Strategic Liquidity Provision in Uniswap v3

TL;DR

This paper introduces a neural network-based framework for optimizing liquidity provision strategies in Uniswap v3, enhancing earnings by strategically allocating liquidity within price intervals amid dynamic market conditions.

Contribution

It formalizes the strategic liquidity provision problem and develops a neural network approach to maximize earnings, considering the costs and risks of reallocating liquidity.

Findings

Neural network strategies outperform baseline methods in simulations.

Optimal strategies vary with economic environments and market conditions.

Significant improvements in LP earnings demonstrated using historical data.

Abstract

Uniswap v3 is the largest decentralized exchange for digital currencies. A novelty of its design is that it allows a liquidity provider (LP) to allocate liquidity to one or more closed intervals of the price of an asset instead of the full range of possible prices. An LP earns fee rewards proportional to the amount of its liquidity allocation when prices move in this interval. This induces the problem of {\em strategic liquidity provision}: smaller intervals result in higher concentration of liquidity and correspondingly larger fees when the price remains in the interval, but with higher risk as prices may exit the interval leaving the LP with no fee rewards. Although reallocating liquidity to new intervals can mitigate this loss, it comes at a cost, as LPs must expend gas fees to do so. We formalize the dynamic liquidity provision problem and focus on a general class of strategies for which we provide a neural network-based optimization framework for maximizing LP earnings. We model a single LP that faces an exogenous sequence of price changes that arise from arbitrage and non-arbitrage trades in the decentralized exchange. We present experimental results informed by historical price data that demonstrate large improvements in LP earnings over existing allocation strategy baselines. Moreover we provide insight into qualitative differences in optimal LP behaviour in different economic environments.