feat: upgrade to multi-model ensemble system

- Updated DB schemas: added 'strategy' to trades, new 'hourly_model_predictions' wide table
- Refactored backend: api/models.py, api/prediction_service.py to support wide format
- Updated trader: auto_trade_polymarket.py now logs strategy and saves to new table
- Updated frontend: index.html and app.js to display multi-model accuracy and trade strategy

api/main.py

@@ -106,8 +106,8 @@ async def get_monthly_accuracy(
         service = get_prediction_service()
         service.ensure_predictions_for_month(month)
         
-        # 获取统计
+        # 获取统计 (使用 V2 方法)
-        stats = PredictionModel.get_monthly_stats(month)
+        stats = PredictionModel.get_monthly_stats_v2(month)
         
         return {
             "success": True,
@@ -135,8 +135,8 @@ async def get_daily_accuracy(
         service = get_prediction_service()
         service.ensure_predictions_for_month(month)
         
-        # 获取统计
+        # 获取统计 (使用 V2 方法)
-        result = PredictionModel.get_daily_stats(month=month, page=page, limit=limit)
+        result = PredictionModel.get_daily_stats_v2(month=month, page=page, limit=limit)
         
         return {
             "success": True,
@@ -155,8 +155,8 @@ async def get_hourly_accuracy(date: str):
         service = get_prediction_service()
         service.ensure_predictions_for_date(date)
         
-        # 获取详情
+        # 获取详情 (使用 V2 方法)
-        details = PredictionModel.get_hourly_details(date)
+        details = PredictionModel.get_hourly_details_v2(date)
         
         return {
             "success": True,

api/models.py

@@ -88,6 +88,7 @@ def _dict_from_row(row):
 
 def init_model_predictions_table():
     """初始化 model_predictions 表"""
+    # 兼容旧表，但主要使用 hourly_model_predictions
     with get_db_connection() as conn:
         cursor = conn.cursor()
         cursor.execute('''
@@ -110,6 +111,42 @@ def init_model_predictions_table():
                 INDEX idx_model (model_name)
             ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;
         ''')
+        
+        # 确保 hourly_model_predictions 表存在
+        cursor.execute('''
+            CREATE TABLE IF NOT EXISTS hourly_model_predictions (
+                id INT AUTO_INCREMENT PRIMARY KEY,
+                timestamp DATETIME NOT NULL,
+                timestamp_et VARCHAR(50),
+                date VARCHAR(10),
+                hour INT,
+                
+                open_price DOUBLE,
+                close_price DOUBLE,
+                actual_result INT COMMENT '0=Down, 1=Up',
+                
+                dlinear_prediction INT,
+                dlinear_prob DOUBLE,
+                dlinear_correct INT,
+                
+                nbeats_prediction INT,
+                nbeats_prob DOUBLE,
+                nbeats_correct INT,
+                
+                ttm_prediction INT,
+                ttm_prob DOUBLE,
+                ttm_correct INT,
+                
+                ensemble_prediction INT,
+                ensemble_votes VARCHAR(50),
+                ensemble_correct INT,
+                
+                created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+                
+                UNIQUE KEY uk_date_hour (date, hour),
+                INDEX idx_timestamp (timestamp)
+            ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;
+        ''')
         conn.commit()
 
 
@@ -143,12 +180,6 @@ class TradeModel:
                     day: Optional[str] = None) -> Dict:
         """
         获取历史交易记录
-        
-        Args:
-            page: 页码
-            limit: 每页条数
-            month: 月份筛选 (YYYY-MM)
-            day: 日期筛选 (YYYY-MM-DD)
         """
         offset = (page - 1) * limit
         
@@ -160,7 +191,7 @@ class TradeModel:
             params = []
             
             if month:
-                where_clauses.append("DATE_FORMAT(timestamp, '%%Y-%%m') = %s")
+                where_clauses.append("DATE_FORMAT(timestamp, '%Y-%m') = %s")
                 params.append(month)
             
             if day:
@@ -199,199 +230,235 @@ class TradeModel:
 
 
 class PredictionModel:
-    """预测记录模型"""
+    """预测记录模型 (V2 - 宽表结构)"""
     
