Peter Stone's Selected Publications

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Designing Safe, Profitable Automated Stock Trading Agents Using Evolutionary Algorithms

Harish Subramanian, Subramanian Ramamoorthy, Peter Stone, and Ben Kuipers. Designing Safe, Profitable Automated Stock Trading Agents Using Evolutionary Algorithms. In Proceedings of the Genetic and Evolutionary Computation Conference, July 2006.
GECCO 2006

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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.

BibTeX Entry

@InProceedings{GECCO06-trading,
	author="Harish Subramanian and Subramanian Ramamoorthy and Peter Stone and Ben Kuipers",
	title="Designing Safe, Profitable Automated Stock Trading Agents Using Evolutionary Algorithms",
	booktitle="Proceedings of the Genetic and Evolutionary Computation Conference",
	month="July",year="2006",
	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.
		  },
        wwwnote={<a href="http://www.sigevo.org/gecco-2006/">GECCO 2006</a>},
}

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