Sparse Graph Attention for Trading Models

Article MQL5 articles

Summary

This article discusses implementing the attention module of a Sparse Adaptive Graph Diffusion Network (SAGDFN) for financial time-series analysis using MQL5 and OpenCL. The framework samples significant neighbors, then applies sparse multi-head attention to model relationships among selected series or data points. It describes α-Entmax as a sparse alternative to Softmax and balances local relationships with broader context. A central implementation technique exploits shared weights: project each node embedding into query-like and key-like components once, then add those projections for each selected pair instead of repeatedly processing concatenated embeddings.

The author argues this reduces the dense computation from scaling with the number of node-neighbor pairs times embedding width to reusable node projections plus lighter pairwise work, while also avoiding storage of every concatenated pair vector. The article focuses on architecture and computational design; it does not report a completed historical-data evaluation in this installment. Forecast accuracy and practical robustness remain unverified here, despite broad claims about the model’s potential. The implementation is a proposed adaptation, so its benefits depend on graph size, dimensions, and actual workload.

Key ideas

Significant-neighbor sampling aims to focus graph attention on selected relationships while limiting noise.
Sparse multi-head attention is intended to reduce computation while representing local and global structure.
Shared query and key projections can be computed once per node and reused across its neighbor pairs.
The projection reuse approach reduces repeated dense work and avoids storing all concatenated pair vectors.
The article describes implementation choices but leaves historical forecast and robustness evaluation to later work.

Optimizing Sparse Graph Attention for Financial Time-Series Models

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