European Binomial Model in q and Python

In the previous post, we created a binomial probability mass function (pmf). We can use that to easily evaluate European-style options:

EBM:{[P;S;K;T;r;b;v;n]                 / European Binomial Model (CRR)
 t:T%n;                                / time interval
 u:exp v*sqrt t;                       / up
 d:1%u;                                / down
 p:(exp[b*t]-d)%(u-d);                 / probability of up
 ns:til n+1;                           / 0, 1, 2, ..., n
 us:u xexp ns;                         / u**0, u**1, ...
 ds:d xexp ns;                         / d**0, d**1, ...
 Ss:S*ds*reverse us;                   / prices at tree leaves
 ps:pmf[n;p];                          / probabilities at tree leaves
 exp[neg r*T]*sum P[Ss;K]*ps }

Note that P is the payoff, S is the current price, K is the strike price, T is the time to maturity, r is the risk-free rate, v is the volatility, b is the cost of carry and n is the depth of the binomial tree. The Python version using NumPy and SciPy actually looks quite similar:

def EuropeanBinomialModel(P, S, K, T, r, b, v, n):
  n = int(n)
  t = float(T)/n                              # time interval
  u = np.exp(v * np.sqrt(t))                  # up
  d = 1/u                                     # down
  p = (np.exp(b*t)-d)/(u-d)                   # probability of up
  ns = np.arange(0, n+1, 1)                   # 0, 1, 2, ..., n
  us = u**ns                                  # u**0, u**1, ...
  ds = d**ns                                  # d**0, d**1, ...
  Ss = S*us*ds[::-1]                          # prices at leaves
  ps = binom_pmf(ns, n, p)                    # probabilities at leaves
  return np.exp(-r*T) * np.sum(P(Ss,K) * ps)

As we can see, both code has no explicit loops. This is possible in Python as NumPy and SciPy are array-oriented. NumPy and SciPy’s idea of “broadcasting” has some similarity with k/q’s concept of “atomic functions” (definition: a function f of any number of arguments is atomic if f is identical to f’).


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