Time Matters: Exploring the Effects of Urgency and Reaction Speed in Automated Traders
Pages 149 - 170
Abstract
We consider issues of time in automated trading strategies in simulated financial markets containing a single exchange with public limit order book and continuous double auction matching. In particular, we explore two effects: (i) reaction speed - the time taken for trading strategies to calculate a response to market events; and (ii) trading urgency - the sensitivity of trading strategies to approaching deadlines. Much of the literature on trading agents focuses on optimising pricing strategies only and ignores the effects of time, while real-world markets continue to experience a race to zero latency, as automated trading systems compete to quickly access information and act in the market ahead of others. We demonstrate that modelling reaction speed can significantly alter previously published results, with simple strategies such as SHVR outperforming more complex adaptive algorithms such as AA. We also show that adding a pace parameter to ZIP traders (ZIP-Pace, or ZIPP) can create a sense of urgency that significantly improves profitability.
References
[1]
Baxter, G., Cartlidge, J.: Flying by the seat of their pants: what can high frequency trading learn from aviation? In: Proceedings of the 3rd International Conference on Application and Theory of Automation in Command and Control Systems, ATACCS, pp. 56–65. (2013). https://dl.acm.org/doi/10.1145/2494493.2494501
[2]
BSE: The Bristol Stock Exchange. GitHub public source-code repository (2012). https://github.com/davecliff/BristolStockExchange
[3]
Cartea Á, Donnelly R, and Jaimungal S Enhancing trading strategies with order book signals Appl. Math. Financ. 2018 25 1 1-35
[4]
Cartlidge, J., Cliff, D.: Exploring the ‘robot phase transition’ in experimental human-algorithmic markets. In: The Future of Computer Trading in Financial Markets, Driver Review DR25. Foresight, Government Office for Science, London (2012). https://bit.ly/2llHjbh+
[5]
Cartlidge J and Cliff D Chen S-H, Kao Y-F, Venkatachalam R, and Du Y-R Modelling complex financial markets using real-time human–agent trading experiments Complex Systems Modeling and Simulation in Economics and Finance 2018 Cham Springer 35-69
[6]
Cartlidge, J., Smart, N.P., Alaoui, Y.T.: MPC joins the dark side. In: Proceedings of the 14th ACM Asia Conference on Computer and Communications Security, pp. 148–159. AsiaCCS (2019).
[7]
Cartlidge, J., Smart, N.P., Alaoui, Y.T.: Multi-party computation mechanism for anonymous equity block trading: a secure implementation of Turquoise Plato Uncross. Cryptology ePrint Archive, Report 2020/662 (2020), https://ia.cr/2020/662
[8]
Cartlidge, J., Szostek, C., De Luca, M., Cliff, D.: Too fast too furious: faster financial-market trading agents can give less efficient markets. In: Proceedings of the 4th International Conference on Agents and Artificial Intelligence, volume 2, pp. 126–135. ICAART (2012).
[9]
Cliff, D.: Minimal-intelligence agents for bargaining behaviours in market-based environments. Technical report HPL-97-91, Hewlett-Packard Labs (1997). https://www.hpl.hp.com/techreports/97/HPL-97-91.html
[10]
Cliff, D.: An open-source limit-order-book exchange for teaching and research. In: IEEE Symposium Series on Computational Intelligence, SSCI, pp. 1853–1860 (2018).
[11]
Cliff, D.: Exhaustive testing of trader-agents in realistically dynamic continuous double auction markets: AA does not dominate. In: Proceedings of the 11th International Conference on Agents and Artificial Intelligence, ICAART, volume 2, pp. 224–236 (2019).
[12]
Das, R., Hanson, J.E., Kephart, J.O., Tesauro, G.: Agent-human interactions in the continuous double auction. In: Proceedings of the 17th International Joint Conference on Artificial Intelligence, IJCAI, volume 2, pp. 1169–1176 (2001). https://dl.acm.org/doi/10.1145/501158.501183
[13]
De Luca, M., Cliff, D.: Human-agent auction interactions: adaptive aggressive agents dominate. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence, IJCAI, volume 1, pp. 178–185 (2011).
[14]
De Luca, M., Szostek, C., Cartlidge, J., Cliff, D.: Studies of interactions between human traders and algorithmic trading systems. In: The Future of Computer Trading In Financial Markets, Driver Review DR13. Foresight, Government Office for Science, London (2011). https://bit.ly/2llv52c+
[15]
Duffin, M., Cartlidge, J.: Agent-based model exploration of latency arbitrage in fragmented financial markets. In: IEEE Symposium Series on Computational Intelligence, SSCI, pp. 2312–2320 (2018).
[16]
ExPo: The Exchange Portal. SourceForge public source-code repository (2011). https://sourceforge.net/projects/exchangeportal/
[17]
Gjerstad, S.: The strategic impact of pace in double auction bargaining. In: Econometric Society 2004 North American Winter Meetings 190. Econometric Society (2004). https://ideas.repec.org/p/ecm/nawm04/190.html
[18]
Gjerstad S and Dickhaut J Price formation in double auctions Games Econ. Behav. 1998 22 1 1-29
[19]
Gode, D.K., Sunder, S.: Allocative efficiency of markets with zero-intelligence traders: market as a partial substitute for individual rationality. J. Polit. Econ. 101(1), 119–137 (1993).
