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
Search in scripts for "rma"
Swing Stock Market Multi MA Correlation This is a swing strategy adapted to stock market using correlation with either SP500 or Nasdaq, so its best to trade stocks from this region.
Its components are
Correlation Candle
Fast moving average to choose from SMA , EMA , SMMA (RMA), WMA and VWMA
Medium moving Average to choose from SMA , EMA , SMMA (RMA), WMA and VWMA
Slow moving average to choose from SMA , EMA , SMMA (RMA), WMA and VWMA
Rules for entry
Long: fast ma > medium ma and medium ma > slow ma
Short: fast ma< medium ma and medium ma < slow ma.
Rules for exit
We exit when we receive an inverse condition.
Caution:
This strategy use no risk management inside, so be careful with it .
If you have any questions, let me know !
+ JMA KDJ with RSI OB/OS SignalsSo, what is the KDJ indicator? If you're familiar with the Stochastic, then you'll know that the two oscillating lines are called the 'K' and 'D' lines. Now you know that this is some sort of implementation of the Stochastic. But, then, what is the J? The 'J' is simply the measure of convergence/divergence of the 'K' and 'D' lines, and the 'J' crossing the 'K' and 'D' lines is representational of the 'K' and 'D' lines themselves crossing. Is this an improvement over simply using the Stochastic as it is? Beats me. I don't use the Stochastic. I stumbled upon the KDJ while surfing around the web, and it sounded cool, so I thought I'd look at it. I do like it a bit more as the 'J' line being far overextended from the other two (usually into overbought/sold territory) does give a clear visual representation of the divergence of the 'K' and 'D' lines, which you might not notice otherwise. So, from that perspective I suppose it is nicer.
But let's get to the good stuff now, shall we? What did I do here?
Well, first thing you're wondering is why there are only two lines when based on my explanation (or your previous experience with the indicator) there should be three. I found this script here on TV, by x4random, who took the 'K' and 'D' lines and made an average of them, so there is only one line instead of the two. So, fewer lines on the indicator, but still the same usefulness. It was in older TV code, so I took it to version4 and cleaned up the code slightly. His indicator included the RSI ob/os plots, and I thought this was neat (even though the RSI being os/ob doesn't tell you much except that the trend is strong, and you should be buying pullback or selling rallies) so I kept them in. His indicator was also the most visually appealing one that I saw on here, so that attracted me too. Credit to x4random for the indicator, though.
Aside from code cleanup and adding the usual bells and whistles (which I will get to) the big thing I did here was change is RMA that he was using for the 'K' and 'D' lines to a Jurik MA's, which smooth a lot of the noise of other moving averages while maintaining responsiveness. This eliminates noise (false signals) while keeping the signals of significance. It took me a while to figure out how to substitute the JMA for the RMA, but thanks to QuantTherapy's "Jurik PPO" indicator I was able to nail down the implementation. One thing you might notice is that there is no input to change signal length. I fiddled with this for a time before sticking to using the period, instead of the signal (thus eliminating the use of the signal input altogether), length to generate the 'K' and 'D' calculations. To make any adjustments other than the period length use the Jurik Power input. You can use the phase input as well, but it has much less of an effect.
Everything else I changed is pretty much cosmetic.
Candle coloring with the option to color candles based on either the 'J' line or the 'KD' line.
color.from_gradients with color inputs to make it beautiful (this is probably my best looking indicator, imo)
plots for when crosses occur (really wish there was a way to plot these over candlesticks! If anyone has any suggestions I'd love to see!)
I think that's about it. Alerts of course.
Enjoy!
Below is a comparison chart of my JMA implementation to the original RMA script.
You can see how much smoother the JMA version is. Both of these had the default period of 55 set, and the JMA version is using the default settings, while the original version is using a length of 3 for the signal line.
Volume MAs Oscillator | Lyro RS// This Pine Script® code is subject to the terms of the Mozilla Public License 2.0 at mozilla.org
// © LyroRS
//@version=6
indicator("Volume MAs Oscillator | Lyro RS")
import LyroRS/LMAs/1 as DynamicMAs
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//─────────────────────────────────────────────────────────────────────────────────────────────────────────────
// LyroRS v1.0
// Groups
ma_g = "𝗠𝗢𝗩𝗜𝗡𝗚 𝗔𝗩𝗘𝗥𝗔𝗚𝗘"
bands_g = "𝗕𝗔𝗡𝗗"
display_g = '𝗗𝗜𝗦𝗣𝗟𝗔𝗬'
// Inputs
// -- Moving Average
source = input.source(close, "Source", group= ma_g, tooltip= "Select where the data originates (open, high, low, close, etc..).")
ma_type = input.string("EMA", "Select Moving Average", options= , group=ma_g, tooltip="Choose a moving average type to apply to the price multiplied by volume. VWMA already incorporates volume directly.")
ma_length = input.int(30, "Moving Average Length", group= ma_g, tooltip= "Defines the length or period of the selected moving average.")
// -- Bands
band_length = input.int(27, "Band Length", group=bands_g, tooltip="Number of bars used to calculate standard deviation.")
band_smoothing = input.float(0.8, "Band Smoothing", group=bands_g, minval=0, tooltip="Smooths the band edges to reduce noise.")
pbm = input.float(1.8, "Positive Band Multiplier", group=bands_g, minval=0, tooltip="Multiplier for the upper band distance.")
nbm = input.float(-0.85, "Negative Band Multiplier", group=bands_g, maxval=0, tooltip="Multiplier for the lower band distance.")
// Color Inputs
signal_type = input.string("Trend", "Select Signal Type", options= , group=display_g, tooltip="Select which way to use the indicator.")
ColMode = input.string("Mystic", "Custom Color Palette", inline="drop", options= , display=display.none, group= display_g, tooltip="Select a predefined color scheme for the indicator display. (Major Themes color mode automatically switches colors based on the major asset you picked for valuation analysis.)")
cpyn = input.bool (true, "Use Custom Palette", group= display_g, display=display.none, tooltip="Enables custom color selection for signals.")
cp_UpC = input.color (#00ff00, "Custom Up", inline = "Custom Palette", group= display_g, display=display.none, tooltip="")
cp_DnC = input.color (#ff0000, "Custom Down", inline = "Custom Palette", group= display_g, display=display.none, tooltip="User specifed bullish and bearish colors.")
d_obos_sigs = input.bool (true, "Display Oversold/Overbought Signs", group= display_g, display=display.none, tooltip="Enables triangle signs to be displayed.")
d_signs = input.bool (true, "Display Signs", group= display_g, display=display.none, tooltip="Enables signs for Trend mode.")
d_bgcol = input.bool (true, "Display Background Color", group= display_g, display=display.none, tooltip="Enables background color for Reversion & Valuation mode.")
// Colors
color UpC = na
color DnC = na
// -- Predefined Colors
switch ColMode
"Classic" =>
UpC := #00E676
DnC := #880E4F
"Mystic" =>
UpC := #30FDCF
DnC := #E117B7
"Accented" =>
UpC := #9618F7
DnC := #FF0078
"Royal" =>
UpC := #FFC107
DnC := #673AB7
// -- Custom Colors
if cpyn
UpC := cp_UpC
DnC := cp_DnC
// Coloring Function for Valuation
coloring(src) =>
color.from_gradient(src, ta.lowest(src, band_length), ta.highest(src, band_length), UpC, DnC)
// Moving Average Switch
float ma = na
switch ma_type
"SMA" => ma := DynamicMAs.SMA(source * volume, ma_length) / DynamicMAs.SMA(volume, ma_length)
"EMA" => ma := DynamicMAs.EMA(source * volume, ma_length) / DynamicMAs.EMA(volume, ma_length)
"WMA" => ma := DynamicMAs.WMA(source * volume, ma_length) / DynamicMAs.WMA(volume, ma_length)
"VWMA" => ma := DynamicMAs.VWMA(source, volume, ma_length) // Already Volume Based MA
"DEMA" => ma := DynamicMAs.DEMA(source * volume, ma_length) / DynamicMAs.DEMA(volume, ma_length)
"TEMA" => ma := DynamicMAs.TEMA(source * volume, ma_length) / DynamicMAs.TEMA(volume, ma_length)
"RMA" => ma := DynamicMAs.RMA(source * volume, ma_length) / DynamicMAs.RMA(volume, ma_length)
"HMA" => ma := DynamicMAs.HMA(source * volume, ma_length) / DynamicMAs.HMA(volume, ma_length)
"LSMA" => ma := DynamicMAs.LSMA(source * volume, ma_length, 0) / DynamicMAs.LSMA(volume, ma_length, 0)
"SMMA" => ma := DynamicMAs.SMMA(source * volume, ma_length) / DynamicMAs.SMMA(volume, ma_length)
"ALMA" => ma := DynamicMAs.ALMA(source * volume, ma_length, 0, 20) / DynamicMAs.ALMA(volume, ma_length, 0, 20)
"ZLSMA" => ma := DynamicMAs.ZLSMA(source * volume, ma_length) / DynamicMAs.ZLSMA(volume, ma_length)
"FRAMA" => ma := DynamicMAs.FRAMA(source * volume, ma_length) / DynamicMAs.FRAMA(volume, ma_length)
"KAMA" => ma := DynamicMAs.KAMA(source * volume, ma_length) / DynamicMAs.KAMA(volume, ma_length)
"JMA" => ma := DynamicMAs.JMA(source * volume, ma_length, 0) / DynamicMAs.JMA(volume, ma_length, 0)
"T3" => ma := DynamicMAs.T3(source * volume, ma_length, 0.5) / DynamicMAs.T3(volume, ma_length, 0.5)
price_diff = ((source - ma) / ma) * 100 // Percentage Difference between Source and the Moving Average of the source
// Calculations for the Bands
std = ta.stdev(price_diff, band_length)
upperBandRaw = std * pbm
lowerBandRaw = std * nbm
var float upperBand = na
var float lowerBand = na
// Smooth
upperBand := upperBandRaw * band_smoothing + nz(upperBand ) * (1 - band_smoothing)
lowerBand := lowerBandRaw * band_smoothing + nz(lowerBand ) * (1 - band_smoothing)
// Plot Color
var color pc = na
var color uB_color = na
var color lB_color = na
var int signal = 0
if signal_type == "Trend"
uB_color := UpC
lB_color := DnC
if price_diff > upperBand
pc := UpC
signal := 1
if price_diff < lowerBand
pc := DnC
signal := -1
if signal_type == "Reversion"
uB_color := DnC
lB_color := UpC
if price_diff > upperBand
pc := DnC
signal := -1
else if price_diff < lowerBand
pc := UpC
signal := 1
else
pc := color.gray
signal := 0
if signal_type == "Valuation"
uB_color := UpC
lB_color := DnC
pc := coloring(price_diff)
// Plot
plot(price_diff, color= pc, linewidth = 2, title= "Volume MAs Oscillator")
plot(upperBand, color= color.new(uB_color, 50), title= "Upper Band")
plot(lowerBand, color= color.new(lB_color, 50), title= "Lower Band")
plot(0, color= color.new(pc, 60), linewidth = 2, display= display.pane, title= "Mid Line")
plot(0, color= color.new(pc, 75), linewidth = 5, display= display.pane, title= "Mid Line Glow 1")
plot(0, color= color.new(pc, 85), linewidth = 10, display= display.pane, title= "Mid Line Glow 2")
plotchar(upperBand + 0.5, char='▼', color= ta.crossunder(price_diff, upperBand) ? DnC : na, location=location.absolute, title= "Overbought Signal", display= d_obos_sigs ? display.pane : display.none, size= size.tiny)
plotchar(lowerBand - 0.5, char='▲', color= ta.crossover(price_diff, lowerBand) ? UpC : na, location=location.absolute, title= "Oversold Signal", display= d_obos_sigs ? display.pane : display.none, size= size.tiny)
reversion_enable = signal_type == "Reversion"
valuation_enable = signal_type == "Valuation"
bgcolor(d_bgcol and ((valuation_enable and price_diff > upperBand) or (reversion_enable and ta.crossunder(price_diff, upperBand))) ? color.new(DnC, 85) : na, title= "BG Color OB")
bgcolor(d_bgcol and ((valuation_enable and price_diff > upperBand) or (reversion_enable and ta.crossunder(price_diff, upperBand))) ? color.new(DnC, 85) : na, title= "BG Color OB Overlay", force_overlay = true)
bgcolor(d_bgcol and ((valuation_enable and price_diff < lowerBand) or (reversion_enable and ta.crossover(price_diff, lowerBand))) ? color.new(UpC, 85) : na, title= "BG Color OS")
bgcolor(d_bgcol and ((valuation_enable and price_diff < lowerBand) or (reversion_enable and ta.crossover(price_diff, lowerBand))) ? color.new(UpC, 85) : na, title= "BG Color OS Overlay", force_overlay = true)
plotshape(ta.crossover(signal, 0), title="Buy Signal", location=location.belowbar,
style=shape.labelup, text="𝓛𝓸𝓷𝓰", textcolor=#000000, size=size.small,
color=UpC, force_overlay=true, display= signal_type == "Trend" and d_signs == true ? display.pane : display.none)
plotshape(ta.crossunder(signal, 0), title="Sell Signal", location=location.abovebar,
style=shape.labeldown, text="𝓢𝓱𝓸𝓻𝓽", textcolor=#000000, size=size.small,
color=DnC, force_overlay=true, display= signal_type == "Trend" and d_signs == true ? display.pane : display.none)
plotcandle(open, high, low, close, color= pc, wickcolor = pc, bordercolor = pc, force_overlay = true, display= display.pane, title= "Plot Candle")
barcolor(pc, title= "Barcolor")
// ==========================================================================================
// === Dashboard with Telegram Link ===
var table myTable = table.new(position.top_center, 1, 1, border_width=1, frame_color=color.black, bgcolor=color.white)
// Add Telegram Message to Dashboard
table.cell(myTable, 0, 0, "Join Telegram @mrexpert_ai", bgcolor=color.blue, text_color=color.white, text_size=size.normal)
Combined: Net Volume, RSI & ATR# Combined: Net Volume, RSI & ATR Indicator
## Overview
This custom TradingView indicator overlays **Net Volume** and **RSI (Relative Strength Index)** on the same chart panel, with RSI scaled to match the visual range of volume spikes. It also displays **ATR (Average True Range)** values in a table.
## Key Features
### Net Volume
- Calculates buying vs selling pressure by analyzing lower timeframe data
- Displays as a **yellow line** centered around zero
- Automatically selects optimal timeframe or allows manual override
- Shows net buying pressure (positive values) and selling pressure (negative values)
### RSI (Relative Strength Index)
- Traditional 14-period RSI displayed as a **blue line**
- **Overlays directly on the volume chart** - scaled to match volume spike heights
- Includes **70/30 overbought/oversold levels** (shown as dotted red/green lines)
- Adjustable scale factor to fine-tune visual sizing relative to volume
- Optional **smoothing** with multiple moving average types (SMA, EMA, RMA, WMA, VWMA)
- Optional **Bollinger Bands** around RSI smoothing line
- **Divergence detection** - identifies regular bullish/bearish divergences with labels
### ATR (Average True Range)
- Displays current ATR value in a **table at top-right corner**
- Configurable period length (default: 50)
- Multiple smoothing methods: RMA, SMA, EMA, or WMA
- Helps assess current market volatility
## Use Cases
- **Momentum & Volume Confirmation**: See if RSI trends align with net volume flows
- **Divergence Trading**: Automatically spots when price makes new highs/lows but RSI doesn't
- **Volatility Assessment**: Monitor ATR for position sizing and stop-loss placement
- **Overbought/Oversold + Volume**: Identify exhaustion when RSI hits extremes with volume spikes
## Customization
All components can be toggled on/off independently. RSI scale factor allows you to adjust how prominent the RSI line appears relative to volume bars.
Kịch bản của tôi//@version=6
indicator(title="Relative Strength Index", shorttitle="Gấu Trọc RSI", format=format.price, precision=2, timeframe="", timeframe_gaps=true)
rsiLengthInput = input.int(14, minval=1, title="RSI Length", group="RSI Settings")
rsiSourceInput = input.source(close, "Source", group="RSI Settings")
calculateDivergence = input.bool(false, title="Calculate Divergence", group="RSI Settings", display = display.data_window, tooltip = "Calculating divergences is needed in order for divergence alerts to fire.")
change = ta.change(rsiSourceInput)
up = ta.rma(math.max(change, 0), rsiLengthInput)
down = ta.rma(-math.min(change, 0), rsiLengthInput)
rsi = down == 0 ? 100 : up == 0 ? 0 : 100 - (100 / (1 + up / down))
rsiPlot = plot(rsi, "RSI", color=#7E57C2)
rsiUpperBand1 = hline(98, "RSI Upper Band1", color=#787B86)
rsiUpperBand = hline(70, "RSI Upper Band", color=#787B86)
midline = hline(50, "RSI Middle Band", color=color.new(#787B86, 50))
rsiLowerBand = hline(30, "RSI Lower Band", color=#787B86)
rsiLowerBand2 = hline(14, "RSI Lower Band2", color=#787B86)
fill(rsiUpperBand, rsiLowerBand, color=color.rgb(126, 87, 194, 90), title="RSI Background Fill")
midLinePlot = plot(50, color = na, editable = false, display = display.none)
fill(rsiPlot, midLinePlot, 100, 70, top_color = color.new(color.green, 0), bottom_color = color.new(color.green, 100), title = "Overbought Gradient Fill")
fill(rsiPlot, midLinePlot, 30, 0, top_color = color.new(color.red, 100), bottom_color = color.new(color.red, 0), title = "Oversold Gradient Fill")
// Smoothing MA inputs
GRP = "Smoothing"
TT_BB = "Only applies when 'SMA + Bollinger Bands' is selected. Determines the distance between the SMA and the bands."
maTypeInput = input.string("SMA", "Type", options = , group = GRP, display = display.data_window)
var isBB = maTypeInput == "SMA + Bollinger Bands"
maLengthInput = input.int(14, "Length", group = GRP, display = display.data_window, active = maTypeInput != "None")
bbMultInput = input.float(2.0, "BB StdDev", minval = 0.001, maxval = 50, step = 0.5, tooltip = TT_BB, group = GRP, display = display.data_window, active = isBB)
var enableMA = maTypeInput != "None"
// Smoothing MA Calculation
ma(source, length, MAtype) =>
switch MAtype
"SMA" => ta.sma(source, length)
"SMA + Bollinger Bands" => ta.sma(source, length)
"EMA" => ta.ema(source, length)
"SMMA (RMA)" => ta.rma(source, length)
"WMA" => ta.wma(source, length)
"VWMA" => ta.vwma(source, length)
// Smoothing MA plots
smoothingMA = enableMA ? ma(rsi, maLengthInput, maTypeInput) : na
smoothingStDev = isBB ? ta.stdev(rsi, maLengthInput) * bbMultInput : na
plot(smoothingMA, "RSI-based MA", color=color.yellow, display = enableMA ? display.all : display.none, editable = enableMA)
bbUpperBand = plot(smoothingMA + smoothingStDev, title = "Upper Bollinger Band", color=color.green, display = isBB ? display.all : display.none, editable = isBB)
bbLowerBand = plot(smoothingMA - smoothingStDev, title = "Lower Bollinger Band", color=color.green, display = isBB ? display.all : display.none, editable = isBB)
fill(bbUpperBand, bbLowerBand, color= isBB ? color.new(color.green, 90) : na, title="Bollinger Bands Background Fill", display = isBB ? display.all : display.none, editable = isBB)
// Divergence
lookbackRight = 5
lookbackLeft = 5
rangeUpper = 60
rangeLower = 5
bearColor = color.red
bullColor = color.green
textColor = color.white
noneColor = color.new(color.white, 100)
_inRange(bool cond) =>
bars = ta.barssince(cond)
rangeLower <= bars and bars <= rangeUpper
plFound = false
phFound = false
bullCond = false
bearCond = false
rsiLBR = rsi
if calculateDivergence
//------------------------------------------------------------------------------
// Regular Bullish
// rsi: Higher Low
plFound := not na(ta.pivotlow(rsi, lookbackLeft, lookbackRight))
rsiHL = rsiLBR > ta.valuewhen(plFound, rsiLBR, 1) and _inRange(plFound )
// Price: Lower Low
lowLBR = low
priceLL = lowLBR < ta.valuewhen(plFound, lowLBR, 1)
bullCond := priceLL and rsiHL and plFound
//------------------------------------------------------------------------------
// Regular Bearish
// rsi: Lower High
phFound := not na(ta.pivothigh(rsi, lookbackLeft, lookbackRight))
rsiLH = rsiLBR < ta.valuewhen(phFound, rsiLBR, 1) and _inRange(phFound )
// Price: Higher High
highLBR = high
priceHH = highLBR > ta.valuewhen(phFound, highLBR, 1)
bearCond := priceHH and rsiLH and phFound
plot(
plFound ? rsiLBR : na,
offset = -lookbackRight,
title = "Regular Bullish",
linewidth = 2,
color = (bullCond ? bullColor : noneColor),
display = display.pane,
editable = calculateDivergence)
plotshape(
bullCond ? rsiLBR : na,
offset = -lookbackRight,
title = "Regular Bullish Label",
text = " Bull ",
style = shape.labelup,
location = location.absolute,
color = bullColor,
textcolor = textColor,
display = display.pane,
editable = calculateDivergence)
plot(
phFound ? rsiLBR : na,
offset = -lookbackRight,
title = "Regular Bearish",
linewidth = 2,
color = (bearCond ? bearColor : noneColor),
display = display.pane,
editable = calculateDivergence)
plotshape(
bearCond ? rsiLBR : na,
offset = -lookbackRight,
title = "Regular Bearish Label",
text = " Bear ",
style = shape.labeldown,
location = location.absolute,
color = bearColor,
textcolor = textColor,
display = display.pane,
editable = calculateDivergence)
alertcondition(bullCond, title='Regular Bullish Divergence', message="Found a new Regular Bullish Divergence, `Pivot Lookback Right` number of bars to the left of the current bar.")
alertcondition(bearCond, title='Regular Bearish Divergence', message='Found a new Regular Bearish Divergence, `Pivot Lookback Right` number of bars to the left of the current bar.')
