Using evolutionary game-theory to analyse the performance of trading strategies in a continuous double auction market

Authors:
Kai Cai;Jinzhong Niu;Simon Parsons
Affiliations:
Department of Computer Science, Graduate Center, City University of New York, New York, NY;Department of Computer Science, Graduate Center, City University of New York, New York, NY;Department of Computer Science, Graduate Center, City University of New York, New York, NY and Department of Computer and Information Science, Brooklyn College, City University of New York, Brookl ...
Venue:
ALAMAS'05/ALAMAS'06/ALAMAS'07 Proceedings of the 5th , 6th and 7th European conference on Adaptive and learning agents and multi-agent systems: adaptation and multi-agent learning
Year:
2005

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Abstract

In agent-based computational economics, many different trading strategies have been proposed. Given the kinds of market that such trading strategies are employed in, it is clear that the performance of the strategies depends heavily on the behavior of other traders. However, most trading strategies are studied in homogeneous populations, and those tests that have been carried out on heterogeneous populations are limited to a small number of strategies. In this paper we extend the range of strategies that have been exposed to a more extensive analysis, measuring the performance of eight trading strategies using an approach based on evolutionary game theory.