Implementing Sparse Graph Convolution for Adaptive Trading Forecasts

Summary

The article concludes an implementation series on the SAGDFN adaptive graph forecasting framework, focusing on its OneStepFastGConv component. The framework constructs a sparse graph by selecting significant neighbors, then applies sparse multi-head attention to weight their contributions. The article describes an OpenCL implementation of sparse-matrix-by-dense-matrix multiplication and its gradient calculation, which support forward and backward passes in graph convolution. The design aims to reduce redundant computation and memory use compared with processing dense relationships through cascaded operations.

The implementation modifies parts of the proposed framework, including using Sparse-SoftMax in place of iterative α-Entmax. The reported trading tests show resilience to sharp price moves and moderate drawdown, but overall returns are negative; the author says more parameter tuning and training data may be needed. Thus, the article offers engineering detail on sparse graph neural networks, but its reported results do not demonstrate a profitable trading method. Performance claims are limited to the described implementation and test setup.

Key ideas

• SAGDFN selects relevant graph neighbors before applying sparse attention and convolution.
• OneStepFastGConv aggregates sparse relationships in a single transformation step.
• OpenCL kernels implement sparse-dense multiplication and gradient propagation.
• The implementation substitutes Sparse-SoftMax for the more computationally demanding α-Entmax procedure.
• Reported tests had negative overall returns despite moderate drawdown and resilience to sharp fluctuations.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.