Graph Theory: Heuristic Search Algorithm (A-Star) Applied in Trading
The article applies the A* heuristic to market structure by modeling validated swing highs and lows as graph nodes and weighting edges with ATR‑normalized distance, spread, and noise penalties. The engine searches the most efficient route to infer trade direction and targets, then filters signals by directional ratio, total path cost, and opposing swings. It anchors TP to the final node and SL to prior structure, with on‑chart visualization and configurable inputs.
Creating a Trading Administrator Panel in MQL5 (Part I): Building a Messaging Interface
This article discusses the creation of a Messaging Interface for MetaTrader 5, aimed at System Administrators, to facilitate communication with other traders directly within the platform. Recent integrations of social platforms with MQL5 allow for quick signal broadcasting across different channels. Imagine being able to validate sent signals with just a click—either "YES" or "NO." Read on to learn more.
Continuous walk-forward optimization (Part 8): Program improvements and fixes
The program has been modified based on comments and requests from users and readers of this article series. This article contains a new version of the auto optimizer. This version implements requested features and provides other improvements, which I found when working with the program.
Introduction to MQL5 (Part 29): Mastering API and WebRequest Function in MQL5 (III)
In this article, we continue mastering API and WebRequest in MQL5 by retrieving candlestick data from an external source. We focus on splitting the server response, cleaning the data, and extracting essential elements such as opening time and OHLC values for multiple daily candles, preparing the data for further analysis.
Improve Your Trading Charts With Interactive GUI's in MQL5 (Part II): Movable GUI (II)
Unlock the potential of dynamic data representation in your trading strategies and utilities with our in-depth guide to creating movable GUIs in MQL5. Delve into the fundamental principles of object-oriented programming and discover how to design and implement single or multiple movable GUIs on the same chart with ease and efficiency.
Advanced Memory Management and Optimization Techniques 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.
Integrating MQL5 with data processing packages (Part 5): Adaptive Learning and Flexibility
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.
Building AI-Powered Trading Systems in MQL5 (Part 3): Upgrading to a Scrollable Single Chat-Oriented UI
In this article, we upgrade the ChatGPT-integrated program in MQL5 to a scrollable single chat-oriented UI, enhancing conversation history display with timestamps and dynamic scrolling. The system builds on JSON parsing to manage multi-turn messages, supporting customizable scrollbar modes and hover effects for improved user interaction.
Portfolio Risk Model using Kelly Criterion and Monte Carlo Simulation
For decades, traders have been using the Kelly Criterion formula to determine the optimal proportion of capital to allocate to an investment or bet to maximize long-term growth while minimizing the risk of ruin. However, blindly following Kelly Criterion using the result of a single backtest is often dangerous for individual traders, as in live trading, trading edge diminishes over time, and past performance is no predictor of future result. In this article, I will present a realistic approach to applying the Kelly Criterion for one or more EA's risk allocation in MetaTrader 5, incorporating Monte Carlo simulation results from Python.
Modified Grid-Hedge EA in MQL5 (Part III): Optimizing Simple Hedge Strategy (I)
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.
Developing a multi-currency Expert Advisor (Part 22): Starting the transition to hot swapping of settings
If we are going to automate periodic optimization, we need to think about auto updates of the settings of the EAs already running on the trading account. This should also allow us to run the EA in the strategy tester and change its settings within a single run.
From Novice to Expert: Predictive Price Pathways
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.
Developing a robot in Python and MQL5 (Part 2): Model selection, creation and training, Python custom tester
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.
Building a Candlestick Trend Constraint Model (Part 9): Multiple Strategies Expert Advisor (III)
Welcome to the third installment of our trend series! Today, we’ll delve into the use of divergence as a strategy for identifying optimal entry points within the prevailing daily trend. We’ll also introduce a custom profit-locking mechanism, similar to a trailing stop-loss, but with unique enhancements. In addition, we’ll upgrade the Trend Constraint Expert to a more advanced version, incorporating a new trade execution condition to complement the existing ones. As we move forward, we’ll continue to explore the practical application of MQL5 in algorithmic development, providing you with more in-depth insights and actionable techniques.
Building A Candlestick Trend Constraint Model (Part 9): Multiple Strategies Expert Advisor (I)
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.
Black-Scholes Greeks: Gamma and Delta
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.
