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ENH: Use ddof=0 and nanstd, remove skipped test #36

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ENH: Use ddof=0 and nanstd, remove skipped test #36

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8 changes: 4 additions & 4 deletions empyrical/stats.py
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Original file line number Diff line number Diff line change
Expand Up @@ -405,7 +405,7 @@ def annual_volatility(returns, period=DAILY, alpha=2.0,

ann_factor = annualization_factor(period, annualization)

volatility = nanstd(returns, ddof=1) * (ann_factor ** (1.0 / alpha))
volatility = nanstd(returns) * (ann_factor ** (1.0 / alpha))

return volatility

Expand Down Expand Up @@ -558,10 +558,10 @@ def sharpe_ratio(returns, risk_free=0, period=DAILY, annualization=None):
returns_risk_adj = np.asanyarray(_adjust_returns(returns, risk_free))
returns_risk_adj = returns_risk_adj[~np.isnan(returns_risk_adj)]

if np.std(returns_risk_adj, ddof=1) == 0:
if nanstd(returns_risk_adj) == 0:
return np.nan

return np.mean(returns_risk_adj) / np.std(returns_risk_adj, ddof=1) * \
return np.mean(returns_risk_adj) / nanstd(returns_risk_adj) * \
np.sqrt(ann_factor)


Expand Down Expand Up @@ -695,7 +695,7 @@ def information_ratio(returns, factor_returns):
return np.nan

active_return = _adjust_returns(returns, factor_returns)
tracking_error = nanstd(active_return, ddof=1)
tracking_error = nanstd(active_return)
if np.isnan(tracking_error):
return 0.0
if tracking_error == 0:
Expand Down
40 changes: 10 additions & 30 deletions empyrical/tests/test_stats.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@
import random
from copy import copy
from operator import attrgetter
from unittest import TestCase, skip, SkipTest
from unittest import TestCase, SkipTest

from nose_parameterized import parameterized
import numpy as np
Expand Down Expand Up @@ -210,26 +210,6 @@ def test_max_drawdown(self, returns, expected):
expected,
DECIMAL_PLACES)

# Multiplying returns by a positive constant larger than 1 will increase
# the maximum drawdown by a factor greater than or equal to the constant.
# Similarly, a positive constant smaller than 1 will decrease maximum
# drawdown by at least the constant.
@parameterized.expand([
(noise_uniform, 1.1),
(noise, 2),
(noise_uniform, 10),
(noise_uniform, 0.99),
(noise, 0.5)
])
@skip("Randomly fails")
def test_max_drawdown_transformation(self, returns, constant):
max_dd = self.empyrical.max_drawdown(returns)
transformed_dd = self.empyrical.max_drawdown(constant*returns)
if constant >= 1:
assert constant*max_dd <= transformed_dd
else:
assert constant*max_dd >= transformed_dd

# Maximum drawdown is always less than or equal to zero. Translating
# returns by a positive constant should increase the maximum
# drawdown to a maximum of zero. Translating by a negative constant
Expand Down Expand Up @@ -265,9 +245,9 @@ def test_annual_ret(self, returns, period, expected):

@parameterized.expand([
(flat_line_1_tz, empyrical.DAILY, 0.0),
(mixed_returns, empyrical.DAILY, 0.9136465399704637),
(weekly_returns, empyrical.WEEKLY, 0.38851569394870583),
(monthly_returns, empyrical.MONTHLY, 0.18663690238892558)
(mixed_returns, empyrical.DAILY, 0.8546380812952347),
(weekly_returns, empyrical.WEEKLY, 0.36629610905136956),
(monthly_returns, empyrical.MONTHLY, 0.17596295906514803)
])
def test_annual_volatility(self, returns, period, expected):
assert_almost_equal(
Expand Down Expand Up @@ -333,10 +313,10 @@ def test_omega_returns(self, returns, required_return_less,
(empty_returns, 0.0, np.nan),
(one_return, 0.0, np.nan),
(mixed_returns, mixed_returns, np.nan),
(mixed_returns, 0.0, 1.7238613961706866),
(mixed_returns, simple_benchmark, 0.34111411441060574),
(positive_returns, 0.0, 52.915026221291804),
(negative_returns, 0.0, -24.406808633910085)
(mixed_returns, 0.0, 1.842885350501853),
(mixed_returns, simple_benchmark, 0.36466632740494126),
(positive_returns, 0.0, 56.124860801609117),
(negative_returns, 0.0, -25.887329838240298)
])
def test_sharpe_ratio(self, returns, risk_free, expected):
assert_almost_equal(
Expand Down Expand Up @@ -638,8 +618,8 @@ def test_sortino_translation_diff(self, returns, required_return,
(empty_returns, 0.0, np.nan),
(one_return, 0.0, np.nan),
(pos_line, pos_line, np.nan),
(mixed_returns, 0.0, 0.10859306069076737),
(mixed_returns, flat_line_1, -0.06515583641446039),
(mixed_returns, 0.0, 0.11609086505314305),
(mixed_returns, flat_line_1, -0.0696545190318858),
])
def test_information_ratio(self, returns, factor_returns, expected):
assert_almost_equal(
Expand Down