vollib
Python library for calculating option prices, implied volatility and greeks.
Decision gist · record as of 2026-08-14
Yes. vollib is actively maintained, has no known vulnerabilities, installs with low friction, and is the canonical package for classical option pricing in Python. Use it if you need Black, Black-Scholes, or Black-Scholes-Merton pricing or implied volatility. Verify that the pure-Python implementation meets your performance requirements before deploying to latency-critical systems.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Strike prices (K) must be strictly positive; K ≤ 0 raises ZeroDivisionError or ValueError.
- Requires Python 3.9 or later.
- Low friction install with pure-Python wheels.
License · maintenance · safety
MIT (permissive) — MIT license is permissive; no restrictions on commercial use, modification, or redistribution. Includes Peter Jaeckel's LetsBeRational code under a similar permissive license with warranty disclaimer.
last release 2026-06-01 (74 days) · last repo commit 2026-05-29 · 424 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 124,688 downloads/mo, #11,855 on PyPI
Alternatives
Verify before relying
pip install vollib
from vollib.black_scholes import black_scholes
from vollib.black_scholes.implied_volatility import implied_volatility
price = black_scholes('c', S=100, K=100, t=0.5, r=0.01, sigma=0.2)
iv = implied_volatility(price, S=100, K=100, t=0.5, r=0.01, flag='c')- Numerical accuracy and performance characteristics compared to other option pricing libraries.
- Whether analytical vs. numerical Greeks implementation choice affects typical use cases.
What it is and what it does
vollib is a Python library for quantitative finance that computes option prices, implied volatility, and Greeks (sensitivity measures) using three classical pricing models: Black, Black-Scholes, and Black-Scholes-Merton. At its core is an extremely fast algorithm from Peter Jaeckel's "Let's Be Rational" paper for deriving implied volatility from observed option prices—a computationally intensive inverse problem that vollib solves to high precision in just two iterations. The library provides both analytical and numerical implementations of Greeks for each model, letting users choose between speed and numerical robustness.
The package depends on numpy, scipy, pandas, and simplejson for numeric computation, plus specialized finance libraries cody-special and piecewise-rational. It is actively maintained, supports Python 3.9–3.13, and installs cleanly as a pure-Python wheel. Strike prices must be strictly positive; the library does not special-case zero or negative strikes and will raise an error if given them. Existing code using the deprecated py_vollib namespace will still work for now, but new code should import directly from vollib.
Use it for
- Calculate option prices and Greeks for hedging and risk management in derivatives trading.
- Infer implied volatility from market option prices to assess market expectations of future volatility.
- Backtest option trading strategies by computing prices and sensitivities across different market scenarios.
- Validate or calibrate volatility models by comparing theoretical prices to market data.
- Build financial analytics dashboards that require real-time Greeks and price updates for options portfolios.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
vollib is actively maintained, has no known vulnerabilities, installs with low friction, and is the canonical package for classical option pricing in Python. Use it if you need Black, Black-Scholes, or Black-Scholes-Merton pricing or implied volatility. Verify that the pure-Python implementation meets your performance requirements before deploying to latency-critical systems.
Install
vollib on PyPI
Before you install
Low friction install with pure-Python wheels. Actively maintained as of 2026-05-29 with recent release (1.0.11 on 2026-06-01). Supports Python 3.9–3.13 and depends on standard numeric libraries (numpy, scipy, pandas) plus specialized finance packages.
Strike prices (K) must be strictly positive; K ≤ 0 raises ZeroDivisionError or ValueError. Requires Python 3.9 or later.
License in practice
MIT license is permissive; no restrictions on commercial use, modification, or redistribution. Includes Peter Jaeckel's LetsBeRational code under a similar permissive license with warranty disclaimer.
Quickstart
pip install vollib
from vollib.black_scholes import black_scholes
from vollib.black_scholes.implied_volatility import implied_volatility
price = black_scholes('c', S=100, K=100, t=0.5, r=0.01, sigma=0.2)
iv = implied_volatility(price, S=100, K=100, t=0.5, r=0.01, flag='c')Verify before relying
- Numerical accuracy and performance characteristics compared to other option pricing libraries.
- Whether analytical vs. numerical Greeks implementation choice affects typical use cases.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release <4,>=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 7 packagescody-speciallets-be-rationalnumpypandaspiecewise-rationalscipysimplejson |
| Maintenance | Actively maintained 74 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 124,688 / month, #11,855 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: MIT LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.9Topic :: Office/Business :: Financial |
Evidence: vollib-1.0.11-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “greeks option pricing”
- vollibvollib calculates option prices, implied volatility, and Greeks using…
- py-vollibCalculates option prices, implied volatility, and Greeks using Black,…
- py-lets-be-rationalComputes implied volatility from option prices using Peter Jaeckel's…
Give your agent the search over MCP, or paste the wish link into any chat.
More Financial packages
statsmodels provides statistical models, inference methods, and descriptive statistics for Python, complementing scipy with regression, time series, discrete choice, survival analysis, and multivariate methods.
Install it if you need publication-quality statistical models, hypothesis tests, or time series analysis beyond what scipy or pandas provide.
Generates country- and subdivision-specific government holiday calendars on demand, supporting 250 country codes with optional language localization and holiday categories.
Fetches financial and market data from Yahoo Finance's public APIs, including ticker information, historical prices, and live streaming data.
However, do not use it for commercial applications or high-volume data collection without confirming compliance with Yahoo's terms of service.
Bokeh is an interactive visualization library that creates browser-based plots, dashboards, and data applications from Python code, with support for large and streaming datasets.
Install it if you need browser-based interactivity.
Parses, validates, and reformats standard numbers and codes across many countries and industries—tax IDs, bank accounts, identity numbers, VAT codes, and financial identifiers.
Install it if you validate or reformat standardized numbers.
Provides elementary financial functions (IRR, NPV, PMT, and others) that were deprecated and removed from NumPy, offering a dedicated replacement for financial calculations.
However, given the aging status (no releases since 2019) and unclear compatibility with modern Python and recent numpy versions, verify that it works with your target…
See also py-vollib · py-lets-be-rational · volatility3 · ta · pyportfolioopt · QuantLib · quantstats · linearmodels