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聚烯烃PP期货预测

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workpc 2025-08-05 11:37:12 +08:00
parent 97ef8b63a3
commit f3971a94ec
5 changed files with 273 additions and 281 deletions
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3
config_juxiting.py
View File

@ -397,6 +397,9 @@ get_waring_data_value_list_data = {
# 八大维度数据项编码 # 八大维度数据项编码
bdwd_items = { bdwd_items = {
'ciri': 'jxtppbdwdcr', 'ciri': 'jxtppbdwdcr',
'cierri': 'jxtppbdwdcer',
'cisanri': 'jxtppbdwdcsanr',
'cisiri': 'jxtppbdwdcsir',
'benzhou': 'jxtppbdwdbz', 'benzhou': 'jxtppbdwdbz',
'cizhou': 'jxtppbdwdcz', 'cizhou': 'jxtppbdwdcz',
'gezhou': 'jxtppbdwdgz', 'gezhou': 'jxtppbdwdgz',

3
config_juxiting_zhoudu.py
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@ -86,11 +86,14 @@ bdwdname = [
'次周', '次周',
'隔周', '隔周',
] ]
# 数据库预测结果表八大维度列名 # 数据库预测结果表八大维度列名
price_columns = [ price_columns = [
'day_price', 'week_price', 'second_week_price', 'next_week_price', 'day_price', 'week_price', 'second_week_price', 'next_week_price',
'next_month_price', 'next_february_price', 'next_march_price', 'next_april_price' 'next_month_price', 'next_february_price', 'next_march_price', 'next_april_price'
] ]
modelsindex = [{ modelsindex = [{
"NHITS": "SELF0000231", "NHITS": "SELF0000231",
"Informer": "SELF0000232", "Informer": "SELF0000232",

70
main_juxiting.py
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@ -2,7 +2,7 @@
from lib.dataread import * from lib.dataread import *
from config_juxiting import * from config_juxiting import *
from lib.tools import SendMail, exception_logger, convert_df_to_pydantic_pp, exception_logger, get_modelsname, plot_pp_predict_result from lib.tools import SendMail, exception_logger, convert_df_to_pydantic_pp, exception_logger, find_best_models, get_modelsname, plot_pp_predict_result
from models.nerulforcastmodels import ex_Model_Juxiting, model_losss_juxiting, pp_export_pdf from models.nerulforcastmodels import ex_Model_Juxiting, model_losss_juxiting, pp_export_pdf
import datetime import datetime
import torch import torch
@ -103,49 +103,53 @@ global_config.update({
def push_market_value(): def push_market_value():
config.logger.info('发送预测结果到市场信息平台') config.logger.info('发送预测结果到市场信息平台')
current_end_time = global_config['end_time']
previous_trading_day = (pd.Timestamp(current_end_time) -
pd.tseries.offsets.BusinessDay(1)).strftime('%Y-%m-%d')
# 读取预测数据和模型评估数据 # 读取预测数据和模型评估数据
predict_file_path = os.path.join(config.dataset, 'predict.csv') best_bdwd_price = find_best_models(
model_eval_file_path = os.path.join(config.dataset, 'model_evaluation.csv') date=previous_trading_day, global_config=global_config)
try:
predictdata_df = pd.read_csv(predict_file_path) # 获取本周最佳模型的五日预测价格
top_models_df = pd.read_csv(model_eval_file_path) five_days_predict_price = pd.read_csv('juxitingdataset/predict.csv')
except FileNotFoundError as e: week_price_modelname = best_bdwd_price['week_price']['model_name']
config.logger.error(f"文件未找到: {e}") five_days_predict_price = five_days_predict_price[['ds',week_price_modelname]]
return five_days_predict_price['ds'] = pd.to_datetime(five_days_predict_price['ds'])
five_days_predict_price.rename(columns={week_price_modelname:'predictresult'},inplace=True)
predictdata = predictdata_df.copy() # 设置索引 次日 次二日 次三日 次四日 次五日
