An algorithm for Elliott waves pattern detection

Abstract

The examination of the ElliottWave theory is the main motivation of this contribution. All of the fundamental features of an proper ElliottWave pattern (EW pattern) are reviewed and explained. Based on this knowledge, an algorithm for detection of these patterns is designed, developed and tested. Under several different algorithm settings, several EW pattern sets are obtained. They differ in amount of found EW patterns, quality and size.

The following application of the developed detection algorithm was based on recognition of an incomplete EW patterns with aim of the prediction of the following progress of the time set. The Random Decision Forest and the Support Vector Machine are the machine learning algorithms employed for this task. The accuracy of trend prediction above 70% proves the relevancy of EW patterns on stock market data as well as the validity of the algorithm as a tool for detection of such patterns.