克隆策略

    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    In [ ]:
    # 本代码由可视化策略环境自动生成 2021年6月22日06:08
# 本代码单元只能在可视化模式下编辑。您也可以拷贝代码，粘贴到新建的代码单元或者策略，然后修改。


def cal_DMA(df_total, close,volume, market_cap):
    import pandas as pd
    df_total['factor'] = df_total['amount_0']/df_total['market_cap_float_0']
    
    def calDMA(df,index):
        if(index==0):
            df.loc[index,'dma'] = df.loc[0,'close_0']
        else:
            predma = calDMA(df,index-1)
            df.loc[index,'dma'] = df.loc[index,'close_0'] * df.loc[index,'factor']+(1-df.loc[index,'factor'])*predma
        return df.loc[index,'dma']
    
    def getDMA(df):
        df = df.reset_index()
        calDMA(df,df.index[-1])
        return df
    
    df1=df_total.groupby(["instrument"]).apply(getDMA)
    df1 = df1.droplevel('instrument')
    merge_df = pd.merge(df_total, df1[['date',"instrument",'dma']], on=['date','instrument'], how='left')
    return merge_df['dma']

 
m16_user_functions_bigquant_run = {
    'cal_DMA':cal_DMA
}


m1 = M.instruments.v2(
    start_date='2014-01-01',
    end_date='2015-01-01',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m3 = M.input_features.v1(
    features="""dma = cal_DMA(close_0,amount_0, market_cap_float_0)
 """
)

m15 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m16 = M.derived_feature_extractor.v3(
    input_data=m15.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions=m16_user_functions_bigquant_run
)