克隆策略
单因子分析¶
版本v1.0
1、新建空白可视化策略
2、从左侧的模块列表“用户模块”——“共享模块”中拖入“单因子分析”模块，输入指定的因子表达式和回测起止时间
3、计算后可以获取指定因子在测试时间段内：
- 月度因子暴露值： 按月度频率使用全市场股票数据计算因子暴露值
- 因子暴露时序序列：按月度频率使用全市场股票数据计算的因子暴露值序列
- 指定因子与九大类因子的相关系数：九大类因子分别为规模因子、成长因子、贝塔因子、流动性因子、杠杆因子、价值因子、反转因子、动量因子和波动性因子
- 因子暴露值在各行业的分布：各行业内股票计算各行业的因子暴露值
- 因子的Rank_IC值序列：按月度频率使用全市场股票数据计算因子的Rank_IC值序列
- 因子分组收益：按股票因子值降序排列，利用因子值的分位数将全市场股票分为5组，计算分别持有5组股票组合的净值曲线
- 多空组合收益：按股票因子值降序排列，利用因子值的分位数将全市场股票分为5组，计算做多第一组股票同时做空第五组股票(多空组合)的净值曲线
- 因子收益：按月度频率使用全市场股票数据计算每月的因子收益
- 因子收益t检验：对因子收益计算t检验统计量的值
{"Description":"实验创建于2017/8/26","Summary":"","Graph":{"EdgesInternal":[],"ModuleNodes":[{"Id":"-10","ModuleId":"BigQuantSpace.single_factor_analysis.single_factor_analysis-v4","ModuleParameters":[{"Name":"factor","Value":"market_cap_0","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"start_date","Value":"2016-01-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"2018-12-31","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[],"OutputPortsInternal":[{"Name":"data","NodeId":"-10","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":2,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true}],"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><NodePosition Node='-10' Position='-12,96,200,200'/></NodePositions><NodeGroups /></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":true}
In [3]:
# 本代码由可视化策略环境自动生成 2020年4月28日 14:37
# 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.single_factor_analysis.v4(
factor='market_cap_0',
start_date='2016-01-01',
end_date='2018-12-31'
)
日志 19 条，错误日志
0 条
[2020-04-28 14:28:37.763719] INFO: moduleinvoker: single_factor_analysis.v4 开始运行..
[2020-04-28 14:29:03.169327] INFO: moduleinvoker: instruments.v2 开始运行..
[2020-04-28 14:29:03.430013] INFO: moduleinvoker: 命中缓存
[2020-04-28 14:29:03.431405] INFO: moduleinvoker: instruments.v2 运行完成[0.262045s].
[2020-04-28 14:29:03.433010] INFO: moduleinvoker: input_features.v1 开始运行..
[2020-04-28 14:29:03.773402] INFO: moduleinvoker: 命中缓存
[2020-04-28 14:29:03.774984] INFO: moduleinvoker: input_features.v1 运行完成[0.341962s].
[2020-04-28 14:29:03.879835] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..
[2020-04-28 14:29:09.780154] INFO: 基础特征抽取: 年份 2016, 特征行数=641546
[2020-04-28 14:29:23.971966] INFO: 基础特征抽取: 年份 2017, 特征行数=743233
[2020-04-28 14:29:46.666714] INFO: 基础特征抽取: 年份 2018, 特征行数=816987
[2020-04-28 14:29:51.285458] INFO: 基础特征抽取: 总行数: 2201766
[2020-04-28 14:29:51.299553] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[47.419705s].
[2020-04-28 14:29:51.308311] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2020-04-28 14:29:52.934009] INFO: derived_feature_extractor: /y_2016, 641546
[2020-04-28 14:29:53.906010] INFO: derived_feature_extractor: /y_2017, 743233
[2020-04-28 14:29:54.961247] INFO: derived_feature_extractor: /y_2018, 816987
[2020-04-28 14:29:56.544224] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[5.2359s].
[2020-04-28 14:37:20.551628] INFO: moduleinvoker: single_factor_analysis.v4 运行完成[522.787794s].