High resolution microprice estimates from limit orderbook data using hyperdimensional vector Tsetlin Machines

TL;DR

This paper introduces a novel hyperdimensional vector Tsetlin machine framework to accurately estimate high-resolution microprices from limit orderbook data, improving future price prediction in high-frequency trading.

Contribution

It presents a new error-correcting model for microprice estimation that leverages hyperdimensional Tsetlin machines for fast and robust predictions.

Findings

The estimator provides accurate future price predictions.

It demonstrates robustness across different market conditions.

The approach is computationally efficient.

Abstract

We propose an error-correcting model for the microprice, a high-frequency estimator of future prices given higher order information of imbalances in the orderbook. The model takes into account a current microprice estimate given the spread and best bid to ask imbalance, and adjusts the microprice based on recent dynamics of higher price rank imbalances. We introduce a computationally fast estimator using a recently proposed hyperdimensional vector Tsetlin machine framework and demonstrate empirically that this estimator can provide a robust estimate of future prices in the orderbook.