There's a paper by B. Mandelbrot and N. Taleb Mild vs Wild Randomness that says that Pareto distributions is a better fit for modelling price changes.
P(X>x) = Kx^-α where P(X>x) is the probability of exceeding a variable x and α is the asymptotic power law exponent for x large enough α ~ 3 for stocks
Is there a more detailed, practical example how it can be used? To estimate price distribution from historical prices? Ideally with some scripts in Python, R, Java etc.