Qlib 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
Open-source Python platform for quant investment research using machine learning.
Open source · MIT
Python framework for building quant trading systems, with market gateways, backtesting and ML research tools.
The short answer
Choose Qlib if you need…
- Quant researchers who want a structured ML workflow for stock data
- Python developers experimenting with financial machine learning models
- Students learning how quantitative investment research is organized
- Teams that want to reuse published quant models such as those listed in the README
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
| Qlib | VeighNa | |
|---|---|---|
| Type | Library | Framework |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 49.9k | 46.2k |
| Setup difficulty | Advanced | Advanced |
| How you get it | Install with a package manager | See the README |
| Main language | Python | Python |
| Last code update | 7 days ago | 10 days ago |
What each one is
Qlib
Qlib is a Python platform for quantitative investment research, where analysts build models that try to predict market behavior from historical data. It covers the workflow from exploring ideas to running models, and supports supervised learning, market dynamics modeling, and reinforcement learning. The README also says it now connects to RD-Agent, a separate Microsoft project for automating factor mining and model optimization.
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
Qlib
- Quant researchers who want a structured ML workflow for stock data
- Python developers experimenting with financial machine learning models
- Students learning how quantitative investment research is organized
- Teams that want to reuse published quant models such as those listed in the README
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
Qlib
- Open source under the MIT license with a large community (about 49,800 stars at time of analysis)
- Covers multiple modeling approaches in one framework
- Includes documentation, tutorials, and an active CI test badge in the README
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
Qlib
- People looking for a ready-made trading bot, since the README describes a research platform rather than a turnkey strategy
- Anyone who needs guaranteed returns or investment advice; the project does not provide this
- Users who are not comfortable with Python and command-line setup
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
Qlib
- The README warns of an unreleased breaking change to how saved models and datasets are loaded, which requires reading a migration guide before upgrading
- Recent source builds restrict loading artifacts unless users opt in with trusted=True, so careful review is needed
- Requires a working understanding of quant finance and machine learning to use meaningfully
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
VeighNa
Clone the repository
git clone https://github.com/vnpy/vnpy.gitFrequently asked questions
What is the difference between Qlib and VeighNa?
Qlib: Open-source Python platform for quant investment research using machine learning. VeighNa: Python framework for building quant trading systems, with market gateways, backtesting and ML research tools. Qlib suits quant researchers who want a structured ml workflow for stock data, while VeighNa suits python developers who want to build custom quant trading systems.
Are Qlib and VeighNa free?
Qlib: Free and open source. VeighNa: Free and open source.
Is Qlib or VeighNa more popular?
Qlib has more GitHub stars (49.9k) than VeighNa (46.2k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to Qlib and VeighNa?
Other options people compare them with include FinRL.
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.