Identifying trade entry and exit timing using mathematical technical indicators in XCS

Preen, Richard

Abstract

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. 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 four of five 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. © 2010 Springer-Verlag Berlin Heidelberg.

• Presentation Conference Type: Conference Paper (published)
• Publication Date: Dec 1, 2010
• Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
• Print ISSN: 0302-9743
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 6471 LNAI
• Pages: 166-184
• Series Title: LNCS
• DOI: https://doi.org/10.1007/978-3-642-17508-4_11
• Keywords: computational finance, learning classifier systems, XCS
• Public URL: https://uwe-repository.worktribe.com/output/987730
• Publisher URL: http://dx.doi.org/10.1007/978-3-642-17508-4_11