  The availability of data on digital traces is growing to unprecedented sizes,
but inferring actionable knowledge from large-scale data is far from being
trivial. This is especially important for computational finance, where digital
traces of human behavior offer a great potential to drive trading strategies.
We contribute to this by providing a consistent approach that integrates
various datasources in the design of algorithmic traders. This allows us to
derive insights into the principles behind the profitability of our trading
strategies. We illustrate our approach through the analysis of Bitcoin, a
cryptocurrency known for its large price fluctuations. In our analysis, we
include economic signals of volume and price of exchange for USD, adoption of
the Bitcoin technology, and transaction volume of Bitcoin. We add social
signals related to information search, word of mouth volume, emotional valence,
and opinion polarization as expressed in tweets related to Bitcoin for more
than 3 years. Our analysis reveals that increases in opinion polarization and
exchange volume precede rising Bitcoin prices, and that emotional valence
precedes opinion polarization and rising exchange volumes. We apply these
insights to design algorithmic trading strategies for Bitcoin, reaching very
high profits in less than a year. We verify this high profitability with robust
statistical methods that take into account risk and trading costs, confirming
the long-standing hypothesis that trading based social media sentiment has the
potential to yield positive returns on investment.
