因子测试错误

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{"Description":"实验创建于2017/8/26","Summary":"","Graph":{"EdgesInternal":[{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-15:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-29:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data1","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-15:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-29:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-35:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-76:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-43:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-70:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-35:input_data","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-29:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data2","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-35:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:model","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-43:model"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-84:input_data","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-81:options_data","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:predictions"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-70:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-81:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-76:input_data","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-70:data"},{"DestinationInputPortId":"-86:input_data","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-76:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-43:training_ds","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-84:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:data","SourceOutputPortId":"-86:data"}],"ModuleNodes":[{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2010-01-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"2015-01-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"market","Value":"CN_STOCK_A","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_list","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"max_count","Value":"0","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"rolling_conf","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":1,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15","ModuleId":"BigQuantSpace.advanced_auto_labeler.advanced_auto_labeler-v2","ModuleParameters":[{"Name":"label_expr","Value":"# 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\nta_atr(high_0,low_0,close_0,5)\n((-1*((low_0-close_0)*(open_0**5)))/((low_0-high_0)*(close_0**5)))\nreturn_6\n-1*delta(((close_0-low_0)-(high_0-close_0))/(high_0-low_0),1)\nreturn_3\nstd(volume_0,10)\nta_ema(((high_0+low_0-0)/2-(delay(high_0,1)+delay(low_0,1))/2)*(high_0-low_0)/volume_0,15)\n(close_0-mean(close_0,12))/mean(close_0,12)*100\n(close_0-delay(close_0,6))/delay(close_0,6)*volume_0\n(volume_0-delay(volume_0,5))/delay(volume_0,5)*100\nsum(((close_0-low_0)-(high_0-close_0))/(high_0-low_0)*volume_0,20)\n(close_0-mean(close_0,24))/mean(close_0,24)*100\n((sum(close_0,7)/7)-close_0)+correlation(amount_0/volume_0*adjust_factor_0,delay(close_0,5),230)\nreturn_15\nrank((-1*((1-(open_0/close_0))**1))) \nmean(close_0,12)/close_0\nta_ema((close_0-ts_min(low_0,9))/(ts_max(high_0,9)-ts_min(low_0,9))*100,3)\nreturn_20\n(close_0-delay(close_0,20))/delay(close_0,20)*100\nclose_0-delay(close_0,5)\nta_ema(volume_0, 21)\nclose_0/delay(close_0,5)\nstd(amount_0,20)\nsum(((close_0-low_0)-(high_0-close_0))/(high_0-low_0)*volume_0,6)\n((high_0+low_0+close_0)/3-mean((high_0+low_0+close_0)/3,12))/(0.015*mean(abs(close_0-mean((high_0+low_0+close_0)/3,12)),12))\nstd(amount_0,6)\nta_ema(((ts_max(high_0,6)-close_0)/(ts_max(high_0,6)-ts_min(low_0,6))*100),20)\nta_ema(ta_ema((close_0-ts_min(low_0,9))/(ts_max(high_0,9)-ts_min(low_0,9))*100,3),3)\n(close_0-delay(close_0,6))/delay(close_0,6)*100\n(((high_0*low_0)**0.5)-amount_0/volume_0*adjust_factor_0)\n(mean(close_0,3)+mean(close_0,6)+mean(close_0,12)+mean(close_0,24))/(4*close_0)\nta_ema(close_0-delay(close_0,5),5)\nta_ema(high_0-low_0,10)/ta_ema(ta_ema(high_0-low_0,10),10)\n((high_0-ta_ema(close_0,15))-(low_0-ta_ema(close_0,15)))/close_0\n(close_0+high_0+low_0)/3\nstd(volume_0,20)\nopen_0/shift(close_0,1)-1 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    In [2]:
    # 本代码由可视化策略环境自动生成 2017年11月7日 23:34
    # 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='2015-01-01',
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m2 = M.advanced_auto_labeler.v2(
        instruments=m1.data,
        label_expr="""# #号开始的表示注释
    # 0. 每行一个,顺序执行,从第二个开始,可以使用label字段
    # 1. 可用数据字段见 https://bigquant.com/docs/data_history_data.html
    #   添加benchmark_前缀,可使用对应的benchmark数据
    # 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/big_expr.html>`_
    
