Accurately forecasting the direction of financial returns poses a formidable
challenge, given the inherent unpredictability of financial time series. The
task becomes even more arduous when applied to cryptocurrency returns, given
the chaotic and intricately complex nature of crypto markets. In this study, we
present a novel prediction algorithm using limit order book (LOB) data rooted
in the Hawkes model, a category of point processes. Coupled with a continuous
output error (COE) model, our approach offers a precise forecast of return
signs by leveraging predictions of future financial interactions. Capitalizing
on the non-uniformly sampled structure of the original time series, our
strategy surpasses benchmark models in both prediction accuracy and cumulative
profit when implemented in a trading environment. The efficacy of our approach
is validated through Monte Carlo simulations across 50 scenarios. The research
draws on LOB measurements from a centralized cryptocurrency exchange where the
stablecoin Tether is exchanged against the U.S. dollar.

该研究提出了一种基于 Hawkes 模型的极限订单簿（LOB）数据的新型预测算法，通过利用未来财务交互的预测，精确预测收益方向，并利用原始时间序列的非均匀采样结构，该策略在交易环境中实现了比基准模型更高的预测准确性和累积利润。

利用限价盘数据的基于 Hawkes 过程的加密货币预测

Hawkes-based cryptocurrency forecasting via Limit Order Book data

Mid-price movement prediction based on limit order book (LOB) data is a
challenging task due to the complexity and dynamics of the LOB. So far, there
have been very limited attempts for extracting relevant features based on LOB
data. In this paper, we address this problem by designing a new set of
handcrafted features and performing an extensive experimental evaluation on
both liquid and illiquid stocks. More specifically, we implement a new set of
econometrical features that capture statistical properties of the underlying
securities for the task of mid-price prediction. Moreover, we develop a new
experimental protocol for online learning that treats the task as a
multi-objective optimization problem and predicts i) the direction of the next
price movement and ii) the number of order book events that occur until the
change takes place. In order to predict the mid-price movement, the features
are fed into nine different deep learning models based on multi-layer
perceptrons (MLP), convolutional neural networks (CNN) and long short-term
memory (LSTM) neural networks. The performance of the proposed method is then
evaluated on liquid and illiquid stocks, which are based on TotalView-ITCH US
and Nordic stocks, respectively. For some stocks, results suggest that the
correct choice of a feature set and a model can lead to the successful
prediction of how long it takes to have a stock price movement.

本文通过设计一种新的手工特征并对流动性和非流动性股票进行广泛的实验评估，解决了基于限价单簿（LOB）数据的中间价格运动预测的问题，并将特征馈入 9 种基于多层感知器（MLP）、卷积神经网络（CNN）和长短期记忆（LSTM）神经网络的不同深度学习模型，以预测下一个价格趋势的方向和直到变化发生的订单簿事件数量，实验结果表明，正确选择特征集和模型可以成功预测股票价格变动的时间。

深度学习的中价预测特征工程

Feature Engineering for Mid-Price Prediction with Deep Learning

The existing literature provides evidence that limit order book data can be
used to predict short-term price movements in stock markets. This paper
proposes a new neural network architecture for predicting return jump arrivals
in equity markets with high-frequency limit order book data. This new
architecture, based on Convolutional Long Short-Term Memory with Attention, is
introduced to apply time series representation learning with memory and to
focus the prediction attention on the most important features to improve
performance. The data set consists of order book data on five liquid U.S.
stocks. The use of the attention mechanism makes it possible to analyze the
importance of the inclusion limit order book data and other input variables. By
using this mechanism, we provide evidence that the use of limit order book data
was found to improve the performance of the proposed model in jump prediction,
either clearly or marginally, depending on the underlying stock. This suggests
that path-dependence in limit order book markets is a stock specific feature.
Moreover, we find that the proposed approach with an attention mechanism
outperforms the multi-layer perceptron network as well as the convolutional
neural network and Long Short-Term memory model.

本论文提出了一种基于 Convolutional Long Short-Term Memory 网络和注意力机制的新的神经网络架构，可以使用高频限价委托数据预测股票市场中的收益跳跃到达事件，并发现限价委托数据可以对预测精度产生积极影响，这表明限价委托市场的路径依赖是股票特定的特性。