btcvol 1.2.0

Bitcoin Implied Volatility Prediction Competition - Participant Package

btcvol

Python package for building and testing Bitcoin implied volatility prediction models for CrunchDAO competitions.

Installation

# Install from GitHub
pip install git+https://github.com/jberros/btcvol-python.git

# Or from PyPI (coming soon)
pip install btcvol

Quick Start

from btcvol import TrackerBase, test_model_locally
from btcvol.examples import GARCHVolatilityModel
import numpy as np

# Option 1: Use the built-in GARCH model
model = GARCHVolatilityModel(base_vol=0.42)
predictions = model.predict("BTC", horizon=3600, step=900)
print(f"Generated {len(predictions)} predictions: {predictions}")

# Option 2: Create your own custom model
class MyVolatilityModel(TrackerBase):
    def predict(self, asset: str, horizon: int, step: int):
        """
        Generate volatility predictions.
        
        Args:
            asset: Asset to predict (e.g., "BTC")
            horizon: Time horizon in seconds (e.g., 3600 for 1h, 86400 for 24h)
            step: Prediction resolution in seconds (e.g., 900 for 15 min)
        
        Returns:
            List of predicted volatility values (0-1 range, e.g., 0.40 = 40%)
        """
        num_predictions = horizon // step
        base_vol = 0.42
        
        # Apply mean reversion based on horizon
        horizon_hours = horizon / 3600
        adjustment = 1.0 / np.sqrt(horizon_hours / 24.0)
        
        return [base_vol * adjustment] * num_predictions

# Test your model locally
if __name__ == "__main__":
    test_model_locally(MyVolatilityModel)

Features

• TrackerBase: Abstract base class for building volatility prediction models
• test_model_locally(): Validate your model before submission
• Example Models: Pre-built models to help you get started
  • GARCHVolatilityModel: Mean-reverting GARCH-style predictions
  • RandomWalkModel: Random walk with configurable volatility
  • LinearDecayModel: Linear decay from start to end volatility

Example Models

GARCH-Style Model

from btcvol.examples import GARCHVolatilityModel

model = GARCHVolatilityModel(base_vol=0.42)
predictions = model.predict("BTC", horizon=3600, step=900)
# Returns 4 predictions for 1-hour horizon with 15-min steps

Custom Model

import numpy as np
from btcvol import TrackerBase

class AdvancedModel(TrackerBase):
    def __init__(self):
        super().__init__()
        # Initialize your model parameters
        
    def predict(self, asset: str, horizon: int, step: int):
        num_predictions = horizon // step
        # Your sophisticated prediction logic here
        return [0.40] * num_predictions

Competition Details

This package is designed for CrunchDAO's Bitcoin DVOL prediction competition:

• Target: Deribit 30-day Bitcoin Implied Volatility (DVOL)
• Prediction Frequency: Every 15 minutes
• Horizons: 1 hour (3600s) and 24 hours (86400s)
• Evaluation: Mean Squared Error (MSE) over 30-day period

Testing Your Model

from btcvol import test_model_locally
from your_module import YourModel

# Run validation tests
test_model_locally(YourModel)

The test validates:

• ✓ Model inherits from TrackerBase
• ✓ Predict method is implemented
• ✓ Returns correct number of predictions
• ✓ All predictions are numeric
• ✓ Values are in reasonable range

API Reference

TrackerBase

Base class for all volatility prediction models.

Methods:

• predict(asset, horizon, step) - [Abstract] Generate predictions
• fetch_price_data(asset, days) - Helper to fetch historical prices
• fetch_dvol_data(days) - Helper to fetch historical DVOL data

test_model_locally(model_class, verbose=True)

Validate your model implementation.

Parameters:

• model_class: Your model class (not an instance)
• verbose: Print detailed test results

Returns:

• bool: True if all tests pass

Development

# Clone the repository
git clone https://github.com/yourusername/btcvol-python.git
cd btcvol-python

# Install in development mode
pip install -e ".[dev]"

# Run tests
pytest tests/

Requirements

• Python >= 3.9
• pandas >= 2.0.0
• numpy >= 1.24.0

License

MIT License - see LICENSE file for details.

Links

• Competition Platform: CrunchDAO
• Documentation: Full Docs
• Issue Tracker: GitHub Issues

Support

For competition-specific questions, visit the CrunchDAO Discord.

For package issues, please open an issue on GitHub.