benedictbrady

benedictbrady / amm-challenge

Public

Compete to build the most profitable AMM

80
63
100% credibility
Found Feb 07, 2026 at 28 stars 3x -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A playground for designing and testing adaptive fee rules in simulated trading pools to outperform a fixed-fee competitor.

How It Works

1
🔍 Discover the Challenge

You find an exciting online competition to create smarter trading fees for digital marketplaces.

2
📖 Learn the Game

You read simple explanations of how market prices wiggle, smart traders fix prices, and everyday buyers show up.

3
✏️ Craft Your Strategy

Using a ready example, you design rules for fees that change after each trade to grab more profits.

4
🛠️ Prepare Your Tester

You grab the free testing playground and set it up on your computer to mimic real markets.

5
▶️ Run Market Simulations

You hit play and watch 10,000 pretend market moments unfold, seeing your profits versus the standard approach.

6
🔄 Tweak and Retest

You adjust your fee ideas based on results and rerun tests until you consistently win.

🏆 Join the Leaderboard

You share your winning strategy online and celebrate your ranking among other creators.

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Star Growth

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AI-Generated Review

What is amm-challenge?

This Python framework lets you compete to build the most profitable AMM by writing Solidity smart contracts for dynamic fee strategies. Submit a .sol file via CLI—it validates, compiles, and runs head-to-head simulations against a 30bps baseline, modeling real markets with drifting prices, retail traders, and arbitrageurs. Your score is the "Edge": average profit advantage over thousands of randomized runs.

Why is it gaining traction?

Unlike static fee models, it pits your design against competition in a full market sim, revealing if your challenger AMM captures more retail flow or resists arbs. Rust-powered parallel execution cranks through 1000+ sims fast, while EVM enforcement keeps strategies realistic—no cheating with external data. Developers dig the gamified benchmark for profitable Python-AMM experiments.

Who should use this?

DeFi protocol engineers optimizing fees beyond constant spreads. Solidity devs prototyping adaptive strategies without deploying testnets. Quant researchers simulating MEV dynamics or bodybuilder-compete style tournaments for AMM designs.

Verdict

Grab it for quick AMM fee benchmarking—CLI is polished, sims are solid. 66 stars and 1.0% credibility signal early days with thin docs/tests, so fork and contribute if you build something profitable.

(187 words)

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