提示：derived_feature_extractor: 提取失败 wr10=ta_willr(close_0, 10): WILLR() takes at least 3 positional arguments (1 given)
因子 ta_willr(close_0, 10) 无法正确得到值
snryang
(snryang)
#3
克隆策略
In [25]:
m4.data.read_df().tail()
Out[25]:
{"Description":"实验创建于2019/11/27","Summary":"","Graph":{"EdgesInternal":[{"DestinationInputPortId":"-288:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"DestinationInputPortId":"-295:input_data","SourceOutputPortId":"-288:data"},{"DestinationInputPortId":"-1143:input_data","SourceOutputPortId":"-295:data"},{"DestinationInputPortId":"-288:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-295:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"}],"ModuleNodes":[{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2019-11-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"2019-11-27","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-bdf8-104b137c8601-62"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":1,"Comment":"预测数据，用于回测和模拟","CommentCollapsed":true},{"Id":"-288","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":"80","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"-288"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-288"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-288","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":2,"Comment":"","CommentCollapsed":true},{"Id":"-295","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":"-295"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-295"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-295","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":3,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24","ModuleId":"BigQuantSpace.input_features.input_features-v1","ModuleParameters":[{"Name":"features","Value":"k=ta_kdj_k(high_0, low_0, close_0, 9, 3)\nd=ta_kdj_d(high_0, low_0, close_0, 9, 3)\nj=ta_kdj_j(high_0, low_0, close_0, 9, 3)\n\nwr10=ta_willr(close_0, 10)","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features_ds","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":5,"Comment":"","CommentCollapsed":true},{"Id":"-1143","ModuleId":"BigQuantSpace.dropnan.dropnan-v1","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-1143"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-1143","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":4,"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='287d2cb0-f53c-4101-bdf8-104b137c8601-62' Position='410,101,200,200'/><NodePosition Node='-288' Position='331,235,200,200'/><NodePosition Node='-295' Position='382,336,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-24' Position='768,128,200,200'/><NodePosition Node='-1143' Position='378,442,200,200'/></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 [9]:
# 本代码由可视化策略环境自动生成 2019年11月29日 10:53
# 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='2019-11-01',
end_date='2019-11-27',
market='CN_STOCK_A',
instrument_list='',
max_count=0
)
m5 = M.input_features.v1(
features="""k=ta_kdj_k(high_0, low_0, close_0, 9, 3)
d=ta_kdj_d(high_0, low_0, close_0, 9, 3)
j=ta_kdj_j(high_0, low_0, close_0, 9, 3)
wr10=ta_willr(close_0, 10)"""
)
m2 = M.general_feature_extractor.v7(
instruments=m1.data,
features=m5.data,
start_date='',
end_date='',
before_start_days=80
)
m3 = M.derived_feature_extractor.v3(
input_data=m2.data,
features=m5.data,
date_col='date',
instrument_col='instrument',
drop_na=False,
remove_extra_columns=False
)
m4 = M.dropnan.v1(
input_data=m3.data
)
日志 19 条，错误日志
0 条
[2019-11-29 10:52:13.069605] INFO: bigquant: instruments.v2 开始运行..
[2019-11-29 10:52:13.111347] INFO: bigquant: 命中缓存
[2019-11-29 10:52:13.113105] INFO: bigquant: instruments.v2 运行完成[0.043501s].
[2019-11-29 10:52:13.115073] INFO: bigquant: input_features.v1 开始运行..
[2019-11-29 10:52:13.212348] INFO: bigquant: input_features.v1 运行完成[0.097254s].
[2019-11-29 10:52:13.323161] INFO: bigquant: general_feature_extractor.v7 开始运行..
[2019-11-29 10:52:13.369318] INFO: bigquant: 命中缓存
[2019-11-29 10:52:13.370994] INFO: bigquant: general_feature_extractor.v7 运行完成[0.047841s].
[2019-11-29 10:52:13.373328] INFO: bigquant: derived_feature_extractor.v3 开始运行..
[2019-11-29 10:52:22.427697] INFO: derived_feature_extractor: 提取完成 k=ta_kdj_k(high_0, low_0, close_0, 9, 3), 8.889s
[2019-11-29 10:52:22.430504] INFO: derived_feature_extractor: 提取失败 d=ta_kdj_d(high_0, low_0, close_0, 9, 3): ta_kdj_d() takes exactly 7 positional arguments (6 given)
[2019-11-29 10:52:22.432938] INFO: derived_feature_extractor: 提取失败 j=ta_kdj_j(high_0, low_0, close_0, 9, 3): ta_kdj_j() takes exactly 7 positional arguments (6 given)
[2019-11-29 10:52:22.522244] INFO: derived_feature_extractor: 提取失败 wr10=ta_willr(close_0, 10): WILLR() takes at least 3 positional arguments (1 given)
[2019-11-29 10:52:22.736796] INFO: derived_feature_extractor: /y_2019, 260836
[2019-11-29 10:52:23.170458] INFO: bigquant: derived_feature_extractor.v3 运行完成[9.797104s].
[2019-11-29 10:52:23.177065] INFO: bigquant: dropnan.v1 开始运行..
