QUANTAXIS vs VeighNa
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
Chinese open-source Python quant trading framework for stocks, futures and options, covering data, backtests a
Open source · MIT
Python framework for building quant trading systems, with market gateways, backtesting and ML research tools.
The short answer
Choose QUANTAXIS if you need…
- 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
Choose VeighNa if you need…
- Python developers who want to build custom quant trading systems
- Quant traders working with the Chinese futures and securities markets the README lists
- Researchers who want a workflow covering data, model training, signals and backtests
- Users who want a modular framework with reusable trading apps and database adapters
Side-by-side
| QUANTAXIS | VeighNa | |
|---|---|---|
| Type | Library | Framework |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 11.3k | 46.2k |
| Setup difficulty | Intermediate | Advanced |
| How you get it | Clone and build from source | See the README |
| Main language | Python | Python |
| Last code update | 49 days ago | 10 days ago |
What each one is
QUANTAXIS
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.
VeighNa
VeighNa is an open-source Python framework for building quantitative trading software. It bundles connections to many trading interfaces, a set of ready-made trading apps such as strategy backtesting, risk management and data management, and a newer vnpy.alpha module for machine-learning-based strategy research. People use it to build and run automated trading systems in their own setup.
Best for
QUANTAXIS
- 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
VeighNa
- Python developers who want to build custom quant trading systems
- Quant traders working with the Chinese futures and securities markets the README lists
- Researchers who want a workflow covering data, model training, signals and backtests
- Users who want a modular framework with reusable trading apps and database adapters
Strengths
QUANTAXIS
- 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
VeighNa
- Modular design: trading gateways, apps and database adapters can be combined as needed
- Wide coverage of the listed Chinese and overseas trading interfaces and databases
- Active project with a documented ecosystem of separate plugin repositories and a community forum
Not ideal for
QUANTAXIS
- 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
VeighNa
- Complete beginners with no Python or trading background, since it requires setup and coding and the README has no beginner walkthrough
- People who need a plug-and-play trading product with no configuration, since many gateways require broker accounts and vendor-specific setup
- Anyone needing guaranteed returns or a turnkey strategy, since the README describes tooling, not performance
Trade-offs
QUANTAXIS
- 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
VeighNa
- Many gateways depend on third-party broker or data vendor access, which the user must arrange separately
- The README is partly in Chinese and the primary documentation is Chinese-language, which may be a barrier for some readers
- Trading with real money carries risk, and the README gives no performance or safety guarantees
Getting started
QUANTAXIS
Clone the repository
git clone https://github.com/QUANTAXIS/QUANTAXIS.gitEnter the project folder
cd QUANTAXISVeighNa
Clone the repository
git clone https://github.com/vnpy/vnpy.gitFrequently asked questions
What is the difference between QUANTAXIS and VeighNa?
QUANTAXIS: Chinese open-source Python quant trading framework for stocks, futures and options, covering data, backtests a. VeighNa: Python framework for building quant trading systems, with market gateways, backtesting and ML research tools. QUANTAXIS suits python developers building trading strategies for chinese stocks or futures, while VeighNa suits python developers who want to build custom quant trading systems.
Are QUANTAXIS and VeighNa free?
QUANTAXIS: Free and open source. VeighNa: Free and open source.
Which is easier to set up, QUANTAXIS or VeighNa?
QUANTAXIS is easier to get started with (rated intermediate); VeighNa is rated advanced and usually needs more setup.
Is QUANTAXIS or VeighNa more popular?
VeighNa has more GitHub stars (46.2k) than QUANTAXIS (11.3k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to QUANTAXIS and VeighNa?
Other options people compare them with include Qlib.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.