    # 计算收益:5日收盘价(作为卖出价格)除以明日开盘价(作为买入价格)
    shift(close, -5) / shift(open, -1)
    
    # 极值处理:用1%和99%分位的值做clip
    clip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))
    
    # 将分数映射到分类,这里使用20个分类
    all_wbins(label, 20)
    
    # 过滤掉一字涨停的情况 (设置label为NaN,在后续处理和训练中会忽略NaN的label)
    where(shift(high, -1) == shift(low, -1), NaN, label)
    """,
        start_date='',
        end_date='',
        benchmark='000300.SHA',
        drop_na_label=True,
        cast_label_int=True
    )
    
    m3 = M.input_features.v1(
        features="""return_6
    fs_roe_0
    fs_eps_0
    fs_bps_0
    fs_roa_0
    return_20
    rank_turn_0
    rank_turn_9
    #'ta_rsi(close_0,28)
    rank_pb_lf_0
    fs_roa_ttm_0
    fs_roe_ttm_0
    high_0/low_0
    fs_eps_yoy_0
    sqrt(high_0*low_0)-amount_0/volume_0*adjust_factor_0
    sum(max(0,high_0-delay(close_0,1)),20)/sum(max(0,delay(close_0, 1)-low_0),20)*100
    ((close_0-open_0)/((high_0-low_0)+.001))
    return_9
    ta_ema(((high_0+low_0)/2-(delay(high_0,1)+delay(low_0,1))/2)*(high_0-low_0)/volume_0,7)
    return_1
    fs_operating_revenue_yoy_0
    fs_operating_revenue_qoq_0
    fs_net_profit_margin_ttm_0
    fs_gross_profit_margin_ttm_0
    rank_pe_lyr_0
    rank_pe_ttm_0
    rank_ps_ttm_0
    rank_return_9
    rank_fs_bps_0
    rank_return_6
    rank_return_15
    close_1/open_0
    open_0/close_0
    high_0/close_1
    close_0/open_0
    rank_return_30
    rank_return_20
    rank_avg_turn_1
    close_9/close_0
    rank_avg_turn_6
    fs_cash_ratio_0
    close_4/close_0
    close_6/close_0
    close_2/close_0
    close_3/close_0
    close_5/close_0
    close_1/close_0
    rank_avg_turn_0
    volume_0/mean(volume_0, 3)*100 
    rank_avg_turn_3
    rank_avg_turn_9
    close_20/close_0
    rank_avg_turn_15
    close_15/close_0
    rank_avg_turn_20
    rank_market_cap_0
    amount_2/amount_0
    rank_fs_eps_yoy_0
    return_5/return_0
    amount_4/amount_0
    rank_fs_roe_ttm_0
    return_9/return_0
    amount_3/amount_0
    amount_5/amount_0
    (-1*correlation(open_0,volume_0,10))
    (-1*delta((((close_0-low_0)-(high_0-close_0))/(close_0-low_0)),9)) 
    ta_atr(high_0,low_0,close_0,5)
    ((-1*((low_0-close_0)*(open_0**5)))/((low_0-high_0)*(close_0**5)))
    return_6
    -1*delta(((close_0-low_0)-(high_0-close_0))/(high_0-low_0),1)
    return_3
    std(volume_0,10)
    ta_ema(((high_0+low_0-0)/2-(delay(high_0,1)+delay(low_0,1))/2)*(high_0-low_0)/volume_0,15)
    (close_0-mean(close_0,12))/mean(close_0,12)*100
    (close_0-delay(close_0,6))/delay(close_0,6)*volume_0
    (volume_0-delay(volume_0,5))/delay(volume_0,5)*100
    sum(((close_0-low_0)-(high_0-close_0))/(high_0-low_0)*volume_0,20)
    (close_0-mean(close_0,24))/mean(close_0,24)*100
    ((sum(close_0,7)/7)-close_0)+correlation(amount_0/volume_0*adjust_factor_0,delay(close_0,5),230)
    return_15
    rank((-1*((1-(open_0/close_0))**1))) 
    mean(close_0,12)/close_0
    ta_ema((close_0-ts_min(low_0,9))/(ts_max(high_0,9)-ts_min(low_0,9))*100,3)
    return_20
    (close_0-delay(close_0,20))/delay(close_0,20)*100
    close_0-delay(close_0,5)
    ta_ema(volume_0, 21)
    close_0/delay(close_0,5)
    std(amount_0,20)
    sum(((close_0-low_0)-(high_0-close_0))/(high_0-low_0)*volume_0,6)
