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
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@@ -88,6 +88,7 @@ def _dict_from_row(row):
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def init_model_predictions_table():
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"""初始化 model_predictions 表"""
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# 兼容旧表，但主要使用 hourly_model_predictions
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with get_db_connection() as conn:
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cursor = conn.cursor()
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cursor.execute('''
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@@ -110,6 +111,42 @@ def init_model_predictions_table():
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INDEX idx_model (model_name)
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) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;
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''')
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# 确保 hourly_model_predictions 表存在
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cursor.execute('''
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CREATE TABLE IF NOT EXISTS hourly_model_predictions (
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id INT AUTO_INCREMENT PRIMARY KEY,
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timestamp DATETIME NOT NULL,
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timestamp_et VARCHAR(50),
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date VARCHAR(10),
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hour INT,
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open_price DOUBLE,
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close_price DOUBLE,
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actual_result INT COMMENT '0=Down, 1=Up',
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dlinear_prediction INT,
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dlinear_prob DOUBLE,
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dlinear_correct INT,
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nbeats_prediction INT,
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nbeats_prob DOUBLE,
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nbeats_correct INT,
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ttm_prediction INT,
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ttm_prob DOUBLE,
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ttm_correct INT,
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ensemble_prediction INT,
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ensemble_votes VARCHAR(50),
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ensemble_correct INT,
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created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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UNIQUE KEY uk_date_hour (date, hour),
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INDEX idx_timestamp (timestamp)
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) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;
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''')
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conn.commit()
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@@ -143,12 +180,6 @@ class TradeModel:
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day: Optional[str] = None) -> Dict:
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"""
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获取历史交易记录
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Args:
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page: 页码
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limit: 每页条数
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month: 月份筛选 (YYYY-MM)
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day: 日期筛选 (YYYY-MM-DD)
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"""
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offset = (page - 1) * limit
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@@ -160,7 +191,7 @@ class TradeModel:
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params = []
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if month:
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where_clauses.append("DATE_FORMAT(timestamp, '%%Y-%%m') = %s")
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where_clauses.append("DATE_FORMAT(timestamp, '%Y-%m') = %s")
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params.append(month)
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if day:
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@@ -199,274 +230,328 @@ class TradeModel:
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class PredictionModel:
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"""预测记录模型"""
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"""预测记录模型 (V2 - 宽表结构)"""
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@staticmethod
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def get_prediction(date: str, hour: int, model_name: str) -> Optional[Dict]:
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"""获取指定时间和模型的预测记录"""
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with get_db_connection() as conn:
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cursor = conn.cursor()
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cursor.execute("""
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SELECT * FROM model_predictions
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WHERE date = %s AND hour = %s AND model_name = %s
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""", (date, hour, model_name))
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row = cursor.fetchone()
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return _dict_from_row(row)
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@staticmethod
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def save_prediction(data: Dict) -> int:
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def save_hourly_prediction(data: Dict) -> int:
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"""
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保存预测记录
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Args:
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data: 预测数据字典
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Returns:
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插入的记录ID
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保存每小时综合预测记录 (宽表)
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"""
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with get_db_connection() as conn:
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cursor = conn.cursor()
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sql = """
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INSERT INTO model_predictions
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(timestamp, timestamp_et, date, hour, model_name,
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prediction, probability, actual_result, is_correct,
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open_price, close_price)
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VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
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INSERT INTO hourly_model_predictions
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(timestamp, timestamp_et, date, hour, open_price, close_price, actual_result,
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dlinear_prediction, dlinear_prob, dlinear_correct,
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nbeats_prediction, nbeats_prob, nbeats_correct,
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ttm_prediction, ttm_prob, ttm_correct,
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ensemble_prediction, ensemble_votes, ensemble_correct)
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VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
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ON DUPLICATE KEY UPDATE
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prediction = VALUES(prediction),
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probability = VALUES(probability),
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actual_result = VALUES(actual_result),
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is_correct = VALUES(is_correct),
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open_price = VALUES(open_price),
