We propose the integration of sentiment analysis and deep-reinforcement learning ensemble algorithms for stock trading, and design a strategy capable of dynamically altering its employed agent given concurrent market sentiment. In particular, we create a simple-yet-effective method for extracting news sentiment and combine this with general improvements upon existing works, resulting in automated trading agents that effectively consider both qualitative market factors and quantitative stock data. We show that our approach results in a strategy that is profitable, robust, and risk-minimal -- outperforming the traditional ensemble strategy as well as single agent algorithms and market metrics. Our findings determine that the conventional practice of switching ensemble agents every fixed-number of months is sub-optimal, and that a dynamic sentiment-based framework greatly unlocks additional performance within these agents. Furthermore, as we have designed our algorithm with simplicity and efficiency in mind, we hypothesize that the transition of our method from historical evaluation towards real-time trading with live data should be relatively simple.

翻译：我们提出将情感分析与深度强化学习集成算法相结合用于股票交易，并设计一种能够根据当前市场情绪动态调整其采用智能体的策略。具体而言，我们创建了一种简单而有效的新闻情感提取方法，并将其与对现有工作的一般性改进相结合，从而形成能够同时考虑定性市场因素和定量股票数据的自动化交易智能体。我们证明，该方法产生的策略具有盈利性、稳健性和风险最小化特点——优于传统集成策略、单一智能体算法以及市场指标。我们的研究结果表明，每固定月份切换集成智能体的传统做法并非最优，而基于动态情绪框架能极大释放这些智能体的额外性能。此外，由于我们以简洁高效为设计原则，我们推测该方法从历史评估向实时交易（基于实时数据）的过渡应当相对简单。