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Strategic Liquidity Provision in Uniswap V3

Authors Zhou Fan, Francisco Marmolejo-Cossio, Daniel Moroz, Michael Neuder, Rithvik Rao, David C. Parkes

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Author Details

Zhou Fan
  • Harvard University, Cambridge, MA, USA
Francisco Marmolejo-Cossio
  • Harvard University, Cambridge, MA, USA
  • IOG, USA
Daniel Moroz
  • Harvard University, Cambridge, MA, USA
Michael Neuder
  • Harvard University, Cambridge, MA, USA
Rithvik Rao
  • Harvard University, Cambridge, MA, USA
David C. Parkes
  • Harvard University, Cambridge, MA, USA

Funding

This work is supported in part by two generous gifts to the Center for Research on Computation and Society at Harvard University, both to support research on applied cryptography and society.

Cite AsGet BibTex

Zhou Fan, Francisco Marmolejo-Cossio, Daniel Moroz, Michael Neuder, Rithvik Rao, and David C. Parkes. Strategic Liquidity Provision in Uniswap V3. In 5th Conference on Advances in Financial Technologies (AFT 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 282, pp. 25:1-25:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.AFT.2023.25

Abstract

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 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.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Modeling methodologies
  • Computing methodologies → Neural networks
Keywords
  • blockchain
  • decentralized finance
  • Uniswap v3
  • liquidity provision
  • stochastic gradient descent

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