Algorithmic trading on Forex market with help of a Twitter

In this thesis we study the performance of electronic trading algorithms with a help of machine learning methods. We compare the performance of developed trading algorithms that trade based on posts (tweets) on Twitter with those that trade based on historic foreign exchange values and technical indicators. Besides the well known methods for text transformation to attribute notation we also use word2vec word vectors. We evaluate all the developed text transformation methods and their parameters, first on simpler but related tweet sentiment detection problem and later with trading in simulation environment. We improve developed models' predictions with the prediction combining techniques and we achieve up to 250% of initial funds at simulation in the period of last five years. The results show that Twitter is a better source of trading information than foreign exchange rates and technical indicators