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update pay function

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zzlgreat
2025-11-21 13:41:49 +08:00
parent a1c1a36f6a
commit 12d104cc22
3 changed files with 6362 additions and 305 deletions
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@@ -2497,272 +2497,12 @@ def api_get_events():
paginated = query.paginate(page=page, per_page=per_page, error_out=False)
# ==================== 批量获取股票行情数据（优化版） ====================
# 1. 收集当前页所有事件的ID
event_ids = [event.id for event in paginated.items]
# 2. 获取所有相关股票
all_related_stocks = {}
if event_ids:
related_stocks = RelatedStock.query.filter(
RelatedStock.event_id.in_(event_ids)
).all()
# 按事件ID分组
for stock in related_stocks:
if stock.event_id not in all_related_stocks:
all_related_stocks[stock.event_id] = []
all_related_stocks[stock.event_id].append(stock)
# 3. 收集所有股票代码
all_stock_codes = []
stock_code_mapping = {} # 清理后的代码 -> 原始代码的映射
for stocks in all_related_stocks.values():
for stock in stocks:
clean_code = stock.stock_code.replace('.SH', '').replace('.SZ', '').replace('.BJ', '')
all_stock_codes.append(clean_code)
stock_code_mapping[clean_code] = stock.stock_code
# 去重
all_stock_codes = list(set(all_stock_codes))
# 4. 批量查询最近7个交易日的数据（用于计算日涨跌和周涨跌）
stock_price_data = {}
if all_stock_codes:
# 构建SQL查询 - 获取最近7个交易日的数据
codes_str = "'" + "', '".join(all_stock_codes) + "'"
# 获取最近7个交易日的数据
recent_trades_sql = f"""
SELECT
SECCODE,
SECNAME,
F007N as close_price,
F010N as daily_change,
TRADEDATE,
ROW_NUMBER() OVER (PARTITION BY SECCODE ORDER BY TRADEDATE DESC) as rn
FROM ea_trade
WHERE SECCODE IN ({codes_str})
AND F007N IS NOT NULL
AND TRADEDATE >= DATE_SUB(CURDATE(), INTERVAL 10 DAY)
ORDER BY SECCODE, TRADEDATE DESC
"""
result = db.session.execute(text(recent_trades_sql))
# 整理数据
for row in result.fetchall():
sec_code = row[0]
if sec_code not in stock_price_data:
stock_price_data[sec_code] = {
'stock_name': row[1],
'prices': []
}
stock_price_data[sec_code]['prices'].append({
'close_price': float(row[2]) if row[2] else 0,
'daily_change': float(row[3]) if row[3] else 0,
'trade_date': row[4],
'rank': row[5]
})
# 5. 计算日涨跌和周涨跌
stock_changes = {}
for sec_code, data in stock_price_data.items():
prices = data['prices']
# 最新日涨跌（第1条记录）
daily_change = 0
if prices and prices[0]['rank'] == 1:
daily_change = prices[0]['daily_change']
# 计算周涨跌（最新价 vs 5个交易日前的价格）
week_change = 0
if len(prices) >= 2:
latest_price = prices[0]['close_price']
# 找到第5个交易日的数据（如果有）
week_ago_price = None
for price_data in prices:
if price_data['rank'] >= 5:
week_ago_price = price_data['close_price']
break
# 如果没有第5天的数据，使用最早的数据
if week_ago_price is None and len(prices) > 1:
week_ago_price = prices[-1]['close_price']
if week_ago_price and week_ago_price > 0:
week_change = (latest_price - week_ago_price) / week_ago_price * 100
stock_changes[sec_code] = {
'stock_name': data['stock_name'],
'daily_change': daily_change,
'week_change': week_change
}
# ==================== 获取整体统计信息（应用所有筛选条件） ====================
overall_distribution = {
'limit_down': 0,
'down_over_5': 0,
'down_5_to_1': 0,
'down_within_1': 0,
'flat': 0,
'up_within_1': 0,
'up_1_to_5': 0,
'up_over_5': 0,
'limit_up': 0
}
# 使用当前筛选条件的query，但不应用分页限制，获取所有符合条件的事件
# 这样统计数据会跟随用户的筛选条件变化
all_filtered_events = query.limit(1000).all() # 限制最多1000个事件，避免查询过慢
