SMA/pivot/Bollinger/MACD/RSI en pantalla gráficoMulti-indicador con los indicadores que empleo más pero sin añadir ventanas abajo.
Contiene:
Cruce de 3 medias móviles
La idea es no tenerlas en pantalla, pero están dibujadas también. Yo las dejo ocultas salvo que las quiera mirar para algo.
Lo que presento en pantalla es la media lenta con verde si el cruce de las 3 marca alcista, amarillo si no está claro y rojo si marca bajista.
Pivot
Normalmente los tengo ocultos pero los muestro cuando me interesa. Están todos aunque aparezcan 2 seguidos.
Bandas de Bollinger
No dibujo la línea central porque empleo la media como tal.
Parabollic SAR
Lo empleo para dibujar las ondas de Elliott como postula Matías Menéndez Larre en el capítulo 11 de su libro "Las ondas de Elliott". Así que, aunque se puede mostrar, lo mantengo oculto y lo que muestro es dónde cambia (SAR cambio).
MACD
No está dibujado porque necesitaría sacarlo del gráfico.
Marco en la parte superior cuándo la señal sobrepasa al MACD hacia arriba o hacia abajo con un flecha indicando el sentido de esta señal.
RSI
Similar al MACD pero en la parte inferior.
Probablemente, programe otro indicador para visualizar en una ventanita MACD, RSI y volumen todo junto. El volumen en la principal hay veces que no te permite ver bien alguna sombra y los otros 2 te quitan mucho espacio para graficar si los tienes permanentemente en 2 ventanas separadas.
Cerca negli script per "algo"
DFT - Dominant Cycle Period 8-50 bars - John EhlerThis is the translation of discret cosine tranform (DCT) usage by John Ehler for finding dominant cycle period (DC).
The price is first filtered to remove aliasing noise(bellow 8 bars) and trend informations(above 50 bars), then the power is computed.
The trick here is to use a normalisation against the maximum power in order to get a good frequency resolution.
Current limitation in tradingview does not allow to display all of the periods, still the DC period is plot after beeing computed based on the center of gravity algo.
The DC period can be used to tune all of the indicators based on the cycles of the markets. For instance one can use this (DC period)/2 as an input for RSI.
Hope you find this of some interrest.
META: STDEV Study (Scripting Exercise)While trying to figure out how to make the STDEV function use an exponential moving average instead of simple moving average , I discovered the builtin function doesn't really use either.
Check it out, it's amazing how different the two-pass algorithm is from the builtin!
Eventually I reverse-engineered and discovered that STDEV uses the Naiive algorithm and doesn't apply "Bessel's Correction". K can be 0, it doesn't seem to change the data although having it included should make it a little more precise.
en.wikipedia.org
[naoligo] Simple ADXI'm publishing this indicator just for study purposes, because the result is exactly the same as DMI without the smoothing factor. It is exactly the same as ADX Wilder from MT5.
I was looking for the algorithm all over and it was a pain to find the right formula, meaning: one that would match with the built-in ones. After several study and comparison, I still didn't find the algorithm that match with the MT5's built-in simple ADX ...
Enjoy!
Patrones de entrada/salida V.1.0 -BETA-Este algoritmo intenta identificar patrones o fractales dentro de los movimientos de precios para dar señales de compra o venta de activos.
Zero Lag MACD Enhanced - Version 1.1ENHANCED ZERO LAG MACD
Version 1.1
Based on ZeroLag EMA - see Technical Analysis of Stocks and Commodities, April 2000
Original version by user Glaz. Thanks !
Ideas and code from @yassotreyo version.
Tweaked by Albert Callisto (AC)
New features:
Added original signal line formula
Added optional EMA on MACD
Added filling between the MACD and signal line
I looked at other versions of the zero lag and noticed that the histogram was slightly different. After looking at other zero lags on TV, I noticed that the algorithm implementation of Glanz generated a modified signal line. I decided to add the old version to be compliant with the original algorithm that you will find in other platforms like MT4, FXCM, etc.
So now you can choose if you want the original algorithm or Glanz version. It's up to you then to choose which one you prefer. I also added an extra EMA applied on the MACD. This is used in a system I am currently studying and can be of some interest to filter out false signals.
Acc/Dist. Cloud with Fractal Deviation Bands by @XeL_ArjonaACCUMULATION / DISTRIBUTION CLOUD with MORPHIC DEVIATION BANDS
Ver. 2.0.beta.23:08:2015
by Ricardo M. Arjona @XeL_Arjona
DISCLAIMER
The Following indicator/code IS NOT intended to be a formal investment advice or recommendation by the author, nor should be construed as such. Users will be fully responsible by their use regarding their own trading vehicles/assets.
The embedded code and ideas within this work are FREELY AND PUBLICLY available on the Web for NON LUCRATIVE ACTIVITIES and must remain as is.
Pine Script code MOD's and adaptations by @XeL_Arjona with special mention in regard of:
Buy (Bull) and Sell (Bear) "Power Balance Algorithm by Vadim Gimelfarb published at Stocks & Commodities V. 21:10 (68-72).
Custom Weighting Coefficient for Exponential Moving Average (nEMA) adaptation work by @XeL_Arjona with contribution help from @RicardoSantos at TradingView @pinescript chat room.
Morphic Numbers (PHI & Plastic) Pine Script adaptation from it's algebraic generation formulas by @XeL_Arjona
Fractal Deviation Bands idea by @XeL_Arjona
CHANGE LOG:
ACCUMULATION / DISTRIBUTION CLOUD: I decided to change it's name from the Buy to Sell Pressure. The code is essentially the same as older versions and they are the center core (VORTEX?) of all derived New stuff which are:
MORPHIC NUMBERS: The "Golden Ratio" expressed by the result of the constant "PHI" and the newer and same in characteristics "Plastic Number" expressed as "PN". For more information about this regard take a look at: HERE!
CUSTOM(K) EXPONENTIAL MOVING AVERAGE: Some code has cleaned from last version to include as custom function the nEMA , which use an additional input (K) to customise the way the "exponentially" is weighted from the custom array. For the purpose of this indicator, I implement a volatility algorithm using the Average True Range of last 9 periods multiplied by the morphic number used in the fractal study. (Golden Ratio as default) The result is very similar in response to classic EMA but tend to accelerate or decelerate much more responsive with wider bars presented in trending average.
FRACTAL DEVIATION BANDS: The main idea is based on the so useful Standard Deviation process to create Bands in favor of a multiplier (As John Bollinger used in it's own bands) from a custom array, in which for this case is the "Volume Pressure Moving Average" as the main Vortex for the "Fractallitly", so then apply as many "Child bands" using the older one as the new calculation array using the same morphic constant as multiplier (Like Fibonacci but with other approach rather than %ratios). Results are AWSOME! Market tend to accelerate or decelerate their Trend in favor of a Fractal approach. This bands try to catch them, so please experiment and feedback me your own observations.
EXTERNAL TICKER FOR VOLUME DATA: I Added a way to input volume data for this kind of study from external tickers. This is just a quicky-hack given that currently TradingView is not adding Volume to their Indexes so; maybe this is temporary by now. It seems that this part of the code is conflicting with intraday timeframes, so You are advised.
This CODE is versioned as BETA FOR TESTING PROPOSES. By now TradingView Admins are changing lot's of things internally, so maybe this could conflict with correct rendering of this study with special tickers or timeframes. I will try to code by itself just the core parts of this study in order to use them at discretion in other areas. ALL NEW IDEAS OR MODIFICATIONS to these indicator(s) are Welcome in favor to deploy a better and more accurate readings. I will be very glad to be notified at Twitter or TradingView accounts at: @XeL_Arjona
Markov Chain Trend ProbabilityA Markov Chain is a mathematical model that predicts future states based on the current state, assuming that the future depends only on the present (not the past). Originally developed by Russian mathematician Andrey Markov, this concept is widely used in:
Finance: Risk modeling, portfolio optimization, credit scoring, algorithmic trading
Weather Forecasting: Predicting sunny/rainy days, temperature patterns, storm tracking
Here's an example of a Markov chain: If the weather is sunny, the probability that will be sunny 30 min later is say 90%. However, if the state changes, i.e. it starts raining, how the probability that will be raining 30 min later is say 70% and only 30% sunny.
Similar concept can be applied to markets price action and trends.
Mathematical Foundation
The core principle follows the Markov Property: P(X_{t+1}|X_t, X_{t-1}, ..., X_0) = P(X_{t+1}|X_t)
Transition Matrix :
-------------Next State
Current----
--------P11 P12
-----P21 P22
Probability Calculations:
P(Up→Up) = Count(Up→Up) / Count(Up states)
P(Down→Down) = Count(Down→Down) / Count(Down states)
Steady-state probability: π = πP (where π is the stationary distribution)
State Definition:
State = UPTREND if (Price_t - Price_{t-n})/ATR > threshold
State = DOWNTREND if (Price_t - Price_{t-n})/ATR < -threshold
How It Works in Trading
This indicator applies Markov Chain theory to market trends by:
Defining States: Classifies market conditions as UPTREND or DOWNTREND based on price movement relative to ATR (Average True Range)
Learning Transitions: Analyzes historical data to calculate probabilities of moving from one state to another
Predicting Probabilities: Estimates the likelihood of future trend continuation or reversal
How to Use
Parameters:
Lookback Period: Number of bars to analyze for trend detection (default: 14)
ATR Threshold: Sensitivity multiplier for state changes (default: 0.5)
Historical Periods: Sample size for probability calculations (default: 33)
Trading Applications:
Trend confirmation for entry/exit decisions
Risk assessment through probability analysis
Market regime identification
Early warning system for potential trend reversals
The indicator works on any timeframe and asset class. Enjoy!
DR V966 - Smart Money Concepts// This Pine Script™ code is subject to the terms of the Mozilla Public License 2.0 at mozilla.org
// © DR.BASL
//
//@version=5
indicator("DR V966 - Smart Money Concepts", "DR V966 - Smart Money Concepts ",
overlay = true,
max_bars_back = 5000,
max_boxes_count = 500,
max_labels_count = 500,
max_lines_count = 500,
max_polylines_count = 100)
plot(na)
//
MSG = "MARKET STRUCTURE"
VBG = "VOLUMETRIC ORDER BLOCKS"
MST = "Limit market structure calculation to improve memory speed time"
SLT = " Limit swing structure to tot bars back"
IDT = " Start date of the internal structure"
CST = "Color candle based on trend detection system"
OBT = "Display internal buy and sell activity"
OBD = "Show Last number of orderblock"
OBMT = " Use Length to adjust cordinate of the orderblocks\n Use whole candle body"
_ ='
------------
–––––––––––––––––––––––––– INPUTS –––––––––––––––––––––––––––
------------ '//{
bool windowsis = input.bool(true, "Window", inline="kla", group=MSG)
int mswindow = input.int(5000, "", tooltip=MST,group=MSG, inline="kla", minval=1000)
bool showSwing = input.bool(true, "Swing", inline="scss", group=MSG)
int swingLimit = input.int(100, "", tooltip=SLT, inline="scss", group=MSG, minval=10, maxval=200)
color swingcssup = input.color(#089981, "", inline="scss", group=MSG)
color swingcssdn = input.color(#f23645, "", inline="scss", group=MSG)
bool showMapping = input.bool(false, "Mapping Structure", inline="mapping", group=MSG)
string mappingStyle = input.string("----", "", options= , inline="mapping", group=MSG)
color mappingcss = input.color(color.silver, "", tooltip="Display Mapping Structure", inline="mapping", group=MSG)
bool candlecss = input.bool(false, "Color Candles", tooltip=CST, group=MSG, inline="txt")
string mstext = input.string("Tiny", "", options= ,
inline="txt", group=MSG)
string msmode = input.string("Adjusted Points", "Algorithmic Logic", options=
, inline="node", group=MSG)
int mslen = input.int(5, "", inline="node", group=MSG, minval=2)
bool buildsweep = input.bool(true, "Build Sweep (x)", "Build sweep on market structure", "znc", MSG)
bool msbubble = input.bool(true, "Bubbles", tooltip="Display Circle Bubbles", inline="bubbles", group=MSG)
bool obshow = input.bool(true, "Show Last", tooltip=OBD, group=VBG, inline="obshow")
