ZeroSwap: Data-driven Optimal Market Making in DeFi
Viraj Nadkarni Jiachen Hu Ranvir Rana Chi Jin Sanjeev Kulkarni Pramod Viswanath
Automated Market Makers (AMMs) are major centers of matching liquidity supply
and demand in Decentralized Finance. Their functioning relies primarily on the
presence of liquidity providers (LPs) incentivized to invest their assets into
a liquidity pool. However, the prices at which a pooled asset is traded is
often more stale than the prices on centralized and more liquid exchanges. This
leads to the LPs suffering losses to arbitrage. This problem is addressed by
adapting market prices to trader behavior, captured via the classical market
microstructure model of Glosten and Milgrom. In this paper, we propose the
first optimal Bayesian and the first model-free data-driven algorithm to
optimally track the external price of the asset. The notion of optimality that
we use enforces a zero-profit condition on the prices of the market maker,
hence the name ZeroSwap. This ensures that the market maker balances losses to
informed traders with profits from noise traders. The key property of our
approach is the ability to estimate the external market price without the need
for price oracles or loss oracles. Our theoretical guarantees on the
performance of both these algorithms, ensuring the stability and convergence of
their price recommendations, are of independent interest in the theory of
reinforcement learning. We empirically demonstrate the robustness of our
algorithms to changing market conditions.