  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.
