MQL4 and MQL5 Programming Articles

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.

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.

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.

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.

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 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.

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.

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.

Feature importance often understates correlated predictors by spreading one signal across many engineered copies, while unrelated noise can appear higher. We measure this effect against a known ground truth and compare four remedies: permutation importance with purged cross-validation, single-feature models, and clustered impurity versus clustered accuracy. The results include per-method rankings and a per-cluster dilution ratio that help identify true signals and avoid deleting valuable features.