Articles on trading system automation in MQL5

The article explains how to use MQL5 structures with binary files to persist Expert Advisor parameters. It covers defining structures, accessing members, and distinguishing simple from complex layouts, then writing and reading entire records using FileWriteStruct and FileReadStruct in FILE BIN mode. You will learn safe patterns for fixed-size data and how shared storage (FILE COMMON) enables reuse across sessions and terminals.

This article builds an order-flow footprint indicator in MQL5 that aggregates tick-by-tick volume into quantized price levels and supports Bid vs Ask and Delta display modes. A canvas overlay renders color-scaled volume text aligned with the candles and updates on every tick. You will learn sorting of price levels, max-value normalization for color mapping, and responsive redraws on zoom, scroll, and resize to read volume distribution and aggressor dominance inside each bar.

In this article, we expand the MQL5 graphing tool to support seventeen statistical distributions with interactive cycling via a header switch icon. We add type-specific data loading, discrete and continuous histogram computation, and theoretical density functions for each model, with dynamic titles, axis labels, and parameter panels that adapt automatically. The result lets you overlay distribution models on the same sample and compare fit across families without reloading the tool.

This article lays the system architecture for a multi‑account algorithmic trading setup that operates cryptocurrency CFDs on MetaTrader 5 while respecting prop‑firm constraints. It defines three core principles—fixed dollar risk, one script per account, and centralized configuration—then details the Python–MQL5 split, the 60‑second processing loop, and JSON-based signaling. Readers get practical lot‑size computation, safety checks, and position management patterns for reliable deployment.

Build an MQL5 Expert Advisor that automates Larry Williams Hidden Smash Day reversals. It reads confirmed signals from a custom indicator, applies context filters (Supertrend alignment and optional trading‑day rules), and manages risk with stop‑loss models based on smash‑bar structure or ATR and a fixed or risk‑based position size. The result is a reproducible framework ready for testing and extension.

GridSearchCV and RandomizedSearchCV share a fundamental limitation in financial ML: each trial is independent, so search quality does not improve with additional compute. This article integrates Optuna — using the Tree-structured Parzen Estimator — with PurgedKFold cross-validation, HyperbandPruner early stopping, and a dual-weight convention that separates training weights from evaluation weights. The result is a five-component system: an objective function with fold-level pruning, a suggestion layer that optimizes the weighting scheme jointly with model hyperparameters, a financially-calibrated pruner, a resumable SQLite-backed orchestrator, and a converter to scikit-learn cv_results_ format. The article also establishes the boundary — drawn from Timothy Masters — between statistical objectives where directed search is beneficial and financial objectives where it is harmful.

In this article, we advance the binomial distribution graphing tool in MQL5 by integrating DirectX for 3D visualization, enabling switchable 2D/3D modes with camera-controlled rotation, zoom, and auto-fitting for immersive analysis. We render 3D histogram bars, ground planes, and axes alongside the theoretical probability mass function curve, while preserving 2D elements like statistics panels, legends, and customizable themes, gradients, and labels

This is an article about a specialized trend-following EA that aims to clearly elaborate how to frame and utilize trading setups that occur from imbalances found in PD arrays. This article will explore in detail an EA that is specifically designed for traders who are keen on optimizing and utilizing PD arrays and imbalances as entry criteria for their trades and trading decisions. It will also explore how to correctly determine and profile premium and discount arrays and how to validate and utilize each of them when they occur in their respective market conditions, thus trying to maximize opportunities that occur from such scenarios.

During sideways price movements, traders face excessive signals from multiple moving average crossovers. Today, we discuss how ALGLIB preprocesses raw price data to produce filtered crossover layers, which can also generate alerts when they occur. Join this discussion to learn how a mathematical library can be leveraged in MQL5 programs.

In this part, we will integrate a real-time correlation matrix into a multi-symbol Expert Advisor to prevent redundant or risk-stacked trades. By dynamically measuring cross-pair relationships, the EA will filter entries that conflict with existing exposure, improving portfolio balance, reducing systemic risk, and enhancing overall trade quality.

In this part of the Price Action Analysis Toolkit Development series, we develop an MQL5 indicator that automatically detects rising and falling wedge patterns in real time. The system confirms pivot structures, validates boundary convergence mathematically, prevents overlapping formations, and monitors breakout and failure conditions with precise visual feedback. Built using a clean object-oriented architecture, this implementation converts subjective wedge recognition into a structured, state-aware analytical component designed to strengthen disciplined price action analysis.

This article presents an adaptive parallel channel detection and breakout system in MQL5. It explains how swing points are identified, channels are constructed and dynamically recalculated, and breakouts are confirmed and visualized with persistent signals. The framework integrates trendline geometry, ATR-based filtering, and retest validation to provide reliable, real-time price action analysis for professional charting and trading decisions.

The article shows how to pair SQLite (OLTP) with DuckDB (OLAP) for statistical arbitrage data processing. DuckDB’s columnar engine, ASOF JOIN, and array functions accelerate core tasks such as quote–trade alignment and RWEC, with measured speedups from 2x to 23x versus SQLite on larger inputs. You get simpler queries and faster analytics while keeping trade execution in SQLite.

The article extends a liquidity-based strategy with a simple trend constraint: trade liquidity zones only in the direction of the EMA(50). It explains filtering rules, presents a reusable TrendFilter.mqh class and EA integration in MQL5, and compares baseline versus filtered tests. Readers gain a clear directional bias, reduced overtrading in countertrend phases, and ready-to-use source files.

In this article, we create a canvas-based graphing tool in MQL5 for statistical correlation and linear regression analysis between two symbols, with draggable and resizable features. We incorporate ALGLIB for regression calculations, dynamic tick labels, data points, and a stats panel displaying slope, intercept, correlation, and R-squared. This interactive visualization aids in pair trading insights, supporting customizable themes, borders, and real-time updates on new bars

In the latest installment of this series, we move beyond individual machine learning techniques to address the "Research Chaos" that plagues many quantitative traders. This article focuses on the transition from ad-hoc notebook experiments to a principled, production-grade pipeline that ensures reproducibility, traceability, and efficiency.

Discipline becomes reliable when it is produced by system design, not willpower. Using MQL5, the article implements real-time constraints—trade-frequency caps and daily equity-based stops—that monitor behavior and trigger actions on breach. Readers gain a practical template for governance layers that stabilize execution under market pressure.

In this article, we build an interactive tools palette in MQL5 for chart drawing, with draggable, resizable panels and theme switching. We add buttons for tools like crosshair, trendlines, lines, rectangles, Fibonacci, text, and arrows, handling mouse events for activation and instructions. This system improves trading analysis through a customizable UI, supporting real-time interactions on charts