Articles with examples of trading robots developed in MQL5

This article shows how to build a synthetic custom symbol in MQL5 by averaging OHLC data from multiple instruments into a single derived price series. It covers symbol collection and validation, custom symbol creation and configuration, timestamp alignment, historical reconstruction, and lightweight live updates. The result is a reusable method for creating synthetic instruments suitable for correlation analysis, index-style modeling, indicator development, and strategy testing.

In this article, we will explore the ST-Expert framework, which ensures the robustness of forecasts under market uncertainty by taking local and global dependencies in time series into account. Its flexible architecture promotes model adaptability and improves the accuracy of predictions.

In this article, we explore the HiSSD framework, which combines hierarchical learning and multi-agent approaches to create adaptive systems. We examine in detail how this innovative methodology helps uncover hidden patterns in financial markets and optimize trading strategies in decentralized environments.

In this article, we replace the embedded CSV snapshot with a SQLite layer that persists calendar events and triggered trade IDs across restarts. The database lives in the common terminal folder and is shared by live charts and the strategy tester, so both modes read the same data without recompiling. An on-demand downloader with a canvas progress bar fetches history from the calendar API and stores it for offline reuse.

In this article, we begin our exploration of the SSCNN framework — a modern architectural solution for time series analysis that combines accuracy, a structured design, and high computational efficiency. We will systematically examine its theoretical aspects, highlight the key differences from its predecessors, and begin the practical implementation of its basic components in the MQL5 environment.

The article examines the architecture of an Expert Advisor based on a cellular automaton with 10,000 adaptive parameters and an independent binary predictor with a 10-bar prediction horizon. The article demonstrates a three-level online learning process, the evolution of strategies, and validation using a ring buffer and a confusion matrix. The input parameters are reduced to Magic Number, while trading settings are calculated from ATR and five genomes. The EURUSD H1 test yielded an approximate hit rate of 58%, compared to roughly 51% for the fixed MLP.

The article provides a critical analysis of an LLM strategy in which forecasting the direction is separated from trading decisions, and demonstrates why this leads to a disconnect between metrics and PnL. We will describe procedures for dataset balancing, feature engineering, prompt and response preparation, fine-tuning configuration in Ollama, and reliable parsing. Backtesting and forward testing reveal systematic degradation. The practical conclusion is that the problem must be formulated as a direct optimization of trading outcomes.

We are pleased to present the final stage of the TQNet framework’s development and testing, where theory meets real-world trading practice. We will move from historical training to a stress test using recent market data, evaluating the model's robustness and accuracy. The final results are not just dry statistics, but also a clear demonstration of the practical value of the proposed approach.