mlsanityData to 7 Oct 2026

Neural Augmented Kalman Filtering with Bollinger Bands for Pairs Trading

arXiv 2210.15448 ↗First posted 19 Oct 2022q-fin.TR cs.LG eess.SPcited by 2

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Abstract (arXiv)

Pairs trading is a family of trading techniques that determine their policies based on monitoring the relationships between pairs of assets. A common pairs trading approach relies on describing the pair-wise relationship as a linear Space State (SS) model with Gaussian noise. This representation facilitates extracting financial indicators with low complexity and latency using a Kalman Filter (KF), that are then 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 KalmenNet-aided Bollinger bands Pairs Trading (KBPT), a deep learning aided policy that augments the operation of KF-aided BB trading. KBPT is designed by formulating an extended SS model for pairs trading that approximates their relationship as holding partial co-integration. This SS model is utilized by a trading policy that augments KF-BB trading with a dedicated neural network based on the KalmanNet architecture. The resulting KBPT is trained in a two-stage manner which first tunes the tracking algorithm in an unsupervised manner independently of the trading task, followed by its adaptation to track the financial indicators to maximize revenue while approximating BB with a differentiable mapping. KBPT thus leverages data to overcome the approximated nature of the SS model, converting the KF-BB policy into a trainable model. We empirically demonstrate that our proposed KBPT systematically yields improved revenue compared with model-based and data-driven benchmarks over various different assets.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 15 of its 76 references that are in the map. First posted on arXiv 19 Oct 2022.

Builds on

15 of 76 references.

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3 more
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Citing papers per year: 2024: 2.

2 papers.

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