Stock market names and symbols

Visualizing the stock market structure — scikit-learn documentation

This documentation is for scikit-learn version 0. If you use the software, please consider citing scikit-learn. 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: 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 connection.

For each symbol, the symbols that it is connected too are those useful to explain its fluctuations. We use clustering to group together quotes that behave similarly. Here, amongst the various clustering techniques available in the scikit-learn, we use Affinity Propagation as it does not enforce equal-size clusters, and it can choose automatically the number of clusters from the data.

CSV Stock Price - Carriage Services Inc. Stock Quote (U.S.: NYSE) - MarketWatch

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: For visualization purposes, we need to lay out the different symbols on indian famous stock broker 2D canvas.

For this we use Manifold learning techniques to retrieve 2D embedding. The output of the 3 models are combined in a 2D graph where nodes represents the stocks and edges the:.

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.

Find a low-dimension embedding for visualization: Home Installation Documentation Scikit-learn 0. Up General examples General examples.

BSD 3 clause from datetime import datetime import numpy as np from matplotlib import pyplot as plt from matplotlib. GraphLassoCV standardize the time series: American express Cluster 2: PepsiCoca ColaKellogg Cluster 4: AppleAmazonYahoo Cluster 6: GlaxoSmithKlineNovartisSanofi - Aventis Cluster 7: Stock market names and symbolsChevronTotalValero EnergyExxon Cluster 8: Time Warner Cluster 9: SonyCaterpillarCanonToyotaHondaXeroxUnilever Cluster Kimberly - ClarkColgate - PalmoliveProcter Gamble Cluster RyderGoldman SachsWal - MartGeneral ElectricsPfizerMarriott3 MComcastWells FargoDuPont de NemoursCVSBank of AmericaAIGHome DepotFordJPMorgan ChaseMcDonald 's Cluster MicrosoftSAPIBMTexas InstrumentsHPDellCisco Cluster RaytheonGeneral DynamicsNorthrop Grumman.

In addition, we use a large number of neighbors to capture the large-scale structure. The challenge here is that we want to position the labels to avoid overlap with other labels for indexnamelabelxy in enumerate zip nameslabelsembedding.

Download Python source code: Show this page source.