D

#### DarthXander

function on and then plot the results for many values of t to see what

if any time lag exists between the data.

Thus far my code is;

import csv

import pylab

from pylab import *

from numpy import *

from numpy import array

HSBC=csv.reader(open("HSBC data.csv"))

Barclays=csv.reader(open("Barclays data.csv"))

x=[]

a=[]

y=[]

b=[]

g=[]

h=[]

d=[]

for Date, Close in HSBC:

x.append(Date)

a.append(float(Close))

for Date, Close in Barclays:

y.append(Date)

b.append(float(Close))

for index in range(len(a)):

g.append(a[index]-mean(a))

for index in range(len(b)):

h.append(b[index]-mean(b))

r=std(a)

s=std(b)

So I have all the necessary components for the DCF.

However I'm not faced with the challenge of performing the DCF for t

in the range of potentially 0-700 or so.

Currently I could do it individually for each value of tau ie;

t1=[]

for index in range(len(g)-1):

j=(g[index]*h[index+1])/(r*s)

t1.append(j)

d.append(mean(t1))

However to do this 700 times seems ridiculous. How would I get python

to perform this for me for t in a range of roughly 0-700?

Thanks

Alex