Designing Safe, Profitable Automated Stock Trading Agents Using Evolutionary Algorithms

Harish Subramanian, Subramanian Ramamoorthy, Peter Stone, Benjamin J. Kuipers

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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.
Original languageEnglish
Title of host publicationGECCO '06 Proceedings of the 8th annual conference on Genetic and evolutionary computation
Place of PublicationNew York, NY, USA
Number of pages8
ISBN (Print)1-59593-186-4
Publication statusPublished - 2006

Publication series

NameGECCO '06

Keywords / Materials (for Non-textual outputs)

  • application, finance, fitness evaluation, genetic algorithms, genetic programming


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