Programming Articles on MQL5.com

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.

The article describes the transition from four-voice debates to the Council of Fifteen: ten analysts, four independent risk managers, and a Chair, with strict voting rules. It examines the roles of the participants, the three-phase architecture, and the parallel execution of a full cycle in 10–15 seconds. It shows the operational log, the risk-gate rules, and backward compatibility so you can quickly connect the system to an Expert Advisor.

Many traders know what they want to analyze or automate but cannot navigate MetaTrader 5, use MetaEditor, or translate an idea into working MQL5 code. This article shows how the AI Assistant and MCP turn one natural-language prompt into a complete workflow: strategy development, editing, debugging, chart interaction, testing, and permission-controlled trading. We apply the process by building MCP_Accessibility_Assistant.mq5, leaving you with a working, accessible EA and a reusable prompt-driven development method.

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.

This article describes an MQL5 Expert Advisor that models the market as a random graph rather than a fixed structure. Bars are encoded into discrete states; bar-to-bar moves form a decaying, Laplace-smoothed transition matrix, and a k-step random walk yields a bounded directional signal gated by an Erdos–Renyi null-model test. A separate trade-outcome graph over R-milestones learns survival probabilities to manage exits, turning position management into evidence-based rules.

This MQL5 dashboard aggregates closed deals by symbol and UTC hour, calculates win rate and average reward‑to‑risk, and displays the results as two color‑coded heatmaps. A companion table in the Experts tab highlights the strongest and weakest symbol/hour cells, with an option to require a minimum trade count to reduce noise.

In this article, we continue implementing the ST-Expert framework approaches, focusing on the practical aspects of applying them using MQL5. Earlier, we examined the theoretical foundations and key components of the model; now we move on to working directly with graph attention algorithms and local and global attention distribution. The main goal of this work is to demonstrate how ST-Expert's conceptual ideas are transformed into workable solutions for analyzing and forecasting financial time series.

The article extracts the series' divergence logic into DivergenceEngine.mqh, a reusable header for MQL5 indicators and Expert Advisors. It details the struct-based design, oscillator options (MPO4 or RSI), pivot and state handling, and the minimal access API. A Parabolic SAR EA demonstrates integration by adapting the acceleration factor from the detected divergence, providing a clear pattern you can reuse without duplicating code.

The series develops state persistence for MQL5 Expert Advisors. Part 1 delivers a crash-safe key-value store: a CStateStore class that saves through a temporary file and a rename, carries a versioned header with a checksum, and stores integers, doubles, strings, booleans, and double arrays, plus a demo advisor that resumes a counter and a rolling window after a restart. Readers get a compact include file and a pattern that protects the live state file if the process dies mid-save.

We implement Symbolic Fourier Approximation in MQL5 and compare it to SAX under a shared harness on identical price windows. SFA keeps low‑frequency Fourier coefficients and learns per‑position bins (MCB), with a proven, sound lower bound. The measurements show how the same bit budget behaves under different splits of word length and alphabet, and give a practical rule for choosing settings for your symbol.

The second part of the series examines the mathematical framework for multivariate random variables, which is necessary for analyzing the dependence and joint behavior of market assets. This section describes joint distribution functions, the concepts of marginal and conditional distributions, and the conditions for dependence and independence of variables. The theoretical material is based on extending the analogy between probability and mass to multidimensional space. Particular attention is given to measures of association: from classical linear covariance and correlation to modern tools such as copulas and Shannon mutual information.

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.

The article presents a compact dashboard that relates trade duration to net profit using MQL5 and CCanvas. It pulls closed deals, derives duration in minutes, and renders a log‑scaled scatter by symbol, with an overlaid least‑squares line and R². A bucketed duration view identifies which hold‑time range produced the highest average result, helping assess exit timing.

In this article, we will explore the ST-Expert framework, which ensures the robustness of forecasts under market uncertainty by taking local and global dependencies in time series into account. Its flexible architecture promotes model adaptability and improves the accuracy of predictions.

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.

The article implements intrinsic-time analysis in MQL5: an event-based directional-change operator that splits ticks into directional-change and overshoot sections. We reproduce the core scaling laws on 17.8 million live EUR/USD ticks and compare them to a random-walk baseline. Finally, we build a hedging-account Expert Advisor that trades the Alpha Engine with limit orders, detailing thresholds, inventory skew, and liquidity control for practical reuse.

