  There's a long tradition of research using computational intelligence
(methods from artificial intelligence (AI) and machine learning (ML)), to
automatically discover, implement, and fine-tune strategies for autonomous
adaptive automated trading in financial markets, with a sequence of research
papers on this topic published at AI conferences such as IJCAI and in journals
such as Artificial Intelligence: we show here that this strand of research has
taken a number of methodological mis-steps and that actually some of the
reportedly best-performing public-domain AI/ML trading strategies can routinely
be out-performed by extremely simple trading strategies that involve no AI or
ML at all. The results that we highlight here could easily have been revealed
at the time that the relevant key papers were published, more than a decade
ago, but the accepted methodology at the time of those publications involved a
somewhat minimal approach to experimental evaluation of trader-agents, making
claims on the basis of a few thousand test-sessions of the trader-agent in a
small number of market scenarios. In this paper we present results from
exhaustive testing over wide ranges of parameter values, using parallel
cloud-computing facilities, where we conduct millions of tests and thereby
create much richer data from which firmer conclusions can be drawn. We show
that the best public-domain AI/ML traders in the published literature can be
routinely outperformed by a "sub-zero-intelligence" trading strategy that at
face value appears to be so simple as to be financially ruinous, but which
interacts with the market in such a way that in practice it is more profitable
than the well-known AI/ML strategies from the research literature. That such a
simple strategy can outperform established AI/ML-based strategies is a sign
that perhaps the AI/ML trading strategies were good answers to the wrong
question.
