1. Proceedings
  2. I3M
  3. 2019
  4. EMSS
  5. 10.46354/i3m.2019.emss.041

A simulator for studying automated block trading on a coupled dark/lit financial exchange with reputation tracking

  • a George Church  ,
  • b Dave Cliff  
  • a,b Department of Computer Science, University of Bristol, Bristol BS8 1UB, UK
Cite as
Church G., Cliff D. (2019). A simulator for studying automated block trading on a coupled dark/lit financial exchange with reputation tracking. Proceedings of the 31st European Modeling & Simulation Symposium (EMSS 2019), pp. 284-293. DOI: https://doi.org/10.46354/i3m.2019.emss.041.

Abstract

We describe a novel simulation of a contemporary realworld financial exchange: London Stock Exchange (LSE) Turquoise, and we also introduce a newly-created adaptive automated trading strategy called ISHV, which exhibits realistic behavior in situations where large orders can radically shift prices before transactions occur. LSE Turquoise is a recently-introduced platform where buying and selling takes place on a pair of coupled trading pools: a lit pool that is visible to all traders; and a dark pool where large "block" orders are hidden from sight until they are automatically matched with a counterparty, after which the transaction is then revealed. Orders from traders are routed to the lit or dark pool depending on their size, and on the reputation of the trader issuing the order. Unlike all other public-domain adaptive trading strategies, ISHV can alter the prices it quotes in anticipation of adverse price changes that are likely to occur when orders for block-trades are publicly visible: so-called market impact. LSE Turquoise is intended to reduce the negative effects of market impact; something that we test with our simulator. We extend the existing BSE open-source exchange simulator to incorporate coupled lit and dark pools, naming the new system BSELD. We show ISHV exhibiting market impact in a lit-only pool, and discuss how a Turquoisestyle coupled dark pool reduces or eliminates that impact. We also show results from a Turquoise-style reputationtracking mechanism, which can be used for modulating trader access control to the dark pool.

References

  1. Arnuk, S., & Saluzzi, J., 2012. Broken Market: How High-Frequency Trading and Predatory Practices on Wall St are Destroying Investor Confidence and your Portfolio. Financial Times / Prentice Hall.
  2. Bodek, H. & Dolgopolov, S, 2015. The Market Structure Crisis: Electronic Stock Markets, High Frequency Trading, and Dark Pools. CreateSpace Publishing.
  3. BSE, 2012. The Bristol Stock Exchange. On GitHub at github.com/davecliff/BristolStockExchange
  4. Cartea, A., Jaimungal, S., & Penalva, J., 2015. Algorithmic and High-Frequency Trading. Cambridge University Press.
  5. Church, G., 2019. Extending the Bristol Stock Exchange with a Dark Pool. Master's Thesis, Department of Computer Science, University of Bristol.
  6. Cliff, D., 1997. Minimal-Intelligence Agents for Bargaining Behaviours in Market-Based Environments. Hewlett-Packard Labs Technical Report HPL-97-91.
  7. Cliff, D., 2018a. BSE: A Minimal Simulation of a Limit-Order-Book Stock Exchange. In: M. Affenzeller, A. Bruzzone, et al. (eds) Proceedings. of the European Modelling and Simulation Symposium (EMSS2018), pp.194–203.
  8. Cliff, D., 2018b. “An Open-Source Limit-Order-Book Exchange for Teaching and Research.” Proc. IEEE Symposium on Computational Intelligence in Financial Engineering (CIFEr), pp.1853–1860.
  9. Cliff, D., 2019. “Exhaustive Testing of Trader-agents in Realistically Dynamic Continuous Double Auction Markets: AA Does Not Dominate”. In: Proceedings ICAART 2019, Vol.2: 224–236; ScitePress.
  10. Das, R., Hanson, J., Kephart, J., & Tesauro, G., 2001. “Agent-Human Interactions in the Continuous Double Auction”. Proc. International Joint Conference on AI. (IJCAI’01), pp.1169–1176.
  11. De Luca, M., & Cliff, D., 2011. Human-Agent Auction Interactions Adaptive-Aggressive Agents Dominate. Proceedings of the International Joint Conference on AI (IJCAI-2011), pp.178–185.
  12. De Luca, M., 2015. Adaptive Algorithmic Trading Systems. PhD Thesis, Department of Computer Science, University of Bristol, UK.
  13. Gjerstad, S. & Dickhaut, J., 1998. Price Formation in Double Auctions. Games & Economic Behavior, 22(1):1–29.
  14. Gjerstad, S., 2003. The Impact of Pace in Double Auction Bargaining. Working Paper, Department of Economics, University of Arizona.
  15. 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.
  16. Harris, L., 2002. Trading and Exchanges: Market Microstructure for Practitioners. Oxford University Press.
  17. London Stock Exchange Group, 2019a. Turquoise Plato Block Discovery (V2.26.2): bit.ly/2ZPJAi0.
  18. London Stock Exchange Group, 2019b. Turquoise Trading Service Description (V3.34.9j):bit.ly/2Y1jWFi.
  19. Narang, R., 2013. Inside the Black Box: The Simple Truth about Quantitative Trading. Second Edition. Wiley Finance.
  20. Palmer R., Rust, J., & Miller, J. 1992. Behavior of Trading Automata in a Computerized Double Auction Market. In D. Friedman, & J. Rust (Eds.), Double Auction Markets: Theory, Institutions, Laboratory Evidence. Addison Wesley.
  21. Patterson, S., 2013. Dark Pools: The Rise of AI Trading Machines. Random House.
  22. Pentapalli, M., 2008. A comparative study of Roth-Erev and Modified Roth-Erev reinforcement learning algorithms for uniform-price double auctions. PhD Thesis, Iowa State University.
  23. Rodgers, K. 2016. Why Aren’t They Shouting? A Banker's Tale of Change, Computers, and Perpetual Crisis. RH Business Books.
  24. Stotter, S., Cartlidge, J., and Cliff, D. 2013. “Exploring assignment-adaptive (ASAD) trading agents in financial market experiments”, Proceedings ICAART2013, 1:77–88.
  25. Tesauro, G. and Das, R. 2001. “High-performance Bidding Agents for the Continuous Double Auction”. Proceedings of the 3rd ACM Conference on Electronic Commerce, pp.206–209.
  26. Tesauro, G. and Bredin, J., 2002. “Sequential Strategic Bidding in Auctions using Dynamic Programming”. In Proceedings AAMAS 2002.
  27. Vach, D., 2015. Comparison of Double Auction Bidding Strategies for Automated Trading Agents. Master's Thesis, Charles University in Prague.
  28. Vytelingum,P., 2006. The Structure and Behaviour of the Continuous Double Auction. PhD Thesis, University of Southampton.
  29. Vytelingum, P., Cliff, D., & Jennings, N., 2008. “Strategic Bidding in Continuous Double Auctions”. Artificial Intelligence, 172(14):1700–1729.