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    In [2]:
    # 本代码由可视化策略环境自动生成 2022年6月28日 10:56
# 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='2022-05-01',
    end_date='2022-05-31',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m7 = M.use_datasource.v1(
    instruments=m1.data,
    datasource_id='basic_info_IndustrySw',
    start_date='',
    end_date=''
)

m9 = M.select_columns.v3(
    input_ds=m7.data,
    columns='code,name',
    reverse_select=False
)

m2 = M.input_features.v1(
    features="""
code=industry_sw_level1_0.astype('str')
# 同时按日期和key做分组，求平均
pjzf=group_mean(industry_sw_level1_0, return_0)
pjzf20=mean(pjzf,20)

# 同时按日期和key做分组，求和
# group_sum(industry_level1_0, return_0)

# 同时按日期和key做分组，求当前值在分组内的排名百分比
hypm=group_rank(industry_sw_level3_0,return_0)
#"""
)

m3 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m2.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m4 = M.derived_feature_extractor.v3(
    input_data=m3.data,
    features=m2.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

m10 = M.join.v3(
    data1=m4.data,
    data2=m9.data,
    on='code',
    how='left',
    sort=False
)

m8 = M.dropnan.v2(
    input_data=m10.data
)

m6 = M.filter.v3(
    input_data=m8.data,
    expr='date==\'2022-05-17\'',
    output_left_data=False
)

m5 = M.sort.v5(
    input_ds=m6.data,
    sort_by='pjzf',
    group_by='date',
    keep_columns='--',
    ascending=False
)