# Uniswap Liquidity Provision: An Online Learning Approach

arXiv: https://arxiv.org/abs/2302.00610 · HTML: https://mlsanity.com/paper/2302.00610/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 1 Feb 2023.

## Builds on (most cited first, 7 of 7)

- [Market Making via Reinforcement Learning](https://mlsanity.com/paper/1804.04216/) (2018)
- [An analysis of Uniswap markets](https://mlsanity.com/paper/1911.03380/) (2019)
- [Improved Price Oracles: Constant Function Market Makers](https://mlsanity.com/paper/2003.10001/) (2020)
- [Strategic Liquidity Provision in Uniswap v3](https://mlsanity.com/paper/2106.12033/) (2021)
- [Differential Liquidity Provision in Uniswap v3 and Implications for Contract Design](https://mlsanity.com/paper/2204.00464/) (2022)
- [Constant Function Market Makers: Multi-Asset Trades via Convex Optimization](https://mlsanity.com/paper/2107.12484/) (2021)
- [Composing Networks of Automated Market Makers](https://mlsanity.com/paper/2106.00083/) (2021)

## Built on by (most cited first, 5 of 5)

- [Adaptive Liquidity Provision in Uniswap V3 with Deep Reinforcement Learning](https://mlsanity.com/paper/2309.10129/) (2023)
- [Non-stochastic Bandits With Evolving Observations](https://mlsanity.com/paper/2405.16843/) (2024)
- [The Invisible Handshake: Persistent Overpricing by Adaptive Market Agents](https://mlsanity.com/paper/2510.15995/) (2025)
- [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)

## Abstract (arXiv)

Decentralized Exchanges (DEXs) are new types of marketplaces leveraging Blockchain technology. They allow users to trade assets with Automatic Market Makers (AMM), using funds provided by liquidity providers, removing the need for order books. One such DEX, Uniswap v3, allows liquidity providers to allocate funds more efficiently by specifying an active price interval for their funds. This introduces the problem of finding an optimal strategy for choosing price intervals. We formalize this problem as an online learning problem with non-stochastic rewards. We use regret-minimization methods to show a liquidity provision strategy that guarantees a lower bound on the reward. This is true even for non-stochastic changes to asset pricing, and we express this bound in terms of the trading volume.