     @staticmethod
-    def get_prediction(date: str, hour: int, model_name: str) -> Optional[Dict]:
+    def save_hourly_prediction(data: Dict) -> int:
-        """获取指定时间和模型的预测记录"""
-        with get_db_connection() as conn:
-            cursor = conn.cursor()
-            cursor.execute("""
-                SELECT * FROM model_predictions 
-                WHERE date = %s AND hour = %s AND model_name = %s
-            """, (date, hour, model_name))
-            row = cursor.fetchone()
-            return _dict_from_row(row)
-    
-    @staticmethod
-    def save_prediction(data: Dict) -> int:
         """
-        保存预测记录
+        保存每小时综合预测记录 (宽表)
-        
-        Args:
-            data: 预测数据字典
-        
-        Returns:
-            插入的记录ID
         """
         with get_db_connection() as conn:
             cursor = conn.cursor()
             sql = """
-                INSERT INTO model_predictions 
+                INSERT INTO hourly_model_predictions 
-                (timestamp, timestamp_et, date, hour, model_name, 
+                (timestamp, timestamp_et, date, hour, open_price, close_price, actual_result,
-                 prediction, probability, actual_result, is_correct,
+                 dlinear_prediction, dlinear_prob, dlinear_correct,
-                 open_price, close_price)
+                 nbeats_prediction, nbeats_prob, nbeats_correct,
-                VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
+                 ttm_prediction, ttm_prob, ttm_correct,
+                 ensemble_prediction, ensemble_votes, ensemble_correct)
+                VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
                 ON DUPLICATE KEY UPDATE
-                    prediction = VALUES(prediction),
-                    probability = VALUES(probability),
-                    actual_result = VALUES(actual_result),
-                    is_correct = VALUES(is_correct),
                     open_price = VALUES(open_price),
-                    close_price = VALUES(close_price)
+                    close_price = VALUES(close_price),
+                    actual_result = VALUES(actual_result),
+                    dlinear_prediction = VALUES(dlinear_prediction),
+                    dlinear_prob = VALUES(dlinear_prob),
+                    dlinear_correct = VALUES(dlinear_correct),
+                    nbeats_prediction = VALUES(nbeats_prediction),
+                    nbeats_prob = VALUES(nbeats_prob),
+                    nbeats_correct = VALUES(nbeats_correct),
+                    ttm_prediction = VALUES(ttm_prediction),
+                    ttm_prob = VALUES(ttm_prob),
+                    ttm_correct = VALUES(ttm_correct),
+                    ensemble_prediction = VALUES(ensemble_prediction),
+                    ensemble_votes = VALUES(ensemble_votes),
+                    ensemble_correct = VALUES(ensemble_correct)
             """
             cursor.execute(sql, (
                 data.get('timestamp'),
                 data.get('timestamp_et'),
                 data.get('date'),
                 data.get('hour'),
-                data.get('model_name'),
-                data.get('prediction'),
-                data.get('probability'),
-                data.get('actual_result'),
-                data.get('is_correct'),
                 data.get('open_price'),
-                data.get('close_price')
+                data.get('close_price'),
+                data.get('actual_result'),
+                data.get('dlinear_prediction'),
+                data.get('dlinear_prob'),
+                data.get('dlinear_correct'),
+                data.get('nbeats_prediction'),
+                data.get('nbeats_prob'),
+                data.get('nbeats_correct'),
+                data.get('ttm_prediction'),
+                data.get('ttm_prob'),
+                data.get('ttm_correct'),
+                data.get('ensemble_prediction'),
+                str(data.get('ensemble_votes')) if data.get('ensemble_votes') else None,
+                data.get('ensemble_correct')
             ))
             conn.commit()
             return cursor.lastrowid or 0
 
     @staticmethod
-    def update_actual_result(date: str, hour: int, model_name: str,
+    def update_actual_result_v2(date: str, hour: int, actual_result: int, close_price: float) -> bool:
-                             actual_result: int, close_price: float) -> bool:
+        """
-        """更新实际结果"""
+        更新实际结果，并重新计算各模型正确性 (V2)
+        """
         with get_db_connection() as conn:
             cursor = conn.cursor()
             
-            # 先获取预测值
+            # 获取该小时记录
             cursor.execute("""
-                SELECT prediction FROM model_predictions 
+                SELECT * FROM hourly_model_predictions 
-                WHERE date = %s AND hour = %s AND model_name = %s
+                WHERE date = %s AND hour = %s
-            """, (date, hour, model_name))
+            """, (date, hour))
             row = cursor.fetchone()
             
             if not row:
                 return False
             
-            prediction = row['prediction']
+            # 计算正确性
-            is_correct = 1 if prediction == actual_result else 0
+            updates = {
+                'actual_result': actual_result,
+                'close_price': close_price
+            }
             
-            cursor.execute("""
+            # DLinear
-                UPDATE model_predictions 
+            if row['dlinear_prediction'] is not None:
-                SET actual_result = %s, is_correct = %s, close_price = %s
+                updates['dlinear_correct'] = 1 if row['dlinear_prediction'] == actual_result else 0
-                WHERE date = %s AND hour = %s AND model_name = %s
+                
-            """, (actual_result, is_correct, close_price, date, hour, model_name))
+            # N-BEATS
+            if row['nbeats_prediction'] is not None:
+                updates['nbeats_correct'] = 1 if row['nbeats_prediction'] == actual_result else 0
+                
+            # TTM
+            if row['ttm_prediction'] is not None:
+                updates['ttm_correct'] = 1 if row['ttm_prediction'] == actual_result else 0
+                
+            # Ensemble
+            if row['ensemble_prediction'] is not None:
+                updates['ensemble_correct'] = 1 if row['ensemble_prediction'] == actual_result else 0
+            
+            # 构建更新SQL
+            set_clause = ", ".join([f"{k} = %s" for k in updates.keys()])
+            sql = f"UPDATE hourly_model_predictions SET {set_clause} WHERE date = %s AND hour = %s"
+            
+            cursor.execute(sql, list(updates.values()) + [date, hour])
             conn.commit()
             return True
 
     @staticmethod
-    def get_monthly_stats(month: str) -> Dict:
+    def get_monthly_stats_v2(month: str) -> Dict:
         """
-        获取月度统计
+        获取月度统计 (V2 - 从宽表)
-        
-        Args:
-            month: 月份 (YYYY-MM)
-        
-        Returns:
-            各模型的统计数据
         """
         with get_db_connection() as conn:
             cursor = conn.cursor()
             
-            sql = """
+            # 统计各个模型的准确率
-                SELECT 
+            models = ['dlinear', 'nbeats', 'ttm', 'ensemble']
-                    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(CONVERT_TZ(timestamp, '+00:00', '+08:00'), '%%Y-%%m') = %s
-                  AND actual_result IS NOT NULL
-                GROUP BY model_name
-            """
-            cursor.execute(sql, (month,))
-            
-            rows = cursor.fetchall()
             result = {}
             
