# -*- coding:utf-8 -*-
"""
@author:code37
@file:ROIC.py
@time:2018/3/217:39
"""
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
[docs]class ROIC(BaseFactor):
"""
:名称: 投资资本回报率
:计算方法: 投资资本回报率 = （净利润（不含少数股东权益）_TTM + 财务费用 _TTM）/ 投资资本_TTM，投资资本 = 资产总计 - 流动负债 + 应付票据 + 短期借款 + 一年内到期的长期负债，净利润_TTM为最近4个季度报告期的净利润之和，投资资本_TTM为最近5个季度报告期总资产的平均值。
:应用: 一般而言，资本回报率较高表明公司强健或者管理有方。但同时，也可能管理者过分强调营收，忽略成长机会，牺牲长期价值。
"""
def __init__(self, factor_name='ROIC', tickers='000016.SH', data_source='', factor_parameters={}, save_dir=None):
# Initialize super class.
super(ROIC, 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) # 取到2年之前的数据
# Invested Capital = 资产总计121 - 流动负债101+ 应付票据68 + 短期借款109 + 一年内到期的长期负债0
bs = cp.concat_fund(self.data_source, self.tickers, 'BS').loc[shifted_begin_date:end_date,['ticker', 121, 101, 68, 109, 0]]
bs['IC'] = bs[121] - bs[101] + bs[68] + bs[109] + bs[0]
bs = bs.drop([121, 101, 68, 109, 0], axis=1)
self.bs = bs.dropna()
# EBT = 归母净利润40 + 财务费用56
inst = cp.concat_fund(self.data_source, self.tickers, 'IS').loc[shifted_begin_date:end_date,['ticker', 40, 56]]
inst = inst[(inst[56] > 1) | (inst[56] < -1)].copy()
inst['return'] = inst[40] + inst[56]
inst = inst.drop([40, 56], axis=1)
inst.dropna(inplace=True)
inst['release_date'] = inst.index
inst['report_date'] = inst.index
returnTTM_ls = []
for ticker in inst['ticker'].unique():
try: # 财务数据不足4条会有异常
return_df = ttmContinues(inst[inst['ticker'] == ticker], 'return')
return_df['ticker'] = ticker
except:
# print(ticker + ': revenue error')
continue
returnTTM_ls.append(return_df)
self.inst = pd.concat(returnTTM_ls)
self.inst.set_index('datetime', inplace=True)
[docs] def generate_factor(self, end_day):
inst = self.inst.loc[:end_day]
inst.sort_index(ascending=True, inplace=True)
inst = inst.groupby('ticker').apply(lambda x: x['return' + '_TTM'].iloc[-1])
bs = self.bs.loc[:end_day]
bs.sort_index(ascending=True, inplace=True)
bs = bs.groupby('ticker')['IC'].rolling(5).mean().groupby('ticker').tail(1)
ret = inst.dropna() / bs.reset_index(level=1, drop=True).dropna()
return ret.dropna()
if __name__ == '__main__':
from_dt = '2017-06-15'
to_dt = '2018-03-09'
# 取沪深300
hs300 = ts.get_hs300s()
hs300.code = hs300.code.apply(code_to_symbol)
ROIC = ROIC(
factor_name='ROIC',
factor_parameters={},
tickers=hs300.code.tolist(),
save_dir='',
data_source='D:\\idwzx\\project\\factorset\\data',
)
ROIC.generate_factor_and_store(from_dt, to_dt)
print('因子构建完成，并已成功入库!')