DarkPool's Squeeze Momentum @author LazyBearDarkPool's Squeeze Momentum Pro is a comprehensive overhaul of the classic volatility indicator, designed for the modern trader who requires deeper market insight. While staying true to the core logic of the original TTM Squeeze, this version introduces advanced features like automatic divergence detection, dynamic moving average selection, and main-chart integration to help you time entries and exits with precision.
Credit: This script is built upon the foundational "Squeeze Momentum Indicator" originally developed by LazyBear. This version expands on that legacy with enhanced visualization, alert systems, and divergence logic.
Key Features
1. Advanced Divergence Detection
The indicator automatically scans for Regular Bullish and Regular Bearish divergences between price action and momentum.
Bullish Divergence (Green "BULL" Label): Occurs when Price makes a Lower Low, but Momentum makes a Higher Low. This often precedes a bullish reversal.
Bearish Divergence (Red "BEAR" Label): Occurs when Price makes a Higher High, but Momentum makes a Lower High. This often precedes a bearish reversal.
2. Multi-Mode Squeeze Detection
The central dots on the zero line tell you the state of market volatility:
Red Dot (Squeeze ON): Volatility is compressed. The Bollinger Bands are inside the Keltner Channels. The market is "coiling" and preparing for an explosive move. Do not trade yet—wait for the fire.
Grey Dot (Squeeze OFF): The squeeze has "fired." Volatility is expanding, and price is moving.
Blue Dot (Wide Bands): Volatility is extremely high. The bands are exceptionally wide, often indicating the end of a trend or a period of high risk.
3. "Ghost" Histogram & Visual Depth
The momentum histogram features a "Ghost" fill (transparent background) to help visualize the volume of momentum without cluttering the screen.
Bright Green: Strong Bullish Momentum (Rising).
Dark Green: Weakening Bullish Momentum (Fading).
Bright Red: Strong Bearish Momentum (Falling).
Dark Red: Weakening Bearish Momentum (Recovering).
4. Dynamic Candle Coloring
Enabled by default, this feature colors the candles on your main chart to match the momentum histogram. This allows you to instantly gauge the trend strength without looking down at the oscillator pane.
5. Adaptive Calculation Engines
Unlike standard versions fixed to SMA, you can now select the moving average algorithm that drives the Bollinger Bands and Keltner Channels:
SMA: Standard, stable signals.
EMA: More reactive to recent price action.
WMA/RMA: Weighted options for specific strategies.
🛠 How to Operate
The "Squeeze & Fire" Strategy
Identify the Squeeze: Look for a series of Red Dots on the zero line. This indicates the market is resting and building energy.
The Trigger: Wait for the dot to turn Gray AND for the histogram to expand clearly in one direction.
Long Signal: Squeeze fires (Red -> Gray) + Histogram turns Green.
Short Signal: Squeeze fires (Red -> Gray) + Histogram turns Red.
The "Divergence Reversal" Strategy
Watch for "BULL" or "BEAR" labels appearing near the peaks or valleys of the histogram.
Confirmation: A divergence is a warning. Wait for the histogram color to change (e.g., from Bright Red to Dark Red) before entering a reversal trade.
⚙️ Settings Guide
Basis MA Type: Choose between SMA, EMA, WMA, or RMA to tune the sensitivity of the squeeze.
BB/KC Settings: Fully customizable Length and Multipliers to adapt to different assets (Crypto, Forex, or Stocks).
Pivot Lookback: Controls how strict the divergence detection is. Higher numbers = fewer, more significant signals.
Colour Main Chart Candles: Toggle this OFF if you prefer your standard candle colours.
Disclaimer
Trading involves a high level of risk and is not suitable for all investors. This indicator is a tool for technical analysis and does not constitute financial advice. Past performance is not indicative of future results. Always use proper risk management and do not trade based solely on a single indicator.
Mambo MA & HAMambo MA & HA is a combined trend-view indicator that overlays Heikin Ashi direction markers and up to eight customizable moving averages on any chart.
The goal is to give a clear, uncluttered visual summary of short-term and long-term trend direction using both regular chart data and Heikin Ashi structure.
This indicator displays:
Heikin Ashi (HA) directional markers on the chart timeframe
Optional Heikin Ashi markers from a second, higher timeframe
Up to eight different moving averages (SMA, EMA, SMMA/RMA, WMA, VWMA)
Adjustable colors and transparency for visual layering
Offset controls for HA markers to prevent overlap with price candles
It is designed for visual clarity without altering the underlying price candles.
Heikin Ashi Direction Markers (Chart Timeframe)
The indicator generates HA OHLC values internally and compares the HA open and close:
Green (bullish) HA candle → triangle-up marker plotted above the bar
Red (bearish) HA candle → triangle-down marker plotted above the bar
The triangles use soft pastel colors for minimal obstruction:
Up marker: light green (rgb 204, 232, 204)
Down marker: light red (rgb 255, 204, 204)
The “HA Offset (chart TF ticks)” input lets users shift the triangle vertically in price terms to avoid overlapping the real candles or MAs.
Heikin Ashi Markers from a Second Timeframe
An optional second timeframe (default: 60m) shows additional HA direction:
Green HA (higher timeframe) → tiny triangle-up below the bar
Red HA (higher timeframe) → tiny triangle-down below the bar
This allows a trader to see higher-timeframe HA structure without switching charts.
The offset for the second timeframe is independent (“HA Offset (extra TF ticks)”).
Custom Moving Averages (Up to Eight)
The indicator includes eight individually configurable MAs, each with:
On/off visibility toggle
MA type
SMA
EMA
SMMA / RMA
WMA
VWMA
Source
Length
Color (with preset 70% transparency for visual stacking)
The default MA lengths are: 10, 20, 50, 100, 150, 200, 250, 300.
All MA colors are slightly transparent by design to avoid obscuring price bars and HA markers.
Purpose of the Indicator
This tool provides a simple combined view of:
Immediate trend direction (chart-TF HA markers)
Higher-timeframe HA trend bias (extra-TF markers)
Overall moving-average structure from short to very long periods
It is particularly useful for:
Monitoring trend continuation vs. reversal
Confirming entries with multi-TF Heikin Ashi direction
Identifying pullbacks relative to layered moving averages
Viewing trend context without switching timeframes
There are no signals, alerts, or strategy components.
It is strictly a visual trend-context tool.
Key Features Summary
Two-timeframe Heikin Ashi direction
Separate offsets for HA markers
Eight fully configurable MAs
Clean color scheme with low opacity
Non-intrusive overlays
Compatible with all markets and chart types
Average True Range with MAKey features
ATR calculation: true range (ta.tr(true)) is smoothed using a selectable method to produce the ATR.
ATR smoothing options: RMA, SMA, EMA, or WMA for the ATR calculation.
MA-on-ATR: a separate moving average computed on the ATR values with its own length and smoothing method.
Display controls: toggles to show/hide the ATR and the ATR MA independently.
Appearance controls: separate color inputs for the ATR and the ATR MA, and a thicker line for the MA (linewidth=2).
Inputs
ATR Length (default 14): length used to smooth true range into the ATR.
ATR Smoothing (default RMA): smoothing method applied to the true range to form ATR.
MA Length (on ATR) (default 14): length for the moving average applied to the ATR series.
MA Smoothing (default SMA): smoothing method used for the MA applied to ATR.
Show ATR / Show ATR MA: booleans to toggle visibility.
ATR Color / ATR MA Color: choose plot colors.
How to interpret
ATR line: shows current volatility (average true range). Rising ATR indicates increasing volatility; falling ATR indicates decreasing volatility.
ATR MA line: smooths the ATR to reveal trend direction and reduce noise.
Use crossovers: ATR crossing above its MA may signal volatility is picking up; ATR crossing below its MA suggests volatility is subsiding.
Combine with price action or other indicators (e.g., breakout systems, position sizing rules) to make decisions based on volatility regime.
Sigma Trinity ModelAbstract
Sigma Trinity Model is an educational framework that studies how three layers of market behavior interact within the same trend: (1) structural momentum (Rasta), (2) internal strength (RSI), and (3) continuation/compounding structure (Pyramid). The model deliberately combines bar-close momentum logic with intrabar, wick-aware strength checks to help users see how reversals form, confirm, and extend. It is not a signal service or automation tool; it is a transparent learning instrument for chart study and backtesting.
Why this is not “just a mashup”
Many scripts merge indicators without explaining the purpose. Sigma Trinity is a coordinated, three-engine study designed for a specific learning goal:
Rasta (structure): defines when momentum actually flips using a dual-line EMA vs smoothed EMA. It gives the entry/exit framework on bar close for clean historical study.
RSI (energy): measures internal strength with wick-aware triggers. It uses RSI of LOW (for bottom touches/reclaims) and RSI of HIGH (for top touches/exhaustion) so users can see intrabar strength/weakness that the close can hide.
Pyramid (progression): demonstrates how continuation behaves once momentum and strength align. It shows the logic of adds (compounding) as a didactic layer, also on bar close to keep historical alignment consistent.
These three roles are complementary, not redundant: structure → strength → progression.
Architecture Overview
Execution model
Rasta & Pyramid: bar close only by default (historically stable, easy to audit).
RSI: per tick (realtime) with bar-close backup by default, using RSI of LOW for entries and RSI of HIGH for exits. This makes the module sensitive to intra-bar wicks while still giving a close-based safety net for backtests.
Stops (optional in strategy builds): wick-accurate: trail arms/ratchets on HIGH; stop hit checks with LOW (or Close if selected) with a small undershoot buffer to avoid micro-noise hits.
Visual model
Dual lines (EMA vs smoothed EMA) for Rasta + color fog to see direction and compression/expansion.
Rungs (small vertical lines) drawn between the two Rasta lines to visualize wave spacing and rhythm.
Clean labels for Entry/Exit/Pyramid Add/RSI events. Everything is state-locked to avoid spamming.
Module 1 — Rasta (Structural Momentum Layer)
Goal: Identify structural momentum reversals and maintain a consistent, replayable backbone for study.
Method:
Compute an EMA of a chosen price source (default Close), and a smoothed version (SMA/EMA/RMA/WMA/None selectable).
Flip points occur when the EMA line crosses the smoothed line.
Optional EMA 8/21 trend filter can gate entries (long-bias when EMA8 > EMA21). A small “adaptive on flip” option lets an entry fire when the filter itself flips to ON and the EMA is already above the smoothed line—useful for trend resumption.
Why bar close only?
Bar-close Rasta gives a stable, auditable timeline for the structure of the trend. It teaches users to separate “structure” (close-resolved) from “energy” (intrabar, via RSI).
Visuals:
Fog between the lines (green/red) to show regime.
Rungs between lines to show spread (compression vs expansion).
Optional plotting of EMA8/EMA21 so users can see the gating effect.
Module 2 — RSI (Internal Strength / Energy Layer)
Goal: Reveal the intrabar strength/weakness that often precedes or confirms structural flips.
Method:
Standard RSI with adjustable length and signal smoothing for the panel view.
Logic uses wick-aware sources:
Entry trigger: RSI of LOW (same RSI length) touching or below a lower band (default 15). Think of it as intraband reactivation from the bottom, using the candle’s deepest excursion.
Exit trigger: RSI of HIGH touching or above an upper band (default 85). Think of it as exhaustion at the top, using the candle’s highest excursion.
Realtime + Close Backup: fires intrabar on tick, but if the realtime event was missed, the close backup will note it at bar end.
Cooldown control: optional bars-between-signals to avoid rapid re-triggers on choppy sequences.
Why wick-aware RSI?
A close-only RSI can miss the true micro-extremes that cause reversals. Using LOW/HIGH for triggers captures the behavior that traders actually react to during the bar, while the bar-close backup preserves historical reproducibility.
Module 3 — Pyramid (Continuation / Compounding Layer)
Goal: Teach how continuation behaves once a trend is underway, and how adds can be structured.
Method:
Same dual-line logic as Rasta (EMA vs smoothed EMA), but only fires when already in a position (or after prior entry conditions).
Supports the same EMA 8/21 filter and optional adaptive-on-flip behavior.
Bar close only to maintain historical cohesion.
What it teaches:
Adds tend to cluster when momentum persists.
Students can experiment with add spacing and compare “one-shot entries” vs “laddered adds” during strong regimes.
How the Pieces Work Together
Rasta establishes the structural frame (when the wave flip is real enough to record at close).
RSI validates or challenges that structure by tracking intrabar energy at the extremes (low/high touches).
Pyramid shows what sustained continuation looks like once (1) and (2) align.
This produces a layered view: Structure → Energy → Progression. Users can see when all three line up (strongest phases) and when they diverge (riskier phases or transitions).
How to Use It (Step-by-Step)
Quick Start
Apply script to any symbol/timeframe.
In Strategy/Indicator Properties:
Enable On every tick (recommended).
If available, enable Using bar magnifier and choose a lower resolution (e.g., 1m) to simulate intrabar fills more realistically.
Keep On bar close unchecked if you want to observe realtime logic in live charts (strategies still place orders on close by platform design).
Default behavior: Rasta & Pyramid = bar close; RSI = per tick with close backup.
Reading the Chart
Watch for Rasta Entry/Exit labels: they define clean structural turns on close.
Watch RSI Entry (LOW touch at/below lower band) and RSI Exit (HIGH touch at/above upper band) to gauge internal energy extremes.
Pyramid Add labels reveal continuation phases once a move is already in progress.
Tuning
Rasta smoothing: choose SMA/EMA/RMA/WMA or None. Higher smoothing → later but cleaner flips; lower smoothing → earlier but choppier.
RSI bands: a common educational setting is 15/85 for strong extremes; 20/80 is a bit looser.
Cooldown: increase if you see too many RSI re-fires in chop.
EMA 8/21 filter: toggle ON to study “trend-gated” entries, OFF to study raw momentum flips.
Backtesting Notes (for Strategy Builds)
Stops (optional): trail is armed when price advances by a trigger (default D–F₀), ratchets only upward from HIGH, and hits from LOW (or Close if chosen) with a tiny undershoot buffer to avoid micro-wicks.
Order sequencing per bar (mirrors the script’s code comments):
Trail ratchet via HIGH
Intrabar stop hit via LOW/CLOSE → immediate close
If still in position at bar close: process exits (Rasta/RSI)
If still in position at bar close: process Pyramid Add
If flat at bar close: process entries (Rasta/RSI)
Platform reality: strategies place orders at bar close in historical testing; the intrabar logic improves realism for stops and event marking but final order timestamps are still close-resolved.
Inputs Reference (common)
Modules: enable/disable RSI and Pyramid learning layers.
Rasta: EMA length, smoothing type/length, EMA8/21 filter & adaptive flip, fog opacity, rungs on/off & limit.
RSI: RSI length, signal MA length (panel), Entry band (LOW), Exit band (HIGH), cooldown bars, labels.
Pyramid: EMA length, smoothing, EMA8/21 filter & adaptive adds.
Execution: toggle Bar Close Only for Rasta/Pyramid; toggle Realtime + Close Backup for RSI.
Stops (strategy): Fixed Stop % (first), Fixed Stop % (add), Trail Distance %, Trigger rule (auto D–F₀ or custom), undershoot buffer %, and hit source (LOW/CLOSE).
What to Study With It
Convergence: how often RSI-LOW entry touches precede the next Rasta flip.
Divergence: cases where RSI screams exhaustion (HIGH >= upper band) but Rasta hasn’t flipped yet—often transition zones.
Continuation: how Pyramid adds cluster in strong moves; how spacing changes with smoothing/filter choices.
Regime changes: use EMA8/21 filter toggles to see what happens at macro turns vs chop.
Limitations & Scope
This is a learning tool, not a trade copier. It does not provide financial advice or automated execution.
Intrabar results depend on data granularity; bar magnifier (when available) can help simulate lower-resolution ticks, but true tick-by-tick fills are a platform-level feature and not guaranteed across all symbols.
Suggested Publication Settings (Strategy)
Initial capital: 100
Order size: 100 USD (cash)
Pyramiding: 10
Commission: 0.25%
Slippage: 3 ticks
Recalculate: ✓ On every tick
Fill orders: ✓ Using bar magnifier (choose 1m or similar); leave On bar close unchecked for live viewing.
Educational License
Released under the Michael Culpepper Gratitude License (2025).
Use and modify freely for education and research with attribution. No resale. No promises of profitability. Purpose is understanding, not signals.