    ((high_0+low_0+close_0)/3-mean((high_0+low_0+close_0)/3,12))/(0.015*mean(abs(close_0-mean((high_0+low_0+close_0)/3,12)),12))
    std(amount_0,6)
    ta_ema(((ts_max(high_0,6)-close_0)/(ts_max(high_0,6)-ts_min(low_0,6))*100),20)
    ta_ema(ta_ema((close_0-ts_min(low_0,9))/(ts_max(high_0,9)-ts_min(low_0,9))*100,3),3)
    (close_0-delay(close_0,6))/delay(close_0,6)*100
    (((high_0*low_0)**0.5)-amount_0/volume_0*adjust_factor_0)
    (mean(close_0,3)+mean(close_0,6)+mean(close_0,12)+mean(close_0,24))/(4*close_0)
    ta_ema(close_0-delay(close_0,5),5)
    ta_ema(high_0-low_0,10)/ta_ema(ta_ema(high_0-low_0,10),10)
    ((high_0-ta_ema(close_0,15))-(low_0-ta_ema(close_0,15)))/close_0
    (close_0+high_0+low_0)/3
    std(volume_0,20)
    open_0/shift(close_0,1)-1   
    return_9
    (mean(close_0,3)+mean(close_0,6)+mean(close_0,12)+mean(close_0,24))/4
    rank(delta(((((high_0+low_0)/2)*0.2)+(amount_0/volume_0*adjust_factor_0*0.8)),4)*-1)
    (rank(sign(delta((((open_0*0.85)+(high_0 *0.15))),4)))*-1)
    (-1*correlation(close_0,volume_0, 10))
    close_0-delay(close_0,20)
    (close_0-delay(close_0,1))/delay(close_0,1)*volume_0
    (close_0-delay(close_0,12))/delay(close_0,12)*volume_0
    return_3
    return_0
    (high_0-low_0-ta_ema(high_0-low_0, 11))/ta_ema(high_0-low_0, 11)*100
    return_1
    mean(abs(close_0-mean(close_0,6)),6)
    -1*((low_0-close_0*(open_0**5)))/((close_0-high_0)*(close_0**5))
    mean(amount_0,20)
    return_30
    return_15
    (rank((amount_0/volume_0*adjust_factor_0-close_0))/rank((amount_0/volume_0*adjust_factor_0 + close_0))) 
    ((rank(max((amount_0/volume_0*adjust_factor_0-close_0),3))+rank(min((amount_0/volume_0*adjust_factor_0-close_0), 3)))*rank(delta(volume_0, 3)))
    ta_beta(high_0,low_0,12)
    correlation(amount_0/volume_0*adjust_factor_0,volume_0,5)
    ta_adx(high_0,low_0,close_0,14)
    rank_turn_3
    rank_turn_1
    correlation(high_0/low_0,volume_0,4)
    rank_turn_6
    #'ta_rsi(close_0,14)
    rank_turn_15
    rank_turn_20
    rank_fs_roa_0
    rank_fs_roe_0
    rank_fs_eps_0
    rank_return_3
    rank_return_1
    rank_return_0
    low_0/close_1
    return_4/return_0
    rank_fs_roa_ttm_0
    amount_1/amount_0
    ta_wma(close_0,5)/close_0
    mean(close_0,5)/close_0
    ta_ema(close_0,5)/close_0
    ta_atr(high_0,low_0,close_0,14)/close_0
    avg_turn_9/turn_0
    avg_turn_1/turn_0
    ta_wma(close_0,30)/close_0
    return_9/return_5
    avg_turn_6/turn_0
    return_3/return_0
    ta_atr(high_0,low_0,close_0,28)/close_0
    close_0/mean(close_0,10)
    return_1/return_5
    return_0/return_3
    mean(close_0,30)/close_0
    return_1/return_0
    return_9/return_3
    ta_ema(close_0,30)/close_0
    avg_turn_3/turn_0
    return_1/return_3
    close_0/mean(close_0,30)
    return_6/return_5
    return_6/return_0
    close_0/mean(close_0,20)
    return_0/return_5
    return_6/return_3
    fs_net_profit_yoy_0
    fs_net_profit_qoq_0
    return_90/return_5
    return_15/return_0
    avg_turn_15/turn_0
    return_20/return_5
    return_50/return_5
    rank_sh_holder_num_0
    return_30/return_5
    avg_turn_20/turn_0
    return_30/return_0
    return_30/return_3
