.. _sphx_glr_auto_examples_plot_hmm_stock_analysis.py:


Gaussian HMM of stock data
--------------------------

This script shows how to use Gaussian HMM on stock price data from
Yahoo! finance. For more information on how to visualize stock prices
with matplotlib, please refer to ``date_demo1.py`` of matplotlib.



.. code-block:: python


    from __future__ import print_function

    import datetime

    import numpy as np
    from matplotlib import cm, pyplot as plt
    from matplotlib.dates import YearLocator, MonthLocator
    try:
        from matplotlib.finance import quotes_historical_yahoo_ochl
    except ImportError:
        # For Matplotlib prior to 1.5.
        from matplotlib.finance import (
            quotes_historical_yahoo as quotes_historical_yahoo_ochl
        )

    from hmmlearn.hmm import GaussianHMM


    print(__doc__)


Get quotes from Yahoo! finance



.. code-block:: python

    quotes = quotes_historical_yahoo_ochl(
        "INTC", datetime.date(1995, 1, 1), datetime.date(2012, 1, 6))

    # Unpack quotes
    dates = np.array([q[0] for q in quotes], dtype=int)
    close_v = np.array([q[2] for q in quotes])
    volume = np.array([q[5] for q in quotes])[1:]

    # Take diff of close value. Note that this makes
    # ``len(diff) = len(close_t) - 1``, therefore, other quantities also
    # need to be shifted by 1.
    diff = np.diff(close_v)
    dates = dates[1:]
    close_v = close_v[1:]

    # Pack diff and volume for training.
    X = np.column_stack([diff, volume])


Run Gaussian HMM



.. code-block:: python

    print("fitting to HMM and decoding ...", end="")

    # Make an HMM instance and execute fit
    model = GaussianHMM(n_components=4, covariance_type="diag", n_iter=1000).fit(X)

    # Predict the optimal sequence of internal hidden state
    hidden_states = model.predict(X)

    print("done")


Print trained parameters and plot



.. code-block:: python

    print("Transition matrix")
    print(model.transmat_)
    print()

    print("Means and vars of each hidden state")
    for i in range(model.n_components):
        print("{0}th hidden state".format(i))
        print("mean = ", model.means_[i])
        print("var = ", np.diag(model.covars_[i]))
        print()

    fig, axs = plt.subplots(model.n_components, sharex=True, sharey=True)
    colours = cm.rainbow(np.linspace(0, 1, model.n_components))
    for i, (ax, colour) in enumerate(zip(axs, colours)):
        # Use fancy indexing to plot data in each state.
        mask = hidden_states == i
        ax.plot_date(dates[mask], close_v[mask], ".-", c=colour)
        ax.set_title("{0}th hidden state".format(i))

        # Format the ticks.
        ax.xaxis.set_major_locator(YearLocator())
        ax.xaxis.set_minor_locator(MonthLocator())

        ax.grid(True)

    plt.show()

**Total running time of the script:** ( 0 minutes  0.000 seconds)



.. container:: sphx-glr-footer


  .. container:: sphx-glr-download

     :download:`Download Python source code: plot_hmm_stock_analysis.py <plot_hmm_stock_analysis.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: plot_hmm_stock_analysis.ipynb <plot_hmm_stock_analysis.ipynb>`

.. rst-class:: sphx-glr-signature

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