Developing Cryptocurrency Trading Strategy Based on Autoencoder-CNN-GANs Algorithms

ArXiv ID: 2412.18202 “View on arXiv”

Authors: Unknown

Abstract

This paper leverages machine learning algorithms to forecast and analyze financial time series. The process begins with a denoising autoencoder to filter out random noise fluctuations from the main contract price data. Then, one-dimensional convolution reduces the dimensionality of the filtered data and extracts key information. The filtered and dimensionality-reduced price data is fed into a GANs network, and its output serve as input of a fully connected network. Through cross-validation, a model is trained to capture features that precede large price fluctuations. The model predicts the likelihood and direction of significant price changes in real-time price sequences, placing trades at moments of high prediction accuracy. Empirical results demonstrate that using autoencoders and convolution to filter and denoise financial data, combined with GANs, achieves a certain level of predictive performance, validating the capabilities of machine learning algorithms to discover underlying patterns in financial sequences. Keywords - CNN;GANs; Cryptocurrency; Prediction.

Keywords: Autoencoders, GANs, CNN, Time Series Prediction, Crypto, Cryptocurrency

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced deep learning architectures (GANs, CNNs, autoencoders) with mathematical complexity evident in the adversarial training objective and network design, while demonstrating strong empirical rigor through detailed data sourcing from Binance, specific train/validation/test splits, and cross-validation procedures.

  flowchart TD
    A["Research Goal: Forecast Cryptocurrency Price Fluctuations"] --> B["Input: Raw Crypto Price Time Series Data"]
    B --> C["Step 1: Denoising Autoencoder"]
    C --> D["Step 2: 1D CNN<br>Dimensionality Reduction & Feature Extraction"]
    D --> E["Step 3: GANs Network<br>Feature Generation & Pattern Detection"]
    E --> F["Step 4: Fully Connected Network<br>Classification & Prediction"]
    F --> G{"Cross-Validation & Training"}
    G --> H["Outcome: Real-time Trading Strategy<br>Validated Predictive Performance"]
    H --> I["Outcome: Discovery of Underlying Financial Patterns"]