复制链接
克隆策略

    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    In [20]:
    # 本代码由可视化策略环境自动生成 2022年11月29日 20:05
# 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='2013-01-01',
    end_date='2022-11-28',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m3 = M.input_features.v1(
    features="""close_0
high_0
low_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={}
)

m7 = M.input_features.v1(
    features="""buy_cond_1 = where(rank(mf_net_amount_m_0)>0.6, 1, 0)
buy_cond_2 = 1
buy_cond_3 = where(sum(price_limit_status_0==3, 5)==1, 1, 0)




"""
)

m9 = M.general_feature_extractor.v7(
    instruments=m2.data,
    features=m7.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m10 = M.derived_feature_extractor.v3(
    input_data=m9.data,
    features=m7.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

m6 = M.chinaa_stock_filter.v1(
    input_data=m10.data,
    index_constituent_cond=['全部'],
    board_cond=['上证主板', '深证主板'],
    industry_cond=['全部'],
    st_cond=['正常'],
    delist_cond=['非退市'],
    output_left_data=False
)

m8 = M.filter.v3(
    input_data=m6.data,
    expr='buy_cond_1==1&buy_cond_3==1',
    output_left_data=False
)

m11 = M.dropnan.v2(
    input_data=m8.data
)

m13 = M.select_columns.v3(
    input_ds=m11.data,
    columns='date,instrument',
    reverse_select=False
)

m14 = M.join.v3(
    data1=m5.data,
    data2=m13.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m1 = M.genetic_algorithm.v1(
    instruments=m2.data,
    feature_datas=m14.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=5,
    init_ind_num=50,
    ngen=5,
    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='1,20',
    pool_processes_limit=4,
    m_cached=False
)

    -- 开始第「1」次循环第「1」代挖掘 --

    去重前的个体数50
去重后的个体数49

    每代的平均适应度：[-0.2738027653629295]
因子log(low_0)在训练集适应度值为-0.847220597544614
因子ts_argmin(close_0, 8)在训练集适应度值为0.42078871309146804
因子ts_argmin(ts_max(close_0, 18), constant(7))在训练集适应度值为0.07853387426701278
因子min(high_0, low_0)在训练集适应度值为-0.8461702830903864
因子decay_linear(ta_sma(close_0, 12), constant(16))在训练集适应度值为-0.5442676954455469
因子covariance(ts_argmin(close_0, 19), ts_rank(high_0, 3), constant(5))在训练集适应度值为-1.6639930839518235
因子sign(mul(close_0, high_0))在训练集适应度值为nan
因子std(delta(close_0, 10), constant(19))在训练集适应度值为-0.04687075206909976
因子std(rank(close_0), constant(14))在训练集适应度值为0.6193600807392645
因子shift(add(high_0, turn_0), constant(14))在训练集适应度值为-1.2717230224575728
因子delta(delta(high_0, 2), constant(18))在训练集适应度值为-0.20261144134076797
因子div(return_0, close_0)在训练集适应度值为-0.5836768917215
因子add(low_0, turn_0)在训练集适应度值为-0.7920291887922869
因子ta_sma(return_0, 18)在训练集适应度值为-1.9203850273613516
因子max(return_0, close_0)在训练集适应度值为0.14035111021092236
因子product(ts_max(low_0, 9), constant(9))在训练集适应度值为-0.7773842410113363
因子covariance(delta(high_0, 11), rank(close_0), constant(17))在训练集适应度值为nan
因子log(shift(turn_0, 17))在训练集适应度值为-1.8366732019066274
因子sign(turn_0)在训练集适应度值为nan
因子ts_max(sum(turn_0, 1), 3)在训练集适应度值为-0.06266567591521112
因子ts_rank(low_0, 16)在训练集适应度值为0.10490920396445996
因子ta_sma(close_0, 15)在训练集适应度值为-3.7568318205217444
因子ta_sma(ts_max(high_0, 3), constant(10))在训练集适应度值为-0.940452359562614
因子max(high_0, high_0)在训练集适应度值为-0.899352179349475
因子sub(return_0, close_0)在训练集适应度值为-1.475189792486048
因子ta_sma(close_0, constant(1))在训练集适应度值为-0.8476981027065214
因子sign(return_0)在训练集适应度值为nan
因子sign(close_0)在训练集适应度值为nan
因子ts_argmin(return_0, 18)在训练集适应度值为-1.281120670316755
因子abs(close_0)在训练集适应度值为-0.8476981027065214
因子ts_rank(mul(high_0, turn_0), constant(13))在训练集适应度值为-1.1460896941620564
因子abs(high_0)在训练集适应度值为-0.899352179349475
因子sum(ts_argmin(close_0, 6), constant(7))在训练集适应度值为-1.8119965975168904
因子rank(rank(low_0))在训练集适应度值为-1.1049881671487574
因子ts_argmax(close_0, 3)在训练集适应度值为-0.737739969976222
因子sub(close_0, return_0)在训练集适应度值为-0.8404777025764927
因子add(sum(return_0, 6), sign(close_0))在训练集适应度值为-3.3752910680481265
因子shift(div(turn_0, return_0), constant(11))在训练集适应度值为-0.838788711029345
因子std(close_0, 18)在训练集适应度值为-1.2413226905087422
因子sub(return_0, low_0)在训练集适应度值为-1.4306067269442628
因子sub(std(return_0, 7), abs(close_0))在训练集适应度值为-2.1288198025409577
因子std(close_0, 4)在训练集适应度值为-2.923039155975881
因子abs(low_0)在训练集适应度值为-0.8461702830903864
因子ts_rank(min(turn_0, high_0), constant(5))在训练集适应度值为-2.729825259010692
因子abs(close_0)在训练集适应度值为0.14035111021092236
因子div(close_0, high_0)在训练集适应度值为0.05660707655052985
因子normalize(turn_0)在训练集适应度值为-1.0095106446031976
因子ts_rank(close_0, 17)在训练集适应度值为-0.6902404967434357
因子std(return_0, 19)在训练集适应度值为-2.2044815845879175

    pass:0, record:49, population: 49

    下一代挖掘的个体数：50

    -- 开始第「1」次循环第「2」代挖掘 --

    去重前的个体数50
去重后的个体数39

    每代的平均适应度：[-0.2738027653629295, -0.1533942166524678]
因子sign(turn_0)在训练集适应度值为nan
因子std(ts_argmax(high_0, 10), constant(19))在训练集适应度值为-0.8764438650023553
因子std(delta(turn_0, 10), constant(19))在训练集适应度值为nan
因子add(low_0, close_0)在训练集适应度值为-0.8772729954380408
因子shift(div(turn_0, return_0), constant(10))在训练集适应度值为-0.9141177014639519
因子ta_sma(ts_max(high_0, 3), constant(11))在训练集适应度值为-0.7183303537781918
因子ta_sma(turn_0, constant(constant(14)))在训练集适应度值为-3.081813868755839
因子ts_min(high_0, 11)在训练集适应度值为-5.449331629863915
因子ts_rank(mul(close_0, turn_0), constant(16))在训练集适应度值为-0.2286126293780266
因子close_0在训练集适应度值为-0.8476981027065214
因子product(ts_max(low_0, 9), 6)在训练集适应度值为-2.1128120503367915
因子add(sum(return_0, constant(9)), sign(turn_0))在训练集适应度值为-4.435923723312808
因子abs(close_0)在训练集适应度值为0.14035111021092236
因子high_0在训练集适应度值为-0.899352179349475
因子abs(shift(low_0, 13))在训练集适应度值为-0.8903800058377234
因子div(return_0, close_0)在训练集适应度值为-0.5836768917215
因子add(sub(sign(high_0), ts_min(close_0, 12)), turn_0)在训练集适应度值为-1.6055219280681392
因子sign(high_0)在训练集适应度值为nan
因子min(close_0, close_0)在训练集适应度值为-0.8769926368339077
因子close_0在训练集适应度值为0.14035111021092236
因子abs(ts_argmin(abs(low_0), constant(4)))在训练集适应度值为-2.045488390813027
因子ts_argmax(turn_0, 3)在训练集适应度值为-0.8394161576852793
因子ts_rank(low_0, 16)在训练集适应度值为0.10490920396445996
因子std(close_0, 17)在训练集适应度值为-0.6763227054273472
因子decay_linear(ta_sma(close_0, 12), constant(3))在训练集适应度值为-2.7777710946394074
因子log(high_0)在训练集适应度值为-0.6118414256911656
因子std(close_0, 12)在训练集适应度值为0.3558806720017928
因子min(close_0, turn_0)在训练集适应度值为0.16515545625051445
因子std(rank(high_0), 14)在训练集适应度值为-3.4123198007405695
因子sign(close_0)在训练集适应度值为nan
因子product(close_0, constant(10))在训练集适应度值为nan
因子product(ts_max(low_0, 13), constant(9))在训练集适应度值为-0.3511799577797139
因子div(close_0, return_0)在训练集适应度值为-0.9283699579006698
因子sub(high_0, close_0)在训练集适应度值为-1.3345772507359064
因子ta_sma(covariance(turn_0, return_0, 1), 10)在训练集适应度值为nan
因子ts_max(turn_0, 3)在训练集适应度值为-0.06266567591521112
因子return_0在训练集适应度值为2.966799078652266
因子ts_argmin(close_0, constant(7))在训练集适应度值为-1.3115341009466914
因子decay_linear(sub(close_0, close_0), constant(16))在训练集适应度值为nan

    因子return_0在测试集适应度值为2.1723126285272114

    pass:1, record:39, population: 1

    下一代挖掘的个体数：50

    -- 开始第「1」次循环第「3」代挖掘 --

    去重前的个体数50
去重后的个体数16

    每代的平均适应度：[-0.2738027653629295, -0.1533942166524678, -0.05753210349173473]
因子return_0在训练集适应度值为2.966799078652266
因子high_0在训练集适应度值为-0.899352179349475
因子low_0在训练集适应度值为-0.8461702830903864
因子sign(turn_0)在训练集适应度值为nan
因子ta_sma(close_0, 16)在训练集适应度值为-0.06618461723304775
因子turn_0在训练集适应度值为-1.0095106446031976
因子rank(return_0)在训练集适应度值为1.7852849197233474
因子add(return_0, high_0)在训练集适应度值为-0.9122344142764752
因子sum(low_0, 17)在训练集适应度值为-1.5074129446956548
因子close_0在训练集适应度值为0.14035111021092236
因子normalize(high_0)在训练集适应度值为-0.899352179349475
因子mul(close_0, close_0)在训练集适应度值为0.1608372100452606
因子div(high_0, return_0)在训练集适应度值为-0.8780738497862343
因子div(rank(turn_0), shift(close_0, 18))在训练集适应度值为0.23824588697268545
因子close_0在训练集适应度值为-0.8476981027065214
因子sub(ts_argmin(low_0, 9), sum(high_0, 13))在训练集适应度值为-3.9785083535177543

    因子return_0在测试集适应度值为2.1723126285272114

    pass:1, record:16, population: 1

    下一代挖掘的个体数：50

    -- 开始第「1」次循环第「4」代挖掘 --

    去重前的个体数50
去重后的个体数14

    每代的平均适应度：[-0.2738027653629295, -0.1533942166524678, -0.05753210349173473, 0.025449858394693388]
因子return_0在训练集适应度值为2.966799078652266
因子sum(high_0, 6)在训练集适应度值为-2.3394128180590346
因子abs(product(low_0, 7))在训练集适应度值为nan
因子delta(delta(return_0, 10), constant(16))在训练集适应度值为-0.04377193690166693
因子high_0在训练集适应度值为-0.899352179349475
因子ts_max(ts_argmin(turn_0, 1), constant(19))在训练集适应度值为nan
因子ts_argmin(close_0, 16)在训练集适应度值为-1.213333444320314
因子delta(ts_min(turn_0, 14), constant(13))在训练集适应度值为nan
因子ta_sma(product(close_0, 19), 14)在训练集适应度值为nan
因子correlation(low_0, low_0, 3)在训练集适应度值为-3.5603649803835524
因子sign(turn_0)在训练集适应度值为nan
因子ts_rank(close_0, 9)在训练集适应度值为2.7045461120317955
因子ts_max(turn_0, 4)在训练集适应度值为-3.015904057413945
因子decay_linear(abs(close_0), constant(4))在训练集适应度值为-0.026533926533481866

    因子return_0在测试集适应度值为2.1723126285272114
因子ts_rank(close_0, 9)在测试集适应度值为1.3116798891217518

    pass:2, record:14, population: 2

    下一代挖掘的个体数：50

    -- 开始第「1」次循环第「5」代挖掘 --

    去重前的个体数50
去重后的个体数18

    每代的平均适应度：[-0.2738027653629295, -0.1533942166524678, -0.05753210349173473, 0.025449858394693388, -0.07816066142339342]
因子ts_rank(high_0, 3)在训练集适应度值为-1.5764511980677924
因子return_0在训练集适应度值为2.966799078652266
因子close_0在训练集适应度值为0.14035111021092236
因子ts_max(normalize(low_0), 3)在训练集适应度值为-0.5484913318584094
因子ts_rank(turn_0, 9)在训练集适应度值为-2.370529821214508
因子ts_rank(close_0, constant(13))在训练集适应度值为2.6800127722831015
因子ts_rank(close_0, 9)在训练集适应度值为2.7045461120317955
因子product(high_0, 14)在训练集适应度值为nan
因子add(product(turn_0, 1), sum(high_0, 2))在训练集适应度值为-1.463192088023135
因子covariance(high_0, turn_0, 6)在训练集适应度值为-1.484265407073137
因子high_0在训练集适应度值为-0.899352179349475
因子log(low_0)在训练集适应度值为-0.847220597544614
因子decay_linear(turn_0, 16)在训练集适应度值为-1.4896957797821841
因子ts_rank(abs(abs(low_0)), 9)在训练集适应度值为-3.499501917868069
因子ts_rank(rank(high_0), 13)在训练集适应度值为-2.312004024739744
因子product(ts_argmin(low_0, 6), 12)在训练集适应度值为-0.10486795222274098
因子shift(low_0, 10)在训练集适应度值为0.18329108589716633
因子product(add(close_0, high_0), constant(15))在训练集适应度值为nan

