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#
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# The data is from 2003 - 2008. This is reasonably calm: (not too long ago so
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# that we get high-tech firms, and before the 2008 crash). This kind of
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- # historical data can be obtained from APIs like the quandl.com and
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- # alphavantage.co .
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+ # historical data can be obtained from APIs like the
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+ # `data.nasdaq.com <https://data.nasdaq.com/>`_ and
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+ # `alphavantage.co <https://www.alphavantage.co/>`_.
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import sys
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import numpy as np
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#
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# We use sparse inverse covariance estimation to find which quotes are
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# correlated conditionally on the others. Specifically, sparse inverse
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- # covariance gives us a graph, that is a list of connection . For each
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- # symbol, the symbols that it is connected too are those useful to explain
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+ # covariance gives us a graph, that is a list of connections . For each
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+ # symbol, the symbols that it is connected to are those useful to explain
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# its fluctuations.
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from sklearn import covariance
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