Model Explainability Framework

Architecture

A model explainability framework serves as the structural foundation for interpreting complex quantitative models within crypto derivative markets. It integrates transparent feature attribution methods to demystify high-frequency trading algorithms and proprietary pricing engines. By decoupling opaque neural network outputs into understandable variables, the system ensures that traders can verify the logic behind automated position sizing and execution.