我在 cython 代码中的函数之前添加了以下几行,并且从 Cython 获得的结果比 Python 2.7 更快
@cython.boundscheck(False)
@cython.wraparound(False)
@cython.cdivision(True)
我的 10M 积分结果
%timeit PyBlack(BlackPnL, Black_S, Black_Texpiry, Black_strike, Black_volatility, Black_IR, Black_callput)
1 loops, best of 3: 3.49 s per loop
and
%timeit CyBlack(BlackPnL, Black_S, Black_Texpiry, Black_strike, Black_volatility, Black_IR, Black_callput)
1 loops, best of 3: 2.12 s per loop
EDIT
CyBlack.pyx
from numpy cimport ndarray
cimport numpy as np
cimport cython
cdef extern from "math.h":
double exp(double)
double sqrt(double)
double log(double)
double fabs(double)
cdef double a1 = 0.254829592
cdef double a2 = -0.284496736
cdef double a3 = 1.421413741
cdef double a4 = -1.453152027
cdef double a5 = 1.061405429
cdef double p = 0.3275911
@cython.boundscheck(False)
@cython.wraparound(False)
@cython.cdivision(True)
cdef inline double erf(double x):
cdef int sign = 1
if (x < 0):
sign = -1
x = fabs(x)
cdef double t = 1.0/(1.0 + p*x)
cdef double y = 1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x)
return sign*y
@cython.boundscheck(False)
@cython.wraparound(False)
@cython.cdivision(True)
cdef double std_norm_cdf(double x):
return 0.5*(1+erf(x/sqrt(2.0)))
@cython.boundscheck(False)
@cython.wraparound(False)
@cython.cdivision(True)
cpdef CyBlack(ndarray[np.float64_t, ndim=1] BlackPnL, ndarray[np.float64_t, ndim=1] Black_S, ndarray[np.float64_t, ndim=1] Black_Texpiry, ndarray[np.float64_t, ndim=1] Black_strike, ndarray [np.float64_t, ndim=1] Black_volatility, ndarray[np.float64_t, ndim=1] Black_IR, ndarray[np.int64_t, ndim=1] Black_callput):
cdef Py_ssize_t i
cdef Py_ssize_t N = BlackPnL.shape[0]
cdef double d1, d2
for i in range(N):
d1 = ((log(Black_S[i] / Black_strike[i]) + Black_Texpiry[i] * Black_volatility[i] **2 / 2)) / (Black_volatility[i] * sqrt(Black_Texpiry[i]))
d2 = d1 - Black_volatility[i] * sqrt(Black_Texpiry[i])
BlackPnL[i] = exp(-Black_IR[i] * Black_Texpiry[i]) * (Black_callput[i] * Black_S[i] * std_norm_cdf(Black_callput[i] * d1) - Black_callput[i] * Black_strike[i] * std_norm_cdf(Black_callput[i] * d2))
return BlackPnL
setup.py
try:
from setuptools import setup
from setuptools import Extension
except ImportError:
from distutils.core import setup
from distutils.extension import Extension
from Cython.Distutils import build_ext
import numpy as np
ext_modules = [Extension("CyBlack",["CyBlack.pyx"])]
setup(
name= 'Generic model class',
cmdclass = {'build_ext': build_ext},
include_dirs = [np.get_include()],
ext_modules = ext_modules)