1 files changed, 0 insertions, 52 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)