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oil_code oil_name 默认值更新

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workpc 2025-06-27 11:57:10 +08:00
parent 352d30dfa9
commit c85d11866f
4 changed files with 16 additions and 47 deletions
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4
lib/pydantic_models.py
View File

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

1
lib/tools.py
View File

@ -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

27
main_yuanyou.py
View File

@ -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:
row_dict = row._asdict()
yy = df[df['ds'] == row_dict['ds']]['y'].values[0]
LOW = df[df['ds'] == row_dict['ds']]['Brentzdj'].values[0]
HIGH = df[df['ds'] == row_dict['ds']]['Brentzgj'].values[0]
sqlitedb.update_data(
'accuracy', f"y = {yy},LOW_PRICE = {LOW},HIGH_PRICE = {HIGH}", where_condition=f"ds = '{row_dict['ds']}'")
# except:
# logger.info(f'更新accuracy表的y值失败：{row_dict}')
try:
row_dict = row._asdict()
yy = df[df['ds'] == row_dict['ds']]['y'].values[0]
LOW = df[df['ds'] == row_dict['ds']]['Brentzdj'].values[0]
HIGH = df[df['ds'] == row_dict['ds']]['Brentzgj'].values[0]
sqlitedb.update_data(
'accuracy', f"y = {yy},LOW_PRICE = {LOW},HIGH_PRICE = {HIGH}", where_condition=f"ds = '{row_dict['ds']}'")
except:
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'):
# end_time = i_time.strftime('%Y-%m-%d')
# predict_main()
for i_time in pd.date_range('2025-6-11', '2025-6-28', freq='B'):
global_config['end_time'] = i_time.strftime('%Y-%m-%d')
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)

31
models/nerulforcastmodels.py
View File

@ -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():