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.
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Notes
- 1.
Keeping the published version of this paper to the required maximum page-count required us to omit several informative figures and many references. The full original version of this paper is freely available for download: see [2].
- 2.
The Python code in the main BSE GitHub repository [4] has been extended by addition of a minimally simple adaptive PRZI trader, a k-point stochastic hill climber, referred to as PRZI-SHC-k (pronounced prezzy-shuck), for which the \(k=2\) case is a close relative of the AC algorithm described in Sect. 2 and which can readily be used for studies of coevolutionary dynamics. The source-code for our STGP work is available separately at https://github.com/charliefiguero/stgp-trader/.
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Alexandrov, N., Cliff, D., Figuero, C. (2022). Exploring Coevolutionary Dynamics Between Infinitely Diverse Heterogenous Adaptive Automated Trading Agents. In: Czupryna, M., Kamiński, B. (eds) Advances in Social Simulation. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-92843-8_8
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