PyFENG: Python Financial ENGineering

PyFENG is the python implemention of the standard option pricing models in financial engineering.

Models implemented

• Black-Scholes-Merton (and displaced diffusion) model
• Bachelier (Normal) model
• Constant-elasticity-of-variance (CEV) model
• Stochastic-alpha-beta-rho (SABR) model
• Hyperbolic normal stochastic volatility (NSVh) model
• Heston model
• Ornstein-Uhlenbeck driven stochastic volatility model

About the package

• It assumes variables are numpy arrays. So the computations are naturally vectorized.
• It is purely in Python (i.e., no C, C++, cython).
• It is implemented with python class.
• It is intended for, but not limited to, academic use. By providing reference models, it saves researchers' time.

Installation

pip install pyfeng

For upgrade,

pip install pyfeng --upgrade

Code Snippets

In [1]:

import numpy as np
import pyfeng as pf
m = pf.Bsm(sigma=0.2, intr=0.05, divr=0.1)
m.price(strike=np.arange(80, 121, 10), spot=100, texp=1.2)

Out [1]:

array([15.71361973,  9.69250803,  5.52948546,  2.94558338,  1.48139131])

In [2]:

sigma = np.array([[0.2], [0.5]])
m = pf.Bsm(sigma, intr=0.05, divr=0.1) # sigma in axis=0
m.price(strike=[90, 95, 100], spot=100, texp=1.2, cp=[-1,1,1])

Out [2]:

array([[ 5.75927238,  7.38869609,  5.52948546],
       [16.812035  , 18.83878533, 17.10541288]])

Author

• Prof. Jaehyuk Choi (Peking University HSBC Business School). Email: [email protected]

Others

• See also FER: Financial Engineering in R developed by the same author. Not all models in PyFENG is implemented in FER. FER is a subset of PyFENG.