Python for Finance

Options and Futures
Generating our own module p4f
We could combine many small Python progams as one program, such as p4f.py. For
instance, the preceding Python program called bs_call() function is included. Such
a collection of programs offers several benefits. First, when we use the bs_call()
function, we don't have to type those five lines. To save space, we only show a few
functions included in p4f.py. For brevity, we remove all comments included for
each function. Those comments are designed to help future users when issuing the
help() function, such as help(bs_call()):
def bs_call(S,X,T,rf,sigma):
from scipy import log,exp,sqrt,stats
d1=(log(S/X)+(rf+sigma*sigma/2.)*T)/(sigma*sqrt(T))
d2 = d1-sigma*sqrt(T)
return S*stats.norm.cdf(d1)-X*exp(-rf*T)*stats.norm.cdf(d2)
def binomial_grid(n):
import networkx as nx
import matplotlib.pyplot as plt
G=nx.Graph()
for i in range(0,n+1):
for j in range(1,i+2):
if i<n:
G.add_edge((i,j),(i+1,j))
G.add_edge((i,j),(i+1,j+1))
posG={} #dictionary with nodes position
for node in G.nodes():
posG[node]=(node[0],n+2+node[0]-2*node[1])
nx.draw(G,pos=posG)
def delta_call(S,X,T,rf,sigma):
from scipy import log,exp,sqrt,stats
d1=(log(S/X)+(rf+sigma*sigma/2.)*T)/(sigma*sqrt(T))
return(stats.norm.cdf(d1))
def delta_put(S,X,T,rf,sigma):
from scipy import log,exp,sqrt,stats
d1=(log(S/X)+(rf+sigma*sigma/2.)*T)/(sigma*sqrt(T))
return(stats.norm.cdf(d1)-1)
To apply the Black-Scholes-Merton call option model, we simply use the
following codes:
>>>import p4f
>>>c=p4f.bs_call(40,42,0.5,0.015,0.2)
>>>round(c,2)
1.56
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