[2019-11-29 10:52:23.459301] INFO: dropnan: /y_2019, 231088/260836
[2019-11-29 10:52:23.655779] INFO: dropnan: 行数: 231088/260836
[2019-11-29 10:52:23.663323] INFO: bigquant: dropnan.v1 运行完成[0.486259s].
[2019-11-29 10:52:22.427697] INFO: derived_feature_extractor: 提取完成 k=ta_kdj_k(high_0, low_0, close_0, 9, 3), 8.889s
[2019-11-29 10:52:22.430504] INFO: derived_feature_extractor: 提取失败 d=ta_kdj_d(high_0, low_0, close_0, 9, 3): ta_kdj_d() takes exactly 7 positional arguments (6 given)
[2019-11-29 10:52:22.432938] INFO: derived_feature_extractor: 提取失败 j=ta_kdj_j(high_0, low_0, close_0, 9, 3): ta_kdj_j() takes exactly 7 positional arguments (6 given)
[2019-11-29 10:52:22.522244] INFO: derived_feature_extractor: 提取失败 wr10=ta_willr(close_0, 10): WILLR() takes at least 3 positional arguments (1 given)
qhdxlgd
(达达)
#6
大部分不一样啊
克隆策略
In [5]:
df = m4.data.read_df()
In [7]:
df[df.instrument=='000001.SZA']
Out[7]:
{"Description":"实验创建于2019/11/27","Summary":"","Graph":{"EdgesInternal":[{"DestinationInputPortId":"-288:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"DestinationInputPortId":"-295:input_data","SourceOutputPortId":"-288:data"},{"DestinationInputPortId":"-1143:input_data","SourceOutputPortId":"-295:data"},{"DestinationInputPortId":"-288:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-295:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"}],"ModuleNodes":[{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2019-11-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"2019-11-27","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-bdf8-104b137c8601-62"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":1,"IsPartOfPartialRun":null,"Comment":"预测数据，用于回测和模拟","CommentCollapsed":true},{"Id":"-288","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":"80","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"-288"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-288"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-288","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":2,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-295","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":"-295"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-295"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-295","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":3,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24","ModuleId":"BigQuantSpace.input_features.input_features-v1","ModuleParameters":[{"Name":"features","Value":"k=ta_kdj_k(high_0, low_0, close_0, 9, 3)\nd=ta_kdj_d(high_0, low_0, close_0, 9, 3, 3)\nj=ta_kdj_j(high_0, low_0, close_0, 9, 3, 3)\n\nwr10=ta_willr(close_0, 10)","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features_ds","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":5,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-1143","ModuleId":"BigQuantSpace.dropnan.dropnan-v1","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-1143"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-1143","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":4,"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='287d2cb0-f53c-4101-bdf8-104b137c8601-62' Position='410,101,200,200'/><NodePosition Node='-288' Position='331,235,200,200'/><NodePosition Node='-295' Position='382,336,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-24' Position='768,128,200,200'/><NodePosition Node='-1143' Position='378,442,200,200'/></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 2019年11月29日 16:00
# 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='2019-11-01',
end_date='2019-11-27',
market='CN_STOCK_A',
instrument_list='',
max_count=0
)
m5 = M.input_features.v1(
features="""k=ta_kdj_k(high_0, low_0, close_0, 9, 3)
d=ta_kdj_d(high_0, low_0, close_0, 9, 3, 3)
j=ta_kdj_j(high_0, low_0, close_0, 9, 3, 3)
wr10=ta_willr(close_0, 10)"""
)
m2 = M.general_feature_extractor.v7(
instruments=m1.data,
features=m5.data,
start_date='',
end_date='',
before_start_days=80
)
m3 = M.derived_feature_extractor.v3(
input_data=m2.data,
features=m5.data,
date_col='date',
instrument_col='instrument',
drop_na=False,
remove_extra_columns=False
)
m4 = M.dropnan.v1(
input_data=m3.data
)
日志 15 条，错误日志
0 条
[2019-11-29 15:58:55.553406] INFO: bigquant: instruments.v2 开始运行..
[2019-11-29 15:58:55.589870] INFO: bigquant: 命中缓存
[2019-11-29 15:58:55.592008] INFO: bigquant: instruments.v2 运行完成[0.038581s].
[2019-11-29 15:58:55.594974] INFO: bigquant: input_features.v1 开始运行..
[2019-11-29 15:58:55.642139] INFO: bigquant: 命中缓存
[2019-11-29 15:58:55.644287] INFO: bigquant: input_features.v1 运行完成[0.049321s].
[2019-11-29 15:58:55.683087] INFO: bigquant: general_feature_extractor.v7 开始运行..
[2019-11-29 15:58:55.722095] INFO: bigquant: 命中缓存
[2019-11-29 15:58:55.724358] INFO: bigquant: general_feature_extractor.v7 运行完成[0.041277s].
[2019-11-29 15:58:55.728506] INFO: bigquant: derived_feature_extractor.v3 开始运行..
[2019-11-29 15:58:55.776405] INFO: bigquant: 命中缓存
[2019-11-29 15:58:55.778412] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.049905s].
[2019-11-29 15:58:55.781197] INFO: bigquant: dropnan.v1 开始运行..
[2019-11-29 15:58:55.813049] INFO: bigquant: 命中缓存
[2019-11-29 15:58:55.815232] INFO: bigquant: dropnan.v1 运行完成[0.034021s].