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TST: Add test for annualized volatility factor

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This commit is contained in:
Ana Ruelas
2016-11-21 14:24:28 -05:00
parent 10f5cc2cbb
commit 435d5acd14
1 changed files with 55 additions and 2 deletions
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tests/pipeline/test_technical.py
+55 -2
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@@ -17,7 +17,8 @@ from zipline.pipeline.factors import (
LinearWeightedMovingAverage,
RateOfChangePercentage,
TrueRange,
MovingAverageConvergenceDivergence
MovingAverageConvergenceDivergence,
AnnualizedVolatility,
)
from zipline.testing import parameter_space
from zipline.testing.fixtures import ZiplineTestCase
@@ -407,7 +408,7 @@ class TestTrueRange(ZiplineTestCase):
assert_equal(out, np.full((3,), 2.))
class MovingAverageConvergenceDivergenceCase(ZiplineTestCase):
class MovingAverageConvergenceDivergenceTestCase(ZiplineTestCase):
def test_MACD_window_length_generation(self):
signal_period = random_integers(1, 90)
fast_period = random_integers(signal_period+1, signal_period+100)
@@ -480,3 +481,55 @@ class MovingAverageConvergenceDivergenceCase(ZiplineTestCase):
expected_hist,
decimal=8
)
class AnnualizedVolatilityTestCase(ZiplineTestCase):
"""
Test Annualized Volatility
"""
def test_simple_volatility(self):
"""
Simple test for uniform returns should generate 0 volatility
"""
nassets = 3
ann_vol = AnnualizedVolatility()
today = pd.Timestamp('2016', tz='utc')
assets = np.arange(nassets, dtype=np.float)
returns = np.full((ann_vol.window_length, nassets),
0.004,
dtype=np.float64)
out = np.empty(shape=(nassets,), dtype=np.float64)
ann_vol.compute(today, assets, out, returns, 252)
expected_vol = np.array([0] * nassets)
np.testing.assert_almost_equal(
out,
expected_vol,
decimal=8
)
def test_volatility(self):
"""
Check volatility results against values calculated manually
"""
nassets = 3
ann_vol = AnnualizedVolatility()
today = pd.Timestamp('2016', tz='utc')
assets = np.arange(nassets, dtype=np.float)
returns = np.random.normal(loc=0.001,
scale=0.01,
size=(ann_vol.window_length, nassets))
out = np.empty(shape=(nassets,), dtype=np.float64)
ann_vol.compute(today, assets, out, returns, 252)
mean = returns.sum(axis=0) / returns.shape[0]
annualized_variance = ((returns - mean) ** 2).sum(axis=0) / \
returns.shape[0] * 252
expected_vol = np.sqrt(annualized_variance)
np.testing.assert_almost_equal(
out,
expected_vol,
decimal=8
)