making sense of 100's of funds

Ok - thanks for the tips off list Brian and Patrick.

After reading through some of the PerformanceAnalytics docs one of the 
first things I tried to do was convert the daily unit prices to returns. 
  Just looking at the resulting time series is enlightening in its own 
right (I guess I should be surprised).

The next bit it more of an R style question.  I currently have the data 
in one data frame with the fund name as a factor.

 > names(funds)
  [1] "fundname"   "tier"       "region"     "assetClass" "security"
  [6] "style"      "geared"     "hedged"     "pdate"      "EntryPrice"
[11] "ExitPrice"  "Group"

Plotting the raw prices is almost a trivial matter with xyplot's formula 
interface, with or without groups:

print(xyplot(ExitPrice ~ pdate | fundname,  data=funds, type='l',
              layout=c(2,4),
              par.strip.text=list(cex=0.7)))

It may just be my inexperience with lattice but once I start dealing 
with zoo objects, then lattice doesn't seem to be quite so convenient. 
I could cbind the returns back into the the dataframe and continue using 
xyplot but it seems that would be throwing away the features of zoo.

What do people on the list do?

cheers

This is a bit of an open question but the fund manager my super with has 
over two hundred funds I can move my investment around in.

Using R I typically focus on a handful of funds, plotting MACD's and and 
just relying on visualisation methods like that but I was hoping for 
some pointers on more objective measures re risk, return that are 
practical to apply to several hundred investment funds.  To be fair, 
many of the funds are "me too" so it wouldn't hurt to cull this to a 
significantly smaller set.

I have some code which downloads the daily fund entry and exit prices 
into an sqlite database which I read directly with R.

Any tips for me (an engineer not a statistician) would be most appreciated.