Articles with examples of indicators developed in MQL5

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 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.

An MQL5 control system that blocks orders outside scheduled trading hours and during scheduled news releases, converting time rules into executable restrictions. It combines a permissions management mechanism, a transaction-level expert advisor, and a visual dashboard for real-time status and upcoming restrictions. Configuration is accomplished using editable files, with caching and a CSV audit log for traceability.

GoertzelBrain combines Goertzel spectral analysis with an online‑trained neural network ensemble to convert cycle features into a directional confirmation signal. The indicator builds a compact feature vector from the dominant period, amplitude, confidence and their dynamics, plus local volatility, and outputs +1, −1 or 0. The article provides the full MQL5 implementation, explains the architecture and feature engineering, and shows how to use it as a directional filter.

In this article we explore different ways in which the eigenvectors and eigenvalues can be applied in exploratory data analysis to reveal unique relationships in data.

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.

This article introduces a modular Fair Value Gap (FVG) detection engine for MQL5 packaged as a reusable include class, it evaluates imbalance zones on closed bars, applies a Simple True Range average filter to eliminate low-volatility noise, and supports wick-touch and close-through mitigation. A companion diagnostic indicator plots active gaps, and an Expert Advisor template demonstrates automated pullback entries with new-bar execution controls.

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.

The article focuses on the Gopalakrishnan Range Index (GRI/ROCI), which quantitatively assesses the market's "degree of chaos" using the logarithm of the closing price range over a given period. The article shows how to implement GRI in MetaTrader 5, resolve the issue of negative values using a shifted logarithm, and convert the scale to convenient "points" by normalizing it by Point. Next, we examine practical scenarios for using GRI as a filter for volatility and market phases.

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.

We implement a Future Swing Projection indicator in MQL5 that analyzes historical swing structure and estimates the next move from recent price behavior. It locates six alternating swing points, measures five completed legs, and uses their average distance to project a target five bars ahead. The indicator draws swing legs, a projection line, ATR‑based support and resistance zones, and a label with the projected price to keep the process rule‑based and reproducible.

This article builds a white-box classifier in MQL5 using the Tsetlin Machine. It learns human-readable AND-rules instead of weights, trains with integer state updates, and requires no external dependencies. You will assemble the automaton, clause, and multi-class voter, verify on XOR and other boolean tasks, booleanize indicators, label by forward ATR-scaled return, save the model to CSV, and view active rules on a live chart.

A step-by-step guide to building an anchored VWAP indicator with an interactive draggable anchor line in MQL5. The article covers the complete implementation, including calculation methodology, session resets, standard deviation bands, and custom visualization. Learn the architectural design decisions behind stateless boundary detection, multi-instance support, and cross-asset volume handling to build a versatile indicator with benchmarking, technical, and analytical capabilities.

We integrate parameter configuration into the indicator search workflow in MQL5. A central repository describes each indicator's inputs, a dynamic dialog renders controls from those definitions, and the dialog validates entries and converts them to MqlParam. The chart launcher then creates the indicator with IndicatorCreate using the provided values. This streamlines attaching indicators with custom settings on the chosen symbol.

The article introduces the Mapper pipeline in MQL5 by implementing the two fundamental components: CTDAMapperFilter (lens) and CTDAMapperCover (overlapping intervals). It explains three lens options—eccentricity, density, and coordinate—plus cover parameters (resolution and gain), and demonstrates how a price point cloud is reduced to one value per point and interval memberships. Readers obtain ready inputs for subsequent clustering and graph construction.

We implement an interactive Crosshair Volume Profile indicator in MQL5 for MetaTrader 5. Using a right-click-and-drag crosshair, you select a chart region; the indicator then builds adaptive price bins, accumulates tick or real volume, detects the POC, and renders the profile on the chart. You will practice OnChartEvent()-driven interaction, cursor-to-time/price mapping, and price-source models (Close, Median, Typical, Weighted, OHLC4) for on-demand volume analysis.

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.

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.

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.

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 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.

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 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.

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.

In this article, we will examine the Finite Volume Elements (FVE) indicator, which helps identify genuine capital flows in the market. We will implement FVE for MetaTrader 5 and review recommendations for using it in trading.

Build a level-2 path-signature engine in pure MQL5 to read the lead-lag ordering between two data streams without choosing a lag and without a linear model. The article delivers a reusable library, an indicator that plots the Levy‑area oscillator, and a simple rule‑based Expert Advisor. Code is cross‑checked against closed‑form cases, and the components are ready to plug into your projects.

EGARCH models log-variance, avoiding the non-negativity constraints that can distort GARCH estimates and enabling a clear treatment of leverage asymmetry. The article provides a complete MQL5 implementation with logarithmic backcasting, simulation-based multi-step forecasting, and diagnostics including the Engle–Ng Sign Bias, Leverage Correlation, and Volatility Runs tests. Practical outputs include EGARCH Volatility, an Innovation Z-Score, and an Asymmetric Volatility Regime Oscillator to support regime analysis and strategy design.

This article presents a swing-based volume profile indicator in MQL5 that analyzes platform tick volume within completed high-to-low and low-to-high legs. It confirms swing points, partitions each leg's range into ATR(200)‑adaptive price bins, distributes tick volume, and marks the Point of Control. The indicator draws a ZigZag and renders the profile on chart rectangles, helping you study volume concentration within each swing relative to price structure.