.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/applications/plot_stock_market.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. or to run this example in your browser via JupyterLite or Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_applications_plot_stock_market.py: ======================================= Visualizing the stock market structure ======================================= This example employs several unsupervised learning techniques to extract the stock market structure from variations in historical quotes. The quantity that we use is the daily variation in quote price: quotes that are linked tend to fluctuate in relation to each other during a day. .. GENERATED FROM PYTHON SOURCE LINES 12-16 .. code-block:: Python # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause .. GENERATED FROM PYTHON SOURCE LINES 17-25 Retrieve the data from Internet ------------------------------- The data is from 2003 - 2008. This is reasonably calm: (not too long ago so that we get high-tech firms, and before the 2008 crash). This kind of historical data can be obtained from APIs like the `data.nasdaq.com `_ and `alphavantage.co `_. .. GENERATED FROM PYTHON SOURCE LINES 25-109 .. code-block:: Python import sys import numpy as np import pandas as pd symbol_dict = { "TOT": "Total", "XOM": "Exxon", "CVX": "Chevron", "COP": "ConocoPhillips", "VLO": "Valero Energy", "MSFT": "Microsoft", "IBM": "IBM", "TWX": "Time Warner", "CMCSA": "Comcast", "CVC": "Cablevision", "YHOO": "Yahoo", "DELL": "Dell", "HPQ": "HP", "AMZN": "Amazon", "TM": "Toyota", "CAJ": "Canon", "SNE": "Sony", "F": "Ford", "HMC": "Honda", "NAV": "Navistar", "NOC": "Northrop Grumman", "BA": "Boeing", "KO": "Coca Cola", "MMM": "3M", "MCD": "McDonald's", "PEP": "Pepsi", "K": "Kellogg", "UN": "Unilever", "MAR": "Marriott", "PG": "Procter Gamble", "CL": "Colgate-Palmolive", "GE": "General Electrics", "WFC": "Wells Fargo", "JPM": "JPMorgan Chase", "AIG": "AIG", "AXP": "American express", "BAC": "Bank of America", "GS": "Goldman Sachs", "AAPL": "Apple", "SAP": "SAP", "CSCO": "Cisco", "TXN": "Texas Instruments", "XRX": "Xerox", "WMT": "Wal-Mart", "HD": "Home Depot", "GSK": "GlaxoSmithKline", "PFE": "Pfizer", "SNY": "Sanofi-Aventis", "NVS": "Novartis", "KMB": "Kimberly-Clark", "R": "Ryder", "GD": "General Dynamics", "RTN": "Raytheon", "CVS": "CVS", "CAT": "Caterpillar", "DD": "DuPont de Nemours", } symbols, names = np.array(sorted(symbol_dict.items())).T quotes = [] for symbol in symbols: print("Fetching quote history for %r" % symbol, file=sys.stderr) url = ( "https://raw.githubusercontent.com/scikit-learn/examples-data/" "master/financial-data/{}.csv" ) quotes.append(pd.read_csv(url.format(symbol))) close_prices = np.vstack([q["close"] for q in quotes]) open_prices = np.vstack([q["open"] for q in quotes]) # The daily variations of the quotes are what carry the most information variation = close_prices - open_prices .. rst-class:: sphx-glr-script-out .. code-block:: none Fetching quote history for np.str_('AAPL') Fetching quote history for np.str_('AIG') Fetching quote history for np.str_('AMZN') Fetching quote history for np.str_('AXP') Fetching quote history for np.str_('BA') Fetching quote history for np.str_('BAC') Fetching quote history for np.str_('CAJ') Fetching quote history for np.str_('CAT') Fetching quote history for np.str_('CL') Fetching quote history for np.str_('CMCSA') Fetching quote history for np.str_('COP') Fetching quote history for np.str_('CSCO') Fetching quote history for np.str_('CVC') Fetching quote history for np.str_('CVS') Fetching quote history for np.str_('CVX') Fetching quote history for np.str_('DD') Fetching quote history for np.str_('DELL') Fetching quote history for np.str_('F') Fetching quote history for np.str_('GD') Fetching quote history for np.str_('GE') Fetching quote history for np.str_('GS') Fetching quote history for np.str_('GSK') Fetching quote history for np.str_('HD') Fetching quote history for np.str_('HMC') Fetching quote history for np.str_('HPQ') Fetching quote history for np.str_('IBM') Fetching quote history for np.str_('JPM') Fetching quote history for np.str_('K') Fetching quote history for np.str_('KMB') Fetching