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用于加密货币投资组合配置的深度强化学习

文章 arXiv papers · 作者: Zhengyao Jiang et al.
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总结

本文介绍一种无模型卷积神经网络，它接收一组资产的历史价格并输出投资组合权重。模型通过强化学习训练，以累计收益作为奖励。训练数据涵盖某交易所 0.7 年的加密货币价格，所述交易间隔为 30 分钟。

同一市场中的回测报告称，在 1.8 个月期间取得了十倍收益，并将结果与近期发表的若干投资组合选择策略进行了比较。摘录没有说明交易所、资产、基准详情、交易成本，也没有说明评估数据是否独立于训练数据。训练历史较短且只在同一市场测试，因此难以据此判断其稳健性或能否迁移到其他市场；所述方法可应用于其他市场的说法，没有得到这里描述的结果支持。

核心观点

  • 卷积神经网络将资产历史价格映射为投资组合权重。
  • 模型通过强化学习训练，以最大化累计收益。
  • 报告的训练样本涵盖 0.7 年的加密货币价格数据。
  • 回测在同一市场使用 30 分钟交易周期，并报告在 1.8 个月内取得十倍收益。
  • 摘录无法证明其具有样本外稳健性或在其他市场中的表现。

标签

加密资产机器学习投资组合构建回测仓位规模
全文
# Cryptocurrency Portfolio Management with Deep Reinforcement Learning


# Cryptocurrency Portfolio Management with Deep Reinforcement Learning









Portfolio management is the decision-making process of allocating an amount of fund into different financial investment products. Cryptocurrencies are electronic and decentralized alternatives to government-issued money, with Bitcoin as the best-known example of a cryptocurrency. This paper presents a model-less convolutional neural network with historic prices of a set of financial assets as its input, outputting portfolio weights of the set. The network is trained with 0.7 years' price data from a cryptocurrency exchange. The training is done in a reinforcement manner, maximizing the accumulative return, which is regarded as the reward function of the network. Backtest trading experiments with trading period of 30 minutes is conducted in the same market, achieving 10-fold returns in 1.8 months' periods. Some recently published portfolio selection strategies are also used to perform the same back-tests, whose results are compared with the neural network. The network is not limited to cryptocurrency, but can be applied to any other financial markets.

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。