fix: 统一时区处理，准确率统计使用CST时间分组

- 增强 convert_to_cst() 函数，新增 source_tz 参数支持 UTC/ET/CST 输入
- 修改 get_monthly_stats() 使用 CONVERT_TZ 按 CST 月份统计
- 修改 get_daily_stats() 使用 CONVERT_TZ 按 CST 日期分组
- 修改 get_hourly_details() 按 CST 日期筛选和小时排序
- 交易记录使用 source_tz='CST' 避免重复转换
- 前端表头添加'(北京)'时区标注

api/models.py

@@ -17,12 +17,13 @@ TZ_ET = ZoneInfo('America/New_York')  # 美东时间 (Polymarket)
 TZ_CST = ZoneInfo('Asia/Shanghai')     # 中国标准时间 (前端显示)
 
 
-def convert_to_cst(timestamp_val) -> str:
+def convert_to_cst(timestamp_val, source_tz: str = 'UTC') -> str:
     """
     将时间戳转换为 CST 时间字符串
     
     Args:
-        timestamp_val: datetime 对象或 ISO 格式时间戳字符串 (假设是 UTC)
+        timestamp_val: datetime 对象或 ISO 格式时间戳字符串
+        source_tz: 输入时区 - 'UTC'(默认，币安数据), 'ET'(Polymarket), 'CST'(已是北京时间)
     
     Returns:
         CST 时间字符串 (YYYY-MM-DD HH:MM:SS)
@@ -37,10 +38,20 @@ def convert_to_cst(timestamp_val) -> str:
             # 字符串格式
             dt = datetime.fromisoformat(str(timestamp_val).replace('Z', '+00:00'))
         
-        # 如果没有时区信息，假设是 UTC
+        # 根据来源时区设置时区信息
         if dt.tzinfo is None:
+            if source_tz == 'ET':
+                dt = dt.replace(tzinfo=TZ_ET)
+            elif source_tz == 'CST':
+                # 已经是 CST，直接格式化返回
+                return dt.strftime('%Y-%m-%d %H:%M:%S')
+            else:  # 默认 UTC
                 dt = dt.replace(tzinfo=TZ_UTC)
         
+        # 如果来源已经是 CST，直接格式化
+        if source_tz == 'CST':
+            return dt.strftime('%Y-%m-%d %H:%M:%S')
+        
         # 转换为 CST
         dt_cst = dt.astimezone(TZ_CST)
         return dt_cst.strftime('%Y-%m-%d %H:%M:%S')
@@ -122,7 +133,8 @@ class TradeModel:
             row = cursor.fetchone()
             result = _dict_from_row(row)
             if result and result.get('timestamp'):
-                result['timestamp_cst'] = convert_to_cst(result['timestamp'])
+                # 交易记录时间已经是 CST，直接格式化
+                result['timestamp_cst'] = convert_to_cst(result['timestamp'], source_tz='CST')
             return result
     
     @staticmethod
@@ -173,7 +185,8 @@ class TradeModel:
             for row in rows:
                 item = _dict_from_row(row)
                 if item and item.get('timestamp'):
-                    item['timestamp_cst'] = convert_to_cst(item['timestamp'])
+                    # 交易记录时间已经是 CST，直接格式化
+                    item['timestamp_cst'] = convert_to_cst(item['timestamp'], source_tz='CST')
                 data_list.append(item)
             
             return {
@@ -292,7 +305,7 @@ class PredictionModel:
                     SUM(CASE WHEN is_correct = 1 THEN 1 ELSE 0 END) as correct,
                     SUM(CASE WHEN is_correct = 0 THEN 1 ELSE 0 END) as incorrect
                 FROM model_predictions 
-                WHERE DATE_FORMAT(date, '%%Y-%%m') = %s
+                WHERE DATE_FORMAT(CONVERT_TZ(timestamp, '+00:00', '+08:00'), '%%Y-%%m') = %s
                   AND actual_result IS NOT NULL
                 GROUP BY model_name
             """
@@ -333,16 +346,16 @@ class PredictionModel:
             
