# -*- coding:utf-8 -*-
"""
@author:code37
@file:UnleverBeta.py
@time:2018/3/123:35
"""
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
[docs]class LDebt2TA(BaseFactor):
"""
:名称: 长期负债比率（Long-term liability rate）
:计算方法: 长期负债比 = 长期负债 / 资产总额
:应用: 长期负债比率越小，表明公司负债的资本化程度低，长期偿债压力小；反之，则表明公司负债的资本化程度高，长期偿债压力大。
"""
def __init__(self, factor_name='LDebt2TA', tickers='000016.SH', data_source='', factor_parameters={}, save_dir=None):
# Initialize super class.
super().__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
"""
# 获取财务数据:
# 长期负债比 = 长期负债136 / 资产总额121
# LDebt2TA = totalLongLiabilities / totalAssets
shifted_begin_date = shift_date(begin_date, 500)
df = cp.concat_fund(self.data_source, self.tickers, 'BS').loc[shifted_begin_date:end_date, ['ticker', 136, 121]]
# 取出负债，资产总额数据
df['LDebt2TA'] = df[136] / df[121]
df['reportDate'] = df.index
df['reportDate'] = 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
df = df[df['reportDate'].str.endswith('12-31')]
df.drop([136, 121], axis=1)
self.df = df
[docs] def generate_factor(self, end_day):
"""
逐日生成因子数据
Parameters
-----------
end_day:
因子生产的日期
Returns
-----------
ret: pd.Series类型
indx为ticker，value为因子值
"""
df = self.df.loc[:end_day]
df = df.sort_values('reportDate', ascending=False)
# 取最近1年的财报
df = df.groupby('ticker').apply(lambda x: x.head(1))
return df['LDebt2TA'].reset_index(level=1, drop=True).dropna()
if __name__ == '__main__':
# 设定要需要生成的因子数据范围
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)
# 实例化因子
LDebt2TA = LDebt2TA(
factor_name='LDebt2TA',
factor_parameters={},
tickers=hs300.code.tolist(),
save_dir='',
data_source='D:\\idwzx\\project\\factorset\\data',
)
# 生成因子数据并入库
LDebt2TA.generate_factor_and_store(from_dt, to_dt)
print('因子构建完成，并已成功入库!')