Recent years have witnessed the successful marriage of finance innovations
and AI techniques in various finance applications including quantitative
trading (QT). Despite great research efforts devoted to leveraging deep
learning (DL) methods for building better QT strategies, existing studies still
face serious challenges especially from the side of finance, such as the
balance of risk and return, the resistance to extreme loss, and the
interpretability of strategies, which limit the application of DL-based
strategies in real-life financial markets. In this work, we propose AlphaStock,
a novel reinforcement learning (RL) based investment strategy enhanced by
interpretable deep attention networks, to address the above challenges. Our
main contributions are summarized as follows: i) We integrate deep attention
networks with a Sharpe ratio-oriented reinforcement learning framework to
achieve a risk-return balanced investment strategy; ii) We suggest modeling
interrelationships among assets to avoid selection bias and develop a
cross-asset attention mechanism; iii) To our best knowledge, this work is among
the first to offer an interpretable investment strategy using deep
reinforcement learning models. The experiments on long-periodic U.S. and
Chinese markets demonstrate the effectiveness and robustness of AlphaStock over
diverse market states. It turns out that AlphaStock tends to select the stocks
as winners with high long-term growth, low volatility, high intrinsic value,
and being undervalued recently.

本文提出了一种结合解释性深度注意力网络的强化学习投资策略，将深度学习方法应用于量化交易，提高投资策略的风险收益平衡度和可解释性，并实现了资产间关系和高成长、低波动、高内在价值和被低估的股票的选择，经过对美国和中国市场的实验验证，此策略具有较高的效果和稳健性。