  In this paper we propose a deep recurrent model based on the order flow for
the stationary modelling of the high-frequency directional prices movements.
The order flow is the microsecond stream of orders arriving at the exchange,
driving the formation of prices seen on the price chart of a stock or currency.
To test the stationarity of our proposed model we train our model on data
before the 2017 Bitcoin bubble period and test our model during and after the
bubble. We show that without any retraining, the proposed model is temporally
stable even as Bitcoin trading shifts into an extremely volatile "bubble
trouble" period. The significance of the result is shown by benchmarking
against existing state-of-the-art models in the literature for modelling price
formation using deep learning.
