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A general approach named DAuGAN (Data Augmentation using Generative Adversarial Networks) is presented for identifying poorly represented sections of a time series, studying the synthesis and integration of new data points, and performance improvement on a benchmark machine learning model. The problem is studied and applied in the domain of algorithmic trading, whose constraints are presented and taken into consideration. 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