Articles on MetaTrader 5 integration using MQL5

We extend an MQL5 AI assistant from read-only tools to safe trade execution with a human-in-the-loop gate. The article defines action tools for placing, closing, modifying, and deleting trades and for drawing on the chart, validates each request, sizes volume to broker limits, and scopes ownership. We pause the tool loop behind an approval card, queue actions, and resume execution only after approval, which holds while auto-approval stays off, as it is by default.

The article explores the practical application of the L1 Trend Filter for noise reduction and the construction of structural features suitable for live trading. The full workflow is presented: H1 data for 29 instruments from MetaTrader 5, causal filtering, CatBoost with a horizon of three L1 bars, rigorous walk-forward testing, and VaR-based allocation of lots. The reader is provided with a reproducible code pipeline and a portfolio evaluation methodology.

This article presents a single MQL5 include file that adds runtime text-to-speech through Windows SAPI. The library starts a hidden PowerShell process through WinAPI and exposes one function, Speak(), with control of voice, speed, volume, XML tags, and a timeout. It also covers the limits: Windows only, DLL imports required, a blocking call, and refusal in indicators and the tester.

The article continues the series on AI Hedge Fund and addresses three limitations of v4: analysts' reputation scores are now stored persistently in SQLite, the EA has been removed from the critical execution path, and signals from the council of fifteen are distributed to multiple brokers via the TradeMux REST API. The logic behind the council and risk management has remained the same: Python retrieves data via the MetaTrader 5 SDK and executes orders directly. The result is resilience to restarts and scalability across multiple terminals.

This article introduces the foundations of connecting MetaTrader 5 to a cryptocurrency exchange using MQL5 and the MEXC Futures API. It covers terminal configuration, JSON response processing, WebRequest() execution and validation, and conversion of API data into native MQL5 types. Practical examples demonstrate retrieving and processing exchange data. These components establish the basis for a reusable MEXC Futures client, developed progressively throughout the series. The series focuses exclusively on exchange integration; how readers use the retrieved data remains outside its scope.

This part extends the news filter with an ordered tier scale that adds a special tier above the broker's High, per‑event time windows, and one classifier shared by live calendar data and historical CSV. A rule table (with optional CSV overrides) promotes or demotes specific releases and can set custom windows. A diagnostic script prints your broker's calendar, event codes, and server time so your rules match what the terminal actually reports.

We take a closer look at the Color Harmony Algorithm (CHA) — a meta-heuristic optimization method based on Munsell's theory of color harmony. We explore the structure of the hue circle, harmony templates, the alternation between the concentration and dispersion phases, and the role of solution memory. From a theoretical framework to a working implementation in MQL5 and unbiased testing on a standard benchmark.

The article presents a regression-based approach to forecasting Renko bars using CatBoost: the model estimates the logarithmic return of the next bar and the uncertainty of the forecast. We will examine a cascade of residual models with OOF validation using TimeSeriesSplit, shrinkage, and overall early stopping, as well as conditional bias correction. On EURUSD D1, a reduction in OOF-MAE was achieved, along with approximately 65% directional accuracy. A working script for MetaTrader 5 is provided that generates a signal, position size, SL, and TP in brick units.

We present a dynamic news‑sentiment filter for MQL5 Expert Advisors. It uses WebRequest to query a REST API, parses JSON sentiment and confidence, and gates entries and exits alongside Moving Average and RSI signals. You get an example EA, integration steps, timer‑based updates, and Strategy Tester workarounds. The outcome is fewer trades against dominant news flow and earlier risk reduction when sentiment flips.

This update adds manual position adoption and multi-symbol lifecycle management. The manager detects and tracks positions regardless of the chart, applies initial protection, transitions to break-even under fixed or ATR triggers, and validates broker constraints per symbol. Using the MACD sample for context, it demonstrates reusable management across instruments and manually opened trades.

The article introduces a centralized cache that serves indicators to multiple strategy nodes. It encodes full indicator identity in keys, stores Standard Library objects in CArrayObj entries, reuses matching instances, verifies readiness, refreshes data, and deletes each indicator on final release. Readers gain a predictable resource lifecycle and reduced duplication in multi‑symbol Expert Advisors.

This article presents an exception-like model for MQL5 that prevents VM faults through defensive guards and cooperative TRY/CATCH/FINALLY macros. It introduces structured error objects (CException), result propagation (CResult), manual stack tracing, and integrated logging for clear diagnostics. Readers learn how to validate risky operations, propagate failures predictably, and keep EAs stable when facing zero‑divide, null access, invalid handles, and array‑bounds conditions.

This article presents an MQL5 trade-management engine that converts exit rules into proposal generators. A policy engine ranks all proposals by a configurable reason priority with documented tie-breakers. A broker-aware validator checks the candidates in that order and falls back to the next one on rejection, and an execution adapter sends the first accepted request. Reconciliation confirms the result on the position before the internal state changes. The engine uses R-multiples for thresholds, persists intratrade state, and records a decision trace for each managed position on every tick.

The article consolidates trade, basket, and account risk into a modular framework for MQL5 EAs. Risk logic moves into reusable classes and shared types, leaving the EA with its inputs, signal, configuration, and call sequence. The result is a shorter strategy file and a second EA that reuses the basket tier and the shared conversions.

Learn how to bring advanced plotting and data visualization to the MetaTrader 5 terminal by leveraging Matplotlib, one of Python's most powerful and widely used visualization libraries.

We give our AI chat assistant two new abilities in MQL5: sight and tool access. It can now capture the chart as a screenshot, attach it to a message, and view it in the panel, and it can call tools that read live positions, trade history, indicator values, chart objects, and the economic calendar. The assistant reasons from what it sees and queries rather than text alone.

This article presents a practical debugging workflow with MetaEditor's integrated AI Assistant and a comparison to the previous external approach. We fix a controlled set of syntax and API errors in a D1 PriceMarker EA, inspect modifications, and recompile. The result is validated in the Strategy Tester, with clear boundaries between compilation success and required runtime checks.

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.

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.

We outline a modular analyzer for MetaTrader 5 that separates detection, interpretation, and visualization. The engine identifies swing highs and lows, assigns structural labels, evaluates impulses and pullbacks, and stores results in a market state object. An on‑chart dashboard and interactive inspection tools make the latest structure and measurements immediately accessible.

Part 5 adds a fill rule to the rasterizer and a stroke helper to the path. The engine now supports both non-zero and even-odd fills via a single enum parameter, enabling rings, true borders that do not repaint interiors, and glyph counters. AddThickLine() builds strokes from fills in one path and one pass, preserving uniform opacity at joints and showing why strokes require the non-zero rule.

The article describes a competitive architecture for MetaTrader 5 in which ten LLM agents, each with different trading rules, manage their own capital and open independent positions using unique magic numbers. The system prompt and the agent's trading aggressiveness are adjusted based on PnL results and the trade streak. A reproducible framework with operating modes and monitored metrics is presented, suitable for testing and further optimization.

We optimize an MQL5 canvas interface to stay responsive under rapid input without changing its appearance. The article adds a direct-buffer canvas for block region copies, caches text widths and glyph coverage, caps repaints at 60 fps (16 ms), and limits drawing to panes and regions that actually changed. As a result, hover, scroll, and popups render smoothly without full-panel redraws.