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BUG: Make covariance match values in answer key.

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The np.cov call needs a ddof of 0 to match the answer key, which uses
Excel's VAR.

When switching np.cov to use a ddof of 0, the benchmark variance is
no longer the 4th quadrant of the cov result, so use np.var directly.
This commit is contained in:
Eddie Hebert
2013-07-23 11:30:48 -04:00
parent 8e1cdac77d
commit 3164aa9016
3 changed files with 33 additions and 41 deletions
Download Patch File Download Diff File Expand all files Collapse all files
tests/risk/answer_key.py
+7
View File
@@ -173,6 +173,13 @@ class AnswerKey(object):
'year': DataIndex('Sim', 'BE', 34, 34),
}
ALGORITHM_PERIOD_COVARIANCE = {
'Monthly': DataIndex('Sim', 'AX', 23, 34),
'3-Month': DataIndex('Sim', 'AY', 25, 34),
'6-month': DataIndex('Sim', 'AZ', 28, 34),
'year': DataIndex('Sim', 'BA', 34, 34),
}
def __init__(self):
self.workbook = xlrd.open_workbook(ANSWER_KEY_PATH)
tests/risk/test_risk.py
+24 -39
View File
@@ -423,51 +423,36 @@ class TestRisk(unittest.TestCase):
# just to avoid distraction - it is much closer than covariance
# and can probably pass with 6 significant digits instead of 7.
#re-enable variance, alpha, and beta tests once this is resolved
def dtest_algorithm_covariance_06(self):
metric = self.metrics_06.month_periods[3]
print repr(metric)
print "----"
self.assertEqual([round(x.algorithm_covariance, 7)
def test_algorithm_covariance_06(self):
answer_key_month_periods = ANSWER_KEY.get_values(
AnswerKey.ALGORITHM_PERIOD_COVARIANCE['Monthly'],
decimal=7)
self.assertEqual([np.round(x.algorithm_covariance, 7)
for x in self.metrics_06.month_periods],
[0.0000289,
0.0000222,
-0.0000554,
-0.0000192,
0.0000954,
-0.0000333,
-0.0001111,
0.0000322,
-0.0000349,
-0.0000143,
0.0000108,
-0.0000386])
answer_key_month_periods)
self.assertEqual([round(x.algorithm_covariance, 7)
answer_key_three_month_periods = ANSWER_KEY.get_values(
AnswerKey.ALGORITHM_PERIOD_COVARIANCE['3-Month'],
decimal=7)
self.assertEqual([np.round(x.algorithm_covariance, 7)
for x in self.metrics_06.three_month_periods],
[-0.0000026,
-0.0000189,
0.0000049,
0.0000121,
-0.0000158,
-0.000031,
-0.0000336,
-0.0000036,
-0.0000119,
-0.0000122])
answer_key_three_month_periods)
self.assertEqual([round(x.algorithm_covariance, 7)
for x in self.metrics_06.six_month_periods],
[0.000005,
-0.0000172,
-0.0000142,
-0.0000102,
-0.0000089,
-0.0000207,
-0.0000229])
answer_key_six_month_periods = ANSWER_KEY.get_values(
AnswerKey.ALGORITHM_PERIOD_COVARIANCE['6-month'],
decimal=7)
results_six_month_periods = [
np.round(x.algorithm_covariance, 7)
for x in self.metrics_06.six_month_periods]
self.assertEqual(results_six_month_periods,
answer_key_six_month_periods)
self.assertEqual([round(x.algorithm_covariance, 7)
answer_key_year_periods = ANSWER_KEY.get_values(
AnswerKey.ALGORITHM_PERIOD_COVARIANCE['year'],
decimal=7)
self.assertEqual([np.round(x.algorithm_covariance, 7)
for x in self.metrics_06.year_periods],
[-8.75273E-06])
answer_key_year_periods)
def test_benchmark_variance_06(self):
answer_key_month_periods = ANSWER_KEY.get_values(
zipline/finance/risk.py
+2 -2
View File
@@ -482,11 +482,11 @@ class RiskMetricsBase(object):
returns_matrix = np.vstack([self.algorithm_returns,
self.benchmark_returns])
C = np.cov(returns_matrix)
C = np.cov(returns_matrix, ddof=0)
eigen_values = la.eigvals(C)
condition_number = max(eigen_values) / min(eigen_values)
algorithm_covariance = C[0][1]
benchmark_variance = C[1][1]
benchmark_variance = np.var(self.benchmark_returns, ddof=1)
beta = C[0][1] / C[1][1]
return (