quote history for np.str_('KO') Fetching quote history for np.str_('MAR') Fetching quote history for np.str_('MCD') Fetching quote history for np.str_('MMM') Fetching quote history for np.str_('MSFT') Fetching quote history for np.str_('NAV') Fetching quote history for np.str_('NOC') Fetching quote history for np.str_('NVS') Fetching quote history for np.str_('PEP') Fetching quote history for np.str_('PFE') Fetching quote history for np.str_('PG') Fetching quote history for np.str_('R') Fetching quote history for np.str_('RTN') Fetching quote history for np.str_('SAP') Fetching quote history for np.str_('SNE') Fetching quote history for np.str_('SNY') Fetching quote history for np.str_('TM') Fetching quote history for np.str_('TOT') Fetching quote history for np.str_('TWX') Fetching quote history for np.str_('TXN') Fetching quote history for np.str_('UN') Fetching quote history for np.str_('VLO') Fetching quote history for np.str_('WFC') Fetching quote history for np.str_('WMT') Fetching quote history for np.str_('XOM') Fetching quote history for np.str_('XRX') Fetching quote history for np.str_('YHOO') .. GENERATED FROM PYTHON SOURCE LINES 110-120 .. _stock_market: Learning a graph structure -------------------------- We use sparse inverse covariance estimation to find which quotes are correlated conditionally on the others. Specifically, sparse inverse covariance gives us a graph, that is a list of connections. For each symbol, the symbols that it is connected to are those useful to explain its fluctuations. .. GENERATED FROM PYTHON SOURCE LINES 120-132 .. code-block:: Python from sklearn import covariance alphas = np.logspace(-1.5, 1, num=10) edge_model = covariance.GraphicalLassoCV(alphas=alphas) # standardize the time series: using correlations rather than covariance # former is more efficient for structure recovery X = variation.copy().T X /= X.std(axis=0) edge_model.fit(X) .. raw:: html 
GraphicalLassoCV(alphas=array([ 0.03162278,  0.05994843,  0.11364637,  0.21544347,  0.40842387,
            0.77426368,  1.46779927,  2.7825594 ,  5.27499706, 10.        ]))
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.


.. GENERATED FROM PYTHON SOURCE LINES 133-147 Clustering using affinity propagation ------------------------------------- We use clustering to group together quotes that behave similarly. Here, amongst the :ref:`various clustering techniques ` available in the scikit-learn, we use :ref:`affinity_propagation` as it does not enforce equal-size clusters, and it can choose automatically the number of clusters from the data. Note that this gives us a different indication than the graph, as the graph reflects conditional relations between variables, while the clustering reflects marginal properties: variables clustered together can be considered as having a similar impact at the level of the full stock market. .. GENERATED FROM PYTHON SOURCE LINES 147-156 .. code-block:: Python from sklearn import cluster _, labels = cluster.affinity_propagation(edge_model.covariance_, random_state=0) n_labels = labels.max() for i in range(n_labels + 1): print(f"Cluster {i + 1}: {', '.join(names[labels == i])}") .. rst-class:: sphx-glr-script-out .. code-block:: none Cluster 1: Apple, Amazon, Yahoo Cluster 2: Comcast, Cablevision, Time Warner Cluster 3: ConocoPhillips, Chevron, Total, Valero Energy, Exxon Cluster 4: Cisco, Dell, HP, IBM, Microsoft, SAP, Texas Instruments Cluster 5: Boeing, General Dynamics, Northrop Grumman, Raytheon Cluster 6: AIG, American express, Bank of America, Caterpillar, CVS, DuPont de Nemours, Ford, General Electrics, Goldman Sachs, Home Depot, JPMorgan Chase, Marriott, McDonald's, 3M, Ryder, Wells Fargo, Wal-Mart Cluster 7: GlaxoSmithKline, Novartis, Pfizer, Sanofi-Aventis, Unilever Cluster 8: Kellogg, Coca Cola, Pepsi Cluster 9: Colgate-Palmolive, Kimberly-Clark, Procter Gamble Cluster 10: Canon, Honda, Navistar, Sony, Toyota, Xerox .. GENERATED FROM PYTHON SOURCE LINES 157-166 Embedding in 2D space --------------------- For visualization purposes, we need to lay out the different symbols on a 2D canvas. For this we use :ref:`manifold` techniques to retrieve 2D embedding. We use a dense eigen_solver to achieve