Optimizing Portfolio with Two-Sided Transactions and Lending: A Reinforcement Learning Framework
<|reference_start|>Optimizing Portfolio with Two-Sided Transactions and Lending: A Reinforcement Learning Framework: This study presents a Reinforcement Learning (RL)-based portfolio management model tailored for high-risk environments, addressing the limitations of traditional RL models and exploiting market opportunities through two-sided transactions and lending. Our approach integrates a new environmental formulation with a Profit and Loss (PnL)-based reward function, enhancing the RL agent's ability in downside risk management and capital optimization. We implemented the model using the Soft Actor-Critic (SAC) agent with a Convolutional Neural Network with Multi-Head Attention (CNN-MHA). This setup effectively manages a diversified 12-crypto asset portfolio in the Binance perpetual futures market, leveraging USDT for both granting and receiving loans and rebalancing every 4 hours, utilizing market data from the preceding 48 hours. Tested over two 16-month periods of varying market volatility, the model significantly outperformed benchmarks, particularly in high-volatility scenarios, achieving higher return-to-risk ratios and demonstrating robust profitability. These results confirm the model's effectiveness in leveraging market dynamics and managing risks in volatile environments like the cryptocurrency market.<|reference_end|>
@article{habibnia2024optimizing,
    title={Optimizing Portfolio with Two-Sided Transactions and Lending: A
  Reinforcement Learning Framework},
    author={Ali Habibnia and Mahdi Soltanzadeh},
    journal={arXiv preprint arXiv:2408.05382},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.05382},
    primaryClass={q-fin.PM cs.LG}
}