# tatsath/fin-ml

This github repository of "Machine Learning and Data Science Blueprints for Finance". Please star.

Repository: https://github.com/tatsath/fin-ml
Canonical: https://ross.abutalabs.com/products/fin-ml
Language: Jupyter Notebook
License Family: other
Topics: python, machine-learning, finance, fintech, algorithmic-trading
Last push: 2023-01-26T22:03:20+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": 2307, "days_push": 1315, "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 1199, forks 493 (observed 2026-08-28T04:03:57.880789+00:00)

## What it is
A collection of Jupyter notebooks containing case study code from the O'Reilly book 'Machine Learning and Data Science Blueprints for Finance'. It covers machine learning applications across algorithmic trading, portfolio management, and other financial domains.

## Use cases
- learn machine learning for finance
- run algorithmic trading case studies in python
- study portfolio management with unsupervised learning
- explore NLP for financial sentiment analysis
- practice ML case studies from a finance book
- run notebooks in binder or colab without installing anything

## When to choose
- you are reading the book and want its accompanying code
- you want hands-on Jupyter notebook examples of ML in finance
- you want to learn algorithmic trading and portfolio optimization techniques

## When to avoid
- you need production-ready trading software
- you need a maintained library with a license and releases
- you want a framework rather than educational notebooks

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, data-science, trading
- domain: fintech, machine-learning, data-science
- platform: python, cross-platform
- tags: jupyter-notebooks, algorithmic-trading, finance, book-code, oreilly

## Member repositories
- tatsath/fin-ml (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:57.880789+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 2026-08-30T06:21:07.015549+00:00, confidence not recorded.
  - readme: https://github.com/tatsath/fin-ml (fetched 2026-08-28T04:03:57.880789+00:00, sha 4b47ab422261)
- Data as of 2026-08-30T08:39:29.467469+00:00.