This paper applies deep reinforcement learning (DRL) to optimize liquidity provisioning in Uniswap v3, a decentralized finance (DeFi) protocol implementing an automated market maker (AMM) model with concentrated liquidity. We model the liquidity provision task as a Markov Decision Process (MDP) and train an active liquidity provider (LP) agent using the Proximal Policy Optimization (PPO) algorithm. The agent dynamically adjusts liquidity positions by using information about price dynamics to balance fee maximization and impermanent loss mitigation. We use a rolling window approach for training and testing, reflecting realistic market conditions and regime shifts. This study compares the data-driven performance of the DRL-based strategy against common heuristics adopted by small retail LP actors that do not systematically modify their liquidity positions. By promoting more efficient liquidity management, this work aims to make DeFi markets more accessible and inclusive for a broader range of participants. Through a data-driven approach to liquidity management, this work seeks to contribute to the ongoing development of more efficient and user-friendly DeFi markets.

Decentralized exchanges (DEXs) are a cornerstone of decentralized finance (DeFi), allowing users to trade cryptocurrencies without the need for third-party authorization. Investors are incentivized to deposit assets into liquidity pools, against which users can trade directly, while paying fees to liquidity providers (LPs). However, a number of unresolved issues related to capital efficiency and market risk hinder DeFi's further development. Uniswap V3, a leading and groundbreaking DEX project, addresses capital efficiency by enabling LPs to concentrate their liquidity within specific price ranges for deposited assets. Nevertheless, this approach exacerbates market risk, as LPs earn trading fees only when asset prices are within these predetermined brackets. To mitigate this issue, this paper introduces a deep reinforcement learning (DRL) solution designed to adaptively adjust these price ranges, maximizing profits and mitigating market risks. Our approach also neutralizes price-change risks by hedging the liquidity position through a rebalancing portfolio in a centralized futures exchange. The DRL policy aims to optimize trading fees earned by LPs against associated costs, such as gas fees and hedging expenses, which is referred to as loss-versus-rebalancing (LVR). Using simulations with a profit-and-loss (PnL) benchmark, our method demonstrates superior performance in ETH/USDC and ETH/USDT pools compared to existing baselines. We believe that this strategy not only offers investors a valuable asset management tool but also introduces a new incentive mechanism for DEX designers.

This Liquidity Providers on Automated Market Makers generate millions market participant who accepts a bid or ask and thereby withdraws of USD in transaction fees daily. However, the net value of a Liquidity liquidity from the Limit Order Book is referred to as the Market Position is vulnerable to price changes in the underlying assets Taker. in the pool. The dominant measure of loss in a Liquidity Position In the Limit Order Book paradigm, Market Makers are incentivized is Impermanent Loss. Impermanent Loss for Constant Function to add liquidity through exchanges, which provide benefits Market Makers has been widely studied. We propose a new metric for Market Makers such as transaction rebates and reduced transaction to measure Liquidity Position PNL based on price movement fees [1]. In some cases, market participants can agree to become from the underlying assets. Compared to Impermanent Loss, we a Contractual Market Maker, where they are compensated to reliably show how Liquidity Position PNL more appropriately measures provide liquidity so that the difference between the largest the change in the net value of a Liquidity Position as a function of ask and smallest bid is kept to a minimum predetermined range price movement in the assets within the liquidity pool.