Programming Articles on MQL5.com

Ridge regression is a simple technique to reduce model complexity and prevent over-fitting which may result from simple linear regression

Let's consider one of the newest modern optimization algorithms - Grey Wolf Optimization. The original behavior on test functions makes this algorithm one of the most interesting among the ones considered earlier. This is one of the top algorithms for use in training neural networks, smooth functions with many variables.

In this article, we will study the algorithm of an artificial bee colony and supplement our knowledge with new principles of studying functional spaces. In this article, I will showcase my interpretation of the classic version of the algorithm.

In this article, I will make an attempt to consider some ways of building non-linear indicators and their use in trading. There are quite a few indicators in the MetaTrader trading platform that use non-linear approaches.

In this article, I will continue the development of the ProgressBar control. In particular, I will create the functionality for managing the progress bar and visual effects.

Markov chains are a powerful mathematical tool that can be used to model and forecast time series data in various fields, including finance. In financial time series modelling and forecasting, Markov chains are often used to model the evolution of financial assets over time, such as stock prices or exchange rates. One of the main advantages of Markov chain models is their simplicity and ease of use.

In this article, I will complete the development of the ToolTip control and start the development of the ProgressBar WinForms object. While working on objects, I will develop universal functionality for animating controls and their components.

A new article in our series about learning how to design a trading system based on popular technical indicators will be about the Gator Oscillator technical indicator and how to create a trading system through simple strategies.

We got acquainted with the Q-learning method in one of the earlier articles within this series. This method averages rewards for each action. Two works were presented in 2017, which show greater success when studying the reward distribution function. Let's consider the possibility of using such technology to solve our problems.

How do you like the idea of adding a new chart type to the MetaTrader 5 platform? Some people say it lacks a few things that other platforms offer. But the truth is, MetaTrader 5 is a very practical platform as it allows you to do things that can't be done (or at least can't be done easily) in many other platforms.

In this article, I will start developing the Tooltip control, as well as new graphical primitives for the library. Naturally, not every element has a tooltip, but every graphical object has the ability to set it.

This time I will analyze the Ant Colony optimization algorithm. The algorithm is very interesting and complex. In the article, I make an attempt to create a new type of ACO.

Category Theory is a diverse and expanding branch of Mathematics which as of yet is relatively uncovered in the MQL 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.

We continue to remove separate parts from our EA. This is the last article within this series. And the last thing to be removed is the sound system. This can be a bit confusing if you haven't followed these article series.

What is Frames Analyzer? This is a plug-in module for any Expert Advisor for analyzing optimization frames during parameter optimization in the strategy tester, as well as outside the tester, by reading an MQD file or a database that is created immediately after parameter optimization. You will be able to share these optimization results with other users who have the Frames Analyzer tool to discuss the results together.

In the previous article, we started exploring non-gradient optimization methods. We got acquainted with the genetic algorithm. Today, we will continue this topic and will consider another class of evolutionary algorithms.

In this article, I will revise the logic of specifying the base and main objects for all WinForms library objects, develop a new Hint base object and several of its derived classes to indicate the possible direction of moving the separator.

In this article, I will consider several possible approaches to creating adaptive indicators. Adaptive indicators are distinguished by the presence of feedback between the values of the input and output signals. This feedback allows the indicator to independently adjust to the optimal processing of financial time series values.

Todays trader is a philomath who is almost always looking up new ideas, trying them out, choosing to modify them or discard them; an exploratory process that should cost a fair amount of diligence. These series of articles will proposition that the MQL5 wizard should be a mainstay for traders in this effort.

In this article, I will add new mouse events relative to the boundaries of the working areas of WinForms objects and fix some shortcomings in the functioning of the TabControl and SplitContainer controls.

In the current article, I will implement the ability to change the properties and appearance of the newly created SplitContainer control.

Today we are going to use Chart Trade again, but this time it will be an on-chart indicator which may or may not be present on the chart.

Today I want to introduce you to a slightly different learning method. We can say that it is borrowed from Darwin's theory of evolution. It is probably less controllable than the previously discussed methods but it allows training non-differentiable models.

In the previous articles of this series, we have seen two reinforced learning algorithms. Each of them has its own advantages and disadvantages. As often happens in such cases, next comes the idea to combine both methods into an algorithm, using the best of the two. This would compensate for the shortcomings of each of them. One of such methods will be discussed in this article.

In this article, I will consider the popular Particle Swarm Optimization (PSO) algorithm. Previously, we discussed such important characteristics of optimization algorithms as convergence, convergence rate, stability, scalability, as well as developed a test stand and considered the simplest RNG algorithm.

We continue to study reinforcement learning methods. In the previous article, we got acquainted with the Deep Q-Learning method. In this method, the model is trained to predict the upcoming reward depending on the action taken in a particular situation. Then, an action is performed in accordance with the policy and the expected reward. But it is not always possible to approximate the Q-function. Sometimes its approximation does not generate the desired result. In such cases, approximation methods are applied not to utility functions, but to a direct policy (strategy) of actions. One of such methods is Policy Gradient.

In this article, I will create the class of an auxiliary panel separator object for the SplitContainer control.

This is a lazy algorithm that doesn't learn from the training dataset, it stores the dataset instead and acts immediately when it's given a new sample. As simple as it is, it is used in a variety of real-world applications.