{"description":"实验创建于2019/1/21","graph":{"edges":[{"to_node_id":"-797:input_data","from_node_id":"-778:data"},{"to_node_id":"-778:features","from_node_id":"-792:data"},{"to_node_id":"-803:input_data","from_node_id":"-797:data"},{"to_node_id":"-821:input_data","from_node_id":"-803:data"},{"to_node_id":"-803:features","from_node_id":"-811:data"},{"to_node_id":"-761:data1","from_node_id":"-821:data"},{"to_node_id":"-948:input_ds","from_node_id":"-754:data"},{"to_node_id":"-775:input_1","from_node_id":"-761:data"},{"to_node_id":"-784:data1","from_node_id":"-775:data_1"},{"to_node_id":"-778:instruments","from_node_id":"-860:data"},{"to_node_id":"-754:instruments","from_node_id":"-860:data"},{"to_node_id":"-754:features","from_node_id":"-937:data"},{"to_node_id":"-948:columns_ds","from_node_id":"-937:data"},{"to_node_id":"-761:data2","from_node_id":"-948:data"},{"to_node_id":"-784:data2","from_node_id":"-948:data"}],"nodes":[{"node_id":"-778","module_id":"BigQuantSpace.use_datasource.use_datasource-v1","parameters":[{"name":"datasource_id","value":"financial_statement_CN_STOCK_A","type":"Literal","bound_global_parameter":null},{"name":"start_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-778"},{"name":"features","node_id":"-778"}],"output_ports":[{"name":"data","node_id":"-778"}],"cacheable":true,"seq_num":1,"comment":"","comment_collapsed":true},{"node_id":"-784","module_id":"BigQuantSpace.join.join-v3","parameters":[{"name":"on","value":"date,instrument","type":"Literal","bound_global_parameter":null},{"name":"how","value":"inner","type":"Literal","bound_global_parameter":null},{"name":"sort","value":"True","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"data1","node_id":"-784"},{"name":"data2","node_id":"-784"}],"output_ports":[{"name":"data","node_id":"-784"}],"cacheable":true,"seq_num":12,"comment":"","comment_collapsed":true},{"node_id":"-792","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\nfs_roe\nfs_quarter_index","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-792"}],"output_ports":[{"name":"data","node_id":"-792"}],"cacheable":true,"seq_num":3,"comment":"","comment_collapsed":true},{"node_id":"-797","module_id":"BigQuantSpace.filter.filter-v3","parameters":[{"name":"expr","value":"fs_quarter_index==4","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":"-797"}],"output_ports":[{"name":"data","node_id":"-797"},{"name":"left_data","node_id":"-797"}],"cacheable":true,"seq_num":4,"comment":"过滤4季报","comment_collapsed":false},{"node_id":"-803","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":"-803"},{"name":"features","node_id":"-803"}],"output_ports":[{"name":"data","node_id":"-803"}],"cacheable":true,"seq_num":5,"comment":"","comment_collapsed":true},{"node_id":"-811","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"name":"features","value":"\n# 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# 本代码由可视化策略环境自动生成 2021年11月25日 19:33
# 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 m11_run_bigquant_run(input_1, input_2, input_3):
    # 示例代码如下。在这里编写您的代码
    df = input_1.read_df()
    def cal_g(x):
        return x.ffill().bfill()
    result = df.groupby('instrument').apply(cal_g)
    data_1 = DataSource.write_df(result)
    return Outputs(data_1=data_1, data_2=None, data_3=None)

# 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 m11_post_run_bigquant_run(outputs):
    return outputs


m3 = 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
fs_roe
fs_quarter_index"""
)

m6 = 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
fs_eps_avg3=mean(fs_roe,3)"""
)

m2 = M.instruments.v2(
    start_date='2012-01-01',
    end_date='2018-01-01',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m1 = M.use_datasource.v1(
    instruments=m2.data,
    features=m3.data,
    datasource_id='financial_statement_CN_STOCK_A',
    start_date='',
    end_date=''
)

m4 = M.filter.v3(
    input_data=m1.data,
    expr='fs_quarter_index==4',
    output_left_data=False
)

m5 = M.derived_feature_extractor.v3(
    input_data=m4.data,
    features=m6.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

m7 = M.dropnan.v1(
    input_data=m5.data
)

m10 = 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
st_status"""
)

m8 = M.use_datasource.v1(
    instruments=m2.data,
    features=m10.data,
    datasource_id='stock_status_CN_STOCK_A',
    start_date='',
    end_date=''
)

m14 = M.select_columns.v3(
    input_ds=m8.data,
    columns_ds=m10.data,
    columns='st_status',
    reverse_select=True
)

m9 = M.join.v3(
    data1=m7.data,
    data2=m14.data,
    on='date,instrument',
    how='outer',
    sort=False
)

m11 = M.cached.v3(
    input_1=m9.data,
    run=m11_run_bigquant_run,
    post_run=m11_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m12 = M.join.v3(
    data1=m11.data_1,
    data2=m14.data,
    on='date,instrument',
    how='inner',
    sort=True
)

[2021-11-25 19:32:58.878269] INFO: moduleinvoker: input_features.v1 开始运行..

