Adaptive Oscillator constructor [lastguru]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 PulseWire community members (they are also mentioned in the library code) without whom this script would not have been possible. Indicator
Adaptive MA constructor [lastguru]Adaptive Moving Averages are nothing new, however most of them use EMA as their MA of choice once the preferred smoothing length is determined. I have decided to make an experiment and separate length generation from smoothing, offering multiple alternatives to be combined. Some of the combinations are widely known, some are not. 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 Moving Averages.
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.
Length Adaptaton section offer you a choice of Moving Average algorithms. Most of the Adaptations are originally used with EMA, so this is a good starting point for exploration.
SMA - Simple Moving Average
RMA - Running Moving Average
EMA - Exponential Moving Average
HMA - Hull Moving Average
VWMA - Volume Weighted Moving Average
2-pole Super Smoother - 2-pole Super Smoother by John F. Ehlers
3-pole Super Smoother - 3-pole Super Smoother by John F. Ehlers
Filt11 -a variant of 2-pole Super Smoother with error averaging for zero-lag response by John F. Ehlers
Triangle Window - Triangle Window Filter by John F. Ehlers
Hamming Window - Hamming Window Filter by John F. Ehlers
Hann Window - Hann Window Filter by John F. Ehlers
Lowpass - removes cyclic components shorter than length (Price - Highpass)
DSSS - Derivation Scaled Super Smoother by John F. Ehlers
There are two Moving Averages that are drown on the chart, so length for both needs to be selected. If no Adaptation is selected ( None option), you can set Fast Length and Slow Length directly. If an Adaptation is selected, then Cycle multiplier can be selected for Fast and Slow MA.
More information on the algorithms is given in the code for the libraries used. I am also very grateful to other PulseWire community members (they are also mentioned in the library code) without whom this script would not have been possible. Indicator
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PivotsLibrary "Pivots"
This Library focuses in functions related to pivot highs and lows and some of their applications (i.e. divergences, zigzag, harmonics, support and resistance...)
pivots(srcH, srcL, length) Delivers series of pivot highs, lows and zigzag.
Parameters:
srcH : Source series to look for pivot highs. Stricter applications might source from 'close' prices. Oscillators are also another possible source to look for pivot highs and lows. By default 'high'
srcL : Source series to look for pivot lows. By default 'low'
length : This value represents the minimum number of candles between pivots. The lower the number, the more detailed the pivot profile. The higher the number, the more relevant the pivots. By default 10
Returns:
zigzagArray(pivotHigh, pivotLow) Delivers a Zigzag series based on alternating pivots. Ocasionally this line could paint a few consecutive lows or highs without alternating. That happens because it's finding a few consecutive Higher Highs or Lower Lows. If to use lines entities instead of series, that could be easily avoided. But in this one, I'm more interested outputting series rather than painting/deleting line entities.
Parameters:
pivotHigh : Pivot high series
pivotLow : Pivot low series
Returns:
zigzagLine(srcH, srcL, colorLine, widthLine) Delivers a Zigzag based on line entities.
Parameters:
srcH : Source series to look for pivot highs. Stricter applications might source from 'close' prices. Oscillators are also another possible source to look for pivot highs and lows. By default 'high'
srcL : Source series to look for pivot lows. By default 'low'
colorLine : Color of the Zigzag Line. By default Fuchsia
widthLine : Width of the Zigzag Line. By default 4
Returns: Zigzag printed on screen
divergence(h2, l2, h1, l1, length) Calculates divergences between 2 series
Parameters:
h2 : Series in which to locate divs: Highs
l2 : Series in which to locate divs: Lows
h1 : Series in which to locate pivots: Highs. By default high
l1 : Series in which to locate pivots: Lows. By default low
length : Length used to calculate Pivots: By default 10
Returns: Library
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Renko Candles OverlayHello All,
For long time I got many request for Renko Candles and now here it's, Renko Candles Overlay . I tried to make almost everything optional, so you can play with the options as you want.
