复制链接
克隆策略

    {"description":"实验创建于2017/8/26","graph":{"edges":[{"to_node_id":"-215:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"to_node_id":"-215:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-222:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-222:input_data","from_node_id":"-215:data"},{"to_node_id":"-190:input_data","from_node_id":"-222:data"}],"nodes":[{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2022-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2022-06-01","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-bdf8-104b137c8601-8"}],"output_ports":[{"name":"data","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8"}],"cacheable":true,"seq_num":1,"comment":"","comment_collapsed":true},{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"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\nindustry_sw_level1_0\nvolatility_5_0\n","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":3,"comment":"","comment_collapsed":true},{"node_id":"-215","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":90,"type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-215"},{"name":"features","node_id":"-215"}],"output_ports":[{"name":"data","node_id":"-215"}],"cacheable":true,"seq_num":15,"comment":"","comment_collapsed":true},{"node_id":"-222","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":"-222"},{"name":"features","node_id":"-222"}],"output_ports":[{"name":"data","node_id":"-222"}],"cacheable":true,"seq_num":16,"comment":"","comment_collapsed":true},{"node_id":"-190","module_id":"BigQuantSpace.filter.filter-v3","parameters":[{"name":"expr","value":"industry_sw_level1_0 == 710000","type":"Literal","bound_global_parameter":null},{"name":"output_left_data","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-190"}],"output_ports":[{"name":"data","node_id":"-190"},{"name":"left_data","node_id":"-190"}],"cacheable":true,"seq_num":2,"comment":"","comment_collapsed":true}],"node_layout":"<node_postions><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-8' Position='211,64,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-24' Position='603.2093505859375,59.89198684692383,200,200'/><node_position Node='-215' Position='219.2093505859375,177.11024475097656,200,200'/><node_position Node='-222' Position='220.09799194335938,275.3329772949219,200,200'/><node_position Node='-190' Position='224.02340698242188,365.77618408203125,200,200'/></node_postions>"},"nodes_readonly":false,"studio_version":"v2"}
    In [19]:
    # 本代码由可视化策略环境自动生成 2022年6月18日 11:04
# 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='2022-01-01',
    end_date='2022-06-01',
    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
industry_sw_level1_0
volatility_5_0
"""
)

m15 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=90
)

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

m2 = M.filter.v3(
    input_data=m16.data,
    expr='industry_sw_level1_0 == 710000',
    output_left_data=False
)
    In [20]:
    df = m16.data.read()
df.head()
    Out[20]:
    date industry_sw_level1_0 instrument volatility_5_0
    0 2021-10-08 480000 000001.SZA 0.018910
    1 2021-10-11 480000 000001.SZA 0.022105
    2 2021-10-12 480000 000001.SZA 0.024179
    3 2021-10-13 480000 000001.SZA 0.024095
    4 2021-10-14 480000 000001.SZA 0.026141
    In [21]:
    df["industry_sw_level1_0"].unique()
    Out[21]:
    array([480000, 430000, 710000, 410000, 760000, 450000, 640000, 510000,
       630000, 620000, 610000, 330000, 280000, 110000, 270000, 360000,
       350000, 370000, 730000, 720000, 490000, 220000, 750000, 240000,
       420000, 460000, 650000, 770000, 210000, 740000, 340000, 230000,
            0], dtype=int32)
    In [ ]: