{"description":"实验创建于2022/3/30","graph":{"edges":[{"to_node_id":"-1972:instruments","from_node_id":"-184:data"},{"to_node_id":"-1972:features","from_node_id":"-271:data"},{"to_node_id":"-6207:input_1","from_node_id":"-1972:data"},{"to_node_id":"-1123:user_factor_data","from_node_id":"-6207:data_1"},{"to_node_id":"-1123:features","from_node_id":"-804:data"}],"nodes":[{"node_id":"-184","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2015-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2020-12-31","type":"Literal","bound_global_parameter":null},{"name":"market","value":"CN_STOCK_A","type":"Literal","bound_global_parameter":null},{"name":"instrument_list","value":"","type":"Literal","bound_global_parameter":null},{"name":"max_count","value":0,"type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"rolling_conf","node_id":"-184"}],"output_ports":[{"name":"data","node_id":"-184"}],"cacheable":true,"seq_num":1,"comment":"","comment_collapsed":true},{"node_id":"-271","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"name":"features","value":"\n# #号开始的表示注释，注释需单独一行\n# 多个特征，每行一个，可以包含基础特征和衍生特征，特征须为本平台特征\nclose_0","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-271"}],"output_ports":[{"name":"data","node_id":"-271"}],"cacheable":true,"seq_num":2,"comment":"","comment_collapsed":true},{"node_id":"-1972","module_id":"BigQuantSpace.general_feature_extractor.general_feature_extractor-v7","parameters":[{"name":"start_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"before_start_days","value":90,"type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-1972"},{"name":"features","node_id":"-1972"}],"output_ports":[{"name":"data","node_id":"-1972"}],"cacheable":true,"seq_num":3,"comment":"","comment_collapsed":true},{"node_id":"-6207","module_id":"BigQuantSpace.cached.cached-v3","parameters":[{"name":"run","value":"# Python 代码入口函数，input_1/2/3 对应三个输入端，data_1/2/3 对应三个输出端\ndef bigquant_run(input_1, count, input_3):\n #读取输入数据\n df = input_1.read_df()\n \n #计算主函数\n new_df = pd.DataFrame()\n for k,v in df.groupby('instrument'):\n group = v.sort_values('date').reset_index(drop=True) #读取每一只股票的区间行情数据\n close = group['close_0'] #提取历史收盘价\n group['factor'] = (close / close.rolling(count).mean()) - 1 #计算因子值\n new_df = pd.concat([new_df, group],ignore_index=True)\n new_df = new_df.loc[:,['date', 'instrument', 'factor']]\n \n #封装结果\n data_1 = DataSource.write_df(new_df)\n return Outputs(data_1=data_1, data_2=None, data_3=None)","type":"Literal","bound_global_parameter":null},{"name":"post_run","value":"# 后处理函数，可选。输入是主函数的输出，可以在这里对数据做处理，或者返回更友好的outputs数据格式。此函数输出不会被缓存。\ndef bigquant_run(outputs):\n return outputs\n","type":"Literal","bound_global_parameter":null},{"name":"input_ports","value":"","type":"Literal","bound_global_parameter":null},{"name":"params","value":"{\n 'count': 44,\n}","type":"Literal","bound_global_parameter":null},{"name":"output_ports","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_1","node_id":"-6207"},{"name":"input_2","node_id":"-6207"},{"name":"input_3","node_id":"-6207"}],"output_ports":[{"name":"data_1","node_id":"-6207"},{"name":"data_2","node_id":"-6207"},{"name":"data_3","node_id":"-6207"}],"cacheable":true,"seq_num":4,"comment":"计算因子值","comment_collapsed":false},{"node_id":"-804","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"name":"features","value":"\n# #号开始的表示注释，注释需单独一行\n# 多个特征，每行一个，可以包含基础特征和衍生特征，特征须为本平台特征\nfactor\n","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-804"}],"output_ports":[{"name":"data","node_id":"-804"}],"cacheable":true,"seq_num":5,"comment":"","comment_collapsed":true},{"node_id":"-1123","module_id":"BigQuantSpace.factorlens.factorlens-v2","parameters":[{"name":"title","value":"因子分析: {factor_name}","type":"Literal","bound_global_parameter":null},{"name":"start_date","value":"2015-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2021-12-31","type":"Literal","bound_global_parameter":null},{"name":"rebalance_period","value":22,"type":"Literal","bound_global_parameter":null},{"name":"delay_rebalance_days","value":0,"type":"Literal","bound_global_parameter":null},{"name":"rebalance_price","value":"close_0","type":"Literal","bound_global_parameter":null},{"name":"stock_pool","value":"全市场","type":"Literal","bound_global_parameter":null},{"name":"quantile_count","value":"5","type":"Literal","bound_global_parameter":null},{"name":"commission_rate","value":"0","type":"Literal","bound_global_parameter":null},{"name":"returns_calculation_method","value":"累乘","type":"Literal","bound_global_parameter":null},{"name":"benchmark","value":"无","type":"Literal","bound_global_parameter":null},{"name":"drop_new_stocks","value":60,"type":"Literal","bound_global_parameter":null},{"name":"drop_price_limit_stocks","value":"True","type":"Literal","bound_global_parameter":null},{"name":"drop_st_stocks","value":"True","type":"Literal","bound_global_parameter":null},{"name":"drop_suspended_stocks","value":"True","type":"Literal","bound_global_parameter":null},{"name":"cutoutliers","value":"True","type":"Literal","bound_global_parameter":null},{"name":"normalization","value":"True","type":"Literal","bound_global_parameter":null},{"name":"neutralization","value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E8%A1%8C%E4%B8%9A%22%2C%22displayValue%22%3A%22%E8%A1%8C%E4%B8%9A%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%B8%82%E5%80%BC%22%2C%22displayValue%22%3A%22%E5%B8%82%E5%80%BC%22%2C%22selected%22%3Afalse%7D%5D%7D","type":"Literal","bound_global_parameter":null},{"name":"metrics","value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%9B%A0%E5%AD%90%E8%A1%A8%E7%8E%B0%E6%A6%82%E8%A7%88%22%2C%22displayValue%22%3A%22%E5%9B%A0%E5%AD%90%E8%A1%A8%E7%8E%B0%E6%A6%82%E8%A7%88%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%9B%A0%E5%AD%90%E5%88%86%E5%B8%83%22%2C%22displayValue%22%3A%22%E5%9B%A0%E5%AD%90%E5%88%86%E5%B8%83%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%9B%A0%E5%AD%90%E8%A1%8C%E4%B8%9A%E5%88%86%E5%B8%83%22%2C%22displayValue%22%3A%22%E5%9B%A0%E5%AD%90%E8%A1%8C%E4%B8%9A%E5%88%86%E5%B8%83%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%9B%A0%E5%AD%90%E5%B8%82%E5%80%BC%E5%88%86%E5%B8%83%22%2C%22displayValue%22%3A%22%E5%9B%A0%E5%AD%90%E5%B8%82%E5%80%BC%E5%88%86%E5%B8%83%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22IC%E5%88%86%E6%9E%90%22%2C%22displayValue%22%3A%22IC%E5%88%86%E6%9E%90%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E4%B9%B0%E5%85%A5%E4%BF%A1%E5%8F%B7%E9%87%8D%E5%90%88%E5%88%86%E6%9E%90%22%2C%22displayValue%22%3A%22%E4%B9%B0%E5%85%A5%E4%BF%A1%E5%8F%B7%E9%87%8D%E5%90%88%E5%88%86%E6%9E%90%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%9B%A0%E5%AD%90%E4%BC%B0%E5%80%BC%E5%88%86%E6%9E%90%22%2C%22displayValue%22%3A%22%E5%9B%A0%E5%AD%90%E4%BC%B0%E5%80%BC%E5%88%86%E6%9E%90%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%9B%A0%E5%AD%90%E6%8B%A5%E6%8C%A4%E5%BA%A6%E5%88%86%E6%9E%90%22%2C%22displayValue%22%3A%22%E5%9B%A0%E5%AD%90%E6%8B%A5%E6%8C%A4%E5%BA%A6%E5%88%86%E6%9E%90%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%9B%A0%E5%AD%90%E5%80%BC%E6%9C%80%E5%A4%A7%2F%E6%9C%80%E5%B0%8F%E8%82%A1%E7%A5%A8%22%2C%22displayValue%22%3A%22%E5%9B%A0%E5%AD%90%E5%80%BC%E6%9C%80%E5%A4%A7%2F%E6%9C%80%E5%B0%8F%E8%82%A1%E7%A5%A8%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E8%A1%A8%E8%BE%BE%E5%BC%8F%E5%9B%A0%E5%AD%90%E5%80%BC%22%2C%22displayValue%22%3A%22%E8%A1%A8%E8%BE%BE%E5%BC%8F%E5%9B%A0%E5%AD%90%E5%80%BC%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%A4%9A%E5%9B%A0%E5%AD%90%E7%9B%B8%E5%85%B3%E6%80%A7%E5%88%86%E6%9E%90%22%2C%22displayValue%22%3A%22%E5%A4%9A%E5%9B%A0%E5%AD%90%E7%9B%B8%E5%85%B3%E6%80%A7%E5%88%86%E6%9E%90%22%2C%22selected%22%3Atrue%7D%5D%7D","type":"Literal","bound_global_parameter":null},{"name":"factor_coverage","value":0.5,"type":"Literal","bound_global_parameter":null},{"name":"user_data_merge","value":"left","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features","node_id":"-1123"},{"name":"user_factor_data","node_id":"-1123"}],"output_ports":[{"name":"data","node_id":"-1123"},{"name":"save_data","node_id":"-1123"}],"cacheable":true,"seq_num":6,"comment":"","comment_collapsed":true}],"node_layout":"<node_postions><node_position 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# 本代码由可视化策略环境自动生成 2022年3月30日 15:36
# 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。


