Adaptive Market Making via Online Learning

We consider the design of strategies for \emph{market making} in a market like a stock, commodity, or currency exchange. In order to obtain profit guarantees for a market maker one typically requires very particular stochastic assumptions on the sequence of price fluctuations of the asset in question. We propose a class of spread-based market making strategies whose performance can be controlled even under worst-case (adversarial) settings. We prove structural properties of these strategies which allows us to design a master algorithm which obtains low regret relative to the best such strategy in hindsight. We run a set of experiments showing favorable performance on real-world price data.