Realtime market microstructure analysis: online Transaction Cost Analysis

Motivated by the practical challenge in monitoring the performance of a large num-ber of algorithmic trading orders, this paper provides a methodology that leads to automatic discovery of causes that lie behind poor trading performance. It also gives theoretical foundations to a generic framework for real-time trading analysis. The com-mon acronym for investigating the causes of bad and good performance of trading is TCA (Transaction Cost Analysis [Rosenthal, 2009]). Automated algorithms take care of most of the traded flows on electronic markets (more than 70 % in the US, 45 % in Europe and 35 % in Japan in 2012). Academic literature provides different ways to formalize these algorithms and show how optimal they can be from a mean-variance [Almgren and Chriss, 2000], a stochastic control [Guéant et al., 2012], an impulse con-trol [Bouchard et al., 2011] or a statistical learning [Laruelle et al., 2011] viewpoint. This paper is agnostic about the way the algorithm has been built and provides a theoretical formalism to identify in real-time the market conditions that influenced its efficiency or inefficiency. For a given set of characteristics describing the mar