History of MQL5.community development

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.

A complete MQL5 implementation of the Ljung-Box test helps verify independence in trading data and fitted-model residuals. It computes sample autocorrelations, the Q statistic over selected horizons, degrees of freedom with user-controlled adjustments, and right-tail p-values via the regularized incomplete gamma function. Run it on returns, deal outcomes, or external residuals and review decisions directly in the Experts tab.

In this article, we conclude our work on building the SAGDFN framework using MQL5, summarizing the development process and presenting the results of its practical testing. Let's combine the modules we've already implemented into a single system, highlight the strengths of this approach, point out its weaknesses, and discuss possible ways to improve it.

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.

We optimize an MQL5 canvas interface to stay responsive under rapid input without changing its appearance. The article adds a direct-buffer canvas for block region copies, caches text widths and glyph coverage, caps repaints at 60 fps (16 ms), and limits drawing to panes and regions that actually changed. As a result, hover, scroll, and popups render smoothly without full-panel redraws.

This article introduces a lightweight OS-like helper for MQL5 that streamlines file and path operations using a Python-inspired interface. We implement getcwd, listdir, scandir with DirEntry, remove, rmdir, rename, mkdir, stat, and an os.path subset (exists, isfile, isdir, join, split, pardir). You will learn how to work consistently within the terminal Files/common sandbox and simplify everyday filesystem tasks.

FX options are quoted in delta space, not by strike. This article implements an FX-native smile tool for MetaTrader 5: it converts ATM, risk reversal and butterfly quotes into strike-space pillars, prices with the Garman–Kohlhagen model, handles spot/forward and premium-adjusted delta conventions per pair, and draws the smile with a reconstructed strike ladder and Greeks.

The article provides a systematic overview of the theory of random variables, which serves as the basis for analyzing and modeling uncertainty in financial markets. The article covers the definitions and properties of univariate random variables, cumulative distribution functions (CDFs) and probability density functions (PDFs), as well as the differences between discrete, continuous, and mixed models. The theoretical material is based on intuitive analogies with mass and density. The appendix to this article contains practical examples of using the standard MQL5 library to calculate probabilities, quantiles, and moments of distributions. It also demonstrates the graphical capabilities of the MetaTrader 5 platform for visual data analysis by plotting PDF and CDF curves and QQ plots.