Learn QUANTLIB with Real Code Examples

C++ core with modular design

Instrument classes abstract financial products

Pricing engines separate from instruments

Date, calendar, and schedule utilities

Optional language bindings for Python, R, .NET

C++ objects represent instruments, curves, and engines

Python or other bindings provide access to computations

Pricing engines compute NPV and risk metrics

Monte Carlo and numerical methods for simulations

Results can be logged, exported, or visualized externally

Instrument-Engine separation

Handle-Quote pattern for market data

Term structure abstraction

Event-driven recalculation

Modular numerical solvers

Derivative pricing engines for banks

Portfolio risk management platforms

Insurance liability and reserve modeling

Algorithmic trading backtesting

Financial research and academic studies

Open-source and modular architecture

Separation of instruments and pricing engines

Extensible for new models and products

High-performance numerical computations

Cross-platform and multi-language support

Use batch processing for large portfolios

Cache term structures for efficiency

Vectorize computations in Python

Parallelize Monte Carlo simulations

Profile and optimize C++ code paths

Update QuantLib to latest version

Test existing scripts against new APIs

Refactor deprecated functions

Verify model and engine outputs

Update language bindings if necessary