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

Filip Stefaniuk and Robert Ślepaczuk
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Filip Stefaniuk: University of Warsaw, Faculty of Economic Sciences

No 2024-27, Working Papers from Faculty of Economic Sciences, University of Warsaw

Keywords: Machine Learning; Financial Series Forecasting; Automated Trading Strategy; Informer; Transformer; Bitcoin; High Frequency Trading; Statistics; GMADL (search for similar items in EconPapers)
JEL-codes: C14 C4 C45 C53 C58 G13 (search for similar items in EconPapers)
Pages: 44 pages
Date: 2024
New Economics Papers: this item is included in nep-big, nep-cmp and nep-pay
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Persistent link: https://EconPapers.repec.org/RePEc:war:wpaper:2024-27

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