index_labels = ["次日", "次二日", "次三日", "次四日", "次五日"]
# 取模型前十 five_days_predict_price.index = index_labels
top_models = top_models_df['模型(Model)'].head(10).tolist() global_config['logger'].info(f"best_bdwd_price: {best_bdwd_price}")
# 去掉FDBformer
if 'FEDformer' in top_models:
top_models.remove('FEDformer')
# 计算前十模型的均值
predictdata_df['top_models_mean'] = predictdata_df[top_models].mean(axis=1)
# 打印日期和前十模型均值
print(predictdata_df[['ds', 'top_models_mean']])
# 准备要推送的数据
first_mean = predictdata_df['top_models_mean'].iloc[0]
last_mean = predictdata_df['top_models_mean'].iloc[-1]
# 保留两位小数
first_mean = round(first_mean, 2)
last_mean = round(last_mean, 2)
predictdata = [ predictdata = [
{ {
"dataItemNo": global_config['bdwd_items']['ciri'], "dataItemNo": global_config['bdwd_items']['ciri'],
"dataDate": global_config['end_time'].replace('-', ''), "dataDate": global_config['end_time'].replace('-', ''),
"dataStatus": "add", "dataStatus": "add",
"dataValue": first_mean "dataValue": five_days_predict_price.loc['次日','predictresult'].round(2)
},{
"dataItemNo": global_config['bdwd_items']['cierri'],
"dataDate": global_config['end_time'].replace('-', ''),
"dataStatus": "add",
"dataValue": five_days_predict_price.loc['次二日','predictresult'].round(2)
},{
"dataItemNo": global_config['bdwd_items']['cisanri'],
"dataDate": global_config['end_time'].replace('-', ''),
"dataStatus": "add",
"dataValue": five_days_predict_price.loc['次三日','predictresult'].round(2)
},{
"dataItemNo": global_config['bdwd_items']['cisiri'],
"dataDate": global_config['end_time'].replace('-', ''),
"dataStatus": "add",
"dataValue": five_days_predict_price.loc['次四日','predictresult'].round(2)
}, },
{ {
"dataItemNo": global_config['bdwd_items']['benzhou'], "dataItemNo": global_config['bdwd_items']['benzhou'],
"dataDate": global_config['end_time'].replace('-', ''), "dataDate": global_config['end_time'].replace('-', ''),
"dataStatus": "add", "dataStatus": "add",
"dataValue": last_mean "dataValue": five_days_predict_price.loc['次五日','predictresult'].round(2)
} }
] ]
@ -553,7 +557,7 @@ 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'
predict_main() predict_main()
# push_market_value() # push_market_value()

420
main_juxiting_yuedu.py
View File

@ -2,7 +2,7 @@
from lib.dataread import * from lib.dataread import *
from config_juxiting_yuedu import * from config_juxiting_yuedu import *
from lib.tools import SendMail, convert_df_to_pydantic_pp, exception_logger, get_modelsname from lib.tools import SendMail, convert_df_to_pydantic_pp, exception_logger, find_best_models, get_modelsname
from models.nerulforcastmodels import ex_Model, model_losss_juxiting, pp_bdwd_png, pp_export_pdf from models.nerulforcastmodels import ex_Model, model_losss_juxiting, pp_bdwd_png, pp_export_pdf
import datetime import datetime
import torch import torch
@ -93,35 +93,28 @@ global_config.update({
def push_market_value(): def push_market_value():
logger.info('发送预测结果到市场信息平台') logger.info('发送预测结果到市场信息平台')
current_end_time = global_config['end_time']
previous_trading_day = (pd.Timestamp(current_end_time) -
pd.tseries.offsets.BusinessDay(1)).strftime('%Y-%m-%d')
# 读取预测数据和模型评估数据 # 读取预测数据和模型评估数据
predict_file_path = os.path.join(config.dataset, 'predict.csv') best_bdwd_price = find_best_models(
