Python for Finance, 3rd Edition

Part VI. Derivatives Analytics

Derivatives analytics adds a specialized valuation stack on top of the general quant toolkit. This part focuses on reusable valuation frameworks, simulation-based models, and market-based calibration, again relying on an efficient-market view for liquidly traded instruments: market prices are treated as the benchmark that valuation models must match.

This part builds a coherent derivatives toolbox for pricing and risk:

• Chapter 27 establishes the valuation principles and market environment abstractions that underpin the DX implementation.

• Chapter 28 develops simulation techniques for stochastic models used in pricing and risk.

• Chapter 29 applies these models to value derivatives consistently across products.

• Chapter 30 extends the framework to portfolios of derivatives and underlying positions.

• Chapter 31 connects model valuation and calibration to observed market prices, reinforcing the role of efficient markets as the reference point.

The goal is a practical derivatives stack that integrates seamlessly with the rest of the book’s Python infrastructure.