Merge pull request #12 from PyFE/dev-0.1.4

Wrapped around np.array()

Author: jaehyukchoi

README.md

@@ -40,7 +40,7 @@ array([15.71361973,  9.69250803,  5.52948546,  2.94558338,  1.48139131])
 ```python
 sigma = np.array([[0.2], [0.5]])
 m = pf.Bsm(sigma, intr=0.05, divr=0.1) # sigma in axis=0
-m.price(strike=np.array([90, 95, 100]), spot=100, texp=1.2, cp=np.array([-1,1,1]))
+m.price(strike=[90, 95, 100], spot=100, texp=1.2, cp=[-1,1,1])
 ```
 `Out [2]:`
 ```

pyfeng/bsm.py

@@ -46,15 +46,16 @@ def price_formula(strike, spot, sigma, texp, cp=1, intr=0.0, divr=0.0, is_fwd=Fa
             Vanilla option price
         """
         disc_fac = np.exp(-texp * intr)
-        fwd = spot * (1.0 if is_fwd else np.exp(-texp * divr) / disc_fac)
+        fwd = np.array(spot) * (1.0 if is_fwd else np.exp(-texp * divr) / disc_fac)
 
-        sigma_std = np.maximum(sigma * np.sqrt(texp), np.finfo(float).eps)
+        sigma_std = np.maximum(np.array(sigma) * np.sqrt(texp), np.finfo(float).eps)
 
         # don't directly compute d1 just in case sigma_std is infty
         d1 = np.log(fwd / strike) / sigma_std
         d2 = d1 - 0.5*sigma_std
         d1 += 0.5*sigma_std
 
+        cp = np.array(cp)
         price = fwd * spst.norm.cdf(cp * d1) - strike * spst.norm.cdf(cp * d2)
         price *= cp * disc_fac
         return price

pyfeng/norm.py

@@ -63,14 +63,14 @@ def price_formula(strike, spot, sigma, texp, cp=1, intr=0.0, divr=0.0, is_fwd=Fa
         Returns:
             Vanilla option price
         """
+        df = np.exp(-texp * intr)
+        fwd = np.array(spot) * (1.0 if is_fwd else np.exp(-texp * divr) / df)
 
-        disc_fac = np.exp(-texp * intr)
-        fwd = spot * (1.0 if is_fwd else np.exp(-texp * divr) / disc_fac)
-
-        sigma_std = np.maximum(sigma * np.sqrt(texp), np.finfo(float).eps)
+        sigma_std = np.maximum(np.array(sigma) * np.sqrt(texp), np.finfo(float).eps)
         d = (fwd - strike) / sigma_std
 
-        price = disc_fac * (cp*(fwd - strike)*spst.norm.cdf(cp * d) + sigma_std * spst.norm.pdf(d))
+        cp = np.array(cp)
+        price = df * (cp*(fwd - strike)*spst.norm.cdf(cp * d) + sigma_std * spst.norm.pdf(d))
         return price
 
     def _impvol_Choi2009(self, price, strike, spot, texp, cp=1, setval=False):
@@ -94,8 +94,8 @@ def _impvol_Choi2009(self, price, strike, spot, texp, cp=1, setval=False):
             implied volatility
         """
         fwd, df, _ = self._fwd_factor(spot, texp)
-        price_fwd = price / df
-        strike_std = cp*(fwd - strike)
+        price_fwd = np.array(price) / df
+        strike_std = np.array(cp)*(fwd - strike)
 
         time_val = price_fwd - np.maximum(0, strike_std)  # option time value
         strd = 2*price_fwd - strike_std  # straddle value (=call + put)