From c060ce211126cf5c8fe71ff6cce7f12836c7a11b Mon Sep 17 00:00:00 2001 From: zzlgreat Date: Mon, 8 Dec 2025 14:54:35 +0800 Subject: [PATCH 01/20] update pay ui --- app.py | 22 ++++++++++-- src/views/StockOverview/index.js | 60 ++++++++++++++++++++++++++++++-- 2 files changed, 76 insertions(+), 6 deletions(-) diff --git a/app.py b/app.py index 56bc3250..617f508e 100755 --- a/app.py +++ b/app.py @@ -12403,7 +12403,21 @@ def get_daily_top_concepts(): top_concepts = [] for concept in data.get('results', []): - # 保持与 /concept-api/search 相同的字段结构 + # 处理 stocks 字段：兼容 {name, code} 和 {stock_name, stock_code} 两种格式 + raw_stocks = concept.get('stocks', []) + formatted_stocks = [] + for stock in raw_stocks: + # 优先使用 stock_name，其次使用 name + stock_name = stock.get('stock_name') or stock.get('name', '') + stock_code = stock.get('stock_code') or stock.get('code', '') + formatted_stocks.append({ + 'stock_name': stock_name, + 'stock_code': stock_code, + 'name': stock_name, # 兼容旧格式 + 'code': stock_code # 兼容旧格式 + }) + + # 保持与 /concept-api/search 相同的字段结构，并添加新字段 top_concepts.append({ 'concept_id': concept.get('concept_id'), 'concept': concept.get('concept'), # 原始字段名 @@ -12414,8 +12428,10 @@ def get_daily_top_concepts(): 'match_type': concept.get('match_type'), 'price_info': concept.get('price_info', {}), # 完整的价格信息 'change_percent': concept.get('price_info', {}).get('avg_change_pct', 0), # 兼容旧字段 - 'happened_times': concept.get('happened_times', []), # 历史触发时间 - 'stocks': concept.get('stocks', []), # 返回完整股票列表 + 'tags': concept.get('tags', []), # 标签列表 + 'outbreak_dates': concept.get('outbreak_dates', []), # 爆发日期列表 + 'hierarchy': concept.get('hierarchy'), # 层级信息 {lv1, lv2, lv3} + 'stocks': formatted_stocks, # 返回格式化后的股票列表 'hot_score': concept.get('hot_score') }) diff --git a/src/views/StockOverview/index.js b/src/views/StockOverview/index.js index 818093a3..e98686b1 100644 --- a/src/views/StockOverview/index.js +++ b/src/views/StockOverview/index.js @@ -50,7 +50,7 @@ import { SkeletonText, } from '@chakra-ui/react'; import { SearchIcon, CloseIcon, ArrowForwardIcon, TrendingUpIcon, InfoIcon, ChevronRightIcon, CalendarIcon } from '@chakra-ui/icons'; -import { FaChartLine, FaFire, FaRocket, FaBrain, FaCalendarAlt, FaChevronRight, FaArrowUp, FaArrowDown, FaChartBar } from 'react-icons/fa'; +import { FaChartLine, FaFire, FaRocket, FaBrain, FaCalendarAlt, FaChevronRight, FaArrowUp, FaArrowDown, FaChartBar, FaTag, FaLayerGroup, FaBolt } from 'react-icons/fa'; import ConceptStocksModal from '@components/ConceptStocksModal'; import TradeDatePicker from '@components/TradeDatePicker'; import { BsGraphUp, BsLightningFill } from 'react-icons/bs'; @@ -927,16 +927,65 @@ const StockOverview = () => { + {/* 概念名称 */} {concept.concept_name} + {/* 层级信息 */} + {concept.hierarchy && ( + + + + {[concept.hierarchy.lv1, concept.hierarchy.lv2, concept.hierarchy.lv3] + .filter(Boolean) + .join(' > ')} + + + )} + + {/* 描述 */} {concept.description || '暂无描述'} + {/* 标签 */} + {concept.tags && concept.tags.length > 0 && ( + + {concept.tags.slice(0, 4).map((tag, idx) => ( + + + {tag} + + ))} + {concept.tags.length > 4 && ( + + +{concept.tags.length - 4} + + )} + + )} + + {/* 爆发日期 */} + {concept.outbreak_dates && concept.outbreak_dates.length > 0 && ( + + + + 近期爆发: {concept.outbreak_dates.slice(0, 2).join(', ')} + {concept.outbreak_dates.length > 2 && ` 等${concept.outbreak_dates.length}次`} + + + )} + + {/* 相关股票 */} { overflow="hidden" maxH="24px" > - {concept.stocks.map((stock, idx) => ( + {concept.stocks.slice(0, 5).map((stock, idx) => ( { variant="subtle" flexShrink={0} > - {stock.stock_name} + {stock.stock_name || stock.name} ))} + {concept.stocks.length > 5 && ( + + +{concept.stocks.length - 5} + + )} )} From 0735df1aaa03bd6fa73eeee4cd1e2c0a1222f4ed Mon Sep 17 00:00:00 2001 From: zdl <3489966805@qq.com> Date: Mon, 8 Dec 2025 15:40:47 +0800 Subject: [PATCH 02/20] =?UTF-8?q?feat:=20=E6=9B=BF=E6=8D=A2=E5=85=AC?= =?UTF-8?q?=E4=BC=97=E5=8F=B7=E6=96=87=E4=BB=B6?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- public/MP_verify_17Fo4JhapMw6vtNa.txt | 1 + public/MP_verify_k8W6K1RDHiU4U3nw.txt | 1 - 2 files changed, 1 insertion(+), 1 deletion(-) create mode 100644 public/MP_verify_17Fo4JhapMw6vtNa.txt delete mode 100644 public/MP_verify_k8W6K1RDHiU4U3nw.txt diff --git a/public/MP_verify_17Fo4JhapMw6vtNa.txt b/public/MP_verify_17Fo4JhapMw6vtNa.txt new file mode 100644 index 00000000..6792be56 --- /dev/null +++ b/public/MP_verify_17Fo4JhapMw6vtNa.txt @@ -0,0 +1 @@ +17Fo4JhapMw6vtNa diff --git a/public/MP_verify_k8W6K1RDHiU4U3nw.txt b/public/MP_verify_k8W6K1RDHiU4U3nw.txt deleted file mode 100644 index 6bfb00f4..00000000 --- a/public/MP_verify_k8W6K1RDHiU4U3nw.txt +++ /dev/null @@ -1 +0,0 @@ -k8W6K1RDHiU4U3nw \ No newline at end of file From 04f5307e62b8b7d85c40c8e1f330e07b686f2b60 Mon Sep 17 00:00:00 2001 From: zzlgreat Date: Tue, 9 Dec 2025 08:31:18 +0800 Subject: [PATCH 03/20] update pay ui --- app.py | 273 ++ concept_alert_20251208.log | 2823 +++++++++++++++++ concept_alert_alpha.py | 1078 +++++++ concept_alert_alpha_20251208.log | 28 + concept_alert_ml.py | 1625 ++++++++++ concept_alert_realtime.py | 1366 ++++++++ concept_hierarchy_v3.json | 2061 ++++++------ concept_quota_realtime.py | 681 ++++ create_tables.py | 89 + ml/README.md | 112 + ml/__init__.py | 10 + ml/backtest.py | 481 +++ ml/backtest_hybrid.py | 418 +++ ml/detector.py | 571 ++++ ml/enhanced_detector.py | 526 +++ ml/inference.py | 455 +++ ml/model.py | 390 +++ ml/prepare_data.py | 501 +++ ml/requirements.txt | 25 + ml/run_training.sh | 99 + ml/train.py | 808 +++++ sql/concept_minute_alert.sql | 68 + .../components/HotspotOverview/index.js | 539 ++++ src/views/StockOverview/index.js | 6 + 24 files changed, 13972 insertions(+), 1061 deletions(-) create mode 100644 concept_alert_20251208.log create mode 100644 concept_alert_alpha.py create mode 100644 concept_alert_alpha_20251208.log create mode 100644 concept_alert_ml.py create mode 100644 concept_alert_realtime.py create mode 100644 concept_quota_realtime.py create mode 100644 create_tables.py create mode 100644 ml/README.md create mode 100644 ml/__init__.py create mode 100644 ml/backtest.py create mode 100644 ml/backtest_hybrid.py create mode 100644 ml/detector.py create mode 100644 ml/enhanced_detector.py create mode 100644 ml/inference.py create mode 100644 ml/model.py create mode 100644 ml/prepare_data.py create mode 100644 ml/requirements.txt create mode 100644 ml/run_training.sh create mode 100644 ml/train.py create mode 100644 sql/concept_minute_alert.sql create mode 100644 src/views/StockOverview/components/HotspotOverview/index.js diff --git a/app.py b/app.py index 617f508e..b231ea6c 100755 --- a/app.py +++ b/app.py @@ -12458,6 +12458,279 @@ def get_daily_top_concepts(): }), 500 +# ==================== 热点概览 API ==================== + +@app.route('/api/market/hotspot-overview', methods=['GET']) +def get_hotspot_overview(): + """ + 获取热点概览数据（用于个股中心的热点概览图表） + 返回：指数分时数据 + 概念异动标注 + """ + try: + trade_date = request.args.get('date') + index_code = request.args.get('index', '000001.SH') + + # 如果没有指定日期，使用最新交易日 + if not trade_date: + today = date.today() + if today in trading_days_set: + trade_date = today.strftime('%Y-%m-%d') + else: + target_date = get_trading_day_near_date(today) + trade_date = target_date.strftime('%Y-%m-%d') if target_date else today.strftime('%Y-%m-%d') + + # 1. 获取指数分时数据 + client = get_clickhouse_client() + target_date_obj = datetime.strptime(trade_date, '%Y-%m-%d').date() + + index_data = client.execute( + """ + SELECT timestamp, open, high, low, close, volume + FROM index_minute + WHERE code = %(code)s + AND toDate(timestamp) = %(date)s + ORDER BY timestamp + """, + { + 'code': index_code, + 'date': target_date_obj + } + ) + + # 获取昨收价 + code_no_suffix = index_code.split('.')[0] + prev_close = None + with engine.connect() as conn: + prev_result = conn.execute(text(""" + SELECT F006N FROM ea_exchangetrade + WHERE INDEXCODE = :code + AND TRADEDATE < :today + ORDER BY TRADEDATE DESC LIMIT 1 + """), { + 'code': code_no_suffix, + 'today': target_date_obj + }).fetchone() + if prev_result and prev_result[0]: + prev_close = float(prev_result[0]) + + # 格式化指数数据 + index_timeline = [] + for row in index_data: + ts, open_p, high_p, low_p, close_p, vol = row + change_pct = None + if prev_close and close_p: + change_pct = round((float(close_p) - prev_close) / prev_close * 100, 4) + + index_timeline.append({ + 'time': ts.strftime('%H:%M'), + 'timestamp': ts.isoformat(), + 'price': float(close_p) if close_p else None, + 'open': float(open_p) if open_p else None, + 'high': float(high_p) if high_p else None, + 'low': float(low_p) if low_p else None, + 'volume': int(vol) if vol else 0, + 'change_pct': change_pct + }) + + # 2. 获取概念异动数据 + alerts = [] + with engine.connect() as conn: + alert_result = conn.execute(text(""" + SELECT + concept_id, concept_name, alert_time, alert_type, + change_pct, change_delta, limit_up_count, limit_up_delta, + rank_position, index_price, index_change_pct, + stock_count, concept_type, extra_info, + prev_change_pct, zscore, importance_score + FROM concept_minute_alert + WHERE trade_date = :trade_date + ORDER BY alert_time + """), {'trade_date': trade_date}) + + for row in alert_result: + alert_time = row[2] + extra_info = None + if row[13]: + try: + extra_info = json.loads(row[13]) if isinstance(row[13], str) else row[13] + except: + pass + + # 从 extra_info 提取 zscore 和 importance_score（兼容旧数据） + zscore = None + importance_score = None + if len(row) > 15: + zscore = float(row[15]) if row[15] else None + importance_score = float(row[16]) if row[16] else None + if extra_info: + zscore = zscore or extra_info.get('zscore') + importance_score = importance_score or extra_info.get('importance_score') + + alerts.append({ + 'concept_id': row[0], + 'concept_name': row[1], + 'time': alert_time.strftime('%H:%M') if alert_time else None, + 'timestamp': alert_time.isoformat() if alert_time else None, + 'alert_type': row[3], + 'change_pct': float(row[4]) if row[4] else None, + 'change_delta': float(row[5]) if row[5] else None, + 'limit_up_count': row[6], + 'limit_up_delta': row[7], + 'rank_position': row[8], + 'index_price': float(row[9]) if row[9] else None, + 'index_change_pct': float(row[10]) if row[10] else None, + 'stock_count': row[11], + 'concept_type': row[12], + 'extra_info': extra_info, + 'prev_change_pct': float(row[14]) if len(row) > 14 and row[14] else None, + 'zscore': zscore, + 'importance_score': importance_score + }) + + # 计算统计信息 + day_high = max([d['price'] for d in index_timeline if d['price']], default=None) + day_low = min([d['price'] for d in index_timeline if d['price']], default=None) + latest_price = index_timeline[-1]['price'] if index_timeline else None + latest_change_pct = index_timeline[-1]['change_pct'] if index_timeline else None + + return jsonify({ + 'success': True, + 'data': { + 'trade_date': trade_date, + 'index': { + 'code': index_code, + 'name': '上证指数' if index_code == '000001.SH' else index_code, + 'prev_close': prev_close, + 'latest_price': latest_price, + 'change_pct': latest_change_pct, + 'high': day_high, + 'low': day_low, + 'timeline': index_timeline + }, + 'alerts': alerts, + 'alert_count': len(alerts), + 'alert_summary': { + 'surge': len([a for a in alerts if a['alert_type'] == 'surge']), + 'surge_up': len([a for a in alerts if a['alert_type'] == 'surge_up']), + 'surge_down': len([a for a in alerts if a['alert_type'] == 'surge_down']), + 'limit_up': len([a for a in alerts if a['alert_type'] == 'limit_up']), + 'rank_jump': len([a for a in alerts if a['alert_type'] == 'rank_jump']) + } + } + }) + + except Exception as e: + import traceback + logger.error(f"获取热点概览数据失败: {traceback.format_exc()}") + return jsonify({ + 'success': False, + 'error': str(e) + }), 500 + + +@app.route('/api/market/concept-alerts', methods=['GET']) +def get_concept_alerts(): + """ + 获取概念异动列表（支持分页和筛选） + """ + try: + trade_date = request.args.get('date') + alert_type = request.args.get('type') # surge/limit_up/rank_jump + concept_type = request.args.get('concept_type') # leaf/lv1/lv2/lv3 + limit = request.args.get('limit', 50, type=int) + offset = request.args.get('offset', 0, type=int) + + # 构建查询条件 + conditions = [] + params = {'limit': limit, 'offset': offset} + + if trade_date: + conditions.append("trade_date = :trade_date") + params['trade_date'] = trade_date + else: + conditions.append("trade_date = CURDATE()") + + if alert_type: + conditions.append("alert_type = :alert_type") + params['alert_type'] = alert_type + + if concept_type: + conditions.append("concept_type = :concept_type") + params['concept_type'] = concept_type + + where_clause = " AND ".join(conditions) if conditions else "1=1" + + with engine.connect() as conn: + # 获取总数 + count_sql = text(f"SELECT COUNT(*) FROM concept_minute_alert WHERE {where_clause}") + total = conn.execute(count_sql, params).scalar() + + # 获取数据 + query_sql = text(f""" + SELECT + id, concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_price, index_change_pct, + stock_count, concept_type, extra_info + FROM concept_minute_alert + WHERE {where_clause} + ORDER BY alert_time DESC + LIMIT :limit OFFSET :offset + """) + + result = conn.execute(query_sql, params) + + alerts = [] + for row in result: + extra_info = None + if row[19]: + try: + extra_info = json.loads(row[19]) if isinstance(row[19], str) else row[19] + except: + pass + + alerts.append({ + 'id': row[0], + 'concept_id': row[1], + 'concept_name': row[2], + 'alert_time': row[3].isoformat() if row[3] else None, + 'alert_type': row[4], + 'trade_date': row[5].isoformat() if row[5] else None, + 'change_pct': float(row[6]) if row[6] else None, + 'prev_change_pct': float(row[7]) if row[7] else None, + 'change_delta': float(row[8]) if row[8] else None, + 'limit_up_count': row[9], + 'prev_limit_up_count': row[10], + 'limit_up_delta': row[11], + 'rank_position': row[12], + 'prev_rank_position': row[13], + 'rank_delta': row[14], + 'index_price': float(row[15]) if row[15] else None, + 'index_change_pct': float(row[16]) if row[16] else None, + 'stock_count': row[17], + 'concept_type': row[18], + 'extra_info': extra_info + }) + + return jsonify({ + 'success': True, + 'data': alerts, + 'total': total, + 'limit': limit, + 'offset': offset + }) + + except Exception as e: + import traceback + logger.error(f"获取概念异动列表失败: {traceback.format_exc()}") + return jsonify({ + 'success': False, + 'error': str(e) + }), 500 + + @app.route('/api/market/rise-analysis/', methods=['GET']) def get_rise_analysis(seccode): """获取股票涨幅分析数据（从 Elasticsearch 获取）""" diff --git a/concept_alert_20251208.log b/concept_alert_20251208.log new file mode 100644 index 00000000..dffe4ef2 --- /dev/null +++ b/concept_alert_20251208.log @@ -0,0 +1,2823 @@ +2025-12-08 15:43:31,991 - INFO - ============================================================ +2025-12-08 15:43:31,991 - INFO - 🔄 开始回测: 2025-12-08 +2025-12-08 15:43:31,992 - INFO - ============================================================ +2025-12-08 15:43:57,527 - INFO - ============================================================ +2025-12-08 15:43:57,528 - INFO - 🔄 开始回测: 2025-12-08 +2025-12-08 15:43:57,528 - INFO - ============================================================ +2025-12-08 15:47:59,701 - INFO - ============================================================ +2025-12-08 15:47:59,701 - INFO - 🔄 开始回测: 2025-12-08 +2025-12-08 15:47:59,703 - INFO - ============================================================ +2025-12-08 15:47:59,803 - INFO - 已清除 2025-12-08 的已有数据 +2025-12-08 15:47:59,803 - INFO - 加载概念数据... +2025-12-08 15:48:00,021 - INFO - POST http://222.128.1.157:19200/concept_library_v3/_search?scroll=2m [status:200 duration:0.216s] +2025-12-08 15:48:00,218 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.192s] +2025-12-08 15:48:00,442 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.175s] +2025-12-08 15:48:00,656 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.210s] +2025-12-08 15:48:00,845 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.185s] +2025-12-08 15:48:01,025 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.177s] +2025-12-08 15:48:01,209 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.180s] +2025-12-08 15:48:01,377 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.164s] +2025-12-08 15:48:01,486 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.105s] +2025-12-08 15:48:01,496 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.007s] +2025-12-08 15:48:01,505 - INFO - DELETE http://222.128.1.157:19200/_search/scroll [status:200 duration:0.008s] +2025-12-08 15:48:01,505 - INFO - 获取到 865 个叶子概念 +2025-12-08 15:48:01,513 - INFO - 生成了 103 个母概念 +2025-12-08 15:48:01,513 - INFO - 总计 968 个概念 +2025-12-08 15:48:01,516 - INFO - 监控 5938 只股票 +2025-12-08 15:48:02,488 - INFO - 获取到 5132 个基准价格 +2025-12-08 15:48:02,498 - INFO - 指数昨收价: 3902.8076 +2025-12-08 15:48:02,667 - INFO - 找到 241 个分钟时间点 +2025-12-08 15:48:03,435 - ERROR - 保存异动失败: SpaceX - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '54d3a21ee5b8a15d2004b7350bf67e6d', 'concept_name': 'SpaceX', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 11.1678, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 10, 'prev_limit_up_count': 9, 'limit_up_delta': 1, 'rank_position': 2, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 19, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["002149", "688102", "603601", "002792", "300136", "000547", "000901", "688053", "605598", "301568"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:03,442 - ERROR - 保存异动失败: 光纤列阵单元FAU - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '7e7a80554a69b2ff892349f661a0e158', 'concept_name': '光纤列阵单元FAU', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 9.2138, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 3, 'prev_limit_up_count': 2, 'limit_up_delta': 1, 'rank_position': 3, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 10, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["688025", "688143", "300570"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:03,449 - ERROR - 保存异动失败: 海峡两岸福建 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'c0e5202a076100676c901fb6a6fe70fa', 'concept_name': '海峡两岸福建', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 7.3406, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 13, 'prev_limit_up_count': 11, 'limit_up_delta': 2, 'rank_position': 5, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 45, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300300", "000905", "000701", "000632", "000797", "002578", "002961", "300102", "600734", "002679", "002682", "002752", "603122"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:03,454 - ERROR - 保存异动失败: 蓝箭航天朱雀三号 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'd4e69baa804e9adf57bc8fb26807299f', 'concept_name': '蓝箭航天朱雀三号', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 7.0298, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 8, 'prev_limit_up_count': 7, 'limit_up_delta': 1, 'rank_position': 7, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 31, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["688102", "002792", "688333", "301005", "688539", "600343", "688270", "000547"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:03,465 - ERROR - 保存异动失败: 卫星互联网 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '9814b44dd4e4a09363dcfe5c374618f9', 'concept_name': '卫星互联网', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 6.8333, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 11, 'prev_limit_up_count': 8, 'limit_up_delta': 3, 'rank_position': 8, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 48, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300762", "688270", "003031", "300123", "301117", "688102", "688539", "688418", "688311", "300136", "000901"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:03,469 - ERROR - 保存异动失败: 商业航天卫星通信 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '2b570e6ae2e96a7b249dcb0ae68f512d', 'concept_name': '商业航天卫星通信', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 6.7523, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 18, 'prev_limit_up_count': 14, 'limit_up_delta': 4, 'rank_position': 9, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 83, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["600343", "688539", "688102", "301005", "688333", "688270", "300123", "300762", "301117", "002792", "300136", "688418", "688311", "002512", "300102", "000547", "000901", "920665"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:03,480 - ERROR - 保存异动失败: 星网 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '01bf110e2146b364400869c107c83451', 'concept_name': '星网', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 6.5314, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 17, 'prev_limit_up_count': 13, 'limit_up_delta': 4, 'rank_position': 10, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 83, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["600343", "688102", "301005", "688333", "688270", "300123", "300762", "301117", "002792", "300136", "688418", "688311", "002512", "300102", "000547", "000901", "920665"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:03,486 - ERROR - 保存异动失败: 光纤 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '56944fc3bac2fbf44215db12ad2b94b2', 'concept_name': '光纤', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 6.4413, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 3, 'prev_limit_up_count': 2, 'limit_up_delta': 1, 'rank_position': 11, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 14, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["600105", "688143", "300570"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:03,498 - ERROR - 保存异动失败: 商业航天 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '3be4e01f6112192e002c26b6a648e799', 'concept_name': '商业航天', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 6.3055, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 20, 'prev_limit_up_count': 16, 'limit_up_delta': 4, 'rank_position': 12, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 110, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["301005", "688539", "688102", "300900", "000547", "300762", "002977", "600343", "688333", "688270", "003031", "300123", "301117", "688418", "688311", "300136", "002512", "000901", "920665", "600151"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:03,503 - ERROR - 保存异动失败: 空芯光纤 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '973f2ac5c5a1a8770b83e34a8683659c', 'concept_name': '空芯光纤', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 6.2376, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 3, 'prev_limit_up_count': 2, 'limit_up_delta': 1, 'rank_position': 13, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 11, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["000070", "688143", "300570"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:03,551 - ERROR - 保存异动失败: 远程火力 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '271c4f3a775d854954c1a8ea24104821', 'concept_name': '远程火力', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 6.1789, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 11, 'prev_limit_up_count': 6, 'limit_up_delta': 5, 'rank_position': 14, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 57, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["688333", "688282", "688143", "688311", "300123", "002977", "688270", "300427", "603122", "002149", "600302"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:03,577 - ERROR - 保存异动失败: [二级] 陆军装备 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'e78c88c7728af7aa', 'concept_name': '[二级] 陆军装备', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 6.1789, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 11, 'prev_limit_up_count': 6, 'limit_up_delta': 5, 'rank_position': 15, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 57, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["002977", "688311", "688333", "300427", "688143", "600302", "603122", "688270", "002149", "300123", "688282"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:03,601 - ERROR - 保存异动失败: 手机直连卫星 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '2e8d597a0e6bd633695b947e683af5a7', 'concept_name': '手机直连卫星', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 6.1785, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 6, 'prev_limit_up_count': 4, 'limit_up_delta': 2, 'rank_position': 16, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 21, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300762", "002977", "688311", "001270", "688270", "688418"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:03,624 - ERROR - 保存异动失败: 水下军工 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'ddf3e3a0c99aa84b88024fd5e0a2fa6b', 'concept_name': '水下军工', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 5.7954, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 4, 'prev_limit_up_count': 2, 'limit_up_delta': 2, 'rank_position': 19, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 27, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["002149", "688143", "688282", "300123"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:03,630 - ERROR - 保存异动失败: 军工水面水下作战 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '26870549d3469d8b016d58878a4a1694', 'concept_name': '军工水面水下作战', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 5.4306, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 3, 'prev_limit_up_count': 2, 'limit_up_delta': 1, 'rank_position': 22, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 28, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["002149", "688143", "688282"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:03,636 - ERROR - 保存异动失败: 核聚变超导 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '9e0ad5f3de553a074aca639d13094d76', 'concept_name': '核聚变超导', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 5.2595, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 6, 'prev_limit_up_count': 5, 'limit_up_delta': 1, 'rank_position': 26, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 43, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["600105", "920576", "601106", "601399", "002149", "603015"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:03,646 - ERROR - 保存异动失败: 福建国资 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'b59679d54c060fe313b16d6917d9ca93', 'concept_name': '福建国资', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 5.1542, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 8, 'prev_limit_up_count': 6, 'limit_up_delta': 2, 'rank_position': 27, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 50, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["000905", "600734", "000797", "000701", "002682", "002679", "300300", "000632"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:03,654 - ERROR - 保存异动失败: 光通信CPO - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '0b20095da20380417829e5484feac90b', 'concept_name': '光通信CPO', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 5.1014, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 4, 'prev_limit_up_count': 3, 'limit_up_delta': 1, 'rank_position': 28, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 32, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["600105", "688048", "300570", "000070"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:03,660 - ERROR - 保存异动失败: 军工信息化 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '66bd549e8504be635ff0bbad8ce930e0', 'concept_name': '军工信息化', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 4.9328, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 15, 'prev_limit_up_count': 12, 'limit_up_delta': 3, 'rank_position': 33, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 135, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["002977", "300427", "300123", "001270", "000547", "688311", "300762", "688081", "000070", "688270", "301117", "688539", "600973", "300900", "300560"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:03,666 - ERROR - 保存异动失败: 可控核聚变 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'f35622ab5c0d533e0c8da487df982c05', 'concept_name': '可控核聚变', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 4.8832, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 9, 'prev_limit_up_count': 8, 'limit_up_delta': 1, 'rank_position': 35, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 73, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["600105", "600973", "688167", "603015", "601399", "688102", "920576", "601106", "002149"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:03,690 - ERROR - 保存异动失败: 光通信 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'dd05cdaa409ff3dafb3fe19bedbeaa87', 'concept_name': '光通信', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 4.6474, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 4, 'prev_limit_up_count': 3, 'limit_up_delta': 1, 'rank_position': 39, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 36, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300570", "600105", "688048", "000070"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:03,712 - ERROR - 保存异动失败: 北斗信使 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '1808e46d4965b2f8272846d77025716e', 'concept_name': '北斗信使', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 4.5627, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 41, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 15, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["000901", "002512"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:03,717 - ERROR - 保存异动失败: [二级] 无人作战与信息化 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'ab0a7a074639080b', 'concept_name': '[二级] 无人作战与信息化', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 4.4116, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 17, 'prev_limit_up_count': 14, 'limit_up_delta': 3, 'rank_position': 44, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 200, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["688311", "301117", "300560", "002977", "000901", "300900", "688270", "002149", "600973", "001270", "300123", "688539", "300427", "000070", "000547", "688081", "300762"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:03,722 - ERROR - 保存异动失败: [二级] 商业航天 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '3342079af14780e7', 'concept_name': '[二级] 商业航天', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 4.2974, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 27, 'prev_limit_up_count': 23, 'limit_up_delta': 4, 'rank_position': 50, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 238, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["688333", "003031", "300136", "600343", "002977", "000901", "300900", "688270", "001270", "300123", "688539", "688102", "300102", "002512", "002792", "300751", "688311", "301117", "002565", "688418", "301005", "688282", "605598", "000547", "300762", "920665", "600151"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:03,732 - ERROR - 保存异动失败: 信息支援概念整理 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '42e3093f9fc99b4a86374ac455873b9b', 'concept_name': '信息支援概念整理', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 4.1763, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 1, 'prev_limit_up_count': 0, 'limit_up_delta': 1, 'rank_position': 54, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 24, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["688311"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:03,737 - ERROR - 保存异动失败: 军工 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '58ac1fb9ce375d4849893453f9615b49', 'concept_name': '军工', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 4.1592, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 10, 'prev_limit_up_count': 8, 'limit_up_delta': 2, 'rank_position': 56, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 168, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["002977", "000547", "000901", "600343", "688143", "002149", "688333", "688311", "300337", "300123"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:03,745 - ERROR - 保存异动失败: 珠海航展 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'd02ad5be74f6a9c53fb28291a961621c', 'concept_name': '珠海航展', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 4.1462, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 5, 'prev_limit_up_count': 3, 'limit_up_delta': 2, 'rank_position': 57, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 44, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["600343", "000901", "300900", "688311", "300123"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:03,750 - ERROR - 保存异动失败: 6G概念 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '863500a9c8b544d099618a6f355ccfff', 'concept_name': '6G概念', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 4.1245, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 9, 'prev_limit_up_count': 5, 'limit_up_delta': 4, 'rank_position': 59, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 80, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["002093", "002792", "300136", "688270", "300123", "300570", "000547", "300560", "002512"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:03,770 - ERROR - 保存异动失败: 磁悬浮压缩机 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '6ae1f6de3a42693cb613896ba7ce8173', 'concept_name': '磁悬浮压缩机', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 4.1143, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 61, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 14, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["002158", "000811"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:03,775 - ERROR - 保存异动失败: [二级] 海军装备 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '36d8fd5f5f85dffb', 'concept_name': '[二级] 海军装备', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 4.0227, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 9, 'prev_limit_up_count': 6, 'limit_up_delta': 3, 'rank_position': 62, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 99, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["300337", "688143", "688282", "601696", "002512", "000547", "601106", "002149", "300123"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:03,780 - ERROR - 保存异动失败: [一级] 国防军工 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'b2cd2902316a116f', 'concept_name': '[一级] 国防军工', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 4.0186, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 30, 'prev_limit_up_count': 24, 'limit_up_delta': 6, 'rank_position': 63, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 373, 'concept_type': 'lv1', 'extra_info': '{"limit_up_stocks": ["688333", "600343", "300560", "002977", "000901", "300900", "600302", "002682", "688270", "002149", "600973", "001270", "300123" ... (23 characters truncated) ... 300337", "002512", "688311", "301117", "688143", "601696", "603122", "603601", "688282", "000070", "000547", "601106", "688081", "300762", "600151"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:03,786 - ERROR - 保存异动失败: 军用无人机反无人机 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '388136e7bee429da7f0026a26acc953f', 'concept_name': '军用无人机反无人机', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 4.0027, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 67, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 38, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300900", "300123"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:03,814 - ERROR - 保存异动失败: 核电产业链 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '41e18cd109f4f15cd0716e3c6d9955dd', 'concept_name': '核电产业链', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.9503, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 7, 'prev_limit_up_count': 6, 'limit_up_delta': 1, 'rank_position': 69, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 86, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["600973", "600105", "601399", "603015", "920576", "601106", "688167"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:03,845 - ERROR - 保存异动失败: [二级] 综合与主题 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '8dd8fe507c554405', 'concept_name': '[二级] 综合与主题', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.936, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 13, 'prev_limit_up_count': 11, 'limit_up_delta': 2, 'rank_position': 71, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 204, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["688311", "301117", "688333", "688143", "600343", "300560", "002977", "000901", "002149", "300123", "300337", "000547", "600151"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:03,851 - ERROR - 保存异动失败: 航母福建舰240430 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '67d2e2df29052090e9954abeb00d517d', 'concept_name': '航母福建舰240430', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.835, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 4, 'prev_limit_up_count': 3, 'limit_up_delta': 1, 'rank_position': 74, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 40, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300337", "601106", "000547", "002512"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:03,886 - ERROR - 保存异动失败: 铜连接 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '56e10d2abdf5ceb6f13e200e85fef36e', 'concept_name': '铜连接', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.7676, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 3, 'prev_limit_up_count': 2, 'limit_up_delta': 1, 'rank_position': 79, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 23, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["002792", "600973", "300570"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:03,915 - ERROR - 保存异动失败: 博通交换机 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '6f778df3439781376d8c908f8d2b2276', 'concept_name': '博通交换机', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.7567, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 80, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 22, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300570", "301486"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:03,937 - ERROR - 保存异动失败: 卫星出海 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '69d1d811ac734aa07561db06a141bc16', 'concept_name': '卫星出海', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.6151, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 4, 'prev_limit_up_count': 2, 'limit_up_delta': 2, 'rank_position': 91, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 48, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["002792", "300762", "300123", "002512"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:03,942 - ERROR - 保存异动失败: 量子科技产业链 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'f7daaa03d8415d499ca82a4d70ead074', 'concept_name': '量子科技产业链', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.5094, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 4, 'prev_limit_up_count': 2, 'limit_up_delta': 2, 'rank_position': 100, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 63, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["301117", "300123", "002512", "688418"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:03,949 - ERROR - 保存异动失败: 无人机蜂群 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '34f096164ed77a917364457c58fb7b18', 'concept_name': '无人机蜂群', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.484, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 3, 'prev_limit_up_count': 2, 'limit_up_delta': 1, 'rank_position': 102, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 24, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["688311", "301117", "000901"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:03,955 - ERROR - 保存异动失败: 超聚变 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '78a0c55f302a623ae81994e671989229', 'concept_name': '超聚变', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.3262, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 112, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 36, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300290", "600105"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:03,962 - ERROR - 保存异动失败: 对日反制 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'f989d4184984a32d90fa74385399c3eb', 'concept_name': '对日反制', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.3087, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 12, 'prev_limit_up_count': 11, 'limit_up_delta': 1, 'rank_position': 113, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 155, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["688323", "000859", "003031", "002585", "000547", "002682", "300560", "002083", "603122", "603067", "300427", "300123"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:03,970 - ERROR - 保存异动失败: 庭院割草机器人 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '7e531a981bb00d2127f93503ebd29036', 'concept_name': '庭院割草机器人', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.2773, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 119, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 13, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["301125", "002779"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:03,995 - ERROR - 保存异动失败: 华为 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'd63e7ea059dc68e97e2ef16531e47e6c', 'concept_name': '华为', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.2412, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 3, 'prev_limit_up_count': 1, 'limit_up_delta': 2, 'rank_position': 122, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 40, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300290", "301125", "688031"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,001 - ERROR - 保存异动失败: 北斗导航 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'e1cb2d2097ad5505e52914eea10fb93f', 'concept_name': '北斗导航', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.2333, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 123, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 15, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["688282", "000901"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,007 - ERROR - 保存异动失败: 深海经济 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '62c1ff95b594873a91665479c74fa5d5', 'concept_name': '深海经济', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.2094, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 130, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 57, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["920576", "002149"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,014 - ERROR - 保存异动失败: 低空经济亿航智能 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '9e56e5451682848c3c8a440306122ea7', 'concept_name': '低空经济亿航智能', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.1804, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 4, 'prev_limit_up_count': 3, 'limit_up_delta': 1, 'rank_position': 133, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 81, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300900", "002660", "002682", "000701"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,021 - ERROR - 保存异动失败: 海军 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '10bb72970f8ef3c8801898e342d47942', 'concept_name': '海军', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.1182, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 1, 'prev_limit_up_count': 0, 'limit_up_delta': 1, 'rank_position': 140, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 26, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300123"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,034 - ERROR - 保存异动失败: IPV6 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '3fbcd320695e3a01273994471d09cc36', 'concept_name': 'IPV6', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.1161, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 4, 'prev_limit_up_count': 3, 'limit_up_delta': 1, 'rank_position': 142, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 35, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["002885", "688282", "300560", "301529"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,043 - ERROR - 保存异动失败: 国产航母 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '41a3d2f7a25c41eaa2c2e48a520bc380', 'concept_name': '国产航母', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.1147, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 143, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 25, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["601696", "300123"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,056 - ERROR - 保存异动失败: 台资企业 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '814666d2762595328dfddbd3546a1fbf', 'concept_name': '台资企业', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.109, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 6, 'prev_limit_up_count': 5, 'limit_up_delta': 1, 'rank_position': 145, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 54, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["603122", "603886", "603015", "002158", "002578", "002752"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,062 - ERROR - 保存异动失败: 机器人零部件加工设备 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '480f22ef7a1876fb952bbb51afc87210', 'concept_name': '机器人零部件加工设备', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.1047, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 3, 'prev_limit_up_count': 2, 'limit_up_delta': 1, 'rank_position': 147, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 28, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["688577", "301125", "002779"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,071 - ERROR - 保存异动失败: 量子科技 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '41381b4a238daead45abe7866364e44e', 'concept_name': '量子科技', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.0729, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 4, 'prev_limit_up_count': 2, 'limit_up_delta': 2, 'rank_position': 151, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 70, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["688418", "002512", "300123", "301117"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,078 - ERROR - 保存异动失败: 超聚变借壳预期 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'e3f9eb598bd782998ef4d63abf7f914b', 'concept_name': '超聚变借壳预期', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.0691, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 0, 'limit_up_delta': 2, 'rank_position': 152, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 15, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300290", "600302"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,101 - ERROR - 保存异动失败: 5G毫米波 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '331c00a813c599fef1ad0ad65cf90631', 'concept_name': '5G毫米波', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.0402, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 5, 'prev_limit_up_count': 4, 'limit_up_delta': 1, 'rank_position': 155, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 40, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["002792", "300136", "003031", "688270", "002977"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,117 - ERROR - 保存异动失败: [二级] 量子科技 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '24435b1fd612ff3d', 'concept_name': '[二级] 量子科技', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.0325, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 5, 'prev_limit_up_count': 3, 'limit_up_delta': 2, 'rank_position': 158, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 95, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["301117", "300123", "002512", "688418", "002149"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,124 - ERROR - 保存异动失败: [一级] 前沿科技 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '87da2b6fbbcb3e33', 'concept_name': '[一级] 前沿科技', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.0325, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 5, 'prev_limit_up_count': 3, 'limit_up_delta': 2, 'rank_position': 159, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 95, 'concept_type': 'lv1', 'extra_info': '{"limit_up_stocks": ["301117", "300123", "002512", "688418", "002149"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,133 - ERROR - 保存异动失败: 低空经济产业链汇集 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '68aa3c7715416e1ffa19f237d0f95961', 'concept_name': '低空经济产业链汇集', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.9994, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 3, 'prev_limit_up_count': 2, 'limit_up_delta': 1, 'rank_position': 167, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 122, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["000701", "000547", "002792"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,139 - ERROR - 保存异动失败: [一级] 空天经济 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'd1f5d74529f14e29', 'concept_name': '[一级] 空天经济', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.9293, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 37, 'prev_limit_up_count': 31, 'limit_up_delta': 6, 'rank_position': 177, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 690, 'concept_type': 'lv1', 'extra_info': '{"limit_up_stocks": ["300136", "000901", "300900", "002682", "688539", "300102", "002512", "002660", "603601", "301005", "605598", "300570", "920665" ... (93 characters truncated) ... 688102", "002792", "300751", "688311", "301117", "002565", "688418", "600105", "002779", "002093", "688282", "000070", "000547", "600151", "300762"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,147 - ERROR - 保存异动失败: [三级] 核能 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'b44eaa65d1c2425d', 'concept_name': '[三级] 核能', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.91, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 12, 'prev_limit_up_count': 9, 'limit_up_delta': 3, 'rank_position': 178, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 232, 'concept_type': 'lv3', 'extra_info': '{"limit_up_stocks": ["002158", "920576", "600973", "002149", "688102", "600105", "601399", "300290", "000811", "603015", "601106", "688167"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,161 - ERROR - 保存异动失败: [二级] 通信技术 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '22330358ccd65aeb', 'concept_name': '[二级] 通信技术', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.9016, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 14, 'prev_limit_up_count': 10, 'limit_up_delta': 4, 'rank_position': 182, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 189, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["003031", "300136", "300560", "002977", "688270", "300123", "600105", "002093", "000070", "002512", "002792", "000547", "300762", "300570"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,184 - ERROR - 保存异动失败: AEBS - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '4cd10b5ab4805603cd2d7a34f6f6e6b2', 'concept_name': 'AEBS', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.8962, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 1, 'prev_limit_up_count': 0, 'limit_up_delta': 1, 'rank_position': 184, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 38, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300123"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,189 - ERROR - 保存异动失败: 低空经济 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '6c5f0e25b757e8286f7cb93330b36dc8', 'concept_name': '低空经济', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.765, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 4, 'prev_limit_up_count': 3, 'limit_up_delta': 1, 'rank_position': 199, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 120, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300900", "002660", "002682", "000701"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,210 - ERROR - 保存异动失败: 冰雪经济 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '1cf3a25cd1c90c27310c4610d4a3743f', 'concept_name': '冰雪经济', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.7636, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 3, 'prev_limit_up_count': 2, 'limit_up_delta': 1, 'rank_position': 200, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 44, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["000811", "002158", "605299"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,227 - ERROR - 保存异动失败: 英伟达概念 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '650c2a8d881ff609d809cab6379d1593', 'concept_name': '英伟达概念', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.7475, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 202, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 76, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300570", "301117"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,259 - ERROR - 保存异动失败: 养老机器人 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '30af5b5e3a1ec3a15d21497e9b0399e4', 'concept_name': '养老机器人', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.7411, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 1, 'prev_limit_up_count': 0, 'limit_up_delta': 1, 'rank_position': 203, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 28, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300290"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,267 - ERROR - 保存异动失败: 信创概念 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'aeac02717442e5a225bcae32103ee6a4', 'concept_name': '信创概念', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.7404, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 204, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 72, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["301218", "688031"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,294 - ERROR - 保存异动失败: [三级] AI关键组件 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'd125ffc194b42ee4', 'concept_name': '[三级] AI关键组件', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.7179, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 19, 'prev_limit_up_count': 18, 'limit_up_delta': 1, 'rank_position': 212, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 360, 'concept_type': 'lv3', 'extra_info': '{"limit_up_stocks": ["688048", "600973", "603386", "300102", "688025", "002792", "300751", "688143", "002565", "301526", "603122", "301183", "600366", "301486", "688383", "600105", "000070", "300570", "688167"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,300 - ERROR - 保存异动失败: 关键软件 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '7a7fd726a54670b2b3c8bff3b778acf4', 'concept_name': '关键软件', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.705, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 214, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 103, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["688031", "301218"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,314 - ERROR - 保存异动失败: 数据中心液冷 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'f8bbd66624a99da406ceb891aeb6eb3a', 'concept_name': '数据中心液冷', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.6955, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 217, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 65, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["000811", "002158"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,325 - ERROR - 保存异动失败: 国产信创概览 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '22a45db024b3e7d118d2810c2e7bc5bd', 'concept_name': '国产信创概览', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.6884, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 219, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 101, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["688031", "301218"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,336 - ERROR - 保存异动失败: AI-细分延伸更新 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'c6796e52492170dfc6234c299cbc095b', 'concept_name': 'AI-细分延伸更新', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.6801, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 3, 'prev_limit_up_count': 2, 'limit_up_delta': 1, 'rank_position': 220, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 63, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["301526", "002158", "000811"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,341 - ERROR - 保存异动失败: [三级] AI配套设施 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '986593c763801dcc', 'concept_name': '[三级] AI配套设施', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.6794, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 7, 'prev_limit_up_count': 6, 'limit_up_delta': 1, 'rank_position': 222, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 297, 'concept_type': 'lv3', 'extra_info': '{"limit_up_stocks": ["688333", "002158", "002885", "688102", "000811", "002660", "600218"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,347 - ERROR - 保存异动失败: [三级] 制造工艺与设备 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '67341e0ae4ef7dac', 'concept_name': '[三级] 制造工艺与设备', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.6481, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 4, 'prev_limit_up_count': 3, 'limit_up_delta': 1, 'rank_position': 228, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 70, 'concept_type': 'lv3', 'extra_info': '{"limit_up_stocks": ["300136", "002779", "688577", "301125"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,366 - ERROR - 保存异动失败: Minimax - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '380c18e239f03cf9c0a846c7defd029b', 'concept_name': 'Minimax', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.6426, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 229, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 28, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["688383", "605303"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,372 - ERROR - 保存异动失败: 章盟主概念股 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '0ebd312797bf29853df2fb0f54ab9b72', 'concept_name': '章盟主概念股', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.6389, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 1, 'prev_limit_up_count': 0, 'limit_up_delta': 1, 'rank_position': 230, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 22, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["688311"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,377 - ERROR - 保存异动失败: 马斯克Grok3大模型 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'df7573bd0405ab31be0762125a92fdec', 'concept_name': '马斯克Grok3大模型', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.6051, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 243, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 37, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300570", "688661"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,399 - ERROR - 保存异动失败: 华为Pura70 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '7b30055f26aa31e887125520d038e8c9', 'concept_name': '华为Pura70', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.5615, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 4, 'prev_limit_up_count': 3, 'limit_up_delta': 1, 'rank_position': 248, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 68, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300136", "688311", "301183", "688048"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,404 - ERROR - 保存异动失败: 次新股 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '50371a2c5078b757a8f8c75b8877e815', 'concept_name': '次新股', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.5459, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 249, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 51, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["301526", "301568"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,410 - ERROR - 保存异动失败: 华为昇腾 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '1287f5d7e9279690c27706d91bf3000b', 'concept_name': '华为昇腾', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.5455, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 250, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 44, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["301125", "688031"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,415 - ERROR - 保存异动失败: 天太机器人 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '60bd1b38447575a0469438b78c702219', 'concept_name': '天太机器人', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.5369, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 3, 'prev_limit_up_count': 2, 'limit_up_delta': 1, 'rank_position': 251, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 29, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300503", "688160", "300570"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,421 - ERROR - 保存异动失败: 中俄贸易 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '58e58336a55c82ef6e6917be068b2892', 'concept_name': '中俄贸易', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.515, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 4, 'prev_limit_up_count': 3, 'limit_up_delta': 1, 'rank_position': 254, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 72, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300900", "000901", "601106", "301125"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,433 - ERROR - 保存异动失败: [二级] 信创与自主可控 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'df470e2b63c29363', 'concept_name': '[二级] 信创与自主可控', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.4885, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 9, 'prev_limit_up_count': 8, 'limit_up_delta': 1, 'rank_position': 262, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 286, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["301218", "003031", "300136", "688292", "688031", "002792", "301117", "688418", "688081"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,438 - ERROR - 保存异动失败: 整车央企重组 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'c70db13b77d825b0fb1b49cbceae1884', 'concept_name': '整车央企重组', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.4477, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 268, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 34, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["601399", "600302"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,463 - ERROR - 保存异动失败: [二级] 低空经济 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '77979791fa471afe', 'concept_name': '[二级] 低空经济', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.4077, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 7, 'prev_limit_up_count': 6, 'limit_up_delta': 1, 'rank_position': 279, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 349, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["300900", "002682", "000701", "002792", "002660", "002779", "000547"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,469 - ERROR - 保存异动失败: 玻璃基板 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '6a20715a0e59c87740a6bacf5dcdd797', 'concept_name': '玻璃基板', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.4031, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 1, 'prev_limit_up_count': 0, 'limit_up_delta': 1, 'rank_position': 281, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 49, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["301568"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,476 - ERROR - 保存异动失败: [二级] 机器人产业链 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '2e2d410b95c1d2af', 'concept_name': '[二级] 机器人产业链', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.3695, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 11, 'prev_limit_up_count': 10, 'limit_up_delta': 1, 'rank_position': 292, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 311, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["300136", "688160", "301125", "300421", "301526", "301529", "301005", "002779", "300503", "603015", "688577"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,482 - ERROR - 保存异动失败: 设备更新 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'e3f7d1e5f5ea7749d173f9c3d50b494a', 'concept_name': '设备更新', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.3619, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 3, 'prev_limit_up_count': 2, 'limit_up_delta': 1, 'rank_position': 294, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 70, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["600302", "301125", "603122"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,487 - ERROR - 保存异动失败: [二级] AI基础设施 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'a143fdd34018ba6f', 'concept_name': '[二级] AI基础设施', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.3613, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 30, 'prev_limit_up_count': 28, 'limit_up_delta': 2, 'rank_position': 295, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 875, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["002158", "688031", "600973", "300102", "688025", "688143", "002660", "301486", "300290", "605598", "300570", "688167", "688333" ... (23 characters truncated) ... 603386", "688102", "000811", "002792", "300751", "002565", "301526", "600218", "603122", "301183", "600366", "688383", "600105", "000070", "000547"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,497 - ERROR - 保存异动失败: 神经网络 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '40850ccab8cdabc6868888fd85808d0e', 'concept_name': '神经网络', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.3377, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 304, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 30, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["301568", "688081"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,504 - ERROR - 保存异动失败: [二级] 华为产业链 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'a7e8f7a5548a002c', 'concept_name': '[二级] 华为产业链', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.3065, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 17, 'prev_limit_up_count': 16, 'limit_up_delta': 1, 'rank_position': 312, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 498, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["300136", "920592", "688031", "300337", "600734", "300290", "605299", "003031", "688048", "688292", "002885", "002792", "301125", "688311", "301183", "600366", "688383"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,525 - ERROR - 保存异动失败: 工业母机 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '5374db0880e238ea30333257c7707b1f', 'concept_name': '工业母机', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.2962, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 315, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 47, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300503", "600302"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,533 - ERROR - 保存异动失败: [三级] 技术与生态 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '13c15b670f35e095', 'concept_name': '[三级] 技术与生态', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.2884, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 12, 'prev_limit_up_count': 11, 'limit_up_delta': 1, 'rank_position': 317, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 368, 'concept_type': 'lv3', 'extra_info': '{"limit_up_stocks": ["605299", "003031", "300136", "688292", "920592", "688031", "002885", "002792", "301125", "600734", "688383", "300290"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,559 - ERROR - 保存异动失败: [二级] 清洁能源 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '9c928eb8da4b6b99', 'concept_name': '[二级] 清洁能源', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.2863, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 15, 'prev_limit_up_count': 12, 'limit_up_delta': 3, 'rank_position': 318, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 486, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["002158", "920576", "600973", "002149", "688102", "000811", "688025", "300751", "600105", "601399", "300290", "605598", "603015", "601106", "688167"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,564 - ERROR - 保存异动失败: 谷歌概念 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'b0af7bb745342ddb825256f2e6dc07cc', 'concept_name': '谷歌概念', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.2787, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 3, 'prev_limit_up_count': 2, 'limit_up_delta': 1, 'rank_position': 320, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 74, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["688167", "300570", "000070"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,589 - ERROR - 保存异动失败: 机器人-神经网络 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'f550f0183f520fd786dca9c73ae3932d', 'concept_name': '机器人-神经网络', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.275, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 3, 'prev_limit_up_count': 2, 'limit_up_delta': 1, 'rank_position': 322, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 28, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["301568", "002779", "300503"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,594 - ERROR - 保存异动失败: 电子束光刻机“羲之” - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '27e92f2be93742878116b1099c6b9c84', 'concept_name': '电子束光刻机“羲之”', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.2563, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 1, 'prev_limit_up_count': 0, 'limit_up_delta': 1, 'rank_position': 329, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 22, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["301568"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,612 - ERROR - 保存异动失败: 华为Mate70手表 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '0370e22b8849e09f85840b850ff43ff9', 'concept_name': '华为Mate70手表', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.2508, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 4, 'prev_limit_up_count': 3, 'limit_up_delta': 1, 'rank_position': 332, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 95, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300136", "300337", "301183", "688311"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,619 - ERROR - 保存异动失败: [一级] 新能源与电力 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'cedd5e74f7572b73', 'concept_name': '[一级] 新能源与电力', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.2458, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 27, 'prev_limit_up_count': 25, 'limit_up_delta': 2, 'rank_position': 334, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 1054, 'concept_type': 'lv1', 'extra_info': '{"limit_up_stocks": ["300136", "002158", "002585", "600973", "002149", "300427", "688025", "300982", "002660", "603067", "688499", "300062", "300290", "605598", "688167", "301468", "920576", "002885", "688102", "000811", "002083", "300751", "002300", "600105", "601399", "603015", "601106"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,625 - ERROR - 保存异动失败: [一级] 数字经济与金融科技 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '4a5f6ee0da8e5802', 'concept_name': '[一级] 数字经济与金融科技', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.2312, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 11, 'prev_limit_up_count': 10, 'limit_up_delta': 1, 'rank_position': 343, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 440, 'concept_type': 'lv1', 'extra_info': '{"limit_up_stocks": ["301218", "688292", "920592", "688031", "002885", "300377", "002961", "600734", "688282", "688081", "300762"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,635 - ERROR - 保存异动失败: [一级] 机器人 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'a21131132b7ae4ba', 'concept_name': '[一级] 机器人', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.2178, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 20, 'prev_limit_up_count': 16, 'limit_up_delta': 4, 'rank_position': 351, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 671, 'concept_type': 'lv1', 'extra_info': '{"limit_up_stocks": ["300136", "688160", "002682", "300421", "301005", "300503", "688577", "300570", "002589", "002885", "301125", "605287", "301526", "301529", "600366", "600105", "002779", "603015", "301568", "600151"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,642 - ERROR - 保存异动失败: [一级] 消费电子 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '540bff395b82f2c9', 'concept_name': '[一级] 消费电子', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.1646, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 27, 'prev_limit_up_count': 26, 'limit_up_delta': 1, 'rank_position': 370, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 804, 'concept_type': 'lv1', 'extra_info': '{"limit_up_stocks": ["300136", "920592", "688031", "300337", "688025", "600734", "002660", "301486", "300290", "688323", "688167", "605299", "688333", "003031", "688048", "688292", "002885", "000859", "002792", "301125", "688311", "301529", "301183", "600366", "688383", "002779", "688661"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,651 - ERROR - 保存异动失败: [二级] 产业升级与制造 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '5bd863c58a5c878e', 'concept_name': '[二级] 产业升级与制造', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.15, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 4, 'prev_limit_up_count': 3, 'limit_up_delta': 1, 'rank_position': 378, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 175, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["600302", "603122", "300503", "301125"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,655 - ERROR - 保存异动失败: 新型离岸贸易 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'c4fa4f91f9fb625419f317690ea59800', 'concept_name': '新型离岸贸易', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.1436, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 380, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 40, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["000701", "000797"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,687 - ERROR - 保存异动失败: 跨境数据数据要素 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '209d102f174e89a82bfcabc05fbc7a94', 'concept_name': '跨境数据数据要素', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.141, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 5, 'prev_limit_up_count': 4, 'limit_up_delta': 1, 'rank_position': 381, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 173, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["600734", "600734", "688031", "688292", "301218"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,694 - ERROR - 保存异动失败: 杭州六小龙-群核科技 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '43db71cecaa96d2bd95738daa1ea5b29', 'concept_name': '杭州六小龙-群核科技', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.1323, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 3, 'prev_limit_up_count': 2, 'limit_up_delta': 1, 'rank_position': 388, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 84, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300749", "688539", "301218"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,700 - ERROR - 保存异动失败: [三级] 通用生态 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '858106800942746c', 'concept_name': '[三级] 通用生态', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.1289, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 6, 'prev_limit_up_count': 5, 'limit_up_delta': 1, 'rank_position': 390, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 292, 'concept_type': 'lv3', 'extra_info': '{"limit_up_stocks": ["301117", "300503", "000070", "688661", "300570", "688167"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,711 - ERROR - 保存异动失败: [二级] 人形机器人整机 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '5cec0ba414c30f8c', 'concept_name': '[二级] 人形机器人整机', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.1162, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 11, 'prev_limit_up_count': 9, 'limit_up_delta': 2, 'rank_position': 396, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 405, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["688160", "002682", "605287", "300421", "301529", "600105", "301005", "002779", "300503", "688577", "300570"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,744 - ERROR - 保存异动失败: [二级] 机器人软件与AI - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '2c291cfeba422359', 'concept_name': '[二级] 机器人软件与AI', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.1084, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 3, 'prev_limit_up_count': 2, 'limit_up_delta': 1, 'rank_position': 397, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 42, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["002779", "300503", "301568"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,767 - ERROR - 保存异动失败: 并购重组 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '91dd748e27516c02c3f85115558c0cfb', 'concept_name': '并购重组', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.0698, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 5, 'prev_limit_up_count': 4, 'limit_up_delta': 1, 'rank_position': 411, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 149, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["000905", "300102", "300290", "002779", "003031"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,792 - ERROR - 保存异动失败: 外贸出口 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '3f3c9e9578d4a4189f8846eb48f1e0a0', 'concept_name': '外贸出口', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.0469, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 3, 'prev_limit_up_count': 2, 'limit_up_delta': 1, 'rank_position': 424, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 40, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["002083", "605598", "000701"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,800 - ERROR - 保存异动失败: 松延动力机器人 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'a84916ad3c4a624071692dc8d59f0c33', 'concept_name': '松延动力机器人', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.0434, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 1, 'prev_limit_up_count': 0, 'limit_up_delta': 1, 'rank_position': 426, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 31, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["605287"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,807 - ERROR - 保存异动失败: 化工有色元素周期表 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'e2e105e80fa3fe99d2d18494ae8dad9c', 'concept_name': '化工有色元素周期表', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.0119, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 443, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 122, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["002578", "603067"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,813 - ERROR - 保存异动失败: [二级] 并购重组 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '128a4a614ab175f9', 'concept_name': '[二级] 并购重组', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.002, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 13, 'prev_limit_up_count': 11, 'limit_up_delta': 2, 'rank_position': 446, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 555, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["300136", "688160", "300102", "601696", "000035", "301486", "300290", "003031", "600302", "000905", "301183", "002779", "688661"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,840 - ERROR - 保存异动失败: 华为鸿蒙 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '70c532af1fb28f41d335e8ed8fab7039', 'concept_name': '华为鸿蒙', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.9991, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 1, 'prev_limit_up_count': 0, 'limit_up_delta': 1, 'rank_position': 450, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 65, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300290"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,858 - ERROR - 保存异动失败: 央国企AI一张图 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '5072e741fffe4d28ad32bd51710390de', 'concept_name': '央国企AI一张图', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.9947, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 1, 'prev_limit_up_count': 0, 'limit_up_delta': 1, 'rank_position': 452, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 90, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300290"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,875 - ERROR - 保存异动失败: [二级] 数据要素 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '8cac77fc45376080', 'concept_name': '[二级] 数据要素', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.985, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 6, 'prev_limit_up_count': 5, 'limit_up_delta': 1, 'rank_position': 459, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 305, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["301218", "688292", "688031", "002885", "600734", "688282"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,882 - ERROR - 保存异动失败: [二级] 国企改革与市值管理 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '31eaecf0a6fa349b', 'concept_name': '[二级] 国企改革与市值管理', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.9848, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 21, 'prev_limit_up_count': 16, 'limit_up_delta': 5, 'rank_position': 460, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 821, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["000901", "002682", "000663", "000632", "600734", "300290", "002679", "003031", "600343", "000797", "600302", "000701", "000859", "300300", "000905", "600218", "601399", "000070", "000547", "601106", "600151"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,899 - ERROR - 保存异动失败: [二级] AI综合与趋势 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '91ac773adb0a2fc8', 'concept_name': '[二级] AI综合与趋势', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.9603, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 4, 'prev_limit_up_count': 3, 'limit_up_delta': 1, 'rank_position': 471, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 190, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["301526", "002158", "688031", "000811"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,905 - ERROR - 保存异动失败: DeepSeek - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '264d9bb481d458fdda428e70a2bfe2dd', 'concept_name': 'DeepSeek', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.9386, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 3, 'prev_limit_up_count': 2, 'limit_up_delta': 1, 'rank_position': 477, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 53, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300290", "603122", "688031"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,914 - ERROR - 保存异动失败: 政务云政务IT - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'dcd9ab454e81d760bec327f3af236501', 'concept_name': '政务云政务IT', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.9367, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 1, 'prev_limit_up_count': 0, 'limit_up_delta': 1, 'rank_position': 478, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 55, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["301218"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,934 - ERROR - 保存异动失败: [一级] 政策与主题 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'c69c28ca187d7096', 'concept_name': '[一级] 政策与主题', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.9353, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 54, 'prev_limit_up_count': 48, 'limit_up_delta': 6, 'rank_position': 480, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 2332, 'concept_type': 'lv1', 'extra_info': '{"limit_up_stocks": ["301218", "300102", "600734", "003031", "301125", "002565", "000905", "301183", "601399", "002779", "688160", "002682", "688025" ... (263 characters truncated) ... 002149", "001360", "000632", "002679", "688292", "002589", "920576", "000859", "002702", "301117", "603122", "603696", "601106", "688661", "600151"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,940 - ERROR - 保存异动失败: [二级] 国家战略 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '176e364167a853eb', 'concept_name': '[二级] 国家战略', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.9087, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 9, 'prev_limit_up_count': 8, 'limit_up_delta': 1, 'rank_position': 489, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 369, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["920576", "002149", "688102", "001360", "688025", "002565", "002300", "603696", "688081"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,964 - ERROR - 保存异动失败: [二级] 区域发展与自贸区 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '4153baee9169db33', 'concept_name': '[二级] 区域发展与自贸区', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.9039, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 6, 'prev_limit_up_count': 5, 'limit_up_delta': 1, 'rank_position': 492, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 254, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["000632", "688292", "000797", "000701", "603696", "600693"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,970 - ERROR - 保存异动失败: 上海自贸区 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '7619efd113c2722b97fc339a576056ec', 'concept_name': '上海自贸区', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.8776, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 1, 'prev_limit_up_count': 0, 'limit_up_delta': 1, 'rank_position': 507, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 62, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["000701"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:04,997 - ERROR - 保存异动失败: [二级] AI模型与软件 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '2c814fef42bd1255', 'concept_name': '[二级] AI模型与软件', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.8513, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 12, 'prev_limit_up_count': 11, 'limit_up_delta': 1, 'rank_position': 519, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 341, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["688031", "605303", "603122", "300940", "301486", "688383", "002093", "300290", "000070", "688081", "688661", "300570"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:05,002 - ERROR - 保存异动失败: 云手机 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '226e2f919636374374b636ca926974ee', 'concept_name': '云手机', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.8501, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 1, 'prev_limit_up_count': 0, 'limit_up_delta': 1, 'rank_position': 520, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 21, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["600302"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:05,009 - ERROR - 保存异动失败: 科技重组 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '94715a411814ca5d1cba1553ea302fe8', 'concept_name': '科技重组', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.8428, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 521, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 53, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300290", "301486"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:05,029 - ERROR - 保存异动失败: 央国企 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '65ce42095ba531cf3afd04abb20868c2', 'concept_name': '央国企', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.8052, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 0, 'limit_up_delta': 2, 'rank_position': 535, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 149, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["000797", "000663"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:05,043 - ERROR - 保存异动失败: 半导体设备 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '7cf6b644fb26f80f3941b0cbcebb4e07', 'concept_name': '半导体设备', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.8025, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 3, 'prev_limit_up_count': 1, 'limit_up_delta': 2, 'rank_position': 536, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 72, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["688661", "688383", "002158"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:05,069 - ERROR - 保存异动失败: 功率半导体 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'c2354cda068646013b9f8ecc8daf955a', 'concept_name': '功率半导体', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.7791, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 547, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 30, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["600302", "301183"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:05,081 - ERROR - 保存异动失败: [二级] 贸易政策与关系 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'cb524ffba0dd36d0', 'concept_name': '[二级] 贸易政策与关系', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.7596, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 12, 'prev_limit_up_count': 11, 'limit_up_delta': 1, 'rank_position': 554, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 614, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["002158", "000901", "300900", "600973", "000632", "603067", "688323", "300377", "301125", "300751", "601106", "688661"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:05,105 - ERROR - 保存异动失败: 华为海思星闪 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'b6ed4f1111b7800992b9b5614047109e', 'concept_name': '华为海思星闪', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.759, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 556, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 65, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["688383", "688031"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:05,122 - ERROR - 保存异动失败: 杭州算力大会 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'ee44bc8a1b4434d98631bda794220959', 'concept_name': '杭州算力大会', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.7345, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 1, 'prev_limit_up_count': 0, 'limit_up_delta': 1, 'rank_position': 564, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 55, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300290"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:05,131 - ERROR - 保存异动失败: [一级] 全球宏观与贸易 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '8904f63b21322564', 'concept_name': '[一级] 全球宏观与贸易', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.6868, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 16, 'prev_limit_up_count': 15, 'limit_up_delta': 1, 'rank_position': 587, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 886, 'concept_type': 'lv1', 'extra_info': '{"limit_up_stocks": ["002158", "000901", "300900", "002682", "600973", "000632", "603067", "688323", "300123", "600227", "300377", "301125", "300751", "000905", "601106", "688661"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:05,153 - ERROR - 保存异动失败: [二级] 半导体设备 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '2c5cc752e1e328ce', 'concept_name': '[二级] 半导体设备', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.67, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 5, 'prev_limit_up_count': 2, 'limit_up_delta': 3, 'rank_position': 592, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 170, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["002158", "688383", "688661", "688167", "301568"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:05,160 - ERROR - 保存异动失败: [二级] 芯片设计与制造 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'cad6c2087d1d396d', 'concept_name': '[二级] 芯片设计与制造', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.6532, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 6, 'prev_limit_up_count': 5, 'limit_up_delta': 1, 'rank_position': 600, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 244, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["300751", "003031", "300136", "600302", "603122", "301183"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:05,167 - ERROR - 保存异动失败: 高温概念 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '7cb8e6347cdfdc1e834d60887ef30db4', 'concept_name': '高温概念', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.6528, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 3, 'prev_limit_up_count': 2, 'limit_up_delta': 1, 'rank_position': 601, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 74, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["000811", "002158", "600105"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:05,174 - ERROR - 保存异动失败: [一级] 半导体 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '1d2cfd648c370f3e', 'concept_name': '[一级] 半导体', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.6494, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 18, 'prev_limit_up_count': 14, 'limit_up_delta': 4, 'rank_position': 604, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 725, 'concept_type': 'lv1', 'extra_info': '{"limit_up_stocks": ["300136", "002158", "688031", "001360", "688143", "688323", "688167", "003031", "688048", "600302", "000859", "300751", "301117", "603122", "301183", "688383", "688661", "301568"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:05,193 - ERROR - 保存异动失败: [二级] 先进封装 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'c9bee33f0408abfd', 'concept_name': '[二级] 先进封装', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.6381, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 4, 'prev_limit_up_count': 2, 'limit_up_delta': 2, 'rank_position': 613, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 123, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["300751", "688383", "688661", "301568"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:05,206 - ERROR - 保存异动失败: 城市旧改 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '93da0e46ef34f13e0cc170b6e7d195b7', 'concept_name': '城市旧改', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.5557, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 655, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 24, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["605287", "000632"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:05,211 - ERROR - 保存异动失败: 海事反制 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': '47c91f00b40c8a204ca4e693ac52e009', 'concept_name': '海事反制', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.5077, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 1, 'prev_limit_up_count': 0, 'limit_up_delta': 1, 'rank_position': 675, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 53, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300123"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:05,218 - ERROR - 保存异动失败: 医保DRGDIP - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'a52dfb0f6f112392b3ef1ee62fc3950e', 'concept_name': '医保DRGDIP', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.4841, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 688, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 33, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300290", "301117"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:05,225 - ERROR - 保存异动失败: 国产半导体 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'e51800fd2ea5a195005e4af9f0c9b36d', 'concept_name': '国产半导体', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.4012, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 5, 'prev_limit_up_count': 4, 'limit_up_delta': 1, 'rank_position': 731, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 137, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["003031", "688661", "688048", "002158", "000859"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:48:05,230 - ERROR - 保存异动失败: 房地产产业链 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") +[SQL: + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, + %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, + %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, + %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, + %(index_code)s, %(index_price)s, %(index_change_pct)s, + %(stock_count)s, %(concept_type)s, %(extra_info)s) + ] +[parameters: {'concept_id': 'cf102e739fa64e34ccf2c412cbd67b50', 'concept_name': '房地产产业链', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.3825, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 735, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 143, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["000797", "301526"]}'}] +(Background on this error at: https://sqlalche.me/e/14/e3q8) +2025-12-08 15:49:46,800 - INFO - ============================================================ +2025-12-08 15:49:46,800 - INFO - 🔄 开始回测: 2025-12-08 +2025-12-08 15:49:46,802 - INFO - ============================================================ +2025-12-08 15:49:46,898 - INFO - 已清除 2025-12-08 的已有数据 +2025-12-08 15:49:46,898 - INFO - 加载概念数据... +2025-12-08 15:49:47,114 - INFO - POST http://222.128.1.157:19200/concept_library_v3/_search?scroll=2m [status:200 duration:0.215s] +2025-12-08 15:49:47,322 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.204s] +2025-12-08 15:49:47,547 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.188s] +2025-12-08 15:49:47,752 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.202s] +2025-12-08 15:49:47,944 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.189s] +2025-12-08 15:49:48,125 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.176s] +2025-12-08 15:49:48,304 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.176s] +2025-12-08 15:49:48,474 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.166s] +2025-12-08 15:49:48,601 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.123s] +2025-12-08 15:49:48,610 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.006s] +2025-12-08 15:49:48,618 - INFO - DELETE http://222.128.1.157:19200/_search/scroll [status:200 duration:0.007s] +2025-12-08 15:49:48,618 - INFO - 获取到 865 个叶子概念 +2025-12-08 15:49:48,624 - INFO - 生成了 103 个母概念 +2025-12-08 15:49:48,625 - INFO - 总计 968 个概念 +2025-12-08 15:49:48,627 - INFO - 监控 5938 只股票 +2025-12-08 15:49:48,724 - INFO - 获取到 5132 个基准价格 +2025-12-08 15:49:48,733 - INFO - 指数昨收价: 3902.8076 +2025-12-08 15:49:48,844 - INFO - 找到 241 个分钟时间点 +2025-12-08 15:50:04,980 - INFO - 进度: 30/241 (12%), 已检测到 980 条异动 +2025-12-08 15:50:19,301 - INFO - 进度: 60/241 (24%), 已检测到 1644 条异动 +2025-12-08 15:50:33,414 - INFO - 进度: 90/241 (37%), 已检测到 2142 条异动 +2025-12-08 15:50:48,181 - INFO - 进度: 120/241 (49%), 已检测到 3331 条异动 +2025-12-08 15:50:59,775 - INFO - 进度: 150/241 (62%), 已检测到 4678 条异动 +2025-12-08 15:51:08,681 - INFO - 进度: 180/241 (74%), 已检测到 5498 条异动 +2025-12-08 15:51:22,318 - INFO - 进度: 210/241 (87%), 已检测到 7315 条异动 +2025-12-08 15:51:30,566 - INFO - 进度: 240/241 (99%), 已检测到 7969 条异动 +2025-12-08 15:51:31,051 - INFO - ============================================================ +2025-12-08 15:51:31,052 - INFO - ✅ 回测完成! +2025-12-08 15:51:31,053 - INFO - 处理分钟数: 241 +2025-12-08 15:51:31,058 - INFO - 检测到异动: 8043 条 +2025-12-08 15:51:31,058 - INFO - ============================================================ +2025-12-08 15:55:59,803 - INFO - ============================================================ +2025-12-08 15:55:59,803 - INFO - 🔄 开始回测: 2025-12-08 +2025-12-08 15:55:59,805 - INFO - ============================================================ diff --git a/concept_alert_alpha.py b/concept_alert_alpha.py new file mode 100644 index 00000000..0e8c8f54 --- /dev/null +++ b/concept_alert_alpha.py @@ -0,0 +1,1078 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +概念异动检测 - 基于超额收益(Alpha)的方法 + +核心思想： +- 异动 = 概念表现与市场整体的显著偏离 +- Alpha = 概念涨幅 - 大盘涨幅（超额收益） +- 用 Z-Score 衡量 Alpha 是否显著偏离历史正常范围 + +优点： +- 自适应：不同概念有不同波动率，自动适应 +- 相对比较：不看绝对涨跌，看相对表现 +- 无需规则维护：统计方法自动学习"正常范围" +- 通用：可迁移到其他市场 +""" + +import pandas as pd +import numpy as np +from datetime import datetime, timedelta +from sqlalchemy import create_engine, text +from elasticsearch import Elasticsearch +from clickhouse_driver import Client +from collections import deque, defaultdict +import time +import logging +import json +import os +import hashlib +import argparse +from typing import Dict, List, Optional, Tuple +from dataclasses import dataclass, field + +# ==================== 配置 ==================== + +MYSQL_ENGINE = create_engine( + "mysql+pymysql://root:Zzl5588161!@222.128.1.157:33060/stock", + echo=False +) + +ES_CLIENT = Elasticsearch(['http://222.128.1.157:19200']) +INDEX_NAME = 'concept_library_v3' + +CLICKHOUSE_CONFIG = { + 'host': '222.128.1.157', + 'port': 18000, + 'user': 'default', + 'password': 'Zzl33818!', + 'database': 'stock' +} + +HIERARCHY_FILE = 'concept_hierarchy_v3.json' +REFERENCE_INDEX = '000001.SH' + +# ==================== Alpha 异动检测配置 ==================== + +ALPHA_CONFIG = { + # Z-Score 阈值 + 'zscore_threshold': 2.0, # |Z| > 2.0 视为异动（降低阈值） + + # 绝对 Alpha 阈值（当历史数据不足时使用） + 'absolute_alpha_threshold': 1.5, # |Alpha| > 1.5% 视为异动 + + # 历史窗口 + 'history_window': 60, # 保留最近60个数据点用于计算均值/标准差 + 'min_history': 5, # 最少需要5个数据点才用Z-Score（降低要求） + + # 冷却时间 + 'cooldown_minutes': 8, # 同一概念触发异动后的冷却时间 + + # 显示控制 + 'max_alerts_per_minute': 15, # 每分钟最多显示的异动数 + 'min_alpha_abs': 0.5, # 最小超额收益绝对值（过滤微小波动） + + # 重要性评分权重 + 'importance_weights': { + 'alpha_zscore': 0.35, # Alpha Z-Score 绝对值 + 'alpha_abs': 0.25, # 超额收益绝对值 + 'rank_in_minute': 0.20, # 当分钟内的排名 + 'limit_up_count': 0.20, # 涨停数 + } +} + +# ==================== 日志配置 ==================== + +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(levelname)s - %(message)s', + handlers=[ + logging.FileHandler(f'concept_alert_alpha_{datetime.now().strftime("%Y%m%d")}.log', encoding='utf-8'), + logging.StreamHandler() + ] +) +logger = logging.getLogger(__name__) + +# ==================== 数据结构 ==================== + +@dataclass +class AlphaStats: + """概念的Alpha统计信息""" + history: deque = field(default_factory=lambda: deque(maxlen=ALPHA_CONFIG['history_window'])) + mean: float = 0.0 + std: float = 1.0 + last_update: datetime = None + + def update(self, alpha: float, timestamp: datetime): + """更新统计""" + self.history.append(alpha) + self.last_update = timestamp + + if len(self.history) >= 2: + self.mean = np.mean(self.history) + self.std = max(np.std(self.history), 0.1) # 避免除零 + + def get_zscore(self, alpha: float) -> float: + """计算Z-Score""" + if len(self.history) < ALPHA_CONFIG['min_history']: + return 0.0 # 数据不足，不触发 + return (alpha - self.mean) / self.std + + def is_ready(self) -> bool: + """是否有足够数据进行检测""" + return len(self.history) >= ALPHA_CONFIG['min_history'] + + +# ==================== 全局变量 ==================== + +ch_client = None + +# 每个概念的Alpha统计 +alpha_stats: Dict[str, AlphaStats] = defaultdict(AlphaStats) + +# 冷却记录 +cooldown_cache: Dict[str, datetime] = {} + +# 当前分钟的数据缓存（用于计算排名） +current_minute_data: List[dict] = [] + + +def get_ch_client(): + global ch_client + if ch_client is None: + ch_client = Client(**CLICKHOUSE_CONFIG) + return ch_client + + +def generate_id(name: str) -> str: + return hashlib.md5(name.encode('utf-8')).hexdigest()[:16] + + +def code_to_ch_format(code: str) -> str: + if not code or len(code) != 6 or not code.isdigit(): + return None + if code.startswith('6'): + return f"{code}.SH" + elif code.startswith('0') or code.startswith('3'): + return f"{code}.SZ" + else: + return f"{code}.BJ" + + +# ==================== 概念数据获取 ==================== + +def get_all_concepts(): + """从ES获取所有叶子概念""" + concepts = [] + + query = { + "query": {"match_all": {}}, + "size": 100, + "_source": ["concept_id", "concept", "stocks"] + } + + resp = ES_CLIENT.search(index=INDEX_NAME, body=query, scroll='2m') + scroll_id = resp['_scroll_id'] + hits = resp['hits']['hits'] + + while len(hits) > 0: + for hit in hits: + source = hit['_source'] + concept_info = { + 'concept_id': source.get('concept_id'), + 'concept_name': source.get('concept'), + 'stocks': [], + 'concept_type': 'leaf' + } + + if 'stocks' in source and isinstance(source['stocks'], list): + for stock in source['stocks']: + if isinstance(stock, dict) and 'code' in stock and stock['code']: + concept_info['stocks'].append(stock['code']) + + if concept_info['stocks']: + concepts.append(concept_info) + + resp = ES_CLIENT.scroll(scroll_id=scroll_id, scroll='2m') + scroll_id = resp['_scroll_id'] + hits = resp['hits']['hits'] + + ES_CLIENT.clear_scroll(scroll_id=scroll_id) + return concepts + + +def load_hierarchy_concepts(leaf_concepts: list) -> list: + """加载层级结构，生成母概念""" + hierarchy_path = os.path.join(os.path.dirname(__file__), HIERARCHY_FILE) + if not os.path.exists(hierarchy_path): + return [] + + with open(hierarchy_path, 'r', encoding='utf-8') as f: + hierarchy_data = json.load(f) + + concept_to_stocks = {c['concept_name']: set(c['stocks']) for c in leaf_concepts} + parent_concepts = [] + + for lv1 in hierarchy_data.get('hierarchy', []): + lv1_name = lv1.get('lv1', '') + lv1_stocks = set() + + for child in lv1.get('children', []): + lv2_name = child.get('lv2', '') + lv2_stocks = set() + + if 'children' in child: + for lv3_child in child.get('children', []): + lv3_name = lv3_child.get('lv3', '') + lv3_stocks = set() + + for concept_name in lv3_child.get('concepts', []): + if concept_name in concept_to_stocks: + lv3_stocks.update(concept_to_stocks[concept_name]) + + if lv3_stocks: + parent_concepts.append({ + 'concept_id': generate_id(f"lv3_{lv3_name}"), + 'concept_name': f"[三级] {lv3_name}", + 'stocks': list(lv3_stocks), + 'concept_type': 'lv3' + }) + lv2_stocks.update(lv3_stocks) + else: + for concept_name in child.get('concepts', []): + if concept_name in concept_to_stocks: + lv2_stocks.update(concept_to_stocks[concept_name]) + + if lv2_stocks: + parent_concepts.append({ + 'concept_id': generate_id(f"lv2_{lv2_name}"), + 'concept_name': f"[二级] {lv2_name}", + 'stocks': list(lv2_stocks), + 'concept_type': 'lv2' + }) + lv1_stocks.update(lv2_stocks) + + if lv1_stocks: + parent_concepts.append({ + 'concept_id': generate_id(f"lv1_{lv1_name}"), + 'concept_name': f"[一级] {lv1_name}", + 'stocks': list(lv1_stocks), + 'concept_type': 'lv1' + }) + + return parent_concepts + + +# ==================== 价格数据获取 ==================== + +def get_base_prices(stock_codes: list, current_date: str) -> dict: + """获取昨收价""" + if not stock_codes: + return {} + + valid_codes = [code for code in stock_codes if code and len(code) == 6 and code.isdigit()] + if not valid_codes: + return {} + + stock_codes_str = "','".join(valid_codes) + + query = f""" + SELECT SECCODE, F002N + FROM ea_trade + WHERE SECCODE IN ('{stock_codes_str}') + AND TRADEDATE = ( + SELECT MAX(TRADEDATE) FROM ea_trade WHERE TRADEDATE < '{current_date}' + ) + AND F002N IS NOT NULL AND F002N > 0 + """ + + try: + with MYSQL_ENGINE.connect() as conn: + result = conn.execute(text(query)) + return {row[0]: float(row[1]) for row in result if row[1] and float(row[1]) > 0} + except Exception as e: + logger.error(f"获取基准价格失败: {e}") + return {} + + +def get_latest_prices(stock_codes: list) -> dict: + """获取最新价格""" + if not stock_codes: + return {} + + client = get_ch_client() + + ch_codes = [] + code_mapping = {} + for code in stock_codes: + ch_code = code_to_ch_format(code) + if ch_code: + ch_codes.append(ch_code) + code_mapping[ch_code] = code + + if not ch_codes: + return {} + + ch_codes_str = "','".join(ch_codes) + + query = f""" + SELECT code, close, timestamp + FROM ( + SELECT code, close, timestamp, + ROW_NUMBER() OVER (PARTITION BY code ORDER BY timestamp DESC) as rn + FROM stock_minute + WHERE code IN ('{ch_codes_str}') + AND toDate(timestamp) = today() + ) + WHERE rn = 1 + """ + + try: + result = client.execute(query) + prices = {} + for row in result: + ch_code, close, ts = row + if close and close > 0: + pure_code = code_mapping.get(ch_code) + if pure_code: + prices[pure_code] = {'close': float(close), 'timestamp': ts} + return prices + except Exception as e: + logger.error(f"获取最新价格失败: {e}") + return {} + + +def get_prices_at_time(stock_codes: list, timestamp: datetime) -> dict: + """获取指定时间的价格""" + if not stock_codes: + return {} + + client = get_ch_client() + + ch_codes = [] + code_mapping = {} + for code in stock_codes: + ch_code = code_to_ch_format(code) + if ch_code: + ch_codes.append(ch_code) + code_mapping[ch_code] = code + + if not ch_codes: + return {} + + ch_codes_str = "','".join(ch_codes) + ts_str = timestamp.strftime('%Y-%m-%d %H:%M:%S') + + query = f""" + SELECT code, close, timestamp + FROM stock_minute + WHERE code IN ('{ch_codes_str}') + AND timestamp = '{ts_str}' + """ + + try: + result = client.execute(query) + prices = {} + for row in result: + ch_code, close, ts = row + if close and close > 0: + pure_code = code_mapping.get(ch_code) + if pure_code: + prices[pure_code] = {'close': float(close), 'timestamp': ts} + return prices + except Exception as e: + logger.error(f"获取历史价格失败: {e}") + return {} + + +def get_index_change(index_code: str, trade_date: str, timestamp: datetime = None) -> Optional[float]: + """获取指数涨跌幅""" + client = get_ch_client() + + try: + # 获取昨收 + code_no_suffix = index_code.split('.')[0] + with MYSQL_ENGINE.connect() as conn: + prev_result = conn.execute(text(""" + SELECT F006N FROM ea_exchangetrade + WHERE INDEXCODE = :code AND TRADEDATE < :today + ORDER BY TRADEDATE DESC LIMIT 1 + """), {'code': code_no_suffix, 'today': trade_date}).fetchone() + + if not prev_result or not prev_result[0]: + return None + prev_close = float(prev_result[0]) + + # 获取当前价格 + if timestamp: + ts_str = timestamp.strftime('%Y-%m-%d %H:%M:%S') + query = f""" + SELECT close FROM index_minute + WHERE code = '{index_code}' AND timestamp = '{ts_str}' + LIMIT 1 + """ + else: + query = f""" + SELECT close FROM index_minute + WHERE code = '{index_code}' AND toDate(timestamp) = today() + ORDER BY timestamp DESC LIMIT 1 + """ + + result = client.execute(query) + if not result: + return None + + current_price = float(result[0][0]) + return (current_price - prev_close) / prev_close * 100 + + except Exception as e: + logger.error(f"获取指数涨跌幅失败: {e}") + return None + + +def get_index_data(index_code: str, trade_date: str, timestamp: datetime = None) -> Optional[dict]: + """获取指数完整数据""" + client = get_ch_client() + + try: + code_no_suffix = index_code.split('.')[0] + with MYSQL_ENGINE.connect() as conn: + prev_result = conn.execute(text(""" + SELECT F006N FROM ea_exchangetrade + WHERE INDEXCODE = :code AND TRADEDATE < :today + ORDER BY TRADEDATE DESC LIMIT 1 + """), {'code': code_no_suffix, 'today': trade_date}).fetchone() + + if not prev_result or not prev_result[0]: + return None + prev_close = float(prev_result[0]) + + if timestamp: + ts_str = timestamp.strftime('%Y-%m-%d %H:%M:%S') + query = f""" + SELECT close, timestamp FROM index_minute + WHERE code = '{index_code}' AND timestamp = '{ts_str}' + LIMIT 1 + """ + else: + query = f""" + SELECT close, timestamp FROM index_minute + WHERE code = '{index_code}' AND toDate(timestamp) = today() + ORDER BY timestamp DESC LIMIT 1 + """ + + result = client.execute(query) + if not result: + return None + + current_price, ts = result[0] + change_pct = (float(current_price) - prev_close) / prev_close * 100 + + return { + 'code': index_code, + 'price': float(current_price), + 'prev_close': prev_close, + 'change_pct': round(change_pct, 4), + 'timestamp': ts + } + + except Exception as e: + logger.error(f"获取指数数据失败: {e}") + return None + + +# ==================== 概念统计计算 ==================== + +def calculate_concept_stats(concepts: list, stock_changes: dict, index_change: float) -> list: + """ + 计算概念统计，包含超额收益(Alpha) + Alpha = 概念涨幅 - 指数涨幅 + """ + stats = [] + + for concept in concepts: + concept_id = concept['concept_id'] + concept_name = concept['concept_name'] + stock_codes = concept['stocks'] + concept_type = concept.get('concept_type', 'leaf') + + changes = [] + limit_up_count = 0 + limit_down_count = 0 + + for code in stock_codes: + if code in stock_changes: + change_pct = stock_changes[code] + changes.append(change_pct) + + if change_pct >= 9.8: + limit_up_count += 1 + elif change_pct <= -9.8: + limit_down_count += 1 + + if not changes: + continue + + avg_change = np.mean(changes) + + # 核心：计算超额收益 Alpha + alpha = avg_change - index_change + + stats.append({ + 'concept_id': concept_id, + 'concept_name': concept_name, + 'change_pct': round(avg_change, 4), + 'alpha': round(alpha, 4), # 超额收益 + 'index_change': round(index_change, 4), + 'stock_count': len(changes), + 'concept_type': concept_type, + 'limit_up_count': limit_up_count, + 'limit_down_count': limit_down_count, + }) + + # 按Alpha排序 + stats.sort(key=lambda x: x['alpha'], reverse=True) + for i, item in enumerate(stats): + item['alpha_rank'] = i + 1 + + return stats + + +# ==================== Alpha 异动检测 ==================== + +def check_cooldown(concept_id: str, current_time: datetime) -> bool: + """检查冷却""" + if concept_id in cooldown_cache: + last_alert = cooldown_cache[concept_id] + if current_time - last_alert < timedelta(minutes=ALPHA_CONFIG['cooldown_minutes']): + return True + return False + + +def set_cooldown(concept_id: str, current_time: datetime): + """设置冷却""" + cooldown_cache[concept_id] = current_time + + +def calculate_importance( + alpha_zscore: float, + alpha: float, + alpha_rank: int, + total_concepts: int, + limit_up_count: int +) -> float: + """计算重要性评分""" + weights = ALPHA_CONFIG['importance_weights'] + + # Z-Score 分数（归一化到0-1） + zscore_score = min(abs(alpha_zscore) / 5.0, 1.0) + + # Alpha 绝对值分数 + alpha_score = min(abs(alpha) / 3.0, 1.0) + + # 排名分数（越靠前/越靠后越重要） + # alpha_rank 小表示强势，alpha_rank 大表示弱势 + rank_ratio = alpha_rank / max(total_concepts, 1) + rank_score = max(1 - rank_ratio, rank_ratio) # 两端都重要 + + # 涨停分数 + limit_score = min(limit_up_count / 3.0, 1.0) + + total = ( + weights['alpha_zscore'] * zscore_score + + weights['alpha_abs'] * alpha_score + + weights['rank_in_minute'] * rank_score + + weights['limit_up_count'] * limit_score + ) + + return round(total, 4) + + +def detect_alpha_alerts( + stats: list, + index_data: dict, + trade_date: str, + current_time: datetime +) -> list: + """ + 基于Alpha Z-Score的异动检测 + + 检测逻辑： + 1. 如果有足够历史数据（>= min_history）：用 Z-Score 判断 + 2. 如果历史数据不足：用绝对 Alpha 阈值判断 + """ + global alpha_stats + + alerts = [] + total_concepts = len(stats) + + for stat in stats: + concept_id = stat['concept_id'] + concept_name = stat['concept_name'] + alpha = stat['alpha'] + change_pct = stat['change_pct'] + alpha_rank = stat['alpha_rank'] + limit_up_count = stat['limit_up_count'] + limit_down_count = stat['limit_down_count'] + + # 更新该概念的Alpha统计 + concept_stats = alpha_stats[concept_id] + + # 计算Z-Score（在更新前计算，用历史数据） + alpha_zscore = concept_stats.get_zscore(alpha) + + # 更新统计 + concept_stats.update(alpha, current_time) + + # Alpha 太小，不值得关注 + if abs(alpha) < ALPHA_CONFIG['min_alpha_abs']: + continue + + # 检查冷却 + if check_cooldown(concept_id, current_time): + continue + + # 判断是否触发异动 + is_alert = False + trigger_reason = "" + + if concept_stats.is_ready(): + # 方式1：有足够历史数据，用 Z-Score + if abs(alpha_zscore) >= ALPHA_CONFIG['zscore_threshold']: + is_alert = True + trigger_reason = f"Z={alpha_zscore:.2f}" + else: + # 方式2：历史数据不足，用绝对 Alpha 阈值 + if abs(alpha) >= ALPHA_CONFIG['absolute_alpha_threshold']: + is_alert = True + trigger_reason = f"Alpha={alpha:+.2f}%" + alpha_zscore = alpha / 0.5 # 估算一个Z值（假设std=0.5） + + if not is_alert: + continue + + # 判断异动类型 + if alpha > 0: + alert_type = 'alpha_surge' # 超额上涨 + else: + alert_type = 'alpha_drop' # 超额下跌 + + # 计算重要性 + importance = calculate_importance( + alpha_zscore, alpha, alpha_rank, total_concepts, limit_up_count + ) + + alert = { + 'concept_id': concept_id, + 'concept_name': concept_name, + 'alert_type': alert_type, + 'alert_time': current_time, + 'trade_date': trade_date, + 'change_pct': change_pct, + 'alpha': alpha, + 'alpha_zscore': round(alpha_zscore, 4), + 'alpha_mean': round(concept_stats.mean, 4), + 'alpha_std': round(concept_stats.std, 4), + 'index_change_pct': stat['index_change'], + 'alpha_rank': alpha_rank, + 'limit_up_count': limit_up_count, + 'limit_down_count': limit_down_count, + 'stock_count': stat['stock_count'], + 'concept_type': stat['concept_type'], + 'importance_score': importance, + 'index_code': REFERENCE_INDEX, + 'index_price': index_data['price'] if index_data else None, + } + + alerts.append(alert) + set_cooldown(concept_id, current_time) + + # 日志 + direction = "📈 超额上涨" if alert_type == 'alpha_surge' else "📉 超额下跌" + logger.info( + f"{direction}: {concept_name} " + f"Alpha={alpha:+.2f}% ({trigger_reason}) " + f"概念{change_pct:+.2f}% vs 大盘{stat['index_change']:+.2f}%" + ) + + # 按重要性排序，限制数量 + alerts.sort(key=lambda x: x['importance_score'], reverse=True) + return alerts[:ALPHA_CONFIG['max_alerts_per_minute']] + + +# ==================== 数据持久化 ==================== + +def save_alerts_to_mysql(alerts: list): + """保存异动到MySQL""" + if not alerts: + return 0 + + saved = 0 + with MYSQL_ENGINE.begin() as conn: + for alert in alerts: + try: + # 映射 alert_type 到数据库格式 + db_alert_type = 'surge_up' if alert['alert_type'] == 'alpha_surge' else 'surge_down' + + insert_sql = text(""" + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, zscore, importance_score, extra_info) + VALUES + (:concept_id, :concept_name, :alert_time, :alert_type, :trade_date, + :change_pct, :prev_change_pct, :change_delta, + :limit_up_count, 0, 0, + :rank_position, NULL, NULL, + :index_code, :index_price, :index_change_pct, + :stock_count, :concept_type, :zscore, :importance_score, :extra_info) + """) + + params = { + 'concept_id': alert['concept_id'], + 'concept_name': alert['concept_name'], + 'alert_time': alert['alert_time'], + 'alert_type': db_alert_type, + 'trade_date': alert['trade_date'], + 'change_pct': alert['change_pct'], + 'prev_change_pct': alert['index_change_pct'], # 用大盘涨幅作为参照 + 'change_delta': alert['alpha'], # Alpha 作为变化量 + 'limit_up_count': alert['limit_up_count'], + 'rank_position': alert['alpha_rank'], + 'index_code': alert['index_code'], + 'index_price': alert['index_price'], + 'index_change_pct': alert['index_change_pct'], + 'stock_count': alert['stock_count'], + 'concept_type': alert['concept_type'], + 'zscore': alert['alpha_zscore'], + 'importance_score': alert['importance_score'], + 'extra_info': json.dumps({ + 'alpha': alert['alpha'], + 'alpha_zscore': alert['alpha_zscore'], + 'alpha_mean': alert['alpha_mean'], + 'alpha_std': alert['alpha_std'], + 'limit_down_count': alert['limit_down_count'], + }, ensure_ascii=False) + } + + conn.execute(insert_sql, params) + saved += 1 + + except Exception as e: + logger.error(f"保存异动失败: {alert['concept_name']} - {e}") + + return saved + + +def save_index_snapshot(index_data: dict, trade_date: str): + """保存指数快照""" + if not index_data: + return + + try: + with MYSQL_ENGINE.begin() as conn: + conn.execute(text(""" + REPLACE INTO index_minute_snapshot + (index_code, trade_date, snapshot_time, price, prev_close, change_pct) + VALUES (:index_code, :trade_date, :snapshot_time, :price, :prev_close, :change_pct) + """), { + 'index_code': index_data['code'], + 'trade_date': trade_date, + 'snapshot_time': index_data['timestamp'], + 'price': index_data['price'], + 'prev_close': index_data.get('prev_close'), + 'change_pct': index_data.get('change_pct') + }) + except Exception as e: + logger.error(f"保存指数快照失败: {e}") + + +# ==================== 交易时间 ==================== + +def is_trading_time() -> bool: + now = datetime.now() + if now.weekday() >= 5: + return False + + current_time = now.hour * 60 + now.minute + morning = (9 * 60 + 30 <= current_time <= 11 * 60 + 30) + afternoon = (13 * 60 <= current_time <= 15 * 60) + return morning or afternoon + + +def get_next_update_time() -> int: + now = datetime.now() + if is_trading_time(): + return 60 - now.second + + hour, minute = now.hour, now.minute + if hour < 9 or (hour == 9 and minute < 30): + target = now.replace(hour=9, minute=30, second=0) + elif (hour == 11 and minute >= 30) or hour == 12: + target = now.replace(hour=13, minute=0, second=0) + elif hour >= 15: + target = (now + timedelta(days=1)).replace(hour=9, minute=30, second=0) + else: + target = now + timedelta(minutes=1) + + return max(60, int((target - now).total_seconds())) + + +# ==================== 主运行逻辑 ==================== + +def run_once(concepts: list, all_stocks: list, trade_date: str, timestamp: datetime = None) -> Tuple[int, int]: + """执行一次检测""" + timestamp = timestamp or datetime.now() + + # 获取基准价格 + base_prices = get_base_prices(all_stocks, trade_date) + if not base_prices: + logger.warning("无法获取基准价格") + return 0, 0 + + # 获取当前价格 + if timestamp.date() == datetime.now().date(): + latest_prices = get_latest_prices(all_stocks) + else: + latest_prices = get_prices_at_time(all_stocks, timestamp) + + if not latest_prices: + logger.warning("无法获取最新价格") + return 0, 0 + + # 计算股票涨跌幅 + stock_changes = {} + for code, price_data in latest_prices.items(): + if code in base_prices and base_prices[code] > 0: + change = (price_data['close'] - base_prices[code]) / base_prices[code] * 100 + stock_changes[code] = round(change, 4) + + if not stock_changes: + return 0, 0 + + # 获取指数数据 + index_data = get_index_data(REFERENCE_INDEX, trade_date, timestamp) + if not index_data: + logger.warning("无法获取指数数据") + return 0, 0 + + index_change = index_data['change_pct'] + save_index_snapshot(index_data, trade_date) + + # 计算概念统计（包含Alpha） + stats = calculate_concept_stats(concepts, stock_changes, index_change) + + # 检测异动 + alerts = detect_alpha_alerts(stats, index_data, trade_date, timestamp) + + # 保存 + if alerts: + saved = save_alerts_to_mysql(alerts) + logger.info(f"💾 保存了 {saved} 条异动") + + return len(stats), len(alerts) + + +def run_realtime(): + """实时运行""" + logger.info("=" * 60) + logger.info("🚀 启动概念异动检测服务（Alpha Z-Score 方法）") + logger.info("=" * 60) + logger.info(f"配置: Z-Score阈值={ALPHA_CONFIG['zscore_threshold']}, 冷却={ALPHA_CONFIG['cooldown_minutes']}分钟") + + # 加载概念 + leaf_concepts = get_all_concepts() + parent_concepts = load_hierarchy_concepts(leaf_concepts) + all_concepts = leaf_concepts + parent_concepts + + all_stocks = set() + for c in all_concepts: + all_stocks.update(c['stocks']) + all_stocks = list(all_stocks) + + logger.info(f"监控 {len(all_concepts)} 个概念, {len(all_stocks)} 只股票") + + total_alerts = 0 + last_reload = datetime.now() + + while True: + try: + now = datetime.now() + trade_date = now.strftime('%Y-%m-%d') + + # 每小时重载概念 + if (now - last_reload).total_seconds() > 3600: + leaf_concepts = get_all_concepts() + parent_concepts = load_hierarchy_concepts(leaf_concepts) + all_concepts = leaf_concepts + parent_concepts + all_stocks = list(set(s for c in all_concepts for s in c['stocks'])) + last_reload = now + logger.info(f"重载概念: {len(all_concepts)} 个") + + if not is_trading_time(): + wait = get_next_update_time() + logger.info(f"⏰ 非交易时间，等待 {wait//60} 分钟") + time.sleep(min(wait, 300)) + continue + + updated, alert_count = run_once(all_concepts, all_stocks, trade_date) + total_alerts += alert_count + + if alert_count > 0: + logger.info(f"📊 本次 {alert_count} 条异动，累计 {total_alerts} 条") + + time.sleep(60 - datetime.now().second) + + except KeyboardInterrupt: + logger.info("\n停止服务") + break + except Exception as e: + logger.error(f"错误: {e}") + import traceback + traceback.print_exc() + time.sleep(60) + + +def run_backtest(trade_date: str, clear_existing: bool = True): + """回测""" + global alpha_stats, cooldown_cache + + logger.info("=" * 60) + logger.info(f"🔄 回测: {trade_date} (Alpha Z-Score 方法)") + logger.info("=" * 60) + + # 清空状态 + alpha_stats.clear() + cooldown_cache.clear() + + if clear_existing: + with MYSQL_ENGINE.begin() as conn: + conn.execute(text("DELETE FROM concept_minute_alert WHERE trade_date = :date"), {'date': trade_date}) + conn.execute(text("DELETE FROM index_minute_snapshot WHERE trade_date = :date"), {'date': trade_date}) + logger.info(f"已清除 {trade_date} 的数据") + + # 加载概念 + leaf_concepts = get_all_concepts() + parent_concepts = load_hierarchy_concepts(leaf_concepts) + all_concepts = leaf_concepts + parent_concepts + + all_stocks = list(set(s for c in all_concepts for s in c['stocks'])) + logger.info(f"概念: {len(all_concepts)}, 股票: {len(all_stocks)}") + + # 获取分钟时间戳 + client = get_ch_client() + result = client.execute(f""" + SELECT DISTINCT timestamp FROM stock_minute + WHERE toDate(timestamp) = '{trade_date}' + ORDER BY timestamp + """) + timestamps = [row[0] for row in result] + + if not timestamps: + logger.error(f"未找到 {trade_date} 的数据") + return + + logger.info(f"时间点: {len(timestamps)}") + + total_alerts = 0 + + for i, ts in enumerate(timestamps): + updated, alerts = run_once(all_concepts, all_stocks, trade_date, ts) + total_alerts += alerts + + if (i + 1) % 30 == 0: + logger.info(f"进度: {i+1}/{len(timestamps)} ({(i+1)*100//len(timestamps)}%), 异动: {total_alerts}") + + logger.info("=" * 60) + logger.info(f"✅ 回测完成! 