Coding Cross Correlation Function in Python

[DarthXander]

I have two data sets which I wish to perform the discrete correlation
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