Articles on data analysis and statistics in MQL5

The article discusses the Extremal Optimization (EO) algorithm, an optimization method inspired by the Bak-Sneppen self-organized criticality model, where evolution occurs through the elimination of the worst-case components of the system. The modified population version of the algorithm demonstrates a shift away from theoretical principles in favor of practical efficiency, leading to the creation of powerful computational tools.

The article explores the possibility of using regression models in algorithmic trading. Regression models, unlike binary classification, allow for the creation of more flexible trading strategies by quantifying predicted price changes.

The article extends the MQL5 recurrence library to Recurrence Network Analysis (RNA) by treating recurrence matrices as adjacency matrices of undirected graphs. It implements core network metrics—clustering, transitivity, average path length, betweenness, assortativity, and density—and applies them in rolling windows for single-series RNA and Joint RNA (JRNA). A modular metrics engine and two indicators visualize the evolving network structure on MetaTrader 5 charts for practical time-series analysis.

This article builds the Spectrum Fitter: from tau(q) we compute f(alpha) with a discrete Legendre transform, then fit Normal, Binomial, Poisson, and Gamma spectra under box constraints using BLEIC. The best model by SSE is selected, and its parameters (eg, alpha min, alpha max or alpha_0, gamma) become the cascade inputs for multifractal simulation.

The article presents a detailed analysis of the Exchange Market Algorithm (EMA) inspired by the behavior of stock market traders. The algorithm simulates stock trading, where market participants with varying levels of success employ different strategies to maximize profits.

For this article we switch to a custom MQL5 Wizard class implementation that explores Money Management. We are labelling our custom class ‘CMoneySuffixAE’ that we derive by combining the Suffix Automaton algorithm with an Autoencoder neural network. As always, this formulation is testable with MQL5 Wizard Assembled Expert Advisors that can be tuned with various entry signals and trailing stop approaches.

In today's article we will begin studying the use of SQL in MQL5 code. We will also look at how to create a database. Or, more precisely, how to create a SQLite database file using the features built into MQL5. We will also see how to create a table, and then how to establish a relationship between tables by using primary and foreign keys. All of this, once again, will be done with MQL5. We will see how easy it is to create code that can later be migrated to other SQL implementations by using a class that helps hide the implementation being created. And, most importantly, we will see that at various points we may face the risk that something will go wrong when using SQL. This happens because, in MQL5 code, SQL code will always be placed inside a string.

In the previous article, we completed the necessary introduction to SQL. And, in my opinion, we properly clarified what we wanted to show and explain about SQL. This was done so that anyone who comes to look at the market replay/simulation system being built can at least get an idea of what may be happening there. The point is that there is no sense in programming things that SQL handles perfectly.

The article explains how to develop a tool for analyzing recurring price patterns in financial markets — by day of the month (1-31), day of the week (Monday-Sunday), or hour of the day (0-23). The indicator analyzes historical data, calculates the average return for each period, and displays the results as a histogram with a forecast. It includes customizable parameters: seasonality type, number of bars analyzed, display as percentages or absolute values, chart colors.

What if an optimization algorithm could remember its past journeys and use that memory to find better solutions? BSA does just that – balancing exploration with revisiting the tried and true. In this article, we reveal the secrets of the algorithm. A simple idea, minimum parameters and a stable result.

This article explores the grey model, a promising tool that can expand trader's capabilities. We will look at some options for applying this model to technical analysis and building trading strategies.

We extend the RQA library for MetaTrader 5 with JRQA, which detects when two series simultaneously revisit their own past states. The article covers the joint recurrence matrix, twelve JRQA metrics (including TREND and COMPLEXITY), dual-epsilon configuration, and a rolling-window engine with OpenCL acceleration and automatic CPU fallback. A practical indicator plots JRR, JDET, JLAM, JENTR, and JTREND for any symbol pair with timestamp alignment and normalization.

The article explores one of the most interesting non-gradient optimization algorithms, which learns to understand the geometry of the objective function. We will focus on the classical implementation of CMA-ES with a slight modification - replacing the normal distribution with the power one. We will thoroughly examine the math behind the algorithm, as well as practical implementation, and check where CMA-ES is unbeatable and where it should be avoided.