# PyPatel/Quant-Finance-Resources

Courses, Articles and many more which can help beginners or professionals.

Repository: https://github.com/PyPatel/Quant-Finance-Resources
Canonical: https://ross.abutalabs.com/products/quant-finance-resources
License Family: other
Topics: quant, quantitative-finance, algorithms, algorithmic-trading, ai, artificial-intelligence, artificial-intelligence-algorithms, artificial-neural-networks, machine-learning, probability, option-pricing, stock-price-prediction, linear-algebra
Last push: 2021-12-17T07:22:04+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": 2578, "days_push": 1720, "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 1033, forks 155 (observed 2026-08-28T04:03:18.458233+00:00)

## What it is
A curated list of courses, books, and articles for learning quantitative finance, aimed at STEM graduates with strong math and coding backgrounds. It covers mathematics, machine learning, AI, and quant finance topics like market microstructure and option pricing.

## Use cases
- find courses to learn quantitative finance
- learn algorithmic trading from scratch
- study machine learning for finance
- find books on Python for finance
- prepare for a quant trader career
- learn probability and linear algebra for trading

## When to choose
- you are a STEM graduate wanting to enter quantitative finance
- you want deep, math-heavy courses rather than beginner tutorials
- you need a curated starting point for quant trading and ML resources

## When to avoid
- you want beginner-friendly, non-technical finance introductions
- you need runnable software or code libraries
- you expect regularly updated content

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: developer-tools
- domain: fintech, machine-learning, tutorials, mathematics
- platform: cross-platform
- tags: awesome-list, quantitative-finance, algorithmic-trading, curated-resources, stem-education

## Member repositories
- PyPatel/Quant-Finance-Resources (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:18.458233+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 2026-08-30T07:06:38.378611+00:00, confidence not recorded.
  - readme: https://github.com/PyPatel/Quant-Finance-Resources (fetched 2026-08-28T04:03:18.458233+00:00, sha c3428dfb42aa)
- Data as of 2026-08-30T08:39:29.467469+00:00.