Optimizing Concentrated Liquidity Management: A Synthetic-to-Historical Deep Reinforcement Learning Strategy

Abstract

Automated market makers (AMMs) have revolutionized decentralized finance (DeFi), enabling trustless asset exchange through algorithmic liquidity pools. One notable AMM is Uniswap, which improved capital efficiency by introducing concentrated liquidity, allowing Liquidity Providers (LPs) to allocate capital within specific price ranges. However, this flexibility requires active management to maximize fee generation while effectively addressing impermanent loss (IL) and gascosts. We formulate concentrated liquidity management (CLM) as a stochastic control problem and propose deep reinforcement learning (RL) strategies, specifically Proximal Policy Optimization (PPO) and Deep Q-Network (DQN), to optimize liquidity positioning. Unlike heuristics, RL agents adapt dynamically to market conditions. We adopt a two-stage synthetic-to-historical evaluation strategy, involving RL model training on synthetic data and evaluation on historical data. Results show that training on synthetic data and testing historic data allows RL-based strategies to perform well across regimes, capturing more fees while keeping net performance ahead of a passive buy-and-hold strategy. These findings highlight the robustness and practical viability of such strategies for CLM.