Qbot 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
AI-assisted quantitative investment research platform for backtesting and trading strategies.
Open source · MIT
Python framework for building quant trading systems, with market gateways, backtesting and ML research tools.
The short answer
Choose Qbot if you need…
- Python users who want to research quantitative trading strategies
- People experimenting with machine learning models for market prediction
- Students learning backtesting on historical market data
- Individual investors comfortable with some programming and trading basics
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
| Qbot | VeighNa | |
|---|---|---|
| Type | App | Framework |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 18.8k | 46.2k |
| Setup difficulty | Advanced | Advanced |
| How you get it | Clone and build from source | See the README |
| Main language | Jupyter Notebook | Python |
| Last code update | 8 months ago | 10 days ago |
What each one is
Qbot
Qbot is an open-source platform for building and testing automated stock, fund, futures, or crypto trading strategies. It covers data, strategy development, backtesting, simulated trading, and, according to the README, live trading. It emphasizes machine learning and reinforcement learning strategies combined with multi-factor models.
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
Qbot
- Python users who want to research quantitative trading strategies
- People experimenting with machine learning models for market prediction
- Students learning backtesting on historical market data
- Individual investors comfortable with some programming and trading basics
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
Qbot
- Covers the full workflow from data to backtesting to simulated and live trading in one project
- Supports several machine learning paradigms, including supervised learning and reinforcement learning
- Released under the MIT license, so it is free to use and modify
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
Qbot
- Anyone wanting a turnkey, no-code trading product, since the README says some Python knowledge is needed
- Users who need a guaranteed profitable strategy; the README makes no performance claims and live trading carries financial risk
- Users on Python versions other than 3.8 or 3.9, since the README says only those were tested
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
Qbot
- The README states it was only tested on Python 3.8 and 3.9 and documentation is partly in Chinese
- Live trading and financial decisions carry risk, and the README does not document results or accuracy
- Setup depends on several third-party libraries, so installation may be complicated for beginners
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
Qbot
Clone the repository
git clone https://github.com/UFund-Me/Qbot --depth 1Install dependencies
pip install -r dev/requirements.txtVeighNa
Clone the repository
git clone https://github.com/vnpy/vnpy.gitFrequently asked questions
What is the difference between Qbot and VeighNa?
Qbot: AI-assisted quantitative investment research platform for backtesting and trading strategies. VeighNa: Python framework for building quant trading systems, with market gateways, backtesting and ML research tools. Qbot suits python users who want to research quantitative trading strategies, while VeighNa suits python developers who want to build custom quant trading systems.
Are Qbot and VeighNa free?
Qbot: Free and open source. VeighNa: Free and open source.
Is Qbot or VeighNa more popular?
VeighNa has more GitHub stars (46.2k) than Qbot (18.8k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to Qbot and VeighNa?
Other options people compare them with include Qlib, daily_stock_analysis.
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.