liurui/PriceForecast
Template
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

计算最佳模型日期调整为上一交易日

Browse Source
This commit is contained in:
jingboyitiji 2025-08-05 14:42:32 +08:00
parent f3971a94ec
commit 5ff6213717
2 changed files with 28 additions and 20 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

44
lib/tools.py
View File

@ -844,6 +844,8 @@ def find_best_models(date='', global_config=None):
best_models = {} best_models = {}
model_id_name_dict = get_model_id_name_dict(global_config=global_config) model_id_name_dict = get_model_id_name_dict(global_config=global_config)
# 处理日期输入 # 处理日期输入
if not date: if not date:
date = datetime.datetime.now().strftime('%Y-%m-%d') date = datetime.datetime.now().strftime('%Y-%m-%d')
@ -855,8 +857,13 @@ def find_best_models(date='', global_config=None):
global_config['logger'].error( global_config['logger'].error(
f"日期格式错误，期望格式为 '%Y-%m-%d'，实际输入: {date}") f"日期格式错误，期望格式为 '%Y-%m-%d'，实际输入: {date}")
return best_models return best_models
current_date = datetime.datetime.strptime(date, '%Y-%m-%d') current_date = datetime.datetime.strptime(date, '%Y-%m-%d')
# 上一交易日日期 Last trading day
last_trading_day = pd.Timestamp(date) - pd.tseries.offsets.BusinessDay(1)
last_trading_day_str = last_trading_day.strftime('%Y-%m-%d')
# 计算date对应月的一日 # 计算date对应月的一日
first_day_of_month = current_date.replace(day=1) first_day_of_month = current_date.replace(day=1)
# 计算date对应周的周一 # 计算date对应周的周一
@ -943,14 +950,13 @@ def find_best_models(date='', global_config=None):
# 遍历全局配置中的价格列 # 遍历全局配置中的价格列
for i, wd in enumerate(global_config['price_columns']): for i, wd in enumerate(global_config['price_columns']):
global_config['logger'].info( global_config['logger'].info(
f'*********************************************************************************************************计算预测{date}的{wd}最佳模型') f'*********************************************************************************************************计算预测{last_trading_day_str}的{wd}最佳模型')
best_models[wd] = {} best_models[wd] = {}
if i == 0: if i == 0:
# 计算当前日期的前一工作日日期 # 计算当前日期的前一工作日日期
ciridate = (pd.Timestamp(date) - ciridate = last_trading_day_str
pd.tseries.offsets.BusinessDay(1)).strftime('%Y-%m-%d') global_config['logger'].info(f'计算预测{last_trading_day}的次日{last_trading_day}最佳模型')
global_config['logger'].info(f'计算预测{date}的次日{ciridate}最佳模型')
global_config['logger'].info( global_config['logger'].info(
f'{date}真实价格：{true_price[true_price["ds"] == date]["y"].values[0]}') f'{date}真实价格：{true_price[true_price["ds"] == date]["y"].values[0]}')
price = df[['data_date', wd, 'model_id']] price = df[['data_date', wd, 'model_id']]
@ -965,7 +971,7 @@ def find_best_models(date='', global_config=None):
global_config['logger'].info( global_config['logger'].info(
f'{ciridate}预测最准确的模型名称：{best_models[wd]}') f'{ciridate}预测最准确的模型名称：{best_models[wd]}')
predictresult = query_predict_result( predictresult = query_predict_result(
date, best_model_id, global_config, wd) last_trading_day, best_model_id, global_config, wd)
if predictresult: if predictresult:
global_config['logger'].info( global_config['logger'].info(
f'最佳模型{best_models[wd]}在{date}预测结果：{predictresult}') f'最佳模型{best_models[wd]}在{date}预测结果：{predictresult}')
@ -976,9 +982,9 @@ def find_best_models(date='', global_config=None):
elif i == 1: elif i == 1:
# 计算五个工作日之前的日期 # 计算五个工作日之前的日期
benzhoudate = (pd.Timestamp(date) - benzhoudate = (pd.Timestamp(last_trading_day) -
pd.Timedelta(days=7)).strftime('%Y-%m-%d') pd.Timedelta(days=7)).strftime('%Y-%m-%d')
global_config['logger'].info(f'计算预测{date}的五天前{benzhoudate}最佳模型') global_config['logger'].info(f'计算预测{last_trading_day}的五天前{benzhoudate}最佳模型')
global_config['logger'].info( global_config['logger'].info(
f'{date}真实价格：{true_price[true_price["ds"] == date]["y"].values[0]}') f'{date}真实价格：{true_price[true_price["ds"] == date]["y"].values[0]}')
price = df[['data_date', wd, 'model_id']] price = df[['data_date', wd, 'model_id']]
@ -992,7 +998,7 @@ def find_best_models(date='', global_config=None):
global_config['logger'].info( global_config['logger'].info(
f'{benzhoudate}预测最准确的模型名称：{best_models[wd]}') f'{benzhoudate}预测最准确的模型名称：{best_models[wd]}')
predictresult = query_predict_result( predictresult = query_predict_result(
date, best_model_id, global_config, wd) last_trading_day, best_model_id, global_config, wd)
if predictresult: if predictresult:
global_config['logger'].info( global_config['logger'].info(
f'最佳模型{best_models[wd]}在{date}预测结果：{predictresult}') f'最佳模型{best_models[wd]}在{date}预测结果：{predictresult}')
@ -1004,16 +1010,16 @@ def find_best_models(date='', global_config=None):
elif i in [2, 3]: elif i in [2, 3]:
weeks_ago = 1 if i == 2 else 2 weeks_ago = 1 if i == 2 else 2
