fix: timezone handling and prediction logic

- api/models.py: Fix timezone conversion in stats queries by calculating UTC ranges in Python instead of using SQL CONVERT_TZ. Ensure JSON serializable types.
- api/prediction_service.py:
    - Update ensure_predictions_for_month to check previous day (ET) to cover CST month start.
    - Update _generate_prediction_for_hour to skip saving unclosed candles (current hour).
- trader/auto_trade_polymarket.py: Update to use hourly_model_predictions (V2) table for storing live trading predictions.

api/models.py

@@ -4,7 +4,7 @@
 import pymysql
 import os
 from typing import Optional, List, Dict
-from datetime import datetime
+from datetime import datetime, timedelta
 from contextlib import contextmanager
 from zoneinfo import ZoneInfo
 from dotenv import load_dotenv
@@ -340,7 +340,29 @@ class PredictionModel:
     def get_monthly_stats_v2(month: str) -> Dict:
         """
         获取月度统计 (V2 - 从宽表)
+        修改：不使用 SQL 进行时区转换和格式化，而是查询 UTC 时间范围，然后在 Python 中处理
         """
+        # 计算 UTC 时间范围
+        try:
+            year, mon = map(int, month.split('-'))
+            start_date_cst = datetime(year, mon, 1, tzinfo=TZ_CST)
+            # 下个月第一天
+            if mon == 12:
+                end_date_cst = datetime(year + 1, 1, 1, tzinfo=TZ_CST)
+            else:
+                end_date_cst = datetime(year, mon + 1, 1, tzinfo=TZ_CST)
+            
+            # 转换为 UTC
+            start_dt_utc = start_date_cst.astimezone(TZ_UTC)
+            end_dt_utc = end_date_cst.astimezone(TZ_UTC)
+            
+            start_str = start_dt_utc.strftime('%Y-%m-%d %H:%M:%S')
+            end_str = end_dt_utc.strftime('%Y-%m-%d %H:%M:%S')
+            
+        except Exception as e:
+            print(f"日期解析错误: {e}")
+            return {}
+
         with get_db_connection() as conn:
             cursor = conn.cursor()
             
@@ -352,25 +374,26 @@ class PredictionModel:
                 col_pred = f"{model}_prediction"
                 col_correct = f"{model}_correct"
                 
+                # 查询 UTC 时间范围内的所有数据
                 sql = f"""
                     SELECT 
                         COUNT({col_pred}) as total,
                         SUM(CASE WHEN {col_correct} = 1 THEN 1 ELSE 0 END) as correct,
                         SUM(CASE WHEN {col_correct} = 0 THEN 1 ELSE 0 END) as incorrect
                     FROM hourly_model_predictions 
-                    WHERE DATE_FORMAT(CONVERT_TZ(timestamp, '+00:00', '+08:00'), '%%Y-%%m') = %s
+                    WHERE timestamp >= %s AND timestamp < %s
                       AND actual_result IS NOT NULL
                 """
-                cursor.execute(sql, (month,))
+                cursor.execute(sql, (start_str, end_str))
                 row = cursor.fetchone()
                 
                 if row:
-                    total = row['total']
+                    total = int(row['total'])
-                    correct = row['correct'] or 0
+                    correct = int(row['correct'] or 0)
-                    incorrect = row['incorrect'] or 0
+                    incorrect = int(row['incorrect'] or 0)
                     accuracy = (correct / total * 100) if total > 0 else 0
                     
-                    # 格式化键名以匹配前端 (例如 dlinear -> DLinear)
+                    # 格式化键名以匹配前端
                     model_key = {
                         'dlinear': 'DLinear', 
                         'nbeats': 'N-BEATS', 
@@ -391,55 +414,97 @@ class PredictionModel:
     def get_daily_stats_v2(month: str, page: int = 1, limit: int = 10) -> Dict:
         """
         获取每日统计 (V2 - 从宽表)
+        修改：不使用 SQL 进行时区转换，查询 UTC 范围，然后在 Python 中转换
         """
+        # 计算 UTC 时间范围
+        try:
+            year, mon = map(int, month.split('-'))
+            start_date_cst = datetime(year, mon, 1, tzinfo=TZ_CST)
+            if mon == 12:
+                end_date_cst = datetime(year + 1, 1, 1, tzinfo=TZ_CST)
+            else:
+                end_date_cst = datetime(year, mon + 1, 1, tzinfo=TZ_CST)
+            
+            start_dt_utc = start_date_cst.astimezone(TZ_UTC)
+            end_dt_utc = end_date_cst.astimezone(TZ_UTC)
+            
+            start_str = start_dt_utc.strftime('%Y-%m-%d %H:%M:%S')
+            end_str = end_dt_utc.strftime('%Y-%m-%d %H:%M:%S')
+        except Exception:
+            return {"data": [], "page": page, "limit": limit, "total": 0, "total_pages": 0}
+
         with get_db_connection() as conn:
             cursor = conn.cursor()
             
