论文标题
高频交易价格变动的深度概率建模
Deep Probabilistic Modelling of Price Movements for High-Frequency Trading
论文作者
论文摘要
在本文中，我们为高频市场价格的概率建模提出了一个深刻的经常性架构，这对于自动交易系统的风险管理很重要。我们提出的结构将概率混合模型纳入了深度复发的神经网络中。由此产生的深层混合模型同时解决了在文献中以前忽略的自动高频交易策略中重要的几个实际挑战：1）对价格变动的概率预测； 2）单一客观预测价格变动的方向和大小。我们在高频比特币市场数据上训练模型，并根据文献获得的基准模型对其进行评估。我们表明，在基于公制的测试和模拟交易方案中，我们的模型都优于基准模型
In this paper we propose a deep recurrent architecture for the probabilistic modelling of high-frequency market prices, important for the risk management of automated trading systems. Our proposed architecture incorporates probabilistic mixture models into deep recurrent neural networks. The resulting deep mixture models simultaneously address several practical challenges important in the development of automated high-frequency trading strategies that were previously neglected in the literature: 1) probabilistic forecasting of the price movements; 2) single objective prediction of both the direction and size of the price movements. We train our models on high-frequency Bitcoin market data and evaluate them against benchmark models obtained from the literature. We show that our model outperforms the benchmark models in both a metric-based test and in a simulated trading scenario
