Qbot vs Qlib
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
Open-source Python platform for quant investment research using machine learning.
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 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
Side-by-side
| Qbot | Qlib | |
|---|---|---|
| Type | App | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 18.8k | 49.9k |
| Setup difficulty | Advanced | Advanced |
| How you get it | Clone and build from source | Install with a package manager |
| Main language | Jupyter Notebook | Python |
| Last code update | 8 months ago | 7 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.
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.
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
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
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
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
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
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
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
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
Getting started
Qbot
Clone the repository
git clone https://github.com/UFund-Me/Qbot --depth 1Install dependencies
pip install -r dev/requirements.txtFrequently asked questions
What is the difference between Qbot and Qlib?
Qbot: AI-assisted quantitative investment research platform for backtesting and trading strategies. Qlib: Open-source Python platform for quant investment research using machine learning. Qbot suits python users who want to research quantitative trading strategies, while Qlib suits quant researchers who want a structured ml workflow for stock data.
Are Qbot and Qlib free?
Qbot: Free and open source. Qlib: Free and open source.
Is Qbot or Qlib more popular?
Qlib has more GitHub stars (49.9k) 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 Qlib?
Other options people compare them with include VeighNa, daily_stock_analysis, 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.