Skip to main content
Track Exponential smoothing
2 / 41
Library

1984issue C051-3

Residual-impact construction of exponential smoothers

Simple moving averages and exponentially smoothed moving averages are identified as the two calculation methods most often found among widely used trading systems. An observation leaves a finite window on the next interval, while residual impact in an exponential smoother never fully exits. Editorial: TradersWeek teaches the construction as a two-control choice of lookback and leftover share.

  • Simple moving averages and exponentially smoothed moving averages are identified as the two calculation methods most often found among widely used trading systems.
  • An observation leaves an n-period simple or weighted moving average on the next interval, but the same observation never fully exits an exponentially smoothed average.
  • The proposed construction selects the smoothing factor from two controls: the share of an observation's original impact to be used over a span, and the number of days in that span.
  • For a length-only shortcut, 2/(n+1) is preferred to 1/n, and many residual-and-days pairs can yield the same alpha.
Entries in this reading2 entries

Two common calculation methods

Simple moving averages and exponentially smoothed moving averages are identified as the two calculation methods most often found among widely used trading systems.

A moving average is a finite-window average of ordered price, volume, or breadth observations whose contribution ends on the interval after they leave the window. Exponential smoothing is a recursive smoother that mixes the newest ordered observation with the prior output through a constant alpha so earlier observations decay geometrically and never vanish completely.

What leaves a window and what remains

An observation leaves an n-period simple or weighted moving average on the next interval, but the same observation never fully exits an exponentially smoothed average.

Two controls for the smoothing factor

The proposed construction selects the smoothing factor from two controls: the share of an observation's original impact to be used over a span, and the number of days in that span.

Residual impact is the remaining fraction of one observation after a chosen number of intervals. The leftover share after n intervals is (1-alpha) to the n.

How each update weights one observation

Each update is the newest price times alpha plus the previous smoother times one minus alpha. Unfolding that recursion shows the observation's weight on day n is alpha times (1-alpha) to the power n minus one.

The cumulative share of one price over n days is 1 minus (1-alpha) to the n, so the leftover share is (1-alpha) to the n.

A residual-and-span example

Using 95 percent of an observation in the first 10 days, a 5 percent residual, produces alpha equal to 0.25887 and matches the closed form 1 minus residual impact raised to 1 over days.

Length-only mappings

Conventional mappings treat a smoothing factor as equivalent to an n-day simple or weighted average by using either 1/n or 2/(n+1). Those shortcuts are a length-mapped alpha: a conversion of a window length n into alpha with either one over n or two over n plus one.

For a length-only shortcut, 2/(n+1) is preferred to 1/n. As the period lengthens, residuals under those two mappings approach about 36.79 percent and 13.534 percent, and many residual-and-days pairs can yield the same alpha.

Educational research material, not investment advice. Historical source context does not establish present-day performance.
2 of 41 in the Exponential smoothing track
19881-8 pp.Next on Exponential smoothingConstructing simple and exponential moving averages as specified filtersA moving average is a low-pass construction that lets slower variation through and attenuates high-frequency variation.
All readings on this track · 41 readings
  1. 1984Matching average age across price filters
  2. 1984Residual-impact construction of exponential smoothers
  3. 1988Constructing simple and exponential moving averages as specified filters
  4. 1989Evaluating smoothed free reserves and funds-discount spreads
  5. 1990Integer construction of moving averages and exponential smoothing
  6. 1990Constructing incremental moving average filters
  7. 1991Constructing a double-smoothed stochastic oscillator
  8. 1991Constructing an exponential average from a smoothing weight
  9. 1991Constructing a double-smoothed momentum oscillator family
  10. 1991Competing certainty rules from exponential-average conditions
  11. 1991Hybrid construction of a mechanical timing system from smoothed signals
  12. 1991Constructing an exponential smoother from a moving-average length
  13. 1991Equivalent relative-strength-index constructions from a ratio and from smoothed momentum
  14. 1991Constructing the true strength index from double-smoothed momentum
  15. 1991True strength index from signed, double-smoothed momentum
  16. 1992Constructing exponential averages from simple lookbacks
  17. 1992Constructing exponential averages by the smoothing constant, startup length and lag
  18. 1992Nested range smoothing and windowed high-low damping
  19. 1992A weekly equity stance from the discount-bill spread
  20. 1993Double-smoothed stochastics momentum index construction
  21. 1993Constructing the true strength index with double exponential smoothing
  22. 1994Polarized fractal efficiency with a shared exponential smoother
  23. 1994Five-interval exponential smoothing of ten-interval signed path-efficiency
  24. 1994Hand-ranked price-volume series with stacked smoothing and stochastic
  25. 1995Range expansion filter on multi-horizon averages
  26. 1995The exponential smoothing constant from averaging lag
  27. 1997Construct a nested-average rainbow band and oscillator
  28. 1998Filter stacks that combine a moving average with exponential smoothing
  29. 2002Smoothed relative-strength construction from wilder-averaging and a price prefilter
  30. 2003Reverse-engineered RSI price targets
  31. 2004Piecewise trend quality from moving-average reversals
  32. 2004Nested smoothing recipes versus unaudited oscillator experiments
  33. 2008Weight construction beyond the N-day label
  34. 2010A consensus filter for objective RSI and smoothing construction
  35. 2012Sentiment-zone oscillator from signed closes
  36. 2012Classic RSI one-over-N smoothing versus the standard exponential average
  37. 2013Why exponential smoothing uses two over n plus one
  38. 2015Constructing ratio indicators with moving-average and exponential-smoothing weights
  39. 2015Slow relative-strength index from an exponentially smoothed price
  40. 2017Rebuilding the relative strength index as open-scale buy-sell pressure
  41. 2017Comparing Wilder and exponential smoothing inside a relative-strength index
All 183 readings tagged Exponential smoothing
Also on Exponential smoothing5 readings