Discussing the article: "Neural Networks in Trading: Adaptive Perception of Market Dynamics (Encoder)"

[MetaQuotes]

Check out the new article: Neural Networks in Trading: Adaptive Perception of Market Dynamics (Encoder).

The article presents the comprehensive architecture of the STE-FlowNet encoder, which combines stack memory, recurrent processing, and a correlation mechanism to extract hidden market dependencies. The article demonstrates how these modules are sequentially integrated into a single computational chain capable of performing comprehensive time-series analysis.

The next step in our work is to develop a module for learning the correlations between states. It is important to note that, in their work, the authors of the original framework analyzed correlations of the current frame with respect to displacements of the first frame in the analyzed sequence, thereby determining the dynamic momentum of the entire preceding sequence. This approach is entirely justified for the analysis of fixed, finite sequences where the historical context remains accessible.

Our task, however, is fundamentally different: we aim to analyze data in real time. This leads to the analysis of effectively infinite sequences. Moreover, the impact of events diminishes as the distance from them increases, which makes long-term use of a static state ineffective.

The first natural solution is to use a movable point as a reference for analysis. This approach is simple, leaves the rest of the architecture unchanged, and allows the offset size to be controlled as a model hyperparameter, providing a trade-off between analysis speed and accuracy. However, there is also a conceptual limitation: each state gets its own reference, which makes correlations between consecutive states incomparable, not to mention correlations with more distant events.

Author: Dmitriy Gizlyk