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.float64)
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.zeros(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.float64)
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 = np.mean(returns, axis=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)
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.float64)
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.zeros(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.float64)
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 = np.mean(returns, axis=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
)