-            # 分页查询日期
+            # 获取范围内的所有原始数据
-            offset = (page - 1) * limit
+            # 我们需要 timestamp, actual_result, 和各模型的 prediction/correct
-            date_sql = """
+            # 为了按 CST 日期分组，我们需要取出每一行，然后在 Python 中转换时间
-                SELECT DISTINCT DATE(CONVERT_TZ(timestamp, '+00:00', '+08:00')) as date_cst
+            
+            sql = """
+                SELECT 
+                    timestamp,
+                    actual_result,
+                    dlinear_prediction, dlinear_correct,
+                    nbeats_prediction, nbeats_correct,
+                    ttm_prediction, ttm_correct,
+                    ensemble_prediction, ensemble_correct
                 FROM hourly_model_predictions
-                WHERE DATE_FORMAT(CONVERT_TZ(timestamp, '+00:00', '+08:00'), '%%Y-%%m') = %s
+                WHERE timestamp >= %s AND timestamp < %s
-                ORDER BY date_cst DESC
+                  AND actual_result IS NOT NULL
-                LIMIT %s OFFSET %s
+                ORDER BY timestamp DESC
             """
-            cursor.execute(date_sql, (month, limit, offset))
+            cursor.execute(sql, (start_str, end_str))
-            dates = [row['date_cst'] for row in cursor.fetchall()]
+            rows = cursor.fetchall()
             
-            # 总天数
+            # 在 Python 中处理分组
-            count_sql = """
+            daily_stats = {} # { "YYYY-MM-DD": { "date": "...", "dlinear": {...}, ... } }
-                SELECT COUNT(DISTINCT DATE(CONVERT_TZ(timestamp, '+00:00', '+08:00'))) as cnt
-                FROM hourly_model_predictions
-                WHERE DATE_FORMAT(CONVERT_TZ(timestamp, '+00:00', '+08:00'), '%%Y-%%m') = %s
-            """
-            cursor.execute(count_sql, (month,))
-            total_count = cursor.fetchone()['cnt']
             
-            daily_data = []
+            for row in rows:
-            models = ['dlinear', 'nbeats', 'ttm', 'ensemble']
+                # 转换 timestamp 到 CST
+                ts_utc = row['timestamp']
+                # 确保是带时区的 datetime
+                if isinstance(ts_utc, str):
+                    ts_utc = datetime.fromisoformat(str(ts_utc).replace('Z', '+00:00'))
+                if ts_utc.tzinfo is None:
+                    ts_utc = ts_utc.replace(tzinfo=TZ_UTC)
                 
-            for date_obj in dates:
+                ts_cst = ts_utc.astimezone(TZ_CST)
-                date_str = str(date_obj)
+                date_str = ts_cst.strftime('%Y-%m-%d')
-                day_stats = {"date": date_str}
                 
+                if date_str not in daily_stats:
+                    daily_stats[date_str] = {
+                        "date": date_str,
+                        "raw_data": {m: {"total": 0, "correct": 0} for m in ['dlinear', 'nbeats', 'ttm', 'ensemble']}
+                    }
+                
+                # 累加统计
+                models = ['dlinear', 'nbeats', 'ttm', 'ensemble']
                 for model in models:
-                    col_pred = f"{model}_prediction"
+                    pred = row[f"{model}_prediction"]
-                    col_correct = f"{model}_correct"
+                    correct_flag = row[f"{model}_correct"]
                     
-                    sql = f"""
+                    if pred is not None:
-                        SELECT 
+                        daily_stats[date_str]['raw_data'][model]['total'] += 1
-                            COUNT({col_pred}) as total,
+                        if correct_flag == 1:
-                            SUM(CASE WHEN {col_correct} = 1 THEN 1 ELSE 0 END) as correct
+                            daily_stats[date_str]['raw_data'][model]['correct'] += 1
-                        FROM hourly_model_predictions
-                        WHERE DATE(CONVERT_TZ(timestamp, '+00:00', '+08:00')) = %s
-                          AND actual_result IS NOT NULL
-                    """
-                    cursor.execute(sql, (date_str,))
-                    row = cursor.fetchone()
 
