IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1809.06027.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1809.06027
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    3. Dave Cliff, 2021. "BBE: Simulating the Microstructural Dynamics of an In-Play Betting Exchange via Agent-Based Modelling," Papers 2105.08310, arXiv.org.
    4. 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.
    5. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Aaron Wray & Matthew Meades & Dave Cliff, 2020. "Automated Creation of a High-Performing Algorithmic Trader via Deep Learning on Level-2 Limit Order Book Data," Papers 2012.00821, arXiv.org.
    2. Itzhak Rasooly, 2022. "Competitive equilibrium and the double auction," Economics Series Working Papers 974, University of Oxford, Department of Economics.
    3. Sabiou M. Inoua & Vernon L. Smith, 2022. "Perishable goods versus re-tradable assets: A theoretical reappraisal of a fundamental dichotomy," Chapters, in: Sascha Füllbrunn & Ernan Haruvy (ed.), Handbook of Experimental Finance, chapter 15, pages 162-171, Edward Elgar Publishing.
    4. Tai, Chung-Ching & Chen, Shu-Heng & Yang, Lee-Xieng, 2018. "Cognitive ability and earnings performance: Evidence from double auction market experiments," Journal of Economic Dynamics and Control, Elsevier, vol. 91(C), pages 409-440.
    5. Jakob Grazzini, 2013. "Information dissemination in an experimentally based agent-based stock market," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 8(1), pages 179-209, April.
    6. Dave Cliff, 2024. "Parameterised response zero intelligence traders," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 19(3), pages 439-492, July.
    7. Gjerstad, Steven & Dickhaut, John, 1998. "Price Formation in Double Auctions," Games and Economic Behavior, Elsevier, vol. 22(1), pages 1-29, January.
    8. Jason Shachat & Zhenxuan Zhang, 2017. "The Hayek Hypothesis and Long‐run Competitive Equilibrium: An Experimental Investigation," Economic Journal, Royal Economic Society, vol. 127(599), pages 199-228, February.
    9. Brewer, Paul & Ratan, Anmol, 2019. "Profitability, efficiency, and inequality in double auction markets with snipers," Journal of Economic Behavior & Organization, Elsevier, vol. 164(C), pages 486-499.
    10. Großer, Jens & Reuben, Ernesto, 2013. "Redistribution and market efficiency: An experimental study," Journal of Public Economics, Elsevier, vol. 101(C), pages 39-52.
    11. Cason, Timothy N. & Friedman, Daniel, 1996. "Price formation in double auction markets," Journal of Economic Dynamics and Control, Elsevier, vol. 20(8), pages 1307-1337, August.
    12. Marta Posada & Adolfo López-Paredes, 2007. "How to Choose the Bidding Strategy in Continuous Double Auctions: Imitation Versus Take-The-Best Heuristics," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 11(1), pages 1-6.
    13. Armand Mihai Cismaru, 2024. "DeepTraderX: Challenging Conventional Trading Strategies with Deep Learning in Multi-Threaded Market Simulations," Papers 2403.18831, arXiv.org.
    14. Yan Peng & Jason Shachat & Lijia Wei & S. Sarah Zhang, 2024. "Speed traps: algorithmic trader performance under alternative market balances and structures," Experimental Economics, Springer;Economic Science Association, vol. 27(2), pages 325-350, April.
    15. Henry Hanifan & Ben Watson & John Cartlidge & Dave Cliff, 2021. "Time Matters: Exploring the Effects of Urgency and Reaction Speed in Automated Traders," Papers 2103.00600, arXiv.org.
    16. Junhuan Zhang & Peter McBurney & Katarzyna Musial, 2018. "Convergence of trading strategies in continuous double auction markets with boundedly-rational networked traders," Review of Quantitative Finance and Accounting, Springer, vol. 50(1), pages 301-352, January.
    17. Dave Cliff, 2021. "BBE: Simulating the Microstructural Dynamics of an In-Play Betting Exchange via Agent-Based Modelling," Papers 2105.08310, arXiv.org.
    18. Itzhak Rasooly, 2022. "Competitive equilibrium and the double auction," Papers 2209.07532, arXiv.org.
    19. Sean Crockett, 2013. "Price Dynamics In General Equilibrium Experiments," Journal of Economic Surveys, Wiley Blackwell, vol. 27(3), pages 421-438, July.
    20. Duffy, John, 2006. "Agent-Based Models and Human Subject Experiments," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 19, pages 949-1011, Elsevier.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:1809.06027. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.