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# 本代码由可视化策略环境自动生成 2022年9月10日 10:01
# 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。


m2 = M.instruments.v2(
    start_date='2018-01-01',
    end_date='2021-10-20',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m3 = M.input_features.v1(
    features="""close_0
open_0
high_0
low_0 
amount_0
turn_0 
return_0"""
)

m4 = M.general_feature_extractor.v7(
    instruments=m2.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m5 = M.derived_feature_extractor.v3(
    input_data=m4.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

m1 = M.genetic_algorithm.v1(
    instruments=m2.data,
    feature_datas=m5.data,
    all_start_date='',
    all_end_date='',
    short_date_range_ratio=0.7,
    return_field='wap_3_vwap_buy',
    rebalance_period=1,
    train_test_ratio=0.75,
    train_validate_ratio=0.75,
    mtime=2,
    init_ind_num=10,
    ngen=3,
    fitness_func='long_return',
    train_fitness=2,
    test_fitness=1.6,
    ir_type='ir',
    cxpb=0.5,
    mutpb=0.3,
    mutspb=0.3,
    mutnrpb=0.3,
    constant='1,11',
    pool_processes_limit=5,
    m_cached=False
)

[2022-09-10 09:57:41.080953] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-09-10 09:57:41.221859] INFO: moduleinvoker: instruments.v2 运行完成[0.140921s].

[2022-09-10 09:57:41.234762] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-09-10 09:57:41.247651] INFO: moduleinvoker: 命中缓存

[2022-09-10 09:57:41.250020] INFO: moduleinvoker: input_features.v1 运行完成[0.015278s].

[2022-09-10 09:57:41.290176] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-09-10 09:57:43.864533] INFO: 基础特征抽取: 年份 2017, 特征行数=193398

[2022-09-10 09:57:47.348484] INFO: 基础特征抽取: 年份 2018, 特征行数=816987

[2022-09-10 09:57:51.552455] INFO: 基础特征抽取: 年份 2019, 特征行数=884867

[2022-09-10 09:58:00.108053] INFO: 基础特征抽取: 年份 2020, 特征行数=945961

[2022-09-10 09:58:05.725116] INFO: 基础特征抽取: 年份 2021, 特征行数=822395

[2022-09-10 09:58:05.892927] INFO: 基础特征抽取: 总行数: 3663608

[2022-09-10 09:58:05.902517] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[24.612353s].

[2022-09-10 09:58:05.922210] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-09-10 09:58:13.306827] INFO: derived_feature_extractor: /y_2017, 193398

[2022-09-10 09:58:14.909486] INFO: derived_feature_extractor: /y_2018, 816987

[2022-09-10 09:58:16.607879] INFO: derived_feature_extractor: /y_2019, 884867

[2022-09-10 09:58:22.055122] INFO: derived_feature_extractor: /y_2020, 945961

[2022-09-10 09:58:24.826307] INFO: derived_feature_extractor: /y_2021, 822395

[2022-09-10 09:58:26.755562] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[20.833317s].

[2022-09-10 09:58:27.226763] INFO: moduleinvoker: genetic_algorithm.v1 开始运行..

[2022-09-10 09:58:28.986058] INFO: 遗传规划: loading data from 2018-01-01 to 2020-08-30...

[2022-09-10 09:59:43.529243] INFO: 遗传规划: loaded data from 2018-01-01 to 2020-08-30 successfully

[2022-09-10 09:59:43.811149] INFO: 遗传规划: loading data from 2018-01-01 to 2021-10-20...

[2022-09-10 10:00:50.389563] INFO: 遗传规划: loaded data from 2018-01-01 to 2021-10-20 successfully

[2022-09-10 10:00:50.494039] INFO: 遗传规划: 初始化toolbox ...

[2022-09-10 10:00:50.560122] ERROR: moduleinvoker: module name: genetic_algorithm, module version: v1, trackeback: TypeError: issubclass() arg 2 must be a class or tuple of classes

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-1-64cf9ee89408> in <module>
     39 )
     40 
---> 41 m1 = M.genetic_algorithm.v1(
     42     instruments=m2.data,
     43     feature_datas=m5.data,

TypeError: issubclass() arg 2 must be a class or tuple of classes