feat: 添加 pre-commit hook 检查代码规范

- 新增文件必须使用 TypeScript (.ts/.tsx)
- 禁止使用 fetch，提示使用 axios
- 安装 husky 和 lint-staged 依赖

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

.husky/pre-commit

@@ -0,0 +1,110 @@
+#!/bin/sh
+
+# ============================================
+# Git Pre-commit Hook
+# ============================================
+# 规则：
+# 1. src 目录下新增的代码文件必须使用 TypeScript (.ts/.tsx)
+# 2. 修改的代码不能使用 fetch，应使用 axios
+# ============================================
+
+# 颜色定义
+RED='\033[0;31m'
+YELLOW='\033[1;33m'
+GREEN='\033[0;32m'
+NC='\033[0m' # No Color
+
+has_error=0
+
+echo ""
+echo "🔍 正在检查代码规范..."
+echo ""
+
+# ============================================
+# 规则 1: 新文件必须使用 TypeScript
+# ============================================
+
+# 获取新增的文件（只检查 src 目录下的代码文件）
+new_js_files=$(git diff --cached --name-only --diff-filter=A | grep -E '^src/.*\.(js|jsx)$' || true)
+
+if [ -n "$new_js_files" ]; then
+    echo "${RED}❌ 错误: 发现新增的 JavaScript 文件${NC}"
+    echo "${YELLOW}   新文件必须使用 TypeScript (.ts/.tsx)${NC}"
+    echo ""
+    echo "   以下文件需要改为 TypeScript:"
+    echo "$new_js_files" | while read file; do
+        echo "   - $file"
+    done
+    echo ""
+    echo "   💡 提示: 请将文件扩展名改为 .ts 或 .tsx"
+    echo ""
+    has_error=1
+fi
+
+# ============================================
+# 规则 2: 禁止使用 fetch，应使用 axios
+# ============================================
+
+# 获取所有暂存的文件（新增 + 修改）
+staged_files=$(git diff --cached --name-only --diff-filter=AM | grep -E '^src/.*\.(js|jsx|ts|tsx)$' || true)
+
+if [ -n "$staged_files" ]; then
+    # 检查暂存内容中是否包含 fetch 调用
+    # 使用 git diff --cached 检查实际修改的内容
+    fetch_found=""
+
+    for file in $staged_files; do
+        # 检查该文件暂存的更改中是否有 fetch 调用
+        # 排除注释和字符串中的 fetch
+        # 匹配: fetch(, await fetch, .fetch(
+        fetch_matches=$(git diff --cached -U0 "$file" 2>/dev/null | grep -E '^\+.*[^a-zA-Z_]fetch\s*\(' | grep -v '^\+\s*//' || true)
+
+        if [ -n "$fetch_matches" ]; then
+            fetch_found="$fetch_found
+$file"
+        fi
+    done
+
+    if [ -n "$fetch_found" ]; then
+        echo "${RED}❌ 错误: 检测到使用了 fetch API${NC}"
+        echo "${YELLOW}   请使用 axios 进行 HTTP 请求${NC}"
+        echo ""
+        echo "   以下文件包含 fetch 调用:"
+        echo "$fetch_found" | while read file; do
+            if [ -n "$file" ]; then
+                echo "   - $file"
+            fi
+        done
+        echo ""
+        echo "   💡 修改建议:"
+        echo "   ${GREEN}// 替换前${NC}"
+        echo "   fetch('/api/data').then(res => res.json())"
+        echo ""
+        echo "   ${GREEN}// 替换后${NC}"
+        echo "   import axios from 'axios';"
+        echo "   axios.get('/api/data').then(res => res.data)"
+        echo ""
+        has_error=1
+    fi
+fi
+
+# ============================================
+# 检查结果
+# ============================================
+
+if [ $has_error -eq 1 ]; then
+    echo "${RED}========================================${NC}"
+    echo "${RED}提交被阻止，请修复以上问题后重试${NC}"
+    echo "${RED}========================================${NC}"
+    echo ""
+    exit 1
+fi
+
+echo "${GREEN}✅ 代码规范检查通过${NC}"
+echo ""
+
+# 运行 lint-staged（如果配置了）
+# 可选：在 package.json 中添加 "lint-staged" 配置来启用代码格式化
+# if [ -f "package.json" ] && grep -q '"lint-staged"' package.json; then
+#     npx lint-staged
+# fi

app.py

@@ -165,7 +165,7 @@ WECHAT_OPEN_APPID = 'wxa8d74c47041b5f87'
 WECHAT_OPEN_APPSECRET = 'eedef95b11787fd7ca7f1acc6c9061bc'
 
 # 微信公众号配置（H5 网页授权用）
-WECHAT_MP_APPID = 'wx4e4b759f8fa9e43a'
+WECHAT_MP_APPID = 'wx8afd36f7c7b21ba0'
 WECHAT_MP_APPSECRET = 'ef1ca9064af271bb0405330efbc495aa'
 
 # 微信回调地址
@@ -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(
@@ -7819,7 +7823,7 @@ 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),
@@ -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)
@@ -12044,10 +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({
@@ -12055,73 +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)
@@ -12465,6 +12534,10 @@ 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')
@@ -12532,60 +12605,135 @@ 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（优先从 V2 表，fallback 到旧表）
         alerts = []
+        use_v2 = False
+
         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})
+            # 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
 
-            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
+                        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:
+                app.logger.debug(f"V2 表查询失败，使用旧表: {v2_err}")
 
-                # 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')
+            # 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})
 
-                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
-                })
+                    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
+
+                        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[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:
+                    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"])
+                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:
+                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)
@@ -12614,6 +12762,7 @@ 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'])
                 }
             }
@@ -12621,7 +12770,205 @@ 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()
+        app.logger.error(f"获取热点概览数据失败: {error_trace}")
+        return jsonify({
+            'success': False,
+            'error': str(e),
+            'traceback': error_trace  # 临时返回完整错误信息用于调试
+        }), 500
+
+
+@app.route('/api/concept/<concept_id>/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)
+
+        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(
+                    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)
+
+                # 查询当天最新价格
+                query = f"""
+                    SELECT code, close
+                    FROM stock_minute
+                    WHERE code IN ('{ch_codes_str}')
+                    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 = row
+                    if ch_code in code_mapping and close_price:
+                        original_code = code_mapping[ch_code]
+                        current_price_map[original_code] = float(close_price)
+
+            except Exception as ch_err:
+                app.logger.warning(f"ClickHouse 获取价格失败: {ch_err}")
+
+        # 5. 计算涨跌幅并合并数据
+        result_stocks = []
+        for stock in stocks_info:
+            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') if x.get('change_pct') is not None else -999, reverse=True)
+
+        return jsonify({
+            'success': True,
+            'data': {
+                '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:
+        import traceback
+        app.logger.error(f"获取概念股票失败: {traceback.format_exc()}")
         return jsonify({
             'success': False,
             'error': str(e)
@@ -12724,7 +13071,7 @@ 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)

concept_alert_alpha_20251208.log

@@ -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 - ============================================================

concept_quota_realtime.py

@@ -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()

create_tables.py

@@ -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!')

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()

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)

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()

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]
+}

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
+}

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!")

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()

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

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__":

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()

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()

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()

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)

package.json

@@ -131,12 +131,14 @@
     "eslint-plugin-prettier": "3.4.0",
     "gulp": "4.0.2",
     "gulp-append-prepend": "1.0.9",
+    "husky": "^9.1.7",
     "imagemin": "^9.0.1",
     "imagemin-mozjpeg": "^10.0.0",
     "imagemin-pngquant": "^10.0.0",
     "kill-port": "^2.0.1",
     "less": "^4.4.2",
     "less-loader": "^12.3.0",
+    "lint-staged": "^16.2.7",
     "msw": "^2.11.5",
     "prettier": "2.2.1",
     "react-error-overlay": "6.0.9",

src/components/Charts/Stock/MiniTimelineChart.js

@@ -1,4 +1,4 @@
-// src/views/Community/components/StockDetailPanel/components/MiniTimelineChart.js
+// src/components/Charts/Stock/MiniTimelineChart.js
 import React, { useState, useEffect, useMemo, useRef, useCallback } from 'react';
 import ReactECharts from 'echarts-for-react';
 import * as echarts from 'echarts';

src/components/Charts/Stock/hooks/index.js

@@ -0,0 +1,4 @@
+// src/components/Charts/Stock/hooks/index.js
+// 股票图表 Hooks 统一导出
+
+export { useEventStocks } from './useEventStocks';

src/components/Charts/Stock/hooks/useEventStocks.js

@@ -1,4 +1,4 @@
-// src/views/Community/components/StockDetailPanel/hooks/useEventStocks.js
+// src/components/Charts/Stock/hooks/useEventStocks.js
 import { useSelector, useDispatch, shallowEqual } from 'react-redux';
 import { useEffect, useCallback, useMemo } from 'react';
 import {
@@ -8,8 +8,8 @@ import {
   fetchHistoricalEvents,
   fetchChainAnalysis,
   fetchExpectationScore
-} from '../../../../../store/slices/stockSlice';
-import { logger } from '../../../../../utils/logger';
+} from '@store/slices/stockSlice';
+import { logger } from '@utils/logger';
 
 /**
  * 事件股票数据 Hook

src/components/Charts/Stock/index.js

@@ -0,0 +1,5 @@
+// src/components/Charts/Stock/index.js
+// 股票图表组件统一导出
+
+export { default as MiniTimelineChart } from './MiniTimelineChart';
+export { useEventStocks } from './hooks/useEventStocks';

src/components/Citation/CitationMark.js

@@ -121,7 +121,7 @@ const CitationMark = ({ citationId, citation }) => {
             title={`引用来源 [${citationId}]`}
             trigger={triggerType}
             placement="top"
-            overlayInnerStyle={{ maxWidth: 340, padding: '8px' }}
+            styles={{ body: { maxWidth: 340, padding: '8px' } }}
             open={popoverVisible}
             onOpenChange={setPopoverVisible}
             zIndex={2000}

src/components/Citation/CitedContent.js

@@ -146,7 +146,7 @@ const CitedContent = ({
             </div>
 
             {/* 响应式样式 */}
-            <style jsx>{`
+            <style>{`
                 @media (max-width: 768px) {
                     .ai-badge-responsive {
                         font-size: 10px !important;

src/components/EventDetailPanel/CompactMetaBar.js

@@ -10,7 +10,7 @@ import {
   useColorModeValue,
 } from '@chakra-ui/react';
 import { ViewIcon } from '@chakra-ui/icons';
-import EventFollowButton from '@views/Community/components/EventCard/EventFollowButton';
+import { EventFollowButton } from '@views/Community/components/EventCard/atoms';
 
 /**
  * 精简信息栏组件

src/components/EventDetailPanel/DynamicNewsDetailPanel.js

@@ -16,7 +16,7 @@ import {
 } from '@chakra-ui/react';
 import { getImportanceConfig } from '@constants/importanceLevels';
 import { eventService } from '@services/eventService';
-import { useEventStocks } from '@views/Community/components/StockDetailPanel/hooks/useEventStocks';
+import { useEventStocks } from '@components/Charts/Stock';
 import { toggleEventFollow, selectEventFollowStatus } from '@store/slices/communityDataSlice';
 import { useAuth } from '@contexts/AuthContext';
 import EventHeaderInfo from './EventHeaderInfo';

src/components/EventDetailPanel/EventHeaderInfo.js

@@ -15,7 +15,7 @@ import {
 import { ViewIcon } from '@chakra-ui/icons';
 import dayjs from 'dayjs';
 import StockChangeIndicators from '../StockChangeIndicators';
-import EventFollowButton from '@views/Community/components/EventCard/EventFollowButton';
+import { EventFollowButton } from '@views/Community/components/EventCard/atoms';
 
 /**
  * 事件头部信息区组件

src/components/EventDetailPanel/StockListItem.js

@@ -20,7 +20,7 @@ import { StarIcon } from '@chakra-ui/icons';
 import { Tag } from 'antd';
 import { RobotOutlined } from '@ant-design/icons';
 import { selectIsMobile } from '@store/slices/deviceSlice';
-import MiniTimelineChart from '@views/Community/components/StockDetailPanel/components/MiniTimelineChart';
+import { MiniTimelineChart } from '@components/Charts/Stock';
 import MiniKLineChart from './MiniKLineChart';
 import TimelineChartModal from '@components/StockChart/TimelineChartModal';
 import KLineChartModal from '@components/StockChart/KLineChartModal';

src/components/FavoriteButton/index.tsx

@@ -0,0 +1,84 @@
+/**
+ * FavoriteButton - 通用关注/收藏按钮组件（图标按钮）
+ */
+
+import React from 'react';
+import { IconButton, Tooltip, Spinner } from '@chakra-ui/react';
+import { Star } from 'lucide-react';
+
+export interface FavoriteButtonProps {
+  /** 是否已关注 */
+  isFavorite: boolean;
+  /** 加载状态 */
+  isLoading?: boolean;
+  /** 点击回调 */
+  onClick: () => void;
+  /** 按钮大小 */
+  size?: 'sm' | 'md' | 'lg';
+  /** 颜色主题 */
+  colorScheme?: 'gold' | 'default';
+  /** 是否显示 tooltip */
+  showTooltip?: boolean;
+}
+
+// 颜色配置
+const COLORS = {
+  gold: {
+    active: '#F4D03F',    // 已关注 - 亮金色
+    inactive: '#C9A961',  // 未关注 - 暗金色
+    hoverBg: 'whiteAlpha.100',
+  },
+  default: {
+    active: 'yellow.400',
+    inactive: 'gray.400',
+    hoverBg: 'gray.100',
+  },
+};
+
+const FavoriteButton: React.FC<FavoriteButtonProps> = ({
+  isFavorite,
+  isLoading = false,
+  onClick,
+  size = 'sm',
+  colorScheme = 'gold',
+  showTooltip = true,
+}) => {
+  const colors = COLORS[colorScheme];
+  const currentColor = isFavorite ? colors.active : colors.inactive;
+  const label = isFavorite ? '取消关注' : '加入自选';
+
+  const iconButton = (
+    <IconButton
+      aria-label={label}
+      icon={
+        isLoading ? (
+          <Spinner size="sm" color={currentColor} />
+        ) : (
+          <Star
+            size={size === 'sm' ? 18 : size === 'md' ? 20 : 24}
+            fill={isFavorite ? currentColor : 'none'}
+            stroke={currentColor}
+          />
+        )
+      }
+      variant="ghost"
+      color={currentColor}
+      size={size}
+      onClick={onClick}
+      isDisabled={isLoading}
+      _hover={{ bg: colors.hoverBg }}
+    />
+  );
+
+  if (showTooltip) {
+    return (
+      <Tooltip label={label} placement="top">
+        {iconButton}
+      </Tooltip>
+    );
+  }
+
+  return iconButton;
+};
+
+export default FavoriteButton;

src/components/InvestmentCalendar/index.js

@@ -1,5 +1,7 @@
 // src/components/InvestmentCalendar/index.js
-import React, { useState, useEffect, useCallback } from 'react';
+import React, { useState, useEffect, useCallback, useRef } from 'react';
+import { useSelector, useDispatch } from 'react-redux';
+import { loadWatchlist, toggleWatchlist } from '@store/slices/stockSlice';
 import {
     Card, Calendar, Badge, Modal, Table, Tabs, Tag, Button, List, Spin, Empty,
     Drawer, Typography, Divider, Space, Tooltip, message, Alert
@@ -24,6 +26,10 @@ const { TabPane } = Tabs;
 const { Text, Title, Paragraph } = Typography;
 
 const InvestmentCalendar = () => {
+    // Redux 状态
+    const dispatch = useDispatch();
+    const reduxWatchlist = useSelector(state => state.stock.watchlist);
+
     // 权限控制
     const { hasFeatureAccess, getUpgradeRecommendation } = useSubscription();
     const [upgradeModalOpen, setUpgradeModalOpen] = useState(false);
@@ -45,7 +51,6 @@ const InvestmentCalendar = () => {
     const [selectedStock, setSelectedStock] = useState(null);
     const [selectedEventTime, setSelectedEventTime] = useState(null); // 记录事件时间
     const [followingIds, setFollowingIds] = useState([]); // 正在处理关注的事件ID列表
-    const [addingToWatchlist, setAddingToWatchlist] = useState({}); // 正在添加到自选的股票代码
     const [expandedReasons, setExpandedReasons] = useState({}); // 跟踪每个股票关联理由的展开状态
 
     // 加载月度事件统计
@@ -174,10 +179,29 @@ const InvestmentCalendar = () => {
         }
     };
 
+    // 使用 ref 确保只加载一次自选股
+    const watchlistLoadedRef = useRef(false);
+
+    // 组件挂载时加载自选股列表（仅加载一次）
+    useEffect(() => {
+        if (!watchlistLoadedRef.current) {
+            watchlistLoadedRef.current = true;
+            dispatch(loadWatchlist());
+        }
+    }, [dispatch]);
+
     useEffect(() => {
         loadEventCounts(currentMonth);
     }, [currentMonth, loadEventCounts]);
 
+    // 检查股票是否已在自选中
+    const isStockInWatchlist = useCallback((stockCode) => {
+        const sixDigitCode = getSixDigitCode(stockCode);
+        return reduxWatchlist.some(item =>
+            getSixDigitCode(item.stock_code) === sixDigitCode
+        );
+    }, [reduxWatchlist]);
+
     // 自定义日期单元格渲染（Ant Design 5.x API）
     const cellRender = (current, info) => {
         // 只处理日期单元格，月份单元格返回默认
@@ -220,7 +244,12 @@ const InvestmentCalendar = () => {
     };
 
     // 处理日期选择
-    const handleDateSelect = (value) => {
+    // info.source 区分选择来源：'date' = 点击日期，'month'/'year' = 切换月份/年份
+    const handleDateSelect = (value, info) => {
+        // 只有点击日期单元格时才打开弹窗，切换月份/年份时不打开
+        if (info?.source !== 'date') {
+            return;
+        }
         setSelectedDate(value);
         loadDateEvents(value);
         setModalVisible(true);
@@ -379,42 +408,35 @@ const InvestmentCalendar = () => {
         }
     };
 
-    // 添加单只股票到自选（支持新旧格式）
+    // 添加单只股票到自选（乐观更新，无需 loading 状态）
     const addSingleToWatchlist = async (stock) => {
         // 兼容新旧格式
         const code = stock.code || stock[0];
         const name = stock.name || stock[1];
         const stockCode = getSixDigitCode(code);
 
-        setAddingToWatchlist(prev => ({ ...prev, [stockCode]: true }));
+        // 检查是否已在自选中
+        if (isStockInWatchlist(code)) {
+            message.info(`${name} 已在自选中`);
+            return;
+        }
 
         try {
-            const response = await fetch('/api/account/watchlist', {
-                method: 'POST',
-                headers: {
-                    'Content-Type': 'application/json',
-                },
-                credentials: 'include',
-                body: JSON.stringify({
-                    stock_code: stockCode, // 使用六位代码
-                    stock_name: name  // 股票名称
-                })
-            });
+            // 乐观更新：dispatch 后 Redux 立即更新状态，UI 立即响应
+            await dispatch(toggleWatchlist({
+                stockCode,
+                stockName: name,
+                isInWatchlist: false  // false 表示添加
+            })).unwrap();
 
-            const data = await response.json();
-            if (data.success) {
-                message.success(`已将 ${name}(${stockCode}) 添加到自选`);
-            } else {
-                message.error(data.error || '添加失败');
-            }
+            message.success(`已将 ${name}(${stockCode}) 添加到自选`);
         } catch (error) {
+            // 失败时 Redux 会自动回滚状态
             logger.error('InvestmentCalendar', 'addSingleToWatchlist', error, {
                 stockCode,
                 stockName: name
             });
             message.error('添加失败，请重试');
-        } finally {
-            setAddingToWatchlist(prev => ({ ...prev, [stockCode]: false }));
         }
     };
 
@@ -524,7 +546,9 @@ const InvestmentCalendar = () => {
                     {concepts && concepts.length > 0 ? (
                         concepts.slice(0, 3).map((concept, index) => (
                             <Tag key={index} icon={<TagsOutlined />}>
-                                {Array.isArray(concept) ? concept[0] : concept}
+                                {typeof concept === 'string'
+                                    ? concept
+                                    : (concept?.concept || concept?.name || '未知')}
                             </Tag>
                         ))
                     ) : (
@@ -791,17 +815,16 @@ const InvestmentCalendar = () => {
             key: 'action',
             width: 100,
             render: (_, record) => {
-                const stockCode = getSixDigitCode(record.code);
-                const isAdding = addingToWatchlist[stockCode] || false;
+                const inWatchlist = isStockInWatchlist(record.code);
 
                 return (
                     <Button
-                        type="default"
+                        type={inWatchlist ? "primary" : "default"}
                         size="small"
-                        loading={isAdding}
                         onClick={() => addSingleToWatchlist(record)}
+                        disabled={inWatchlist}
                     >
-                        加自选
+                        {inWatchlist ? '已关注' : '加自选'}
                     </Button>
                 );
             }
@@ -919,7 +942,7 @@ const InvestmentCalendar = () => {
                     <Table
                         dataSource={selectedStocks}
                         columns={stockColumns}
-                        rowKey={(record) => record[0]}
+                        rowKey={(record) => record.code}
                         size="middle"
                         pagination={false}
                     />