    return_20/return_0
    return_20/return_3
    return_15/return_5
    rank_fs_cash_ratio_0
    return_70/return_5
    return_60/return_5
    return_80/return_5
    return_15/return_3
    return_30/return_10
    return_70/return_10
    amount_0/avg_amount_5
    return_80/return_10
    return_50/return_10
    return_20/return_10
    return_90/return_10
    amount_0/avg_amount_3
    return_120/return_5
    return_60/return_10
    fs_net_profit_margin_0
    (high_0-low_0)/close_0
    return_120/return_10
    mean(close_0,20)/mean(close_0,30)
    mean(close_0,30)/mean(close_0,60)
    mean(close_0,10)/mean(close_0,60)
    (low_1-close_0)/close_0
    rank_market_cap_float_0
    mean(close_0,10)/mean(close_0,20)
    (low_1-close_1)/close_0
    (close_1-low_0)/close_0
    (low_0-close_1)/close_0
    mean(close_0,10)/mean(close_0,30)
    rank_fs_net_profit_qoq_0
    rank_sh_holder_avg_pct_0
    fs_gross_profit_margin_0
    (high_0-close_1)/close_0
    (high_1-close_0)/close_0
    rank_fs_net_profit_yoy_0
    (open_0-close_0)/close_0
    (close_1-high_0)/close_0
    (high_1-close_1)/close_0
    (high_0-low_0)/(close_0-open_0)
    rank_fs_operating_revenue_yoy_0
    rank_fs_operating_revenue_qoq_0
    (open_0-close_0)/(high_0-low_0)
    rank_sh_holder_avg_pct_6m_chng_0
    rank_sh_holder_avg_pct_3m_chng_0
    mean(close_0,3)/close_0
    mean(amount_0,3)/amount_0
    mean(volume_0,3)/volume_0
    avg_mf_net_amount_6/mf_net_amount_0
    avg_mf_net_amount_9/mf_net_amount_0
    avg_mf_net_amount_3/mf_net_amount_0
    avg_mf_net_amount_20/mf_net_amount_0
    avg_mf_net_amount_15/mf_net_amount_0
    avg_mf_net_amount_12/mf_net_amount_0
    avg_mf_net_amount_9/avg_mf_net_amount_3
    avg_mf_net_amount_6/avg_mf_net_amount_3
    close_0/mean(close_0,3)
    avg_mf_net_amount_20/avg_mf_net_amount_3
    avg_mf_net_amount_12/avg_mf_net_amount_3
    avg_mf_net_amount_15/avg_mf_net_amount_3
    amount_0/mean(amount_0,3)
    ((close_0-low_0)-(high_0-close_0))/(high_0-close_0)
    (high_0-low_0+high_1-low_1+high_2-low_2)/close_0
    mean(close_0,6)/close_0
    mean(amount_0,6)/amount_0
    mean(volume_0,6)/volume_0
    3/1*(high_0-low_0)/(high_0-low_0+high_1-low_1+high_2-low_2)
    mean(close_0,6)/mean(close_0,3)
    mean(close_0,9)/close_0
    mean(amount_0,6)/mean(amount_0,3)
    mean(amount_0,9)/amount_0
    mean(volume_0,9)/volume_0
    (mean(high_0,6)-mean(low_0,6))/close_0
    mean(close_0,9)/mean(close_0,3)
    mean(amount_0,9)/mean(amount_0,3)
    mean(close_0,15)/close_0
    (mean(high_0,9)-mean(low_0,9))/close_0
    mean(amount_0,15)/amount_0
    mean(volume_0,15)/volume_0
    (mean(high_0,6)-mean(low_0,6))/(mean(high_0,3)-mean(low_0,3))
    mean(close_0,15)/mean(close_0,3)
    mean(amount_0,15)/mean(amount_0,3)
    mean(close_0,20)/close_0
    mean(amount_0,20)/amount_0
    mean(volume_0,20)/volume_0
    mean(close_0,20)/mean(close_0,3)
    (mean(high_0,9)-mean(low_0,9))/(mean(high_0,3)-mean(low_0,3))
    mean(amount_0,20)/mean(amount_0,3)
    (sum(high_0,15)-sum(low_0,15))/close_0
    (mean(high_0,15)-mean(low_0,15))/(mean(high_0,3)-mean(low_0,3))
    (sum(high_0,20)-sum(low_0,20))/close_0
    (mean(high_0,20)-mean(low_0,20))/(mean(high_0,3)-mean(low_0,3))"""
    )
    
