Articles on data analysis and statistics in MQL5

Discover how the Boom & Crash Interceptor EA transforms your charts into a proactive alert system-spotting explosive moves with lightning-fast velocity scans, volatility surge checks, trend confirmation, and pivot-zone filters. With crisp green “Boom” and red “Crash” arrows guiding your every decision, this tool cuts through the noise and lets you capitalize on market spikes like never before. Dive in to see how it works and why it can become your next essential edge.

The Fibonacci retracement tool is an essential component of price action analysis, providing critical levels for potential market reactions. However, its effectiveness is often limited by the need for continuous human monitoring, which can lead to missed setups. In this part of our series, we introduce a tool that synchronizes and actively monitors manually drawn Fibonacci levels using MQL5, combining discretionary insight with automated oversight.

ONNX is a great tool for integrating complex AI code between different platforms, it is a great tool that comes with some challenges that one must address to get the most out of it, In this article we discuss the common issues you might face and how to mitigate them.

The article describes the experience of developing a hybrid trading system that combines classical technical analysis with neural networks. The author provides a detailed analysis of the system architecture from basic pattern analysis and neural network structure to the mechanisms behind trading decisions, and shares real code and practical observations.

Forecasting the movements of currency pairs is an important factor in trading success. This article explores various price movement models, analyzes their advantages and disadvantages, and explores their practical application in trading strategies. We will consider approaches that allow us to identify hidden patterns and improve the accuracy of forecasts.

Are you looking for a cutting-edge approach to trading that can help you navigate complex and ever-changing markets? Look no further than Kohonen maps, an innovative form of artificial neural networks that can help you uncover hidden patterns and trends in market data. In this article, we'll explore how Kohonen maps work, and how they can be used to develop smarter, more effective trading strategies. Whether you're a seasoned trader or just starting out, you won't want to miss this exciting new approach to trading.

This article demonstrates a secure way to export MetaTrader data to Google Sheets. Google Sheet is the most valuable solution as it is cloud based and the data saved in there can be accessed anytime and from anywhere. So traders can access trading and related data exported to google sheet and do further analysis for future trading anytime and wherever they are at the moment.

Do the positions of planets and stars affect financial markets? Let's arm ourselves with statistics and big data, and embark on an exciting journey into the world where stars and stock charts intersect.

The article discusses Stochastic Diffusion Search (SDS), which is a very powerful and efficient optimization algorithm based on the principles of random walk. The algorithm allows finding optimal solutions in complex multidimensional spaces, while featuring a high speed of convergence and the ability to avoid local extrema.

In this article, I will consider the Firefly Algorithm (FA) optimization method. Thanks to the modification, the algorithm has turned from an outsider into a real rating table leader.

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.

Matrix serves as the foundation of machine learning algorithms and computers in general because of their ability to effectively handle large mathematical operations, The Standard library has everything one needs but let's see how we can extend it by introducing several functions in the utils file, that are not yet available in the library

Discover how to effortlessly import and utilize the History Manager EX5 library in your MQL5 source code to process trade histories in your MetaTrader 5 account in this series' final article. With simple one-line function calls in MQL5, you can efficiently manage and analyze your trading data. Additionally, you will learn how to create different trade history analytics scripts and develop a price-based Expert Advisor as practical use-case examples. The example EA leverages price data and the History Manager EX5 library to make informed trading decisions, adjust trade volumes, and implement recovery strategies based on previously closed trades.

Category Theory is a diverse and expanding branch of Mathematics which as of yet is relatively uncovered in the MQL5 community. These series of articles look to introduce and examine some of its concepts with the overall goal of establishing an open library that provides insight while hopefully furthering the use of this remarkable field in Traders' strategy development.

In this article, we develop an informational dashboard in MQL5 for monitoring multi-symbol positions and account metrics like balance, equity, and free margin. We implement a sortable grid with real-time updates, CSV export, and a glowing header effect to enhance usability and visual appeal.

In this article, I will show a completely different approach to algorithmic trading I ended up with after quite a long time. Of course, all this has to do with my brute force program, which has undergone a number of changes that allow it to solve several problems simultaneously. Nevertheless, the article has turned out to be more general and as simple as possible, which is why it is also suitable for those who know nothing about brute force.

AdaBoost, a powerful boosting algorithm designed to elevate the performance of your AI models. AdaBoost, short for Adaptive Boosting, is a sophisticated ensemble learning technique that seamlessly integrates weak learners, enhancing their collective predictive strength.

The article describes an innovative approach to forecasting price movements in financial markets using quantum computing. The main focus is on the application of the Quantum Phase Estimation (QPE) algorithm to find prototypes of price patterns allowing traders to significantly speed up the market data analysis.

When working with machine learning models, it’s essential to ensure consistency in the data used for training, validation, and testing. In this article, we will create our own version of the Pandas library in MQL5 to ensure a unified approach for handling machine learning data, for ensuring the same data is applied inside and outside MQL5, where most of the training occurs.

In our series on integrating MQL5 with data processing packages, we delve in to the powerful combination of machine learning and predictive analysis. We will explore how to seamlessly connect MQL5 with popular machine learning libraries, to enable sophisticated predictive models for financial markets.

In this discussion, we will apply a simple Markov Chain on an RSI Indicator, to observe how price behaves after the indicator passes through key levels. We concluded that the strongest buy and sell signals on the NZDJPY pair are generated when the RSI is in the 11-20 range and 71-80 range, respectively. We will demonstrate how you can manipulate your data, to create optimal trading strategies that are learned directly from the data you have. Furthermore, we will demonstrate how to train a deep neural network to learn to use the transition matrix optimally.

The article presents the second half of a structured approach to trading with the Gator Oscillator and Accumulation/Distribution. By introducing five new patterns, the author shows how to filter false moves, detect early reversals, and align signals across timeframes. With clear coding examples and performance tests, the material bridges theory and practice for MQL5 developers.

In this article news filtration for individual news events based on their IDs will be implemented. In addition, previous SQL queries will be improved to provide additional information or reduce the query's runtime. Furthermore, the code built in the previous articles will be made functional.

The next algorithm I will consider is cuckoo search optimization using Levy flights. This is one of the latest optimization algorithms and a new leader in the leaderboard.

In this article, we will look at how to implement part of the receiver code. Here we will implement an Expert Advisor to test and learn how the protocol interaction works. The content presented here is intended solely for educational purposes. Under no circumstances should the application be viewed for any purpose other than to learn and master the concepts presented.

Discover the secrets of algorithmic alchemy as we guide you through the blend of artistry and precision in decoding financial landscapes. Unearth how Random Forests transform data into predictive prowess, offering a unique perspective on navigating the complex terrain of stock markets. Join us on this journey into the heart of financial wizardry, where we demystify the role of Random Forests in shaping market destiny and unlocking the doors to lucrative opportunities

We are creating an adaptive self-learning trading expert advisor based on DQN machine learning, with multidimensional causal inference. The EA will successfully trade simultaneously on 7 currency pairs. And agents of different pairs will exchange information with each other.

This article aims to provide a trader-friendly, gentle introduction to the most common cointegration tests, along with a simple guide to understanding their results. The Engle-Granger and Johansen cointegration tests can reveal statistically significant pairs or groups of assets that share long-term dynamics. The Johansen test is especially useful for portfolios with three or more assets, as it calculates the strength of cointegrating vectors all at once.

Perceptrons, single hidden layer networks, can be a good segue for anyone familiar with basic automated trading and is looking to dip into neural networks. We take a step by step look at how this could be realized in a signal class assembly that is part of the MQL5 Wizard classes for expert advisors.