MQL5 Programs Market

I continue filling the algorithm with the minimum necessary functionality and testing the results. The profitability is quite low but the articles demonstrate the model of the fully automated profitable trading on completely different instruments traded on fundamentally different markets.

In this article I will demonstrate some very interesting and useful techniques for automated trading. Some of them may be familiar to you. I will try to cover the most interesting methods and will explain why they are worth using. Furthermore, I will show what these techniques are apt to in practice. We will create Expert Advisors and test all the described techniques using historic quotes.

In the article, I will start developing the functionality for working with the Depth of Market. I will also create the class of the Depth of Market abstract order object and its descendants.

Perhaps one of the most advanced models among currently existing language neural networks is GPT-3, the maximal variant of which contains 175 billion parameters. Of course, we are not going to create such a monster on our home PCs. However, we can view which architectural solutions can be used in our work and how we can benefit from them.

In this article, I will implement updating tick data in real time and prepare the symbol object class for working with Depth of Market (DOM itself is to be implemented in the next article).

Since a program may use different symbols in its work, a separate list should be created for each of them. In this article, I will combine such lists into a tick data collection. In fact, this will be a regular list based on the class of dynamic array of pointers to instances of CObject class and its descendants of the Standard library.

In this article, I will create the list for storing tick data of a single symbol and check its creation and retrieval of required data in an EA. Tick data lists that are individual for each used symbol will further constitute a collection of tick data.

The popularity of these two methods grows, so a lot of libraries have been developed in Matlab, R, Python, C++ and others, which receive a training set as input and automatically create an appropriate network for the problem. Let us try to understand how the basic neural network type works (including single-neuron perceptron and multilayer perceptron). We will consider an exciting algorithm which is responsible for network training - gradient descent and backpropagation. Existing complex models are often based on such simple network models.

It is impossible to get a truly stable algorithm if we use optimization based on historical data to select parameters. A stable algorithm should be aware of what parameters are needed when working on any trading instrument at any time. It should not forecast or guess, it should know for sure.