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close_price = VALUES(close_price)
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close_price = VALUES(close_price),
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actual_result = VALUES(actual_result),
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dlinear_prediction = VALUES(dlinear_prediction),
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dlinear_prob = VALUES(dlinear_prob),
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dlinear_correct = VALUES(dlinear_correct),
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nbeats_prediction = VALUES(nbeats_prediction),
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nbeats_prob = VALUES(nbeats_prob),
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nbeats_correct = VALUES(nbeats_correct),
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ttm_prediction = VALUES(ttm_prediction),
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ttm_prob = VALUES(ttm_prob),
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ttm_correct = VALUES(ttm_correct),
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ensemble_prediction = VALUES(ensemble_prediction),
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ensemble_votes = VALUES(ensemble_votes),
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ensemble_correct = VALUES(ensemble_correct)
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"""
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cursor.execute(sql, (
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data.get('timestamp'),
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data.get('timestamp_et'),
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data.get('date'),
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data.get('hour'),
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data.get('model_name'),
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data.get('prediction'),
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data.get('probability'),
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data.get('actual_result'),
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data.get('is_correct'),
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data.get('open_price'),
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data.get('close_price')
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data.get('close_price'),
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data.get('actual_result'),
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data.get('dlinear_prediction'),
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data.get('dlinear_prob'),
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data.get('dlinear_correct'),
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data.get('nbeats_prediction'),
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data.get('nbeats_prob'),
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data.get('nbeats_correct'),
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data.get('ttm_prediction'),
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data.get('ttm_prob'),
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data.get('ttm_correct'),
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data.get('ensemble_prediction'),
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str(data.get('ensemble_votes')) if data.get('ensemble_votes') else None,
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data.get('ensemble_correct')
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))
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conn.commit()
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return cursor.lastrowid or 0
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@staticmethod
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def update_actual_result(date: str, hour: int, model_name: str,
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actual_result: int, close_price: float) -> bool:
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"""更新实际结果"""
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def update_actual_result_v2(date: str, hour: int, actual_result: int, close_price: float) -> bool:
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"""
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更新实际结果，并重新计算各模型正确性 (V2)
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"""
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with get_db_connection() as conn:
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cursor = conn.cursor()
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# 先获取预测值
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# 获取该小时记录
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cursor.execute("""
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SELECT prediction FROM model_predictions
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WHERE date = %s AND hour = %s AND model_name = %s
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""", (date, hour, model_name))
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SELECT * FROM hourly_model_predictions
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WHERE date = %s AND hour = %s
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""", (date, hour))
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row = cursor.fetchone()
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if not row:
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return False
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prediction = row['prediction']
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is_correct = 1 if prediction == actual_result else 0
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# 计算正确性
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updates = {
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'actual_result': actual_result,
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'close_price': close_price
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}
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cursor.execute("""
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UPDATE model_predictions
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SET actual_result = %s, is_correct = %s, close_price = %s
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WHERE date = %s AND hour = %s AND model_name = %s
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""", (actual_result, is_correct, close_price, date, hour, model_name))
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# DLinear
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if row['dlinear_prediction'] is not None:
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updates['dlinear_correct'] = 1 if row['dlinear_prediction'] == actual_result else 0
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# N-BEATS
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if row['nbeats_prediction'] is not None:
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updates['nbeats_correct'] = 1 if row['nbeats_prediction'] == actual_result else 0
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# TTM
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if row['ttm_prediction'] is not None:
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updates['ttm_correct'] = 1 if row['ttm_prediction'] == actual_result else 0
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# Ensemble
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if row['ensemble_prediction'] is not None:
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updates['ensemble_correct'] = 1 if row['ensemble_prediction'] == actual_result else 0
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# 构建更新SQL
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set_clause = ", ".join([f"{k} = %s" for k in updates.keys()])
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sql = f"UPDATE hourly_model_predictions SET {set_clause} WHERE date = %s AND hour = %s"
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cursor.execute(sql, list(updates.values()) + [date, hour])
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conn.commit()
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return True
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@staticmethod
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def get_monthly_stats(month: str) -> Dict:
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def get_monthly_stats_v2(month: str) -> Dict:
