External factors in predicting cryptocurrency price with machine learning

Zmožnost napovedovanja gibanja cene finančnih instrumentov predstavlja priložnost za visoke zaslužke. Eni izmed tehnični pristopov, ki se na področju finančnega trgovanja že dalj časa uspešno uporabljajo, so metode strojnega učenja. V diplomski nalogi smo se ukvarjali z napovedovanjem cene kriptovalute Bitcoin. Modeliranje smo začeli s pridobivanjem raznih podatkov, povezanih s ceno kriptovalute, in nato z algoritmom XGBoost izdelali napovedni model. Razumevanje napovedi je ključnega pomena, zato smo uporabili razlagalni algoritem SHAP, s katerim smo dobili globlji vpogled v napovedni model. Izkazalo se je, da imajo podatki, neposredno vezani na ceno kriptovalute, največjo vlogo pri napovedi, temu pa sledi indeks strahu in pohlepa.Ability to forecast the price of financial instruments has a lot of potential for monetary gains through investments. Machine learning methods have been successfully employed in finance as one of technical approaches to financial modelling. The aim of this thesis was to develop a Bitcoin price forecasting model. We started modelling by gathering various data related to Bitcoin. We then used the gathered data with XGBoost algorithm to create a forecast model. To achieve a more in-depth understanding of our model, we used the SHAP algorithm. This allowed us to get more insight into forecasts which are otherwise usually difficult to understand. We concluded that data directly related to cryptocurrency price had the highest importance in forecasting, followed by the fear and greed index