{"description":"实验创建于3/9/2022","graph":{"edges":[{"to_node_id":"-537:features","from_node_id":"-524:data"},{"to_node_id":"-544:features","from_node_id":"-524:data"},{"to_node_id":"-537:instruments","from_node_id":"-528:data"},{"to_node_id":"-544:input_data","from_node_id":"-537:data"}],"nodes":[{"node_id":"-524","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"name":"features","value":"\n# #号开始的表示注释，注释需单独一行\n# 多个特征，每行一个，可以包含基础特征和衍生特征，特征须为本平台特征\nta_ema(close_0, 5).shift(1) \nta_ema(close_0, 10).shift(1)\nta_ema(close_0, 5)\nta_ema(close_0, 10)\ncon = np.where((ta_ema(close_0, 5).shift(1) < ta_ema(close_0, 10).shift(1)) & (ta_ema(close_0, 5) > ta_ema(close_0, 10)),1,0)\ntotal_count = sum(con,20)\n","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-524"}],"output_ports":[{"name":"data","node_id":"-524"}],"cacheable":true,"seq_num":1,"comment":"","comment_collapsed":true},{"node_id":"-528","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2021-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2021-02-01","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":"-528"}],"output_ports":[{"name":"data","node_id":"-528"}],"cacheable":true,"seq_num":2,"comment":"","comment_collapsed":true},{"node_id":"-537","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":"30","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-537"},{"name":"features","node_id":"-537"}],"output_ports":[{"name":"data","node_id":"-537"}],"cacheable":false,"seq_num":3,"comment":"","comment_collapsed":true},{"node_id":"-544","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":"-544"},{"name":"features","node_id":"-544"}],"output_ports":[{"name":"data","node_id":"-544"}],"cacheable":true,"seq_num":4,"comment":"","comment_collapsed":true}],"node_layout":"<node_postions><node_position Node='-524' Position='325,129,200,200'/><node_position Node='-528' Position='5,121,200,200'/><node_position Node='-537' Position='123,319,200,200'/><node_position Node='-544' Position='170,481,200,200'/></node_postions>"},"nodes_readonly":false,"studio_version":"v2"}

# 本代码由可视化策略环境自动生成 2022年3月10日 09:59
# 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="""
# #号开始的表示注释，注释需单独一行
# 多个特征，每行一个，可以包含基础特征和衍生特征，特征须为本平台特征
ta_ema(close_0, 5).shift(1) 
ta_ema(close_0, 10).shift(1)
ta_ema(close_0, 5)
ta_ema(close_0, 10)
con = np.where((ta_ema(close_0, 5).shift(1) < ta_ema(close_0, 10).shift(1)) & (ta_ema(close_0, 5) > ta_ema(close_0, 10)),1,0)
total_count = sum(con,20)
"""
)

m2 = M.instruments.v2(
    start_date='2021-01-01',
    end_date='2021-02-01',
    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=30,
    m_cached=False
)

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,
    user_functions={}
)

[2022-03-10 09:57:45.841984] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-03-10 09:57:45.908944] INFO: moduleinvoker: input_features.v1 运行完成[0.066864s].

[2022-03-10 09:57:45.916038] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-03-10 09:57:45.927849] INFO: moduleinvoker: 命中缓存

[2022-03-10 09:57:45.930040] INFO: moduleinvoker: instruments.v2 运行完成[0.014017s].

[2022-03-10 09:57:45.948155] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-03-10 09:57:48.059926] INFO: 基础特征抽取: 年份 2020, 特征行数=90001

[2022-03-10 09:57:51.270134] INFO: 基础特征抽取: 年份 2021, 特征行数=86766

[2022-03-10 09:57:51.319965] INFO: 基础特征抽取: 总行数: 176767

[2022-03-10 09:57:51.322617] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[5.374502s].

[2022-03-10 09:57:51.354783] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-03-10 09:57:56.251401] INFO: derived_feature_extractor: 提取完成 ta_ema(close_0, 5).shift(1), 4.460s

[2022-03-10 09:58:00.954474] INFO: derived_feature_extractor: 提取完成 ta_ema(close_0, 10).shift(1), 4.701s

[2022-03-10 09:58:04.538146] INFO: derived_feature_extractor: 提取完成 ta_ema(close_0, 5), 3.582s

[2022-03-10 09:58:08.400480] INFO: derived_feature_extractor: 提取完成 ta_ema(close_0, 10), 3.860s

[2022-03-10 09:58:21.937881] INFO: derived_feature_extractor: 提取完成 con = np.where((ta_ema(close_0, 5).shift(1) < ta_ema(close_0, 10).shift(1)) & (ta_ema(close_0, 5) > ta_ema(close_0, 10)),1,0), 13.536s

[2022-03-10 09:58:22.272300] INFO: derived_feature_extractor: 提取完成 total_count = sum(con,20), 0.333s

[2022-03-10 09:58:22.522653] INFO: derived_feature_extractor: /y_2020, 90001

[2022-03-10 09:58:22.752126] INFO: derived_feature_extractor: /y_2021, 86766

[2022-03-10 09:58:22.878642] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[31.523815s].