Discussing the article: "How to Detect and Normalize Chart Objects in MQL5 (Part 5): Fibonacci in Focus"

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Check out the new article: How to Detect and Normalize Chart Objects in MQL5 (Part 5): Fibonacci in Focus.

The article bridges automated placement with manual analysis for the Fibonacci family in MQL5. It scans charts, identifies user Fibonacci objects, and normalizes their level arrays, interaction flags, and visuals per object type while preserving coordinates. With manual-priority enforcement, Expert Advisors can evaluate both human and code-generated tools reliably, without duplicates or runtime indexing issues.

In Part 4 of this series, we built an automated placement layer that detects swing points and programmatically draws analytical objects—trendlines, support/resistance, channels, pitchforks, and Fibonacci tools. That system allowed an Expert Advisor to map out market structure and evaluate price interactions completely on its own.

However, automated object placement introduces a new challenge: reconciling programmatic objects with human discretion. Code-generated objects use predictable names and standardized properties. Manually drawn objects are unpredictable. If an EA places automated objects over an existing manual Fibonacci tool, the chart becomes cluttered with duplicates.

This article addresses that challenge directly. We focus on Fibonacci by building a robust detection and normalization pipeline across all six native MQL5 Fibonacci object types. This system scans the chart for user-drawn Fibonacci tools, forces their underlying properties into a predictable state, and enforces a crucial design rule: manual objects take priority over automated ones. By the end, your EA will seamlessly evaluate both human analysis and code-generated objects without missing a beat.

Author: Clemence Benjamin