             sql = """
                 SELECT 
-                    date,
+                    DATE(CONVERT_TZ(timestamp, '+00:00', '+08:00')) as date_cst,
                     model_name,
                     COUNT(*) as total,
                     SUM(CASE WHEN is_correct = 1 THEN 1 ELSE 0 END) as correct,
                     SUM(CASE WHEN is_correct = 0 THEN 1 ELSE 0 END) as incorrect
                 FROM model_predictions 
-                WHERE DATE_FORMAT(date, '%%Y-%%m') = %s
+                WHERE DATE_FORMAT(CONVERT_TZ(timestamp, '+00:00', '+08:00'), '%%Y-%%m') = %s
                   AND actual_result IS NOT NULL
-                GROUP BY date, model_name
+                GROUP BY date_cst, model_name
-                ORDER BY date DESC
+                ORDER BY date_cst DESC
             """
             cursor.execute(sql, (month,))
             
@@ -352,7 +365,7 @@ class PredictionModel:
             daily_data = {}
             for row in rows:
                 row_dict = _dict_from_row(row)
-                date = row_dict['date']
+                date = str(row_dict['date_cst'])  # CST 日期
                 if date not in daily_data:
                     daily_data[date] = {"date": date}
                 
@@ -391,28 +404,33 @@ class PredictionModel:
         获取指定日期的每小时详情
         
         Args:
-            date: 日期 (YYYY-MM-DD)
+            date: 日期 (YYYY-MM-DD, CST 北京时间)
         """
         with get_db_connection() as conn:
             cursor = conn.cursor()
+            # 按 CST 日期筛选，并获取 CST 小时
             cursor.execute("""
-                SELECT * FROM model_predictions 
+                SELECT 
-                WHERE date = %s
+                    *,
-                ORDER BY hour, model_name
+                    HOUR(CONVERT_TZ(timestamp, '+00:00', '+08:00')) as hour_cst,
+                    CONVERT_TZ(timestamp, '+00:00', '+08:00') as timestamp_cst_dt
+                FROM model_predictions 
+                WHERE DATE(CONVERT_TZ(timestamp, '+00:00', '+08:00')) = %s
+                ORDER BY hour_cst, model_name
             """, (date,))
             
             rows = cursor.fetchall()
             
-            # 按小时重组数据
+            # 按 CST 小时重组数据
             hourly_data = {}
             for row in rows:
                 row_dict = _dict_from_row(row)
-                hour = row_dict['hour']
+                hour_cst = row_dict['hour_cst']  # 使用 CST 小时
-                if hour not in hourly_data:
+                if hour_cst not in hourly_data:
-                    # 将 timestamp 转换为 CST 时间
+                    # 将 timestamp 转换为 CST 时间字符串
                     timestamp_cst = convert_to_cst(row_dict.get('timestamp', ''))
-                    hourly_data[hour] = {
+                    hourly_data[hour_cst] = {
-                        "hour": hour,
+                        "hour": hour_cst,  # 返回 CST 小时
                         "timestamp_et": row_dict['timestamp_et'],
                         "timestamp_cst": timestamp_cst,
                         "open_price": row_dict['open_price'],
@@ -422,13 +440,13 @@ class PredictionModel:
                     }
                 
                 model = row_dict['model_name']
-                hourly_data[hour]["models"][model] = {
+                hourly_data[hour_cst]["models"][model] = {
                     "prediction": row_dict['prediction'],
                     "probability": row_dict['probability'],
                     "is_correct": row_dict['is_correct']
                 }
             
-            # 按小时排序返回
+            # 按 CST 小时排序返回
             return [hourly_data[h] for h in sorted(hourly_data.keys())]
     
     @staticmethod

api/static/index.html

@@ -259,7 +259,7 @@
                             <table class="table table-hover mb-0">
                                 <thead class="table-light">
                                     <tr>
-                                        <th>日期</th>
+                                        <th>日期 (北京)</th>
                                         <th>DLinear 预测</th>
                                         <th>DLinear 准确率</th>
                                         <th>TTM-R2 预测</th>
@@ -341,7 +341,7 @@
                 <div class="modal-content">
                     <div class="modal-header">
                         <h5 class="modal-title">
-                            <i class="bi bi-clock"></i> {{ hourlyModal.date }} 每小时预测详情
+                            <i class="bi bi-clock"></i> {{ hourlyModal.date }} 每小时预测详情 (北京时间)
                         </h5>
                         <button type="button" class="btn-close" data-bs-dismiss="modal"></button>
                     </div>