Discussing the article: "MQL5 Trading Tools (Part 11): Correlation Matrix Dashboard (Pearson, Spearman, Kendall) with Heatmap and Standard Modes"
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Check out the new article: MQL5 Trading Tools (Part 11): Correlation Matrix Dashboard (Pearson, Spearman, Kendall) with Heatmap and Standard Modes.
In this article, we build a correlation matrix dashboard in MQL5 to compute asset relationships using Pearson, Spearman, and Kendall methods over a set timeframe and bars. The system offers standard mode with color thresholds and p-value stars, plus heatmap mode with gradient visuals for correlation strengths. It includes an interactive UI with timeframe selectors, mode toggles, and a dynamic legend for efficient analysis of symbol interdependencies.
The correlation matrix dashboard framework analyzes relationships between financial assets by computing correlation coefficients, helping us identify interdependencies that influence portfolio diversification, hedging, or multi-asset strategies. It processes price changes across user-selected symbols over a defined period and timeframe, applying one of three statistical methods—Pearson for linear relationships, Spearman for rank-based monotonic associations, or Kendall for concordance in rankings—to quantify how assets move together or in opposite directions. Significance is evaluated through p-values to indicate reliability, with visual cues like color thresholds or gradients highlighting strong positive, strong negative, mild, or neutral correlations, enabling quick pattern recognition without manual calculations.
In standard mode, the dashboard uses predefined thresholds to categorize correlations, applying distinct colors for strong positives or negatives and adding stars for p-value significance levels to denote statistical confidence. Heatmap mode employs a continuous color gradient for finer visualization of correlation intensities from negative to positive, making subtle variations more apparent. The interface includes interactive elements such as timeframe selectors for switching analysis periods, toggle buttons for modes or themes, and a dynamic legend to interpret colors and values, all arranged in a grid with symbols on axes and cells showing pairwise correlations.
Our plan is to parse a list of symbols, compute correlations and p-values using the chosen method on price deltas, render a user interface with panels for headers, timeframes, symbols, cells, and legends, and update visuals dynamically based on modes and thresholds. We will incorporate event handling for interactions like mode switching or timeframe changes, ensuring the dashboard refreshes on new data for real-time insights. In brief, here is a visual representation of our objectives.
Author: Allan Munene Mutiiria