Articles with examples of indicators developed in MQL5

The article describes the two-level MarkovMLPOscillator indicator: a three-state Markov chain uses historical data to construct a transition matrix and generate 15 probabilistic features for each bar, and an MLP is trained on these features to predict price direction at a specified forecast horizon. The article covers feature generation, the validation procedure using a holdout set, and parameter settings. The result is an interpretable oscillator with a color histogram, a smoothed signal, and a display of the current transition matrix.

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.

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.

The classic Market Profile from forty years ago is still replicated in dozens of indicators that differ only in the color of their bars. In this article, I examine three conceptual blind spots in the original theory — the monolithic Value Area in bimodal distributions, TPO’s blindness to the aggressor, and the lack of memory between sessions — and build an indicator that addresses each of them: bimodality detection with a dead zone, order flow via CopyTicksRange with absorption detection, and composite market memory using Naked POC and HVN/LVN. The complete source code is included.

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.

We adapt the Savitzky–Golay filter to a strictly causal, real-time form for MQL5 and derive the fixed coefficients for end-point evaluation. The article provides a full indicator and an Expert Advisor, explains the edge-evaluation noise sensitivity, and implements a multi-bar confirmation safeguard. Backtests on several symbols are shown without cherry-picking. You get ready-to-compile code, parameter guidance, and a reduced-lag alternative to moving averages.

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.

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.

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.

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.

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.

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.

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 develops a multi-symbol basket EA that standardizes prices, derives PCA weights with native MQL5 matrices, forms a synthetic spread, and trades z-score deviations from a rolling mean. It validates symbols and synchronized history, stabilizes component orientation, maps signed weights to leg directions, and applies broker-aware volumes, stops, and netting rules. Basket entries run with rollback protection and chart status, with a reproducible testing procedure.

Part 9 adds a second measurement layer to Part 8's micro‑trend signal: pullback quality. It maps Fibonacci retracement depth to a six‑level PULLBACK QUALITY label, adds an H1 range position from a 60‑bar rolling proxy, and uses lag‑1 momentum autocorrelation. These inputs form a single composite entry‑quality score in [0,1] for filtering setups and sizing trades within MicroStructure_Foundation.mqh.

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.

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.

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.

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.

The article discusses the application of Walsh functions in trading. We will explore the basic principles of using these functions to analyze financial markets, forecast prices, and make trading decisions. We will also discuss the advantages and disadvantages of these functions, as well as the prospects for their application in trading and technical analysis.

This article shows how to build an interactive bar replay tool in MQL5 for MetaTrader 5 that reveals historical candles one by one without exposing future data. You will implement custom candles with DRAW COLOR CANDLES, an event-driven engine with OnChartEvent and OnTimer, a dashboard with Play/Pause, a draggable replay anchor, and Buy/Sell paper trading with SL/TP lines, while keeping the active bar in view to practice discretionary analysis and execution.

This article builds a Hull Moving Average Momentum indicator in MQL5 by combining raw price momentum with Hull MA smoothing. We compute momentum as the close-to-close difference over a user-defined length, form 2×Fast WMA − Slow WMA, then apply a final WMA with a square‑root period. The implementation covers inputs, buffers, warm-up/recalculation, and visualization with a color-coded line and zero-line filling, helping interpret positive/negative momentum without treating zero crossings as signals.

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.

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.

This article details a practical framework for converting MetaTrader 5 trendlines from static drawings into managed runtime entities. It covers object discovery, event-driven synchronization of user edits, and confirmation logic based on ATR multipliers and closed candles. A central manager coordinates multiple lines and updates their visual state. Readers can implement consistent, extensible rules for detecting proximity, validating bounces, and confirming breakouts.

This article builds a robust SuperTrend indicator in MQL5 using ATR-based bands, a ratchet mechanism, and strict series indexing to avoid silent recursion errors and repainting on closed bars. We walk through buffer binding, ATR handle management, seeding, and arrow confirmation logic. A companion EA demonstrates practical integration

Trend-scanning supports both forward and backward windows, and the labeling default is unsafe for features: it looks ahead and boosts next-bar agreement well above chance on random walks. We provide a dedicated wrapper, get trend scanning features, that forces computational causal and returns only window, slope, t value, and rsquared. A second analysis quantifies errors introduced by the default log transform on signed series.

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.