Automatic pattern recognition of Head-And-Shoulder
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.
Zitieren als
Marcus Strobel (2024). Automatic pattern recognition of Head-And-Shoulder (https://www.mathworks.com/matlabcentral/fileexchange/66529-automatic-pattern-recognition-of-head-and-shoulder), MATLAB Central File Exchange. Abgerufen .
Kompatibilität der MATLAB-Version
Plattform-Kompatibilität
Windows macOS LinuxKategorien
Tags
Quellenangaben
Inspiriert von: extrema.m, extrema2.m
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!Live Editor erkunden
Erstellen Sie Skripte mit Code, Ausgabe und formatiertem Text in einem einzigen ausführbaren Dokument.
Version | Veröffentlicht | Versionshinweise | |
---|---|---|---|
1.0.0.0 | Summary |