A generative model of a limit order book using recurrent neural networks
开发了一个基于循环神经网络的生成模型，模拟限价订单簿的完整动态，通过分解交易概率为条件概率并用神经网络建模，在纳斯达克斯德哥尔摩交易所数据上验证了有效性。
In this work, a generative model based on recurrent neural networks for the complete dynamics of a limit order book is developed. The model captures the dynamics of the limit order book by decomposing the probability of each transition into a product of conditional probabilities of order type, price level, order size and time delay. Each such conditional probability is modelled by a recurrent neural network. Several evaluation metrics for generative models related to trading execution are introduced. Using these metrics, it is demonstrated that the generative model can be successfully trained to fit both synthetic and real data from the Nasdaq Stockholm exchange.