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WeDeAn – Weather Derivatives Analyzer

WeDeAn (Weather Derivatives Analyzer) is a Python and Streamlit-based application for analyzing weather data and valuing HDD/CDD-based weather derivatives. It integrates DWD (Deutscher Wetterdienst) datasets, computes degree-day indices, and applies burn analysis to estimate the theoretical payoffs and actuarial fair values of weather options.

Watch the video

The source code is available on GitHub.

Features

  • Load and process DWD weather datasets: reads and cleans station data (TMK, RSK, SDK, and others) and prepares it for analysis.

  • Calculate heating and cooling degree days: converts daily mean temperatures (TMK) into HDDs and CDDs, the indices underlying temperature derivatives.

  • Adjust for climatic trends: optionally detrends long-term HDD/CDD series to remove warming or cooling bias.

  • Burn analysis pricing: computes historical payoffs and fair values of HDD/CDD options from actually observed weather.

  • Interactive visualizations: time series plots, rolling averages, payoff curves, and fair value results, all within a single Streamlit dashboard.

The process at a glance

  1. Load the dataset – select a weather station and import its historical data (TMK, SDK, RSK, and so on).
  2. Clean and filter – restrict the data to the chosen observation window (Observation length) and drop years with incomplete records.
  3. Compute the degree-day indices – aggregate daily HDDs and CDDs into seasonal accumulated values.
  4. Trend adjustment (optional) – remove long-term temperature trends.
  5. Calculate the payoffs – compute the option payoff for each season from the accumulated indices.
  6. Price the options (burn analysis) – estimate the fair value as the discounted historical mean of those payoffs.

Core concepts

HDDs (heating degree days)

A measure of how much and for how long the outside air temperature is below a base temperature (commonly 18°C). They are used to estimate heating demand: colder weather means higher HDDs and greater energy use.

Formula:

HDD_i = max(0, T_base - T_i)

Here T_i is the daily mean temperature, and the seasonal index is the sum of the daily HDDs over the contract season.

CDDs (cooling degree days)

A measure of how much and for how long the outside air temperature is above a base temperature (commonly 18°C). They are used to estimate cooling demand: hotter weather means higher CDDs and more electricity use.

Formula:

CDD_i = max(0, T_i - T_base)

As with HDDs, T_i is the daily mean temperature and the daily values are summed over the contract season.

Fair value of HDD/CDD options

The expected (average) payout, discounted to present value. It can be estimated by:

  • Historical simulation (burn analysis)
  • Statistical modeling
  • Monte Carlo simulation

Pricing by burn analysis

A transparent pricing method based on historical weather records.

  1. Compute the HDD/CDD index for each historical year.
  2. Calculate the option payoff (e.g., call/put) for each burn year.
  3. Take the average discounted payoff across all years as the fair value.

Assumption: the historical temperature distribution is representative of the future.

Run the application

1. Clone the repository

git clone https://github.com/trholy/wedean.git
cd wedean

2. Build and run the Docker container

docker compose up --build

3. Open it in your browser

The app is served at http://localhost:8501.

Example output

  • Berlin Marzahn 2020
  • CDDs
  • Trend adjusted
  • Observation length: 12 years
  • Moving average length: 5 years
  • Interest rate: 2.0%
  • Tick size: $100

Screenshot of the CDD analysis Screenshot of the fair value results

Project structure

├── .dockerignore
├── .gitignore
├── Dockerfile
├── LICENSE
├── README.md
├── datasets
├── docker-compose.yml
├── pyproject.toml
├── setup.py
├── src
│   ├── __init__.py
│   ├── wedean
│   │   ├── calculation
│   │   │   ├── __init__.py
│   │   │   └── calculation.py
│   │   ├── data_handling
│   │   │   ├── __init__.py
│   │   │   └── data_handling.py
│   │   ├── plotting
│   │   │   ├── __init__.py
│   │   │   └── plotting.py
│   │   └── utils
│   │       ├── __init__.py
│   │       └── utils.py
└── streamlit-app
    ├── app.py
    ├── requirements.txt
    └── utils.py

License

This project is released under the MIT License. You are free to use, modify, and distribute it, with attribution.