{"description":"实验创建于2022/3/7","graph":{"edges":[{"to_node_id":"-330:instruments","from_node_id":"-142:data"},{"to_node_id":"-593:instruments","from_node_id":"-142:data"},{"to_node_id":"-162:input_data","from_node_id":"-155:data"},{"to_node_id":"-1558:input_1","from_node_id":"-162:data"},{"to_node_id":"-2032:input_data","from_node_id":"-330:data"},{"to_node_id":"-330:features","from_node_id":"-513:data"},{"to_node_id":"-2032:features","from_node_id":"-513:data"},{"to_node_id":"-3114:input_data","from_node_id":"-2032:data"},{"to_node_id":"-155:data1","from_node_id":"-593:data"},{"to_node_id":"-155:data2","from_node_id":"-3114:data"},{"to_node_id":"-69:input_1","from_node_id":"-1558:data_1"},{"to_node_id":"-4742:input_1","from_node_id":"-1558:data_2"}],"nodes":[{"node_id":"-142","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2021-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2022-01-01","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":"-142"}],"output_ports":[{"name":"data","node_id":"-142"}],"cacheable":true,"seq_num":1,"comment":"","comment_collapsed":true},{"node_id":"-155","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":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"data1","node_id":"-155"},{"name":"data2","node_id":"-155"}],"output_ports":[{"name":"data","node_id":"-155"}],"cacheable":true,"seq_num":3,"comment":"","comment_collapsed":true},{"node_id":"-162","module_id":"BigQuantSpace.filter.filter-v3","parameters":[{"name":"expr","value":"cond1 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#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\nhigh_1\nclose_0\ncond1 = ((price_limit_status_1 ==3) & (price_limit_status_2 < 3) & (price_limit_status_3 < 3) & (price_limit_status_4 < 3))\n","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-513"}],"output_ports":[{"name":"data","node_id":"-513"}],"cacheable":true,"seq_num":7,"comment":"","comment_collapsed":true},{"node_id":"-2032","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":"-2032"},{"name":"features","node_id":"-2032"}],"output_ports":[{"name":"data","node_id":"-2032"}],"cacheable":true,"seq_num":6,"comment":"","comment_collapsed":true},{"node_id":"-593","module_id":"BigQuantSpace.use_datasource.use_datasource-v1","parameters":[{"name":"datasource_id","value":"bar1d_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":"-593"},{"name":"features","node_id":"-593"}],"output_ports":[{"name":"data","node_id":"-593"}],"cacheable":true,"seq_num":8,"comment":"","comment_collapsed":true},{"node_id":"-3114","module_id":"BigQuantSpace.chinaa_stock_filter.chinaa_stock_filter-v1","parameters":[{"name":"index_constituent_cond","value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E4%B8%8A%E8%AF%8150%22%2C%22displayValue%22%3A%22%E4%B8%8A%E8%AF%8150%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%B2%AA%E6%B7%B1300%22%2C%22displayValue%22%3A%22%E6%B2%AA%E6%B7%B1300%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%AD%E8%AF%81500%22%2C%22displayValue%22%3A%22%E4%B8%AD%E8%AF%81500%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%AD%E8%AF%81800%22%2C%22displayValue%22%3A%22%E4%B8%AD%E8%AF%81800%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%8A%E8%AF%81180%22%2C%22displayValue%22%3A%22%E4%B8%8A%E8%AF%81180%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%AD%E8%AF%81100%22%2C%22displayValue%22%3A%22%E4%B8%AD%E8%AF%81100%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%B7%B1%E8%AF%81100%22%2C%22displayValue%22%3A%22%E6%B7%B1%E8%AF%81100%22%2C%22selected%22%3Afalse%7D%5D%7D","type":"Literal","bound_global_parameter":null},{"name":"board_cond","value