MQL4 and MQL5 Programming Articles

Uncover the secrets behind these powerful dimensionality reduction techniques as we dissect their applications within the MQL5 trading environment. Delve into the nuances of Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA), gaining a profound understanding of their impact on strategy development and market analysis.

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.

Today we will remove a limitation that has been preventing simulations based on the Last price and will introduce a new entry point specifically for this type of simulation. The entire operating mechanism will be based on the principles of the forex market. The main difference in this procedure is the separation of Bid and Last simulations. However, it is important to note that the methodology used to randomize the time and adjust it to be compatible with the C_Replay class remains identical in both simulations. This is good because changes in one mode lead to automatic improvements in the other, especially when it comes to handling time between ticks.

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.

In the previous article, we got acquainted with the Decision Transformer. But the complex stochastic environment of the foreign exchange market did not allow us to fully implement the potential of the presented method. In this article, I will introduce an algorithm that is aimed at improving the performance of algorithms in stochastic environments.

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.

Unlock the secrets of MQL5 programming in our latest article! Delve into the essentials of structures, classes, and time functions, empowering your coding journey. Whether you're a beginner or an experienced developer, our guide simplifies complex concepts, providing valuable insights for mastering MQL5. Elevate your programming skills and stay ahead in the world of algorithmic trading!

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.

We will continue to build a system for working in the FOREX market. In order to solve this problem, we must first declare the loading of ticks before loading the previous bars. This solves the problem, but at the same time forces the user to follow some structure in the configuration file, which, personally, does not make much sense to me. The reason is that by designing a program that is responsible for analyzing and executing what is in the configuration file, we can allow the user to declare the elements he needs in any order.

We continue to explore reinforcement learning methods. In this article, I will focus on a slightly different algorithm that considers the Agent’s policy in the paradigm of constructing a sequence of actions.

In this article, we will try to provide trading systems using a very important concept in the financial market which is volatility. We will provide a trading system based on the Keltner Channel indicator after understanding it and how we can code it and how we can create a trading system based on a simple trading strategy and then test it on different assets.

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 this reference article, we will look at standard indicators from the Trend Indicators category. We will create ready-to-use templates for indicator use in EAs - declaring and setting parameters, indicator initialization and deinitialization, as well as receiving data and signals from indicator buffers in EAs.

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 article presents a detailed description of the shuffled frog-leaping (SFL) algorithm and its capabilities in solving optimization problems. The SFL algorithm is inspired by the behavior of frogs in their natural environment and offers a new approach to function optimization. The SFL algorithm is an efficient and flexible tool capable of processing a variety of data types and achieving optimal solutions.

DRAKON is a visual programming language designed to simplify interaction between specialists from different fields (biologists, physicists, engineers...) with programmers in Russian space projects (for example, in the Buran reusable spacecraft project). In this article, I will talk about how DRAKON makes the creation of algorithms accessible and intuitive, even if you have never encountered code, and also how it is easier for customers to explain their thoughts when ordering trading robots, and for programmers to make fewer mistakes in complex functions.

In this article, we will consider pair trading, namely what its principles are and if there are any prospects for its practical application. We will also try to create a pair trading strategy.

In this article, we will look at standard indicators of the Volume and Bill Williams' indicators category. We will create ready-to-use templates for indicator use in EAs - declaring and setting parameters, indicator initialization and deinitialization, as well as receiving data and signals from indicator buffers in EAs.

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 takes a quick look at the ALGLIB 3.19 numerical analysis library, its applications and new algorithms that can improve the efficiency of financial data analysis.

The multi-currency expert advisor in this article is an expert advisor or trading robot that uses two RSI indicators with crossing lines, the Fast RSI which crosses with the Slow RSI.

Here I will consider the fairly new Stochastic Marginal Actor-Critic (SMAC) algorithm, which allows building latent variable policies within the framework of entropy maximization.

In this article, we explored the classic grid strategy, detailing its automation using an Expert Advisor in MQL5 and analyzing initial backtest results. We highlighted the strategy's need for high holding capacity and outlined plans for optimizing key parameters like distance, takeProfit, and lot sizes in future installments. The series aims to enhance trading strategy efficiency and adaptability to different market conditions.

Embark on a compelling exploration where financial analysis meets algorithmic trading as we unravel the art of seamlessly uniting R and MetaTrader 5. This article is your guide to bridging the realms of analytical finesse in R with the formidable trading capabilities of MetaTrader 5.

The article considers standard indicators from the oscillator category. We will create ready-to-use templates for their use in EAs - declaring and setting parameters, indicator initialization and deinitialization, as well as receiving data and signals from indicator buffers in EAs.

Embark on an illuminating journey with Part Two of our MQL5 series. These articles are not just tutorials, they're doorways to an enchanted realm where programming novices and wizards alike unite. What makes this journey truly magical? Part Two of our MQL5 series stands out with its refreshing simplicity, making complex concepts accessible to all. Engage with us interactively as we answer your questions, ensuring an enriching and personalized learning experience. Let's build a community where understanding MQL5 is an adventure for everyone. Welcome to the enchantment!

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!

Machine learning models are difficult to interpret and understanding why our models deviate from our expectations is critical if we want to gain any value from using such advanced techniques. Without comprehensive insight into the inner workings of our model, we might fail to spot bugs that are corrupting our model's performance, we may waste time over engineering features that aren't predictive and in the long run we risk underutilizing the power of these models. Fortunately, there is a sophisticated and well maintained all in one solution that allows us to see exactly what our model is doing underneath the hood.