model_eval_file_path = os.path.join(config.dataset, 'model_evaluation.csv') date=previous_trading_day, global_config=global_config)
try:
predictdata_df = pd.read_csv(predict_file_path) # 获取本月最佳模型的预测价格
top_models_df = pd.read_csv(model_eval_file_path) four_month_predict_price = pd.read_csv(os.path.join(global_config['dataset'], 'predict.csv'))
except FileNotFoundError as e: four_month_predict_price['ds'] = pd.to_datetime(four_month_predict_price['ds'])
logger.error(f"文件未找到: {e}") # 设置索引 次月 次二月 次三月 次四月
return index_labels = ["次月", "次二月", "次三月", "次四月"]
four_month_predict_price.index = index_labels
predictdata = predictdata_df.copy() global_config['logger'].info(f"best_bdwd_price: {best_bdwd_price}")
# 取模型前十
top_models = top_models_df['模型(Model)'].head(10).tolist()
# 去掉FDBformer
if 'FEDformer' in top_models:
top_models.remove('FEDformer')
# 计算前十模型的均值
predictdata_df['top_models_mean'] = predictdata_df[top_models].mean(axis=1)
# 打印日期和前十模型均值
print(predictdata_df[['ds', 'top_models_mean']])
# 准备要推送的数据 # 准备要推送的数据
ciyue_mean = predictdata_df['top_models_mean'].iloc[0] ciyue_mean = four_month_predict_price[best_bdwd_price['next_month_price']['model_name']].iloc[0]
cieryue_mean = predictdata_df['top_models_mean'].iloc[1] cieryue_mean = four_month_predict_price[best_bdwd_price['next_february_price']['model_name']].iloc[1]
cisanyue_mean = predictdata_df['top_models_mean'].iloc[2] cisanyue_mean = four_month_predict_price[best_bdwd_price['next_march_price']['model_name']].iloc[2]
cisieryue_mean = predictdata_df['top_models_mean'].iloc[3] cisieryue_mean = four_month_predict_price[best_bdwd_price['next_april_price']['model_name']].iloc[3]
# 保留两位小数 # # 保留两位小数
ciyue_mean = round(ciyue_mean, 2) ciyue_mean = round(ciyue_mean, 2)
cieryue_mean = round(cieryue_mean, 2) cieryue_mean = round(cieryue_mean, 2)
cisanyue_mean = round(cisanyue_mean, 2) cisanyue_mean = round(cisanyue_mean, 2)
@ -292,211 +285,211 @@ def predict_main():
返回: 返回:
None None
""" """
end_time = global_config['end_time'] # end_time = global_config['end_time']
signature = BinanceAPI(APPID, SECRET) # signature = BinanceAPI(APPID, SECRET)
etadata = EtaReader(signature=signature, # etadata = EtaReader(signature=signature,
classifylisturl=global_config['classifylisturl'], # classifylisturl=global_config['classifylisturl'],
classifyidlisturl=global_config['classifyidlisturl'], # classifyidlisturl=global_config['classifyidlisturl'],
edbcodedataurl=global_config['edbcodedataurl'], # edbcodedataurl=global_config['edbcodedataurl'],
edbcodelist=global_config['edbcodelist'], # edbcodelist=global_config['edbcodelist'],
edbdatapushurl=global_config['edbdatapushurl'], # edbdatapushurl=global_config['edbdatapushurl'],
edbdeleteurl=global_config['edbdeleteurl'], # edbdeleteurl=global_config['edbdeleteurl'],
edbbusinessurl=global_config['edbbusinessurl'], # edbbusinessurl=global_config['edbbusinessurl'],
classifyId=global_config['ClassifyId'], # classifyId=global_config['ClassifyId'],
) # )
# 获取数据 # # 获取数据
if is_eta: # if is_eta:
logger.info('从eta获取数据...') # logger.info('从eta获取数据...')
df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_pp_data( # df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_pp_data(
data_set=data_set, dataset=dataset) # 原始数据，未处理 # data_set=data_set, dataset=dataset) # 原始数据，未处理
if is_market: # if is_market:
logger.info('从市场信息平台获取数据...') # logger.info('从市场信息平台获取数据...')