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%8A%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22displayValue%22%3A%22%E4%B8%8A%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E6%B7%B1%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22displayValue%22%3A%22%E6%B7%B1%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%88%9B%E4%B8%9A%E6%9D%BF%22%2C%22displayValue%22%3A%22%E5%88%9B%E4%B8%9A%E6%9D%BF%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E7%A7%91%E5%88%9B%E6%9D%BF%22%2C%22displayValue%22%3A%22%E7%A7%91%E5%88%9B%E6%9D%BF%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%8C%97%E4%BA%A4%E6%89%80%22%2C%22displayValue%22%3A%22%E5%8C%97%E4%BA%A4%E6%89%80%22%2C%22selected%22%3Afalse%7D%5D%7D","type":"Literal","bound_global_parameter":null},{"name":"industry_cond","value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E4%BA%A4%E9%80%9A%E8%BF%90%E8%BE%93%22%2C%22displayValue%22%3A%22%E4%BA%A4%E9%80%9A%E8%BF%90%E8%BE%93%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%BC%91%E9%97%B2%E6%9C%8D%E5%8A%A1%22%2C%22displayValue%22%3A%22%E4%BC%91%E9%97%B2%E6%9C%8D%E5%8A%A1%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%BC%A0%E5%AA%92%2F%E4%BF%A1%E6%81%AF%E6%9C%8D%E5%8A%A1%22%2C%22displayValue%22%3A%22%E4%BC%A0%E5%AA%92%2F%E4%BF%A1%E6%81%AF%E6%9C%8D%E5%8A%A1%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%85%AC%E7%94%A8%E4%BA%8B%E4%B8%9A%22%2C%22displayValue%22%3A%22%E5%85%AC%E7%94%A8%E4%BA%8B%E4%B8%9A%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%86%9C%E6%9E%97%E7%89%A7%E6%B8%94%22%2C%22displayValue%22%3A%22%E5%86%9C%E6%9E%97%E7%89%A7%E6%B8%94%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%8C%96%E5%B7%A5%22%2C%22displayValue%22%3A%22%E5%8C%96%E5%B7%A5%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%8C%BB%E8%8D%AF%E7%94%9F%E7%89%A9%22%2C%22displayValue%22%3A%22%E5%8C%BB%E8%8D%AF%E7%94%9F%E7%89%A9%22%2C%22selected%22%3Afal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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, input_2, input_3):\n\n df = input_1.read_df()\n \n df = df[[\"instrument\",\"high_1\",\"close_0\"]]\n df[\"shouyi\"] = df[\"close_0\"]-df[\"high_1\"]\n \n df[\"a\"] = df[\"shouyi\"] > 0 \n df[\"b\"] = df[\"shouyi\"] <= 0\n df[\"a+b\"] = df[\"a\"] + df[\"b\"]\n df['shouyilv'] = df['shouyi']/df['high_1']\n \n df1 = df.groupby('instrument')['a+b',\"shouyi\",\"high_1\",'a','shouyilv'].sum()\n #df2 = df.groupby('instrument')[\"shouyi\",\"high_1\"].sum()\n #df3 = df.groupby('instrument')['a'].sum()\n \n #df4 = pd.merge(df1,df2,on=\"instrument\",how=\"left\")\n #df5 = pd.merge(df3,df4,on=\"instrument\",how=\"left\")\n \n df1[\"zhong\"] = (df1[\"a+b\"] < 3) *1\n df1[\"hao\"] = ((df1[\"a+b\"] >= 3) & ((df1[\"shouyilv\"]/df1[\"a+b\"]) > 0.02) & (df1[\"a\"]/df1[\"a+b\"] >= 0.70)) *1\n df1[\"cha\"] = ((df1[\"a+b\"] >= 3) & ((df1[\"shouyilv\"]/df1[\"a+b\"]) < -0.02))*1 \n \n df6 = pd.DataFrame(df[\"instrument\"].unique())\n \n #df5[\"gupiao\"] = df6\n #col_name = df5.columns.tolist()\n #df6 = df3['instrument'] #把instrument列单独提出来\n# #col_name.insert(col_name.index('a'),'instrument') # 在 a列前面插入instrument\n# #df5.reindex(columns=col_name)\n# df5['instrument'] = df6\n \n data_1 = DataSource.write_df(df1)\n data_2 = DataSource.write_df(df6)\n data_3 = DataSource.write_df(df)\n return Outputs(data_1=data_1,data_2=data_2,data_3=data_3)\n #return Outputs(data_1=data_1,data_2=data_2)","type":"Literal","bound_global_parameter":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 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# 本代码由可视化策略环境自动生成 2022年3月24日 17:04
# 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。