    m4 = M.general_feature_extractor.v6(
        instruments=m1.data,
        features=m3.data,
        start_date='',
        end_date=''
    )
    
    m5 = M.derived_feature_extractor.v2(
        input_data=m4.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument'
    )
    
    m7 = M.join.v3(
        data1=m2.data,
        data2=m5.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m13 = M.dropnan.v1(
        input_data=m7.data
    )
    
    m6 = M.stock_ranker_train.v5(
        training_ds=m13.data,
        features=m3.data,
        learning_algorithm='排序',
        number_of_leaves=30,
        minimum_docs_per_leaf=1000,
        number_of_trees=20,
        learning_rate=0.1,
        max_bins=1023,
        feature_fraction=1,
        m_lazy_run=False
    )
    
    m9 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2015-01-01'),
        end_date=T.live_run_param('trading_date', '2017-01-01'),
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m10 = M.general_feature_extractor.v6(
        instruments=m9.data,
        features=m3.data,
        start_date='',
        end_date=''
    )
    
    m11 = M.derived_feature_extractor.v2(
        input_data=m10.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument'
    )
    
    m14 = M.dropnan.v1(
        input_data=m11.data
    )
    
    m8 = M.stock_ranker_predict.v5(
        model=m6.model,
        data=m14.data,
        m_lazy_run=False
    )
    
    # 回测引擎:每日数据处理函数,每天执行一次
    def m12_handle_data_bigquant_run(context, data):
        # 按日期过滤得到今日的预测数据
        ranker_prediction = context.ranker_prediction[
            context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]
    
        # 1. 资金分配
        # 平均持仓时间是hold_days,每日都将买入股票,每日预期使用 1/hold_days 的资金
        # 实际操作中,会存在一定的买入误差,所以在前hold_days天,等量使用资金;之后,尽量使用剩余资金(这里设置最多用等量的1.5倍)
        is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间(前 hold_days 天)
        cash_avg = context.portfolio.portfolio_value / context.options['hold_days']
        cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)
        cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)
        positions = {e.symbol: p.amount * p.last_sale_price
                     for e, p in context.perf_tracker.position_tracker.positions.items()}
    
        # 2. 生成卖出订单:hold_days天之后才开始卖出;对持仓的股票,按StockRanker预测的排序末位淘汰
        if not is_staging and cash_for_sell > 0:
            equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}
            instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                    lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))
            # print('rank order for sell %s' % instruments)
            for instrument in instruments:
                context.order_target(context.symbol(instrument), 0)
                cash_for_sell -= positions[instrument]
                if cash_for_sell <= 0:
                    break
    
        # 3. 生成买入订单:按StockRanker预测的排序,买入前面的stock_count只股票
        buy_cash_weights = context.stock_weights
        buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])
        max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument
        for i, instrument in enumerate(buy_instruments):
            cash = cash_for_buy * buy_cash_weights[i]
            if cash > max_cash_per_instrument - positions.get(instrument, 0):
                # 确保股票持仓量不会超过每次股票最大的占用资金量
                cash = max_cash_per_instrument - positions.get(instrument, 0)
            if cash > 0:
                context.order_value(context.symbol(instrument), cash)
    
    # 回测引擎:准备数据,只执行一次
    def m12_prepare_bigquant_run(context):
        pass
    
    # 回测引擎:初始化函数,只执行一次
    def m12_initialize_bigquant_run(context):
        # 加载预测数据
        context.ranker_prediction = context.options['data'].read_df()
    
        # 系统已经设置了默认的交易手续费和滑点,要修改手续费可使用如下函数
        context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))
        # 预测数据,通过options传入进来,使用 read_df 函数,加载到内存 (DataFrame)
        # 设置买入的股票数量,这里买入预测股票列表排名靠前的5只
        stock_count = 5
        # 每只的股票的权重,如下的权重分配会使得靠前的股票分配多一点的资金,[0.339160, 0.213986, 0.169580, ..]
        context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])
        # 设置每只股票占用的最大资金比例
        context.max_cash_per_instrument = 0.2
        context.options['hold_days'] = 5
    
    m12 = M.trade.v3(
        instruments=m9.data,
        options_data=m8.predictions,
        start_date='',
        end_date='',
        handle_data=m12_handle_data_bigquant_run,
        prepare=m12_prepare_bigquant_run,
        initialize=m12_initialize_bigquant_run,
        volume_limit=0.025,
        order_price_field_buy='open',
        order_price_field_sell='close',
        capital_base=1000000,
        benchmark='000300.SHA',
        auto_cancel_non_tradable_orders=True,
        data_frequency='daily',
        plot_charts=True,
        backtest_only=False
    )
    
