Metadata-Version: 2.4
Name: epex_forecast
Version: 0.1.1
Summary: Easy Access to the current EPEX market forecast (day-ahead, week-ahead)
Project-URL: Homepage, https://github.com/LSB-dev/EPEX-Forecast-PyPI
Project-URL: Repository, https://github.com/LSB-dev/EPEX-Forecast-PyPI
Author: Lukas Baur
License-Expression: MIT
License-File: LICENSE
Keywords: day-ahead,energy,epex,pandas,price forecasting,time series,week-ahead
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.9
Requires-Dist: matplotlib>=3.7
Requires-Dist: pandas>=1.5
Requires-Dist: pyyaml>=6.0
Requires-Dist: requests>=2.28
Requires-Dist: tqdm>=4.66
Provides-Extra: dev
Requires-Dist: pytest-cov; extra == 'dev'
Requires-Dist: pytest>=7.0; extra == 'dev'
Provides-Extra: docs
Requires-Dist: mkdocs; extra == 'docs'
Requires-Dist: mkdocs-material; extra == 'docs'
Requires-Dist: mkdocstrings[python]; extra == 'docs'
Description-Content-Type: text/markdown

![A simple logo](images/logo.png)

# EPEX Forecast (PyPi Access)


**Python package: Easy access to current market forecast**

[![PyPI version](https://img.shields.io/pypi/v/epex_forecast.svg)](https://pypi.org/project/epex_forecast/)
[![Python](https://img.shields.io/pypi/pyversions/epex_forecast.svg)](https://pypi.org/project/epex_forecast/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

`epex_forecast` provides a minimal Python API to use the forecast API we provided through our research.

# Quick Start
## Installation

```bash
pip install epex_forecast
```

## Minimal Example
### Code
```python
from epex_forecast import get_epex_forecast, plot_forecast

forecast_df, meta = get_epex_forecast()
plot_forecast(forecast_df, meta)
```
### Output
#### meta
```python
{
    'region': 'DE', 
    'modelId': 'Chr2-prob-7step-[96]'
}
```
#### forecast_df
````csv
                           ground truth           0           1           2           3           4           5           6
2026-06-27 14:30:00+00:00         66.07    7.489024   33.999275   48.606190   59.912212   70.832802   86.320732  116.122849
2026-06-27 14:45:00+00:00         93.83   25.862049   54.465881   69.106926   80.855072   92.888962  110.560532  143.705048
2026-06-27 15:00:00+00:00         61.97    9.305321   34.496605   45.747437   54.106632   62.524593   74.426704  100.204086
2026-06-27 15:15:00+00:00         98.97   45.468853   73.135429   84.800629   92.647545  100.054573  110.290382  133.064301
2026-06-27 15:30:00+00:00        112.08   57.638866   86.084541   98.290726  106.322433  113.863327  125.216148  150.170151
2026-06-27 15:45:00+00:00        129.40   68.099258  100.683945  114.830284  124.534019  134.235596  148.514236  179.282013
...
````
#### plot_forecast
![Output plot of forecast](images/example_request_output.png)

## License
Author: LSB (Fraunhofer IPA)

Licence pip package: MIT

## Acknowledgements
👨‍💻 We'd like to thank Guilherme Schmid for [his work](https://github.com/LSB-dev/PADELF-PIP) on a PyPI integration, which served as the foundation for this work.