Analyzing stock market data using Hidden Markov Models

Let's analyze stock market data using Hidden Markov Models. Stock market data is a good example of time series data where the data is organized in the form of dates. In the dataset that we will use, we can see how the stock values of various companies fluctuate over time. Hidden Markov Models are generative models that are used to analyze such time series data. In this recipe, we will use these models to analyze stock values.

How to do it…

1. Create a new Python file, and import the following packages:

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

2. Get the stock quotes from Yahoo finance. There is a method available in matplotlib to load this directly:

   # Get quotes from Yahoo finance
   quotes = quotes_historical_yahoo_ochl("INTC", 
           datetime.date(1994, 4, 5), datetime.date(2015, 7, 3))

3. There are six values in each quote....