reproducibility (arpack is initiated with the random vectors that we don't control). In addition, we use a large number of neighbors to capture the large-scale structure. .. GENERATED FROM PYTHON SOURCE LINES 166-178 .. code-block:: Python # Finding a low-dimension embedding for visualization: find the best position of # the nodes (the stocks) on a 2D plane from sklearn import manifold node_position_model = manifold.LocallyLinearEmbedding( n_components=2, eigen_solver="dense", n_neighbors=6 ) embedding = node_position_model.fit_transform(X.T).T .. GENERATED FROM PYTHON SOURCE LINES 179-194 Visualization ------------- The output of the 3 models are combined in a 2D graph where nodes represents the stocks and edges the: - cluster labels are used to define the color of the nodes - the sparse covariance model is used to display the strength of the edges - the 2D embedding is used to position the nodes in the plan This example has a fair amount of visualization-related code, as visualization is crucial here to display the graph. One of the challenge is to position the labels minimizing overlap. For this we use an heuristic based on the direction of the nearest neighbor along each axis. .. GENERATED FROM PYTHON SOURCE LINES 194-275 .. code-block:: Python import matplotlib.pyplot as plt from matplotlib.collections import LineCollection plt.figure(1, facecolor="w", figsize=(10, 8)) plt.clf() ax = plt.axes([0.0, 0.0, 1.0, 1.0]) plt.axis("off") # Plot the graph of partial correlations partial_correlations = edge_model.precision_.copy() d = 1 / np.sqrt(np.diag(partial_correlations)) partial_correlations *= d partial_correlations *= d[:, np.newaxis] non_zero = np.abs(np.triu(partial_correlations, k=1)) > 0.02 # Plot the nodes using the coordinates of our embedding plt.scatter( embedding[0], embedding[1], s=100 * d**2, c=labels, cmap=plt.cm.nipy_spectral ) # Plot the edges start_idx, end_idx = np.where(non_zero) # a sequence of (*line0*, *line1*, *line2*), where:: # linen = (x0, y0), (x1, y1), ... (xm, ym) segments = [ [embedding[:, start], embedding[:, stop]] for start, stop in zip(start_idx, end_idx) ] values = np.abs(partial_correlations[non_zero]) lc = LineCollection( segments, zorder=0, cmap=plt.cm.hot_r, norm=plt.Normalize(0, 0.7 * values.max()) ) lc.set_array(values) lc.set_linewidths(15 * values) ax.add_collection(lc) # Add a label to each node. The challenge here is that we want to # position the labels to avoid overlap with other labels for index, (name, label, (x, y)) in enumerate(zip(names, labels, embedding.T)): dx = x - embedding[0] dx[index] = 1 dy = y - embedding[1] dy[index] = 1 this_dx = dx[np.argmin(np.abs(dy))] this_dy = dy[np.argmin(np.abs(dx))] if this_dx > 0: horizontalalignment = "left" x = x + 0.002 else: horizontalalignment = "right" x = x - 0.002 if this_dy > 0: verticalalignment = "bottom" y = y + 0.002 else: verticalalignment = "top" y = y - 0.002 plt.text( x, y, name, size=10, horizontalalignment=horizontalalignment, verticalalignment=verticalalignment, bbox=dict( facecolor="w", edgecolor=plt.cm.nipy_spectral(label / float(n_labels)), alpha=0.6, ), ) plt.xlim( embedding[0].min() - 0.15 * np.ptp(embedding[0]), embedding[0].max() + 0.10 * np.ptp(embedding[0]), ) plt.ylim( embedding[1].min() - 0.03 * np.ptp(embedding[1]), embedding[1].max() + 0.03 * np.ptp(embedding[1]), ) plt.show() .. image-sg:: /auto_examples/applications/images/sphx_glr_plot_stock_market_001.png :alt: plot stock market :srcset: /auto_examples/applications/images/sphx_glr_plot_stock_market_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 7.933 seconds) .. _sphx_glr_download_auto_examples_applications_plot_stock_market.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/1.6.X?urlpath=lab/tree/notebooks/auto_examples/applications/plot_stock_market.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/index.html?path=auto_examples/applications/plot_stock_market.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_stock_market.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_stock_market.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_stock_market.zip ` .. include:: plot_stock_market.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_