Articles on MetaTrader 5 integration using MQL5

The article explores the development of a quantum-inspired trading system, transitioning from a Python prototype to an MQL5 implementation for real-world trading. The system uses quantum computing principles like superposition and entanglement to analyze market states, though it runs on classical computers using quantum simulators. Key features include a three-qubit system for analyzing eight market states simultaneously, 24-hour lookback periods, and seven technical indicators for market analysis. While the accuracy rates might seem modest, they provide a significant edge when combined with proper risk management strategies.

In this article, we will continue to study the remaining optimization methods from the ALGLIB library, paying special attention to their testing on complex multidimensional functions. This will allow us not only to evaluate the efficiency of each algorithm, but also to identify their strengths and weaknesses in different conditions.

Today, we will explore the possibilities of incorporating multiple strategies into an Expert Advisor (EA) using MQL5. Expert Advisors provide broader capabilities than just indicators and scripts, allowing for more sophisticated trading approaches that can adapt to changing market conditions. Find, more in this article discussion.

This study introduces a novel methodology for the development of trend-following trading strategies. This section describes the process of annotating training data and using it to train classifiers. This process yields fully operational trading systems designed to run on MetaTrader 5.

In this article, we enhance the ChatGPT-integrated program in MQL5 overcoming multiline input limitations with improved text rendering, introducing a sidebar for navigating persistent chat storage using AES256 encryption and ZIP compression, and generating initial trade signals through chart data integration.

Todays trader is a philomath who is almost always looking up new ideas, trying them out, choosing to modify them or discard them; an exploratory process that should cost a fair amount of diligence. These series of articles will proposition that the MQL5 wizard should be a mainstay for traders in this effort.

We continue to study unsupervised learning algorithms. This time I suggest that we discuss the features of autoencoders when applied to recurrent model training.

In this article, I will consider the Monkey Algorithm (MA) optimization algorithm. The ability of these animals to overcome difficult obstacles and get to the most inaccessible tree tops formed the basis of the idea of the MA algorithm.

The article presents a new metaheuristic optimization Circle Search Algorithm (CSA) based on the geometric properties of a circle. The algorithm uses the principle of moving points along tangents to find the optimal solution, combining the phases of global exploration and local exploitation.

Hidden Markov Models (HMMs) are powerful statistical tools that identify underlying market states by analyzing observable price movements. In trading, HMMs enhance volatility prediction and inform trend-following strategies by modeling and anticipating shifts in market regimes. In this article, we will present the complete procedure for developing a trend-following strategy that utilizes HMMs to predict volatility as a filter.

In this third part, we revisit the Simple Hedge and Simple Grid Expert Advisors (EAs) developed earlier. Our focus shifts to refining the Simple Hedge EA through mathematical analysis and a brute force approach, aiming for optimal strategy usage. This article delves deep into the mathematical optimization of the strategy, setting the stage for future exploration of coding-based optimization in later installments.

In this article, we will focus on visually styling the graphical user interface (GUI) of our Trading Administrator Panel using MQL5. We’ll explore various techniques and features available in MQL5 that allow for customization and optimization of the interface, ensuring it meets the needs of traders while maintaining an attractive aesthetic.

In this article we demonstrate how Hidden Markov Models trained using Python can be integrated into MetaTrader 5 applications. Hidden Markov Models are a powerful statistical tool used for modeling time series data, where the system being modeled is characterized by unobservable (hidden) states. A fundamental premise of HMMs is that the probability of being in a given state at a particular time depends on the process's state at the previous time slot.

The flow of the market is determined by the forces between bulls and bears. There are specific levels that the market respects due to the forces acting on them. Fibonacci and VWAP levels are especially powerful in influencing market behavior. Join me in this article as we explore a strategy based on VWAP and Fibonacci levels for signal generation.

This part focuses on building a flexible, adaptive trading model trained on historical XAUUSD data, preparing it for ONNX export and potential integration into live trading systems.

We have already implemented the first stage of the automated optimization. We perform optimization for different symbols and timeframes according to several criteria and store information about the results of each pass in the database. Now we are going to select the best groups of parameter sets from those found at the first stage.

We are moving beyond simply viewing analyzed metrics on charts to a broader perspective that includes Telegram integration. This enhancement allows important results to be delivered directly to your mobile device via the Telegram app. Join us as we explore this journey together in this article.

Correlation Pathfinder offers a fresh approach to understanding currency pair dynamics as part of the Price Action Analysis Toolkit Development Series. This tool automates data collection and analysis, providing insight into how pairs like EUR/USD and GBP/USD interact. Enhance your trading strategy with practical, real-time information that helps you manage risk and spot opportunities more effectively.

Correlation Pathfinder offers a fresh approach to understanding currency pair dynamics as part of the Price Action Analysis Toolkit Development Series. This tool automates data collection and analysis, providing insight into how pairs like EUR/USD and GBP/USD interact. Enhance your trading strategy with practical, real-time information that helps you manage risk and spot opportunities more effectively.

We explore how to manipulate databases in MQL5 using the language's native functions. We cover everything from table creation, insertion, updating, and deletion to data import and export, all with sample code. The content serves as a solid foundation for understanding the internal mechanics of data access, paving the way for the discussion of ORM, where we'll build one in MQL5.

Discover practical techniques to optimize memory usage in MQL5 trading systems. Learn to build efficient, stable, and fast-performing Expert Advisors and indicators. We’ll explore how memory really works in MQL5, the common traps that slow your systems down or cause them to fail, and — most importantly — how to fix them.

Candlestick patterns are fundamental to price-action trading, offering valuable insights into potential market reversals or continuations. Envision a reliable tool that continuously monitors each new price bar, identifies key formations such as engulfing patterns, hammers, dojis, and stars, and promptly notifies you when a significant trading setup is detected. This is precisely the functionality we have developed. Whether you are new to trading or an experienced professional, this system provides real-time alerts for candlestick patterns, enabling you to focus on executing trades with greater confidence and efficiency. Continue reading to learn how it operates and how it can enhance your trading strategy.

We will use a quantum computer from IBM to discover all price movement options. Sounds like science fiction? Welcome to the world of quantum computing for trading!

In this article, we refactor the existing code used for sending messages and screenshots from MQL5 to Telegram by organizing it into reusable, modular functions. This will streamline the process, allowing for more efficient execution and easier code management across multiple instances.

Today, we take another step forward by integrating an external news API as the source of headlines for our News Headline EA. In this phase, we’ll explore various news sources—both established and emerging—and learn how to access their APIs effectively. We'll also cover methods for parsing the retrieved data into a format optimized for display within our Expert Advisor. Join the discussion as we explore the benefits of accessing news headlines and the economic calendar directly on the chart, all within a compact, non-intrusive interface.

So far we have considered the automation of launching sequential procedures for optimizing EAs exclusively in the standard strategy tester. But what if we would like to perform some handling of the obtained data using other means between such launches? We will attempt to add the ability to create new optimization stages performed by programs written in Python.