Smoothing a Moving Average with Harmonic Filtering

Summary

This indicator example applies spectral filtering to a moving average time series. It removes higher-frequency components from the series to produce a smoothed output, and the description says the technique has practically zero latency. It presents the result as a cloud between the smoothed moving average and the smoothed closing price, and suggests the same approach can be used to smooth other indicators.

The inputs let the user choose the moving average period, type, and applied price, along with a series length, smoothing coefficient, and horizontal shift. The series length is specified as a power of two. The smoothing coefficient determines which frequencies are suppressed; at its maximum allowed setting, the moving average series is repeated. The document provides no performance tests, market examples, or evidence that the indicator improves trading decisions, so its latency and usefulness claims are not evaluated here.

Key ideas

• The indicator smooths a moving average series by filtering out higher-frequency components.
• The described output is a cloud comparing the smoothed average with smoothed closing prices.
• The smoothing coefficient controls frequency suppression and is bounded by the series length.
• The document claims practically zero latency but provides no empirical validation.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.