2 citations · 3 across the 5 of their papers we have counts for
5 papers · 1 filter
Understanding the Inner Workings of a State-Space Foundation Model for Limit Order Books
Valentin Mohl, Alexandre Bismuth, Aramis Marti-Shahandeh +6
Autoregressive foundation models offer state-of-the-art capabilities to simulate the market by generating the message flow of limit order books (LOBs). Yet, without understanding t…
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…
Painting the market: generative diffusion models for financial limit order book simulation and forecasting
Alfred Backhouse, Kang Li, Jakob Foerster +2
Simulating limit order books (LOBs) has important applications across forecasting and backtesting for financial market data. However, deep generative models struggle in this contex…
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…
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…