The study implements five historical-variance estimators in MQL5 and evaluates their one-session-ahead persistence forecasts for EURUSD D1 sessions using an M1 realized-variance proxy. Deterministic tests cover formulas, chronological order, and target construction. A configurable indicator, comparison scripts, and CSV outputs provide reproducible losses, calibration diagnostics, a common‑target mask, and sensitivity to the estimation window.

This article applies the Kelly criterion to position sizing in native MQL5. It presents a reusable CKelly class that estimates win rate and payoff from closed deals, derives the Kelly fraction, and sizes lots from a stop distance. A Monte Carlo sweep of the Kelly multiplier shows growth peaking at full Kelly while drawdown and ruin increase, motivating fractional Kelly such as half Kelly that preserves most growth with materially lower drawdown.

Part 5 adds a fill rule to the rasterizer and a stroke helper to the path. The engine now supports both non-zero and even-odd fills via a single enum parameter, enabling rings, true borders that do not repaint interiors, and glyph counters. AddThickLine() builds strokes from fills in one path and one pass, preserving uniform opacity at joints and showing why strokes require the non-zero rule.

The article describes a competitive architecture for MetaTrader 5 in which ten LLM agents, each with different trading rules, manage their own capital and open independent positions using unique magic numbers. The system prompt and the agent's trading aggressiveness are adjusted based on PnL results and the trade streak. A reproducible framework with operating modes and monitored metrics is presented, suitable for testing and further optimization.

The Extralonger framework demonstrates a unique ability to integrate spatial and temporal factors into a single model, ensuring high forecast accuracy. Its architecture allows it to adapt to different planning horizons and financial instruments while maintaining the system's transparency and manageability.

A modular MQL5 toolkit computes and visualizes rolling Profit Factor over fixed trade-count windows. It presents the statistical motivation, an incremental algorithm that avoids recomputation, and a dedicated CCanvas rendering pipeline. The dashboard adds reference lines, shading for weak periods, and summary metrics, while a separate test suite validates the math, giving a practical way to monitor stability and detect deterioration in strategy behavior.

This article defines a Protocol Buffers contract for the MetaTrader 5-Python boundary and implements a length-prefixed Protobuf-over-TCP client in MQL5, since MQL5 cannot speak real gRPC natively. A small Python shim relays those frames to a genuine grpc.aio server, unary today, with streaming already live on the backend. You get schema-enforced, strongly-typed messages, explicit errors, retry/backoff, and a Strategy Tester cache for reproducible backtests where sockets don't run.

Volatility arrives in clusters: one large move makes the next large move more likely, and quiet spells stay quiet. This article builds a Hawkes self-exciting point process in pure MQL5 to measure that effect directly, ending in a single number, the branching ratio, that says how reflexive a market currently is. You get a small, tested library, an indicator that plots the fitted intensity live, and a demonstration Expert Advisor, along with the honest limits of all three.

The article describes the architecture of a multi-agent trading system based on the grok-4-fast language model, in which, instead of a single system prompt, four independent analysts with fundamentally different roles operate: a bull, a bear, a risk manager, and an arbiter. Three analysts run in parallel using a ThreadPoolExecutor and, within 3–5 seconds, formulate well-reasoned positions based on the same market data; after that, a deterministic judge renders a final verdict according to strict rules.

This article reframes performance from unconditional win rate to conditional probability given survival time. It introduces an MQL5 library, an on‑chart indicator, and a demo Expert Advisor that read deal history, fit Kaplan–Meier and Aalen–Johansen curves with competing risks, and report forward probabilities over a bar‑based horizon. Readers gain a reproducible way to quantify the chance that the current position reaches its target or stop, and to see the bias of the naive censoring approach.

This article extends the candlestick encoding framework by separating previously unclassified bullish and bearish candlesticks into distinct symbols. Using an MQL5 script, historical data were automatically encoded, sequential patterns were extracted, and frequency statistics were generated. The results show that these newly identified candles represent a significant portion of market activity, revealing structural information previously hidden by the generic underscore notation. The expanded taxonomy improves the completeness of the symbolic representation while preserving an objective, quantitative framework for large-scale market structure analysis.

We are continuing our work on implementing the approaches proposed by the authors of the Extralonger framework. This time, we will focus on building a Global-Local Spatial Attention module using MQL5, examining both its structure and its practical integration into the overall computational process.

The article discusses the Competitive Swarm Optimizer — a swarm optimization algorithm based on an extremely simple idea: agents are randomly paired, and the loser learns from the winner and is drawn toward the center of the swarm. In addition to analyzing CSO, the article describes the modernization of the test bench: visualization of the algorithms’ operation has been moved into 3D space, making it possible to clearly observe the movement of the population on the surface of the test function.

