Eth Global Submission: Chaos LP Uniswap v4 example (Price feeds + Entropy)

[reymom]

Pyth Examples Contribution

Type of Contribution

• New Example Project (Adding a new example to demonstrate Pyth integration)
• Bug Fix (Fixing an issue in existing examples)
• Documentation Update (Improving README, comments, or guides)
• Enhancement (Improving existing functionality or adding features)
• Hackathon Submission (Submitting a project from a hackathon)

Project Information

Project/Example Name: Chaos LP — MEV-Resistant Uniswap v4 Liquidity Using Pyth Price Feeds + Entropy

Pyth Product Used:

• Pyth Price Feeds
• Pyth Entropy
• Multiple Products
• Other: ___________

Blockchain/Platform:

• Ethereum/EVM
• Solana
• Aptos
• Sui
• Fuel
• Starknet
• TON
• Other: ___________

Description

What does this contribution do?

This PR adds a full working example demonstrating how to combine Pyth Price Feeds (pull oracle) and Pyth Entropy to create a MEV-resistant Uniswap v4 Liquidity Provider called Chaos LP.

Chaos LP deploys a Uniswap v4 pool + hook on Base Sepolia and uses:

• Pyth Hermes to pull fresh price update data (bytes[])
• Pyth Price Feed update via getUpdateFee() + updatePriceFeeds()
• Pyth Entropy via getFeeV2() + requestV2()
• Entropy callback to deliver random numbers on-chain
• Randomized liquidity bands that reduce MEV opportunities

The example includes:

• Solidity contracts
• Foundry deployment scripts
• A TypeScript script to perform a Chaos LP request
• A Rust-based Monte Carlo MEV simulator
• Full README documentation, diagrams, and instructions

How does it integrate with Pyth?

1. Pyth Price Feeds (Pull Oracle)
   The hook fetches price update blobs from Hermes:
   https://hermes.pyth.network/v2/updates/price/latest?ids[]=<feed-id>
   Then the hook:

uint256 pythFee = pyth.getUpdateFee(updateData);
pyth.updatePriceFeeds{value: pythFee}(updateData);
PythStructs.Price memory price = pyth.getPriceNoOlderThan(feedId, maxAge);

This provides a fresh, cryptographically signed ETH/USD price for tick calculation.

2. Pyth Entropy
   Chaos LP requests on-chain randomness:

uint128 entropyFee = entropy.getFeeV2();
uint64 seq = entropy.requestV2{value: entropyFee}();

Then in the callback:

function entropyCallback(uint64 seq, address provider, uint256 randomNumber)

The randomness determines the LP band offset, making attacks unpredictable.

What problem does it solve or demonstrate?

Chaos LP demonstrates that price-informed + randomness-informed liquidity dramatically reduces MEV extraction.
The included Rust simulator shows:

• ~96% reduction in attacker expected payoff
• ~92% reduction in MEV success rate
  Highly chaotic, unpredictable LP positions versus standard static LP bands

This example demonstrates a novel, production-relevant use case for Pyth:

• Using price updates + randomness to generate unpredictable DeFi liquidity behavior.

Directory Structure (for new examples)

entropy/chaos-lp-uniswap-v4/
├── contracts/            # Uniswap v4 Hook + Deployers
├── scripts/              # Foundry + TS chaos request script
├── mev-sim/              # Rust Monte Carlo MEV simulator
├── deployments/          # Example JSON deployment artifacts
├── README.md             # Full documentation
└── ...

Testing & Verification

How to Test This Contribution

Smart Contracts: forge test -vv. Then also deploy and run scripts as documented below.

Prerequisites

Node.js ≥ 18
Rust ≥ 1.77
Foundry (forge + anvil)
Base Sepolia RPC (Alchemy/Infura/etc.)

Setup & Run Instructions

cd entropy/chaos-lp-uniswap-v4
cp .env.example .env   # fill RPC + addresses
npm install

Run anvil fork:

anvil \
  --fork-url $BASE_RPC \
  --fork-block-number 20000000 \
  --chain-id 8453

Deploy Chaos LP locally:

forge script scripts/DeployChaosSystem.s.sol:DeployChaosSystem \
  --rpc-url http://127.0.0.1:8545 \
  --private-key $DEPLOYER_PK \
  --broadcast \
  -vv

Trigger Chaos LP request (Pyth + Entropy):

npm run request:chaos:base

Run the MEV simulator:

Run the MEV simulator:

This outputs:

• chaos_vs_normal.csv
• expected_mev_bar.png
• chaos_mev_dashboard.png

Deployment Information (if applicable)

Network: Base Sepolia (Chain ID 84532)

Contract Address(es):
ChaosHook: 0xD4ED3f079E04f681F8c3C16D4589c4eF82AFC800
Token0: 0xE47bC56b07f1E3D4faB5f16bD3Be53A8B6470d2E
Token1: 0x51Ea2928c87328D6Df0e4569DcC60b78EcEc725f

Demo URL: https://www.canva.com/design/DAG4n6h_QZA/tBSK1NuMxQ-6tN-UsrQSfg/view?utm_content=DAG4n6h_QZA&utm_campaign=designshare&utm_medium=link2&utm_source=uniquelinks&utlId=h927271b026

Checklist

Code Quality

• Code follows existing patterns in the repository
• Proper error handling implemented
• No hardcoded values (use environment variables where appropriate)

Testing

• Tested locally and works as expected
• All existing functionality still works (no breaking changes)

Additional Context

Related Issues

Fixes #

Screenshots/Demo (if applicable)

Notes for Reviewers

The project demonstrates a novel, fully working integration of:
Pyth Price Feeds (pull mode)
Pyth Entropy
Randomized Uniswap v4 liquidity strategies
Backed by a quantitative MEV study
This is my submission for ETHGlobal Buenos Aires (Pyth Track: Price Feeds + Entropy).

As being part of a hackathon project, many details can be improved, I can do it in the future.

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