Articles with examples of trading robots developed in MQL5

We continue to implement the DA-CG-LSTM framework, which offers innovative methods for time series analysis and forecasting. The use of CG-LSTM and dual attention allows for more accurate detection of both long-term and short-term dependencies in data, which is particularly useful for working with financial markets.

We add a descriptor-driven property stack and a floating ribbon that binds to the current selection on the drawing layer. The article covers the descriptor list for each tool, the engine get/set API with snapshot-and-restore live preview, and widget renderers for color, opacity, line width, line style, fonts, and level visibility. You get in-place, real-time editing of object appearance via a compact, draggable panel.

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.

This article introduces the DA-CG-LSTM algorithm, which offers new approaches to time series analysis and forecasting. It explains how innovative attention mechanisms and model flexibility can improve forecast accuracy.

The Actor–Director–Critic framework is an evolution of the classic agent learning architecture. The article presents practical experience of its implementation and adaptation to financial market conditions.

This article replaces binary swing validation with a volatility‑normalized pullback model. Retracement depth is measured as a ratio of the prior impulse and calibrated to a rolling ATR regime, while entries require a minimum quality score and confirmation by structure or liquidity signals. The five‑layer design integrates detection, validation, liquidity mapping, regime‑aware scoring, and execution, helping you filter weak corrections and size stops dynamically to current conditions.

We add eight shape tools and nine annotation tools to the canvas and implement a full in-place label-editing system. The article walks through geometry, AA rendering, shared word-wrap and supersampled text helpers, and the caret-driven state machine for typing, navigation, and selection. This yields a complete, consistent annotation toolkit with editable labels that plugs into the prior interaction pipeline.

Build a restart-aware virtual protection layer on top of the SQLite persistence from Part 1. The EA reconstructs hidden stop-loss and take-profit after restart, verifies current price against recovered exits, and closes or continues positions accordingly. The result is a consistent recovery path that detects managed positions and sustains safe runtime management.

We invite you to explore the Actor-Director-Critic framework, which combines hierarchical learning and a multi-component architecture for creating adaptive trading strategies. In this article, we take a detailed look at how using the Director to classify the Actor's actions helps to effectively optimize trading decisions and improve the robustness of models in financial market conditions.

This article develops a class for managing partial position closing in MQL5 and then integrates it into an Order Blocks Expert Advisor. It also presents test results comparing the strategy with and without partial position closing, and analyzes the conditions under which this approach can help provide and maximize profit. In conclusion, partial position closing can be highly beneficial in trading strategies, especially those focused on wider price movements.

The article discusses the practical implementation of the HiSSD framework in algorithmic trading tasks. It explains how the skill hierarchy and adaptive architecture can be used to build sustainable trading strategies.

The article presents a new approach to creating trading systems based on quantum principles and artificial intelligence. The author describes the development of a unique neural network that goes beyond classical machine learning by combining quantum mechanics with modern AI architectures.

This article shows how to build a local stop-loss system in an MQL5 Expert Advisor that keeps stop levels on the terminal side. It walks through the execution logic, event handlers, inputs, and an OOP design using CTrade, CPositionInfo, CHashMap/CHashSet, and chart objects. You will implement multi-position tracking, draggable stops, visual spacers and labels, plus cleanup and disconnection behavior to create a practical risk-control utility.

We extend the canvas drawing layer from the previous part with seven new categories of multi-anchor analytical drawing tools, covering three channel variants, three pitchfork variants, three Gann tools, and the six Fibonacci tools. We work through how each tool encodes its geometry on the canvas, how derived handles let users reshape compound shapes coherently, and how shared helpers handle ray clipping, scanline filling, and anti-aliased arc rendering. By the end, we will have a full set of analytical drawing tools that live on the same interactive canvas alongside the basic line tools from the previous part.

Automated trading systems often focus heavily on signal generation while neglecting the mechanisms required to protect capital during periods of stress. This article presents an Equity Governance Framework in MQL5 that monitors drawdown conditions, evaluates equity pressure, and dynamically controls trading activity through a state-driven risk management model. By combining drawdown analysis, cooldown logic, trade authorization, and execution restrictions, the framework demonstrates how trading discipline can be engineered directly into code using a modular and extensible architecture.

This article shows how to implement a production Z-Score engine in MQL5 using an object-oriented include file, the library computes a rolling mean and population standard deviation, exposes a shift parameter for historical queries, and avoids redundant tick work by running on bar close. An Expert Advisor executes rule-based entries at positive/negative sigma thresholds and closes on mean reversion; a custom indicator provides visual verification.

