LOESS Smoothing

What is LOESS Smoothing?

LOESS (locally estimated scatterplot smoothing) fits a smooth curve through noisy data one point at a time. For each evaluation point it takes a neighborhood of nearby observations, fits a low-degree polynomial (locally linear in the original LOWESS, often quadratic in LOESS) by weighted least squares with nearer points weighted more heavily (a tricube weighting is the classic choice), and records the fitted value at that point. Repeating this across the series traces a flexible curve with no assumed global shape. The method comes from statistician William Cleveland; its span parameter, the fraction of data used in each local fit, controls smoothness.

On a chart, LOESS behaves like a trend curve that bends with the data, where a single linear regression forces one straight line; kernel regression is its close cousin, built from weighted averages rather than local fits. The catch is the right edge: the newest bars have no future neighbors, so their fit is one-sided and gets revised as bars arrive. Historical values look excellent precisely because they were smoothed with hindsight.

How traders use it

• As a flexible trend baseline: the curve's direction and bend give a visual trend read, and subtracting it from price yields a detrended oscillator of local deviations.
• As the centerline of a channel: offsetting the curve by a multiple of the residual standard deviation frames stretch and reversion around the local fit.
• As a smoother for indicator inputs where fixed-length moving averages feel too rigid, accepting the edge instability that comes with recomputation.