  Employing a recent technique which allows the representation of nonstationary
data by means of a juxtaposition of locally stationary patches of different
length, we introduce a comprehensive analysis of the key observables in a
financial market: the trading volume and the price fluctuations. From the
segmentation procedure we are able to introduce a quantitative description of a
group of statistical features (stylizes facts) of the trading volume and price
fluctuations, namely the tails of each distribution, the U-shaped profile of
the volume in a trading session and the evolution of the trading volume
autocorrelation function. The segmentation of the trading volume series
provides evidence of slow evolution of the fluctuating parameters of each
patch, pointing to the mixing scenario. Assuming that long-term features are
the outcome of a statistical mixture of simple local forms, we test and compare
different probability density functions to provide the long-term distribution
of the trading volume, concluding that the log-normal gives the best agreement
with the empirical distribution. Moreover, the segmentation of the magnitude
price fluctuations are quite different from the results for the trading volume,
indicating that changes in the statistics of price fluctuations occur at a
faster scale than in the case of trading volume.
