# -*- coding:utf-8 -*-
"""
@author:code37
@file:EP_LYR.py
@time:2018/3/116:49
"""
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
[docs]class EP_LYR(BaseFactor):
"""
:名称: 静态市盈率的倒数；最近年报的市盈率的倒数
:计算方法: EP_LYR=净利润（不含少数股东权益）_最新年报/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。
:注: 这里用的是市值，而不是价格，这种方法适合计算日度的数据，防止除权除息带来的价格扰动。
"""
def __init__(self, factor_name='EP_LYR', tickers='000016.SH', data_source='', factor_parameters={}, save_dir=None):
# Initialize super class.
super(EP_LYR, 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, 500)
earings_df = cp.concat_fund(self.data_source, self.tickers, 'IS').loc[shifted_begin_date:end_date,['ticker', 40]]
earings_df['motherNetProfit'] = earings_df[40]
earings_df.drop(40, axis=1, inplace=True)
earings_df['reportDate'] = earings_df.index
earings_df['reportDate'] = earings_df['reportDate'].apply(lambda x: x.strftime("%Y-%m-%d"))
# 仅仅取年报, 查找是否reportDate是否以12月31日结尾
self.earings_df = earings_df[earings_df['reportDate'].str.endswith('12-31')]
# 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.earings_df.loc[:trading_day]
earings_df = earings_df.sort_values(by='reportDate', ascending=False)
earings_df = earings_df.groupby('ticker').apply(lambda x: x.head(1)) # 取最近1年的财报
today_mkt_value = self.mkt_value.loc[trading_day]
ret_df = earings_df.merge(today_mkt_value, on='ticker', how='inner')
ret_df['EP_LYR'] = ret_df['motherNetProfit'] / (ret_df['mkt_value'])
return ret_df.set_index('ticker')['EP_LYR']
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_LYR = EP_LYR(
factor_name='EP_LYR',
factor_parameters={},
tickers=hs300.code.tolist(),
save_dir='',
data_source='D:\\idwzx\\project\\factorset\\data',
)
EP_LYR.generate_factor_and_store(from_dt, to_dt)
print('因子构建完成，并已成功入库!')