Hierarchical Graph Learning for Calendar Spread Strategies in Commodity Futures Markets

By Yoonsik Hong, Diego Klabjan

Published 2026-06-25

Everscope rating

1387

Relevance to quantitative trading

9 / 10

Implementation complexity

8 / 10

Reproducibility

3 / 5

About this paper

Methodology: Hierarchical Graph Learning (HGL) for Calendar Spread Trading. Problem types: Time Series Forecasting, Graph Learning, Portfolio Optimization, Pairs Trading, Algorithmic Execution.

arXiv:2606.25811 · Paper rankings

Open the interactive Everscope explorer for full analysis, charts, and paper battles.