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Hybrid Automatic Trading Systems: Technical Analysis & Group Method of Data Handling

  • Marco Corazza
  • Paolo Vanni
  • Umberto Loschi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2486)

Abstract

For building an automatic trading system one needs: a significant variable for characterizing the financial asset behaviours; a suitable algorithm for finding out the information hidden in such a variable; and a proper Trading Strategy for transforming these information in operative indications. Starting from recent results proposed in literature, we have conjectured that the Technical Analysis approach could reasonably extract the information present in prices and volumes. Like tool able to find out the relation existing between the Technical Analysis inputs and an output we properly defined, we use the Group Method of Data Handling, a soft-computing approach which gives back a polynomial approximation of the unknown relationship between the inputs and the output. The automatic Trading Strategy we implement is able both to work in real-time and to return operative signals. The system we create in such a way not only performs pattern recognition, but also generates its own patterns. The results obtained during an intraday operating simulation on the US T-Bond futures is satisfactory, particularly from the point of view of the trend direction detection, and from the net profit standpoint.

Keywords

Automatic Trading Strategy (or Trading System) technical Analisys GMDM 

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References

  1. [1]
    Appel, G.: The Moving Average Convergence-Divergence Method. Signalert, Great Neck (1979)Google Scholar
  2. [2]
    Efron, B., Tibshirani, R. J.: An Introduction to the Bootstrap. Chapman & Hall, London Weinheim New York Tokyo Melbourne Madras (1993)MATHGoogle Scholar
  3. [3]
    Farlow, S.J.: The GMDH Algorithm. In: Farlow, S.J. (ed.): Self-Organizing Methods in Modeling. Marcel Dekker, New York Basel (1984) 1–24Google Scholar
  4. [4]
    Lee, C. M. C, Swaminathan, B.: Price Momentum and Trading Volume. Journal of Finance, LV(5) (2000) 2017–2069CrossRefGoogle Scholar
  5. [5]
    Lo, W. A., Mamaysky, H., Wang, J.: Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation. Journal of Finance, LV(4) (2000) 1705–1769CrossRefGoogle Scholar
  6. [6]
    Nison, S.: Japanese Candlesticks Charting Technique. Prentice Hall Press, New York (1991)Google Scholar
  7. [7]
    Wilder, J. W.: New Concepts in Technical Trading. Trend Research, Greensboro (1978)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Marco Corazza
    • 1
  • Paolo Vanni
    • 2
  • Umberto Loschi
    • 2
  1. 1.Department of Applied MathematicsUniversity Ca’ Foscari of VeniceVeniceItaly
  2. 2.T4T s.r.l. - Tools for TradingPaduaItaly

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