huma008 / qff

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QFF: Quantize Financial Framework

PyPI Python Docker Image Version (latest by date) Documentation Status

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.

Main Features

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.

Installation

Source code

The source code is currently hosted on GitHub at: https://github.com/haijiangxu/qff

General

pip install qff --upgrade

China

pip install qff -i http://mirrors.aliyun.com/pypi/simple/ --upgrade

Docker

Docker image for the QFF is at https://hub.docker.com/r/haijiangxu/qff.

pull docker image

docker pull qff

run docker image

docker run -d -v /root/xxxx:/root/work -p 8765:8765  qff

Document

Documentation for the latest Current release is at https://qff.readthedocs.io/zh_CN/latest/.

Contribution

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

Statement

  1. QFF only supports stocks, but not other financial products such as futures, funds, foreign exchange, bonds, cryptocurrencies, etc.
  2. All data provided by QFF is just for academic research purpose.
  3. The data provided by QFF is for reference only and does not constitute any investment proposal.
  4. Any investor based on QFF research should pay more attention to data risk.
  5. QFF will insist on providing open-source financial data.
  6. Based on some uncontrollable factors, some data interfaces in QFF may be removed.
  7. Please follow the relevant open-source protocol used by QFF.

Acknowledgement

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;

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