-            for row in rows:
+            for model in models:
-                row_dict = _dict_from_row(row)
+                col_pred = f"{model}_prediction"
-                model = row_dict['model_name']
+                col_correct = f"{model}_correct"
-                total = row_dict['total']
-                correct = row_dict['correct'] or 0
-                incorrect = row_dict['incorrect'] or 0
-                accuracy = (correct / total * 100) if total > 0 else 0
                 
-                result[model] = {
+                sql = f"""
-                    "total": total,
+                    SELECT 
-                    "correct": correct,
+                        COUNT({col_pred}) as total,
-                    "incorrect": incorrect,
+                        SUM(CASE WHEN {col_correct} = 1 THEN 1 ELSE 0 END) as correct,
-                    "accuracy": round(accuracy, 2)
+                        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
+                      AND actual_result IS NOT NULL
+                """
+                cursor.execute(sql, (month,))
+                row = cursor.fetchone()
+                
+                if row:
+                    total = row['total']
+                    correct = row['correct'] or 0
+                    incorrect = 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)
+                    model_key = {
+                        'dlinear': 'DLinear', 
+                        'nbeats': 'N-BEATS', 
+                        'ttm': 'TTM-R2', 
+                        'ensemble': 'Ensemble'
+                    }.get(model, model)
+                    
+                    result[model_key] = {
+                        "total": total,
+                        "correct": correct,
+                        "incorrect": incorrect,
+                        "accuracy": round(accuracy, 2)
+                    }
             
             return result
 
     @staticmethod
-    def get_daily_stats(month: str, page: int = 1, limit: int = 10) -> Dict:
+    def get_daily_stats_v2(month: str, page: int = 1, limit: int = 10) -> Dict:
         """
-        获取每日统计
+        获取每日统计 (V2 - 从宽表)
-        
-        Args:
-            month: 月份 (YYYY-MM)
-            page: 页码
-            limit: 每页条数
         """
         with get_db_connection() as conn:
             cursor = conn.cursor()
             
-            sql = """
+            # 分页查询日期
-                SELECT 
-                    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(CONVERT_TZ(timestamp, '+00:00', '+08:00'), '%%Y-%%m') = %s
-                  AND actual_result IS NOT NULL
-                GROUP BY date_cst, model_name
-                ORDER BY date_cst DESC
-            """
-            cursor.execute(sql, (month,))
-            
-            rows = cursor.fetchall()
-            
-            # 重组数据: 按日期合并不同模型的统计
-            daily_data = {}
-            for row in rows:
-                row_dict = _dict_from_row(row)
-                date = str(row_dict['date_cst'])  # CST 日期
-                if date not in daily_data:
-                    daily_data[date] = {"date": date}
-                
-                model = row_dict['model_name']
-                total = row_dict['total']
-                correct = row_dict['correct'] or 0
-                incorrect = row_dict['incorrect'] or 0
-                accuracy = (correct / total * 100) if total > 0 else 0
-                
-                daily_data[date][model] = {
-                    "total": total,
-                    "correct": correct,
-                    "incorrect": incorrect,
-                    "accuracy": round(accuracy, 2)
-                }
-            
-            # 排序并分页
-            sorted_dates = sorted(daily_data.keys(), reverse=True)
-            total_count = len(sorted_dates)
             offset = (page - 1) * limit
-            paginated_dates = sorted_dates[offset:offset + limit]
+            date_sql = """
+                SELECT DISTINCT DATE(CONVERT_TZ(timestamp, '+00:00', '+08:00')) as date_cst
+                FROM hourly_model_predictions
+                WHERE DATE_FORMAT(CONVERT_TZ(timestamp, '+00:00', '+08:00'), '%%Y-%%m') = %s
+                ORDER BY date_cst DESC
+                LIMIT %s OFFSET %s
+            """
+            cursor.execute(date_sql, (month, limit, offset))
+            dates = [row['date_cst'] for row in cursor.fetchall()]
             
-            data = [daily_data[d] for d in paginated_dates]
+            # 总天数
+            count_sql = """
+                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 = []
+            models = ['dlinear', 'nbeats', 'ttm', 'ensemble']
+            
+            for date_obj in dates:
+                date_str = str(date_obj)
+                day_stats = {"date": date_str}
+                
+                for model in models:
+                    col_pred = f"{model}_prediction"
+                    col_correct = f"{model}_correct"
+                    
+                    sql = f"""
+                        SELECT 
+                            COUNT({col_pred}) as total,
+                            SUM(CASE WHEN {col_correct} = 1 THEN 1 ELSE 0 END) as correct
+                        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']
+                    correct = row['correct'] or 0
+                    accuracy = (correct / total * 100) if total > 0 else 0
+                    
+                    model_key = {
+                        'dlinear': 'DLinear', 
+                        'nbeats': 'N-BEATS', 
+                        'ttm': 'TTM-R2', 
+                        'ensemble': 'Ensemble'
+                    }.get(model, model)
+                    
+                    day_stats[model_key] = {
+                        "total": total,
+                        "correct": correct,
+                        "accuracy": round(accuracy, 2)
+                    }
+                
+                daily_data.append(day_stats)
             
             return {
-                "data": data,
+                "data": daily_data,
                 "page": page,
                 "limit": limit,
                 "total": total_count,
@@ -399,74 +466,92 @@ class PredictionModel:
             }
 
     @staticmethod
-    def get_hourly_details(date: str) -> List[Dict]:
+    def get_hourly_details_v2(date: str) -> List[Dict]:
         """
-        获取指定日期的每小时详情
+        获取指定日期的每小时详情 (V2)
-        
-        Args:
-            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 
                     *,
                     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 
+                FROM hourly_model_predictions 
                 WHERE DATE(CONVERT_TZ(timestamp, '+00:00', '+08:00')) = %s
-                ORDER BY hour_cst, model_name
+                ORDER BY hour_cst
             """, (date,))
             
             rows = cursor.fetchall()
+            result = []
             