try: # try:
# 如果是测试环境，最高价最低价取excel文档 # # 如果是测试环境，最高价最低价取excel文档
if server_host == '192.168.100.53': # if server_host == '192.168.100.53':
logger.info('从excel文档获取最高价最低价') # logger.info('从excel文档获取最高价最低价')
df_zhibiaoshuju = get_high_low_data(df_zhibiaoshuju) # df_zhibiaoshuju = get_high_low_data(df_zhibiaoshuju)
else: # else:
logger.info('从市场信息平台获取数据') # logger.info('从市场信息平台获取数据')
df_zhibiaoshuju = get_market_data( # df_zhibiaoshuju = get_market_data(
end_time, df_zhibiaoshuju) # end_time, df_zhibiaoshuju)
except: # except:
logger.info('最高最低价拼接失败') # logger.info('最高最低价拼接失败')
# 保存到xlsx文件的sheet表 # # 保存到xlsx文件的sheet表
with pd.ExcelWriter(os.path.join(dataset, data_set)) as file: # with pd.ExcelWriter(os.path.join(dataset, data_set)) as file:
df_zhibiaoshuju.to_excel(file, sheet_name='指标数据', index=False) # df_zhibiaoshuju.to_excel(file, sheet_name='指标数据', index=False)
df_zhibiaoliebiao.to_excel(file, sheet_name='指标列表', index=False) # df_zhibiaoliebiao.to_excel(file, sheet_name='指标列表', index=False)
# 数据处理 # # 数据处理
df = datachuli_juxiting(df_zhibiaoshuju, df_zhibiaoliebiao, y=global_config['y'], dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture, # df = datachuli_juxiting(df_zhibiaoshuju, df_zhibiaoliebiao, y=global_config['y'], dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
end_time=end_time) # end_time=end_time)
else: # else:
# 读取数据 # # 读取数据
logger.info('读取本地数据：' + os.path.join(dataset, data_set)) # logger.info('读取本地数据：' + os.path.join(dataset, data_set))
df, df_zhibiaoliebiao = getdata_zhoudu_juxiting(filename=os.path.join(dataset, data_set), y=y, dataset=dataset, add_kdj=add_kdj, # df, df_zhibiaoliebiao = getdata_zhoudu_juxiting(filename=os.path.join(dataset, data_set), y=y, dataset=dataset, add_kdj=add_kdj,
is_timefurture=is_timefurture, end_time=end_time) # 原始数据，未处理 # is_timefurture=is_timefurture, end_time=end_time) # 原始数据，未处理
# 更改预测列名称 # # 更改预测列名称
df.rename(columns={y: 'y'}, inplace=True) # df.rename(columns={y: 'y'}, inplace=True)
if is_edbnamelist: # if is_edbnamelist:
df = df[edbnamelist] # df = df[edbnamelist]
df.to_csv(os.path.join(dataset, '指标数据.csv'), index=False) # df.to_csv(os.path.join(dataset, '指标数据.csv'), index=False)
# 保存最新日期的y值到数据库 # # 保存最新日期的y值到数据库
# 取第一行数据存储到数据库中 # # 取第一行数据存储到数据库中
first_row = df[['ds', 'y']].tail(1) # first_row = df[['ds', 'y']].tail(1)
# 判断y的类型是否为float # # 判断y的类型是否为float
if not isinstance(first_row['y'].values[0], float): # if not isinstance(first_row['y'].values[0], float):
logger.info(f'{end_time}预测目标数据为空，跳过') # logger.info(f'{end_time}预测目标数据为空，跳过')
return None # return None
# 将最新真实值保存到数据库 # # 将最新真实值保存到数据库
if not sqlitedb.check_table_exists('trueandpredict'): # if not sqlitedb.check_table_exists('trueandpredict'):
first_row.to_sql('trueandpredict', sqlitedb.connection, index=False) # first_row.to_sql('trueandpredict', sqlitedb.connection, index=False)
else: # else:
for row in first_row.itertuples(index=False): # for row in first_row.itertuples(index=False):
row_dict = row._asdict() # row_dict = row._asdict()
config.logger.info(f'要保存的真实值：{row_dict}') # config.logger.info(f'要保存的真实值：{row_dict}')
# 判断ds是否为字符串类型,如果不是则转换为字符串类型 # # 判断ds是否为字符串类型,如果不是则转换为字符串类型
if isinstance(row_dict['ds'], (pd.Timestamp, datetime.datetime)): # if isinstance(row_dict['ds'], (pd.Timestamp, datetime.datetime)):
row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d') # row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d')
