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用于高频比特币交易的Informer方向损失预测

文章 arXiv papers · 作者: Filip Stefaniuk et al.
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总结

本研究测试Informer预测架构在高频比特币数据自动化策略中的应用。研究使用RMSE、广义平均绝对方向损失（GMADL）和分位数损失训练不同模型，然后将预测的未来收益转化为交易策略。比较对象包括买入并持有策略和两种技术指标策略。评估涵盖六个测试时期的5分钟、15分钟和30分钟数据。

结果取决于损失函数和采样频率。分位数损失模型未能胜过基准；RMSE在较高频率下表现更差，而GMADL受益于更高频数据。根据描述，在5分钟分辨率下，GMADL在大多数测试时期的表现优于其他策略。这些发现表明，在高频交易预测中，强调方向准确性的损失函数可能很重要。证据仅限于所报告的比特币时期和基准；所提供的摘要未说明交易成本、风险调整后收益或测试设置以外的表现。

核心观点

  • 研究将Informer收益预测转化为比特币自动化交易策略。
  • 研究将RMSE、GMADL和分位数损失分别与买入并持有策略及两种指标策略基准进行比较。
  • 在报告的评估中，分位数损失未能胜过基准。
  • RMSE在较高数据频率下表现下降，而GMADL则从中受益。
  • 在研究评估范围内，GMADL在5分钟数据上的表现，在大多数时期优于其他受测策略。

标签

加密资产高频交易机器学习回测
全文
# Informer in Algorithmic Investment Strategies on High Frequency Bitcoin Data


# Informer in Algorithmic Investment Strategies on High Frequency Bitcoin Data









The article investigates the usage of Informer architecture for building automated trading strategies for high frequency Bitcoin data. Three strategies using Informer model with different loss functions: Root Mean Squared Error (RMSE), Generalized Mean Absolute Directional Loss (GMADL) and Quantile loss, are proposed and evaluated against the Buy and Hold benchmark and two benchmark strategies based on technical indicators. The evaluation is conducted using data of various frequencies: 5 minute, 15 minute, and 30 minute intervals, over the 6 different periods. Although the Informer-based model with Quantile loss did not outperform the benchmark, two other models achieved better results. The performance of the model using RMSE loss worsens when used with higher frequency data while the model that uses novel GMADL loss function is benefiting from higher frequency data and when trained on 5 minute interval it beat all the other strategies on most of the testing periods. The primary contribution of this study is the application and assessment of the RMSE, GMADL, and Quantile loss functions with the Informer model to forecast future returns, subsequently using these forecasts to develop automated trading strategies. The research provides evidence that employing an Informer model trained with the GMADL loss function can result in superior trading outcomes compared to the buy-and-hold approach.

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。