克隆策略

    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    In [33]:
    # 本代码由可视化策略环境自动生成 2021年3月24日16:02
# 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。


m2 = M.instruments.v2(
    start_date='2019-01-01',
    end_date='2019-10-31',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m1 = M.advanced_auto_labeler.v2(
    instruments=m2.data,
    label_expr="""# 做一个大盘收益率相对于个股的收益率的3天内相关系数因子
correlation(benchmark_close/shift(benchmark_close,1),close/shift(close,1),3)""",
    start_date='',
    end_date='',
    benchmark='000300.HIX',
    drop_na_label=True,
    cast_label_int=False,
    user_functions={}
)

m4 = M.rename_columns.v5(
    input_ds=m1.data,
    columns='label:factor',
    keep_old_columns=False
)

m5 = 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
bm_ret=close/shift(close,1)"""
)

m3 = M.index_feature_extract.v3(
    input_1=m2.data,
    input_2=m5.data,
    before_days=100,
    index='000300.HIX'
)

m9 = 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
ret=close_0/close_1
"""
)

m8 = M.general_feature_extractor.v7(
    instruments=m2.data,
    features=m9.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m6 = M.derived_feature_extractor.v3(
    input_data=m8.data,
    features=m9.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=True,
    user_functions={}
)

m10 = M.data_join.v3(
    input_1=m3.data_1,
    input_2=m6.data,
    on='date',
    how='right',
    sort=False
)

m13 = 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=correlation(bm_ret,ret,3)"""
)

m11 = M.derived_feature_extractor.v3(
    input_data=m10.data,
    features=m13.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)
    In [31]:
    # 自动标注方法的计算结果factor
m4.data.read()[['date','instrument','factor']].head(10)
    Out[31]:
    date instrument factor
    0 2019-01-07 000001.SZA 0.879492
    1 2019-01-07 000002.SZA 0.934842
    2 2019-01-07 000004.SZA 0.158294
    3 2019-01-07 000005.SZA 0.565058
    4 2019-01-07 000006.SZA 0.892669
    5 2019-01-07 000007.SZA 0.872186
    6 2019-01-07 000008.SZA 0.354121
    7 2019-01-07 000009.SZA 0.887204
    8 2019-01-07 000010.SZA 0.298599
    9 2019-01-07 000011.SZA 0.303374
    In [35]:
    # 逐步抽取法的计算结果
df = m11.data.read()
df[df.date=='2019-01-07'][['date','instrument','factor']].head(10)
    Out[35]:
    date instrument factor
    210564 2019-01-07 000001.SZA 0.879492
    210765 2019-01-07 000002.SZA 0.934842
    210966 2019-01-07 000004.SZA 0.158294
    211157 2019-01-07 000005.SZA 0.565058
    211358 2019-01-07 000006.SZA 0.892669
    211559 2019-01-07 000007.SZA 0.872186
    211760 2019-01-07 000008.SZA 0.354121
    211961 2019-01-07 000009.SZA 0.887204
    212162 2019-01-07 000010.SZA 0.298599
    212362 2019-01-07 000011.SZA 0.303374