论文标题
使用订单流量的固定比特币价格形成的深度复发建模
Deep Recurrent Modelling of Stationary Bitcoin Price Formation Using the Order Flow
论文作者
论文摘要
在本文中，我们提出了一个基于高频定向价格移动的固定建模的顺序流量的深度复发模型。订单流量是到达交易所的订单的微秒流，推动了股票或货币价格图表上的价格形成。为了测试我们提出的模型的平稳性，我们在2017年比特币气泡周期之前对数据进行训练，并在气泡期间和之后测试我们的模型。我们表明，即使比特币交易转变为极为波动的“气泡麻烦”时期，提出的模型在没有任何重新培训的情况下是暂时稳定的。通过对文献中现有的最新模型进行基准测试，可以使用深度学习来建模价格形成，从而显示出结果的重要性。
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.
