@inproceedings{5d6595c76ad6465fa3228f52260029c5,
title = "Kalmanbot: Kalmannet-Aided Bollinger Bands for Pairs Trading",
abstract = "Pairs trading is a family of trading policies based on monitoring the relationships between pairs of assets. A common pairs trading approach relies on state space (SS) modeling, from which financial indicators can be obtained with low complexity and latency using a Kalman filter (KF), and processed using classic policies such as Bollinger bands (BB). However, such SS models are inherently approximated and mismatched, often degrading the revenue. In this work we propose KalmanNet Bollinger Trading (KalmanBOT), a dataaided policy that preserves the advantages of KF-aided BB policies while leveraging data to overcome the approximated nature of the SS model. We adopt the recent KalmanNet architecture, and approximate BB with a differentiable mapping, converting the policy into a trainable model. We empirically demonstrate that KalmanBOT yields improved rewards compared with model-based and data-driven benchmarks.",
keywords = "Kalman filter, KalmanNet, Pairs trading",
author = "Haoran Deng and Guy Revach and Hai Morgenstern and Nir Shlezinger",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023 ; Conference date: 04-06-2023 Through 10-06-2023",
year = "2023",
month = jan,
day = "1",
doi = "10.1109/ICASSP49357.2023.10094933",
language = "English",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers",
booktitle = "ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings",
address = "United States",
}