Articles with MQL5 programming examples
Access a huge collection of articles with code examples showing how to create indicators and trading robots for the MetaTrader platform in the MQL5 language. Source codes are attached to the articles, so you can open them in MetaEditor and run them to see how the applications work.
These articles will be useful both for those who have just started exploring automated trading and for professional traders with programming experience. They feature not only examples, but also contain new ideas.
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Code, Tears, and Algo Forge
This article discusses the transition to MQL5 Algo Forge as a modern and convenient format for publishing program code and article attachments. Using repositories instead of traditional ZIP archives and source code allows you to keep projects up-to-date, make edits quickly, and professionally interact with your readers. Recommendations are provided for quickly migrating developments to the cloud environment via the MetaEditor interface.
Measuring What Matters (Part 1) : Portfolio Risk Decomposition in MQL5
The article establishes a reproducible method to measure portfolio risk for multiple symbols using MQL5 matrices and OpenBLAS. It covers computing log returns, building a covariance matrix, and evaluating wᵀΣw instead of summing individual variances. A complete script prints naive versus true volatility and the cross‑term contribution, enabling you to detect when correlated instruments inflate exposure beyond single‑asset estimates.
Automating Classic Market Methods in MQL5 (Part 2): Wyckoff Cause and Effect—Point and Figure Price Targets
This article builds a self-contained MQL5 Expert Advisor that completes the Wyckoff cycle: it detects accumulation/distribution with a finite state machine, enters at the last point of support/supply, and calculates exit point-and-figure counts under Wyckoff's Cause and Effect. We detail the box size from range ATR, a 1-box reversal, target validation, and a 2R fallback. Readers get runnable code without external dependencies.
Creating an HTML Dashboard for Strategy Tester and Prop Firm Challenge Analysis in MQL5
This article demonstrates how to build a reusable prop‑firm evaluation module for MQL5 Expert Advisors and export results to an HTML dashboard. The module monitors balance and equity during backtests, simulates single or rolling challenges, checks profit target, daily and overall drawdown, and minimum trading days, then outputs both a terminal summary and a browser‑readable report.
Forecasting in Trading Using Grey Models
The article discusses the application of Grey models to forecasting financial time series. We will consider the operating principles of Grey models and the specifics of their application to financial series. We will also discuss the advantages and limitations of using these models in trading.
The MQL5 Standard Library Explorer (Part 13): Implementing the Math Solvers Library in Trading
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.
Interactive Supply and Demand Zone Manager in MQL5 (Part II): Event-Driven Architecture and Persistent Lifecycle Logging
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.
From Basic to Intermediate: Object Events (II)
In this article, we will look at how the last three types of events generated by an object work. Understanding this will be very interesting, because in the end we will do something that may seem crazy to many people, but it is entirely possible and produces a very surprising result.
How to Detect and Normalize Chart Objects in MQL5 (Part 3): Alerting and Automated Trading from Manually Drawn Objects
This article extends the chart‑object detector into a modular monitoring and execution layer. It defines objective interaction rules (touch, cross, breakout) for trendlines, Fibonacci levels, channels, rectangles, and pitchforks, then routes events through an interaction detector, alert manager, and optional trade executor. Orders use object geometry for stop‑loss and take‑profit. The result is a reproducible pipeline that converts static drawings into actionable alerts and, if enabled, trades.
From Basic to Intermediate: Object Events (I)
In this article, we will look at three of the six events that MetaTrader 5 can generate when some change occurs to an object on the chart. These events are very useful from the standpoint of user interaction. This is because, without understanding these events, we would have to put in much more effort to maintain a specific chart configuration when trying to manage objects for particular purposes.
From Basic to Intermediate: Objects (III)
In today's article, we will look at how to implement a very attractive and interesting interaction system, especially for those who are just beginning to practice programming in MQL5. There is nothing fundamentally new here. Thanks to my approach to the topic, it will be much easier to understand everything, because we will see in practice how to develop a program using a structured approach with a practical and engaging goal.
