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Fools Rush In: Competitive Effects of Reaction Time in Automated Trading

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  • Henry Hanifan
  • John Cartlidge

Abstract

We explore the competitive effects of reaction time of automated trading strategies in simulated financial markets containing a single exchange with public limit order book and continuous double auction matching. A large body of research conducted over several decades has been devoted to trading agent design and simulation, but the majority of this work focuses on pricing strategy and does not consider the time taken for these strategies to compute. In real-world financial markets, speed is known to heavily influence the design of automated trading algorithms, with the generally accepted wisdom that faster is better. Here, we introduce increasingly realistic models of trading speed and profile the computation times of a suite of eminent trading algorithms from the literature. Results demonstrate that: (a) trading performance is impacted by speed, but faster is not always better; (b) the Adaptive-Aggressive (AA) algorithm, until recently considered the most dominant trading strategy in the literature, is outperformed by the simplistic Shaver (SHVR) strategy - shave one tick off the current best bid or ask - when relative computation times are accurately simulated.

Suggested Citation

  • Henry Hanifan & John Cartlidge, 2019. "Fools Rush In: Competitive Effects of Reaction Time in Automated Trading," Papers 1912.02775, arXiv.org, revised Nov 2020.
    • Handle: RePEc:arx:papers:1912.02775
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    References listed on IDEAS

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    1. 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.
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    3. Steven Gjerstad, 2003. "The Strategic Impact of Pace in Double Auction Bargaining," Microeconomics 0304001, University Library of Munich, Germany.
    4. Steven Gjerstad, 2003. "The Impact of Pace in Double Auction Bargaining," Levine's Bibliography 666156000000000192, UCLA Department of Economics.
    5. 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.
    6. 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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    Cited by:

    1. 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.

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