Discussing the article: "Neural Networks in Trading: Multi-Task Learning Based on the ResNeXt Model (Final Part)"

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Check out the new article: Neural Networks in Trading: Multi-Task Learning Based on the ResNeXt Model (Final Part).

We continue exploring a multi-task learning framework based on ResNeXt, which is characterized by modularity, high computational efficiency, and the ability to identify stable patterns in data. Using a single encoder and specialized "heads" reduces the risk of model overfitting and improves the quality of forecasts.

The ResNeXt architecture, chosen by the framework authors as the basis for the encoder, is characterized by its modularity and high efficiency. It uses grouped convolutions, which significantly improve model performance without a substantial increase in computational complexity. This is especially important for processing large streams of market data in real time. The flexibility of the architecture also allows model parameters to be tailored to specific tasks: varying network depth, convolutional block configurations, and data normalization methods, making it possible to adapt the system to different operating conditions.

The combination of multi-task learning and the ResNeXt architecture yields a powerful analytical tool capable of efficiently integrating and processing diverse information sources. This approach not only improves forecast accuracy but also allows the system to rapidly adapt to market changes, uncovering hidden dependencies and patterns. Automatic extraction of significant features makes the model more robust to anomalies and helps minimize the impact of random market noise.

In the practical part of the previous article, we examined in detail the implementation of the key components of the ResNeXt architecture using MQL5. During this work, a grouped convolution module with a residual connection was created, implemented as the CNeuronResNeXtBlock object. This approach ensures high system flexibility, scalability, and efficiency in processing financial data.

In the present work, we move away from creating the encoder as a monolithic object. Instead, users will be able to construct the encoder architecture themselves, using the already implemented building blocks. This will not only provide greater flexibility but will also expand the system's ability to adapt to various types of financial data and trading strategies. Today, the primary focus will be on the development and training of models within the multi-task learning framework.

Author: Dmitriy Gizlyk