  We introduce an event based framework of directional changes and overshoots
to map continuous financial data into the so-called Intrinsic Network - a state
based discretisation of intrinsically dissected time series. Defining a method
for state contraction of Intrinsic Network, we show that it has a consistent
hierarchical structure that allows for multi-scale analysis of financial data.
We define an information theoretic measurement termed Liquidity that
characterises the unlikeliness of price trajectories and argue that the new
metric has the ability to detect and predict stress in financial markets. We
show empirical examples within the Foreign Exchange market where the new
measure not only quantifies liquidity but also acts as an early warning signal.
