Informer in Algorithmic Investment Strategies on High Frequency Bitcoin Data

ArXiv ID: 2503.18096 “View on arXiv”

Authors: Unknown

Abstract

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.

Keywords: Informer architecture, Time-series forecasting, GMADL loss, High-frequency trading, Bitcoin, Cryptocurrency

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced machine learning architectures (Informer, Transformer-based) and novel loss functions (GMADL), representing moderate-to-high mathematical density. The empirical work is rigorous, utilizing high-frequency Bitcoin data across multiple timeframes and periods, with thorough backtesting against benchmarks, sensitivity analysis, and open-source implementation for reproducibility.

  flowchart TD
    A["Research Goal: Evaluate Informer architecture<br>for HFT Bitcoin trading strategies"] --> B["Data Processing"]
    B --> C["Model Training & Forecasting"]
    C --> D["Strategy Formulation"]
    D --> E["Performance Evaluation"]

    subgraph B ["Data & Inputs"]
        B1["Bitcoin OHLCV Data"]
        B2["High Frequency Intervals<br>5m, 15m, 30m"]
        B3["Six Distinct Time Periods"]
    end

    subgraph C ["Computational Process"]
        direction LR
        C1["Informer Architecture"] --> C2{"Loss Functions"}
        C2 --> C3["RMSE Loss"]
        C2 --> C4["GMADL Loss"]
        C2 --> C5["Quantile Loss"]
    end

    subgraph D ["Trading Logic"]
        D1["Generate Return Forecasts"]
        D2["Execute Buy/Sell Signals"]
        D3["Benchmarks: Buy & Hold<br>Technical Indicators"]
    end

    subgraph E ["Key Outcomes"]
        E1["RMSE: Performance degrades<br>with higher frequency data"]
        E2["GMADL: Benefits from high frequency<br>Best overall performance"]
        E3["Quantile: Underperformed<br>benchmarks"]
    end