Articles on MetaTrader 5 integration using MQL5

In this article we demonstrate the implementation of the Augmented Dickey-Fuller test, and apply it to conduct cointegration tests using the Engle-Granger method.

In this article, we will discuss one of the possible solutions to what we have been trying to demonstrate—namely, how to allow an Excel user to perform an action in MetaTrader 5 without sending orders or opening or closing positions. The idea is that the user employs Excel to conduct fundamental analysis of a particular symbol. And by using only Excel, they can instruct an expert advisor running in MetaTrader 5 to open or close a specific position.

This discussion covers the updated TradeManagementPanel in our New_Admin_Panel EA. The update enhances the panel by using built-in classes to offer a user-friendly trade management interface. It includes trading buttons for opening positions and controls for managing existing trades and pending orders. A key feature is the integrated risk management that allows setting stop loss and take profit values directly in the interface. This update improves code organization for large programs and simplifies access to order management tools, which are often complex in the terminal.

MetaTrader 5 python package provides an easy way to build trading applications for the MetaTrader 5 platform in the Python language, while being a powerful and useful tool, this module isn't as easy as MQL5 programming language when it comes to making an algorithmic trading solution. In this article, we are going to build trade classes similar to the one offered in MQL5 to create a similar syntax and make it easier to make trading robots in Python as in MQL5.

Let's explore how you can start integrating external code from any repository in the MQL5 Algo Forge storage into your own project. In this article, we finally turn to this promising, yet more complex, task: how to practically connect and use libraries from third-party repositories within MQL5 Algo Forge.

The ADX Oscillator and CCI oscillator are trend following and momentum indicators that can be paired when developing an Expert Advisor. We look at how this can be systemized by using all the 3 main training modes of Machine Learning. Wizard Assembled Expert Advisors allow us to evaluate the patterns presented by these two indicators, and we start by looking at how Supervised-Learning can be applied with these Patterns.

In Part 5 of our MQL5 AI trading system series, we enhance the ChatGPT-integrated Expert Advisor by introducing a collapsible sidebar, improving navigation with small and large history popups for seamless chat selection, while maintaining multiline input handling, persistent encrypted chat storage, and AI-driven trade signal generation from chart data.

In this article we will talk about the importance of APIs (Application Programming Interface) for interaction between different applications and software systems. We will see the role of APIs in simplifying interactions between applications, allowing them to efficiently share data and functionality.

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.

We continue our look at how MetaTrader can be used outside its forex trading ‘comfort-zone’ by looking at another tradable asset in the form of the FXI ETF. Unlike in the last article where we tried to do ‘too-much’ by delving into not just indicator selection, but also considering indicator pattern combinations, for this article we will swim slightly upstream by focusing more on indicator selection. Our end product for this is intended as a form of pipeline that can help recommend indicators for various assets, provided we have a reasonable amount of their price history.

As we progressed, we used more and more simultaneously running instances of trading strategies in one EA. Let's try to figure out how many instances we can get to before we hit resource limitations.

In this article, we enhance the AI-powered trading system in MQL5 with user interface improvements, including loading animations for request preparation and thinking phases, as well as timing metrics displayed in responses for better feedback. We add response management tools like regenerate buttons to re-query the AI and export options to save the last response to a file, streamlining interaction.

In this article, we delve into enhancing the details of trading reports and delivering the final document via email in PDF format. This marks a progression from our previous work, as we continue exploring how to harness the power of MQL5 and Python to generate and schedule trading reports in the most convenient and professional formats. Join us in this discussion to learn more about optimizing trading report generation within the MQL5 ecosystem.