Merge pull request #276 from quantopian/tail_ratio

Tail ratio and common sense ratio

pyfolio/tests/test_timeseries.py

@@ -338,6 +338,18 @@ def test_sortino(self, returns, expected):
             timeseries.sortino_ratio(returns),
             expected, DECIMAL_PLACES)
 
+    def test_tail_ratio(self):
+        returns = np.random.randn(10000)
+        self.assertAlmostEqual(
+            timeseries.tail_ratio(returns),
+            1., 1)
+
+    def test_common_sense_ratio(self):
+        returns = pd.Series(np.random.randn(1000) + .1)
+        self.assertAlmostEqual(
+            timeseries.common_sense_ratio(returns),
+            0.024031933021535612, DECIMAL_PLACES)
+
 
 class TestMultifactor(TestCase):
     simple_rets = pd.Series(

pyfolio/timeseries.py

@@ -497,6 +497,52 @@ def stability_of_timeseries(returns):
     return rhat
 
 
+def tail_ratio(returns):
+    """Determines the ratio between the right (95%) and left tail (5%).
+
+    For example, a ratio of 0.25 means that losses are four times
+    as bad as profits.
+
+    Parameters
+    ----------
+    returns : pd.Series
+        Daily returns of the strategy, noncumulative.
+         - See full explanation in tears.create_full_tear_sheet.
+
+    Returns
+    -------
+    float
+        tail ratio
+
+    """
+
+    return np.abs(np.percentile(returns, 95)) / \
+        np.abs(np.percentile(returns, 5))
+
+
+def common_sense_ratio(returns):
+    """Common sense ratio is the multiplication of the tail ratio and the
+    Gain-to-Pain-Ratio -- sum(profits) / sum(losses).
+
+    See http://bit.ly/1ORzGBk for more information on motivation of
+    this metric.
+
+
+    Parameters
+    ----------
+    returns : pd.Series
+        Daily returns of the strategy, noncumulative.
+         - See full explanation in tears.create_full_tear_sheet.
+
+    Returns
+    -------
+    float
+        common sense ratio
+
+    """
+    return tail_ratio(returns) * (1 + annual_return(returns))
+
+
 SIMPLE_STAT_FUNCS = [
     annual_return,
     annual_volatility,
@@ -508,6 +554,8 @@ def stability_of_timeseries(returns):
     sortino_ratio,
     stats.skew,
     stats.kurtosis,
+    tail_ratio,
+    common_sense_ratio,
 ]
 
 FACTOR_STAT_FUNCS = [