def compute(self, today, assets, out, data, decay_rate):
        weights = self.weights(len(data), decay_rate)

        mean = average(data, axis=0, weights=weights)
        variance = average((mean - data) ** 2, axis=0, weights=weights)

        squared_weight_sum = (np_sum(weights) ** 2)
        bias_correction = (
            squared_weight_sum / (squared_weight_sum - np_sum(weights ** 2))
        )
        out[:] = sqrt(variance * bias_correction)


# Convenience aliases.
dataset/ETHPy150Open quantopian/zipline/zipline/pipeline/factors/technical.py/ExponentialWeightedMovingStdDev.compute/swapped operands of `-`