In financial markets, news about currencies, commodities or companies can give in the next hours, days, weeks or months uptrends or downtrends, depending if the new is positive or negative for one currency, commodity or company. Sometimes no effect is observed due to one new. Statistical methods allow to model different situations in different scenarios incorporating the perception of the user. This is the typical framework in stock markets where is key to incorporate qualitative perceptions such as those given by news. Every new will be analyzed through text analysis techniques, where natural language processing (NLP) will be explored, and will have a value according to some standard in sentiment analysis. This work will study the framework of advanced information searching algorithms and statistical methods to trade and to invest based on news. An original, basic and visual trading and investing system exploring different scenarios is one of the expected outputs of this project.
