Learn QUANTLIB with Real Code Examples

Install a C++ compiler and build tools (GCC, MSVC, Clang)

Download QuantLib source code from GitHub

Build C++ library using CMake or make

Install Python bindings (QuantLib-Python) if needed

Verify installation via example scripts

Install C++ compiler and build tools

Clone QuantLib repository

Build and install C++ library

Install Python/R/.NET bindings if needed

Verify installation with example scripts

*.hpp / *.cpp - source and headers

CMakeLists.txt - build configuration

Python bindings setup.py or pyproject.toml

Market data input files (CSV/JSON)

Optional logging or configuration files

cmake . && make - build C++ library

python setup.py install - install Python bindings

Run example scripts for testing

Use pytest or unit tests for validation

Profile performance with cProfile or valgrind

Supports global date conventions

Multi-currency instruments

Handles regional calendars and holidays

Flexible numerical formatting

Compatible with international financial standards

Open-source BSD license

Python, R, and .NET bindings for accessibility

Documentation and examples available

Community support via mailing lists and forums

Cross-platform support for Linux, Windows, macOS

No GUI provided by default

Use Jupyter notebooks for visualization

Integration with matplotlib or Plotly

Document examples for readability

Organize scripts and code cleanly

Objects maintain parameters and market data

Term structures cached for reuse

Pricing engines stateless between calls

Monte Carlo simulations maintain internal RNG state

Global settings for calendars, evaluation dates

Market quotes for rates, volatilities, and prices

Instrument parameters and attributes

Curve and term structure objects

Simulation paths and results

Exported reports for risk and pricing analysis