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StockRanker多因子选股策略

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    In [20]:
    # 本代码由可视化策略环境自动生成 2022年2月25日 21:02
# 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, input_2, input_3):
    # 新建一个list
    score_lst = {}
    # 写一个循环，依次调用固化的模型来做预测
    for table_name in ['model_stockranker1','model_stockranker2','model_stockranker3','model_stockranker4']:
        data = pd.read_pickle('/home/bigquant/work/userlib/%s.csv'%table_name)
        model_ds = DataSource.write_pickle(data.iloc[0].values[0])
        # 调用预测函数
        m_pred = M.stock_ranker_predict.v5(
            model=model_ds, # 导入训练好的模型
            data=input_1, # 导入input_1作为要预测的数据集使用
            m_lazy_run=False
        )
        score = m_pred.predictions.read() # 读取预测的score
        score_lst[table_name] = score # 放入之前建好的list里面
    data_1 = DataSource.write_pickle(score_lst)
    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 m1_post_run_bigquant_run(outputs):
    return outputs


m3 = M.input_features.v1(
    features="""# #号开始的表示注释
# 多个特征，每行一个，可以包含基础特征和衍生特征
return_5
return_10
return_20
avg_amount_0/avg_amount_5
avg_amount_5/avg_amount_20
rank_avg_amount_0/rank_avg_amount_5
rank_avg_amount_5/rank_avg_amount_10
rank_return_0
rank_return_5
rank_return_10
rank_return_0/rank_return_5
rank_return_5/rank_return_10
pe_ttm_0
"""
)

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
)

m17 = M.general_feature_extractor.v7(
    instruments=m9.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=60
)

m18 = M.derived_feature_extractor.v3(
    input_data=m17.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False
)

m14 = M.dropnan.v1(
    input_data=m18.data
)

m1 = M.cached.v3(
    input_1=m14.data,
    run=m1_run_bigquant_run,
    post_run=m1_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)