RSI Overbought/Oversold + Divergence Indicator (new)//@version=5
indicator('CryptoSignalScanner - RSI Overbought/Oversold + Divergence Indicator (new)',
//---------------------------------------------------------------------------------------------------------------------------------
//--- Define Colors ---------------------------------------------------------------------------------------------------------------
//---------------------------------------------------------------------------------------------------------------------------------
vWhite = #FFFFFF
vViolet = #C77DF3
vIndigo = #8A2BE2
vBlue = #009CDF
vGreen = #5EBD3E
vYellow = #FFB900
vRed = #E23838
longColor = color.green
shortColor = color.red
textColor = color.white
bullishColor = color.rgb(38,166,154,0) //Used in the display table
bearishColor = color.rgb(239,83,79,0) //Used in the display table
nomatchColor = color.silver //Used in the display table
//---------------------------------------------------------------------------------------------------------------------------------------------------------------------
//--- Functions--------------------------------------------------------------------------------------------------------------------------------------------------------
//---------------------------------------------------------------------------------------------------------------------------------------------------------------------
TF2txt(TF) =>
switch TF
"S" => "RSI 1s:"
"5S" => "RSI 5s:"
"10S" => "RSI 10s:"
"15S" => "RSI 15s:"
"30S" => "RSI 30s"
"1" => "RSI 1m:"
"3" => "RSI 3m:"
"5" => "RSI 5m:"
"15" => "RSI 15m:"
"30" => "RSI 30m"
"45" => "RSI 45m"
"60" => "RSI 1h:"
"120" => "RSI 2h:"
"180" => "RSI 3h:"
"240" => "RSI 4h:"
"480" => "RSI 8h:"
"D" => "RSI 1D:"
"1D" => "RSI 1D:"
"2D" => "RSI 2D:"
"3D" => "RSI 2D:"
"3D" => "RSI 3W:"
"W" => "RSI 1W:"
"1W" => "RSI 1W:"
"M" => "RSI 1M:"
"1M" => "RSI 1M:"
"3M" => "RSI 3M:"
"6M" => "RSI 6M:"
"12M" => "RSI 12M:"
//---------------------------------------------------------------------------------------------------------------------------------------------------------------------
//--- Show/Hide Settings ----------------------------------------------------------------------------------------------------------------------------------------------
//---------------------------------------------------------------------------------------------------------------------------------------------------------------------
rsiShowInput = input(true, title='Show RSI', group='Show/Hide Settings')
maShowInput = input(false, title='Show MA', group='Show/Hide Settings')
showRSIMAInput = input(true, title='Show RSIMA Cloud', group='Show/Hide Settings')
rsiBandShowInput = input(true, title='Show Oversold/Overbought Lines', group='Show/Hide Settings')
rsiBandExtShowInput = input(true, title='Show Oversold/Overbought Extended Lines', group='Show/Hide Settings')
rsiHighlightShowInput = input(true, title='Show Oversold/Overbought Highlight Lines', group='Show/Hide Settings')
DivergenceShowInput = input(true, title='Show RSI Divergence Labels', group='Show/Hide Settings')
//---------------------------------------------------------------------------------------------------------------------------------------------------------------------
//--- Table Settings --------------------------------------------------------------------------------------------------------------------------------------------------
//---------------------------------------------------------------------------------------------------------------------------------------------------------------------
rsiShowTable = input(true, title='Show RSI Table Information box', group="RSI Table Settings")
rsiTablePosition = input.string(title='Location', defval='middle_right', options= , group="RSI Table Settings", inline='1')
rsiTextSize = input.string(title=' Size', defval='small', options= , group="RSI Table Settings", inline='1')
rsiShowTF1 = input(true, title='Show TimeFrame1', group="RSI Table Settings", inline='tf1')
rsiTF1 = input.timeframe("15", title=" Time", group="RSI Table Settings", inline='tf1')
rsiShowTF2 = input(true, title='Show TimeFrame2', group="RSI Table Settings", inline='tf2')
rsiTF2 = input.timeframe("60", title=" Time", group="RSI Table Settings", inline='tf2')
rsiShowTF3 = input(true, title='Show TimeFrame3', group="RSI Table Settings", inline='tf3')
rsiTF3 = input.timeframe("240", title=" Time", group="RSI Table Settings", inline='tf3')
rsiShowTF4 = input(true, title='Show TimeFrame4', group="RSI Table Settings", inline='tf4')
rsiTF4 = input.timeframe("D", title=" Time", group="RSI Table Settings", inline='tf4')
rsiShowHist = input(true, title='Show RSI Historical Columns', group="RSI Table Settings", tooltip='Show the information of the 2 previous closed candles')
//---------------------------------------------------------------------------------------------------------------------------------------------------------------------
//--- RSI Input Settings ----------------------------------------------------------------------------------------------------------------------------------------------
//---------------------------------------------------------------------------------------------------------------------------------------------------------------------
rsiSourceInput = input.source(close, 'Source', group='RSI Settings')
rsiLengthInput = input.int(14, minval=1, title='RSI Length', group='RSI Settings', tooltip='Here we set the RSI lenght')
rsiColorInput = input.color(#26a69a, title="RSI Color", group='RSI Settings')
rsimaColorInput = input.color(#ef534f, title="RSIMA Color", group='RSI Settings')
rsiBandColorInput = input.color(#787B86, title="RSI Band Color", group='RSI Settings')
rsiUpperBandExtInput = input.int(title='RSI Overbought Extended Line', defval=80, minval=50, maxval=100, group='RSI Settings')
rsiUpperBandInput = input.int(title='RSI Overbought Line', defval=70, minval=50, maxval=100, group='RSI Settings')
rsiLowerBandInput = input.int(title='RSI Oversold Line', defval=30, minval=0, maxval=50, group='RSI Settings')
rsiLowerBandExtInput = input.int(title='RSI Oversold Extended Line', defval=20, minval=0, maxval=50, group='RSI Settings')
//---------------------------------------------------------------------------------------------------------------------------------------------------------------------
//--- MA Input Settings -----------------------------------------------------------------------------------------------------------------------------------------------
//---------------------------------------------------------------------------------------------------------------------------------------------------------------------
maTypeInput = input.string("EMA", title="MA Type", options= , group="MA Settings")
maLengthInput = input.int(14, title="MA Length", group="MA Settings")
maColorInput = input.color(color.yellow, title="MA Color", group='MA Settings') //#7E57C2
//---------------------------------------------------------------------------------------------------------------------------------------------------------------------
//--- Divergence Input Settings ---------------------------------------------------------------------------------------------------------------------------------------
//---------------------------------------------------------------------------------------------------------------------------------------------------------------------
lbrInput = input(title="Pivot Lookback Right", defval=2, group='RSI Divergence Settings')
lblInput = input(title="Pivot Lookback Left", defval=2, group='RSI Divergence Settings')
lbRangeMaxInput = input(title="Max of Lookback Range", defval=10, group='RSI Divergence Settings')
lbRangeMinInput = input(title="Min of Lookback Range", defval=2, group='RSI Divergence Settings')
plotBullInput = input(title="Plot Bullish", defval=true, group='RSI Divergence Settings')
plotHiddenBullInput = input(title="Plot Hidden Bullish", defval=true, group='RSI Divergence Settings')
plotBearInput = input(title="Plot Bearish", defval=true, group='RSI Divergence Settings')
plotHiddenBearInput = input(title="Plot Hidden Bearish", defval=true, group='RSI Divergence Settings')
//---------------------------------------------------------------------------------------------------------------------------------------------------------------------
//--- RSI Calculation -------------------------------------------------------------------------------------------------------------------------------------------------
//---------------------------------------------------------------------------------------------------------------------------------------------------------------------
rsi = ta.rsi(rsiSourceInput, rsiLengthInput)
rsiprevious = rsi
= request.security(syminfo.tickerid, rsiTF1, [rsi, rsi , rsi ], lookahead=barmerge.lookahead_on)
= request.security(syminfo.tickerid, rsiTF2, [rsi, rsi , rsi ], lookahead=barmerge.lookahead_on)
= request.security(syminfo.tickerid, rsiTF3, [rsi, rsi , rsi ], lookahead=barmerge.lookahead_on)
= request.security(syminfo.tickerid, rsiTF4, [rsi, rsi , rsi ], lookahead=barmerge.lookahead_on)
//---------------------------------------------------------------------------------------------------------------------------------------------------------------------
//--- MA Calculation -------------------------------------------------------------------------------------------------------------------------------------------------
//---------------------------------------------------------------------------------------------------------------------------------------------------------------------
ma(source, length, type) =>
switch type
"SMA" => ta.sma(source, length)
"Bollinger Bands" => ta.sma(source, length)
"EMA" => ta.ema(source, length)
"SMMA (RMA)" => ta.rma(source, length)
"WMA" => ta.wma(source, length)
"VWMA" => ta.vwma(source, length)
rsiMA = ma(rsi, maLengthInput, maTypeInput)
rsiMAPrevious = rsiMA
//---------------------------------------------------------------------------------------------------------------------------------------------------------------------
//--- Stoch RSI Settings + Calculation --------------------------------------------------------------------------------------------------------------------------------
//---------------------------------------------------------------------------------------------------------------------------------------------------------------------
showStochRSI = input(false, title="Show Stochastic RSI", group='Stochastic RSI Settings')
smoothK = input.int(title="Stochastic K", defval=3, minval=1, maxval=10, group='Stochastic RSI Settings')
smoothD = input.int(title="Stochastic D", defval=4, minval=1, maxval=10, group='Stochastic RSI Settings')
lengthRSI = input.int(title="Stochastic RSI Lenght", defval=14, minval=1, group='Stochastic RSI Settings')
lengthStoch = input.int(title="Stochastic Lenght", defval=14, minval=1, group='Stochastic RSI Settings')
colorK = input.color(color.rgb(41,98,255,0), title="K Color", group='Stochastic RSI Settings', inline="1")
colorD = input.color(color.rgb(205,109,0,0), title="D Color", group='Stochastic RSI Settings', inline="1")
StochRSI = ta.rsi(rsiSourceInput, lengthRSI)
k = ta.sma(ta.stoch(StochRSI, StochRSI, StochRSI, lengthStoch), smoothK) //Blue Line
d = ta.sma(k, smoothD) //Red Line
//---------------------------------------------------------------------------------------------------------------------------------------------------------------------
//--- Divergence Settings ------------------------------------------------------------------------------------------------------------------------------------------
//---------------------------------------------------------------------------------------------------------------------------------------------------------------------
bearColor = color.red
bullColor = color.green
hiddenBullColor = color.new(color.green, 50)
hiddenBearColor = color.new(color.red, 50)
//textColor = color.white
noneColor = color.new(color.white, 100)
osc = rsi
plFound = na(ta.pivotlow(osc, lblInput, lbrInput)) ? false : true
phFound = na(ta.pivothigh(osc, lblInput, lbrInput)) ? false : true
_inRange(cond) =>
bars = ta.barssince(cond == true)
lbRangeMinInput <= bars and bars <= lbRangeMaxInput
//---------------------------------------------------------------------------------------------------------------------------------------------------------------------
//--- Define Plot & Line Colors ---------------------------------------------------------------------------------------------------------------------------------------
//---------------------------------------------------------------------------------------------------------------------------------------------------------------------
rsiColor = rsi >= rsiMA ? rsiColorInput : rsimaColorInput
//---------------------------------------------------------------------------------------------------------------------------------------------------------------------
//--- Plot Lines ------------------------------------------------------------------------------------------------------------------------------------------------------
//---------------------------------------------------------------------------------------------------------------------------------------------------------------------
// Create a horizontal line at a specific price level
myLine = line.new(bar_index , 75, bar_index, 75, color = color.rgb(187, 14, 14), width = 2)
bottom = line.new(bar_index , 50, bar_index, 50, color = color.rgb(223, 226, 28), width = 2)
mymainLine = line.new(bar_index , 60, bar_index, 60, color = color.rgb(13, 154, 10), width = 3)
hline(50, title='RSI Baseline', color=color.new(rsiBandColorInput, 50), linestyle=hline.style_solid, editable=false)
hline(rsiBandExtShowInput ? rsiUpperBandExtInput : na, title='RSI Upper Band', color=color.new(rsiBandColorInput, 10), linestyle=hline.style_dashed, editable=false)
hline(rsiBandShowInput ? rsiUpperBandInput : na, title='RSI Upper Band', color=color.new(rsiBandColorInput, 10), linestyle=hline.style_dashed, editable=false)
hline(rsiBandShowInput ? rsiLowerBandInput : na, title='RSI Upper Band', color=color.new(rsiBandColorInput, 10), linestyle=hline.style_dashed, editable=false)
hline(rsiBandExtShowInput ? rsiLowerBandExtInput : na, title='RSI Upper Band', color=color.new(rsiBandColorInput, 10), linestyle=hline.style_dashed, editable=false)
bgcolor(rsiHighlightShowInput ? rsi >= rsiUpperBandExtInput ? color.new(rsiColorInput, 70) : na : na, title="Show Extended Oversold Highlight", editable=false)
bgcolor(rsiHighlightShowInput ? rsi >= rsiUpperBandInput ? rsi < rsiUpperBandExtInput ? color.new(#64ffda, 90) : na : na: na, title="Show Overbought Highlight", editable=false)
bgcolor(rsiHighlightShowInput ? rsi <= rsiLowerBandInput ? rsi > rsiLowerBandExtInput ? color.new(#F43E32, 90) : na : na : na, title="Show Extended Oversold Highlight", editable=false)
bgcolor(rsiHighlightShowInput ? rsi <= rsiLowerBandInput ? color.new(rsimaColorInput, 70) : na : na, title="Show Oversold Highlight", editable=false)
maPlot = plot(maShowInput ? rsiMA : na, title='MA', color=color.new(maColorInput,0), linewidth=1)
rsiMAPlot = plot(showRSIMAInput ? rsiMA : na, title="RSI EMA", color=color.new(rsimaColorInput,0), editable=false, display=display.none)
rsiPlot = plot(rsiShowInput ? rsi : na, title='RSI', color=color.new(rsiColor,0), linewidth=1)
fill(rsiPlot, rsiMAPlot, color=color.new(rsiColor, 60), title="RSIMA Cloud")
plot(showStochRSI ? k : na, title='Stochastic K', color=colorK, linewidth=1)
plot(showStochRSI ? d : na, title='Stochastic D', color=colorD, linewidth=1)
//---------------------------------------------------------------------------------------------------------------------------------------------------------------------
//--- Plot Divergence -------------------------------------------------------------------------------------------------------------------------------------------------
//---------------------------------------------------------------------------------------------------------------------------------------------------------------------
// Regular Bullish
// Osc: Higher Low
oscHL = osc > ta.valuewhen(plFound, osc , 1) and _inRange(plFound )
// Price: Lower Low
priceLL = low < ta.valuewhen(plFound, low , 1)
bullCond = plotBullInput and priceLL and oscHL and plFound
plot(
plFound ? osc : na,
offset=-lbrInput,
title="Regular Bullish",
linewidth=2,
color=(bullCond ? bullColor : noneColor)
)
plotshape(
DivergenceShowInput ? bullCond ? osc : na : na,
offset=-lbrInput,
title="Regular Bullish Label",
text=" Bull ",
style=shape.labelup,
location=location.absolute,
color=bullColor,
textcolor=textColor
)
//------------------------------------------------------------------------------
// Hidden Bullish
// Osc: Lower Low
oscLL = osc < ta.valuewhen(plFound, osc , 1) and _inRange(plFound )
// Price: Higher Low
priceHL = low > ta.valuewhen(plFound, low , 1)
hiddenBullCond = plotHiddenBullInput and priceHL and oscLL and plFound
plot(
plFound ? osc : na,
offset=-lbrInput,
title="Hidden Bullish",
linewidth=2,
color=(hiddenBullCond ? hiddenBullColor : noneColor)
)
plotshape(
DivergenceShowInput ? hiddenBullCond ? osc : na : na,
offset=-lbrInput,
title="Hidden Bullish Label",
text=" H Bull ",
style=shape.labelup,
location=location.absolute,
color=bullColor,
textcolor=textColor
)
//------------------------------------------------------------------------------
// Regular Bearish
// Osc: Lower High
oscLH = osc < ta.valuewhen(phFound, osc , 1) and _inRange(phFound )
// Price: Higher High
priceHH = high > ta.valuewhen(phFound, high , 1)
bearCond = plotBearInput and priceHH and oscLH and phFound
plot(
phFound ? osc : na,
offset=-lbrInput,
title="Regular Bearish",
linewidth=2,
color=(bearCond ? bearColor : noneColor)
)
plotshape(
DivergenceShowInput ? bearCond ? osc : na : na,
offset=-lbrInput,
title="Regular Bearish Label",
text=" Bear ",
style=shape.labeldown,
location=location.absolute,
color=bearColor,
textcolor=textColor
)
//------------------------------------------------------------------------------
// Hidden Bearish
// Osc: Higher High
oscHH = osc > ta.valuewhen(phFound, osc , 1) and _inRange(phFound )
// Price: Lower High
priceLH = high < ta.valuewhen(phFound, high , 1)
hiddenBearCond = plotHiddenBearInput and priceLH and oscHH and phFound
plot(
phFound ? osc : na,
offset=-lbrInput,
title="Hidden Bearish",
linewidth=2,
color=(hiddenBearCond ? hiddenBearColor : noneColor)
)
plotshape(
DivergenceShowInput ? hiddenBearCond ? osc : na : na,
offset=-lbrInput,
title="Hidden Bearish Label",
text=" H Bear ",
style=shape.labeldown,
location=location.absolute,
color=bearColor,
textcolor=textColor
)
//---------------------------------------------------------------------------------------------------------------------------------------------------------------------
//--- Check RSI Lineup ------------------------------------------------------------------------------------------------------------------------------------------------
//---------------------------------------------------------------------------------------------------------------------------------------------------------------------
bullTF = rsi > rsi and rsi > rsi
bearTF = rsi < rsi and rsi < rsi
bullTF1 = rsi1 > rsi1_1 and rsi1_1 > rsi1_2
bearTF1 = rsi1 < rsi1_1 and rsi1_1 < rsi1_2
bullTF2 = rsi2 > rsi2_1 and rsi2_1 > rsi2_2
bearTF2 = rsi2 < rsi2_1 and rsi2_1 < rsi2_2
bullTF3 = rsi3 > rsi3_1 and rsi3_1 > rsi3_2
bearTF3 = rsi3 < rsi3_1 and rsi3_1 < rsi3_2
bullTF4 = rsi4 > rsi4_1 and rsi4_1 > rsi4_2
bearTF4 = rsi4 < rsi4_1 and rsi4_1 < rsi4_2
bbTxt(bull,bear) =>
bull ? "BULLISH" : bear ? "BEARISCH" : 'NO LINEUP'
bbColor(bull,bear) =>
bull ? bullishColor : bear ? bearishColor : nomatchColor
newTC(tBox, col, row, txt, width, txtColor, bgColor, txtHA, txtSize) =>
table.cell(table_id=tBox,column=col, row=row, text=txt, width=width,text_color=txtColor,bgcolor=bgColor, text_halign=txtHA, text_size=txtSize)
//---------------------------------------------------------------------------------------------------------------------------------------------------------------------
//--- Define RSI Table Setting ----------------------------------------------------------------------------------------------------------------------------------------
//---------------------------------------------------------------------------------------------------------------------------------------------------------------------
width_c0 = 0
width_c1 = 0
if rsiShowTable
var tBox = table.new(position=rsiTablePosition, columns=5, rows=6, bgcolor=color.rgb(18,22,33,50), frame_color=color.black, frame_width=1, border_color=color.black, border_width=1)
newTC(tBox, 0,1,"RSI Current",width_c0,color.orange,color.rgb(0,0,0,100),'right',rsiTextSize)
newTC(tBox, 1,1,str.format(" {0,number,#.##} ", rsi),width_c0,vWhite,rsi < 50 ? bearishColor:bullishColor,'left',rsiTextSize)
newTC(tBox, 4,1,bbTxt(bullTF, bearTF),width_c0,vWhite,bbColor(bullTF, bearTF),'center',rsiTextSize)
if rsiShowHist
newTC(tBox, 2,1,str.format(" {0,number,#.##} ", rsi ),width_c0,vWhite,rsi < 50 ? bearishColor:bullishColor,'left',rsiTextSize)
newTC(tBox, 3,1,str.format(" {0,number,#.##} ", rsi ),width_c0,vWhite,rsi < 50 ? bearishColor:bullishColor,'left',rsiTextSize)
if rsiShowTF1
newTC(tBox, 0,2,TF2txt(rsiTF1),width_c0,vWhite,color.rgb(0,0,0,100),'right',rsiTextSize)
newTC(tBox, 1,2,str.format(" {0,number,#.##} ", rsi1),width_c0,vWhite,rsi1 < 50 ? bearishColor:bullishColor,'left',rsiTextSize)
newTC(tBox, 4,2,bbTxt(bullTF1, bearTF1),width_c0,vWhite,bbColor(bullTF1,bearTF1),'center',rsiTextSize)
if rsiShowHist
newTC(tBox, 2,2,str.format(" {0,number,#.##} ", rsi1_1),width_c0,vWhite,rsi1_1 < 50 ? bearishColor:bullishColor,'left',rsiTextSize)
newTC(tBox, 3,2,str.format(" {0,number,#.##} ", rsi1_2),width_c0,vWhite,rsi1_2 < 50 ? bearishColor:bullishColor,'left',rsiTextSize)
if rsiShowTF2
newTC(tBox, 0,3,TF2txt(rsiTF2),width_c0,vWhite,color.rgb(0,0,0,100),'right',rsiTextSize)
newTC(tBox, 1,3,str.format(" {0,number,#.##} ", rsi2),width_c0,vWhite,rsi2 < 50 ? bearishColor:bullishColor,'left',rsiTextSize)
newTC(tBox, 4,3,bbTxt(bullTF2, bearTF2),width_c0,vWhite,bbColor(bullTF2,bearTF2),'center',rsiTextSize)
if rsiShowHist
newTC(tBox, 2,3,str.format(" {0,number,#.##} ", rsi2_1),width_c0,vWhite,rsi2_1 < 50 ? bearishColor:bullishColor,'left',rsiTextSize)
newTC(tBox, 3,3,str.format(" {0,number,#.##} ", rsi2_2),width_c0,vWhite,rsi2_2 < 50 ? bearishColor:bullishColor,'left',rsiTextSize)
if rsiShowTF3
newTC(tBox, 0,4,TF2txt(rsiTF3),width_c0,vWhite,color.rgb(0,0,0,100),'right',rsiTextSize)
newTC(tBox, 1,4,str.format(" {0,number,#.##} ", rsi3),width_c0,vWhite,rsi3 < 50 ? bearishColor:bullishColor,'left',rsiTextSize)
newTC(tBox, 4,4,bbTxt(bullTF3, bearTF3),width_c0,vWhite,bbColor(bullTF3,bearTF3),'center',rsiTextSize)
if rsiShowHist
newTC(tBox, 2,4,str.format(" {0,number,#.##} ", rsi3_1),width_c0,vWhite,rsi3_1 < 50 ? bearishColor:bullishColor,'left',rsiTextSize)
newTC(tBox, 3,4,str.format(" {0,number,#.##} ", rsi3_2),width_c0,vWhite,rsi3_2 < 50 ? bearishColor:bullishColor,'left',rsiTextSize)
if rsiShowTF4
newTC(tBox, 0,5,TF2txt(rsiTF4),width_c0,vWhite,color.rgb(0,0,0,100),'right',rsiTextSize)
newTC(tBox, 1,5,str.format(" {0,number,#.##} ", rsi4),width_c0,vWhite,rsi4 < 50 ? bearishColor:bullishColor,'left',rsiTextSize)
newTC(tBox, 4,5,bbTxt(bullTF4, bearTF4),width_c0,vWhite,bbColor(bullTF4,bearTF4),'center',rsiTextSize)
if rsiShowHist
newTC(tBox, 2,5,str.format(" {0,number,#.##} ", rsi4_1),width_c0,vWhite,rsi4_1 < 50 ? bearishColor:bullishColor,'left',rsiTextSize)
newTC(tBox, 3,5,str.format(" {0,number,#.##} ", rsi4_2),width_c0,vWhite,rsi4_2 < 50 ? bearishColor:bullishColor,'left',rsiTextSize)
//------------------------------------------------------
//--- Alerts -------------------------------------------
//------------------------------------------------------
LibWghtLibrary "LibWght"
This is a library of mathematical and statistical functions
designed for quantitative analysis in Pine Script. Its core
principle is the integration of a custom weighting series
(e.g., volume) into a wide array of standard technical
analysis calculations.
