The LOB Recreation Model: Predicting the Limit Order Book from TAQ History Using an Ordinary Differential Equation Recurrent Neural Network
The LOB Recreation Model: Predicting the Limit Order Book from TAQ History Using an Ordinary Differential Equation Recurrent Neural Network
复制标题
LOB 重建模型：使用常微分方程递归神经网络根据 TAQ 历史预测限价订单簿
DOI：
10.1609/aaai.v35i1.16133
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
J. Cartlidge
中科院分区：
文献类型：
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作者：
Zijian Shi;Yu Chen;J. Cartlidge
在订单驱动的金融市场中，金融资产的价格是通过发布在公开限价订单(LOB)上的订单(以特定价格买入或卖出的请求)的交互来发现的。因此，LOB数据对于模拟市场动态非常有价值。然而，LOB数据不是免费访问的，这对希望利用这些信息的市场参与者和研究人员构成了挑战。幸运的是，交易和报价(Taq)数据-到达LOB顶部的订单，以及在市场中执行的交易-更容易获得。在本文中，我们提出了LOB再创造模型，这是从深度学习的角度首次尝试仅使用Taq数据来重建小盘股LOB的前五个价格水平。通过组合来自以下各项的输出来预测位于LOB深处的订单量：(1)历史编译器，其使用门控递归单元(GRU)模块来选择性地编译预测相关报价历史；(2)市场事件模拟器，其使用常微分式递归神经网络(ODE-RNN)来模拟净订单到达的累积；以及(3)加权方案，以自适应地组合由(1)和(2)生成的预测。通过迁移学习的范例，可以对一只股票上训练的核心编码器进行微调，以使其能够应用于对额外数据要求低得多的同一类别的其他金融资产。在两个真实的日间LOB数据集上进行的综合实验表明，该模型可以在仅使用Taq数据作为输入的情况下以高精度高效地重建LOB。
In an order-driven financial market, the price of a financial asset is discovered through the interaction of orders - requests to buy or sell at a particular price - that are posted to the public limit order book (LOB). Therefore, LOB data is extremely valuable for modelling market dynamics. However, LOB data is not freely accessible, which poses a challenge to market participants and researchers wishing to exploit this information. Fortunately, trades and quotes (TAQ) data - orders arriving at the top of the LOB, and trades executing in the market - are more readily available. In this paper, we present the LOB recreation model, a first attempt from a deep learning perspective to recreate the top five price levels of the LOB for small-tick stocks using only TAQ data. Volumes of orders sitting deep in the LOB are predicted by combining outputs from: (1) a history compiler that uses a Gated Recurrent Unit (GRU) module to selectively compile prediction relevant quote history; (2) a market events simulator, which uses an Ordinary Differential Equation Recurrent Neural Network (ODE-RNN) to simulate the accumulation of net order arrivals; and (3) a weighting scheme to adaptively combine the predictions generated by (1) and (2). By the paradigm of transfer learning, the core encoder trained on one stock can be fine-tuned to enable application to other financial assets of the same class with much lower demand on additional data. Comprehensive experiments conducted on two real world intraday LOB datasets demonstrate that the proposed model can efficiently recreate the LOB with high accuracy using only TAQ data as input.
可解释的多项式神经常微分方程。
DOI：
10.1063/5.0130803
发表时间：
2023
期刊：
Chaos (Woodbury, N.Y.)
影响因子：
--
作者：
Fronk,Colby;Petzold,Linda
通讯作者：
Petzold,Linda