Articles on data analysis and statistics in MQL5

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Articles on mathematical models and laws of probability are interesting for many traders. Mathematics is the basis of technical indicators, and statistics is required to analyze trading results and develop strategies.

Read about the fuzzy logic, digital filters, market profile, Kohonen maps, neural gas and many other tools that can be used for trading.

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MQL5 Trading Tools (Part 38): Adding a Tabbed Settings Window for Editing Object Properties

MQL5 Trading Tools (Part 38): Adding a Tabbed Settings Window for Editing Object Properties

We add a tabbed settings window opened from the ribbon and bound to the selected object. The tabs — Style, Text, Coordinates, and Visibility — are built from the same descriptor system, with scrolling, per-level rows, and shared color/width/style popovers. The article covers layout, rendering, interaction, and inline price/time and numeric editing. You get one place to edit every property with live preview and commit-or-discard on close.
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Linear Regression Prediction Channels in MQL5: Constructing Statistically Grounded Confidence and Prediction Bands

Linear Regression Prediction Channels in MQL5: Constructing Statistically Grounded Confidence and Prediction Bands

The article implements rolling OLS regression channels in MQL5 and computes confidence and prediction bands with Student's t critical values instead of a fixed standard-deviation multiplier. It explains the leverage-driven widening at window edges, contrasts the result with Bollinger and Donchian channels, and reviews OLS assumptions on price data. A five-line rendering is documented to ensure reliable display in MetaTrader 5.
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From Cloud to Complex: The Vietoris-Rips Filtration in MQL5

From Cloud to Complex: The Vietoris-Rips Filtration in MQL5

We turn a price-embedded point cloud into a Vietoris–Rips filtration and its boundary matrix. The article enumerates vertices, edges, and triangles with filtration values, sorts them in entry order, and builds O(1) vertex/edge lookups. You get MQL5 classes CTDARips and CTDABoundary and a sparse Z/2 boundary suitable for the next-step persistence reduction.
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Gaussian Processes in Machine Learning (Part 2): Implementing and Testing a Classification Model in MQL5

Gaussian Processes in Machine Learning (Part 2): Implementing and Testing a Classification Model in MQL5

In this section, we will look at the implementation of the key interfaces of the library of Gaussian processes in MQL5: IKernel, ILikelihood, and IInference. We will also demonstrate its operation on synthetic data and implement indicators for classification and regression, demonstrating its operation in online mode - with retraining of the model on each new bar.