week_event_ids = [e.id for e in all_filtered_events]
if week_event_ids:
# 获取这些事件的所有关联股票
week_related_stocks = RelatedStock.query.filter(
RelatedStock.event_id.in_(week_event_ids)
).all()
# 按事件ID分组
week_stocks_by_event = {}
for stock in week_related_stocks:
if stock.event_id not in week_stocks_by_event:
week_stocks_by_event[stock.event_id] = []
week_stocks_by_event[stock.event_id].append(stock)
# 收集所有股票代码（用于批量查询行情）
week_stock_codes = []
week_code_mapping = {}
for stocks in week_stocks_by_event.values():
for stock in stocks:
clean_code = stock.stock_code.replace('.SH', '').replace('.SZ', '').replace('.BJ', '')
week_stock_codes.append(clean_code)
week_code_mapping[clean_code] = stock.stock_code
week_stock_codes = list(set(week_stock_codes))
# 批量查询这些股票的最新行情数据
week_stock_changes = {}
if week_stock_codes:
codes_str = "'" + "', '".join(week_stock_codes) + "'"
recent_trades_sql = f"""
SELECT
SECCODE,
SECNAME,
F010N as daily_change,
ROW_NUMBER() OVER (PARTITION BY SECCODE ORDER BY TRADEDATE DESC) as rn
FROM ea_trade
WHERE SECCODE IN ({codes_str})
AND F010N IS NOT NULL
AND TRADEDATE >= DATE_SUB(CURDATE(), INTERVAL 3 DAY)
ORDER BY SECCODE, TRADEDATE DESC
"""
result = db.session.execute(text(recent_trades_sql))
for row in result.fetchall():
sec_code = row[0]
if row[3] == 1: # 只取最新的数据（rn=1）
week_stock_changes[sec_code] = {
'stock_name': row[1],
'daily_change': float(row[2]) if row[2] else 0
}
# 按事件统计平均涨跌幅分布
event_avg_changes = {}
for event in all_filtered_events:
event_stocks = week_stocks_by_event.get(event.id, [])
if not event_stocks:
continue
total_change = 0
valid_count = 0
for stock in event_stocks:
clean_code = stock.stock_code.replace('.SH', '').replace('.SZ', '').replace('.BJ', '')
if clean_code in week_stock_changes:
daily_change = week_stock_changes[clean_code]['daily_change']
total_change += daily_change
valid_count += 1
if valid_count > 0:
avg_change = total_change / valid_count
event_avg_changes[event.id] = avg_change
# 统计事件平均涨跌幅的分布
for event_id, avg_change in event_avg_changes.items():
# 对于事件平均涨幅，不使用涨跌停分类，使用通用分类
if avg_change <= -10:
overall_distribution['limit_down'] += 1
elif avg_change >= 10:
overall_distribution['limit_up'] += 1
elif avg_change > 5:
overall_distribution['up_over_5'] += 1
elif avg_change > 1:
overall_distribution['up_1_to_5'] += 1
elif avg_change > 0.1:
overall_distribution['up_within_1'] += 1
elif avg_change >= -0.1:
overall_distribution['flat'] += 1
elif avg_change > -1:
overall_distribution['down_within_1'] += 1
elif avg_change > -5:
overall_distribution['down_5_to_1'] += 1
else:
overall_distribution['down_over_5'] += 1
# ==================== 构建响应数据 ====================
events_data = []
for event in paginated.items:
event_stocks = all_related_stocks.get(event.id, [])
stocks_data = []
total_daily_change = 0
max_daily_change = -999
total_week_change = 0
max_week_change = -999
valid_stocks_count = 0
# 处理每个股票的数据
for stock in event_stocks:
clean_code = stock.stock_code.replace('.SH', '').replace('.SZ', '').replace('.BJ', '')
stock_info = stock_changes.get(clean_code, {})
daily_change = stock_info.get('daily_change', 0)
week_change = stock_info.get('week_change', 0)
if stock_info:
total_daily_change += daily_change
max_daily_change = max(max_daily_change, daily_change)
total_week_change += week_change
