Make sure the black-scholes implementation works

REQUIRE

@@ -0,0 +1 @@
+StatsFuns

src/Quant.jl

@@ -1,3 +1,4 @@
+__precompile__(true)
 
 """
 Quantitative Finance methods for Julia
@@ -7,16 +8,16 @@ want seventeen different versions of the Black-Scholes equation floating
 around, and re-writing a Geometric Brownian Motion simulation for every
 new project is just tedious.
 """
-module Quant.jl
+module Quant
+
 
 export
-
   # Black-Scholes functionality
   d1,
   d2,
   blackscholes_call,
   blackscholes_put,
+end
 
 include("blackscholes.jl")
-
 end

src/blackscholes.jl

@@ -7,48 +7,26 @@
 using StatsFuns
 
 """
-Calculate the value of $d_1$ in the Black-Scholes Formula
+Calculate the value of \$d_1\$ in the Black-Scholes Formula
 """
-d1 = function(σ, T, t, S, K, r)
-  return (σ .* sqrt(T-t)).^-1 * (log(S ./ K) + (r + σ.^2 / 2).*(T-t))
+function d1(S, K, σ, r, T, t=0)
+  return (log(S./K) + (r + σ.^2/2).*(T-t)) ./ (σ.*sqrt(T-t))
 end
 
-d1 = function(σ, T, S, K, r)
-  return d1(σ, T, 0, S, K, r)
-end
-
-d2 = function(d1_val, σ, T, t)
+function d2(d1_val, σ, T, t=0)
   return d1_val - σ .* sqrt(T-t)
 end
 
-d2 = function(d1_val, σ, T)
-  return d2(d1_val, σ, T, 0)
-end
-
-blackscholes_call = function(σ, T, t, S, K, r)
-  d1_val = d1(σ, T, t, S, K, r)
+function blackscholes_call(S, K, σ, r, T, t=0)
+  d1_val = d1(S, K, σ, r, T, t)
   d2_val = d2(d1_val, σ, T, t)
 
   return normcdf(d1_val) .* S - normcdf(d2_val) .* K .* exp(-r .* (T - t))
 end
 
-blackscholes_call = function(σ, T, S, K, r)
-  d1_val = d1(σ, T, S, K, r)
-  d2_val = d2(d1_val, σ, T)
-
-  return normcdf(d1_val) .* S - normcdf(d2_val) .* K .* exp(-r .* T)
-end
-
-blackscholes_put = function(σ, T, t, S, K, r)
-  d1_val = d1(σ, T, t, S, K, r)
+function blackscholes_put(S, K, σ, r, T, t=0)
+  d1_val = d1(S, K, σ, r, T, t)
   d2_val = d2(d1_val, σ, T, t)
 
-  return normcdf(-d2_val).*K.*exp(-r.*(T-t)) - normcdf(-d1).*S
-end
-
-blackscholes_put = function(σ, T, S, K, r)
-  d1_val = d1(σ, T, S, K, r)
-  d2_val = d2(d1_val, σ, T)
-
-  return normcdf(-d2_val).*K.*exp(-r.*T) - normcdf(-d1).*S
+  return normcdf(-d2_val).*K.*exp(-r.*(T-t)) - normcdf(-d1_val).*S
 end

test/blackscholes.jl

@@ -1,5 +1,9 @@
+using Base.Test
+#include("../src/blackscholes.jl")
 using Quant
 
-@test_approx blackscholes_call(.25, 2, 1, 100, 95, .05) 15.047
+@test 15.047 < blackscholes_call(100, 95, .25, .05, 2, 1) < 15.0471
+@test blackscholes_call(100, 95, .25, .05, 2, 1) == blackscholes_call(100, 95, .25, .05, 1)
 
-@test_approx blackscholes_call(.25, 1, 100, 95, .05) blackscholes_call(.25, 2, 1, 100, 95, .05)
+@test 5.413 < blackscholes_put(100, 95, .25, .05, 2, 1) < 5.414
+@test blackscholes_put(100, 95, .25, .05, 2, 1) == blackscholes_put(100, 95, .25, .05, 1)