mlsanityData to 14 Oct 2026

Informer in Algorithmic Investment Strategies on High Frequency Bitcoin Data

arXiv 2503.18096 ↗First posted 23 Mar 2025q-fin.TR cs.LG cs.NEcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 21 of its 76 references that are in the map. First posted on arXiv 23 Mar 2025.

Builds on

21 of 76 references.

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  1. 2026Deep Reinforcement Learning Framework for Diversified Portfolio Management Across Global Equity Marketscited by 0