twsca 0.1.1

Time-Warped Spectral Correlation Analysis for Financial Market Data

Time-Warped Spectral Correlation Analysis (TWSCA)

TWSCA is a Python package for analyzing correlations between time series that may be misaligned in time or have different speeds. It combines dynamic time warping (DTW) with spectral analysis to identify hidden relationships.

Features

• Dynamic Time Warping (DTW) for time series alignment
• Spectral analysis for frequency domain correlation
• Support for multiple time series comparison
• Visualization tools for correlation analysis
• Comprehensive test suite
• Type hints for better IDE support

Installation

# Install from GitHub
pip install git+https://github.com/TheGameStopsNow/twsca.git

# Or install with development dependencies
pip install -e "git+https://github.com/TheGameStopsNow/twsca.git#egg=twsca[dev]"

Quick Start

from twsca import compute_twsca
import numpy as np

# Example with sine waves
t = np.linspace(0, 10, 100)
s1 = np.sin(t)
s2 = np.sin(t + 1)  # Phase-shifted version

# Compute TWSCA
result = compute_twsca(s1, s2)
print(f"Time-domain correlation: {result['time_domain_correlation']}")
print(f"Spectral correlation: {result['spectral_correlation']}")

API Reference

Core Functions

compute_twsca(s1, s2, **kwargs)

Compute Time-Warped Spectral Correlation Analysis between two time series.

Parameters:

• s1 (np.ndarray): First time series
• s2 (np.ndarray): Second time series
• window_size (int, optional): Size of the sliding window for spectral analysis
• dtw_radius (int, optional): Radius for DTW computation
• normalize (bool, optional): Whether to normalize the input series

Returns:

• dict: Dictionary containing:
  • time_domain_correlation: Correlation in time domain
  • spectral_correlation: Correlation in frequency domain
  • dtw_path: DTW alignment path
  • spectral_components: Spectral components of the analysis

Visualization

plot_twsca_results(result, title=None)

Plot TWSCA analysis results.

Parameters:

• result (dict): Results from compute_twsca
• title (str, optional): Plot title

Examples

See the examples directory for detailed examples:

1. Basic Usage: examples/basic_usage.py
2. Market Analysis: examples/market_analysis.py
3. Signal Processing: examples/signal_processing.py

Development

Setup

1. Clone the repository:

git clone https://github.com/TheGameStopsNow/twsca.git
cd twsca

2. Create and activate a virtual environment:

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Install development dependencies:

pip install -e ".[dev]"

Running Tests

pytest

Building Documentation

cd docs
make html

Contributing

1. Fork the repository
2. Create your feature branch: git checkout -b feature/my-new-feature
3. Commit your changes: git commit -am 'Add some feature'
4. Push to the branch: git push origin feature/my-new-feature
5. Submit a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use TWSCA in your research, please cite:

@software{twsca2024,
  author = {Dennis Nedry},
  title = {TWSCA: Time-Warped Spectral Correlation Analysis},
  year = {2024},
  publisher = {GitHub},
  url = {https://github.com/TheGameStopsNow/twsca}
}