35 DAYS TO GO

Gator Quant Hacks

Name: Gator Quant Hacks
Start: 2026-10-02T00:00:00.000-0400
End: 2026-10-04T17:00:00.000-0400

GQQuantED and AlgoGators Investment Fund

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dateOct 2 – Oct 4, 2026

locationReitz Union Grand Ballroom

priceTBA

formatHybrid

organiserQuantED and AlgoGators Investment Fund

about this event

Gator Quant Hacks is a national quantitative finance competition hosted by QuantED and the AlgoGators Investment Fund at the University of Florida. From October 2 to 4, 2026, students from universities across the country will spend 48 hours doing the work quants actually do: forming a hypothesis, testing it against real market data, and defending the result in front of people who do this for a living.

The event runs in a hybrid format. In-person teams compete in the Reitz Union Grand Ballroom in Gainesville, Florida, and virtual teams compete remotely on equal footing, with the same tracks, the same deadlines, and the same judging.

You do not need a finance background. One of our three tracks requires no dataset and no prior markets knowledge at all. If you can write code or reason through a hard math problem, there is a place for you here.

Who should compete: Students curious about quant research, trading, or systematic investing, with no prior experience needed. CS, math, statistics, physics, and engineering students who like hard problems. Finance and economics students who want to test ideas with data instead of slides. Anyone who has read about quant finance and wanted a real reason to try it.

Open to currently enrolled students at any U.S. university, undergraduate and graduate.

Tracks: Track 01: Research and Alpha Discovery — Pick a question about markets and answer it properly. Teams form a hypothesis, test it against data, and submit a written analysis alongside a live presentation to judges. Judged on the quality of the question, the rigor of the testing, honesty about what the data does and does not support, and clarity of communication. Best fit for finance, economics, and statistics students.

Track 02: Quantitative Puzzles and Brainteasers — A continuous point ladder running the full 48 hours. Problems in probability, combinatorics, expected value, market making, and algorithmic reasoning unlock progressively, and teams climb the leaderboard by solving them. No dataset, no setup, no finance background required. Best fit for CS and math students, and the easiest way to try quant for the first time.

Track 03: Systematic Trading — Build a trading strategy and backtest it on historical data provided by our data partners. Submissions are evaluated on risk-adjusted performance, including Sharpe ratio, maximum drawdown, and turnover, rather than raw return. Overfitting is the thing to beat here, and the judges will look for it. Best fit for anyone who wants to know whether their idea survives contact with out-of-sample data.

Teams may enter one track. Pick the one that fits how you think. Registration and event details: gqhacks.com. Join the participant Discord: https://discord.gg/PhEnUQXCp

tracks

Research and Alpha Discovery

Pick a question about markets and answer it properly. Teams form a hypothesis, test it against data, and submit a written analysis alongside a live presentation to judges. Judged on the quality of the question, the rigor of the testing, honesty about what the data does and does not support, and clarity of communication. Best fit for finance, economics, and statistics students.

Quantitative Puzzles and Brainteasers

A continuous point ladder running the full 48 hours. Problems in probability, combinatorics, expected value, market making, and algorithmic reasoning unlock progressively, and teams climb the leaderboard by solving them. No dataset, no setup, no finance background required. Best fit for CS and math students, and the easiest way to try quant for the first time.

Systematic Trading

Build a trading strategy and backtest it on historical data provided by our data partners. Submissions are evaluated on risk-adjusted performance, including Sharpe ratio, maximum drawdown, and turnover, rather than raw return. Overfitting is the thing to beat here, and the judges will look for it. Best fit for anyone who wants to know whether their idea survives contact with out-of-sample data.

schedule

Fri 2 OctTBA