  We report on a series of experiments in which we study the coevolutionary
"arms-race" dynamics among groups of agents that engage in adaptive automated
trading in an accurate model of contemporary financial markets. At any one
time, every trader in the market is trying to make as much profit as possible
given the current distribution of different other trading strategies that it
finds itself pitched against in the market; but the distribution of trading
strategies and their observable behaviors is constantly changing, and changes
in any one trader are driven to some extent by the changes in all the others.
Prior studies of coevolutionary dynamics in markets have concentrated on
systems where traders can choose one of a small number of fixed pure
strategies, and can change their choice occasionally, thereby giving a market
with a discrete phase-space, made up of a finite set of possible system states.
Here we present first results from two independent sets of experiments, where
we use minimal-intelligence trading-agents but in which the space of possible
strategies is continuous and hence infinite. Our work reveals that by taking
only a small step in the direction of increased realism we move immediately
into high-dimensional phase-spaces, which then present difficulties in
visualising and understanding the coevolutionary dynamics unfolding within the
system. We conclude that further research is required to establish better
analytic tools for monitoring activity and progress in co-adapting markets. We
have released relevant Python code as open-source on GitHub, to enable others
to continue this work.
