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TST: Use benchmark returns from spreadsheet.

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The risk unit tests were using the public Yahoo! data instead
of the returns from the answer key spreadsheet, change the RiskPeriod's
created in tests to use the values in the benchmark returns
column of the answer key.

Also, change the spreadsheet's benchmark volatility calculation
to use sample.
The use of population was exposed when the input values were
corrected.
This commit is contained in:
Eddie Hebert
2014-04-09 15:39:05 -04:00
parent 4bf8ab0f8d
commit 618d554da1
2 changed files with 17 additions and 13 deletions
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tests/risk/risk-answer-key-checksums
+1
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@@ -12,3 +12,4 @@ cc507b6fca18aabadac69657181edd4e
37e3ea4a1788f1aa6f3ee0986bc625ae
651e611e723e2a58b1ded91d0cd39b66
d62fce39ec78f032165d8f356bba5c2c
97632f6f64dfc4a2de09882419a79421
tests/risk/test_risk_period.py
+16 -13
View File
@@ -23,6 +23,7 @@ from zipline.utils import factory
from zipline.finance.trading import SimulationParameters
import answer_key
from . answer_key import AnswerKey
ANSWER_KEY = AnswerKey()
@@ -54,9 +55,13 @@ class TestRisk(unittest.TestCase):
self.sim_params
)
self.benchmark_returns_06 = \
answer_key.RETURNS_DATA['Benchmark Returns']
self.metrics_06 = risk.RiskReport(
self.algo_returns_06,
self.sim_params
self.sim_params,
benchmark_returns=self.benchmark_returns_06,
)
start_08 = datetime.datetime(
@@ -98,23 +103,22 @@ class TestRisk(unittest.TestCase):
self.assertEqual(metrics.max_drawdown, 0.505)
def test_benchmark_returns_06(self):
returns = factory.create_returns_from_range(self.sim_params)
metrics = risk.RiskReport(returns, self.sim_params)
np.testing.assert_almost_equal(
[x.benchmark_period_returns
for x in metrics.month_periods],
for x in self.metrics_06.month_periods],
ANSWER_KEY.BENCHMARK_PERIOD_RETURNS['Monthly'])
np.testing.assert_almost_equal(
[x.benchmark_period_returns
for x in metrics.three_month_periods],
for x in self.metrics_06.three_month_periods],
ANSWER_KEY.BENCHMARK_PERIOD_RETURNS['3-Month'])
np.testing.assert_almost_equal(
[x.benchmark_period_returns
for x in metrics.six_month_periods],
for x in self.metrics_06.six_month_periods],
ANSWER_KEY.BENCHMARK_PERIOD_RETURNS['6-month'])
np.testing.assert_almost_equal(
[x.benchmark_period_returns
for x in metrics.year_periods],
for x in self.metrics_06.year_periods],
ANSWER_KEY.BENCHMARK_PERIOD_RETURNS['year'])
def test_trading_days_06(self):
@@ -126,23 +130,22 @@ class TestRisk(unittest.TestCase):
[20, 19, 23, 19, 22, 22, 20, 23, 20, 22, 21, 20])
def test_benchmark_volatility_06(self):
returns = factory.create_returns_from_range(self.sim_params)
metrics = risk.RiskReport(returns, self.sim_params)
np.testing.assert_almost_equal(
[x.benchmark_volatility
for x in metrics.month_periods],
for x in self.metrics_06.month_periods],
ANSWER_KEY.BENCHMARK_PERIOD_VOLATILITY['Monthly'])
np.testing.assert_almost_equal(
[x.benchmark_volatility
for x in metrics.three_month_periods],
for x in self.metrics_06.three_month_periods],
ANSWER_KEY.BENCHMARK_PERIOD_VOLATILITY['3-Month'])
np.testing.assert_almost_equal(
[x.benchmark_volatility
for x in metrics.six_month_periods],
for x in self.metrics_06.six_month_periods],
ANSWER_KEY.BENCHMARK_PERIOD_VOLATILITY['6-month'])
np.testing.assert_almost_equal(
[x.benchmark_volatility
for x in metrics.year_periods],
for x in self.metrics_06.year_periods],
ANSWER_KEY.BENCHMARK_PERIOD_VOLATILITY['year'])
def test_algorithm_returns_06(self):