Introduction: The paper addresses the challenging problem of predicting the short-term realized volatility of the Bitcoin price using order flow information. The inherent stochastic nature and anti-persistence of price pose difficulties in accurate prediction. Methods: To address this, we propose a method that transforms order flow data over a fixed time interval (snapshots) into images. The order flow includes trade sizes, trade directions, and limit order book, and is mapped into image colour channels. These images are then used to train both a simple 3-layer Convolutional Neural Network (CNN) and more advanced ResNet-18 and ConvMixer, with additionally supplementing them with hand-crafted features. The models are evaluated against classical GARCH, Multilayer Perceptron trained on raw data, and a naive guess method that considers current volatility as a prediction. Results: The experiments are conducted using price data from January 2021 and evaluate model performance in terms of root mean square error (RMSPE). The results show that our order flow representation with a CNN as a predictive model achieves the best performance, with an RMSPE of 0.85+/-1.1 for the model with aggregated features and 1.0+/-1.4 for the model without feature supplementation. ConvMixer with feature supplementation follows closely. In comparison, the RMSPE for the naive guess method was 1.4+/-3.0.

翻译：引言：本文利用订单流信息解决比特币价格短期已实现波动率预测的挑战性问题。价格固有的随机性和反持久性给准确预测带来了困难。方法：为解决该问题，我们提出一种方法，将固定时间间隔内的订单流数据（快照）转换为图像。订单流包含交易规模、交易方向和限价订单簿，并将其映射到图像颜色通道。这些图像随后用于训练简单的三层卷积神经网络（CNN）及更先进的ResNet-18和ConvMixer，并额外补充手工设计的特征。模型与经典GARCH、基于原始数据训练的多层感知机以及将当前波动率视为预测值的朴素猜测方法进行了对比评估。结果：实验使用2021年1月的价格数据，并以均方根百分比误差（RMSPE）评估模型性能。结果表明，我们的订单流表示结合CNN作为预测模型取得了最佳性能：对于聚合特征模型，RMSPE为0.85±1.1；对于无特征补充的模型，RMSPE为1.0±1.4。补充特征的ConvMixer紧随其后。相比之下，朴素猜测方法的RMSPE为1.4±3.0。