Generative Adversarial Networks (GANs) are crucial for enhancing financial data reliability in algorithmic trading. Introduced by Ian Goodfellow in 2014, GANs generate synthetic data to address issues of unbalanced samples, enhancing dataset diversity. In finance, GANs help modelers overcome data scarcity and noise by producing realistic copies of stock price sequences. However, training GANs is resource-intensive, requiring careful validation to maintain relevance to real market conditions.

The GAN framework consists of two neural networks: the Generator and Discriminator, which engage in an adversarial interaction. The Generator mimics actual data using random noise inputs, while the Discriminator differentiates real from synthetic data, boosting the Generator's improvement. This process relies on iterative feedback to achieve high-quality synthetic data.

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