spb/tools-spboucher-ai

1{2  "slug": "quantlib",3  "name": "QuantLib",4  "aliases": [5    "QuantLib-Python",6    "QuantLibXL"7  ],8  "vendor": {9    "name": "QuantLib project (Luigi Ballabio and contributors)",10    "slug": "quantlib-project",11    "website": "https://www.quantlib.org"12  },13  "homepage_url": "https://www.quantlib.org/",14  "docs_url": "https://www.quantlib.org/docs.shtml",15  "repo_url": "https://github.com/lballabio/QuantLib",16  "license_url": "https://www.quantlib.org/license.shtml",17  "product_type": "library",18  "product_status": "active",19  "summary": "Open-source C++ library for quantitative finance covering instruments, term structures, pricing engines and calendars, with Python and other bindings",20  "description": "QuantLib is an open-source C++ library for quantitative finance, maintained since 2000 by Luigi Ballabio and a community of contributors, and belongs to the financial analysis category. It models the building blocks of derivative pricing and fixed-income analytics: dates and business-day calendars, day counters, yield and volatility term structures, interest-rate and equity instruments, stochastic processes, and pricing engines based on analytic formulas, trees, finite differences and Monte Carlo.\n\nThe library is organised around instruments that are priced by interchangeable engines, so a bond, swap or option can be revalued under different models without rewriting the instrument. Bindings generated with SWIG expose the same objects to Python (the QuantLib package on PyPI), C#, Java and R, and QuantLibXL brings the functionality into Excel. Most practitioners today call it from Python notebooks to bootstrap curves, price vanilla and exotic options, compute sensitivities or run calibration experiments.\n\nIt is used in banks, asset managers, consultancies and university courses, both directly and as the reference implementation that other libraries are checked against. Releases are frequent and the license is a modified BSD, so it can be embedded in proprietary systems.\n\nThe cost is complexity: the object model is large, documentation is thin outside the generated reference and community books, Python bindings mirror C++ idioms rather than feeling native, and performance-critical users may need to build from source and tune compilation. It is a pricing library, not a market-data or risk platform, so data feeds, position storage and reporting are left to the user.",21  "capabilities": [22    "Dates, calendars and day-count conventions for many markets",23    "Yield-curve bootstrapping and interpolation",24    "Interest-rate, equity, FX and credit instruments",25    "Pricing engines: analytic, lattice, finite-difference and Monte Carlo",26    "Short-rate and stochastic-volatility models with calibration helpers",27    "Bindings for Python, C#, Java and R, plus an Excel add-in"28  ],29  "workflows": [30    "Bootstrap a discount curve from deposits, futures and swaps, then price a swap book",31    "Calibrate a Heston or Hull-White model to market quotes",32    "Teach option pricing in a notebook with QuantLib-Python"33  ],34  "advantages": [35    "Mature, widely scrutinised reference implementation",36    "Permissive modified-BSD license",37    "Engine/instrument separation eases model comparison"38  ],39  "limitations": [40    "Steep learning curve and large object model",41    "Sparse narrative documentation outside third-party books",42    "Python API mirrors C++ idioms",43    "No market data, risk aggregation or reporting layers"44  ],45  "prerequisites": [46    "C++ toolchain and Boost for building from source, or a prebuilt Python wheel",47    "Familiarity with fixed-income and derivatives conventions"48  ],49  "audiences": [50    "Quantitative analysts and developers",51    "Finance faculty and graduate students",52    "Risk and valuation teams"53  ],54  "alternatives": [55    "pyportfolioopt",56    "riskfolio-lib",57    "scipy",58    "numpy"59  ],60  "comparison_notes": "PyPortfolioOpt and Riskfolio-Lib solve portfolio-allocation problems and do not price instruments; QuantLib prices instruments and builds curves but has no allocation layer. For simple Black-Scholes or curve math, SciPy and NumPy suffice, whereas QuantLib provides market conventions, calendars and calibrated models that would otherwise be rebuilt by hand.",61  "categories": [62    {63      "slug": "financial-analysis-investing",64      "primary": true65    },66    {67      "slug": "math-stats-tools",68      "label": "Numerical methods for derivative pricing"69    }70  ],71  "tags": [72    "quantitative-finance",73    "derivatives",74    "fixed-income",75    "cpp",76    "python",77    "open-source"78  ],79  "use_cases": [80    "price-derivatives",81    "build-yield-curves",82    "write-papers"83  ],84  "operating_systems": [85    "macos",86    "linux",87    "windows"88  ],89  "execution_modes": [90    "local"91  ],92  "interfaces": [93    "library"94  ],95  "integrations": [96    "Python",97    "Excel (QuantLibXL)",98    "R",99    "Java",100    "C#",101    "Boost"102  ],103  "data_formats": [104    "In-memory objects"105  ],106  "languages": [107    "C++",108    "Python",109    "C#",110    "Java",111    "R"112  ],113  "source_model": "open-source",114  "license_spdx": "BSD-3-Clause",115  "pricing_model": [116    "open-source",117    "free"118  ],119  "self_hostable": true,120  "sources": [121    {122      "url": "https://www.quantlib.org/",123      "type": "official_site",124      "supports": [125        "name",126        "summary",127        "product_type"128      ],129      "checked_at": "2026-11-02",130      "title": "QuantLib, a free/open-source library for quantitative finance"131    },132    {133      "url": "https://www.quantlib.org/docs.shtml",134      "type": "official_docs",135      "supports": [136        "capabilities",137        "languages"138      ],139      "checked_at": "2026-11-02",140      "title": "QuantLib documentation"141    },142    {143      "url": "https://www.quantlib.org/license.shtml",144      "type": "license_page",145      "supports": [146        "license_spdx",147        "source_model"148      ],149      "checked_at": "2026-11-02",150      "title": "QuantLib license (modified BSD)"151    },152    {153      "url": "https://github.com/lballabio/QuantLib",154      "type": "repository",155      "supports": [156        "product_status"157      ],158      "checked_at": "2026-11-02",159      "title": "lballabio/QuantLib"160    },161    {162      "url": "https://pypi.org/project/QuantLib/",163      "type": "package_registry",164      "supports": [165        "interfaces",166        "languages"167      ],168      "checked_at": "2026-11-02",169      "title": "QuantLib on PyPI"170    }171  ],172  "confidence": "high",173  "depth": "deep",174  "featured": true,175  "deployment_models": [],176  "discovery_method": "editorial-research",177  "editorial_status": "reviewed",178  "visibility": "public",179  "author": "batch-F"180}181