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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+6
-6
@@ -106,8 +106,8 @@ async def get_monthly_accuracy(
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service = get_prediction_service()
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service.ensure_predictions_for_month(month)
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# 获取统计
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stats = PredictionModel.get_monthly_stats(month)
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# 获取统计 (使用 V2 方法)
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stats = PredictionModel.get_monthly_stats_v2(month)
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return {
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"success": True,
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@@ -135,8 +135,8 @@ async def get_daily_accuracy(
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service = get_prediction_service()
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service.ensure_predictions_for_month(month)
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# 获取统计
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result = PredictionModel.get_daily_stats(month=month, page=page, limit=limit)
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# 获取统计 (使用 V2 方法)
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result = PredictionModel.get_daily_stats_v2(month=month, page=page, limit=limit)
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return {
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"success": True,
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@@ -155,8 +155,8 @@ async def get_hourly_accuracy(date: str):
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service = get_prediction_service()
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service.ensure_predictions_for_date(date)
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# 获取详情
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details = PredictionModel.get_hourly_details(date)
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# 获取详情 (使用 V2 方法)
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details = PredictionModel.get_hourly_details_v2(date)
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return {
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"success": True,
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+272
-187
@@ -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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@@ -199,199 +230,235 @@ 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
|
||||
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
|
||||
|
||||
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']
|
||||
}
|
||||
result.append(hour_data)
|
||||
|
||||
# 按 CST 小时排序返回
|
||||
return [hourly_data[h] for h in sorted(hourly_data.keys())]
|
||||
return result
|
||||
|
||||
@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
|
||||
|
||||
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}
|
||||
|
||||
+44
-54
@@ -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)
|
||||
# 为了保持兼容性，还是主要检查这两个，或者可以增加 N-BEATS
|
||||
models_to_check = ["DLinear", "TTM-R2", "N-BEATS"]
|
||||
# 检查是否有缺失的小时 (只检查宽表是否有记录)
|
||||
missing_hours = PredictionModel.get_missing_hours(date)
|
||||
|
||||
for model_name in models_to_check:
|
||||
# 数据库中模型名字对应
|
||||
db_model_name = model_name
|
||||
if not missing_hours:
|
||||
return
|
||||
|
||||
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:
|
||||
continue
|
||||
# 获取足够的历史数据
|
||||
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
|
||||
target_date = datetime.strptime(date, '%Y-%m-%d')
|
||||
df = self._get_historical_data(target_date)
|
||||
|
||||
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)
|
||||
# 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)
|
||||
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,
|
||||
hour: int, model_name: str) -> None:
|
||||
def _generate_prediction_for_hour(self, df: pd.DataFrame, date: str, hour: int) -> 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
|
||||
prediction = None
|
||||
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
|
||||
# 6. 调用多模型预测
|
||||
preds = self.inference_manager.predict_all(df_input)
|
||||
|
||||
# 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
|
||||
is_correct = 1 if prediction == actual_result else 0
|
||||
|
||||
# 9. 保存预测结果
|
||||
# 8. 组装数据 (宽表格式)
|
||||
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:
|
||||
"""
|
||||
|
||||
+131
-77
@@ -74,8 +74,8 @@
|
||||
</div>
|
||||
<div class="col-md-2">
|
||||
<div class="stat-card">
|
||||
<div class="stat-label">价格</div>
|
||||
