activity
20232026
most citedJAX-LOB: A GPU-Accelerated limit order book simulator to unlock large scale reinforcement learning for trading

2 citations · 3 across the 4 of their papers we have counts for

collaborators

5 papers

astro-ph.SR2026

20,000 Rotation Periods in Nearby Open Clusters and Moving Groups from ZTF and ASAS-SN

Yuxi Lu

Open clusters provide uniquely powerful laboratories for studying stellar evolution because their members share a common age and chemical composition. Stellar rotation of low-mass…

q-fin.TR2025

JaxMARL-HFT: GPU-Accelerated Large-Scale Multi-Agent Reinforcement Learning for High-Frequency Trading

Valentin Mohl, Sascha Frey, Reuben Leyland +6

Agent-based modelling (ABM) approaches for high-frequency financial markets are difficult to calibrate and validate, partly due to the large parameter space created by defining fix…

cs.LG2025

LOB-Bench: Benchmarking Generative AI for Finance -- an Application to Limit Order Book Data

Peer Nagy, Sascha Frey, Kang Li +5

While financial data presents one of the most challenging and interesting sequence modelling tasks due to high noise, heavy tails, and strategic interactions, progress in this area…

q-fin.TR2023★ 2 cited

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

Sascha Frey, Kang Li, Peer Nagy +5

Financial exchanges across the world use limit order books (LOBs) to process orders and match trades. For research purposes it is important to have large scale efficient simulators…

q-fin.TR2023★ 1 cited

Generative AI for End-to-End Limit Order Book Modelling: A Token-Level Autoregressive Generative Model of Message Flow Using a Deep State Space Network

Peer Nagy, Sascha Frey, Silvia Sapora +4

Developing a generative model of realistic order flow in financial markets is a challenging open problem, with numerous applications for market participants. Addressing this, we pr…