Python for Finance Cookbook

How to do it...

Execute the following steps to use the exponential smoothing methods to create forecasts of Google's stock prices.

1. Import the libraries:

import pandas as pdimport numpy as npimport yfinance as yffrom datetime import datefrom statsmodels.tsa.holtwinters import (ExponentialSmoothing,                                          SimpleExpSmoothing,                                          Holt)

2. Download the adjusted stock prices for Google:

df = yf.download('GOOG',                 start='2010-01-01',                 end='2018-12-31',                 adjusted=True,                 progress=False)

3. Aggregate to monthly frequency:

goog = df.resample('M') \         .last() \         .rename(columns={'Adj Close': 'adj_close'}) \         .adj_close 

4. Create the training/test split:

train_indices = goog.index.year < 2018goog_train = goog[train_indices]goog_test = goog[~train_indices]test_length ...