10 citations · 12 across the 6 of their papers we have counts for
8 papers
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…
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…
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…
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…
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…
Asynchronous Deep Double Duelling Q-Learning for Trading-Signal Execution in Limit Order Book Markets
Peer Nagy, Jan-Peter Calliess, Stefan Zohren
We employ deep reinforcement learning (RL) to train an agent to successfully translate a high-frequency trading signal into a trading strategy that places individual limit orders.…
Fast Agent-Based Simulation Framework with Applications to Reinforcement Learning and the Study of Trading Latency Effects
Peter Belcak, Jan-Peter Calliess, Stefan Zohren
We introduce a new software toolbox for agent-based simulation. Facilitating rapid prototyping by offering a user-friendly Python API, its core rests on an efficient C++ implementa…