Articles on the MQL5 programming and use of technical indicators

We introduce a Partial Information Decomposition library for MQL5 that decomposes two sources about a target into four atoms: unique to each, shared, and synergy. The implementation uses quantile binning, tabulated logarithms, and a maximum-entropy fit (for I_ccs), and it pairs results with a block-permutation null because atoms sit above zero on finite samples. Use it to screen indicator pairs and judge significance, including family-wise correction.

We implement a five-stage MQL5 pipeline that quantifies market structure, liquidity interaction, and price behavior on four timeframes, then resolves them into a 0–100 Market Intent Score. Decision states (WAIT/WATCH/ACTION) are driven by explicit weights plus hard gates. The analytical core feeds a concise dashboard and, when AutoTrade is on, an execution layer with entry zones, invalidation and liquidity‑based targets.

This article implements a self-contained Isolation Forest library for MetaTrader 5 with no labels, no distribution assumptions and no external dependencies. It details a reproducible 64‑bit generator, tree/forest construction, scoring and feature design, then verifies results against Python and market data with two null models. The package includes an indicator that plots the decision variable and a gate example. Readers get a validated library, clear limits of applicability and a practical way to calibrate thresholds.

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 presents a compact MQL5 library for market-complexity analysis: LZ76 complexity and Normalized Compression Distance built on a SAX symbolizer, exposed through a simple facade and an efficiency indicator. It explains the discretization choices, normalization, and distance formulation, and validates the code with unit checks and an independent cross-check. You get a ready-to-use library and indicator, plus a disciplined way to interpret readings with a shuffle null and a direction check.

This article is a practical walk-through of the MetaEditor debugger using a rolling z‑score indicator with two planted bugs: an off‑by‑one array access and a silent wrong‑denominator variance. We show how to set breakpoints, step through code, read the call stack, and inspect values in the Watch window. You will learn a repeatable method to catch both crashing index errors and tiny numerical biases that charts cannot reveal.

This article develops a dynamic ATR-based trend channel indicator in MQL5 that responds to current market volatility. It derives True Range, applies a two-step ATR smoothing, and constructs adaptive upper and lower boundaries to track trend shifts. The tool also renders a trailing trend line, trend-colored candles, and reversal arrows, offering a usable code base for volatility-aware analysis and further indicator design.

An innovative indicator based on prime number theory helps identify strong reversal levels that other traders overlook. Testing on 10 assets showed that reversals in mathematically significant zones occur 1.5 to 1.8 times more frequently. Five practical application scenarios with specific rules for filtering out false breakouts and making precise market entries.

This article builds a Kolmogorov–Arnold Network (KAN) in MQL5, where every edge carries a learnable B‑spline curve rather than a scalar weight. We construct the spline basis, assemble edges and a layer, and fit all coefficients by ridge‑regularized least‑squares in a single solve. The model is delivered as an indicator that visualizes the learned curves and an Expert Advisor that acts on the prediction, providing an interpretable, reusable codebase.

Although the functions we will discuss have been known for quite some time, their application in the field of trading remains terra incognita to this day. In this article, we will explore some of the opportunities these old-but-new functions offer for developing trading strategies and assess their potential.

We continue enhancing our modular indicator search panel by adding symbol selection capabilities. The implementation allows users to search for built-in indicators, choose a destination symbol, and attach the selected indicator without opening multiple charts or running separate Expert Advisor instances.

This article benchmarks CUSUM_Breakpoint.mq5 against the Siegmund ARL₀ prediction on live‑like data. The empirical false‑alarm rate is about five times higher than theory for all tested symbols and timeframes, and confirmations show sensitivity to variance changes over mean changes. Practitioners should calibrate h and k on the target instrument's history and apply the signal to manage volatility regimes, not to infer directional shifts.

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.

A modular indicator search system for MetaTrader 5 that replaces manual navigation through built-in indicator categories with a searchable interface. The application integrates an indicator catalog, search engine, chart launcher, and graphical panel, allowing indicators to be located, filtered, and attached to the appropriate chart window from a single interface.

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 build a CSV exporter for MQL5 custom indicators that preserves the exact values seen on the chart. The script creates the indicator handle with iCustom, waits for BarsCalculated, aligns buffers to CopyRates, and writes a locale-safe CSV that pandas loads with parsed dates and NaN for warm-up bars. It addresses compile-time argument limits, jagged-array workarounds, and EMPTY_VALUE handling, enabling reliable Python backtests without re-coding the indicator.

This article implements an MQL5 Liquidity Heatmap that infers likely liquidation zones from price and volume. It qualifies bars with a rolling volume SMA, computes leverage-based liquidation levels from candle extremes, ranks signals across two volume modes, and manages chart objects (lines and bubbles) that extend until price crosses them, allowing you to highlight potential stop-hunt areas and strengthen structural analysis.

This article implements BOSS from scratch in MQL5 and applies it to regime classification: SFA turns windows into words, bags record word frequencies, and an ensemble over window lengths votes on labels. We cover the encoding steps, the BOSS distance, training with auto-generated regime labels, and practical parameters. A BTCUSD benchmark versus DTW shows higher macro accuracy on clean data and markedly faster inference.

Symbolic Aggregate approXimation (SAX) encodes price windows as short words to enable fast, sound similarity search on history. We implement SAX in pure MQL5, including Gaussian breakpoints, PAA, and the lower-bounding MINDIST, and validate it with a test harness. An indicator applies a no-lookahead, two-stage search, summarizes forward paths in ATR units, and draws a forecast fan, explicitly indicating when the sample shows no edge.

In this article we make the case for pairing the Discrete Fourier Transform with a Spiking Neural Network in a Trading Robot. The Fourier Transform helps represent data as oscillations instead of its raw values. To govern how we interpret these cycles, we engage a Spiking Neural Network that unlike regular networks, uses time dependent electrical charges to accumulate potential and only "spike" when a target threshold is met. Combining these two engines allows us better control on the timing of discrete market movements, that in theory should give us entry signals with rigorous mathematical confirmation.

This article profiles a rolling z-score indicator with bands using MetaEditor's built-in sampling profiler. We read the Total CPU and Self CPU columns and follow the heat‑mapped source to the true hotspots, replace window rescans with sliding accumulators, remove a redundant array copy, and honor prev_calculated. The result is the same output with measured samples reduced from roughly 7,050 to 59.

This article describes a prototype reusable market structure framework for MQL5, built with a clean modular architecture and an internal event queue. It shows how to detect swing points, classify break-of-structure and change-of-character events, maintain a deterministic market state, and persist data to CSV. The focus is entirely on software engineering, component separation, and extensibility, not on trading signals. The prototype is a foundation for further development, not a production-ready library.

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 walks through creating an MT5 indicator that ingests option chains from native symbols or CSV, inverts prices to implied volatility via a hybrid Newton–Raphson/bisection method, and assembles a clean strike–expiry grid. It then renders a shaded, rotatable 3D surface with the platform's DirectX layer, enabling clear, in-terminal analysis of skew and term structure using live or file-based data.

The article presents a production-oriented Order Block engine for MQL5 packaged as an include class, it validates zones via displacement and market structure break, maintains mitigation state only on closed bars, and avoids heavy copies by passing data by reference. A diagnostic indicator plots zones, and an EA gates logic to new bars for stable performance and reproducible tests.

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.

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.