In this paper we apply evolutionary optimization techniques to compute
optimal rule-based trading strategies based on financial sentiment data. The
sentiment data was extracted from the social media service StockTwits to
accommodate the level of bullishness or bearishness of the online trading
community towards certain stocks. Numerical results for all stocks from the Dow
Jones Industrial Average (DJIA) index are presented and a comparison to
classical risk-return portfolio selection is provided