# -*- 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, market_value
from factorset.data import CSVParser as cp
from factorset.Util.finance import ttmContinues,ttmDiscrete
[docs]class CATurnover(BaseFactor):
"""
:名称: 流动资产周转率
:计算方法: 流动资产周转率 = 营业收入_TTM / 流动资产总计_TTM，营业收入_TTM为最近4个季度报告期的营业收入之和，流动资产总计_TTM为最近5个季度报告期总资产的平均值。
:应用: 流动资产周转率越高，表明企业流动资产周转速度越快，利用越好。在较快的周转速度下，流动资产会相对节约，其意义相当于流动资产投入的扩大，在某种程度上增强了企业的创收能力。
"""
def __init__(self, factor_name='CATurnover', tickers='000016.SH', data_source='', factor_parameters={}, save_dir=None):
# Initialize super class.
super(CATurnover, 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):
"""
数据预处理
"""
# 获取财务数据:
# CATurnover = currentAssets 103 / revenue 0
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', 103]]
bs['release_date'] = bs.index
bs['report_date'] = bs.index
bs['currentAssets'] = bs[103]
bs.drop(103, axis=1, inplace=True)
inst = cp.concat_fund(self.data_source, self.tickers, 'IS').loc[shifted_begin_date:end_date, ['ticker', 0]]
inst['release_date'] = inst.index
inst['report_date'] = inst.index
inst['revenue'] = inst[0]
inst.drop([0], axis=1, inplace=True)
# TTM Continues处理
revenueTTM_ls = []
for ticker in inst['ticker'].unique():
try: # 财务数据不足4条会有异常
reven_df = ttmContinues(inst[inst['ticker'] == ticker], 'revenue')
reven_df['ticker'] = ticker
except:
print(ticker + ': revenue error')
continue
revenueTTM_ls.append(reven_df)
# TTM Discrete 取近期平均
currentAssetsTTM_ls = []
for ticker in bs['ticker'].unique():
try:
currentAssets_df = ttmDiscrete(bs[bs['ticker'] == ticker], 'currentAssets')
currentAssets_df['ticker'] = ticker
except:
print(ticker + ': current asset error')
continue
currentAssetsTTM_ls.append(currentAssets_df)
self.revenueTTM = pd.concat(revenueTTM_ls)
self.currentAssetsTTM = pd.concat(currentAssetsTTM_ls)
# 仅仅取年报, 查找是否reportDate是否以12月31日结尾
# self.df = df[df['reportDate'].str.endswith('1231')]
[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
"""
revenue_ttm_df = self.revenueTTM[self.revenueTTM['datetime'] <= end_day]
current_assets_ttm_df = self.currentAssetsTTM[self.currentAssetsTTM['datetime'] <= end_day]
# ttm 的值顺序排列，取最底部的数据即为最新的数据
revenue_se = revenue_ttm_df.groupby('ticker').apply(lambda x: x['revenue' + '_TTM'].iloc[-1])
current_assets_se = current_assets_ttm_df.groupby('ticker').apply(lambda x: x['currentAssets' + '_TTM'].iloc[-1])
return revenue_se / current_assets_se
if __name__ == '__main__':
# 设定要需要生成的因子数据范围
from_dt = '2017-06-01'
to_dt = '2018-04-09'
# 取沪深300
hs300 = ts.get_hs300s()
hs300.code = hs300.code.apply(code_to_symbol)
CATurnover = CATurnover(
factor_name='CATurnover',
factor_parameters={},
tickers=hs300.code.tolist(),
save_dir='',
data_source='D:\\idwzx\\project\\factorset\\data',
)
CATurnover.generate_factor_and_store(from_dt, to_dt)
print('因子构建完成，并已成功入库!')