Exercise

# Practice with PyPortfolioOpt: returns

Modern Portfolio Theory is the cornerstone of portfolio risk management, because the efficient frontier is a standard method of assessing both investor risk appetite and market risk-return tradeoffs. In this exercise you'll develop powerful tools to explore a portfolio's efficient frontier, using the **PyPortfolioOpt** `pypfopt`

Python library.

To compute the efficient frontier, both expected returns and the covariance matrix of the portfolio are required.

After some practice loading the investment bank price data, you'll use `pypfopt.expected_returns`

's `mean_historical_return`

method to compute and visualize the annualized average returns of each bank from daily asset prices. The following exercise will then cover the covariance matrix.

Instructions 1/2

**undefined XP**

- Load portfolio data
`portfolio.csv`

into`prices`

using the`.read_csv()`

method. - Convert the
`'Date'`

column in`prices`

to the`datetime`

format, and make it the index using`prices`

's`.set_index()`

method.