In a new article, the integration of k-means clustering within MQL5 is examined, addressing common limitations when using built-in applications without source codes, particularly with custom indicators. This examination focuses on using OpenCL for parallel computing to enhance performance in model construction and implementation.

The article details the procedures for organizing parallel computation via OpenCL. This encompasses writing kernel programs for calculating distances between data points and cluster centers, clustering data points, updating cluster centers, and calculating the loss function. Each step leverages parallel processing to optimize efficiency and speed.

Additionally, the preparatory work for integrating these processes into a main program through an MQL5 class is outlined. The approach ensures compatibility and readiness for future int...

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