# Python 代码入口函数，input_1/2/3 对应三个输入端，data_1/2/3 对应三个输出端
def m4_run_bigquant_run(input_1, count, input_3):
    #读取输入数据
    df = input_1.read_df()
    
    #计算主函数
    new_df = pd.DataFrame()
    for k,v in df.groupby('instrument'):
        group = v.sort_values('date').reset_index(drop=True)      #读取每一只股票的区间行情数据
        close = group['close_0']        #提取历史收盘价
        group['factor'] = (close / close.rolling(count).mean()) - 1        #计算因子值
        new_df = pd.concat([new_df, group],ignore_index=True)
    new_df = new_df.loc[:,['date', 'instrument', 'factor']]
    
    #封装结果
    data_1 = DataSource.write_df(new_df)
    return Outputs(data_1=data_1, data_2=None, data_3=None)
# 后处理函数，可选。输入是主函数的输出，可以在这里对数据做处理，或者返回更友好的outputs数据格式。此函数输出不会被缓存。
def m4_post_run_bigquant_run(outputs):
    return outputs


m1 = M.instruments.v2(
    start_date='2015-01-01',
    end_date='2020-12-31',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m2 = M.input_features.v1(
    features="""
# #号开始的表示注释，注释需单独一行
# 多个特征，每行一个，可以包含基础特征和衍生特征，特征须为本平台特征
close_0"""
)

m3 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m2.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m4 = M.cached.v3(
    input_1=m3.data,
    run=m4_run_bigquant_run,
    post_run=m4_post_run_bigquant_run,
    input_ports='',
    params="""{
    'count': 44,
}""",
    output_ports=''
)

m5 = M.input_features.v1(
    features="""
# #号开始的表示注释，注释需单独一行
# 多个特征，每行一个，可以包含基础特征和衍生特征，特征须为本平台特征
factor
"""
)

m6 = M.factorlens.v2(
    features=m5.data,
    user_factor_data=m4.data_1,
    title='因子分析: {factor_name}',
    start_date='2015-01-01',
    end_date='2021-12-31',
    rebalance_period=22,
    delay_rebalance_days=0,
    rebalance_price='close_0',
    stock_pool='全市场',
    quantile_count=5,
    commission_rate=0,
    returns_calculation_method='累乘',
    benchmark='无',
    drop_new_stocks=60,
    drop_price_limit_stocks=True,
    drop_st_stocks=True,
    drop_suspended_stocks=True,
    cutoutliers=True,
    normalization=True,
    neutralization=[],
    metrics=['因子表现概览', '因子分布', '因子行业分布', '因子市值分布', 'IC分析', '买入信号重合分析', '因子估值分析', '因子拥挤度分析', '因子值最大/最小股票', '表达式因子值', '多因子相关性分析'],
    factor_coverage=0.5,
    user_data_merge='left'
)

[2022-03-30 15:25:39.509867] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-03-30 15:25:39.518603] INFO: moduleinvoker: 命中缓存

[2022-03-30 15:25:39.521450] INFO: moduleinvoker: instruments.v2 运行完成[0.011598s].

