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    In [3]:
    # 本代码由可视化策略环境自动生成 2022年11月24日 20:17
# 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。


def ts_delay(df,f,d):
    return df[f].shift(d)
def ts_std(df,f,d):
    return df[f].rolling(d).std()
def ts_mean(df,f,d):
    return df[f].rolling(d).mean()
def beta(df,d):
    benchmark = ['000300.SHA']  # 以沪深300为基准计算beta值
    benchmark_df=D.history_data(benchmark,fields=['close'],start_date='2015-01-01',end_date='2020-01-01')
    df["close_pct"]=df['close'].pct_change()
    benchmark_df["close_pct"]=benchmark_df['close'].pct_change()
    return (df['close_pct'].rolling(d).cov(benchmark_df['close_pct']))/benchmark_df['close_pct'].rolling(d).var()
m9_user_functions_bigquant_run = {
    'beta':beta,
    'ts_delay':ts_delay,
    'ts_mean':ts_mean,
    'ts_std':ts_std,
}

m6 = M.use_datasource.v2(
    datasource_id='bar1d_index_CN_STOCK_A',
    start_date='2013-01-01',
    end_date='2022-11-01',
    before_start_days=1
)

m7 = M.filter.v3(
    input_data=m6.data,
    expr='instrument==\'000001.HIX\'',
    output_left_data=False
)

m8 = M.input_features.v1(
    features="""volume=volume
#close 衍生特征
close
ma_10=ts_mean('close',10)
ma_20=ts_mean('close',20)
ma_50=ts_mean('close',50)
#return 衍生特征
return_1=close/ts_delay('close',1)
return_5=close/ts_delay('close',5)
return_10=close/ts_delay('close',10)
return_20=close/ts_delay('close',20)
r1r5=return_1/return_5
r5r10=return_5/return_10
r10r20=return_10/return_20
# Beta
beta_10=beta(10)
beta_20=beta(20)
beta_50=beta(50)"""
)

m9 = M.derived_feature_extractor.v3(
    input_data=m7.data,
    features=m8.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=True,
    remove_extra_columns=False,
    user_functions=m9_user_functions_bigquant_run
)