Machine Learning for Trading 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
Code companion to a book on building, testing, and running ML-driven trading strategies.
Open source · MIT
Open-source Python platform for quant investment research using machine learning.
The short answer
Choose Machine Learning for Trading if you need…
- Readers of the Machine Learning for Trading book who want to run its code
- Students learning applied machine learning on financial time series
- Quantitative researchers who want a worked example of an end-to-end strategy workflow
- Developers who are comfortable with Python and Jupyter notebooks
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
| Machine Learning for Trading | Qlib | |
|---|---|---|
| Type | Learning resource | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 21.7k | 49.9k |
| Setup difficulty | Intermediate | Advanced |
| How you get it | Clone and build from source | Install with a package manager |
| Main language | Jupyter Notebook | Python |
| Last code update | 8 days ago | 7 days ago |
What each one is
Machine Learning for Trading
This repository holds the code for the third edition of the book Machine Learning for Trading by Stefan Jansen, with 27 chapters and nine case studies. It walks through one workflow: sourcing market data, researching strategy ideas, evaluating them with backtests, and deploying them to live markets. People use it to learn how machine learning is applied to financial data and to run the book's examples themselves.
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
Machine Learning for Trading
- Readers of the Machine Learning for Trading book who want to run its code
- Students learning applied machine learning on financial time series
- Quantitative researchers who want a worked example of an end-to-end strategy workflow
- Developers who are comfortable with Python and Jupyter notebooks
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
Machine Learning for Trading
- Covers the full path from data to live execution in one consistent workflow
- Includes nine case studies across ETFs, futures, FX, options, crypto, and equities
- Uses reproducible Docker environments for each chapter, per the README
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
Machine Learning for Trading
- People looking for a ready-made profitable trading strategy: the README presents code for learning and research, not a tested investment product
- Complete beginners to Python or finance, since the material assumes working knowledge of both
- Readers who want the second edition: the README says it lives on a separate branch
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
Machine Learning for Trading
- Focused on learning and research code; the README does not promise trading results
- Requires setup of data files and environments before the notebooks run, which may take effort
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
Machine Learning for Trading
Clone the repository
git clone https://github.com/stefan-jansen/machine-learning-for-tradingFrequently asked questions
What is the difference between Machine Learning for Trading and Qlib?
Machine Learning for Trading: Code companion to a book on building, testing, and running ML-driven trading strategies. Qlib: Open-source Python platform for quant investment research using machine learning. Machine Learning for Trading suits readers of the machine learning for trading book who want to run its code, while Qlib suits quant researchers who want a structured ml workflow for stock data.
Are Machine Learning for Trading and Qlib free?
Machine Learning for Trading: Free and open source. Qlib: Free and open source.
Which is easier to set up, Machine Learning for Trading or Qlib?
Machine Learning for Trading is easier to get started with (rated intermediate); Qlib is rated advanced and usually needs more setup.
Is Machine Learning for Trading or Qlib more popular?
Qlib has more GitHub stars (49.9k) than Machine Learning for Trading (21.7k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to Machine Learning for Trading and Qlib?
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.