计算最佳模型日期调整为上一交易日
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jingboyitiji
parent
f3971a94ec
commit
5ff6213717
44
lib/tools.py
44
lib/tools.py
@ -843,6 +843,8 @@ def convert_df_to_pydantic_pp(df_predict, model_id_name_dict, global_config):
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def find_best_models(date='', global_config=None):
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best_models = {}
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model_id_name_dict = get_model_id_name_dict(global_config=global_config)
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# 处理日期输入
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if not date:
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@ -855,8 +857,13 @@ def find_best_models(date='', global_config=None):
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global_config['logger'].error(
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f"日期格式错误，期望格式为 '%Y-%m-%d'，实际输入: {date}")
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return best_models
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current_date = datetime.datetime.strptime(date, '%Y-%m-%d')
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# 上一交易日日期 Last trading day
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last_trading_day = pd.Timestamp(date) - pd.tseries.offsets.BusinessDay(1)
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last_trading_day_str = last_trading_day.strftime('%Y-%m-%d')
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# 计算date对应月的一日
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first_day_of_month = current_date.replace(day=1)
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# 计算date对应周的周一
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@ -943,14 +950,13 @@ def find_best_models(date='', global_config=None):
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# 遍历全局配置中的价格列
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for i, wd in enumerate(global_config['price_columns']):
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global_config['logger'].info(
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f'*********************************************************************************************************计算预测{date}的{wd}最佳模型')
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f'*********************************************************************************************************计算预测{last_trading_day_str}的{wd}最佳模型')
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best_models[wd] = {}
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if i == 0:
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# 计算当前日期的前一工作日日期
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ciridate = (pd.Timestamp(date) -
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pd.tseries.offsets.BusinessDay(1)).strftime('%Y-%m-%d')
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global_config['logger'].info(f'计算预测{date}的次日{ciridate}最佳模型')
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ciridate = last_trading_day_str
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global_config['logger'].info(f'计算预测{last_trading_day}的次日{last_trading_day}最佳模型')
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global_config['logger'].info(
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f'{date}真实价格：{true_price[true_price["ds"] == date]["y"].values[0]}')
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price = df[['data_date', wd, 'model_id']]
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@ -965,7 +971,7 @@ def find_best_models(date='', global_config=None):
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global_config['logger'].info(
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f'{ciridate}预测最准确的模型名称：{best_models[wd]}')
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predictresult = query_predict_result(
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date, best_model_id, global_config, wd)
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last_trading_day, best_model_id, global_config, wd)
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if predictresult:
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global_config['logger'].info(
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f'最佳模型{best_models[wd]}在{date}预测结果：{predictresult}')
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@ -976,9 +982,9 @@ def find_best_models(date='', global_config=None):
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elif i == 1:
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# 计算五个工作日之前的日期
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benzhoudate = (pd.Timestamp(date) -
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benzhoudate = (pd.Timestamp(last_trading_day) -
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pd.Timedelta(days=7)).strftime('%Y-%m-%d')
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global_config['logger'].info(f'计算预测{date}的五天前{benzhoudate}最佳模型')
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global_config['logger'].info(f'计算预测{last_trading_day}的五天前{benzhoudate}最佳模型')
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global_config['logger'].info(
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f'{date}真实价格：{true_price[true_price["ds"] == date]["y"].values[0]}')
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price = df[['data_date', wd, 'model_id']]
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@ -992,7 +998,7 @@ def find_best_models(date='', global_config=None):
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global_config['logger'].info(
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f'{benzhoudate}预测最准确的模型名称：{best_models[wd]}')
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predictresult = query_predict_result(
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date, best_model_id, global_config, wd)
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last_trading_day, best_model_id, global_config, wd)
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if predictresult:
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global_config['logger'].info(
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f'最佳模型{best_models[wd]}在{date}预测结果：{predictresult}')
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@ -1004,16 +1010,16 @@ def find_best_models(date='', global_config=None):
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elif i in [2, 3]:
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weeks_ago = 1 if i == 2 else 2
