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Pairs Trading using Unsupervised Clustering and Deep Reinforcement Learning

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# Pairs Trading using Unsupervised Clustering and Deep Reinforcement Learning (IAQF 2023)

Conventional pairs trading strategies are based on concepts of mean reversion and stationary stochastic processes, where pairs are assumed to have linear relationships. However, empirical evidence suggest that asset prices in equity markets frequently exhibit non-linear dynamics. We aim to tackle this issue by exploring pairs trading from a deep learning perspective to leverage these non-linear relationships. Our proposed workflow includes two steps, first clustering equity indices and then using a Reinforcement Learning based trading strategy on pairs selected from these clusters. Using a combination of fundamental and technical signals we extract a set of 10 latent risk factors using a convolutional auto-encoder and create 10 clusters of indices using these risk factors. We test our approach on a sample of 13 pairs to train and test our trading strategy and compare our performance against the S&P 500. From the period starting from April 2017 to December 2022, our best performing strategy consisting of the NASDAQ Composite and MSCI World IT Index earns an annualized return of 21.86% with a Sharpe ratio of 1.15, while generating an alpha of 20%.

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