# 读取配置 from lib.dataread import * from lib.tools import * from models.nerulforcastmodels import ex_Model,model_losss,brent_export_pdf,tansuanli_export_pdf,pp_export_pdf,model_losss_juxiting import glob import torch torch.set_float32_matmul_precision("high") sqlitedb = SQLiteHandler(db_name) sqlitedb.connect() def predict_main(): signature = BinanceAPI(APPID, SECRET) etadata = EtaReader(signature=signature, classifylisturl = classifylisturl, classifyidlisturl=classifyidlisturl, edbcodedataurl=edbcodedataurl, edbcodelist=edbcodelist, edbdatapushurl=edbdatapushurl, edbdeleteurl=edbdeleteurl, edbbusinessurl=edbbusinessurl ) # 获取数据 if is_eta: # eta数据 logger.info('从eta获取数据...') signature = BinanceAPI(APPID, SECRET) etadata = EtaReader(signature=signature, classifylisturl = classifylisturl, classifyidlisturl=classifyidlisturl, edbcodedataurl=edbcodedataurl, edbcodelist=edbcodelist, edbdatapushurl=edbdatapushurl, edbdeleteurl=edbdeleteurl, edbbusinessurl=edbbusinessurl, ) df_zhibiaoshuju,df_zhibiaoliebiao = etadata.get_eta_api_pp_data(data_set=data_set,dataset=dataset) # 原始数据，未处理 # 数据处理 df = datachuli_juxiting(df_zhibiaoshuju,df_zhibiaoliebiao,y = y,dataset=dataset,add_kdj=add_kdj,is_timefurture=is_timefurture,end_time=end_time) else: logger.info('读取本地数据：'+os.path.join(dataset,data_set)) df = getdata_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 # 更改预测列名称 df.rename(columns={y:'y'},inplace=True) if is_edbnamelist: df = df[edbnamelist] 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 # 取第一行数据存储到数据库中 first_row = df[['ds','y']].tail(1) # 将最新真实值保存到数据库 if not sqlitedb.check_table_exists('trueandpredict'): first_row.to_sql('trueandpredict',sqlitedb.connection,index=False) else: for row in first_row.itertuples(index=False): row_dict = row._asdict() row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d %H:%M:%S') check_query = sqlitedb.select_data('trueandpredict',where_condition = f"ds = '{row.ds}'") if len(check_query) > 0: set_clause = ", ".join([f"{key} = '{value}'" for key, value in row_dict.items()]) sqlitedb.update_data('trueandpredict',set_clause,where_condition = f"ds = '{row.ds}'") continue sqlitedb.insert_data('trueandpredict',tuple(row_dict.values()),columns=row_dict.keys()) import datetime # 判断当前日期是不是周一 is_weekday = datetime.datetime.now().weekday() == 0 if is_weekday: logger.info('今天是周一，更新预测模型') # 计算最近20天预测残差最低的模型名称 model_results = sqlitedb.select_data('trueandpredict',order_by = "ds DESC",limit = "20") # 删除空值率为40%以上的列,删除空行 model_results = model_results.dropna(thresh=len(model_results)*0.6,axis=1) model_results = model_results.dropna() modelnames = model_results.columns.to_list()[2:] for col in model_results[modelnames].select_dtypes(include=['object']).columns: 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 for model in modelnames: model_results[f'{model}_abs_error_rate'] = abs(model_results['y'] - model_results[model]) / model_results['y'] # 获取每行对应的最小偏差率值 min_abs_error_rate_values = model_results.apply(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 min_abs_error_rate_column_name = model_results.apply(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 min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(lambda x: x.split('_')[0]) # 取出现次数最多的模型名称 most_common_model = min_abs_error_rate_column_name.value_counts().idxmax() logger.info(f"最近20天预测残差最低的模型名称：{most_common_model}") # 保存结果到数据库 if not sqlitedb.check_table_exists('most_model'): sqlitedb.create_table('most_model',columns="ds datetime, most_common_model TEXT") sqlitedb.insert_data('most_model',(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'),most_common_model,),columns=('ds','most_common_model',)) if is_corr: 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 logger.info(f"开始训练模型...") row,col = df.shape now = datetime.datetime.now().strftime('%Y%m%d%H%M%S') ex_Model(df, horizon=horizon, input_size=input_size, train_steps=train_steps, val_check_steps=val_check_steps, early_stop_patience_steps=early_stop_patience_steps, is_debug=is_debug, dataset=dataset, is_train=is_train, is_fivemodels=is_fivemodels, val_size=val_size, test_size=test_size, settings=settings, now=now, etadata = etadata, modelsindex = modelsindex, data = data, is_eta=is_eta, ) logger.info('模型训练完成') # # 模型评估 logger.info('训练数据绘图ing') model_results3 = model_losss_juxiting(sqlitedb) logger.info('训练数据绘图end') # 模型报告 logger.info('制作报告ing') title = f'{settings}--{now}-预测报告' # 报告标题 pp_export_pdf(dataset=dataset,num_models = 5 if is_fivemodels else 22,time=end_time, reportname=reportname,sqlitedb=sqlitedb), logger.info('制作报告end') logger.info('模型训练完成') # tansuanli_export_pdf(dataset=dataset,num_models = 5 if is_fivemodels else 22,end_time=end_time,reportname=reportname) # # LSTM 单变量模型 # ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset) # # lstm 多变量模型 # ex_Lstm_M(df,n_days=input_size,out_days=horizon,is_debug=is_debug,datasetpath=dataset) # # GRU 模型 # # ex_GRU(df) # 发送邮件 m = SendMail( username=username, passwd=passwd, recv=recv, title=title, content=content, file=max(glob.glob(os.path.join(dataset,'*.pdf')), key=os.path.getctime), ssl=ssl, ) m.send_mail() if __name__ == '__main__': predict_main()