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ago_monday = current_date - \
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datetime.timedelta(days=current_date.weekday() + 7 * weeks_ago)
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ago_monday = last_trading_day - \
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datetime.timedelta(days=last_trading_day.weekday() + 7 * weeks_ago)
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ago_sunday = ago_monday + datetime.timedelta(days=6)
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ago_date_str = f"{ago_monday.strftime('%Y-%m-%d')} - {ago_sunday.strftime('%Y-%m-%d')}"
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global_config['logger'].info(
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f'计算预测{date}的前{weeks_ago}周{ago_date_str}最佳模型')
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weektrueprice = true_price[(true_price['ds'] >= date_monday.strftime(
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'%Y-%m-%d')) & (true_price['ds'] <= date)]['y'].mean()
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weektrueprice = true_price[(true_price['ds'] >= ago_monday.strftime('%Y-%m-%d')) & (true_price['ds'] <= ago_sunday.strftime('%Y-%m-%d'))]['y'].mean()
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global_config['logger'].info(
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f'当周{date_monday.strftime("%Y-%m-%d")}---{date}真实价格的周均价：{weektrueprice}')
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f'当周{date_monday.strftime("%Y-%m-%d")}---{last_trading_day_str}真实价格的周均价：{weektrueprice}')
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price = df[['data_date', wd, 'model_id']]
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price = price[(price['data_date'] >= ago_monday) &
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(price['data_date'] <= ago_sunday)]
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@ -1025,7 +1031,8 @@ def find_best_models(date='', global_config=None):
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global_config['logger'].info(
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f'{ago_date_str}预测最准确的模型名称：{best_models[wd]}')
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predictresult = query_predict_result(
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date, best_model_id, global_config, wd)
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last_trading_day_str, best_model_id, global_config, wd)
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if predictresult:
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global_config['logger'].info(
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f'最佳模型{best_models[wd]}在{date}预测结果：{predictresult}')
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@ -1039,9 +1046,8 @@ def find_best_models(date='', global_config=None):
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elif i in [4, 5, 6, 7]:
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months_ago = i - 3
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current_date_ts = pd.Timestamp(date)
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last_month_first_day = (
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current_date_ts - pd.offsets.MonthBegin(months_ago)).strftime('%Y-%m-%d')
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last_trading_day - pd.offsets.MonthBegin(months_ago)).strftime('%Y-%m-%d')
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last_month_last_day = (pd.Timestamp(
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last_month_first_day) + pd.offsets.MonthEnd(0)).strftime('%Y-%m-%d')
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global_config['logger'].info(
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@ -1049,7 +1055,7 @@ def find_best_models(date='', global_config=None):
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monthtrueprice = true_price[(true_price['ds'] >= first_day_of_month.strftime(
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'%Y-%m-%d')) & (true_price['ds'] <= date)]['y'].mean()
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global_config['logger'].info(
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f'当月{first_day_of_month.strftime("%Y-%m-%d")}-{date}真实价格的月均价：{monthtrueprice}')
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f'当月{first_day_of_month.strftime("%Y-%m-%d")}-{last_trading_day}真实价格的月均价：{monthtrueprice}')
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price = df[['data_date', wd, 'model_id']]
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price = price[(price['data_date'] >= last_month_first_day) & (
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price['data_date'] <= last_month_last_day)]
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@ -1061,7 +1067,7 @@ def find_best_models(date='', global_config=None):
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global_config['logger'].info(
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f'{last_month_first_day}-{last_month_last_day}预测最准确的模型名称：{best_models[wd]}')
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predictresult = query_predict_result(
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date, best_model_id, global_config, wd)
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last_trading_day, best_model_id, global_config, wd)
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if predictresult:
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global_config['logger'].info(
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f'最佳模型{best_models[wd]}在{date}预测结果：{predictresult}')
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@ -557,8 +557,10 @@ if __name__ == '__main__':
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# except Exception as e:
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# logger.info(f'预测失败：{e}')
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# continue
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global_config['end_time'] = '2025-08-04'
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global_config['end_time'] = '2025-08-01'
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global_config['db_mysql'].connect()
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predict_main()
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# global_config['end_time'] = '2025-08-01'
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# push_market_value()
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# sql_inset_predict(global_config)
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