Articles on data analysis and statistics in MQL5

We continue studying the environment in reinforcement learning models. And in this article we will look at another algorithm – Go-Explore, which allows you to effectively explore the environment at the model training stage.

With the rapid development of artificial intelligence today, language models (LLMs) are an important part of artificial intelligence, so we should think about how to integrate powerful LLMs into our algorithmic trading. For most people, it is difficult to fine-tune these powerful models according to their needs, deploy them locally, and then apply them to algorithmic trading. This series of articles will take a step-by-step approach to achieve this goal.

Saplings Sowing and Growing up (SSG) algorithm is inspired by one of the most resilient organisms on the planet demonstrating outstanding capability for survival in a wide variety of conditions.

The article presents a new metaheuristic method based on a fractal approach to partitioning the search space for solving optimization problems. The algorithm sequentially identifies and separates promising areas, creating a self-similar fractal structure that concentrates computing resources on the most promising areas. A unique mutation mechanism aimed at better solutions ensures an optimal balance between exploration and exploitation of the search space, significantly increasing the efficiency of the algorithm.

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 attracts comments and discussion while hopefully furthering the use of this remarkable field in Traders' strategy development.

This article discusses an approach to trading only in the chosen direction (buy or sell). For this purpose, the technique of causal inference and machine learning are used.

Here we are going to develop a script from scratch that simplifies unloading print screens of deals for analyzing trading entries. All the necessary information on a single deal is to be conveniently displayed on one chart with the ability to draw different timeframes.

We will look at price discretization methods using Python + MQL5. In this article, I will share my practical experience developing a Python library that implements a wide range of approaches to bar formation — from classic Volume and Range bars to more exotic methods like Renko and Kagi. We will consider three-line breakout candles and range bars analyzing their statistics and trying to define how else the prices can be represented discretely.

We continue studying distributed Q-learning algorithms. In previous articles, we have considered distributed and quantile Q-learning algorithms. In the first algorithm, we trained the probabilities of given ranges of values. In the second algorithm, we trained ranges with a given probability. In both of them, we used a priori knowledge of one distribution and trained another one. In this article, we will consider an algorithm which allows the model to train for both distributions.

The Black Hole Algorithm (BHA) uses the principles of black hole gravity to optimize solutions. In this article, we will look at how BHA attracts the best solutions while avoiding local extremes, and why this algorithm has become a powerful tool for solving complex problems. Learn how simple ideas can lead to impressive results in the world of optimization.

In this article, we will look at the binary genetic algorithm (BGA), which models the natural processes that occur in the genetic material of living things in nature.

In this article, I will consider the Bat Algorithm (BA), which shows good convergence on smooth functions.

What is angular analysis of financial markets? How to use price action angles and machine learning to make accurate forecasts with 67% accuracy? How to combine a regression and classification model with angular features and obtain a working algorithm? What does Gann have to do with it? Why are price movement angles a good indicator for machine learning?

In the ever-changing world of trading, adapting to market shifts is not just a choice—it's a necessity. New patterns and trends emerge everyday, making it harder even the most advanced machine learning models to stay effective in the face of evolving conditions. In this article, we’ll explore how to keep your models relevant and responsive to new market data by automatically retraining.

The EURUSD forecasting system with the use of computer vision and deep learning. Learn how convolutional neural networks can recognize complex price patterns in the foreign exchange market and predict exchange rate movements with up to 54% accuracy. The article shares the methodology for creating an algorithm that uses artificial intelligence technologies for visual analysis of charts instead of traditional technical indicators. The author demonstrates the process of transforming price data into "images", their processing by a neural network, and a unique opportunity to peer into the "consciousness" of AI through activation maps and attention heatmaps. Practical Python code using the MetaTrader 5 library allows readers to reproduce the system and apply it in their own trading.