elif not isinstance(row_dict['ds'], str): # elif not isinstance(row_dict['ds'], str):
try: # try:
row_dict['ds'] = pd.to_datetime( # row_dict['ds'] = pd.to_datetime(
row_dict['ds']).strftime('%Y-%m-%d') # row_dict['ds']).strftime('%Y-%m-%d')
except: # except:
logger.warning(f"无法解析的时间格式: {row_dict['ds']}") # logger.warning(f"无法解析的时间格式: {row_dict['ds']}")
# row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d') # # row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d')
# row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d %H:%M:%S') # # row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d %H:%M:%S')
check_query = sqlitedb.select_data( # check_query = sqlitedb.select_data(
'trueandpredict', where_condition=f"ds = '{row.ds}'") # 'trueandpredict', where_condition=f"ds = '{row.ds}'")
if len(check_query) > 0: # if len(check_query) > 0:
set_clause = ", ".join( # set_clause = ", ".join(
[f"{key} = '{value}'" for key, value in row_dict.items()]) # [f"{key} = '{value}'" for key, value in row_dict.items()])
sqlitedb.update_data( # sqlitedb.update_data(
'trueandpredict', set_clause, where_condition=f"ds = '{row.ds}'") # 'trueandpredict', set_clause, where_condition=f"ds = '{row.ds}'")
continue # continue
sqlitedb.insert_data('trueandpredict', tuple( # sqlitedb.insert_data('trueandpredict', tuple(
row_dict.values()), columns=row_dict.keys()) # row_dict.values()), columns=row_dict.keys())
# 更新accuracy表的y值 # # 更新accuracy表的y值
if not sqlitedb.check_table_exists('accuracy'): # if not sqlitedb.check_table_exists('accuracy'):
pass # pass
else: # else:
update_y = sqlitedb.select_data( # update_y = sqlitedb.select_data(
'accuracy', where_condition="y is null") # 'accuracy', where_condition="y is null")
if len(update_y) > 0: # if len(update_y) > 0:
logger.info('更新accuracy表的y值') # logger.info('更新accuracy表的y值')
# 找到update_y 中ds且df中的y的行 # # 找到update_y 中ds且df中的y的行
update_y = update_y[update_y['ds'] <= end_time] # update_y = update_y[update_y['ds'] <= end_time]
logger.info(f'要更新y的信息：{update_y}') # logger.info(f'要更新y的信息：{update_y}')
# try: # # try:
for row in update_y.itertuples(index=False): # 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: # # except Exception as e:
# logger.info(f'更新accuracy表的y值失败：{e}') # # logger.info(f'更新accuracy表的y值失败：{e}')
# 判断当前日期是不是周一 # # 判断当前日期是不是周一
is_weekday = datetime.datetime.now().weekday() == 0 # is_weekday = datetime.datetime.now().weekday() == 0
if is_weekday: # if is_weekday:
logger.info('今天是周一，更新预测模型') # logger.info('今天是周一，更新预测模型')
# 计算最近60天预测残差最低的模型名称 # # 计算最近60天预测残差最低的模型名称
model_results = sqlitedb.select_data( # model_results = sqlitedb.select_data(
'trueandpredict', order_by="ds DESC", limit="60") # 'trueandpredict', order_by="ds DESC", limit="60")
# 删除空值率为90%以上的列 # # 删除空值率为90%以上的列
if len(model_results) > 10: # if len(model_results) > 10:
model_results = model_results.dropna( # model_results = model_results.dropna(
thresh=len(model_results)*0.1, axis=1) # thresh=len(model_results)*0.1, axis=1)
# 删除空行 # # 删除空行
model_results = model_results.dropna() # model_results = model_results.dropna()
modelnames = model_results.columns.to_list()[2:-1] # modelnames = model_results.columns.to_list()[2:-1]
for col in model_results[modelnames].select_dtypes(include=['object']).columns: # for col in model_results[modelnames].select_dtypes(include=['object']).columns:
