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# 本代码由可视化策略环境自动生成 2018年7月21日 09:07
# 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。


m1 = M.instruments.v2(
    start_date='2017-01-01',
    end_date='2018-01-01',
    market='CN_STOCK_A',
    instrument_list="""600000.SHA
600010.SHA
600015.SHA
600016.SHA
600018.SHA
600028.SHA
600029.SHA
600030.SHA
600036.SHA
600048.SHA

 """,
    max_count=0
)

m2 = M.input_features.v1(
    features="""return_5
sum(mf_net_amount_l_0, 5)
close_0
market_cap_float_0
close_0/close_5-1

 
"""
)

m7 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m2.data,
    start_date='',
    end_date='',
    m_cached=False
)

m4 = M.input_features.v1(
    features='relative_ret(close_0)'
)

def calcu_relative_ret(df):
    # 先获取中证800指数数据
    start_date = min(m7.data.read_df().date).strftime('%Y-%m-%d')
    end_date = max(m7.data.read_df().date).strftime('%Y-%m-%d')
    hs800_df = D.history_data(
    '000906.SHA',
    start_date=(pd.to_datetime(start_date) - datetime.timedelta(days=10)).strftime('%Y-%m-%d'),  # 多取几天的数据
    end_date=end_date)[['date', 'close']].rename(columns={'close': 'hs800_close'})
    
    # 与个股数据合并
    df = df[['date', 'close_0']].reset_index().merge(hs800_df, on='date', how='left').set_index('index')
    
    # 返回超额收益率
    return df['close_0'].pct_change() - df['hs800_close'].pct_change()


# 按股票代码groupby计算个股超额收益率数据
def relative_ret(df, close_0):
    return df.groupby('instrument', group_keys=False).apply(calcu_relative_ret)


m8_user_functions_bigquant_run = {
    'relative_ret':  relative_ret
}


m8 = M.derived_feature_extractor.v3(
    input_data=m7.data,
    features=m4.data,
    date_col='date',
    instrument_col='instrument',
    user_functions=m8_user_functions_bigquant_run
)

m9 = M.derived_feature_extractor.v3(
    input_data=m8.data,
    features=m2.data,
    date_col='date',
    instrument_col='instrument',
    user_functions={}
)

[2018-07-21 09:07:06.514970] INFO: bigquant: instruments.v2 开始运行..
[2018-07-21 09:07:06.528198] INFO: bigquant: 命中缓存
[2018-07-21 09:07:06.529552] INFO: bigquant: instruments.v2 运行完成[0.014616s].
[2018-07-21 09:07:06.535163] INFO: bigquant: input_features.v1 开始运行..
[2018-07-21 09:07:06.586750] INFO: bigquant: 命中缓存
[2018-07-21 09:07:06.588032] INFO: bigquant: input_features.v1 运行完成[0.052862s].
[2018-07-21 09:07:06.597259] INFO: bigquant: general_feature_extractor.v7 开始运行..
[2018-07-21 09:07:08.381895] INFO: 基础特征抽取: 年份 2017, 特征行数=2434
[2018-07-21 09:07:09.516820] INFO: 基础特征抽取: 年份 2018, 特征行数=0
[2018-07-21 09:07:09.525082] INFO: 基础特征抽取: 总行数: 2434
[2018-07-21 09:07:09.526209] INFO: bigquant: general_feature_extractor.v7 运行完成[2.92896s].
[2018-07-21 09:07:09.529049] INFO: bigquant: input_features.v1 开始运行..
[2018-07-21 09:07:09.533583] INFO: bigquant: 命中缓存
[2018-07-21 09:07:09.534437] INFO: bigquant: input_features.v1 运行完成[0.005389s].
[2018-07-21 09:07:09.733215] INFO: bigquant: derived_feature_extractor.v3 开始运行..
[2018-07-21 09:07:10.531858] INFO: derived_feature_extractor: 提取完成 relative_ret(close_0), 0.775s
[2018-07-21 09:07:10.552099] INFO: derived_feature_extractor: /y_2017, 2434
[2018-07-21 09:07:10.574013] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.8408s].
[2018-07-21 09:07:10.577004] INFO: bigquant: derived_feature_extractor.v3 开始运行..
[2018-07-21 09:07:10.602979] INFO: derived_feature_extractor: 提取完成 close_0/close_5-1, 0.002s
[2018-07-21 09:07:10.614650] INFO: derived_feature_extractor: 提取完成 sum(mf_net_amount_l_0, 5), 0.011s
[2018-07-21 09:07:10.634869] INFO: derived_feature_extractor: /y_2017, 2434
[2018-07-21 09:07:10.655059] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.07805s].