Modeling order book dynamics
Optimal trade execution is a well-known problem in quantitative finance. It helps financial actors who trade large quantities of a given asset minimize their risk and their adverse price impact. The problem’s complexity is multiplied when
considering highly fragmented markets, such as those existing today for digital assets. The most recent advances in reinforcement learning and deep learning open the door to a new class of execution algorithms. This data-driven algorithm class reliefs many assumptions from the classical solutions coming from stochastic optimal control theory. The biggest challenge of the project relates to the high dimensionality of the data being used. This problem is circumvented by using deep learning methods for market simulation and the learning of optimal actions.