int oblast = input.int(5, "", group=VBG, inline="obshow", minval=0)
color obupcs = input.color(color.new(#089981, 90), "", inline="obshow", group=VBG)
color obdncs = input.color(color.new(#f23645, 90), "", inline="obshow", group=VBG)
bool obshowactivity = input.bool(true, "Show Buy/Sell Activity", inline="act", group=VBG, tooltip=OBT)
color obactup = input.color(color.new(#089981, 50), "", inline="act", group=VBG)
color obactdn = input.color(color.new(#f23645, 50), "", inline="act", group=VBG)
obshowbb = input.bool(false, "Show Breakers", inline="bb", group=VBG, tooltip="Display Breakers")
color bbup = input.color(color.new(#089981, 100), "", inline="bb", group=VBG)
color bbdn = input.color(color.new(#f23645, 100), "", inline="bb", group=VBG)
obmode = input.string("Length", "Construction", options= , tooltip=OBMT, inline="atr", group=VBG)
len = input.int(5, "", inline="atr", group=VBG, minval=1)
obmiti = input.string("Close", "Mitigation Method", options= ,
tooltip="Mitigation method for when to trigger order blocks", group=VBG)
obtxt = input.string("Normal", "Metric Size", options= ,
tooltip="Order block Metrics text size", inline="txt", group=VBG)
showmetric = input.bool(true, "Show Metrics", group=VBG)
showline = input.bool(true, "Show Mid-Line", group=VBG)
overlap = input.bool(true, "Hide Overlap", group=VBG, inline="ov")
wichlap = input.string("Recent", "", options= , inline="ov", group=VBG)
fvg_enable = input.bool(false, "", inline="1", group="FAIR VALUE GAP", tooltip="Display fair value gap")
what_fvg = input.string("FVG", "", inline="1", group="FAIR VALUE GAP", tooltip="Display fair value gap",
options= )
fvg_num = input.int(5, "Show Last", inline="1a", group="FAIR VALUE GAP", tooltip="Number of fvg to show", minval=0)
fvg_upcss = input.color(color.new(#089981, 80), "", inline="1", group="FAIR VALUE GAP")
fvg_dncss = input.color(color.new(#f23645, 80), "", inline="1", group="FAIR VALUE GAP")
fvgbbup = input.color(color.new(#089981, 100), "", inline="1", group="FAIR VALUE GAP")
fvgbbdn = input.color(color.new(#f23645, 100), "", inline="1", group="FAIR VALUE GAP")
fvg_src = input.string("Close", "Mitigation",
inline="3",
group="FAIR VALUE GAP",
tooltip=" Use the close of the body as trigger\n\n Use the extreme point of the body as trigger",
options= )
fvgthresh = input.float(0, "Threshold", tooltip="Filter out non significative FVG", group="FAIR VALUE GAP",
inline="asd", minval=0, maxval=2, step=0.1)
fvgoverlap = input.bool(true, "Hide Overlap", "Hide overlapping FVG", group="FAIR VALUE GAP")
fvgline = input.bool(true, "Show Mid-Line", group="FAIR VALUE GAP")
fvgextend = input.bool(false, "Extend FVG", group="FAIR VALUE GAP")
dispraid = input.bool(false, "Display Raids", inline="raid", group="FAIR VALUE GAP")
// إعدادات تفعيل/تعطيل وتخصيص لكل مستوى فيبوناتشي
show_fib_0 = input.bool(true, "إظهار 0.0" , group="Fibonacci")
show_fib_236 = input.bool(true, "إظهار 0.236" , group="Fibonacci")
show_fib_382 = input.bool(true, "إظهار 0.382" , group="Fibonacci")
show_fib_5 = input.bool(true, "إظهار 0.5" , group="Fibonacci")
show_fib_618 = input.bool(true, "إظهار 0.618" , group="Fibonacci")
show_fib_786 = input.bool(true, "إظهار 0.786" , group="Fibonacci")
show_fib_1 = input.bool(true, "إظهار 1.0" , group="Fibonacci")
show_fib_1272 = input.bool(true, "إظهار 1.272" , group="Fibonacci")
show_fib_1618 = input.bool(true, "إظهار 1.618" , group="Fibonacci")
show_fib_180 = input.bool(true, "إظهار 1.80" , group="Fibonacci")
show_fib_2 = input.bool(true, "إظهار 2.0" , group="Fibonacci")
show_fib_2272 = input.bool(true, "إظهار 2.272" , group="Fibonacci")
show_fib_2618 = input.bool(true, "إظهار 2.618" , group="Fibonacci")
fib_color_0 = input.color(color.white, "لون 0.0" , group="Fibonacci")
fib_color_236 = input.color(color.white, "لون 0.236" , group="Fibonacci")
fib_color_382 = input.color(color.white, "لون 0.382" , group="Fibonacci")
fib_color_5 = input.color(color.white, "لون 0.5" , group="Fibonacci")
fib_color_618 = input.color(color.white, "لون 0.618" , group="Fibonacci")
fib_color_786 = input.color(color.white, "لون 0.786" , group="Fibonacci")
fib_color_1 = input.color(color.white, "لون 1.0" , group="Fibonacci")
fib_color_1272 = input.color(color.white, "لون 1.272" , group="Fibonacci")
fib_color_1618 = input.color(color.white, "لون 1.618" , group="Fibonacci")
fib_color_180 = input.color(color.white, "لون 1.80" , group="Fibonacci")
fib_color_2 = input.color(color.white, "لون 2.0" , group="Fibonacci")
fib_color_2272 = input.color(color.white, "لون 2.272" , group="Fibonacci")
fib_color_2618 = input.color(color.white, "لون 2.618" , group="Fibonacci")
fib_size = input.string("normal", "حجم الخط", options= , group="Fibonacci")
fib_shift = input.int(0, "تحريك خطوط الفيبوناتشي إلى اليمين", minval=0, maxval=100, group="Fibonacci")
//}
_ ='
------------
–––––––––––––––––––––––––– UDT –––––––––––––––––––––––––––
------------ '//{
type hqlzone
box pbx
box ebx
box lbx
label plb
label elb
label lbl
type Zphl
line top
line bottom
label top_label
label bottom_label
bool stopcross
bool sbottomcross
bool itopcross
bool ibottomcross
string txtup
string txtdn
float topy
float bottomy
float topx
float bottomx
float tup
float tdn
int tupx
int tdnx
float itopy
float itopx
float ibottomy
float ibottomx
float uV
float dV
type entered
bool normal = false
bool breaker = false
type store
line ln
label lb
box bx
linefill lf
type structure
int zn
float zz
float bos
float choch
int loc
int temp
int trend
int start
float main
int xloc
bool upsweep
bool dnsweep
string txt = na
type drawms
int x1
int x2
float y
string txt
color css
string style
type ob
bool bull
float top
float btm
float avg
int loc
color css
float vol
int dir
int move
int blPOS
int brPOS
int xlocbl
int xlocbr
bool isbb = false
int bbloc
type FVG
float top = na
float btm = na
int loc = bar_index
bool isbb = false
int bbloc = na
bool israid = false
float raidy = na
int raidloc = na
int raidx2 = na
bool active = false
color raidcs = na
type SFP
float y
int loc
float ancor
type sfpbuildlbl
int x
float y
string style
color css
string txt
type sfpbuildline
int x1
int x2
float y
color css
float ancor
int loc
type equalbuild
int x1
float y1
int x2
float y2
color css
string style
type equalname
int x
float y
string txt
color css
string style
type ehl
float pt
int t
float pb
int b
type sellbuyside
float top
float btm
int loc
color css
string txt
float vol
type timer
bool start = false
int count = 0
//}
_ ='
------------
–––––––––––––––––––––––––– SETUP –––––––––––––––––––––––––––
------------ '//{
var store bin = store.new(
array.new< line >()
, array.new< label >()
, array.new< box >()
, array.new()
)
var entered blobenter = entered.new()
var entered brobenter = entered.new()
var entered blfvgenter = entered.new()
var entered brfvgenter = entered.new()
var entered blarea = entered.new()
var entered brarea = entered.new()
var timer lc = timer.new ()
if barstate.islast
for obj in bin.ln
obj.delete()
for obj in bin.lb
obj.delete()
for obj in bin.bx
obj.delete()
for obj in bin.lf
obj.delete()
bin.ln.clear()
bin.lb.clear()
bin.bx.clear()
bin.lf.clear()
invcol = #ffffff00
float atr = (ta.atr(200) / (5/len))
//}
_ ='
------------
–––––––––––––––––––––––––– UTILITY –––––––––––––––––––––––––––
------------ '//{
method txSz(string s) =>
out = switch s
"Tiny" => size.tiny
"Small" => size.small
"Normal" => size.normal
"Large" => size.large
"Huge" => size.huge
"Auto" => size.auto
out
method lstyle(string style) =>
out = switch style
'⎯⎯⎯⎯' => line.style_solid
'----' => line.style_dashed
'····' => line.style_dotted
ghl() => [high , low , close , open , close, open, high, low, high , low , ta.atr(200)]
method IDMIDX(bool use_max, int loc) =>
min = 99999999.
max = 0.
idx = 0
if use_max
for i = 0 to (bar_index - loc)
max := math.max(high , max)
min := max == high ? low : min
idx := max == high ? i : idx
else
for i = 0 to (bar_index - loc)
min := math.min(low , min)
max := min == low ? high : max
idx := min == low ? i : idx
idx
SFPData() => [high, high , high , low, low , low , close, volume, time, bar_index , time ]
SFPcords() =>
RealTF = barstate.isrealtime ? 0 : 1
= SFPData()
[h , h1 , h2 , l , l1 , l2 , c , v , t , n , t1 ]
method find(structure ms, bool use_max, bool sweep, bool useob) =>
min = 99999999.
max = 0.
idx = 0
if not sweep
if ((bar_index - ms.loc) - 1) > 0
if use_max
for i = 0 to (bar_index - ms.loc) - 1
max := math.max(high , max)
min := max == high ? low : min
idx := max == high ? i : idx
if useob
if high > high
max := high
min := low
idx := idx + 1
else
for i = 0 to (bar_index - ms.loc) - 1
min := math.min(low , min)
max := min == low ? high : max
idx := min == low ? i : idx
if useob
if low < low
max := high
min := low
idx := idx + 1
else
if use_max
for i = 0 to (bar_index - ms.loc)
max := math.max(high , max)
min := max == high ? low : min
idx := max == high ? i : idx
if useob
if high > high
max := high
min := low
idx := idx + 1
else
for i = 0 to (bar_index - ms.loc)
min := math.min(low , min)
max := min == low ? high : max
idx := min == low ? i : idx
if useob
if low < low
max := high
min := low
idx := idx + 1
else
if ((bar_index - ms.xloc) - 1) > 0
if use_max
for i = 0 to (bar_index - ms.xloc) - 1
max := math.max(high , max)
min := max == high ? low : min
idx := max == high ? i : idx
if useob
if high > high
max := high
min := low
idx := idx + 1
else
for i = 0 to (bar_index - ms.xloc) - 1
min := math.min(low , min)
max := min == low ? high : max
idx := min == low ? i : idx
if useob
if low < low
max := high
min := low
idx := idx + 1
else
if use_max
for i = 0 to (bar_index - ms.xloc)
max := math.max(high , max)
min := max == high ? low : min
idx := max == high ? i : idx
if useob
if high > high
max := high
min := low
idx := idx + 1
else
for i = 0 to (bar_index - ms.xloc)
min := math.min(low , min)
max := min == low ? high : max
idx := min == low ? i : idx
if useob
if low < low
max := high
min := low
idx := idx + 1
idx
method fnOB(ob block, bool bull, float cords, int idx) =>
switch bull
true =>
blobenter.normal := false
blobenter.breaker := false
block.unshift(
ob.new(
true
, cords
, low
, math.avg(cords, low )
, time
, obupcs
, volume
, close > open ? 1 : -1
, 1
, 1
, 1
, time
)
)
false =>
brobenter.normal := false
brobenter.breaker := false
block.unshift(
ob.new(
false
, high
, cords
, math.avg(cords, high )
, time
, obdncs
, volume
, close > open ? 1 : -1
, 1
, 1
, 1
, time
)
)
method mitigated(ob block) =>
if barstate.isconfirmed
for in block
if not stuff.isbb
switch stuff.bull
true =>
if obmiti == "Close" ? math.min(close, open) < stuff.btm : obmiti == "Wick" ? low < stuff.btm : obmiti == "Avg" ? low < stuff.avg : na
stuff.isbb := true
stuff.bbloc := time
if not obshowbb
block.remove(i)
false =>
if obmiti == "Close" ? math.max(close, open) > stuff.top : obmiti == "Wick" ? high > stuff.top : obmiti == "Avg" ? high > stuff.avg : na
stuff.isbb := true
stuff.bbloc := time
if not obshowbb
block.remove(i)
else
switch stuff.bull
true =>
if obmiti == "Close" ? math.max(close, open) > stuff.top : obmiti == "Wick" ? high > stuff.top : obmiti == "Avg" ? high > stuff.avg : na
block.remove(i)
false =>
if obmiti == "Close" ? math.min(close, open) < stuff.btm : obmiti == "Wick" ? low < stuff.btm : obmiti == "Avg" ? low < stuff.avg : na
block.remove(i)
overlap(ob bull, ob bear) =>
if bull.size() > 1
for i = bull.size() - 1 to 1
stuff = bull.get(i)
current = bull.get(0)
v = wichlap == "Recent" ? i : 0
switch
stuff.btm > current.btm and stuff.btm < current.top => bull.remove(v)
stuff.top < current.top and stuff.btm > current.btm => bull.remove(v)
stuff.top > current.top and stuff.btm < current.btm => bull.remove(v)
stuff.top < current.top and stuff.top > current.btm => bull.remove(v)
if bear.size() > 1
for i = bear.size() - 1 to 1
stuff = bear.get(i)
current = bear.get(0)
v = wichlap == "Recent" ? i : 0
switch
stuff.btm > current.btm and stuff.btm < current.top => bear.remove(v)
stuff.top < current.top and stuff.btm > current.btm => bear.remove(v)
stuff.top > current.top and stuff.btm < current.btm => bear.remove(v)
stuff.top < current.top and stuff.top > current.btm => bear.remove(v)
if bull.size() > 0 and bear.size() > 0
for i = bull.size() - 1 to 0
stuff = bull.get(i)
current = bear.get(0)
v = wichlap == "Recent" ? 0 : i
switch
stuff.btm > current.btm and stuff.btm < current.top => bull.remove(v)
stuff.top < current.top and stuff.btm > current.btm => bull.remove(v)
stuff.top > current.top and stuff.btm < current.btm => bull.remove(v)
stuff.top < current.top and stuff.top > current.btm => bull.remove(v)
if bull.size() > 0 and bear.size() > 0
for i = bear.size() - 1 to 0
stuff = bear.get(i)
current = bull.get(0)
v = wichlap == "Recent" ? 0 : i
switch
