  In order to use the advanced inference techniques available for Ising models,
we transform complex data (real vectors) into binary strings, by local
averaging and thresholding. This transformation introduces parameters, which
must be varied to characterize the behaviour of the system. The approach is
illustrated on financial data, using three inference methods -- equilibrium,
synchronous and asynchronous inference -- to construct functional connections
between stocks. We show that the traded volume information is enough to obtain
well known results about financial markets, which use however the presumably
richer price information: collective behaviour ("market mode") and strong
interactions within industry sectors. Synchronous and asynchronous Ising
inference methods give results which are coherent with equilibrium ones, and
more detailed since the obtained interaction networks are directed.
