Deep transformer q-learning based reinforcement learning for portfolio optimization of cryptocurrencies
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M Zepeda, W Kristjanpoller, MC Minutolo
Applied Soft Computing, 2026
Elsevier
Portfolio optimization remains a central theme in financial research, with recent advances in machine learning (ML) and computational power enabling more sophisticated forecasting models. Cryptocurrencies, characterized by extreme volatility and rapidly changing dynamics, pose particular challenges for portfolio allocation. This study proposes a novel Double Deep Transformer Q-Learning (DDTQL) framework for optimizing a portfolio of 15 leading cryptocurrencies. To the best of our knowledge, this is the first work to apply a …