Let see the options:
Method: the option for brick scaling method: ATR, ATR/2, ATR/4, Percent, Traditional
- ATR Period: period for Average True Range and it's valid if the method is ATR
- ATR/2 Period: period for Average True Range and it's valid if the method is ATR/2
- ATR/4 Period: period for Average True Range and it's valid if the method is ATR/4
- Traditional: User-defined brick size, it's valid if the method is Traditional
- Percent: Percent of Close price, it's valid if the method is Percent
if the method is not Traditional (fixed brick size) then Brick size is calculated/updated when new bricks added. so The box sizes may be different because of the calculation is dynamic.
Levels & Lines for new Bricks: if you enable this option then the script shows the levels for new brick
Change Bar Color: optionally the script changes the bar color by using direction of the bricks
and some other options for coloring.
The script shows the bricks for visible area, which is approximately 280 candles. so if you change the width and number of the bricks then number of bricks that is shown is adjusted automatically to fit the screen. you can see the examples below:
The script shows the levels to new brick as a line and label:
Because of real-time bar is not confirmed until the candle close, the script shows the bricks as Unconfirmed , and unconfirmed bricks shown in different color:
You can change the width of the bricks (width is 10 in following example):
Optionally candle colors are changde by the direction of the bricks:
If you have any recommendation then please drop a comment under the script ;)
Enjoy! Indicator
Modified QQE-ZigZag [Non Repaint During Candle Building]V V V V V V V Please Read V V V V V V V
I ask Peter and he is fine, that im published this script
Tell me if you have some ideas or criticism about that sricpt
>>>>>>>>>> This is a modified Version of Peter_O's Momentum Based ZigZag <<<<<<<<<<<
This is only a test, and i want to share it with the community
It works like other ZigZags
Because Peters_O's original Version is only non repaint on closed historical Data ,
during a Candle building process it can still repaint (signal appears / 21 seconds later signal disapears / 42 seconds later signal appears again in the same candle / etc.),
but that isnt important for backtesting, its only important for realtime PivotPoints during a candle.
My goal for this zigzag was to make it absolute non repaint neither during a candle building process (current candle),
so once the signal is shown there is no chance that it disapers and shown a few seconds later again on that same candle, it can only show up one time per candle an thats it,
and that makes it absolute non repaint in all time frames.
Credits to:
==> Thanks to @glaz , for bringing the QQE to Tradingview <3
==> Thanks to @Peter_O , for sharing his idea to use the QQE as base for a Zigzag
and for sharing his MTF RSI with the Community <3
Changes:
- I changed the MTF RSI a little bit, you can choose between two version
- I changed the QQE a little bit, its now using the MTF RSI , and its using High and Low values as Source to make it absolute non repaint during a candle is building
- I added a little Divergence Calculation beween price and the MTF RSI that is used for the ZigZag
Colors :
- Green for HH / HL Continuation
- Red for LL / LH Continuation
- Yellow for Positive Divergence
- Purple for Negative Divergence
Important:
It is not possible to backtest this script correctly with historical Data, its only possible in Realtime,
because the QQE is using crossunders with RSILowSource and the QQE Line to find the Tops and,
because the QQE is using crossovers with RSIHighSource and the QQE Line to find the Bottoms,
and that means it is not possible to find the correct Time/Moment when that crossovers / crossunders happens in historical Data
=============> So please be sure you understand the Calculation and Backtest it in Realtime when you want to use it,
because i didn't published this script for real trading
=============> Im not a financial advisor and youre using this script at your own risk
=============> Please do your own research Indicator
Cyclic RSI High Low With Noise Filter█ OVERVIEW
This indicator displays Cyclic Relative Strength Index based on Decoding the Hidden Market Rhythm, Part 1 written by Lars von Thienen.
To determine true or false for Overbought / Oversold are unnecessary, therefore these should be either strong or weak.
Noise for weak Overbought / Oversold can be filtered, especially for smaller timeframe.
█ FEATURES
Display calculated Cyclic Relative Strength Index.
