This article extends the MQL5 RQA library to Cross-Recurrence Quantification Analysis (CRQA) for comparing two time series. We implement dual‑series embedding, cross‑recurrence matrix construction, adapted metrics (CRR, CDET, CLAM, CENTR, and others), and rolling‑window analysis, with optional GPU acceleration via OpenCL. A ready-to-use indicator compares two symbols in real time, supporting timestamp alignment and normalization for practical inter-market analysis.
This article explores the practical application of L1 trend filtering in MetaTrader 5, covering both its mathematical foundations and usage in MQL5 programs. The L1 filter enables extraction of piecewise-linear trends that preserve essential market structure while reducing price noise. The study analyzes parameter scaling, trend estimation behavior, and integration of the method into algorithmic trading strategies. Experimental results demonstrate how L1 trend filtering can enhance signal stability, trade timing, and overall robustness of trading systems.
The article adds a self-adaptive layer to the Malaysian Engulfing indicator by optimizing the retest bar range with a constrained brute-force search scored by MFE and MAE. It details the data model, helper routines, and an MQL5 implementation that gathers historical setups, computes excursions, and selects the best parameter. Readers learn how to remove manual tuning and run the indicator with context-appropriate settings across symbols and timeframes.
This article shows how to convert subjective flag recognition into reproducible MQL5 logic for live charts. It combines ATR-normalized pole strength, retracement limits, consolidation structure checks, breakout confirmation, and overlap control. Readers gain a workable approach that renders adaptive channels and zones, updates active setups efficiently, and provides optional alerts for newly confirmed patterns.
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.
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.
A GPH‑based estimator for d, the key ARFIMA parameter, is added to MicroStructure_Foundation.mqh. GPHEstimator() computes d via log‑periodogram regression, while PopulateARFIMAAnalysis() stores d with an R² confidence score and validates the theoretical relationship H = d + 0.5. An empirical study on 72 US100 M1 sessions confirms pooled d = −0.006, consistent with the random walk boundary established in Part 2.
Build a megaphone pattern indicator in MQL5 that detects expanding structures on the chart. The article walks through swing identification and refinement, trend line validation, breakout confirmation, and SL/TP projection, with chart objects for lines, labels, and signals. As a result, you get a rule-based implementation that automates pattern detection and produces actionable levels directly in MetaTrader 5.
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.
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.
The article implements GJR-GARCH and TARCH in an MQL5 volatility library and explains why asymmetry improves on standard ARCH/GARCH. It covers model formulation, parameterization, and usage through derived classes and scripts. Readers get code examples for calibration and one-step-ahead forecasting on real data to support risk and diagnostics.
This article presents the design and MetaTrader 5 implementation of the Candle Pressure Index (CPI)—a CLV-based overlay that visualizes intra-Bar buying and selling pressure directly on price charts. The discussion focuses on candle structure, pressure classification, visualization mechanics, and a non-repainting, transition-based alert system designed for consistent behavior across timeframes and instruments.