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]}<br/>现价：<span style="color: ${priceColor}">¥${prices[d]?.toFixed(2)} (${priceChangePercent >= 0 ? '+' : ''}${priceChangePercent.toFixed(2)}%)</span><br/>均价：<span style="color: ${avgColor}">¥${avgPrices[d]?.toFixed(2)} (${avgChangePercent >= 0 ? '+' : ''}${avgChangePercent.toFixed(2)}%)</span><br/>昨收：¥${prevClose?.toFixed(2)}<br/>成交量：${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] || '-'}<br/>现价：<span style="color: ${priceColor}">¥${safeFixed(price)} (${formatPercent(priceChangePercent)})</span><br/>均价：<span style="color: ${avgColor}">¥${safeFixed(avgPrice)} (${formatPercent(avgChangePercent)})</span><br/>昨收：¥${safeFixed(prevClose)}<br/>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 },

src/components/StockChart/TimelineChartModal.tsx

@@ -217,27 +217,34 @@ const TimelineChartModal: React.FC<TimelineChartModalProps> = ({
           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 `
             <div style="padding: 8px;">
-              <div style="font-weight: bold; margin-bottom: 8px;">${item.time}</div>
+              <div style="font-weight: bold; margin-bottom: 8px;">${item.time || '-'}</div>
               <div style="display: flex; justify-content: space-between; margin-bottom: 4px;">
                 <span>价格:</span>
-                <span style="color: ${changeColor}; font-weight: bold; margin-left: 20px;">${item.price.toFixed(2)}</span>
+                <span style="color: ${changeColor}; font-weight: bold; margin-left: 20px;">${safeFixed(item.price)}</span>
               </div>
               <div style="display: flex; justify-content: space-between; margin-bottom: 4px;">
                 <span>均价:</span>
-                <span style="color: #ffa726; margin-left: 20px;">${item.avg_price.toFixed(2)}</span>
+                <span style="color: #ffa726; margin-left: 20px;">${safeFixed(item.avg_price)}</span>
               </div>
               <div style="display: flex; justify-content: space-between; margin-bottom: 4px;">
                 <span>涨跌幅:</span>
-                <span style="color: ${changeColor}; margin-left: 20px;">${changeSign}${item.change_percent.toFixed(2)}%</span>
+                <span style="color: ${changeColor}; margin-left: 20px;">${changeSign}${safeFixed(changePercent)}%</span>
               </div>
               <div style="display: flex; justify-content: space-between;">
                 <span>成交量:</span>
-                <span style="margin-left: 20px;">${(item.volume / 100).toFixed(0)}手</span>
+                <span style="margin-left: 20px;">${item.volume != null ? (item.volume / 100).toFixed(0) : '-'}手</span>
               </div>
             </div>
           `;
@@ -314,7 +321,7 @@ const TimelineChartModal: React.FC<TimelineChartModalProps> = ({
           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<TimelineChartModalProps> = ({
             color: '#999',
             fontSize: isMobile ? 10 : 12,
             formatter: (value: number) => {
+              if (value == null || isNaN(value)) return '-';
               if (value >= 10000) {
                 return (value / 10000).toFixed(1) + '万';
               }

src/components/SubTabContainer/index.tsx

@@ -0,0 +1,232 @@
+/**
+ * SubTabContainer - 二级导航容器组件
+ *
+ * 用于模块内的子功能切换（如公司档案下的股权结构、管理团队等）
+ * 与 TabContainer（一级导航）区分：无 Card 包裹，直接融入父容器
+ *
+ * @example
+ * ```tsx
+ * <SubTabContainer
+ *   tabs={[
+ *     { key: 'tab1', name: 'Tab 1', icon: FaHome, component: Tab1 },
+ *     { key: 'tab2', name: 'Tab 2', icon: FaUser, component: Tab2 },
+ *   ]}
+ *   componentProps={{ stockCode: '000001' }}
+ *   onTabChange={(index, key) => console.log('切换到', key)}
+ * />
+ * ```
+ */
+
+import React, { useState, useCallback, memo } from 'react';
+import {
+  Box,
+  Tabs,
+  TabList,
+  TabPanels,
+  Tab,
+  TabPanel,
+  Icon,
+  HStack,
+  Text,
+  Spacer,
+} from '@chakra-ui/react';
+import type { ComponentType } from 'react';
+import type { IconType } from 'react-icons';
+
+/**
+ * Tab 配置项
+ */
+export interface SubTabConfig {
+  key: string;
+  name: string;
+  icon?: IconType | ComponentType;
+  component?: ComponentType<any>;
+}
+
+/**
+ * 主题配置
+ */
+export interface SubTabTheme {
+  bg: string;
+  borderColor: string;
+  tabSelectedBg: string;
+  tabSelectedColor: string;
+  tabUnselectedColor: string;
+  tabHoverBg: string;
+}
+
+/**
+ * 预设主题
+ */
+const THEME_PRESETS: Record<string, SubTabTheme> = {
+  blackGold: {
+    bg: 'gray.900',
+    borderColor: 'rgba(212, 175, 55, 0.3)',
+    tabSelectedBg: '#D4AF37',
+    tabSelectedColor: 'gray.900',
+    tabUnselectedColor: '#D4AF37',
+    tabHoverBg: 'gray.600',
+  },
+  default: {
+    bg: 'white',
+    borderColor: 'gray.200',
+    tabSelectedBg: 'blue.500',
+    tabSelectedColor: 'white',
+    tabUnselectedColor: 'gray.600',
+    tabHoverBg: 'gray.100',
+  },
+};
+
+export interface SubTabContainerProps {
+  /** Tab 配置数组 */
+  tabs: SubTabConfig[];
+  /** 传递给 Tab 内容组件的 props */
+  componentProps?: Record<string, any>;
+  /** 默认选中的 Tab 索引 */
+  defaultIndex?: number;
+  /** 受控模式下的当前索引 */
+  index?: number;
+  /** Tab 变更回调 */
+  onTabChange?: (index: number, tabKey: string) => void;
+  /** 主题预设 */
+  themePreset?: 'blackGold' | 'default';
+  /** 自定义主题（优先级高于预设） */
+  theme?: Partial<SubTabTheme>;
+  /** 内容区内边距 */
+  contentPadding?: number;
+  /** 是否懒加载 */
+  isLazy?: boolean;
+  /** TabList 右侧自定义内容 */
+  rightElement?: React.ReactNode;
+}
+
+const SubTabContainer: React.FC<SubTabContainerProps> = memo(({
+  tabs,
+  componentProps = {},
+  defaultIndex = 0,
+  index: controlledIndex,
+  onTabChange,
+  themePreset = 'blackGold',
+  theme: customTheme,
+  contentPadding = 4,
+  isLazy = true,
+  rightElement,
+}) => {
+  // 内部状态（非受控模式）
+  const [internalIndex, setInternalIndex] = useState(defaultIndex);
+
+  // 当前索引
+  const currentIndex = controlledIndex ?? internalIndex;
+
+  // 记录已访问的 Tab 索引（用于真正的懒加载）
+  const [visitedTabs, setVisitedTabs] = useState<Set<number>>(
+    () => new Set([controlledIndex ?? defaultIndex])
+  );
+
+  // 合并主题
+  const theme: SubTabTheme = {
+    ...THEME_PRESETS[themePreset],
+    ...customTheme,
+  };
+
+  /**
+   * 处理 Tab 切换
+   */
+  const handleTabChange = useCallback(
+    (newIndex: number) => {
+      const tabKey = tabs[newIndex]?.key || '';
+      onTabChange?.(newIndex, tabKey);
+
+      // 记录已访问的 Tab（用于懒加载）
+      setVisitedTabs(prev => {
+        if (prev.has(newIndex)) return prev;
+        return new Set(prev).add(newIndex);
+      });
+
+      if (controlledIndex === undefined) {
+        setInternalIndex(newIndex);
+      }
+    },
+    [tabs, onTabChange, controlledIndex]
+  );
+
+  return (
+    <Box>
+      <Tabs
+        isLazy={isLazy}
+        variant="unstyled"
+        index={currentIndex}
+        onChange={handleTabChange}
+      >
+        <TabList
+          bg={theme.bg}
+          borderBottom="1px solid"
+          borderColor={theme.borderColor}
+          pl={0}
+          pr={2}
+          py={1.5}
+          flexWrap="nowrap"
+          gap={1}
+          alignItems="center"
+          overflowX="auto"
+          css={{
+            '&::-webkit-scrollbar': { display: 'none' },
+            scrollbarWidth: 'none',
+          }}
+        >
+          {tabs.map((tab) => (
+            <Tab
+              key={tab.key}
+              color={theme.tabUnselectedColor}
+              borderRadius="full"
+              px={2.5}
+              py={1.5}
+              fontSize="xs"
+              whiteSpace="nowrap"
+              flexShrink={0}
+              _selected={{
+                bg: theme.tabSelectedBg,
+                color: theme.tabSelectedColor,
+                fontWeight: 'bold',
+              }}
+              _hover={{
+                bg: theme.tabHoverBg,
+              }}
+            >
+              <HStack spacing={1}>
+                {tab.icon && <Icon as={tab.icon} boxSize={3} />}
+                <Text>{tab.name}</Text>
+              </HStack>
+            </Tab>
+          ))}
+          {rightElement && (
+            <>
+              <Spacer />
+              <Box flexShrink={0}>{rightElement}</Box>
+            </>
+          )}
+        </TabList>
+
+        <TabPanels p={contentPadding}>
+          {tabs.map((tab, idx) => {
+            const Component = tab.component;
+            // 懒加载：只渲染已访问过的 Tab
+            const shouldRender = !isLazy || visitedTabs.has(idx);
+
+            return (
+              <TabPanel key={tab.key} p={0}>
+                {shouldRender && Component ? (
+                  <Component {...componentProps} />
+                ) : null}
+              </TabPanel>
+            );
+          })}
+        </TabPanels>
+      </Tabs>
+    </Box>
+  );
+});
+
+SubTabContainer.displayName = 'SubTabContainer';
+
+export default SubTabContainer;

src/components/TabContainer/TabNavigation.tsx

@@ -0,0 +1,56 @@
+/**
+ * TabNavigation 通用导航组件
+ *
+ * 渲染 Tab 按钮列表，支持图标 + 文字
+ */
+
+import React from 'react';
+import { TabList, Tab, HStack, Icon, Text } from '@chakra-ui/react';
+import type { TabNavigationProps } from './types';
+
+const TabNavigation: React.FC<TabNavigationProps> = ({
+  tabs,
+  themeColors,
+  borderRadius = 'lg',
+}) => {
+  return (
+    <TabList
+      bg={themeColors.bg}
+      borderBottom="1px solid"
+      borderColor={themeColors.dividerColor}
+      borderTopLeftRadius={borderRadius}
+      borderTopRightRadius={borderRadius}
+      pl={0}
+      pr={4}
+      py={2}
+      flexWrap="wrap"
+      gap={2}
+    >
+      {tabs.map((tab) => (
+        <Tab
+          key={tab.key}
+          color={themeColors.unselectedText}
+          borderRadius="full"
+          px={4}
+          py={2}
+          fontSize="sm"
+          _selected={{
+            bg: themeColors.selectedBg,
+            color: themeColors.selectedText,
+            fontWeight: 'bold',
+          }}
+          _hover={{
+            bg: 'whiteAlpha.100',
+          }}
+        >
+          <HStack spacing={2}>
+            {tab.icon && <Icon as={tab.icon} boxSize={4} />}
+            <Text>{tab.name}</Text>
+          </HStack>
+        </Tab>
+      ))}
+    </TabList>
+  );
+};
+
+export default TabNavigation;

src/components/TabContainer/constants.ts

@@ -0,0 +1,55 @@
+/**
+ * TabContainer 常量和主题预设
+ */
+
+import type { ThemeColors, ThemePreset } from './types';
+
+/**
+ * 主题预设配置
+ */
+export const THEME_PRESETS: Record<ThemePreset, Required<ThemeColors>> = {
+  // 黑金主题（原 Company 模块风格）
+  blackGold: {
+    bg: '#1A202C',
+    selectedBg: '#C9A961',
+    selectedText: '#FFFFFF',
+    unselectedText: '#D4AF37',
+    dividerColor: 'gray.600',
+  },
+  // 默认主题（Chakra 风格）
+  default: {
+    bg: 'white',
+    selectedBg: 'blue.500',
+    selectedText: 'white',
+    unselectedText: 'gray.600',
+    dividerColor: 'gray.200',
+  },
+  // 深色主题
+  dark: {
+    bg: 'gray.800',
+    selectedBg: 'blue.400',
+    selectedText: 'white',
+    unselectedText: 'gray.300',
+    dividerColor: 'gray.600',
+  },
+  // 浅色主题
+  light: {
+    bg: 'gray.50',
+    selectedBg: 'blue.500',
+    selectedText: 'white',
+    unselectedText: 'gray.700',
+    dividerColor: 'gray.300',
+  },
+};
+
+/**
+ * 默认配置
+ */
+export const DEFAULT_CONFIG = {
+  themePreset: 'blackGold' as ThemePreset,
+  isLazy: true,
+  size: 'lg' as const,
+  borderRadius: 'lg',
+  shadow: 'lg',
+  panelPadding: 0,
+};

src/components/TabContainer/index.tsx

@@ -0,0 +1,134 @@
+/**
+ * TabContainer 通用 Tab 容器组件
+ *
+ * 功能：
+ * - 管理 Tab 切换状态（支持受控/非受控模式）
+ * - 动态渲染 Tab 导航和内容
+ * - 支持多种主题预设（黑金、默认、深色、浅色）
+ * - 支持自定义主题颜色
+ * - 支持懒加载
+ *
+ * @example
+ * // 基础用法（传入 components）
+ * <TabContainer
+ *   tabs={[
+ *     { key: 'tab1', name: 'Tab 1', icon: FaHome, component: Tab1Content },
+ *     { key: 'tab2', name: 'Tab 2', icon: FaUser, component: Tab2Content },
+ *   ]}
+ *   componentProps={{ userId: '123' }}
+ *   onTabChange={(index, key) => console.log('切换到', key)}
+ * />
+ *
+ * @example
+ * // 自定义渲染用法（使用 children）
+ * <TabContainer tabs={tabs} themePreset="dark">
+ *   <TabPanel>自定义内容 1</TabPanel>
+ *   <TabPanel>自定义内容 2</TabPanel>
+ * </TabContainer>
+ */
+
+import React, { useState, useCallback, useMemo } from 'react';
+import {
+  Card,
+  CardBody,
+  Tabs,
+  TabPanels,
+  TabPanel,
+} from '@chakra-ui/react';
+
+import TabNavigation from './TabNavigation';
+import { THEME_PRESETS, DEFAULT_CONFIG } from './constants';
+import type { TabContainerProps, ThemeColors } from './types';
+
+// 导出类型和常量
+export type { TabConfig, ThemeColors, ThemePreset, TabContainerProps } from './types';
+export { THEME_PRESETS } from './constants';
+
+const TabContainer: React.FC<TabContainerProps> = ({
+  tabs,
+  componentProps = {},
+  onTabChange,
+  defaultIndex = 0,
+  index: controlledIndex,
+  themePreset = DEFAULT_CONFIG.themePreset,
+  themeColors: customThemeColors,
+  isLazy = DEFAULT_CONFIG.isLazy,
+  size = DEFAULT_CONFIG.size,
+  borderRadius = DEFAULT_CONFIG.borderRadius,
+  shadow = DEFAULT_CONFIG.shadow,
+  panelPadding = DEFAULT_CONFIG.panelPadding,
+  children,
+}) => {
+  // 内部状态（非受控模式）
+  const [internalIndex, setInternalIndex] = useState(defaultIndex);
+
+  // 当前索引（支持受控/非受控）
+  const currentIndex = controlledIndex ?? internalIndex;
+
+  // 合并主题颜色（自定义颜色优先）
+  const themeColors: Required<ThemeColors> = useMemo(() => ({
+    ...THEME_PRESETS[themePreset],
+    ...customThemeColors,
+  }), [themePreset, customThemeColors]);
+
+  /**
+   * 处理 Tab 切换
+   */
+  const handleTabChange = useCallback((newIndex: number) => {
+    const tabKey = tabs[newIndex]?.key || '';
+
+    // 触发回调
+    onTabChange?.(newIndex, tabKey, currentIndex);
+
+    // 非受控模式下更新内部状态
+    if (controlledIndex === undefined) {
+      setInternalIndex(newIndex);
+    }
+  }, [tabs, onTabChange, currentIndex, controlledIndex]);
+
+  /**
+   * 渲染 Tab 内容
+   */
+  const renderTabPanels = () => {
+    // 如果传入了 children，直接渲染 children
+    if (children) {
+      return children;
+    }
+
+    // 否则根据 tabs 配置渲染
+    return tabs.map((tab) => {
+      const Component = tab.component;
+      return (
+        <TabPanel key={tab.key} px={panelPadding} py={panelPadding}>
+          {Component ? <Component {...componentProps} /> : null}
+        </TabPanel>
+      );
+    });
+  };
+
+  return (
+    <Card shadow={shadow} bg={themeColors.bg} borderRadius={borderRadius}>
+      <CardBody p={0}>
+        <Tabs
+          isLazy={isLazy}
+          variant="unstyled"
+          size={size}
+          index={currentIndex}
+          onChange={handleTabChange}
+        >
+          {/* Tab 导航 */}
+          <TabNavigation
+            tabs={tabs}
+            themeColors={themeColors}
+            borderRadius={borderRadius}
+          />
+
+          {/* Tab 内容面板 */}
+          <TabPanels>{renderTabPanels()}</TabPanels>
+        </Tabs>
+      </CardBody>
+    </Card>
+  );
+};
+
+export default TabContainer;

src/components/TabContainer/types.ts

@@ -0,0 +1,85 @@
+/**
+ * TabContainer 通用 Tab 容器组件类型定义
+ */
+
+import type { ComponentType, ReactNode } from 'react';
+import type { IconType } from 'react-icons';
+
+/**
+ * Tab 配置项
+ */
+export interface TabConfig {
+  /** Tab 唯一标识 */
+  key: string;
+  /** Tab 显示名称 */
+  name: string;
+  /** Tab 图标（可选） */
+  icon?: IconType | ComponentType;
+  /** Tab 内容组件（可选，如果不传则使用 children 渲染） */
+  component?: ComponentType<any>;
+}
+
+/**
+ * 主题颜色配置
+ */
+export interface ThemeColors {
+  /** 容器背景色 */
+  bg?: string;
+  /** 选中 Tab 背景色 */
+  selectedBg?: string;
+  /** 选中 Tab 文字颜色 */
+  selectedText?: string;
+  /** 未选中 Tab 文字颜色 */
+  unselectedText?: string;
+  /** 分割线颜色 */
+  dividerColor?: string;
+}
+
+/**
+ * 预设主题类型
+ */
+export type ThemePreset = 'blackGold' | 'default' | 'dark' | 'light';
+
+/**
+ * TabContainer 组件 Props
+ */
+export interface TabContainerProps {
+  /** Tab 配置数组 */
+  tabs: TabConfig[];
+  /** 传递给 Tab 内容组件的通用 props */
+  componentProps?: Record<string, any>;
+  /** Tab 变更回调 */
+  onTabChange?: (index: number, tabKey: string, prevIndex: number) => void;
+  /** 默认选中的 Tab 索引 */
+  defaultIndex?: number;
+  /** 受控模式下的当前索引 */
+  index?: number;
+  /** 主题预设 */
+  themePreset?: ThemePreset;
+  /** 自定义主题颜色（优先级高于预设） */
+  themeColors?: ThemeColors;
+  /** 是否启用懒加载 */
+  isLazy?: boolean;
+  /** Tab 尺寸 */
+  size?: 'sm' | 'md' | 'lg';
+  /** 容器圆角 */
+  borderRadius?: string;
+  /** 容器阴影 */
+  shadow?: string;
+  /** 自定义 Tab 面板内边距 */
+  panelPadding?: number | string;
+  /** 子元素（用于自定义渲染 Tab 内容） */
+  children?: ReactNode;
+}
+
+/**
+ * TabNavigation 组件 Props
+ */
+export interface TabNavigationProps {
+  /** Tab 配置数组 */
+  tabs: TabConfig[];
+  /** 主题颜色 */
+  themeColors: Required<ThemeColors>;
+  /** 容器圆角 */
+  borderRadius?: string;
+}

src/components/TabPanelContainer/index.tsx

@@ -0,0 +1,100 @@
+/**
+ * TabPanelContainer - Tab 面板通用容器组件
+ *
+ * 提供统一的：
+ * - Loading 状态处理
+ * - VStack 布局
+ * - 免责声明（可选）
+ *
+ * @example
+ * ```tsx
+ * <TabPanelContainer loading={loading} showDisclaimer>
+ *   <YourContent />
+ * </TabPanelContainer>
+ * ```
+ */
+
+import React, { memo } from 'react';
+import { VStack, Center, Spinner, Text, Box } from '@chakra-ui/react';
+
+// 默认免责声明文案
+const DEFAULT_DISCLAIMER =
+  '免责声明：本内容由AI模型基于新闻、公告、研报等公开信息自动分析和生成，未经许可严禁转载。所有内容仅供参考，不构成任何投资建议，请投资者注意风险，独立审慎决策。';
+
+export interface TabPanelContainerProps {
+  /** 是否处于加载状态 */
+  loading?: boolean;
+  /** 加载状态显示的文案 */
+  loadingMessage?: string;
+  /** 加载状态高度 */
+  loadingHeight?: string;
+  /** 子组件间距，默认 6 */
+  spacing?: number;
+  /** 内边距，默认 4 */
+  padding?: number;
+  /** 是否显示免责声明，默认 false */
+  showDisclaimer?: boolean;
+  /** 自定义免责声明文案 */
+  disclaimerText?: string;
+  /** 子组件 */
+  children: React.ReactNode;
+}
+
+/**
+ * 加载状态组件
+ */
+const LoadingState: React.FC<{ message: string; height: string }> = ({
+  message,
+  height,
+}) => (
+  <Center h={height}>
+    <VStack spacing={3}>
+      <Spinner size="lg" color="#D4AF37" thickness="3px" />
+      <Text fontSize="sm" color="gray.500">
+        {message}
+      </Text>
+    </VStack>
+  </Center>
+);
+
+/**
+ * 免责声明组件
+ */
+const DisclaimerText: React.FC<{ text: string }> = ({ text }) => (
+  <Text mt={4} color="gray.500" fontSize="12px" lineHeight="1.5">
+    {text}
+  </Text>
+);
+
+/**
+ * Tab 面板通用容器
+ */
+const TabPanelContainer: React.FC<TabPanelContainerProps> = memo(
+  ({
+    loading = false,
+    loadingMessage = '加载中...',
+    loadingHeight = '200px',
+    spacing = 6,
+    padding = 4,
+    showDisclaimer = false,
+    disclaimerText = DEFAULT_DISCLAIMER,
+    children,
+  }) => {
+    if (loading) {
+      return <LoadingState message={loadingMessage} height={loadingHeight} />;
+    }
+
+    return (
+      <Box p={padding}>
+        <VStack spacing={spacing} align="stretch">
+          {children}
+        </VStack>
+        {showDisclaimer && <DisclaimerText text={disclaimerText} />}
+      </Box>
+    );
+  }
+);
+
+TabPanelContainer.displayName = 'TabPanelContainer';
+
+export default TabPanelContainer;

src/contexts/NotificationContext.js

@@ -661,6 +661,12 @@ export const NotificationProvider = ({ children }) => {
 
     // ========== 连接到 Socket 服务（⚡ 异步初始化，不阻塞首屏） ==========
     useEffect(() => {
+        // ⚡ Mock 模式下跳过 Socket 连接（避免连接生产服务器失败的错误）
+        if (process.env.REACT_APP_ENABLE_MOCK === 'true') {
+            logger.debug('NotificationContext', 'Mock 模式，跳过 Socket 连接');
+            return;
+        }
+
         // ⚡ 防止 React Strict Mode 导致的重复初始化
         if (socketInitialized) {
             logger.debug('NotificationContext', 'Socket 已初始化，跳过重复执行（Strict Mode 保护）');

src/hooks/useFirstScreenMetrics.ts

@@ -16,6 +16,8 @@
 import { useState, useEffect, useCallback, useRef } from 'react';
 import { initWebVitalsTracking, getCachedMetrics } from '@utils/performance/webVitals';
 import { collectResourceStats, collectApiStats } from '@utils/performance/resourceMonitor';
+import { performanceMonitor } from '@utils/performanceMonitor';
+import { usePerformanceMark } from '@hooks/usePerformanceTracker';
 import posthog from 'posthog-js';
 import type {
   FirstScreenMetrics,
@@ -44,11 +46,17 @@ export const useFirstScreenMetrics = (
   const [isLoading, setIsLoading] = useState(true);
   const [metrics, setMetrics] = useState<FirstScreenMetrics | null>(null);
 
