MQL5 Programs Market

Gradients are introduced by making the source a polymorphic paint: the rasterizer calls ColorAt(x, y) for each pixel. This enables solid, linear and radial fills on any path with a single inner‑loop change, keeping Path.mqh untouched. The article details color stops and padding, corner‑based normalization for linear gradients, normalized centers/radii for radial ones, a CCairoFillStyle handle, and compatibility with the legacy uint Fill().

This article explains how to track and handle the removal of objects from the chart in MQL5 using MetaTrader 5 events. When the code detects that an object created by an indicator has been deleted, it deletes the corresponding instance to avoid inconsistencies and prevent subwindows from being left behind. This approach uses unique names and a header file, prioritizing simplicity and predictable behavior.

The third article in this series introduces a trainable feature graph in the Cellular10K architecture: the weights of the feature → feature connections are strengthened online after correct predictions and weakened after errors. The article covers soft initialization, the message-passing step, a local Hebbian learning rule, weight clipping, normalization, and decay. The article demonstrates integration with a cellular automaton and a binary predictor, along with diagnostics metrics and practical activation thresholds for controlling overfitting.