pyfeng's Introduction

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.

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