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.

翻译：Uniswap v3是最大的数字货币去中心化交易所。其设计创新在于允许流动性提供者(LP)将流动性配置到资产价格的单个或多个封闭区间，而非全部可能价格区间。当价格在该区间内波动时，LP可获得与其流动性配置数量成比例的费用奖励。这引出了"战略性流动性提供"问题：较小的区间可实现更高的流动性集中度，当价格维持在区间内时可获得更高费用，但同时也面临价格可能脱离区间导致LP无法获得费用奖励的更高风险。虽然将流动性重新配置至新区间可减轻这种损失，但LP需为此支付gas费用。我们形式化了动态流动性提供问题，聚焦于一类通用策略，并为此提出基于神经网络的优化框架以最大化LP收益。我们对单一LP建模，该LP面临由去中心化交易所中的套利与非套利交易产生的外生价格序列。基于历史价格数据的实验结果表明，与现有配置策略基线相比，LP收益有显著提升。此外，我们揭示了不同经济环境下最优LP行为的定性差异。