Hybrid Deep Learning Architectures for Multi-Target Bitcoin Forecasting and Algorithmic Trading: A Comparative Study of Attention-Based Models with Mamdani Fuzzy Inference

This study aims to develop a robust, multi-target algorithmic trading strategy for Bitcoin by leveraging advanced hybrid deep learning architectures. Our methodology first involves training and rigorously evaluating ten distinct models – including Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Temporal Convolutional Network (TCN), and various attention-based encoder-decoders – on hourly data augmented with critical technical and on-chain metrics. Models were optimized across multi-target regression (forecasting metrics like pullback depth, and volatility regimes) and multi-target classification (predicting directional signs and breakout signals). Feature selection utilized Mutual Information (MI) and Permutation Importance (PI) to ensure model relevance. Models incorporating both LSTM and Attention mechanisms, particularly the Encoder-Decoder structure, demonstrated superior performance in the regression tasks, while the standard LSTM-Attention model led in the classification tasks. Finally, we translated these top-performing models into four distinct algorithmic trading strategies, integrating their outputs through Mamdani Fuzzy Inference Systems and Average True Range (ATR)-based risk management protocols. Walk-forward backtesting revealed that while the classification-based strategies achieved modest profitability, translating complex regression forecasts directly into profitable trading decisions remains challenging in highly volatile markets. The results underscore the power of Attention mechanisms in capturing market dynamics and confirm the utility of Fuzzy logic in synthesizing diverse ML outputs for actionable trading decisions.