Articles on data analysis and statistics in MQL5

Now the creation occurs at the same point where we converted ticks into bars. This way, if something goes wrong during the conversion process, we will immediately notice the error. This is because the same code that places 1-minute bars on the chart during fast forwarding is also used for the positioning system to place bars during normal performance. In other words, the code that is responsible for this task is not duplicated anywhere else. This way we get a much better system for both maintenance and improvement.

CatBoost is a powerful tree-based machine learning model that specializes in decision-making based on stationary features. Other tree-based models like XGBoost and Random Forest share similar traits in terms of their robustness, ability to handle complex patterns, and interpretability. These models have a wide range of uses, from feature analysis to risk management. In this article, we're going to walk through the procedure of utilizing a trained CatBoost model as a filter for a classic moving average cross trend-following strategy. This article is meant to provide insights into the strategy development process while addressing the challenges one may face along the way. I will introduce my workflow of fetching data from MetaTrader 5, training machine learning model in Python, and integrating back to MetaTrader 5 Expert Advisors. By the end of this article, we will validate the strategy through statistical testing and discuss future aspirations extending from the current approach.

Although this is the third article on this topic, I must explain for those who have not yet understood the difference between the stock market and the foreign exchange market: the big difference is that in the Forex there is no, or rather, we are not given information about some points that actually occurred during the course of trading.

This article discusses the use of the Go-Explore algorithm over a long training period, since the random action selection strategy may not lead to a profitable pass as training time increases.

Moving Averages are a very common indicator that are used and understood by most Traders. We explore possible use cases that may not be so common within MQL5 Wizard assembled Expert Advisors.

In this article, we discuss the concept of dynamic time warping as a means of identifying predictive patterns in financial time series. We will look into how it works as well as present its implementation in pure MQL5.

In this article, we will finally begin to do what we wanted to do much earlier. However, due to the lack of "solid ground", I did not feel confident to present this part publicly. Now I have the basis to do this. I suggest that you focus as much as possible on understanding the content of this article. I mean not simply reading it. I want to emphasize that if you do not understand this article, you can completely give up hope of understanding the content of the following ones.

In this article we present the implementation of several risk return metrics billed as alternatives to the Sharpe ratio and examine hypothetical equity curves to analyze their characteristics.

In this article, we will look into one of the most difficult issues in the field of MQL5 programming: how to correctly obtain a chart ID, and why objects are sometimes not plotted on the chart. The materials presented here are for didactic purposes only. Under no circumstances should the application be viewed for any purpose other than to learn and master the concepts presented.

Let's create something more interesting. I don't want to spoil the surprise, so follow the article for a better understanding. From the very beginning of this series on developing the replay/simulator system, I was saying that the idea is to use the MetaTrader 5 platform in the same way both in the system we are developing and in the real market. It is important that this is done properly. No one wants to train and learn to fight using one tool while having to use another one during the fight.

Candlestick patterns offer valuable insights into potential market moves. Some single candles signal continuation of the current trend, while others foreshadow reversals, depending on their position within the price action. This article introduces an EA that automatically identifies four key candlestick formations. Explore the following sections to learn how this tool can enhance your price-action analysis.

The article considers the algorithm of the MEC family called the simple mind evolutionary computation algorithm (Simple MEC, SMEC). The algorithm is distinguished by the beauty of its idea and ease of implementation.

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.

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.

In this article, we will consider the algorithm that demonstrates the most controversial results of all those discussed previously - the differential evolution (DE) algorithm.

In this article, we will look at how to put together the first of a number of truly functional modules for use in the replay/simulator system that will also be of general purpose to serve other purposes. We are talking about the mouse module.

The Ichimuko Kinko Hyo is a renown Japanese indicator that serves as a trend identification system. We examine this, on a pattern by pattern basis, as has been the case in previous similar articles, and also assess its strategies & test reports with the help of the MQL5 wizard library classes and assembly.

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 article proposes the method of creating bots using machine learning.

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.

In this article, we will continue exploring the Artificial Bee Hive Algorithm (ABHA) by diving into the code and considering the remaining methods. As you might remember, each bee in the model is represented as an individual agent whose behavior depends on internal and external information, as well as motivational state. We will test the algorithm on various functions and summarize the results by presenting them in the rating table.

In this article, we will look at how to use the CustomBookAdd function correctly and effectively. Despite its apparent simplicity, it has many nuances. For example, it allows you to tell the mouse indicator whether a custom symbol is on auction, being traded, or the market is closed. 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.

When people started creating the first systems capable of computing, everything required the participation of engineers, who had to know the project very well. We are talking about the dawn of computer technology, a time when there were not even terminals for programming. As it developed and more people got interested in being able to create something, new ideas and ways of programming emerged which replaced the previous-style changing of connector positions. This is when the first terminals appeared.

In this article we continue with our theme in the last of tackling everyday trading indicators viewed in a ‘new’ light. We are handling horizontal composition of natural transformations for this piece and the best indicator for this, that expands on what we just covered, is the double exponential moving average (DEMA).

The article examines the impact of changing the shape of probability distributions on the performance of optimization algorithms. We will conduct experiments using the Smart Cephalopod (SC) test algorithm to evaluate the efficiency of various probability distributions in the context of optimization problems.

In this article, we will look at changes that will allow the replay/simulation system to operate more efficiently and securely. I will also not leave without attention those who want to get the most out of using classes. In addition, we will consider a specific problem in MQL5 that reduces code performance when working with classes, and explain how to solve it.

Number Walls are a variant of Linear Shift Back Registers that prescreen sequences for predictability by checking for convergence. We look at how these ideas could be of use in MQL5.

The Simulated Annealing algorithm is a metaheuristic inspired by the metal annealing process. In the article, we will conduct a thorough analysis of the algorithm and debunk a number of common beliefs and myths surrounding this widely known optimization method. The second part of the article will consider the custom Simulated Isotropic Annealing (SIA) algorithm.

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.

In this article, we complete the first phase of developing our replay and simulation system. Dear reader, with this achievement I confirm that the system has reached an advanced level, paving the way for the introduction of new functionality. The goal is to enrich the system even further, turning it into a powerful tool for research and development of market analysis.