跳至正文
返回文库全部文档

用于加密货币投资组合管理的深度强化学习

文章 arXiv papers · 作者: Zhengyao Jiang et al.
阅读原文

总结

本文提出一种无模型强化学习框架，用于在金融资产之间重新配置投资组合。其组成部分包括独立评估器集成、对先前组合权重的记忆、在线随机批量学习，以及显式奖励函数。该框架采用卷积神经网络、循环神经网络和长短期记忆神经网络实现。

报告的评估采用加密货币数据进行三次回测，交易周期为30分钟。作者将模型与其他投资组合选择策略进行比较，并报告称，尽管手续费为0.25%，其三种实现仍在各项实验中位列前三，且50天内的回报至少达到四倍。这些是历史回测结果，并非未来表现的证据。摘录未说明资产、数据划分、风险指标或稳健性检验，因此难以据此判断过拟合风险或实盘交易可行性。

核心观点

  • 该框架无需依赖显式金融市场模型，即可学习投资组合再配置。
  • 该方法结合独立资产评估器与对先前组合权重的记忆。
  • 在线随机批量学习和显式奖励函数是该方法的核心组成部分。
  • 作者在加密货币回测中测试了CNN、RNN和LSTM种实现。
  • 报告的回测排名和回报不能证明实盘表现。

标签

加密资产机器学习投资组合构建回测
全文
# A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem


# A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem









Financial portfolio management is the process of constant redistribution of a fund into different financial products. This paper presents a financial-model-free Reinforcement Learning framework to provide a deep machine learning solution to the portfolio management problem. The framework consists of the Ensemble of Identical Independent Evaluators (EIIE) topology, a Portfolio-Vector Memory (PVM), an Online Stochastic Batch Learning (OSBL) scheme, and a fully exploiting and explicit reward function. This framework is realized in three instants in this work with a Convolutional Neural Network (CNN), a basic Recurrent Neural Network (RNN), and a Long Short-Term Memory (LSTM). They are, along with a number of recently reviewed or published portfolio-selection strategies, examined in three back-test experiments with a trading period of 30 minutes in a cryptocurrency market. Cryptocurrencies are electronic and decentralized alternatives to government-issued money, with Bitcoin as the best-known example of a cryptocurrency. All three instances of the framework monopolize the top three positions in all experiments, outdistancing other compared trading algorithms. Although with a high commission rate of 0.25% in the backtests, the framework is able to achieve at least 4-fold returns in 50 days.

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。