Articles on trading system automation 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.

We build a robust MQL5 detector for double tops and double bottoms that first confirms the H4 trend, then validates six conditions (point equality, neckline placement, ordering, width, height, and ATR‑based tolerances). The neckline break is timed on the chart's timeframe, and a three-state machine ensures each pattern trades once. The measured‑move target translates structure into clear exits.

The article discusses the application of Walsh functions in trading. We will explore the basic principles of using these functions to analyze financial markets, forecast prices, and make trading decisions. We will also discuss the advantages and disadvantages of these functions, as well as the prospects for their application in trading and technical analysis.

The article provides a detailed explanation of the SCNN architecture and one way to implement it using MQL5. We will show how time series decomposition can be combined with neural network methods and attention mechanisms.

The article presents the full integration of the 3D-bar module into a quantum-enhanced trading system for forecasting the movement of currency pairs. The system combines stationary four-dimensional features, an 8-qubit quantum encoder, and CatBoost gradient boosting with 52+ features. The system is implemented in Python using MetaTrader 5, Qiskit, CatBoost, and optional integration with the Llama 3.2 LLM for interpreting forecasts.

We implement a five-stage MQL5 pipeline that quantifies market structure, liquidity interaction, and price behavior on four timeframes, then resolves them into a 0–100 Market Intent Score. Decision states (WAIT/WATCH/ACTION) are driven by explicit weights plus hard gates. The analytical core feeds a concise dashboard and, when AutoTrade is on, an execution layer with entry zones, invalidation and liquidity‑based targets.

This part implements risk-based position sizing for the EA. Lot size is derived from account balance, a chosen risk percent, and ATR-based stop distance, then confined and rounded to the broker's volume rules and minimum stop levels. An optional drawdown-aware layer reduces risk during equity declines. Readers get a reproducible sizing function that keeps per-trade risk consistent and orders acceptable to the server.

We invite you to explore the next stage in implementing the SCNN framework, which combines flexibility and interpretability, allowing structural components of a time series to be identified precisely. The article provides a detailed explanation of the mechanisms of adaptive normalization and attention, which ensure the model's resilience to changing market conditions.

This article develops a market‑structure‑driven intraday breakout system based on Bill Williams fractals. We define session bounds, derive volatility‑scaled stops, use fixed risk and take‑profit multipliers, and limit trades to one per direction. An MQL5 Expert Advisor, visualization and statistics, tick-level backtests, an ORB comparison, and a cross-asset forward test provide a complete, replicable workflow.

We introduce a persistent bookmark layer for the MetaTrader 5 History Navigator. Bookmarks capture a chart's symbol, timeframe, and historical position with a name and notes, write them to a CSV file, and reload them later without manual date entry. The implementation integrates bookmark management into the current navigation engine, enabling quick creation, selection, navigation, and deletion for efficient historical study.

We invite you to explore the innovative SCNN framework, which takes time series analysis to a new level by clearly separating data into long-term, seasonal, short-term, and residual components. This approach significantly improves forecasting accuracy by allowing the model to adapt to complex and changing market dynamics.