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# 本代码由可视化策略环境自动生成 2022年11月29日 18:49
# 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):
    # 示例代码如下。在这里编写您的代码
#     input_df = input_1.read_df()
#     input_df.index=input_df.date
#     columns+ =['date','instrument']
#     df =input_df[columns]
#     resample_df=df.groupby(['instrument',pd.Grouper(freq=resample_period)]).agg(how_key).reset_index('instrument')
#     resample_df.reset_index('date',drop=True,inplace=True)
#     data = DataSource.write_df(resample_df)
#     return Outputs(data=data)
def m4_run_bigquant_run(input_1,input_2,col_name):
    df_resample = input_1.read()
    df_origin = input_2.read()
    
    new_column = 'rolling_' +col_name
    def cal(x):
        return pd.Series([[x.values[j-i] for i in range(5) if j-i >0 ] for j in range(len(x))],index=x.index)
    df_resample[new_column] = df_resample.groupby('instrument' ,group_keys=False)[col_name].apply(lambda x: cal(x))
    start_ = df_resample.date.min( )
    df_merge = df_origin[df_origin.date>=start_].merge(df_resample[[ 'date' , 'instrument ' ,new_column]],
                                                       on=['date','instrument'],how='left')
    df_merge[new_column] += df_merge['close'].apply(lambda x:[x])
    df_merge['bolling_up'] = df_merge[new_column].apply(lambda x:np.mean(x)+np.std(x))
    data_1 =DataSource.write_df(df_merge)
    return Outputs(data_1=data_1)

# 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='2015-01-01',
    end_date='2017-01-01',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m2 = M.use_datasource.v2(
    instruments=m1.data,
    datasource_id='bar1d_CN_STOCK_A',
    start_date='',
    end_date='',
    before_start_days=90
)

m5 = M.resample_df.v15(
    input_1=m2.data,
    columns=["open", "high", "low", "close","amount","volume"],
    resample_period='W-FRI',
    how_key={"date": "last",
 "volume": "sum",
 "amount": "sum",
 "close": "last",
 "high": "max",
 "low": "min",
 "open": "first"}
)

m4 = M.cached.v3(
    input_1=m5.data,
    input_2=m2.data,
    run=m4_run_bigquant_run,
    post_run=m4_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

[2022-11-29 18:49:21.064440] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-11-29 18:49:21.245080] INFO: moduleinvoker: 命中缓存

[2022-11-29 18:49:21.247694] INFO: moduleinvoker: instruments.v2 运行完成[0.183268s].

[2022-11-29 18:49:21.255865] INFO: moduleinvoker: use_datasource.v2 开始运行..

[2022-11-29 18:49:21.268715] INFO: moduleinvoker: 命中缓存

[2022-11-29 18:49:21.273295] INFO: moduleinvoker: use_datasource.v2 运行完成[0.017365s].

[2022-11-29 18:49:21.330299] INFO: moduleinvoker: resample_df.v15 开始运行..

[2022-11-29 18:49:21.343699] INFO: moduleinvoker: 命中缓存

[2022-11-29 18:49:21.345935] INFO: moduleinvoker: resample_df.v15 运行完成[0.015663s].

[2022-11-29 18:49:21.363187] INFO: moduleinvoker: cached.v3 开始运行..

[2022-11-29 18:49:27.315811] ERROR: moduleinvoker: module name: cached, module version: v3, trackeback: TypeError: can only concatenate str (not "NoneType") to str

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-50-7f5c1151ae84> in <module>
     64 )
     65 
---> 66 m4 = M.cached.v3(
     67     input_1=m5.data,
     68     input_2=m2.data,

<ipython-input-50-7f5c1151ae84> in m4_run_bigquant_run(input_1, input_2, col_name)
     18     df_origin = input_2.read()
     19 
---> 20     new_column = 'rolling_' +col_name
     21     def cal(x):
     22         return pd.Series([[x.values[j-i] for i in range(5) if j-i >0 ] for j in range(len(x))],index=x.index)

TypeError: can only concatenate str (not "NoneType") to str