Part 8 adds bar-by-bar micro-trend scoring for NQ M1. GetMicroTrendStrength() builds a continuous [-1, +1] composite from EMA alignment, ATR‑normalized price position, slope consistency, and volume, with a contradiction penalty to suppress alignment/price conflicts. Session-adaptive thresholds scale by Part 7 confidence to modulate signal frequency across regimes. Outputs include a seven-state label, a binary signal, and a persistence check, calibrated on 514 New York sessions (May 2024–May 2026).
We implement an interactive, anchored multi-timeframe volume profile in MQL5 for MetaTrader 5. The indicator draws the current-timeframe profile on the main chart and a higher timeframe profile in a subwindow, both aligned by a draggable anchor and the visible range. You will learn keyboard-driven bin editing (E/S double-click), robust timeframe validation, viewport-aware updates, and object restoration to build a reliable, synchronized volume workflow.
This article turns the verified TDA pipeline into a live MQL5 indicator. It reduces each price window to two persistence-entropy lines (H0 and H1), computes a normalized loop-strength metric with an adaptive percentile band, and places fade marks only when loop strength is high and price hits a window extreme. You can attach the indicator, read six buffers from an Expert Advisor, and tune key window, ranking, and performance parameters.
The ProSpread seasonality index indicator with a Moving Average is a technical analysis tool that identifies seasonal patterns in price movements, analyzes price behavior during specific trading hours and is able to work with either a single instrument or a spread between two assets. It also visualizes the statistical probability of directional movements.
We add SQLite persistence to the canvas tools, saving every drawing and the entire UI session per symbol, then restoring them on startup so the workspace resumes exactly where you left it. The article builds versioned object serialization, a load/save lifecycle with dirty writes, and a timeframe-visibility editor that drives render-time filtering. The toolkit also runs as an indicator, so it can sit alongside other indicators or an Expert Advisor.
This article develops a rule-based daily pivot point indicator in MQL5 that uses the previous trading day's high, low, and close values to generate pivot, support, and resistance levels. It details historical data retrieval, pivot computation, chart object management, configurable label rendering, and automatic level updates as new trading days begin. The completed indicator displays multiple historical pivot sessions on the main chart for technical analysis on daily and lower timeframes.
The article upgrades SuperTrend by integrating a divergence engine (MPO4 or RSI) the dynamically reduces the ATR multiplier during weakening momentum. It covers the shrinking formula, non-repainting state propagation with dedicated buffers, and a step-by-step MQL5 implementation on the price chart. You will learn how to interpret arrows and line flips, adjust inputs, and apply the indicator for disciplined trailing and earlier confirmations.
This article demonstrates how to build the UT Bot Alerts indicator in MQL5 using a clear, step-by-step approach. The tutorial explains how to implement an ATR-based trailing stop system, compute a custom EMA for signal detection, and generate buy and sell signals without repainting. The final indicator provides well-structured buffers that enable easy integration with Expert Advisors, automated trading systems, and other algorithmic tools within the MetaTrader 5 platform.
We complete persistent homology for MQL5 by reducing the Vietoris–Rips boundary matrix to a persistence diagram. The article implements Z/2 column reduction (CTDAReduction), a diagram container with analytics (CTDADiagram), and a facade that runs the six-stage pipeline in one call (CTDA). Outputs are cross-checked against Ripser to numerical agreement, enabling reliable diagram-based metrics.
We build a Support and Resistance Volume Profile indicator that adapts to the current viewport in MetaTrader 5. You will learn viewport detection, dynamic SnR identification, zoom‑driven bin sizing, min‑max volume scaling, and fast on‑chart rendering controlled by OnChartEvent. This approach expresses the relative strength of SnR levels with volume, keeping the chart focused on actionable reaction zones.
The article presents a structured approach to external and internal market structure in MQL5, from swing identification to CHoCH/BoS validation within an established trend. It explains refining true highs/lows, enforcing “first internal signal” logic, and rendering lines, labels, and markers on the chart. The outcome is a consistent indicator that converts price structure into defined entries, stop losses, and 1.5R targets.
This article finalizes the Forward Simulation Engine for MetaTrader 5 by calibrating synthetic candles to recent market volatility instead of using slope-only sizing. It samples average body, upper wick, and lower wick from closed bars, applies a sine-envelope with decay, proportional wicks, gaps between candles, and periodic counter-trend injections. The result is a live projection that advances one bar ahead, with code you can reuse for calibrated, anchor-based forward rendering and automatic cleanup.
This article shows how to implement a session vwap in MQL5 as a reusable include class with a strict daily reset at broker midnight. The engine computes VWAP and volume‑weighted deviation bands only on closed bars and anchors accumulation with MqlDateTime to avoid distortions from missing candles. A companion indicator plots the baseline and bands, while an Expert Advisor reads signals once per bar for consistent, CPU‑efficient execution and reliable testing.
The article discusses the application of Grey models to forecasting financial time series. We will consider the operating principles of Grey models and the specifics of their application to financial series. We will also discuss the advantages and limitations of using these models in trading.