stuff.btm > current.btm and stuff.btm < current.top => bear.remove(v)
stuff.top < current.top and stuff.btm > current.btm => bear.remove(v)
stuff.top > current.top and stuff.btm < current.btm => bear.remove(v)
stuff.top < current.top and stuff.top > current.btm => bear.remove(v)
overlapFVG(FVG blFVG, FVG brFVG) =>
if blFVG.size() > 1
for i = blFVG.size() - 1 to 1
stuff = blFVG.get(i)
current = blFVG.get(0)
switch
stuff.btm > current.btm and stuff.btm < current.top => blFVG.remove(i)
stuff.top < current.top and stuff.btm > current.btm => blFVG.remove(i)
stuff.top > current.top and stuff.btm < current.btm => blFVG.remove(i)
stuff.top < current.top and stuff.top > current.btm => blFVG.remove(i)
if brFVG.size() > 1
for i = brFVG.size() - 1 to 1
stuff = brFVG.get(i)
current = brFVG.get(0)
switch
stuff.btm > current.btm and stuff.btm < current.top => brFVG.remove(i)
stuff.top < current.top and stuff.btm > current.btm => brFVG.remove(i)
stuff.top > current.top and stuff.btm < current.btm => brFVG.remove(i)
stuff.top < current.top and stuff.top > current.btm => brFVG.remove(i)
if blFVG.size() > 0 and brFVG.size() > 0
for i = blFVG.size() - 1 to 0
stuff = blFVG.get(i)
current = brFVG.get(0)
switch
stuff.btm > current.btm and stuff.btm < current.top => blFVG.remove(i)
stuff.top < current.top and stuff.btm > current.btm => blFVG.remove(i)
stuff.top > current.top and stuff.btm < current.btm => blFVG.remove(i)
stuff.top < current.top and stuff.top > current.btm => blFVG.remove(i)
if blFVG.size() > 0 and brFVG.size() > 0
for i = brFVG.size() - 1 to 0
stuff = brFVG.get(i)
current = blFVG.get(0)
switch
stuff.btm > current.btm and stuff.btm < current.top => brFVG.remove(i)
stuff.top < current.top and stuff.btm > current.btm => brFVG.remove(i)
stuff.top > current.top and stuff.btm < current.btm => brFVG.remove(i)
stuff.top < current.top and stuff.top > current.btm => brFVG.remove(i)
method umt(ob metric) =>
switch metric.dir
1 =>
switch metric.move
1 => metric.blPOS := metric.blPOS + 1, metric.move := 2
2 => metric.blPOS := metric.blPOS + 1, metric.move := 3
3 => metric.brPOS := metric.brPOS + 1, metric.move := 1
-1 =>
switch metric.move
1 => metric.brPOS := metric.brPOS + 1, metric.move := 2
2 => metric.brPOS := metric.brPOS + 1, metric.move := 3
3 => metric.blPOS := metric.blPOS + 1, metric.move := 1
if (time - time ) == (time - time )
metric.xlocbl := metric.loc + (time - time ) * metric.blPOS
metric.xlocbr := metric.loc + (time - time ) * metric.brPOS
method display(ob id, ob full, int i) =>
if not id.isbb
bin.bx.unshift(box.new (top = id.top, bottom = id.btm, left = id.loc, right = time , border_color = na , bgcolor = id.css, xloc = xloc.bar_time))
bin.bx.unshift(box.new (top = id.top, bottom = id.btm, left = time , right = time + 1 , border_color = na , bgcolor = id.css, xloc = xloc.bar_time, extend = extend.right))
else
bin.bx.unshift(box.new (top = id.top, bottom = id.btm, left = id.loc , right = id.bbloc , border_color = na , bgcolor = id.css , xloc = xloc.bar_time))
bin.bx.unshift(box.new (top = id.top, bottom = id.btm, left = id.bbloc , right = time , border_color = id.css , bgcolor = id.bull ? bbup : bbdn , xloc = xloc.bar_time, border_width = 2))
bin.bx.unshift(box.new (top = id.top, bottom = id.btm, left = time , right = time + 1 , border_color = id.css , bgcolor = id.bull ? bbup : bbdn , xloc = xloc.bar_time, extend = extend.right))
if obshowactivity
bin.bx.unshift(box.new (top = id.top, bottom = id.avg, left = id.loc , right = id.xlocbl, border_color = na , bgcolor = obactup, xloc = xloc.bar_time))
bin.bx.unshift(box.new (top = id.avg, bottom = id.btm, left = id.loc , right = id.xlocbr, border_color = na , bgcolor = obactdn, xloc = xloc.bar_time))
if showline
bin.ln.unshift(line.new(
x1 = id.loc
, x2 = time
, y1 = id.avg
, y2 = id.avg
, color = color.new(id.css, 0)
, xloc = xloc.bar_time
, style = line.style_dashed
)
)
if showmetric
if i == math.min(oblast - 1, full.size() - 1)
float tV = 0
float dV = array.new()
seq = math.min(oblast - 1, full.size() - 1)
for j = 0 to seq
cV = full.get(j)
tV += cV.vol
if j == seq
for y = 0 to seq
dV.push(
math.floor(
(full.get(y).vol / tV) * 100)
)
ids = full.get(y)
bin.lb.unshift(label.new(
bar_index - 1
, ids.avg
, textcolor = color.new(ids.css, 0)
, style = label.style_label_left
, size = obtxt.txSz()
, color = #ffffff00
, text =
str.tostring(
math.round(full.get(y).vol, 3), format = format.volume) + " (" + str.tostring(dV.get(y)) + "%)"
)
)
method dispFVG(FVG fvg, int i, bool bull) =>
ext = fvgextend ? extend.right : extend.none
if not fvg.isbb
bin.bx.unshift(box .new(top = fvg.top, bottom = fvg.btm, left = fvg.loc , right = time , border_color = na , bgcolor = bull ? fvg_upcss : fvg_dncss , xloc = xloc.bar_time, extend = ext))
if fvgline
bin.ln.unshift(line.new(x1 = fvg.loc, x2 = time , y1 = math.avg(fvg.top, fvg.btm), y2 = math.avg(fvg.top, fvg.btm), xloc = xloc.bar_time, color = color.new(bull ? fvg_upcss : fvg_dncss, 0) , extend = ext))
if dispraid
bin.ln.unshift(line.new(x1 = fvg.raidloc, x2 = fvg.raidx2, y1 = fvg.raidy, y2 = fvg.raidy, xloc = xloc.bar_time, color = fvg.raidcs))
bin.lb.unshift(label.new(x = int(math.avg(fvg.raidloc, fvg.raidx2)), y = fvg.raidy, text = "x", xloc = xloc.bar_time, textcolor = fvg.raidcs, style = bull ? label.style_label_up : label.style_label_down, size = size.small, color = #ffffff00))
else
bin.bx.unshift(box .new(top = fvg.top , bottom = fvg.btm, left = fvg.loc , right = fvg.bbloc , border_color = na , bgcolor = bull ? fvg_upcss : fvg_dncss, xloc = xloc.bar_time))
bin.bx.unshift(box .new(top = fvg.top , bottom = fvg.btm, left = fvg.bbloc , right = time , border_color = bull ? fvg_dncss : fvg_upcss , bgcolor = bull ? fvg_dncss : fvg_upcss, xloc = xloc.bar_time, extend = ext))
if fvgline
bin.ln.unshift(line.new(x1 = fvg.loc , x2 = fvg.bbloc , y1 = math.avg(fvg.top, fvg.btm), y2 = math.avg(fvg.top, fvg.btm), color = color.new(bull ? fvg_upcss : fvg_dncss, 0) , xloc = xloc.bar_time))
bin.ln.unshift(line.new(x1 = fvg.bbloc, x2 = time , y1 = math.avg(fvg.top, fvg.btm), y2 = math.avg(fvg.top, fvg.btm), color = color.new(bull ? fvg_dncss : fvg_upcss, 0) , xloc = xloc.bar_time, extend = ext, style = line.style_dashed))
//}
_ ='
------------
–––––––––––––––––––––––––– FUNCTION –––––––––––––––––––––––––––
------------ '//{
mapping() =>
var float up = na
var float dn = na
var float point = na
var int trend = 0
var int idx = na
var int sum = na
var int project = na
var chart.point charts = array.new()
if na(up)
up := high
idx := bar_index
if na(dn)
dn := low
idx := bar_index
if high > up
if trend == -1
id = IDMIDX(false, idx)
charts.unshift(
chart.point.from_time(
time
, low
)
)
idx := bar_index
point := low
sum := time
up := high
dn := low
project := time
trend := 1
if low < dn
if trend == 1
id = IDMIDX(true, idx)
charts.unshift(
chart.point.from_time(
time
, high
)
)
idx := bar_index
point := high
sum := time
up := high
dn := low
project := time
trend := -1
if barstate.islast
var line ln = na
var polyline pl = na
ln.delete()
pl.delete()
ln := na
pl := na
ln := line.new(
x1 = sum
, x2 = project
, y1 = point
, y2 = trend == 1 ? up : dn
, xloc = xloc.bar_time
, color = color.red
)
pl := polyline.new(
charts
, line_color = mappingcss
, xloc = xloc.bar_time
, line_style = mappingStyle.lstyle()
)
dFVG() =>
= ghl()
var FVG blFVG = array.new()
var FVG brFVG = array.new()
bool upfvg = false
bool dnfvg = false
float blth = l1 + (fvatr * fvgthresh)
float brth = h1 - (fvatr * fvgthresh)
cc = timeframe.change()
switch
what_fvg == "FVG" or what_fvg == "Breakers" =>
if l > h2 and cc and c1 > blth
upfvg := true
if l2 > h and cc and c1 < brth
dnfvg := true
if upfvg
if blFVG.size() > 0
fvg = blFVG.get(0)
if fvg.israid == true and fvg.active == false
fvg.active := true
fvg.raidloc := na
fvg.raidx2 := na
fvg.raidy := na
fvg.raidcs := #ffffff00
blFVG.unshift(
FVG.new(
l
, h2
, time
, false
, na
)
)
if dnfvg
if brFVG.size() > 0
fvg = brFVG.get(0)
if fvg.israid == true and fvg.active == false
fvg = brFVG.get(0)
fvg.active := true
fvg.active := true
fvg.raidloc := na
fvg.raidx2 := na
fvg.raidy := na
fvg.raidcs := #ffffff00
brFVG.unshift(
FVG.new(
l2
, h
, time
, false
, na
)
)
if blFVG.size() > 0
for in blFVG
if not fvg.isbb
if fvg_src == "Close" ? math.min(c, o) < fvg.btm : fvg_src == "Wick" ? l < fvg.btm : fvg_src == "Avg" ? l < math.avg(fvg.top, fvg.btm) : na
fvg.isbb := true
fvg.bbloc := time
if what_fvg == "FVG"
blFVG.remove(i)
else
if (fvg_src == "Close" ? math.max(c, o) > fvg.top : fvg_src == "Wick" ? h > fvg.top : fvg_src == "Avg" ? h > math.avg(fvg.top, fvg.btm) : na) and what_fvg == "Breakers"
blFVG.remove(i)
if brFVG.size() > 0
for in brFVG
if not fvg.isbb
if (fvg_src == "Close" ? math.max(c, o) > fvg.top : fvg_src == "Wick" ? h > fvg.top : fvg_src == "Avg" ? h > math.avg(fvg.top, fvg.btm) : na)
fvg.isbb := true
fvg.bbloc := time
if what_fvg == "FVG"
brFVG.remove(i)
else
if (fvg_src == "Close" ? math.min(c, o) < fvg.btm : fvg_src == "Wick" ? l < fvg.btm : fvg_src == "Avg" ? l < math.avg(fvg.top, fvg.btm) : na) and what_fvg == "Breakers"
brFVG.remove(i)
if fvgoverlap
overlapFVG(blFVG, brFVG)
if dispraid
for in blFVG
if not fvg.israid and not fvg.isbb
if low < fvg.top and close > fvg.top
fvg.israid := true
fvg.raidloc := time
fvg.raidx2 := time
fvg.raidy := low
fvg.raidcs := chart.fg_color
else
if low <= fvg.raidy and fvg.active == false and not fvg.isbb
fvg.active := true
fvg.raidx2 := time
else
if fvg.active == false and not fvg.isbb
fvg.raidx2 := time
for in brFVG
if not fvg.israid and not fvg.isbb
if high > fvg.btm and close < fvg.btm and not fvg.isbb
fvg.israid := true
fvg.raidloc := time
fvg.raidy := high
fvg.raidx2 := time
fvg.raidcs := chart.fg_color
else
if high >= fvg.raidy and fvg.active == false and not fvg.isbb
fvg.active := true
fvg.raidx2 := time
else
if fvg.active == false and not fvg.isbb
fvg.raidx2 := time
if barstate.islast
if blFVG.size() > 0 and fvg_num > 0
for i = 0 to math.min(fvg_num - 1, blFVG.size() - 1)
fvg = blFVG.get(i)
dispFVG(fvg, i, true)
if brFVG.size() > 0 and fvg_num > 0
for i = 0 to math.min(fvg_num - 1, brFVG.size() - 1)
fvg = brFVG.get(i)
dispFVG(fvg, i, false)
structure(color upcss, color dncss, bool draw, bool internal, int limit) =>
var structure ms = structure.new(start = 0)
var ob blob = array.new< ob >()
var ob brob = array.new< ob >()
var drawms bldw = array.new< drawms >()
var drawms brdw = array.new< drawms >()
var sellbuyside sellside = array.new()
var sellbuyside buyside = array.new()
bool crossup = false
bool crossdn = false
var float up = na
var float dn = na
idbull = ms.find(false, false, true)
idbear = ms.find(true , false, true)
btmP = obmode == "Length" ? (high - 1 * atr ) < low ? low : (high - 1 * atr ) : low
topP = obmode == "Length" ? (low + 1 * atr ) > high ? high : (low + 1 * atr ) : high
atr = ta.atr (200)
buy = low + atr
sel = high - atr
ph = ta.pivothigh(high, mslen, mslen)
pl = ta.pivotlow (low , mslen, mslen)
var int phn = array.new< int >(1, na)
var int pln = array.new< int >(1, na)
var float php = array.new(1, na)
var float plp = array.new(1, na)
if internal
blob.clear()
brob.clear()
if ph
phn.unshift(bar_index )
php.unshift(high )
if pl
pln.unshift(bar_index )
plp.unshift(low )
if php.size() > 0
if high > php.get(0)
php.clear()
phn.clear()
if plp.size() > 0
if low < plp.get(0)
plp.clear()
pln.clear()
if na(up)
up := high
if na(dn)
dn := low
if high > up
up := high
dn := low
crossup := true
if low < dn
up := high
dn := low
crossdn := true
if ms.start == 0
ms := structure.new(bar_index, na, high, low , bar_index, bar_index, 0, 1, na, bar_index)
if draw
bldw.unshift(drawms.new(time, time, high , "CHoCH" , upcss, line.style_dashed))
brdw.unshift(drawms.new(time, time, low , "CHoCH" , dncss, line.style_dashed))
ms.upsweep := false
ms.dnsweep := false
if ms.start == 1
switch
low <= ms.choch and close >= ms.choch and buildsweep =>
ms.dnsweep := true
ms.choch := low
ms.xloc := bar_index
if draw
dw = brdw.get(0)
dw.x2 := time
dw.style := line.style_dotted
dw.txt := "x"
brdw.unshift(
drawms.new(
time
, time
, low
, "CHoCH"
, dncss
, line.style_dashed
)
)
high >= ms.bos and close <= ms.bos and buildsweep =>
ms.upsweep := true
ms.bos := high
ms.xloc := bar_index
if draw
dw = bldw.get(0)
dw.x2 := time
dw.style := line.style_dotted
dw.txt := "x"
bldw.unshift(
drawms.new(
time
, time
, high
, "CHoCH"
, upcss
, line.style_dashed
)
)
close <= ms.choch =>
ms.txt := "choch"
lc.start := true
lc.count := 0
blob.fnOB(true, topP, idbull)
ms.trend := -1
ms.choch := ms.bos
ms.bos := na
ms.start := 2
ms.loc := bar_index
ms.main := low
ms.temp := ms.loc
ms.xloc := bar_index
if draw
dw = brdw.get(0)
dw.x2 := time
dw.style := internal ? line.style_dashed : line.style_solid
close >= ms.bos =>
