Python for Finance Cookbook

How to do it...

Execute the following steps to simulate Microsoft's stock prices one month ahead.

1. Import the libraries:

import numpy as npimport pandas as pdimport yfinance as yf

2. Define the parameters for downloading the data:

RISKY_ASSET = 'MSFT'START_DATE = '2019-01-01'END_DATE = '2019-07-31'

3. Download data from Yahoo Finance:

df = yf.download(RISKY_ASSET, start=START_DATE,                  end=END_DATE, adjusted=True)

4. Calculate the daily returns:

adj_close = df['Adj Close']returns = adj_close.pct_change().dropna()print(f'Average return: {100 * returns.mean():.2f}%')returns.plot(title=f'{RISKY_ASSET} returns: {START_DATE} - {END_DATE}')

The code produces the following plot:

And the following line:

Average return: 0.24%

5. Split the data into training ...