# wilsonfreitas/awesome-quant

A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)

Repository: https://github.com/wilsonfreitas/awesome-quant
Canonical: https://ross.abutalabs.com/products/awesome-quant
Homepage: https://wilsonfreitas.github.io/awesome-quant/
Language: HTML
License Family: other
Topics: finance, financial-data, stock-data, awesome, awesome-list, quantitative-finance, quant, quantitative-trading, yahoo-finance, finance-api, google-finance, trading-strategies, financial-instruments, trading-algorithms, algotrading, algorithmic-trading-engine, algorithmic-trading-library, technical-analysis, trading-bot, arbitrage-bot
Last push: 2026-08-26T01:28:52+00:00

## Health v2 (maintenance only)
Score: 77/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 35, longevity 100
- inputs: {"age_days": 3990, "days_push": 8, "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 29207, forks 3904 (observed 2026-08-28T04:11:53.152265+00:00)

## What it is
A curated awesome-list of libraries, packages, and resources for quantitative finance, covering trading, backtesting, pricing, risk analysis, and market data across many languages. It indexes 675+ projects in 35 languages with a browsable web interface.

## Use cases
- find python libraries for backtesting trading strategies
- discover open-source tools for quantitative finance
- find market data sources for stock prices
- compare portfolio optimization and risk analysis libraries
- find technical analysis indicator libraries
- learn about algorithmic trading frameworks
- find resources for reproducing quant research papers and books

## When to choose
- you want a broad, curated index of quant finance tooling across languages
- you are exploring which libraries exist for a specific quant task like backtesting or factor analysis
- you want a regularly updated reference maintained by the community

## When to avoid
- you need a working library rather than a list of links
- you need detailed comparisons, benchmarks, or tutorials for each project
- you need a single opinionated stack rather than many options

## Facets
- artifact type: learning-resource
- maturity: active
- function: developer-tools
- domain: fintech, awesome-lists, data-science
- platform: cross-platform
- tags: awesome-list, quantitative-finance, curated-list, trading, backtesting, market-data, portfolio-optimization, technical-analysis

## Member repositories
- wilsonfreitas/awesome-quant (main) score 77

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:53.152265+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 2026-08-29T16:53:08.970653+00:00, confidence not recorded.
  - readme: https://github.com/wilsonfreitas/awesome-quant (fetched 2026-08-28T04:11:53.152265+00:00, sha 162c4710e7a9)
  - homepage: https://wilsonfreitas.github.io/awesome-quant/ (fetched 2026-08-29T07:50:15.208502+00:00, sha 3add9bfe5fbe)
- Data as of 2026-08-30T08:39:29.467469+00:00.