A High Frequency Trade Execution Model for Supervised Learning

TL;DR

This paper proposes a novel high frequency trade execution model that uses a trade information matrix to evaluate the economic impact of supervised learning predictions, specifically assessing how prediction errors affect profit and loss in market making strategies.

Contribution

It introduces the trade information matrix extending the confusion matrix concept to attribute P&L to prediction accuracy under execution constraints, enabling direct performance sensitivity analysis.

Findings

Demonstrates the model on E-mini S&P 500 futures data

Estimates P&L sensitivity to RNN prediction errors

Augments traditional market simulation testing

Abstract

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.