PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Designing Safe, Profitable Automated Stock Trading Agents Using Evolutionary Algorithms
Harish Subramanian, Subramanian Ramamoorthy, Peter Stone and Benjamin Kuipers
In: Proceedings of the Genetic and Evolutionary Computation Conference, 8 - 12 July 2006, Seattle, USA.

Abstract

Trading rules are widely used by practitioners as an effective means to mechanize aspects of their reasoning about stock price trends. However, due to the simplicity of these rules, each rule is susceptible to poor behavior in specific types of adverse market conditions. Naive combinations of such rules are not very effective in mitigating the weaknesses of component rules. We demonstrate that sophisticated approaches to combining these trading rules enable us to overcome these problems and gainfully utilize them in autonomous agents. We achieve this combination through the use of genetic algorithms and genetic programs. Further, we show that it is possible to use qualitative characterizations of stochastic dynamics to improve the performance of these agents by delineating safe, or feasible, regions. We present the results of experiments conducted within the Penn-Lehman Automated Trading project. In this way we are able to demonstrate that autonomous agents can achieve consistent profitability in a variety of market conditions, in ways that are human competitive.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:4834
Deposited By:Subramanian Ramamoorthy
Deposited On:24 March 2009