  In this article, 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.