[2022-03-30 15:25:39.535823] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-03-30 15:25:39.545764] INFO: moduleinvoker: 命中缓存

[2022-03-30 15:25:39.548843] INFO: moduleinvoker: input_features.v1 运行完成[0.01304s].

[2022-03-30 15:25:39.567278] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-03-30 15:25:39.575546] INFO: moduleinvoker: 命中缓存

[2022-03-30 15:25:39.578020] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.010766s].

[2022-03-30 15:25:39.599435] INFO: moduleinvoker: cached.v3 开始运行..

[2022-03-30 15:25:39.608291] INFO: moduleinvoker: 命中缓存

[2022-03-30 15:25:39.610249] INFO: moduleinvoker: cached.v3 运行完成[0.010854s].

[2022-03-30 15:25:39.616335] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-03-30 15:25:39.624532] INFO: moduleinvoker: 命中缓存

[2022-03-30 15:25:39.626352] INFO: moduleinvoker: input_features.v1 运行完成[0.010119s].

[2022-03-30 15:25:39.635380] INFO: moduleinvoker: factorlens.v2 开始运行..

[2022-03-30 15:25:59.884596] INFO: 因子分析: batch_process start

[2022-03-30 15:25:59.886535] INFO: 因子分析: load_instruments 2015-01-01, 2021-12-31

[2022-03-30 15:26:02.088609] INFO: 因子分析: load_instruments, 4758 rows.

[2022-03-30 15:26:02.091121] INFO: 因子分析: load_benchmark_data 2015-01-01, 2021-12-31

[2022-03-30 15:26:02.279810] INFO: 因子分析: load_benchmark_data, 5115 rows.

[2022-03-30 15:26:02.282398] INFO: 因子分析: StockPool.before_load_general_feature_data

[2022-03-30 15:26:02.283945] INFO: 因子分析: UserDataMerge.before_load_general_feature_data

[2022-03-30 15:26:02.285350] INFO: 因子分析: DropSTStocks.before_load_general_feature_data

[2022-03-30 15:26:02.286671] INFO: 因子分析: DropNewStocks.before_load_general_feature_data

[2022-03-30 15:26:02.287846] INFO: 因子分析: Neutralization.before_load_general_feature_data

[2022-03-30 15:26:02.288951] INFO: 因子分析: DelayRebalanceDays.before_load_general_feature_data

[2022-03-30 15:26:02.290062] INFO: 因子分析: RebalancePeriod.before_load_general_feature_data

[2022-03-30 15:26:02.291297] INFO: 因子分析: RebalancePrice.before_load_general_feature_data

[2022-03-30 15:26:02.292562] INFO: 因子分析: FactorCoverage.before_load_general_feature_data

[2022-03-30 15:26:02.293683] INFO: 因子分析: Industry.before_load_general_feature_data

[2022-03-30 15:26:02.294743] INFO: 因子分析: PBRatio.before_load_general_feature_data

[2022-03-30 15:26:02.295855] INFO: 因子分析: Turnover.before_load_general_feature_data

[2022-03-30 15:26:02.296952] INFO: 因子分析: MarketCap.before_load_general_feature_data

[2022-03-30 15:26:02.298262] INFO: 因子分析: load_general_feature_data, load data

[2022-03-30 15:32:19.267059] INFO: 因子分析: RebalancePeriod.after_load_general_feature_data

[2022-03-30 15:32:19.696241] INFO: 因子分析: RebalancePeriodsReturns.after_load_general_feature_data

[2022-03-30 15:33:07.610166] INFO: 因子分析: RebalancePrice.after_load_general_feature_data

[2022-03-30 15:33:07.611821] INFO: 因子分析: load_general_feature_data, 6233737 rows.

