Streamline PyPortfolioOpt Library
- Status: Closed
- Prize: $150
- Entries Received: 1
- Winner: ruiteixeira93
Contest Brief
The task? To extract and clean the PyPortfolioOpt library. You've got to remove classes and distill the features in question to simple, functions – using only Python and numpy. However, cvxpy can remain throughout the process.
What to deliver:
A colab notebook prooving how you code (as short as possible) results in the same answer as the following code:
ef = EfficientFrontier(Means, Cov_Matrix, weight_bounds=(0, 0.1))
weights = ef.max_sharpe()
source codes:
https://pyportfolioopt.readthedocs.io/en/latest/_modules/pypfopt/efficient_frontier/efficient_frontier.html#EfficientFrontier.max_sharpe
https://pyportfolioopt.readthedocs.io/en/latest/_modules/pypfopt/base_optimizer.html
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“Great, fast and efficient”
raphalutfalla7, Brazil.
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ruiteixeira93 United Kingdom
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