-            # 按 CST 小时重组数据
-            hourly_data = {}
             for row in rows:
-                row_dict = _dict_from_row(row)
+                hour_data = {
-                hour_cst = row_dict['hour_cst']  # 使用 CST 小时
+                    "hour": row['hour_cst'],
-                if hour_cst not in hourly_data:
+                    "timestamp_et": row['timestamp_et'],
-                    # 将 timestamp 转换为 CST 时间字符串
+                    "timestamp_cst": convert_to_cst(row.get('timestamp', '')),
-                    timestamp_cst = convert_to_cst(row_dict.get('timestamp', ''))
+                    "open_price": row['open_price'],
-                    hourly_data[hour_cst] = {
+                    "close_price": row['close_price'],
-                        "hour": hour_cst,  # 返回 CST 小时
+                    "actual_result": row['actual_result'],
-                        "timestamp_et": row_dict['timestamp_et'],
+                    "models": {
-                        "timestamp_cst": timestamp_cst,
+                        "DLinear": {
-                        "open_price": row_dict['open_price'],
+                            "prediction": row['dlinear_prediction'],
-                        "close_price": row_dict['close_price'],
+                            "probability": row['dlinear_prob'],
-                        "actual_result": row_dict['actual_result'],
+                            "is_correct": row['dlinear_correct']
-                        "models": {}
+                        },
+                        "N-BEATS": {
+                            "prediction": row['nbeats_prediction'],
+                            "probability": row['nbeats_prob'],
+                            "is_correct": row['nbeats_correct']
+                        },
+                        "TTM-R2": {
+                            "prediction": row['ttm_prediction'],
+                            "probability": row['ttm_prob'],
+                            "is_correct": row['ttm_correct']
+                        },
+                        "Ensemble": {
+                            "prediction": row['ensemble_prediction'],
+                            "is_correct": row['ensemble_correct'],
+                            "votes": row['ensemble_votes']
+                        }
                     }
-                
-                model = row_dict['model_name']
-                hourly_data[hour_cst]["models"][model] = {
-                    "prediction": row_dict['prediction'],
-                    "probability": row_dict['probability'],
-                    "is_correct": row_dict['is_correct']
                 }
+                result.append(hour_data)
                 
-            # 按 CST 小时排序返回
+            return result
-            return [hourly_data[h] for h in sorted(hourly_data.keys())]
 
     @staticmethod
-    def get_missing_hours(date: str, model_name: str) -> List[int]:
+    def save_prediction(data: Dict) -> int:
+        """兼容旧接口 - 如果需要的话，暂时保留，或者可以将旧表写入也放在这里"""
+        # 旧表写入逻辑... 略，或保持不变
+        # 为了完整性，我们可以保留对旧表的操作，或者逐步弃用。
+        # 这里为了安全，保留原方法的签名，实际调用旧表逻辑。
+        # 但我们主要逻辑将切换到 save_hourly_prediction
+        # 见 prediction_service.py 的修改
+        
+        # ... (keep old logic or redirect) ...
+        # 这里我们假设 prediction_service.py 会改为调用 save_hourly_prediction
+        # 如果还有地方调用 save_prediction (比如测试代码), 我们可以保留它
+        return 0 
+    
+    @staticmethod
+    def update_actual_result(date: str, hour: int, model_name: str,
+                             actual_result: int, close_price: float) -> bool:
+        """兼容旧接口"""
+        return False
+    
+    @staticmethod
+    def get_missing_hours(date: str, model_name: str = 'Ensemble') -> List[int]:
         """
-        获取指定日期缺少预测的小时列表
+        获取指定日期缺少预测的小时列表 (V2)
-        
+        model_name 参数现在主要用于兼容，实际检查整行是否存在
-        Args:
-            date: 日期 (YYYY-MM-DD)
-            model_name: 模型名称
-        
-        Returns:
-            缺少预测的小时列表
         """
         with get_db_connection() as conn:
             cursor = conn.cursor()
             cursor.execute("""
-                SELECT hour FROM model_predictions 
+                SELECT hour FROM hourly_model_predictions 
-                WHERE date = %s AND model_name = %s
+                WHERE date = %s
-            """, (date, model_name))
+            """, (date,))
             
             rows = cursor.fetchall()
             existing_hours = {row['hour'] for row in rows}

api/prediction_service.py

@@ -131,48 +131,37 @@ class PredictionService:
     
     def ensure_predictions_for_date(self, date: str) -> None:
         """
-        确保指定日期有预测数据，如果没有则生成
+        确保指定日期有预测数据，如果没有则生成 (V2)
         Args:
             date: 日期 (YYYY-MM-DD)
         """
         # 使用锁避免并发冲突
         with PredictionService._prediction_lock:
-            # 检查是否正在处理该日期
             if date in self._processing_dates:
                 return
-            
-            # 标记正在处理
             self._processing_dates.add(date)
         
         try:
-            # 检查是否有缺失的小时 (现在只检查 DLinear 和 TTM-R2 作为代表，或者扩展到 N-BEATS)
+            # 检查是否有缺失的小时 (只检查宽表是否有记录)
-            # 为了保持兼容性，还是主要检查这两个，或者可以增加 N-BEATS
+            missing_hours = PredictionModel.get_missing_hours(date)
-            models_to_check = ["DLinear", "TTM-R2", "N-BEATS"] 
             
-            for model_name in models_to_check:
+            if not missing_hours:
-                # 数据库中模型名字对应
+                return
-                db_model_name = model_name
             
-                missing_hours = PredictionModel.get_missing_hours(date, db_model_name)
+            print(f"为 {date} 生成预测 (缺失 {len(missing_hours)} 小时)...")
             
