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In this paper we propose a deep recurrent model based on the order flow forthe 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 databefore the 2017 Bitcoin bubble period and test our model during and after thebubble. We show that without any retraining, the proposed model is temporallystable even as Bitcoin trading shifts into an extremely volatile "bubbletrouble" period. The significance of the result is shown by benchmarkingagainst existing state-of-the-art models in the literature for modelling priceformation using deep learning.

Deep Recurrent Modelling of Stationary Bitcoin Price Formation Using the Order Flow