Key Capabilities:
1. **Universal Weighting:** All exported functions accept a `weight`
parameter. This allows standard calculations (like moving
averages, RSI, and standard deviation) to be influenced by an
external data series, such as volume or tick count.
2. **Weighted Averages and Indicators:** Includes a comprehensive
collection of weighted functions:
- **Moving Averages:** `wSma`, `wEma`, `wWma`, `wRma` (Wilder's),
`wHma` (Hull), and `wLSma` (Least Squares / Linear Regression).
- **Oscillators & Ranges:** `wRsi`, `wAtr` (Average True Range),
`wTr` (True Range), and `wR` (High-Low Range).
3. **Volatility Decomposition:** Provides functions to decompose
total variance into distinct components for market analysis.
- **Two-Way Decomposition (`wTotVar`):** Separates variance into
**between-bar** (directional) and **within-bar** (noise)
components.
- **Three-Way Decomposition (`wLRTotVar`):** Decomposes variance
relative to a linear regression into **Trend** (explained by
the LR slope), **Residual** (mean-reversion around the
LR line), and **Within-Bar** (noise) components.
- **Local Volatility (`wLRLocTotStdDev`):** Measures the total
"noise" (within-bar + residual) around the trend line.
4. **Weighted Statistics and Regression:** Provides a robust
function for Weighted Linear Regression (`wLinReg`) and a
full suite of related statistical measures:
- **Between-Bar Stats:** `wBtwVar`, `wBtwStdDev`, `wBtwStdErr`.
- **Residual Stats:** `wResVar`, `wResStdDev`, `wResStdErr`.
5. **Fallback Mechanism:** All functions are designed for reliability.
If the total weight over the lookback period is zero (e.g., in
a no-volume period), the algorithms automatically fall back to
their unweighted, uniform-weight equivalents (e.g., `wSma`
becomes a standard `ta.sma`), preventing errors and ensuring
continuous calculation.
---
**DISCLAIMER**
This library is provided "AS IS" and for informational and
educational purposes only. It does not constitute financial,
investment, or trading advice.
The author assumes no liability for any errors, inaccuracies,
or omissions in the code. Using this library to build
trading indicators or strategies is entirely at your own risk.
As a developer using this library, you are solely responsible
for the rigorous testing, validation, and performance of any
scripts you create based on these functions. The author shall
not be held liable for any financial losses incurred directly
or indirectly from the use of this library or any scripts
derived from it.
wSma(source, weight, length)
Weighted Simple Moving Average (linear kernel).
Parameters:
source (float) : series float Data to average.
weight (float) : series float Weight series.
length (int) : series int Look-back length ≥ 1.
Returns: series float Linear-kernel weighted mean; falls back to
the arithmetic mean if Σweight = 0.
wEma(source, weight, length)
Weighted EMA (exponential kernel).
Parameters:
source (float) : series float Data to average.
weight (float) : series float Weight series.
length (simple int) : simple int Look-back length ≥ 1.
Returns: series float Exponential-kernel weighted mean; falls
back to classic EMA if Σweight = 0.
wWma(source, weight, length)
Weighted WMA (linear kernel).
Parameters:
source (float) : series float Data to average.
weight (float) : series float Weight series.
length (int) : series int Look-back length ≥ 1.
Returns: series float Linear-kernel weighted mean; falls back to
classic WMA if Σweight = 0.
wRma(source, weight, length)
Weighted RMA (Wilder kernel, α = 1/len).
Parameters:
source (float) : series float Data to average.
weight (float) : series float Weight series.
length (simple int) : simple int Look-back length ≥ 1.
Returns: series float Wilder-kernel weighted mean; falls back to
classic RMA if Σweight = 0.
wHma(source, weight, length)
Weighted HMA (linear kernel).
Parameters:
source (float) : series float Data to average.
weight (float) : series float Weight series.
length (int) : series int Look-back length ≥ 1.
Returns: series float Linear-kernel weighted mean; falls back to
classic HMA if Σweight = 0.
wRsi(source, weight, length)
Weighted Relative Strength Index.
Parameters:
source (float) : series float Price series.
weight (float) : series float Weight series.
length (simple int) : simple int Look-back length ≥ 1.
Returns: series float Weighted RSI; uniform if Σw = 0.
wAtr(tr, weight, length)
Weighted ATR (Average True Range).
Implemented as WRMA on *true range*.
Parameters:
tr (float) : series float True Range series.
weight (float) : series float Weight series.
length (simple int) : simple int Look-back length ≥ 1.
Returns: series float Weighted ATR; uniform weights if Σw = 0.
wTr(tr, weight, length)
Weighted True Range over a window.
Parameters:
tr (float) : series float True Range series.
weight (float) : series float Weight series.
length (int) : series int Look-back length ≥ 1.
Returns: series float Weighted mean of TR; uniform if Σw = 0.
wR(r, weight, length)
Weighted High-Low Range over a window.
Parameters:
r (float) : series float High-Low per bar.
weight (float) : series float Weight series.
length (int) : series int Look-back length ≥ 1.
Returns: series float Weighted mean of range; uniform if Σw = 0.
wBtwVar(source, weight, length, biased)
Weighted Between Variance (biased/unbiased).
Parameters:
source (float) : series float Data series.
weight (float) : series float Weight series.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population (biased); false → sample.
Returns:
variance series float The calculated between-bar variance (σ²btw), either biased or unbiased.
sumW series float The sum of weights over the lookback period (Σw).
sumW2 series float The sum of squared weights over the lookback period (Σw²).
wBtwStdDev(source, weight, length, biased)
Weighted Between Standard Deviation.
Parameters:
source (float) : series float Data series.
weight (float) : series float Weight series.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population (biased); false → sample.
Returns: series float σbtw uniform if Σw = 0.
wBtwStdErr(source, weight, length, biased)
Weighted Between Standard Error.
Parameters:
source (float) : series float Data series.
weight (float) : series float Weight series.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population (biased); false → sample.
Returns: series float √(σ²btw / N_eff) uniform if Σw = 0.
wTotVar(mu, sigma, weight, length, biased)
Weighted Total Variance (= between-group + within-group).
Useful when each bar represents an aggregate with its own
mean* and pre-estimated σ (e.g., second-level ranges inside a
1-minute bar). Assumes the *weight* series applies to both the
group means and their σ estimates.
Parameters:
mu (float) : series float Group means (e.g., HL2 of 1-second bars).
sigma (float) : series float Pre-estimated σ of each group (same basis).
weight (float) : series float Weight series (volume, ticks, …).
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population (biased); false → sample.
Returns:
varBtw series float The between-bar variance component (σ²btw).
varWtn series float The within-bar variance component (σ²wtn).
sumW series float The sum of weights over the lookback period (Σw).
sumW2 series float The sum of squared weights over the lookback period (Σw²).
wTotStdDev(mu, sigma, weight, length, biased)
Weighted Total Standard Deviation.
Parameters:
mu (float) : series float Group means (e.g., HL2 of 1-second bars).
sigma (float) : series float Pre-estimated σ of each group (same basis).
weight (float) : series float Weight series (volume, ticks, …).
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population (biased); false → sample.
Returns: series float σtot.
wTotStdErr(mu, sigma, weight, length, biased)
Weighted Total Standard Error.
SE = √( total variance / N_eff ) with the same effective sample
size logic as `wster()`.
Parameters:
mu (float) : series float Group means (e.g., HL2 of 1-second bars).
sigma (float) : series float Pre-estimated σ of each group (same basis).
weight (float) : series float Weight series (volume, ticks, …).
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population (biased); false → sample.
Returns: series float √(σ²tot / N_eff).
wLinReg(source, weight, length)
Weighted Linear Regression.
Parameters:
source (float) : series float Data series.
weight (float) : series float Weight series.
length (int) : series int Look-back length ≥ 2.
Returns:
mid series float The estimated value of the regression line at the most recent bar.
slope series float The slope of the regression line.
intercept series float The intercept of the regression line.
wResVar(source, weight, midLine, slope, length, biased)
Weighted Residual Variance.
linear regression – optionally biased (population) or
unbiased (sample).
Parameters:
source (float) : series float Data series.
weight (float) : series float Weighting series (volume, etc.).
midLine (float) : series float Regression value at the last bar.
slope (float) : series float Slope per bar.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population variance (σ²_P), denominator ≈ N_eff.
false → sample variance (σ²_S), denominator ≈ N_eff - 2.
(Adjusts for 2 degrees of freedom lost to the regression).
Returns:
variance series float The calculated residual variance (σ²res), either biased or unbiased.
sumW series float The sum of weights over the lookback period (Σw).
sumW2 series float The sum of squared weights over the lookback period (Σw²).
wResStdDev(source, weight, midLine, slope, length, biased)
Weighted Residual Standard Deviation.
Parameters:
source (float) : series float Data series.
weight (float) : series float Weight series.
midLine (float) : series float Regression value at the last bar.
slope (float) : series float Slope per bar.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population (biased); false → sample.
Returns: series float σres; uniform if Σw = 0.
wResStdErr(source, weight, midLine, slope, length, biased)
Weighted Residual Standard Error.
Parameters:
source (float) : series float Data series.
weight (float) : series float Weight series.
midLine (float) : series float Regression value at the last bar.
slope (float) : series float Slope per bar.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population (biased); false → sample.
Returns: series float √(σ²res / N_eff); uniform if Σw = 0.
wLRTotVar(mu, sigma, weight, midLine, slope, length, biased)
Weighted Linear-Regression Total Variance **around the
window’s weighted mean μ**.
σ²_tot = E_w ⟶ *within-group variance*
+ Var_w ⟶ *residual variance*
+ Var_w ⟶ *trend variance*
where each bar i in the look-back window contributes
m_i = *mean* (e.g. 1-sec HL2)
σ_i = *sigma* (pre-estimated intrabar σ)
w_i = *weight* (volume, ticks, …)
ŷ_i = b₀ + b₁·x (value of the weighted LR line)
r_i = m_i − ŷ_i (orthogonal residual)
Parameters:
mu (float) : series float Per-bar mean m_i.
sigma (float) : series float Pre-estimated σ_i of each bar.
weight (float) : series float Weight series w_i (≥ 0).
midLine (float) : series float Regression value at the latest bar (ŷₙ₋₁).
slope (float) : series float Slope b₁ of the regression line.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population; false → sample.
Returns:
varRes series float The residual variance component (σ²res).
varWtn series float The within-bar variance component (σ²wtn).
varTrd series float The trend variance component (σ²trd), explained by the linear regression.
sumW series float The sum of weights over the lookback period (Σw).
sumW2 series float The sum of squared weights over the lookback period (Σw²).
wLRTotStdDev(mu, sigma, weight, midLine, slope, length, biased)
Weighted Linear-Regression Total Standard Deviation.
Parameters:
mu (float) : series float Per-bar mean m_i.
sigma (float) : series float Pre-estimated σ_i of each bar.
weight (float) : series float Weight series w_i (≥ 0).
midLine (float) : series float Regression value at the latest bar (ŷₙ₋₁).
slope (float) : series float Slope b₁ of the regression line.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population; false → sample.
Returns: series float √(σ²tot).
wLRTotStdErr(mu, sigma, weight, midLine, slope, length, biased)
Weighted Linear-Regression Total Standard Error.
SE = √( σ²_tot / N_eff ) with N_eff = Σw² / Σw² (like in wster()).
Parameters:
mu (float) : series float Per-bar mean m_i.
sigma (float) : series float Pre-estimated σ_i of each bar.
weight (float) : series float Weight series w_i (≥ 0).
midLine (float) : series float Regression value at the latest bar (ŷₙ₋₁).
slope (float) : series float Slope b₁ of the regression line.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population; false → sample.
Returns: series float √((σ²res, σ²wtn, σ²trd) / N_eff).
wLRLocTotStdDev(mu, sigma, weight, midLine, slope, length, biased)
Weighted Linear-Regression Local Total Standard Deviation.
Measures the total "noise" (within-bar + residual) around the trend.
Parameters:
mu (float) : series float Per-bar mean m_i.
sigma (float) : series float Pre-estimated σ_i of each bar.
weight (float) : series float Weight series w_i (≥ 0).
midLine (float) : series float Regression value at the latest bar (ŷₙ₋₁).
slope (float) : series float Slope b₁ of the regression line.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population; false → sample.
Returns: series float √(σ²wtn + σ²res).
wLRLocTotStdErr(mu, sigma, weight, midLine, slope, length, biased)
Weighted Linear-Regression Local Total Standard Error.
Parameters:
mu (float) : series float Per-bar mean m_i.
sigma (float) : series float Pre-estimated σ_i of each bar.
weight (float) : series float Weight series w_i (≥ 0).
midLine (float) : series float Regression value at the latest bar (ŷₙ₋₁).
slope (float) : series float Slope b₁ of the regression line.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population; false → sample.
Returns: series float √((σ²wtn + σ²res) / N_eff).
wLSma(source, weight, length)
Weighted Least Square Moving Average.
Parameters:
source (float) : series float Data series.
weight (float) : series float Weight series.
length (int) : series int Look-back length ≥ 2.
Returns: series float Least square weighted mean. Falls back
to unweighted regression if Σw = 0.
Luxy Adaptive MA Cloud - Trend Strength & Signal Tracker V2Luxy Adaptive MA Cloud - Professional Trend Strength & Signal Tracker
Next-generation moving average cloud indicator combining ultra-smooth gradient visualization with intelligent momentum detection. Built for traders who demand clarity, precision, and actionable insights.
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WHAT MAKES THIS INDICATOR SPECIAL?
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Unlike traditional MA indicators that show static lines, Luxy Adaptive MA Cloud creates a living, breathing visualization of market momentum. Here's what sets it apart:
Exponential Gradient Technology
This isn't just a simple fill between two lines. It's a professionally engineered gradient system with 26 precision layers using exponential density distribution. The result? An organic, cloud-like appearance where the center is dramatically darker (15% transparency - where crossovers and price action occur), while edges fade gracefully (75% transparency). Think of it as a visual "heat map" of trend strength.
Dynamic Momentum Intelligence
Most MA clouds only show structure (which MA is on top). This indicator shows momentum strength in real-time through four intelligent states:
- 🟢 Bright Green = Explosive bullish momentum (both MAs rising strongly)
- 🔵 Blue = Weakening bullish (structure intact, but momentum fading)
- 🟠 Orange = Caution zone (bearish structure forming, weak momentum)
- 🔴 Deep Red = Strong bearish momentum (both MAs falling)
The cloud literally tells you when trends are accelerating or losing steam.
Conditional Performance Architecture
Every calculation is optimized for speed. Disable a feature? It stops calculating entirely—not just hidden, but not computed . The 26-layer gradient only renders when enabled. Toggle signals off? Those crossover checks don't run. This makes it one of the most efficient cloud indicators available, even with its advanced visual system.
Zero Repaint Guarantee
All signals and momentum states are based on confirmed bar data only . What you see in historical data is exactly what you would have seen trading live. No lookahead bias. No repainting tricks. No signals that "magically" appear perfect in hindsight. If a signal shows in history, it would have triggered in real-time at that exact moment.
