# -*- 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
from factorset.Util.finance import ttmContinues, ttmDiscrete
[docs]class NATurnover(BaseFactor):
"""
:名称: 净资产周转率
:计算方法: NATurnover = revenue_TTM / netAsset_TTM，净资产周转率 = 营业收入_TTM / 净资产总计_TTM，营业收入_TTM为最近4个季度报告期的营业收入之和，净资产总计_TTM为最近5个季度报告期总资产的平均值。
:应用: 资产周转率越高，表明企业总资产周转速度越快。销售能力越强，资产利用效率越高。
"""
def __init__(self, factor_name='NATurnover', 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
"""
# 净资产周转率 = 营业收入_TTM / 净资产总计_TTM
# 净资产总计=总资产-负债总额
# 营业收入_TTM为最近4个季度报告期的营业收入之和，
# 净资产总计_TTM为最近5个季度报告期总资产的平均值。
# Net asset turnover ratio = netAssets / totalLiabilities
# 获取财务数据:
shifted_begin_date = shift_date(begin_date, 500)
#117负债, 121资产
netAssets = cp.concat_fund(self.data_source, self.tickers, 'BS').loc[shifted_begin_date:end_date, ['ticker', 117, 121]]
netAssets['netAssets'] = netAssets[121] - netAssets[117]
netAssets.drop([117, 121], axis=1, inplace=True)
netAssets = netAssets[netAssets['netAssets'] :0]
netAssets['report_date'] = netAssets.index
netAssets['release_date'] = netAssets.index
netAssetsTTM_ls = []
for ticker in netAssets['ticker'].unique():
try:
netAssets_df = ttmDiscrete(netAssets[netAssets['ticker'] == ticker], 'netAssets')
netAssets_df['ticker'] = ticker
except:
# print(ticker + ': net asset error')
continue
netAssetsTTM_ls.append(netAssets_df)
#0营业收入
revenue = cp.concat_fund(self.data_source, self.tickers, 'IS').loc[shifted_begin_date:end_date, ['ticker', 0]]
revenue['revenue'] = revenue[0]
revenue.drop([0], axis=1, inplace=True)
revenue['report_date'] = revenue.index
revenue['release_date'] = revenue.index
revenueTTM_ls = []
for ticker in revenue['ticker'].unique():
try: # 财务数据不足4条会有异常
reven_df = ttmContinues(revenue[revenue['ticker'] == ticker], 'revenue')
reven_df['ticker'] = ticker
except:
# print(ticker + ': revenue error')
continue
revenueTTM_ls.append(reven_df)
self.revenueTTM = pd.concat(revenueTTM_ls)
self.netAssetsTTM = pd.concat(netAssetsTTM_ls)
# 仅仅取年报, 查找是否report_date是否以12月31日结尾
# self.df = df[df['report_date'].str.endswith('1231')]
[docs] def generate_factor(self, end_day):
revenue_ttm_df = self.revenueTTM[self.revenueTTM['datetime'] <= end_day]
net_assets_ttm_df = self.netAssetsTTM[self.netAssetsTTM['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])
net_assets_se = net_assets_ttm_df.groupby('ticker').apply(lambda x: x['netAssets' + '_TTM'].iloc[-1])
return revenue_se / net_assets_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
NATurnover = NATurnover(
factor_name='NATurnover',
factor_parameters={},
tickers=hs300.code.tolist(),
save_dir='',
data_source='D:\\idwzx\\project\\factorset\\data',
)
# 生成因子数据并入库
NATurnover.generate_factor_and_store(from_dt, to_dt)
print('因子构建完成，并已成功入库!')