model_results[col] = model_results[col].astype(np.float32) # model_results[col] = model_results[col].astype(np.float32)
# 计算每个预测值与真实值之间的偏差率 # # 计算每个预测值与真实值之间的偏差率
for model in modelnames: # for model in modelnames:
model_results[f'{model}_abs_error_rate'] = abs( # model_results[f'{model}_abs_error_rate'] = abs(
model_results['y'] - model_results[model]) / model_results['y'] # model_results['y'] - model_results[model]) / model_results['y']
# 获取每行对应的最小偏差率值 # # 获取每行对应的最小偏差率值
min_abs_error_rate_values = model_results.apply( # min_abs_error_rate_values = model_results.apply(
lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1) # lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)
# 获取每行对应的最小偏差率值对应的列名 # # 获取每行对应的最小偏差率值对应的列名
min_abs_error_rate_column_name = model_results.apply( # min_abs_error_rate_column_name = model_results.apply(
lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1) # lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1)
# 将列名索引转换为列名 # # 将列名索引转换为列名
min_abs_error_rate_column_name = min_abs_error_rate_column_name.map( # min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(
lambda x: x.split('_')[0]) # lambda x: x.split('_')[0])
# 取出现次数最多的模型名称 # # 取出现次数最多的模型名称
most_common_model = min_abs_error_rate_column_name.value_counts().idxmax() # most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()
logger.info(f"最近60天预测残差最低的模型名称：{most_common_model}") # logger.info(f"最近60天预测残差最低的模型名称：{most_common_model}")
# 保存结果到数据库 # # 保存结果到数据库
if not sqlitedb.check_table_exists('most_model'): # if not sqlitedb.check_table_exists('most_model'):
sqlitedb.create_table( # sqlitedb.create_table(
'most_model', columns="ds datetime, most_common_model TEXT") # 'most_model', columns="ds datetime, most_common_model TEXT")
sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime( # sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime(
'%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',)) # '%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
if is_corr: # if is_corr:
df = corr_feature(df=df) # df = corr_feature(df=df)
df1 = df.copy() # 备份一下，后面特征筛选完之后加入ds y 列用 # df1 = df.copy() # 备份一下，后面特征筛选完之后加入ds y 列用
logger.info(f"开始训练模型...") # logger.info(f"开始训练模型...")
row, col = df.shape # row, col = df.shape
now = datetime.datetime.now().strftime('%Y%m%d%H%M%S') # now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
ex_Model(df, # ex_Model(df,
horizon=global_config['horizon'], # horizon=global_config['horizon'],
input_size=global_config['input_size'], # input_size=global_config['input_size'],
train_steps=global_config['train_steps'], # train_steps=global_config['train_steps'],
val_check_steps=global_config['val_check_steps'], # val_check_steps=global_config['val_check_steps'],
early_stop_patience_steps=global_config['early_stop_patience_steps'], # early_stop_patience_steps=global_config['early_stop_patience_steps'],
is_debug=global_config['is_debug'], # is_debug=global_config['is_debug'],
dataset=global_config['dataset'], # dataset=global_config['dataset'],
is_train=global_config['is_train'], # is_train=global_config['is_train'],
is_fivemodels=global_config['is_fivemodels'], # is_fivemodels=global_config['is_fivemodels'],
val_size=global_config['val_size'], # val_size=global_config['val_size'],
test_size=global_config['test_size'], # test_size=global_config['test_size'],
settings=global_config['settings'], # settings=global_config['settings'],
