复制链接
克隆策略

    {"description":"实验创建于2017/8/26","graph":{"edges":[{"to_node_id":"-288:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"to_node_id":"-295:input_data","from_node_id":"-288:data"},{"to_node_id":"-288:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-295:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"}],"nodes":[{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2022-06-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2022-09-20","type":"Literal","bound_global_parameter":null},{"name":"market","value":"CN_STOCK_A","type":"Literal","bound_global_parameter":null},{"name":"instrument_list","value":"","type":"Literal","bound_global_parameter":null},{"name":"max_count","value":"0","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"rolling_conf","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62"}],"output_ports":[{"name":"data","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62"}],"cacheable":true,"seq_num":1,"comment":"预测数据,用于回测和模拟","comment_collapsed":true},{"node_id":"-288","module_id":"BigQuantSpace.general_feature_extractor.general_feature_extractor-v7","parameters":[{"name":"start_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"before_start_days","value":"60","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-288"},{"name":"features","node_id":"-288"}],"output_ports":[{"name":"data","node_id":"-288"}],"cacheable":true,"seq_num":2,"comment":"","comment_collapsed":true},{"node_id":"-295","module_id":"BigQuantSpace.derived_feature_extractor.derived_feature_extractor-v3","parameters":[{"name":"date_col","value":"date","type":"Literal","bound_global_parameter":null},{"name":"instrument_col","value":"instrument","type":"Literal","bound_global_parameter":null},{"name":"drop_na","value":"False","type":"Literal","bound_global_parameter":null},{"name":"remove_extra_columns","value":"False","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-295"},{"name":"features","node_id":"-295"}],"output_ports":[{"name":"data","node_id":"-295"}],"cacheable":true,"seq_num":3,"comment":"","comment_collapsed":true},{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"name":"features","value":"ta_macd_hist(close_0)","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24"}],"output_ports":[{"name":"data","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24"}],"cacheable":true,"seq_num":4,"comment":"","comment_collapsed":true}],"node_layout":"<node_postions><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-62' Position='751.9052124023438,-0.8107814788818359,200,200'/><node_position Node='-288' Position='570,461,200,200'/><node_position Node='-295' Position='589,565,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-24' Position='346,-16,200,200'/></node_postions>"},"nodes_readonly":false,"studio_version":"v2"}
    In [12]:
    # 本代码由可视化策略环境自动生成 2022年9月20日 23:39
    # 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='2022-06-01',
        end_date='2022-09-20',
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m4 = M.input_features.v1(
        features='ta_macd_hist(close_0)'
    )
    
    m2 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m4.data,
        start_date='',
        end_date='',
        before_start_days=60
    )
    
    m3 = M.derived_feature_extractor.v3(
        input_data=m2.data,
        features=m4.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False
    )
    
    In [13]:
    test = m3.data.read_df()
    print(test[(test['date']=='2022-09-19') & (test['instrument']=='300480.SZA')])
    
              close_0       date  instrument  ta_macd_hist(close_0)
    223017  61.837372 2022-09-19  300480.SZA               0.476263