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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, July 2006.
GECCO 2006
[PDF]214.6kB [postscript]813.6kB
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.
@InProceedings{GECCO06-trading,
author="Harish Subramanian and Subramanian Ramamoorthy and Peter Stone and Benjamin 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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