Dynamic Liquidity Provision in Decentralized Markets: Strategy Optimization and Performance Evaluation in Concentrated Liquidity AMMs

Paper: arXiv 2505.15338

Authors: Andrey Urusov, Rostislav Berezovskiy, Anatoly Krestenko, Andrei Kornilov, Yury Yanovich

Abstract

Concentrated Liquidity Market Makers (CLMMs) represent a fundamental innovation in market microstructure, transforming liquidity provision from passive portfolio allocation to active risk management. This evolution creates significant challenges for performance evaluation and strategy optimization, particularly due to the absence of comprehensive historical liquidity data. We address these challenges through a novel methodological framework that reconstructs historical liquidity states from swap transaction data, enabling rigorous backtesting of dynamic liquidity provision strategies. Our parametric reconstruction method achieves high accuracy (approximation errors averaging around 2%) without relying on historical liquidity snapshots, addressing a critical data gap in decentralized finance research. We apply this framework to evaluate tau-reset strategies–dynamic liquidity reallocation approaches that respond to market movements–across multiple Uniswap v3 pools. Using machine learning to optimize strategy parameters based on market conditions, we identify consistent outperformance (13–23% higher fees) compared to uniform allocation benchmarks. Our analysis reveals important insights into the risk-return tradeoffs in automated market making, including the critical role of impermanent loss as a dominant risk factor and the effectiveness of asymmetric strategy modifications for capital preservation. These findings contribute to the broader understanding of market microstructure in decentralized exchanges, providing both methodological innovations for performance evaluation and practical insights for liquidity providers navigating this evolving financial landscape.

Complexity vs Empirical Score

• Math Complexity: 5.5/10
• Empirical Rigor: 7.5/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents a solid methodological framework for addressing the historical data gap in Concentrated Liquidity AMMs (Uniswap V3) by reconstructing liquidity profiles directly from swap transaction data. It combines formal market microstructure modeling with empirical backtesting and machine learning parameter optimization, achieving strong practical results.

Research Flowchart

  flowchart TD
    A[Research Goal: Optimize Dynamic Liquidity Provision & Evaluate Risk-Return in CLMMs] --> B[Data Input: Raw Swap Transaction Data from Uniswap v3 Pools]
    B --> C[Parametric Reconstruction: Rebuild Historical Liquidity States ~2% Error]
    C --> D[Strategy Backtesting: Simulate Dynamic Tau-Reset Strategies]
    D --> E[ML Optimization: Tune Parameters Based on Market Conditions]
    E --> F[Key Outcomes: 13-23% Higher Fees vs. Benchmarks & Asymmetric Loss Mitigation]