diff --git a/tests/test_transforms.py b/tests/test_transforms.py
index f0d2d420..100da652 100644
--- a/tests/test_transforms.py
+++ b/tests/test_transforms.py
@@ -293,7 +293,6 @@ class TestTALIB(TestCase):
         self.source, self.panel = \
             factory.create_test_panel_ohlc_source(sim_params)
 
-    @unittest.skip
     def test_talib_with_default_params(self):
         BLACKLIST = ['make_transform', 'BatchTransform',
                      # TODO: Figure out why MAVP generates a KeyError
@@ -345,9 +344,6 @@ class TestTALIB(TestCase):
             # self.source, self.panel = \
                 # factory.create_test_panel_ohlc_source(self.sim_params)
 
-    # TODO: Remove this skip, after debugging why sometimes the talib_results
-    #       contain to many leading nans.
-    @unittest.skip
     def test_multiple_talib_with_args(self):
         zipline_transforms = [ta.MA(0, window_length=10),
                               ta.MA(0, window_length=25)]
@@ -355,10 +351,12 @@ class TestTALIB(TestCase):
         algo = TALIBAlgorithm(talib=zipline_transforms)
         algo.run(self.source)
         # Test if computed values match those computed by pandas rolling mean.
-        np.testing.assert_array_equal(np.array(algo.talib_results.values()[0]),
+        talib_values = np.array(algo.talib_results[zipline_transforms[0]])
+        np.testing.assert_array_equal(talib_values,
                                       pd.rolling_mean(self.panel[0]['price'],
                                                       10).values)
-        np.testing.assert_array_equal(np.array(algo.talib_results.values()[1]),
+        talib_values = np.array(algo.talib_results[zipline_transforms[1]])
+        np.testing.assert_array_equal(talib_values,
                                       pd.rolling_mean(self.panel[0]['price'],
                                                       25).values)
         for t in zipline_transforms: