ENH: Annualize information ratio.

Use annualized values for information, so that it is calculated
using the same units as sharpe, etc.

tests/risk/answer_key.py

@@ -238,6 +238,9 @@ class AnswerKey(object):
         'CUMULATIVE_SORTINO': DataIndex(
             'Sim Cumulative', 'V', 4, 254),
 
+        'CUMULATIVE_INFORMATION': DataIndex(
+            'Sim Cumulative', 'Y', 4, 254),
+
     }
 
     def __init__(self):
@@ -300,4 +303,6 @@ RISK_CUMULATIVE = pd.DataFrame({
     'downside_risk': pd.Series(dict(zip(
         DATES, ANSWER_KEY.CUMULATIVE_DOWNSIDE_RISK))),
     'sortino': pd.Series(dict(zip(
-        DATES, ANSWER_KEY.CUMULATIVE_SORTINO)))})
+        DATES, ANSWER_KEY.CUMULATIVE_SORTINO))),
+    'information': pd.Series(dict(zip(
+        DATES, ANSWER_KEY.CUMULATIVE_INFORMATION)))})

tests/risk/test_risk_cumulative.py

@@ -86,3 +86,11 @@ class TestRisk(unittest.TestCase):
                 value,
                 decimal=2,
                 err_msg="Mismatch at %s" % (dt,))
+
+    def test_information_06(self):
+        for dt, value in answer_key.RISK_CUMULATIVE.information.iterkv():
+            np.testing.assert_almost_equal(
+                self.cumulative_metrics_06.metrics.information[dt],
+                value,
+                decimal=2,
+                err_msg="Mismatch at %s" % (dt,))