QFF is a Python package of quantitative financial framework, which is used to provide a localized backtesting and simulation trading environment for individuals, so that users can focus more on trading strategy writing.
Here are just a few of the things that QFF does well:
- Provide one-stop solutions such as data crawling, data cleaning, data storage, strategy writing, strategy analysis, strategy backtest and simulated trade.
- Provide graceful interface for strategy writing (similar to JoinQuant), facilitate users to get started quickly.
- Provide a local running environment to improve the strategy running efficiency.
- Provide rich interfaces to obtain free stock data, such as fundamental data, real-time and historical market data etc.
- Provide practical auxiliary functions to simplify strategy writing, such as indicator calculation, trading system framework, etc.
The source code is currently hosted on GitHub at: https://github.com/haijiangxu/qff
pip install qff --upgrade
pip install qff -i http://mirrors.aliyun.com/pypi/simple/ --upgrade
Docker image for the QFF is at https://hub.docker.com/r/haijiangxu/qff.
docker pull qff
docker run -d -v /root/xxxx:/root/work -p 8765:8765 qff
Documentation for the latest Current release is at https://qff.readthedocs.io/zh_CN/latest/.
QFF is still under developing, feel free to open issues and pull requests:
- Report or fix bugs
- Require or publish interface
- Write or fix documentation
- Add test cases
- QFF only supports stocks, but not other financial products such as futures, funds, foreign exchange, bonds, cryptocurrencies, etc.
- All data provided by QFF is just for academic research purpose.
- The data provided by QFF is for reference only and does not constitute any investment proposal.
- Any investor based on QFF research should pay more attention to data risk.
- QFF will insist on providing open-source financial data.
- Based on some uncontrollable factors, some data interfaces in QFF may be removed.
- Please follow the relevant open-source protocol used by QFF.
Special thanks QUANTAXIS for the opportunity of learning from the project;
Special thanks AKShare for the opportunity of learning from the project;
Special thanks JoinQuant for the opportunity of learning from the project;