# Strategic Liquidity Provision in Uniswap v3

arXiv: https://arxiv.org/abs/2106.12033 · HTML: https://mlsanity.com/paper/2106.12033/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2021.

## Built on by (most cited first, 11 of 11)

- [Adaptive Liquidity Provision in Uniswap V3 with Deep Reinforcement Learning](https://mlsanity.com/paper/2309.10129/) (2023)
- [Uniswap Liquidity Provision: An Online Learning Approach](https://mlsanity.com/paper/2302.00610/) (2023)
- [Improving DeFi Accessibility through Efficient Liquidity Provisioning with Deep Reinforcement Learning](https://mlsanity.com/paper/2501.07508/) (2025)
- [Quantum Adaptive Self-Attention for Financial Rebalancing: An Empirical Study on Automated Market Makers in Decentralized Finance](https://mlsanity.com/paper/2509.16955/) (2025)
- [Predictive Crypto-Asset Automated Market Making Architecture for Decentralized Finance using Deep Reinforcement Learning](https://mlsanity.com/paper/2211.01346/) (2022)
- [Delta Hedging Liquidity Positions on Automated Market Makers](https://mlsanity.com/paper/2208.03318/) (2022)
- [Adaptive Dueling Double Deep Q-networks in Uniswap V3 Replication and Extension with Mamba](https://mlsanity.com/paper/2511.22101/) (2025)
- [RAmmStein: Regime Adaptation in Mean-reverting Markets with Stein Thresholds -- Optimal Impulse Control in Concentrated AMMs](https://mlsanity.com/paper/2602.19419/) (2026)
- [Towards Event-Aware Forecasting in DeFi: Insights from On-chain Automated Market Maker Protocols](https://mlsanity.com/paper/2604.20374/) (2026)
- [Concentrated Liquidity Provision: a Reinforcement Learning Perspective](https://mlsanity.com/paper/2608.19389/) (2026)
- [SAiFE-gym: Model-based Environments for Automated Market Making with Concentrated Liquidity](https://mlsanity.com/paper/2609.17788/) (2026)

## Abstract (arXiv)

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.