pandas.ewmstd¶

pandas.ewmstd(arg, com=None, span=None, halflife=None, min_periods=0, bias=False, ignore_na=False, adjust=True)

Exponentially-weighted moving std

Parameters: arg : Series, DataFrame com : float. optional Center of mass: , span : float, optional Specify decay in terms of span, halflife : float, optional Specify decay in terms of halflife, min_periods : int, default 0 Minimum number of observations in window required to have a value (otherwise result is NA). freq : None or string alias / date offset object, default=None Frequency to conform to before computing statistic adjust : boolean, default True Divide by decaying adjustment factor in beginning periods to account for imbalance in relative weightings (viewing EWMA as a moving average) how : string, default ‘mean’ Method for down- or re-sampling ignore_na : boolean, default False Ignore missing values when calculating weights; specify True to reproduce pre-0.15.0 behavior bias : boolean, default False Use a standard estimation bias correction y : type of input argument

Notes

Either center of mass, span or halflife must be specified

EWMA is sometimes specified using a “span” parameter s, we have that the decay parameter is related to the span as

where c is the center of mass. Given a span, the associated center of mass is

So a “20-day EWMA” would have center 9.5.

When adjust is True (default), weighted averages are calculated using weights
(1-alpha)**(n-1), (1-alpha)**(n-2), ..., 1-alpha, 1.
When adjust is False, weighted averages are calculated recursively as:
weighted_average[0] = arg[0]; weighted_average[i] = (1-alpha)*weighted_average[i-1] + alpha*arg[i].

When ignore_na is False (default), weights are based on absolute positions. For example, the weights of x and y used in calculating the final weighted average of [x, None, y] are (1-alpha)**2 and 1 (if adjust is True), and (1-alpha)**2 and alpha (if adjust is False).

When ignore_na is True (reproducing pre-0.15.0 behavior), weights are based on relative positions. For example, the weights of x and y used in calculating the final weighted average of [x, None, y] are 1-alpha and 1 (if adjust is True), and 1-alpha and alpha (if adjust is False).

More details can be found at http://pandas.pydata.org/pandas-docs/stable/computation.html#exponentially-weighted-moment-functions