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QuantLib-Python

library1.18pypypi✓ verified 21d ago

QuantLib-Python provides official Python bindings for the open-source Quantitative Finance Library (QuantLib C++). It offers a comprehensive suite of models and tools for pricing, risk management, and analysis of financial instruments. As of version 1.41, the library is actively maintained with regular updates, making advanced financial analytics accessible in Python.

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pip install QuantLib-Python

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INSTALL

IMPORT

SIG · QUANTLIB-PYTHON

QuantLib-Python

datapythonv1.18

Install

2.5s avg

Import

1104ms

Disk

102MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v1.18 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.95 runs

installs and imports cleanly · install 0.0s · import 1.158s · 108.1MB

glibc

py 3.10–3.95 runs

installs and imports cleanly · install 2.5s · import 1.050s · 99MB

102MB installed

● package 102MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

QuantLib

✓ import QuantLib as ql

It is common practice to import QuantLib with the alias 'ql' for brevity.

This quickstart demonstrates how to import QuantLib, set a global evaluation date, create QuantLib Date objects, and use a calendar function. Setting the evaluation date is a fundamental first step for most QuantLib applications.

import QuantLib as ql # Set a global evaluation date (crucial for many QuantLib calculations) todaysDate = ql.Date(15, ql.May, 2023) ql.Settings.instance().evaluationDate = todaysDate # Create another QuantLib Date object another_date = ql.Date(15, ql.May, 2024) # Print dates and check a simple calendar function (e.g., TARGET) print(f"Evaluation date: {todaysDate}") print(f"Another date: {another_date}") print(f"Is evaluation date a business day (TARGET calendar)? {ql.TARGET().isBusinessDay(todaysDate)}")

Debug

Known issues

gotchaQuantLib uses its own robust date, calendar, and day counter system (e.g., `ql.Date`, `ql.Calendar`). Mixing directly with Python's `datetime` objects without proper conversion or misunderstanding QuantLib's specific business day conventions can lead to incorrect or unexpected results.

fix

Always use `QuantLib.Date` and `QuantLib.Calendar` for internal calculations, converting to/from `datetime` explicitly only when interacting with external Python libraries or data sources. Consult QuantLib documentation for specific date arithmetic and calendar rules.

affects: All versions

gotchaQuantLib heavily relies on an 'observable' pattern for dependency management (e.g., an instrument observing a yield curve). If underlying data or objects change, dependent objects might not automatically update their calculated values unless explicitly refreshed or properly observing these changes.

fix

Ensure proper observer registration for dependent objects. In scenarios where values seem stale, explicit calls to `update()` on observable objects might be necessary to trigger recalculations or verify that the observation chain is correctly configured.

affects: All versions

gotchaWhile robust, the Python bindings introduce a performance overhead compared to direct C++ QuantLib usage. For very high-frequency calculations, large-scale Monte Carlo simulations, or iterative optimizations, this overhead can become significant.

fix

Profile critical sections of your code to identify performance bottlenecks. For highly performance-sensitive tasks, consider offloading computations to a C++ extension or pre-calculating results where feasible, or explore alternative pure-Python libraries for less complex tasks.

affects: All versions

Errors

Common errors & fixes

ModuleNotFoundError: No module named 'QuantLib'

The QuantLib-Python package is not installed or the Python environment cannot locate it.

fix

Ensure that QuantLib-Python is installed in your environment by running 'pip install QuantLib'.

ModuleNotFoundError: No module named '_QuantLib'

The '_QuantLib' module, a core component of QuantLib-Python, is missing or not properly installed.

fix

Verify that QuantLib-Python is correctly installed and that your Python environment is set up to locate the '_QuantLib' module.

ImportError: DLL load failed: The specified module could not be found.

A required DLL for QuantLib-Python is missing or not accessible.

fix

Ensure that all necessary dependencies, such as the Microsoft Visual C++ Redistributable, are installed and that the system's PATH includes the directory containing the required DLLs.

ImportError: DLL load failed while importing gurobipy: The specified module could not be found.

The 'gurobipy' module is missing a required DLL or the DLL is not in the system's PATH.

fix

Add the directory containing the 'gurobi90.dll' to the system's PATH or copy the DLL to the 'gurobipy' package directory.

ImportError: DLL load failed while importing shell: The specified module could not be found.

The 'shell' module is missing a required DLL or the DLL is not in the system's PATH.

fix

Ensure that all necessary dependencies are installed and that the system's PATH includes the directory containing the required DLLs.

Upgrade

Version history

1.18latest on PyPI · released Mar 23, 2020

Audit

Dependencies

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Agent activity

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OpenAI (training)

Resources

packagehttps://pypi.org/project/QuantLib-Python/ ↗

homepagehttp://quantlib.org ↗