# mfrdixon/ML_Finance_Codes

Machine Learning in Finance: From Theory to Practice Book

Repository: https://github.com/mfrdixon/ML_Finance_Codes
Canonical: https://ross.abutalabs.com/products/ml_finance_codes
Homepage: https://www.springer.com/gp/book/9783030410674
Language: Jupyter Notebook
License Family: other
Last push: 2020-06-13T21:20:27+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2532, "days_push": 2272, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2635, forks 642 (observed 2026-08-28T04:07:06.231375+00:00)

## What it is
Official Python source code repository accompanying the textbook 'Machine Learning in Finance: From Theory to Practice' by Dixon, Halperin, and Bilokon. It contains Jupyter notebooks organized by chapter that implement the book's machine learning methods for financial applications.

## Use cases
- learn machine learning applied to finance
- reproduce textbook examples for ML in finance
- study quantitative finance notebooks in Python
- find reference code for financial ML models
- supplement reading the Machine Learning in Finance book
- explore reinforcement learning for trading

## When to choose
- you are reading the textbook and want its companion code
- you want worked Python examples of ML techniques in finance
- you need educational notebooks covering supervised and reinforcement learning for finance

## When to avoid
- you need production-ready trading software
- you want a maintained library with a stable API
- you require a project with an explicit open-source license file

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, data-science, trading
- domain: machine-learning, fintech, education, tutorials
- platform: python, cross-platform
- tags: jupyter-notebooks, finance, textbook, quantitative-finance, supervised-learning, reinforcement-learning

## Member repositories
- mfrdixon/ML_Finance_Codes (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:06.231375+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 2026-08-30T02:19:21.195089+00:00, confidence not recorded.
  - readme: https://github.com/mfrdixon/ML_Finance_Codes (fetched 2026-08-28T04:07:06.231375+00:00, sha fe08cc3ae3aa)
- Data as of 2026-08-30T08:39:29.467469+00:00.