[2021-11-25 19:32:58.888130] INFO: moduleinvoker: 命中缓存

[2021-11-25 19:32:58.890017] INFO: moduleinvoker: input_features.v1 运行完成[0.011756s].

[2021-11-25 19:32:58.896345] INFO: moduleinvoker: input_features.v1 开始运行..

[2021-11-25 19:32:58.906948] INFO: moduleinvoker: 命中缓存

[2021-11-25 19:32:58.908602] INFO: moduleinvoker: input_features.v1 运行完成[0.012271s].

[2021-11-25 19:32:58.913964] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-11-25 19:32:58.922908] INFO: moduleinvoker: 命中缓存

[2021-11-25 19:32:58.924377] INFO: moduleinvoker: instruments.v2 运行完成[0.01041s].

[2021-11-25 19:32:58.930107] INFO: moduleinvoker: use_datasource.v1 开始运行..

[2021-11-25 19:32:58.940695] INFO: moduleinvoker: 命中缓存

[2021-11-25 19:32:58.943638] INFO: moduleinvoker: use_datasource.v1 运行完成[0.013542s].

[2021-11-25 19:32:58.957747] INFO: moduleinvoker: filter.v3 开始运行..

[2021-11-25 19:32:58.967657] INFO: moduleinvoker: 命中缓存

[2021-11-25 19:32:58.969959] INFO: moduleinvoker: filter.v3 运行完成[0.012213s].

[2021-11-25 19:32:58.978273] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2021-11-25 19:32:58.988813] INFO: moduleinvoker: 命中缓存

[2021-11-25 19:32:58.990279] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.01201s].

[2021-11-25 19:32:58.999350] INFO: moduleinvoker: dropnan.v1 开始运行..

[2021-11-25 19:32:59.009350] INFO: moduleinvoker: 命中缓存

[2021-11-25 19:32:59.011105] INFO: moduleinvoker: dropnan.v1 运行完成[0.011741s].

[2021-11-25 19:32:59.018471] INFO: moduleinvoker: input_features.v1 开始运行..

[2021-11-25 19:32:59.030308] INFO: moduleinvoker: 命中缓存

[2021-11-25 19:32:59.031762] INFO: moduleinvoker: input_features.v1 运行完成[0.013303s].

[2021-11-25 19:32:59.036996] INFO: moduleinvoker: use_datasource.v1 开始运行..

[2021-11-25 19:32:59.058400] INFO: moduleinvoker: 命中缓存

[2021-11-25 19:32:59.065728] INFO: moduleinvoker: use_datasource.v1 运行完成[0.028717s].

[2021-11-25 19:32:59.095095] INFO: moduleinvoker: select_columns.v3 开始运行..

[2021-11-25 19:33:00.121378] INFO: moduleinvoker: select_columns.v3 运行完成[1.026293s].

[2021-11-25 19:33:00.130553] INFO: moduleinvoker: join.v3 开始运行..

[2021-11-25 19:33:04.266022] INFO: join: 行数=4185553/10094/4183479, 耗时=1.895689s

[2021-11-25 19:33:04.295470] INFO: join: 最终行数: 4185553

[2021-11-25 19:33:04.305199] INFO: moduleinvoker: join.v3 运行完成[4.17464s].

[2021-11-25 19:33:04.318275] INFO: moduleinvoker: cached.v3 开始运行..

[2021-11-25 19:33:19.273666] INFO: moduleinvoker: cached.v3 运行完成[14.95539s].

[2021-11-25 19:33:19.283496] INFO: moduleinvoker: join.v3 开始运行..

[2021-11-25 19:33:37.527656] INFO: join: /data, 行数=4183479/4183479, 耗时=10.999596s

[2021-11-25 19:33:37.637461] INFO: join: 最终行数: 4183479

[2021-11-25 19:33:37.660367] INFO: moduleinvoker: join.v3 运行完成[18.376864s].

列： ['st_status', 'st_status']
/data: 4183479