Clemence Benjamin / Perfil

This article extends the chart‑object detector into a modular monitoring and execution layer. It defines objective interaction rules (touch, cross, breakout) for trendlines, Fibonacci levels, channels, rectangles, and pitchforks, then routes events through an interaction detector, alert manager, and optional trade executor. Orders use object geometry for stop‑loss and take‑profit. The result is a reproducible pipeline that converts static drawings into actionable alerts and, if enabled, trades.

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.

Manually drawn analytical object tools like Fibonacci tools, and Andrews Pitchforks are invisible to automated trading logic. This article extends a base detector to extract anchor points, level arrays, and geometric offsets from complex objects. You will implement a reusable collector that normalizes the raw chart data into structured memory arrays, ready for strategy decisions.

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.

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.

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.

Learn to engineer an MQL5 indicator that converts trend, momentum, and volatility into a single raw score using a matrix.mqh (ALGLIB). The article covers a separate‑window oscillator to validate the core mathematics, then a main‑chart indicator that plots non‑repainting buy/sell arrows when the score crosses user‑defined thresholds. An optional long‑term EMA filter, a minimum‑bar cooldown, and built‑in alerts make the tool practical for live trading.

Higher-timeframe CRT ranges are informative, yet traders often execute on lower timeframes without that context. We implement an MQL5 indicator that reads higher-timeframe OHLC, projects the full candle range, body, and wicks onto the active lower-timeframe chart, and marks entries, stops, and targets. This improves situational awareness and removes the need to switch windows.

This article implements an MQL5 module that analyzes the lower‑timeframe bars inside each liquidity‑zone base candle. It detects swing points and applies objective rules to classify the internal structure as an ascending, descending, or symmetrical triangle; a rectangle; M; W; or undefined. The indicator displays geometry labels on the chart and adds the pattern to alerts, reducing manual lower‑timeframe inspection.

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.