  Derivative traders are usually required to scan through hundreds, even
thousands of possible trades on a daily basis. Up to now, not a single solution
is available to aid in their job. Hence, this work aims to develop a trading
recommendation system, and apply this system to the so-called Mid-Curve
Calendar Spread (MCCS), an exotic swaption-based derivatives package. In
summary, our trading recommendation system follows this pipeline: (i) on a
certain trade date, we compute metrics and sensitivities related to an MCCS;
(ii) these metrics are feed in a model that can predict its expected return for
a given holding period; and after repeating (i) and (ii) for all trades we
(iii) rank the trades using some dominance criteria. To suggest that such
approach is feasible, we used a list of 35 different types of MCCS; a total of
11 predictive models; and 4 benchmark models. Our results suggest that in
general linear regression with lasso regularisation compared favourably to
other approaches from a predictive and interpretability perspective.
