Interpretable ML for High-Frequency Execution

TL;DR

This paper develops a neural network-based framework for high-frequency order execution, leveraging microstructural features to predict fill probabilities and optimize order placement strategies across different markets.

Contribution

It introduces a simple neural network architecture with a loss weighting method for censored data, enabling accurate fill probability estimation in high-frequency trading.

Findings

Strong state dependence of fill probability functions identified

Model performs well across crypto and equity markets

Effective in optimizing order placement and estimating clean-up costs

Abstract

Order placement tactics play a crucial role in high-frequency trading algorithms and their design is based on understanding the dynamics of the order book. Using high quality high-frequency data and a set of microstructural features, we exhibit strong state dependence properties of the fill probability function. We train a neural network to infer the fill probability function for a fixed horizon. Since we aim at providing a high-frequency execution framework, we use a simple architecture. A weighting method is applied to the loss function such that the model learns from censored data. By comparing numerical results obtained on both digital asset centralized exchanges (CEXs) and stock markets, we are able to analyze dissimilarities between feature importances of the fill probability of small tick crypto pairs and Euronext equities. The practical use of this model is illustrated with a fixed time horizon execution problem in which both the decision to post a limit order or to immediately execute and the optimal distance of placement are characterized. We discuss the importance of accurately estimating the clean-up cost that occurs in the case of a non-execution and we show it can be well approximated by a smooth function of market features. We finally assess the performance of our model with a backtesting approach that avoids the insertion of hypothetical orders and makes possible to test the order placement algorithm with orders that realistically impact the price formation process.