Zigzag high low based on Cyclic Relative Strength Index.
Able to filter noise for high low.
█ LEGENDS
◍ Weak Overbought / Oversold
OB ▼ = Strong Overbought
OS ▲ = Strong Oversold
█ USAGE / TIPS
Recommend to be used for Harmonic Patterns such as XABCD and ABCD.
Condition 1 (XABCD) : When ▼ and ▲ exist side by side, usually this outline XA, while the next two ◍ can be BC.
Condition 2 (ABCD) : When ▼ and ▲ exist side by side, usually this outline AB, while the next one ◍ can be BC, strong ABCD.
Condition 3 (ABCD) : When ▼ or ▲ exist at Point A, the next two ◍ can be Point B and Point C, medium ABCD.
Condition 4 (ABCD) : When ◍ exist at Point a, the next two ◍ can be Point b and Point c, weak ABCD usually used as lower case as abcd.
█ CREDITS
LoneSomeTheBlue
WhenToTrade Indicator
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Jeges JigsThis is a combination of all my old indicators, with an added feature for trend lines (inspiration for this came from Wedge Maker script thanks to veryfid, I hope he doesn't mind).
This script looks for a period with increased volatility , as measured by ATR ( Average True Range ), then it looks for a high or a low in that area.
When price is above EMA (400 is default, can be changed), it looks for the highs and adds multiples of ATR to the high. Default values for multipliers are 3,9 and 27, meaning that the script will show 3xATR level above the high, 9xATR above the high and 27xATR above the high.
When price is below EMA it looks for the lows and subtracts multiples of ATR from the low.The script will show 3xATR level below the low, 9xATR below the low and 27xATR below the low.
Multipliers values can be changed as well, making it a versatile tool that shows potential levels of suppport/resistance based on the volatility .
Possible use cases:
Breakout trading, when price crosses a certain level, it may show potential profit targets for trades opened at a breakout.
Stoploss helper. Many traders use ATR for their stoplosses, 1 ATR below the swing low for long trades and 1 ATR above the swing high for short trades are common values used by many traders. In this case, the Lookback value comes handy, if we want to look maybe at a more recent value for swing high/low point.
It highlights ATR peaks, it also displays Bollinger bands of SMA400 (or Ema), breakouts for upper/lower bands.
Another thing you get is Parabolic SAR and Zigzag based on SAR.
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supertrendHere is an extensive library on different variations of supertrend.
Library "supertrend"
supertrend : Library dedicated to different variations of supertrend
supertrend_atr(length, multiplier, atrMaType, source, highSource, lowSource, waitForClose, delayed) supertrend_atr: Simple supertrend based on atr but also takes into consideration of custom MA Type, sources
Parameters:
length : : ATR Length
multiplier : : ATR Multiplier
atrMaType : : Moving Average type for ATR calculation. This can be sma, ema, hma, rma, wma, vwma, swma
source : : Default is close. Can Chose custom source
highSource : : Default is high. Can also use close price for both high and low source
lowSource : : Default is low. Can also use close price for both high and low source
waitForClose : : Considers source for direction change crossover if checked. Else, uses highSource and lowSource.
delayed : : if set to true lags supertrend atr stop based on target levels.
Returns: dir : Supertrend direction
supertrend : BuyStop if direction is 1 else SellStop
supertrend_bands(bandType, maType, length, multiplier, source, highSource, lowSource, waitForClose, useTrueRange, useAlternateSource, alternateSource, sticky) supertrend_bands: Simple supertrend based on atr but also takes into consideration of custom MA Type, sources
Parameters:
bandType : : Type of band used - can be bb, kc or dc
maType : : Moving Average type for Bands. This can be sma, ema, hma, rma, wma, vwma, swma
length : : Band Length
multiplier : : Std deviation or ATR multiplier for Bollinger Bands and Keltner Channel
source : : Default is close. Can Chose custom source
highSource : : Default is high. Can also use close price for both high and low source
lowSource : : Default is low. Can also use close price for both high and low source
waitForClose : : Considers source for direction change crossover if checked. Else, uses highSource and lowSource.
useTrueRange : : Used for Keltner channel. If set to false, then high-low is used as range instead of true range
useAlternateSource : - Custom source is used for Donchian Chanbel only if useAlternateSource is set to true
alternateSource : - Custom source for Donchian channel
sticky : : if set to true borders change only when price is beyond borders.