ms.txt := "choch"
lc.start := true
lc.count := 0
brob.fnOB(false, btmP, idbear)
ms.trend := 1
ms.choch := ms.choch
ms.bos := na
ms.start := 2
ms.loc := bar_index
ms.main := high
ms.temp := ms.loc
ms.xloc := bar_index
if draw
dw = bldw.get(0)
dw.x2 := time
dw.style := internal ? line.style_dashed : line.style_solid
if ms.start == 2
switch ms.trend
-1 =>
if low <= ms.main
ms.main := low
ms.temp := bar_index
if bar_index % mslen * 2 == 0
if not na(ms.bos) and msmode == "Adjusted Points" and php.size() > 0
if php.get(0) < ms.choch
// ms.xloc := phn.get(0)
ms.choch := php.get(0)
ms.loc := phn.get(0)
ms.xloc := phn.get(0)
ms.temp := phn.get(0)
if draw
choch = bldw.get(0)
choch.x1 := time
choch.x2 := time
choch.y := php.get(0)
if na(ms.bos)
if crossup and close > open and close > open
ms.bos := ms.main
ms.loc := ms.temp
ms.xloc := ms.loc
if draw
brdw.unshift(
drawms.new(
time
, time
, low
, "BOS"
, dncss
, line.style_dashed
)
)
if not na(ms.bos) and draw
dw = brdw.get(0)
dw.x2 := time
if draw
choch = bldw.get(0)
choch.x2 := time
switch
low <= ms.bos and close >= ms.bos and not na(ms.bos) and buildsweep =>
ms.dnsweep := true
ms.bos := low
if draw
dw = brdw.get(0)
dw.x2 := time
dw.style := line.style_dotted
dw.txt := "x"
brdw.unshift(
drawms.new(
time
, time
, low
, "BOS"
, dncss
, line.style_dashed
)
)
ms.xloc := bar_index
close <= ms.bos and not na(ms.bos) =>
ms.txt := "bos"
ms.zz := ms.bos
ms.zn := bar_index
lc.start := true
lc.count := 0
brob.fnOB(false, btmP, idbear)
id = ms.find(true, false, false)
ms.xloc := bar_index
ms.bos := na
ms.choch := high
ms.loc := bar_index
if draw
dw = brdw.get(0)
dw.x2 := time
dw.style := internal ? line.style_dashed : line.style_solid
choch = bldw.get(0)
choch.x1 := time
choch.x2 := time
choch.y := high
switch
high >= ms.choch and close <= ms.choch and buildsweep =>
ms.upsweep := true
ms.choch := high
ms.xloc := bar_index
if draw
dw = bldw.get(0)
dw.x2 := time
dw.style := line.style_dotted
dw.txt := "x"
bldw.unshift(
drawms.new(
time
, time
, high
, "CHoCH"
, upcss
, line.style_dashed
)
)
close >= ms.choch =>
ms.txt := "choch"
ms.zz := ms.choch
ms.zn := bar_index
lc.start := true
lc.count := 0
blob.fnOB(true, topP, idbull)
id = ms.find(false, false, false)
switch
na(ms.bos) =>
ms.choch := low
if draw
brdw.unshift(
drawms.new(
time
, time
, low
, "BOS"
, dncss
, line.style_dashed
)
)
choch = brdw.get(0)
choch.x1 := time
=> ms.choch := ms.bos//low < low ? low : low
ms.bos := na
ms.main := high
ms.trend := 1
ms.loc := bar_index
ms.xloc := bar_index
ms.temp := ms.loc
if draw
dw = bldw.get(0)
dw.x2 := time
dw.txt := "CHoCH"
dw.style := internal ? line.style_dashed : line.style_solid
choch = brdw.get(0)
choch.x2 := time
choch.y := ms.choch
choch.txt := "CHoCH"
ms.xloc := bar_index
blarea.normal := false
1 =>
if high >= ms.main
ms.main := high
ms.temp := bar_index
if na(ms.bos)
if crossdn and close < open and close < open
ms.bos := ms.main
ms.loc := ms.temp
ms.xloc := ms.loc
if draw
bldw.unshift(
drawms.new(
time
, time
, high
, "BOS"
, upcss
, line.style_dashed
)
)
if bar_index % mslen * 2 == 0
if not na(ms.bos) and msmode == "Adjusted Points" and plp.size() > 0
if plp.get(0) > ms.choch
// ms.xloc := pln.get(0)
ms.choch := plp.get(0)
ms.loc := pln.get(0)
ms.xloc := pln.get(0)
ms.temp := pln.get(0)
// ms.loc := pln.get(0)
if draw
choch = brdw.get(0)
choch.x1 := time
choch.x2 := time
choch.y := plp.get(0)
if not na(ms.bos) and draw
dw = bldw.get(0)
dw.x2 := time
if draw
choch = brdw.get(0)
choch.x2 := time
switch
high >= ms.bos and close <= ms.bos and not na(ms.bos) and buildsweep =>
ms.upsweep := true
ms.bos := high
if draw
dw = bldw.get(0)
dw.x2 := time
dw.style := line.style_dotted
dw.txt := "x"
bldw.unshift(
drawms.new(
time
, time
, high
, "BOS"
, upcss
, line.style_dashed
)
)
ms.xloc := bar_index
close >= ms.bos and not na(ms.bos) =>
ms.txt := "bos"
ms.zz := ms.bos
ms.zn := bar_index
lc.start := true
lc.count := 0
blob.fnOB(true, topP, idbull)
id = ms.find(false, false, false)
ms.xloc := bar_index
ms.bos := na
ms.choch := low
ms.loc := bar_index
if draw
dw = bldw.get(0)
dw.x2 := time
dw.style := internal ? line.style_dashed : line.style_solid
choch = brdw.get(0)
choch.x1 := time
choch.x2 := time
choch.y := low
switch
low <= ms.choch and close >= ms.choch and buildsweep =>
ms.dnsweep := true
ms.choch := low
ms.xloc := bar_index
if draw
dw = brdw.get(0)
dw.x2 := time
dw.style := line.style_dotted
dw.txt := "x"
brdw.unshift(
drawms.new(
time
, time
, low
, "CHoCH"
, dncss
, line.style_dashed
)
)
close <= ms.choch =>
ms.txt := "choch"
ms.zz := ms.choch
ms.zn := bar_index
lc.start := true
lc.count := 0
brob.fnOB(false, btmP, idbear)
id = ms.find(true, false, false)
switch
na(ms.bos) =>
ms.choch := high
if draw
bldw.unshift(
drawms.new(
time
, time
, high
, "BOS"
, upcss
, line.style_dashed
)
)
choch = bldw.get(0)
choch.x1 := time
=> ms.choch := ms.bos//high > high ? high : high
ms.bos := na
ms.main := low
ms.trend := -1
ms.loc := bar_index
ms.temp := ms.loc
if draw
dw = brdw.get(0)
dw.x2 := time
dw.txt := "CHoCH"
dw.style := internal ? line.style_dashed : line.style_solid
choch = bldw.get(0)
choch.y := ms.choch
choch.x2 := time
choch.txt := "CHoCH"
ms.xloc := bar_index
if blob.size() > 0
ob = blob.get(0)
if not ob.isbb
if low < ob.top
if blobenter.normal == false
blobenter.normal := true
else
if high > ob.btm
if blobenter.breaker == false
blobenter.breaker := true
if brob.size() > 0
ob = brob.get(0)
if not ob.isbb
if high > ob.btm
if brobenter.normal == false
brobenter.normal := true
else
if low < ob.top
if brobenter.breaker == false
brobenter.breaker := true
if obshow and oblast > 0
if barstate.isconfirmed
blob.mitigated()
brob.mitigated()
if overlap
overlap(blob, brob)
if blob.size() > 0
for in blob
metric.umt()
if brob.size() > 0
for in brob
metric.umt()
if barstate.islast
if blob.size() > 0
for i = 0 to math.min(oblast - 1, blob.size() - 1)
obs = blob.get(i)
display(obs, blob, i)
if brob.size() > 0
for i = 0 to math.min(oblast - 1, brob.size() - 1)
obs = brob.get(i)
display(obs, brob, i)
if barstate.islast and draw and bldw.size() > 0 and brdw.size() > 0
for i = 0 to bldw.size() - 1
obj = bldw.get(i)
if i <= limit
bin.ln.unshift(
line.new(
x1 = obj.x1
, x2 = obj.x2
, y1 = obj.y
, y2 = obj.y
, color = obj.css
, style = obj.style
, xloc = xloc.bar_time
)
)
bin.lb.unshift(
label.new(
x = int(math.avg(bin.ln.get(0).get_x1(), bin.ln.get(0).get_x2()))
, y = obj.y
, xloc = xloc.bar_time
, color = #ffffff00
, style = label.style_label_down
, textcolor = obj.css
, size = mstext.txSz()
, text = obj.txt
)
)
if msbubble
bin.lb.unshift(
label.new(
x = obj.x1
, y = obj.y
, xloc = xloc.bar_time
, color = color.new(obj.css, 80)
, style = label.style_circle
, size = size.tiny
)
)
for i = 0 to brdw.size() - 1
obj = brdw.get(i)
if i <= limit
bin.ln.unshift(
line.new(
x1 = obj.x1
, x2 = obj.x2
, y1 = obj.y
, y2 = obj.y
, color = obj.css
, style = obj.style
, xloc = xloc.bar_time
)
)
bin.lb.unshift(
label.new(
x = int(math.avg(bin.ln.get(0).get_x1(), bin.ln.get(0).get_x2()))
, y = obj.y
, xloc = xloc.bar_time
, color = #ffffff00
, style = label.style_label_up
, textcolor = obj.css
, size = mstext.txSz()
, text = obj.txt
)
)
if msbubble
bin.lb.unshift(
label.new(
x = obj.x1
, y = obj.y
, xloc = xloc.bar_time
, color = color.new(obj.css, 80)
, style = label.style_circle
, size = size.tiny
)
)
ms
//}
_ ='
------------
–––––––––––––––––––––––––– EXECUTION –––––––––––––––––––––––––––
------------ '//{
structure ms = na
if windowsis
if (bar_index > last_bar_index - mswindow)
ms := structure(swingcssup , swingcssdn , showSwing , false, swingLimit)
if windowsis == false
ms := structure(swingcssup , swingcssdn , showSwing , false, swingLimit)
// if showInternal and inZone
// structure ims = structure(interncssup, interncssdn, showInternal, true , swingLimit)
color css = na
method darkcss(color css, float factor) =>
blue = color.b(css) * (1 - factor)
red = color.r(css) * (1 - factor)
green = color.g(css) * (1 - factor)
color.rgb(red, green, blue, 0)
if windowsis ? (bar_index > last_bar_index - mswindow) : true
css := ms.trend == 1 ? swingcssup : swingcssdn
css := (ms.txt == "bos" ? css : css.darkcss(0.3))
barcolor(candlecss ? css : na)
if fvg_enable
dFVG()
if showMapping
mapping()
var phl = Zphl.new(
na
, na
, label.new(na , na , color = invcol , textcolor = swingcssdn , style = label.style_label_down , size = size.tiny , text = "")
, label.new(na , na , color = invcol , textcolor = swingcssup , style = label.style_label_up , size = size.tiny , text = "")
, true
, true
, true
, true
, ""
, ""
, 0
, 0
, 0
, 0
, high
, low
, 0
, 0
, 0
, 0
, 0
, 0
, na
, na
)
// إعدادات تفعيل/تعطيل وتخصيص لكل مستوى فيبوناتشي
// حساب آخر قمة وقاع محوري
int fib_pivot_len = 5
ph = ta.pivothigh(high, fib_pivot_len, fib_pivot_len)
pl = ta.pivotlow(low, fib_pivot_len, fib_pivot_len)
var float last_high = na
var int last_high_x = na
var float last_low = na
var int last_low_x = na
if not na(ph)
last_high := high
last_high_x := bar_index - fib_pivot_len
if not na(pl)
last_low := low
last_low_x := bar_index - fib_pivot_len
var float fib_top = na
var float fib_bottom = na
var int fib_x1 = na
var int fib_x2 = na
if not na(last_high) and not na(last_low)
if last_high_x > last_low_x
fib_top := last_high
fib_bottom := last_low
fib_x1 := last_low_x
fib_x2 := last_high_x
else
fib_top := last_high
fib_bottom := last_low
fib_x1 := last_high_x
fib_x2 := last_low_x
var line fib_lines_pivot = array.new()
var label fib_labels_pivot = array.new()
if not na(fib_top) and not na(fib_bottom) and not na(fib_x1) and not na(fib_x2)
if barstate.islast
// حذف الخطوط والليبلات القديمة
for l in fib_lines_pivot
l.delete()
fib_lines_pivot.clear()
for lb in fib_labels_pivot
lb.delete()
fib_labels_pivot.clear()
// ...existing code...
fib_vals = array.from(0.0, 0.236, 0.382, 0.5, 0.618, 0.786, 1.0, 1.272, 1.618, 1.80, 2.0, 2.272, 2.618)
fib_shows = array.from(show_fib_0, show_fib_236, show_fib_382, show_fib_5, show_fib_618, show_fib_786, show_fib_1, show_fib_1272, show_fib_1618, show_fib_180, show_fib_2, show_fib_2272, show_fib_2618)
fib_colors = array.from(fib_color_0, fib_color_236, fib_color_382, fib_color_5, fib_color_618, fib_color_786, fib_color_1, fib_color_1272, fib_color_1618, fib_color_180, fib_color_2, fib_color_2272, fib_color_2618)
fib_texts = array.from("0.0", "23.6%", "38.2%", "50.0%", "61.8%", "78.6%", "100%", "127.2%", "161.8%", "180%", "200%", "227.2%", "261.8%")
// عند رسم الخطوط والليبلات:
for i = 0 to array.size(fib_vals) - 1
level_val = array.get(fib_vals, i)
level_show = array.get(fib_shows, i)
level_color = array.get(fib_colors, i)
level_txt = array.get(fib_texts, i)
if level_show
price = fib_bottom + (fib_top - fib_bottom) * level_val
l = line.new(x1=fib_x1, y1=price, x2=fib_x2 + fib_shift, y2=price, color=level_color, width=1, style=line.style_dotted, xloc=xloc.bar_index)
array.push(fib_lines_pivot, l)
lb = label.new(x=fib_x2 + fib_shift, y=price, text=level_txt + " | " + str.tostring(price, format.mintick), color=#ffffff00, textcolor=level_color, size=fib_size, style=label.style_label_left, xloc=xloc.bar_index)
array.push(fib_labels_pivot, lb)
SMT Divergence x outofoptions🔍 SMT Divergence — Advanced Market Correlation Analysis
This was created with and approved by @outofoptions to bring you smaller SMTs based on his original SMT Divergence indicator
SMT Divergence is a sophisticated technical analysis indicator designed to identify high-probability reversal and continuation signals through intelligent correlation analysis between related markets. This powerful tool reveals hidden market dynamics by comparing price action divergences across correlated instruments, providing traders with institutional-level market insight.
🎯 Core Capabilities:
Multi-Market Analysis : Automatically compares your chart with a correlated instrument to identify divergence patterns and market inefficiencies
Smart Liquidity Detection : Advanced algorithms identify key liquidity levels and sweep patterns for enhanced signal accuracy
Dynamic Divergence Mapping : Real-time visualization of bullish and bearish divergences with customizable line styles and colors
Intelligent Signal Validation : Optional candle-based confirmation system to filter high-probability setups from noise
Automated Line Management : Smart removal of invalidated divergences to maintain clean, actionable chart analysis
📊 Professional Features:
The SMT Divergence indicator excels at revealing market structure imbalances that often precede significant price movements. By analyzing the relationship between correlated markets, it identifies when institutional money may be positioned differently than retail sentiment suggests, providing early warning signals for potential reversals.
