  Systematic trading strategies are algorithmic procedures that allocate assets
aiming to optimize a certain performance criterion. To obtain an edge in a
highly competitive environment, the analyst needs to proper fine-tune its
strategy, or discover how to combine weak signals in novel alpha creating
manners. Both aspects, namely fine-tuning and combination, have been
extensively researched using several methods, but emerging techniques such as
Generative Adversarial Networks can have an impact into such aspects.
Therefore, our work proposes the use of Conditional Generative Adversarial
Networks (cGANs) for trading strategies calibration and aggregation. To this
purpose, we provide a full methodology on: (i) the training and selection of a
cGAN for time series data; (ii) how each sample is used for strategies
calibration; and (iii) how all generated samples can be used for ensemble
modelling. To provide evidence that our approach is well grounded, we have
designed an experiment with multiple trading strategies, encompassing 579
assets. We compared cGAN with an ensemble scheme and model validation methods,
both suited for time series. Our results suggest that cGANs are a suitable
alternative for strategies calibration and combination, providing
outperformance when the traditional techniques fail to generate any alpha.
