{"description":"实验创建于2022/11/9","graph":{"edges":[{"to_node_id":"-216:instruments","from_node_id":"-203:data"},{"to_node_id":"-216:features","from_node_id":"-211:data"},{"to_node_id":"-226:input_1","from_node_id":"-216:data"},{"to_node_id":"-174:input_data","from_node_id":"-226:data_1"}],"nodes":[{"node_id":"-203","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2023-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2050-01-01","type":"Literal","bound_global_parameter":"交易日期"},{"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":"-203"}],"output_ports":[{"name":"data","node_id":"-203"}],"cacheable":true,"seq_num":1,"comment":"","comment_collapsed":true},{"node_id":"-211","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"name":"features","value":"price_limit_status_0","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-211"}],"output_ports":[{"name":"data","node_id":"-211"}],"cacheable":true,"seq_num":2,"comment":"","comment_collapsed":true},{"node_id":"-216","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":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-216"},{"name":"features","node_id":"-216"}],"output_ports":[{"name":"data","node_id":"-216"}],"cacheable":true,"seq_num":3,"comment":"","comment_collapsed":true},{"node_id":"-226","module_id":"BigQuantSpace.cached.cached-v3","parameters":[{"name":"run","value":"# 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 # 示例代码如下。在这里编写您的代码\n df = input_1.read()\n dates = df.date.unique()\n dates.sort()\n limitup_df = pd.DataFrame({'date': dates, 'limitup_nums':0, 'limitup_percent':0,\"limitdown_nums\":0,'limitdown_percent':0})\n def cpt_limitup_nums(x):\n date = x.date.values[0]\n limitup_df.loc[limitup_df.date==date, 'limitup_nums'] = len(x[x.price_limit_status_0==1])\n limitup_df.loc[limitup_df.date==date, 'limitup_percent'] = round((len(x[x.price_limit_status_0==1]) / len(x)) * 100, 2)\n limitup_df.loc[limitup_df.date==date, 'limitdown_nums'] = len(x[x.price_limit_status_0==1])\n limitup_df.loc[limitup_df.date==date, 'limitdown_percent'] = round((len(x[x.price_limit_status_0==1]) / len(x)) * 100, 2)\n df.groupby('date').apply(cpt_limitup_nums)\n print(m1.data.read()['end_date'])\n data_1 = DataSource.write_df(limitup_df)\n return Outputs(data_1=data_1, data_2=None, data_3=None)\n","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 outputs\n","type":"Literal","bound_global_parameter":null},{"name":"input_ports","value":"","type":"Literal","bound_global_parameter":null},{"name":"params","value":"{}","type":"Literal","bound_global_parameter":null},{"name":"output_ports","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_1","node_id":"-226"},{"name":"input_2","node_id":"-226"},{"name":"input_3","node_id":"-226"}],"output_ports":[{"name":"data_1","node_id":"-226"},{"name":"data_2","node_id":"-226"},{"name":"data_3","node_id":"-226"}],"cacheable":true,"seq_num":6,"comment":"","comment_collapsed":true},{"node_id":"-174","module_id":"BigQuantSpace.datahub_update_datasource2.datahub_update_datasource2-v1","parameters":[{"name":"alias","value":"hxie_limitStatics","type":"Literal","bound_global_parameter":null},{"name":"date_field","value":"date","type":"Literal","bound_global_parameter":null},{"name":"primary_key","value":"# #号开始的表示注释，注释需单独一行\n# 主键字段, 每个字段为一行\n# 主键字段主要用于数据去重, 建议填写, 避免过多重复数据\n# 示例如下: \n# col_xxx\n# col_yyy\ndate\n","type":"Literal","bound_global_parameter":null},{"name":"friendly_name","value":"涨跌停统计","type":"Literal","bound_global_parameter":null},{"name":"desc","value":"涨跌停统计","type":"Literal","bound_global_parameter":null},{"name":"public","value":"True","type":"Literal","bound_global_parameter":null},{"name":"rewrite","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-174"}],"output_ports":[{"name":"data","node_id":"-174"}],"cacheable":false,"seq_num":5,"comment":"","comment_collapsed":true},{"node_id":"-390","module_id":"BigQuantSpace.datahub_usertask2.datahub_usertask2-v3","parameters":[{"name":"task_name","value":"hxie_limit_task","type":"Literal","bound_global_parameter":null},{"name":"description","value":"更新涨跌停","type":"Literal","bound_global_parameter":null},{"name":"run_time","value":"16:08","type":"Literal","bound_global_parameter":null},{"name":"day_of_week","value":"每天","type":"Literal","bound_global_parameter":null},{"name":"day_of_month","value":"每天","type":"Literal","bound_global_parameter":null},{"name":"not_run_weekly","value":"True","type":"Literal","bound_global_parameter":null},{"name":"update_mode","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[],"output_ports":[{"name":"task","node_id":"-390"}],"cacheable":false,"seq_num":4,"comment":"","comment_collapsed":true}],"node_layout":"<node_postions><node_position Node='-203' Position='33,243,200,200'/><node_position Node='-211' Position='398,249,200,200'/><node_position Node='-216' Position='164,355,200,200'/><node_position Node='-226' Position='237,473,200,200'/><node_position Node='-174' Position='240.631591796875,596.3157958984375,200,200'/><node_position Node='-390' Position='147,100,200,200'/></node_postions>"},"nodes_readonly":false,"studio_version":"v2"}

