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.

翻译：配对交易是一类基于监控资产对间关系制定策略的交易技术。常见方法将资产间关系描述为具有高斯噪声的线性状态空间模型。这种表示通过卡尔曼滤波器可低复杂度、低延迟地提取金融指标，并利用布林带等经典策略进行处理。然而此类状态空间模型存在固有近似误差与失配问题，常导致收益下降。本文提出卡尔曼网络辅助的布林带配对交易方法——一种深度学习增强策略，通过扩展配对交易的状态空间模型（将资产关系近似为部分协整关系）实现。该模型被用于构建交易策略：在卡尔曼滤波器-布林带交易基础上，集成基于KalmanNet架构的专用神经网络。所提方法采用两阶段训练机制：首先以无监督方式独立于交易任务优化跟踪算法，随后通过可微分映射近似布林带，调整算法使其在跟踪金融指标的同时最大化收益。该方法利用数据克服状态空间模型的近似特性，将传统卡尔曼滤波-布林带策略转化为可训练模型。实验表明，相较于基于模型和数据驱动的基准方法，我们的方法在多类资产上系统性地获得了更优收益。