{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/3"},{"label":"Financial","url":"https://skillfed.io/packages/category/office-business-financial"}],"enrichment":{"capability":"QuantLib provides Python bindings to a C++ library for quantitative finance, offering tools for pricing derivatives, managing portfolios, and modeling financial instruments.","skillfed_tags":["quantitative-finance","derivatives-pricing","fixed-income"],"use_cases":["Price derivatives (options, swaps, bonds) using standard models like Black-Scholes and Hull-White","Build and manage yield curves for fixed-income analysis and valuation","Perform portfolio risk calculations and scenario analysis across multiple instruments","Develop quantitative trading strategies that require instrument pricing and Greeks computation","Conduct financial research and backtesting with historical or synthetic market data"],"what_it_does":"QuantLib is a Python wrapper around a mature C++ quantitative finance library. It provides a comprehensive framework for modeling and pricing financial instruments\u2014bonds, options, swaps, and other derivatives\u2014along with tools for portfolio analysis, curve construction, and risk management. The package is designed for financial analysts, researchers, and developers who need production-grade quantitative finance calculations.\n\nThe library ships with pre-built wheels for modern Python versions and multiple platforms, though installation still carries medium friction due to its compiled nature. A key limitation is that the underlying C++ code is not thread-safe; it maintains global state (notably the evaluation date) that cannot be isolated per-thread in the current wheel implementation. Users should avoid sharing objects across threads and instead give each thread its own set of curves and instruments.","worth_installing":"Yes, if you work in quantitative finance and need a mature, production-grade pricing and analytics library. The permissive BSD-3-Clause license, active maintenance, and broad platform support make it a solid choice. However, be aware of the threading limitations documented in the description\u2014single-threaded or carefully partitioned multi-threaded designs are required. Verify that the pre-built wheels match your Python version before installing."},"id":"quantlib","links":{"html":"https://skillfed.io/packages/quantlib","md":"https://skillfed.io/packages/quantlib.md","pypi":"https://pypi.org/project/quantlib/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-14","license_spdx":null,"license_treatment":"permissive","name":"QuantLib","python_support":"unspecified","summary":"Python bindings for the QuantLib library"},"popularity":{"monthly_downloads":844444,"position":4923,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"1.43"}