Informer in Algorithmic Investment Strategies on High Frequency Bitcoin Data

Paper: arXiv 2503.18096

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.

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: 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.

Research Flowchart

  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

Cited References (77)

1. Generalized Mean Absolute Directional Loss as a Solution to Overfitting and High Transaction Costs in Machine Learning Models Used in High-Frequency Algorithmic Investment Strategies (2024) — link
2. The efficiency of various types of input layers of LSTM model in investment strategies on S&P500 index (2022) — link
3. Bitcoin: A Peer-to-Peer Electronic Cash System (2009)
4. A Random Walk Down Wall Street (1973)
5. Efficient Capital Markets: A Review of Theory and Empirical Work (1970)
6. Efficient Capital Markets: II (1991) — link
7. Reflections on the Efficient Market Hypothesis: 30 Years Later (2005) — link
8. The Evolution of Technical Analysis: Financial Prediction from Babylonian Tablets to Bloomberg Terminals (2011) — link

2 of 77 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.