Deep Recurrent Modelling of Stationary Bitcoin Price Formation Using the Order Flow
This provides a robust method for financial traders and analysts to predict Bitcoin price formation under volatile conditions, though it is incremental as it builds on existing deep learning approaches.
The authors tackled the problem of modeling high-frequency directional Bitcoin price movements using a deep recurrent model based on order flow, and demonstrated that their model remains temporally stable without retraining during and after the 2017 Bitcoin bubble, outperforming existing state-of-the-art models.
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.