-  // 使用 ref 记录页面加载开始时间
-  const pageLoadStartRef = useRef<number>(performance.now());
-  const skeletonStartRef = useRef<number>(performance.now());
+  // 使用 ref 避免重复标记
+  const hasMarkedRef = useRef(false);
   const hasInitializedRef = useRef(false);
 
+  // 在组件首次渲染时标记开始时间点
+  if (!hasMarkedRef.current) {
+    hasMarkedRef.current = true;
+    performanceMonitor.mark(`${pageType}-page-load-start`);
+    performanceMonitor.mark(`${pageType}-skeleton-start`);
+  }
+
   /**
    * 收集所有首屏指标
    */
@@ -82,12 +90,20 @@ export const useFirstScreenMetrics = (
         customProperties,
       });
 
-      // 5. 计算首屏可交互时间（TTI）
-      const now = performance.now();
-      const timeToInteractive = now - pageLoadStartRef.current;
+      // 5. 标记可交互时间点，并计算 TTI
+      performanceMonitor.mark(`${pageType}-interactive`);
+      const timeToInteractive = performanceMonitor.measure(
+        `${pageType}-page-load-start`,
+        `${pageType}-interactive`,
+        `${pageType} TTI`
+      ) || 0;
 
       // 6. 计算骨架屏展示时长
-      const skeletonDisplayDuration = now - skeletonStartRef.current;
+      const skeletonDisplayDuration = performanceMonitor.measure(
+        `${pageType}-skeleton-start`,
+        `${pageType}-interactive`,
+        `${pageType} 骨架屏时长`
+      ) || 0;
 
       const firstScreenMetrics: FirstScreenMetrics = {
         webVitals,
@@ -143,9 +159,9 @@ export const useFirstScreenMetrics = (
   const remeasure = useCallback(() => {
     setIsLoading(true);
 
-    // 重置计时器
-    pageLoadStartRef.current = performance.now();
-    skeletonStartRef.current = performance.now();
+    // 重置性能标记
+    performanceMonitor.mark(`${pageType}-page-load-start`);
+    performanceMonitor.mark(`${pageType}-skeleton-start`);
 
     // 延迟收集指标（等待 Web Vitals 完成）
     setTimeout(() => {
@@ -167,7 +183,7 @@ export const useFirstScreenMetrics = (
         setIsLoading(false);
       }
     }, 1000); // 延迟 1 秒收集
-  }, [collectAllMetrics, trackFirstScreenInteractive, enableConsoleLog]);
+  }, [pageType, collectAllMetrics, trackFirstScreenInteractive, enableConsoleLog]);
 
   /**
    * 导出指标为 JSON
@@ -247,7 +263,7 @@ export const useFirstScreenMetrics = (
  *
  * 使用示例：
  * ```tsx
- * const { markSkeletonEnd } = useSkeletonTiming();
+ * const { markSkeletonEnd } = useSkeletonTiming('home-skeleton');
  *
  * useEffect(() => {
  *   if (!loading) {
@@ -256,27 +272,32 @@ export const useFirstScreenMetrics = (
  * }, [loading, markSkeletonEnd]);
  * ```
  */
-export const useSkeletonTiming = () => {
-  const skeletonStartRef = useRef<number>(performance.now());
-  const skeletonEndRef = useRef<number | null>(null);
+export const useSkeletonTiming = (prefix = 'skeleton') => {
+  const { mark, getMeasure } = usePerformanceMark(prefix);
+  const hasMarkedEndRef = useRef(false);
+  const hasMarkedStartRef = useRef(false);
+
+  // 在组件首次渲染时标记开始
+  if (!hasMarkedStartRef.current) {
+    hasMarkedStartRef.current = true;
+    mark('start');
+  }
 
   const markSkeletonEnd = useCallback(() => {
-    if (!skeletonEndRef.current) {
-      skeletonEndRef.current = performance.now();
-      const duration = skeletonEndRef.current - skeletonStartRef.current;
+    if (!hasMarkedEndRef.current) {
+      hasMarkedEndRef.current = true;
+      mark('end');
+      const duration = getMeasure('start', 'end');
 
-      if (process.env.NODE_ENV === 'development') {
+      if (process.env.NODE_ENV === 'development' && duration) {
         console.log(`⏱️ Skeleton Display Duration: ${(duration / 1000).toFixed(2)}s`);
       }
     }
-  }, []);
+  }, [mark, getMeasure]);
 
   const getSkeletonDuration = useCallback((): number | null => {
-    if (skeletonEndRef.current) {
-      return skeletonEndRef.current - skeletonStartRef.current;
-    }
-    return null;
-  }, []);
+    return getMeasure('start', 'end');
+  }, [getMeasure]);
 
   return {
     markSkeletonEnd,

src/hooks/usePerformanceTracker.ts

@@ -0,0 +1,129 @@
+/**
+ * React 性能追踪 Hooks
+ * 封装 performanceMonitor 工具，提供 React 友好的性能追踪 API
+ */
+
+import { useEffect, useRef, useCallback } from 'react';
+import { performanceMonitor } from '@utils/performanceMonitor';
+
+/**
+ * usePerformanceMark 返回值类型
+ */
+export interface UsePerformanceMarkReturn {
+  /** 标记时间点 */
+  mark: (suffix: string) => void;
+  /** 测量并记录到 performanceMonitor */
+  measure: (startSuffix: string, endSuffix: string, name?: string) => number | null;
+  /** 获取测量值（不记录） */
+  getMeasure: (startSuffix: string, endSuffix: string) => number | null;
+}
+
+/**
+ * usePerformanceTracker - 自动追踪组件渲染性能
+ *
+ * @param componentName - 组件名称，用于标记
+ * @param options - 配置选项
+ *
+ * @example
+ * ```tsx
+ * function MyComponent() {
+ *   usePerformanceTracker('MyComponent');
+ *   return <div>...</div>;
+ * }
+ * ```
+ *
+ * 自动标记：
+ * - {componentName}-mount: 组件挂载时
+ * - {componentName}-rendered: 首次渲染完成
+ * - {componentName}-unmount: 组件卸载时
+ */
+export function usePerformanceTracker(
+  componentName: string,
+  options: { trackRender?: boolean } = {}
+): void {
+  const { trackRender = true } = options;
+  const hasMounted = useRef(false);
+
+  // 首次渲染时立即标记（同步）
+  if (!hasMounted.current) {
+    performanceMonitor.mark(`${componentName}-mount`);
+  }
+
+  useEffect(() => {
+    if (!hasMounted.current) {
+      hasMounted.current = true;
+
+      // 渲染完成标记（在 useEffect 中，表示 DOM 已更新）
+      if (trackRender) {
+        performanceMonitor.mark(`${componentName}-rendered`);
+        performanceMonitor.measure(
+          `${componentName}-mount`,
+          `${componentName}-rendered`,
+          `${componentName} 渲染`
+        );
+      }
+    }
+
+    // 组件卸载时标记
+    return () => {
+      performanceMonitor.mark(`${componentName}-unmount`);
+    };
+  }, [componentName, trackRender]);
+}
+
+/**
+ * usePerformanceMark - 手动标记自定义操作的性能
+ *
+ * @param prefix - 标记前缀，用于区分不同操作
+ * @returns 包含 mark、measure、getMeasure 方法的对象
+ *
+ * @example
+ * ```tsx
+ * function MyComponent() {
+ *   const { mark, getMeasure } = usePerformanceMark('api-call');
+ *
+ *   const handleFetch = async () => {
+ *     mark('start');
+ *     await fetchData();
+ *     mark('end');
+ *     const duration = getMeasure('start', 'end');
+ *     console.log('API耗时:', duration, 'ms');
+ *   };
+ *
+ *   return <button onClick={handleFetch}>加载数据</button>;
+ * }
+ * ```
+ */
+export function usePerformanceMark(prefix: string): UsePerformanceMarkReturn {
+  const mark = useCallback(
+    (suffix: string) => {
+      performanceMonitor.mark(`${prefix}-${suffix}`);
+    },
+    [prefix]
+  );
+
+  const measure = useCallback(
+    (startSuffix: string, endSuffix: string, name?: string) => {
+      return performanceMonitor.measure(
+        `${prefix}-${startSuffix}`,
+        `${prefix}-${endSuffix}`,
+        name || `${prefix}: ${startSuffix} → ${endSuffix}`
+      );
+    },
+    [prefix]
+  );
+
+  const getMeasure = useCallback(
+    (startSuffix: string, endSuffix: string) => {
+      return performanceMonitor.measure(
+        `${prefix}-${startSuffix}`,
+        `${prefix}-${endSuffix}`
+      );
+    },
+    [prefix]
+  );
+
+  return { mark, measure, getMeasure };
+}
+
+export default usePerformanceTracker;

src/hooks/useSearchEvents.js

@@ -1,244 +0,0 @@
-// src/hooks/useSearchEvents.js
-// 全局搜索功能事件追踪 Hook
-
-import { useCallback } from 'react';
-import { usePostHogTrack } from './usePostHogRedux';
-import { RETENTION_EVENTS } from '../lib/constants';
-import { logger } from '../utils/logger';
-
-/**
- * 全局搜索事件追踪 Hook
- * @param {Object} options - 配置选项
- * @param {string} options.context - 搜索上下文 ('global' | 'stock' | 'news' | 'concept' | 'simulation')
- * @returns {Object} 事件追踪处理函数集合
- */
-export const useSearchEvents = ({ context = 'global' } = {}) => {
-  const { track } = usePostHogTrack();
-
-  /**
-   * 追踪搜索开始（聚焦搜索框）
-   * @param {string} placeholder - 搜索框提示文本
-   */
-  const trackSearchInitiated = useCallback((placeholder = '') => {
-    track(RETENTION_EVENTS.SEARCH_INITIATED, {
-      context,
-      placeholder,
-      timestamp: new Date().toISOString(),
-    });
-
-    logger.debug('useSearchEvents', '🔍 Search Initiated', {
-      context,
-      placeholder,
-    });
-  }, [track, context]);
-
-  /**
-   * 追踪搜索查询提交
-   * @param {string} query - 搜索查询词
-   * @param {number} resultCount - 搜索结果数量
-   * @param {Object} filters - 应用的筛选条件
-   */
-  const trackSearchQuerySubmitted = useCallback((query, resultCount = 0, filters = {}) => {
-    if (!query) {
-      logger.warn('useSearchEvents', 'trackSearchQuerySubmitted: query is required');
-      return;
-    }
-
-    track(RETENTION_EVENTS.SEARCH_QUERY_SUBMITTED, {
-      query,
-      query_length: query.length,
-      result_count: resultCount,
-      has_results: resultCount > 0,
-      context,
-      filters: filters,
-      filter_count: Object.keys(filters).length,
-      timestamp: new Date().toISOString(),
-    });
-
-    // 如果没有搜索结果，额外追踪
-    if (resultCount === 0) {
-      track(RETENTION_EVENTS.SEARCH_NO_RESULTS, {
-        query,
-        context,
-        filters,
-        timestamp: new Date().toISOString(),
-      });
-
-      logger.debug('useSearchEvents', '❌ Search No Results', {
-        query,
-        context,
-      });
-    } else {
-      logger.debug('useSearchEvents', '✅ Search Query Submitted', {
-        query,
-        resultCount,
-        context,
-      });
-    }
-  }, [track, context]);
-
-  /**
-   * 追踪搜索结果点击
-   * @param {Object} result - 被点击的搜索结果
-   * @param {string} result.type - 结果类型 ('stock' | 'news' | 'concept' | 'event')
-   * @param {string} result.id - 结果ID
-   * @param {string} result.title - 结果标题
-   * @param {number} position - 在搜索结果中的位置
-   * @param {string} query - 搜索查询词
-   */
-  const trackSearchResultClicked = useCallback((result, position = 0, query = '') => {
-    if (!result || !result.type) {
-      logger.warn('useSearchEvents', 'trackSearchResultClicked: result object with type is required');
-      return;
-    }
-
-    track(RETENTION_EVENTS.SEARCH_RESULT_CLICKED, {
-      result_type: result.type,
-      result_id: result.id || result.code || '',
-      result_title: result.title || result.name || '',
-      position,
-      query,
-      context,
-      timestamp: new Date().toISOString(),
-    });
-
-    logger.debug('useSearchEvents', '🎯 Search Result Clicked', {
-      type: result.type,
-      id: result.id || result.code,
-      position,
-      context,
-    });
-  }, [track, context]);
-
-  /**
-   * 追踪搜索筛选应用
-   * @param {Object} filters - 应用的筛选条件
-   * @param {string} filterType - 筛选类型 ('sort' | 'category' | 'date_range' | 'price_range')
-   * @param {any} filterValue - 筛选值
-   */
-  const trackSearchFilterApplied = useCallback((filterType, filterValue, filters = {}) => {
-    if (!filterType) {
-      logger.warn('useSearchEvents', 'trackSearchFilterApplied: filterType is required');
-      return;
-    }
-
-    track(RETENTION_EVENTS.SEARCH_FILTER_APPLIED, {
-      filter_type: filterType,
-      filter_value: String(filterValue),
-      all_filters: filters,
-      context,
-      timestamp: new Date().toISOString(),
-    });
-
-    logger.debug('useSearchEvents', '🔍 Search Filter Applied', {
-      filterType,
-      filterValue,
-      context,
-    });
-  }, [track, context]);
-
-  /**
-   * 追踪搜索建议点击（自动完成）
-   * @param {string} suggestion - 被点击的搜索建议
-   * @param {number} position - 在建议列表中的位置
-   * @param {string} source - 建议来源 ('history' | 'popular' | 'related')
-   */
-  const trackSearchSuggestionClicked = useCallback((suggestion, position = 0, source = 'popular') => {
-    if (!suggestion) {
-      logger.warn('useSearchEvents', 'trackSearchSuggestionClicked: suggestion is required');
-      return;
-    }
-
-    track('Search Suggestion Clicked', {
-      suggestion,
-      position,
-      source,
-      context,
-      timestamp: new Date().toISOString(),
-    });
-
-    logger.debug('useSearchEvents', '💡 Search Suggestion Clicked', {
-      suggestion,
-      position,
-      source,
-      context,
-    });
-  }, [track, context]);
-
-  /**
-   * 追踪搜索历史查看
-   * @param {number} historyCount - 历史记录数量
-   */
-  const trackSearchHistoryViewed = useCallback((historyCount = 0) => {
-    track('Search History Viewed', {
-      history_count: historyCount,
-      has_history: historyCount > 0,
-      context,
-      timestamp: new Date().toISOString(),
-    });
-
-    logger.debug('useSearchEvents', '📜 Search History Viewed', {
-      historyCount,
-      context,
-    });
-  }, [track, context]);
-
-  /**
-   * 追踪搜索历史清除
-   */
-  const trackSearchHistoryCleared = useCallback(() => {
-    track('Search History Cleared', {
-      context,
-      timestamp: new Date().toISOString(),
-    });
-
-    logger.debug('useSearchEvents', '🗑️ Search History Cleared', {
-      context,
-    });
-  }, [track, context]);
-
-  /**
-   * 追踪热门搜索词点击
-   * @param {string} keyword - 被点击的热门关键词
-   * @param {number} position - 在列表中的位置
-   * @param {number} heatScore - 热度分数
-   */
-  const trackPopularKeywordClicked = useCallback((keyword, position = 0, heatScore = 0) => {
-    if (!keyword) {
-      logger.warn('useSearchEvents', 'trackPopularKeywordClicked: keyword is required');
-      return;
-    }
-
-    track('Popular Keyword Clicked', {
-      keyword,
-      position,
-      heat_score: heatScore,
-      context,
-      timestamp: new Date().toISOString(),
-    });
-
-    logger.debug('useSearchEvents', '🔥 Popular Keyword Clicked', {
-      keyword,
-      position,
-      context,
-    });
-  }, [track, context]);
-
-  return {
-    // 搜索流程事件
-    trackSearchInitiated,
-    trackSearchQuerySubmitted,
-    trackSearchResultClicked,
-
-    // 筛选和建议
-    trackSearchFilterApplied,
-    trackSearchSuggestionClicked,
-
-    // 历史和热门
-    trackSearchHistoryViewed,
-    trackSearchHistoryCleared,
-    trackPopularKeywordClicked,
-  };
-};
-
-export default useSearchEvents;

src/index.js

@@ -5,6 +5,17 @@ import { BrowserRouter as Router } from 'react-router-dom';
 
 // ⚡ 性能监控：在应用启动时尽早标记
 import { performanceMonitor } from './utils/performanceMonitor';
+
+// T0: HTML 加载完成时间点
+if (document.readyState === 'complete') {
+  performanceMonitor.mark('html-loaded');
+} else {
+  window.addEventListener('load', () => {
+    performanceMonitor.mark('html-loaded');
+  });
+}
+
+// T1: React 开始初始化
 performanceMonitor.mark('app-start');
 
 // ⚡ 已删除 brainwave.css（项目未安装 Tailwind CSS，该文件无效）

src/mocks/data/account.js

@@ -128,6 +128,13 @@ export const mockRealtimeQuotes = [
 
 // ==================== 关注事件数据 ====================
 
+// 事件关注内存存储（Set 存储已关注的事件 ID）
+export const followedEventsSet = new Set();
+
+// 关注事件完整数据存储（Map: eventId -> eventData）
+export const followedEventsMap = new Map();
+
+// 初始关注事件列表（用于初始化）
 export const mockFollowingEvents = [
   {
     id: 101,
@@ -231,6 +238,74 @@ export const mockFollowingEvents = [
   }
 ];
 
+// 初始化：将 mockFollowingEvents 的数据加入内存存储
+mockFollowingEvents.forEach(event => {
+  followedEventsSet.add(event.id);
+  followedEventsMap.set(event.id, event);
+});
+
+/**
+ * 切换事件关注状态
+ * @param {number} eventId - 事件 ID
+ * @param {Object} eventData - 事件数据（关注时需要）
+ * @returns {{ isFollowing: boolean, followerCount: number }}
+ */
+export function toggleEventFollowStatus(eventId, eventData = null) {
+  const wasFollowing = followedEventsSet.has(eventId);
+
+  if (wasFollowing) {
+    // 取消关注
+    followedEventsSet.delete(eventId);
+    followedEventsMap.delete(eventId);
+  } else {
+    // 添加关注
+    followedEventsSet.add(eventId);
+    if (eventData) {
+      followedEventsMap.set(eventId, {
+        ...eventData,
+        followed_at: new Date().toISOString()
+      });
+    } else {
+      // 如果没有提供事件数据，创建基础数据
+      followedEventsMap.set(eventId, {
+        id: eventId,
+        title: `事件 ${eventId}`,
+        tags: [],
+        followed_at: new Date().toISOString()
+      });
+    }
+  }
+
+  const isFollowing = !wasFollowing;
+  const followerCount = isFollowing ? Math.floor(Math.random() * 500) + 100 : Math.floor(Math.random() * 500) + 50;
+
+  console.log('[Mock Data] 切换事件关注状态:', {
+    eventId,
+    wasFollowing,
+    isFollowing,
+    followedEventsCount: followedEventsSet.size
+  });
+
+  return { isFollowing, followerCount };
+}
+
+/**
+ * 检查事件是否已关注
+ * @param {number} eventId - 事件 ID
+ * @returns {boolean}
+ */
+export function isEventFollowed(eventId) {
+  return followedEventsSet.has(eventId);
+}
+
+/**
+ * 获取所有已关注的事件列表
+ * @returns {Array}
+ */
+export function getFollowedEvents() {
+  return Array.from(followedEventsMap.values());
+}
+
 // ==================== 评论数据 ====================
 
 export const mockEventComments = [

src/mocks/data/events.js

@@ -874,8 +874,20 @@ export function generateMockEvents(params = {}) {
             e.title.toLowerCase().includes(query) ||
             e.description.toLowerCase().includes(query) ||
             // keywords 是对象数组 { concept, score, ... }，需要访问 concept 属性
-            e.keywords.some(k => k.concept && k.concept.toLowerCase().includes(query))
+            e.keywords.some(k => k.concept && k.concept.toLowerCase().includes(query)) ||
+            // 搜索 related_stocks 中的股票名称和代码
+            (e.related_stocks && e.related_stocks.some(stock =>
+                (stock.stock_name && stock.stock_name.toLowerCase().includes(query)) ||
+                (stock.stock_code && stock.stock_code.toLowerCase().includes(query))
+            )) ||
+            // 搜索行业
+            (e.industry && e.industry.toLowerCase().includes(query))
         );
+
+        // 如果搜索结果为空，返回所有事件（宽松模式）
+        if (filteredEvents.length === 0) {
+            filteredEvents = allEvents;
+        }
     }
 
