# -*- coding:utf-8 -*-
"""
@author:code37
@file:EP_TTM.py
@time:2018/2/2717:54
"""
import pandas as pd
import tushare as ts
from factorset.factors import BaseFactor
from factorset.data.OtherData import code_to_symbol, shift_date, market_value
from factorset.data import CSVParser as cp
from factorset.Util.finance import ttmContinues
[docs]class EP_TTM(BaseFactor):
"""
:名称: 过去滚动4个季度（12月）市盈率的倒数
:计算方法: EP_TTM = 净利润（不含少数股东权益）_TTM /总市值
:应用: 市盈率越低，代表投资者能够以相对较低价格购入股票。
"""
def __init__(self, factor_name='EP_TTM', tickers='000016.SH', data_source='', factor_parameters={}, save_dir=None):
# Initialize super class.
super(EP_TTM, 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):
"""
数据预处理
"""
shifted_begin_date = shift_date(begin_date, 500)
inst = cp.concat_fund(self.data_source, self.tickers, 'IS').loc[shifted_begin_date:end_date,['ticker', 40]]
inst['motherNetProfit'] = inst[40]
inst.drop(40, axis=1, inplace=True)
inst['release_date'] = inst.index
inst['report_date'] = inst.index
profitTTM_ls = []
for ticker in inst['ticker'].unique():
try: # 财务数据不足4条会有异常
reven_df = ttmContinues(inst[inst['ticker'] == ticker], 'motherNetProfit')
reven_df['ticker'] = ticker
except:
continue
profitTTM_ls.append(reven_df)
# 净利润ttm
self.profitTTM = pd.concat(profitTTM_ls)
# self.profitTTM.set_index('datetime', inplace=True)
# 总市值
# Tushare的市值数据只有17年-now
df = market_value(self.data_source + '\\other\\otherdata.csv', self.tickers)
self.mkt_value = df.drop(['price', 'totals'], axis=1)
[docs] def generate_factor(self, trading_day):
earings_df = self.profitTTM[self.profitTTM['datetime'] <= trading_day]
earings_df = earings_df.sort_values(by=['datetime', 'report_date'], ascending=[False, False])
# 取最近1年的财报
earings_df = earings_df.groupby('ticker').apply(lambda x: x.head(1))
# print(self.mkt_value)
today_mkt_value = self.mkt_value.loc[trading_day]
ret_df = earings_df.merge(today_mkt_value, on='ticker', how='inner')
ret_df['EP_TTM'] = ret_df['motherNetProfit_TTM'] / ret_df['mkt_value']
return ret_df.set_index('ticker')['EP_TTM']
if __name__ == '__main__':
from_dt = '2017-07-15'
to_dt = '2018-04-09'
# 取沪深300
hs300 = ts.get_hs300s()
hs300.code = hs300.code.apply(code_to_symbol)
EP_TTM = EP_TTM(
factor_name='EP_TTM',
factor_parameters={},
tickers=hs300.code.tolist(),
save_dir='',
data_source='D:\\idwzx\\project\\factorset\\data',
)
EP_TTM.generate_factor_and_store(from_dt, to_dt)
print('因子构建完成，并已成功入库!')