检测到 {total_alerts} 条异动") + logger.info("=" * 60) + + +def show_status(): + """显示状态""" + print("\n" + "=" * 60) + print("概念异动检测服务 - Alpha Z-Score 方法") + print("=" * 60) + + print(f"\n当前时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}") + print(f"交易时间: {'是' if is_trading_time() else '否'}") + print(f"Z-Score阈值: {ALPHA_CONFIG['zscore_threshold']}") + + try: + with MYSQL_ENGINE.connect() as conn: + result = conn.execute(text(""" + SELECT alert_type, COUNT(*), AVG(change_delta) + FROM concept_minute_alert + WHERE trade_date = CURDATE() + GROUP BY alert_type + """)) + + print("\n今日异动统计:") + rows = list(result) + if rows: + for row in rows: + type_name = {'surge_up': '超额上涨', 'surge_down': '超额下跌'}.get(row[0], row[0]) + avg_alpha = f"{row[2]:+.2f}%" if row[2] else "-" + print(f" {type_name}: {row[1]} 条 (平均Alpha: {avg_alpha})") + else: + print(" 暂无异动") + + print("\n最新异动 (前10条):") + result = conn.execute(text(""" + SELECT concept_name, alert_type, alert_time, change_pct, + change_delta, index_change_pct, zscore + FROM concept_minute_alert + WHERE trade_date = CURDATE() + ORDER BY alert_time DESC + LIMIT 10 + """)) + + rows = list(result) + if rows: + print(f" {'概念':<18} | {'类型':<8} | {'时间':<5} | {'涨幅':>6} | {'Alpha':>6} | {'大盘':>5} | {'Z':>5}") + print(" " + "-" * 75) + for row in rows: + name = (row[0][:16] + '..') if len(row[0]) > 18 else row[0] + t = {'surge_up': '超额涨', 'surge_down': '超额跌'}.get(row[1], row[1][:6]) + time_s = row[2].strftime('%H:%M') if row[2] else '-' + chg = f"{row[3]:+.2f}%" if row[3] else '-' + alpha = f"{row[4]:+.2f}%" if row[4] else '-' + idx = f"{row[5]:+.1f}%" if row[5] else '-' + z = f"{row[6]:.1f}" if row[6] else '-' + print(f" {name:<18} | {t:<8} | {time_s:<5} | {chg:>6} | {alpha:>6} | {idx:>5} | {z:>5}") + + except Exception as e: + print(f" 查询失败: {e}") + + +def main(): + parser = argparse.ArgumentParser(description='概念异动检测 - Alpha Z-Score 方法') + parser.add_argument('command', nargs='?', default='realtime', + choices=['realtime', 'once', 'status', 'backtest'], + help='命令') + parser.add_argument('--date', '-d', type=str, default=None, help='回测日期') + parser.add_argument('--keep', '-k', action='store_true', help='保留已有数据') + + args = parser.parse_args() + + if args.command == 'realtime': + run_realtime() + elif args.command == 'once': + leaf_concepts = get_all_concepts() + parent_concepts = load_hierarchy_concepts(leaf_concepts) + all_concepts = leaf_concepts + parent_concepts + all_stocks = list(set(s for c in all_concepts for s in c['stocks'])) + trade_date = datetime.now().strftime('%Y-%m-%d') + run_once(all_concepts, all_stocks, trade_date) + elif args.command == 'status': + show_status() + elif args.command == 'backtest': + trade_date = args.date or datetime.now().strftime('%Y-%m-%d') + run_backtest(trade_date, not args.keep) + + +if __name__ == "__main__": + main() diff --git a/concept_alert_alpha_20251208.log b/concept_alert_alpha_20251208.log new file mode 100644 index 00000000..656199bf --- /dev/null +++ b/concept_alert_alpha_20251208.log @@ -0,0 +1,28 @@ +2025-12-08 16:40:41,567 - INFO - ============================================================ +2025-12-08 16:40:41,567 - INFO - 🔄 回测: 2025-12-08 (Alpha Z-Score 方法) +2025-12-08 16:40:41,569 - INFO - ============================================================ +2025-12-08 16:40:41,679 - INFO - 已清除 2025-12-08 的数据 +2025-12-08 16:40:41,903 - INFO - POST http://222.128.1.157:19200/concept_library_v3/_search?scroll=2m [status:200 duration:0.224s] +2025-12-08 16:40:42,105 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.197s] +2025-12-08 16:40:42,330 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.178s] +2025-12-08 16:40:42,518 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.183s] +2025-12-08 16:40:42,704 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.182s] +2025-12-08 16:40:42,894 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.186s] +2025-12-08 16:40:43,060 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.162s] +2025-12-08 16:40:43,234 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.171s] +2025-12-08 16:40:43,383 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.145s] +2025-12-08 16:40:43,394 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.008s] +2025-12-08 16:40:43,399 - INFO - DELETE http://222.128.1.157:19200/_search/scroll [status:200 duration:0.005s] +2025-12-08 16:40:43,409 - INFO - 概念: 968, 股票: 5938 +2025-12-08 16:40:43,505 - INFO - 时间点: 241 +2025-12-08 16:41:02,028 - INFO - 进度: 30/241 (12%), 异动: 0 +2025-12-08 16:41:20,851 - INFO - 进度: 60/241 (24%), 异动: 0 +2025-12-08 16:41:39,396 - INFO - 进度: 90/241 (37%), 异动: 0 +2025-12-08 16:41:58,687 - INFO - 进度: 120/241 (49%), 异动: 0 +2025-12-08 16:43:08,124 - INFO - 进度: 150/241 (62%), 异动: 0 +2025-12-08 16:43:26,973 - INFO - 进度: 180/241 (74%), 异动: 0 +2025-12-08 16:43:45,746 - INFO - 进度: 210/241 (87%), 异动: 0 +2025-12-08 16:44:04,479 - INFO - 进度: 240/241 (99%), 异动: 0 +2025-12-08 16:44:05,123 - INFO - ============================================================ +2025-12-08 16:44:05,123 - INFO - ✅ 回测完成! 检测到 0 条异动 +2025-12-08 16:44:05,125 - INFO - ============================================================ diff --git a/concept_alert_ml.py b/concept_alert_ml.py new file mode 100644 index 00000000..dbe50ea0 --- /dev/null +++ b/concept_alert_ml.py @@ -0,0 +1,1625 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +概念异动智能检测服务 - 基于 Z-Score + SVM +- Z-Score: 动态阈值，根据历史波动率判断异常 +- SVM: 多特征分类，综合判断是否为有意义的异动 +- 支持暴涨和暴跌检测 +- 异动重要性评分，避免图表过于密集 +""" + +import pandas as pd +import numpy as np +from datetime import datetime, timedelta +from sqlalchemy import create_engine, text +from elasticsearch import Elasticsearch +from clickhouse_driver import Client +from collections import deque +import time +import logging +import json +import os +import hashlib +import argparse +import pickle +from typing import Dict, List, Optional, Tuple + +# 尝试导入sklearn，如果不存在则提示安装 +try: + from sklearn.svm import OneClassSVM + from sklearn.preprocessing import StandardScaler + from sklearn.ensemble import IsolationForest + SKLEARN_AVAILABLE = True +except ImportError: + SKLEARN_AVAILABLE = False + print("警告: sklearn 未安装，将使用纯 Z-Score 方法") + print("安装命令: pip install scikit-learn") + +# ==================== 配置 ==================== + +# MySQL配置 +MYSQL_ENGINE = create_engine( + "mysql+pymysql://root:Zzl5588161!@222.128.1.157:33060/stock", + echo=False +) + +# Elasticsearch配置 +ES_CLIENT = Elasticsearch(['http://222.128.1.157:19200']) +INDEX_NAME = 'concept_library_v3' + +# ClickHouse配置 +CLICKHOUSE_CONFIG = { + 'host': '222.128.1.157', + 'port': 18000, + 'user': 'default', + 'password': 'Zzl33818!', + 'database': 'stock' +} + +# 层级结构文件 +HIERARCHY_FILE = 'concept_hierarchy_v3.json' + +# 模型保存路径 +MODEL_DIR = 'models' +os.makedirs(MODEL_DIR, exist_ok=True) + +# ==================== 智能异动检测配置 ==================== + +SMART_ALERT_CONFIG = { + # Z-Score 配置 + 'zscore': { + 'enabled': True, + 'lookback_days': 20, # 历史数据回看天数 + 'threshold_up': 2.5, # 上涨异动阈值（标准差倍数） + 'threshold_down': -2.5, # 下跌异动阈值（标准差倍数） + 'min_data_points': 10, # 最少数据点数 + }, + + # SVM 异常检测配置 + 'svm': { + 'enabled': SKLEARN_AVAILABLE, + 'nu': 0.05, # 异常比例（预期5%为异常） + 'kernel': 'rbf', # 核函数 + 'gamma': 'auto', + 'retrain_days': 7, # 每N天重新训练 + }, + + # 特征配置（用于SVM） + 'features': [ + 'change_pct', # 当前涨跌幅 + 'change_delta_5min', # 5分钟涨跌幅变化 + 'change_delta_10min', # 10分钟涨跌幅变化 + 'rank_delta_5min', # 5分钟排名变化 + 'limit_up_ratio', # 涨停股占比 + 'volume_ratio', # 成交量比率（预留） + 'index_correlation', # 与指数相关性 + ], + + # 重要性评分权重 + 'importance_weights': { + 'zscore_abs': 0.3, # Z-Score 绝对值 + 'rank_position': 0.2, # 排名位置（越靠前越重要） + 'limit_up_count': 0.2, # 涨停数 + 'stock_count': 0.1, # 概念股票数 + 'change_magnitude': 0.2, # 涨跌幅度 + }, + + # 显示控制 + 'display': { + 'max_alerts_per_hour': 20, # 每小时最多显示异动数 + 'min_importance_score': 0.3, # 最低重要性分数 + 'cooldown_minutes': 15, # 同一概念冷却时间 + }, +} + +# 参考指数 +REFERENCE_INDEX = '000001.SH' + +# ==================== 日志配置 ==================== + +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(levelname)s - %(message)s', + handlers=[ + logging.FileHandler(f'concept_alert_ml_{datetime.now().strftime("%Y%m%d")}.log', encoding='utf-8'), + logging.StreamHandler() + ] +) +logger = logging.getLogger(__name__) + +# ==================== 全局变量 ==================== + +ch_client = None + +# 历史统计数据缓存 +# 结构: {concept_id: {'mean': float, 'std': float, 'history': deque}} +stats_cache: Dict[str, dict] = {} + +# 分钟级历史缓存（用于计算变化率） +minute_cache: Dict[str, deque] = {} +MINUTE_WINDOW = 15 # 保留15分钟数据 + +# 冷却记录 +cooldown_cache: Dict[Tuple[str, str], datetime] = {} + +# SVM 模型 +svm_model = None +svm_scaler = None +svm_last_train = None + + +def get_ch_client(): + """获取ClickHouse客户端""" + global ch_client + if ch_client is None: + ch_client = Client(**CLICKHOUSE_CONFIG) + return ch_client + + +def generate_id(name: str) -> str: + """生成概念ID""" + return hashlib.md5(name.encode('utf-8')).hexdigest()[:16] + + +def code_to_ch_format(code: str) -> str: + """将6位股票代码转换为ClickHouse格式""" + if not code or len(code) != 6 or not code.isdigit(): + return None + if code.startswith('6'): + return f"{code}.SH" + elif code.startswith('0') or code.startswith('3'): + return f"{code}.SZ" + else: + return f"{code}.BJ" + + +# ==================== 概念数据获取（复用原有代码）==================== + +def get_all_concepts(): + """从ES获取所有叶子概念及其股票列表""" + concepts = [] + + query = { + "query": {"match_all": {}}, + "size": 100, + "_source": ["concept_id", "concept", "stocks"] + } + + resp = ES_CLIENT.search(index=INDEX_NAME, body=query, scroll='2m') + scroll_id = resp['_scroll_id'] + hits = resp['hits']['hits'] + + while len(hits) > 0: + for hit in hits: + source = hit['_source'] + concept_info = { + 'concept_id': source.get('concept_id'), + 'concept_name': source.get('concept'), + 'stocks': [], + 'concept_type': 'leaf' + } + + if 'stocks' in source and isinstance(source['stocks'], list): + for stock in source['stocks']: + if isinstance(stock, dict) and 'code' in stock and stock['code']: + concept_info['stocks'].append(stock['code']) + + if concept_info['stocks']: + concepts.append(concept_info) + + resp = ES_CLIENT.scroll(scroll_id=scroll_id, scroll='2m') + scroll_id = resp['_scroll_id'] + hits = resp['hits']['hits'] + + ES_CLIENT.clear_scroll(scroll_id=scroll_id) + return concepts + + +def load_hierarchy_concepts(leaf_concepts: list) -> list: + """加载层级结构，生成母概念""" + hierarchy_path = os.path.join(os.path.dirname(__file__), HIERARCHY_FILE) + if not os.path.exists(hierarchy_path): + logger.warning(f"层级文件不存在: {hierarchy_path}") + return [] + + with open(hierarchy_path, 'r', encoding='utf-8') as f: + hierarchy_data = json.load(f) + + concept_to_stocks = {} + for c in leaf_concepts: + concept_to_stocks[c['concept_name']] = set(c['stocks']) + + parent_concepts = [] + + for lv1 in hierarchy_data.get('hierarchy', []): + lv1_name = lv1.get('lv1', '') + lv1_stocks = set() + + for child in lv1.get('children', []): + lv2_name = child.get('lv2', '') + lv2_stocks = set() + + if 'children' in child: + for lv3_child in child.get('children', []): + lv3_name = lv3_child.get('lv3', '') + lv3_stocks = set() + + for concept_name in lv3_child.get('concepts', []): + if concept_name in concept_to_stocks: + lv3_stocks.update(concept_to_stocks[concept_name]) + + if lv3_stocks: + parent_concepts.append({ + 'concept_id': generate_id(f"lv3_{lv3_name}"), + 'concept_name': f"[三级] {lv3_name}", + 'stocks': list(lv3_stocks), + 'concept_type': 'lv3' + }) + + lv2_stocks.update(lv3_stocks) + else: + for concept_name in child.get('concepts', []): + if concept_name in concept_to_stocks: + lv2_stocks.update(concept_to_stocks[concept_name]) + + if lv2_stocks: + parent_concepts.append({ + 'concept_id': generate_id(f"lv2_{lv2_name}"), + 'concept_name': f"[二级] {lv2_name}", + 'stocks': list(lv2_stocks), + 'concept_type': 'lv2' + }) + + lv1_stocks.update(lv2_stocks) + + if lv1_stocks: + parent_concepts.append({ + 'concept_id': generate_id(f"lv1_{lv1_name}"), + 'concept_name': f"[一级] {lv1_name}", + 'stocks': list(lv1_stocks), + 'concept_type': 'lv1' + }) + + return parent_concepts + + +# ==================== 价格数据获取 ==================== + +def get_base_prices(stock_codes: list, current_date: str) -> dict: + """获取昨收价作为基准""" + if not stock_codes: + return {} + + valid_codes = [code for code in stock_codes if code and len(code) == 6 and code.isdigit()] + if not valid_codes: + return {} + + stock_codes_str = "','".join(valid_codes) + + query = f""" + SELECT SECCODE, F002N + FROM ea_trade + WHERE SECCODE IN ('{stock_codes_str}') + AND TRADEDATE = ( + SELECT MAX(TRADEDATE) + FROM ea_trade + WHERE TRADEDATE < '{current_date}' + ) + AND F002N IS NOT NULL AND F002N > 0 + """ + + try: + with MYSQL_ENGINE.connect() as conn: + result = conn.execute(text(query)) + base_prices = {row[0]: float(row[1]) for row in result if row[1] and float(row[1]) > 0} + return base_prices + except Exception as e: + logger.error(f"获取基准价格失败: {e}") + return {} + + +def get_latest_prices(stock_codes: list) -> dict: + """从ClickHouse获取最新价格""" + if not stock_codes: + return {} + + client = get_ch_client() + + ch_codes = [] + code_mapping = {} + for code in stock_codes: + ch_code = code_to_ch_format(code) + if ch_code: + ch_codes.append(ch_code) + code_mapping[ch_code] = code + + if not ch_codes: + return {} + + ch_codes_str = "','".join(ch_codes) + + query = f""" + SELECT code, close, timestamp + FROM ( + SELECT code, close, timestamp, + ROW_NUMBER() OVER (PARTITION BY code ORDER BY timestamp DESC) as rn + FROM stock_minute + WHERE code IN ('{ch_codes_str}') + AND toDate(timestamp) = today() + ) + WHERE rn = 1 + """ + + try: + result = client.execute(query) + if not result: + return {} + + latest_prices = {} + for row in result: + ch_code, close, ts = row + if close and close > 0: + pure_code = code_mapping.get(ch_code) + if pure_code: + latest_prices[pure_code] = { + 'close': float(close), + 'timestamp': ts + } + + return latest_prices + except Exception as e: + logger.error(f"获取最新价格失败: {e}") + return {} + + +def get_prices_at_time(stock_codes: list, timestamp: datetime) -> dict: + """获取指定时间点的股票价格""" + if not stock_codes: + return {} + + client = get_ch_client() + + ch_codes = [] + code_mapping = {} + for code in stock_codes: + ch_code = code_to_ch_format(code) + if ch_code: + ch_codes.append(ch_code) + code_mapping[ch_code] = code + + if not ch_codes: + return {} + + ch_codes_str = "','".join(ch_codes) + + query = f""" + SELECT code, close, timestamp + FROM stock_minute + WHERE code IN ('{ch_codes_str}') + AND timestamp = '{timestamp.strftime('%Y-%m-%d %H:%M:%S')}' + """ + + try: + result = client.execute(query) + prices = {} + for row in result: + ch_code, close, ts = row + if close and close > 0: + pure_code = code_mapping.get(ch_code) + if pure_code: + prices[pure_code] = { + 'close': float(close), + 'timestamp': ts + } + return prices + except Exception as e: + logger.error(f"获取历史价格失败: {e}") + return {} + + +def get_index_realtime(index_code: str = REFERENCE_INDEX) -> dict: + """获取指数实时数据""" + client = get_ch_client() + + try: + query = f""" + SELECT close, timestamp + FROM index_minute + WHERE code = '{index_code}' + AND toDate(timestamp) = today() + ORDER BY timestamp DESC + LIMIT 1 + """ + result = client.execute(query) + + if not result: + return None + + close, ts = result[0] + + # 获取昨收价 + prev_close = get_index_prev_close(index_code, datetime.now().strftime('%Y-%m-%d')) + + change_pct = None + if close and prev_close and prev_close > 0: + change_pct = (float(close) - prev_close) / prev_close * 100 + + return { + 'code': index_code, + 'price': float(close), + 'prev_close': prev_close, + 'change_pct': round(change_pct, 4) if change_pct else None, + 'timestamp': ts + } + + except Exception as e: + logger.error(f"获取指数数据失败: {e}") + return None + + +def get_index_at_time(index_code: str, timestamp: datetime, prev_close: float) -> dict: + """获取指定时间点的指数数据""" + client = get_ch_client() + + query = f""" + SELECT close, timestamp + FROM index_minute + WHERE code = '{index_code}' + AND timestamp = '{timestamp.strftime('%Y-%m-%d %H:%M:%S')}' + LIMIT 1 + """ + + try: + result = client.execute(query) + if not result: + return None + + close, ts = result[0] + change_pct = None + if close and prev_close and prev_close > 0: + change_pct = (float(close) - prev_close) / prev_close * 100 + + return { + 'code': index_code, + 'price': float(close), + 'prev_close': prev_close, + 'change_pct': round(change_pct, 4) if change_pct else None, + 'timestamp': ts + } + except Exception as e: + logger.error(f"获取指数数据失败: {e}") + return None + + +def get_index_prev_close(index_code: str, trade_date: str) -> float: + """获取指数昨收价""" + code_no_suffix = index_code.split('.')[0] + + with MYSQL_ENGINE.connect() as conn: + result = conn.execute(text(""" + SELECT F006N FROM ea_exchangetrade + WHERE INDEXCODE = :code + AND TRADEDATE < :today + ORDER BY TRADEDATE DESC LIMIT 1 + """), { + 'code': code_no_suffix, + 'today': trade_date + }).fetchone() + + if result and result[0]: + return float(result[0]) + return None + + +# ==================== 涨跌幅计算 ==================== + +def calculate_change_pct(base_prices: dict, latest_prices: dict) -> dict: + """计算涨跌幅""" + changes = {} + for code, latest in latest_prices.items(): + if code in base_prices and base_prices[code] > 0: + base = base_prices[code] + close = latest['close'] + change_pct = (close - base) / base * 100 + changes[code] = { + 'change_pct': round(change_pct, 4), + 'close': close, + 'base': base + } + return changes + + +def calculate_concept_stats(concepts: list, stock_changes: dict) -> list: + """计算概念统计""" + stats = [] + + for concept in concepts: + concept_id = concept['concept_id'] + concept_name = concept['concept_name'] + stock_codes = concept['stocks'] + concept_type = concept.get('concept_type', 'leaf') + + changes = [] + limit_up_count = 0 + limit_down_count = 0 + limit_up_stocks = [] + limit_down_stocks = [] + + for code in stock_codes: + if code in stock_changes: + change_info = stock_changes[code] + change_pct = change_info['change_pct'] + changes.append(change_pct) + + # 涨停判断（涨幅 >= 9.8%） + if change_pct >= 9.8: + limit_up_count += 1 + limit_up_stocks.append(code) + # 跌停判断（跌幅 <= -9.8%） + elif change_pct <= -9.8: + limit_down_count += 1 + limit_down_stocks.append(code) + + if not changes: + continue + + avg_change_pct = round(np.mean(changes), 4) + std_change_pct = round(np.std(changes), 4) if len(changes) > 1 else 0 + + stats.append({ + 'concept_id': concept_id, + 'concept_name': concept_name, + 'avg_change_pct': avg_change_pct, + 'std_change_pct': std_change_pct, + 'stock_count': len(changes), + 'concept_type': concept_type, + 'limit_up_count': limit_up_count, + 'limit_down_count': limit_down_count, + 'limit_up_stocks': limit_up_stocks, + 'limit_down_stocks': limit_down_stocks, + 'limit_up_ratio': limit_up_count / len(changes) if changes else 0, + 'limit_down_ratio': limit_down_count / len(changes) if changes else 0, + }) + + # 按涨幅排序并添加排名 + stats.sort(key=lambda x: x['avg_change_pct'], reverse=True) + for i, item in enumerate(stats): + item['rank'] = i + 1 + + return stats + + +# ==================== Z-Score 计算 ==================== + +def load_historical_stats(concept_id: str, lookback_days: int = 20) -> dict: + """ + 加载概念的历史统计数据（用于计算Z-Score） + 从历史分钟数据中计算每日的平均涨跌幅变化 + """ + if concept_id in stats_cache and stats_cache[concept_id].get('loaded'): + return stats_cache[concept_id] + + # 查询历史异动记录计算统计 + try: + with MYSQL_ENGINE.connect() as conn: + # 获取历史每日变化统计 + result = conn.execute(text(""" + SELECT + trade_date, + AVG(change_delta) as avg_delta, + MAX(change_delta) as max_delta, + MIN(change_delta) as min_delta + FROM concept_minute_alert + WHERE concept_id = :concept_id + AND trade_date >= DATE_SUB(CURDATE(), INTERVAL :days DAY) + AND alert_type = 'surge' + GROUP BY trade_date + """), {'concept_id': concept_id, 'days': lookback_days}) + + rows = list(result) + + if len(rows) >= SMART_ALERT_CONFIG['zscore']['min_data_points']: + deltas = [float(r[1]) for r in rows if r[1]] + stats_cache[concept_id] = { + 'mean': np.mean(deltas), + 'std': np.std(deltas) if len(deltas) > 1 else 1.0, + 'count': len(deltas), + 'loaded': True + } + else: + # 数据不足，使用默认值 + stats_cache[concept_id] = { + 'mean': 0.5, # 默认平均变化0.5% + 'std': 0.8, # 默认标准差0.8% + 'count': 0, + 'loaded': True + } + except Exception as e: + logger.error(f"加载历史统计失败: {e}") + stats_cache[concept_id] = { + 'mean': 0.5, + 'std': 0.8, + 'count': 0, + 'loaded': True + } + + return stats_cache[concept_id] + + +def calculate_zscore(concept_id: str, change_delta: float) -> float: + """ + 计算涨跌幅变化的Z-Score + Z = (X - μ) / σ + """ + stats = load_historical_stats(concept_id) + + mean = stats['mean'] + std = stats['std'] + + # 避免除零 + if std < 0.1: + std = 0.1 + + zscore = (change_delta - mean) / std + return round(zscore, 4) + + +def update_minute_cache(concept_id: str, timestamp: datetime, data: dict): + """更新分钟级缓存""" + if concept_id not in minute_cache: + minute_cache[concept_id] = deque(maxlen=MINUTE_WINDOW) + + minute_cache[concept_id].append({ + 'timestamp': timestamp, + **data + }) + + +def get_minute_history(concept_id: str, minutes_ago: int) -> Optional[dict]: + """获取N分钟前的数据""" + if concept_id not in minute_cache: + return None + + history = minute_cache[concept_id] + if not history: + return None + + # 获取当前最新时间 + current_time = history[-1]['timestamp'] if history else datetime.now() + target_time = current_time - timedelta(minutes=minutes_ago) + + # 找到最接近目标时间的记录 + for record in reversed(list(history)): + if record['timestamp'] <= target_time: + return record + + return None + + +# ==================== SVM 异常检测 ==================== + +def extract_features(stat: dict, index_data: dict) -> np.ndarray: + """ + 提取用于SVM的特征向量 + """ + concept_id = stat['concept_id'] + + # 获取历史数据 + prev_5min = get_minute_history(concept_id, 5) + prev_10min = get_minute_history(concept_id, 10) + + features = [] + + # 1. 当前涨跌幅 + features.append(stat['avg_change_pct']) + + # 2. 5分钟涨跌幅变化 + if prev_5min: + features.append(stat['avg_change_pct'] - prev_5min.get('change_pct', 0)) + else: + features.append(0) + + # 3. 10分钟涨跌幅变化 + if prev_10min: + features.append(stat['avg_change_pct'] - prev_10min.get('change_pct', 0)) + else: + features.append(0) + + # 4. 5分钟排名变化 + if prev_5min: + features.append(prev_5min.get('rank', stat['rank']) - stat['rank']) + else: + features.append(0) + + # 5. 涨停股占比 + features.append(stat.get('limit_up_ratio', 0) * 100) + + # 6. 成交量比率（预留，暂用0） + features.append(0) + + # 7. 与指数相关性（简化：涨跌方向一致性） + if index_data and index_data.get('change_pct'): + index_change = index_data['change_pct'] + concept_change = stat['avg_change_pct'] + # 同向为正，反向为负 + correlation = 1 if (index_change * concept_change > 0) else -1 + features.append(correlation * abs(concept_change - index_change)) + else: + features.append(0) + + return np.array(features) + + +def train_svm_model(training_data: List[np.ndarray]): + """ + 训练 OneClass SVM 模型 + 用于检测异常模式 + """ + global svm_model, svm_scaler, svm_last_train + + if not SKLEARN_AVAILABLE: + return False + + if len(training_data) < 100: + logger.warning(f"训练数据不足: {len(training_data)} 条") + return False + + try: + X = np.array(training_data) + + # 标准化 + svm_scaler = StandardScaler() + X_scaled = svm_scaler.fit_transform(X) + + # 训练 OneClass SVM + svm_model = OneClassSVM( + nu=SMART_ALERT_CONFIG['svm']['nu'], + kernel=SMART_ALERT_CONFIG['svm']['kernel'], + gamma=SMART_ALERT_CONFIG['svm']['gamma'] + ) + svm_model.fit(X_scaled) + + svm_last_train = datetime.now() + + # 保存模型 + model_path = os.path.join(MODEL_DIR, 'svm_model.pkl') + scaler_path = os.path.join(MODEL_DIR, 'svm_scaler.pkl') + + with open(model_path, 'wb') as f: + pickle.dump(svm_model, f) + with open(scaler_path, 'wb') as f: + pickle.dump(svm_scaler, f) + + logger.info(f"SVM模型训练完成，使用 {len(training_data)} 条数据") + return True + + except Exception as e: + logger.error(f"SVM模型训练失败: {e}") + return False + + +def load_svm_model(): + """加载已保存的SVM模型""" + global svm_model, svm_scaler + + if not SKLEARN_AVAILABLE: + return False + + model_path = os.path.join(MODEL_DIR, 'svm_model.pkl') + scaler_path = os.path.join(MODEL_DIR, 'svm_scaler.pkl') + + if os.path.exists(model_path) and os.path.exists(scaler_path): + try: + with open(model_path, 'rb') as f: + svm_model = pickle.load(f) + with open(scaler_path, 'rb') as f: + svm_scaler = pickle.load(f) + logger.info("SVM模型加载成功") + return True + except Exception as e: + logger.error(f"SVM模型加载失败: {e}") + + return False + + +def predict_anomaly(features: np.ndarray) -> Tuple[bool, float]: + """ + 使用SVM预测是否为异常 + 返回: (是否异常, 异常分数) + """ + global svm_model, svm_scaler + + if svm_model is None or svm_scaler is None: + return False, 0.0 + + try: + X_scaled = svm_scaler.transform(features.reshape(1, -1)) + prediction = svm_model.predict(X_scaled)[0] + score = svm_model.decision_function(X_scaled)[0] + + # prediction: 1 = 正常, -1 = 异常 + is_anomaly = prediction == -1 + + return is_anomaly, float(score) + except Exception as e: + logger.error(f"SVM预测失败: {e}") + return False, 0.0 + + +# ==================== 重要性评分 ==================== + +def calculate_importance_score( + zscore: float, + rank: int, + limit_up_count: int, + stock_count: int, + change_pct: float, + total_concepts: int +) -> float: + """ + 计算异动的重要性分数（0-1） + 综合多个因素判断这条异动是否值得显示 + """ + weights = SMART_ALERT_CONFIG['importance_weights'] + + scores = {} + + # 1. Z-Score 绝对值（越大越重要） + zscore_score = min(abs(zscore) / 5.0, 1.0) # 5倍标准差为满分 + scores['zscore_abs'] = zscore_score + + # 2. 排名位置（越靠前越重要） + rank_score = max(0, 1 - (rank - 1) / min(100, total_concepts)) + scores['rank_position'] = rank_score + + # 3. 涨停数（越多越重要） + limit_score = min(limit_up_count / 5.0, 1.0) # 5个涨停为满分 + scores['limit_up_count'] = limit_score + + # 4. 概念股票数（适中最好） + if stock_count < 10: + stock_score = stock_count / 10.0 + elif stock_count > 100: + stock_score = max(0.5, 1 - (stock_count - 100) / 200) + else: + stock_score = 1.0 + scores['stock_count'] = stock_score + + # 5. 涨跌幅度 + change_score = min(abs(change_pct) / 5.0, 1.0) # 5%为满分 + scores['change_magnitude'] = change_score + + # 加权求和 + total_score = sum(scores[k] * weights[k] for k in weights) + + return round(total_score, 4) + + +# ==================== 智能异动检测 ==================== + +def check_cooldown(concept_id: str, alert_type: str) -> bool: + """检查是否在冷却期""" + key = (concept_id, alert_type) + cooldown_minutes = SMART_ALERT_CONFIG['display']['cooldown_minutes'] + + if key in cooldown_cache: + last_alert = cooldown_cache[key] + if datetime.now() - last_alert < timedelta(minutes=cooldown_minutes): + return True + return False + + +def set_cooldown(concept_id: str, alert_type: str, alert_time: datetime = None): + """设置冷却""" + cooldown_cache[(concept_id, alert_type)] = alert_time or datetime.now() + + +def detect_smart_alerts( + current_stats: list, + index_data: dict, + trade_date: str, + current_time: datetime = None +) -> list: + """ + 智能异动检测 + 结合 Z-Score + SVM 进行检测 + """ + alerts = [] + current_time = current_time or datetime.now() + total_concepts = len(current_stats) + + for stat in current_stats: + concept_id = stat['concept_id'] + concept_name = stat['concept_name'] + change_pct = stat['avg_change_pct'] + rank = stat['rank'] + limit_up_count = stat['limit_up_count'] + limit_down_count = stat['limit_down_count'] + stock_count = stat['stock_count'] + concept_type = stat['concept_type'] + + # 更新分钟缓存 + update_minute_cache(concept_id, current_time, { + 'change_pct': change_pct, + 'rank': rank, + 'limit_up_count': limit_up_count, + 'limit_down_count': limit_down_count + }) + + # 获取历史数据计算变化 + prev_5min = get_minute_history(concept_id, 5) + if not prev_5min: + continue + + change_delta = change_pct - prev_5min.get('change_pct', 0) + + # ========== Z-Score 检测 ========== + zscore = calculate_zscore(concept_id, abs(change_delta)) + + # 判断是涨还是跌 + is_surge_up = change_delta > 0 and zscore >= SMART_ALERT_CONFIG['zscore']['threshold_up'] + is_surge_down = change_delta < 0 and zscore >= abs(SMART_ALERT_CONFIG['zscore']['threshold_down']) + + if not (is_surge_up or is_surge_down): + continue + + alert_type = 'surge_up' if is_surge_up else 'surge_down' + + # 检查冷却 + if check_cooldown(concept_id, alert_type): + continue + + # ========== SVM 验证（可选）========== + svm_is_anomaly = False + svm_score = 0.0 + + if SMART_ALERT_CONFIG['svm']['enabled'] and svm_model is not None: + features = extract_features(stat, index_data) + svm_is_anomaly, svm_score = predict_anomaly(features) + + # 如果SVM认为不是异常，降低Z-Score要求 + if not svm_is_anomaly and abs(zscore) < 3.5: + continue + + # ========== 计算重要性分数 ========== + importance = calculate_importance_score( + zscore=zscore, + rank=rank, + limit_up_count=limit_up_count if is_surge_up else limit_down_count, + stock_count=stock_count, + change_pct=change_pct, + total_concepts=total_concepts + ) + + # 过滤低重要性 + if importance < SMART_ALERT_CONFIG['display']['min_importance_score']: + continue + + # ========== 创建异动记录 ========== + alert = { + 'concept_id': concept_id, + 'concept_name': concept_name, + 'alert_type': alert_type, + 'alert_time': current_time, + 'trade_date': trade_date, + 'change_pct': change_pct, + 'prev_change_pct': prev_5min.get('change_pct'), + 'change_delta': round(change_delta, 4), + 'zscore': zscore, + 'svm_score': svm_score, + 'importance_score': importance, + 'limit_up_count': limit_up_count, + 'limit_down_count': limit_down_count, + 'prev_limit_up_count': prev_5min.get('limit_up_count', 0), + 'rank_position': rank, + 'prev_rank_position': prev_5min.get('rank'), + 'rank_delta': (prev_5min.get('rank', rank) - rank) if prev_5min else 0, + 'stock_count': stock_count, + 'concept_type': concept_type, + 'index_code': REFERENCE_INDEX, + 'index_price': index_data['price'] if index_data else None, + 'index_change_pct': index_data['change_pct'] if index_data else None, + 'extra_info': { + 'limit_up_stocks': stat.get('limit_up_stocks', []), + 'limit_down_stocks': stat.get('limit_down_stocks', []), + } + } + + alerts.append(alert) + set_cooldown(concept_id, alert_type, current_time) + + # 日志 + direction = "🔥 暴涨" if is_surge_up else "💧 暴跌" + logger.info( + f"{direction}: {concept_name} " + f"涨幅 {prev_5min.get('change_pct', 0):.2f}% -> {change_pct:.2f}% " + f"(Δ{change_delta:+.2f}%, Z={zscore:.2f}, 重要性={importance:.2f})" + ) + + # 按重要性排序，限制数量 + alerts.sort(key=lambda x: x['importance_score'], reverse=True) + max_alerts = SMART_ALERT_CONFIG['display']['max_alerts_per_hour'] + + return alerts[:max_alerts] + + +# ==================== 数据持久化 ==================== + +def save_alerts_to_mysql(alerts: list): + """保存异动数据到MySQL""" + if not alerts: + return 0 + + saved = 0 + with MYSQL_ENGINE.begin() as conn: + for alert in alerts: + try: + insert_sql = text(""" + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (:concept_id, :concept_name, :alert_time, :alert_type, :trade_date, + :change_pct, :prev_change_pct, :change_delta, + :limit_up_count, :prev_limit_up_count, :limit_up_delta, + :rank_position, :prev_rank_position, :rank_delta, + :index_code, :index_price, :index_change_pct, + :stock_count, :concept_type, :extra_info) + """) + + # 计算 limit_up_delta + limit_up_delta = alert.get('limit_up_count', 0) - alert.get('prev_limit_up_count', 0) + + params = { + 'concept_id': alert['concept_id'], + 'concept_name': alert['concept_name'], + 'alert_time': alert['alert_time'], + 'alert_type': alert['alert_type'], + 'trade_date': alert['trade_date'], + 'change_pct': alert.get('change_pct'), + 'prev_change_pct': alert.get('prev_change_pct'), + 'change_delta': alert.get('change_delta'), + 'limit_up_count': alert.get('limit_up_count', 0), + 'prev_limit_up_count': alert.get('prev_limit_up_count', 0), + 'limit_up_delta': limit_up_delta, + 'rank_position': alert.get('rank_position'), + 'prev_rank_position': alert.get('prev_rank_position'), + 'rank_delta': alert.get('rank_delta'), + 'index_code': alert.get('index_code', REFERENCE_INDEX), + 'index_price': alert.get('index_price'), + 'index_change_pct': alert.get('index_change_pct'), + 'stock_count': alert.get('stock_count'), + 'concept_type': alert.get('concept_type', 'leaf'), + 'extra_info': json.dumps({ + **alert.get('extra_info', {}), + 'zscore': alert.get('zscore'), + 'svm_score': alert.get('svm_score'), + 'importance_score': alert.get('importance_score'), + }, ensure_ascii=False) if alert.get('extra_info') else None + } + + conn.execute(insert_sql, params) + saved += 1 + + except Exception as e: + logger.error(f"保存异动失败: {alert['concept_name']} - {e}") + + return saved + + +def save_index_snapshot(index_data: dict, trade_date: str): + """保存指数快照""" + if not index_data: + return + + try: + with MYSQL_ENGINE.begin() as conn: + upsert_sql = text(""" + REPLACE INTO index_minute_snapshot + (index_code, trade_date, snapshot_time, price, prev_close, change_pct) + VALUES (:index_code, :trade_date, :snapshot_time, :price, :prev_close, :change_pct) + """) + + conn.execute(upsert_sql, { + 'index_code': index_data['code'], + 'trade_date': trade_date, + 'snapshot_time': index_data['timestamp'], + 'price': index_data['price'], + 'prev_close': index_data.get('prev_close'), + 'change_pct': index_data.get('change_pct') + }) + except Exception as e: + logger.error(f"保存指数快照失败: {e}") + + +# ==================== 交易时间判断 ==================== + +def is_trading_time() -> bool: + """判断当前是否为交易时间""" + now = datetime.now() + weekday = now.weekday() + + if weekday >= 5: + return False + + hour, minute = now.hour, now.minute + current_time = hour * 60 + minute + + morning_start = 9 * 60 + 30 + morning_end = 11 * 60 + 30 + afternoon_start = 13 * 60 + afternoon_end = 15 * 60 + + return (morning_start <= current_time <= morning_end) or \ + (afternoon_start <= current_time <= afternoon_end) + + +def get_next_update_time() -> int: + """获取距离下次更新的秒数""" + now = datetime.now() + + if is_trading_time(): + return 60 - now.second + else: + hour, minute = now.hour, now.minute + + if hour < 9 or (hour == 9 and minute < 30): + target = now.replace(hour=9, minute=30, second=0, microsecond=0) + elif (hour == 11 and minute >= 30) or hour == 12: + target = now.replace(hour=13, minute=0, second=0, microsecond=0) + elif hour >= 15: + target = (now + timedelta(days=1)).replace(hour=9, minute=30, second=0, microsecond=0) + else: + target = now + timedelta(minutes=1) + + wait_seconds = (target - now).total_seconds() + return max(60, int(wait_seconds)) + + +# ==================== 主运行逻辑 ==================== + +def run_once(concepts: list, all_stocks: list) -> tuple: + """执行一次检测""" + now = datetime.now() + trade_date = now.strftime('%Y-%m-%d') + + # 获取基准价格 + base_prices = get_base_prices(all_stocks, trade_date) + if not base_prices: + logger.warning("无法获取基准价格") + return 0, 0 + + # 获取最新价格 + latest_prices = get_latest_prices(all_stocks) + if not latest_prices: + logger.warning("无法获取最新价格") + return 0, 0 + + # 获取指数数据 + index_data = get_index_realtime(REFERENCE_INDEX) + if index_data: + save_index_snapshot(index_data, trade_date) + + # 计算涨跌幅 + stock_changes = calculate_change_pct(base_prices, latest_prices) + if not stock_changes: + logger.warning("无涨跌幅数据") + return 0, 0 + + logger.info(f"获取到 {len(stock_changes)} 只股票的涨跌幅") + + # 计算概念统计 + stats = calculate_concept_stats(concepts, stock_changes) + logger.info(f"计算了 {len(stats)} 个概念的涨跌幅") + + # 智能异动检测 + alerts = detect_smart_alerts(stats, index_data, trade_date, now) + + # 保存异动 + if alerts: + saved = save_alerts_to_mysql(alerts) + logger.info(f"💾 保存了 {saved} 条异动记录") + + return len(stats), len(alerts) + + +def run_realtime(): + """实时检测主循环""" + logger.info("=" * 60) + logger.info("🚀 启动智能概念异动检测服务 (Z-Score + SVM)") + logger.info("=" * 60) + logger.info(f"配置: {json.dumps(SMART_ALERT_CONFIG, indent=2, ensure_ascii=False, default=str)}") + + # 尝试加载SVM模型 + if SKLEARN_AVAILABLE: + load_svm_model() + + # 加载概念数据 + logger.info("加载概念数据...") + leaf_concepts = get_all_concepts() + logger.info(f"获取到 {len(leaf_concepts)} 个叶子概念") + + parent_concepts = load_hierarchy_concepts(leaf_concepts) + logger.info(f"生成了 {len(parent_concepts)} 个母概念") + + all_concepts = leaf_concepts + parent_concepts + logger.info(f"总计 {len(all_concepts)} 个概念") + + # 收集所有股票代码 + all_stocks = set() + for c in all_concepts: + all_stocks.update(c['stocks']) + all_stocks = list(all_stocks) + logger.info(f"监控 {len(all_stocks)} 只股票") + + last_concept_update = datetime.now() + total_alerts = 0 + + while True: + try: + now = datetime.now() + + # 每小时重新加载概念数据 + if (now - last_concept_update).total_seconds() > 3600: + logger.info("重新加载概念数据...") + leaf_concepts = get_all_concepts() + parent_concepts = load_hierarchy_concepts(leaf_concepts) + all_concepts = leaf_concepts + parent_concepts + all_stocks = set() + for c in all_concepts: + all_stocks.update(c['stocks']) + all_stocks = list(all_stocks) + last_concept_update = now + logger.info(f"更新完成: {len(all_concepts)} 个概念, {len(all_stocks)} 只股票") + + # 检查是否交易时间 + if not is_trading_time(): + wait_sec = get_next_update_time() + wait_min = wait_sec // 60 + logger.info(f"⏰ 非交易时间，等待 {wait_min} 分钟后重试...") + time.sleep(min(wait_sec, 300)) + continue + + # 执行检测 + logger.info(f"\n{'=' * 40}") + logger.info(f"🔍 检测时间: {now.strftime('%Y-%m-%d %H:%M:%S')}") + updated, alert_count = run_once(all_concepts, all_stocks) + total_alerts += alert_count + + if alert_count > 0: + logger.info(f"📊 本次检测到 {alert_count} 条异动，累计 {total_alerts} 条") + + # 等待下一分钟 + sleep_sec = 60 - datetime.now().second + logger.info(f"⏳ 等待 {sleep_sec} 秒后继续...") + time.sleep(sleep_sec) + + except KeyboardInterrupt: + logger.info("\n收到退出信号，停止服务...") + break + except Exception as e: + logger.error(f"发生错误: {e}") + import traceback + traceback.print_exc() + time.sleep(60) + + +def run_single(): + """单次运行""" + logger.info("单次检测模式") + + if SKLEARN_AVAILABLE: + load_svm_model() + + leaf_concepts = get_all_concepts() + parent_concepts = load_hierarchy_concepts(leaf_concepts) + all_concepts = leaf_concepts + parent_concepts + + all_stocks = set() + for c in all_concepts: + all_stocks.update(c['stocks']) + all_stocks = list(all_stocks) + + logger.info(f"概念数: {len(all_concepts)}, 股票数: {len(all_stocks)}") + + updated, alerts = run_once(all_concepts, all_stocks) + logger.info(f"检测完成: {updated} 个概念, {alerts} 条异动") + + +# ==================== 回测功能 ==================== + +def get_minute_timestamps(trade_date: str) -> list: + """获取指定交易日的所有分钟时间戳""" + client = get_ch_client() + + query = f""" + SELECT DISTINCT timestamp + FROM stock_minute + WHERE toDate(timestamp) = '{trade_date}' + ORDER BY timestamp + """ + + result = client.execute(query) + return [row[0] for row in result] + + +def run_backtest(trade_date: str, clear_existing: bool = True): + """ + 回测指定日期的异动检测 + """ + global minute_cache, cooldown_cache, stats_cache + + logger.info("=" * 60) + logger.info(f"🔄 开始智能回测: {trade_date}") + logger.info("=" * 60) + + # 清空缓存 + minute_cache = {} + cooldown_cache = {} + stats_cache = {} + + # 清除已有数据 + if clear_existing: + with MYSQL_ENGINE.begin() as conn: + conn.execute(text("DELETE FROM concept_minute_alert WHERE trade_date = :date"), {'date': trade_date}) + conn.execute(text("DELETE FROM index_minute_snapshot WHERE trade_date = :date"), {'date': trade_date}) + logger.info(f"已清除 {trade_date} 的已有数据") + + # 加载概念数据 + logger.info("加载概念数据...") + leaf_concepts = get_all_concepts() + logger.info(f"获取到 {len(leaf_concepts)} 个叶子概念") + + parent_concepts = load_hierarchy_concepts(leaf_concepts) + logger.info(f"生成了 {len(parent_concepts)} 个母概念") + + all_concepts = leaf_concepts + parent_concepts + logger.info(f"总计 {len(all_concepts)} 个概念") + + # 收集所有股票代码 + all_stocks = set() + for c in all_concepts: + all_stocks.update(c['stocks']) + all_stocks = list(all_stocks) + logger.info(f"监控 {len(all_stocks)} 只股票") + + # 获取基准价格（昨收价） + base_prices = get_base_prices(all_stocks, trade_date) + if not base_prices: + logger.error("无法获取基准价格，退出回测") + return + logger.info(f"获取到 {len(base_prices)} 个基准价格") + + # 获取指数昨收价 + index_prev_close = get_index_prev_close(REFERENCE_INDEX, trade_date) + logger.info(f"指数昨收价: {index_prev_close}") + + # 获取所有分钟时间戳 + timestamps = get_minute_timestamps(trade_date) + if not timestamps: + logger.error(f"未找到 {trade_date} 的分钟数据") + return + logger.info(f"找到 {len(timestamps)} 个分钟时间点") + + total_alerts = 0 + processed = 0 + + # 逐分钟处理 + for ts in timestamps: + processed += 1 + + # 获取该时间点的价格 + latest_prices = get_prices_at_time(all_stocks, ts) + if not latest_prices: + continue + + # 获取指数数据 + index_data = get_index_at_time(REFERENCE_INDEX, ts, index_prev_close) + if index_data: + save_index_snapshot(index_data, trade_date) + + # 计算涨跌幅 + stock_changes = calculate_change_pct(base_prices, latest_prices) + if not stock_changes: + continue + + # 计算概念统计 + stats = calculate_concept_stats(all_concepts, stock_changes) + + # 智能异动检测 + alerts = detect_smart_alerts(stats, index_data, trade_date, ts) + + # 保存异动 + if alerts: + saved = save_alerts_to_mysql(alerts) + total_alerts += saved + + # 进度显示 + if processed % 30 == 0: + logger.info(f"进度: {processed}/{len(timestamps)} ({processed*100//len(timestamps)}%), 已检测到 {total_alerts} 条异动") + + logger.info("=" * 60) + logger.info(f"✅ 回测完成!") + logger.info(f" 处理分钟数: {processed}") + logger.info(f" 检测到异动: {total_alerts} 条") + logger.info("=" * 60) + + +def show_status(): + """显示状态""" + print("\n" + "=" * 60) + print("智能概念异动检测服务 (Z-Score + SVM) - 状态") + print("=" * 60) + + now = datetime.now() + print(f"\n当前时间: {now.strftime('%Y-%m-%d %H:%M:%S')}") + print(f"是否交易时间: {'是' if is_trading_time() else '否'}") + print(f"sklearn可用: {'是' if SKLEARN_AVAILABLE else '否'}") + + # 模型状态 + model_path = os.path.join(MODEL_DIR, 'svm_model.pkl') + print(f"SVM模型: {'已加载' if os.path.exists(model_path) else '未训练'}") + + # 今日异动统计 + print("\n今日异动统计:") + try: + with MYSQL_ENGINE.connect() as conn: + result = conn.execute(text(""" + SELECT alert_type, COUNT(*) as cnt, AVG(change_delta) as avg_delta + FROM concept_minute_alert + WHERE trade_date = CURDATE() + GROUP BY alert_type + """)) + rows = list(result) + if rows: + for row in rows: + alert_type_name = { + 'surge_up': '暴涨', + 'surge_down': '暴跌', + 'surge': '急涨(旧)', + 'limit_up': '涨停增加', + 'rank_jump': '排名跃升' + }.get(row[0], row[0]) + avg_delta = f"{row[2]:.2f}%" if row[2] else "-" + print(f" {alert_type_name}: {row[1]} 条 (平均变化: {avg_delta})") + else: + print(" 今日暂无异动") + + # 最新异动 + print("\n最新异动 (前10条):") + result = conn.execute(text(""" + SELECT concept_name, alert_type, alert_time, change_pct, change_delta, extra_info + FROM concept_minute_alert + WHERE trade_date = CURDATE() + ORDER BY alert_time DESC + LIMIT 10 + """)) + rows = list(result) + if rows: + print(f" {'概念':<20} | {'类型':<6} | {'时间':<8} | {'涨幅':>6} | {'变化':>6} | {'Z分':>5}") + print(" " + "-" * 70) + for row in rows: + name = row[0][:18] if len(row[0]) > 18 else row[0] + alert_type = {'surge_up': '暴涨', 'surge_down': '暴跌'}.get(row[1], row[1][:4]) + time_str = row[2].strftime('%H:%M') if row[2] else '-' + change = f"{row[3]:.2f}%" if row[3] else '-' + delta = f"{row[4]:+.2f}%" if row[4] else '-' + + # 解析extra_info获取zscore + zscore = '-' + if row[5]: + try: + extra = json.loads(row[5]) if isinstance(row[5], str) else row[5] + zscore = f"{extra.get('zscore', 0):.1f}" + except: + pass + + print(f" {name:<20} | {alert_type:<6} | {time_str:<8} | {change:>6} | {delta:>6} | {zscore:>5}") + else: + print(" 暂无异动记录") + + except Exception as e: + print(f" 查询失败: {e}") + + +def train_model(): + """训练SVM模型""" + if not SKLEARN_AVAILABLE: + print("错误: sklearn未安装，无法训练模型") + print("安装命令: pip install scikit-learn") + return + + logger.info("=" * 60) + logger.info("🎓 开始训练SVM模型") + logger.info("=" * 60) + + # 加载概念数据 + leaf_concepts = get_all_concepts() + parent_concepts = load_hierarchy_concepts(leaf_concepts) + all_concepts = leaf_concepts + parent_concepts + + all_stocks = set() + for c in all_concepts: + all_stocks.update(c['stocks']) + all_stocks = list(all_stocks) + + logger.info(f"概念数: {len(all_concepts)}, 股票数: {len(all_stocks)}") + + # 收集训练数据（使用历史异动数据） + training_features = [] + + # 从最近N天的数据中提取特征 + lookback_days = 30 + + try: + with MYSQL_ENGINE.connect() as conn: + result = conn.execute(text(""" + SELECT change_pct, change_delta, rank_position, limit_up_count, + stock_count, index_change_pct, extra_info + FROM concept_minute_alert + WHERE trade_date >= DATE_SUB(CURDATE(), INTERVAL :days DAY) + """), {'days': lookback_days}) + + for row in result: + features = [ + float(row[0]) if row[0] else 0, # change_pct + float(row[1]) if row[1] else 0, # change_delta + 0, # change_delta_10min (not available) + -float(row[2]) if row[2] else 0, # rank_delta (approximation) + float(row[3]) / max(1, float(row[4])) * 100 if row[3] and row[4] else 0, # limit_up_ratio + 0, # volume_ratio + float(row[0]) - float(row[5]) if row[0] and row[5] else 0, # index_correlation + ] + training_features.append(features) + + logger.info(f"收集到 {len(training_features)} 条训练数据") + + if len(training_features) >= 100: + success = train_svm_model(training_features) + if success: + logger.info("✅ 模型训练成功!") + else: + logger.error("❌ 模型训练失败") + else: + logger.warning("训练数据不足100条，跳过训练") + + except Exception as e: + logger.error(f"训练失败: {e}") + import traceback + traceback.print_exc() + + +# ==================== 主函数 ==================== + +def main(): + parser = argparse.ArgumentParser(description='智能概念异动检测服务 (Z-Score + SVM)') + parser.add_argument('command', nargs='?', default='realtime', + choices=['realtime', 'once', 'status', 'backtest', 'train'], + help='命令: realtime(实时运行), once(单次运行), status(状态), backtest(回测), train(训练模型)') + parser.add_argument('--date', '-d', type=str, default=None, + help='回测日期，格式: YYYY-MM-DD，默认为今天') + parser.add_argument('--keep', '-k', action='store_true', + help='回测时保留已有数据（默认会清除）') + + args = parser.parse_args() + + if args.command == 'realtime': + run_realtime() + elif args.command == 'once': + run_single() + elif args.command == 'status': + show_status() + elif args.command == 'backtest': + trade_date = args.date or datetime.now().strftime('%Y-%m-%d') + clear_existing = not args.keep + run_backtest(trade_date, clear_existing) + elif args.command == 'train': + train_model() + + +if __name__ == "__main__": + main() diff --git a/concept_alert_realtime.py b/concept_alert_realtime.py new file mode 100644 index 00000000..6e931a91 --- /dev/null +++ b/concept_alert_realtime.py @@ -0,0 +1,1366 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +概念异动实时检测服务 +- 基于 concept_quota_realtime.py 扩展 +- 检测概念板块的异动（急涨、涨停增加、排名跃升） +- 记录异动时的指数位置，用于热点概览图表展示 +""" + +import pandas as pd +import numpy as np +from datetime import datetime, timedelta +from sqlalchemy import create_engine, text +from elasticsearch import Elasticsearch +from clickhouse_driver import Client +from collections import deque +import time +import logging +import json +import os +import hashlib +import argparse + +# ==================== 配置 ==================== + +# MySQL配置 +MYSQL_ENGINE = create_engine( + "mysql+pymysql://root:Zzl5588161!@222.128.1.157:33060/stock", + echo=False +) + +# Elasticsearch配置 +ES_CLIENT = Elasticsearch(['http://222.128.1.157:19200']) +INDEX_NAME = 'concept_library_v3' + +# ClickHouse配置 +CLICKHOUSE_CONFIG = { + 'host': '222.128.1.157', + 'port': 18000, + 'user': 'default', + 'password': 'Zzl33818!', + 'database': 'stock' +} + +# 层级结构文件 +HIERARCHY_FILE = 'concept_hierarchy_v3.json' + +# ==================== 异动检测阈值配置 ==================== + +ALERT_CONFIG = { + # 急涨检测：N分钟内涨幅变化超过阈值 + 'surge': { + 'enabled': True, + 'window_minutes': 5, # 检测窗口（分钟） + 'threshold_pct': 1.0, # 涨幅变化阈值（%） + 'min_change_pct': 0.5, # 最低涨幅要求（避免负涨幅的噪音） + 'cooldown_minutes': 10, # 同一概念冷却时间（避免重复报警） + }, + # 涨停数增加检测 + 'limit_up': { + 'enabled': True, + 'threshold_count': 1, # 涨停数增加阈值 + 'cooldown_minutes': 15, # 冷却时间 + }, + # 排名跃升检测 + 'rank_jump': { + 'enabled': True, + 'window_minutes': 5, # 检测窗口 + 'threshold_rank': 15, # 排名上升阈值 + 'max_rank': 50, # 只关注前N名的变化 + 'cooldown_minutes': 15, + }, +} + +# 参考指数 +REFERENCE_INDEX = '000001.SH' # 上证指数 + +# ==================== 日志配置 ==================== + +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(levelname)s - %(message)s', + handlers=[ + logging.FileHandler(f'concept_alert_{datetime.now().strftime("%Y%m%d")}.log', encoding='utf-8'), + logging.StreamHandler() + ] +) +logger = logging.getLogger(__name__) + +# ==================== 全局变量 ==================== + +ch_client = None + +# 历史数据缓存（用于异动检测） +# 结构: {concept_id: deque([(timestamp, change_pct, rank, limit_up_count), ...])} +history_cache = {} +HISTORY_WINDOW = 10 # 保留最近10分钟的数据 + +# 冷却记录（避免重复报警） +# 结构: {(concept_id, alert_type): last_alert_time} +cooldown_cache = {} + +# 当前排名缓存 +current_rankings = {} + + +def get_ch_client(): + """获取ClickHouse客户端""" + global ch_client + if ch_client is None: + ch_client = Client(**CLICKHOUSE_CONFIG) + return ch_client + + +def generate_id(name: str) -> str: + """生成概念ID""" + return hashlib.md5(name.encode('utf-8')).hexdigest()[:16] + + +def code_to_ch_format(code: str) -> str: + """将6位股票代码转换为ClickHouse格式""" + if not code or len(code) != 6 or not code.isdigit(): + return None + if code.startswith('6'): + return f"{code}.SH" + elif code.startswith('0') or code.startswith('3'): + return f"{code}.SZ" + else: + return f"{code}.BJ" + + +# ==================== 概念数据获取 ==================== + +def get_all_concepts(): + """从ES获取所有叶子概念及其股票列表""" + concepts = [] + + query = { + "query": {"match_all": {}}, + "size": 100, + "_source": ["concept_id", "concept", "stocks"] + } + + resp = ES_CLIENT.search(index=INDEX_NAME, body=query, scroll='2m') + scroll_id = resp['_scroll_id'] + hits = resp['hits']['hits'] + + while len(hits) > 0: + for hit in hits: + source = hit['_source'] + concept_info = { + 'concept_id': source.get('concept_id'), + 'concept_name': source.get('concept'), + 'stocks': [], + 'concept_type': 'leaf' + } + + if 'stocks' in source and isinstance(source['stocks'], list): + for stock in source['stocks']: + if isinstance(stock, dict) and 'code' in stock and stock['code']: + concept_info['stocks'].append(stock['code']) + + if concept_info['stocks']: + concepts.append(concept_info) + + resp = ES_CLIENT.scroll(scroll_id=scroll_id, scroll='2m') + scroll_id = resp['_scroll_id'] + hits = resp['hits']['hits'] + + ES_CLIENT.clear_scroll(scroll_id=scroll_id) + return concepts + + +def load_hierarchy_concepts(leaf_concepts: list) -> list: + """加载层级结构，生成母概念""" + hierarchy_path = os.path.join(os.path.dirname(__file__), HIERARCHY_FILE) + if not os.path.exists(hierarchy_path): + logger.warning(f"层级文件不存在: {hierarchy_path}") + return [] + + with open(hierarchy_path, 'r', encoding='utf-8') as f: + hierarchy_data = json.load(f) + + concept_to_stocks = {} + for c in leaf_concepts: + concept_to_stocks[c['concept_name']] = set(c['stocks']) + + parent_concepts = [] + + for lv1 in hierarchy_data.get('hierarchy', []): + lv1_name = lv1.get('lv1', '') + lv1_stocks = set() + + for child in lv1.get('children', []): + lv2_name = child.get('lv2', '') + lv2_stocks = set() + + if 'children' in child: + for lv3_child in child.get('children', []): + lv3_name = lv3_child.get('lv3', '') + lv3_stocks = set() + + for concept_name in lv3_child.get('concepts', []): + if concept_name in concept_to_stocks: + lv3_stocks.update(concept_to_stocks[concept_name]) + + if lv3_stocks: + parent_concepts.append({ + 'concept_id': generate_id(f"lv3_{lv3_name}"), + 'concept_name': f"[三级] {lv3_name}", + 'stocks': list(lv3_stocks), + 'concept_type': 'lv3' + }) + + lv2_stocks.update(lv3_stocks) + else: + for concept_name in child.get('concepts', []): + if concept_name in concept_to_stocks: + lv2_stocks.update(concept_to_stocks[concept_name]) + + if lv2_stocks: + parent_concepts.append({ + 'concept_id': generate_id(f"lv2_{lv2_name}"), + 'concept_name': f"[二级] {lv2_name}", + 'stocks': list(lv2_stocks), + 'concept_type': 'lv2' + }) + + lv1_stocks.update(lv2_stocks) + + if lv1_stocks: + parent_concepts.append({ + 'concept_id': generate_id(f"lv1_{lv1_name}"), + 'concept_name': f"[一级] {lv1_name}", + 'stocks': list(lv1_stocks), + 'concept_type': 'lv1' + }) + + return parent_concepts + + +# ==================== 价格数据获取 ==================== + +def get_base_prices(stock_codes: list, current_date: str) -> dict: + """获取昨收价作为基准""" + if not stock_codes: + return {} + + valid_codes = [code for code in stock_codes if code and len(code) == 6 and code.isdigit()] + if not valid_codes: + return {} + + stock_codes_str = "','".join(valid_codes) + + query = f""" + SELECT SECCODE, F002N + FROM ea_trade + WHERE SECCODE IN ('{stock_codes_str}') + AND TRADEDATE = ( + SELECT MAX(TRADEDATE) + FROM ea_trade + WHERE TRADEDATE <= '{current_date}' + ) + AND F002N IS NOT NULL AND F002N > 0 + """ + + try: + with MYSQL_ENGINE.connect() as conn: + result = conn.execute(text(query)) + base_prices = {row[0]: float(row[1]) for row in result if row[1] and float(row[1]) > 0} + return base_prices + except Exception as e: + logger.error(f"获取基准价格失败: {e}") + return {} + + +def get_latest_prices(stock_codes: list) -> dict: + """从ClickHouse获取最新价格""" + if not stock_codes: + return {} + + client = get_ch_client() + + ch_codes = [] + code_mapping = {} + for code in stock_codes: + ch_code = code_to_ch_format(code) + if ch_code: + ch_codes.append(ch_code) + code_mapping[ch_code] = code + + if not ch_codes: + return {} + + ch_codes_str = "','".join(ch_codes) + + query = f""" + SELECT code, close, timestamp + FROM ( + SELECT code, close, timestamp, + ROW_NUMBER() OVER (PARTITION BY code ORDER BY timestamp DESC) as rn + FROM stock_minute + WHERE code IN ('{ch_codes_str}') + AND toDate(timestamp) = today() + ) + WHERE rn = 1 + """ + + try: + result = client.execute(query) + if not result: + return {} + + latest_prices = {} + for row in result: + ch_code, close, ts = row + if close and close > 0: + pure_code = code_mapping.get(ch_code) + if pure_code: + latest_prices[pure_code] = { + 'close': float(close), + 'timestamp': ts + } + + return latest_prices + except Exception as e: + logger.error(f"获取最新价格失败: {e}") + return {} + + +def get_index_realtime(index_code: str = REFERENCE_INDEX) -> dict: + """获取指数实时数据""" + client = get_ch_client() + + try: + # 从 index_minute 表获取最新数据 + query = f""" + SELECT close, timestamp + FROM index_minute + WHERE code = '{index_code}' + AND toDate(timestamp) = today() + ORDER BY timestamp DESC + LIMIT 1 + """ + result = client.execute(query) + + if not result: + return None + + close, ts = result[0] + + # 获取昨收价 + prev_close = None + code_no_suffix = index_code.split('.')[0] + + with MYSQL_ENGINE.connect() as conn: + prev_result = conn.execute(text(""" + SELECT F006N FROM ea_exchangetrade + WHERE INDEXCODE = :code + AND TRADEDATE < CURDATE() + ORDER BY TRADEDATE DESC LIMIT 1 + """), {'code': code_no_suffix}).fetchone() + + if prev_result and prev_result[0]: + prev_close = float(prev_result[0]) + + change_pct = None + if close and prev_close and prev_close > 0: + change_pct = (float(close) - prev_close) / prev_close * 100 + + return { + 'code': index_code, + 'price': float(close), + 'prev_close': prev_close, + 'change_pct': round(change_pct, 4) if change_pct else None, + 'timestamp': ts + } + + except Exception as e: + logger.error(f"获取指数数据失败: {e}") + return None + + +# ==================== 涨跌幅计算 ==================== + +def calculate_change_pct(base_prices: dict, latest_prices: dict) -> dict: + """计算涨跌幅""" + changes = {} + for code, latest in latest_prices.items(): + if code in base_prices and base_prices[code] > 0: + base = base_prices[code] + close = latest['close'] + change_pct = (close - base) / base * 100 + changes[code] = { + 'change_pct': round(change_pct, 4), + 'close': close, + 'base': base + } + return changes + + +def calculate_concept_stats(concepts: list, stock_changes: dict) -> list: + """计算概念统计（包含涨停数）""" + stats = [] + + for concept in concepts: + concept_id = concept['concept_id'] + concept_name = concept['concept_name'] + stock_codes = concept['stocks'] + concept_type = concept.get('concept_type', 'leaf') + + changes = [] + limit_up_count = 0 + limit_up_stocks = [] + + for code in stock_codes: + if code in stock_changes: + change_info = stock_changes[code] + change_pct = change_info['change_pct'] + changes.append(change_pct) + + # 涨停判断（涨幅 >= 9.8%） + if change_pct >= 9.8: + limit_up_count += 1 + limit_up_stocks.append(code) + + if not changes: + continue + + avg_change_pct = round(np.mean(changes), 4) + + stats.append({ + 'concept_id': concept_id, + 'concept_name': concept_name, + 'avg_change_pct': avg_change_pct, + 'stock_count': len(changes), + 'concept_type': concept_type, + 'limit_up_count': limit_up_count, + 'limit_up_stocks': limit_up_stocks + }) + + # 按涨幅排序并添加排名 + stats.sort(key=lambda x: x['avg_change_pct'], reverse=True) + for i, item in enumerate(stats): + item['rank'] = i + 1 + + return stats + + +# ==================== 异动检测 ==================== + +def check_cooldown(concept_id: str, alert_type: str, cooldown_minutes: int) -> bool: + """检查是否在冷却期内""" + key = (concept_id, alert_type) + if key in cooldown_cache: + last_alert = cooldown_cache[key] + if datetime.now() - last_alert < timedelta(minutes=cooldown_minutes): + return True + return False + + +def set_cooldown(concept_id: str, alert_type: str): + """设置冷却""" + cooldown_cache[(concept_id, alert_type)] = datetime.now() + + +def update_history(concept_id: str, timestamp: datetime, change_pct: float, rank: int, limit_up_count: int): + """更新历史缓存""" + if concept_id not in history_cache: + history_cache[concept_id] = deque(maxlen=HISTORY_WINDOW) + + history_cache[concept_id].append({ + 'timestamp': timestamp, + 'change_pct': change_pct, + 'rank': rank, + 'limit_up_count': limit_up_count + }) + + +def get_history(concept_id: str, minutes_ago: int) -> dict: + """获取N分钟前的历史数据""" + if concept_id not in history_cache: + return None + + history = history_cache[concept_id] + if not history: + return None + + target_time = datetime.now() - timedelta(minutes=minutes_ago) + + # 找到最接近目标时间的记录 + for record in history: + if record['timestamp'] <= target_time: + return record + + # 如果没有足够早的数据，返回最早的记录 + return history[0] if history else None + + +def detect_alerts(current_stats: list, index_data: dict, trade_date: str) -> list: + """检测异动""" + alerts = [] + now = datetime.now() + + for stat in current_stats: + concept_id = stat['concept_id'] + concept_name = stat['concept_name'] + change_pct = stat['avg_change_pct'] + rank = stat['rank'] + limit_up_count = stat['limit_up_count'] + stock_count = stat['stock_count'] + concept_type = stat['concept_type'] + + # 更新历史 + update_history(concept_id, now, change_pct, rank, limit_up_count) + + # 1. 急涨检测 + if ALERT_CONFIG['surge']['enabled']: + cfg = ALERT_CONFIG['surge'] + if change_pct >= cfg['min_change_pct']: # 最低涨幅要求 + if not check_cooldown(concept_id, 'surge', cfg['cooldown_minutes']): + prev_data = get_history(concept_id, cfg['window_minutes']) + if prev_data: + change_delta = change_pct - prev_data['change_pct'] + if change_delta >= cfg['threshold_pct']: + alerts.append({ + 'concept_id': concept_id, + 'concept_name': concept_name, + 'alert_type': 'surge', + 'alert_time': now, + 'trade_date': trade_date, + 'change_pct': change_pct, + 'prev_change_pct': prev_data['change_pct'], + 'change_delta': round(change_delta, 4), + 'limit_up_count': limit_up_count, + 'rank_position': rank, + 'stock_count': stock_count, + 'concept_type': concept_type, + 'index_price': index_data['price'] if index_data else None, + 'index_change_pct': index_data['change_pct'] if index_data else None, + }) + set_cooldown(concept_id, 'surge') + logger.info(f"🔥 急涨异动: {concept_name} 涨幅 {prev_data['change_pct']:.2f}% -> {change_pct:.2f}% (+{change_delta:.2f}%)") + + # 2. 涨停数增加检测 + if ALERT_CONFIG['limit_up']['enabled']: + cfg = ALERT_CONFIG['limit_up'] + if limit_up_count > 0: + if not check_cooldown(concept_id, 'limit_up', cfg['cooldown_minutes']): + prev_data = get_history(concept_id, 1) # 对比上一分钟 + if prev_data: + limit_up_delta = limit_up_count - prev_data['limit_up_count'] + if limit_up_delta >= cfg['threshold_count']: + alerts.append({ + 'concept_id': concept_id, + 'concept_name': concept_name, + 'alert_type': 'limit_up', + 'alert_time': now, + 'trade_date': trade_date, + 'change_pct': change_pct, + 'limit_up_count': limit_up_count, + 'prev_limit_up_count': prev_data['limit_up_count'], + 'limit_up_delta': limit_up_delta, + 'rank_position': rank, + 'stock_count': stock_count, + 'concept_type': concept_type, + 'index_price': index_data['price'] if index_data else None, + 'index_change_pct': index_data['change_pct'] if index_data else None, + 'extra_info': {'limit_up_stocks': stat.get('limit_up_stocks', [])} + }) + set_cooldown(concept_id, 'limit_up') + logger.info(f"🚀 涨停异动: {concept_name} 涨停数 {prev_data['limit_up_count']} -> {limit_up_count} (+{limit_up_delta})") + + # 3. 排名跃升检测 + if ALERT_CONFIG['rank_jump']['enabled']: + cfg = ALERT_CONFIG['rank_jump'] + if rank <= cfg['max_rank']: # 只关注前N名 + if not check_cooldown(concept_id, 'rank_jump', cfg['cooldown_minutes']): + prev_data = get_history(concept_id, cfg['window_minutes']) + if prev_data and prev_data['rank'] > cfg['max_rank']: # 从榜外进入前N + rank_delta = prev_data['rank'] - rank # 正数表示上升 + if rank_delta >= cfg['threshold_rank']: + alerts.append({ + 'concept_id': concept_id, + 'concept_name': concept_name, + 'alert_type': 'rank_jump', + 'alert_time': now, + 'trade_date': trade_date, + 'change_pct': change_pct, + 'rank_position': rank, + 'prev_rank_position': prev_data['rank'], + 'rank_delta': -rank_delta, # 负数表示上升 + 'limit_up_count': limit_up_count, + 'stock_count': stock_count, + 'concept_type': concept_type, + 'index_price': index_data['price'] if index_data else None, + 'index_change_pct': index_data['change_pct'] if index_data else None, + }) + set_cooldown(concept_id, 'rank_jump') + logger.info(f"📈 排名跃升: {concept_name} 排名 {prev_data['rank']} -> {rank} (上升{rank_delta}名)") + + return alerts + + +# ==================== 数据持久化 ==================== + +def save_alerts_to_mysql(alerts: list): + """保存异动数据到MySQL""" + if not alerts: + return 0 + + saved = 0 + with MYSQL_ENGINE.begin() as conn: + for alert in alerts: + try: + insert_sql = text(""" + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, prev_change_pct, change_delta, + limit_up_count, prev_limit_up_count, limit_up_delta, + rank_position, prev_rank_position, rank_delta, + index_code, index_price, index_change_pct, + stock_count, concept_type, extra_info) + VALUES + (:concept_id, :concept_name, :alert_time, :alert_type, :trade_date, + :change_pct, :prev_change_pct, :change_delta, + :limit_up_count, :prev_limit_up_count, :limit_up_delta, + :rank_position, :prev_rank_position, :rank_delta, + :index_code, :index_price, :index_change_pct, + :stock_count, :concept_type, :extra_info) + """) + + params = { + 'concept_id': alert['concept_id'], + 'concept_name': alert['concept_name'], + 'alert_time': alert['alert_time'], + 'alert_type': alert['alert_type'], + 'trade_date': alert['trade_date'], + 'change_pct': alert.get('change_pct'), + 'prev_change_pct': alert.get('prev_change_pct'), + 'change_delta': alert.get('change_delta'), + 'limit_up_count': alert.get('limit_up_count', 0), + 'prev_limit_up_count': alert.get('prev_limit_up_count', 0), + 'limit_up_delta': alert.get('limit_up_delta', 0), + 'rank_position': alert.get('rank_position'), + 'prev_rank_position': alert.get('prev_rank_position'), + 'rank_delta': alert.get('rank_delta'), + 'index_code': REFERENCE_INDEX, + 'index_price': alert.get('index_price'), + 'index_change_pct': alert.get('index_change_pct'), + 'stock_count': alert.get('stock_count'), + 'concept_type': alert.get('concept_type', 'leaf'), + 'extra_info': json.dumps(alert.get('extra_info')) if alert.get('extra_info') else None + } + + conn.execute(insert_sql, params) + saved += 1 + + except Exception as e: + logger.error(f"保存异动失败: {alert['concept_name']} - {e}") + + return saved + + +def save_index_snapshot(index_data: dict, trade_date: str): + """保存指数快照""" + if not index_data: + return + + try: + with MYSQL_ENGINE.begin() as conn: + upsert_sql = text(""" + REPLACE INTO index_minute_snapshot + (index_code, trade_date, snapshot_time, price, prev_close, change_pct) + VALUES (:index_code, :trade_date, :snapshot_time, :price, :prev_close, :change_pct) + """) + + conn.execute(upsert_sql, { + 'index_code': index_data['code'], + 'trade_date': trade_date, + 'snapshot_time': index_data['timestamp'], + 'price': index_data['price'], + 'prev_close': index_data.get('prev_close'), + 'change_pct': index_data.get('change_pct') + }) + except Exception as e: + logger.error(f"保存指数快照失败: {e}") + + +# ==================== 交易时间判断 ==================== + +def is_trading_time() -> bool: + """判断当前是否为交易时间""" + now = datetime.now() + weekday = now.weekday() + + if weekday >= 5: + return False + + hour, minute = now.hour, now.minute + current_time = hour * 60 + minute + + morning_start = 9 * 60 + 30 + morning_end = 11 * 60 + 30 + afternoon_start = 13 * 60 + afternoon_end = 15 * 60 + + return (morning_start <= current_time <= morning_end) or \ + (afternoon_start <= current_time <= afternoon_end) + + +def get_next_update_time() -> int: + """获取距离下次更新的秒数""" + now = datetime.now() + + if is_trading_time(): + return 60 - now.second + else: + hour, minute = now.hour, now.minute + + if hour < 9 or (hour == 9 and minute < 30): + target = now.replace(hour=9, minute=30, second=0, microsecond=0) + elif (hour == 11 and minute >= 30) or hour == 12: + target = now.replace(hour=13, minute=0, second=0, microsecond=0) + elif hour >= 15: + target = (now + timedelta(days=1)).replace(hour=9, minute=30, second=0, microsecond=0) + else: + target = now + timedelta(minutes=1) + + wait_seconds = (target - now).total_seconds() + return max(60, int(wait_seconds)) + + +# ==================== 主运行逻辑 ==================== + +def run_once(concepts: list, all_stocks: list) -> tuple: + """执行一次检测，返回 (更新数, 异动数)""" + now = datetime.now() + trade_date = now.strftime('%Y-%m-%d') + + # 获取基准价格 + base_prices = get_base_prices(all_stocks, trade_date) + if not base_prices: + logger.warning("无法获取基准价格") + return 0, 0 + + # 获取最新价格 + latest_prices = get_latest_prices(all_stocks) + if not latest_prices: + logger.warning("无法获取最新价格") + return 0, 0 + + # 获取指数数据 + index_data = get_index_realtime(REFERENCE_INDEX) + if index_data: + save_index_snapshot(index_data, trade_date) + + # 计算涨跌幅 + stock_changes = calculate_change_pct(base_prices, latest_prices) + if not stock_changes: + logger.warning("无涨跌幅数据") + return 0, 0 + + logger.info(f"获取到 {len(stock_changes)} 只股票的涨跌幅") + + # 计算概念统计 + stats = calculate_concept_stats(concepts, stock_changes) + logger.info(f"计算了 {len(stats)} 个概念的涨跌幅") + + # 检测异动 + alerts = detect_alerts(stats, index_data, trade_date) + + # 保存异动 + if alerts: + saved = save_alerts_to_mysql(alerts) + logger.info(f"💾 保存了 {saved} 条异动记录") + + return len(stats), len(alerts) + + +def run_realtime(): + """实时检测主循环""" + logger.info("=" * 60) + logger.info("🚀 启动概念异动实时检测服务") + logger.info("=" * 60) + logger.info(f"异动配置: {json.dumps(ALERT_CONFIG, indent=2, ensure_ascii=False)}") + + # 加载概念数据 + logger.info("加载概念数据...") + leaf_concepts = get_all_concepts() + logger.info(f"获取到 {len(leaf_concepts)} 个叶子概念") + + parent_concepts = load_hierarchy_concepts(leaf_concepts) + logger.info(f"生成了 {len(parent_concepts)} 个母概念") + + all_concepts = leaf_concepts + parent_concepts + logger.info(f"总计 {len(all_concepts)} 个概念") + + # 收集所有股票代码 + all_stocks = set() + for c in all_concepts: + all_stocks.update(c['stocks']) + all_stocks = list(all_stocks) + logger.info(f"监控 {len(all_stocks)} 只股票") + + last_concept_update = datetime.now() + total_alerts = 0 + + while True: + try: + now = datetime.now() + + # 每小时重新加载概念数据 + if (now - last_concept_update).total_seconds() > 3600: + logger.info("重新加载概念数据...") + leaf_concepts = get_all_concepts() + parent_concepts = load_hierarchy_concepts(leaf_concepts) + all_concepts = leaf_concepts + parent_concepts + all_stocks = set() + for c in all_concepts: + all_stocks.update(c['stocks']) + all_stocks = list(all_stocks) + last_concept_update = now + logger.info(f"更新完成: {len(all_concepts)} 个概念, {len(all_stocks)} 只股票") + + # 检查是否交易时间 + if not is_trading_time(): + wait_sec = get_next_update_time() + wait_min = wait_sec // 60 + logger.info(f"⏰ 非交易时间，等待 {wait_min} 分钟后重试...") + time.sleep(min(wait_sec, 300)) + continue + + # 执行检测 + logger.info(f"\n{'=' * 40}") + logger.info(f"🔍 检测时间: {now.strftime('%Y-%m-%d %H:%M:%S')}") + updated, alert_count = run_once(all_concepts, all_stocks) + total_alerts += alert_count + + if alert_count > 0: + logger.info(f"📊 本次检测到 {alert_count} 条异动，累计 {total_alerts} 条") + + # 等待下一分钟 + sleep_sec = 60 - datetime.now().second + logger.info(f"⏳ 等待 {sleep_sec} 秒后继续...") + time.sleep(sleep_sec) + + except KeyboardInterrupt: + logger.info("\n收到退出信号，停止服务...") + break + except Exception as e: + logger.error(f"发生错误: {e}") + import traceback + traceback.print_exc() + time.sleep(60) + + +def run_single(): + """单次运行""" + logger.info("单次检测模式") + + leaf_concepts = get_all_concepts() + parent_concepts = load_hierarchy_concepts(leaf_concepts) + all_concepts = leaf_concepts + parent_concepts + + all_stocks = set() + for c in all_concepts: + all_stocks.update(c['stocks']) + all_stocks = list(all_stocks) + + logger.info(f"概念数: {len(all_concepts)}, 股票数: {len(all_stocks)}") + + updated, alerts = run_once(all_concepts, all_stocks) + logger.info(f"检测完成: {updated} 个概念, {alerts} 条异动") + + +def show_status(): + """显示状态""" + print("\n" + "=" * 60) + print("概念异动实时检测服务 - 状态") + print("=" * 60) + + now = datetime.now() + print(f"\n当前时间: {now.strftime('%Y-%m-%d %H:%M:%S')}") + print(f"是否交易时间: {'是' if is_trading_time() else '否'}") + + # 今日异动统计 + print("\n今日异动统计:") + try: + with MYSQL_ENGINE.connect() as conn: + result = conn.execute(text(""" + SELECT alert_type, COUNT(*) as cnt + FROM concept_minute_alert + WHERE trade_date = CURDATE() + GROUP BY alert_type + """)) + rows = list(result) + if rows: + for row in rows: + alert_type_name = { + 'surge': '急涨', + 'limit_up': '涨停增加', + 'rank_jump': '排名跃升' + }.get(row[0], row[0]) + print(f" {alert_type_name}: {row[1]} 条") + else: + print(" 今日暂无异动") + + # 最新异动 + print("\n最新异动 (前10条):") + result = conn.execute(text(""" + SELECT concept_name, alert_type, alert_time, change_pct, limit_up_count, index_price + FROM concept_minute_alert + WHERE trade_date = CURDATE() + ORDER BY alert_time DESC + LIMIT 10 + """)) + rows = list(result) + if rows: + print(f" {'概念':<20} | {'类型':<8} | {'时间':<8} | {'涨幅':>6} | {'涨停':>4} | {'指数':>8}") + print(" " + "-" * 70) + for row in rows: + name = row[0][:18] if len(row[0]) > 18 else row[0] + alert_type = {'surge': '急涨', 'limit_up': '涨停', 'rank_jump': '排名'}.get(row[1], row[1]) + time_str = row[2].strftime('%H:%M') if row[2] else '-' + change = f"{row[3]:.2f}%" if row[3] else '-' + limit_up = str(row[4]) if row[4] else '-' + index_p = f"{row[5]:.2f}" if row[5] else '-' + print(f" {name:<20} | {alert_type:<8} | {time_str:<8} | {change:>6} | {limit_up:>4} | {index_p:>8}") + else: + print(" 暂无异动记录") + + except Exception as e: + print(f" 查询失败: {e}") + + +def init_tables(): + """初始化数据库表""" + print("初始化数据库表...") + + sql_file = os.path.join(os.path.dirname(__file__), 'sql', 'concept_minute_alert.sql') + + if not os.path.exists(sql_file): + print(f"SQL文件不存在: {sql_file}") + return + + with open(sql_file, 'r', encoding='utf-8') as f: + sql_content = f.read() + + # 分割多个语句 + statements = [s.strip() for s in sql_content.split(';') if s.strip() and not s.strip().startswith('--')] + + with MYSQL_ENGINE.begin() as conn: + for stmt in statements: + if stmt: + try: + conn.execute(text(stmt)) + print(f"✅ 执行成功") + except Exception as e: + print(f"❌ 执行失败: {e}") + + print("初始化完成") + + +# ==================== 回测功能 ==================== + +def get_minute_timestamps(trade_date: str) -> list: + """获取指定交易日的所有分钟时间戳""" + client = get_ch_client() + + query = f""" + SELECT DISTINCT timestamp + FROM stock_minute + WHERE toDate(timestamp) = '{trade_date}' + ORDER BY timestamp + """ + + result = client.execute(query) + return [row[0] for row in result] + + +def get_prices_at_time(stock_codes: list, timestamp: datetime) -> dict: + """获取指定时间点的股票价格 + + Args: + stock_codes: 纯6位股票代码列表 + timestamp: 指定的时间点 + + Returns: + dict: {纯6位代码: {'close': 价格, 'timestamp': 时间}} + """ + if not stock_codes: + return {} + + client = get_ch_client() + + # 转换为ClickHouse格式 + ch_codes = [] + code_mapping = {} + for code in stock_codes: + ch_code = code_to_ch_format(code) + if ch_code: + ch_codes.append(ch_code) + code_mapping[ch_code] = code + + if not ch_codes: + return {} + + ch_codes_str = "','".join(ch_codes) + + # 获取指定时间点的数据 + query = f""" + SELECT code, close, timestamp + FROM stock_minute + WHERE code IN ('{ch_codes_str}') + AND timestamp = '{timestamp.strftime('%Y-%m-%d %H:%M:%S')}' + """ + + try: + result = client.execute(query) + prices = {} + for row in result: + ch_code, close, ts = row + if close and close > 0: + pure_code = code_mapping.get(ch_code) + if pure_code: + prices[pure_code] = { + 'close': float(close), + 'timestamp': ts + } + return prices + except Exception as e: + logger.error(f"获取历史价格失败: {e}") + return {} + + +def get_index_at_time(index_code: str, timestamp: datetime, prev_close: float) -> dict: + """获取指定时间点的指数数据""" + client = get_ch_client() + + query = f""" + SELECT close, timestamp + FROM index_minute + WHERE code = '{index_code}' + AND timestamp = '{timestamp.strftime('%Y-%m-%d %H:%M:%S')}' + LIMIT 1 + """ + + try: + result = client.execute(query) + if not result: + return None + + close, ts = result[0] + change_pct = None + if close and prev_close and prev_close > 0: + change_pct = (float(close) - prev_close) / prev_close * 100 + + return { + 'code': index_code, + 'price': float(close), + 'prev_close': prev_close, + 'change_pct': round(change_pct, 4) if change_pct else None, + 'timestamp': ts + } + except Exception as e: + logger.error(f"获取指数数据失败: {e}") + return None + + +def get_index_prev_close(index_code: str, trade_date: str) -> float: + """获取指数昨收价""" + code_no_suffix = index_code.split('.')[0] + + with MYSQL_ENGINE.connect() as conn: + result = conn.execute(text(""" + SELECT F006N FROM ea_exchangetrade + WHERE INDEXCODE = :code + AND TRADEDATE < :today + ORDER BY TRADEDATE DESC LIMIT 1 + """), { + 'code': code_no_suffix, + 'today': trade_date + }).fetchone() + + if result and result[0]: + return float(result[0]) + return None + + +def run_backtest(trade_date: str, clear_existing: bool = True): + """ + 回测指定日期的异动检测 + + Args: + trade_date: 交易日期，格式 'YYYY-MM-DD' + clear_existing: 是否清除该日期已有的异动数据 + """ + global history_cache, cooldown_cache + + logger.info("=" * 60) + logger.info(f"🔄 开始回测: {trade_date}") + logger.info("=" * 60) + + # 清空缓存 + history_cache = {} + cooldown_cache = {} + + # 清除已有数据 + if clear_existing: + with MYSQL_ENGINE.begin() as conn: + conn.execute(text("DELETE FROM concept_minute_alert WHERE trade_date = :date"), {'date': trade_date}) + conn.execute(text("DELETE FROM index_minute_snapshot WHERE trade_date = :date"), {'date': trade_date}) + logger.info(f"已清除 {trade_date} 的已有数据") + + # 加载概念数据 + logger.info("加载概念数据...") + leaf_concepts = get_all_concepts() + logger.info(f"获取到 {len(leaf_concepts)} 个叶子概念") + + parent_concepts = load_hierarchy_concepts(leaf_concepts) + logger.info(f"生成了 {len(parent_concepts)} 个母概念") + + all_concepts = leaf_concepts + parent_concepts + logger.info(f"总计 {len(all_concepts)} 个概念") + + # 收集所有股票代码 + all_stocks = set() + for c in all_concepts: + all_stocks.update(c['stocks']) + all_stocks = list(all_stocks) + logger.info(f"监控 {len(all_stocks)} 只股票") + + # 获取基准价格（昨收价） + base_prices = get_base_prices(all_stocks, trade_date) + if not base_prices: + logger.error("无法获取基准价格，退出回测") + return + logger.info(f"获取到 {len(base_prices)} 个基准价格") + + # 获取指数昨收价 + index_prev_close = get_index_prev_close(REFERENCE_INDEX, trade_date) + logger.info(f"指数昨收价: {index_prev_close}") + + # 获取所有分钟时间戳 + timestamps = get_minute_timestamps(trade_date) + if not timestamps: + logger.error(f"未找到 {trade_date} 的分钟数据") + return + logger.info(f"找到 {len(timestamps)} 个分钟时间点") + + total_alerts = 0 + processed = 0 + + # 逐分钟处理 + for ts in timestamps: + processed += 1 + + # 获取该时间点的价格 + latest_prices = get_prices_at_time(all_stocks, ts) + if not latest_prices: + continue + + # 获取指数数据 + index_data = get_index_at_time(REFERENCE_INDEX, ts, index_prev_close) + if index_data: + save_index_snapshot(index_data, trade_date) + + # 计算涨跌幅 + stock_changes = calculate_change_pct(base_prices, latest_prices) + if not stock_changes: + continue + + # 计算概念统计 + stats = calculate_concept_stats(all_concepts, stock_changes) + + # 检测异动（使用回测专用函数） + alerts = detect_alerts_backtest(stats, index_data, trade_date, ts) + + # 保存异动 + if alerts: + saved = save_alerts_to_mysql(alerts) + total_alerts += saved + + # 进度显示 + if processed % 30 == 0: + logger.info(f"进度: {processed}/{len(timestamps)} ({processed*100//len(timestamps)}%), 已检测到 {total_alerts} 条异动") + + logger.info("=" * 60) + logger.info(f"✅ 回测完成!") + logger.info(f" 处理分钟数: {processed}") + logger.info(f" 检测到异动: {total_alerts} 条") + logger.info("=" * 60) + + +def detect_alerts_backtest(current_stats: list, index_data: dict, trade_date: str, current_time: datetime) -> list: + """ + 回测模式的异动检测（使用指定时间而非当前时间） + """ + alerts = [] + + for stat in current_stats: + concept_id = stat['concept_id'] + concept_name = stat['concept_name'] + change_pct = stat['avg_change_pct'] + rank = stat['rank'] + limit_up_count = stat['limit_up_count'] + stock_count = stat['stock_count'] + concept_type = stat['concept_type'] + + # 更新历史（使用指定时间） + if concept_id not in history_cache: + history_cache[concept_id] = deque(maxlen=HISTORY_WINDOW) + + history_cache[concept_id].append({ + 'timestamp': current_time, + 'change_pct': change_pct, + 'rank': rank, + 'limit_up_count': limit_up_count + }) + + # 获取历史数据的辅助函数（回测专用） + def get_history_backtest(concept_id: str, minutes_ago: int): + if concept_id not in history_cache: + return None + history = history_cache[concept_id] + if not history: + return None + + target_time = current_time - timedelta(minutes=minutes_ago) + for record in reversed(list(history)): + if record['timestamp'] <= target_time: + return record + return None + + # 检查冷却（回测专用） + def check_cooldown_backtest(concept_id: str, alert_type: str, cooldown_minutes: int) -> bool: + key = (concept_id, alert_type) + if key in cooldown_cache: + last_alert = cooldown_cache[key] + if current_time - last_alert < timedelta(minutes=cooldown_minutes): + return True + return False + + def set_cooldown_backtest(concept_id: str, alert_type: str): + cooldown_cache[(concept_id, alert_type)] = current_time + + # 1. 急涨检测 + if ALERT_CONFIG['surge']['enabled']: + cfg = ALERT_CONFIG['surge'] + if change_pct >= cfg['min_change_pct']: + if not check_cooldown_backtest(concept_id, 'surge', cfg['cooldown_minutes']): + prev_data = get_history_backtest(concept_id, cfg['window_minutes']) + if prev_data: + change_delta = change_pct - prev_data['change_pct'] + if change_delta >= cfg['threshold_pct']: + alerts.append({ + 'concept_id': concept_id, + 'concept_name': concept_name, + 'alert_type': 'surge', + 'alert_time': current_time, + 'trade_date': trade_date, + 'change_pct': change_pct, + 'prev_change_pct': prev_data['change_pct'], + 'change_delta': round(change_delta, 4), + 'limit_up_count': limit_up_count, + 'rank_position': rank, + 'stock_count': stock_count, + 'concept_type': concept_type, + 'index_price': index_data['price'] if index_data else None, + 'index_change_pct': index_data['change_pct'] if index_data else None, + }) + set_cooldown_backtest(concept_id, 'surge') + logger.debug(f"🔥 急涨: {concept_name} {prev_data['change_pct']:.2f}% -> {change_pct:.2f}%") + + # 2. 涨停数增加检测 + if ALERT_CONFIG['limit_up']['enabled']: + cfg = ALERT_CONFIG['limit_up'] + if limit_up_count > 0: + if not check_cooldown_backtest(concept_id, 'limit_up', cfg['cooldown_minutes']): + prev_data = get_history_backtest(concept_id, 1) + if prev_data: + limit_up_delta = limit_up_count - prev_data['limit_up_count'] + if limit_up_delta >= cfg['threshold_count']: + alerts.append({ + 'concept_id': concept_id, + 'concept_name': concept_name, + 'alert_type': 'limit_up', + 'alert_time': current_time, + 'trade_date': trade_date, + 'change_pct': change_pct, + 'limit_up_count': limit_up_count, + 'prev_limit_up_count': prev_data['limit_up_count'], + 'limit_up_delta': limit_up_delta, + 'rank_position': rank, + 'stock_count': stock_count, + 'concept_type': concept_type, + 'index_price': index_data['price'] if index_data else None, + 'index_change_pct': index_data['change_pct'] if index_data else None, + 'extra_info': {'limit_up_stocks': stat.get('limit_up_stocks', [])} + }) + set_cooldown_backtest(concept_id, 'limit_up') + logger.debug(f"🚀 涨停: {concept_name} 涨停数 +{limit_up_delta}") + + # 3. 排名跃升检测 + if ALERT_CONFIG['rank_jump']['enabled']: + cfg = ALERT_CONFIG['rank_jump'] + if rank <= cfg['max_rank']: + if not check_cooldown_backtest(concept_id, 'rank_jump', cfg['cooldown_minutes']): + prev_data = get_history_backtest(concept_id, cfg['window_minutes']) + if prev_data and prev_data['rank'] > cfg['max_rank']: + rank_delta = prev_data['rank'] - rank + if rank_delta >= cfg['threshold_rank']: + alerts.append({ + 'concept_id': concept_id, + 'concept_name': concept_name, + 'alert_type': 'rank_jump', + 'alert_time': current_time, + 'trade_date': trade_date, + 'change_pct': change_pct, + 'rank_position': rank, + 'prev_rank_position': prev_data['rank'], + 'rank_delta': -rank_delta, + 'limit_up_count': limit_up_count, + 'stock_count': stock_count, + 'concept_type': concept_type, + 'index_price': index_data['price'] if index_data else None, + 'index_change_pct': index_data['change_pct'] if index_data else None, + }) + set_cooldown_backtest(concept_id, 'rank_jump') + logger.debug(f"📈 排名跃升: {concept_name} 排名 {prev_data['rank']} -> {rank}") + + return alerts + + +# ==================== 主函数 ==================== + +def main(): + parser = argparse.ArgumentParser(description='概念异动实时检测服务') + parser.add_argument('command', nargs='?', default='realtime', + choices=['realtime', 'once', 'status', 'init', 'backtest'], + help='命令: realtime(实时运行), once(单次运行), status(状态查看), init(初始化表), backtest(回测历史)') + parser.add_argument('--date', '-d', type=str, default=None, + help='回测日期，格式: YYYY-MM-DD，默认为今天') + parser.add_argument('--keep', '-k', action='store_true', + help='回测时保留已有数据（默认会清除）') + + args = parser.parse_args() + + if args.command == 'realtime': + run_realtime() + elif args.command == 'once': + run_single() + elif args.command == 'status': + show_status() + elif args.command == 'init': + init_tables() + elif args.command == 'backtest': + # 回测模式 + trade_date = args.date or datetime.now().strftime('%Y-%m-%d') + clear_existing = not args.keep + run_backtest(trade_date, clear_existing) + + +if __name__ == "__main__": + main() diff --git a/concept_hierarchy_v3.json b/concept_hierarchy_v3.json index a7eb2200..e5b74263 100644 --- a/concept_hierarchy_v3.json +++ b/concept_hierarchy_v3.json @@ -2,435 +2,602 @@ "hierarchy": [ { "lv1": "人工智能", - "lv1_id": "lv1_1", + "lv1_id": "ai", "children": [ { - "lv2": "AI基础设施", - "lv2_id": "lv2_1_1", + "lv2": "AI芯片与存储", + "lv2_id": "ai_chips_memory", + "concepts": [ + "AI算力芯片", + "AI芯片", + "GPU概念股", + "HBM", + "SRAM存储", + "RISC-V", + "TPU芯片", + "中昊芯英概念股", + "国产算力芯片", + "国产GPU", + "国产芯片参股公司", + "摩尔线程", + "摩尔线程IPO", + "沐曦集成", + "超威半导体AMD", + "英伟达概念", + "英伟达H20", + "芯片替代", + "谷歌", + "谷歌概念", + "阿里AI芯片", + "利基型存储DDR4", + "存储芯片产业", + "存储", + "存储芯片" + ] + }, + { + "lv2": "AI硬件与基础设施", + "lv2_id": "ai_hardware_infra", "children": [ { - "lv3": "AI算力硬件", - "lv3_id": "lv3_1_1_1", - "concepts": [ - "AI一体机", - "AI算力芯片", - "AI芯片", - "DeepSeek智算一体机", - "GPU概念股", - "TPU芯片", - "一体机核心标的弹性测算", - "中昊芯英概念股", - "国产算力芯片", - "国产GPU", - "服务器", - "昇腾推理一体机", - "摩尔线程", - "摩尔线程IPO", - "沐曦集成", - "阿里AI芯片" - ] - }, - { - "lv3": "AI关键组件", - "lv3_id": "lv3_1_1_2", + "lv3": "AI PCB与封装", + "lv3_id": "ai_pcb_packaging", "concepts": [ "AI PCB", "AI PCB英伟达M9", - "AI服务器钽电容", - "HBM", - "OCS光电路交换机", "PCB", "PCB设备及耗材", - "SRAM存储", - "光纤", - "光通信CPO", - "光通信", - "光芯片", - "光纤列阵单元FAU", - "博通交换机", - "改良型半加成工艺mSAP", - "服务器零部件", - "硅光技术", - "空芯光纤", - "薄膜铌酸锂", - "存储", - "存储芯片", - "存储芯片产业", - "铜互连", - "铜连接", - "钽电容", - "忆阻器", - "利基型存储DDR4" + "改良型半加成工艺mSAP" ] }, { - "lv3": "AI配套设施", - "lv3_id": "lv3_1_1_3", + "lv3": "AI一体机", + "lv3_id": "ai_integrated_machines", + "concepts": [ + "AI一体机", + "DeepSeek智算一体机", + "一体机核心标的弹性测算", + "昇腾推理一体机" + ] + }, + { + "lv3": "AI服务器组件", + "lv3_id": "ai_server_components", + "concepts": [ + "AI服务器钽电容", + "钽电容", + "功率半导体", + "电池备份单元", + "超级电容器" + ] + }, + { + "lv3": "AI数据中心与电力", + "lv3_id": "ai_data_center_power", "concepts": [ "AIDC供配电设备弹性", "数据中心", - "数据中心液冷", "数据中心电力设备", - "微泵液冷", - "微通道水冷板", - "液冷", - "液冷数据中心", - "液态金属散热", - "电池备份单元", - "电磁屏蔽", - "超级电容器", "柴油发电机", - "钻石散热", "英伟达电源方案" ] }, { - "lv3": "算力网络与服务", - "lv3_id": "lv3_1_1_4", + "lv3": "AI散热", + "lv3_id": "ai_cooling", "concepts": [ - "中国星际之门芜湖", - "上海算力", - "四川算力", - "大厂算力订单", - "字节算力", - "星际之门概念", - "杭州算力大会", - "核心城市智算算力", - "毫秒用算", - "算力" + "微泵液冷", + "微通道水冷板", + "数据中心液冷", + "液态金属散热", + "液冷", + "钻石散热", + "电磁屏蔽" + ] + }, + { + "lv3": "AI网络设备", + "lv3_id": "ai_network_equipment", + "concepts": [ + "博通交换机", + "铜连接", + "铜互连", + "铜互联" ] } ] }, { - "lv2": "AI模型与软件", - "lv2_id": "lv2_1_2", + "lv2": "AI光通信", + "lv2_id": "ai_optical_comm", + "concepts": [ + "光纤", + "光通信CPO", + "光通信", + "光芯片", + "光纤列阵单元FAU", + "OCS光电路交换机", + "硅光技术", + "空芯光纤", + "薄膜铌酸锂" + ] + }, + { + "lv2": "AI算力基础设施", + "lv2_id": "ai_computing_power_infra", + "concepts": [ + "中国星际之门芜湖", + "上海算力", + "四川算力", + "大厂算力订单", + "字节算力", + "核心城市智算算力", + "毫秒用算", + "星际之门概念", + "杭州算力大会", + "甲骨文概念股", + "算力" + ] + }, + { + "lv2": "AI模型与算法", + "lv2_id": "ai_models_algorithms", "concepts": [ "DeepSeek FP8", "DeepSeek", "DeepSeek、国产算力", - "KIMI", "MOE模型", "Minimax", - "Nano Banana", "SORA概念", "国产大模型", + "阶跃星辰" + ] + }, + { + "lv2": "AI应用与服务", + "lv2_id": "ai_applications_services", + "concepts": [ + "AI4S", + "AI应用AI语料", + "AI编程", + "Nano Banana", + "KIMI", + "低代码", + "内容审核概念", "文生视频", - "秘塔AI", - "阶跃星辰", + "智象未来", + "版权", + "秘塔AI" + ] + }, + { + "lv2": "AI智能体与陪伴", + "lv2_id": "ai_agents_companions", + "concepts": [ + "AI伴侣", + "AI成人陪伴", + "AI应用陪伴智能体", + "AI智能体", + "AI应用智能体", + "AI智能体AI应用", + "AI语音助手", + "AI陪伴", + "Manus", + "字节AI陪伴", + "开发智能体" + ] + }, + { + "lv2": "AI通用概念", + "lv2_id": "ai_general_concepts", + "concepts": [ + "AI-细分延伸更新", + "AI合集", + "物理AI", "马斯克Grok3大模型" ] }, { - "lv2": "AI应用", - "lv2_id": "lv2_1_3", - "children": [ - { - "lv3": "智能体与陪伴", - "lv3_id": "lv3_1_3_1", - "concepts": [ - "AI伴侣", - "AI成人陪伴", - "AI应用陪伴智能体", - "AI智能体", - "AI应用智能体", - "AI智能体AI应用", - "AI语音助手", - "AI陪伴", - "Manus", - "字节AI陪伴", - "开发智能体" - ] - }, - { - "lv3": "行业应用", - "lv3_id": "lv3_1_3_2", - "concepts": [ - "AI4S", - "AI应用AI语料", - "AI编程", - "低代码", - "内容审核概念", - "物理AI" - ] - } - ] - }, - { - "lv2": "AI生态系统", - "lv2_id": "lv2_1_4", - "children": [ - { - "lv3": "通用生态", - "lv3_id": "lv3_1_4_1", - "concepts": [ - "云计算各厂商云", - "微软Azure云平台", - "甲骨文概念股", - "英伟达代理", - "英伟达概念", - "英伟达H20", - "超威半导体AMD", - "谷歌", - "谷歌概念" - ] - }, - { - "lv3": "阿里生态", - "lv3_id": "lv3_1_4_2", - "concepts": [ - "通义千问阿里云", - "阿里云", - "阿里云通义千问", - "阿里AI千问、灵光", - "阿里“千问”项目", - "阿里AI来听" - ] - }, - { - "lv3": "腾讯生态", - "lv3_id": "lv3_1_4_3", - "concepts": [ - "腾讯元宝", - "腾讯大模型", - "腾讯混元大模型", - "腾讯云及大模型合作公司" - ] - }, - { - "lv3": "字节跳动生态", - "lv3_id": "lv3_1_4_4", - "concepts": [ - "努比亚手机", - "华为抖音支付", - "字节概念豆包AI手机", - "字节豆包概念股", - "抖音概念", - "豆包大模型" - ] - } - ] - }, - { - "lv2": "AI综合与趋势", - "lv2_id": "lv2_1_5", + "lv2": "云平台与大模型", + "lv2_id": "cloud_platforms_large_models", "concepts": [ - "AI-细分延伸更新", - "AI合集" + "云手机", + "云计算各厂商云", + "微软Azure云平台", + "腾讯元宝", + "腾讯大模型", + "腾讯混元大模型", + "腾讯云及大模型合作公司", + "通义千问阿里云", + "阿里云通义千问", + "阿里AI千问、灵光", + "阿里“千问”项目", + "阿里AI来听", + "阿里云" ] } ] }, { "lv1": "半导体", - "lv1_id": "lv1_2", + "lv1_id": "semiconductor", "children": [ { - "lv2": "半导体设备", - "lv2_id": "lv2_2_1", + "lv2": "半导体制造与材料", + "lv2_id": "semiconductor_mfg_materials", "concepts": [ "EDA", + "GAA晶体管", + "PSPI", "上海微电子", - "光刻机", + "先进陶瓷", + "光刻胶", "光刻机宇量昇", + "光刻机", + "半导体抛光液", + "半导体封测", + "半导体设计", + "半导体材料", + "半导体产业链", "半导体设备", + "半导体混合键合技术", + "国产光刻胶", + "国产半导体", "大湾区芯片展览会-新凯莱", "新凯来概念股", "新凯来示波器", - "电子束光刻机“羲之”" - ] - }, - { - "lv2": "半导体材料", - "lv2_id": "lv2_2_2", - "concepts": [ - "PSPI", - "先进陶瓷", - "光刻胶", - "半导体抛光液", - "半导体材料", - "国产光刻胶", - "石英砂", - "磷化铟" - ] - }, - { - "lv2": "芯片设计与制造", - "lv2_id": "lv2_2_3", - "concepts": [ - "ASIC", - "GAA晶体管", - "ISP视觉", - "RISC-V", - "功率半导体", - "半导体设计", - "第三代半导体", - "端侧AI芯片", - "碳化硅", - "英诺赛科概念股" - ] - }, - { - "lv2": "先进封装", - "lv2_id": "lv2_2_4", - "concepts": [ - "半导体封测", - "半导体混合键合技术", + "电子束光刻机“羲之”", "玻璃基板", + "石英砂", + "磷化铟", + "盛合晶微概念股", "盛合晶微", - "盛合晶微概念股" - ] - }, - { - "lv2": "重点企业与IPO", - "lv2_id": "lv2_2_5", - "concepts": [ - "地平线", - "地平线概念", + "第三代半导体", "紫光展锐IPO", + "碳化硅", + "科特估半导体", + "英诺赛科概念股", + "超硬材料", "长鑫存储", - "长鑫、长江产业链", - "高通概念" + "长鑫、长江产业链" ] }, { - "lv2": "综合与政策", - "lv2_id": "lv2_2_6", + "lv2": "半导体政策与地缘", + "lv2_id": "semiconductor_policy_geopolitics", "concepts": [ - "半导体产业链", - "国产半导体", - "国产芯片参股公司", - "科特估半导体", - "芯片替代", - "英特尔概念股" + "中美关系", + "出口管制" ] } ] }, { - "lv1": "机器人", - "lv1_id": "lv1_3", + "lv1": "机器人与智能制造", + "lv1_id": "robotics_smart_mfg", "children": [ { - "lv2": "人形机器人整机", - "lv2_id": "lv2_3_1", + "lv2": "人形机器人整机与生态", + "lv2_id": "humanoid_robot_systems_ecosystems", "concepts": [ "Optimus特斯拉机器人", "乐聚机器人", - "人形机器人", + "人形机器产业链", "人形机器人Figure", + "人形机器人", "云深处", "优必选", + "人形机器人万向节", + "人形机器人核心标的概览", + "人形机器人核心标的估值弹性测算", "优必选机器人", "华为人形机器人", "各厂商机器人", "奇瑞机器人潜在产业链", "天太机器人", - "天工机器人", "宇树人形机器人", "宇树机器人", + "小米智元机器人产业链机构机构版", + "天工机器人", "小鹏机器人", "小米机器人", - "小米智元机器人产业链机构版", "开普勒机器人", - "松延动力机器人", - "特斯拉人形机器人", + "工业机器人", "智元机器人", + "松延动力机器人", + "机器狗四足机器人", + "特斯拉人形机器人价值量", + "特斯拉人形机器人", + "特斯拉产业链", + "特斯拉人形机器人弹性测算", "荣耀华为人形机器人", + "美的库卡机器人", "赛力斯机器人" ] }, { - "lv2": "机器人核心零部件", - "lv2_id": "lv2_3_2", + "lv2": "机器人核心部件与材料", + "lv2_id": "robotics_core_components_materials", "concepts": [ + "MIM概念", "PCB轴向磁通电机", "人形机器人-滚柱丝杆丝杠", - "人形机器人万向节", + "人形机器人轻量化-PEEK材料", "人形机器人腱绳", - "机电", - "机器人皮肤仿生皮肤", + "冷锻产业链", + "机器人轻量化-PEEK", + "机器人轻量化—碳纤维", + "机器人零部件加工设备", + "机器人轻量化-镁铝合金", "摆线减速器", - "电子皮肤", - "轴向磁通电机" + "金属粉末注射成形MIM", + "超高分子量聚乙烯纤维", + "机器人氮化镓", + "轴向磁通电机", + "机电" ] }, { - "lv2": "机器人产业链", - "lv2_id": "lv2_3_3", - "children": [ - { - "lv3": "综合与价值链", - "lv3_id": "lv3_3_3_1", - "concepts": [ - "人形机器产业链", - "人形机器人核心标的估值弹性测算", - "人形机器人核心标的概览", - "特斯拉人形机器人价值量", - "特斯拉人形机器人弹性测算", - "特斯拉产业链" - ] - }, - { - "lv3": "轻量化材料", - "lv3_id": "lv3_3_3_2", - "concepts": [ - "人形机器人轻量化-PEEK", - "人形机器人轻量化-PEEK材料", - "机器人轻量化-镁铝合金", - "机器人轻量化—碳纤维", - "超高分子量聚乙烯纤维" - ] - }, - { - "lv3": "制造工艺与设备", - "lv3_id": "lv3_3_3_3", - "concepts": [ - "MIM概念", - "冷锻产业链", - "机器人零部件加工设备", - "金属粉末注射成形MIM" - ] - } + "lv2": "机器人感知与交互", + "lv2_id": "robotics_perception_interaction", + "concepts": [ + "机器人动作捕捉", + "机器人皮肤仿生皮肤", + "电子皮肤" ] }, { - "lv2": "机器人软件与AI", - "lv2_id": "lv2_3_4", + "lv2": "机器人AI与控制", + "lv2_id": "robotics_ai_control", "concepts": [ "机器人-神经网络", - "机器人动作捕捉" + "神经网络" ] }, { - "lv2": "其他类型机器人", - "lv2_id": "lv2_3_5", + "lv2": "信创与工业自动化", + "lv2_id": "xinchuang_industrial_automation", "concepts": [ - "外骨骼机器人", - "工业机器人", - "机器狗四足机器人", - "美的库卡机器人" + "信创概念", + "关键软件", + "国产信创概览", + "工业设备更新", + "工业软件", + "工业母机", + "灯塔工厂", + "自主可控", + "软件自主可控", + "设备更新" ] } ] }, { - "lv1": "消费电子", - "lv1_id": "lv1_4", + "lv1": "新能源与电力", + "lv1_id": "new_energy_power", "children": [ { - "lv2": "智能终端(端侧AI)", - "lv2_id": "lv2_4_1", + "lv2": "光伏产业", + "lv2_id": "photovoltaic_industry", "concepts": [ - "2025CES参展公司", - "AI PC", - "AIPC", - "AI手机" + "N型产业链", + "光伏", + "光伏行业兼并重组", + "光伏产业链", + "反内卷光伏", + "叠层钙钛矿", + "钙钛矿电池" ] }, { - "lv2": "XR与空间计算", - "lv2_id": "lv2_4_2", + "lv2": "先进电池技术", + "lv2_id": "advanced_battery_tech", + "concepts": [ + "固态电池-硅基负极", + "固态电池-硫化物", + "固态电池", + "固态电池设备", + "固态电池负极集流体材料-铜箔", + "固态电池产业链", + "复合集流体", + "富锂锰基材料", + "硅基负极材料", + "钠离子电池", + "隔膜", + "陶瓷隔膜骨架膜" + ] + }, + { + "lv2": "充电与换电", + "lv2_id": "charging_battery_swapping", + "concepts": [ + "充电桩", + "华为智能充电", + "华为智能充电网络", + "换电", + "换电重卡", + "比亚迪兆瓦闪充" + ] + }, + { + "lv2": "电力基础设施与AI能源", + "lv2_id": "power_infra_ai_energy", + "concepts": [ + "北美缺电AI电力", + "变压器出海", + "固体氧化物燃料电池-SOFC", + "固态变压器SST", + "燃料电池", + "燃气设备", + "电力产业链", + "燃气轮机HRSG", + "电力电网", + "电力设备", + "电力" + ] + }, + { + "lv2": "核能技术与应用", + "lv2_id": "nuclear_energy_applications", + "concepts": [ + "可控核聚变", + "微型核电", + "核电钍基熔盐堆", + "核聚变超导", + "核电产业链", + "超聚变" + ] + }, + { + "lv2": "核能通用概念", + "lv2_id": "nuclear_energy_general_concepts", + "concepts": [ + "高温概念" + ] + } + ] + }, + { + "lv1": "智能出行与交通", + "lv1_id": "smart_mobility_trans", + "children": [ + { + "lv2": "智能驾驶", + "lv2_id": "autonomous_driving", + "concepts": [ + "Robotaxi", + "小马智行", + "无人驾驶公交", + "文远知行", + "智能驾驶产业链", + "无人驾驶-线控转向", + "无人驾驶", + "特斯拉FSD", + "特斯拉RoboTaxi概念", + "矿山智驾", + "特斯拉无人驾驶出租车Robotaxi", + "自动驾驶" + ] + }, + { + "lv2": "无人物流", + "lv2_id": "unmanned_logistics", + "concepts": [ + "京东物流Robovan", + "无人物流", + "无人环卫车", + "无人物流车九识智能", + "菜鸟无人物流车" + ] + }, + { + "lv2": "智能驾驶基础设施", + "lv2_id": "autonomous_driving_infra", + "concepts": [ + "机器人充电", + "汽车无线充电" + ] + }, + { + "lv2": "智能出行服务", + "lv2_id": "smart_mobility_services", + "concepts": [ + "网约车" + ] + }, + { + "lv2": "智能驾驶与整车", + "lv2_id": "smart_driving_oems", + "concepts": [ + "mobileye替代概念", + "地平线概念", + "地平线", + "比亚迪产业链", + "比亚迪智驾", + "理想汽车" + ] + }, + { + "lv2": "低空经济", + "lv2_id": "low_altitude_economy", + "concepts": [ + "eVTOL材料", + "亿航智能订单量", + "低空经济&飞行汽车", + "低空经济", + "低空管控", + "低空物流", + "低空经济亿航智能", + "低空设计", + "低空经济产业链汇集", + "小鹏汇天", + "小鹏汇天供应商", + "空中成像", + "长安飞行汽车机器人概念", + "飞行汽车eVTOL" + ] + } + ] + }, + { + "lv1": "通信与航天", + "lv1_id": "comm_aerospace", + "children": [ + { + "lv2": "5G与未来通信", + "lv2_id": "5g_future_comm", + "concepts": [ + "5G毫米波", + "5G-A", + "5.5G", + "eSIM概念", + "通感一体" + ] + }, + { + "lv2": "卫星通信与应用", + "lv2_id": "satellite_comm_applications", + "concepts": [ + "6G概念", + "低轨卫星通信华为", + "北斗信使", + "北斗导航", + "商业航天卫星通信", + "卫星出海", + "卫星互联网", + "商业航天", + "太空行走", + "太空旅行", + "太空算力", + "手机直连卫星", + "星河动力", + "星网", + "蓝箭航天朱雀三号", + "长征十二号甲", + "SpaceX", + "卫星能源太阳翼" + ] + }, + { + "lv2": "航空航天", + "lv2_id": "aviation_space", + "concepts": [ + "九天无人机", + "凌空天行", + "大飞机", + "珠海航展", + "空客合作" + ] + } + ] + }, + { + "lv1": "消费电子与XR", + "lv1_id": "consumer_electronics_xr", + "children": [ + { + "lv2": "智能穿戴与XR", + "lv2_id": "smart_wearables_xr", "concepts": [ "AI手势识别", "AI眼镜", @@ -440,665 +607,56 @@ "META智能眼镜", "MR", "Rokid AR", - "消费电子-玄玑感知系统", + "小米眼镜", "智能穿戴", "智能眼镜", - "小米眼镜", - "阿里夸克AI眼镜", - "谷歌AI眼镜-合作XREAL" - ] - }, - { - "lv2": "华为产业链", - "lv2_id": "lv2_4_3", - "children": [ - { - "lv3": "终端产品", - "lv3_id": "lv3_4_3_1", - "concepts": [ - "华为P70", - "华为Mate80", - "华为Pura70", - "华为Mate70手表", - "华为MATE70" - ] - }, - { - "lv3": "技术与生态", - "lv3_id": "lv3_4_3_2", - "concepts": [ - "华为", - "华为5G", - "华为AI容器", - "华为云", - "华为昇腾", - "华为昇腾超节点", - "华为通信大模型", - "华为海思星闪", - "华为鸿蒙", - "华为鸿蒙甄选与支付", - "华字辈", - "昇腾异构计算架构-CANN", - "鸿蒙PC" - ] - }, - { - "lv3": "芯片与算力", - "lv3_id": "lv3_4_3_3", - "concepts": [ - "华为910C", - "华为AI存储", - "华为存储OceanStor", - "华为麒麟芯片", - "磁电存储" - ] - } - ] - }, - { - "lv2": "苹果产业链", - "lv2_id": "lv2_4_4", - "concepts": [ - "果链OPEN AI复用", + "消费电子-玄玑感知系统", "苹果MR产业链", + "谷歌AI眼镜-合作XREAL", + "阿里夸克AI眼镜", + "雷鸟创新光波导" + ] + }, + { + "lv2": "AI终端与芯片", + "lv2_id": "ai_devices_chips", + "concepts": [ + "2025CES参展公司", + "AI PC", + "AIPC", + "AI手机", + "ASIC", + "ISP视觉", + "果链OPEN AI复用", + "端侧AI芯片", "苹果供应商核心公司", "苹果机器人", - "苹果手机产业链" + "苹果手机产业链", + "高通概念" ] }, { "lv2": "新型显示技术", - "lv2_id": "lv2_4_5", + "lv2_id": "new_display_technologies", "concepts": [ "华为三折叠屏", "折叠屏", "显影液及硅基OLED", "苹果OLED潜在受益", "苹果折叠屏", - "面板" + "面板", + "TV面板LCD" ] } ] }, { - "lv1": "智能驾驶与汽车", - "lv1_id": "lv1_5", + "lv1": "文化传媒与娱乐", + "lv1_id": "culture_media_ent", "children": [ { - "lv2": "自动驾驶解决方案", - "lv2_id": "lv2_5_1", - "concepts": [ - "Robotaxi", - "京东物流Robovan", - "小马智行", - "文远知行", - "无人物流", - "无人物流车九识智能", - "无人驾驶", - "特斯拉FSD", - "特斯拉RoboTaxi概念", - "特斯拉无人驾驶出租车Robotaxi", - "自动驾驶", - "菜鸟无人物流车" - ] - }, - { - "lv2": "智能汽车产业链", - "lv2_id": "lv2_5_2", - "concepts": [ - "比亚迪产业链", - "比亚迪智驾", - "孙潇雅团队概念股", - "小米YU7供应链弹性测算", - "小米大模型", - "小米概念", - "小米算力AI互联", - "小米汽车产业链", - "小米汽车产业链弹性", - "小米汽车产业链弹性测算", - "小鹏产业链", - "理想汽车" - ] - }, - { - "lv2": "车路协同与特定场景", - "lv2_id": "lv2_5_3", - "concepts": [ - "无人驾驶-线控转向", - "无人驾驶公交", - "无人环卫车", - "矿山智驾", - "车路云-车路协同运营建设", - "车路云一体化", - "车路协同" - ] - }, - { - "lv2": "产业链综合", - "lv2_id": "lv2_5_4", - "concepts": [ - "mobileye替代概念", - "智能驾驶产业链", - "汽车安全" - ] - }, - { - "lv2": "出行服务", - "lv2_id": "lv2_5_5", - "concepts": [ - "网约车" - ] - }, - { - "lv2": "智能座舱", - "lv2_id": "lv2_5_6", - "concepts": [ - "空中成像" - ] - } - ] - }, - { - "lv1": "新能源与电力", - "lv1_id": "lv1_6", - "children": [ - { - "lv2": "新型电池技术", - "lv2_id": "lv2_6_1", - "children": [ - { - "lv3": "固态电池", - "lv3_id": "lv3_6_1_1", - "concepts": [ - "固态电池", - "固态电池-硅基负极", - "固态电池-硫化物", - "固态电池产业链", - "固态电池设备", - "固态电池负极集流体材料-铜箔", - "陶瓷隔膜骨架膜" - ] - }, - { - "lv3": "其他材料与技术", - "lv3_id": "lv3_6_1_2", - "concepts": [ - "复合集流体", - "富锂锰基材料", - "硅基负极材料", - "钠离子电池", - "隔膜" - ] - } - ] - }, - { - "lv2": "电力设备与电网", - "lv2_id": "lv2_6_2", - "concepts": [ - "北美缺电AI电力", - "变压器出海", - "固体氧化物燃料电池-SOFC", - "固态变压器SST", - "燃料电池", - "燃气设备", - "燃气轮机HRSG", - "电力", - "电力产业链", - "电力电网", - "电力设备" - ] - }, - { - "lv2": "清洁能源", - "lv2_id": "lv2_6_3", - "children": [ - { - "lv3": "光伏", - "lv3_id": "lv3_6_3_1", - "concepts": [ - "N型产业链", - "光伏", - "光伏行业兼并重组", - "光伏产业链", - "反内卷光伏", - "叠层钙钛矿", - "钙钛矿电池" - ] - }, - { - "lv3": "核能", - "lv3_id": "lv3_6_3_2", - "concepts": [ - "可控核聚变", - "微型核电", - "核电产业链", - "核电钍基熔盐堆", - "核聚变超导", - "超聚变", - "高温概念" - ] - } - ] - }, - { - "lv2": "充电桩与补能", - "lv2_id": "lv2_6_4", - "concepts": [ - "充电桩", - "华为智能充电", - "华为智能充电网络", - "换电", - "换电重卡", - "机器人充电", - "汽车无线充电", - "比亚迪兆瓦闪充" - ] - } - ] - }, - { - "lv1": "空天经济", - "lv1_id": "lv1_7", - "children": [ - { - "lv2": "低空经济", - "lv2_id": "lv2_7_1", - "concepts": [ - "eVTOL材料", - "九天无人机", - "亿航智能订单量", - "低空经济", - "低空经济&飞行汽车", - "低空经济产业链汇集", - "低空经济亿航智能", - "低空管控", - "低空物流", - "低空设计", - "小鹏汇天", - "小鹏汇天供应商", - "飞行汽车eVTOL", - "长安飞行汽车机器人概念" - ] - }, - { - "lv2": "商业航天", - "lv2_id": "lv2_7_2", - "concepts": [ - "凌空天行", - "北斗信使", - "北斗导航", - "商业航天", - "商业航天卫星通信", - "卫星出海", - "卫星互联网", - "太空行走", - "太空旅行", - "太空算力", - "手机直连卫星", - "星河动力", - "星网", - "蓝箭航天朱雀三号" - ] - }, - { - "lv2": "通信技术", - "lv2_id": "lv2_7_3", - "concepts": [ - "5.5G", - "5G-A", - "5G毫米波", - "6G概念", - "eSIM概念", - "通感一体" - ] - }, - { - "lv2": "民用航空", - "lv2_id": "lv2_7_4", - "concepts": [ - "大飞机", - "空客合作" - ] - }, - { - "lv2": "综合与主题", - "lv2_id": "lv2_7_5", - "concepts": [ - "珠海航展" - ] - } - ] - }, - { - "lv1": "国防军工", - "lv1_id": "lv1_8", - "children": [ - { - "lv2": "无人作战与信息化", - "lv2_id": "lv2_8_1", - "concepts": [ - "AI军工", - "AI无人机军工信息化", - "信息支援概念整理", - "军工信息化", - "军用无人机反无人机", - "无人机蜂群" - ] - }, - { - "lv2": "海军装备", - "lv2_id": "lv2_8_2", - "concepts": [ - "军工水面水下作战", - "国产航母", - "水下军工", - "海军", - "电磁弹射概念股", - "电磁发射设备", - "航母福建舰240430" - ] - }, - { - "lv2": "空军装备", - "lv2_id": "lv2_8_3", - "concepts": [ - "军机" - ] - }, - { - "lv2": "陆军装备", - "lv2_id": "lv2_8_4", - "concepts": [ - "远程火力" - ] - }, - { - "lv2": "军贸出海", - "lv2_id": "lv2_8_5", - "concepts": [ - "军贸", - "巴印军贸", - "巴黎航展" - ] - }, - { - "lv2": "综合与主题", - "lv2_id": "lv2_8_6", - "concepts": [ - "军工", - "军工-阅兵", - "国防军工" - ] - } - ] - }, - { - "lv1": "政策与主题", - "lv1_id": "lv1_9", - "children": [ - { - "lv2": "国企改革与市值管理", - "lv2_id": "lv2_9_1", - "concepts": [ - "中兵集团并购重组", - "中字头", - "中字头央企", - "中船合并", - "央企市值管理", - "央国企", - "央国企地产", - "央国企重组", - "安徽国资", - "地面兵装", - "整车央企重组", - "河南国资能源集团重组", - "市值管理16条-破净股", - "珠海国资", - "破净央国企", - "破净股合集", - "福建国资", - "湖北三资改革", - "国资高息股", - "高送转概念股", - "央国企AI一张图" - ] - }, - { - "lv2": "并购重组", - "lv2_id": "lv2_9_2", - "concepts": [ - "IPO终止相关企业重组预期", - "上海并购重组", - "券商合并预期", - "宝德计算机", - "并购重组", - "并购重组预期", - "消费医疗重组预期", - "湘财合并大智慧", - "科创板并购重组", - "秦淮数据", - "科技重组", - "超聚变借壳预期", - "证券", - "荣耀股改", - "重组-中科院系&海光系" - ] - }, - { - "lv2": "信创与自主可控", - "lv2_id": "lv2_9_3", - "concepts": [ - "信创概念", - "关键软件", - "国产信创概览", - "工业软件", - "自主可控", - "软件自主可控", - "信息安全", - "安全概念股", - "网络安全", - "通信设备", - "通信安全" - ] - }, - { - "lv2": "重大基建", - "lv2_id": "lv2_9_4", - "concepts": [ - "三峡水运新通道", - "新藏铁路", - "新疆概念", - "水利", - "水利工程", - "混凝土减水剂、砂石设备", - "节水产业240423", - "西部大开发", - "西部大开发240424", - "西南水电", - "西南水电站", - "西南水电站-机构测算", - "隧洞设备盾构机", - "雅下水电对电力设备增量测算-机构", - "雅下水电站", - "雅下水电站大件物流" - ] - }, - { - "lv2": "供给侧改革", - "lv2_id": "lv2_9_5", - "concepts": [ - "反内卷", - "反内卷造纸", - "反内卷食用盐", - "反内卷快递", - "反内卷合集", - "生猪", - "物流", - "牛肉", - "统一大市场", - "钢铁" - ] - }, - { - "lv2": "产业升级与制造", - "lv2_id": "lv2_9_6", - "concepts": [ - "工业设备更新", - "工业母机", - "设备更新", - "灯塔工厂" - ] - }, - { - "lv2": "国家战略", - "lv2_id": "lv2_9_7", - "concepts": [ - "2025年政府工作报告利好行业及个股", - "新型经济", - "新质生产力", - "深海数智化", - "深海经济", - "深地经济", - "首发经济" - ] - }, - { - "lv2": "区域发展与自贸区", - "lv2_id": "lv2_9_8", - "concepts": [ - "上海自贸区", - "免税离境退税", - "新型离岸贸易", - "海南", - "海南自贸区", - "海南自贸港", - "零售消费免税" - ] - }, - { - "lv2": "行业监管与规范", - "lv2_id": "lv2_9_9", - "concepts": [ - "充电宝", - "农药证件厂家", - "食品安全", - "食品安全全链条", - "预制菜" - ] - } - ] - }, - { - "lv1": "周期与材料", - "lv1_id": "lv1_10", - "children": [ - { - "lv2": "化工", - "lv2_id": "lv2_10_1", - "children": [ - { - "lv3": "行业趋势", - "lv3_id": "lv3_10_1_1", - "concepts": [ - "化工", - "化工概念", - "化工品涨价", - "涨价概念" - ] - }, - { - "lv3": "具体品种", - "lv3_id": "lv3_10_1_2", - "concepts": [ - "TMA偏苯三酸酐", - "乙烷", - "光引发剂", - "六氟磷酸锂", - "农药杀虫剂-氯虫苯甲酰胺", - "双季戊四醇", - "己内酰胺", - "有机硅", - "正丙醇", - "烧碱", - "涤纶长丝", - "电解液添加剂", - "甲苯二异氰酸酯-TDI", - "环氧丙烷", - "纯碱", - "磷化工", - "磷化工六氟磷酸锂", - "聚酯产业", - "维生素", - "苯酚丙酮", - "超硬材料", - "除草剂-烯草酮" - ] - }, - { - "lv3": "产业链", - "lv3_id": "lv3_10_1_3", - "concepts": [ - "有机硅产业链", - "电解液产业链" - ] - } - ] - }, - { - "lv2": "有色金属", - "lv2_id": "lv2_10_2", - "concepts": [ - "化工有色元素周期表", - "有色金属", - "电解铝", - "稀土", - "白银", - "铅酸电池", - "铜", - "铜产业", - "钨金属", - "钴", - "钴金属", - "钼金属", - "锡矿" - ] - } - ] - }, - { - "lv1": "大消费", - "lv1_id": "lv1_11", - "children": [ - { - "lv2": "文化传媒", - "lv2_id": "lv2_11_1", - "concepts": [ - "AI游戏", - "乙游", - "传媒出海", - "出版传媒", - "国产游戏黑神话", - "周杰伦概念股", - "幻兽帕鲁", - "影视", - "影视IP", - "影视传媒", - "影视院线", - "春节档重点影片（哪吒2）", - "智象未来", - "漫剧", - "游戏", - "游戏出海", - "疯狂动物城2", - "短剧", - "诡秘之主", - "腾讯短剧重点名单" - ] - }, - { - "lv2": "新消费", - "lv2_id": "lv2_11_2", + "lv2": "潮玩与IP衍生", + "lv2_id": "trendy_play_ip_derivatives", "concepts": [ "上市潮玩盲盒公司", "卡游文创玩具", @@ -1112,8 +670,263 @@ ] }, { - "lv2": "人口与社会", - "lv2_id": "lv2_11_3", + "lv2": "影视与IP内容", + "lv2_id": "film_tv_ip_content", + "concepts": [ + "出版传媒", + "周杰伦概念股", + "影视院线", + "影视IP", + "影视", + "影视传媒", + "春节档重点影片（哪吒2）", + "疯狂动物城2", + "诡秘之主" + ] + }, + { + "lv2": "体育赛事与经济", + "lv2_id": "sports_events_economy", + "concepts": [ + "体育", + "体育产业", + "冰雪经济", + "川超联赛", + "第十五届全运会", + "足球-苏超联赛、体彩", + "足球" + ] + }, + { + "lv2": "游戏与AI赋能", + "lv2_id": "gaming_ai_empowerment", + "concepts": [ + "AI游戏", + "乙游", + "国产游戏黑神话", + "幻兽帕鲁", + "游戏" + ] + }, + { + "lv2": "内容出海与新业态", + "lv2_id": "content_export_new_formats", + "concepts": [ + "传媒出海", + "漫剧", + "游戏出海", + "短剧", + "腾讯短剧重点名单" + ] + }, + { + "lv2": "社交媒体与跨境电商", + "lv2_id": "social_media_cross_border_ecomm", + "concepts": [ + "TikTok", + "小红书概念", + "小红书概念股", + "敦煌网跨境电商" + ] + } + ] + }, + { + "lv1": "宏观经济与政策", + "lv1_id": "macro_policy", + "children": [ + { + "lv2": "国家战略与新经济", + "lv2_id": "national_strategy_new_economy", + "concepts": [ + "2025年政府工作报告利好行业及个股", + "新型经济", + "新质生产力", + "深海数智化", + "深海经济", + "深地经济", + "首发经济" + ] + }, + { + "lv2": "国企改革与市值管理", + "lv2_id": "soe_reform_market_value", + "concepts": [ + "中兵集团并购重组", + "中字头", + "中字头央企", + "中船合并", + "国资高息股", + "央企市值管理", + "央国企", + "地面兵装", + "央国企地产", + "央国企重组", + "安徽国资", + "市值管理16条-破净股", + "整车央企重组", + "河南国资能源集团重组", + "珠海国资", + "湖北三资改革", + "破净央国企", + "破净股合集", + "福建国资" + ] + }, + { + "lv2": "并购重组", + "lv2_id": "mergers_acquisitions", + "concepts": [ + "IPO终止相关企业重组预期", + "上海并购重组", + "券商合并预期", + "宝德计算机", + "并购重组预期", + "并购重组", + "消费医疗重组预期", + "湘财合并大智慧", + "科创板并购重组", + "秦淮数据", + "科技重组", + "超聚变借壳预期", + "证券", + "荣耀股改", + "重组-中科院系&海光系" + ] + }, + { + "lv2": "供给侧改革与周期", + "lv2_id": "supply_side_reform_cycles", + "concepts": [ + "反内卷", + "反内卷造纸", + "反内卷食用盐", + "反内卷快递", + "反内卷合集", + "己内酰胺", + "涤纶长丝", + "生猪", + "牛肉", + "聚酯产业", + "钢铁", + "BOPET膜" + ] + }, + { + "lv2": "区域经济与自贸区", + "lv2_id": "regional_economy_free_trade_zones", + "concepts": [ + "上海自贸区", + "免税离境退税", + "新型离岸贸易", + "海南", + "海南自贸区", + "海南自贸港", + "零售消费免税" + ] + } + ] + }, + { + "lv1": "地缘政治与国际关系", + "lv1_id": "geopolitics_intl", + "children": [ + { + "lv2": "贸易政策与供应链", + "lv2_id": "trade_policy_supply_chain", + "concepts": [ + "中俄贸易", + "乙烷", + "中欧贸易", + "二轮车全地形车", + "关税豁免", + "关税减免出口链", + "后关税战受益", + "反制关税涨价预期", + "墨西哥汽车零部件", + "绒毛浆", + "芬太尼管制", + "转口贸易出口转内销", + "越南工厂" + ] + }, + { + "lv2": "国际冲突与重建", + "lv2_id": "intl_conflicts_reconstruction", + "concepts": [ + "乌克兰战后重建概念", + "乌克兰重建", + "俄乌重建", + "柬泰战争", + "黄岩岛概念股" + ] + }, + { + "lv2": "两岸关系与区域发展", + "lv2_id": "cross_strait_regional_dev", + "concepts": [ + "两岸融合", + "台资企业", + "海峡两岸福建", + "厦门“十五五规划”" + ] + } + ] + }, + { + "lv1": "数字经济与数据要素", + "lv1_id": "digital_economy_data", + "children": [ + { + "lv2": "数字金融与Web3", + "lv2_id": "digital_finance_web3", + "concepts": [ + "上海浦江数链", + "互联网金融", + "复星稳定币", + "数字货币", + "树图链概念", + "稳定币RWA概念股", + "稳定币-蚂蚁国际", + "稳定币一体机", + "蚂蚁金服", + "香港金融牌照" + ] + }, + { + "lv2": "数据要素与基础设施", + "lv2_id": "data_elements_infra", + "concepts": [ + "RWA上链— IoT设备数据采集", + "RDA概念股", + "数据可信", + "数据交易所", + "数据要素", + "跨境数据数据要素" + ] + }, + { + "lv2": "网络与数据安全", + "lv2_id": "network_data_security", + "concepts": [ + "信息安全", + "地理信息", + "安全概念股", + "汽车安全", + "网络安全", + "通信设备", + "通信安全" + ] + } + ] + }, + { + "lv1": "社会民生", + "lv1_id": "social_livelihood", + "children": [ + { + "lv2": "生育与教育", + "lv2_id": "fertility_education", "concepts": [ "三胎", "多胎", @@ -1124,148 +937,274 @@ ] }, { - "lv2": "体育产业", - "lv2_id": "lv2_11_4", + "lv2": "行业监管与安全", + "lv2_id": "industry_regulation_safety", "concepts": [ - "体育", - "体育产业", - "冰雪经济", - "川超联赛", - "第十五届全运会", - "足球", - "足球-苏超联赛、体彩" + "充电宝", + "农药证件厂家", + "食品安全", + "食品安全全链条", + "预制菜" ] } ] }, { - "lv1": "数字经济与金融科技", - "lv1_id": "lv1_12", + "lv1": "医药与生物科技", + "lv1_id": "pharma_biotech", "children": [ { - "lv2": "数据要素", - "lv2_id": "lv2_12_1", - "concepts": [ - "RDA概念股", - "RWA上链— IoT设备数据采集", - "版权", - "地理信息", - "数据可信", - "数据交易所", - "数据要素", - "政务云政务IT", - "跨境数据数据要素" - ] - }, - { - "lv2": "数字金融", - "lv2_id": "lv2_12_2", - "concepts": [ - "上海浦江数链", - "互联网金融", - "复星稳定币", - "数字货币", - "树图链概念", - "稳定币-蚂蚁国际", - "稳定币RWA概念股", - "稳定币一体机", - "蚂蚁金服", - "香港金融牌照" - ] - } - ] - }, - { - "lv1": "全球宏观与贸易", - "lv1_id": "lv1_13", - "children": [ - { - "lv2": "地缘政治与冲突", - "lv2_id": "lv2_13_1", - "concepts": [ - "以伊冲突-天然气", - "以伊冲突-油运仓储", - "以伊冲突-航运", - "以伊冲突-资源化工", - "乌克兰战后重建概念", - "乌克兰重建", - "俄乌重建", - "油气", - "海外港口", - "海事反制", - "石油", - "航运", - "远洋航运", - "柬泰战争", - "黄岩岛概念股" - ] - }, - { - "lv2": "贸易政策与关系", - "lv2_id": "lv2_13_2", - "concepts": [ - "中美关系", - "中俄贸易", - "中欧贸易", - "出口管制", - "反制关税涨价预期", - "后关税战受益", - "关税减免出口链", - "关税豁免", - "绒毛浆", - "芬太尼管制", - "转口贸易出口转内销" - ] - }, - { - "lv2": "供应链重构", - "lv2_id": "lv2_13_3", - "concepts": [ - "二轮车全地形车", - "墨西哥汽车零部件", - "越南工厂" - ] - } - ] - }, - { - "lv1": "医药健康", - "lv1_id": "lv1_14", - "children": [ - { - "lv2": "创新药", - "lv2_id": "lv2_14_1", + "lv2": "创新药与医疗器械", + "lv2_id": "innovative_drugs_medical_devices", "concepts": [ "AI制药", - "创新药", - "创新药双抗", "创新药相关", - "医药" + "创新药双抗", + "医疗器械", + "创新药", + "医药", + "医药外包CXO" + ] + } + ] + }, + { + "lv1": "基础材料与化工", + "lv1_id": "basic_materials_chem", + "children": [ + { + "lv2": "化工产品与周期", + "lv2_id": "chemical_products_cycles", + "concepts": [ + "TMA偏苯三酸酐", + "光引发剂", + "六氟磷酸锂", + "农药杀虫剂-氯虫苯甲酰胺", + "化工品涨价", + "化工概念", + "化工", + "双季戊四醇", + "正丙醇", + "有机硅", + "有机硅产业链", + "烧碱", + "涨价概念", + "电解液添加剂", + "电解液产业链", + "甲苯二异氰酸酯-TDI", + "环氧丙烷", + "电解铝", + "纯碱", + "磷化工六氟磷酸锂", + "磷化工", + "维生素", + "苯酚丙酮", + "铅酸电池", + "除草剂-烯草酮" ] }, { - "lv2": "细胞治疗", - "lv2_id": "lv2_14_2", + "lv2": "有色金属与稀有矿产", + "lv2_id": "non_ferrous_rare_minerals", "concepts": [ - "干细胞", - "干细胞概念股" + "化工有色元素周期表", + "有色金属", + "稀土", + "白银", + "铜产业", + "钨金属", + "钴", + "铜", + "钼金属", + "钴金属", + "锡矿" + ] + } + ] + }, + { + "lv1": "基础设施与工程", + "lv1_id": "infra_engineering", + "children": [ + { + "lv2": "重大基建与水利", + "lv2_id": "major_infra_water", + "concepts": [ + "三峡水运新通道", + "新藏铁路", + "新疆概念", + "水利", + "水利工程", + "混凝土减水剂、砂石设备", + "节水产业240423", + "西部大开发", + "西南水电站", + "西部大开发240424", + "西南水电站-机构测算", + "西南水电", + "隧洞设备盾构机", + "雅下水电对电力设备增量测算-机构", + "雅下水电站", + "雅下水电站大件物流" ] } ] }, { "lv1": "前沿科技", - "lv1_id": "lv1_15", + "lv1_id": "frontier_tech", "children": [ { - "lv2": "量子科技", - "lv2_id": "lv2_15_1", + "lv2": "量子技术与应用", + "lv2_id": "quantum_tech_applications", "concepts": [ "量子材料钛酸锶", "量子科技", "量子科技产业链", - "量子科技参股公司", - "量子计算" + "量子计算", + "量子科技参股公司" + ] + } + ] + }, + { + "lv1": "市场情绪与概念", + "lv1_id": "market_sentiment", + "children": [ + { + "lv2": "叙事与情绪驱动", + "lv2_id": "narrative_emotion_driven", + "concepts": [ + "“马”字辈", + "人造肉", + "长安的荔枝", + "韦神概念股" + ] + } + ] + }, + { + "lv1": "华为生态", + "lv1_id": "huawei_eco", + "children": [ + { + "lv2": "华为终端与芯片", + "lv2_id": "huawei_devices_chips", + "concepts": [ + "华为P70", + "华为Mate80", + "华为Pura70", + "华为Mate70手表", + "华为MATE70", + "华为麒麟芯片" + ] + }, + { + "lv2": "华为通信与软件", + "lv2_id": "huawei_comm_software", + "concepts": [ + "华为5G", + "华为通信大模型", + "华为海思星闪", + "华为鸿蒙", + "华为鸿蒙甄选与支付", + "鸿蒙PC" + ] + }, + { + "lv2": "华为AI与云计算", + "lv2_id": "huawei_ai_cloud", + "concepts": [ + "华为存储OceanStor", + "华为910C", + "华为云", + "华为AI存储", + "华为AI容器", + "华为昇腾", + "华为昇腾超节点", + "昇腾异构计算架构-CANN", + "磁电存储" + ] + }, + { + "lv2": "华为通用概念", + "lv2_id": "huawei_general_concepts", + "concepts": [ + "华为", + "华字辈" + ] + } + ] + }, + { + "lv1": "小米生态", + "lv1_id": "xiaomi_eco", + "children": [ + { + "lv2": "小米生态与智能汽车", + "lv2_id": "xiaomi_eco_smart_vehicles", + "concepts": [ + "小米算力AI互联", + "孙潇雅团队概念股", + "小米YU7供应链弹性测算", + "小米大模型", + "小鹏产业链", + "小米概念", + "小米汽车产业链弹性", + "小米汽车产业链", + "小米汽车产业链弹性测算" + ] + } + ] + }, + { + "lv1": "字节跳动生态", + "lv1_id": "bytedance_eco", + "children": [ + { + "lv2": "字节生态与AI终端", + "lv2_id": "bytedance_eco_ai_devices", + "concepts": [ + "努比亚手机", + "华为抖音支付", + "字节概念豆包AI手机", + "字节豆包概念股", + "抖音概念", + "豆包大模型" + ] + } + ] + }, + { + "lv1": "国防军工", + "lv1_id": "national_defense_military", + "children": [ + { + "lv2": "军工装备与信息化", + "lv2_id": "military_equipment_informatization", + "concepts": [ + "AI军工", + "AI无人机军工信息化", + "信息支援概念整理", + "军工-阅兵", + "军工信息化", + "军工", + "军工水面水下作战", + "军贸", + "军用无人机反无人机", + "军机", + "国防军工", + "国产航母", + "巴印军贸", + "巴黎航展", + "无人机蜂群", + "水下军工", + "海军", + "电磁弹射概念股", + "电磁发射设备", + "航母福建舰240430", + "远程火力", + "福建-军工" ] } ] diff --git a/concept_quota_realtime.py b/concept_quota_realtime.py new file mode 100644 index 00000000..103b586e --- /dev/null +++ b/concept_quota_realtime.py @@ -0,0 +1,681 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +概念涨跌幅实时更新服务 +- 在交易时间段每分钟从ClickHouse获取最新分钟数据 +- 计算涨跌幅后更新MySQL的concept_daily_stats表 +- 支持叶子概念和母概念（lv1/lv2/lv3） +""" + +import pandas as pd +import numpy as np +from datetime import datetime, timedelta +from sqlalchemy import create_engine, text +from elasticsearch import Elasticsearch +from clickhouse_driver import Client +import time +import logging +import json +import os +import hashlib +import argparse + +# ==================== 配置 ==================== + +# MySQL配置 +MYSQL_ENGINE = create_engine( + "mysql+pymysql://root:Zzl5588161!@222.128.1.157:33060/stock", + echo=False +) + +# Elasticsearch配置 +ES_CLIENT = Elasticsearch(['http://222.128.1.157:19200']) +INDEX_NAME = 'concept_library_v3' + +# ClickHouse配置 +CLICKHOUSE_CONFIG = { + 'host': '222.128.1.157', + 'port': 18000, + 'user': 'default', + 'password': 'Zzl33818!', + 'database': 'stock' +} + +# 层级结构文件 +HIERARCHY_FILE = 'concept_hierarchy_v3.json' + +# 交易时间配置 +TRADING_HOURS = { + 'morning_start': (9, 30), + 'morning_end': (11, 30), + 'afternoon_start': (13, 0), + 'afternoon_end': (15, 0), +} + +# ==================== 日志配置 ==================== + +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(levelname)s - %(message)s', + handlers=[ + logging.FileHandler(f'concept_realtime_{datetime.now().strftime("%Y%m%d")}.log', encoding='utf-8'), + logging.StreamHandler() + ] +) +logger = logging.getLogger(__name__) + +# ClickHouse客户端 +ch_client = None + + +def get_ch_client(): + """获取ClickHouse客户端""" + global ch_client + if ch_client is None: + ch_client = Client(**CLICKHOUSE_CONFIG) + return ch_client + + +def generate_id(name: str) -> str: + """生成概念ID""" + return hashlib.md5(name.encode('utf-8')).hexdigest()[:16] + + +def code_to_ch_format(code: str) -> str: + """将6位股票代码转换为ClickHouse格式（带后缀） + + 规则： + - 6开头 -> .SH（上海） + - 0或3开头 -> .SZ（深圳） + - 其他 -> .BJ（北京） + - 非6位数字的忽略（可能是港股） + """ + if not code or len(code) != 6 or not code.isdigit(): + return None + + if code.startswith('6'): + return f"{code}.SH" + elif code.startswith('0') or code.startswith('3'): + return f"{code}.SZ" + else: + return f"{code}.BJ" + + +def ch_code_to_pure(ch_code: str) -> str: + """将ClickHouse格式的股票代码转回纯6位代码""" + if not ch_code: + return None + return ch_code.split('.')[0] + + +# ==================== 概念数据获取 ==================== + +def get_all_concepts(): + """从ES获取所有叶子概念及其股票列表""" + concepts = [] + + query = { + "query": {"match_all": {}}, + "size": 100, + "_source": ["concept_id", "concept", "stocks"] + } + + resp = ES_CLIENT.search(index=INDEX_NAME, body=query, scroll='2m') + scroll_id = resp['_scroll_id'] + hits = resp['hits']['hits'] + + while len(hits) > 0: + for hit in hits: + source = hit['_source'] + concept_info = { + 'concept_id': source.get('concept_id'), + 'concept_name': source.get('concept'), + 'stocks': [], + 'concept_type': 'leaf' + } + + # v3索引的stocks字段是 [{name, code}, ...] + if 'stocks' in source and isinstance(source['stocks'], list): + for stock in source['stocks']: + if isinstance(stock, dict) and 'code' in stock and stock['code']: + concept_info['stocks'].append(stock['code']) + + if concept_info['stocks']: + concepts.append(concept_info) + + resp = ES_CLIENT.scroll(scroll_id=scroll_id, scroll='2m') + scroll_id = resp['_scroll_id'] + hits = resp['hits']['hits'] + + ES_CLIENT.clear_scroll(scroll_id=scroll_id) + return concepts + + +def load_hierarchy_concepts(leaf_concepts: list) -> list: + """加载层级结构，生成母概念（lv1/lv2/lv3）""" + hierarchy_path = os.path.join(os.path.dirname(__file__), HIERARCHY_FILE) + if not os.path.exists(hierarchy_path): + logger.warning(f"层级文件不存在: {hierarchy_path}") + return [] + + with open(hierarchy_path, 'r', encoding='utf-8') as f: + hierarchy_data = json.load(f) + + # 建立概念名称到股票的映射 + concept_to_stocks = {} + for c in leaf_concepts: + concept_to_stocks[c['concept_name']] = set(c['stocks']) + + parent_concepts = [] + + for lv1 in hierarchy_data.get('hierarchy', []): + lv1_name = lv1.get('lv1', '') + lv1_stocks = set() + + for child in lv1.get('children', []): + lv2_name = child.get('lv2', '') + lv2_stocks = set() + + if 'children' in child: + for lv3_child in child.get('children', []): + lv3_name = lv3_child.get('lv3', '') + lv3_stocks = set() + + for concept_name in lv3_child.get('concepts', []): + if concept_name in concept_to_stocks: + lv3_stocks.update(concept_to_stocks[concept_name]) + + if lv3_stocks: + parent_concepts.append({ + 'concept_id': generate_id(f"lv3_{lv3_name}"), + 'concept_name': f"[三级] {lv3_name}", + 'stocks': list(lv3_stocks), + 'concept_type': 'lv3' + }) + + lv2_stocks.update(lv3_stocks) + else: + for concept_name in child.get('concepts', []): + if concept_name in concept_to_stocks: + lv2_stocks.update(concept_to_stocks[concept_name]) + + if lv2_stocks: + parent_concepts.append({ + 'concept_id': generate_id(f"lv2_{lv2_name}"), + 'concept_name': f"[二级] {lv2_name}", + 'stocks': list(lv2_stocks), + 'concept_type': 'lv2' + }) + + lv1_stocks.update(lv2_stocks) + + if lv1_stocks: + parent_concepts.append({ + 'concept_id': generate_id(f"lv1_{lv1_name}"), + 'concept_name': f"[一级] {lv1_name}", + 'stocks': list(lv1_stocks), + 'concept_type': 'lv1' + }) + + return parent_concepts + + +# ==================== 基准价格获取 ==================== + +def get_base_prices(stock_codes: list, current_date: str) -> dict: + """获取当日的昨收价作为基准（从ea_trade的F002N字段） + + ea_trade表字段说明： + - F002N: 昨日收盘价 + - F007N: 最近成交价（收盘价） + - F010N: 涨跌幅 + """ + if not stock_codes: + return {} + + # 过滤出有效的6位股票代码 + valid_codes = [code for code in stock_codes if code and len(code) == 6 and code.isdigit()] + if not valid_codes: + return {} + + stock_codes_str = "','".join(valid_codes) + + # 获取当日数据中的昨收价（F002N） + query = f""" + SELECT SECCODE, F002N + FROM ea_trade + WHERE SECCODE IN ('{stock_codes_str}') + AND TRADEDATE = ( + SELECT MAX(TRADEDATE) + FROM ea_trade + WHERE TRADEDATE <= '{current_date}' + ) + AND F002N IS NOT NULL AND F002N > 0 + """ + + try: + with MYSQL_ENGINE.connect() as conn: + result = conn.execute(text(query)) + base_prices = {row[0]: float(row[1]) for row in result if row[1] and float(row[1]) > 0} + logger.info(f"获取到 {len(base_prices)} 个基准价格") + return base_prices + except Exception as e: + logger.error(f"获取基准价格失败: {e}") + return {} + + +# ==================== 实时价格获取 ==================== + +def get_latest_prices(stock_codes: list) -> dict: + """从ClickHouse获取最新分钟数据的收盘价 + + Args: + stock_codes: 纯6位股票代码列表（如 ['000001', '600000']） + + Returns: + dict: {纯6位代码: {'close': 价格, 'timestamp': 时间}} + """ + if not stock_codes: + return {} + + client = get_ch_client() + + # 转换为ClickHouse格式的代码（带后缀） + ch_codes = [] + code_mapping = {} # ch_code -> pure_code + for code in stock_codes: + ch_code = code_to_ch_format(code) + if ch_code: + ch_codes.append(ch_code) + code_mapping[ch_code] = code + + if not ch_codes: + logger.warning("没有有效的股票代码可查询") + return {} + + ch_codes_str = "','".join(ch_codes) + + # 获取今日最新的分钟数据 + query = f""" + SELECT code, close, timestamp + FROM ( + SELECT code, close, timestamp, + ROW_NUMBER() OVER (PARTITION BY code ORDER BY timestamp DESC) as rn + FROM stock_minute + WHERE code IN ('{ch_codes_str}') + AND toDate(timestamp) = today() + ) + WHERE rn = 1 + """ + + try: + result = client.execute(query) + if not result: + return {} + + latest_prices = {} + for row in result: + ch_code, close, ts = row + if close and close > 0: + # 转回纯6位代码 + pure_code = code_mapping.get(ch_code) + if pure_code: + latest_prices[pure_code] = { + 'close': float(close), + 'timestamp': ts + } + + return latest_prices + except Exception as e: + logger.error(f"获取最新价格失败: {e}") + return {} + + +# ==================== 涨跌幅计算 ==================== + +def calculate_change_pct(base_prices: dict, latest_prices: dict) -> dict: + """计算涨跌幅""" + changes = {} + for code, latest in latest_prices.items(): + if code in base_prices and base_prices[code] > 0: + base = base_prices[code] + close = latest['close'] + change_pct = (close - base) / base * 100 + changes[code] = round(change_pct, 4) + return changes + + +def calculate_concept_stats(concepts: list, stock_changes: dict, trade_date: str) -> list: + """计算所有概念的涨跌幅统计""" + stats = [] + + for concept in concepts: + concept_id = concept['concept_id'] + concept_name = concept['concept_name'] + stock_codes = concept['stocks'] + concept_type = concept.get('concept_type', 'leaf') + + # 获取该概念股票的涨跌幅 + changes = [stock_changes[code] for code in stock_codes if code in stock_changes] + + if not changes: + continue + + avg_change_pct = round(np.mean(changes), 4) + stock_count = len(changes) + + stats.append({ + 'concept_id': concept_id, + 'concept_name': concept_name, + 'trade_date': trade_date, + 'avg_change_pct': avg_change_pct, + 'stock_count': stock_count, + 'concept_type': concept_type + }) + + return stats + + +# ==================== MySQL更新 ==================== + +def update_mysql_stats(stats: list): + """更新MySQL的concept_daily_stats表""" + if not stats: + return 0 + + with MYSQL_ENGINE.begin() as conn: + updated = 0 + for item in stats: + upsert_sql = text(""" + REPLACE INTO concept_daily_stats + (concept_id, concept_name, trade_date, avg_change_pct, stock_count, concept_type) + VALUES (:concept_id, :concept_name, :trade_date, :avg_change_pct, :stock_count, :concept_type) + """) + conn.execute(upsert_sql, item) + updated += 1 + + return updated + + +# ==================== 交易时间判断 ==================== + +def is_trading_time() -> bool: + """判断当前是否为交易时间""" + now = datetime.now() + weekday = now.weekday() + + # 周末不交易 + if weekday >= 5: + return False + + hour, minute = now.hour, now.minute + current_time = hour * 60 + minute + + # 上午 9:30 - 11:30 + morning_start = 9 * 60 + 30 + morning_end = 11 * 60 + 30 + + # 下午 13:00 - 15:00 + afternoon_start = 13 * 60 + afternoon_end = 15 * 60 + + return (morning_start <= current_time <= morning_end) or \ + (afternoon_start <= current_time <= afternoon_end) + + +def get_next_update_time() -> int: + """获取距离下次更新的秒数""" + now = datetime.now() + + if is_trading_time(): + # 交易时间内，等到下一分钟 + return 60 - now.second + else: + # 非交易时间 + hour, minute = now.hour, now.minute + + # 计算距离下次交易开始的时间 + if hour < 9 or (hour == 9 and minute < 30): + # 等到9:30 + target = now.replace(hour=9, minute=30, second=0, microsecond=0) + elif (hour == 11 and minute >= 30) or hour == 12: + # 等到13:00 + target = now.replace(hour=13, minute=0, second=0, microsecond=0) + elif hour >= 15: + # 等到明天9:30 + target = (now + timedelta(days=1)).replace(hour=9, minute=30, second=0, microsecond=0) + else: + target = now + timedelta(minutes=1) + + wait_seconds = (target - now).total_seconds() + return max(60, int(wait_seconds)) + + +# ==================== 主运行逻辑 ==================== + +def run_once(concepts: list, all_stocks: list) -> int: + """执行一次更新""" + now = datetime.now() + trade_date = now.strftime('%Y-%m-%d') + + # 获取基准价格（昨日收盘价） + base_prices = get_base_prices(all_stocks, trade_date) + if not base_prices: + logger.warning("无法获取基准价格") + return 0 + + # 获取最新价格 + latest_prices = get_latest_prices(all_stocks) + if not latest_prices: + logger.warning("无法获取最新价格") + return 0 + + # 计算涨跌幅 + stock_changes = calculate_change_pct(base_prices, latest_prices) + if not stock_changes: + logger.warning("无涨跌幅数据") + return 0 + + logger.info(f"获取到 {len(stock_changes)} 只股票的涨跌幅") + + # 计算概念统计 + stats = calculate_concept_stats(concepts, stock_changes, trade_date) + logger.info(f"计算了 {len(stats)} 个概念的涨跌幅") + + # 更新MySQL + updated = update_mysql_stats(stats) + logger.info(f"更新了 {updated} 条记录到MySQL") + + return updated + + +def run_realtime(): + """实时更新主循环""" + logger.info("=" * 60) + logger.info("启动概念涨跌幅实时更新服务") + logger.info("=" * 60) + + # 加载概念数据 + logger.info("加载概念数据...") + leaf_concepts = get_all_concepts() + logger.info(f"获取到 {len(leaf_concepts)} 个叶子概念") + + parent_concepts = load_hierarchy_concepts(leaf_concepts) + logger.info(f"生成了 {len(parent_concepts)} 个母概念") + + all_concepts = leaf_concepts + parent_concepts + logger.info(f"总计 {len(all_concepts)} 个概念") + + # 收集所有股票代码 + all_stocks = set() + for c in all_concepts: + all_stocks.update(c['stocks']) + all_stocks = list(all_stocks) + logger.info(f"监控 {len(all_stocks)} 只股票") + + last_concept_update = datetime.now() + + while True: + try: + now = datetime.now() + + # 每小时重新加载概念数据 + if (now - last_concept_update).total_seconds() > 3600: + logger.info("重新加载概念数据...") + leaf_concepts = get_all_concepts() + parent_concepts = load_hierarchy_concepts(leaf_concepts) + all_concepts = leaf_concepts + parent_concepts + all_stocks = set() + for c in all_concepts: + all_stocks.update(c['stocks']) + all_stocks = list(all_stocks) + last_concept_update = now + logger.info(f"更新完成: {len(all_concepts)} 个概念, {len(all_stocks)} 只股票") + + # 检查是否交易时间 + if not is_trading_time(): + wait_sec = get_next_update_time() + wait_min = wait_sec // 60 + logger.info(f"非交易时间，等待 {wait_min} 分钟后重试...") + time.sleep(min(wait_sec, 300)) # 最多等5分钟再检查 + continue + + # 执行更新 + logger.info(f"\n{'=' * 40}") + logger.info(f"更新时间: {now.strftime('%Y-%m-%d %H:%M:%S')}") + updated = run_once(all_concepts, all_stocks) + + # 等待下一分钟 + sleep_sec = 60 - datetime.now().second + logger.info(f"完成，等待 {sleep_sec} 秒后继续...") + time.sleep(sleep_sec) + + except KeyboardInterrupt: + logger.info("\n收到退出信号，停止服务...") + break + except Exception as e: + logger.error(f"发生错误: {e}") + import traceback + traceback.print_exc() + time.sleep(60) + + +def run_single(): + """单次运行（不循环）""" + logger.info("单次更新模式") + + leaf_concepts = get_all_concepts() + parent_concepts = load_hierarchy_concepts(leaf_concepts) + all_concepts = leaf_concepts + parent_concepts + + all_stocks = set() + for c in all_concepts: + all_stocks.update(c['stocks']) + all_stocks = list(all_stocks) + + logger.info(f"概念数: {len(all_concepts)}, 股票数: {len(all_stocks)}") + + updated = run_once(all_concepts, all_stocks) + logger.info(f"更新完成: {updated} 条记录") + + +def show_status(): + """显示当前状态""" + print("\n" + "=" * 60) + print("概念涨跌幅实时更新服务 - 状态") + print("=" * 60) + + # 当前时间 + now = datetime.now() + print(f"\n当前时间: {now.strftime('%Y-%m-%d %H:%M:%S')}") + print(f"是否交易时间: {'是' if is_trading_time() else '否'}") + + # MySQL数据状态 + print("\nMySQL数据状态:") + try: + with MYSQL_ENGINE.connect() as conn: + # 今日数据量 + result = conn.execute(text(""" + SELECT concept_type, COUNT(*) as cnt + FROM concept_daily_stats + WHERE trade_date = CURDATE() + GROUP BY concept_type + """)) + rows = list(result) + if rows: + print(" 今日数据:") + for row in rows: + print(f" {row[0]}: {row[1]} 条") + else: + print(" 今日暂无数据") + + # 最新更新时间 + result = conn.execute(text(""" + SELECT MAX(updated_at) FROM concept_daily_stats WHERE trade_date = CURDATE() + """)) + row = result.fetchone() + if row and row[0]: + print(f" 最后更新: {row[0]}") + except Exception as e: + print(f" 查询失败: {e}") + + # ClickHouse数据状态 + print("\nClickHouse数据状态:") + try: + client = get_ch_client() + result = client.execute(""" + SELECT COUNT(*), MAX(timestamp) + FROM stock_minute + WHERE toDate(timestamp) = today() + """) + if result: + count, max_ts = result[0] + print(f" 今日分钟数据: {count:,} 条") + print(f" 最新时间戳: {max_ts}") + except Exception as e: + print(f" 查询失败: {e}") + + # 今日涨跌幅TOP10 + print("\n今日涨跌幅 TOP10:") + try: + with MYSQL_ENGINE.connect() as conn: + result = conn.execute(text(""" + SELECT concept_name, avg_change_pct, stock_count, concept_type + FROM concept_daily_stats + WHERE trade_date = CURDATE() AND concept_type = 'leaf' + ORDER BY avg_change_pct DESC + LIMIT 10 + """)) + rows = list(result) + if rows: + print(f" {'概念':<25} | {'涨跌幅':>8} | {'股票数':>6}") + print(" " + "-" * 50) + for row in rows: + name = row[0][:25] if len(row[0]) > 25 else row[0] + print(f" {name:<25} | {row[1]:>7.2f}% | {row[2]:>6}") + else: + print(" 暂无数据") + except Exception as e: + print(f" 查询失败: {e}") + + +# ==================== 主函数 ==================== + +def main(): + parser = argparse.ArgumentParser(description='概念涨跌幅实时更新服务') + parser.add_argument('command', nargs='?', default='realtime', + choices=['realtime', 'once', 'status'], + help='命令: realtime(实时运行), once(单次运行), status(状态查看)') + + args = parser.parse_args() + + if args.command == 'realtime': + run_realtime() + elif args.command == 'once': + run_single() + elif args.command == 'status': + show_status() + + +if __name__ == "__main__": + main() diff --git a/create_tables.py b/create_tables.py new file mode 100644 index 00000000..f5c8912e --- /dev/null +++ b/create_tables.py @@ -0,0 +1,89 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +"""创建异动检测所需的数据库表""" + +import sys +from sqlalchemy import create_engine, text + +engine = create_engine('mysql+pymysql://root:Zzl5588161!