还原原油周度执行文件
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workpc
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@ -436,42 +436,41 @@ def predict_main():
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sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime(
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'%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
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# try:
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# if is_weekday:
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if True:
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logger.info('发送特征预警')
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# 获取取消订阅的指标ID
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quxiaodingyueidlist = get_waring_data()
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# 上传预警信息到数据库
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warning_data_df = df_zhibiaoliebiao.copy()
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warning_data_df = warning_data_df[warning_data_df['停更周期'] > 3][[
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'指标名称', '指标id', '频度', '更新周期', '指标来源', '最后更新时间', '停更周期']]
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# 重命名列名
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warning_data_df = warning_data_df.rename(columns={'指标名称': 'indicatorName', '指标id': 'indicatorId', '频度': 'frequency',
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'更新周期': 'UPDATE_FREQUENCY', '指标来源': 'DATA_SOURCE', '最后更新时间': 'LAST_UPDATE_DATE', '停更周期': 'updateSuspensionCycle'})
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try:
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if True:
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logger.info('发送特征预警')
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# 获取取消订阅的指标ID
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quxiaodingyueidlist = get_waring_data()
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# 上传预警信息到数据库
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warning_data_df = df_zhibiaoliebiao.copy()
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warning_data_df = warning_data_df[warning_data_df['停更周期'] > 3][[
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'指标名称', '指标id', '频度', '更新周期', '指标来源', '最后更新时间', '停更周期']]
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# 重命名列名
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warning_data_df = warning_data_df.rename(columns={'指标名称': 'indicatorName', '指标id': 'indicatorId', '频度': 'frequency',
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'更新周期': 'UPDATE_FREQUENCY', '指标来源': 'DATA_SOURCE', '最后更新时间': 'LAST_UPDATE_DATE', '停更周期': 'updateSuspensionCycle'})
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warning_data_df['warningDate'] = datetime.date.today().strftime(
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"%Y-%m-%d %H:%M:%S")
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warning_data_df['dataSource'] = 9
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warning_data_df['warningDate'] = datetime.date.today().strftime(
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"%Y-%m-%d %H:%M:%S")
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warning_data_df['dataSource'] = 9
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if len(quxiaodingyueidlist) > 0:
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# 去掉取消订阅的指标
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print(warning_data_df.shape)
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warning_data_df = warning_data_df[~warning_data_df['indicatorId'].isin(
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quxiaodingyueidlist)]
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print(warning_data_df.shape)
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warning_data = warning_data_df.to_json(
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orient='records', force_ascii=False)
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warning_data = warning_data.replace('日度', '1')
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warning_data = warning_data.replace('周度', '2')
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warning_data = warning_data.replace('月度', '3')
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warning_data = json.loads(warning_data)
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push_waring_market_data(
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warning_data, dataSource=warning_data_df['dataSource'].values[0])
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# if is_update_warning_data:
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# upload_warning_info(len(warning_data_df))
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# except:
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# logger.info('上传预警信息到数据库失败')
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if len(quxiaodingyueidlist) > 0:
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# 去掉取消订阅的指标
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print(warning_data_df.shape)
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warning_data_df = warning_data_df[~warning_data_df['indicatorId'].isin(
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quxiaodingyueidlist)]
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print(warning_data_df.shape)
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warning_data = warning_data_df.to_json(
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orient='records', force_ascii=False)
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warning_data = warning_data.replace('日度', '1')
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warning_data = warning_data.replace('周度', '2')
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warning_data = warning_data.replace('月度', '3')
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warning_data = json.loads(warning_data)
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push_waring_market_data(
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warning_data, dataSource=warning_data_df['dataSource'].values[0])
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# if is_update_warning_data:
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# upload_warning_info(len(warning_data_df))
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except:
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logger.info('上传预警信息到数据库失败')
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if is_corr:
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df = corr_feature(df=df)
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@ -1,9 +1,9 @@
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# 读取配置
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from lib.dataread import *
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from config_juxiting_zhoudu import *
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from lib.tools import SendMail, exception_logger, convert_df_to_pydantic, exception_logger, get_modelsname
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from models.nerulforcastmodels import ex_Model_Juxiting, model_losss_juxiting, pp_export_pdf
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from config_jingbo_zhoudu import *
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from lib.tools import SendMail, convert_df_to_pydantic, exception_logger, get_modelsname
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from models.nerulforcastmodels import ex_Model, model_losss, brent_export_pdf
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import datetime
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import torch
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torch.set_float32_matmul_precision("high")
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@ -13,10 +13,6 @@ global_config.update({
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'logger': logger,
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'dataset': dataset,
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'y': y,
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# 'offsite_col': offsite_col,
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# 'avg_cols': avg_cols,
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# 'offsite': offsite,
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'edbcodenamedict': edbcodenamedict,
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'is_debug': is_debug,
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'is_train': is_train,
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'is_fivemodels': is_fivemodels,
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@ -67,14 +63,6 @@ global_config.update({
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'push_data_value_list_url': push_data_value_list_url,
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'push_data_value_list_data': push_data_value_list_data,
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# 上传预警数据
