pyquantlib 0.6.0

Python bindings for QuantLib

PyQuantLib: Modern Python bindings for QuantLib

Overview

PyQuantLib provides Python bindings for QuantLib, the open-source library for quantitative finance. Built with pybind11, it offers a more Pythonic API than existing alternatives.

Features

• Pythonic API: Pass Python objects directly to functions (implicit conversion)
• Zero-copy NumPy: Buffer protocol for efficient, bidirectional data exchange
• Type hints: IDE-friendly with complete .pyi stub files
• Python subclassing: Extend QuantLib classes without C++ recompilation
• Modern build: scikit-build-core, CMake presets, cross-platform CI/CD

Installation

pip install pyquantlib

Pre-built wheels are available for Python 3.10--3.13 on Linux (x86_64), macOS (ARM), and Windows (x64). QuantLib is statically linked -- no separate installation required.

From Source

Building from source requires QuantLib built with specific CMake flags. See CONTRIBUTING.md for detailed instructions.

Prerequisites

• Python 3.10+
• CMake 3.18+
• C++17 compatible compiler
• Boost headers
• QuantLib 1.40+ built with std::shared_ptr support (see below)

QuantLib Build Requirement

Important: PyQuantLib requires QuantLib built from source with specific settings.

Required CMake flags:

cmake -DBUILD_SHARED_LIBS=OFF \
      -DCMAKE_POSITION_INDEPENDENT_CODE=ON \
      -DQL_USE_STD_SHARED_PTR=ON \
      -DCMAKE_MSVC_RUNTIME_LIBRARY=MultiThreadedDLL \  # Windows only
      -DCMAKE_BUILD_TYPE=Release \
      ...

Flag	Why Required
BUILD_SHARED_LIBS=OFF	Static build prevents Settings singleton issues on Linux/macOS
CMAKE_POSITION_INDEPENDENT_CODE=ON	Required for static libs in Python modules
QL_USE_STD_SHARED_PTR=ON	pybind11 uses std::shared_ptr as default holder
CMAKE_MSVC_RUNTIME_LIBRARY=MultiThreadedDLL	Windows only: Python extensions require dynamic runtime (/MD)

Note: Pre-built packages (Homebrew, vcpkg, apt) use shared builds and boost::shared_ptr -- they are not compatible. You must build QuantLib from source. See CONTRIBUTING.md for detailed build instructions.

pip install git+https://github.com/quantales/pyquantlib.git

Quick Start

import pyquantlib as ql

# Set evaluation date
today = ql.Date(15, 6, 2025)
ql.Settings.evaluationDate = today

# Market data
spot = ql.SimpleQuote(100.0)
rate = ql.SimpleQuote(0.05)
vol = ql.SimpleQuote(0.20)

# Term structures (pass quotes directly, handles created internally)
dc = ql.Actual365Fixed()
risk_free = ql.FlatForward(today, rate, dc)
dividend = ql.FlatForward(today, 0.0, dc)
volatility = ql.BlackConstantVol(today, ql.TARGET(), vol, dc)

# Black-Scholes process (pass objects directly)
process = ql.GeneralizedBlackScholesProcess(spot, dividend, risk_free, volatility)

# European call option
payoff = ql.PlainVanillaPayoff(ql.Call, 100.0)
exercise = ql.EuropeanExercise(today + ql.Period("1Y"))
option = ql.VanillaOption(payoff, exercise)

# Price with analytic Black-Scholes
option.setPricingEngine(ql.AnalyticEuropeanEngine(process))

print(f"NPV:   {option.NPV():.4f}")
print(f"Delta: {option.delta():.4f}")
print(f"Gamma: {option.gamma():.4f}")
print(f"Vega:  {option.vega():.4f}")
print(f"Theta: {option.theta():.4f}")

Output:

NPV:   10.4506
Delta: 0.6368
Gamma: 0.0188
Vega:  37.5240
Theta: -6.4140

Module Organization

import pyquantlib as ql          # Concrete classes
from pyquantlib.base import ...  # Abstract base classes (for subclassing)

Coverage includes dates and calendars, market quotes, yield and volatility term structures, stochastic processes, instruments, and pricing engines. See the API Reference for the complete list.

Development

See CONTRIBUTING.md for development setup and guidelines.

# Clone and install in development mode
git clone https://github.com/quantales/pyquantlib.git
cd pyquantlib
python -m venv venv
source venv/bin/activate  # or venv\Scripts\activate on Windows
python -m pip install --upgrade pip
pip install -r requirements-dev.txt
pip install -e .

# Run tests
pytest

Documentation

Full documentation is available at pyquantlib.readthedocs.io. For the latest additions, see the changelog.

Examples

See the examples directory for Jupyter notebooks demonstrating PyQuantLib usage.

License

BSD 3-Clause License. See LICENSE for details.

Acknowledgments

• QuantLib - Quantitative finance library
• pybind11 - C++/Python bindings
• scikit-build-core - Build system