Trading Options Without Options (Part 2): Use in Real Trading
The article considers simple options strategies and their implementation in MQL5. We will develop a basic EA that will be modernized and become more complex.
From Basic to Intermediate: Handling Mouse Events
This article belongs to the category of materials where simply looking through and studying the code is definitely not enough to understand the processes involved. In fact, you need to create an executable application and run it on any chart. This is done so that you can understand small details that would otherwise be extremely difficult to grasp, such as using the keyboard and mouse together to create certain elements.
Overcoming Accessibility Problems in MQL5 Trading Tools (Part V): Gesture-Based Trading With Computer Vision
This article shows how to build a hands-free trading workflow for MetaTrader 5 by translating webcam-tracked hand gestures into MQL5 trade commands. We cover the architecture (MediaPipe/OpenCV in Python plus an MQL5 EA), gesture-to-action mapping, and interprocess communication via Global Variables or HTTP polling. You will implement the EA, execute BUY/SELL/CLOSE actions, and validate latency and reliability under real‑time conditions.
Graph Theory: Network Flow of Commodities (Ford-Fulkerson Algorithm), Used as a Liquidity-Capacity Engine
The article presents an MQL5 Expert Advisor that adapts the Ford–Fulkerson max-flow method into a liquidity-capacity filter. Market structures—Swing Highs/Lows, Fair Value Gaps, Order Blocks, and Liquidity Pools—form a directed graph with edge capacities from volume, price reaction, distance, and structure quality. Maximum flow qualifies ICT setups, filters weak paths, and drives dynamic position sizing for a consistent, two-stage decision process.
Swing Extremes and Pullbacks (Part 4): Dynamic Pullback Depth Using Volatility Models
This article replaces binary swing validation with a volatility‑normalized pullback model. Retracement depth is measured as a ratio of the prior impulse and calibrated to a rolling ATR regime, while entries require a minimum quality score and confirmation by structure or liquidity signals. The five‑layer design integrates detection, validation, liquidity mapping, regime‑aware scoring, and execution, helping you filter weak corrections and size stops dynamically to current conditions.
Step-by-Step Implementation of a Local Stop Loss System in MQL5
This article shows how to build a local stop-loss system in an MQL5 Expert Advisor that keeps stop levels on the terminal side. It walks through the execution logic, event handlers, inputs, and an OOP design using CTrade, CPositionInfo, CHashMap/CHashSet, and chart objects. You will implement multi-position tracking, draggable stops, visual spacers and labels, plus cleanup and disconnection behavior to create a practical risk-control utility.
How to Detect and Normalize Chart Objects in MQL5 (Part 2): Collecting and Structuring Data from Complex Analytical Objects
Manually drawn analytical object tools like Fibonacci tools, and Andrews Pitchforks are invisible to automated trading logic. This article extends a base detector to extract anchor points, level arrays, and geometric offsets from complex objects. You will implement a reusable collector that normalizes the raw chart data into structured memory arrays, ready for strategy decisions.
Developing a Multi-Currency Expert Advisor (Part 28): Adding a Position Closing Manager
When running multiple strategies in parallel, you may want to periodically close all open positions and start the strategies over again. The existing code only allows this behavior to be implemented through manual intervention. Let's try to automate this part.
Implementing a Breakeven Mechanism in MQL5 (Part 2): ATR- and RRR-Based Breakeven
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.
Interactive Supply and Demand Zone Manager in MQL5 (Part I): From Manual to Automated Lifecycle
Replace static drawings with automated, stateful zones controlled by a CZone wrapper. The system synchronizes user rectangles, sizes zones by ATR, validates breakouts using consecutive closes, applies ghost/deactivation rules, merges nearby structures by a 1.5×ATR threshold, and projects edges forward. Traders gain durable levels that update themselves and reduce repetitive chart management.