[2022-03-30 15:33:07.613617] INFO: 因子分析: load_derived_feature_data, 6233737 rows, 24 columns.

[2022-03-30 15:33:07.615069] INFO: 因子分析: process, factor

[2022-03-30 15:33:07.616220] INFO: 因子分析: calculate_factor, factor

[2022-03-30 15:33:09.103575] INFO: 因子分析: calculate_factor, done

[2022-03-30 15:33:09.979507] INFO: 因子分析: QuantileReturns.before_process

[2022-03-30 15:33:09.980953] INFO: 因子分析: IC.before_process

[2022-03-30 15:33:09.982775] INFO: 因子分析: BasicDescription.before_process

[2022-03-30 15:33:09.984008] INFO: 因子分析: Industry.before_process

[2022-03-30 15:33:09.985120] INFO: 因子分析: RebalanceOverlap.before_process

[2022-03-30 15:33:09.986215] INFO: 因子分析: PBRatio.before_process

[2022-03-30 15:33:09.987354] INFO: 因子分析: Turnover.before_process

[2022-03-30 15:33:09.988477] INFO: 因子分析: Stocks.before_process

[2022-03-30 15:33:09.989585] INFO: 因子分析: MarketCap.before_process

[2022-03-30 15:33:09.990655] INFO: 因子分析: FactorValue.before_process

[2022-03-30 15:33:09.991740] INFO: 因子分析: FactorPairwiseCorrelationMerged.before_process

[2022-03-30 15:33:10.429590] INFO: 因子分析: process metrics, start ..

[2022-03-30 15:33:11.491774] INFO: 因子分析: process, 4273373/5663819 rows ..

[2022-03-30 15:33:11.493335] INFO: 因子分析: BacktestInterval.process, 0.000s

[2022-03-30 15:33:11.494527] INFO: 因子分析: Benchmark.process, 0.000s

[2022-03-30 15:33:11.495582] INFO: 因子分析: StockPool.process, 0.000s

[2022-03-30 15:33:11.496654] INFO: 因子分析: UserDataMerge.process, 0.000s

[2022-03-30 15:33:11.497631] INFO: 因子分析: DropSTStocks.process, 0.000s

[2022-03-30 15:33:11.498560] INFO: 因子分析: DropPriceLimitStocks.process, 0.000s

[2022-03-30 15:33:11.499521] INFO: 因子分析: DropNewStocks.process, 0.000s

[2022-03-30 15:33:11.500452] INFO: 因子分析: DropSuspendedStocks.process, 0.000s

[2022-03-30 15:33:11.501403] INFO: 因子分析: QuantileCount.process, 0.000s

[2022-03-30 15:33:11.502495] INFO: 因子分析: CommissionRates.process, 0.000s

[2022-03-30 15:33:13.047416] INFO: 因子分析: Cutoutliers.process, 1.544s

[2022-03-30 15:33:14.248082] INFO: 因子分析: Normalization.process, 1.199s

[2022-03-30 15:33:14.249717] INFO: 因子分析: Neutralization.process, 0.000s

[2022-03-30 15:33:14.250932] INFO: 因子分析: DelayRebalanceDays.process, 0.000s

[2022-03-30 15:34:55.913278] INFO: 因子分析: RebalancePeriod.process, 101.661s

[2022-03-30 15:34:55.915142] INFO: 因子分析: RebalancePeriodsReturns.process, 0.000s

[2022-03-30 15:34:55.916632] INFO: 因子分析: RebalancePrice.process, 0.000s

[2022-03-30 15:34:55.917887] INFO: 因子分析: ReturnsCalculationMethod.process, 0.000s