⚙️ Advanced Customization:
Flexible correlation pair selection for any market combination
Customizable visual styling with multiple line types and color schemes
Adjustable validation criteria for different trading styles
Professional alert system with detailed message customization
Automatic cleanup of broken or invalidated divergences
🎨 Visual Excellence:
Clean, professional line drawing with customizable styling
Dynamic labeling system with size and color options
Real-time divergence tracking and management
Institutional-grade chart presentation
Optimized performance for extended analysis periods
📈 Ideal For:
Swing traders seeking high-probability reversal signals
Multi-market analysts comparing correlated instruments
Institutional-style traders using correlation analysis
Advanced technical analysts studying market structure
Those seeking early warning signals for trend changes
🔔 Smart Alerts:
Comprehensive alert system with customizable messaging allows you to stay informed of new divergences across multiple timeframes and market sessions, ensuring you never miss critical market developments.
💡 Market Intelligence:
SMT Divergence transforms complex inter-market relationships into clear, actionable signals, giving you the same analytical edge used by professional trading institutions to identify market turning points before they become obvious to retail traders.
Educational Tool: This indicator is designed for educational and analytical purposes. Divergence analysis requires understanding of market correlation principles. Always combine with proper risk management and additional analysis methods.
Lorentzian Theory Classifier🧮 Lorentzian Theory Classifier: An Observatory for Market Spacetime
Transcend the flat plane of traditional charting. Enter the curved, dynamic reality of market spacetime. The Lorentzian Theory Classifier (LTC) is not an indicator; it is a computational observatory. It is an instrument engineered to decode the geometry of market behavior, revealing the hidden curvatures and resonant frequencies that precede significant turning points.
We discard the outdated tools of Euclidean simplicity and embrace a more profound truth: financial markets, much like the cosmos described by general relativity, are governed by a fabric that is warped by the mass of participation and the energy of volatility. The LTC is your lens to perceive this fabric, to move beyond predicting lines on a chart and begin reading the very architecture of probability.
The Resonance Manifold: Standard Euclidean models search for historical analogues within a rigid sphere, missing the crucial outliers that define market extremes. The LTC's Lorentzian Resonance engine operates in a curved, non-Euclidean space, allowing it to connect with these "fat-tail" events—the true genesis points of major reversals.
🌌 THE THEORETICAL FRAMEWORK: A new Grand Unified Theory of Market Analysis
The LTC is built upon a revolutionary synthesis of concepts from special relativity, quantum mechanics, and information theory. It reframes market analysis not as a problem of forecasting, but as a problem of state recognition in a non-Euclidean manifold.
1. The Lorentzian Kernel: The Mathematics of Reality
Financial markets are not Gaussian. Their reality is one of "fat tails"—sudden, high-impact events that standard models dismiss as anomalies. The LTC acknowledges this reality by using the mathematically pure and robust Lorentzian kernel as its core engine:
Similarity(x, y) = 1 / (1 + (||x − y||² / γ²))
||x − y||²: The squared distance between the current market state (x) and a historical state (y) in our 8-dimensional feature space.
γ (Gamma): A dynamic bandwidth parameter, our "Lorentz factor," which adapts to market entropy (chaos). In calm markets, gamma is small, demanding precise resonance. In chaotic markets, gamma expands, intelligently seeking broader patterns.
This heavy-tailed function is revolutionary. It correctly assigns profound significance to the rare, extreme events that truly define market structure, while gracefully tuning out the noise of mundane price action. It doesn't just calculate; it understands context.
2. The 8-Dimensional State Vector: The Market's Quantum Fingerprint
To achieve a holistic view, the LTC projects the market onto an 8-dimensional Hilbert space, where each dimension represents a critical "observable":
Momentum & Acceleration (f_rsi, f_roc): The market's velocity and its rate of change.
Cyclical Position (f_stoch, f_cci): The market's location within its recent oscillation cycles.
Energy & Participation (f_vol, f_cor): The force of capital flow and its harmony with price.
Chaos & Uncertainty (f_ent, f_mom): The degree of randomness and the standardized force of price changes.
These are not eight separate indicators. They are entangled properties of a single "market wavefunction." The LTC's genius lies in measuring the geometric distance between these complete quantum states.
3. The k-NN Oracle: A Council of Past Universes
The LTC employs a k-Nearest Neighbors algorithm, but in our curved Lorentzian spacetime. It poses a constant, profound question: " Which moments in history are most geometrically congruent to the present moment across all eight dimensions? "
It then summons a "council" of these historical neighbors. Each neighbor's future outcome (did price ascend or descend?) casts a vote, weighted by its resonant similarity. The result is a probabilistic forecast of stunning clarity:
Prognosis: The final weighted consensus on future direction.
Assurance: The degree of unanimity within the council—a direct measure of the prediction's confidence.
The Funnel of Conviction: The LTC's process is a rigorous distillation of information. Raw, chaotic market data is resolved into a clean 8-dimensional state vector. The Lorentzian Kernel filters these states for resonance, which are then passed to the k-NN Oracle for a vote. Noise is eliminated at each stage, resulting in a single, validated, high-conviction signal.
⚙️ THE COMMAND CONSOLE: A Guide to Calibrating Your Observatory
Mastering the LTC's inputs is to become an architect of your own analytical universe. Each parameter is a dial that tunes the observatory's focus, from galactic structures to subatomic fluctuations. The tooltips in-script—over 6,000 words of documentation—provide immediate reference; this guide provides the philosophy.
A summarized guide to the Core, Signal, Supreme, and Visual controls is included directly in the indicator's code and tooltips. We encourage all users to explore these settings to tune the LTC to their unique analytical style.
🏆 THE SUPREME DASHBOARD: Your Mission Control
The dashboard is not a data table; it is your command interface with market reality. It translates the intricate dance of probabilities and vectors into clear, actionable intelligence.
⚡ ORACLE STATUS
Prognosis: The primary directional vector. Its color, magnitude, and emoji (⚡) reveal the strength and conviction of the Oracle's forward guidance.
Assurance: A real-time gauge of prediction quality, from "LOW" (high uncertainty) to "ELITE" (overwhelming statistical consensus). Interpret this as your core risk metric: trade with conviction when Assurance is ELITE; trade with caution when it is LOW.
🔮 RESONANCE ANALYSIS
Chaos: A direct measurement of market entropy. "LOW CHAOS" signifies a predictable, orderly regime. "HIGH CHAOS" is a warning of randomness and unpredictability, where trend-following logic may fail.
Turbulence: A measure of raw volatility. When the market is "TURBULENT," expect wider price swings and increased risk. Use this metric to adjust stop-loss distances and profit targets dynamically.
🏆 PERFORMANCE & ⚔️ GUARD METRICS
These sections provide illustrative statistics on the script's recent historical behavior. Metrics like Yield Ratio and Guard Index offer a quick heuristic on the prevailing risk-reward environment. Crucially, these are for observational context only and are not a substitute for your own rigorous testing and analysis.
🎨 THE VISUAL MANIFESTATION: Charting the Unseen
The LTC's visuals are designed to transform your chart from a 2D price graph into a 4D informational battlespace.
The Dynamic Aura (Background Color): This is the ambient energy field of the market. A luminous green (Ascend) signifies a bullish resonance field; a deep red (Descend) indicates bearish pressure.
The Assurance Shroud (Blue Bands): A visualization of confidence. When the shroud is wide and expansive , the Oracle's vision is clear and its predictions are robust.
The Prognosis Arc (Curved Line): A geodesic projection of the market's most likely path, based on the current Prognosis.
The Turbulence Cloud (Orange Mist): A visual warning system for market chaos. When this entropic mist expands , it is a clear sign that you are navigating a nebula of high unpredictability.
Oracle Markers (▲▼): The final, validated signals. These are not merely pivot points. They are moments in spacetime where a structural pivot has been confirmed and then ratified by a high-conviction vote from the Lorentzian Oracle. They are the pinnacles of confluence.
The Analyst's Observatory: The LTC transforms your chart into a command center for market analysis, providing a complete, at-a-glance view of market state, risk, and probabilistic trajectory.
🔧 THE ARCHITECT'S VISION: From a Blank Slate to a New Cosmos
The LTC was not assembled; it was derived. It began not with code, but with first principles, asking: "If we were to build an instrument to measure the market today, unbound by the technical dogmas of the 20th century, what would it look like?" The answer was clear: it must be multi-dimensional, it must be adaptive, and it must be built on a mathematical framework that respects the "fat-tailed" nature of reality.
The decision to use a pure Lorentzian kernel was non-negotiable. It represented a commitment to intellectual honesty over computational ease. The development of the Supreme Dashboard was driven by the philosophy of the "glass cockpit"—a belief that a trader's greatest asset is not a black box signal, but a transparent and intuitive flow of high-quality information. This script is the result of that unwavering vision: to create not just another indicator, but a new lens through which to perceive the market.
⚠️ RISK DISCLOSURE & PHILOSOPHY OF USE
The Lorentzian Theory Classifier is an instrument of profound analytical power, intended for the serious, discerning trader. It does not generate infallible signals. It generates high-probability, data-driven hypotheses based on a rigorous and transparent methodology. All trading involves substantial risk, and the future is fundamentally unknowable. Past performance, whether real or simulated, is no guarantee of future results. Use this tool to augment your own skill, to confirm your own analysis, and to manage your own risk within a well-defined trading plan.
"The effort to understand the universe is one of the very few things that lifts human life a little above the level of farce, and gives it some of the grace of tragedy."
— Steven Weinberg, Nobel Laureate in Physics
Trade with rigor. Trade with perspective. Trade with enlightenment. Trade with insight. Trade with anticipation.
— Dskyz, for DAFE Trading Systems
[Pandora] Laguerre Ultimate Explorations MulticatorIt's time to begin demonstrations differentiating the difference between known and actual feasibility beyond imagination... Welcome to my algorithmic twilight zone .
INTRODUCTION:
Hot off my press, I present this Laguerre multicator employing PSv6.0, originally formulated by John Ehlers for TASC - July 2025 Traders Tips. Basically I transcended Ehlers' notions of transversal filtration with an overhaul of his Laguerre design with my "what if" Pandora notions included. Striving beyond John Ehlers' original intended design. This action packed indicator is a radically revamped version of his original filter using novel techniques. My aim was to explore whether providing even more enhanced responsiveness and lesser lag is possible and how. Presented here is my mind warping results to witness.
EHLERS' LAGUERRE EXPLAINED:
First and foremost, the concept of Ehlers' Laguerre-izing method deserves a comprehensive deep dive. Ehlers' Laguerre filter design, as it functions originally, begins with his Ultimate Smoother (US) followed by a gang of four LERP (jargon for Linear intERPolation) filters. Following a myriad of cascading LERPs is a window-like FIR filter tapped into the LERP delay values to provide extra smoothness via the output.
On a side note, damping factor controlled LERP filters resemble EMAs indeed, but aren't exactly "periodic" filters that would have a period/length parameter and their subsequent calculations. I won't go into fine-grained relationship details, but EMA and LERP are indeed related in approach, being cousins of similar pedigree.
EXAMINING LAGUERRE:
I focused firstly on US initialization obstacles at Pine's bar_index==0 with nz() in abundance. The next primary notion of intrigue I mostly wondered about was, why are there four LERP elements instead of fewer or more. Why not three or why not two LERPs, etc... 1-4-6-4-1, I remember seeing those coefficients before in high pass filters.
Gathering my thoughts from that highpass knowledge base, I devised other tapped configuration modes to inspect their behavior out of curiosity. Eureka! There is actually more to Laguerre than Ehlers' mind provided, now that I had formulated additional modes. Each mode exhibits it's own lag/smoothness characteristics better than the quad LERPed version. I narrowed it down to a total of 5 modes for exploration. Mode 0 is just the raw US by itself.
ANALYZING FILTER BEHAVIORS:
Which option might be possibly superior, and how may I determine that? Fortunately, I have a custom-built analyzer allowing me to thoroughly examine transient responses across multiple periodicities simultaneously, providing remarkable visual insights.
While Ehlers has meagerly touched upon presenting general frequency responses in his books, I have excelled far beyond that. This robust filter analysis capability enables me to observe finer aspects hidden to others, ultimately leading to the deprecation of numerous existing filters. Not only this, but inventing entirely new species of filtration whether lowpass, highpass, or bandpass is already possible with a thorough comprehensive evaluation.
Revealing what's quirky with each filter and having the ability to discover what filters may be lacking in performance, is one of it's implications. I'm just going to explain this: For example US has a little too much overshoot to my liking, along with nonconformant cutoff frequency compliance with the period parameter. Perhaps Ehlers should inspect US coefficients a bit closer... I hope stating this is not received in an ill manner, as it's not my intention here.
What this technically eludes to is that UltimateSmoother can be further improved, analogous to my Laguerre alterations described above. I will also state Laguerre can indeed be reformulated to an even greater extent concerning group delay, from what I have already discussed. Another exciting time though... More investigative research is warranted.
LAGUERRE CONCLUSIONS:
After analyzing Laguerre's frequency compliance, transient responses, amplitudes, lag, symmetry across periodicities, noise rejection, and smoothness... I favor mode 3 for a multitude of reasons over the mode 4 configuration, but mostly superb smoothing with less lag, AND I also appreciated mode 1 & 2 for it's lower lag performance options.
Each mode and lag (phase shift) damping value has it's own unique characteristics at extremes, yet they demonstrate additional finesse in it's new hybrid form without adding too much more complexity. This multicator has a bunch of Laguerre filters in the overlay chart over many periodicities so you can easily witness it's differing periodic symmetries on an input signal while adjusting lag and mode.
LAGUERRE OSCILLATOR:
The oscillator is integrated into the laguerreMulti() function for the intention of posterity only. I performed no evaluation on it, only providing the code in Pine. That wasn't part of my intended exploration adventure, as I'm more TREND oriented for the time being, focusing my efforts there.
Market analysis has two primary aspects in my observations, one cyclic while the other is trending dynamics... There's endless oscillators, but my expectations for trend analysis seems a little lesser explored in my opinion, hence my laborious trend endeavors. Ehlers provided both indicator facets this time around, and I hope you find the filtration aspect more intriguing after absorption of this reading.
FUNCTION MODULES EXPLAINED:
The Ultimate Smoother is an advanced IIR lowpass smoothing filter intended to minimize noise in time series data with minimal group delay, similar to a traditional biquad filter. This calculation helps to create a smoother version of the original signal without the distortions of short-term fluctuations and with minimal lag, adjustable by period.
