Metadata-Version: 2.4
Name: fractime
Version: 0.1.0
Summary: Python library for advanced time series forecasting using fractal geometry and chaos theory
Project-URL: Homepage, https://github.com/Wayy-Research/fracTime
Project-URL: Documentation, https://wayy-research.github.io/fracTime
Project-URL: Repository, https://github.com/Wayy-Research/fracTime
Project-URL: Issues, https://github.com/Wayy-Research/fracTime/issues
Author-email: RC Galbo <rcgalbo@gmail.com>
License-Expression: MIT
Keywords: chaos-theory,finance,forecasting,fractals,hurst-exponent,quantitative-finance,time-series
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Office/Business :: Financial
Classifier: Topic :: Scientific/Engineering :: Mathematics
Requires-Python: >=3.10
Requires-Dist: arch>=6.0.0
Requires-Dist: arviz>=0.18.0
Requires-Dist: matplotlib>=3.10.0
Requires-Dist: numba>=0.61.0
Requires-Dist: numpy>=2.1.3
Requires-Dist: pandas>=2.2.3
Requires-Dist: plotly>=6.0.0
Requires-Dist: pmdarima>=2.0.0
Requires-Dist: polars>=1.22.0
Requires-Dist: prophet>=1.1.0
Requires-Dist: pyarrow>=19.0.1
Requires-Dist: pymc>=5.10.0
Requires-Dist: pytensor>=2.18.0
Requires-Dist: requests>=2.32.3
Requires-Dist: scikit-learn>=1.6.1
Requires-Dist: scipy>=1.15.2
Requires-Dist: statsmodels>=0.14.4
Requires-Dist: xgboost>=2.0.0
Requires-Dist: yfinance>=0.2.54
Provides-Extra: dev
Requires-Dist: black>=24.2.0; extra == 'dev'
Requires-Dist: mypy>=1.8.0; extra == 'dev'
Requires-Dist: pytest-cov>=4.1.0; extra == 'dev'
Requires-Dist: pytest>=8.0.0; extra == 'dev'
Requires-Dist: ruff>=0.3.0; extra == 'dev'
Provides-Extra: docs
Requires-Dist: mkdocs-gen-files>=0.5.0; extra == 'docs'
Requires-Dist: mkdocs-literate-nav>=0.6.0; extra == 'docs'
Requires-Dist: mkdocs-material>=9.5.0; extra == 'docs'
Requires-Dist: mkdocs>=1.6.0; extra == 'docs'
Requires-Dist: mkdocstrings[python]>=0.24.0; extra == 'docs'
Description-Content-Type: text/markdown

# FracTime

Fractal-based time series forecasting with ensemble methods and rigorous backtesting.

[![Python 3.10+](https://img.shields.io/badge/python-3.10%2B-blue.svg)](https://www.python.org/downloads/)
[![License: MIT](https://img.shields.io/badge/license-MIT-green.svg)](LICENSE)
[![Documentation](https://img.shields.io/badge/docs-mkdocs-blue.svg)](https://wayy-research.github.io/fracTime)

## Installation

```bash
pip install fractime
```

All dependencies (ARIMA, GARCH, Prophet, PyMC, XGBoost) are included.

## Quick Start

```python
import fractime as ft
import numpy as np

# Load or create data
prices = np.random.randn(500).cumsum() + 100

# Analyze fractal properties
analyzer = ft.FractalAnalyzer()
hurst = analyzer.compute_hurst(prices)
print(f"Hurst: {hurst:.3f} ({'trending' if hurst > 0.5 else 'mean-reverting'})")

# Forecast
forecaster = ft.FractalForecaster()
forecaster.fit(prices)
result = forecaster.predict(n_steps=30)

print(f"Forecast: {result['forecast'][-1]:.2f}")
print(f"95% CI: [{result['lower'][-1]:.2f}, {result['upper'][-1]:.2f}]")

# Visualize
fig = ft.plot_forecast(prices, result['forecast'], result['paths'])
fig.show()
```

## Features

- **Fractal Forecasting**: Hurst exponent, fractal dimension, long-term memory
- **Baseline Models**: ARIMA, ETS, GARCH, Prophet, VAR, LSTM
- **Ensemble Methods**: Stacking and boosting
- **Backtesting**: Walk-forward validation with comprehensive metrics
- **Model Selection**: Automatic selection with statistical testing

## Documentation

Full documentation: [https://wayy-research.github.io/fracTime](https://wayy-research.github.io/fracTime)

- [Installation Guide](https://wayy-research.github.io/fracTime/getting-started/installation/)
- [Quick Start Tutorial](https://wayy-research.github.io/fracTime/getting-started/quickstart/)
- [API Reference](https://wayy-research.github.io/fracTime/api/core/)
- [Examples](https://wayy-research.github.io/fracTime/examples/basic/)

## License

MIT
