  Algorithmic trading, due to its inherent nature, is a difficult problem to
tackle; there are too many variables involved in the real world which make it
almost impossible to have reliable algorithms for automated stock trading. The
lack of reliable labelled data that considers physical and physiological
factors that dictate the ups and downs of the market, has hindered the
supervised learning attempts for dependable predictions. To learn a good policy
for trading, we formulate an approach using reinforcement learning which uses
traditional time series stock price data and combines it with news headline
sentiments, while leveraging knowledge graphs for exploiting news about
implicit relationships.