    [2017-11-07 23:31:33.483984] INFO: bigquant: instruments.v2 开始运行..
    [2017-11-07 23:31:33.490981] INFO: bigquant: 命中缓存
    [2017-11-07 23:31:33.491862] INFO: bigquant: instruments.v2 运行完成[0.007901s].
    [2017-11-07 23:31:33.498837] INFO: bigquant: advanced_auto_labeler.v2 开始运行..
    [2017-11-07 23:31:33.502238] INFO: bigquant: 命中缓存
    [2017-11-07 23:31:33.503031] INFO: bigquant: advanced_auto_labeler.v2 运行完成[0.00419s].
    [2017-11-07 23:31:33.507164] INFO: bigquant: input_features.v1 开始运行..
    [2017-11-07 23:31:33.520571] INFO: bigquant: 命中缓存
    [2017-11-07 23:31:33.521337] INFO: bigquant: input_features.v1 运行完成[0.014167s].
    [2017-11-07 23:31:33.540290] INFO: bigquant: general_feature_extractor.v6 开始运行..
    [2017-11-07 23:31:33.542791] INFO: bigquant: 命中缓存
    [2017-11-07 23:31:33.543736] INFO: bigquant: general_feature_extractor.v6 运行完成[0.00345s].
    [2017-11-07 23:31:33.549802] INFO: bigquant: derived_feature_extractor.v2 开始运行..
    [2017-11-07 23:31:33.552431] INFO: bigquant: 命中缓存
    [2017-11-07 23:31:33.553267] INFO: bigquant: derived_feature_extractor.v2 运行完成[0.003455s].
    [2017-11-07 23:31:33.558882] INFO: bigquant: join.v3 开始运行..
    [2017-11-07 23:31:33.560965] INFO: bigquant: 命中缓存
    [2017-11-07 23:31:33.561808] INFO: bigquant: join.v3 运行完成[0.002915s].
    [2017-11-07 23:31:33.567197] INFO: bigquant: dropnan.v1 开始运行..
    [2017-11-07 23:31:33.569854] INFO: bigquant: 命中缓存
    [2017-11-07 23:31:33.570643] INFO: bigquant: dropnan.v1 运行完成[0.003435s].
    [2017-11-07 23:31:33.577952] INFO: bigquant: stock_ranker_train.v5 开始运行..
    [2017-11-07 23:31:33.580841] INFO: bigquant: 命中缓存
    [2017-11-07 23:31:33.581708] INFO: bigquant: stock_ranker_train.v5 运行完成[0.003729s].
    [2017-11-07 23:31:33.586908] INFO: bigquant: instruments.v2 开始运行..
    [2017-11-07 23:31:33.589924] INFO: bigquant: 命中缓存
    [2017-11-07 23:31:33.590737] INFO: bigquant: instruments.v2 运行完成[0.003821s].
    [2017-11-07 23:31:33.609783] INFO: bigquant: general_feature_extractor.v6 开始运行..
    [2017-11-07 23:31:33.612941] INFO: bigquant: 命中缓存
    [2017-11-07 23:31:33.613782] INFO: bigquant: general_feature_extractor.v6 运行完成[0.004063s].
    [2017-11-07 23:31:33.621272] INFO: bigquant: derived_feature_extractor.v2 开始运行..
    [2017-11-07 23:31:33.623906] INFO: bigquant: 命中缓存
    [2017-11-07 23:31:33.624765] INFO: bigquant: derived_feature_extractor.v2 运行完成[0.003508s].
    [2017-11-07 23:31:33.631871] INFO: bigquant: dropnan.v1 开始运行..
    [2017-11-07 23:31:33.635048] INFO: bigquant: 命中缓存
    [2017-11-07 23:31:33.636112] INFO: bigquant: dropnan.v1 运行完成[0.004241s].
    [2017-11-07 23:31:33.645823] INFO: bigquant: stock_ranker_predict.v5 开始运行..
    [2017-11-07 23:31:33.652937] INFO: bigquant: 命中缓存
    [2017-11-07 23:31:33.653858] INFO: bigquant: stock_ranker_predict.v5 运行完成[0.008047s].
    [2017-11-07 23:31:33.677590] INFO: bigquant: backtest.v7 开始运行..
    [2017-11-07 23:31:33.680097] INFO: bigquant: 命中缓存
    
