# PacktPublishing/Hands-On-Machine-Learning-for-Algorithmic-Trading

Hands-On Machine Learning for Algorithmic Trading, published by Packt

Repository: https://github.com/PacktPublishing/Hands-On-Machine-Learning-for-Algorithmic-Trading
Canonical: https://ross.abutalabs.com/products/hands-on-machine-learning-for-algorithmic-trading
Language: Jupyter Notebook
License: MIT
License Family: permissive
Last push: 2023-01-18T09:16:47+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2675, "days_push": 1323, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1914, forks 683 (observed 2026-08-28T04:05:53.662723+00:00)

## What it is
The official code repository for the Packt book 'Hands-On Machine Learning for Algorithmic Trading', containing Jupyter Notebook examples in Python. It demonstrates applying supervised, unsupervised, and reinforcement learning to build investment and trading strategies using pandas, NumPy, and scikit-learn.

## Use cases
- learn machine learning for algorithmic trading
- build trading strategies with python and ml
- research alpha factors from market and alternative data
- optimize portfolio risk with scikit-learn
- example code for reinforcement learning in trading
- study quantitative finance notebooks

## When to choose
- you are reading the book and want its companion code
- you want hands-on Jupyter examples of ML applied to trading
- you are a data scientist or analyst exploring quantitative finance

## When to avoid
- you need production-ready trading software or a live trading engine
- you want a maintained library rather than book example code
- you need a beginner introduction to Python or ML without finance context

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, data-science, trading, etl
- domain: fintech, machine-learning, data-science, tutorials
- platform: python, cross-platform
- tags: algorithmic-trading, jupyter-notebooks, book-code, quantitative-finance, reinforcement-learning, portfolio-optimization, packt

## Member repositories
- PacktPublishing/Hands-On-Machine-Learning-for-Algorithmic-Trading (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:53.662723+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:10:25.018359+00:00, confidence not recorded.
  - readme: https://github.com/PacktPublishing/Hands-On-Machine-Learning-for-Algorithmic-Trading (fetched 2026-08-28T04:05:53.662723+00:00, sha 83aea328497b)
- Data as of 2026-08-30T08:39:29.467469+00:00.