Articles on data analysis and statistics in MQL5

In this article, we develop a Crab Harmonic Pattern system in MQL5 that identifies bullish and bearish Crab harmonic patterns using pivot points and Fibonacci ratios, triggering trades with precise entry, stop loss, and take-profit levels. We incorporate visual feedback through chart objects like triangles and trendlines to display the XABCD pattern structure and trade levels.

Candlestick patterns help traders understand market psychology and identify trends in financial markets, they enable more informed trading decisions that can lead to better outcomes. In this article, we will explore how to use candlestick patterns with AI models to achieve optimal trading performance.

Discover the indispensable role of Support Vector Machines (SVM) in shaping the future of trading. This comprehensive guide explores how SVM can elevate your trading strategies, enhance decision-making, and unlock new opportunities in the financial markets. Dive into the world of SVM with real-world applications, step-by-step tutorials, and expert insights. Equip yourself with the essential tool that can help you navigate the complexities of modern trading. Elevate your trading game with SVM—a must-have for every trader's toolbox.

In this article, we will rethink code locks, transforming them from security mechanisms into tools for solving complex optimization problems. Discover the world of code locks viewed not as simple security devices, but as inspiration for a new approach to optimization. We will create a whole population of "locks", where each lock represents a unique solution to the problem. We will then develop an algorithm that will "pick" these locks and find optimal solutions in a variety of areas, from machine learning to trading systems development.

MQL5 offers endless opportunities to develop automated trading systems tailored to your preferences. Did you know it can even perform complex mathematical calculations? In this article, we introduce the Japanese Heikin-Ashi technique as an automated trading strategy.

Long Short-Term Memory (LSTM) is a type of recurrent neural network (RNN) designed to model sequential data by effectively capturing long-term dependencies and addressing the vanishing gradient problem. In this article, we will explore how to utilize LSTM to predict future trends, enhancing the performance of trend-following strategies. The article will cover the introduction of key concepts and the motivation behind development, fetching data from MetaTrader 5, using that data to train the model in Python, integrating the machine learning model into MQL5, and reflecting on the results and future aspirations based on statistical backtesting.

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.

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.

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.

Master the power of VWAP with our comprehensive guide! Learn how to integrate VWAP analysis into your trading strategy using MQL5 and Python. Maximize your market insights and improve your trading decisions today.

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.

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.

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.

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.

Just because ticks are constantly flowing in doesn’t mean every moment is an opportunity to trade. Today, we take an in-depth study into the art of timing—focusing on developing a time isolation algorithm to help traders identify and trade within their most favorable market windows. Cultivating this discipline allows retail traders to synchronize more closely with institutional timing, where precision and patience often define success. Join this discussion as we explore the science of timing and selective trading through the analytical capabilities of MQL5.

Geometric patterns offer traders a concise way to interpret price action. Many analysts draw trend lines, rectangles, and other shapes by hand, and then base trading decisions on the formations they see. In this article, we explore an automated alternative: harnessing MQL5 to detect and analyze the most popular geometric patterns. We’ll break down the methodology, discuss implementation details, and highlight how automated pattern recognition can sharpen a trader's market insights.

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).

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.

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.

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.

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 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.

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.

This article describes the fundamentals of portfolio-level statistical arbitrage. Its goal is to facilitate the understanding of the principles of statistical arbitrage to readers without deep math knowledge and propose a starting point conceptual framework. The article includes a working Expert Advisor, some notes about its one-year backtest, and the respective backtest configuration settings (.ini file) for the reproduction of the experiment.

Fibonacci retracements are a popular tool in technical analysis, helping traders identify potential reversal zones. In this article, we’ll explore how these retracement levels can be transformed into target variables for machine learning models to help them understand the market better using this powerful tool.

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!

The Market Structure Flip Detector Expert Advisor (EA) acts as your vigilant partner, constantly observing shifts in market sentiment. By utilizing Average True Range (ATR)-based thresholds, it effectively detects structure flips and labels each Higher Low and Lower High with clear indicators. Thanks to MQL5’s swift execution and flexible API, this tool offers real-time analysis that adjusts the display for optimal readability and provides a live dashboard to monitor flip counts and timings. Furthermore, customizable sound and push notifications guarantee that you stay informed of critical signals, allowing you to see how straightforward inputs and helper routines can transform price movements into actionable strategies.

We are going to develop a professional indicator for currency strength analysis in MQL5. This step-by-step guide will show you how to develop a powerful trading tool with a visual dashboard for MetaTrader 5. You will learn how to calculate the strength of currency pairs across multiple timeframes (H1, H4, D1), implement dynamic data updates, and create a user-friendly interface.

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.

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.