Discussing the article: "Neural Networks in Trading: Heterogeneity-Informed Meta-Parameter Learning (Key Components)"

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Check out the new article: Neural Networks in Trading: Heterogeneity-Informed Meta-Parameter Learning (Key Components).

In this article, we take a detailed look at the algorithms used to implement the key components of the HimNet framework. We demonstrate how, with a minimal number of trainable components, a high degree of consistency and controllability can be achieved throughout the entire system. The presented implementation is compact and transparent, which makes it easier to adapt to real-world market tasks.

The practical section of the first article confirmed that the idea works in a real-world engineering environment. We moved the computationally intensive operations for constructing Chebyshev polynomials and error backpropagation to the GPU by implementing the ChebStep and ChebStepGrad kernels in OpenCL.

On the main program side, the CChebPolinom object was implemented, which wraps the kernels and provides a unified interface for inference and gradient computation. The result is a hybrid pipeline: fast training and reliable inference.

In this article, we continue our work on implementing the approaches of the HimNet framework using MQL5.

Author: Dmitriy Gizlyk