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Fast Agent-Based Simulation Framework with Applications to Reinforcement Learning and the Study of Trading Latency Effects

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  • Peter Belcak
  • Jan-Peter Calliess
  • Stefan Zohren

Abstract

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++ implementation to support simulation of large-scale multi-agent systems. Our software environment benefits from a versatile message-driven architecture. Originally developed to support research on financial markets, it offers the flexibility to simulate a wide-range of different (easily customisable) market rules and to study the effect of auxiliary factors, such as delays, on the market dynamics. As a simple illustration, we employ our toolbox to investigate the role of the order processing delay in normal trading and for the scenario of a significant price change. Owing to its general architecture, our toolbox can also be employed as a generic multi-agent system simulator. We provide an example of such a non-financial application by simulating a mechanism for the coordination of no-regret learning agents in a multi-agent network routing scenario previously proposed in the literature.

Suggested Citation

  • Peter Belcak & Jan-Peter Calliess & Stefan Zohren, 2020. "Fast Agent-Based Simulation Framework with Applications to Reinforcement Learning and the Study of Trading Latency Effects," Papers 2008.07871, arXiv.org, revised Sep 2022.
    • Handle: RePEc:arx:papers:2008.07871
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    References listed on IDEAS

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    1. Field, Jonathan & Large, Jeremy, 2008. "Pro-rata matching and one-tick futures markets," CFS Working Paper Series 2008/40, Center for Financial Studies (CFS).
    2. Rama Cont, 2007. "Volatility Clustering in Financial Markets: Empirical Facts and Agent-Based Models," Springer Books, in: Gilles Teyssière & Alan P. Kirman (ed.), Long Memory in Economics, pages 289-309, Springer.
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    Cited by:

    1. Samuel N. Cohen & Derek Snow & Lukasz Szpruch, 2021. "Black-box model risk in finance," Papers 2102.04757, arXiv.org.
    2. Zijian Shi & John Cartlidge, 2021. "The Limit Order Book Recreation Model (LOBRM): An Extended Analysis," Papers 2107.00534, arXiv.org.
    3. Zhenglong Li & Vincent Tam & Kwan L. Yeung, 2024. "Developing A Multi-Agent and Self-Adaptive Framework with Deep Reinforcement Learning for Dynamic Portfolio Risk Management," Papers 2402.00515, arXiv.org, revised Sep 2024.
    4. Sascha Frey & Kang Li & Peer Nagy & Silvia Sapora & Chris Lu & Stefan Zohren & Jakob Foerster & Anisoara Calinescu, 2023. "JAX-LOB: A GPU-Accelerated limit order book simulator to unlock large scale reinforcement learning for trading," Papers 2308.13289, arXiv.org.
    5. Zihao Zhang & Bryan Lim & Stefan Zohren, 2021. "Deep Learning for Market by Order Data," Papers 2102.08811, arXiv.org, revised Jul 2021.

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