def test_expanding_consistency(consistency_data, min_periods):
x, is_constant, no_nans = consistency_data
# suppress warnings about empty slices, as we are deliberately testing
# with empty/0-length Series/DataFrames
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=".*(empty slice|0 for slice).*",
category=RuntimeWarning)
# test consistency between different expanding_* moments
moments_consistency_mock_mean(
x=x,
mean=lambda x: x.expanding(min_periods=min_periods).mean(),
mock_mean=lambda x: x.expanding(min_periods=min_periods).sum() / x.
expanding().count(),
)
moments_consistency_is_constant(
x=x,
is_constant=is_constant,
min_periods=min_periods,
count=lambda x: x.expanding().count(),
mean=lambda x: x.expanding(min_periods=min_periods).mean(),
corr=lambda x, y: x.expanding(min_periods=min_periods).corr(y),
)
moments_consistency_var_debiasing_factors(
x=x,
var_unbiased=lambda x: x.expanding(min_periods=min_periods).var(),
var_biased=lambda x: x.expanding(min_periods=min_periods).var(ddof=
0),
var_debiasing_factors=lambda x:
(x.expanding().count() /
(x.expanding().count() - 1.0).replace(0.0, np.nan)),
)
def test_ewm_consistency(consistency_data, min_periods, adjust, ignore_na):
def _weights(s, com, adjust, ignore_na):
if isinstance(s, DataFrame):
if not len(s.columns):
return DataFrame(index=s.index, columns=s.columns)
w = concat(
[
_weights(s.iloc[:, i],
com=com,
adjust=adjust,
ignore_na=ignore_na)
for i, _ in enumerate(s.columns)
],
axis=1,
)
w.index = s.index
w.columns = s.columns
return w
w = Series(np.nan, index=s.index)
alpha = 1.0 / (1.0 + com)
if ignore_na:
w[s.notna()] = _weights(s[s.notna()],
com=com,
adjust=adjust,
ignore_na=False)
elif adjust:
for i in range(len(s)):
if s.iat[i] == s.iat[i]:
w.iat[i] = pow(1.0 / (1.0 - alpha), i)
else:
sum_wts = 0.0
prev_i = -1
for i in range(len(s)):
if s.iat[i] == s.iat[i]:
if prev_i == -1:
w.iat[i] = 1.0
else:
w.iat[i] = alpha * sum_wts / pow(
1.0 - alpha, i - prev_i)
sum_wts += w.iat[i]
prev_i = i
return w
def _variance_debiasing_factors(s, com, adjust, ignore_na):
weights = _weights(s, com=com, adjust=adjust, ignore_na=ignore_na)
cum_sum = weights.cumsum().fillna(method="ffill")
cum_sum_sq = (weights * weights).cumsum().fillna(method="ffill")
numerator = cum_sum * cum_sum
denominator = numerator - cum_sum_sq
denominator[denominator <= 0.0] = np.nan
return numerator / denominator
def _ewma(s, com, min_periods, adjust, ignore_na):
weights = _weights(s, com=com, adjust=adjust, ignore_na=ignore_na)
result = (s.multiply(weights).cumsum().divide(
weights.cumsum()).fillna(method="ffill"))
result[s.expanding().count() < (
max(min_periods, 1) if min_periods else 1)] = np.nan
return result
x, is_constant, no_nans = consistency_data
com = 3.0
moments_consistency_mock_mean(
x=x,
mean=lambda x: x.ewm(com=com,
min_periods=min_periods,
adjust=adjust,
ignore_na=ignore_na).mean(),
mock_mean=lambda x: _ewma(x,
com=com,
min_periods=min_periods,
adjust=adjust,
ignore_na=ignore_na),
)
moments_consistency_is_constant(
x=x,
is_constant=is_constant,
min_periods=min_periods,
count=lambda x: x.expanding().count(),
mean=lambda x: x.ewm(com=com,
min_periods=min_periods,
adjust=adjust,
ignore_na=ignore_na).mean(),
corr=lambda x, y: x.ewm(com=com,
min_periods=min_periods,
adjust=adjust,
ignore_na=ignore_na).corr(y),
)
moments_consistency_var_debiasing_factors(
x=x,
var_unbiased=lambda x: (x.ewm(com=com,
min_periods=min_periods,
adjust=adjust,
ignore_na=ignore_na).var(bias=False)),
var_biased=lambda x: (x.ewm(com=com,
min_periods=min_periods,
adjust=adjust,
ignore_na=ignore_na).var(bias=True)),
var_debiasing_factors=lambda x: (_variance_debiasing_factors(
x, com=com, adjust=adjust, ignore_na=ignore_na)),
)