Artificial Algae Algorithm (AAA)
The article considers the Artificial Algae Algorithm (AAA) based on biological processes characteristic of microalgae. The algorithm includes spiral motion, evolutionary process and adaptation, which allows it to solve optimization problems. The article provides an in-depth analysis of the working principles of AAA and its potential in mathematical modeling, highlighting the connection between nature and algorithmic solutions.
Developing a trading Expert Advisor from scratch (Part 17): Accessing data on the web (III)
In this article we continue considering how to obtain data from the web and to use it in an Expert Advisor. This time we will proceed to developing an alternative system.
Analyzing all price movement options on the IBM quantum computer
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!
From Novice to Expert: Reporting EA — Setting up the work flow
Brokerages often provide trading account reports at regular intervals, based on a predefined schedule. These firms, through their API technologies, have access to your account activity and trading history, allowing them to generate performance reports on your behalf. Similarly, the MetaTrader 5 terminal stores detailed records of your trading activity, which can be leveraged using MQL5 to create fully customized reports and define personalized delivery methods.
From Novice to Expert: Animated News Headline Using MQL5 (II)
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.
Creating an MQL5-Telegram Integrated Expert Advisor (Part 6): Adding Responsive Inline Buttons
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.
From Novice to Expert: Statistical Validation of Supply and Demand Zones
Today, we uncover the often overlooked statistical foundation behind supply and demand trading strategies. By combining MQL5 with Python through a Jupyter Notebook workflow, we conduct a structured, data-driven investigation aimed at transforming visual market assumptions into measurable insights. This article covers the complete research process, including data collection, Python-based statistical analysis, algorithm design, testing, and final conclusions. To explore the methodology and findings in detail, read the full article.
Mastering Quick Trades: Overcoming Execution Paralysis
The UT BOT ATR Trailing Indicator is a personal and customizable indicator that is very effective for traders who like to make quick decisions and make money from differences in price referred to as short-term trading (scalpers) and also proves to be vital and very effective for long-term traders (positional traders).
Price Action Analysis Toolkit Development (Part 12): External Flow (III) TrendMap
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.
MetaTrader 5 Machine Learning Blueprint (Part 8): Bayesian Hyperparameter Optimization with Purged Cross-Validation and Trial Pruning
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.
ALGLIB library optimization methods (Part II)
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.
Moving to MQL5 Algo Forge (Part 1): Creating the Main Repository
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.
Neural networks made easy (Part 37): Sparse Attention
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.
Population optimization algorithms: Invasive Weed Optimization (IWO)
The amazing ability of weeds to survive in a wide variety of conditions has become the idea for a powerful optimization algorithm. IWO is one of the best algorithms among the previously reviewed ones.
Mastering JSON: Create Your Own JSON Reader from Scratch in MQL5
Experience a step-by-step guide on creating a custom JSON parser in MQL5, complete with object and array handling, error checking, and serialization. Gain practical insights into bridging your trading logic and structured data with this flexible solution for handling JSON in MetaTrader 5.
MQL5 Wizard Techniques you should know (Part 38): Bollinger Bands
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.
Creating an MQL5-Telegram Integrated Expert Advisor (Part 4): Modularizing Code Functions for Enhanced Reusability
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.
Population optimization algorithms: Monkey algorithm (MA)
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.
From Novice to Expert: Animated News Headline Using MQL5 (IX) — Multiple Symbol Management on a single chart for News Trading
News trading often requires managing multiple positions and symbols within a very short time due to heightened volatility. In today’s discussion, we address the challenges of multi-symbol trading by integrating this feature into our News Headline EA. Join us as we explore how algorithmic trading with MQL5 makes multi-symbol trading more efficient and powerful.
Developing a Multi-Currency Expert Advisor (Part 26): Informer for Trading Instruments
Before moving forward with the development of multi-currency EAs, let's try to switch to creating a new project using the developed library. This example will demonstrate how to best organize source code storage and how using the new code repository from MetaQuotes can help us.
Creating a Trading Administrator Panel in MQL5 (Part III): Enhancing the GUI with Visual Styling (I)
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.
Multilayer perceptron and backpropagation algorithm (Part 3): Integration with the Strategy Tester - Overview (I).
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.
Neural networks made easy (Part 22): Unsupervised learning of recurrent models
We continue to study unsupervised learning algorithms. This time I suggest that we discuss the features of autoencoders when applied to recurrent model training.
Creating 3D bars based on time, price and volume
The article dwells on multivariate 3D price charts and their creation. We will also consider how 3D bars predict price reversals, and how Python and MetaTrader 5 allow us to plot these volume bars in real time.