Articles on data analysis and statistics in MQL5

In this discussion, we will set the foundation for using powerful linear, algebra tools that are implemented in the MQL5 matrix and vector API. For us to make proficient use of this API, we need to have a firm understanding of the principles in linear algebra that govern intelligent use of these methods. This article aims to get the reader an intuitive level of understanding of some of the most important rules of linear algebra that we, as algorithmic traders in MQL5 need,to get started, taking advantage of this powerful library.

In our previous article, we introduced a simple script called "The Quarters Drawer." Building on that foundation, we are now taking the next step by creating a monitor Expert Advisor (EA) to track these quarters and provide oversight regarding potential market reactions at these levels. Join us as we explore the process of developing a zone detection tool in this article.

Discover how to fix a critical flaw in financial machine learning that causes overfit models and poor live performance—label concurrency. When using the triple-barrier method, your training labels overlap in time, violating the core IID assumption of most ML algorithms. This article provides a hands-on solution through sample weighting. You will learn how to quantify temporal overlap between trading signals, calculate sample weights that reflect each observation's unique information, and implement these weights in scikit-learn to build more robust classifiers. Learning these essential techniques will make your trading models more robust, reliable and profitable.

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.

Many traders have experienced this situation, often stick to their entry criteria but struggle with trade management. Even with the right setups, emotional decision-making—such as panic exits before trades reach their take-profit or stop-loss levels—can lead to a declining equity curve. How can traders overcome this issue and improve their results? This article will address these questions by examining random win-rates and demonstrating, through Monte Carlo simulation, how traders can refine their strategies by taking profits at reasonable levels before the original target is reached.

The article presents an implementation of the Levenberg-Marquardt algorithm for training feedforward neural networks. A comparative analysis of performance with algorithms from the scikit-learn Python library has been conducted. Simpler learning methods, such as gradient descent, gradient descent with momentum, and stochastic gradient descent are preliminarily discussed.

Detecting patterns in financial markets is challenging because it involves seeing what's on the chart, something that's difficult to undertake in MQL5 due to image limitations. In this article, we are going to discuss a decent model made in Python that helps us detect patterns present on the chart with minimal effort.

The article explores the possibility of improving price forecasting based on trading volume analysis by integrating technical analysis principles with LSTM neural network architecture. Particular attention is paid to the detection and interpretation of anomalous volumes, the use of clustering and the creation of features based on volumes and their definition in the context of machine learning.

The initial goal of this article is not to cover all the possibilities of Forex trading, but rather to adapt the system so that you can perform at least one market replay. We'll leave simulation for another moment. However, if we don't have ticks and only bars, with a little effort we can simulate possible trades that could happen in the Forex market. This will be the case until we look at how to adapt the simulator. An attempt to work with Forex data inside the system without modifying it leads to a range of errors.

The article considers a new population optimization algorithm - Cyclic Parthenogenesis Algorithm (CPA), inspired by the unique reproductive strategy of aphids. The algorithm combines two reproduction mechanisms — parthenogenesis and sexual reproduction — and also utilizes the colonial structure of the population with the possibility of migration between colonies. The key features of the algorithm are adaptive switching between different reproductive strategies and a system of information exchange between colonies through the flight mechanism.

Do you know what really drives a currency pair’s direction? It’s the strength of each individual currency. In this article, we’ll measure a currency’s strength by looping through every pair it appears in. That insight lets us predict how those pairs may move based on their relative strengths. Read on to learn more.

The ATR oscillator is a very popular indicator for acting as a volatility proxy, especially in the forex markets where volume data is scarce. We examine this, on a pattern basis as we have with prior indicators, and share strategies & test reports thanks to the MQL5 wizard library classes and assembly.

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.

This article explores advanced portfolio optimization techniques using Python and MQL5 with MetaTrader 5. It demonstrates how to develop algorithms for data analysis, asset allocation, and trading signal generation, emphasizing the importance of data-driven decision-making in modern financial management and risk mitigation.

In this article we discuss methods related to the analysis of timeseries in the frequency domain. Emphasizing the utility of examining the power spectra of time series when building predictive models. In this article we will discuss some of the useful perspectives to be gained by analyzing time series in the frequency domain using the discrete fourier transform (dft).

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.

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.

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.

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.

Let's continue creating the system and controls. Without the ability to control the service, it is difficult to move forward and improve the system.

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.

As a continuation of this series of articles, let's consider another type of problems within unsupervised learning methods: mining association rules. This problem type was first used in retail, namely supermarkets, to analyze market baskets. In this article, we will talk about the applicability of such algorithms in trading.

The article examines the possibilities of generating and providing reporting data on the use of the seasonality factor when trading spreads on Forex.

In this article, we develop a Bat Pattern system in MQL5 that identifies bullish and bearish Bat harmonic patterns using pivot points and Fibonacci ratios, triggering trades with precise entry, stop loss, and take-profit levels, enhanced with visual feedback through chart objects

This series of articles introduces several time series labeling methods, which can create data that meets most artificial intelligence models, and targeted data labeling according to needs can make the trained artificial intelligence model more in line with the expected design, improve the accuracy of our model, and even help the model make a qualitative leap!

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.

In this article we will complete the development of a simulator for our system. The main goal here will be to configure the algorithm discussed in the previous article. This algorithm aims to create a RANDOM WALK movement. Therefore, to understand today's material, it is necessary to understand the content of previous articles. If you have not followed the development of the simulator, I advise you to read this sequence from the very beginning. Otherwise, you may get confused about what will be explained here.