{"repo":"quants-net/PyFENG","free":true,"listed":false,"github":"https://github.com/quants-net/PyFENG","clone":"git clone https://github.com/quants-net/PyFENG.git","description":"Python Financial ENGineering (PyFENG package in PyPI.org)","language":"Python","stars":184,"topics":["financial-engineering","mathematical-finance","quantitative-finance","option-pricing","derivatives","black-scholes","heston-model","sabr-model","bachelier-model"],"license":"GPL-2.0","category":"trading","readme_excerpt":"PyFENG: [Py]thon [F]inancial [ENG]ineering PyFENG provides an implementation of the standard financial engineering models for derivative pricing. Implemented Models Black-Scholes-Merton (BSM) and displaced BSM models: Analytic option price, Greeks, and implied volatility. Bachelier (Normal) model Analytic option price, Greeks, and implied volatility. Constant-elasticity-of-variance (CEV) model Analytic option price, Greeks, and implied volatility. Stochastic-alpha-beta-rho (SABR) model Hagan's BSM vol approximation. Choi & Wu's CEV vol approximation. Analytic integral for the normal SABR. Closed-form MC simulation for the normal SABR. Hyperbolic normal stochastic volatility (NSVh) model Analytic option pricing. Heston model FFT option pricing. Almost exact MC simulation by Glasserman & Kim and Choi & Kwok. Schobel-Zhu (OUSV) model FFT option pricing. Almost exact MC simulation by Choi Rough volatility models Rough Heston MC by Ma & Wu About the Package Uses numpy arrays as basic datatype so computations are naturally vectorized. Purely Python without C/C++ extensisons. Implemented with Python class. Intended for academic use. By providing reference models, it saves researchers' time. See PyFENG for Papers in Related Projects below. Installation For upgrade, Code Snippets In [1]: Out [1]: In [2]: Out [2]: Author Prof. Jaehyuk Choi (Columbia University MAFN Program Director). Email Jaehyuk Choi. Related Projects Commercial versions (implemented and optimized in C/C++) for some ","default_branch":null,"files":null,"tree":[],"storefront":"/r/quants-net","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/quants-net/PyFENG/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}