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Portfolio optimization using pandas to calculate covariance Description Modeling a small QP problem to perform portfolio optimization
using pandas Python library to calculate covariance matrix. Further explanation of this example: see Chapter 'Python' in the 'Mosel Language Reference Manual'
Source Files By clicking on a file name, a preview is opened at the bottom of this page.
Data Files folioqp.dat
! Data file for `folioqp.mos'
! trs haw thr tel brw hgw car bnk sof elc
RET: [(1) 5 17 26 12 8 9 7 6 31 21]
! trs haw thr tel brw hgw car bnk sof elc
VAR: [ (1 1) 0.1 0 0 0 0 0 0 0 0 0 ! treasury
(2 1) 0 19 -2 4 1 1 1 0.5 10 5 ! hardware
(3 1) 0 -2 28 1 2 1 1 0 -2 -1 ! theater
(4 1) 0 4 1 22 0 1 2 0 3 4 ! telecom
(5 1) 0 1 2 0 4 -1.5 -2 -1 1 1 ! brewery
(6 1) 0 1 1 1 -1.5 3.5 2 0.5 1 1.5 ! highways
(7 1) 0 1 1 2 -2 2 5 0.5 1 2.5 ! cars
(8 1) 0 0.5 0 0 -1 0.5 0.5 1 0.5 0.5 ! bank
(9 1) 0 10 -2 3 1 1 1 0.5 25 8 ! software
(10 1) 0 5 -1 4 1 1.5 2.5 0.5 8 16 ! electronics
]
RISK: [2 3 4 9 10]
NA: [1 2 3 4]
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