用强化学习动态调整币种投资组合，提升收益并控制风险。
Cryptocurrency Portfolio Management with Reinforcement Learning: Soft Actor--Critic and Deep Deterministic Policy Gradient Algorithms
- 用SAC和DDPG算法让智能体从历史数据中学习连续交易策略。
- SAC在噪声市场中表现更稳定，收益高于等权重和均值-方差策略。
- 适合关注量化交易与加密资产配置的研究者和从业者。
本文提出一种基于强化学习的加密货币投资组合管理框架，采用软演员-评论家（SAC）和深度确定性策略梯度（DDPG）算法。传统投资组合优化方法难以适应加密货币市场高度波动和非线性的动态特征。为此，我们设计了一个智能体，通过与模拟交易环境交互，直接从历史市场数据中学习连续交易动作。该智能体旨在优化投资组合权重，以最大化累计收益，同时最小化下行风险和交易成本。在多个加密货币上的实验表明，SAC和DDPG智能体均优于等权重和均值-方差基准策略。其中，具有熵正则化目标的SAC算法在噪声市场条件下展现出更强的稳定性和鲁棒性。结果表明，深度强化学习在加密货币市场的自适应、数据驱动投资组合管理中具有巨大潜力。
原文摘要 · Abstract (English)
This paper proposes a reinforcement learning--based framework for cryptocurrency portfolio management using the Soft Actor--Critic (SAC) and Deep Deterministic Policy Gradient (DDPG) algorithms. Traditional portfolio optimization methods often struggle to adapt to the highly volatile and nonlinear dynamics of cryptocurrency markets. To address this, we design an agent that learns continuous trading actions directly from historical market data through interaction with a simulated trading environment. The agent optimizes portfolio weights to maximize cumulative returns while minimizing downside risk and transaction costs. Experimental evaluations on multiple cryptocurrencies demonstrate that the SAC and DDPG agents outperform baseline strategies such as equal-weighted and mean--variance portfolios. The SAC algorithm, with its entropy-regularized objective, shows greater stability and robustness in noisy market conditions compared to DDPG. These results highlight the potential of deep reinforcement learning for adaptive and data-driven portfolio management in cryptocurrency markets.
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