One way to compute an alpha profile is to use a classification technique such as Naive Bayes: find in the historical record a collection of orders with similar features, compute the impact-free price for each case, and take the simple average return from trade start over the next few days. This method is robust and transparent: each order is attached to a class of orders that share specific features that can be shown to the user as part of an explanation for the proposed optimal decision. However, an alpha profiling model based on classifying trades by similarity has limited generalization power. New orders do not always behave in the same way as other orders with similar features behaved in the past. A more accurate estimation of alpha profiles can be accomplished using Machine Learning (ML) methods to learn the probabilities of future price scenarios given the order and the state of the market. Alpha profiles are then computed as the statistical average of the security price under various scenarios, weighted by scenario probabilities.
