High resolution microprice estimates from limit orderbook data using hyperdimensional vector Tsetlin Machines
This work addresses high-frequency trading by improving microprice estimation, though it appears incremental as it builds on existing Tsetlin machine frameworks.
The paper tackles the problem of estimating future prices from limit order book data by proposing an error-correcting model for the microprice, which adjusts based on recent dynamics of higher price rank imbalances, and demonstrates that their estimator provides a robust estimate of future prices.
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.