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    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    In [69]:
    # 本代码由可视化策略环境自动生成 2022年3月24日 12:44
# 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='2018-02-01',
    end_date='2020-12-31',
    market='CN_STOCK_A',
    instrument_list=' ',
    max_count=0
)

m3 = M.use_datasource.v1(
    instruments=m1.data,
    datasource_id='bar1d_CN_STOCK_A',
    start_date='',
    end_date=''
)

m6 = M.input_features.v1(
    features="""
cond4= abs((close_0-close_1)/close_1) >0.03

cond6=st_status_0==0
"""
)

m5 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m6.data,
    start_date='',
    end_date='',
    before_start_days=58
)

m2 = M.derived_feature_extractor.v3(
    input_data=m5.data,
    features=m6.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=True,
    remove_extra_columns=False
)

m4 = M.filter.v3(
    input_data=m2.data,
    expr='cond4 and cond6',
    output_left_data=False
)

m8 = M.join.v3(
    data1=m4.data,
    data2=m3.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m7 = M.select_columns.v3(
    input_ds=m8.data,
    columns='date,instrument',
    reverse_select=False
)

m9 = M.input_features.v1(
    features="""turn_0
ts_max(amount_0,10)
mean(close_0,5)"""
)

m10 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m9.data,
    start_date='',
    end_date='',
    before_start_days=58
)

m11 = M.derived_feature_extractor.v3(
    input_data=m10.data,
    features=m9.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=True,
    remove_extra_columns=False
)

m12 = M.join.v3(
    data1=m7.data,
    data2=m11.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m13 = M.standardlize.v8(
    input_1=m12.data,
    columns_input=''
)

    列： ['date', 'instrument']
/y_2017: 0
/y_2018: 154561
/y_2019: 166854
/y_2020: 215682
    In [72]:
    m12.data.read_df()
    Out[72]:
    amount_0 close_0 date instrument turn_0 ts_max(amount_0,10) mean(close_0,5)
    0 3.326355e+09 1546.792969 2018-02-05 000001.SZA 1.378413 3.838735e+09 1495.339478
    1 3.641295e+09 1488.323120 2018-02-06 000001.SZA 1.526702 3.838735e+09 1502.781104
    2 4.521587e+09 1373.509521 2018-02-07 000001.SZA 1.977609 4.521587e+09 1478.755298
    3 3.308554e+09 1242.749756 2018-02-09 000001.SZA 1.669791 4.521587e+09 1396.897510
    4 1.573563e+09 1324.607544 2018-02-22 000001.SZA 0.749738 4.521587e+09 1271.665747
    ... ... ... ... ... ... ... ...
    213394 5.636281e+08 73.099998 2020-12-24 689009.SHA 12.703243 7.293031e+08 74.874001
    213395 6.488905e+08 80.099998 2020-12-28 689009.SHA 14.095573 7.293031e+08 76.779999
    213396 4.975511e+08 77.459999 2020-12-29 689009.SHA 10.811251 7.293031e+08 77.171999
    213397 5.506528e+08 80.910004 2020-12-30 689009.SHA 11.944640 7.293031e+08 77.334000
    213398 5.421623e+08 85.820000 2020-12-31 689009.SHA 11.130113 7.293031e+08 79.878000

    532388 rows × 7 columns

    In [71]:
    m13.data.read_df()
    Out[71]:
    amount_0 close_0 date instrument turn_0 ts_max(amount_0,10) mean(close_0,5)
    0 3.326355e+09 1546.792969 2018-02-05 000001.SZA 1.378413 3.838735e+09 1495.339478
    1 3.641295e+09 1488.323120 2018-02-06 000001.SZA 1.526702 3.838735e+09 1502.781104
    2 4.521587e+09 1373.509521 2018-02-07 000001.SZA 1.977609 4.521587e+09 1478.755298
    3 3.308554e+09 1242.749756 2018-02-09 000001.SZA 1.669791 4.521587e+09 1396.897510
    4 1.573563e+09 1324.607544 2018-02-22 000001.SZA 0.749738 4.521587e+09 1271.665747
    ... ... ... ... ... ... ... ...
    213394 5.636281e+08 73.099998 2020-12-24 689009.SHA 12.703243 7.293031e+08 74.874001
    213395 6.488905e+08 80.099998 2020-12-28 689009.SHA 14.095573 7.293031e+08 76.779999
    213396 4.975511e+08 77.459999 2020-12-29 689009.SHA 10.811251 7.293031e+08 77.171999
    213397 5.506528e+08 80.910004 2020-12-30 689009.SHA 11.944640 7.293031e+08 77.334000
    213398 5.421623e+08 85.820000 2020-12-31 689009.SHA 11.130113 7.293031e+08 79.878000

    532388 rows × 7 columns