{"description":"实验创建于2022/7/29","graph":{"edges":[{"to_node_id":"-124:instruments","from_node_id":"-111:data"},{"to_node_id":"-124:features","from_node_id":"-119:data"},{"to_node_id":"-45:input_1","from_node_id":"-124:data"}],"nodes":[{"node_id":"-111","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2018-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2020-01-01","type":"Literal","bound_global_parameter":null},{"name":"market","value":"CN_STOCK_A","type":"Literal","bound_global_parameter":null},{"name":"instrument_list","value":"000002.SZA\n000063.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":"-111"}],"output_ports":[{"name":"data","node_id":"-111"}],"cacheable":true,"seq_num":1,"comment":"","comment_collapsed":true},{"node_id":"-119","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\nvolume_0\nclose_0\nclose_1\n","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-119"}],"output_ports":[{"name":"data","node_id":"-119"}],"cacheable":true,"seq_num":2,"comment":"","comment_collapsed":true},{"node_id":"-124","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":90,"type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-124"},{"name":"features","node_id":"-124"}],"output_ports":[{"name":"data","node_id":"-124"}],"cacheable":true,"seq_num":3,"comment":"","comment_collapsed":true},{"node_id":"-45","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 df = input_1.read()\n result = dict()\n sd = 50\n ed = 20\n def _get_max_vols(x2):\n x = x2.copy()\n x.sort_values(\"date\", ascending=True, inplace=True)\n code = x.instrument.values[0]\n print(code)\n x[\"shift_date\"] = x.date.shift(ed)\n x[\"shift_vol\"] = x.volume_0.shift(ed)\n x.dropna(inplace=True)\n max_vol_df = dict()\n k = 0\n for i in x.rolling(window=30):\n if len(i)<30:\n continue\n _i = i.sort_values(\"shift_vol\", ascending=False)\n max_vol = _i.iloc[0,:]\n i[\"max_date\"] = max_vol[\"shift_date\"]\n i[\"max_vol\"] = max_vol[\"shift_vol\"]\n max_vol_df[str(k)] = i.iloc[-1,:]\n k = k+1\n result[code] = pd.DataFrame(max_vol_df).T\n df.groupby(\"instrument\").apply(_get_max_vols)\n \n res_df = pd.DataFrame()\n for k,v in result.items():\n res_df = pd.concat([res_df, v], axis=0)\n \n res_df.reset_index(drop=True, inplace=True)\n data_1 = DataSource.write_df(res_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":"-45"},{"name":"input_2","node_id":"-45"},{"name":"input_3","node_id":"-45"}],"output_ports":[{"name":"data_1","node_id":"-45"},{"name":"data_2","node_id":"-45"},{"name":"data_3","node_id":"-45"}],"cacheable":true,"seq_num":4,"comment":"","comment_collapsed":true}],"node_layout":"<node_postions><node_position Node='-111' Position='115,153,200,200'/><node_position Node='-119' Position='516,153,200,200'/><node_position Node='-124' Position='310,282,200,200'/><node_position Node='-45' Position='306,394,200,200'/></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7月29日 18:32
# 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 m4_run_bigquant_run(input_1, input_2, input_3):
    df = input_1.read()
    result = dict()
    sd = 50
    ed = 20
    def _get_max_vols(x2):
        x = x2.copy()
        x.sort_values("date", ascending=True, inplace=True)
        code = x.instrument.values[0]
        print(code)
        x["shift_date"] = x.date.shift(ed)
        x["shift_vol"] = x.volume_0.shift(ed)
        x.dropna(inplace=True)
        max_vol_df = dict()
        k = 0
        for i in x.rolling(window=30):
            if len(i)<30:
                continue
            _i = i.sort_values("shift_vol", ascending=False)
            max_vol = _i.iloc[0,:]
            i["max_date"] = max_vol["shift_date"]
            i["max_vol"] = max_vol["shift_vol"]
            max_vol_df[str(k)] = i.iloc[-1,:]
            k = k+1
        result[code] = pd.DataFrame(max_vol_df).T
    df.groupby("instrument").apply(_get_max_vols)
    
    res_df = pd.DataFrame()
    for k,v in result.items():
        res_df = pd.concat([res_df, v], axis=0)
    
    res_df.reset_index(drop=True, inplace=True)
    data_1 = DataSource.write_df(res_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 m4_post_run_bigquant_run(outputs):
    return outputs


m1 = M.instruments.v2(
    start_date='2018-01-01',
    end_date='2020-01-01',
    market='CN_STOCK_A',
    instrument_list="""000002.SZA
000063.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
volume_0
close_0
close_1
"""
)

m3 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m2.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m4 = M.cached.v3(
    input_1=m3.data,
    run=m4_run_bigquant_run,
    post_run=m4_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

[2022-07-29 18:31:10.511553] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-07-29 18:31:10.540270] INFO: moduleinvoker: 命中缓存

[2022-07-29 18:31:10.542814] INFO: moduleinvoker: instruments.v2 运行完成[0.030848s].

[2022-07-29 18:31:10.547865] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-07-29 18:31:10.625661] INFO: moduleinvoker: input_features.v1 运行完成[0.077801s].

[2022-07-29 18:31:10.641593] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-07-29 18:31:12.723003] INFO: 基础特征抽取: 年份 2017, 特征行数=120

[2022-07-29 18:31:17.848163] INFO: 基础特征抽取: 年份 2018, 特征行数=447

[2022-07-29 18:31:22.266564] INFO: 基础特征抽取: 年份 2019, 特征行数=488

[2022-07-29 18:31:25.018501] INFO: 基础特征抽取: 年份 2020, 特征行数=0

[2022-07-29 18:31:25.056939] INFO: 基础特征抽取: 总行数: 1055

[2022-07-29 18:31:25.062351] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[14.420754s].

[2022-07-29 18:31:25.095295] INFO: moduleinvoker: cached.v3 开始运行..

[2022-07-29 18:31:28.326553] INFO: moduleinvoker: cached.v3 运行完成[3.231262s].

000002.SZA
000063.SZA