Discussing the article: "How to Detect and Normalize Chart Objects in MQL5 (Part 3): Alerting and Automated Trading from Manually Drawn Objects"

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Check out the new article: How to Detect and Normalize Chart Objects in MQL5 (Part 3): Alerting and Automated Trading from Manually Drawn Objects.

This article extends the chart‑object detector into a modular monitoring and execution layer. It defines objective interaction rules (touch, cross, breakout) for trendlines, Fibonacci levels, channels, rectangles, and pitchforks, then routes events through an interaction detector, alert manager, and optional trade executor. Orders use object geometry for stop‑loss and take‑profit. The result is a reproducible pipeline that converts static drawings into actionable alerts and, if enabled, trades.

Consider a typical trading session: a trader has drawn a pitchfork on the H1 chart, a Fibonacci retracement on the M15, and an equidistant channel on the M5. While monitoring, price approaches the pitchfork’s median line, touches the 61.8% Fibonacci level, and then breaks out of the channel. The trader is momentarily distracted, and by the time they return, the opportunity—or the stop-loss—has already passed. This scenario is not an edge case; it is a regular occurrence for any technical analyst who relies on manually drawn instruments. These limitations are not merely theoretical; they manifest daily as missed entries, unintended stops, and erratic equity curves.

Manual monitoring suffers from three fundamental limitations that directly impact profitability and risk management:

• Latency: Even a disciplined trader checking every few minutes reacts slower than an automated system. Price–object interactions that last only a few ticks can be missed entirely.
• Scalability: As the number of drawn objects grows—multiple timeframes, several symbols, dozens of pitchforks and Fibonacci levels—human attention cannot track every boundary simultaneously.
• Subjectivity: Manual interpretation of whether price “touched” a line, “closed beyond” a rectangle, or “rejected” a channel boundary varies between traders and even across sessions for the same trader. Inconsistent rules lead to inconsistent results.

The core problem is that chart objects are static geometric definitions, while price is a dynamic stream. Bridging this gap previously required either constant human vigilance or expensive third‑party tools. In earlier installments (Part 1 and Part 2 of this series), we built a solid foundation: a system that can detect any chart object present on a chart, then normalize its coordinates into a consistent internal representation stored inside the structures SChartObjectInfo and SComplexObjectInfo. That gave us the ability to answer “What objects exist?” and “Where are their anchor points?”. However, we stopped short of the truly valuable question: “When and how does price interact with these objects?”

This article completes the loop. We extend the detection and normalization engine into a fully automated monitoring and execution layer. The goal is to turn every drawn pitchfork, Fibonacci, channel, trendline, rectangle, horizontal line, and arc into a trigger that can generate alerts or even send market orders—all without a single manual check. The trader can step away, knowing that every price–boundary interaction will be caught and acted upon in real time.

Author: Clemence Benjamin