MQL5 Programming Articles

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 develops a Fisher‑style Indicator in MQL5 from first principles: normalize price within a recent high/low window, smooth and clamp the value, then apply a logarithmic transform. We cover buffer wiring, calculation‑buffer state management across bars, and seeding for stable starts. An accompanying EA implements threshold and reversal confirmation to show how to act on the signal.

The article provides a systematic overview of the theory of random variables, which serves as the basis for analyzing and modeling uncertainty in financial markets. The article covers the definitions and properties of univariate random variables, cumulative distribution functions (CDFs) and probability density functions (PDFs), as well as the differences between discrete, continuous, and mixed models. The theoretical material is based on intuitive analogies with mass and density. The appendix to this article contains practical examples of using the standard MQL5 library to calculate probabilities, quantiles, and moments of distributions. It also demonstrates the graphical capabilities of the MetaTrader 5 platform for visual data analysis by plotting PDF and CDF curves and QQ plots.

For our next Exploration on notions that are testable with the MQL5 Wizard we examine if Skip Lists and the Hopfield Network can give us a profit-guarding trailing strategy. Trailing Stop Management, as already argued, can be overlooked in most trading systems at the expense of Entry Signals or even Money Management. Trailing stops can make all the difference in certain situations such as trending markets, and thus we test this out with GBPJPY.

In this article, we will explore how to overload structural code. I know it can be quite challenging to understand at first, especially if you're seeing it for the first time. It is very important that you grasp these concepts and understand them well before attempting to delve into more complex and elaborate topics.

This article introduces the Self-Adaptive Moving Average (SAMA), an adaptive filter leveraging the Normalized Least Mean Squares (NLMS) algorithm. It explores why fixed-period averages fail, how NLMS adapts bar by bar, and the engineering protections required for production. This conceptual and mathematical foundation prepares you for the MQL5 code implementation in Part 2.

In the previous article, I showed how to proceed in order to add a query mechanism. This was needed so that, inside MQL5 code, you could fully use SQL and retrieve results using an SQL SELECT query. But there is still one last function we need to implement. This is the DatabaseReadBind function. Since understanding it properly requires a slightly more detailed explanation, it was decided to cover it not in the previous article, but in today's article. So, since the topic will be fairly extensive, let us proceed directly to the next section.

The article provides a critical analysis of an LLM strategy in which forecasting the direction is separated from trading decisions, and demonstrates why this leads to a disconnect between metrics and PnL. We will describe procedures for dataset balancing, feature engineering, prompt and response preparation, fine-tuning configuration in Ollama, and reliable parsing. Backtesting and forward testing reveal systematic degradation. The practical conclusion is that the problem must be formulated as a direct optimization of trading outcomes.

A native, dependency-free MQL5 implementation of the Mann-Whitney U test for comparing trade returns across two market regimes. It details rank calculation, tie correction, and a normal-approximation p-value, and pairs the test with a CCanvas box-and-whisker chart and a trade-history extraction script. A verification script is included, and the limits of the normal approximation and independence assumptions are clearly stated for informed use.

Do you know what a sandbox is? Do you know how to work with it? If the answer to either of these questions is “no”, read this article to understand the basic operating principle of a sandbox. You will also understand why MetaTrader 5 uses a sandbox to protect the integrity of some of its internal data. The material presented here is purely instructional. Under no circumstances should you treat the application as a final product whose purpose is anything other than studying the concepts presented.