用深度强化学习优化Uniswap v3的流动性提供，提升收益并降低风险。
Improving DeFi Accessibility through Efficient Liquidity Provisioning with Deep Reinforcement Learning
- 将流动性提供建模为马尔可夫决策过程，用PPO算法动态调整仓位。
- 相比散户常用策略，收益提升23.7%，损失减少18.4%。
- 适合希望自动化管理流动性的DeFi投资者和开发者参考。
本文将深度强化学习（DRL）应用于Uniswap v3，一种采用集中流动性机制的去中心化金融（DeFi）自动做市商（AMM）协议。将流动性提供任务建模为马尔可夫决策过程（MDP），并使用近端策略优化（PPO）算法训练主动做市商（LP）代理。该代理利用价格动态信息，动态调整流动性位置，以平衡手续费收益与无常损失的控制。采用滚动窗口方法进行训练与测试，模拟真实市场条件与周期性变化。研究对比了基于数据驱动的DRL策略与小规模零售LP常用静态策略的表现。结果表明，该方法显著提升了流动性管理效率，有助于使更广泛的参与者更容易接入和使用DeFi市场。本工作旨在推动高效、用户友好的下一代DeFi市场的持续发展。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。