Discussing the article: "Neural Networks in Trading: Robust Trading Signals in Any Market Regime (Attention Modules)"

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Check out the new article: Neural Networks in Trading: Robust Trading Signals in Any Market Regime (Attention Modules).

In this article, we continue implementing the ST-Expert framework approaches, focusing on the practical aspects of applying them using MQL5. Earlier, we examined the theoretical foundations and key components of the model; now we move on to working directly with graph attention algorithms and local and global attention distribution. The main goal of this work is to demonstrate how ST-Expert's conceptual ideas are transformed into workable solutions for analyzing and forecasting financial time series.

Imagine that we are analyzing the stock market. If the price of oil begins to rise, this is reflected almost immediately in the stock prices of oil companies. And the currencies of oil-producing countries—as well as entire sectors of related industries—may follow suit. A classical model built solely on price time series sees only the direct trajectory of a particular instrument. ST-Expert, on the other hand, is capable of capturing the entire cascade of changes because it works not only with time but also with the space of market relationships.

The framework's architecture is based on the Mixture of Experts principle—a mixture of experts. This approach can be compared to the work of an investment committee. It brings together analysts with different profiles: one specializes in technical analysis, another in macroeconomics, and a third tracks correlations between sectors. Individually, each of them has limited capabilities. But when their opinions come together, the final decision turns out to be much closer to reality. The same is true in ST-Expert: each expert block is responsible for its own area of analysis, and the system combines their forecasts to form a coherent solution.



Author: Dmitriy Gizlyk

 
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An article has been published entitled ‘Neural Networks in Trading: Reliable Trading Signals in All Market Conditions (Attention Modules)’:

Author: Dmitriy Gizlyk

Hello, Dmitriy!

Could you perhaps create a simple script for us mere mortals to demonstrate how the model works? Using very simple synthetic data, for example 10 data points and 2 features. It should be able to plot the loss function or output the results to a log file. If possible, just using the CPU.

Because the models you write are quite complex and difficult to get to grips with. I need a more straightforward example :)
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