  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.