-                    total = row['total']
+            # 整理为最终格式
-                    correct = row['correct'] or 0
+            sorted_dates = sorted(daily_stats.keys(), reverse=True)
+            total_count = len(sorted_dates)
+            
+            # 分页
+            offset = (page - 1) * limit
+            paginated_dates = sorted_dates[offset:offset + limit]
+            
+            result_data = []
+            for date_key in paginated_dates:
+                day_item = daily_stats[date_key]
+                formatted_item = {"date": day_item["date"]}
+                
+                for model in ['dlinear', 'nbeats', 'ttm', 'ensemble']:
+                    raw = day_item['raw_data'][model]
+                    total = int(raw['total'])
+                    correct = int(raw['correct'])
                     accuracy = (correct / total * 100) if total > 0 else 0
                     
                     model_key = {
@@ -449,16 +514,15 @@ class PredictionModel:
                         'ensemble': 'Ensemble'
                     }.get(model, model)
                     
-                    day_stats[model_key] = {
+                    formatted_item[model_key] = {
                         "total": total,
                         "correct": correct,
                         "accuracy": round(accuracy, 2)
                     }
-                
+                result_data.append(formatted_item)
-                daily_data.append(day_stats)
             
             return {
-                "data": daily_data,
+                "data": result_data,
                 "page": page,
                 "limit": limit,
                 "total": total_count,
@@ -469,25 +533,46 @@ class PredictionModel:
     def get_hourly_details_v2(date: str) -> List[Dict]:
         """
         获取指定日期的每小时详情 (V2)
+        修改：根据传入的 CST 日期计算 UTC 范围，查询该范围内的数据
         """
+        try:
+            # date 是 'YYYY-MM-DD' (CST)
+            date_cst_start = datetime.strptime(date, '%Y-%m-%d').replace(tzinfo=TZ_CST)
+            date_cst_end = date_cst_start + timedelta(days=1)
+            
+            start_dt_utc = date_cst_start.astimezone(TZ_UTC)
+            end_dt_utc = date_cst_end.astimezone(TZ_UTC)
+            
+            start_str = start_dt_utc.strftime('%Y-%m-%d %H:%M:%S')
+            end_str = end_dt_utc.strftime('%Y-%m-%d %H:%M:%S')
+        except Exception as e:
+            print(f"日期处理错误: {e}")
+            return []
+
         with get_db_connection() as conn:
             cursor = conn.cursor()
             cursor.execute("""
-                SELECT 
+                SELECT *
-                    *,
-                    HOUR(CONVERT_TZ(timestamp, '+00:00', '+08:00')) as hour_cst,
-                    CONVERT_TZ(timestamp, '+00:00', '+08:00') as timestamp_cst_dt
                 FROM hourly_model_predictions 
-                WHERE DATE(CONVERT_TZ(timestamp, '+00:00', '+08:00')) = %s
+                WHERE timestamp >= %s AND timestamp < %s
-                ORDER BY hour_cst
+                ORDER BY timestamp ASC
-            """, (date,))
+            """, (start_str, end_str))
             
             rows = cursor.fetchall()
             result = []
             
             for row in rows:
+                # 计算 CST 小时用于显示排序 (虽然 SQL 已经按 timestamp 排序了，但为了确保前端显示 hour 字段正确)
+                ts_utc = row['timestamp']
+                if isinstance(ts_utc, str):
+                    ts_utc = datetime.fromisoformat(str(ts_utc).replace('Z', '+00:00'))
+                if ts_utc.tzinfo is None:
+                    ts_utc = ts_utc.replace(tzinfo=TZ_UTC)
+                ts_cst = ts_utc.astimezone(TZ_CST)
+                hour_cst = ts_cst.hour
+                
                 hour_data = {
-                    "hour": row['hour_cst'],
+                    "hour": hour_cst,
                     "timestamp_et": row['timestamp_et'],
                     "timestamp_cst": convert_to_cst(row.get('timestamp', '')),
                     "open_price": row['open_price'],

api/prediction_service.py

@@ -226,6 +226,17 @@ class PredictionService:
             preds = self.inference_manager.predict_all(df_input)
             
             # 7. 判断实际涨跌
+            # Check if the candle is closed (current time > candle open time + 1 hour)
+            current_now_utc = datetime.now(ZoneInfo('UTC')).replace(tzinfo=None)
+            candle_close_time = target_time_utc + timedelta(hours=1)
+            
+            is_closed = current_now_utc >= candle_close_time
+            
+            if not is_closed:
+                # 仅保存已完成的K线预测，未完成的跳过 (等待完成后再生成)
+                # 实时交易逻辑在 auto_trade_polymarket.py 中单独处理
+                return
+
             actual_result = 1 if target_close > prev_close else 0
             
