  A paradigm of statistical mechanics of financial markets (SMFM) is fit to
multivariate financial markets using Adaptive Simulated Annealing (ASA), a
global optimization algorithm, to perform maximum likelihood fits of
Lagrangians defined by path integrals of multivariate conditional
probabilities. Canonical momenta are thereby derived and used as technical
indicators in a recursive ASA optimization process to tune trading rules. These
trading rules are then used on out-of-sample data, to demonstrate that they can
profit from the SMFM model, to illustrate that these markets are likely not
efficient. This methodology can be extended to other systems, e.g.,
electroencephalography. This approach to complex systems emphasizes the utility
of blending an intuitive and powerful mathematical-physics formalism to
generate indicators which are used by AI-type rule-based models of management.
