  Spoofing is an illegal act of artificially modifying the supply to drive
temporarily prices in a given direction for profit. In practice, detection of
such an act is challenging due to the complexity of modern electronic platforms
and the high frequency at which orders are channeled. We present a
micro-structural study of spoofing in a simple static setting. A multilevel
imbalance which influences the resulting price movement is introduced upon
which we describe the optimization strategy of a potential spoofer. We provide
conditions under which a market is more likely to admit spoofing behavior as a
function of the characteristics of the market. We describe the optimal spoofing
strategy after optimization which allows us to quantify the resulting impact on
the imbalance after spoofing. Based on these results we calibrate the model to
real Level 2 datasets from TMX, and provide some monitoring procedures based on
the Wasserstein distance to detect spoofing strategies in real time.
