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用于风险感知型加密货币投资组合管理的深度强化学习

文章 arXiv papers · 作者: Wonsup Shin et al.
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总结

本文提出一个用于投资组合管理的深度强化学习智能体，在追求收益的同时控制风险。其目标策略通过可调节的贪婪度参数，调整智能体偏好最优行动的程度，以鼓励其选择较低风险的方案。作者使用加密货币市场数据评估该方法，选择这类数据是因为其分钟级观测丰富且波动率较高。

据报告，在测试期间，该智能体的收益为 1800%，且在所比较的方法中风险最低。进一步实验表明，该方法在市场波动率较高和训练期较短时表现稳健。摘录没有说明风险指标、比较方法、资产、交易成本或评估设计，因此无法详细评估这些表现主张，也不能假定其能推广至所述实验之外。

核心观点

  • 所提出的智能体在关注利润的同时，也兼顾风险控制，以优化投资组合管理。
  • 可调节的目标策略控制对最优行动的偏好程度，旨在鼓励选择较低风险的行动。
  • 该方法使用包含分钟级观测的加密货币市场数据进行评估。
  • 作者报告称，测试期收益为 1800%，且在比较方法中风险最低。
  • 补充实验显示，该方法可能适用于高波动率环境和较短训练期，但摘录未提供评估细节。

标签

加密资产机器学习风险管理投资组合构建回测
全文
# Automatic Financial Trading Agent for Low-risk Portfolio Management using Deep Reinforcement Learning


# Automatic Financial Trading Agent for Low-risk Portfolio Management using Deep Reinforcement Learning









The autonomous trading agent is one of the most actively studied areas of artificial intelligence to solve the capital market portfolio management problem. The two primary goals of the portfolio management problem are maximizing profit and restrainting risk. However, most approaches to this problem solely take account of maximizing returns. Therefore, this paper proposes a deep reinforcement learning based trading agent that can manage the portfolio considering not only profit maximization but also risk restraint. We also propose a new target policy to allow the trading agent to learn to prefer low-risk actions. The new target policy can be reflected in the update by adjusting the greediness for the optimal action through the hyper parameter. The proposed trading agent verifies the performance through the data of the cryptocurrency market. The Cryptocurrency market is the best test-ground for testing our trading agents because of the huge amount of data accumulated every minute and the market volatility is extremely large. As a experimental result, during the test period, our agents achieved a return of 1800% and provided the least risky investment strategy among the existing methods. And, another experiment shows that the agent can maintain robust generalized performance even if market volatility is large or training period is short.

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。