Designing Safe, Profitable Automated Stock Trading Agents Using …
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itable automated stock trading achieves this using a combination of … technical rules for automated stock trading. M.S. Thesis, …
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Designing Safe, Profitable Automated Stock Trading
Agents Using Evolutionary Algorithms
Harish Subramanian, Subramanian Ramamoorthy, Peter Stone, Benjamin J. Kuipers
Artificial Intelligence Laboratory, Department of Computer Sciences
The University of Texas at Austin
Austin, TX 78712, USA
harish.subramanian@alumni.utexas.net, s.ramamoorthy@mail.utexas.edu
pstone@cs.utexas.edu, kuipers@cs.utexas.edu
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…
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