@222.128.1.157:33060/stock', echo=False) + +# 删除旧表 +drop_sql1 = 'DROP TABLE IF EXISTS concept_minute_alert' +drop_sql2 = 'DROP TABLE IF EXISTS index_minute_snapshot' + +# 创建 concept_minute_alert 表 +# 支持 Z-Score + SVM 智能检测 +sql1 = ''' +CREATE TABLE concept_minute_alert ( + id BIGINT AUTO_INCREMENT PRIMARY KEY, + concept_id VARCHAR(32) NOT NULL, + concept_name VARCHAR(100) NOT NULL, + alert_time DATETIME NOT NULL, + alert_type VARCHAR(20) NOT NULL COMMENT 'surge_up=暴涨, surge_down=暴跌, limit_up=涨停增加, rank_jump=排名跃升', + trade_date DATE NOT NULL, + change_pct DECIMAL(10,4) COMMENT '当前涨跌幅', + prev_change_pct DECIMAL(10,4) COMMENT '之前涨跌幅', + change_delta DECIMAL(10,4) COMMENT '涨跌幅变化', + limit_up_count INT DEFAULT 0 COMMENT '涨停数', + prev_limit_up_count INT DEFAULT 0, + limit_up_delta INT DEFAULT 0, + limit_down_count INT DEFAULT 0 COMMENT '跌停数', + rank_position INT COMMENT '当前排名', + prev_rank_position INT COMMENT '之前排名', + rank_delta INT COMMENT '排名变化(负数表示上升)', + index_code VARCHAR(20) DEFAULT '000001.SH', + index_price DECIMAL(12,4), + index_change_pct DECIMAL(10,4), + stock_count INT, + concept_type VARCHAR(20) DEFAULT 'leaf', + zscore DECIMAL(8,4) COMMENT 'Z-Score值', + importance_score DECIMAL(6,4) COMMENT '重要性评分(0-1)', + extra_info JSON COMMENT '扩展信息(包含zscore,svm_score等)', + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + INDEX idx_trade_date (trade_date), + INDEX idx_alert_time (alert_time), + INDEX idx_concept_id (concept_id), + INDEX idx_alert_type (alert_type), + INDEX idx_trade_date_time (trade_date, alert_time), + INDEX idx_importance (importance_score) +) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COMMENT='概念异动记录表(智能版)' +''' + +# 创建 index_minute_snapshot 表 +sql2 = ''' +CREATE TABLE index_minute_snapshot ( + id BIGINT AUTO_INCREMENT PRIMARY KEY, + index_code VARCHAR(20) NOT NULL, + trade_date DATE NOT NULL, + snapshot_time DATETIME NOT NULL, + price DECIMAL(12,4), + open_price DECIMAL(12,4), + high_price DECIMAL(12,4), + low_price DECIMAL(12,4), + prev_close DECIMAL(12,4), + change_pct DECIMAL(10,4), + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + UNIQUE KEY uk_index_time (index_code, snapshot_time), + INDEX idx_trade_date (trade_date) +) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 +''' + +if __name__ == '__main__': + print('正在重建数据库表...\n') + + with engine.begin() as conn: + # 先删除旧表 + print('删除旧表...') + conn.execute(text(drop_sql1)) + print(' - concept_minute_alert 已删除') + conn.execute(text(drop_sql2)) + print(' - index_minute_snapshot 已删除') + + # 创建新表 + print('\n创建新表...') + conn.execute(text(sql1)) + print(' ✅ concept_minute_alert 表创建成功') + conn.execute(text(sql2)) + print(' ✅ index_minute_snapshot 表创建成功') + + print('\n✅ 所有表创建完成!') diff --git a/ml/README.md b/ml/README.md new file mode 100644 index 00000000..7651277c --- /dev/null +++ b/ml/README.md @@ -0,0 +1,112 @@ +# 概念异动检测 ML 模块 + +基于 Transformer Autoencoder 的概念异动检测系统。 + +## 环境要求 + +- Python 3.8+ +- PyTorch 2.0+ (CUDA 12.x for 5090 GPU) +- ClickHouse, MySQL, Elasticsearch + +## 数据库配置 + +当前配置（`prepare_data.py`）: +- MySQL: `192.168.1.5:3306` +- Elasticsearch: `127.0.0.1:9200` +- ClickHouse: `127.0.0.1:9000` + +## 快速开始 + +```bash +# 1. 安装依赖 +pip install -r ml/requirements.txt + +# 2. 安装 PyTorch (5090 需要 CUDA 12.4) +pip install torch --index-url https://download.pytorch.org/whl/cu124 + +# 3. 运行训练 +chmod +x ml/run_training.sh +./ml/run_training.sh +``` + +## 文件说明 + +| 文件 | 说明 | +|------|------| +| `model.py` | Transformer Autoencoder 模型定义 | +| `prepare_data.py` | 数据提取和特征计算 | +| `train.py` | 模型训练脚本 | +| `inference.py` | 推理服务 | +| `enhanced_detector.py` | 增强版检测器（融合 Alpha + ML） | + +## 训练参数 + +```bash +# 完整参数 +./ml/run_training.sh --start 2022-01-01 --end 2024-12-01 --epochs 100 --batch_size 256 + +# 只准备数据 +python ml/prepare_data.py --start 2022-01-01 + +# 只训练（数据已准备好） +python ml/train.py --epochs 100 --batch_size 256 --lr 1e-4 +``` + +## 模型架构 + +``` +输入: (batch, 30, 6) # 30分钟序列，6个特征 + ↓ +Positional Encoding + ↓ +Transformer Encoder (4层, 8头, d=128) + ↓ +Bottleneck (压缩到 32 维) + ↓ +Transformer Decoder (4层) + ↓ +输出: (batch, 30, 6) # 重构序列 + +异动判断: reconstruction_error > threshold +``` + +## 6维特征 + +1. `alpha` - 超额收益（概念涨幅 - 大盘涨幅） +2. `alpha_delta` - Alpha 5分钟变化 +3. `amt_ratio` - 成交额 / 20分钟均值 +4. `amt_delta` - 成交额变化率 +5. `rank_pct` - Alpha 排名百分位 +6. `limit_up_ratio` - 涨停股占比 + +## 训练产出 + +训练完成后，`ml/checkpoints/` 包含: +- `best_model.pt` - 最佳模型权重 +- `thresholds.json` - 异动阈值 (P90/P95/P99) +- `normalization_stats.json` - 数据标准化参数 +- `config.json` - 训练配置 + +## 使用示例 + +```python +from ml.inference import ConceptAnomalyDetector + +detector = ConceptAnomalyDetector('ml/checkpoints') + +# 实时检测 +is_anomaly, score = detector.detect( + concept_name="人工智能", + features={ + 'alpha': 2.5, + 'alpha_delta': 0.8, + 'amt_ratio': 1.5, + 'amt_delta': 0.3, + 'rank_pct': 0.95, + 'limit_up_ratio': 0.15, + } +) + +if is_anomaly: + print(f"检测到异动！分数: {score}") +``` diff --git a/ml/__init__.py b/ml/__init__.py new file mode 100644 index 00000000..348015d0 --- /dev/null +++ b/ml/__init__.py @@ -0,0 +1,10 @@ +# -*- coding: utf-8 -*- +""" +概念异动检测 ML 模块 + +提供基于 Transformer Autoencoder 的异动检测功能 +""" + +from .inference import ConceptAnomalyDetector, MLAnomalyService + +__all__ = ['ConceptAnomalyDetector', 'MLAnomalyService'] diff --git a/ml/backtest.py b/ml/backtest.py new file mode 100644 index 00000000..57d49e30 --- /dev/null +++ b/ml/backtest.py @@ -0,0 +1,481 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +历史异动回测脚本 + +使用训练好的模型，对历史数据进行异动检测，生成异动记录 + +使用方法： + # 回测指定日期范围 + python backtest.py --start 2024-01-01 --end 2024-12-01 + + # 回测单天 + python backtest.py --start 2024-11-01 --end 2024-11-01 + + # 只生成结果，不写入数据库 + python backtest.py --start 2024-01-01 --dry-run +""" + +import os +import sys +import argparse +import json +from datetime import datetime, timedelta +from pathlib import Path +from typing import Dict, List, Tuple, Optional +from collections import defaultdict + +import numpy as np +import pandas as pd +import torch +from tqdm import tqdm +from sqlalchemy import create_engine, text + +# 添加父目录到路径 +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +from model import TransformerAutoencoder + + +# ==================== 配置 ==================== + +MYSQL_ENGINE = create_engine( + "mysql+pymysql://root:Zzl5588161!@192.168.1.5:3306/stock", + echo=False +) + +# 特征列表（与训练一致） +FEATURES = [ + 'alpha', + 'alpha_delta', + 'amt_ratio', + 'amt_delta', + 'rank_pct', + 'limit_up_ratio', +] + +# 回测配置 +BACKTEST_CONFIG = { + 'seq_len': 30, # 序列长度 + 'threshold_key': 'p95', # 使用的阈值 + 'min_alpha_abs': 0.5, # 最小 Alpha 绝对值（过滤微小波动） + 'cooldown_minutes': 8, # 同一概念冷却时间 + 'max_alerts_per_minute': 15, # 每分钟最多异动数 + 'clip_value': 10.0, # 极端值截断 +} + + +# ==================== 模型加载 ==================== + +class AnomalyDetector: + """异动检测器""" + + def __init__(self, checkpoint_dir: str = 'ml/checkpoints', device: str = 'auto'): + self.checkpoint_dir = Path(checkpoint_dir) + + # 设备 + if device == 'auto': + self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + else: + self.device = torch.device(device) + + # 加载配置 + self._load_config() + + # 加载模型 + self._load_model() + + # 加载阈值 + self._load_thresholds() + + print(f"AnomalyDetector 初始化完成") + print(f" 设备: {self.device}") + print(f" 阈值 ({BACKTEST_CONFIG['threshold_key']}): {self.threshold:.6f}") + + def _load_config(self): + config_path = self.checkpoint_dir / 'config.json' + with open(config_path, 'r') as f: + self.config = json.load(f) + + def _load_model(self): + model_path = self.checkpoint_dir / 'best_model.pt' + checkpoint = torch.load(model_path, map_location=self.device) + + model_config = self.config['model'].copy() + model_config['use_instance_norm'] = self.config.get('use_instance_norm', True) + + self.model = TransformerAutoencoder(**model_config) + self.model.load_state_dict(checkpoint['model_state_dict']) + self.model.to(self.device) + self.model.eval() + + def _load_thresholds(self): + thresholds_path = self.checkpoint_dir / 'thresholds.json' + with open(thresholds_path, 'r') as f: + thresholds = json.load(f) + + self.threshold = thresholds[BACKTEST_CONFIG['threshold_key']] + + @torch.no_grad() + def compute_anomaly_scores(self, sequences: np.ndarray) -> np.ndarray: + """ + 计算异动分数 + + Args: + sequences: (n_sequences, seq_len, n_features) + Returns: + scores: (n_sequences,) 每个序列最后时刻的异动分数 + """ + # 截断极端值 + sequences = np.clip(sequences, -BACKTEST_CONFIG['clip_value'], BACKTEST_CONFIG['clip_value']) + + # 转为 tensor + x = torch.FloatTensor(sequences).to(self.device) + + # 计算重构误差 + errors = self.model.compute_reconstruction_error(x, reduction='none') + + # 取最后一个时刻的误差 + scores = errors[:, -1].cpu().numpy() + + return scores + + def is_anomaly(self, score: float) -> bool: + """判断是否异动""" + return score > self.threshold + + +# ==================== 数据加载 ==================== + +def load_daily_features(data_dir: str, date: str) -> Optional[pd.DataFrame]: + """加载单天的特征数据""" + file_path = Path(data_dir) / f"features_{date}.parquet" + + if not file_path.exists(): + return None + + df = pd.read_parquet(file_path) + return df + + +def get_available_dates(data_dir: str, start_date: str, end_date: str) -> List[str]: + """获取可用的日期列表""" + data_path = Path(data_dir) + all_files = sorted(data_path.glob("features_*.parquet")) + + dates = [] + for f in all_files: + date = f.stem.replace('features_', '') + if start_date <= date <= end_date: + dates.append(date) + + return dates + + +# ==================== 回测逻辑 ==================== + +def backtest_single_day( + detector: AnomalyDetector, + df: pd.DataFrame, + date: str, + seq_len: int = 30 +) -> List[Dict]: + """ + 回测单天数据 + + Args: + detector: 异动检测器 + df: 当天的特征数据 + date: 日期 + seq_len: 序列长度 + + Returns: + alerts: 异动列表 + """ + alerts = [] + + # 按概念分组 + grouped = df.groupby('concept_id', sort=False) + + # 冷却记录 {concept_id: last_alert_timestamp} + cooldown = {} + + # 获取所有时间点 + all_timestamps = sorted(df['timestamp'].unique()) + + if len(all_timestamps) < seq_len: + return alerts + + # 对每个时间点进行检测（从第 seq_len 个开始） + for t_idx in range(seq_len - 1, len(all_timestamps)): + current_time = all_timestamps[t_idx] + window_start_time = all_timestamps[t_idx - seq_len + 1] + + minute_alerts = [] + + # 收集该时刻所有概念的序列 + concept_sequences = [] + concept_infos = [] + + for concept_id, concept_df in grouped: + # 获取该概念在时间窗口内的数据 + mask = (concept_df['timestamp'] >= window_start_time) & (concept_df['timestamp'] <= current_time) + window_df = concept_df[mask].sort_values('timestamp') + + if len(window_df) < seq_len: + continue + + # 取最后 seq_len 个点 + window_df = window_df.tail(seq_len) + + # 提取特征 + features = window_df[FEATURES].values + + # 处理缺失值 + features = np.nan_to_num(features, nan=0.0, posinf=0.0, neginf=0.0) + + # 获取当前时刻的信息 + current_row = window_df.iloc[-1] + + concept_sequences.append(features) + concept_infos.append({ + 'concept_id': concept_id, + 'timestamp': current_time, + 'alpha': current_row.get('alpha', 0), + 'alpha_delta': current_row.get('alpha_delta', 0), + 'amt_ratio': current_row.get('amt_ratio', 1), + 'limit_up_ratio': current_row.get('limit_up_ratio', 0), + 'limit_down_ratio': current_row.get('limit_down_ratio', 0), + 'rank_pct': current_row.get('rank_pct', 0.5), + 'stock_count': current_row.get('stock_count', 0), + 'total_amt': current_row.get('total_amt', 0), + }) + + if not concept_sequences: + continue + + # 批量计算异动分数 + sequences_array = np.array(concept_sequences) + scores = detector.compute_anomaly_scores(sequences_array) + + # 检测异动 + for i, (info, score) in enumerate(zip(concept_infos, scores)): + concept_id = info['concept_id'] + alpha = info['alpha'] + + # 过滤小波动 + if abs(alpha) < BACKTEST_CONFIG['min_alpha_abs']: + continue + + # 检查冷却 + if concept_id in cooldown: + last_alert = cooldown[concept_id] + if isinstance(current_time, datetime): + time_diff = (current_time - last_alert).total_seconds() / 60 + else: + # timestamp 是字符串或其他格式 + time_diff = BACKTEST_CONFIG['cooldown_minutes'] + 1 # 跳过冷却检查 + + if time_diff < BACKTEST_CONFIG['cooldown_minutes']: + continue + + # 判断是否异动 + if not detector.is_anomaly(score): + continue + + # 记录异动 + alert_type = 'surge_up' if alpha > 0 else 'surge_down' + + alert = { + 'concept_id': concept_id, + 'alert_time': current_time, + 'trade_date': date, + 'alert_type': alert_type, + 'anomaly_score': float(score), + 'threshold': detector.threshold, + **info + } + + minute_alerts.append(alert) + cooldown[concept_id] = current_time + + # 按分数排序，限制数量 + minute_alerts.sort(key=lambda x: x['anomaly_score'], reverse=True) + alerts.extend(minute_alerts[:BACKTEST_CONFIG['max_alerts_per_minute']]) + + return alerts + + +# ==================== 数据库写入 ==================== + +def save_alerts_to_mysql(alerts: List[Dict], dry_run: bool = False) -> int: + """保存异动到 MySQL""" + if not alerts: + return 0 + + if dry_run: + print(f" [Dry Run] 将写入 {len(alerts)} 条异动") + return len(alerts) + + saved = 0 + with MYSQL_ENGINE.begin() as conn: + for alert in alerts: + try: + # 检查是否已存在 + check_sql = text(""" + SELECT id FROM concept_minute_alert + WHERE concept_id = :concept_id + AND alert_time = :alert_time + AND trade_date = :trade_date + """) + exists = conn.execute(check_sql, { + 'concept_id': alert['concept_id'], + 'alert_time': alert['alert_time'], + 'trade_date': alert['trade_date'], + }).fetchone() + + if exists: + continue + + # 插入新记录 + insert_sql = text(""" + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, zscore, importance_score, stock_count, extra_info) + VALUES + (:concept_id, :concept_name, :alert_time, :alert_type, :trade_date, + :change_pct, :zscore, :importance_score, :stock_count, :extra_info) + """) + + conn.execute(insert_sql, { + 'concept_id': alert['concept_id'], + 'concept_name': alert.get('concept_name', ''), + 'alert_time': alert['alert_time'], + 'alert_type': alert['alert_type'], + 'trade_date': alert['trade_date'], + 'change_pct': alert.get('alpha', 0), + 'zscore': alert['anomaly_score'], + 'importance_score': alert['anomaly_score'], + 'stock_count': alert.get('stock_count', 0), + 'extra_info': json.dumps({ + 'detection_method': 'ml_autoencoder', + 'threshold': alert['threshold'], + 'alpha': alert.get('alpha', 0), + 'amt_ratio': alert.get('amt_ratio', 1), + }, ensure_ascii=False) + }) + + saved += 1 + + except Exception as e: + print(f" 保存失败: {alert['concept_id']} - {e}") + + return saved + + +def export_alerts_to_csv(alerts: List[Dict], output_path: str): + """导出异动到 CSV""" + if not alerts: + return + + df = pd.DataFrame(alerts) + df.to_csv(output_path, index=False, encoding='utf-8-sig') + print(f"已导出到: {output_path}") + + +# ==================== 主函数 ==================== + +def main(): + parser = argparse.ArgumentParser(description='历史异动回测') + parser.add_argument('--data_dir', type=str, default='ml/data', + help='特征数据目录') + parser.add_argument('--checkpoint_dir', type=str, default='ml/checkpoints', + help='模型检查点目录') + parser.add_argument('--start', type=str, required=True, + help='开始日期 (YYYY-MM-DD)') + parser.add_argument('--end', type=str, required=True, + help='结束日期 (YYYY-MM-DD)') + parser.add_argument('--dry-run', action='store_true', + help='只计算，不写入数据库') + parser.add_argument('--export-csv', type=str, default=None, + help='导出 CSV 文件路径') + parser.add_argument('--device', type=str, default='auto', + help='设备 (auto/cuda/cpu)') + + args = parser.parse_args() + + print("=" * 60) + print("历史异动回测") + print("=" * 60) + print(f"日期范围: {args.start} ~ {args.end}") + print(f"数据目录: {args.data_dir}") + print(f"模型目录: {args.checkpoint_dir}") + print(f"Dry Run: {args.dry_run}") + print("=" * 60) + + # 初始化检测器 + detector = AnomalyDetector(args.checkpoint_dir, args.device) + + # 获取可用日期 + dates = get_available_dates(args.data_dir, args.start, args.end) + + if not dates: + print(f"未找到 {args.start} ~ {args.end} 范围内的数据") + return + + print(f"\n找到 {len(dates)} 天的数据") + + # 回测 + all_alerts = [] + total_saved = 0 + + for date in tqdm(dates, desc="回测进度"): + # 加载数据 + df = load_daily_features(args.data_dir, date) + + if df is None or df.empty: + continue + + # 回测单天 + alerts = backtest_single_day( + detector, df, date, + seq_len=BACKTEST_CONFIG['seq_len'] + ) + + if alerts: + all_alerts.extend(alerts) + + # 写入数据库 + saved = save_alerts_to_mysql(alerts, dry_run=args.dry_run) + total_saved += saved + + if not args.dry_run: + tqdm.write(f" {date}: 检测到 {len(alerts)} 个异动，保存 {saved} 条") + + # 导出 CSV + if args.export_csv and all_alerts: + export_alerts_to_csv(all_alerts, args.export_csv) + + # 汇总 + print("\n" + "=" * 60) + print("回测完成！") + print("=" * 60) + print(f"总计检测到: {len(all_alerts)} 个异动") + print(f"保存到数据库: {total_saved} 条") + + # 统计 + if all_alerts: + df_alerts = pd.DataFrame(all_alerts) + print(f"\n异动类型分布:") + print(df_alerts['alert_type'].value_counts()) + + print(f"\n异动分数统计:") + print(f" Mean: {df_alerts['anomaly_score'].mean():.4f}") + print(f" Max: {df_alerts['anomaly_score'].max():.4f}") + print(f" Min: {df_alerts['anomaly_score'].min():.4f}") + + print("=" * 60) + + +if __name__ == "__main__": + main() diff --git a/ml/backtest_hybrid.py b/ml/backtest_hybrid.py new file mode 100644 index 00000000..7bb4c808 --- /dev/null +++ b/ml/backtest_hybrid.py @@ -0,0 +1,418 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +融合异动回测脚本 + +使用 HybridAnomalyDetector 进行回测： +- 规则评分 + LSTM Autoencoder 融合判断 +- 输出更丰富的异动信息 + +使用方法： + python backtest_hybrid.py --start 2024-01-01 --end 2024-12-01 + python backtest_hybrid.py --start 2024-11-01 --dry-run +""" + +import os +import sys +import argparse +import json +from datetime import datetime +from pathlib import Path +from typing import Dict, List, Optional +from collections import defaultdict + +import numpy as np +import pandas as pd +from tqdm import tqdm +from sqlalchemy import create_engine, text + +# 添加父目录到路径 +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +from detector import HybridAnomalyDetector, create_detector + + +# ==================== 配置 ==================== + +MYSQL_ENGINE = create_engine( + "mysql+pymysql://root:Zzl5588161!@192.168.1.5:3306/stock", + echo=False +) + +FEATURES = [ + 'alpha', + 'alpha_delta', + 'amt_ratio', + 'amt_delta', + 'rank_pct', + 'limit_up_ratio', +] + +BACKTEST_CONFIG = { + 'seq_len': 30, + 'min_alpha_abs': 0.3, # 降低阈值，让规则也能发挥作用 + 'cooldown_minutes': 8, + 'max_alerts_per_minute': 20, + 'clip_value': 10.0, +} + + +# ==================== 数据加载 ==================== + +def load_daily_features(data_dir: str, date: str) -> Optional[pd.DataFrame]: + """加载单天的特征数据""" + file_path = Path(data_dir) / f"features_{date}.parquet" + + if not file_path.exists(): + return None + + df = pd.read_parquet(file_path) + return df + + +def get_available_dates(data_dir: str, start_date: str, end_date: str) -> List[str]: + """获取可用的日期列表""" + data_path = Path(data_dir) + all_files = sorted(data_path.glob("features_*.parquet")) + + dates = [] + for f in all_files: + date = f.stem.replace('features_', '') + if start_date <= date <= end_date: + dates.append(date) + + return dates + + +# ==================== 融合回测 ==================== + +def backtest_single_day_hybrid( + detector: HybridAnomalyDetector, + df: pd.DataFrame, + date: str, + seq_len: int = 30 +) -> List[Dict]: + """ + 使用融合检测器回测单天数据 + """ + alerts = [] + + # 按概念分组 + grouped = df.groupby('concept_id', sort=False) + + # 冷却记录 + cooldown = {} + + # 获取所有时间点 + all_timestamps = sorted(df['timestamp'].unique()) + + if len(all_timestamps) < seq_len: + return alerts + + # 对每个时间点进行检测 + for t_idx in range(seq_len - 1, len(all_timestamps)): + current_time = all_timestamps[t_idx] + window_start_time = all_timestamps[t_idx - seq_len + 1] + + minute_alerts = [] + + for concept_id, concept_df in grouped: + # 获取时间窗口内的数据 + mask = (concept_df['timestamp'] >= window_start_time) & (concept_df['timestamp'] <= current_time) + window_df = concept_df[mask].sort_values('timestamp') + + if len(window_df) < seq_len: + continue + + window_df = window_df.tail(seq_len) + + # 提取特征序列（给 ML 模型） + sequence = window_df[FEATURES].values + sequence = np.nan_to_num(sequence, nan=0.0, posinf=0.0, neginf=0.0) + sequence = np.clip(sequence, -BACKTEST_CONFIG['clip_value'], BACKTEST_CONFIG['clip_value']) + + # 当前时刻特征（给规则系统） + current_row = window_df.iloc[-1] + current_features = { + 'alpha': current_row.get('alpha', 0), + 'alpha_delta': current_row.get('alpha_delta', 0), + 'amt_ratio': current_row.get('amt_ratio', 1), + 'amt_delta': current_row.get('amt_delta', 0), + 'rank_pct': current_row.get('rank_pct', 0.5), + 'limit_up_ratio': current_row.get('limit_up_ratio', 0), + } + + # 过滤微小波动 + if abs(current_features['alpha']) < BACKTEST_CONFIG['min_alpha_abs']: + continue + + # 检查冷却 + if concept_id in cooldown: + last_alert = cooldown[concept_id] + if isinstance(current_time, datetime): + time_diff = (current_time - last_alert).total_seconds() / 60 + else: + time_diff = BACKTEST_CONFIG['cooldown_minutes'] + 1 + + if time_diff < BACKTEST_CONFIG['cooldown_minutes']: + continue + + # 融合检测 + result = detector.detect(current_features, sequence) + + if not result.is_anomaly: + continue + + # 记录异动 + alert = { + 'concept_id': concept_id, + 'alert_time': current_time, + 'trade_date': date, + 'alert_type': result.anomaly_type, + 'final_score': result.final_score, + 'rule_score': result.rule_score, + 'ml_score': result.ml_score, + 'trigger_reason': result.trigger_reason, + 'triggered_rules': list(result.rule_details.keys()), + **current_features, + 'stock_count': current_row.get('stock_count', 0), + 'total_amt': current_row.get('total_amt', 0), + } + + minute_alerts.append(alert) + cooldown[concept_id] = current_time + + # 按最终得分排序 + minute_alerts.sort(key=lambda x: x['final_score'], reverse=True) + alerts.extend(minute_alerts[:BACKTEST_CONFIG['max_alerts_per_minute']]) + + return alerts + + +# ==================== 数据库写入 ==================== + +def save_alerts_to_mysql(alerts: List[Dict], dry_run: bool = False) -> int: + """保存异动到 MySQL（增强版字段）""" + if not alerts: + return 0 + + if dry_run: + print(f" [Dry Run] 将写入 {len(alerts)} 条异动") + return len(alerts) + + saved = 0 + with MYSQL_ENGINE.begin() as conn: + for alert in alerts: + try: + # 检查是否已存在 + check_sql = text(""" + SELECT id FROM concept_minute_alert + WHERE concept_id = :concept_id + AND alert_time = :alert_time + AND trade_date = :trade_date + """) + exists = conn.execute(check_sql, { + 'concept_id': alert['concept_id'], + 'alert_time': alert['alert_time'], + 'trade_date': alert['trade_date'], + }).fetchone() + + if exists: + continue + + # 插入新记录 + insert_sql = text(""" + INSERT INTO concept_minute_alert + (concept_id, concept_name, alert_time, alert_type, trade_date, + change_pct, zscore, importance_score, stock_count, extra_info) + VALUES + (:concept_id, :concept_name, :alert_time, :alert_type, :trade_date, + :change_pct, :zscore, :importance_score, :stock_count, :extra_info) + """) + + extra_info = { + 'detection_method': 'hybrid', + 'final_score': alert['final_score'], + 'rule_score': alert['rule_score'], + 'ml_score': alert['ml_score'], + 'trigger_reason': alert['trigger_reason'], + 'triggered_rules': alert['triggered_rules'], + 'alpha': alert.get('alpha', 0), + 'alpha_delta': alert.get('alpha_delta', 0), + 'amt_ratio': alert.get('amt_ratio', 1), + } + + conn.execute(insert_sql, { + 'concept_id': alert['concept_id'], + 'concept_name': alert.get('concept_name', ''), + 'alert_time': alert['alert_time'], + 'alert_type': alert['alert_type'], + 'trade_date': alert['trade_date'], + 'change_pct': alert.get('alpha', 0), + 'zscore': alert['final_score'], # 用最终得分作为 zscore + 'importance_score': alert['final_score'], + 'stock_count': alert.get('stock_count', 0), + 'extra_info': json.dumps(extra_info, ensure_ascii=False) + }) + + saved += 1 + + except Exception as e: + print(f" 保存失败: {alert['concept_id']} - {e}") + + return saved + + +def export_alerts_to_csv(alerts: List[Dict], output_path: str): + """导出异动到 CSV""" + if not alerts: + return + + df = pd.DataFrame(alerts) + df.to_csv(output_path, index=False, encoding='utf-8-sig') + print(f"已导出到: {output_path}") + + +# ==================== 统计分析 ==================== + +def analyze_alerts(alerts: List[Dict]): + """分析异动结果""" + if not alerts: + print("无异动数据") + return + + df = pd.DataFrame(alerts) + + print("\n" + "=" * 60) + print("异动统计分析") + print("=" * 60) + + # 1. 基本统计 + print(f"\n总异动数: {len(alerts)}") + + # 2. 按类型统计 + print(f"\n异动类型分布:") + print(df['alert_type'].value_counts()) + + # 3. 得分统计 + print(f"\n得分统计:") + print(f" 最终得分 - Mean: {df['final_score'].mean():.1f}, Max: {df['final_score'].max():.1f}") + print(f" 规则得分 - Mean: {df['rule_score'].mean():.1f}, Max: {df['rule_score'].max():.1f}") + print(f" ML得分 - Mean: {df['ml_score'].mean():.1f}, Max: {df['ml_score'].max():.1f}") + + # 4. 触发来源分析 + print(f"\n触发来源分析:") + trigger_counts = df['trigger_reason'].apply( + lambda x: '规则' if '规则' in x else ('ML' if 'ML' in x else '融合') + ).value_counts() + print(trigger_counts) + + # 5. 规则触发频率 + all_rules = [] + for rules in df['triggered_rules']: + if isinstance(rules, list): + all_rules.extend(rules) + + if all_rules: + print(f"\n最常触发的规则 (Top 10):") + from collections import Counter + rule_counts = Counter(all_rules) + for rule, count in rule_counts.most_common(10): + print(f" {rule}: {count}") + + +# ==================== 主函数 ==================== + +def main(): + parser = argparse.ArgumentParser(description='融合异动回测') + parser.add_argument('--data_dir', type=str, default='ml/data', + help='特征数据目录') + parser.add_argument('--checkpoint_dir', type=str, default='ml/checkpoints', + help='模型检查点目录') + parser.add_argument('--start', type=str, required=True, + help='开始日期 (YYYY-MM-DD)') + parser.add_argument('--end', type=str, default=None, + help='结束日期 (YYYY-MM-DD)，默认=start') + parser.add_argument('--dry-run', action='store_true', + help='只计算，不写入数据库') + parser.add_argument('--export-csv', type=str, default=None, + help='导出 CSV 文件路径') + parser.add_argument('--rule-weight', type=float, default=0.6, + help='规则权重 (0-1)') + parser.add_argument('--ml-weight', type=float, default=0.4, + help='ML权重 (0-1)') + + args = parser.parse_args() + + if args.end is None: + args.end = args.start + + print("=" * 60) + print("融合异动回测 (规则 + LSTM)") + print("=" * 60) + print(f"日期范围: {args.start} ~ {args.end}") + print(f"数据目录: {args.data_dir}") + print(f"模型目录: {args.checkpoint_dir}") + print(f"规则权重: {args.rule_weight}") + print(f"ML权重: {args.ml_weight}") + print(f"Dry Run: {args.dry_run}") + print("=" * 60) + + # 初始化融合检测器 + config = { + 'rule_weight': args.rule_weight, + 'ml_weight': args.ml_weight, + } + detector = create_detector(args.checkpoint_dir, config) + + # 获取可用日期 + dates = get_available_dates(args.data_dir, args.start, args.end) + + if not dates: + print(f"未找到 {args.start} ~ {args.end} 范围内的数据") + return + + print(f"\n找到 {len(dates)} 天的数据") + + # 回测 + all_alerts = [] + total_saved = 0 + + for date in tqdm(dates, desc="回测进度"): + df = load_daily_features(args.data_dir, date) + + if df is None or df.empty: + continue + + alerts = backtest_single_day_hybrid( + detector, df, date, + seq_len=BACKTEST_CONFIG['seq_len'] + ) + + if alerts: + all_alerts.extend(alerts) + + saved = save_alerts_to_mysql(alerts, dry_run=args.dry_run) + total_saved += saved + + if not args.dry_run: + tqdm.write(f" {date}: 检测到 {len(alerts)} 个异动，保存 {saved} 条") + + # 导出 CSV + if args.export_csv and all_alerts: + export_alerts_to_csv(all_alerts, args.export_csv) + + # 统计分析 + analyze_alerts(all_alerts) + + # 汇总 + print("\n" + "=" * 60) + print("回测完成！") + print("=" * 60) + print(f"总计检测到: {len(all_alerts)} 个异动") + print(f"保存到数据库: {total_saved} 条") + print("=" * 60) + + +if __name__ == "__main__": + main() diff --git a/ml/detector.py b/ml/detector.py new file mode 100644 index 00000000..0c14880d --- /dev/null +++ b/ml/detector.py @@ -0,0 +1,571 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +概念异动检测器 - 融合版 + +结合两种方法的优势： +1. 规则评分系统：可解释、稳定、覆盖已知模式 +2. LSTM Autoencoder：发现未知的异常模式 + +融合策略： +┌─────────────────────────────────────────────────────────┐ +│ 输入特征 │ +│ (alpha, alpha_delta, amt_ratio, amt_delta, rank_pct, │ +│ limit_up_ratio) │ +├─────────────────────────────────────────────────────────┤ +│ │ +│ ┌──────────────┐ ┌──────────────┐ │ +│ │ 规则评分系统 │ │ LSTM Autoencoder │ │ +│ │ (0-100分) │ │ (重构误差) │ │ +│ └──────┬───────┘ └──────┬───────┘ │ +│ │ │ │ +│ ▼ ▼ │ +│ rule_score (0-100) ml_score (标准化后 0-100) │ +│ │ +├─────────────────────────────────────────────────────────┤ +│ 融合策略 │ +│ │ +│ final_score = w1 * rule_score + w2 * ml_score │ +│ │ +│ 异动判定： │ +│ - rule_score >= 60 → 直接触发（规则强信号） │ +│ - ml_score >= 80 → 直接触发（ML强信号） │ +│ - final_score >= 50 → 融合触发 │ +│ │ +└─────────────────────────────────────────────────────────┘ + +优势： +- 规则系统保证已知模式的检出率 +- ML模型捕捉规则未覆盖的异常 +- 两者互相验证，减少误报 +""" + +import json +from pathlib import Path +from typing import Dict, List, Optional, Tuple +from dataclasses import dataclass + +import numpy as np +import torch + +# 尝试导入模型（可能不存在） +try: + from model import LSTMAutoencoder, create_model + HAS_MODEL = True +except ImportError: + HAS_MODEL = False + + +@dataclass +class AnomalyResult: + """异动检测结果""" + is_anomaly: bool + final_score: float # 最终得分 (0-100) + rule_score: float # 规则得分 (0-100) + ml_score: float # ML得分 (0-100) + trigger_reason: str # 触发原因 + rule_details: Dict # 规则明细 + anomaly_type: str # 异动类型: surge_up / surge_down / volume_spike / unknown + + +class RuleBasedScorer: + """ + 基于规则的评分系统 + + 设计原则： + - 每个规则独立打分 + - 分数可叠加 + - 阈值可配置 + """ + + # 默认规则配置 + DEFAULT_RULES = { + # Alpha 相关（超额收益） + 'alpha_strong': { + 'condition': lambda r: abs(r.get('alpha', 0)) >= 3.0, + 'score': 35, + 'description': 'Alpha强信号(|α|≥3%)' + }, + 'alpha_medium': { + 'condition': lambda r: 2.0 <= abs(r.get('alpha', 0)) < 3.0, + 'score': 25, + 'description': 'Alpha中等(2%≤|α|<3%)' + }, + 'alpha_weak': { + 'condition': lambda r: 1.5 <= abs(r.get('alpha', 0)) < 2.0, + 'score': 15, + 'description': 'Alpha轻微(1.5%≤|α|<2%)' + }, + + # Alpha 变化率（加速度） + 'alpha_delta_strong': { + 'condition': lambda r: abs(r.get('alpha_delta', 0)) >= 1.0, + 'score': 30, + 'description': 'Alpha加速强(|Δα|≥1%)' + }, + 'alpha_delta_medium': { + 'condition': lambda r: 0.5 <= abs(r.get('alpha_delta', 0)) < 1.0, + 'score': 20, + 'description': 'Alpha加速中(0.5%≤|Δα|<1%)' + }, + + # 成交额比率（放量） + 'volume_spike_strong': { + 'condition': lambda r: r.get('amt_ratio', 1) >= 5.0, + 'score': 30, + 'description': '极度放量(≥5倍)' + }, + 'volume_spike_medium': { + 'condition': lambda r: 3.0 <= r.get('amt_ratio', 1) < 5.0, + 'score': 20, + 'description': '显著放量(3-5倍)' + }, + 'volume_spike_weak': { + 'condition': lambda r: 2.0 <= r.get('amt_ratio', 1) < 3.0, + 'score': 10, + 'description': '轻微放量(2-3倍)' + }, + + # 成交额变化率 + 'amt_delta_strong': { + 'condition': lambda r: abs(r.get('amt_delta', 0)) >= 1.0, + 'score': 15, + 'description': '成交额急变(|Δamt|≥100%)' + }, + + # 排名跳变 + 'rank_top': { + 'condition': lambda r: r.get('rank_pct', 0.5) >= 0.95, + 'score': 25, + 'description': '排名前5%' + }, + 'rank_bottom': { + 'condition': lambda r: r.get('rank_pct', 0.5) <= 0.05, + 'score': 25, + 'description': '排名后5%' + }, + 'rank_high': { + 'condition': lambda r: 0.9 <= r.get('rank_pct', 0.5) < 0.95, + 'score': 15, + 'description': '排名前10%' + }, + + # 涨停比例 + 'limit_up_high': { + 'condition': lambda r: r.get('limit_up_ratio', 0) >= 0.2, + 'score': 25, + 'description': '涨停比例≥20%' + }, + 'limit_up_medium': { + 'condition': lambda r: 0.1 <= r.get('limit_up_ratio', 0) < 0.2, + 'score': 15, + 'description': '涨停比例10-20%' + }, + + # 组合条件（更可靠的信号） + 'alpha_with_volume': { + 'condition': lambda r: abs(r.get('alpha', 0)) >= 1.5 and r.get('amt_ratio', 1) >= 2.0, + 'score': 20, # 额外加分 + 'description': 'Alpha+放量组合' + }, + 'acceleration_with_rank': { + 'condition': lambda r: abs(r.get('alpha_delta', 0)) >= 0.5 and (r.get('rank_pct', 0.5) >= 0.9 or r.get('rank_pct', 0.5) <= 0.1), + 'score': 15, # 额外加分 + 'description': '加速+排名异常组合' + }, + } + + def __init__(self, rules: Dict = None): + """ + 初始化规则评分器 + + Args: + rules: 自定义规则，格式同 DEFAULT_RULES + """ + self.rules = rules or self.DEFAULT_RULES + + def score(self, features: Dict) -> Tuple[float, Dict]: + """ + 计算规则得分 + + Args: + features: 特征字典，包含 alpha, alpha_delta, amt_ratio 等 + Returns: + score: 总分 (0-100) + details: 触发的规则明细 + """ + total_score = 0 + triggered_rules = {} + + for rule_name, rule_config in self.rules.items(): + try: + if rule_config['condition'](features): + total_score += rule_config['score'] + triggered_rules[rule_name] = { + 'score': rule_config['score'], + 'description': rule_config['description'] + } + except Exception: + # 忽略规则计算错误 + pass + + # 限制在 0-100 + total_score = min(100, max(0, total_score)) + + return total_score, triggered_rules + + def get_anomaly_type(self, features: Dict) -> str: + """判断异动类型""" + alpha = features.get('alpha', 0) + amt_ratio = features.get('amt_ratio', 1) + + if alpha >= 1.5: + return 'surge_up' + elif alpha <= -1.5: + return 'surge_down' + elif amt_ratio >= 3.0: + return 'volume_spike' + else: + return 'unknown' + + +class MLScorer: + """ + 基于 LSTM Autoencoder 的评分器 + + 将重构误差转换为 0-100 的分数 + """ + + def __init__( + self, + checkpoint_dir: str = 'ml/checkpoints', + device: str = 'auto' + ): + self.checkpoint_dir = Path(checkpoint_dir) + + # 设备 + if device == 'auto': + self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + else: + self.device = torch.device(device) + + self.model = None + self.thresholds = None + self.config = None + + # 尝试加载模型 + self._load_model() + + def _load_model(self): + """加载模型和阈值""" + if not HAS_MODEL: + print("警告: 无法导入模型模块") + return + + model_path = self.checkpoint_dir / 'best_model.pt' + thresholds_path = self.checkpoint_dir / 'thresholds.json' + config_path = self.checkpoint_dir / 'config.json' + + if not model_path.exists(): + print(f"警告: 模型文件不存在 {model_path}") + return + + try: + # 加载配置 + if config_path.exists(): + with open(config_path, 'r') as f: + self.config = json.load(f) + + # 加载模型 + checkpoint = torch.load(model_path, map_location=self.device) + + model_config = self.config.get('model', {}) if self.config else {} + self.model = create_model(model_config) + self.model.load_state_dict(checkpoint['model_state_dict']) + self.model.to(self.device) + self.model.eval() + + # 加载阈值 + if thresholds_path.exists(): + with open(thresholds_path, 'r') as f: + self.thresholds = json.load(f) + + print(f"MLScorer 加载成功 (设备: {self.device})") + + except Exception as e: + print(f"警告: 模型加载失败 - {e}") + self.model = None + + def is_ready(self) -> bool: + """检查模型是否就绪""" + return self.model is not None + + @torch.no_grad() + def score(self, sequence: np.ndarray) -> float: + """ + 计算 ML 得分 + + Args: + sequence: (seq_len, n_features) 或 (batch, seq_len, n_features) + Returns: + score: 0-100 的分数，越高越异常 + """ + if not self.is_ready(): + return 0.0 + + # 确保是 3D + if sequence.ndim == 2: + sequence = sequence[np.newaxis, ...] + + # 转为 tensor + x = torch.FloatTensor(sequence).to(self.device) + + # 计算重构误差 + output, _ = self.model(x) + mse = ((output - x) ** 2).mean(dim=-1) # (batch, seq_len) + + # 取最后时刻的误差 + error = mse[:, -1].cpu().numpy() + + # 转换为 0-100 分数 + # 使用 p95 阈值作为参考 + if self.thresholds: + p95 = self.thresholds.get('p95', 0.1) + p99 = self.thresholds.get('p99', 0.2) + else: + p95, p99 = 0.1, 0.2 + + # 线性映射：p95 -> 50分, p99 -> 80分 + # error=0 -> 0分, error>=p99*1.5 -> 100分 + score = np.clip(error / p95 * 50, 0, 100) + + return float(score[0]) if len(score) == 1 else score.tolist() + + +class HybridAnomalyDetector: + """ + 融合异动检测器 + + 结合规则系统和 ML 模型 + """ + + # 默认配置 + DEFAULT_CONFIG = { + # 权重配置 + 'rule_weight': 0.6, # 规则权重 + 'ml_weight': 0.4, # ML权重 + + # 触发阈值 + 'rule_trigger': 60, # 规则直接触发阈值 + 'ml_trigger': 80, # ML直接触发阈值 + 'fusion_trigger': 50, # 融合触发阈值 + + # 特征列表 + 'features': [ + 'alpha', 'alpha_delta', 'amt_ratio', + 'amt_delta', 'rank_pct', 'limit_up_ratio' + ], + + # 序列长度（ML模型需要） + 'seq_len': 30, + } + + def __init__( + self, + config: Dict = None, + checkpoint_dir: str = 'ml/checkpoints', + device: str = 'auto' + ): + self.config = {**self.DEFAULT_CONFIG, **(config or {})} + + # 初始化评分器 + self.rule_scorer = RuleBasedScorer() + self.ml_scorer = MLScorer(checkpoint_dir, device) + + print(f"HybridAnomalyDetector 初始化完成") + print(f" 规则权重: {self.config['rule_weight']}") + print(f" ML权重: {self.config['ml_weight']}") + print(f" ML模型: {'就绪' if self.ml_scorer.is_ready() else '未加载'}") + + def detect( + self, + features: Dict, + sequence: np.ndarray = None + ) -> AnomalyResult: + """ + 检测异动 + + Args: + features: 当前时刻的特征字典 + sequence: 历史序列 (seq_len, n_features)，ML模型需要 + Returns: + AnomalyResult: 检测结果 + """ + # 1. 规则评分 + rule_score, rule_details = self.rule_scorer.score(features) + + # 2. ML评分 + ml_score = 0.0 + if sequence is not None and self.ml_scorer.is_ready(): + ml_score = self.ml_scorer.score(sequence) + + # 3. 融合得分 + w1 = self.config['rule_weight'] + w2 = self.config['ml_weight'] + + # 如果ML不可用，全部权重给规则 + if not self.ml_scorer.is_ready(): + w1, w2 = 1.0, 0.0 + + final_score = w1 * rule_score + w2 * ml_score + + # 4. 判断是否异动 + is_anomaly = False + trigger_reason = '' + + if rule_score >= self.config['rule_trigger']: + is_anomaly = True + trigger_reason = f'规则强信号({rule_score:.0f}分)' + elif ml_score >= self.config['ml_trigger']: + is_anomaly = True + trigger_reason = f'ML强信号({ml_score:.0f}分)' + elif final_score >= self.config['fusion_trigger']: + is_anomaly = True + trigger_reason = f'融合触发({final_score:.0f}分)' + + # 5. 判断异动类型 + anomaly_type = self.rule_scorer.get_anomaly_type(features) if is_anomaly else '' + + return AnomalyResult( + is_anomaly=is_anomaly, + final_score=final_score, + rule_score=rule_score, + ml_score=ml_score, + trigger_reason=trigger_reason, + rule_details=rule_details, + anomaly_type=anomaly_type + ) + + def detect_batch( + self, + features_list: List[Dict], + sequences: np.ndarray = None + ) -> List[AnomalyResult]: + """ + 批量检测 + + Args: + features_list: 特征字典列表 + sequences: (batch, seq_len, n_features) + Returns: + List[AnomalyResult] + """ + results = [] + + for i, features in enumerate(features_list): + seq = sequences[i] if sequences is not None else None + result = self.detect(features, seq) + results.append(result) + + return results + + +# ==================== 便捷函数 ==================== + +def create_detector( + checkpoint_dir: str = 'ml/checkpoints', + config: Dict = None +) -> HybridAnomalyDetector: + """创建融合检测器""" + return HybridAnomalyDetector(config, checkpoint_dir) + + +def quick_detect(features: Dict) -> bool: + """ + 快速检测（只用规则，不需要ML模型） + + 适用于： + - 实时检测 + - ML模型未训练完成时 + """ + scorer = RuleBasedScorer() + score, _ = scorer.score(features) + return score >= 50 + + +# ==================== 测试 ==================== + +if __name__ == "__main__": + print("=" * 60) + print("融合异动检测器测试") + print("=" * 60) + + # 创建检测器 + detector = create_detector() + + # 测试用例 + test_cases = [ + { + 'name': '正常情况', + 'features': { + 'alpha': 0.5, + 'alpha_delta': 0.1, + 'amt_ratio': 1.2, + 'amt_delta': 0.1, + 'rank_pct': 0.5, + 'limit_up_ratio': 0.02 + } + }, + { + 'name': 'Alpha异动', + 'features': { + 'alpha': 3.5, + 'alpha_delta': 0.8, + 'amt_ratio': 2.5, + 'amt_delta': 0.5, + 'rank_pct': 0.92, + 'limit_up_ratio': 0.05 + } + }, + { + 'name': '放量异动', + 'features': { + 'alpha': 1.2, + 'alpha_delta': 0.3, + 'amt_ratio': 6.0, + 'amt_delta': 1.5, + 'rank_pct': 0.85, + 'limit_up_ratio': 0.08 + } + }, + { + 'name': '涨停潮', + 'features': { + 'alpha': 2.5, + 'alpha_delta': 0.6, + 'amt_ratio': 3.5, + 'amt_delta': 0.8, + 'rank_pct': 0.98, + 'limit_up_ratio': 0.25 + } + }, + ] + + print("\n测试结果:") + print("-" * 60) + + for case in test_cases: + result = detector.detect(case['features']) + + print(f"\n{case['name']}:") + print(f" 异动: {'是' if result.is_anomaly else '否'}") + print(f" 最终得分: {result.final_score:.1f}") + print(f" 规则得分: {result.rule_score:.1f}") + print(f" ML得分: {result.ml_score:.1f}") + if result.is_anomaly: + print(f" 触发原因: {result.trigger_reason}") + print(f" 异动类型: {result.anomaly_type}") + print(f" 触发规则: {list(result.rule_details.keys())}") + + print("\n" + "=" * 60) + print("测试完成!") diff --git a/ml/enhanced_detector.py b/ml/enhanced_detector.py new file mode 100644 index 00000000..b64c63a8 --- /dev/null +++ b/ml/enhanced_detector.py @@ -0,0 +1,526 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +增强版概念异动检测器 + +融合两种检测方法： +1. Alpha-based Z-Score（规则方法，实时性好） +2. Transformer Autoencoder（ML方法，更准确） + +使用策略： +- 当 ML 模型可用且历史数据足够时，优先使用 ML 方法 +- 否则回退到 Alpha-based 方法 +- 可以配置两种方法的融合权重 +""" + +import os +import sys +import logging +from datetime import datetime +from pathlib import Path +from typing import Dict, List, Tuple, Optional +from dataclasses import dataclass, field +from collections import deque +import numpy as np + +# 添加父目录到路径 +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +logger = logging.getLogger(__name__) + + +# ==================== 配置 ==================== + +ENHANCED_CONFIG = { + # 融合策略 + 'fusion_mode': 'adaptive', # 'ml_only', 'alpha_only', 'adaptive', 'ensemble' + + # ML 权重（在 ensemble 模式下） + 'ml_weight': 0.6, + 'alpha_weight': 0.4, + + # ML 模型配置 + 'ml_checkpoint_dir': 'ml/checkpoints', + 'ml_threshold_key': 'p95', # p90, p95, p99 + + # Alpha 配置（与 concept_alert_alpha.py 一致） + 'alpha_zscore_threshold': 2.0, + 'alpha_absolute_threshold': 1.5, + 'alpha_history_window': 60, + 'alpha_min_history': 5, + + # 共享配置 + 'cooldown_minutes': 8, + 'max_alerts_per_minute': 15, + 'min_alpha_abs': 0.5, +} + +# 特征配置（与训练一致） +FEATURE_NAMES = [ + 'alpha', + 'alpha_delta', + 'amt_ratio', + 'amt_delta', + 'rank_pct', + 'limit_up_ratio', +] + + +# ==================== 数据结构 ==================== + +@dataclass +class AlphaStats: + """概念的Alpha统计信息""" + history: deque = field(default_factory=lambda: deque(maxlen=ENHANCED_CONFIG['alpha_history_window'])) + mean: float = 0.0 + std: float = 1.0 + + def update(self, alpha: float): + self.history.append(alpha) + if len(self.history) >= 2: + self.mean = np.mean(self.history) + self.std = max(np.std(self.history), 0.1) + + def get_zscore(self, alpha: float) -> float: + if len(self.history) < ENHANCED_CONFIG['alpha_min_history']: + return 0.0 + return (alpha - self.mean) / self.std + + def is_ready(self) -> bool: + return len(self.history) >= ENHANCED_CONFIG['alpha_min_history'] + + +@dataclass +class ConceptFeatures: + """概念的实时特征""" + alpha: float = 0.0 + alpha_delta: float = 0.0 + amt_ratio: float = 1.0 + amt_delta: float = 0.0 + rank_pct: float = 0.5 + limit_up_ratio: float = 0.0 + + def to_dict(self) -> Dict[str, float]: + return { + 'alpha': self.alpha, + 'alpha_delta': self.alpha_delta, + 'amt_ratio': self.amt_ratio, + 'amt_delta': self.amt_delta, + 'rank_pct': self.rank_pct, + 'limit_up_ratio': self.limit_up_ratio, + } + + +# ==================== 增强检测器 ==================== + +class EnhancedAnomalyDetector: + """ + 增强版异动检测器 + + 融合 Alpha-based 和 ML 两种方法 + """ + + def __init__( + self, + config: Dict = None, + ml_enabled: bool = True + ): + self.config = config or ENHANCED_CONFIG + self.ml_enabled = ml_enabled + self.ml_detector = None + + # Alpha 统计 + self.alpha_stats: Dict[str, AlphaStats] = {} + + # 特征历史（用于计算 delta） + self.feature_history: Dict[str, deque] = {} + + # 冷却记录 + self.cooldown_cache: Dict[str, datetime] = {} + + # 尝试加载 ML 模型 + if ml_enabled: + self._load_ml_model() + + logger.info(f"EnhancedAnomalyDetector 初始化完成") + logger.info(f" 融合模式: {self.config['fusion_mode']}") + logger.info(f" ML 可用: {self.ml_detector is not None}") + + def _load_ml_model(self): + """加载 ML 模型""" + try: + from inference import ConceptAnomalyDetector + checkpoint_dir = Path(__file__).parent / 'checkpoints' + + if (checkpoint_dir / 'best_model.pt').exists(): + self.ml_detector = ConceptAnomalyDetector( + checkpoint_dir=str(checkpoint_dir), + threshold_key=self.config['ml_threshold_key'] + ) + logger.info("ML 模型加载成功") + else: + logger.warning(f"ML 模型不存在: {checkpoint_dir / 'best_model.pt'}") + except Exception as e: + logger.warning(f"ML 模型加载失败: {e}") + self.ml_detector = None + + def _get_alpha_stats(self, concept_id: str) -> AlphaStats: + """获取或创建 Alpha 统计""" + if concept_id not in self.alpha_stats: + self.alpha_stats[concept_id] = AlphaStats() + return self.alpha_stats[concept_id] + + def _get_feature_history(self, concept_id: str) -> deque: + """获取特征历史""" + if concept_id not in self.feature_history: + self.feature_history[concept_id] = deque(maxlen=10) + return self.feature_history[concept_id] + + def _check_cooldown(self, concept_id: str, current_time: datetime) -> bool: + """检查冷却""" + if concept_id not in self.cooldown_cache: + return False + + last_alert = self.cooldown_cache[concept_id] + cooldown_td = (current_time - last_alert).total_seconds() / 60 + + return cooldown_td < self.config['cooldown_minutes'] + + def _set_cooldown(self, concept_id: str, current_time: datetime): + """设置冷却""" + self.cooldown_cache[concept_id] = current_time + + def compute_features( + self, + concept_id: str, + alpha: float, + amt_ratio: float, + rank_pct: float, + limit_up_ratio: float + ) -> ConceptFeatures: + """ + 计算概念的完整特征 + + Args: + concept_id: 概念ID + alpha: 当前超额收益 + amt_ratio: 成交额比率 + rank_pct: 排名百分位 + limit_up_ratio: 涨停股占比 + + Returns: + 完整特征 + """ + history = self._get_feature_history(concept_id) + + # 计算变化率 + alpha_delta = 0.0 + amt_delta = 0.0 + + if len(history) > 0: + last_features = history[-1] + alpha_delta = alpha - last_features.alpha + if last_features.amt_ratio > 0: + amt_delta = (amt_ratio - last_features.amt_ratio) / last_features.amt_ratio + + features = ConceptFeatures( + alpha=alpha, + alpha_delta=alpha_delta, + amt_ratio=amt_ratio, + amt_delta=amt_delta, + rank_pct=rank_pct, + limit_up_ratio=limit_up_ratio, + ) + + # 更新历史 + history.append(features) + + return features + + def detect_alpha_anomaly( + self, + concept_id: str, + alpha: float + ) -> Tuple[bool, float, str]: + """ + Alpha-based 异动检测 + + Returns: + is_anomaly: 是否异动 + score: 异动分数（Z-Score 绝对值） + reason: 触发原因 + """ + stats = self._get_alpha_stats(concept_id) + + # 计算 Z-Score（在更新前） + zscore = stats.get_zscore(alpha) + + # 更新统计 + stats.update(alpha) + + # 判断 + if stats.is_ready(): + if abs(zscore) >= self.config['alpha_zscore_threshold']: + return True, abs(zscore), f"Z={zscore:.2f}" + else: + if abs(alpha) >= self.config['alpha_absolute_threshold']: + fake_zscore = alpha / 0.5 + return True, abs(fake_zscore), f"Alpha={alpha:+.2f}%" + + return False, abs(zscore) if zscore else 0.0, "" + + def detect_ml_anomaly( + self, + concept_id: str, + features: ConceptFeatures + ) -> Tuple[bool, float]: + """ + ML-based 异动检测 + + Returns: + is_anomaly: 是否异动 + score: 异动分数（重构误差） + """ + if self.ml_detector is None: + return False, 0.0 + + try: + is_anomaly, score = self.ml_detector.detect( + concept_id, + features.to_dict() + ) + return is_anomaly, score or 0.0 + except Exception as e: + logger.warning(f"ML 检测失败: {e}") + return False, 0.0 + + def detect( + self, + concept_id: str, + concept_name: str, + alpha: float, + amt_ratio: float, + rank_pct: float, + limit_up_ratio: float, + change_pct: float, + index_change: float, + current_time: datetime, + **extra_data + ) -> Optional[Dict]: + """ + 融合检测 + + Args: + concept_id: 概念ID + concept_name: 概念名称 + alpha: 超额收益 + amt_ratio: 成交额比率 + rank_pct: 排名百分位 + limit_up_ratio: 涨停股占比 + change_pct: 概念涨跌幅 + index_change: 大盘涨跌幅 + current_time: 当前时间 + **extra_data: 其他数据（limit_up_count, stock_count 等） + + Returns: + 异动信息（如果触发），否则 None + """ + # Alpha 太小，不关注 + if abs(alpha) < self.config['min_alpha_abs']: + return None + + # 检查冷却 + if self._check_cooldown(concept_id, current_time): + return None + + # 计算特征 + features = self.compute_features( + concept_id, alpha, amt_ratio, rank_pct, limit_up_ratio + ) + + # 执行检测 + fusion_mode = self.config['fusion_mode'] + + alpha_anomaly, alpha_score, alpha_reason = self.detect_alpha_anomaly(concept_id, alpha) + ml_anomaly, ml_score = False, 0.0 + + if fusion_mode in ('ml_only', 'adaptive', 'ensemble'): + ml_anomaly, ml_score = self.detect_ml_anomaly(concept_id, features) + + # 根据融合模式判断 + is_anomaly = False + final_score = 0.0 + detection_method = '' + + if fusion_mode == 'alpha_only': + is_anomaly = alpha_anomaly + final_score = alpha_score + detection_method = 'alpha' + + elif fusion_mode == 'ml_only': + is_anomaly = ml_anomaly + final_score = ml_score + detection_method = 'ml' + + elif fusion_mode == 'adaptive': + # 优先 ML，回退 Alpha + if self.ml_detector and ml_score > 0: + is_anomaly = ml_anomaly + final_score = ml_score + detection_method = 'ml' + else: + is_anomaly = alpha_anomaly + final_score = alpha_score + detection_method = 'alpha' + + elif fusion_mode == 'ensemble': + # 加权融合 + # 归一化分数 + norm_alpha = min(alpha_score / 5.0, 1.0) # Z > 5 视为 1.0 + norm_ml = min(ml_score / (self.ml_detector.threshold if self.ml_detector else 1.0), 1.0) + + final_score = ( + self.config['alpha_weight'] * norm_alpha + + self.config['ml_weight'] * norm_ml + ) + is_anomaly = final_score > 0.5 or alpha_anomaly or ml_anomaly + detection_method = 'ensemble' + + if not is_anomaly: + return None + + # 构建异动记录 + self._set_cooldown(concept_id, current_time) + + alert_type = 'surge_up' if alpha > 0 else 'surge_down' + + alert = { + 'concept_id': concept_id, + 'concept_name': concept_name, + 'alert_type': alert_type, + 'alert_time': current_time, + 'change_pct': change_pct, + 'alpha': alpha, + 'alpha_zscore': alpha_score, + 'index_change_pct': index_change, + 'detection_method': detection_method, + 'alpha_score': alpha_score, + 'ml_score': ml_score, + 'final_score': final_score, + **extra_data + } + + return alert + + def batch_detect( + self, + concepts_data: List[Dict], + current_time: datetime + ) -> List[Dict]: + """ + 批量检测 + + Args: + concepts_data: 概念数据列表 + current_time: 当前时间 + + Returns: + 异动列表（按分数排序，限制数量） + """ + alerts = [] + + for data in concepts_data: + alert = self.detect( + concept_id=data['concept_id'], + concept_name=data['concept_name'], + alpha=data.get('alpha', 0), + amt_ratio=data.get('amt_ratio', 1.0), + rank_pct=data.get('rank_pct', 0.5), + limit_up_ratio=data.get('limit_up_ratio', 0), + change_pct=data.get('change_pct', 0), + index_change=data.get('index_change', 0), + current_time=current_time, + limit_up_count=data.get('limit_up_count', 0), + limit_down_count=data.get('limit_down_count', 0), + stock_count=data.get('stock_count', 0), + concept_type=data.get('concept_type', 'leaf'), + ) + + if alert: + alerts.append(alert) + + # 排序并限制数量 + alerts.sort(key=lambda x: x['final_score'], reverse=True) + return alerts[:self.config['max_alerts_per_minute']] + + def reset(self): + """重置所有状态（新交易日）""" + self.alpha_stats.clear() + self.feature_history.clear() + self.cooldown_cache.clear() + + if self.ml_detector: + self.ml_detector.clear_history() + + logger.info("检测器状态已重置") + + +# ==================== 测试 ==================== + +if __name__ == "__main__": + import random + + print("测试 EnhancedAnomalyDetector...") + + # 初始化 + detector = EnhancedAnomalyDetector(ml_enabled=False) # 不加载 ML（可能不存在） + + # 模拟数据 + concepts = [ + {'concept_id': 'ai_001', 'concept_name': '人工智能'}, + {'concept_id': 'chip_002', 'concept_name': '芯片半导体'}, + {'concept_id': 'car_003', 'concept_name': '新能源汽车'}, + ] + + print("\n模拟实时检测...") + current_time = datetime.now() + + for minute in range(50): + concepts_data = [] + + for c in concepts: + # 生成随机数据 + alpha = random.gauss(0, 0.8) + amt_ratio = max(0.3, random.gauss(1, 0.3)) + rank_pct = random.random() + limit_up_ratio = random.random() * 0.1 + + # 模拟异动（第30分钟人工智能暴涨） + if minute == 30 and c['concept_id'] == 'ai_001': + alpha = 4.5 + amt_ratio = 2.5 + limit_up_ratio = 0.3 + + concepts_data.append({ + **c, + 'alpha': alpha, + 'amt_ratio': amt_ratio, + 'rank_pct': rank_pct, + 'limit_up_ratio': limit_up_ratio, + 'change_pct': alpha + 0.5, + 'index_change': 0.5, + }) + + # 检测 + alerts = detector.batch_detect(concepts_data, current_time) + + if alerts: + for alert in alerts: + print(f" t={minute:02d} 🔥 {alert['concept_name']} " + f"Alpha={alert['alpha']:+.2f}% " + f"Score={alert['final_score']:.2f} " + f"Method={alert['detection_method']}") + + current_time = current_time.replace(minute=current_time.minute + 1 if current_time.minute < 59 else 0) + + print("\n测试完成！") diff --git a/ml/inference.py b/ml/inference.py new file mode 100644 index 00000000..4e704f4c --- /dev/null +++ b/ml/inference.py @@ -0,0 +1,455 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +概念异动检测推理服务 + +在实时场景中使用训练好的 Transformer Autoencoder 进行异动检测 + +使用方法： + from ml.inference import ConceptAnomalyDetector + + detector = ConceptAnomalyDetector('ml/checkpoints') + + # 检测异动 + features = {...} # 实时特征数据 + is_anomaly, score = detector.detect(features) +""" + +import os +import json +from pathlib import Path +from typing import Dict, List, Tuple, Optional +from collections import deque + +import numpy as np +import torch + +from model import TransformerAutoencoder + + +class ConceptAnomalyDetector: + """ + 概念异动检测器 + + 使用训练好的 Transformer Autoencoder 进行实时异动检测 + """ + + def __init__( + self, + checkpoint_dir: str = 'ml/checkpoints', + device: str = 'auto', + threshold_key: str = 'p95' + ): + """ + 初始化检测器 + + Args: + checkpoint_dir: 模型检查点目录 + device: 设备 (auto/cuda/cpu) + threshold_key: 使用的阈值键 (p90/p95/p99) + """ + self.checkpoint_dir = Path(checkpoint_dir) + self.threshold_key = threshold_key + + # 设备选择 + if device == 'auto': + self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + else: + self.device = torch.device(device) + + # 加载配置 + self._load_config() + + # 加载模型 + self._load_model() + + # 加载阈值 + self._load_thresholds() + + # 加载标准化统计量 + self._load_normalization_stats() + + # 概念历史数据缓存 + # {concept_name: deque(maxlen=seq_len)} + self.history_cache: Dict[str, deque] = {} + + print(f"ConceptAnomalyDetector 初始化完成") + print(f" 设备: {self.device}") + print(f" 阈值: {self.threshold_key} = {self.threshold:.6f}") + print(f" 序列长度: {self.seq_len}") + + def _load_config(self): + """加载配置""" + config_path = self.checkpoint_dir / 'config.json' + if not config_path.exists(): + raise FileNotFoundError(f"配置文件不存在: {config_path}") + + with open(config_path, 'r') as f: + self.config = json.load(f) + + self.features = self.config['features'] + self.seq_len = self.config['seq_len'] + self.model_config = self.config['model'] + + def _load_model(self): + """加载模型""" + model_path = self.checkpoint_dir / 'best_model.pt' + if not model_path.exists(): + raise FileNotFoundError(f"模型文件不存在: {model_path}") + + # 创建模型 + self.model = TransformerAutoencoder(**self.model_config) + + # 加载权重 + checkpoint = torch.load(model_path, map_location=self.device) + self.model.load_state_dict(checkpoint['model_state_dict']) + self.model.to(self.device) + self.model.eval() + + print(f"模型已加载: {model_path}") + + def _load_thresholds(self): + """加载阈值""" + thresholds_path = self.checkpoint_dir / 'thresholds.json' + if not thresholds_path.exists(): + raise FileNotFoundError(f"阈值文件不存在: {thresholds_path}") + + with open(thresholds_path, 'r') as f: + self.thresholds = json.load(f) + + if self.threshold_key not in self.thresholds: + available_keys = list(self.thresholds.keys()) + raise KeyError(f"阈值键 '{self.threshold_key}' 不存在，可用: {available_keys}") + + self.threshold = self.thresholds[self.threshold_key] + + def _load_normalization_stats(self): + """加载标准化统计量""" + stats_path = self.checkpoint_dir / 'normalization_stats.json' + if not stats_path.exists(): + raise FileNotFoundError(f"标准化统计量文件不存在: {stats_path}") + + with open(stats_path, 'r') as f: + stats = json.load(f) + + self.norm_mean = np.array(stats['mean']) + self.norm_std = np.array(stats['std']) + + def normalize(self, features: np.ndarray) -> np.ndarray: + """标准化特征""" + return (features - self.norm_mean) / self.norm_std + + def update_history( + self, + concept_name: str, + features: Dict[str, float] + ): + """ + 更新概念历史数据 + + Args: + concept_name: 概念名称 + features: 当前时刻的特征字典 + """ + # 初始化历史缓存 + if concept_name not in self.history_cache: + self.history_cache[concept_name] = deque(maxlen=self.seq_len) + + # 提取特征向量 + feature_vector = np.array([ + features.get(f, 0.0) for f in self.features + ]) + + # 处理异常值 + feature_vector = np.nan_to_num(feature_vector, nan=0.0, posinf=0.0, neginf=0.0) + + # 添加到历史 + self.history_cache[concept_name].append(feature_vector) + + def get_history_length(self, concept_name: str) -> int: + """获取概念的历史数据长度""" + if concept_name not in self.history_cache: + return 0 + return len(self.history_cache[concept_name]) + + @torch.no_grad() + def detect( + self, + concept_name: str, + features: Dict[str, float] = None, + return_score: bool = True + ) -> Tuple[bool, Optional[float]]: + """ + 检测概念是否异动 + + Args: + concept_name: 概念名称 + features: 当前时刻的特征（如果提供，会先更新历史） + return_score: 是否返回异动分数 + + Returns: + is_anomaly: 是否异动 + score: 异动分数（如果 return_score=True） + """ + # 更新历史 + if features is not None: + self.update_history(concept_name, features) + + # 检查历史数据是否足够 + if concept_name not in self.history_cache: + return False, None + + history = self.history_cache[concept_name] + if len(history) < self.seq_len: + return False, None + + # 构建输入序列 + sequence = np.array(list(history)) # (seq_len, n_features) + + # 标准化 + sequence = self.normalize(sequence) + + # 转为 tensor + x = torch.FloatTensor(sequence).unsqueeze(0) # (1, seq_len, n_features) + x = x.to(self.device) + + # 计算重构误差 + error = self.model.compute_reconstruction_error(x, reduction='none') + + # 取最后一个时刻的误差作为当前分数 + score = error[0, -1].item() + + # 判断是否异动 + is_anomaly = score > self.threshold + + if return_score: + return is_anomaly, score + else: + return is_anomaly, None + + @torch.no_grad() + def batch_detect( + self, + concept_features: Dict[str, Dict[str, float]] + ) -> Dict[str, Tuple[bool, float]]: + """ + 批量检测多个概念 + + Args: + concept_features: {concept_name: {feature_name: value}} + + Returns: + results: {concept_name: (is_anomaly, score)} + """ + results = {} + + for concept_name, features in concept_features.items(): + is_anomaly, score = self.detect(concept_name, features) + results[concept_name] = (is_anomaly, score) + + return results + + def get_anomaly_type( + self, + concept_name: str, + features: Dict[str, float] + ) -> str: + """ + 判断异动类型 + + Args: + concept_name: 概念名称 + features: 当前特征 + + Returns: + anomaly_type: 'surge_up' / 'surge_down' / 'normal' + """ + is_anomaly, score = self.detect(concept_name, features) + + if not is_anomaly: + return 'normal' + + # 根据 alpha 判断涨跌 + alpha = features.get('alpha', 0.0) + + if alpha > 0: + return 'surge_up' + else: + return 'surge_down' + + def get_top_anomalies( + self, + concept_features: Dict[str, Dict[str, float]], + top_k: int = 10 + ) -> List[Tuple[str, float, str]]: + """ + 获取异动分数最高的 top_k 个概念 + + Args: + concept_features: {concept_name: {feature_name: value}} + top_k: 返回数量 + + Returns: + anomalies: [(concept_name, score, anomaly_type), ...] + """ + results = self.batch_detect(concept_features) + + # 按分数排序 + sorted_results = sorted( + [(name, is_anomaly, score) for name, (is_anomaly, score) in results.items() if score is not None], + key=lambda x: x[2], + reverse=True + ) + + # 取 top_k + top_anomalies = [] + for name, is_anomaly, score in sorted_results[:top_k]: + if is_anomaly: + alpha = concept_features[name].get('alpha', 0.0) + anomaly_type = 'surge_up' if alpha > 0 else 'surge_down' + top_anomalies.append((name, score, anomaly_type)) + + return top_anomalies + + def clear_history(self, concept_name: str = None): + """ + 清除历史缓存 + + Args: + concept_name: 概念名称（如果为 None，清除所有） + """ + if concept_name is None: + self.history_cache.clear() + elif concept_name in self.history_cache: + del self.history_cache[concept_name] + + +# ==================== 集成到现有系统 ==================== + +class MLAnomalyService: + """ + ML 异动检测服务 + + 用于替换或增强现有的 Alpha-based 检测 + """ + + def __init__( + self, + checkpoint_dir: str = 'ml/checkpoints', + fallback_to_alpha: bool = True + ): + """ + Args: + checkpoint_dir: 模型检查点目录 + fallback_to_alpha: 当 ML 模型不可用时是否回退到 Alpha 方法 + """ + self.fallback_to_alpha = fallback_to_alpha + self.ml_detector = None + + try: + self.ml_detector = ConceptAnomalyDetector(checkpoint_dir) + print("ML 异动检测服务初始化成功") + except Exception as e: + print(f"ML 模型加载失败: {e}") + if not fallback_to_alpha: + raise + print("将回退到 Alpha-based 检测") + + def is_ml_available(self) -> bool: + """检查 ML 模型是否可用""" + return self.ml_detector is not None + + def detect_anomaly( + self, + concept_name: str, + features: Dict[str, float], + alpha_threshold: float = 2.0 + ) -> Tuple[bool, float, str]: + """ + 检测异动 + + Args: + concept_name: 概念名称 + features: 特征字典（需包含 alpha, amt_ratio 等） + alpha_threshold: Alpha Z-Score 阈值（用于回退） + + Returns: + is_anomaly: 是否异动 + score: 异动分数 + method: 检测方法 ('ml' / 'alpha') + """ + # 优先使用 ML 检测 + if self.ml_detector is not None: + history_len = self.ml_detector.get_history_length(concept_name) + + # 历史数据足够时使用 ML + if history_len >= self.ml_detector.seq_len - 1: + is_anomaly, score = self.ml_detector.detect(concept_name, features) + if score is not None: + return is_anomaly, score, 'ml' + else: + # 更新历史但使用 Alpha 方法 + self.ml_detector.update_history(concept_name, features) + + # 回退到 Alpha 方法 + if self.fallback_to_alpha: + alpha = features.get('alpha', 0.0) + alpha_zscore = features.get('alpha_zscore', 0.0) + + is_anomaly = abs(alpha_zscore) > alpha_threshold + score = abs(alpha_zscore) + + return is_anomaly, score, 'alpha' + + return False, 0.0, 'none' + + +# ==================== 测试 ==================== + +if __name__ == "__main__": + import random + + print("测试 ConceptAnomalyDetector...") + + # 检查模型是否存在 + checkpoint_dir = Path('ml/checkpoints') + if not (checkpoint_dir / 'best_model.pt').exists(): + print("模型文件不存在，跳过测试") + print("请先运行 train.py 训练模型") + exit(0) + + # 初始化检测器 + detector = ConceptAnomalyDetector('ml/checkpoints') + + # 模拟数据 + print("\n模拟实时检测...") + concept_name = "人工智能" + + for i in range(40): + # 生成随机特征 + features = { + 'alpha': random.gauss(0, 1), + 'alpha_delta': random.gauss(0, 0.5), + 'amt_ratio': random.gauss(1, 0.3), + 'amt_delta': random.gauss(0, 0.2), + 'rank_pct': random.random(), + 'limit_up_ratio': random.random() * 0.1, + } + + # 在第 35 分钟模拟异动 + if i == 35: + features['alpha'] = 5.0 + features['alpha_delta'] = 2.0 + features['amt_ratio'] = 3.0 + + is_anomaly, score = detector.detect(concept_name, features) + + history_len = detector.get_history_length(concept_name) + + if score is not None: + status = "🔥 异动！" if is_anomaly else "正常" + print(f" t={i:02d} | 历史={history_len} | 分数={score:.4f} | {status}") + else: + print(f" t={i:02d} | 历史={history_len} | 数据不足") + + print("\n测试完成！") diff --git a/ml/model.py b/ml/model.py new file mode 100644 index 00000000..2eb63d87 --- /dev/null +++ b/ml/model.py @@ -0,0 +1,390 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +LSTM Autoencoder 模型定义 + +用于概念异动检测： +- 学习"正常"市场模式 +- 重构误差大的时刻 = 异动 + +模型结构（简洁有效）: +┌─────────────────────────────────────┐ +│ 输入: (batch, seq_len, n_features) │ +│ 过去30分钟的特征序列 │ +├─────────────────────────────────────┤ +│ LSTM Encoder │ +│ - 双向 LSTM │ +│ - 输出最后隐藏状态 │ +├─────────────────────────────────────┤ +│ Bottleneck (压缩层) │ +│ 降维到 latent_dim（关键！） │ +├─────────────────────────────────────┤ +│ LSTM Decoder │ +│ - 单向 LSTM │ +│ - 重构序列 │ +├─────────────────────────────────────┤ +│ 输出: (batch, seq_len, n_features) │ +│ 重构的特征序列 │ +└─────────────────────────────────────┘ + +为什么用 LSTM 而不是 Transformer: +1. 参数更少，不容易过拟合 +2. 对于 6 维特征足够用 +3. 训练更稳定 +4. 瓶颈约束更容易控制 +""" + +import torch +import torch.nn as nn +import torch.nn.functional as F +from typing import Optional, Tuple + + +class LSTMAutoencoder(nn.Module): + """ + LSTM Autoencoder for Anomaly Detection + + 设计原则： + - 足够简单，避免过拟合 + - 瓶颈层严格限制，迫使模型只学习主要模式 + - 异常难以通过狭窄瓶颈，重构误差大 + """ + + def __init__( + self, + n_features: int = 6, + hidden_dim: int = 32, # LSTM 隐藏维度（小！） + latent_dim: int = 4, # 瓶颈维度（非常小！关键参数） + num_layers: int = 1, # LSTM 层数 + dropout: float = 0.2, + bidirectional: bool = True, # 双向编码器 + ): + super().__init__() + + self.n_features = n_features + self.hidden_dim = hidden_dim + self.latent_dim = latent_dim + self.num_layers = num_layers + self.bidirectional = bidirectional + self.num_directions = 2 if bidirectional else 1 + + # Encoder: 双向 LSTM + self.encoder = nn.LSTM( + input_size=n_features, + hidden_size=hidden_dim, + num_layers=num_layers, + batch_first=True, + dropout=dropout if num_layers > 1 else 0, + bidirectional=bidirectional + ) + + # Bottleneck: 压缩到极小的 latent space + encoder_output_dim = hidden_dim * self.num_directions + self.bottleneck_down = nn.Sequential( + nn.Linear(encoder_output_dim, latent_dim), + nn.Tanh(), # 限制范围，增加约束 + ) + + self.bottleneck_up = nn.Sequential( + nn.Linear(latent_dim, hidden_dim), + nn.ReLU(), + ) + + # Decoder: 单向 LSTM + self.decoder = nn.LSTM( + input_size=hidden_dim, + hidden_size=hidden_dim, + num_layers=num_layers, + batch_first=True, + dropout=dropout if num_layers > 1 else 0, + bidirectional=False # 解码器用单向 + ) + + # 输出层 + self.output_layer = nn.Linear(hidden_dim, n_features) + + # Dropout + self.dropout = nn.Dropout(dropout) + + # 初始化 + self._init_weights() + + def _init_weights(self): + """初始化权重""" + for name, param in self.named_parameters(): + if 'weight_ih' in name: + nn.init.xavier_uniform_(param) + elif 'weight_hh' in name: + nn.init.orthogonal_(param) + elif 'bias' in name: + nn.init.zeros_(param) + + def encode(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + """ + 编码器 + + Args: + x: (batch, seq_len, n_features) + Returns: + latent: (batch, seq_len, latent_dim) 每个时间步的压缩表示 + encoder_outputs: (batch, seq_len, hidden_dim * num_directions) + """ + # LSTM 编码 + encoder_outputs, (h_n, c_n) = self.encoder(x) + # encoder_outputs: (batch, seq_len, hidden_dim * num_directions) + + encoder_outputs = self.dropout(encoder_outputs) + + # 压缩到 latent space（对每个时间步） + latent = self.bottleneck_down(encoder_outputs) + # latent: (batch, seq_len, latent_dim) + + return latent, encoder_outputs + + def decode(self, latent: torch.Tensor, seq_len: int) -> torch.Tensor: + """ + 解码器 + + Args: + latent: (batch, seq_len, latent_dim) + seq_len: 序列长度 + Returns: + output: (batch, seq_len, n_features) + """ + # 从 latent space 恢复 + decoder_input = self.bottleneck_up(latent) + # decoder_input: (batch, seq_len, hidden_dim) + + # LSTM 解码 + decoder_outputs, _ = self.decoder(decoder_input) + # decoder_outputs: (batch, seq_len, hidden_dim) + + decoder_outputs = self.dropout(decoder_outputs) + + # 投影到原始特征空间 + output = self.output_layer(decoder_outputs) + # output: (batch, seq_len, n_features) + + return output + + def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + """ + 前向传播 + + Args: + x: (batch, seq_len, n_features) + Returns: + output: (batch, seq_len, n_features) 重构结果 + latent: (batch, seq_len, latent_dim) 隐向量 + """ + batch_size, seq_len, _ = x.shape + + # 编码 + latent, _ = self.encode(x) + + # 解码 + output = self.decode(latent, seq_len) + + return output, latent + + def compute_reconstruction_error( + self, + x: torch.Tensor, + reduction: str = 'none' + ) -> torch.Tensor: + """ + 计算重构误差 + + Args: + x: (batch, seq_len, n_features) + reduction: 'none' | 'mean' | 'sum' + Returns: + error: 重构误差 + """ + output, _ = self.forward(x) + + # MSE per feature per timestep + error = F.mse_loss(output, x, reduction='none') + + if reduction == 'none': + # (batch, seq_len, n_features) -> (batch, seq_len) + return error.mean(dim=-1) + elif reduction == 'mean': + return error.mean() + elif reduction == 'sum': + return error.sum() + else: + raise ValueError(f"Unknown reduction: {reduction}") + + def detect_anomaly( + self, + x: torch.Tensor, + threshold: float = None, + return_scores: bool = True + ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: + """ + 检测异动 + + Args: + x: (batch, seq_len, n_features) + threshold: 异动阈值（如果为 None，只返回分数） + return_scores: 是否返回异动分数 + Returns: + is_anomaly: (batch, seq_len) bool tensor (if threshold is not None) + scores: (batch, seq_len) 异动分数 (if return_scores) + """ + scores = self.compute_reconstruction_error(x, reduction='none') + + is_anomaly = None + if threshold is not None: + is_anomaly = scores > threshold + + if return_scores: + return is_anomaly, scores + else: + return is_anomaly, None + + +# 为了兼容性，创建别名 +TransformerAutoencoder = LSTMAutoencoder + + +# ==================== 损失函数 ==================== + +class AnomalyDetectionLoss(nn.Module): + """ + 异动检测损失函数 + + 简单的 MSE 重构损失 + """ + + def __init__( + self, + feature_weights: torch.Tensor = None, + ): + super().__init__() + self.feature_weights = feature_weights + + def forward( + self, + output: torch.Tensor, + target: torch.Tensor, + latent: torch.Tensor = None + ) -> Tuple[torch.Tensor, dict]: + """ + Args: + output: (batch, seq_len, n_features) 重构结果 + target: (batch, seq_len, n_features) 原始输入 + latent: (batch, seq_len, latent_dim) 隐向量（未使用） + Returns: + loss: 总损失 + loss_dict: 各项损失详情 + """ + # 重构损失 (MSE) + mse = F.mse_loss(output, target, reduction='none') + + # 特征加权（可选） + if self.feature_weights is not None: + weights = self.feature_weights.to(mse.device) + mse = mse * weights + + reconstruction_loss = mse.mean() + + loss_dict = { + 'total': reconstruction_loss.item(), + 'reconstruction': reconstruction_loss.item(), + } + + return reconstruction_loss, loss_dict + + +# ==================== 工具函数 ==================== + +def count_parameters(model: nn.Module) -> int: + """统计模型参数量""" + return sum(p.numel() for p in model.parameters() if p.requires_grad) + + +def create_model(config: dict = None) -> LSTMAutoencoder: + """ + 创建模型 + + 默认使用小型 LSTM 配置，适合异动检测 + """ + default_config = { + 'n_features': 6, + 'hidden_dim': 32, # 小！ + 'latent_dim': 4, # 非常小！关键 + 'num_layers': 1, + 'dropout': 0.2, + 'bidirectional': True, + } + + if config: + # 兼容旧的 Transformer 配置键名 + if 'd_model' in config: + config['hidden_dim'] = config.pop('d_model') // 2 + if 'num_encoder_layers' in config: + config['num_layers'] = config.pop('num_encoder_layers') + if 'num_decoder_layers' in config: + config.pop('num_decoder_layers') + if 'nhead' in config: + config.pop('nhead') + if 'dim_feedforward' in config: + config.pop('dim_feedforward') + if 'max_seq_len' in config: + config.pop('max_seq_len') + if 'use_instance_norm' in config: + config.pop('use_instance_norm') + + default_config.update(config) + + model = LSTMAutoencoder(**default_config) + param_count = count_parameters(model) + print(f"模型参数量: {param_count:,}") + + if param_count > 100000: + print(f"⚠️ 警告: 参数量较大（{param_count:,}），可能过拟合") + else: + print(f"✓ 参数量适中（LSTM Autoencoder）") + + return model + + +if __name__ == "__main__": + # 测试模型 + print("测试 LSTM Autoencoder...") + + # 创建模型 + model = create_model() + + # 测试输入 + batch_size = 32 + seq_len = 30 + n_features = 6 + + x = torch.randn(batch_size, seq_len, n_features) + + # 前向传播 + output, latent = model(x) + + print(f"输入形状: {x.shape}") + print(f"输出形状: {output.shape}") + print(f"隐向量形状: {latent.shape}") + + # 计算重构误差 + error = model.compute_reconstruction_error(x) + print(f"重构误差形状: {error.shape}") + print(f"平均重构误差: {error.mean().item():.4f}") + + # 测试异动检测 + is_anomaly, scores = model.detect_anomaly(x, threshold=0.5) + print(f"异动检测结果形状: {is_anomaly.shape if is_anomaly is not None else 'None'}") + print(f"异动分数形状: {scores.shape}") + + # 测试损失函数 + criterion = AnomalyDetectionLoss() + loss, loss_dict = criterion(output, x, latent) + print(f"损失: {loss.item():.4f}") + + print("\n测试通过！") diff --git a/ml/prepare_data.py b/ml/prepare_data.py new file mode 100644 index 00000000..4734f582 --- /dev/null +++ b/ml/prepare_data.py @@ -0,0 +1,501 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +数据准备脚本 - 为 Transformer Autoencoder 准备训练数据 + +从 ClickHouse 提取历史分钟数据，计算以下特征： +1. alpha - 超额收益（概念涨幅 - 大盘涨幅） +2. alpha_delta - Alpha 变化率（5分钟） +3. amt_ratio - 成交额相对均值（当前/过去20分钟均值） +4. amt_delta - 成交额变化率 +5. rank_pct - Alpha 排名百分位 +6. limit_up_ratio - 涨停股占比 + +输出：按交易日存储的特征文件（parquet格式） +""" + +import os +import sys +import numpy as np +import pandas as pd +from datetime import datetime, timedelta, date +from sqlalchemy import create_engine, text +from elasticsearch import Elasticsearch +from clickhouse_driver import Client +import hashlib +import json +import logging +from typing import Dict, List, Set, Tuple +from concurrent.futures import ThreadPoolExecutor, as_completed +import warnings +warnings.filterwarnings('ignore') + +# ==================== 配置 ==================== + +MYSQL_ENGINE = create_engine( + "mysql+pymysql://root:Zzl5588161!@192.168.1.5:3306/stock", + echo=False +) + +ES_CLIENT = Elasticsearch(['http://127.0.0.1:9200']) +ES_INDEX = 'concept_library_v3' + +CLICKHOUSE_CONFIG = { + 'host': '127.0.0.1', + 'port': 9000, + 'user': 'default', + 'password': 'Zzl33818!', + 'database': 'stock' +} + +# 输出目录 +OUTPUT_DIR = os.path.join(os.path.dirname(__file__), 'data') +os.makedirs(OUTPUT_DIR, exist_ok=True) + +# 特征计算参数 +FEATURE_CONFIG = { + 'alpha_delta_window': 5, # Alpha变化窗口（分钟） + 'amt_ma_window': 20, # 成交额均值窗口（分钟） + 'limit_up_threshold': 9.8, # 涨停阈值（%） + 'limit_down_threshold': -9.8, # 跌停阈值（%） +} + +REFERENCE_INDEX = '000001.SH' + +# ==================== 日志 ==================== + +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(levelname)s - %(message)s' +) +logger = logging.getLogger(__name__) + +# ==================== 工具函数 ==================== + +def get_ch_client(): + return Client(**CLICKHOUSE_CONFIG) + + +def generate_id(name: str) -> str: + return hashlib.md5(name.encode('utf-8')).hexdigest()[:16] + + +def code_to_ch_format(code: str) -> str: + if not code or len(code) != 6 or not code.isdigit(): + return None + if code.startswith('6'): + return f"{code}.SH" + elif code.startswith('0') or code.startswith('3'): + return f"{code}.SZ" + else: + return f"{code}.BJ" + + +# ==================== 获取概念列表 ==================== + +def get_all_concepts() -> List[dict]: + """从ES获取所有叶子概念""" + concepts = [] + + query = { + "query": {"match_all": {}}, + "size": 100, + "_source": ["concept_id", "concept", "stocks"] + } + + resp = ES_CLIENT.search(index=ES_INDEX, body=query, scroll='2m') + scroll_id = resp['_scroll_id'] + hits = resp['hits']['hits'] + + while len(hits) > 0: + for hit in hits: + source = hit['_source'] + stocks = [] + if 'stocks' in source and isinstance(source['stocks'], list): + for stock in source['stocks']: + if isinstance(stock, dict) and 'code' in stock and stock['code']: + stocks.append(stock['code']) + + if stocks: + concepts.append({ + 'concept_id': source.get('concept_id'), + 'concept_name': source.get('concept'), + 'stocks': stocks + }) + + resp = ES_CLIENT.scroll(scroll_id=scroll_id, scroll='2m') + scroll_id = resp['_scroll_id'] + hits = resp['hits']['hits'] + + ES_CLIENT.clear_scroll(scroll_id=scroll_id) + logger.info(f"获取到 {len(concepts)} 个概念") + return concepts + + +# ==================== 获取交易日列表 ==================== + +def get_trading_days(start_date: str, end_date: str) -> List[str]: + """获取交易日列表""" + client = get_ch_client() + + query = f""" + SELECT DISTINCT toDate(timestamp) as trade_date + FROM stock_minute + WHERE toDate(timestamp) >= '{start_date}' + AND toDate(timestamp) <= '{end_date}' + ORDER BY trade_date + """ + + result = client.execute(query) + days = [row[0].strftime('%Y-%m-%d') for row in result] + logger.info(f"找到 {len(days)} 个交易日: {days[0]} ~ {days[-1]}") + return days + + +# ==================== 获取单日数据 ==================== + +def get_daily_stock_data(trade_date: str, stock_codes: List[str]) -> pd.DataFrame: + """获取单日所有股票的分钟数据""" + client = get_ch_client() + + # 转换代码格式 + ch_codes = [] + code_map = {} + for code in stock_codes: + ch_code = code_to_ch_format(code) + if ch_code: + ch_codes.append(ch_code) + code_map[ch_code] = code + + if not ch_codes: + return pd.DataFrame() + + ch_codes_str = "','".join(ch_codes) + + query = f""" + SELECT + code, + timestamp, + close, + volume, + amt + FROM stock_minute + WHERE toDate(timestamp) = '{trade_date}' + AND code IN ('{ch_codes_str}') + ORDER BY code, timestamp + """ + + result = client.execute(query) + + if not result: + return pd.DataFrame() + + df = pd.DataFrame(result, columns=['ch_code', 'timestamp', 'close', 'volume', 'amt']) + df['code'] = df['ch_code'].map(code_map) + df = df.dropna(subset=['code']) + + return df[['code', 'timestamp', 'close', 'volume', 'amt']] + + +def get_daily_index_data(trade_date: str, index_code: str = REFERENCE_INDEX) -> pd.DataFrame: + """获取单日指数分钟数据""" + client = get_ch_client() + + query = f""" + SELECT + timestamp, + close, + volume, + amt + FROM index_minute + WHERE toDate(timestamp) = '{trade_date}' + AND code = '{index_code}' + ORDER BY timestamp + """ + + result = client.execute(query) + + if not result: + return pd.DataFrame() + + df = pd.DataFrame(result, columns=['timestamp', 'close', 'volume', 'amt']) + return df + + +def get_prev_close(stock_codes: List[str], trade_date: str) -> Dict[str, float]: + """获取昨收价""" + valid_codes = [c for c in stock_codes if c and len(c) == 6 and c.isdigit()] + if not valid_codes: + return {} + + codes_str = "','".join(valid_codes) + + query = f""" + SELECT SECCODE, F002N + FROM ea_trade + WHERE SECCODE IN ('{codes_str}') + AND TRADEDATE = ( + SELECT MAX(TRADEDATE) FROM ea_trade WHERE TRADEDATE < '{trade_date}' + ) + AND F002N IS NOT NULL AND F002N > 0 + """ + + try: + with MYSQL_ENGINE.connect() as conn: + result = conn.execute(text(query)) + return {row[0]: float(row[1]) for row in result if row[1]} + except Exception as e: + logger.error(f"获取昨收价失败: {e}") + return {} + + +def get_index_prev_close(trade_date: str, index_code: str = REFERENCE_INDEX) -> float: + """获取指数昨收价""" + code_no_suffix = index_code.split('.')[0] + + try: + with MYSQL_ENGINE.connect() as conn: + result = conn.execute(text(""" + SELECT F006N FROM ea_exchangetrade + WHERE INDEXCODE = :code AND TRADEDATE < :today + ORDER BY TRADEDATE DESC LIMIT 1 + """), {'code': code_no_suffix, 'today': trade_date}).fetchone() + + if result and result[0]: + return float(result[0]) + except Exception as e: + logger.error(f"获取指数昨收失败: {e}") + + return None + + +# ==================== 计算特征 ==================== + +def compute_daily_features( + trade_date: str, + concepts: List[dict], + all_stocks: List[str] +) -> pd.DataFrame: + """ + 计算单日所有概念的特征 + + 返回 DataFrame: + - index: (timestamp, concept_id) + - columns: alpha, alpha_delta, amt_ratio, amt_delta, rank_pct, limit_up_ratio + """ + + # 1. 获取数据 + logger.info(f" 获取股票数据...") + stock_df = get_daily_stock_data(trade_date, all_stocks) + if stock_df.empty: + logger.warning(f" 无股票数据") + return pd.DataFrame() + + logger.info(f" 获取指数数据...") + index_df = get_daily_index_data(trade_date) + if index_df.empty: + logger.warning(f" 无指数数据") + return pd.DataFrame() + + # 2. 获取昨收价 + logger.info(f" 获取昨收价...") + prev_close = get_prev_close(all_stocks, trade_date) + index_prev_close = get_index_prev_close(trade_date) + + if not prev_close or not index_prev_close: + logger.warning(f" 无昨收价数据") + return pd.DataFrame() + + # 3. 计算股票涨跌幅和成交额 + stock_df['prev_close'] = stock_df['code'].map(prev_close) + stock_df = stock_df.dropna(subset=['prev_close']) + stock_df['change_pct'] = (stock_df['close'] - stock_df['prev_close']) / stock_df['prev_close'] * 100 + + # 4. 计算指数涨跌幅 + index_df['change_pct'] = (index_df['close'] - index_prev_close) / index_prev_close * 100 + index_change_map = dict(zip(index_df['timestamp'], index_df['change_pct'])) + + # 5. 获取所有时间点 + timestamps = sorted(stock_df['timestamp'].unique()) + logger.info(f" 时间点数: {len(timestamps)}") + + # 6. 按时间点计算概念特征 + results = [] + + # 概念到股票的映射 + concept_stocks = {c['concept_id']: set(c['stocks']) for c in concepts} + concept_names = {c['concept_id']: c['concept_name'] for c in concepts} + + # 历史数据缓存（用于计算变化率） + concept_history = {cid: {'alpha': [], 'amt': []} for cid in concept_stocks} + + for ts in timestamps: + ts_stock_data = stock_df[stock_df['timestamp'] == ts] + index_change = index_change_map.get(ts, 0) + + # 股票涨跌幅和成交额字典 + stock_change = dict(zip(ts_stock_data['code'], ts_stock_data['change_pct'])) + stock_amt = dict(zip(ts_stock_data['code'], ts_stock_data['amt'])) + + concept_features = [] + + for concept_id, stocks in concept_stocks.items(): + # 该概念的股票数据 + concept_changes = [stock_change[s] for s in stocks if s in stock_change] + concept_amts = [stock_amt.get(s, 0) for s in stocks if s in stock_change] + + if not concept_changes: + continue + + # 基础统计 + avg_change = np.mean(concept_changes) + total_amt = sum(concept_amts) + + # Alpha = 概念涨幅 - 指数涨幅 + alpha = avg_change - index_change + + # 涨停/跌停股占比 + limit_up_count = sum(1 for c in concept_changes if c >= FEATURE_CONFIG['limit_up_threshold']) + limit_down_count = sum(1 for c in concept_changes if c <= FEATURE_CONFIG['limit_down_threshold']) + limit_up_ratio = limit_up_count / len(concept_changes) + limit_down_ratio = limit_down_count / len(concept_changes) + + # 更新历史 + history = concept_history[concept_id] + history['alpha'].append(alpha) + history['amt'].append(total_amt) + + # 计算变化率 + alpha_delta = 0 + if len(history['alpha']) > FEATURE_CONFIG['alpha_delta_window']: + alpha_delta = alpha - history['alpha'][-FEATURE_CONFIG['alpha_delta_window']-1] + + # 成交额相对均值 + amt_ratio = 1.0 + amt_delta = 0 + if len(history['amt']) > FEATURE_CONFIG['amt_ma_window']: + amt_ma = np.mean(history['amt'][-FEATURE_CONFIG['amt_ma_window']-1:-1]) + if amt_ma > 0: + amt_ratio = total_amt / amt_ma + amt_delta = total_amt - history['amt'][-2] if len(history['amt']) > 1 else 0 + + concept_features.append({ + 'concept_id': concept_id, + 'alpha': alpha, + 'alpha_delta': alpha_delta, + 'amt_ratio': amt_ratio, + 'amt_delta': amt_delta, + 'limit_up_ratio': limit_up_ratio, + 'limit_down_ratio': limit_down_ratio, + 'total_amt': total_amt, + 'stock_count': len(concept_changes), + }) + + if not concept_features: + continue + + # 计算排名百分位 + concept_df = pd.DataFrame(concept_features) + concept_df['rank_pct'] = concept_df['alpha'].rank(pct=True) + + # 添加时间戳 + concept_df['timestamp'] = ts + results.append(concept_df) + + if not results: + return pd.DataFrame() + + # 合并所有时间点 + final_df = pd.concat(results, ignore_index=True) + + # 标准化成交额变化率 + if 'amt_delta' in final_df.columns: + amt_delta_std = final_df['amt_delta'].std() + if amt_delta_std > 0: + final_df['amt_delta'] = final_df['amt_delta'] / amt_delta_std + + logger.info(f" 计算完成: {len(final_df)} 条记录") + return final_df + + +# ==================== 主流程 ==================== + +def process_single_day(trade_date: str, concepts: List[dict], all_stocks: List[str]) -> str: + """处理单个交易日""" + output_file = os.path.join(OUTPUT_DIR, f'features_{trade_date}.parquet') + + # 检查是否已处理 + if os.path.exists(output_file): + logger.info(f"[{trade_date}] 已存在，跳过") + return output_file + + logger.info(f"[{trade_date}] 开始处理...") + + try: + df = compute_daily_features(trade_date, concepts, all_stocks) + + if df.empty: + logger.warning(f"[{trade_date}] 无数据") + return None + + # 保存 + df.to_parquet(output_file, index=False) + logger.info(f"[{trade_date}] 保存完成: {output_file}") + return output_file + + except Exception as e: + logger.error(f"[{trade_date}] 处理失败: {e}") + import traceback + traceback.print_exc() + return None + + +def main(): + import argparse + + parser = argparse.ArgumentParser(description='准备训练数据') + parser.add_argument('--start', type=str, default='2022-01-01', help='开始日期') + parser.add_argument('--end', type=str, default=None, help='结束日期（默认今天）') + parser.add_argument('--workers', type=int, default=1, help='并行数（建议1，避免数据库压力）') + + args = parser.parse_args() + + end_date = args.end or datetime.now().strftime('%Y-%m-%d') + + logger.info("=" * 60) + logger.info("数据准备 - Transformer Autoencoder 训练数据") + logger.info("=" * 60) + logger.info(f"日期范围: {args.start} ~ {end_date}") + + # 1. 获取概念列表 + concepts = get_all_concepts() + + # 收集所有股票 + all_stocks = list(set(s for c in concepts for s in c['stocks'])) + logger.info(f"股票总数: {len(all_stocks)}") + + # 2. 获取交易日列表 + trading_days = get_trading_days(args.start, end_date) + + if not trading_days: + logger.error("无交易日数据") + return + + # 3. 处理每个交易日 + logger.info(f"\n开始处理 {len(trading_days)} 个交易日...") + + success_count = 0 + for i, trade_date in enumerate(trading_days): + logger.info(f"\n[{i+1}/{len(trading_days)}] {trade_date}") + result = process_single_day(trade_date, concepts, all_stocks) + if result: + success_count += 1 + + logger.info("\n" + "=" * 60) + logger.info(f"处理完成: {success_count}/{len(trading_days)} 个交易日") + logger.info(f"数据保存在: {OUTPUT_DIR}") + logger.info("=" * 60) + + +if __name__ == "__main__": + main() diff --git a/ml/requirements.txt b/ml/requirements.txt new file mode 100644 index 00000000..7b052bb8 --- /dev/null +++ b/ml/requirements.txt @@ -0,0 +1,25 @@ +# 概念异动检测 ML 模块依赖 +# 安装: pip install -r ml/requirements.txt + +# PyTorch (根据 CUDA 版本选择) +# 5090 显卡需要 CUDA 12.x +# pip install torch --index-url https://download.pytorch.org/whl/cu124 +torch>=2.0.0 + +# 数据处理 +numpy>=1.24.0 +pandas>=2.0.0 +pyarrow>=14.0.0 + +# 数据库 +clickhouse-driver>=0.2.6 +elasticsearch>=7.0.0,<8.0.0 +sqlalchemy>=2.0.0 +pymysql>=1.1.0 + +# 训练工具 +tqdm>=4.65.0 + +# 可选: 可视化 +# matplotlib>=3.7.0 +# tensorboard>=2.14.0 diff --git a/ml/run_training.sh b/ml/run_training.sh new file mode 100644 index 00000000..7f7f2dcd --- /dev/null +++ b/ml/run_training.sh @@ -0,0 +1,99 @@ +#!/bin/bash +# 概念异动检测模型训练脚本 (Linux) +# +# 使用方法: +# chmod +x run_training.sh +# ./run_training.sh +# +# 或指定参数: +# ./run_training.sh --start 2022-01-01 --epochs 100 + +set -e + +echo "============================================================" +echo "概念异动检测模型训练流程" +echo "============================================================" +echo "" + +# 获取脚本所在目录 +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +cd "$SCRIPT_DIR/.." + +echo "[1/4] 检查环境..." +python3 --version || { echo "Python3 未找到！"; exit 1; } + +# 检查 GPU +if python3 -c "import torch; print(f'CUDA: {torch.cuda.is_available()}')" 2>/dev/null; then + echo "PyTorch GPU 检测完成" +else + echo "警告: PyTorch 未安装或无法检测 GPU" +fi + +echo "" +echo "[2/4] 检查依赖..." +pip3 install -q torch pandas numpy pyarrow tqdm clickhouse-driver elasticsearch sqlalchemy pymysql + +echo "" +echo "[3/4] 准备训练数据..." +echo "从 ClickHouse 提取历史数据，这可能需要较长时间..." +echo "" + +# 解析参数 +START_DATE="2022-01-01" +END_DATE="" +EPOCHS=100 +BATCH_SIZE=256 +TRAIN_END="2025-06-30" +VAL_END="2025-09-30" + +while [[ $# -gt 0 ]]; do + case $1 in + --start) + START_DATE="$2" + shift 2 + ;; + --end) + END_DATE="$2" + shift 2 + ;; + --epochs) + EPOCHS="$2" + shift 2 + ;; + --batch_size) + BATCH_SIZE="$2" + shift 2 + ;; + --train_end) + TRAIN_END="$2" + shift 2 + ;; + --val_end) + VAL_END="$2" + shift 2 + ;; + *) + shift + ;; + esac +done + +# 数据准备 +if [ -n "$END_DATE" ]; then + python3 ml/prepare_data.py --start "$START_DATE" --end "$END_DATE" +else + python3 ml/prepare_data.py --start "$START_DATE" +fi + +echo "" +echo "[4/4] 训练模型..." +echo "使用 GPU 加速训练..." +echo "" + +python3 ml/train.py --epochs "$EPOCHS" --batch_size "$BATCH_SIZE" --train_end "$TRAIN_END" --val_end "$VAL_END" + +echo "" +echo "============================================================" +echo "训练完成！" +echo "模型保存在: ml/checkpoints/" +echo "============================================================" diff --git a/ml/train.py b/ml/train.py new file mode 100644 index 00000000..b93120ac --- /dev/null +++ b/ml/train.py @@ -0,0 +1,808 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Transformer Autoencoder 训练脚本 (修复版) + +修复问题： +1. 按概念分组构建序列，避免跨概念切片 +2. 按时间（日期）切分数据集，避免数据泄露 +3. 使用 RobustScaler + Clipping 处理非平稳性 +4. 使用验证集计算阈值 + +训练流程： +1. 加载预处理好的特征数据（parquet 文件） +2. 按概念分组，在每个概念内部构建序列 +3. 按日期划分训练/验证/测试集 +4. 训练 Autoencoder（最小化重构误差） +5. 保存模型和阈值 + +使用方法： + python train.py --data_dir ml/data --epochs 100 --batch_size 256 +""" + +import os +import sys +import argparse +import json +from datetime import datetime +from pathlib import Path +from typing import List, Tuple, Dict + +import numpy as np +import pandas as pd +import torch +import torch.nn as nn +from torch.utils.data import Dataset, DataLoader +from torch.optim import AdamW +from torch.optim.lr_scheduler import CosineAnnealingWarmRestarts +from tqdm import tqdm + +from model import TransformerAutoencoder, AnomalyDetectionLoss, count_parameters + +# 性能优化：启用 cuDNN benchmark（对固定输入尺寸自动选择最快算法） +torch.backends.cudnn.benchmark = True +# 启用 TF32（RTX 30/40 系列特有，提速约 3 倍） +torch.backends.cuda.matmul.allow_tf32 = True +torch.backends.cudnn.allow_tf32 = True + +# 可视化（可选） +try: + import matplotlib + matplotlib.use('Agg') # 无头模式，不需要显示器 + import matplotlib.pyplot as plt + HAS_MATPLOTLIB = True +except ImportError: + HAS_MATPLOTLIB = False + + +# ==================== 配置 ==================== + +TRAIN_CONFIG = { + # 数据配置 + 'seq_len': 30, # 输入序列长度（30分钟） + 'stride': 5, # 滑动窗口步长 + + # 时间切分（按日期） + 'train_end_date': '2024-06-30', # 训练集截止日期 + 'val_end_date': '2024-09-30', # 验证集截止日期（之后为测试集） + + # 特征配置 + 'features': [ + 'alpha', # 超额收益 + 'alpha_delta', # Alpha 变化率 + 'amt_ratio', # 成交额比率 + 'amt_delta', # 成交额变化率 + 'rank_pct', # Alpha 排名百分位 + 'limit_up_ratio', # 涨停比例 + ], + + # 训练配置（针对 4x RTX 4090 优化） + 'batch_size': 4096, # 256 -> 4096（大幅增加，充分利用显存） + 'epochs': 100, + 'learning_rate': 3e-4, # 1e-4 -> 3e-4（大 batch 需要更大学习率） + 'weight_decay': 1e-5, + 'gradient_clip': 1.0, + + # 早停配置 + 'patience': 10, + 'min_delta': 1e-6, + + # 模型配置（LSTM Autoencoder，简洁有效） + 'model': { + 'n_features': 6, + 'hidden_dim': 32, # LSTM 隐藏维度（小） + 'latent_dim': 4, # 瓶颈维度（非常小！关键） + 'num_layers': 1, # LSTM 层数 + 'dropout': 0.2, + 'bidirectional': True, # 双向编码器 + }, + + # 标准化配置 + 'use_instance_norm': True, # 模型内部使用 Instance Norm（推荐） + 'clip_value': 10.0, # 简单截断极端值 + + # 阈值配置 + 'threshold_percentiles': [90, 95, 99], +} + + +# ==================== 数据加载（修复版）==================== + +def load_data_by_date(data_dir: str, features: List[str]) -> Dict[str, pd.DataFrame]: + """ + 按日期加载数据，返回 {date: DataFrame} 字典 + + 每个 DataFrame 包含该日所有概念的所有时间点数据 + """ + data_path = Path(data_dir) + parquet_files = sorted(data_path.glob("features_*.parquet")) + + if not parquet_files: + raise FileNotFoundError(f"未找到 parquet 文件: {data_dir}") + + print(f"找到 {len(parquet_files)} 个数据文件") + + date_data = {} + + for pf in tqdm(parquet_files, desc="加载数据"): + # 提取日期 + date = pf.stem.replace('features_', '') + + df = pd.read_parquet(pf) + + # 检查必要列 + required_cols = features + ['concept_id', 'timestamp'] + missing_cols = [c for c in required_cols if c not in df.columns] + if missing_cols: + print(f"警告: {date} 缺少列: {missing_cols}, 跳过") + continue + + date_data[date] = df + + print(f"成功加载 {len(date_data)} 天的数据") + return date_data + + +def split_data_by_date( + date_data: Dict[str, pd.DataFrame], + train_end: str, + val_end: str +) -> Tuple[Dict[str, pd.DataFrame], Dict[str, pd.DataFrame], Dict[str, pd.DataFrame]]: + """ + 按日期严格划分数据集 + + - 训练集: <= train_end + - 验证集: train_end < date <= val_end + - 测试集: > val_end + """ + train_data = {} + val_data = {} + test_data = {} + + for date, df in date_data.items(): + if date <= train_end: + train_data[date] = df + elif date <= val_end: + val_data[date] = df + else: + test_data[date] = df + + print(f"数据集划分（按日期）:") + print(f" 训练集: {len(train_data)} 天 (<= {train_end})") + print(f" 验证集: {len(val_data)} 天 ({train_end} ~ {val_end})") + print(f" 测试集: {len(test_data)} 天 (> {val_end})") + + return train_data, val_data, test_data + + +def build_sequences_by_concept( + date_data: Dict[str, pd.DataFrame], + features: List[str], + seq_len: int, + stride: int +) -> np.ndarray: + """ + 按概念分组构建序列（性能优化版） + + 使用 groupby 一次性分组，避免重复扫描大数组 + + 1. 将所有日期的数据合并 + 2. 使用 groupby 按 concept_id 分组 + 3. 在每个概念内部，按时间排序并滑动窗口 + 4. 合并所有序列 + """ + # 合并所有日期的数据 + all_dfs = [] + for date, df in sorted(date_data.items()): + df = df.copy() + df['date'] = date + all_dfs.append(df) + + if not all_dfs: + return np.array([]) + + combined = pd.concat(all_dfs, ignore_index=True) + + # 预先排序（按概念、日期、时间），这样 groupby 会更快 + combined = combined.sort_values(['concept_id', 'date', 'timestamp']) + + # 使用 groupby 一次性分组（性能关键！） + all_sequences = [] + grouped = combined.groupby('concept_id', sort=False) + n_concepts = len(grouped) + + for concept_id, concept_df in tqdm(grouped, desc="构建序列", total=n_concepts, leave=False): + # 已经排序过了，直接提取特征 + feature_data = concept_df[features].values + + # 处理缺失值 + feature_data = np.nan_to_num(feature_data, nan=0.0, posinf=0.0, neginf=0.0) + + # 在该概念内部滑动窗口 + n_points = len(feature_data) + for start in range(0, n_points - seq_len + 1, stride): + seq = feature_data[start:start + seq_len] + all_sequences.append(seq) + + if not all_sequences: + return np.array([]) + + sequences = np.array(all_sequences) + print(f" 构建序列: {len(sequences):,} 条 (来自 {n_concepts} 个概念)") + + return sequences + + +# ==================== 数据集 ==================== + +class SequenceDataset(Dataset): + """序列数据集（已经构建好的序列）""" + + def __init__(self, sequences: np.ndarray): + self.sequences = torch.FloatTensor(sequences) + + def __len__(self) -> int: + return len(self.sequences) + + def __getitem__(self, idx: int) -> torch.Tensor: + return self.sequences[idx] + + +# ==================== 训练器 ==================== + +class EarlyStopping: + """早停机制""" + + def __init__(self, patience: int = 10, min_delta: float = 1e-6): + self.patience = patience + self.min_delta = min_delta + self.counter = 0 + self.best_loss = float('inf') + self.early_stop = False + + def __call__(self, val_loss: float) -> bool: + if val_loss < self.best_loss - self.min_delta: + self.best_loss = val_loss + self.counter = 0 + else: + self.counter += 1 + if self.counter >= self.patience: + self.early_stop = True + + return self.early_stop + + +class Trainer: + """模型训练器（支持 AMP 混合精度加速）""" + + def __init__( + self, + model: nn.Module, + train_loader: DataLoader, + val_loader: DataLoader, + config: Dict, + device: torch.device, + save_dir: str = 'ml/checkpoints' + ): + self.model = model.to(device) + self.train_loader = train_loader + self.val_loader = val_loader + self.config = config + self.device = device + self.save_dir = Path(save_dir) + self.save_dir.mkdir(parents=True, exist_ok=True) + + # 优化器 + self.optimizer = AdamW( + model.parameters(), + lr=config['learning_rate'], + weight_decay=config['weight_decay'] + ) + + # 学习率调度器 + self.scheduler = CosineAnnealingWarmRestarts( + self.optimizer, + T_0=10, + T_mult=2, + eta_min=1e-6 + ) + + # 损失函数（简化版，只用 MSE） + self.criterion = AnomalyDetectionLoss() + + # 早停 + self.early_stopping = EarlyStopping( + patience=config['patience'], + min_delta=config['min_delta'] + ) + + # AMP 混合精度训练（大幅提速 + 省显存） + self.use_amp = torch.cuda.is_available() + self.scaler = torch.cuda.amp.GradScaler() if self.use_amp else None + if self.use_amp: + print(" ✓ 启用 AMP 混合精度训练") + + # 训练历史 + self.history = { + 'train_loss': [], + 'val_loss': [], + 'learning_rate': [], + } + + self.best_val_loss = float('inf') + + def train_epoch(self) -> float: + """训练一个 epoch（使用 AMP 混合精度）""" + self.model.train() + total_loss = 0.0 + n_batches = 0 + + pbar = tqdm(self.train_loader, desc="Training", leave=False) + for batch in pbar: + batch = batch.to(self.device, non_blocking=True) # 异步传输 + + self.optimizer.zero_grad(set_to_none=True) # 更快的梯度清零 + + # AMP 混合精度前向传播 + if self.use_amp: + with torch.cuda.amp.autocast(): + output, latent = self.model(batch) + loss, loss_dict = self.criterion(output, batch, latent) + + # AMP 反向传播 + self.scaler.scale(loss).backward() + + # 梯度裁剪（需要 unscale） + self.scaler.unscale_(self.optimizer) + torch.nn.utils.clip_grad_norm_( + self.model.parameters(), + self.config['gradient_clip'] + ) + + self.scaler.step(self.optimizer) + self.scaler.update() + else: + # 非 AMP 模式 + output, latent = self.model(batch) + loss, loss_dict = self.criterion(output, batch, latent) + + loss.backward() + torch.nn.utils.clip_grad_norm_( + self.model.parameters(), + self.config['gradient_clip'] + ) + self.optimizer.step() + + total_loss += loss.item() + n_batches += 1 + + pbar.set_postfix({'loss': f"{loss.item():.4f}"}) + + return total_loss / n_batches + + @torch.no_grad() + def validate(self) -> float: + """验证（使用 AMP）""" + self.model.eval() + total_loss = 0.0 + n_batches = 0 + + for batch in self.val_loader: + batch = batch.to(self.device, non_blocking=True) + + if self.use_amp: + with torch.cuda.amp.autocast(): + output, latent = self.model(batch) + loss, _ = self.criterion(output, batch, latent) + else: + output, latent = self.model(batch) + loss, _ = self.criterion(output, batch, latent) + + total_loss += loss.item() + n_batches += 1 + + return total_loss / n_batches + + def save_checkpoint(self, epoch: int, val_loss: float, is_best: bool = False): + """保存检查点""" + # 处理 DataParallel 包装 + model_to_save = self.model.module if hasattr(self.model, 'module') else self.model + + checkpoint = { + 'epoch': epoch, + 'model_state_dict': model_to_save.state_dict(), + 'optimizer_state_dict': self.optimizer.state_dict(), + 'scheduler_state_dict': self.scheduler.state_dict(), + 'val_loss': val_loss, + 'config': self.config, + } + + # 保存最新检查点 + torch.save(checkpoint, self.save_dir / 'last_checkpoint.pt') + + # 保存最佳模型 + if is_best: + torch.save(checkpoint, self.save_dir / 'best_model.pt') + print(f" ✓ 保存最佳模型 (val_loss: {val_loss:.6f})") + + def train(self, epochs: int): + """完整训练流程""" + print(f"\n开始训练 ({epochs} epochs)...") + print(f"设备: {self.device}") + print(f"模型参数量: {count_parameters(self.model):,}") + + for epoch in range(1, epochs + 1): + print(f"\nEpoch {epoch}/{epochs}") + + # 训练 + train_loss = self.train_epoch() + + # 验证 + val_loss = self.validate() + + # 更新学习率 + self.scheduler.step() + current_lr = self.optimizer.param_groups[0]['lr'] + + # 记录历史 + self.history['train_loss'].append(train_loss) + self.history['val_loss'].append(val_loss) + self.history['learning_rate'].append(current_lr) + + # 打印进度 + print(f" Train Loss: {train_loss:.6f}") + print(f" Val Loss: {val_loss:.6f}") + print(f" LR: {current_lr:.2e}") + + # 保存检查点 + is_best = val_loss < self.best_val_loss + if is_best: + self.best_val_loss = val_loss + self.save_checkpoint(epoch, val_loss, is_best) + + # 早停检查 + if self.early_stopping(val_loss): + print(f"\n早停触发！验证损失已 {self.early_stopping.patience} 个 epoch 未改善") + break + + print(f"\n训练完成！最佳验证损失: {self.best_val_loss:.6f}") + + # 保存训练历史 + self.save_history() + + return self.history + + def save_history(self): + """保存训练历史""" + history_path = self.save_dir / 'training_history.json' + with open(history_path, 'w') as f: + json.dump(self.history, f, indent=2) + print(f"训练历史已保存: {history_path}") + + # 绘制训练曲线 + self.plot_training_curves() + + def plot_training_curves(self): + """绘制训练曲线""" + if not HAS_MATPLOTLIB: + print("matplotlib 未安装，跳过绘图") + return + + fig, axes = plt.subplots(1, 2, figsize=(14, 5)) + + epochs = range(1, len(self.history['train_loss']) + 1) + + # 1. Loss 曲线 + ax1 = axes[0] + ax1.plot(epochs, self.history['train_loss'], 'b-', label='Train Loss', linewidth=2) + ax1.plot(epochs, self.history['val_loss'], 'r-', label='Val Loss', linewidth=2) + ax1.set_xlabel('Epoch', fontsize=12) + ax1.set_ylabel('Loss', fontsize=12) + ax1.set_title('Training & Validation Loss', fontsize=14) + ax1.legend(fontsize=11) + ax1.grid(True, alpha=0.3) + + # 标记最佳点 + best_epoch = np.argmin(self.history['val_loss']) + 1 + best_val_loss = min(self.history['val_loss']) + ax1.axvline(x=best_epoch, color='g', linestyle='--', alpha=0.7, label=f'Best Epoch: {best_epoch}') + ax1.scatter([best_epoch], [best_val_loss], color='g', s=100, zorder=5) + ax1.annotate(f'Best: {best_val_loss:.6f}', xy=(best_epoch, best_val_loss), + xytext=(best_epoch + 2, best_val_loss + 0.0005), + fontsize=10, color='green') + + # 2. 学习率曲线 + ax2 = axes[1] + ax2.plot(epochs, self.history['learning_rate'], 'g-', linewidth=2) + ax2.set_xlabel('Epoch', fontsize=12) + ax2.set_ylabel('Learning Rate', fontsize=12) + ax2.set_title('Learning Rate Schedule', fontsize=14) + ax2.set_yscale('log') + ax2.grid(True, alpha=0.3) + + plt.tight_layout() + + # 保存图片 + plot_path = self.save_dir / 'training_curves.png' + plt.savefig(plot_path, dpi=150, bbox_inches='tight') + plt.close() + print(f"训练曲线已保存: {plot_path}") + + +# ==================== 阈值计算（使用验证集）==================== + +@torch.no_grad() +def compute_thresholds( + model: nn.Module, + data_loader: DataLoader, + device: torch.device, + percentiles: List[float] = [90, 95, 99] +) -> Dict[str, float]: + """ + 在验证集上计算重构误差的百分位数阈值 + + 注：使用验证集而非测试集，避免数据泄露 + """ + model.eval() + all_errors = [] + + print("计算异动阈值（使用验证集）...") + for batch in tqdm(data_loader, desc="Computing thresholds"): + batch = batch.to(device) + errors = model.compute_reconstruction_error(batch, reduction='none') + + # 取每个序列的最后一个时刻误差（预测当前时刻） + seq_errors = errors[:, -1] # (batch,) + all_errors.append(seq_errors.cpu().numpy()) + + all_errors = np.concatenate(all_errors) + + thresholds = {} + for p in percentiles: + threshold = np.percentile(all_errors, p) + thresholds[f'p{p}'] = float(threshold) + print(f" P{p}: {threshold:.6f}") + + # 额外统计 + thresholds['mean'] = float(np.mean(all_errors)) + thresholds['std'] = float(np.std(all_errors)) + thresholds['median'] = float(np.median(all_errors)) + + print(f" Mean: {thresholds['mean']:.6f}") + print(f" Median: {thresholds['median']:.6f}") + print(f" Std: {thresholds['std']:.6f}") + + return thresholds + + +# ==================== 主函数 ==================== + +def main(): + parser = argparse.ArgumentParser(description='训练概念异动检测模型') + parser.add_argument('--data_dir', type=str, default='ml/data', + help='数据目录路径') + parser.add_argument('--epochs', type=int, default=100, + help='训练轮数') + parser.add_argument('--batch_size', type=int, default=4096, + help='批次大小（4x RTX 4090 推荐 4096~8192）') + parser.add_argument('--lr', type=float, default=3e-4, + help='学习率（大 batch 推荐 3e-4）') + parser.add_argument('--device', type=str, default='auto', + help='设备 (auto/cuda/cpu)') + parser.add_argument('--save_dir', type=str, default='ml/checkpoints', + help='模型保存目录') + parser.add_argument('--train_end', type=str, default='2024-06-30', + help='训练集截止日期') + parser.add_argument('--val_end', type=str, default='2024-09-30', + help='验证集截止日期') + + args = parser.parse_args() + + # 更新配置 + config = TRAIN_CONFIG.copy() + config['batch_size'] = args.batch_size + config['epochs'] = args.epochs + config['learning_rate'] = args.lr + config['train_end_date'] = args.train_end + config['val_end_date'] = args.val_end + + # 设备选择 + if args.device == 'auto': + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + else: + device = torch.device(args.device) + + print("=" * 60) + print("概念异动检测模型训练（修复版）") + print("=" * 60) + print(f"配置:") + print(f" 数据目录: {args.data_dir}") + print(f" 设备: {device}") + print(f" 批次大小: {config['batch_size']}") + print(f" 学习率: {config['learning_rate']}") + print(f" 训练轮数: {config['epochs']}") + print(f" 训练集截止: {config['train_end_date']}") + print(f" 验证集截止: {config['val_end_date']}") + print("=" * 60) + + # 1. 按日期加载数据 + print("\n[1/6] 加载数据...") + date_data = load_data_by_date(args.data_dir, config['features']) + + # 2. 按日期划分 + print("\n[2/6] 按日期划分数据集...") + train_data, val_data, test_data = split_data_by_date( + date_data, + config['train_end_date'], + config['val_end_date'] + ) + + # 3. 按概念构建序列 + print("\n[3/6] 按概念构建序列...") + print("训练集:") + train_sequences = build_sequences_by_concept( + train_data, config['features'], config['seq_len'], config['stride'] + ) + print("验证集:") + val_sequences = build_sequences_by_concept( + val_data, config['features'], config['seq_len'], config['stride'] + ) + print("测试集:") + test_sequences = build_sequences_by_concept( + test_data, config['features'], config['seq_len'], config['stride'] + ) + + if len(train_sequences) == 0: + print("错误: 训练集为空！请检查数据和日期范围") + return + + # 4. 数据预处理（简单截断极端值，标准化在模型内部通过 Instance Norm 完成） + print("\n[4/6] 数据预处理...") + print(" 注意: 使用 Instance Norm，每个序列在模型内部单独标准化") + print(" 这样可以处理不同概念波动率差异（银行 vs 半导体）") + + clip_value = config['clip_value'] + print(f" 截断极端值: ±{clip_value}") + + # 简单截断极端值（防止异常数据影响训练） + train_sequences = np.clip(train_sequences, -clip_value, clip_value) + if len(val_sequences) > 0: + val_sequences = np.clip(val_sequences, -clip_value, clip_value) + if len(test_sequences) > 0: + test_sequences = np.clip(test_sequences, -clip_value, clip_value) + + # 保存配置 + save_dir = Path(args.save_dir) + save_dir.mkdir(parents=True, exist_ok=True) + + preprocess_params = { + 'features': config['features'], + 'normalization': 'instance_norm', # 在模型内部完成 + 'clip_value': clip_value, + 'note': '标准化在模型内部通过 InstanceNorm1d 完成，无需外部 Scaler' + } + + with open(save_dir / 'normalization_stats.json', 'w') as f: + json.dump(preprocess_params, f, indent=2) + print(f" 预处理参数已保存") + + # 5. 创建数据集和加载器 + print("\n[5/6] 创建数据加载器...") + train_dataset = SequenceDataset(train_sequences) + val_dataset = SequenceDataset(val_sequences) if len(val_sequences) > 0 else None + test_dataset = SequenceDataset(test_sequences) if len(test_sequences) > 0 else None + + print(f" 训练序列: {len(train_dataset):,}") + print(f" 验证序列: {len(val_dataset) if val_dataset else 0:,}") + print(f" 测试序列: {len(test_dataset) if test_dataset else 0:,}") + + # 多卡时增加 num_workers（Linux 上可以用更多） + n_gpus = torch.cuda.device_count() if torch.cuda.is_available() else 1 + num_workers = min(32, 8 * n_gpus) if sys.platform != 'win32' else 0 + print(f" DataLoader workers: {num_workers}") + print(f" Batch size: {config['batch_size']}") + + # 大 batch + 多 worker + prefetch 提速 + train_loader = DataLoader( + train_dataset, + batch_size=config['batch_size'], + shuffle=True, + num_workers=num_workers, + pin_memory=True, + prefetch_factor=4 if num_workers > 0 else None, # 预取更多 batch + persistent_workers=True if num_workers > 0 else False, # 保持 worker 存活 + drop_last=True # 丢弃不完整的最后一批，避免 batch 大小不一致 + ) + + val_loader = DataLoader( + val_dataset, + batch_size=config['batch_size'] * 2, # 验证时可以用更大 batch（无梯度） + shuffle=False, + num_workers=num_workers, + pin_memory=True, + prefetch_factor=4 if num_workers > 0 else None, + persistent_workers=True if num_workers > 0 else False, + ) if val_dataset else None + + test_loader = DataLoader( + test_dataset, + batch_size=config['batch_size'] * 2, + shuffle=False, + num_workers=num_workers, + pin_memory=True, + prefetch_factor=4 if num_workers > 0 else None, + persistent_workers=True if num_workers > 0 else False, + ) if test_dataset else None + + # 6. 训练 + print("\n[6/6] 训练模型...") + model_config = config['model'].copy() + model = TransformerAutoencoder(**model_config) + + # 多卡并行 + if torch.cuda.device_count() > 1: + print(f" 使用 {torch.cuda.device_count()} 张 GPU 并行训练") + model = nn.DataParallel(model) + + if val_loader is None: + print("警告: 验证集为空，将使用训练集的一部分作为验证") + # 简单处理：用训练集的后 10% 作为验证 + split_idx = int(len(train_dataset) * 0.9) + train_subset = torch.utils.data.Subset(train_dataset, range(split_idx)) + val_subset = torch.utils.data.Subset(train_dataset, range(split_idx, len(train_dataset))) + + train_loader = DataLoader(train_subset, batch_size=config['batch_size'], shuffle=True, num_workers=num_workers, pin_memory=True) + val_loader = DataLoader(val_subset, batch_size=config['batch_size'], shuffle=False, num_workers=num_workers, pin_memory=True) + + trainer = Trainer( + model=model, + train_loader=train_loader, + val_loader=val_loader, + config=config, + device=device, + save_dir=args.save_dir + ) + + history = trainer.train(config['epochs']) + + # 7. 计算阈值（使用验证集） + print("\n[额外] 计算异动阈值...") + + # 加载最佳模型 + best_checkpoint = torch.load( + save_dir / 'best_model.pt', + map_location=device + ) + model.load_state_dict(best_checkpoint['model_state_dict']) + model.to(device) + + # 使用验证集计算阈值（避免数据泄露） + thresholds = compute_thresholds( + model, + val_loader, + device, + config['threshold_percentiles'] + ) + + # 保存阈值 + with open(save_dir / 'thresholds.json', 'w') as f: + json.dump(thresholds, f, indent=2) + print(f"阈值已保存") + + # 保存完整配置 + with open(save_dir / 'config.json', 'w') as f: + json.dump(config, f, indent=2) + + print("\n" + "=" * 60) + print("训练完成！") + print("=" * 60) + print(f"模型保存位置: {args.save_dir}") + print(f" - best_model.pt: 最佳模型权重") + print(f" - thresholds.json: 异动阈值") + print(f" - normalization_stats.json: 标准化参数") + print(f" - config.json: 训练配置") + print("=" * 60) + + +if __name__ == "__main__": + main() diff --git a/sql/concept_minute_alert.sql b/sql/concept_minute_alert.sql new file mode 100644 index 00000000..d54cc7ff --- /dev/null +++ b/sql/concept_minute_alert.sql @@ -0,0 +1,68 @@ +-- 概念分钟级异动数据表 +-- 用于存储概念板块的实时异动信息，支持热点概览图表展示 + +CREATE TABLE IF NOT EXISTS concept_minute_alert ( + id BIGINT AUTO_INCREMENT PRIMARY KEY, + concept_id VARCHAR(32) NOT NULL COMMENT '概念ID', + concept_name VARCHAR(100) NOT NULL COMMENT '概念名称', + alert_time DATETIME NOT NULL COMMENT '异动时间（精确到分钟）', + alert_type VARCHAR(20) NOT NULL COMMENT '异动类型：surge(急涨)/limit_up(涨停增加)/rank_jump(排名跃升)', + trade_date DATE NOT NULL COMMENT '交易日期', + + -- 涨跌幅相关 + change_pct DECIMAL(10,4) COMMENT '当时涨跌幅(%)', + prev_change_pct DECIMAL(10,4) COMMENT '之前涨跌幅(%)', + change_delta DECIMAL(10,4) COMMENT '涨幅变化量(%)', + + -- 涨停相关 + limit_up_count INT DEFAULT 0 COMMENT '当前涨停数量', + prev_limit_up_count INT DEFAULT 0 COMMENT '之前涨停数量', + limit_up_delta INT DEFAULT 0 COMMENT '涨停变化数量', + + -- 排名相关 + rank_position INT COMMENT '当前涨幅排名', + prev_rank_position INT COMMENT '之前涨幅排名', + rank_delta INT COMMENT '排名变化（负数表示上升）', + + -- 指数位置（用于图表Y轴定位） + index_code VARCHAR(20) DEFAULT '000001.SH' COMMENT '参考指数代码', + index_price DECIMAL(12,4) COMMENT '异动时的指数点位', + index_change_pct DECIMAL(10,4) COMMENT '异动时的指数涨跌幅(%)', + + -- 概念详情 + stock_count INT COMMENT '概念包含股票数', + concept_type VARCHAR(20) DEFAULT 'leaf' COMMENT '概念类型：leaf/lv1/lv2/lv3', + + -- 额外信息（JSON格式，存储涨停股票列表等） + extra_info JSON COMMENT '额外信息', + + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + + -- 索引 + INDEX idx_trade_date (trade_date), + INDEX idx_alert_time (alert_time), + INDEX idx_concept_id (concept_id), + INDEX idx_alert_type (alert_type), + INDEX idx_trade_date_time (trade_date, alert_time) +) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COMMENT='概念分钟级异动数据表'; + + +-- 创建指数分时快照表（用于异动时获取指数位置） +CREATE TABLE IF NOT EXISTS index_minute_snapshot ( + id BIGINT AUTO_INCREMENT PRIMARY KEY, + index_code VARCHAR(20) NOT NULL COMMENT '指数代码', + trade_date DATE NOT NULL COMMENT '交易日期', + snapshot_time DATETIME NOT NULL COMMENT '快照时间', + + price DECIMAL(12,4) COMMENT '指数点位', + open_price DECIMAL(12,4) COMMENT '开盘价', + high_price DECIMAL(12,4) COMMENT '最高价', + low_price DECIMAL(12,4) COMMENT '最低价', + prev_close DECIMAL(12,4) COMMENT '昨收价', + change_pct DECIMAL(10,4) COMMENT '涨跌幅(%)', + + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + + UNIQUE KEY uk_index_time (index_code, snapshot_time), + INDEX idx_trade_date (trade_date) +) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COMMENT='指数分时快照表'; diff --git a/src/views/StockOverview/components/HotspotOverview/index.js b/src/views/StockOverview/components/HotspotOverview/index.js new file mode 100644 index 00000000..856d752d --- /dev/null +++ b/src/views/StockOverview/components/HotspotOverview/index.js @@ -0,0 +1,539 @@ +/** + * 热点概览组件 + * 展示大盘分时走势 + 概念异动标注 + */ +import React, { useState, useEffect, useRef, useCallback } from 'react'; +import { + Box, + Card, + CardBody, + Heading, + Text, + HStack, + VStack, + Badge, + Spinner, + Center, + Icon, + Flex, + Spacer, + Tooltip, + useColorModeValue, + Stat, + StatLabel, + StatNumber, + StatHelpText, + StatArrow, + SimpleGrid, +} from '@chakra-ui/react'; +import { FaFire, FaRocket, FaChartLine, FaBolt, FaArrowDown } from 'react-icons/fa'; +import { InfoIcon } from '@chakra-ui/icons'; +import * as echarts from 'echarts'; +import { logger } from '@utils/logger'; + +const HotspotOverview = ({ selectedDate }) => { + const chartRef = useRef(null); + const chartInstance = useRef(null); + const [loading, setLoading] = useState(true); + const [data, setData] = useState(null); + const [error, setError] = useState(null); + + // 颜色主题 + const cardBg = useColorModeValue('white', '#1a1a1a'); + const borderColor = useColorModeValue('gray.200', '#333333'); + const textColor = useColorModeValue('gray.800', 'white'); + const subTextColor = useColorModeValue('gray.600', 'gray.400'); + + // 获取数据 + const fetchData = useCallback(async () => { + setLoading(true); + setError(null); + + try { + const dateParam = selectedDate + ? `?date=${selectedDate.toISOString().split('T')[0]}` + : ''; + const response = await fetch(`/api/market/hotspot-overview${dateParam}`); + const result = await response.json(); + + if (result.success) { + setData(result.data); + } else { + setError(result.error || '获取数据失败'); + } + } catch (err) { + logger.error('HotspotOverview', 'fetchData', err); + setError('网络请求失败'); + } finally { + setLoading(false); + } + }, [selectedDate]); + + useEffect(() => { + fetchData(); + }, [fetchData]); + + // 渲染图表 + const renderChart = useCallback(() => { + if (!chartRef.current || !data) return; + + if (!chartInstance.current) { + chartInstance.current = echarts.init(chartRef.current); + } + + const { index, alerts } = data; + const timeline = index.timeline || []; + + // 准备数据 + const times = timeline.map((d) => d.time); + const prices = timeline.map((d) => d.price); + const volumes = timeline.map((d) => d.volume); + const changePcts = timeline.map((d) => d.change_pct); + + // 计算Y轴范围 + const priceMin = Math.min(...prices.filter(Boolean)); + const priceMax = Math.max(...prices.filter(Boolean)); + const priceRange = priceMax - priceMin; + const yAxisMin = priceMin - priceRange * 0.1; + const yAxisMax = priceMax + priceRange * 0.2; // 上方留更多空间给标注 + + // 准备异动标注 - 按重要性排序，限制显示数量 + const sortedAlerts = [...alerts] + .sort((a, b) => (b.importance_score || 0) - (a.importance_score || 0)) + .slice(0, 15); // 最多显示15个标注，避免图表过于密集 + + const markPoints = sortedAlerts.map((alert) => { + // 找到对应时间的价格 + const timeIndex = times.indexOf(alert.time); + const price = timeIndex >= 0 ? prices[timeIndex] : (alert.index_price || priceMax); + + // 根据异动类型设置颜色和符号 + let color = '#ff6b6b'; + let symbol = 'pin'; + let symbolSize = 35; + + // 暴涨 + if (alert.alert_type === 'surge_up' || alert.alert_type === 'surge') { + color = '#ff4757'; + symbol = 'triangle'; + symbolSize = 30 + Math.min((alert.importance_score || 0.5) * 20, 15); // 根据重要性调整大小 + } + // 暴跌 + else if (alert.alert_type === 'surge_down') { + color = '#2ed573'; + symbol = 'path://M0,0 L10,0 L5,10 Z'; // 向下三角形 + symbolSize = 30 + Math.min((alert.importance_score || 0.5) * 20, 15); + } + // 涨停增加 + else if (alert.alert_type === 'limit_up') { + color = '#ff6348'; + symbol = 'diamond'; + symbolSize = 28; + } + // 排名跃升 + else if (alert.alert_type === 'rank_jump') { + color = '#3742fa'; + symbol = 'circle'; + symbolSize = 25; + } + + // 格式化标签 - 简化显示 + let label = alert.concept_name; + // 截断过长的名称 + if (label.length > 8) { + label = label.substring(0, 7) + '...'; + } + + // 添加变化信息 + const changeDelta = alert.change_delta; + if (changeDelta) { + const sign = changeDelta > 0 ? '+' : ''; + label += `\n${sign}${changeDelta.toFixed(1)}%`; + } + + return { + name: alert.concept_name, + coord: [alert.time, price], + value: label, + symbol: symbol, + symbolSize: symbolSize, + itemStyle: { + color: color, + borderColor: '#fff', + borderWidth: 1, + shadowBlur: 3, + shadowColor: 'rgba(0,0,0,0.2)', + }, + label: { + show: true, + position: alert.alert_type === 'surge_down' ? 'bottom' : 'top', // 暴跌标签在下方 + formatter: '{b}', + fontSize: 9, + color: textColor, + backgroundColor: alert.alert_type === 'surge_down' + ? 'rgba(46, 213, 115, 0.9)' + : 'rgba(255,255,255,0.9)', + padding: [2, 4], + borderRadius: 2, + borderColor: color, + borderWidth: 1, + }, + // 存储额外信息用于 tooltip + alertData: alert, + }; + }); + + // 渐变色 - 根据涨跌 + const latestChangePct = changePcts[changePcts.length - 1] || 0; + const areaColorStops = latestChangePct >= 0 + ? [ + { offset: 0, color: 'rgba(255, 77, 77, 0.4)' }, + { offset: 1, color: 'rgba(255, 77, 77, 0.05)' }, + ] + : [ + { offset: 0, color: 'rgba(34, 197, 94, 0.4)' }, + { offset: 1, color: 'rgba(34, 197, 94, 0.05)' }, + ]; + + const lineColor = latestChangePct >= 0 ? '#ff4d4d' : '#22c55e'; + + const option = { + backgroundColor: 'transparent', + tooltip: { + trigger: 'axis', + axisPointer: { + type: 'cross', + crossStyle: { + color: '#999', + }, + }, + formatter: function (params) { + if (!params || params.length === 0) return ''; + + const dataIndex = params[0].dataIndex; + const time = times[dataIndex]; + const price = prices[dataIndex]; + const changePct = changePcts[dataIndex]; + const volume = volumes[dataIndex]; + + let html = ` +
+
${time}
+
指数: ${price?.toFixed(2)}
+
涨跌: ${changePct >= 0 ? '+' : ''}${changePct?.toFixed(2)}%
+
成交量: ${(volume / 10000).toFixed(0)}万手
+
+ `; + + // 检查是否有异动 + const alertsAtTime = alerts.filter((a) => a.time === time); + if (alertsAtTime.length > 0) { + html += '
'; + html += '
概念异动:
'; + alertsAtTime.forEach((alert) => { + const typeLabel = { + surge: '急涨', + surge_up: '暴涨', + surge_down: '暴跌', + limit_up: '涨停增加', + rank_jump: '排名跃升', + }[alert.alert_type] || alert.alert_type; + const typeColor = alert.alert_type === 'surge_down' ? '#2ed573' : '#ff6b6b'; + const delta = alert.change_delta ? ` (${alert.change_delta > 0 ? '+' : ''}${alert.change_delta.toFixed(2)}%)` : ''; + const zscore = alert.zscore ? ` Z=${alert.zscore.toFixed(1)}` : ''; + html += `
• ${alert.concept_name} (${typeLabel}${delta}${zscore})
`; + }); + html += '
'; + } + + return html; + }, + }, + legend: { + show: false, + }, + grid: [ + { + left: '8%', + right: '3%', + top: '8%', + height: '55%', + }, + { + left: '8%', + right: '3%', + top: '70%', + height: '20%', + }, + ], + xAxis: [ + { + type: 'category', + data: times, + axisLine: { lineStyle: { color: '#ddd' } }, + axisLabel: { + color: subTextColor, + fontSize: 10, + interval: Math.floor(times.length / 6), + }, + axisTick: { show: false }, + splitLine: { show: false }, + }, + { + type: 'category', + gridIndex: 1, + data: times, + axisLine: { lineStyle: { color: '#ddd' } }, + axisLabel: { show: false }, + axisTick: { show: false }, + splitLine: { show: false }, + }, + ], + yAxis: [ + { + type: 'value', + min: yAxisMin, + max: yAxisMax, + axisLine: { show: false }, + axisLabel: { + color: subTextColor, + fontSize: 10, + formatter: (val) => val.toFixed(0), + }, + splitLine: { + lineStyle: { color: '#eee', type: 'dashed' }, + }, + // 右侧显示涨跌幅 + axisPointer: { + label: { + formatter: function (params) { + const pct = ((params.value - index.prev_close) / index.prev_close) * 100; + return `${params.value.toFixed(2)} (${pct >= 0 ? '+' : ''}${pct.toFixed(2)}%)`; + }, + }, + }, + }, + { + type: 'value', + gridIndex: 1, + axisLine: { show: false }, + axisLabel: { show: false }, + splitLine: { show: false }, + }, + ], + series: [ + // 分时线 + { + name: '上证指数', + type: 'line', + data: prices, + smooth: true, + symbol: 'none', + lineStyle: { + color: lineColor, + width: 1.5, + }, + areaStyle: { + color: new echarts.graphic.LinearGradient(0, 0, 0, 1, areaColorStops), + }, + markPoint: { + symbol: 'pin', + symbolSize: 40, + data: markPoints, + animation: true, + }, + }, + // 成交量 + { + name: '成交量', + type: 'bar', + xAxisIndex: 1, + yAxisIndex: 1, + data: volumes.map((v, i) => ({ + value: v, + itemStyle: { + color: changePcts[i] >= 0 ? 'rgba(255, 77, 77, 0.6)' : 'rgba(34, 197, 94, 0.6)', + }, + })), + barWidth: '60%', + }, + ], + }; + + chartInstance.current.setOption(option, true); + }, [data, textColor, subTextColor]); + + // 数据变化时重新渲染 + useEffect(() => { + if (data) { + renderChart(); + } + }, [data, renderChart]); + + // 窗口大小变化时重新渲染 + useEffect(() => { + const handleResize = () => { + if (chartInstance.current) { + chartInstance.current.resize(); + } + }; + + window.addEventListener('resize', handleResize); + return () => { + window.removeEventListener('resize', handleResize); + if (chartInstance.current) { + chartInstance.current.dispose(); + chartInstance.current = null; + } + }; + }, []); + + // 异动类型标签 + const AlertTypeBadge = ({ type, count }) => { + const config = { + surge: { label: '急涨', color: 'red', icon: FaBolt }, + surge_up: { label: '暴涨', color: 'red', icon: FaBolt }, + surge_down: { label: '暴跌', color: 'green', icon: FaArrowDown }, + limit_up: { label: '涨停', color: 'orange', icon: FaRocket }, + rank_jump: { label: '排名跃升', color: 'blue', icon: FaChartLine }, + }; + + const cfg = config[type] || { label: type, color: 'gray', icon: FaFire }; + + return ( + + + + {cfg.label} + {count} + + + ); + }; + + if (loading) { + return ( + + +
+ + + 加载热点概览数据... + +
+ + + ); + } + + if (error) { + return ( + + +
+ + + {error} + +
+ + + ); + } + + if (!data) { + return null; + } + + const { index, alerts, alert_summary } = data; + + return ( + + + {/* 头部信息 */} + + + + + 热点概览 + + + + + + + + + {/* 指数统计 */} + + + {index.name} + = 0 ? 'red.500' : 'green.500'} + > + {index.latest_price?.toFixed(2)} + + + = 0 ? 'increase' : 'decrease'} /> + {index.change_pct?.toFixed(2)}% + + + + + 最高 + + {index.high?.toFixed(2)} + + + + + 最低 + + {index.low?.toFixed(2)} + + + + + 异动次数 + + {alerts.length} + + + + + {/* 异动类型统计 */} + {alerts.length > 0 && ( + + {(alert_summary.surge_up > 0 || alert_summary.surge > 0) && ( + + )} + {alert_summary.surge_down > 0 && ( + + )} + {alert_summary.limit_up > 0 && ( + + )} + {alert_summary.rank_jump > 0 && ( + + )} + + )} + + {/* 图表 */} + + + {/* 无异动提示 */} + {alerts.length === 0 && ( +
+ + 当日暂无概念异动数据 + +
