The Agent-Based Hedge Fund

In this article we describe the implementation of a diversified investment strategy using 25 intelligent agents. Each agent utilizes several data mining models and other artificial intelligence techniques to autonomously day trade an American stock. The agents were individually tested with out-of-sample data corresponding to the period between February of 2006 and June of 2010, and most achieved an acceptable performance. By integrating the 25 agents in a multi-agent system, we were able to obtain much better results (according to the return and maximum drawdown metrics); this leads us to believe that it might be possible to use one such system in the creation of a profitable hedge fund in which the investment decisions can be made without human intervention.

[1]  E. Fama EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK* , 1970 .

[2]  Luis E. Ortiz,et al.  The Penn-Lehman Automated Trading Project , 2003, IEEE Intell. Syst..

[3]  Marc-André Mittermayer,et al.  Forecasting Intraday stock price trends with text mining techniques , 2004, 37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of the.

[4]  Ajith Abraham,et al.  Analysis of hybrid soft and hard computing techniques for forex monitoring systems , 2002, 2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE'02. Proceedings (Cat. No.02CH37291).

[5]  Orlando Belo,et al.  Algorithmic Trading Using Intelligent Agents , 2008, IC-AI.

[6]  Jingtao Yao,et al.  A case study on using neural networks to perform technical forecasting of forex , 2000, Neurocomputing.