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

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.

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

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.

Paper Structure

This paper contains 57 sections, 27 equations, 24 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Key Concepts and External Data
  4. Prices.
  5. Volumes and TVL.
  6. Baseline Strategy Type: $\tau$-Reset Strategies
  7. Bucket partition
  8. Strategy algorithm.
  9. Concept of Liquidity
  10. Related Work
  11. Problem Statement and Proposed Solution
  12. Problem Statement
  13. Approach to Pool Reward Calculation.
  14. Approach to LP Reward Calculation Within the Pool.
  15. State Assumptions and the LP-balance Equation.
  16. ...and 42 more sections

Figures (24)

  • Figure 1: Visualization of the LP-liquidity reallocation process under the $\tau$-reset strategy ($\tau=1$) on a fixed bucket partition $\beta$ over eight epochs.
  • Figure 2: Overall architecture of the integral model.
  • Figure 3: Calibration procedure for the approximating liquidity profile, specified by the weight vector $\bm{\alpha}_{e_i}^{\Sigma} = f_{\mathcal{G}}\!\bigl(\mu_{e_i},\sigma_{e_i}\bigr)$ within epoch $e_i$.
  • Figure 4: Liquidity state in each bucket under the parametric approximation of the pool’s historical liquidity.
  • Figure 5: Target state for Approach 1: the parametric liquidity profile characterized by the weight vector $\bm{\alpha}_{e_i}^{\Sigma} = f_{\mathcal{G}}\!\bigl(\mu_{e_i}^{\ast},\sigma_{e_i}^{\ast}\bigr)$ within epoch $e_i$.
  • ...and 19 more figures