# 本代码由可视化策略环境自动生成 2023年2月23日 18:13
# 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 m6_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()
    dates = df.date.unique()
    dates.sort()
    limitup_df = pd.DataFrame({'date': dates, 'limitup_nums':0, 'limitup_percent':0,"limitdown_nums":0,'limitdown_percent':0})
    def cpt_limitup_nums(x):
        date = x.date.values[0]
        limitup_df.loc[limitup_df.date==date, 'limitup_nums'] = len(x[x.price_limit_status_0==1])
        limitup_df.loc[limitup_df.date==date, 'limitup_percent'] = round((len(x[x.price_limit_status_0==1]) / len(x))  * 100, 2)
        limitup_df.loc[limitup_df.date==date, 'limitdown_nums'] = len(x[x.price_limit_status_0==1])
        limitup_df.loc[limitup_df.date==date, 'limitdown_percent'] = round((len(x[x.price_limit_status_0==1]) / len(x))  * 100, 2)
    df.groupby('date').apply(cpt_limitup_nums)
    print(m1.data.read()['end_date'])
    data_1 = DataSource.write_df(limitup_df)
    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 m6_post_run_bigquant_run(outputs):
    return outputs


m1 = M.instruments.v2(
    start_date='2023-01-01',
    end_date=T.live_run_param('trading_date', '2050-01-01'),
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m2 = M.input_features.v1(
    features='price_limit_status_0'
)

m3 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m2.data,
    start_date='',
    end_date=''
)

m6 = M.cached.v3(
    input_1=m3.data,
    run=m6_run_bigquant_run,
    post_run=m6_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m5 = M.datahub_update_datasource2.v1(
    input_data=m6.data_1,
    alias='hxie_limitStatics',
    date_field='date',
    primary_key="""# #号开始的表示注释，注释需单独一行
# 主键字段, 每个字段为一行
# 主键字段主要用于数据去重, 建议填写, 避免过多重复数据
# 示例如下: 
# col_xxx
# col_yyy
date
""",
    friendly_name='涨跌停统计',
    desc='涨跌停统计',
    public=True,
    rewrite=False
)

m4 = M.datahub_usertask2.v3(
    task_name='hxie_limit_task',
    description='更新涨跌停',
    run_time='16:08',
    day_of_week='每天',
    day_of_month='每天',
    not_run_weekly=True,
    update_mode=False
)

[2023-02-22 16:21:45.826187] INFO: moduleinvoker: instruments.v2 开始运行..

[2023-02-22 16:21:45.981418] INFO: moduleinvoker: instruments.v2 运行完成[0.155266s].

[2023-02-22 16:21:45.996506] INFO: moduleinvoker: input_features.v1 开始运行..

[2023-02-22 16:21:46.009422] INFO: moduleinvoker: 命中缓存

[2023-02-22 16:21:46.011936] INFO: moduleinvoker: input_features.v1 运行完成[0.015455s].

[2023-02-22 16:21:46.033593] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2023-02-22 16:21:47.056589] INFO: 基础特征抽取: 年份 2022, 特征行数=299536