+ )} + + + ); +}; + +export default HotspotOverview; diff --git a/src/views/StockOverview/index.js b/src/views/StockOverview/index.js index e98686b1..13fc3cd9 100644 --- a/src/views/StockOverview/index.js +++ b/src/views/StockOverview/index.js @@ -53,6 +53,7 @@ import { SearchIcon, CloseIcon, ArrowForwardIcon, TrendingUpIcon, InfoIcon, Chev import { FaChartLine, FaFire, FaRocket, FaBrain, FaCalendarAlt, FaChevronRight, FaArrowUp, FaArrowDown, FaChartBar, FaTag, FaLayerGroup, FaBolt } from 'react-icons/fa'; import ConceptStocksModal from '@components/ConceptStocksModal'; import TradeDatePicker from '@components/TradeDatePicker'; +import HotspotOverview from './components/HotspotOverview'; import { BsGraphUp, BsLightningFill } from 'react-icons/bs'; import * as echarts from 'echarts'; import { logger } from '../../utils/logger'; @@ -840,6 +841,11 @@ const StockOverview = () => { )} + {/* 热点概览 - 大盘走势 + 概念异动 */} + + + + {/* 今日热门概念 */} From ceb537e9447edfaee24e4823b4724b3c14f126af Mon Sep 17 00:00:00 2001 From: zzlgreat Date: Tue, 9 Dec 2025 16:27:56 +0800 Subject: [PATCH 04/20] update pay ui --- concept_alert_20251208.log | 2823 ----------------- concept_alert_alpha.py | 1078 ------- concept_alert_alpha_20251208.log | 28 - concept_alert_ml.py | 1625 ---------- concept_alert_realtime.py | 1366 -------- concept_quota_realtime.py | 681 ---- create_tables.py | 89 - .../realtime_detector.cpython-310.pyc | Bin 0 -> 32449 bytes ml/backtest_fast.py | 859 +++++ ml/backtest_hybrid.py | 145 +- ml/detector.py | 72 +- ml/realtime_detector.py | 1518 +++++++++ src/mocks/handlers/market.js | 168 +- .../components/AlertSummary.js | 147 + .../components/ConceptAlertList.js | 194 ++ .../components/IndexMinuteChart.js | 264 ++ .../HotspotOverview/components/index.js | 3 + .../components/HotspotOverview/hooks/index.js | 1 + .../HotspotOverview/hooks/useHotspotData.js | 53 + .../components/HotspotOverview/index.js | 539 +--- .../HotspotOverview/utils/chartHelpers.js | 159 + .../components/HotspotOverview/utils/index.js | 1 + 22 files changed, 3637 insertions(+), 8176 deletions(-) delete mode 100644 concept_alert_20251208.log delete mode 100644 concept_alert_alpha.py delete mode 100644 concept_alert_alpha_20251208.log delete mode 100644 concept_alert_ml.py delete mode 100644 concept_alert_realtime.py delete mode 100644 concept_quota_realtime.py delete mode 100644 create_tables.py create mode 100644 ml/__pycache__/realtime_detector.cpython-310.pyc create mode 100644 ml/backtest_fast.py create mode 100644 ml/realtime_detector.py create mode 100644 src/views/StockOverview/components/HotspotOverview/components/AlertSummary.js create mode 100644 src/views/StockOverview/components/HotspotOverview/components/ConceptAlertList.js create mode 100644 src/views/StockOverview/components/HotspotOverview/components/IndexMinuteChart.js create mode 100644 src/views/StockOverview/components/HotspotOverview/components/index.js create mode 100644 src/views/StockOverview/components/HotspotOverview/hooks/index.js create mode 100644 src/views/StockOverview/components/HotspotOverview/hooks/useHotspotData.js create mode 100644 src/views/StockOverview/components/HotspotOverview/utils/chartHelpers.js create mode 100644 src/views/StockOverview/components/HotspotOverview/utils/index.js diff --git a/concept_alert_20251208.log b/concept_alert_20251208.log deleted file mode 100644 index dffe4ef2..00000000 --- a/concept_alert_20251208.log +++ /dev/null @@ -1,2823 +0,0 @@ -2025-12-08 15:43:31,991 - INFO - ============================================================ -2025-12-08 15:43:31,991 - INFO - 🔄 开始回测: 2025-12-08 -2025-12-08 15:43:31,992 - INFO - ============================================================ -2025-12-08 15:43:57,527 - INFO - ============================================================ -2025-12-08 15:43:57,528 - INFO - 🔄 开始回测: 2025-12-08 -2025-12-08 15:43:57,528 - INFO - ============================================================ -2025-12-08 15:47:59,701 - INFO - ============================================================ -2025-12-08 15:47:59,701 - INFO - 🔄 开始回测: 2025-12-08 -2025-12-08 15:47:59,703 - INFO - ============================================================ -2025-12-08 15:47:59,803 - INFO - 已清除 2025-12-08 的已有数据 -2025-12-08 15:47:59,803 - INFO - 加载概念数据... -2025-12-08 15:48:00,021 - INFO - POST http://222.128.1.157:19200/concept_library_v3/_search?scroll=2m [status:200 duration:0.216s] -2025-12-08 15:48:00,218 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.192s] -2025-12-08 15:48:00,442 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.175s] -2025-12-08 15:48:00,656 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.210s] -2025-12-08 15:48:00,845 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.185s] -2025-12-08 15:48:01,025 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.177s] -2025-12-08 15:48:01,209 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.180s] -2025-12-08 15:48:01,377 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.164s] -2025-12-08 15:48:01,486 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.105s] -2025-12-08 15:48:01,496 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.007s] -2025-12-08 15:48:01,505 - INFO - DELETE http://222.128.1.157:19200/_search/scroll [status:200 duration:0.008s] -2025-12-08 15:48:01,505 - INFO - 获取到 865 个叶子概念 -2025-12-08 15:48:01,513 - INFO - 生成了 103 个母概念 -2025-12-08 15:48:01,513 - INFO - 总计 968 个概念 -2025-12-08 15:48:01,516 - INFO - 监控 5938 只股票 -2025-12-08 15:48:02,488 - INFO - 获取到 5132 个基准价格 -2025-12-08 15:48:02,498 - INFO - 指数昨收价: 3902.8076 -2025-12-08 15:48:02,667 - INFO - 找到 241 个分钟时间点 -2025-12-08 15:48:03,435 - ERROR - 保存异动失败: SpaceX - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '54d3a21ee5b8a15d2004b7350bf67e6d', 'concept_name': 'SpaceX', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 11.1678, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 10, 'prev_limit_up_count': 9, 'limit_up_delta': 1, 'rank_position': 2, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 19, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["002149", "688102", "603601", "002792", "300136", "000547", "000901", "688053", "605598", "301568"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:03,442 - ERROR - 保存异动失败: 光纤列阵单元FAU - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '7e7a80554a69b2ff892349f661a0e158', 'concept_name': '光纤列阵单元FAU', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 9.2138, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 3, 'prev_limit_up_count': 2, 'limit_up_delta': 1, 'rank_position': 3, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 10, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["688025", "688143", "300570"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:03,449 - ERROR - 保存异动失败: 海峡两岸福建 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'c0e5202a076100676c901fb6a6fe70fa', 'concept_name': '海峡两岸福建', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 7.3406, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 13, 'prev_limit_up_count': 11, 'limit_up_delta': 2, 'rank_position': 5, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 45, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300300", "000905", "000701", "000632", "000797", "002578", "002961", "300102", "600734", "002679", "002682", "002752", "603122"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:03,454 - ERROR - 保存异动失败: 蓝箭航天朱雀三号 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'd4e69baa804e9adf57bc8fb26807299f', 'concept_name': '蓝箭航天朱雀三号', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 7.0298, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 8, 'prev_limit_up_count': 7, 'limit_up_delta': 1, 'rank_position': 7, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 31, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["688102", "002792", "688333", "301005", "688539", "600343", "688270", "000547"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:03,465 - ERROR - 保存异动失败: 卫星互联网 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '9814b44dd4e4a09363dcfe5c374618f9', 'concept_name': '卫星互联网', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 6.8333, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 11, 'prev_limit_up_count': 8, 'limit_up_delta': 3, 'rank_position': 8, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 48, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300762", "688270", "003031", "300123", "301117", "688102", "688539", "688418", "688311", "300136", "000901"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:03,469 - ERROR - 保存异动失败: 商业航天卫星通信 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '2b570e6ae2e96a7b249dcb0ae68f512d', 'concept_name': '商业航天卫星通信', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 6.7523, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 18, 'prev_limit_up_count': 14, 'limit_up_delta': 4, 'rank_position': 9, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 83, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["600343", "688539", "688102", "301005", "688333", "688270", "300123", "300762", "301117", "002792", "300136", "688418", "688311", "002512", "300102", "000547", "000901", "920665"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:03,480 - ERROR - 保存异动失败: 星网 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '01bf110e2146b364400869c107c83451', 'concept_name': '星网', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 6.5314, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 17, 'prev_limit_up_count': 13, 'limit_up_delta': 4, 'rank_position': 10, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 83, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["600343", "688102", "301005", "688333", "688270", "300123", "300762", "301117", "002792", "300136", "688418", "688311", "002512", "300102", "000547", "000901", "920665"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:03,486 - ERROR - 保存异动失败: 光纤 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '56944fc3bac2fbf44215db12ad2b94b2', 'concept_name': '光纤', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 6.4413, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 3, 'prev_limit_up_count': 2, 'limit_up_delta': 1, 'rank_position': 11, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 14, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["600105", "688143", "300570"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:03,498 - ERROR - 保存异动失败: 商业航天 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '3be4e01f6112192e002c26b6a648e799', 'concept_name': '商业航天', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 6.3055, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 20, 'prev_limit_up_count': 16, 'limit_up_delta': 4, 'rank_position': 12, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 110, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["301005", "688539", "688102", "300900", "000547", "300762", "002977", "600343", "688333", "688270", "003031", "300123", "301117", "688418", "688311", "300136", "002512", "000901", "920665", "600151"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:03,503 - ERROR - 保存异动失败: 空芯光纤 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '973f2ac5c5a1a8770b83e34a8683659c', 'concept_name': '空芯光纤', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 6.2376, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 3, 'prev_limit_up_count': 2, 'limit_up_delta': 1, 'rank_position': 13, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 11, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["000070", "688143", "300570"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:03,551 - ERROR - 保存异动失败: 远程火力 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '271c4f3a775d854954c1a8ea24104821', 'concept_name': '远程火力', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 6.1789, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 11, 'prev_limit_up_count': 6, 'limit_up_delta': 5, 'rank_position': 14, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 57, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["688333", "688282", "688143", "688311", "300123", "002977", "688270", "300427", "603122", "002149", "600302"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:03,577 - ERROR - 保存异动失败: [二级] 陆军装备 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'e78c88c7728af7aa', 'concept_name': '[二级] 陆军装备', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 6.1789, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 11, 'prev_limit_up_count': 6, 'limit_up_delta': 5, 'rank_position': 15, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 57, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["002977", "688311", "688333", "300427", "688143", "600302", "603122", "688270", "002149", "300123", "688282"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:03,601 - ERROR - 保存异动失败: 手机直连卫星 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '2e8d597a0e6bd633695b947e683af5a7', 'concept_name': '手机直连卫星', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 6.1785, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 6, 'prev_limit_up_count': 4, 'limit_up_delta': 2, 'rank_position': 16, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 21, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300762", "002977", "688311", "001270", "688270", "688418"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:03,624 - ERROR - 保存异动失败: 水下军工 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'ddf3e3a0c99aa84b88024fd5e0a2fa6b', 'concept_name': '水下军工', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 5.7954, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 4, 'prev_limit_up_count': 2, 'limit_up_delta': 2, 'rank_position': 19, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 27, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["002149", "688143", "688282", "300123"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:03,630 - ERROR - 保存异动失败: 军工水面水下作战 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '26870549d3469d8b016d58878a4a1694', 'concept_name': '军工水面水下作战', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 5.4306, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 3, 'prev_limit_up_count': 2, 'limit_up_delta': 1, 'rank_position': 22, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 28, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["002149", "688143", "688282"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:03,636 - ERROR - 保存异动失败: 核聚变超导 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '9e0ad5f3de553a074aca639d13094d76', 'concept_name': '核聚变超导', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 5.2595, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 6, 'prev_limit_up_count': 5, 'limit_up_delta': 1, 'rank_position': 26, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 43, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["600105", "920576", "601106", "601399", "002149", "603015"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:03,646 - ERROR - 保存异动失败: 福建国资 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'b59679d54c060fe313b16d6917d9ca93', 'concept_name': '福建国资', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 5.1542, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 8, 'prev_limit_up_count': 6, 'limit_up_delta': 2, 'rank_position': 27, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 50, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["000905", "600734", "000797", "000701", "002682", "002679", "300300", "000632"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:03,654 - ERROR - 保存异动失败: 光通信CPO - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '0b20095da20380417829e5484feac90b', 'concept_name': '光通信CPO', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 5.1014, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 4, 'prev_limit_up_count': 3, 'limit_up_delta': 1, 'rank_position': 28, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 32, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["600105", "688048", "300570", "000070"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:03,660 - ERROR - 保存异动失败: 军工信息化 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '66bd549e8504be635ff0bbad8ce930e0', 'concept_name': '军工信息化', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 4.9328, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 15, 'prev_limit_up_count': 12, 'limit_up_delta': 3, 'rank_position': 33, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 135, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["002977", "300427", "300123", "001270", "000547", "688311", "300762", "688081", "000070", "688270", "301117", "688539", "600973", "300900", "300560"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:03,666 - ERROR - 保存异动失败: 可控核聚变 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'f35622ab5c0d533e0c8da487df982c05', 'concept_name': '可控核聚变', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 4.8832, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 9, 'prev_limit_up_count': 8, 'limit_up_delta': 1, 'rank_position': 35, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 73, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["600105", "600973", "688167", "603015", "601399", "688102", "920576", "601106", "002149"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:03,690 - ERROR - 保存异动失败: 光通信 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'dd05cdaa409ff3dafb3fe19bedbeaa87', 'concept_name': '光通信', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 4.6474, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 4, 'prev_limit_up_count': 3, 'limit_up_delta': 1, 'rank_position': 39, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 36, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300570", "600105", "688048", "000070"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:03,712 - ERROR - 保存异动失败: 北斗信使 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '1808e46d4965b2f8272846d77025716e', 'concept_name': '北斗信使', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 4.5627, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 41, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 15, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["000901", "002512"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:03,717 - ERROR - 保存异动失败: [二级] 无人作战与信息化 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'ab0a7a074639080b', 'concept_name': '[二级] 无人作战与信息化', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 4.4116, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 17, 'prev_limit_up_count': 14, 'limit_up_delta': 3, 'rank_position': 44, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 200, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["688311", "301117", "300560", "002977", "000901", "300900", "688270", "002149", "600973", "001270", "300123", "688539", "300427", "000070", "000547", "688081", "300762"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:03,722 - ERROR - 保存异动失败: [二级] 商业航天 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '3342079af14780e7', 'concept_name': '[二级] 商业航天', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 4.2974, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 27, 'prev_limit_up_count': 23, 'limit_up_delta': 4, 'rank_position': 50, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 238, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["688333", "003031", "300136", "600343", "002977", "000901", "300900", "688270", "001270", "300123", "688539", "688102", "300102", "002512", "002792", "300751", "688311", "301117", "002565", "688418", "301005", "688282", "605598", "000547", "300762", "920665", "600151"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:03,732 - ERROR - 保存异动失败: 信息支援概念整理 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '42e3093f9fc99b4a86374ac455873b9b', 'concept_name': '信息支援概念整理', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 4.1763, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 1, 'prev_limit_up_count': 0, 'limit_up_delta': 1, 'rank_position': 54, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 24, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["688311"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:03,737 - ERROR - 保存异动失败: 军工 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '58ac1fb9ce375d4849893453f9615b49', 'concept_name': '军工', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 4.1592, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 10, 'prev_limit_up_count': 8, 'limit_up_delta': 2, 'rank_position': 56, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 168, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["002977", "000547", "000901", "600343", "688143", "002149", "688333", "688311", "300337", "300123"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:03,745 - ERROR - 保存异动失败: 珠海航展 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'd02ad5be74f6a9c53fb28291a961621c', 'concept_name': '珠海航展', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 4.1462, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 5, 'prev_limit_up_count': 3, 'limit_up_delta': 2, 'rank_position': 57, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 44, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["600343", "000901", "300900", "688311", "300123"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:03,750 - ERROR - 保存异动失败: 6G概念 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '863500a9c8b544d099618a6f355ccfff', 'concept_name': '6G概念', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 4.1245, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 9, 'prev_limit_up_count': 5, 'limit_up_delta': 4, 'rank_position': 59, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 80, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["002093", "002792", "300136", "688270", "300123", "300570", "000547", "300560", "002512"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:03,770 - ERROR - 保存异动失败: 磁悬浮压缩机 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '6ae1f6de3a42693cb613896ba7ce8173', 'concept_name': '磁悬浮压缩机', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 4.1143, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 61, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 14, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["002158", "000811"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:03,775 - ERROR - 保存异动失败: [二级] 海军装备 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '36d8fd5f5f85dffb', 'concept_name': '[二级] 海军装备', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 4.0227, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 9, 'prev_limit_up_count': 6, 'limit_up_delta': 3, 'rank_position': 62, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 99, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["300337", "688143", "688282", "601696", "002512", "000547", "601106", "002149", "300123"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:03,780 - ERROR - 保存异动失败: [一级] 国防军工 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'b2cd2902316a116f', 'concept_name': '[一级] 国防军工', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 4.0186, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 30, 'prev_limit_up_count': 24, 'limit_up_delta': 6, 'rank_position': 63, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 373, 'concept_type': 'lv1', 'extra_info': '{"limit_up_stocks": ["688333", "600343", "300560", "002977", "000901", "300900", "600302", "002682", "688270", "002149", "600973", "001270", "300123" ... (23 characters truncated) ... 300337", "002512", "688311", "301117", "688143", "601696", "603122", "603601", "688282", "000070", "000547", "601106", "688081", "300762", "600151"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:03,786 - ERROR - 保存异动失败: 军用无人机反无人机 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '388136e7bee429da7f0026a26acc953f', 'concept_name': '军用无人机反无人机', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 4.0027, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 67, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 38, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300900", "300123"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:03,814 - ERROR - 保存异动失败: 核电产业链 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '41e18cd109f4f15cd0716e3c6d9955dd', 'concept_name': '核电产业链', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.9503, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 7, 'prev_limit_up_count': 6, 'limit_up_delta': 1, 'rank_position': 69, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 86, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["600973", "600105", "601399", "603015", "920576", "601106", "688167"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:03,845 - ERROR - 保存异动失败: [二级] 综合与主题 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '8dd8fe507c554405', 'concept_name': '[二级] 综合与主题', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.936, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 13, 'prev_limit_up_count': 11, 'limit_up_delta': 2, 'rank_position': 71, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 204, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["688311", "301117", "688333", "688143", "600343", "300560", "002977", "000901", "002149", "300123", "300337", "000547", "600151"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:03,851 - ERROR - 保存异动失败: 航母福建舰240430 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '67d2e2df29052090e9954abeb00d517d', 'concept_name': '航母福建舰240430', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.835, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 4, 'prev_limit_up_count': 3, 'limit_up_delta': 1, 'rank_position': 74, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 40, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300337", "601106", "000547", "002512"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:03,886 - ERROR - 保存异动失败: 铜连接 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '56e10d2abdf5ceb6f13e200e85fef36e', 'concept_name': '铜连接', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.7676, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 3, 'prev_limit_up_count': 2, 'limit_up_delta': 1, 'rank_position': 79, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 23, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["002792", "600973", "300570"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:03,915 - ERROR - 保存异动失败: 博通交换机 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '6f778df3439781376d8c908f8d2b2276', 'concept_name': '博通交换机', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.7567, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 80, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 22, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300570", "301486"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:03,937 - ERROR - 保存异动失败: 卫星出海 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '69d1d811ac734aa07561db06a141bc16', 'concept_name': '卫星出海', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.6151, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 4, 'prev_limit_up_count': 2, 'limit_up_delta': 2, 'rank_position': 91, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 48, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["002792", "300762", "300123", "002512"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:03,942 - ERROR - 保存异动失败: 量子科技产业链 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'f7daaa03d8415d499ca82a4d70ead074', 'concept_name': '量子科技产业链', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.5094, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 4, 'prev_limit_up_count': 2, 'limit_up_delta': 2, 'rank_position': 100, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 63, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["301117", "300123", "002512", "688418"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:03,949 - ERROR - 保存异动失败: 无人机蜂群 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '34f096164ed77a917364457c58fb7b18', 'concept_name': '无人机蜂群', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.484, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 3, 'prev_limit_up_count': 2, 'limit_up_delta': 1, 'rank_position': 102, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 24, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["688311", "301117", "000901"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:03,955 - ERROR - 保存异动失败: 超聚变 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '78a0c55f302a623ae81994e671989229', 'concept_name': '超聚变', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.3262, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 112, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 36, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300290", "600105"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:03,962 - ERROR - 保存异动失败: 对日反制 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'f989d4184984a32d90fa74385399c3eb', 'concept_name': '对日反制', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.3087, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 12, 'prev_limit_up_count': 11, 'limit_up_delta': 1, 'rank_position': 113, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 155, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["688323", "000859", "003031", "002585", "000547", "002682", "300560", "002083", "603122", "603067", "300427", "300123"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:03,970 - ERROR - 保存异动失败: 庭院割草机器人 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '7e531a981bb00d2127f93503ebd29036', 'concept_name': '庭院割草机器人', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.2773, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 119, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 13, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["301125", "002779"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:03,995 - ERROR - 保存异动失败: 华为 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'd63e7ea059dc68e97e2ef16531e47e6c', 'concept_name': '华为', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.2412, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 3, 'prev_limit_up_count': 1, 'limit_up_delta': 2, 'rank_position': 122, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 40, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300290", "301125", "688031"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,001 - ERROR - 保存异动失败: 北斗导航 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'e1cb2d2097ad5505e52914eea10fb93f', 'concept_name': '北斗导航', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.2333, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 123, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 15, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["688282", "000901"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,007 - ERROR - 保存异动失败: 深海经济 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '62c1ff95b594873a91665479c74fa5d5', 'concept_name': '深海经济', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.2094, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 130, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 57, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["920576", "002149"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,014 - ERROR - 保存异动失败: 低空经济亿航智能 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '9e56e5451682848c3c8a440306122ea7', 'concept_name': '低空经济亿航智能', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.1804, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 4, 'prev_limit_up_count': 3, 'limit_up_delta': 1, 'rank_position': 133, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 81, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300900", "002660", "002682", "000701"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,021 - ERROR - 保存异动失败: 海军 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '10bb72970f8ef3c8801898e342d47942', 'concept_name': '海军', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.1182, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 1, 'prev_limit_up_count': 0, 'limit_up_delta': 1, 'rank_position': 140, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 26, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300123"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,034 - ERROR - 保存异动失败: IPV6 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '3fbcd320695e3a01273994471d09cc36', 'concept_name': 'IPV6', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.1161, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 4, 'prev_limit_up_count': 3, 'limit_up_delta': 1, 'rank_position': 142, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 35, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["002885", "688282", "300560", "301529"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,043 - ERROR - 保存异动失败: 国产航母 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '41a3d2f7a25c41eaa2c2e48a520bc380', 'concept_name': '国产航母', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.1147, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 143, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 25, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["601696", "300123"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,056 - ERROR - 保存异动失败: 台资企业 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '814666d2762595328dfddbd3546a1fbf', 'concept_name': '台资企业', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.109, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 6, 'prev_limit_up_count': 5, 'limit_up_delta': 1, 'rank_position': 145, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 54, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["603122", "603886", "603015", "002158", "002578", "002752"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,062 - ERROR - 保存异动失败: 机器人零部件加工设备 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '480f22ef7a1876fb952bbb51afc87210', 'concept_name': '机器人零部件加工设备', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.1047, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 3, 'prev_limit_up_count': 2, 'limit_up_delta': 1, 'rank_position': 147, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 28, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["688577", "301125", "002779"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,071 - ERROR - 保存异动失败: 量子科技 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '41381b4a238daead45abe7866364e44e', 'concept_name': '量子科技', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.0729, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 4, 'prev_limit_up_count': 2, 'limit_up_delta': 2, 'rank_position': 151, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 70, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["688418", "002512", "300123", "301117"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,078 - ERROR - 保存异动失败: 超聚变借壳预期 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'e3f9eb598bd782998ef4d63abf7f914b', 'concept_name': '超聚变借壳预期', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.0691, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 0, 'limit_up_delta': 2, 'rank_position': 152, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 15, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300290", "600302"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,101 - ERROR - 保存异动失败: 5G毫米波 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '331c00a813c599fef1ad0ad65cf90631', 'concept_name': '5G毫米波', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.0402, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 5, 'prev_limit_up_count': 4, 'limit_up_delta': 1, 'rank_position': 155, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 40, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["002792", "300136", "003031", "688270", "002977"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,117 - ERROR - 保存异动失败: [二级] 量子科技 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '24435b1fd612ff3d', 'concept_name': '[二级] 量子科技', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.0325, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 5, 'prev_limit_up_count': 3, 'limit_up_delta': 2, 'rank_position': 158, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 95, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["301117", "300123", "002512", "688418", "002149"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,124 - ERROR - 保存异动失败: [一级] 前沿科技 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '87da2b6fbbcb3e33', 'concept_name': '[一级] 前沿科技', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 3.0325, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 5, 'prev_limit_up_count': 3, 'limit_up_delta': 2, 'rank_position': 159, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 95, 'concept_type': 'lv1', 'extra_info': '{"limit_up_stocks": ["301117", "300123", "002512", "688418", "002149"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,133 - ERROR - 保存异动失败: 低空经济产业链汇集 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '68aa3c7715416e1ffa19f237d0f95961', 'concept_name': '低空经济产业链汇集', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.9994, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 3, 'prev_limit_up_count': 2, 'limit_up_delta': 1, 'rank_position': 167, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 122, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["000701", "000547", "002792"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,139 - ERROR - 保存异动失败: [一级] 空天经济 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'd1f5d74529f14e29', 'concept_name': '[一级] 空天经济', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.9293, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 37, 'prev_limit_up_count': 31, 'limit_up_delta': 6, 'rank_position': 177, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 690, 'concept_type': 'lv1', 'extra_info': '{"limit_up_stocks": ["300136", "000901", "300900", "002682", "688539", "300102", "002512", "002660", "603601", "301005", "605598", "300570", "920665" ... (93 characters truncated) ... 688102", "002792", "300751", "688311", "301117", "002565", "688418", "600105", "002779", "002093", "688282", "000070", "000547", "600151", "300762"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,147 - ERROR - 保存异动失败: [三级] 核能 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'b44eaa65d1c2425d', 'concept_name': '[三级] 核能', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.91, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 12, 'prev_limit_up_count': 9, 'limit_up_delta': 3, 'rank_position': 178, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 232, 'concept_type': 'lv3', 'extra_info': '{"limit_up_stocks": ["002158", "920576", "600973", "002149", "688102", "600105", "601399", "300290", "000811", "603015", "601106", "688167"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,161 - ERROR - 保存异动失败: [二级] 通信技术 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '22330358ccd65aeb', 'concept_name': '[二级] 通信技术', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.9016, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 14, 'prev_limit_up_count': 10, 'limit_up_delta': 4, 'rank_position': 182, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 189, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["003031", "300136", "300560", "002977", "688270", "300123", "600105", "002093", "000070", "002512", "002792", "000547", "300762", "300570"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,184 - ERROR - 保存异动失败: AEBS - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '4cd10b5ab4805603cd2d7a34f6f6e6b2', 'concept_name': 'AEBS', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.8962, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 1, 'prev_limit_up_count': 0, 'limit_up_delta': 1, 'rank_position': 184, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 38, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300123"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,189 - ERROR - 保存异动失败: 低空经济 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '6c5f0e25b757e8286f7cb93330b36dc8', 'concept_name': '低空经济', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.765, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 4, 'prev_limit_up_count': 3, 'limit_up_delta': 1, 'rank_position': 199, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 120, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300900", "002660", "002682", "000701"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,210 - ERROR - 保存异动失败: 冰雪经济 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '1cf3a25cd1c90c27310c4610d4a3743f', 'concept_name': '冰雪经济', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.7636, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 3, 'prev_limit_up_count': 2, 'limit_up_delta': 1, 'rank_position': 200, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 44, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["000811", "002158", "605299"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,227 - ERROR - 保存异动失败: 英伟达概念 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '650c2a8d881ff609d809cab6379d1593', 'concept_name': '英伟达概念', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.7475, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 202, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 76, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300570", "301117"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,259 - ERROR - 保存异动失败: 养老机器人 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '30af5b5e3a1ec3a15d21497e9b0399e4', 'concept_name': '养老机器人', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.7411, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 1, 'prev_limit_up_count': 0, 'limit_up_delta': 1, 'rank_position': 203, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 28, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300290"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,267 - ERROR - 保存异动失败: 信创概念 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'aeac02717442e5a225bcae32103ee6a4', 'concept_name': '信创概念', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.7404, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 204, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 72, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["301218", "688031"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,294 - ERROR - 保存异动失败: [三级] AI关键组件 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'd125ffc194b42ee4', 'concept_name': '[三级] AI关键组件', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.7179, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 19, 'prev_limit_up_count': 18, 'limit_up_delta': 1, 'rank_position': 212, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 360, 'concept_type': 'lv3', 'extra_info': '{"limit_up_stocks": ["688048", "600973", "603386", "300102", "688025", "002792", "300751", "688143", "002565", "301526", "603122", "301183", "600366", "301486", "688383", "600105", "000070", "300570", "688167"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,300 - ERROR - 保存异动失败: 关键软件 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '7a7fd726a54670b2b3c8bff3b778acf4', 'concept_name': '关键软件', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.705, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 214, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 103, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["688031", "301218"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,314 - ERROR - 保存异动失败: 数据中心液冷 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'f8bbd66624a99da406ceb891aeb6eb3a', 'concept_name': '数据中心液冷', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.6955, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 217, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 65, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["000811", "002158"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,325 - ERROR - 保存异动失败: 国产信创概览 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '22a45db024b3e7d118d2810c2e7bc5bd', 'concept_name': '国产信创概览', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.6884, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 219, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 101, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["688031", "301218"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,336 - ERROR - 保存异动失败: AI-细分延伸更新 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'c6796e52492170dfc6234c299cbc095b', 'concept_name': 'AI-细分延伸更新', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.6801, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 3, 'prev_limit_up_count': 2, 'limit_up_delta': 1, 'rank_position': 220, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 63, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["301526", "002158", "000811"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,341 - ERROR - 保存异动失败: [三级] AI配套设施 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '986593c763801dcc', 'concept_name': '[三级] AI配套设施', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.6794, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 7, 'prev_limit_up_count': 6, 'limit_up_delta': 1, 'rank_position': 222, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 297, 'concept_type': 'lv3', 'extra_info': '{"limit_up_stocks": ["688333", "002158", "002885", "688102", "000811", "002660", "600218"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,347 - ERROR - 保存异动失败: [三级] 制造工艺与设备 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '67341e0ae4ef7dac', 'concept_name': '[三级] 制造工艺与设备', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.6481, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 4, 'prev_limit_up_count': 3, 'limit_up_delta': 1, 'rank_position': 228, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 70, 'concept_type': 'lv3', 'extra_info': '{"limit_up_stocks": ["300136", "002779", "688577", "301125"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,366 - ERROR - 保存异动失败: Minimax - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '380c18e239f03cf9c0a846c7defd029b', 'concept_name': 'Minimax', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.6426, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 229, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 28, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["688383", "605303"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,372 - ERROR - 保存异动失败: 章盟主概念股 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '0ebd312797bf29853df2fb0f54ab9b72', 'concept_name': '章盟主概念股', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.6389, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 1, 'prev_limit_up_count': 0, 'limit_up_delta': 1, 'rank_position': 230, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 22, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["688311"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,377 - ERROR - 保存异动失败: 马斯克Grok3大模型 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'df7573bd0405ab31be0762125a92fdec', 'concept_name': '马斯克Grok3大模型', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.6051, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 243, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 37, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300570", "688661"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,399 - ERROR - 保存异动失败: 华为Pura70 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '7b30055f26aa31e887125520d038e8c9', 'concept_name': '华为Pura70', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.5615, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 4, 'prev_limit_up_count': 3, 'limit_up_delta': 1, 'rank_position': 248, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 68, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300136", "688311", "301183", "688048"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,404 - ERROR - 保存异动失败: 次新股 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '50371a2c5078b757a8f8c75b8877e815', 'concept_name': '次新股', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.5459, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 249, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 51, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["301526", "301568"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,410 - ERROR - 保存异动失败: 华为昇腾 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '1287f5d7e9279690c27706d91bf3000b', 'concept_name': '华为昇腾', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.5455, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 250, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 44, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["301125", "688031"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,415 - ERROR - 保存异动失败: 天太机器人 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '60bd1b38447575a0469438b78c702219', 'concept_name': '天太机器人', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.5369, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 3, 'prev_limit_up_count': 2, 'limit_up_delta': 1, 'rank_position': 251, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 29, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300503", "688160", "300570"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,421 - ERROR - 保存异动失败: 中俄贸易 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '58e58336a55c82ef6e6917be068b2892', 'concept_name': '中俄贸易', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.515, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 4, 'prev_limit_up_count': 3, 'limit_up_delta': 1, 'rank_position': 254, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 72, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300900", "000901", "601106", "301125"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,433 - ERROR - 保存异动失败: [二级] 信创与自主可控 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'df470e2b63c29363', 'concept_name': '[二级] 信创与自主可控', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.4885, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 9, 'prev_limit_up_count': 8, 'limit_up_delta': 1, 'rank_position': 262, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 286, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["301218", "003031", "300136", "688292", "688031", "002792", "301117", "688418", "688081"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,438 - ERROR - 保存异动失败: 整车央企重组 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'c70db13b77d825b0fb1b49cbceae1884', 'concept_name': '整车央企重组', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.4477, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 268, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 34, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["601399", "600302"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,463 - ERROR - 保存异动失败: [二级] 低空经济 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '77979791fa471afe', 'concept_name': '[二级] 低空经济', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.4077, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 7, 'prev_limit_up_count': 6, 'limit_up_delta': 1, 'rank_position': 279, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 349, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["300900", "002682", "000701", "002792", "002660", "002779", "000547"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,469 - ERROR - 保存异动失败: 玻璃基板 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '6a20715a0e59c87740a6bacf5dcdd797', 'concept_name': '玻璃基板', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.4031, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 1, 'prev_limit_up_count': 0, 'limit_up_delta': 1, 'rank_position': 281, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 49, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["301568"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,476 - ERROR - 保存异动失败: [二级] 机器人产业链 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '2e2d410b95c1d2af', 'concept_name': '[二级] 机器人产业链', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.3695, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 11, 'prev_limit_up_count': 10, 'limit_up_delta': 1, 'rank_position': 292, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 311, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["300136", "688160", "301125", "300421", "301526", "301529", "301005", "002779", "300503", "603015", "688577"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,482 - ERROR - 保存异动失败: 设备更新 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'e3f7d1e5f5ea7749d173f9c3d50b494a', 'concept_name': '设备更新', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.3619, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 3, 'prev_limit_up_count': 2, 'limit_up_delta': 1, 'rank_position': 294, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 70, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["600302", "301125", "603122"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,487 - ERROR - 保存异动失败: [二级] AI基础设施 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'a143fdd34018ba6f', 'concept_name': '[二级] AI基础设施', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.3613, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 30, 'prev_limit_up_count': 28, 'limit_up_delta': 2, 'rank_position': 295, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 875, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["002158", "688031", "600973", "300102", "688025", "688143", "002660", "301486", "300290", "605598", "300570", "688167", "688333" ... (23 characters truncated) ... 603386", "688102", "000811", "002792", "300751", "002565", "301526", "600218", "603122", "301183", "600366", "688383", "600105", "000070", "000547"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,497 - ERROR - 保存异动失败: 神经网络 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '40850ccab8cdabc6868888fd85808d0e', 'concept_name': '神经网络', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.3377, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 304, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 30, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["301568", "688081"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,504 - ERROR - 保存异动失败: [二级] 华为产业链 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'a7e8f7a5548a002c', 'concept_name': '[二级] 华为产业链', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.3065, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 17, 'prev_limit_up_count': 16, 'limit_up_delta': 1, 'rank_position': 312, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 498, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["300136", "920592", "688031", "300337", "600734", "300290", "605299", "003031", "688048", "688292", "002885", "002792", "301125", "688311", "301183", "600366", "688383"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,525 - ERROR - 保存异动失败: 工业母机 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '5374db0880e238ea30333257c7707b1f', 'concept_name': '工业母机', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.2962, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 315, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 47, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300503", "600302"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,533 - ERROR - 保存异动失败: [三级] 技术与生态 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '13c15b670f35e095', 'concept_name': '[三级] 技术与生态', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.2884, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 12, 'prev_limit_up_count': 11, 'limit_up_delta': 1, 'rank_position': 317, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 368, 'concept_type': 'lv3', 'extra_info': '{"limit_up_stocks": ["605299", "003031", "300136", "688292", "920592", "688031", "002885", "002792", "301125", "600734", "688383", "300290"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,559 - ERROR - 保存异动失败: [二级] 清洁能源 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '9c928eb8da4b6b99', 'concept_name': '[二级] 清洁能源', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.2863, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 15, 'prev_limit_up_count': 12, 'limit_up_delta': 3, 'rank_position': 318, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 486, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["002158", "920576", "600973", "002149", "688102", "000811", "688025", "300751", "600105", "601399", "300290", "605598", "603015", "601106", "688167"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,564 - ERROR - 保存异动失败: 谷歌概念 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'b0af7bb745342ddb825256f2e6dc07cc', 'concept_name': '谷歌概念', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.2787, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 3, 'prev_limit_up_count': 2, 'limit_up_delta': 1, 'rank_position': 320, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 74, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["688167", "300570", "000070"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,589 - ERROR - 保存异动失败: 机器人-神经网络 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'f550f0183f520fd786dca9c73ae3932d', 'concept_name': '机器人-神经网络', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.275, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 3, 'prev_limit_up_count': 2, 'limit_up_delta': 1, 'rank_position': 322, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 28, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["301568", "002779", "300503"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,594 - ERROR - 保存异动失败: 电子束光刻机“羲之” - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '27e92f2be93742878116b1099c6b9c84', 'concept_name': '电子束光刻机“羲之”', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.2563, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 1, 'prev_limit_up_count': 0, 'limit_up_delta': 1, 'rank_position': 329, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 22, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["301568"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,612 - ERROR - 保存异动失败: 华为Mate70手表 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '0370e22b8849e09f85840b850ff43ff9', 'concept_name': '华为Mate70手表', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.2508, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 4, 'prev_limit_up_count': 3, 'limit_up_delta': 1, 'rank_position': 332, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 95, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300136", "300337", "301183", "688311"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,619 - ERROR - 保存异动失败: [一级] 新能源与电力 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'cedd5e74f7572b73', 'concept_name': '[一级] 新能源与电力', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.2458, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 27, 'prev_limit_up_count': 25, 'limit_up_delta': 2, 'rank_position': 334, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 1054, 'concept_type': 'lv1', 'extra_info': '{"limit_up_stocks": ["300136", "002158", "002585", "600973", "002149", "300427", "688025", "300982", "002660", "603067", "688499", "300062", "300290", "605598", "688167", "301468", "920576", "002885", "688102", "000811", "002083", "300751", "002300", "600105", "601399", "603015", "601106"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,625 - ERROR - 保存异动失败: [一级] 数字经济与金融科技 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '4a5f6ee0da8e5802', 'concept_name': '[一级] 数字经济与金融科技', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.2312, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 11, 'prev_limit_up_count': 10, 'limit_up_delta': 1, 'rank_position': 343, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 440, 'concept_type': 'lv1', 'extra_info': '{"limit_up_stocks": ["301218", "688292", "920592", "688031", "002885", "300377", "002961", "600734", "688282", "688081", "300762"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,635 - ERROR - 保存异动失败: [一级] 机器人 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'a21131132b7ae4ba', 'concept_name': '[一级] 机器人', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.2178, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 20, 'prev_limit_up_count': 16, 'limit_up_delta': 4, 'rank_position': 351, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 671, 'concept_type': 'lv1', 'extra_info': '{"limit_up_stocks": ["300136", "688160", "002682", "300421", "301005", "300503", "688577", "300570", "002589", "002885", "301125", "605287", "301526", "301529", "600366", "600105", "002779", "603015", "301568", "600151"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,642 - ERROR - 保存异动失败: [一级] 消费电子 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '540bff395b82f2c9', 'concept_name': '[一级] 消费电子', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.1646, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 27, 'prev_limit_up_count': 26, 'limit_up_delta': 1, 'rank_position': 370, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 804, 'concept_type': 'lv1', 'extra_info': '{"limit_up_stocks": ["300136", "920592", "688031", "300337", "688025", "600734", "002660", "301486", "300290", "688323", "688167", "605299", "688333", "003031", "688048", "688292", "002885", "000859", "002792", "301125", "688311", "301529", "301183", "600366", "688383", "002779", "688661"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,651 - ERROR - 保存异动失败: [二级] 产业升级与制造 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '5bd863c58a5c878e', 'concept_name': '[二级] 产业升级与制造', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.15, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 4, 'prev_limit_up_count': 3, 'limit_up_delta': 1, 'rank_position': 378, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 175, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["600302", "603122", "300503", "301125"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,655 - ERROR - 保存异动失败: 新型离岸贸易 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'c4fa4f91f9fb625419f317690ea59800', 'concept_name': '新型离岸贸易', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.1436, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 380, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 40, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["000701", "000797"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,687 - ERROR - 保存异动失败: 跨境数据数据要素 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '209d102f174e89a82bfcabc05fbc7a94', 'concept_name': '跨境数据数据要素', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.141, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 5, 'prev_limit_up_count': 4, 'limit_up_delta': 1, 'rank_position': 381, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 173, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["600734", "600734", "688031", "688292", "301218"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,694 - ERROR - 保存异动失败: 杭州六小龙-群核科技 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '43db71cecaa96d2bd95738daa1ea5b29', 'concept_name': '杭州六小龙-群核科技', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.1323, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 3, 'prev_limit_up_count': 2, 'limit_up_delta': 1, 'rank_position': 388, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 84, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300749", "688539", "301218"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,700 - ERROR - 保存异动失败: [三级] 通用生态 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '858106800942746c', 'concept_name': '[三级] 通用生态', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.1289, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 6, 'prev_limit_up_count': 5, 'limit_up_delta': 1, 'rank_position': 390, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 292, 'concept_type': 'lv3', 'extra_info': '{"limit_up_stocks": ["301117", "300503", "000070", "688661", "300570", "688167"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,711 - ERROR - 保存异动失败: [二级] 人形机器人整机 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '5cec0ba414c30f8c', 'concept_name': '[二级] 人形机器人整机', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.1162, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 11, 'prev_limit_up_count': 9, 'limit_up_delta': 2, 'rank_position': 396, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 405, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["688160", "002682", "605287", "300421", "301529", "600105", "301005", "002779", "300503", "688577", "300570"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,744 - ERROR - 保存异动失败: [二级] 机器人软件与AI - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '2c291cfeba422359', 'concept_name': '[二级] 机器人软件与AI', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.1084, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 3, 'prev_limit_up_count': 2, 'limit_up_delta': 1, 'rank_position': 397, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 42, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["002779", "300503", "301568"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,767 - ERROR - 保存异动失败: 并购重组 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '91dd748e27516c02c3f85115558c0cfb', 'concept_name': '并购重组', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.0698, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 5, 'prev_limit_up_count': 4, 'limit_up_delta': 1, 'rank_position': 411, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 149, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["000905", "300102", "300290", "002779", "003031"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,792 - ERROR - 保存异动失败: 外贸出口 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '3f3c9e9578d4a4189f8846eb48f1e0a0', 'concept_name': '外贸出口', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.0469, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 3, 'prev_limit_up_count': 2, 'limit_up_delta': 1, 'rank_position': 424, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 40, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["002083", "605598", "000701"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,800 - ERROR - 保存异动失败: 松延动力机器人 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'a84916ad3c4a624071692dc8d59f0c33', 'concept_name': '松延动力机器人', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.0434, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 1, 'prev_limit_up_count': 0, 'limit_up_delta': 1, 'rank_position': 426, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 31, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["605287"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,807 - ERROR - 保存异动失败: 化工有色元素周期表 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'e2e105e80fa3fe99d2d18494ae8dad9c', 'concept_name': '化工有色元素周期表', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.0119, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 443, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 122, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["002578", "603067"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,813 - ERROR - 保存异动失败: [二级] 并购重组 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '128a4a614ab175f9', 'concept_name': '[二级] 并购重组', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 2.002, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 13, 'prev_limit_up_count': 11, 'limit_up_delta': 2, 'rank_position': 446, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 555, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["300136", "688160", "300102", "601696", "000035", "301486", "300290", "003031", "600302", "000905", "301183", "002779", "688661"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,840 - ERROR - 保存异动失败: 华为鸿蒙 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '70c532af1fb28f41d335e8ed8fab7039', 'concept_name': '华为鸿蒙', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.9991, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 1, 'prev_limit_up_count': 0, 'limit_up_delta': 1, 'rank_position': 450, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 65, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300290"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,858 - ERROR - 保存异动失败: 央国企AI一张图 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '5072e741fffe4d28ad32bd51710390de', 'concept_name': '央国企AI一张图', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.9947, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 1, 'prev_limit_up_count': 0, 'limit_up_delta': 1, 'rank_position': 452, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 90, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300290"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,875 - ERROR - 保存异动失败: [二级] 数据要素 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '8cac77fc45376080', 'concept_name': '[二级] 数据要素', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.985, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 6, 'prev_limit_up_count': 5, 'limit_up_delta': 1, 'rank_position': 459, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 305, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["301218", "688292", "688031", "002885", "600734", "688282"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,882 - ERROR - 保存异动失败: [二级] 国企改革与市值管理 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '31eaecf0a6fa349b', 'concept_name': '[二级] 国企改革与市值管理', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.9848, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 21, 'prev_limit_up_count': 16, 'limit_up_delta': 5, 'rank_position': 460, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 821, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["000901", "002682", "000663", "000632", "600734", "300290", "002679", "003031", "600343", "000797", "600302", "000701", "000859", "300300", "000905", "600218", "601399", "000070", "000547", "601106", "600151"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,899 - ERROR - 保存异动失败: [二级] AI综合与趋势 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '91ac773adb0a2fc8', 'concept_name': '[二级] AI综合与趋势', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.9603, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 4, 'prev_limit_up_count': 3, 'limit_up_delta': 1, 'rank_position': 471, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 190, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["301526", "002158", "688031", "000811"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,905 - ERROR - 保存异动失败: DeepSeek - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '264d9bb481d458fdda428e70a2bfe2dd', 'concept_name': 'DeepSeek', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.9386, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 3, 'prev_limit_up_count': 2, 'limit_up_delta': 1, 'rank_position': 477, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 53, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300290", "603122", "688031"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,914 - ERROR - 保存异动失败: 政务云政务IT - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'dcd9ab454e81d760bec327f3af236501', 'concept_name': '政务云政务IT', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.9367, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 1, 'prev_limit_up_count': 0, 'limit_up_delta': 1, 'rank_position': 478, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 55, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["301218"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,934 - ERROR - 保存异动失败: [一级] 政策与主题 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'c69c28ca187d7096', 'concept_name': '[一级] 政策与主题', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.9353, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 54, 'prev_limit_up_count': 48, 'limit_up_delta': 6, 'rank_position': 480, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 2332, 'concept_type': 'lv1', 'extra_info': '{"limit_up_stocks": ["301218", "300102", "600734", "003031", "301125", "002565", "000905", "301183", "601399", "002779", "688160", "002682", "688025" ... (263 characters truncated) ... 002149", "001360", "000632", "002679", "688292", "002589", "920576", "000859", "002702", "301117", "603122", "603696", "601106", "688661", "600151"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,940 - ERROR - 保存异动失败: [二级] 国家战略 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '176e364167a853eb', 'concept_name': '[二级] 国家战略', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.9087, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 9, 'prev_limit_up_count': 8, 'limit_up_delta': 1, 'rank_position': 489, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 369, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["920576", "002149", "688102", "001360", "688025", "002565", "002300", "603696", "688081"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,964 - ERROR - 保存异动失败: [二级] 区域发展与自贸区 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '4153baee9169db33', 'concept_name': '[二级] 区域发展与自贸区', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.9039, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 6, 'prev_limit_up_count': 5, 'limit_up_delta': 1, 'rank_position': 492, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 254, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["000632", "688292", "000797", "000701", "603696", "600693"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,970 - ERROR - 保存异动失败: 上海自贸区 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '7619efd113c2722b97fc339a576056ec', 'concept_name': '上海自贸区', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.8776, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 1, 'prev_limit_up_count': 0, 'limit_up_delta': 1, 'rank_position': 507, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 62, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["000701"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:04,997 - ERROR - 保存异动失败: [二级] AI模型与软件 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '2c814fef42bd1255', 'concept_name': '[二级] AI模型与软件', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.8513, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 12, 'prev_limit_up_count': 11, 'limit_up_delta': 1, 'rank_position': 519, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 341, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["688031", "605303", "603122", "300940", "301486", "688383", "002093", "300290", "000070", "688081", "688661", "300570"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:05,002 - ERROR - 保存异动失败: 云手机 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '226e2f919636374374b636ca926974ee', 'concept_name': '云手机', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.8501, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 1, 'prev_limit_up_count': 0, 'limit_up_delta': 1, 'rank_position': 520, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 21, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["600302"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:05,009 - ERROR - 保存异动失败: 科技重组 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '94715a411814ca5d1cba1553ea302fe8', 'concept_name': '科技重组', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.8428, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 521, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 53, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300290", "301486"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:05,029 - ERROR - 保存异动失败: 央国企 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '65ce42095ba531cf3afd04abb20868c2', 'concept_name': '央国企', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.8052, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 0, 'limit_up_delta': 2, 'rank_position': 535, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 149, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["000797", "000663"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:05,043 - ERROR - 保存异动失败: 半导体设备 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '7cf6b644fb26f80f3941b0cbcebb4e07', 'concept_name': '半导体设备', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.8025, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 3, 'prev_limit_up_count': 1, 'limit_up_delta': 2, 'rank_position': 536, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 72, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["688661", "688383", "002158"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:05,069 - ERROR - 保存异动失败: 功率半导体 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'c2354cda068646013b9f8ecc8daf955a', 'concept_name': '功率半导体', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.7791, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 547, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 30, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["600302", "301183"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:05,081 - ERROR - 保存异动失败: [二级] 贸易政策与关系 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'cb524ffba0dd36d0', 'concept_name': '[二级] 贸易政策与关系', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.7596, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 12, 'prev_limit_up_count': 11, 'limit_up_delta': 1, 'rank_position': 554, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 614, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["002158", "000901", "300900", "600973", "000632", "603067", "688323", "300377", "301125", "300751", "601106", "688661"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:05,105 - ERROR - 保存异动失败: 华为海思星闪 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'b6ed4f1111b7800992b9b5614047109e', 'concept_name': '华为海思星闪', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.759, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 556, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 65, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["688383", "688031"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:05,122 - ERROR - 保存异动失败: 杭州算力大会 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'ee44bc8a1b4434d98631bda794220959', 'concept_name': '杭州算力大会', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.7345, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 1, 'prev_limit_up_count': 0, 'limit_up_delta': 1, 'rank_position': 564, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 55, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300290"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:05,131 - ERROR - 保存异动失败: [一级] 全球宏观与贸易 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '8904f63b21322564', 'concept_name': '[一级] 全球宏观与贸易', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.6868, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 16, 'prev_limit_up_count': 15, 'limit_up_delta': 1, 'rank_position': 587, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 886, 'concept_type': 'lv1', 'extra_info': '{"limit_up_stocks": ["002158", "000901", "300900", "002682", "600973", "000632", "603067", "688323", "300123", "600227", "300377", "301125", "300751", "000905", "601106", "688661"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:05,153 - ERROR - 保存异动失败: [二级] 半导体设备 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '2c5cc752e1e328ce', 'concept_name': '[二级] 半导体设备', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.67, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 5, 'prev_limit_up_count': 2, 'limit_up_delta': 3, 'rank_position': 592, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 170, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["002158", "688383", "688661", "688167", "301568"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:05,160 - ERROR - 保存异动失败: [二级] 芯片设计与制造 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'cad6c2087d1d396d', 'concept_name': '[二级] 芯片设计与制造', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.6532, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 6, 'prev_limit_up_count': 5, 'limit_up_delta': 1, 'rank_position': 600, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 244, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["300751", "003031", "300136", "600302", "603122", "301183"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:05,167 - ERROR - 保存异动失败: 高温概念 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '7cb8e6347cdfdc1e834d60887ef30db4', 'concept_name': '高温概念', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.6528, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 3, 'prev_limit_up_count': 2, 'limit_up_delta': 1, 'rank_position': 601, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 74, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["000811", "002158", "600105"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:05,174 - ERROR - 保存异动失败: [一级] 半导体 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '1d2cfd648c370f3e', 'concept_name': '[一级] 半导体', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.6494, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 18, 'prev_limit_up_count': 14, 'limit_up_delta': 4, 'rank_position': 604, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 725, 'concept_type': 'lv1', 'extra_info': '{"limit_up_stocks": ["300136", "002158", "688031", "001360", "688143", "688323", "688167", "003031", "688048", "600302", "000859", "300751", "301117", "603122", "301183", "688383", "688661", "301568"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:05,193 - ERROR - 保存异动失败: [二级] 先进封装 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'c9bee33f0408abfd', 'concept_name': '[二级] 先进封装', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.6381, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 4, 'prev_limit_up_count': 2, 'limit_up_delta': 2, 'rank_position': 613, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 123, 'concept_type': 'lv2', 'extra_info': '{"limit_up_stocks": ["300751", "688383", "688661", "301568"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:05,206 - ERROR - 保存异动失败: 城市旧改 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '93da0e46ef34f13e0cc170b6e7d195b7', 'concept_name': '城市旧改', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.5557, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 655, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 24, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["605287", "000632"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:05,211 - ERROR - 保存异动失败: 海事反制 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': '47c91f00b40c8a204ca4e693ac52e009', 'concept_name': '海事反制', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.5077, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 1, 'prev_limit_up_count': 0, 'limit_up_delta': 1, 'rank_position': 675, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 53, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300123"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:05,218 - ERROR - 保存异动失败: 医保DRGDIP - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'a52dfb0f6f112392b3ef1ee62fc3950e', 'concept_name': '医保DRGDIP', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.4841, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 688, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 33, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["300290", "301117"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:05,225 - ERROR - 保存异动失败: 国产半导体 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'e51800fd2ea5a195005e4af9f0c9b36d', 'concept_name': '国产半导体', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.4012, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 5, 'prev_limit_up_count': 4, 'limit_up_delta': 1, 'rank_position': 731, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 137, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["003031", "688661", "688048", "002158", "000859"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:48:05,230 - ERROR - 保存异动失败: 房地产产业链 - (pymysql.err.OperationalError) (1054, "Unknown column 'limit_up_delta' in 'field list'") -[SQL: - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (%(concept_id)s, %(concept_name)s, %(alert_time)s, %(alert_type)s, %(trade_date)s, - %(change_pct)s, %(prev_change_pct)s, %(change_delta)s, - %(limit_up_count)s, %(prev_limit_up_count)s, %(limit_up_delta)s, - %(rank_position)s, %(prev_rank_position)s, %(rank_delta)s, - %(index_code)s, %(index_price)s, %(index_change_pct)s, - %(stock_count)s, %(concept_type)s, %(extra_info)s) - ] -[parameters: {'concept_id': 'cf102e739fa64e34ccf2c412cbd67b50', 'concept_name': '房地产产业链', 'alert_time': datetime.datetime(2025, 12, 8, 9, 31, tzinfo=), 'alert_type': 'limit_up', 'trade_date': '2025-12-08', 'change_pct': 1.3825, 'prev_change_pct': None, 'change_delta': None, 'limit_up_count': 2, 'prev_limit_up_count': 1, 'limit_up_delta': 1, 'rank_position': 735, 'prev_rank_position': None, 'rank_delta': None, 'index_code': '000001.SH', 'index_price': 3912.76, 'index_change_pct': 0.255, 'stock_count': 143, 'concept_type': 'leaf', 'extra_info': '{"limit_up_stocks": ["000797", "301526"]}'}] -(Background on this error at: https://sqlalche.me/e/14/e3q8) -2025-12-08 15:49:46,800 - INFO - ============================================================ -2025-12-08 15:49:46,800 - INFO - 🔄 开始回测: 2025-12-08 -2025-12-08 15:49:46,802 - INFO - ============================================================ -2025-12-08 15:49:46,898 - INFO - 已清除 2025-12-08 的已有数据 -2025-12-08 15:49:46,898 - INFO - 加载概念数据... -2025-12-08 15:49:47,114 - INFO - POST http://222.128.1.157:19200/concept_library_v3/_search?scroll=2m [status:200 duration:0.215s] -2025-12-08 15:49:47,322 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.204s] -2025-12-08 15:49:47,547 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.188s] -2025-12-08 15:49:47,752 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.202s] -2025-12-08 15:49:47,944 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.189s] -2025-12-08 15:49:48,125 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.176s] -2025-12-08 15:49:48,304 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.176s] -2025-12-08 15:49:48,474 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.166s] -2025-12-08 15:49:48,601 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.123s] -2025-12-08 15:49:48,610 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.006s] -2025-12-08 15:49:48,618 - INFO - DELETE http://222.128.1.157:19200/_search/scroll [status:200 duration:0.007s] -2025-12-08 15:49:48,618 - INFO - 获取到 865 个叶子概念 -2025-12-08 15:49:48,624 - INFO - 生成了 103 个母概念 -2025-12-08 15:49:48,625 - INFO - 总计 968 个概念 -2025-12-08 15:49:48,627 - INFO - 监控 5938 只股票 -2025-12-08 15:49:48,724 - INFO - 获取到 5132 个基准价格 -2025-12-08 15:49:48,733 - INFO - 指数昨收价: 3902.8076 -2025-12-08 15:49:48,844 - INFO - 找到 241 个分钟时间点 -2025-12-08 15:50:04,980 - INFO - 进度: 30/241 (12%), 已检测到 980 条异动 -2025-12-08 15:50:19,301 - INFO - 进度: 60/241 (24%), 已检测到 1644 条异动 -2025-12-08 15:50:33,414 - INFO - 进度: 90/241 (37%), 已检测到 2142 条异动 -2025-12-08 15:50:48,181 - INFO - 进度: 120/241 (49%), 已检测到 3331 条异动 -2025-12-08 15:50:59,775 - INFO - 进度: 150/241 (62%), 已检测到 4678 条异动 -2025-12-08 15:51:08,681 - INFO - 进度: 180/241 (74%), 已检测到 5498 条异动 -2025-12-08 15:51:22,318 - INFO - 进度: 210/241 (87%), 已检测到 7315 条异动 -2025-12-08 15:51:30,566 - INFO - 进度: 240/241 (99%), 已检测到 7969 条异动 -2025-12-08 15:51:31,051 - INFO - ============================================================ -2025-12-08 15:51:31,052 - INFO - ✅ 回测完成! -2025-12-08 15:51:31,053 - INFO - 处理分钟数: 241 -2025-12-08 15:51:31,058 - INFO - 检测到异动: 8043 条 -2025-12-08 15:51:31,058 - INFO - ============================================================ -2025-12-08 15:55:59,803 - INFO - ============================================================ -2025-12-08 15:55:59,803 - INFO - 🔄 开始回测: 2025-12-08 -2025-12-08 15:55:59,805 - INFO - ============================================================ diff --git a/concept_alert_alpha.py b/concept_alert_alpha.py deleted file mode 100644 index 0e8c8f54..00000000 --- a/concept_alert_alpha.py +++ /dev/null @@ -1,1078 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -概念异动检测 - 基于超额收益(Alpha)的方法 - -核心思想： -- 异动 = 概念表现与市场整体的显著偏离 -- Alpha = 概念涨幅 - 大盘涨幅（超额收益） -- 用 Z-Score 衡量 Alpha 是否显著偏离历史正常范围 - -优点： -- 自适应：不同概念有不同波动率，自动适应 -- 相对比较：不看绝对涨跌，看相对表现 -- 无需规则维护：统计方法自动学习"正常范围" -- 通用：可迁移到其他市场 -""" - -import pandas as pd -import numpy as np -from datetime import datetime, timedelta -from sqlalchemy import create_engine, text -from elasticsearch import Elasticsearch -from clickhouse_driver import Client -from collections import deque, defaultdict -import time -import logging -import json -import os -import hashlib -import argparse -from typing import Dict, List, Optional, Tuple -from dataclasses import dataclass, field - -# ==================== 配置 ==================== - -MYSQL_ENGINE = create_engine( - "mysql+pymysql://root:Zzl5588161!@222.128.1.157:33060/stock", - echo=False -) - -ES_CLIENT = Elasticsearch(['http://222.128.1.157:19200']) -INDEX_NAME = 'concept_library_v3' - -CLICKHOUSE_CONFIG = { - 'host': '222.128.1.157', - 'port': 18000, - 'user': 'default', - 'password': 'Zzl33818!', - 'database': 'stock' -} - -HIERARCHY_FILE = 'concept_hierarchy_v3.json' -REFERENCE_INDEX = '000001.SH' - -# ==================== Alpha 异动检测配置 ==================== - -ALPHA_CONFIG = { - # Z-Score 阈值 - 'zscore_threshold': 2.0, # |Z| > 2.0 视为异动（降低阈值） - - # 绝对 Alpha 阈值（当历史数据不足时使用） - 'absolute_alpha_threshold': 1.5, # |Alpha| > 1.5% 视为异动 - - # 历史窗口 - 'history_window': 60, # 保留最近60个数据点用于计算均值/标准差 - 'min_history': 5, # 最少需要5个数据点才用Z-Score（降低要求） - - # 冷却时间 - 'cooldown_minutes': 8, # 同一概念触发异动后的冷却时间 - - # 显示控制 - 'max_alerts_per_minute': 15, # 每分钟最多显示的异动数 - 'min_alpha_abs': 0.5, # 最小超额收益绝对值（过滤微小波动） - - # 重要性评分权重 - 'importance_weights': { - 'alpha_zscore': 0.35, # Alpha Z-Score 绝对值 - 'alpha_abs': 0.25, # 超额收益绝对值 - 'rank_in_minute': 0.20, # 当分钟内的排名 - 'limit_up_count': 0.20, # 涨停数 - } -} - -# ==================== 日志配置 ==================== - -logging.basicConfig( - level=logging.INFO, - format='%(asctime)s - %(levelname)s - %(message)s', - handlers=[ - logging.FileHandler(f'concept_alert_alpha_{datetime.now().strftime("%Y%m%d")}.log', encoding='utf-8'), - logging.StreamHandler() - ] -) -logger = logging.getLogger(__name__) - -# ==================== 数据结构 ==================== - -@dataclass -class AlphaStats: - """概念的Alpha统计信息""" - history: deque = field(default_factory=lambda: deque(maxlen=ALPHA_CONFIG['history_window'])) - mean: float = 0.0 - std: float = 1.0 - last_update: datetime = None - - def update(self, alpha: float, timestamp: datetime): - """更新统计""" - self.history.append(alpha) - self.last_update = timestamp - - if len(self.history) >= 2: - self.mean = np.mean(self.history) - self.std = max(np.std(self.history), 0.1) # 避免除零 - - def get_zscore(self, alpha: float) -> float: - """计算Z-Score""" - if len(self.history) < ALPHA_CONFIG['min_history']: - return 0.0 # 数据不足，不触发 - return (alpha - self.mean) / self.std - - def is_ready(self) -> bool: - """是否有足够数据进行检测""" - return len(self.history) >= ALPHA_CONFIG['min_history'] - - -# ==================== 全局变量 ==================== - -ch_client = None - -# 每个概念的Alpha统计 -alpha_stats: Dict[str, AlphaStats] = defaultdict(AlphaStats) - -# 冷却记录 -cooldown_cache: Dict[str, datetime] = {} - -# 当前分钟的数据缓存（用于计算排名） -current_minute_data: List[dict] = [] - - -def get_ch_client(): - global ch_client - if ch_client is None: - ch_client = Client(**CLICKHOUSE_CONFIG) - return ch_client - - -def generate_id(name: str) -> str: - return hashlib.md5(name.encode('utf-8')).hexdigest()[:16] - - -def code_to_ch_format(code: str) -> str: - if not code or len(code) != 6 or not code.isdigit(): - return None - if code.startswith('6'): - return f"{code}.SH" - elif code.startswith('0') or code.startswith('3'): - return f"{code}.SZ" - else: - return f"{code}.BJ" - - -# ==================== 概念数据获取 ==================== - -def get_all_concepts(): - """从ES获取所有叶子概念""" - concepts = [] - - query = { - "query": {"match_all": {}}, - "size": 100, - "_source": ["concept_id", "concept", "stocks"] - } - - resp = ES_CLIENT.search(index=INDEX_NAME, body=query, scroll='2m') - scroll_id = resp['_scroll_id'] - hits = resp['hits']['hits'] - - while len(hits) > 0: - for hit in hits: - source = hit['_source'] - concept_info = { - 'concept_id': source.get('concept_id'), - 'concept_name': source.get('concept'), - 'stocks': [], - 'concept_type': 'leaf' - } - - if 'stocks' in source and isinstance(source['stocks'], list): - for stock in source['stocks']: - if isinstance(stock, dict) and 'code' in stock and stock['code']: - concept_info['stocks'].append(stock['code']) - - if concept_info['stocks']: - concepts.append(concept_info) - - resp = ES_CLIENT.scroll(scroll_id=scroll_id, scroll='2m') - scroll_id = resp['_scroll_id'] - hits = resp['hits']['hits'] - - ES_CLIENT.clear_scroll(scroll_id=scroll_id) - return concepts - - -def load_hierarchy_concepts(leaf_concepts: list) -> list: - """加载层级结构，生成母概念""" - hierarchy_path = os.path.join(os.path.dirname(__file__), HIERARCHY_FILE) - if not os.path.exists(hierarchy_path): - return [] - - with open(hierarchy_path, 'r', encoding='utf-8') as f: - hierarchy_data = json.load(f) - - concept_to_stocks = {c['concept_name']: set(c['stocks']) for c in leaf_concepts} - parent_concepts = [] - - for lv1 in hierarchy_data.get('hierarchy', []): - lv1_name = lv1.get('lv1', '') - lv1_stocks = set() - - for child in lv1.get('children', []): - lv2_name = child.get('lv2', '') - lv2_stocks = set() - - if 'children' in child: - for lv3_child in child.get('children', []): - lv3_name = lv3_child.get('lv3', '') - lv3_stocks = set() - - for concept_name in lv3_child.get('concepts', []): - if concept_name in concept_to_stocks: - lv3_stocks.update(concept_to_stocks[concept_name]) - - if lv3_stocks: - parent_concepts.append({ - 'concept_id': generate_id(f"lv3_{lv3_name}"), - 'concept_name': f"[三级] {lv3_name}", - 'stocks': list(lv3_stocks), - 'concept_type': 'lv3' - }) - lv2_stocks.update(lv3_stocks) - else: - for concept_name in child.get('concepts', []): - if concept_name in concept_to_stocks: - lv2_stocks.update(concept_to_stocks[concept_name]) - - if lv2_stocks: - parent_concepts.append({ - 'concept_id': generate_id(f"lv2_{lv2_name}"), - 'concept_name': f"[二级] {lv2_name}", - 'stocks': list(lv2_stocks), - 'concept_type': 'lv2' - }) - lv1_stocks.update(lv2_stocks) - - if lv1_stocks: - parent_concepts.append({ - 'concept_id': generate_id(f"lv1_{lv1_name}"), - 'concept_name': f"[一级] {lv1_name}", - 'stocks': list(lv1_stocks), - 'concept_type': 'lv1' - }) - - return parent_concepts - - -# ==================== 价格数据获取 ==================== - -def get_base_prices(stock_codes: list, current_date: str) -> dict: - """获取昨收价""" - if not stock_codes: - return {} - - valid_codes = [code for code in stock_codes if code and len(code) == 6 and code.isdigit()] - if not valid_codes: - return {} - - stock_codes_str = "','".join(valid_codes) - - query = f""" - SELECT SECCODE, F002N - FROM ea_trade - WHERE SECCODE IN ('{stock_codes_str}') - AND TRADEDATE = ( - SELECT MAX(TRADEDATE) FROM ea_trade WHERE TRADEDATE < '{current_date}' - ) - AND F002N IS NOT NULL AND F002N > 0 - """ - - try: - with MYSQL_ENGINE.connect() as conn: - result = conn.execute(text(query)) - return {row[0]: float(row[1]) for row in result if row[1] and float(row[1]) > 0} - except Exception as e: - logger.error(f"获取基准价格失败: {e}") - return {} - - -def get_latest_prices(stock_codes: list) -> dict: - """获取最新价格""" - if not stock_codes: - return {} - - client = get_ch_client() - - ch_codes = [] - code_mapping = {} - for code in stock_codes: - ch_code = code_to_ch_format(code) - if ch_code: - ch_codes.append(ch_code) - code_mapping[ch_code] = code - - if not ch_codes: - return {} - - ch_codes_str = "','".join(ch_codes) - - query = f""" - SELECT code, close, timestamp - FROM ( - SELECT code, close, timestamp, - ROW_NUMBER() OVER (PARTITION BY code ORDER BY timestamp DESC) as rn - FROM stock_minute - WHERE code IN ('{ch_codes_str}') - AND toDate(timestamp) = today() - ) - WHERE rn = 1 - """ - - try: - result = client.execute(query) - prices = {} - for row in result: - ch_code, close, ts = row - if close and close > 0: - pure_code = code_mapping.get(ch_code) - if pure_code: - prices[pure_code] = {'close': float(close), 'timestamp': ts} - return prices - except Exception as e: - logger.error(f"获取最新价格失败: {e}") - return {} - - -def get_prices_at_time(stock_codes: list, timestamp: datetime) -> dict: - """获取指定时间的价格""" - if not stock_codes: - return {} - - client = get_ch_client() - - ch_codes = [] - code_mapping = {} - for code in stock_codes: - ch_code = code_to_ch_format(code) - if ch_code: - ch_codes.append(ch_code) - code_mapping[ch_code] = code - - if not ch_codes: - return {} - - ch_codes_str = "','".join(ch_codes) - ts_str = timestamp.strftime('%Y-%m-%d %H:%M:%S') - - query = f""" - SELECT code, close, timestamp - FROM stock_minute - WHERE code IN ('{ch_codes_str}') - AND timestamp = '{ts_str}' - """ - - try: - result = client.execute(query) - prices = {} - for row in result: - ch_code, close, ts = row - if close and close > 0: - pure_code = code_mapping.get(ch_code) - if pure_code: - prices[pure_code] = {'close': float(close), 'timestamp': ts} - return prices - except Exception as e: - logger.error(f"获取历史价格失败: {e}") - return {} - - -def get_index_change(index_code: str, trade_date: str, timestamp: datetime = None) -> Optional[float]: - """获取指数涨跌幅""" - client = get_ch_client() - - try: - # 获取昨收 - code_no_suffix = index_code.split('.')[0] - with MYSQL_ENGINE.connect() as conn: - prev_result = conn.execute(text(""" - SELECT F006N FROM ea_exchangetrade - WHERE INDEXCODE = :code AND TRADEDATE < :today - ORDER BY TRADEDATE DESC LIMIT 1 - """), {'code': code_no_suffix, 'today': trade_date}).fetchone() - - if not prev_result or not prev_result[0]: - return None - prev_close = float(prev_result[0]) - - # 获取当前价格 - if timestamp: - ts_str = timestamp.strftime('%Y-%m-%d %H:%M:%S') - query = f""" - SELECT close FROM index_minute - WHERE code = '{index_code}' AND timestamp = '{ts_str}' - LIMIT 1 - """ - else: - query = f""" - SELECT close FROM index_minute - WHERE code = '{index_code}' AND toDate(timestamp) = today() - ORDER BY timestamp DESC LIMIT 1 - """ - - result = client.execute(query) - if not result: - return None - - current_price = float(result[0][0]) - return (current_price - prev_close) / prev_close * 100 - - except Exception as e: - logger.error(f"获取指数涨跌幅失败: {e}") - return None - - -def get_index_data(index_code: str, trade_date: str, timestamp: datetime = None) -> Optional[dict]: - """获取指数完整数据""" - client = get_ch_client() - - try: - code_no_suffix = index_code.split('.')[0] - with MYSQL_ENGINE.connect() as conn: - prev_result = conn.execute(text(""" - SELECT F006N FROM ea_exchangetrade - WHERE INDEXCODE = :code AND TRADEDATE < :today - ORDER BY TRADEDATE DESC LIMIT 1 - """), {'code': code_no_suffix, 'today': trade_date}).fetchone() - - if not prev_result or not prev_result[0]: - return None - prev_close = float(prev_result[0]) - - if timestamp: - ts_str = timestamp.strftime('%Y-%m-%d %H:%M:%S') - query = f""" - SELECT close, timestamp FROM index_minute - WHERE code = '{index_code}' AND timestamp = '{ts_str}' - LIMIT 1 - """ - else: - query = f""" - SELECT close, timestamp FROM index_minute - WHERE code = '{index_code}' AND toDate(timestamp) = today() - ORDER BY timestamp DESC LIMIT 1 - """ - - result = client.execute(query) - if not result: - return None - - current_price, ts = result[0] - change_pct = (float(current_price) - prev_close) / prev_close * 100 - - return { - 'code': index_code, - 'price': float(current_price), - 'prev_close': prev_close, - 'change_pct': round(change_pct, 4), - 'timestamp': ts - } - - except Exception as e: - logger.error(f"获取指数数据失败: {e}") - return None - - -# ==================== 概念统计计算 ==================== - -def calculate_concept_stats(concepts: list, stock_changes: dict, index_change: float) -> list: - """ - 计算概念统计，包含超额收益(Alpha) - Alpha = 概念涨幅 - 指数涨幅 - """ - stats = [] - - for concept in concepts: - concept_id = concept['concept_id'] - concept_name = concept['concept_name'] - stock_codes = concept['stocks'] - concept_type = concept.get('concept_type', 'leaf') - - changes = [] - limit_up_count = 0 - limit_down_count = 0 - - for code in stock_codes: - if code in stock_changes: - change_pct = stock_changes[code] - changes.append(change_pct) - - if change_pct >= 9.8: - limit_up_count += 1 - elif change_pct <= -9.8: - limit_down_count += 1 - - if not changes: - continue - - avg_change = np.mean(changes) - - # 核心：计算超额收益 Alpha - alpha = avg_change - index_change - - stats.append({ - 'concept_id': concept_id, - 'concept_name': concept_name, - 'change_pct': round(avg_change, 4), - 'alpha': round(alpha, 4), # 超额收益 - 'index_change': round(index_change, 4), - 'stock_count': len(changes), - 'concept_type': concept_type, - 'limit_up_count': limit_up_count, - 'limit_down_count': limit_down_count, - }) - - # 按Alpha排序 - stats.sort(key=lambda x: x['alpha'], reverse=True) - for i, item in enumerate(stats): - item['alpha_rank'] = i + 1 - - return stats - - -# ==================== Alpha 异动检测 ==================== - -def check_cooldown(concept_id: str, current_time: datetime) -> bool: - """检查冷却""" - if concept_id in cooldown_cache: - last_alert = cooldown_cache[concept_id] - if current_time - last_alert < timedelta(minutes=ALPHA_CONFIG['cooldown_minutes']): - return True - return False - - -def set_cooldown(concept_id: str, current_time: datetime): - """设置冷却""" - cooldown_cache[concept_id] = current_time - - -def calculate_importance( - alpha_zscore: float, - alpha: float, - alpha_rank: int, - total_concepts: int, - limit_up_count: int -) -> float: - """计算重要性评分""" - weights = ALPHA_CONFIG['importance_weights'] - - # Z-Score 分数（归一化到0-1） - zscore_score = min(abs(alpha_zscore) / 5.0, 1.0) - - # Alpha 绝对值分数 - alpha_score = min(abs(alpha) / 3.0, 1.0) - - # 排名分数（越靠前/越靠后越重要） - # alpha_rank 小表示强势，alpha_rank 大表示弱势 - rank_ratio = alpha_rank / max(total_concepts, 1) - rank_score = max(1 - rank_ratio, rank_ratio) # 两端都重要 - - # 涨停分数 - limit_score = min(limit_up_count / 3.0, 1.0) - - total = ( - weights['alpha_zscore'] * zscore_score + - weights['alpha_abs'] * alpha_score + - weights['rank_in_minute'] * rank_score + - weights['limit_up_count'] * limit_score - ) - - return round(total, 4) - - -def detect_alpha_alerts( - stats: list, - index_data: dict, - trade_date: str, - current_time: datetime -) -> list: - """ - 基于Alpha Z-Score的异动检测 - - 检测逻辑： - 1. 如果有足够历史数据（>= min_history）：用 Z-Score 判断 - 2. 如果历史数据不足：用绝对 Alpha 阈值判断 - """ - global alpha_stats - - alerts = [] - total_concepts = len(stats) - - for stat in stats: - concept_id = stat['concept_id'] - concept_name = stat['concept_name'] - alpha = stat['alpha'] - change_pct = stat['change_pct'] - alpha_rank = stat['alpha_rank'] - limit_up_count = stat['limit_up_count'] - limit_down_count = stat['limit_down_count'] - - # 更新该概念的Alpha统计 - concept_stats = alpha_stats[concept_id] - - # 计算Z-Score（在更新前计算，用历史数据） - alpha_zscore = concept_stats.get_zscore(alpha) - - # 更新统计 - concept_stats.update(alpha, current_time) - - # Alpha 太小，不值得关注 - if abs(alpha) < ALPHA_CONFIG['min_alpha_abs']: - continue - - # 检查冷却 - if check_cooldown(concept_id, current_time): - continue - - # 判断是否触发异动 - is_alert = False - trigger_reason = "" - - if concept_stats.is_ready(): - # 方式1：有足够历史数据，用 Z-Score - if abs(alpha_zscore) >= ALPHA_CONFIG['zscore_threshold']: - is_alert = True - trigger_reason = f"Z={alpha_zscore:.2f}" - else: - # 方式2：历史数据不足，用绝对 Alpha 阈值 - if abs(alpha) >= ALPHA_CONFIG['absolute_alpha_threshold']: - is_alert = True - trigger_reason = f"Alpha={alpha:+.2f}%" - alpha_zscore = alpha / 0.5 # 估算一个Z值（假设std=0.5） - - if not is_alert: - continue - - # 判断异动类型 - if alpha > 0: - alert_type = 'alpha_surge' # 超额上涨 - else: - alert_type = 'alpha_drop' # 超额下跌 - - # 计算重要性 - importance = calculate_importance( - alpha_zscore, alpha, alpha_rank, total_concepts, limit_up_count - ) - - alert = { - 'concept_id': concept_id, - 'concept_name': concept_name, - 'alert_type': alert_type, - 'alert_time': current_time, - 'trade_date': trade_date, - 'change_pct': change_pct, - 'alpha': alpha, - 'alpha_zscore': round(alpha_zscore, 4), - 'alpha_mean': round(concept_stats.mean, 4), - 'alpha_std': round(concept_stats.std, 4), - 'index_change_pct': stat['index_change'], - 'alpha_rank': alpha_rank, - 'limit_up_count': limit_up_count, - 'limit_down_count': limit_down_count, - 'stock_count': stat['stock_count'], - 'concept_type': stat['concept_type'], - 'importance_score': importance, - 'index_code': REFERENCE_INDEX, - 'index_price': index_data['price'] if index_data else None, - } - - alerts.append(alert) - set_cooldown(concept_id, current_time) - - # 日志 - direction = "📈 超额上涨" if alert_type == 'alpha_surge' else "📉 超额下跌" - logger.info( - f"{direction}: {concept_name} " - f"Alpha={alpha:+.2f}% ({trigger_reason}) " - f"概念{change_pct:+.2f}% vs 大盘{stat['index_change']:+.2f}%" - ) - - # 按重要性排序，限制数量 - alerts.sort(key=lambda x: x['importance_score'], reverse=True) - return alerts[:ALPHA_CONFIG['max_alerts_per_minute']] - - -# ==================== 数据持久化 ==================== - -def save_alerts_to_mysql(alerts: list): - """保存异动到MySQL""" - if not alerts: - return 0 - - saved = 0 - with MYSQL_ENGINE.begin() as conn: - for alert in alerts: - try: - # 映射 alert_type 到数据库格式 - db_alert_type = 'surge_up' if alert['alert_type'] == 'alpha_surge' else 'surge_down' - - insert_sql = text(""" - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, zscore, importance_score, extra_info) - VALUES - (:concept_id, :concept_name, :alert_time, :alert_type, :trade_date, - :change_pct, :prev_change_pct, :change_delta, - :limit_up_count, 0, 0, - :rank_position, NULL, NULL, - :index_code, :index_price, :index_change_pct, - :stock_count, :concept_type, :zscore, :importance_score, :extra_info) - """) - - params = { - 'concept_id': alert['concept_id'], - 'concept_name': alert['concept_name'], - 'alert_time': alert['alert_time'], - 'alert_type': db_alert_type, - 'trade_date': alert['trade_date'], - 'change_pct': alert['change_pct'], - 'prev_change_pct': alert['index_change_pct'], # 用大盘涨幅作为参照 - 'change_delta': alert['alpha'], # Alpha 作为变化量 - 'limit_up_count': alert['limit_up_count'], - 'rank_position': alert['alpha_rank'], - 'index_code': alert['index_code'], - 'index_price': alert['index_price'], - 'index_change_pct': alert['index_change_pct'], - 'stock_count': alert['stock_count'], - 'concept_type': alert['concept_type'], - 'zscore': alert['alpha_zscore'], - 'importance_score': alert['importance_score'], - 'extra_info': json.dumps({ - 'alpha': alert['alpha'], - 'alpha_zscore': alert['alpha_zscore'], - 'alpha_mean': alert['alpha_mean'], - 'alpha_std': alert['alpha_std'], - 'limit_down_count': alert['limit_down_count'], - }, ensure_ascii=False) - } - - conn.execute(insert_sql, params) - saved += 1 - - except Exception as e: - logger.error(f"保存异动失败: {alert['concept_name']} - {e}") - - return saved - - -def save_index_snapshot(index_data: dict, trade_date: str): - """保存指数快照""" - if not index_data: - return - - try: - with MYSQL_ENGINE.begin() as conn: - conn.execute(text(""" - REPLACE INTO index_minute_snapshot - (index_code, trade_date, snapshot_time, price, prev_close, change_pct) - VALUES (:index_code, :trade_date, :snapshot_time, :price, :prev_close, :change_pct) - """), { - 'index_code': index_data['code'], - 'trade_date': trade_date, - 'snapshot_time': index_data['timestamp'], - 'price': index_data['price'], - 'prev_close': index_data.get('prev_close'), - 'change_pct': index_data.get('change_pct') - }) - except Exception as e: - logger.error(f"保存指数快照失败: {e}") - - -# ==================== 交易时间 ==================== - -def is_trading_time() -> bool: - now = datetime.now() - if now.weekday() >= 5: - return False - - current_time = now.hour * 60 + now.minute - morning = (9 * 60 + 30 <= current_time <= 11 * 60 + 30) - afternoon = (13 * 60 <= current_time <= 15 * 60) - return morning or afternoon - - -def get_next_update_time() -> int: - now = datetime.now() - if is_trading_time(): - return 60 - now.second - - hour, minute = now.hour, now.minute - if hour < 9 or (hour == 9 and minute < 30): - target = now.replace(hour=9, minute=30, second=0) - elif (hour == 11 and minute >= 30) or hour == 12: - target = now.replace(hour=13, minute=0, second=0) - elif hour >= 15: - target = (now + timedelta(days=1)).replace(hour=9, minute=30, second=0) - else: - target = now + timedelta(minutes=1) - - return max(60, int((target - now).total_seconds())) - - -# ==================== 主运行逻辑 ==================== - -def run_once(concepts: list, all_stocks: list, trade_date: str, timestamp: datetime = None) -> Tuple[int, int]: - """执行一次检测""" - timestamp = timestamp or datetime.now() - - # 获取基准价格 - base_prices = get_base_prices(all_stocks, trade_date) - if not base_prices: - logger.warning("无法获取基准价格") - return 0, 0 - - # 获取当前价格 - if timestamp.date() == datetime.now().date(): - latest_prices = get_latest_prices(all_stocks) - else: - latest_prices = get_prices_at_time(all_stocks, timestamp) - - if not latest_prices: - logger.warning("无法获取最新价格") - return 0, 0 - - # 计算股票涨跌幅 - stock_changes = {} - for code, price_data in latest_prices.items(): - if code in base_prices and base_prices[code] > 0: - change = (price_data['close'] - base_prices[code]) / base_prices[code] * 100 - stock_changes[code] = round(change, 4) - - if not stock_changes: - return 0, 0 - - # 获取指数数据 - index_data = get_index_data(REFERENCE_INDEX, trade_date, timestamp) - if not index_data: - logger.warning("无法获取指数数据") - return 0, 0 - - index_change = index_data['change_pct'] - save_index_snapshot(index_data, trade_date) - - # 计算概念统计（包含Alpha） - stats = calculate_concept_stats(concepts, stock_changes, index_change) - - # 检测异动 - alerts = detect_alpha_alerts(stats, index_data, trade_date, timestamp) - - # 保存 - if alerts: - saved = save_alerts_to_mysql(alerts) - logger.info(f"💾 保存了 {saved} 条异动") - - return len(stats), len(alerts) - - -def run_realtime(): - """实时运行""" - logger.info("=" * 60) - logger.info("🚀 启动概念异动检测服务（Alpha Z-Score 方法）") - logger.info("=" * 60) - logger.info(f"配置: Z-Score阈值={ALPHA_CONFIG['zscore_threshold']}, 冷却={ALPHA_CONFIG['cooldown_minutes']}分钟") - - # 加载概念 - leaf_concepts = get_all_concepts() - parent_concepts = load_hierarchy_concepts(leaf_concepts) - all_concepts = leaf_concepts + parent_concepts - - all_stocks = set() - for c in all_concepts: - all_stocks.update(c['stocks']) - all_stocks = list(all_stocks) - - logger.info(f"监控 {len(all_concepts)} 个概念, {len(all_stocks)} 只股票") - - total_alerts = 0 - last_reload = datetime.now() - - while True: - try: - now = datetime.now() - trade_date = now.strftime('%Y-%m-%d') - - # 每小时重载概念 - if (now - last_reload).total_seconds() > 3600: - leaf_concepts = get_all_concepts() - parent_concepts = load_hierarchy_concepts(leaf_concepts) - all_concepts = leaf_concepts + parent_concepts - all_stocks = list(set(s for c in all_concepts for s in c['stocks'])) - last_reload = now - logger.info(f"重载概念: {len(all_concepts)} 个") - - if not is_trading_time(): - wait = get_next_update_time() - logger.info(f"⏰ 非交易时间，等待 {wait//60} 分钟") - time.sleep(min(wait, 300)) - continue - - updated, alert_count = run_once(all_concepts, all_stocks, trade_date) - total_alerts += alert_count - - if alert_count > 0: - logger.info(f"📊 本次 {alert_count} 条异动，累计 {total_alerts} 条") - - time.sleep(60 - datetime.now().second) - - except KeyboardInterrupt: - logger.info("\n停止服务") - break - except Exception as e: - logger.error(f"错误: {e}") - import traceback - traceback.print_exc() - time.sleep(60) - - -def run_backtest(trade_date: str, clear_existing: bool = True): - """回测""" - global alpha_stats, cooldown_cache - - logger.info("=" * 60) - logger.info(f"🔄 回测: {trade_date} (Alpha Z-Score 方法)") - logger.info("=" * 60) - - # 清空状态 - alpha_stats.clear() - cooldown_cache.clear() - - if clear_existing: - with MYSQL_ENGINE.begin() as conn: - conn.execute(text("DELETE FROM concept_minute_alert WHERE trade_date = :date"), {'date': trade_date}) - conn.execute(text("DELETE FROM index_minute_snapshot WHERE trade_date = :date"), {'date': trade_date}) - logger.info(f"已清除 {trade_date} 的数据") - - # 加载概念 - leaf_concepts = get_all_concepts() - parent_concepts = load_hierarchy_concepts(leaf_concepts) - all_concepts = leaf_concepts + parent_concepts - - all_stocks = list(set(s for c in all_concepts for s in c['stocks'])) - logger.info(f"概念: {len(all_concepts)}, 股票: {len(all_stocks)}") - - # 获取分钟时间戳 - client = get_ch_client() - result = client.execute(f""" - SELECT DISTINCT timestamp FROM stock_minute - WHERE toDate(timestamp) = '{trade_date}' - ORDER BY timestamp - """) - timestamps = [row[0] for row in result] - - if not timestamps: - logger.error(f"未找到 {trade_date} 的数据") - return - - logger.info(f"时间点: {len(timestamps)}") - - total_alerts = 0 - - for i, ts in enumerate(timestamps): - updated, alerts = run_once(all_concepts, all_stocks, trade_date, ts) - total_alerts += alerts - - if (i + 1) % 30 == 0: - logger.info(f"进度: {i+1}/{len(timestamps)} ({(i+1)*100//len(timestamps)}%), 异动: {total_alerts}") - - logger.info("=" * 60) - logger.info(f"✅ 回测完成! 检测到 {total_alerts} 条异动") - logger.info("=" * 60) - - -def show_status(): - """显示状态""" - print("\n" + "=" * 60) - print("概念异动检测服务 - Alpha Z-Score 方法") - print("=" * 60) - - print(f"\n当前时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}") - print(f"交易时间: {'是' if is_trading_time() else '否'}") - print(f"Z-Score阈值: {ALPHA_CONFIG['zscore_threshold']}") - - try: - with MYSQL_ENGINE.connect() as conn: - result = conn.execute(text(""" - SELECT alert_type, COUNT(*), AVG(change_delta) - FROM concept_minute_alert - WHERE trade_date = CURDATE() - GROUP BY alert_type - """)) - - print("\n今日异动统计:") - rows = list(result) - if rows: - for row in rows: - type_name = {'surge_up': '超额上涨', 'surge_down': '超额下跌'}.get(row[0], row[0]) - avg_alpha = f"{row[2]:+.2f}%" if row[2] else "-" - print(f" {type_name}: {row[1]} 条 (平均Alpha: {avg_alpha})") - else: - print(" 暂无异动") - - print("\n最新异动 (前10条):") - result = conn.execute(text(""" - SELECT concept_name, alert_type, alert_time, change_pct, - change_delta, index_change_pct, zscore - FROM concept_minute_alert - WHERE trade_date = CURDATE() - ORDER BY alert_time DESC - LIMIT 10 - """)) - - rows = list(result) - if rows: - print(f" {'概念':<18} | {'类型':<8} | {'时间':<5} | {'涨幅':>6} | {'Alpha':>6} | {'大盘':>5} | {'Z':>5}") - print(" " + "-" * 75) - for row in rows: - name = (row[0][:16] + '..') if len(row[0]) > 18 else row[0] - t = {'surge_up': '超额涨', 'surge_down': '超额跌'}.get(row[1], row[1][:6]) - time_s = row[2].strftime('%H:%M') if row[2] else '-' - chg = f"{row[3]:+.2f}%" if row[3] else '-' - alpha = f"{row[4]:+.2f}%" if row[4] else '-' - idx = f"{row[5]:+.1f}%" if row[5] else '-' - z = f"{row[6]:.1f}" if row[6] else '-' - print(f" {name:<18} | {t:<8} | {time_s:<5} | {chg:>6} | {alpha:>6} | {idx:>5} | {z:>5}") - - except Exception as e: - print(f" 查询失败: {e}") - - -def main(): - parser = argparse.ArgumentParser(description='概念异动检测 - Alpha Z-Score 方法') - parser.add_argument('command', nargs='?', default='realtime', - choices=['realtime', 'once', 'status', 'backtest'], - help='命令') - parser.add_argument('--date', '-d', type=str, default=None, help='回测日期') - parser.add_argument('--keep', '-k', action='store_true', help='保留已有数据') - - args = parser.parse_args() - - if args.command == 'realtime': - run_realtime() - elif args.command == 'once': - leaf_concepts = get_all_concepts() - parent_concepts = load_hierarchy_concepts(leaf_concepts) - all_concepts = leaf_concepts + parent_concepts - all_stocks = list(set(s for c in all_concepts for s in c['stocks'])) - trade_date = datetime.now().strftime('%Y-%m-%d') - run_once(all_concepts, all_stocks, trade_date) - elif args.command == 'status': - show_status() - elif args.command == 'backtest': - trade_date = args.date or datetime.now().strftime('%Y-%m-%d') - run_backtest(trade_date, not args.keep) - - -if __name__ == "__main__": - main() diff --git a/concept_alert_alpha_20251208.log b/concept_alert_alpha_20251208.log deleted file mode 100644 index 656199bf..00000000 --- a/concept_alert_alpha_20251208.log +++ /dev/null @@ -1,28 +0,0 @@ -2025-12-08 16:40:41,567 - INFO - ============================================================ -2025-12-08 16:40:41,567 - INFO - 🔄 回测: 2025-12-08 (Alpha Z-Score 方法) -2025-12-08 16:40:41,569 - INFO - ============================================================ -2025-12-08 16:40:41,679 - INFO - 已清除 2025-12-08 的数据 -2025-12-08 16:40:41,903 - INFO - POST http://222.128.1.157:19200/concept_library_v3/_search?scroll=2m [status:200 duration:0.224s] -2025-12-08 16:40:42,105 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.197s] -2025-12-08 16:40:42,330 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.178s] -2025-12-08 16:40:42,518 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.183s] -2025-12-08 16:40:42,704 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.182s] -2025-12-08 16:40:42,894 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.186s] -2025-12-08 16:40:43,060 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.162s] -2025-12-08 16:40:43,234 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.171s] -2025-12-08 16:40:43,383 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.145s] -2025-12-08 16:40:43,394 - INFO - POST http://222.128.1.157:19200/_search/scroll [status:200 duration:0.008s] -2025-12-08 16:40:43,399 - INFO - DELETE http://222.128.1.157:19200/_search/scroll [status:200 duration:0.005s] -2025-12-08 16:40:43,409 - INFO - 概念: 968, 股票: 5938 -2025-12-08 16:40:43,505 - INFO - 时间点: 241 -2025-12-08 16:41:02,028 - INFO - 进度: 30/241 (12%), 异动: 0 -2025-12-08 16:41:20,851 - INFO - 进度: 60/241 (24%), 异动: 0 -2025-12-08 16:41:39,396 - INFO - 进度: 90/241 (37%), 异动: 0 -2025-12-08 16:41:58,687 - INFO - 进度: 120/241 (49%), 异动: 0 -2025-12-08 16:43:08,124 - INFO - 进度: 150/241 (62%), 异动: 0 -2025-12-08 16:43:26,973 - INFO - 进度: 180/241 (74%), 异动: 0 -2025-12-08 16:43:45,746 - INFO - 进度: 210/241 (87%), 异动: 0 -2025-12-08 16:44:04,479 - INFO - 进度: 240/241 (99%), 异动: 0 -2025-12-08 16:44:05,123 - INFO - ============================================================ -2025-12-08 16:44:05,123 - INFO - ✅ 回测完成! 检测到 0 条异动 -2025-12-08 16:44:05,125 - INFO - ============================================================ diff --git a/concept_alert_ml.py b/concept_alert_ml.py deleted file mode 100644 index dbe50ea0..00000000 --- a/concept_alert_ml.py +++ /dev/null @@ -1,1625 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -概念异动智能检测服务 - 基于 Z-Score + SVM -- Z-Score: 动态阈值，根据历史波动率判断异常 -- SVM: 多特征分类，综合判断是否为有意义的异动 -- 支持暴涨和暴跌检测 -- 异动重要性评分，避免图表过于密集 -""" - -import pandas as pd -import numpy as np -from datetime import datetime, timedelta -from sqlalchemy import create_engine, text -from elasticsearch import Elasticsearch -from clickhouse_driver import Client -from collections import deque -import time -import logging -import json -import os -import hashlib -import argparse -import pickle -from typing import Dict, List, Optional, Tuple - -# 尝试导入sklearn，如果不存在则提示安装 -try: - from sklearn.svm import OneClassSVM - from sklearn.preprocessing import StandardScaler - from sklearn.ensemble import IsolationForest - SKLEARN_AVAILABLE = True -except ImportError: - SKLEARN_AVAILABLE = False - print("警告: sklearn 未安装，将使用纯 Z-Score 方法") - print("安装命令: pip install scikit-learn") - -# ==================== 配置 ==================== - -# MySQL配置 -MYSQL_ENGINE = create_engine( - "mysql+pymysql://root:Zzl5588161!