High frequency trading has been characterized as an arms race with 'Red Queen' characteristics Farmer,2012%DIFDELCMD < ]%%% . It is improbable, even impossible, that many market participants can sustain a competitive advantage through the sole reliance on low latency trade execution systems. The growth in volume of market data, advances in computer hardware and commensurate prominence of machine learning in other disciplines, have spurred the exploration of machine learning for price discovery. Even though the application of machine learning to price prediction has been extensively researched, the merit of this approach for high frequency market making has received little attention. This paper introduces a  trade execution model to evaluate the economic impact of classifiers through backtesting . Extending the concept of a confusion matrix, we present a 'trade information matrix' to attribute the expected profit and loss of tick level predictive classifiers under execution constraints, such as fill probabilities and position dependent trade rules, to correct and incorrect predictions. We apply the  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 classification error . Our approach directly evaluates the performance sensitivity of a market making strategy to classifier error and augments traditional market simulation based testing.
 %DIFDELCMD < ]%%% 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.
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High frequency trading has been characterized as an arms race with 'Red Queen' characteristics
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Farmer,2012
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. It is improbable, even impossible, that many market participants can sustain a competitive advantage through the sole reliance on low latency trade execution systems. The growth in volume of market data, advances in computer hardware and commensurate prominence of machine learning in other disciplines, have spurred the exploration of machine learning for price discovery. Even though the application of machine learning to price prediction has been extensively researched, the merit of this approach for high frequency market making has received little attention.
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high frequency
R
classifiers through backtesting
supervised machine learners
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tick level predictive classifiers
the high frequency strategy
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trade
R
classification error
the error of a Recurrent Neural Network
R
classifier
prediction