Returns: dir : Supertrend direction
supertrend : BuyStop if direction is 1 else SellStop
supertrend_zigzag(length, history, useAlternateSource, alternateSource, source, highSource, lowSource, waitForClose, atrlength, multiplier, atrMaType) supertrend_zigzag: Zigzag pivot based supertrend
Parameters:
length : : Zigzag Length
history : : number of historical pivots to consider
useAlternateSource : - Custom source is used for Zigzag only if useAlternateSource is set to true
alternateSource : - Custom source for Zigzag
source : : Default is close. Can Chose custom source
highSource : : Default is high. Can also use close price for both high and low source
lowSource : : Default is low. Can also use close price for both high and low source
waitForClose : : Considers source for direction change crossover if checked. Else, uses highSource and lowSource.
atrlength : : ATR Length
multiplier : : ATR Multiplier
atrMaType : : Moving Average type for ATR calculation. This can be sma, ema, hma, rma, wma, vwma, swma
Returns: dir : Supertrend direction
supertrend : BuyStop if direction is 1 else SellStop Library
zigzag⬜ Zigzag at your fingertips.
Creating zigzag array is more simpler than ever. All you need to do is:
▶ Import library:
import HeWhoMustNotBeNamed// as zgi
▶ And invoke zigzag to get all the details.
zgi.drawzigzag(zigzagLength)
More examples in the code where you can get retracement ratios, zigzag direction, divergence etc.
Library "zigzag"
Library dedicated to zigzags and related indicators
zigzag(length, numberOfPivots, useAlternativeSource, source, oscillatorSource, directionBias) zigzag: Calculates zigzag pivots and generates an array
Parameters:
length : : Zigzag Length
numberOfPivots : : Max number of pivots to return in the array. Default is 20
useAlternativeSource : : If set uses the source for genrating zigzag. Default is false
source : : Alternative source used only if useAlternativeSource is set to true. Default is close
oscillatorSource : : Oscillator source for calculating divergence
directionBias : : Direction bias for calculating divergence
Returns:
zigzagpivots : Array containing zigzag pivots
zigzagpivotbars : Array containing zigzag pivot bars
zigzagpivotdirs : Array containing zigzag pivot directions (Lower High : 1, Higher High : 2, Lower Low : -2 and Higher Low : -1)
zigzagpivotratios : Array containing zigzag retracement ratios for each pivot
zigzagoscillators : Array of oscillator values at pivots. Will have valid values only if valid oscillatorSource is provided as per input.
zigzagoscillatordirs : Array of oscillator directions (HH, HL, LH, LL) at pivots. Will have valid values only if valid oscillatorSource is provided as per input.
zigzagtrendbias : Array of trend bias at pivots. Will have valid value only if directionBias series is sent in input parameters
zigzagdivergence : Array of divergence sentiment at each pivot. Will have valid values only if oscillatorSource and directionBias inputs are provided
newPivot : Returns true if new pivot created
doublePivot : Returns true if two new pivots are created on same bar (Happens in case of candles with long wicks and shorter zigzag lengths)
drawzigzag(length, numberOfPivots, , source, linecolor, linewidth, linestyle, oscillatorSource, directionBias, showHighLow, showRatios, showDivergence) drawzigzag: Calculates and draws zigzag pivots
Parameters:
length : : Zigzag Length
numberOfPivots : : Max number of pivots to return in the array. Default is 20
: useAlternativeSource: If set uses the source for genrating zigzag. Default is false
source : : Alternative source used only if useAlternativeSource is set to true. Default is close
linecolor : : zigzag line color
linewidth : : zigzag line width
linestyle : : zigzag line style
oscillatorSource : : Oscillator source for calculating divergence
directionBias : : Direction bias for calculating divergence
showHighLow : : show highlow label
showRatios : : show retracement ratios
showDivergence : : Show divergence on label (Only works if divergence data is available - that is if we pass valid oscillatorSource and directionBias input)
Returns:
zigzagpivots : Array containing zigzag pivots
zigzagpivotbars : Array containing zigzag pivot bars
zigzagpivotdirs : Array containing zigzag pivot directions (Lower High : 1, Higher High : 2, Lower Low : -2 and Higher Low : -1)
zigzagpivotratios : Array containing zigzag retracement ratios for each pivot
zigzagoscillators : Array of oscillator values at pivots. Will have valid values only if valid oscillatorSource is provided as per input.
