Dimensionality reduction is critical in today's data-heavy environment, easing storage and computational needs. By simplifying data structures, methods like Principal Component Analysis (PCA) maintain essential information while reducing complexity. In trading, PCA can help streamline model inputs, making real-time decisions faster, and improving system efficiency. PCA, introduced by Karl Pearson, identifies principal components to capture data variance optimally. Through singular value decomposition, we derive orthogonal vectors ensuring minimal correlation and enhanced model learning. When implementing PCA, data normalization is paramount. In MQL5, matrix operations aid the process, ensuring effective dimensional reduction while preserving 99% of original data information.

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