From 29a80c2f9868a53e797110f64e21c5d46ef73b71 Mon Sep 17 00:00:00 2001
From: Eddie Hebert <ehebert@quantopian.com>
Date: Thu, 19 Sep 2013 15:52:53 -0400
Subject: [PATCH] MAINT: Store sharpe values in a DataFrame instead of list.

Eventually, all cumulative metrics, (alpha, beta, etc.) will be
stored in the same DataFrame

For easier tracking of dt to values during debugging, but should be
some performance gains as well.
---
 tests/test_events_through_risk.py  |  2 +-
 zipline/finance/risk/cumulative.py | 15 ++++++++++++---
 2 files changed, 13 insertions(+), 4 deletions(-)

diff --git a/tests/test_events_through_risk.py b/tests/test_events_through_risk.py
index 8c463fa6..7a8b42b0 100644
--- a/tests/test_events_through_risk.py
+++ b/tests/test_events_through_risk.py
@@ -160,7 +160,7 @@ class TestEventsThroughRisk(unittest.TestCase):
 
             np.testing.assert_almost_equal(
                 expected_sharpe[current_dt],
-                crm.sharpe[-1],
+                crm.metrics.sharpe[current_dt],
                 decimal=6)
 
     def test_minute_buy_and_hold(self):
diff --git a/zipline/finance/risk/cumulative.py b/zipline/finance/risk/cumulative.py
index 9271e21c..3d9eb710 100644
--- a/zipline/finance/risk/cumulative.py
+++ b/zipline/finance/risk/cumulative.py
@@ -92,7 +92,12 @@ class RiskMetricsCumulative(object):
         self.algorithm_period_returns = []
         self.benchmark_period_returns = []
 
-        self.sharpe = []
+        self.latest_dt = cont_index[0]
+
+        metric_names = ('sharpe',)
+
+        self.metrics = pd.DataFrame(index=cont_index, columns=metric_names)
+
         self.sortino = []
         self.information = []
         self.beta = []
@@ -168,11 +173,15 @@ algorithm_returns ({algo_count}) in range {start} : {end} on {dt}"
             self.algorithm_period_returns[-1] - self.treasury_period_return)
         self.beta.append(self.calculate_beta())
         self.alpha.append(self.calculate_alpha())
-        self.sharpe.append(self.calculate_sharpe())
+        self.metrics.sharpe[dt] = self.calculate_sharpe()
         self.sortino.append(self.calculate_sortino())
         self.information.append(self.calculate_information())
         self.max_drawdown = self.calculate_max_drawdown()
 
+        # Keep track of latest dt for use in to_dict and other methods
+        # that report current state.
+        self.latest_dt = dt
+
     def to_dict(self):
         """
         Creates a dictionary representing the state of the risk report.
@@ -193,7 +202,7 @@ algorithm_returns ({algo_count}) in range {start} : {end} on {dt}"
             'period_label': period_label
         }
 
-        rval['sharpe'] = self.sharpe[-1]
+        rval['sharpe'] = self.metrics.sharpe[self.latest_dt]
         rval['sortino'] = self.sortino[-1]
         rval['information'] = self.information[-1]