Lets have a look at some garden-variety Python code for the Black Scholes formula.

Taken from here,

```
import numpy as np
import scipy.stats as ss
import time
#Black and Scholes
def d1(S0, K, r, sigma, T):
return (np.log(S0/K) + (r + sigma**2 / 2) * T)/(sigma * np.sqrt(T))
def d2(S0, K, r, sigma, T):
return (np.log(S0 / K) + (r - sigma**2 / 2) * T) / (sigma * np.sqrt(T))
def BlackScholes(type, S0, K, r, sigma, T):
if type=="C":
return S0 * ss.norm.cdf(d1(S0, K, r, sigma, T)) - K * np.exp(-r * T) * ss.norm.cdf(d2(S0, K, r, sigma, T))
else:
return K * np.exp(-r * T) * ss.norm.cdf(-d2(S0, K, r, sigma, T)) - S0 * ss.norm.cdf(-d1(S0, K, r, sigma, T))
```

Pricing goes like this,

```
S0 = 100.0
K = 100.0
r = 0.1
sigma = 0.30
T = 3
Otype = 'C'
print "S0\tstock price at time 0:", S0
print "K\tstrike price:", K
print "r\tcontinuously compounded risk-free rate:", r
print "sigma\tvolatility of the stock price per year:", sigma
print "T\ttime to maturity in trading years:", T
t = time.time()
c_BS = BlackScholes(Otype,S0, K, r, sigma, T)
elapsed = time.time()-t
print "c_BS\tBlack-Scholes price:", c_BS, elapsed
```

The exercise is to rewrite this, but not to use any variables or functions, *only* lambdas!

If you do it quickly, try to do it as elegantly as possible.

Have fun.