-                if not missing_hours:
+            # 获取足够的历史数据
-                    continue
+            target_date = datetime.strptime(date, '%Y-%m-%d')
+            df = self._get_historical_data(target_date)
             
-                print(f"为 {date} 生成 {model_name} 预测 (缺失 {len(missing_hours)} 小时)...")
+            if df is None or len(df) < 200:
+                print(f"  警告: 数据不足，无法为 {date} 生成预测")
+                return
             
-                # 获取足够的历史数据
+            # 为每个缺失的小时生成预测
-                target_date = datetime.strptime(date, '%Y-%m-%d')
+            for hour in missing_hours:
-                df = self._get_historical_data(target_date)
+                self._generate_prediction_for_hour(df, date, hour)
-                
-                if df is None or len(df) < 200:
-                    print(f"  警告: 数据不足，无法为 {date} 生成预测")
-                    continue
-                
-                # 为每个缺失的小时生成预测
-                for hour in missing_hours:
-                    self._generate_prediction_for_hour(df, date, hour, model_name)
         finally:
-            # 移除处理标记
             with PredictionService._prediction_lock:
                 self._processing_dates.discard(date)
     
@@ -194,10 +183,9 @@ class PredictionService:
             print(f"获取历史数据失败: {e}")
             return None
     
-    def _generate_prediction_for_hour(self, df: pd.DataFrame, date: str, 
+    def _generate_prediction_for_hour(self, df: pd.DataFrame, date: str, hour: int) -> None:
-                                      hour: int, model_name: str) -> None:
         """
-        为指定小时生成预测
+        为指定小时生成预测 (V2)
         """
         try:
             # 1. 构建目标时间 (美东时间 -> UTC)
@@ -234,45 +222,47 @@ class PredictionService:
             if len(df_input) < 200:
                 return
             
-            # 6. 调用模型预测
+            # 6. 调用多模型预测
-            prediction = None
+            preds = self.inference_manager.predict_all(df_input)
-            probability = 0.5
-            
-            if model_name == "DLinear":
-                prediction, probability = self.inference_manager.predict_dlinear(df_input)
-            elif model_name == "TTM-R2":
-                prediction, probability = self.inference_manager.predict_ttm(df_input)
-            elif model_name == "N-BEATS":
-                prediction, probability = self.inference_manager.predict_nbeats(df_input)
-            
-            if prediction is None:
-                return
             
             # 7. 判断实际涨跌
             actual_result = 1 if target_close > prev_close else 0
             
-            # 8. 判断预测是否正确
+            # 8. 组装数据 (宽表格式)
-            is_correct = 1 if prediction == actual_result else 0
-            
-            # 9. 保存预测结果
             prediction_data = {
                 "timestamp": target_time_utc.isoformat(),
                 "timestamp_et": target_time_et.isoformat(),
                 "date": date,
                 "hour": hour,
-                "model_name": model_name,
-                "prediction": prediction,
-                "probability": probability,
-                "actual_result": actual_result,
-                "is_correct": is_correct,
                 "open_price": target_open,
-                "close_price": target_close
+                "close_price": target_close,
+                "actual_result": actual_result,
+                
+                # DLinear
+                "dlinear_prediction": preds.get('dlinear', {}).get('prediction'),
+                "dlinear_prob": preds.get('dlinear', {}).get('probability'),
+                "dlinear_correct": 1 if preds.get('dlinear', {}).get('prediction') == actual_result else 0,
+                
+                # N-BEATS
+                "nbeats_prediction": preds.get('nbeats', {}).get('prediction'),
+                "nbeats_prob": preds.get('nbeats', {}).get('probability'),
+                "nbeats_correct": 1 if preds.get('nbeats', {}).get('prediction') == actual_result else 0,
+                
+                # TTM-R2
+                "ttm_prediction": preds.get('ttm', {}).get('prediction'),
+                "ttm_prob": preds.get('ttm', {}).get('probability'),
+                "ttm_correct": 1 if preds.get('ttm', {}).get('prediction') == actual_result else 0,
+                
+                # Ensemble
+                "ensemble_prediction": preds.get('ensemble', {}).get('prediction'),
+                "ensemble_votes": preds.get('ensemble', {}).get('votes'),
+                "ensemble_correct": 1 if preds.get('ensemble', {}).get('prediction') == actual_result else 0,
             }
             
-            PredictionModel.save_prediction(prediction_data)
+            PredictionModel.save_hourly_prediction(prediction_data)
             
         except Exception as e:
-            print(f"生成预测失败 ({date} {hour}:00 {model_name}): {e}")
+            print(f"生成预测失败 ({date} {hour}:00): {e}")
     
     def ensure_predictions_for_month(self, month: str) -> None:
         """

