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WeDeAn – The Weather Derivatives Analyzer

Back in 2018, I wrote my bachelor's thesis on weather derivatives - exploring how temperature data can be used to price financial contracts that hedge against extreme weather.

At that time, I built the prototype entirely in Excel. 😅

Two years later, when I started learning Python, I revisited the project and created my first simple GUI using Tkinter. 🧑‍💻

Now, a few years (and many lines of code) later, I've revisited this old project with new skills: I rebuilt the tool as a web application using Streamlit and containerized it with Docker to make it easily accessible and deployable. 🚀

🔹 What WeDeAn does:

WeDeAn connects weather data from the German Weather Service (DWD) with financial derivatives—tools used by energy companies, utilities, and insurers to manage risks from temperature fluctuations.

👉 In short: It converts temperature data into data-driven valuations for weather derivatives.

Key features:

  • 📈 Visualizes historical weather trends (temperature, sunshine, rainfall)
  • ❄️ Calculates Heating Degree Days (HDD) - how cold a season was
  • ☀️ Calculates Cooling Degree Days (CDD) - how hot a season was
  • 💰 Estimates fair values of HDD/CDD options using burn analysis - a data-driven pricing approach

💡 Why this matters:

Energy traders, utilities, and insurers rely on weather derivatives to hedge against weather-driven demand or revenue swings.

🧠 How it works:

  • 1️⃣ Load historical weather data from a chosen DWD station
  • 2️⃣ Compute how hot or cold each year was (HDDs & CDDs)
  • 3️⃣ Simulate how an option based on that weather would have performed historically
  • 4️⃣ Estimate a fair value for that option based on those simulated payouts

Watch the demo

Watch the video

⚙️ Try it yourself:

👉 WeDeAn is open source and runs in a Docker container.