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

Abstract:

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 of LOB dynamics. LOB simulators have previously been implemented in the context of agent-based models (ABMs), reinforcement learning (RL) environments, and generative models, processing order flows from historical data sets and hand-crafted agents alike. For many applications, there is a requirement for processing ...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1145/3604237.3626880

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Role:
Author
ORCID:
0009-0001-6265-9607
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Role:
Author
ORCID:
0009-0007-7847-9292
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Role:
Author
ORCID:
0000-0001-7669-481X
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Role:
Author
ORCID:
0009-0000-0715-983X
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Author
ORCID:
0009-0006-4730-3633
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Name:
Engineering and Physical Sciences Research Council
Grant:
EP/W002949/1
Publisher:
Association for Computing Machinery
Pages:
583-591
Publication date:
2023-11-25
Acceptance date:
2023-09-28
Event title:
4th ACM International Conference on AI in Finance (ICAIF 2023)
DOI:
ISBN:
9798400702402
Language:
English
Keywords:
Pubs id:
1569298
Local pid:
pubs:1569298
Deposit date:
2024-01-17

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