# -*- coding:utf-8 -*-
"""
@author:code37
@file:CurrentRatio.py
@time:2018/3/1615:22
"""
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, ttmDiscrete
[docs]class CurrentRatio(BaseFactor):
"""
:名称: 流动比率（Current Ratio）；营运资金比率（Working Capital Ratio）；真实比率（Real Ratio）
:计算方法: 流动比率 = 流动资产合计_最新财报 / 流动负债合计_最新财报
:应用: 流动比率越高，说明资产的流动性越大，短期偿债能力越强。
"""
def __init__(self, factor_name='CurrentRatio', 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):
shifted_begin_date = shift_date(begin_date, 500)
bs = cp.concat_fund(self.data_source, self.tickers, 'BS').loc[shifted_begin_date:end_date,
['ticker', 101, 103]]
bs['CurrentRatio'] = bs[101] / bs[103]
self.bs = bs.drop([101, 103], axis=1)
# bs['report_date'] = bs.index
# bs['release_date'] = bs.index
[docs] def generate_factor(self, date_str):
balance_df = self.bs[:date_str]
balance_df = balance_df.dropna()
result_se = balance_df.groupby('ticker').apply(lambda x: x['CurrentRatio'].iloc[-1])
return result_se
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
CurrentRatio = CurrentRatio(
factor_name='CurrentRatio',
factor_parameters={},
tickers=hs300.code.tolist(),
save_dir='',
data_source='D:\\idwzx\\project\\factorset\\data',
)
# 生成因子数据并入库
CurrentRatio.generate_factor_and_store(from_dt, to_dt)
print('因子构建完成，并已成功入库!')