     // 行业筛选
@@ -1042,7 +1054,7 @@ function generateTransmissionChain(industry, index) {
 
         let nodeName;
         if (nodeType === 'company' && industryStock) {
-            nodeName = industryStock.name;
+            nodeName = industryStock.stock_name;
         } else if (nodeType === 'industry') {
             nodeName = `${industry}产业`;
         } else if (nodeType === 'policy') {
@@ -1133,7 +1145,7 @@ export function generateDynamicNewsEvents(timeRange = null, count = 30) {
                 const stock = industryStocks[j % industryStocks.length];
                 relatedStocks.push({
                     stock_code: stock.stock_code,
-                    stock_name: stock.name,
+                    stock_name: stock.stock_name,
                     relation_desc: relationDescriptions[j % relationDescriptions.length]
                 });
             }
@@ -1145,7 +1157,7 @@ export function generateDynamicNewsEvents(timeRange = null, count = 30) {
             if (!relatedStocks.some(s => s.stock_code === randomStock.stock_code)) {
                 relatedStocks.push({
                     stock_code: randomStock.stock_code,
-                    stock_name: randomStock.name,
+                    stock_name: randomStock.stock_name,
                     relation_desc: relationDescriptions[relatedStocks.length % relationDescriptions.length]
                 });
             }

src/mocks/data/financial.js

@@ -10,73 +10,323 @@ export const generateFinancialData = (stockCode) => {
 
         // 股票基本信息
         stockInfo: {
-            code: stockCode,
-            name: stockCode === '000001' ? '平安银行' : '示例公司',
+            stock_code: stockCode,
+            stock_name: stockCode === '000001' ? '平安银行' : '示例公司',
             industry: stockCode === '000001' ? '银行' : '制造业',
             list_date: '1991-04-03',
-            market: 'SZ'
+            market: 'SZ',
+            // 关键指标
+            key_metrics: {
+                eps: 2.72,
+                roe: 16.23,
+                gross_margin: 71.92,
+                net_margin: 32.56,
+                roa: 1.05
+            },
+            // 增长率
+            growth_rates: {
+                revenue_growth: 8.2,
+                profit_growth: 12.5,
+                asset_growth: 5.6,
+                equity_growth: 6.8
+            },
+            // 财务概要
+            financial_summary: {
+                revenue: 162350,
+                net_profit: 52860,
+                total_assets: 5024560,
+                total_liabilities: 4698880
+            },
+            // 最新业绩预告
+            latest_forecast: {
+                forecast_type: '预增',
+                content: '预计全年净利润同比增长10%-17%'
+            }
         },
 
-        // 资产负债表
+        // 资产负债表 - 嵌套结构
         balanceSheet: periods.map((period, i) => ({
             period,
-            total_assets: 5024560 - i * 50000, // 百万元
-            total_liabilities: 4698880 - i * 48000,
-            shareholders_equity: 325680 - i * 2000,
-            current_assets: 2512300 - i * 25000,
-            non_current_assets: 2512260 - i * 25000,
-            current_liabilities: 3456780 - i * 35000,
-            non_current_liabilities: 1242100 - i * 13000
+            assets: {
+                current_assets: {
+                    cash: 856780 - i * 10000,
+                    trading_financial_assets: 234560 - i * 5000,
+                    notes_receivable: 12340 - i * 200,
+                    accounts_receivable: 45670 - i * 1000,
+                    prepayments: 8900 - i * 100,
+                    other_receivables: 23450 - i * 500,
+                    inventory: 156780 - i * 3000,
+                    contract_assets: 34560 - i * 800,
+                    other_current_assets: 67890 - i * 1500,
+                    total: 2512300 - i * 25000
+                },
+                non_current_assets: {
+                    long_term_equity_investments: 234560 - i * 5000,
+                    investment_property: 45670 - i * 1000,
+                    fixed_assets: 678900 - i * 15000,
+                    construction_in_progress: 123450 - i * 3000,
+                    right_of_use_assets: 34560 - i * 800,
+                    intangible_assets: 89012 - i * 2000,
+                    goodwill: 45670 - i * 1000,
+                    deferred_tax_assets: 12340 - i * 300,
+                    other_non_current_assets: 67890 - i * 1500,
+                    total: 2512260 - i * 25000
+                },
+                total: 5024560 - i * 50000
+            },
+            liabilities: {
+                current_liabilities: {
+                    short_term_borrowings: 456780 - i * 10000,
+                    notes_payable: 23450 - i * 500,
+                    accounts_payable: 234560 - i * 5000,
+                    advance_receipts: 12340 - i * 300,
+                    contract_liabilities: 34560 - i * 800,
+                    employee_compensation_payable: 45670 - i * 1000,
+                    taxes_payable: 23450 - i * 500,
+                    other_payables: 78900 - i * 1500,
+                    non_current_liabilities_due_within_one_year: 89012 - i * 2000,
+                    total: 3456780 - i * 35000
+                },
+                non_current_liabilities: {
+                    long_term_borrowings: 678900 - i * 15000,
+                    bonds_payable: 234560 - i * 5000,
+                    lease_liabilities: 45670 - i * 1000,
+                    deferred_tax_liabilities: 12340 - i * 300,
+                    other_non_current_liabilities: 89012 - i * 2000,
+                    total: 1242100 - i * 13000
+                },
+                total: 4698880 - i * 48000
+            },
+            equity: {
+                share_capital: 19405,
+                capital_reserve: 89012 - i * 2000,
+                surplus_reserve: 45670 - i * 1000,
+                undistributed_profit: 156780 - i * 3000,
+                treasury_stock: 0,
+                other_comprehensive_income: 12340 - i * 300,
+                parent_company_equity: 315680 - i * 1800,
+                minority_interests: 10000 - i * 200,
+                total: 325680 - i * 2000
+            }
         })),
 
-        // 利润表
+        // 利润表 - 嵌套结构
         incomeStatement: periods.map((period, i) => ({
             period,
-            revenue: 162350 - i * 4000, // 百万元
-            operating_cost: 45620 - i * 1200,
-            gross_profit: 116730 - i * 2800,
-            operating_profit: 68450 - i * 1500,
-            net_profit: 52860 - i * 1200,
-            eps: 2.72 - i * 0.06
+            revenue: {
+                total_operating_revenue: 162350 - i * 4000,
+                operating_revenue: 158900 - i * 3900,
+                other_income: 3450 - i * 100
+            },
+            costs: {
+                total_operating_cost: 93900 - i * 2500,
+                operating_cost: 45620 - i * 1200,
+                taxes_and_surcharges: 4560 - i * 100,
+                selling_expenses: 12340 - i * 300,
+                admin_expenses: 15670 - i * 400,
+                rd_expenses: 8900 - i * 200,
+                financial_expenses: 6810 - i * 300,
+                interest_expense: 8900 - i * 200,
+                interest_income: 2090 - i * 50,
+                three_expenses_total: 34820 - i * 1000,
+                four_expenses_total: 43720 - i * 1200,
+                asset_impairment_loss: 1200 - i * 50,
+                credit_impairment_loss: 2340 - i * 100
+            },
+            other_gains: {
+                fair_value_change: 1230 - i * 50,
+                investment_income: 3450 - i * 100,
+                investment_income_from_associates: 890 - i * 20,
+                exchange_income: 560 - i * 10,
+                asset_disposal_income: 340 - i * 10
+            },
+            profit: {
+                operating_profit: 68450 - i * 1500,
+                total_profit: 69500 - i * 1500,
+                income_tax_expense: 16640 - i * 300,
+                net_profit: 52860 - i * 1200,
+                parent_net_profit: 51200 - i * 1150,
+                minority_profit: 1660 - i * 50,
+                continuing_operations_net_profit: 52860 - i * 1200,
+                discontinued_operations_net_profit: 0
+            },
+            non_operating: {
+                non_operating_income: 1050 - i * 20,
+                non_operating_expenses: 450 - i * 10
+            },
+            per_share: {
+                basic_eps: 2.72 - i * 0.06,
+                diluted_eps: 2.70 - i * 0.06
+            },
+            comprehensive_income: {
+                other_comprehensive_income: 890 - i * 20,
+                total_comprehensive_income: 53750 - i * 1220,
+                parent_comprehensive_income: 52050 - i * 1170,
+                minority_comprehensive_income: 1700 - i * 50
+            }
         })),
 
-        // 现金流量表
+        // 现金流量表 - 嵌套结构
         cashflow: periods.map((period, i) => ({
             period,
-            operating_cashflow: 125600 - i * 3000, // 百万元
-            investing_cashflow: -45300 - i * 1000,
-            financing_cashflow: -38200 + i * 500,
-            net_cashflow: 42100 - i * 1500,
-            cash_ending: 456780 - i * 10000
+            operating_activities: {
+                inflow: {
+                    cash_from_sales: 178500 - i * 4500
+                },
+                outflow: {
+                    cash_for_goods: 52900 - i * 1500
+                },
+                net_flow: 125600 - i * 3000
+            },
+            investment_activities: {
+                net_flow: -45300 - i * 1000
+            },
+            financing_activities: {
+                net_flow: -38200 + i * 500
+            },
+            cash_changes: {
+                net_increase: 42100 - i * 1500,
+                ending_balance: 456780 - i * 10000
+            },
+            key_metrics: {
+                free_cash_flow: 80300 - i * 2000
+            }
         })),
 
-        // 财务指标
+        // 财务指标 - 嵌套结构
         financialMetrics: periods.map((period, i) => ({
             period,
-            roe: 16.23 - i * 0.3, // %
-            roa: 1.05 - i * 0.02,
-            gross_margin: 71.92 - i * 0.5,
-            net_margin: 32.56 - i * 0.3,
-            current_ratio: 0.73 + i * 0.01,
-            quick_ratio: 0.71 + i * 0.01,
-            debt_ratio: 93.52 + i * 0.05,
-            asset_turnover: 0.41 - i * 0.01,
-            inventory_turnover: 0, // 银行无库存
-            receivable_turnover: 0 // 银行特殊
+            profitability: {
+                roe: 16.23 - i * 0.3,
+                roe_deducted: 15.89 - i * 0.3,
+                roe_weighted: 16.45 - i * 0.3,
+                roa: 1.05 - i * 0.02,
+                gross_margin: 71.92 - i * 0.5,
+                net_profit_margin: 32.56 - i * 0.3,
+                operating_profit_margin: 42.16 - i * 0.4,
+                cost_profit_ratio: 115.8 - i * 1.2,
+                ebit: 86140 - i * 1800
+            },
+            per_share_metrics: {
+                eps: 2.72 - i * 0.06,
+                basic_eps: 2.72 - i * 0.06,
+                diluted_eps: 2.70 - i * 0.06,
+                deducted_eps: 2.65 - i * 0.06,
+                bvps: 16.78 - i * 0.1,
+                operating_cash_flow_ps: 6.47 - i * 0.15,
+                capital_reserve_ps: 4.59 - i * 0.1,
+                undistributed_profit_ps: 8.08 - i * 0.15
+            },
+            growth: {
+                revenue_growth: 8.2 - i * 0.5,
+                net_profit_growth: 12.5 - i * 0.8,
+                deducted_profit_growth: 11.8 - i * 0.7,
+                parent_profit_growth: 12.3 - i * 0.75,
+                operating_cash_flow_growth: 15.6 - i * 1.0,
+                total_asset_growth: 5.6 - i * 0.3,
+                equity_growth: 6.8 - i * 0.4,
+                fixed_asset_growth: 4.2 - i * 0.2
+            },
+            operational_efficiency: {
+                total_asset_turnover: 0.41 - i * 0.01,
+                fixed_asset_turnover: 2.35 - i * 0.05,
+                current_asset_turnover: 0.82 - i * 0.02,
+                receivable_turnover: 12.5 - i * 0.3,
+                receivable_days: 29.2 + i * 0.7,
+                inventory_turnover: 0, // 银行无库存
+                inventory_days: 0,
+                working_capital_turnover: 1.68 - i * 0.04
+            },
+            solvency: {
+                current_ratio: 0.73 + i * 0.01,
+                quick_ratio: 0.71 + i * 0.01,
+                cash_ratio: 0.25 + i * 0.005,
+                conservative_quick_ratio: 0.68 + i * 0.01,
+                asset_liability_ratio: 93.52 + i * 0.05,
+                interest_coverage: 8.56 - i * 0.2,
+                cash_to_maturity_debt_ratio: 0.45 - i * 0.01,
+                tangible_asset_debt_ratio: 94.12 + i * 0.05
+            },
+            expense_ratios: {
+                selling_expense_ratio: 7.60 + i * 0.1,
+                admin_expense_ratio: 9.65 + i * 0.1,
+                financial_expense_ratio: 4.19 + i * 0.1,
+                rd_expense_ratio: 5.48 + i * 0.1,
+                three_expense_ratio: 21.44 + i * 0.3,
+                four_expense_ratio: 26.92 + i * 0.4,
+                cost_ratio: 28.10 + i * 0.2
+            }
         })),
 
-        // 主营业务
+        // 主营业务 - 按产品/业务分类
         mainBusiness: {
-            by_product: [
-                { name: '对公业务', revenue: 68540, ratio: 42.2, yoy_growth: 6.8 },
-                { name: '零售业务', revenue: 81320, ratio: 50.1, yoy_growth: 11.2 },
-                { name: '金融市场业务', revenue: 12490, ratio: 7.7, yoy_growth: 3.5 }
+            product_classification: [
+                {
+                    period: '2024-09-30',
+                    report_type: '2024年三季报',
+                    products: [
+                        { content: '零售金融业务', revenue: 81320000000, gross_margin: 68.5, profit_margin: 42.3, profit: 34398160000 },
+                        { content: '对公金融业务', revenue: 68540000000, gross_margin: 62.8, profit_margin: 38.6, profit: 26456440000 },
+                        { content: '金融市场业务', revenue: 12490000000, gross_margin: 75.2, profit_margin: 52.1, profit: 6507290000 },
+                        { content: '合计', revenue: 162350000000, gross_margin: 67.5, profit_margin: 41.2, profit: 66883200000 },
+                    ]
+                },
+                {
+                    period: '2024-06-30',
+                    report_type: '2024年中报',
+                    products: [
+                        { content: '零售金融业务', revenue: 78650000000, gross_margin: 67.8, profit_margin: 41.5, profit: 32639750000 },
+                        { content: '对公金融业务', revenue: 66280000000, gross_margin: 61.9, profit_margin: 37.8, profit: 25053840000 },
+                        { content: '金融市场业务', revenue: 11870000000, gross_margin: 74.5, profit_margin: 51.2, profit: 6077440000 },
+                        { content: '合计', revenue: 156800000000, gross_margin: 66.8, profit_margin: 40.5, profit: 63504000000 },
+                    ]
+                },
+                {
+                    period: '2024-03-31',
+                    report_type: '2024年一季报',
+                    products: [
+                        { content: '零售金融业务', revenue: 38920000000, gross_margin: 67.2, profit_margin: 40.8, profit: 15879360000 },
+                        { content: '对公金融业务', revenue: 32650000000, gross_margin: 61.2, profit_margin: 37.1, profit: 12113150000 },
+                        { content: '金融市场业务', revenue: 5830000000, gross_margin: 73.8, profit_margin: 50.5, profit: 2944150000 },
+                        { content: '合计', revenue: 77400000000, gross_margin: 66.1, profit_margin: 39.8, profit: 30805200000 },
+                    ]
+                },
+                {
+                    period: '2023-12-31',
+                    report_type: '2023年年报',
+                    products: [
+                        { content: '零售金融业务', revenue: 152680000000, gross_margin: 66.5, profit_margin: 40.2, profit: 61377360000 },
+                        { content: '对公金融业务', revenue: 128450000000, gross_margin: 60.5, profit_margin: 36.5, profit: 46884250000 },
+                        { content: '金融市场业务', revenue: 22870000000, gross_margin: 73.2, profit_margin: 49.8, profit: 11389260000 },
+                        { content: '合计', revenue: 304000000000, gross_margin: 65.2, profit_margin: 39.2, profit: 119168000000 },
+                    ]
+                },
             ],
-            by_region: [
-                { name: '华南地区', revenue: 56800, ratio: 35.0, yoy_growth: 9.2 },
-                { name: '华东地区', revenue: 48705, ratio: 30.0, yoy_growth: 8.5 },
-                { name: '华北地区', revenue: 32470, ratio: 20.0, yoy_growth: 7.8 },
-                { name: '其他地区', revenue: 24375, ratio: 15.0, yoy_growth: 6.5 }
+            industry_classification: [
+                {
+                    period: '2024-09-30',
+                    report_type: '2024年三季报',
+                    industries: [
+                        { content: '华南地区', revenue: 56817500000, gross_margin: 69.2, profit_margin: 43.5, profit: 24715612500 },
+                        { content: '华东地区', revenue: 48705000000, gross_margin: 67.8, profit_margin: 41.2, profit: 20066460000 },
+                        { content: '华北地区', revenue: 32470000000, gross_margin: 65.5, profit_margin: 38.8, profit: 12598360000 },
+                        { content: '西南地区', revenue: 16235000000, gross_margin: 64.2, profit_margin: 37.5, profit: 6088125000 },
+                        { content: '其他地区', revenue: 8122500000, gross_margin: 62.8, profit_margin: 35.2, profit: 2859120000 },
+                        { content: '合计', revenue: 162350000000, gross_margin: 67.5, profit_margin: 41.2, profit: 66883200000 },
+                    ]
+                },
+                {
+                    period: '2024-06-30',
+                    report_type: '2024年中报',
+                    industries: [
+                        { content: '华南地区', revenue: 54880000000, gross_margin: 68.5, profit_margin: 42.8, profit: 23488640000 },
+                        { content: '华东地区', revenue: 47040000000, gross_margin: 67.1, profit_margin: 40.5, profit: 19051200000 },
+                        { content: '华北地区', revenue: 31360000000, gross_margin: 64.8, profit_margin: 38.1, profit: 11948160000 },
+                        { content: '西南地区', revenue: 15680000000, gross_margin: 63.5, profit_margin: 36.8, profit: 5770240000 },
+                        { content: '其他地区', revenue: 7840000000, gross_margin: 62.1, profit_margin: 34.5, profit: 2704800000 },
+                        { content: '合计', revenue: 156800000000, gross_margin: 66.8, profit_margin: 40.5, profit: 63504000000 },
+                    ]
+                },
             ]
         },
 
@@ -92,48 +342,74 @@ export const generateFinancialData = (stockCode) => {
             publish_date: '2024-10-15'
         },
 
-        // 行业排名
-        industryRank: {
-            industry: '银行',
-            total_companies: 42,
-            rankings: [
-                { metric: '总资产', rank: 8, value: 5024560, percentile: 19 },
-                { metric: '营业收入', rank: 9, value: 162350, percentile: 21 },
-                { metric: '净利润', rank: 8, value: 52860, percentile: 19 },
-                { metric: 'ROE', rank: 12, value: 16.23, percentile: 29 },
-                { metric: '不良贷款率', rank: 18, value: 1.02, percentile: 43 }
-            ]
-        },
+        // 行业排名（数组格式，符合 IndustryRankingView 组件要求）
+        industryRank: [
+            {
+                period: '2024-09-30',
+                report_type: '三季报',
+                rankings: [
+                    {
+                        industry_name: stockCode === '000001' ? '银行' : '制造业',
+                        level_description: '一级行业',
+                        metrics: {
+                            eps: { value: 2.72, rank: 8, industry_avg: 1.85 },
+                            bvps: { value: 15.23, rank: 12, industry_avg: 12.50 },
+                            roe: { value: 16.23, rank: 10, industry_avg: 12.00 },
+                            revenue_growth: { value: 8.2, rank: 15, industry_avg: 5.50 },
+                            profit_growth: { value: 12.5, rank: 9, industry_avg: 8.00 },
+                            operating_margin: { value: 32.56, rank: 6, industry_avg: 25.00 },
+                            debt_ratio: { value: 92.5, rank: 35, industry_avg: 88.00 },
+                            receivable_turnover: { value: 5.2, rank: 18, industry_avg: 4.80 }
+                        }
+                    }
+                ]
+            }
+        ],
 
-        // 期间对比
-        periodComparison: {
-            periods: ['Q3-2024', 'Q2-2024', 'Q1-2024', 'Q4-2023'],
-            metrics: [
-                {
-                    name: '营业收入',
-                    unit: '百万元',
-                    values: [41500, 40800, 40200, 40850],
-                    yoy: [8.2, 7.8, 8.5, 9.2]
-                },
-                {
-                    name: '净利润',
-                    unit: '百万元',
-                    values: [13420, 13180, 13050, 13210],
-                    yoy: [12.5, 11.2, 10.8, 12.3]
-                },
-                {
-                    name: 'ROE',
-                    unit: '%',
-                    values: [16.23, 15.98, 15.75, 16.02],
-                    yoy: [1.2, 0.8, 0.5, 1.0]
-                },
-                {
-                    name: 'EPS',
-                    unit: '元',
-                    values: [0.69, 0.68, 0.67, 0.68],
-                    yoy: [12.3, 11.5, 10.5, 12.0]
+        // 期间对比 - 营收与利润趋势数据
+        periodComparison: [
+            {
+                period: '2024-09-30',
+                performance: {
+                    revenue: 41500000000,   // 415亿
+                    net_profit: 13420000000  // 134.2亿
                 }
-            ]
-        }
+            },
+            {
+                period: '2024-06-30',
+                performance: {
+                    revenue: 40800000000,   // 408亿
+                    net_profit: 13180000000  // 131.8亿
+                }
+            },
+            {
+                period: '2024-03-31',
+                performance: {
+                    revenue: 40200000000,   // 402亿
+                    net_profit: 13050000000  // 130.5亿
+                }
+            },
+            {
+                period: '2023-12-31',
+                performance: {
+                    revenue: 40850000000,   // 408.5亿
+                    net_profit: 13210000000  // 132.1亿
+                }
+            },
+            {
+                period: '2023-09-30',
+                performance: {
+                    revenue: 38500000000,   // 385亿
+                    net_profit: 11920000000  // 119.2亿
+                }
+            },
+            {
+                period: '2023-06-30',
+                performance: {
+                    revenue: 37800000000,   // 378亿
+                    net_profit: 11850000000  // 118.5亿
+                }
+            }
+        ]
     };
 };

src/mocks/data/market.js

@@ -24,8 +24,9 @@ export const generateMarketData = (stockCode) => {
                     low: parseFloat(low.toFixed(2)),
                     volume: Math.floor(Math.random() * 500000000) + 100000000, // 1-6亿股
                     amount: Math.floor(Math.random() * 7000000000) + 1300000000, // 13-80亿元
-                    turnover_rate: (Math.random() * 2 + 0.5).toFixed(2), // 0.5-2.5%
-                    change_pct: (Math.random() * 6 - 3).toFixed(2) // -3% to +3%
+                    turnover_rate: parseFloat((Math.random() * 2 + 0.5).toFixed(2)), // 0.5-2.5%
+                    change_percent: parseFloat((Math.random() * 6 - 3).toFixed(2)), // -3% to +3%
+                    pe_ratio: parseFloat((Math.random() * 3 + 4).toFixed(2)) // 4-7
                 };
             })
         },
@@ -78,73 +79,119 @@ export const generateMarketData = (stockCode) => {
             }))
         },
 
