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JerBouma/AlgorithmicTrading resource

This repository contains three ways to obtain arbitrage which are Dual Listing, Options and Statistical Arbitrage. These are projects in collaboration with Optiver and have been peer-reviewed by staff members of Optiver. observed · 2026-08-28

github.com/JerBouma/AlgorithmicTrading · homepage · Jupyter Notebook · MIT (permissive) · archived observed · 2026-08-28

Health v2 · maintenance only

10/100

  • Activity 0
  • Release rhythm 35
  • Longevity 100

Flags: no_releases archived

How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.

  • gap_med: n/a
  • age_days: 2729
  • days_rel: n/a
  • days_push: 1116
  • n_releases_24m: 0

Full methodology

Adoption not part of the score

1102 stars · 201 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of Jupyter Notebook projects demonstrating three arbitrage strategies (dual listing, options, and statistical arbitrage), developed in collaboration with and peer-reviewed by Optiver. It serves as an educational reference for quantitative trading analysis rather than a production trading system.

Use cases

  • learn how statistical arbitrage and pairs trading work
  • study cointegration-based trading strategies with Python notebooks
  • understand dual listing and options arbitrage concepts
  • find example quantitative finance analysis for coursework
  • explore arbitrage strategies peer-reviewed by Optiver
  • get a starting point for building trading strategy research

When to choose

  • you want to learn or teach arbitrage and pairs trading concepts
  • you need peer-reviewed example notebooks for quantitative finance education
  • you want Python-based analysis of cointegration and options arbitrage

When to avoid

  • you need a production-ready, low-latency trading system (the author notes real arbitrage requires C++ and nanosecond connections)
  • you expect the strategies to be profitable for retail investors as-is
  • you need a maintained trading library with an API rather than educational notebooks

Facets

learning-resource · maturity maintenance

trading data-science analytics fintech data-science education python algorithmic-trading arbitrage statistical-arbitrage pairs-trading cointegration options-arbitrage jupyter-notebooks quantitative-finance optiver

3 sources

Member repositories

RepositoryRoleHealth v2
JerBouma/AlgorithmicTradingmain10

For agents

markdown · JSON · MCP: product_card(name="JerBouma/AlgorithmicTrading")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem