My current recommendation is to get yourself set up with poetry then just run.Build your first container with make build; run tests with make test.
![]() ![]() Discrete allocation: GOOG: 0, AAPL: 5, FB: 11, BABA: 5, AMZN: 1. The key insight is that by combining assets with different expected returns and volatilities, one can decide on a mathematically optimal allocation which minimises the risk for a target return the set of all such optimal portfolios is referred to as the efficient frontier. The main drawback of mean-variance optimization is that the theoretical treatment requires knowledge of the expected returns and the future risk-characteristics (covariance) of the assets. As a substitute, we can derive estimates of the expected return and covariance based on historical data though we do lose the theoretical guarantees provided by Markowitz, the closer our estimates are to the real values, the better our portfolio will be. This is important because in order to reap the benefits of diversification (and thus increase return per unit risk), the assets in the portfolio should be as uncorrelated as possible. PyPortfolioOpt provides wrappers around the efficient vectorised implementations provided by sklearn.covariance. We offer three shrinkage targets: constantvariance, singlefactor, and constantcorrelation. This is the default option because it finds the optimal return per unit risk. You can provide your own risk-aversion level and compute the appropriate portfolio. This is not possible for the max Sharpe portfolio and the min volatility portfolio because in those cases because they are not invariant with respect to leverage. Essentially, it adds a penalty (parameterised by gamma ) on small weights, with a term that looks just like L2 regularisation in machine learning. It may be necessary to try several gamma values to achieve the desired number of non-negligible weights. For the test portfolio of 20 securities, gamma 1 is sufficient. Check out the docs for a discussion of the theory, as well as advice. Run the tests by navigating to the package directory and simply running pytest on the command line. If youre not sure which to choose, learn more about installing packages.
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