# S&P500 Sharpe ratio

In this exercise, you're going to **calculate the Sharpe ratio** of the S&P500, starting with **pricing data only**. In the next exercise, you'll do the same for the portfolio data, such that you can compare the Sharpe ratios of the two.

Available for you is the price data from the S&P500 under `sp500_value`

. The risk-free rate is available under `rfr`

, which is conveniently set to zero. Let's give it a try!

This is a part of the course

## “Introduction to Portfolio Analysis in Python”

### Exercise instructions

- Calculate the total return of the S&P500 pricing data
`sp500_value`

using indexing and annualize the total return number; the data spans 4 years. - Calculate the daily returns from the S&P500 pricing data, you'll need this for the volatility calculation.
- Calculate the standard deviation from the returns data and annualize the number using 250 trading days.
- Finally, calculate the Sharpe ratio using the annualized return and the annualized volatility and print the results.

### Hands-on interactive exercise

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

```
# Calculate total return and annualized return from price data
total_return = (sp500_value[____] - ____[____]) / ____[____]
# Annualize the total return over 4 year
annualized_return = ((____ + ____)**(____/____))-1
# Create the returns data
returns_sp500 = ____.____()
# Calculate annualized volatility from the standard deviation
vol_sp500 = ____.____() * np.sqrt(____)
# Calculate the Sharpe ratio
sharpe_ratio = ((____ - rfr) / ____)
print (sharpe_ratio)
```

This exercise is part of the course

## Introduction to Portfolio Analysis in Python

Learn how to calculate meaningful measures of risk and performance, and how to compile an optimal portfolio for the desired risk and return trade-off.

Chapter 2 goes deeper into how to measure returns and risk accurately. The two most important measures of return, annualized returns, and risk-adjusted returns, are covered in the first part of the chapter. In the second part, you’ll learn how to look at risk from different perspectives. This part focuses on skewness and kurtosis of a distribution, as well as downside risk.

Exercise 1: Annualized returnsExercise 2: Annualizing portfolio returnsExercise 3: Comparing annualized rates of returnExercise 4: Risk adjusted returnsExercise 5: Interpreting the Sharpe ratioExercise 6: S&P500 Sharpe ratioExercise 7: Portfolio Sharpe ratioExercise 8: Non-normal distribution of returnsExercise 9: Skewness of the S&P500Exercise 10: Calculating skewness and kurtosisExercise 11: Comparing distributions of stock returnsExercise 12: Alternative measures of riskExercise 13: Sortino ratioExercise 14: Maximum draw-down portfolio### What is DataCamp?

Learn the data skills you need online at your own pace—from non-coding essentials to data science and machine learning.