ago_monday = current_date - \ ago_monday = last_trading_day - \
datetime.timedelta(days=current_date.weekday() + 7 * weeks_ago) datetime.timedelta(days=last_trading_day.weekday() + 7 * weeks_ago)
ago_sunday = ago_monday + datetime.timedelta(days=6) ago_sunday = ago_monday + datetime.timedelta(days=6)
ago_date_str = f"{ago_monday.strftime('%Y-%m-%d')} - {ago_sunday.strftime('%Y-%m-%d')}" ago_date_str = f"{ago_monday.strftime('%Y-%m-%d')} - {ago_sunday.strftime('%Y-%m-%d')}"
global_config['logger'].info( global_config['logger'].info(
f'计算预测{date}的前{weeks_ago}周{ago_date_str}最佳模型') f'计算预测{date}的前{weeks_ago}周{ago_date_str}最佳模型')
weektrueprice = true_price[(true_price['ds'] >= date_monday.strftime( weektrueprice = true_price[(true_price['ds'] >= ago_monday.strftime('%Y-%m-%d')) & (true_price['ds'] <= ago_sunday.strftime('%Y-%m-%d'))]['y'].mean()
'%Y-%m-%d')) & (true_price['ds'] <= date)]['y'].mean()
global_config['logger'].info( global_config['logger'].info(
f'当周{date_monday.strftime("%Y-%m-%d")}---{date}真实价格的周均价：{weektrueprice}') f'当周{date_monday.strftime("%Y-%m-%d")}---{last_trading_day_str}真实价格的周均价：{weektrueprice}')
price = df[['data_date', wd, 'model_id']] price = df[['data_date', wd, 'model_id']]
price = price[(price['data_date'] >= ago_monday) & price = price[(price['data_date'] >= ago_monday) &
(price['data_date'] <= ago_sunday)] (price['data_date'] <= ago_sunday)]
@ -1025,7 +1031,8 @@ def find_best_models(date='', global_config=None):
global_config['logger'].info( global_config['logger'].info(
f'{ago_date_str}预测最准确的模型名称：{best_models[wd]}') f'{ago_date_str}预测最准确的模型名称：{best_models[wd]}')
predictresult = query_predict_result( predictresult = query_predict_result(
date, best_model_id, global_config, wd) last_trading_day_str, best_model_id, global_config, wd)
if predictresult: if predictresult:
global_config['logger'].info( global_config['logger'].info(
f'最佳模型{best_models[wd]}在{date}预测结果：{predictresult}') f'最佳模型{best_models[wd]}在{date}预测结果：{predictresult}')
@ -1039,9 +1046,8 @@ def find_best_models(date='', global_config=None):
elif i in [4, 5, 6, 7]: elif i in [4, 5, 6, 7]:
months_ago = i - 3 months_ago = i - 3
current_date_ts = pd.Timestamp(date)
last_month_first_day = ( last_month_first_day = (
current_date_ts - pd.offsets.MonthBegin(months_ago)).strftime('%Y-%m-%d') last_trading_day - pd.offsets.MonthBegin(months_ago)).strftime('%Y-%m-%d')
last_month_last_day = (pd.Timestamp( last_month_last_day = (pd.Timestamp(
last_month_first_day) + pd.offsets.MonthEnd(0)).strftime('%Y-%m-%d') last_month_first_day) + pd.offsets.MonthEnd(0)).strftime('%Y-%m-%d')
global_config['logger'].info( global_config['logger'].info(
@ -1049,7 +1055,7 @@ def find_best_models(date='', global_config=None):
monthtrueprice = true_price[(true_price['ds'] >= first_day_of_month.strftime( monthtrueprice = true_price[(true_price['ds'] >= first_day_of_month.strftime(
'%Y-%m-%d')) & (true_price['ds'] <= date)]['y'].mean() '%Y-%m-%d')) & (true_price['ds'] <= date)]['y'].mean()
global_config['logger'].info( global_config['logger'].info(
f'当月{first_day_of_month.strftime("%Y-%m-%d")}-{date}真实价格的月均价：{monthtrueprice}') f'当月{first_day_of_month.strftime("%Y-%m-%d")}-{last_trading_day}真实价格的月均价：{monthtrueprice}')
price = df[['data_date', wd, 'model_id']] price = df[['data_date', wd, 'model_id']]
price = price[(price['data_date'] >= last_month_first_day) & ( price = price[(price['data_date'] >= last_month_first_day) & (
price['data_date'] <= last_month_last_day)] price['data_date'] <= last_month_last_day)]
@ -1061,7 +1067,7 @@ def find_best_models(date='', global_config=None):
global_config['logger'].info( global_config['logger'].info(
f'{last_month_first_day}-{last_month_last_day}预测最准确的模型名称：{best_models[wd]}') f'{last_month_first_day}-{last_month_last_day}预测最准确的模型名称：{best_models[wd]}')
predictresult = query_predict_result( predictresult = query_predict_result(
date, best_model_id, global_config, wd) last_trading_day, best_model_id, global_config, wd)
if predictresult: if predictresult:
global_config['logger'].info( global_config['logger'].info(
f'最佳模型{best_models[wd]}在{date}预测结果：{predictresult}') f'最佳模型{best_models[wd]}在{date}预测结果：{predictresult}')

4
main_juxiting.py
View File

@ -557,8 +557,10 @@ if __name__ == '__main__':
# except Exception as e: # except Exception as e:
# logger.info(f'预测失败：{e}') # logger.info(f'预测失败：{e}')
# continue # continue
global_config['end_time'] = '2025-08-04' global_config['end_time'] = '2025-08-01'
global_config['db_mysql'].connect()
predict_main() predict_main()
# global_config['end_time'] = '2025-08-01'
# push_market_value() # push_market_value()
# sql_inset_predict(global_config) # sql_inset_predict(global_config)