Educational by Design
Every single input includes comprehensive tooltips with:
- Clear explanations of what each parameter does
- Practical examples of when to use different settings
- Recommended configurations for scalping, day trading, and swing trading
- Real-world trading impact ("This affects entry timing" vs "This is visual only")
You're not just getting an indicator—you're learning how to use it effectively .
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THE GRADIENT CLOUD - TECHNICAL DETAILS
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Architecture:
26 precision layers for silk-smooth transitions
Exponential density curve - layers packed tightly near center (where crossovers happen), spread wider at edges
75%-15% transparency range - center is highly opaque (15%), edges fade gracefully (75%)
V-Gradient design - emphasizes the action zone between Fast and Medium MAs
The Four Momentum States:
🟢 GREEN - Strong Bullish
Fast MA above Medium MA
Both MAs rising with momentum > 0.02%
Action: Enter/hold LONG positions, strong uptrend confirmed
🔵 BLUE - Weak Bullish
Fast MA above Medium MA
Weak or flat momentum
Action: Caution - bullish structure but losing strength, consider trailing stops
🟠 ORANGE - Weak Bearish
Medium MA above Fast MA
Weak or flat momentum
Action: Warning - bearish structure developing, consider exits
🔴 RED - Strong Bearish
Medium MA above Fast MA
Both MAs falling with momentum < -0.02%
Action: Enter/hold SHORT positions, strong downtrend confirmed
Smooth Transitions: The momentum score is smoothed using an 8-bar EMA to eliminate noise and prevent whipsaws. You see the true trend , not every minor fluctuation.
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FLEXIBLE MOVING AVERAGE SYSTEM
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Three Customizable MAs:
Fast MA (default: EMA 10) - Reacts quickly to price changes, defines short-term momentum
Medium MA (default: EMA 20) - Balances responsiveness with stability, core trend reference
Slow MA (default: SMA 200, optional) - Long-term trend filter, major support/resistance
Six MA Types Available:
EMA - Exponential; faster response, ideal for momentum and day trading
SMA - Simple; smooth and stable, best for swing trading and trend following
WMA - Weighted; middle ground between EMA and SMA
VWMA - Volume-weighted; reflects market participation, useful for liquid markets
RMA - Wilder's smoothing; used in RSI/ADX, excellent for trend filters
HMA - Hull; extremely responsive with minimal lag, aggressive option
Recommended Settings by Trading Style:
Scalping (1m-5m):
Fast: EMA(5-8)
Medium: EMA(10-15)
Slow: Not needed or EMA(50)
Day Trading (5m-1h):
Fast: EMA(10-12)
Medium: EMA(20-21)
Slow: SMA(200) for bias
Swing Trading (4h-1D):
Fast: EMA(10-20)
Medium: EMA(34-50)
Slow: SMA(200)
Pro Tip: Start with Fast < Medium < Slow lengths. The gradient works best when there's clear separation between Fast and Medium MAs.
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CROSSOVER SIGNALS - CLEAN & RELIABLE
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Golden Cross ⬆ LONG Signal
Fast MA crosses above Medium MA
Classic bullish reversal or trend continuation signal
Most reliable when accompanied by GREEN cloud (strong momentum)
Death Cross ⬇ SHORT Signal
Fast MA crosses below Medium MA
Classic bearish reversal or trend continuation signal
Most reliable when accompanied by RED cloud (strong momentum)
Signal Intelligence:
Anti-spam filter - Minimum 5 bars between signals prevents noise
Clean labels - Placed precisely at crossover points
Alert-ready - Built-in ALERTS for automated trading systems
No repainting - Signals based on confirmed bars only
Signal Quality Assessment:
High-Quality Entry:
Golden Cross + GREEN cloud + Price above both MAs
= Strong bullish setup ✓
Low-Quality Entry (skip or wait):
Golden Cross + ORANGE cloud + Choppy price action
= Weak bullish setup, likely whipsaw ✗
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REAL-TIME INFO PANEL
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An at-a-glance dashboard showing:
Trend Strength Indicator:
Visual display of current momentum state
Color-coded header matching cloud color
Instant recognition of market bias
MA Distance Table:
Shows percentage distance of price from each enabled MA:
Green rows : Price ABOVE MA (bullish)
Red rows : Price BELOW MA (bearish)
Gray rows : Price AT MA (rare, decision point)
Distance Interpretation:
+2% to +5%: Healthy uptrend
+5% to +10%: Getting extended, caution
+10%+: Overextended, expect pullback
-2% to -5%: Testing support
-5% to -10%: Oversold zone
-10%+: Deep correction or downtrend
Customization:
4 corner positions
5 font sizes (Tiny to Huge)
Toggle visibility on/off
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HOW TO USE - PRACTICAL TRADING GUIDE
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STRATEGY 1: Trend Following
Identify trend : Wait for GREEN (bullish) or RED (bearish) cloud
Enter on signal : Golden Cross in GREEN cloud = LONG, Death Cross in RED cloud = SHORT
Hold position : While cloud maintains color
Exit signals :
• Cloud turns ORANGE/BLUE = momentum weakening, tighten stops
• Opposite crossover = close position
• Cloud turns opposite color = full reversal
STRATEGY 2: Pullback Entries
Confirm trend : GREEN cloud established (bullish bias)
Wait for pullback : Price touches or crosses below Fast MA
Enter when : Price rebounds back above Fast MA with cloud still GREEN
Stop loss : Below Medium MA or recent swing low
Target : Previous high or when cloud weakens
STRATEGY 3: Momentum Confirmation
Your setup triggers : (e.g., chart pattern, support/resistance)
Check cloud color :
• GREEN = proceed with LONG
• RED = proceed with SHORT
• BLUE/ORANGE = skip or reduce size
Use gradient as confluence : Not as primary signal, but as momentum filter
Risk Management Tips:
Never enter against the cloud color (don't LONG in RED cloud)
Reduce position size during BLUE/ORANGE (transition periods)
Place stops beyond Medium MA for swing trades
Use Slow MA (200) as final trend filter - don't SHORT above it in uptrends
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PERFORMANCE & OPTIMIZATION
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Tested On:
Crypto: BTC, ETH, major altcoins
Stocks: SPY, AAPL, TSLA, QQQ
Forex: EUR/USD, GBP/USD, USD/JPY
Indices: S&P 500, NASDAQ, DJI
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TRANSPARENCY & RELIABILITY
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Educational Focus:
Detailed tooltips on every input
Clear documentation of methodology
Practical examples in descriptions
Teaches you why , not just what
Open Logic:
Momentum calculation: (Fast slope + Medium slope) / 2
Smoothing: 8-bar EMA to reduce noise
Thresholds: ±0.02% for strong momentum classification
Everything is transparent and explainable
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COMPLETE FEATURE LIST
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Visual Components:
26-layer exponential gradient cloud
3 customizable moving average lines
Golden Cross / Death Cross labels
Real-time info panel with trend strength
MA distance table
Calculation Features:
6 MA types (EMA, SMA, WMA, VWMA, RMA, HMA)
Momentum-based cloud coloring
Smoothed trend strength scoring
Conditional performance optimization
Customization Options:
All MA lengths adjustable
All colors customizable (when gradient disabled)
Panel position (4 corners)
Font sizes (5 options)
Toggle any feature on/off
Signal Features:
Anti-spam filter (configurable gap)
Clean, non-overlapping labels
Built-in alert conditions
No repainting guarantee
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IMPORTANT DISCLAIMERS
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This indicator is for educational and informational purposes only
Not financial advice - always do your own research
Past performance does not guarantee future results
Use proper risk management - never risk more than you can afford to lose
Test on paper/demo accounts before using with real money
Combine with other analysis methods - no single indicator is perfect
Works best in trending markets; less effective in choppy/sideways conditions
Signals may perform differently in different timeframes and market conditions
The indicator uses historical data for MA calculations - allow sufficient lookback period
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CREDITS & TECHNICAL INFO
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Version: 2.0
Release: October 2025
Special Thanks:
TradingView community for feedback and testing
Pine Script documentation for technical reference
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SUPPORT & UPDATES
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Found a bug? Comment below with:
Ticker symbol
Timeframe
Screenshot if possible
Steps to reproduce
Feature requests? I'm always looking to improve! Share your ideas in the comments.
Questions? Check the tooltips first (hover over any input) - most answers are there. If still stuck, ask in comments.
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Happy Trading!
Remember: The best indicator is the one you understand and use consistently. Take time to learn how the cloud behaves in different market conditions. Practice on paper before going live. Trade smart, manage risk, and may the trends be with you! 🚀
Composite Stochastic Oscillator (CSO) [SharpStrat]Composite Stochastic Oscillator (CSO)
The Composite Stochastic Oscillator (CSO) is a refined momentum tool designed to improve on the limitations of the traditional stochastic indicator. Standard stochastics are often too sensitive, producing choppy signals and frequent false turns. CSO tackles this problem by combining multiple stochastic calculations, each with different lengths and smoothing settings, into a single, balanced output.
The goal of combining these stochastic variants is to create a more stable and reliable reading of market momentum. Each version of the stochastic captures different aspects of price behavior like shorter ones react faster, while longer ones filter noise. CSO brings them together mathematically to form a composite oscillator that reacts smoothly and consistently across varying market conditions. This makes it a useful improvement over the standard stochastic, providing traders with a more dependable signal while retaining the familiar interpretation framework.
How It Works
Calculates five independent stochastic oscillators with customizable K, D, and slowing parameters.
Each stochastic contributes to the final composite value according to its assigned weight, allowing the user to emphasize faster or slower reactions.
The resulting composite K is then smoothed into a D line using a chosen moving average method (SMA, EMA, WMA, or RMA).
The oscillator is plotted along with optional overbought/oversold levels and a color fill to enhance visual interpretation.
A compact on-chart table displays the current K and D readings for quick reference.
Comparison with normal Stochastic
Compared to a standard stochastic, the CSO generally produces smoother lines and fewer false flips. As evident in the comparison chart, this improves upon the normal stochastic by reducing noise and making signals more reliable, although results depend on parameter settings too.
How To Use It
Use the CSO exactly like a normal stochastic: look for crossovers, overbought/oversold zones, and divergences.
In practice, CSO should provides smoother and more consistent signals than the regular stochastic, especially in sideways or volatile markets.
When plotted beside a standard stochastic, you’ll notice CSO avoids many of the false reversals that clutter traditional readings.
Customization Options
Choice of smoothing method (SMA, EMA, WMA, RMA).
Full control over each stochastic component’s parameters and weights.
Adjustable overbought/oversold levels and display preferences.
Option to enable or disable the on-chart table and zone fills.
Note
This indicator is shared purely for educational and research purposes. It is not financial advice and should not be treated as a ready-made trading system.
I encourage you to experiment with different parameter values (periods, weights, smoothing) to explore how the behavior changes and to learn from the results.
Relative Strength Index Remastered [CHE]Relative Strength Index Remastered — Enhanced RSI with robust divergence detection using price-based pivots and line-of-sight validation to reduce false signals compared to the standard RSI indicator.
Summary
RSI Remastered builds on the classic Relative Strength Index by adding a more reliable divergence detection system that relies on price pivots rather than RSI pivots alone, incorporating a line-of-sight check to ensure the RSI path between points remains clear. This approach filters out many false divergences that occur in the original RSI indicator due to its volatile pivot detection on the RSI line itself. Users benefit from clearer reversal and continuation signals, especially in noisy markets, with optional hidden divergence support for trend confirmation. The core RSI calculation and smoothing options remain familiar, but the divergence logic provides materially fewer alerts while maintaining sensitivity.
Motivation: Why this design?
The standard RSI indicator often generates misleading divergence signals because it detects pivots directly on the RSI values, which can fluctuate erratically in volatile conditions, leading to frequent false positives that confuse traders during ranging or choppy price action. RSI Remastered addresses this by shifting pivot detection to the underlying price highs and lows, which are more stable, and adding a validation step that confirms the RSI line does not cross the direct path between pivot points. This design targets the real problem of over-signaling in the original, promoting more actionable insights without altering the RSI's core momentum measurement.
What’s different vs. standard approaches?
- Reference baseline: The classical TradingView RSI indicator, which uses simple RSI-based pivot detection for divergences.
- Architecture differences:
- Pivot identification on price extremes (highs and lows) instead of RSI values, extracting RSI levels at those points for comparison.
- Addition of a line-of-sight validation that checks the RSI path bar by bar between pivots to prevent signals where the line is interrupted.
- Inclusion of hidden divergence types alongside regular ones, using the same robust framework.
- Configurable drawing of connecting lines between validated pivot RSI points for visual clarity.
- Practical effect: Charts show fewer but higher-quality divergence markers and lines, reducing clutter from the original's frequent RSI pivot triggers; this matters for avoiding whipsaws in intraday trading, where the standard version might flag dozens of invalid setups per session.
Key Comparison Aspects
Aspect: Title/Shorttitle
Original RSI: "Relative Strength Index" / "RSI"
Robust Variant: "Relative Strength Index Remastered " / "RSI RM"
Aspect: Max. Lines/Labels
Original RSI: No specification (Standard: 50/50)
Robust Variant: max_lines_count=200, max_labels_count=200 (for more lines/markers in divergences)
Aspect: RSI Calculation & Plots
Original RSI: Identical: RSI with RMA, Plots (line, bands, gradient fills)
Robust Variant: Identical: RSI with RMA, Plots (line, bands, gradient fills)
Aspect: Smoothing (MA)
Original RSI: Identical: Inputs for MA types (SMA, EMA etc.), Bollinger Bands optional
Robust Variant: Identical: Inputs for MA types (SMA, EMA etc.), Bollinger Bands optional
Aspect: Divergence Activation
Original RSI: input.bool(false, "Calculate Divergence") (disabled by default)
Robust Variant: input.bool(true, "Calculate Divergence") (enabled by default, with tooltip)
Aspect: Pivot Calculation
Original RSI: Pivots on RSI (ta.pivotlow/high on RSI values)
Robust Variant: Pivots on price (ta.pivotlow/high on low/high), RSI values then extracted
Aspect: Lookback Values
Original RSI: Fixed: lookbackLeft=5, lookbackRight=5
Robust Variant: Input: L=5 (Pivot Left), R=5 (Pivot Right), adjustable (min=1, max=50)
Aspect: Range Between Pivots
Original RSI: Fixed: rangeUpper=60, rangeLower=5 (via _inRange function)
Robust Variant: Input: rangeUpper=60 (Max Bars), rangeLower=5 (Min Bars), adjustable (min=1–6, max=100–300)
Aspect: Divergence Types
Original RSI: Only Regular Bullish/Bearish: - Bull: Price LL + RSI HL - Bear: Price HH + RSI LH
Robust Variant: Regular + Hidden (optional via showHidden=true): - Regular Bull: Price LL + RSI HL - Regular Bear: Price HH + RSI LH - Hidden Bull: Price HL + RSI LL - Hidden Bear: Price LH + RSI HH
Aspect: Validation
Original RSI: No additional check (only pivot + range check)
Robust Variant: Line-of-Sight Check: RSI line must not cross the connecting line between pivots (line_clear function with slope calculation and loop for each bar in between)
Aspect: Signals (Plots/Shapes)
Original RSI: - Plot of pivot points (if divergence) - Shapes: "Bull"/"Bear" at RSI value, offset=-5
Robust Variant: - No pivot plots, instead shapes at RSI , offset=-R (adjustable) - Shapes: "Bull"/"Bear" (Regular), "HBull"/"HBear" (Hidden) - Colors: Lime/Red (Regular), Teal/Orange (Hidden)
Aspect: Line Drawing
Original RSI: No lines
Robust Variant: Optional (showLines=true): Lines between RSI pivots (thick for regular, dashed/thin for hidden), extend=none
Aspect: Alerts
Original RSI: Only Regular Bullish/Bearish (with pivot lookback reference)
Robust Variant: Regular Bullish/Bearish + Hidden Bullish/Bearish (specific "at latest pivot low/high")
Aspect: Robustness
Original RSI: Simple, prone to false signals (RSI pivots can be volatile)
Robust Variant: Higher: Price pivots are more stable, line-of-sight filters "broken" divergences, hidden support for trend continuations
Aspect: Code Length/Structure
Original RSI: ~100 lines, simple if-blocks for bull/bear
Robust Variant: ~150 lines, extended helper functions (e.g., inRange, line_clear), var group for inputs
How it works (technical)
The indicator first computes the core RSI value based on recent price changes, separating upward and downward movements over the specified length and smoothing them to derive a momentum reading scaled between zero and one hundred. This value is then plotted in a separate pane with fixed upper and lower reference lines at seventy and thirty, along with optional gradient fills to highlight overbought and oversold zones.
For smoothing, a moving average type is applied to the RSI if enabled, with an option to add bands around it based on the variability of recent RSI values scaled by a multiplier. Divergence detection activates on confirmed price pivots: lows for bullish checks and highs for bearish. At each new pivot, the system retrieves the bar index and values (price and RSI) for the current and prior pivot, ensuring they fall within a configurable bar range to avoid unrelated points.
Comparisons then assess whether the price has made a lower low (or higher high) while the RSI at those points moves in the opposite direction—higher for bullish regular, lower for bearish regular. For hidden types, the directions reverse to capture trend strength. The line-of-sight check calculates the straight path between the two RSI points and verifies that the actual RSI values in between stay entirely above (for bullish) or below (for bearish) that path, breaking the signal if any bar violates it. Valid signals trigger shapes at the RSI level of the new pivot and optional lines connecting the points. Initialization uses built-in functions to track prior occurrences, with states persisting across bars for accurate historical comparisons. No higher timeframe data is used, so confirmation occurs after the right pivot bars close, minimizing live-bar repaints.
Parameter Guide
Length — Controls the period for measuring price momentum changes — Default: 14 — Trade-offs/Tips: Shorter values increase responsiveness but add noise and more false signals; longer smooths trends but delays entries in fast markets.
Source — Selects the price input for RSI calculation — Default: Close — Trade-offs/Tips: Use high or low for volatility focus, but close works best for most assets; mismatches can skew overbought/oversold reads.
Calculate Divergence — Enables the enhanced divergence logic — Default: True — Trade-offs/Tips: Disable for pure RSI view to save computation; essential for signal reliability over the standard method.
Type (Smoothing) — Chooses the moving average applied to RSI — Default: SMA — Trade-offs/Tips: None for raw RSI; EMA for quicker adaptation, but SMA reduces whipsaws; Bollinger Bands option adds volatility context at cost of added lines.
Length (Smoothing) — Period for the smoothing average — Default: 14 — Trade-offs/Tips: Match RSI length for consistency; shorter boosts signal speed but amplifies noise in the smoothed line.
BB StdDev — Multiplier for band width around smoothed RSI — Default: 2.0 — Trade-offs/Tips: Lower narrows bands for tighter signals, risking more touches; higher widens for fewer but stronger breakouts.
Pivot Left — Bars to the left for confirming price pivots — Default: 5 — Trade-offs/Tips: Increase for stricter pivots in noisy data, reducing signals; too high delays confirmation excessively.
Pivot Right — Bars to the right for confirming price pivots — Default: 5 — Trade-offs/Tips: Balances with left for symmetry; longer right ensures maturity but shifts signals backward.
Max Bars Between Pivots — Upper limit on distance for valid pivot pairs — Default: 60 — Trade-offs/Tips: Tighten for short-term trades to focus recent action; widen for swing setups but risks unrelated comparisons.
Min Bars Between Pivots — Lower limit to avoid clustered pivots — Default: 5 — Trade-offs/Tips: Raise to filter micro-moves; too low invites overlapping signals like the original RSI.
