Abstract
In technical analysis, a trading strategy can trigger either buy or sell signals whenever the specified conditions are satisfied. When a set of strategies are applied to a particular stock, a trader often receives conflicting recommendations from each strategy. In this paper, we propose a unified data mining approach in which the outcomes from all strategies are taken into consideration for decision making. First, we develop a framework for compo- sing complex trading strategies. Next, we show how to perform simulation analysis on constructed strategies using extracted historical prices. The result of the simulation analysis is then used for training classifiers which can be used for recommending stock trading actions. Experiments conducted with the price data from Hong Kong Stock Market show promising results.
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Si, YW., Lei, WL., Chiu, CC. (2010). Strategy Modeling and Classifier Training for Share Trading. In: Zhang, Y., Cuzzocrea, A., Ma, J., Chung, Ki., Arslan, T., Song, X. (eds) Database Theory and Application, Bio-Science and Bio-Technology. BSBT DTA 2010 2010. Communications in Computer and Information Science, vol 118. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17622-7_7
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DOI: https://doi.org/10.1007/978-3-642-17622-7_7
Publisher Name: Springer, Berlin, Heidelberg
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