Search

V delu predstavimo algoritmično trgovanje. Gre za trgovanje, kjer je trgovalna strategija, ki je zaporedje pravil, ki določajo kaj, kdaj in koliko nekega sredstva kupimo oziroma prodamo, opisljiva z algoritmi.Predstavljeni so tudi avtomatski trgovalni sistemi, ki za avtomatizacijo potrebujejo algoritmično trgovanje. Navadno je algoritmično trgovalna strategija narejena na podlagi temeljne in tehnične analize, zato definiramo različne tehnične indikatorje s katerimi to naredimo. Poleg slednjih dveh pa lahko strategijo sestavimo tudi z metodami strojnega učenja s katerimi naredimo napovedni model. V ta namen definiramo nekaj glavnih metod in vse potrebne funkcije, da lahko uspešnost modela izračunamo. V nadaljevanju potem naredimo napovedni model za trgovanje s kriptovalutami. Podatke izboljšamo s tem, ko dodamo še tehnične indikatorje kot napovedne spremenljivke. Na teh podatkih potem naučimo modele, ki smo jih spoznali in jih med seboj primerjamo. In the thesis we introduce the concept of algorithmic trading, which is the type of trading that uses algorithms to execute trading strategies. Strategy is a set of rules that define what, when and how much of an asset we should trade. Furthermore, we give an overview of the automated trading system, which requires algorithmic trading for automation. Algorithmic strategy is usually built on the basis of fundamental and technical analysis, which is why we define several technical indicators, that are used in trading algorithms. Moreover it can be constructed with machine learning methods. We present several of them as well as all the necessary functions to evaluate prediction models. Then we follow up with the practical part, where we create our own trading strategy based on the machine learning methods we covered, using data enhanced with technical indicators. Finally we evaluate the models and present the results.