Abstract
In this paper we present a novel approach for identifying the head-and-shoulders technical analysis pattern based on neural networks. For training the network we use actual patterns that were identified in stochastically simulated price series by means of a rule-based algorithm. Then the patterns are being converted to binary images, in a manner similar to the one used in hand-written character and digit recognition. Our approach is tested on new simulated price series using a rolling window of variable size. The results are very promising with an overall correct classification rate of 97.1%.
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Zapranis, A., Tsinaslanidis, P. (2010). Identification of the Head-and-Shoulders Technical Analysis Pattern with Neural Networks. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6354. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15825-4_17
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DOI: https://doi.org/10.1007/978-3-642-15825-4_17
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