@222.128.1.157:33060/stock", - echo=False -) - -# Elasticsearch配置 -ES_CLIENT = Elasticsearch(['http://222.128.1.157:19200']) -INDEX_NAME = 'concept_library_v3' - -# ClickHouse配置 -CLICKHOUSE_CONFIG = { - 'host': '222.128.1.157', - 'port': 18000, - 'user': 'default', - 'password': 'Zzl33818!', - 'database': 'stock' -} - -# 层级结构文件 -HIERARCHY_FILE = 'concept_hierarchy_v3.json' - -# 模型保存路径 -MODEL_DIR = 'models' -os.makedirs(MODEL_DIR, exist_ok=True) - -# ==================== 智能异动检测配置 ==================== - -SMART_ALERT_CONFIG = { - # Z-Score 配置 - 'zscore': { - 'enabled': True, - 'lookback_days': 20, # 历史数据回看天数 - 'threshold_up': 2.5, # 上涨异动阈值（标准差倍数） - 'threshold_down': -2.5, # 下跌异动阈值（标准差倍数） - 'min_data_points': 10, # 最少数据点数 - }, - - # SVM 异常检测配置 - 'svm': { - 'enabled': SKLEARN_AVAILABLE, - 'nu': 0.05, # 异常比例（预期5%为异常） - 'kernel': 'rbf', # 核函数 - 'gamma': 'auto', - 'retrain_days': 7, # 每N天重新训练 - }, - - # 特征配置（用于SVM） - 'features': [ - 'change_pct', # 当前涨跌幅 - 'change_delta_5min', # 5分钟涨跌幅变化 - 'change_delta_10min', # 10分钟涨跌幅变化 - 'rank_delta_5min', # 5分钟排名变化 - 'limit_up_ratio', # 涨停股占比 - 'volume_ratio', # 成交量比率（预留） - 'index_correlation', # 与指数相关性 - ], - - # 重要性评分权重 - 'importance_weights': { - 'zscore_abs': 0.3, # Z-Score 绝对值 - 'rank_position': 0.2, # 排名位置（越靠前越重要） - 'limit_up_count': 0.2, # 涨停数 - 'stock_count': 0.1, # 概念股票数 - 'change_magnitude': 0.2, # 涨跌幅度 - }, - - # 显示控制 - 'display': { - 'max_alerts_per_hour': 20, # 每小时最多显示异动数 - 'min_importance_score': 0.3, # 最低重要性分数 - 'cooldown_minutes': 15, # 同一概念冷却时间 - }, -} - -# 参考指数 -REFERENCE_INDEX = '000001.SH' - -# ==================== 日志配置 ==================== - -logging.basicConfig( - level=logging.INFO, - format='%(asctime)s - %(levelname)s - %(message)s', - handlers=[ - logging.FileHandler(f'concept_alert_ml_{datetime.now().strftime("%Y%m%d")}.log', encoding='utf-8'), - logging.StreamHandler() - ] -) -logger = logging.getLogger(__name__) - -# ==================== 全局变量 ==================== - -ch_client = None - -# 历史统计数据缓存 -# 结构: {concept_id: {'mean': float, 'std': float, 'history': deque}} -stats_cache: Dict[str, dict] = {} - -# 分钟级历史缓存（用于计算变化率） -minute_cache: Dict[str, deque] = {} -MINUTE_WINDOW = 15 # 保留15分钟数据 - -# 冷却记录 -cooldown_cache: Dict[Tuple[str, str], datetime] = {} - -# SVM 模型 -svm_model = None -svm_scaler = None -svm_last_train = None - - -def get_ch_client(): - """获取ClickHouse客户端""" - global ch_client - if ch_client is None: - ch_client = Client(**CLICKHOUSE_CONFIG) - return ch_client - - -def generate_id(name: str) -> str: - """生成概念ID""" - return hashlib.md5(name.encode('utf-8')).hexdigest()[:16] - - -def code_to_ch_format(code: str) -> str: - """将6位股票代码转换为ClickHouse格式""" - if not code or len(code) != 6 or not code.isdigit(): - return None - if code.startswith('6'): - return f"{code}.SH" - elif code.startswith('0') or code.startswith('3'): - return f"{code}.SZ" - else: - return f"{code}.BJ" - - -# ==================== 概念数据获取（复用原有代码）==================== - -def get_all_concepts(): - """从ES获取所有叶子概念及其股票列表""" - concepts = [] - - query = { - "query": {"match_all": {}}, - "size": 100, - "_source": ["concept_id", "concept", "stocks"] - } - - resp = ES_CLIENT.search(index=INDEX_NAME, body=query, scroll='2m') - scroll_id = resp['_scroll_id'] - hits = resp['hits']['hits'] - - while len(hits) > 0: - for hit in hits: - source = hit['_source'] - concept_info = { - 'concept_id': source.get('concept_id'), - 'concept_name': source.get('concept'), - 'stocks': [], - 'concept_type': 'leaf' - } - - if 'stocks' in source and isinstance(source['stocks'], list): - for stock in source['stocks']: - if isinstance(stock, dict) and 'code' in stock and stock['code']: - concept_info['stocks'].append(stock['code']) - - if concept_info['stocks']: - concepts.append(concept_info) - - resp = ES_CLIENT.scroll(scroll_id=scroll_id, scroll='2m') - scroll_id = resp['_scroll_id'] - hits = resp['hits']['hits'] - - ES_CLIENT.clear_scroll(scroll_id=scroll_id) - return concepts - - -def load_hierarchy_concepts(leaf_concepts: list) -> list: - """加载层级结构，生成母概念""" - hierarchy_path = os.path.join(os.path.dirname(__file__), HIERARCHY_FILE) - if not os.path.exists(hierarchy_path): - logger.warning(f"层级文件不存在: {hierarchy_path}") - return [] - - with open(hierarchy_path, 'r', encoding='utf-8') as f: - hierarchy_data = json.load(f) - - concept_to_stocks = {} - for c in leaf_concepts: - concept_to_stocks[c['concept_name']] = set(c['stocks']) - - parent_concepts = [] - - for lv1 in hierarchy_data.get('hierarchy', []): - lv1_name = lv1.get('lv1', '') - lv1_stocks = set() - - for child in lv1.get('children', []): - lv2_name = child.get('lv2', '') - lv2_stocks = set() - - if 'children' in child: - for lv3_child in child.get('children', []): - lv3_name = lv3_child.get('lv3', '') - lv3_stocks = set() - - for concept_name in lv3_child.get('concepts', []): - if concept_name in concept_to_stocks: - lv3_stocks.update(concept_to_stocks[concept_name]) - - if lv3_stocks: - parent_concepts.append({ - 'concept_id': generate_id(f"lv3_{lv3_name}"), - 'concept_name': f"[三级] {lv3_name}", - 'stocks': list(lv3_stocks), - 'concept_type': 'lv3' - }) - - lv2_stocks.update(lv3_stocks) - else: - for concept_name in child.get('concepts', []): - if concept_name in concept_to_stocks: - lv2_stocks.update(concept_to_stocks[concept_name]) - - if lv2_stocks: - parent_concepts.append({ - 'concept_id': generate_id(f"lv2_{lv2_name}"), - 'concept_name': f"[二级] {lv2_name}", - 'stocks': list(lv2_stocks), - 'concept_type': 'lv2' - }) - - lv1_stocks.update(lv2_stocks) - - if lv1_stocks: - parent_concepts.append({ - 'concept_id': generate_id(f"lv1_{lv1_name}"), - 'concept_name': f"[一级] {lv1_name}", - 'stocks': list(lv1_stocks), - 'concept_type': 'lv1' - }) - - return parent_concepts - - -# ==================== 价格数据获取 ==================== - -def get_base_prices(stock_codes: list, current_date: str) -> dict: - """获取昨收价作为基准""" - if not stock_codes: - return {} - - valid_codes = [code for code in stock_codes if code and len(code) == 6 and code.isdigit()] - if not valid_codes: - return {} - - stock_codes_str = "','".join(valid_codes) - - query = f""" - SELECT SECCODE, F002N - FROM ea_trade - WHERE SECCODE IN ('{stock_codes_str}') - AND TRADEDATE = ( - SELECT MAX(TRADEDATE) - FROM ea_trade - WHERE TRADEDATE < '{current_date}' - ) - AND F002N IS NOT NULL AND F002N > 0 - """ - - try: - with MYSQL_ENGINE.connect() as conn: - result = conn.execute(text(query)) - base_prices = {row[0]: float(row[1]) for row in result if row[1] and float(row[1]) > 0} - return base_prices - except Exception as e: - logger.error(f"获取基准价格失败: {e}") - return {} - - -def get_latest_prices(stock_codes: list) -> dict: - """从ClickHouse获取最新价格""" - if not stock_codes: - return {} - - client = get_ch_client() - - ch_codes = [] - code_mapping = {} - for code in stock_codes: - ch_code = code_to_ch_format(code) - if ch_code: - ch_codes.append(ch_code) - code_mapping[ch_code] = code - - if not ch_codes: - return {} - - ch_codes_str = "','".join(ch_codes) - - query = f""" - SELECT code, close, timestamp - FROM ( - SELECT code, close, timestamp, - ROW_NUMBER() OVER (PARTITION BY code ORDER BY timestamp DESC) as rn - FROM stock_minute - WHERE code IN ('{ch_codes_str}') - AND toDate(timestamp) = today() - ) - WHERE rn = 1 - """ - - try: - result = client.execute(query) - if not result: - return {} - - latest_prices = {} - for row in result: - ch_code, close, ts = row - if close and close > 0: - pure_code = code_mapping.get(ch_code) - if pure_code: - latest_prices[pure_code] = { - 'close': float(close), - 'timestamp': ts - } - - return latest_prices - except Exception as e: - logger.error(f"获取最新价格失败: {e}") - return {} - - -def get_prices_at_time(stock_codes: list, timestamp: datetime) -> dict: - """获取指定时间点的股票价格""" - if not stock_codes: - return {} - - client = get_ch_client() - - ch_codes = [] - code_mapping = {} - for code in stock_codes: - ch_code = code_to_ch_format(code) - if ch_code: - ch_codes.append(ch_code) - code_mapping[ch_code] = code - - if not ch_codes: - return {} - - ch_codes_str = "','".join(ch_codes) - - query = f""" - SELECT code, close, timestamp - FROM stock_minute - WHERE code IN ('{ch_codes_str}') - AND timestamp = '{timestamp.strftime('%Y-%m-%d %H:%M:%S')}' - """ - - try: - result = client.execute(query) - prices = {} - for row in result: - ch_code, close, ts = row - if close and close > 0: - pure_code = code_mapping.get(ch_code) - if pure_code: - prices[pure_code] = { - 'close': float(close), - 'timestamp': ts - } - return prices - except Exception as e: - logger.error(f"获取历史价格失败: {e}") - return {} - - -def get_index_realtime(index_code: str = REFERENCE_INDEX) -> dict: - """获取指数实时数据""" - client = get_ch_client() - - try: - query = f""" - SELECT close, timestamp - FROM index_minute - WHERE code = '{index_code}' - AND toDate(timestamp) = today() - ORDER BY timestamp DESC - LIMIT 1 - """ - result = client.execute(query) - - if not result: - return None - - close, ts = result[0] - - # 获取昨收价 - prev_close = get_index_prev_close(index_code, datetime.now().strftime('%Y-%m-%d')) - - change_pct = None - if close and prev_close and prev_close > 0: - change_pct = (float(close) - prev_close) / prev_close * 100 - - return { - 'code': index_code, - 'price': float(close), - 'prev_close': prev_close, - 'change_pct': round(change_pct, 4) if change_pct else None, - 'timestamp': ts - } - - except Exception as e: - logger.error(f"获取指数数据失败: {e}") - return None - - -def get_index_at_time(index_code: str, timestamp: datetime, prev_close: float) -> dict: - """获取指定时间点的指数数据""" - client = get_ch_client() - - query = f""" - SELECT close, timestamp - FROM index_minute - WHERE code = '{index_code}' - AND timestamp = '{timestamp.strftime('%Y-%m-%d %H:%M:%S')}' - LIMIT 1 - """ - - try: - result = client.execute(query) - if not result: - return None - - close, ts = result[0] - change_pct = None - if close and prev_close and prev_close > 0: - change_pct = (float(close) - prev_close) / prev_close * 100 - - return { - 'code': index_code, - 'price': float(close), - 'prev_close': prev_close, - 'change_pct': round(change_pct, 4) if change_pct else None, - 'timestamp': ts - } - except Exception as e: - logger.error(f"获取指数数据失败: {e}") - return None - - -def get_index_prev_close(index_code: str, trade_date: str) -> float: - """获取指数昨收价""" - code_no_suffix = index_code.split('.')[0] - - with MYSQL_ENGINE.connect() as conn: - result = conn.execute(text(""" - SELECT F006N FROM ea_exchangetrade - WHERE INDEXCODE = :code - AND TRADEDATE < :today - ORDER BY TRADEDATE DESC LIMIT 1 - """), { - 'code': code_no_suffix, - 'today': trade_date - }).fetchone() - - if result and result[0]: - return float(result[0]) - return None - - -# ==================== 涨跌幅计算 ==================== - -def calculate_change_pct(base_prices: dict, latest_prices: dict) -> dict: - """计算涨跌幅""" - changes = {} - for code, latest in latest_prices.items(): - if code in base_prices and base_prices[code] > 0: - base = base_prices[code] - close = latest['close'] - change_pct = (close - base) / base * 100 - changes[code] = { - 'change_pct': round(change_pct, 4), - 'close': close, - 'base': base - } - return changes - - -def calculate_concept_stats(concepts: list, stock_changes: dict) -> list: - """计算概念统计""" - stats = [] - - for concept in concepts: - concept_id = concept['concept_id'] - concept_name = concept['concept_name'] - stock_codes = concept['stocks'] - concept_type = concept.get('concept_type', 'leaf') - - changes = [] - limit_up_count = 0 - limit_down_count = 0 - limit_up_stocks = [] - limit_down_stocks = [] - - for code in stock_codes: - if code in stock_changes: - change_info = stock_changes[code] - change_pct = change_info['change_pct'] - changes.append(change_pct) - - # 涨停判断（涨幅 >= 9.8%） - if change_pct >= 9.8: - limit_up_count += 1 - limit_up_stocks.append(code) - # 跌停判断（跌幅 <= -9.8%） - elif change_pct <= -9.8: - limit_down_count += 1 - limit_down_stocks.append(code) - - if not changes: - continue - - avg_change_pct = round(np.mean(changes), 4) - std_change_pct = round(np.std(changes), 4) if len(changes) > 1 else 0 - - stats.append({ - 'concept_id': concept_id, - 'concept_name': concept_name, - 'avg_change_pct': avg_change_pct, - 'std_change_pct': std_change_pct, - 'stock_count': len(changes), - 'concept_type': concept_type, - 'limit_up_count': limit_up_count, - 'limit_down_count': limit_down_count, - 'limit_up_stocks': limit_up_stocks, - 'limit_down_stocks': limit_down_stocks, - 'limit_up_ratio': limit_up_count / len(changes) if changes else 0, - 'limit_down_ratio': limit_down_count / len(changes) if changes else 0, - }) - - # 按涨幅排序并添加排名 - stats.sort(key=lambda x: x['avg_change_pct'], reverse=True) - for i, item in enumerate(stats): - item['rank'] = i + 1 - - return stats - - -# ==================== Z-Score 计算 ==================== - -def load_historical_stats(concept_id: str, lookback_days: int = 20) -> dict: - """ - 加载概念的历史统计数据（用于计算Z-Score） - 从历史分钟数据中计算每日的平均涨跌幅变化 - """ - if concept_id in stats_cache and stats_cache[concept_id].get('loaded'): - return stats_cache[concept_id] - - # 查询历史异动记录计算统计 - try: - with MYSQL_ENGINE.connect() as conn: - # 获取历史每日变化统计 - result = conn.execute(text(""" - SELECT - trade_date, - AVG(change_delta) as avg_delta, - MAX(change_delta) as max_delta, - MIN(change_delta) as min_delta - FROM concept_minute_alert - WHERE concept_id = :concept_id - AND trade_date >= DATE_SUB(CURDATE(), INTERVAL :days DAY) - AND alert_type = 'surge' - GROUP BY trade_date - """), {'concept_id': concept_id, 'days': lookback_days}) - - rows = list(result) - - if len(rows) >= SMART_ALERT_CONFIG['zscore']['min_data_points']: - deltas = [float(r[1]) for r in rows if r[1]] - stats_cache[concept_id] = { - 'mean': np.mean(deltas), - 'std': np.std(deltas) if len(deltas) > 1 else 1.0, - 'count': len(deltas), - 'loaded': True - } - else: - # 数据不足，使用默认值 - stats_cache[concept_id] = { - 'mean': 0.5, # 默认平均变化0.5% - 'std': 0.8, # 默认标准差0.8% - 'count': 0, - 'loaded': True - } - except Exception as e: - logger.error(f"加载历史统计失败: {e}") - stats_cache[concept_id] = { - 'mean': 0.5, - 'std': 0.8, - 'count': 0, - 'loaded': True - } - - return stats_cache[concept_id] - - -def calculate_zscore(concept_id: str, change_delta: float) -> float: - """ - 计算涨跌幅变化的Z-Score - Z = (X - μ) / σ - """ - stats = load_historical_stats(concept_id) - - mean = stats['mean'] - std = stats['std'] - - # 避免除零 - if std < 0.1: - std = 0.1 - - zscore = (change_delta - mean) / std - return round(zscore, 4) - - -def update_minute_cache(concept_id: str, timestamp: datetime, data: dict): - """更新分钟级缓存""" - if concept_id not in minute_cache: - minute_cache[concept_id] = deque(maxlen=MINUTE_WINDOW) - - minute_cache[concept_id].append({ - 'timestamp': timestamp, - **data - }) - - -def get_minute_history(concept_id: str, minutes_ago: int) -> Optional[dict]: - """获取N分钟前的数据""" - if concept_id not in minute_cache: - return None - - history = minute_cache[concept_id] - if not history: - return None - - # 获取当前最新时间 - current_time = history[-1]['timestamp'] if history else datetime.now() - target_time = current_time - timedelta(minutes=minutes_ago) - - # 找到最接近目标时间的记录 - for record in reversed(list(history)): - if record['timestamp'] <= target_time: - return record - - return None - - -# ==================== SVM 异常检测 ==================== - -def extract_features(stat: dict, index_data: dict) -> np.ndarray: - """ - 提取用于SVM的特征向量 - """ - concept_id = stat['concept_id'] - - # 获取历史数据 - prev_5min = get_minute_history(concept_id, 5) - prev_10min = get_minute_history(concept_id, 10) - - features = [] - - # 1. 当前涨跌幅 - features.append(stat['avg_change_pct']) - - # 2. 5分钟涨跌幅变化 - if prev_5min: - features.append(stat['avg_change_pct'] - prev_5min.get('change_pct', 0)) - else: - features.append(0) - - # 3. 10分钟涨跌幅变化 - if prev_10min: - features.append(stat['avg_change_pct'] - prev_10min.get('change_pct', 0)) - else: - features.append(0) - - # 4. 5分钟排名变化 - if prev_5min: - features.append(prev_5min.get('rank', stat['rank']) - stat['rank']) - else: - features.append(0) - - # 5. 涨停股占比 - features.append(stat.get('limit_up_ratio', 0) * 100) - - # 6. 成交量比率（预留，暂用0） - features.append(0) - - # 7. 与指数相关性（简化：涨跌方向一致性） - if index_data and index_data.get('change_pct'): - index_change = index_data['change_pct'] - concept_change = stat['avg_change_pct'] - # 同向为正，反向为负 - correlation = 1 if (index_change * concept_change > 0) else -1 - features.append(correlation * abs(concept_change - index_change)) - else: - features.append(0) - - return np.array(features) - - -def train_svm_model(training_data: List[np.ndarray]): - """ - 训练 OneClass SVM 模型 - 用于检测异常模式 - """ - global svm_model, svm_scaler, svm_last_train - - if not SKLEARN_AVAILABLE: - return False - - if len(training_data) < 100: - logger.warning(f"训练数据不足: {len(training_data)} 条") - return False - - try: - X = np.array(training_data) - - # 标准化 - svm_scaler = StandardScaler() - X_scaled = svm_scaler.fit_transform(X) - - # 训练 OneClass SVM - svm_model = OneClassSVM( - nu=SMART_ALERT_CONFIG['svm']['nu'], - kernel=SMART_ALERT_CONFIG['svm']['kernel'], - gamma=SMART_ALERT_CONFIG['svm']['gamma'] - ) - svm_model.fit(X_scaled) - - svm_last_train = datetime.now() - - # 保存模型 - model_path = os.path.join(MODEL_DIR, 'svm_model.pkl') - scaler_path = os.path.join(MODEL_DIR, 'svm_scaler.pkl') - - with open(model_path, 'wb') as f: - pickle.dump(svm_model, f) - with open(scaler_path, 'wb') as f: - pickle.dump(svm_scaler, f) - - logger.info(f"SVM模型训练完成，使用 {len(training_data)} 条数据") - return True - - except Exception as e: - logger.error(f"SVM模型训练失败: {e}") - return False - - -def load_svm_model(): - """加载已保存的SVM模型""" - global svm_model, svm_scaler - - if not SKLEARN_AVAILABLE: - return False - - model_path = os.path.join(MODEL_DIR, 'svm_model.pkl') - scaler_path = os.path.join(MODEL_DIR, 'svm_scaler.pkl') - - if os.path.exists(model_path) and os.path.exists(scaler_path): - try: - with open(model_path, 'rb') as f: - svm_model = pickle.load(f) - with open(scaler_path, 'rb') as f: - svm_scaler = pickle.load(f) - logger.info("SVM模型加载成功") - return True - except Exception as e: - logger.error(f"SVM模型加载失败: {e}") - - return False - - -def predict_anomaly(features: np.ndarray) -> Tuple[bool, float]: - """ - 使用SVM预测是否为异常 - 返回: (是否异常, 异常分数) - """ - global svm_model, svm_scaler - - if svm_model is None or svm_scaler is None: - return False, 0.0 - - try: - X_scaled = svm_scaler.transform(features.reshape(1, -1)) - prediction = svm_model.predict(X_scaled)[0] - score = svm_model.decision_function(X_scaled)[0] - - # prediction: 1 = 正常, -1 = 异常 - is_anomaly = prediction == -1 - - return is_anomaly, float(score) - except Exception as e: - logger.error(f"SVM预测失败: {e}") - return False, 0.0 - - -# ==================== 重要性评分 ==================== - -def calculate_importance_score( - zscore: float, - rank: int, - limit_up_count: int, - stock_count: int, - change_pct: float, - total_concepts: int -) -> float: - """ - 计算异动的重要性分数（0-1） - 综合多个因素判断这条异动是否值得显示 - """ - weights = SMART_ALERT_CONFIG['importance_weights'] - - scores = {} - - # 1. Z-Score 绝对值（越大越重要） - zscore_score = min(abs(zscore) / 5.0, 1.0) # 5倍标准差为满分 - scores['zscore_abs'] = zscore_score - - # 2. 排名位置（越靠前越重要） - rank_score = max(0, 1 - (rank - 1) / min(100, total_concepts)) - scores['rank_position'] = rank_score - - # 3. 涨停数（越多越重要） - limit_score = min(limit_up_count / 5.0, 1.0) # 5个涨停为满分 - scores['limit_up_count'] = limit_score - - # 4. 概念股票数（适中最好） - if stock_count < 10: - stock_score = stock_count / 10.0 - elif stock_count > 100: - stock_score = max(0.5, 1 - (stock_count - 100) / 200) - else: - stock_score = 1.0 - scores['stock_count'] = stock_score - - # 5. 涨跌幅度 - change_score = min(abs(change_pct) / 5.0, 1.0) # 5%为满分 - scores['change_magnitude'] = change_score - - # 加权求和 - total_score = sum(scores[k] * weights[k] for k in weights) - - return round(total_score, 4) - - -# ==================== 智能异动检测 ==================== - -def check_cooldown(concept_id: str, alert_type: str) -> bool: - """检查是否在冷却期""" - key = (concept_id, alert_type) - cooldown_minutes = SMART_ALERT_CONFIG['display']['cooldown_minutes'] - - if key in cooldown_cache: - last_alert = cooldown_cache[key] - if datetime.now() - last_alert < timedelta(minutes=cooldown_minutes): - return True - return False - - -def set_cooldown(concept_id: str, alert_type: str, alert_time: datetime = None): - """设置冷却""" - cooldown_cache[(concept_id, alert_type)] = alert_time or datetime.now() - - -def detect_smart_alerts( - current_stats: list, - index_data: dict, - trade_date: str, - current_time: datetime = None -) -> list: - """ - 智能异动检测 - 结合 Z-Score + SVM 进行检测 - """ - alerts = [] - current_time = current_time or datetime.now() - total_concepts = len(current_stats) - - for stat in current_stats: - concept_id = stat['concept_id'] - concept_name = stat['concept_name'] - change_pct = stat['avg_change_pct'] - rank = stat['rank'] - limit_up_count = stat['limit_up_count'] - limit_down_count = stat['limit_down_count'] - stock_count = stat['stock_count'] - concept_type = stat['concept_type'] - - # 更新分钟缓存 - update_minute_cache(concept_id, current_time, { - 'change_pct': change_pct, - 'rank': rank, - 'limit_up_count': limit_up_count, - 'limit_down_count': limit_down_count - }) - - # 获取历史数据计算变化 - prev_5min = get_minute_history(concept_id, 5) - if not prev_5min: - continue - - change_delta = change_pct - prev_5min.get('change_pct', 0) - - # ========== Z-Score 检测 ========== - zscore = calculate_zscore(concept_id, abs(change_delta)) - - # 判断是涨还是跌 - is_surge_up = change_delta > 0 and zscore >= SMART_ALERT_CONFIG['zscore']['threshold_up'] - is_surge_down = change_delta < 0 and zscore >= abs(SMART_ALERT_CONFIG['zscore']['threshold_down']) - - if not (is_surge_up or is_surge_down): - continue - - alert_type = 'surge_up' if is_surge_up else 'surge_down' - - # 检查冷却 - if check_cooldown(concept_id, alert_type): - continue - - # ========== SVM 验证（可选）========== - svm_is_anomaly = False - svm_score = 0.0 - - if SMART_ALERT_CONFIG['svm']['enabled'] and svm_model is not None: - features = extract_features(stat, index_data) - svm_is_anomaly, svm_score = predict_anomaly(features) - - # 如果SVM认为不是异常，降低Z-Score要求 - if not svm_is_anomaly and abs(zscore) < 3.5: - continue - - # ========== 计算重要性分数 ========== - importance = calculate_importance_score( - zscore=zscore, - rank=rank, - limit_up_count=limit_up_count if is_surge_up else limit_down_count, - stock_count=stock_count, - change_pct=change_pct, - total_concepts=total_concepts - ) - - # 过滤低重要性 - if importance < SMART_ALERT_CONFIG['display']['min_importance_score']: - continue - - # ========== 创建异动记录 ========== - alert = { - 'concept_id': concept_id, - 'concept_name': concept_name, - 'alert_type': alert_type, - 'alert_time': current_time, - 'trade_date': trade_date, - 'change_pct': change_pct, - 'prev_change_pct': prev_5min.get('change_pct'), - 'change_delta': round(change_delta, 4), - 'zscore': zscore, - 'svm_score': svm_score, - 'importance_score': importance, - 'limit_up_count': limit_up_count, - 'limit_down_count': limit_down_count, - 'prev_limit_up_count': prev_5min.get('limit_up_count', 0), - 'rank_position': rank, - 'prev_rank_position': prev_5min.get('rank'), - 'rank_delta': (prev_5min.get('rank', rank) - rank) if prev_5min else 0, - 'stock_count': stock_count, - 'concept_type': concept_type, - 'index_code': REFERENCE_INDEX, - 'index_price': index_data['price'] if index_data else None, - 'index_change_pct': index_data['change_pct'] if index_data else None, - 'extra_info': { - 'limit_up_stocks': stat.get('limit_up_stocks', []), - 'limit_down_stocks': stat.get('limit_down_stocks', []), - } - } - - alerts.append(alert) - set_cooldown(concept_id, alert_type, current_time) - - # 日志 - direction = "🔥 暴涨" if is_surge_up else "💧 暴跌" - logger.info( - f"{direction}: {concept_name} " - f"涨幅 {prev_5min.get('change_pct', 0):.2f}% -> {change_pct:.2f}% " - f"(Δ{change_delta:+.2f}%, Z={zscore:.2f}, 重要性={importance:.2f})" - ) - - # 按重要性排序，限制数量 - alerts.sort(key=lambda x: x['importance_score'], reverse=True) - max_alerts = SMART_ALERT_CONFIG['display']['max_alerts_per_hour'] - - return alerts[:max_alerts] - - -# ==================== 数据持久化 ==================== - -def save_alerts_to_mysql(alerts: list): - """保存异动数据到MySQL""" - if not alerts: - return 0 - - saved = 0 - with MYSQL_ENGINE.begin() as conn: - for alert in alerts: - try: - insert_sql = text(""" - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (:concept_id, :concept_name, :alert_time, :alert_type, :trade_date, - :change_pct, :prev_change_pct, :change_delta, - :limit_up_count, :prev_limit_up_count, :limit_up_delta, - :rank_position, :prev_rank_position, :rank_delta, - :index_code, :index_price, :index_change_pct, - :stock_count, :concept_type, :extra_info) - """) - - # 计算 limit_up_delta - limit_up_delta = alert.get('limit_up_count', 0) - alert.get('prev_limit_up_count', 0) - - params = { - 'concept_id': alert['concept_id'], - 'concept_name': alert['concept_name'], - 'alert_time': alert['alert_time'], - 'alert_type': alert['alert_type'], - 'trade_date': alert['trade_date'], - 'change_pct': alert.get('change_pct'), - 'prev_change_pct': alert.get('prev_change_pct'), - 'change_delta': alert.get('change_delta'), - 'limit_up_count': alert.get('limit_up_count', 0), - 'prev_limit_up_count': alert.get('prev_limit_up_count', 0), - 'limit_up_delta': limit_up_delta, - 'rank_position': alert.get('rank_position'), - 'prev_rank_position': alert.get('prev_rank_position'), - 'rank_delta': alert.get('rank_delta'), - 'index_code': alert.get('index_code', REFERENCE_INDEX), - 'index_price': alert.get('index_price'), - 'index_change_pct': alert.get('index_change_pct'), - 'stock_count': alert.get('stock_count'), - 'concept_type': alert.get('concept_type', 'leaf'), - 'extra_info': json.dumps({ - **alert.get('extra_info', {}), - 'zscore': alert.get('zscore'), - 'svm_score': alert.get('svm_score'), - 'importance_score': alert.get('importance_score'), - }, ensure_ascii=False) if alert.get('extra_info') else None - } - - conn.execute(insert_sql, params) - saved += 1 - - except Exception as e: - logger.error(f"保存异动失败: {alert['concept_name']} - {e}") - - return saved - - -def save_index_snapshot(index_data: dict, trade_date: str): - """保存指数快照""" - if not index_data: - return - - try: - with MYSQL_ENGINE.begin() as conn: - upsert_sql = text(""" - REPLACE INTO index_minute_snapshot - (index_code, trade_date, snapshot_time, price, prev_close, change_pct) - VALUES (:index_code, :trade_date, :snapshot_time, :price, :prev_close, :change_pct) - """) - - conn.execute(upsert_sql, { - 'index_code': index_data['code'], - 'trade_date': trade_date, - 'snapshot_time': index_data['timestamp'], - 'price': index_data['price'], - 'prev_close': index_data.get('prev_close'), - 'change_pct': index_data.get('change_pct') - }) - except Exception as e: - logger.error(f"保存指数快照失败: {e}") - - -# ==================== 交易时间判断 ==================== - -def is_trading_time() -> bool: - """判断当前是否为交易时间""" - now = datetime.now() - weekday = now.weekday() - - if weekday >= 5: - return False - - hour, minute = now.hour, now.minute - current_time = hour * 60 + minute - - morning_start = 9 * 60 + 30 - morning_end = 11 * 60 + 30 - afternoon_start = 13 * 60 - afternoon_end = 15 * 60 - - return (morning_start <= current_time <= morning_end) or \ - (afternoon_start <= current_time <= afternoon_end) - - -def get_next_update_time() -> int: - """获取距离下次更新的秒数""" - now = datetime.now() - - if is_trading_time(): - return 60 - now.second - else: - hour, minute = now.hour, now.minute - - if hour < 9 or (hour == 9 and minute < 30): - target = now.replace(hour=9, minute=30, second=0, microsecond=0) - elif (hour == 11 and minute >= 30) or hour == 12: - target = now.replace(hour=13, minute=0, second=0, microsecond=0) - elif hour >= 15: - target = (now + timedelta(days=1)).replace(hour=9, minute=30, second=0, microsecond=0) - else: - target = now + timedelta(minutes=1) - - wait_seconds = (target - now).total_seconds() - return max(60, int(wait_seconds)) - - -# ==================== 主运行逻辑 ==================== - -def run_once(concepts: list, all_stocks: list) -> tuple: - """执行一次检测""" - now = datetime.now() - trade_date = now.strftime('%Y-%m-%d') - - # 获取基准价格 - base_prices = get_base_prices(all_stocks, trade_date) - if not base_prices: - logger.warning("无法获取基准价格") - return 0, 0 - - # 获取最新价格 - latest_prices = get_latest_prices(all_stocks) - if not latest_prices: - logger.warning("无法获取最新价格") - return 0, 0 - - # 获取指数数据 - index_data = get_index_realtime(REFERENCE_INDEX) - if index_data: - save_index_snapshot(index_data, trade_date) - - # 计算涨跌幅 - stock_changes = calculate_change_pct(base_prices, latest_prices) - if not stock_changes: - logger.warning("无涨跌幅数据") - return 0, 0 - - logger.info(f"获取到 {len(stock_changes)} 只股票的涨跌幅") - - # 计算概念统计 - stats = calculate_concept_stats(concepts, stock_changes) - logger.info(f"计算了 {len(stats)} 个概念的涨跌幅") - - # 智能异动检测 - alerts = detect_smart_alerts(stats, index_data, trade_date, now) - - # 保存异动 - if alerts: - saved = save_alerts_to_mysql(alerts) - logger.info(f"💾 保存了 {saved} 条异动记录") - - return len(stats), len(alerts) - - -def run_realtime(): - """实时检测主循环""" - logger.info("=" * 60) - logger.info("🚀 启动智能概念异动检测服务 (Z-Score + SVM)") - logger.info("=" * 60) - logger.info(f"配置: {json.dumps(SMART_ALERT_CONFIG, indent=2, ensure_ascii=False, default=str)}") - - # 尝试加载SVM模型 - if SKLEARN_AVAILABLE: - load_svm_model() - - # 加载概念数据 - logger.info("加载概念数据...") - leaf_concepts = get_all_concepts() - logger.info(f"获取到 {len(leaf_concepts)} 个叶子概念") - - parent_concepts = load_hierarchy_concepts(leaf_concepts) - logger.info(f"生成了 {len(parent_concepts)} 个母概念") - - all_concepts = leaf_concepts + parent_concepts - logger.info(f"总计 {len(all_concepts)} 个概念") - - # 收集所有股票代码 - all_stocks = set() - for c in all_concepts: - all_stocks.update(c['stocks']) - all_stocks = list(all_stocks) - logger.info(f"监控 {len(all_stocks)} 只股票") - - last_concept_update = datetime.now() - total_alerts = 0 - - while True: - try: - now = datetime.now() - - # 每小时重新加载概念数据 - if (now - last_concept_update).total_seconds() > 3600: - logger.info("重新加载概念数据...") - leaf_concepts = get_all_concepts() - parent_concepts = load_hierarchy_concepts(leaf_concepts) - all_concepts = leaf_concepts + parent_concepts - all_stocks = set() - for c in all_concepts: - all_stocks.update(c['stocks']) - all_stocks = list(all_stocks) - last_concept_update = now - logger.info(f"更新完成: {len(all_concepts)} 个概念, {len(all_stocks)} 只股票") - - # 检查是否交易时间 - if not is_trading_time(): - wait_sec = get_next_update_time() - wait_min = wait_sec // 60 - logger.info(f"⏰ 非交易时间，等待 {wait_min} 分钟后重试...") - time.sleep(min(wait_sec, 300)) - continue - - # 执行检测 - logger.info(f"\n{'=' * 40}") - logger.info(f"🔍 检测时间: {now.strftime('%Y-%m-%d %H:%M:%S')}") - updated, alert_count = run_once(all_concepts, all_stocks) - total_alerts += alert_count - - if alert_count > 0: - logger.info(f"📊 本次检测到 {alert_count} 条异动，累计 {total_alerts} 条") - - # 等待下一分钟 - sleep_sec = 60 - datetime.now().second - logger.info(f"⏳ 等待 {sleep_sec} 秒后继续...") - time.sleep(sleep_sec) - - except KeyboardInterrupt: - logger.info("\n收到退出信号，停止服务...") - break - except Exception as e: - logger.error(f"发生错误: {e}") - import traceback - traceback.print_exc() - time.sleep(60) - - -def run_single(): - """单次运行""" - logger.info("单次检测模式") - - if SKLEARN_AVAILABLE: - load_svm_model() - - leaf_concepts = get_all_concepts() - parent_concepts = load_hierarchy_concepts(leaf_concepts) - all_concepts = leaf_concepts + parent_concepts - - all_stocks = set() - for c in all_concepts: - all_stocks.update(c['stocks']) - all_stocks = list(all_stocks) - - logger.info(f"概念数: {len(all_concepts)}, 股票数: {len(all_stocks)}") - - updated, alerts = run_once(all_concepts, all_stocks) - logger.info(f"检测完成: {updated} 个概念, {alerts} 条异动") - - -# ==================== 回测功能 ==================== - -def get_minute_timestamps(trade_date: str) -> list: - """获取指定交易日的所有分钟时间戳""" - client = get_ch_client() - - query = f""" - SELECT DISTINCT timestamp - FROM stock_minute - WHERE toDate(timestamp) = '{trade_date}' - ORDER BY timestamp - """ - - result = client.execute(query) - return [row[0] for row in result] - - -def run_backtest(trade_date: str, clear_existing: bool = True): - """ - 回测指定日期的异动检测 - """ - global minute_cache, cooldown_cache, stats_cache - - logger.info("=" * 60) - logger.info(f"🔄 开始智能回测: {trade_date}") - logger.info("=" * 60) - - # 清空缓存 - minute_cache = {} - cooldown_cache = {} - stats_cache = {} - - # 清除已有数据 - if clear_existing: - with MYSQL_ENGINE.begin() as conn: - conn.execute(text("DELETE FROM concept_minute_alert WHERE trade_date = :date"), {'date': trade_date}) - conn.execute(text("DELETE FROM index_minute_snapshot WHERE trade_date = :date"), {'date': trade_date}) - logger.info(f"已清除 {trade_date} 的已有数据") - - # 加载概念数据 - logger.info("加载概念数据...") - leaf_concepts = get_all_concepts() - logger.info(f"获取到 {len(leaf_concepts)} 个叶子概念") - - parent_concepts = load_hierarchy_concepts(leaf_concepts) - logger.info(f"生成了 {len(parent_concepts)} 个母概念") - - all_concepts = leaf_concepts + parent_concepts - logger.info(f"总计 {len(all_concepts)} 个概念") - - # 收集所有股票代码 - all_stocks = set() - for c in all_concepts: - all_stocks.update(c['stocks']) - all_stocks = list(all_stocks) - logger.info(f"监控 {len(all_stocks)} 只股票") - - # 获取基准价格（昨收价） - base_prices = get_base_prices(all_stocks, trade_date) - if not base_prices: - logger.error("无法获取基准价格，退出回测") - return - logger.info(f"获取到 {len(base_prices)} 个基准价格") - - # 获取指数昨收价 - index_prev_close = get_index_prev_close(REFERENCE_INDEX, trade_date) - logger.info(f"指数昨收价: {index_prev_close}") - - # 获取所有分钟时间戳 - timestamps = get_minute_timestamps(trade_date) - if not timestamps: - logger.error(f"未找到 {trade_date} 的分钟数据") - return - logger.info(f"找到 {len(timestamps)} 个分钟时间点") - - total_alerts = 0 - processed = 0 - - # 逐分钟处理 - for ts in timestamps: - processed += 1 - - # 获取该时间点的价格 - latest_prices = get_prices_at_time(all_stocks, ts) - if not latest_prices: - continue - - # 获取指数数据 - index_data = get_index_at_time(REFERENCE_INDEX, ts, index_prev_close) - if index_data: - save_index_snapshot(index_data, trade_date) - - # 计算涨跌幅 - stock_changes = calculate_change_pct(base_prices, latest_prices) - if not stock_changes: - continue - - # 计算概念统计 - stats = calculate_concept_stats(all_concepts, stock_changes) - - # 智能异动检测 - alerts = detect_smart_alerts(stats, index_data, trade_date, ts) - - # 保存异动 - if alerts: - saved = save_alerts_to_mysql(alerts) - total_alerts += saved - - # 进度显示 - if processed % 30 == 0: - logger.info(f"进度: {processed}/{len(timestamps)} ({processed*100//len(timestamps)}%), 已检测到 {total_alerts} 条异动") - - logger.info("=" * 60) - logger.info(f"✅ 回测完成!") - logger.info(f" 处理分钟数: {processed}") - logger.info(f" 检测到异动: {total_alerts} 条") - logger.info("=" * 60) - - -def show_status(): - """显示状态""" - print("\n" + "=" * 60) - print("智能概念异动检测服务 (Z-Score + SVM) - 状态") - print("=" * 60) - - now = datetime.now() - print(f"\n当前时间: {now.strftime('%Y-%m-%d %H:%M:%S')}") - print(f"是否交易时间: {'是' if is_trading_time() else '否'}") - print(f"sklearn可用: {'是' if SKLEARN_AVAILABLE else '否'}") - - # 模型状态 - model_path = os.path.join(MODEL_DIR, 'svm_model.pkl') - print(f"SVM模型: {'已加载' if os.path.exists(model_path) else '未训练'}") - - # 今日异动统计 - print("\n今日异动统计:") - try: - with MYSQL_ENGINE.connect() as conn: - result = conn.execute(text(""" - SELECT alert_type, COUNT(*) as cnt, AVG(change_delta) as avg_delta - FROM concept_minute_alert - WHERE trade_date = CURDATE() - GROUP BY alert_type - """)) - rows = list(result) - if rows: - for row in rows: - alert_type_name = { - 'surge_up': '暴涨', - 'surge_down': '暴跌', - 'surge': '急涨(旧)', - 'limit_up': '涨停增加', - 'rank_jump': '排名跃升' - }.get(row[0], row[0]) - avg_delta = f"{row[2]:.2f}%" if row[2] else "-" - print(f" {alert_type_name}: {row[1]} 条 (平均变化: {avg_delta})") - else: - print(" 今日暂无异动") - - # 最新异动 - print("\n最新异动 (前10条):") - result = conn.execute(text(""" - SELECT concept_name, alert_type, alert_time, change_pct, change_delta, extra_info - FROM concept_minute_alert - WHERE trade_date = CURDATE() - ORDER BY alert_time DESC - LIMIT 10 - """)) - rows = list(result) - if rows: - print(f" {'概念':<20} | {'类型':<6} | {'时间':<8} | {'涨幅':>6} | {'变化':>6} | {'Z分':>5}") - print(" " + "-" * 70) - for row in rows: - name = row[0][:18] if len(row[0]) > 18 else row[0] - alert_type = {'surge_up': '暴涨', 'surge_down': '暴跌'}.get(row[1], row[1][:4]) - time_str = row[2].strftime('%H:%M') if row[2] else '-' - change = f"{row[3]:.2f}%" if row[3] else '-' - delta = f"{row[4]:+.2f}%" if row[4] else '-' - - # 解析extra_info获取zscore - zscore = '-' - if row[5]: - try: - extra = json.loads(row[5]) if isinstance(row[5], str) else row[5] - zscore = f"{extra.get('zscore', 0):.1f}" - except: - pass - - print(f" {name:<20} | {alert_type:<6} | {time_str:<8} | {change:>6} | {delta:>6} | {zscore:>5}") - else: - print(" 暂无异动记录") - - except Exception as e: - print(f" 查询失败: {e}") - - -def train_model(): - """训练SVM模型""" - if not SKLEARN_AVAILABLE: - print("错误: sklearn未安装，无法训练模型") - print("安装命令: pip install scikit-learn") - return - - logger.info("=" * 60) - logger.info("🎓 开始训练SVM模型") - logger.info("=" * 60) - - # 加载概念数据 - leaf_concepts = get_all_concepts() - parent_concepts = load_hierarchy_concepts(leaf_concepts) - all_concepts = leaf_concepts + parent_concepts - - all_stocks = set() - for c in all_concepts: - all_stocks.update(c['stocks']) - all_stocks = list(all_stocks) - - logger.info(f"概念数: {len(all_concepts)}, 股票数: {len(all_stocks)}") - - # 收集训练数据（使用历史异动数据） - training_features = [] - - # 从最近N天的数据中提取特征 - lookback_days = 30 - - try: - with MYSQL_ENGINE.connect() as conn: - result = conn.execute(text(""" - SELECT change_pct, change_delta, rank_position, limit_up_count, - stock_count, index_change_pct, extra_info - FROM concept_minute_alert - WHERE trade_date >= DATE_SUB(CURDATE(), INTERVAL :days DAY) - """), {'days': lookback_days}) - - for row in result: - features = [ - float(row[0]) if row[0] else 0, # change_pct - float(row[1]) if row[1] else 0, # change_delta - 0, # change_delta_10min (not available) - -float(row[2]) if row[2] else 0, # rank_delta (approximation) - float(row[3]) / max(1, float(row[4])) * 100 if row[3] and row[4] else 0, # limit_up_ratio - 0, # volume_ratio - float(row[0]) - float(row[5]) if row[0] and row[5] else 0, # index_correlation - ] - training_features.append(features) - - logger.info(f"收集到 {len(training_features)} 条训练数据") - - if len(training_features) >= 100: - success = train_svm_model(training_features) - if success: - logger.info("✅ 模型训练成功!") - else: - logger.error("❌ 模型训练失败") - else: - logger.warning("训练数据不足100条，跳过训练") - - except Exception as e: - logger.error(f"训练失败: {e}") - import traceback - traceback.print_exc() - - -# ==================== 主函数 ==================== - -def main(): - parser = argparse.ArgumentParser(description='智能概念异动检测服务 (Z-Score + SVM)') - parser.add_argument('command', nargs='?', default='realtime', - choices=['realtime', 'once', 'status', 'backtest', 'train'], - help='命令: realtime(实时运行), once(单次运行), status(状态), backtest(回测), train(训练模型)') - parser.add_argument('--date', '-d', type=str, default=None, - help='回测日期，格式: YYYY-MM-DD，默认为今天') - parser.add_argument('--keep', '-k', action='store_true', - help='回测时保留已有数据（默认会清除）') - - args = parser.parse_args() - - if args.command == 'realtime': - run_realtime() - elif args.command == 'once': - run_single() - elif args.command == 'status': - show_status() - elif args.command == 'backtest': - trade_date = args.date or datetime.now().strftime('%Y-%m-%d') - clear_existing = not args.keep - run_backtest(trade_date, clear_existing) - elif args.command == 'train': - train_model() - - -if __name__ == "__main__": - main() diff --git a/concept_alert_realtime.py b/concept_alert_realtime.py deleted file mode 100644 index 6e931a91..00000000 --- a/concept_alert_realtime.py +++ /dev/null @@ -1,1366 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -概念异动实时检测服务 -- 基于 concept_quota_realtime.py 扩展 -- 检测概念板块的异动（急涨、涨停增加、排名跃升） -- 记录异动时的指数位置，用于热点概览图表展示 -""" - -import pandas as pd -import numpy as np -from datetime import datetime, timedelta -from sqlalchemy import create_engine, text -from elasticsearch import Elasticsearch -from clickhouse_driver import Client -from collections import deque -import time -import logging -import json -import os -import hashlib -import argparse - -# ==================== 配置 ==================== - -# MySQL配置 -MYSQL_ENGINE = create_engine( - "mysql+pymysql://root:Zzl5588161!@222.128.1.157:33060/stock", - echo=False -) - -# Elasticsearch配置 -ES_CLIENT = Elasticsearch(['http://222.128.1.157:19200']) -INDEX_NAME = 'concept_library_v3' - -# ClickHouse配置 -CLICKHOUSE_CONFIG = { - 'host': '222.128.1.157', - 'port': 18000, - 'user': 'default', - 'password': 'Zzl33818!', - 'database': 'stock' -} - -# 层级结构文件 -HIERARCHY_FILE = 'concept_hierarchy_v3.json' - -# ==================== 异动检测阈值配置 ==================== - -ALERT_CONFIG = { - # 急涨检测：N分钟内涨幅变化超过阈值 - 'surge': { - 'enabled': True, - 'window_minutes': 5, # 检测窗口（分钟） - 'threshold_pct': 1.0, # 涨幅变化阈值（%） - 'min_change_pct': 0.5, # 最低涨幅要求（避免负涨幅的噪音） - 'cooldown_minutes': 10, # 同一概念冷却时间（避免重复报警） - }, - # 涨停数增加检测 - 'limit_up': { - 'enabled': True, - 'threshold_count': 1, # 涨停数增加阈值 - 'cooldown_minutes': 15, # 冷却时间 - }, - # 排名跃升检测 - 'rank_jump': { - 'enabled': True, - 'window_minutes': 5, # 检测窗口 - 'threshold_rank': 15, # 排名上升阈值 - 'max_rank': 50, # 只关注前N名的变化 - 'cooldown_minutes': 15, - }, -} - -# 参考指数 -REFERENCE_INDEX = '000001.SH' # 上证指数 - -# ==================== 日志配置 ==================== - -logging.basicConfig( - level=logging.INFO, - format='%(asctime)s - %(levelname)s - %(message)s', - handlers=[ - logging.FileHandler(f'concept_alert_{datetime.now().strftime("%Y%m%d")}.log', encoding='utf-8'), - logging.StreamHandler() - ] -) -logger = logging.getLogger(__name__) - -# ==================== 全局变量 ==================== - -ch_client = None - -# 历史数据缓存（用于异动检测） -# 结构: {concept_id: deque([(timestamp, change_pct, rank, limit_up_count), ...])} -history_cache = {} -HISTORY_WINDOW = 10 # 保留最近10分钟的数据 - -# 冷却记录（避免重复报警） -# 结构: {(concept_id, alert_type): last_alert_time} -cooldown_cache = {} - -# 当前排名缓存 -current_rankings = {} - - -def get_ch_client(): - """获取ClickHouse客户端""" - global ch_client - if ch_client is None: - ch_client = Client(**CLICKHOUSE_CONFIG) - return ch_client - - -def generate_id(name: str) -> str: - """生成概念ID""" - return hashlib.md5(name.encode('utf-8')).hexdigest()[:16] - - -def code_to_ch_format(code: str) -> str: - """将6位股票代码转换为ClickHouse格式""" - if not code or len(code) != 6 or not code.isdigit(): - return None - if code.startswith('6'): - return f"{code}.SH" - elif code.startswith('0') or code.startswith('3'): - return f"{code}.SZ" - else: - return f"{code}.BJ" - - -# ==================== 概念数据获取 ==================== - -def get_all_concepts(): - """从ES获取所有叶子概念及其股票列表""" - concepts = [] - - query = { - "query": {"match_all": {}}, - "size": 100, - "_source": ["concept_id", "concept", "stocks"] - } - - resp = ES_CLIENT.search(index=INDEX_NAME, body=query, scroll='2m') - scroll_id = resp['_scroll_id'] - hits = resp['hits']['hits'] - - while len(hits) > 0: - for hit in hits: - source = hit['_source'] - concept_info = { - 'concept_id': source.get('concept_id'), - 'concept_name': source.get('concept'), - 'stocks': [], - 'concept_type': 'leaf' - } - - if 'stocks' in source and isinstance(source['stocks'], list): - for stock in source['stocks']: - if isinstance(stock, dict) and 'code' in stock and stock['code']: - concept_info['stocks'].append(stock['code']) - - if concept_info['stocks']: - concepts.append(concept_info) - - resp = ES_CLIENT.scroll(scroll_id=scroll_id, scroll='2m') - scroll_id = resp['_scroll_id'] - hits = resp['hits']['hits'] - - ES_CLIENT.clear_scroll(scroll_id=scroll_id) - return concepts - - -def load_hierarchy_concepts(leaf_concepts: list) -> list: - """加载层级结构，生成母概念""" - hierarchy_path = os.path.join(os.path.dirname(__file__), HIERARCHY_FILE) - if not os.path.exists(hierarchy_path): - logger.warning(f"层级文件不存在: {hierarchy_path}") - return [] - - with open(hierarchy_path, 'r', encoding='utf-8') as f: - hierarchy_data = json.load(f) - - concept_to_stocks = {} - for c in leaf_concepts: - concept_to_stocks[c['concept_name']] = set(c['stocks']) - - parent_concepts = [] - - for lv1 in hierarchy_data.get('hierarchy', []): - lv1_name = lv1.get('lv1', '') - lv1_stocks = set() - - for child in lv1.get('children', []): - lv2_name = child.get('lv2', '') - lv2_stocks = set() - - if 'children' in child: - for lv3_child in child.get('children', []): - lv3_name = lv3_child.get('lv3', '') - lv3_stocks = set() - - for concept_name in lv3_child.get('concepts', []): - if concept_name in concept_to_stocks: - lv3_stocks.update(concept_to_stocks[concept_name]) - - if lv3_stocks: - parent_concepts.append({ - 'concept_id': generate_id(f"lv3_{lv3_name}"), - 'concept_name': f"[三级] {lv3_name}", - 'stocks': list(lv3_stocks), - 'concept_type': 'lv3' - }) - - lv2_stocks.update(lv3_stocks) - else: - for concept_name in child.get('concepts', []): - if concept_name in concept_to_stocks: - lv2_stocks.update(concept_to_stocks[concept_name]) - - if lv2_stocks: - parent_concepts.append({ - 'concept_id': generate_id(f"lv2_{lv2_name}"), - 'concept_name': f"[二级] {lv2_name}", - 'stocks': list(lv2_stocks), - 'concept_type': 'lv2' - }) - - lv1_stocks.update(lv2_stocks) - - if lv1_stocks: - parent_concepts.append({ - 'concept_id': generate_id(f"lv1_{lv1_name}"), - 'concept_name': f"[一级] {lv1_name}", - 'stocks': list(lv1_stocks), - 'concept_type': 'lv1' - }) - - return parent_concepts - - -# ==================== 价格数据获取 ==================== - -def get_base_prices(stock_codes: list, current_date: str) -> dict: - """获取昨收价作为基准""" - if not stock_codes: - return {} - - valid_codes = [code for code in stock_codes if code and len(code) == 6 and code.isdigit()] - if not valid_codes: - return {} - - stock_codes_str = "','".join(valid_codes) - - query = f""" - SELECT SECCODE, F002N - FROM ea_trade - WHERE SECCODE IN ('{stock_codes_str}') - AND TRADEDATE = ( - SELECT MAX(TRADEDATE) - FROM ea_trade - WHERE TRADEDATE <= '{current_date}' - ) - AND F002N IS NOT NULL AND F002N > 0 - """ - - try: - with MYSQL_ENGINE.connect() as conn: - result = conn.execute(text(query)) - base_prices = {row[0]: float(row[1]) for row in result if row[1] and float(row[1]) > 0} - return base_prices - except Exception as e: - logger.error(f"获取基准价格失败: {e}") - return {} - - -def get_latest_prices(stock_codes: list) -> dict: - """从ClickHouse获取最新价格""" - if not stock_codes: - return {} - - client = get_ch_client() - - ch_codes = [] - code_mapping = {} - for code in stock_codes: - ch_code = code_to_ch_format(code) - if ch_code: - ch_codes.append(ch_code) - code_mapping[ch_code] = code - - if not ch_codes: - return {} - - ch_codes_str = "','".join(ch_codes) - - query = f""" - SELECT code, close, timestamp - FROM ( - SELECT code, close, timestamp, - ROW_NUMBER() OVER (PARTITION BY code ORDER BY timestamp DESC) as rn - FROM stock_minute - WHERE code IN ('{ch_codes_str}') - AND toDate(timestamp) = today() - ) - WHERE rn = 1 - """ - - try: - result = client.execute(query) - if not result: - return {} - - latest_prices = {} - for row in result: - ch_code, close, ts = row - if close and close > 0: - pure_code = code_mapping.get(ch_code) - if pure_code: - latest_prices[pure_code] = { - 'close': float(close), - 'timestamp': ts - } - - return latest_prices - except Exception as e: - logger.error(f"获取最新价格失败: {e}") - return {} - - -def get_index_realtime(index_code: str = REFERENCE_INDEX) -> dict: - """获取指数实时数据""" - client = get_ch_client() - - try: - # 从 index_minute 表获取最新数据 - query = f""" - SELECT close, timestamp - FROM index_minute - WHERE code = '{index_code}' - AND toDate(timestamp) = today() - ORDER BY timestamp DESC - LIMIT 1 - """ - result = client.execute(query) - - if not result: - return None - - close, ts = result[0] - - # 获取昨收价 - prev_close = None - code_no_suffix = index_code.split('.')[0] - - with MYSQL_ENGINE.connect() as conn: - prev_result = conn.execute(text(""" - SELECT F006N FROM ea_exchangetrade - WHERE INDEXCODE = :code - AND TRADEDATE < CURDATE() - ORDER BY TRADEDATE DESC LIMIT 1 - """), {'code': code_no_suffix}).fetchone() - - if prev_result and prev_result[0]: - prev_close = float(prev_result[0]) - - change_pct = None - if close and prev_close and prev_close > 0: - change_pct = (float(close) - prev_close) / prev_close * 100 - - return { - 'code': index_code, - 'price': float(close), - 'prev_close': prev_close, - 'change_pct': round(change_pct, 4) if change_pct else None, - 'timestamp': ts - } - - except Exception as e: - logger.error(f"获取指数数据失败: {e}") - return None - - -# ==================== 涨跌幅计算 ==================== - -def calculate_change_pct(base_prices: dict, latest_prices: dict) -> dict: - """计算涨跌幅""" - changes = {} - for code, latest in latest_prices.items(): - if code in base_prices and base_prices[code] > 0: - base = base_prices[code] - close = latest['close'] - change_pct = (close - base) / base * 100 - changes[code] = { - 'change_pct': round(change_pct, 4), - 'close': close, - 'base': base - } - return changes - - -def calculate_concept_stats(concepts: list, stock_changes: dict) -> list: - """计算概念统计（包含涨停数）""" - stats = [] - - for concept in concepts: - concept_id = concept['concept_id'] - concept_name = concept['concept_name'] - stock_codes = concept['stocks'] - concept_type = concept.get('concept_type', 'leaf') - - changes = [] - limit_up_count = 0 - limit_up_stocks = [] - - for code in stock_codes: - if code in stock_changes: - change_info = stock_changes[code] - change_pct = change_info['change_pct'] - changes.append(change_pct) - - # 涨停判断（涨幅 >= 9.8%） - if change_pct >= 9.8: - limit_up_count += 1 - limit_up_stocks.append(code) - - if not changes: - continue - - avg_change_pct = round(np.mean(changes), 4) - - stats.append({ - 'concept_id': concept_id, - 'concept_name': concept_name, - 'avg_change_pct': avg_change_pct, - 'stock_count': len(changes), - 'concept_type': concept_type, - 'limit_up_count': limit_up_count, - 'limit_up_stocks': limit_up_stocks - }) - - # 按涨幅排序并添加排名 - stats.sort(key=lambda x: x['avg_change_pct'], reverse=True) - for i, item in enumerate(stats): - item['rank'] = i + 1 - - return stats - - -# ==================== 异动检测 ==================== - -def check_cooldown(concept_id: str, alert_type: str, cooldown_minutes: int) -> bool: - """检查是否在冷却期内""" - key = (concept_id, alert_type) - if key in cooldown_cache: - last_alert = cooldown_cache[key] - if datetime.now() - last_alert < timedelta(minutes=cooldown_minutes): - return True - return False - - -def set_cooldown(concept_id: str, alert_type: str): - """设置冷却""" - cooldown_cache[(concept_id, alert_type)] = datetime.now() - - -def update_history(concept_id: str, timestamp: datetime, change_pct: float, rank: int, limit_up_count: int): - """更新历史缓存""" - if concept_id not in history_cache: - history_cache[concept_id] = deque(maxlen=HISTORY_WINDOW) - - history_cache[concept_id].append({ - 'timestamp': timestamp, - 'change_pct': change_pct, - 'rank': rank, - 'limit_up_count': limit_up_count - }) - - -def get_history(concept_id: str, minutes_ago: int) -> dict: - """获取N分钟前的历史数据""" - if concept_id not in history_cache: - return None - - history = history_cache[concept_id] - if not history: - return None - - target_time = datetime.now() - timedelta(minutes=minutes_ago) - - # 找到最接近目标时间的记录 - for record in history: - if record['timestamp'] <= target_time: - return record - - # 如果没有足够早的数据，返回最早的记录 - return history[0] if history else None - - -def detect_alerts(current_stats: list, index_data: dict, trade_date: str) -> list: - """检测异动""" - alerts = [] - now = datetime.now() - - for stat in current_stats: - concept_id = stat['concept_id'] - concept_name = stat['concept_name'] - change_pct = stat['avg_change_pct'] - rank = stat['rank'] - limit_up_count = stat['limit_up_count'] - stock_count = stat['stock_count'] - concept_type = stat['concept_type'] - - # 更新历史 - update_history(concept_id, now, change_pct, rank, limit_up_count) - - # 1. 急涨检测 - if ALERT_CONFIG['surge']['enabled']: - cfg = ALERT_CONFIG['surge'] - if change_pct >= cfg['min_change_pct']: # 最低涨幅要求 - if not check_cooldown(concept_id, 'surge', cfg['cooldown_minutes']): - prev_data = get_history(concept_id, cfg['window_minutes']) - if prev_data: - change_delta = change_pct - prev_data['change_pct'] - if change_delta >= cfg['threshold_pct']: - alerts.append({ - 'concept_id': concept_id, - 'concept_name': concept_name, - 'alert_type': 'surge', - 'alert_time': now, - 'trade_date': trade_date, - 'change_pct': change_pct, - 'prev_change_pct': prev_data['change_pct'], - 'change_delta': round(change_delta, 4), - 'limit_up_count': limit_up_count, - 'rank_position': rank, - 'stock_count': stock_count, - 'concept_type': concept_type, - 'index_price': index_data['price'] if index_data else None, - 'index_change_pct': index_data['change_pct'] if index_data else None, - }) - set_cooldown(concept_id, 'surge') - logger.info(f"🔥 急涨异动: {concept_name} 涨幅 {prev_data['change_pct']:.2f}% -> {change_pct:.2f}% (+{change_delta:.2f}%)") - - # 2. 涨停数增加检测 - if ALERT_CONFIG['limit_up']['enabled']: - cfg = ALERT_CONFIG['limit_up'] - if limit_up_count > 0: - if not check_cooldown(concept_id, 'limit_up', cfg['cooldown_minutes']): - prev_data = get_history(concept_id, 1) # 对比上一分钟 - if prev_data: - limit_up_delta = limit_up_count - prev_data['limit_up_count'] - if limit_up_delta >= cfg['threshold_count']: - alerts.append({ - 'concept_id': concept_id, - 'concept_name': concept_name, - 'alert_type': 'limit_up', - 'alert_time': now, - 'trade_date': trade_date, - 'change_pct': change_pct, - 'limit_up_count': limit_up_count, - 'prev_limit_up_count': prev_data['limit_up_count'], - 'limit_up_delta': limit_up_delta, - 'rank_position': rank, - 'stock_count': stock_count, - 'concept_type': concept_type, - 'index_price': index_data['price'] if index_data else None, - 'index_change_pct': index_data['change_pct'] if index_data else None, - 'extra_info': {'limit_up_stocks': stat.get('limit_up_stocks', [])} - }) - set_cooldown(concept_id, 'limit_up') - logger.info(f"🚀 涨停异动: {concept_name} 涨停数 {prev_data['limit_up_count']} -> {limit_up_count} (+{limit_up_delta})") - - # 3. 排名跃升检测 - if ALERT_CONFIG['rank_jump']['enabled']: - cfg = ALERT_CONFIG['rank_jump'] - if rank <= cfg['max_rank']: # 只关注前N名 - if not check_cooldown(concept_id, 'rank_jump', cfg['cooldown_minutes']): - prev_data = get_history(concept_id, cfg['window_minutes']) - if prev_data and prev_data['rank'] > cfg['max_rank']: # 从榜外进入前N - rank_delta = prev_data['rank'] - rank # 正数表示上升 - if rank_delta >= cfg['threshold_rank']: - alerts.append({ - 'concept_id': concept_id, - 'concept_name': concept_name, - 'alert_type': 'rank_jump', - 'alert_time': now, - 'trade_date': trade_date, - 'change_pct': change_pct, - 'rank_position': rank, - 'prev_rank_position': prev_data['rank'], - 'rank_delta': -rank_delta, # 负数表示上升 - 'limit_up_count': limit_up_count, - 'stock_count': stock_count, - 'concept_type': concept_type, - 'index_price': index_data['price'] if index_data else None, - 'index_change_pct': index_data['change_pct'] if index_data else None, - }) - set_cooldown(concept_id, 'rank_jump') - logger.info(f"📈 排名跃升: {concept_name} 排名 {prev_data['rank']} -> {rank} (上升{rank_delta}名)") - - return alerts - - -# ==================== 数据持久化 ==================== - -def save_alerts_to_mysql(alerts: list): - """保存异动数据到MySQL""" - if not alerts: - return 0 - - saved = 0 - with MYSQL_ENGINE.begin() as conn: - for alert in alerts: - try: - insert_sql = text(""" - INSERT INTO concept_minute_alert - (concept_id, concept_name, alert_time, alert_type, trade_date, - change_pct, prev_change_pct, change_delta, - limit_up_count, prev_limit_up_count, limit_up_delta, - rank_position, prev_rank_position, rank_delta, - index_code, index_price, index_change_pct, - stock_count, concept_type, extra_info) - VALUES - (:concept_id, :concept_name, :alert_time, :alert_type, :trade_date, - :change_pct, :prev_change_pct, :change_delta, - :limit_up_count, :prev_limit_up_count, :limit_up_delta, - :rank_position, :prev_rank_position, :rank_delta, - :index_code, :index_price, :index_change_pct, - :stock_count, :concept_type, :extra_info) - """) - - params = { - 'concept_id': alert['concept_id'], - 'concept_name': alert['concept_name'], - 'alert_time': alert['alert_time'], - 'alert_type': alert['alert_type'], - 'trade_date': alert['trade_date'], - 'change_pct': alert.get('change_pct'), - 'prev_change_pct': alert.get('prev_change_pct'), - 'change_delta': alert.get('change_delta'), - 'limit_up_count': alert.get('limit_up_count', 0), - 'prev_limit_up_count': alert.get('prev_limit_up_count', 0), - 'limit_up_delta': alert.get('limit_up_delta', 0), - 'rank_position': alert.get('rank_position'), - 'prev_rank_position': alert.get('prev_rank_position'), - 'rank_delta': alert.get('rank_delta'), - 'index_code': REFERENCE_INDEX, - 'index_price': alert.get('index_price'), - 'index_change_pct': alert.get('index_change_pct'), - 'stock_count': alert.get('stock_count'), - 'concept_type': alert.get('concept_type', 'leaf'), - 'extra_info': json.dumps(alert.get('extra_info')) if alert.get('extra_info') else None - } - - conn.execute(insert_sql, params) - saved += 1 - - except Exception as e: - logger.error(f"保存异动失败: {alert['concept_name']} - {e}") - - return saved - - -def save_index_snapshot(index_data: dict, trade_date: str): - """保存指数快照""" - if not index_data: - return - - try: - with MYSQL_ENGINE.begin() as conn: - upsert_sql = text(""" - REPLACE INTO index_minute_snapshot - (index_code, trade_date, snapshot_time, price, prev_close, change_pct) - VALUES (:index_code, :trade_date, :snapshot_time, :price, :prev_close, :change_pct) - """) - - conn.execute(upsert_sql, { - 'index_code': index_data['code'], - 'trade_date': trade_date, - 'snapshot_time': index_data['timestamp'], - 'price': index_data['price'], - 'prev_close': index_data.get('prev_close'), - 'change_pct': index_data.get('change_pct') - }) - except Exception as e: - logger.error(f"保存指数快照失败: {e}") - - -# ==================== 交易时间判断 ==================== - -def is_trading_time() -> bool: - """判断当前是否为交易时间""" - now = datetime.now() - weekday = now.weekday() - - if weekday >= 5: - return False - - hour, minute = now.hour, now.minute - current_time = hour * 60 + minute - - morning_start = 9 * 60 + 30 - morning_end = 11 * 60 + 30 - afternoon_start = 13 * 60 - afternoon_end = 15 * 60 - - return (morning_start <= current_time <= morning_end) or \ - (afternoon_start <= current_time <= afternoon_end) - - -def get_next_update_time() -> int: - """获取距离下次更新的秒数""" - now = datetime.now() - - if is_trading_time(): - return 60 - now.second - else: - hour, minute = now.hour, now.minute - - if hour < 9 or (hour == 9 and minute < 30): - target = now.replace(hour=9, minute=30, second=0, microsecond=0) - elif (hour == 11 and minute >= 30) or hour == 12: - target = now.replace(hour=13, minute=0, second=0, microsecond=0) - elif hour >= 15: - target = (now + timedelta(days=1)).replace(hour=9, minute=30, second=0, microsecond=0) - else: - target = now + timedelta(minutes=1) - - wait_seconds = (target - now).total_seconds() - return max(60, int(wait_seconds)) - - -# ==================== 主运行逻辑 ==================== - -def run_once(concepts: list, all_stocks: list) -> tuple: - """执行一次检测，返回 (更新数, 异动数)""" - now = datetime.now() - trade_date = now.strftime('%Y-%m-%d') - - # 获取基准价格 - base_prices = get_base_prices(all_stocks, trade_date) - if not base_prices: - logger.warning("无法获取基准价格") - return 0, 0 - - # 获取最新价格 - latest_prices = get_latest_prices(all_stocks) - if not latest_prices: - logger.warning("无法获取最新价格") - return 0, 0 - - # 获取指数数据 - index_data = get_index_realtime(REFERENCE_INDEX) - if index_data: - save_index_snapshot(index_data, trade_date) - - # 计算涨跌幅 - stock_changes = calculate_change_pct(base_prices, latest_prices) - if not stock_changes: - logger.warning("无涨跌幅数据") - return 0, 0 - - logger.info(f"获取到 {len(stock_changes)} 只股票的涨跌幅") - - # 计算概念统计 - stats = calculate_concept_stats(concepts, stock_changes) - logger.info(f"计算了 {len(stats)} 个概念的涨跌幅") - - # 检测异动 - alerts = detect_alerts(stats, index_data, trade_date) - - # 保存异动 - if alerts: - saved = save_alerts_to_mysql(alerts) - logger.info(f"💾 保存了 {saved} 条异动记录") - - return len(stats), len(alerts) - - -def run_realtime(): - """实时检测主循环""" - logger.info("=" * 60) - logger.info("🚀 启动概念异动实时检测服务") - logger.info("=" * 60) - logger.info(f"异动配置: {json.dumps(ALERT_CONFIG, indent=2, ensure_ascii=False)}") - - # 加载概念数据 - logger.info("加载概念数据...") - leaf_concepts = get_all_concepts() - logger.info(f"获取到 {len(leaf_concepts)} 个叶子概念") - - parent_concepts = load_hierarchy_concepts(leaf_concepts) - logger.info(f"生成了 {len(parent_concepts)} 个母概念") - - all_concepts = leaf_concepts + parent_concepts - logger.info(f"总计 {len(all_concepts)} 个概念") - - # 收集所有股票代码 - all_stocks = set() - for c in all_concepts: - all_stocks.update(c['stocks']) - all_stocks = list(all_stocks) - logger.info(f"监控 {len(all_stocks)} 只股票") - - last_concept_update = datetime.now() - total_alerts = 0 - - while True: - try: - now = datetime.now() - - # 每小时重新加载概念数据 - if (now - last_concept_update).total_seconds() > 3600: - logger.info("重新加载概念数据...") - leaf_concepts = get_all_concepts() - parent_concepts = load_hierarchy_concepts(leaf_concepts) - all_concepts = leaf_concepts + parent_concepts - all_stocks = set() - for c in all_concepts: - all_stocks.update(c['stocks']) - all_stocks = list(all_stocks) - last_concept_update = now - logger.info(f"更新完成: {len(all_concepts)} 个概念, {len(all_stocks)} 只股票") - - # 检查是否交易时间 - if not is_trading_time(): - wait_sec = get_next_update_time() - wait_min = wait_sec // 60 - logger.info(f"⏰ 非交易时间，等待 {wait_min} 分钟后重试...") - time.sleep(min(wait_sec, 300)) - continue - - # 执行检测 - logger.info(f"\n{'=' * 40}") - logger.info(f"🔍 检测时间: {now.strftime('%Y-%m-%d %H:%M:%S')}") - updated, alert_count = run_once(all_concepts, all_stocks) - total_alerts += alert_count - - if alert_count > 0: - logger.info(f"📊 本次检测到 {alert_count} 条异动，累计 {total_alerts} 条") - - # 等待下一分钟 - sleep_sec = 60 - datetime.now().second - logger.info(f"⏳ 等待 {sleep_sec} 秒后继续...") - time.sleep(sleep_sec) - - except KeyboardInterrupt: - logger.info("\n收到退出信号，停止服务...") - break - except Exception as e: - logger.error(f"发生错误: {e}") - import traceback - traceback.print_exc() - time.sleep(60) - - -def run_single(): - """单次运行""" - logger.info("单次检测模式") - - leaf_concepts = get_all_concepts() - parent_concepts = load_hierarchy_concepts(leaf_concepts) - all_concepts = leaf_concepts + parent_concepts - - all_stocks = set() - for c in all_concepts: - all_stocks.update(c['stocks']) - all_stocks = list(all_stocks) - - logger.info(f"概念数: {len(all_concepts)}, 股票数: {len(all_stocks)}") - - updated, alerts = run_once(all_concepts, all_stocks) - logger.info(f"检测完成: {updated} 个概念, {alerts} 条异动") - - -def show_status(): - """显示状态""" - print("\n" + "=" * 60) - print("概念异动实时检测服务 - 状态") - print("=" * 60) - - now = datetime.now() - print(f"\n当前时间: {now.strftime('%Y-%m-%d %H:%M:%S')}") - print(f"是否交易时间: {'是' if is_trading_time() else '否'}") - - # 今日异动统计 - print("\n今日异动统计:") - try: - with MYSQL_ENGINE.connect() as conn: - result = conn.execute(text(""" - SELECT alert_type, COUNT(*) as cnt - FROM concept_minute_alert - WHERE trade_date = CURDATE() - GROUP BY alert_type - """)) - rows = list(result) - if rows: - for row in rows: - alert_type_name = { - 'surge': '急涨', - 'limit_up': '涨停增加', - 'rank_jump': '排名跃升' - }.get(row[0], row[0]) - print(f" {alert_type_name}: {row[1]} 条") - else: - print(" 今日暂无异动") - - # 最新异动 - print("\n最新异动 (前10条):") - result = conn.execute(text(""" - SELECT concept_name, alert_type, alert_time, change_pct, limit_up_count, index_price - FROM concept_minute_alert - WHERE trade_date = CURDATE() - ORDER BY alert_time DESC - LIMIT 10 - """)) - rows = list(result) - if rows: - print(f" {'概念':<20} | {'类型':<8} | {'时间':<8} | {'涨幅':>6} | {'涨停':>4} | {'指数':>8}") - print(" " + "-" * 70) - for row in rows: - name = row[0][:18] if len(row[0]) > 18 else row[0] - alert_type = {'surge': '急涨', 'limit_up': '涨停', 'rank_jump': '排名'}.get(row[1], row[1]) - time_str = row[2].strftime('%H:%M') if row[2] else '-' - change = f"{row[3]:.2f}%" if row[3] else '-' - limit_up = str(row[4]) if row[4] else '-' - index_p = f"{row[5]:.2f}" if row[5] else '-' - print(f" {name:<20} | {alert_type:<8} | {time_str:<8} | {change:>6} | {limit_up:>4} | {index_p:>8}") - else: - print(" 暂无异动记录") - - except Exception as e: - print(f" 查询失败: {e}") - - -def init_tables(): - """初始化数据库表""" - print("初始化数据库表...") - - sql_file = os.path.join(os.path.dirname(__file__), 'sql', 'concept_minute_alert.sql') - - if not os.path.exists(sql_file): - print(f"SQL文件不存在: {sql_file}") - return - - with open(sql_file, 'r', encoding='utf-8') as f: - sql_content = f.read() - - # 分割多个语句 - statements = [s.strip() for s in sql_content.split(';') if s.strip() and not s.strip().startswith('--')] - - with MYSQL_ENGINE.begin() as conn: - for stmt in statements: - if stmt: - try: - conn.execute(text(stmt)) - print(f"✅ 执行成功") - except Exception as e: - print(f"❌ 执行失败: {e}") - - print("初始化完成") - - -# ==================== 回测功能 ==================== - -def get_minute_timestamps(trade_date: str) -> list: - """获取指定交易日的所有分钟时间戳""" - client = get_ch_client() - - query = f""" - SELECT DISTINCT timestamp - FROM stock_minute - WHERE toDate(timestamp) = '{trade_date}' - ORDER BY timestamp - """ - - result = client.execute(query) - return [row[0] for row in result] - - -def get_prices_at_time(stock_codes: list, timestamp: datetime) -> dict: - """获取指定时间点的股票价格 - - Args: - stock_codes: 纯6位股票代码列表 - timestamp: 指定的时间点 - - Returns: - dict: {纯6位代码: {'close': 价格, 'timestamp': 时间}} - """ - if not stock_codes: - return {} - - client = get_ch_client() - - # 转换为ClickHouse格式 - ch_codes = [] - code_mapping = {} - for code in stock_codes: - ch_code = code_to_ch_format(code) - if ch_code: - ch_codes.append(ch_code) - code_mapping[ch_code] = code - - if not ch_codes: - return {} - - ch_codes_str = "','".join(ch_codes) - - # 获取指定时间点的数据 - query = f""" - SELECT code, close, timestamp - FROM stock_minute - WHERE code IN ('{ch_codes_str}') - AND timestamp = '{timestamp.strftime('%Y-%m-%d %H:%M:%S')}' - """ - - try: - result = client.execute(query) - prices = {} - for row in result: - ch_code, close, ts = row - if close and close > 0: - pure_code = code_mapping.get(ch_code) - if pure_code: - prices[pure_code] = { - 'close': float(close), - 'timestamp': ts - } - return prices - except Exception as e: - logger.error(f"获取历史价格失败: {e}") - return {} - - -def get_index_at_time(index_code: str, timestamp: datetime, prev_close: float) -> dict: - """获取指定时间点的指数数据""" - client = get_ch_client() - - query = f""" - SELECT close, timestamp - FROM index_minute - WHERE code = '{index_code}' - AND timestamp = '{timestamp.strftime('%Y-%m-%d %H:%M:%S')}' - LIMIT 1 - """ - - try: - result = client.execute(query) - if not result: - return None - - close, ts = result[0] - change_pct = None - if close and prev_close and prev_close > 0: - change_pct = (float(close) - prev_close) / prev_close * 100 - - return { - 'code': index_code, - 'price': float(close), - 'prev_close': prev_close, - 'change_pct': round(change_pct, 4) if change_pct else None, - 'timestamp': ts - } - except Exception as e: - logger.error(f"获取指数数据失败: {e}") - return None - - -def get_index_prev_close(index_code: str, trade_date: str) -> float: - """获取指数昨收价""" - code_no_suffix = index_code.split('.')[0] - - with MYSQL_ENGINE.connect() as conn: - result = conn.execute(text(""" - SELECT F006N FROM ea_exchangetrade - WHERE INDEXCODE = :code - AND TRADEDATE < :today - ORDER BY TRADEDATE DESC LIMIT 1 - """), { - 'code': code_no_suffix, - 'today': trade_date - }).fetchone() - - if result and result[0]: - return float(result[0]) - return None - - -def run_backtest(trade_date: str, clear_existing: bool = True): - """ - 回测指定日期的异动检测 - - Args: - trade_date: 交易日期，格式 'YYYY-MM-DD' - clear_existing: 是否清除该日期已有的异动数据 - """ - global history_cache, cooldown_cache - - logger.info("=" * 60) - logger.info(f"🔄 开始回测: {trade_date}") - logger.info("=" * 60) - - # 清空缓存 - history_cache = {} - cooldown_cache = {} - - # 清除已有数据 - if clear_existing: - with MYSQL_ENGINE.begin() as conn: - conn.execute(text("DELETE FROM concept_minute_alert WHERE trade_date = :date"), {'date': trade_date}) - conn.execute(text("DELETE FROM index_minute_snapshot WHERE trade_date = :date"), {'date': trade_date}) - logger.info(f"已清除 {trade_date} 的已有数据") - - # 加载概念数据 - logger.info("加载概念数据...") - leaf_concepts = get_all_concepts() - logger.info(f"获取到 {len(leaf_concepts)} 个叶子概念") - - parent_concepts = load_hierarchy_concepts(leaf_concepts) - logger.info(f"生成了 {len(parent_concepts)} 个母概念") - - all_concepts = leaf_concepts + parent_concepts - logger.info(f"总计 {len(all_concepts)} 个概念") - - # 收集所有股票代码 - all_stocks = set() - for c in all_concepts: - all_stocks.update(c['stocks']) - all_stocks = list(all_stocks) - logger.info(f"监控 {len(all_stocks)} 只股票") - - # 获取基准价格（昨收价） - base_prices = get_base_prices(all_stocks, trade_date) - if not base_prices: - logger.error("无法获取基准价格，退出回测") - return - logger.info(f"获取到 {len(base_prices)} 个基准价格") - - # 获取指数昨收价 - index_prev_close = get_index_prev_close(REFERENCE_INDEX, trade_date) - logger.info(f"指数昨收价: {index_prev_close}") - - # 获取所有分钟时间戳 - timestamps = get_minute_timestamps(trade_date) - if not timestamps: - logger.error(f"未找到 {trade_date} 的分钟数据") - return - logger.info(f"找到 {len(timestamps)} 个分钟时间点") - - total_alerts = 0 - processed = 0 - - # 逐分钟处理 - for ts in timestamps: - processed += 1 - - # 获取该时间点的价格 - latest_prices = get_prices_at_time(all_stocks, ts) - if not latest_prices: - continue - - # 获取指数数据 - index_data = get_index_at_time(REFERENCE_INDEX, ts, index_prev_close) - if index_data: - save_index_snapshot(index_data, trade_date) - - # 计算涨跌幅 - stock_changes = calculate_change_pct(base_prices, latest_prices) - if not stock_changes: - continue - - # 计算概念统计 - stats = calculate_concept_stats(all_concepts, stock_changes) - - # 检测异动（使用回测专用函数） - alerts = detect_alerts_backtest(stats, index_data, trade_date, ts) - - # 保存异动 - if alerts: - saved = save_alerts_to_mysql(alerts) - total_alerts += saved - - # 进度显示 - if processed % 30 == 0: - logger.info(f"进度: {processed}/{len(timestamps)} ({processed*100//len(timestamps)}%), 已检测到 {total_alerts} 条异动") - - logger.info("=" * 60) - logger.info(f"✅ 回测完成!") - logger.info(f" 处理分钟数: {processed}") - logger.info(f" 检测到异动: {total_alerts} 条") - logger.info("=" * 60) - - -def detect_alerts_backtest(current_stats: list, index_data: dict, trade_date: str, current_time: datetime) -> list: - """ - 回测模式的异动检测（使用指定时间而非当前时间） - """ - alerts = [] - - for stat in current_stats: - concept_id = stat['concept_id'] - concept_name = stat['concept_name'] - change_pct = stat['avg_change_pct'] - rank = stat['rank'] - limit_up_count = stat['limit_up_count'] - stock_count = stat['stock_count'] - concept_type = stat['concept_type'] - - # 更新历史（使用指定时间） - if concept_id not in history_cache: - history_cache[concept_id] = deque(maxlen=HISTORY_WINDOW) - - history_cache[concept_id].append({ - 'timestamp': current_time, - 'change_pct': change_pct, - 'rank': rank, - 'limit_up_count': limit_up_count - }) - - # 获取历史数据的辅助函数（回测专用） - def get_history_backtest(concept_id: str, minutes_ago: int): - if concept_id not in history_cache: - return None - history = history_cache[concept_id] - if not history: - return None - - target_time = current_time - timedelta(minutes=minutes_ago) - for record in reversed(list(history)): - if record['timestamp'] <= target_time: - return record - return None - - # 检查冷却（回测专用） - def check_cooldown_backtest(concept_id: str, alert_type: str, cooldown_minutes: int) -> bool: - key = (concept_id, alert_type) - if key in cooldown_cache: - last_alert = cooldown_cache[key] - if current_time - last_alert < timedelta(minutes=cooldown_minutes): - return True - return False - - def set_cooldown_backtest(concept_id: str, alert_type: str): - cooldown_cache[(concept_id, alert_type)] = current_time - - # 1. 急涨检测 - if ALERT_CONFIG['surge']['enabled']: - cfg = ALERT_CONFIG['surge'] - if change_pct >= cfg['min_change_pct']: - if not check_cooldown_backtest(concept_id, 'surge', cfg['cooldown_minutes']): - prev_data = get_history_backtest(concept_id, cfg['window_minutes']) - if prev_data: - change_delta = change_pct - prev_data['change_pct'] - if change_delta >= cfg['threshold_pct']: - alerts.append({ - 'concept_id': concept_id, - 'concept_name': concept_name, - 'alert_type': 'surge', - 'alert_time': current_time, - 'trade_date': trade_date, - 'change_pct': change_pct, - 'prev_change_pct': prev_data['change_pct'], - 'change_delta': round(change_delta, 4), - 'limit_up_count': limit_up_count, - 'rank_position': rank, - 'stock_count': stock_count, - 'concept_type': concept_type, - 'index_price': index_data['price'] if index_data else None, - 'index_change_pct': index_data['change_pct'] if index_data else None, - }) - set_cooldown_backtest(concept_id, 'surge') - logger.debug(f"🔥 急涨: {concept_name} {prev_data['change_pct']:.2f}% -> {change_pct:.2f}%") - - # 2. 涨停数增加检测 - if ALERT_CONFIG['limit_up']['enabled']: - cfg = ALERT_CONFIG['limit_up'] - if limit_up_count > 0: - if not check_cooldown_backtest(concept_id, 'limit_up', cfg['cooldown_minutes']): - prev_data = get_history_backtest(concept_id, 1) - if prev_data: - limit_up_delta = limit_up_count - prev_data['limit_up_count'] - if limit_up_delta >= cfg['threshold_count']: - alerts.append({ - 'concept_id': concept_id, - 'concept_name': concept_name, - 'alert_type': 'limit_up', - 'alert_time': current_time, - 'trade_date': trade_date, - 'change_pct': change_pct, - 'limit_up_count': limit_up_count, - 'prev_limit_up_count': prev_data['limit_up_count'], - 'limit_up_delta': limit_up_delta, - 'rank_position': rank, - 'stock_count': stock_count, - 'concept_type': concept_type, - 'index_price': index_data['price'] if index_data else None, - 'index_change_pct': index_data['change_pct'] if index_data else None, - 'extra_info': {'limit_up_stocks': stat.get('limit_up_stocks', [])} - }) - set_cooldown_backtest(concept_id, 'limit_up') - logger.debug(f"🚀 涨停: {concept_name} 涨停数 +{limit_up_delta}") - - # 3. 排名跃升检测 - if ALERT_CONFIG['rank_jump']['enabled']: - cfg = ALERT_CONFIG['rank_jump'] - if rank <= cfg['max_rank']: - if not check_cooldown_backtest(concept_id, 'rank_jump', cfg['cooldown_minutes']): - prev_data = get_history_backtest(concept_id, cfg['window_minutes']) - if prev_data and prev_data['rank'] > cfg['max_rank']: - rank_delta = prev_data['rank'] - rank - if rank_delta >= cfg['threshold_rank']: - alerts.append({ - 'concept_id': concept_id, - 'concept_name': concept_name, - 'alert_type': 'rank_jump', - 'alert_time': current_time, - 'trade_date': trade_date, - 'change_pct': change_pct, - 'rank_position': rank, - 'prev_rank_position': prev_data['rank'], - 'rank_delta': -rank_delta, - 'limit_up_count': limit_up_count, - 'stock_count': stock_count, - 'concept_type': concept_type, - 'index_price': index_data['price'] if index_data else None, - 'index_change_pct': index_data['change_pct'] if index_data else None, - }) - set_cooldown_backtest(concept_id, 'rank_jump') - logger.debug(f"📈 排名跃升: {concept_name} 排名 {prev_data['rank']} -> {rank}") - - return alerts - - -# ==================== 主函数 ==================== - -def main(): - parser = argparse.ArgumentParser(description='概念异动实时检测服务') - parser.add_argument('command', nargs='?', default='realtime', - choices=['realtime', 'once', 'status', 'init', 'backtest'], - help='命令: realtime(实时运行), once(单次运行), status(状态查看), init(初始化表), backtest(回测历史)') - parser.add_argument('--date', '-d', type=str, default=None, - help='回测日期，格式: YYYY-MM-DD，默认为今天') - parser.add_argument('--keep', '-k', action='store_true', - help='回测时保留已有数据（默认会清除）') - - args = parser.parse_args() - - if args.command == 'realtime': - run_realtime() - elif args.command == 'once': - run_single() - elif args.command == 'status': - show_status() - elif args.command == 'init': - init_tables() - elif args.command == 'backtest': - # 回测模式 - trade_date = args.date or datetime.now().strftime('%Y-%m-%d') - clear_existing = not args.keep - run_backtest(trade_date, clear_existing) - - -if __name__ == "__main__": - main() diff --git a/concept_quota_realtime.py b/concept_quota_realtime.py deleted file mode 100644 index 103b586e..00000000 --- a/concept_quota_realtime.py +++ /dev/null @@ -1,681 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -概念涨跌幅实时更新服务 -- 在交易时间段每分钟从ClickHouse获取最新分钟数据 -- 计算涨跌幅后更新MySQL的concept_daily_stats表 -- 支持叶子概念和母概念（lv1/lv2/lv3） -""" - -import pandas as pd -import numpy as np -from datetime import datetime, timedelta -from sqlalchemy import create_engine, text -from elasticsearch import Elasticsearch -from clickhouse_driver import Client -import time -import logging -import json -import os -import hashlib -import argparse - -# ==================== 配置 ==================== - -# MySQL配置 -MYSQL_ENGINE = create_engine( - "mysql+pymysql://root:Zzl5588161!@222.128.1.157:33060/stock", - echo=False -) - -# Elasticsearch配置 -ES_CLIENT = Elasticsearch(['http://222.128.1.157:19200']) -INDEX_NAME = 'concept_library_v3' - -# ClickHouse配置 -CLICKHOUSE_CONFIG = { - 'host': '222.128.1.157', - 'port': 18000, - 'user': 'default', - 'password': 'Zzl33818!', - 'database': 'stock' -} - -# 层级结构文件 -HIERARCHY_FILE = 'concept_hierarchy_v3.json' - -# 交易时间配置 -TRADING_HOURS = { - 'morning_start': (9, 30), - 'morning_end': (11, 30), - 'afternoon_start': (13, 0), - 'afternoon_end': (15, 0), -} - -# ==================== 日志配置 ==================== - -logging.basicConfig( - level=logging.INFO, - format='%(asctime)s - %(levelname)s - %(message)s', - handlers=[ - logging.FileHandler(f'concept_realtime_{datetime.now().strftime("%Y%m%d")}.log', encoding='utf-8'), - logging.StreamHandler() - ] -) -logger = logging.getLogger(__name__) - -# ClickHouse客户端 -ch_client = None - - -def get_ch_client(): - """获取ClickHouse客户端""" - global ch_client - if ch_client is None: - ch_client = Client(**CLICKHOUSE_CONFIG) - return ch_client - - -def generate_id(name: str) -> str: - """生成概念ID""" - return hashlib.md5(name.encode('utf-8')).hexdigest()[:16] - - -def code_to_ch_format(code: str) -> str: - """将6位股票代码转换为ClickHouse格式（带后缀） - - 规则： - - 6开头 -> .SH（上海） - - 0或3开头 -> .SZ（深圳） - - 其他 -> .BJ（北京） - - 非6位数字的忽略（可能是港股） - """ - if not code or len(code) != 6 or not code.isdigit(): - return None - - if code.startswith('6'): - return f"{code}.SH" - elif code.startswith('0') or code.startswith('3'): - return f"{code}.SZ" - else: - return f"{code}.BJ" - - -def ch_code_to_pure(ch_code: str) -> str: - """将ClickHouse格式的股票代码转回纯6位代码""" - if not ch_code: - return None - return ch_code.split('.')[0] - - -# ==================== 概念数据获取 ==================== - -def get_all_concepts(): - """从ES获取所有叶子概念及其股票列表""" - concepts = [] - - query = { - "query": {"match_all": {}}, - "size": 100, - "_source": ["concept_id", "concept", "stocks"] - } - - resp = ES_CLIENT.search(index=INDEX_NAME, body=query, scroll='2m') - scroll_id = resp['_scroll_id'] - hits = resp['hits']['hits'] - - while len(hits) > 0: - for hit in hits: - source = hit['_source'] - concept_info = { - 'concept_id': source.get('concept_id'), - 'concept_name': source.get('concept'), - 'stocks': [], - 'concept_type': 'leaf' - } - - # v3索引的stocks字段是 [{name, code}, ...] - if 'stocks' in source and isinstance(source['stocks'], list): - for stock in source['stocks']: - if isinstance(stock, dict) and 'code' in stock and stock['code']: - concept_info['stocks'].append(stock['code']) - - if concept_info['stocks']: - concepts.append(concept_info) - - resp = ES_CLIENT.scroll(scroll_id=scroll_id, scroll='2m') - scroll_id = resp['_scroll_id'] - hits = resp['hits']['hits'] - - ES_CLIENT.clear_scroll(scroll_id=scroll_id) - return concepts - - -def load_hierarchy_concepts(leaf_concepts: list) -> list: - """加载层级结构，生成母概念（lv1/lv2/lv3）""" - hierarchy_path = os.path.join(os.path.dirname(__file__), HIERARCHY_FILE) - if not os.path.exists(hierarchy_path): - logger.warning(f"层级文件不存在: {hierarchy_path}") - return [] - - with open(hierarchy_path, 'r', encoding='utf-8') as f: - hierarchy_data = json.load(f) - - # 建立概念名称到股票的映射 - concept_to_stocks = {} - for c in leaf_concepts: - concept_to_stocks[c['concept_name']] = set(c['stocks']) - - parent_concepts = [] - - for lv1 in hierarchy_data.get('hierarchy', []): - lv1_name = lv1.get('lv1', '') - lv1_stocks = set() - - for child in lv1.get('children', []): - lv2_name = child.get('lv2', '') - lv2_stocks = set() - - if 'children' in child: - for lv3_child in child.get('children', []): - lv3_name = lv3_child.get('lv3', '') - lv3_stocks = set() - - for concept_name in lv3_child.get('concepts', []): - if concept_name in concept_to_stocks: - lv3_stocks.update(concept_to_stocks[concept_name]) - - if lv3_stocks: - parent_concepts.append({ - 'concept_id': generate_id(f"lv3_{lv3_name}"), - 'concept_name': f"[三级] {lv3_name}", - 'stocks': list(lv3_stocks), - 'concept_type': 'lv3' - }) - - lv2_stocks.update(lv3_stocks) - else: - for concept_name in child.get('concepts', []): - if concept_name in concept_to_stocks: - lv2_stocks.update(concept_to_stocks[concept_name]) - - if lv2_stocks: - parent_concepts.append({ - 'concept_id': generate_id(f"lv2_{lv2_name}"), - 'concept_name': f"[二级] {lv2_name}", - 'stocks': list(lv2_stocks), - 'concept_type': 'lv2' - }) - - lv1_stocks.update(lv2_stocks) - - if lv1_stocks: - parent_concepts.append({ - 'concept_id': generate_id(f"lv1_{lv1_name}"), - 'concept_name': f"[一级] {lv1_name}", - 'stocks': list(lv1_stocks), - 'concept_type': 'lv1' - }) - - return parent_concepts - - -# ==================== 基准价格获取 ==================== - -def get_base_prices(stock_codes: list, current_date: str) -> dict: - """获取当日的昨收价作为基准（从ea_trade的F002N字段） - - ea_trade表字段说明： - - F002N: 昨日收盘价 - - F007N: 最近成交价（收盘价） - - F010N: 涨跌幅 - """ - if not stock_codes: - return {} - - # 过滤出有效的6位股票代码 - valid_codes = [code for code in stock_codes if code and len(code) == 6 and code.isdigit()] - if not valid_codes: - return {} - - stock_codes_str = "','".join(valid_codes) - - # 获取当日数据中的昨收价（F002N） - query = f""" - SELECT SECCODE, F002N - FROM ea_trade - WHERE SECCODE IN ('{stock_codes_str}') - AND TRADEDATE = ( - SELECT MAX(TRADEDATE) - FROM ea_trade - WHERE TRADEDATE <= '{current_date}' - ) - AND F002N IS NOT NULL AND F002N > 0 - """ - - try: - with MYSQL_ENGINE.connect() as conn: - result = conn.execute(text(query)) - base_prices = {row[0]: float(row[1]) for row in result if row[1] and float(row[1]) > 0} - logger.info(f"获取到 {len(base_prices)} 个基准价格") - return base_prices - except Exception as e: - logger.error(f"获取基准价格失败: {e}") - return {} - - -# ==================== 实时价格获取 ==================== - -def get_latest_prices(stock_codes: list) -> dict: - """从ClickHouse获取最新分钟数据的收盘价 - - Args: - stock_codes: 纯6位股票代码列表（如 ['000001', '600000']） - - Returns: - dict: {纯6位代码: {'close': 价格, 'timestamp': 时间}} - """ - if not stock_codes: - return {} - - client = get_ch_client() - - # 转换为ClickHouse格式的代码（带后缀） - ch_codes = [] - code_mapping = {} # ch_code -> pure_code - for code in stock_codes: - ch_code = code_to_ch_format(code) - if ch_code: - ch_codes.append(ch_code) - code_mapping[ch_code] = code - - if not ch_codes: - logger.warning("没有有效的股票代码可查询") - return {} - - ch_codes_str = "','".join(ch_codes) - - # 获取今日最新的分钟数据 - query = f""" - SELECT code, close, timestamp - FROM ( - SELECT code, close, timestamp, - ROW_NUMBER() OVER (PARTITION BY code ORDER BY timestamp DESC) as rn - FROM stock_minute - WHERE code IN ('{ch_codes_str}') - AND toDate(timestamp) = today() - ) - WHERE rn = 1 - """ - - try: - result = client.execute(query) - if not result: - return {} - - latest_prices = {} - for row in result: - ch_code, close, ts = row - if close and close > 0: - # 转回纯6位代码 - pure_code = code_mapping.get(ch_code) - if pure_code: - latest_prices[pure_code] = { - 'close': float(close), - 'timestamp': ts - } - - return latest_prices - except Exception as e: - logger.error(f"获取最新价格失败: {e}") - return {} - - -# ==================== 涨跌幅计算 ==================== - -def calculate_change_pct(base_prices: dict, latest_prices: dict) -> dict: - """计算涨跌幅""" - changes = {} - for code, latest in latest_prices.items(): - if code in base_prices and base_prices[code] > 0: - base = base_prices[code] - close = latest['close'] - change_pct = (close - base) / base * 100 - changes[code] = round(change_pct, 4) - return changes - - -def calculate_concept_stats(concepts: list, stock_changes: dict, trade_date: str) -> list: - """计算所有概念的涨跌幅统计""" - stats = [] - - for concept in concepts: - concept_id = concept['concept_id'] - concept_name = concept['concept_name'] - stock_codes = concept['stocks'] - concept_type = concept.get('concept_type', 'leaf') - - # 获取该概念股票的涨跌幅 - changes = [stock_changes[code] for code in stock_codes if code in stock_changes] - - if not changes: - continue - - avg_change_pct = round(np.mean(changes), 4) - stock_count = len(changes) - - stats.append({ - 'concept_id': concept_id, - 'concept_name': concept_name, - 'trade_date': trade_date, - 'avg_change_pct': avg_change_pct, - 'stock_count': stock_count, - 'concept_type': concept_type - }) - - return stats - - -# ==================== MySQL更新 ==================== - -def update_mysql_stats(stats: list): - """更新MySQL的concept_daily_stats表""" - if not stats: - return 0 - - with MYSQL_ENGINE.begin() as conn: - updated = 0 - for item in stats: - upsert_sql = text(""" - REPLACE INTO concept_daily_stats - (concept_id, concept_name, trade_date, avg_change_pct, stock_count, concept_type) - VALUES (:concept_id, :concept_name, :trade_date, :avg_change_pct, :stock_count, :concept_type) - """) - conn.execute(upsert_sql, item) - updated += 1 - - return updated - - -# ==================== 交易时间判断 ==================== - -def is_trading_time() -> bool: - """判断当前是否为交易时间""" - now = datetime.now() - weekday = now.weekday() - - # 周末不交易 - if weekday >= 5: - return False - - hour, minute = now.hour, now.minute - current_time = hour * 60 + minute - - # 上午 9:30 - 11:30 - morning_start = 9 * 60 + 30 - morning_end = 11 * 60 + 30 - - # 下午 13:00 - 15:00 - afternoon_start = 13 * 60 - afternoon_end = 15 * 60 - - return (morning_start <= current_time <= morning_end) or \ - (afternoon_start <= current_time <= afternoon_end) - - -def get_next_update_time() -> int: - """获取距离下次更新的秒数""" - now = datetime.now() - - if is_trading_time(): - # 交易时间内，等到下一分钟 - return 60 - now.second - else: - # 非交易时间 - hour, minute = now.hour, now.minute - - # 计算距离下次交易开始的时间 - if hour < 9 or (hour == 9 and minute < 30): - # 等到9:30 - target = now.replace(hour=9, minute=30, second=0, microsecond=0) - elif (hour == 11 and minute >= 30) or hour == 12: - # 等到13:00 - target = now.replace(hour=13, minute=0, second=0, microsecond=0) - elif hour >= 15: - # 等到明天9:30 - target = (now + timedelta(days=1)).replace(hour=9, minute=30, second=0, microsecond=0) - else: - target = now + timedelta(minutes=1) - - wait_seconds = (target - now).total_seconds() - return max(60, int(wait_seconds)) - - -# ==================== 主运行逻辑 ==================== - -def run_once(concepts: list, all_stocks: list) -> int: - """执行一次更新""" - now = datetime.now() - trade_date = now.strftime('%Y-%m-%d') - - # 获取基准价格（昨日收盘价） - base_prices = get_base_prices(all_stocks, trade_date) - if not base_prices: - logger.warning("无法获取基准价格") - return 0 - - # 获取最新价格 - latest_prices = get_latest_prices(all_stocks) - if not latest_prices: - logger.warning("无法获取最新价格") - return 0 - - # 计算涨跌幅 - stock_changes = calculate_change_pct(base_prices, latest_prices) - if not stock_changes: - logger.warning("无涨跌幅数据") - return 0 - - logger.info(f"获取到 {len(stock_changes)} 只股票的涨跌幅") - - # 计算概念统计 - stats = calculate_concept_stats(concepts, stock_changes, trade_date) - logger.info(f"计算了 {len(stats)} 个概念的涨跌幅") - - # 更新MySQL - updated = update_mysql_stats(stats) - logger.info(f"更新了 {updated} 条记录到MySQL") - - return updated - - -def run_realtime(): - """实时更新主循环""" - logger.info("=" * 60) - logger.info("启动概念涨跌幅实时更新服务") - logger.info("=" * 60) - - # 加载概念数据 - logger.info("加载概念数据...") - leaf_concepts = get_all_concepts() - logger.info(f"获取到 {len(leaf_concepts)} 个叶子概念") - - parent_concepts = load_hierarchy_concepts(leaf_concepts) - logger.info(f"生成了 {len(parent_concepts)} 个母概念") - - all_concepts = leaf_concepts + parent_concepts - logger.info(f"总计 {len(all_concepts)} 个概念") - - # 收集所有股票代码 - all_stocks = set() - for c in all_concepts: - all_stocks.update(c['stocks']) - all_stocks = list(all_stocks) - logger.info(f"监控 {len(all_stocks)} 只股票") - - last_concept_update = datetime.now() - - while True: - try: - now = datetime.now() - - # 每小时重新加载概念数据 - if (now - last_concept_update).total_seconds() > 3600: - logger.info("重新加载概念数据...") - leaf_concepts = get_all_concepts() - parent_concepts = load_hierarchy_concepts(leaf_concepts) - all_concepts = leaf_concepts + parent_concepts - all_stocks = set() - for c in all_concepts: - all_stocks.update(c['stocks']) - all_stocks = list(all_stocks) - last_concept_update = now - logger.info(f"更新完成: {len(all_concepts)} 个概念, {len(all_stocks)} 只股票") - - # 检查是否交易时间 - if not is_trading_time(): - wait_sec = get_next_update_time() - wait_min = wait_sec // 60 - logger.info(f"非交易时间，等待 {wait_min} 分钟后重试...") - time.sleep(min(wait_sec, 300)) # 最多等5分钟再检查 - continue - - # 执行更新 - logger.info(f"\n{'=' * 40}") - logger.info(f"更新时间: {now.strftime('%Y-%m-%d %H:%M:%S')}") - updated = run_once(all_concepts, all_stocks) - - # 等待下一分钟 - sleep_sec = 60 - datetime.now().second - logger.info(f"完成，等待 {sleep_sec} 秒后继续...") - time.sleep(sleep_sec) - - except KeyboardInterrupt: - logger.info("\n收到退出信号，停止服务...") - break - except Exception as e: - logger.error(f"发生错误: {e}") - import traceback - traceback.print_exc() - time.sleep(60) - - -def run_single(): - """单次运行（不循环）""" - logger.info("单次更新模式") - - leaf_concepts = get_all_concepts() - parent_concepts = load_hierarchy_concepts(leaf_concepts) - all_concepts = leaf_concepts + parent_concepts - - all_stocks = set() - for c in all_concepts: - all_stocks.update(c['stocks']) - all_stocks = list(all_stocks) - - logger.info(f"概念数: {len(all_concepts)}, 股票数: {len(all_stocks)}") - - updated = run_once(all_concepts, all_stocks) - logger.info(f"更新完成: {updated} 条记录") - - -def show_status(): - """显示当前状态""" - print("\n" + "=" * 60) - print("概念涨跌幅实时更新服务 - 状态") - print("=" * 60) - - # 当前时间 - now = datetime.now() - print(f"\n当前时间: {now.strftime('%Y-%m-%d %H:%M:%S')}") - print(f"是否交易时间: {'是' if is_trading_time() else '否'}") - - # MySQL数据状态 - print("\nMySQL数据状态:") - try: - with MYSQL_ENGINE.connect() as conn: - # 今日数据量 - result = conn.execute(text(""" - SELECT concept_type, COUNT(*) as cnt - FROM concept_daily_stats - WHERE trade_date = CURDATE() - GROUP BY concept_type - """)) - rows = list(result) - if rows: - print(" 今日数据:") - for row in rows: - print(f" {row[0]}: {row[1]} 条") - else: - print(" 今日暂无数据") - - # 最新更新时间 - result = conn.execute(text(""" - SELECT MAX(updated_at) FROM concept_daily_stats WHERE trade_date = CURDATE() - """)) - row = result.fetchone() - if row and row[0]: - print(f" 最后更新: {row[0]}") - except Exception as e: - print(f" 查询失败: {e}") - - # ClickHouse数据状态 - print("\nClickHouse数据状态:") - try: - client = get_ch_client() - result = client.execute(""" - SELECT COUNT(*), MAX(timestamp) - FROM stock_minute - WHERE toDate(timestamp) = today() - """) - if result: - count, max_ts = result[0] - print(f" 今日分钟数据: {count:,} 条") - print(f" 最新时间戳: {max_ts}") - except Exception as e: - print(f" 查询失败: {e}") - - # 今日涨跌幅TOP10 - print("\n今日涨跌幅 TOP10:") - try: - with MYSQL_ENGINE.connect() as conn: - result = conn.execute(text(""" - SELECT concept_name, avg_change_pct, stock_count, concept_type - FROM concept_daily_stats - WHERE trade_date = CURDATE() AND concept_type = 'leaf' - ORDER BY avg_change_pct DESC - LIMIT 10 - """)) - rows = list(result) - if rows: - print(f" {'概念':<25} | {'涨跌幅':>8} | {'股票数':>6}") - print(" " + "-" * 50) - for row in rows: - name = row[0][:25] if len(row[0]) > 25 else row[0] - print(f" {name:<25} | {row[1]:>7.2f}% | {row[2]:>6}") - else: - print(" 暂无数据") - except Exception as e: - print(f" 查询失败: {e}") - - -# ==================== 主函数 ==================== - -def main(): - parser = argparse.ArgumentParser(description='概念涨跌幅实时更新服务') - parser.add_argument('command', nargs='?', default='realtime', - choices=['realtime', 'once', 'status'], - help='命令: realtime(实时运行), once(单次运行), status(状态查看)') - - args = parser.parse_args() - - if args.command == 'realtime': - run_realtime() - elif args.command == 'once': - run_single() - elif args.command == 'status': - show_status() - - -if __name__ == "__main__": - main() diff --git a/create_tables.py b/create_tables.py deleted file mode 100644 index f5c8912e..00000000 --- a/create_tables.py +++ /dev/null @@ -1,89 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -"""创建异动检测所需的数据库表""" - -import sys -from sqlalchemy import create_engine, text - -engine = create_engine('mysql+pymysql://root:Zzl5588161!@222.128.1.157:33060/stock', echo=False) - -# 删除旧表 -drop_sql1 = 'DROP TABLE IF EXISTS concept_minute_alert' -drop_sql2 = 'DROP TABLE IF EXISTS index_minute_snapshot' - -# 创建 concept_minute_alert 表 -# 支持 Z-Score + SVM 智能检测 -sql1 = ''' -CREATE TABLE concept_minute_alert ( - id BIGINT AUTO_INCREMENT PRIMARY KEY, - concept_id VARCHAR(32) NOT NULL, - concept_name VARCHAR(100) NOT NULL, - alert_time DATETIME NOT NULL, - alert_type VARCHAR(20) NOT NULL COMMENT 'surge_up=暴涨, surge_down=暴跌, limit_up=涨停增加, rank_jump=排名跃升', - trade_date DATE NOT NULL, - change_pct DECIMAL(10,4) COMMENT '当前涨跌幅', - prev_change_pct DECIMAL(10,4) COMMENT '之前涨跌幅', - change_delta DECIMAL(10,4) COMMENT '涨跌幅变化', - limit_up_count INT DEFAULT 0 COMMENT '涨停数', - prev_limit_up_count INT DEFAULT 0, - limit_up_delta INT DEFAULT 0, - limit_down_count INT DEFAULT 0 COMMENT '跌停数', - rank_position INT COMMENT '当前排名', - prev_rank_position INT COMMENT '之前排名', - rank_delta INT COMMENT '排名变化(负数表示上升)', - index_code VARCHAR(20) DEFAULT '000001.SH', - index_price DECIMAL(12,4), - index_change_pct DECIMAL(10,4), - stock_count INT, - concept_type VARCHAR(20) DEFAULT 'leaf', - zscore DECIMAL(8,4) COMMENT 'Z-Score值', - importance_score DECIMAL(6,4) COMMENT '重要性评分(0-1)', - extra_info JSON COMMENT '扩展信息(包含zscore,svm_score等)', - created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, - INDEX idx_trade_date (trade_date), - INDEX idx_alert_time (alert_time), - INDEX idx_concept_id (concept_id), - INDEX idx_alert_type (alert_type), - INDEX idx_trade_date_time (trade_date, alert_time), - INDEX idx_importance (importance_score) -) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COMMENT='概念异动记录表(智能版)' -''' - -# 创建 index_minute_snapshot 表 -sql2 = ''' -CREATE TABLE index_minute_snapshot ( - id BIGINT AUTO_INCREMENT PRIMARY KEY, - index_code VARCHAR(20) NOT NULL, - trade_date DATE NOT NULL, - snapshot_time DATETIME NOT NULL, - price DECIMAL(12,4), - open_price DECIMAL(12,4), - high_price DECIMAL(12,4), - low_price DECIMAL(12,4), - prev_close DECIMAL(12,4), - change_pct DECIMAL(10,4), - created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, - UNIQUE KEY uk_index_time (index_code, snapshot_time), - INDEX idx_trade_date (trade_date) -) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 -''' - -if __name__ == '__main__': - print('正在重建数据库表...\n') - - with engine.begin() as conn: - # 先删除旧表 - print('删除旧表...') - conn.execute(text(drop_sql1)) - print(' - concept_minute_alert 已删除') - conn.execute(text(drop_sql2)) - print(' - index_minute_snapshot 已删除') - - # 创建新表 - print('\n创建新表...') - conn.execute(text(sql1)) - print(' ✅ concept_minute_alert 表创建成功') - conn.execute(text(sql2)) - print(' ✅ index_minute_snapshot 表创建成功') - - print('\n✅ 所有表创建完成!') diff --git a/ml/__pycache__/realtime_detector.cpython-310.pyc b/ml/__pycache__/realtime_detector.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b926f280bcfab51579e91387bc8db82a9917abaa GIT binary patch literal 32449 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zl@yEvKGT6aWAvAQZfC&{(Ycb&F*@VywH!s%N&?1RMMv<+cKQTy2$~c^eFx(X(c!L0 zzAmNp4G%K-Fr7!}yh(?b!{{4V#iCTUOh0BEh?1wA5YjHRoH$4(P~)@)Ak@(mZnMzW z;L!F|o9W;Rr%r8hqY-LcIPe;J<=ug6Ofyj9iG#04L!{{SFzGqKNQZ;3n~_>d;~D&= znJ^9Ojc~ZjO2iVeuE@$YT}xMXF5T3-bWQuZvBY>{ukQ~MV`eYPya}Lu1NHwFb>hP% a=5K<*=!M>D;%eU?0GnPCT?~%=&HoSaPj0~g literal 0 HcmV?d00001 diff --git a/ml/backtest_fast.py b/ml/backtest_fast.py new file mode 100644 index 00000000..e1c06254 --- /dev/null +++ b/ml/backtest_fast.py @@ -0,0 +1,859 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +快速融合异动回测脚本 + +优化策略： +1. 预先构建所有序列（向量化），避免循环内重复切片 +2. 批量 ML 推理（一次推理所有候选） +3. 使用 NumPy 向量化操作替代 Python 循环 + +性能对比： +- 原版：5分钟/天 +- 优化版：预计 10-30秒/天 +""" + +import os +import sys +import argparse +import json +from datetime import datetime +from pathlib import Path +from typing import Dict, List, Optional, Tuple +from collections import defaultdict + +import numpy as np +import pandas as pd +import torch +from tqdm import tqdm +from sqlalchemy import create_engine, text + +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + + +# ==================== 配置 ==================== + +MYSQL_ENGINE = create_engine( + "mysql+pymysql://root:Zzl5588161!@192.168.1.5:3306/stock", + echo=False +) + +FEATURES = ['alpha', 'alpha_delta', 'amt_ratio', 'amt_delta', 'rank_pct', 'limit_up_ratio'] + +CONFIG = { + 'seq_len': 15, # 序列长度（支持跨日后可从 9:30 检测） + 'min_alpha_abs': 0.3, # 最小 alpha 过滤 + 'cooldown_minutes': 8, + 'max_alerts_per_minute': 20, + 'clip_value': 10.0, + # === 融合权重：均衡 === + 'rule_weight': 0.5, + 'ml_weight': 0.5, + # === 触发阈值 === + 'rule_trigger': 65, # 60 -> 65，略提高规则门槛 + 'ml_trigger': 70, # 75 -> 70，略降低 ML 门槛 + 'fusion_trigger': 45, +} + + +# ==================== 规则评分（向量化版）==================== + +def get_size_adjusted_thresholds(stock_count: np.ndarray) -> dict: + """ + 根据概念股票数量计算动态阈值 + + 设计思路： + - 小概念（<10 只）：波动大是正常的，需要更高阈值 + - 中概念（10-50 只）：标准阈值 + - 大概念（>50 只）：能有明显波动说明是真异动，降低阈值 + + 返回各指标的调整系数（乘以基准阈值） + """ + n = len(stock_count) + + # 基于股票数量的调整系数 + # 小概念：系数 > 1（提高阈值，更难触发） + # 大概念：系数 < 1（降低阈值，更容易触发） + size_factor = np.ones(n) + + # 微型概念（<5 只）：阈值 × 1.8 + tiny = stock_count < 5 + size_factor[tiny] = 1.8 + + # 小概念（5-10 只）：阈值 × 1.4 + small = (stock_count >= 5) & (stock_count < 10) + size_factor[small] = 1.4 + + # 中小概念（10-20 只）：阈值 × 1.2 + medium_small = (stock_count >= 10) & (stock_count < 20) + size_factor[medium_small] = 1.2 + + # 中概念（20-50 只）：标准阈值 × 1.0 + medium = (stock_count >= 20) & (stock_count < 50) + size_factor[medium] = 1.0 + + # 大概念（50-100 只）：阈值 × 0.85 + large = (stock_count >= 50) & (stock_count < 100) + size_factor[large] = 0.85 + + # 超大概念（>100 只）：阈值 × 0.7 + xlarge = stock_count >= 100 + size_factor[xlarge] = 0.7 + + return size_factor + + +def score_rules_batch(df: pd.DataFrame) -> Tuple[np.ndarray, List[List[str]]]: + """ + 批量计算规则得分（向量化）- 考虑概念规模版 + + 设计原则： + - 规则作为辅助信号，不应单独主导决策 + - 根据概念股票数量动态调整阈值 + - 大概念异动更有价值，小概念需要更大波动才算异动 + + Args: + df: DataFrame，包含所有特征列（必须包含 stock_count） + Returns: + scores: (n,) 规则得分数组 + triggered_rules: 每行触发的规则列表 + """ + n = len(df) + scores = np.zeros(n) + triggered = [[] for _ in range(n)] + + alpha = df['alpha'].values + alpha_delta = df['alpha_delta'].values + amt_ratio = df['amt_ratio'].values + amt_delta = df['amt_delta'].values + rank_pct = df['rank_pct'].values + limit_up_ratio = df['limit_up_ratio'].values + stock_count = df['stock_count'].values if 'stock_count' in df.columns else np.full(n, 20) + + alpha_abs = np.abs(alpha) + alpha_delta_abs = np.abs(alpha_delta) + + # 获取基于规模的调整系数 + size_factor = get_size_adjusted_thresholds(stock_count) + + # ========== Alpha 规则（动态阈值）========== + # 基准阈值：极强 5%，强 4%，中等 3% + # 实际阈值 = 基准 × size_factor + + # 极强信号 + alpha_extreme_thresh = 5.0 * size_factor + mask = alpha_abs >= alpha_extreme_thresh + scores[mask] += 20 + for i in np.where(mask)[0]: triggered[i].append('alpha_extreme') + + # 强信号 + alpha_strong_thresh = 4.0 * size_factor + mask = (alpha_abs >= alpha_strong_thresh) & (alpha_abs < alpha_extreme_thresh) + scores[mask] += 15 + for i in np.where(mask)[0]: triggered[i].append('alpha_strong') + + # 中等信号 + alpha_medium_thresh = 3.0 * size_factor + mask = (alpha_abs >= alpha_medium_thresh) & (alpha_abs < alpha_strong_thresh) + scores[mask] += 10 + for i in np.where(mask)[0]: triggered[i].append('alpha_medium') + + # ========== Alpha 加速度规则（动态阈值）========== + delta_strong_thresh = 2.0 * size_factor + mask = alpha_delta_abs >= delta_strong_thresh + scores[mask] += 15 + for i in np.where(mask)[0]: triggered[i].append('alpha_delta_strong') + + delta_medium_thresh = 1.5 * size_factor + mask = (alpha_delta_abs >= delta_medium_thresh) & (alpha_delta_abs < delta_strong_thresh) + scores[mask] += 10 + for i in np.where(mask)[0]: triggered[i].append('alpha_delta_medium') + + # ========== 成交额规则（不受规模影响，放量就是放量）========== + mask = amt_ratio >= 10.0 + scores[mask] += 20 + for i in np.where(mask)[0]: triggered[i].append('volume_extreme') + + mask = (amt_ratio >= 6.0) & (amt_ratio < 10.0) + scores[mask] += 12 + for i in np.where(mask)[0]: triggered[i].append('volume_strong') + + # ========== 排名规则 ========== + mask = rank_pct >= 0.98 + scores[mask] += 15 + for i in np.where(mask)[0]: triggered[i].append('rank_top') + + mask = rank_pct <= 0.02 + scores[mask] += 15 + for i in np.where(mask)[0]: triggered[i].append('rank_bottom') + + # ========== 涨停规则（动态阈值）========== + # 大概念有涨停更有意义 + limit_high_thresh = 0.30 * size_factor + mask = limit_up_ratio >= limit_high_thresh + scores[mask] += 20 + for i in np.where(mask)[0]: triggered[i].append('limit_up_high') + + limit_medium_thresh = 0.20 * size_factor + mask = (limit_up_ratio >= limit_medium_thresh) & (limit_up_ratio < limit_high_thresh) + scores[mask] += 12 + for i in np.where(mask)[0]: triggered[i].append('limit_up_medium') + + # ========== 概念规模加分（大概念异动更有价值）========== + # 大概念（50+）额外加分 + large_concept = stock_count >= 50 + has_signal = scores > 0 # 至少触发了某个规则 + mask = large_concept & has_signal + scores[mask] += 10 + for i in np.where(mask)[0]: triggered[i].append('large_concept_bonus') + + # 超大概念（100+）再加分 + xlarge_concept = stock_count >= 100 + mask = xlarge_concept & has_signal + scores[mask] += 10 + for i in np.where(mask)[0]: triggered[i].append('xlarge_concept_bonus') + + # ========== 组合规则（动态阈值）========== + combo_alpha_thresh = 3.0 * size_factor + + # Alpha + 放量 + 排名（三重验证） + mask = (alpha_abs >= combo_alpha_thresh) & (amt_ratio >= 5.0) & ((rank_pct >= 0.95) | (rank_pct <= 0.05)) + scores[mask] += 20 + for i in np.where(mask)[0]: triggered[i].append('triple_signal') + + # Alpha + 涨停（强组合） + mask = (alpha_abs >= combo_alpha_thresh) & (limit_up_ratio >= 0.15 * size_factor) + scores[mask] += 15 + for i in np.where(mask)[0]: triggered[i].append('alpha_with_limit') + + # ========== 小概念惩罚（过滤噪音）========== + # 微型概念（<5 只）如果只有单一信号，减分 + tiny_concept = stock_count < 5 + single_rule = np.array([len(t) <= 1 for t in triggered]) + mask = tiny_concept & single_rule & (scores > 0) + scores[mask] *= 0.5 # 减半 + for i in np.where(mask)[0]: triggered[i].append('tiny_concept_penalty') + + scores = np.clip(scores, 0, 100) + return scores, triggered + + +# ==================== ML 评分器 ==================== + +class FastMLScorer: + """快速 ML 评分器""" + + def __init__(self, checkpoint_dir: str = 'ml/checkpoints', device: str = 'auto'): + self.checkpoint_dir = Path(checkpoint_dir) + + if device == 'auto': + self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + elif device == 'cuda' and not torch.cuda.is_available(): + print("警告: CUDA 不可用，使用 CPU") + self.device = torch.device('cpu') + else: + self.device = torch.device(device) + + self.model = None + self.thresholds = None + self._load_model() + + def _load_model(self): + model_path = self.checkpoint_dir / 'best_model.pt' + thresholds_path = self.checkpoint_dir / 'thresholds.json' + config_path = self.checkpoint_dir / 'config.json' + + if not model_path.exists(): + print(f"警告: 模型不存在 {model_path}") + return + + try: + from model import LSTMAutoencoder + + config = {} + if config_path.exists(): + with open(config_path) as f: + config = json.load(f).get('model', {}) + + # 处理旧配置键名 + if 'd_model' in config: + config['hidden_dim'] = config.pop('d_model') // 2 + for key in ['num_encoder_layers', 'num_decoder_layers', 'nhead', 'dim_feedforward', 'max_seq_len', 'use_instance_norm']: + config.pop(key, None) + if 'num_layers' not in config: + config['num_layers'] = 1 + + checkpoint = torch.load(model_path, map_location='cpu') + self.model = LSTMAutoencoder(**config) + self.model.load_state_dict(checkpoint['model_state_dict']) + self.model.to(self.device) + self.model.eval() + + if thresholds_path.exists(): + with open(thresholds_path) as f: + self.thresholds = json.load(f) + + print(f"ML模型加载成功 (设备: {self.device})") + except Exception as e: + print(f"ML模型加载失败: {e}") + self.model = None + + def is_ready(self): + return self.model is not None + + @torch.no_grad() + def score_batch(self, sequences: np.ndarray) -> np.ndarray: + """ + 批量计算 ML 得分 + + Args: + sequences: (batch, seq_len, n_features) + Returns: + scores: (batch,) 0-100 分数 + """ + if not self.is_ready() or len(sequences) == 0: + return np.zeros(len(sequences)) + + x = torch.FloatTensor(sequences).to(self.device) + output, _ = self.model(x) + mse = ((output - x) ** 2).mean(dim=-1) + errors = mse[:, -1].cpu().numpy() + + p95 = self.thresholds.get('p95', 0.1) if self.thresholds else 0.1 + scores = np.clip(errors / p95 * 50, 0, 100) + return scores + + +# ==================== 快速回测 ==================== + +def build_sequences_fast( + df: pd.DataFrame, + seq_len: int = 30, + prev_df: pd.DataFrame = None +) -> Tuple[np.ndarray, pd.DataFrame]: + """ + 快速构建所有有效序列 + + 支持跨日序列：用前一天收盘数据 + 当天开盘数据拼接，实现 9:30 就能检测 + + Args: + df: 当天数据 + seq_len: 序列长度 + prev_df: 前一天数据（可选，用于构建开盘时的序列） + + 返回： + sequences: (n_valid, seq_len, n_features) 所有有效序列 + info_df: 对应的元信息 DataFrame + """ + # 确保按概念和时间排序 + df = df.sort_values(['concept_id', 'timestamp']).reset_index(drop=True) + + # 如果有前一天数据，按概念构建尾部缓存（取每个概念最后 seq_len-1 条） + prev_cache = {} + if prev_df is not None and len(prev_df) > 0: + prev_df = prev_df.sort_values(['concept_id', 'timestamp']) + for concept_id, gdf in prev_df.groupby('concept_id'): + tail_data = gdf.tail(seq_len - 1) + if len(tail_data) > 0: + feat_matrix = tail_data[FEATURES].values + feat_matrix = np.nan_to_num(feat_matrix, nan=0.0, posinf=0.0, neginf=0.0) + feat_matrix = np.clip(feat_matrix, -CONFIG['clip_value'], CONFIG['clip_value']) + prev_cache[concept_id] = feat_matrix + + # 按概念分组 + groups = df.groupby('concept_id') + + sequences = [] + infos = [] + + for concept_id, gdf in groups: + gdf = gdf.reset_index(drop=True) + + # 获取特征矩阵 + feat_matrix = gdf[FEATURES].values + feat_matrix = np.nan_to_num(feat_matrix, nan=0.0, posinf=0.0, neginf=0.0) + feat_matrix = np.clip(feat_matrix, -CONFIG['clip_value'], CONFIG['clip_value']) + + # 如果有前一天缓存，拼接到当天数据前面 + if concept_id in prev_cache: + prev_data = prev_cache[concept_id] + combined_matrix = np.vstack([prev_data, feat_matrix]) + # 计算偏移量：前一天数据的长度 + offset = len(prev_data) + else: + combined_matrix = feat_matrix + offset = 0 + + # 滑动窗口构建序列 + n_total = len(combined_matrix) + if n_total < seq_len: + continue + + for i in range(n_total - seq_len + 1): + seq = combined_matrix[i:i + seq_len] + + # 计算对应当天数据的索引 + # 序列最后一个点的位置 = i + seq_len - 1 + # 对应当天数据的索引 = (i + seq_len - 1) - offset + today_idx = i + seq_len - 1 - offset + + # 只要序列的最后一个点是当天的数据，就记录 + if today_idx < 0 or today_idx >= len(gdf): + continue + + sequences.append(seq) + + # 记录最后一个时间步的信息（当天的） + row = gdf.iloc[today_idx] + infos.append({ + 'concept_id': concept_id, + 'timestamp': row['timestamp'], + 'alpha': row['alpha'], + 'alpha_delta': row.get('alpha_delta', 0), + 'amt_ratio': row.get('amt_ratio', 1), + 'amt_delta': row.get('amt_delta', 0), + 'rank_pct': row.get('rank_pct', 0.5), + 'limit_up_ratio': row.get('limit_up_ratio', 0), + 'stock_count': row.get('stock_count', 0), + 'total_amt': row.get('total_amt', 0), + }) + + if not sequences: + return np.array([]), pd.DataFrame() + + return np.array(sequences), pd.DataFrame(infos) + + +def backtest_single_day_fast( + ml_scorer: FastMLScorer, + df: pd.DataFrame, + date: str, + config: Dict, + prev_df: pd.DataFrame = None +) -> List[Dict]: + """ + 快速回测单天（向量化版本） + + Args: + ml_scorer: ML 评分器 + df: 当天数据 + date: 日期 + config: 配置 + prev_df: 前一天数据（用于 9:30 开始检测） + """ + seq_len = config.get('seq_len', 30) + + # 1. 构建所有序列（支持跨日） + sequences, info_df = build_sequences_fast(df, seq_len, prev_df) + + if len(sequences) == 0: + return [] + + # 2. 过滤小波动 + alpha_abs = np.abs(info_df['alpha'].values) + valid_mask = alpha_abs >= config['min_alpha_abs'] + + sequences = sequences[valid_mask] + info_df = info_df[valid_mask].reset_index(drop=True) + + if len(sequences) == 0: + return [] + + # 3. 批量规则评分 + rule_scores, triggered_rules = score_rules_batch(info_df) + + # 4. 批量 ML 评分（分批处理避免显存溢出） + batch_size = 2048 + ml_scores = [] + for i in range(0, len(sequences), batch_size): + batch_seq = sequences[i:i+batch_size] + batch_scores = ml_scorer.score_batch(batch_seq) + ml_scores.append(batch_scores) + ml_scores = np.concatenate(ml_scores) if ml_scores else np.zeros(len(sequences)) + + # 5. 融合得分 + w1, w2 = config['rule_weight'], config['ml_weight'] + final_scores = w1 * rule_scores + w2 * ml_scores + + # 6. 判断异动 + is_anomaly = ( + (rule_scores >= config['rule_trigger']) | + (ml_scores >= config['ml_trigger']) | + (final_scores >= config['fusion_trigger']) + ) + + # 7. 应用冷却期（按概念+时间排序后处理） + info_df['rule_score'] = rule_scores + info_df['ml_score'] = ml_scores + info_df['final_score'] = final_scores + info_df['is_anomaly'] = is_anomaly + info_df['triggered_rules'] = triggered_rules + + # 只保留异动 + anomaly_df = info_df[info_df['is_anomaly']].copy() + + if len(anomaly_df) == 0: + return [] + + # 应用冷却期 + anomaly_df = anomaly_df.sort_values(['concept_id', 'timestamp']) + cooldown = {} + keep_mask = [] + + for _, row in anomaly_df.iterrows(): + cid = row['concept_id'] + ts = row['timestamp'] + + if cid in cooldown: + try: + diff = (ts - cooldown[cid]).total_seconds() / 60 + except: + diff = config['cooldown_minutes'] + 1 + + if diff < config['cooldown_minutes']: + keep_mask.append(False) + continue + + cooldown[cid] = ts + keep_mask.append(True) + + anomaly_df = anomaly_df[keep_mask] + + # 8. 按时间分组，每分钟最多 max_alerts_per_minute 个 + alerts = [] + for ts, group in anomaly_df.groupby('timestamp'): + group = group.nlargest(config['max_alerts_per_minute'], 'final_score') + + for _, row in group.iterrows(): + alpha = row['alpha'] + if alpha >= 1.5: + atype = 'surge_up' + elif alpha <= -1.5: + atype = 'surge_down' + elif row['amt_ratio'] >= 3.0: + atype = 'volume_spike' + else: + atype = 'unknown' + + rule_score = row['rule_score'] + ml_score = row['ml_score'] + final_score = row['final_score'] + + if rule_score >= config['rule_trigger']: + trigger = f'规则强信号({rule_score:.0f}分)' + elif ml_score >= config['ml_trigger']: + trigger = f'ML强信号({ml_score:.0f}分)' + else: + trigger = f'融合触发({final_score:.0f}分)' + + alerts.append({ + 'concept_id': row['concept_id'], + 'alert_time': row['timestamp'], + 'trade_date': date, + 'alert_type': atype, + 'final_score': final_score, + 'rule_score': rule_score, + 'ml_score': ml_score, + 'trigger_reason': trigger, + 'triggered_rules': row['triggered_rules'], + 'alpha': alpha, + 'alpha_delta': row['alpha_delta'], + 'amt_ratio': row['amt_ratio'], + 'amt_delta': row['amt_delta'], + 'rank_pct': row['rank_pct'], + 'limit_up_ratio': row['limit_up_ratio'], + 'stock_count': row['stock_count'], + 'total_amt': row['total_amt'], + }) + + return alerts + + +# ==================== 数据加载 ==================== + +def load_daily_features(data_dir: str, date: str) -> Optional[pd.DataFrame]: + file_path = Path(data_dir) / f"features_{date}.parquet" + if not file_path.exists(): + return None + return pd.read_parquet(file_path) + + +def get_available_dates(data_dir: str, start: str, end: str) -> List[str]: + data_path = Path(data_dir) + dates = [] + for f in sorted(data_path.glob("features_*.parquet")): + d = f.stem.replace('features_', '') + if start <= d <= end: + dates.append(d) + return dates + + +def get_prev_trading_day(data_dir: str, date: str) -> Optional[str]: + """获取给定日期之前最近的有数据的交易日""" + data_path = Path(data_dir) + all_dates = sorted([f.stem.replace('features_', '') for f in data_path.glob("features_*.parquet")]) + + for i, d in enumerate(all_dates): + if d == date and i > 0: + return all_dates[i - 1] + return None + + +def export_to_csv(alerts: List[Dict], path: str): + if alerts: + pd.DataFrame(alerts).to_csv(path, index=False, encoding='utf-8-sig') + print(f"已导出: {path}") + + +# ==================== 数据库写入 ==================== + +def init_db_table(): + """ + 初始化数据库表（如果不存在则创建） + + 表结构说明： + - concept_id: 概念ID + - alert_time: 异动时间（精确到分钟） + - trade_date: 交易日期 + - alert_type: 异动类型（surge_up/surge_down/volume_spike/unknown） + - final_score: 最终得分（0-100） + - rule_score: 规则得分（0-100） + - ml_score: ML得分（0-100） + - trigger_reason: 触发原因 + - alpha: 超额收益率 + - alpha_delta: alpha变化速度 + - amt_ratio: 成交额放大倍数 + - rank_pct: 排名百分位 + - stock_count: 概念股票数量 + - triggered_rules: 触发的规则列表（JSON） + """ + create_sql = text(""" + CREATE TABLE IF NOT EXISTS concept_anomaly_hybrid ( + id INT AUTO_INCREMENT PRIMARY KEY, + concept_id VARCHAR(64) NOT NULL, + alert_time DATETIME NOT NULL, + trade_date DATE NOT NULL, + alert_type VARCHAR(32) NOT NULL, + final_score FLOAT NOT NULL, + rule_score FLOAT NOT NULL, + ml_score FLOAT NOT NULL, + trigger_reason VARCHAR(64), + alpha FLOAT, + alpha_delta FLOAT, + amt_ratio FLOAT, + amt_delta FLOAT, + rank_pct FLOAT, + limit_up_ratio FLOAT, + stock_count INT, + total_amt FLOAT, + triggered_rules JSON, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + UNIQUE KEY uk_concept_time (concept_id, alert_time, trade_date), + INDEX idx_trade_date (trade_date), + INDEX idx_concept_id (concept_id), + INDEX idx_final_score (final_score), + INDEX idx_alert_type (alert_type) + ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COMMENT='概念异动检测结果（融合版）' + """) + + with MYSQL_ENGINE.begin() as conn: + conn.execute(create_sql) + print("数据库表已就绪: concept_anomaly_hybrid") + + +def save_alerts_to_mysql(alerts: List[Dict], dry_run: bool = False) -> int: + """ + 保存异动到 MySQL + + Args: + alerts: 异动列表 + dry_run: 是否只模拟，不实际写入 + + Returns: + 实际保存的记录数 + """ + if not alerts: + return 0 + + if dry_run: + print(f" [Dry Run] 将写入 {len(alerts)} 条异动") + return len(alerts) + + saved = 0 + skipped = 0 + + with MYSQL_ENGINE.begin() as conn: + for alert in alerts: + try: + # 检查是否已存在（使用 INSERT IGNORE 更高效） + insert_sql = text(""" + INSERT IGNORE INTO concept_anomaly_hybrid + (concept_id, alert_time, trade_date, alert_type, + final_score, rule_score, ml_score, trigger_reason, + alpha, alpha_delta, amt_ratio, amt_delta, + rank_pct, limit_up_ratio, stock_count, total_amt, + triggered_rules) + VALUES + (:concept_id, :alert_time, :trade_date, :alert_type, + :final_score, :rule_score, :ml_score, :trigger_reason, + :alpha, :alpha_delta, :amt_ratio, :amt_delta, + :rank_pct, :limit_up_ratio, :stock_count, :total_amt, + :triggered_rules) + """) + + result = conn.execute(insert_sql, { + 'concept_id': alert['concept_id'], + 'alert_time': alert['alert_time'], + 'trade_date': alert['trade_date'], + 'alert_type': alert['alert_type'], + 'final_score': alert['final_score'], + 'rule_score': alert['rule_score'], + 'ml_score': alert['ml_score'], + 'trigger_reason': alert['trigger_reason'], + 'alpha': alert.get('alpha', 0), + 'alpha_delta': alert.get('alpha_delta', 0), + 'amt_ratio': alert.get('amt_ratio', 1), + 'amt_delta': alert.get('amt_delta', 0), + 'rank_pct': alert.get('rank_pct', 0.5), + 'limit_up_ratio': alert.get('limit_up_ratio', 0), + 'stock_count': alert.get('stock_count', 0), + 'total_amt': alert.get('total_amt', 0), + 'triggered_rules': json.dumps(alert.get('triggered_rules', []), ensure_ascii=False), + }) + + if result.rowcount > 0: + saved += 1 + else: + skipped += 1 + + except Exception as e: + print(f" 保存失败: {alert['concept_id']} @ {alert['alert_time']} - {e}") + + if skipped > 0: + print(f" 跳过 {skipped} 条重复记录") + + return saved + + +def clear_alerts_by_date(trade_date: str) -> int: + """清除指定日期的异动记录（用于重新回测）""" + with MYSQL_ENGINE.begin() as conn: + result = conn.execute( + text("DELETE FROM concept_anomaly_hybrid WHERE trade_date = :trade_date"), + {'trade_date': trade_date} + ) + return result.rowcount + + +def analyze_alerts(alerts: List[Dict]): + if not alerts: + print("无异动") + return + + df = pd.DataFrame(alerts) + print(f"\n总异动: {len(alerts)}") + print(f"\n类型分布:\n{df['alert_type'].value_counts()}") + print(f"\n得分统计:") + print(f" 最终: {df['final_score'].mean():.1f} (max: {df['final_score'].max():.1f})") + print(f" 规则: {df['rule_score'].mean():.1f} (max: {df['rule_score'].max():.1f})") + print(f" ML: {df['ml_score'].mean():.1f} (max: {df['ml_score'].max():.1f})") + + trigger_type = df['trigger_reason'].apply( + lambda x: '规则' if '规则' in x else ('ML' if 'ML' in x else '融合') + ) + print(f"\n触发来源:\n{trigger_type.value_counts()}") + + +# ==================== 主函数 ==================== + +def main(): + parser = argparse.ArgumentParser(description='快速融合异动回测') + parser.add_argument('--data_dir', default='ml/data') + parser.add_argument('--checkpoint_dir', default='ml/checkpoints') + parser.add_argument('--start', required=True) + parser.add_argument('--end', default=None) + parser.add_argument('--dry-run', action='store_true', help='模拟运行，不写入数据库') + parser.add_argument('--export-csv', default=None, help='导出 CSV 文件路径') + parser.add_argument('--save-db', action='store_true', help='保存结果到数据库') + parser.add_argument('--clear-first', action='store_true', help='写入前先清除该日期的旧数据') + parser.add_argument('--device', default='auto') + + args = parser.parse_args() + if args.end is None: + args.end = args.start + + print("=" * 60) + print("快速融合异动回测") + print("=" * 60) + print(f"日期: {args.start} ~ {args.end}") + print(f"设备: {args.device}") + print(f"保存数据库: {args.save_db}") + print("=" * 60) + + # 初始化数据库表（如果需要保存） + if args.save_db and not args.dry_run: + init_db_table() + + # 初始化 ML 评分器 + ml_scorer = FastMLScorer(args.checkpoint_dir, args.device) + + # 获取日期 + dates = get_available_dates(args.data_dir, args.start, args.end) + if not dates: + print("无数据") + return + + print(f"找到 {len(dates)} 天数据\n") + + # 回测（支持跨日序列） + all_alerts = [] + total_saved = 0 + prev_df = None # 缓存前一天数据 + + for i, date in enumerate(tqdm(dates, desc="回测")): + df = load_daily_features(args.data_dir, date) + if df is None or df.empty: + prev_df = None # 当天无数据，清空缓存 + continue + + # 第一天需要加载前一天数据（如果存在） + if i == 0 and prev_df is None: + prev_date = get_prev_trading_day(args.data_dir, date) + if prev_date: + prev_df = load_daily_features(args.data_dir, prev_date) + if prev_df is not None: + tqdm.write(f" 加载前一天数据: {prev_date}") + + alerts = backtest_single_day_fast(ml_scorer, df, date, CONFIG, prev_df) + all_alerts.extend(alerts) + + # 保存到数据库 + if args.save_db and alerts: + if args.clear_first and not args.dry_run: + cleared = clear_alerts_by_date(date) + if cleared > 0: + tqdm.write(f" 清除 {date} 旧数据: {cleared} 条") + + saved = save_alerts_to_mysql(alerts, dry_run=args.dry_run) + total_saved += saved + tqdm.write(f" {date}: {len(alerts)} 个异动, 保存 {saved} 条") + elif alerts: + tqdm.write(f" {date}: {len(alerts)} 个异动") + + # 当天数据成为下一天的 prev_df + prev_df = df + + # 导出 CSV + if args.export_csv: + export_to_csv(all_alerts, args.export_csv) + + # 分析 + analyze_alerts(all_alerts) + + print(f"\n总计: {len(all_alerts)} 个异动") + if args.save_db: + print(f"已保存到数据库: {total_saved} 条") + + +if __name__ == "__main__": + main() diff --git a/ml/backtest_hybrid.py b/ml/backtest_hybrid.py index 7bb4c808..6913f204 100644 --- a/ml/backtest_hybrid.py +++ b/ml/backtest_hybrid.py @@ -93,12 +93,12 @@ def backtest_single_day_hybrid( seq_len: int = 30 ) -> List[Dict]: """ - 使用融合检测器回测单天数据 + 使用融合检测器回测单天数据（批量优化版） """ alerts = [] - # 按概念分组 - grouped = df.groupby('concept_id', sort=False) + # 按概念分组，预先构建字典 + grouped_dict = {cid: cdf for cid, cdf in df.groupby('concept_id', sort=False)} # 冷却记录 cooldown = {} @@ -114,27 +114,46 @@ def backtest_single_day_hybrid( current_time = all_timestamps[t_idx] window_start_time = all_timestamps[t_idx - seq_len + 1] - minute_alerts = [] + # 批量收集该时刻所有候选概念 + batch_sequences = [] + batch_features = [] + batch_infos = [] + + for concept_id, concept_df in grouped_dict.items(): + # 检查冷却（提前过滤） + if concept_id in cooldown: + last_alert = cooldown[concept_id] + if isinstance(current_time, datetime): + time_diff = (current_time - last_alert).total_seconds() / 60 + else: + time_diff = BACKTEST_CONFIG['cooldown_minutes'] + 1 + if time_diff < BACKTEST_CONFIG['cooldown_minutes']: + continue - for concept_id, concept_df in grouped: # 获取时间窗口内的数据 mask = (concept_df['timestamp'] >= window_start_time) & (concept_df['timestamp'] <= current_time) - window_df = concept_df[mask].sort_values('timestamp') + window_df = concept_df.loc[mask] if len(window_df) < seq_len: continue - window_df = window_df.tail(seq_len) + window_df = window_df.sort_values('timestamp').tail(seq_len) - # 提取特征序列（给 ML 模型） + # 当前时刻特征 + current_row = window_df.iloc[-1] + alpha = current_row.get('alpha', 0) + + # 过滤微小波动（提前过滤） + if abs(alpha) < BACKTEST_CONFIG['min_alpha_abs']: + continue + + # 提取特征序列 sequence = window_df[FEATURES].values sequence = np.nan_to_num(sequence, nan=0.0, posinf=0.0, neginf=0.0) sequence = np.clip(sequence, -BACKTEST_CONFIG['clip_value'], BACKTEST_CONFIG['clip_value']) - # 当前时刻特征（给规则系统） - current_row = window_df.iloc[-1] current_features = { - 'alpha': current_row.get('alpha', 0), + 'alpha': alpha, 'alpha_delta': current_row.get('alpha_delta', 0), 'amt_ratio': current_row.get('amt_ratio', 1), 'amt_delta': current_row.get('amt_delta', 0), @@ -142,41 +161,79 @@ def backtest_single_day_hybrid( 'limit_up_ratio': current_row.get('limit_up_ratio', 0), } - # 过滤微小波动 - if abs(current_features['alpha']) < BACKTEST_CONFIG['min_alpha_abs']: + batch_sequences.append(sequence) + batch_features.append(current_features) + batch_infos.append({ + 'concept_id': concept_id, + 'stock_count': current_row.get('stock_count', 0), + 'total_amt': current_row.get('total_amt', 0), + }) + + if not batch_sequences: + continue + + # 批量 ML 推理 + sequences_array = np.array(batch_sequences) + ml_scores = detector.ml_scorer.score(sequences_array) if detector.ml_scorer.is_ready() else [0.0] * len(batch_sequences) + if isinstance(ml_scores, float): + ml_scores = [ml_scores] + + # 批量规则评分 + 融合 + minute_alerts = [] + for i, (features, info) in enumerate(zip(batch_features, batch_infos)): + concept_id = info['concept_id'] + + # 规则评分 + rule_score, rule_details = detector.rule_scorer.score(features) + + # ML 评分 + ml_score = ml_scores[i] if i < len(ml_scores) else 0.0 + + # 融合 + w1 = detector.config['rule_weight'] + w2 = detector.config['ml_weight'] + final_score = w1 * rule_score + w2 * ml_score + + # 判断是否异动 + is_anomaly = False + trigger_reason = '' + + if rule_score >= detector.config['rule_trigger']: + is_anomaly = True + trigger_reason = f'规则强信号({rule_score:.0f}分)' + elif ml_score >= detector.config['ml_trigger']: + is_anomaly = True + trigger_reason = f'ML强信号({ml_score:.0f}分)' + elif final_score >= detector.config['fusion_trigger']: + is_anomaly = True + trigger_reason = f'融合触发({final_score:.0f}分)' + + if not is_anomaly: continue - # 检查冷却 - if concept_id in cooldown: - last_alert = cooldown[concept_id] - if isinstance(current_time, datetime): - time_diff = (current_time - last_alert).total_seconds() / 60 - else: - time_diff = BACKTEST_CONFIG['cooldown_minutes'] + 1 + # 异动类型 + alpha = features.get('alpha', 0) + if alpha >= 1.5: + anomaly_type = 'surge_up' + elif alpha <= -1.5: + anomaly_type = 'surge_down' + elif features.get('amt_ratio', 1) >= 3.0: + anomaly_type = 'volume_spike' + else: + anomaly_type = 'unknown' - if time_diff < BACKTEST_CONFIG['cooldown_minutes']: - continue - - # 融合检测 - result = detector.detect(current_features, sequence) - - if not result.is_anomaly: - continue - - # 记录异动 alert = { 'concept_id': concept_id, 'alert_time': current_time, 'trade_date': date, - 'alert_type': result.anomaly_type, - 'final_score': result.final_score, - 'rule_score': result.rule_score, - 'ml_score': result.ml_score, - 'trigger_reason': result.trigger_reason, - 'triggered_rules': list(result.rule_details.keys()), - **current_features, - 'stock_count': current_row.get('stock_count', 0), - 'total_amt': current_row.get('total_amt', 0), + 'alert_type': anomaly_type, + 'final_score': final_score, + 'rule_score': rule_score, + 'ml_score': ml_score, + 'trigger_reason': trigger_reason, + 'triggered_rules': list(rule_details.keys()), + **features, + **info, } minute_alerts.append(alert) @@ -341,6 +398,8 @@ def main(): help='规则权重 (0-1)') parser.add_argument('--ml-weight', type=float, default=0.4, help='ML权重 (0-1)') + parser.add_argument('--device', type=str, default='cuda', + help='设备 (cuda/cpu)，默认 cuda') args = parser.parse_args() @@ -355,15 +414,19 @@ def main(): print(f"模型目录: {args.checkpoint_dir}") print(f"规则权重: {args.rule_weight}") print(f"ML权重: {args.ml_weight}") + print(f"设备: {args.device}") print(f"Dry Run: {args.dry_run}") print("=" * 60) - # 初始化融合检测器 + # 初始化融合检测器（使用 GPU） config = { 'rule_weight': args.rule_weight, 'ml_weight': args.ml_weight, } - detector = create_detector(args.checkpoint_dir, config) + + # 修改 detector.py 中 MLScorer 的设备 + from detector import HybridAnomalyDetector + detector = HybridAnomalyDetector(config, args.checkpoint_dir, device=args.device) # 获取可用日期 dates = get_available_dates(args.data_dir, args.start, args.end) diff --git a/ml/detector.py b/ml/detector.py index 0c14880d..c5184771 100644 --- a/ml/detector.py +++ b/ml/detector.py @@ -243,9 +243,12 @@ class MLScorer: ): self.checkpoint_dir = Path(checkpoint_dir) - # 设备 + # 设备检测 if device == 'auto': self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + elif device == 'cuda' and not torch.cuda.is_available(): + print("警告: CUDA 不可用，使用 CPU") + self.device = torch.device('cpu') else: self.device = torch.device(device) @@ -276,8 +279,8 @@ class MLScorer: with open(config_path, 'r') as f: self.config = json.load(f) - # 加载模型 - checkpoint = torch.load(model_path, map_location=self.device) + # 先用 CPU 加载模型（避免 CUDA 不可用问题），再移动到目标设备 + checkpoint = torch.load(model_path, map_location='cpu') model_config = self.config.get('model', {}) if self.config else {} self.model = create_model(model_config) @@ -294,6 +297,8 @@ class MLScorer: except Exception as e: print(f"警告: 模型加载失败 - {e}") + import traceback + traceback.print_exc() self.model = None def is_ready(self) -> bool: @@ -551,7 +556,8 @@ if __name__ == "__main__": }, ] - print("\n测试结果:") + print("\n" + "-" * 60) + print("测试1: 只用规则（无序列数据）") print("-" * 60) for case in test_cases: @@ -567,5 +573,63 @@ if __name__ == "__main__": print(f" 异动类型: {result.anomaly_type}") print(f" 触发规则: {list(result.rule_details.keys())}") + # 测试2: 带序列数据的融合检测 + print("\n" + "-" * 60) + print("测试2: 融合检测（规则 + ML）") + print("-" * 60) + + # 生成模拟序列数据 + seq_len = 30 + n_features = 6 + + # 正常序列：小幅波动 + normal_sequence = np.random.randn(seq_len, n_features) * 0.3 + normal_sequence[:, 0] = np.linspace(0, 0.5, seq_len) # alpha 缓慢上升 + normal_sequence[:, 2] = np.abs(normal_sequence[:, 2]) + 1 # amt_ratio > 0 + + # 异常序列：最后几个时间步突然变化 + anomaly_sequence = np.random.randn(seq_len, n_features) * 0.3 + anomaly_sequence[-5:, 0] = np.linspace(1, 4, 5) # alpha 突然飙升 + anomaly_sequence[-5:, 1] = np.linspace(0.2, 1.5, 5) # alpha_delta 加速 + anomaly_sequence[-5:, 2] = np.linspace(2, 6, 5) # amt_ratio 放量 + anomaly_sequence[:, 2] = np.abs(anomaly_sequence[:, 2]) + 1 + + # 测试正常序列 + normal_features = { + 'alpha': float(normal_sequence[-1, 0]), + 'alpha_delta': float(normal_sequence[-1, 1]), + 'amt_ratio': float(normal_sequence[-1, 2]), + 'amt_delta': float(normal_sequence[-1, 3]), + 'rank_pct': 0.5, + 'limit_up_ratio': 0.02 + } + + result = detector.detect(normal_features, normal_sequence) + print(f"\n正常序列:") + print(f" 异动: {'是' if result.is_anomaly else '否'}") + print(f" 最终得分: {result.final_score:.1f}") + print(f" 规则得分: {result.rule_score:.1f}") + print(f" ML得分: {result.ml_score:.1f}") + + # 测试异常序列 + anomaly_features = { + 'alpha': float(anomaly_sequence[-1, 0]), + 'alpha_delta': float(anomaly_sequence[-1, 1]), + 'amt_ratio': float(anomaly_sequence[-1, 2]), + 'amt_delta': float(anomaly_sequence[-1, 3]), + 'rank_pct': 0.95, + 'limit_up_ratio': 0.15 + } + + result = detector.detect(anomaly_features, anomaly_sequence) + print(f"\n异常序列:") + print(f" 异动: {'是' if result.is_anomaly else '否'}") + print(f" 最终得分: {result.final_score:.1f}") + print(f" 规则得分: {result.rule_score:.1f}") + print(f" ML得分: {result.ml_score:.1f}") + if result.is_anomaly: + print(f" 触发原因: {result.trigger_reason}") + print(f" 异动类型: {result.anomaly_type}") + print("\n" + "=" * 60) print("测试完成!") diff --git a/ml/realtime_detector.py b/ml/realtime_detector.py new file mode 100644 index 00000000..0144add3 --- /dev/null +++ b/ml/realtime_detector.py @@ -0,0 +1,1518 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +实时概念异动检测服务（实盘可用） + +盘中每分钟运行一次，检测概念异动并写入数据库 + +数据流程： +1. 启动时从 ES 获取概念列表，从 MySQL 获取昨收价 +2. 自动预热：从 ClickHouse 加载当天已有的历史分钟数据 +3. 每分钟增量获取最新分钟数据 +4. 在内存中实时计算概念特征（无前瞻偏差） +5. 使用规则+ML融合评分检测异动 +6. 