Articles on MetaTrader 5 integration using MQL5

In the previous article, we discussed relational models which use attention mechanisms in their architecture. One of the specific features of these models is the intensive utilization of computing resources. In this article, we will consider one of the mechanisms for reducing the number of computational operations inside the Self-Attention block. This will increase the general performance of the model.

In this article, we integrate interactive inline buttons into an MQL5 Expert Advisor, allowing real-time control via Telegram. Each button press triggers specific actions and sends responses back to the user. We also modularize functions for handling Telegram messages and callback queries efficiently.

We continue the series of articles on developing a trading robot in Python and MQL5. Today we will solve the problem of selecting and training a model, testing it, implementing cross-validation, grid search, as well as the problem of model ensemble.

Gamma and Delta measure how an option’s value reacts to changes in the underlying asset’s price. Delta represents the rate of change of the option’s price relative to the underlying, while Gamma measures how Delta itself changes as price moves. Together, they describe an option’s directional sensitivity and convexity—critical for dynamic hedging and volatility-based trading strategies.

Fibonacci levels provide a practical framework that markets often respect, highlighting price zones where reactions are more likely. In this article, we build an expert advisor that applies Fibonacci retracement logic to anticipate likely future moves and trade retracements with pending orders. Explore the full workflow—from swing detection to level plotting, risk controls, and execution.

The multilayer perceptron is an evolution of the simple perceptron which can solve non-linear separable problems. Together with the backpropagation algorithm, this neural network can be effectively trained. In Part 3 of the Multilayer Perceptron and Backpropagation series, we'll see how to integrate this technique into the Strategy Tester. This integration will allow the use of complex data analysis aimed at making better decisions to optimize your trading strategies. In this article, we will discuss the advantages and problems of this technique.

In this article we will talk about how MQL5 can interact with Python and FastAPI, using HTTP calls in MQL5 to interact with the tic-tac-toe game in Python. The article discusses the creation of an API using FastAPI for this integration and provides a test script in MQL5, highlighting the versatility of MQL5, the simplicity of Python, and the effectiveness of FastAPI in connecting different technologies to create innovative solutions.

In Part 6 of our MQL5 AI trading system series, we advance the ChatGPT-integrated Expert Advisor by introducing chat deletion functionality through interactive delete buttons in the sidebar, small/large history popups, and a new search popup, allowing traders to manage and organize persistent conversations efficiently while maintaining encrypted storage and AI-driven signals from chart data.

When working on projects in MetaEditor, developers often face the need to manage code versions. MetaQuotes recently announced migration to GIT and the launch of MQL5 Algo Forge with code versioning and collaboration capabilities. In this article, we will discuss how to use the new and previously existing tools more efficiently.

We turn the MQL5 AI trading assistant into a dispatch-driven system that routes seven trading actions through a single central dispatcher. A line-based key-value protocol constrains AI output, while each action maps to market or pending orders and instrument-aware stop levels. A canvas-based UI with a custom prompt editor and pixel-accurate text fitting makes signals consistent, auditable, and ready to render on the chart

This article follows up ‘Part-74’, where we examined the pairing of Ichimoku and the ADX under a Supervised Learning framework, by moving our focus to Reinforcement Learning. Ichimoku and ADX form a complementary combination of support/resistance mapping and trend strength spotting. In this installment, we indulge in how the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm can be used with this indicator set. As with earlier parts of the series, the implementation is carried out in a custom signal class designed for integration with the MQL5 Wizard, which facilitates seamless Expert Advisor assembly.

Bollinger Bands are a very common Envelope Indicator used by a lot of traders to manually place and close trades. We examine this indicator by considering as many of the different possible signals it does generate, and see how they could be put to use in a wizard assembled Expert Advisor.

This article reports the first attempts in the development of a native MQTT client for MQL5. MQTT is a Client Server publish/subscribe messaging transport protocol. It is lightweight, open, simple, and designed to be easy to implement. These characteristics make it ideal for use in many situations.

GridSearchCV and RandomizedSearchCV share a fundamental limitation in financial ML: each trial is independent, so search quality does not improve with additional compute. This article integrates Optuna — using the Tree-structured Parzen Estimator — with PurgedKFold cross-validation, HyperbandPruner early stopping, and a dual-weight convention that separates training weights from evaluation weights. The result is a five-component system: an objective function with fold-level pruning, a suggestion layer that optimizes the weighting scheme jointly with model hyperparameters, a financially-calibrated pruner, a resumable SQLite-backed orchestrator, and a converter to scikit-learn cv_results_ format. The article also establishes the boundary — drawn from Timothy Masters — between statistical objectives where directed search is beneficial and financial objectives where it is harmful.

This article describes the implementation of a regression model based on a decision tree. The model should predict prices of financial assets. We have already prepared the data, trained and evaluated the model, as well as adjusted and optimized it. However, it is important to note that this model is intended for study purposes only and should not be used in real trading.

In this series of articles, we have already mentioned Transfer Learning more than once. However, this was only mentioning. in this article, I suggest filling this gap and taking a closer look at Transfer Learning.