# paperswithbacktest/awesome-systematic-trading

A curated list of awesome libraries, packages, strategies, books, blogs, tutorials for systematic trading.

Repository: https://github.com/paperswithbacktest/awesome-systematic-trading
Canonical: https://ross.abutalabs.com/products/awesome-systematic-trading
Homepage: https://paperswithbacktest.com
Language: Python
License Family: other
Topics: finance, awesome, book, paper, trading-bot, algotrading, quant, awesome-list, trading-strategies, trading-algorithms, quantitative-finance, algorithmic-trading, quantitative-trading, arbitrage-bot, futures-historical-data, alpha, futures, futures-market, futuresmarkets
Last push: 2026-08-26T22:31:39+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": 1670, "days_push": 7, "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 13985, forks 1689 (observed 2026-08-28T04:11:05.244125+00:00)

## What it is
A curated awesome-list of libraries, packages, strategies, books, blogs, videos, and courses for systematic (quantitative/algorithmic) trading. It aggregates 97+ libraries, 40+ documented strategies, 55 books, and other learning resources for researching, developing, and running trading strategies.

## Use cases
- find python libraries for backtesting trading strategies
- learn algorithmic trading from books and courses
- discover academic papers describing quantitative trading strategies
- find market data sources for backtesting
- compare event-driven vs vector-based backtesting frameworks
- find broker APIs and crypto trading tools
- get started with quantitative finance as a beginner

## When to choose
- you want a curated starting point for systematic trading resources
- you are researching which backtesting or live-trading library to adopt
- you want a reading list of trading books, papers, and courses

## When to avoid
- you need runnable trading software rather than a list of links
- you want a maintained backtesting engine or data API itself
- you need guaranteed-accurate or vetted strategy performance claims

## Facets
- artifact type: learning-resource
- maturity: active
- function: developer-tools, documentation
- domain: fintech, tutorials, awesome-lists
- platform: cross-platform, python
- tags: awesome-list, systematic-trading, quantitative-finance, algorithmic-trading, backtesting, trading-strategies, curated-resources

## Member repositories
- paperswithbacktest/awesome-systematic-trading (main) score 77

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:05.244125+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 2026-08-29T17:12:53.816322+00:00, confidence not recorded.
  - readme: https://github.com/paperswithbacktest/awesome-systematic-trading (fetched 2026-08-28T04:11:05.244125+00:00, sha 7e4603b392e4)
  - homepage: https://paperswithbacktest.com (fetched 2026-08-29T08:06:56.922613+00:00, sha 8a01e5f5d66e)
  - site_page: https://paperswithbacktest.com/docs (fetched 2026-08-29T08:06:56.927528+00:00, sha 693419469aa3)
  - site_page: https://paperswithbacktest.com/pricing (fetched 2026-08-29T08:06:56.925536+00:00, sha d969dcc9b8df)
- Data as of 2026-08-30T08:39:29.467469+00:00.