-        // 股权质押
+        // 股权质押 - 匹配 PledgeData[] 类型
         pledgeData: {
             success: true,
-            data: {
-                total_pledged: 25.6, // 质押比例%
-                major_shareholders: [
-                    { name: '中国平安保险集团', pledged_shares: 0, total_shares: 10168542300, pledge_ratio: 0 },
-                    { name: '深圳市投资控股', pledged_shares: 50000000, total_shares: 382456100, pledge_ratio: 13.08 }
-                ],
-                update_date: '2024-09-30'
-            }
+            data: Array(12).fill(null).map((_, i) => {
+                const date = new Date();
+                date.setMonth(date.getMonth() - (11 - i));
+                return {
+                    end_date: date.toISOString().split('T')[0].slice(0, 7) + '-01',
+                    unrestricted_pledge: Math.floor(Math.random() * 1000000000) + 500000000,
+                    restricted_pledge: Math.floor(Math.random() * 200000000) + 50000000,
+                    total_pledge: Math.floor(Math.random() * 1200000000) + 550000000,
+                    total_shares: 19405918198,
+                    pledge_ratio: parseFloat((Math.random() * 3 + 6).toFixed(2)), // 6-9%
+                    pledge_count: Math.floor(Math.random() * 50) + 100 // 100-150
+                };
+            })
         },
 
-        // 市场摘要
+        // 市场摘要 - 匹配 MarketSummary 类型
         summaryData: {
             success: true,
             data: {
-                current_price: basePrice,
-                change: 0.25,
-                change_pct: 1.89,
-                open: 13.35,
-                high: 13.68,
-                low: 13.28,
-                volume: 345678900,
-                amount: 4678900000,
-                turnover_rate: 1.78,
-                pe_ratio: 4.96,
-                pb_ratio: 0.72,
-                total_market_cap: 262300000000,
-                circulating_market_cap: 262300000000
+                stock_code: stockCode,
+                stock_name: stockCode === '000001' ? '平安银行' : '示例股票',
+                latest_trade: {
+                    close: basePrice,
+                    change_percent: 1.89,
+                    volume: 345678900,
+                    amount: 4678900000,
+                    turnover_rate: 1.78,
+                    pe_ratio: 4.96
+                },
+                latest_funding: {
+                    financing_balance: 5823000000,
+                    securities_balance: 125600000
+                },
+                latest_pledge: {
+                    pledge_ratio: 8.25
+                }
             }
         },
 
-        // 涨停分析
+        // 涨停分析 - 返回数组格式，每个元素对应一个交易日
         riseAnalysisData: {
             success: true,
-            data: {
-                is_limit_up: false,
-                limit_up_price: basePrice * 1.10,
-                current_price: basePrice,
-                distance_to_limit: 8.92, // %
-                consecutive_days: 0,
-                reason: '',
-                concept_tags: ['银行', '深圳国资', 'MSCI', '沪深300']
-            }
+            data: Array(30).fill(null).map((_, i) => {
+                const tradeDate = new Date(Date.now() - (29 - i) * 24 * 60 * 60 * 1000).toISOString().split('T')[0];
+                const isLimitUp = Math.random() < 0.05; // 5%概率涨停
+                return {
+                    trade_date: tradeDate,
+                    is_limit_up: isLimitUp,
+                    limit_up_price: (basePrice * 1.10).toFixed(2),
+                    current_price: (basePrice + (Math.random() - 0.5) * 0.5).toFixed(2),
+                    distance_to_limit: (Math.random() * 10).toFixed(2), // %
+                    consecutive_days: isLimitUp ? Math.floor(Math.random() * 3) + 1 : 0,
+                    reason: isLimitUp ? '业绩超预期' : '',
+                    concept_tags: ['银行', '深圳国资', 'MSCI', '沪深300'],
+                    analysis: isLimitUp ? '股价触及涨停板，资金流入明显' : '股价正常波动，交投活跃'
+                };
+            })
         },
 
-        // 最新分时数据
+        // 最新分时数据 - 匹配 MinuteData 类型
         latestMinuteData: {
             success: true,
-            data: Array(240).fill(null).map((_, i) => {
-                const minute = 9 * 60 + 30 + i; // 从9:30开始
-                const hour = Math.floor(minute / 60);
-                const min = minute % 60;
-                const time = `${hour.toString().padStart(2, '0')}:${min.toString().padStart(2, '0')}`;
-                const randomChange = (Math.random() - 0.5) * 0.1;
-                return {
-                    time,
-                    price: (basePrice + randomChange).toFixed(2),
-                    volume: Math.floor(Math.random() * 2000000) + 500000,
-                    avg_price: (basePrice + randomChange * 0.8).toFixed(2)
-                };
-            }),
+            data: (() => {
+                const minuteData = [];
+                // 上午 9:30-11:30 (120分钟)
+                for (let i = 0; i < 120; i++) {
+                    const hour = 9 + Math.floor((30 + i) / 60);
+                    const min = (30 + i) % 60;
+                    const time = `${hour.toString().padStart(2, '0')}:${min.toString().padStart(2, '0')}`;
+                    const randomChange = (Math.random() - 0.5) * 0.1;
+                    const open = parseFloat((basePrice + randomChange).toFixed(2));
+                    const close = parseFloat((basePrice + randomChange + (Math.random() - 0.5) * 0.05).toFixed(2));
+                    const high = parseFloat(Math.max(open, close, open + Math.random() * 0.05).toFixed(2));
+                    const low = parseFloat(Math.min(open, close, close - Math.random() * 0.05).toFixed(2));
+                    minuteData.push({
+                        time,
+                        open,
+                        close,
+                        high,
+                        low,
+                        volume: Math.floor(Math.random() * 2000000) + 500000,
+                        amount: Math.floor(Math.random() * 30000000) + 5000000
+                    });
+                }
+                // 下午 13:00-15:00 (120分钟)
+                for (let i = 0; i < 120; i++) {
+                    const hour = 13 + Math.floor(i / 60);
+                    const min = i % 60;
+                    const time = `${hour.toString().padStart(2, '0')}:${min.toString().padStart(2, '0')}`;
+                    const randomChange = (Math.random() - 0.5) * 0.1;
+                    const open = parseFloat((basePrice + randomChange).toFixed(2));
+                    const close = parseFloat((basePrice + randomChange + (Math.random() - 0.5) * 0.05).toFixed(2));
+                    const high = parseFloat(Math.max(open, close, open + Math.random() * 0.05).toFixed(2));
+                    const low = parseFloat(Math.min(open, close, close - Math.random() * 0.05).toFixed(2));
+                    minuteData.push({
+                        time,
+                        open,
+                        close,
+                        high,
+                        low,
+                        volume: Math.floor(Math.random() * 1500000) + 400000,
+                        amount: Math.floor(Math.random() * 25000000) + 4000000
+                    });
+                }
+                return minuteData;
+            })(),
             code: stockCode,
             name: stockCode === '000001' ? '平安银行' : '示例股票',
             trade_date: new Date().toISOString().split('T')[0],
-            type: 'minute'
+            type: '1min'
         }
     };
 };

src/mocks/handlers/account.js

@@ -9,7 +9,8 @@ import {
   mockInvestmentPlans,
   mockCalendarEvents,
   mockSubscriptionCurrent,
-  getCalendarEventsByDateRange
+  getCalendarEventsByDateRange,
+  getFollowedEvents
 } from '../data/account';
 
 // 模拟网络延迟（毫秒）
@@ -250,7 +251,7 @@ export const accountHandlers = [
 
   // ==================== 事件关注管理 ====================
 
-  // 8. 获取关注的事件
+  // 8. 获取关注的事件（使用内存状态动态返回）
   http.get('/api/account/events/following', async () => {
     await delay(NETWORK_DELAY);
 
@@ -262,11 +263,14 @@ export const accountHandlers = [
       );
     }
 
-    console.log('[Mock] 获取关注的事件');
+    // 从内存存储获取已关注的事件列表
+    const followedEvents = getFollowedEvents();
+
+    console.log('[Mock] 获取关注的事件, 数量:', followedEvents.length);
 
     return HttpResponse.json({
       success: true,
-      data: mockFollowingEvents
+      data: followedEvents
     });
   }),
 

src/mocks/handlers/bytedesk.js

@@ -1,16 +1,28 @@
 // src/mocks/handlers/bytedesk.js
 /**
  * Bytedesk 客服 Widget MSW Handler
- * 使用 passthrough 让请求通过到真实服务器，消除 MSW 警告
+ * Mock 模式下返回模拟数据
  */
 
-import { http, passthrough } from 'msw';
+import { http, HttpResponse, passthrough } from 'msw';
 
 export const bytedeskHandlers = [
-    // Bytedesk API 请求 - 直接 passthrough
-    // 匹配 /bytedesk/* 路径（通过代理访问后端）
+    // 未读消息数量
+    http.get('/bytedesk/visitor/api/v1/message/unread/count', () => {
+        return HttpResponse.json({
+            code: 200,
+            message: 'success',
+            data: { count: 0 },
+        });
+    }),
+
+    // 其他 Bytedesk API - 返回通用成功响应
     http.all('/bytedesk/*', () => {
-        return passthrough();
+        return HttpResponse.json({
+            code: 200,
+            message: 'success',
+            data: null,
+        });
     }),
 
     // Bytedesk 外部 CDN/服务请求

src/mocks/handlers/company.js

@@ -43,12 +43,10 @@ export const companyHandlers = [
         const { stockCode } = params;
         const data = getCompanyData(stockCode);
 
+        // 直接返回 keyFactorsTimeline 对象（包含 key_factors 和 development_timeline）
         return HttpResponse.json({
             success: true,
-            data: {
-                timeline: data.keyFactorsTimeline,
-                total: data.keyFactorsTimeline.length
-            }
+            data: data.keyFactorsTimeline
         });
     }),
 
@@ -69,10 +67,19 @@ export const companyHandlers = [
         await delay(150);
         const { stockCode } = params;
         const data = getCompanyData(stockCode);
+        const raw = data.actualControl;
+
+        // 数据已经是数组格式，只做数值转换（holding_ratio 从 0-100 转为 0-1）
+        const formatted = Array.isArray(raw)
+            ? raw.map(item => ({
+                ...item,
+                holding_ratio: item.holding_ratio > 1 ? item.holding_ratio / 100 : item.holding_ratio,
+            }))
+            : [];
 
         return HttpResponse.json({
             success: true,
-            data: data.actualControl
+            data: formatted
         });
     }),
 
@@ -81,10 +88,19 @@ export const companyHandlers = [
         await delay(150);
         const { stockCode } = params;
         const data = getCompanyData(stockCode);
+        const raw = data.concentration;
+
+        // 数据已经是数组格式，只做数值转换（holding_ratio 从 0-100 转为 0-1）
+        const formatted = Array.isArray(raw)
+            ? raw.map(item => ({
+                ...item,
+                holding_ratio: item.holding_ratio > 1 ? item.holding_ratio / 100 : item.holding_ratio,
+            }))
+            : [];
 
         return HttpResponse.json({
             success: true,
-            data: data.concentration
+            data: formatted
         });
     }),
 
@@ -212,4 +228,64 @@ export const companyHandlers = [
             data: data.forecastReport || null
         });
     }),
+
+    // 14. 价值链关联公司
+    http.get('/api/company/value-chain/related-companies', async ({ request }) => {
+        await delay(300);
+
+        const url = new URL(request.url);
+        const nodeName = url.searchParams.get('node_name') || '';
+
+        console.log('[Mock] 获取价值链关联公司:', nodeName);
+
+        // 生成模拟的关联公司数据
+        const relatedCompanies = [
+            {
+                stock_code: '601318',
+                stock_name: '中国平安',
+                industry: '保险',
+                relation: '同业竞争',
+                market_cap: 9200,
+                change_pct: 1.25
+            },
+            {
+                stock_code: '600036',
+                stock_name: '招商银行',
+                industry: '银行',
+                relation: '核心供应商',
+                market_cap: 8500,
+                change_pct: 0.85
+            },
+            {
+                stock_code: '601166',
+                stock_name: '兴业银行',
+                industry: '银行',
+                relation: '同业竞争',
+                market_cap: 4200,
+                change_pct: -0.32
+            },
+            {
+                stock_code: '601398',
+                stock_name: '工商银行',
+                industry: '银行',
+                relation: '同业竞争',
+                market_cap: 15000,
+                change_pct: 0.15
+            },
+            {
+                stock_code: '601288',
+                stock_name: '农业银行',
+                industry: '银行',
+                relation: '同业竞争',
+                market_cap: 12000,
+                change_pct: -0.08
+            }
+        ];
+
+        return HttpResponse.json({
+            success: true,
+            data: relatedCompanies,
+            node_name: nodeName
+        });
+    }),
 ];

src/mocks/handlers/event.js

@@ -3,7 +3,7 @@
 
 import { http, HttpResponse } from 'msw';
 import { getEventRelatedStocks, generateMockEvents, generateHotEvents, generatePopularKeywords, generateDynamicNewsEvents } from '../data/events';
-import { getMockFutureEvents, getMockEventCountsForMonth } from '../data/account';
+import { getMockFutureEvents, getMockEventCountsForMonth, toggleEventFollowStatus, isEventFollowed } from '../data/account';
 import { generatePopularConcepts } from './concept';
 
 // 模拟网络延迟
@@ -119,9 +119,12 @@ export const eventHandlers = [
         try {
             const result = generateMockEvents(params);
 
+            // 返回格式兼容 NewsPanel 期望的结构
+            // NewsPanel 期望: { success, data: [], pagination: {} }
             return HttpResponse.json({
                 success: true,
-                data: result,
+                data: result.events,           // 事件数组
+                pagination: result.pagination, // 分页信息
                 message: '获取成功'
             });
         } catch (error) {
@@ -135,16 +138,14 @@ export const eventHandlers = [
                 {
                     success: false,
                     error: '获取事件列表失败',
-                    data: {
-                        events: [],
-                        pagination: {
-                            page: 1,
-                            per_page: 10,
-                            total: 0,
-                            pages: 0,              // ← 对齐后端字段名
-                            has_prev: false,       // ← 对齐后端
-                            has_next: false        // ← 对齐后端
-                        }
+                    data: [],
+                    pagination: {
+                        page: 1,
+                        per_page: 10,
+                        total: 0,
+                        pages: 0,
+                        has_prev: false,
+                        has_next: false
                     }
                 },
                 { status: 500 }
@@ -260,15 +261,19 @@ export const eventHandlers = [
         await delay(200);
 
         const { eventId } = params;
+        const numericEventId = parseInt(eventId, 10);
 
-        console.log('[Mock] 获取事件详情, eventId:', eventId);
+        console.log('[Mock] 获取事件详情, eventId:', numericEventId);
 
         try {
+            // 检查是否已关注
+            const isFollowing = isEventFollowed(numericEventId);
+
             // 返回模拟的事件详情数据
             return HttpResponse.json({
                 success: true,
                 data: {
-                    id: parseInt(eventId),
+                    id: numericEventId,
                     title: `测试事件 ${eventId} - 重大政策发布`,
                     description: '这是一个模拟的事件描述，用于开发测试。该事件涉及重要政策变化，可能对相关板块产生显著影响。建议关注后续发展动态。',
                     importance: ['S', 'A', 'B', 'C'][Math.floor(Math.random() * 4)],
@@ -278,7 +283,7 @@ export const eventHandlers = [
                     related_avg_chg: parseFloat((Math.random() * 10 - 5).toFixed(2)),
                     follower_count: Math.floor(Math.random() * 500) + 50,
                     view_count: Math.floor(Math.random() * 5000) + 100,
-                    is_following: false,
+                    is_following: isFollowing,  // 使用内存状态
                     post_count: Math.floor(Math.random() * 50),
                     expectation_surprise_score: parseFloat((Math.random() * 100).toFixed(1)),
                 },
@@ -297,6 +302,45 @@ export const eventHandlers = [
         }
     }),
 
+    // 获取事件超预期得分
+    http.get('/api/events/:eventId/expectation-score', async ({ params }) => {
+        await delay(200);
+
+        const { eventId } = params;
+
+        console.log('[Mock] 获取事件超预期得分, eventId:', eventId);
+
+        try {
+            // 生成模拟的超预期得分数据
+            const score = parseFloat((Math.random() * 100).toFixed(1));
+            const avgChange = parseFloat((Math.random() * 10 - 2).toFixed(2));
+            const maxChange = parseFloat((Math.random() * 15).toFixed(2));
+
+            return HttpResponse.json({
+                success: true,
+                data: {
+                    event_id: parseInt(eventId),
+                    expectation_score: score,
+                    avg_change: avgChange,
+                    max_change: maxChange,
+                    stock_count: Math.floor(Math.random() * 20) + 5,
+                    updated_at: new Date().toISOString(),
+                },
+                message: '获取成功'
+            });
+        } catch (error) {
+            console.error('[Mock] 获取事件超预期得分失败:', error);
+            return HttpResponse.json(
+                {
+                    success: false,
+                    error: '获取事件超预期得分失败',
+                    data: null
+                },
+                { status: 500 }
+            );
+        }
+    }),
+
     // 获取事件相关股票
     http.get('/api/events/:eventId/stocks', async ({ params }) => {
         await delay(300);
@@ -356,19 +400,29 @@ export const eventHandlers = [
         }
     }),
 
-    // 切换事件关注状态
-    http.post('/api/events/:eventId/follow', async ({ params }) => {
+    // 切换事件关注状态（使用内存状态管理）
+    http.post('/api/events/:eventId/follow', async ({ params, request }) => {
         await delay(200);
 
         const { eventId } = params;
+        const numericEventId = parseInt(eventId, 10);
 
-        console.log('[Mock] 切换事件关注状态, eventId:', eventId);
+        console.log('[Mock] 切换事件关注状态, eventId:', numericEventId);
 
         try {
-            // 模拟切换逻辑：随机生成关注状态
-            // 实际应用中，这里应该从某个状态存储中读取和更新
-            const isFollowing = Math.random() > 0.5;
-            const followerCount = Math.floor(Math.random() * 1000) + 100;
+            // 尝试从请求体获取事件数据（用于新关注时保存完整信息）
+            let eventData = null;
+            try {
+                const body = await request.json();
+                if (body && body.title) {
+                    eventData = body;
+                }
+            } catch {
+                // 没有请求体或解析失败，忽略
+            }
+
+            // 使用内存状态管理切换关注
+            const { isFollowing, followerCount } = toggleEventFollowStatus(numericEventId, eventData);
 
             return HttpResponse.json({
                 success: true,

src/mocks/handlers/market.js

@@ -8,6 +8,53 @@ import { generateMarketData } from '../data/market';
 const delay = (ms) => new Promise(resolve => setTimeout(resolve, ms));
 
 export const marketHandlers = [
+    // 0. 指数实时行情数据
+    http.get('/api/index/:indexCode/realtime', async ({ params }) => {
+        await delay(100);
+        const { indexCode } = params;
+
+        console.log('[Mock] 获取指数实时行情, indexCode:', indexCode);
+
+        // 指数基础数据
+        const indexData = {
+            '000001': { name: '上证指数', basePrice: 3200, baseVolume: 3500 },
+            '399001': { name: '深证成指', basePrice: 10500, baseVolume: 4200 },
+            '399006': { name: '创业板指', basePrice: 2100, baseVolume: 1800 },
+            '000300': { name: '沪深300', basePrice: 3800, baseVolume: 2800 },
+            '000016': { name: '上证50', basePrice: 2600, baseVolume: 1200 },
+            '000905': { name: '中证500', basePrice: 5800, baseVolume: 1500 },
+        };
+
+        const baseData = indexData[indexCode] || { name: `指数${indexCode}`, basePrice: 3000, baseVolume: 2000 };
+
+        // 生成随机波动
+        const changePercent = parseFloat((Math.random() * 4 - 2).toFixed(2)); // -2% ~ +2%
+        const price = parseFloat((baseData.basePrice * (1 + changePercent / 100)).toFixed(2));
+        const change = parseFloat((price - baseData.basePrice).toFixed(2));
+        const volume = parseFloat((baseData.baseVolume * (0.8 + Math.random() * 0.4)).toFixed(2)); // 80%-120% of base
+        const amount = parseFloat((volume * price / 10000).toFixed(2)); // 亿元
+
+        return HttpResponse.json({
+            success: true,
+            data: {
+                index_code: indexCode,
+                index_name: baseData.name,
+                current_price: price,
+                change: change,
+                change_percent: changePercent,
+                open_price: parseFloat((baseData.basePrice * (1 + (Math.random() * 0.01 - 0.005))).toFixed(2)),
+                high_price: parseFloat((price * (1 + Math.random() * 0.01)).toFixed(2)),
+                low_price: parseFloat((price * (1 - Math.random() * 0.01)).toFixed(2)),
+                prev_close: baseData.basePrice,
+                volume: volume, // 亿手
+                amount: amount, // 亿元
+                update_time: new Date().toISOString(),
+                market_status: 'trading', // trading, closed, pre-market, after-hours
+            },
+            message: '获取成功'
+        });
+    }),
+
     // 1. 成交数据
     http.get('/api/market/trade/:stockCode', async ({ params, request }) => {
         await delay(200);
@@ -299,7 +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);

src/mocks/handlers/stock.js

@@ -341,6 +341,68 @@ export const stockHandlers = [
         }
     }),
 
+    // 获取股票业绩预告
+    http.get('/api/stock/:stockCode/forecast', async ({ params }) => {
+        await delay(200);
+
+        const { stockCode } = params;
+        console.log('[Mock Stock] 获取业绩预告:', { stockCode });
+
+        // 生成股票列表用于查找名称
+        const stockList = generateStockList();
+        const stockInfo = stockList.find(s => s.code === stockCode.replace(/\.(SH|SZ)$/i, ''));
+        const stockName = stockInfo?.name || `股票${stockCode}`;
+
+        // 业绩预告类型列表
+        const forecastTypes = ['预增', '预减', '略增', '略减', '扭亏', '续亏', '首亏', '续盈'];
+
+        // 生成业绩预告数据
+        const forecasts = [
+            {
+                forecast_type: '预增',
+                report_date: '2024年年报',
+                content: `${stockName}预计2024年度归属于上市公司股东的净利润为58亿元至62亿元，同比增长10%至17%。`,
+                reason: '报告期内，公司主营业务收入稳步增长，产品结构持续优化，毛利率提升；同时公司加大研发投入，新产品市场表现良好。',
+                change_range: {
+                    lower: 10,
+                    upper: 17
+                },
+                publish_date: '2024-10-15'
+            },
+            {
+                forecast_type: '略增',
+                report_date: '2024年三季报',
+                content: `${stockName}预计2024年1-9月归属于上市公司股东的净利润为42亿元至45亿元，同比增长5%至12%。`,
+                reason: '公司积极拓展市场渠道，销售规模持续扩大，经营效益稳步提升。',
+                change_range: {
+                    lower: 5,
+                    upper: 12
+                },
+                publish_date: '2024-07-12'
+            },
+            {
+                forecast_type: forecastTypes[Math.floor(Math.random() * forecastTypes.length)],
+                report_date: '2024年中报',
+                content: `${stockName}预计2024年上半年归属于上市公司股东的净利润为28亿元至30亿元。`,
+                reason: '受益于行业景气度回升及公司降本增效措施效果显现，经营业绩同比有所改善。',
+                change_range: {
+                    lower: 3,
+                    upper: 8
+                },
+                publish_date: '2024-04-20'
+            }
+        ];
+
+        return HttpResponse.json({
+            success: true,
+            data: {
+                stock_code: stockCode,
+                stock_name: stockName,
+                forecasts: forecasts
+            }
+        });
+    }),
+
     // 获取股票报价（批量）
     http.post('/api/stock/quotes', async ({ request }) => {
         await delay(200);
@@ -368,6 +430,25 @@ export const stockHandlers = [
                 stockMap[s.code] = s.name;
             });
 
