Uniswap Liquidity Provision: An Online Learning Approach
This addresses the challenge for liquidity providers in decentralized exchanges to maximize returns efficiently, though it appears incremental as it applies existing regret-minimization methods to a specific domain.
The paper tackles the problem of finding optimal price intervals for liquidity provision in Uniswap v3 by formalizing it as an online learning problem with non-stochastic rewards, and shows a strategy that guarantees a lower bound on reward expressed in terms of trading volume.
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.