Testing trading capabilities of the model

We have done quite a bit of work studying various architectural solutions for organizing neural networks. We have created a library for building various neural layers, and now with its help, we can create different neural network models to find the best solution for our tasks. This is all very good and useful, of course. However, we are doing this not just for the sake of science or self-enlightenment, although that is certainly not a bad reason to study something. In this case, we embarked on the study of the organization of neural networks and their architectural solutions with a practical purpose to find a solution for use in the financial markets. There are two visions for such a solution:

• Creating an indicator based on a neural network model.
• Creating an Expert Advisor capable of executing trading operations based on the signals of the neural network model.

We will not discuss which of the above options is preferable. In fact, this is a rhetorical question because it depends on the user's personal preferences. In any case, we need to organize the correct operation of the model and the interpretation of its signals.

At the same time, we would like to assess the expected profitability of our model. To conduct such work, the MetaTrader 5 terminal offers the use of the Strategy Tester.

In this chapter, we move from the theoretical study and creation of neural networks to the practical application of the developed models in the financial sector. Our goal is to evaluate the effectiveness of neural networks for creating indicators and Expert Advisors capable of performing trading operations in financial markets. We'll start by examining the functionality of the MetaTrader 5 Strategy Tester, which is a key tool for evaluating the performance of our models.

Next, we will move on to creating an Expert Advisor template using the MQL5 programming language. This will allow us to apply our models in real trading conditions. Then we will focus on creating a model for testing. In this part, we will see how to properly prepare and configure the model to produce the most accurate and useful results.

After that, we will discuss the definition of Expert Advisor parameters, which includes setting various parameters and options that optimize the Expert Advisor operation in accordance with the user's trading strategies and goals. Finally, we will test the model using new data, which is a critical step in assessing the model's ability to adapt to changing market conditions and predict future trading signals.

This chapter focuses on the practical application of the developed neural networks in real-world trading strategies, covering model testing and optimization stages.