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Financial exchanges across the world use limit order books (LOBs) to processorders and match trades. For research purposes it is important to have largescale efficient simulators of LOB dynamics. LOB simulators have previously beenimplemented in the context of agent-based models (ABMs), reinforcement learning(RL) environments, and generative models, processing order flows fromhistorical data sets and hand-crafted agents alike. For many applications,there is a requirement for processing multiple books, either for thecalibration of ABMs or for the training of RL agents. We showcase the firstGPU-enabled LOB simulator designed to process thousands of books in parallel,with a notably reduced per-message processing time. The implementation of oursimulator - JAX-LOB - is based on design choices that aim to best exploit thepowers of JAX without compromising on the realism of LOB-related mechanisms. Weintegrate JAX-LOB with other JAX packages, to provide an example of how one mayaddress an optimal execution problem with reinforcement learning, and to sharesome preliminary results from end-to-end RL training on GPUs.

JAX-LOB: A GPU-Accelerated limit order book simulator to unlock large scale reinforcement learning for trading