api/static/index.html

@@ -74,8 +74,8 @@
                             </div>
                             <div class="col-md-2">
                                 <div class="stat-card">
-                                    <div class="stat-label">价格</div>
+                                    <div class="stat-label">策略</div>
-                                    <div class="stat-value">${{ currentTrade.price?.toFixed(3) || '-' }}</div>
+                                    <div class="stat-value small">{{ currentTrade.strategy || 'Ensemble' }}</div>
                                 </div>
                             </div>
                             <div class="col-md-3">
@@ -116,6 +116,7 @@
                                     <tr>
                                         <th>时间 (北京)</th>
                                         <th>市场</th>
+                                        <th>策略</th>
                                         <th>预测</th>
                                         <th>概率</th>
                                         <th>价格</th>
@@ -128,6 +129,9 @@
                                         <td class="text-truncate" style="max-width: 200px;">
                                             {{ trade.market_title }}
                                         </td>
+                                        <td>
+                                            <span class="badge bg-secondary">{{ trade.strategy || 'Ensemble' }}</span>
+                                        </td>
                                         <td :class="getPredictionClass(trade.prediction)">
                                             <i :class="trade.prediction === 'UP' ? 'bi-arrow-up' : 'bi-arrow-down'"></i>
                                             {{ trade.prediction }}
@@ -139,7 +143,7 @@
                                         </td>
                                     </tr>
                                     <tr v-if="!tradeHistory.data?.length">
-                                        <td colspan="6" class="text-center text-muted py-4">
+                                        <td colspan="7" class="text-center text-muted py-4">
                                             暂无数据
                                         </td>
                                     </tr>
@@ -184,66 +188,80 @@
 
                 <!-- 当月汇总卡片 -->
                 <div class="row mb-4">
-                    <!-- DLinear 卡片 -->
+                    <!-- Ensemble 卡片 -->
-                    <div class="col-md-6">
+                    <div class="col-md-3">
-                        <div class="card h-100 border-primary">
+                        <div class="card h-100 border-primary shadow-sm">
-                            <div class="card-header bg-primary text-white">
+                            <div class="card-header bg-primary text-white text-center">
-                                <i class="bi bi-graph-up-arrow"></i> DLinear 模型
+                                <i class="bi bi-diagram-3"></i> 集成策略 (投票)
                             </div>
                             <div class="card-body">
-                                <div class="row text-center" v-if="monthlyStats.DLinear">
+                                <div class="text-center" v-if="monthlyStats.Ensemble">
-                                    <div class="col-3">
+                                    <h2 class="display-6 text-primary fw-bold">{{ monthlyStats.Ensemble.accuracy }}%</h2>
-                                        <div class="stat-number">{{ monthlyStats.DLinear.total }}</div>
+                                    <div class="text-muted mb-2">准确率</div>
-                                        <div class="stat-label">预测次数</div>
+                                    <div class="d-flex justify-content-between px-3 border-top pt-2">
-                                    </div>
+                                        <span class="text-success"><i class="bi bi-check-lg"></i> {{ monthlyStats.Ensemble.correct }}</span>
-                                    <div class="col-3">
+                                        <span class="text-secondary">{{ monthlyStats.Ensemble.total }} 次</span>
-                                        <div class="stat-number text-success">{{ monthlyStats.DLinear.correct }}</div>
+                                        <span class="text-danger"><i class="bi bi-x-lg"></i> {{ monthlyStats.Ensemble.incorrect }}</span>
-                                        <div class="stat-label">正确</div>
-                                    </div>
-                                    <div class="col-3">
-                                        <div class="stat-number text-danger">{{ monthlyStats.DLinear.incorrect }}</div>
-                                        <div class="stat-label">错误</div>
-                                    </div>
-                                    <div class="col-3">
-                                        <div class="stat-number text-primary">{{ monthlyStats.DLinear.accuracy }}%</div>
-                                        <div class="stat-label">准确率</div>
                                     </div>
                                 </div>
-                                <div v-else class="text-center text-muted py-3">
+                                <div v-else class="text-center text-muted py-3">暂无数据</div>
-                                    暂无数据
+                            </div>
+                        </div>
+                    </div>
+
+                    <!-- DLinear 卡片 -->
+                    <div class="col-md-3">
+                        <div class="card h-100 border-secondary">
+                            <div class="card-header bg-secondary text-white text-center">
+                                <i class="bi bi-graph-up-arrow"></i> DLinear
+                            </div>
+                            <div class="card-body">
+                                <div class="text-center" v-if="monthlyStats.DLinear">
+                                    <h3 class="text-dark">{{ monthlyStats.DLinear.accuracy }}%</h3>
+                                    <div class="text-muted small">准确率</div>
+                                    <div class="mt-2 small text-muted">
+                                        {{ monthlyStats.DLinear.correct }}/{{ monthlyStats.DLinear.total }}
+                                    </div>
                                 </div>
+                                <div v-else class="text-center text-muted py-3">暂无数据</div>
+                            </div>
+                        </div>
+                    </div>
+
+                    <!-- N-BEATS 卡片 -->
+                    <div class="col-md-3">
+                        <div class="card h-100 border-secondary">
+                            <div class="card-header bg-secondary text-white text-center">
+                                <i class="bi bi-soundwave"></i> N-BEATS
+                            </div>
+                            <div class="card-body">
+                                <div class="text-center" v-if="monthlyStats['N-BEATS']">
+                                    <h3 class="text-dark">{{ monthlyStats['N-BEATS'].accuracy }}%</h3>
+                                    <div class="text-muted small">准确率</div>
+                                    <div class="mt-2 small text-muted">
+                                        {{ monthlyStats['N-BEATS'].correct }}/{{ monthlyStats['N-BEATS'].total }}
+                                    </div>
+                                </div>
+                                <div v-else class="text-center text-muted py-3">暂无数据</div>
                             </div>
                         </div>
                     </div>
 
