# -*- coding:utf-8 -*-
"""
@author:code37
@file:UnleverBeta.py
@time:2018/3/123:35
"""
import pandas as pd
import numpy as np
import tushare as ts
from scipy import stats
from factorset.factors import BaseFactor
from factorset.data.OtherData import code_to_symbol, shift_date
from factorset.data import CSVParser as cp
from factorset.data import StockSaver as sp
from factorset.Util.finance import ttmContinues, ttmDiscrete
[docs]class UnleverBeta(BaseFactor):
"""
:名称: UnleverBeta因子，剔除财务杠杆比率的Beta（账面价值比）
:计算方法: UnleverBeta = Beta / (1 + 总负债 / 股东权益)
:应用: Unlevered beta本意是作为一个实际beta估算的中间值，排除资本结构差异的影响。无财务杠杆的企业只有经营风险，没有财务风险，无财务杠杆的贝塔系数是企业经营风险的衡量，该贝塔系数越大，企业经营风险就越大，投资者要求的投资回报率就越大，市盈率就越低。
"""
def __init__(self, factor_name='UnleverBeta_60D', tickers='000016.SH',
factor_parameters={'lagTradeDays': 60, 'benchmark': '000300'},
data_source='', save_dir=None):
# Initialize super class.
super(UnleverBeta, self).__init__(factor_name=factor_name, tickers=tickers,
factor_parameters=factor_parameters,
data_source=data_source, save_dir=save_dir)
self.lagTradeDays = self.factor_param['lagTradeDays']
self.benchmark = self.factor_param['benchmark']
[docs] def prepare_data(self, begin_date, end_date):
"""
数据预处理
"""
# 多取一些数据做填充
shifted_begin_date = shift_date(begin_date, self.factor_param['lagTradeDays'])
# 获取股票行情
hq = cp.concat_stock(self.data_source, self.tickers).loc[shifted_begin_date:end_date, ['code', 'close']]
self.hq = cp.hconcat_stock_series(hq, self.tickers)
# 获取指数Benchmark
# b = sp.get_index(self.benchmark).loc[shifted_begin_date:end_date,['close']]
b = pd.read_csv(self.data_source + '\\hq\\' + self.benchmark + '.csv', index_col=0).loc[
shifted_begin_date:end_date, ['close']]
self.b = b.fillna(method='ffill')
# 获取财务数据
# 按账面价值比 1/(1+负债总额/股东权益)
# Dbequrt: Debt to Equity Ratio 产权比率=负债总额/股东权益*100%
shifted_begin_date = shift_date(begin_date, 500)
# 117负债, 121资产
Dbequrt_df = cp.concat_fund(self.data_source, self.tickers, 'BS').loc[shifted_begin_date:end_date, ['ticker', 117, 121]]
Dbequrt_df['totalLiabilities'] = Dbequrt_df[121]
Dbequrt_df['totalEquity'] = Dbequrt_df[117]
Dbequrt_df['Dbequrt'] = Dbequrt_df['totalLiabilities'] / Dbequrt_df['totalEquity']
Dbequrt_df.drop([117, 121], axis=1, inplace=True)
Dbequrt_df = Dbequrt_df[Dbequrt_df['Dbequrt'] :0]
Dbequrt_df['report_date'] = Dbequrt_df.index
Dbequrt_df['release_date'] = Dbequrt_df.index
self.Dbequrt_df = Dbequrt_df.drop(['totalLiabilities', 'totalEquity'], axis=1)
# 仅仅取年报, 查找是否reportDate是否以12月31日结尾
# self.Dbequrt_df = Dbequrt_df[Dbequrt_df['reportDate'].str.endswith('1231')]
[docs] def generate_factor(self, end_day):
# 根据因子计算需要的数据量，计算出起始日期
begin_day = shift_date(end_day, self.lagTradeDays)
close_df = self.hq.loc[begin_day:end_day]
close_b = self.b.loc[begin_day:end_day]
ret = close_df / close_df.shift(1) - 1
bret = close_b / close_b.shift(1) - 1
bret.dropna(axis=0, how='any', inplace=True)
# 根据因子滚动的数据时点，取最新一年的财报数据
Dbequrt_df = self.Dbequrt_df[self.Dbequrt_df['release_date'] <= end_day]
Dbequrt_df = Dbequrt_df.sort_values(by=['report_date', 'release_date'], ascending=[False, False])
# 取最近1年的财报
Dbequrt_df = Dbequrt_df.groupby('ticker').apply(lambda x: x.head(1))
Dbequrt_df = Dbequrt_df['Dbequrt']
# 查找无数据Ticker
if not len(ret.dropna(axis=1, how='all').columns) == len(ret.columns):
nonticker = list(set(ret.columns) - set(ret.dropna(axis=1, how='all').columns))
else:
nonticker = []
beta = []
for columns in ret:
if columns in nonticker:
beta.append(np.nan)
else:
# 每只股票数据量与Benchmark数据量对应
retseries = ret[columns].dropna(axis=0, how='any')
bseries = bret.iloc[len(bret) - len(retseries.index):len(bret)]
OLSresult = stats.linregress(retseries.values, bseries.values.flatten())
try:
beta.append(OLSresult[0])
except IndexError:
beta.append(np.nan)
beta_df = pd.DataFrame(beta, index=ret.columns, columns=['Beta'])
beta_df = pd.concat((beta_df, Dbequrt_df.to_frame().reset_index(level=1, drop=True)), axis=1, join='inner')
beta_df['UnleverBeta'] = beta_df['Beta'] / (1 + beta_df['Dbequrt'])
return beta_df['UnleverBeta'].dropna()
if __name__ == '__main__':
# 设定要需要生成的因子数据范围
# 最多取到6-30
from_dt = '2017-06-30'
to_dt = '2018-04-20'
# 取沪深300
hs300 = ts.get_hs300s()
hs300.code = hs300.code.apply(code_to_symbol)
# 实例化因子
UnleverBeta_60D = UnleverBeta(
factor_name='UnleverBeta_60D',
factor_parameters={'lagTradeDays': 60, 'benchmark': '000300'},
tickers=hs300.code.tolist(),
save_dir='',
data_source='D:\\idwzx\\project\\factorset\\data',
)
# 生成因子数据并入库
UnleverBeta_60D.generate_factor_and_store(from_dt, to_dt)
print('因子构建完成，并已成功入库!')