The Modified Laguerre Lowpass Filter (MLLF) enhances the functionality of US by introducing a Laguerre mode parameter along side the lag parameter to refine control over the amount of additional smoothing/lag applied to the signal. By tethering US with this LERPed lag mechanism, MLLF achieves an effective balance between responsiveness and smoothness, allowing for customizable lag adjustments via multiple inputs. This filter ends with selecting from a choice of weighted averages derived from a gang of up to four cascading LERP calculations, resulting with smoother representations of the data.
The Laguerre Oscillator is a momentum-like indicator derived from the output of US and a singular LERPed lowpass filter. It calculates the difference between the US data and Laguerre filter data, normalizing it by the root mean square (RMS). This quasi-normalization technique helps to assess the intensity of the momentum on any timeframe within an expected bound range centered around 0.0. When the Laguerre Oscillator is positive, it suggests that the smoothed data is trending upward, while a negative value indicates a downward trend. Adjustability is controlled with period, lag, Laguerre mode, and RMS period.
Gabriel's Dynamic Sentiment RSI📊 Dynamic Sentiment RSI with Velocity, Acceleration & Divergence Detection
Created by GabrielAmadeusLau
This advanced Pine Script indicator fuses multiple layers of market insight into a unified momentum and sentiment tool. It is designed to extract nuanced sentiment signals from price action using a hybridized RSI model enhanced with stochastic dynamics, volatility weighting, and divergence tracking. It adapts to a wide range of asset classes including equities, crypto, gold, and forex.
🔍 Core Components
✅ 1. Dynamic Sentiment RSI
A normalized, stochastic-based RSI that adjusts its sensitivity using the Sentiment Factor.
Smoothed using a Jurik Moving Average for precision noise filtering.
Weighted using volume, volatility (VIX-like), HL extremes, and trend-based adaptive weighting, giving it a powerful multi-dimensional response.
✅ 2. Hann-Window RSI Calculation
Leverages a Hann Window and Levy Flight transformation to amplify cyclical behavior in RSI inputs.
Applies power-based weighting to directional movement, ideal for assets with cyclic or fractal-like structure.
✅ 3. Velocity & Acceleration Engine
Measures the rate of RSI change over a customizable period, and then the rate of that rate (acceleration).
Both are plotted with adaptive coloring to visually represent momentum shifts.
This dynamic structure aids in anticipating breakout strength or exhaustion.
✅ 4. Sentiment Heat Background
Background shading reflects bullish (teal) or bearish (silver) sentiment using smoothed stochastic RSI outputs.
Creates an intuitive market "mood" indicator for quick-glance visual analysis.
🔁 Smoothing & Weighting Customizations
You can toggle between different weighting modes:
Volume Weighted: Uses volume or ATR if unavailable.
VIX Weighted: Incorporates a volatility-based weight via a WVF-like formula.
HL Weighted: High-Low range smoothed.
Linear Weighted: Applies linear regression to the signal.
Trend Adaptive: Squares rolling maximums/minimums for dynamic strength adaptation.
🔎 Divergence Detection System
Supports Regular & Hidden Divergence using any of the following pivots:
Raw RSI
Smoothed K% RSI
Sentiment RSI
Velocity
Acceleration
Allows divergence tracking on custom timeframes and Heikin Ashi data.
Custom line styles, colors, and optional “last signal only” visibility.
Alerts are provided for all four divergence types.
📌 Built-in Alerts
✅ Bullish/Bearish Regular Divergence
✅ Bullish/Bearish Hidden Divergence
✅ General Divergence Summary Alerts
⚙️ Highly Configurable Settings
Sentiment Factor scaling (default ~2.2)
Levy exponent (ideal between 0.4 to 3.2 depending on asset class)
Velocity & Acceleration scaling inputs
Pivot lookback controls
Toggle smoothing methods and weighting logic
🧠 Ideal Use Cases
Swing and Trend Trading: The dynamic structure identifies both trend continuations and reversals with precision.
Divergence Confirmation: Confirm entries or exits using regular/hidden divergence alongside acceleration/velocity overlays.
Adaptive Strategy Building: Integrate this tool as a sentiment engine for algorithmic trading strategies.
🔬 Recommended Settings by Asset Class
Asset Type Levy Sentiment Factor
Crypto 0.6–1.2 2.0–2.5
Gold 0.4–1.0 2.0–2.2
Stocks 0.9–1.2 2.2–2.5
Forex 2.5–3.2 1.8–2.3
*A sentiment Factor of 9.5 can tell the larger trend apart on Daily and up.*
🧩 Technical Notes
Uses Jurik MA (Power 2, Phase 50) for minimal lag smoothing.
Employs Chebyshev filters (pre-Stochastic) for advanced sentiment smoothing.
Weighted RSI is normalized from -100 to 100, with color-coded velocity and acceleration histograms.
Breaker Blocks with Signals + Dynamic 5-Day SMA FilterBased on Lux Algos Script with a dynamic 5d SMA which filters out trades that are not in line with the local HTF trend.
Vortex strategy🧠 Overview
The Vortex Strategy is a sophisticated trend-following and volatility-based trading algorithm designed for precision entries during directional market phases. It leverages Jurik smoothing, velocity filtering, and Vortex Indicator derivatives, enhanced with advanced risk management and hourly optimization. This strategy is best suited for futures and high-volume intraday markets like the CME E-Mini S&P 500 (ES1!).
🔧 Core Features
🔄 Vortex Indicator with Velocity Filter
Uses a velocity-enhanced form of the classic Vortex Indicator (VIP and VIM) to capture directional momentum.
Smooth VMP/VMM via SS & BW and pass that through a custom velocity function using RMS-scaled filtering.
📉 Super Smoother Trend Filter
Implements Ehlers' Super Smoother Filter to define trend bias dynamically.
Acts as a directional filter for long/short entry conditions, avoiding whipsaws.
🔊 Volume Velocity Confirmation
Entry signals require increasing volume velocity, calculated using RMS scaling and normalized via bandpass filtering.
🧠 Signal Conditions
Long Entry: VIP crosses above VIM, price above smoothed trend filter, and volume velocity increasing.
Short Entry: VIM crosses above VIP, price below trend filter, and volume velocity increasing.
🛡️ Risk & Trade Management
🎯 Dynamic Stop Loss & Take Profit
SL and TP are calculated using ATR smoothed with Jurik MA.
TP multiplier is customizable by hour, optimizing profit capture per trading session.
🕐 Hourly Optimization
Define up to 3 specific trade hours, each with:
Unique risk % allocation
Unique TP multipliers
Falls back to user-defined default settings if outside optimized hours, (Asia, New York).
🪙 Position Sizing
Risk-based sizing per trade.
Contracts are calculated dynamically using user/hourly risk and stop distance.
Rounded down to whole units to comply with futures contract rules.
⚙️ Advanced Tools
Jurik Moving Average (JMA): Smoothing with minimal lag
T3 Moving Average: Tilson’s smoother with configurable alpha
Laguerre Ultimate Smoother: Custom low-pass filter for signal confirmation
Hann Window FIR Filter: Optional for fine-tuned smoothing
Butterworth High-Pass & Bandpass Filters: For noise reduction and signal isolation
Z-Score Normalization: Detect extreme moves
Modular MA Framework: Plug-and-play MA type selector for quick experiments
⚙️ User Inputs (Grouped)
📌 Strategy Settings
Trade Direction: Long, Short, or Both
User-defined base risk (%)
📌 Entry Filters
Source type (hl2 default)
Vortex Length and Velocity Length
Super Smoother length
Volume Velocity length
📌 Risk & TP Settings
ATR Length, SL Multiplier, TP Multiplier
Per-hour TP/SL & risk percentages
📌 Time Filter
Toggle & configure 3 active trade hours
✅ Execution Logic
Strategy orders are submitted using strategy entry with calculated qty and a stop trigger.
Exits are handled by strategy exit with both SL and TP conditions.
Ensures each entry aligns with direction, trend filter, and volume momentum.
📈 Ideal For
Intraday futures traders (e.g., CME ES1!)
Traders needing hour-by-hour performance tuning
Strategies requiring advanced smoothing and signal validation
Quantitative backtesters analyzing risk-adjusted performance
Reversal Point Dynamics⇋ Reversal Point Dynamics (RPD)
This is not an indicator; it is a complete system for deconstructing the mechanics of a market reversal. Reversal Point Dynamics (RPD) moves far beyond simplistic pattern recognition, venturing into a deep analysis of the underlying forces that cause trends to exhaust, pause, and turn. It is engineered from the ground up to identify high-probability reversal points by quantifying the confluence of market dynamics in real-time.
Where other tools provide a static signal, RPD delivers a dynamic probability. It understands that a true market turning point is not a single event, but a cascade of failing momentum, structural breakdown, and a shift in market order. RPD's core engine meticulously analyzes each of these dynamic components—the market's underlying state, its velocity and acceleration, its degree of chaos (entropy), and its structural framework. These forces are synthesized into a single, unified Probability Score, offering you an unprecedented, transparent view into the conviction behind every potential reversal.
This is not a "black box" system. It is an open-architecture engine designed to empower the discerning trader. Featuring real-time signal projection, an integrated Fibonacci R2R Target Engine, and a comprehensive dashboard that acts as your Dynamics Control Center , RPD gives you a complete, holistic view of the market's state.
The Theoretical Core: Deconstructing Market Dynamics
RPD's analytical power is born from the intelligent synthesis of multiple, distinct theoretical models. Each pillar of the engine analyzes a different facet of market behavior. The convergence of these analyses—the "Singularity" event referenced in the dashboard—is what generates the final, high-conviction probability score.
1. Pillar One: Quantum State Analysis (QSA)
This is the foundational analysis of the market's current state within its recent context. Instead of treating price as a random walk, QSA quantizes it into a finite number of discrete "states."
Formulaic Concept: The engine establishes a price range using the highest high and lowest low over the Adaptive Analysis Period. This range is then divided into a user-defined number of Analysis Levels. The current price is mapped to one of these states (e.g., in a 9-level system, State 0 is the absolute low, and State 8 is the absolute high).
Analytical Edge: This acts as a powerful foundational filter. The engine will only begin searching for reversal signals when the market has reached a statistically stretched, extreme state (e.g., State 0 or 8). The Edge Sensitivity input allows you to control exactly how close to this extreme edge the price must be, ensuring you are trading from points of maximum potential exhaustion.
2. Pillar Two: Price State Roc (PSR) - The Dynamics of Momentum
This pillar analyzes the kinetic forces of the market: its velocity and acceleration. It understands that it’s not just where the price is, but how it got there that matters.
Formulaic Concept: The psr function calculates two derivatives of price.
Velocity: (price - price ). This measures the speed and direction of the current move.
Acceleration: (velocity - velocity ). This measures the rate of change in that speed. A negative acceleration (deceleration) during a strong rally is a critical pre-reversal warning, indicating momentum is fading even as price may be pushing higher.
Analytical Edge: The engine specifically hunts for exhaustion patterns where momentum is clearly decelerating as price reaches an extreme state. This is the mechanical signature of a weakening trend.
3. Pillar Three: Market Entropy Analysis - The Dynamics of Order & Chaos
This is RPD's chaos filter, a concept borrowed from information theory. Entropy measures the degree of randomness or disorder in the market's price action.
Formulaic Concept: The calculateEntropy function analyzes recent price changes. A market moving directionally and smoothly has low entropy (high order). A market chopping back and forth without direction has high entropy (high chaos). The value is normalized between 0 and 1.
Analytical Edge: The most reliable trades occur in low-entropy, ordered environments. RPD uses the Entropy Threshold to disqualify signals that attempt to form in chaotic, unpredictable conditions, providing a powerful shield against whipsaw markets.
4. Pillar Four: The Synthesis Engine & Probability Calculation
This is where all the dynamic forces converge. The final probability score is a weighted calculation that heavily rewards confluence.
Formulaic Concept: The calculateProbability function intelligently assembles the final score:
A Base Score is established from trend strength and entropy.
An Entropy Score adds points for low entropy (order) and subtracts for high entropy (chaos).
A significant Divergence Bonus is awarded for a classic momentum divergence.
RSI & Volume Bonuses are added if momentum oscillators are in extreme territory or a volume spike confirms institutional interest.
MTF & Adaptive Bonuses add further weight for alignment with higher timeframe structure.
Analytical Edge: A signal backed by multiple dynamic forces (e.g., extreme state + decelerating momentum + low entropy + volume spike) will receive an exponentially higher probability score. This is the very essence of analyzing reversal point dynamics.
The Command Center: Mastering the Inputs
Every input is a precise lever of control, allowing you to fine-tune the RPD engine to your exact trading style, market, and timeframe.
🧠 Core Algorithm
Predictive Mode (Early Detection):
What It Is: Enables the engine to search for potential reversals on the current, unclosed bar.
How It Works: Analyzes intra-bar acceleration and state to identify developing exhaustion. These signals are marked with a ' ? ' and are tentative.
How To Use It: Enable for scalping or very aggressive day trading to get the earliest possible indication. Disable for swing trading or a more conservative approach that waits for full bar confirmation.
Live Signal Mode (Current Bar):
What It Is: A highly aggressive mode that plots tentative signals with a ' ! ' on the live bar based on projected price and momentum. These signals repaint intra-bar.
How It Works: Uses a linear regression projection of the close to anticipate a reversal.
How To Use It: For advanced users who use intra-bar dynamics for execution and understand the nature of repainting signals.
Adaptive Analysis Period:
What It Is: The main lookback period for the QSA, PSR, and Entropy calculations. This is the engine's "memory."
How It Works: A shorter period makes the engine highly sensitive to local price swings. A longer period makes it focus only on major, significant market structure.
How To Use It: Scalping (1-5m): 15-25. Day Trading (15m-1H): 25-40. Swing Trading (4H+): 40-60.
Fractal Strength (Bars):
What It Is: Defines the strength of the pivot detection used for confirming reversal events.
How It Works: A value of '2' requires a candle's high/low to be more extreme than the two bars to its left and right.
How To Use It: '2' is a robust standard. Increase to '3' for an even stricter definition of a structural pivot, which will result in fewer signals.
MTF Multiplier:
What It Is: Integrates pivot data from a higher timeframe for confluence.
How It Works: A multiplier of '4' on a 15-minute chart will pull pivot data from the 1-hour chart (15 * 4 = 60m).
How To Use It: Set to a multiple that corresponds to your preferred higher timeframe for contextual analysis.
🎯 Signal Settings
Min Probability %:
What It Is: Your master quality filter. A signal is only plotted if its score exceeds this threshold.
How It Works: Directly filters the output of the final probability calculation.
How To Use It: High-Quality (80-95): For A+ setups only. Balanced (65-75): For day trading. Aggressive (50-60): For scalping.
