An agent task force for stock trading

Abstract

In this article the authors present the simulated trading results of a system consisting of 60 intelligent agents, each being responsible for day trading a stock listed on the NYSE or the NASDAQ stock exchange. These agents were implemented according to an architecture that was previously applied to currency trading with interesting results. The performance of the stock trading agents, once integrated in a diversified investment system, showed similar promise. The trading simulation was done using out-of-sample price data for the period between February of 2006 and October of 2010. Throughout this period, the system's performance compared favorably with that of the buy-and-hold strategy, both in terms of return and maximum drawdown. These results indicate that agent technology might be of use for this particular practical application, a conclusion that should interest the investment industry. © 2011 Springer-Verlag Berlin Heidelberg.