10 citations · 13 across the 4 of their papers we have counts for
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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…
q-fin.TR2023★ 10 cited
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.…