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Informer in Algorithmic Investment Strategies on High Frequency Bitcoin Data

Author

Listed:
  • Filip Stefaniuk

    (University of Warsaw, Faculty of Economic Sciences)

  • Robert Ślepaczuk

    (University of Warsaw, Faculty of Economic Sciences, Department of Quantitative Finance and Machine Learning, Quantitative Finance Research Group)

Abstract

The thesis investigates the usage of Informer architecture for building automated trading strategies for high frequency Bitcoin data. Two strategies using Informer models with different loss functions, Quantile loss and Generalized Mean Absolute Directional Loss (GMADL), 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 manage to outperform the benchmark, the model that uses novel GMADL loss function turned out to be benefiting from higher frequency data and beat all the other strategies on most of the testing periods. The primary contribution of this study is the application and assessment of the Quantile and GMADL 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.

Suggested Citation

  • Filip Stefaniuk & Robert Ślepaczuk, 2024. "Informer in Algorithmic Investment Strategies on High Frequency Bitcoin Data," Working Papers 2024-27, Faculty of Economic Sciences, University of Warsaw.
    • Handle: RePEc:war:wpaper:2024-27
    as

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    File URL: https://www.wne.uw.edu.pl/download_file/5067/0
    File Function: First version, 2024
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    References listed on IDEAS

    as
    1. Sima Siami-Namini & Akbar Siami Namin, 2018. "Forecasting Economics and Financial Time Series: ARIMA vs. LSTM," Papers 1803.06386, arXiv.org.
    2. Pai, Ping-Feng & Lin, Chih-Sheng, 2005. "A hybrid ARIMA and support vector machines model in stock price forecasting," Omega, Elsevier, vol. 33(6), pages 497-505, December.
    3. Yuze Lu & Hailong Zhang & Qiwen Guo, 2023. "Stock and market index prediction using Informer network," Papers 2305.14382, arXiv.org.
    4. Kian‐Ping Lim & Robert Brooks, 2011. "The Evolution Of Stock Market Efficiency Over Time: A Survey Of The Empirical Literature," Journal of Economic Surveys, Wiley Blackwell, vol. 25(1), pages 69-108, February.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Machine Learning; Financial Series Forecasting; Automated Trading Strategy; Informer; Transformer; Bitcoin; High Frequency Trading; Statistics; GMADL;
    All these keywords.

    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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