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

ArXiv ID: 2408.05382 “View on arXiv”

Authors: Unknown

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.

Keywords: Reinforcement Learning, Soft Actor-Critic (SAC), Convolutional Neural Network, Multi-Head Attention, Portfolio Management, Cryptocurrency

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 9.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematics, including reinforcement learning (SAC with CNN-MHA), dynamic programming, and stochastic optimization frameworks. It demonstrates high empirical rigor with specific implementation details (Binance perpetual futures, 12-asset portfolio, 4-hour rebalancing), two backtested periods (16 months each), and comparative performance metrics.

  flowchart TD
    A["Research Goal: High-Risk Portfolio Optimization via RL"] --> B["Methodology: SAC with CNN-MHA Architecture"]
    B --> C["Data: 12 Crypto Assets, 48h Window, 4h Rebalance"]
    C --> D["Computations: PnL-based Reward & Two-Sided Transactions"]
    D --> E["Outcome: Superior Risk-Adjusted Returns in High Volatility"]