Adaptive Oscillator constructor [lastguru]

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Adaptive Oscillators use the same principle as Adaptive Moving Averages. This is an experiment to separate length generation from oscillators, offering multiple alternatives to be combined. Some of the combinations are widely known, some are not. Note that all Oscillators here are normalized to -1..1 range. This indicator is based on my previously published public libraries and also serve as a usage demonstration for them. I will try to expand the collection (suggestions are welcome), however it is not meant as an encyclopaedic resource , so you are encouraged to experiment yourself: by looking on the source code of this indicator, I am sure you will see how trivial it is to use the provided libraries and expand them with your own ideas and combinations. I give no recommendation on what settings to use, but if you find some useful setting, combination or application ideas (or bugs in my code), I would be happy to read about them in the comments section.

The indicator works in three stages: Prefiltering, Length Adaptation and Oscillators.

Prefiltering is a fast smoothing to get rid of high-frequency (2, 3 or 4 bar) noise.

Adaptation algorithms are roughly subdivided in two categories: classic Length Adaptations and Cycle Estimators (they are also implemented in separate libraries), all are selected in Adaptation dropdown. Length Adaptation used in the Adaptive Moving Averages and the Adaptive Oscillators try to follow price movements and accelerate/decelerate accordingly (usually quite rapidly with a huge range). Cycle Estimators, on the other hand, try to measure the cycle period of the current market, which does not reflect price movement or the rate of change (the rate of change may also differ depending on the cycle phase, but the cycle period itself usually changes slowly).
  • Chande (Price) - based on Chande's Dynamic Momentum Index (CDMI or DYMOI), which is dynamic RSI with this length
  • Chande (Volume) - a variant of Chande's algorithm, where volume is used instead of price
  • VIDYA - based on VIDYA algorithm. The period oscillates from the Lower Bound up (slow)
  • VIDYA-RS - based on Vitali Apirine's modification of VIDYA algorithm (he calls it Relative Strength Moving Average). The period oscillates from the Upper Bound down (fast)
  • Kaufman Efficiency Scaling - based on Efficiency Ratio calculation originally used in KAMA
  • Deviation Scaling - based on DSSS by John F. Ehlers
  • Median Average - based on Median Average Adaptive Filter by John F. Ehlers
  • Fractal Adaptation - based on FRAMA by John F. Ehlers
  • MESA MAMA Alpha - based on MESA Adaptive Moving Average by John F. Ehlers
  • MESA MAMA Cycle - based on MESA Adaptive Moving Average by John F. Ehlers , but unlike Alpha calculation, this adaptation estimates cycle period
  • Pearson Autocorrelation* - based on Pearson Autocorrelation Periodogram by John F. Ehlers
  • DFT Cycle* - based on Discrete Fourier Transform Spectrum estimator by John F. Ehlers
  • Phase Accumulation* - based on Dominant Cycle from Phase Accumulation by John F. Ehlers

Length Adaptation usually take two parameters: Bound From (lower bound) and To (upper bound). These are the limits for Adaptation values. Note that the Cycle Estimators marked with asterisks(*) are very computationally intensive, so the bounds should not be set much higher than 50, otherwise you may receive a timeout error (also, it does not seem to be a useful thing to do, but you may correct me if I'm wrong).

The Cycle Estimators marked with asterisks(*) also have 3 checkboxes: HP (Highpass Filter), SS (Super Smoother) and HW (Hann Window). These enable or disable their internal prefilters, which are recommended by their author - John F. Ehlers . I do not know, which combination works best, so you can experiment.

Chande's Adaptations also have 3 additional parameters: SD Length (lookback length of Standard deviation), Smooth (smoothing length of Standard deviation) and Power ( exponent of the length adaptation - lower is smaller variation). These are internal tweaks for the calculation.

Oscillators section offer you a choice of Oscillator algorithms:
  • Stochastic - Stochastic
  • Super Smooth Stochastic - Super Smooth Stochastic (part of MESA Stochastic) by John F. Ehlers
  • CMO - Chande Momentum Oscillator
  • RSI - Relative Strength Index
  • Volume-scaled RSI - my own version of RSI. It scales price movements by the proportion of RMS of volume
  • Momentum RSI - RSI of price momentum
  • Rocket RSI - inspired by RocketRSI by John F. Ehlers (not an exact implementation)
  • MFI - Money Flow Index
  • LRSI - Laguerre RSI by John F. Ehlers
  • LRSI with Fractal Energy - a combo oscillator that uses Fractal Energy to tune LRSI gamma
  • Fractal Energy - Fractal Energy or Choppiness Index by E. W. Dreiss
  • Efficiency ratio - based on Kaufman Adaptive Moving Average calculation
  • DMI - Directional Movement Index (only ADX is drawn)
  • Fast DMI - same as DMI, but without secondary smoothing

If no Adaptation is selected (None option), you can set Length directly. If an Adaptation is selected, then Cycle multiplier can be set.

Before an Oscillator, a High Pass filter may be executed to remove cyclic components longer than the provided Highpass Length (no High Pass filter, if Highpass Length = 0). Both before and after the Oscillator a Moving Average can be applied. The following Moving Averages are included: SMA, RMA, EMA, HMA , VWMA, 2-pole Super Smoother, 3-pole Super Smoother, Filt11, Triangle Window, Hamming Window, Hann Window, Lowpass, DSSS. For more details on these Moving Averages, you can check my other Adaptive Constructor indicator:
The Oscillator output may be renormalized and postprocessed with the following Normalization algorithms:
  • Stochastic - Stochastic
  • Super Smooth Stochastic - Super Smooth Stochastic (part of MESA Stochastic) by John F. Ehlers
  • Inverse Fisher Transform - Inverse Fisher Transform
  • Noise Elimination Technology - a simplified Kendall correlation algorithm "Noise Elimination Technology" by John F. Ehlers

Except for Inverse Fisher Transform, all Normalization algorithms can have Length parameter. If it is not specified (set to 0), then the calculated Oscillator length is used.

More information on the algorithms is given in the code for the libraries used. I am also very grateful to other TradingView community members (they are also mentioned in the library code) without whom this script would not have been possible.
Phát hành các Ghi chú:
Added Momentum Postfilter
Phát hành các Ghi chú:
variable name fix
Phát hành các Ghi chú:
Fixed Windowing algorithm selection. Removed an unused function.
Phát hành các Ghi chú:
Added Average Vortex Index (ADX calculated from Vortex Indicator) and Hurst Exponent oscillators
Added Fast Default windowing, that is the same as Default, but with half the length
Phát hành các Ghi chú:
Added simple Fractal Dimension (derived from FRAMA)
Added FVE and VFI by Markos Katsanos.
Phát hành các Ghi chú:
Added Relative Vigor Index (RVI)
Phát hành các Ghi chú:
library update: fixed vidyaRS calculation

Tips in TradingView Coins are appreciated
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