Crypto Signal Stacking Pipeline

Overview

Features

• Sentiment Analysis: Incorporates market sentiment data for enhanced prediction accuracy
• Technical Indicators: Utilizes various technical analysis indicators
• Stacking Models: Implements meta-learning with boosting trees for superior performance
• Model Ensemble: Combines multiple algorithms including:
  • CatBoost
  • LightGBM
  • TabNet
  • Transformer-based models
  • Logistic Regression (Meta-learner)

Project Structure

├── Code/
│   └── Code_KarbitSlayer.ipynb    # Main Jupyter notebook with complete pipeline
├── Model/
│   ├── catboost_checkpoint.cbm
│   ├── lightgbm_checkpoint.txt
│   ├── tabnet_checkpoint.pkl
│   ├── transformer_checkpoint.pth
│   ├── meta_logreg_checkpoint.pkl
│   └── isotonic_calibrator_checkpoint.pkl
├── requirements.txt               # Python dependencies
└── README.md                     # This file

Installation

1. Clone the repository:

1. Install dependencies:

1. Run the Jupyter notebook:

Usage

• Data preprocessing and feature engineering
• Technical indicator calculation
• Sentiment analysis integration
• Model training and hyperparameter optimization
• Ensemble stacking implementation
• Performance evaluation and visualization

Models

• CatBoost: Gradient boosting with categorical features support
• LightGBM: Light gradient boosting machine
• TabNet: Deep learning for tabular data
• Transformer: Attention-based neural network
• Meta-Learner: Logistic regression for ensemble combination

Requirements

• Python 3.8+
• See requirements.txt for detailed package versions

License

Author

license: mit

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