Adaptive Portfolio Optimization using Deep Reinforcement Learning and Generative Models

Cryptocurrency financial markets are characterized by high volatility and non-stationary price dynamics, posing significant challenges to traditional portfolio optimization techniques that rely on static risk–return assumptions. In such environments, existing methods often struggle to generalize and adapt effectively, leading to suboptimal performance and increased downside risk. To address these limitations, this paper proposes a novel adaptive portfolio optimization framework that integrates Generative Adversarial Networks (GANs) for synthetic data augmentation with a state-of-the-art Soft Actor-Critic (SAC) deep reinforcement learning (DRL) agent. By augmenting real historical OHLC data with realistic TimeGAN-generated price sequences, the proposed approach exposes the DRL agent to a broader range of market scenarios, thereby improving generalization and mitigating overfitting. A convolutional neural network (CNN) feature extractor captures deep temporal dependencies, while causal and dilated convolutions model complex inter-asset correlations. Empirical results demonstrate that the proposed GAN–SAC hybrid consistently outperforms conventional strategies and the baseline Deep Portfolio Optimization (DPO) model, achieving a higher Accumulative Portfolio Value (APV) of 53.72, an improved Sharpe Ratio of 0.0980, and a reduced Maximum Drawdown (MDD) of 28.5%. These findings confirm the effectiveness of combining generative models and DRL to develop robust, adaptive portfolio strategies capable of navigating highly volatile cryptocurrency markets, with practical implications for next-generation algorithmic trading systems requiring enhanced resilience and dynamic risk control.