TY - GEN
T1 - Designing Safe, Profitable Automated Stock Trading Agents Using Evolutionary Algorithms
AU - Subramanian, Harish
AU - Ramamoorthy, Subramanian
AU - Stone, Peter
AU - Kuipers, Benjamin J.
PY - 2006
Y1 - 2006
N2 - 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.
AB - 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.
KW - application, finance, fitness evaluation, genetic algorithms, genetic programming
U2 - 10.1145/1143997.1144285
DO - 10.1145/1143997.1144285
M3 - Conference contribution
SN - 1-59593-186-4
T3 - GECCO '06
SP - 1777
EP - 1784
BT - GECCO '06 Proceedings of the 8th annual conference on Genetic and evolutionary computation
PB - ACM
CY - New York, NY, USA
ER -