Articles with examples of trading robots developed in MQL5

The article provides a fascinating look at how SwiGLU embedding reveals hidden market patterns, and how a sparse Mixture of Experts within a Decoder-Only Transformer makes forecasts more accurate at reasonable computational cost. We take an in-depth look at the integration of Time‑MoE into MQL5 and OpenCL, and provide a step-by-step guide to configuring and training the model.

The article bridges automated placement with manual analysis for the Fibonacci family in MQL5. It scans charts, identifies user Fibonacci objects, and normalizes their level arrays, interaction flags, and visuals per object type while preserving coordinates. With manual-priority enforcement, Expert Advisors can evaluate both human and code-generated tools reliably, without duplicates or runtime indexing issues.

We invite you to explore the practical implementation of a sparse mixture of experts block for time series in the OpenCL computing environment. This article provides a step-by-step explanation of how masked multi-window convolution works, as well as how gradient-based training is organized in the presence of multiple information streams.

A hybrid exit engine for MQL5 replaces static TPs with CRT-derived structural levels. The CRT_ProfitConserve class secures a partial at the first level and then trails the remaining position by structural anchors rather than fixed pips. The article walks through the class API, essential methods, and example usage in EAs, providing a clear path to embed CRT-based exits into existing strategies.

We build a tCISD program in MQL5 that pairs Quarterly Theory cycles anchored to New York time with a correlated-symbol SSMT divergence to time reversals. The article shows how to map cycles and quarters, detect the cross-symbol sweep disagreement, and derive the tCISD level whose break confirms the change in delivery. You will get a working entry logic that arms on divergence and executes on a confirmation close or a retest.

We invite you to explore the modern Time-MoE framework, which has been adapted for time series forecasting tasks. In this article, we will implement the key components of the architecture step by step, providing explanations and practical examples along the way. This approach will allow you not only to understand how the model works, but also to apply those principles to real-world trading scenarios.

The article focuses on the practical implementation of the TimeFound model for time series forecasting. The key stages of implementing the framework's main approaches using MQL5 are examined.

Build a level-2 path-signature engine in pure MQL5 to read the lead-lag ordering between two data streams without choosing a lag and without a linear model. The article delivers a reusable library, an indicator that plots the Levy‑area oscillator, and a simple rule‑based Expert Advisor. Code is cross‑checked against closed‑form cases, and the components are ready to plug into your projects.

The article describes the development of a multi-agent machine learning system for algorithmic trading on MetaTrader 5 based on reinforcement learning. The system has a three-tier architecture: memory neurons store experience, agents make independent decisions, and the collective mind combines them through weighted voting. The system is continuously improved through Q-learning, pruning of ineffective neurons, and evolutionary reduction of exploration.

In this article, we build the core of the TimeFound intelligent model step by step, adapting it to real-world time series forecasting tasks. If you are interested in the practical implementation of neural network patching algorithms in MQL5, you have come to the right place.

The article implements a self-sufficient Adaptive SuperTrend EA with internal calculations on a selectable timeframe, avoiding external buffers and indicator files. It includes risk-based lot sizing, ATR stops, stepwise RR trailing, optional anti-repainting confirmation, and session control. Practitioners can reuse the structure for consistent new‑bar signal handling and broker‑compliant order validation.

In this article, we will examine the application of mathematics to grid strategies. We will consider the basic principles of the strategy, as well as its advantages and disadvantages. You will learn how to build a trading grid, set optimal parameters, and manage risks effectively.

This article introduces a modular Fair Value Gap (FVG) detection engine for MQL5 packaged as a reusable include class, it evaluates imbalance zones on closed bars, applies a Simple True Range average filter to eliminate low-volatility noise, and supports wick-touch and close-through mitigation. A companion diagnostic indicator plots active gaps, and an Expert Advisor template demonstrates automated pullback entries with new-bar execution controls.

We build a session-based reversal program in MQL5 using the Bread and Butter Judas Swing model. It derives a higher-timeframe daily bias, defines New York kill zones, maps each session's premium and discount from the live range, and requires a sweep before a market structure shift confirms entry. Readers get a ready approach to arm setups only during active sessions and execute in the bias direction with clear, testable rules.

The Mantis framework transforms complex time series into informative tokens and serves as a reliable foundation for an intelligent trading agent capable of operating in real time.

Kohonen's self-organizing maps transform the chaos of market data into an ordered two-dimensional map, where similar patterns are grouped together. The article demonstrates a complete implementation of a SOM in an MQL5 Expert Advisor with 400 neurons and continuous learning. We break down the Best Matching Unit search algorithm, weight updates using a Gaussian neighborhood function, integration with quantum effects, and the generation of trading signals. The code is open-source, the math is clear, and the results are verifiable.

We implement a Hierarchical Risk Parity allocator in MQL5 as a single class, validate each stage against an independent Python reference, and package it in a rebalancing Expert Advisor. The pipeline covers returns, covariance/correlation, clustering, quasi-diagonalization, and recursive bisection, and contrasts HRP with Markowitz on stressed data. You finish with a verified allocator and an EA ready for basket-level testing.

