Hi,
I'm an R newbie and I've been struggling with a optimization problem for
the past couple of days now.
Here's the problem - I have a matrix of expected payouts from different
stock option strategies. Each column in my matrix represents a different
stock and each row represents the return to the strategy given a certain
market move. So the rows are not a time series of percentage returns but a
dollar payout in different expected scenarios, i.e.
Expected Return Matrix (ER) = stock1 stock2 .... stockn
scenario1 $ $
$
scenario2 $ $
$
scenario3 $ $
$
...
I want to create an optimal portfolio of these strategies by applying a
vector of weights. The weights will be the number of contracts of each to
buy and won't be a percentage weighting. There are a few constraints I need
it comply with:
- The weights have to be integers
- The minimum portfolio return (ER* Weights) across the scenarios has to
be greater than some negative number I specify
- There has to be a certain minimum number of stocks in the portfolio so
length(weights)>some number I specify.
Any help is GREATLY appreciated since I have tried so many different
functions and packages. Even if someone can just lead me to the correct
function to use that would be a great help as I've looked at optim,
solveLP, ROI package and many others.
Thanks,
S
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