Discussing the article: "Introduction to MQL5 (Part 19): Automating Wolfe Wave Detection"

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Check out the new article: Introduction to MQL5 (Part 19): Automating Wolfe Wave Detection.

This article shows how to programmatically identify bullish and bearish Wolfe Wave patterns and trade them using MQL5. We’ll explore how to identify Wolfe Wave structures programmatically and execute trades based on them using MQL5. This includes detecting key swing points, validating pattern rules, and preparing the EA to act on the signals it finds.

In the last article, we already discussed the structure and rules of the bearish Wolfe Wave pattern in detail. Now, in this section, we will focus on how to implement that logic programmatically. As previously mentioned, the bearish Wolfe Wave is made up of five waves that have to adhere to a certain sequence and fulfill certain structural requirements. Wave 2 must be a swing low that is situated beneath Wave 1, and Wave 1 must be a swing high. Another swing high, but this time above wave 1 and inside a certain Fibonacci extension of the wave 1 to wave 2 legs, is then formed by wave 3. Wave 4, a swing low that falls below wave 3 but stays above wave 2, is then recognized. Wave 5 completes the pattern by reaching a swing high above wave 3 and falling inside a predetermined Fibonacci extension of the wave 3 to wave 4 motions.

Remembering that the legs of waves one and two must be comparable in size to those of waves three and four is also crucial. Waves 3–4 should ideally be at least 70% as long as waves 1–2. The structure gains credibility from this symmetry, which also serves to validate the validity of the pattern. This section will locate the five locations using swing detection functions and include checks to make sure their distances and connections meet Wolfe Wave requirements.

Author: Israel Pelumi Abioye