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Automatic pattern recognition of Head-And-Shoulder

version (5.6 KB) by Marcus Strobel
Implementation for Head-And-Shoulder (Lo et al., 2000, Journal of Finance) with simulated data


Updated 17 Mar 2018

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This prototype shows a reduced approach, how to implement an automated pattern recognition algorithm for the Head-And-Shoulders pattern (Lo et al., 2000, Journal of Finance) in MATLAB.
Lo et al. (2000) use a moving window consisting of 38 price observations. With every step, the oldest price will be deleted and a new price appended to the price queue. A kernel regression smooths the raw prices (observations 4.-38.) in order to filter noise. The pattern recognition algorithm identifies a specific visual pattern, which is described by the position of the last five local extrema (called E1, ..., E5) in the smoothed prices. For example, the arrangement E1 < E3 > E5 and (E2, E4) < (E1, E3, E5) constitutes a Head-And-Shoulder TOP pattern, if the 4. price is a local extrema. The remaining newer prices (3. – 1.) protect again look-ahead bias. A TOP pattern implies a decreasing price and BOTTOM pattern vice versa. A timer generates 80 random prices. The command-line and a figure present the output.

Cite As

Marcus Strobel (2022). Automatic pattern recognition of Head-And-Shoulder (, MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2015b
Compatible with any release
Platform Compatibility
Windows macOS Linux

Inspired by: extrema.m, extrema2.m

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