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There's a long tradition of research using computational intelligence(methods from artificial intelligence (AI) and machine learning (ML)), toautomatically discover, implement, and fine-tune strategies for autonomousadaptive automated trading in financial markets, with a sequence of researchpapers on this topic published at AI conferences such as IJCAI and in journalssuch as Artificial Intelligence: we show here that this strand of research hastaken a number of methodological mis-steps and that actually some of thereportedly best-performing public-domain AI/ML trading strategies can routinelybe out-performed by extremely simple trading strategies that involve no AI orML at all. The results that we highlight here could easily have been revealedat the time that the relevant key papers were published, more than a decadeago, but the accepted methodology at the time of those publications involved asomewhat minimal approach to experimental evaluation of trader-agents, makingclaims on the basis of a few thousand test-sessions of the trader-agent in asmall number of market scenarios. In this paper we present results fromexhaustive testing over wide ranges of parameter values, using parallelcloud-computing facilities, where we conduct millions of tests and therebycreate much richer data from which firmer conclusions can be drawn. We showthat the best public-domain AI/ML traders in the published literature can beroutinely outperformed by a "sub-zero-intelligence" trading strategy that atface value appears to be so simple as to be financially ruinous, but whichinteracts with the market in such a way that in practice it is more profitablethan the well-known AI/ML strategies from the research literature. That such asimple strategy can outperform established AI/ML-based strategies is a signthat perhaps the AI/ML trading strategies were good answers to the wrongquestion.

Methods Matter: A Trading Agent with No Intelligence Routinely Outperforms AI-Based Traders