Detect Hidden — Includes trend-continuation hidden types — Default: True — Trade-offs/Tips: Enable for full trend analysis; disable simplifies to reversals only, akin to basic RSI.
Draw Lines — Shows connecting lines between valid pivots — Default: True — Trade-offs/Tips: Turn off for cleaner charts; helps visually confirm line-of-sight in backtests.
Reading & Interpretation
The main RSI line oscillates between zero and one hundred, crossing above fifty suggesting building momentum and below indicating weakness; touches near seventy or thirty flag potential extremes. The optional smoothed line and bands provide a filtered view—price above the upper band on the RSI pane hints at overextension. Divergence shapes appear as upward labels for bullish (lime for regular, teal for hidden) and downward for bearish (red regular, orange hidden) at the pivot's RSI level, signaling a mismatch only after validation. Connecting lines, if drawn, slope between points without RSI interference, their color matching the shape type; a dashed style denotes hidden. Fewer shapes overall compared to the standard RSI mean higher conviction, but always confirm with price structure.
Practical Workflows & Combinations
- Trend following: Enter longs on regular bullish shapes near support with higher highs in price; filter hidden bullish for pullback buys in uptrends, pairing with a rising smoothed RSI above fifty.
- Exits/Stops: Use bearish regular as reversal warnings to tighten stops; hidden bearish in downtrends confirms continuation—exit if lines show RSI crossing the path.
- Multi-asset/Multi-TF: Defaults suit forex and stocks on one-hour charts; for crypto volatility, widen pivot ranges to ten; scale min/max bars proportionally on daily for swings, avoiding the original's intraday spam.
Behavior, Constraints & Performance
Signals confirm only after the right pivot bars close, so live bars may show tentative pivots that vanish on close, unlike the standard RSI's immediate RSI-pivot triggers—plan for this delay in automation. No higher timeframe calls, so no security-related repaints. Resources include up to two hundred lines and labels for dense charts, with a loop in validation scanning up to three hundred bars between pivots, which is efficient but could slow on very long histories. Known limits: Slight lag at pivot confirmation in trending markets; volatile RSI might rarely miss fine path violations; not ideal for gap-heavy assets where pivots skip.
Sensible Defaults & Quick Tuning
Start with defaults for balanced momentum and divergence on most timeframes. For too many signals (like the original), raise pivot left/right to eight and min bars to ten to filter noise. If sluggish in trends, shorten RSI length to nine and enable EMA smoothing for faster adaptation. In high-volatility assets, widen max bars to one hundred but disable hidden to focus essentials. For clean reversal hunts, set smoothing to none and lines on.
What this indicator is—and isn’t
RSI Remastered serves as a refined momentum and divergence visualization tool, enhancing the standard RSI for better signal quality in technical analysis setups. It is not a standalone trading system, nor does it predict price moves—pair it with volume, structure breaks, and risk rules for decisions. Use alongside position sizing and broader context, not in isolation.
Disclaimer
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts.
Best regards and happy trading
Chervolino
T3 ATR [DCAUT]█ T3 ATR
📊 ORIGINALITY & INNOVATION
The T3 ATR indicator represents an important enhancement to the traditional Average True Range (ATR) indicator by incorporating the T3 (Tilson Triple Exponential Moving Average) smoothing algorithm. While standard ATR uses fixed RMA (Running Moving Average) smoothing, T3 ATR introduces a configurable volume factor parameter that allows traders to adjust the smoothing characteristics from highly responsive to heavily smoothed output.
This innovation addresses a fundamental limitation of traditional ATR: the inability to adapt smoothing behavior without changing the calculation period. With T3 ATR, traders can maintain a consistent ATR period while adjusting the responsiveness through the volume factor, making the indicator adaptable to different trading styles, market conditions, and timeframes through a single unified implementation.
The T3 algorithm's triple exponential smoothing with volume factor control provides improved signal quality by reducing noise while maintaining better responsiveness compared to traditional smoothing methods. This makes T3 ATR particularly valuable for traders who need to adapt their volatility measurement approach to varying market conditions without switching between multiple indicator configurations.
📐 MATHEMATICAL FOUNDATION
The T3 ATR calculation process involves two distinct stages:
Stage 1: True Range Calculation
The True Range (TR) is calculated using the standard formula:
TR = max(high - low, |high - close |, |low - close |)
This captures the greatest of the current bar's range, the gap from the previous close to the current high, or the gap from the previous close to the current low, providing a comprehensive measure of price movement that accounts for gaps and limit moves.
Stage 2: T3 Smoothing Application
The True Range values are then smoothed using the T3 algorithm, which applies six exponential moving averages in succession:
First Layer: e1 = EMA(TR, period), e2 = EMA(e1, period)
Second Layer: e3 = EMA(e2, period), e4 = EMA(e3, period)
Third Layer: e5 = EMA(e4, period), e6 = EMA(e5, period)
Final Calculation: T3 = c1×e6 + c2×e5 + c3×e4 + c4×e3
The coefficients (c1, c2, c3, c4) are derived from the volume factor (VF) parameter:
a = VF / 2
c1 = -a³
c2 = 3a² + 3a³
c3 = -6a² - 3a - 3a³
c4 = 1 + 3a + a³ + 3a²
The volume factor parameter (0.0 to 1.0) controls the weighting of these coefficients, directly affecting the balance between responsiveness and smoothness:
Lower VF values (approaching 0.0): Coefficients favor recent data, resulting in faster response to volatility changes with minimal lag but potentially more noise
Higher VF values (approaching 1.0): Coefficients distribute weight more evenly across the smoothing layers, producing smoother output with reduced noise but slightly increased lag
📊 COMPREHENSIVE SIGNAL ANALYSIS
Volatility Level Interpretation:
High Absolute Values: Indicate strong price movements and elevated market activity, suggesting larger position risks and wider stop-loss requirements, often associated with trending markets or significant news events
Low Absolute Values: Indicate subdued price movements and quiet market conditions, suggesting smaller position risks and tighter stop-loss opportunities, often associated with consolidation phases or low-volume periods
Rapid Increases: Sharp spikes in T3 ATR often signal the beginning of significant price moves or market regime changes, providing early warning of increased trading risk
Sustained High Levels: Extended periods of elevated T3 ATR indicate sustained trending conditions with persistent volatility, suitable for trend-following strategies
Sustained Low Levels: Extended periods of low T3 ATR indicate range-bound conditions with suppressed volatility, suitable for mean-reversion strategies
Volume Factor Impact on Signals:
Low VF Settings (0.0-0.3): Produce responsive signals that quickly capture volatility changes, suitable for short-term trading but may generate more frequent color changes during minor fluctuations
Medium VF Settings (0.4-0.7): Provide balanced signal quality with moderate responsiveness, filtering out minor noise while capturing significant volatility changes, suitable for swing trading
High VF Settings (0.8-1.0): Generate smooth, stable signals that filter out most noise and focus on major volatility trends, suitable for position trading and long-term analysis
🎯 STRATEGIC APPLICATIONS
Position Sizing Strategy:
Determine your risk per trade (e.g., 1% of account capital - adjust based on your risk tolerance and experience)
Decide your stop-loss distance multiplier (e.g., 2.0x T3 ATR - this varies by market and strategy, test different values)
Calculate stop-loss distance: Stop Distance = Multiplier × Current T3 ATR
Calculate position size: Position Size = (Account × Risk %) / Stop Distance
Example: $10,000 account, 1% risk, T3 ATR = 50 points, 2x multiplier → Position Size = ($10,000 × 0.01) / (2 × 50) = $100 / 100 points = 1 unit per point
Important: The ATR multiplier (1.5x - 3.0x) should be determined through backtesting for your specific instrument and strategy - using inappropriate multipliers may result in stops that are too tight (frequent stop-outs) or too wide (excessive losses)
Adjust the volume factor to match your trading style: lower VF for responsive stop distances in short-term trading, higher VF for stable stop distances in position trading
Dynamic Stop-Loss Placement:
Determine your risk tolerance multiplier (typically 1.5x to 3.0x T3 ATR)
For long positions: Set stop-loss at entry price minus (multiplier × current T3 ATR value)
For short positions: Set stop-loss at entry price plus (multiplier × current T3 ATR value)
Trail stop-losses by recalculating based on current T3 ATR as the trade progresses
Adjust the volume factor based on desired stop-loss stability: higher VF for less frequent adjustments, lower VF for more adaptive stops
Market Regime Identification:
Calculate a reference volatility level using a longer-period moving average of T3 ATR (e.g., 50-period SMA)
High Volatility Regime: Current T3 ATR significantly above reference (e.g., 120%+) - favor trend-following strategies, breakout trades, and wider targets
Normal Volatility Regime: Current T3 ATR near reference (e.g., 80-120%) - employ standard trading strategies appropriate for prevailing market structure
Low Volatility Regime: Current T3 ATR significantly below reference (e.g., <80%) - favor mean-reversion strategies, range trading, and prepare for potential volatility expansion
Monitor T3 ATR trend direction and compare current values to recent history to identify regime transitions early
Risk Management Implementation:
Establish your maximum portfolio heat (total risk across all positions, typically 2-6% of capital)
For each position: Calculate position size using the formula Position Size = (Account × Individual Risk %) / (ATR Multiplier × Current T3 ATR)
When T3 ATR increases: Position sizes automatically decrease (same risk %, larger stop distance = smaller position)
When T3 ATR decreases: Position sizes automatically increase (same risk %, smaller stop distance = larger position)
This approach maintains constant dollar risk per trade regardless of market volatility changes
Use consistent volume factor settings across all positions to ensure uniform risk measurement
📋 DETAILED PARAMETER CONFIGURATION
ATR Length Parameter:
Default Setting: 14 periods
This is the standard ATR calculation period established by Welles Wilder, providing balanced volatility measurement that captures both short-term fluctuations and medium-term trends across most markets and timeframes
Selection Principles:
Shorter periods increase sensitivity to recent volatility changes and respond faster to market shifts, but may produce less stable readings
Longer periods emphasize sustained volatility trends and filter out short-term noise, but respond more slowly to genuine regime changes
The optimal period depends on your holding time, trading frequency, and the typical volatility cycle of your instrument
Consider the timeframe you trade: Intraday traders typically use shorter periods, swing traders use intermediate periods, position traders use longer periods
Practical Approach:
Start with the default 14 periods and observe how well it captures volatility patterns relevant to your trading decisions
If ATR seems too reactive to minor price movements: Increase the period until volatility readings better reflect meaningful market changes
If ATR lags behind obvious volatility shifts that affect your trades: Decrease the period for faster response
Match the period roughly to your typical holding time - if you hold positions for N bars, consider ATR periods in a similar range
Test different periods using historical data for your specific instrument and strategy before committing to live trading
T3 Volume Factor Parameter:
Default Setting: 0.7
This setting provides a reasonable balance between responsiveness and smoothness for most market conditions and trading styles
Understanding the Volume Factor:
Lower values (closer to 0.0) reduce smoothing, allowing T3 ATR to respond more quickly to volatility changes but with less noise filtering
Higher values (closer to 1.0) increase smoothing, producing more stable readings that focus on sustained volatility trends but respond more slowly
The trade-off is between immediacy and stability - there is no universally optimal setting
Selection Principles:
Match to your decision speed: If you need to react quickly to volatility changes for entries/exits, use lower VF; if you're making longer-term risk assessments, use higher VF
Match to market character: Noisier, choppier markets may benefit from higher VF for clearer signals; cleaner trending markets may work well with lower VF for faster response
Match to your preference: Some traders prefer responsive indicators even with occasional false signals, others prefer stable indicators even with some delay
Practical Adjustment Guidelines:
Start with default 0.7 and observe how T3 ATR behavior aligns with your trading needs over multiple sessions
If readings seem too unstable or noisy for your decisions: Try increasing VF toward 0.9-1.0 for heavier smoothing
If the indicator lags too much behind volatility changes you care about: Try decreasing VF toward 0.3-0.5 for faster response
Make meaningful adjustments (0.2-0.3 changes) rather than small increments - subtle differences are often imperceptible in practice
Test adjustments in simulation or paper trading before applying to live positions
📈 PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES
Responsiveness Characteristics:
The T3 smoothing algorithm provides improved responsiveness compared to traditional RMA smoothing used in standard ATR. The triple exponential design with volume factor control allows the indicator to respond more quickly to genuine volatility changes while maintaining the ability to filter noise through appropriate VF settings. This results in earlier detection of volatility regime changes compared to standard ATR, particularly valuable for risk management and position sizing adjustments.
Signal Stability:
Unlike simple smoothing methods that may produce erratic signals during transitional periods, T3 ATR's multi-layer exponential smoothing provides more stable signal progression. The volume factor parameter allows traders to tune signal stability to their preference, with higher VF settings producing remarkably smooth volatility profiles that help avoid overreaction to temporary market fluctuations.
Comparison with Standard ATR:
Adaptability: T3 ATR allows adjustment of smoothing characteristics through the volume factor without changing the ATR period, whereas standard ATR requires changing the period length to alter responsiveness, potentially affecting the fundamental volatility measurement
Lag Reduction: At lower volume factor settings, T3 ATR responds more quickly to volatility changes than standard ATR with equivalent periods, providing earlier signals for risk management adjustments
Noise Filtering: At higher volume factor settings, T3 ATR provides superior noise filtering compared to standard ATR, producing cleaner signals for long-term analysis without sacrificing volatility measurement accuracy
Flexibility: A single T3 ATR configuration can serve multiple trading styles by adjusting only the volume factor, while standard ATR typically requires multiple instances with different periods for different trading applications
Suitable Use Cases:
T3 ATR is well-suited for the following scenarios:
Dynamic Risk Management: When position sizing and stop-loss placement need to adapt quickly to changing volatility conditions
Multi-Style Trading: When a single volatility indicator must serve different trading approaches (day trading, swing trading, position trading)
Volatile Markets: When standard ATR produces too many false volatility signals during choppy conditions
Systematic Trading: When algorithmic systems require a single, configurable volatility input that can be optimized for different instruments
Market Regime Analysis: When clear identification of volatility expansion and contraction phases is critical for strategy selection
Known Limitations:
Like all technical indicators, T3 ATR has limitations that users should understand:
Historical Nature: T3 ATR is calculated from historical price data and cannot predict future volatility with certainty
Smoothing Trade-offs: The volume factor setting involves a trade-off between responsiveness and smoothness - no single setting is optimal for all market conditions
Extreme Events: During unprecedented market events or gaps, T3 ATR may not immediately reflect the full scope of volatility until sufficient data is processed
Relative Measurement: T3 ATR values are most meaningful in relative context (compared to recent history) rather than as absolute thresholds
Market Context Required: T3 ATR measures volatility magnitude but does not indicate price direction or trend quality - it should be used in conjunction with directional analysis
Performance Expectations:
T3 ATR is designed to help traders measure and adapt to changing market volatility conditions. When properly configured and applied:
It can help reduce position risk during volatile periods through appropriate position sizing
It can help identify optimal times for more aggressive position sizing during stable periods
It can improve stop-loss placement by adapting to current market conditions
It can assist in strategy selection by identifying volatility regimes
However, volatility measurement alone does not guarantee profitable trading. T3 ATR should be integrated into a comprehensive trading approach that includes directional analysis, proper risk management, and sound trading psychology.
USAGE NOTES
This indicator is designed for technical analysis and educational purposes. T3 ATR provides adaptive volatility measurement but has limitations and should not be used as the sole basis for trading decisions. The indicator measures historical volatility patterns, and past volatility characteristics do not guarantee future volatility behavior. Market conditions can change rapidly, and extreme events may produce volatility readings that fall outside historical norms.
Traders should combine T3 ATR with directional analysis tools, support/resistance analysis, and other technical indicators to form a complete trading strategy. Proper backtesting and forward testing with appropriate risk management is essential before applying T3 ATR-based strategies to live trading. The volume factor parameter should be optimized for specific instruments and trading styles through careful testing rather than assuming default settings are optimal for all applications.
Michal D. Lagless Moving Average | MisinkoMasterThe 𝕸𝖎𝖈𝖍𝖆𝖑 𝕯. 𝕷𝖆𝖌𝖑𝖊𝖘𝖘 𝕸𝖔𝖛𝖎𝖓𝖌 𝕬𝖛𝖊𝖗𝖆𝖌𝖊 is my latest creation of a trend following tool, which is a bit different from the rest. By trying to de-lag the classical moving average, it gives you fast signals on changes in trend as fast as possible, keeping traders & investors always in check for potential risks they might want to avoid.
How does it work?
First we need to calculate lengths. The lengths are calcuted using a user defined input called the "Length Multiplier" and we of course need as well the length input too.
The indicator uses 10 lengths, 5 for an average price, 5 for median price.
The length for the average is the following:
length_2_avg = length_1_avg * length_multiplier
length_3_avg = length_2_avg * length_multiplier
...
and for the median lengths:
length_1_median = length_2_avg
length_2_median = length_3_avg
Here applies this rule
length_x_median < length_x_avg
This is intentional, and it is because the average is a little more reactive, while the median is a bit slower. To make up for the "slowness" of the median, we simple reduce the length of it a bit more than the average.
Now that we have our length we are ready to calculate averages and medians over their respective period. This is the a normal average from elementary school, nothing too fancy.
Now that we have all of them we match the pairs using another user defined input called "Median Weight" like so:
(Average_x * (2-median_weight) + Median_x * median_weight)/2
This gives more weight to the average (also due to the max value limit set to avoid breaking the fundational logic behind it).
After doing it to all the pairs we now average those pairs using another input called "Exponential Weight Multiplier".
The Exponential Weight Multiplier is used for weights which I will cover soon:
weight1 = weight
weight2 = weight * weight
weight3 = weight * weight * weight....
This is done until we have all the weights calculated
This gives exponentially more weight to the less lagging indicators, which is how we delag the indicator.
Then we sum all the pairs like so:
sum = pair1 * weight1 + pair2 * weight2 + pair3 * weight3 + pair4 * weight4 + pair5 * weight5
Then the sum is divided by the sum of weights, this results in us getting the final value.
Methodology & What is the actual point & how was it made?
I want to cover this one a bit deeper:
The methodology behind this was creating an indicator that would not be lagging, and would be able to avoid lag while not producing signals too often.
In many attempts in the first part, I tried using EMA, RMA, DEMA, TEMA, HMA, SMA and so on, but they were too noisy (except for SMA & RMA, but those had their flaws), so I tried the classical average taught in elementary school. This one worked better, but the noise was too high still after all this time. This made me include the median, which helped the noise, but made it far too lagging.
Here came the idea of making the median length lower and adding weights to counter the lag of the median, but it was still too lagging. This made me make the weights for lengths more exponential, while previously they were calculated using a little bit amplified sums that were alright, but nowhere near my desired result.
Using the new weights I got further, and after a bit of testing I was sattisfied with the results.
The logic for the trend was a big part in my development part, there were many I could think of, but not enough time to try them, so I stuck to the usual one, and I leave it up to YOU to beat my trend logic and get even better results.
Use Cases:
- Price/MA Crossovers
Simple, effective, useful
- Source for other indicators
This I tried myself, and it worked in a cool way, making the signals of for example RSI much smoother, so definitely try it out if you know how to code, or just simply put it in the source of the RSI.
- ROC
This trend logic stuck with me, I think you could find a way to make it good, but mainly for the people that can code in pine, trying out to combine the trend logic with ROC could work very well, do not sleep on it!
