克隆策略

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    In [55]:
    # 本代码由可视化策略环境自动生成 2021年6月23日16:38
# 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 m2_run_bigquant_run(input_1, input_2, col_name):
    # 示例代码如下。在这里编写您的代码
    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-1] 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.groupby('instrument')[new_column].ffill()
    
    df_merge[new_column] = df_merge[col_name].apply(lambda x:[x]) + df_merge[new_column]
    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 m2_post_run_bigquant_run(outputs):
    return outputs


m9 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2015-01-01'),
    end_date=T.live_run_param('trading_date', '2017-01-01'),
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m3 = M.use_datasource.v1(
    instruments=m9.data,
    datasource_id='bar1d_CN_STOCK_A',
    start_date='',
    end_date=''
)

m1 = M.resample_df.v15(
    input_1=m3.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'}
)

m2 = M.cached.v3(
    input_1=m1.data,
    input_2=m3.data,
    run=m2_run_bigquant_run,
    post_run=m2_post_run_bigquant_run,
    input_ports='input_1,input_2',
    params='{\'col_name\':\'close\'}',
    output_ports='data_1'
)