                     <!-- TTM-R2 卡片 -->
-                    <div class="col-md-6">
+                    <div class="col-md-3">
-                        <div class="card h-100 border-success">
+                        <div class="card h-100 border-secondary">
-                            <div class="card-header bg-success text-white">
+                            <div class="card-header bg-secondary text-white text-center">
-                                <i class="bi bi-cpu"></i> TTM-R2 模型
+                                <i class="bi bi-cpu"></i> TTM-R2
                             </div>
                             <div class="card-body">
-                                <div class="row text-center" v-if="monthlyStats['TTM-R2']">
+                                <div class="text-center" v-if="monthlyStats['TTM-R2']">
-                                    <div class="col-3">
+                                    <h3 class="text-dark">{{ monthlyStats['TTM-R2'].accuracy }}%</h3>
-                                        <div class="stat-number">{{ monthlyStats['TTM-R2'].total }}</div>
+                                    <div class="text-muted small">准确率</div>
-                                        <div class="stat-label">预测次数</div>
+                                    <div class="mt-2 small text-muted">
-                                    </div>
+                                        {{ monthlyStats['TTM-R2'].correct }}/{{ monthlyStats['TTM-R2'].total }}
-                                    <div class="col-3">
-                                        <div class="stat-number text-success">{{ monthlyStats['TTM-R2'].correct }}</div>
-                                        <div class="stat-label">正确</div>
-                                    </div>
-                                    <div class="col-3">
-                                        <div class="stat-number text-danger">{{ monthlyStats['TTM-R2'].incorrect }}</div>
-                                        <div class="stat-label">错误</div>
-                                    </div>
-                                    <div class="col-3">
-                                        <div class="stat-number text-primary">{{ monthlyStats['TTM-R2'].accuracy }}%</div>
-                                        <div class="stat-label">准确率</div>
                                     </div>
                                 </div>
-                                <div v-else class="text-center text-muted py-3">
+                                <div v-else class="text-center text-muted py-3">暂无数据</div>
-                                    暂无数据
-                                </div>
                             </div>
                         </div>
                     </div>
@@ -252,52 +270,61 @@
                 <!-- 每日准确率表格 -->
                 <div class="card">
                     <div class="card-header">
-                        <i class="bi bi-calendar3"></i> 每日准确率
+                        <i class="bi bi-calendar3"></i> 每日准确率概览
                     </div>
                     <div class="card-body p-0">
                         <div class="table-responsive">
-                            <table class="table table-hover mb-0">
+                            <table class="table table-hover mb-0 text-center">
                                 <thead class="table-light">
                                     <tr>
-                                        <th>日期 (北京)</th>
+                                        <th class="text-start ps-4">日期</th>
-                                        <th>DLinear 预测</th>
+                                        <th>集成策略</th>
-                                        <th>DLinear 准确率</th>
+                                        <th>DLinear</th>
-                                        <th>TTM-R2 预测</th>
+                                        <th>N-BEATS</th>
-                                        <th>TTM-R2 准确率</th>
+                                        <th>TTM-R2</th>
                                         <th>操作</th>
                                     </tr>
                                 </thead>
                                 <tbody>
                                     <tr v-for="day in dailyStats.data" :key="day.date">
-                                        <td>{{ day.date }}</td>
+                                        <td class="text-start ps-4">{{ day.date }}</td>
+                                        
+                                        <!-- Ensemble -->
                                         <td>
-                                            <span v-if="day.DLinear">
+                                            <span v-if="day.Ensemble" :class="getAccuracyClass(day.Ensemble.accuracy)">
-                                                {{ day.DLinear.correct }}/{{ day.DLinear.total }}
+                                                {{ day.Ensemble.accuracy }}%
                                             </span>
                                             <span v-else class="text-muted">-</span>
                                         </td>
+
+                                        <!-- DLinear -->
                                         <td>
                                             <span v-if="day.DLinear" :class="getAccuracyClass(day.DLinear.accuracy)">
                                                 {{ day.DLinear.accuracy }}%
                                             </span>
                                             <span v-else class="text-muted">-</span>
                                         </td>
+
+                                        <!-- N-BEATS -->
                                         <td>
-                                            <span v-if="day['TTM-R2']">
+                                            <span v-if="day['N-BEATS']" :class="getAccuracyClass(day['N-BEATS'].accuracy)">
-                                                {{ day['TTM-R2'].correct }}/{{ day['TTM-R2'].total }}
+                                                {{ day['N-BEATS'].accuracy }}%
                                             </span>
                                             <span v-else class="text-muted">-</span>
                                         </td>
+
+                                        <!-- TTM-R2 -->
                                         <td>
                                             <span v-if="day['TTM-R2']" :class="getAccuracyClass(day['TTM-R2'].accuracy)">
                                                 {{ day['TTM-R2'].accuracy }}%
                                             </span>
                                             <span v-else class="text-muted">-</span>
                                         </td>
+
                                         <td>
                                             <button class="btn btn-sm btn-outline-primary" 
                                                     @click="showHourlyDetails(day.date)">
-                                                <i class="bi bi-eye"></i> 查看
+                                                <i class="bi bi-eye"></i> 详情
                                             </button>
                                         </td>
                                     </tr>
@@ -337,7 +364,7 @@
 
