# -*- coding:utf-8 -*-
"""
@author:code37
@file:Accruals2price.py
@time:2018/3/1911:32
"""
import os
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
from factorset.Util.configutil import GetConfig
[docs]class Accruals2price(BaseFactor):
"""
:Name: 应计收入与价格比率（Accruals-to-price）
:计算方法: 应计收入与价格比率 =（净利润_TTM - 经营活动产生的现金流量金额_TTM） / 总市值
:应用: 若应计收入与价格比率较高，公司可能夸大销售额，未来可能产生亏损，损害股价。
:注: O'Shaughnessy J P. What works on Wall Street: The classic guide to the best-performing investment strategies of all time[M]. McGraw Hill Professional, 2011.
"""
def __init__(self, factor_name='Accruals2price', tickers='000016.SH', data_source='', factor_parameters={}, save_dir=None):
# Initialize super class.
super(Accruals2price, 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)
# motherNetProfit 40
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
# cash_flows_yield 133
cf = cp.concat_fund(self.data_source, self.tickers, 'CF').loc[shifted_begin_date:end_date,['ticker', 133]]
cf['release_date'] = cf.index
cf['report_date'] = cf.index
self.accrual_df = cf.merge(inst, on=['ticker', 'release_date', 'report_date'])
self.accrual_df['accr'] = self.accrual_df[40] - self.accrual_df[133]
cash_flow_ls = []
for ticker in self.accrual_df['ticker'].unique():
try: # 财务数据不足4条会有异常
reven_df = ttmContinues(self.accrual_df[self.accrual_df['ticker'] == ticker], 'accr')
reven_df['ticker'] = ticker
except:
continue
cash_flow_ls.append(reven_df)
self.accrual_ttm = pd.concat(cash_flow_ls)
# 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):
accr_df = self.accrual_ttm[self.accrual_ttm['datetime'] <= trading_day]
accr_df = accr_df.sort_values(by=['datetime', 'report_date'], ascending=[False, False])
accr_se = accr_df.groupby('ticker')['accr_TTM'].apply(lambda x: x.iloc[0]) # 取最近1年的财报
today_mkt_value = self.mkt_value.loc[trading_day]
mkt_value = today_mkt_value.set_index('ticker')['mkt_value']
ret_se = accr_se / mkt_value
return ret_se.dropna()
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)
Accruals2price = Accruals2price(
factor_name='Accruals2price',
factor_parameters={},
tickers=hs300.code.tolist(),
save_dir='',
data_source=os.path.abspath('..'),
)
Accruals2price.generate_factor_and_store(from_dt, to_dt)
print('因子构建完成，并已成功入库!')