  Understanding the emergence of universal features such as the stylized facts
in markets is a long-standing challenge that has drawn much attention from
economists and physicists. Most existing models, such as stochastic volatility
models, focus mainly on price changes, neglecting the complex trading dynamics.
Recently, there are increasing studies on order books, thanks to the
availability of large-scale trading datasets, aiming to understand the
underlying mechanisms governing the market dynamics. In this paper, we collect
order-book datasets of Bitcoin platforms across three countries over millions
of users and billions of daily turnovers. We find a 1+1D field theory, govern
by a set of KPZ-like stochastic equations, predicts precisely the order book
dynamics observed in empirical data. Despite the microscopic difference of
markets, we argue the proposed effective field theory captures the correct
universality class of market dynamics. We also show that the model agrees with
the existing stochastic volatility models at the long-wavelength limit.
