Optimizing Portfolio with Two-Sided Transactions and Lending: A Reinforcement Learning Framework

• URL: http://arxiv.org/abs/2408.05382v1
• Date: Fri, 9 Aug 2024 23:36:58 GMT
• Title: Optimizing Portfolio with Two-Sided Transactions and Lending: A Reinforcement Learning Framework
• Authors: Ali Habibnia, Mahdi Soltanzadeh,
• Abstract summary: This study presents a Reinforcement Learning-based portfolio management model tailored for high-risk environments. We implement the model using the Soft Actor-Critic (SAC) agent with a Convolutional Neural Network with Multi-Head Attention. Tested over two 16-month periods of varying market volatility, the model significantly outperformed benchmarks.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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