Articles on trading system automation in MQL5

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.

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.

We build an MQL5 program that detects and trades Double Top and Double Bottom patterns from confirmed swing pivots with rule-based logic. The detector matches peaks within a tolerance, derives the neckline, and applies leg-balance and spacing filters to reject weak shapes. It supports entry on a neckline break or an optional pullback, with a stop beyond the extreme and targets by measured move or reward-to-risk.

In this article, we will take a detailed look at the practical implementation of the key components of the SAGDFN framework. We will show how sparse attention and the selection of significant neighbors are organized for time series forecasting. The approaches presented strike a balance between forecast accuracy and computational efficiency.

The article examines three key obstacles to integrating LLMs with MetaTrader 5: the lack of direct access, strict rate limits, and API key security given the architectural limitations of MQL5. A configuration is proposed that uses a local Python server as a bridge between the Expert Advisor and OpenRouter. The article covers WebSocket and fallback to TCP, storing the key on the server, batch processing of multiple symbols, and constructing a technical prompt. Readers get a ready-made architecture that reduces latency and costs.

I know that many of you may have thought I would publish a few more articles to explain other aspects of this system. The missing elements are easy to implement. Even so, developing them will let you see how prepared you really are.

In this article, I will present our system for simulating market operations. Although everything is practically finished, there are still a few things to implement and a few changes to make. However, I have to admit that, after everything we've already developed, I'm tired of still being stuck on implementing this system.

Code and build Python-based trading robots just like MQL5 Expert Advisors (EAs). In this article, we develop a Python-based replica of the MetaTrader 5 Python package, providing methods that closely resemble those of MetaTrader 5 during simulation. This allows us to backtest Python EAs in a simplified environment, using an approach similar to developing and testing Expert Advisors in MQL5.

We replace the embedded bitmap icons in our MQL5 canvas interface with a resolution-independent vector icon system. A small set of anti-aliased primitives (strokes, discs, rings, rounded rectangles, and polygons) draws every logo and sidebar glyph procedurally, then plugs into the header, sidebar, and theme toggle. You get smaller builds, theme-aware recoloring, and icons that stay sharp at any size.

Article revisits Trading Robot that merged Discrete Fourier Transform with Leaky Integrate-and-Fire Spiking Neural Network. Evaluation is made over the seven operating modes with different symbols, timeframes, and test windows. We use two-thirds of the test window to optimize while the one-third does a forward walk run. This study tries to detail parameter interactions, input scaling, gating effects, and outlines practical checks that include rolling windows, frozen inputs, and others. The goal remains identifying settings worth further testing or paper trading.

One of the issues that bothered me the most was that the `C_ElementsTrade` class contains code for accessing positions. Don't take this as a mistake, because it really isn't one. However, this increases the risk of errors in some of the tasks we will need to handle later. All work on implementing the position indicator was carried out with a view to its use in the replay/simulation service. However, when running in this environment, we will have no access to actual positions. Consequently, any call to the MQL5 library intended to retrieve position data will have no effect in this environment.

The article describes a practical implementation of the HimNet framework based on MQL5, ready for integration into automated trading. We demonstrate how heterogeneity-adapted meta-parameters transform the model into a universal tool capable of handling fluctuating volatility.

In this article, we take a detailed look at the algorithms used to implement the key components of the HimNet framework. We demonstrate how, with a minimal number of trainable components, a high degree of consistency and controllability can be achieved throughout the entire system. The presented implementation is compact and transparent, which makes it easier to adapt to real-world market tasks.

A file-based news filter for MQL5 reads a pre-downloaded Forex Factory CSV from MQL5/Files, avoiding fragile web scraping and paid APIs. It provides a modular CNewsFilter with a quote-aware CSV parser, suffix-robust currency extraction, an inclusive time-window checker with clear block reasons, and chart zones for today's events. A demo EA and assertion tests help you integrate and verify offline filtering around scheduled releases.

The article presents a full MQL5 implementation of a multi-symbol trend system: dual EMA crossovers for entries, ADX to avoid ranges, ATR to normalize position size, and a heat monitor to cap total portfolio risk. We explain the architecture, calculation details, and entry/exit logic on daily bars. The result is a practical EA template for systematic, risk-aware portfolio trading.

We invite you to explore the HimNet framework, which combines the flexibility of spatio-temporal adaptation with high computational efficiency, enabling accurate and stable forecasts for financial time series. The article explains in detail how its key components interact with one another, transforming complex algorithms into a manageable architecture.

The series develops machine learning in 100% native MQL5 with no external dependencies. Part 1 delivers logistic regression from first principles: a CLogReg class with standardization, a stable sigmoid, SGD training, and model persistence, plus a script that builds ATR-normalized features, labels the next bar, and tests out-of-sample against a baseline. Readers get a compact include file and a clear template for leakage-free evaluation.