- Education
This concept is not really that complex, so for people looking for new ideas, inspiration, or just watching how trend following tools behave in general this is something that could benefit anyone, as the concept can be applied to ANYTHING, even the classical RSI, MACD, you could try even the Parabolic SAR, maybe STC or VZO, there is no limit to imagination.
- Strategy creation
Filtering this indicator with "and" conditions, or maybe even "or" or anything really could be very useful in a strategy that desires fast signals.
- Price Distance from bands
I noticed this while looking at past performance:
The stronger the trend the higher the distance from the Moving Average.
Final Notes
Watch out for mean reverting markets, as this is trend following you could get easily screwed in them.
Play around with this if it fits your desired outcome, you might find something I did not.
Hope you find it useful,
See you next time!
Stochastic Enhanced [DCAUT]█ Stochastic Enhanced
📊 ORIGINALITY & INNOVATION
The Stochastic Enhanced indicator builds upon George Lane's classic momentum oscillator (developed in the late 1950s) by providing comprehensive smoothing algorithm flexibility. While traditional implementations limit users to Simple Moving Average (SMA) smoothing, this enhanced version offers 21 advanced smoothing algorithms, allowing traders to optimize the indicator's characteristics for different market conditions and trading styles.
Key Improvements:
Extended from single SMA smoothing to 21 professional-grade algorithms including adaptive filters (KAMA, FRAMA), zero-lag methods (ZLEMA, T3), and advanced digital filters (Kalman, Laguerre)
Maintains backward compatibility with traditional Stochastic calculations through SMA default setting
Unified smoothing algorithm applies to both %K and %D lines for consistent signal processing characteristics
Enhanced visual feedback with clear color distinction and background fill highlighting for intuitive signal recognition
Comprehensive alert system covering crossovers and zone entries for systematic trade management
Differentiation from Traditional Stochastic:
Traditional Stochastic indicators use fixed SMA smoothing, which introduces consistent lag regardless of market volatility. This enhanced version addresses the limitation by offering adaptive algorithms that adjust to market conditions (KAMA, FRAMA), reduce lag without sacrificing smoothness (ZLEMA, T3, HMA), or provide superior noise filtering (Kalman Filter, Laguerre filters). The flexibility helps traders balance responsiveness and stability according to their specific needs.
📐 MATHEMATICAL FOUNDATION
Core Stochastic Calculation:
The Stochastic Oscillator measures the position of the current close relative to the high-low range over a specified period:
Step 1: Raw %K Calculation
%K_raw = 100 × (Close - Lowest Low) / (Highest High - Lowest Low)
Where:
Close = Current closing price
Lowest Low = Lowest low over the %K Length period
Highest High = Highest high over the %K Length period
Result ranges from 0 (close at period low) to 100 (close at period high)
Step 2: Smoothed %K Calculation
%K = MA(%K_raw, K Smoothing Period, MA Type)
Where:
MA = Selected moving average algorithm (SMA, EMA, etc.)
K Smoothing = 1 for Fast Stochastic, 3+ for Slow Stochastic
Traditional Fast Stochastic uses %K_raw directly without smoothing
Step 3: Signal Line %D Calculation
%D = MA(%K, D Smoothing Period, MA Type)
Where:
%D acts as a signal line and moving average of %K
D Smoothing typically set to 3 periods in traditional implementations
Both %K and %D use the same MA algorithm for consistent behavior
Available Smoothing Algorithms (21 Options):
Standard Moving Averages:
SMA (Simple): Equal-weighted average, traditional default, consistent lag characteristics
EMA (Exponential): Recent price emphasis, faster response to changes, exponential decay weighting
RMA (Rolling/Wilder's): Smoothed average used in RSI, less reactive than EMA
WMA (Weighted): Linear weighting favoring recent data, moderate responsiveness
VWMA (Volume-Weighted): Incorporates volume data, reflects market participation intensity
Advanced Moving Averages:
HMA (Hull): Reduced lag with smoothness, uses weighted moving averages and square root period
ALMA (Arnaud Legoux): Gaussian distribution weighting, minimal lag with good noise reduction
LSMA (Least Squares): Linear regression based, fits trend line to data points
DEMA (Double Exponential): Reduced lag compared to EMA, uses double smoothing technique
TEMA (Triple Exponential): Further lag reduction, triple smoothing with lag compensation
ZLEMA (Zero-Lag Exponential): Lag elimination attempt using error correction, very responsive
TMA (Triangular): Double-smoothed SMA, very smooth but slower response
Adaptive & Intelligent Filters:
T3 (Tilson T3): Six-pass exponential smoothing with volume factor adjustment, excellent smoothness
FRAMA (Fractal Adaptive): Adapts to market fractal dimension, faster in trends, slower in ranges
KAMA (Kaufman Adaptive): Efficiency ratio based adaptation, responds to volatility changes
McGinley Dynamic: Self-adjusting mechanism following price more accurately, reduced whipsaws
Kalman Filter: Optimal estimation algorithm from aerospace engineering, dynamic noise filtering
Advanced Digital Filters:
Ultimate Smoother: Advanced digital filter design, superior noise rejection with minimal lag
Laguerre Filter: Time-domain filter with N-order implementation, adjustable lag characteristics
Laguerre Binomial Filter: 6-pole Laguerre filter, extremely smooth output for long-term analysis
Super Smoother: Butterworth filter implementation, removes high-frequency noise effectively
📊 COMPREHENSIVE SIGNAL ANALYSIS
Absolute Level Interpretation (%K Line):
%K Above 80: Overbought condition, price near period high, potential reversal or pullback zone, caution for new long entries
%K in 70-80 Range: Strong upward momentum, bullish trend confirmation, uptrend likely continuing
%K in 50-70 Range: Moderate bullish momentum, neutral to positive outlook, consolidation or mild uptrend
%K in 30-50 Range: Moderate bearish momentum, neutral to negative outlook, consolidation or mild downtrend
%K in 20-30 Range: Strong downward momentum, bearish trend confirmation, downtrend likely continuing
%K Below 20: Oversold condition, price near period low, potential bounce or reversal zone, caution for new short entries
Crossover Signal Analysis:
%K Crosses Above %D (Bullish Cross): Momentum shifting bullish, faster line overtakes slower signal, consider long entry especially in oversold zone, strongest when occurring below 20 level
%K Crosses Below %D (Bearish Cross): Momentum shifting bearish, faster line falls below slower signal, consider short entry especially in overbought zone, strongest when occurring above 80 level
Crossover in Midrange (40-60): Less reliable signals, often in choppy sideways markets, require additional confirmation from trend or volume analysis
Multiple Failed Crosses: Indicates ranging market or choppy conditions, reduce position sizes or avoid trading until clear directional move
Advanced Divergence Patterns (%K Line vs Price):
Bullish Divergence: Price makes lower low while %K makes higher low, indicates weakening bearish momentum, potential trend reversal upward, more reliable when %K in oversold zone
Bearish Divergence: Price makes higher high while %K makes lower high, indicates weakening bullish momentum, potential trend reversal downward, more reliable when %K in overbought zone
Hidden Bullish Divergence: Price makes higher low while %K makes lower low, indicates trend continuation in uptrend, bullish trend strength confirmation
Hidden Bearish Divergence: Price makes lower high while %K makes higher high, indicates trend continuation in downtrend, bearish trend strength confirmation
Momentum Strength Analysis (%K Line Slope):
Steep %K Slope: Rapid momentum change, strong directional conviction, potential for extended moves but also increased reversal risk
Gradual %K Slope: Steady momentum development, sustainable trends more likely, lower probability of sharp reversals
Flat or Horizontal %K: Momentum stalling, potential reversal or consolidation ahead, wait for directional break before committing
%K Oscillation Within Range: Indicates ranging market, sideways price action, better suited for range-trading strategies than trend following
🎯 STRATEGIC APPLICATIONS
Mean Reversion Strategy (Range-Bound Markets):
Identify ranging market conditions using price action or Bollinger Bands
Wait for Stochastic to reach extreme zones (above 80 for overbought, below 20 for oversold)
Enter counter-trend position when %K crosses %D in extreme zone (sell on bearish cross above 80, buy on bullish cross below 20)
Set profit targets near opposite extreme or midline (50 level)
Use tight stop-loss above recent swing high/low to protect against breakout scenarios
Exit when Stochastic reaches opposite extreme or %K crosses %D in opposite direction
Trend Following with Momentum Confirmation:
Identify primary trend direction using higher timeframe analysis or moving averages
Wait for Stochastic pullback to oversold zone (<20) in uptrend or overbought zone (>80) in downtrend
Enter in trend direction when %K crosses %D confirming momentum shift (bullish cross in uptrend, bearish cross in downtrend)
Use wider stops to accommodate normal trend volatility
Add to position on subsequent pullbacks showing similar Stochastic pattern
Exit when Stochastic shows opposite extreme with failed cross or bearish/bullish divergence
Divergence-Based Reversal Strategy:
Scan for divergence between price and Stochastic at swing highs/lows
Confirm divergence with at least two price pivots showing divergent Stochastic readings
Wait for %K to cross %D in direction of anticipated reversal as entry trigger
Enter position in divergence direction with stop beyond recent swing extreme
Target profit at key support/resistance levels or Fibonacci retracements
Scale out as Stochastic reaches opposite extreme zone
Multi-Timeframe Momentum Alignment:
Analyze Stochastic on higher timeframe (4H or Daily) for primary trend bias
Switch to lower timeframe (1H or 15M) for precise entry timing
Only take trades where lower timeframe Stochastic signal aligns with higher timeframe momentum direction
Higher timeframe Stochastic in bullish zone (>50) = only take long entries on lower timeframe
Higher timeframe Stochastic in bearish zone (<50) = only take short entries on lower timeframe
Exit when lower timeframe shows counter-signal or higher timeframe momentum reverses
Zone Transition Strategy:
Monitor Stochastic for transitions between zones (oversold to neutral, neutral to overbought, etc.)
Enter long when Stochastic crosses above 20 (exiting oversold), signaling momentum shift from bearish to neutral/bullish
Enter short when Stochastic crosses below 80 (exiting overbought), signaling momentum shift from bullish to neutral/bearish
Use zone midpoint (50) as dynamic support/resistance for position management
Trail stops as Stochastic advances through favorable zones
Exit when Stochastic fails to maintain momentum and reverses back into prior zone
📋 DETAILED PARAMETER CONFIGURATION
%K Length (Default: 14):
Lower Values (5-9): Highly sensitive to price changes, generates more frequent signals, increased false signals in choppy markets, suitable for very short-term trading and scalping
Standard Values (10-14): Balanced sensitivity and reliability, traditional default (14) widely used,适合 swing trading and intraday strategies
Higher Values (15-21): Reduced sensitivity, smoother oscillations, fewer but potentially more reliable signals, better for position trading and lower timeframe noise reduction
Very High Values (21+): Slow response, long-term momentum measurement, fewer trading signals, suitable for weekly or monthly analysis
%K Smoothing (Default: 3):
Value 1: Fast Stochastic, uses raw %K calculation without additional smoothing, most responsive to price changes, generates earliest signals with higher noise
Value 3: Slow Stochastic (default), traditional smoothing level, reduces false signals while maintaining good responsiveness, widely accepted standard
Values 5-7: Very slow response, extremely smooth oscillations, significantly reduced whipsaws but delayed entry/exit timing
Recommendation: Default value 3 suits most trading scenarios, active short-term traders may use 1, conservative long-term positions use 5+
%D Smoothing (Default: 3):
Lower Values (1-2): Signal line closely follows %K, frequent crossover signals, useful for active trading but requires strict filtering
Standard Value (3): Traditional setting providing balanced signal line behavior, optimal for most trading applications
Higher Values (4-7): Smoother signal line, fewer crossover signals, reduced whipsaws but slower confirmation, better for trend trading
Very High Values (8+): Signal line becomes slow-moving reference, crossovers rare and highly significant, suitable for long-term position changes only
Smoothing Type Algorithm Selection:
For Trending Markets:
ZLEMA, DEMA, TEMA: Reduced lag for faster trend entry, quick response to momentum shifts, suitable for strong directional moves
HMA, ALMA: Good balance of smoothness and responsiveness, effective for clean trend following without excessive noise
EMA: Classic choice for trending markets, faster than SMA while maintaining reasonable stability
For Ranging/Choppy Markets:
Kalman Filter, Super Smoother: Superior noise filtering, reduces false signals in sideways action, helps identify genuine reversal points
Laguerre Filters: Smooth oscillations with adjustable lag, excellent for mean reversion strategies in ranges
T3, TMA: Very smooth output, filters out market noise effectively, clearer extreme zone identification
For Adaptive Market Conditions:
KAMA: Automatically adjusts to market efficiency, fast in trends and slow in congestion, reduces whipsaws during transitions
FRAMA: Adapts to fractal market structure, responsive during directional moves, conservative during uncertainty
McGinley Dynamic: Self-adjusting smoothing, follows price naturally, minimizes lag in trending markets while filtering noise in ranges
For Conservative Long-Term Analysis:
SMA: Traditional choice, predictable behavior, widely understood characteristics
RMA (Wilder's): Smooth oscillations, reduced sensitivity to outliers, consistent behavior across market conditions
Laguerre Binomial Filter: Extremely smooth output, ideal for weekly/monthly timeframe analysis, eliminates short-term noise completely
Source Selection:
Close (Default): Standard choice using closing prices, most common and widely tested
HLC3 or OHLC4: Incorporates more price information, reduces impact of sudden spikes or gaps, smoother oscillator behavior
HL2: Midpoint of high-low range, emphasizes intrabar volatility, useful for markets with wide intraday ranges
Custom Source: Can use other indicators as input (e.g., Heikin Ashi close, smoothed price), creates derivative momentum indicators
📈 PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES
Responsiveness Characteristics:
Traditional SMA-Based Stochastic:
Fixed lag regardless of market conditions, consistent delay of approximately (K Smoothing + D Smoothing) / 2 periods
Equal treatment of trending and ranging markets, no adaptation to volatility changes
Predictable behavior but suboptimal in varying market regimes
Enhanced Version with Adaptive Algorithms:
KAMA and FRAMA reduce lag by up to 40-60% in strong trends compared to SMA while maintaining similar smoothness in ranges
ZLEMA and T3 provide near-zero lag characteristics for early entry signals with acceptable noise levels
Kalman Filter and Super Smoother offer superior noise rejection, reducing false signals in choppy conditions by estimations of 30-50% compared to SMA
Performance improvements vary by algorithm selection and market conditions
Signal Quality Improvements:
Adaptive algorithms help reduce whipsaw trades in ranging markets by adjusting sensitivity dynamically
Advanced filters (Kalman, Laguerre, Super Smoother) provide clearer extreme zone readings for mean reversion strategies
Zero-lag methods (ZLEMA, DEMA, TEMA) generate earlier crossover signals in trending markets for improved entry timing
Smoother algorithms (T3, Laguerre Binomial) reduce false extreme zone touches for more reliable overbought/oversold signals
Comparison with Standard Implementations:
Versus Basic Stochastic: Enhanced version offers 21 smoothing options versus single SMA, allowing optimization for specific market characteristics and trading styles
Versus RSI: Stochastic provides range-bound measurement (0-100) with clear extreme zones, RSI measures momentum speed, Stochastic offers clearer visual overbought/oversold identification
Versus MACD: Stochastic bounded oscillator suitable for mean reversion, MACD unbounded indicator better for trend strength, Stochastic excels in range-bound and oscillating markets
Versus CCI: Stochastic has fixed bounds (0-100) for consistent interpretation, CCI unbounded with variable extremes, Stochastic provides more standardized extreme readings across different instruments
Flexibility Advantages:
Single indicator adaptable to multiple strategies through algorithm selection rather than requiring different indicator variants
Ability to optimize smoothing characteristics for specific instruments (e.g., smoother for crypto volatility, faster for forex trends)
Multi-timeframe analysis with consistent algorithm across timeframes for coherent momentum picture
Backtesting capability with algorithm as optimization parameter for strategy development
Limitations and Considerations:
Increased complexity from multiple algorithm choices may lead to over-optimization if parameters are curve-fitted to historical data
Adaptive algorithms (KAMA, FRAMA) have adjustment periods during market regime changes where signals may be less reliable
Zero-lag algorithms sacrifice some smoothness for responsiveness, potentially increasing noise sensitivity in very choppy conditions
Performance characteristics vary significantly across algorithms, requiring understanding and testing before live implementation
Like all oscillators, Stochastic can remain in extreme zones for extended periods during strong trends, generating premature reversal signals
USAGE NOTES
This indicator is designed for technical analysis and educational purposes to provide traders with enhanced flexibility in momentum analysis. The Stochastic Oscillator has limitations and should not be used as the sole basis for trading decisions.
Important Considerations:
Algorithm performance varies with market conditions - no single smoothing method is optimal for all scenarios
Extreme zone signals (overbought/oversold) indicate potential reversal areas but not guaranteed turning points, especially in strong trends
Crossover signals may generate false entries during sideways choppy markets regardless of smoothing algorithm
Divergence patterns require confirmation from price action or additional indicators before trading
Past indicator characteristics and backtested results do not guarantee future performance
Always combine Stochastic analysis with proper risk management, position sizing, and multi-indicator confirmation
Test selected algorithm on historical data of specific instrument and timeframe before live trading
Market regime changes may require algorithm adjustment for optimal performance
The enhanced smoothing options are intended to provide tools for optimizing the indicator's behavior to match individual trading styles and market characteristics, not to create a perfect predictive tool. Responsible usage includes understanding the mathematical properties of selected algorithms and their appropriate application contexts.
1m Scalping ATR (with SL & Zones)A universal ATR indicator that anchors volatility to your stop-loss.
Read any market (FX, JPY pairs, Gold/Silver, indices, crypto) consistently—regardless of pip/point conventions and timeframe.
Why this indicator?
Classic ATR is absolute (pips/points) and feels different across markets/TFs. ATR Takeoff normalizes ATR to your stop-loss in pips and highlights clear zones for “quiet / ideal / too volatile,” so you instantly know if a 10-pip SL fits current conditions.
Key features
Auto pip detection (FX, JPY, XAU/XAG, indices, BTC/ETH).
Selectable ATR source: chart timeframe or fixed ATR TF (e.g., “15”, “30”, “60”).
Display modes:
Percent of SL – ATR relative to SL in %, great for M1 (typical 10–30%).
Multiple of SL – ATR as a multiple of SL (e.g., 0.6× / 1.0× / 1.2×).
Panel zones:
Green = “Ready for takeoff” (≤ Low), Yellow = reference (Mid), Red = too volatile (≥ High).
Status badge (top-right): Quiet / ATR ok / Wild, current ATR/SL value, ATR TF used.
Direction-agnostic: Works the same for longs and shorts.
Inputs (at a glance)
Length / Smoothing (RMA/SMA/EMA/WMA): ATR base settings.
Your Stop-Loss (Pips): Reference SL (e.g., 10).
ATR Timeframe (empty = chart): Use chart TF or a fixed TF.
Display Mode: “Percent of SL” or “Multiple of SL.”
Low/Mid/High (Percent Mode): Zone thresholds in % of SL.
Low/Mid/High (Multiple Mode): Zone thresholds in ×SL.
Recommended defaults
Length 14, Smoothing RMA, SL 10 pips
Display Mode: Percent of SL
Low/Mid/High (%): 15 / 20 / 25
ATR Timeframe: empty (= chart) for reactive, or “30” for smoother M30 context with M1 entries.
How to use
Set SL (pips). 2) Choose display mode. 3) Optionally pick ATR TF.
Interpretation:
≤ Low (green): setups allowed.
≈ Mid (yellow): neutral reference.
