利用强化学习管理含借贷与做空的加密货币期货投资组合

文章 arXiv papers · 作者: Ali Habibnia et al.

总结

这项研究针对高风险场景提出一种强化学习投资组合管理器。它结合双向交易与借贷，使用损益作为奖励，旨在改善下行风险管理和资金使用。其实现将软演员评论家智能体与卷积网络和多头注意力结合起来。投资组合包含通过永续期货交易的加密资产，使用USDT进行借贷，并根据近期市场数据定期再平衡。

模型在涵盖不同波动率条件的两个时期内接受评估，作者报告称其表现优于基准，尤其是在高波动率情景下。摘录没有说明基准的具体身份、详细风险指标、交易成本处理方式，也没有提供超出这些历史测试的证据。因此，结果仅描述所报告的评估，并不能证明该方法在其他时期或实盘交易中仍能盈利。

核心观点

投资组合管理器将多头和空头交易与借贷结合起来。
损益奖励旨在支持下行风险管理和资金配置。
实现方案采用软演员评论家、卷积网络和多头注意力。
模型在分散化的加密货币永续期货投资组合上进行评估，并定期再平衡。
报告的基准超额表现最明显于高波动率测试期，但结果能否推广仍不明确。

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


# 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.

在遵守原作品许可的前提下，附作者信息全文展示。许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。

利用强化学习管理含借贷与做空的加密货币期货投资组合

总结

核心观点

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