We turn a price-embedded point cloud into a Vietoris–Rips filtration and its boundary matrix. The article enumerates vertices, edges, and triangles with filtration values, sorts them in entry order, and builds O(1) vertex/edge lookups. You get MQL5 classes CTDARips and CTDABoundary and a sparse Z/2 boundary suitable for the next-step persistence reduction.
This article implements a custom Point and Figure indicator in MQL5 that maps price movement into X/O columns using a fixed box size and three-box reversal logic. We define the base price, convert prices into box intervals, manage trends and reversals, auto-scale the indicator window, and render symbols with objects, providing a clean, time-independent view of trends, breakouts, and support/resistance.
We introduce MPO4, a pressure-based oscillator that emphasizes the body and direction of candles in the context of current volatility. The article details its mathematics, normalization into a bounded range, and the EMA smoothing, then builds a pivot-driven divergence module designed not to repaint. You get complete MQL5 implementation and practical guidance for interpreting signals, including a comparison with RSI as an alternative source.
The article presents a practical foundation for shape analysis of price series in MQL5. It implements Takens time‑delay embedding to build a phase‑space point cloud and computes the full pairwise distance matrix under selectable norms. The CTDAPointCloud and CTDADistance classes are provided with a demo script that embeds chart data and outputs results, preparing inputs for downstream topological tools.
We extend the weekend gap toolkit with an indicator that turns gap structure into tradeable signals. When price confirms back into the gap, the indicator issues buy/sell arrows, sets TP at the opposite edge, and places SL using current-week extremes. It maintains non-repainting behavior, reconstructs historical signals, updates live, and provides EA-ready buffers for entry markers and TP/SL to support automation.
An EA-ready weekend gap-fill tool for MetaTrader 5 that detects gaps, confirms complete fills, and posts deterministic buy/sell values to indicator buffers. It reconstructs historical events, monitors live markets without repainting, and visualizes gap structure directly on the chart. Configurable alerts and clear object graphics support both manual review and automated execution.
The article provides a detailed guide to creating the innovative 3DBarCustomSymbol.mq5 indicator, which generates custom symbols in MetaTrader 5 that combine price, time, volume, and volatility into a single three-dimensional representation. The mathematical foundations, system architecture, practical aspects of implementation and application in trading strategies are considered.
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.
The article delivers an object-based MQL5 implementation that detects weekend gaps from time discontinuities and renders them directly on the chart. It manages graphical objects, tracks state transitions (fresh, partial, reaction, filled), and preserves completed gaps as historical zones. The result is a reproducible framework for monitoring how price revisits and fills weekend gap structures.
A detailed guide on how to create a heat map indicator for MetaTrader 5 that visualizes the price distribution over time. The article reveals the mathematical basis of time density analysis, where each price level is colored from red (minimum stay time) to blue (maximum stay time).
The article explains how to develop a tool for analyzing recurring price patterns in financial markets — by day of the month (1-31), day of the week (Monday-Sunday), or hour of the day (0-23). The indicator analyzes historical data, calculates the average return for each period, and displays the results as a histogram with a forecast. It includes customizable parameters: seasonality type, number of bars analyzed, display as percentages or absolute values, chart colors.
Time gap analysis helps traders identify potential market reversal points. The article discusses what a time gap is, how to interpret it, and how it can be used to detect large volume influxes into the market.
We implement an EquiVolume indicator in MQL5 that converts standard candlesticks into volume-weighted boxes. The workflow includes selecting volume type, detecting the maximum volume within a lookback range, normalizing all values against it, and mapping them into proportional box widths. The result is a chart-based structure that visualizes trading activity intensity alongside price movement in MetaTrader 5.
This article develops an object-oriented framework for MQL5 indicators by evolving a primitive example into reusable modules. It formalizes partial buffer recalculation in OnCalculate, moves logic into header-based classes (CAppliedPrice, CSma), and introduces CSubIndiBase, CIndicatorBase, and a registry to centralize requirements. You get portable components, isolated inputs, and clean buffers with minimal boilerplate, making new indicators faster to assemble and easier to maintain.
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.
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.
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.
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 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.
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.
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.
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.
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.
A custom forward simulation engine detects fast/slow EMA crossovers and immediately projects synthetic candles ahead of the signal bar. It generates bodies and wicks using controlled logic, draws them with chart objects, and refreshes on every new signal or anchor change. You get a clear forward-looking view to test timing, visualize scenarios, and manage invalidation on the chart.
We present a rule‑based alphabet for candlestick price action that maps measurable shape and direction to letter codes (A/a, H/h, E/e, G/g, D). The article shows an MQL5 implementation: classifying candles, building two‑bar sequences via permutations, and scanning charts with an indicator and alerts. Readers gain a practical template for objective pattern detection and systematic testing.
Implement the Malaysian Engulfing concept in MQL5 with two coordinated indicators. One applies strict, body‑based engulfing rules for precise pattern detection; the other uses a state-driven model to monitor what follows—pullbacks and timed retests—directly on the chart. The result is a repeatable, rule-based workflow that replaces visual guesswork with programmable logic.
