Articles on trading system automation in MQL5

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Read articles on the trading systems with a wide variety of ideas at the core. Learn how to use statistical methods and patterns on candlestick charts, how to filter signals and where to use semaphore indicators.

The MQL5 Wizard will help you create robots without programming to quickly check your trading ideas. Use the Wizard to learn about genetic algorithms.

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Engineering Trading Discipline into Code (Part 7): Automating Equity Protection Through Governance Logic

Engineering Trading Discipline into Code (Part 7): Automating Equity Protection Through Governance Logic

Automated trading systems often focus heavily on signal generation while neglecting the mechanisms required to protect capital during periods of stress. This article presents an Equity Governance Framework in MQL5 that monitors drawdown conditions, evaluates equity pressure, and dynamically controls trading activity through a state-driven risk management model. By combining drawdown analysis, cooldown logic, trade authorization, and execution restrictions, the framework demonstrates how trading discipline can be engineered directly into code using a modular and extensible architecture.
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Carry Trade Logic in MQL5: Building an EA That Factors Swap Rates Into Position Sizing and Holding Decisions

Carry Trade Logic in MQL5: Building an EA That Factors Swap Rates Into Position Sizing and Holding Decisions

Most retail traders ignore overnight swap rates, but for long-term positions, these interest payments can make or break your strategy. This article shows you how to build a dynamic MQL5 module that retrieves real-time swap data and converts it into actual profit or loss in your account currency. You will learn how to program an Expert Advisor that automatically calculates if a trade is worth holding based on carry income and adjusts your position size to account for expected interest. It is a practical guide to turning a hidden cost into a mathematical advantage for your trading systems.
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Feature Engineering for ML (Part 5): Microstructural Features in Python

Feature Engineering for ML (Part 5): Microstructural Features in Python

This article implements the Chapter 19 microstructure suite in afml.features.microstructure and explains a two-layer design for OHLCV-only and tick-augmented workflows. We cover Roll and Corwin–Schultz spread/volatility, Kyle's, Amihud's, and Hasbrouck's lambdas, VPIN, and bar‑level imbalance features, all in Numba‑accelerated kernels. A single np.searchsorted pass resolves bar boundaries, enabling prange parallelization and producing a bar‑indexed feature matrix ready for downstream ML models.
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MQL5 Trading Tools (Part 35): Adding Channel, Pitchfork, Gann, and Fibonacci Tools to the Canvas Drawing Layer

MQL5 Trading Tools (Part 35): Adding Channel, Pitchfork, Gann, and Fibonacci Tools to the Canvas Drawing Layer

We extend the canvas drawing layer from the previous part with seven new categories of multi-anchor analytical drawing tools, covering three channel variants, three pitchfork variants, three Gann tools, and the six Fibonacci tools. We work through how each tool encodes its geometry on the canvas, how derived handles let users reshape compound shapes coherently, and how shared helpers handle ray clipping, scanline filling, and anti-aliased arc rendering. By the end, we will have a full set of analytical drawing tools that live on the same interactive canvas alongside the basic line tools from the previous part.
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Position Management: A Reusable Trade Journal with Live Maximum Adverse Excursion, Maximum Favorable Excursion, and R-Multiple Tracking in MQL5

Position Management: A Reusable Trade Journal with Live Maximum Adverse Excursion, Maximum Favorable Excursion, and R-Multiple Tracking in MQL5

This article presents CTradeJournal, a self-contained MQL5 class for live tracking of open positions at tick frequency. It maintains MAE, MFE, and initial risk in money, calculates the R-multiple when a position closes, and writes a complete CSV record. The text explains the design choices, provides the implementation, and shows simple EA integration so you can analyze entries, stop placement, and outcome distribution.