now=now, # now=now,
etadata=etadata, # etadata=etadata,
modelsindex=global_config['modelsindex'], # modelsindex=global_config['modelsindex'],
data=data, # data=data,
is_eta=global_config['is_eta'], # is_eta=global_config['is_eta'],
end_time=global_config['end_time'], # end_time=global_config['end_time'],
) # )
logger.info('模型训练完成') # logger.info('模型训练完成')
logger.info('训练数据绘图ing') # logger.info('训练数据绘图ing')
model_results3 = model_losss_juxiting( # model_results3 = model_losss_juxiting(
sqlitedb, end_time=global_config['end_time'], is_fivemodels=global_config['is_fivemodels']) # sqlitedb, end_time=global_config['end_time'], is_fivemodels=global_config['is_fivemodels'])
logger.info('训练数据绘图end') # logger.info('训练数据绘图end')
push_market_value() # push_market_value()
sql_inset_predict(global_config) # sql_inset_predict(global_config)
# 模型报告 # # 模型报告
logger.info('制作报告ing') # logger.info('制作报告ing')
title = f'{settings}--{end_time}-预测报告' # 报告标题 # title = f'{settings}--{end_time}-预测报告' # 报告标题
reportname = f'聚烯烃PP大模型月度预测--{end_time}.pdf' # 报告文件名 # reportname = f'聚烯烃PP大模型月度预测--{end_time}.pdf' # 报告文件名
reportname = reportname.replace(':', '-') # 替换冒号 # reportname = reportname.replace(':', '-') # 替换冒号
pp_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time, # pp_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
reportname=reportname, sqlitedb=sqlitedb), # reportname=reportname, sqlitedb=sqlitedb),
logger.info('制作报告end') # logger.info('制作报告end')
# 图片报告 # 图片报告
logger.info('图片报告ing') logger.info('图片报告ing')
@ -537,7 +530,8 @@ if __name__ == '__main__':
# logger.info(f'预测失败：{e}') # logger.info(f'预测失败：{e}')
# continue # continue
predict_main() predict_main()
# push_market_value()
# 图片报告 # 图片报告
# global_config['end_time'] = '2025-07-31' # global_config['end_time'] = '2025-07-31'

58
main_juxiting_zhoudu.py
View File

@ -2,8 +2,8 @@
from lib.dataread import * from lib.dataread import *
from config_juxiting_zhoudu import * from config_juxiting_zhoudu import *
from lib.tools import SendMail, exception_logger, convert_df_to_pydantic_pp, exception_logger, get_modelsname from lib.tools import SendMail, exception_logger, convert_df_to_pydantic_pp, exception_logger, find_best_models, get_modelsname
from models.nerulforcastmodels import ex_Model_Juxiting, model_losss_juxiting, pp_bdwd_png, pp_export_pdf from models.nerulforcastmodels import ex_Model_Juxiting, model_losss_juxiting, pp_export_pdf
import datetime import datetime
import torch import torch
torch.set_float32_matmul_precision("high") torch.set_float32_matmul_precision("high")
@ -23,7 +23,6 @@ global_config.update({
'is_update_report': is_update_report, 'is_update_report': is_update_report,
'settings': settings, 'settings': settings,
'bdwdname': bdwdname, 'bdwdname': bdwdname,
'columnsrename': columnsrename,
'price_columns': price_columns, 'price_columns': price_columns,
@ -102,32 +101,26 @@ global_config.update({
def push_market_value(): def push_market_value():
config.logger.info('发送预测结果到市场信息平台') config.logger.info('发送预测结果到市场信息平台')
current_end_time = global_config['end_time']
previous_trading_day = (pd.Timestamp(current_end_time) -
pd.tseries.offsets.BusinessDay(1)).strftime('%Y-%m-%d')
# 读取预测数据和模型评估数据 # 读取预测数据和模型评估数据
predict_file_path = os.path.join(config.dataset, 'predict.csv') best_bdwd_price = find_best_models(
