MQL5 Programming Articles

This article builds the foundation layer of a twelve-part MQL5 market microstructure toolkit. It implements guarded math helpers (SafeDivide, SafeLog, SafeSqrt, SafeExp, SafeTanh), robust data validation (ValidateSymbolV2, SafeCopyClose), trimmed statistical estimators (robust mean var), a linear regression slope, shared structs, and an FFT. You compile a single include file that hardens indicators and expert advisors against silent numerical failures and standardizes data flow for later parts.

The article presents the Artificial Showering Algorithm (ASHA), a new metaheuristic method developed for solving general optimization problems. Based on simulation of water flow and accumulation processes, this algorithm constructs the concept of an ideal field, in which each unit of resource (water) is called upon to find an optimal solution. We will find out how ASHA adapts flow and accumulation principles to efficiently allocate resources in a search space, and see its implementation and test results.

In this discussion, we follow up on the previously developed multi-signal Expert Advisor with the objective of exploring and applying available optimization methods. The aim is to determine whether the trading performance of the EA can be meaningfully improved through systematic optimization based on historical data.

The article considers an optimization method based on the principles of the body's immune system - Micro Artificial Immune System (Micro-AIS) - a modification of AIS. Micro-AIS uses a simpler model of the immune system and simple immune information processing operations. The article also discusses the advantages and disadvantages of Micro-AIS compared to conventional AIS.

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.

This article introduces the basic concepts behind HMAC-SHA256 and API signatures in MQL5, explaining how messages and secret keys are combined to securely authenticate requests. It lays the foundation for signing API calls without exposing sensitive data.

In this article, we explore the challenge of understanding how AI works. AI models often make decisions in ways that are hard to explain, leading to what's known as the "disagreement problem". This issue is key to making AI more transparent and trustworthy.

The article introduces a unified MQL5 discipline framework that consolidates the symbol whitelist, trading‑hours and news filters, and daily trade‑limit modules under CDisciplineEngine.mqh. It explains centralized trade validation and state synchronization shared by a chart dashboard and an enforcement Expert Advisor. Readers learn how to authorize orders through a single gate, monitor permissions in real time, and automatically enforce rules across the terminal.

Today we will learn a technique that can help us a lot in different stages of our professional life as a programmer. Often it is not the platform itself that is limited, but the knowledge of the person who talks about the limitations. This article will tell you that with common sense and creativity you can make the MetaTrader 5 platform much more interesting and versatile without resorting to creating crazy programs or anything like that, and create simple yet safe and reliable code. We will use our creativity to modify existing code without deleting or adding a single line to the source code.

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.

In this article, we will look at how to fix two errors in the code. However, I will try to explain them in a way that will help you, beginner programmers, understand that things don't always go as you expect. Anyway, this is an opportunity to learn. The content presented here is intended solely for educational purposes. In no way should this application be considered as a final document with any purpose other than to explore the concepts presented.

In this article, we present the Arithmetic Optimization Algorithm (AOA) based on simple arithmetic operations: addition, subtraction, multiplication and division. These basic mathematical operations serve as the foundation for finding optimal solutions to various problems.

High-frequency MQL5 indicators that instantiate objects on every tick accumulate allocation overhead and timing jitter in OnCalculate(). This article constructs a generic templated object pool using a free-list index array, delivering O(1) Acquire() and Release() operations. The design includes double-release protection, strict separation of payload state from pool metadata in Reset(), and a fixed-capacity free list with no heap fallback. A dual-path custom indicator benchmark measures per-tick overhead difference using GetMicrosecondCount().

Applying Python session boundaries to MQL5 broker timestamps misclassifies session membership by two to three hours on any non-UTC broker, corrupting session flags across the full backtest history. We implement CTimeFeatures.mqh, containing CRingBuffer and CTimeFeatures, with three EA-facing methods: Initialize (UTC offset capture and frequency gate configuration), Update (log return push to session-conditional ring buffers), and Calculate (cyclical encoding, session flags, and session volatility). The output is a flat double array drop-compatible with Python's get_time_features for sub-hourly, hourly, and daily timeframes.

