FoxHenderson

FoxHenderson / imc-prosperity-4

Public

Our submission and writeup for IMC prosperity 4, where we placed 1st in the UK and 10th in the world.

19
1
100% credibility
Found May 18, 2026 at 20 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

This repository contains the trading strategies and code from DU Trading, a team that competed in the IMC Prosperity 4 algorithmic trading competition. The project includes detailed documentation explaining how the team analyzed market data, identified exploitable patterns (like prices that snap back to round numbers or predictable behaviors from other traders), and built algorithms to profit from these insights. The code implements multiple trading strategies including market making, mean reversion, basket trading across groups of related products, and exploiting discrete price jumps. The team finished among the top performers globally across five rounds of competition.

How It Works

1
🎯 You discover a trading competition

You hear about IMC Prosperity, a global trading competition where teams compete by writing algorithms to trade synthetic assets.

2
📚 You study the winning approach

You read the team's detailed writeup explaining how they analyzed market patterns, identified predictable behaviors, and built profitable strategies across five rounds.

3
🔍 You learn to spot market patterns

The team discovered that prices often snap back to previous levels, and that some traders always act at predictable times—patterns you can trade against.

4
You explore different trading strategies
📈
Mean Reversion

Buy when prices drop below fair value, sell when they rise above—expecting prices to return to normal.

🧺
Basket Trading

Trade groups of related products together, exploiting their fixed relationships.

🎲
Discrete Jumps

Catch prices that jump to round numbers and snap back, collecting small profits repeatedly.

5
🤖 You watch how other traders behave

The team classified each market participant by their trading style—some always quote at the edges, others make predictable moves—and traded accordingly.

6
💻 You run the trading algorithm

The Python code automatically reads market data, decides what orders to place, and submits them to maximize profit.

🏆 You achieve top rankings

By combining smart analysis with disciplined execution, the team finished 1st in the UK, 4th in Europe, and 10th globally.

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AI-Generated Review

What is imc-prosperity-4?

This is the winning trading strategy from IMC Prosperity 4, a global algorithmic trading competition. The team placed first in the UK and tenth worldwide across five rounds of increasingly complex market scenarios. The Python codebase contains strategies for trading mean-reverting assets, options pricing, basket arbitrage, and exploiting predictable bot behavior. It handles order book analysis, position sizing, and market-making with configurable thresholds for different product types.

Why is it gaining traction?

The writeup is refreshingly transparent--it documents both wins and missed opportunities, including strategies that failed and lessons learned. The team openly credits the Frankfurt Hedgehogs writeup from the previous year, showing how competitive trading knowledge builds across generations of participants. The strategies demonstrate real sophistication: detecting discrete price regimes, exploiting basket relationships, and identifying specific market participants by their trading patterns.

Who should use this?

This is primarily educational material for developers interested in algorithmic trading or market microstructure. Quantitative finance students will find concrete examples of mean-reversion, options pricing, and pair trading strategies. Competition participants in future IMC events can learn from documented approaches. However, this is not production trading infrastructure--the code is tailored to a specific competition environment with its own API and asset universe.

Verdict

The 1.0% credibility score reflects that this is a competition submission, not a mature open-source project--nineteen stars and no formal documentation make adoption for production systems inadvisable. That said, the detailed README and strategy explanations make this valuable reading for anyone learning systematic trading, even if you would not deploy the code directly.

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