  In recent years, state-of-the-art methods for supervised learning have
exploited increasingly gradient boosting techniques, with mainstream efficient
implementations such as xgboost or lightgbm. One of the key points in
generating proficient methods is Feature Selection (FS). It consists in
selecting the right valuable effective features. When facing hundreds of these
features, it becomes critical to select best features. While filter and
wrappers methods have come to some maturity, embedded methods are truly
necessary to find the best features set as they are hybrid methods combining
features filtering and wrapping. In this work, we tackle the problem of finding
through machine learning best a priori trades from an algorithmic strategy. We
derive this new method using coordinate ascent optimization and using block
variables. We compare our method to Recursive Feature Elimination (RFE) and
Binary Coordinate Ascent (BCA). We show on a real life example the capacity of
this method to select good trades a priori. Not only this method outperforms
the initial trading strategy as it avoids taking loosing trades, it also
surpasses other method, having the smallest feature set and the highest score
at the same time. The interest of this method goes beyond this simple trade
classification problem as it is a very general method to determine the optimal
feature set using some information about features relationship as well as using
coordinate ascent optimization.