Min Signal Distance (Bars):
What It Is: A noise filter that prevents signals from clustering in choppy conditions.
How It Works: Enforces a "cooldown" period of N bars after a signal.
How To Use It: Increase in ranging markets to focus on major swings. Decrease on lower timeframes.
Entropy Threshold:
What It Is: Your "chaos shield." Sets the maximum allowable market randomness for a signal.
How It Works: If calculated entropy is above this value, the signal is invalidated.
How To Use It: Lower values (0.1-0.5): Extremely strict. Higher values (0.7-1.0): More lenient. 0.85 is a good balance.
Adaptive Entropy & Aggressive Mode:
What It Is: Toggles for dynamically adjusting the engine's core parameters.
How It Works: Adaptive Entropy can slightly lower the required probability in strong trends. Aggressive Mode uses more lenient settings across the board.
How To Use It: Keep Adaptive on. Use Aggressive Mode sparingly, primarily for scalping highly volatile assets.
📊 State Analysis
Analysis Levels:
What It Is: The number of discrete "states" for the QSA.
How It Works: More levels create a finer-grained analysis of price location.
How To Use It: 6-7 levels are ideal. Increasing to 9 can provide more precision on very volatile assets.
Edge Sensitivity:
What It Is: Defines how close to the absolute top/bottom of the range price must be.
How It Works: '0' means price must be in the absolute highest/lowest state. '3' allows a signal within the top/bottom 3 states.
How To Use It: '3' provides a good balance. Lower it to '1' or '0' if you only want to trade extreme exhaustion.
The Dashboard: Your Dynamics Control Center
The dashboard provides a transparent, real-time view into the engine's brain. Use it to understand the context behind every signal and to gauge the current market environment at a glance.
🎯 UNIFIED PROB SCORE
TOTAL SCORE: The highest probability score (either Peak or Valley) the engine is currently calculating. This is your main at-a-glance conviction metric. The "Singularity" header refers to the event where market dynamics align—the event RPD is built to detect.
Quality: A human-readable interpretation of the Total Score. "EXCEPTIONAL" (🌟) is a rare, A+ confluence event. "STRONG" (💪) is a high-quality, tradable setup.
📊 ORDER FLOW & COMPONENT ANALYSIS
Volume Spike: Shows if the current volume is significantly higher than average (YES/NO). A 'YES' adds major confirmation.
Peak/Valley Conf: This breaks down the probability score into its directional components, showing you the separate confidence levels for a potential top (Peak) versus a bottom (Valley).
🌌 MARKET STRUCTURE
HTF Trend: Shows the direction of the underlying trend based on a Supertrend calculation.
Entropy: The current market chaos reading. "🔥 LOW" is an ideal, ordered state for trading. "😴 HIGH" is a warning of choppy, unpredictable conditions.
🔮 FIB & R2R ZONE (Large Dashboard)
This section gives you the status of the Fibonacci Target Engine. It shows if an Active Channel (entry zone) or Stop Zone (invalidation zone) is active and displays the precise price levels for the static entry, target, and stop calculated at the time of the signal.
🛡️ FILTERS & PREDICTIVES (Large Dashboard)
This panel provides a status check on all the bonus filters. It shows the current RSI Status, whether a Divergence is present, and if a Live Pending signal is forming.
The Visual Interface: A Symphony of Data
Every visual element is designed for instant, intuitive interpretation of market dynamics.
Signal Markers: These are the primary outputs of the engine.
▼/▲ b: A fully confirmed signal that has passed all filters.
? b: A tentative signal generated in Predictive Mode, indicating developing dynamics.
◈ b: This diamond icon replaces the standard triangle when the signal is confirmed by a strong momentum divergence, highlighting it as a superior setup where dynamics are misaligned with price.
Harmonic Wave: The flowing, colored wave around the price.
What It Represents: The market's "flow dynamic" and volatility.
How to Interpret It: Expanding waves show increasing volatility. The color is tied to the "Quantum Color" in your theme, representing the underlying energy field of the market.
Entropy Particles: The small dots appearing above/below price.
What They Represent: A direct visualization of the "order dynamic."
How to Interpret Them: Their presence signifies a low-entropy, ordered state ideal for trading. Their color indicates the direction of momentum (PSR velocity). Their absence means the market is too chaotic (high entropy).
The Fibonacci Target Engine: The dynamic R2R system appearing post-signal.
Static Fib Levels: Colored horizontal lines representing the market's "structural dynamic."
The Green "Active Channel" Box: Your zone of consideration. An area to manage a potential entry.
Development Philosophy
Reversal Point Dynamics was engineered to answer a fundamental question: can we objectively measure the forces behind a market turn? It is a synthesis of concepts from market microstructure, statistics, and information theory. The objective was never to create a "perfect" system, but to build a robust decision-support tool that provides a measurable, statistical edge by focusing on the principle of confluence.
By demanding that multiple, independent market dynamics align simultaneously, RPD filters out the vast majority of market noise. It is designed for the trader who thinks in terms of probability and risk management, not in terms of certainties. It is a tool to help you discount the obvious and bet on the unexpected alignment of market forces.
"Markets are constantly in a state of uncertainty and flux and money is made by discounting the obvious and betting on the unexpected."
— George Soros
Trade with insight. Trade with anticipation.
— Dskyz, for DAFE Trading Systems
Smart MTF S/R Levels[BullByte]
Smart MTF S/R Levels
Introduction & Motivation
Support and Resistance (S/R) levels are the backbone of technical analysis. However, most traders face two major challenges:
Manual S/R Marking: Drawing S/R levels by hand is time-consuming, subjective, and often inconsistent.
Multi-Timeframe Blind Spots: Key S/R levels from higher or lower timeframes are often missed, leading to surprise reversals or missed opportunities.
Smart MTF S/R Levels was created to solve these problems. It is a fully automated, multi-timeframe, multi-method S/R detection and visualization tool, designed to give traders a complete, objective, and actionable view of the market’s most important price zones.
What Makes This Indicator Unique?
Multi-Timeframe Analysis: Simultaneously analyzes up to three user-selected timeframes, ensuring you never miss a critical S/R level from any timeframe.
Multi-Method Confluence: Integrates several respected S/R detection methods—Swings, Pivots, Fibonacci, Order Blocks, and Volume Profile—into a single, unified system.
Zone Clustering: Automatically merges nearby levels into “zones” to reduce clutter and highlight areas of true market consensus.
Confluence Scoring: Each zone is scored by the number of methods and timeframes in agreement, helping you instantly spot the most significant S/R areas.
Reaction Counting: Tracks how many times price has recently interacted with each zone, providing a real-world measure of its importance.
Customizable Dashboard: A real-time, on-chart table summarizes all key S/R zones, their origins, confluence, and proximity to price.
Smart Alerts: Get notified when price approaches high-confluence zones, so you never miss a critical trading opportunity.
Why Should a Trader Use This?
Objectivity: Removes subjectivity from S/R analysis by using algorithmic detection and clustering.
Efficiency: Saves hours of manual charting and reduces analysis fatigue.
Comprehensiveness: Ensures you are always aware of the most relevant S/R zones, regardless of your trading timeframe.
Actionability: The dashboard and alerts make it easy to act on the most important levels, improving trade timing and risk management.
Adaptability: Works for all asset classes (stocks, forex, crypto, futures) and all trading styles (scalping, swing, position).
The Gap This Indicator Fills
Most S/R indicators focus on a single method or timeframe, leading to incomplete analysis. Manual S/R marking is error-prone and inconsistent. This indicator fills the gap by:
Automating S/R detection across multiple timeframes and methods
Objectively scoring and ranking zones by confluence and reaction
Presenting all this information in a clear, actionable dashboard
How Does It Work? (Technical Logic)
1. Level Detection
For each selected timeframe, the script detects S/R levels using:
SW (Swing High/Low): Recent price pivots where reversals occurred.
Pivot: Classic floor trader pivots (P, S1, R1).
Fib (Fibonacci): Key retracement levels (0.236, 0.382, 0.5, 0.618, 0.786) over the last 50 bars.
Bull OB / Bear OB: Institutional price zones based on bullish/bearish engulfing patterns.
VWAP / POC: Volume Weighted Average Price and Point of Control over the last 50 bars.
2. Level Clustering
Levels within a user-defined % distance are merged into a single “zone.”
Each zone records which methods and timeframes contributed to it.
3. Confluence & Reaction Scoring
Confluence: The number of unique methods/timeframes in agreement for a zone.
Reactions: The number of times price has touched or reversed at the zone in the recent past (user-defined lookback).
4. Filtering & Sorting
Only zones within a user-defined % of the current price are shown (to focus on actionable areas).
Zones can be sorted by confluence, reaction count, or proximity to price.
5. Visualization
Zones: Shaded boxes on the chart (green for support, red for resistance, blue for mixed).
Lines: Mark the exact level of each zone.
Labels: Show level, methods by timeframe (e.g., 15m (3 SW), 30m (1 VWAP)), and (if applicable) Fibonacci ratios.
Dashboard Table: Lists all nearby zones with full details.
6. Alerts
Optional alerts trigger when price approaches a zone with confluence above a user-set threshold.
Inputs & Customization (Explained for All Users)
Show Timeframe 1/2/3: Enable/disable analysis for each timeframe (e.g., 15m, 30m, 1h).
Show Swings/Pivots/Fibonacci/Order Blocks/Volume Profile: Select which S/R methods to include.
Show levels within X% of price: Only display zones near the current price (default: 3%).
How many swing highs/lows to show: Number of recent swings to include (default: 3).
Cluster levels within X%: Merge levels close together into a single zone (default: 0.25%).
Show Top N Zones: Limit the number of zones displayed (default: 8).
Bars to check for reactions: How far back to count price reactions (default: 100).
Sort Zones By: Choose how to rank zones in the dashboard (Confluence, Reactions, Distance).
Alert if Confluence >=: Set the minimum confluence score for alerts (default: 3).
Zone Box Width/Line Length/Label Offset: Control the appearance of zones and labels.
Dashboard Size/Location: Customize the dashboard table.
How to Read the Output
Shaded Boxes: Represent S/R zones. The color indicates type (green = support, red = resistance, blue = mixed).
Lines: Mark the precise level of each zone.
Labels: Show the level, methods by timeframe (e.g., 15m (3 SW), 30m (1 VWAP)), and (if applicable) Fibonacci ratios.
Dashboard Table: Columns include:
Level: Price of the zone
Methods (by TF): Which S/R methods and how many, per timeframe (see abbreviation key below)
Type: Support, Resistance, or Mixed
Confl.: Confluence score (higher = more significant)
React.: Number of recent price reactions
Dist %: Distance from current price (in %)
Abbreviations Used
SW = Swing High/Low (recent price pivots where reversals occurred)
Fib = Fibonacci Level (key retracement levels such as 0.236, 0.382, 0.5, 0.618, 0.786)
VWAP = Volume Weighted Average Price (price level weighted by volume)
POC = Point of Control (price level with the highest traded volume)
Bull OB = Bullish Order Block (institutional support zone from bullish price action)
Bear OB = Bearish Order Block (institutional resistance zone from bearish price action)
Pivot = Pivot Point (classic floor trader pivots: P, S1, R1)
These abbreviations appear in the dashboard and chart labels for clarity.
Example: How to Read the Dashboard and Labels (from the chart above)
Suppose you are trading BTCUSDT on a 15-minute chart. The dashboard at the top right shows several S/R zones, each with a breakdown of which timeframes and methods contributed to their detection:
Resistance zone at 119257.11:
The dashboard shows:
5m (1 SW), 15m (2 SW), 1h (3 SW)
This means the level 119257.11 was identified as a resistance zone by one swing high (SW) on the 5-minute timeframe, two swing highs on the 15-minute timeframe, and three swing highs on the 1-hour timeframe. The confluence score is 6 (total number of method/timeframe hits), and there has been 1 recent price reaction at this level. This suggests 119257.11 is a strong resistance zone, confirmed by multiple swing highs across all selected timeframes.
Mixed zone at 118767.97:
The dashboard shows:
5m (2 SW), 15m (2 SW)
This means the level 118767.97 was identified by two swing points on both the 5-minute and 15-minute timeframes. The confluence score is 4, and there have been 19 recent price reactions at this level, indicating it is a highly reactive zone.
Support zone at 117411.35:
The dashboard shows:
5m (2 SW), 1h (2 SW)
This means the level 117411.35 was identified as a support zone by two swing lows on the 5-minute timeframe and two swing lows on the 1-hour timeframe. The confluence score is 4, and there have been 2 recent price reactions at this level.
Mixed zone at 118291.45:
The dashboard shows:
15m (1 SW, 1 VWAP), 5m (1 VWAP), 1h (1 VWAP)
This means the level 118291.45 was identified by a swing and VWAP on the 15-minute timeframe, and by VWAP on both the 5-minute and 1-hour timeframes. The confluence score is 4, and there have been 12 recent price reactions at this level.
Support zone at 117103.10:
The dashboard shows:
15m (1 SW), 1h (1 SW)
This means the level 117103.10 was identified by a single swing low on both the 15-minute and 1-hour timeframes. The confluence score is 2, and there have been no recent price reactions at this level.
Resistance zone at 117899.33:
The dashboard shows:
5m (1 SW)
This means the level 117899.33 was identified by a single swing high on the 5-minute timeframe. The confluence score is 1, and there have been no recent price reactions at this level.
How to use this:
Zones with higher confluence (more methods and timeframes in agreement) and more recent reactions are generally more significant. For example, the resistance at 119257.11 is much stronger than the resistance at 117899.33, and the mixed zone at 118767.97 has shown the most recent price reactions, making it a key area to watch for potential reversals or breakouts.
Tip:
“SW” stands for Swing High/Low, and “VWAP” stands for Volume Weighted Average Price.
The format 15m (2 SW) means two swing points were detected on the 15-minute timeframe.
Best Practices & Recommendations
Use with Other Tools: This indicator is most powerful when combined with your own price action analysis and risk management.
Adjust Settings: Experiment with timeframes, clustering, and methods to suit your trading style and the asset’s volatility.
Watch for High Confluence: Zones with higher confluence and more reactions are generally more significant.
Limitations
No Future Prediction: The indicator does not predict future price movement; it highlights areas where price is statistically more likely to react.
Not a Standalone System: Should be used as part of a broader trading plan.
Historical Data: Reaction counts are based on historical price action and may not always repeat.
Disclaimer
This indicator is a technical analysis tool and does not constitute financial advice or a recommendation to buy or sell any asset. Trading involves risk, and past performance is not indicative of future results. Always use proper risk management and consult a financial advisor if needed.
