Statistics

Methods which provide common statistics.

signal.z_score(*, num_periods, min_periods=None, delay_periods=1)

Given a stationary time series (signal), calculate a rolling window z-score. The signal is assumed to be stationary and normally distributed.

Parameters:

  • num_periods – The number of time-periods of the signal to include in the estimate. That is, for a daily signal like close_price.relative_change(days=1), this argument is the number of days.

  • min_periods – The minimum number of actual data-points before estimate is produced. If min_periods is not specified, then it is set equal to num_periods.

  • delay_periods – The number of periods before the estimated model is applied to the current data point.

Example:

Calculate the z-scores of the price movements over the past 90 days:

close_price.relative_change(days=1).z_score(num_periods=90)
signal.p_value(num_periods, min_periods=None, delay_periods=1, p_cap=0.0)

Given a stationary time series (signal), calculate rolling p-values. The signal is assumed to be stationary and normally distributed.

Parameters:

  • num_periods – The number of time-periods of the signal to include in the estimate. That is, for a daily signal like close_price.relative_change(days=1), this argument is the number of days.

  • min_periods – The minimum number of actual data-points before estimate is produced. If min_periods is not specified, then it is set equal to num_periods.

  • delay_periods – The number of periods before the estimated model is applied to the current data point.

  • p_cap – A lower threshold on the p-values to be returned (lower values are removed)

Examples:

Calculate the p-values of the price movements over the past 90 days:

close_price.relative_change(days=1).p_value(num_periods=90, min_periods=50)

A simple outlier detector:

close_price.relative_change(days=1).p_value(num_periods=90, min_periods=50, p_cap=0.9999)