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    In [6]:
    # 本代码由可视化策略环境自动生成 2022年6月9日 19:37
# 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。


import datetime
# Python 代码入口函数，input_1/2/3 对应三个输入端，data_1/2/3 对应三个输出端
def m11_run_bigquant_run(input_1, input_2, input_3):
    # 示例代码如下。在这里编写您的代码
    d=datetime.datetime.now()
    dstr=str(d.year)+'-%02d-%02d'%(d.month,d.day)
    print (dstr)
    n=50000
    
    df = input_1.read_df()
    
    for x in range(0,len(df),n):
        print (df[x:x+n])
        #df[x:x+n].to_hdf("userlib/data_"+str(x)+'.csv',key="data",mode="w",complevel=9)
        df[x:x+n].to_csv("userlib/etf_"+str(x)+'.csv',index=False)
    return Outputs(data_1=df)

# 后处理函数，可选。输入是主函数的输出，可以在这里对数据做处理，或者返回更友好的outputs数据格式。此函数输出不会被缓存。
def m11_post_run_bigquant_run(outputs):
    return outputs


m3 = M.input_features.v1(
    features="""return_0
open_0"""
)

m9 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2021-01-15'),
    end_date=T.live_run_param('trading_date', '2022-06-07'),
    market='CN_FUND',
    instrument_list='510050.HOF',
    max_count=0,
    m_cached=False
)

m17 = M.general_feature_extractor.v7(
    instruments=m9.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=30
)

m18 = M.derived_feature_extractor.v3(
    input_data=m17.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=True,
    remove_extra_columns=True
)

m11 = M.cached.v3(
    input_1=m18.data,
    run=m11_run_bigquant_run,
    post_run=m11_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

    ---------------------------------------------------------------------------
Exception                                 Traceback (most recent call last)
<ipython-input-6-debe64f32341> in <module>
     39 )
     40 
---> 41 m17 = M.general_feature_extractor.v7(
     42     instruments=m9.data,
     43     features=m3.data,

Exception: no features extracted.