  We propose the application of a high-speed maximum likelihood clustering
algorithm to detect temporal financial market states, using correlation
matrices estimated from intraday market microstructure features. We first
determine the ex-ante intraday temporal cluster configurations to identify
market states, and then study the identified temporal state features to extract
state signature vectors which enable online state detection. The state
signature vectors serve as low-dimensional state descriptors which can be used
in learning algorithms for optimal planning in the high-frequency trading
domain. We present a feasible scheme for real-time intraday state detection
from streaming market data feeds. This study identifies an interesting
hierarchy of system behaviour which motivates the need for time-scale-specific
state space reduction for participating agents.
