QUANTAXIS
Chinese open-source Python quant trading framework for stocks, futures and options, covering data, backtests a
- Stars
- 11.3k
- Forks
- 3.5k
- Licence
- MIT
- Language
- Python
- Last update
- 2 months ago
- Type
- Library
What it is
QUANTAXIS is a Python framework for quantitative trading, mainly aimed at the Chinese markets. The README describes it as covering market data storage, backtesting, simulated and live trading, account management and scheduled tasks, with an emphasis on running locally. Its README is written primarily in Chinese and centers on a recent 2.x release that adds a Rust core for speed.
- Store and update historical market data in MongoDB or ClickHouse
- Backtest a trading strategy against daily price data
- Track a simulated stock or futures account with QIFI-format account records
- Schedule background data-update or trading tasks
Great for
- Python developers building trading strategies for Chinese stocks or futures
- Quant researchers who want backtesting with a local database like MongoDB or ClickHouse
- Teams that need multi-account management and scheduled data updates
Not ideal for
- Readers who need English documentation, since the README and many docs are in Chinese
- Beginners with no programming background, since setup involves databases and optional compiled components
- Anyone needing a guaranteed-working setup on Python versions outside 3.9-3.12
How to get it
- 1
Clone and build from source
You'll need: Python 3.9-3.12 · MongoDB 4.0+ or ClickHouse 20.0+ (optional)
Clone the repository
git clone https://github.com/QUANTAXIS/QUANTAXIS.gitEnter the project folder
cd QUANTAXISInstall dependencies
pip install -e . - 2
Run it
- Install Python 3.9-3.12 and clone the repository
- Run pip install -e . from the project folder
- Verify the install by importing QUANTAXIS and printing its version
- Configure a MongoDB connection if you want to store market data
- Create a QARSAccount and call buy() or sell() to test a trade
- 3
Get the most out of it
- Use the optional extras (pip install -e .[rust] or .[full]) to enable the Rust-backed account and backtest components
- Connect ClickHouse as a storage backend for larger datasets
- Use QAPubSub with RabbitMQ for distributed task distribution, as described in the README
- Read the linked documentation hub for API references on QAFetch and QAData
Downloads
Strengths
- Covers data, backtesting, simulation and account management in one Python package
- Open source under the MIT license
- Large community following, with over 11,000 GitHub stars at the time of this metadata
Trade-offs
- Documentation is mostly in Chinese, and the README is truncated
- Several features are marked as in development (for example options and the factor optimizer)
- The README's performance figures (such as 100x) are the project's own claims and are not independently verified here
How it compares
| Project | Stars | Type | Level | Updated | Best for |
|---|---|---|---|---|---|
| QUANTAXIS (this) | 11.3k | Library | intermediate | 2 months ago | Python developers building trading strategies for Chinese stocks or futures |
| vnpy | 46.2k | Framework | advanced | 10 days ago | Python developers who want to build custom quant trading systems |
| qlib | 49.9k | Library | advanced | 7 days ago | Quant researchers who want a structured ML workflow for stock data |
| daily_stock_analysis | 67.4k | App | intermediate | 4 days ago | Individual investors who follow a personal watchlist of A-shares, HK, US, or other listed stocks and want a daily summary |
| akshare | 23.2k | Library | beginner | 4 days ago | Python users doing academic or personal research on Chinese financial markets |
Stats synced from GitHub. The assessment above was drafted with AI from the project's README on 2026-10-29 and may miss recent changes — check the README before relying on a command. MIT licence: free to use, modify and distribute, including commercially, as long as you keep the copyright notice.