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We report on a series of experiments in which we study the coevolutionary"arms-race" dynamics among groups of agents that engage in adaptive automatedtrading in an accurate model of contemporary financial markets. At any onetime, every trader in the market is trying to make as much profit as possiblegiven the current distribution of different other trading strategies that itfinds itself pitched against in the market; but the distribution of tradingstrategies and their observable behaviors is constantly changing, and changesin any one trader are driven to some extent by the changes in all the others.Prior studies of coevolutionary dynamics in markets have concentrated onsystems where traders can choose one of a small number of fixed purestrategies, and can change their choice occasionally, thereby giving a marketwith 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, wherewe use minimal-intelligence trading-agents but in which the space of possiblestrategies is continuous and hence infinite. Our work reveals that by takingonly a small step in the direction of increased realism we move immediatelyinto high-dimensional phase-spaces, which then present difficulties invisualising and understanding the coevolutionary dynamics unfolding within thesystem. We conclude that further research is required to establish betteranalytic tools for monitoring activity and progress in co-adapting markets. Wehave released relevant Python code as open-source on GitHub, to enable othersto continue this work.

Exploring Coevolutionary Dynamics of Competitive Arms-Races Between Infinitely Diverse Heterogenous Adaptive Automated Trader-Agents