<div class="stat-value">${{ currentTrade.price?.toFixed(3) || '-' }}</div>
|
||||
<div class="stat-label">策略</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 -->
|
||||
<div class="col-md-6">
|
||||
<div class="card h-100 border-primary">
|
||||
<div class="card-header bg-primary text-white">
|
||||
<i class="bi bi-graph-up-arrow"></i> DLinear 模型
|
||||
<!-- Ensemble 卡片 -->
|
||||
<div class="col-md-3">
|
||||
<div class="card h-100 border-primary shadow-sm">
|
||||
<div class="card-header bg-primary text-white text-center">
|
||||
<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="col-3">
|
||||
<div class="stat-number">{{ monthlyStats.DLinear.total }}</div>
|
||||
<div class="stat-label">预测次数</div>
|
||||
</div>
|
||||
<div class="col-3">
|
||||
<div class="stat-number text-success">{{ monthlyStats.DLinear.correct }}</div>
|
||||
<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 class="text-center" v-if="monthlyStats.Ensemble">
|
||||
<h2 class="display-6 text-primary fw-bold">{{ monthlyStats.Ensemble.accuracy }}%</h2>
|
||||
<div class="text-muted mb-2">准确率</div>
|
||||
<div class="d-flex justify-content-between px-3 border-top pt-2">
|
||||
<span class="text-success"><i class="bi bi-check-lg"></i> {{ monthlyStats.Ensemble.correct }}</span>
|
||||
<span class="text-secondary">{{ monthlyStats.Ensemble.total }} 次</span>
|
||||
<span class="text-danger"><i class="bi bi-x-lg"></i> {{ monthlyStats.Ensemble.incorrect }}</span>
|
||||
</div>
|
||||
</div>
|
||||
<div v-else class="text-center text-muted py-3">
|
||||
暂无数据
|
||||
<div v-else class="text-center text-muted py-3">暂无数据</div>
|
||||
</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="card h-100 border-success">
|
||||
<div class="card-header bg-success text-white">
|
||||
<i class="bi bi-cpu"></i> TTM-R2 模型
|
||||
<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-cpu"></i> TTM-R2
|
||||
</div>
|
||||
<div class="card-body">
|
||||
<div class="row text-center" v-if="monthlyStats['TTM-R2']">
|
||||
<div class="col-3">
|
||||
<div class="stat-number">{{ monthlyStats['TTM-R2'].total }}</div>
|
||||
<div class="stat-label">预测次数</div>
|
||||
</div>
|
||||
<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 class="text-center" v-if="monthlyStats['TTM-R2']">
|
||||
<h3 class="text-dark">{{ monthlyStats['TTM-R2'].accuracy }}%</h3>
|
||||
<div class="text-muted small">准确率</div>
|
||||
<div class="mt-2 small text-muted">
|
||||
{{ monthlyStats['TTM-R2'].correct }}/{{ monthlyStats['TTM-R2'].total }}
|
||||
</div>
|
||||
</div>
|
||||
<div v-else class="text-center text-muted py-3">
|
||||
暂无数据
|
||||
</div>
|
||||
<div v-else class="text-center text-muted py-3">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>DLinear 预测</th>
|
||||
<th>DLinear 准确率</th>
|
||||
<th>TTM-R2 预测</th>
|
||||
<th>TTM-R2 准确率</th>
|
||||
<th class="text-start ps-4">日期</th>
|
||||
<th>集成策略</th>
|
||||
<th>DLinear</th>
|
||||
<th>N-BEATS</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">
|
||||
{{ day.DLinear.correct }}/{{ day.DLinear.total }}
|
||||
<span v-if="day.Ensemble" :class="getAccuracyClass(day.Ensemble.accuracy)">
|
||||
{{ 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']">
|
||||
{{ day['TTM-R2'].correct }}/{{ day['TTM-R2'].total }}
|
||||
<span v-if="day['N-BEATS']" :class="getAccuracyClass(day['N-BEATS'].accuracy)">
|
||||
{{ 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>实际涨跌</th>
|
||||
<th>时间</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 :class="hour.actual_result === 1 ? 'text-success' : 'text-danger'">
|
||||
<i :class="hour.actual_result === 1 ? 'bi-arrow-up' : 'bi-arrow-down'"></i>
|
||||
{{ hour.actual_result === 1 ? 'UP' : 'DOWN' }}
|
||||
<td>{{ hour.timestamp_cst ? hour.timestamp_cst.split(' ')[1] : (hour.hour + ':00') }}</td>
|
||||
<td>${{ hour.open_price?.toFixed(0) || '-' }}</td>
|
||||
<td>${{ hour.close_price?.toFixed(0) || '-' }}</td>
|
||||
|
||||
<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' }}
|
||||
<i :class="hour.models.DLinear.is_correct ? 'bi-check-circle' : 'bi-x-circle'"></i>
|
||||
<span class="small text-muted">({{ (hour.models.DLinear.probability * 100).toFixed(0) }}%)</span>
|
||||
<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 :class="hour.models['TTM-R2'].is_correct ? 'bi-check-circle' : 'bi-x-circle'"></i>
|
||||
<i v-if="hour.actual_result !== null" :class="hour.models['TTM-R2'].is_correct ? 'bi-check' : 'bi-x'"></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>
|
||||
|
||||
+3
-2
@@ -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
|
||||
) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
|
||||
status, strategy, error_message, raw_data
|
||||
) 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
|
||||
))
|
||||
|
||||
Reference in new issue
Block a user