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.
In this article, we explore the HiSSD framework, which combines hierarchical learning and multi-agent approaches to create adaptive systems. We examine in detail how this innovative methodology helps uncover hidden patterns in financial markets and optimize trading strategies in decentralized environments.
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 replace the embedded CSV snapshot with a SQLite layer that persists calendar events and triggered trade IDs across restarts. The database lives in the common terminal folder and is shared by live charts and the strategy tester, so both modes read the same data without recompiling. An on-demand downloader with a canvas progress bar fetches history from the calendar API and stores it for offline reuse.
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.
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 MQL5 AI trading assistant into a dispatch-driven system that routes seven trading actions through a single central dispatcher. A line-based key-value protocol constrains AI output, while each action maps to market or pending orders and instrument-aware stop levels. A canvas-based UI with a custom prompt editor and pixel-accurate text fitting makes signals consistent, auditable, and ready to render on the chart
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
The article describes the arrangement of a coordinated ML pipeline in MetaTrader 5 with separation of roles: Python trains and exports the model to ONNX, MQL5 reproduces normalization and PCA via matrix/vector and performs inference. This approach makes the model's inputs stable and verifiable, and the MetaTrader 5 strategy tester provides metrics for analyzing the system behavior.
Learn how to implement a tick-based chart in MQL5 where each bar is built from a fixed number of ticks instead of time. The article covers creating and configuring a custom symbol, capturing real-time ticks, forming OHLC values, and pushing data with CustomRatesUpdate. This approach produces activity-driven candles that better reflect market intensity and short-term momentum for precise intraday analysis.
The article is dedicated to the event-driven architecture in MQL5 and describes the transition from the monolithic OnTick model to distributed processing. We will consider predefined and custom events, services and messaging between programs, as well as common architectural errors. A practical example demonstrates how to organize interactions between indicators and an EA to reduce load, improve readability, and simplify maintenance.
The article replaces hardcoded cost assumptions in triple-barrier labeling with measured inputs. An MQL5 script captures spread distribution, swap rates, and symbol metadata from your broker, and a Python model converts them into a broker-calibrated min ret you can pass to get events. Labels then reflect the actual round-trip friction for your instrument and holding period.
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.
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.
The article presents a minimal working set for maintaining a trading journal in MQL5 using SQLite: a table structure for trades, signals, and events, indices, prepared statements and trades, as well as standard analytical SQL queries. Integration with the statistics dashboard in MetaTrader 5 and working with the database via MetaEditor are demonstrated. The approach allows automating the journal, accelerating calculations, and performing analysis without complicating the EA code.
In this article, we expand our butterfly animation program with a four-stage animation pipeline: sequential curve drawing, smooth wing fill fading, detailed body rendering, and continuous flight. We implement a timer-driven state machine, four oscillators for wing flapping, vertical bobbing, horizontal sway, and tilt, as well as a neon glow around the wing outlines and a cyclical color change based on hue. You will learn how to structure these effects on the MetaTrader 5 canvas for clean and controlled playback.
Adaptation of the classical CAPM model for the Forex currency market in MQL5. The indicator calculates expected return and risk premium based on historical volatility. The indicators rise at peaks and bottoms, reflecting the fundamental principles of pricing. Practical application for counter-trend and trend-following strategies, taking into account the dynamics of the risk-reward ratio in real time. The article includes mathematical apparatus and technical implementation.
The article describes a variant of options emulation through an underlying asset implemented in the MQL5 programming language. The pros and cons of the chosen approach are compared with real exchange options using the example of the FORTS futures market of the MOEX Moscow exchange and the Bybit crypto exchange.
This article explains how to implement parameter versioning in MQL5 using binary files and packed structures. It shows how to write and read fixed-size records with FileWriteStruct and FileReadStruct in FILE_BIN mode, including version numbers, timestamps, and a checksum. You will also see how to detect changes via checksums, append records safely, and load the latest configuration without overwriting prior settings.
We build a lightweight bridge that captures closed trades in MetaTrader 5 and sends them to an external backend over HTTP as JSON. It uses OnTradeTransaction for event detection, reads details from deal history, assembles a JSON payload, and posts it via WebRequest. A local Flask API is used to test the flow, delivering a working path to move trade data outside the terminal.
We implement an MQL5 expert advisor that detects order blocks formed after consolidation breakouts and confirms them with fair value gaps. Each zone is validated by a break of structure and a preceding inducement, then filtered by the higher-timeframe trend. The program adds mitigation tracking, risk-based lot sizing, and two trailing stop modes, providing clear on-chart visuals and backtest-ready trade execution logic.
The CATCH framework combines Fourier transform and frequency patching to accurately identify market anomalies beyond the reach of traditional methods. Let us examine how this approach reveals hidden patterns in financial data.
We continue to build the algorithms that form the basis of the DADA framework, which is an advanced tool for detecting anomalies in time series. This approach enables effective distinguishing random fluctuations from significant deviations. Unlike classical methods, DADA dynamically adapts to different data types, choosing the optimal compression level in each specific case.
We expand the capabilities of the MetaTrader 5 butterfly curve canvas by adding multi-layered wing fills, vein lines, scale dots, and a full body (abdomen, thorax, head, eyes, antennae). This article implements polygon fills with vertical and radial gradients, as well as filled circles and ellipses, all using supersampling antialiasing. You will also receive reusable MQL5 helper functions and a rendering order that transforms a simple curve into a customizable, detailed chart illustration.
Self-training EA with a neural network based on a state matrix. We combine Markov chains with a multilayer neural network MLP developed using the ALGLIB MQL5 library. How can Markov chains and neural networks be combined for Forex forecasting?
We are going to create a matrix forecasting model based on a Markov chain. What are Markov chains, and how can we use a Markov chain for Forex trading?
In this article, we explore the butterfly curve, a parametric mathematical equation, and render it visually on a MQL5 canvas. We build an interactive display with a draggable, resizable canvas window, supersampled curve rendering, gradient backgrounds, and a color-segmented legend. By the end, we have a fully functional visual tool that plots the butterfly curve directly on the MetaTrader 5 chart.
Build a local, bidirectional voice interface for MetaTrader 5 using MQL5 WebRequest and two Python services. The article implements offline speech recognition with Vosk, wake‑word detection, an HTTP command endpoint, and a text‑to‑speech server on localhost. You will wire an Expert Advisor that fetches commands, executes trades, and returns spoken confirmations for hands‑free operation.
