MQL5 Programming Articles

The article reveals the potential of the ANS algorithm as an important step in the development of flexible and intelligent optimization methods that can take into account the specifics of the problem and the dynamics of the environment in the search space.

In this article, we discuss and demonstrate how to convert nominal predictors into numerical formats that are suitable for machine learning algorithms, using both Python and MQL5.

The article presents an optimization algorithm based on the patterns of constructing spiral trajectories in nature, such as mollusk shells - the spiral dynamics optimization (SDO) algorithm. I have thoroughly revised and modified the algorithm proposed by the authors. The article will consider the necessity of these changes.

Replay buffers in Reinforcement Learning are particularly important with off-policy algorithms like DQN or SAC. This then puts the spotlight on the sampling process of this memory-buffer. While default options with SAC, for instance, use random selection from this buffer, Prioritized Experience Replay buffers fine tune this by sampling from the buffer based on a TD-score. We review the importance of Reinforcement Learning, and, as always, examine just this hypothesis (not the cross-validation) in a wizard assembled Expert Advisor.

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.

This article is somewhat different from my earlier publications. In this article, we will talk about an alternative representation of time series. Piecewise linear representation of time series is a method of approximating a time series using linear functions over small intervals.

The RSI is a popular momentum oscillator that measures pace and size of a security’s recent price change to evaluate over-and-under valued situations in the security’s price. These insights in speed and magnitude are key in defining reversal points. We put this oscillator to work in another custom signal class and examine the traits of some of its signals. We start, though, by wrapping up what we started previously on Bollinger Bands.

In this article, we take our second attempt to convert the changes in price levels on any market, into a corresponding change in angle. This time around, we selected a more mathematically sophisticated approach than we selected in our first attempt, and the results we obtained suggest that our change in approach may have been the right decision. Join us today, as we discuss how we can use Polar coordinates to calculate the angle formed by changes in price levels, in a meaningful way, regardless of which market you are analyzing.

In this article, we'll look at how you can do something very similar to what's implemented in languages like C, C++, and Java. I am talking about passing a virtually infinite number of parameters inside a function or procedure. While this may seem like a fairly advanced topic, in my opinion, what will be shown here can be easily implemented by anyone who has understood the previous concepts. Provided that they were really properly understood.

Support Vector Machines classify data based on predefined classes by exploring the effects of increasing its dimensionality. It is a supervised learning method that is fairly complex given its potential to deal with multi-dimensioned data. For this article we consider how it’s very basic implementation of 2-dimensioned data can be done more efficiently with Newton’s Polynomial when classifying price-action.

By studying the FEDformer method, we opened the door to the frequency domain of time series representation. In this new article, we will continue the topic we started. We will consider a method with which we can not only conduct an analysis, but also predict subsequent states in a particular area.

In this article, we present the implementation of several ensemble learning methods in MQL5 and examine their effectiveness across different scenarios.

Spatial Temporal Fusion which is using both ‘space’ and time metrics in modelling data is primarily useful in remote-sensing, and a host of other visual based activities in gaining a better understanding of our surroundings. Thanks to a published paper, we take a novel approach in using it by examining its potential to traders.

How to use World Bank economic data for forecasts? What happens when you combine AI models and economics?

This article explores a feature selection algorithm introduced in the paper 'Local Feature Selection for Data Classification' by Narges Armanfard et al. The algorithm is implemented in Python to build binary classifier models that can be integrated with MetaTrader 5 applications for inference.

In this article, we will change the mouse pointer to enable the interaction with the control indicator to ensure reliable and stable operation.

We will consider the principle of constructing multi-population algorithms. As an example of this type of algorithm, we will have a look at the new custom algorithm - Evolution of Social Groups (ESG). We will analyze the basic concepts, population interaction mechanisms and advantages of this algorithm, as well as examine its performance in optimization problems.

This article is a continuation in a series that look at Category Theory implementation in MQL5. In here we examine how Graph-Theory could be integrated with monoids and other data structures when developing a close-out strategy to a trading system.

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.

This article considers two aspects. First, how the standard library can convert binary values to other representations such as octal, decimal, and hexadecimal. Second, we will talk about how we can determine the width of our password based on the secret phrase, using the knowledge we have already acquired.

This is a continuation of the previous article considering the idea of social groups. The article explores the evolution of social groups using movement and memory algorithms. The results will help to understand the evolution of social systems and apply them in optimization and search for solutions.

In this article, we will practically understand the difference between passing by value and passing by reference. Although this seems like something simple and common and not causing any problems, many experienced programmers often face real failures in working on the code precisely because of this small detail. Knowing when, how, and why to use pass by value or pass by reference will make a huge difference in our lives as programmers. 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.

In this article, we will look at how to implement and solve the mouse pointer issue when using it in conjunction with a replay/simulation application. 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.

In this article we are creating a synthetic symbol using a Generative Adversarial Network (GAN) involves generating realistic Financial data that mimics the behavior of actual market instruments, such as EURUSD. The GAN model learns patterns and volatility from historical market data and creates synthetic price data with similar characteristics.

Temporal Difference is another algorithm in reinforcement learning that updates Q-Values basing on the difference between predicted and actual rewards during agent training. It specifically dwells on updating Q-Values without minding their state-action pairing. We therefore look to see how to apply this, as we have with previous articles, in a wizard assembled Expert Advisor.

The article presents the African Buffalo Optimization (ABO) algorithm, a metaheuristic approach developed in 2015 based on the unique behavior of these animals. The article describes in detail the stages of the algorithm implementation and its efficiency in finding solutions to complex problems, which makes it a valuable tool in the field of optimization.

In this article, we will get acquainted with the ALGLIB library optimization methods for MQL5. The article includes simple and clear examples of using ALGLIB to solve optimization problems, which will make mastering the methods as accessible as possible. We will take a detailed look at the connection of such algorithms as BLEIC, L-BFGS and NS, and use them to solve a simple test problem.

Imagine a malicious actor infiltrating the Trading Administrator room, gaining access to the computers and the Admin Panel used to communicate valuable insights to millions of traders worldwide. Such an intrusion could lead to disastrous consequences, such as the unauthorized sending of misleading messages or random clicks on buttons that trigger unintended actions. In this discussion, we will explore the security measures in MQL5 and the new security features we have implemented in our Admin Panel to safeguard against these threats. By enhancing our security protocols, we aim to protect our communication channels and maintain the trust of our global trading community. Find more insights in this article discussion.

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.