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BSE: A Minimal Simulation of a Limit-Order-Book Stock Exchange

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  • Dave Cliff

Abstract

This paper describes the design, implementation, and successful use of the Bristol Stock Exchange (BSE), a novel minimal simulation of a centralised financial market, based on a Limit Order Book (LOB) such as is common in major stock exchanges. Construction of BSE was motivated by the fact that most of the world's major financial markets have automated, with trading activity that previously was the responsibility of human traders now being performed by high-speed autonomous automated trading systems. Research aimed at understanding the dynamics of this new style of financial market is hampered by the fact that no operational real-world exchange is ever likely to allow experimental probing of that market while it is open and running live, forcing researchers to work primarily from time-series of past trading data. Similarly, university-level education of the engineers who can create next-generation automated trading systems requires that they have hands-on learning experience in a sufficiently realistic teaching environment. BSE as described here addresses both those needs: it has been successfully used for teaching and research in a leading UK university since 2012, and the BSE program code is freely available as open-source on GitHuB.

Suggested Citation

  • Dave Cliff, 2018. "BSE: A Minimal Simulation of a Limit-Order-Book Stock Exchange," Papers 1809.06027, arXiv.org.
    • Handle: RePEc:arx:papers:1809.06027
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    References listed on IDEAS

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    1. Gjerstad, Steven & Dickhaut, John, 1998. "Price Formation in Double Auctions," Games and Economic Behavior, Elsevier, vol. 22(1), pages 1-29, January.
    2. Gode, Dhananjay K & Sunder, Shyam, 1993. "Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality," Journal of Political Economy, University of Chicago Press, vol. 101(1), pages 119-137, February.
    3. Vernon L. Smith, 1962. "An Experimental Study of Competitive Market Behavior," Journal of Political Economy, University of Chicago Press, vol. 70(2), pages 111-111.
    4. Arthur le Calvez & Dave Cliff, 2018. "Deep Learning can Replicate Adaptive Traders in a Limit-Order-Book Financial Market," Papers 1811.02880, arXiv.org.
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    Cited by:

    1. Kenneth Lomas & Dave Cliff, 2020. "Exploring Narrative Economics: An Agent-Based-Modeling Platform that Integrates Automated Traders with Opinion Dynamics," Papers 2012.08840, arXiv.org.
    2. Dave Cliff, 2021. "BBE: Simulating the Microstructural Dynamics of an In-Play Betting Exchange via Agent-Based Modelling," Papers 2105.08310, arXiv.org.
    3. Priyanka Shinde & Ioannis Boukas & David Radu & Miguel Manuel de Villena & Mikael Amelin, 2021. "Analyzing Trade in Continuous Intra-Day Electricity Market: An Agent-Based Modeling Approach," Energies, MDPI, vol. 14(13), pages 1-31, June.
    4. Colin M. Van Oort & Ethan Ratliff-Crain & Brian F. Tivnan & Safwan Wshah, 2023. "Adaptive Agents and Data Quality in Agent-Based Financial Markets," Papers 2311.15974, arXiv.org.
    5. Nik Alexandrov & Dave Cliff & Charlie Figuero, 2021. "Exploring Coevolutionary Dynamics of Competitive Arms-Races Between Infinitely Diverse Heterogenous Adaptive Automated Trader-Agents," Papers 2109.10429, arXiv.org.

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