Articles with MQL5 programming examples

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.

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.

The article develops a multi-symbol basket EA that standardizes prices, derives PCA weights with native MQL5 matrices, forms a synthetic spread, and trades z-score deviations from a rolling mean. It validates symbols and synchronized history, stabilizes component orientation, maps signed weights to leg directions, and applies broker-aware volumes, stops, and netting rules. Basket entries run with rollback protection and chart status, with a reproducible testing procedure.

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 presents a reproducible MetaTrader 5 workflow: collect history, engineer nine context features, label simulated EMA crossover trades, train with FLAML, and export to ONNX with fixed opset and plain probabilities. The Expert Advisor loads the model natively, mirrors the Python feature contract, and uses a tunable confidence threshold as a trade filter. Readers can swap signals and features to reuse the same pipeline.

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.

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.

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.

We convert the Part 15 decision‑forest classifier into a regime‑adaptive Expert Advisor that decouples statistical inference from trading authority. The EA trains on completed bars, scores each new completed bar, and confirms stable bullish, neutral, or bearish regimes before acting. It then applies spread, ownership, risk, and execution checks to authorize opening, holding, closing, or blocking a position.

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.

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.

The article presents a full MQL5 implementation of a multi-symbol trend system: dual EMA crossovers for entries, ADX to avoid ranges, ATR to normalize position size, and a heat monitor to cap total portfolio risk. We explain the architecture, calculation details, and entry/exit logic on daily bars. The result is a practical EA template for systematic, risk-aware portfolio trading.

A practical bridge from the options market into MetaTrader 5 for gold. We compute near-the-money implied volatility by solving Black-Scholes from quoted prices, compare it with 30-day realized volatility, and use the ratio as a regime proxy. A Python feed publishes the value, an MQL5 script consumes it with WebRequest, and a background service keeps a panel current and alerts on changes. Source code for all parts is provided.

Part 5 moves risk control from single trades to a basket-level framework. The EA aggregates its own positions, computes volume‑weighted entry, floating P/L including swap, and used margin, then enforces limits on combined loss, margin, position count, and time underwater, while logging maximum adverse excursion. A companion mean‑reversion EA demonstrates target‑based sizing and caps on implied risk that remains hidden when trades are evaluated in isolation.

In this article, we will examine the Finite Volume Elements (FVE) indicator, which helps identify genuine capital flows in the market. We will implement FVE for MetaTrader 5 and review recommendations for using it in trading.

Many traders know what they want to analyze or automate but cannot navigate MetaTrader 5, use MetaEditor, or translate an idea into working MQL5 code. This article shows how the AI Assistant and MCP turn one natural-language prompt into a complete workflow: strategy development, editing, debugging, chart interaction, testing, and permission-controlled trading. We apply the process by building MCP_Accessibility_Assistant.mq5, leaving you with a working, accessible EA and a reusable prompt-driven development method.

This article will explain a very simple and easy-to-understand mechanism whose purpose is to sort any array. In it, we will see that the result obtained does not always meet expectations, so the implementation itself will need to be adapted to achieve the desired results.

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.

This article is intended to be as educational as possible, since the topic we will be discussing often causes considerable confusion in itself. Therefore, dear reader, please try to put what is explained here into practice. If you have any questions, be sure to leave a comment—after all, understanding destructors is no easy task.

Risk-based lot sizing can still exceed what free margin allows. The article adds a margin-aware cap using OrderCalcMargin(), an optional adaptive cap that scales with ACCOUNT MARGIN LEVEL, and a single pre-trade validation gate that unifies position limits, risk sizing, and margin checks. Readers get concrete code to prevent order rejections and over-committing margin, with clear logs when a trade is reduced or skipped.

EGARCH models log-variance, avoiding the non-negativity constraints that can distort GARCH estimates and enabling a clear treatment of leverage asymmetry. The article provides a complete MQL5 implementation with logarithmic backcasting, simulation-based multi-step forecasting, and diagnostics including the Engle–Ng Sign Bias, Leverage Correlation, and Volatility Runs tests. Practical outputs include EGARCH Volatility, an Innovation Z-Score, and an Asymmetric Volatility Regime Oscillator to support regime analysis and strategy design.

This article presents a reusable CTable class for building chart-based tables in MQL5. It covers table architecture, creation and destruction of objects, coordinates and sizing, cell properties, horizontal/vertical headers, dynamic row/column edits, object naming, index conversion, and efficient refreshing. You will be able to assemble consistent, aligned on-chart dashboards for market data, indicators, and signals with minimal boilerplate.

This article describes an MQL5 Expert Advisor that models the market as a random graph rather than a fixed structure. Bars are encoded into discrete states; bar-to-bar moves form a decaying, Laplace-smoothed transition matrix, and a k-step random walk yields a bounded directional signal gated by an Erdos–Renyi null-model test. A separate trade-outcome graph over R-milestones learns survival probabilities to manage exits, turning position management into evidence-based rules.

The article extracts the series' divergence logic into DivergenceEngine.mqh, a reusable header for MQL5 indicators and Expert Advisors. It details the struct-based design, oscillator options (MPO4 or RSI), pivot and state handling, and the minimal access API. A Parabolic SAR EA demonstrates integration by adapting the acceleration factor from the detected divergence, providing a clear pattern you can reuse without duplicating code.

In this article, we will examine how to build and apply Markov chains in market conditions: from selecting states and counting transitions to generating forecasts of trajectories and levels. We will also see how Markov chains can be applied to qualitative and quantitative data, ways to account for rare events, and the impact of the forecast horizon. Examples are provided using prices and indicators, as well as an option for evaluating a sequence of trades, with ready-to-use implementations in MQL5.

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.