Comments (5)
Thanks a lot, I will have a look as soon as I find the time.
NB: I can think of NaNs happening in case the covariance matrix is not (semi) positive definite, but I would expect the algorithm to throw at some point instead of outputing NaNs, so, I am definitely intrigued :-) !
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Thanks for your reply!
I thought so too about the precondition of the covariance matrix.
While debugging, it seemed that the NaNs happened here.
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I confirm the issue is related to the covariance matrix being semi-definite positive (i.e., determinant equal to 0).
In this case, it might happen that the variance is (numerically) negative zero (e.g. -0,000000000001), from which it is not possible to compute a volatility.
I added a proper management of this specific case on my local computer (more tricky than it seems due to the convergence condition of the algorithm which needs to be slightly amended), and it is now working OK on your example.
Commit will follow with non-regression tests, and will be included in the next release 0.0.7.
Thanks again for reporting.
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Thank you very much, looking forward to the new release :)
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Solved with today's release of v0.0.7 of the library.
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Related Issues (7)
- Real world example HOT 1
- TypeError: PortfolioAllocation.maximumSharpeRatioWeights is not a function HOT 4
- Internal error in meanVarianceOptimizationWeights / maximumTargetVolatility method HOT 2
- Maximum number of iterations reached, while simple problem to solve HOT 4
- README Google Sheets Example Not Working as Expected HOT 5
- Mean Variance Optimization not working as expected HOT 6
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