Discussing the article: "How to Detect and Normalize Chart Objects in MQL5 (Part 4): Fully Automated Analytical Objects System"
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Check out the new article: How to Detect and Normalize Chart Objects in MQL5 (Part 4): Fully Automated Analytical Objects System.
This part extends the series with a modular, event-driven MQL5 pipeline: swing detection feeds an object placer for trendlines, SR, Fibonacci, channels, and pitchforks; evaluators monitor interactions and generate signals; adaptive logic executes trades with valid stops per instrument. The topology manager synchronizes placement, scanning, and processing. The code is structured into reusable components for easy reuse and scaling.
Manual drawing is time‑consuming and inconsistent. A trader may spot a swing pattern and draw a pitchfork, but if they are away from the screen, the opportunity is missed. Moreover, the same trader might draw slightly different pitchforks at different times, leading to inconsistent signals. To overcome these limitations, we need the ability to programmatically create analytical objects based on market structure. This part of the series introduces automatic object placement: the EA will scan the chart for swing points and automatically draw pitchforks, trendlines, and support/resistance lines. These objects are then immediately monitored by the same interaction pipeline, generating alerts and trades without any human intervention.
We will also address the latency introduced by timer‑based polling. In Part 3, interactions are evaluated every two seconds. If an object is created or modified by the EA, it would not be detected until the next polling cycle. To solve this, the modular system uses a topology manager to refresh objects on demand. This ensures immediate responsiveness when an object is created, deleted, or modified. The combination of automatic placement and event‑driven updates makes the system fully autonomous and responsive.
Author: Clemence Benjamin