Black Litterman weights should use posterior covariance

[jrvarma]

Describe the bug
In the bl_weights function, the raw_weights are computed using the self.cov_matrix or Sigma

Expected behavior
bl_weights function should use the posterior covariance Sigma+M returned by bl_cov().

[jrvarma]

The relevant equations are Eq(10) and Eq(13) in He, G. and Litterman, R., 2002. The intuition behind Black-Litterman model portfolios. Available at SSRN 334304.

[heykav]

Looked into this before writing any code, because it's genuinely not a clear-cut bug.

bl_cov() (this file, ~line 445) returns Sigma + M, where M is the posterior covariance of the estimate of the mean (the uncertainty introduced by the views themselves — it shrinks toward zero as omega/view confidence tightens). bl_weights() currently reverse-optimizes against Sigma alone.

Whether the final weight/optimization step should use Sigma or Sigma + M is an actual point of disagreement in the Black-Litterman literature, not a settled question with one right answer — some treatments argue M represents estimation risk in the mean and shouldn't be folded into the asset covariance used for portfolio construction (since that conflates parameter uncertainty with return variance and can produce oddly conservative weights as confidence increases), while others include it for exactly the reason raised here. Different well-regarded implementations and surveys of the model land on different sides of this.

Practically, this also isn't a backward-compatible fix either way: test_bl_weights's hardcoded numeric assertions were computed against the current Sigma-only formula, so switching to Sigma + M changes real output numbers for every existing user, not just closes a bug.

Given the genuine ambiguity plus the breaking-change surface, I don't think this is something to just code up unilaterally — happy to implement whichever convention @lballabio (or whoever owns this module) wants, possibly as an opt-in parameter similar to #763/#764 rather than a silent behavior change, if that's useful context for deciding.

[lballabio]

I'm not involved with this project :)

[heykav]

Sorry about that — @lballabio tag was a copy-paste mistake on my end from an unrelated PR I had open elsewhere, not this project's maintainer. Correcting: @fkiraly / @robertmartin8, tagging you both since I see you've been merging recently — same question stands whenever you get a chance: should bl_weights() use Sigma or Sigma + M, and if there's no clear answer, would an opt-in parameter (rather than a silent behavior change) be the preferred way to let users pick?