Mantis is a versatile tool for in-depth time series analysis that can be flexibly scaled to accommodate any financial scenario. Learn how a combination of patching, local convolutions, and cross-attention enables a highly accurate interpretation of market patterns.

Meet Mantis — a lightweight foundation model for time series classification based on a Transformer architecture, featuring contrastive pre-training and hybrid attention that deliver record-breaking accuracy and scalability.

We continue our acquaintance with the Mamba4Cast framework. Today, we will delve into the practical implementation of the proposed approaches. Mamba4Cast was designed not for lengthy warm-up on every new time series, but for immediate deployment. Thanks to the concept of Zero-Shot Forecasting, the model can produce high-quality forecasts on real-world data without additional training or hyperparameter tuning.

Learn to assemble an MT5 Expert Advisor that hosts a chart management dashboard written in MQL5. The guide walks through shared definitions, symbol acquisition and filtering, chart lifecycle functions, and a UI panel with search, scrolling, and state indicators, all driven by events and a timer. The result is a reproducible tool that reduces clicks and accelerates multi-symbol analysis.

This article demonstrates a practical approach to researching trading ideas using a range breakout strategy as an example. We will go through the entire process, from formalizing trading rules and building a baseline model to parameter optimization, forward testing, and evaluating the robustness of the results. The main goal of the article is to develop an understanding of how statistics and testing can be used to identify, validate, and evaluate trading hypotheses.

We enhance the manual backtesting EA with real-time lot adjustment, an order module for buy/sell stops and limits, and a Trade Manager to modify TP/SL and close positions individually. The article explains control setup with CButton/CBmpButton/CEdit, logic in OnTick, and workarounds for Strategy Tester input constraints. Readers can reuse these components to speed up testing workflows and implement robust trade management.

We demonstrate a practical customization path for a built-in MetaTrader 5 EA using BullishBearish MeetingLines Stoch. The workflow covers baseline testing in the Strategy Tester, parameter optimization, and code-level changes. Two modifications are implemented: exposing Stochastic thresholds as inputs and adding an optional Moving Average filter to limit counter‑trend signals. The article includes the full modified code for replication.

This article introduces ap.mqh, the ALGLIB port for MQL5, and demonstrates its use in multi‑asset workflows that require robust linear algebra. It covers why built-in indicators fall short, then implements polynomial regression, a rolling correlation matrix indicator, and an adaptive hedge ratio estimator using ridge regression with Cholesky. Practical code shows how to compute spread z‑scores and execute coordinated pairs trades entirely within MetaTrader 5.

In this article, triangular arbitrage is presented as a problem of finding cycles in a directed graph, where the vertices are currencies and the edges are currency pairs with weight rates. Profitable cycle: product of weights >1. Our Floyd-Warshall and DFS algorithms find optimal currency exchange paths that return to the starting point with a profit.

We describe an MQL5 framework that aligns entries with session rhythms and scheduled clock events. A script identifies the broker's time zone and DST by detecting NFP spikes on EURUSD and matching them to EU/US/AU transition dates, producing EA‑ready settings. Session-to-broker time conversion and 15-minute marks constrain execution. A multi‑timeframe AMA signal aggregates trends for strategy selection and optimization.

The article presents a complete implementation of TimeGPT, a specialized Transformer-based architecture for forecasting financial time series on the MetaTrader 5 platform. Adaptation of the attention mechanism to financial data, selective tokenization of price changes, hardware-aware optimizations, and advanced learning techniques are discussed. Included are practical testing results showing 87% forecast accuracy over a 24-bar horizon with a training time of 15 minutes on the CPU. We also present a ready-made trading EA with automatic retraining.

The article presents the revolutionary architecture of PatchTST, a tailored transformer for financial time series analysis that breaks market data into 16-bar patches for efficient processing. We will discuss the full implementation of a trading robot in MQL5 covering everything from mathematical fundamentals and data structures to a ready-made EA with risk management and continuous learning systems.

We build an automated MQL5 program that trades Turtle Soup by fading false breakouts of the N-bar high and low. The article implements liquidity-sweep detection, confirmation closes back inside the level, sweep-depth and extreme-age filters, and an optional reversal-candle body check. It adds configurable dynamic or static stops, two take-profit modes, points-based trailing, and clear chart visuals, providing a ready baseline for backtesting and further customization.

We implement a symbol resolution framework that abstracts broker naming differences in MetaTrader 5. Using a persistent mapping store, layered resolution with validation, a hash-indexed registry, and a cache, it returns selectable symbols with live market data and logs unresolved cases. Practically, you can deploy the same EA across brokers and keep symbol access consistent at low runtime cost.

In this article, we introduce the Mamba4Cast framework and take a closer look at one of its key components: timestamp-based positional encoding. The article shows shows how time embedding is formed taking into account the calendar structure of the data.