克隆策略

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    In [1]:
    # 本代码由可视化策略环境自动生成 2020年10月15日 16:51
# 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 m1_run_bigquant_run(input_1,N):
    # 示例代码如下。在这里编写您的代码
    
    df = input_1.read_df().sort_values(by=['date','factor'],ascending=True)
    df['ranker'] = df.groupby('date',group_keys=False)['factor'].rank(pct=True)
    groups = [ 0.1 * k for k in range(11)]
    df['group'] = df.groupby('date',group_keys=False)['ranker'].apply(lambda x:pd.cut(x, groups,labels=[k for k in range(1,11)]))
    grouped_processed_df = df.groupby(['date','group'])['ret'].agg(np.mean).fillna(0).reset_index()
    
    ret_df = pd.pivot_table(grouped_processed_df,values='ret',index='date',columns='group').fillna(0)
    
    ret_filter = ret_df.iloc[0::N]
    
    results = (1+ret_filter).cumprod()/(1+ret_filter.iloc[0])
    
    data_2 = DataSource.write_pickle(['factor'])
    data_1 = DataSource.write_df(results)
    return Outputs(data_1=data_1,data_2=data_2)

# 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 m1_post_run_bigquant_run(outputs):
    results = outputs.data_1.read()
    factor = outputs.data_2.read_pickle()[0]
    T.plot(results[[1,2,3,4,5]], 
           options={
               'legend':{'enabled': True},
               'chart': {'type': 'line'},
               'title': {'text': factor+' :因子分组收益'}})
    return outputs


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
factor=pe_ttm_0
ret=shift(return_0-1,-1)"""
)

m4 = M.instruments.v2(
    start_date='2019-01-01',
    end_date='2019-03-01',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m3 = M.general_feature_extractor.v7(
    instruments=m4.data,
    features=m2.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m5 = 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={}
)

m1 = M.cached.v3(
    input_1=m5.data,
    run=m1_run_bigquant_run,
    post_run=m1_post_run_bigquant_run,
    input_ports='input_1',
    params='{"N":1}',
    output_ports='',
    m_cached=False
)