用深度强化学习管理投资组合，不依赖金融模型。
A Deep Reinforcement Learning Framework For Financial Portfolio Management
- 设计无金融模型的强化学习框架，结合多评估器与记忆机制。
- 在加密货币市场实现优于传统策略的回报率。
- 在股票市场表现一般，适合研究非传统资产配置场景。
本文研究了一种名为《基于深度强化学习的金融投资组合管理框架》的论文（arXiv:1706.10059），该框架采用无金融模型的强化学习方法，包含同质独立评估器集成（EIIE）结构、投资组合向量记忆（PVM）、在线随机批量学习（OSBL）机制及显式奖励函数。通过卷积神经网络（CNN）、循环神经网络（RNN）和长短期记忆网络（LSTM）三种结构实现。实验对比了多种近期发表的投资组合选择策略，成功复现并验证了原论文结果：在加密货币市场中表现优异，获得显著超额收益；但在股票市场应用时性能下降，未达预期效果。
原文摘要 · Abstract (English)
In this research paper, we investigate into a paper named "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem" [arXiv:1706.10059]. It is a portfolio management problem which is solved by deep learning techniques. The original paper proposes a financial-model-free reinforcement learning framework, which 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. Three different instants are used to realize this framework, namely a Convolutional Neural Network (CNN), a basic Recurrent Neural Network (RNN), and a Long Short-Term Memory (LSTM). The performance is then examined by comparing to a number of recently reviewed or published portfolio-selection strategies. We have successfully replicated their implementations and evaluations. Besides, we further apply this framework in the stock market, instead of the cryptocurrency market that the original paper uses. The experiment in the cryptocurrency market is consistent with the original paper, which achieve superior returns. But it doesn't perform as well when applied in the stock market.
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