异动写入 MySQL + +特征计算说明（无 Looking Forward）： +- alpha: 当前时间点的概念超额收益 +- alpha_delta: 使用过去 5 分钟的 alpha 变化 +- amt_ratio: 使用过去 20 分钟的成交额均值 +- rank_pct: 当前时间点所有概念的 alpha 排名 +- limit_up_ratio: 当前时间点的涨停股占比 + +使用方法： + # 实盘模式（推荐）- 自动预热，不依赖 prepare_data.py + python realtime_detector.py + + # 单次检测 + python realtime_detector.py --once + + # 回补历史异动到数据库（需要 prepare_data.py 生成 parquet） + python realtime_detector.py --backfill-only + + # 实盘模式 + 启动时回补历史 + python realtime_detector.py --backfill + +最小数据量要求： +- ML 评分需要 seq_len=15 分钟的序列 +- amt_ratio 需要 amt_ma_window=20 分钟的历史 +- 即：开盘后约 35 分钟才能正常工作 +""" + +import os +import sys +import time +import json +import argparse +import schedule +from datetime import datetime, timedelta +from pathlib import Path +from typing import Dict, List, Optional, Tuple, Set +from collections import defaultdict + +import numpy as np +import pandas as pd +import torch +from sqlalchemy import create_engine, text +from elasticsearch import Elasticsearch +from clickhouse_driver import Client as CHClient + +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + + +# ==================== 配置 ==================== + +MYSQL_ENGINE = create_engine( + "mysql+pymysql://root:Zzl5588161!@192.168.1.5:3306/stock", + echo=False, + pool_pre_ping=True, + pool_recycle=3600, +) + +ES_CLIENT = Elasticsearch(['http://127.0.0.1:9200']) +ES_INDEX = 'concept_library_v3' + +CLICKHOUSE_CONFIG = { + 'host': '127.0.0.1', + 'port': 9000, + 'user': 'default', + 'password': 'Zzl33818!', + 'database': 'stock' +} + +REFERENCE_INDEX = '000001.SH' + +FEATURES = ['alpha', 'alpha_delta', 'amt_ratio', 'amt_delta', 'rank_pct', 'limit_up_ratio'] + +# 特征计算参数 +FEATURE_CONFIG = { + 'alpha_delta_window': 5, + 'amt_ma_window': 20, + 'limit_up_threshold': 9.8, + 'limit_down_threshold': -9.8, +} + +# 检测配置（与 backtest_fast.py 保持一致） +CONFIG = { + 'seq_len': 15, + 'min_alpha_abs': 0.3, + 'cooldown_minutes': 8, + 'max_alerts_per_minute': 20, + 'clip_value': 10.0, + # === 融合权重：与 backtest_fast.py 一致 === + 'rule_weight': 0.5, + 'ml_weight': 0.5, + # === 触发阈值：与 backtest_fast.py 一致 === + 'rule_trigger': 65, + 'ml_trigger': 70, + 'fusion_trigger': 45, +} + +TRADING_PERIODS = [ + ('09:30', '11:30'), + ('13:00', '15:00'), +] + + +# ==================== 工具函数 ==================== + +def get_ch_client(): + return CHClient(**CLICKHOUSE_CONFIG) + + +def code_to_ch_format(code: str) -> str: + if not code or len(code) != 6 or not code.isdigit(): + return None + if code.startswith('6'): + return f"{code}.SH" + elif code.startswith('0') or code.startswith('3'): + return f"{code}.SZ" + else: + return f"{code}.BJ" + + +def is_trading_time() -> bool: + now = datetime.now() + if now.weekday() >= 5: + return False + current_time = now.strftime('%H:%M') + for start, end in TRADING_PERIODS: + if start <= current_time <= end: + return True + return False + + +def get_current_trade_date() -> str: + now = datetime.now() + if now.hour < 9: + now = now - timedelta(days=1) + return now.strftime('%Y-%m-%d') + + +# ==================== 数据获取 ==================== + +def get_all_concepts() -> List[dict]: + """从 ES 获取所有概念""" + concepts = [] + query = { + "query": {"match_all": {}}, + "size": 100, + "_source": ["concept_id", "concept", "stocks"] + } + + resp = ES_CLIENT.search(index=ES_INDEX, body=query, scroll='2m') + scroll_id = resp['_scroll_id'] + hits = resp['hits']['hits'] + + while len(hits) > 0: + for hit in hits: + source = hit['_source'] + stocks = [] + if 'stocks' in source and isinstance(source['stocks'], list): + for stock in source['stocks']: + if isinstance(stock, dict) and 'code' in stock and stock['code']: + stocks.append(stock['code']) + + if stocks: + concepts.append({ + 'concept_id': source.get('concept_id'), + 'concept_name': source.get('concept'), + 'stocks': stocks + }) + + resp = ES_CLIENT.scroll(scroll_id=scroll_id, scroll='2m') + scroll_id = resp['_scroll_id'] + hits = resp['hits']['hits'] + + ES_CLIENT.clear_scroll(scroll_id=scroll_id) + print(f"获取到 {len(concepts)} 个概念") + return concepts + + +def get_prev_close(stock_codes: List[str], trade_date: str) -> Dict[str, float]: + """获取昨收价""" + valid_codes = [c for c in stock_codes if c and len(c) == 6 and c.isdigit()] + if not valid_codes: + return {} + + codes_str = "','".join(valid_codes) + query = f""" + SELECT SECCODE, F002N + FROM ea_trade + WHERE SECCODE IN ('{codes_str}') + AND TRADEDATE = ( + SELECT MAX(TRADEDATE) FROM ea_trade WHERE TRADEDATE < '{trade_date}' + ) + AND F002N IS NOT NULL AND F002N > 0 + """ + + try: + with MYSQL_ENGINE.connect() as conn: + result = conn.execute(text(query)) + return {row[0]: float(row[1]) for row in result if row[1]} + except Exception as e: + print(f"获取昨收价失败: {e}") + return {} + + +def get_index_prev_close(trade_date: str) -> float: + """获取指数昨收价""" + code_no_suffix = REFERENCE_INDEX.split('.')[0] + try: + with MYSQL_ENGINE.connect() as conn: + result = conn.execute(text(""" + SELECT F006N FROM ea_exchangetrade + WHERE INDEXCODE = :code AND TRADEDATE < :today + ORDER BY TRADEDATE DESC LIMIT 1 + """), {'code': code_no_suffix, 'today': trade_date}).fetchone() + if result and result[0]: + return float(result[0]) + except Exception as e: + print(f"获取指数昨收失败: {e}") + return None + + +def get_stock_minute_data(trade_date: str, stock_codes: List[str], since_time: datetime = None) -> pd.DataFrame: + """ + 从 ClickHouse 获取股票分钟数据 + + Args: + trade_date: 交易日期 + stock_codes: 股票代码列表 + since_time: 只获取该时间之后的数据（增量获取） + """ + client = get_ch_client() + + ch_codes = [] + code_map = {} + for code in stock_codes: + ch_code = code_to_ch_format(code) + if ch_code: + ch_codes.append(ch_code) + code_map[ch_code] = code + + if not ch_codes: + return pd.DataFrame() + + ch_codes_str = "','".join(ch_codes) + + time_filter = "" + if since_time: + time_filter = f"AND timestamp > '{since_time.strftime('%Y-%m-%d %H:%M:%S')}'" + + query = f""" + SELECT code, timestamp, close, volume, amt + FROM stock_minute + WHERE toDate(timestamp) = '{trade_date}' + AND code IN ('{ch_codes_str}') + {time_filter} + ORDER BY code, timestamp + """ + + result = client.execute(query) + if not result: + return pd.DataFrame() + + df = pd.DataFrame(result, columns=['ch_code', 'timestamp', 'close', 'volume', 'amt']) + df['code'] = df['ch_code'].map(code_map) + df = df.dropna(subset=['code']) + return df[['code', 'timestamp', 'close', 'volume', 'amt']] + + +def get_index_minute_data(trade_date: str, since_time: datetime = None) -> pd.DataFrame: + """从 ClickHouse 获取指数分钟数据""" + client = get_ch_client() + + time_filter = "" + if since_time: + time_filter = f"AND timestamp > '{since_time.strftime('%Y-%m-%d %H:%M:%S')}'" + + query = f""" + SELECT timestamp, close, volume, amt + FROM index_minute + WHERE toDate(timestamp) = '{trade_date}' + AND code = '{REFERENCE_INDEX}' + {time_filter} + ORDER BY timestamp + """ + + result = client.execute(query) + if not result: + return pd.DataFrame() + + return pd.DataFrame(result, columns=['timestamp', 'close', 'volume', 'amt']) + + +# ==================== 规则评分 ==================== + +def get_size_adjusted_thresholds(stock_count: np.ndarray) -> np.ndarray: + """根据概念股票数量计算动态阈值""" + n = len(stock_count) + size_factor = np.ones(n) + + size_factor[stock_count < 5] = 1.8 + size_factor[(stock_count >= 5) & (stock_count < 10)] = 1.4 + size_factor[(stock_count >= 10) & (stock_count < 20)] = 1.2 + size_factor[(stock_count >= 20) & (stock_count < 50)] = 1.0 + size_factor[(stock_count >= 50) & (stock_count < 100)] = 0.85 + size_factor[stock_count >= 100] = 0.7 + + return size_factor + + +def score_rules_batch(df: pd.DataFrame) -> Tuple[np.ndarray, List[List[str]]]: + """批量计算规则得分""" + n = len(df) + scores = np.zeros(n) + triggered = [[] for _ in range(n)] + + alpha = df['alpha'].values + alpha_delta = df['alpha_delta'].values + amt_ratio = df['amt_ratio'].values + rank_pct = df['rank_pct'].values + limit_up_ratio = df['limit_up_ratio'].values + stock_count = df['stock_count'].values if 'stock_count' in df.columns else np.full(n, 20) + + alpha_abs = np.abs(alpha) + alpha_delta_abs = np.abs(alpha_delta) + size_factor = get_size_adjusted_thresholds(stock_count) + + # Alpha 规则 + alpha_extreme_thresh = 5.0 * size_factor + mask = alpha_abs >= alpha_extreme_thresh + scores[mask] += 20 + for i in np.where(mask)[0]: triggered[i].append('alpha_extreme') + + alpha_strong_thresh = 4.0 * size_factor + mask = (alpha_abs >= alpha_strong_thresh) & (alpha_abs < alpha_extreme_thresh) + scores[mask] += 15 + for i in np.where(mask)[0]: triggered[i].append('alpha_strong') + + alpha_medium_thresh = 3.0 * size_factor + mask = (alpha_abs >= alpha_medium_thresh) & (alpha_abs < alpha_strong_thresh) + scores[mask] += 10 + for i in np.where(mask)[0]: triggered[i].append('alpha_medium') + + # Alpha 加速度 + delta_strong_thresh = 2.0 * size_factor + mask = alpha_delta_abs >= delta_strong_thresh + scores[mask] += 15 + for i in np.where(mask)[0]: triggered[i].append('alpha_delta_strong') + + delta_medium_thresh = 1.5 * size_factor + mask = (alpha_delta_abs >= delta_medium_thresh) & (alpha_delta_abs < delta_strong_thresh) + scores[mask] += 10 + for i in np.where(mask)[0]: triggered[i].append('alpha_delta_medium') + + # 成交额 + mask = amt_ratio >= 10.0 + scores[mask] += 20 + for i in np.where(mask)[0]: triggered[i].append('volume_extreme') + + mask = (amt_ratio >= 6.0) & (amt_ratio < 10.0) + scores[mask] += 12 + for i in np.where(mask)[0]: triggered[i].append('volume_strong') + + # 排名 + mask = rank_pct >= 0.98 + scores[mask] += 15 + for i in np.where(mask)[0]: triggered[i].append('rank_top') + + mask = rank_pct <= 0.02 + scores[mask] += 15 + for i in np.where(mask)[0]: triggered[i].append('rank_bottom') + + # 涨停 + limit_high_thresh = 0.30 * size_factor + mask = limit_up_ratio >= limit_high_thresh + scores[mask] += 20 + for i in np.where(mask)[0]: triggered[i].append('limit_up_high') + + limit_medium_thresh = 0.20 * size_factor + mask = (limit_up_ratio >= limit_medium_thresh) & (limit_up_ratio < limit_high_thresh) + scores[mask] += 12 + for i in np.where(mask)[0]: triggered[i].append('limit_up_medium') + + # 概念规模加分 + large_concept = stock_count >= 50 + has_signal = scores > 0 + mask = large_concept & has_signal + scores[mask] += 10 + for i in np.where(mask)[0]: triggered[i].append('large_concept_bonus') + + xlarge_concept = stock_count >= 100 + mask = xlarge_concept & has_signal + scores[mask] += 10 + for i in np.where(mask)[0]: triggered[i].append('xlarge_concept_bonus') + + # 组合规则 + combo_alpha_thresh = 3.0 * size_factor + mask = (alpha_abs >= combo_alpha_thresh) & (amt_ratio >= 5.0) & ((rank_pct >= 0.95) | (rank_pct <= 0.05)) + scores[mask] += 20 + for i in np.where(mask)[0]: triggered[i].append('triple_signal') + + mask = (alpha_abs >= combo_alpha_thresh) & (limit_up_ratio >= 0.15 * size_factor) + scores[mask] += 15 + for i in np.where(mask)[0]: triggered[i].append('alpha_with_limit') + + # 小概念惩罚 + tiny_concept = stock_count < 5 + single_rule = np.array([len(t) <= 1 for t in triggered]) + mask = tiny_concept & single_rule & (scores > 0) + scores[mask] *= 0.5 + for i in np.where(mask)[0]: triggered[i].append('tiny_concept_penalty') + + scores = np.clip(scores, 0, 100) + return scores, triggered + + +def rule_score_with_details(features: Dict, stock_count: int = 50) -> Tuple[float, Dict[str, float]]: + """ + 单条记录的规则评分（带详情） + + Args: + features: 特征字典，包含 alpha, alpha_delta, amt_ratio, rank_pct, limit_up_ratio + stock_count: 概念股票数量 + + Returns: + (score, details): 总分和各规则触发详情 + """ + score = 0.0 + details = {} + + alpha = features.get('alpha', 0) + alpha_delta = features.get('alpha_delta', 0) + amt_ratio = features.get('amt_ratio', 1) + rank_pct = features.get('rank_pct', 0.5) + limit_up_ratio = features.get('limit_up_ratio', 0) + + alpha_abs = abs(alpha) + alpha_delta_abs = abs(alpha_delta) + size_factor = get_size_adjusted_thresholds(np.array([stock_count]))[0] + + # Alpha 规则 + alpha_extreme_thresh = 5.0 * size_factor + alpha_strong_thresh = 4.0 * size_factor + alpha_medium_thresh = 3.0 * size_factor + + if alpha_abs >= alpha_extreme_thresh: + score += 20 + details['alpha_extreme'] = 20 + elif alpha_abs >= alpha_strong_thresh: + score += 15 + details['alpha_strong'] = 15 + elif alpha_abs >= alpha_medium_thresh: + score += 10 + details['alpha_medium'] = 10 + + # Alpha 加速度 + delta_strong_thresh = 2.0 * size_factor + delta_medium_thresh = 1.5 * size_factor + + if alpha_delta_abs >= delta_strong_thresh: + score += 15 + details['alpha_delta_strong'] = 15 + elif alpha_delta_abs >= delta_medium_thresh: + score += 10 + details['alpha_delta_medium'] = 10 + + # 成交额 + if amt_ratio >= 10.0: + score += 20 + details['volume_extreme'] = 20 + elif amt_ratio >= 6.0: + score += 12 + details['volume_strong'] = 12 + + # 排名 + if rank_pct >= 0.98: + score += 15 + details['rank_top'] = 15 + elif rank_pct <= 0.02: + score += 15 + details['rank_bottom'] = 15 + + # 涨停 + limit_high_thresh = 0.30 * size_factor + limit_medium_thresh = 0.20 * size_factor + + if limit_up_ratio >= limit_high_thresh: + score += 20 + details['limit_up_high'] = 20 + elif limit_up_ratio >= limit_medium_thresh: + score += 12 + details['limit_up_medium'] = 12 + + # 概念规模加分 + if score > 0: + if stock_count >= 50: + score += 10 + details['large_concept_bonus'] = 10 + if stock_count >= 100: + score += 10 + details['xlarge_concept_bonus'] = 10 + + # 组合规则 + combo_alpha_thresh = 3.0 * size_factor + + if alpha_abs >= combo_alpha_thresh and amt_ratio >= 5.0 and (rank_pct >= 0.95 or rank_pct <= 0.05): + score += 20 + details['triple_signal'] = 20 + + if alpha_abs >= combo_alpha_thresh and limit_up_ratio >= 0.15 * size_factor: + score += 15 + details['alpha_with_limit'] = 15 + + # 小概念惩罚 + if stock_count < 5 and len(details) <= 1 and score > 0: + penalty = score * 0.5 + score *= 0.5 + details['tiny_concept_penalty'] = -penalty + + score = min(max(score, 0), 100) + return score, details + + +# ==================== ML 评分器 ==================== + +class MLScorer: + def __init__(self, checkpoint_dir: str = 'ml/checkpoints', device: str = 'auto'): + self.checkpoint_dir = Path(checkpoint_dir) + if device == 'auto': + self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + else: + self.device = torch.device(device) + + self.model = None + self.thresholds = None + self._load_model() + + def _load_model(self): + model_path = self.checkpoint_dir / 'best_model.pt' + thresholds_path = self.checkpoint_dir / 'thresholds.json' + config_path = self.checkpoint_dir / 'config.json' + + if not model_path.exists(): + print(f"警告: 模型不存在 {model_path}") + return + + try: + from model import LSTMAutoencoder + + config = {} + if config_path.exists(): + with open(config_path) as f: + config = json.load(f).get('model', {}) + + if 'd_model' in config: + config['hidden_dim'] = config.pop('d_model') // 2 + for key in ['num_encoder_layers', 'num_decoder_layers', 'nhead', 'dim_feedforward', 'max_seq_len', 'use_instance_norm']: + config.pop(key, None) + if 'num_layers' not in config: + config['num_layers'] = 1 + + checkpoint = torch.load(model_path, map_location='cpu') + self.model = LSTMAutoencoder(**config) + self.model.load_state_dict(checkpoint['model_state_dict']) + self.model.to(self.device) + self.model.eval() + + if thresholds_path.exists(): + with open(thresholds_path) as f: + self.thresholds = json.load(f) + + print(f"ML模型加载成功 (设备: {self.device})") + except Exception as e: + print(f"ML模型加载失败: {e}") + + def is_ready(self): + return self.model is not None + + @torch.no_grad() + def score_batch(self, sequences: np.ndarray, debug: bool = False) -> np.ndarray: + if not self.is_ready() or len(sequences) == 0: + return np.zeros(len(sequences)) + + x = torch.FloatTensor(sequences).to(self.device) + output, _ = self.model(x) + mse = ((output - x) ** 2).mean(dim=-1) + errors = mse[:, -1].cpu().numpy() + + p95 = self.thresholds.get('p95', 0.1) if self.thresholds else 0.1 + scores = np.clip(errors / p95 * 50, 0, 100) + + if debug and len(errors) > 0: + print(f"[ML调试] p95={p95:.4f}, errors: min={errors.min():.4f}, max={errors.max():.4f}, mean={errors.mean():.4f}") + print(f"[ML调试] scores: min={scores.min():.0f}, max={scores.max():.0f}, mean={scores.mean():.0f}, =100占比={100*(scores>=100).mean():.1f}%") + + return scores + + +# ==================== 内存数据管理器 ==================== + +class RealtimeDataManager: + """ + 内存数据管理器 + + - 缓存股票分钟数据和指数数据 + - 增量获取新数据 + - 实时计算概念特征 + """ + + def __init__(self, concepts: List[dict], prev_close: Dict[str, float], index_prev_close: float): + self.concepts = concepts + self.prev_close = prev_close + self.index_prev_close = index_prev_close + + # 概念到股票的映射 + self.concept_stocks = {c['concept_id']: set(c['stocks']) for c in concepts} + self.all_stocks = list(set(s for c in concepts for s in c['stocks'])) + + # 内存缓存：股票分钟数据 + self.stock_data = pd.DataFrame() # code, timestamp, close, volume, amt, change_pct + self.index_data = pd.DataFrame() # timestamp, close, change_pct + + # 最后更新时间 + self.last_update_time = None + + # 概念历史（用于计算变化率） + self.concept_history = defaultdict(lambda: {'alpha': [], 'amt': []}) + + # 概念特征时间序列（用于 ML） + self.concept_features_history = defaultdict(list) # concept_id -> list of feature dicts + + def update(self, trade_date: str) -> int: + """ + 增量更新数据 + + Returns: + 新增的时间点数量 + """ + # 获取增量数据 + new_stock_df = get_stock_minute_data(trade_date, self.all_stocks, self.last_update_time) + new_index_df = get_index_minute_data(trade_date, self.last_update_time) + + if new_stock_df.empty and new_index_df.empty: + return 0 + + # 计算涨跌幅 + if not new_stock_df.empty: + new_stock_df['prev_close'] = new_stock_df['code'].map(self.prev_close) + new_stock_df = new_stock_df.dropna(subset=['prev_close']) + new_stock_df['change_pct'] = (new_stock_df['close'] - new_stock_df['prev_close']) / new_stock_df['prev_close'] * 100 + + # 合并到缓存 + self.stock_data = pd.concat([self.stock_data, new_stock_df], ignore_index=True) + self.stock_data = self.stock_data.drop_duplicates(subset=['code', 'timestamp'], keep='last') + + if not new_index_df.empty: + new_index_df['change_pct'] = (new_index_df['close'] - self.index_prev_close) / self.index_prev_close * 100 + + # 调试：打印指数涨跌幅范围 + if len(self.index_data) == 0: # 第一次 + print(f"[调试] 指数 close 范围: {new_index_df['close'].min():.2f} ~ {new_index_df['close'].max():.2f}") + print(f"[调试] 指数 change_pct 范围: {new_index_df['change_pct'].min():.2f}% ~ {new_index_df['change_pct'].max():.2f}%") + + self.index_data = pd.concat([self.index_data, new_index_df], ignore_index=True) + self.index_data = self.index_data.drop_duplicates(subset=['timestamp'], keep='last') + + # 更新最后时间 + if not new_stock_df.empty: + self.last_update_time = new_stock_df['timestamp'].max() + elif not new_index_df.empty: + self.last_update_time = new_index_df['timestamp'].max() + + # 获取新时间点 + new_timestamps = sorted(new_stock_df['timestamp'].unique()) if not new_stock_df.empty else [] + + # 计算新时间点的概念特征 + for ts in new_timestamps: + self._compute_features_for_timestamp(ts) + + return len(new_timestamps) + + def _compute_features_for_timestamp(self, ts): + """计算单个时间点的概念特征""" + ts_stock_data = self.stock_data[self.stock_data['timestamp'] == ts] + index_row = self.index_data[self.index_data['timestamp'] == ts] + + if ts_stock_data.empty or index_row.empty: + return + + index_change = index_row['change_pct'].values[0] + stock_change = dict(zip(ts_stock_data['code'], ts_stock_data['change_pct'])) + stock_amt = dict(zip(ts_stock_data['code'], ts_stock_data['amt'])) + + for concept_id, stocks in self.concept_stocks.items(): + concept_changes = [stock_change[s] for s in stocks if s in stock_change] + concept_amts = [stock_amt.get(s, 0) for s in stocks if s in stock_change] + + if not concept_changes: + continue + + avg_change = np.mean(concept_changes) + total_amt = sum(concept_amts) + alpha = avg_change - index_change + + # 涨停比例 + limit_up_count = sum(1 for c in concept_changes if c >= FEATURE_CONFIG['limit_up_threshold']) + limit_up_ratio = limit_up_count / len(concept_changes) + + # 更新历史 + history = self.concept_history[concept_id] + history['alpha'].append(alpha) + history['amt'].append(total_amt) + + # 计算变化率 + alpha_delta = 0 + if len(history['alpha']) > FEATURE_CONFIG['alpha_delta_window']: + alpha_delta = alpha - history['alpha'][-FEATURE_CONFIG['alpha_delta_window'] - 1] + + amt_ratio = 1.0 + amt_delta = 0 + if len(history['amt']) > FEATURE_CONFIG['amt_ma_window']: + amt_ma = np.mean(history['amt'][-FEATURE_CONFIG['amt_ma_window'] - 1:-1]) + if amt_ma > 0: + amt_ratio = total_amt / amt_ma + amt_delta = total_amt - history['amt'][-2] if len(history['amt']) > 1 else 0 + + features = { + 'timestamp': ts, + 'concept_id': concept_id, + 'alpha': alpha, + 'alpha_delta': alpha_delta, + 'amt_ratio': amt_ratio, + 'amt_delta': amt_delta, + 'limit_up_ratio': limit_up_ratio, + 'stock_count': len(concept_changes), + 'total_amt': total_amt, + } + + self.concept_features_history[concept_id].append(features) + + def get_latest_features(self) -> pd.DataFrame: + """获取最新时间点的所有概念特征""" + if not self.concept_features_history: + return pd.DataFrame() + + latest_features = [] + for concept_id, history in self.concept_features_history.items(): + if history: + latest_features.append(history[-1]) + + if not latest_features: + return pd.DataFrame() + + df = pd.DataFrame(latest_features) + + # 计算排名百分位 + if len(df) > 1: + df['rank_pct'] = df['alpha'].rank(pct=True) + else: + df['rank_pct'] = 0.5 + + return df + + def get_sequences_for_concepts(self, seq_len: int) -> Tuple[np.ndarray, pd.DataFrame]: + """获取所有概念的特征序列（用于 ML 评分）""" + sequences = [] + infos = [] + + for concept_id, history in self.concept_features_history.items(): + if len(history) < seq_len: + continue + + # 取最近 seq_len 个时间点 + recent = history[-seq_len:] + + # 构建序列 + seq = np.array([[ + f['alpha'], + f['alpha_delta'], + f['amt_ratio'], + f['amt_delta'], + f.get('rank_pct', 0.5), + f['limit_up_ratio'] + ] for f in recent]) + + seq = np.nan_to_num(seq, nan=0.0, posinf=0.0, neginf=0.0) + seq = np.clip(seq, -CONFIG['clip_value'], CONFIG['clip_value']) + + sequences.append(seq) + infos.append(recent[-1]) # 最新特征 + + if not sequences: + return np.array([]), pd.DataFrame() + + # 补充 rank_pct + info_df = pd.DataFrame(infos) + if 'rank_pct' not in info_df.columns and len(info_df) > 1: + info_df['rank_pct'] = info_df['alpha'].rank(pct=True) + + return np.array(sequences), info_df + + def get_all_timestamps(self) -> List: + """获取所有时间点""" + if self.stock_data.empty: + return [] + return sorted(self.stock_data['timestamp'].unique()) + + def get_concept_features_df(self) -> pd.DataFrame: + """获取概念特征的 DataFrame 形式（用于批量回测）""" + if not self.concept_features_history: + return pd.DataFrame() + + rows = [] + for concept_id, history in self.concept_features_history.items(): + for f in history: + row = { + 'concept_id': concept_id, + 'timestamp': f['timestamp'], + 'alpha': f['alpha'], + 'alpha_delta': f['alpha_delta'], + 'amt_ratio': f['amt_ratio'], + 'amt_delta': f.get('amt_delta', 0), + 'limit_up_ratio': f['limit_up_ratio'], + 'stock_count': f.get('stock_count', 0), + 'total_amt': f.get('total_amt', 0), + } + rows.append(row) + + if not rows: + return pd.DataFrame() + + df = pd.DataFrame(rows) + + # 按时间点计算 rank_pct（每个时间点内部排名） + df['rank_pct'] = df.groupby('timestamp')['alpha'].rank(pct=True) + + return df + + +# ==================== 冷却期管理 ==================== + +class CooldownManager: + def __init__(self, cooldown_minutes: int = 8): + self.cooldown_minutes = cooldown_minutes + self.last_alert_time = {} + + def is_in_cooldown(self, concept_id: str, current_time: datetime) -> bool: + if concept_id not in self.last_alert_time: + return False + last_time = self.last_alert_time[concept_id] + diff = (current_time - last_time).total_seconds() / 60 + return diff < self.cooldown_minutes + + def record_alert(self, concept_id: str, alert_time: datetime): + self.last_alert_time[concept_id] = alert_time + + def cleanup_old(self, current_time: datetime): + cutoff = current_time - timedelta(minutes=self.cooldown_minutes * 2) + self.last_alert_time = {cid: t for cid, t in self.last_alert_time.items() if t > cutoff} + + +# ==================== 异动检测 ==================== + +def detect_anomalies( + ml_scorer: MLScorer, + data_mgr: RealtimeDataManager, + cooldown_mgr: CooldownManager, + trade_date: str, + config: Dict +) -> List[Dict]: + """检测当前时刻的异动""" + + # 获取最新特征 + latest_df = data_mgr.get_latest_features() + if latest_df.empty: + return [] + + # 获取 ML 序列 + sequences, info_df = data_mgr.get_sequences_for_concepts(config['seq_len']) + + if len(sequences) == 0: + return [] + + # 获取当前时间 + current_time = pd.to_datetime(info_df['timestamp'].iloc[0]) + + # 清理过期冷却 + cooldown_mgr.cleanup_old(current_time) + + # 过滤冷却中的概念 + valid_mask = [] + for _, row in info_df.iterrows(): + in_cooldown = cooldown_mgr.is_in_cooldown(row['concept_id'], current_time) + valid_mask.append(not in_cooldown) + + valid_mask = np.array(valid_mask) + sequences = sequences[valid_mask] + info_df = info_df[valid_mask].reset_index(drop=True) + + if len(sequences) == 0: + return [] + + # 过滤小波动 + alpha_mask = np.abs(info_df['alpha'].values) >= config['min_alpha_abs'] + sequences = sequences[alpha_mask] + info_df = info_df[alpha_mask].reset_index(drop=True) + + if len(sequences) == 0: + return [] + + # 规则评分 + rule_scores, triggered_rules = score_rules_batch(info_df) + + # ML 评分 + ml_scores = ml_scorer.score_batch(sequences) + + # 融合得分 + w1, w2 = config['rule_weight'], config['ml_weight'] + final_scores = w1 * rule_scores + w2 * ml_scores + + # 判断异动 + alerts = [] + for i, row in info_df.iterrows(): + rule_score = rule_scores[i] + ml_score = ml_scores[i] + final_score = final_scores[i] + + is_anomaly = ( + rule_score >= config['rule_trigger'] or + ml_score >= config['ml_trigger'] or + final_score >= config['fusion_trigger'] + ) + + if not is_anomaly: + continue + + # 触发原因 + if rule_score >= config['rule_trigger']: + trigger = f'规则强信号({rule_score:.0f}分)' + elif ml_score >= config['ml_trigger']: + trigger = f'ML强信号({ml_score:.0f}分)' + else: + trigger = f'融合触发({final_score:.0f}分)' + + # 异动类型 + alpha = row['alpha'] + if alpha >= 1.5: + alert_type = 'surge_up' + elif alpha <= -1.5: + alert_type = 'surge_down' + elif row['amt_ratio'] >= 3.0: + alert_type = 'volume_spike' + else: + alert_type = 'unknown' + + alert = { + 'concept_id': row['concept_id'], + 'alert_time': row['timestamp'], + 'trade_date': trade_date, + 'alert_type': alert_type, + 'final_score': final_score, + 'rule_score': rule_score, + 'ml_score': ml_score, + 'trigger_reason': trigger, + 'triggered_rules': triggered_rules[i], + 'alpha': row['alpha'], + 'alpha_delta': row['alpha_delta'], + 'amt_ratio': row['amt_ratio'], + 'amt_delta': row.get('amt_delta', 0), + 'rank_pct': row.get('rank_pct', 0.5), + 'limit_up_ratio': row['limit_up_ratio'], + 'stock_count': row['stock_count'], + 'total_amt': row['total_amt'], + } + + alerts.append(alert) + cooldown_mgr.record_alert(row['concept_id'], current_time) + + # 按得分排序 + alerts.sort(key=lambda x: x['final_score'], reverse=True) + return alerts[:config['max_alerts_per_minute']] + + +# ==================== 数据库写入 ==================== + +def save_alerts_to_mysql(alerts: List[Dict]) -> int: + if not alerts: + return 0 + + saved = 0 + with MYSQL_ENGINE.begin() as conn: + for alert in alerts: + try: + insert_sql = text(""" + INSERT IGNORE INTO concept_anomaly_hybrid + (concept_id, alert_time, trade_date, alert_type, + final_score, rule_score, ml_score, trigger_reason, + alpha, alpha_delta, amt_ratio, amt_delta, + rank_pct, limit_up_ratio, stock_count, total_amt, + triggered_rules) + VALUES + (:concept_id, :alert_time, :trade_date, :alert_type, + :final_score, :rule_score, :ml_score, :trigger_reason, + :alpha, :alpha_delta, :amt_ratio, :amt_delta, + :rank_pct, :limit_up_ratio, :stock_count, :total_amt, + :triggered_rules) + """) + + result = conn.execute(insert_sql, { + 'concept_id': alert['concept_id'], + 'alert_time': alert['alert_time'], + 'trade_date': alert['trade_date'], + 'alert_type': alert['alert_type'], + 'final_score': alert['final_score'], + 'rule_score': alert['rule_score'], + 'ml_score': alert['ml_score'], + 'trigger_reason': alert['trigger_reason'], + 'alpha': alert.get('alpha', 0), + 'alpha_delta': alert.get('alpha_delta', 0), + 'amt_ratio': alert.get('amt_ratio', 1), + 'amt_delta': alert.get('amt_delta', 0), + 'rank_pct': alert.get('rank_pct', 0.5), + 'limit_up_ratio': alert.get('limit_up_ratio', 0), + 'stock_count': alert.get('stock_count', 0), + 'total_amt': alert.get('total_amt', 0), + 'triggered_rules': json.dumps(alert.get('triggered_rules', []), ensure_ascii=False), + }) + + if result.rowcount > 0: + saved += 1 + except Exception as e: + print(f"保存失败: {alert['concept_id']} - {e}") + + return saved + + +# ==================== 主服务 ==================== + +class RealtimeDetectorService: + def __init__(self, checkpoint_dir: str = 'ml/checkpoints', device: str = 'auto'): + self.checkpoint_dir = checkpoint_dir + self.device = device + + # 初始化 ML 评分器 + self.ml_scorer = MLScorer(checkpoint_dir, device) + + # 这些在 init_for_trade_date 中初始化 + self.data_mgr = None + self.cooldown_mgr = None + self.trade_date = None + + def init_for_trade_date(self, trade_date: str, preload_history: bool = True): + """ + 为指定交易日初始化 + + Args: + trade_date: 交易日期 + preload_history: 是否预加载当天已有的历史数据（实盘必须为 True） + """ + if self.trade_date == trade_date and self.data_mgr is not None: + return + + print(f"[初始化] 交易日: {trade_date}") + + # 获取概念列表 + print(f"[初始化] 获取概念列表...") + concepts = get_all_concepts() + + # 获取所有股票 + all_stocks = list(set(s for c in concepts for s in c['stocks'])) + print(f"[初始化] 共 {len(all_stocks)} 只股票") + + # 获取昨收价 + print(f"[初始化] 获取昨收价...") + prev_close = get_prev_close(all_stocks, trade_date) + index_prev_close = get_index_prev_close(trade_date) + print(f"[初始化] 获取到 {len(prev_close)} 只股票的昨收价") + print(f"[初始化] 指数昨收价: {index_prev_close}") + + # 创建数据管理器 + self.data_mgr = RealtimeDataManager(concepts, prev_close, index_prev_close) + self.cooldown_mgr = CooldownManager(CONFIG['cooldown_minutes']) + self.trade_date = trade_date + + # 预加载当天已有的历史数据（实盘关键） + if preload_history: + self._preload_today_history(trade_date) + + def _preload_today_history(self, trade_date: str): + """ + 预加载当天已有的历史数据到内存 + + 这是实盘运行的关键： + - 在盘中任意时刻启动服务时，需要先加载当天已有的数据 + - 这样才能正确计算 alpha_delta（需要过去 5 分钟）和 amt_ratio（需要过去 20 分钟） + - 以及构建 ML 所需的序列（需要 seq_len=15 分钟） + + 整个过程不依赖 prepare_data.py，直接从 ClickHouse 读取原始数据计算 + """ + print(f"[预热] 加载当天历史数据...") + + # 直接调用 update，但不设置 last_update_time，会获取当天所有数据 + # data_mgr.last_update_time 初始为 None，会获取全部数据 + n_updates = self.data_mgr.update(trade_date) + + if n_updates > 0: + print(f"[预热] 加载完成，共 {n_updates} 个时间点") + + # 检查是否满足 ML 所需的最小数据量 + min_required = CONFIG['seq_len'] + FEATURE_CONFIG['amt_ma_window'] + if n_updates < min_required: + print(f"[预热] 警告：数据量 {n_updates} < 最小需求 {min_required}，部分特征可能不准确") + else: + print(f"[预热] 数据充足，可以正常检测") + else: + print(f"[预热] 当天暂无历史数据（可能是开盘前）") + + def backfill_today(self): + """ + 补齐当天历史数据并检测异动（回补模式） + + 使用与 backtest_fast.py 完全相同的逻辑： + 1. 先用 prepare_data.py 生成当天的 parquet 文件 + 2. 读取 parquet 文件进行回测 + + 注意：这个方法用于回补历史异动记录，不是实盘必须的 + 实盘模式下，init_for_trade_date 会自动预热历史数据 + """ + trade_date = get_current_trade_date() + print(f"[补齐] 交易日: {trade_date}") + + # 1. 生成当天的 parquet 文件 + parquet_path = Path('ml/data') / f'features_{trade_date}.parquet' + + if not parquet_path.exists(): + print(f"[补齐] 生成当天特征数据...") + self._generate_today_parquet(trade_date) + + if not parquet_path.exists(): + print(f"[补齐] 无法生成特征数据，跳过") + return + + # 2. 读取 parquet 文件 + df = pd.read_parquet(parquet_path) + print(f"[补齐] 读取到 {len(df)} 条特征数据") + + if df.empty: + print("[补齐] 无数据") + return + + # 打印特征分布（调试） + print(f"[调试] alpha 分布: min={df['alpha'].min():.2f}, max={df['alpha'].max():.2f}, mean={df['alpha'].mean():.2f}") + print(f"[调试] |alpha| >= 0.3 的数量: {(df['alpha'].abs() >= 0.3).sum()}") + + # 3. 使用 backtest_fast.py 相同的回测逻辑 + alerts = self._backtest_from_parquet(df, trade_date) + + # 4. 保存结果 + if alerts: + saved = save_alerts_to_mysql(alerts) + print(f"[补齐] 完成！共 {len(alerts)} 个异动, 保存 {saved} 条") + + # 统计触发来源 + trigger_stats = {'规则': 0, 'ML': 0, '融合': 0} + for a in alerts: + reason = a['trigger_reason'] + if '规则' in reason: + trigger_stats['规则'] += 1 + elif 'ML' in reason: + trigger_stats['ML'] += 1 + else: + trigger_stats['融合'] += 1 + print(f"[补齐] 触发来源: {trigger_stats}") + else: + print("[补齐] 无异动") + + def _generate_today_parquet(self, trade_date: str): + """ + 生成当天的 parquet 文件（调用 prepare_data.py 的逻辑） + """ + import subprocess + cmd = ['python', 'ml/prepare_data.py', '--start', trade_date, '--end', trade_date] + print(f"[补齐] 执行: {' '.join(cmd)}") + try: + result = subprocess.run(cmd, capture_output=True, text=True, timeout=300) + if result.returncode != 0: + print(f"[补齐] prepare_data.py 执行失败: {result.stderr}") + except Exception as e: + print(f"[补齐] prepare_data.py 执行异常: {e}") + + def _backtest_from_parquet(self, df: pd.DataFrame, trade_date: str) -> List[Dict]: + """ + 从 parquet 数据回测（与 backtest_fast.py 完全一致的逻辑） + """ + seq_len = CONFIG['seq_len'] + now = datetime.now() + + # 确保按概念和时间排序 + df = df.sort_values(['concept_id', 'timestamp']).reset_index(drop=True) + + # 获取所有时间点 + all_timestamps = sorted(df['timestamp'].unique()) + + # 只处理当前时间之前的 + past_timestamps = [] + for ts in all_timestamps: + try: + ts_dt = pd.to_datetime(ts) + if ts_dt.tzinfo is not None: + ts_dt = ts_dt.tz_localize(None) + if ts_dt < now: + past_timestamps.append(ts) + except Exception: + continue + + print(f"[补齐] 处理 {len(past_timestamps)} 个历史时间点...") + + if len(past_timestamps) < seq_len: + print(f"[补齐] 时间点不足 {seq_len}，跳过") + return [] + + # 构建序列（与 backtest_fast.py 的 build_sequences_fast 一致） + sequences = [] + infos = [] + + groups = df.groupby('concept_id') + + for concept_id, gdf in groups: + gdf = gdf.reset_index(drop=True) + feat_matrix = gdf[FEATURES].values + feat_matrix = np.nan_to_num(feat_matrix, nan=0.0, posinf=0.0, neginf=0.0) + feat_matrix = np.clip(feat_matrix, -CONFIG['clip_value'], CONFIG['clip_value']) + + n_total = len(feat_matrix) + if n_total < seq_len: + continue + + for i in range(n_total - seq_len + 1): + seq = feat_matrix[i:i + seq_len] + row = gdf.iloc[i + seq_len - 1] + + # 只保留当前时间之前的 + ts = row['timestamp'] + try: + ts_dt = pd.to_datetime(ts) + if ts_dt.tzinfo is not None: + ts_dt = ts_dt.tz_localize(None) + if ts_dt >= now: + continue + except Exception: + continue + + sequences.append(seq) + infos.append({ + 'concept_id': concept_id, + 'timestamp': row['timestamp'], + 'alpha': row['alpha'], + 'alpha_delta': row.get('alpha_delta', 0), + 'amt_ratio': row.get('amt_ratio', 1), + 'amt_delta': row.get('amt_delta', 0), + 'rank_pct': row.get('rank_pct', 0.5), + 'limit_up_ratio': row.get('limit_up_ratio', 0), + 'stock_count': row.get('stock_count', 0), + 'total_amt': row.get('total_amt', 0), + }) + + if not sequences: + return [] + + sequences = np.array(sequences) + info_df = pd.DataFrame(infos) + + print(f"[补齐] 构建了 {len(sequences)} 个序列") + + # 过滤小波动 + alpha_abs = np.abs(info_df['alpha'].values) + valid_mask = alpha_abs >= CONFIG['min_alpha_abs'] + sequences = sequences[valid_mask] + info_df = info_df[valid_mask].reset_index(drop=True) + + if len(sequences) == 0: + return [] + + print(f"[补齐] 过滤后 {len(sequences)} 个序列") + + # 批量规则评分 + rule_scores, triggered_rules = score_rules_batch(info_df) + + # 批量 ML 评分 + batch_size = 2048 + ml_scores = [] + for i in range(0, len(sequences), batch_size): + batch_seq = sequences[i:i+batch_size] + batch_scores = self.ml_scorer.score_batch(batch_seq) + ml_scores.append(batch_scores) + ml_scores = np.concatenate(ml_scores) if ml_scores else np.zeros(len(sequences)) + + # 融合得分 + w1, w2 = CONFIG['rule_weight'], CONFIG['ml_weight'] + final_scores = w1 * rule_scores + w2 * ml_scores + + # 判断异动 + is_anomaly = ( + (rule_scores >= CONFIG['rule_trigger']) | + (ml_scores >= CONFIG['ml_trigger']) | + (final_scores >= CONFIG['fusion_trigger']) + ) + + # 添加分数到 info_df + info_df['rule_score'] = rule_scores + info_df['ml_score'] = ml_scores + info_df['final_score'] = final_scores + info_df['is_anomaly'] = is_anomaly + info_df['triggered_rules'] = triggered_rules + + # 只保留异动 + anomaly_df = info_df[info_df['is_anomaly']].copy() + + if len(anomaly_df) == 0: + return [] + + print(f"[补齐] 发现 {len(anomaly_df)} 个候选异动") + + # 应用冷却期 + anomaly_df = anomaly_df.sort_values(['concept_id', 'timestamp']) + cooldown = {} + keep_mask = [] + + for _, row in anomaly_df.iterrows(): + cid = row['concept_id'] + ts = row['timestamp'] + + if cid in cooldown: + try: + diff = (pd.to_datetime(ts) - pd.to_datetime(cooldown[cid])).total_seconds() / 60 + except: + diff = CONFIG['cooldown_minutes'] + 1 + + if diff < CONFIG['cooldown_minutes']: + keep_mask.append(False) + continue + + cooldown[cid] = ts + keep_mask.append(True) + + anomaly_df = anomaly_df[keep_mask] + + print(f"[补齐] 冷却后 {len(anomaly_df)} 个异动") + + # 按时间分组，每分钟最多 max_alerts_per_minute 个 + alerts = [] + for ts, group in anomaly_df.groupby('timestamp'): + group = group.nlargest(CONFIG['max_alerts_per_minute'], 'final_score') + + for _, row in group.iterrows(): + alpha = row['alpha'] + if alpha >= 1.5: + atype = 'surge_up' + elif alpha <= -1.5: + atype = 'surge_down' + elif row['amt_ratio'] >= 3.0: + atype = 'volume_spike' + else: + atype = 'unknown' + + rule_score = row['rule_score'] + ml_score = row['ml_score'] + final_score = row['final_score'] + + if rule_score >= CONFIG['rule_trigger']: + trigger = f'规则强信号({rule_score:.0f}分)' + elif ml_score >= CONFIG['ml_trigger']: + trigger = f'ML强信号({ml_score:.0f}分)' + else: + trigger = f'融合触发({final_score:.0f}分)' + + alerts.append({ + 'concept_id': row['concept_id'], + 'alert_time': row['timestamp'], + 'trade_date': trade_date, + 'alert_type': atype, + 'final_score': final_score, + 'rule_score': rule_score, + 'ml_score': ml_score, + 'trigger_reason': trigger, + 'triggered_rules': row['triggered_rules'], + 'alpha': alpha, + 'alpha_delta': row['alpha_delta'], + 'amt_ratio': row['amt_ratio'], + 'amt_delta': row['amt_delta'], + 'rank_pct': row['rank_pct'], + 'limit_up_ratio': row['limit_up_ratio'], + 'stock_count': row['stock_count'], + 'total_amt': row['total_amt'], + }) + + return alerts + + def run_once(self): + """执行一次检测""" + now = datetime.now() + trade_date = get_current_trade_date() + + if not is_trading_time(): + print(f"[{now.strftime('%H:%M:%S')}] 非交易时间，跳过") + return + + # 初始化 + self.init_for_trade_date(trade_date) + + print(f"[{now.strftime('%H:%M:%S')}] 获取新数据...") + + # 增量更新 + n_updates = self.data_mgr.update(trade_date) + print(f" 新增 {n_updates} 个时间点") + + if n_updates == 0: + print(f" 无新数据") + return + + # 检测 + alerts = detect_anomalies( + self.ml_scorer, + self.data_mgr, + self.cooldown_mgr, + trade_date, + CONFIG + ) + + if alerts: + saved = save_alerts_to_mysql(alerts) + print(f" 检测到 {len(alerts)} 个异动, 保存 {saved} 条") + + for alert in alerts[:5]: + print(f" - {alert['concept_id']}: {alert['alert_type']} " + f"(final={alert['final_score']:.0f}, rule={alert['rule_score']:.0f}, ml={alert['ml_score']:.0f})") + else: + print(f" 无异动") + + def run_loop(self, backfill: bool = False): + """ + 持续运行（实盘模式） + + Args: + backfill: 是否回补历史异动到数据库（使用 prepare_data.py 方式） + 默认 False，因为实盘模式下 init_for_trade_date 会自动预热数据 + """ + print("=" * 60) + print("实时概念异动检测服务（实盘模式）") + print("=" * 60) + print(f"模型目录: {self.checkpoint_dir}") + print(f"交易时段: {TRADING_PERIODS}") + print(f"ML 序列长度: {CONFIG['seq_len']} 分钟") + print(f"成交额均值窗口: {FEATURE_CONFIG['amt_ma_window']} 分钟") + print("=" * 60) + + # 立即初始化并预热（即使不在交易时间也预热，方便测试） + trade_date = get_current_trade_date() + print(f"\n[启动] 初始化交易日 {trade_date}...") + self.init_for_trade_date(trade_date, preload_history=True) + + # 可选：回补历史异动记录到数据库 + if backfill and is_trading_time(): + print("\n[启动] 回补历史异动...") + self.backfill_today() + + # 每分钟第 10 秒执行 + schedule.every().minute.at(":10").do(self.run_once) + + print("\n服务已启动，等待下一分钟...") + + while True: + schedule.run_pending() + time.sleep(1) + + +# ==================== 主函数 ==================== + +def main(): + parser = argparse.ArgumentParser(description='实时概念异动检测') + parser.add_argument('--checkpoint_dir', default='ml/checkpoints', help='模型目录') + parser.add_argument('--device', default='auto', help='设备 (auto/cpu/cuda)') + parser.add_argument('--once', action='store_true', help='只运行一次检测') + parser.add_argument('--backfill', action='store_true', help='启动时回补历史异动到数据库') + parser.add_argument('--backfill-only', action='store_true', help='只回补历史（不持续运行）') + + args = parser.parse_args() + + service = RealtimeDetectorService( + checkpoint_dir=args.checkpoint_dir, + device=args.device + ) + + if args.once: + # 单次检测模式 + service.run_once() + elif args.backfill_only: + # 仅回补历史模式（需要 prepare_data.py） + service.backfill_today() + else: + # 实盘持续运行模式（自动预热，不依赖 prepare_data.py） + service.run_loop(backfill=args.backfill) + + +if __name__ == "__main__": + main() diff --git a/src/mocks/handlers/market.js b/src/mocks/handlers/market.js index 19857e45..4e15a72d 100644 --- a/src/mocks/handlers/market.js +++ b/src/mocks/handlers/market.js @@ -346,7 +346,173 @@ export const marketHandlers = [ }); }), - // 11. 市场统计数据（个股中心页面使用） + // 11. 热点概览数据（大盘分时 + 概念异动） + http.get('/api/market/hotspot-overview', async ({ request }) => { + await delay(300); + const url = new URL(request.url); + const date = url.searchParams.get('date'); + + const tradeDate = date || new Date().toISOString().split('T')[0]; + + // 生成分时数据（240个点，9:30-11:30 + 13:00-15:00） + const timeline = []; + const basePrice = 3900 + Math.random() * 100; // 基准价格 3900-4000 + const prevClose = basePrice; + let currentPrice = basePrice; + let cumulativeVolume = 0; + + // 上午时段 9:30-11:30 (120分钟) + for (let i = 0; i < 120; i++) { + const hour = 9 + Math.floor((i + 30) / 60); + const minute = (i + 30) % 60; + const time = `${hour.toString().padStart(2, '0')}:${minute.toString().padStart(2, '0')}`; + + // 模拟价格波动 + const volatility = 0.002; // 0.2%波动 + const drift = (Math.random() - 0.5) * 0.001; // 微小趋势 + currentPrice = currentPrice * (1 + (Math.random() - 0.5) * volatility + drift); + + const volume = Math.floor(Math.random() * 500000 + 100000); // 成交量 + cumulativeVolume += volume; + + timeline.push({ + time, + price: parseFloat(currentPrice.toFixed(2)), + volume: cumulativeVolume, + change_pct: parseFloat(((currentPrice - prevClose) / prevClose * 100).toFixed(2)) + }); + } + + // 下午时段 13:00-15:00 (120分钟) + for (let i = 0; i < 120; i++) { + const hour = 13 + Math.floor(i / 60); + const minute = i % 60; + const time = `${hour.toString().padStart(2, '0')}:${minute.toString().padStart(2, '0')}`; + + // 下午波动略小 + const volatility = 0.0015; + const drift = (Math.random() - 0.5) * 0.0008; + currentPrice = currentPrice * (1 + (Math.random() - 0.5) * volatility + drift); + + const volume = Math.floor(Math.random() * 400000 + 80000); + cumulativeVolume += volume; + + timeline.push({ + time, + price: parseFloat(currentPrice.toFixed(2)), + volume: cumulativeVolume, + change_pct: parseFloat(((currentPrice - prevClose) / prevClose * 100).toFixed(2)) + }); + } + + // 生成概念异动数据 + const conceptNames = [ + '人工智能', 'AI眼镜', '机器人', '核电', '国企', '卫星导航', + '福建自贸区', '两岸融合', 'CRO', '三季报增长', '百货零售', + '人形机器人', '央企', '数据中心', 'CPO', '新能源', '电网设备', + '氢能源', '算力租赁', '厦门国资', '乳业', '低空安防', '创新药', + '商业航天', '控制权变更', '文化传媒', '海峡两岸' + ]; + + const alertTypes = ['surge_up', 'surge_down', 'volume_spike', 'limit_up', 'rank_jump']; + + // 生成 15-25 个异动 + const alertCount = Math.floor(Math.random() * 10) + 15; + const alerts = []; + const usedTimes = new Set(); + + for (let i = 0; i < alertCount; i++) { + // 随机选择一个时间点 + let timeIdx; + let attempts = 0; + do { + timeIdx = Math.floor(Math.random() * timeline.length); + attempts++; + } while (usedTimes.has(timeIdx) && attempts < 50); + + if (attempts >= 50) continue; + + // 同一时间可以有多个异动 + const time = timeline[timeIdx].time; + const conceptName = conceptNames[Math.floor(Math.random() * conceptNames.length)]; + const alertType = alertTypes[Math.floor(Math.random() * alertTypes.length)]; + + // 根据类型生成 alpha + let alpha; + if (alertType === 'surge_up') { + alpha = parseFloat((Math.random() * 3 + 2).toFixed(2)); // +2% ~ +5% + } else if (alertType === 'surge_down') { + alpha = parseFloat((-Math.random() * 3 - 1.5).toFixed(2)); // -1.5% ~ -4.5% + } else { + alpha = parseFloat((Math.random() * 4 - 1).toFixed(2)); // -1% ~ +3% + } + + const finalScore = Math.floor(Math.random() * 40 + 45); // 45-85分 + const ruleScore = Math.floor(Math.random() * 30 + 40); + const mlScore = Math.floor(Math.random() * 30 + 40); + + alerts.push({ + concept_id: `CONCEPT_${1000 + i}`, + concept_name: conceptName, + time, + alert_type: alertType, + alpha, + alpha_delta: parseFloat((Math.random() * 2 - 0.5).toFixed(2)), + amt_ratio: parseFloat((Math.random() * 5 + 1).toFixed(2)), + limit_up_count: alertType === 'limit_up' ? Math.floor(Math.random() * 5 + 1) : 0, + limit_up_ratio: parseFloat((Math.random() * 0.3).toFixed(3)), + final_score: finalScore, + rule_score: ruleScore, + ml_score: mlScore, + trigger_reason: finalScore >= 65 ? '规则强信号' : (mlScore >= 70 ? 'ML强信号' : '融合触发'), + importance_score: parseFloat((finalScore / 100).toFixed(2)), + index_price: timeline[timeIdx].price + }); + } + + // 按时间排序 + alerts.sort((a, b) => a.time.localeCompare(b.time)); + + // 统计异动类型 + const alertSummary = alerts.reduce((acc, alert) => { + acc[alert.alert_type] = (acc[alert.alert_type] || 0) + 1; + return acc; + }, {}); + + // 计算指数统计 + const prices = timeline.map(t => t.price); + const latestPrice = prices[prices.length - 1]; + const highPrice = Math.max(...prices); + const lowPrice = Math.min(...prices); + const changePct = ((latestPrice - prevClose) / prevClose * 100); + + console.log('[Mock Market] 获取热点概览数据:', { + date: tradeDate, + timelinePoints: timeline.length, + alertCount: alerts.length + }); + + return HttpResponse.json({ + success: true, + data: { + index: { + code: '000001.SH', + name: '上证指数', + latest_price: latestPrice, + prev_close: prevClose, + high: highPrice, + low: lowPrice, + change_pct: parseFloat(changePct.toFixed(2)), + timeline + }, + alerts, + alert_summary: alertSummary + }, + trade_date: tradeDate + }); + }), + + // 12. 市场统计数据（个股中心页面使用） http.get('/api/market/statistics', async ({ request }) => { await delay(200); const url = new URL(request.url); diff --git a/src/views/StockOverview/components/HotspotOverview/components/AlertSummary.js b/src/views/StockOverview/components/HotspotOverview/components/AlertSummary.js new file mode 100644 index 00000000..37a871c6 --- /dev/null +++ b/src/views/StockOverview/components/HotspotOverview/components/AlertSummary.js @@ -0,0 +1,147 @@ +/** + * 异动统计摘要组件 + * 展示指数统计和异动类型统计 + */ +import React from 'react'; +import { + Box, + HStack, + VStack, + Text, + Badge, + Icon, + Stat, + StatLabel, + StatNumber, + StatHelpText, + StatArrow, + SimpleGrid, + useColorModeValue, +} from '@chakra-ui/react'; +import { FaBolt, FaArrowDown, FaRocket, FaChartLine, FaFire, FaVolumeUp } from 'react-icons/fa'; + +/** + * 异动类型徽章 + */ +const AlertTypeBadge = ({ type, count }) => { + const config = { + surge: { label: '急涨', color: 'red', icon: FaBolt }, + surge_up: { label: '暴涨', color: 'red', icon: FaBolt }, + surge_down: { label: '暴跌', color: 'green', icon: FaArrowDown }, + limit_up: { label: '涨停', color: 'orange', icon: FaRocket }, + rank_jump: { label: '排名跃升', color: 'blue', icon: FaChartLine }, + volume_spike: { label: '放量', color: 'purple', icon: FaVolumeUp }, + }; + + const cfg = config[type] || { label: type, color: 'gray', icon: FaFire }; + + return ( + + + + {cfg.label} + {count} + + + ); +}; + +/** + * 指数统计卡片 + */ +const IndexStatCard = ({ indexData }) => { + const cardBg = useColorModeValue('white', '#1a1a1a'); + const borderColor = useColorModeValue('gray.200', '#333'); + const subTextColor = useColorModeValue('gray.600', 'gray.400'); + + if (!indexData) return null; + + const changePct = indexData.change_pct || 0; + const isUp = changePct >= 0; + + return ( + + + {indexData.name || '上证指数'} + + {indexData.latest_price?.toFixed(2) || '-'} + + + + {changePct?.toFixed(2)}% + + + + + 最高 + + {indexData.high?.toFixed(2) || '-'} + + + + + 最低 + + {indexData.low?.toFixed(2) || '-'} + + + + + 振幅 + + {indexData.high && indexData.low && indexData.prev_close + ? (((indexData.high - indexData.low) / indexData.prev_close) * 100).toFixed(2) + '%' + : '-'} + + + + ); +}; + +/** + * 异动统计摘要 + * @param {Object} props + * @param {Object} props.indexData - 指数数据 + * @param {Array} props.alerts - 异动数组 + * @param {Object} props.alertSummary - 异动类型统计 + */ +const AlertSummary = ({ indexData, alerts = [], alertSummary = {} }) => { + const cardBg = useColorModeValue('white', '#1a1a1a'); + const borderColor = useColorModeValue('gray.200', '#333'); + + // 如果没有 alertSummary，从 alerts 中统计 + const summary = alertSummary && Object.keys(alertSummary).length > 0 + ? alertSummary + : alerts.reduce((acc, alert) => { + const type = alert.alert_type || 'unknown'; + acc[type] = (acc[type] || 0) + 1; + return acc; + }, {}); + + const totalAlerts = alerts.length; + + return ( + + {/* 指数统计 */} + + + {/* 异动统计 */} + {totalAlerts > 0 && ( + + + 异动 {totalAlerts} 次: + + {(summary.surge_up > 0 || summary.surge > 0) && ( + + )} + {summary.surge_down > 0 && } + {summary.limit_up > 0 && } + {summary.volume_spike > 0 && } + {summary.rank_jump > 0 && } + + )} + + ); +}; + +export default AlertSummary; diff --git a/src/views/StockOverview/components/HotspotOverview/components/ConceptAlertList.js b/src/views/StockOverview/components/HotspotOverview/components/ConceptAlertList.js new file mode 100644 index 00000000..9c846122 --- /dev/null +++ b/src/views/StockOverview/components/HotspotOverview/components/ConceptAlertList.js @@ -0,0 +1,194 @@ +/** + * 概念异动列表组件 + * 展示当日的概念异动记录 + */ +import React from 'react'; +import { + Box, + VStack, + HStack, + Text, + Badge, + Icon, + Tooltip, + useColorModeValue, + Flex, + Divider, +} from '@chakra-ui/react'; +import { FaBolt, FaArrowUp, FaArrowDown, FaChartLine, FaFire, FaVolumeUp } from 'react-icons/fa'; +import { getAlertTypeLabel, formatScore, getScoreColor } from '../utils/chartHelpers'; + +/** + * 单个异动项组件 + */ +const AlertItem = ({ alert, onClick, isSelected }) => { + const bgColor = useColorModeValue('white', '#1a1a1a'); + const hoverBg = useColorModeValue('gray.50', '#2a2a2a'); + const borderColor = useColorModeValue('gray.200', '#333'); + const selectedBg = useColorModeValue('purple.50', '#2a2a3a'); + + const isUp = alert.alert_type !== 'surge_down'; + const typeColor = isUp ? 'red' : 'green'; + + // 获取异动类型图标 + const getTypeIcon = (type) => { + switch (type) { + case 'surge_up': + case 'surge': + return FaArrowUp; + case 'surge_down': + return FaArrowDown; + case 'limit_up': + return FaFire; + case 'volume_spike': + return FaVolumeUp; + case 'rank_jump': + return FaChartLine; + default: + return FaBolt; + } + }; + + return ( + onClick?.(alert)} + > + + {/* 左侧：概念名称和时间 */} + + + + + {alert.concept_name} + + + + {alert.time} + + {getAlertTypeLabel(alert.alert_type)} + + + + + {/* 右侧：分数和关键指标 */} + + {/* 综合得分 */} + {alert.final_score !== undefined && ( + + + {formatScore(alert.final_score)}分 + + + )} + + {/* Alpha 值 */} + {alert.alpha !== undefined && ( + = 0 ? 'red.500' : 'green.500'} fontWeight="medium"> + α {alert.alpha >= 0 ? '+' : ''}{alert.alpha.toFixed(2)}% + + )} + + {/* 涨停数量 */} + {alert.limit_up_count > 0 && ( + + + + 涨停 {alert.limit_up_count} + + + )} + + + + ); +}; + +/** + * 概念异动列表 + * @param {Object} props + * @param {Array} props.alerts - 异动数据数组 + * @param {Function} props.onAlertClick - 点击异动的回调 + * @param {Object} props.selectedAlert - 当前选中的异动 + * @param {number} props.maxHeight - 最大高度 + */ +const ConceptAlertList = ({ alerts = [], onAlertClick, selectedAlert, maxHeight = '400px' }) => { + const textColor = useColorModeValue('gray.800', 'white'); + const subTextColor = useColorModeValue('gray.500', 'gray.400'); + + if (!alerts || alerts.length === 0) { + return ( + + + 当日暂无概念异动 + + + ); + } + + // 按时间分组 + const groupedAlerts = alerts.reduce((acc, alert) => { + const time = alert.time || '未知时间'; + if (!acc[time]) { + acc[time] = []; + } + acc[time].push(alert); + return acc; + }, {}); + + // 按时间排序 + const sortedTimes = Object.keys(groupedAlerts).sort(); + + return ( + + + {sortedTimes.map((time, timeIndex) => ( + + {/* 时间分隔线 */} + {timeIndex > 0 && } + + {/* 时间标签 */} + + + + {time} + + + ({groupedAlerts[time].length}个异动) + + + + {/* 该时间点的异动 */} + + {groupedAlerts[time].map((alert, idx) => ( + + ))} + + + ))} + + + ); +}; + +export default ConceptAlertList; diff --git a/src/views/StockOverview/components/HotspotOverview/components/IndexMinuteChart.js b/src/views/StockOverview/components/HotspotOverview/components/IndexMinuteChart.js new file mode 100644 index 00000000..a7c360a5 --- /dev/null +++ b/src/views/StockOverview/components/HotspotOverview/components/IndexMinuteChart.js @@ -0,0 +1,264 @@ +/** + * 指数分时图组件 + * 展示大盘分时走势，支持概念异动标注 + */ +import React, { useRef, useEffect, useCallback, useMemo } from 'react'; +import { Box, useColorModeValue } from '@chakra-ui/react'; +import * as echarts from 'echarts'; +import { getAlertMarkPoints } from '../utils/chartHelpers'; + +/** + * @param {Object} props + * @param {Object} props.indexData - 指数数据 { timeline, prev_close, name, ... } + * @param {Array} props.alerts - 异动数据数组 + * @param {Function} props.onAlertClick - 点击异动标注的回调 + * @param {string} props.height - 图表高度 + */ +const IndexMinuteChart = ({ indexData, alerts = [], onAlertClick, height = '350px' }) => { + const chartRef = useRef(null); + const chartInstance = useRef(null); + + const textColor = useColorModeValue('gray.800', 'white'); + const subTextColor = useColorModeValue('gray.600', 'gray.400'); + const gridLineColor = useColorModeValue('#eee', '#333'); + + // 计算图表配置 + const chartOption = useMemo(() => { + if (!indexData || !indexData.timeline || indexData.timeline.length === 0) { + return null; + } + + const timeline = indexData.timeline || []; + const times = timeline.map((d) => d.time); + const prices = timeline.map((d) => d.price); + const volumes = timeline.map((d) => d.volume); + const changePcts = timeline.map((d) => d.change_pct); + + // 计算Y轴范围 + const validPrices = prices.filter(Boolean); + if (validPrices.length === 0) return null; + + const priceMin = Math.min(...validPrices); + const priceMax = Math.max(...validPrices); + const priceRange = priceMax - priceMin; + const yAxisMin = priceMin - priceRange * 0.1; + const yAxisMax = priceMax + priceRange * 0.25; // 上方留更多空间给标注 + + // 准备异动标注 + const markPoints = getAlertMarkPoints(alerts, times, prices, priceMax); + + // 渐变色 - 根据涨跌 + const latestChangePct = changePcts[changePcts.length - 1] || 0; + const isUp = latestChangePct >= 0; + const lineColor = isUp ? '#ff4d4d' : '#22c55e'; + const areaColorStops = isUp + ? [ + { offset: 0, color: 'rgba(255, 77, 77, 0.4)' }, + { offset: 1, color: 'rgba(255, 77, 77, 0.05)' }, + ] + : [ + { offset: 0, color: 'rgba(34, 197, 94, 0.4)' }, + { offset: 1, color: 'rgba(34, 197, 94, 0.05)' }, + ]; + + return { + backgroundColor: 'transparent', + tooltip: { + trigger: 'axis', + axisPointer: { + type: 'cross', + crossStyle: { color: '#999' }, + }, + formatter: (params) => { + if (!params || params.length === 0) return ''; + + const dataIndex = params[0].dataIndex; + const time = times[dataIndex]; + const price = prices[dataIndex]; + const changePct = changePcts[dataIndex]; + const volume = volumes[dataIndex]; + + let html = ` +
+
${time}
+
指数: ${price?.toFixed(2)}
+
涨跌: ${changePct >= 0 ? '+' : ''}${changePct?.toFixed(2)}%
+
成交量: ${(volume / 10000).toFixed(0)}万手
+
+ `; + + // 检查是否有异动 + const alertsAtTime = alerts.filter((a) => a.time === time); + if (alertsAtTime.length > 0) { + html += '
'; + html += '
概念异动:
'; + alertsAtTime.forEach((alert) => { + const typeLabel = { + surge: '急涨', + surge_up: '暴涨', + surge_down: '暴跌', + limit_up: '涨停增加', + rank_jump: '排名跃升', + volume_spike: '放量', + }[alert.alert_type] || alert.alert_type; + const typeColor = alert.alert_type === 'surge_down' ? '#2ed573' : '#ff6b6b'; + const alpha = alert.alpha ? ` (α${alert.alpha > 0 ? '+' : ''}${alert.alpha.toFixed(2)}%)` : ''; + html += `
• ${alert.concept_name} (${typeLabel}${alpha})
`; + }); + html += '
'; + } + + return html; + }, + }, + legend: { show: false }, + grid: [ + { left: '8%', right: '3%', top: '8%', height: '58%' }, + { left: '8%', right: '3%', top: '72%', height: '18%' }, + ], + xAxis: [ + { + type: 'category', + data: times, + axisLine: { lineStyle: { color: gridLineColor } }, + axisLabel: { + color: subTextColor, + fontSize: 10, + interval: Math.floor(times.length / 6), + }, + axisTick: { show: false }, + splitLine: { show: false }, + }, + { + type: 'category', + gridIndex: 1, + data: times, + axisLine: { lineStyle: { color: gridLineColor } }, + axisLabel: { show: false }, + axisTick: { show: false }, + splitLine: { show: false }, + }, + ], + yAxis: [ + { + type: 'value', + min: yAxisMin, + max: yAxisMax, + axisLine: { show: false }, + axisLabel: { + color: subTextColor, + fontSize: 10, + formatter: (val) => val.toFixed(0), + }, + splitLine: { lineStyle: { color: gridLineColor, type: 'dashed' } }, + axisPointer: { + label: { + formatter: (params) => { + if (!indexData.prev_close) return params.value.toFixed(2); + const pct = ((params.value - indexData.prev_close) / indexData.prev_close) * 100; + return `${params.value.toFixed(2)} (${pct >= 0 ? '+' : ''}${pct.toFixed(2)}%)`; + }, + }, + }, + }, + { + type: 'value', + gridIndex: 1, + axisLine: { show: false }, + axisLabel: { show: false }, + splitLine: { show: false }, + }, + ], + series: [ + // 分时线 + { + name: indexData.name || '上证指数', + type: 'line', + data: prices, + smooth: true, + symbol: 'none', + lineStyle: { color: lineColor, width: 1.5 }, + areaStyle: { + color: new echarts.graphic.LinearGradient(0, 0, 0, 1, areaColorStops), + }, + markPoint: { + symbol: 'pin', + symbolSize: 40, + data: markPoints, + animation: true, + }, + }, + // 成交量 + { + name: '成交量', + type: 'bar', + xAxisIndex: 1, + yAxisIndex: 1, + data: volumes.map((v, i) => ({ + value: v, + itemStyle: { + color: changePcts[i] >= 0 ? 'rgba(255, 77, 77, 0.6)' : 'rgba(34, 197, 94, 0.6)', + }, + })), + barWidth: '60%', + }, + ], + }; + }, [indexData, alerts, subTextColor, gridLineColor]); + + // 渲染图表 + const renderChart = useCallback(() => { + if (!chartRef.current || !chartOption) return; + + if (!chartInstance.current) { + chartInstance.current = echarts.init(chartRef.current); + } + + chartInstance.current.setOption(chartOption, true); + + // 点击事件 + if (onAlertClick) { + chartInstance.current.off('click'); + chartInstance.current.on('click', 'series.line.markPoint', (params) => { + if (params.data && params.data.alertData) { + onAlertClick(params.data.alertData); + } + }); + } + }, [chartOption, onAlertClick]); + + // 数据变化时重新渲染 + useEffect(() => { + renderChart(); + }, [renderChart]); + + // 窗口大小变化时重新渲染 + useEffect(() => { + const handleResize = () => { + if (chartInstance.current) { + chartInstance.current.resize(); + } + }; + + window.addEventListener('resize', handleResize); + return () => { + window.removeEventListener('resize', handleResize); + if (chartInstance.current) { + chartInstance.current.dispose(); + chartInstance.current = null; + } + }; + }, []); + + if (!chartOption) { + return ( + + 暂无数据 + + ); + } + + return ; +}; + +export default IndexMinuteChart; diff --git a/src/views/StockOverview/components/HotspotOverview/components/index.js b/src/views/StockOverview/components/HotspotOverview/components/index.js new file mode 100644 index 00000000..2401b9bd --- /dev/null +++ b/src/views/StockOverview/components/HotspotOverview/components/index.js @@ -0,0 +1,3 @@ +export { default as IndexMinuteChart } from './IndexMinuteChart'; +export { default as ConceptAlertList } from './ConceptAlertList'; +export { default as AlertSummary } from './AlertSummary'; diff --git a/src/views/StockOverview/components/HotspotOverview/hooks/index.js b/src/views/StockOverview/components/HotspotOverview/hooks/index.js new file mode 100644 index 00000000..43a31148 --- /dev/null +++ b/src/views/StockOverview/components/HotspotOverview/hooks/index.js @@ -0,0 +1 @@ +export { useHotspotData } from './useHotspotData'; diff --git a/src/views/StockOverview/components/HotspotOverview/hooks/useHotspotData.js b/src/views/StockOverview/components/HotspotOverview/hooks/useHotspotData.js new file mode 100644 index 00000000..2fbca02a --- /dev/null +++ b/src/views/StockOverview/components/HotspotOverview/hooks/useHotspotData.js @@ -0,0 +1,53 @@ +/** + * 热点概览数据获取 Hook + * 负责获取指数分时数据和概念异动数据 + */ +import { useState, useEffect, useCallback } from 'react'; +import { logger } from '@utils/logger'; + +/** + * @param {Date|null} selectedDate - 选中的交易日期 + * @returns {Object} 数据和状态 + */ +export const useHotspotData = (selectedDate) => { + const [loading, setLoading] = useState(true); + const [error, setError] = useState(null); + const [data, setData] = useState(null); + + const fetchData = useCallback(async () => { + setLoading(true); + setError(null); + + try { + const dateParam = selectedDate + ? `?date=${selectedDate.toISOString().split('T')[0]}` + : ''; + const response = await fetch(`/api/market/hotspot-overview${dateParam}`); + const result = await response.json(); + + if (result.success) { + setData(result.data); + } else { + setError(result.error || '获取数据失败'); + } + } catch (err) { + logger.error('useHotspotData', 'fetchData', err); + setError('网络请求失败'); + } finally { + setLoading(false); + } + }, [selectedDate]); + + useEffect(() => { + fetchData(); + }, [fetchData]); + + return { + loading, + error, + data, + refetch: fetchData, + }; +}; + +export default useHotspotData; diff --git a/src/views/StockOverview/components/HotspotOverview/index.js b/src/views/StockOverview/components/HotspotOverview/index.js index 856d752d..ccef8cbb 100644 --- a/src/views/StockOverview/components/HotspotOverview/index.js +++ b/src/views/StockOverview/components/HotspotOverview/index.js @@ -1,8 +1,15 @@ /** * 热点概览组件 * 展示大盘分时走势 + 概念异动标注 + * + * 模块化结构： + * - hooks/useHotspotData.js - 数据获取 + * - components/IndexMinuteChart.js - 分时图 + * - components/ConceptAlertList.js - 异动列表 + * - components/AlertSummary.js - 统计摘要 + * - utils/chartHelpers.js - 图表辅助函数 */ -import React, { useState, useEffect, useRef, useCallback } from 'react'; +import React, { useState, useCallback } from 'react'; import { Box, Card, @@ -11,7 +18,6 @@ import { Text, HStack, VStack, - Badge, Spinner, Center, Icon, @@ -19,24 +25,29 @@ import { Spacer, Tooltip, useColorModeValue, - Stat, - StatLabel, - StatNumber, - StatHelpText, - StatArrow, - SimpleGrid, + Grid, + GridItem, + Divider, + IconButton, + Collapse, } from '@chakra-ui/react'; -import { FaFire, FaRocket, FaChartLine, FaBolt, FaArrowDown } from 'react-icons/fa'; +import { FaFire, FaList, FaChartArea, FaChevronDown, FaChevronUp } from 'react-icons/fa'; import { InfoIcon } from '@chakra-ui/icons'; -import * as echarts from 'echarts'; -import { logger } from '@utils/logger'; +import { useHotspotData } from './hooks'; +import { IndexMinuteChart, ConceptAlertList, AlertSummary } from './components'; + +/** + * 热点概览主组件 + * @param {Object} props + * @param {Date|null} props.selectedDate - 选中的交易日期 + */ const HotspotOverview = ({ selectedDate }) => { - const chartRef = useRef(null); - const chartInstance = useRef(null); - const [loading, setLoading] = useState(true); - const [data, setData] = useState(null); - const [error, setError] = useState(null); + const [selectedAlert, setSelectedAlert] = useState(null); + const [showAlertList, setShowAlertList] = useState(true); + + // 获取数据 + const { loading, error, data } = useHotspotData(selectedDate); // 颜色主题 const cardBg = useColorModeValue('white', '#1a1a1a'); @@ -44,373 +55,13 @@ const HotspotOverview = ({ selectedDate }) => { const textColor = useColorModeValue('gray.800', 'white'); const subTextColor = useColorModeValue('gray.600', 'gray.400'); - // 获取数据 - const fetchData = useCallback(async () => { - setLoading(true); - setError(null); - - try { - const dateParam = selectedDate - ? `?date=${selectedDate.toISOString().split('T')[0]}` - : ''; - const response = await fetch(`/api/market/hotspot-overview${dateParam}`); - const result = await response.json(); - - if (result.success) { - setData(result.data); - } else { - setError(result.error || '获取数据失败'); - } - } catch (err) { - logger.error('HotspotOverview', 'fetchData', err); - setError('网络请求失败'); - } finally { - setLoading(false); - } - }, [selectedDate]); - - useEffect(() => { - fetchData(); - }, [fetchData]); - - // 渲染图表 - const renderChart = useCallback(() => { - if (!chartRef.current || !data) return; - - if (!chartInstance.current) { - chartInstance.current = echarts.init(chartRef.current); - } - - const { index, alerts } = data; - const timeline = index.timeline || []; - - // 准备数据 - const times = timeline.map((d) => d.time); - const prices = timeline.map((d) => d.price); - const volumes = timeline.map((d) => d.volume); - const changePcts = timeline.map((d) => d.change_pct); - - // 计算Y轴范围 - const priceMin = Math.min(...prices.filter(Boolean)); - const priceMax = Math.max(...prices.filter(Boolean)); - const priceRange = priceMax - priceMin; - const yAxisMin = priceMin - priceRange * 0.1; - const yAxisMax = priceMax + priceRange * 0.2; // 上方留更多空间给标注 - - // 准备异动标注 - 按重要性排序，限制显示数量 - const sortedAlerts = [...alerts] - .sort((a, b) => (b.importance_score || 0) - (a.importance_score || 0)) - .slice(0, 15); // 最多显示15个标注，避免图表过于密集 - - const markPoints = sortedAlerts.map((alert) => { - // 找到对应时间的价格 - const timeIndex = times.indexOf(alert.time); - const price = timeIndex >= 0 ? prices[timeIndex] : (alert.index_price || priceMax); - - // 根据异动类型设置颜色和符号 - let color = '#ff6b6b'; - let symbol = 'pin'; - let symbolSize = 35; - - // 暴涨 - if (alert.alert_type === 'surge_up' || alert.alert_type === 'surge') { - color = '#ff4757'; - symbol = 'triangle'; - symbolSize = 30 + Math.min((alert.importance_score || 0.5) * 20, 15); // 根据重要性调整大小 - } - // 暴跌 - else if (alert.alert_type === 'surge_down') { - color = '#2ed573'; - symbol = 'path://M0,0 L10,0 L5,10 Z'; // 向下三角形 - symbolSize = 30 + Math.min((alert.importance_score || 0.5) * 20, 15); - } - // 涨停增加 - else if (alert.alert_type === 'limit_up') { - color = '#ff6348'; - symbol = 'diamond'; - symbolSize = 28; - } - // 排名跃升 - else if (alert.alert_type === 'rank_jump') { - color = '#3742fa'; - symbol = 'circle'; - symbolSize = 25; - } - - // 格式化标签 - 简化显示 - let label = alert.concept_name; - // 截断过长的名称 - if (label.length > 8) { - label = label.substring(0, 7) + '...'; - } - - // 添加变化信息 - const changeDelta = alert.change_delta; - if (changeDelta) { - const sign = changeDelta > 0 ? '+' : ''; - label += `\n${sign}${changeDelta.toFixed(1)}%`; - } - - return { - name: alert.concept_name, - coord: [alert.time, price], - value: label, - symbol: symbol, - symbolSize: symbolSize, - itemStyle: { - color: color, - borderColor: '#fff', - borderWidth: 1, - shadowBlur: 3, - shadowColor: 'rgba(0,0,0,0.2)', - }, - label: { - show: true, - position: alert.alert_type === 'surge_down' ? 'bottom' : 'top', // 暴跌标签在下方 - formatter: '{b}', - fontSize: 9, - color: textColor, - backgroundColor: alert.alert_type === 'surge_down' - ? 'rgba(46, 213, 115, 0.9)' - : 'rgba(255,255,255,0.9)', - padding: [2, 4], - borderRadius: 2, - borderColor: color, - borderWidth: 1, - }, - // 存储额外信息用于 tooltip - alertData: alert, - }; - }); - - // 渐变色 - 根据涨跌 - const latestChangePct = changePcts[changePcts.length - 1] || 0; - const areaColorStops = latestChangePct >= 0 - ? [ - { offset: 0, color: 'rgba(255, 77, 77, 0.4)' }, - { offset: 1, color: 'rgba(255, 77, 77, 0.05)' }, - ] - : [ - { offset: 0, color: 'rgba(34, 197, 94, 0.4)' }, - { offset: 1, color: 'rgba(34, 197, 94, 0.05)' }, - ]; - - const lineColor = latestChangePct >= 0 ? '#ff4d4d' : '#22c55e'; - - const option = { - backgroundColor: 'transparent', - tooltip: { - trigger: 'axis', - axisPointer: { - type: 'cross', - crossStyle: { - color: '#999', - }, - }, - formatter: function (params) { - if (!params || params.length === 0) return ''; - - const dataIndex = params[0].dataIndex; - const time = times[dataIndex]; - const price = prices[dataIndex]; - const changePct = changePcts[dataIndex]; - const volume = volumes[dataIndex]; - - let html = ` -
-
${time}
-
指数: ${price?.toFixed(2)}
-
涨跌: ${changePct >= 0 ? '+' : ''}${changePct?.toFixed(2)}%
-
成交量: ${(volume / 10000).toFixed(0)}万手
-
- `; - - // 检查是否有异动 - const alertsAtTime = alerts.filter((a) => a.time === time); - if (alertsAtTime.length > 0) { - html += '
'; - html += '
概念异动:
'; - alertsAtTime.forEach((alert) => { - const typeLabel = { - surge: '急涨', - surge_up: '暴涨', - surge_down: '暴跌', - limit_up: '涨停增加', - rank_jump: '排名跃升', - }[alert.alert_type] || alert.alert_type; - const typeColor = alert.alert_type === 'surge_down' ? '#2ed573' : '#ff6b6b'; - const delta = alert.change_delta ? ` (${alert.change_delta > 0 ? '+' : ''}${alert.change_delta.toFixed(2)}%)` : ''; - const zscore = alert.zscore ? ` Z=${alert.zscore.toFixed(1)}` : ''; - html += `
• ${alert.concept_name} (${typeLabel}${delta}${zscore})
`; - }); - html += '
'; - } - - return html; - }, - }, - legend: { - show: false, - }, - grid: [ - { - left: '8%', - right: '3%', - top: '8%', - height: '55%', - }, - { - left: '8%', - right: '3%', - top: '70%', - height: '20%', - }, - ], - xAxis: [ - { - type: 'category', - data: times, - axisLine: { lineStyle: { color: '#ddd' } }, - axisLabel: { - color: subTextColor, - fontSize: 10, - interval: Math.floor(times.length / 6), - }, - axisTick: { show: false }, - splitLine: { show: false }, - }, - { - type: 'category', - gridIndex: 1, - data: times, - axisLine: { lineStyle: { color: '#ddd' } }, - axisLabel: { show: false }, - axisTick: { show: false }, - splitLine: { show: false }, - }, - ], - yAxis: [ - { - type: 'value', - min: yAxisMin, - max: yAxisMax, - axisLine: { show: false }, - axisLabel: { - color: subTextColor, - fontSize: 10, - formatter: (val) => val.toFixed(0), - }, - splitLine: { - lineStyle: { color: '#eee', type: 'dashed' }, - }, - // 右侧显示涨跌幅 - axisPointer: { - label: { - formatter: function (params) { - const pct = ((params.value - index.prev_close) / index.prev_close) * 100; - return `${params.value.toFixed(2)} (${pct >= 0 ? '+' : ''}${pct.toFixed(2)}%)`; - }, - }, - }, - }, - { - type: 'value', - gridIndex: 1, - axisLine: { show: false }, - axisLabel: { show: false }, - splitLine: { show: false }, - }, - ], - series: [ - // 分时线 - { - name: '上证指数', - type: 'line', - data: prices, - smooth: true, - symbol: 'none', - lineStyle: { - color: lineColor, - width: 1.5, - }, - areaStyle: { - color: new echarts.graphic.LinearGradient(0, 0, 0, 1, areaColorStops), - }, - markPoint: { - symbol: 'pin', - symbolSize: 40, - data: markPoints, - animation: true, - }, - }, - // 成交量 - { - name: '成交量', - type: 'bar', - xAxisIndex: 1, - yAxisIndex: 1, - data: volumes.map((v, i) => ({ - value: v, - itemStyle: { - color: changePcts[i] >= 0 ? 'rgba(255, 77, 77, 0.6)' : 'rgba(34, 197, 94, 0.6)', - }, - })), - barWidth: '60%', - }, - ], - }; - - chartInstance.current.setOption(option, true); - }, [data, textColor, subTextColor]); - - // 数据变化时重新渲染 - useEffect(() => { - if (data) { - renderChart(); - } - }, [data, renderChart]); - - // 窗口大小变化时重新渲染 - useEffect(() => { - const handleResize = () => { - if (chartInstance.current) { - chartInstance.current.resize(); - } - }; - - window.addEventListener('resize', handleResize); - return () => { - window.removeEventListener('resize', handleResize); - if (chartInstance.current) { - chartInstance.current.dispose(); - chartInstance.current = null; - } - }; + // 点击异动标注 + const handleAlertClick = useCallback((alert) => { + setSelectedAlert(alert); + // 可以在这里添加滚动到对应位置的逻辑 }, []); - // 异动类型标签 - const AlertTypeBadge = ({ type, count }) => { - const config = { - surge: { label: '急涨', color: 'red', icon: FaBolt }, - surge_up: { label: '暴涨', color: 'red', icon: FaBolt }, - surge_down: { label: '暴跌', color: 'green', icon: FaArrowDown }, - limit_up: { label: '涨停', color: 'orange', icon: FaRocket }, - rank_jump: { label: '排名跃升', color: 'blue', icon: FaChartLine }, - }; - - const cfg = config[type] || { label: type, color: 'gray', icon: FaFire }; - - return ( - - - - {cfg.label} - {count} - - - ); - }; - + // 渲染加载状态 if (loading) { return ( @@ -426,6 +77,7 @@ const HotspotOverview = ({ selectedDate }) => { ); } + // 渲染错误状态 if (error) { return ( @@ -441,6 +93,7 @@ const HotspotOverview = ({ selectedDate }) => { ); } + // 无数据 if (!data) { return null; } @@ -450,7 +103,7 @@ const HotspotOverview = ({ selectedDate }) => { return ( - {/* 头部信息 */} + {/* 头部 */} @@ -459,69 +112,75 @@ const HotspotOverview = ({ selectedDate }) => { - - - + + + : } + size="sm" + variant="ghost" + onClick={() => setShowAlertList(!showAlertList)} + aria-label="切换异动列表" + /> + + + + + - {/* 指数统计 */} - - - {index.name} - = 0 ? 'red.500' : 'green.500'} - > - {index.latest_price?.toFixed(2)} - - - = 0 ? 'increase' : 'decrease'} /> - {index.change_pct?.toFixed(2)}% - - + {/* 统计摘要 */} + + + - - 最高 - - {index.high?.toFixed(2)} - - + - - 最低 - - {index.low?.toFixed(2)} - - + {/* 主体内容：图表 + 异动列表 */} + + {/* 分时图 */} + + + + + + 大盘分时走势 + + + + + - - 异动次数 - - {alerts.length} - - - - - {/* 异动类型统计 */} - {alerts.length > 0 && ( - - {(alert_summary.surge_up > 0 || alert_summary.surge > 0) && ( - - )} - {alert_summary.surge_down > 0 && ( - - )} - {alert_summary.limit_up > 0 && ( - - )} - {alert_summary.rank_jump > 0 && ( - - )} - - )} - - {/* 图表 */} - + {/* 异动列表（可收起） */} + + + + + + + 异动记录 + + + ({alerts.length}) + + + + + + + {/* 无异动提示 */} {alerts.length === 0 && ( diff --git a/src/views/StockOverview/components/HotspotOverview/utils/chartHelpers.js b/src/views/StockOverview/components/HotspotOverview/utils/chartHelpers.js new file mode 100644 index 00000000..96ba2d5b --- /dev/null +++ b/src/views/StockOverview/components/HotspotOverview/utils/chartHelpers.js @@ -0,0 +1,159 @@ +/** + * 图表辅助函数 + * 用于处理异动标注等图表相关逻辑 + */ + +/** + * 获取异动标注的配色和符号 + * @param {string} alertType - 异动类型 + * @param {number} importanceScore - 重要性得分 + * @returns {Object} { color, symbol, symbolSize } + */ +export const getAlertStyle = (alertType, importanceScore = 0.5) => { + let color = '#ff6b6b'; + let symbol = 'pin'; + let symbolSize = 35; + + switch (alertType) { + case 'surge_up': + case 'surge': + color = '#ff4757'; + symbol = 'triangle'; + symbolSize = 30 + Math.min(importanceScore * 20, 15); + break; + case 'surge_down': + color = '#2ed573'; + symbol = 'path://M0,0 L10,0 L5,10 Z'; // 向下三角形 + symbolSize = 30 + Math.min(importanceScore * 20, 15); + break; + case 'limit_up': + color = '#ff6348'; + symbol = 'diamond'; + symbolSize = 28; + break; + case 'rank_jump': + color = '#3742fa'; + symbol = 'circle'; + symbolSize = 25; + break; + case 'volume_spike': + color = '#ffa502'; + symbol = 'rect'; + symbolSize = 25; + break; + default: + break; + } + + return { color, symbol, symbolSize }; +}; + +/** + * 获取异动类型的显示标签 + * @param {string} alertType - 异动类型 + * @returns {string} 显示标签 + */ +export const getAlertTypeLabel = (alertType) => { + const labels = { + surge: '急涨', + surge_up: '暴涨', + surge_down: '暴跌', + limit_up: '涨停增加', + rank_jump: '排名跃升', + volume_spike: '放量', + unknown: '异动', + }; + return labels[alertType] || alertType; +}; + +/** + * 生成图表标注点数据 + * @param {Array} alerts - 异动数据数组 + * @param {Array} times - 时间数组 + * @param {Array} prices - 价格数组 + * @param {number} priceMax - 最高价格（用于无法匹配时间时的默认位置） + * @param {number} maxCount - 最大显示数量 + * @returns {Array} ECharts markPoint data + */ +export const getAlertMarkPoints = (alerts, times, prices, priceMax, maxCount = 15) => { + if (!alerts || alerts.length === 0) return []; + + // 按重要性排序，限制显示数量 + const sortedAlerts = [...alerts] + .sort((a, b) => (b.final_score || b.importance_score || 0) - (a.final_score || a.importance_score || 0)) + .slice(0, maxCount); + + return sortedAlerts.map((alert) => { + // 找到对应时间的价格 + const timeIndex = times.indexOf(alert.time); + const price = timeIndex >= 0 ? prices[timeIndex] : (alert.index_price || priceMax); + + const { color, symbol, symbolSize } = getAlertStyle( + alert.alert_type, + alert.final_score / 100 || alert.importance_score || 0.5 + ); + + // 格式化标签 + let label = alert.concept_name || ''; + if (label.length > 6) { + label = label.substring(0, 5) + '...'; + } + + // 添加涨停数量（如果有） + if (alert.limit_up_count > 0) { + label += `\n涨停: ${alert.limit_up_count}`; + } + + const isDown = alert.alert_type === 'surge_down'; + + return { + name: alert.concept_name, + coord: [alert.time, price], + value: label, + symbol, + symbolSize, + itemStyle: { + color, + borderColor: '#fff', + borderWidth: 1, + shadowBlur: 3, + shadowColor: 'rgba(0,0,0,0.2)', + }, + label: { + show: true, + position: isDown ? 'bottom' : 'top', + formatter: '{b}', + fontSize: 9, + color: '#333', + backgroundColor: isDown ? 'rgba(46, 213, 115, 0.9)' : 'rgba(255,255,255,0.9)', + padding: [2, 4], + borderRadius: 2, + borderColor: color, + borderWidth: 1, + }, + alertData: alert, // 存储原始数据 + }; + }); +}; + +/** + * 格式化分数显示 + * @param {number} score - 分数 + * @returns {string} 格式化后的分数 + */ +export const formatScore = (score) => { + if (score === null || score === undefined) return '-'; + return Math.round(score).toString(); +}; + +/** + * 获取分数对应的颜色 + * @param {number} score - 分数 (0-100) + * @returns {string} 颜色代码 + */ +export const getScoreColor = (score) => { + if (score >= 80) return '#ff4757'; + if (score >= 60) return '#ff6348'; + if (score >= 40) return '#ffa502'; + return '#747d8c'; +}; diff --git a/src/views/StockOverview/components/HotspotOverview/utils/index.js b/src/views/StockOverview/components/HotspotOverview/utils/index.js new file mode 100644 index 00000000..133f3c2d --- /dev/null +++ b/src/views/StockOverview/components/HotspotOverview/utils/index.js @@ -0,0 +1 @@ +export * from './chartHelpers'; From bebe58c99f3b9992d28444a1a82b9d9173af8710 Mon Sep 17 00:00:00 2001 From: zzlgreat Date: Tue, 9 Dec 2025 17:07:47 +0800 Subject: [PATCH 05/20] update pay ui --- app.py | 118 +++++++++++++++++++++++++++++++++++++++------------------ 1 file changed, 81 insertions(+), 37 deletions(-) diff --git a/app.py b/app.py index b231ea6c..e1d71850 100755 --- a/app.py +++ b/app.py @@ -12465,6 +12465,10 @@ def get_hotspot_overview(): """ 获取热点概览数据（用于个股中心的热点概览图表） 返回：指数分时数据 + 概念异动标注 + + 数据来源： + - 指数分时：ClickHouse index_minute 表 + - 概念异动：MySQL concept_anomaly_hybrid 表（来自 realtime_detector.py） """ try: trade_date = request.args.get('date') @@ -12532,61 +12536,100 @@ def get_hotspot_overview(): 'change_pct': change_pct }) - # 2. 获取概念异动数据 + # 2. 获取概念异动数据（从 concept_anomaly_hybrid 表） alerts = [] with engine.connect() as conn: + # 查询 concept_anomaly_hybrid 表 alert_result = conn.execute(text(""" SELECT - concept_id, concept_name, alert_time, alert_type, - change_pct, change_delta, limit_up_count, limit_up_delta, - rank_position, index_price, index_change_pct, - stock_count, concept_type, extra_info, - prev_change_pct, zscore, importance_score - FROM concept_minute_alert - WHERE trade_date = :trade_date - ORDER BY alert_time + a.concept_id, + a.alert_time, + a.trade_date, + a.alert_type, + a.final_score, + a.rule_score, + a.ml_score, + a.trigger_reason, + a.alpha, + a.alpha_delta, + a.amt_ratio, + a.amt_delta, + a.rank_pct, + a.limit_up_ratio, + a.stock_count, + a.total_amt, + a.triggered_rules + FROM concept_anomaly_hybrid a + WHERE a.trade_date = :trade_date + ORDER BY a.alert_time """), {'trade_date': trade_date}) + # 获取概念名称映射（从 ES 或缓存） + concept_names = {} + for row in alert_result: - alert_time = row[2] - extra_info = None - if row[13]: + concept_id = row[0] + alert_time = row[1] + triggered_rules = None + if row[16]: try: - extra_info = json.loads(row[13]) if isinstance(row[13], str) else row[13] + triggered_rules = json.loads(row[16]) if isinstance(row[16], str) else row[16] except: pass - # 从 extra_info 提取 zscore 和 importance_score（兼容旧数据） - zscore = None - importance_score = None - if len(row) > 15: - zscore = float(row[15]) if row[15] else None - importance_score = float(row[16]) if row[16] else None - if extra_info: - zscore = zscore or extra_info.get('zscore') - importance_score = importance_score or extra_info.get('importance_score') + # 获取概念名称（优先从缓存，否则使用 concept_id） + concept_name = concept_names.get(concept_id) or concept_id + + # 计算涨停数量（从 limit_up_ratio 和 stock_count 估算） + limit_up_ratio = float(row[13]) if row[13] else 0 + stock_count = int(row[14]) if row[14] else 0 + limit_up_count = int(limit_up_ratio * stock_count) if stock_count > 0 else 0 alerts.append({ - 'concept_id': row[0], - 'concept_name': row[1], + 'concept_id': concept_id, + 'concept_name': concept_name, 'time': alert_time.strftime('%H:%M') if alert_time else None, 'timestamp': alert_time.isoformat() if alert_time else None, 'alert_type': row[3], - 'change_pct': float(row[4]) if row[4] else None, - 'change_delta': float(row[5]) if row[5] else None, - 'limit_up_count': row[6], - 'limit_up_delta': row[7], - 'rank_position': row[8], - 'index_price': float(row[9]) if row[9] else None, - 'index_change_pct': float(row[10]) if row[10] else None, - 'stock_count': row[11], - 'concept_type': row[12], - 'extra_info': extra_info, - 'prev_change_pct': float(row[14]) if len(row) > 14 and row[14] else None, - 'zscore': zscore, - 'importance_score': importance_score + 'final_score': float(row[4]) if row[4] else None, + 'rule_score': float(row[5]) if row[5] else None, + 'ml_score': float(row[6]) if row[6] else None, + 'trigger_reason': row[7], + 'alpha': float(row[8]) if row[8] else None, + 'alpha_delta': float(row[9]) if row[9] else None, + 'amt_ratio': float(row[10]) if row[10] else None, + 'amt_delta': float(row[11]) if row[11] else None, + 'rank_pct': float(row[12]) if row[12] else None, + 'limit_up_ratio': limit_up_ratio, + 'limit_up_count': limit_up_count, + 'stock_count': stock_count, + 'total_amt': float(row[15]) if row[15] else None, + 'triggered_rules': triggered_rules, + # 兼容旧字段 + 'importance_score': float(row[4]) / 100 if row[4] else None, }) + # 尝试批量获取概念名称 + if alerts: + concept_ids = list(set(a['concept_id'] for a in alerts)) + try: + from elasticsearch import Elasticsearch + es_client = Elasticsearch(['http://127.0.0.1:9200']) + es_result = es_client.mget( + index='concept_library_v3', + body={'ids': concept_ids}, + _source=['concept'] + ) + for doc in es_result.get('docs', []): + if doc.get('found') and doc.get('_source'): + concept_names[doc['_id']] = doc['_source'].get('concept', doc['_id']) + # 更新 alerts 中的概念名称 + for alert in alerts: + if alert['concept_id'] in concept_names: + alert['concept_name'] = concept_names[alert['concept_id']] + except Exception as e: + logger.warning(f"获取概念名称失败: {e}") + # 计算统计信息 day_high = max([d['price'] for d in index_timeline if d['price']], default=None) day_low = min([d['price'] for d in index_timeline if d['price']], default=None) @@ -12614,6 +12657,7 @@ def get_hotspot_overview(): 'surge_up': len([a for a in alerts if a['alert_type'] == 'surge_up']), 'surge_down': len([a for a in alerts if a['alert_type'] == 'surge_down']), 'limit_up': len([a for a in alerts if a['alert_type'] == 'limit_up']), + 'volume_spike': len([a for a in alerts if a['alert_type'] == 'volume_spike']), 'rank_jump': len([a for a in alerts if a['alert_type'] == 'rank_jump']) } } From c9b521b9010caeb46319f4af15c1d65479acb285 Mon Sep 17 00:00:00 2001 From: zzlgreat Date: Tue, 9 Dec 2025 17:09:01 +0800 Subject: [PATCH 06/20] update pay ui --- app.py | 34 ++++++++++++++++++++++++++++++++++ 1 file changed, 34 insertions(+) diff --git a/app.py b/app.py index e1d71850..76932722 100755 --- a/app.py +++ b/app.py @@ -12539,6 +12539,40 @@ def get_hotspot_overview(): # 2. 获取概念异动数据（从 concept_anomaly_hybrid 表） alerts = [] with engine.connect() as conn: + # 首先确保表存在 + try: + conn.execute(text(""" + CREATE TABLE IF NOT EXISTS concept_anomaly_hybrid ( + id INT AUTO_INCREMENT PRIMARY KEY, + concept_id VARCHAR(64) NOT NULL, + alert_time DATETIME NOT NULL, + trade_date DATE NOT NULL, + alert_type VARCHAR(32) NOT NULL, + final_score FLOAT NOT NULL, + rule_score FLOAT NOT NULL, + ml_score FLOAT NOT NULL, + trigger_reason VARCHAR(64), + alpha FLOAT, + alpha_delta FLOAT, + amt_ratio FLOAT, + amt_delta FLOAT, + rank_pct FLOAT, + limit_up_ratio FLOAT, + stock_count INT, + total_amt FLOAT, + triggered_rules JSON, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + UNIQUE KEY uk_concept_time (concept_id, alert_time, trade_date), + INDEX idx_trade_date (trade_date), + INDEX idx_concept_id (concept_id), + INDEX idx_final_score (final_score), + INDEX idx_alert_type (alert_type) + ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COMMENT='概念异动检测结果（融合版）' + """)) + conn.commit() + except Exception as create_err: + logger.debug(f"创建表检查: {create_err}") + # 查询 concept_anomaly_hybrid 表 alert_result = conn.execute(text(""" SELECT From a2e773a1c3b383621c5d4dc15de2c81d68864666 Mon Sep 17 00:00:00 2001 From: zzlgreat Date: Tue, 9 Dec 2025 17:13:23 +0800 Subject: [PATCH 07/20] update pay ui --- app.py | 19 +++++++++++-------- 1 file changed, 11 insertions(+), 8 deletions(-) diff --git a/app.py b/app.py index 76932722..3d1b59e2 100755 --- a/app.py +++ b/app.py @@ -12538,9 +12538,10 @@ def get_hotspot_overview(): # 2. 获取概念异动数据（从 concept_anomaly_hybrid 表） alerts = [] - with engine.connect() as conn: - # 首先确保表存在 - try: + + # 首先确保表存在（使用 begin() 来自动提交） + try: + with engine.begin() as conn: conn.execute(text(""" CREATE TABLE IF NOT EXISTS concept_anomaly_hybrid ( id INT AUTO_INCREMENT PRIMARY KEY, @@ -12569,10 +12570,10 @@ def get_hotspot_overview(): INDEX idx_alert_type (alert_type) ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COMMENT='概念异动检测结果（融合版）' """)) - conn.commit() - except Exception as create_err: - logger.debug(f"创建表检查: {create_err}") + except Exception as create_err: + logger.debug(f"创建表检查: {create_err}") + with engine.connect() as conn: # 查询 concept_anomaly_hybrid 表 alert_result = conn.execute(text(""" SELECT @@ -12699,10 +12700,12 @@ def get_hotspot_overview(): except Exception as e: import traceback - logger.error(f"获取热点概览数据失败: {traceback.format_exc()}") + error_trace = traceback.format_exc() + logger.error(f"获取热点概览数据失败: {error_trace}") return jsonify({ 'success': False, - 'error': str(e) + 'error': str(e), + 'traceback': error_trace # 临时返回完整错误信息用于调试 }), 500 From 813d416499e0483286f92fb0dccfa2ce4ea1c781 Mon Sep 17 00:00:00 2001 From: zzlgreat Date: Tue, 9 Dec 2025 17:15:22 +0800 Subject: [PATCH 08/20] update pay ui --- app.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/app.py b/app.py index 3d1b59e2..07769534 100755 --- a/app.py +++ b/app.py @@ -12649,7 +12649,7 @@ def get_hotspot_overview(): concept_ids = list(set(a['concept_id'] for a in alerts)) try: from elasticsearch import Elasticsearch - es_client = Elasticsearch(['http://127.0.0.1:9200']) + es_client = Elasticsearch(["http://222.128.1.157:19200"]) es_result = es_client.mget( index='concept_library_v3', body={'ids': concept_ids}, From e56f62506d9db6dcb84a073977cc6d9af9d0c5b4 Mon Sep 17 00:00:00 2001 From: zzlgreat Date: Wed, 10 Dec 2025 11:02:09 +0800 Subject: [PATCH 09/20] update pay ui --- app.py | 198 ++--- ml/backtest_v2.py | 294 +++++++ ml/checkpoints_v2/config.json | 31 + ml/checkpoints_v2/thresholds.json | 8 + ml/detector_v2.py | 716 +++++++++++++++++ ml/model.py | 5 +- ml/prepare_data.py | 126 +-- ml/prepare_data_v2.py | 715 +++++++++++++++++ ml/realtime_detector.py | 8 +- ml/realtime_detector_v2.py | 729 ++++++++++++++++++ ml/train_v2.py | 622 +++++++++++++++ ml/update_baseline.py | 132 ++++ .../components/MiniTimelineChart.js | 267 +++++++ .../FlexScreen/components/OrderBookPanel.js | 275 +++++++ .../FlexScreen/components/QuoteTile.js | 270 +++++++ .../components/FlexScreen/components/index.js | 3 + .../components/FlexScreen/hooks/index.js | 1 + .../FlexScreen/hooks/useRealtimeQuote.js | 692 +++++++++++++++++ .../components/FlexScreen/index.js | 463 +++++++++++ .../components/ConceptAlertList.js | 104 ++- src/views/StockOverview/index.js | 6 + 21 files changed, 5514 insertions(+), 151 deletions(-) create mode 100644 ml/backtest_v2.py create mode 100644 ml/checkpoints_v2/config.json create mode 100644 ml/checkpoints_v2/thresholds.json create mode 100644 ml/detector_v2.py create mode 100644 ml/prepare_data_v2.py create mode 100644 ml/realtime_detector_v2.py create mode 100644 ml/train_v2.py create mode 100644 ml/update_baseline.py create mode 100644 src/views/StockOverview/components/FlexScreen/components/MiniTimelineChart.js create mode 100644 src/views/StockOverview/components/FlexScreen/components/OrderBookPanel.js create mode 100644 src/views/StockOverview/components/FlexScreen/components/QuoteTile.js create mode 100644 src/views/StockOverview/components/FlexScreen/components/index.js create mode 100644 src/views/StockOverview/components/FlexScreen/hooks/index.js create mode 100644 src/views/StockOverview/components/FlexScreen/hooks/useRealtimeQuote.js create mode 100644 src/views/StockOverview/components/FlexScreen/index.js diff --git a/app.py b/app.py index 07769534..0980cf9f 100755 --- a/app.py +++ b/app.py @@ -12536,113 +12536,113 @@ def get_hotspot_overview(): 'change_pct': change_pct }) - # 2. 获取概念异动数据（从 concept_anomaly_hybrid 表） + # 2. 获取概念异动数据（优先从 V2 表，fallback 到旧表） alerts = [] - - # 首先确保表存在（使用 begin() 来自动提交） - try: - with engine.begin() as conn: - conn.execute(text(""" - CREATE TABLE IF NOT EXISTS concept_anomaly_hybrid ( - id INT AUTO_INCREMENT PRIMARY KEY, - concept_id VARCHAR(64) NOT NULL, - alert_time DATETIME NOT NULL, - trade_date DATE NOT NULL, - alert_type VARCHAR(32) NOT NULL, - final_score FLOAT NOT NULL, - rule_score FLOAT NOT NULL, - ml_score FLOAT NOT NULL, - trigger_reason VARCHAR(64), - alpha FLOAT, - alpha_delta FLOAT, - amt_ratio FLOAT, - amt_delta FLOAT, - rank_pct FLOAT, - limit_up_ratio FLOAT, - stock_count INT, - total_amt FLOAT, - triggered_rules JSON, - created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, - UNIQUE KEY uk_concept_time (concept_id, alert_time, trade_date), - INDEX idx_trade_date (trade_date), - INDEX idx_concept_id (concept_id), - INDEX idx_final_score (final_score), - INDEX idx_alert_type (alert_type) - ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COMMENT='概念异动检测结果（融合版）' - """)) - except Exception as create_err: - logger.debug(f"创建表检查: {create_err}") + use_v2 = False with engine.connect() as conn: - # 查询 concept_anomaly_hybrid 表 - alert_result = conn.execute(text(""" - SELECT - a.concept_id, - a.alert_time, - a.trade_date, - a.alert_type, - a.final_score, - a.rule_score, - a.ml_score, - a.trigger_reason, - a.alpha, - a.alpha_delta, - a.amt_ratio, - a.amt_delta, - a.rank_pct, - a.limit_up_ratio, - a.stock_count, - a.total_amt, - a.triggered_rules - FROM concept_anomaly_hybrid a - WHERE a.trade_date = :trade_date - ORDER BY a.alert_time - """), {'trade_date': trade_date}) + # 尝试查询 V2 表（时间片对齐 + 持续确认版本） + try: + v2_result = conn.execute(text(""" + SELECT + concept_id, alert_time, trade_date, alert_type, + final_score, rule_score, ml_score, trigger_reason, confirm_ratio, + alpha, alpha_zscore, amt_zscore, rank_zscore, + momentum_3m, momentum_5m, limit_up_ratio, triggered_rules + FROM concept_anomaly_v2 + WHERE trade_date = :trade_date + ORDER BY alert_time + """), {'trade_date': trade_date}) + v2_rows = v2_result.fetchall() + if v2_rows: + use_v2 = True + for row in v2_rows: + triggered_rules = None + if row[16]: + try: + triggered_rules = json.loads(row[16]) if isinstance(row[16], str) else row[16] + except: + pass - # 获取概念名称映射（从 ES 或缓存） - concept_names = {} + alerts.append({ + 'concept_id': row[0], + 'concept_name': row[0], # 后面会填充 + 'time': row[1].strftime('%H:%M') if row[1] else None, + 'timestamp': row[1].isoformat() if row[1] else None, + 'alert_type': row[3], + 'final_score': float(row[4]) if row[4] else None, + 'rule_score': float(row[5]) if row[5] else None, + 'ml_score': float(row[6]) if row[6] else None, + 'trigger_reason': row[7], + # V2 新增字段 + 'confirm_ratio': float(row[8]) if row[8] else None, + 'alpha': float(row[9]) if row[9] else None, + 'alpha_zscore': float(row[10]) if row[10] else None, + 'amt_zscore': float(row[11]) if row[11] else None, + 'rank_zscore': float(row[12]) if row[12] else None, + 'momentum_3m': float(row[13]) if row[13] else None, + 'momentum_5m': float(row[14]) if row[14] else None, + 'limit_up_ratio': float(row[15]) if row[15] else 0, + 'triggered_rules': triggered_rules, + # 兼容字段 + 'importance_score': float(row[4]) / 100 if row[4] else None, + 'is_v2': True, + }) + except Exception as v2_err: + logger.debug(f"V2 表查询失败，使用旧表: {v2_err}") - for row in alert_result: - concept_id = row[0] - alert_time = row[1] - triggered_rules = None - if row[16]: - try: - triggered_rules = json.loads(row[16]) if isinstance(row[16], str) else row[16] - except: - pass + # Fallback: 查询旧表 + if not use_v2: + try: + alert_result = conn.execute(text(""" + SELECT + a.concept_id, a.alert_time, a.trade_date, a.alert_type, + a.final_score, a.rule_score, a.ml_score, a.trigger_reason, + a.alpha, a.alpha_delta, a.amt_ratio, a.amt_delta, + a.rank_pct, a.limit_up_ratio, a.stock_count, a.total_amt, + a.triggered_rules + FROM concept_anomaly_hybrid a + WHERE a.trade_date = :trade_date + ORDER BY a.alert_time + """), {'trade_date': trade_date}) - # 获取概念名称（优先从缓存，否则使用 concept_id） - concept_name = concept_names.get(concept_id) or concept_id + for row in alert_result: + triggered_rules = None + if row[16]: + try: + triggered_rules = json.loads(row[16]) if isinstance(row[16], str) else row[16] + except: + pass - # 计算涨停数量（从 limit_up_ratio 和 stock_count 估算） - limit_up_ratio = float(row[13]) if row[13] else 0 - stock_count = int(row[14]) if row[14] else 0 - limit_up_count = int(limit_up_ratio * stock_count) if stock_count > 0 else 0 + limit_up_ratio = float(row[13]) if row[13] else 0 + stock_count = int(row[14]) if row[14] else 0 + limit_up_count = int(limit_up_ratio * stock_count) if stock_count > 0 else 0 - alerts.append({ - 'concept_id': concept_id, - 'concept_name': concept_name, - 'time': alert_time.strftime('%H:%M') if alert_time else None, - 'timestamp': alert_time.isoformat() if alert_time else None, - 'alert_type': row[3], - 'final_score': float(row[4]) if row[4] else None, - 'rule_score': float(row[5]) if row[5] else None, - 'ml_score': float(row[6]) if row[6] else None, - 'trigger_reason': row[7], - 'alpha': float(row[8]) if row[8] else None, - 'alpha_delta': float(row[9]) if row[9] else None, - 'amt_ratio': float(row[10]) if row[10] else None, - 'amt_delta': float(row[11]) if row[11] else None, - 'rank_pct': float(row[12]) if row[12] else None, - 'limit_up_ratio': limit_up_ratio, - 'limit_up_count': limit_up_count, - 'stock_count': stock_count, - 'total_amt': float(row[15]) if row[15] else None, - 'triggered_rules': triggered_rules, - # 兼容旧字段 - 'importance_score': float(row[4]) / 100 if row[4] else None, - }) + alerts.append({ + 'concept_id': row[0], + 'concept_name': row[0], + 'time': row[1].strftime('%H:%M') if row[1] else None, + 'timestamp': row[1].isoformat() if row[1] else None, + 'alert_type': row[3], + 'final_score': float(row[4]) if row[4] else None, + 'rule_score': float(row[5]) if row[5] else None, + 'ml_score': float(row[6]) if row[6] else None, + 'trigger_reason': row[7], + 'alpha': float(row[8]) if row[8] else None, + 'alpha_delta': float(row[9]) if row[9] else None, + 'amt_ratio': float(row[10]) if row[10] else None, + 'amt_delta': float(row[11]) if row[11] else None, + 'rank_pct': float(row[12]) if row[12] else None, + 'limit_up_ratio': limit_up_ratio, + 'limit_up_count': limit_up_count, + 'stock_count': stock_count, + 'total_amt': float(row[15]) if row[15] else None, + 'triggered_rules': triggered_rules, + 'importance_score': float(row[4]) / 100 if row[4] else None, + 'is_v2': False, + }) + except Exception as old_err: + logger.debug(f"旧表查询也失败: {old_err}") # 尝试批量获取概念名称 if alerts: diff --git a/ml/backtest_v2.py b/ml/backtest_v2.py new file mode 100644 index 00000000..84524fd9 --- /dev/null +++ b/ml/backtest_v2.py @@ -0,0 +1,294 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +V2 回测脚本 - 验证时间片对齐 + 持续性确认的效果 + +回测指标： +1. 准确率：异动后 N 分钟内 alpha 是否继续上涨/下跌 +2. 虚警率：多少异动是噪音 +3. 持续性：平均异动持续时长 +""" + +import os +import sys +import json +import argparse +from datetime import datetime +from pathlib import Path +from typing import Dict, List, Tuple +from collections import defaultdict + +import numpy as np +import pandas as pd +from tqdm import tqdm +from sqlalchemy import create_engine, text + +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +from ml.detector_v2 import AnomalyDetectorV2, CONFIG + + +# ==================== 配置 ==================== + +MYSQL_ENGINE = create_engine( + "mysql+pymysql://root:Zzl5588161!@192.168.1.5:3306/stock", + echo=False +) + + +# ==================== 回测评估 ==================== + +def evaluate_alerts( + alerts: List[Dict], + raw_data: pd.DataFrame, + lookahead_minutes: int = 10 +) -> Dict: + """ + 评估异动质量 + + 指标： + 1. 方向正确率：异动后 N 分钟 alpha 方向是否一致 + 2. 持续率：异动后 N 分钟内有多少时刻 alpha 保持同向 + 3. 峰值收益：异动后 N 分钟内的最大 alpha + """ + if not alerts: + return {'accuracy': 0, 'sustained_rate': 0, 'avg_peak': 0, 'total_alerts': 0} + + results = [] + + for alert in alerts: + concept_id = alert['concept_id'] + alert_time = alert['alert_time'] + alert_alpha = alert['alpha'] + is_up = alert_alpha > 0 + + # 获取该概念在异动后的数据 + concept_data = raw_data[ + (raw_data['concept_id'] == concept_id) & + (raw_data['timestamp'] > alert_time) + ].head(lookahead_minutes) + + if len(concept_data) < 3: + continue + + future_alphas = concept_data['alpha'].values + + # 方向正确：未来 alpha 平均值与当前同向 + avg_future_alpha = np.mean(future_alphas) + direction_correct = (is_up and avg_future_alpha > 0) or (not is_up and avg_future_alpha < 0) + + # 持续率：有多少时刻保持同向 + if is_up: + sustained_count = sum(1 for a in future_alphas if a > 0) + else: + sustained_count = sum(1 for a in future_alphas if a < 0) + sustained_rate = sustained_count / len(future_alphas) + + # 峰值收益 + if is_up: + peak = max(future_alphas) + else: + peak = min(future_alphas) + + results.append({ + 'direction_correct': direction_correct, + 'sustained_rate': sustained_rate, + 'peak': peak, + 'alert_alpha': alert_alpha, + }) + + if not results: + return {'accuracy': 0, 'sustained_rate': 0, 'avg_peak': 0, 'total_alerts': 0} + + return { + 'accuracy': np.mean([r['direction_correct'] for r in results]), + 'sustained_rate': np.mean([r['sustained_rate'] for r in results]), + 'avg_peak': np.mean([abs(r['peak']) for r in results]), + 'total_alerts': len(alerts), + 'evaluated_alerts': len(results), + } + + +def save_alerts_to_mysql(alerts: List[Dict], dry_run: bool = False) -> int: + """保存异动到 MySQL""" + if not alerts or dry_run: + return 0 + + # 确保表存在 + with MYSQL_ENGINE.begin() as conn: + conn.execute(text(""" + CREATE TABLE IF NOT EXISTS concept_anomaly_v2 ( + id INT AUTO_INCREMENT PRIMARY KEY, + concept_id VARCHAR(64) NOT NULL, + alert_time DATETIME NOT NULL, + trade_date DATE NOT NULL, + alert_type VARCHAR(32) NOT NULL, + final_score FLOAT NOT NULL, + rule_score FLOAT NOT NULL, + ml_score FLOAT NOT NULL, + trigger_reason VARCHAR(128), + confirm_ratio FLOAT, + alpha FLOAT, + alpha_zscore FLOAT, + amt_zscore FLOAT, + rank_zscore FLOAT, + momentum_3m FLOAT, + momentum_5m FLOAT, + limit_up_ratio FLOAT, + triggered_rules JSON, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + UNIQUE KEY uk_concept_time (concept_id, alert_time, trade_date), + INDEX idx_trade_date (trade_date), + INDEX idx_final_score (final_score) + ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COMMENT='概念异动 V2（时间片对齐+持续确认）' + """)) + + # 插入数据 + saved = 0 + with MYSQL_ENGINE.begin() as conn: + for alert in alerts: + try: + conn.execute(text(""" + INSERT IGNORE INTO concept_anomaly_v2 + (concept_id, alert_time, trade_date, alert_type, + final_score, rule_score, ml_score, trigger_reason, confirm_ratio, + alpha, alpha_zscore, amt_zscore, rank_zscore, + momentum_3m, momentum_5m, limit_up_ratio, triggered_rules) + VALUES + (:concept_id, :alert_time, :trade_date, :alert_type, + :final_score, :rule_score, :ml_score, :trigger_reason, :confirm_ratio, + :alpha, :alpha_zscore, :amt_zscore, :rank_zscore, + :momentum_3m, :momentum_5m, :limit_up_ratio, :triggered_rules) + """), { + 'concept_id': alert['concept_id'], + 'alert_time': alert['alert_time'], + 'trade_date': alert['trade_date'], + 'alert_type': alert['alert_type'], + 'final_score': alert['final_score'], + 'rule_score': alert['rule_score'], + 'ml_score': alert['ml_score'], + 'trigger_reason': alert['trigger_reason'], + 'confirm_ratio': alert.get('confirm_ratio', 0), + 'alpha': alert['alpha'], + 'alpha_zscore': alert.get('alpha_zscore', 0), + 'amt_zscore': alert.get('amt_zscore', 0), + 'rank_zscore': alert.get('rank_zscore', 0), + 'momentum_3m': alert.get('momentum_3m', 0), + 'momentum_5m': alert.get('momentum_5m', 0), + 'limit_up_ratio': alert.get('limit_up_ratio', 0), + 'triggered_rules': json.dumps(alert.get('triggered_rules', [])), + }) + saved += 1 + except Exception as e: + print(f"保存失败: {e}") + + return saved + + +# ==================== 主函数 ==================== + +def main(): + parser = argparse.ArgumentParser(description='V2 回测') + parser.add_argument('--start', type=str, required=True, help='开始日期') + parser.add_argument('--end', type=str, default=None, help='结束日期') + parser.add_argument('--model_dir', type=str, default='ml/checkpoints_v2') + parser.add_argument('--baseline_dir', type=str, default='ml/data_v2/baselines') + parser.add_argument('--save', action='store_true', help='保存到数据库') + parser.add_argument('--lookahead', type=int, default=10, help='评估前瞻时间（分钟）') + + args = parser.parse_args() + + end_date = args.end or args.start + + print("=" * 60) + print("V2 回测 - 时间片对齐 + 持续性确认") + print("=" * 60) + print(f"日期范围: {args.start} ~ {end_date}") + print(f"模型目录: {args.model_dir}") + print(f"评估前瞻: {args.lookahead} 分钟") + + # 初始化检测器 + detector = AnomalyDetectorV2( + model_dir=args.model_dir, + baseline_dir=args.baseline_dir + ) + + # 获取交易日 + from prepare_data_v2 import get_trading_days + trading_days = get_trading_days(args.start, end_date) + + if not trading_days: + print("无交易日") + return + + print(f"交易日数: {len(trading_days)}") + + # 回测统计 + total_stats = { + 'total_alerts': 0, + 'accuracy_sum': 0, + 'sustained_sum': 0, + 'peak_sum': 0, + 'day_count': 0, + } + + all_alerts = [] + + for trade_date in tqdm(trading_days, desc="回测进度"): + # 检测异动 + alerts = detector.detect(trade_date) + + if not alerts: + continue + + all_alerts.extend(alerts) + + # 评估 + raw_data = detector._compute_raw_features(trade_date) + if raw_data.empty: + continue + + stats = evaluate_alerts(alerts, raw_data, args.lookahead) + + if stats['evaluated_alerts'] > 0: + total_stats['total_alerts'] += stats['total_alerts'] + total_stats['accuracy_sum'] += stats['accuracy'] * stats['evaluated_alerts'] + total_stats['sustained_sum'] += stats['sustained_rate'] * stats['evaluated_alerts'] + total_stats['peak_sum'] += stats['avg_peak'] * stats['evaluated_alerts'] + total_stats['day_count'] += 1 + + print(f"\n[{trade_date}] 异动: {stats['total_alerts']}, " + f"准确率: {stats['accuracy']:.1%}, " + f"持续率: {stats['sustained_rate']:.1%}, " + f"峰值: {stats['avg_peak']:.2f}%") + + # 汇总 + print("\n" + "=" * 60) + print("回测汇总") + print("=" * 60) + + if total_stats['total_alerts'] > 0: + avg_accuracy = total_stats['accuracy_sum'] / total_stats['total_alerts'] + avg_sustained = total_stats['sustained_sum'] / total_stats['total_alerts'] + avg_peak = total_stats['peak_sum'] / total_stats['total_alerts'] + + print(f"总异动数: {total_stats['total_alerts']}") + print(f"回测天数: {total_stats['day_count']}") + print(f"平均每天: {total_stats['total_alerts'] / max(1, total_stats['day_count']):.1f} 个") + print(f"方向准确率: {avg_accuracy:.1%}") + print(f"持续率: {avg_sustained:.1%}") + print(f"平均峰值: {avg_peak:.2f}%") + else: + print("无异动检测结果") + + # 保存 + if args.save and all_alerts: + print(f"\n保存 {len(all_alerts)} 条异动到数据库...") + saved = save_alerts_to_mysql(all_alerts) + print(f"保存完成: {saved} 条") + + print("=" * 60) + + +if __name__ == "__main__": + main() diff --git a/ml/checkpoints_v2/config.json b/ml/checkpoints_v2/config.json new file mode 100644 index 00000000..007997f5 --- /dev/null +++ b/ml/checkpoints_v2/config.json @@ -0,0 +1,31 @@ +{ + "seq_len": 10, + "stride": 2, + "train_end_date": "2025-06-30", + "val_end_date": "2025-09-30", + "features": [ + "alpha_zscore", + "amt_zscore", + "rank_zscore", + "momentum_3m", + "momentum_5m", + "limit_up_ratio" + ], + "batch_size": 32768, + "epochs": 150, + "learning_rate": 0.0006, + "weight_decay": 1e-05, + "gradient_clip": 1.0, + "patience": 15, + "min_delta": 1e-06, + "model": { + "n_features": 6, + "hidden_dim": 32, + "latent_dim": 4, + "num_layers": 1, + "dropout": 0.2, + "bidirectional": true + }, + "clip_value": 5.0, + "threshold_percentiles": [90, 95, 99] +} diff --git a/ml/checkpoints_v2/thresholds.json b/ml/checkpoints_v2/thresholds.json new file mode 100644 index 00000000..47a6fe63 --- /dev/null +++ b/ml/checkpoints_v2/thresholds.json @@ -0,0 +1,8 @@ +{ + "p90": 0.15, + "p95": 0.25, + "p99": 0.50, + "mean": 0.08, + "std": 0.12, + "median": 0.06 +} diff --git a/ml/detector_v2.py b/ml/detector_v2.py new file mode 100644 index 00000000..e4e6f1ae --- /dev/null +++ b/ml/detector_v2.py @@ -0,0 +1,716 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +异动检测器 V2 - 基于时间片对齐 + 持续性确认 + +核心改进： +1. Z-Score 特征：相对于同时间片历史的偏离 +2. 短序列 LSTM：10分钟序列，开盘即可用 +3. 持续性确认：5分钟窗口内60%时刻超标才确认为异动 + +检测流程： +1. 计算当前时刻的 Z-Score（对比同时间片历史基线） +2. 构建最近10分钟的 Z-Score 序列 +3. LSTM 计算重构误差（ML分数） +4. 规则评分（基于 Z-Score 的规则） +5. 滑动窗口确认：最近5分钟内是否有足够多的时刻超标 +6. 只有通过持续性确认的才输出为异动 +""" + +import os +import sys +import json +import pickle +from datetime import datetime, timedelta +from typing import Dict, List, Optional, Tuple +from collections import defaultdict, deque + +import numpy as np +import pandas as pd +import torch +from sqlalchemy import create_engine, text +from elasticsearch import Elasticsearch +from clickhouse_driver import Client + +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +from ml.model import TransformerAutoencoder + +# ==================== 配置 ==================== + +MYSQL_ENGINE = create_engine( + "mysql+pymysql://root:Zzl5588161!@192.168.1.5:3306/stock", + echo=False +) + +ES_CLIENT = Elasticsearch(['http://127.0.0.1:9200']) +ES_INDEX = 'concept_library_v3' + +CLICKHOUSE_CONFIG = { + 'host': '127.0.0.1', + 'port': 9000, + 'user': 'default', + 'password': 'Zzl33818!', + 'database': 'stock' +} + +REFERENCE_INDEX = '000001.SH' + +# 检测配置 +CONFIG = { + # 序列配置 + 'seq_len': 10, # LSTM 序列长度（分钟） + + # 持续性确认配置（核心！） + 'confirm_window': 5, # 确认窗口（分钟） + 'confirm_ratio': 0.6, # 确认比例（60%时刻需要超标） + + # Z-Score 阈值 + 'alpha_zscore_threshold': 2.0, # Alpha Z-Score 阈值 + 'amt_zscore_threshold': 2.5, # 成交额 Z-Score 阈值 + + # 融合权重 + 'rule_weight': 0.5, + 'ml_weight': 0.5, + + # 触发阈值 + 'rule_trigger': 60, + 'ml_trigger': 70, + 'fusion_trigger': 50, + + # 冷却期 + 'cooldown_minutes': 10, + 'max_alerts_per_minute': 15, + + # Z-Score 截断 + 'zscore_clip': 5.0, +} + +# V2 特征列表 +FEATURES_V2 = [ + 'alpha_zscore', 'amt_zscore', 'rank_zscore', + 'momentum_3m', 'momentum_5m', 'limit_up_ratio' +] + + +# ==================== 工具函数 ==================== + +def get_ch_client(): + return Client(**CLICKHOUSE_CONFIG) + + +def code_to_ch_format(code: str) -> str: + if not code or len(code) != 6 or not code.isdigit(): + return None + if code.startswith('6'): + return f"{code}.SH" + elif code.startswith('0') or code.startswith('3'): + return f"{code}.SZ" + else: + return f"{code}.BJ" + + +def time_to_slot(ts) -> str: + """时间戳转时间片（HH:MM）""" + if isinstance(ts, str): + return ts + return ts.strftime('%H:%M') + + +# ==================== 基线加载 ==================== + +def load_baselines(baseline_dir: str = 'ml/data_v2/baselines') -> Dict[str, pd.DataFrame]: + """加载时间片基线""" + baseline_file = os.path.join(baseline_dir, 'baselines.pkl') + if os.path.exists(baseline_file): + with open(baseline_file, 'rb') as f: + return pickle.load(f) + return {} + + +# ==================== 规则评分（基于 Z-Score）==================== + +def score_rules_zscore(row: Dict) -> Tuple[float, List[str]]: + """ + 基于 Z-Score 的规则评分 + + 设计思路：Z-Score 已经标准化，直接用阈值判断 + """ + score = 0.0 + triggered = [] + + alpha_zscore = row.get('alpha_zscore', 0) + amt_zscore = row.get('amt_zscore', 0) + rank_zscore = row.get('rank_zscore', 0) + momentum_3m = row.get('momentum_3m', 0) + momentum_5m = row.get('momentum_5m', 0) + limit_up_ratio = row.get('limit_up_ratio', 0) + + alpha_zscore_abs = abs(alpha_zscore) + amt_zscore_abs = abs(amt_zscore) + + # ========== Alpha Z-Score 规则 ========== + if alpha_zscore_abs >= 4.0: + score += 25 + triggered.append('alpha_zscore_extreme') + elif alpha_zscore_abs >= 3.0: + score += 18 + triggered.append('alpha_zscore_strong') + elif alpha_zscore_abs >= 2.0: + score += 10 + triggered.append('alpha_zscore_moderate') + + # ========== 成交额 Z-Score 规则 ========== + if amt_zscore >= 4.0: + score += 20 + triggered.append('amt_zscore_extreme') + elif amt_zscore >= 3.0: + score += 12 + triggered.append('amt_zscore_strong') + elif amt_zscore >= 2.0: + score += 6 + triggered.append('amt_zscore_moderate') + + # ========== 排名 Z-Score 规则 ========== + if abs(rank_zscore) >= 3.0: + score += 15 + triggered.append('rank_zscore_extreme') + elif abs(rank_zscore) >= 2.0: + score += 8 + triggered.append('rank_zscore_strong') + + # ========== 动量规则 ========== + if momentum_3m >= 1.0: + score += 12 + triggered.append('momentum_3m_strong') + elif momentum_3m >= 0.5: + score += 6 + triggered.append('momentum_3m_moderate') + + if momentum_5m >= 1.5: + score += 10 + triggered.append('momentum_5m_strong') + + # ========== 涨停比例规则 ========== + if limit_up_ratio >= 0.3: + score += 20 + triggered.append('limit_up_extreme') + elif limit_up_ratio >= 0.15: + score += 12 + triggered.append('limit_up_strong') + elif limit_up_ratio >= 0.08: + score += 5 + triggered.append('limit_up_moderate') + + # ========== 组合规则 ========== + # Alpha Z-Score + 成交额放大 + if alpha_zscore_abs >= 2.0 and amt_zscore >= 2.0: + score += 15 + triggered.append('combo_alpha_amt') + + # Alpha Z-Score + 涨停 + if alpha_zscore_abs >= 2.0 and limit_up_ratio >= 0.1: + score += 12 + triggered.append('combo_alpha_limitup') + + return min(score, 100), triggered + + +# ==================== ML 评分器 ==================== + +class MLScorerV2: + """V2 ML 评分器""" + + def __init__(self, model_dir: str = 'ml/checkpoints_v2'): + self.model_dir = model_dir + self.model = None + self.thresholds = None + self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + self._load_model() + + def _load_model(self): + """加载模型和阈值""" + model_path = os.path.join(self.model_dir, 'best_model.pt') + threshold_path = os.path.join(self.model_dir, 'thresholds.json') + config_path = os.path.join(self.model_dir, 'config.json') + + if not os.path.exists(model_path): + print(f"警告: 模型文件不存在: {model_path}") + return + + # 加载配置 + with open(config_path, 'r') as f: + config = json.load(f) + + # 创建模型 + model_config = config.get('model', {}) + self.model = TransformerAutoencoder(**model_config) + + # 加载权重 + checkpoint = torch.load(model_path, map_location=self.device) + self.model.load_state_dict(checkpoint['model_state_dict']) + self.model.to(self.device) + self.model.eval() + + # 加载阈值 + if os.path.exists(threshold_path): + with open(threshold_path, 'r') as f: + self.thresholds = json.load(f) + + print(f"V2 模型加载完成: {model_path}") + + @torch.no_grad() + def score_batch(self, sequences: np.ndarray) -> np.ndarray: + """ + 批量计算 ML 分数 + + 返回 0-100 的分数，越高越异常 + """ + if self.model is None: + return np.zeros(len(sequences)) + + # 转换为 tensor + x = torch.FloatTensor(sequences).to(self.device) + + # 计算重构误差 + errors = self.model.compute_reconstruction_error(x, reduction='none') + # 取最后一个时刻的误差 + last_errors = errors[:, -1].cpu().numpy() + + # 转换为 0-100 分数 + if self.thresholds: + p50 = self.thresholds.get('median', 0.1) + p99 = self.thresholds.get('p99', 1.0) + + # 线性映射：p50 -> 50分，p99 -> 99分 + scores = 50 + (last_errors - p50) / (p99 - p50) * 49 + scores = np.clip(scores, 0, 100) + else: + # 没有阈值时，简单归一化 + scores = last_errors * 100 + scores = np.clip(scores, 0, 100) + + return scores + + +# ==================== 实时数据管理器 ==================== + +class RealtimeDataManagerV2: + """ + V2 实时数据管理器 + + 维护： + 1. 每个概念的历史 Z-Score 序列（用于 LSTM 输入） + 2. 每个概念的异动候选队列（用于持续性确认） + """ + + def __init__(self, concepts: List[dict], baselines: Dict[str, pd.DataFrame]): + self.concepts = {c['concept_id']: c for c in concepts} + self.baselines = baselines + + # 概念到股票的映射 + self.concept_stocks = {c['concept_id']: set(c['stocks']) for c in concepts} + + # 历史 Z-Score 序列（每个概念） + # {concept_id: deque([(timestamp, features_dict), ...], maxlen=seq_len)} + self.zscore_history = defaultdict(lambda: deque(maxlen=CONFIG['seq_len'])) + + # 异动候选队列（用于持续性确认） + # {concept_id: deque([(timestamp, score), ...], maxlen=confirm_window)} + self.anomaly_candidates = defaultdict(lambda: deque(maxlen=CONFIG['confirm_window'])) + + # 冷却期记录 + self.cooldown = {} + + # 上一次更新的时间戳 + self.last_timestamp = None + + def compute_zscore_features( + self, + concept_id: str, + timestamp, + alpha: float, + total_amt: float, + rank_pct: float, + limit_up_ratio: float + ) -> Optional[Dict]: + """计算单个概念单个时刻的 Z-Score 特征""" + if concept_id not in self.baselines: + return None + + baseline = self.baselines[concept_id] + time_slot = time_to_slot(timestamp) + + # 查找对应时间片的基线 + bl_row = baseline[baseline['time_slot'] == time_slot] + if bl_row.empty: + return None + + bl = bl_row.iloc[0] + + # 检查样本量 + if bl.get('sample_count', 0) < 10: + return None + + # 计算 Z-Score + alpha_zscore = (alpha - bl['alpha_mean']) / bl['alpha_std'] + amt_zscore = (total_amt - bl['amt_mean']) / bl['amt_std'] + rank_zscore = (rank_pct - bl['rank_mean']) / bl['rank_std'] + + # 截断 + clip = CONFIG['zscore_clip'] + alpha_zscore = np.clip(alpha_zscore, -clip, clip) + amt_zscore = np.clip(amt_zscore, -clip, clip) + rank_zscore = np.clip(rank_zscore, -clip, clip) + + # 计算动量（需要历史） + history = self.zscore_history[concept_id] + momentum_3m = 0 + momentum_5m = 0 + + if len(history) >= 3: + recent_alphas = [h[1]['alpha'] for h in list(history)[-3:]] + older_alphas = [h[1]['alpha'] for h in list(history)[-6:-3]] if len(history) >= 6 else [alpha] + momentum_3m = np.mean(recent_alphas) - np.mean(older_alphas) + + if len(history) >= 5: + recent_alphas = [h[1]['alpha'] for h in list(history)[-5:]] + older_alphas = [h[1]['alpha'] for h in list(history)[-10:-5]] if len(history) >= 10 else [alpha] + momentum_5m = np.mean(recent_alphas) - np.mean(older_alphas) + + return { + 'alpha': alpha, + 'alpha_zscore': alpha_zscore, + 'amt_zscore': amt_zscore, + 'rank_zscore': rank_zscore, + 'momentum_3m': momentum_3m, + 'momentum_5m': momentum_5m, + 'limit_up_ratio': limit_up_ratio, + 'total_amt': total_amt, + 'rank_pct': rank_pct, + } + + def update(self, concept_id: str, timestamp, features: Dict): + """更新概念的历史数据""" + self.zscore_history[concept_id].append((timestamp, features)) + + def get_sequence(self, concept_id: str) -> Optional[np.ndarray]: + """获取用于 LSTM 的序列""" + history = self.zscore_history[concept_id] + + if len(history) < CONFIG['seq_len']: + return None + + # 提取特征 + feature_list = [] + for _, features in history: + feature_list.append([ + features['alpha_zscore'], + features['amt_zscore'], + features['rank_zscore'], + features['momentum_3m'], + features['momentum_5m'], + features['limit_up_ratio'], + ]) + + return np.array(feature_list) + + def add_anomaly_candidate(self, concept_id: str, timestamp, score: float): + """添加异动候选""" + self.anomaly_candidates[concept_id].append((timestamp, score)) + + def check_sustained_anomaly(self, concept_id: str, threshold: float) -> Tuple[bool, float]: + """ + 检查是否为持续性异动 + + 返回：(是否确认, 确认比例) + """ + candidates = self.anomaly_candidates[concept_id] + + if len(candidates) < CONFIG['confirm_window']: + return False, 0.0 + + # 统计超过阈值的时刻数量 + exceed_count = sum(1 for _, score in candidates if score >= threshold) + ratio = exceed_count / len(candidates) + + return ratio >= CONFIG['confirm_ratio'], ratio + + def check_cooldown(self, concept_id: str, timestamp) -> bool: + """检查是否在冷却期""" + if concept_id not in self.cooldown: + return False + + last_alert = self.cooldown[concept_id] + try: + diff = (timestamp - last_alert).total_seconds() / 60 + return diff < CONFIG['cooldown_minutes'] + except: + return False + + def set_cooldown(self, concept_id: str, timestamp): + """设置冷却期""" + self.cooldown[concept_id] = timestamp + + +# ==================== 异动检测器 V2 ==================== + +class AnomalyDetectorV2: + """ + V2 异动检测器 + + 核心流程： + 1. 获取实时数据 + 2. 计算 Z-Score 特征 + 3. 规则评分 + ML 评分 + 4. 持续性确认 + 5. 输出异动 + """ + + def __init__( + self, + model_dir: str = 'ml/checkpoints_v2', + baseline_dir: str = 'ml/data_v2/baselines' + ): + # 加载概念 + self.concepts = self._load_concepts() + + # 加载基线 + self.baselines = load_baselines(baseline_dir) + print(f"加载了 {len(self.baselines)} 个概念的基线") + + # 初始化 ML 评分器 + self.ml_scorer = MLScorerV2(model_dir) + + # 初始化数据管理器 + self.data_manager = RealtimeDataManagerV2(self.concepts, self.baselines) + + # 收集所有股票 + self.all_stocks = list(set(s for c in self.concepts for s in c['stocks'])) + + def _load_concepts(self) -> List[dict]: + """从 ES 加载概念""" + concepts = [] + query = {"query": {"match_all": {}}, "size": 100, "_source": ["concept_id", "concept", "stocks"]} + + resp = ES_CLIENT.search(index=ES_INDEX, body=query, scroll='2m') + scroll_id = resp['_scroll_id'] + hits = resp['hits']['hits'] + + while len(hits) > 0: + for hit in hits: + source = hit['_source'] + stocks = [] + if 'stocks' in source and isinstance(source['stocks'], list): + for stock in source['stocks']: + if isinstance(stock, dict) and 'code' in stock and stock['code']: + stocks.append(stock['code']) + if stocks: + concepts.append({ + 'concept_id': source.get('concept_id'), + 'concept_name': source.get('concept'), + 'stocks': stocks + }) + + resp = ES_CLIENT.scroll(scroll_id=scroll_id, scroll='2m') + scroll_id = resp['_scroll_id'] + hits = resp['hits']['hits'] + + ES_CLIENT.clear_scroll(scroll_id=scroll_id) + print(f"加载了 {len(concepts)} 个概念") + return concepts + + def detect(self, trade_date: str) -> List[Dict]: + """ + 检测指定日期的异动 + + 返回异动列表 + """ + print(f"\n检测 {trade_date} 的异动...") + + # 获取原始数据 + raw_features = self._compute_raw_features(trade_date) + if raw_features.empty: + print("无数据") + return [] + + # 按时间排序 + timestamps = sorted(raw_features['timestamp'].unique()) + print(f"时间点数: {len(timestamps)}") + + all_alerts = [] + + for ts in timestamps: + ts_data = raw_features[raw_features['timestamp'] == ts] + ts_alerts = self._process_timestamp(ts, ts_data, trade_date) + all_alerts.extend(ts_alerts) + + print(f"共检测到 {len(all_alerts)} 个异动") + return all_alerts + + def _compute_raw_features(self, trade_date: str) -> pd.DataFrame: + """计算原始特征（同 prepare_data_v2）""" + # 这里简化处理，直接调用数据准备逻辑 + from prepare_data_v2 import compute_raw_concept_features + return compute_raw_concept_features(trade_date, self.concepts, self.all_stocks) + + def _process_timestamp(self, timestamp, ts_data: pd.DataFrame, trade_date: str) -> List[Dict]: + """处理单个时间戳""" + alerts = [] + candidates = [] # (concept_id, features, rule_score, triggered_rules) + + for _, row in ts_data.iterrows(): + concept_id = row['concept_id'] + + # 计算 Z-Score 特征 + features = self.data_manager.compute_zscore_features( + concept_id, timestamp, + row['alpha'], row['total_amt'], row['rank_pct'], row['limit_up_ratio'] + ) + + if features is None: + continue + + # 更新历史 + self.data_manager.update(concept_id, timestamp, features) + + # 规则评分 + rule_score, triggered_rules = score_rules_zscore(features) + + # 收集候选 + candidates.append((concept_id, features, rule_score, triggered_rules)) + + if not candidates: + return [] + + # 批量 ML 评分 + sequences = [] + valid_candidates = [] + + for concept_id, features, rule_score, triggered_rules in candidates: + seq = self.data_manager.get_sequence(concept_id) + if seq is not None: + sequences.append(seq) + valid_candidates.append((concept_id, features, rule_score, triggered_rules)) + + if not sequences: + return [] + + sequences = np.array(sequences) + ml_scores = self.ml_scorer.score_batch(sequences) + + # 融合评分 + 持续性确认 + for i, (concept_id, features, rule_score, triggered_rules) in enumerate(valid_candidates): + ml_score = ml_scores[i] + final_score = CONFIG['rule_weight'] * rule_score + CONFIG['ml_weight'] * ml_score + + # 判断是否触发 + is_triggered = ( + rule_score >= CONFIG['rule_trigger'] or + ml_score >= CONFIG['ml_trigger'] or + final_score >= CONFIG['fusion_trigger'] + ) + + # 添加到候选队列 + self.data_manager.add_anomaly_candidate(concept_id, timestamp, final_score) + + if not is_triggered: + continue + + # 检查冷却期 + if self.data_manager.check_cooldown(concept_id, timestamp): + continue + + # 持续性确认 + is_sustained, confirm_ratio = self.data_manager.check_sustained_anomaly( + concept_id, CONFIG['fusion_trigger'] + ) + + if not is_sustained: + continue + + # 确认为异动！ + self.data_manager.set_cooldown(concept_id, timestamp) + + # 确定异动类型 + alpha = features['alpha'] + if alpha >= 1.5: + alert_type = 'surge_up' + elif alpha <= -1.5: + alert_type = 'surge_down' + elif features['amt_zscore'] >= 3.0: + alert_type = 'volume_spike' + else: + alert_type = 'surge' + + # 确定触发原因 + if rule_score >= CONFIG['rule_trigger']: + trigger_reason = f'规则({rule_score:.0f})+持续确认({confirm_ratio:.0%})' + elif ml_score >= CONFIG['ml_trigger']: + trigger_reason = f'ML({ml_score:.0f})+持续确认({confirm_ratio:.0%})' + else: + trigger_reason = f'融合({final_score:.0f})+持续确认({confirm_ratio:.0%})' + + alerts.append({ + 'concept_id': concept_id, + 'concept_name': self.data_manager.concepts.get(concept_id, {}).get('concept_name', concept_id), + 'alert_time': timestamp, + 'trade_date': trade_date, + 'alert_type': alert_type, + 'final_score': final_score, + 'rule_score': rule_score, + 'ml_score': ml_score, + 'trigger_reason': trigger_reason, + 'confirm_ratio': confirm_ratio, + 'alpha': alpha, + 'alpha_zscore': features['alpha_zscore'], + 'amt_zscore': features['amt_zscore'], + 'rank_zscore': features['rank_zscore'], + 'momentum_3m': features['momentum_3m'], + 'momentum_5m': features['momentum_5m'], + 'limit_up_ratio': features['limit_up_ratio'], + 'triggered_rules': triggered_rules, + }) + + # 每分钟最多 N 个 + if len(alerts) > CONFIG['max_alerts_per_minute']: + alerts = sorted(alerts, key=lambda x: x['final_score'], reverse=True) + alerts = alerts[:CONFIG['max_alerts_per_minute']] + + return alerts + + +# ==================== 主函数 ==================== + +def main(): + import argparse + + parser = argparse.ArgumentParser(description='V2 异动检测器') + parser.add_argument('--date', type=str, default=None, help='检测日期（默认今天）') + parser.add_argument('--model_dir', type=str, default='ml/checkpoints_v2') + parser.add_argument('--baseline_dir', type=str, default='ml/data_v2/baselines') + + args = parser.parse_args() + + trade_date = args.date or datetime.now().strftime('%Y-%m-%d') + + detector = AnomalyDetectorV2( + model_dir=args.model_dir, + baseline_dir=args.baseline_dir + ) + + alerts = detector.detect(trade_date) + + print(f"\n检测结果:") + for alert in alerts[:20]: + print(f" [{alert['alert_time'].strftime('%H:%M') if hasattr(alert['alert_time'], 'strftime') else alert['alert_time']}] " + f"{alert['concept_name']} ({alert['alert_type']}) " + f"分数={alert['final_score']:.0f} " + f"确认率={alert['confirm_ratio']:.0%}") + + if len(alerts) > 20: + print(f" ... 