From Basic to Intermediate: Objects (II)
In today's article, we will look at how to control some object properties in a simple way using code. We will also see how a custom application can place more than one object on the same chart. In addition, we will begin to understand the importance of assigning a short name to any indicator we plan to implement.
From Basic to Intermediate: Function Pointers
You have probably already heard about pointers when it comes to programming. But did you know that we can use this kind of data here in MQL5? Of course, this must be done in a way that keeps us in control and avoids strange program behavior during execution. Still, because this is a feature with a very specific purpose and aimed at particular kinds of tasks, it is rare to hear anyone discuss what a pointer is and how to use it in MQL5.
From Basic to Intermediate: Objects (I)
In this article, we will begin looking at how to work with objects directly on the chart. This is done using code specially developed for demonstration purposes. Working with objects is very interesting and can be a lot of fun. Since this will be our first contact with the topic, we will start with something very simple.
From Basic to Intermediate: Indicator (V)
In this article, we will look at how to handle user requests to change the chart plotting mode. This is necessary so that an indicator designed for the current chart plotting mode does not look strange or differ from what a MetaTrader 5 user expects.
Formulating Dynamic Multi-Pair EA (Part 9): Market Microstructure Execution Noise Filtering
This article presents a multi-symbol execution filter that scores real-time market quality before any trade is allowed. It measures spread behavior, tick velocity, quote gaps, micro-volatility, and a slippage estimate, then classifies the state to block degraded conditions. Once noise settles, a liquidity sweep continuation model evaluates structure shifts so entries occur only when execution is mechanically stable.
Modular Indicator Architecture in MQL5 (Part 1): Stop Copy-Pasting and Start Writing Scalable, Reusable Code
This article develops an object-oriented framework for MQL5 indicators by evolving a primitive example into reusable modules. It formalizes partial buffer recalculation in OnCalculate, moves logic into header-based classes (CAppliedPrice, CSma), and introduces CSubIndiBase, CIndicatorBase, and a registry to centralize requirements. You get portable components, isolated inputs, and clean buffers with minimal boilerplate, making new indicators faster to assemble and easier to maintain.
Meta-Labeling the Classics (Part 1): Filtering and Sizing RSI Trades
RSI accumulates losses in trending conditions by firing at every threshold crossing regardless of market regime. A Random Forest secondary classifier trained on 12 contextual features — RSI momentum slope, EMA50 trend velocity, ATR-normalised trend stretch, and nine others — filters raw signals and scales position size by classifier confidence on EURUSD H1. Results compare plain RSI, meta-filtered RSI, and bet-sized RSI across a 16-month out-of-sample period with per-trade metrics and drawdown diagnostics.
Building a Dynamic STF Liquidity Sweep Indicator in MQL5
The article delivers a dynamic MetaTrader 5 indicator that detects liquidity sweeps via swing‑point logic, wick‑ratio thresholds, and engulfing confirmation. It recognizes single‑wick and dual‑candle patterns without a fixed window, updates buy‑/sell‑side targets as price evolves, and invalidates broken levels to maintain a reliable liquidity map.
Integrating AI into 3 Smart Money Concepts (SMC): OB, BOS, and FVG
This guide integrates a trained XGBoost model (ONNX) into an SMC EA to evaluate trade setups before execution. The Python pipeline labels historical XAUUSD events and produces a 12-feature representation aligned with the EA. The result is a reproducible method to train, export, and embed the model so the EA can filter OB, FVG, and BOS signals programmatically.
Building the Market Structure Sentinel Indicator in MQL5
This article builds a Market Structure Sentinel indicator in MQL5 that detects and visualizes Smart Money Concepts (SMC) events, including Break of Structure (BOS) and Change of Character (CHOCH), in real time. It explains swing detection, structural validation, and trend classification, and adds a compact dashboard to track bullish, bearish, or ranging states for faster on‑chart interpretation.
