Paper: arXiv 2411.05829

Abstract

This study explores the use of Recurrent Neural Networks (RNN) for real-time cryptocurrency price prediction and optimized trading strategies. Given the high volatility of the cryptocurrency market, traditional forecasting models often fall short. By leveraging RNNs’ capability to capture long-term patterns in time-series data, this research aims to improve accuracy in price prediction and develop effective trading strategies. The project follows a structured approach involving data collection, preprocessing, and model refinement, followed by rigorous backtesting for profitability and risk assessment. This work contributes to both the academic and practical fields by providing a robust predictive model and optimized trading strategies that address the challenges of cryptocurrency trading.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced neural network architectures (LSTM, GRU, Bi-LSTM) and rigorous time-series forecasting methodology with standard metrics, and includes a detailed backtesting framework on real cryptocurrency data.

Research Flowchart

  flowchart TD
  A["Research Goal: Improve crypto price prediction & trading strategy using RNN"] --> B["Data Collection<br>Historical Crypto Price Data"]
  B --> C["Preprocessing & Feature Engineering<br>Normalization, Time-series Structuring"]
  C --> D["Model Building: RNN Architecture<br>LSTM/GRU for long-term pattern capture"]
  D --> E["Training & Validation<br>Optimize parameters on historical data"]
  E --> F["Strategy Development<br>Generate trading signals from predictions"]
  F --> G["Backtesting & Evaluation<br>Simulate trading, assess profitability & risk"]
  G --> H["Key Outcomes<br>Robust predictive model & optimized trading strategy"]