Bação, Fernando José Ferreira LucasOliveira, Catarina Alexandra Gouveia Andrade de2025-11-172025-11-172025-10-31http://hdl.handle.net/10362/190835Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThis study presents a hybrid financial modeling framework that combines technical indicators and sentiment analysis to generate trading signals for the BTC-USD market using SelfOrganizing Maps (SOMs). The proposed pipeline integrates two data sources: numerical price data and financial news, from which sentiment scores are extracted using FinBERT. After feature engineering and normalization, three SOMs are trained independently, one using only technical features, one using only sentiment features, and one combining both. A comprehensive grid search is performed to optimize the feature selection and SOM hyperparameters. The resulting cluster assignments are translated into trading signals (buy, hold, sell) and evaluated using four ensemble strategies: majority, unanimous, weighted, and aggressive voting, and, among these, the weighted ensemble is further optimized by testing various weight combinations for each signal source. The framework is tested over a defined out-of-sample period, and its performance is assessed using metrics such as cumulative return, sharpe ratio, and maximum drawdown. The results demonstrate that combining SOMbased clustering with ensemble decision strategies can yield interpretable and competitive trading signals in volatile markets like cryptocurrency. This work highlights the relevance of unsupervised learning techniques and multi-source data integration in financial forecasting and trading automation.engBitcoinEnsemble LearningSentiment AnalysisSelf-Organizing MapsTechnical IndicatorsSDG 8 - Decent work and economic growthmaster thesis204071429