  We provide an empirical investigation aimed at uncovering the statistical
properties of intricate stock trading networks based on the order flow data of
a highly liquid stock (Shenzhen Development Bank) listed on Shenzhen Stock
Exchange during the whole year of 2003. By reconstructing the limit order book,
we can extract detailed information of each executed order for each trading day
and demonstrate that the trade size distributions for different trading days
exhibit power-law tails and that most of the estimated power-law exponents are
well within the L{\'e}vy stable regime. Based on the records of order matching
among investors, we can construct a stock trading network for each trading day,
in which the investors are mapped into nodes and each transaction is translated
as a direct edge from the seller to the buyer with the trade size as its
weight. We find that all the trading networks comprise a giant component and
have power-law degree distributions and disassortative architectures. In
particular, the degrees are correlated with order sizes by a power-law
function. By regarding the size executed order as its fitness, the fitness
model can reproduce the empirical power-law degree distribution.
