Articles with examples of indicators developed in MQL5

An on-chart MQL5 indicator generates a live stream of encoded candlestick symbols and a frequency report for the last N bars. It supports color customization and type filtering, and uses A/a, H/h, E/e, G/g, D, and N/n classifications. Traders can quickly review occurrences and common sequences directly on the chart.

Learn how Smart Trendline Manager separates creation from lifecycle control while handling both manual and auto-generated trendlines. It demonstrates discovery, registration, proximity and touch handling, bounce/break confirmation, post-break resurrection, expiration, and state-driven visualization. This gives you a consistent, configurable way to manage multiple lines through one orchestrated update process.

In this article, we will examine how to build and apply Markov chains in market conditions: from selecting states and counting transitions to generating forecasts of trajectories and levels. We will also see how Markov chains can be applied to qualitative and quantitative data, ways to account for rare events, and the impact of the forecast horizon. Examples are provided using prices and indicators, as well as an option for evaluating a sequence of trades, with ready-to-use implementations in MQL5.

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.

Build a responsive session volume profile indicator in MQL5 from tick data with draggable session bounds and dual anchoring. The article details hash-based bucket aggregation for historical and live ticks, a timer-driven rendering loop with partial updates, automatic or custom bucket sizing, pixel-anchored proportional bars, POC highlighting, and multi‑instance safety—so you can implement, optimize, and run multiple profiles on one chart without UI lag.

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 outline a modular analyzer for MetaTrader 5 that separates detection, interpretation, and visualization. The engine identifies swing highs and lows, assigns structural labels, evaluates impulses and pullbacks, and stores results in a market state object. An on‑chart dashboard and interactive inspection tools make the latest structure and measurements immediately accessible.

The article presents a session analysis workflow in MQL5 that standardizes sessions as structured data with timing, OHLC, range, net move, and the sequence of extremes. It measures how range builds at 25%, 50%, 75%, and 100% of session duration. The chart shows session markers, a compact comparison panel, and an interactive inspector to review individual occurrences.

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.

The article develops a practical MetaTrader 5 tool that synchronizes charts of the same symbol across timeframes, starting from a script and moving to an interactive indicator. It uses time as a common reference, converts it to bar indices with iBarShift(), and navigates charts with ChartNavigate(). The result is a synchronized workspace for efficient MTF analysis. This is Part 1 of a three-part development series.

This article presents a reusable CTable class for building chart-based tables in MQL5. It covers table architecture, creation and destruction of objects, coordinates and sizing, cell properties, horizontal/vertical headers, dynamic row/column edits, object naming, index conversion, and efficient refreshing. You will be able to assemble consistent, aligned on-chart dashboards for market data, indicators, and signals with minimal boilerplate.

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.

An evidence-first workflow for Range Breakout in MQL5: a non-trading logger captures session/time windows and records 61 fields per breakout to CSV, with a strict split between entry features and hindsight labels. The MT5 AI Assistant processes the dataset in nine phases, helping validate data, quantify baselines, and locate stable, entry-usable thresholds for subsequent EA coding.

The article introduces the APARCH volatility process to the MQL5 library via the CAparchProcess class, estimating the power exponent (delta) jointly with other parameters. It details the recursion, parameter bounds, stationarity constraints, and starting values and reports SLSQP solver updates that streamline optimization. Implementation correctness is partially validated by reproducing approximations of GARCH and GJR-GARCH conditional volatility under parameter restrictions. A companion APARCH indicator visualizes conditional volatility, standardized residuals, and delta to track volatility dynamics and parameter drift.

This article implements the scalar Kalman Filter for price smoothing in MQL5 using the standard local-level model and the exact prediction–update equations. It explains how process and measurement noise (Q, R) shape the Kalman gain, builds two independently configured filters into a fast/slow crossover, and provides a complete indicator and Expert Advisor that trade a stability-confirmed signal via iCustom, giving a reproducible baseline for testing and tuning.

We build a Two‑Pole Momentum Oscillator in MQL5 that measures deviation from a 25‑period SMA, normalizes it by its mean and standard deviation, and smooths the result with two EMA‑style passes. The code defines inputs, buffers, warm‑up and recalculation logic, signal rules, and colorized plots, and derives dynamic targets from the 100‑period average range. It helps detect momentum shifts around the zero line and display price levels tied to recent volatility.

The article guides you through building a real‑time Hilbert Spectrum indicator in MQL5: EMD produces IMFs, a discrete FIR Hilbert transform yields analytical signals, and a CCanvas renderer draws a time–frequency–energy heatmap. Implementation details include extrema detection, SD stopping, phase unwrapping, Nyquist clipping, and bin accumulation. Use the panel to identify active cycle bands and regime shifts while keeping trade rules separate.

This article extends the A‑B‑C‑D constraint model with a measurement layer. We capture confirmation timing, breakout beyond Candle C, and post‑signal MFE/MAE; aggregate results by A‑B relationship; and write one CSV row per completed D window while showing concise CCanvas comments. The separation of detection, replay, visualization, measurement, and optional execution provides clean, analyzable data for further research.

The article details a complete MQL5 implementation of a draggable slider for controlling ranges on the chart. It introduces the CDragHandle class, private state, public APIs for dimensions, colors, range, and value, plus Refresh* and UpdateHandlePosition logic and event processing. A working example changes CHART_SCALE, demonstrating how to connect the control to platform properties.

Oscillator thresholds are often treated as direct reversal instructions, causing traders to sell strong bullish markets or buy persistent bearish declines too early. We develop an analytical Expert Advisor that interprets Relative Strength Index events through confirmed swing structure. The model distinguishes continuation, reversal, pending, and expired conditions by combining closed-bar RSI crossings with higher highs, higher lows, lower highs, lower lows, and breaks of structure. It displays each validated interpretation without placing trades.

This article presents a Trade Replay Tool for MetaTrader 5 implemented as an MQL5 indicator. It replays historical candles within an anchor-defined range and automatically reconstructs historical trades as paper positions with entry, SL, and TP. Users control start/end anchors and playback speed via UI buttons and hotkeys, enabling focused study of price evolution and trade lifecycle in a precise, event-driven environment.

Migrating 3D bars from Python to native MQL5: instead of Plotly and a bridge to the terminal — a scene using CCanvas3D and DirectX 11 directly on the chart. Price, time, and tick volume are plotted along three axes; the geometry is assembled manually from vertices and triangles; and a mouse-controlled orbit camera allows interactive exploration without external dependencies.

We implement an adaptive trend stop indicator in MQL5 that reseeds on every flip, then accelerates toward price, with speed boosted by distance from VWAP and all distances scaled by ATR. The article covers the acceleration engine, the VWAP agreement filter that grades each flip, the anti-aliased stop line with its cloud, and persistent flip levels and arrows. The result highlights where the trend turned and whether VWAP agreed.

The Session Analyzer is upgraded to define sessions by local market time and to translate those windows into broker/server time with date-aware DST logic. We describe the session model, broker offset treatment, and efficient bar selection, then add a relationship layer that measures temporal overlap, shared price range, range breaks, and size ratios. This provides a clear, reusable basis for session-based research and chart inspection