    因子return_0在测试集适应度值为2.1723126285272114
因子ts_rank(close_0, constant(13))在测试集适应度值为1.3850911276549203
因子ts_rank(close_0, 9)在测试集适应度值为1.3116798891217518

    pass:3, record:18, population: 3

    下一代挖掘的个体数：50

    -- 开始第「2」次循环第「1」代挖掘 --

    去重前的个体数50
去重后的个体数49

    每代的平均适应度：[-0.22162481765687597]
因子decay_linear(ts_argmax(return_0, 15), constant(5))在训练集适应度值为-3.255916945171633
因子ts_min(close_0, 14)在训练集适应度值为-2.9749603943114997
因子mul(high_0, high_0)在训练集适应度值为-1.0634617832595352
因子delta(high_0, constant(6))在训练集适应度值为-2.763583386899871
因子correlation(close_0, low_0, 16)在训练集适应度值为nan
因子product(return_0, 7)在训练集适应度值为nan
因子ta_sma(sum(return_0, 18), constant(14))在训练集适应度值为-1.5819005821900975
因子min(low_0, return_0)在训练集适应度值为2.966799078652266
因子sub(sign(close_0), decay_linear(high_0, 17))在训练集适应度值为-2.2919324879921392
因子mul(sub(high_0, high_0), ts_max(turn_0, 11))在训练集适应度值为nan
因子max(close_0, turn_0)在训练集适应度值为-0.9578157279076831
因子covariance(min(high_0, close_0), normalize(low_0), constant(5))在训练集适应度值为-1.9456774535258874
因子ts_max(sum(close_0, 16), constant(19))在训练集适应度值为-0.9455432247114686
因子sub(return_0, close_0)在训练集适应度值为-1.3805321686331393
因子add(normalize(low_0), log(return_0))在训练集适应度值为0.06002265857071834
因子correlation(close_0, return_0, 11)在训练集适应度值为-2.9163817073416767
因子sign(close_0)在训练集适应度值为nan
因子sign(high_0)在训练集适应度值为nan
因子ts_max(low_0, 14)在训练集适应度值为-3.2741322369498986
因子product(high_0, 5)在训练集适应度值为0.04568362491213082
因子covariance(low_0, turn_0, 15)在训练集适应度值为-2.4822536311708405
因子product(close_0, 8)在训练集适应度值为0.28544347516176594
因子normalize(close_0)在训练集适应度值为-0.8476981027065214
因子sub(product(return_0, 16), ta_sma(close_0, 2))在训练集适应度值为nan
因子ts_argmax(return_0, 15)在训练集适应度值为-4.510281164611354
因子ta_sma(high_0, 1)在训练集适应度值为-0.899352179349475
因子product(ts_argmin(turn_0, 11), constant(8))在训练集适应度值为-1.285002624627623
因子add(low_0, high_0)在训练集适应度值为-0.9330158117378282
因子log(ts_min(high_0, 18))在训练集适应度值为-1.2514850494837677
因子max(close_0, return_0)在训练集适应度值为-0.8734474736300971
因子ts_rank(low_0, 11)在训练集适应度值为-2.9455933024584957
因子correlation(turn_0, return_0, 18)在训练集适应度值为-1.0652736710618553
因子covariance(close_0, return_0, 5)在训练集适应度值为-3.0638004228753037
因子log(close_0)在训练集适应度值为-0.7388599896612504
因子normalize(turn_0)在训练集适应度值为-1.0095106446031976
因子abs(normalize(return_0))在训练集适应度值为2.3035683440953063
因子ts_min(close_0, 17)在训练集适应度值为-0.05878917230029014
因子div(product(turn_0, 10), shift(turn_0, 19))在训练集适应度值为nan
因子decay_linear(product(high_0, 3), constant(1))在训练集适应度值为-3.3202068154424724
因子ts_argmax(mul(return_0, close_0), constant(2))在训练集适应度值为-0.9642428654357448
因子abs(high_0)在训练集适应度值为-0.899352179349475
因子std(return_0, 16)在训练集适应度值为-1.6437414136796569
因子correlation(high_0, close_0, 14)在训练集适应度值为-3.579523123831351
因子covariance(sub(high_0, low_0), div(return_0, close_0), constant(7))在训练集适应度值为-2.3214873572281984
因子abs(return_0)在训练集适应度值为2.966799078652266
因子ts_argmin(high_0, 10)在训练集适应度值为-3.5020479795797477
因子sum(close_0, 14)在训练集适应度值为-3.4152529294804266
因子ts_max(mul(turn_0, low_0), constant(17))在训练集适应度值为-1.499814787548442
因子ts_min(std(low_0, 2), constant(16))在训练集适应度值为nan

    因子min(low_0, return_0)在测试集适应度值为2.1723126285272114
因子abs(normalize(return_0))在测试集适应度值为1.1823197177888314
因子abs(return_0)在测试集适应度值为2.1723126285272114

    pass:3, record:49, population: 3

    下一代挖掘的个体数：50

    -- 开始第「2」次循环第「2」代挖掘 --

    去重前的个体数50
去重后的个体数22

    每代的平均适应度：[-0.22162481765687597, 0.009896131336176913]
因子min(low_0, return_0)在训练集适应度值为2.966799078652266
因子min(low_0, div(close_0, close_0))在训练集适应度值为-0.7189301118248982
因子sign(low_0)在训练集适应度值为nan
因子min(return_0, return_0)在训练集适应度值为2.966799078652266
因子close_0在训练集适应度值为-0.8476981027065214
因子abs(high_0)在训练集适应度值为-0.899352179349475
因子min(close_0, return_0)在训练集适应度值为2.966799078652266
因子abs(close_0)在训练集适应度值为0.14035111021092236
因子abs(return_0)在训练集适应度值为2.966799078652266
因子min(low_0, close_0)在训练集适应度值为-0.8461702830903864
因子abs(div(decay_linear(return_0, 11), sign(close_0)))在训练集适应度值为-3.8582073812226394
因子min(low_0, low_0)在训练集适应度值为-0.8461702830903864
因子abs(close_0)在训练集适应度值为-0.8476981027065214
因子low_0在训练集适应度值为-0.8461702830903864
因子min(div(close_0, high_0), return_0)在训练集适应度值为0.5238838373047799
因子abs(low_0)在训练集适应度值为-0.8461702830903864
因子min(low_0, turn_0)在训练集适应度值为-1.0206958199547311
因子abs(turn_0)在训练集适应度值为-1.0095106446031976
因子min(low_0, ts_max(div(low_0, close_0), constant(5)))在训练集适应度值为-0.008109751271399648
因子abs(max(ts_rank(close_0, 1), ts_argmin(low_0, 14)))在训练集适应度值为-3.5487702993626535
因子log(normalize(return_0))在训练集适应度值为nan
因子abs(normalize(low_0))在训练集适应度值为-1.8110428924569142

    因子min(low_0, return_0)在测试集适应度值为2.1723126285272114
因子min(return_0, return_0)在测试集适应度值为2.1723126285272114
因子min(close_0, return_0)在测试集适应度值为2.1723126285272114
因子abs(return_0)在测试集适应度值为2.1723126285272114

    pass:4, record:22, population: 4

    下一代挖掘的个体数：50

    -- 开始第「2」次循环第「3」代挖掘 --

    去重前的个体数50
去重后的个体数24

    每代的平均适应度：[-0.22162481765687597, 0.009896131336176913, 0.047010060860965916]
因子abs(turn_0)在训练集适应度值为-1.0095106446031976
因子min(add(covariance(low_0, low_0, 14), abs(low_0)), return_0)在训练集适应度值为-1.2723635994027398
因子min(return_0, turn_0)在训练集适应度值为3.2711429509050887
因子min(turn_0, close_0)在训练集适应度值为-1.043982278327278
因子min(low_0, close_0)在训练集适应度值为-0.8461702830903864
因子min(return_0, return_0)在训练集适应度值为2.966799078652266
因子min(low_0, low_0)在训练集适应度值为-0.8461702830903864
因子min(sub(turn_0, return_0), return_0)在训练集适应度值为3.2619590214153305
因子min(close_0, return_0)在训练集适应度值为2.966799078652266
因子min(low_0, return_0)在训练集适应度值为2.966799078652266
因子sum(turn_0, 16)在训练集适应度值为-1.4949517055855637
因子min(abs(correlation(high_0, low_0, 10)), low_0)在训练集适应度值为-3.1172042944212017
因子abs(close_0)在训练集适应度值为-0.8476981027065214
因子abs(return_0)在训练集适应度值为2.966799078652266
因子std(div(low_0, high_0), constant(2))在训练集适应度值为-0.15731177357560613
因子min(return_0, min(close_0, high_0))在训练集适应度值为2.966799078652266
因子abs(delta(return_0, 18))在训练集适应度值为-2.8927327209011424
因子abs(sum(ts_max(turn_0, 1), constant(5)))在训练集适应度值为-2.9946197138296333
因子ta_sma(return_0, 8)在训练集适应度值为0.35384982669713777
因子return_0在训练集适应度值为2.966799078652266
因子min(close_0, ts_max(correlation(high_0, turn_0, 17), constant(7)))在训练集适应度值为-0.8766776948515331
因子std(high_0, 1)在训练集适应度值为nan
因子std(return_0, 15)在训练集适应度值为-4.038787098547623
因子add(decay_linear(close_0, 19), std(low_0, 17))在训练集适应度值为-1.7325018938315833

    因子min(return_0, turn_0)在测试集适应度值为1.9628287395834418
因子min(return_0, return_0)在测试集适应度值为2.1723126285272114
因子min(sub(turn_0, return_0), return_0)在测试集适应度值为1.3114825088664688
因子min(close_0, return_0)在测试集适应度值为2.1723126285272114
因子min(low_0, return_0)在测试集适应度值为2.1723126285272114
因子abs(return_0)在测试集适应度值为2.1723126285272114
因子min(return_0, min(close_0, high_0))在测试集适应度值为2.1723126285272114
因子return_0在测试集适应度值为2.1723126285272114

    pass:8, record:24, population: 8

    下一代挖掘的个体数：50

    -- 开始第「2」次循环第「4」代挖掘 --

    去重前的个体数50
去重后的个体数30

    每代的平均适应度：[-0.22162481765687597, 0.009896131336176913, 0.047010060860965916, 0.2097928184381592]
因子min(low_0, close_0)在训练集适应度值为-0.8461702830903864
因子min(return_0, close_0)在训练集适应度值为2.966799078652266
因子min(turn_0, return_0)在训练集适应度值为3.2711429509050887
因子min(delta(ts_max(high_0, 19), constant(7)), return_0)在训练集适应度值为-0.08023304999185518
因子min(return_0, min(high_0, return_0))在训练集适应度值为2.966799078652266
因子min(high_0, return_0)在训练集适应度值为2.966799078652266
因子min(low_0, return_0)在训练集适应度值为2.966799078652266
因子min(return_0, min(turn_0, high_0))在训练集适应度值为3.2711429509050887
因子min(rank(close_0), return_0)在训练集适应度值为2.963728880828274
因子min(return_0, return_0)在训练集适应度值为2.966799078652266
因子abs(return_0)在训练集适应度值为2.966799078652266
因子sub(close_0, return_0)在训练集适应度值为-0.8829170997042474
因子low_0在训练集适应度值为-0.8461702830903864
因子add(ts_argmax(low_0, 1), ts_argmax(high_0, 7))在训练集适应度值为-3.409735852526271
因子return_0在训练集适应度值为2.966799078652266
因子min(close_0, return_0)在训练集适应度值为2.966799078652266
因子abs(abs(low_0))在训练集适应度值为-0.8461702830903864
因子min(low_0, turn_0)在训练集适应度值为-1.0206958199547311
因子abs(ts_min(sign(return_0), constant(6)))在训练集适应度值为nan
因子close_0在训练集适应度值为-0.8476981027065214
因子min(covariance(close_0, close_0, 11), correlation(close_0, close_0, 6))在训练集适应度值为-2.6332952595239063
因子abs(log(covariance(low_0, high_0, 19)))在训练集适应度值为-2.2181267182800313
因子ts_argmax(covariance(return_0, return_0, 1), constant(19))在训练集适应度值为nan
因子min(min(close_0, high_0), return_0)在训练集适应度值为0.17082496878096806
因子mul(low_0, abs(close_0))在训练集适应度值为-0.9817661644831495
因子min(return_0, min(close_0, correlation(turn_0, turn_0, 18)))在训练集适应度值为2.9550271039749743
因子ts_argmin(turn_0, 18)在训练集适应度值为-1.2446830011968495
因子abs(ts_max(div(close_0, high_0), constant(9)))在训练集适应度值为-1.7352932308711693
因子ts_argmin(abs(high_0), constant(10))在训练集适应度值为-3.5020479795797477
因子min(close_0, return_0)在训练集适应度值为0.17082496878096806

