MQL4 and MQL5 Programming Articles

This article discusses the transition from procedural coding to object-oriented programming (OOP) in MQL5 with an emphasis on integration with the REST API. Today we will discuss how to organize HTTP request functions (GET and POST) into classes. We will take a closer look at code refactoring and show how to replace isolated functions with class methods. The article contains practical examples and tests.

This article is a continuation in a series that look at Category Theory implementation in MQL5. In here we examine how Graph-Theory could be integrated with monoids and other data structures when developing a close-out strategy to a trading system.

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.

Trend-scanning supports both forward and backward windows, and the labeling default is unsafe for features: it looks ahead and boosts next-bar agreement well above chance on random walks. We provide a dedicated wrapper, get trend scanning features, that forces computational causal and returns only window, slope, t value, and rsquared. A second analysis quantifies errors introduced by the default log transform on signed series.

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.

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).

This is a continuation of the previous article considering the idea of social groups. The article explores the evolution of social groups using movement and memory algorithms. The results will help to understand the evolution of social systems and apply them in optimization and search for solutions.

This article advances the stateful supply and demand zone framework for MetaTrader 5 by replacing polling with an event-driven model based on OnChartEvent(). We split synchronization into dedicated handlers for creation, modification, and deletion, and separate market logic in OnTick() from user interactions in OnChartEvent(). A persistent, append-only CSV logger records all lifecycle events, improving responsiveness, state consistency, and recoverable history for downstream analysis.

In this article, we will take a very close look at how to solve the problem posed at the end of the previous article. There was an attempt to create a template of such type so that to be able to create a template for data union.

We continue to implement the DA-CG-LSTM framework, which offers innovative methods for time series analysis and forecasting. The use of CG-LSTM and dual attention allows for more accurate detection of both long-term and short-term dependencies in data, which is particularly useful for working with financial markets.

In this article we explore different ways in which the eigenvectors and eigenvalues can be applied in exploratory data analysis to reveal unique relationships in data.

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.

We bridge Python-native artifacts to MQL5 for tick-accurate CPCV backtesting. The export script converts the ONNX model, calibrator, feature spec, and path masks to flat files, while the expert advisor rebuilds features, performs ONNX inference with calibration, and trades on real ticks. The Strategy Tester runs each combinatorial path, and Python aggregates per-path equities into a path Sharpe distribution to assess robustness after spread, slippage, and commission.

Volatile-market failures such as requotes, connection drops, and partial fills expose a common weakness in EAs: unclassified retries and no cumulative failure control. This article introduces CRetryExecutor with exponential backoff and explicit error classification, plus a three-state CCircuitBreaker with cooldown and half-open probes, unified in CExecutionGateway. You can plug it into an EA to stop futile retries, prevent duplicate submissions, and improve diagnostics.

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.

In this article we will do things that many will find strange and completely out of context, but which, if used correctly, will make your learning much more fun and interesting: we will be able to build quite interesting things based on what is shown here. This will allow you to better understand the syntax of the MQL5 language. The materials provided here are for educational purposes only. It should not be considered in any way as a final application. Its purpose is not to explore the concepts presented.

A market-neutral trading strategy based on the empirical return distribution offers an alternative to traditional technical analysis methods, replacing price direction forecasting with the statistical placement of orders at levels the price is likely to reach. This article provides a detailed analysis of the mathematical framework for calculating percentiles, algorithms for weighting position sizes based on the probability of an order being triggered, and mechanisms for adapting to changing market conditions through grid expiration. A complete implementation in MQL5 is provided.

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.

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.

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.

We extend the Part 9 setup wizard to build a canvas-based, in-chart documentation system for MetaTrader 5. The panel is tabbed and scrollable, supports inline styling, images, and interactive controls, and renders with supersampled anti-aliasing. The result is a reusable engine that any MQL5 program can embed to deliver self-contained documentation directly on the chart.

We build a tCISD program in MQL5 that pairs Quarterly Theory cycles anchored to New York time with a correlated-symbol SSMT divergence to time reversals. The article shows how to map cycles and quarters, detect the cross-symbol sweep disagreement, and derive the tCISD level whose break confirms the change in delivery. You will get a working entry logic that arms on divergence and executes on a confirmation close or a retest.

We build a lightweight bridge that captures closed trades in MetaTrader 5 and sends them to an external backend over HTTP as JSON. It uses OnTradeTransaction for event detection, reads details from deal history, assembles a JSON payload, and posts it via WebRequest. A local Flask API is used to test the flow, delivering a working path to move trade data outside the terminal.

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.

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

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.

We present a complete workflow for adaptive filtering in MQL5 using the CNlEq Levenberg–Marquardt–like solver. The EA fits a VAMAC model—two EWMAs with an ATR‑based scaling—by supplying residuals and a Jacobian through CNlEq's reverse‑communication loop, with optional numerical or analytical derivatives. Code, setup instructions, and GBPUSD H1 tests show how to replace static thresholds with on‑bar re‑estimation.

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

An MQL5 control system that blocks orders outside scheduled trading hours and during scheduled news releases, converting time rules into executable restrictions. It combines a permissions management mechanism, a transaction-level expert advisor, and a visual dashboard for real-time status and upcoming restrictions. Configuration is accomplished using editable files, with caching and a CSV audit log for traceability.

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.

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.

An innovative indicator based on prime number theory helps identify strong reversal levels that other traders overlook. Testing on 10 assets showed that reversals in mathematically significant zones occur 1.5 to 1.8 times more frequently. Five practical application scenarios with specific rules for filtering out false breakouts and making precise market entries.