max_week_change = max(max_week_change, week_change)
valid_stocks_count += 1
stocks_data.append({
"stock_code": stock.stock_code,
"stock_name": stock.stock_name,
"sector": stock.sector,
"week_change": round(week_change, 2),
"daily_change": round(daily_change, 2)
})
avg_daily_change = total_daily_change / valid_stocks_count if valid_stocks_count > 0 else 0
avg_week_change = total_week_change / valid_stocks_count if valid_stocks_count > 0 else 0
if max_daily_change == -999:
max_daily_change = 0
if max_week_change == -999:
max_week_change = 0
# 构建事件数据
# 构建事件数据（简化版，只包含基本信息和涨跌幅）
event_dict = {
'id': event.id,
'title': event.title,
@@ -2774,34 +2514,26 @@ def api_get_events():
'updated_at': event.updated_at.isoformat() if event.updated_at else None,
'start_time': event.start_time.isoformat() if event.start_time else None,
'end_time': event.end_time.isoformat() if event.end_time else None,
'related_stocks': stocks_data,
'stocks_stats': {
'stocks_count': len(event_stocks),
'valid_stocks_count': valid_stocks_count,
# 周涨跌统计
'avg_week_change': round(avg_week_change, 2),
'max_week_change': round(max_week_change, 2),
# 日涨跌统计
'avg_daily_change': round(avg_daily_change, 2),
'max_daily_change': round(max_daily_change, 2)
}
# 涨跌幅数据（从数据库字段直接获取）
'related_avg_chg': event.related_avg_chg, # 平均涨跌幅
'related_max_chg': event.related_max_chg, # 最大涨跌幅
'related_week_chg': event.related_week_chg, # 周涨跌幅
# 关联股票数量（固定值）
'stocks_count': 10
}
# 统计信息
# 统计信息（可选）
if include_stats:
event_dict.update({
'hot_score': event.hot_score,
'view_count': event.view_count,
'post_count': event.post_count,
'follower_count': event.follower_count,
'related_avg_chg': event.related_avg_chg,
'related_max_chg': event.related_max_chg,
'related_week_chg': event.related_week_chg,
'invest_score': event.invest_score,
'trending_score': event.trending_score,
})
# 创建者信息
# 创建者信息（可选）
if include_creator:
event_dict['creator'] = {
'id': event.creator.id if event.creator else None,
@@ -2815,25 +2547,6 @@ def api_get_events():
event_dict['keywords'] = event.keywords if isinstance(event.keywords, list) else []
event_dict['related_industries'] = event.related_industries
# 包含统计信息
if include_stats:
event_dict['stats'] = {
'related_stocks_count': len(event_stocks),
'historical_events_count': 0, # 需要额外查询
'related_data_count': 0, # 需要额外查询
'related_concepts_count': 0 # 需要额外查询
}
# 包含关联数据
if include_related_data:
event_dict['related_stocks'] = [{
'id': stock.id,
'stock_code': stock.stock_code,
'stock_name': stock.stock_name,
'sector': stock.sector,
'correlation': float(stock.correlation) if stock.correlation else 0
} for stock in event_stocks[:5]] # 限制返回5个
events_data.append(event_dict)
# ==================== 构建筛选信息 ====================
@@ -2860,7 +2573,7 @@ def api_get_events():
applied_filters['search_query'] = search_query
applied_filters['search_type'] = search_type
# ==================== 返回结果（保持完全兼容） ====================
# ==================== 返回结果（简化版） ====================
return jsonify({
'success': True,
@@ -2884,14 +2597,6 @@ def api_get_events():
'sort': sort_by,
'order': order
}
},
# 整体股票涨跌幅分布统计
'overall_stats': {
'total_stocks': len(all_stocks_for_stats) if 'all_stocks_for_stats' in locals() else 0,
'change_distribution': overall_distribution,
'change_distribution_percentages': {
k: v for k, v in overall_distribution.items()
}
}
}
})