    因子min(return_0, close_0)在测试集适应度值为2.1723126285272114
因子min(turn_0, return_0)在测试集适应度值为1.9628287395834418
因子min(return_0, min(high_0, return_0))在测试集适应度值为2.1723126285272114
因子min(high_0, return_0)在测试集适应度值为2.1723126285272114
因子min(low_0, return_0)在测试集适应度值为2.1723126285272114
因子min(return_0, min(turn_0, high_0))在测试集适应度值为1.9628287395834418
因子min(rank(close_0), return_0)在测试集适应度值为2.1575357966465836
因子min(return_0, return_0)在测试集适应度值为2.1723126285272114
因子abs(return_0)在测试集适应度值为2.1723126285272114
因子return_0在测试集适应度值为2.1723126285272114
因子min(close_0, return_0)在测试集适应度值为2.1723126285272114
因子min(return_0, min(close_0, correlation(turn_0, turn_0, 18)))在测试集适应度值为2.1652870594547

    pass:12, record:30, population: 12

    下一代挖掘的个体数：50

    -- 开始第「2」次循环第「5」代挖掘 --

    去重前的个体数50
去重后的个体数31

    每代的平均适应度：[-0.22162481765687597, 0.009896131336176913, 0.047010060860965916, 0.2097928184381592, 0.21774420625238206]
因子min(turn_0, close_0)在训练集适应度值为-1.043982278327278
因子min(return_0, min(return_0, turn_0))在训练集适应度值为3.2711429509050887
因子min(low_0, close_0)在训练集适应度值为-0.8461702830903864
因子min(return_0, return_0)在训练集适应度值为2.966799078652266
因子min(high_0, return_0)在训练集适应度值为2.966799078652266
因子min(turn_0, high_0)在训练集适应度值为-1.0412213003259672
因子return_0在训练集适应度值为2.966799078652266
因子min(high_0, sum(ts_min(low_0, 8), constant(14)))在训练集适应度值为-0.899352179349475
因子ts_rank(turn_0, 1)在训练集适应度值为nan
因子high_0在训练集适应度值为-0.899352179349475
因子sign(return_0)在训练集适应度值为nan
因子min(rank(return_0), return_0)在训练集适应度值为2.963082671909163
因子min(turn_0, return_0)在训练集适应度值为3.2711429509050887
因子abs(return_0)在训练集适应度值为2.966799078652266
因子min(return_0, high_0)在训练集适应度值为2.966799078652266
因子min(return_0, close_0)在训练集适应度值为2.966799078652266
因子close_0在训练集适应度值为-0.8476981027065214
因子ts_argmax(low_0, 7)在训练集适应度值为-0.8036525730054513
因子low_0在训练集适应度值为-0.8461702830903864
因子ts_argmax(ts_max(turn_0, 3), 18)在训练集适应度值为-0.6942503873747207
因子turn_0在训练集适应度值为-1.0095106446031976
因子min(return_0, min(low_0, close_0))在训练集适应度值为2.966799078652266
因子min(return_0, min(high_0, correlation(turn_0, high_0, 17)))在训练集适应度值为2.957291807050486
因子min(high_0, high_0)在训练集适应度值为-0.899352179349475
因子min(low_0, return_0)在训练集适应度值为2.966799078652266
因子min(return_0, correlation(return_0, delta(log(low_0), constant(18)), 18))在训练集适应度值为2.966799078652266
因子abs(low_0)在训练集适应度值为-0.8461702830903864
因子min(return_0, mul(high_0, return_0))在训练集适应度值为2.966799078652266
因子min(return_0, min(high_0, return_0))在训练集适应度值为2.966799078652266
因子min(low_0, sum(sum(close_0, 16), constant(15)))在训练集适应度值为-0.8461702830903864
因子min(return_0, low_0)在训练集适应度值为2.966799078652266

    因子min(return_0, min(return_0, turn_0))在测试集适应度值为1.9628287395834418
因子min(return_0, return_0)在测试集适应度值为2.1723126285272114
因子min(high_0, return_0)在测试集适应度值为2.1723126285272114
因子return_0在测试集适应度值为2.1723126285272114
因子min(rank(return_0), return_0)在测试集适应度值为2.1723126285272114
因子min(turn_0, return_0)在测试集适应度值为1.9628287395834418
因子abs(return_0)在测试集适应度值为2.1723126285272114
因子min(return_0, high_0)在测试集适应度值为2.1723126285272114
因子min(return_0, close_0)在测试集适应度值为2.1723126285272114
因子min(return_0, min(low_0, close_0))在测试集适应度值为2.1723126285272114
因子min(return_0, min(high_0, correlation(turn_0, high_0, 17)))在测试集适应度值为2.152618627170182
因子min(low_0, return_0)在测试集适应度值为2.1723126285272114
因子min(return_0, correlation(return_0, delta(log(low_0), constant(18)), 18))在测试集适应度值为2.1723126285272114
因子min(return_0, mul(high_0, return_0))在测试集适应度值为2.1723126285272114
因子min(return_0, min(high_0, return_0))在测试集适应度值为2.1723126285272114
因子min(return_0, low_0)在测试集适应度值为2.1723126285272114

    pass:16, record:31, population: 16

    下一代挖掘的个体数：50

    -- 开始第「3」次循环第「1」代挖掘 --

    去重前的个体数50
去重后的个体数48

    每代的平均适应度：[-0.15718277761270463]
因子correlation(close_0, high_0, 8)在训练集适应度值为-0.14620283666192374
因子ta_sma(close_0, 17)在训练集适应度值为-0.07597288567346074
因子ta_sma(turn_0, 18)在训练集适应度值为-0.3000703798474867
因子ts_argmax(close_0, 16)在训练集适应度值为-0.9016420815689645
因子log(low_0)在训练集适应度值为-0.847220597544614
因子ta_sma(ts_min(return_0, 17), constant(7))在训练集适应度值为-1.7258049287656432
因子std(shift(turn_0, 19), constant(6))在训练集适应度值为-0.5948990474972838
因子min(correlation(close_0, low_0, 13), ts_max(turn_0, 19))在训练集适应度值为-3.413139819925143
因子normalize(min(low_0, high_0))在训练集适应度值为-0.8461702830903864
因子rank(high_0)在训练集适应度值为-1.107530300523381
因子sub(shift(return_0, 2), std(high_0, 18))在训练集适应度值为-0.8014266550915874
因子ts_max(min(turn_0, high_0), constant(6))在训练集适应度值为-2.1605006802125235
因子shift(return_0, 17)在训练集适应度值为-3.1806715171522106
因子min(return_0, low_0)在训练集适应度值为2.966799078652266
因子delta(turn_0, 9)在训练集适应度值为-2.452685464777236
因子decay_linear(turn_0, 15)在训练集适应度值为-4.019256897340393
因子ts_argmin(close_0, 18)在训练集适应度值为0.10988614498469906
因子div(ts_max(return_0, 3), shift(close_0, 15))在训练集适应度值为-1.212965748738417
因子decay_linear(sub(turn_0, return_0), constant(12))在训练集适应度值为-2.512605426593555
因子product(div(close_0, low_0), constant(16))在训练集适应度值为nan
因子std(close_0, 2)在训练集适应度值为1.913742971232113
因子sum(ts_min(low_0, 14), constant(18))在训练集适应度值为-1.3817208918870223
因子shift(sub(high_0, close_0), constant(5))在训练集适应度值为-0.6946585741529455
因子ts_argmin(turn_0, 19)在训练集适应度值为-2.0348627299267292
因子rank(close_0)在训练集适应度值为0.48771240495264867
因子ts_argmin(div(return_0, close_0), constant(12))在训练集适应度值为-3.652992631444229
因子sum(turn_0, 3)在训练集适应度值为-1.8452283843741886
因子correlation(return_0, close_0, 14)在训练集适应度值为nan
因子ts_rank(high_0, 5)在训练集适应度值为-2.3728923053792563
因子sub(close_0, low_0)在训练集适应度值为0.4082658046298504
因子ts_rank(low_0, 10)在训练集适应度值为-1.7511366035563576
因子ta_sma(ts_min(close_0, 3), constant(9))在训练集适应度值为-0.7694522737217591
因子min(low_0, high_0)在训练集适应度值为-0.8461702830903864
因子shift(low_0, 3)在训练集适应度值为-0.6610020890895318
因子normalize(close_0)在训练集适应度值为-0.8476981027065214
因子ta_sma(shift(close_0, 3), constant(16))在训练集适应度值为-0.07507120860751741
因子ts_argmax(normalize(low_0), constant(11))在训练集适应度值为-4.377335056887826
因子ta_sma(turn_0, 6)在训练集适应度值为-2.484513488533871
因子normalize(sum(turn_0, 1))在训练集适应度值为-1.0095106446031976
因子product(close_0, constant(18))在训练集适应度值为0.38405903216333487
因子normalize(high_0)在训练集适应度值为-0.899352179349475
因子sum(close_0, 19)在训练集适应度值为-2.383105769571088
因子ts_max(std(return_0, 1), constant(15))在训练集适应度值为nan
因子sign(high_0)在训练集适应度值为nan
因子ts_argmax(ta_sma(close_0, 14), constant(16))在训练集适应度值为-0.1849891624271755
因子ts_rank(add(close_0, turn_0), constant(17))在训练集适应度值为-0.6766329763601696
因子log(close_0)在训练集适应度值为-0.7388599896612504
因子shift(return_0, 13)在训练集适应度值为-0.7262835580686147

    因子min(return_0, low_0)在测试集适应度值为2.1723126285272114

    pass:1, record:48, population: 1

    下一代挖掘的个体数：50

    -- 开始第「3」次循环第「2」代挖掘 --

    去重前的个体数50
去重后的个体数19

    每代的平均适应度：[-0.15718277761270463, 0.19400110052137878]
因子min(return_0, low_0)在训练集适应度值为2.966799078652266
因子min(return_0, high_0)在训练集适应度值为2.966799078652266
因子min(low_0, low_0)在训练集适应度值为-0.8461702830903864
因子min(shift(sign(high_0), constant(15)), return_0)在训练集适应度值为-1.115962756053952
因子min(turn_0, low_0)在训练集适应度值为-1.0206958199547311
因子min(return_0, return_0)在训练集适应度值为2.966799078652266
因子min(return_0, ta_sma(add(low_0, close_0), constant(4)))在训练集适应度值为2.966799078652266
因子min(std(turn_0, 16), low_0)在训练集适应度值为-0.9073958133141969
因子ts_max(close_0, 11)在训练集适应度值为-4.434674352212621
因子min(return_0, close_0)在训练集适应度值为2.966799078652266
因子min(return_0, close_0)在训练集适应度值为2.966799078652266
因子mul(close_0, high_0)在训练集适应度值为-0.9559744753698158
因子mul(high_0, turn_0)在训练集适应度值为-1.0079381449829803
因子min(close_0, low_0)在训练集适应度值为-0.8461702830903864
因子min(high_0, turn_0)在训练集适应度值为-1.0412213003259672
因子div(ta_sma(close_0, 17), min(turn_0, close_0))在训练集适应度值为-2.419425246353318
因子min(return_0, turn_0)在训练集适应度值为3.2711429509050887
因子min(ts_min(ts_min(high_0, 1), constant(9)), low_0)在训练集适应度值为-1.5137317513185056
因子min(return_0, add(correlation(close_0, turn_0, 8), ts_rank(close_0, 8)))在训练集适应度值为2.94848522670707

