Articles with MQL5 programming examples

We translate the EMA‑50 retest idea into a behavior‑driven Expert Advisor for intraday trading. The study formalizes trend bias, EMA interaction (pierce and close), reaction confirmation, and optional filters, then implements them in MQL5 with modular functions and resource‑safe handles. Visual testing in the Strategy Tester verifies signal correctness. The result is a clear template for coding discretionary bounces.

In this article, we will implement the risk management system developed in previous publications and add the Order Blocks indicator described in other articles. In addition, we will run a backtest so we can compare results with the risk management system enabled and evaluate the impact of dynamic risk.

This article turns Market Memory Zones from a chart-only concept into a complete MQL5 Expert Advisor. It automates Displacement, Structure Transition (CHoCH), and Liquidity Sweep zones using ATR- and candle-structure filters, applies lower-timeframe confirmation, and enforces risk-based position sizing with dynamic SL and structure-based TP. You will get the code architecture for detection, entries, trade management, and visualization, plus a brief backtest review.

This article shows how to build rounded speech bubbles in MQL5 by combining a rounded rectangle with a pointer triangle and controlling orientation (up, down, left, right). It details geometry precomputation, supersampled filling, rounded apex arcs, and segmented borders with an extension ratio for seamless joins. Readers get configurable code for size, radii, colors, opacity, and thickness, ready for alerts or tooltips in trading interfaces.

We present a slanted trendline breakout tool that relies on three‑swing validation to generate objective, price‑action signals. The system automates swing detection, trendline construction, and breakout confirmation using crossing logic to reduce noise and standardize execution. The article explains the strategy rules, shows the MQL5 implementation, and reviews testing results; the tool is intended for analysis and signal confirmation, not automated trading.

This is Part 4 of our series on risk management in MQL5, where we continue exploring advanced methods for protecting and optimizing trading strategies. Having laid important foundations in earlier articles, we will now focus on completing all remaining methods postponed in Part 3, including functions for checking whether specific profit or loss levels have been reached. In addition, we will introduce new key events that enable more accurate and flexible risk management.

In this article, we will explore how to create so-called structural code, where the entire context and methods for manipulating variables and information are placed within a structure to create a suitable context for implementing any code. Therefore, we will examine the necessity of using a private section of the code to separate what is public from what is not, thereby adhering to the rule of encapsulation and preserving the context for which the data structure was created.

In this article, we will explore how to approach the implementation of a common structural code base. The goal is to reduce the programming workload and leverage the full potential of the programming language itself—in this case, MQL5.

We are beginning to implement the connection between Excel and MetaTrader 5, but first we need to understand some key points. This way, you won't have to rack your brains trying to figure out why something works or doesn't. And before you frown at the prospect of integrating Python and Excel, let's see how we can (to some extent) control MetaTrader 5 through Excel using xlwings. What we demonstrate here will primarily focus on educational objectives. However, don't think that we can only do what will be covered here.

Many programmers might assume we should abandon using Excel and move directly to Python, using some packages that allow Python to generate an Excel file for later analysis of results. However, as mentioned in the previous article, although this solution is the simplest for many programmers, it will not be accepted by some users. And in this particular case, the user is always right. As programmers, we must find a way to make everything work.

In this article, we will explore what structured code is. Many people confuse structured code with organized code, but there is a difference between these two concepts. This is exactly what will be discussed in this article. Despite the apparent complexity you may feel when first encountering this type of code writing, I have tried to approach the topic as simply as possible. However, this article is just the first step toward something greater.

The article presents a complete Python–MQL5 integration for multi‑agent trading: MT5 data ingestion, indicator computation, per‑agent decisions, and a weighted consensus that outputs a single action. Signals are stored to JSON, served by Flask, and consumed by an MQL5 Expert Advisor for execution with position sizing and ATR‑derived SL/TP. Flask routes provide safe lifecycle control and status monitoring.

In this article, we explore the File Operations classes of the MQL5 Standard Library to build a robust reporting module that automatically generates Excel-ready CSV files. Along the way, we clearly distinguish between manually executed trades and algorithmically executed orders, laying the groundwork for reliable, auditable trade reporting.

This article explores an accessibility-focused enhancement that goes beyond default terminal alerts by leveraging MQL5 resource management to deliver contextual voice feedback. Instead of generic tones, the indicator communicates what has occurred and why, allowing traders to understand market events without relying solely on visual observation. This approach is especially valuable for visually impaired traders, but it also benefits busy or multitasking users who prefer hands-free interaction.

While studying a wide range of breakout setups, I noticed that failed breakouts were rarely caused by a lack of volatility, but more often by weak internal structure. That observation led to the framework presented in this article. The approach identifies patterns where the final price leg shows superior length, steepness, and speed—clear signs of momentum accumulation ahead of directional expansion. By detecting these subtle geometric imbalances within consolidation, traders can anticipate higher-probability breakouts before price exits the range. Continue reading to see how this fractal-based, geometric framework translates structural imbalance into precise breakout signals.

The extent of liquidity zones and the magnitude of the breakout range are key variables that substantially affect the probability of a retest occurring. In this discussion, we outline the complete process for developing an indicator that incorporates these ratios.

In this article, we write an example of visualizing the optimization process and display the top three passes for the four optimization criteria. We will also provide an opportunity to select one of the three best passes for displaying its data in tables and on a chart.

This article presents a structured way to manage SQLite data in MQL5 through an ORM layer for MetaTrader 5. It introduces core classes for entity modeling and database access, a fluent CRUD API, reflection hooks for OnGet/OnSet, and macros to define models quickly. Practical code shows creating tables, binding fields, inserting, updating, querying, and deleting records. Developers gain reusable, type-safe components that minimize repetitive SQL.

Today, we uncover the often overlooked statistical foundation behind supply and demand trading strategies. By combining MQL5 with Python through a Jupyter Notebook workflow, we conduct a structured, data-driven investigation aimed at transforming visual market assumptions into measurable insights. This article covers the complete research process, including data collection, Python-based statistical analysis, algorithm design, testing, and final conclusions. To explore the methodology and findings in detail, read the full article.

Breadth First Search (BFS) uses level-order traversal to model market structure as a directed graph of price swings evolving through time. By analyzing historical bars or sessions layer by layer, BFS prioritizes recent price behavior while still respecting deeper market memory.

In this part, we will focus on designing an intelligent execution layer that continuously monitors and evaluates real-time spread conditions across multiple symbols. The EA dynamically adapts its symbol selection by enabling or disabling trading based on spread efficiency rather than fixed rules. This approach allows high-frequency multi-pair systems to prioritize cost-effective symbols.

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.

In this article, we will begin creating a core risk management class that will be key to controlling risks in the system. We will focus on building the foundations, defining the basic structures, variables and functions. In addition, we will implement the necessary methods for setting maximum profit and loss values, thereby laying the foundation for risk management.