股票连续上涨后的持续上涨的概率是多少

[think]

在BigQuant上做股票数据分析和可视化展示真是太方便了。只需要写两行代码，其他都是搭积木。

在交易中是否我们常常会想一个已经连续涨的股票（比如3天），继续上涨的概率大么？（好吧，这很散户思维，只考虑了一个维度）。除了看连续上涨的股票上涨概率，我们还需要用全市场股票上涨情况做为对比（不然就很业余了）。

• 构造如下模型
  
  
• 特征输入
  • cond = (close_0 / close_1 > 1.01) & (close_1 / close_2 > 1.01) & (close_2 / close_3 > 1.01)：结果为True这三日都在上涨（超过1%），否则为False
  • label = where(shift(return_0, -1) > 1.01, 1, 0)：1 表示明天上涨超过1%，0表示明天下跌
• 用自定义模块计算：主要是使用 pandas groupby
  g1 = df[‘label’].groupby(pd.Grouper(freq=‘M’)).apply(lambda x: x.sum() / len(x))
  g2 = df[‘label’][df[‘cond’]].groupby(pd.Grouper(freq=‘M’)).apply(lambda x: x.sum() / len(x))
• 最后使用绘制DataFrame模块
• 结果：初步看起来没有必然上涨关系，和市场强弱有关系
• 改进：
  • 可以看更长时间周期
  • 考虑不同日期参数等
  • 考虑引入其他维度

克隆策略

{"Description":"实验创建于2017/8/26","Summary":"","Graph":{"EdgesInternal":[{"DestinationInputPortId":"-215:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"DestinationInputPortId":"-222:input_data","SourceOutputPortId":"-215:data"},{"DestinationInputPortId":"-191:input_1","SourceOutputPortId":"-222:data"},{"DestinationInputPortId":"-215:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-222:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-202:input_data","SourceOutputPortId":"-191:data_1"}],"ModuleNodes":[{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2016-01-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"2019-03-01","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":"287d2cb0-f53c-4101-bdf8-104b137c8601-8"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":1,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-215","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":"-215"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-215"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-215","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":15,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-222","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":"True","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":"-222"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-222"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-222","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":16,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24","ModuleId":"BigQuantSpace.input_features.input_features-v1","ModuleParameters":[{"Name":"features","Value":"# #号开始的表示注释\n# 多个特征，每行一个，可以包含基础特征和衍生特征\ncond = (close_0 / close_1 > 1.01) & (close_1 / close_2 > 1.01) & (close_2 / close_3 > 1.01)\nlabel = where(shift(return_0, -1) > 1.01, 1, 0)\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":3,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-191","ModuleId":"BigQuantSpace.cached.cached-v3","ModuleParameters":[{"Name":"run","Value":"# 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\ndef bigquant_run(input_1):\n # 示例代码如下。在这里编写您的代码\n df = input_1.read()\n df.set_index('date', inplace=True)\n g1 = df['label'].groupby(pd.Grouper(freq='M')).apply(lambda x: x.sum() / len(x))\n g2 = df['label'][df['cond']].groupby(pd.Grouper(freq='M')).apply(lambda x: x.sum() / len(x))\n ds = DataSource.write_df(pd.DataFrame({'全市场上涨比例': g1, '连续上涨三天后上涨比例': g2}))\n return Outputs(data_1=ds)\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"post_run","Value":"# 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。\ndef bigquant_run(outputs):\n return outputs\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"input_ports","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"params","Value":"{}","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"output_ports","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-191"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-191"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_3","NodeId":"-191"}],"OutputPortsInternal":[{"Name":"data_1","NodeId":"-191","OutputType":null},{"Name":"data_2","NodeId":"-191","OutputType":null},{"Name":"data_3","NodeId":"-191","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":2,"IsPartOfPartialRun":null,"Comment":"数据聚合计算","CommentCollapsed":false},{"Id":"-202","ModuleId":"BigQuantSpace.plot_dataframe.plot_dataframe-v1","ModuleParameters":[{"Name":"title","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"chart_type","Value":"column","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"x","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"y","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"options","Value":"{\n 'chart': {\n 'height': 400\n }\n}","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"candlestick","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"pane_1","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"pane_2","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"pane_3","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"pane_4","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-202"}],"OutputPortsInternal":[],"UsePreviousResults":false,"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-bdf8-104b137c8601-8' Position='264,36,200,200'/><NodePosition Node='-215' Position='434,160,200,200'/><NodePosition Node='-222' Position='438,252,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-24' Position='626,38,200,200'/><NodePosition Node='-191' Position='498,339,200,200'/><NodePosition Node='-202' Position='567,458,200,200'/></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 [52]:

