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    In [1]:
    # 本代码由可视化策略环境自动生成 2022年12月1日 11:56
# 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。


m2 = M.instruments.v2(
    start_date='2020-01-01',
    end_date='2022-09-30',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m3 = M.input_features.v1(
    features="""open_0
close_0
high_0
low_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=1,
    init_ind_num=10,
    ngen=3,
    fitness_func='longshort_sharpe',
    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=10
)

    -- 开始第「1」次循环第「1」代挖掘 --

    去重前的个体数10
去重后的个体数10

    每代的平均适应度：[3.4096430239905153]
因子sum(correlation(turn_0, high_0, 9), 3)在训练集适应度值为6.310481979551782
因子shift(ts_min(low_0, 9), constant(3))在训练集适应度值为0.140416160024857
因子rank(rank(return_0))在训练集适应度值为18.548475050098798
因子add(open_0, open_0)在训练集适应度值为1.9513986668092793
因子rank(return_0)在训练集适应度值为18.548475050098798
因子product(turn_0, 10)在训练集适应度值为3.4264508104830806
因子ta_sma(open_0, 2)在训练集适应度值为1.6741345626553934
因子sub(ts_max(low_0, 10), div(high_0, turn_0))在训练集适应度值为0.8527036854812011
因子log(open_0)在训练集适应度值为2.996697032985282
因子ts_argmin(ts_argmax(close_0, 6), constant(2))在训练集适应度值为-11.637948686201309

    因子sum(correlation(turn_0, high_0, 9), 3)在测试集适应度值为13.247008748096649
因子rank(rank(return_0))在测试集适应度值为20.10745431252832
因子rank(return_0)在测试集适应度值为20.10745431252832
因子product(turn_0, 10)在测试集适应度值为3.9495198315135758
因子log(open_0)在测试集适应度值为3.385025032794358

    pass:5, record:10, population: 5

    下一代挖掘的个体数：10

    -- 开始第「1」次循环第「2」代挖掘 --

    去重前的个体数10
去重后的个体数9

    每代的平均适应度：[3.4096430239905153, 1.0642603811719904]
因子sum(covariance(turn_0, return_0, 9), 3)在训练集适应度值为10.685030795911299
因子close_0在训练集适应度值为2.00781685076183
因子min(ts_min(open_0, 6), low_0)在训练集适应度值为0.6493639801409912
因子mul(turn_0, low_0)在训练集适应度值为5.538666963658825
因子ts_min(close_0, 9)在训练集适应度值为0.4835494028468293
因子rank(return_0)在训练集适应度值为-1.9227668174485841
因子sum(turn_0, 3)在训练集适应度值为5.517185945257751
因子sub(open_0, ts_argmin(low_0, 4))在训练集适应度值为1.673188404975073
因子ts_rank(open_0, 7)在训练集适应度值为6.200496440115069

    因子sum(covariance(turn_0, return_0, 9), 3)在测试集适应度值为13.368608096005152
因子close_0在测试集适应度值为0.8744108341181166
因子mul(turn_0, low_0)在测试集适应度值为13.193938128833839
因子sum(turn_0, 3)在测试集适应度值为9.087290635820452
因子ts_rank(open_0, 7)在测试集适应度值为4.978111398912166

    pass:5, record:9, population: 5

    下一代挖掘的个体数：10

    -- 开始第「1」次循环第「3」代挖掘 --

    去重前的个体数10
去重后的个体数7

    每代的平均适应度：[3.4096430239905153, 1.0642603811719904, 2.5630107849997565]
因子sum(covariance(turn_0, return_0, 9), 9)在训练集适应度值为-2.124111546080266
因子sum(close_0, 3)在训练集适应度值为1.7202293637946655
因子mul(ts_min(std(open_0, 3), constant(1)), low_0)在训练集适应度值为6.468297500541676
因子sum(return_0, 3)在训练集适应度值为15.50835508512944
因子sum(turn_0, 3)在训练集适应度值为4.92184744208693
因子mul(return_0, low_0)在训练集适应度值为2.8263082258389223
因子mul(normalize(ta_sma(close_0, 1)), low_0)在训练集适应度值为0.7377957713617624

    因子mul(ts_min(std(open_0, 3), constant(1)), low_0)在测试集适应度值为5.834494660281935
因子sum(return_0, 3)在测试集适应度值为14.524900991273437
因子sum(turn_0, 3)在测试集适应度值为7.260887159873579
因子mul(return_0, low_0)在测试集适应度值为2.1719676569843673

    pass:4, record:7, population: 4

    下一代挖掘的个体数：10
    In [ ]: