【新功能上线】因子研究看板和因子分析功能

新手专区
标签: #<Tag:0x00007fcf663ba5c8>

(NIKOBELLIC) #5

请问在点开因子过后,为什么看不到自定义分析的按钮?


(WoodMan2019) #6

几个问题:
1是看不到自定义分析的按钮.
2某些因子能给出表达式吗?比如短周期191因子,不然自定义分析里怎么分析这些因子
3.自定义的因子怎么使用因子分析功能


(iQuant) #7

收到提问,已提交至策略工程师,会尽快进行回复。


(yangziriver) #8

因子分析模板使用出现问题 我用的因子是 rank_return_0 时间20170101-20200303 其它因子也不行

WARNING: unknown fields: [‘全部’]


(iQuant) #9

应该可以跑了吧?

克隆策略

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{factor_name}","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"start_date","Value":"2017-01-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"2020-03-03","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"rebalance_period","Value":22,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"stock_pool","Value":"全市场","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"quantile_count","Value":5,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"commission_rate","Value":0.0016,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"drop_price_limit_stocks","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"drop_st_stocks","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"drop_new_stocks","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"normalization","Value":"True","ValueType":"Literal","LinkedGlobalParameter":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%3Atrue%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%3Atrue%7D%5D%7D","ValueType":"Literal","LinkedGlobalParameter":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%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","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-310"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"user_factor_data","NodeId":"-310"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-310","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":1,"Comment":"","CommentCollapsed":true},{"Id":"-326","ModuleId":"BigQuantSpace.input_features.input_features-v1","ModuleParameters":[{"Name":"features","Value":"\n# 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/></DataV1>"},"IsDraft":true,"ParentExperimentId":null,"WebService":{"IsWebServiceExperiment":false,"Inputs":[],"Outputs":[],"Parameters":[{"Name":"交易日期","Value":"","ParameterDefinition":{"Name":"交易日期","FriendlyName":"交易日期","DefaultValue":"","ParameterType":"String","HasDefaultValue":true,"IsOptional":true,"ParameterRules":[],"HasRules":false,"MarkupType":0,"CredentialDescriptor":null}}],"WebServiceGroupId":null,"SerializedClientData":"<?xml version='1.0' encoding='utf-16'?><DataV1 xmlns:xsd='http://www.w3.org/2001/XMLSchema' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance'><Meta /><NodePositions></NodePositions><NodeGroups /></DataV1>"},"DisableNodesUpdate":false,"Category":"user","Tags":[],"IsPartialRun":false}
    In [1]:
    # 本代码由可视化策略环境自动生成 2020年3月5日 15:49
    # 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.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_return_0"""
    )
    
    m1 = M.factorlens.v1(
        features=m2.data,
        title='因子分析: {factor_name}',
        start_date='2017-01-01',
        end_date='2020-03-03',
        rebalance_period=22,
        stock_pool='全市场',
        quantile_count=5,
        commission_rate=0.0016,
        drop_price_limit_stocks=True,
        drop_st_stocks=True,
        drop_new_stocks=True,
        normalization=True,
        neutralization=['行业', '市值'],
        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', '多因子相关性分析']
    )
    

    因子分析: rank_return_0

    因子表现概览

      累计收益 近1年收益 近3月收益 近1月收益 近1周收益 昨日收益 最大回撤 夏普比率 收益波动率
    最小分位 -61.55% -2.81% 8.59% 6.14% -2.68% 1.05% 66.28% -1.37 23.42%
    最大分位 -63.57% -13.42% 5.35% 3.27% -2.32% 0.90% 67.09% -1.49 22.73%
    多空组合 2.89% 6.08% 1.57% 1.41% -0.15% 0.08% 5.91% -1.08 2.35%

    基本特征分析

    IC分析

    IC均值

    -0.01

    IC标准差

    0.07

    IR值

    -0.08

    |IC| > 0.02比率

    70.59%

    买入信号重合分析

    因子估值分析

    因子拥挤度分析

    因子值最小的20只股票 (2020-03-03)

