Articles on data analysis and statistics in MQL5

As we explained in the first article about SQL, there is no point in spending time programming procedures to do what is already built into SQL. However, without knowing the basics, you won’t be able to do anything with SQL or take full advantage of everything this tool offers. Therefore, in this article, we will look at how to perform basic tasks in databases.

It doesn't matter which SQL program we use: MySQL, SQL Server, SQLite, OpenSQL, or another. They all have something in common, and the common element is the SQL language. Even if we do not intend to use Workbench, we can manipulate or work with the database directly in MetaEditor or through MQL5 to perform actions in MetaTrader 5, but to do so, you will need knowledge of SQL. So here, we will learn at least the basics.

In this article, we develop a frequency analysis tool in MQL5 that bins price data into histograms, computes entropy for information content, and applies chi-square tests for distribution goodness-of-fit, with interactive logs and statistical panels for market insights. We integrate per-bar or per-tick computation modes, supersampled rendering for smooth visuals, and draggable/resizable canvases with auto-scrolling logs to enhance usability in trading analysis.

The article explores the Battle Royale Optimizer algorithm — a metaheuristic in which solutions compete with their nearest neighbors, accumulate “damage,” are replaced when a threshold is exceeded, and periodically shrink the search space around the current best solution. It presents both pseudocode and an MQL5 implementation of the CAOBRO class, including neighbor search, movement toward the best solution, and an adaptive delta interval. Test results on the Hilly, Forest, and Megacity functions highlight the strengths and limitations of the approach. The reader is provided with a ready-to-use foundation for experimentation and tuning key parameters such as popSize and maxDamage.

In this article, we expand the MQL5 graphing tool to support seventeen statistical distributions with interactive cycling via a header switch icon. We add type-specific data loading, discrete and continuous histogram computation, and theoretical density functions for each model, with dynamic titles, axis labels, and parameter panels that adapt automatically. The result lets you overlay distribution models on the same sample and compare fit across families without reloading the tool.

This article shows how to represent market structure as a graph in MQL5, turning swing highs/lows into nodes with features and linking them by edges. It trains a Graph Neural Network to score potential liquidity zones, exports the model to ONNX, and runs real-time inference in an Expert Advisor. Readers learn how to build the data pipeline, integrate the model, visualize zones on the chart, and use the signals for rule-based execution.

In this article, we advance the binomial distribution graphing tool in MQL5 by integrating DirectX for 3D visualization, enabling switchable 2D/3D modes with camera-controlled rotation, zoom, and auto-fitting for immersive analysis. We render 3D histogram bars, ground planes, and axes alongside the theoretical probability mass function curve, while preserving 2D elements like statistics panels, legends, and customizable themes, gradients, and labels

This article implements a box‑constrained Truncated Newton Conjugate‑Gradient (TNC) optimizer in MQL5 and details its core components: scaling, projection to bounds, line search, and Hessian‑vector products via finite differences. It provides an objective wrapper supporting analytic or numerical derivatives and validates the solver on the Rosenbrock benchmark. A logistic regression example shows how to use TNC as a drop‑in alternative to LBFGS.

This article develops an interactive MQL5 plot for the binomial distribution, combining a histogram of simulated outcomes with the theoretical probability mass function. It implements mean, standard deviation, skewness, kurtosis, percentiles, and confidence intervals, along with configurable themes and labels, and supports dragging, resizing, and live parameter changes. Use it to assess expected wins, likely drawdowns, and confidence ranges when validating trading strategies.

The article shows how to pair SQLite (OLTP) with DuckDB (OLAP) for statistical arbitrage data processing. DuckDB’s columnar engine, ASOF JOIN, and array functions accelerate core tasks such as quote–trade alignment and RWEC, with measured speedups from 2x to 23x versus SQLite on larger inputs. You get simpler queries and faster analytics while keeping trade execution in SQLite.

In this article, we create a canvas-based graphing tool in MQL5 for statistical correlation and linear regression analysis between two symbols, with draggable and resizable features. We incorporate ALGLIB for regression calculations, dynamic tick labels, data points, and a stats panel displaying slope, intercept, correlation, and R-squared. This interactive visualization aids in pair trading insights, supporting customizable themes, borders, and real-time updates on new bars

In this article, we will discuss one of the possible solutions to what we have been trying to demonstrate—namely, how to allow an Excel user to perform an action in MetaTrader 5 without sending orders or opening or closing positions. The idea is that the user employs Excel to conduct fundamental analysis of a particular symbol. And by using only Excel, they can instruct an expert advisor running in MetaTrader 5 to open or close a specific position.

Learn how to read a CSV file in MQL5 and organize its trading data into dynamic arrays. This article shows step by step how to count file elements, store all data in a single array, and separate each column into dedicated arrays, laying the foundation for advanced analysis and trading performance visualization.

In this article, we will look at how to solve certain problems and issues that arise when using Python code within other programs. More specifically, we will demonstrate a common issue encountered when using Excel in conjunction with MetaTrader 5, although we will be using Python to facilitate this interaction. However, this implementation has a minor drawback. It does not occur in all cases, but only in certain specific situations. When it does happen, it is necessary to understand the cause. In today’s article, we will begin explaining how to resolve this issue.

Create a CSV trading journal in MQL5 by reading account history over a defined period and writing structured records to file. The article explains deal counting, ticket retrieval, symbol and order type decoding, and capturing entry (lot, time, price, SL/TP) and exit (time, price, profit, result) data with dynamic arrays. The result is an organized, persistent log suitable for analysis and reporting.

What is quantitative trend analysis in the Forex market? We collect statistics on trends, their magnitude and distribution across the EURUSD currency pair. How quantitative trend analysis can help you create a profitable trading expert advisor.

The article extends our copula toolkit with mixed copulas implemented natively in MQL5. We construct Clayton–Frank–Gumbel and Clayton–Student–t–Gumbel mixtures, estimate them via EM, and enable sparsity control through SCAD with cross‑validation. Provided scripts tune hyperparameters, compare mixtures using information criteria, and save trained models. Practitioners can apply these components to capture asymmetric tail dependence and embed the selected model in indicators or Expert Advisors.