Hybridizing Data Stream Mining and Technical Indicators in Automated Trading Systems

  • Michael Mayo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6820)


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.


Trading Strategy Closing Price Technical Indicator Market Direction Baseline Strategy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Michael Mayo
    • 1
  1. 1.Department of Computer ScienceUniversity of WaikatoHamiltonNew Zealand

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