Articles with examples of trading robots developed in MQL5

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.

The article describes a practical approach to synchronizing bars between instruments in a portfolio in MQL5. Classes are provided for loading, storing, and aligning OHLCV data, with options to use an empty bar or carry over values from the previous bar, select a synchronization symbol, and process new bars asynchronously. Examples of use in multi-chart and basket indicators are shown. Readers receive a ready-to-use API for reliable portfolio calculations.

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.

The article proposes a hybrid approach to algorithmic trading based on quantum encoding of market states, Double DQN with a prioritized experience replay buffer, and an LLM acting as a contextual EA. The SEAL methodology enables asynchronous continued training of the agent without halting trading. A lightweight Q-learning filter (USE/SKIP/REDUCE) controls signal execution at the meta-level. Practical details are provided on integrating the system with the MetaTrader 5 trading platform, along with a scheme for adapting it to market regime shifts.

This part focuses on practical data analysis in MQL5 with dataanalysis.mqh. We prepare a labeled dataset from bars, apply normalization, explore redundancy with PCA, and train a decision forest to classify future bar regimes. The article shows how to obtain out-of-bag estimates and permutation importance, helping you validate the model and understand which inputs matter most.

This article presents reusable MQL5 utilities for managing trailing and break-even stops. It covers fixed-point, moving average, ATR, Parabolic SAR, money-based, and time-periodic trailing, plus break-even by points and by money, with activation thresholds, step logic, reverse-move protection, and broker-level validation. Code examples and Bootstrap classes show how to integrate these helpers into Expert Advisors to standardize position control and reduce duplicate code.

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.

We invite you to explore an innovative approach to forecasting time series with missing data using the GinAR framework. The article demonstrates the implementation of key components using OpenCL, which ensures high performance. In our next publication, we will take a detailed look at how to integrate these solutions into MQL5. This will help understand how to apply the method in practice in trading.