A High Frequency Trade Execution Model for Supervised Learning

Forthcoming in High Frequency

27 Pages Posted: 15 Nov 2016 Last revised: 6 Dec 2017

Date Written: December 5th, 2017

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.

Keywords: Supervised Learning, High-Frequency Trading, Market Making, Algorithmic Finace

JEL Classification: C38, C45, C53

Suggested Citation

Dixon, Matthew Francis, A High Frequency Trade Execution Model for Supervised Learning (December 5th, 2017). Forthcoming in High Frequency. Available at SSRN: https://ssrn.com/abstract=2868473 or http://dx.doi.org/10.2139/ssrn.2868473

Matthew Francis Dixon (Contact Author)

Illinois Institute of Technology ( email )

Department of Math
W 32nd St., E1 room 208, 10 S Wabash Ave, Chicago,
Chicago, IL 60616
United States

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