BSE: A minimal simulation of a Limit-Order-Book stock exchange

Abstract

This paper describes the design, implementation, and successful use of the Bristol Stock Exchange (BSE) a novel minimal simulation of a centralized financial market, based on a Limit Order Book (LOB) such as is commonly 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 financial 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 experiences in a sufficiently realistic teaching environment. BSE as described here addresses both 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.

Simulation for Education | Financial Markets | Automated Trading 

References

1. BSE, 2012. GitHub open-source code repository at http://github.com/davecliff/BristolStockExchange/
2. Cartlidge, J. & Cliff, D., 2012. Exploring the ‘robot phase transition’ in experimental humanalgorithmic markets. UK Government Office for Scence, Foresight Project: Future of Computer Trading in the Financial Markets, Driver Review DR 25. https://bit.ly/2llHjbh.
3. Cliff, D., 1997. Minimal-Intelligence Agents for Bargaining Behaviors in Market-Based
   Environments. HP Labs Tech Report HPL-97-91. www.hpl.hp.com/techreports/97/HPL-97-91.pdf
4. Cliff, D., 2018. A Free Open-Source Limit-Order-Book Simulator for Teaching and Research. Submitted to Computational Intelligence in Financial Engineering (CIFEr) track at IEEE Symposium Series on Computational Intelligence, Bengaluru, India, November 2018.
5. Das, R., Hanson, J., Kephart, J., & Tesauro, G., 2001. Agent-Human Interactions in the Continuous Double Auction. Proceedings International Joint Conference on Artificial Intelligence (IJCAI’01).
6. le Calvez, A., 2018. Learning to be a Financial Trader: An Exploration of Neural Networks in a Continuous Double Auction. Masters Thesis, Department of Computer Science, University of Bristol.
7. le Calvez, A. & Cliff, D., 2018. Deep Learning can Replicate Adaptive Traders in a Limit-Order-Book Financial Market. Submitted to Computational Intelligence in Financial Engineering (CIFEr) track at IEEE Symposium Series on Computational Intelligence, Bengaluru, India, November 2018.
8. De Luca, M., & Cliff, D., 2011. Human-Agent Auction Interactions Adaptive-Aggressive Agents
   Dominate. Proceedings International Joint Conference on Artificial Intelligence (IJCAI-2011).
9. De Luca, M., Cartlidge, J., Szostek, C., & Cliff, D., 2012. Studies of interactions between human traders and algorithmic trading systems. UK Government Office for Science, Foresight Project: Future of Computer Trading in Financial Markets, Driver Review DR13. https://bit.ly/2llv52c.
10. De Luca, M., 2016. Adaptive Algorithmic Trading Systems. PhD thesis, Department of Computer
    Science, University of Bristol.
11. Gjerstad, S. & Dickhaut, J., 1998. Price Formation in Double Auctions. Games & Economic Behavior, 22(1):1-29.
12. Gode, D. & Sunder, S., 1993. Allocative efficiency of markets with zero-intelligence traders: Market as a partial substitute for individual rationality. Journal of Political Economy, 101(1):119-137.
13. Krizhevsky, A., Sutskever, I, & Hinton, G., 2012. ImageNet classification with deep convolutional
    neural networks. In Proc. 25th International Conf. on Neural Information Processing Systems, Vol.1 (NIPS'12), F. Pereira, et al. (eds), pp.1097-1105.
14. Rust, J., Miller, J., & Palmer, R., 1992. Behaviour of trading automata in a computerized double auction market. in D. Friedman & J. Rust (eds) The Double Auction Market: Institutions, Theories, & Evidence. Addison Wesley, pp.155-198.
15. Smith, V., 1962. An experimental study of competitive market behavior. J. Polit. Economy, 70(2):111-137.
16. Stotter, S., Cartlidge, J., & Cliff, D., 2013. Exploring assignment-adaptive (ASAD) trading agents in financial market experiments, in Proceedings of the 5th International Conference on Agents and Artificial Intelligence, (ICAART). J. Filipe & A. Fred, (eds). Barcelona: SciTePress, Vol.1.pp.77-88.
17. Stotter, S., Cartlidge, J., & Cliff, D., 2014. Behavioural investigations of financial trading agents using Exchange Portal (ExPo). In: N. Nguyen, et al. (eds) Transactions on Computational Collective Intelligence XVII. Springer, pp. 22-45
18. Tibrewal, K., 2017. Can Neural Networks be Traders? Explorations of Machine Learning Using the Bristol Stock Exchange. MEng Thesis, Department of Computer Science, University of Bristol.
19. Vytelingum, P., 2006, The Structure and Behaviour of the Continuous Double Auction. PhD Thesis, School of Electronics and Computer Science, University of Southampton.
20. Vytelingum, P., Cliff, D., & Jennings, N. 2008. “Strategic Bidding in Continuous Double
    Auctions”. Artificial Intelligence, 172(14):1700-1729.