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'push_waring_data_value_list_url': push_waring_data_value_list_url,
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'push_waring_data_value_list_data': push_waring_data_value_list_data,
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# 获取取消订阅的数据
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'get_waring_data_value_list_url': get_waring_data_value_list_url,
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'get_waring_data_value_list_data': get_waring_data_value_list_data,
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# eta 配置
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'APPID': APPID,
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'SECRET': SECRET,
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@ -91,7 +79,6 @@ global_config.update({
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# 数据库配置
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'sqlitedb': sqlitedb,
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'bdwd_items': bdwd_items,
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'is_bdwd': is_bdwd,
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'db_mysql': db_mysql,
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'DEFAULT_CONFIG': DEFAULT_CONFIG,
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@ -99,7 +86,7 @@ global_config.update({
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def push_market_value():
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config.logger.info('发送预测结果到市场信息平台')
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logger.info('发送预测结果到市场信息平台')
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# 读取预测数据和模型评估数据
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predict_file_path = os.path.join(config.dataset, 'predict.csv')
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model_eval_file_path = os.path.join(config.dataset, 'model_evaluation.csv')
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@ -107,7 +94,7 @@ def push_market_value():
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predictdata_df = pd.read_csv(predict_file_path)
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top_models_df = pd.read_csv(model_eval_file_path)
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except FileNotFoundError as e:
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config.logger.error(f"文件未找到: {e}")
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logger.error(f"文件未找到: {e}")
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return
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predictdata = predictdata_df.copy()
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@ -151,7 +138,7 @@ def push_market_value():
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try:
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push_market_data(predictdata)
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except Exception as e:
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config.logger.error(f"推送数据失败: {e}")
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logger.error(f"推送数据失败: {e}")
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def sql_inset_predict(global_config):
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@ -167,7 +154,7 @@ def sql_inset_predict(global_config):
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model_name_list, model_id_name_dict = get_modelsname(df, global_config)
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PRICE_COLUMNS = [
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'day_price', 'week_price', 'second_week_price', 'next_week_price',
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'day_price', 'week_price', 'second_week_price', 'next_week_price',
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'next_month_price', 'next_february_price', 'next_march_price', 'next_april_price'
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]
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@ -217,8 +204,8 @@ def sql_inset_predict(global_config):
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params = (
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result.feature_factor_frequency,
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result.strategy_id,
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global_config['DEFAULT_CONFIG']['oil_code'],
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global_config['DEFAULT_CONFIG']['oil_name'],
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result.oil_code,
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result.oil_name,
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next_day_df['created_dt'].values[0],
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result.market_price,
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*price_values,
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@ -279,6 +266,7 @@ def predict_main():
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None
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"""
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end_time = global_config['end_time']
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signature = BinanceAPI(APPID, SECRET)
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etadata = EtaReader(signature=signature,
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classifylisturl=global_config['classifylisturl'],
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@ -294,7 +282,7 @@ def predict_main():
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if is_eta:
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logger.info('从eta获取数据...')
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df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_pp_data(
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df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_yuanyou_data(
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data_set=data_set, dataset=dataset) # 原始数据，未处理
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if is_market:
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@ -302,7 +290,7 @@ def predict_main():
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try:
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# 如果是测试环境，最高价最低价取excel文档
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if server_host == '192.168.100.53':
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logger.info('从excel文档获取市场信息平台指标')
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logger.info('从excel文档获取最高价最低价')
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df_zhibiaoshuju = get_high_low_data(df_zhibiaoshuju)
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else:
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logger.info('从市场信息平台获取数据')
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@ -310,7 +298,7 @@ def predict_main():
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end_time, df_zhibiaoshuju)
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except:
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logger.info('市场信息平台数据项-eta数据项 拼接失败')
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logger.info('最高最低价拼接失败')
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# 保存到xlsx文件的sheet表
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with pd.ExcelWriter(os.path.join(dataset, data_set)) as file:
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@ -318,14 +306,14 @@ def predict_main():
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df_zhibiaoliebiao.to_excel(file, sheet_name='指标列表', index=False)
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# 数据处理
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df = datachuli_juxiting(df_zhibiaoshuju, df_zhibiaoliebiao, y=global_config['y'], dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
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end_time=end_time)
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df = datachuli(df_zhibiaoshuju, df_zhibiaoliebiao, y=y, dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
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end_time=end_time)
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else:
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# 读取数据
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logger.info('读取本地数据：' + os.path.join(dataset, data_set))
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df, df_zhibiaoliebiao = getdata_zhoudu_juxiting(filename=os.path.join(dataset, data_set), y=y, dataset=dataset, add_kdj=add_kdj,
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is_timefurture=is_timefurture, end_time=end_time) # 原始数据，未处理
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df, df_zhibiaoliebiao = getdata(filename=os.path.join(dataset, data_set), y=y, dataset=dataset, add_kdj=add_kdj,
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is_timefurture=is_timefurture, end_time=end_time) # 原始数据，未处理
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# 更改预测列名称
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df.rename(columns={y: 'y'}, inplace=True)
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@ -395,8 +383,8 @@ def predict_main():
