• Portfolio optimization, particularly within the framework of Reinforcement Learning (RL), requires high-quality data to simulate future returns and facilitates decision-making based on risk and reward. Traditional approaches often rely on historical data; however, this data is frequently limited or incomplete, especially when stress-testing strategies under various market conditions. By utilizing generative models such as Variational Autoencoders (VaE) and Generative Adversarial Networks (GAN), it becomes possible to simulate realistic stock return paths, thereby providing a more comprehensive dataset for portfolio management models, particularly for RL agents.​
• The combined use of GAN and VaE methodologies proves especially advantageous. GANs excel at generating high-quality, realistic samples, while VaEs offer a latent space that allows for smooth interpolation between various financial conditions. Together, these models can produce stock and option return paths that are virtually indistinguishable from actual market returns, significantly enhancing the training process for RL agents engaged in portfolio optimization.​

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