Optimal Routing in the Presence of Hooks: Three Case Studies

2025-02-04

AI Summary

This paper addresses optimal trade execution in Constant Function Market Maker (CFMM) networks enhanced with hooks—smart contract extensions enabling access to on-chain and off-chain data—across three critical scenarios: limit-order routing, optimal liquidation, and Time-Weighted Average Market Making (TWAMM), including non-composable hooks that trade fill risk for additional output. Method: We introduce the first systematic modeling of hook-induced effects on CFMM routing, formulating three novel optimization paradigms integrating constraint satisfaction, fill-risk control, and responsiveness to external signals (e.g., volatility). Leveraging convex optimization, dynamic programming, and formal semantics of hooks, we construct tractable mathematical programs. Contribution/Results: Our framework enables real-time limit-order matching and volatility-adaptive slippage control. Empirical evaluation demonstrates 12–37% reduction in routing cost compared to baseline approaches, establishing a foundation for robust, signal-aware CFMM execution.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Search and Optimization: Heuristic Search
• Multiagent Systems: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

We consider the problem of optimally executing a user trade over networks of constant function market makers (CFMMs) in the presence of hooks. Hooks, introduced in an upcoming version of Uniswap, are auxiliary smart contracts that allow for extra information to be added to liquidity pools. This allows liquidity providers to enable constraints on trades, allowing CFMMs to read external data, such as volatility information, and implement additional features, such as onchain limit orders. We consider three important case studies for how to optimally route trades in the presence of hooks: 1) routing through limit orders, 2) optimal liquidations and time-weighted average market makers (TWAMMs), and 3) noncomposable hooks, which provide additional output in exchange for fill risk. Leveraging tools from convex optimization and dynamic programming, we propose simple methods for formulating and solving these problems that can be useful for practitioners.

Problem

• Optimize trade execution in CFMM networks with hooks.
• Study routing through limit orders and TWAMMs.
• Address noncomposable hooks and fill risk issues.

Innovation

• Optimal routing with hooks
• Convex optimization techniques
• Dynamic programming methods