日内涨幅因子策略

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克隆策略

    {"Description":"实验创建于8/16/2019","Summary":"","Graph":{"EdgesInternal":[{"DestinationInputPortId":"-391:instruments","SourceOutputPortId":"-382:data"},{"DestinationInputPortId":"-406:input_data","SourceOutputPortId":"-391:data"},{"DestinationInputPortId":"-391:features","SourceOutputPortId":"-401:data"},{"DestinationInputPortId":"-406:features","SourceOutputPortId":"-401:data"},{"DestinationInputPortId":"-760:input_data","SourceOutputPortId":"-406:data"},{"DestinationInputPortId":"-6007:input_2","SourceOutputPortId":"-583:data"},{"DestinationInputPortId":"-1995:features","SourceOutputPortId":"-583:data"},{"DestinationInputPortId":"-975:input_data","SourceOutputPortId":"-760:data"},{"DestinationInputPortId":"-1995:input_data","SourceOutputPortId":"-975:data"},{"DestinationInputPortId":"-6007:input_1","SourceOutputPortId":"-1995:data"}],"ModuleNodes":[{"Id":"-382","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2016-01-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"2019-07-01","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":"-382"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-382","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":1,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-391","ModuleId":"BigQuantSpace.general_feature_extractor.general_feature_extractor-v7","ModuleParameters":[{"Name":"start_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"before_start_days","Value":90,"ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"-391"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-391"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-391","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":2,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-401","ModuleId":"BigQuantSpace.input_features.input_features-v1","ModuleParameters":[{"Name":"features","Value":"\n# 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close_1/adjust_factor_1\nprice_limit_status_1\nret0=shift(close_1,-2)/shift(open_0,-1)-1","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features_ds","NodeId":"-401"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-401","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":4,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-406","ModuleId":"BigQuantSpace.derived_feature_extractor.derived_feature_extractor-v3","ModuleParameters":[{"Name":"date_col","Value":"date","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_col","Value":"instrument","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"drop_na","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"remove_extra_columns","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_functions","Value":"{}","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-406"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-406"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-406","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":5,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-583","ModuleId":"BigQuantSpace.input_features.input_features-v1","ModuleParameters":[{"Name":"features","Value":"\n# 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    In [31]:
    # 本代码由可视化策略环境自动生成 2019年8月20日 14:55
    # 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 m7_run_bigquant_run(input_1, input_2):
        # 示例代码如下。在这里编写您的代码
        df = input_1.read_df()
        factor = input_2.read_pickle()[0]
        df['group'] = pd.cut(df[factor],bins=[0.0,0.02,0.4,0.6,0.8,1.0],labels=[1,2,3,4,5])
        grouped_processed_df = df.groupby(['date','group'])['ret0'].agg(np.mean).fillna(0).reset_index()
        results = grouped_processed_df.groupby('group').apply(lambda df: np.cumprod(1+df.set_index('date')['ret0'])).T
        data_2 = DataSource.write_pickle([factor])
        data_1 = DataSource.write_df(results)
    
        return Outputs(data_1=data_1,data_2=data_2)
    
    # 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 m7_post_run_bigquant_run(outputs):
        results = outputs.data_1.read()
        factor = outputs.data_2.read_pickle()[0]
        T.plot(results,
               options={
                   'legend':{'enabled': True},
                   'chart': {'type': 'line'},
                   'title': {'text': factor+' :因子分组收益'}})
        return outputs
    
    
    m1 = M.instruments.v2(
        start_date='2016-01-01',
        end_date='2019-07-01',
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m4 = 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_1
    open_0
    alpha = open_0/adjust_factor_0 - close_1/adjust_factor_1
    price_limit_status_1
    ret0=shift(close_1,-2)/shift(open_0,-1)-1"""
    )
    
    m2 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m4.data,
        start_date='',
        end_date='',
        before_start_days=90
    )
    
    m5 = M.derived_feature_extractor.v3(
        input_data=m2.data,
        features=m4.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False,
        user_functions={}
    )
    
    m6 = M.chinaa_stock_filter.v1(
        input_data=m5.data,
        index_constituent_cond=['全部'],
        board_cond=['全部'],
        industry_cond=['全部'],
        st_cond=['正常'],
        output_left_data=False
    )
    
    m9 = M.filter.v3(
        input_data=m6.data,
        expr='price_limit_status_1<3',
        output_left_data=False
    )
    
    m3 = 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
    rank(alpha)"""
    )
    
    m8 = M.derived_feature_extractor.v3(
        input_data=m9.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False,
        user_functions={}
    )
    
    m7 = M.cached.v3(
        input_1=m8.data,
        input_2=m3.data,
        run=m7_run_bigquant_run,
        post_run=m7_post_run_bigquant_run,
        input_ports='input_1,input_2',
        params='{}',
        output_ports='data_1,data_2'
    )
    

    希望官方能出个鉴定特征因子间兼容性的检测工具,以提升效率。
    (supertrim258) #2

    是不是return0就可以替代这个日内涨幅因子了?