This paper explores the application of the General Unary Hypotheses Automaton (GUHA) framework to automate the principles of technical analysis, focusing on Bitcoin price prediction. By leveraging technical indicators, such as the Relative Strength Index (RSI), Average Directional Index (ADX), and Bollinger Bands, this study employs data mining techniques to automate insights from technical analysis. The 4 ft-Miner and CF-Miner procedures, implemented through the CleverMiner Python framework, were applied to Bitcoin price data spanning from 2014 to 2024. The findings demonstrate the effectiveness of GUHA in identifying robust predictive patterns. Rules mined via 4 ft-Miner achieved 80.65% confidence in predicting next-day price decreases and 96.15% confidence in forecasting significant upward movements (next-day high exceeding a 1% increase over the opening price). CF-Miner further categorized price return probabilities, enhancing interpretability and revealing ordered insights into the prediction of positive returns. These results underscore GUHA’s potential to automate and enhance the efficiency of technical analysis, providing actionable intelligence for automated trading and financial risk management. By offering interpretable patterns and statistically significant relationships, the GUHA framework bridges the gap between traditional technical analysis and modern algorithmic trading strategies. Future research may extend this approach to longer-term trend predictions and multi-asset portfolios, further advancing the automation of financial market analysis.