How to Detect and Normalize Chart Objects in MQL5 (Part 1): Building a Chart Object Detection Engine
This article addresses the interpretative gap between visual chart objects and algorithmic execution. You will build a systematic detector that iterates over all chart objects, identifies analytical types, and normalises their geometric data (time and price coordinates) into a structured SChartObjectInfo array. The implementation uses raw MQL5 functions, a filter‑extract‑store pipeline, and a timer‑driven test EA, resulting in a reusable framework for rule‑based trading inputs.
Publish Your Article Code to MQL5 Algo Forge in 10 Minutes: A Step-by-Step Guide
The article provides a step-by-step guide on how to migrate code from a published project into a fully-fledged MQL5 Algo Forge project. You will set up the environment and authentication in MetaEditor, create a project in Shared Projects, select the type, arrange the files, add README.md, check the encoding and build, commit the changes to Git, and open the repository publicly. The article helps to build a working structure and preserve version history for the convenience of readers.
Overcoming Accessibility Problems in MQL5 Trading Tools (Part IV): Remote voice trading
Learn a practical way to execute MetaTrader 5 trades from Telegram voice notes using a Python middleware and an MQL5 EA acting as an HTTP client. The article covers architecture, WebRequest polling, in-memory queuing, JSON parsing with null-terminator stripping, and a constrained command grammar with a 0.001-lot default. You will configure the environment and validate round‑trip latency suitable for mobile data connections.
3D Visualization Without External Libraries: How MetaTrader 5 Reveals Optimization Results via MQL5 + DX11
The article describes the practical application of DirectX 11 and built-in MQL5 tools for creating 3D visualizations and interactive interfaces in MetaTrader 5. The focus is on cognitive efficiency - the ability of 3D charts and guided scenes to help in understanding optimization data, liquidity clusters, and multi-dimensional trading scenarios. The basics of the DX pipeline, working with shaders, binding mouse and keyboard events, and objective technological limitations are discussed in detail. The article is intended for MQL5 developers and algorithmic traders who are ready to transform strategy metrics into understandable 3D analytical landscapes, where the visual layer accelerates decision-making.
Engineering Trading Discipline into Code (Part 6): Building a Unified Discipline Framework in MQL5
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.
MetaTrader 5: Build a Market to Suit Your Strategy — Renko/Range/Volume, Synthetics, and Stress Tests on Custom Symbols
In this article, we demonstrate how to use API of the MetaTrader 5 custom symbols to transform your terminal into a data constructor for generating timeless Renko, Range, and Equal-Volume charts and assembling synthetic instruments. We will analyze tick aggregation and history modification for stress tests (spread widening, stop level changes) taking into account platform limitations. Besides, you will get some practice of handling CiCustomSymbol and routing orders to a real symbol through the CustomOrder wrapper with ready-made code fragments.
The MQL5 Standard Library Explorer (Part 12): Multi-Timeframe Composite-Score Dashboard
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.
Price Action Analysis Toolkit Development (Part 69): Flag Pattern Detection in MQL5
This article shows how to convert subjective flag recognition into reproducible MQL5 logic for live charts. It combines ATR-normalized pole strength, retracement limits, consolidation structure checks, breakout confirmation, and overlap control. Readers gain a workable approach that renders adaptive channels and zones, updates active setups efficiently, and provides optional alerts for newly confirmed patterns.
The Power of MetaTrader 5: From Step-by-Step Debugging to EX5 Protection in a Unified Environment
This article examines a comprehensive approach to developing trading algorithms: from project setup and logic debugging to protecting the finished product. We will explore MetaEditor's built-in tools, including step-by-step debugging using real ticks, performance profiling, and direct integration with C++ DLLs to speed up calculations. The article also explains how to protect intellectual property using MQL5 Cloud Protector. The application of the described techniques will transform Expert Advisor development from a chaotic search for solutions into a systematic process, significantly reducing the time required to develop a strategy.