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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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 of a Breakeven Mechanism in MQL5 (Part 2): ATR- and RRR-Based Breakeven
This article completes the implementation of ATR- and RRR-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: 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.
RiskGate: Centralized Risk Management for Multiple EAs
Many MetaTrader 5 setups run several EAs on one account, so risk gets fragmented and correlated exposure slips through. The article introduces RiskGate, a centralized Service that evaluates EA intents account‑wide: EAs send a JSON signal, the Service returns approved, lot and reason. You will see the client/server wiring, example rules (daily loss, exposure and correlation caps), unit‑tested handler design, and an EA example. The result is consistent portfolio‑level risk with simpler EAs.
Integrating MQL5 with Data Processing Packages (Part 9): Entropy-Based Adaptive Volatility
This work presents an end-to-end pipeline: collect MetaTrader 5 data, engineer entropy/volatility/trend features, train a PyTorch classifier, and expose predictions through a Flask API. An MQL5 EA posts rolling prices each tick, receives probability and regime, and applies adaptive position sizing and stop distances. The result is a clear recipe for integrating ML inference with MetaTrader 5.
Adaptive Malaysian Engulfing Indicator (Part 2): Optimized Retest Bar Range
The article adds a self-adaptive layer to the Malaysian Engulfing indicator by optimizing the retest bar range with a constrained brute-force search scored by MFE and MAE. It details the data model, helper routines, and an MQL5 implementation that gathers historical setups, computes excursions, and selects the best parameter. Readers learn how to remove manual tuning and run the indicator with context-appropriate settings across symbols and timeframes.
Creating an EMA Crossover Forward Simulation Indicator in MQL5
A custom forward simulation engine detects fast/slow EMA crossovers and immediately projects synthetic candles ahead of the signal bar. It generates bodies and wicks using controlled logic, draws them with chart objects, and refreshes on every new signal or anchor change. You get a clear forward-looking view to test timing, visualize scenarios, and manage invalidation on the chart.
Adaptive Malaysian Engulfing Indicator (Part 1): Pattern Detection and Retest Validation
Implement the Malaysian Engulfing concept in MQL5 with two coordinated indicators. One applies strict, body‑based engulfing rules for precise pattern detection; the other uses a state-driven model to monitor what follows—pullbacks and timed retests—directly on the chart. The result is a repeatable, rule-based workflow that replaces visual guesswork with programmable logic.
Event-Driven Architecture in MQL5: How to Turn an Expert Advisor into a Full-Fledged Trading System
The article is dedicated to the event-driven architecture in MQL5 and describes the transition from the monolithic OnTick model to distributed processing. We will consider predefined and custom events, services and messaging between programs, as well as common architectural errors. A practical example demonstrates how to organize interactions between indicators and an EA to reduce load, improve readability, and simplify maintenance.
MetaTrader 5 and the MQL5 Economic Calendar: How to Turn News into a Reproducible Trading System
The article presents a systematic approach to news trading in MetaTrader 5 using the built-in economic calendar: data structure, API functions, time synchronization rules, and event filtering. Methods of caching and incremental updating without overloading the server are described. The article also provides a working mechanism for exporting history to an .EX5 resource for deterministic testing using the same algorithm.
Engineering Trading Discipline into Code (Part 5): Account-Level Risk Enforcement in MQL5
We introduce an MQL5 discipline engine that enforces risk consistently at the account level. It continuously scans positions from any source, validates SL/TP, equity-based exposure, and target R:R, and automatically corrects deviations by setting levels or adjusting volume. The result is uniform risk structure across manual and EA trades, supported by on-chart feedback and mode-based control.
How to implement AutoARIMA forecasting in MQL5
This article presents an MQL5 implementation of AutoARIMA that builds ARIMA models without manual tuning. It estimates d via a variance-based heuristic, fits ARMA(p,q) by gradient optimization with Adam, and selects p and q using AICc. The code returns a one-step-ahead price forecast by differencing, model estimation, and integration back to price level, ready to call on a Close series.
Graph Theory: Heuristic Search Algorithm (A-Star) Applied in Trading
The article applies the A* heuristic to market structure by modeling validated swing highs and lows as graph nodes and weighting edges with ATR‑normalized distance, spread, and noise penalties. The engine searches the most efficient route to infer trade direction and targets, then filters signals by directional ratio, total path cost, and opposing swings. It anchors TP to the final node and SL to prior structure, with on‑chart visualization and configurable inputs.
Price Action Analysis Toolkit Development (Part 68): Price-Attached RSI Panel in MQL5
We present a chart-embedded RSI panel that removes the need for a separate window by attaching momentum directly to live price. The article explains the design and MQL5 code: real-time RSI retrieval, slope-based signal classification, and adaptive positioning. Traders get RSI value, state, and signal strength where decisions are made, improving clarity across timeframes.
Algorithmic Trading Without the Routine: Quick Trade Analysis in MetaTrader 5 with SQLite
The article presents a minimal working set for maintaining a trading journal in MQL5 using SQLite: a table structure for trades, signals, and events, indices, prepared statements and trades, as well as standard analytical SQL queries. Integration with the statistics dashboard in MetaTrader 5 and working with the database via MetaEditor are demonstrated. The approach allows automating the journal, accelerating calculations, and performing analysis without complicating the EA code.
Developing a Multi-Currency Advisor (Part 27): Component for Displaying Multi-Line Text
If there is a need to display text on a chart, we can use the Comment() function. But its capabilities are quite limited. Therefore, in this article, we will create our own component - a full-screen dialog window capable of displaying multi-line text with flexible font settings and scrolling support.
Price Action Analysis Toolkit Development (Part 67): Automating Support and Resistance Monitoring in MQL5
This article implements a complete MQL5 Expert Advisor that monitors manually drawn support and resistance levels in real time. It synchronizes horizontal lines, detects approaches, touches, breakouts, reversals, and retests, and adds optional candlestick pattern checks. Alerts and on‑chart markers provide clear, repeatable feedback, allowing you to keep manual analysis while automating the surveillance of key price levels.
Building Volatility Models in MQL5 (Part II): Implementing GJR-GARCH and TARCH in MQL5
The article implements GJR-GARCH and TARCH in an MQL5 volatility library and explains why asymmetry improves on standard ARCH/GARCH. It covers model formulation, parameterization, and usage through derived classes and scripts. Readers get code examples for calibration and one-step-ahead forecasting on real data to support risk and diagnostics.
From Novice to Expert: Automating Base-Candle Geometry for Liquidity Zones in MQL5
This article implements an MQL5 module that analyzes the lower‑timeframe bars inside each liquidity‑zone base candle. It detects swing points and applies objective rules to classify the internal structure as an ascending, descending, or symmetrical triangle; a rectangle; M; W; or undefined. The indicator displays geometry labels on the chart and adds the pattern to alerts, reducing manual lower‑timeframe inspection.
Automating Market Entropy Indicator: Trading System Based on Information Theory
This article presents an EA that automates the previously introduced Market Entropy methodology. It computes fast and slow entropy, momentum, and compression states, validates signals, and executes orders with SL/TP and optional position reversal. The result is a practical, configurable tool that applies information-theoretic signals without manual interpretation.
Account Audit System in MQL5 (Part 1): Designing the User Interface
This article builds the user interface layer of an Account Audit System in MQL5 using CChartObject classes. We construct an on-chart dashboard that displays key metrics such as start/end balance, net profit, total trades, wins/losses, win rate, withdrawals, and a star-based performance rating. A menu button lets you show or hide the panel and restores one-click trading, delivering a clean, usable foundation for the broader audit pipeline.