model_eval_file_path = os.path.join(config.dataset, 'model_evaluation.csv') date=previous_trading_day, global_config=global_config)
try:
predictdata_df = pd.read_csv(predict_file_path) # 获取次周，隔周 最佳模型的预测价格
top_models_df = pd.read_csv(model_eval_file_path) weeks_predict_price = pd.read_csv(os.path.join(global_config['dataset'], 'predict.csv'))
except FileNotFoundError as e: weeks_predict_price['ds'] = pd.to_datetime(weeks_predict_price['ds'])
config.logger.error(f"文件未找到: {e}") # 设置索引 次周 隔周
return index_labels = ["次周", "隔周"]
weeks_predict_price.index = index_labels
predictdata = predictdata_df.copy() global_config['logger'].info(f"best_bdwd_price: {best_bdwd_price}")
# 取模型前十
top_models = top_models_df['模型(Model)'].head(10).tolist()
# 去掉FDBformer
if 'FEDformer' in top_models:
top_models.remove('FEDformer')
# 计算前十模型的均值
predictdata_df['top_models_mean'] = predictdata_df[top_models].mean(axis=1)
# 打印日期和前十模型均值
print(predictdata_df[['ds', 'top_models_mean']])
# 准备要推送的数据 # 准备要推送的数据
first_mean = predictdata_df['top_models_mean'].iloc[0] first_mean = weeks_predict_price[best_bdwd_price['second_week_price']['model_name']].iloc[0]
last_mean = predictdata_df['top_models_mean'].iloc[-1] last_mean = weeks_predict_price[best_bdwd_price['next_week_price']['model_name']].iloc[-1]
# 保留两位小数 # 保留两位小数
first_mean = round(first_mean, 2) first_mean = round(first_mean, 2)
last_mean = round(last_mean, 2) last_mean = round(last_mean, 2)
@ -159,7 +152,7 @@ def push_market_value():
def sql_inset_predict(global_config): def sql_inset_predict(global_config):
df = pd.read_csv(os.path.join(config.dataset, 'predict.csv')) df = pd.read_csv(os.path.join(config.dataset, 'predict.csv'))
df['created_dt'] = pd.to_datetime(df['created_dt']) df['created_dt'] = pd.to_datetime(df['created_dt'])
df['ds'] = pd.to_datetime(df['ds']) df['ds'] = pd.to_datetime(df['ds'])
# 获取次周预测结果 # 获取次周预测结果
second_week_price_df = df[df['ds'] == df['ds'].min()] second_week_price_df = df[df['ds'] == df['ds'].min()]
# 获取隔周周预测结果 # 获取隔周周预测结果
@ -472,9 +465,6 @@ def predict_main():
# sqlitedb, end_time=global_config['end_time'], is_fivemodels=global_config['is_fivemodels']) # sqlitedb, end_time=global_config['end_time'], is_fivemodels=global_config['is_fivemodels'])
# logger.info('训练数据绘图end') # logger.info('训练数据绘图end')
push_market_value()
sql_inset_predict(global_config)
# # # 模型报告 # # # 模型报告
# logger.info('制作报告ing') # logger.info('制作报告ing')
# title = f'{settings}--{end_time}-预测报告' # 报告标题 # title = f'{settings}--{end_time}-预测报告' # 报告标题
@ -485,10 +475,8 @@ def predict_main():
# logger.info('制作报告end') # logger.info('制作报告end')
# 图片报告 push_market_value()
logger.info('图片报告ing') sql_inset_predict(global_config)
pp_bdwd_png(global_config=global_config)
logger.info('图片报告end')
# # LSTM 单变量模型 # # LSTM 单变量模型
# ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset) # ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset)
@ -515,7 +503,7 @@ def predict_main():
if __name__ == '__main__': if __name__ == '__main__':
# global end_time # global end_time
# 遍历2024-11-25 到 2024-12-3 之间的工作日日期 # 遍历2024-11-25 到 2024-12-3 之间的工作日日期
# for i_time in pd.date_range('2025-7-18', '2025-7-23', freq='B'): # for i_time in pd.date_range('2025-7-28', '2025-7-28', freq='B'):
# try: # try:
# global_config['end_time'] = i_time.strftime('%Y-%m-%d') # global_config['end_time'] = i_time.strftime('%Y-%m-%d')
# global_config['db_mysql'].connect() # global_config['db_mysql'].connect()