共 {len(alerts)} 个异动") + + +if __name__ == "__main__": + main() diff --git a/ml/model.py b/ml/model.py index 2eb63d87..90c0d61b 100644 --- a/ml/model.py +++ b/ml/model.py @@ -85,9 +85,12 @@ class LSTMAutoencoder(nn.Module): nn.Tanh(), # 限制范围，增加约束 ) + # 使用 LeakyReLU 替代 ReLU + # 原因：Z-Score 数据范围是 [-5, +5]，ReLU 会截断负值，丢失跌幅信息 + # LeakyReLU 保留负值信号（乘以 0.1） self.bottleneck_up = nn.Sequential( nn.Linear(latent_dim, hidden_dim), - nn.ReLU(), + nn.LeakyReLU(negative_slope=0.1), ) # Decoder: 单向 LSTM diff --git a/ml/prepare_data.py b/ml/prepare_data.py index 4734f582..cb905d42 100644 --- a/ml/prepare_data.py +++ b/ml/prepare_data.py @@ -26,7 +26,9 @@ import hashlib import json import logging from typing import Dict, List, Set, Tuple -from concurrent.futures import ThreadPoolExecutor, as_completed +from concurrent.futures import ProcessPoolExecutor, as_completed +from multiprocessing import Manager +import multiprocessing import warnings warnings.filterwarnings('ignore') @@ -128,7 +130,7 @@ def get_all_concepts() -> List[dict]: hits = resp['hits']['hits'] ES_CLIENT.clear_scroll(scroll_id=scroll_id) - logger.info(f"获取到 {len(concepts)} 个概念") + print(f"获取到 {len(concepts)} 个概念") return concepts @@ -148,7 +150,7 @@ def get_trading_days(start_date: str, end_date: str) -> List[str]: result = client.execute(query) days = [row[0].strftime('%Y-%m-%d') for row in result] - logger.info(f"找到 {len(days)} 个交易日: {days[0]} ~ {days[-1]}") + print(f"找到 {len(days)} 个交易日: {days[0]} ~ {days[-1]}") return days @@ -223,21 +225,23 @@ def get_daily_index_data(trade_date: str, index_code: str = REFERENCE_INDEX) -> def get_prev_close(stock_codes: List[str], trade_date: str) -> Dict[str, float]: - """获取昨收价""" + """获取昨收价（上一交易日的收盘价 F007N）""" valid_codes = [c for c in stock_codes if c and len(c) == 6 and c.isdigit()] if not valid_codes: return {} codes_str = "','".join(valid_codes) + # 注意：F007N 是"最近成交价"即当日收盘价，F002N 是"昨日收盘价" + # 我们需要查上一交易日的 F007N（那天的收盘价）作为今天的昨收 query = f""" - SELECT SECCODE, F002N + SELECT SECCODE, F007N FROM ea_trade WHERE SECCODE IN ('{codes_str}') AND TRADEDATE = ( SELECT MAX(TRADEDATE) FROM ea_trade WHERE TRADEDATE < '{trade_date}' ) - AND F002N IS NOT NULL AND F002N > 0 + AND F007N IS NOT NULL AND F007N > 0 """ try: @@ -245,7 +249,7 @@ def get_prev_close(stock_codes: List[str], trade_date: str) -> Dict[str, float]: result = conn.execute(text(query)) return {row[0]: float(row[1]) for row in result if row[1]} except Exception as e: - logger.error(f"获取昨收价失败: {e}") + print(f"获取昨收价失败: {e}") return {} @@ -264,7 +268,7 @@ def get_index_prev_close(trade_date: str, index_code: str = REFERENCE_INDEX) -> if result and result[0]: return float(result[0]) except Exception as e: - logger.error(f"获取指数昨收失败: {e}") + print(f"获取指数昨收失败: {e}") return None @@ -285,25 +289,19 @@ def compute_daily_features( """ # 1. 获取数据 - logger.info(f" 获取股票数据...") stock_df = get_daily_stock_data(trade_date, all_stocks) if stock_df.empty: - logger.warning(f" 无股票数据") return pd.DataFrame() - logger.info(f" 获取指数数据...") index_df = get_daily_index_data(trade_date) if index_df.empty: - logger.warning(f" 无指数数据") return pd.DataFrame() # 2. 获取昨收价 - logger.info(f" 获取昨收价...") prev_close = get_prev_close(all_stocks, trade_date) index_prev_close = get_index_prev_close(trade_date) if not prev_close or not index_prev_close: - logger.warning(f" 无昨收价数据") return pd.DataFrame() # 3. 计算股票涨跌幅和成交额 @@ -317,7 +315,6 @@ def compute_daily_features( # 5. 获取所有时间点 timestamps = sorted(stock_df['timestamp'].unique()) - logger.info(f" 时间点数: {len(timestamps)}") # 6. 按时间点计算概念特征 results = [] @@ -414,87 +411,126 @@ def compute_daily_features( if amt_delta_std > 0: final_df['amt_delta'] = final_df['amt_delta'] / amt_delta_std - logger.info(f" 计算完成: {len(final_df)} 条记录") return final_df # ==================== 主流程 ==================== -def process_single_day(trade_date: str, concepts: List[dict], all_stocks: List[str]) -> str: - """处理单个交易日""" +def process_single_day(args) -> Tuple[str, bool]: + """ + 处理单个交易日（多进程版本） + + Args: + args: (trade_date, concepts, all_stocks) 元组 + + Returns: + (trade_date, success) 元组 + """ + trade_date, concepts, all_stocks = args output_file = os.path.join(OUTPUT_DIR, f'features_{trade_date}.parquet') # 检查是否已处理 if os.path.exists(output_file): - logger.info(f"[{trade_date}] 已存在，跳过") - return output_file + print(f"[{trade_date}] 已存在，跳过") + return (trade_date, True) - logger.info(f"[{trade_date}] 开始处理...") + print(f"[{trade_date}] 开始处理...") try: df = compute_daily_features(trade_date, concepts, all_stocks) if df.empty: - logger.warning(f"[{trade_date}] 无数据") - return None + print(f"[{trade_date}] 无数据") + return (trade_date, False) # 保存 df.to_parquet(output_file, index=False) - logger.info(f"[{trade_date}] 保存完成: {output_file}") - return output_file + print(f"[{trade_date}] 保存完成") + return (trade_date, True) except Exception as e: - logger.error(f"[{trade_date}] 处理失败: {e}") + print(f"[{trade_date}] 处理失败: {e}") import traceback traceback.print_exc() - return None + return (trade_date, False) def main(): import argparse + from tqdm import tqdm parser = argparse.ArgumentParser(description='准备训练数据') parser.add_argument('--start', type=str, default='2022-01-01', help='开始日期') parser.add_argument('--end', type=str, default=None, help='结束日期（默认今天）') - parser.add_argument('--workers', type=int, default=1, help='并行数（建议1，避免数据库压力）') + parser.add_argument('--workers', type=int, default=18, help='并行进程数（默认18）') + parser.add_argument('--force', action='store_true', help='强制重新处理已存在的文件') args = parser.parse_args() end_date = args.end or datetime.now().strftime('%Y-%m-%d') - logger.info("=" * 60) - logger.info("数据准备 - Transformer Autoencoder 训练数据") - logger.info("=" * 60) - logger.info(f"日期范围: {args.start} ~ {end_date}") + print("=" * 60) + print("数据准备 - Transformer Autoencoder 训练数据") + print("=" * 60) + print(f"日期范围: {args.start} ~ {end_date}") + print(f"并行进程数: {args.workers}") # 1. 获取概念列表 concepts = get_all_concepts() # 收集所有股票 all_stocks = list(set(s for c in concepts for s in c['stocks'])) - logger.info(f"股票总数: {len(all_stocks)}") + print(f"股票总数: {len(all_stocks)}") # 2. 获取交易日列表 trading_days = get_trading_days(args.start, end_date) if not trading_days: - logger.error("无交易日数据") + print("无交易日数据") return - # 3. 处理每个交易日 - logger.info(f"\n开始处理 {len(trading_days)} 个交易日...") + # 如果强制模式，删除已有文件 + if args.force: + for trade_date in trading_days: + output_file = os.path.join(OUTPUT_DIR, f'features_{trade_date}.parquet') + if os.path.exists(output_file): + os.remove(output_file) + print(f"删除已有文件: {output_file}") + # 3. 准备任务参数 + tasks = [(trade_date, concepts, all_stocks) for trade_date in trading_days] + + print(f"\n开始处理 {len(trading_days)} 个交易日（{args.workers} 进程并行）...") + + # 4. 多进程处理 success_count = 0 - for i, trade_date in enumerate(trading_days): - logger.info(f"\n[{i+1}/{len(trading_days)}] {trade_date}") - result = process_single_day(trade_date, concepts, all_stocks) - if result: - success_count += 1 + failed_dates = [] - logger.info("\n" + "=" * 60) - logger.info(f"处理完成: {success_count}/{len(trading_days)} 个交易日") - logger.info(f"数据保存在: {OUTPUT_DIR}") - logger.info("=" * 60) + with ProcessPoolExecutor(max_workers=args.workers) as executor: + # 提交所有任务 + futures = {executor.submit(process_single_day, task): task[0] for task in tasks} + + # 使用 tqdm 显示进度 + with tqdm(total=len(futures), desc="处理进度", unit="天") as pbar: + for future in as_completed(futures): + trade_date = futures[future] + try: + result_date, success = future.result() + if success: + success_count += 1 + else: + failed_dates.append(result_date) + except Exception as e: + print(f"\n[{trade_date}] 进程异常: {e}") + failed_dates.append(trade_date) + pbar.update(1) + + print("\n" + "=" * 60) + print(f"处理完成: {success_count}/{len(trading_days)} 个交易日") + if failed_dates: + print(f"失败日期: {failed_dates[:10]}{'...' if len(failed_dates) > 10 else ''}") + print(f"数据保存在: {OUTPUT_DIR}") + print("=" * 60) if __name__ == "__main__": diff --git a/ml/prepare_data_v2.py b/ml/prepare_data_v2.py new file mode 100644 index 00000000..5a3ad02c --- /dev/null +++ b/ml/prepare_data_v2.py @@ -0,0 +1,715 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +数据准备 V2 - 基于时间片对齐的特征计算（修复版） + +核心改进： +1. 时间片对齐：9:35 和历史的 9:35 比，而不是和前30分钟比 +2. Z-Score 特征：相对于同时间片历史分布的偏离程度 +3. 滚动窗口基线：每个日期使用它之前 N 天的数据作为基线（不是固定的最后 N 天！） +4. 基于 Z-Score 的动量：消除一天内波动率异构性 + +修复： +- 滚动窗口基线：避免未来数据泄露 +- Z-Score 动量：消除早盘/尾盘波动率差异 +- 进程级数据库单例：避免连接池爆炸 +""" + +import os +import sys +import numpy as np +import pandas as pd +from datetime import datetime, timedelta +from sqlalchemy import create_engine, text +from elasticsearch import Elasticsearch +from clickhouse_driver import Client +from concurrent.futures import ProcessPoolExecutor, as_completed +from typing import Dict, List, Tuple, Optional +from tqdm import tqdm +from collections import defaultdict +import warnings +import pickle + +warnings.filterwarnings('ignore') + +# ==================== 配置 ==================== + +MYSQL_URL = "mysql+pymysql://root:Zzl5588161!@192.168.1.5:3306/stock" +ES_HOST = 'http://127.0.0.1:9200' +ES_INDEX = 'concept_library_v3' + +CLICKHOUSE_CONFIG = { + 'host': '127.0.0.1', + 'port': 9000, + 'user': 'default', + 'password': 'Zzl33818!', + 'database': 'stock' +} + +REFERENCE_INDEX = '000001.SH' + +# 输出目录 +OUTPUT_DIR = os.path.join(os.path.dirname(__file__), 'data_v2') +BASELINE_DIR = os.path.join(OUTPUT_DIR, 'baselines') +RAW_CACHE_DIR = os.path.join(OUTPUT_DIR, 'raw_cache') +os.makedirs(OUTPUT_DIR, exist_ok=True) +os.makedirs(BASELINE_DIR, exist_ok=True) +os.makedirs(RAW_CACHE_DIR, exist_ok=True) + +# 特征配置 +CONFIG = { + 'baseline_days': 20, # 滚动窗口大小 + 'min_baseline_samples': 10, # 最少需要10个样本才算有效基线 + 'limit_up_threshold': 9.8, + 'limit_down_threshold': -9.8, + 'zscore_clip': 5.0, +} + +# 特征列表 +FEATURES_V2 = [ + 'alpha', 'alpha_zscore', 'amt_zscore', 'rank_zscore', + 'momentum_3m', 'momentum_5m', 'limit_up_ratio', +] + +# ==================== 进程级单例（避免连接池爆炸）==================== + +# 进程级全局变量 +_process_mysql_engine = None +_process_es_client = None +_process_ch_client = None + + +def init_process_connections(): + """进程初始化时调用，创建连接（单例）""" + global _process_mysql_engine, _process_es_client, _process_ch_client + _process_mysql_engine = create_engine(MYSQL_URL, echo=False, pool_pre_ping=True, pool_size=5) + _process_es_client = Elasticsearch([ES_HOST]) + _process_ch_client = Client(**CLICKHOUSE_CONFIG) + + +def get_mysql_engine(): + """获取进程级 MySQL Engine（单例）""" + global _process_mysql_engine + if _process_mysql_engine is None: + _process_mysql_engine = create_engine(MYSQL_URL, echo=False, pool_pre_ping=True, pool_size=5) + return _process_mysql_engine + + +def get_es_client(): + """获取进程级 ES 客户端（单例）""" + global _process_es_client + if _process_es_client is None: + _process_es_client = Elasticsearch([ES_HOST]) + return _process_es_client + + +def get_ch_client(): + """获取进程级 ClickHouse 客户端（单例）""" + global _process_ch_client + if _process_ch_client is None: + _process_ch_client = Client(**CLICKHOUSE_CONFIG) + return _process_ch_client + + +# ==================== 工具函数 ==================== + +def code_to_ch_format(code: str) -> str: + if not code or len(code) != 6 or not code.isdigit(): + return None + if code.startswith('6'): + return f"{code}.SH" + elif code.startswith('0') or code.startswith('3'): + return f"{code}.SZ" + else: + return f"{code}.BJ" + + +def time_to_slot(ts) -> str: + """将时间戳转换为时间片（HH:MM格式）""" + if isinstance(ts, str): + return ts + return ts.strftime('%H:%M') + + +# ==================== 获取概念列表 ==================== + +def get_all_concepts() -> List[dict]: + """从ES获取所有叶子概念""" + es_client = get_es_client() + concepts = [] + + query = { + "query": {"match_all": {}}, + "size": 100, + "_source": ["concept_id", "concept", "stocks"] + } + + resp = es_client.search(index=ES_INDEX, body=query, scroll='2m') + scroll_id = resp['_scroll_id'] + hits = resp['hits']['hits'] + + while len(hits) > 0: + for hit in hits: + source = hit['_source'] + stocks = [] + if 'stocks' in source and isinstance(source['stocks'], list): + for stock in source['stocks']: + if isinstance(stock, dict) and 'code' in stock and stock['code']: + stocks.append(stock['code']) + + if stocks: + concepts.append({ + 'concept_id': source.get('concept_id'), + 'concept_name': source.get('concept'), + 'stocks': stocks + }) + + resp = es_client.scroll(scroll_id=scroll_id, scroll='2m') + scroll_id = resp['_scroll_id'] + hits = resp['hits']['hits'] + + es_client.clear_scroll(scroll_id=scroll_id) + print(f"获取到 {len(concepts)} 个概念") + return concepts + + +# ==================== 获取交易日列表 ==================== + +def get_trading_days(start_date: str, end_date: str) -> List[str]: + """获取交易日列表""" + client = get_ch_client() + + query = f""" + SELECT DISTINCT toDate(timestamp) as trade_date + FROM stock_minute + WHERE toDate(timestamp) >= '{start_date}' + AND toDate(timestamp) <= '{end_date}' + ORDER BY trade_date + """ + + result = client.execute(query) + days = [row[0].strftime('%Y-%m-%d') for row in result] + if days: + print(f"找到 {len(days)} 个交易日: {days[0]} ~ {days[-1]}") + return days + + +# ==================== 获取昨收价 ==================== + +def get_prev_close(stock_codes: List[str], trade_date: str) -> Dict[str, float]: + """获取昨收价（上一交易日的收盘价 F007N）""" + valid_codes = [c for c in stock_codes if c and len(c) == 6 and c.isdigit()] + if not valid_codes: + return {} + + codes_str = "','".join(valid_codes) + query = f""" + SELECT SECCODE, F007N + FROM ea_trade + WHERE SECCODE IN ('{codes_str}') + AND TRADEDATE = ( + SELECT MAX(TRADEDATE) FROM ea_trade WHERE TRADEDATE < '{trade_date}' + ) + AND F007N IS NOT NULL AND F007N > 0 + """ + + try: + engine = get_mysql_engine() + with engine.connect() as conn: + result = conn.execute(text(query)) + return {row[0]: float(row[1]) for row in result if row[1]} + except Exception as e: + print(f"获取昨收价失败: {e}") + return {} + + +def get_index_prev_close(trade_date: str, index_code: str = REFERENCE_INDEX) -> float: + """获取指数昨收价""" + code_no_suffix = index_code.split('.')[0] + + try: + engine = get_mysql_engine() + with engine.connect() as conn: + result = conn.execute(text(""" + SELECT F006N FROM ea_exchangetrade + WHERE INDEXCODE = :code AND TRADEDATE < :today + ORDER BY TRADEDATE DESC LIMIT 1 + """), {'code': code_no_suffix, 'today': trade_date}).fetchone() + + if result and result[0]: + return float(result[0]) + except Exception as e: + print(f"获取指数昨收失败: {e}") + + return None + + +# ==================== 获取分钟数据 ==================== + +def get_daily_stock_data(trade_date: str, stock_codes: List[str]) -> pd.DataFrame: + """获取单日所有股票的分钟数据""" + client = get_ch_client() + + ch_codes = [] + code_map = {} + for code in stock_codes: + ch_code = code_to_ch_format(code) + if ch_code: + ch_codes.append(ch_code) + code_map[ch_code] = code + + if not ch_codes: + return pd.DataFrame() + + ch_codes_str = "','".join(ch_codes) + + query = f""" + SELECT code, timestamp, close, volume, amt + FROM stock_minute + WHERE toDate(timestamp) = '{trade_date}' + AND code IN ('{ch_codes_str}') + ORDER BY code, timestamp + """ + + result = client.execute(query) + if not result: + return pd.DataFrame() + + df = pd.DataFrame(result, columns=['ch_code', 'timestamp', 'close', 'volume', 'amt']) + df['code'] = df['ch_code'].map(code_map) + df = df.dropna(subset=['code']) + + return df[['code', 'timestamp', 'close', 'volume', 'amt']] + + +def get_daily_index_data(trade_date: str, index_code: str = REFERENCE_INDEX) -> pd.DataFrame: + """获取单日指数分钟数据""" + client = get_ch_client() + + query = f""" + SELECT timestamp, close, volume, amt + FROM index_minute + WHERE toDate(timestamp) = '{trade_date}' + AND code = '{index_code}' + ORDER BY timestamp + """ + + result = client.execute(query) + if not result: + return pd.DataFrame() + + df = pd.DataFrame(result, columns=['timestamp', 'close', 'volume', 'amt']) + return df + + +# ==================== 计算原始概念特征（单日）==================== + +def compute_raw_concept_features( + trade_date: str, + concepts: List[dict], + all_stocks: List[str] +) -> pd.DataFrame: + """计算单日概念的原始特征（alpha, amt, rank_pct, limit_up_ratio）""" + # 检查缓存 + cache_file = os.path.join(RAW_CACHE_DIR, f'raw_{trade_date}.parquet') + if os.path.exists(cache_file): + return pd.read_parquet(cache_file) + + # 获取数据 + stock_df = get_daily_stock_data(trade_date, all_stocks) + if stock_df.empty: + return pd.DataFrame() + + index_df = get_daily_index_data(trade_date) + if index_df.empty: + return pd.DataFrame() + + # 获取昨收价 + prev_close = get_prev_close(all_stocks, trade_date) + index_prev_close = get_index_prev_close(trade_date) + + if not prev_close or not index_prev_close: + return pd.DataFrame() + + # 计算涨跌幅 + stock_df['prev_close'] = stock_df['code'].map(prev_close) + stock_df = stock_df.dropna(subset=['prev_close']) + stock_df['change_pct'] = (stock_df['close'] - stock_df['prev_close']) / stock_df['prev_close'] * 100 + + index_df['change_pct'] = (index_df['close'] - index_prev_close) / index_prev_close * 100 + index_change_map = dict(zip(index_df['timestamp'], index_df['change_pct'])) + + # 获取所有时间点 + timestamps = sorted(stock_df['timestamp'].unique()) + + # 概念到股票的映射 + concept_stocks = {c['concept_id']: set(c['stocks']) for c in concepts} + + results = [] + + for ts in timestamps: + ts_stock_data = stock_df[stock_df['timestamp'] == ts] + index_change = index_change_map.get(ts, 0) + + stock_change = dict(zip(ts_stock_data['code'], ts_stock_data['change_pct'])) + stock_amt = dict(zip(ts_stock_data['code'], ts_stock_data['amt'])) + + concept_features = [] + + for concept_id, stocks in concept_stocks.items(): + concept_changes = [stock_change[s] for s in stocks if s in stock_change] + concept_amts = [stock_amt.get(s, 0) for s in stocks if s in stock_change] + + if not concept_changes: + continue + + avg_change = np.mean(concept_changes) + total_amt = sum(concept_amts) + alpha = avg_change - index_change + + limit_up_count = sum(1 for c in concept_changes if c >= CONFIG['limit_up_threshold']) + limit_up_ratio = limit_up_count / len(concept_changes) + + concept_features.append({ + 'concept_id': concept_id, + 'alpha': alpha, + 'total_amt': total_amt, + 'limit_up_ratio': limit_up_ratio, + 'stock_count': len(concept_changes), + }) + + if not concept_features: + continue + + concept_df = pd.DataFrame(concept_features) + concept_df['rank_pct'] = concept_df['alpha'].rank(pct=True) + concept_df['timestamp'] = ts + concept_df['time_slot'] = time_to_slot(ts) + concept_df['trade_date'] = trade_date + + results.append(concept_df) + + if not results: + return pd.DataFrame() + + result_df = pd.concat(results, ignore_index=True) + + # 保存缓存 + result_df.to_parquet(cache_file, index=False) + + return result_df + + +# ==================== 滚动窗口基线计算 ==================== + +def compute_rolling_baseline( + historical_data: pd.DataFrame, + concept_id: str +) -> Dict[str, Dict]: + """ + 计算单个概念的滚动基线 + + 返回: {time_slot: {alpha_mean, alpha_std, amt_mean, amt_std, rank_mean, rank_std, sample_count}} + """ + if historical_data.empty: + return {} + + concept_data = historical_data[historical_data['concept_id'] == concept_id] + if concept_data.empty: + return {} + + baseline_dict = {} + + for time_slot, group in concept_data.groupby('time_slot'): + if len(group) < CONFIG['min_baseline_samples']: + continue + + alpha_std = group['alpha'].std() + amt_std = group['total_amt'].std() + rank_std = group['rank_pct'].std() + + baseline_dict[time_slot] = { + 'alpha_mean': group['alpha'].mean(), + 'alpha_std': max(alpha_std if pd.notna(alpha_std) else 1.0, 0.1), + 'amt_mean': group['total_amt'].mean(), + 'amt_std': max(amt_std if pd.notna(amt_std) else group['total_amt'].mean() * 0.5, 1.0), + 'rank_mean': group['rank_pct'].mean(), + 'rank_std': max(rank_std if pd.notna(rank_std) else 0.2, 0.05), + 'sample_count': len(group), + } + + return baseline_dict + + +# ==================== 计算单日 Z-Score 特征（带滚动基线）==================== + +def compute_zscore_features_rolling( + trade_date: str, + concepts: List[dict], + all_stocks: List[str], + historical_raw_data: pd.DataFrame # 该日期之前 N 天的原始数据 +) -> pd.DataFrame: + """ + 计算单日的 Z-Score 特征（使用滚动窗口基线） + + 关键改进： + 1. 基线只使用 trade_date 之前的数据（无未来泄露） + 2. 动量基于 Z-Score 计算（消除波动率异构性） + """ + # 计算当日原始特征 + raw_df = compute_raw_concept_features(trade_date, concepts, all_stocks) + + if raw_df.empty: + return pd.DataFrame() + + zscore_records = [] + + for concept_id, group in raw_df.groupby('concept_id'): + # 计算该概念的滚动基线（只用历史数据） + baseline_dict = compute_rolling_baseline(historical_raw_data, concept_id) + + if not baseline_dict: + continue + + # 按时间排序 + group = group.sort_values('timestamp').reset_index(drop=True) + + # Z-Score 历史（用于计算基于 Z-Score 的动量） + zscore_history = [] + + for idx, row in group.iterrows(): + time_slot = row['time_slot'] + + if time_slot not in baseline_dict: + continue + + bl = baseline_dict[time_slot] + + # 计算 Z-Score + alpha_zscore = (row['alpha'] - bl['alpha_mean']) / bl['alpha_std'] + amt_zscore = (row['total_amt'] - bl['amt_mean']) / bl['amt_std'] + rank_zscore = (row['rank_pct'] - bl['rank_mean']) / bl['rank_std'] + + # 截断极端值 + clip = CONFIG['zscore_clip'] + alpha_zscore = np.clip(alpha_zscore, -clip, clip) + amt_zscore = np.clip(amt_zscore, -clip, clip) + rank_zscore = np.clip(rank_zscore, -clip, clip) + + # 记录 Z-Score 历史 + zscore_history.append(alpha_zscore) + + # 基于 Z-Score 计算动量（消除波动率异构性） + momentum_3m = 0.0 + momentum_5m = 0.0 + + if len(zscore_history) >= 3: + recent_3 = zscore_history[-3:] + older_3 = zscore_history[-6:-3] if len(zscore_history) >= 6 else [zscore_history[0]] + momentum_3m = np.mean(recent_3) - np.mean(older_3) + + if len(zscore_history) >= 5: + recent_5 = zscore_history[-5:] + older_5 = zscore_history[-10:-5] if len(zscore_history) >= 10 else [zscore_history[0]] + momentum_5m = np.mean(recent_5) - np.mean(older_5) + + zscore_records.append({ + 'concept_id': concept_id, + 'timestamp': row['timestamp'], + 'time_slot': time_slot, + 'trade_date': trade_date, + # 原始特征 + 'alpha': row['alpha'], + 'total_amt': row['total_amt'], + 'limit_up_ratio': row['limit_up_ratio'], + 'stock_count': row['stock_count'], + 'rank_pct': row['rank_pct'], + # Z-Score 特征 + 'alpha_zscore': alpha_zscore, + 'amt_zscore': amt_zscore, + 'rank_zscore': rank_zscore, + # 基于 Z-Score 的动量 + 'momentum_3m': momentum_3m, + 'momentum_5m': momentum_5m, + }) + + if not zscore_records: + return pd.DataFrame() + + return pd.DataFrame(zscore_records) + + +# ==================== 多进程处理 ==================== + +def process_single_day_v2(args) -> Tuple[str, bool]: + """处理单个交易日（多进程版本）""" + trade_date, day_index, concepts, all_stocks, all_trading_days = args + output_file = os.path.join(OUTPUT_DIR, f'features_v2_{trade_date}.parquet') + + if os.path.exists(output_file): + return (trade_date, True) + + try: + # 计算滚动窗口范围（该日期之前的 N 天） + baseline_days = CONFIG['baseline_days'] + + # 找出 trade_date 之前的交易日 + start_idx = max(0, day_index - baseline_days) + end_idx = day_index # 不包含当天 + + if end_idx <= start_idx: + # 没有足够的历史数据 + return (trade_date, False) + + historical_days = all_trading_days[start_idx:end_idx] + + # 加载历史原始数据 + historical_dfs = [] + for hist_date in historical_days: + cache_file = os.path.join(RAW_CACHE_DIR, f'raw_{hist_date}.parquet') + if os.path.exists(cache_file): + historical_dfs.append(pd.read_parquet(cache_file)) + else: + # 需要计算 + hist_df = compute_raw_concept_features(hist_date, concepts, all_stocks) + if not hist_df.empty: + historical_dfs.append(hist_df) + + if not historical_dfs: + return (trade_date, False) + + historical_raw_data = pd.concat(historical_dfs, ignore_index=True) + + # 计算当日 Z-Score 特征（使用滚动基线） + df = compute_zscore_features_rolling(trade_date, concepts, all_stocks, historical_raw_data) + + if df.empty: + return (trade_date, False) + + df.to_parquet(output_file, index=False) + return (trade_date, True) + + except Exception as e: + print(f"[{trade_date}] 处理失败: {e}") + import traceback + traceback.print_exc() + return (trade_date, False) + + +# ==================== 主流程 ==================== + +def main(): + import argparse + + parser = argparse.ArgumentParser(description='准备训练数据 V2（滚动窗口基线 + Z-Score 动量）') + parser.add_argument('--start', type=str, default='2022-01-01', help='开始日期') + parser.add_argument('--end', type=str, default=None, help='结束日期（默认今天）') + parser.add_argument('--workers', type=int, default=18, help='并行进程数') + parser.add_argument('--baseline-days', type=int, default=20, help='滚动基线窗口大小') + parser.add_argument('--force', action='store_true', help='强制重新计算（忽略缓存）') + + args = parser.parse_args() + + end_date = args.end or datetime.now().strftime('%Y-%m-%d') + CONFIG['baseline_days'] = args.baseline_days + + print("=" * 60) + print("数据准备 V2 - 滚动窗口基线 + Z-Score 动量") + print("=" * 60) + print(f"日期范围: {args.start} ~ {end_date}") + print(f"并行进程数: {args.workers}") + print(f"滚动基线窗口: {args.baseline_days} 天") + + # 初始化主进程连接 + init_process_connections() + + # 1. 获取概念列表 + concepts = get_all_concepts() + all_stocks = list(set(s for c in concepts for s in c['stocks'])) + print(f"股票总数: {len(all_stocks)}") + + # 2. 获取交易日列表 + trading_days = get_trading_days(args.start, end_date) + + if not trading_days: + print("无交易日数据") + return + + # 3. 第一阶段：预计算所有原始特征（用于缓存） + print(f"\n{'='*60}") + print("第一阶段：预计算原始特征（用于滚动基线）") + print(f"{'='*60}") + + # 如果强制重新计算，删除缓存 + if args.force: + import shutil + if os.path.exists(RAW_CACHE_DIR): + shutil.rmtree(RAW_CACHE_DIR) + os.makedirs(RAW_CACHE_DIR, exist_ok=True) + if os.path.exists(OUTPUT_DIR): + for f in os.listdir(OUTPUT_DIR): + if f.startswith('features_v2_'): + os.remove(os.path.join(OUTPUT_DIR, f)) + + # 单线程预计算原始特征（因为需要顺序缓存） + print(f"预计算 {len(trading_days)} 天的原始特征...") + for trade_date in tqdm(trading_days, desc="预计算原始特征"): + cache_file = os.path.join(RAW_CACHE_DIR, f'raw_{trade_date}.parquet') + if not os.path.exists(cache_file): + compute_raw_concept_features(trade_date, concepts, all_stocks) + + # 4. 第二阶段：计算 Z-Score 特征（多进程） + print(f"\n{'='*60}") + print("第二阶段：计算 Z-Score 特征（滚动基线）") + print(f"{'='*60}") + + # 从第 baseline_days 天开始（前面的没有足够历史） + start_idx = args.baseline_days + processable_days = trading_days[start_idx:] + + if not processable_days: + print(f"错误：需要至少 {args.baseline_days + 1} 天的数据") + return + + print(f"可处理日期: {processable_days[0]} ~ {processable_days[-1]} ({len(processable_days)} 天)") + print(f"跳过前 {start_idx} 天（基线预热期）") + + # 构建任务 + tasks = [] + for i, trade_date in enumerate(trading_days): + if i >= start_idx: + tasks.append((trade_date, i, concepts, all_stocks, trading_days)) + + print(f"开始处理 {len(tasks)} 个交易日（{args.workers} 进程并行）...") + + success_count = 0 + failed_dates = [] + + # 使用进程池初始化器 + with ProcessPoolExecutor(max_workers=args.workers, initializer=init_process_connections) as executor: + futures = {executor.submit(process_single_day_v2, task): task[0] for task in tasks} + + with tqdm(total=len(futures), desc="处理进度", unit="天") as pbar: + for future in as_completed(futures): + trade_date = futures[future] + try: + result_date, success = future.result() + if success: + success_count += 1 + else: + failed_dates.append(result_date) + except Exception as e: + print(f"\n[{trade_date}] 进程异常: {e}") + failed_dates.append(trade_date) + pbar.update(1) + + print("\n" + "=" * 60) + print(f"处理完成: {success_count}/{len(tasks)} 个交易日") + if failed_dates: + print(f"失败日期: {failed_dates[:10]}{'...' if len(failed_dates) > 10 else ''}") + print(f"数据保存在: {OUTPUT_DIR}") + print("=" * 60) + + +if __name__ == "__main__": + main() diff --git a/ml/realtime_detector.py b/ml/realtime_detector.py index 0144add3..3af09828 100644 --- a/ml/realtime_detector.py +++ b/ml/realtime_detector.py @@ -190,20 +190,22 @@ def get_all_concepts() -> List[dict]: def get_prev_close(stock_codes: List[str], trade_date: str) -> Dict[str, float]: - """获取昨收价""" + """获取昨收价（上一交易日的收盘价 F007N）""" valid_codes = [c for c in stock_codes if c and len(c) == 6 and c.isdigit()] if not valid_codes: return {} codes_str = "','".join(valid_codes) + # 注意：F007N 是"最近成交价"即当日收盘价，F002N 是"昨日收盘价" + # 我们需要查上一交易日的 F007N（那天的收盘价）作为今天的昨收 query = f""" - SELECT SECCODE, F002N + SELECT SECCODE, F007N FROM ea_trade WHERE SECCODE IN ('{codes_str}') AND TRADEDATE = ( SELECT MAX(TRADEDATE) FROM ea_trade WHERE TRADEDATE < '{trade_date}' ) - AND F002N IS NOT NULL AND F002N > 0 + AND F007N IS NOT NULL AND F007N > 0 """ try: diff --git a/ml/realtime_detector_v2.py b/ml/realtime_detector_v2.py new file mode 100644 index 00000000..e5ccc942 --- /dev/null +++ b/ml/realtime_detector_v2.py @@ -0,0 +1,729 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +V2 实时异动检测器 + +使用方法： + # 作为模块导入 + from ml.realtime_detector_v2 import RealtimeDetectorV2 + + detector = RealtimeDetectorV2() + alerts = detector.detect_realtime() # 检测当前时刻 + + # 或命令行测试 + python ml/realtime_detector_v2.py --date 2025-12-09 +""" + +import os +import sys +import json +import pickle +from datetime import datetime, timedelta +from typing import Dict, List, Optional, Tuple +from collections import defaultdict, deque + +import numpy as np +import pandas as pd +import torch +from sqlalchemy import create_engine, text +from elasticsearch import Elasticsearch +from clickhouse_driver import Client + +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +from ml.model import TransformerAutoencoder + +# ==================== 配置 ==================== + +MYSQL_URL = "mysql+pymysql://root:Zzl5588161!@192.168.1.5:3306/stock" +ES_HOST = 'http://127.0.0.1:9200' +ES_INDEX = 'concept_library_v3' + +CLICKHOUSE_CONFIG = { + 'host': '127.0.0.1', + 'port': 9000, + 'user': 'default', + 'password': 'Zzl33818!', + 'database': 'stock' +} + +REFERENCE_INDEX = '000001.SH' +BASELINE_FILE = 'ml/data_v2/baselines/realtime_baseline.pkl' +MODEL_DIR = 'ml/checkpoints_v2' + +# 检测配置 +CONFIG = { + 'seq_len': 10, # LSTM 序列长度 + 'confirm_window': 5, # 持续确认窗口 + 'confirm_ratio': 0.6, # 确认比例 + 'rule_weight': 0.5, + 'ml_weight': 0.5, + 'rule_trigger': 60, + 'ml_trigger': 70, + 'fusion_trigger': 50, + 'cooldown_minutes': 10, + 'max_alerts_per_minute': 15, + 'zscore_clip': 5.0, + 'limit_up_threshold': 9.8, +} + +FEATURES = ['alpha_zscore', 'amt_zscore', 'rank_zscore', 'momentum_3m', 'momentum_5m', 'limit_up_ratio'] + + +# ==================== 数据库连接 ==================== + +_mysql_engine = None +_es_client = None +_ch_client = None + + +def get_mysql_engine(): + global _mysql_engine + if _mysql_engine is None: + _mysql_engine = create_engine(MYSQL_URL, echo=False, pool_pre_ping=True) + return _mysql_engine + + +def get_es_client(): + global _es_client + if _es_client is None: + _es_client = Elasticsearch([ES_HOST]) + return _es_client + + +def get_ch_client(): + global _ch_client + if _ch_client is None: + _ch_client = Client(**CLICKHOUSE_CONFIG) + return _ch_client + + +def code_to_ch_format(code: str) -> str: + if not code or len(code) != 6 or not code.isdigit(): + return None + if code.startswith('6'): + return f"{code}.SH" + elif code.startswith('0') or code.startswith('3'): + return f"{code}.SZ" + return f"{code}.BJ" + + +def time_to_slot(ts) -> str: + if isinstance(ts, str): + return ts + return ts.strftime('%H:%M') + + +# ==================== 规则评分 ==================== + +def score_rules_zscore(features: Dict) -> Tuple[float, List[str]]: + """基于 Z-Score 的规则评分""" + score = 0.0 + triggered = [] + + alpha_z = abs(features.get('alpha_zscore', 0)) + amt_z = features.get('amt_zscore', 0) + rank_z = abs(features.get('rank_zscore', 0)) + mom_3m = features.get('momentum_3m', 0) + mom_5m = features.get('momentum_5m', 0) + limit_up = features.get('limit_up_ratio', 0) + + # Alpha Z-Score + if alpha_z >= 4.0: + score += 25 + triggered.append('alpha_extreme') + elif alpha_z >= 3.0: + score += 18 + triggered.append('alpha_strong') + elif alpha_z >= 2.0: + score += 10 + triggered.append('alpha_moderate') + + # 成交额 Z-Score + if amt_z >= 4.0: + score += 20 + triggered.append('amt_extreme') + elif amt_z >= 3.0: + score += 12 + triggered.append('amt_strong') + elif amt_z >= 2.0: + score += 6 + triggered.append('amt_moderate') + + # 排名 Z-Score + if rank_z >= 3.0: + score += 15 + triggered.append('rank_extreme') + elif rank_z >= 2.0: + score += 8 + triggered.append('rank_strong') + + # 动量（基于 Z-Score 的） + if mom_3m >= 1.0: + score += 12 + triggered.append('momentum_3m_strong') + elif mom_3m >= 0.5: + score += 6 + triggered.append('momentum_3m_moderate') + + if mom_5m >= 1.5: + score += 10 + triggered.append('momentum_5m_strong') + + # 涨停比例 + if limit_up >= 0.3: + score += 20 + triggered.append('limit_up_extreme') + elif limit_up >= 0.15: + score += 12 + triggered.append('limit_up_strong') + elif limit_up >= 0.08: + score += 5 + triggered.append('limit_up_moderate') + + # 组合规则 + if alpha_z >= 2.0 and amt_z >= 2.0: + score += 15 + triggered.append('combo_alpha_amt') + + if alpha_z >= 2.0 and limit_up >= 0.1: + score += 12 + triggered.append('combo_alpha_limitup') + + return min(score, 100), triggered + + +# ==================== 实时检测器 ==================== + +class RealtimeDetectorV2: + """V2 实时异动检测器""" + + def __init__(self, model_dir: str = MODEL_DIR, baseline_file: str = BASELINE_FILE): + print("初始化 V2 实时检测器...") + + # 加载概念 + self.concepts = self._load_concepts() + self.concept_stocks = {c['concept_id']: set(c['stocks']) for c in self.concepts} + self.all_stocks = list(set(s for c in self.concepts for s in c['stocks'])) + + # 加载基线 + self.baselines = self._load_baselines(baseline_file) + + # 加载模型 + self.model, self.thresholds, self.device = self._load_model(model_dir) + + # 状态管理 + self.zscore_history = defaultdict(lambda: deque(maxlen=CONFIG['seq_len'])) + self.anomaly_candidates = defaultdict(lambda: deque(maxlen=CONFIG['confirm_window'])) + self.cooldown = {} + + print(f"初始化完成: {len(self.concepts)} 概念, {len(self.baselines)} 基线") + + def _load_concepts(self) -> List[dict]: + """从 ES 加载概念""" + es = get_es_client() + concepts = [] + + query = {"query": {"match_all": {}}, "size": 100, "_source": ["concept_id", "concept", "stocks"]} + resp = es.search(index=ES_INDEX, body=query, scroll='2m') + scroll_id = resp['_scroll_id'] + hits = resp['hits']['hits'] + + while hits: + for hit in hits: + src = hit['_source'] + stocks = [s['code'] for s in src.get('stocks', []) if isinstance(s, dict) and s.get('code')] + if stocks: + concepts.append({ + 'concept_id': src.get('concept_id'), + 'concept_name': src.get('concept'), + 'stocks': stocks + }) + resp = es.scroll(scroll_id=scroll_id, scroll='2m') + scroll_id = resp['_scroll_id'] + hits = resp['hits']['hits'] + + es.clear_scroll(scroll_id=scroll_id) + return concepts + + def _load_baselines(self, baseline_file: str) -> Dict: + """加载基线""" + if not os.path.exists(baseline_file): + print(f"警告: 基线文件不存在: {baseline_file}") + print("请先运行: python ml/update_baseline.py") + return {} + + with open(baseline_file, 'rb') as f: + data = pickle.load(f) + + print(f"基线日期范围: {data.get('date_range', 'unknown')}") + print(f"更新时间: {data.get('update_time', 'unknown')}") + + return data.get('baselines', {}) + + def _load_model(self, model_dir: str): + """加载模型""" + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + + config_path = os.path.join(model_dir, 'config.json') + model_path = os.path.join(model_dir, 'best_model.pt') + threshold_path = os.path.join(model_dir, 'thresholds.json') + + if not os.path.exists(model_path): + print(f"警告: 模型不存在: {model_path}") + return None, {}, device + + with open(config_path) as f: + config = json.load(f) + + model = TransformerAutoencoder(**config['model']) + checkpoint = torch.load(model_path, map_location=device) + model.load_state_dict(checkpoint['model_state_dict']) + model.to(device) + model.eval() + + thresholds = {} + if os.path.exists(threshold_path): + with open(threshold_path) as f: + thresholds = json.load(f) + + print(f"模型已加载: {model_path}") + return model, thresholds, device + + def _get_realtime_data(self, trade_date: str) -> pd.DataFrame: + """获取实时数据并计算原始特征""" + ch = get_ch_client() + + # 获取股票数据 + ch_codes = [code_to_ch_format(c) for c in self.all_stocks if code_to_ch_format(c)] + ch_codes_str = "','".join(ch_codes) + + stock_query = f""" + SELECT code, timestamp, close, amt + FROM stock_minute + WHERE toDate(timestamp) = '{trade_date}' + AND code IN ('{ch_codes_str}') + ORDER BY timestamp + """ + stock_result = ch.execute(stock_query) + if not stock_result: + return pd.DataFrame() + + stock_df = pd.DataFrame(stock_result, columns=['ch_code', 'timestamp', 'close', 'amt']) + + # 映射回原始代码 + ch_to_code = {code_to_ch_format(c): c for c in self.all_stocks if code_to_ch_format(c)} + stock_df['code'] = stock_df['ch_code'].map(ch_to_code) + stock_df = stock_df.dropna(subset=['code']) + + # 获取指数数据 + index_query = f""" + SELECT timestamp, close + FROM index_minute + WHERE toDate(timestamp) = '{trade_date}' + AND code = '{REFERENCE_INDEX}' + ORDER BY timestamp + """ + index_result = ch.execute(index_query) + if not index_result: + return pd.DataFrame() + + index_df = pd.DataFrame(index_result, columns=['timestamp', 'close']) + + # 获取昨收价 + engine = get_mysql_engine() + codes_str = "','".join([c for c in self.all_stocks if c and len(c) == 6]) + + with engine.connect() as conn: + prev_result = conn.execute(text(f""" + SELECT SECCODE, F007N FROM ea_trade + WHERE SECCODE IN ('{codes_str}') + AND TRADEDATE = (SELECT MAX(TRADEDATE) FROM ea_trade WHERE TRADEDATE < '{trade_date}') + AND F007N > 0 + """)) + prev_close = {row[0]: float(row[1]) for row in prev_result if row[1]} + + idx_result = conn.execute(text(""" + SELECT F006N FROM ea_exchangetrade + WHERE INDEXCODE = '000001' AND TRADEDATE < :today + ORDER BY TRADEDATE DESC LIMIT 1 + """), {'today': trade_date}).fetchone() + index_prev_close = float(idx_result[0]) if idx_result else None + + if not prev_close or not index_prev_close: + return pd.DataFrame() + + # 计算涨跌幅 + stock_df['prev_close'] = stock_df['code'].map(prev_close) + stock_df = stock_df.dropna(subset=['prev_close']) + stock_df['change_pct'] = (stock_df['close'] - stock_df['prev_close']) / stock_df['prev_close'] * 100 + + index_df['change_pct'] = (index_df['close'] - index_prev_close) / index_prev_close * 100 + index_map = dict(zip(index_df['timestamp'], index_df['change_pct'])) + + # 按时间聚合概念特征 + results = [] + for ts in sorted(stock_df['timestamp'].unique()): + ts_data = stock_df[stock_df['timestamp'] == ts] + idx_chg = index_map.get(ts, 0) + + stock_chg = dict(zip(ts_data['code'], ts_data['change_pct'])) + stock_amt = dict(zip(ts_data['code'], ts_data['amt'])) + + for cid, stocks in self.concept_stocks.items(): + changes = [stock_chg[s] for s in stocks if s in stock_chg] + amts = [stock_amt.get(s, 0) for s in stocks if s in stock_chg] + + if not changes: + continue + + alpha = np.mean(changes) - idx_chg + total_amt = sum(amts) + limit_up_ratio = sum(1 for c in changes if c >= CONFIG['limit_up_threshold']) / len(changes) + + results.append({ + 'concept_id': cid, + 'timestamp': ts, + 'time_slot': time_to_slot(ts), + 'alpha': alpha, + 'total_amt': total_amt, + 'limit_up_ratio': limit_up_ratio, + 'stock_count': len(changes), + }) + + if not results: + return pd.DataFrame() + + df = pd.DataFrame(results) + + # 计算排名 + for ts in df['timestamp'].unique(): + mask = df['timestamp'] == ts + df.loc[mask, 'rank_pct'] = df.loc[mask, 'alpha'].rank(pct=True) + + return df + + def _compute_zscore(self, concept_id: str, time_slot: str, alpha: float, total_amt: float, rank_pct: float) -> Optional[Dict]: + """计算 Z-Score""" + if concept_id not in self.baselines: + return None + + baseline = self.baselines[concept_id] + if time_slot not in baseline: + return None + + bl = baseline[time_slot] + + alpha_z = np.clip((alpha - bl['alpha_mean']) / bl['alpha_std'], -5, 5) + amt_z = np.clip((total_amt - bl['amt_mean']) / bl['amt_std'], -5, 5) + rank_z = np.clip((rank_pct - bl['rank_mean']) / bl['rank_std'], -5, 5) + + # 动量（基于 Z-Score 历史） + history = list(self.zscore_history[concept_id]) + mom_3m = 0.0 + mom_5m = 0.0 + + if len(history) >= 3: + recent = [h['alpha_zscore'] for h in history[-3:]] + older = [h['alpha_zscore'] for h in history[-6:-3]] if len(history) >= 6 else [history[0]['alpha_zscore']] + mom_3m = np.mean(recent) - np.mean(older) + + if len(history) >= 5: + recent = [h['alpha_zscore'] for h in history[-5:]] + older = [h['alpha_zscore'] for h in history[-10:-5]] if len(history) >= 10 else [history[0]['alpha_zscore']] + mom_5m = np.mean(recent) - np.mean(older) + + return { + 'alpha_zscore': float(alpha_z), + 'amt_zscore': float(amt_z), + 'rank_zscore': float(rank_z), + 'momentum_3m': float(mom_3m), + 'momentum_5m': float(mom_5m), + } + + @torch.no_grad() + def _ml_score(self, sequences: np.ndarray) -> np.ndarray: + """批量 ML 评分""" + if self.model is None or len(sequences) == 0: + return np.zeros(len(sequences)) + + x = torch.FloatTensor(sequences).to(self.device) + errors = self.model.compute_reconstruction_error(x, reduction='none') + last_errors = errors[:, -1].cpu().numpy() + + # 转换为 0-100 分数 + if self.thresholds: + p50 = self.thresholds.get('median', 0.001) + p99 = self.thresholds.get('p99', 0.05) + scores = 50 + (last_errors - p50) / (p99 - p50 + 1e-6) * 49 + else: + scores = last_errors * 1000 + + return np.clip(scores, 0, 100) + + def detect(self, trade_date: str = None) -> List[Dict]: + """检测指定日期的异动""" + trade_date = trade_date or datetime.now().strftime('%Y-%m-%d') + print(f"\n检测 {trade_date} 的异动...") + + # 重置状态 + self.zscore_history.clear() + self.anomaly_candidates.clear() + self.cooldown.clear() + + # 获取数据 + raw_df = self._get_realtime_data(trade_date) + if raw_df.empty: + print("无数据") + return [] + + timestamps = sorted(raw_df['timestamp'].unique()) + print(f"时间点数: {len(timestamps)}") + + all_alerts = [] + + for ts in timestamps: + ts_data = raw_df[raw_df['timestamp'] == ts] + time_slot = time_to_slot(ts) + + candidates = [] + + # 计算每个概念的 Z-Score + for _, row in ts_data.iterrows(): + cid = row['concept_id'] + + zscore = self._compute_zscore( + cid, time_slot, + row['alpha'], row['total_amt'], row['rank_pct'] + ) + + if zscore is None: + continue + + # 完整特征 + features = { + **zscore, + 'alpha': row['alpha'], + 'limit_up_ratio': row['limit_up_ratio'], + 'total_amt': row['total_amt'], + } + + # 更新历史 + self.zscore_history[cid].append(zscore) + + # 规则评分 + rule_score, triggered = score_rules_zscore(features) + + candidates.append((cid, features, rule_score, triggered)) + + if not candidates: + continue + + # 批量 ML 评分 + sequences = [] + valid_candidates = [] + + for cid, features, rule_score, triggered in candidates: + history = list(self.zscore_history[cid]) + if len(history) >= CONFIG['seq_len']: + seq = np.array([[h['alpha_zscore'], h['amt_zscore'], h['rank_zscore'], + h['momentum_3m'], h['momentum_5m'], features['limit_up_ratio']] + for h in history]) + sequences.append(seq) + valid_candidates.append((cid, features, rule_score, triggered)) + + if not sequences: + continue + + ml_scores = self._ml_score(np.array(sequences)) + + # 融合 + 确认 + for i, (cid, features, rule_score, triggered) in enumerate(valid_candidates): + ml_score = ml_scores[i] + final_score = CONFIG['rule_weight'] * rule_score + CONFIG['ml_weight'] * ml_score + + # 判断触发 + is_triggered = ( + rule_score >= CONFIG['rule_trigger'] or + ml_score >= CONFIG['ml_trigger'] or + final_score >= CONFIG['fusion_trigger'] + ) + + self.anomaly_candidates[cid].append((ts, final_score)) + + if not is_triggered: + continue + + # 冷却期 + if cid in self.cooldown: + if (ts - self.cooldown[cid]).total_seconds() < CONFIG['cooldown_minutes'] * 60: + continue + + # 持续确认 + recent = list(self.anomaly_candidates[cid]) + if len(recent) < CONFIG['confirm_window']: + continue + + exceed = sum(1 for _, s in recent if s >= CONFIG['fusion_trigger']) + ratio = exceed / len(recent) + + if ratio < CONFIG['confirm_ratio']: + continue + + # 确认异动！ + self.cooldown[cid] = ts + + alpha = features['alpha'] + alert_type = 'surge_up' if alpha >= 1.5 else 'surge_down' if alpha <= -1.5 else 'surge' + + concept_name = next((c['concept_name'] for c in self.concepts if c['concept_id'] == cid), cid) + + all_alerts.append({ + 'concept_id': cid, + 'concept_name': concept_name, + 'alert_time': ts, + 'trade_date': trade_date, + 'alert_type': alert_type, + 'final_score': float(final_score), + 'rule_score': float(rule_score), + 'ml_score': float(ml_score), + 'confirm_ratio': float(ratio), + 'alpha': float(alpha), + 'alpha_zscore': float(features['alpha_zscore']), + 'amt_zscore': float(features['amt_zscore']), + 'rank_zscore': float(features['rank_zscore']), + 'momentum_3m': float(features['momentum_3m']), + 'momentum_5m': float(features['momentum_5m']), + 'limit_up_ratio': float(features['limit_up_ratio']), + 'triggered_rules': triggered, + 'trigger_reason': f"融合({final_score:.0f})+确认({ratio:.0%})", + }) + + print(f"检测到 {len(all_alerts)} 个异动") + return all_alerts + + +# ==================== 数据库存储 ==================== + +def create_v2_table(): + """创建 V2 异动表（如果不存在）""" + engine = get_mysql_engine() + with engine.begin() as conn: + conn.execute(text(""" + CREATE TABLE IF NOT EXISTS concept_anomaly_v2 ( + id INT AUTO_INCREMENT PRIMARY KEY, + concept_id VARCHAR(50) NOT NULL, + alert_time DATETIME NOT NULL, + trade_date DATE NOT NULL, + alert_type VARCHAR(20) NOT NULL, + final_score FLOAT, + rule_score FLOAT, + ml_score FLOAT, + trigger_reason VARCHAR(200), + confirm_ratio FLOAT, + alpha FLOAT, + alpha_zscore FLOAT, + amt_zscore FLOAT, + rank_zscore FLOAT, + momentum_3m FLOAT, + momentum_5m FLOAT, + limit_up_ratio FLOAT, + triggered_rules TEXT, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + UNIQUE KEY uk_concept_time (concept_id, alert_time), + INDEX idx_trade_date (trade_date), + INDEX idx_alert_type (alert_type) + ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 + """)) + print("concept_anomaly_v2 表已就绪") + + +def save_alerts_to_db(alerts: List[Dict]) -> int: + """保存异动到数据库""" + if not alerts: + return 0 + + engine = get_mysql_engine() + saved = 0 + + with engine.begin() as conn: + for alert in alerts: + try: + insert_sql = text(""" + INSERT IGNORE INTO concept_anomaly_v2 + (concept_id, alert_time, trade_date, alert_type, + final_score, rule_score, ml_score, trigger_reason, confirm_ratio, + alpha, alpha_zscore, amt_zscore, rank_zscore, + momentum_3m, momentum_5m, limit_up_ratio, triggered_rules) + VALUES + (:concept_id, :alert_time, :trade_date, :alert_type, + :final_score, :rule_score, :ml_score, :trigger_reason, :confirm_ratio, + :alpha, :alpha_zscore, :amt_zscore, :rank_zscore, + :momentum_3m, :momentum_5m, :limit_up_ratio, :triggered_rules) + """) + + result = conn.execute(insert_sql, { + 'concept_id': alert['concept_id'], + 'alert_time': alert['alert_time'], + 'trade_date': alert['trade_date'], + 'alert_type': alert['alert_type'], + 'final_score': alert['final_score'], + 'rule_score': alert['rule_score'], + 'ml_score': alert['ml_score'], + 'trigger_reason': alert['trigger_reason'], + 'confirm_ratio': alert['confirm_ratio'], + 'alpha': alert['alpha'], + 'alpha_zscore': alert['alpha_zscore'], + 'amt_zscore': alert['amt_zscore'], + 'rank_zscore': alert['rank_zscore'], + 'momentum_3m': alert['momentum_3m'], + 'momentum_5m': alert['momentum_5m'], + 'limit_up_ratio': alert['limit_up_ratio'], + 'triggered_rules': json.dumps(alert.get('triggered_rules', []), ensure_ascii=False), + }) + + if result.rowcount > 0: + saved += 1 + except Exception as e: + print(f"保存失败: {alert['concept_id']} - {e}") + + return saved + + +def main(): + import argparse + parser = argparse.ArgumentParser() + parser.add_argument('--date', type=str, default=None) + parser.add_argument('--no-save', action='store_true', help='不保存到数据库，只打印') + args = parser.parse_args() + + # 确保表存在 + if not args.no_save: + create_v2_table() + + detector = RealtimeDetectorV2() + alerts = detector.detect(args.date) + + print(f"\n{'='*60}") + print(f"检测结果 ({len(alerts)} 个异动)") + print('='*60) + + for a in alerts[:20]: + print(f"[{a['alert_time'].strftime('%H:%M') if hasattr(a['alert_time'], 'strftime') else a['alert_time']}] " + f"{a['concept_name']} | {a['alert_type']} | " + f"分数={a['final_score']:.0f} 确认={a['confirm_ratio']:.0%} " + f"α={a['alpha']:.2f}% αZ={a['alpha_zscore']:.1f}") + + if len(alerts) > 20: + print(f"... 共 {len(alerts)} 个") + + # 保存到数据库 + if not args.no_save and alerts: + saved = save_alerts_to_db(alerts) + print(f"\n✅ 已保存 {saved}/{len(alerts)} 条到 concept_anomaly_v2 表") + elif args.no_save: + print(f"\n⚠️ --no-save 模式，未保存到数据库") + + +if __name__ == "__main__": + main() diff --git a/ml/train_v2.py b/ml/train_v2.py new file mode 100644 index 00000000..47e6cdb7 --- /dev/null +++ b/ml/train_v2.py @@ -0,0 +1,622 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +训练脚本 V2 - 基于 Z-Score 特征的 LSTM Autoencoder + +改进点： +1. 使用 Z-Score 特征（相对于同时间片历史的偏离） +2. 短序列：10分钟（不需要30分钟预热） +3. 开盘即可检测：9:30 直接有特征 + +模型输入： +- 过去10分钟的 Z-Score 特征序列 +- 特征：alpha_zscore, amt_zscore, rank_zscore, momentum_3m, momentum_5m, limit_up_ratio + +模型学习： +- 学习 Z-Score 序列的"正常演化模式" +- 异动 = Z-Score 序列的异常演化（重构误差大） +""" + +import os +import sys +import argparse +import json +from datetime import datetime +from pathlib import Path +from typing import List, Tuple, Dict + +import numpy as np +import pandas as pd +import torch +import torch.nn as nn +from torch.utils.data import Dataset, DataLoader +from torch.optim import AdamW +from torch.optim.lr_scheduler import CosineAnnealingWarmRestarts +from tqdm import tqdm + +from model import TransformerAutoencoder, AnomalyDetectionLoss, count_parameters + +# 性能优化 +torch.backends.cudnn.benchmark = True +torch.backends.cuda.matmul.allow_tf32 = True +torch.backends.cudnn.allow_tf32 = True + +try: + import matplotlib + matplotlib.use('Agg') + import matplotlib.pyplot as plt + HAS_MATPLOTLIB = True +except ImportError: + HAS_MATPLOTLIB = False + + +# ==================== 配置 ==================== + +TRAIN_CONFIG = { + # 数据配置（改进！） + 'seq_len': 10, # 10分钟序列（不是30分钟！） + 'stride': 2, # 步长2分钟 + + # 时间切分 + 'train_end_date': '2024-06-30', + 'val_end_date': '2024-09-30', + + # V2 特征（Z-Score 为主） + 'features': [ + 'alpha_zscore', # Alpha 的 Z-Score + 'amt_zscore', # 成交额的 Z-Score + 'rank_zscore', # 排名的 Z-Score + 'momentum_3m', # 3分钟动量 + 'momentum_5m', # 5分钟动量 + 'limit_up_ratio', # 涨停占比 + ], + + # 训练配置 + 'batch_size': 4096, + 'epochs': 100, + 'learning_rate': 3e-4, + 'weight_decay': 1e-5, + 'gradient_clip': 1.0, + + # 早停配置 + 'patience': 15, + 'min_delta': 1e-6, + + # 模型配置（小型 LSTM） + 'model': { + 'n_features': 6, + 'hidden_dim': 32, + 'latent_dim': 4, + 'num_layers': 1, + 'dropout': 0.2, + 'bidirectional': True, + }, + + # 标准化配置 + 'clip_value': 5.0, # Z-Score 已经标准化，clip 5.0 足够 + + # 阈值配置 + 'threshold_percentiles': [90, 95, 99], +} + + +# ==================== 数据加载 ==================== + +def load_data_by_date(data_dir: str, features: List[str]) -> Dict[str, pd.DataFrame]: + """按日期加载 V2 数据""" + data_path = Path(data_dir) + parquet_files = sorted(data_path.glob("features_v2_*.parquet")) + + if not parquet_files: + raise FileNotFoundError(f"未找到 V2 数据文件: {data_dir}") + + print(f"找到 {len(parquet_files)} 个 V2 数据文件") + + date_data = {} + + for pf in tqdm(parquet_files, desc="加载数据"): + date = pf.stem.replace('features_v2_', '') + + df = pd.read_parquet(pf) + + required_cols = features + ['concept_id', 'timestamp'] + missing_cols = [c for c in required_cols if c not in df.columns] + if missing_cols: + print(f"警告: {date} 缺少列: {missing_cols}, 跳过") + continue + + date_data[date] = df + + print(f"成功加载 {len(date_data)} 天的数据") + return date_data + + +def split_data_by_date( + date_data: Dict[str, pd.DataFrame], + train_end: str, + val_end: str +) -> Tuple[Dict[str, pd.DataFrame], Dict[str, pd.DataFrame], Dict[str, pd.DataFrame]]: + """按日期划分数据集""" + train_data = {} + val_data = {} + test_data = {} + + for date, df in date_data.items(): + if date <= train_end: + train_data[date] = df + elif date <= val_end: + val_data[date] = df + else: + test_data[date] = df + + print(f"数据集划分:") + print(f" 训练集: {len(train_data)} 天 (<= {train_end})") + print(f" 验证集: {len(val_data)} 天 ({train_end} ~ {val_end})") + print(f" 测试集: {len(test_data)} 天 (> {val_end})") + + return train_data, val_data, test_data + + +def build_sequences_by_concept( + date_data: Dict[str, pd.DataFrame], + features: List[str], + seq_len: int, + stride: int +) -> np.ndarray: + """按概念分组构建序列""" + all_dfs = [] + for date, df in sorted(date_data.items()): + df = df.copy() + df['date'] = date + all_dfs.append(df) + + if not all_dfs: + return np.array([]) + + combined = pd.concat(all_dfs, ignore_index=True) + combined = combined.sort_values(['concept_id', 'date', 'timestamp']) + + all_sequences = [] + grouped = combined.groupby('concept_id', sort=False) + n_concepts = len(grouped) + + for concept_id, concept_df in tqdm(grouped, desc="构建序列", total=n_concepts, leave=False): + feature_data = concept_df[features].values + feature_data = np.nan_to_num(feature_data, nan=0.0, posinf=0.0, neginf=0.0) + + n_points = len(feature_data) + for start in range(0, n_points - seq_len + 1, stride): + seq = feature_data[start:start + seq_len] + all_sequences.append(seq) + + if not all_sequences: + return np.array([]) + + sequences = np.array(all_sequences) + print(f" 构建序列: {len(sequences):,} 条 (来自 {n_concepts} 个概念)") + + return sequences + + +# ==================== 数据集 ==================== + +class SequenceDataset(Dataset): + def __init__(self, sequences: np.ndarray): + self.sequences = torch.FloatTensor(sequences) + + def __len__(self) -> int: + return len(self.sequences) + + def __getitem__(self, idx: int) -> torch.Tensor: + return self.sequences[idx] + + +# ==================== 训练器 ==================== + +class EarlyStopping: + def __init__(self, patience: int = 10, min_delta: float = 1e-6): + self.patience = patience + self.min_delta = min_delta + self.counter = 0 + self.best_loss = float('inf') + self.early_stop = False + + def __call__(self, val_loss: float) -> bool: + if val_loss < self.best_loss - self.min_delta: + self.best_loss = val_loss + self.counter = 0 + else: + self.counter += 1 + if self.counter >= self.patience: + self.early_stop = True + return self.early_stop + + +class Trainer: + def __init__( + self, + model: nn.Module, + train_loader: DataLoader, + val_loader: DataLoader, + config: Dict, + device: torch.device, + save_dir: str = 'ml/checkpoints_v2' + ): + self.model = model.to(device) + self.train_loader = train_loader + self.val_loader = val_loader + self.config = config + self.device = device + self.save_dir = Path(save_dir) + self.save_dir.mkdir(parents=True, exist_ok=True) + + self.optimizer = AdamW( + model.parameters(), + lr=config['learning_rate'], + weight_decay=config['weight_decay'] + ) + + self.scheduler = CosineAnnealingWarmRestarts( + self.optimizer, T_0=10, T_mult=2, eta_min=1e-6 + ) + + self.criterion = AnomalyDetectionLoss() + + self.early_stopping = EarlyStopping( + patience=config['patience'], + min_delta=config['min_delta'] + ) + + self.use_amp = torch.cuda.is_available() + self.scaler = torch.cuda.amp.GradScaler() if self.use_amp else None + if self.use_amp: + print(" ✓ 启用 AMP 混合精度训练") + + self.history = {'train_loss': [], 'val_loss': [], 'learning_rate': []} + self.best_val_loss = float('inf') + + def train_epoch(self) -> float: + self.model.train() + total_loss = 0.0 + n_batches = 0 + + pbar = tqdm(self.train_loader, desc="Training", leave=False) + for batch in pbar: + batch = batch.to(self.device, non_blocking=True) + self.optimizer.zero_grad(set_to_none=True) + + if self.use_amp: + with torch.cuda.amp.autocast(): + output, latent = self.model(batch) + loss, _ = self.criterion(output, batch, latent) + + self.scaler.scale(loss).backward() + self.scaler.unscale_(self.optimizer) + torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.config['gradient_clip']) + self.scaler.step(self.optimizer) + self.scaler.update() + else: + output, latent = self.model(batch) + loss, _ = self.criterion(output, batch, latent) + loss.backward() + torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.config['gradient_clip']) + self.optimizer.step() + + total_loss += loss.item() + n_batches += 1 + pbar.set_postfix({'loss': f"{loss.item():.4f}"}) + + return total_loss / n_batches + + @torch.no_grad() + def validate(self) -> float: + self.model.eval() + total_loss = 0.0 + n_batches = 0 + + for batch in self.val_loader: + batch = batch.to(self.device, non_blocking=True) + + if self.use_amp: + with torch.cuda.amp.autocast(): + output, latent = self.model(batch) + loss, _ = self.criterion(output, batch, latent) + else: + output, latent = self.model(batch) + loss, _ = self.criterion(output, batch, latent) + + total_loss += loss.item() + n_batches += 1 + + return total_loss / n_batches + + def save_checkpoint(self, epoch: int, val_loss: float, is_best: bool = False): + model_to_save = self.model.module if hasattr(self.model, 'module') else self.model + + checkpoint = { + 'epoch': epoch, + 'model_state_dict': model_to_save.state_dict(), + 'optimizer_state_dict': self.optimizer.state_dict(), + 'scheduler_state_dict': self.scheduler.state_dict(), + 'val_loss': val_loss, + 'config': self.config, + } + + torch.save(checkpoint, self.save_dir / 'last_checkpoint.pt') + + if is_best: + torch.save(checkpoint, self.save_dir / 'best_model.pt') + print(f" ✓ 保存最佳模型 (val_loss: {val_loss:.6f})") + + def train(self, epochs: int): + print(f"\n开始训练 ({epochs} epochs)...") + print(f"设备: {self.device}") + print(f"模型参数量: {count_parameters(self.model):,}") + + for epoch in range(1, epochs + 1): + print(f"\nEpoch {epoch}/{epochs}") + + train_loss = self.train_epoch() + val_loss = self.validate() + + self.scheduler.step() + current_lr = self.optimizer.param_groups[0]['lr'] + + self.history['train_loss'].append(train_loss) + self.history['val_loss'].append(val_loss) + self.history['learning_rate'].append(current_lr) + + print(f" Train Loss: {train_loss:.6f}") + print(f" Val Loss: {val_loss:.6f}") + print(f" LR: {current_lr:.2e}") + + is_best = val_loss < self.best_val_loss + if is_best: + self.best_val_loss = val_loss + self.save_checkpoint(epoch, val_loss, is_best) + + if self.early_stopping(val_loss): + print(f"\n早停触发！") + break + + print(f"\n训练完成！最佳验证损失: {self.best_val_loss:.6f}") + self.save_history() + + return self.history + + def save_history(self): + history_path = self.save_dir / 'training_history.json' + with open(history_path, 'w') as f: + json.dump(self.history, f, indent=2) + print(f"训练历史已保存: {history_path}") + + if HAS_MATPLOTLIB: + self.plot_training_curves() + + def plot_training_curves(self): + fig, axes = plt.subplots(1, 2, figsize=(14, 5)) + epochs = range(1, len(self.history['train_loss']) + 1) + + ax1 = axes[0] + ax1.plot(epochs, self.history['train_loss'], 'b-', label='Train Loss', linewidth=2) + ax1.plot(epochs, self.history['val_loss'], 'r-', label='Val Loss', linewidth=2) + ax1.set_xlabel('Epoch') + ax1.set_ylabel('Loss') + ax1.set_title('Training & Validation Loss (V2)') + ax1.legend() + ax1.grid(True, alpha=0.3) + + best_epoch = np.argmin(self.history['val_loss']) + 1 + best_val_loss = min(self.history['val_loss']) + ax1.axvline(x=best_epoch, color='g', linestyle='--', alpha=0.7) + ax1.scatter([best_epoch], [best_val_loss], color='g', s=100, zorder=5) + + ax2 = axes[1] + ax2.plot(epochs, self.history['learning_rate'], 'g-', linewidth=2) + ax2.set_xlabel('Epoch') + ax2.set_ylabel('Learning Rate') + ax2.set_title('Learning Rate Schedule') + ax2.set_yscale('log') + ax2.grid(True, alpha=0.3) + + plt.tight_layout() + plt.savefig(self.save_dir / 'training_curves.png', dpi=150, bbox_inches='tight') + plt.close() + print(f"训练曲线已保存") + + +# ==================== 阈值计算 ==================== + +@torch.no_grad() +def compute_thresholds( + model: nn.Module, + data_loader: DataLoader, + device: torch.device, + percentiles: List[float] = [90, 95, 99] +) -> Dict[str, float]: + """在验证集上计算阈值""" + model.eval() + all_errors = [] + + print("计算异动阈值...") + for batch in tqdm(data_loader, desc="Computing thresholds"): + batch = batch.to(device) + errors = model.compute_reconstruction_error(batch, reduction='none') + seq_errors = errors[:, -1] # 最后一个时刻 + all_errors.append(seq_errors.cpu().numpy()) + + all_errors = np.concatenate(all_errors) + + thresholds = {} + for p in percentiles: + threshold = np.percentile(all_errors, p) + thresholds[f'p{p}'] = float(threshold) + print(f" P{p}: {threshold:.6f}") + + thresholds['mean'] = float(np.mean(all_errors)) + thresholds['std'] = float(np.std(all_errors)) + thresholds['median'] = float(np.median(all_errors)) + + return thresholds + + +# ==================== 主函数 ==================== + +def main(): + parser = argparse.ArgumentParser(description='训练 V2 模型') + parser.add_argument('--data_dir', type=str, default='ml/data_v2', help='V2 数据目录') + parser.add_argument('--epochs', type=int, default=100) + parser.add_argument('--batch_size', type=int, default=4096) + parser.add_argument('--lr', type=float, default=3e-4) + parser.add_argument('--device', type=str, default='auto') + parser.add_argument('--save_dir', type=str, default='ml/checkpoints_v2') + parser.add_argument('--train_end', type=str, default='2024-06-30') + parser.add_argument('--val_end', type=str, default='2024-09-30') + parser.add_argument('--seq_len', type=int, default=10, help='序列长度（分钟）') + + args = parser.parse_args() + + config = TRAIN_CONFIG.copy() + config['batch_size'] = args.batch_size + config['epochs'] = args.epochs + config['learning_rate'] = args.lr + config['train_end_date'] = args.train_end + config['val_end_date'] = args.val_end + config['seq_len'] = args.seq_len + + if args.device == 'auto': + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + else: + device = torch.device(args.device) + + print("=" * 60) + print("概念异动检测模型训练 V2（Z-Score 特征）") + print("=" * 60) + print(f"数据目录: {args.data_dir}") + print(f"设备: {device}") + print(f"序列长度: {config['seq_len']} 分钟") + print(f"批次大小: {config['batch_size']}") + print(f"特征: {config['features']}") + print("=" * 60) + + # 1. 加载数据 + print("\n[1/6] 加载 V2 数据...") + date_data = load_data_by_date(args.data_dir, config['features']) + + # 2. 划分数据集 + print("\n[2/6] 划分数据集...") + train_data, val_data, test_data = split_data_by_date( + date_data, config['train_end_date'], config['val_end_date'] + ) + + # 3. 构建序列 + print("\n[3/6] 构建序列...") + print("训练集:") + train_sequences = build_sequences_by_concept( + train_data, config['features'], config['seq_len'], config['stride'] + ) + print("验证集:") + val_sequences = build_sequences_by_concept( + val_data, config['features'], config['seq_len'], config['stride'] + ) + + if len(train_sequences) == 0: + print("错误: 训练集为空！") + return + + # 4. 预处理 + print("\n[4/6] 数据预处理...") + clip_value = config['clip_value'] + print(f" Z-Score 特征已标准化，截断: ±{clip_value}") + + train_sequences = np.clip(train_sequences, -clip_value, clip_value) + if len(val_sequences) > 0: + val_sequences = np.clip(val_sequences, -clip_value, clip_value) + + # 保存配置 + save_dir = Path(args.save_dir) + save_dir.mkdir(parents=True, exist_ok=True) + + with open(save_dir / 'config.json', 'w') as f: + json.dump(config, f, indent=2) + + # 5. 创建数据加载器 + print("\n[5/6] 创建数据加载器...") + train_dataset = SequenceDataset(train_sequences) + val_dataset = SequenceDataset(val_sequences) if len(val_sequences) > 0 else None + + print(f" 训练序列: {len(train_dataset):,}") + print(f" 验证序列: {len(val_dataset) if val_dataset else 0:,}") + + n_gpus = torch.cuda.device_count() if torch.cuda.is_available() else 1 + num_workers = min(32, 8 * n_gpus) if sys.platform != 'win32' else 0 + + train_loader = DataLoader( + train_dataset, + batch_size=config['batch_size'], + shuffle=True, + num_workers=num_workers, + pin_memory=True, + prefetch_factor=4 if num_workers > 0 else None, + persistent_workers=True if num_workers > 0 else False, + drop_last=True + ) + + val_loader = DataLoader( + val_dataset, + batch_size=config['batch_size'] * 2, + shuffle=False, + num_workers=num_workers, + pin_memory=True, + ) if val_dataset else None + + # 6. 训练 + print("\n[6/6] 训练模型...") + model = TransformerAutoencoder(**config['model']) + + if torch.cuda.device_count() > 1: + print(f" 使用 {torch.cuda.device_count()} 张 GPU 并行训练") + model = nn.DataParallel(model) + + if val_loader is None: + print("警告: 验证集为空，使用训练集的 10% 作为验证") + split_idx = int(len(train_dataset) * 0.9) + train_subset = torch.utils.data.Subset(train_dataset, range(split_idx)) + val_subset = torch.utils.data.Subset(train_dataset, range(split_idx, len(train_dataset))) + train_loader = DataLoader(train_subset, batch_size=config['batch_size'], shuffle=True, num_workers=num_workers, pin_memory=True) + val_loader = DataLoader(val_subset, batch_size=config['batch_size'], shuffle=False, num_workers=num_workers, pin_memory=True) + + trainer = Trainer( + model=model, + train_loader=train_loader, + val_loader=val_loader, + config=config, + device=device, + save_dir=args.save_dir + ) + + trainer.train(config['epochs']) + + # 计算阈值 + print("\n[额外] 计算异动阈值...") + best_checkpoint = torch.load(save_dir / 'best_model.pt', map_location=device) + + # 创建新的单 GPU 模型用于计算阈值（避免 DataParallel 问题） + threshold_model = TransformerAutoencoder(**config['model']) + threshold_model.load_state_dict(best_checkpoint['model_state_dict']) + threshold_model.to(device) + threshold_model.eval() + + thresholds = compute_thresholds(threshold_model, val_loader, device, config['threshold_percentiles']) + + with open(save_dir / 'thresholds.json', 'w') as f: + json.dump(thresholds, f, indent=2) + + print("\n" + "=" * 60) + print("训练完成！") + print(f"模型保存位置: {args.save_dir}") + print("=" * 60) + + +if __name__ == "__main__": + main() diff --git a/ml/update_baseline.py b/ml/update_baseline.py new file mode 100644 index 00000000..7ee7e3cc --- /dev/null +++ b/ml/update_baseline.py @@ -0,0 +1,132 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +每日盘后运行：更新滚动基线 + +使用方法： + python ml/update_baseline.py + +建议加入 crontab，每天 15:30 后运行： + 30 15 * * 1-5 cd /path/to/project && python ml/update_baseline.py +""" + +import os +import sys +import pickle +import pandas as pd +import numpy as np +from datetime import datetime, timedelta +from pathlib import Path +from tqdm import tqdm + +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +from ml.prepare_data_v2 import ( + get_all_concepts, get_trading_days, compute_raw_concept_features, + init_process_connections, CONFIG, RAW_CACHE_DIR, BASELINE_DIR +) + + +def update_rolling_baseline(baseline_days: int = 20): + """ + 更新滚动基线（用于实盘检测） + + 基线 = 最近 N 个交易日每个时间片的统计量 + """ + print("=" * 60) + print("更新滚动基线（用于实盘）") + print("=" * 60) + + # 初始化连接 + init_process_connections() + + # 获取概念列表 + concepts = get_all_concepts() + all_stocks = list(set(s for c in concepts for s in c['stocks'])) + + # 获取最近的交易日 + today = datetime.now().strftime('%Y-%m-%d') + start_date = (datetime.now() - timedelta(days=60)).strftime('%Y-%m-%d') # 多取一些 + + trading_days = get_trading_days(start_date, today) + + if len(trading_days) < baseline_days: + print(f"错误：交易日不足 {baseline_days} 天") + return + + # 只取最近 N 天 + recent_days = trading_days[-baseline_days:] + print(f"使用 {len(recent_days)} 天数据: {recent_days[0]} ~ {recent_days[-1]}") + + # 加载原始数据 + all_data = [] + for trade_date in tqdm(recent_days, desc="加载数据"): + cache_file = os.path.join(RAW_CACHE_DIR, f'raw_{trade_date}.parquet') + + if os.path.exists(cache_file): + df = pd.read_parquet(cache_file) + else: + df = compute_raw_concept_features(trade_date, concepts, all_stocks) + + if not df.empty: + all_data.append(df) + + if not all_data: + print("错误：无数据") + return + + combined = pd.concat(all_data, ignore_index=True) + print(f"总数据量: {len(combined):,} 条") + + # 按概念计算基线 + baselines = {} + + for concept_id, group in tqdm(combined.groupby('concept_id'), desc="计算基线"): + baseline_dict = {} + + for time_slot, slot_group in group.groupby('time_slot'): + if len(slot_group) < CONFIG['min_baseline_samples']: + continue + + alpha_std = slot_group['alpha'].std() + amt_std = slot_group['total_amt'].std() + rank_std = slot_group['rank_pct'].std() + + baseline_dict[time_slot] = { + 'alpha_mean': float(slot_group['alpha'].mean()), + 'alpha_std': float(max(alpha_std if pd.notna(alpha_std) else 1.0, 0.1)), + 'amt_mean': float(slot_group['total_amt'].mean()), + 'amt_std': float(max(amt_std if pd.notna(amt_std) else slot_group['total_amt'].mean() * 0.5, 1.0)), + 'rank_mean': float(slot_group['rank_pct'].mean()), + 'rank_std': float(max(rank_std if pd.notna(rank_std) else 0.2, 0.05)), + 'sample_count': len(slot_group), + } + + if baseline_dict: + baselines[concept_id] = baseline_dict + + print(f"计算了 {len(baselines)} 个概念的基线") + + # 保存 + os.makedirs(BASELINE_DIR, exist_ok=True) + baseline_file = os.path.join(BASELINE_DIR, 'realtime_baseline.pkl') + + with open(baseline_file, 'wb') as f: + pickle.dump({ + 'baselines': baselines, + 'update_time': datetime.now().isoformat(), + 'date_range': [recent_days[0], recent_days[-1]], + 'baseline_days': baseline_days, + }, f) + + print(f"基线已保存: {baseline_file}") + print("=" * 60) + + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser() + parser.add_argument('--days', type=int, default=20, help='基线天数') + args = parser.parse_args() + + update_rolling_baseline(args.days) diff --git a/src/views/StockOverview/components/FlexScreen/components/MiniTimelineChart.js b/src/views/StockOverview/components/FlexScreen/components/MiniTimelineChart.js new file mode 100644 index 00000000..46c15e58 --- /dev/null +++ b/src/views/StockOverview/components/FlexScreen/components/MiniTimelineChart.js @@ -0,0 +1,267 @@ +/** + * 迷你分时图组件 + * 用于灵活屏中显示证券的日内走势 + */ +import React, { useEffect, useRef, useState, useMemo } from 'react'; +import { Box, Spinner, Center, Text } from '@chakra-ui/react'; +import * as echarts from 'echarts'; + +/** + * 生成交易时间刻度（用于 X 轴） + * A股交易时间：9:30-11:30, 13:00-15:00 + */ +const generateTimeTicks = () => { + const ticks = []; + // 上午 + for (let h = 9; h <= 11; h++) { + for (let m = (h === 9 ? 30 : 0); m < 60; m++) { + if (h === 11 && m > 30) break; + ticks.push(`${String(h).padStart(2, '0')}:${String(m).padStart(2, '0')}`); + } + } + // 下午 + for (let h = 13; h <= 15; h++) { + for (let m = 0; m < 60; m++) { + if (h === 15 && m > 0) break; + ticks.push(`${String(h).padStart(2, '0')}:${String(m).padStart(2, '0')}`); + } + } + return ticks; +}; + +const TIME_TICKS = generateTimeTicks(); + +/** + * MiniTimelineChart 组件 + * @param {Object} props + * @param {string} props.code - 证券代码 + * @param {boolean} props.isIndex - 是否为指数 + * @param {number} props.prevClose - 昨收价 + * @param {number} props.currentPrice - 当前价（实时） + * @param {number} props.height - 图表高度 + */ +const MiniTimelineChart = ({ + code, + isIndex = false, + prevClose, + currentPrice, + height = 120, +}) => { + const chartRef = useRef(null); + const chartInstance = useRef(null); + const [timelineData, setTimelineData] = useState([]); + const [loading, setLoading] = useState(true); + const [error, setError] = useState(null); + + // 获取分钟数据 + useEffect(() => { + if (!code) return; + + const fetchData = async () => { + setLoading(true); + setError(null); + + try { + const apiPath = isIndex + ? `/api/index/${code}/kline?type=minute` + : `/api/stock/${code}/kline?type=minute`; + + const response = await fetch(apiPath); + const result = await response.json(); + + if (result.success !== false && result.data) { + // 格式化数据 + const formatted = result.data.map(item => ({ + time: item.time || item.timestamp, + price: item.close || item.price, + })); + setTimelineData(formatted); + } else { + setError(result.error || '暂无数据'); + } + } catch (e) { + setError('加载失败'); + } finally { + setLoading(false); + } + }; + + fetchData(); + + // 交易时间内每分钟刷新 + const now = new Date(); + const hours = now.getHours(); + const minutes = now.getMinutes(); + const currentMinutes = hours * 60 + minutes; + const isTrading = (currentMinutes >= 570 && currentMinutes <= 690) || + (currentMinutes >= 780 && currentMinutes <= 900); + + let intervalId; + if (isTrading) { + intervalId = setInterval(fetchData, 60000); // 1分钟刷新 + } + + return () => { + if (intervalId) clearInterval(intervalId); + }; + }, [code, isIndex]); + + // 合并实时价格到数据中 + const chartData = useMemo(() => { + if (!timelineData.length) return []; + + const data = [...timelineData]; + + // 如果有实时价格，添加到最新点 + if (currentPrice && data.length > 0) { + const now = new Date(); + const timeStr = `${String(now.getHours()).padStart(2, '0')}:${String(now.getMinutes()).padStart(2, '0')}`; + const lastItem = data[data.length - 1]; + + // 如果实时价格的时间比最后一条数据新，添加新点 + if (lastItem.time !== timeStr) { + data.push({ time: timeStr, price: currentPrice }); + } else { + // 更新最后一条 + data[data.length - 1] = { ...lastItem, price: currentPrice }; + } + } + + return data; + }, [timelineData, currentPrice]); + + // 渲染图表 + useEffect(() => { + if (!chartRef.current || loading || !chartData.length) return; + + if (!chartInstance.current) { + chartInstance.current = echarts.init(chartRef.current); + } + + const baseLine = prevClose || chartData[0]?.price || 0; + + // 计算价格范围 + const prices = chartData.map(d => d.price).filter(p => p > 0); + const minPrice = Math.min(...prices, baseLine); + const maxPrice = Math.max(...prices, baseLine); + const range = Math.max(maxPrice - baseLine, baseLine - minPrice) * 1.1; + + // 准备数据 + const times = chartData.map(d => d.time); + const values = chartData.map(d => d.price); + + // 判断涨跌 + const lastPrice = values[values.length - 1] || baseLine; + const isUp = lastPrice >= baseLine; + + const option = { + grid: { + top: 5, + right: 5, + bottom: 5, + left: 5, + containLabel: false, + }, + xAxis: { + type: 'category', + data: times, + show: false, + boundaryGap: false, + }, + yAxis: { + type: 'value', + min: baseLine - range, + max: baseLine + range, + show: false, + }, + series: [ + { + type: 'line', + data: values, + smooth: false, + symbol: 'none', + lineStyle: { + width: 1.5, + color: isUp ? '#ef4444' : '#22c55e', + }, + areaStyle: { + color: { + type: 'linear', + x: 0, + y: 0, + x2: 0, + y2: 1, + colorStops: [ + { offset: 0, color: isUp ? 'rgba(239, 68, 68, 0.3)' : 'rgba(34, 197, 94, 0.3)' }, + { offset: 1, color: isUp ? 'rgba(239, 68, 68, 0.05)' : 'rgba(34, 197, 94, 0.05)' }, + ], + }, + }, + markLine: { + silent: true, + symbol: 'none', + data: [ + { + yAxis: baseLine, + lineStyle: { + color: '#666', + type: 'dashed', + width: 1, + }, + label: { show: false }, + }, + ], + }, + }, + ], + animation: false, + }; + + chartInstance.current.setOption(option); + + return () => { + // 不在这里销毁，只在组件卸载时销毁 + }; + }, [chartData, prevClose, loading]); + + // 组件卸载时销毁图表 + useEffect(() => { + return () => { + if (chartInstance.current) { + chartInstance.current.dispose(); + chartInstance.current = null; + } + }; + }, []); + + // 窗口 resize 处理 + useEffect(() => { + const handleResize = () => { + chartInstance.current?.resize(); + }; + window.addEventListener('resize', handleResize); + return () => window.removeEventListener('resize', handleResize); + }, []); + + if (loading) { + return ( +
+ +
+ ); + } + + if (error || !chartData.length) { + return ( +
+ + {error || '暂无数据'} + +
+ ); + } + + return ; +}; + +export default MiniTimelineChart; diff --git a/src/views/StockOverview/components/FlexScreen/components/OrderBookPanel.js b/src/views/StockOverview/components/FlexScreen/components/OrderBookPanel.js new file mode 100644 index 00000000..a3cf92fc --- /dev/null +++ b/src/views/StockOverview/components/FlexScreen/components/OrderBookPanel.js @@ -0,0 +1,275 @@ +/** + * 盘口行情面板组件 + * 支持显示 5 档或 10 档买卖盘数据 + * + * 上交所: 5 档行情 + * 深交所: 10 档行情 + */ +import React, { useState } from 'react'; +import { + Box, + VStack, + HStack, + Text, + Button, + ButtonGroup, + useColorModeValue, + Tooltip, + Badge, +} from '@chakra-ui/react'; + +/** + * 格式化成交量 + * @param {number} volume - 成交量（股） + * @returns {string} 格式化后的字符串 + */ +const formatVolume = (volume) => { + if (!volume || volume === 0) return '-'; + if (volume >= 10000) { + return `${(volume / 10000).toFixed(0)}万`; + } + if (volume >= 1000) { + return `${(volume / 1000).toFixed(1)}k`; + } + return String(volume); +}; + +/** + * 格式化价格 + * @param {number} price - 价格 + * @param {number} prevClose - 昨收价 + * @returns {Object} { text, color } + */ +const formatPrice = (price, prevClose) => { + if (!price || price === 0) { + return { text: '-', color: 'gray.400' }; + } + + const text = price.toFixed(2); + + if (!prevClose || prevClose === 0) { + return { text, color: 'gray.600' }; + } + + if (price > prevClose) { + return { text, color: 'red.500' }; + } + if (price < prevClose) { + return { text, color: 'green.500' }; + } + return { text, color: 'gray.600' }; +}; + +/** + * 单行盘口 + */ +const OrderRow = ({ label, price, volume, prevClose, isBid, maxVolume, isLimitPrice }) => { + const bgColor = useColorModeValue( + isBid ? 'red.50' : 'green.50', + isBid ? 'rgba(239, 68, 68, 0.1)' : 'rgba(34, 197, 94, 0.1)' + ); + const barColor = useColorModeValue( + isBid ? 'red.200' : 'green.200', + isBid ? 'rgba(239, 68, 68, 0.3)' : 'rgba(34, 197, 94, 0.3)' + ); + const limitColor = useColorModeValue('orange.500', 'orange.300'); + + const priceInfo = formatPrice(price, prevClose); + const volumeText = formatVolume(volume); + + // 计算成交量条宽度 + const barWidth = maxVolume > 0 ? Math.min((volume / maxVolume) * 100, 100) : 0; + + return ( + + {/* 成交量条 */} + + + {/* 内容 */} + + {label} + + + + {priceInfo.text} + + {isLimitPrice && ( + + + {isBid ? '跌' : '涨'} + + + )} + + + {volumeText} + + + ); +}; + +/** + * OrderBookPanel 组件 + * @param {Object} props + * @param {number[]} props.bidPrices - 买档价格（最多10档） + * @param {number[]} props.bidVolumes - 买档量 + * @param {number[]} props.askPrices - 卖档价格（最多10档） + * @param {number[]} props.askVolumes - 卖档量 + * @param {number} props.prevClose - 昨收价 + * @param {number} props.upperLimit - 涨停价 + * @param {number} props.lowerLimit - 跌停价 + * @param {number} props.defaultLevels - 默认显示档数（5 或 10） + */ +const OrderBookPanel = ({ + bidPrices = [], + bidVolumes = [], + askPrices = [], + askVolumes = [], + prevClose, + upperLimit, + lowerLimit, + defaultLevels = 5, +}) => { + const borderColor = useColorModeValue('gray.200', 'gray.700'); + const buttonBg = useColorModeValue('gray.100', 'gray.700'); + + // 可切换显示的档位数 + const maxAvailableLevels = Math.max(bidPrices.length, askPrices.length, 1); + const [showLevels, setShowLevels] = useState(Math.min(defaultLevels, maxAvailableLevels)); + + // 计算最大成交量（用于条形图比例） + const displayBidVolumes = bidVolumes.slice(0, showLevels); + const displayAskVolumes = askVolumes.slice(0, showLevels); + const allVolumes = [...displayBidVolumes, ...displayAskVolumes].filter(v => v > 0); + const maxVolume = allVolumes.length > 0 ? Math.max(...allVolumes) : 0; + + // 判断是否为涨跌停价 + const isUpperLimit = (price) => upperLimit && Math.abs(price - upperLimit) < 0.001; + const isLowerLimit = (price) => lowerLimit && Math.abs(price - lowerLimit) < 0.001; + + // 卖盘（从卖N到卖1，即价格从高到低） + const askRows = []; + for (let i = showLevels - 1; i >= 0; i--) { + askRows.push( + + ); + } + + // 买盘（从买1到买N，即价格从高到低） + const bidRows = []; + for (let i = 0; i < showLevels; i++) { + bidRows.push( + + ); + } + + // 没有数据时的提示 + const hasData = bidPrices.length > 0 || askPrices.length > 0; + + if (!hasData) { + return ( + + + 暂无盘口数据 + + + ); + } + + return ( + + {/* 档位切换（只有当有超过5档数据时才显示） */} + {maxAvailableLevels > 5 && ( + + + + + + + )} + + {/* 卖盘 */} + {askRows} + + {/* 分隔线 + 当前价信息 */} + + {prevClose && ( + + 昨收 {prevClose.toFixed(2)} + + )} + + + {/* 买盘 */} + {bidRows} + + {/* 涨跌停价信息 */} + {(upperLimit || lowerLimit) && ( + + {lowerLimit && 跌停 {lowerLimit.toFixed(2)}} + {upperLimit && 涨停 {upperLimit.toFixed(2)}} + + )} + + ); +}; + +export default OrderBookPanel; diff --git a/src/views/StockOverview/components/FlexScreen/components/QuoteTile.js b/src/views/StockOverview/components/FlexScreen/components/QuoteTile.js new file mode 100644 index 00000000..f5b5c389 --- /dev/null +++ b/src/views/StockOverview/components/FlexScreen/components/QuoteTile.js @@ -0,0 +1,270 @@ +/** + * 行情瓷砖组件 + * 单个证券的实时行情展示卡片，包含分时图和五档盘口 + */ +import React, { useState } from 'react'; +import { + Box, + VStack, + HStack, + Text, + IconButton, + Tooltip, + useColorModeValue, + Collapse, + Badge, + Flex, + Spacer, +} from '@chakra-ui/react'; +import { CloseIcon, ChevronDownIcon, ChevronUpIcon, ExternalLinkIcon } from '@chakra-ui/icons'; +import { useNavigate } from 'react-router-dom'; +import MiniTimelineChart from './MiniTimelineChart'; +import OrderBookPanel from './OrderBookPanel'; + +/** + * 格式化价格显示 + */ +const formatPrice = (price) => { + if (!price || isNaN(price)) return '-'; + return price.toFixed(2); +}; + +/** + * 格式化涨跌幅 + */ +const formatChangePct = (pct) => { + if (!pct || isNaN(pct)) return '0.00%'; + const sign = pct > 0 ? '+' : ''; + return `${sign}${pct.toFixed(2)}%`; +}; + +/** + * 格式化涨跌额 + */ +const formatChange = (change) => { + if (!change || isNaN(change)) return '-'; + const sign = change > 0 ? '+' : ''; + return `${sign}${change.toFixed(2)}`; +}; + +/** + * 格式化成交额 + */ +const formatAmount = (amount) => { + if (!amount || isNaN(amount)) return '-'; + if (amount >= 100000000) { + return `${(amount / 100000000).toFixed(2)}亿`; + } + if (amount >= 10000) { + return `${(amount / 10000).toFixed(0)}万`; + } + return amount.toFixed(0); +}; + +/** + * QuoteTile 组件 + * @param {Object} props + * @param {string} props.code - 证券代码 + * @param {string} props.name - 证券名称 + * @param {Object} props.quote - 实时行情数据 + * @param {boolean} props.isIndex - 是否为指数 + * @param {Function} props.onRemove - 移除回调 + */ +const QuoteTile = ({ + code, + name, + quote = {}, + isIndex = false, + onRemove, +}) => { + const navigate = useNavigate(); + const [expanded, setExpanded] = useState(true); + + // 颜色主题 + const cardBg = useColorModeValue('white', '#1a1a1a'); + const borderColor = useColorModeValue('gray.200', '#333'); + const hoverBorderColor = useColorModeValue('purple.300', '#666'); + const textColor = useColorModeValue('gray.800', 'white'); + const subTextColor = useColorModeValue('gray.500', 'gray.400'); + + // 涨跌色 + const { price, prevClose, change, changePct, amount } = quote; + const priceColor = useColorModeValue( + !prevClose || price === prevClose ? 'gray.800' : + price > prevClose ? 'red.500' : 'green.500', + !prevClose || price === prevClose ? 'gray.200' : + price > prevClose ? 'red.400' : 'green.400' + ); + + // 涨跌幅背景色 + const changeBgColor = useColorModeValue( + !changePct || changePct === 0 ? 'gray.100' : + changePct > 0 ? 'red.100' : 'green.100', + !changePct || changePct === 0 ? 'gray.700' : + changePct > 0 ? 'rgba(239, 68, 68, 0.2)' : 'rgba(34, 197, 94, 0.2)' + ); + + // 跳转到详情页 + const handleNavigate = () => { + if (isIndex) { + // 指数暂无详情页 + return; + } + navigate(`/company?scode=${code}`); + }; + + return ( + + {/* 头部 */} + setExpanded(!expanded)} + > + {/* 名称和代码 */} + + + { + e.stopPropagation(); + handleNavigate(); + }} + > + {name || code} + + {isIndex && ( + + 指数 + + )} + + + {code} + + + + {/* 价格信息 */} + + + {formatPrice(price)} + + + + {formatChangePct(changePct)} + + + {formatChange(change)} + + + + + {/* 操作按钮 */} + + : } + size="xs" + variant="ghost" + aria-label={expanded ? '收起' : '展开'} + onClick={(e) => { + e.stopPropagation(); + setExpanded(!expanded); + }} + /> + + } + size="xs" + variant="ghost" + colorScheme="red" + aria-label="移除" + onClick={(e) => { + e.stopPropagation(); + onRemove?.(code); + }} + /> + + + + + {/* 可折叠内容 */} + + + {/* 统计信息 */} + + + 昨收: + {formatPrice(prevClose)} + + + 今开: + {formatPrice(quote.open)} + + + 成交额: + {formatAmount(amount)} + + + + {/* 分时图 */} + + + + + {/* 盘口（指数没有盘口） */} + {!isIndex && ( + + + 盘口 {quote.bidPrices?.length > 5 ? '(10档)' : '(5档)'} + + + + )} + + + + ); +}; + +export default QuoteTile; diff --git a/src/views/StockOverview/components/FlexScreen/components/index.js b/src/views/StockOverview/components/FlexScreen/components/index.js new file mode 100644 index 00000000..46c2c9f5 --- /dev/null +++ b/src/views/StockOverview/components/FlexScreen/components/index.js @@ -0,0 +1,3 @@ +export { default as MiniTimelineChart } from './MiniTimelineChart'; +export { default as OrderBookPanel } from './OrderBookPanel'; +export { default as QuoteTile } from './QuoteTile'; diff --git a/src/views/StockOverview/components/FlexScreen/hooks/index.js b/src/views/StockOverview/components/FlexScreen/hooks/index.js new file mode 100644 index 00000000..492690a3 --- /dev/null +++ b/src/views/StockOverview/components/FlexScreen/hooks/index.js @@ -0,0 +1 @@ +export { useRealtimeQuote } from './useRealtimeQuote'; diff --git a/src/views/StockOverview/components/FlexScreen/hooks/useRealtimeQuote.js b/src/views/StockOverview/components/FlexScreen/hooks/useRealtimeQuote.js new file mode 100644 index 00000000..476563ed --- /dev/null +++ b/src/views/StockOverview/components/FlexScreen/hooks/useRealtimeQuote.js @@ -0,0 +1,692 @@ +/** + * 实时行情 Hook + * 管理上交所和深交所 WebSocket 连接，获取实时行情数据 + * + * 上交所 (SSE): ws://49.232.185.254:8765 - 需主动订阅，提供五档行情 + * 深交所 (SZSE): ws://222.128.1.157:8765 - 自动推送，提供十档行情 + * + * 深交所支持的数据类型 (category): + * - stock (300111): 股票快照，含10档买卖盘 + * - bond (300211): 债券快照 + * - afterhours_block (300611): 盘后定价大宗交易 + * - afterhours_trading (303711): 盘后定价交易 + * - hk_stock (306311): 港股快照（深港通） + * - index (309011): 指数快照 + * - volume_stats (309111): 成交量统计 + * - fund_nav (309211): 基金净值 + */ +import { useState, useEffect, useRef, useCallback } from 'react'; +import { logger } from '@utils/logger'; + +// WebSocket 地址配置 +const WS_CONFIG = { + SSE: 'ws://49.232.185.254:8765', // 上交所 + SZSE: 'ws://222.128.1.157:8765', // 深交所 +}; + +// 心跳间隔 (ms) +const HEARTBEAT_INTERVAL = 30000; + +// 重连间隔 (ms) +const RECONNECT_INTERVAL = 3000; + +/** + * 判断证券代码属于哪个交易所 + * @param {string} code - 证券代码（可带或不带后缀） + * @returns {'SSE'|'SZSE'} 交易所标识 + */ +const getExchange = (code) => { + const baseCode = code.split('.')[0]; + + // 6开头为上海股票 + if (baseCode.startsWith('6')) { + return 'SSE'; + } + + // 000开头的6位数可能是上证指数或深圳股票 + if (baseCode.startsWith('000') && baseCode.length === 6) { + // 000001-000999 是上证指数范围，但 000001 也是平安银行 + // 这里需要更精确的判断，暂时把 000 开头当深圳 + return 'SZSE'; + } + + // 399开头是深证指数 + if (baseCode.startsWith('399')) { + return 'SZSE'; + } + + // 0、3开头是深圳股票 + if (baseCode.startsWith('0') || baseCode.startsWith('3')) { + return 'SZSE'; + } + + // 5开头是上海 ETF + if (baseCode.startsWith('5')) { + return 'SSE'; + } + + // 1开头是深圳 ETF/债券 + if (baseCode.startsWith('1')) { + return 'SZSE'; + } + + // 默认上海 + return 'SSE'; +}; + +/** + * 标准化证券代码为无后缀格式 + */ +const normalizeCode = (code) => { + return code.split('.')[0]; +}; + +/** + * 从深交所 bids/asks 数组提取价格和量数组 + * @param {Array} orderBook - [{price, volume}, ...] + * @returns {{ prices: number[], volumes: number[] }} + */ +const extractOrderBook = (orderBook) => { + if (!orderBook || !Array.isArray(orderBook)) { + return { prices: [], volumes: [] }; + } + const prices = orderBook.map(item => item.price || 0); + const volumes = orderBook.map(item => item.volume || 0); + return { prices, volumes }; +}; + +/** + * 实时行情 Hook + * @param {string[]} codes - 订阅的证券代码列表 + * @returns {Object} { quotes, connected, subscribe, unsubscribe } + */ +export const useRealtimeQuote = (codes = []) => { + // 行情数据 { [code]: QuoteData } + const [quotes, setQuotes] = useState({}); + // 连接状态 { SSE: boolean, SZSE: boolean } + const [connected, setConnected] = useState({ SSE: false, SZSE: false }); + + // WebSocket 实例引用 + const wsRefs = useRef({ SSE: null, SZSE: null }); + // 心跳定时器 + const heartbeatRefs = useRef({ SSE: null, SZSE: null }); + // 重连定时器 + const reconnectRefs = useRef({ SSE: null, SZSE: null }); + // 当前订阅的代码（按交易所分组） + const subscribedCodes = useRef({ SSE: new Set(), SZSE: new Set() }); + + /** + * 创建 WebSocket 连接 + */ + const createConnection = useCallback((exchange) => { + // 清理现有连接 + if (wsRefs.current[exchange]) { + wsRefs.current[exchange].close(); + } + + const ws = new WebSocket(WS_CONFIG[exchange]); + wsRefs.current[exchange] = ws; + + ws.onopen = () => { + logger.info('FlexScreen', `${exchange} WebSocket 已连接`); + setConnected(prev => ({ ...prev, [exchange]: true })); + + // 上交所需要主动订阅 + if (exchange === 'SSE') { + const codes = Array.from(subscribedCodes.current.SSE); + if (codes.length > 0) { + ws.send(JSON.stringify({ + action: 'subscribe', + channels: ['stock', 'index'], + codes: codes, + })); + } + } + + // 启动心跳 + startHeartbeat(exchange); + }; + + ws.onmessage = (event) => { + try { + const msg = JSON.parse(event.data); + handleMessage(exchange, msg); + } catch (e) { + logger.warn('FlexScreen', `${exchange} 消息解析失败`, e); + } + }; + + ws.onerror = (error) => { + logger.error('FlexScreen', `${exchange} WebSocket 错误`, error); + }; + + ws.onclose = () => { + logger.info('FlexScreen', `${exchange} WebSocket 断开`); + setConnected(prev => ({ ...prev, [exchange]: false })); + stopHeartbeat(exchange); + + // 自动重连 + scheduleReconnect(exchange); + }; + }, []); + + /** + * 处理 WebSocket 消息 + */ + const handleMessage = useCallback((exchange, msg) => { + // 处理 pong + if (msg.type === 'pong') { + return; + } + + if (exchange === 'SSE') { + // 上交所消息格式 + if (msg.type === 'stock' || msg.type === 'index') { + const data = msg.data || {}; + setQuotes(prev => { + const updated = { ...prev }; + Object.entries(data).forEach(([code, quote]) => { + // 只更新订阅的代码 + if (subscribedCodes.current.SSE.has(code)) { + updated[code] = { + code: quote.security_id, + name: quote.security_name, + price: quote.last_price, + prevClose: quote.prev_close, + open: quote.open_price, + high: quote.high_price, + low: quote.low_price, + volume: quote.volume, + amount: quote.amount, + change: quote.last_price - quote.prev_close, + changePct: quote.prev_close ? ((quote.last_price - quote.prev_close) / quote.prev_close * 100) : 0, + bidPrices: quote.bid_prices || [], + bidVolumes: quote.bid_volumes || [], + askPrices: quote.ask_prices || [], + askVolumes: quote.ask_volumes || [], + updateTime: quote.trade_time, + exchange: 'SSE', + }; + } + }); + return updated; + }); + } + } else if (exchange === 'SZSE') { + // 深交所消息格式（更新后的 API） + if (msg.type === 'realtime') { + const { category, data } = msg; + const code = data.security_id; + + // 只更新订阅的代码 + if (!subscribedCodes.current.SZSE.has(code)) { + return; + } + + if (category === 'stock') { + // 股票行情 - 含 10 档买卖盘 + const { prices: bidPrices, volumes: bidVolumes } = extractOrderBook(data.bids); + const { prices: askPrices, volumes: askVolumes } = extractOrderBook(data.asks); + + setQuotes(prev => ({ + ...prev, + [code]: { + code: code, + name: prev[code]?.name || '', + price: data.last_px, + prevClose: data.prev_close, + open: data.open_px, + high: data.high_px, + low: data.low_px, + volume: data.volume, + amount: data.amount, + numTrades: data.num_trades, + upperLimit: data.upper_limit, // 涨停价 + lowerLimit: data.lower_limit, // 跌停价 + change: data.last_px - data.prev_close, + changePct: data.prev_close ? ((data.last_px - data.prev_close) / data.prev_close * 100) : 0, + bidPrices, + bidVolumes, + askPrices, + askVolumes, + tradingPhase: data.trading_phase, + updateTime: msg.timestamp, + exchange: 'SZSE', + }, + })); + } else if (category === 'index') { + // 指数行情 + setQuotes(prev => ({ + ...prev, + [code]: { + code: code, + name: prev[code]?.name || '', + price: data.current_index, + prevClose: data.prev_close, + open: data.open_index, + high: data.high_index, + low: data.low_index, + close: data.close_index, + volume: data.volume, + amount: data.amount, + numTrades: data.num_trades, + change: data.current_index - data.prev_close, + changePct: data.prev_close ? ((data.current_index - data.prev_close) / data.prev_close * 100) : 0, + bidPrices: [], + bidVolumes: [], + askPrices: [], + askVolumes: [], + tradingPhase: data.trading_phase, + updateTime: msg.timestamp, + exchange: 'SZSE', + }, + })); + } else if (category === 'bond') { + // 债券行情 + setQuotes(prev => ({ + ...prev, + [code]: { + code: code, + name: prev[code]?.name || '', + price: data.last_px, + prevClose: data.prev_close, + open: data.open_px, + high: data.high_px, + low: data.low_px, + volume: data.volume, + amount: data.amount, + numTrades: data.num_trades, + weightedAvgPx: data.weighted_avg_px, + change: data.last_px - data.prev_close, + changePct: data.prev_close ? ((data.last_px - data.prev_close) / data.prev_close * 100) : 0, + bidPrices: [], + bidVolumes: [], + askPrices: [], + askVolumes: [], + tradingPhase: data.trading_phase, + updateTime: msg.timestamp, + exchange: 'SZSE', + isBond: true, + }, + })); + } else if (category === 'hk_stock') { + // 港股行情（深港通） + const { prices: bidPrices, volumes: bidVolumes } = extractOrderBook(data.bids); + const { prices: askPrices, volumes: askVolumes } = extractOrderBook(data.asks); + + setQuotes(prev => ({ + ...prev, + [code]: { + code: code, + name: prev[code]?.name || '', + price: data.last_px, + prevClose: data.prev_close, + open: data.open_px, + high: data.high_px, + low: data.low_px, + volume: data.volume, + amount: data.amount, + numTrades: data.num_trades, + nominalPx: data.nominal_px, // 按盘价 + referencePx: data.reference_px, // 参考价 + change: data.last_px - data.prev_close, + changePct: data.prev_close ? ((data.last_px - data.prev_close) / data.prev_close * 100) : 0, + bidPrices, + bidVolumes, + askPrices, + askVolumes, + tradingPhase: data.trading_phase, + updateTime: msg.timestamp, + exchange: 'SZSE', + isHK: true, + }, + })); + } else if (category === 'afterhours_block' || category === 'afterhours_trading') { + // 盘后交易 + setQuotes(prev => ({ + ...prev, + [code]: { + ...prev[code], + afterhours: { + bidPx: data.bid_px, + bidSize: data.bid_size, + offerPx: data.offer_px, + offerSize: data.offer_size, + volume: data.volume, + amount: data.amount, + numTrades: data.num_trades, + }, + updateTime: msg.timestamp, + }, + })); + } + // fund_nav 和 volume_stats 暂不处理 + } else if (msg.type === 'snapshot') { + // 深交所初始快照 + const { stocks = [], indexes = [], bonds = [] } = msg.data || {}; + setQuotes(prev => { + const updated = { ...prev }; + + stocks.forEach(s => { + if (subscribedCodes.current.SZSE.has(s.security_id)) { + const { prices: bidPrices, volumes: bidVolumes } = extractOrderBook(s.bids); + const { prices: askPrices, volumes: askVolumes } = extractOrderBook(s.asks); + + updated[s.security_id] = { + code: s.security_id, + name: s.security_name || '', + price: s.last_px, + prevClose: s.prev_close, + open: s.open_px, + high: s.high_px, + low: s.low_px, + volume: s.volume, + amount: s.amount, + numTrades: s.num_trades, + upperLimit: s.upper_limit, + lowerLimit: s.lower_limit, + change: s.last_px - s.prev_close, + changePct: s.prev_close ? ((s.last_px - s.prev_close) / s.prev_close * 100) : 0, + bidPrices, + bidVolumes, + askPrices, + askVolumes, + exchange: 'SZSE', + }; + } + }); + + indexes.forEach(i => { + if (subscribedCodes.current.SZSE.has(i.security_id)) { + updated[i.security_id] = { + code: i.security_id, + name: i.security_name || '', + price: i.current_index, + prevClose: i.prev_close, + open: i.open_index, + high: i.high_index, + low: i.low_index, + volume: i.volume, + amount: i.amount, + numTrades: i.num_trades, + change: i.current_index - i.prev_close, + changePct: i.prev_close ? ((i.current_index - i.prev_close) / i.prev_close * 100) : 0, + bidPrices: [], + bidVolumes: [], + askPrices: [], + askVolumes: [], + exchange: 'SZSE', + }; + } + }); + + bonds.forEach(b => { + if (subscribedCodes.current.SZSE.has(b.security_id)) { + updated[b.security_id] = { + code: b.security_id, + name: b.security_name || '', + price: b.last_px, + prevClose: b.prev_close, + open: b.open_px, + high: b.high_px, + low: b.low_px, + volume: b.volume, + amount: b.amount, + change: b.last_px - b.prev_close, + changePct: b.prev_close ? ((b.last_px - b.prev_close) / b.prev_close * 100) : 0, + bidPrices: [], + bidVolumes: [], + askPrices: [], + askVolumes: [], + exchange: 'SZSE', + isBond: true, + }; + } + }); + + return updated; + }); + } + } + }, []); + + /** + * 启动心跳 + */ + const startHeartbeat = useCallback((exchange) => { + stopHeartbeat(exchange); + heartbeatRefs.current[exchange] = setInterval(() => { + const ws = wsRefs.current[exchange]; + if (ws && ws.readyState === WebSocket.OPEN) { + if (exchange === 'SSE') { + ws.send(JSON.stringify({ action: 'ping' })); + } else { + ws.send(JSON.stringify({ type: 'ping' })); + } + } + }, HEARTBEAT_INTERVAL); + }, []); + + /** + * 停止心跳 + */ + const stopHeartbeat = useCallback((exchange) => { + if (heartbeatRefs.current[exchange]) { + clearInterval(heartbeatRefs.current[exchange]); + heartbeatRefs.current[exchange] = null; + } + }, []); + + /** + * 安排重连 + */ + const scheduleReconnect = useCallback((exchange) => { + if (reconnectRefs.current[exchange]) { + return; // 已有重连计划 + } + + reconnectRefs.current[exchange] = setTimeout(() => { + reconnectRefs.current[exchange] = null; + // 只有还有订阅的代码才重连 + if (subscribedCodes.current[exchange].size > 0) { + createConnection(exchange); + } + }, RECONNECT_INTERVAL); + }, [createConnection]); + + /** + * 订阅证券 + */ + const subscribe = useCallback((code) => { + const baseCode = normalizeCode(code); + const exchange = getExchange(code); + + // 添加到订阅列表 + subscribedCodes.current[exchange].add(baseCode); + + // 如果连接已建立，发送订阅消息（仅上交所需要） + const ws = wsRefs.current[exchange]; + if (exchange === 'SSE' && ws && ws.readyState === WebSocket.OPEN) { + ws.send(JSON.stringify({ + action: 'subscribe', + channels: ['stock', 'index'], + codes: [baseCode], + })); + } + + // 如果连接未建立，创建连接 + if (!ws || ws.readyState !== WebSocket.OPEN) { + createConnection(exchange); + } + }, [createConnection]); + + /** + * 取消订阅 + */ + const unsubscribe = useCallback((code) => { + const baseCode = normalizeCode(code); + const exchange = getExchange(code); + + // 从订阅列表移除 + subscribedCodes.current[exchange].delete(baseCode); + + // 从 quotes 中移除 + setQuotes(prev => { + const updated = { ...prev }; + delete updated[baseCode]; + return updated; + }); + + // 如果该交易所没有订阅了，关闭连接 + if (subscribedCodes.current[exchange].size === 0) { + const ws = wsRefs.current[exchange]; + if (ws) { + ws.close(); + wsRefs.current[exchange] = null; + } + } + }, []); + + /** + * 初始化订阅 + */ + useEffect(() => { + if (!codes || codes.length === 0) { + return; + } + + // 按交易所分组 + const sseCodesSet = new Set(); + const szseCodesSet = new Set(); + + codes.forEach(code => { + const baseCode = normalizeCode(code); + const exchange = getExchange(code); + if (exchange === 'SSE') { + sseCodesSet.add(baseCode); + } else { + szseCodesSet.add(baseCode); + } + }); + + // 更新订阅列表 + subscribedCodes.current.SSE = sseCodesSet; + subscribedCodes.current.SZSE = szseCodesSet; + + // 建立连接 + if (sseCodesSet.size > 0) { + createConnection('SSE'); + } + if (szseCodesSet.size > 0) { + createConnection('SZSE'); + } + + // 清理 + return () => { + ['SSE', 'SZSE'].forEach(exchange => { + stopHeartbeat(exchange); + if (reconnectRefs.current[exchange]) { + clearTimeout(reconnectRefs.current[exchange]); + } + const ws = wsRefs.current[exchange]; + if (ws) { + ws.close(); + } + }); + }; + }, []); // 只在挂载时执行 + + /** + * 处理 codes 变化 + */ + useEffect(() => { + if (!codes) return; + + // 计算新的订阅列表 + const newSseCodes = new Set(); + const newSzseCodes = new Set(); + + codes.forEach(code => { + const baseCode = normalizeCode(code); + const exchange = getExchange(code); + if (exchange === 'SSE') { + newSseCodes.add(baseCode); + } else { + newSzseCodes.add(baseCode); + } + }); + + // 找出需要新增和删除的代码 + const oldSseCodes = subscribedCodes.current.SSE; + const oldSzseCodes = subscribedCodes.current.SZSE; + + // 更新上交所订阅 + const sseToAdd = [...newSseCodes].filter(c => !oldSseCodes.has(c)); + const sseToRemove = [...oldSseCodes].filter(c => !newSseCodes.has(c)); + + if (sseToAdd.length > 0 || sseToRemove.length > 0) { + subscribedCodes.current.SSE = newSseCodes; + + const ws = wsRefs.current.SSE; + if (ws && ws.readyState === WebSocket.OPEN && sseToAdd.length > 0) { + ws.send(JSON.stringify({ + action: 'subscribe', + channels: ['stock', 'index'], + codes: sseToAdd, + })); + } + + // 如果新增了代码但连接未建立 + if (sseToAdd.length > 0 && (!ws || ws.readyState !== WebSocket.OPEN)) { + createConnection('SSE'); + } + + // 如果没有订阅了，关闭连接 + if (newSseCodes.size === 0 && ws) { + ws.close(); + wsRefs.current.SSE = null; + } + } + + // 更新深交所订阅 + const szseToAdd = [...newSzseCodes].filter(c => !oldSzseCodes.has(c)); + const szseToRemove = [...oldSzseCodes].filter(c => !newSzseCodes.has(c)); + + if (szseToAdd.length > 0 || szseToRemove.length > 0) { + subscribedCodes.current.SZSE = newSzseCodes; + + // 深交所是自动推送，只需要管理连接 + const ws = wsRefs.current.SZSE; + + if (szseToAdd.length > 0 && (!ws || ws.readyState !== WebSocket.OPEN)) { + createConnection('SZSE'); + } + + if (newSzseCodes.size === 0 && ws) { + ws.close(); + wsRefs.current.SZSE = null; + } + } + + // 清理已取消订阅的 quotes + const removedCodes = [...sseToRemove, ...szseToRemove]; + if (removedCodes.length > 0) { + setQuotes(prev => { + const updated = { ...prev }; + removedCodes.forEach(code => { + delete updated[code]; + }); + return updated; + }); + } + }, [codes, createConnection]); + + return { + quotes, + connected, + subscribe, + unsubscribe, + }; +}; + +export default useRealtimeQuote; diff --git a/src/views/StockOverview/components/FlexScreen/index.js b/src/views/StockOverview/components/FlexScreen/index.js new file mode 100644 index 00000000..aeace485 --- /dev/null +++ b/src/views/StockOverview/components/FlexScreen/index.js @@ -0,0 +1,463 @@ +/** + * 灵活屏组件 + * 用户可自定义添加关注的指数/个股，实时显示行情 + * + * 功能： + * 1. 添加/删除自选证券 + * 2. 显示实时行情（通过 WebSocket） + * 3. 显示分时走势（结合 ClickHouse 历史数据） + * 4. 显示五档盘口（上交所完整五档，深交所买一卖一） + * 5. 本地存储自选列表 + */ +import React, { useState, useEffect, useCallback, useMemo } from 'react'; +import { + Box, + Card, + CardBody, + VStack, + HStack, + Heading, + Text, + Input, + InputGroup, + InputLeftElement, + InputRightElement, + IconButton, + Button, + SimpleGrid, + Flex, + Spacer, + Icon, + useColorModeValue, + useToast, + Badge, + Tooltip, + Collapse, + List, + ListItem, + Spinner, + Center, + Menu, + MenuButton, + MenuList, + MenuItem, + Divider, + Tag, + TagLabel, +} from '@chakra-ui/react'; +import { + SearchIcon, + CloseIcon, + AddIcon, + ChevronDownIcon, + ChevronUpIcon, + SettingsIcon, +} from '@chakra-ui/icons'; +import { FaDesktop, FaPlus, FaTrash, FaSync, FaWifi, FaExclamationCircle } from 'react-icons/fa'; + +import { useRealtimeQuote } from './hooks'; +import { QuoteTile } from './components'; +import { logger } from '@utils/logger'; + +// 本地存储 key +const STORAGE_KEY = 'flexscreen_watchlist'; + +// 默认自选列表 +const DEFAULT_WATCHLIST = [ + { code: '000001', name: '上证指数', isIndex: true }, + { code: '399001', name: '深证成指', isIndex: true }, + { code: '399006', name: '创业板指', isIndex: true }, +]; + +// 热门推荐 +const HOT_RECOMMENDATIONS = [ + { code: '000001', name: '上证指数', isIndex: true }, + { code: '399001', name: '深证成指', isIndex: true }, + { code: '399006', name: '创业板指', isIndex: true }, + { code: '399300', name: '沪深300', isIndex: true }, + { code: '600519', name: '贵州茅台', isIndex: false }, + { code: '000858', name: '五粮液', isIndex: false }, + { code: '300750', name: '宁德时代', isIndex: false }, + { code: '002594', name: '比亚迪', isIndex: false }, +]; + +/** + * FlexScreen 组件 + */ +const FlexScreen = () => { + const toast = useToast(); + + // 自选列表 + const [watchlist, setWatchlist] = useState([]); + // 搜索状态 + const [searchQuery, setSearchQuery] = useState(''); + const [searchResults, setSearchResults] = useState([]); + const [isSearching, setIsSearching] = useState(false); + const [showResults, setShowResults] = useState(false); + // 面板状态 + const [isCollapsed, setIsCollapsed] = useState(false); + + // 颜色主题 + const cardBg = useColorModeValue('white', '#1a1a1a'); + const borderColor = useColorModeValue('gray.200', '#333'); + const textColor = useColorModeValue('gray.800', 'white'); + const subTextColor = useColorModeValue('gray.600', 'gray.400'); + const searchBg = useColorModeValue('gray.50', '#2a2a2a'); + const hoverBg = useColorModeValue('gray.100', '#333'); + + // 获取订阅的证券代码列表 + const subscribedCodes = useMemo(() => { + return watchlist.map(item => item.code); + }, [watchlist]); + + // WebSocket 实时行情 + const { quotes, connected } = useRealtimeQuote(subscribedCodes); + + // 从本地存储加载自选列表 + useEffect(() => { + try { + const saved = localStorage.getItem(STORAGE_KEY); + if (saved) { + const parsed = JSON.parse(saved); + if (Array.isArray(parsed) && parsed.length > 0) { + setWatchlist(parsed); + return; + } + } + } catch (e) { + logger.warn('FlexScreen', '加载自选列表失败', e); + } + // 使用默认列表 + setWatchlist(DEFAULT_WATCHLIST); + }, []); + + // 保存自选列表到本地存储 + useEffect(() => { + if (watchlist.length > 0) { + try { + localStorage.setItem(STORAGE_KEY, JSON.stringify(watchlist)); + } catch (e) { + logger.warn('FlexScreen', '保存自选列表失败', e); + } + } + }, [watchlist]); + + // 搜索证券 + const searchSecurities = useCallback(async (query) => { + if (!query.trim()) { + setSearchResults([]); + setShowResults(false); + return; + } + + setIsSearching(true); + try { + const response = await fetch(`/api/stocks/search?q=${encodeURIComponent(query)}&limit=10`); + const data = await response.json(); + + if (data.success) { + setSearchResults(data.data || []); + setShowResults(true); + } else { + setSearchResults([]); + } + } catch (e) { + logger.error('FlexScreen', '搜索失败', e); + setSearchResults([]); + } finally { + setIsSearching(false); + } + }, []); + + // 防抖搜索 + useEffect(() => { + const timer = setTimeout(() => { + searchSecurities(searchQuery); + }, 300); + return () => clearTimeout(timer); + }, [searchQuery, searchSecurities]); + + // 添加证券 + const addSecurity = useCallback((security) => { + const code = security.stock_code || security.code; + const name = security.stock_name || security.name; + const isIndex = security.isIndex || code.startsWith('000') || code.startsWith('399'); + + // 检查是否已存在 + if (watchlist.some(item => item.code === code)) { + toast({ + title: '已在自选列表中', + status: 'info', + duration: 2000, + isClosable: true, + }); + return; + } + + // 添加到列表 + setWatchlist(prev => [...prev, { code, name, isIndex }]); + + toast({ + title: `已添加 ${name}`, + status: 'success', + duration: 2000, + isClosable: true, + }); + + // 清空搜索 + setSearchQuery(''); + setShowResults(false); + }, [watchlist, toast]); + + // 移除证券 + const removeSecurity = useCallback((code) => { + setWatchlist(prev => prev.filter(item => item.code !== code)); + }, []); + + // 清空自选列表 + const clearWatchlist = useCallback(() => { + setWatchlist([]); + localStorage.removeItem(STORAGE_KEY); + toast({ + title: '已清空自选列表', + status: 'info', + duration: 2000, + isClosable: true, + }); + }, [toast]); + + // 重置为默认列表 + const resetWatchlist = useCallback(() => { + setWatchlist(DEFAULT_WATCHLIST); + toast({ + title: '已重置为默认列表', + status: 'success', + duration: 2000, + isClosable: true, + }); + }, [toast]); + + // 连接状态指示 + const isAnyConnected = connected.SSE || connected.SZSE; + const connectionStatus = useMemo(() => { + if (connected.SSE && connected.SZSE) { + return { color: 'green', text: '上交所/深交所 已连接' }; + } + if (connected.SSE) { + return { color: 'yellow', text: '上交所 已连接' }; + } + if (connected.SZSE) { + return { color: 'yellow', text: '深交所 已连接' }; + } + return { color: 'red', text: '未连接' }; + }, [connected]); + + return ( + + + {/* 头部 */} + + + + + 灵活屏 + + + + + {isAnyConnected ? '实时' : '离线'} + + + + + + {/* 操作菜单 */} + + } + size="sm" + variant="ghost" + aria-label="设置" + /> + + } onClick={resetWatchlist}> + 重置为默认 + + } onClick={clearWatchlist} color="red.500"> + 清空列表 + + + + {/* 折叠按钮 */} + : } + size="sm" + variant="ghost" + onClick={() => setIsCollapsed(!isCollapsed)} + aria-label={isCollapsed ? '展开' : '收起'} + /> + + + + {/* 可折叠内容 */} + + {/* 搜索框 */} + + + + + + setSearchQuery(e.target.value)} + bg={searchBg} + borderRadius="lg" + _focus={{ + borderColor: 'purple.400', + boxShadow: '0 0 0 1px var(--chakra-colors-purple-400)', + }} + /> + {searchQuery && ( + + } + variant="ghost" + onClick={() => { + setSearchQuery(''); + setShowResults(false); + }} + aria-label="清空" + /> + + )} + + + {/* 搜索结果下拉 */} + + + {isSearching ? ( +
+ +
+ ) : searchResults.length > 0 ? ( + + {searchResults.map((stock, index) => ( + addSecurity(stock)} + borderBottomWidth={index < searchResults.length - 1 ? '1px' : '0'} + borderColor={borderColor} + > + + + + {stock.stock_name} + + + {stock.stock_code} + + + } + size="xs" + colorScheme="purple" + variant="ghost" + aria-label="添加" + /> + + + ))} + + ) : ( +
+ + 未找到相关证券 + +
+ )} + + + + + {/* 快捷添加 */} + {watchlist.length === 0 && ( + + + 热门推荐（点击添加） + + + {HOT_RECOMMENDATIONS.map((item) => ( + addSecurity(item)} + > + {item.name} + + ))} + + + )} + + {/* 自选列表 */} + {watchlist.length > 0 ? ( + + {watchlist.map((item) => ( + + ))} + + ) : ( +
+ + + + 自选列表为空，请搜索添加证券 + + +
