A High Frequency Trade Execution Model for Supervised Learning

By Matthew F. Dixon. 2017

22 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Economics and Econometrics, Management Science and Operations Research

Citations in the corpus

• Universal features of price formation in financial markets: perspectives from deep learning (2018, neutral): “This is one of the reasons why, except in a few instances (Sirignano et al. 2016, Kolanovic and Krishnamachari 2017, Dixon 2018a, 2018b, Sirignano 2019), largescale machine learning methods such as Deep Learning (Goodfellow et al. 2017) have not yet been deployed for quantitative modeling in…”