    因子min(return_0, low_0)在测试集适应度值为2.1723126285272114
因子min(return_0, high_0)在测试集适应度值为2.1723126285272114
因子min(return_0, return_0)在测试集适应度值为2.1723126285272114
因子min(return_0, ta_sma(add(low_0, close_0), constant(4)))在测试集适应度值为2.1723126285272114
因子min(return_0, close_0)在测试集适应度值为2.1723126285272114
因子min(return_0, close_0)在测试集适应度值为2.1723126285272114
因子min(return_0, turn_0)在测试集适应度值为1.9628287395834418
因子min(return_0, add(correlation(close_0, turn_0, 8), ts_rank(close_0, 8)))在测试集适应度值为2.172857538528948

    pass:8, record:19, population: 8

    下一代挖掘的个体数：50

    -- 开始第「3」次循环第「3」代挖掘 --

    去重前的个体数50
去重后的个体数29

    每代的平均适应度：[-0.15718277761270463, 0.19400110052137878, 0.3235587031398346]
因子min(close_0, return_0)在训练集适应度值为0.17082496878096806
因子min(low_0, add(close_0, ts_rank(close_0, 8)))在训练集适应度值为-0.8496101811725715
因子min(turn_0, return_0)在训练集适应度值为3.2711429509050887
因子min(return_0, correlation(close_0, turn_0, 8))在训练集适应度值为2.9519967578356985
因子min(return_0, close_0)在训练集适应度值为2.966799078652266
因子min(return_0, high_0)在训练集适应度值为2.966799078652266
因子min(close_0, correlation(close_0, turn_0, 8))在训练集适应度值为-0.7956654797947154
因子min(return_0, ta_sma(add(low_0, close_0), constant(4)))在训练集适应度值为2.966799078652266
因子min(return_0, add(low_0, close_0))在训练集适应度值为2.966799078652266
因子min(product(return_0, 5), close_0)在训练集适应度值为-0.0366315584030273
因子min(low_0, add(correlation(close_0, turn_0, 8), ts_rank(close_0, 8)))在训练集适应度值为-0.759877602876318
因子sum(close_0, 15)在训练集适应度值为-3.24810344904179
因子min(return_0, low_0)在训练集适应度值为2.966799078652266
因子min(log(low_0), high_0)在训练集适应度值为-0.847220597544614
因子ts_rank(ts_min(close_0, 7), constant(19))在训练集适应度值为0.251413500376999
因子min(ts_rank(close_0, 8), return_0)在训练集适应度值为-3.510771226320033
因子min(return_0, add(correlation(close_0, turn_0, 8), return_0))在训练集适应度值为2.9649808618537046
因子min(ts_argmax(turn_0, 17), sign(turn_0))在训练集适应度值为-1.8619090263657319
因子add(div(close_0, turn_0), mul(low_0, low_0))在训练集适应度值为-0.9782696442850973
因子min(close_0, close_0)在训练集适应度值为0.140686457362151
因子min(return_0, add(return_0, ts_rank(close_0, 8)))在训练集适应度值为2.844081612663561
因子min(return_0, close_0)在训练集适应度值为2.966799078652266
因子low_0在训练集适应度值为-0.8461702830903864
因子min(return_0, add(turn_0, ts_rank(close_0, 8)))在训练集适应度值为2.8551649417356257
因子min(return_0, add(correlation(close_0, turn_0, 8), ts_rank(close_0, 10)))在训练集适应度值为2.940714056110555
因子min(sum(close_0, 3), close_0)在训练集适应度值为-0.6122625886604794
因子min(return_0, add(correlation(close_0, turn_0, 18), ts_rank(close_0, 8)))在训练集适应度值为2.966799078652266
因子min(low_0, return_0)在训练集适应度值为2.966799078652266
因子min(return_0, sub(correlation(close_0, turn_0, 8), ts_rank(close_0, 8)))在训练集适应度值为2.95801578602637

    因子min(turn_0, return_0)在测试集适应度值为1.9628287395834418
因子min(return_0, correlation(close_0, turn_0, 8))在测试集适应度值为2.172857538528948
因子min(return_0, close_0)在测试集适应度值为2.1723126285272114
因子min(return_0, high_0)在测试集适应度值为2.1723126285272114
因子min(return_0, ta_sma(add(low_0, close_0), constant(4)))在测试集适应度值为2.1723126285272114
因子min(return_0, add(low_0, close_0))在测试集适应度值为2.1723126285272114
因子min(return_0, low_0)在测试集适应度值为2.1723126285272114
因子min(return_0, add(correlation(close_0, turn_0, 8), return_0))在测试集适应度值为2.1723126285272114
因子min(return_0, add(return_0, ts_rank(close_0, 8)))在测试集适应度值为2.044636043350991
因子min(return_0, close_0)在测试集适应度值为2.1723126285272114
因子min(return_0, add(turn_0, ts_rank(close_0, 8)))在测试集适应度值为2.2319814604395725
因子min(return_0, add(correlation(close_0, turn_0, 8), ts_rank(close_0, 10)))在测试集适应度值为2.1723126285272114
因子min(return_0, add(correlation(close_0, turn_0, 18), ts_rank(close_0, 8)))在测试集适应度值为2.1723126285272114
因子min(low_0, return_0)在测试集适应度值为2.1723126285272114
因子min(return_0, sub(correlation(close_0, turn_0, 8), ts_rank(close_0, 8)))在测试集适应度值为2.170581542633562

    pass:15, record:29, population: 15

    下一代挖掘的个体数：50

    -- 开始第「3」次循环第「4」代挖掘 --

    去重前的个体数50
去重后的个体数33

    每代的平均适应度：[-0.15718277761270463, 0.19400110052137878, 0.3235587031398346, 0.5447230934101001]
因子min(return_0, ts_rank(close_0, 8))在训练集适应度值为2.5840894992305894
因子min(ts_argmax(close_0, 9), close_0)在训练集适应度值为-0.3393257344854195
因子min(return_0, add(turn_0, close_0))在训练集适应度值为2.966799078652266
因子min(return_0, high_0)在训练集适应度值为2.966799078652266
因子min(return_0, turn_0)在训练集适应度值为3.2711429509050887
因子min(low_0, return_0)在训练集适应度值为2.966799078652266
因子min(low_0, correlation(close_0, turn_0, 8))在训练集适应度值为-0.759877602876318
因子min(return_0, add(correlation(close_0, turn_0, 18), ts_rank(close_0, constant(constant(13)))))在训练集适应度值为2.966799078652266
因子min(return_0, correlation(close_0, turn_0, 8))在训练集适应度值为2.9519967578356985
因子min(return_0, max(correlation(close_0, return_0, 8), return_0))在训练集适应度值为2.966799078652266
因子high_0在训练集适应度值为-0.899352179349475
因子min(return_0, add(high_0, return_0))在训练集适应度值为2.966799078652266
因子min(ts_rank(close_0, 8), high_0)在训练集适应度值为-3.820469502549138
因子min(turn_0, return_0)在训练集适应度值为3.2711429509050887
因子min(return_0, add(correlation(close_0, low_0, 8), ts_rank(correlation(ts_argmin(return_0, 19), ta_sma(low_0, 9), constant(8)), 10)))在训练集适应度值为2.966799078652266
因子min(return_0, min(correlation(close_0, turn_0, 8), low_0))在训练集适应度值为2.9519967578356985
因子min(close_0, return_0)在训练集适应度值为2.966799078652266
因子min(return_0, log(turn_0))在训练集适应度值为2.0564543978466947
因子min(return_0, add(turn_0, ts_rank(close_0, 8)))在训练集适应度值为2.8551649417356257
因子min(return_0, add(correlation(close_0, turn_0, 18), ts_rank(close_0, 18)))在训练集适应度值为2.966799078652266
因子min(return_0, add(correlation(close_0, turn_0, 8), ts_rank(close_0, 8)))在训练集适应度值为2.94848522670707
因子min(return_0, correlation(return_0, turn_0, 8))在训练集适应度值为2.4060009257825916
因子min(return_0, close_0)在训练集适应度值为2.966799078652266
因子min(close_0, low_0)在训练集适应度值为0.144803210441691
因子min(return_0, add(turn_0, turn_0))在训练集适应度值为3.081364302089742
因子min(return_0, add(correlation(close_0, ts_rank(close_0, 8), 9), high_0))在训练集适应度值为2.966799078652266
因子min(return_0, add(correlation(close_0, turn_0, 14), ts_rank(close_0, 8)))在训练集适应度值为2.966799078652266
因子min(return_0, add(correlation(close_0, turn_0, 8), ts_rank(close_0, 10)))在训练集适应度值为2.810692975213698
因子min(return_0, return_0)在训练集适应度值为2.966799078652266
因子min(return_0, add(close_0, ts_rank(close_0, 8)))在训练集适应度值为2.966799078652266
因子min(return_0, correlation(close_0, turn_0, constant(13)))在训练集适应度值为2.966799078652266
因子min(return_0, log(covariance(close_0, close_0, 7)))在训练集适应度值为2.0520534728926103
因子min(return_0, ts_rank(close_0, 8))在训练集适应度值为-1.3309010501314837

    因子min(return_0, ts_rank(close_0, 8))在测试集适应度值为2.057394946038863
因子min(return_0, add(turn_0, close_0))在测试集适应度值为2.1723126285272114
因子min(return_0, high_0)在测试集适应度值为2.1723126285272114
因子min(return_0, turn_0)在测试集适应度值为1.9628287395834418
因子min(low_0, return_0)在测试集适应度值为2.1723126285272114
因子min(return_0, add(correlation(close_0, turn_0, 18), ts_rank(close_0, constant(constant(13)))))在测试集适应度值为2.1723126285272114
因子min(return_0, correlation(close_0, turn_0, 8))在测试集适应度值为2.172857538528948
因子min(return_0, max(correlation(close_0, return_0, 8), return_0))在测试集适应度值为2.1723126285272114
因子min(return_0, add(high_0, return_0))在测试集适应度值为2.1723126285272114
因子min(turn_0, return_0)在测试集适应度值为1.9628287395834418
因子min(return_0, add(correlation(close_0, low_0, 8), ts_rank(correlation(ts_argmin(return_0, 19), ta_sma(low_0, 9), constant(8)), 10)))在测试集适应度值为2.1723126285272114
因子min(return_0, min(correlation(close_0, turn_0, 8), low_0))在测试集适应度值为2.172857538528948
因子min(close_0, return_0)在测试集适应度值为2.1723126285272114
因子min(return_0, log(turn_0))在测试集适应度值为0.9452525382748316
因子min(return_0, add(turn_0, ts_rank(close_0, 8)))在测试集适应度值为2.2319814604395725
因子min(return_0, add(correlation(close_0, turn_0, 18), ts_rank(close_0, 18)))在测试集适应度值为2.1723126285272114
因子min(return_0, add(correlation(close_0, turn_0, 8), ts_rank(close_0, 8)))在测试集适应度值为2.172857538528948
因子min(return_0, correlation(return_0, turn_0, 8))在测试集适应度值为1.9272439505260477
因子min(return_0, close_0)在测试集适应度值为2.1723126285272114
因子min(return_0, add(turn_0, turn_0))在测试集适应度值为2.1480745878886385
因子min(return_0, add(correlation(close_0, ts_rank(close_0, 8), 9), high_0))在测试集适应度值为2.1723126285272114
因子min(return_0, add(correlation(close_0, turn_0, 14), ts_rank(close_0, 8)))在测试集适应度值为2.1723126285272114
因子min(return_0, add(correlation(close_0, turn_0, 8), ts_rank(close_0, 10)))在测试集适应度值为2.1594610094274094
因子min(return_0, return_0)在测试集适应度值为2.1723126285272114
因子min(return_0, add(close_0, ts_rank(close_0, 8)))在测试集适应度值为2.1723126285272114
因子min(return_0, correlation(close_0, turn_0, constant(13)))在测试集适应度值为2.1723126285272114
因子min(return_0, log(covariance(close_0, close_0, 7)))在测试集适应度值为1.8303539639593456

    pass:27, record:33, population: 27

    下一代挖掘的个体数：50

    -- 开始第「3」次循环第「5」代挖掘 --

    去重前的个体数50
去重后的个体数39

    每代的平均适应度：[-0.15718277761270463, 0.19400110052137878, 0.3235587031398346, 0.5447230934101001, 0.5272465559492385]
因子min(close_0, max(correlation(close_0, return_0, 8), return_0))在训练集适应度值为2.960898005422229
因子min(return_0, add(sum(log(close_0), constant(13)), ts_rank(correlation(ts_argmin(return_0, 19), ta_sma(low_0, 9), constant(8)), 10)))在训练集适应度值为2.966799078652266
因子min(decay_linear(ts_max(turn_0, 3), constant(8)), add(turn_0, ts_rank(close_0, 8)))在训练集适应度值为-0.27740356177680137
因子min(return_0, return_0)在训练集适应度值为2.966799078652266
因子min(return_0, correlation(close_0, turn_0, 13))在训练集适应度值为2.966799078652266
因子min(return_0, low_0)在训练集适应度值为2.966799078652266
因子min(return_0, add(close_0, close_0))在训练集适应度值为2.966799078652266
因子min(low_0, div(return_0, close_0))在训练集适应度值为-0.4680979790560583
因子min(return_0, add(correlation(close_0, low_0, 8), ts_rank(correlation(return_0, ta_sma(low_0, 9), constant(8)), 10)))在训练集适应度值为2.966799078652266
因子min(return_0, add(correlation(close_0, turn_0, 10), ts_rank(close_0, 10)))在训练集适应度值为2.8959114056419373
因子min(return_0, turn_0)在训练集适应度值为3.2711429509050887
因子min(turn_0, sign(return_0))在训练集适应度值为-2.435104638769661
因子min(return_0, shift(min(close_0, close_0), constant(5)))在训练集适应度值为2.966799078652266
因子min(return_0, add(high_0, return_0))在训练集适应度值为2.966799078652266
因子min(return_0, max(correlation(close_0, return_0, constant(13)), return_0))在训练集适应度值为2.966799078652266
因子min(return_0, add(correlation(std(turn_0, 5), ts_rank(close_0, 8), 9), close_0))在训练集适应度值为2.966799078652266
因子min(return_0, add(correlation(high_0, low_0, 8), ts_rank(correlation(ts_argmin(return_0, 19), ts_rank(log(close_0), constant(17)), constant(8)), 10)))在训练集适应度值为2.9520717461656645
因子min(return_0, add(correlation(close_0, turn_0, 6), ts_rank(ts_rank(close_0, 8), 8)))在训练集适应度值为2.9424745245985306
因子min(return_0, add(correlation(close_0, turn_0, 8), close_0))在训练集适应度值为2.966799078652266
因子min(return_0, correlation(close_0, turn_0, constant(13)))在训练集适应度值为2.966799078652266
因子ts_argmax(high_0, 4)在训练集适应度值为-2.8635977752070687
因子min(return_0, add(correlation(close_0, close_0, constant(constant(18))), close_0))在训练集适应度值为2.966799078652266
因子min(low_0, high_0)在训练集适应度值为-0.8461702830903864
因子min(return_0, correlation(close_0, low_0, 8))在训练集适应度值为2.954534975232355
因子min(return_0, add(turn_0, ts_rank(close_0, 13)))在训练集适应度值为2.978874739426301
因子min(return_0, ts_min(add(close_0, low_0), 14))在训练集适应度值为2.966799078652266
因子min(return_0, max(close_0, ts_min(low_0, 10)))在训练集适应度值为2.966799078652266
因子ts_rank(low_0, 17)在训练集适应度值为-0.3115016234437932
因子min(high_0, add(turn_0, close_0))在训练集适应度值为-0.8278705199240984
因子min(return_0, add(close_0, turn_0))在训练集适应度值为2.966799078652266
因子min(return_0, correlation(close_0, turn_0, 8))在训练集适应度值为2.9519967578356985
因子min(close_0, add(turn_0, ts_rank(close_0, 8)))在训练集适应度值为-0.8369725612320132
因子min(return_0, correlation(ts_min(return_0, 9), abs(low_0), constant(4)))在训练集适应度值为3.0084688892574323
因子min(add(turn_0, ts_rank(close_0, 8)), return_0)在训练集适应度值为-3.4856288130150888
因子min(return_0, close_0)在训练集适应度值为2.966799078652266
因子min(return_0, sub(correlation(close_0, turn_0, 8), close_0))在训练集适应度值为2.934338459232893
因子min(return_0, close_0)在训练集适应度值为2.966799078652266
因子min(low_0, covariance(close_0, turn_0, 5))在训练集适应度值为-0.08788094904206695
因子min(return_0, add(correlation(close_0, turn_0, 18), close_0))在训练集适应度值为2.966799078652266

