Background
Description
This repository implements a production-style quantitative valuation pipeline for equity options, combining high-performance pricing models with a full data and calibration workflow.
The system goes beyond a standalone pricer: it integrates market data ingestion, structured storage, numerical pricing, and volatility surface calibration into a single reproducible framework.
The goal of this project
The goal of this project is to serve as a modular foundation for quantitative modeling and experimentation in option pricing and financial time series.
Mathematical Framework
Read about what the option pricing engine actually does → Option Pricing Engine Mathematical Framework
Roadmap
The roadmap is outlined in the following flow chart
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Design
Rough planned class diagram sketch for qengine
📊 Observations and further analysis
- Find out how the implied volatility was reconstructed from real world market data using this system and what challenges were faced along the way: → Option Price Engine Implied Volatility Analysis
- Investigate research based techniques how to compute the local volatility from the implied volatility surface data: → Local volatility computations
Bonus projects
- Realize a battery storage optimization schedule based on a real-world timeseries of electricity prices. → Battery Storage Optimization
Testing strategy
Unit tests
- Payoff
- Black-Scholes Process unit test
- Test with a given result
- Random number generator tests
- Statistics unit tests
Written by David Doebel 03.04.2026 LinkedIn: https://www.linkedin.com/in/david-doebel-b1b9b1339/