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"""
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获取月度统计
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Args:
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month: 月份 (YYYY-MM)
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Returns:
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各模型的统计数据
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获取月度统计 (V2 - 从宽表)
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"""
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with get_db_connection() as conn:
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cursor = conn.cursor()
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sql = """
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SELECT
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model_name,
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COUNT(*) as total,
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SUM(CASE WHEN is_correct = 1 THEN 1 ELSE 0 END) as correct,
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SUM(CASE WHEN is_correct = 0 THEN 1 ELSE 0 END) as incorrect
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FROM model_predictions
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WHERE DATE_FORMAT(CONVERT_TZ(timestamp, '+00:00', '+08:00'), '%%Y-%%m') = %s
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AND actual_result IS NOT NULL
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GROUP BY model_name
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"""
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cursor.execute(sql, (month,))
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rows = cursor.fetchall()
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# 统计各个模型的准确率
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models = ['dlinear', 'nbeats', 'ttm', 'ensemble']
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result = {}
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for row in rows:
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row_dict = _dict_from_row(row)
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model = row_dict['model_name']
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total = row_dict['total']
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correct = row_dict['correct'] or 0
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incorrect = row_dict['incorrect'] or 0
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accuracy = (correct / total * 100) if total > 0 else 0
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for model in models:
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col_pred = f"{model}_prediction"
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col_correct = f"{model}_correct"
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result[model] = {
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"total": total,
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"correct": correct,
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"incorrect": incorrect,
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"accuracy": round(accuracy, 2)
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}
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sql = f"""
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SELECT
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COUNT({col_pred}) as total,
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SUM(CASE WHEN {col_correct} = 1 THEN 1 ELSE 0 END) as correct,
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SUM(CASE WHEN {col_correct} = 0 THEN 1 ELSE 0 END) as incorrect
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FROM hourly_model_predictions
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WHERE DATE_FORMAT(CONVERT_TZ(timestamp, '+00:00', '+08:00'), '%%Y-%%m') = %s
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AND actual_result IS NOT NULL
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"""
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cursor.execute(sql, (month,))
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row = cursor.fetchone()
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if row:
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total = row['total']
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correct = row['correct'] or 0
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incorrect = row['incorrect'] or 0
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accuracy = (correct / total * 100) if total > 0 else 0
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# 格式化键名以匹配前端 (例如 dlinear -> DLinear)
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model_key = {
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'dlinear': 'DLinear',
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'nbeats': 'N-BEATS',
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'ttm': 'TTM-R2',
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'ensemble': 'Ensemble'
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}.get(model, model)
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result[model_key] = {
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"total": total,
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"correct": correct,
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"incorrect": incorrect,
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"accuracy": round(accuracy, 2)
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}
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return result
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@staticmethod
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def get_daily_stats(month: str, page: int = 1, limit: int = 10) -> Dict:
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def get_daily_stats_v2(month: str, page: int = 1, limit: int = 10) -> Dict:
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"""
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获取每日统计
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Args:
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month: 月份 (YYYY-MM)
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page: 页码
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limit: 每页条数
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获取每日统计 (V2 - 从宽表)
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"""
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with get_db_connection() as conn:
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cursor = conn.cursor()
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sql = """
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SELECT
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DATE(CONVERT_TZ(timestamp, '+00:00', '+08:00')) as date_cst,
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model_name,
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COUNT(*) as total,
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SUM(CASE WHEN is_correct = 1 THEN 1 ELSE 0 END) as correct,
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SUM(CASE WHEN is_correct = 0 THEN 1 ELSE 0 END) as incorrect
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FROM model_predictions
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WHERE DATE_FORMAT(CONVERT_TZ(timestamp, '+00:00', '+08:00'), '%%Y-%%m') = %s
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AND actual_result IS NOT NULL
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GROUP BY date_cst, model_name
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ORDER BY date_cst DESC
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"""
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cursor.execute(sql, (month,))
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rows = cursor.fetchall()
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# 重组数据: 按日期合并不同模型的统计
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daily_data = {}
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for row in rows:
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row_dict = _dict_from_row(row)
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date = str(row_dict['date_cst']) # CST 日期
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if date not in daily_data:
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daily_data[date] = {"date": date}
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model = row_dict['model_name']
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total = row_dict['total']