zigzagoscillatordirs : Array of oscillator directions (HH, HL, LH, LL) at pivots. Will have valid values only if valid oscillatorSource is provided as per input.
zigzagtrendbias : Array of trend bias at pivots. Will have valid value only if directionBias series is sent in input parameters
zigzagdivergence : Array of divergence sentiment at each pivot. Will have valid values only if oscillatorSource and directionBias inputs are provided
zigzaglines : Returns array of zigzag lines
zigzaglabels : Returns array of zigzag labels
Library
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Multi-ZigZag Multi-Oscillator Trend DetectorThis table is intended to give you snapshot of how price and oscillators are moving along with zigzag pivots.
This is done in the same lines of Zigzag-Trend-Divergence-Detector
But, here are the differences
Table shows multiple oscillator movements at a same time instead of one selected oscillator
Divergence is not calculated and also supertrend based trend. Trend can be calculated based on zigzag movements. However, lets keep this for future enhancements.
This system also uses multiple zigzags instead of just one.
⬜ Process
▶ Derive multiple zigzags - Code is taken from Multi-ZigZag
▶ Along with zigzags - also calculate different oscillators and attach it to zigzag pivot.
▶ Calculate directions of zigzag pivots and corresponding oscillators.
▶ Plot everything in the table on last bar.
⬜ Table components
Table contains following data:
Directional legends are:
⇈ - Higher High (Green)
⇊ - Lower Low (Red)
⭡- Lower High (Orange)
⭣ - Higher Low (Lime)
⬜ Input Parameters
▶ Source : Default is close. If Unchecked - uses high/low data for calculating pivots. Can also use external input such as OBV
▶ Stats : Gives option to select the depth of output (History) and also lets you chose text size and table position.
▶ Oscillators : Oscillator length is derived by multiplying multiplier to zigzag length. For example, for zigzag 5, with 4 as multiplier, all oscillators are calculated with length 20. But, same for zigzag 8 will be 32 and so on.
▶ Available oscillators :
CCI - Commodity Channel Index
CMO - Chande Momentum Oscillator
COG - Center Of Gravity
MFI - Money Flow Index (Shows only if volume is present)
MOM - Momentum oscillator
ROC - Rate Of Change
RSI - Relative Strength Index
TSI - Total Strength Index
WPR - William Percent R
BB - Bollinger Percent B
KC - Keltner Channel Percent K
DC - Donchian Channel Percent D
ADC - Adoptive Donchian Channel Percent D ( Adoptive-Donchian-Channel )
⬜ Challenges
There are 12 oscillators and each zigzag has different length. Which means, there are 48 combinations of the ocillators.
First challenge was generating these values without creating lots of static initialization. Also, note, if the functions are not called on each bar, then they will not yield correct result. This is achieved through initializer function which runs on every bar and stores the oscillator values in an array which emulates multi dimensional array oscillator X zigzag length.
Next challenge was getting these values within function when we need it. While doing so I realized that values stored in array also have historical series and calling array.get will actully get you the entire series and not just the value. This is an important takeaway for me and this can be used for further complex implementations.
Thanks to @LonesomeTheBlue and @LucF for some timely suggestions and interesting technical discussions :) Indicator