    股票名称 股票代码 因子值
    宜安科技 300328.SZA 0.0003
    恺英网络 002517.SZA 0.0005
    融捷健康 300247.SZA 0.0008
    宝通科技 300031.SZA 0.0011
    恒通股份 603223.SHA 0.0013
    国农科技 000004.SZA 0.0016
    金盾股份 300411.SZA 0.0019
    五洋停车 300420.SZA 0.0021
    斯莱克 300382.SZA 0.0024
    游族网络 002174.SZA 0.0026
    京威股份 002662.SZA 0.0029
    延江股份 300658.SZA 0.0034
    科斯伍德 300192.SZA 0.0040
    国网信通 600131.SHA 0.0042
    再升科技 603601.SHA 0.0045
    奥佳华 002614.SZA 0.0048
    盐津铺子 002847.SZA 0.0050
    英唐智控 300131.SZA 0.0053
    金科股份 000656.SZA 0.0056
    中钢国际 000928.SZA 0.0058

    因子值最大的20只股票 (2020-03-03)

    股票名称 股票代码 因子值
    佳云科技 300242.SZA 0.9669
    智慧松德 300173.SZA 0.9672
    日丰股份 002953.SZA 0.9677
    用友网络 600588.SHA 0.9680
    长园集团 600525.SHA 0.9683
    道恩股份 002838.SZA 0.9685
    金山办公 688111.SHA 0.9688
    复旦复华 600624.SHA 0.9691
    理邦仪器 300206.SZA 0.9693
    万孚生物 300482.SZA 0.9696
    新力金融 600318.SHA 0.9698
    鹏博士 600804.SHA 0.9701
    佳都科技 600728.SHA 0.9704
    朗新科技 300682.SZA 0.9706
    楚江新材 002171.SZA 0.9709
    上海新阳 300236.SZA 0.9712
    宁波高发 603788.SHA 0.9714
    天龙集团 300063.SZA 0.9717
    永悦科技 603879.SHA 0.9720
    亚士创能 603378.SHA 0.9746

    (yangziriver) #10

    好的,谢谢!


    (GOOSIE) #11

    请问输出的IC时序图如何将IC值保存下来呢?


    (公孙睿) #12

    请问老师:系统默认的分组数量为5组,是不是把全市场(3765只股票)总共分成5组,每组(3765÷5=753只)股票?


    (达达) #13

    是,你可以增加组数然后观察头部效应


    (公孙睿) #14

    达达老师,请问什么是“头部效应”?


    (达达) #15

    主要就是是否因子值最大/最小的组对应的收益曲线和别的组有显著区分度,分组收益分的越开说明因子的区分能力强,再有就是如果因子最小/最大的组不是最高收益曲线,那就说明这里的收益和因子之间有非线性规律,需要进一步用stockranker等算法来训练找到这种非线性关系。


    (公孙睿) #16

    明白了,谢谢老师!


    (zhrh88) #18

    单因子分析里,‘NoneType’ object is not subscriptable这个错误如何处理


    (iQuant) #19

    可以参考一下这篇帖子,将策略分享到社区,我们帮您看一下:遇到问题如何才能得到快速处理?


    (侯) #20

    IC和IR,是越大越好还是越小越好呢?


    (iQuant) #21

    越大越好


    (scolo) #22

    我在这个模板,测试30日的收益率。用了return_21 和 close_0/close_(30+1) 2种表达式都会报错。


    (adhaha111) #23

    您好,return_21 和 close_0/close_(30+1) 并不属于预计算因子,您可以参考下相关文档:A股预计算因子


    (scolo) #24

    似乎这个预计算因子 只能通过基础特征抽取模块来获得数据,但是这样的数据不是不能输入因子分析模块了吗?那如果要做因子分析,就只能自己构建因子分析框架吗?


    (adhaha111) #25

    您也可以通过 shift(return_0, 21) 来达到相同的效果:

    克隆策略

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      In [ ]:
      # 本代码由可视化策略环境自动生成 2020年9月14日 16:13
      # 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.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
      shift(close_0, 21)
      close_0/shift(close_0, 31)
      close_0/close_(30+1)"""
      )
      
      m1 = M.factorlens.v1(
          features=m2.data,
          title='因子分析: {factor_name}',
          start_date='2019-01-01',
          end_date='2019-12-31',
          rebalance_period=22,
          stock_pool='全市场',
          quantile_count=5,
          commission_rate=0.0016,
          returns_calculation_method='累乘',
          benchmark='无',
          drop_price_limit_stocks=True,
          drop_st_stocks=True,
          drop_new_stocks=True,
          normalization=True,
          neutralization=['行业', '市值'],
          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']
      )