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# 本代码由可视化策略环境自动生成 2018年2月3日 13:33
# 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='2018-01-01',
    end_date='2018-01-31',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m2 = M.input_features.v1(
    features="""# 最近的营业总收入复合年增长率(利用最近3年的年报数据)
fu_gross_revenues_cagr(fs_quarter_year_0,fs_quarter_index_0)
"""
)

m3 = M.general_feature_extractor.v6(
    instruments=m1.data,
    features=m2.data,
    start_date='',
    end_date='',
    before_start_days=0
)

def fu_gross_revenues_cagr(df,fs_quarter_year_0,fs_quarter_index_0):
    
    end_date = max(df.date).strftime('%Y-%m-%d')

    instruments = D.instruments(start_date='2005-01-01', end_date=end_date)

    dff = D.financial_statements(instruments, start_date='2005-01-01', end_date=end_date, 
                                fields=['instrument','fs_quarter_year','fs_quarter_index','fs_gross_revenues'])
    # 仅保留年报
    dff = dff[dff.fs_quarter_index==4]

    # 对年报数据进行双索引化
    tmp_df = dff.set_index(["fs_quarter_year","instrument"])

    # 对营业总收入,  双索引化 =>  行索引fs_quarter_year + 列索引instrument
    gross_revenues = tmp_df["fs_gross_revenues"].unstack().astype(np.float64)

    # 计算最近3年区间的营业总收入复合年增长率
    gross_revenues_cagr= ((gross_revenues/gross_revenues.shift(3))**(1/3)-1)*100

    # 将算出的营业总收入复合年增长率合并到双索引化的年报数据中
    tmp_df["u_gross_revenues_cagr"] =  gross_revenues_cagr.stack()

    # 恢复成无双索引的年报数据
    dff = tmp_df.reset_index(["fs_quarter_year","instrument"])

    # 最近的年报
    last_quarter_year = (df["fs_quarter_year_0"] - (df["fs_quarter_index_0"]<4))

    # 根据最新年报查询营业总收入复合年增长率
    tmp_df = df[["date","instrument"]]
    tmp_df["fs_quarter_year"] = last_quarter_year
    tmp_df = pd.merge(tmp_df,dff[['instrument','fs_quarter_year','u_gross_revenues_cagr']],
                  on=['instrument','fs_quarter_year'],
                  how='left')

    # 目标因子
    return tmp_df['u_gross_revenues_cagr']

m4_user_functions_bigquant_run = {
    'fu_gross_revenues_cagr':  fu_gross_revenues_cagr
}


m4 = M.derived_feature_extractor.v2(
    input_data=m3.data,
    features=m2.data,
    date_col='date',
    instrument_col='instrument',
    user_functions=m4_user_functions_bigquant_run
)

[2018-02-03 13:32:36.570319] INFO: bigquant: instruments.v2 开始运行..
[2018-02-03 13:32:36.576159] INFO: bigquant: 命中缓存
[2018-02-03 13:32:36.577293] INFO: bigquant: instruments.v2 运行完成[0.006995s].
[2018-02-03 13:32:36.585773] INFO: bigquant: input_features.v1 开始运行..
[2018-02-03 13:32:36.588507] INFO: bigquant: 命中缓存
[2018-02-03 13:32:36.589640] INFO: bigquant: input_features.v1 运行完成[0.003873s].
[2018-02-03 13:32:36.609553] INFO: bigquant: general_feature_extractor.v6 开始运行..
[2018-02-03 13:32:36.611735] INFO: bigquant: 命中缓存
[2018-02-03 13:32:36.612642] INFO: bigquant: general_feature_extractor.v6 运行完成[0.003048s].
[2018-02-03 13:32:36.674144] INFO: bigquant: derived_feature_extractor.v2 开始运行..
[2018-02-03 13:32:36.677020] INFO: bigquant: 命中缓存
[2018-02-03 13:32:36.677977] INFO: bigquant: derived_feature_extractor.v2 运行完成[0.00385s].