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correct = row_dict['correct'] or 0
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incorrect = row_dict['incorrect'] or 0
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accuracy = (correct / total * 100) if total > 0 else 0
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daily_data[date][model] = {
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"total": total,
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"correct": correct,
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"incorrect": incorrect,
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"accuracy": round(accuracy, 2)
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}
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# 排序并分页
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sorted_dates = sorted(daily_data.keys(), reverse=True)
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total_count = len(sorted_dates)
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# 分页查询日期
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offset = (page - 1) * limit
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paginated_dates = sorted_dates[offset:offset + limit]
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date_sql = """
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SELECT DISTINCT DATE(CONVERT_TZ(timestamp, '+00:00', '+08:00')) as date_cst
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FROM hourly_model_predictions
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WHERE DATE_FORMAT(CONVERT_TZ(timestamp, '+00:00', '+08:00'), '%%Y-%%m') = %s
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ORDER BY date_cst DESC
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LIMIT %s OFFSET %s
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"""
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cursor.execute(date_sql, (month, limit, offset))
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dates = [row['date_cst'] for row in cursor.fetchall()]
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data = [daily_data[d] for d in paginated_dates]
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# 总天数
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count_sql = """
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SELECT COUNT(DISTINCT DATE(CONVERT_TZ(timestamp, '+00:00', '+08:00'))) as cnt
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FROM hourly_model_predictions
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WHERE DATE_FORMAT(CONVERT_TZ(timestamp, '+00:00', '+08:00'), '%%Y-%%m') = %s
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"""
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cursor.execute(count_sql, (month,))
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total_count = cursor.fetchone()['cnt']
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daily_data = []
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models = ['dlinear', 'nbeats', 'ttm', 'ensemble']
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for date_obj in dates:
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date_str = str(date_obj)
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day_stats = {"date": date_str}
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for model in models:
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col_pred = f"{model}_prediction"
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col_correct = f"{model}_correct"
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sql = f"""
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SELECT
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COUNT({col_pred}) as total,
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SUM(CASE WHEN {col_correct} = 1 THEN 1 ELSE 0 END) as correct
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FROM hourly_model_predictions
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WHERE DATE(CONVERT_TZ(timestamp, '+00:00', '+08:00')) = %s
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AND actual_result IS NOT NULL
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"""
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cursor.execute(sql, (date_str,))
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row = cursor.fetchone()
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total = row['total']
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correct = row['correct'] or 0
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accuracy = (correct / total * 100) if total > 0 else 0
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model_key = {
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'dlinear': 'DLinear',
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'nbeats': 'N-BEATS',
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'ttm': 'TTM-R2',
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'ensemble': 'Ensemble'
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}.get(model, model)
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day_stats[model_key] = {
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"total": total,
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"correct": correct,
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"accuracy": round(accuracy, 2)
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}
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daily_data.append(day_stats)
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return {
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"data": data,
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"data": daily_data,
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"page": page,
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"limit": limit,
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"total": total_count,
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"total_pages": (total_count + limit - 1) // limit if limit > 0 else 0
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}
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@staticmethod
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def get_hourly_details(date: str) -> List[Dict]:
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def get_hourly_details_v2(date: str) -> List[Dict]:
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"""
|
||||
获取指定日期的每小时详情
|
||||
|
||||
Args:
|
||||
date: 日期 (YYYY-MM-DD, CST 北京时间)
|
||||
获取指定日期的每小时详情 (V2)
|
||||
"""
|
||||
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_cst = row_dict['hour_cst'] # 使用 CST 小时
|
||||
if hour_cst not in hourly_data:
|
||||
# 将 timestamp 转换为 CST 时间字符串
|
||||
timestamp_cst = convert_to_cst(row_dict.get('timestamp', ''))
|
||||
hourly_data[hour_cst] = {
|
||||
"hour": hour_cst, # 返回 CST 小时
|
||||
"timestamp_et": row_dict['timestamp_et'],
|
||||
"timestamp_cst": timestamp_cst,
|
||||
"open_price": row_dict['open_price'],
|
||||
"close_price": row_dict['close_price'],
|
||||
"actual_result": row_dict['actual_result'],
|
||||
"models": {}
|
||||
hour_data = {
|
||||
"hour": row['hour_cst'],
|
||||
"timestamp_et": row['timestamp_et'],
|
||||
"timestamp_cst": convert_to_cst(row.get('timestamp', '')),
|
||||
"open_price": row['open_price'],
|
||||
"close_price": row['close_price'],
|
||||
"actual_result": row['actual_result'],
|
||||
"models": {
|
||||
"DLinear": {
|
||||
"prediction": row['dlinear_prediction'],
|
||||
"probability": row['dlinear_prob'],
|
||||
"is_correct": row['dlinear_correct']
|
||||
},
|
||||
"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']
|
||||
}
|
||||
|
||||
# 按 CST 小时排序返回
|
||||
return [hourly_data[h] for h in sorted(hourly_data.keys())]
|
||||
result.append(hour_data)
|
||||
|
||||
return result
|
||||
|
||||
@staticmethod
|
||||
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 get_missing_hours(date: str, model_name: str) -> List[int]:
|
||||
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
|
||||
|
||||
Args:
|
||||
date: 日期 (YYYY-MM-DD)
|
||||
model_name: 模型名称
|
||||
|
||||
Returns:
|
||||
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
|
||||
"""
|
||||
with get_db_connection() as conn:
|
||||
cursor = conn.cursor()
|
||||
cursor.execute("""
|
||||
SELECT hour FROM model_predictions
|
||||
WHERE date = %s AND model_name = %s
|
||||
""", (date, model_name))
|
||||
SELECT hour FROM hourly_model_predictions
|
||||
WHERE date = %s
|
||||
""", (date,))
|
||||
|
||||
rows = cursor.fetchall()
|
||||
existing_hours = {row['hour'] for row in rows}
|
||||
|
||||
Reference in new issue
Block a user