    因子min(close_0, max(correlation(close_0, return_0, 8), return_0))在测试集适应度值为2.1723126285272114
因子min(return_0, add(sum(log(close_0), constant(13)), ts_rank(correlation(ts_argmin(return_0, 19), ta_sma(low_0, 9), constant(8)), 10)))在测试集适应度值为2.1723126285272114
因子min(return_0, return_0)在测试集适应度值为2.1723126285272114
因子min(return_0, correlation(close_0, turn_0, 13))在测试集适应度值为2.1723126285272114
因子min(return_0, low_0)在测试集适应度值为2.1723126285272114
因子min(return_0, add(close_0, close_0))在测试集适应度值为2.1723126285272114
因子min(return_0, add(correlation(close_0, low_0, 8), ts_rank(correlation(return_0, ta_sma(low_0, 9), constant(8)), 10)))在测试集适应度值为2.1723126285272114
因子min(return_0, add(correlation(close_0, turn_0, 10), ts_rank(close_0, 10)))在测试集适应度值为2.135890427432
因子min(return_0, turn_0)在测试集适应度值为1.9628287395834418
因子min(return_0, shift(min(close_0, close_0), constant(5)))在测试集适应度值为2.1723126285272114
因子min(return_0, add(high_0, return_0))在测试集适应度值为2.1723126285272114
因子min(return_0, max(correlation(close_0, return_0, constant(13)), return_0))在测试集适应度值为2.1723126285272114
因子min(return_0, add(correlation(std(turn_0, 5), ts_rank(close_0, 8), 9), close_0))在测试集适应度值为2.1723126285272114
因子min(return_0, add(correlation(high_0, low_0, 8), ts_rank(correlation(ts_argmin(return_0, 19), ts_rank(log(close_0), constant(17)), constant(8)), 10)))在测试集适应度值为2.1723126285272114
因子min(return_0, add(correlation(close_0, turn_0, 6), ts_rank(ts_rank(close_0, 8), 8)))在测试集适应度值为2.1723126285272114
因子min(return_0, add(correlation(close_0, turn_0, 8), close_0))在测试集适应度值为2.1723126285272114
因子min(return_0, correlation(close_0, turn_0, constant(13)))在测试集适应度值为2.1723126285272114
因子min(return_0, add(correlation(close_0, close_0, constant(constant(18))), close_0))在测试集适应度值为2.1723126285272114
因子min(return_0, correlation(close_0, low_0, 8))在测试集适应度值为2.170581542633562
因子min(return_0, add(turn_0, ts_rank(close_0, 13)))在测试集适应度值为2.2144794024239918
因子min(return_0, ts_min(add(close_0, low_0), 14))在测试集适应度值为2.1723126285272114
因子min(return_0, max(close_0, ts_min(low_0, 10)))在测试集适应度值为2.1723126285272114
因子min(return_0, add(close_0, turn_0))在测试集适应度值为2.1723126285272114
因子min(return_0, correlation(close_0, turn_0, 8))在测试集适应度值为2.172857538528948
因子min(return_0, correlation(ts_min(return_0, 9), abs(low_0), constant(4)))在测试集适应度值为2.210458418652508
因子min(return_0, close_0)在测试集适应度值为2.1723126285272114
因子min(return_0, sub(correlation(close_0, turn_0, 8), close_0))在测试集适应度值为2.172857538528948
因子min(return_0, close_0)在测试集适应度值为2.1723126285272114
因子min(return_0, add(correlation(close_0, turn_0, 18), close_0))在测试集适应度值为2.1723126285272114

    pass:29, record:39, population: 29

    下一代挖掘的个体数：50

    -- 开始第「4」次循环第「1」代挖掘 --

    去重前的个体数50
去重后的个体数50

    每代的平均适应度：[-0.13359826356502566]
因子abs(low_0)在训练集适应度值为-0.8461702830903864
因子std(low_0, 12)在训练集适应度值为-3.594164414955865
因子ts_argmax(close_0, 19)在训练集适应度值为-0.2652075414845032
因子ts_max(turn_0, 19)在训练集适应度值为-1.3858233058894833
因子add(sub(low_0, low_0), product(high_0, 17))在训练集适应度值为nan
因子ts_max(return_0, 8)在训练集适应度值为-1.2069292222681953
因子ts_argmin(normalize(high_0), constant(12))在训练集适应度值为-2.7601629077970364
因子correlation(mul(turn_0, close_0), ts_rank(close_0, 13), constant(6))在训练集适应度值为-5.170582799031485
因子ts_min(turn_0, 4)在训练集适应度值为-2.384600016408193
因子delta(return_0, 7)在训练集适应度值为-3.9773448895268007
因子normalize(add(low_0, close_0))在训练集适应度值为-0.8772729954380408
因子div(turn_0, return_0)在训练集适应度值为-1.239954345350399
因子std(ta_sma(close_0, 6), constant(14))在训练集适应度值为-2.025874834252821
因子std(turn_0, 5)在训练集适应度值为-2.80957391780679
因子abs(close_0)在训练集适应度值为-0.8476981027065214
因子ta_sma(log(close_0), constant(4))在训练集适应度值为-2.536851472674113
因子add(div(close_0, return_0), abs(turn_0))在训练集适应度值为-0.855853506674452
因子product(rank(close_0), constant(18))在训练集适应度值为nan
因子mul(turn_0, close_0)在训练集适应度值为-1.0700548550648357
因子log(high_0)在训练集适应度值为-0.6118414256911656
因子std(close_0, 17)在训练集适应度值为0.11055936236388753
因子ts_max(close_0, constant(17))在训练集适应度值为-1.6057843449891747
因子delta(turn_0, 19)在训练集适应度值为-1.7842914138017967
因子ts_argmin(low_0, 11)在训练集适应度值为-2.501662954021381
因子ts_argmax(ts_rank(high_0, 1), constant(8))在训练集适应度值为-1.1844539783881658
因子correlation(add(return_0, close_0), ts_argmin(close_0, 14), constant(12))在训练集适应度值为nan
因子log(return_0)在训练集适应度值为2.9263085908876127
因子shift(low_0, 5)在训练集适应度值为-0.21567138532920396
因子correlation(turn_0, high_0, 17)在训练集适应度值为-2.0404863523884846
因子ta_sma(return_0, 12)在训练集适应度值为-3.0684400769021303
因子log(ts_rank(close_0, 17))在训练集适应度值为nan
因子ts_rank(low_0, 3)在训练集适应度值为-1.9465236894928155
因子sign(covariance(low_0, close_0, 12))在训练集适应度值为0.0341069515139254
因子sign(turn_0)在训练集适应度值为nan
因子min(return_0, shift(close_0, 19))在训练集适应度值为2.966799078652266
因子decay_linear(close_0, constant(12))在训练集适应度值为-3.189156633296345
因子add(abs(close_0), ta_sma(close_0, 14))在训练集适应度值为-0.46090771018698373
因子decay_linear(high_0, constant(15))在训练集适应度值为-3.760176812145961
因子mul(close_0, close_0)在训练集适应度值为-0.82655527898439
因子normalize(turn_0)在训练集适应度值为-1.0095106446031976
因子mul(ta_sma(low_0, 12), ts_argmin(high_0, 12))在训练集适应度值为-4.239030734711274
因子covariance(return_0, return_0, 4)在训练集适应度值为-1.009287713018236
因子ts_max(div(low_0, close_0), constant(5))在训练集适应度值为-2.3957372298913606
因子sub(std(high_0, 14), ts_rank(turn_0, 11))在训练集适应度值为-1.1357748756675081
因子div(abs(high_0), sub(close_0, close_0))在训练集适应度值为nan
因子ts_rank(close_0, 2)在训练集适应度值为-2.183648180958447
因子sum(return_0, 1)在训练集适应度值为2.966799078652266
因子shift(return_0, 2)在训练集适应度值为-0.6448278650041174
因子normalize(ts_rank(close_0, 19))在训练集适应度值为-1.153634345029874
因子abs(turn_0)在训练集适应度值为-1.0095106446031976

    因子log(return_0)在测试集适应度值为2.2029442082923274
因子min(return_0, shift(close_0, 19))在测试集适应度值为2.1723126285272114
因子sum(return_0, 1)在测试集适应度值为2.1723126285272114

    pass:3, record:50, population: 3

    下一代挖掘的个体数：50

    -- 开始第「4」次循环第「2」代挖掘 --

    去重前的个体数50
去重后的个体数20

    每代的平均适应度：[-0.13359826356502566, 0.06778499964455141]
因子log(return_0)在训练集适应度值为2.9263085908876127
因子log(close_0)在训练集适应度值为-0.7388599896612504
因子log(high_0)在训练集适应度值为-0.6118414256911656
因子log(turn_0)在训练集适应度值为-1.4561224619584097
因子min(return_0, close_0)在训练集适应度值为2.966799078652266
因子log(low_0)在训练集适应度值为-0.847220597544614
因子sum(return_0, constant(constant(12)))在训练集适应度值为-3.2741804638469403
因子ts_argmax(abs(high_0), constant(16))在训练集适应度值为-1.051450697806151
因子sum(return_0, 1)在训练集适应度值为2.966799078652266
因子sum(low_0, 1)在训练集适应度值为-0.8461702830903864
因子log(sum(low_0, 16))在训练集适应度值为-2.0838591594685103
因子sum(ta_sma(high_0, 4), 1)在训练集适应度值为-2.160326741306502
因子log(close_0)在训练集适应度值为0.365794883218713
因子sum(turn_0, 1)在训练集适应度值为-1.0095106446031976
因子sum(close_0, 1)在训练集适应度值为0.14035111021092236
因子sum(return_0, 16)在训练集适应度值为-1.3132802014353375
因子log(min(turn_0, return_0))在训练集适应度值为3.2631937646060774
因子add(min(high_0, low_0), max(return_0, high_0))在训练集适应度值为-0.9330158117378282
因子sum(std(close_0, 19), constant(17))在训练集适应度值为-0.09378399096673105
因子min(return_0, shift(close_0, 19))在训练集适应度值为2.966799078652266

    因子log(return_0)在测试集适应度值为2.2029442082923274
因子min(return_0, close_0)在测试集适应度值为2.1723126285272114
因子sum(return_0, 1)在测试集适应度值为2.1723126285272114
因子log(min(turn_0, return_0))在测试集适应度值为1.9884439676607377
因子min(return_0, shift(close_0, 19))在测试集适应度值为2.1723126285272114

    pass:5, record:20, population: 5

    下一代挖掘的个体数：50

    -- 开始第「4」次循环第「3」代挖掘 --

    去重前的个体数50
去重后的个体数22

    每代的平均适应度：[-0.13359826356502566, 0.06778499964455141, 0.15852469186738494]
因子sum(return_0, 1)在训练集适应度值为2.966799078652266
因子log(close_0)在训练集适应度值为0.365794883218713
因子min(return_0, return_0)在训练集适应度值为2.966799078652266
因子min(return_0, close_0)在训练集适应度值为2.966799078652266
因子log(return_0)在训练集适应度值为2.9263085908876127
因子min(return_0, shift(high_0, 19))在训练集适应度值为2.966799078652266
因子min(high_0, close_0)在训练集适应度值为-0.8734474736300971
因子close_0在训练集适应度值为0.14035111021092236
因子log(low_0)在训练集适应度值为-0.847220597544614
因子log(high_0)在训练集适应度值为-0.6118414256911656
因子min(return_0, mul(ts_argmax(turn_0, 5), delta(low_0, 16)))在训练集适应度值为2.9077846682609634
因子mul(log(return_0), covariance(close_0, high_0, 6))在训练集适应度值为-4.096421419772917
因子log(turn_0)在训练集适应度值为-1.4561224619584097
因子min(div(turn_0, close_0), shift(close_0, 19))在训练集适应度值为-0.05949675617599686
因子log(mul(low_0, close_0))在训练集适应度值为-0.8138468433137203
因子log(shift(close_0, 19))在训练集适应度值为0.16362307775273208
因子min(low_0, return_0)在训练集适应度值为2.966799078652266
因子sum(close_0, 1)在训练集适应度值为-0.8476981027065214
因子log(close_0)在训练集适应度值为-0.7388599896612504
因子sum(low_0, 1)在训练集适应度值为-0.8461702830903864
因子min(turn_0, return_0)在训练集适应度值为3.2711429509050887
因子min(turn_0, close_0)在训练集适应度值为-1.0143454887402648