+ )} + + + + ); +}; + +export default FlexScreen; diff --git a/src/views/StockOverview/components/HotspotOverview/components/ConceptAlertList.js b/src/views/StockOverview/components/HotspotOverview/components/ConceptAlertList.js index 9c846122..1c375bce 100644 --- a/src/views/StockOverview/components/HotspotOverview/components/ConceptAlertList.js +++ b/src/views/StockOverview/components/HotspotOverview/components/ConceptAlertList.js @@ -18,6 +18,69 @@ import { import { FaBolt, FaArrowUp, FaArrowDown, FaChartLine, FaFire, FaVolumeUp } from 'react-icons/fa'; import { getAlertTypeLabel, formatScore, getScoreColor } from '../utils/chartHelpers'; +/** + * Z-Score 指示器组件 + */ +const ZScoreIndicator = ({ value, label, tooltip }) => { + if (value === null || value === undefined) return null; + + // Z-Score 颜色：越大越红，越小越绿 + const getZScoreColor = (z) => { + const absZ = Math.abs(z); + if (absZ >= 3) return z > 0 ? 'red.600' : 'green.600'; + if (absZ >= 2) return z > 0 ? 'red.500' : 'green.500'; + if (absZ >= 1) return z > 0 ? 'orange.400' : 'teal.400'; + return 'gray.400'; + }; + + // Z-Score 强度条宽度（最大 5σ） + const barWidth = Math.min(Math.abs(value) / 5 * 100, 100); + + return ( + + + {label} + + = 0 ? '50%' : `${50 - barWidth / 2}%`} + w={`${barWidth / 2}%`} + h="100%" + bg={getZScoreColor(value)} + borderRadius="full" + /> + + + {value >= 0 ? '+' : ''}{value.toFixed(1)} + + + + ); +}; + +/** + * 持续确认率指示器 + */ +const ConfirmRatioIndicator = ({ ratio }) => { + if (ratio === null || ratio === undefined) return null; + + const percent = Math.round(ratio * 100); + const color = percent >= 80 ? 'green' : percent >= 60 ? 'orange' : 'red'; + + return ( + + + + + + + {percent}% + + + + ); +}; + /** * 单个异动项组件 */ @@ -29,6 +92,7 @@ const AlertItem = ({ alert, onClick, isSelected }) => { const isUp = alert.alert_type !== 'surge_down'; const typeColor = isUp ? 'red' : 'green'; + const isV2 = alert.is_v2; // 获取异动类型图标 const getTypeIcon = (type) => { @@ -69,13 +133,30 @@ const AlertItem = ({ alert, onClick, isSelected }) => { {alert.concept_name} + {isV2 && ( + + V2 + + )} {alert.time} {getAlertTypeLabel(alert.alert_type)} + {/* V2: 持续确认率 */} + {isV2 && alert.confirm_ratio !== undefined && ( + + )} + + {/* V2: Z-Score 指标行 */} + {isV2 && (alert.alpha_zscore !== undefined || alert.amt_zscore !== undefined) && ( + + + + + )} {/* 右侧：分数和关键指标 */} @@ -104,12 +185,29 @@ const AlertItem = ({ alert, onClick, isSelected }) => { )} - {/* 涨停数量 */} - {alert.limit_up_count > 0 && ( + {/* V2: 动量指标 */} + {isV2 && alert.momentum_5m !== undefined && Math.abs(alert.momentum_5m) > 0.3 && ( + + 0 ? FaArrowUp : FaArrowDown} + color={alert.momentum_5m > 0 ? 'red.400' : 'green.400'} + boxSize={3} + /> + 0 ? 'red.400' : 'green.400'}> + 动量 {alert.momentum_5m > 0 ? '+' : ''}{alert.momentum_5m.toFixed(2)} + + + )} + + {/* 涨停数量 / 涨停比例 */} + {(alert.limit_up_count > 0 || (alert.limit_up_ratio > 0.05 && isV2)) && ( - 涨停 {alert.limit_up_count} + {alert.limit_up_count > 0 + ? `涨停 ${alert.limit_up_count}` + : `涨停 ${Math.round(alert.limit_up_ratio * 100)}%` + } )} diff --git a/src/views/StockOverview/index.js b/src/views/StockOverview/index.js index a888ec09..dacae01b 100644 --- a/src/views/StockOverview/index.js +++ b/src/views/StockOverview/index.js @@ -54,6 +54,7 @@ import { FaChartLine, FaFire, FaRocket, FaBrain, FaCalendarAlt, FaChevronRight, import ConceptStocksModal from '@components/ConceptStocksModal'; import TradeDatePicker from '@components/TradeDatePicker'; import HotspotOverview from './components/HotspotOverview'; +import FlexScreen from './components/FlexScreen'; import { BsGraphUp, BsLightningFill } from 'react-icons/bs'; import * as echarts from 'echarts'; import { logger } from '../../utils/logger'; @@ -846,6 +847,11 @@ const StockOverview = () => { + {/* 灵活屏 - 实时行情监控 */} + + + + {/* 今日热门概念 */} From 3c1860598802e36bafbf29a1affdcf03f8c4e4f1 Mon Sep 17 00:00:00 2001 From: zzlgreat Date: Wed, 10 Dec 2025 11:19:02 +0800 Subject: [PATCH 10/20] update pay ui --- ...TimelineChart.js => MiniTimelineChart.tsx} | 63 +- .../{OrderBookPanel.js => OrderBookPanel.tsx} | 62 +- .../{QuoteTile.js => QuoteTile.tsx} | 82 ++- .../components/{index.js => index.ts} | 4 + .../components/FlexScreen/hooks/constants.ts | 17 + .../components/FlexScreen/hooks/index.js | 1 - .../components/FlexScreen/hooks/index.ts | 7 + .../FlexScreen/hooks/useRealtimeQuote.js | 692 ------------------ .../FlexScreen/hooks/useRealtimeQuote.ts | 594 +++++++++++++++ .../components/FlexScreen/hooks/utils.ts | 83 +++ .../FlexScreen/{index.js => index.tsx} | 122 +-- .../components/FlexScreen/types.ts | 322 ++++++++ 12 files changed, 1229 insertions(+), 820 deletions(-) rename src/views/StockOverview/components/FlexScreen/components/{MiniTimelineChart.js => MiniTimelineChart.tsx} (80%) rename src/views/StockOverview/components/FlexScreen/components/{OrderBookPanel.js => OrderBookPanel.tsx} (84%) rename src/views/StockOverview/components/FlexScreen/components/{QuoteTile.js => QuoteTile.tsx} (72%) rename src/views/StockOverview/components/FlexScreen/components/{index.js => index.ts} (85%) create mode 100644 src/views/StockOverview/components/FlexScreen/hooks/constants.ts delete mode 100644 src/views/StockOverview/components/FlexScreen/hooks/index.js create mode 100644 src/views/StockOverview/components/FlexScreen/hooks/index.ts delete mode 100644 src/views/StockOverview/components/FlexScreen/hooks/useRealtimeQuote.js create mode 100644 src/views/StockOverview/components/FlexScreen/hooks/useRealtimeQuote.ts create mode 100644 src/views/StockOverview/components/FlexScreen/hooks/utils.ts rename src/views/StockOverview/components/FlexScreen/{index.js => index.tsx} (83%) create mode 100644 src/views/StockOverview/components/FlexScreen/types.ts diff --git a/src/views/StockOverview/components/FlexScreen/components/MiniTimelineChart.js b/src/views/StockOverview/components/FlexScreen/components/MiniTimelineChart.tsx similarity index 80% rename from src/views/StockOverview/components/FlexScreen/components/MiniTimelineChart.js rename to src/views/StockOverview/components/FlexScreen/components/MiniTimelineChart.tsx index 46c15e58..0730b24f 100644 --- a/src/views/StockOverview/components/FlexScreen/components/MiniTimelineChart.js +++ b/src/views/StockOverview/components/FlexScreen/components/MiniTimelineChart.tsx @@ -5,16 +5,19 @@ import React, { useEffect, useRef, useState, useMemo } from 'react'; import { Box, Spinner, Center, Text } from '@chakra-ui/react'; import * as echarts from 'echarts'; +import type { ECharts, EChartsOption } from 'echarts'; + +import type { MiniTimelineChartProps, TimelineDataPoint } from '../types'; /** * 生成交易时间刻度（用于 X 轴） * A股交易时间：9:30-11:30, 13:00-15:00 */ -const generateTimeTicks = () => { - const ticks = []; +const generateTimeTicks = (): string[] => { + const ticks: string[] = []; // 上午 for (let h = 9; h <= 11; h++) { - for (let m = (h === 9 ? 30 : 0); m < 60; m++) { + for (let m = h === 9 ? 30 : 0; m < 60; m++) { if (h === 11 && m > 30) break; ticks.push(`${String(h).padStart(2, '0')}:${String(m).padStart(2, '0')}`); } @@ -31,33 +34,42 @@ const generateTimeTicks = () => { const TIME_TICKS = generateTimeTicks(); +/** API 返回的分钟数据结构 */ +interface MinuteKLineItem { + time?: string; + timestamp?: string; + close?: number; + price?: number; +} + +/** API 响应结构 */ +interface KLineApiResponse { + success?: boolean; + data?: MinuteKLineItem[]; + error?: string; +} + /** * MiniTimelineChart 组件 - * @param {Object} props - * @param {string} props.code - 证券代码 - * @param {boolean} props.isIndex - 是否为指数 - * @param {number} props.prevClose - 昨收价 - * @param {number} props.currentPrice - 当前价（实时） - * @param {number} props.height - 图表高度 */ -const MiniTimelineChart = ({ +const MiniTimelineChart: React.FC = ({ code, isIndex = false, prevClose, currentPrice, height = 120, }) => { - const chartRef = useRef(null); - const chartInstance = useRef(null); - const [timelineData, setTimelineData] = useState([]); + const chartRef = useRef(null); + const chartInstance = useRef(null); + const [timelineData, setTimelineData] = useState([]); const [loading, setLoading] = useState(true); - const [error, setError] = useState(null); + const [error, setError] = useState(null); // 获取分钟数据 useEffect(() => { if (!code) return; - const fetchData = async () => { + const fetchData = async (): Promise => { setLoading(true); setError(null); @@ -67,13 +79,13 @@ const MiniTimelineChart = ({ : `/api/stock/${code}/kline?type=minute`; const response = await fetch(apiPath); - const result = await response.json(); + const result: KLineApiResponse = await response.json(); if (result.success !== false && result.data) { // 格式化数据 - const formatted = result.data.map(item => ({ - time: item.time || item.timestamp, - price: item.close || item.price, + const formatted: TimelineDataPoint[] = result.data.map(item => ({ + time: item.time || item.timestamp || '', + price: item.close || item.price || 0, })); setTimelineData(formatted); } else { @@ -93,10 +105,11 @@ const MiniTimelineChart = ({ const hours = now.getHours(); const minutes = now.getMinutes(); const currentMinutes = hours * 60 + minutes; - const isTrading = (currentMinutes >= 570 && currentMinutes <= 690) || - (currentMinutes >= 780 && currentMinutes <= 900); + const isTrading = + (currentMinutes >= 570 && currentMinutes <= 690) || + (currentMinutes >= 780 && currentMinutes <= 900); - let intervalId; + let intervalId: NodeJS.Timeout | undefined; if (isTrading) { intervalId = setInterval(fetchData, 60000); // 1分钟刷新 } @@ -107,7 +120,7 @@ const MiniTimelineChart = ({ }, [code, isIndex]); // 合并实时价格到数据中 - const chartData = useMemo(() => { + const chartData = useMemo((): TimelineDataPoint[] => { if (!timelineData.length) return []; const data = [...timelineData]; @@ -154,7 +167,7 @@ const MiniTimelineChart = ({ const lastPrice = values[values.length - 1] || baseLine; const isUp = lastPrice >= baseLine; - const option = { + const option: EChartsOption = { grid: { top: 5, right: 5, @@ -236,7 +249,7 @@ const MiniTimelineChart = ({ // 窗口 resize 处理 useEffect(() => { - const handleResize = () => { + const handleResize = (): void => { chartInstance.current?.resize(); }; window.addEventListener('resize', handleResize); diff --git a/src/views/StockOverview/components/FlexScreen/components/OrderBookPanel.js b/src/views/StockOverview/components/FlexScreen/components/OrderBookPanel.tsx similarity index 84% rename from src/views/StockOverview/components/FlexScreen/components/OrderBookPanel.js rename to src/views/StockOverview/components/FlexScreen/components/OrderBookPanel.tsx index a3cf92fc..4da68ca0 100644 --- a/src/views/StockOverview/components/FlexScreen/components/OrderBookPanel.js +++ b/src/views/StockOverview/components/FlexScreen/components/OrderBookPanel.tsx @@ -18,12 +18,18 @@ import { Badge, } from '@chakra-ui/react'; +import type { OrderBookPanelProps } from '../types'; + +/** 格式化价格返回值 */ +interface FormattedPrice { + text: string; + color: string; +} + /** * 格式化成交量 - * @param {number} volume - 成交量（股） - * @returns {string} 格式化后的字符串 */ -const formatVolume = (volume) => { +const formatVolume = (volume: number): string => { if (!volume || volume === 0) return '-'; if (volume >= 10000) { return `${(volume / 10000).toFixed(0)}万`; @@ -36,11 +42,8 @@ const formatVolume = (volume) => { /** * 格式化价格 - * @param {number} price - 价格 - * @param {number} prevClose - 昨收价 - * @returns {Object} { text, color } */ -const formatPrice = (price, prevClose) => { +const formatPrice = (price: number, prevClose?: number): FormattedPrice => { if (!price || price === 0) { return { text: '-', color: 'gray.400' }; } @@ -60,10 +63,29 @@ const formatPrice = (price, prevClose) => { return { text, color: 'gray.600' }; }; +/** OrderRow 组件 Props */ +interface OrderRowProps { + label: string; + price: number; + volume: number; + prevClose?: number; + isBid: boolean; + maxVolume: number; + isLimitPrice: boolean; +} + /** * 单行盘口 */ -const OrderRow = ({ label, price, volume, prevClose, isBid, maxVolume, isLimitPrice }) => { +const OrderRow: React.FC = ({ + label, + price, + volume, + prevClose, + isBid, + maxVolume, + isLimitPrice, +}) => { const bgColor = useColorModeValue( isBid ? 'red.50' : 'green.50', isBid ? 'rgba(239, 68, 68, 0.1)' : 'rgba(34, 197, 94, 0.1)' @@ -129,17 +151,8 @@ const OrderRow = ({ label, price, volume, prevClose, isBid, maxVolume, isLimitPr /** * OrderBookPanel 组件 - * @param {Object} props - * @param {number[]} props.bidPrices - 买档价格（最多10档） - * @param {number[]} props.bidVolumes - 买档量 - * @param {number[]} props.askPrices - 卖档价格（最多10档） - * @param {number[]} props.askVolumes - 卖档量 - * @param {number} props.prevClose - 昨收价 - * @param {number} props.upperLimit - 涨停价 - * @param {number} props.lowerLimit - 跌停价 - * @param {number} props.defaultLevels - 默认显示档数（5 或 10） */ -const OrderBookPanel = ({ +const OrderBookPanel: React.FC = ({ bidPrices = [], bidVolumes = [], askPrices = [], @@ -151,6 +164,7 @@ const OrderBookPanel = ({ }) => { const borderColor = useColorModeValue('gray.200', 'gray.700'); const buttonBg = useColorModeValue('gray.100', 'gray.700'); + const bgColor = useColorModeValue('white', '#1a1a1a'); // 可切换显示的档位数 const maxAvailableLevels = Math.max(bidPrices.length, askPrices.length, 1); @@ -163,11 +177,13 @@ const OrderBookPanel = ({ const maxVolume = allVolumes.length > 0 ? Math.max(...allVolumes) : 0; // 判断是否为涨跌停价 - const isUpperLimit = (price) => upperLimit && Math.abs(price - upperLimit) < 0.001; - const isLowerLimit = (price) => lowerLimit && Math.abs(price - lowerLimit) < 0.001; + const isUpperLimit = (price: number): boolean => + !!upperLimit && Math.abs(price - upperLimit) < 0.001; + const isLowerLimit = (price: number): boolean => + !!lowerLimit && Math.abs(price - lowerLimit) < 0.001; // 卖盘（从卖N到卖1，即价格从高到低） - const askRows = []; + const askRows: React.ReactNode[] = []; for (let i = showLevels - 1; i >= 0; i--) { askRows.push( 昨收 {prevClose.toFixed(2)} diff --git a/src/views/StockOverview/components/FlexScreen/components/QuoteTile.js b/src/views/StockOverview/components/FlexScreen/components/QuoteTile.tsx similarity index 72% rename from src/views/StockOverview/components/FlexScreen/components/QuoteTile.js rename to src/views/StockOverview/components/FlexScreen/components/QuoteTile.tsx index f5b5c389..d5a51be5 100644 --- a/src/views/StockOverview/components/FlexScreen/components/QuoteTile.js +++ b/src/views/StockOverview/components/FlexScreen/components/QuoteTile.tsx @@ -13,18 +13,18 @@ import { useColorModeValue, Collapse, Badge, - Flex, - Spacer, } from '@chakra-ui/react'; -import { CloseIcon, ChevronDownIcon, ChevronUpIcon, ExternalLinkIcon } from '@chakra-ui/icons'; +import { CloseIcon, ChevronDownIcon, ChevronUpIcon } from '@chakra-ui/icons'; import { useNavigate } from 'react-router-dom'; + import MiniTimelineChart from './MiniTimelineChart'; import OrderBookPanel from './OrderBookPanel'; +import type { QuoteTileProps, QuoteData } from '../types'; /** * 格式化价格显示 */ -const formatPrice = (price) => { +const formatPrice = (price?: number): string => { if (!price || isNaN(price)) return '-'; return price.toFixed(2); }; @@ -32,7 +32,7 @@ const formatPrice = (price) => { /** * 格式化涨跌幅 */ -const formatChangePct = (pct) => { +const formatChangePct = (pct?: number): string => { if (!pct || isNaN(pct)) return '0.00%'; const sign = pct > 0 ? '+' : ''; return `${sign}${pct.toFixed(2)}%`; @@ -41,7 +41,7 @@ const formatChangePct = (pct) => { /** * 格式化涨跌额 */ -const formatChange = (change) => { +const formatChange = (change?: number): string => { if (!change || isNaN(change)) return '-'; const sign = change > 0 ? '+' : ''; return `${sign}${change.toFixed(2)}`; @@ -50,7 +50,7 @@ const formatChange = (change) => { /** * 格式化成交额 */ -const formatAmount = (amount) => { +const formatAmount = (amount?: number): string => { if (!amount || isNaN(amount)) return '-'; if (amount >= 100000000) { return `${(amount / 100000000).toFixed(2)}亿`; @@ -63,14 +63,8 @@ const formatAmount = (amount) => { /** * QuoteTile 组件 - * @param {Object} props - * @param {string} props.code - 证券代码 - * @param {string} props.name - 证券名称 - * @param {Object} props.quote - 实时行情数据 - * @param {boolean} props.isIndex - 是否为指数 - * @param {Function} props.onRemove - 移除回调 */ -const QuoteTile = ({ +const QuoteTile: React.FC = ({ code, name, quote = {}, @@ -80,6 +74,9 @@ const QuoteTile = ({ const navigate = useNavigate(); const [expanded, setExpanded] = useState(true); + // 类型断言，确保类型安全 + const quoteData = quote as Partial; + // 颜色主题 const cardBg = useColorModeValue('white', '#1a1a1a'); const borderColor = useColorModeValue('gray.200', '#333'); @@ -88,24 +85,36 @@ const QuoteTile = ({ const subTextColor = useColorModeValue('gray.500', 'gray.400'); // 涨跌色 - const { price, prevClose, change, changePct, amount } = quote; + const { price, prevClose, change, changePct, amount } = quoteData; const priceColor = useColorModeValue( - !prevClose || price === prevClose ? 'gray.800' : - price > prevClose ? 'red.500' : 'green.500', - !prevClose || price === prevClose ? 'gray.200' : - price > prevClose ? 'red.400' : 'green.400' + !prevClose || price === prevClose + ? 'gray.800' + : price && price > prevClose + ? 'red.500' + : 'green.500', + !prevClose || price === prevClose + ? 'gray.200' + : price && price > prevClose + ? 'red.400' + : 'green.400' ); // 涨跌幅背景色 const changeBgColor = useColorModeValue( - !changePct || changePct === 0 ? 'gray.100' : - changePct > 0 ? 'red.100' : 'green.100', - !changePct || changePct === 0 ? 'gray.700' : - changePct > 0 ? 'rgba(239, 68, 68, 0.2)' : 'rgba(34, 197, 94, 0.2)' + !changePct || changePct === 0 + ? 'gray.100' + : changePct > 0 + ? 'red.100' + : 'green.100', + !changePct || changePct === 0 + ? 'gray.700' + : changePct > 0 + ? 'rgba(239, 68, 68, 0.2)' + : 'rgba(34, 197, 94, 0.2)' ); // 跳转到详情页 - const handleNavigate = () => { + const handleNavigate = (): void => { if (isIndex) { // 指数暂无详情页 return; @@ -113,6 +122,15 @@ const QuoteTile = ({ navigate(`/company?scode=${code}`); }; + // 获取盘口数据（带类型安全） + const bidPrices = 'bidPrices' in quoteData ? (quoteData.bidPrices as number[]) : []; + const bidVolumes = 'bidVolumes' in quoteData ? (quoteData.bidVolumes as number[]) : []; + const askPrices = 'askPrices' in quoteData ? (quoteData.askPrices as number[]) : []; + const askVolumes = 'askVolumes' in quoteData ? (quoteData.askVolumes as number[]) : []; + const upperLimit = 'upperLimit' in quoteData ? (quoteData.upperLimit as number | undefined) : undefined; + const lowerLimit = 'lowerLimit' in quoteData ? (quoteData.lowerLimit as number | undefined) : undefined; + const openPrice = 'open' in quoteData ? (quoteData.open as number | undefined) : undefined; + return ( 今开: - {formatPrice(quote.open)} + {formatPrice(openPrice)} 成交额: @@ -248,16 +266,16 @@ const QuoteTile = ({ {!isIndex && ( - 盘口 {quote.bidPrices?.length > 5 ? '(10档)' : '(5档)'} + 盘口 {bidPrices.length > 5 ? '(10档)' : '(5档)'} )} diff --git a/src/views/StockOverview/components/FlexScreen/components/index.js b/src/views/StockOverview/components/FlexScreen/components/index.ts similarity index 85% rename from src/views/StockOverview/components/FlexScreen/components/index.js rename to src/views/StockOverview/components/FlexScreen/components/index.ts index 46c2c9f5..4777c0f1 100644 --- a/src/views/StockOverview/components/FlexScreen/components/index.js +++ b/src/views/StockOverview/components/FlexScreen/components/index.ts @@ -1,3 +1,7 @@ +/** + * 组件导出文件 + */ + export { default as MiniTimelineChart } from './MiniTimelineChart'; export { default as OrderBookPanel } from './OrderBookPanel'; export { default as QuoteTile } from './QuoteTile'; diff --git a/src/views/StockOverview/components/FlexScreen/hooks/constants.ts b/src/views/StockOverview/components/FlexScreen/hooks/constants.ts new file mode 100644 index 00000000..6f1717b3 --- /dev/null +++ b/src/views/StockOverview/components/FlexScreen/hooks/constants.ts @@ -0,0 +1,17 @@ +/** + * WebSocket 配置常量 + */ + +import type { Exchange } from '../types'; + +/** WebSocket 服务地址 */ +export const WS_CONFIG: Record = { + SSE: 'ws://49.232.185.254:8765', // 上交所 + SZSE: 'ws://222.128.1.157:8765', // 深交所 +}; + +/** 心跳间隔 (ms) */ +export const HEARTBEAT_INTERVAL = 30000; + +/** 重连间隔 (ms) */ +export const RECONNECT_INTERVAL = 3000; diff --git a/src/views/StockOverview/components/FlexScreen/hooks/index.js b/src/views/StockOverview/components/FlexScreen/hooks/index.js deleted file mode 100644 index 492690a3..00000000 --- a/src/views/StockOverview/components/FlexScreen/hooks/index.js +++ /dev/null @@ -1 +0,0 @@ -export { useRealtimeQuote } from './useRealtimeQuote'; diff --git a/src/views/StockOverview/components/FlexScreen/hooks/index.ts b/src/views/StockOverview/components/FlexScreen/hooks/index.ts new file mode 100644 index 00000000..ffe3399e --- /dev/null +++ b/src/views/StockOverview/components/FlexScreen/hooks/index.ts @@ -0,0 +1,7 @@ +/** + * Hooks 导出文件 + */ + +export { useRealtimeQuote } from './useRealtimeQuote'; +export * from './constants'; +export * from './utils'; diff --git a/src/views/StockOverview/components/FlexScreen/hooks/useRealtimeQuote.js b/src/views/StockOverview/components/FlexScreen/hooks/useRealtimeQuote.js deleted file mode 100644 index 476563ed..00000000 --- a/src/views/StockOverview/components/FlexScreen/hooks/useRealtimeQuote.js +++ /dev/null @@ -1,692 +0,0 @@ -/** - * 实时行情 Hook - * 管理上交所和深交所 WebSocket 连接，获取实时行情数据 - * - * 上交所 (SSE): ws://49.232.185.254:8765 - 需主动订阅，提供五档行情 - * 深交所 (SZSE): ws://222.128.1.157:8765 - 自动推送，提供十档行情 - * - * 深交所支持的数据类型 (category): - * - stock (300111): 股票快照，含10档买卖盘 - * - bond (300211): 债券快照 - * - afterhours_block (300611): 盘后定价大宗交易 - * - afterhours_trading (303711): 盘后定价交易 - * - hk_stock (306311): 港股快照（深港通） - * - index (309011): 指数快照 - * - volume_stats (309111): 成交量统计 - * - fund_nav (309211): 基金净值 - */ -import { useState, useEffect, useRef, useCallback } from 'react'; -import { logger } from '@utils/logger'; - -// WebSocket 地址配置 -const WS_CONFIG = { - SSE: 'ws://49.232.185.254:8765', // 上交所 - SZSE: 'ws://222.128.1.157:8765', // 深交所 -}; - -// 心跳间隔 (ms) -const HEARTBEAT_INTERVAL = 30000; - -// 重连间隔 (ms) -const RECONNECT_INTERVAL = 3000; - -/** - * 判断证券代码属于哪个交易所 - * @param {string} code - 证券代码（可带或不带后缀） - * @returns {'SSE'|'SZSE'} 交易所标识 - */ -const getExchange = (code) => { - const baseCode = code.split('.')[0]; - - // 6开头为上海股票 - if (baseCode.startsWith('6')) { - return 'SSE'; - } - - // 000开头的6位数可能是上证指数或深圳股票 - if (baseCode.startsWith('000') && baseCode.length === 6) { - // 000001-000999 是上证指数范围，但 000001 也是平安银行 - // 这里需要更精确的判断，暂时把 000 开头当深圳 - return 'SZSE'; - } - - // 399开头是深证指数 - if (baseCode.startsWith('399')) { - return 'SZSE'; - } - - // 0、3开头是深圳股票 - if (baseCode.startsWith('0') || baseCode.startsWith('3')) { - return 'SZSE'; - } - - // 5开头是上海 ETF - if (baseCode.startsWith('5')) { - return 'SSE'; - } - - // 1开头是深圳 ETF/债券 - if (baseCode.startsWith('1')) { - return 'SZSE'; - } - - // 默认上海 - return 'SSE'; -}; - -/** - * 标准化证券代码为无后缀格式 - */ -const normalizeCode = (code) => { - return code.split('.')[0]; -}; - -/** - * 从深交所 bids/asks 数组提取价格和量数组 - * @param {Array} orderBook - [{price, volume}, ...] - * @returns {{ prices: number[], volumes: number[] }} - */ -const extractOrderBook = (orderBook) => { - if (!orderBook || !Array.isArray(orderBook)) { - return { prices: [], volumes: [] }; - } - const prices = orderBook.map(item => item.price || 0); - const volumes = orderBook.map(item => item.volume || 0); - return { prices, volumes }; -}; - -/** - * 实时行情 Hook - * @param {string[]} codes - 订阅的证券代码列表 - * @returns {Object} { quotes, connected, subscribe, unsubscribe } - */ -export const useRealtimeQuote = (codes = []) => { - // 行情数据 { [code]: QuoteData } - const [quotes, setQuotes] = useState({}); - // 连接状态 { SSE: boolean, SZSE: boolean } - const [connected, setConnected] = useState({ SSE: false, SZSE: false }); - - // WebSocket 实例引用 - const wsRefs = useRef({ SSE: null, SZSE: null }); - // 心跳定时器 - const heartbeatRefs = useRef({ SSE: null, SZSE: null }); - // 重连定时器 - const reconnectRefs = useRef({ SSE: null, SZSE: null }); - // 当前订阅的代码（按交易所分组） - const subscribedCodes = useRef({ SSE: new Set(), SZSE: new Set() }); - - /** - * 创建 WebSocket 连接 - */ - const createConnection = useCallback((exchange) => { - // 清理现有连接 - if (wsRefs.current[exchange]) { - wsRefs.current[exchange].close(); - } - - const ws = new WebSocket(WS_CONFIG[exchange]); - wsRefs.current[exchange] = ws; - - ws.onopen = () => { - logger.info('FlexScreen', `${exchange} WebSocket 已连接`); - setConnected(prev => ({ ...prev, [exchange]: true })); - - // 上交所需要主动订阅 - if (exchange === 'SSE') { - const codes = Array.from(subscribedCodes.current.SSE); - if (codes.length > 0) { - ws.send(JSON.stringify({ - action: 'subscribe', - channels: ['stock', 'index'], - codes: codes, - })); - } - } - - // 启动心跳 - startHeartbeat(exchange); - }; - - ws.onmessage = (event) => { - try { - const msg = JSON.parse(event.data); - handleMessage(exchange, msg); - } catch (e) { - logger.warn('FlexScreen', `${exchange} 消息解析失败`, e); - } - }; - - ws.onerror = (error) => { - logger.error('FlexScreen', `${exchange} WebSocket 错误`, error); - }; - - ws.onclose = () => { - logger.info('FlexScreen', `${exchange} WebSocket 断开`); - setConnected(prev => ({ ...prev, [exchange]: false })); - stopHeartbeat(exchange); - - // 自动重连 - scheduleReconnect(exchange); - }; - }, []); - - /** - * 处理 WebSocket 消息 - */ - const handleMessage = useCallback((exchange, msg) => { - // 处理 pong - if (msg.type === 'pong') { - return; - } - - if (exchange === 'SSE') { - // 上交所消息格式 - if (msg.type === 'stock' || msg.type === 'index') { - const data = msg.data || {}; - setQuotes(prev => { - const updated = { ...prev }; - Object.entries(data).forEach(([code, quote]) => { - // 只更新订阅的代码 - if (subscribedCodes.current.SSE.has(code)) { - updated[code] = { - code: quote.security_id, - name: quote.security_name, - price: quote.last_price, - prevClose: quote.prev_close, - open: quote.open_price, - high: quote.high_price, - low: quote.low_price, - volume: quote.volume, - amount: quote.amount, - change: quote.last_price - quote.prev_close, - changePct: quote.prev_close ? ((quote.last_price - quote.prev_close) / quote.prev_close * 100) : 0, - bidPrices: quote.bid_prices || [], - bidVolumes: quote.bid_volumes || [], - askPrices: quote.ask_prices || [], - askVolumes: quote.ask_volumes || [], - updateTime: quote.trade_time, - exchange: 'SSE', - }; - } - }); - return updated; - }); - } - } else if (exchange === 'SZSE') { - // 深交所消息格式（更新后的 API） - if (msg.type === 'realtime') { - const { category, data } = msg; - const code = data.security_id; - - // 只更新订阅的代码 - if (!subscribedCodes.current.SZSE.has(code)) { - return; - } - - if (category === 'stock') { - // 股票行情 - 含 10 档买卖盘 - const { prices: bidPrices, volumes: bidVolumes } = extractOrderBook(data.bids); - const { prices: askPrices, volumes: askVolumes } = extractOrderBook(data.asks); - - setQuotes(prev => ({ - ...prev, - [code]: { - code: code, - name: prev[code]?.name || '', - price: data.last_px, - prevClose: data.prev_close, - open: data.open_px, - high: data.high_px, - low: data.low_px, - volume: data.volume, - amount: data.amount, - numTrades: data.num_trades, - upperLimit: data.upper_limit, // 涨停价 - lowerLimit: data.lower_limit, // 跌停价 - change: data.last_px - data.prev_close, - changePct: data.prev_close ? ((data.last_px - data.prev_close) / data.prev_close * 100) : 0, - bidPrices, - bidVolumes, - askPrices, - askVolumes, - tradingPhase: data.trading_phase, - updateTime: msg.timestamp, - exchange: 'SZSE', - }, - })); - } else if (category === 'index') { - // 指数行情 - setQuotes(prev => ({ - ...prev, - [code]: { - code: code, - name: prev[code]?.name || '', - price: data.current_index, - prevClose: data.prev_close, - open: data.open_index, - high: data.high_index, - low: data.low_index, - close: data.close_index, - volume: data.volume, - amount: data.amount, - numTrades: data.num_trades, - change: data.current_index - data.prev_close, - changePct: data.prev_close ? ((data.current_index - data.prev_close) / data.prev_close * 100) : 0, - bidPrices: [], - bidVolumes: [], - askPrices: [], - askVolumes: [], - tradingPhase: data.trading_phase, - updateTime: msg.timestamp, - exchange: 'SZSE', - }, - })); - } else if (category === 'bond') { - // 债券行情 - setQuotes(prev => ({ - ...prev, - [code]: { - code: code, - name: prev[code]?.name || '', - price: data.last_px, - prevClose: data.prev_close, - open: data.open_px, - high: data.high_px, - low: data.low_px, - volume: data.volume, - amount: data.amount, - numTrades: data.num_trades, - weightedAvgPx: data.weighted_avg_px, - change: data.last_px - data.prev_close, - changePct: data.prev_close ? ((data.last_px - data.prev_close) / data.prev_close * 100) : 0, - bidPrices: [], - bidVolumes: [], - askPrices: [], - askVolumes: [], - tradingPhase: data.trading_phase, - updateTime: msg.timestamp, - exchange: 'SZSE', - isBond: true, - }, - })); - } else if (category === 'hk_stock') { - // 港股行情（深港通） - const { prices: bidPrices, volumes: bidVolumes } = extractOrderBook(data.bids); - const { prices: askPrices, volumes: askVolumes } = extractOrderBook(data.asks); - - setQuotes(prev => ({ - ...prev, - [code]: { - code: code, - name: prev[code]?.name || '', - price: data.last_px, - prevClose: data.prev_close, - open: data.open_px, - high: data.high_px, - low: data.low_px, - volume: data.volume, - amount: data.amount, - numTrades: data.num_trades, - nominalPx: data.nominal_px, // 按盘价 - referencePx: data.reference_px, // 参考价 - change: data.last_px - data.prev_close, - changePct: data.prev_close ? ((data.last_px - data.prev_close) / data.prev_close * 100) : 0, - bidPrices, - bidVolumes, - askPrices, - askVolumes, - tradingPhase: data.trading_phase, - updateTime: msg.timestamp, - exchange: 'SZSE', - isHK: true, - }, - })); - } else if (category === 'afterhours_block' || category === 'afterhours_trading') { - // 盘后交易 - setQuotes(prev => ({ - ...prev, - [code]: { - ...prev[code], - afterhours: { - bidPx: data.bid_px, - bidSize: data.bid_size, - offerPx: data.offer_px, - offerSize: data.offer_size, - volume: data.volume, - amount: data.amount, - numTrades: data.num_trades, - }, - updateTime: msg.timestamp, - }, - })); - } - // fund_nav 和 volume_stats 暂不处理 - } else if (msg.type === 'snapshot') { - // 深交所初始快照 - const { stocks = [], indexes = [], bonds = [] } = msg.data || {}; - setQuotes(prev => { - const updated = { ...prev }; - - stocks.forEach(s => { - if (subscribedCodes.current.SZSE.has(s.security_id)) { - const { prices: bidPrices, volumes: bidVolumes } = extractOrderBook(s.bids); - const { prices: askPrices, volumes: askVolumes } = extractOrderBook(s.asks); - - updated[s.security_id] = { - code: s.security_id, - name: s.security_name || '', - price: s.last_px, - prevClose: s.prev_close, - open: s.open_px, - high: s.high_px, - low: s.low_px, - volume: s.volume, - amount: s.amount, - numTrades: s.num_trades, - upperLimit: s.upper_limit, - lowerLimit: s.lower_limit, - change: s.last_px - s.prev_close, - changePct: s.prev_close ? ((s.last_px - s.prev_close) / s.prev_close * 100) : 0, - bidPrices, - bidVolumes, - askPrices, - askVolumes, - exchange: 'SZSE', - }; - } - }); - - indexes.forEach(i => { - if (subscribedCodes.current.SZSE.has(i.security_id)) { - updated[i.security_id] = { - code: i.security_id, - name: i.security_name || '', - price: i.current_index, - prevClose: i.prev_close, - open: i.open_index, - high: i.high_index, - low: i.low_index, - volume: i.volume, - amount: i.amount, - numTrades: i.num_trades, - change: i.current_index - i.prev_close, - changePct: i.prev_close ? ((i.current_index - i.prev_close) / i.prev_close * 100) : 0, - bidPrices: [], - bidVolumes: [], - askPrices: [], - askVolumes: [], - exchange: 'SZSE', - }; - } - }); - - bonds.forEach(b => { - if (subscribedCodes.current.SZSE.has(b.security_id)) { - updated[b.security_id] = { - code: b.security_id, - name: b.security_name || '', - price: b.last_px, - prevClose: b.prev_close, - open: b.open_px, - high: b.high_px, - low: b.low_px, - volume: b.volume, - amount: b.amount, - change: b.last_px - b.prev_close, - changePct: b.prev_close ? ((b.last_px - b.prev_close) / b.prev_close * 100) : 0, - bidPrices: [], - bidVolumes: [], - askPrices: [], - askVolumes: [], - exchange: 'SZSE', - isBond: true, - }; - } - }); - - return updated; - }); - } - } - }, []); - - /** - * 启动心跳 - */ - const startHeartbeat = useCallback((exchange) => { - stopHeartbeat(exchange); - heartbeatRefs.current[exchange] = setInterval(() => { - const ws = wsRefs.current[exchange]; - if (ws && ws.readyState === WebSocket.OPEN) { - if (exchange === 'SSE') { - ws.send(JSON.stringify({ action: 'ping' })); - } else { - ws.send(JSON.stringify({ type: 'ping' })); - } - } - }, HEARTBEAT_INTERVAL); - }, []); - - /** - * 停止心跳 - */ - const stopHeartbeat = useCallback((exchange) => { - if (heartbeatRefs.current[exchange]) { - clearInterval(heartbeatRefs.current[exchange]); - heartbeatRefs.current[exchange] = null; - } - }, []); - - /** - * 安排重连 - */ - const scheduleReconnect = useCallback((exchange) => { - if (reconnectRefs.current[exchange]) { - return; // 已有重连计划 - } - - reconnectRefs.current[exchange] = setTimeout(() => { - reconnectRefs.current[exchange] = null; - // 只有还有订阅的代码才重连 - if (subscribedCodes.current[exchange].size > 0) { - createConnection(exchange); - } - }, RECONNECT_INTERVAL); - }, [createConnection]); - - /** - * 订阅证券 - */ - const subscribe = useCallback((code) => { - const baseCode = normalizeCode(code); - const exchange = getExchange(code); - - // 添加到订阅列表 - subscribedCodes.current[exchange].add(baseCode); - - // 如果连接已建立，发送订阅消息（仅上交所需要） - const ws = wsRefs.current[exchange]; - if (exchange === 'SSE' && ws && ws.readyState === WebSocket.OPEN) { - ws.send(JSON.stringify({ - action: 'subscribe', - channels: ['stock', 'index'], - codes: [baseCode], - })); - } - - // 如果连接未建立，创建连接 - if (!ws || ws.readyState !== WebSocket.OPEN) { - createConnection(exchange); - } - }, [createConnection]); - - /** - * 取消订阅 - */ - const unsubscribe = useCallback((code) => { - const baseCode = normalizeCode(code); - const exchange = getExchange(code); - - // 从订阅列表移除 - subscribedCodes.current[exchange].delete(baseCode); - - // 从 quotes 中移除 - setQuotes(prev => { - const updated = { ...prev }; - delete updated[baseCode]; - return updated; - }); - - // 如果该交易所没有订阅了，关闭连接 - if (subscribedCodes.current[exchange].size === 0) { - const ws = wsRefs.current[exchange]; - if (ws) { - ws.close(); - wsRefs.current[exchange] = null; - } - } - }, []); - - /** - * 初始化订阅 - */ - useEffect(() => { - if (!codes || codes.length === 0) { - return; - } - - // 按交易所分组 - const sseCodesSet = new Set(); - const szseCodesSet = new Set(); - - codes.forEach(code => { - const baseCode = normalizeCode(code); - const exchange = getExchange(code); - if (exchange === 'SSE') { - sseCodesSet.add(baseCode); - } else { - szseCodesSet.add(baseCode); - } - }); - - // 更新订阅列表 - subscribedCodes.current.SSE = sseCodesSet; - subscribedCodes.current.SZSE = szseCodesSet; - - // 建立连接 - if (sseCodesSet.size > 0) { - createConnection('SSE'); - } - if (szseCodesSet.size > 0) { - createConnection('SZSE'); - } - - // 清理 - return () => { - ['SSE', 'SZSE'].forEach(exchange => { - stopHeartbeat(exchange); - if (reconnectRefs.current[exchange]) { - clearTimeout(reconnectRefs.current[exchange]); - } - const ws = wsRefs.current[exchange]; - if (ws) { - ws.close(); - } - }); - }; - }, []); // 只在挂载时执行 - - /** - * 处理 codes 变化 - */ - useEffect(() => { - if (!codes) return; - - // 计算新的订阅列表 - const newSseCodes = new Set(); - const newSzseCodes = new Set(); - - codes.forEach(code => { - const baseCode = normalizeCode(code); - const exchange = getExchange(code); - if (exchange === 'SSE') { - newSseCodes.add(baseCode); - } else { - newSzseCodes.add(baseCode); - } - }); - - // 找出需要新增和删除的代码 - const oldSseCodes = subscribedCodes.current.SSE; - const oldSzseCodes = subscribedCodes.current.SZSE; - - // 更新上交所订阅 - const sseToAdd = [...newSseCodes].filter(c => !oldSseCodes.has(c)); - const sseToRemove = [...oldSseCodes].filter(c => !newSseCodes.has(c)); - - if (sseToAdd.length > 0 || sseToRemove.length > 0) { - subscribedCodes.current.SSE = newSseCodes; - - const ws = wsRefs.current.SSE; - if (ws && ws.readyState === WebSocket.OPEN && sseToAdd.length > 0) { - ws.send(JSON.stringify({ - action: 'subscribe', - channels: ['stock', 'index'], - codes: sseToAdd, - })); - } - - // 如果新增了代码但连接未建立 - if (sseToAdd.length > 0 && (!ws || ws.readyState !== WebSocket.OPEN)) { - createConnection('SSE'); - } - - // 如果没有订阅了，关闭连接 - if (newSseCodes.size === 0 && ws) { - ws.close(); - wsRefs.current.SSE = null; - } - } - - // 更新深交所订阅 - const szseToAdd = [...newSzseCodes].filter(c => !oldSzseCodes.has(c)); - const szseToRemove = [...oldSzseCodes].filter(c => !newSzseCodes.has(c)); - - if (szseToAdd.length > 0 || szseToRemove.length > 0) { - subscribedCodes.current.SZSE = newSzseCodes; - - // 深交所是自动推送，只需要管理连接 - const ws = wsRefs.current.SZSE; - - if (szseToAdd.length > 0 && (!ws || ws.readyState !== WebSocket.OPEN)) { - createConnection('SZSE'); - } - - if (newSzseCodes.size === 0 && ws) { - ws.close(); - wsRefs.current.SZSE = null; - } - } - - // 清理已取消订阅的 quotes - const removedCodes = [...sseToRemove, ...szseToRemove]; - if (removedCodes.length > 0) { - setQuotes(prev => { - const updated = { ...prev }; - removedCodes.forEach(code => { - delete updated[code]; - }); - return updated; - }); - } - }, [codes, createConnection]); - - return { - quotes, - connected, - subscribe, - unsubscribe, - }; -}; - -export default useRealtimeQuote; diff --git a/src/views/StockOverview/components/FlexScreen/hooks/useRealtimeQuote.ts b/src/views/StockOverview/components/FlexScreen/hooks/useRealtimeQuote.ts new file mode 100644 index 00000000..dfb69bfc --- /dev/null +++ b/src/views/StockOverview/components/FlexScreen/hooks/useRealtimeQuote.ts @@ -0,0 +1,594 @@ +/** + * 实时行情 Hook + * 管理上交所和深交所 WebSocket 连接，获取实时行情数据 + * + * 上交所 (SSE): ws://49.232.185.254:8765 - 需主动订阅，提供五档行情 + * 深交所 (SZSE): ws://222.128.1.157:8765 - 自动推送，提供十档行情 + */ + +import { useState, useEffect, useRef, useCallback } from 'react'; +import { logger } from '@utils/logger'; +import { WS_CONFIG, HEARTBEAT_INTERVAL, RECONNECT_INTERVAL } from './constants'; +import { getExchange, normalizeCode, extractOrderBook, calcChangePct } from './utils'; +import type { + Exchange, + ConnectionStatus, + QuotesMap, + QuoteData, + SSEMessage, + SSEQuoteItem, + SZSEMessage, + SZSERealtimeMessage, + SZSESnapshotMessage, + SZSEStockData, + SZSEIndexData, + SZSEBondData, + SZSEHKStockData, + SZSEAfterhoursData, + UseRealtimeQuoteReturn, +} from '../types'; + +/** + * 处理上交所消息 + */ +const handleSSEMessage = ( + msg: SSEMessage, + subscribedCodes: Set, + prevQuotes: QuotesMap +): QuotesMap | null => { + if (msg.type !== 'stock' && msg.type !== 'index') { + return null; + } + + const data = msg.data || {}; + const updated: QuotesMap = { ...prevQuotes }; + let hasUpdate = false; + + Object.entries(data).forEach(([code, quote]: [string, SSEQuoteItem]) => { + if (subscribedCodes.has(code)) { + hasUpdate = true; + updated[code] = { + code: quote.security_id, + name: quote.security_name, + price: quote.last_price, + prevClose: quote.prev_close, + open: quote.open_price, + high: quote.high_price, + low: quote.low_price, + volume: quote.volume, + amount: quote.amount, + change: quote.last_price - quote.prev_close, + changePct: calcChangePct(quote.last_price, quote.prev_close), + bidPrices: quote.bid_prices || [], + bidVolumes: quote.bid_volumes || [], + askPrices: quote.ask_prices || [], + askVolumes: quote.ask_volumes || [], + updateTime: quote.trade_time, + exchange: 'SSE', + } as QuoteData; + } + }); + + return hasUpdate ? updated : null; +}; + +/** + * 处理深交所实时消息 + */ +const handleSZSERealtimeMessage = ( + msg: SZSERealtimeMessage, + subscribedCodes: Set, + prevQuotes: QuotesMap +): QuotesMap | null => { + const { category, data, timestamp } = msg; + const code = data.security_id; + + if (!subscribedCodes.has(code)) { + return null; + } + + const updated: QuotesMap = { ...prevQuotes }; + + switch (category) { + case 'stock': { + const stockData = data as SZSEStockData; + const { prices: bidPrices, volumes: bidVolumes } = extractOrderBook(stockData.bids); + const { prices: askPrices, volumes: askVolumes } = extractOrderBook(stockData.asks); + + updated[code] = { + code, + name: prevQuotes[code]?.name || '', + price: stockData.last_px, + prevClose: stockData.prev_close, + open: stockData.open_px, + high: stockData.high_px, + low: stockData.low_px, + volume: stockData.volume, + amount: stockData.amount, + numTrades: stockData.num_trades, + upperLimit: stockData.upper_limit, + lowerLimit: stockData.lower_limit, + change: stockData.last_px - stockData.prev_close, + changePct: calcChangePct(stockData.last_px, stockData.prev_close), + bidPrices, + bidVolumes, + askPrices, + askVolumes, + tradingPhase: stockData.trading_phase, + updateTime: timestamp, + exchange: 'SZSE', + } as QuoteData; + break; + } + + case 'index': { + const indexData = data as SZSEIndexData; + updated[code] = { + code, + name: prevQuotes[code]?.name || '', + price: indexData.current_index, + prevClose: indexData.prev_close, + open: indexData.open_index, + high: indexData.high_index, + low: indexData.low_index, + close: indexData.close_index, + volume: indexData.volume, + amount: indexData.amount, + numTrades: indexData.num_trades, + change: indexData.current_index - indexData.prev_close, + changePct: calcChangePct(indexData.current_index, indexData.prev_close), + bidPrices: [], + bidVolumes: [], + askPrices: [], + askVolumes: [], + tradingPhase: indexData.trading_phase, + updateTime: timestamp, + exchange: 'SZSE', + } as QuoteData; + break; + } + + case 'bond': { + const bondData = data as SZSEBondData; + updated[code] = { + code, + name: prevQuotes[code]?.name || '', + price: bondData.last_px, + prevClose: bondData.prev_close, + open: bondData.open_px, + high: bondData.high_px, + low: bondData.low_px, + volume: bondData.volume, + amount: bondData.amount, + numTrades: bondData.num_trades, + weightedAvgPx: bondData.weighted_avg_px, + change: bondData.last_px - bondData.prev_close, + changePct: calcChangePct(bondData.last_px, bondData.prev_close), + bidPrices: [], + bidVolumes: [], + askPrices: [], + askVolumes: [], + tradingPhase: bondData.trading_phase, + updateTime: timestamp, + exchange: 'SZSE', + isBond: true, + } as QuoteData; + break; + } + + case 'hk_stock': { + const hkData = data as SZSEHKStockData; + const { prices: bidPrices, volumes: bidVolumes } = extractOrderBook(hkData.bids); + const { prices: askPrices, volumes: askVolumes } = extractOrderBook(hkData.asks); + + updated[code] = { + code, + name: prevQuotes[code]?.name || '', + price: hkData.last_px, + prevClose: hkData.prev_close, + open: hkData.open_px, + high: hkData.high_px, + low: hkData.low_px, + volume: hkData.volume, + amount: hkData.amount, + numTrades: hkData.num_trades, + nominalPx: hkData.nominal_px, + referencePx: hkData.reference_px, + change: hkData.last_px - hkData.prev_close, + changePct: calcChangePct(hkData.last_px, hkData.prev_close), + bidPrices, + bidVolumes, + askPrices, + askVolumes, + tradingPhase: hkData.trading_phase, + updateTime: timestamp, + exchange: 'SZSE', + isHK: true, + } as QuoteData; + break; + } + + case 'afterhours_block': + case 'afterhours_trading': { + const afterhoursData = data as SZSEAfterhoursData; + const existing = prevQuotes[code]; + if (existing) { + updated[code] = { + ...existing, + afterhours: { + bidPx: afterhoursData.bid_px, + bidSize: afterhoursData.bid_size, + offerPx: afterhoursData.offer_px, + offerSize: afterhoursData.offer_size, + volume: afterhoursData.volume, + amount: afterhoursData.amount, + numTrades: afterhoursData.num_trades || 0, + }, + updateTime: timestamp, + } as QuoteData; + } + break; + } + + default: + return null; + } + + return updated; +}; + +/** + * 处理深交所快照消息 + */ +const handleSZSESnapshotMessage = ( + msg: SZSESnapshotMessage, + subscribedCodes: Set, + prevQuotes: QuotesMap +): QuotesMap | null => { + const { stocks = [], indexes = [], bonds = [] } = msg.data || {}; + const updated: QuotesMap = { ...prevQuotes }; + let hasUpdate = false; + + stocks.forEach((s: SZSEStockData) => { + if (subscribedCodes.has(s.security_id)) { + hasUpdate = true; + const { prices: bidPrices, volumes: bidVolumes } = extractOrderBook(s.bids); + const { prices: askPrices, volumes: askVolumes } = extractOrderBook(s.asks); + + updated[s.security_id] = { + code: s.security_id, + name: '', + price: s.last_px, + prevClose: s.prev_close, + open: s.open_px, + high: s.high_px, + low: s.low_px, + volume: s.volume, + amount: s.amount, + numTrades: s.num_trades, + upperLimit: s.upper_limit, + lowerLimit: s.lower_limit, + change: s.last_px - s.prev_close, + changePct: calcChangePct(s.last_px, s.prev_close), + bidPrices, + bidVolumes, + askPrices, + askVolumes, + exchange: 'SZSE', + } as QuoteData; + } + }); + + indexes.forEach((i: SZSEIndexData) => { + if (subscribedCodes.has(i.security_id)) { + hasUpdate = true; + updated[i.security_id] = { + code: i.security_id, + name: '', + price: i.current_index, + prevClose: i.prev_close, + open: i.open_index, + high: i.high_index, + low: i.low_index, + volume: i.volume, + amount: i.amount, + numTrades: i.num_trades, + change: i.current_index - i.prev_close, + changePct: calcChangePct(i.current_index, i.prev_close), + bidPrices: [], + bidVolumes: [], + askPrices: [], + askVolumes: [], + exchange: 'SZSE', + } as QuoteData; + } + }); + + bonds.forEach((b: SZSEBondData) => { + if (subscribedCodes.has(b.security_id)) { + hasUpdate = true; + updated[b.security_id] = { + code: b.security_id, + name: '', + price: b.last_px, + prevClose: b.prev_close, + open: b.open_px, + high: b.high_px, + low: b.low_px, + volume: b.volume, + amount: b.amount, + change: b.last_px - b.prev_close, + changePct: calcChangePct(b.last_px, b.prev_close), + bidPrices: [], + bidVolumes: [], + askPrices: [], + askVolumes: [], + exchange: 'SZSE', + isBond: true, + } as QuoteData; + } + }); + + return hasUpdate ? updated : null; +}; + +/** + * 实时行情 Hook + * @param codes - 订阅的证券代码列表 + */ +export const useRealtimeQuote = (codes: string[] = []): UseRealtimeQuoteReturn => { + const [quotes, setQuotes] = useState({}); + const [connected, setConnected] = useState({ SSE: false, SZSE: false }); + + const wsRefs = useRef>({ SSE: null, SZSE: null }); + const heartbeatRefs = useRef>({ SSE: null, SZSE: null }); + const reconnectRefs = useRef>({ SSE: null, SZSE: null }); + const subscribedCodes = useRef>>({ + SSE: new Set(), + SZSE: new Set(), + }); + + const stopHeartbeat = useCallback((exchange: Exchange) => { + if (heartbeatRefs.current[exchange]) { + clearInterval(heartbeatRefs.current[exchange]!); + heartbeatRefs.current[exchange] = null; + } + }, []); + + const startHeartbeat = useCallback((exchange: Exchange) => { + stopHeartbeat(exchange); + heartbeatRefs.current[exchange] = setInterval(() => { + const ws = wsRefs.current[exchange]; + if (ws && ws.readyState === WebSocket.OPEN) { + const msg = exchange === 'SSE' ? { action: 'ping' } : { type: 'ping' }; + ws.send(JSON.stringify(msg)); + } + }, HEARTBEAT_INTERVAL); + }, [stopHeartbeat]); + + const handleMessage = useCallback((exchange: Exchange, msg: SSEMessage | SZSEMessage) => { + if (msg.type === 'pong') return; + + if (exchange === 'SSE') { + const result = handleSSEMessage( + msg as SSEMessage, + subscribedCodes.current.SSE, + {} // Will be merged with current state + ); + if (result) { + setQuotes(prev => ({ ...prev, ...result })); + } + } else { + if (msg.type === 'realtime') { + setQuotes(prev => { + const result = handleSZSERealtimeMessage( + msg as SZSERealtimeMessage, + subscribedCodes.current.SZSE, + prev + ); + return result || prev; + }); + } else if (msg.type === 'snapshot') { + setQuotes(prev => { + const result = handleSZSESnapshotMessage( + msg as SZSESnapshotMessage, + subscribedCodes.current.SZSE, + prev + ); + return result || prev; + }); + } + } + }, []); + + const createConnection = useCallback((exchange: Exchange) => { + if (wsRefs.current[exchange]) { + wsRefs.current[exchange]!.close(); + } + + const ws = new WebSocket(WS_CONFIG[exchange]); + wsRefs.current[exchange] = ws; + + ws.onopen = () => { + logger.info('FlexScreen', `${exchange} WebSocket 已连接`); + setConnected(prev => ({ ...prev, [exchange]: true })); + + if (exchange === 'SSE') { + const codes = Array.from(subscribedCodes.current.SSE); + if (codes.length > 0) { + ws.send(JSON.stringify({ + action: 'subscribe', + channels: ['stock', 'index'], + codes, + })); + } + } + + startHeartbeat(exchange); + }; + + ws.onmessage = (event: MessageEvent) => { + try { + const msg = JSON.parse(event.data); + handleMessage(exchange, msg); + } catch (e) { + logger.warn('FlexScreen', `${exchange} 消息解析失败`, e); + } + }; + + ws.onerror = (error: Event) => { + logger.error('FlexScreen', `${exchange} WebSocket 错误`, error); + }; + + ws.onclose = () => { + logger.info('FlexScreen', `${exchange} WebSocket 断开`); + setConnected(prev => ({ ...prev, [exchange]: false })); + stopHeartbeat(exchange); + + // 自动重连 + if (!reconnectRefs.current[exchange] && subscribedCodes.current[exchange].size > 0) { + reconnectRefs.current[exchange] = setTimeout(() => { + reconnectRefs.current[exchange] = null; + if (subscribedCodes.current[exchange].size > 0) { + createConnection(exchange); + } + }, RECONNECT_INTERVAL); + } + }; + }, [startHeartbeat, stopHeartbeat, handleMessage]); + + const subscribe = useCallback((code: string) => { + const baseCode = normalizeCode(code); + const exchange = getExchange(code); + + subscribedCodes.current[exchange].add(baseCode); + + const ws = wsRefs.current[exchange]; + if (exchange === 'SSE' && ws && ws.readyState === WebSocket.OPEN) { + ws.send(JSON.stringify({ + action: 'subscribe', + channels: ['stock', 'index'], + codes: [baseCode], + })); + } + + if (!ws || ws.readyState !== WebSocket.OPEN) { + createConnection(exchange); + } + }, [createConnection]); + + const unsubscribe = useCallback((code: string) => { + const baseCode = normalizeCode(code); + const exchange = getExchange(code); + + subscribedCodes.current[exchange].delete(baseCode); + + setQuotes(prev => { + const updated = { ...prev }; + delete updated[baseCode]; + return updated; + }); + + if (subscribedCodes.current[exchange].size === 0) { + const ws = wsRefs.current[exchange]; + if (ws) { + ws.close(); + wsRefs.current[exchange] = null; + } + } + }, []); + + // 初始化和 codes 变化处理 + useEffect(() => { + if (!codes || codes.length === 0) return; + + const newSseCodes = new Set(); + const newSzseCodes = new Set(); + + codes.forEach(code => { + const baseCode = normalizeCode(code); + const exchange = getExchange(code); + if (exchange === 'SSE') { + newSseCodes.add(baseCode); + } else { + newSzseCodes.add(baseCode); + } + }); + + // 更新上交所订阅 + const oldSseCodes = subscribedCodes.current.SSE; + const sseToAdd = [...newSseCodes].filter(c => !oldSseCodes.has(c)); + + if (sseToAdd.length > 0 || newSseCodes.size !== oldSseCodes.size) { + subscribedCodes.current.SSE = newSseCodes; + const ws = wsRefs.current.SSE; + + if (ws && ws.readyState === WebSocket.OPEN && sseToAdd.length > 0) { + ws.send(JSON.stringify({ + action: 'subscribe', + channels: ['stock', 'index'], + codes: sseToAdd, + })); + } + + if (sseToAdd.length > 0 && (!ws || ws.readyState !== WebSocket.OPEN)) { + createConnection('SSE'); + } + + if (newSseCodes.size === 0 && ws) { + ws.close(); + wsRefs.current.SSE = null; + } + } + + // 更新深交所订阅 + const oldSzseCodes = subscribedCodes.current.SZSE; + const szseToAdd = [...newSzseCodes].filter(c => !oldSzseCodes.has(c)); + + if (szseToAdd.length > 0 || newSzseCodes.size !== oldSzseCodes.size) { + subscribedCodes.current.SZSE = newSzseCodes; + const ws = wsRefs.current.SZSE; + + if (szseToAdd.length > 0 && (!ws || ws.readyState !== WebSocket.OPEN)) { + createConnection('SZSE'); + } + + if (newSzseCodes.size === 0 && ws) { + ws.close(); + wsRefs.current.SZSE = null; + } + } + + // 清理已取消订阅的 quotes + const allNewCodes = new Set([...newSseCodes, ...newSzseCodes]); + setQuotes(prev => { + const updated: QuotesMap = {}; + Object.keys(prev).forEach(code => { + if (allNewCodes.has(code)) { + updated[code] = prev[code]; + } + }); + return updated; + }); + }, [codes, createConnection]); + + // 清理 + useEffect(() => { + return () => { + (['SSE', 'SZSE'] as Exchange[]).forEach(exchange => { + stopHeartbeat(exchange); + if (reconnectRefs.current[exchange]) { + clearTimeout(reconnectRefs.current[exchange]!); + } + const ws = wsRefs.current[exchange]; + if (ws) { + ws.close(); + } + }); + }; + }, [stopHeartbeat]); + + return { quotes, connected, subscribe, unsubscribe }; +}; + +export default useRealtimeQuote; diff --git a/src/views/StockOverview/components/FlexScreen/hooks/utils.ts b/src/views/StockOverview/components/FlexScreen/hooks/utils.ts new file mode 100644 index 00000000..8f71de36 --- /dev/null +++ b/src/views/StockOverview/components/FlexScreen/hooks/utils.ts @@ -0,0 +1,83 @@ +/** + * 实时行情相关工具函数 + */ + +import type { Exchange, OrderBookLevel } from '../types'; + +/** + * 判断证券代码属于哪个交易所 + * @param code - 证券代码（可带或不带后缀） + * @returns 交易所标识 + */ +export const getExchange = (code: string): Exchange => { + const baseCode = code.split('.')[0]; + + // 6开头为上海股票 + if (baseCode.startsWith('6')) { + return 'SSE'; + } + + // 000开头的6位数是深圳股票（如平安银行000001） + if (baseCode.startsWith('000') && baseCode.length === 6) { + return 'SZSE'; + } + + // 399开头是深证指数 + if (baseCode.startsWith('399')) { + return 'SZSE'; + } + + // 0、3开头是深圳股票 + if (baseCode.startsWith('0') || baseCode.startsWith('3')) { + return 'SZSE'; + } + + // 5开头是上海 ETF + if (baseCode.startsWith('5')) { + return 'SSE'; + } + + // 1开头是深圳 ETF/债券 + if (baseCode.startsWith('1')) { + return 'SZSE'; + } + + // 默认上海 + return 'SSE'; +}; + +/** + * 标准化证券代码为无后缀格式 + * @param code - 原始代码 + * @returns 无后缀代码 + */ +export const normalizeCode = (code: string): string => { + return code.split('.')[0]; +}; + +/** + * 从深交所 bids/asks 数组提取价格和量数组 + * @param orderBook - 盘口数组 [{price, volume}, ...] + * @returns { prices, volumes } + */ +export const extractOrderBook = ( + orderBook: OrderBookLevel[] | undefined +): { prices: number[]; volumes: number[] } => { + if (!orderBook || !Array.isArray(orderBook)) { + return { prices: [], volumes: [] }; + } + const prices = orderBook.map(item => item.price || 0); + const volumes = orderBook.map(item => item.volume || 0); + return { prices, volumes }; +}; + +/** + * 计算涨跌幅 + * @param price - 当前价 + * @param prevClose - 昨收价 + * @returns 涨跌幅百分比 + */ +export const calcChangePct = (price: number, prevClose: number): number => { + if (!prevClose || prevClose === 0) return 0; + return ((price - prevClose) / prevClose) * 100; +}; diff --git a/src/views/StockOverview/components/FlexScreen/index.js b/src/views/StockOverview/components/FlexScreen/index.tsx similarity index 83% rename from src/views/StockOverview/components/FlexScreen/index.js rename to src/views/StockOverview/components/FlexScreen/index.tsx index aeace485..59132aa9 100644 --- a/src/views/StockOverview/components/FlexScreen/index.js +++ b/src/views/StockOverview/components/FlexScreen/index.tsx @@ -6,7 +6,7 @@ * 1. 添加/删除自选证券 * 2. 显示实时行情（通过 WebSocket） * 3. 显示分时走势（结合 ClickHouse 历史数据） - * 4. 显示五档盘口（上交所完整五档，深交所买一卖一） + * 4. 显示五档盘口（上交所5档，深交所10档） * 5. 本地存储自选列表 */ import React, { useState, useEffect, useCallback, useMemo } from 'react'; @@ -23,7 +23,6 @@ import { InputLeftElement, InputRightElement, IconButton, - Button, SimpleGrid, Flex, Spacer, @@ -41,7 +40,6 @@ import { MenuButton, MenuList, MenuItem, - Divider, Tag, TagLabel, } from '@chakra-ui/react'; @@ -53,24 +51,31 @@ import { ChevronUpIcon, SettingsIcon, } from '@chakra-ui/icons'; -import { FaDesktop, FaPlus, FaTrash, FaSync, FaWifi, FaExclamationCircle } from 'react-icons/fa'; +import { + FaDesktop, + FaTrash, + FaSync, + FaWifi, + FaExclamationCircle, +} from 'react-icons/fa'; import { useRealtimeQuote } from './hooks'; -import { QuoteTile } from './components'; +import QuoteTile from './components/QuoteTile'; import { logger } from '@utils/logger'; +import type { WatchlistItem, ConnectionStatus } from './types'; // 本地存储 key const STORAGE_KEY = 'flexscreen_watchlist'; // 默认自选列表 -const DEFAULT_WATCHLIST = [ +const DEFAULT_WATCHLIST: WatchlistItem[] = [ { code: '000001', name: '上证指数', isIndex: true }, { code: '399001', name: '深证成指', isIndex: true }, { code: '399006', name: '创业板指', isIndex: true }, ]; // 热门推荐 -const HOT_RECOMMENDATIONS = [ +const HOT_RECOMMENDATIONS: WatchlistItem[] = [ { code: '000001', name: '上证指数', isIndex: true }, { code: '399001', name: '深证成指', isIndex: true }, { code: '399006', name: '创业板指', isIndex: true }, @@ -81,17 +86,38 @@ const HOT_RECOMMENDATIONS = [ { code: '002594', name: '比亚迪', isIndex: false }, ]; +/** 搜索结果项 */ +interface SearchResultItem { + stock_code: string; + stock_name: string; + isIndex?: boolean; + code?: string; + name?: string; +} + +/** 搜索 API 响应 */ +interface SearchApiResponse { + success: boolean; + data?: SearchResultItem[]; +} + +/** 连接状态信息 */ +interface ConnectionStatusInfo { + color: string; + text: string; +} + /** * FlexScreen 组件 */ -const FlexScreen = () => { +const FlexScreen: React.FC = () => { const toast = useToast(); // 自选列表 - const [watchlist, setWatchlist] = useState([]); + const [watchlist, setWatchlist] = useState([]); // 搜索状态 const [searchQuery, setSearchQuery] = useState(''); - const [searchResults, setSearchResults] = useState([]); + const [searchResults, setSearchResults] = useState([]); const [isSearching, setIsSearching] = useState(false); const [showResults, setShowResults] = useState(false); // 面板状态 @@ -118,7 +144,7 @@ const FlexScreen = () => { try { const saved = localStorage.getItem(STORAGE_KEY); if (saved) { - const parsed = JSON.parse(saved); + const parsed = JSON.parse(saved) as WatchlistItem[]; if (Array.isArray(parsed) && parsed.length > 0) { setWatchlist(parsed); return; @@ -143,7 +169,7 @@ const FlexScreen = () => { }, [watchlist]); // 搜索证券 - const searchSecurities = useCallback(async (query) => { + const searchSecurities = useCallback(async (query: string): Promise => { if (!query.trim()) { setSearchResults([]); setShowResults(false); @@ -153,7 +179,7 @@ const FlexScreen = () => { setIsSearching(true); try { const response = await fetch(`/api/stocks/search?q=${encodeURIComponent(query)}&limit=10`); - const data = await response.json(); + const data: SearchApiResponse = await response.json(); if (data.success) { setSearchResults(data.data || []); @@ -178,44 +204,48 @@ const FlexScreen = () => { }, [searchQuery, searchSecurities]); // 添加证券 - const addSecurity = useCallback((security) => { - const code = security.stock_code || security.code; - const name = security.stock_name || security.name; - const isIndex = security.isIndex || code.startsWith('000') || code.startsWith('399'); + const addSecurity = useCallback( + (security: SearchResultItem | WatchlistItem): void => { + const code = 'stock_code' in security ? security.stock_code : security.code; + const name = 'stock_name' in security ? security.stock_name : security.name; + const isIndex = + security.isIndex || code.startsWith('000') || code.startsWith('399'); + + // 检查是否已存在 + if (watchlist.some(item => item.code === code)) { + toast({ + title: '已在自选列表中', + status: 'info', + duration: 2000, + isClosable: true, + }); + return; + } + + // 添加到列表 + setWatchlist(prev => [...prev, { code, name, isIndex }]); - // 检查是否已存在 - if (watchlist.some(item => item.code === code)) { toast({ - title: '已在自选列表中', - status: 'info', + title: `已添加 ${name}`, + status: 'success', duration: 2000, isClosable: true, }); - return; - } - // 添加到列表 - setWatchlist(prev => [...prev, { code, name, isIndex }]); - - toast({ - title: `已添加 ${name}`, - status: 'success', - duration: 2000, - isClosable: true, - }); - - // 清空搜索 - setSearchQuery(''); - setShowResults(false); - }, [watchlist, toast]); + // 清空搜索 + setSearchQuery(''); + setShowResults(false); + }, + [watchlist, toast] + ); // 移除证券 - const removeSecurity = useCallback((code) => { + const removeSecurity = useCallback((code: string): void => { setWatchlist(prev => prev.filter(item => item.code !== code)); }, []); // 清空自选列表 - const clearWatchlist = useCallback(() => { + const clearWatchlist = useCallback((): void => { setWatchlist([]); localStorage.removeItem(STORAGE_KEY); toast({ @@ -227,7 +257,7 @@ const FlexScreen = () => { }, [toast]); // 重置为默认列表 - const resetWatchlist = useCallback(() => { + const resetWatchlist = useCallback((): void => { setWatchlist(DEFAULT_WATCHLIST); toast({ title: '已重置为默认列表', @@ -239,7 +269,7 @@ const FlexScreen = () => { // 连接状态指示 const isAnyConnected = connected.SSE || connected.SZSE; - const connectionStatus = useMemo(() => { + const connectionStatus = useMemo((): ConnectionStatusInfo => { if (connected.SSE && connected.SZSE) { return { color: 'green', text: '上交所/深交所 已连接' }; } @@ -317,7 +347,7 @@ const FlexScreen = () => { setSearchQuery(e.target.value)} + onChange={e => setSearchQuery(e.target.value)} bg={searchBg} borderRadius="lg" _focus={{ @@ -413,7 +443,7 @@ const FlexScreen = () => { 热门推荐（点击添加） - {HOT_RECOMMENDATIONS.map((item) => ( + {HOT_RECOMMENDATIONS.map(item => ( { {/* 自选列表 */} {watchlist.length > 0 ? ( - {watchlist.map((item) => ( + {watchlist.map(item => ( {
- - 自选列表为空，请搜索添加证券 - + 自选列表为空，请搜索添加证券
)} diff --git a/src/views/StockOverview/components/FlexScreen/types.ts b/src/views/StockOverview/components/FlexScreen/types.ts new file mode 100644 index 00000000..796290f3 --- /dev/null +++ b/src/views/StockOverview/components/FlexScreen/types.ts @@ -0,0 +1,322 @@ +/** + * 灵活屏组件类型定义 + */ + +// ==================== WebSocket 相关类型 ==================== + +/** 交易所标识 */ +export type Exchange = 'SSE' | 'SZSE'; + +/** WebSocket 连接状态 */ +export interface ConnectionStatus { + SSE: boolean; + SZSE: boolean; +} + +/** 盘口档位数据 */ +export interface OrderBookLevel { + price: number; + volume: number; +} + +// ==================== 行情数据类型 ==================== + +/** 盘后交易数据 */ +export interface AfterhoursData { + bidPx: number; + bidSize: number; + offerPx: number; + offerSize: number; + volume: number; + amount: number; + numTrades: number; +} + +/** 基础行情数据 */ +export interface BaseQuoteData { + code: string; + name: string; + price: number; + prevClose: number; + open: number; + high: number; + low: number; + volume: number; + amount: number; + change: number; + changePct: number; + updateTime?: string; + exchange: Exchange; +} + +/** 股票行情数据 */ +export interface StockQuoteData extends BaseQuoteData { + numTrades?: number; + upperLimit?: number; // 涨停价 + lowerLimit?: number; // 跌停价 + bidPrices: number[]; + bidVolumes: number[]; + askPrices: number[]; + askVolumes: number[]; + tradingPhase?: string; + afterhours?: AfterhoursData; // 盘后交易数据 +} + +/** 指数行情数据 */ +export interface IndexQuoteData extends BaseQuoteData { + close?: number; + numTrades?: number; + bidPrices: number[]; + bidVolumes: number[]; + askPrices: number[]; + askVolumes: number[]; + tradingPhase?: string; +} + +/** 债券行情数据 */ +export interface BondQuoteData extends BaseQuoteData { + numTrades?: number; + weightedAvgPx?: number; + bidPrices: number[]; + bidVolumes: number[]; + askPrices: number[]; + askVolumes: number[]; + tradingPhase?: string; + isBond: true; +} + +/** 港股行情数据 */ +export interface HKStockQuoteData extends BaseQuoteData { + numTrades?: number; + nominalPx?: number; // 按盘价 + referencePx?: number; // 参考价 + bidPrices: number[]; + bidVolumes: number[]; + askPrices: number[]; + askVolumes: number[]; + tradingPhase?: string; + isHK: true; +} + +/** 统一行情数据类型 */ +export type QuoteData = StockQuoteData | IndexQuoteData | BondQuoteData | HKStockQuoteData; + +/** 行情数据字典 */ +export interface QuotesMap { + [code: string]: QuoteData; +} + +// ==================== 上交所 WebSocket 消息类型 ==================== + +/** 上交所行情数据 */ +export interface SSEQuoteItem { + security_id: string; + security_name: string; + prev_close: number; + open_price: number; + high_price: number; + low_price: number; + last_price: number; + close_price: number; + volume: number; + amount: number; + bid_prices?: number[]; + bid_volumes?: number[]; + ask_prices?: number[]; + ask_volumes?: number[]; + trading_status?: string; + trade_time?: string; + update_time?: string; +} + +/** 上交所消息 */ +export interface SSEMessage { + type: 'stock' | 'index' | 'etf' | 'bond' | 'option' | 'subscribed' | 'pong' | 'error'; + timestamp?: string; + data?: Record; + channels?: string[]; + message?: string; +} + +// ==================== 深交所 WebSocket 消息类型 ==================== + +/** 深交所数据类别 */ +export type SZSECategory = + | 'stock' // 300111 股票快照 + | 'bond' // 300211 债券快照 + | 'afterhours_block' // 300611 盘后定价大宗交易 + | 'afterhours_trading' // 303711 盘后定价交易 + | 'hk_stock' // 306311 港股快照 + | 'index' // 309011 指数快照 + | 'volume_stats' // 309111 成交量统计 + | 'fund_nav'; // 309211 基金净值 + +/** 深交所股票行情数据 */ +export interface SZSEStockData { + security_id: string; + orig_time?: number; + channel_no?: number; + trading_phase?: string; + last_px: number; + open_px: number; + high_px: number; + low_px: number; + prev_close: number; + volume: number; + amount: number; + num_trades?: number; + upper_limit?: number; + lower_limit?: number; + bids?: OrderBookLevel[]; + asks?: OrderBookLevel[]; +} + +/** 深交所指数行情数据 */ +export interface SZSEIndexData { + security_id: string; + orig_time?: number; + channel_no?: number; + trading_phase?: string; + current_index: number; + open_index: number; + high_index: number; + low_index: number; + close_index?: number; + prev_close: number; + volume: number; + amount: number; + num_trades?: number; +} + +/** 深交所债券行情数据 */ +export interface SZSEBondData { + security_id: string; + orig_time?: number; + channel_no?: number; + trading_phase?: string; + last_px: number; + open_px: number; + high_px: number; + low_px: number; + prev_close: number; + weighted_avg_px?: number; + volume: number; + amount: number; + num_trades?: number; + auction_volume?: number; + auction_amount?: number; +} + +/** 深交所港股行情数据 */ +export interface SZSEHKStockData { + security_id: string; + orig_time?: number; + channel_no?: number; + trading_phase?: string; + last_px: number; + open_px: number; + high_px: number; + low_px: number; + prev_close: number; + nominal_px?: number; + reference_px?: number; + volume: number; + amount: number; + num_trades?: number; + vcm_start_time?: number; + vcm_end_time?: number; + bids?: OrderBookLevel[]; + asks?: OrderBookLevel[]; +} + +/** 深交所盘后交易数据 */ +export interface SZSEAfterhoursData { + security_id: string; + orig_time?: number; + channel_no?: number; + trading_phase?: string; + prev_close: number; + bid_px: number; + bid_size: number; + offer_px: number; + offer_size: number; + volume: number; + amount: number; + num_trades?: number; +} + +/** 深交所实时消息 */ +export interface SZSERealtimeMessage { + type: 'realtime'; + category: SZSECategory; + msg_type?: number; + timestamp: string; + data: SZSEStockData | SZSEIndexData | SZSEBondData | SZSEHKStockData | SZSEAfterhoursData; +} + +/** 深交所快照消息 */ +export interface SZSESnapshotMessage { + type: 'snapshot'; + timestamp: string; + data: { + stocks?: SZSEStockData[]; + indexes?: SZSEIndexData[]; + bonds?: SZSEBondData[]; + }; +} + +/** 深交所消息类型 */ +export type SZSEMessage = SZSERealtimeMessage | SZSESnapshotMessage | { type: 'pong' }; + +// ==================== 组件 Props 类型 ==================== + +/** 自选证券项 */ +export interface WatchlistItem { + code: string; + name: string; + isIndex: boolean; +} + +/** QuoteTile 组件 Props */ +export interface QuoteTileProps { + code: string; + name: string; + quote: Partial; + isIndex?: boolean; + onRemove?: (code: string) => void; +} + +/** OrderBookPanel 组件 Props */ +export interface OrderBookPanelProps { + bidPrices?: number[]; + bidVolumes?: number[]; + askPrices?: number[]; + askVolumes?: number[]; + prevClose?: number; + upperLimit?: number; + lowerLimit?: number; + defaultLevels?: number; +} + +/** MiniTimelineChart 组件 Props */ +export interface MiniTimelineChartProps { + code: string; + isIndex?: boolean; + prevClose?: number; + currentPrice?: number; + height?: number; +} + +/** 分时数据点 */ +export interface TimelineDataPoint { + time: string; + price: number; +} + +/** useRealtimeQuote Hook 返回值 */ +export interface UseRealtimeQuoteReturn { + quotes: QuotesMap; + connected: ConnectionStatus; + subscribe: (code: string) => void; + unsubscribe: (code: string) => void; +} From 5dcf1fa5744e2fd13e3985b63d0b1261f8e84618 Mon Sep 17 00:00:00 2001 From: zzlgreat Date: Wed, 10 Dec 2025 11:30:19 +0800 Subject: [PATCH 11/20] update pay ui --- app.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/app.py b/app.py index 0980cf9f..ec33fc87 100755 --- a/app.py +++ b/app.py @@ -7819,7 +7819,7 @@ def get_index_realtime(index_code): }) except Exception as e: - logger.error(f"获取指数实时行情失败: {index_code}, 错误: {str(e)}") + app.logger.error(f"获取指数实时行情失败: {index_code}, 错误: {str(e)}") return jsonify({ 'success': False, 'error': str(e), @@ -12589,7 +12589,7 @@ def get_hotspot_overview(): 'is_v2': True, }) except Exception as v2_err: - logger.debug(f"V2 表查询失败，使用旧表: {v2_err}") + app.logger.debug(f"V2 表查询失败，使用旧表: {v2_err}") # Fallback: 查询旧表 if not use_v2: @@ -12642,11 +12642,12 @@ def get_hotspot_overview(): 'is_v2': False, }) except Exception as old_err: - logger.debug(f"旧表查询也失败: {old_err}") + app.logger.debug(f"旧表查询也失败: {old_err}") # 尝试批量获取概念名称 if alerts: concept_ids = list(set(a['concept_id'] for a in alerts)) + concept_names = {} # 初始化 concept_names 字典 try: from elasticsearch import Elasticsearch es_client = Elasticsearch(["http://222.128.1.157:19200"]) @@ -12663,7 +12664,7 @@ def get_hotspot_overview(): if alert['concept_id'] in concept_names: alert['concept_name'] = concept_names[alert['concept_id']] except Exception as e: - logger.warning(f"获取概念名称失败: {e}") + app.logger.warning(f"获取概念名称失败: {e}") # 计算统计信息 day_high = max([d['price'] for d in index_timeline if d['price']], default=None) @@ -12701,7 +12702,7 @@ def get_hotspot_overview(): except Exception as e: import traceback error_trace = traceback.format_exc() - logger.error(f"获取热点概览数据失败: {error_trace}") + app.logger.error(f"获取热点概览数据失败: {error_trace}") return jsonify({ 'success': False, 'error': str(e), @@ -12805,7 +12806,7 @@ def get_concept_alerts(): except Exception as e: import traceback - logger.error(f"获取概念异动列表失败: {traceback.format_exc()}") + app.logger.error(f"获取概念异动列表失败: {traceback.format_exc()}") return jsonify({ 'success': False, 'error': str(e) From 2a492a94914800f4f205c92e87c353c0773b58a4 Mon Sep 17 00:00:00 2001 From: zzlgreat Date: Wed, 10 Dec 2025 11:42:34 +0800 Subject: [PATCH 12/20] update pay ui --- .../FlexScreen/hooks/useRealtimeQuote.ts | 110 ++--- sse_html.html | 378 ++++++++++++++++++ szse_html.html | 289 +++++++++++++ valuefrontier.conf | 83 ++-- 4 files changed, 784 insertions(+), 76 deletions(-) create mode 100644 sse_html.html create mode 100644 szse_html.html diff --git a/src/views/StockOverview/components/FlexScreen/hooks/useRealtimeQuote.ts b/src/views/StockOverview/components/FlexScreen/hooks/useRealtimeQuote.ts index dfb69bfc..1167614a 100644 --- a/src/views/StockOverview/components/FlexScreen/hooks/useRealtimeQuote.ts +++ b/src/views/StockOverview/components/FlexScreen/hooks/useRealtimeQuote.ts @@ -402,59 +402,77 @@ export const useRealtimeQuote = (codes: string[] = []): UseRealtimeQuoteReturn = }, []); const createConnection = useCallback((exchange: Exchange) => { + // 检查是否是 HTTPS 页面尝试连接 ws://（Mixed Content） + const isHttps = typeof window !== 'undefined' && window.location.protocol === 'https:'; + const wsUrl = WS_CONFIG[exchange]; + const isInsecureWs = wsUrl.startsWith('ws://'); + + if (isHttps && isInsecureWs) { + logger.warn( + 'FlexScreen', + `${exchange} WebSocket 连接被跳过：HTTPS 页面无法连接不安全的 ws:// 端点` + ); + return; + } + if (wsRefs.current[exchange]) { wsRefs.current[exchange]!.close(); } - const ws = new WebSocket(WS_CONFIG[exchange]); - wsRefs.current[exchange] = ws; + try { + const ws = new WebSocket(wsUrl); + wsRefs.current[exchange] = ws; - ws.onopen = () => { - logger.info('FlexScreen', `${exchange} WebSocket 已连接`); - setConnected(prev => ({ ...prev, [exchange]: true })); + ws.onopen = () => { + logger.info('FlexScreen', `${exchange} WebSocket 已连接`); + setConnected(prev => ({ ...prev, [exchange]: true })); - if (exchange === 'SSE') { - const codes = Array.from(subscribedCodes.current.SSE); - if (codes.length > 0) { - ws.send(JSON.stringify({ - action: 'subscribe', - channels: ['stock', 'index'], - codes, - })); - } - } - - startHeartbeat(exchange); - }; - - ws.onmessage = (event: MessageEvent) => { - try { - const msg = JSON.parse(event.data); - handleMessage(exchange, msg); - } catch (e) { - logger.warn('FlexScreen', `${exchange} 消息解析失败`, e); - } - }; - - ws.onerror = (error: Event) => { - logger.error('FlexScreen', `${exchange} WebSocket 错误`, error); - }; - - ws.onclose = () => { - logger.info('FlexScreen', `${exchange} WebSocket 断开`); - setConnected(prev => ({ ...prev, [exchange]: false })); - stopHeartbeat(exchange); - - // 自动重连 - if (!reconnectRefs.current[exchange] && subscribedCodes.current[exchange].size > 0) { - reconnectRefs.current[exchange] = setTimeout(() => { - reconnectRefs.current[exchange] = null; - if (subscribedCodes.current[exchange].size > 0) { - createConnection(exchange); + if (exchange === 'SSE') { + const codes = Array.from(subscribedCodes.current.SSE); + if (codes.length > 0) { + ws.send(JSON.stringify({ + action: 'subscribe', + channels: ['stock', 'index'], + codes, + })); } - }, RECONNECT_INTERVAL); - } - }; + } + + startHeartbeat(exchange); + }; + + ws.onmessage = (event: MessageEvent) => { + try { + const msg = JSON.parse(event.data); + handleMessage(exchange, msg); + } catch (e) { + logger.warn('FlexScreen', `${exchange} 消息解析失败`, e); + } + }; + + ws.onerror = (error: Event) => { + logger.error('FlexScreen', `${exchange} WebSocket 错误`, error); + }; + + ws.onclose = () => { + logger.info('FlexScreen', `${exchange} WebSocket 断开`); + setConnected(prev => ({ ...prev, [exchange]: false })); + stopHeartbeat(exchange); + + // 自动重连（仅在非 HTTPS + ws:// 场景下） + if (!reconnectRefs.current[exchange] && subscribedCodes.current[exchange].size > 0) { + reconnectRefs.current[exchange] = setTimeout(() => { + reconnectRefs.current[exchange] = null; + if (subscribedCodes.current[exchange].size > 0) { + createConnection(exchange); + } + }, RECONNECT_INTERVAL); + } + }; + } catch (e) { + logger.error('FlexScreen', `${exchange} WebSocket 连接失败`, e); + setConnected(prev => ({ ...prev, [exchange]: false })); + } }, [startHeartbeat, stopHeartbeat, handleMessage]); const subscribe = useCallback((code: string) => { diff --git a/sse_html.html b/sse_html.html new file mode 100644 index 00000000..8723bda7 --- /dev/null +++ b/sse_html.html @@ -0,0 +1,378 @@ + + + + + + VDE 实时行情 - WebSocket 测试 + + + +
+

VDE 实时行情

+ +
+
+ 未连接 +
+
更新次数: 0
+
最后更新: -
+
+
+ + + +
+
+ +
+ +
+

📊 指数行情

+ + + + + + + + + + + +
代码/名称最新涨跌涨跌幅成交额(亿)
+
+ + +
+

📈 股票行情 (前20)

+ + + + + + + + + + + + +
代码/名称最新涨跌幅买一卖一成交额(万)
+
+
+ + +
+
+ + + + diff --git a/szse_html.html b/szse_html.html new file mode 100644 index 00000000..baf8e8f0 --- /dev/null +++ b/szse_html.html @@ -0,0 +1,289 @@ + + + + + + 深交所行情 WebSocket 测试 + + + +
+

深交所行情 WebSocket 测试

+ +
+ + + + + + 未连接 +
+ +
+
+
0
+
消息总数
+
+
+
0
+
股票 (300111)
+
+
+
0
+
指数 (309011)
+
+
+
0
+
债券 (300211)
+
+
+
0
+
港股 (306311)
+
+
+
0
+
其他类型
+
+
+ +
+
+
+ 实时行情 + -- +
+
+ + + + + + + + + + + +
代码类型最新价涨跌幅成交量
+
+
+
+
+ 消息日志 + 0 条 +
+
+
+
+
+ + + + diff --git a/valuefrontier.conf b/valuefrontier.conf index 9f4e2992..76fac284 100644 --- a/valuefrontier.conf +++ b/valuefrontier.conf @@ -112,6 +112,42 @@ server { proxy_buffering off; } + # ============================================ + # 实时行情 WebSocket 代理（灵活屏功能） + # ============================================ + + # 上交所实时行情 WebSocket + location /ws/sse { + proxy_pass http://49.232.185.254:8765; + proxy_http_version 1.1; + proxy_set_header Upgrade $http_upgrade; + proxy_set_header Connection $connection_upgrade; + proxy_set_header Host $host; + proxy_set_header X-Real-IP $remote_addr; + proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; + proxy_set_header X-Forwarded-Proto $scheme; + proxy_connect_timeout 7d; + proxy_send_timeout 7d; + proxy_read_timeout 7d; + proxy_buffering off; + } + + # 深交所实时行情 WebSocket + location /ws/szse { + proxy_pass http://222.128.1.157:8765; + proxy_http_version 1.1; + proxy_set_header Upgrade $http_upgrade; + proxy_set_header Connection $connection_upgrade; + proxy_set_header Host $host; + proxy_set_header X-Real-IP $remote_addr; + proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; + proxy_set_header X-Forwarded-Proto $scheme; + proxy_connect_timeout 7d; + proxy_send_timeout 7d; + proxy_read_timeout 7d; + proxy_buffering off; + } + location /mcp/ { proxy_pass http://127.0.0.1:8900/; proxy_http_version 1.1; @@ -142,7 +178,6 @@ server { } } - # 概念板块API代理 location /concept-api/ { proxy_pass http://222.128.1.157:16801/; @@ -158,6 +193,7 @@ server { proxy_send_timeout 60s; proxy_read_timeout 60s; } + # Elasticsearch API代理（价值论坛） location /es-api/ { proxy_pass http://222.128.1.157:19200/; @@ -223,36 +259,7 @@ server { proxy_send_timeout 86400s; proxy_read_timeout 86400s; } - # AI Chat 应用 (Next.js) - MCP 集成 - # AI Chat 静态资源（图片、CSS、JS） - location ~ ^/ai-chat/(images|_next/static|_next/image|favicon.ico) { - proxy_pass http://127.0.0.1:3000; - proxy_http_version 1.1; - proxy_set_header Host $host; - proxy_set_header X-Real-IP $remote_addr; - proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; - proxy_set_header X-Forwarded-Proto $scheme; - # 缓存设置 - expires 30d; - add_header Cache-Control "public, immutable"; - } - - # AI Chat 主应用 - location /ai-chat { - proxy_pass http://127.0.0.1:3000; - proxy_http_version 1.1; - proxy_set_header Host $host; - proxy_set_header X-Real-IP $remote_addr; - proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; - proxy_set_header X-Forwarded-Proto $scheme; - proxy_set_header Cookie $http_cookie; - proxy_pass_request_headers on; - proxy_set_header Upgrade $http_upgrade; - proxy_set_header Connection "upgrade"; - proxy_buffering off; - proxy_cache off; - } # iframe 内部资源代理（Bytedesk 聊天窗口的 CSS/JS） location /chat/ { proxy_pass http://43.143.189.195/chat/; @@ -326,6 +333,22 @@ server { add_header Cache-Control "public, max-age=86400"; } + # Bytedesk 文件访问代理（仅 2025 年文件） + location ^~ /file/2025/ { + proxy_pass http://43.143.189.195/file/2025/; + proxy_http_version 1.1; + proxy_set_header Host $host; + proxy_set_header X-Real-IP $remote_addr; + proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; + proxy_set_header X-Forwarded-Proto $scheme; + + # 缓存配置 + proxy_cache_valid 200 1d; + expires 1d; + add_header Cache-Control "public, max-age=86400"; + add_header Access-Control-Allow-Origin *; + } + # Visitor API 代理（Bytedesk 初始化接口） location /visitor/ { From 794542eeeebe330a41abebf5f7caa65e3e0e1787 Mon Sep 17 00:00:00 2001 From: zzlgreat Date: Wed, 10 Dec 2025 11:43:56 +0800 Subject: [PATCH 13/20] update pay ui --- .../components/FlexScreen/hooks/constants.ts | 37 +++++++++++++++++-- .../FlexScreen/hooks/useRealtimeQuote.ts | 13 +++++-- 2 files changed, 42 insertions(+), 8 deletions(-) diff --git a/src/views/StockOverview/components/FlexScreen/hooks/constants.ts b/src/views/StockOverview/components/FlexScreen/hooks/constants.ts index 6f1717b3..f9d41b30 100644 --- a/src/views/StockOverview/components/FlexScreen/hooks/constants.ts +++ b/src/views/StockOverview/components/FlexScreen/hooks/constants.ts @@ -4,12 +4,41 @@ import type { Exchange } from '../types'; -/** WebSocket 服务地址 */ -export const WS_CONFIG: Record = { - SSE: 'ws://49.232.185.254:8765', // 上交所 - SZSE: 'ws://222.128.1.157:8765', // 深交所 +/** + * 获取 WebSocket 配置 + * - 生产环境 (HTTPS): 通过 Nginx 代理使用 wss:// + * - 开发环境 (HTTP): 直连 ws:// + */ +const getWsConfig = (): Record => { + // 服务端渲染或测试环境使用默认配置 + if (typeof window === 'undefined') { + return { + SSE: 'ws://49.232.185.254:8765', + SZSE: 'ws://222.128.1.157:8765', + }; + } + + const isHttps = window.location.protocol === 'https:'; + const host = window.location.host; + + if (isHttps) { + // 生产环境：通过 Nginx 代理 + return { + SSE: `wss://${host}/ws/sse`, // 上交所 - Nginx 代理 + SZSE: `wss://${host}/ws/szse`, // 深交所 - Nginx 代理 + }; + } + + // 开发环境：直连 + return { + SSE: 'ws://49.232.185.254:8765', // 上交所 + SZSE: 'ws://222.128.1.157:8765', // 深交所 + }; }; +/** WebSocket 服务地址 */ +export const WS_CONFIG: Record = getWsConfig(); + /** 心跳间隔 (ms) */ export const HEARTBEAT_INTERVAL = 30000; diff --git a/src/views/StockOverview/components/FlexScreen/hooks/useRealtimeQuote.ts b/src/views/StockOverview/components/FlexScreen/hooks/useRealtimeQuote.ts index 1167614a..73f243c2 100644 --- a/src/views/StockOverview/components/FlexScreen/hooks/useRealtimeQuote.ts +++ b/src/views/StockOverview/components/FlexScreen/hooks/useRealtimeQuote.ts @@ -2,8 +2,12 @@ * 实时行情 Hook * 管理上交所和深交所 WebSocket 连接，获取实时行情数据 * - * 上交所 (SSE): ws://49.232.185.254:8765 - 需主动订阅，提供五档行情 - * 深交所 (SZSE): ws://222.128.1.157:8765 - 自动推送，提供十档行情 + * 连接方式： + * - 生产环境 (HTTPS): 通过 Nginx 代理使用 wss:// (如 wss://valuefrontier.cn/ws/sse) + * - 开发环境 (HTTP): 直连 ws:// + * + * 上交所 (SSE): 需主动订阅，提供五档行情 + * 深交所 (SZSE): 自动推送，提供十档行情 */ import { useState, useEffect, useRef, useCallback } from 'react'; @@ -402,7 +406,8 @@ export const useRealtimeQuote = (codes: string[] = []): UseRealtimeQuoteReturn = }, []); const createConnection = useCallback((exchange: Exchange) => { - // 检查是否是 HTTPS 页面尝试连接 ws://（Mixed Content） + // 防御性检查：确保 HTTPS 页面不会意外连接 ws://（Mixed Content 安全错误） + // 正常情况下 WS_CONFIG 会自动根据协议返回正确的 URL，这里是备用保护 const isHttps = typeof window !== 'undefined' && window.location.protocol === 'https:'; const wsUrl = WS_CONFIG[exchange]; const isInsecureWs = wsUrl.startsWith('ws://'); @@ -410,7 +415,7 @@ export const useRealtimeQuote = (codes: string[] = []): UseRealtimeQuoteReturn = if (isHttps && isInsecureWs) { logger.warn( 'FlexScreen', - `${exchange} WebSocket 连接被跳过：HTTPS 页面无法连接不安全的 ws:// 端点` + `${exchange} WebSocket 配置错误：HTTPS 页面尝试连接 ws:// 端点，请检查 Nginx 代理配置` ); return; } From 3f7d0e597e95c806d25480b7ec55c3012f8dac04 Mon Sep 17 00:00:00 2001 From: zzlgreat Date: Wed, 10 Dec 2025 12:06:13 +0800 Subject: [PATCH 14/20] update pay ui --- app.py | 139 ++++- .../FlexScreen/hooks/useRealtimeQuote.ts | 116 +++-- .../components/FlexScreen/hooks/utils.ts | 40 +- .../components/FlexScreen/index.tsx | 28 +- .../components/ConceptAlertList.js | 490 ++++++++++-------- 5 files changed, 532 insertions(+), 281 deletions(-) diff --git a/app.py b/app.py index ec33fc87..4266207c 100755 --- a/app.py +++ b/app.py @@ -6412,6 +6412,10 @@ def get_stock_kline(stock_code): except ValueError: return jsonify({'error': 'Invalid event_time format'}), 400 + # 确保股票代码包含后缀（ClickHouse 中数据带后缀） + if '.' not in stock_code: + stock_code = f"{stock_code}.SH" if stock_code.startswith('6') else f"{stock_code}.SZ" + # 获取股票名称 with engine.connect() as conn: result = conn.execute(text( @@ -7837,8 +7841,13 @@ def get_index_kline(index_code): except ValueError: return jsonify({'error': 'Invalid event_time format'}), 400 + # 确保指数代码包含后缀（ClickHouse 中数据带后缀） + # 399xxx -> 深交所, 其他（000xxx等）-> 上交所 + if '.' not in index_code: + index_code = f"{index_code}.SZ" if index_code.startswith('39') else f"{index_code}.SH" + # 指数名称（暂无索引表，先返回代码本身） - index_name = index_code + index_name = index_code.split('.')[0] if chart_type == 'minute': return get_index_minute_kline(index_code, event_datetime, index_name) @@ -12710,6 +12719,134 @@ def get_hotspot_overview(): }), 500 +@app.route('/api/concept//stocks', methods=['GET']) +def get_concept_stocks(concept_id): + """ + 获取概念的相关股票列表（带实时涨跌幅） + + Args: + concept_id: 概念 ID（来自 ES concept_library_v3） + + Returns: + - stocks: 股票列表 [{code, name, reason, change_pct}, ...] + """ + try: + from elasticsearch import Elasticsearch + from clickhouse_driver import Client + + # 1. 从 ES 获取概念的股票列表 + es_client = Elasticsearch(["http://222.128.1.157:19200"]) + es_result = es_client.get(index='concept_library_v3', id=concept_id) + + if not es_result.get('found'): + return jsonify({ + 'success': False, + 'error': f'概念 {concept_id} 不存在' + }), 404 + + source = es_result.get('_source', {}) + concept_name = source.get('concept', concept_id) + raw_stocks = source.get('stocks', []) + + if not raw_stocks: + return jsonify({ + 'success': True, + 'data': { + 'concept_id': concept_id, + 'concept_name': concept_name, + 'stocks': [] + } + }) + + # 提取股票代码和原因 + stocks_info = [] + stock_codes = [] + for s in raw_stocks: + if isinstance(s, dict): + code = s.get('code', '') + if code and len(code) == 6: + stocks_info.append({ + 'code': code, + 'name': s.get('name', ''), + 'reason': s.get('reason', '') + }) + stock_codes.append(code) + + # 2. 从 ClickHouse 获取最新涨跌幅 + change_map = {} + if stock_codes: + try: + ch_client = Client( + host='127.0.0.1', + port=9000, + user='default', + password='Zzl33818!', + database='stock' + ) + + # 转换为 ClickHouse 格式 + ch_codes = [] + code_mapping = {} + for code in stock_codes: + if code.startswith('6'): + ch_code = f"{code}.SH" + elif code.startswith('0') or code.startswith('3'): + ch_code = f"{code}.SZ" + else: + ch_code = f"{code}.BJ" + ch_codes.append(ch_code) + code_mapping[ch_code] = code + + ch_codes_str = "','".join(ch_codes) + + # 查询最新分钟数据 + query = f""" + SELECT code, close, pre_close + FROM stock_minute + WHERE code IN ('{ch_codes_str}') + AND timestamp >= today() + ORDER BY timestamp DESC + LIMIT 1 BY code + """ + result = ch_client.execute(query) + + for row in result: + ch_code, close_price, pre_close = row + if ch_code in code_mapping and pre_close and pre_close > 0: + original_code = code_mapping[ch_code] + change_pct = (float(close_price) - float(pre_close)) / float(pre_close) * 100 + change_map[original_code] = round(change_pct, 2) + + except Exception as ch_err: + app.logger.warning(f"ClickHouse 获取涨跌幅失败: {ch_err}") + + # 3. 合并数据 + result_stocks = [] + for stock in stocks_info: + stock['change_pct'] = change_map.get(stock['code']) + result_stocks.append(stock) + + # 按涨跌幅排序（涨停优先） + result_stocks.sort(key=lambda x: x.get('change_pct') or -999, reverse=True) + + return jsonify({ + 'success': True, + 'data': { + 'concept_id': concept_id, + 'concept_name': concept_name, + 'stock_count': len(result_stocks), + 'stocks': result_stocks + } + }) + + except Exception as e: + app.logger.error(f"获取概念股票失败: {e}") + return jsonify({ + 'success': False, + 'error': str(e) + }), 500 + + @app.route('/api/market/concept-alerts', methods=['GET']) def get_concept_alerts(): """ diff --git a/src/views/StockOverview/components/FlexScreen/hooks/useRealtimeQuote.ts b/src/views/StockOverview/components/FlexScreen/hooks/useRealtimeQuote.ts index 73f243c2..7ba99be8 100644 --- a/src/views/StockOverview/components/FlexScreen/hooks/useRealtimeQuote.ts +++ b/src/views/StockOverview/components/FlexScreen/hooks/useRealtimeQuote.ts @@ -34,6 +34,8 @@ import type { /** * 处理上交所消息 + * 注意：上交所返回的 code 不带后缀，但通过 msg.type 区分 'stock' 和 'index' + * 存储时使用带后缀的完整代码作为 key（如 000001.SH） */ const handleSSEMessage = ( msg: SSEMessage, @@ -47,12 +49,16 @@ const handleSSEMessage = ( const data = msg.data || {}; const updated: QuotesMap = { ...prevQuotes }; let hasUpdate = false; + const isIndex = msg.type === 'index'; Object.entries(data).forEach(([code, quote]: [string, SSEQuoteItem]) => { - if (subscribedCodes.has(code)) { + // 生成带后缀的完整代码（上交所统一用 .SH） + const fullCode = code.includes('.') ? code : `${code}.SH`; + + if (subscribedCodes.has(code) || subscribedCodes.has(fullCode)) { hasUpdate = true; - updated[code] = { - code: quote.security_id, + updated[fullCode] = { + code: fullCode, name: quote.security_name, price: quote.last_price, prevClose: quote.prev_close, @@ -78,6 +84,8 @@ const handleSSEMessage = ( /** * 处理深交所实时消息 + * 注意：深交所返回的 security_id 可能带后缀也可能不带 + * 存储时统一使用带后缀的完整代码作为 key（如 000001.SZ） */ const handleSZSERealtimeMessage = ( msg: SZSERealtimeMessage, @@ -85,9 +93,11 @@ const handleSZSERealtimeMessage = ( prevQuotes: QuotesMap ): QuotesMap | null => { const { category, data, timestamp } = msg; - const code = data.security_id; + const rawCode = data.security_id; + // 生成带后缀的完整代码（深交所统一用 .SZ） + const fullCode = rawCode.includes('.') ? rawCode : `${rawCode}.SZ`; - if (!subscribedCodes.has(code)) { + if (!subscribedCodes.has(rawCode) && !subscribedCodes.has(fullCode)) { return null; } @@ -99,9 +109,9 @@ const handleSZSERealtimeMessage = ( const { prices: bidPrices, volumes: bidVolumes } = extractOrderBook(stockData.bids); const { prices: askPrices, volumes: askVolumes } = extractOrderBook(stockData.asks); - updated[code] = { - code, - name: prevQuotes[code]?.name || '', + updated[fullCode] = { + code: fullCode, + name: prevQuotes[fullCode]?.name || '', price: stockData.last_px, prevClose: stockData.prev_close, open: stockData.open_px, @@ -127,9 +137,9 @@ const handleSZSERealtimeMessage = ( case 'index': { const indexData = data as SZSEIndexData; - updated[code] = { - code, - name: prevQuotes[code]?.name || '', + updated[fullCode] = { + code: fullCode, + name: prevQuotes[fullCode]?.name || '', price: indexData.current_index, prevClose: indexData.prev_close, open: indexData.open_index, @@ -154,9 +164,9 @@ const handleSZSERealtimeMessage = ( case 'bond': { const bondData = data as SZSEBondData; - updated[code] = { - code, - name: prevQuotes[code]?.name || '', + updated[fullCode] = { + code: fullCode, + name: prevQuotes[fullCode]?.name || '', price: bondData.last_px, prevClose: bondData.prev_close, open: bondData.open_px, @@ -185,9 +195,9 @@ const handleSZSERealtimeMessage = ( const { prices: bidPrices, volumes: bidVolumes } = extractOrderBook(hkData.bids); const { prices: askPrices, volumes: askVolumes } = extractOrderBook(hkData.asks); - updated[code] = { - code, - name: prevQuotes[code]?.name || '', + updated[fullCode] = { + code: fullCode, + name: prevQuotes[fullCode]?.name || '', price: hkData.last_px, prevClose: hkData.prev_close, open: hkData.open_px, @@ -215,9 +225,9 @@ const handleSZSERealtimeMessage = ( case 'afterhours_block': case 'afterhours_trading': { const afterhoursData = data as SZSEAfterhoursData; - const existing = prevQuotes[code]; + const existing = prevQuotes[fullCode]; if (existing) { - updated[code] = { + updated[fullCode] = { ...existing, afterhours: { bidPx: afterhoursData.bid_px, @@ -243,6 +253,7 @@ const handleSZSERealtimeMessage = ( /** * 处理深交所快照消息 + * 存储时统一使用带后缀的完整代码作为 key */ const handleSZSESnapshotMessage = ( msg: SZSESnapshotMessage, @@ -254,13 +265,16 @@ const handleSZSESnapshotMessage = ( let hasUpdate = false; stocks.forEach((s: SZSEStockData) => { - if (subscribedCodes.has(s.security_id)) { + const rawCode = s.security_id; + const fullCode = rawCode.includes('.') ? rawCode : `${rawCode}.SZ`; + + if (subscribedCodes.has(rawCode) || subscribedCodes.has(fullCode)) { hasUpdate = true; const { prices: bidPrices, volumes: bidVolumes } = extractOrderBook(s.bids); const { prices: askPrices, volumes: askVolumes } = extractOrderBook(s.asks); - updated[s.security_id] = { - code: s.security_id, + updated[fullCode] = { + code: fullCode, name: '', price: s.last_px, prevClose: s.prev_close, @@ -284,10 +298,13 @@ const handleSZSESnapshotMessage = ( }); indexes.forEach((i: SZSEIndexData) => { - if (subscribedCodes.has(i.security_id)) { + const rawCode = i.security_id; + const fullCode = rawCode.includes('.') ? rawCode : `${rawCode}.SZ`; + + if (subscribedCodes.has(rawCode) || subscribedCodes.has(fullCode)) { hasUpdate = true; - updated[i.security_id] = { - code: i.security_id, + updated[fullCode] = { + code: fullCode, name: '', price: i.current_index, prevClose: i.prev_close, @@ -309,10 +326,13 @@ const handleSZSESnapshotMessage = ( }); bonds.forEach((b: SZSEBondData) => { - if (subscribedCodes.has(b.security_id)) { + const rawCode = b.security_id; + const fullCode = rawCode.includes('.') ? rawCode : `${rawCode}.SZ`; + + if (subscribedCodes.has(rawCode) || subscribedCodes.has(fullCode)) { hasUpdate = true; - updated[b.security_id] = { - code: b.security_id, + updated[fullCode] = { + code: fullCode, name: '', price: b.last_px, prevClose: b.prev_close, @@ -433,12 +453,14 @@ export const useRealtimeQuote = (codes: string[] = []): UseRealtimeQuoteReturn = setConnected(prev => ({ ...prev, [exchange]: true })); if (exchange === 'SSE') { - const codes = Array.from(subscribedCodes.current.SSE); - if (codes.length > 0) { + // subscribedCodes 存的是带后缀的完整代码，发送给 WS 需要去掉后缀 + const fullCodes = Array.from(subscribedCodes.current.SSE); + const baseCodes = fullCodes.map(c => normalizeCode(c)); + if (baseCodes.length > 0) { ws.send(JSON.stringify({ action: 'subscribe', channels: ['stock', 'index'], - codes, + codes: baseCodes, })); } } @@ -481,17 +503,19 @@ export const useRealtimeQuote = (codes: string[] = []): UseRealtimeQuoteReturn = }, [startHeartbeat, stopHeartbeat, handleMessage]); const subscribe = useCallback((code: string) => { - const baseCode = normalizeCode(code); const exchange = getExchange(code); + // 确保使用带后缀的完整代码 + const fullCode = code.includes('.') ? code : `${code}.${exchange === 'SSE' ? 'SH' : 'SZ'}`; + const baseCode = normalizeCode(code); - subscribedCodes.current[exchange].add(baseCode); + subscribedCodes.current[exchange].add(fullCode); const ws = wsRefs.current[exchange]; if (exchange === 'SSE' && ws && ws.readyState === WebSocket.OPEN) { ws.send(JSON.stringify({ action: 'subscribe', channels: ['stock', 'index'], - codes: [baseCode], + codes: [baseCode], // 发送给 WS 用不带后缀的代码 })); } @@ -501,14 +525,15 @@ export const useRealtimeQuote = (codes: string[] = []): UseRealtimeQuoteReturn = }, [createConnection]); const unsubscribe = useCallback((code: string) => { - const baseCode = normalizeCode(code); const exchange = getExchange(code); + // 确保使用带后缀的完整代码 + const fullCode = code.includes('.') ? code : `${code}.${exchange === 'SSE' ? 'SH' : 'SZ'}`; - subscribedCodes.current[exchange].delete(baseCode); + subscribedCodes.current[exchange].delete(fullCode); setQuotes(prev => { const updated = { ...prev }; - delete updated[baseCode]; + delete updated[fullCode]; // 删除时也用带后缀的 key return updated; }); @@ -522,35 +547,40 @@ export const useRealtimeQuote = (codes: string[] = []): UseRealtimeQuoteReturn = }, []); // 初始化和 codes 变化处理 + // 注意：codes 现在是带后缀的完整代码（如 000001.SH） useEffect(() => { if (!codes || codes.length === 0) return; + // 使用带后缀的完整代码作为内部 key const newSseCodes = new Set(); const newSzseCodes = new Set(); codes.forEach(code => { - const baseCode = normalizeCode(code); const exchange = getExchange(code); + // 确保代码带后缀 + const fullCode = code.includes('.') ? code : `${code}.${exchange === 'SSE' ? 'SH' : 'SZ'}`; if (exchange === 'SSE') { - newSseCodes.add(baseCode); + newSseCodes.add(fullCode); } else { - newSzseCodes.add(baseCode); + newSzseCodes.add(fullCode); } }); // 更新上交所订阅 const oldSseCodes = subscribedCodes.current.SSE; const sseToAdd = [...newSseCodes].filter(c => !oldSseCodes.has(c)); + // 发送给 WebSocket 的代码需要去掉后缀 + const sseToAddBase = sseToAdd.map(c => normalizeCode(c)); if (sseToAdd.length > 0 || newSseCodes.size !== oldSseCodes.size) { subscribedCodes.current.SSE = newSseCodes; const ws = wsRefs.current.SSE; - if (ws && ws.readyState === WebSocket.OPEN && sseToAdd.length > 0) { + if (ws && ws.readyState === WebSocket.OPEN && sseToAddBase.length > 0) { ws.send(JSON.stringify({ action: 'subscribe', channels: ['stock', 'index'], - codes: sseToAdd, + codes: sseToAddBase, })); } @@ -582,7 +612,7 @@ export const useRealtimeQuote = (codes: string[] = []): UseRealtimeQuoteReturn = } } - // 清理已取消订阅的 quotes + // 清理已取消订阅的 quotes（使用带后缀的完整代码） const allNewCodes = new Set([...newSseCodes, ...newSzseCodes]); setQuotes(prev => { const updated: QuotesMap = {}; diff --git a/src/views/StockOverview/components/FlexScreen/hooks/utils.ts b/src/views/StockOverview/components/FlexScreen/hooks/utils.ts index 8f71de36..28712193 100644 --- a/src/views/StockOverview/components/FlexScreen/hooks/utils.ts +++ b/src/views/StockOverview/components/FlexScreen/hooks/utils.ts @@ -7,19 +7,25 @@ import type { Exchange, OrderBookLevel } from '../types'; /** * 判断证券代码属于哪个交易所 * @param code - 证券代码（可带或不带后缀） + * @param isIndex - 是否为指数（用于区分同代码的指数和股票，如 000001） * @returns 交易所标识 */ -export const getExchange = (code: string): Exchange => { - const baseCode = code.split('.')[0]; +export const getExchange = (code: string, isIndex?: boolean): Exchange => { + // 如果已带后缀，直接判断 + if (code.includes('.')) { + return code.endsWith('.SH') ? 'SSE' : 'SZSE'; + } + + const baseCode = code; // 6开头为上海股票 if (baseCode.startsWith('6')) { return 'SSE'; } - // 000开头的6位数是深圳股票（如平安银行000001） - if (baseCode.startsWith('000') && baseCode.length === 6) { - return 'SZSE'; + // 5开头是上海 ETF + if (baseCode.startsWith('5')) { + return 'SSE'; } // 399开头是深证指数 @@ -27,16 +33,16 @@ export const getExchange = (code: string): Exchange => { return 'SZSE'; } + // 000开头：如果是指数则为上交所（上证指数000001），否则为深交所（平安银行000001） + if (baseCode.startsWith('000')) { + return isIndex ? 'SSE' : 'SZSE'; + } + // 0、3开头是深圳股票 if (baseCode.startsWith('0') || baseCode.startsWith('3')) { return 'SZSE'; } - // 5开头是上海 ETF - if (baseCode.startsWith('5')) { - return 'SSE'; - } - // 1开头是深圳 ETF/债券 if (baseCode.startsWith('1')) { return 'SZSE'; @@ -46,6 +52,20 @@ export const getExchange = (code: string): Exchange => { return 'SSE'; }; +/** + * 获取证券代码的完整格式（带交易所后缀） + * @param code - 原始代码 + * @param isIndex - 是否为指数 + * @returns 带后缀的代码 + */ +export const getFullCode = (code: string, isIndex?: boolean): string => { + if (code.includes('.')) { + return code; // 已带后缀 + } + const exchange = getExchange(code, isIndex); + return `${code}.${exchange === 'SSE' ? 'SH' : 'SZ'}`; +}; + /** * 标准化证券代码为无后缀格式 * @param code - 原始代码 diff --git a/src/views/StockOverview/components/FlexScreen/index.tsx b/src/views/StockOverview/components/FlexScreen/index.tsx index 59132aa9..57b4d189 100644 --- a/src/views/StockOverview/components/FlexScreen/index.tsx +++ b/src/views/StockOverview/components/FlexScreen/index.tsx @@ -60,6 +60,7 @@ import { } from 'react-icons/fa'; import { useRealtimeQuote } from './hooks'; +import { getFullCode } from './hooks/utils'; import QuoteTile from './components/QuoteTile'; import { logger } from '@utils/logger'; import type { WatchlistItem, ConnectionStatus } from './types'; @@ -131,9 +132,9 @@ const FlexScreen: React.FC = () => { const searchBg = useColorModeValue('gray.50', '#2a2a2a'); const hoverBg = useColorModeValue('gray.100', '#333'); - // 获取订阅的证券代码列表 + // 获取订阅的证券代码列表（带后缀，用于区分上证指数000001.SH和平安银行000001.SZ） const subscribedCodes = useMemo(() => { - return watchlist.map(item => item.code); + return watchlist.map(item => getFullCode(item.code, item.isIndex)); }, [watchlist]); // WebSocket 实时行情 @@ -463,16 +464,19 @@ const FlexScreen: React.FC = () => { {/* 自选列表 */} {watchlist.length > 0 ? ( - {watchlist.map(item => ( - - ))} + {watchlist.map(item => { + const fullCode = getFullCode(item.code, item.isIndex); + return ( + + ); + })} ) : (
diff --git a/src/views/StockOverview/components/HotspotOverview/components/ConceptAlertList.js b/src/views/StockOverview/components/HotspotOverview/components/ConceptAlertList.js index 1c375bce..75d0f7cf 100644 --- a/src/views/StockOverview/components/HotspotOverview/components/ConceptAlertList.js +++ b/src/views/StockOverview/components/HotspotOverview/components/ConceptAlertList.js @@ -1,8 +1,8 @@ /** - * 概念异动列表组件 - * 展示当日的概念异动记录 + * 概念异动列表组件 - V2 + * 展示当日的概念异动记录，点击可展开显示相关股票 */ -import React from 'react'; +import React, { useState, useCallback } from 'react'; import { Box, VStack, @@ -13,222 +13,304 @@ import { Tooltip, useColorModeValue, Flex, - Divider, + Collapse, + Spinner, + Progress, + Table, + Thead, + Tbody, + Tr, + Th, + Td, + TableContainer, } from '@chakra-ui/react'; -import { FaBolt, FaArrowUp, FaArrowDown, FaChartLine, FaFire, FaVolumeUp } from 'react-icons/fa'; +import { FaArrowUp, FaArrowDown, FaFire, FaChevronDown, FaChevronRight } from 'react-icons/fa'; +import { useNavigate } from 'react-router-dom'; +import axios from 'axios'; import { getAlertTypeLabel, formatScore, getScoreColor } from '../utils/chartHelpers'; /** - * Z-Score 指示器组件 + * 紧凑型异动卡片 */ -const ZScoreIndicator = ({ value, label, tooltip }) => { - if (value === null || value === undefined) return null; - - // Z-Score 颜色：越大越红，越小越绿 - const getZScoreColor = (z) => { - const absZ = Math.abs(z); - if (absZ >= 3) return z > 0 ? 'red.600' : 'green.600'; - if (absZ >= 2) return z > 0 ? 'red.500' : 'green.500'; - if (absZ >= 1) return z > 0 ? 'orange.400' : 'teal.400'; - return 'gray.400'; - }; - - // Z-Score 强度条宽度（最大 5σ） - const barWidth = Math.min(Math.abs(value) / 5 * 100, 100); - - return ( - - - {label} - - = 0 ? '50%' : `${50 - barWidth / 2}%`} - w={`${barWidth / 2}%`} - h="100%" - bg={getZScoreColor(value)} - borderRadius="full" - /> - - - {value >= 0 ? '+' : ''}{value.toFixed(1)} - - - - ); -}; - -/** - * 持续确认率指示器 - */ -const ConfirmRatioIndicator = ({ ratio }) => { - if (ratio === null || ratio === undefined) return null; - - const percent = Math.round(ratio * 100); - const color = percent >= 80 ? 'green' : percent >= 60 ? 'orange' : 'red'; - - return ( - - - - - - - {percent}% - - - - ); -}; - -/** - * 单个异动项组件 - */ -const AlertItem = ({ alert, onClick, isSelected }) => { +const AlertCard = ({ alert, isExpanded, onToggle, stocks, loadingStocks }) => { + const navigate = useNavigate(); const bgColor = useColorModeValue('white', '#1a1a1a'); - const hoverBg = useColorModeValue('gray.50', '#2a2a2a'); + const hoverBg = useColorModeValue('gray.50', '#252525'); const borderColor = useColorModeValue('gray.200', '#333'); - const selectedBg = useColorModeValue('purple.50', '#2a2a3a'); + const expandedBg = useColorModeValue('purple.50', '#1e1e2e'); const isUp = alert.alert_type !== 'surge_down'; const typeColor = isUp ? 'red' : 'green'; const isV2 = alert.is_v2; - // 获取异动类型图标 - const getTypeIcon = (type) => { - switch (type) { - case 'surge_up': - case 'surge': - return FaArrowUp; - case 'surge_down': - return FaArrowDown; - case 'limit_up': - return FaFire; - case 'volume_spike': - return FaVolumeUp; - case 'rank_jump': - return FaChartLine; - default: - return FaBolt; - } + // 点击股票跳转 + const handleStockClick = (e, stockCode) => { + e.stopPropagation(); + navigate(`/company?scode=${stockCode}`); }; return ( onClick?.(alert)} + _hover={{ borderColor: 'purple.300' }} > - - {/* 左侧：概念名称和时间 */} - - - - + {/* 主卡片 - 点击展开 */} + + + {/* 左侧：名称 + 类型 */} + + + + {alert.concept_name} {isV2 && ( - + V2 )} - + + {/* 右侧：分数 */} + + {formatScore(alert.final_score)}分 + + + + {/* 第二行：时间 + 关键指标 */} + + {alert.time} {getAlertTypeLabel(alert.alert_type)} - {/* V2: 持续确认率 */} + {/* 确认率 */} {isV2 && alert.confirm_ratio !== undefined && ( - + + + = 0.8 ? 'green.500' : 'orange.500'} + /> + + {Math.round(alert.confirm_ratio * 100)}% + )} - {/* V2: Z-Score 指标行 */} - {isV2 && (alert.alpha_zscore !== undefined || alert.amt_zscore !== undefined) && ( - - - + {/* Alpha + Z-Score 简化显示 */} + + {alert.alpha !== undefined && ( + = 0 ? 'red.500' : 'green.500'} fontWeight="medium"> + α {alert.alpha >= 0 ? '+' : ''}{alert.alpha.toFixed(2)}% + + )} + {isV2 && alert.alpha_zscore !== undefined && ( + + + + = 0 ? '50%' : undefined} + right={alert.alpha_zscore < 0 ? '50%' : undefined} + w={`${Math.min(Math.abs(alert.alpha_zscore) / 5 * 50, 50)}%`} + h="100%" + bg={alert.alpha_zscore >= 0 ? 'red.500' : 'green.500'} + /> + + = 0 ? 'red.400' : 'green.400'}> + {alert.alpha_zscore >= 0 ? '+' : ''}{alert.alpha_zscore.toFixed(1)}σ + + + + )} + {alert.limit_up_ratio > 0.05 && ( + + + {Math.round(alert.limit_up_ratio * 100)}% + + )} + + + + + {/* 展开的股票列表 */} + + + {loadingStocks ? ( + + + 加载相关股票... - )} - - - {/* 右侧：分数和关键指标 */} - - {/* 综合得分 */} - {alert.final_score !== undefined && ( - - - {formatScore(alert.final_score)}分 - - - )} - - {/* Alpha 值 */} - {alert.alpha !== undefined && ( - = 0 ? 'red.500' : 'green.500'} fontWeight="medium"> - α {alert.alpha >= 0 ? '+' : ''}{alert.alpha.toFixed(2)}% + ) : stocks && stocks.length > 0 ? ( + + + + + + + + + + + {stocks.slice(0, 10).map((stock, idx) => ( + handleStockClick(e, stock.code || stock.stock_code)} + > + + + + + ))} + +
股票涨跌原因
+ + {stock.name || stock.stock_name} + + + 0 ? 'red.400' : + stock.change_pct < 0 ? 'green.400' : 'gray.400' + } + > + {stock.change_pct !== undefined + ? `${stock.change_pct > 0 ? '+' : ''}${stock.change_pct.toFixed(2)}%` + : '-' + } + + + + {stock.reason || '-'} + +
+ {stocks.length > 10 && ( + + 共 {stocks.length} 只相关股票，显示前 10 只 + + )} + + ) : ( + + 暂无相关股票数据 )} - - {/* V2: 动量指标 */} - {isV2 && alert.momentum_5m !== undefined && Math.abs(alert.momentum_5m) > 0.3 && ( - - 0 ? FaArrowUp : FaArrowDown} - color={alert.momentum_5m > 0 ? 'red.400' : 'green.400'} - boxSize={3} - /> - 0 ? 'red.400' : 'green.400'}> - 动量 {alert.momentum_5m > 0 ? '+' : ''}{alert.momentum_5m.toFixed(2)} - - - )} - - {/* 涨停数量 / 涨停比例 */} - {(alert.limit_up_count > 0 || (alert.limit_up_ratio > 0.05 && isV2)) && ( - - - - {alert.limit_up_count > 0 - ? `涨停 ${alert.limit_up_count}` - : `涨停 ${Math.round(alert.limit_up_ratio * 100)}%` - } - - - )} - - + + ); }; /** * 概念异动列表 - * @param {Object} props - * @param {Array} props.alerts - 异动数据数组 - * @param {Function} props.onAlertClick - 点击异动的回调 - * @param {Object} props.selectedAlert - 当前选中的异动 - * @param {number} props.maxHeight - 最大高度 */ const ConceptAlertList = ({ alerts = [], onAlertClick, selectedAlert, maxHeight = '400px' }) => { - const textColor = useColorModeValue('gray.800', 'white'); + const [expandedId, setExpandedId] = useState(null); + const [conceptStocks, setConceptStocks] = useState({}); + const [loadingConcepts, setLoadingConcepts] = useState({}); + const subTextColor = useColorModeValue('gray.500', 'gray.400'); + // 获取概念相关股票 + const fetchConceptStocks = useCallback(async (conceptId) => { + if (conceptStocks[conceptId] || loadingConcepts[conceptId]) { + return; + } + + setLoadingConcepts(prev => ({ ...prev, [conceptId]: true })); + + try { + // 调用后端 API 获取概念股票 + const response = await axios.get(`/api/concept/${conceptId}/stocks`); + if (response.data?.success && response.data?.data?.stocks) { + setConceptStocks(prev => ({ + ...prev, + [conceptId]: response.data.data.stocks + })); + } + } catch (error) { + console.error('获取概念股票失败:', error); + // 如果 API 失败，尝试从 ES 直接获取 + try { + const esResponse = await axios.get(`/api/es/concept/${conceptId}`); + if (esResponse.data?.stocks) { + setConceptStocks(prev => ({ + ...prev, + [conceptId]: esResponse.data.stocks + })); + } + } catch (esError) { + console.error('ES 获取也失败:', esError); + setConceptStocks(prev => ({ ...prev, [conceptId]: [] })); + } + } finally { + setLoadingConcepts(prev => ({ ...prev, [conceptId]: false })); + } + }, [conceptStocks, loadingConcepts]); + + // 切换展开状态 + const handleToggle = useCallback((alert) => { + const alertKey = `${alert.concept_id}-${alert.time}`; + + if (expandedId === alertKey) { + setExpandedId(null); + } else { + setExpandedId(alertKey); + // 获取股票数据 + if (alert.concept_id) { + fetchConceptStocks(alert.concept_id); + } + } + + // 通知父组件 + onAlertClick?.(alert); + }, [expandedId, fetchConceptStocks, onAlertClick]); + if (!alerts || alerts.length === 0) { return ( @@ -239,51 +321,29 @@ const ConceptAlertList = ({ alerts = [], onAlertClick, selectedAlert, maxHeight ); } - // 按时间分组 - const groupedAlerts = alerts.reduce((acc, alert) => { - const time = alert.time || '未知时间'; - if (!acc[time]) { - acc[time] = []; - } - acc[time].push(alert); - return acc; - }, {}); - - // 按时间排序 - const sortedTimes = Object.keys(groupedAlerts).sort(); + // 按时间倒序排列 + const sortedAlerts = [...alerts].sort((a, b) => { + const timeA = a.time || '00:00'; + const timeB = b.time || '00:00'; + return timeB.localeCompare(timeA); + }); return ( - - - {sortedTimes.map((time, timeIndex) => ( - - {/* 时间分隔线 */} - {timeIndex > 0 && } - - {/* 时间标签 */} - - - - {time} - - - ({groupedAlerts[time].length}个异动) - - - - {/* 该时间点的异动 */} - - {groupedAlerts[time].map((alert, idx) => ( - - ))} - - - ))} + + + {sortedAlerts.map((alert, idx) => { + const alertKey = `${alert.concept_id}-${alert.time}`; + return ( + handleToggle(alert)} + stocks={conceptStocks[alert.concept_id]} + loadingStocks={loadingConcepts[alert.concept_id]} + /> + ); + })} ); From ad97b5e7695b96804c2c165280c60abe30ce1e91 Mon Sep 17 00:00:00 2001 From: zzlgreat Date: Wed, 10 Dec 2025 12:22:40 +0800 Subject: [PATCH 15/20] update pay ui --- app.py | 174 ++++++++++++------ .../components/FlexScreen/index.tsx | 30 ++- .../components/ConceptAlertList.js | 106 ++++++----- .../HotspotOverview/utils/chartHelpers.js | 7 +- 4 files changed, 197 insertions(+), 120 deletions(-) diff --git a/app.py b/app.py index 4266207c..6adbdfbb 100755 --- a/app.py +++ b/app.py @@ -12053,10 +12053,11 @@ def get_market_summary(seccode): @app.route('/api/stocks/search', methods=['GET']) def search_stocks(): - """搜索股票（支持股票代码、股票简称、拼音首字母）""" + """搜索股票和指数（支持代码、名称搜索）""" try: query = request.args.get('q', '').strip() limit = request.args.get('limit', 20, type=int) + search_type = request.args.get('type', 'all') # all, stock, index if not query: return jsonify({ @@ -12064,73 +12065,132 @@ def search_stocks(): 'error': '请输入搜索关键词' }), 400 + results = [] + with engine.connect() as conn: - test_sql = text(""" - SELECT SECCODE, SECNAME, F001V, F003V, F010V, F011V - FROM ea_stocklist - WHERE SECCODE = '300750' - OR F001V LIKE '%ndsd%' LIMIT 5 - """) - test_result = conn.execute(test_sql).fetchall() + # 搜索指数（优先显示指数，因为通常用户搜索代码时指数更常用） + if search_type in ('all', 'index'): + index_sql = text(""" + SELECT DISTINCT + INDEXCODE as stock_code, + SECNAME as stock_name, + INDEXNAME as full_name, + F018V as exchange + FROM ea_exchangeindex + WHERE ( + UPPER(INDEXCODE) LIKE UPPER(:query_pattern) + OR UPPER(SECNAME) LIKE UPPER(:query_pattern) + OR UPPER(INDEXNAME) LIKE UPPER(:query_pattern) + ) + ORDER BY CASE + WHEN UPPER(INDEXCODE) = UPPER(:exact_query) THEN 1 + WHEN UPPER(SECNAME) = UPPER(:exact_query) THEN 2 + WHEN UPPER(INDEXCODE) LIKE UPPER(:prefix_pattern) THEN 3 + WHEN UPPER(SECNAME) LIKE UPPER(:prefix_pattern) THEN 4 + ELSE 5 + END, + INDEXCODE + LIMIT :limit + """) - # 构建搜索SQL - 支持股票代码、股票简称、拼音简称搜索 - search_sql = text(""" - SELECT DISTINCT SECCODE as stock_code, - SECNAME as stock_name, - F001V as pinyin_abbr, - F003V as security_type, - F005V as exchange, - F011V as listing_status - FROM ea_stocklist - WHERE ( - UPPER(SECCODE) LIKE UPPER(:query_pattern) - OR UPPER(SECNAME) LIKE UPPER(:query_pattern) - OR UPPER(F001V) LIKE UPPER(:query_pattern) - ) - -- 基本过滤条件：只搜索正常的A股和B股 - AND (F011V = '正常上市' OR F010V = '013001') -- 正常上市状态 - AND F003V IN ('A股', 'B股') -- 只搜索A股和B股 - ORDER BY CASE - WHEN UPPER(SECCODE) = UPPER(:exact_query) THEN 1 - WHEN UPPER(SECNAME) = UPPER(:exact_query) THEN 2 - WHEN UPPER(F001V) = UPPER(:exact_query) THEN 3 - WHEN UPPER(SECCODE) LIKE UPPER(:prefix_pattern) THEN 4 - WHEN UPPER(SECNAME) LIKE UPPER(:prefix_pattern) THEN 5 - WHEN UPPER(F001V) LIKE UPPER(:prefix_pattern) THEN 6 - ELSE 7 - END, - SECCODE LIMIT :limit - """) + index_result = conn.execute(index_sql, { + 'query_pattern': f'%{query}%', + 'exact_query': query, + 'prefix_pattern': f'{query}%', + 'limit': limit + }).fetchall() - result = conn.execute(search_sql, { - 'query_pattern': f'%{query}%', - 'exact_query': query, - 'prefix_pattern': f'{query}%', - 'limit': limit - }).fetchall() + for row in index_result: + results.append({ + 'stock_code': row.stock_code, + 'stock_name': row.stock_name, + 'full_name': row.full_name, + 'exchange': row.exchange, + 'isIndex': True, + 'security_type': '指数' + }) - stocks = [] - for row in result: - # 获取当前价格 - current_price, _ = get_latest_price_from_clickhouse(row.stock_code) + # 搜索股票 + if search_type in ('all', 'stock'): + stock_sql = text(""" + SELECT DISTINCT SECCODE as stock_code, + SECNAME as stock_name, + F001V as pinyin_abbr, + F003V as security_type, + F005V as exchange, + F011V as listing_status + FROM ea_stocklist + WHERE ( + UPPER(SECCODE) LIKE UPPER(:query_pattern) + OR UPPER(SECNAME) LIKE UPPER(:query_pattern) + OR UPPER(F001V) LIKE UPPER(:query_pattern) + ) + AND (F011V = '正常上市' OR F010V = '013001') + AND F003V IN ('A股', 'B股') + ORDER BY CASE + WHEN UPPER(SECCODE) = UPPER(:exact_query) THEN 1 + WHEN UPPER(SECNAME) = UPPER(:exact_query) THEN 2 + WHEN UPPER(F001V) = UPPER(:exact_query) THEN 3 + WHEN UPPER(SECCODE) LIKE UPPER(:prefix_pattern) THEN 4 + WHEN UPPER(SECNAME) LIKE UPPER(:prefix_pattern) THEN 5 + WHEN UPPER(F001V) LIKE UPPER(:prefix_pattern) THEN 6 + ELSE 7 + END, + SECCODE + LIMIT :limit + """) - stocks.append({ - 'stock_code': row.stock_code, - 'stock_name': row.stock_name, - 'current_price': current_price or 0, # 添加当前价格 - 'pinyin_abbr': row.pinyin_abbr, - 'security_type': row.security_type, - 'exchange': row.exchange, - 'listing_status': row.listing_status - }) + stock_result = conn.execute(stock_sql, { + 'query_pattern': f'%{query}%', + 'exact_query': query, + 'prefix_pattern': f'{query}%', + 'limit': limit + }).fetchall() + + for row in stock_result: + results.append({ + 'stock_code': row.stock_code, + 'stock_name': row.stock_name, + 'pinyin_abbr': row.pinyin_abbr, + 'security_type': row.security_type, + 'exchange': row.exchange, + 'listing_status': row.listing_status, + 'isIndex': False + }) + + # 如果搜索全部，按相关性重新排序（精确匹配优先） + if search_type == 'all': + def sort_key(item): + code = item['stock_code'].upper() + name = item['stock_name'].upper() + q = query.upper() + # 精确匹配代码优先 + if code == q: + return (0, not item['isIndex'], code) # 指数优先 + # 精确匹配名称 + if name == q: + return (1, not item['isIndex'], code) + # 前缀匹配代码 + if code.startswith(q): + return (2, not item['isIndex'], code) + # 前缀匹配名称 + if name.startswith(q): + return (3, not item['isIndex'], code) + return (4, not item['isIndex'], code) + + results.sort(key=sort_key) + + # 限制总数 + results = results[:limit] return jsonify({ 'success': True, - 'data': stocks, - 'count': len(stocks) + 'data': results, + 'count': len(results) }) except Exception as e: + app.logger.error(f"搜索股票/指数错误: {e}") return jsonify({ 'success': False, 'error': str(e) diff --git a/src/views/StockOverview/components/FlexScreen/index.tsx b/src/views/StockOverview/components/FlexScreen/index.tsx index 57b4d189..dfda551e 100644 --- a/src/views/StockOverview/components/FlexScreen/index.tsx +++ b/src/views/StockOverview/components/FlexScreen/index.tsx @@ -209,11 +209,14 @@ const FlexScreen: React.FC = () => { (security: SearchResultItem | WatchlistItem): void => { const code = 'stock_code' in security ? security.stock_code : security.code; const name = 'stock_name' in security ? security.stock_name : security.name; - const isIndex = - security.isIndex || code.startsWith('000') || code.startsWith('399'); + // 优先使用 API 返回的 isIndex 字段 + const isIndex = security.isIndex === true; - // 检查是否已存在 - if (watchlist.some(item => item.code === code)) { + // 生成唯一标识（带后缀的完整代码） + const fullCode = getFullCode(code, isIndex); + + // 检查是否已存在（使用带后缀的代码比较，避免上证指数和平安银行冲突） + if (watchlist.some(item => getFullCode(item.code, item.isIndex) === fullCode)) { toast({ title: '已在自选列表中', status: 'info', @@ -227,7 +230,7 @@ const FlexScreen: React.FC = () => { setWatchlist(prev => [...prev, { code, name, isIndex }]); toast({ - title: `已添加 ${name}`, + title: `已添加 ${name}${isIndex ? '（指数）' : ''}`, status: 'success', duration: 2000, isClosable: true, @@ -397,7 +400,7 @@ const FlexScreen: React.FC = () => { {searchResults.map((stock, index) => ( { > - - {stock.stock_name} - + + + {stock.stock_name} + + + {stock.isIndex ? '指数' : '股票'} + + {stock.stock_code} diff --git a/src/views/StockOverview/components/HotspotOverview/components/ConceptAlertList.js b/src/views/StockOverview/components/HotspotOverview/components/ConceptAlertList.js index 75d0f7cf..c1613aba 100644 --- a/src/views/StockOverview/components/HotspotOverview/components/ConceptAlertList.js +++ b/src/views/StockOverview/components/HotspotOverview/components/ConceptAlertList.js @@ -112,29 +112,29 @@ const AlertCard = ({ alert, isExpanded, onToggle, stocks, loadingStocks }) => { {getAlertTypeLabel(alert.alert_type)} {/* 确认率 */} - {isV2 && alert.confirm_ratio !== undefined && ( + {isV2 && alert.confirm_ratio != null && ( = 0.8 ? 'green.500' : 'orange.500'} + bg={(alert.confirm_ratio || 0) >= 0.8 ? 'green.500' : 'orange.500'} /> - {Math.round(alert.confirm_ratio * 100)}% + {Math.round((alert.confirm_ratio || 0) * 100)}% )} {/* Alpha + Z-Score 简化显示 */} - {alert.alpha !== undefined && ( - = 0 ? 'red.500' : 'green.500'} fontWeight="medium"> - α {alert.alpha >= 0 ? '+' : ''}{alert.alpha.toFixed(2)}% + {alert.alpha != null && ( + = 0 ? 'red.500' : 'green.500'} fontWeight="medium"> + α {(alert.alpha || 0) >= 0 ? '+' : ''}{(alert.alpha || 0).toFixed(2)}% )} - {isV2 && alert.alpha_zscore !== undefined && ( - + {isV2 && alert.alpha_zscore != null && ( + { > = 0 ? '50%' : undefined} - right={alert.alpha_zscore < 0 ? '50%' : undefined} - w={`${Math.min(Math.abs(alert.alpha_zscore) / 5 * 50, 50)}%`} + left={(alert.alpha_zscore || 0) >= 0 ? '50%' : undefined} + right={(alert.alpha_zscore || 0) < 0 ? '50%' : undefined} + w={`${Math.min(Math.abs(alert.alpha_zscore || 0) / 5 * 50, 50)}%`} h="100%" - bg={alert.alpha_zscore >= 0 ? 'red.500' : 'green.500'} + bg={(alert.alpha_zscore || 0) >= 0 ? 'red.500' : 'green.500'} /> - = 0 ? 'red.400' : 'green.400'}> - {alert.alpha_zscore >= 0 ? '+' : ''}{alert.alpha_zscore.toFixed(1)}σ + = 0 ? 'red.400' : 'green.400'}> + {(alert.alpha_zscore || 0) >= 0 ? '+' : ''}{(alert.alpha_zscore || 0).toFixed(1)}σ )} - {alert.limit_up_ratio > 0.05 && ( + {(alert.limit_up_ratio || 0) > 0.05 && ( - {Math.round(alert.limit_up_ratio * 100)}% + {Math.round((alert.limit_up_ratio || 0) * 100)}% )} @@ -193,40 +193,44 @@ const AlertCard = ({ alert, isExpanded, onToggle, stocks, loadingStocks }) => { - {stocks.slice(0, 10).map((stock, idx) => ( - handleStockClick(e, stock.code || stock.stock_code)} - > - - - {stock.name || stock.stock_name} - - - - 0 ? 'red.400' : - stock.change_pct < 0 ? 'green.400' : 'gray.400' - } - > - {stock.change_pct !== undefined - ? `${stock.change_pct > 0 ? '+' : ''}${stock.change_pct.toFixed(2)}%` - : '-' - } - - - - - {stock.reason || '-'} - - - - ))} + {stocks.slice(0, 10).map((stock, idx) => { + const changePct = stock.change_pct; + const hasChange = changePct != null && !isNaN(changePct); + return ( + handleStockClick(e, stock.code || stock.stock_code)} + > + + + {stock.name || stock.stock_name || '-'} + + + + 0 ? 'red.400' : + hasChange && changePct < 0 ? 'green.400' : 'gray.400' + } + > + {hasChange + ? `${changePct > 0 ? '+' : ''}${changePct.toFixed(2)}%` + : '-' + } + + + + + {stock.reason || '-'} + + + + ); + })} {stocks.length > 10 && ( diff --git a/src/views/StockOverview/components/HotspotOverview/utils/chartHelpers.js b/src/views/StockOverview/components/HotspotOverview/utils/chartHelpers.js index 96ba2d5b..54ff9eba 100644 --- a/src/views/StockOverview/components/HotspotOverview/utils/chartHelpers.js +++ b/src/views/StockOverview/components/HotspotOverview/utils/chartHelpers.js @@ -152,8 +152,9 @@ export const formatScore = (score) => { * @returns {string} 颜色代码 */ export const getScoreColor = (score) => { - if (score >= 80) return '#ff4757'; - if (score >= 60) return '#ff6348'; - if (score >= 40) return '#ffa502'; + const s = score || 0; + if (s >= 80) return '#ff4757'; + if (s >= 60) return '#ff6348'; + if (s >= 40) return '#ffa502'; return '#747d8c'; }; From 7379533ebdcc275451ce760e145c69c9f8530285 Mon Sep 17 00:00:00 2001 From: zzlgreat Date: Wed, 10 Dec 2025 13:23:49 +0800 Subject: [PATCH 16/20] update pay ui --- app.py | 98 ++++++++++++++++--- .../FlexScreen/hooks/useRealtimeQuote.ts | 83 +++++++++++++++- .../components/FlexScreen/hooks/utils.ts | 57 +++++++++-- 3 files changed, 213 insertions(+), 25 deletions(-) diff --git a/app.py b/app.py index 6adbdfbb..cdfdbe86 100755 --- a/app.py +++ b/app.py @@ -12832,8 +12832,61 @@ def get_concept_stocks(concept_id): }) stock_codes.append(code) - # 2. 从 ClickHouse 获取最新涨跌幅 - change_map = {} + if not stock_codes: + return jsonify({ + 'success': True, + 'data': { + 'concept_id': concept_id, + 'concept_name': concept_name, + 'stocks': stocks_info + } + }) + + # 2. 获取最新交易日和前一交易日 + today = datetime.now().date() + trading_day = None + prev_trading_day = None + + with engine.connect() as conn: + # 获取最新交易日 + result = conn.execute(text(""" + SELECT EXCHANGE_DATE FROM trading_days + WHERE EXCHANGE_DATE <= :today + ORDER BY EXCHANGE_DATE DESC LIMIT 1 + """), {"today": today}).fetchone() + if result: + trading_day = result[0].date() if hasattr(result[0], 'date') else result[0] + + # 获取前一交易日 + if trading_day: + result = conn.execute(text(""" + SELECT EXCHANGE_DATE FROM trading_days + WHERE EXCHANGE_DATE < :date + ORDER BY EXCHANGE_DATE DESC LIMIT 1 + """), {"date": trading_day}).fetchone() + if result: + prev_trading_day = result[0].date() if hasattr(result[0], 'date') else result[0] + + # 3. 从 MySQL ea_trade 获取前一交易日收盘价（F007N） + prev_close_map = {} + if prev_trading_day and stock_codes: + with engine.connect() as conn: + placeholders = ','.join([f':code{i}' for i in range(len(stock_codes))]) + params = {f'code{i}': code for i, code in enumerate(stock_codes)} + params['trade_date'] = prev_trading_day + + result = conn.execute(text(f""" + SELECT SECCODE, F007N + FROM ea_trade + WHERE SECCODE IN ({placeholders}) + AND TRADEDATE = :trade_date + AND F007N > 0 + """), params).fetchall() + + prev_close_map = {row[0]: float(row[1]) for row in result if row[1]} + + # 4. 从 ClickHouse 获取最新价格 + current_price_map = {} if stock_codes: try: ch_client = Client( @@ -12859,35 +12912,48 @@ def get_concept_stocks(concept_id): ch_codes_str = "','".join(ch_codes) - # 查询最新分钟数据 + # 查询当天最新价格 query = f""" - SELECT code, close, pre_close + SELECT code, close FROM stock_minute WHERE code IN ('{ch_codes_str}') - AND timestamp >= today() + AND toDate(timestamp) = today() ORDER BY timestamp DESC LIMIT 1 BY code """ result = ch_client.execute(query) for row in result: - ch_code, close_price, pre_close = row - if ch_code in code_mapping and pre_close and pre_close > 0: + ch_code, close_price = row + if ch_code in code_mapping and close_price: original_code = code_mapping[ch_code] - change_pct = (float(close_price) - float(pre_close)) / float(pre_close) * 100 - change_map[original_code] = round(change_pct, 2) + current_price_map[original_code] = float(close_price) except Exception as ch_err: - app.logger.warning(f"ClickHouse 获取涨跌幅失败: {ch_err}") + app.logger.warning(f"ClickHouse 获取价格失败: {ch_err}") - # 3. 合并数据 + # 5. 计算涨跌幅并合并数据 result_stocks = [] for stock in stocks_info: - stock['change_pct'] = change_map.get(stock['code']) - result_stocks.append(stock) + code = stock['code'] + prev_close = prev_close_map.get(code) + current_price = current_price_map.get(code) + + change_pct = None + if prev_close and current_price and prev_close > 0: + change_pct = round((current_price - prev_close) / prev_close * 100, 2) + + result_stocks.append({ + 'code': code, + 'name': stock['name'], + 'reason': stock['reason'], + 'change_pct': change_pct, + 'price': current_price, + 'prev_close': prev_close + }) # 按涨跌幅排序（涨停优先） - result_stocks.sort(key=lambda x: x.get('change_pct') or -999, reverse=True) + result_stocks.sort(key=lambda x: x.get('change_pct') if x.get('change_pct') is not None else -999, reverse=True) return jsonify({ 'success': True, @@ -12895,12 +12961,14 @@ def get_concept_stocks(concept_id): 'concept_id': concept_id, 'concept_name': concept_name, 'stock_count': len(result_stocks), + 'trading_day': str(trading_day) if trading_day else None, 'stocks': result_stocks } }) except Exception as e: - app.logger.error(f"获取概念股票失败: {e}") + import traceback + app.logger.error(f"获取概念股票失败: {traceback.format_exc()}") return jsonify({ 'success': False, 'error': str(e) diff --git a/src/views/StockOverview/components/FlexScreen/hooks/useRealtimeQuote.ts b/src/views/StockOverview/components/FlexScreen/hooks/useRealtimeQuote.ts index 7ba99be8..09687696 100644 --- a/src/views/StockOverview/components/FlexScreen/hooks/useRealtimeQuote.ts +++ b/src/views/StockOverview/components/FlexScreen/hooks/useRealtimeQuote.ts @@ -106,8 +106,48 @@ const handleSZSERealtimeMessage = ( switch (category) { case 'stock': { const stockData = data as SZSEStockData; - const { prices: bidPrices, volumes: bidVolumes } = extractOrderBook(stockData.bids); - const { prices: askPrices, volumes: askVolumes } = extractOrderBook(stockData.asks); + // eslint-disable-next-line @typescript-eslint/no-explicit-any + const rawData = data as any; // 用于检查替代字段名 + + // 调试日志：检查深交所返回的盘口原始数据（临时使用 warn 级别方便调试） + if (!stockData.bids || stockData.bids.length === 0) { + logger.warn('FlexScreen', `SZSE股票数据无盘口 ${fullCode}`, { + hasBids: !!stockData.bids, + hasAsks: !!stockData.asks, + bidsLength: stockData.bids?.length || 0, + asksLength: stockData.asks?.length || 0, + // 检查替代字段名 + hasBidPrices: !!rawData.bid_prices, + hasAskPrices: !!rawData.ask_prices, + dataKeys: Object.keys(stockData), // 查看服务端实际返回了哪些字段 + }); + } + + // 优先使用 bids/asks 对象数组格式，如果不存在则尝试 bid_prices/ask_prices 分离数组格式 + let bidPrices: number[] = []; + let bidVolumes: number[] = []; + let askPrices: number[] = []; + let askVolumes: number[] = []; + + if (stockData.bids && stockData.bids.length > 0) { + const extracted = extractOrderBook(stockData.bids); + bidPrices = extracted.prices; + bidVolumes = extracted.volumes; + } else if (rawData.bid_prices && Array.isArray(rawData.bid_prices)) { + // 替代格式：bid_prices 和 bid_volumes 分离 + bidPrices = rawData.bid_prices; + bidVolumes = rawData.bid_volumes || []; + } + + if (stockData.asks && stockData.asks.length > 0) { + const extracted = extractOrderBook(stockData.asks); + askPrices = extracted.prices; + askVolumes = extracted.volumes; + } else if (rawData.ask_prices && Array.isArray(rawData.ask_prices)) { + // 替代格式：ask_prices 和 ask_volumes 分离 + askPrices = rawData.ask_prices; + askVolumes = rawData.ask_volumes || []; + } updated[fullCode] = { code: fullCode, @@ -270,8 +310,43 @@ const handleSZSESnapshotMessage = ( if (subscribedCodes.has(rawCode) || subscribedCodes.has(fullCode)) { hasUpdate = true; - const { prices: bidPrices, volumes: bidVolumes } = extractOrderBook(s.bids); - const { prices: askPrices, volumes: askVolumes } = extractOrderBook(s.asks); + // eslint-disable-next-line @typescript-eslint/no-explicit-any + const rawData = s as any; // 用于检查替代字段名 + + // 调试日志：检查快照消息中的盘口数据（无盘口时警告） + if (!s.bids || s.bids.length === 0) { + logger.warn('FlexScreen', `SZSE快照股票数据无盘口 ${fullCode}`, { + hasBids: !!s.bids, + hasAsks: !!s.asks, + hasBidPrices: !!rawData.bid_prices, + hasAskPrices: !!rawData.ask_prices, + dataKeys: Object.keys(s), + }); + } + + // 优先使用 bids/asks 对象数组格式，如果不存在则尝试 bid_prices/ask_prices 分离数组格式 + let bidPrices: number[] = []; + let bidVolumes: number[] = []; + let askPrices: number[] = []; + let askVolumes: number[] = []; + + if (s.bids && s.bids.length > 0) { + const extracted = extractOrderBook(s.bids); + bidPrices = extracted.prices; + bidVolumes = extracted.volumes; + } else if (rawData.bid_prices && Array.isArray(rawData.bid_prices)) { + bidPrices = rawData.bid_prices; + bidVolumes = rawData.bid_volumes || []; + } + + if (s.asks && s.asks.length > 0) { + const extracted = extractOrderBook(s.asks); + askPrices = extracted.prices; + askVolumes = extracted.volumes; + } else if (rawData.ask_prices && Array.isArray(rawData.ask_prices)) { + askPrices = rawData.ask_prices; + askVolumes = rawData.ask_volumes || []; + } updated[fullCode] = { code: fullCode, diff --git a/src/views/StockOverview/components/FlexScreen/hooks/utils.ts b/src/views/StockOverview/components/FlexScreen/hooks/utils.ts index 28712193..dcc54890 100644 --- a/src/views/StockOverview/components/FlexScreen/hooks/utils.ts +++ b/src/views/StockOverview/components/FlexScreen/hooks/utils.ts @@ -75,20 +75,65 @@ export const normalizeCode = (code: string): string => { return code.split('.')[0]; }; +/** + * 盘口数据可能的格式（根据不同的 WebSocket 服务端实现） + */ +type OrderBookInput = + | OrderBookLevel[] // 格式1: [{price, volume}, ...] + | Array<[number, number]> // 格式2: [[price, volume], ...] + | { prices: number[]; volumes: number[] } // 格式3: {prices: [...], volumes: [...]} + | undefined; + /** * 从深交所 bids/asks 数组提取价格和量数组 - * @param orderBook - 盘口数组 [{price, volume}, ...] + * 支持多种可能的数据格式 + * @param orderBook - 盘口数据，支持多种格式 * @returns { prices, volumes } */ export const extractOrderBook = ( - orderBook: OrderBookLevel[] | undefined + orderBook: OrderBookInput ): { prices: number[]; volumes: number[] } => { - if (!orderBook || !Array.isArray(orderBook)) { + if (!orderBook) { return { prices: [], volumes: [] }; } - const prices = orderBook.map(item => item.price || 0); - const volumes = orderBook.map(item => item.volume || 0); - return { prices, volumes }; + + // 格式3: 已经是 {prices, volumes} 结构 + if (!Array.isArray(orderBook) && 'prices' in orderBook && 'volumes' in orderBook) { + return { + prices: orderBook.prices || [], + volumes: orderBook.volumes || [], + }; + } + + // 必须是数组才能继续 + if (!Array.isArray(orderBook) || orderBook.length === 0) { + return { prices: [], volumes: [] }; + } + + const firstItem = orderBook[0]; + + // 格式2: [[price, volume], ...] + if (Array.isArray(firstItem)) { + const prices = orderBook.map((item: unknown) => { + const arr = item as [number, number]; + return arr[0] || 0; + }); + const volumes = orderBook.map((item: unknown) => { + const arr = item as [number, number]; + return arr[1] || 0; + }); + return { prices, volumes }; + } + + // 格式1: [{price, volume}, ...] （标准格式） + if (typeof firstItem === 'object' && firstItem !== null) { + const typedBook = orderBook as OrderBookLevel[]; + const prices = typedBook.map(item => item.price || 0); + const volumes = typedBook.map(item => item.volume || 0); + return { prices, volumes }; + } + + return { prices: [], volumes: [] }; }; /** From 46818b733c5a4e88809b57402f6e0ba7b1d38b32 Mon Sep 17 00:00:00 2001 From: zdl <3489966805@qq.com> Date: Wed, 10 Dec 2025 13:29:08 +0800 Subject: [PATCH 17/20] =?UTF-8?q?fix:=20=E4=B8=AA=E8=82=A1=E4=B8=AD?= =?UTF-8?q?=E5=BF=83bug=E4=BF=AE=E5=A4=8D?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/components/InvestmentCalendar/index.js | 18 +++++++++++++----- src/views/Company/CompanyOverview.js | 2 +- 2 files changed, 14 insertions(+), 6 deletions(-) diff --git a/src/components/InvestmentCalendar/index.js b/src/components/InvestmentCalendar/index.js index a84a9c1f..fc689cce 100644 --- a/src/components/InvestmentCalendar/index.js +++ b/src/components/InvestmentCalendar/index.js @@ -544,11 +544,19 @@ const InvestmentCalendar = () => { render: (concepts) => ( {concepts && concepts.length > 0 ? ( - concepts.slice(0, 3).map((concept, index) => ( - }> - {Array.isArray(concept) ? concept[0] : concept} - - )) + concepts.slice(0, 3).map((concept, index) => { + // 兼容多种数据格式：字符串、数组、对象 + const conceptName = typeof concept === 'string' + ? concept + : Array.isArray(concept) + ? concept[0] + : concept?.concept || concept?.name || ''; + return ( + }> + {conceptName} + + ); + }) ) : ( 无 )} diff --git a/src/views/Company/CompanyOverview.js b/src/views/Company/CompanyOverview.js index 15fa08ee..d5db0be8 100644 --- a/src/views/Company/CompanyOverview.js +++ b/src/views/Company/CompanyOverview.js @@ -2426,7 +2426,7 @@ const CompanyAnalysisComplete = ({ stockCode: propStockCode }) => { <> {event.keywords.slice(0, 4).map((keyword, kidx) => ( - {keyword} + {typeof keyword === 'string' ? keyword : keyword.concept} ))} From 0da543b13d4b6b5d708e0573941a26feff7796ee Mon Sep 17 00:00:00 2001 From: zzlgreat Date: Wed, 10 Dec 2025 14:01:38 +0800 Subject: [PATCH 18/20] update pay ui --- .../StockChart/StockChartAntdModal.js | 27 ++++++++++++++++--- src/views/Community/components/HeroPanel.js | 19 +++++++------ 2 files changed, 34 insertions(+), 12 deletions(-) diff --git a/src/components/StockChart/StockChartAntdModal.js b/src/components/StockChart/StockChartAntdModal.js index 8cddda26..bb1a0257 100644 --- a/src/components/StockChart/StockChartAntdModal.js +++ b/src/components/StockChart/StockChartAntdModal.js @@ -313,12 +313,29 @@ const StockChartAntdModal = ({ axisPointer: { type: 'cross' }, formatter: function(params) { const d = params[0]?.dataIndex ?? 0; - const priceChangePercent = ((prices[d] - prevClose) / prevClose * 100); - const avgChangePercent = ((avgPrices[d] - prevClose) / prevClose * 100); + const price = prices[d]; + const avgPrice = avgPrices[d]; + const volume = volumes[d]; + + // 安全计算涨跌幅，处理 undefined/null/0 的情况 + const safeCalcPercent = (val, base) => { + if (val == null || base == null || base === 0) return 0; + return ((val - base) / base * 100); + }; + + const priceChangePercent = safeCalcPercent(price, prevClose); + const avgChangePercent = safeCalcPercent(avgPrice, prevClose); const priceColor = priceChangePercent >= 0 ? '#ef5350' : '#26a69a'; const avgColor = avgChangePercent >= 0 ? '#ef5350' : '#26a69a'; - return `时间：${times[d]}
现价：¥${prices[d]?.toFixed(2)} (${priceChangePercent >= 0 ? '+' : ''}${priceChangePercent.toFixed(2)}%)
均价：¥${avgPrices[d]?.toFixed(2)} (${avgChangePercent >= 0 ? '+' : ''}${avgChangePercent.toFixed(2)}%)
昨收：¥${prevClose?.toFixed(2)}
成交量：${Math.round(volumes[d]/100)}手`; + // 安全格式化数字 + const safeFixed = (val, digits = 2) => (val != null && !isNaN(val)) ? val.toFixed(digits) : '-'; + const formatPercent = (val) => { + if (val == null || isNaN(val)) return '-'; + return (val >= 0 ? '+' : '') + val.toFixed(2) + '%'; + }; + + return `时间：${times[d] || '-'}
现价：¥${safeFixed(price)} (${formatPercent(priceChangePercent)})
均价：¥${safeFixed(avgPrice)} (${formatPercent(avgChangePercent)})
昨收：¥${safeFixed(prevClose)}
成交量：${volume != null ? Math.round(volume/100) + '手' : '-'}`; } }, grid: [ @@ -337,6 +354,7 @@ const StockChartAntdModal = ({ position: 'left', axisLabel: { formatter: function(value) { + if (value == null || isNaN(value)) return '-'; return (value >= 0 ? '+' : '') + value.toFixed(2) + '%'; } }, @@ -354,11 +372,12 @@ const StockChartAntdModal = ({ position: 'right', axisLabel: { formatter: function(value) { + if (value == null || isNaN(value)) return '-'; return (value >= 0 ? '+' : '') + value.toFixed(2) + '%'; } } }, - { type: 'value', gridIndex: 1, scale: true, axisLabel: { formatter: v => Math.round(v/100) + '手' } } + { type: 'value', gridIndex: 1, scale: true, axisLabel: { formatter: v => (v != null && !isNaN(v)) ? Math.round(v/100) + '手' : '-' } } ], dataZoom: [ { type: 'inside', xAxisIndex: [0, 1], start: 0, end: 100 }, diff --git a/src/views/Community/components/HeroPanel.js b/src/views/Community/components/HeroPanel.js index 40a69a2a..3c957fe0 100644 --- a/src/views/Community/components/HeroPanel.js +++ b/src/views/Community/components/HeroPanel.js @@ -207,9 +207,12 @@ const CompactIndexCard = ({ indexCode, indexName }) => { const raw = chartData.rawData[idx]; if (!raw) return ''; + // 安全格式化数字 + const safeFixed = (val, digits = 2) => (val != null && !isNaN(val)) ? val.toFixed(digits) : '-'; + // 计算涨跌 const prevClose = raw.prev_close || (idx > 0 ? chartData.rawData[idx - 1]?.close : raw.open) || raw.open; - const changeAmount = raw.close - prevClose; + const changeAmount = (raw.close != null && prevClose != null) ? (raw.close - prevClose) : 0; const changePct = prevClose ? ((changeAmount / prevClose) * 100) : 0; const isUp = changeAmount >= 0; const color = isUp ? '#ef5350' : '#26a69a'; @@ -218,22 +221,22 @@ const CompactIndexCard = ({ indexCode, indexName }) => { return `
- 📅 ${raw.time} + 📅 ${raw.time || '-'}
开盘 - ${raw.open.toFixed(2)} + ${safeFixed(raw.open)} 收盘 - ${raw.close.toFixed(2)} + ${safeFixed(raw.close)} 最高 - ${raw.high.toFixed(2)} + ${safeFixed(raw.high)} 最低 - ${raw.low.toFixed(2)} + ${safeFixed(raw.low)}
涨跌幅 - ${sign}${changeAmount.toFixed(2)} (${sign}${changePct.toFixed(2)}%) + ${sign}${safeFixed(changeAmount)} (${sign}${safeFixed(changePct)}%)
@@ -529,7 +532,7 @@ const FlowingConcepts = () => { color={colors.text} whiteSpace="nowrap" > - {concept.change_pct > 0 ? '+' : ''}{concept.change_pct.toFixed(2)}% + {concept.change_pct > 0 ? '+' : ''}{concept.change_pct?.toFixed(2) ?? '-'}% From bd09d2986f35dafb70bc5d68b8693ce1eebbbdcc Mon Sep 17 00:00:00 2001 From: zzlgreat Date: Wed, 10 Dec 2025 14:12:11 +0800 Subject: [PATCH 19/20] update pay ui --- .../StockChart/TimelineChartModal.tsx | 24 ++++++++++++------- 1 file changed, 16 insertions(+), 8 deletions(-) diff --git a/src/components/StockChart/TimelineChartModal.tsx b/src/components/StockChart/TimelineChartModal.tsx index 7981b098..eb9eb809 100644 --- a/src/components/StockChart/TimelineChartModal.tsx +++ b/src/components/StockChart/TimelineChartModal.tsx @@ -217,27 +217,34 @@ const TimelineChartModal: React.FC = ({ if (dataIndex === undefined) return ''; const item = data[dataIndex]; - const changeColor = item.change_percent >= 0 ? '#ef5350' : '#26a69a'; - const changeSign = item.change_percent >= 0 ? '+' : ''; + if (!item) return ''; + + // 安全格式化数字 + const safeFixed = (val: any, digits = 2) => + val != null && !isNaN(val) ? Number(val).toFixed(digits) : '-'; + + const changePercent = item.change_percent ?? 0; + const changeColor = changePercent >= 0 ? '#ef5350' : '#26a69a'; + const changeSign = changePercent >= 0 ? '+' : ''; return `
-
${item.time}
+
${item.time || '-'}
价格: - ${item.price.toFixed(2)} + ${safeFixed(item.price)}
均价: - ${item.avg_price.toFixed(2)} + ${safeFixed(item.avg_price)}
涨跌幅: - ${changeSign}${item.change_percent.toFixed(2)}% + ${changeSign}${safeFixed(changePercent)}%
成交量: - ${(item.volume / 100).toFixed(0)}手 + ${item.volume != null ? (item.volume / 100).toFixed(0) : '-'}手
`; @@ -314,7 +321,7 @@ const TimelineChartModal: React.FC = ({ axisLabel: { color: '#999', fontSize: isMobile ? 10 : 12, - formatter: (value: number) => value.toFixed(2), + formatter: (value: number) => (value != null && !isNaN(value)) ? value.toFixed(2) : '-', }, }, { @@ -333,6 +340,7 @@ const TimelineChartModal: React.FC = ({ color: '#999', fontSize: isMobile ? 10 : 12, formatter: (value: number) => { + if (value == null || isNaN(value)) return '-'; if (value >= 10000) { return (value / 10000).toFixed(1) + '万'; } From 461533281c210f5af5b55f1a3dbde40b7799783a Mon Sep 17 00:00:00 2001 From: zdl <3489966805@qq.com> Date: Wed, 10 Dec 2025 14:26:16 +0800 Subject: [PATCH 20/20] =?UTF-8?q?feat:=20Company=20=E9=A1=B5=E9=9D=A2?= =?UTF-8?q?=E6=90=9C=E7=B4=A2=E6=A1=86=E6=B7=BB=E5=8A=A0=E8=82=A1=E7=A5=A8?= =?UTF-8?q?=E6=A8=A1=E7=B3=8A=E6=90=9C=E7=B4=A2=E5=8A=9F=E8=83=BD?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - 添加 AutoComplete 组件替换原 Input，支持下拉选择 - 集成 stockService.fuzzySearch 实现按代码/名称模糊匹配 - 从 Redux 获取 allStocks 数据，自动加载保障 - 选中股票自动触发查询并更新 URL 参数 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude --- src/views/Company/index.js | 75 ++++++++++++++++++++++++++++---------- 1 file changed, 56 insertions(+), 19 deletions(-) diff --git a/src/views/Company/index.js b/src/views/Company/index.js index cf5b3723..0eb747e2 100644 --- a/src/views/Company/index.js +++ b/src/views/Company/index.js @@ -1,5 +1,9 @@ import React, { useState, useEffect, useCallback } from 'react'; import { useSearchParams } from 'react-router-dom'; +import { useSelector, useDispatch } from 'react-redux'; +import { loadAllStocks } from '@store/slices/stockSlice'; +import { AutoComplete } from 'antd'; +import { stockService } from '@services/stockService'; import { Container, Heading, @@ -12,10 +16,7 @@ import { TabPanel, HStack, VStack, - Input, Button, - InputGroup, - InputLeftElement, Text, Badge, Divider, @@ -41,10 +42,22 @@ const CompanyIndex = () => { const [searchParams, setSearchParams] = useSearchParams(); const [stockCode, setStockCode] = useState(searchParams.get('scode') || '000001'); const [inputCode, setInputCode] = useState(stockCode); + const [stockOptions, setStockOptions] = useState([]); const { colorMode, toggleColorMode } = useColorMode(); const toast = useToast(); const { isAuthenticated } = useAuth(); + // 从 Redux 获取股票列表数据 + const dispatch = useDispatch(); + const allStocks = useSelector((state) => state.stock.allStocks); + + // 确保股票数据已加载 + useEffect(() => { + if (!allStocks || allStocks.length === 0) { + dispatch(loadAllStocks()); + } + }, [dispatch, allStocks]); + // 🎯 PostHog 事件追踪 const { trackStockSearched, @@ -113,6 +126,31 @@ const CompanyIndex = () => { } }; + // 模糊搜索股票（由 onSearch 触发） + const handleStockSearch = (value) => { + if (!value || !allStocks || allStocks.length === 0) { + setStockOptions([]); + return; + } + const results = stockService.fuzzySearch(value, allStocks, 10); + const options = results.map((stock) => ({ + value: stock.code, + label: `${stock.code} ${stock.name}`, + })); + setStockOptions(options); + }; + + // 选中股票 + const handleStockSelect = (value) => { + setInputCode(value); + setStockOptions([]); + if (value !== stockCode) { + trackStockSearched(value, stockCode); + setStockCode(value); + setSearchParams({ scode: value }); + } + }; + const handleWatchlistToggle = async () => { if (!stockCode) { logger.warn('CompanyIndex', 'handleWatchlistToggle', '无效的股票代码', { stockCode }); @@ -190,22 +228,21 @@ const CompanyIndex = () => { - - - - - setInputCode(e.target.value)} - onKeyPress={handleKeyPress} - borderRadius="md" - _focus={{ - borderColor: 'blue.500', - boxShadow: '0 0 0 1px #3182ce' - }} - /> - + setInputCode(value)} + placeholder="输入股票代码或名称" + style={{ width: 260 }} + size="large" + onKeyDown={(e) => { + if (e.key === 'Enter') { + handleSearch(); + } + }} + />