    • 收益率20.73%
    • 年化收益率10.22%
    • 基准收益率-6.33%
    • 阿尔法0.09
    • 贝塔0.33
    • 夏普比率0.25
    • 收益波动率27.72%
    • 信息比率0.41
    • 最大回撤29.22%
    [2017-11-07 23:31:34.731142] INFO: bigquant: backtest.v7 运行完成[1.053546s].
    
    In [4]:
    m6.feature_gains.read_df()
    
    Out[4]:
    feature gain
    0 ta_atr(high_0,low_0,close_0,14)/close_0 500.986090
    1 rank_pb_lf_0 387.905144
    2 rank_return_3 165.377296
    3 mean(amount_0,20) 162.665266
    4 ta_atr(high_0,low_0,close_0,28)/close_0 156.039341
    5 rank_return_30 145.930485
    6 ta_ema(((high_0+low_0)/2-(delay(high_0,1)+dela... 139.499215
    7 rank_market_cap_0 121.716013
    8 rank(delta(((((high_0+low_0)/2)*0.2)+(amount_0... 117.350771
    9 std(amount_0,20) 113.203059
    10 fs_operating_revenue_yoy_0 84.809674
    11 rank_return_20 80.188019
    12 mean(close_0,30)/mean(close_0,60) 67.960342
    13 return_30 49.147825
    14 std(amount_0,6) 47.296348
    15 rank_market_cap_float_0 46.649346
    16 ((sum(close_0,7)/7)-close_0)+correlation(amoun... 42.040619
    17 close_0-delay(close_0,20) 39.499578
    18 fs_eps_yoy_0 38.686575
    19 mean(close_0,20)/mean(close_0,30) 38.435181
    20 mean(close_0,10)/mean(close_0,60) 29.479249
    21 ta_ema(close_0,30)/close_0 27.772079
    22 return_80/return_5 27.073237
    23 rank_return_9 25.365799
    24 return_80/return_10 24.237828
    25 rank_return_1 23.919731
    26 rank_sh_holder_avg_pct_3m_chng_0 23.872813
    27 sum(((close_0-low_0)-(high_0-close_0))/(high_0... 22.704252
    28 ta_ema(((ts_max(high_0,6)-close_0)/(ts_max(hig... 20.999721
    29 mean(close_0,6)/mean(close_0,3) 20.607918
    ... ... ...
    232 return_1/return_3 0.000000
    233 return_1/return_5 0.000000
    234 return_15 0.000000
    235 return_15/return_0 0.000000
    236 return_20 0.000000
    237 return_20/return_0 0.000000
    238 return_20/return_3 0.000000
    239 return_3 0.000000
    240 return_3/return_0 0.000000
    241 return_30/return_3 0.000000
    242 return_5/return_0 0.000000
    243 return_50/return_10 0.000000
    244 return_50/return_5 0.000000
    245 return_6 0.000000
    246 return_6/return_0 0.000000
    247 return_6/return_3 0.000000
    248 return_6/return_5 0.000000
    249 return_9 0.000000
    250 return_9/return_0 0.000000
    251 return_9/return_3 0.000000
    252 return_9/return_5 0.000000
    253 return_90/return_10 0.000000
    254 sum(((close_0-low_0)-(high_0-close_0))/(high_0... 0.000000
    255 sum(max(0,high_0-delay(close_0,1)),20)/sum(max... 0.000000
    256 ta_ema(((high_0+low_0-0)/2-(delay(high_0,1)+de... 0.000000
    257 ta_ema(close_0,5)/close_0 0.000000
    258 ta_ema(close_0-delay(close_0,5),5) 0.000000
    259 ta_ema(ta_ema((close_0-ts_min(low_0,9))/(ts_ma... 0.000000
    260 ta_wma(close_0,30)/close_0 0.000000
    261 volume_0/mean(volume_0, 3)*100 0.000000

    262 rows × 2 columns


    (iQuant) #2

    该错误已经稳定复现,我们看看详细是什么原因。


    (iQuant) #3

    你现在再试试呢,应该没有问题。


    这是我测试的截图。