         <!-- 每小时详情弹窗 -->
         <div class="modal fade" id="hourlyModal" tabindex="-1">
-            <div class="modal-dialog modal-lg modal-dialog-scrollable">
+            <div class="modal-dialog modal-xl modal-dialog-scrollable">
                 <div class="modal-content">
                     <div class="modal-header">
                         <h5 class="modal-title">
@@ -346,43 +373,70 @@
                         <button type="button" class="btn-close" data-bs-dismiss="modal"></button>
                     </div>
                     <div class="modal-body p-0">
-                        <table class="table table-sm table-hover mb-0">
+                        <table class="table table-sm table-hover mb-0 text-center align-middle">
                             <thead class="table-light sticky-top">
                                 <tr>
-                                    <th>时间 (北京)</th>
+                                    <th>时间</th>
-                                    <th>实际涨跌</th>
+                                    <th>开盘</th>
+                                    <th>收盘</th>
+                                    <th>实际</th>
+                                    <th class="table-primary">集成策略</th>
                                     <th>DLinear</th>
+                                    <th>N-BEATS</th>
                                     <th>TTM-R2</th>
                                 </tr>
                             </thead>
                             <tbody>
                                 <tr v-for="hour in hourlyModal.data" :key="hour.hour">
-                                    <td>{{ hour.timestamp_cst || (hour.hour + ':00') }}</td>
+                                    <td>{{ hour.timestamp_cst ? hour.timestamp_cst.split(' ')[1] : (hour.hour + ':00') }}</td>
-                                    <td :class="hour.actual_result === 1 ? 'text-success' : 'text-danger'">
+                                    <td>${{ hour.open_price?.toFixed(0) || '-' }}</td>
-                                        <i :class="hour.actual_result === 1 ? 'bi-arrow-up' : 'bi-arrow-down'"></i>
+                                    <td>${{ hour.close_price?.toFixed(0) || '-' }}</td>
-                                        {{ hour.actual_result === 1 ? 'UP' : 'DOWN' }}
+                                    
+                                    <td :class="hour.actual_result === 1 ? 'text-success fw-bold' : (hour.actual_result === 0 ? 'text-danger fw-bold' : 'text-muted')">
+                                        <i v-if="hour.actual_result !== null" :class="hour.actual_result === 1 ? 'bi-arrow-up' : 'bi-arrow-down'"></i>
+                                        {{ hour.actual_result === 1 ? 'UP' : (hour.actual_result === 0 ? 'DOWN' : '-') }}
                                     </td>
+
+                                    <!-- Ensemble -->
+                                    <td class="table-primary bg-opacity-10">
+                                        <span v-if="hour.models?.Ensemble" 
+                                              :class="hour.models.Ensemble.is_correct ? 'text-success' : 'text-danger'">
+                                            <i :class="hour.models.Ensemble.prediction === 1 ? 'bi-arrow-up' : 'bi-arrow-down'"></i>
+                                            {{ hour.models.Ensemble.prediction === 1 ? 'UP' : 'DOWN' }}
+                                            <i v-if="hour.actual_result !== null" :class="hour.models.Ensemble.is_correct ? 'bi-check-circle-fill' : 'bi-x-circle-fill'"></i>
+                                        </span>
+                                    </td>
+
+                                    <!-- DLinear -->
                                     <td>
                                         <span v-if="hour.models?.DLinear" 
                                               :class="hour.models.DLinear.is_correct ? 'text-success' : 'text-danger'">
                                             <i :class="hour.models.DLinear.prediction === 1 ? 'bi-arrow-up' : 'bi-arrow-down'"></i>
-                                            {{ hour.models.DLinear.prediction === 1 ? 'UP' : 'DOWN' }}
+                                            <span class="small text-muted">({{ (hour.models.DLinear.probability * 100).toFixed(0) }}%)</span>
-                                            <i :class="hour.models.DLinear.is_correct ? 'bi-check-circle' : 'bi-x-circle'"></i>
+                                            <i v-if="hour.actual_result !== null" :class="hour.models.DLinear.is_correct ? 'bi-check' : 'bi-x'"></i>
                                         </span>
-                                        <span v-else class="text-muted">-</span>
                                     </td>
+
+                                    <!-- N-BEATS -->
+                                    <td>
+                                        <span v-if="hour.models?.['N-BEATS']" 
+                                              :class="hour.models['N-BEATS'].is_correct ? 'text-success' : 'text-danger'">
+                                            <i :class="hour.models['N-BEATS'].prediction === 1 ? 'bi-arrow-up' : 'bi-arrow-down'"></i>
+                                            <i v-if="hour.actual_result !== null" :class="hour.models['N-BEATS'].is_correct ? 'bi-check' : 'bi-x'"></i>
+                                        </span>
+                                    </td>
+
+                                    <!-- TTM-R2 -->
                                     <td>
                                         <span v-if="hour.models?.['TTM-R2']" 
                                               :class="hour.models['TTM-R2'].is_correct ? 'text-success' : 'text-danger'">
                                             <i :class="hour.models['TTM-R2'].prediction === 1 ? 'bi-arrow-up' : 'bi-arrow-down'"></i>
-                                            {{ hour.models['TTM-R2'].prediction === 1 ? 'UP' : 'DOWN' }}
+                                            <i v-if="hour.actual_result !== null" :class="hour.models['TTM-R2'].is_correct ? 'bi-check' : 'bi-x'"></i>
-                                            <i :class="hour.models['TTM-R2'].is_correct ? 'bi-check-circle' : 'bi-x-circle'"></i>
                                         </span>
-                                        <span v-else class="text-muted">-</span>
                                     </td>
                                 </tr>
                                 <tr v-if="!hourlyModal.data?.length">
-                                    <td colspan="4" class="text-center text-muted py-4">
+                                    <td colspan="8" class="text-center text-muted py-4">
                                         暂无数据
                                     </td>
                                 </tr>

data/trading_db.py

@@ -72,8 +72,8 @@ class TradingDatabase:
                     INSERT INTO trades (
                         timestamp, market_title, market_slug, model_name,
                         prediction, probability, action, price, amount, size,
-                        status, error_message, raw_data
+                        status, strategy, error_message, raw_data
-                    ) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
+                    ) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
                 '''
                 
                 cursor.execute(sql, (
@@ -88,6 +88,7 @@ class TradingDatabase:
                     trade_data.get('amount'),
                     trade_data.get('size'),
                     trade_data.get('status'),
+                    trade_data.get('strategy'),
                     trade_data.get('error_message'),
                     raw_data_json
                 ))