# 本代码由可视化策略环境自动生成 2019年3月13日 17:16
# 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 m2_run_bigquant_run(input_1):
    # 示例代码如下。在这里编写您的代码
    df = input_1.read()
    df.set_index('date', inplace=True)
    g1 = df['label'].groupby(pd.Grouper(freq='M')).apply(lambda x: x.sum() / len(x))
    g2 = df['label'][df['cond']].groupby(pd.Grouper(freq='M')).apply(lambda x: x.sum() / len(x))
    ds = DataSource.write_df(pd.DataFrame({'全市场上涨比例': g1, '连续上涨三天后上涨比例': g2}))
    return Outputs(data_1=ds)

# 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 m2_post_run_bigquant_run(outputs):
    return outputs


m1 = M.instruments.v2(
    start_date='2016-01-01',
    end_date='2019-03-01',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m3 = M.input_features.v1(
    features="""# #号开始的表示注释
# 多个特征，每行一个，可以包含基础特征和衍生特征
cond = (close_0 / close_1 > 1.01) & (close_1 / close_2 > 1.01) & (close_2 / close_3 > 1.01)
label = where(shift(return_0, -1) > 1.01, 1, 0)
"""
)

m15 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=0
)

m16 = M.derived_feature_extractor.v3(
    input_data=m15.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=True,
    remove_extra_columns=False
)

m2 = M.cached.v3(
    input_1=m16.data,
    run=m2_run_bigquant_run,
    post_run=m2_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m4 = M.plot_dataframe.v1(
    input_data=m2.data_1,
    title='',
    chart_type='column',
    x='',
    y='',
    options={
    'chart': {
        'height': 400
    }
},
    candlestick=False,
    pane_1='',
    pane_2='',
    pane_3='',
    pane_4=''
)

日志 23 条，错误日志  0 条 

2019-03-13 17:14:56.423313 INFO: bigquant: instruments.v2 开始运行.

2019-03-13 17:14:56.429720 INFO: bigquant: 命中缓

2019-03-13 17:14:56.431378 INFO: bigquant: instruments.v2 运行完成[0.008065s]

2019-03-13 17:14:56.434499 INFO: bigquant: input_features.v1 开始运行.

2019-03-13 17:14:56.439364 INFO: bigquant: 命中缓

2019-03-13 17:14:56.441131 INFO: bigquant: input_features.v1 运行完成[0.006617s]

2019-03-13 17:14:56.452588 INFO: bigquant: general_feature_extractor.v7 开始运行.

2019-03-13 17:14:57.818612 INFO: 基础特征抽取: 年份 2016, 特征行数=64154

2019-03-13 17:15:00.428573 INFO: 基础特征抽取: 年份 2017, 特征行数=74323

2019-03-13 17:15:02.978060 INFO: 基础特征抽取: 年份 2018, 特征行数=81698

2019-03-13 17:15:03.878210 INFO: 基础特征抽取: 年份 2019, 特征行数=13542

2019-03-13 17:15:03.936392 INFO: 基础特征抽取: 总行数: 233718

2019-03-13 17:15:03.940612 INFO: bigquant: general_feature_extractor.v7 运行完成[7.488016s]

2019-03-13 17:15:03.944955 INFO: bigquant: derived_feature_extractor.v3 开始运行.

2019-03-13 17:15:05.083051 INFO: general_feature_extractor: 提取完成 cond = (close_0 / close_1 > 1.01) & (close_1 / close_2 > 1.01) & (close_2 / close_3 > 1.01), 0.009

2019-03-13 17:15:05.654309 INFO: general_feature_extractor: 提取完成 label = where(shift(return_0, -1) > 1.01, 1, 0), 0.564

2019-03-13 17:15:06.075270 INFO: general_feature_extractor: /y_2016, 64154

2019-03-13 17:15:06.814127 INFO: general_feature_extractor: /y_2017, 74323

2019-03-13 17:15:07.814837 INFO: general_feature_extractor: /y_2018, 81698

2019-03-13 17:15:08.673295 INFO: general_feature_extractor: /y_2019, 13542

2019-03-13 17:15:08.855577 INFO: bigquant: derived_feature_extractor.v3 运行完成[4.910603s]

2019-03-13 17:15:08.893505 INFO: bigquant: cached.v3 开始运行.

2019-03-13 17:15:10.962748 INFO: bigquant: cached.v3 运行完成[2.069224s]

[wujunjun]

我的pandas什么时候才能用的和你一样优秀

[think]

跟着BigQuant平台学的 :)