Plot efficient frontier

We can finally plot the results of our MPT portfolios, which shows the "efficient frontier". This is a plot of the volatility vs the returns. This can help us visualize our risk-return possibilities for portfolios. The upper left boundary of the points is the best we can do (highest return for a given risk), and that is the efficient frontier.

To create this plot, we will use the latest date in our covariances dictionary which we created a few exercises ago. This has dates as keys, so we'll get the sorted keys using sorted() and .keys(), then get the last entry with Python indexing ([-1]). Lastly we'll use matplotlib to scatter variance vs returns and see the efficient frontier for the latest date in the data.

Diese Übung ist Teil des Kurses

<Kurs>Machine Learning for Finance in Python</Kurs>

Kurs ansehen

Übungsanweisungen

Get the latest date from the covariances dictionary -- remember the dates are the keys.
Plot the volatility vs returns (portfolio_returns) for the latest date in a scatter plot, and set the alpha value for transparency to be 0.1.

Interaktive praktische Übung

Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.

# Get latest date of available data
date = sorted(covariances.____)[____]  

# Plot efficient frontier
# warning: this can take at least 10s for the plot to execute...
plt.scatter(x=portfolio_volatility[date], y=____,  alpha=____)
plt.xlabel('Volatility')
plt.ylabel('Returns')
plt.show()

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