Strategy Modeling and Classifier Training for Share Trading

  • Yain-Whar Si
  • Weng-Lon Lei
  • Chi-Chong Chiu
Part of the Communications in Computer and Information Science book series (CCIS, volume 118)


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.


Technical indicators trading signals trading strategies 


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  1. 1.
    AmCharts, (last accessed April 27, 2010)
  2. 2.
    Weka, (last accessed April 27, 2010)
  3. 3.
    Kirkpatrick, C.D.: Technical Analysis, The Complete Resource for Financial Market Technicians. FT Press (2007)Google Scholar
  4. 4.
    Murphy, J.J.: Technical Analysis of the Financial Markets, A Comprehensive Guide to Trading Methods and Applications. Institute of Finance Press, New York (1999)Google Scholar
  5. 5.
    Fyfe, C., Marney, J., Tarbert, H.: Technical Analysis Versus Market Efficiency - a Genetic Programming Approach. Applied Financial Economics 9, 183–191 (1999)CrossRefGoogle Scholar
  6. 6.
    Atsalakis, G.S., Valavanis, K.P.: Surveying Stock Market Forecasting Techniques - Part II: Soft Computing Methods. Expert Systems with Applications 36(3), Part 2, 5932–5941 (2009)CrossRefGoogle Scholar
  7. 7.
    Abraham, A., Philip, N.S., Saratchandran, P.: Modelling Chaotic Behaviour of Stock Indices Using Intelligent Paradigms. Neural, Parallel & Scientific Computations Archive 11, 143–160 (2003)zbMATHGoogle Scholar
  8. 8.
    Doeksen, B., Abraham, A., Thomas, J., Paprzycki, M.: Real Stock Trading Using Soft Computing Models. In: Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC 2005), vol. II, pp. 162–167 (2005)Google Scholar
  9. 9.
    Dourra, H., Siy, P.: Investment Using Technical Analysis and Fuzzy Logic. Fuzzy Sets and Systems 127, 221–240 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Motiwalla, L., Wahab, M.: Predictable Variation and Profitable Rrading of US equities: a Trading Simulation Using Neural Networks. Computers and Operations Research 27(11-12), 1111–1129 (2000)CrossRefzbMATHGoogle Scholar
  11. 11.
    Pérez-Rodríguez, J.V., Torra, S., Andrada-Félix, J.: STAR and ANN models: Forecasting Performance on the Spanish “Ibex-35” Stock Index. Journal of Empirical Finance 12(3), 490–509 (2005)CrossRefGoogle Scholar
  12. 12.
    Tabrizi, H.A., Panahian, H.: Stock Price Prediction by Artificial Neural Networks: A Study of Tehran’s Stock Exchange (T.S.E), (last accessed April 27, 2010)
  13. 13.
    Cheng, J.H., Chen, H.P., Lin, Y.M.: A Hybrid Forecast Marketing Timing Model Based on Probabilistic Neural Network. Rough Set and C4.5. Expert Systems with Applications 37(3), 1814–1820 (2010)CrossRefGoogle Scholar
  14. 14.
    Robertson, C., Geva, S., Wolff, R.: Predicting the Short-Term Market Reaction to Asset Specific News: Is Time Against Us? In: Washio, T., Zhou, Z.-H., Huang, J.Z., Hu, X., Li, J., Xie, C., He, J., Zou, D., Li, K.-C., Freire, M.M. (eds.) PAKDD 2007. LNCS (LNAI), vol. 4819, pp. 1–13. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  15. 15.
    Huang, T.T., Chang, C.H.: Intelligent Stock Selecting via Bayesian Naive Classifiers on the Hybrid Use of Scientific and Humane Attributes. In: Proceedings of 8th International Conference on Intelligent Systems Design and Applications (ISDA 2008), pp. 617–621 (2008)Google Scholar
  16. 16.
    Ou, P., Wang, H.: Prediction of Stock Market Index Movement by Ten Data Mining Techniques. Modern Applied Science 3(12) (2009)Google Scholar
  17. 17.
    Sehgal, V., Song, C.: SOPS: Stock Prediction Using Web Sentiment. In: Proceedings of the 7th IEEE International Conference on Data Mining Workshops (ICDMW 2007), pp. 21–26 (2007)Google Scholar
  18. 18.
    Bodas-Sagi, D.J., Fernández, P., Hidalgo, J.I., Soltero, F.J., Risco-Martín, J.L.: Multiobjective Optimization of Technical Market Indicators. In: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers, pp. 1999–2004 (2009)Google Scholar
  19. 19.
    Ni, J., Zhang, C.: Mining Better Technical Trading Strategies with Genetic Algorithms. In: Proceedings of the International Workshop on Integrating AI and Data Mining (AIDM 2006), pp. 26–33 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Yain-Whar Si
    • 1
  • Weng-Lon Lei
    • 1
  • Chi-Chong Chiu
    • 1
  1. 1.Faculty of Science and TechnologyUniversity of MacauChina

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