def test_to_excess_returns():
rf = 0.05
r = df.to_returns()
np.allclose(r.to_excess_returns(0), r)
np.allclose(r.to_excess_returns(rf, nperiods=252),
r.to_excess_returns(ffn.deannualize(rf, 252)))
np.allclose(r.to_excess_returns(rf), r - rf)
def test_to_excess_returns():
rf = 0.05
r = df.to_returns()
np.allclose(r.to_excess_returns(0), r)
np.allclose(r.to_excess_returns(rf, nperiods=252),
r.to_excess_returns(ffn.deannualize(rf, 252)))
np.allclose(r.to_excess_returns(rf), r - rf)
def test_calc_sharpe():
x = pd.Series()
assert np.isnan(x.calc_sharpe())
r = df.to_returns()
res = r.calc_sharpe()
assert np.allclose(res, r.mean() / r.std())
res = r.calc_sharpe(rf=0.05, nperiods=252)
drf = ffn.deannualize(0.05, 252)
ar = r - drf
assert np.allclose(res, ar.mean() / ar.std() * np.sqrt(252))
def test_calc_sharpe():
x = pd.Series()
assert np.isnan(x.calc_sharpe())
r = df.to_returns()
res = r.calc_sharpe()
assert np.allclose(res, r.mean() / r.std())
res = r.calc_sharpe(rf=0.05, nperiods=252)
drf = ffn.deannualize(0.05, 252)
ar = r - drf
assert np.allclose(res, ar.mean() / ar.std() * np.sqrt(252))
def test_deannualize():
res = ffn.deannualize(0.05, 252)
assert np.allclose(res, np.power(1.05, 1 / 252.) - 1)
def test_deannualize():
res = ffn.deannualize(0.05, 252)
assert np.allclose(res, np.power(1.05, 1 / 252.) - 1)