             # 8. 组装数据 (宽表格式)
@@ -266,7 +277,11 @@ class PredictionService:
     
     def ensure_predictions_for_month(self, month: str) -> None:
         """
-        确保指定月份有预测数据
+        确保指定月份有预测数据 (CST)
+        需要检查:
+        1. 该月的所有日期 (ET)
+        2. 上个月的最后一天 (ET) -> 因为 ET 上个月最后一天 11:00-23:00 对应 CST 该月第一天 00:00-12:00
+        
         Args:
             month: 月份 (YYYY-MM)
         """
@@ -281,8 +296,15 @@ class PredictionService:
             from calendar import monthrange
             _, last_day = monthrange(year, mon)
             
+            # 1. 确保上个月最后一天有数据 (为了 CST 的开头)
+            first_day_of_month = datetime(year, mon, 1).date()
+            prev_day = first_day_of_month - timedelta(days=1)
+            prev_day_str = prev_day.strftime('%Y-%m-%d')
+            self.ensure_predictions_for_date(prev_day_str)
+            
             today = datetime.now(self.et_tz).date()
             
+            # 2. 确保本月所有日期
             for day in range(1, last_day + 1):
                 date = f"{year}-{mon:02d}-{day:02d}"
                 date_obj = datetime.strptime(date, '%Y-%m-%d').date()

trader/auto_trade_polymarket.py

@@ -541,41 +541,43 @@ class PolymarketBTCTrader:
                 final_probability = all_preds['dlinear']['probability']
                 logger.warning("⚠️ 集成预测不可用，降级为 DLinear")
 
-        # 保存所有预测结果到 model_predictions 表
+        # 保存所有预测结果到 hourly_model_predictions 表 (V2)
         try:
             et_tz = ZoneInfo('America/New_York')
             current_time_utc = pd.Timestamp(current_time)
             current_time_et = current_time_utc.tz_localize('UTC').tz_convert(et_tz)
             
-            common_data = {
+            prediction_data = {
                 "timestamp": current_time_utc.isoformat(),
                 "timestamp_et": current_time_et.isoformat(),
                 "date": current_time_et.strftime('%Y-%m-%d'),
                 "hour": current_time_et.hour,
-                "actual_result": None,
-                "is_correct": None,
                 "open_price": float(df['open'].iloc[-1]),
                 "close_price": None,
+                "actual_result": None,
+                
+                # DLinear
+                "dlinear_prediction": all_preds.get('dlinear', {}).get('prediction'),
+                "dlinear_prob": all_preds.get('dlinear', {}).get('probability'),
+                "dlinear_correct": None,
+                
+                # N-BEATS
+                "nbeats_prediction": all_preds.get('nbeats', {}).get('prediction'),
+                "nbeats_prob": all_preds.get('nbeats', {}).get('probability'),
+                "nbeats_correct": None,
+                
+                # TTM-R2
+                "ttm_prediction": all_preds.get('ttm', {}).get('prediction'),
+                "ttm_prob": all_preds.get('ttm', {}).get('probability'),
+                "ttm_correct": None,
+                
+                # Ensemble
+                "ensemble_prediction": all_preds.get('ensemble', {}).get('prediction'),
+                "ensemble_votes": all_preds.get('ensemble', {}).get('votes'),
+                "ensemble_correct": None,
             }
             
-            # 辅助函数
+            PredictionModel.save_hourly_prediction(prediction_data)
-            def save_single_pred(name, pred_res):
-                data = common_data.copy()
-                data['model_name'] = name
-                data['prediction'] = pred_res['prediction']
-                data['probability'] = pred_res.get('probability', 0.5)
-                PredictionModel.save_prediction(data)
-
-            if 'dlinear' in all_preds:
-                save_single_pred("DLinear", all_preds['dlinear'])
-            if 'nbeats' in all_preds:
-                save_single_pred("N-BEATS", all_preds['nbeats'])
-            if 'ttm' in all_preds:
-                save_single_pred("TTM-R2", all_preds['ttm'])
-            if 'ensemble' in all_preds and all_preds['ensemble']:
-                 # 集成也保存
-                 save_single_pred("Ensemble", all_preds['ensemble'])
-
             logger.info(f"💾 所有模型预测记录已保存 (ET {current_time_et.strftime('%Y-%m-%d %H:%M')})")
         except Exception as e:
             logger.error(f"保存预测记录失败: {e}")
@@ -599,22 +601,17 @@ class PolymarketBTCTrader:
             prev_close = float(prev_row['close'])
             actual_result = 1 if prev_close > prev_open else 0
 
