  The distribution of intertrade durations, defined as the waiting times
between two consecutive transactions, is investigated based upon the limit
order book data of 23 liquid Chinese stocks listed on the Shenzhen Stock
Exchange in the whole year 2003. A scaling pattern is observed in the
distributions of intertrade durations, where the empirical density functions of
the normalized intertrade durations of all 23 stocks collapse onto a single
curve. The scaling pattern is also observed in the intertrade duration
distributions for filled and partially filled trades and in the conditional
distributions. The ensemble distributions for all stocks are modeled by the
Weibull and the Tsallis $q$-exponential distributions. Maximum likelihood
estimation shows that the Weibull distribution outperforms the $q$-exponential
for not-too-large intertrade durations which account for more than 98.5% of the
data. Alternatively, nonlinear least-squares estimation selects the
$q$-exponential as a better model, in which the optimization is conducted on
the distance between empirical and theoretical values of the logarithmic
probability densities. The distribution of intertrade durations is Weibull
followed by a power-law tail with an asymptotic tail exponent close to 3.
