10 citations · 12 across the 5 of their papers we have counts for
4 papers · 1 filter
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…
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.…