Balaraman G., Ballabio L. QuantLib Python Cookbook

New York: Leanpub, 2017. — 208 p.The choice of the QuantLib Python bindings and the IPython Notebook was due to their interactivity, which make it easier to demonstrate features, and the fact that the platform provides out of the box excellent modules like matplotlib for graphing and pandas for data analysis.
This choice might seem to leave C++ users out in the cold. However, it’s easy enough to translate the Python code shown here into the corresponding C++ code. An example of such translation is shown in the appendix.Basics
Quantlib Basics
Instruments and pricing engines
Numerical Greeks calculation
Market quotes
Interest-rate curves
Term structures and their reference dates
EONIA curve bootstrapping
Euribor curve bootstrapping
Constructing Yield Curve
Implied term structures
Interest-rate sensitivities via zero spread
A glitch in forward-rate curves
Interest-rate models
Simulating Interest Rates using Hull White Model
Thoughts on the Convergence of Hull-White Model Monte-Carlo Simulations
Short Interest Rate Model Calibration
Par versus indexed coupons
Caps and Floors
Equity models
Valuing European Option Using the Heston Model
Valuing European and American Options
Valuing Options on Commodity Futures Using The Black Formula
Defining rho for the Black process
Bonds
Modeling Fixed Rate Bonds
Modeling Callable Bonds
Duration of floating-rate bonds
Treasury Futures Contract
Mischievous pricing conventions
More mischievous conventionsTranslating QuantLib Python examples to C++