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# 本代码由可视化策略环境自动生成 2019年7月15日 10:18
# 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='2010-01-01',
    end_date='2016-12-31',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m2 = M.input_features.v1(
    features="""
# #号开始的表示注释，注释需单独一行
# 多个特征，每行一个，可以包含基础特征和衍生特征，特征须为本平台特征
alpha1='+'.join(['close_{}*turn_{}'.format(k,k) for k in range(3)])
alpha2='-'.join(['close_{}*turn_{}'.format(k,j) for k,j in zip(range(0,3),range(1,7,2))])
alpha3='+'.join(['shift(close_0,{})/shift(close_0,{})-1'.format(k,j) for k,j in zip(range(0,66,22),range(22,88,22))])"""
)

m3 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m2.data,
    start_date='',
    end_date='',
    before_start_days=200
)

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={}
)

[2019-07-15 10:15:52.164226] INFO: bigquant: instruments.v2 开始运行..

[2019-07-15 10:15:52.216280] INFO: bigquant: 命中缓存

[2019-07-15 10:15:52.221470] INFO: bigquant: instruments.v2 运行完成[0.057217s].

[2019-07-15 10:15:52.228019] INFO: bigquant: input_features.v1 开始运行..

[2019-07-15 10:15:52.290075] INFO: bigquant: 命中缓存

[2019-07-15 10:15:52.293288] INFO: bigquant: input_features.v1 运行完成[0.06526s].

[2019-07-15 10:15:52.381459] INFO: bigquant: general_feature_extractor.v7 开始运行..

[2019-07-15 10:15:53.532672] INFO: 基础特征抽取: 年份 2009, 特征行数=213944

[2019-07-15 10:15:54.505036] INFO: 基础特征抽取: 年份 2010, 特征行数=431567

[2019-07-15 10:15:55.737958] INFO: 基础特征抽取: 年份 2011, 特征行数=511455

[2019-07-15 10:15:57.235641] INFO: 基础特征抽取: 年份 2012, 特征行数=565675

[2019-07-15 10:15:58.607983] INFO: 基础特征抽取: 年份 2013, 特征行数=564168

[2019-07-15 10:16:00.048254] INFO: 基础特征抽取: 年份 2014, 特征行数=569948

[2019-07-15 10:16:01.781399] INFO: 基础特征抽取: 年份 2015, 特征行数=569698

[2019-07-15 10:16:03.253450] INFO: 基础特征抽取: 年份 2016, 特征行数=641546

[2019-07-15 10:16:03.570671] INFO: 基础特征抽取: 总行数: 4068001

[2019-07-15 10:16:03.583817] INFO: bigquant: general_feature_extractor.v7 运行完成[11.202368s].

[2019-07-15 10:16:03.589761] INFO: bigquant: derived_feature_extractor.v3 开始运行..

[2019-07-15 10:16:06.500452] INFO: derived_feature_extractor: 提取完成 alpha1=close_0*turn_0+close_1*turn_1+close_2*turn_2, 0.043s

[2019-07-15 10:16:06.539609] INFO: derived_feature_extractor: 提取完成 alpha2=close_0*turn_1-close_1*turn_3-close_2*turn_5, 0.036s

[2019-07-15 10:16:11.954975] INFO: derived_feature_extractor: 提取完成 alpha3=shift(close_0,0)/shift(close_0,22)-1+shift(close_0,22)/shift(close_0,44)-1+shift(close_0,44)/shift(close_0,66)-1, 5.412s

[2019-07-15 10:16:12.877111] INFO: derived_feature_extractor: /y_2009, 213944

[2019-07-15 10:16:13.136811] INFO: derived_feature_extractor: /y_2010, 431567

[2019-07-15 10:16:13.883000] INFO: derived_feature_extractor: /y_2011, 511455

[2019-07-15 10:16:14.455743] INFO: derived_feature_extractor: /y_2012, 565675

[2019-07-15 10:16:15.047998] INFO: derived_feature_extractor: /y_2013, 564168

[2019-07-15 10:16:15.670760] INFO: derived_feature_extractor: /y_2014, 569948

[2019-07-15 10:16:16.384204] INFO: derived_feature_extractor: /y_2015, 569698

[2019-07-15 10:16:17.132131] INFO: derived_feature_extractor: /y_2016, 641546

[2019-07-15 10:16:17.838109] INFO: bigquant: derived_feature_extractor.v3 运行完成[14.248337s].