From 84d20fd551bf0193bb5518e8dc1ba368d0a878b6 Mon Sep 17 00:00:00 2001
From: Eddie Hebert <ehebert@quantopian.com>
Date: Wed, 18 Sep 2013 15:13:26 -0400
Subject: [PATCH] MAINT: Remove unused values during beta calculation.

The eigen_values, condition_number, algorithm_covariance, and
benchmark variance, which were easy to calculate alongside beta,
since they share the same inputs, but were not passed along to performance.

Remove to trim down the number of risk report members as well as
number of calcluations done.

Can add back in if there is an expressed need for eigen_values etc.,
perhaps in an 'opt-in' type configuration.
---
 zipline/finance/risk/cumulative.py | 24 +++---------------------
 1 file changed, 3 insertions(+), 21 deletions(-)

diff --git a/zipline/finance/risk/cumulative.py b/zipline/finance/risk/cumulative.py
index d38d6ad8..ea06abc3 100644
--- a/zipline/finance/risk/cumulative.py
+++ b/zipline/finance/risk/cumulative.py
@@ -17,7 +17,6 @@
 import logbook
 import math
 import numpy as np
-import numpy.linalg as la
 
 import zipline.finance.trading as trading
 
@@ -79,11 +78,6 @@ class RiskMetricsCumulative(object):
         self.algorithm_period_returns = []
         self.benchmark_period_returns = []
 
-        self.algorithm_covariance = None
-        self.benchmark_variance = None
-        self.condition_number = None
-        self.eigen_values = None
-
         self.sharpe = []
         self.sortino = []
         self.information = []
@@ -163,7 +157,7 @@ algorithm_returns ({algo_count}) in range {start} : {end} on {dt}"
             self.daily_treasury[treasury_end]
         self.excess_returns.append(
             self.algorithm_period_returns[-1] - self.treasury_period_return)
-        self.beta.append(self.calculate_beta()[0])
+        self.beta.append(self.calculate_beta())
         self.alpha.append(self.calculate_alpha())
         self.sharpe.append(self.calculate_sharpe())
         self.sortino.append(self.calculate_sortino())
@@ -210,15 +204,11 @@ algorithm_returns ({algo_count}) in range {start} : {end} on {dt}"
             "sharpe",
             "sortino",
             "information",
-            "algorithm_covariance",
-            "benchmark_variance",
             "beta",
             "alpha",
             "max_drawdown",
             "algorithm_returns",
             "benchmark_returns",
-            "condition_number",
-            "eigen_values"
         ]
 
         for metric in metrics:
@@ -325,21 +315,13 @@ algorithm_returns ({algo_count}) in range {start} : {end} on {dt}"
         # it doesn't make much sense to calculate beta for less than two days,
         # so return none.
         if len(self.algorithm_returns) < 2:
-            return 0.0, 0.0, 0.0, 0.0, []
+            return 0.0
 
         returns_matrix = np.vstack([self.algorithm_returns,
                                     self.benchmark_returns])
         C = np.cov(returns_matrix, ddof=1)
-        eigen_values = la.eigvals(C)
-        condition_number = max(eigen_values) / min(eigen_values)
         algorithm_covariance = C[0][1]
         benchmark_variance = C[1][1]
         beta = algorithm_covariance / benchmark_variance
 
-        return (
-            beta,
-            algorithm_covariance,
-            benchmark_variance,
-            condition_number,
-            eigen_values
-        )
+        return beta