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Adaptive-Aggressive Traders Don't Dominate

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationAgents and Artificial Intelligence
Subtitle of host publicationRevised selected papers from ICAART2019
EditorsJaap van Herik, Ana Paula Rocha, Luc Steels
Publisher or commissioning bodySpringer Berlin Heidelberg
Pages246-269
Number of pages24
ISBN (Electronic)978-3-030-37494-5
ISBN (Print)978-3-030-37493-8
DOIs
DateAccepted/In press - 9 Oct 2019
DateE-pub ahead of print (current) - 15 Dec 2019

Publication series

NameLecture Notes in Artificial Intelligence
PublisherSpringer
Volume11978
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Abstract

For more than a decade Vytelingum’s Adaptive-Aggressive (AA) algorithm has been recognized as the best-performing automated auction-market trading-agent strategy currently known in the AI/Agents literature; in this paper, we demonstrate that it is in fact routinely outperformed by another algorithm when exhaustively tested across a sufficiently wide range of market scenarios. The novel step taken here is to use large-scale compute facilities to brute-force exhaustively evaluate AA in a variety of market environments based on those used for testing it in the original publications. Our results show that even in these simple environments AA is consistently outperformed by IBM's GDX algorithm, first published in 2002. We summarize here results from more than one million market simulation experiments, orders of magnitude more testing than was reported in the original publications that first introduced AA. A 2019 ICAART paper by Cliff claimed that AA's failings were revealed by testing it in more realistic experiments, with conditions closer to those found in real financial markets,
but here we demonstrate that even in the simple experiment conditions that were used in the original AA papers, exhaustive testing shows AA to be outperformed by GDX. We close this paper with a discussion of the methodological implications of our work: any results from previous papers where any one trading algorithm is claimed to be superior to others on the basis of only a few thousand trials are probably best treated with some suspicion now. The rise of cloud computing means that the compute-power necessary to subject trading algorithms to millions of trials over a wide range of conditions is readily available at reasonable cost: we should make use of this; exhaustive testing such as is shown here should be the norm in future evaluations and comparisons of new trading algorithms.

    Research areas

  • Automated Trading, Auction Markets, Adaptive Bidding Agents

Documents

Documents

  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via Springer Nature at https://link.springer.com/chapter/10.1007/978-3-030-37494-5_13. Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 941 KB, PDF document

    Embargo ends: 15/12/20

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