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2011issue C1277-79

Constructing an inverse Fisher stochastic with bands and averages

This archive article presents a construction pipeline that first smooths price, then bounds a stochastic with an inverse Fisher transform, and keeps moving averages and Bollinger Bands as independent confirmation layers.

  • Smooth price first by leaving the number of rainbow-average iterations as a free parameter, then compute the stochastic on that iteratively smoothed series rather than the raw close.
  • Apply an inverse Fisher transform so the oscillator is compressed toward the extremes of a bounded range.
  • Keep Bollinger Bands and 20-day, 50-day, and 100-day simple moving averages as separate confirmation layers rather than substitutes for the oscillator.
  • Search for a supporting filter, such as a declining-market gate, alongside the core oscillator instead of replacing it.
Entries in this reading3 entries
Current trackBollinger Bands84 readingsStochastic oscillator170 readingsMoving average449 readings

What the construction separates

The archive construction applies an inverse Fisher transform to a stochastic oscillator whose input is iteratively smoothed price rather than the raw close.

Editorial reading: treat that sequence as a pipeline with separate jobs. Smooth the price series first. Bound the stochastic second. Keep moving averages and Bollinger Bands as independent confirmation layers so each layer has a limited decision.

Smoothing as a free stage

The smoothing stage can be specified so that the number of successive rainbow-average iterations is a free parameter rather than a fixed count.

A rainbow-average iteration is a repeated smoothing pass in which each stage averages the previous stage before the stochastic is computed.

Bounding the oscillator

After that smoothing, the inverse-fisher-stochastic is the stochastic oscillator whose iteratively smoothed price input is passed through an inverse Fisher transform so the output is compressed toward the extremes of a bounded range.

The same construction was packaged both as a standalone oscillator and as an automated strategy that consumes that oscillator.

Inverse Fisher stochastic versus the unsquashed 30,5 stochastic on daily S&P 500

The inverse-Fisher line parks on the 0 or 100 rail for weeks, while the ordinary 30-period stochastic (smoothed 5) still drifts through the middle of the band. That is the bounding step: a mid-range wiggle is not a flip until the transform leaves the rail. Points were read off the printed Updata pane (last index 1120.27; last oscillator print 85.54), not taken from a vendor file.
The inverse-Fisher line parks on the 0 or 100 rail for weeks, while the ordinary 30-period stochastic (smoothed 5) still drifts through the middle of the band. That is the bounding step: a mid-range wiggle is not a flip until the transform leaves the rail. Points were read off the printed Updata pane (last index 1120.27; last oscillator print 85.54), not taken from a vendor file.S&P 500 · daily · 2004-06-02T00:00:00.000Z to 2004-09-08T00:00:00.000Z

Pane label is Stochastic (30, 5) and Stochastic IFT. Values are approximate readings from the magazine raster, honest to about five oscillator points. The screenshot is dated 11 October 2011; the visible June–September window and the 1060–1150 index scale match the summer 2004 S&P 500 path named in the Updata note.

Independent confirmation layers

A spreadsheet reconstruction places Bollinger Bands, a conventional stochastic, and the inverse-Fisher stochastic in one calculation set.

Bollinger Bands are used here as a separate confirmation layer rather than as a substitute for the oscillator.

That reconstruction also plots 20-day, 50-day, and 100-day simple moving averages of the close beside the oscillator stack. Each simple-moving-average is an equal-weighted average of closing prices over a fixed lookback.

Rules that sit beside the oscillator

The published rule set was used to drive a simple transaction-level simulation of entries and exits.

Complementary rules, including a filter intended to block signals in a declining market, were searched for alongside the core oscillator rather than replacing it.

A supporting-filter is an extra rule, such as a declining-market gate, that can suppress oscillator signals instead of replacing the oscillator itself.

Worked series

Worked examples applied the oscillator to a daily equity-index series and to an hourly currency pair.

Educational research material, not investment advice. Historical source context does not establish present-day performance.
31 of 45 in the Bollinger Bands track
201262-71 pp.Next on Bollinger BandsConstructing a Bollinger Band indicator suiteSession definition decides whether bands or envelopes are the starting overlay. Both ask whether prices are high or low.
All readings on this track · 45 readings
  1. 1992Constructing volatility-scaled bands with relative strength index confirmation
  2. 1994Implied volatility as a band-defined regime filter for index options
  3. 1995Constructing projection bands from least-squares slopes
  4. 1995Constructing regression projection bands and range oscillators
  5. 1996Constructing Bollinger bands, percent-b, and stochastics
  6. 1996Constructing mechanical rules from Bollinger Bands and stochastics
  7. 1996Constructing a standard-error envelope around a linear regression
  8. 1996Dual-horizon ratio envelopes and regression error channels
  9. 1997Rational group structure with a trend screen, RSI, and bands
  10. 1997Asymmetric volatility band construction
  11. 1998Constructing three-state filters from Bollinger band envelopes
  12. 1999Combination filters with Bollinger Bands and the relative strength index
  13. 1999Constructing stochastic timed exits and band-RSI reversals
  14. 1999Evaluating Bollinger Bands against fixed-width and range-based envelopes
  15. 2000Constructing a Bollinger Band target as a forward price
  16. 2001Numeric candlestick encoding with local size bands
  17. 2001Ranked candlestick sentiment to band-cross entries
  18. 2002Combining Bollinger Bands, RSI, and a stop-loss
  19. 2002Bollinger Bands remain filters, not forecasts
  20. 2002Constructing a stochastic RSI with Bollinger bands
  21. 2002Constructing a StochRSI and Bollinger mechanical system
  22. 2003Constructing volatility-scaled Bollinger envelopes
  23. 2003Why tick breadth fails as a market personality
  24. 2005Constructing Bollinger bands versus fixed trading bands
  25. 2006Squared versus absolute deviation in envelope construction
  26. 2006Confirming yen crossovers with implied volatility and bands
  27. 2006A daily candle reversal is a hypothesis until shorter sessions fail at the same zone
  28. 2008Rebuild the Relative Strength Index as price-scale bands
  29. 2008Reading Relative Strength Index extremes on one price axis with Bollinger Bands and moving averages
  30. 2011Three-filter confirmation for short-swing futures
  31. 2011Constructing an inverse Fisher stochastic with bands and averages
  32. 2012Constructing a Bollinger Band indicator suite
  33. 2012Stacking price extremes, crossovers, bands, and MACD
  34. 2012Adaptive Bollinger band impulse, trend, and momentum filters
  35. 2013Rescaling stochastic, percent-B, and wave-count parameters
  36. 2014Industry-group quartile pivots as a Bollinger Bands case study
  37. 2014Bollinger Bands as adaptive price envelopes: a 2014 classroom case
  38. 2016Trend-channel entry rules from stacked moving averages
  39. 2016A permission stack for Bollinger, RSI, and the 50-period average
  40. 2017Constructing weighted Bollinger bands and volume averages
  41. 2017Four swing-entry rules that share a timed exit
  42. 2017Two-wave monthly cycles as a regime filter
  43. 2019Constructing exponential-deviation-bands from a midline-average
  44. 2020Critiquing exponential variants of Bollinger Bands
  45. 2020Constructing selectable volatility and moving-average bands
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1996Constructing Bollinger bands, percent-b, and stochastics1995Constructing projection bands from least-squares slopes1995Constructing regression projection bands and range oscillators1996Constructing mechanical rules from Bollinger Bands and stochastics1999Constructing stochastic timed exits and band-RSI reversals