factorset

pypi version status travis status Documentation Status

Factorset is a financial factors construction factory of Chinese A-share. 提供中国A股市场因子集合，包含各类常用及特异因子计算方法，持续更新中。 提供轻量级因子计算框架，高可扩展。持续更新中。

Features

  • Ease of Use: factorset tries to get out of your way so that you can focus on algorithm development. See below for a code example.

Installation

Installing With pip

Assuming you have all required (see note below) non-Python dependencies, you can install factorset with pip via:

$ pip install factorset

Note: Installing factorset via pip is slightly more involved than the average Python package. Simply running pip install factorset will likely fail if you’ve never installed any scientific Python packages before.

The FundCrawler class in factorset depends on proxy_pool, a powerful proxy crawler.

Quickstart

The following code generate a EP_TTM factor class.

import os
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

class EP_TTM(BaseFactor):
"""
:名称: 过去滚动4个季度（12月）市盈率的倒数
:计算方法: EP_TTM = 净利润（不含少数股东权益）_TTM /总市值
:应用: 市盈率越低，代表投资者能够以相对较低价格购入股票。

"""

def __init__(self, factor_name='EP_TTM', tickers='000016.SH', data_source='', factor_parameters={}, save_dir=None):
    # Initialize super class.
    super(EP_TTM, self).__init__(factor_name=factor_name, tickers=tickers,
                                 factor_parameters=factor_parameters,
                                 data_source=data_source, save_dir=save_dir)

def prepare_data(self, begin_date, end_date):
    """
    数据预处理
    """

    shifted_begin_date = shift_date(begin_date, 500) # 向前取500个交易日

    # 取利润表中“归属于母公司股东的净利润”项目，项目名称及数字详见FundDict
    inst = cp.concat_fund(self.data_source, self.tickers, 'IS').loc[shifted_begin_date:end_date,['ticker', 40]]
    inst['motherNetProfit'] = inst[40]
    inst.drop(40, axis=1, inplace=True)

    # ttm算法需要“财报发布日”与“财报报告日”两个日期作为参数
    inst['release_date'] = inst.index
    inst['report_date'] = inst.index

    # 净利润ttm
    profitTTM_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], 'motherNetProfit')
            reven_df['ticker'] = ticker
        except:
            continue
        profitTTM_ls.append(reven_df)
    self.profitTTM = pd.concat(profitTTM_ls)

    # 取“OtherData”中总市值数据
    # Tushare的市值数据只有17年6月->now
    df = market_value(self.data_source + '\\other\\otherdata.csv', self.tickers)
    self.mkt_value = df.drop(['price', 'totals'], axis=1)

def generate_factor(self, trading_day):
    # generate_factor会遍历交易日区间, 即生成每个交易日所有股票的因子值
    earings_df = self.profitTTM[self.profitTTM['datetime'] <= trading_day]
    earings_df = earings_df.sort_values(by=['datetime', 'report_date'], ascending=[False, False])

    # 取最近1期ttm数据
    earings_df = earings_df.groupby('ticker').apply(lambda x: x.head(1))

    # 取当前交易日市值数据
    today_mkt_value = self.mkt_value.loc[trading_day]

    ret_df = earings_df.merge(today_mkt_value, on='ticker', how='inner')
    ret_df['EP_TTM'] = ret_df['motherNetProfit_TTM'] / ret_df['mkt_value']
    return ret_df.set_index('ticker')['EP_TTM']

if __name__ == '__main__':
    from_dt = '2017-07-15'
    to_dt = '2018-04-09'

    # 取沪深300
    hs300 = ts.get_hs300s()
    hs300.code = hs300.code.apply(code_to_symbol)

    EP_TTM = EP_TTM(
        factor_name='EP_TTM',
        factor_parameters={},
        tickers=hs300.code.tolist(),
        save_dir='',
        data_source=os.path.abspath('.'),
    )

    EP_TTM.generate_factor_and_store(from_dt, to_dt)
    print('因子构建完成，并已成功入库!')

You can find other factors in the factorset/factors directory.

Questions?

If you find a bug, feel free to open an issue and fill out the issue template.