+            // 行业和指数映射表
+            const stockIndustryMap = {
+                '000001': { industry_l1: '金融', industry: '银行', index_tags: ['沪深300', '上证180'] },
+                '600519': { industry_l1: '消费', industry: '白酒', index_tags: ['沪深300', '上证50'] },
+                '300750': { industry_l1: '工业', industry: '电池', index_tags: ['创业板50'] },
+                '601318': { industry_l1: '金融', industry: '保险', index_tags: ['沪深300', '上证50'] },
+                '600036': { industry_l1: '金融', industry: '银行', index_tags: ['沪深300', '上证50'] },
+                '000858': { industry_l1: '消费', industry: '白酒', index_tags: ['沪深300'] },
+                '002594': { industry_l1: '汽车', industry: '乘用车', index_tags: ['沪深300', '创业板指'] },
+            };
+
+            const defaultIndustries = [
+                { industry_l1: '科技', industry: '软件' },
+                { industry_l1: '医药', industry: '化学制药' },
+                { industry_l1: '消费', industry: '食品' },
+                { industry_l1: '金融', industry: '证券' },
+                { industry_l1: '工业', industry: '机械' },
+            ];
+
             // 为每只股票生成报价数据
             const quotesData = {};
             codes.forEach(stockCode => {
@@ -380,6 +461,11 @@ export const stockHandlers = [
                 // 昨收
                 const prevClose = parseFloat((basePrice - change).toFixed(2));
 
+                // 获取行业和指数信息
+                const codeWithoutSuffix = stockCode.replace(/\.(SH|SZ)$/i, '');
+                const industryInfo = stockIndustryMap[codeWithoutSuffix] ||
+                    defaultIndustries[Math.floor(Math.random() * defaultIndustries.length)];
+
                 quotesData[stockCode] = {
                     code: stockCode,
                     name: stockMap[stockCode] || `股票${stockCode}`,
@@ -393,7 +479,23 @@ export const stockHandlers = [
                     volume: Math.floor(Math.random() * 100000000),
                     amount: parseFloat((Math.random() * 10000000000).toFixed(2)),
                     market: stockCode.startsWith('6') ? 'SH' : 'SZ',
-                    update_time: new Date().toISOString()
+                    update_time: new Date().toISOString(),
+                    // 行业和指数标签
+                    industry_l1: industryInfo.industry_l1,
+                    industry: industryInfo.industry,
+                    index_tags: industryInfo.index_tags || [],
+                    // 关键指标
+                    pe: parseFloat((Math.random() * 50 + 5).toFixed(2)),
+                    eps: parseFloat((Math.random() * 5 + 0.1).toFixed(3)),
+                    pb: parseFloat((Math.random() * 8 + 0.5).toFixed(2)),
+                    market_cap: `${(Math.random() * 5000 + 100).toFixed(0)}亿`,
+                    week52_low: parseFloat((basePrice * 0.7).toFixed(2)),
+                    week52_high: parseFloat((basePrice * 1.3).toFixed(2)),
+                    // 主力动态
+                    main_net_inflow: parseFloat((Math.random() * 10 - 5).toFixed(2)),
+                    institution_holding: parseFloat((Math.random() * 50 + 10).toFixed(2)),
+                    buy_ratio: parseFloat((Math.random() * 40 + 30).toFixed(2)),
+                    sell_ratio: parseFloat((100 - (Math.random() * 40 + 30)).toFixed(2))
                 };
             });
 

src/routes/lazy-components.js

@@ -35,9 +35,9 @@ export const lazyComponents = {
 
     // 公司相关模块
     CompanyIndex: React.lazy(() => import('@views/Company')),
-    ForecastReport: React.lazy(() => import('@views/Company/ForecastReport')),
-    FinancialPanorama: React.lazy(() => import('@views/Company/FinancialPanorama')),
-    MarketDataView: React.lazy(() => import('@views/Company/MarketDataView')),
+    ForecastReport: React.lazy(() => import('@views/Company/components/ForecastReport')),
+    FinancialPanorama: React.lazy(() => import('@views/Company/components/FinancialPanorama')),
+    MarketDataView: React.lazy(() => import('@views/Company/components/MarketDataView')),
 
     // Agent模块
     AgentChat: React.lazy(() => import('@views/AgentChat')),

src/store/slices/communityDataSlice.js

@@ -608,14 +608,40 @@ const communityDataSlice = createSlice({
         state.error[stateKey] = action.payload;
         logger.error('CommunityData', `${stateKey} 加载失败`, new Error(action.payload));
       })
-      // toggleEventFollow
+      // ===== toggleEventFollow（乐观更新）=====
+      // pending: 立即切换状态
+      .addCase(toggleEventFollow.pending, (state, action) => {
+        const eventId = action.meta.arg;
+        const current = state.eventFollowStatus[eventId];
+        // 乐观切换：如果当前已关注则变为未关注，反之亦然
+        state.eventFollowStatus[eventId] = {
+          isFollowing: !(current?.isFollowing),
+          followerCount: current?.followerCount ?? 0
+        };
+        logger.debug('CommunityData', 'toggleEventFollow pending (乐观更新)', {
+          eventId,
+          newIsFollowing: !(current?.isFollowing)
+        });
+      })
+      // rejected: 回滚状态
+      .addCase(toggleEventFollow.rejected, (state, action) => {
+        const eventId = action.meta.arg;
+        const current = state.eventFollowStatus[eventId];
+        // 回滚：恢复到之前的状态（再次切换回去）
+        state.eventFollowStatus[eventId] = {
+          isFollowing: !(current?.isFollowing),
+          followerCount: current?.followerCount ?? 0
+        };
+        logger.error('CommunityData', 'toggleEventFollow rejected (已回滚)', {
+          eventId,
+          error: action.payload
+        });
+      })
+      // fulfilled: 使用 API 返回的准确数据覆盖
       .addCase(toggleEventFollow.fulfilled, (state, action) => {
         const { eventId, isFollowing, followerCount } = action.payload;
         state.eventFollowStatus[eventId] = { isFollowing, followerCount };
         logger.debug('CommunityData', 'toggleEventFollow fulfilled', { eventId, isFollowing, followerCount });
-      })
-      .addCase(toggleEventFollow.rejected, (_state, action) => {
-        logger.error('CommunityData', 'toggleEventFollow rejected', action.payload);
       });
   }
 });

src/store/slices/stockSlice.js

@@ -4,6 +4,56 @@ import { eventService, stockService } from '../../services/eventService';
 import { logger } from '../../utils/logger';
 import { getApiBase } from '../../utils/apiConfig';
 
+// ==================== Watchlist 缓存配置 ====================
+const WATCHLIST_CACHE_KEY = 'watchlist_cache';
+const WATCHLIST_CACHE_DURATION = 7 * 24 * 60 * 60 * 1000; // 7天
+
+/**
+ * 从 localStorage 读取自选股缓存
+ */
+const loadWatchlistFromCache = () => {
+  try {
+    const cached = localStorage.getItem(WATCHLIST_CACHE_KEY);
+    if (!cached) return null;
+
+    const { data, timestamp } = JSON.parse(cached);
+    const now = Date.now();
+
+    // 检查缓存是否过期（7天）
+    if (now - timestamp > WATCHLIST_CACHE_DURATION) {
+      localStorage.removeItem(WATCHLIST_CACHE_KEY);
+      logger.debug('stockSlice', '自选股缓存已过期');
+      return null;
+    }
+
+    logger.debug('stockSlice', '自选股 localStorage 缓存命中', {
+      count: data?.length || 0,
+      age: Math.round((now - timestamp) / 1000 / 60) + '分钟前'
+    });
+    return data;
+  } catch (error) {
+    logger.error('stockSlice', 'loadWatchlistFromCache', error);
+    return null;
+  }
+};
+
+/**
+ * 保存自选股到 localStorage
+ */
+const saveWatchlistToCache = (data) => {
+  try {
+    localStorage.setItem(WATCHLIST_CACHE_KEY, JSON.stringify({
+      data,
+      timestamp: Date.now()
+    }));
+    logger.debug('stockSlice', '自选股已缓存到 localStorage', {
+      count: data?.length || 0
+    });
+  } catch (error) {
+    logger.error('stockSlice', 'saveWatchlistToCache', error);
+  }
+};
+
 // ==================== Async Thunks ====================
 
 /**
@@ -153,13 +203,28 @@ export const fetchExpectationScore = createAsyncThunk(
 
 /**
  * 加载用户自选股列表（包含完整信息）
+ * 缓存策略：Redux 内存缓存 → localStorage 持久缓存（7天） → API 请求
  */
 export const loadWatchlist = createAsyncThunk(
   'stock/loadWatchlist',
-  async () => {
+  async (_, { getState }) => {
     logger.debug('stockSlice', 'loadWatchlist');
 
     try {
+      // 1. 先检查 Redux 内存缓存
+      const reduxCached = getState().stock.watchlist;
+      if (reduxCached && reduxCached.length > 0) {
+        logger.debug('stockSlice', 'Redux watchlist 缓存命中', { count: reduxCached.length });
+        return reduxCached;
+      }
+
+      // 2. 再检查 localStorage 持久缓存（7天有效期）
+      const localCached = loadWatchlistFromCache();
+      if (localCached && localCached.length > 0) {
+        return localCached;
+      }
+
+      // 3. 缓存无效，调用 API
       const apiBase = getApiBase();
       const response = await fetch(`${apiBase}/api/account/watchlist`, {
         credentials: 'include'
@@ -172,6 +237,10 @@ export const loadWatchlist = createAsyncThunk(
           stock_code: item.stock_code,
           stock_name: item.stock_name,
         }));
+
+        // 保存到 localStorage 缓存
+        saveWatchlistToCache(watchlistData);
+
         logger.debug('stockSlice', '自选股列表加载成功', {
           count: watchlistData.length
         });
@@ -340,6 +409,26 @@ const stockSlice = createSlice({
       delete state.historicalEventsCache[eventId];
       delete state.chainAnalysisCache[eventId];
       delete state.expectationScores[eventId];
+    },
+
+    /**
+     * 乐观更新：添加自选股（同步）
+     */
+    optimisticAddWatchlist: (state, action) => {
+      const { stockCode, stockName } = action.payload;
+      // 避免重复添加
+      const exists = state.watchlist.some(item => item.stock_code === stockCode);
+      if (!exists) {
+        state.watchlist.push({ stock_code: stockCode, stock_name: stockName || '' });
+      }
+    },
+
+    /**
+     * 乐观更新：移除自选股（同步）
+     */
+    optimisticRemoveWatchlist: (state, action) => {
+      const { stockCode } = action.payload;
+      state.watchlist = state.watchlist.filter(item => item.stock_code !== stockCode);
     }
   },
   extraReducers: (builder) => {
@@ -440,9 +529,10 @@ const stockSlice = createSlice({
         state.loading.allStocks = false;
       })
 
-      // ===== toggleWatchlist =====
-      .addCase(toggleWatchlist.fulfilled, (state, action) => {
-        const { stockCode, stockName, isInWatchlist } = action.payload;
+      // ===== toggleWatchlist（乐观更新）=====
+      // pending: 立即更新状态
+      .addCase(toggleWatchlist.pending, (state, action) => {
+        const { stockCode, stockName, isInWatchlist } = action.meta.arg;
         if (isInWatchlist) {
           // 移除
           state.watchlist = state.watchlist.filter(item => item.stock_code !== stockCode);
@@ -453,6 +543,26 @@ const stockSlice = createSlice({
             state.watchlist.push({ stock_code: stockCode, stock_name: stockName });
           }
         }
+      })
+      // rejected: 回滚状态
+      .addCase(toggleWatchlist.rejected, (state, action) => {
+        const { stockCode, stockName, isInWatchlist } = action.meta.arg;
+        // 回滚：与 pending 操作相反
+        if (isInWatchlist) {
+          // 之前移除了，现在加回来
+          const exists = state.watchlist.some(item => item.stock_code === stockCode);
+          if (!exists) {
+            state.watchlist.push({ stock_code: stockCode, stock_name: stockName });
+          }
+        } else {
+          // 之前添加了，现在移除
+          state.watchlist = state.watchlist.filter(item => item.stock_code !== stockCode);
+        }
+      })
+      // fulfilled: 同步更新 localStorage 缓存
+      .addCase(toggleWatchlist.fulfilled, (state) => {
+        // 状态已在 pending 时更新，这里同步到 localStorage
+        saveWatchlistToCache(state.watchlist);
       });
   }
 });
@@ -461,7 +571,9 @@ export const {
   updateQuote,
   updateQuotes,
   clearQuotes,
-  clearEventCache
+  clearEventCache,
+  optimisticAddWatchlist,
+  optimisticRemoveWatchlist
 } = stockSlice.actions;
 
 export default stockSlice.reducer;

src/utils/performanceMonitor.ts

@@ -2,6 +2,7 @@
 // 性能监控工具 - 统计白屏时间和性能指标
 
 import { logger } from './logger';
+import { reportPerformanceMetrics } from '../lib/posthog';
 
 /**
  * 性能指标接口
@@ -208,6 +209,9 @@ class PerformanceMonitor {
     // 性能分析建议
     this.analyzePerformance();
 
+    // 上报性能指标到 PostHog
+    reportPerformanceMetrics(this.metrics);
+
     return this.metrics;
   }
 

src/utils/priceFormatters.js

@@ -103,3 +103,71 @@ export const PriceArrow = ({ value }) => {
 
     return <Icon color={color} boxSize="16px" />;
 };
+
+// ==================== 货币/数值格式化 ====================
+
+/**
+ * 格式化货币金额（自动选择单位：亿元/万元/元）
+ * @param {number|null|undefined} value - 金额（单位：元）
+ * @returns {string} 格式化后的金额字符串
+ */
+export const formatCurrency = (value) => {
+    if (value === null || value === undefined) return '-';
+    const absValue = Math.abs(value);
+    if (absValue >= 100000000) {
+        return (value / 100000000).toFixed(2) + '亿元';
+    } else if (absValue >= 10000) {
+        return (value / 10000).toFixed(2) + '万元';
+    }
+    return value.toFixed(2) + '元';
+};
+
+/**
+ * 格式化业务营收（支持指定单位）
+ * @param {number|null|undefined} value - 营收金额
+ * @param {string} [unit] - 原始单位（元/万元/亿元）
+ * @returns {string} 格式化后的营收字符串
+ */
+export const formatBusinessRevenue = (value, unit) => {
+    if (value === null || value === undefined) return '-';
+    if (unit) {
+        if (unit === '元') {
+            const absValue = Math.abs(value);
+            if (absValue >= 100000000) {
+                return (value / 100000000).toFixed(2) + '亿元';
+            } else if (absValue >= 10000) {
+                return (value / 10000).toFixed(2) + '万元';
+            }
+            return value.toFixed(0) + '元';
+        } else if (unit === '万元') {
+            const absValue = Math.abs(value);
+            if (absValue >= 10000) {
+                return (value / 10000).toFixed(2) + '亿元';
+            }
+            return value.toFixed(2) + '万元';
+        } else if (unit === '亿元') {
+            return value.toFixed(2) + '亿元';
+        } else {
+            return value.toFixed(2) + unit;
+        }
+    }
+    // 无单位时，假设为元
+    const absValue = Math.abs(value);
+    if (absValue >= 100000000) {
+        return (value / 100000000).toFixed(2) + '亿元';
+    } else if (absValue >= 10000) {
+        return (value / 10000).toFixed(2) + '万元';
+    }
+    return value.toFixed(2) + '元';
+};
+
+/**
+ * 格式化百分比
+ * @param {number|null|undefined} value - 百分比值
+ * @param {number} [decimals=2] - 小数位数
+ * @returns {string} 格式化后的百分比字符串
+ */
+export const formatPercentage = (value, decimals = 2) => {
+    if (value === null || value === undefined) return '-';
+    return value.toFixed(decimals) + '%';
+};

src/views/Community/README.md

@@ -0,0 +1,184 @@
+# Community 模块说明
+
+本目录包含社区页面的所有组件，按功能模块进行组织。
+
+## 目录结构
+
+```
+src/views/Community/
+├── index.js                 # 页面入口
+├── components/              # 组件目录
+│   ├── SearchFilters/       # 搜索筛选模块
+│   │   ├── CompactSearchBox.js
+│   │   ├── CompactSearchBox.css
+│   │   ├── TradingTimeFilter.js
+│   │   └── index.js
+│   ├── EventCard/           # 事件卡片模块
+│   │   ├── atoms/           # 原子组件
+│   │   └── index.js
+│   ├── HotEvents/           # 热点事件模块
+│   │   ├── HotEvents.js
+│   │   ├── HotEvents.css
+│   │   ├── HotEventsSection.js
+│   │   └── index.js
+│   ├── DynamicNews/         # 动态新闻模块
+│   │   ├── layouts/
+│   │   ├── hooks/
+│   │   └── index.js
+│   ├── EventDetailModal/    # 事件详情弹窗模块
+│   │   ├── EventDetailModal.tsx
+│   │   ├── EventDetailModal.less
+│   │   └── index.ts
+│   └── HeroPanel.js         # 英雄面板（独立组件）
+└── hooks/                   # 页面级 Hooks
+    ├── useEventData.js
+    ├── useEventFilters.js
+    └── useCommunityEvents.js
+```
+
+---
+
+## 1. SearchFilters 模块（搜索筛选）
+
+路径：`components/SearchFilters/`
+
+| 文件 | 行数 | 功能 |
+|------|------|------|
+| `CompactSearchBox.js` | 612 | 紧凑搜索框，集成关键词搜索、概念/行业筛选 |
+| `TradingTimeFilter.js` | 491 | 交易时间筛选器，被 CompactSearchBox 引用 |
+
+**使用方式**：
+```javascript
+import { CompactSearchBox } from './components/SearchFilters';
+```
+
+---
+
+## 2. EventCard 模块（事件卡片）
+
+路径：`components/EventCard/`
+
+### 主卡片组件
+
+| 文件 | 功能 |
+|------|------|
+| `CompactEventCard.js` | 紧凑事件卡片（列表模式） |
+| `DetailedEventCard.js` | 详细事件卡片（展开模式） |
+| `DynamicNewsEventCard.js` | 动态新闻事件卡片 |
+| `HorizontalDynamicNewsEventCard.js` | 水平布局新闻卡片 |
+
+### 原子组件（atoms/）
+
+| 文件 | 功能 |
+|------|------|
+| `EventHeader.js` | 事件标题头部 |
+| `EventDescription.js` | 事件描述文本 |
+| `EventStats.js` | 事件统计数据 |
+| `EventPriceDisplay.js` | 股价显示 |
+| `EventTimeline.js` | 事件时间线 |
+| `EventFollowButton.js` | 关注按钮 |
+| `EventImportanceBadge.js` | 重要性徽章 |
+| `ImportanceBadge.js` | 通用重要性徽章 |
+| `ImportanceStamp.js` | 重要性印章 |
+| `KeywordsCarousel.js` | 关键词轮播 |
+
+**使用方式**：
+```javascript
+// 使用主卡片组件
+import EventCard from './components/EventCard';
+
+// 使用原子组件
+import { EventHeader, EventTimeline } from './components/EventCard/atoms';
+```
+
+---
+
+## 3. HotEvents 模块（热点事件）
+
+路径：`components/HotEvents/`
+
+| 文件 | 功能 |
+|------|------|
+| `HotEvents.js` | 热点事件列表渲染 |
+| `HotEventsSection.js` | 热点事件区块容器 |
+
+**使用方式**：
+```javascript
+import { HotEventsSection } from './components/HotEvents';
+```
+
+---
+
+## 4. DynamicNews 模块（动态新闻）
+
+路径：`components/DynamicNews/`
+
+### 主组件
+
+| 文件 | 功能 |
+|------|------|
+| `DynamicNewsCard.js` | 主列表容器（695行） |
+| `EventScrollList.js` | 事件滚动列表 |
+| `EventDetailScrollPanel.js` | 事件详情滚动面板 |
+| `ModeToggleButtons.js` | 模式切换按钮 |
+| `PaginationControl.js` | 分页控制器 |
+| `constants.js` | 常量配置 |
+
+### 布局组件（layouts/）
+
+| 文件 | 功能 |
+|------|------|
+| `VerticalModeLayout.js` | 垂直布局模式 |
+| `VirtualizedFourRowGrid.js` | 虚拟滚动四行网格（性能优化） |
+
+### Hooks（hooks/）
+
+| 文件 | 功能 |
+|------|------|
+| `usePagination.js` | 分页逻辑 Hook |
+
+**使用方式**：
+```javascript
+import { DynamicNewsCard } from './components/DynamicNews';
+import { usePagination } from './components/DynamicNews/hooks';
+```
+
+---
+
+## 5. EventDetailModal 模块（事件详情弹窗）
+
+路径：`components/EventDetailModal/`
+
+| 文件 | 功能 |
+|------|------|
+| `EventDetailModal.tsx` | 事件详情弹窗（TypeScript） |
+| `EventDetailModal.less` | 弹窗样式 |
+
+**使用方式**：
+```javascript
+import EventDetailModal from './components/EventDetailModal';
+```
+
+---
+
+## 6. 独立组件
+
+路径：`components/`
+
+| 文件 | 行数 | 功能 |
+|------|------|------|
+| `HeroPanel.js` | 972 | 首页英雄面板（指数K线 + 概念词云） |
+
+**说明**：
+- `HeroPanel.js` 使用懒加载，包含 ECharts (~600KB)
+
+---
+
+## 更新日志
+
+- **2024-12-09**: 目录结构重组
+  - 创建 `SearchFilters/` 模块（含 CSS）
+  - 创建 `EventCard/atoms/` 原子组件目录
+  - 创建 `HotEvents/` 模块（含 CSS）
+  - 重组 `DynamicNews/` 模块（含 layouts/ 和 hooks/）
+  - 创建 `EventDetailModal/` 模块

