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

TL;DR

This paper investigates the complex coevolutionary arms-race dynamics among adaptive automated trading agents in realistic financial market models with continuous strategy spaces, revealing high-dimensional challenges and the need for new analytical tools.

Contribution

It introduces experiments with minimal-intelligence agents in continuous strategy spaces, highlighting the complexity of high-dimensional coevolutionary market dynamics.

Findings

High-dimensional phase-spaces complicate visualization and understanding of coevolution.

Continuous strategies lead to more realistic and complex market dynamics.

Open-source Python code is provided for further research.

Abstract

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.