克隆策略
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In [23]:
# 本代码由可视化策略环境自动生成 2019年11月25日 00:30
# 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.input_features.v1(
features="""amount_0
close_0
shift(open_0, -1)
shift(close_0, -1)
shift(open_0, -2)
shift(close_0, -2)
"""
)
m2 = M.instruments.v2(
start_date='2019-11-14',
end_date='2019-11-15',
market='CN_STOCK_A',
instrument_list='',
max_count=0
)
m3 = M.general_feature_extractor.v7(
instruments=m2.data,
features=m1.data,
start_date='',
end_date='',
before_start_days=0
)
m4 = M.derived_feature_extractor.v3(
input_data=m3.data,
features=m1.data,
date_col='date',
instrument_col='instrument',
drop_na=False,
remove_extra_columns=False
)
m5 = M.dropnan.v1(
input_data=m4.data
)
日志 15 条，错误日志
1 条
[2019-11-25 00:30:17.317507] INFO: bigquant: input_features.v1 开始运行..
[2019-11-25 00:30:17.400444] INFO: bigquant: 命中缓存
[2019-11-25 00:30:17.402300] INFO: bigquant: input_features.v1 运行完成[0.084811s].
[2019-11-25 00:30:17.404652] INFO: bigquant: instruments.v2 开始运行..
[2019-11-25 00:30:17.481666] INFO: bigquant: 命中缓存
[2019-11-25 00:30:17.483395] INFO: bigquant: instruments.v2 运行完成[0.078733s].
[2019-11-25 00:30:17.599703] INFO: bigquant: general_feature_extractor.v7 开始运行..
[2019-11-25 00:30:17.645625] INFO: bigquant: 命中缓存
[2019-11-25 00:30:17.648289] INFO: bigquant: general_feature_extractor.v7 运行完成[0.048608s].
[2019-11-25 00:30:17.656236] INFO: bigquant: derived_feature_extractor.v3 开始运行..
[2019-11-25 00:30:17.754621] INFO: bigquant: 命中缓存
[2019-11-25 00:30:17.759757] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.103504s].
[2019-11-25 00:30:17.763322] INFO: bigquant: dropnan.v1 开始运行..
[2019-11-25 00:30:17.911203] INFO: dropnan: /y_2019, 0/7404
[2019-11-25 00:30:18.007863] ERROR: bigquant: module name: dropnan, module version: v1, trackeback: Traceback (most recent call last): Exception: no data left after dropnan
In [ ]:
克隆策略
{"Description":"实验创建于2019/11/4","Summary":"","Graph":{"EdgesInternal":[{"DestinationInputPortId":"-288:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-295:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-288:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"DestinationInputPortId":"-295:input_data","SourceOutputPortId":"-288:data"},{"DestinationInputPortId":"-86:input_data","SourceOutputPortId":"-295:data"}],"ModuleNodes":[{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24","ModuleId":"BigQuantSpace.input_features.input_features-v1","ModuleParameters":[{"Name":"features","Value":"amount_0\nclose_0\nshift(open_0, -1)\nshift(close_0, -1)\nshift(open_0, -2)\nshift(close_0, -2)\n","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":1,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2019-11-14","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"2019-11-15","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":2,"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":"0","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":3,"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":4,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-86","ModuleId":"BigQuantSpace.dropnan.dropnan-v1","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-86"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-86","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":5,"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-bdf8-104b137c8601-24' Position='398.3380126953125,178,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-62' Position='719,281,200,200'/><NodePosition Node='-288' Position='721,413.3310241699219,200,200'/><NodePosition Node='-295' Position='722,505,200,200'/><NodePosition Node='-86' Position='719,593,200,200'/></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":false}
In [23]:
# 本代码由可视化策略环境自动生成 2019年11月25日 00:29
# 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.input_features.v1(
features="""amount_0
close_0
shift(open_0, -1)
shift(close_0, -1)
shift(open_0, -2)
shift(close_0, -2)
"""
)
m2 = M.instruments.v2(
start_date='2019-11-14',
end_date='2019-11-15',
market='CN_STOCK_A',
instrument_list='',
max_count=0
)
m3 = M.general_feature_extractor.v7(
instruments=m2.data,
features=m1.data,
start_date='',
end_date='',
before_start_days=0
)
m4 = M.derived_feature_extractor.v3(
input_data=m3.data,
features=m1.data,
date_col='date',
instrument_col='instrument',
drop_na=False,
remove_extra_columns=False
)
m5 = M.dropnan.v1(
input_data=m4.data
)
日志 15 条，错误日志
1 条
[2019-11-25 00:30:17.317507] INFO: bigquant: input_features.v1 开始运行..
[2019-11-25 00:30:17.400444] INFO: bigquant: 命中缓存
[2019-11-25 00:30:17.402300] INFO: bigquant: input_features.v1 运行完成[0.084811s].
[2019-11-25 00:30:17.404652] INFO: bigquant: instruments.v2 开始运行..
[2019-11-25 00:30:17.481666] INFO: bigquant: 命中缓存
[2019-11-25 00:30:17.483395] INFO: bigquant: instruments.v2 运行完成[0.078733s].
[2019-11-25 00:30:17.599703] INFO: bigquant: general_feature_extractor.v7 开始运行..
[2019-11-25 00:30:17.645625] INFO: bigquant: 命中缓存
[2019-11-25 00:30:17.648289] INFO: bigquant: general_feature_extractor.v7 运行完成[0.048608s].
[2019-11-25 00:30:17.656236] INFO: bigquant: derived_feature_extractor.v3 开始运行..
[2019-11-25 00:30:17.754621] INFO: bigquant: 命中缓存
[2019-11-25 00:30:17.759757] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.103504s].
[2019-11-25 00:30:17.763322] INFO: bigquant: dropnan.v1 开始运行..
[2019-11-25 00:30:17.911203] INFO: dropnan: /y_2019, 0/7404
[2019-11-25 00:30:18.007863] ERROR: bigquant: module name: dropnan, module version: v1, trackeback: Traceback (most recent call last): Exception: no data left after dropnan
In [22]:
以上策略中，我取19年14-15日数据，提示：
Exception: no data left after dropnan
但是取到14-18日数据，就没有问题