Älysopimuksista älysovelluksiin: Hajautettujen rahoitusmarkkinoiden ja koneoppimisen hyödyntäminen yhdistelmästrategian luomiseksi

Abstract

This Master's thesis investigates the application of machine learning methods to cryptocurrency market prediction and the development of hybrid trading strategies that combine predictive signals with decentralized finance yield components. The study addresses how machine learning models can predict directional shifts in cryptocurrency markets and whether integrating DeFi yield elements can improve risk-adjusted portfolio returns compared to traditional buy-and-hold approaches. The empirical investigation examined multiple machine learning architectures for binary directional forecasting of Bitcoin price movements. Models were trained on data spanning January 2018 to August 2024 using walk-forward validation. LightGBMRegressor achieved 53 % directional accuracy, while Random Forest reached 52 % accuracy. Other tested models, including LSTM networks and MLP, performed within the 51-56 % accuracy range. These results indicate that while machine learning methods demonstrate potential for market direction prediction when combined with properly formatted datasets and appropriate technical indicators, achieving high prediction accuracy remains challenging. A composed trading strategy was developed that integrated LSTM predictions with real-world DeFi yield rates from liquidity pools. The strategy utilized actual yield data to provide realistic performance assessment. Despite modest directional prediction accuracy of 53 %, the hybrid approach reduced drawdown by 50 % compared to the benchmark buy-and-hold strategy. The DeFi yield component compensated for imperfect directional signals, demonstrating that yield-enhanced strategies can achieve adequate risk-adjusted returns even without superior prediction accuracy. The study also examined structural differences between decentralized and traditional financial systems. DeFi offers global accessibility, programmable infrastructure, and fast settlement, but faces challenges including security vulnerabilities and regulatory uncertainty. However, the primary contribution lies in demonstrating that hybrid strategies combining machine learning signals with DeFi yield mechanisms represent a viable approach to portfolio management, when effective risk management is implemented.