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

@@ -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