  This paper introduces a high frequency trade execution model to evaluate the
economic impact of supervised machine learners. Extending the concept of a
confusion matrix, we present a 'trade information matrix' to attribute the
expected profit and loss of the high frequency strategy under execution
constraints, such as fill probabilities and position dependent trade rules, to
correct and incorrect predictions. We apply the trade execution model and trade
information matrix to Level II E-mini S&P 500 futures history and demonstrate
an estimation approach for measuring the sensitivity of the P&L to the error of
a Recurrent Neural Network. Our approach directly evaluates the performance
sensitivity of a market making strategy to prediction error and augments
traditional market simulation based testing.