    因子sum(return_0, 1)在测试集适应度值为2.1723126285272114
因子min(return_0, return_0)在测试集适应度值为2.1723126285272114
因子min(return_0, close_0)在测试集适应度值为2.1723126285272114
因子log(return_0)在测试集适应度值为2.2029442082923274
因子min(return_0, shift(high_0, 19))在测试集适应度值为2.1723126285272114
因子min(return_0, mul(ts_argmax(turn_0, 5), delta(low_0, 16)))在测试集适应度值为2.1526844941257908
因子min(low_0, return_0)在测试集适应度值为2.1723126285272114
因子min(turn_0, return_0)在测试集适应度值为1.9628287395834418

    pass:8, record:22, population: 8

    下一代挖掘的个体数：50

    -- 开始第「4」次循环第「4」代挖掘 --

    去重前的个体数50
去重后的个体数28

    每代的平均适应度：[-0.13359826356502566, 0.06778499964455141, 0.15852469186738494, 0.24422809308826285]
因子return_0在训练集适应度值为2.966799078652266
因子min(close_0, low_0)在训练集适应度值为0.144803210441691
因子min(return_0, mul(turn_0, ts_max(close_0, 16)))在训练集适应度值为2.954652813794514
因子sum(return_0, constant(constant(3)))在训练集适应度值为-2.9926885420109017
因子min(return_0, shift(high_0, constant(18)))在训练集适应度值为2.966799078652266
因子sum(return_0, 1)在训练集适应度值为2.966799078652266
因子min(return_0, return_0)在训练集适应度值为2.966799078652266
因子sum(close_0, 1)在训练集适应度值为-0.8476981027065214
因子log(normalize(high_0))在训练集适应度值为-0.7072671046802772
因子log(close_0)在训练集适应度值为0.365794883218713
因子log(return_0)在训练集适应度值为2.9263085908876127
因子log(low_0)在训练集适应度值为-0.847220597544614
因子min(return_0, high_0)在训练集适应度值为2.966799078652266
因子ts_min(return_0, 14)在训练集适应度值为-3.0099862589443562
因子min(high_0, return_0)在训练集适应度值为2.966799078652266
因子log(shift(high_0, 19))在训练集适应度值为-1.4180014382133372
因子min(turn_0, return_0)在训练集适应度值为3.2711429509050887
因子min(product(turn_0, 3), delta(low_0, 18))在训练集适应度值为-3.733755816731241
因子min(return_0, shift(return_0, 19))在训练集适应度值为2.897081039087316
因子min(return_0, low_0)在训练集适应度值为2.966799078652266
因子min(return_0, ts_rank(low_0, constant(2)))在训练集适应度值为-2.2772383278766926
因子min(close_0, return_0)在训练集适应度值为0.17082496878096806
因子sub(decay_linear(close_0, 13), covariance(low_0, low_0, 11))在训练集适应度值为-4.476916275872022
因子log(turn_0)在训练集适应度值为-1.4561224619584097
因子log(shift(high_0, 2))在训练集适应度值为-1.6019417994172531
因子min(return_0, abs(ta_sma(turn_0, 11)))在训练集适应度值为2.983813429250927
因子turn_0在训练集适应度值为-1.0095106446031976
因子high_0在训练集适应度值为-0.899352179349475

    因子return_0在测试集适应度值为2.1723126285272114
因子min(return_0, mul(turn_0, ts_max(close_0, 16)))在测试集适应度值为2.1723126285272114
因子min(return_0, shift(high_0, constant(18)))在测试集适应度值为2.1723126285272114
因子sum(return_0, 1)在测试集适应度值为2.1723126285272114
因子min(return_0, return_0)在测试集适应度值为2.1723126285272114
因子log(return_0)在测试集适应度值为2.2029442082923274
因子min(return_0, high_0)在测试集适应度值为2.1723126285272114
因子min(high_0, return_0)在测试集适应度值为2.1723126285272114
因子min(turn_0, return_0)在测试集适应度值为1.9628287395834418
因子min(return_0, shift(return_0, 19))在测试集适应度值为2.2645145481452214
因子min(return_0, low_0)在测试集适应度值为2.1723126285272114
因子min(return_0, abs(ta_sma(turn_0, 11)))在测试集适应度值为2.170771875137197

    pass:12, record:28, population: 12

    下一代挖掘的个体数：50

    -- 开始第「4」次循环第「5」代挖掘 --

    去重前的个体数50
去重后的个体数30

    每代的平均适应度：[-0.13359826356502566, 0.06778499964455141, 0.15852469186738494, 0.24422809308826285, 0.3184399019388734]
因子min(return_0, close_0)在训练集适应度值为2.966799078652266
因子min(return_0, low_0)在训练集适应度值为2.966799078652266
因子log(low_0)在训练集适应度值为-0.847220597544614
因子min(ts_min(return_0, 13), shift(return_0, 19))在训练集适应度值为-3.2904404262094857
因子sum(return_0, 1)在训练集适应度值为2.966799078652266
因子min(return_0, return_0)在训练集适应度值为2.966799078652266
因子log(return_0)在训练集适应度值为2.9263085908876127
因子min(return_0, shift(return_0, 19))在训练集适应度值为2.897081039087316
因子delta(close_0, 2)在训练集适应度值为3.476861165293347
因子min(return_0, close_0)在训练集适应度值为2.966799078652266
因子min(return_0, high_0)在训练集适应度值为2.966799078652266
因子min(return_0, mul(turn_0, close_0))在训练集适应度值为2.982629953332209
因子abs(close_0)在训练集适应度值为-0.8476981027065214
因子min(close_0, return_0)在训练集适应度值为2.966799078652266
因子sum(return_0, 19)在训练集适应度值为-2.4603579690902704
因子min(return_0, shift(high_0, constant(18)))在训练集适应度值为2.966799078652266
因子sum(low_0, constant(14))在训练集适应度值为-2.67937569173984
因子log(shift(return_0, 19))在训练集适应度值为-1.3471365258232537
因子sum(close_0, 1)在训练集适应度值为0.14035111021092236
因子min(product(covariance(close_0, low_0, 12), constant(18)), return_0)在训练集适应度值为nan
因子min(covariance(sign(high_0), product(return_0, 10), constant(16)), return_0)在训练集适应度值为nan
因子log(turn_0)在训练集适应度值为-1.4561224619584097
因子ta_sma(ts_max(close_0, 1), constant(18))在训练集适应度值为-1.9571873566030458
因子min(turn_0, low_0)在训练集适应度值为-1.0206958199547311
因子min(return_0, mul(turn_0, max(ts_max(turn_0, 12), ts_rank(high_0, 5))))在训练集适应度值为0.4887626870423723
因子min(return_0, shift(return_0, 16))在训练集适应度值为2.688490568561795
因子min(return_0, shift(ts_min(low_0, 8), 19))在训练集适应度值为2.966799078652266
因子log(high_0)在训练集适应度值为-0.6118414256911656
因子min(turn_0, mul(turn_0, ts_max(close_0, 16)))在训练集适应度值为-1.0095106446031976
因子max(ts_rank(return_0, 2), correlation(close_0, turn_0, 19))在训练集适应度值为-1.561184153531764

    因子min(return_0, close_0)在测试集适应度值为2.1723126285272114
因子min(return_0, low_0)在测试集适应度值为2.1723126285272114
因子sum(return_0, 1)在测试集适应度值为2.1723126285272114
因子min(return_0, return_0)在测试集适应度值为2.1723126285272114
因子log(return_0)在测试集适应度值为2.2029442082923274
因子min(return_0, shift(return_0, 19))在测试集适应度值为2.2645145481452214
因子delta(close_0, 2)在测试集适应度值为1.6909389903239054
因子min(return_0, close_0)在测试集适应度值为2.1723126285272114
因子min(return_0, high_0)在测试集适应度值为2.1723126285272114
因子min(return_0, mul(turn_0, close_0))在测试集适应度值为2.1723126285272114
因子min(close_0, return_0)在测试集适应度值为2.1723126285272114
因子min(return_0, shift(high_0, constant(18)))在测试集适应度值为2.1723126285272114
因子min(return_0, shift(return_0, 16))在测试集适应度值为2.095321815696886
因子min(return_0, shift(ts_min(low_0, 8), 19))在测试集适应度值为2.1723126285272114

    pass:14, record:30, population: 14

    下一代挖掘的个体数：50

    -- 开始第「5」次循环第「1」代挖掘 --

    去重前的个体数50
去重后的个体数49

    每代的平均适应度：[-0.04861216167402308]
因子std(low_0, 6)在训练集适应度值为-1.6593528273098832
因子shift(turn_0, 10)在训练集适应度值为-0.6238947936698077
因子decay_linear(low_0, 9)在训练集适应度值为-2.060339093881102
因子ts_argmin(close_0, 14)在训练集适应度值为0.27953442119981947
因子ts_argmax(ts_max(return_0, 16), constant(16))在训练集适应度值为-1.327094127554072
因子ts_min(close_0, 11)在训练集适应度值为-4.740895749146302
因子rank(sign(return_0))在训练集适应度值为nan
因子sign(sub(turn_0, close_0))在训练集适应度值为0.08375547129936799
因子sum(covariance(return_0, high_0, 1), constant(5))在训练集适应度值为nan
因子sign(close_0)在训练集适应度值为nan
因子delta(sign(return_0), constant(6))在训练集适应度值为nan
因子covariance(close_0, high_0, 16)在训练集适应度值为-1.5788937263379081
因子min(max(turn_0, low_0), ts_rank(close_0, 8))在训练集适应度值为-1.360191585690583
因子sum(low_0, 18)在训练集适应度值为-1.7028640895557463
因子div(high_0, low_0)在训练集适应度值为2.765437476966865
因子delta(close_0, 12)在训练集适应度值为1.2048322011331953
因子mul(turn_0, low_0)在训练集适应度值为-1.0181403043791781
因子sum(close_0, 3)在训练集适应度值为-1.463251480706445
因子add(high_0, shift(low_0, 6))在训练集适应度值为-3.858334143122769
因子sub(ts_argmin(high_0, 14), covariance(return_0, high_0, 3))在训练集适应度值为-2.020983274712671
因子sub(ts_argmax(close_0, 6), return_0)在训练集适应度值为-0.9887524336396489
因子correlation(decay_linear(turn_0, 17), ts_min(high_0, 3), constant(17))在训练集适应度值为nan
因子min(low_0, low_0)在训练集适应度值为-0.8461702830903864
因子ts_rank(min(close_0, turn_0), constant(16))在训练集适应度值为2.4326314192139944
因子product(shift(close_0, 1), constant(8))在训练集适应度值为0.2568256482297317
因子product(ta_sma(low_0, 17), constant(10))在训练集适应度值为-0.39185706366426365
因子abs(low_0)在训练集适应度值为-0.8461702830903864
因子sub(close_0, close_0)在训练集适应度值为0.24196857645824432
因子correlation(rank(return_0), ts_max(high_0, 16), constant(7))在训练集适应度值为nan
因子delta(log(return_0), constant(19))在训练集适应度值为-3.0457343398943384
因子std(turn_0, 2)在训练集适应度值为-0.013376060034441988
因子delta(turn_0, 17)在训练集适应度值为-3.143530401988067
因子min(return_0, return_0)在训练集适应度值为2.966799078652266
因子min(correlation(close_0, high_0, 4), add(close_0, return_0))在训练集适应度值为-0.5893892057810065
因子shift(low_0, 13)在训练集适应度值为-0.8903800058377234
因子abs(log(close_0))在训练集适应度值为-0.7388599896612504
因子log(high_0)在训练集适应度值为-0.6118414256911656
因子ts_min(normalize(return_0), constant(5))在训练集适应度值为-0.7874950744181626
因子ts_argmin(return_0, 17)在训练集适应度值为-1.1256389743547008
因子correlation(turn_0, return_0, 4)在训练集适应度值为-0.6692376154284261
因子delta(close_0, 13)在训练集适应度值为-2.5780058773486108
因子log(turn_0)在训练集适应度值为-1.4561224619584097
因子rank(close_0)在训练集适应度值为-1.0977782305532073
因子normalize(rank(turn_0))在训练集适应度值为-0.7849678654703479
因子product(log(return_0), constant(9))在训练集适应度值为nan
因子ta_sma(high_0, 7)在训练集适应度值为-1.7072431804175154
因子shift(close_0, 1)在训练集适应度值为-1.3638672369982188
因子ts_argmin(low_0, 12)在训练集适应度值为-3.1921573630370887
因子delta(turn_0, 10)在训练集适应度值为-1.5180477985489909

