  We present a simple transformation of the formulation of the log-periodic
power law formula of the Johansen-Ledoit-Sornette model of financial bubbles
that reduces it to a function of only three nonlinear parameters. The
transformation significantly decreases the complexity of the fitting procedure
and improves its stability tremendously because the modified cost function is
now characterized by good smooth properties with in general a single minimum in
the case where the model is appropriate to the empirical data. We complement
the approach with an additional subordination procedure that slaves two of the
nonlinear parameters to what can be considered to be the most crucial nonlinear
parameter, the critical time $t_c$ defined as the end of the bubble and the
most probably time for a crash to occur. This further decreases the complexity
of the search and provides an intuitive representation of the results of the
calibration. With our proposed methodology, metaheuristic searches are not
longer necessary and one can resort solely to rigorous controlled local search
algorithms, leading to dramatic increase in efficiency. Empirical tests on the
Shanghai Composite index (SSE) from January 2007 to March 2008 illustrate our
findings.
