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[DOCS] Update UserGuide.rst recipe link #699

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06d1de7
Update UserGuide.rst recipe link
aral190 Jan 29, 2026
3cbdfcf
Another place where the link uses a different spelling now
aral190 Jan 29, 2026
32c0dc8
Update GeneralEfficientFrontier.rst
aral190 Jan 29, 2026
9911d83
Update FAQ.rs
aral190 Jan 29, 2026
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2 changes: 1 addition & 1 deletion docs/FAQ.rst
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Expand Up @@ -7,7 +7,7 @@ FAQs
Constraining a score
--------------------

Suppose that for each asset you have some "score" – it could be an ESG metric, or some custom risk/return metric. It is simple to specify linear constraints, like "portfolio ESG score must be greater than x": you simply create
Suppose that for each asset you have some "score" – it could be an ESG (Environmental, Social, and Governance) metric, or some custom risk/return metric. It is simple to specify linear constraints, like "portfolio ESG score must be greater than x": you simply create
a vector of scores, add a constraint on the dot product of those scores with the portfolio weights, then optimize your objective::

esg_scores = [0.3, 0.1, 0.4, 0.1, 0.5, 0.9, 0.2]
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2 changes: 1 addition & 1 deletion docs/GeneralEfficientFrontier.rst
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Expand Up @@ -63,7 +63,7 @@ annual return of 20%::
from pypfopt import expected_returns, EfficientSemivariance

df = ... # your dataframe of prices
mu = expected_returns.mean_historical_returns(df)
mu = expected_returns.mean_historical_return(df)
historical_returns = expected_returns.returns_from_prices(df)

es = EfficientSemivariance(mu, historical_returns)
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2 changes: 1 addition & 1 deletion docs/MeanVariance.rst
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Expand Up @@ -32,7 +32,7 @@ language for convex optimization upon which PyPortfolioOpt's efficient frontier

.. tip::

You can find complete examples in the relevant cookbook `recipe <https://github.com/PyPortfolio/PyPortfolioOpt/blob/main/cookbook/2-Mean-Variance-Optimization.ipynb>`_.
You can find complete examples in the relevant cookbook `recipe <https://github.com/PyPortfolio/PyPortfolioOpt/blob/main/cookbook/2-Mean-Variance-Optimisation.ipynb>`_.


Structure
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2 changes: 1 addition & 1 deletion docs/UserGuide.rst
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Expand Up @@ -301,7 +301,7 @@ in the sidebar to learn more about the parameters and theoretical details of the
different models offered by PyPortfolioOpt. If you have any questions, please
raise an issue on GitHub and I will try to respond promptly.

If you'd like even more examples, check out the cookbook `recipe <https://github.com/PyPortfolio/PyPortfolioOpt/blob/main/cookbook/2-Mean-Variance-Optimization.ipynb>`_.
If you'd like even more examples, check out the cookbook `recipe <https://github.com/PyPortfolio/PyPortfolioOpt/blob/main/cookbook/2-Mean-Variance-Optimisation.ipynb>`_.


References
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