# 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):

    df =  input_1.read_df()
    
    df = df[["instrument","high_1","close_0"]]
    df["shouyi"] = df["close_0"]-df["high_1"]
    
    df["a"] = df["shouyi"] > 0 
    df["b"] = df["shouyi"] <= 0
    df["a+b"] = df["a"] + df["b"]
    df['shouyilv'] = df['shouyi']/df['high_1']
    
    df1 = df.groupby('instrument')['a+b',"shouyi","high_1",'a','shouyilv'].sum()
    #df2 = df.groupby('instrument')["shouyi","high_1"].sum()
    #df3 = df.groupby('instrument')['a'].sum()
    
    #df4 = pd.merge(df1,df2,on="instrument",how="left")
    #df5 = pd.merge(df3,df4,on="instrument",how="left")
    
    df1["zhong"] = (df1["a+b"] < 3) *1
    df1["hao"] = ((df1["a+b"] >= 3) & ((df1["shouyilv"]/df1["a+b"]) > 0.02) & (df1["a"]/df1["a+b"] >= 0.70)) *1
    df1["cha"] = ((df1["a+b"] >= 3) & ((df1["shouyilv"]/df1["a+b"]) < -0.02))*1 
    
    df6 = pd.DataFrame(df["instrument"].unique())
    
    #df5["gupiao"] = df6
    #col_name = df5.columns.tolist()
    #df6 = df3['instrument']   #把instrument列单独提出来
#     #col_name.insert(col_name.index('a'),'instrument')  # 在 a列前面插入instrument
#     #df5.reindex(columns=col_name)
#     df5['instrument'] = df6
    
    data_1 = DataSource.write_df(df1)
    data_2 = DataSource.write_df(df6)
    data_3 = DataSource.write_df(df)
    return Outputs(data_1=data_1,data_2=data_2,data_3=data_3)
    #return Outputs(data_1=data_1,data_2=data_2)
# 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


m1 = M.instruments.v2(
    start_date='2021-01-01',
    end_date='2022-01-01',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m8 = M.use_datasource.v1(
    instruments=m1.data,
    datasource_id='bar1d_CN_STOCK_A',
    start_date='',
    end_date=''
)

m7 = 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
high_1
close_0
cond1 = ((price_limit_status_1 ==3) & (price_limit_status_2 < 3) & (price_limit_status_3 < 3) & (price_limit_status_4 < 3))
"""
)

m5 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m7.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m6 = M.derived_feature_extractor.v3(
    input_data=m5.data,
    features=m7.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

m10 = M.chinaa_stock_filter.v1(
    input_data=m6.data,
    index_constituent_cond=['全部'],
    board_cond=['上证主板', '深证主板'],
    industry_cond=['全部'],
    st_cond=['正常'],
    delist_cond=['非退市'],
    output_left_data=False
)

m3 = M.join.v3(
    data1=m8.data,
    data2=m10.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m4 = M.filter.v3(
    input_data=m3.data,
    expr='cond1 ',
    output_left_data=False
)

m11 = M.cached.v3(
    input_1=m4.data,
    run=m11_run_bigquant_run,
    post_run=m11_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m12 = M.df_save_to_csv.v1(
    input_1=m11.data_1,
    name='评价股票最终数据.csv'
)

m2 = M.df_save_to_csv.v1(
    input_1=m11.data_2,
    name='股票名称.csv'
)

[2022-03-24 14:41:33.105115] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-03-24 14:41:33.129685] INFO: moduleinvoker: 命中缓存

[2022-03-24 14:41:33.131809] INFO: moduleinvoker: instruments.v2 运行完成[0.026723s].

