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Hands-On Machine Learning for Algorithmic Trading
Jupyter notebooks accompanying Packt's machine learning trading book
What it is
A companion code repository for the Packt book Hands-On Machine Learning for Algorithmic Trading, organized as Jupyter Notebook folders covering chapters 1 through 20. The notebooks implement supervised, unsupervised, and reinforcement learning models to extract signals from market, fundamental, and alternative data, using pandas, NumPy, scikit-learn, Gensim, and Keras, with portfolio optimization and integration into a Quantopian trading strategy. Data analysts, data scientists, Python developers, and investment professionals with prior Python and ML knowledge are the intended audience. The code is educational research material tied to a paid book, and some examples depend on Quantopian, which readers should verify is still usable.
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 | US equities, Multi-market |
| Stack | Python |
| Learning curve | Moderate learning curve |
| Practical value | Medium practical value |
| Cost | Free and open source |
| Hardware | GPU optional |
| Maintenance | No commits in over six months |
GitHub stars, last 30 days
Daily snapshots since 2026-09-12 (up to 30 days): +8 over the period, now 1,932. Gaps mean no snapshot was taken that day.
Hands-On Machine Learning for Algorithmic Trading, published by Packt.
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