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In order to use the advanced inference techniques available for Ising models, we transform complex data (real vectors) into binary strings, using 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 that use, however, presumably richer price information: collective behaviour ('market mode') and strong interactions within industry sectors. Synchronous and asynchronous Ising inference methods give results that are coherent with equilibrium ones and are more detailed since the obtained interaction networks are directed.