ENH: Adds quantopian-quandl bundle as new default.

This data bundle will use the quantopian mirror of the quandl WIKI data
instead of downloading from quandl directly. This dramatically improves
the speed because we do not pay the rate limiting for quandl and we can
send the data in the format zipline expects.

docs/source/beginner-tutorial.rst

@@ -127,7 +127,7 @@ on OSX):
      --capital-base FLOAT            The starting capital for the simulation.
                                      [default: 10000000.0]
      -b, --bundle BUNDLE-NAME        The data bundle to use for the simulation.
-                                     [default: quandl]
+                                     [default: quantopian-quandl]
      --bundle-timestamp TIMESTAMP    The date to lookup data on or before.
                                      [default: <current-time>]
      -s, --start DATE                The start date of the simulation.
@@ -140,8 +140,8 @@ on OSX):
 
 As you can see there are a couple of flags that specify where to find your
 algorithm (``-f``) as well as parameters specifying which data to use,
-defaulting to the :ref:`quandl-data-bundle`. There are also arguments for the
-date range to run the algorithm over (``--start`` and ``--end``). Finally,
+defaulting to the :ref:`quantopian-quandl-mirror`. There are also arguments for
+the date range to run the algorithm over (``--start`` and ``--end``). Finally,
 you'll want to save the performance metrics of your algorithm so that you can
 analyze how it performed. This is done via the ``--output`` flag and will cause
 it to write the performance ``DataFrame`` in the pickle Python file format.