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Neural Pattern Recognition with Self-organizing Maps for Efficient Processing of Forex Market Data Streams

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 6113)

Abstract

The paper addresses the problem of using Japanese candlestick methodology to analyze stock or forex market data by neural nets. Self organizing maps are presented as tools for providing maps of known candlestick formations. They may be used to visualize these patterns, and as inputs for more complex trading decision systems. In that case their role is preprocessing, coding and pre-classification of price data. An example of a profitable system based on this method is presented. Simplicity and efficiency of training and network simulating algorithms is emphasized in the context of processing streams of market data.

Keywords

  • Open Trade
  • Trading System
  • Price Data
  • Trading Signal
  • Trading Platform

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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  • DOI: 10.1007/978-3-642-13208-7_39
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References

  1. McNelis, P.D.: Neural Networks in Finance: Gaining predictive edge in the market. Elsevier, Amsterdam (2005)

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  2. Nison, S.: Japanese Candlestick Charting Techniques, 2nd edn. Prentice Hall Press, Englewood Cliffs (2001)

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  3. Li, S.-T., Kuo, S.-C.: Knowledge discovery in financial investment for forecasting and trading strategy through wavelet-based SOM networks. Expert Systems with Applications 32(2), 935–951 (2008)

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  4. Vanstone, B., Finnie, G.: An empirical methodology for developing stockmarket trading systems using artificial neural networks. Expert Systems with Applications 36(3), 6668–6680 (2009)

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© 2010 Springer-Verlag Berlin Heidelberg

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Ciskowski, P., Zaton, M. (2010). Neural Pattern Recognition with Self-organizing Maps for Efficient Processing of Forex Market Data Streams. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2010. Lecture Notes in Computer Science(), vol 6113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13208-7_39

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  • DOI: https://doi.org/10.1007/978-3-642-13208-7_39

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13207-0

  • Online ISBN: 978-3-642-13208-7

  • eBook Packages: Computer ScienceComputer Science (R0)