# -*- coding:utf-8 -*-
"""
@author:code37
@file:RoeGrowth223_new.py
@time:2018/3/1917:31
"""
import pandas as pd
import tushare as ts
from factorset.factors import BaseFactor
from factorset.data.OtherData import code_to_symbol, shift_date
from factorset.data import CSVParser as cp
from factorset.Util.finance import ttmContinues, ttmDiscrete
[docs]class RoeGrowth2(BaseFactor):
"""
:名称: ROE(TTM)增长（去年同期）
:计算方法: ROE增长 = 当期ROE(TTM) - 去年同期ROE(TTM)
"""
def __init__(self, factor_name='RoeGrowth2', tickers='000016.SH', data_source='', factor_parameters={}, save_dir=None):
# Initialize super class.
super(RoeGrowth2, self).__init__(factor_name=factor_name, tickers=tickers,
factor_parameters=factor_parameters,
data_source=data_source, save_dir=save_dir)
[docs] def prepare_data(self, begin_date, end_date):
shifted_begin_date = shift_date(begin_date, 800)
bs = cp.concat_fund(self.data_source, self.tickers, 'BS').loc[shifted_begin_date:end_date,['ticker', 86]]
bs['release_date'] = bs.index
bs['report_date'] = bs.index
bs['motherEquity'] = bs[86]
# 归母权益
equity_mean = []
for ticker in bs['ticker'].unique():
try:
tmp_equity = ttmDiscrete(bs[bs['ticker'] == ticker], 'motherEquity', 5)
tmp_equity['ticker'] = ticker
except:
continue
equity_mean.append(tmp_equity)
equity_mean = pd.concat(equity_mean)
inst = cp.concat_fund(self.data_source, self.tickers, 'IS').loc[shifted_begin_date:end_date,['ticker', 40]]
inst['release_date'] = inst.index
inst['report_date'] = inst.index
inst['motherNetProfit'] = inst[40]
# 归母净利润
net_profit = []
for ticker in inst['ticker'].unique():
try:
tmp_profit = ttmContinues(inst[inst['ticker'] == ticker], 'motherNetProfit')
tmp_profit['ticker'] = ticker
except:
continue
net_profit.append(tmp_profit)
net_profit = pd.concat(net_profit)
equity_mean['report_date'] = equity_mean['report_date'].apply(lambda x: x.strftime("%Y-%m-%d"))
net_profit['report_date'] = net_profit['report_date'].apply(lambda x: x.strftime("%Y-%m-%d"))
self.equity_mean = equity_mean.sort_values(by=['report_date', 'datetime'], ascending=[False, False])
self.net_profit = net_profit.sort_values(by=['report_date', 'datetime'], ascending=[False, False])
[docs] def generate_factor(self, end_day):
net_profit_df = self.net_profit[self.net_profit['datetime'] <= end_day]
print(net_profit_df)
equity_mean_df = self.equity_mean[self.equity_mean['datetime'] <= end_day]
net_profit_se = net_profit_df.groupby('ticker').apply(lambda x: x['motherNetProfit' + '_TTM'].iloc[0])
equity_mean_se = equity_mean_df.groupby('ticker').apply(lambda x: x['motherEquity' + '_TTM'].iloc[0])
roe = net_profit_se / equity_mean_se
# 去年同期的因子计算
latest_report_date = net_profit_df['report_date'].head(1).iloc[0]
last_year_report_date = str(int(latest_report_date[0:4]) - 1) + latest_report_date[4:]
# 取每个ticker中最新日期的去年财报, 若取不到则会变成NA被剔除
last_y_net_profit = net_profit_df[net_profit_df.report_date == last_year_report_date].groupby('ticker').apply(
lambda x: x['motherNetProfit' + '_TTM'].iloc[0])
last_y_equity = equity_mean_df[equity_mean_df.report_date == last_year_report_date].groupby('ticker').apply(
lambda x: x['motherEquity' + '_TTM'].iloc[0])
last_year_roe = last_y_net_profit / last_y_equity
return roe - last_year_roe
if __name__ == '__main__':
# 设定要需要生成的因子数据范围
from_dt = '2017-06-30'
to_dt = '2018-04-09'
# 取沪深300
hs300 = ts.get_hs300s()
hs300.code = hs300.code.apply(code_to_symbol)
RoeGrowth2 = RoeGrowth2(
factor_name='RoeGrowth2',
factor_parameters={},
tickers=hs300.code.tolist(),
save_dir='',
data_source='D:\\idwzx\\project\\factorset\\data',
)
RoeGrowth2.generate_factor_and_store(from_dt, to_dt)
print('因子构建完成，并已成功入库!')