Machine Learning – A Machine’s Perspective on Positioning
By Mark Keenan, Head of Research and Strategy at Engelhart Commodity Trading Partners and Member of the GCARD’s Editorial Advisory Board
This digest article discusses how machine learning can be applied to studying positioning dynamics in commodities. The article introduces decision trees and random forests as ways of potentially uncovering relationships between changes in positioning and changes in commodity prices.
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