# JerBouma/AlgorithmicTrading

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.

Repository: https://github.com/JerBouma/AlgorithmicTrading
Canonical: https://ross.abutalabs.com/products/algorithmictrading
Homepage: https://www.jeroenbouma.com/
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: cointegration, statistical-arbitrage, algorithmic-trading, pairs-trading, python, dual-listing, options-arbitrage, analysis, algorithm, optiver, arbitrage, finance
Archived: true
Last push: 2023-08-13T07:15:17+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2729, "days_push": 1116, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1102, forks 201 (observed 2026-08-28T04:03:35.926565+00:00)

## What it is
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
- artifact type: learning-resource
- maturity: maintenance
- function: trading, data-science, analytics
- domain: fintech, data-science, education
- platform: python
- tags: algorithmic-trading, arbitrage, statistical-arbitrage, pairs-trading, cointegration, options-arbitrage, jupyter-notebooks, quantitative-finance, optiver

## Member repositories
- JerBouma/AlgorithmicTrading (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:35.926565+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 2026-08-30T06:45:35.688832+00:00, confidence not recorded.
  - readme: https://github.com/JerBouma/AlgorithmicTrading (fetched 2026-08-28T04:03:35.926565+00:00, sha a36accfaa676)
  - homepage: https://www.jeroenbouma.com/ (fetched 2026-08-29T12:48:37.800472+00:00, sha 6d1c4d15ef40)
  - site_page: https://www.jeroenbouma.com/modelling/getting-started (fetched 2026-08-29T12:48:37.803033+00:00, sha 781e2373cbdf)
- Data as of 2026-08-30T08:39:29.467469+00:00.