Reinforcement Learning Pair Trading: A Dynamic Scaling approach
<|reference_start|>Reinforcement Learning Pair Trading: A Dynamic Scaling approach: Cryptocurrency is a cryptography-based digital asset with extremely volatile prices. Around $70 billion worth of crypto-currency is traded daily on exchanges. Trading crypto-currency is difficult due to the inherent volatility of the crypto-market. In this work, we want to test the hypothesis: "Can techniques from artificial intelligence help with algorithmically trading cryptocurrencies?". In order to address this question, we combine Reinforcement Learning (RL) with pair trading. Pair trading is a statistical arbitrage trading technique which exploits the price difference between statistically correlated assets. We train reinforcement learners to determine when and how to trade pairs of cryptocurrencies. We develop new reward shaping and observation/action spaces for reinforcement learning. We performed experiments with the developed reinforcement learner on pairs of BTC-GBP and BTC-EUR data separated by 1-minute intervals (n = 263,520). The traditional non-RL pair trading technique achieved an annualised profit of 8.33%, while the proposed RL-based pair trading technique achieved annualised profits from 9.94% - 31.53%, depending upon the RL learner. Our results show that RL can significantly outperform manual and traditional pair trading techniques when applied to volatile markets such as cryptocurrencies.<|reference_end|>
@article{yang2024reinforcement,
    title={Reinforcement Learning Pair Trading: A Dynamic Scaling approach},
    author={Hongshen Yang, Avinash Malik},
    journal={arXiv preprint arXiv:2407.16103},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.16103},
    primaryClass={q-fin.CP cs.LG q-fin.TR}
}