Alpha profiling[1][2] is an application of machine learning to optimize the execution of large orders in financial markets by means of algorithmic trading.
For example, a portfolio manager specialized in value investing may have a behavioral bias to place orders to buy while an asset is still declining in value.
But this same portfolio manager will occasionally place an order after the asset price has already begun to rise in which case it should best be handled with urgency; this example illustrates the fact that Alpha Profiling must combine public information such as market data with private information including as the identity of the portfolio manager and the size and origin of the order, to identify the optimal execution schedule.
A method for deriving optimal execution schedules that minimize a risk-adjusted cost function was proposed by Bertsimas and Lo.
[5] Almgren and Chriss provided closed-form solutions of the basic risk-adjusted cost optimization problem with a linear impact model and trivial alpha profile.
[6] More recent solutions have been proposed based on a propagator model for market impact,[7] but here again the alpha profile is assumed to be trivial.