    因子div(high_0, low_0)在测试集适应度值为1.1568439185348383
因子ts_rank(min(close_0, turn_0), constant(16))在测试集适应度值为1.423178740836047
因子min(return_0, return_0)在测试集适应度值为2.1723126285272114

    pass:3, record:49, population: 3

    下一代挖掘的个体数：50

    -- 开始第「5」次循环第「2」代挖掘 --

    去重前的个体数50
去重后的个体数27

    每代的平均适应度：[-0.04861216167402308, 0.333500623554049]
因子ts_rank(min(close_0, return_0), constant(16))在训练集适应度值为2.458861785286965
因子min(return_0, turn_0)在训练集适应度值为3.2711429509050887
因子min(return_0, return_0)在训练集适应度值为2.966799078652266
因子min(ta_sma(ts_min(turn_0, 10), constant(3)), return_0)在训练集适应度值为-2.233066386974195
因子min(high_0, return_0)在训练集适应度值为2.966799078652266
因子ts_rank(close_0, constant(16))在训练集适应度值为2.590562983329258
因子ts_rank(add(close_0, turn_0), constant(16))在训练集适应度值为0.597498375814866
因子ts_rank(min(close_0, turn_0), 16)在训练集适应度值为2.4326314192139944
因子ts_rank(min(close_0, return_0), constant(6))在训练集适应度值为2.3081837394100355
因子min(return_0, low_0)在训练集适应度值为2.966799078652266
因子ts_max(add(close_0, high_0), 10)在训练集适应度值为-4.9708964256485775
因子delta(decay_linear(return_0, 15), constant(1))在训练集适应度值为-2.7270082358957333
因子ts_rank(min(return_0, turn_0), 16)在训练集适应度值为-0.5367074360266523
因子ts_rank(min(ts_min(close_0, 9), turn_0), constant(16))在训练集适应度值为-3.1731240002097127
因子min(ts_min(max(close_0, high_0), constant(3)), high_0)在训练集适应度值为-0.6827409582151237
因子min(close_0, return_0)在训练集适应度值为2.966799078652266
因子min(turn_0, return_0)在训练集适应度值为3.2711429509050887
因子ts_rank(add(close_0, turn_0), 16)在训练集适应度值为0.597498375814866
因子ts_rank(min(low_0, turn_0), constant(16))在训练集适应度值为-0.5474348626899693
因子div(high_0, log(low_0))在训练集适应度值为-0.8886694010350215
因子ts_rank(abs(close_0), constant(16))在训练集适应度值为2.590562983329258
因子min(return_0, close_0)在训练集适应度值为2.966799078652266
因子min(return_0, correlation(correlation(return_0, turn_0, 4), add(close_0, turn_0), constant(15)))在训练集适应度值为2.959473945706857
因子ts_rank(min(close_0, return_0), constant(5))在训练集适应度值为2.1380561667908267
因子min(low_0, return_0)在训练集适应度值为2.966799078652266
因子div(high_0, return_0)在训练集适应度值为-0.8780738497862343
因子ts_rank(min(return_0, turn_0), constant(16))在训练集适应度值为-0.5367074360266523

    因子ts_rank(min(close_0, return_0), constant(16))在测试集适应度值为1.4241556516140776
因子min(return_0, turn_0)在测试集适应度值为1.9628287395834418
因子min(return_0, return_0)在测试集适应度值为2.1723126285272114
因子min(high_0, return_0)在测试集适应度值为2.1723126285272114
因子ts_rank(close_0, constant(16))在测试集适应度值为1.3417793970259018
因子ts_rank(min(close_0, turn_0), 16)在测试集适应度值为1.423178740836047
因子ts_rank(min(close_0, return_0), constant(6))在测试集适应度值为1.2417506663072688
因子min(return_0, low_0)在测试集适应度值为2.1723126285272114
因子min(close_0, return_0)在测试集适应度值为2.1723126285272114
因子min(turn_0, return_0)在测试集适应度值为1.9628287395834418
因子ts_rank(abs(close_0), constant(16))在测试集适应度值为1.3417793970259018
因子min(return_0, close_0)在测试集适应度值为2.1723126285272114
因子min(return_0, correlation(correlation(return_0, turn_0, 4), add(close_0, turn_0), constant(15)))在测试集适应度值为2.176481242699318
因子ts_rank(min(close_0, return_0), constant(5))在测试集适应度值为1.2222315224838403
因子min(low_0, return_0)在测试集适应度值为2.1723126285272114

    pass:15, record:27, population: 15

    下一代挖掘的个体数：50

    -- 开始第「5」次循环第「3」代挖掘 --

    去重前的个体数50
去重后的个体数29

    每代的平均适应度：[-0.04861216167402308, 0.333500623554049, 0.5114537101273361]
因子min(low_0, return_0)在训练集适应度值为2.966799078652266
因子min(return_0, low_0)在训练集适应度值为2.966799078652266
因子min(return_0, add(close_0, close_0))在训练集适应度值为2.966799078652266
因子min(turn_0, return_0)在训练集适应度值为3.2711429509050887
因子min(return_0, correlation(correlation(low_0, turn_0, 4), add(close_0, turn_0), constant(15)))在训练集适应度值为2.959473945706857
因子min(log(sub(low_0, close_0)), return_0)在训练集适应度值为nan
因子min(close_0, return_0)在训练集适应度值为0.17082496878096806
因子min(high_0, return_0)在训练集适应度值为2.966799078652266
因子min(return_0, return_0)在训练集适应度值为2.966799078652266
因子ts_rank(close_0, constant(16))在训练集适应度值为2.590562983329258
因子ts_rank(min(product(return_0, 14), return_0), constant(16))在训练集适应度值为nan
因子min(return_0, correlation(correlation(return_0, close_0, 4), add(close_0, turn_0), constant(15)))在训练集适应度值为2.977468978476626
因子abs(max(return_0, low_0))在训练集适应度值为-0.8461702830903864
因子min(high_0, delta(close_0, constant(9)))在训练集适应度值为-1.5007396882198472
因子min(low_0, low_0)在训练集适应度值为-0.8461702830903864
因子min(close_0, return_0)在训练集适应度值为2.966799078652266
因子shift(normalize(high_0), constant(7))在训练集适应度值为-0.15250794716652577
因子min(return_0, close_0)在训练集适应度值为2.966799078652266
因子min(return_0, correlation(correlation(return_0, high_0, 4), add(close_0, turn_0), constant(15)))在训练集适应度值为2.9626727168568703
因子min(close_0, div(product(low_0, 5), std(return_0, 17)))在训练集适应度值为-0.8476981027065214
因子min(return_0, correlation(correlation(return_0, turn_0, constant(1)), add(close_0, turn_0), constant(15)))在训练集适应度值为2.966799078652266
因子ts_rank(min(low_0, turn_0), 16)在训练集适应度值为-0.5474348626899693
因子min(return_0, close_0)在训练集适应度值为2.966799078652266
因子ts_rank(min(close_0, low_0), 16)在训练集适应度值为2.5924668483215254
因子min(return_0, high_0)在训练集适应度值为2.966799078652266
因子min(return_0, correlation(correlation(return_0, turn_0, 4), add(close_0, turn_0), constant(constant(15))))在训练集适应度值为2.959473945706857
因子min(return_0, correlation(correlation(div(return_0, close_0), turn_0, 4), add(close_0, turn_0), 15))在训练集适应度值为2.962882431289037
因子min(return_0, turn_0)在训练集适应度值为3.2711429509050887
因子min(return_0, min(add(close_0, high_0), min(return_0, close_0)))在训练集适应度值为2.966799078652266

    因子min(low_0, return_0)在测试集适应度值为2.1723126285272114
因子min(return_0, low_0)在测试集适应度值为2.1723126285272114
因子min(return_0, add(close_0, close_0))在测试集适应度值为2.1723126285272114
因子min(turn_0, return_0)在测试集适应度值为1.9628287395834418
因子min(return_0, correlation(correlation(low_0, turn_0, 4), add(close_0, turn_0), constant(15)))在测试集适应度值为2.176481242699318
因子min(high_0, return_0)在测试集适应度值为2.1723126285272114
因子min(return_0, return_0)在测试集适应度值为2.1723126285272114
因子ts_rank(close_0, constant(16))在测试集适应度值为1.3417793970259018
因子min(return_0, correlation(correlation(return_0, close_0, 4), add(close_0, turn_0), constant(15)))在测试集适应度值为2.176481242699318
因子min(close_0, return_0)在测试集适应度值为2.1723126285272114
因子min(return_0, close_0)在测试集适应度值为2.1723126285272114
因子min(return_0, correlation(correlation(return_0, high_0, 4), add(close_0, turn_0), constant(15)))在测试集适应度值为2.176481242699318
因子min(return_0, correlation(correlation(return_0, turn_0, constant(1)), add(close_0, turn_0), constant(15)))在测试集适应度值为2.1723126285272114
因子min(return_0, close_0)在测试集适应度值为2.1723126285272114
因子ts_rank(min(close_0, low_0), 16)在测试集适应度值为1.3429207104210883
因子min(return_0, high_0)在测试集适应度值为2.1723126285272114
因子min(return_0, correlation(correlation(return_0, turn_0, 4), add(close_0, turn_0), constant(constant(15))))在测试集适应度值为2.176481242699318
因子min(return_0, correlation(correlation(div(return_0, close_0), turn_0, 4), add(close_0, turn_0), 15))在测试集适应度值为2.1723126285272114
因子min(return_0, turn_0)在测试集适应度值为1.9628287395834418
因子min(return_0, min(add(close_0, high_0), min(return_0, close_0)))在测试集适应度值为2.1723126285272114

    pass:20, record:29, population: 20

    下一代挖掘的个体数：50

    -- 开始第「5」次循环第「4」代挖掘 --

    去重前的个体数50
去重后的个体数36

    每代的平均适应度：[-0.04861216167402308, 0.333500623554049, 0.5114537101273361, 0.4419329939210573]
因子min(turn_0, low_0)在训练集适应度值为-1.0206958199547311
因子min(return_0, correlation(turn_0, add(close_0, return_0), constant(16)))在训练集适应度值为2.957688451813969
因子min(return_0, close_0)在训练集适应度值为2.966799078652266
因子min(return_0, max(sum(low_0, 13), ts_argmax(return_0, 6)))在训练集适应度值为2.6720913468154714
因子min(return_0, add(close_0, high_0))在训练集适应度值为2.966799078652266
因子min(return_0, correlation(correlation(return_0, close_0, 4), add(log(ts_argmin(close_0, 4)), turn_0), constant(15)))在训练集适应度值为2.9776681970939074
因子min(return_0, correlation(correlation(return_0, sum(low_0, 15), 4), low_0, constant(constant(15))))在训练集适应度值为2.966799078652266
因子min(return_0, add(close_0, turn_0))在训练集适应度值为2.966799078652266
因子min(turn_0, return_0)在训练集适应度值为3.2711429509050887
因子min(return_0, correlation(low_0, turn_0, 4))在训练集适应度值为1.212362499333556
因子min(turn_0, close_0)在训练集适应度值为-1.043982278327278
因子min(return_0, correlation(correlation(low_0, min(close_0, low_0), 4), add(close_0, turn_0), constant(15)))在训练集适应度值为2.966799078652266
因子min(return_0, correlation(correlation(return_0, close_0, 4), turn_0, constant(constant(7))))在训练集适应度值为2.852982475879074
因子min(return_0, correlation(correlation(return_0, turn_0, 4), add(close_0, turn_0), constant(15)))在训练集适应度值为2.959473945706857
因子min(abs(close_0), correlation(correlation(return_0, high_0, 4), add(close_0, turn_0), constant(15)))在训练集适应度值为-0.8636302607355644
因子min(return_0, correlation(correlation(return_0, high_0, 4), add(turn_0, turn_0), constant(15)))在训练集适应度值为2.964789489779659
因子min(product(close_0, 14), correlation(correlation(low_0, turn_0, 4), add(close_0, close_0), constant(15)))在训练集适应度值为nan
因子min(low_0, correlation(ts_min(close_0, 15), add(close_0, turn_0), constant(15)))在训练集适应度值为-0.8461702830903864
因子min(return_0, correlation(high_0, add(close_0, turn_0), constant(4)))在训练集适应度值为2.33476374410298
因子min(close_0, return_0)在训练集适应度值为2.966799078652266
因子min(return_0, correlation(correlation(return_0, turn_0, constant(1)), add(close_0, turn_0), 15))在训练集适应度值为2.966799078652266
因子min(return_0, correlation(correlation(low_0, close_0, 4), add(close_0, close_0), constant(15)))在训练集适应度值为2.966799078652266
因子min(return_0, correlation(correlation(low_0, turn_0, 4), turn_0, constant(15)))在训练集适应度值为2.972948763921149
因子min(close_0, close_0)在训练集适应度值为-0.8769926368339077
因子div(close_0, high_0)在训练集适应度值为-1.5628533128038389
因子min(return_0, high_0)在训练集适应度值为2.966799078652266
因子min(return_0, min(add(close_0, high_0), min(return_0, close_0)))在训练集适应度值为2.966799078652266
因子min(return_0, correlation(correlation(return_0, return_0, 4), return_0, constant(constant(15))))在训练集适应度值为2.966799078652266
因子min(return_0, correlation(correlation(turn_0, close_0, 4), add(close_0, return_0), constant(15)))在训练集适应度值为2.9760509407712474
因子min(low_0, return_0)在训练集适应度值为2.966799078652266
因子min(close_0, correlation(correlation(return_0, close_0, 4), add(close_0, turn_0), constant(15)))在训练集适应度值为-0.8636302607355644
因子min(return_0, correlation(covariance(return_0, turn_0, 4), add(close_0, turn_0), constant(constant(15))))在训练集适应度值为2.959473945706857
因子max(close_0, turn_0)在训练集适应度值为-0.8744566455570912
因子min(return_0, correlation(correlation(return_0, high_0, constant(15)), add(close_0, turn_0), constant(15)))在训练集适应度值为2.9486859677854813
因子min(return_0, covariance(return_0, turn_0, 4))在训练集适应度值为-0.5561099512115687
因子min(close_0, correlation(correlation(return_0, low_0, 4), add(add(close_0, close_0), turn_0), constant(15)))在训练集适应度值为0.14337032821572354

