Hybridizing data stream mining and technical indicators in automated trading systems
Mayo, M. (2011). Hybridizing data stream mining and technical indicators in automated trading systems. In V. Torra, Y. Narukawa, J. Yin & J. Long (Eds.), Modeling Decision for Artificial Intelligence, Proceedings 8th International Conference, MDAI 2011, Changsha, Hunan, China, July 28-30, 2011. Lecture Notes in Computer Science, Volume 6820/2011 (pp. 79-90). Berlin, Germany: Springer-Verlag Berlin Heidelberg.
Permanent Research Commons link: https://hdl.handle.net/10289/5602
Automated trading systems for financial markets can use data mining techniques for future price movement prediction. However, classifier accuracy is only one important component in such a system: the other is a decision procedure utilizing the prediction in order to be long, short or out of the market. In this paper, we investigate the use of technical indicators as a means of deciding when to trade in the direction of a classifier’s prediction. We compare this “hybrid” technical/data stream mining-based system with a naive system that always trades in the direction of predicted price movement. We are able to show via evaluations across five financial market datasets that our novel hybrid technique frequently outperforms the naive system. To strengthen our conclusions, we also include in our evaluation several “simple” trading strategies without any data mining component that provide a much stronger baseline for comparison than traditional buy-and-hold or sell-and-hold strategies.
Springer-Verlag Berlin Heidelberg
This is an author’s accepted version. The original publication is available at www.springerlink.com.