Python for Finance, 3rd Edition

Chapter 27. Valuation Framework

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Derivatives analytics starts with a surprisingly “unsexy” problem: you need a consistent way to represent time, discounting, and market inputs before you can simulate anything or value any payoff. This chapter introduces a compact valuation framework that turns the risk-neutral pricing idea into a small set of reusable Python building blocks that are used throughout Part VI.

This chapter develops the foundation of a small derivatives analytics library called dxlib. It implements time-axis helpers that turn maturities into year fractions, discount curves that turn future cashflows into present values, and a market environment container that bundles valuation inputs into a single object. These components keep later simulation and valuation code small, explicit, and testable. The examples assume a working Python environment with numpy, basic familiarity with dataclasses and type hints, and the ability to run scripts from the directory that contains code/ so it ...