Execute the following steps to create and evaluate the 1/n portfolio.

1. Import the libraries:

import yfinance as yf
import numpy as np
import pandas as pd
import pyfolio as pf

2. Set up the parameters:

RISKY_ASSETS = ['AAPL', 'IBM', 'MSFT', 'TWTR']
START_DATE = '2017-01-01'
END_DATE = '2018-12-31'

n_assets = len(RISKY_ASSETS)

3. Download the stock prices from Yahoo Finance:

prices_df = yf.download(RISKY_ASSETS, start=START_DATE, 
                        end=END_DATE, adjusted=True)

4. Calculate individual asset returns:

returns = prices_df['Adj Close'].pct_change().dropna()

5. Define the weights:

portfolio_weights = n_assets * [1 / n_assets]

6. Calculate the portfolio returns:

portfolio_returns = pd.Series(np.dot(portfolio_weights, returns.T), 
                              index=returns.index)

7. Create the tear sheet (simple variant):

pf.create_simple_tear_sheet(portfolio_returns)

The preceding line of code generates the simple tear sheet, and the first table looks like this:

We describe the metrics presented in the first table of the tear sheet in the following section.