-            models_to_update = ["DLinear", "N-BEATS", "TTM-R2", "Ensemble"]
+            # 使用 V2 更新方法 (宽表)
+            updated = PredictionModel.update_actual_result_v2(
+                date=prev_date,
+                hour=prev_hour,
+                actual_result=actual_result,
+                close_price=prev_close
+            )
             
-            for model_name in models_to_update:
+            if updated:
-                updated = PredictionModel.update_actual_result(
+                logger.info(f"📝 回填上一小时结果: ET {prev_date} {prev_hour}:00, "
-                    date=prev_date,
+                            f"实际{'涨' if actual_result == 1 else '跌'} (O:{prev_open:.0f} C:{prev_close:.0f})")
-                    hour=prev_hour,
-                    model_name=model_name,
-                    actual_result=actual_result,
-                    close_price=prev_close
-                )
-                if updated:
-                    # 仅打印一条日志代表更新成功，避免刷屏
-                    pass
-            
-            logger.info(f"📝 回填上一小时结果: ET {prev_date} {prev_hour}:00, "
-                        f"实际{'涨' if actual_result == 1 else '跌'} (O:{prev_open:.0f} C:{prev_close:.0f})")
 
         except Exception as e:
             logger.error(f"回填上一小时结果失败: {e}")
@@ -801,30 +798,38 @@ class PolymarketBTCTrader:
             current_time_utc = pd.Timestamp(current_time)
             current_time_et = current_time_utc.tz_localize('UTC').tz_convert(et_tz)
             
-            # 1. 保存预测到 model_predictions
+            # 1. 保存预测到 hourly_model_predictions (V2)
-            common_data = {
+            prediction_data = {
                 "timestamp": current_time_utc.isoformat(),
                 "timestamp_et": current_time_et.isoformat(),
                 "date": current_time_et.strftime('%Y-%m-%d'),
                 "hour": current_time_et.hour,
-                "actual_result": None,
-                "is_correct": None,
                 "open_price": float(df['open'].iloc[-1]),
                 "close_price": None,
+                "actual_result": None,
+                
+                # DLinear
+                "dlinear_prediction": all_preds.get('dlinear', {}).get('prediction'),
+                "dlinear_prob": all_preds.get('dlinear', {}).get('probability'),
+                "dlinear_correct": None,
+                
+                # N-BEATS
+                "nbeats_prediction": all_preds.get('nbeats', {}).get('prediction'),
+                "nbeats_prob": all_preds.get('nbeats', {}).get('probability'),
+                "nbeats_correct": None,
+                
+                # TTM-R2
+                "ttm_prediction": all_preds.get('ttm', {}).get('prediction'),
+                "ttm_prob": all_preds.get('ttm', {}).get('probability'),
+                "ttm_correct": None,
+                
+                # Ensemble
+                "ensemble_prediction": all_preds.get('ensemble', {}).get('prediction'),
+                "ensemble_votes": all_preds.get('ensemble', {}).get('votes'),
+                "ensemble_correct": None,
             }
             
-            def save_single_pred(name, pred_res):
+            PredictionModel.save_hourly_prediction(prediction_data)
-                data = common_data.copy()
-                data['model_name'] = name
-                data['prediction'] = pred_res['prediction']
-                data['probability'] = pred_res.get('probability', 0.5)
-                PredictionModel.save_prediction(data)
-
-            if 'dlinear' in all_preds: save_single_pred("DLinear", all_preds['dlinear'])
-            if 'nbeats' in all_preds: save_single_pred("N-BEATS", all_preds['nbeats'])
-            if 'ttm' in all_preds: save_single_pred("TTM-R2", all_preds['ttm'])
-            if 'ensemble' in all_preds and all_preds['ensemble']: save_single_pred("Ensemble", all_preds['ensemble'])
-            
             logger.info(f"💾 [Init] 初始预测已保存到数据库")
 
             # 2. 记录当前监控状态到 trades (作为 'in_progress')

trading_log.json

@@ -9,5 +9,14 @@
     "side": "DOWN",
     "price": 0.505,
     "probability": 0.5563886165618896
+  },
+  {
+    "timestamp": "2026-02-11T22:32:46.922211",
+    "success": false,
+    "error": "该市场已存在成功交易记录",
+    "prediction": "DOWN",
+    "probability": 0.0,
+    "market": "Bitcoin Up or Down - February 11, 9AM ET",
+    "market_slug": "bitcoin-up-or-down-february-11-9am-et"
   }
 ]