标题： 算法投资策略中的Informer在高频比特币数据中的应用 显示英文标题

标题： Informer in Algorithmic Investment Strategies on High Frequency Bitcoin Data

摘要： 文章研究了Informer架构在构建高频比特币数据的自动化交易策略中的应用。 三种使用Informer模型且具有不同损失函数的策略：均方根误差（RMSE）、广义平均绝对方向损失（GMADL）和分位数损失，与买入并持有基准以及基于技术指标的两种基准策略进行了比较和评估。 评估使用了不同频率的数据：5分钟、15分钟和30分钟间隔，在6个不同的时间段内进行。 尽管使用分位数损失的Informer模型没有超越基准，但其他两个模型取得了更好的结果。 使用RMSE损失的模型在使用更高频数据时表现变差，而使用新型GMADL损失函数的模型则从更高频数据中受益，当在5分钟间隔上训练时，在大多数测试时间段内击败了其他所有策略。 本研究的主要贡献是将RMSE、GMADL和分位数损失函数应用于Informer模型以预测未来收益，并随后利用这些预测开发自动化交易策略。 该研究提供了证据，表明使用GMADL损失函数训练的Informer模型可以产生优于买入并持有的交易结果。 显示英文摘要

摘要： 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.