Clustering algorithms in machine learning are important unsupervised learning algorithms that can divide the original data into groups with similar observations. By using these groups, you can analyze the market for a specific cluster, search for the most stable clusters using new data, and make causal inferences. The article proposes an original method for time series clustering in Python.

The AI breakthroughs dominating headlines, from ChatGPT to self-driving cars, aren’t built from isolated models but through cumulative knowledge transferred from various models or common fields. Now, this same "learn once, apply everywhere" approach can be applied to help us transform our AI models in algorithmic trading. In this article, we are going to learn how we can leverage the information gained across various instruments to help in improving predictions on others using transfer learning.

This article presents a sample Expert Advisor implementation for trading a basket of four Nasdaq stocks. The stocks were initially filtered based on Pearson correlation tests. The filtered group was then tested for cointegration with Johansen tests. Finally, the cointegrated spread was tested for stationarity with the ADF and KPSS tests. Here we will see some notes about this process and the results of the backtests after a small optimization.

We continue our dive into chaos theory in financial markets. This time I will consider its applicability to the analysis of currencies and other assets.

This tool is a Correlation Dashboard that calculates and displays real-time correlation coefficients across multiple currency pairs. By visualizing how pairs move in relation to one another, it adds valuable context to your price-action analysis and helps you anticipate inter-market dynamics. Read on to explore its features and applications.

The first of a series of articles looking at the mathematics of Custom Criteria with a specific focus on non-linear functions used in Neural Networks, MQL5 code for implementation and the use of targeted and correctional offsets.

In this article, we enhance the hybrid Time Price Opportunity (TPO) market profile indicator in MQL5 by integrating volume data to calculate volume-based point of control, value areas, and volume-weighted average price with customizable highlighting options. The system introduces advanced features like initial balance detection, key level extension lines, split profiles, and alternative TPO characters such as squares or circles for improved visual analysis across multiple timeframes.

In this article we will implement the C_Mouse class. It provides the ability to program at the highest level. However, talking about high-level or low-level programming languages is not about including obscene words or jargon in the code. It's the other way around. When we talk about high-level or low-level programming, we mean how easy or difficult the code is for other programmers to understand.

Market sentiment is one of the most overlooked yet powerful forces influencing price movement. While most traders rely on lagging indicators or guesswork, the Sentiment Tilt Meter (STM) EA transforms raw market data into clear, visual guidance, showing whether the market is leaning bullish, bearish, or staying neutral in real-time. This makes it easier to confirm trades, avoid false entries, and time market participation more effectively.

N-BEATS is a revolutionary deep learning model designed for time series forecasting. It was released to surpass classical models for time series forecasting such as ARIMA, PROPHET, VAR, etc. In this article, we are going to discuss this model and use it in predicting the stock market.

Linear Kernels are the simplest matrix of its kind used in machine learning for linear regression and support vector machines. The Matérn kernel on the other hand is a more versatile version of the Radial Basis Function we looked at in an earlier article, and it is adept at mapping functions that are not as smooth as the RBF would assume. We build a custom signal class that utilizes both kernels in forecasting long and short conditions.

This is a unique optimization algorithm inspired by the evolution of the turtle shell. The TSEA algorithm emulates the gradual formation of keratinized skin areas, which represent optimal solutions to a problem. The best solutions become "harder" and are located closer to the outer surface, while the less successful solutions remain "softer" and are located inside. The algorithm uses clustering of solutions by quality and distance, allowing to preserve less successful options and providing flexibility and adaptability.

This article presents a study of the interaction of different activation functions with optimization algorithms in the context of neural network training. Particular attention is paid to the comparison of the classical ADAM and its population version when working with a wide range of activation functions, including the oscillating ACON and Snake functions. Using a minimalistic MLP (1-1-1) architecture and a single training example, the influence of activation functions on the optimization is isolated from other factors. The article proposes an approach to manage network weights through the boundaries of activation functions and a weight reflection mechanism, which allows avoiding problems with saturation and stagnation in training.