Articles with MQL5 programming examples

A step-by-step guide to building an anchored VWAP indicator with an interactive draggable anchor line in MQL5. The article covers the complete implementation, including calculation methodology, session resets, standard deviation bands, and custom visualization. Learn the architectural design decisions behind stateless boundary detection, multi-instance support, and cross-asset volume handling to build a versatile indicator with benchmarking, technical, and analytical capabilities.

This article focuses on EA architecture rather than signal design. Starting with a flawed Moving Average crossover EA, we add new‑bar detection to prevent duplicate entries, Magic Number and position awareness, ATR‑based risk levels, and data and trade result validation, along with basic safeguards. You obtain a practical base to build and test advanced systems.

This article demonstrates a practical approach to researching trading ideas using a range breakout strategy as an example. We will go through the entire process, from formalizing trading rules and building a baseline model to parameter optimization, forward testing, and evaluating the robustness of the results. The main goal of the article is to develop an understanding of how statistics and testing can be used to identify, validate, and evaluate trading hypotheses.

We enhance the manual backtesting EA with real-time lot adjustment, an order module for buy/sell stops and limits, and a Trade Manager to modify TP/SL and close positions individually. The article explains control setup with CButton/CBmpButton/CEdit, logic in OnTick, and workarounds for Strategy Tester input constraints. Readers can reuse these components to speed up testing workflows and implement robust trade management.

We demonstrate a practical customization path for a built-in MetaTrader 5 EA using BullishBearish MeetingLines Stoch. The workflow covers baseline testing in the Strategy Tester, parameter optimization, and code-level changes. Two modifications are implemented: exposing Stochastic thresholds as inputs and adding an optional Moving Average filter to limit counter‑trend signals. The article includes the full modified code for replication.

The article quantifies correlation and portfolio risk in MetaTrader 5: from time-aligned returns to a covariance matrix, true portfolio variance against the independent-sum assumption, and position-level risk attribution. A MetaTrader 5 service runs in the background, shows the metrics on a small chart panel, and pushes alerts when risk thresholds are crossed. Source code is provided for an example script, a reusable risk engine class, and the service.

A structured MQL5 implementation of a multi‑symbol trading panel with clear separation of concerns: symbol handling, trading logic, and GUI. Integrated into an Expert Advisor, it validates symbols, exposes centralized controls for opening and positions managing across symbols, and applies SL/TP changes. Real‑time account and portfolio metrics help streamline routine operations from a single chart.

Your Expert Advisor runs clean on your demo, then throws errors on a client's broker and quietly stops trading - and the code never changed. What changed is the broker's rulebook. This first article of the Broker Reality Check series builds a diagnostic EA that reads every relevant symbol trading condition - filling policy, stops and freeze levels, volume step, trade mode, swap and the triple-swap day - and flags the ones that silently break EAs, in plain language. It shows a green/amber/red panel, prints a report and dumps every Market Watch symbol to CSV, so you see why an OrderSend fails (10030, invalid stops, invalid volume) before it costs you a trade.

This article introduces ap.mqh, the ALGLIB port for MQL5, and demonstrates its use in multi‑asset workflows that require robust linear algebra. It covers why built-in indicators fall short, then implements polynomial regression, a rolling correlation matrix indicator, and an adaptive hedge ratio estimator using ridge regression with Cholesky. Practical code shows how to compute spread z‑scores and execute coordinated pairs trades entirely within MetaTrader 5.

We implement an interactive, anchored multi-timeframe volume profile in MQL5 for MetaTrader 5. The indicator draws the current-timeframe profile on the main chart and a higher timeframe profile in a subwindow, both aligned by a draggable anchor and the visible range. You will learn keyboard-driven bin editing (E/S double-click), robust timeframe validation, viewport-aware updates, and object restoration to build a reliable, synchronized volume workflow.

In today’s article, we will look at several ways to work with the FileSave and FileLoad library functions. Although many people consider them of limited use because of certain limitations or difficulties they create in specific scenarios, properly understanding how these two functions work can save us a great deal of effort at certain points. They are also an excellent way to work with log files.

We extend the stateful supply and demand framework for MetaTrader 5 with a quantitative admission model and a dedicated interaction engine. Candidate zones are scored by structural symmetry, volume participation, and ATR‑normalized displacement, then classified into objective tiers. Admitted zones follow a deterministic lifecycle that tracks first touch, validates bounces, or confirms breakouts, with full telemetry for analysis and reproducibility.

In Part 2, we introduce a reusable CCovarianceMatrix class that computes and stores a covariance matrix from raw return series using MQL5's native Cov() method. We verify symmetry, print a labeled matrix grid, and call Eig() to obtain eigenvalues and eigenvectors. Readers see how symbols co-move and which factors drive variance, enabling clearer portfolio diagnostics and reuse in scripts or EAs.

This article will illustrate to the reader how to implement a mean-reverting strategy for the EURUSD pair. The strategy follows contrarian trading rules. Our strategy implements a weekly moving average channel, with one moving average on the high-price feed and the latter on the low-price feed. We enter short positions when the price falls beneath the low moving average and long positions when the price rises above the high moving average. Additionally, we will export daily market data to build a simple ONNX model of the market to provide an additional filter for our entries. This provides the reader with a reproducible template for strategy development and backtesting.

This is perhaps the most entertaining article so far. The reason is that here we will modify an object already available in MetaTrader 5 in order to create another one that is not originally present on the platform. Of course, what we are going to look at here may seem a little crazy, but it works and serves a very interesting purpose.

This article demonstrates a working prototype integrating Brain-Computer Interface technology with MetaTrader 5, proving thought-based trading is feasible at the software level. A Python Flask server simulates neural command generation, communicating with an MQL5 Expert Advisor via JSON-over-HTTP. The complete pipeline—from signal generation to trade execution—is validated through WebRequest and CTrade. While BCI hardware remains clinically restricted, this simulation establishes a reference architecture for future accessibility options, enabling direct intention-based trading that expands how traders can interact with financial markets.

In this article, we will prepare the foundation for what will be covered in the next publication. We will also look at how to make an OBJ_LABEL object fully interactive for editing and moving. In other words, we can change both the text and the position of the OBJ_LABEL object without opening the Object Properties dialog.

We build a Support and Resistance Volume Profile indicator that adapts to the current viewport in MetaTrader 5. You will learn viewport detection, dynamic SnR identification, zoom‑driven bin sizing, min‑max volume scaling, and fast on‑chart rendering controlled by OnChartEvent. This approach expresses the relative strength of SnR levels with volume, keeping the chart focused on actionable reaction zones.

The article delivers MQL5 implementations of FIGARCH and HARCH and updates the volatility library for long‑memory processes. It provides code for Hurst and GPH testing, parameter setup (truncation and horizons), and scripts for fitting, forecasting, and simulations. Readers learn how to apply and compare the models on market data to select an appropriate specification.

This article finalizes the Forward Simulation Engine for MetaTrader 5 by calibrating synthetic candles to recent market volatility instead of using slope-only sizing. It samples average body, upper wick, and lower wick from closed bars, applies a sine-envelope with decay, proportional wicks, gaps between candles, and periodic counter-trend injections. The result is a live projection that advances one bar ahead, with code you can reuse for calibrated, anchor-based forward rendering and automatic cleanup.

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.

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.

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.

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.

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.

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.

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.

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.