1 files changed, 0 insertions, 69 deletions
diff --git a/python/analytics/cms_spread_utils.pyx b/python/analytics/cms_spread_utils.pyx
deleted file mode 100644
index 3a71f566..00000000
--- a/python/analytics/cms_spread_utils.pyx
+++ /dev/null
@@ -1,69 +0,0 @@
-# cython: language_level=3, cdivision=True, boundscheck=False, wraparound=False
-from libc.math cimport log, exp, sqrt
-from .black cimport cnd_erf
-cimport cython
-import numpy as np
-cimport numpy as np
-
-cdef api double h1(int n, double* data) nogil:
-    # z = (y - mu_y) / sigma_y
-    cdef:
-       double z = data[0]
-       double K = data[1]
-       double S1 = data[2]
-       double S2 = data[3]
-       double mu_x = data[4]
-       double mu_y = data[5]
-       double sigma_x = data[6]
-       double sigma_y = data[7]
-       double rho = data[8]
-       double u1, u2, Ktilde, v, v2, x
-
-    u1 = mu_x + rho * sigma_x * z
-    Ktilde = K + S2 * exp(mu_y + sigma_y * z)
-    u2 = log(Ktilde / S1)
-
-    v = sigma_x * sqrt(1 - rho * rho)
-    v2 = sigma_x * sigma_x * (1 - rho * rho)
-    x = (u1 - u2) / v
-    return (
-        0.5
-        * (S1 * exp(u1 + 0.5 * v2) * cnd_erf(x + v) - Ktilde * cnd_erf(x))
-        * exp(-0.5 * z * z)
-    )
-
-
-cpdef np.ndarray h_call(double[::1] z, double K, double S1, double S2, double mu_x, double mu_y, double sigma_x, double sigma_y, double rho):
-    # conditionned on S2, integral wrt S1
-    # z = (y - mu_y) / sigma_y
-    cdef:
-       np.ndarray[np.float_t, ndim=1] r = np.empty_like(z)
-       double u1, u2, Ktilde, x
-       double v = sigma_x * sqrt(1 - rho * rho)
-       double v2 = sigma_x * sigma_x * (1 - rho * rho)
-       int i
-
-    for i in range(z.shape[0]):
-        u1 = mu_x + rho * sigma_x * z[i]
-        Ktilde = K + S2 * exp(mu_y + sigma_y * z[i])
-        u2 = log(Ktilde / S1)
-        x = (u1 - u2) / v
-        r[i] = 0.5 * (S1 * exp(u1 + 0.5 * v2) * cnd_erf(x + v) - Ktilde * cnd_erf(x))
-    return r
-
-cpdef double[::1] h_put(double[::1] z, double K, double S1, double S2, double mu_x, double mu_y, double sigma_x, double sigma_y, double rho):
-    # z = (y - mu_y) / sigma_y
-    cdef:
-       double[::1] r = np.empty_like(z)
-       double u1, u2, Ktilde, x
-       double v = sigma_x * sqrt(1 - rho * rho)
-       double v2 = sigma_x * sigma_x * (1 - rho * rho)
-       int i
-
-    for i in range(z.shape[0]):
-        u1 = mu_x + rho * sigma_x * z[i]
-        Ktilde = K + S2 * exp(mu_y + sigma_y * z[i])
-        u2 = log(Ktilde / S1)
-        x = (u2 - u1) / v
-        r[i] = 0.5 * (Ktilde * cnd_erf(x) - S1 * exp(u1 + 0.5 * v2) * cnd_erf(x - v))
-    return r