oil_code oil_name 默认值更新

lib/pydantic_models.py

@@ -9,8 +9,8 @@ from decimal import Decimal
 class PredictionResult(BaseModel):
     feature_factor_frequency: str
     strategy_id: int
-    oil_code: Optional[str] = None
+    oil_code: Optional[str] = 'CRUDE'
-    oil_name: Optional[str] = None
+    oil_name: Optional[str] = '原油'
     data_date: Optional[datetime] = None
     market_price: Optional[Decimal] = None
     day_price: Optional[Decimal] = None

lib/tools.py

@@ -686,7 +686,6 @@ def get_modelsname(df, global_config):
     model_name_list = [row['model_name'] for row in modelsname]
     model_name_list = set(columns) & set(model_name_list)
     model_name_list = list(model_name_list)
-    global_config['db_mysql'].close()
     return model_name_list, model_id_name_dict
 
 

main_yuanyou.py

@@ -381,15 +381,15 @@ def predict_main():
             logger.info(f'要更新y的信息：{update_y}')
             # try:
             for row in update_y.itertuples(index=False):
-                # try:
+                try:
-                row_dict = row._asdict()
+                    row_dict = row._asdict()
-                yy = df[df['ds'] == row_dict['ds']]['y'].values[0]
+                    yy = df[df['ds'] == row_dict['ds']]['y'].values[0]
-                LOW = df[df['ds'] == row_dict['ds']]['Brentzdj'].values[0]
+                    LOW = df[df['ds'] == row_dict['ds']]['Brentzdj'].values[0]
-                HIGH = df[df['ds'] == row_dict['ds']]['Brentzgj'].values[0]
+                    HIGH = df[df['ds'] == row_dict['ds']]['Brentzgj'].values[0]
-                sqlitedb.update_data(
+                    sqlitedb.update_data(
-                    'accuracy', f"y = {yy},LOW_PRICE = {LOW},HIGH_PRICE = {HIGH}", where_condition=f"ds = '{row_dict['ds']}'")
+                        'accuracy', f"y = {yy},LOW_PRICE = {LOW},HIGH_PRICE = {HIGH}", where_condition=f"ds = '{row_dict['ds']}'")
-                # except:
+                except:
-                #     logger.info(f'更新accuracy表的y值失败：{row_dict}')
+                    logger.info(f'更新accuracy表的y值失败：{row_dict}')
             # except Exception as e:
             #     logger.info(f'更新accuracy表的y值失败：{e}')
 
@@ -581,10 +581,11 @@ def predict_main():
 if __name__ == '__main__':
     # global end_time
     # # 遍历2024-11-25 到 2024-12-3 之间的工作日日期
-    # for i_time in pd.date_range('2024-12-1', '2025-2-26', freq='W'):
+    for i_time in pd.date_range('2025-6-11', '2025-6-28', freq='B'):
-    #     end_time = i_time.strftime('%Y-%m-%d')
+        global_config['end_time'] = i_time.strftime('%Y-%m-%d')
-    #     predict_main()
+        global_config['db_mysql'].connect()
+        predict_main()
 
     # predict_main()
     # push_market_value()
-    sql_inset_predict(global_config=global_config)
+    # sql_inset_predict(global_config=global_config)

models/nerulforcastmodels.py

@@ -241,37 +241,6 @@ def ex_Model(df, horizon, input_size, train_steps, val_check_steps, early_stop_p
     if config.is_update_eta:
         df_predict['ds'] = pd.to_datetime(df_predict['ds'])
 
-        # 按行遍历df_predict
-        IndexName = data['IndexName']
-        for index, row in df_predict.iterrows():
-            try:
-                yy = config.bdwdname[index]
-            except IndexError as e:
-                break
-            for m in modelsindex[index].keys():
-                if m not in df_predict.columns:
-                    continue
-                if m == 'FEDformer':
-                    continue
-                list = [{'Date': config.end_time, 'Value': round(row[m], 2)}]
-                data['DataList'] = list
-                data['IndexCode'] = modelsindex[index][m]
-                # data['IndexName'] = f'价格预测{m}模型'
-                data['IndexName'] = data['IndexName'].replace('xx', m)
-                data['IndexName'] = data['IndexName'].replace('yy', yy)
-                data['Remark'] = m
-                print('预测数据上传到eta:')
-                etadata.push_data(data)
-                # print(data)
-                data['IndexName'] = IndexName
-
-    # 把预测值上传到市场信息平台
-    if config.is_update_market:
-        '''
-        预测结果整理，写入到数据表 v_tbl_predict_prediction_results 
-        '''
-        df_predict['ds'] = pd.to_datetime(df_predict['ds'])
-
         # 按行遍历df_predict
         IndexName = data['IndexName']
         for index, row in df_predict.iterrows():