[20]
Hanifan, H.: Investigating the impact speed has on the performance of algorithmic traders within the BSE simulation. Master’s thesis, Department of Computer Science, University of Bristol, UK (2019)
[21]
Hanifan, H., Cartlidge, J.: Fools rush. In: Competitive Effects of Reaction Time in Automated Trading. In: Proceedings of the 12th International Conference on Agents and Artificial Intelligence, ICAART, volume 1, pp. 82–93 (2020).
[22]
Imaev, D.D., Imaev, D.H.: Automated trading systems based on order book imbalance. In: XX IEEE International Conference on Soft Computing and Measurements (SCM), pp. 815–819 (2017).
[23]
Johnson N et al. Abrupt rise of new machine ecology beyond human response time Sci. Rep. 2013 3 2627 1-7
[24]
McGroarty F, Booth A, Gerding E, and Chinthalapati VLR High frequency trading strategies, market fragility and price spikes: an agent based model perspective Ann. Oper. Res. 2019 282 1 217-244
[25]
Miles, B., Cliff, D.: A cloud-native globally distributed financial exchange simulator for studying real-world trading-latency issues at planetary scale. In: Proceedings of the 31st European Modelling and Simulation Symposium, EMSS, pp. 294–303 (2019). https://arxiv.org/abs/1909.12926
[26]
Ockenfels, A., Roth, A.E.: Ending rules in internet auctions. In: Vulkan, N., Roth, A.E., Neeman, Z. (eds.) The Handbook of Market Design, chap. 13. Oxford University Press (2013).
[27]
OpEx: The Open Exchange. SourceForge public source-code repository (2011). https://sourceforge.net/projects/open-exchange/
[28]
Rust J, Miller JH, and Palmer R Characterizing effective trading strategies: insights from the computerized double auction tournament Econ. Dyn. Control 1994 18 1 61-96
[29]
Smith VL An experimental study of competitive market behavior J. Polit. Econ. 1962 70 2 111-137
[30]
Snashall D and Cliff D van den Herik J, Rocha AP, and Steels L Adaptive-aggressive traders don’t dominate Agents and Artificial Intelligence 2019 Cham Springer 246-269
[31]
Stotter, S., Cartlidge, J., Cliff, D.: Exploring assignment-adaptive (ASAD) trading agents in financial market experiments. In: Proceedings of the 5th International Conference on Agents and Artificial Intelligence, ICAART, volume 1, pp. 77–88 (2013).
[32]
Stotter S, Cartlidge J, and Cliff D Nguyen NT, Kowalczyk R, Fred A, and Joaquim F Behavioural investigations of financial trading agents using exchange portal (ExPo) Transactions on Computational Collective Intelligence XVII 2014 Heidelberg Springer 22-45
[33]
Tesauro, G., Bredin, J.L.: Strategic sequential bidding in auctions using dynamic programming. In: Proceedings of the 1st International Joint Conference on Autonomous Agents and Multiagent Systems: Part 2, AAMAS, pp. 591–598 (2002). http://doi.acm.org/10.1145/544862.544885
[34]
Tesauro, G., Das, R.: High-performance bidding agents for the continuous double auction. In: Proceedings of the 3rd ACM Conference on Electronic Commerce, pp. 206–209 (2001).
[35]
Vach, D.: Comparison of double auction bidding strategies for automated trading agents. Master’s thesis, Faculty of Social Sciences, Charles University in Prague, CZ (2015). https://is.cuni.cz/webapps/zzp/detail/152184
[36]
Vytelingum, P.: The structure and behaviour of the continuous double auction. Ph.D. thesis, School of Electronics and Computer Science, University of Southampton, UK (2006). https://eprints.soton.ac.uk/263234/
[37]
Vytelingum P, Cliff D, and Jennings NR Strategic bidding in CDAs Artif. Intell. 2008 172 14 1700-1729
[38]
Watson, B.A.: Algorithmic trading on multiple trading platforms. Master’s thesis, Department of Computer Science, University of Bristol, UK (2019)
Recommendations
Exploring Narrative Economics: An Agent-Based Co-Evolutionary Model Featuring Nonlinear Continuous-Time Opinion Dynamics
Agents and Artificial IntelligenceAutomated traders in commodities markets: Case of producer-consumer institution
Automatizing commodities' price negotiation was hard to achieve in practice, mainly because of logistical complications. The purpose of our work is to show that it is possible to automatize thoroughly commodities' trading in the futures market by ...
Next-generation securities market systems: an experimental investigation of quote-driven and order-driven trading
Special section: Strategic and competitive information systemsSeveral major securities markets including Nasdaq in the United States and the London Stock Exchange's SEAQ are organized as dealer markets that use computer screen displays of competitive dealer quotes to establish fair trade prices. To improve their ...
Comments
Information & Contributors
Information
Published In
![cover image Guide Proceedings](/cms/asset/d4a236c4-4ade-48ae-b6d4-8535087c4ace/978-3-030-71158-0.cover.jpg)
Feb 2020
519 pages
ISBN:978-3-030-71157-3
DOI:10.1007/978-3-030-71158-0
- Editors:
- Ana Paula Rocha,
- Luc Steels,
- Jaap van den Herik
© Springer Nature Switzerland AG 2021.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 22 February 2020
Author Tags
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 05 Feb 2025