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-rw-r--r--python/analytics/black.py52
-rw-r--r--python/analytics/black.pyx34
-rw-r--r--python/analytics/sabr.py16
3 files changed, 34 insertions, 68 deletions
diff --git a/python/analytics/black.py b/python/analytics/black.py
deleted file mode 100644
index 781732d9..00000000
--- a/python/analytics/black.py
+++ /dev/null
@@ -1,52 +0,0 @@
-from math import log, sqrt, erf
-from numba import jit, float64, boolean
-from scipy.stats import norm
-import math
-
-
-def d1(F, K, sigma, T):
- return (log(F / K) + sigma ** 2 * T / 2) / (sigma * math.sqrt(T))
-
-
-def d2(F, K, sigma, T):
- return d1(F, K, sigma, T) - sigma * math.sqrt(T)
-
-
-@jit(cache=True, nopython=True)
-def d12(F, K, sigma, T):
- sigmaT = sigma * sqrt(T)
- d1 = log(F / K) / sigmaT
- d2 = d1
- d1 += 0.5 * sigmaT
- d2 -= 0.5 * sigmaT
- return d1, d2
-
-
-@jit(float64(float64), cache=True, nopython=True)
-def cnd_erf(d):
- """ 2 * Phi where Phi is the cdf of a Normal """
- RSQRT2 = 0.7071067811865475
- return 1 + erf(RSQRT2 * d)
-
-
-@jit(float64(float64, float64, float64, float64, boolean), cache=True, nopython=True)
-def black(F, K, T, sigma, payer=True):
- d1, d2 = d12(F, K, sigma, T)
- if payer:
- return 0.5 * (F * cnd_erf(d1) - K * cnd_erf(d2))
- else:
- return 0.5 * (K * cnd_erf(-d2) - F * cnd_erf(-d1))
-
-
-@jit(float64(float64, float64, float64, float64), cache=True, nopython=True)
-def Nx(F, K, sigma, T):
- return cnd_erf((log(F / K) - sigma ** 2 * T / 2) / (sigma * sqrt(T))) / 2
-
-
-def bachelier(F, K, T, sigma):
- """ Bachelier formula for normal dynamics
-
- need to multiply by discount factor
- """
- d1 = (F - K) / (sigma * sqrt(T))
- return 0.5 * (F - K) * cnd_erf(d1) + sigma * sqrt(T) * norm.pdf(d1)
diff --git a/python/analytics/black.pyx b/python/analytics/black.pyx
new file mode 100644
index 00000000..80d13a1a
--- /dev/null
+++ b/python/analytics/black.pyx
@@ -0,0 +1,34 @@
+# cython: language_level=3, cdivision=True
+from libc.math cimport log, sqrt, erf
+from scipy.stats import norm
+import cython
+
+cpdef double cnd_erf(double d):
+ """ 2 * Phi where Phi is the cdf of a Normal """
+ cdef double RSQRT2 = 0.7071067811865475
+ return 1 + erf(RSQRT2 * d)
+
+
+cpdef double black(double F, double K, double T, double sigma, bint payer=True):
+ cdef:
+ double x = log(F / K)
+ double sigmaT = sigma * sqrt(T)
+ double d1 = (x + 0.5 * sigmaT * sigmaT) / sigmaT
+ double d2 = (x - 0.5 * sigmaT * sigmaT) / sigmaT
+ if payer:
+ return 0.5 * (F * cnd_erf(d1) - K * cnd_erf(d2))
+ else:
+ return 0.5 * (K * cnd_erf(-d2) - F * cnd_erf(-d1))
+
+
+cpdef double Nx(double F, double K, double sigma, double T):
+ return cnd_erf((log(F / K) - sigma ** 2 * T / 2) / (sigma * sqrt(T))) / 2
+
+
+cpdef double bachelier(double F, double K, double T, double sigma):
+ """ Bachelier formula for normal dynamics
+
+ need to multiply by discount factor
+ """
+ cdef double d1 = (F - K) / (sigma * sqrt(T))
+ return 0.5 * (F - K) * cnd_erf(d1) + sigma * sqrt(T) * norm.pdf(d1)
diff --git a/python/analytics/sabr.py b/python/analytics/sabr.py
index 7d66f1da..71b42cad 100644
--- a/python/analytics/sabr.py
+++ b/python/analytics/sabr.py
@@ -1,14 +1,8 @@
import datetime
import math
import numpy as np
-from numba import jit, float64
-@jit(
- float64(float64, float64, float64, float64, float64, float64),
- cache=True,
- nopython=True,
-)
def sabr_lognormal(alpha, rho, nu, F, K, T):
A = 1 + (0.25 * (alpha * nu * rho) + nu * nu * (2 - 3 * rho * rho) / 24.0) * T
if F == K:
@@ -21,11 +15,6 @@ def sabr_lognormal(alpha, rho, nu, F, K, T):
return VOL
-@jit(
- float64(float64, float64, float64, float64, float64, float64),
- cache=True,
- nopython=True,
-)
def sabr_normal(alpha, rho, nu, F, K, T):
if F == K:
V = F
@@ -55,11 +44,6 @@ def sabr_normal(alpha, rho, nu, F, K, T):
return VOL
-@jit(
- float64(float64, float64, float64, float64, float64, float64, float64),
- cache=True,
- nopython=True,
-)
def sabr(alpha, beta, rho, nu, F, K, T):
if beta == 0.0:
return sabr_normal(alpha, rho, nu, F, K, T)