[2023-02-22 16:21:47.389248] INFO: 基础特征抽取: 年份 2023, 特征行数=157159

[2023-02-22 16:21:47.471522] INFO: 基础特征抽取: 年份 2024, 特征行数=0

[2023-02-22 16:21:47.551007] INFO: 基础特征抽取: 年份 2025, 特征行数=0

[2023-02-22 16:21:47.660037] INFO: 基础特征抽取: 年份 2026, 特征行数=0

[2023-02-22 16:21:47.738337] INFO: 基础特征抽取: 年份 2027, 特征行数=0

[2023-02-22 16:21:47.808570] INFO: 基础特征抽取: 年份 2028, 特征行数=0

[2023-02-22 16:21:47.893505] INFO: 基础特征抽取: 年份 2029, 特征行数=0

[2023-02-22 16:21:47.990599] INFO: 基础特征抽取: 年份 2030, 特征行数=0

[2023-02-22 16:21:48.058527] INFO: 基础特征抽取: 年份 2031, 特征行数=0

[2023-02-22 16:21:48.133340] INFO: 基础特征抽取: 年份 2032, 特征行数=0

[2023-02-22 16:21:48.203985] INFO: 基础特征抽取: 年份 2033, 特征行数=0

[2023-02-22 16:21:48.272067] INFO: 基础特征抽取: 年份 2034, 特征行数=0

[2023-02-22 16:21:48.355000] INFO: 基础特征抽取: 年份 2035, 特征行数=0

[2023-02-22 16:21:48.430738] INFO: 基础特征抽取: 年份 2036, 特征行数=0

[2023-02-22 16:21:48.516784] INFO: 基础特征抽取: 年份 2037, 特征行数=0

[2023-02-22 16:21:48.593842] INFO: 基础特征抽取: 年份 2038, 特征行数=0

[2023-02-22 16:21:48.686289] INFO: 基础特征抽取: 年份 2039, 特征行数=0

[2023-02-22 16:21:48.751966] INFO: 基础特征抽取: 年份 2040, 特征行数=0

[2023-02-22 16:21:48.819832] INFO: 基础特征抽取: 年份 2041, 特征行数=0

[2023-02-22 16:21:48.889751] INFO: 基础特征抽取: 年份 2042, 特征行数=0

[2023-02-22 16:21:48.959056] INFO: 基础特征抽取: 年份 2043, 特征行数=0

[2023-02-22 16:21:49.034889] INFO: 基础特征抽取: 年份 2044, 特征行数=0

[2023-02-22 16:21:49.097425] INFO: 基础特征抽取: 年份 2045, 特征行数=0

[2023-02-22 16:21:49.153109] INFO: 基础特征抽取: 年份 2046, 特征行数=0

[2023-02-22 16:21:49.196710] INFO: 基础特征抽取: 年份 2047, 特征行数=0

[2023-02-22 16:21:49.254486] INFO: 基础特征抽取: 年份 2048, 特征行数=0

[2023-02-22 16:21:49.329721] INFO: 基础特征抽取: 年份 2049, 特征行数=0

[2023-02-22 16:21:49.391267] INFO: 基础特征抽取: 年份 2050, 特征行数=0

[2023-02-22 16:21:49.465218] INFO: 基础特征抽取: 总行数: 456695

[2023-02-22 16:21:49.471616] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[3.438015s].

[2023-02-22 16:21:49.498970] INFO: moduleinvoker: cached.v3 开始运行..

[2023-02-22 16:21:51.504079] INFO: moduleinvoker: cached.v3 运行完成[2.00512s].

[2023-02-22 16:21:51.649644] INFO: moduleinvoker: plot_dataframe.v1 运行完成[0.132516s].

[2023-02-22 16:21:51.871872] INFO: 数据保存/更新(数据平台): 数据更新中, 请稍候 ... 

[2023-02-22 16:22:32.336546] INFO: 数据保存/更新(数据平台): 数据更新完成, 可以使用  DataSource('hxie_limitStatics_U').read() 点击跳转 -> [url="https://bigquant.com/data-platform/categories/myData" style="display: inline-block;padding: 5px 7px;border-radius: 2px;background: #dc3022;color: white"]我的数据[/url]

[2023-02-22 16:22:32.339019] INFO: moduleinvoker: datahub_update_datasource2.v1 运行完成[40.673551s].

[2023-02-22 16:22:32.367000] INFO: 定时任务(数据平台): 任务名称: hxie_limit_task 定时设置: 8 16 * * 1-5 任务描述: 更新涨跌停

[2023-02-22 16:22:32.475631] INFO: 定时任务(数据平台): 创建任务成功: hxie_limit_task 新建任务 点击跳转 -> [url="https://bigquant.com/data-platform/categories/myTAsk" style="display: inline-block;padding: 5px 7px;border-radius: 2px;background: #dc3022;color: white"]我的任务[/url]

[2023-02-22 16:22:32.477916] INFO: moduleinvoker: datahub_usertask2.v3 运行完成[0.117449s].

2050-01-01