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    In [8]:
    # 本代码由可视化策略环境自动生成 2022年9月4日 00:27
    # 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。
    
    
    m1 = M.instruments.v2(
        start_date='2021-01-01',
        end_date='2021-12-31',
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m3 = M.input_features.v1(
        features='sum(avg_turn_10 > 0.05, 20)'
    )
    
    m15 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    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
    )
    
    In [9]:
    df = m16.data.read()
    
    In [13]:
    df.tail()
    
    Out[13]:
    avg_turn_10 date instrument sum(avg_turn_10 > 0.05, 20)
    1061522 1.521125 2021-12-27 872925.BJA 20.0
    1061523 1.437943 2021-12-28 872925.BJA 20.0
    1061524 1.454976 2021-12-29 872925.BJA 20.0
    1061525 2.382988 2021-12-30 872925.BJA 20.0
    1061526 3.185065 2021-12-31 872925.BJA 20.0
    In [14]:
    T.plot(df.groupby("date")["sum(avg_turn_10 > 0.05, 20)"].agg(["mean", "min", "std"]))
    
    In [ ]: