Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic Algorithms and Genetic Programming in Computational Finance
Genetic Algorithms and Genetic Programming in Computational Finance
Explorations in LCS Models of Stock Trading
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
Strong, Stable, and Reliable Fitness Pressure in XCS due to Tournament Selection
Genetic Programming and Evolvable Machines
An efficient fuzzy based neuro: genetic algorithm for stock market prediction
International Journal of Hybrid Intelligent Systems
Portfolio allocation using XCS experts in technical analysis, market conditions and options market
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Zcs: A zeroth level classifier system
Evolutionary Computation
Classifier fitness based on accuracy
Evolutionary Computation
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This paper extends current LCS research into financial time series forecasting by analysing the performance of agents utilising mathematical technical indicators for both environment classification and in selecting actions to be executed in the environment. It compares these agents with traditional models which only use such indicators to classify the environment and exit at the close of the next day. It is proposed that XCS agents utilising mathematical technical indicators for exit conditions will not only outperform similar agents which close the trade at the end of the next day, but also result in fewer trades and consequently lower commissions paid. The results show that in five out of six assets, agents using indicator exit conditions outperformed those exiting at the close of the next day, before commissions were factored in. After commissions are factored in, the performance gap between the two agent classes further widens. Additionally, the agent's best results are continuously able to outperform a buy and hold strategy.