    因子min(return_0, correlation(turn_0, add(close_0, return_0), constant(16)))在测试集适应度值为2.1514180437807644
因子min(return_0, close_0)在测试集适应度值为2.1723126285272114
因子min(return_0, max(sum(low_0, 13), ts_argmax(return_0, 6)))在测试集适应度值为2.010908278825105
因子min(return_0, add(close_0, high_0))在测试集适应度值为2.1723126285272114
因子min(return_0, correlation(correlation(return_0, close_0, 4), add(log(ts_argmin(close_0, 4)), turn_0), constant(15)))在测试集适应度值为2.18505175714241
因子min(return_0, correlation(correlation(return_0, sum(low_0, 15), 4), low_0, constant(constant(15))))在测试集适应度值为2.1723126285272114
因子min(return_0, add(close_0, turn_0))在测试集适应度值为2.1723126285272114
因子min(turn_0, return_0)在测试集适应度值为1.9628287395834418
因子min(return_0, correlation(correlation(low_0, min(close_0, low_0), 4), add(close_0, turn_0), constant(15)))在测试集适应度值为2.1723126285272114
因子min(return_0, correlation(correlation(return_0, close_0, 4), turn_0, constant(constant(7))))在测试集适应度值为2.19260216347347
因子min(return_0, correlation(correlation(return_0, turn_0, 4), add(close_0, turn_0), constant(15)))在测试集适应度值为2.176481242699318
因子min(return_0, correlation(correlation(return_0, high_0, 4), add(turn_0, turn_0), constant(15)))在测试集适应度值为2.1783443872185138
因子min(return_0, correlation(high_0, add(close_0, turn_0), constant(4)))在测试集适应度值为2.2914317967490816
因子min(close_0, return_0)在测试集适应度值为2.1723126285272114
因子min(return_0, correlation(correlation(return_0, turn_0, constant(1)), add(close_0, turn_0), 15))在测试集适应度值为2.1723126285272114
因子min(return_0, correlation(correlation(low_0, close_0, 4), add(close_0, close_0), constant(15)))在测试集适应度值为2.1723126285272114
因子min(return_0, correlation(correlation(low_0, turn_0, 4), turn_0, constant(15)))在测试集适应度值为2.179714420141761
因子min(return_0, high_0)在测试集适应度值为2.1723126285272114
因子min(return_0, min(add(close_0, high_0), min(return_0, close_0)))在测试集适应度值为2.1723126285272114
因子min(return_0, correlation(correlation(return_0, return_0, 4), return_0, constant(constant(15))))在测试集适应度值为2.1723126285272114
因子min(return_0, correlation(correlation(turn_0, close_0, 4), add(close_0, return_0), constant(15)))在测试集适应度值为2.1723126285272114
因子min(low_0, return_0)在测试集适应度值为2.1723126285272114
因子min(return_0, correlation(covariance(return_0, turn_0, 4), add(close_0, turn_0), constant(constant(15))))在测试集适应度值为2.176481242699318
因子min(return_0, correlation(correlation(return_0, high_0, constant(15)), add(close_0, turn_0), constant(15)))在测试集适应度值为2.1723126285272114

    pass:24, record:36, population: 24

    下一代挖掘的个体数：50

    -- 开始第「5」次循环第「5」代挖掘 --

    去重前的个体数50
去重后的个体数36

    每代的平均适应度：[-0.04861216167402308, 0.333500623554049, 0.5114537101273361, 0.4419329939210573, 0.6454272749118504]
因子min(return_0, close_0)在训练集适应度值为2.966799078652266
因子min(return_0, correlation(high_0, add(correlation(return_0, high_0, 4), turn_0), constant(4)))在训练集适应度值为2.505094361272668
因子min(correlation(correlation(low_0, close_0, 4), add(close_0, close_0), constant(15)), correlation(correlation(ts_max(ta_sma(high_0, 19), constant(18)), close_0, 4), turn_0, constant(constant(7))))在训练集适应度值为nan
因子min(return_0, return_0)在训练集适应度值为2.966799078652266
因子min(return_0, correlation(correlation(return_0, close_0, 4), add(ts_argmin(close_0, 4), turn_0), constant(15)))在训练集适应度值为2.980276816211829
因子min(return_0, correlation(correlation(return_0, high_0, constant(15)), add(close_0, turn_0), constant(15)))在训练集适应度值为2.9486859677854813
因子rank(add(close_0, close_0))在训练集适应度值为-1.2564311245425528
因子min(return_0, normalize(product(return_0, 8)))在训练集适应度值为2.966799078652266
因子min(return_0, correlation(correlation(return_0, turn_0, 4), min(sum(delta(close_0, 14), constant(13)), turn_0), constant(15)))在训练集适应度值为2.868159201957424
因子min(return_0, correlation(correlation(low_0, turn_0, 4), turn_0, constant(15)))在训练集适应度值为2.972948763921149
因子sum(max(close_0, low_0), constant(19))在训练集适应度值为-2.3377657992740892
因子min(return_0, min(add(close_0, high_0), min(return_0, close_0)))在训练集适应度值为2.966799078652266
因子min(low_0, correlation(correlation(return_0, high_0, 4), add(turn_0, turn_0), constant(15)))在训练集适应度值为-0.8682771678723349
因子min(return_0, correlation(correlation(return_0, turn_0, 4), add(close_0, add(ts_argmax(return_0, 2), product(close_0, 3))), constant(4)))在训练集适应度值为2.8487267967514684
因子min(return_0, correlation(correlation(return_0, close_0, 15), turn_0, constant(15)))在训练集适应度值为2.9434245661892158
因子min(return_0, correlation(correlation(return_0, close_0, 4), turn_0, constant(4)))在训练集适应度值为2.569501006716563
因子min(return_0, correlation(correlation(high_0, min(close_0, low_0), constant(7)), add(close_0, turn_0), constant(15)))在训练集适应度值为2.970699033176901
因子min(return_0, correlation(correlation(return_0, close_0, constant(15)), add(log(ts_argmin(close_0, 4)), turn_0), constant(15)))在训练集适应度值为2.9747979489313776
因子min(return_0, correlation(correlation(return_0, high_0, constant(15)), add(close_0, turn_0), 4))在训练集适应度值为2.968101578703134
因子min(return_0, correlation(correlation(return_0, high_0, 4), add(turn_0, high_0), constant(15)))在训练集适应度值为2.9626727168568703
因子min(return_0, covariance(correlation(return_0, close_0, 4), add(log(close_0), turn_0), constant(15)))在训练集适应度值为2.9151634642775694
因子min(return_0, correlation(correlation(low_0, min(close_0, low_0), 4), add(close_0, turn_0), constant(13)))在训练集适应度值为2.966799078652266
因子min(return_0, correlation(correlation(return_0, turn_0, 4), add(close_0, turn_0), constant(19)))在训练集适应度值为2.966799078652266
因子min(return_0, correlation(correlation(return_0, max(close_0, turn_0), 4), low_0, constant(constant(15))))在训练集适应度值为2.9626727168568703
因子sub(high_0, close_0)在训练集适应度值为-1.3345772507359064
因子min(return_0, correlation(high_0, add(close_0, turn_0), constant(6)))在训练集适应度值为1.8745224240863885
因子min(return_0, min(add(turn_0, high_0), close_0))在训练集适应度值为2.966799078652266
因子min(return_0, correlation(correlation(return_0, high_0, 4), add(turn_0, product(turn_0, 1)), 15))在训练集适应度值为2.964789489779659
因子min(return_0, turn_0)在训练集适应度值为3.2711429509050887
因子min(return_0, min(turn_0, min(return_0, close_0)))在训练集适应度值为3.2711429509050887
因子min(return_0, correlation(correlation(return_0, close_0, 15), add(log(ts_argmin(close_0, 4)), turn_0), constant(15)))在训练集适应度值为2.9747979489313776
因子min(return_0, correlation(correlation(return_0, turn_0, 4), add(close_0, turn_0), constant(18)))在训练集适应度值为2.966799078652266
因子min(return_0, correlation(close_0, turn_0, constant(4)))在训练集适应度值为1.515779763523224
因子min(return_0, correlation(high_0, add(close_0, turn_0), constant(4)))在训练集适应度值为2.33476374410298
因子min(return_0, correlation(correlation(return_0, close_0, 1), turn_0, constant(constant(7))))在训练集适应度值为2.966799078652266
因子min(return_0, correlation(correlation(low_0, min(close_0, low_0), 4), ts_min(high_0, 17), constant(15)))在训练集适应度值为2.966799078652266

    因子min(return_0, close_0)在测试集适应度值为2.1723126285272114
因子min(return_0, correlation(high_0, add(correlation(return_0, high_0, 4), turn_0), constant(4)))在测试集适应度值为2.0852533474683965
因子min(return_0, return_0)在测试集适应度值为2.1723126285272114
因子min(return_0, correlation(correlation(return_0, close_0, 4), add(ts_argmin(close_0, 4), turn_0), constant(15)))在测试集适应度值为2.18505175714241
因子min(return_0, correlation(correlation(return_0, high_0, constant(15)), add(close_0, turn_0), constant(15)))在测试集适应度值为2.1723126285272114
因子min(return_0, normalize(product(return_0, 8)))在测试集适应度值为2.1723126285272114
因子min(return_0, correlation(correlation(return_0, turn_0, 4), min(sum(delta(close_0, 14), constant(13)), turn_0), constant(15)))在测试集适应度值为2.148689102719969
因子min(return_0, correlation(correlation(low_0, turn_0, 4), turn_0, constant(15)))在测试集适应度值为2.179714420141761
因子min(return_0, min(add(close_0, high_0), min(return_0, close_0)))在测试集适应度值为2.1723126285272114
因子min(return_0, correlation(correlation(return_0, turn_0, 4), add(close_0, add(ts_argmax(return_0, 2), product(close_0, 3))), constant(4)))在测试集适应度值为2.1857125371022694
因子min(return_0, correlation(correlation(return_0, close_0, 15), turn_0, constant(15)))在测试集适应度值为2.1723126285272114
因子min(return_0, correlation(correlation(return_0, close_0, 4), turn_0, constant(4)))在测试集适应度值为2.153683143940772
因子min(return_0, correlation(correlation(high_0, min(close_0, low_0), constant(7)), add(close_0, turn_0), constant(15)))在测试集适应度值为2.1723126285272114
因子min(return_0, correlation(correlation(return_0, close_0, constant(15)), add(log(ts_argmin(close_0, 4)), turn_0), constant(15)))在测试集适应度值为2.1723126285272114
因子min(return_0, correlation(correlation(return_0, high_0, constant(15)), add(close_0, turn_0), 4))在测试集适应度值为2.1832887080343206
因子min(return_0, correlation(correlation(return_0, high_0, 4), add(turn_0, high_0), constant(15)))在测试集适应度值为2.176481242699318
因子min(return_0, covariance(correlation(return_0, close_0, 4), add(log(close_0), turn_0), constant(15)))在测试集适应度值为2.1993659861755304
因子min(return_0, correlation(correlation(low_0, min(close_0, low_0), 4), add(close_0, turn_0), constant(13)))在测试集适应度值为2.1723126285272114
因子min(return_0, correlation(correlation(return_0, turn_0, 4), add(close_0, turn_0), constant(19)))在测试集适应度值为2.1723126285272114
因子min(return_0, correlation(correlation(return_0, max(close_0, turn_0), 4), low_0, constant(constant(15))))在测试集适应度值为2.176481242699318
因子min(return_0, min(add(turn_0, high_0), close_0))在测试集适应度值为2.1723126285272114
因子min(return_0, correlation(correlation(return_0, high_0, 4), add(turn_0, product(turn_0, 1)), 15))在测试集适应度值为2.1783443872185138
因子min(return_0, turn_0)在测试集适应度值为1.9628287395834418
因子min(return_0, min(turn_0, min(return_0, close_0)))在测试集适应度值为1.9628287395834418
因子min(return_0, correlation(correlation(return_0, close_0, 15), add(log(ts_argmin(close_0, 4)), turn_0), constant(15)))在测试集适应度值为2.1723126285272114
因子min(return_0, correlation(correlation(return_0, turn_0, 4), add(close_0, turn_0), constant(18)))在测试集适应度值为2.1723126285272114
因子min(return_0, correlation(high_0, add(close_0, turn_0), constant(4)))在测试集适应度值为2.2914317967490816
因子min(return_0, correlation(correlation(return_0, close_0, 1), turn_0, constant(constant(7))))在测试集适应度值为2.1723126285272114
因子min(return_0, correlation(correlation(low_0, min(close_0, low_0), 4), ts_min(high_0, 17), constant(15)))在测试集适应度值为2.1723126285272114

    pass:29, record:36, population: 29

    下一代挖掘的个体数：50
    In [23]:
    m1.factors.read()
    Out[23]:
    {}
    In [ ]: