Plot efficient frontier with best Sharpe ratio

Let's now plot the efficient frontier again, but add a marker for the portfolio with the best Sharpe index. Visualizing our data is always a good idea to better understand it.

Recall the efficient frontier is plotted in a scatter plot of portfolio volatility on the x-axis, and portfolio returns on the y-axis. We'll get the latest date we have in our data from covariances.keys(), although any of the portfolio_returns, etc, dictionaries could be used as well to get the date. Then we get volatilities and returns for the latest date we have from our portfolio_volatility and portfolio_returns. Finally we get the index of the portfolio with the best Sharpe index from max_sharpe_idxs[date], and plot everything with plt.scatter().

This exercise is part of the course

Machine Learning for Finance in Python

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Exercise instructions

Set cur_volatility to be the portfolio volatilities for the latest date.
Construct the "efficient frontier" plot by plotting volatility on the x-axis and returns on the y-axis.
Get the best portfolio index for the latest date from max_sharpe_idxs.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Get most recent (current) returns and volatility
date = sorted(covariances.keys())[-1]
cur_returns = portfolio_returns[date]
cur_volatility = ____

# Plot efficient frontier with sharpe as point
plt.scatter(x=____, y=____, alpha=0.1, color='blue')
best_idx = max_sharpe_idxs[____]

# Place an orange "X" on the point with the best Sharpe ratio
plt.scatter(x=cur_volatility[best_idx], y=cur_returns[best_idx], marker='x', color='orange')
plt.xlabel('Volatility')
plt.ylabel('Returns')
plt.show()

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