We develop and implement an approach using deep neural networks to process financial and macroeconomic signals to help identify key inflection points in equity market-based factor performance such as momentum and volatility. The model may be used to calibrate factor rotation strategies and better assess portfolio risks associated with factor-based exposures. The machine learning algorithm relies on the GPU for high-performance computations to drive an optimization framework within a deep neural network.