{"description":"实验创建于2022/8/9","graph":{"edges":[{"to_node_id":"-243:instruments","from_node_id":"-214:data"},{"to_node_id":"-243:features","from_node_id":"-222:data"},{"to_node_id":"-374:features","from_node_id":"-222:data"},{"to_node_id":"-374:input_data","from_node_id":"-243:data"},{"to_node_id":"-6195:input_1","from_node_id":"-374:data"}],"nodes":[{"node_id":"-214","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2020-01-01","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":"000002.SZA","type":"Literal","bound_global_parameter":null},{"name":"max_count","value":0,"type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"rolling_conf","node_id":"-214"}],"output_ports":[{"name":"data","node_id":"-214"}],"cacheable":true,"seq_num":1,"comment":"","comment_collapsed":true},{"node_id":"-222","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\nclose_0\nmax_close_shift_date = ts_argmax(close_0, 30)\nmacd = ta_macd_hist(close_0, fastperiod=12, slowperiod=26, signalperiod=9)\n","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-222"}],"output_ports":[{"name":"data","node_id":"-222"}],"cacheable":true,"seq_num":2,"comment":"","comment_collapsed":true},{"node_id":"-243","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":"0","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-243"},{"name":"features","node_id":"-243"}],"output_ports":[{"name":"data","node_id":"-243"}],"cacheable":true,"seq_num":3,"comment":"","comment_collapsed":true},{"node_id":"-374","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":"-374"},{"name":"features","node_id":"-374"}],"output_ports":[{"name":"data","node_id":"-374"}],"cacheable":true,"seq_num":4,"comment":"","comment_collapsed":true},{"node_id":"-6195","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_df()\n def func(_x):\n x = _x.copy()\n x.sort_values(\"date\", inplace=True)\n x.reset_index(drop=True, inplace=True)\n x[\"index2\"] = x.index - 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# 本代码由可视化策略环境自动生成 2022年8月9日 11:47
# 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 m5_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 func(_x):
        x = _x.copy()
        x.sort_values("date", inplace=True)
        x.reset_index(drop=True, inplace=True)
        x["index2"] = x.index - (30 - x.max_close_shift_date) + 1
        def del_neg(x):
            x = np.nan if x < 0 else x
            return x
        x["index2"] = x["index2"].apply(del_neg)
        x.replace(np.nan, 0, inplace=True)
        x["max_date"] = x.iloc[x.index2.values,:]["date"].values
        x["macd2"] = x.iloc[x.index2.values,:]["macd"].values
        x = x.iloc[30:,]
        return x
    result = df.groupby("instrument").apply(func)
    
    result.reset_index(drop=True, inplace=True)
    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 m5_post_run_bigquant_run(outputs):
    return outputs


m1 = M.instruments.v2(
    start_date='2020-01-01',
    end_date='2022-01-01',
    market='CN_STOCK_A',
    instrument_list='000002.SZA',
    max_count=0
)

m2 = 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
close_0
max_close_shift_date = ts_argmax(close_0, 30)
macd = ta_macd_hist(close_0, fastperiod=12, slowperiod=26, signalperiod=9)
"""
)

m3 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m2.data,
    start_date='',
    end_date='',
    before_start_days=0
)

m4 = M.derived_feature_extractor.v3(
    input_data=m3.data,
    features=m2.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

m5 = M.cached.v3(
    input_1=m4.data,
    run=m5_run_bigquant_run,
    post_run=m5_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

[2022-08-09 11:44:46.285336] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-08-09 11:44:46.293374] INFO: moduleinvoker: 命中缓存

[2022-08-09 11:44:46.295797] INFO: moduleinvoker: instruments.v2 运行完成[0.010473s].

[2022-08-09 11:44:46.323259] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-08-09 11:44:46.368024] INFO: moduleinvoker: input_features.v1 运行完成[0.044767s].

[2022-08-09 11:44:46.392543] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-08-09 11:44:46.401445] INFO: moduleinvoker: 命中缓存

[2022-08-09 11:44:46.404436] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.011927s].

[2022-08-09 11:44:46.424478] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-08-09 11:44:46.514485] INFO: derived_feature_extractor: 提取完成 max_close_shift_date = ts_argmax(close_0, 30), 0.006s

[2022-08-09 11:44:46.523618] INFO: derived_feature_extractor: 提取完成 macd = ta_macd_hist(close_0, fastperiod=12, slowperiod=26, signalperiod=9), 0.007s

[2022-08-09 11:44:46.568379] INFO: derived_feature_extractor: /y_2020, 243

[2022-08-09 11:44:46.635001] INFO: derived_feature_extractor: /y_2021, 243

[2022-08-09 11:44:46.800469] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.375972s].

[2022-08-09 11:44:46.816954] INFO: moduleinvoker: cached.v3 开始运行..

[2022-08-09 11:44:46.992170] INFO: moduleinvoker: cached.v3 运行完成[0.175224s].