≥ High (red): too volatile → adjust SL/size or wait.
Note: Auto-pip relies on common ticker naming; verify on exotic symbols.
Disclaimer: For research/education. Not financial advice.
John Bollinger's Bollinger BandsJapanese below / 日本語説明は下記
This indicator replicates how John Bollinger, the inventor of Bollinger Bands, uses Bollinger Bands, displaying Bollinger Bands, %B and Bandwidth in one indicator with alerts and signals.
Bollinger Bands is created by John Bollinger in 1980s who is an American financial trader and analyst. He introduced %B and Bandwidth 30 years later.
🟦 What's different from other Bollinger Bands indicator?
Unlike the default Bollinger Bands or other custom Bollinger Bands indicators on TradingView, this indicator enables to display three Bollinger Bands tools into a single indicator with signals and alerts capability.
You can plot the classic Bollinger Bands together with either %B or Bandwidth or three tools altogether which requires the specific setting(see below settings).
This makes it easy to quantitatively monitor volatility changes and price position in relation to Bollinger Bands in one place.
🟦 Features:
Plots Bollinger Bands (Upper, Basis, Lower) with fill between bands.
Option to display %B or Bandwidth with Bollinger Bands.
Plots highest and lowest Bandwidth levels over a customizable lookback period.
Adds visual markers when Bandwidth reaches its highest (Bulge) or lowest (Squeeze) value.
Includes ready-to-use alert conditions for Bulge and Squeeze events.
📈Chart
Green triangles and red triangles in the bottom chart mark Bulges and Squeezes respectively.
🟦 Settings:
Length: Number of bars used for Bollinger Band middleline calculation.
Basis MA Type: Choose SMA, EMA, SMMA (RMA), WMA, or VWMA for the midline.
StdDev: Standard deviation multiplier (default = 2.0).
Option: Select "Bandwidth" or "%B" (add the indicator twice if you want to display both).
Period for Squeeze and Bulge: Lookback period for detecting the highest and lowest Bandwidth levels.(default = 125 as specified by John Bollinger )
Style Settings: Colors, line thickness, and transparency can be customized.
📈Chart
The chart below shows an example of three Bollinger Bands tools: Bollinger Band, %B and Bandwidth are in display.
To do this, you need to add this indicator TWICE where you select %B from Option in the first addition of this indicator and Bandwidth from Option in the second addition.
🟦 Usage:
🟠Monitor Volatility:
Watch Bandwidth values to spot volatility contractions (Squeeze) and expansions (Bulge) that often precede strong price moves.
John Bollinger defines Squeeze and Bulge as follows;
Squeeze:
The lowest bandwidth in the past 125 period, where trend is born.
Bulge:
The highest bandwidth in the past 125 period where trend is going to die.
According to John Bollinger, this 125 period can be used in any timeframe.
📈Chart1
Example of Squeeze
You can see uptrends start after squeeze(red triangles)
📈Chart2
Example of Bulge
You can see the trend reversal from downtrend to uptrends at the bulge(green triangles)
📈Chart3
Bulge DOES NOT NECESSARILY mean the beginning of a trend in opposite direction.
For example, you can see a bulge happening in the right side of the chart where green triangles are marked. Nevertheless, uptrend still continues after the bulge.
In this case, the bulge marks the beginning of a consolidation which lead to the continuation of the trend. It means that a phase of the trend highlighted in the light blue box came to an end.
Note: light blue box is not drawn by the indicator.
Like other technical analysis methods or tools, these setups do not guarantee birth of new trends and trend reversals. Traders should be carefully observing these setups along with other factors for making decisions.
🟠Track Price Position:
Use %B to see where price is located in relation to the Bollinger Bands.
If %B is close to 1, the price is near upper band while %B is close to 0, the price is near lower band.
🟠Set Alerts:
Receive alerts when Bandwidth hits highest and lowest values of bandwidth, helping you prepare for potential breakout, ending of trends and trend reversal opportunities.
🟠Combine with Other Tools:
This indicator would work best when combined with price action, trend analysis, or
market environmental analysis.
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このインジケーターはボリンジャーバンドの考案者であるジョン・ボリンジャー氏が提唱するボリンジャーバンドの使い方を再現するために、ボリンジャーバンド、%B、バンドウィズ(Bandwidth) の3つを1つのインジケーターで表示可能にしたものです。シグナルやアラートにも対応しています。
ボリンジャーバンドは1980年代にアメリカ人トレーダー兼アナリストのジョン・ボリンジャー氏によって開発されました。彼はその30年後に%Bとバンドウィズを導入しました。
🟦 他のボリンジャーバンドとの違い
TradingView標準のボリンジャーバンドや他のボリンジャーバンドとは異なり、このインジケーターでは3つのボリンジャーバンドツールを1つのインジケーターで表示し、シグナルやアラート機能も利用できるようになっています。
一般的に知られている通常のボリンジャーバンドに加え、%Bやバンドウィズを組み合わせて表示でき、設定次第では3つすべてを同時にモニターすることも可能です。これにより、価格とボリンジャーバンドの位置関係とボラティリティ変化をひと目で、かつ定量的に把握することができます。
🟦 機能:
ボリンジャーバンド（アッパーバンド・基準線・ロワーバンド）を描画し、バンド間を塗りつぶし表示。
オプションで%Bまたはバンドウィズを追加表示可能。
バンドウィズの最高値・最安値を、任意の期間で検出して表示。
バンドウィズが指定期間の最高値（バルジ※）または最安値（スクイーズ）に達した際にシグナルを表示。
※バルジは一般的にボリンジャーバンドで用いられるエクスパンションとほぼ同じ意味ですが、定義が異なります。(下記参照)
バルジおよびスクイーズ発生時のアラート設定が可能。
📈 チャート例
下記チャートの緑の三角と赤の三角は、それぞれバルジとスクイーズを示しています。
🟦 設定:
Length: ボリンジャーバンドの基準線計算に使う期間。
Basis MA Type: SMA, EMA, SMMA (RMA), WMA, VWMAから選択可能。
StdDev: 標準偏差の乗数（デフォルト2.0）。
Option: 「Bandwidth」または「%B」を選択（両方表示するにはこのインジケーターを2回追加）。
Period for Squeeze and Bulge: Bandwidthの最高値・最安値を検出する期間（デフォルトはジョン・ボリンジャー氏が推奨する125）。
Style Settings: 色、線の太さ、透明度などをカスタマイズ可能。
📈 チャート例
下のチャートは「ボリンジャーバンド」「%B」「バンドウィズ」の3つを同時に表示した例です。
この場合、インジケーターを2回追加し、最初に追加した方ではOptionを「%B」に、次に追加した方では「Bandwidth」を選択します。
🟦 使い方:
🟠 ボラティリティを監視する:
バンドウィズの値を見ることで、価格変動の収縮（スクイーズ）や拡大（バルジ）を確認できます。
これらはしばしば強い値動きの前兆となります。
ジョン・ボリンジャー氏はスクイーズとバルジを次のように定義しています：
スクイーズ: 過去125期間の中で最も低いバンドウィズ→ 新しいトレンドが生まれる場所。
バルジ: 過去125期間の中で最も高いバンドウィズ → トレンドが終わりを迎える場所。
この「125期間」はどのタイムフレームでも利用可能とされています。
📈 チャート1
スクイーズの例
赤い三角のスクイーズの後に上昇トレンドが始まっているのが確認できます。
📈 チャート2
バルジの例
緑の三角のバルジの箇所で下降トレンドから上昇トレンドへの反転が見られます。
📈 チャート3
バルジが必ずしも反転を意味しない例
下記のチャート右側の緑の三角で示されたバルジの後も、上昇トレンドが継続しています。
この場合、バルジは反転ではなく「トレンド一時的な調整（レンジ入り）」を示しており、結果的に上昇トレンドが継続しています。
この場合、バルジは水色のボックスで示されたトレンドのフェーズの終わりを示しています。
※水色のボックスはインジケーターが描画したものではありません。
また、他のテクニカル分析と同様に、これらのセットアップは必ず新しいトレンドの発生やトレンド転換を保証するものではありません。トレーダーは他の要素も考慮し、慎重に意思決定する必要があります。
🟠 価格とボリンジャーバンドの位置関係を確認する:
%Bを利用すれば、価格がバンドのどこに位置しているかを簡単に把握できます。
%Bが1に近ければ価格はアッパーバンド付近、0に近ければロワーバンド付近にあります。
🟠 アラートを設定する:
バンドウィズが一定期間の最高値または最安値に到達した際にアラートを設定することで、ブレイクアウトやトレンド終了、反転の可能性に備えることができます。
🟠 他のツールと組み合わせる:
このインジケーターは、プライスアクション、トレンド分析、環境認識などと組み合わせて活用すると最も効果的です。
MA Dist% Screener [Pineify]MA Distance Screener: Multi-Asset Market Scanner for TradingView
Screen multiple symbols and multiple timeframes on TradingView with the MA Distance Screener. Compare asset prices to flexible moving average types. Visual table view, custom assets, timeframes, and MA types. Supercharge your TradingView screener, optimize your workflow, and catch opportunities across assets in real time.
Key Features
Screen up to 10 custom symbols simultaneously across four configurable timeframes.
Choose from multiple Moving Average types: EMA, SMA, WMA, HMA, RMA, VWMA for flexible market context.
Visualize real-time % distance between price and moving average per asset/timeframe in a clean, color-coded table.
Highly customizable: Set your own symbol list, timeframes, MA length and type.
Alerts for symbol/MA deviations—instantly see overbought/oversold status with intuitive background coloring.
Optimized for crypto, FX, and traditional assets – all asset types supported.
How It Works
The MA Distance Screener acts as a dynamic multi-symbol, multi-timeframe scanner. For each selected symbol and timeframe, it calculates the percentage distance between the latest close price and the selected type of moving average (EMA/SMA/etc.). This is achieved by making secure `request.security` calls per asset/timeframe combination, retrieving updated values for each matrix cell. The computed distance (%) is displayed in a color-coded table: a positive value signals price above the MA (potential trend strength), while negatives indicate price below the MA (potential weakness or retracement). Custom colors highlight extreme overbought/oversold readings for quick visual cues.
Trading Ideas and Insights
Quickly spot assets showing the largest deviation from their moving averages – ideal for mean reversion or trend-following entries.
Identify clusters of assets and timeframes lining up in overbought or oversold states; optimize entries with multi-timeframe confirmation.
Scan the market in one glance—reduce chart-hopping and never miss an opportunity when multiple assets align for signals.
The ability to scan distance-to-MA across assets and periods gives traders a statistical edge, surfacing hidden pivots, breakouts, and mean-reversion trades that single-chart analysis may miss.
How Multiple Indicators Work Together
At its core, this screener allows the trader to configure what gets scanned—pick your top 10 assets and favorite 4 timeframes. With each matrix cell, the selected MA (e.g., 14-period EMA) is recalculated, and the current price's distance (%) from that value is computed. By offering six distinct moving average algorithms (EMA, SMA, RMA, HMA, WMA, VWMA), traders can choose their preferred method, adapting the screener for trend, swing, or mean-reversion style. All values are visualized in a single table, creating a true "market dashboard" effect for real-time cross-asset assessment.
Unique Aspects
True cross-asset, cross-timeframe screening in a unified table—rare for Pine Script indicators.
Full flexibility—customizable list of assets, timeframes, and MA parameters to suit any market/trading plan.
Intuitive color-coding and table display eliminates guesswork, enabling “at-a-glance” screening and rapid decision-making.
Efficient, optimized Pine v6 codebase—minimal lag even with 40+ concurrent streams.
How to Use
Add the indicator to your TradingView chart (overlay: off, use a clean chart).
In the settings panel, enter up to 10 symbols (tickers) you want to screen—crypto, stocks, FX, or indices.
Set the 4 timeframes to scan (e.g., 1m, 5m, 15m, 1h), plus your preferred moving average length and type.
Review the results in the pop-up table, where each cell shows "% Distance" from MA for each symbol/timeframe.
Monitor table background/text color for overbought vs. oversold cues.
Customization
Symbol List: Track any asset by typing its TradingView ticker.
Timeframes: Full freedom to select 4 timeframes per scan, from 1min to monthly.
MA Config: Choose period length and MA algorithm (classic or exotic types).
Color Themes: Easily spot signals with dynamic color backgrounds and customizable thresholds.
Conclusion
The MA Distance Screener is a must-have tool for systematic traders, portfolio managers, and retail chartists seeking a true multi-asset edge. With real-time cross-checking against multiple moving averages and timeframes, it empowers faster, more confident decision-making, while reducing chart fatigue and missed setups.
Unlock new insights, catch broad and hidden opportunities, and optimize your market workflow—all in a single TradingView panel.
Synthetic Implied APROverview
The Synthetic Implied APR is an artificial implied APR, designed to imitate the implied APR seen when trading cryptocurrency funding rates. It combines real-time funding rates with premium data to calculate an artificial market expectation of the annualized funding rate.
The (actual) implied APR is the market's expectation of the annualized funding rate. This is dependent on bid/ask impacts of the implied APR, something which is currently unavailable to fetch with TradingView. In essence, an implied APR of X% means traders believe that asset's funding fees to average X% when annualized.
What's important to understand, is that the actual value of the synthetic implied APR is not relevant. We only simply use its relative changes when we trade (i.e if it crosses above/below its MA for a given weight). Even for the same asset, the implied APRs will change depending on days to maturity.
How it calculates
The synthetic implied APR is calculated with these steps:
Collects premium data from perpetual futures markets using optimized lower timeframe requests (check my 'Predicted Funding Rates' indicator)
Calculates the funding rate by adding the premium to an interest rate component (clamped within exchange limits)
Derives the underlying APR from the 8-hour funding rate (funding rate × 3 × 365)
Apply a weighed formula that imitates both the direction (underlying APR) with the volatility of prices (from the premium index and funding)
premium_component = (prem_avg / 50 ) * 365
weighedprem = (weight * fr) + ((1 - weight) * apr) + (premium_component * 0.3)
impliedAPR = math.avg(weighedprem, ta.sma(apr, maLength))
How to use it: Generally
Preface: Funding rates are an indication of market sentiment
If funding is positive, generally the market is bullish as longs are willing to pay shorts funding
If funding is negative, generally the market is bearish as shorts are willing to pay longs funding
So, this script can be used like a typical oscillator:
Bullish: If implied APR > MA OR if implied APR MA is green
Bearish: If implied APR < MA OR if implied APR MA is red
The components:
Synthetic Implied APR: The main metric. At current setting of 0.7, it imitates volatility
Weight: The higher the value, the smoother the synthetic implied APR is (and MA too). This value is very important to the imitation. At 0.7, it imitates the actual volatility of the implied APR. At weight = 1, it becomes very smooth. Perfect for trading
Synthetic Implied APR Moving Average: A moving average of the Synthetic implied APR. Can choose from multiple selections, (SMA, EMA, WMA, HMA, VWMA, RMA)
How to use it: Trading Funding
When trading funding there're multiple ways to use it with different settings
Trade funding rates with trend changes
Settings: Weight = 1
Method 1: When the implied APR MA turns green, long funding rates (or short if red)
Method 2: When the implied APR crosses above the MA, long funding rates (or short when crosses below)
Trade funding rates with MA pullbacks
Settings: Weight = 0.7, timeframe 15m
In an uptrend: When implied APR crosses below then above the script, long funding opportunity
In an downtrend: When implied APR crosses above then below the script, shortfunding opportunity
You can determine the trend with the method before, using a weight of 1
To trade funding rates, it's best to have these 3 scripts at these settings:
Predicted Funding Rates: This allows you to see the predicted funding rates and see if they've maxxed out for added confluence too (+/-0.01% usually for Binance BTC futures)
Synthetic implied APR: At weight 1, the MA provides a good trend (whether close above/below or colour change)
Synthetic implied APR: At weight 0.7, it provides a good imitation of volatility
How to use it: Trading Futures
When trading futures:
You can determine roughly what the trend is, if the assumption is made that funding rates can help identify trends if used as a sentiment indicator. It should be supplemented with traditional trend trading methods
To prevent whipsaws, weight should remain high
Long trend: When the implied APR MA turns green OR when it crosses above its MA
Short trend: When the implied APR MA turns red OR when it below above its MA
Why it's original
This indicator introduces a unique synthetic weighting system that combines funding rates, underlying APR, and premium components in a way not found in existing TradingView scripts. Trading funding rates is a niche area, there aren't that many scripts currently available. And to my knowledge, there's no synthetic implied APR scripts available on TradingView either. So I believe this script to be original in that sense.
Notes
Because it depends on my triangular weighting algos, optimal accuracy is found on timeframes that are 4H or less. On higher timeframes, the accuracy drops off. Best timeframes for intraday trading using this are 15m or 1 hour
The higher the timeframe, the lower the MA one should use. At 1 hour, 200 or higher is best. At say, 4h, length of 50 is best
Only works for coins that have a Binance premium index
Inputs
Funding Period - Select between "1 Hour" or "8 Hour" funding cycles. 8 hours is standard for Binance
Table - Toggle the information dashboard on/off to show or hide real-time metrics including funding rate, premium, and APR value
Weight - Controls the balance between funding rate (higher values = smoother) and APR (lower values = more responsive) in the calculation, ranging from 0.0 to 1.0. Default is 0.7, this imitates the volatility
Auto Timeframe Implied Length - Automatically calculates optimal smoothing length based on your chart timeframe for consistent behavior across different time periods
Manual Implied Length - Sets a fixed smoothing length (in bars) when auto mode is disabled, with lower values being more responsive and higher values being smoother
Show Implied APR MA - Displays an additional moving average line of the Synthetic Implied APR to help identify trend direction and crossover signals
MA Type for Implied APR - Selects the calculation method (SMA, EMA, WMA, HMA, VWMA, or RMA) for the moving average, each offering different responsiveness and lag characteristics
MA Length for Implied APR - Sets the lookback period (1-500 bars) for the moving average, with shorter lengths providing more signals and longer lengths filtering noise
Show Underlying APR - Displays the raw APR calculation (without synthetic weighting) as a reference line to compare against the main indicator
Bullish Color - Sets the color for positive values in the table and rising MA line
Bearish Color - Sets the color for negative values in the table and falling MA line
Table Background - Customizes the background color and transparency of the information dashboard
Table Text Color - Sets the color for label text in the left column of the information table
Table Text Size - Controls the font size of table text with options from Tiny to Huge
Clean MA + Signals (overlay)//@version=5
indicator("Clean MA + Signals (overlay)", overlay=true)
// Inputs
maLen = input.int(50, "MA Length", minval=1)
maType = input.string("EMA", "MA Type", options= )
// MA
maCalc(src, len, typ) =>
switch typ
"SMA" => ta.sma(src, len)
"EMA" => ta.ema(src, len)
"RMA" => ta.rma(src, len)
"WMA" => ta.wma(src, len)
maLine = maCalc(close, maLen, maType)
plot(maLine, "MA", color=color.new(color.teal, 0), linewidth=2)
// Siqnallar — yalnız kəsişmə anında
longCond = ta.crossover(close, maLine)
shortCond = ta.crossunder(close, maLine)
plotshape(longCond, "LONG", location=location.belowbar, style=shape.triangleup, color=color.lime, size=size.small, text="LONG")
plotshape(shortCond, "SHORT", location=location.abovebar, style=shape.triangledown, color=color.red, size=size.small, text="SHORT")
alertcondition(longCond, "LONG Signal", "LONG signal on {{ticker}} {{interval}}")
alertcondition(shortCond, "SHORT Signal", "SHORT signal on {{ticker}} {{interval}}")
