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Time Matters: Exploring the Effects of Urgency and Reaction Speed in Automated Traders

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  • Henry Hanifan
  • Ben Watson
  • John Cartlidge
  • Dave Cliff

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.

Suggested Citation

  • 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.
  • Handle: RePEc:arx:papers:2103.00600
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    References listed on IDEAS

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    1. Vulkan, Nir & Roth, Alvin E. & Neeman, Zvika (ed.), 2013. "The Handbook of Market Design," OUP Catalogue, Oxford University Press, number 9780199570515.
    2. Rust, John & Miller, John H. & Palmer, Richard, 1994. "Characterizing effective trading strategies : Insights from a computerized double auction tournament," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 61-96, January.
    3. Álvaro Cartea & Ryan Donnelly & Sebastian Jaimungal, 2018. "Enhancing trading strategies with order book signals," Applied Mathematical Finance, Taylor & Francis Journals, vol. 25(1), pages 1-35, January.
    4. Gjerstad, Steven & Dickhaut, John, 1998. "Price Formation in Double Auctions," Games and Economic Behavior, Elsevier, vol. 22(1), pages 1-29, January.
    5. 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.
    6. 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.
    7. Steven Gjerstad, 2003. "The Strategic Impact of Pace in Double Auction Bargaining," Microeconomics 0304001, University Library of Munich, Germany.
    8. Bradley Miles & Dave Cliff, 2019. "A Cloud-Native Globally Distributed Financial Exchange Simulator for Studying Real-World Trading-Latency Issues at Planetary Scale," Papers 1909.12926, arXiv.org.
    9. Henry Hanifan & John Cartlidge, 2019. "Fools Rush In: Competitive Effects of Reaction Time in Automated Trading," Papers 1912.02775, arXiv.org, revised Nov 2020.
    10. Frank McGroarty & Ash Booth & Enrico Gerding & V. L. Raju Chinthalapati, 2019. "High frequency trading strategies, market fragility and price spikes: an agent based model perspective," Annals of Operations Research, Springer, vol. 282(1), pages 217-244, November.
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