We turn the ONNX reader into an interactive graph viewer on a MetaTrader 5 chart. The parser is extended to carry tensor shapes and node attributes, and a canvas with anti-aliased drawing renders nodes and labeled connections. The layout arranges layers by depth, while zoom, pan, and clicks let you inspect operators and the tensors they read. This makes a model's structure and data flow visible directly in MQL5.

This article implements a lightweight feature-space drift guard in MQL5 using 1D Wasserstein‑1: sort-and-pair scoring on equal windows, IQR normalization per feature, and gating via both a weighted composite and a max-statistic. Configuration comes from a JSON manifest (window sizes, weights, warn/critical thresholds, actions). It runs next to an ONNX classifier and is validated with Strategy Tester results and explicit caveats.

We implement three MQL5 classes that replace fixed 1R/2R/3R targets with data-driven scale-out levels. CExcursionTracker records each closed trade's maximum favorable excursion in R, CExitLadderCalibrator derives runs from distribution percentiles with lookback and minimum-sample controls, and CLadderExecutor executes them on open positions. The ladder recalibrates as trades accumulate and uses a fallback until enough samples exist.

This article builds a usable roughness index from rough volatility theory by fitting local H via a structure-function regression on blocked returns, all in native MQL5. We combine it with vol-of-vol features, train a gradient-boosted classifier in Python, export to ONNX, and call it from an EA. You will be able to compute H on every bar and use the model as a regime-aware entry filter.

Standard HMMs assume geometric, memoryless state durations, which poorly match real market phases. This piece implements a duration‑explicit Hidden Semi‑Markov Model natively in MQL5, with per‑state sojourn distributions and a residual‑time forward filter. Parameters are fit offline via EM and loaded through a compact JSON manifest. The EA for XAUUSD M5 uses expected remaining duration to gate entries and exits, helping hold trends while avoiding late entries near regime exhaustion.

The rasterizer now accumulates exact span coverage in X and sampled coverage in Y, and blends it via CairoBlendOver on straight ARGB. CairoAaSamples sets the number of vertical samples at runtime, making the cost nearly linear and localized to edges. Readers get smoother boundaries, correct compositing of translucent shapes, and controllable performance.

The Extralonger framework demonstrates an approach to integrating spatial and temporal factors into a single model, which makes it possible to account for both local patterns and long-term cycles simultaneously. This architecture makes time series forecasting more resilient to market noise and enables data analysis across different time horizons. The article takes a detailed look at how these ideas are put into practice using OpenCL and MQL5.

The article discusses the implementation of the Artificial Searching Swarm Algorithm (ASSA) in MQL5 as part of a unified test bench. The article examines three behavioral movement rules, the signal and global bulletin board mechanisms, space normalization, and the stepRatio and Pc parameters. Readers will receive a ready-made foundation for integrating ASSA, as well as an answer to the question of how successful the tactical metaphor proved to be as a basis for the competitiveness of the optimization algorithm.

Aggregate metrics like win rate or profit factor miss sequence-dependent behavior, such as sizing up right after a loss. This MQL5 script trains a small native neural network on closed-deal history to estimate loss probability from behavioral and market-context features. It reports accuracy uplift over a baseline, probability calibration, and permutation feature importance, then combines them into a configurable A-F grade with concise, plain-language recommendations.

Part 3 implements a scanline rasterizer in pure MQL5. We convert path segments into top‑down edges, sweep each row, sort crossings, and apply the non‑zero winding rule to decide which pixels are inside. The result is a single Fill(path, color, buffer, w, h) routine that draws rectangles, stars, glyphs, or 2000‑edge shapes without per‑shape code, ready to integrate into your MQL5 projects.

Sample correlation matrices can look precise yet be mostly noise. This article implements a dependency-free RMT cleaner in MQL5: Jacobi eigendecomposition, Marchenko–Pastur eigenvalue screening, and average-noise reconstruction that preserves the matrix trace and unit diagonal. It explains integration into a basket EA so the denoised matrix improves stability of hedge ratios and weights between rebalances, while keeping the code portable and auditable.

We build a drawdown analytics dashboard that derives the equity curve from deals and finds every episode's depth and recovery duration. Results appear on a CCanvas timeline spaced by point index with alternating bold annotations, and in a terminal table sorted by duration, allowing you to prioritize risk by time spent underwater rather than depth alone.