Meva contains two mean-variance portfolio optimizers: an analytical optimizer and a numerical optimizer. Both are single-period optimizers.
Analytical optimizer
The analytical optimizer aopt() handles linear equality constraints and
soft linear equality constraints (a penalty proportional to the squared
deviation from equality is subtracted from the objective function).
Numerical optimizer
The numerical optimizer nopt() is a long-short optimizer that allows you
to separately specify the sum of the negative portfolio weights and the sum
of the positive weights. It handles soft linear equality constraints,
inequality constraints (implemented as iterative soft constraints),
turnover constraints (iteratively multiplying linear transaction costs),
and (linear and quadratic) transaction costs.
Covariance estimation
Both portfolio optimizers need an estimate of the covariance matrix of
asset returns. Meva contains two algorithms to estimate the covariance
matrix: cov_pca() which is base on principal component analysis and
cov_fa() which is based on factor analysis.
Requirements:
| meva | python, numpy |
| speed | accelerated BLAS such as ATLAS |
| unit tests | nose |
Meva is a pure Python package. To install, all you have to do is to make sure Python can find the meva directory. Or you can install the traditional way:
$ python setup.py build $ sudo python setup.py install
After you have installed meva, run the unit test suite:
>>> import meva >>> meva.test() <snip> Ran 33 tests in 7.038s OK (KNOWNFAIL=1) <nose.result.TextTestResult run=33 errors=0 failures=0>