Investigating the Effectiveness of Machine Learning Algorithms in Predicting Bitcoin Prices and Improving Trading Strategies

This bachelor thesis investigates the effectiveness of machine learning algorithms in predicting Bitcoin prices and improving trading strategies. The investigation begins with a comprehensive review of the existing literature and approaches in this topic. Various machine learning models written in Python are used to forecast Bitcoin price movements. The models are trained and assessed using historical Bitcoin price data. Metrics such as Mean Squared Error (MSE) and R^2 score are used to evaluate the models performance. The thesis investigates the outcomes of machine learning models and provides insights into their utility in predicting Bitcoin prices. The findings demonstrate the benefits and drawbacks of various algorithms, allowing for a complete understanding of their performance in this specific context.