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利用订单流图像预测比特币波动率

文章 arXiv papers · 作者: Artem Lensky et al.
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总结

本文将订单流数据快照编码为图像，以预测比特币短期已实现波动率。研究将交易规模、方向和限价订单簿信息映射至颜色通道，再用于训练三层卷积神经网络、ResNet-18和ConvMixer。研究还测试加入人工构造特征，并将基于图像的模型与GARCH、在原始数据上训练的多层感知机以及基于当前波动率的朴素预测进行比较。

实验使用2021年1月的价格数据，并以RMSPE评估预测误差。在所述方法中，基于图像的CNN表现最佳；加入聚合特征后，报告的误差更低，带特征的ConvMixer紧随其后。基于当前波动率的朴素预测报告的误差较高。这些结果表明，图像表示可能使订单流模式可用于短期波动率预测，但证据仅涵盖一个月的比特币数据。说明中没有提供验证设计细节，也没有报告其他时期和市场的表现。

核心观点

  • 订单流快照将交易规模、交易方向和订单簿信息组合为图像通道。
  • 研究评估了CNN、ResNet-18和ConvMixer模型，并比较是否加入人工构造特征。
  • 报告的CNN预测优于所列的GARCH、原始数据MLP及朴素对比方法。
  • 加入聚合特征后，报告的CNN结果有所改善。
  • 证据仅限于2021的比特币数据。

标签

加密资产波动率机器学习市场微观结构回测
全文
# Learning to Predict Short-Term Volatility with Order Flow Image Representation


# Learning to Predict Short-Term Volatility with Order Flow Image Representation









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.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。