Articles on manual and algorithmic trading in MetaTrader 5

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.

We refactor the Tools Palette from a flat, function-based panel into a modular, class-driven sidebar in MQL5. The design introduces supersampled canvas rendering for anti-aliased shapes, theme control, a category registry, snap alignment, and selective corner rounding. The result is a reusable, scalable sidebar foundation that you can extend with tool selection, dragging, and fly-out menus in future steps.

Deterministic Oscillatory Search (DOS) algorithm is an innovative global optimization method that combines the advantages of gradient and swarm algorithms without the use of random numbers. The fitness oscillation and slope mechanism allows DOS to explore complex search spaces in a deterministic manner.

The Camel Algorithm, developed in 2016, simulates the behavior of camels in the desert to solve optimization problems, taking into account temperature, supply, and endurance. This article also presents a modified version of the algorithm (CAm) with key improvements: the use of a Gaussian distribution in generating solutions and the optimization of the oasis effect parameters.

This article explores the development of a Market Entropy Indicator based on principles from Information Theory to measure the uncertainty and information content within financial markets. By applying concepts such as Shannon Entropy to price movements, the indicator quantifies whether the market is structured (trending), transitioning, or chaotic.

The article explores the Battle Royale Optimizer algorithm — a metaheuristic in which solutions compete with their nearest neighbors, accumulate “damage,” are replaced when a threshold is exceeded, and periodically shrink the search space around the current best solution. It presents both pseudocode and an MQL5 implementation of the CAOBRO class, including neighbor search, movement toward the best solution, and an adaptive delta interval. Test results on the Hilly, Forest, and Megacity functions highlight the strengths and limitations of the approach. The reader is provided with a ready-to-use foundation for experimentation and tuning key parameters such as popSize and maxDamage.

This article builds an order-flow footprint indicator in MQL5 that aggregates tick-by-tick volume into quantized price levels and supports Bid vs Ask and Delta display modes. A canvas overlay renders color-scaled volume text aligned with the candles and updates on every tick. You will learn sorting of price levels, max-value normalization for color mapping, and responsive redraws on zoom, scroll, and resize to read volume distribution and aggressor dominance inside each bar.

This is an article about a specialized trend-following EA that aims to clearly elaborate how to frame and utilize trading setups that occur from imbalances found in PD arrays. This article will explore in detail an EA that is specifically designed for traders who are keen on optimizing and utilizing PD arrays and imbalances as entry criteria for their trades and trading decisions. It will also explore how to correctly determine and profile premium and discount arrays and how to validate and utilize each of them when they occur in their respective market conditions, thus trying to maximize opportunities that occur from such scenarios.

This article develops an interactive MQL5 plot for the binomial distribution, combining a histogram of simulated outcomes with the theoretical probability mass function. It implements mean, standard deviation, skewness, kurtosis, percentiles, and confidence intervals, along with configurable themes and labels, and supports dragging, resizing, and live parameter changes. Use it to assess expected wins, likely drawdowns, and confidence ranges when validating trading strategies.

In this article, we build an interactive tools palette in MQL5 for chart drawing, with draggable, resizable panels and theme switching. We add buttons for tools like crosshair, trendlines, lines, rectangles, Fibonacci, text, and arrows, handling mouse events for activation and instructions. This system improves trading analysis through a customizable UI, supporting real-time interactions on charts

In this article, we explore how to build a position information visualization tool using the MQL5 Standard Library’s CCanvas. This project strengthens your skills in working with library modules while providing traders with a practical tool to visualize and interact with open positions directly on a live chart. Join the discussion to learn more.

In this article, we enhance the MQL5 canvas dashboard with advanced visual effects, including blur gradients for fog overlays, shadow rendering for headers, and antialiased drawing for smoother lines and curves. We add smooth mouse wheel scrolling to the text panel that does not interfere with the chart zoom scale, technically an upgrade.

The new proprietary optimization algorithm NOA2 (Neuroboids Optimization Algorithm 2) combines the principles of swarm intelligence with neural control. NOA2 combines the mechanics of a neuroboid swarm with an adaptive neural system that allows agents to self-correct their behavior while searching for the optimum. The algorithm is under active development and demonstrates potential for solving complex optimization problems.

In this article, we develop a Nick Rypock Trailing Reverse (NRTR) trading system in MQL5 that uses channel indicators for reversal signals, enabling trend-following entries with hedging support for buys and sells. We incorporate risk management features like auto lot sizing based on equity or balance, fixed or dynamic stop-loss and take-profit levels using ATR multipliers, and position limits.