PriceForecast/main.py

# 读取配置
# from config_jingbo import *
# from config_tansuanli import *
from config_juxiting import *
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,
                                    )
        if 'Brent' in y:
            df_zhibiaoshuju,df_zhibiaoliebiao = etadata.get_eta_api_yuanyou_data(data_set=data_set,dataset=dataset)  # 原始数据，未处理
        else:
            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(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(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")
        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
    brent_export_pdf(dataset=dataset,num_models = 5 if is_fivemodels else 22,time=end_time,
                reportname=reportname,sqlitedb=sqlitedb),
    # pp_export_pdf(dataset=dataset,num_models = 5 if is_fivemodels else 22,time=end_time,
                # reportname=reportname),
    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()