History of MQL5.community development

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.

We rebuild the MQL5 Economic Calendar dashboard from a monolithic object-based panel into a modular canvas-based system split across four files. The update adds a dual light and dark theme, collapsible day groups, a resizable layout with pixel-based scrolling, revised value markers, and a live countdown with toast notifications. A candidate event cache and a fast-path timer that repaints only changed cells improve responsiveness and make the codebase easier to extend.

Applying Python session boundaries to MQL5 broker timestamps misclassifies session membership by two to three hours on any non-UTC broker, corrupting session flags across the full backtest history. We implement CTimeFeatures.mqh, containing CRingBuffer and CTimeFeatures, with three EA-facing methods: Initialize (UTC offset capture and frequency gate configuration), Update (log return push to session-conditional ring buffers), and Calculate (cyclical encoding, session flags, and session volatility). The output is a flat double array drop-compatible with Python's get_time_features for sub-hourly, hourly, and daily timeframes.

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.

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.

Part 2 focuses on practical long-memory detection for intraday data. Three complementary Hurst estimators are implemented and combined into a confidence‑weighted composite, with confidence tied to valid regression scales. The final H and confidence populate the shared analysis struct, enabling indicators to act only when H departs from the neutral 0.40–0.60 band and to select trend‑following above 0.60 or mean‑reversion below 0.40.

This article extends the existing Flask backend to compute performance analytics from stored MetaTrader 5 closed trades and deliver them as both JSON and a simple web view. It calculates total trades, total profit, win rate, average profit, and trade duration metrics, returning JSON at /api/v1/analytics/summary and rendering a dashboard at /api/v1. The result provides a quick, consistent way to review trading performance from persisted SQLite records.

The article examines the quality of a seasonal trading approach on a daily timeframe, both for individual symbols and for spreads. Particular attention is paid to identifying recurring monthly cycles and the possibilities of their application in trading within the current year.

The article examines an engineering approach to optimizing an Expert Advisor in MetaTrader 5: from collecting custom metrics through Optimization Frames to parameter surface analysis. A simple event-driven EMA/RSI model demonstrates CSV export, smoothing, and local stability assessment in Python. The goal is to find stable areas of configurations and validate them with forward optimization for reliable implementation.

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.

In this article, we extend the Tools Palette with a precision crosshair for MQL5 charts: reticle tick marks, full-width and full-height lines with axis labels, and a circular magnifier that renders zoomed candles. A double-click measure mode adds anchor markers, a diagonal connector, and a floating label with bars, pips, and price difference. Implementation details include a crosshair manager, eleven canvas layers, Bresenham line drawing, and theme-aware behavior that hides near the sidebar and fly out.

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.

With the multifractal parameters from Part 2 in hand, this article builds the full MMAR process. We construct the multiplicative cascade for trading time, generate Fractional Brownian Motion via Davies-Harte FFT, and combine both into X(t) = B_H[theta(t)]. A 100-path Monte Carlo simulation produces the volatility forecast, which we then pit against GARCH on the same EURUSD M5 data. Does Mandelbrot's fractal architecture outforecast Engle's conditional variance framework? Part 3 of a eight-part series leading to a native MQL5 library and Expert Advisor.