We present fast-vollib, an open-source Python library that provides high-performance European option pricing, implied volatility (IV) computation, and Greeks under the Black-76, Black-Scholes, and Black-Scholes-Merton models. The library is designed as a drop-in alternative to the de-facto-standard py_vollib and py_vollib_vectorized packages, with pluggable PyTorch and JAX execution backends, a CUDA fused-kernel Triton contribution for batched IV workloads, and a compatibility-first public API. In addition to a vectorized Halley-method IV solver, fast-vollib ships an experimental, fully-vectorized implementation of Jäckel's "Let's Be Rational" (LBR) algorithm with NumPy/Numba, torch.compile, JAX, and Triton single-pass GPU kernels for batched option chains. This note announces the library and describes its public API surface, with source, documentation, and packaging artifacts available at: GitHub (https://github.com/raeidsaqur/fast-vollib), Docs (https://raeidsaqur.github.io/fast-vollib/), PyPI (https://pypi.org/project/fast-vollib/).

翻译：我们推出了 fast-vollib 这一开源 Python 库，该库在 Black-76、Black-Scholes 和 Black-Scholes-Merton 模型下，提供高性能的欧式期权定价、隐含波动率计算以及希腊值运算。该库旨在作为业界标准 py_vollib 和 py_vollib_vectorized 包的即插即用替代方案，具备可插拔的 PyTorch 和 JAX 执行后端、面向批量隐含波动率工作负载的 CUDA 融合内核 Triton 组件，以及优先保持兼容性的公共 API。除了向量化的 Halley 方法隐含波动率求解器外，fast-vollib 还内置了一个实验性的全向量化 Jäckel "Let's Be Rational" 算法实现，该实现依托 NumPy/Numba、torch.compile、JAX 以及用于批量期权链的 Triton 单趟 GPU 内核。本公告正式发布该库，并描述其公共 API 接口。源代码、文档及打包工件可通过以下链接获取：GitHub (https://github.com/raeidsaqur/fast-vollib)、文档 (https://raeidsaqur.github.io/fast-vollib/)、PyPI (https://pypi.org/project/fast-vollib/)。