A detailed guide on how to create a heat map indicator for MetaTrader 5 that visualizes the price distribution over time. The article reveals the mathematical basis of time density analysis, where each price level is colored from red (minimum stay time) to blue (maximum stay time).

We invite you to explore the Actor-Director-Critic framework, which combines hierarchical learning and a multi-component architecture for creating adaptive trading strategies. In this article, we take a detailed look at how using the Director to classify the Actor's actions helps to effectively optimize trading decisions and improve the robustness of models in financial market conditions.

The article demonstrates spurious regression occurring when attempting to apply regression analysis to non-stationary processes using Monte Carlo simulation.

We introduce MPO4, a pressure-based oscillator that emphasizes the body and direction of candles in the context of current volatility. The article details its mathematics, normalization into a bounded range, and the EMA smoothing, then builds a pivot-driven divergence module designed not to repaint. You get complete MQL5 implementation and practical guidance for interpreting signals, including a comparison with RSI as an alternative source.

GoertzelBrain combines Goertzel spectral analysis with an online‑trained neural network ensemble to convert cycle features into a directional confirmation signal. The indicator builds a compact feature vector from the dominant period, amplitude, confidence and their dynamics, plus local volatility, and outputs +1, −1 or 0. The article provides the full MQL5 implementation, explains the architecture and feature engineering, and shows how to use it as a directional filter.

This is the second part of the article devoted to the implementation of the table model in MQL5 using the MVC (Model-View-Controller) architectural paradigm. The article discusses the development of table classes and the table header based on a previously created table model. The developed classes will form the basis for further implementation of View and Controller components, which will be discussed in the following articles.

The article implements CMultiTimeframeMatrix, a reusable dashboard that maps symbols vs. timeframes and displays a numeric, colour‑coded score. The score combines trend, momentum, and volatility, updates by timer, and respects performance constraints. You will learn how to build the UI with CAppDialog/CLabel, compute metrics via CMatrixDouble, and embed the component into a thin EA for a consistent, real-time overview.

Discover how to detect user actions in MetaTrader 5, send requests to an AI API, extract responses, and implement scrolling text in your panel.

In this series of articles, we revisit well-known trading strategies to see if we can improve them using AI. In today's discussion, join us as we test whether there is a reliable relationship between precious metals and currencies.

In this article we make the case for pairing the Discrete Fourier Transform with a Spiking Neural Network in a Trading Robot. The Fourier Transform helps represent data as oscillations instead of its raw values. To govern how we interpret these cycles, we engage a Spiking Neural Network that unlike regular networks, uses time dependent electrical charges to accumulate potential and only "spike" when a target threshold is met. Combining these two engines allows us better control on the timing of discrete market movements, that in theory should give us entry signals with rigorous mathematical confirmation.

Today we have a very funny and quite interesting article. We will look at Union and will try to solve the problem discussed earlier. We'll also explore some unusual situations that can arise when using union in applications. The materials presented here are intended for didactic purposes only. Under no circumstances should the application be viewed for any purpose other than to learn and master the concepts presented.

This article completes the implementation of ATR- and RRRR-based breakeven mechanisms in MQL5 and develops, from scratch, a class that makes it easy to switch breakeven modes without having to enter the parameters again. To evaluate the effectiveness of each breakeven type, several backtests are run, analyzing their advantages and disadvantages in the context of algorithmic trading.

The article presents the revolutionary architecture of PatchTST, a tailored transformer for financial time series analysis that breaks market data into 16-bar patches for efficient processing. We will discuss the full implementation of a trading robot in MQL5 covering everything from mathematical fundamentals and data structures to a ready-made EA with risk management and continuous learning systems.

This article shows how to configure a black-box model to automatically uncover strong trading strategies using a data-driven approach. By using Mutual Information to prioritize the most learnable signals, we can build smarter and more adaptive models that outperform conventional methods. Readers will also learn to avoid common pitfalls like overreliance on surface-level metrics, and instead develop strategies rooted in meaningful statistical insight.