  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.
