MQL4 and MQL5 Programming Articles

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.

The article presents a complete implementation of TimeGPT, a specialized Transformer-based architecture for forecasting financial time series on the MetaTrader 5 platform. Adaptation of the attention mechanism to financial data, selective tokenization of price changes, hardware-aware optimizations, and advanced learning techniques are discussed. Included are practical testing results showing 87% forecast accuracy over a 24-bar horizon with a training time of 15 minutes on the CPU. We also present a ready-made trading EA with automatic retraining.

This article walks through creating an MT5 indicator that ingests option chains from native symbols or CSV, inverts prices to implied volatility via a hybrid Newton–Raphson/bisection method, and assembles a clean strike–expiry grid. It then renders a shaded, rotatable 3D surface with the platform's DirectX layer, enabling clear, in-terminal analysis of skew and term structure using live or file-based data.

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.

This article turns the verified TDA pipeline into a live MQL5 indicator. It reduces each price window to two persistence-entropy lines (H0 and H1), computes a normalized loop-strength metric with an adaptive percentile band, and places fade marks only when loop strength is high and price hits a window extreme. You can attach the indicator, read six buffers from an Expert Advisor, and tune key window, ranking, and performance parameters.

The article presents a production-oriented Order Block engine for MQL5 packaged as an include class, it validates zones via displacement and market structure break, maintains mitigation state only on closed bars, and avoids heavy copies by passing data by reference. A diagnostic indicator plots zones, and an EA gates logic to new bars for stable performance and reproducible tests.

This article extends single-candlestick analysis to ordered double-candlestick patterns using an MQL5 script. The script encodes candles into symbols, extracts every consecutive two-symbol sequence (treating Aa and aA as different), counts occurrences and percentages, and writes sorted frequency tables to a text file. Readers can quickly identify the most recurrent transitions by symbol, timeframe, and lookback for further statistical testing.

The article presents the revolutionary architecture of PatchTST, a tailored transformer for financial time series analysis that breaks market data into 16-bar patches for efficient processing. We will discuss the full implementation of a trading robot in MQL5 covering everything from mathematical fundamentals and data structures to a ready-made EA with risk management and continuous learning systems.

The ProSpread seasonality index indicator with a Moving Average is a technical analysis tool that identifies seasonal patterns in price movements, analyzes price behavior during specific trading hours and is able to work with either a single instrument or a spread between two assets. It also visualizes the statistical probability of directional movements.

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 develops a rule-based daily pivot point indicator in MQL5 that uses the previous trading day's high, low, and close values to generate pivot, support, and resistance levels. It details historical data retrieval, pivot computation, chart object management, configurable label rendering, and automatic level updates as new trading days begin. The completed indicator displays multiple historical pivot sessions on the main chart for technical analysis on daily and lower timeframes.

In previous articles, we mentioned that sometimes we need to set a value for the ZOrder property. But why? The reason is that many pieces of code that add objects to a chart simply do not use, or more precisely do not define, a value for this property. The point is that I am not here to say what every programmer should or should not do, or how they should or should not write their code. I am here to show you, dear reader, and everyone who truly wants to understand how these processes work internally, what actually happens behind the scenes.

The article presents CJsonConfigLoader and a typed SStrategyConfig that move EA inputs to a shared JSON file. A hand-written, quote-aware tokenizer parses a flat object without any DLLs. A hotkey triggers reload so all instances can pick up new lot size, SL/TP, and spread limits without reattaching the EA. On malformed input, the loader falls back to safe defaults and keeps the previous configuration.

Volatile-market failures such as requotes, connection drops, and partial fills expose a common weakness in EAs: unclassified retries and no cumulative failure control. This article introduces CRetryExecutor with exponential backoff and explicit error classification, plus a three-state CCircuitBreaker with cooldown and half-open probes, unified in CExecutionGateway. You can plug it into an EA to stop futile retries, prevent duplicate submissions, and improve diagnostics.

In this article, I will show how to use an indicator to track open positions on the trading server in the simplest and most practical way possible. I am doing this step by step to show that you do not necessarily have to move all of this into an Expert Advisor. Many of you have probably become used to doing that for one reason or another. In fact, that is not really justified, because as this implementation evolves, it will become clear that you can create or implement different types of indicators for this purpose.

The article upgrades SuperTrend by integrating a divergence engine (MPO4 or RSI) the dynamically reduces the ATR multiplier during weakening momentum. It covers the shrinking formula, non-repainting state propagation with dedicated buffers, and a step-by-step MQL5 implementation on the price chart. You will learn how to interpret arrows and line flips, adjust inputs, and apply the indicator for disciplined trailing and earlier confirmations.

A templated CCircularBuffer class for MQL5 replaces the O(n) ArrayCopy array-shift pattern with O(1) insertion using a fixed-capacity ring buffer. The implementation is shown end to end and integrated into a rolling standard deviation indicator. Benchmarks across multiple window sizes compare both approaches and quantify the impact on real-time indicator calculations.

We build an automated MQL5 program that trades Turtle Soup by fading false breakouts of the N-bar high and low. The article implements liquidity-sweep detection, confirmation closes back inside the level, sweep-depth and extreme-age filters, and an optional reversal-candle body check. It adds configurable dynamic or static stops, two take-profit modes, points-based trailing, and clear chart visuals, providing a ready baseline for backtesting and further customization.

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.

Standard MQL5 risk tools read risk from recent history and miss how heavy the downside tail can be. We implement Extreme Value Theory in MetaTrader 5: a Peaks‑Over‑Threshold fit of the Generalized Pareto Distribution via ALGLIB, a live indicator that reports EVT VaR/ES and tail shape, and an EA that sizes positions from the tail estimate. A controlled backtest illustrates reduced drawdown for unchanged entries.

This article demonstrates how to build the UT Bot Alerts indicator in MQL5 using a clear, step-by-step approach. The tutorial explains how to implement an ATR-based trailing stop system, compute a custom EMA for signal detection, and generate buy and sell signals without repainting. The final indicator provides well-structured buffers that enable easy integration with Expert Advisors, automated trading systems, and other algorithmic tools within the MetaTrader 5 platform.

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 extends Part 1 by giving an AI access to the development lifecycle on MQL5 Algo Forge. We implement an MCP server over the Forgejo REST API so an agent can create repositories, commit Expert Advisors, branch from main, open pull requests, file issues, and tag releases. You will get a ready-to-run Python server, clear tools, and a safer, reversible workflow.

The article builds a reusable validation layer for Expert Advisors in MQL5. It implements lot-size rules and normalization, SL/TP and freeze-level guards, price digit normalization, margin sufficiency checks, unchanged-level filtering on modifications, account order-limit control, new-bar detection, symbol tradability checks, economic-calendar news windows, and session detectors. The result is cleaner code and fewer terminal errors in live trading.