Smoothing Moving Averages by Filtering High-Frequency Harmonics

Summary

This indicator demonstrates smoothing a moving average by transforming its time series into a spectrum and suppressing frequencies above a chosen cutoff. The document presents the approach as a way to smooth the output of other indicators as well. Its stated advantage is practically zero latency, though no measurements, comparisons, or test results are provided to support that claim.

Users can choose the moving-average period, method, applied price, series length, smoothing coefficient, and horizontal shift. The series length is constrained to powers of two, and the coefficient controls which spectral frequencies are removed; at its maximum, the setting leaves the moving-average series unchanged. The description supplies configuration details but does not explain the transform or provide evidence about how smoothing affects signal quality, lag in practice, or trading outcomes. It should therefore be read as an indicator implementation concept rather than a validated trading strategy.

Key ideas

• The method smooths a moving average by filtering higher-frequency components of its spectrum.
• The same spectral filtering approach may be applied to other indicator time series.
• The series length must be a power of two, and the smoothing setting determines the frequency cutoff.
• At the maximum smoothing coefficient, the output repeats the original moving-average series.
• The document claims very low latency but provides no empirical validation or trading results.

Tags

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