src/views/Community/components/DynamicNews/DynamicNewsCard.js

@@ -1,4 +1,4 @@
-// src/views/Community/components/DynamicNewsCard.js
+// src/views/Community/components/DynamicNews/DynamicNewsCard.js
 // 横向滚动事件卡片组件（实时要闻·动态追踪）
 
 import React, { forwardRef, useState, useEffect, useMemo, useCallback, useRef, useImperativeHandle } from 'react';
@@ -30,23 +30,23 @@ import {
   Icon,
 } from '@chakra-ui/react';
 import { TimeIcon, BellIcon } from '@chakra-ui/icons';
-import { useNotification } from '../../../contexts/NotificationContext';
-import EventScrollList from './DynamicNewsCard/EventScrollList';
-import ModeToggleButtons from './DynamicNewsCard/ModeToggleButtons';
-import PaginationControl from './DynamicNewsCard/PaginationControl';
+import { useNotification } from '@contexts/NotificationContext';
+import EventScrollList from './EventScrollList';
+import ModeToggleButtons from './ModeToggleButtons';
+import PaginationControl from './PaginationControl';
 import DynamicNewsDetailPanel from '@components/EventDetailPanel';
-import CompactSearchBox from './CompactSearchBox';
+import CompactSearchBox from '../SearchFilters/CompactSearchBox';
 import {
   fetchDynamicNews,
   toggleEventFollow,
   selectEventFollowStatus,
   selectVerticalEventsWithLoading,
   selectFourRowEventsWithLoading
-} from '../../../store/slices/communityDataSlice';
-import { usePagination } from './DynamicNewsCard/hooks/usePagination';
-import { PAGINATION_CONFIG, DISPLAY_MODES, REFRESH_DEBOUNCE_DELAY } from './DynamicNewsCard/constants';
-import { PROFESSIONAL_COLORS } from '../../../constants/professionalTheme';
-import { debounce } from '../../../utils/debounce';
+} from '@store/slices/communityDataSlice';
+import { usePagination } from './hooks/usePagination';
+import { PAGINATION_CONFIG, DISPLAY_MODES, REFRESH_DEBOUNCE_DELAY } from './constants';
+import { PROFESSIONAL_COLORS } from '@constants/professionalTheme';
+import { debounce } from '@utils/debounce';
 import { useDevice } from '@hooks/useDevice';
 
 // 🔍 调试：渲染计数器

src/views/Community/components/DynamicNews/EventDetailScrollPanel.js

@@ -1,4 +1,4 @@
-// src/views/Community/components/DynamicNewsCard/EventDetailScrollPanel.js
+// src/views/Community/components/DynamicNews/EventDetailScrollPanel.js
 // 事件详情滚动面板组件
 
 import React from 'react';

src/views/Community/components/DynamicNews/EventScrollList.js

@@ -1,4 +1,4 @@
-// src/views/Community/components/DynamicNewsCard/EventScrollList.js
+// src/views/Community/components/DynamicNews/EventScrollList.js
 // 横向滚动事件列表组件
 
 import React, { useRef, useCallback } from 'react';
@@ -6,8 +6,8 @@ import {
   Box,
   useColorModeValue
 } from '@chakra-ui/react';
-import VirtualizedFourRowGrid from './VirtualizedFourRowGrid';
-import VerticalModeLayout from './VerticalModeLayout';
+import VirtualizedFourRowGrid from './layouts/VirtualizedFourRowGrid';
+import VerticalModeLayout from './layouts/VerticalModeLayout';
 
 /**
  * 事件列表组件 - 支持纵向和平铺两种展示模式

src/views/Community/components/DynamicNews/ModeToggleButtons.js

@@ -1,4 +1,4 @@
-// src/views/Community/components/DynamicNewsCard/ModeToggleButtons.js
+// src/views/Community/components/DynamicNews/ModeToggleButtons.js
 // 事件列表模式切换按钮组
 
 import React from 'react';

src/views/Community/components/DynamicNews/PaginationControl.js

@@ -1,4 +1,4 @@
-// src/views/Community/components/DynamicNewsCard/PaginationControl.js
+// src/views/Community/components/DynamicNews/PaginationControl.js
 // 分页控制器组件
 
 import React, { useState } from 'react';

src/views/Community/components/DynamicNews/constants.js

@@ -1,4 +1,4 @@
-// src/views/Community/components/DynamicNewsCard/constants.js
+// src/views/Community/components/DynamicNews/constants.js
 // 动态新闻卡片组件 - 常量配置
 
 // ========== 分页配置常量 ==========

src/views/Community/components/DynamicNews/hooks/index.js

@@ -0,0 +1,4 @@
+// src/views/Community/components/DynamicNews/hooks/index.js
+// Hooks
+
+export { usePagination } from './usePagination';

src/views/Community/components/DynamicNews/hooks/usePagination.js

@@ -1,9 +1,9 @@
-// src/views/Community/components/DynamicNewsCard/hooks/usePagination.js
+// src/views/Community/components/DynamicNews/hooks/usePagination.js
 // 分页逻辑自定义 Hook
 
 import { useState, useMemo, useCallback, useRef } from 'react';
-import { fetchDynamicNews, updatePaginationPage } from '../../../../../store/slices/communityDataSlice';
-import { logger } from '../../../../../utils/logger';
+import { fetchDynamicNews, updatePaginationPage } from '@store/slices/communityDataSlice';
+import { logger } from '@utils/logger';
 import {
   PAGINATION_CONFIG,
   DISPLAY_MODES,

src/views/Community/components/DynamicNews/index.js

@@ -0,0 +1,21 @@
+// src/views/Community/components/DynamicNews/index.js
+// 动态新闻模块
+
+// 主组件
+export { default as DynamicNewsCard } from './DynamicNewsCard';
+
+// 布局组件
+export { default as VerticalModeLayout } from './layouts/VerticalModeLayout';
+export { default as VirtualizedFourRowGrid } from './layouts/VirtualizedFourRowGrid';
+
+// 子组件
+export { default as EventScrollList } from './EventScrollList';
+export { default as EventDetailScrollPanel } from './EventDetailScrollPanel';
+export { default as ModeToggleButtons } from './ModeToggleButtons';
+export { default as PaginationControl } from './PaginationControl';
+
+// Hooks
+export { usePagination } from './hooks/usePagination';
+
+// 常量
+export * from './constants';

src/views/Community/components/DynamicNews/layouts/VerticalModeLayout.js

@@ -1,4 +1,4 @@
-// src/views/Community/components/DynamicNewsCard/VerticalModeLayout.js
+// src/views/Community/components/DynamicNews/layouts/VerticalModeLayout.js
 // 纵向分栏模式布局组件
 
 import React, { useState } from 'react';
@@ -12,10 +12,10 @@ import {
   useDisclosure
 } from '@chakra-ui/react';
 import { InfoIcon } from '@chakra-ui/icons';
-import HorizontalDynamicNewsEventCard from '../EventCard/HorizontalDynamicNewsEventCard';
-import EventDetailScrollPanel from './EventDetailScrollPanel';
-import EventDetailModal from '../EventDetailModal';
-import PaginationControl from './PaginationControl';
+import HorizontalDynamicNewsEventCard from '../../EventCard/HorizontalDynamicNewsEventCard';
+import EventDetailScrollPanel from '../EventDetailScrollPanel';
+import EventDetailModal from '../../EventDetailModal';
+import PaginationControl from '../PaginationControl';
 
 /**
  * 纵向分栏模式布局

src/views/Community/components/DynamicNews/layouts/VirtualizedFourRowGrid.js

@@ -1,4 +1,4 @@
-// src/views/Community/components/DynamicNewsCard/VirtualizedFourRowGrid.js
+// src/views/Community/components/DynamicNews/layouts/VirtualizedFourRowGrid.js
 // 虚拟化网格组件（支持多列布局 + 纵向滚动 + 无限滚动）
 
 import React, { useRef, useMemo, useEffect, forwardRef, useImperativeHandle } from 'react';
@@ -6,7 +6,7 @@ import { useVirtualizer } from '@tanstack/react-virtual';
 import { Box, Grid, Spinner, Text, VStack, Center, HStack, IconButton, useBreakpointValue } from '@chakra-ui/react';
 import { RepeatIcon } from '@chakra-ui/icons';
 import { useColorModeValue } from '@chakra-ui/react';
-import DynamicNewsEventCard from '../EventCard/DynamicNewsEventCard';
+import DynamicNewsEventCard from '../../EventCard/DynamicNewsEventCard';
 
 /**
  * 虚拟化网格组件（支持多列布局 + 无限滚动）

src/views/Community/components/DynamicNews/layouts/index.js

@@ -0,0 +1,5 @@
+// src/views/Community/components/DynamicNews/layouts/index.js
+// 布局组件
+
+export { default as VerticalModeLayout } from './VerticalModeLayout';
+export { default as VirtualizedFourRowGrid } from './VirtualizedFourRowGrid';

src/views/Community/components/DynamicNewsCard/PageNavigationButton.js

@@ -1,83 +0,0 @@
-// src/views/Community/components/DynamicNewsCard/PageNavigationButton.js
-// 翻页导航按钮组件
-
-import React from 'react';
-import { IconButton, useColorModeValue } from '@chakra-ui/react';
-import { ChevronLeftIcon, ChevronRightIcon } from '@chakra-ui/icons';
-
-/**
- * 翻页导航按钮组件
- * @param {Object} props
- * @param {'prev'|'next'} props.direction - 按钮方向（prev=上一页，next=下一页）
- * @param {number} props.currentPage - 当前页码
- * @param {number} props.totalPages - 总页数
- * @param {Function} props.onPageChange - 翻页回调
- * @param {string} props.mode - 显示模式（只在carousel/grid模式下显示）
- */
-const PageNavigationButton = ({
-  direction,
-  currentPage,
-  totalPages,
-  onPageChange,
-  mode
-}) => {
-  // 主题适配
-  const arrowBtnBg = useColorModeValue('rgba(255, 255, 255, 0.9)', 'rgba(0, 0, 0, 0.6)');
-  const arrowBtnHoverBg = useColorModeValue('rgba(255, 255, 255, 1)', 'rgba(0, 0, 0, 0.8)');
-
-  // 根据方向计算配置
-  const isPrev = direction === 'prev';
-  const isNext = direction === 'next';
-
-  const Icon = isPrev ? ChevronLeftIcon : ChevronRightIcon;
-  const position = isPrev ? 'left' : 'right';
-  const label = isPrev ? '上一页' : '下一页';
-  const targetPage = isPrev ? currentPage - 1 : currentPage + 1;
-  const shouldShow = isPrev
-    ? currentPage > 1
-    : currentPage < totalPages;
-  const isDisabled = isNext ? currentPage >= totalPages : false;
-
-  // 判断是否显示（只在单排/双排模式显示）
-  const shouldRender = shouldShow && (mode === 'carousel' || mode === 'grid');
-
-  if (!shouldRender) return null;
-
-  const handleClick = () => {
-    console.log(
-      `%c🔵 [翻页] 点击${label}: 当前页${currentPage} → 目标页${targetPage} (共${totalPages}页)`,
-      'color: #3B82F6; font-weight: bold;'
-    );
-    onPageChange(targetPage);
-  };
-
-  return (
-    <IconButton
-      icon={<Icon boxSize={6} color="blue.500" />}
-      position="absolute"
-      {...{ [position]: 0 }}
-      top="50%"
-      transform="translateY(-50%)"
-      zIndex={2}
-      onClick={handleClick}
-      variant="ghost"
-      size="md"
-      w="40px"
-      h="40px"
-      minW="40px"
-      borderRadius="full"
-      bg={arrowBtnBg}
-      boxShadow="0 2px 8px rgba(0, 0, 0, 0.15)"
-      _hover={{
-        bg: arrowBtnHoverBg,
-        boxShadow: '0 4px 12px rgba(0, 0, 0, 0.2)',
-        transform: 'translateY(-50%) scale(1.05)'
-      }}
-      isDisabled={isDisabled}
-      aria-label={label}
-      title={label}
-    />
-  );
-};
-
-export default PageNavigationButton;

src/views/Community/components/DynamicNewsCard/hooks/useInfiniteScroll.js

@@ -1,88 +0,0 @@
-// src/views/Community/components/DynamicNewsCard/hooks/useInfiniteScroll.js
-// 无限滚动 Hook
-
-import { useEffect, useRef, useCallback } from 'react';
-
-/**
- * 无限滚动 Hook
- * 监听容器滚动事件，当滚动到底部附近时触发加载更多数据
- *
- * @param {Object} options - 配置选项
- * @param {Function} options.onLoadMore - 加载更多回调函数（返回 Promise）
- * @param {boolean} options.hasMore - 是否还有更多数据
- * @param {boolean} options.isLoading - 是否正在加载
- * @param {number} options.threshold - 触发阈值（距离底部多少像素时触发，默认200px）
- * @returns {Object} { containerRef } - 容器引用
- */
-export const useInfiniteScroll = ({
-  onLoadMore,
-  hasMore = true,
-  isLoading = false,
-  threshold = 200
-}) => {
-  const containerRef = useRef(null);
-  const isLoadingRef = useRef(false);
-
-  // 滚动处理函数
-  const handleScroll = useCallback(() => {
-    const container = containerRef.current;
-
-    // 检查条件：容器存在、未加载中、还有更多数据
-    if (!container || isLoadingRef.current || !hasMore) {
-      return;
-    }
-
-    const { scrollTop, scrollHeight, clientHeight } = container;
-    const distanceToBottom = scrollHeight - scrollTop - clientHeight;
-
-    // 距离底部小于阈值时触发加载
-    if (distanceToBottom < threshold) {
-      console.log(
-        '%c⬇️ [懒加载] 触发加载下一页',
-        'color: #8B5CF6; font-weight: bold;',
-        {
-          scrollTop,
-          scrollHeight,
-          clientHeight,
-          distanceToBottom,
-          threshold
-        }
-      );
-
-      isLoadingRef.current = true;
-
-      // 调用加载函数并更新状态
-      onLoadMore()
-        .then(() => {
-          console.log('%c✅ [懒加载] 加载完成', 'color: #10B981; font-weight: bold;');
-        })
-        .catch((error) => {
-          console.error('%c❌ [懒加载] 加载失败', 'color: #DC2626; font-weight: bold;', error);
-        })
-        .finally(() => {
-          isLoadingRef.current = false;
-        });
-    }
-  }, [onLoadMore, hasMore, threshold]);
-
-  // 绑定滚动事件
-  useEffect(() => {
-    const container = containerRef.current;
-    if (!container) return;
-
-    // 添加滚动监听
-    container.addEventListener('scroll', handleScroll, { passive: true });
-
-    // 清理函数
-    return () => {
-      container.removeEventListener('scroll', handleScroll);
-    };
-  }, [handleScroll]);
-
-  // 更新 loading 状态的 ref
-  useEffect(() => {
-    isLoadingRef.current = isLoading;
-  }, [isLoading]);
-
-  return { containerRef };
-};

src/views/Community/components/EventCard/CompactEventCard.js

@@ -12,13 +12,15 @@ import {
     useColorModeValue,
 } from '@chakra-ui/react';
 import dayjs from 'dayjs';
-import { getImportanceConfig } from '../../../../constants/importanceLevels';
+import { getImportanceConfig } from '@constants/importanceLevels';
 
 // 导入子组件
-import EventTimeline from './EventTimeline';
-import EventHeader from './EventHeader';
-import EventStats from './EventStats';
-import EventFollowButton from './EventFollowButton';
+import {
+    EventTimeline,
+    EventHeader,
+    EventStats,
+    EventFollowButton,
+} from './atoms';
 
 /**
  * 紧凑模式事件卡片组件

src/views/Community/components/EventCard/DetailedEventCard.js

@@ -10,15 +10,17 @@ import {
     useColorModeValue,
 } from '@chakra-ui/react';
 import dayjs from 'dayjs';
-import { getImportanceConfig } from '../../../../constants/importanceLevels';
+import { getImportanceConfig } from '@constants/importanceLevels';
 
 // 导入子组件
-import EventTimeline from './EventTimeline';
-import EventHeader from './EventHeader';
-import EventStats from './EventStats';
-import EventFollowButton from './EventFollowButton';
-import EventPriceDisplay from './EventPriceDisplay';
-import EventDescription from './EventDescription';
+import {
+    EventTimeline,
+    EventHeader,
+    EventStats,
+    EventFollowButton,
+    EventPriceDisplay,
+    EventDescription,
+} from './atoms';
 
 /**
  * 详细模式事件卡片组件

src/views/Community/components/EventCard/DynamicNewsEventCard.js

@@ -12,12 +12,12 @@ import {
     useColorModeValue,
 } from '@chakra-ui/react';
 import dayjs from 'dayjs';
-import { getImportanceConfig } from '../../../../constants/importanceLevels';
-import { getChangeColor } from '../../../../utils/colorUtils';
+import { getImportanceConfig } from '@constants/importanceLevels';
+import { getChangeColor } from '@utils/colorUtils';
 
 // 导入子组件
-import EventFollowButton from './EventFollowButton';
-import StockChangeIndicators from '../../../../components/StockChangeIndicators';
+import { EventFollowButton } from './atoms';
+import StockChangeIndicators from '@components/StockChangeIndicators';
 
 /**
  * 动态新闻事件卡片组件（极简版）

src/views/Community/components/EventCard/HorizontalDynamicNewsEventCard.js

@@ -13,17 +13,19 @@ import {
     useColorModeValue,
     useBreakpointValue,
 } from '@chakra-ui/react';
-import { getImportanceConfig } from '../../../../constants/importanceLevels';
-import { PROFESSIONAL_COLORS } from '../../../../constants/professionalTheme';
+import { getImportanceConfig } from '@constants/importanceLevels';
+import { PROFESSIONAL_COLORS } from '@constants/professionalTheme';
 import { useDevice } from '@hooks/useDevice';
 import dayjs from 'dayjs';
 
 // 导入子组件
-import ImportanceStamp from './ImportanceStamp';
-import EventTimeline from './EventTimeline';
-import EventFollowButton from './EventFollowButton';
-import StockChangeIndicators from '../../../../components/StockChangeIndicators';
-import KeywordsCarousel from './KeywordsCarousel';
+import {
+    ImportanceStamp,
+    EventTimeline,
+    EventFollowButton,
+    KeywordsCarousel,
+} from './atoms';
+import StockChangeIndicators from '@components/StockChangeIndicators';
 
 /**
  * 横向布局的动态新闻事件卡片组件

src/views/Community/components/EventCard/atoms/EventFollowButton.js

@@ -1,4 +1,4 @@
-// src/views/Community/components/EventCard/EventFollowButton.js
+// src/views/Community/components/EventCard/atoms/EventFollowButton.js
 import React from 'react';
 import { IconButton, Box } from '@chakra-ui/react';
 import { AiFillStar, AiOutlineStar } from 'react-icons/ai';

src/views/Community/components/EventCard/atoms/EventImportanceBadge.js

@@ -1,8 +1,8 @@
-// src/views/Community/components/EventCard/EventImportanceBadge.js
+// src/views/Community/components/EventCard/atoms/EventImportanceBadge.js
 import React from 'react';
 import { Badge, Tooltip, VStack, HStack, Text, Divider, Circle } from '@chakra-ui/react';
 import { InfoIcon } from '@chakra-ui/icons';
-import { getImportanceConfig, getAllImportanceLevels } from '../../../../constants/importanceLevels';
+import { getImportanceConfig, getAllImportanceLevels } from '@constants/importanceLevels';
 