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# except Exception as e:
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# logger.info(f'更新accuracy表的y值失败：{e}')
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# 判断当前日期是不是周一
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is_weekday = datetime.datetime.now().weekday() == 0
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# 判断当前日期是不是周一 预测目标周度许转换，暂注释
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# is_weekday = datetime.datetime.strptime(global_config['end_time'], "%Y-%m-%d").weekday() == 0
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# if is_weekday:
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# logger.info('今天是周一，更新预测模型')
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# # 计算最近60天预测残差最低的模型名称
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@ -442,41 +430,45 @@ def predict_main():
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row, col = df.shape
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now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
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ex_Model_Juxiting(df,
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horizon=global_config['horizon'],
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input_size=global_config['input_size'],
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train_steps=global_config['train_steps'],
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val_check_steps=global_config['val_check_steps'],
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early_stop_patience_steps=global_config['early_stop_patience_steps'],
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is_debug=global_config['is_debug'],
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dataset=global_config['dataset'],
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is_train=global_config['is_train'],
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is_fivemodels=global_config['is_fivemodels'],
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val_size=global_config['val_size'],
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test_size=global_config['test_size'],
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settings=global_config['settings'],
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now=now,
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etadata=etadata,
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modelsindex=global_config['modelsindex'],
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data=data,
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is_eta=global_config['is_eta'],
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end_time=global_config['end_time'],
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)
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ex_Model(df,
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horizon=global_config['horizon'],
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input_size=global_config['input_size'],
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train_steps=global_config['train_steps'],
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val_check_steps=global_config['val_check_steps'],
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early_stop_patience_steps=global_config['early_stop_patience_steps'],
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is_debug=global_config['is_debug'],
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dataset=global_config['dataset'],
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is_train=global_config['is_train'],
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is_fivemodels=global_config['is_fivemodels'],
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val_size=global_config['val_size'],
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test_size=global_config['test_size'],
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settings=global_config['settings'],
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now=now,
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etadata=etadata,
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modelsindex=global_config['modelsindex'],
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data=data,
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is_eta=global_config['is_eta'],
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end_time=global_config['end_time'],
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)
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logger.info('模型训练完成')
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logger.info('训练数据绘图ing')
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model_results3 = model_losss_juxiting(
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sqlitedb, end_time=global_config['end_time'], is_fivemodels=global_config['is_fivemodels'])
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model_results3 = model_losss(sqlitedb, end_time=end_time)
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logger.info('训练数据绘图end')
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# # 模型报告
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logger.info('制作报告ing')
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title = f'{settings}--{end_time}-预测报告' # 报告标题
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reportname = f'聚烯烃PP大模型周度预测--{end_time}.pdf' # 报告文件名
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reportname = f'Brent原油大模型周度预测--{end_time}.pdf' # 报告文件名
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reportname = reportname.replace(':', '-') # 替换冒号
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pp_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
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reportname=reportname, sqlitedb=sqlitedb),
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brent_export_pdf(dataset=dataset,
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num_models=5 if is_fivemodels else 22,
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time=end_time,
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reportname=reportname,
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inputsize=global_config['horizon'],
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sqlitedb=sqlitedb
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),
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logger.info('制作报告end')
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logger.info('模型训练完成')
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@ -484,15 +476,6 @@ def predict_main():
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push_market_value()
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sql_inset_predict(global_config)
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# # LSTM 单变量模型
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# ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset)
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# # lstm 多变量模型
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# ex_Lstm_M(df,n_days=input_size,out_days=horizon,is_debug=is_debug,datasetpath=dataset)
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# # GRU 模型
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# # ex_GRU(df)
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# 发送邮件
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# m = SendMail(
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# username=username,
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@ -509,15 +492,10 @@ def predict_main():
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if __name__ == '__main__':
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# global end_time
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# 遍历2024-11-25 到 2024-12-3 之间的工作日日期
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# for i_time in pd.date_range('2025-4-14', '2025-4-15', freq='B'):
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# try:
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# global_config['end_time'] = i_time.strftime('%Y-%m-%d')
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# predict_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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for i_time in pd.date_range('2025-6-23', '2025-6-30', freq='B'):
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global_config['end_time'] = i_time.strftime('%Y-%m-%d')
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global_config['db_mysql'].connect()
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predict_main()
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predict_main()
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# push_market_value()
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# sql_inset_predict(global_config)
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# predict_main()
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# sql_inset_predict(global_config=global_config)
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