Discussing the article: "Neural Networks in Trading: Decomposition Instead of Scaling — Building Modules"

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Check out the new article: Neural Networks in Trading: Decomposition Instead of Scaling — Building Modules.

In this article, we continue our hands-on exploration of SSCNN — a next-generation architectural solution capable of processing fragmented time series. Instead of blind scaling — smart modularity, attention to detail, and targeted normalization. Step by step, we are creating computational blocks in the MQL5 environment and laying the foundation for reliable predictive analysis.

In the practical section of the previous article, we focused on implementing a normalization kernel with attention weights in the OpenCL environment. This work allowed us to lay the groundwork for the fast, parallel execution of key operations: calculating mean values and standard deviations across data segments while accounting for attention weights, as well as directly normalizing the input signal. All of this is performed within the GPU context, which ensures high performance and scalability of the solution.

Today, we are naturally continuing the work we started earlier and shifting our focus to how the main program interacts with these OpenCL kernels. We need to implement the algorithms for handling them, including buffer preparation, configuration of call parameters, synchronization, and verification of the integrity of the transmitted data. This is an important step: after all, the effectiveness of the SSCNN framework depends largely not only on the mathematical accuracy of the kernel, but also on how competently and coherently data exchange between the CPU and the GPU is organized.

Author: Dmitriy Gizlyk