We continue to work on implementing the CATCH framework, which combines the Fourier transform and frequency patching mechanisms, ensuring accurate detection of market anomalies. In this article, we complete the implementation of our own vision of the proposed approaches and test the new models on real historical data.

We replace native MetaTrader chart objects with a canvas-based drawing engine that renders tools pixel-by-pixel on a full-chart bitmap layer. The article implements persistent object storage with per-tool style memory, precise hit testing, selection, whole-object dragging, and handle manipulation. It also adds new line tools, a reorganized category system with a one-click delete action, and a rubber-band preview for multi-click placement.

We bridge Python-native artifacts to MQL5 for tick-accurate CPCV backtesting. The export script converts the ONNX model, calibrator, feature spec, and path masks to flat files, while the expert advisor rebuilds features, performs ONNX inference with calibration, and trades on real ticks. The Strategy Tester runs each combinatorial path, and Python aggregates per-path equities into a path Sharpe distribution to assess robustness after spread, slippage, and commission.

The article describes an MQL5 EA that automates Wyckoff accumulation and distribution via a finite state machine. It confirms spring to SOS and upthrust to SOW before placing LPS or LPSY entries, using relative tick volume as the confirmation metric. Readers get the state model, detection criteria, code organization, and MetaTrader 5 testing procedure.

This article presents a multi-symbol execution filter that scores real-time market quality before any trade is allowed. It measures spread behavior, tick velocity, quote gaps, micro-volatility, and a slippage estimate, then classifies the state to block degraded conditions. Once noise settles, a liquidity sweep continuation model evaluates structure shifts so entries occur only when execution is mechanically stable.

We extend the Part 9 setup wizard to build a canvas-based, in-chart documentation system for MetaTrader 5. The panel is tabbed and scrollable, supports inline styling, images, and interactive controls, and renders with supersampled anti-aliasing. The result is a reusable engine that any MQL5 program can embed to deliver self-contained documentation directly on the chart.

This article demonstrates how to build the persistence foundation of a self-healing Expert Advisor in MQL5 using SQLite. Readers will learn how to create a permanent trade-state storage layer capable of surviving terminal restarts, shutdowns, and unexpected interruptions. The article covers SQLite integration in MetaTrader 5, database lifecycle management, persistent trade-state structures, and runtime state recovery using practical MQL5 implementations.

RSI accumulates losses in trending conditions by firing at every threshold crossing regardless of market regime. A Random Forest secondary classifier trained on 12 contextual features — RSI momentum slope, EMA50 trend velocity, ATR-normalised trend stretch, and nine others — filters raw signals and scales position size by classifier confidence on EURUSD H1. Results compare plain RSI, meta-filtered RSI, and bet-sized RSI across a 16-month out-of-sample period with per-trade metrics and drawdown diagnostics.

This article addresses the interpretative gap between visual chart objects and algorithmic execution. You will build a systematic detector that iterates over all chart objects, identifies analytical types, and normalises their geometric data (time and price coordinates) into a structured SChartObjectInfo array. The implementation uses raw MQL5 functions, a filter‑extract‑store pipeline, and a timer‑driven test EA, resulting in a reusable framework for rule‑based trading inputs.

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.

Many MetaTrader 5 setups run several EAs on one account, so risk gets fragmented and correlated exposure slips through. The article introduces RiskGate, a centralized Service that evaluates EA intents account‑wide: EAs send a JSON signal, the Service returns approved, lot and reason. You will see the client/server wiring, example rules (daily loss, exposure and correlation caps), unit‑tested handler design, and an EA example. The result is consistent portfolio‑level risk with simpler EAs.

We turn the Tools Palette sidebar from a static shell into an interactive MQL5 system. The article implements flyout menus per category, a chart event handler, a multi-click drawing engine (one-, two-, and three-click tools), and mouse interactions including drag, bottom-edge resize, scrolling, hover states, and live theme toggling. You will be able to select a tool and place chart objects directly from the palette for analysis

This article presents an MQL5 implementation of AutoARIMA that builds ARIMA models without manual tuning. It estimates d via a variance-based heuristic, fits ARMA(p,q) by gradient optimization with Adam, and selects p and q using AICc. The code returns a one-step-ahead price forecast by differencing, model estimation, and integration back to price level, ready to call on a Close series.