Summary
In this chapter, we introduced GANs that learn a probability distribution over the input data and are thus capable of generating synthetic samples that are representative of the target data.
While there are many practical applications for this very recent innovation, they could be particularly valuable for algorithmic trading if the success in generating time-series training data in the medical domain can be transferred to financial market data. We learned how to set up adversarial training using TensorFlow. We also explored TimeGAN, a recent example of such a model, tailored to generating synthetic time-series data.
In the next chapter, we focus on reinforcement learning where we will build agents that interactively learn from their (market) environment.
