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In this thesis a method for generating option portfolios using machine learning, more specifically WGAN-GP (Wasserstein Generative Adversarial Networks with Gradient Penalty), is presented. To reduce the complexity however, the model does not immediately generate portfolios with option series, but instead option classes, which includes the underlying asset, option type and direction of position. The generated portfolios are then transformed such that they include option series. A comparison between the real and generated portfolios was conducted, using a range of different metrics, such as number of positions, total market value and margin. Which concluded in that the model, presented in this thesis, effectively functions as a portfolio generator.