 /**
  * 事件重要性等级标签组件

src/views/Community/components/EventCard/atoms/EventPriceDisplay.js

@@ -1,7 +1,7 @@
-// src/views/Community/components/EventCard/EventPriceDisplay.js
+// src/views/Community/components/EventCard/atoms/EventPriceDisplay.js
 import React, { useState } from 'react';
 import { HStack, Box, Text, Tooltip, Progress } from '@chakra-ui/react';
-import { PriceArrow } from '../../../../utils/priceFormatters';
+import { PriceArrow } from '@utils/priceFormatters';
 
 /**
  * 事件价格变动显示组件

src/views/Community/components/EventCard/atoms/ImportanceBadge.js

@@ -1,4 +1,4 @@
-// src/views/Community/components/EventCard/ImportanceBadge.js
+// src/views/Community/components/EventCard/atoms/ImportanceBadge.js
 // 重要性标签通用组件
 
 import React from 'react';
@@ -14,7 +14,7 @@ import {
     PopoverArrow,
     Portal,
 } from '@chakra-ui/react';
-import { getImportanceConfig, getAllImportanceLevels } from '../../../../constants/importanceLevels';
+import { getImportanceConfig, getAllImportanceLevels } from '@constants/importanceLevels';
 
 /**
  * 重要性标签组件（实心样式）

src/views/Community/components/EventCard/atoms/ImportanceStamp.js

@@ -1,4 +1,4 @@
-// src/views/Community/components/EventCard/ImportanceStamp.js
+// src/views/Community/components/EventCard/atoms/ImportanceStamp.js
 // 重要性印章组件
 
 import React from 'react';
@@ -7,7 +7,7 @@ import {
   Text,
   useColorModeValue,
 } from '@chakra-ui/react';
-import { getImportanceConfig } from '../../../../constants/importanceLevels';
+import { getImportanceConfig } from '@constants/importanceLevels';
 
 /**
  * 重要性印章组件（模拟盖章效果）

src/views/Community/components/EventCard/atoms/KeywordsCarousel.js

@@ -1,8 +1,8 @@
-// src/views/Community/components/EventCard/KeywordsCarousel.js
+// src/views/Community/components/EventCard/atoms/KeywordsCarousel.js
 // Keywords标签组件（点击切换）
 import React, { useState } from 'react';
 import { Box, Text, Tooltip } from '@chakra-ui/react';
-import { PROFESSIONAL_COLORS } from '../../../../constants/professionalTheme';
+import { PROFESSIONAL_COLORS } from '@constants/professionalTheme';
 
 /**
  * Keywords标签组件（点击切换下一个）

src/views/Community/components/EventCard/atoms/index.js

@@ -0,0 +1,13 @@
+// src/views/Community/components/EventCard/atoms/index.js
+// 事件卡片原子组件
+
+export { default as EventDescription } from './EventDescription';
+export { default as EventFollowButton } from './EventFollowButton';
+export { default as EventHeader } from './EventHeader';
+export { default as EventImportanceBadge } from './EventImportanceBadge';
+export { default as EventPriceDisplay } from './EventPriceDisplay';
+export { default as EventStats } from './EventStats';
+export { default as EventTimeline } from './EventTimeline';
+export { default as ImportanceBadge } from './ImportanceBadge';
+export { default as ImportanceStamp } from './ImportanceStamp';
+export { default as KeywordsCarousel } from './KeywordsCarousel';

src/views/Community/components/EventDetailModal/index.ts

@@ -0,0 +1,4 @@
+// src/views/Community/components/EventDetailModal/index.ts
+// 事件详情弹窗模块
+
+export { default } from './EventDetailModal';

src/views/Community/components/EventDiscussionModal.js

@@ -1,614 +0,0 @@
-import React, { useState, useEffect } from 'react';
-import {
-  Modal,
-  ModalOverlay,
-  ModalContent,
-  ModalHeader,
-  ModalBody,
-  ModalCloseButton,
-  Box,
-  Text,
-  VStack,
-  HStack,
-  Avatar,
-  Textarea,
-  Button,
-  Divider,
-  useToast,
-  Badge,
-  Flex,
-  IconButton,
-  Menu,
-  MenuButton,
-  MenuList,
-  MenuItem,
-  useColorModeValue,
-  Spinner,
-  Center,
-  Collapse,
-  Input,
-} from '@chakra-ui/react';
-import {
-  ChatIcon,
-  TimeIcon,
-  DeleteIcon,
-  EditIcon,
-  ChevronDownIcon,
-  TriangleDownIcon,
-  TriangleUpIcon,
-} from '@chakra-ui/icons';
-import { FaHeart, FaRegHeart, FaComment } from 'react-icons/fa';
-import { format } from 'date-fns';
-import { zhCN } from 'date-fns/locale';
-import { eventService } from '../../../services/eventService';
-import { logger } from '../../../utils/logger';
-
-const EventDiscussionModal = ({ isOpen, onClose, eventId, eventTitle, discussionType = '事件讨论' }) => {
-  const [posts, setPosts] = useState([]);
-  const [newPostContent, setNewPostContent] = useState('');
-  const [newPostTitle, setNewPostTitle] = useState('');
-  const [loading, setLoading] = useState(false);
-  const [submitting, setSubmitting] = useState(false);
-  const [expandedPosts, setExpandedPosts] = useState({});
-  const [postComments, setPostComments] = useState({});
-  const [replyContents, setReplyContents] = useState({});
-  const [loadingComments, setLoadingComments] = useState({});
-  
-  const toast = useToast();
-  const bgColor = useColorModeValue('white', 'gray.800');
-  const borderColor = useColorModeValue('gray.200', 'gray.600');
-  const hoverBg = useColorModeValue('gray.50', 'gray.700');
-
-  // 加载帖子列表
-  const loadPosts = async () => {
-    if (!eventId) return;
-    
-    setLoading(true);
-    try {
-      const response = await fetch(`/api/events/${eventId}/posts?sort=latest&page=1&per_page=20`, {
-        method: 'GET',
-        headers: { 'Content-Type': 'application/json' },
-        credentials: 'include'
-      });
-      const result = await response.json();
-      
-      if (response.ok && result.success) {
-        setPosts(result.data || []);
-        logger.debug('EventDiscussionModal', '帖子列表加载成功', {
-          eventId,
-          postsCount: result.data?.length || 0
-        });
-      } else {
-        logger.error('EventDiscussionModal', 'loadPosts', new Error('API返回错误'), {
-          eventId,
-          status: response.status,
-          message: result.message
-        });
-        toast({
-          title: '加载帖子失败',
-          status: 'error',
-          duration: 3000,
-          isClosable: true,
-        });
-      }
-    } catch (error) {
-      logger.error('EventDiscussionModal', 'loadPosts', error, { eventId });
-      toast({
-        title: '加载帖子失败',
-        status: 'error',
-        duration: 3000,
-        isClosable: true,
-      });
-    } finally {
-      setLoading(false);
-    }
-  };
-
-  // 加载帖子的评论
-  const loadPostComments = async (postId) => {
-    setLoadingComments(prev => ({ ...prev, [postId]: true }));
-    try {
-      const response = await fetch(`/api/posts/${postId}/comments?sort=latest`, {
-        method: 'GET',
-        headers: { 'Content-Type': 'application/json' },
-        credentials: 'include'
-      });
-      const result = await response.json();
-      
-      if (response.ok && result.success) {
-        setPostComments(prev => ({ ...prev, [postId]: result.data || [] }));
-        logger.debug('EventDiscussionModal', '评论加载成功', {
-          postId,
-          commentsCount: result.data?.length || 0
-        });
-      }
-    } catch (error) {
-      logger.error('EventDiscussionModal', 'loadPostComments', error, { postId });
-    } finally {
-      setLoadingComments(prev => ({ ...prev, [postId]: false }));
-    }
-  };
-
-  // 切换展开/收起评论
-  const togglePostComments = async (postId) => {
-    const isExpanded = expandedPosts[postId];
-    if (!isExpanded) {
-      // 展开时加载评论
-      await loadPostComments(postId);
-    }
-    setExpandedPosts(prev => ({ ...prev, [postId]: !isExpanded }));
-  };
-
-  // 提交新帖子
-  const handleSubmitPost = async () => {
-    if (!newPostContent.trim()) return;
-    
-    setSubmitting(true);
-    try {
-      const response = await fetch(`/api/events/${eventId}/posts`, {
-        method: 'POST',
-        headers: { 'Content-Type': 'application/json' },
-        credentials: 'include',
-        body: JSON.stringify({
-          title: newPostTitle.trim(),
-          content: newPostContent.trim(),
-          content_type: 'text',
-        })
-      });
-      const result = await response.json();
-      
-      if (response.ok && result.success) {
-        setNewPostContent('');
-        setNewPostTitle('');
-        loadPosts();
-        logger.info('EventDiscussionModal', '帖子发布成功', {
-          eventId,
-          postId: result.data?.id
-        });
-        toast({
-          title: '帖子发布成功',
-          status: 'success',
-          duration: 2000,
-          isClosable: true,
-        });
-      } else {
-        logger.error('EventDiscussionModal', 'handleSubmitPost', new Error('API返回错误'), {
-          eventId,
-          message: result.message
-        });
-        toast({
-          title: result.message || '帖子发布失败',
-          status: 'error',
-          duration: 3000,
-          isClosable: true,
-        });
-      }
-    } catch (error) {
-      logger.error('EventDiscussionModal', 'handleSubmitPost', error, { eventId });
-      toast({
-        title: '帖子发布失败',
-        status: 'error',
-        duration: 3000,
-        isClosable: true,
-      });
-    } finally {
-      setSubmitting(false);
-    }
-  };
-
-  // 删除帖子
-  const handleDeletePost = async (postId) => {
-    if (!window.confirm('确定要删除这个帖子吗？')) return;
-    
-    try {
-      const response = await fetch(`/api/posts/${postId}`, {
-        method: 'DELETE',
-        headers: { 'Content-Type': 'application/json' },
-        credentials: 'include'
-      });
-      const result = await response.json();
-      
-      if (response.ok && result.success) {
-        loadPosts();
-        logger.info('EventDiscussionModal', '帖子删除成功', { postId });
-        toast({
-          title: '帖子已删除',
-          status: 'success',
-          duration: 2000,
-          isClosable: true,
-        });
-      } else {
-        logger.error('EventDiscussionModal', 'handleDeletePost', new Error('API返回错误'), {
-          postId,
-          message: result.message
-        });
-        toast({
-          title: result.message || '删除失败',
-          status: 'error',
-          duration: 3000,
-          isClosable: true,
-        });
-      }
-    } catch (error) {
-      logger.error('EventDiscussionModal', 'handleDeletePost', error, { postId });
-      toast({
-        title: '删除失败',
-        status: 'error',
-        duration: 3000,
-        isClosable: true,
-      });
-    }
-  };
-
-  // 点赞帖子
-  const handleLikePost = async (postId) => {
-    try {
-      const response = await fetch(`/api/posts/${postId}/like`, {
-        method: 'POST',
-        headers: { 'Content-Type': 'application/json' },
-        credentials: 'include'
-      });
-      const result = await response.json();
-      
-      if (response.ok && result.success) {
-        // 更新帖子列表中的点赞状态
-        setPosts(prev => prev.map(post =>
-          post.id === postId
-            ? { ...post, likes_count: result.likes_count, liked: result.liked }
-            : post
-        ));
-        logger.debug('EventDiscussionModal', '点赞操作成功', {
-          postId,
-          liked: result.liked,
-          likesCount: result.likes_count
-        });
-      }
-    } catch (error) {
-      logger.error('EventDiscussionModal', 'handleLikePost', error, { postId });
-      toast({
-        title: '操作失败',
-        status: 'error',
-        duration: 2000,
-        isClosable: true,
-      });
-    }
-  };
-
-  // 提交评论
-  const handleSubmitComment = async (postId) => {
-    const content = replyContents[postId];
-    if (!content?.trim()) return;
-    
-    try {
-      const response = await fetch(`/api/posts/${postId}/comments`, {
-        method: 'POST',
-        headers: { 'Content-Type': 'application/json' },
-        credentials: 'include',
-        body: JSON.stringify({
-          content: content.trim(),
-        })
-      });
-      const result = await response.json();
-      
-      if (response.ok && result.success) {
-        setReplyContents(prev => ({ ...prev, [postId]: '' }));
-        // 重新加载该帖子的评论
-        await loadPostComments(postId);
-        // 更新帖子的评论数
-        setPosts(prev => prev.map(post =>
-          post.id === postId
-            ? { ...post, comments_count: (post.comments_count || 0) + 1 }
-            : post
-        ));
-        logger.info('EventDiscussionModal', '评论发布成功', {
-          postId,
-          commentId: result.data?.id
-        });
-        toast({
-          title: '评论发布成功',
-          status: 'success',
-          duration: 2000,
-          isClosable: true,
-        });
-      }
-    } catch (error) {
-      logger.error('EventDiscussionModal', 'handleSubmitComment', error, { postId });
-      toast({
-        title: '评论发布失败',
-        status: 'error',
-        duration: 3000,
-        isClosable: true,
-      });
-    }
-  };
-
-  // 删除评论
-  const handleDeleteComment = async (commentId, postId) => {
-    if (!window.confirm('确定要删除这条评论吗？')) return;
-    
-    try {
-      const response = await fetch(`/api/comments/${commentId}`, {
-        method: 'DELETE',
-        headers: { 'Content-Type': 'application/json' },
-        credentials: 'include'
-      });
-      const result = await response.json();
-      
-      if (response.ok && result.success) {
-        // 重新加载该帖子的评论
-        await loadPostComments(postId);
-        // 更新帖子的评论数
-        setPosts(prev => prev.map(post =>
-          post.id === postId
-            ? { ...post, comments_count: Math.max(0, (post.comments_count || 0) - 1) }
-            : post
-        ));
-        logger.info('EventDiscussionModal', '评论删除成功', { commentId, postId });
-        toast({
-          title: '评论已删除',
-          status: 'success',
-          duration: 2000,
-          isClosable: true,
-        });
-      }
-    } catch (error) {
-      logger.error('EventDiscussionModal', 'handleDeleteComment', error, { commentId, postId });
-      toast({
-        title: '删除失败',
-        status: 'error',
-        duration: 3000,
-        isClosable: true,
-      });
-    }
-  };
-
-  useEffect(() => {
-    if (isOpen) {
-      loadPosts();
-    }
-  }, [isOpen, eventId]);
-
-  return (
-    <Modal isOpen={isOpen} onClose={onClose} size="xl">
-      <ModalOverlay />
-      <ModalContent maxH="80vh">
-        <ModalHeader>
-          <VStack align="start" spacing={1}>
-            <HStack>
-              <ChatIcon />
-              <Text>{discussionType}</Text>
-            </HStack>
-            {eventTitle && (
-              <Text fontSize="sm" color="gray.500" fontWeight="normal">
-                {eventTitle}
-              </Text>
-            )}
-          </VStack>
-        </ModalHeader>
-        <ModalCloseButton />
-        
-        <ModalBody overflowY="auto">
-          {/* 发布新帖子 */}
-          <Box mb={4}>
-            <Input
-              value={newPostTitle}
-              onChange={(e) => setNewPostTitle(e.target.value)}
-              placeholder="帖子标题（可选）"
-              size="sm"
-              mb={2}
-            />
-            <Textarea
-              value={newPostContent}
-              onChange={(e) => setNewPostContent(e.target.value)}
-              placeholder="分享您的观点..."
-              size="sm"
-              resize="vertical"
-              minH="80px"
-            />
-            <Flex justify="flex-end" mt={2}>
-              <Button
-                colorScheme="blue"
-                size="sm"
-                onClick={handleSubmitPost}
-                isLoading={submitting}
-                isDisabled={!newPostContent.trim()}
-              >
-                发布帖子
-              </Button>
-            </Flex>
-          </Box>
-
-          <Divider mb={4} />
-
-          {/* 帖子列表 */}
-          {loading ? (
-            <Center py={8}>
-              <Spinner size="lg" />
-            </Center>
-          ) : posts.length > 0 ? (
-            <VStack spacing={4} align="stretch">
-              {posts.map((post) => (
-                <Box
-                  key={post.id}
-                  p={4}
-                  borderWidth="1px"
-                  borderColor={borderColor}
-                  borderRadius="md"
-                  transition="background 0.2s"
-                >
-                  {/* 帖子头部 */}
-                  <Flex justify="space-between" align="start" mb={3}>
-                    <HStack align="start" spacing={3}>
-                      <Avatar
-                        size="sm"
-                        name={post.user?.username || '匿名用户'}
-                        src={post.user?.avatar_url}
-                      />
-                      <VStack align="start" spacing={1} flex={1}>
-                        <HStack>
-                          <Text fontWeight="semibold" fontSize="sm">
-                            {post.user?.username || '匿名用户'}
-                          </Text>
-                        </HStack>
-                        <HStack fontSize="xs" color="gray.500">
-                          <TimeIcon />
-                          <Text>
-                            {format(new Date(post.created_at), 'MM月dd日 HH:mm', {
-                              locale: zhCN,
-                            })}
-                          </Text>
-                        </HStack>
-                      </VStack>
-                    </HStack>
-                    
-                    {/* 操作菜单 */}
-                    <Menu>
-                      <MenuButton
-                        as={IconButton}
-                        icon={<ChevronDownIcon />}
-                        variant="ghost"
-                        size="sm"
-                      />
-                      <MenuList>
-                        <MenuItem
-                          icon={<DeleteIcon />}
-                          color="red.500"
-                          onClick={() => handleDeletePost(post.id)}
-                        >
-                          删除帖子
-                        </MenuItem>
-                      </MenuList>
-                    </Menu>
-                  </Flex>
-
-                  {/* 帖子标题 */}
-                  {post.title && (
-                    <Text fontSize="md" fontWeight="bold" mb={2}>
-                      {post.title}
-                    </Text>
-                  )}
-
-                  {/* 帖子内容 */}
-                  <Text fontSize="sm" whiteSpace="pre-wrap" mb={3}>
-                    {post.content}
-                  </Text>
-
-                  {/* 帖子操作栏 */}
-                  <HStack spacing={4} mb={3}>
-                    <Button
-                      size="sm"
-                      variant="ghost"
-                      leftIcon={post.liked ? <FaHeart /> : <FaRegHeart />}
-                      color={post.liked ? 'red.500' : 'gray.600'}
-                      onClick={() => handleLikePost(post.id)}
-                    >
-                      {post.likes_count || 0}
-                    </Button>
-                    <Button
-                      size="sm"
-                      variant="ghost"
-                      leftIcon={<FaComment />}
-                      onClick={() => togglePostComments(post.id)}
-                      rightIcon={expandedPosts[post.id] ? <TriangleUpIcon /> : <TriangleDownIcon />}
-                    >
-                      {post.comments_count || 0} 评论
-                    </Button>
-                  </HStack>
-
-                  {/* 评论区 */}
-                  <Collapse in={expandedPosts[post.id]} animateOpacity>
-                    <Box borderTopWidth="1px" borderColor={borderColor} pt={3}>
-                      {/* 评论输入框 */}
-                      <HStack mb={3}>
-                        <Textarea
-                          size="sm"
-                          placeholder="写下你的评论..."
-                          value={replyContents[post.id] || ''}
-                          onChange={(e) => setReplyContents(prev => ({ ...prev, [post.id]: e.target.value }))}
-                          rows={2}
-                          flex={1}
-                        />
-                        <Button
-                          size="sm"
-                          colorScheme="blue"
-                          onClick={() => handleSubmitComment(post.id)}
-                          isDisabled={!replyContents[post.id]?.trim()}
-                        >
-                          评论
-                        </Button>
-                      </HStack>
-
-                      {/* 评论列表 */}
-                      {loadingComments[post.id] ? (
-                        <Center py={4}>
-                          <Spinner size="sm" />
-                        </Center>
-                      ) : (
-                        <VStack align="stretch" spacing={2}>
-                          {postComments[post.id]?.map((comment) => (
-                            <Box key={comment.id} pl={4} borderLeftWidth="2px" borderColor="gray.200">
-                              <HStack justify="space-between" mb={1}>
-                                <HStack spacing={2}>
-                                  <Avatar size="xs" name={comment.user?.username} src={comment.user?.avatar_url} />
-                                  <Text fontSize="sm" fontWeight="medium">
-                                    {comment.user?.username || '匿名用户'}
-                                  </Text>
-                                  <Text fontSize="xs" color="gray.500">
-                                    {format(new Date(comment.created_at), 'MM-dd HH:mm')}
-                                  </Text>
-                                </HStack>
-                                <IconButton
-                                  size="xs"
-                                  icon={<DeleteIcon />}
-                                  variant="ghost"
-                                  onClick={() => handleDeleteComment(comment.id, post.id)}
-                                />
-                              </HStack>
-                              <Text fontSize="sm" pl={7}>
-                                {comment.content}
-                              </Text>
-                              
-                              {/* 显示回复 */}
-                              {comment.replies && comment.replies.length > 0 && (
-                                <VStack align="stretch" mt={2} spacing={1} pl={4}>
-                                  {comment.replies.map((reply) => (
-                                    <Box key={reply.id}>
-                                      <HStack spacing={1}>
-                                        <Text fontSize="xs" fontWeight="medium">
-                                          {reply.user?.username}:
-                                        </Text>
-                                        <Text fontSize="xs">{reply.content}</Text>
-                                      </HStack>
-                                    </Box>
-                                  ))}
-                                </VStack>
-                              )}
-                            </Box>
-                          ))}
-                          {(!postComments[post.id] || postComments[post.id].length === 0) && (
-                            <Text fontSize="sm" color="gray.500" textAlign="center" py={2}>
-                              暂无评论
-                            </Text>
-                          )}
-                        </VStack>
-                      )}
-                    </Box>
-                  </Collapse>
-                </Box>
-              ))}
-            </VStack>
-          ) : (
-            <Center py={8}>
-              <VStack>
-                <ChatIcon boxSize={8} color="gray.400" />
-                <Text color="gray.500">暂无帖子，快来发表您的观点吧！</Text>
-              </VStack>
-            </Center>
-          )}
-        </ModalBody>
-      </ModalContent>
-    </Modal>
-  );
-};
-
-export default EventDiscussionModal;