Market making is one of the most important aspects of algorithmic trading, and it has been studied quite extensively from a theoretical point of view. The practical implementation of so-called “optimal strategies” however suffers from the failure of most order-book models to faithfully reproduce the behavior of real market participants.
This paper is two-fold. First, some important statistical properties of order-driven markets are identified, advocating against the use of purely Markovian order-book models. Then, market making strategies are designed and their performances are compared, based on simulation as well as backtesting. We find that incorporating some simple non-Markovian features in the limit order book greatly improves the performances of market making strategies in a realistic context.