We will analyze stock market data in this section using Hidden Markov Models. This is an example where the data is already organized timestamped. We will use the dataset available in the matplotlib package. The dataset contains the stock values of various companies over the years. Hidden Markov models are generative models that can analyze such time series data and extract the underlying structure. We will use this model to analyze stock price variations and generate the outputs.

Create a new python file and import the following packages:

import datetime 
import warnings 
 
import numpy as np 
import matplotlib.pyplot as plt 
from matplotlib.finance import quotes_historical_yahoo_ochl\ 
        as quotes_yahoo 
from hmmlearn.hmm import GaussianHMM 

Load historical stock market quotes from September 4, 1970 to May 17, 2016. You are free to choose any date range you wish.

# Load historical stock quotes from matplotlib package  
...