[2022-03-30 15:34:55.919039] INFO: 因子分析: FactorCoverage.process, 0.000s

[2022-03-30 15:34:56.192281] INFO: 因子分析: QuantileReturns.process, 0.272s

[2022-03-30 15:34:58.030523] INFO: 因子分析: IC.process, 1.837s

[2022-03-30 15:34:58.270490] INFO: 因子分析: BasicDescription.process, 0.238s

[2022-03-30 15:34:58.621490] INFO: 因子分析: Industry.process, 0.349s

[2022-03-30 15:34:58.838509] INFO: 因子分析: RebalanceOverlap.process, 0.215s

[2022-03-30 15:34:59.256821] INFO: 因子分析: PBRatio.process, 0.417s

[2022-03-30 15:34:59.508703] INFO: 因子分析: Turnover.process, 0.250s

[2022-03-30 15:34:59.560841] INFO: 因子分析: Stocks.process, 0.051s

[2022-03-30 15:34:59.848153] INFO: 因子分析: MarketCap.process, 0.286s

[2022-03-30 15:35:00.233931] INFO: 因子分析: FactorValue.process, 0.384s

[2022-03-30 15:35:00.356657] INFO: 因子分析: process metrics, 109.927s

[2022-03-30 15:35:00.415672] INFO: 因子分析: QuantileReturns.after_process

[2022-03-30 15:35:00.417764] INFO: 因子分析: IC.after_process

[2022-03-30 15:35:00.419015] INFO: 因子分析: BasicDescription.after_process

[2022-03-30 15:35:00.420070] INFO: 因子分析: Industry.after_process

[2022-03-30 15:35:00.421158] INFO: 因子分析: RebalanceOverlap.after_process

[2022-03-30 15:35:00.422297] INFO: 因子分析: PBRatio.after_process

[2022-03-30 15:35:00.423546] INFO: 因子分析: Turnover.after_process

[2022-03-30 15:35:00.424713] INFO: 因子分析: Stocks.after_process

[2022-03-30 15:35:00.425798] INFO: 因子分析: MarketCap.after_process

[2022-03-30 15:35:00.426818] INFO: 因子分析: FactorValue.after_process

[2022-03-30 15:35:00.427810] INFO: 因子分析: FactorPairwiseCorrelationMerged.after_process

[2022-03-30 15:35:00.428939] INFO: 因子分析: QuantileReturns.before_merged_process

[2022-03-30 15:35:00.430157] INFO: 因子分析: IC.before_merged_process

[2022-03-30 15:35:00.431383] INFO: 因子分析: BasicDescription.before_merged_process

[2022-03-30 15:35:00.432763] INFO: 因子分析: Industry.before_merged_process

[2022-03-30 15:35:00.434117] INFO: 因子分析: RebalanceOverlap.before_merged_process

[2022-03-30 15:35:00.435599] INFO: 因子分析: PBRatio.before_merged_process

[2022-03-30 15:35:00.436981] INFO: 因子分析: Turnover.before_merged_process

[2022-03-30 15:35:00.438258] INFO: 因子分析: Stocks.before_merged_process

[2022-03-30 15:35:00.439506] INFO: 因子分析: MarketCap.before_merged_process

[2022-03-30 15:35:00.440827] INFO: 因子分析: FactorValue.before_merged_process

[2022-03-30 15:35:00.442173] INFO: 因子分析: FactorPairwiseCorrelationMerged.before_merged_process

[2022-03-30 15:35:00.443697] INFO: 因子分析: QuantileReturns.after_merged_process

[2022-03-30 15:35:00.445356] INFO: 因子分析: IC.after_merged_process

[2022-03-30 15:35:00.447131] INFO: 因子分析: BasicDescription.after_merged_process

[2022-03-30 15:35:00.448701] INFO: 因子分析: Industry.after_merged_process

[2022-03-30 15:35:00.450213] INFO: 因子分析: RebalanceOverlap.after_merged_process

[2022-03-30 15:35:00.451744] INFO: 因子分析: PBRatio.after_merged_process

[2022-03-30 15:35:00.453285] INFO: 因子分析: Turnover.after_merged_process

[2022-03-30 15:35:00.455010] INFO: 因子分析: Stocks.after_merged_process

[2022-03-30 15:35:00.456812] INFO: 因子分析: MarketCap.after_merged_process

[2022-03-30 15:35:00.458701] INFO: 因子分析: FactorValue.after_merged_process

[2022-03-30 15:35:00.460396] INFO: 因子分析: FactorPairwiseCorrelationMerged.after_merged_process

[2022-03-30 15:35:00.462720] INFO: 因子分析: batch_process ended, 540.578s

[2022-03-30 15:35:01.611964] INFO: moduleinvoker: factorlens.v2 运行完成[561.976578s].