This article implements the Darvas Box method as a complete MQL5 Expert Advisor. We code box detection with a three-session hold, volume contraction during consolidation, and volume-confirmed breakouts, plus a staircase pyramid with a shared, rolling stop at the latest box floor. The EA uses a state machine to run box scanning and trade management in parallel, providing a ready-to-compile system with configurable inputs and clear on-chart diagnostics.

This article implements a typed message bus over Windows named pipes to replace MetaTrader's untyped GlobalVariables for inter‑EA communication. A broker EA manages the server and registry, serves multiple slave EAs, and responds with a live, per‑symbol‑attributed portfolio risk measure. It also explains the non-blocking accept pattern that preserves terminal responsiveness, and includes a dashboard and a test script.

A hybrid architecture based on Llama 3.2 and SEAL is being tested on eight currency pairs (M15), with forward-period data isolation and information leakage control. The methodology combines adversarial self-play, curriculum learning, and class balancing to ensure stable training. The experiments confirm the gap between forecast accuracy and actual returns, providing readers with practical guidelines for testing strategies and accurately assessing their generalizability.

The TQNet framework opens up new possibilities for modeling and forecasting financial time series by combining modularity, flexibility, and high performance. The article explores the possibility of implementing complex mechanisms for handling global correlations, including advanced parameter initialization methods.

The article outlines the complete process of creating a multi-currency Expert Advisor using the Adwizard library for MetaTrader 5: from setting up the environment for creating optimization projects to obtaining the final multi-currency Expert Advisors, which combine multiple instances of a simple trading strategy. We will walk through setting up the necessary input parameters, conventions for convenient file names, and launching three instances of the final Expert Advisors on different trading accounts with different parameters.

We turn eigenvalues from a covariance matrix into a normalized spectral‑entropy score that measures how evenly variance is spread across factors. SpectralEntropyCalculator.mq5 compares two portfolios in one run, using native vector summation, ArraySort()-based ordering, element‑wise division, and the Shannon entropy formula. The report makes dominant factors visible and enables quick, repeatable checks of diversification quality.

This part implements PortfolioRisk.mqh, a shared library that shifts risk management to the account level. It scans positions and pending orders, computes margin and floating results, counts symbols, and decomposes pairs into currencies to detect concentration, then validates each new trade against portfolio limits. The Series EA example illustrates configuring scope (account-wide or magic-filtered), registering magics, and integrating the pre-trade gate.

Establishes the persistence foundation for a prop-firm compliance EA in MetaTrader 5. It introduces rule inputs and status enums, separates live account state from stored settings, validates percentages and thresholds, and implements SQLite open/close, schema creation, and prepared save/load operations. Using the account login and server as a composite key, the EA restores existing settings and updates them when inputs change.

We invite you to learn about an algorithm for decomposing a time series into meaningful layers and using them to build a parsimonious model. We systematically present the architecture, the practical implementation in MQL5/OpenCL, and real-world tests using historical market data.

In this article, we continue our hands-on exploration of SSCNN — a next-generation architectural solution capable of processing fragmented time series. Instead of blind scaling — smart modularity, attention to detail, and targeted normalization. Step by step, we are creating computational blocks in the MQL5 environment and laying the foundation for reliable predictive analysis.

This article presents a modular position sizing engine for MetaTrader 5 that operates on normalized R-multiples. A layered pipeline combines enriched trade statistics, a generalized Kelly edge estimate, volatility-aware adjustment, Monte Carlo calibration under ruin and drawdown limits, a continuous risk policy, an exposure guard, and a broker-aware lot calculator. The output is a broker-valid lot size with an optional CSV audit trail, providing a transparent prototype for implementing modern risk controls in native MQL5.

This article presents a complete MQL5 Expert Advisor that implements Jesse Livermore's market key as a deterministic state machine. It detects pivotal levels from consolidations using ATR and volume expansion, scales in across four tranches, and exits on abnormal behavior defined by range and volume. The EA validates inputs in OnInit, requires a hedging account, and compiles out of the box for testing Livermore's rules on daily data.

Risk-based lot sizing can still exceed what free margin allows. The article adds a margin-aware cap using OrderCalcMargin(), an optional adaptive cap that scales with ACCOUNT MARGIN LEVEL, and a single pre-trade validation gate that unifies position limits, risk sizing, and margin checks. Readers get concrete code to prevent order rejections and over-committing margin, with clear logs when a trade is reduced or skipped.

The article will show you how Mamba4Cast turns theory into a working trading algorithm and lays the groundwork for your own experiments. Do not miss this opportunity to gain a full range of knowledge and inspiration for developing your own strategy.