[2022-03-24 14:41:33.143267] INFO: moduleinvoker: use_datasource.v1 开始运行..

[2022-03-24 14:41:38.305142] INFO: moduleinvoker: use_datasource.v1 运行完成[5.161857s].

[2022-03-24 14:41:38.321358] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-03-24 14:41:38.377417] INFO: moduleinvoker: input_features.v1 运行完成[0.056062s].

[2022-03-24 14:41:38.396912] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-03-24 14:41:40.546694] INFO: 基础特征抽取: 年份 2020, 特征行数=243745

[2022-03-24 14:41:50.810153] INFO: 基础特征抽取: 年份 2021, 特征行数=1061527

[2022-03-24 14:41:51.470300] INFO: 基础特征抽取: 年份 2022, 特征行数=0

[2022-03-24 14:41:51.954022] INFO: 基础特征抽取: 总行数: 1305272

[2022-03-24 14:41:51.960134] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[13.563239s].

[2022-03-24 14:41:52.009522] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-03-24 14:41:54.777779] INFO: derived_feature_extractor: 提取完成 cond1 = ((price_limit_status_1 ==3) & (price_limit_status_2 < 3) & (price_limit_status_3 < 3) & (price_limit_status_4 < 3)), 0.008s

[2022-03-24 14:41:55.276250] INFO: derived_feature_extractor: /y_2020, 243745

[2022-03-24 14:41:58.026852] INFO: derived_feature_extractor: /y_2021, 1061527

[2022-03-24 14:41:58.437519] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[6.427991s].

[2022-03-24 14:41:58.450577] INFO: moduleinvoker: chinaa_stock_filter.v1 开始运行..

[2022-03-24 14:41:59.444785] INFO: A股股票过滤: 过滤 /y_2020, 167622/0/243745

[2022-03-24 14:42:03.019874] INFO: A股股票过滤: 过滤 /y_2021, 706124/0/1061527

[2022-03-24 14:42:03.023956] INFO: A股股票过滤: 过滤完成, 873746 + 0

[2022-03-24 14:42:03.054180] INFO: moduleinvoker: chinaa_stock_filter.v1 运行完成[4.6036s].

[2022-03-24 14:42:03.071375] INFO: moduleinvoker: join.v3 开始运行..

[2022-03-24 14:42:05.651629] INFO: join: /y_2020, 行数=0/167622, 耗时=0.637331s

[2022-03-24 14:42:07.835307] INFO: join: /y_2021, 行数=706124/706124, 耗时=2.181211s

[2022-03-24 14:42:07.896010] INFO: join: 最终行数: 706124

[2022-03-24 14:42:07.913333] INFO: moduleinvoker: join.v3 运行完成[4.841961s].

[2022-03-24 14:42:07.925576] INFO: moduleinvoker: filter.v3 开始运行..

[2022-03-24 14:42:07.941883] INFO: filter: 使用表达式 cond1  过滤

[2022-03-24 14:42:08.008679] INFO: filter: 过滤 /y_2020, 0/0/0

[2022-03-24 14:42:08.463299] INFO: filter: 过滤 /y_2021, 9563/0/706124

[2022-03-24 14:42:08.490789] INFO: moduleinvoker: filter.v3 运行完成[0.565217s].

[2022-03-24 14:42:08.516356] INFO: moduleinvoker: cached.v3 开始运行..

[2022-03-24 14:42:08.850463] INFO: moduleinvoker: cached.v3 运行完成[0.334122s].

[2022-03-24 14:42:08.980702] INFO: moduleinvoker: df_save_to_csv.v1 运行完成[0.056812s].

[2022-03-24 14:42:09.033485] INFO: moduleinvoker: df_save_to_csv.v1 运行完成[0.040554s].