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Companion notebooks for the Machine Learning in Finance textbook
What it is
A collection of Jupyter notebooks providing the official Python source code accompanying the textbook Machine Learning in Finance: From Theory to Practice by Dixon, Halperin and Bilokon. Each chapter folder contains notebooks with individual details, and a SETUP.md guides readers through installing a virtual environment with dependencies matching the authors' setup. This repository suits readers and students working through the book who want to reproduce its methods in code. As research code accompanying a textbook rather than a trading product, it is intended for study, and readers should consult the official repo for the latest revisions.
At a glance
Research onlyOur rating, based on popularity, maintenance and how ready it is for real use.
| Best for | Professional quants |
|---|---|
| Used for | Strategy research, Data analysis |
| Markets | Multi-market |
| Stack | Python |
| Learning curve | Steep learning curve |
| Practical value | Medium practical value |
| Cost | Free and open source |
| Hardware | No GPU needed |
| Maintenance | No commits in over six months |
GitHub stars, last 30 days
Daily snapshots since 2026-09-12 (up to 30 days): +17 over the period, now 2,678. Gaps mean no snapshot was taken that day.
Machine Learning in Finance: From Theory to Practice Book.
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