Abstract: In this work, we provide a flexible framework for optimal trading in an asset listed on different venues. We take into account the dependencies between the imbalance and spread of the venues, and allow for partial execution of limit orders at different limits as well as market orders. We present a Bayesian update of the model parameters to take into account possibly changing market conditions and propose extensions to include short/long trading signals, market impact or hidden liquidity. To solve the stochastic control problem of the trader we apply the finite difference method and also develop a deep reinforcement learning algorithm allowing to consider more complex settings.

Bio: Iuliia is a Post-Doctoral researcher at École Polytechnique, under the supervision of Mathieu Rosenbaum. Her research is focused on the applications of machine learning to control problems in finance. During her Ph.D. supervised by Olivier Guéant, Iuliia has collaborated with HSBC and J.P. Morgan. She holds an MSc in Economics from Higher School of Economics and a Bachelor in Applied Mathematics and Computer Science from Moscow State University. Iuliia is also the recipient of the 2020 Risk Rising Star in Quant Finance Awards for her work on smart-order routing.