Defensive Rebalancing for Automated Market Makers

TL;DR

This paper proposes a new defensive rebalancing mechanism for CFMMs that prevents arbitrage-induced value leakage by transforming configurations into arbitrage-free states through convex optimization, enhancing liquidity protection.

Contribution

It introduces the concept of defensive rebalancing, proves its effectiveness in eliminating arbitrage, and formulates the search for optimal rebalancing as a convex optimization problem.

Findings

Rebalancing can strictly increase CFMM liquidity without harming others.

Arbitrage-free configurations are Pareto efficient under rebalancing.

Optimal rebalancing solutions are computationally tractable and unique.

Abstract

This paper introduces and analyzes \emph{defensive rebalancing}, a novel mechanism for protecting constant-function market makers (CFMMs) from value leakage due to arbitrage. A \emph{rebalancing} transfers assets directly from one CFMM's pool to another's, bypassing the CFMMs' standard trading protocols. In any \emph{arbitrage-prone} configuration, we prove there exists a rebalancing to an \textit{arbitrage-free} configuration that strictly increases some CFMMs' liquidities without reducing the liquidities of the others. Moreover, we prove that a configuration is arbitrage-free if and only if it is \emph{Pareto efficient} under rebalancing, meaning that any further direct asset transfers must decrease some CFMM's liquidity. We prove that for any log-concave trading function, including the ubiquitous constant product market maker, the search for an optimal, arbitrage-free rebalancing that maximizes global liquidity while ensuring no participant is worse off can be cast as a convex optimization problem with a unique, computationally tractable solution. We extend this framework to \emph{mixed rebalancing}, where a subset of participating CFMMs use a combination of direct transfers and standard trades to transition to an arbitrage-free configuration while harvesting arbitrage profits from non-participating CFMMs, and from price oracle market makers such as centralized exchanges. Our results provide a rigorous foundation for future AMM protocols that proactively defend liquidity providers against arbitrage.