Learn QUANTLIB with Real Code Examples

Price a European call option using Black-Scholes

Construct a zero-coupon yield curve

Price a vanilla interest rate swap

Compute Greeks for options sensitivity

Monte Carlo simulation of path-dependent derivatives

Check market data inputs and calendar settings

Ensure consistent date conventions

Verify correct model and engine selection

Debug convergence issues in Monte Carlo simulations

Validate calibration parameters

Unit test individual instruments

Validate pricing against benchmarks

Test term structures and curves

Check Monte Carlo convergence

Perform regression tests on updates

Local desktop for research and prototyping

Server-side deployment in risk engines

Python notebooks for interactive analysis

Integration into trading or pricing platforms

Batch processing for portfolio evaluation

QuantLib C++ core library

QuantLib-Python bindings

Jupyter notebooks for Python experimentation

Financial data sources (Bloomberg, Quandl)

CMake, Visual Studio, or other build tools

Python data analysis libraries (NumPy, pandas)

Risk management systems

Trading platforms and backtesting frameworks

Database connectivity for market data

Reporting and visualization tools

Use Python bindings for rapid prototyping

Cache frequently used market data

Modularize instruments and engines

Reuse curves and term structures

Write example scripts for benchmarking

Complexity of instrument modeling

Numerical stability in pricing algorithms

Calibrating models to market data

Integration into enterprise systems

Maintaining performance in Python bindings