Trend Range Detector (Zeiierman)█ Overview
Trend Range Detector (Zeiierman) is a market structure tool that identifies and tracks periods of price compression by forming adaptive range boxes based on volatility and price movement. When prices remain stable within a defined band, the script dynamically draws a range box; when prices break out of that structure, the box highlights the breakout in real-time.
By combining a volatility-based envelope with a custom weighted centerline, this tool filters out noise and isolates truly stable zones — providing a clean framework for traders who focus on accumulation, distribution, breakout anticipation, and reversion opportunities.
Whether you're range trading, spotting trend consolidations, or looking for volatility contractions before major moves, the Trend Range Detector gives you a mathematically adaptive, visually intuitive structure that maps the heartbeat of the market.
█ How It Works
⚪ Range Formation Engine
The core of this indicator revolves around two conditions:
Distance Filter: The maximum distance between all recent closes and a dynamic centerline must remain within a volatility envelope.
Volatility Envelope: Based on an ATR(2000) multiplied by a user-defined factor to account for broader market volatility trends.
If both conditions are satisfied over the most recent length bars, a range box is drawn to visually anchor the zone.
⚪ Dynamic Breakout Coloring
When price breaks out of the top or bottom of the active range box, the box color shifts in real-time:
Blue Boxes represent areas where price has remained within a defined volatility envelope over a sustained number of bars. These zones reflect stable, low-volatility periods, often associated with consolidation, equilibrium, or market indecision.
Green Boxes for bullish breakouts.
Red Boxes for bearish breakdowns.
This allows traders to visually spot transitions from consolidation to expansion phases without relying on lagging signals.
█ Why Use a Weighted Close Instead of SMA?
A standard Simple Moving Average (SMA) treats all past closes equally, which works well in theory, but not in dynamic, fast-shifting markets. In this script, we replace the traditional SMA with a speed-weighted average that reflects how aggressively the market has moved bar-to-bar.
⚪ Here's why it matters:
Bars with higher momentum (larger price differences between closes) are given more weight.
Slow, sideways candles (typical in noise or low volume) contribute less to the calculated centerline.
This method creates a more accurate snapshot of market behavior, especially during volatile phases. As a result, the indicator adapts to market conditions more effectively, helping traders identify real consolidation zones, not just average lines distorted by flat bars or noise.
█ How to Use
⚪ Range Detection
Boxes form only when price remains consistently close to the speed-weighted mean.
Helps identify sideways zones, consolidations, and low-volatility structures where price is “charging up.”
⚪ Breakout Confirmation
Once price exits the top or bottom boundary, the box immediately highlights the direction of the break.
Use this signal in conjunction with your own momentum, volume, or trend filters for higher-confidence trades.
█ Settings
Minimum Range Length: Number of candles required for a valid range to form.
Range Width Multiplier: Adjusts the envelope around the weighted average using ATR(2000).
Highlight Box Breaks: Enables real-time coloring of breakouts and breakdowns for immediate visual feedback.
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Disclaimer
The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
Market Structure🏗️ Market Structure Indicator for TradingView (Pine Script v6)
Overview:
The Market Structure indicator is a robust tool for identifying swing highs and swing lows across multiple structural levels:
🟤 Short-Term Swings
🟠 Intermediate-Term Swings
🟢 Long-Term Swings
It helps traders visually interpret market structure by detecting confirmed pivot points and promoting them through a hierarchical system. This provides a clear picture of trend direction, breakouts, and reversals.
⚙️ Features and Functionality:
✅ Multi-level Swing Detection:
The indicator promotes confirmed swing points from short-term to intermediate and long-term levels using a structured algorithm.
✅ Full Customization:
Toggle visibility of each swing level independently.
Choose custom colors for short, intermediate, and long-term swings.
✅ Transparent and Commented Logic:
The code contains well-structured functions for identifying and confirming swing highs and lows.
Label arrays are used for precise control over swing detection and display.
✅ Detailed and Open Source Code:
Every function is thoroughly explained with inline comments.
Designed to be easy to understand, modify, and extend — perfect for learning or integrating into more advanced systems.
📜 Open and Documented Source Code
The script is entirely open-source, written in Pine Script v6, and includes full documentation inside the code itself. Key sections include:
🔧 Input settings and visual configuration
🧠 Swing detection and confirmation methods
🔁 Promotion logic between structure levels
📈 Real-time label drawing on each bar
Everything is fully accessible and explained — no obfuscation, no hidden logic.
Gabriel's Weibull Stdv. SuperTrend📈 Gabriel's Weibull Stdv. SuperTrend
Description:
Gabriel’s Weibull Stdv. SuperTrend is a custom trend-following indicator that blends the statistical rigor of the Weibull Moving Average with the adaptive nature of the Standard Deviation-based SuperTrend.
This hybrid system dynamically adjusts its trend bands using a Weibull-weighted average, emphasizing more recent price action while allowing the curve to flexibly adapt based on two key Weibull parameters: Shape (k) and Scale (λ). The bands themselves are shifted by a multiple of standard deviation, offering a volatility-sensitive approach to trend detection.
🔧 Key Components:
Weibull Moving Average (WMA):
A smoothing function that assigns weights to historical prices using the Weibull distribution, controlled via Shape and Scale parameters.
SuperTrend Logic with Adaptive Bands:
Standard deviation is calculated over a user-defined length and scaled with a factor to set upper and lower thresholds around the WMA.
Trend Direction Detection:
The algorithm identifies bullish or bearish states based on crossover logic relative to the dynamic bands.
Visual Enhancements:
Bright green/red lines for SuperTrend direction.
Midpoint overlay and color-coded candles for clarity.
Filled zones between price and trend for visual emphasis.
⚙️ User Inputs:
Source: Price data to analyze (default: close).
Stdv. Length: Period for calculating standard deviation.
Factor: Multiplier to widen or narrow the SuperTrend bands.
Window Length: Lookback period for the Weibull MA.
Shape (k): Controls the skewness of the Weibull distribution.
Scale (λ): Stretches or compresses the weighting curve.
🔔 Alerts:
Long Entry Alert: Triggered when the trend flips bullish.
Short Entry Alert: Triggered when the trend flips bearish.
🧠 Use Cases:
Catch early reversals using custom-tailored smoothing.
Identify high-confidence trend shifts with dynamic volatility.
Combine with other confirmation indicators for enhanced entries.
FVG 9:31–10:00 AM ETFVG 9:31–10:00 AM ET - Script Description
What This Script Does
This indicator finds **Fair Value Gaps (FVGs)** that form during the first 29 minutes of the U.S. stock market (9:31 AM to 10:00 AM Eastern Time). A Fair Value Gap is a price imbalance where there's a gap between candles that often becomes an important support or resistance level.
Key Features:
- **Time Window**: Only looks for FVGs between 9:31-10:00 AM ET (most important opening period)
- **One Per Day**: Finds only the first FVG that forms in this time window each day
- **Visual Display**: Draws a purple box around the gap with a clear "FVG" label
- **Price Tracking**: Monitors when price comes back to test the gap level
- **Alert System**: Sends notifications when price returns to the FVG zone
How FVGs Are Detected:
- **Bullish FVG**: When there's a gap up (low of middle candle is above high of 3rd candle back)
- **Bearish FVG**: When there's a gap down (high of middle candle is below low of 3rd candle back)
The 9:31-10:00 AM window is chosen because this is when institutions and algorithms create their biggest price moves right after market open, making these gaps very reliable.
Customization Options
User Settings
Extend FVG Box (Bars)
- **What it does**: Makes the purple box longer to the right
- **Default**: 0 (box ends right after the gap forms)
- **Options**: Any number from 0 to 100+
- **When to use**:
- Keep at 0 for clean historical view
- Set to 10-20 to track the gap during the current session
- Set higher for longer reference
Code Settings (Can Be Changed)
Time Window
- **Start**: 9:31 AM Eastern Time
- **End**: 10:00 AM Eastern Time
- **Can modify**: Change the hour/minute numbers in the code
Visual Style
- **Color**: Purple with see-through background
- **Label**: Shows "FVG" text in white
- **Can modify**: Change colors and transparency in the code
How to Use:
Setup
Chart Settings
1. Use 1-minute, 5-minute, or 15-minute charts (works best on these timeframes)
2. Apply to liquid markets like ES, NQ, major stocks, or forex pairs
3. Set the "Extend FVG Box" to your preference (start with 0 or 10)
What You'll See
- A purple box appears when an FVG forms during 9:31-10:00 AM
- Box shows the exact price levels of the gap
- "FVG" label appears on the box
- Only one FVG per day will be marked
Trading Strategies
Basic FVG Trading
1. **Wait for Formation**: Let the purple box appear during 9:31-10:00 AM
2. **Watch Price Movement**: See if price moves away from the gap
3. **Enter on Retest**: When price comes back to the purple box area, consider entering
4. **Trade Direction**:
- Bullish FVG = look for long opportunities when price retests
- Bearish FVG = look for short opportunities when price retests
Entry Methods
- **Bounce Play**: Enter when price touches the FVG box and bounces away
- **Break Play**: Enter if price strongly breaks through the FVG box
- **Rejection Play**: Enter opposite direction if price gets rejected at the FVG
Risk Management
Stop Losses
- Place stops just outside the FVG box (a few ticks beyond the gap)
- If trading a bounce, stop goes on opposite side of the gap
- If trading a break, stop goes back inside the gap
Position Sizing
- Start small until you understand how FVGs work in your market
- Bigger gaps = smaller position size (more risk)
- Smaller gaps = can use larger position size
Profit Targets
- Take profits at obvious levels like round numbers, previous highs/lows
- Consider taking half profits at 1:1 risk/reward ratio
- Let some position run if the move is strong
Best Practices
When It Works Best
- High-volume stocks and futures (ES, NQ work great)
- Normal market days without major news during the 9:31-10:00 window
- When there's clear institutional activity in the opening period
When to Be Careful
- Low-volume stocks or markets
- Major economic news releases during the time window
- Market holidays when volume is low
- Very choppy or sideways days
Alert Usage
- The script will alert you when price comes back to test the FVG
- Don't trade the alert blindly - always check the current market situation
- Use the alert as a heads-up to start watching the setup more closely
Tips for Success
- The earlier the FVG forms in the 9:31-10:00 window, often the more significant it is
- FVGs that form with high volume are usually more reliable
- Always consider the overall market direction - don't fight the main trend
- Practice on paper first to understand how FVGs behave in your chosen market
🔗 Works Best With:
✅ Liquidity Levels — Smart Swing Lows: Spot key structural lows that can fuel stop hunts and reversals.
✅ ICT Turtle Soup — Liquidity Reversal: Add a classic reversal pattern to your toolkit to catch fakeouts cleanly.
✅ ICT SMC Liquidity Grabs and OBs- Liquidity Grabs, Order Block Zones, and Fibonacci OTE Levels, allowing traders to identify institutional entry models with clean, rule-based visual signals.
This script is most valuable for day traders who want to catch institutional moves right after market open, but it can also help swing traders identify important intraday levels.
✅ ICT Macro Zones (Grey Box Version)- It tracks real-time highs and lows for each Silver Bullet session.
✅ Weekly Opening Gap (cryptonnnite)
XABCD_HarmonicsLibrary for detecting harmonic patterns using ZigZag pivots or custom swing points. Supports Butterfly, Gartley, Bat, and Crab patterns with automatic Fibonacci ratio validation and optional D-point projection using extremes. Returns detailed PatternResult including structure points and target projection. Ideal for technical analysis, algorithmic detection, or overlay visualizations.
Quantum Dip Hunter | AlphaNattQuantum Dip Hunter | AlphaNatt
🎯 Overview
The Quantum Dip Hunter is an advanced technical indicator designed to identify high-probability buying opportunities when price temporarily dips below dynamic support levels. Unlike simple oversold indicators, this system uses a sophisticated quality scoring algorithm to filter out low-quality dips and highlight only the best entry points.
"Buy the dip" - but only the right dips. Not all dips are created equal.
⚡ Key Features
5 Detection Methods: Choose from Dynamic, Fibonacci, Volatility, Volume Profile, or Hybrid modes
Quality Scoring System: Each dip is scored from 0-100% based on multiple factors
Smart Filtering: Only signals above your quality threshold are displayed
Visual Effects: Glow, Pulse, and Wave animations for the support line
Risk Management: Automatic stop-loss and take-profit calculations
Real-time Statistics: Live dashboard showing current market conditions
📊 How It Works
The indicator calculates a dynamic support line using your selected method
When price dips below this line, it evaluates the dip quality
Quality score is calculated based on: trend alignment (30%), volume (20%), RSI (20%), momentum (15%), and dip depth (15%)
If the score exceeds your minimum threshold, a buy signal arrow appears
Stop-loss and take-profit levels are automatically calculated and displayed
🚀 Detection Methods Explained
Dynamic Support
Adapts to recent price action
Best for: Trending markets
Uses ATR-adjusted lowest points
Fibonacci Support
Based on 61.8% and 78.6% retracement levels
Best for: Pullbacks in strong trends
Automatically switches between fib levels
Volatility Support
Uses Bollinger Band methodology
Best for: Range-bound markets
Adapts to changing volatility
Volume Profile Support
Finds high-volume price levels
Best for: Identifying institutional support
Updates dynamically as volume accumulates
Hybrid Mode
Combines all methods for maximum accuracy
Best for: All market conditions
Takes the most conservative support level
⚙️ Key Settings
Dip Detection Engine
Detection Method: Choose your preferred support calculation
Sensitivity: Higher = more sensitive to price movements (0.5-3.0)
Lookback Period: How far back to analyze (20-200 bars)
Dip Depth %: Minimum dip size to consider (0.5-10%)
Quality Filters
Trend Filter: Only buy dips in uptrends when enabled
Minimum Dip Score: Quality threshold for signals (0-100%)
Trend Strength: Required trend score when filter is on
📈 Trading Strategies
Conservative Approach
Use Dynamic method with Trend Filter ON
Set minimum score to 80%
Risk:Reward ratio of 2:1 or higher
Best for: Swing trading
Aggressive Approach
Use Hybrid method with Trend Filter OFF
Set minimum score to 60%
Risk:Reward ratio of 1:1
Best for: Day trading
Scalping Setup
Use Volatility method
Set sensitivity to 2.0+
Focus on Target 1 only
Best for: Quick trades
🎨 Visual Customization
Color Themes:
Neon: Bright cyan/magenta for dark backgrounds
Ocean: Cool blues and teals
Solar: Warm yellows and oranges
Matrix: Classic green terminal look
Gradient: Smooth color transitions
Line Styles:
Solid: Clean, simple line
Glow: Adds depth with glow effect
Pulse: Animated breathing effect
Wave: Oscillating wave pattern
💡 Pro Tips
Start with the Trend Filter ON to avoid catching falling knives
Higher quality scores (80%+) have better win rates but fewer signals
Use Volume Profile method near major support/resistance levels
Combine with your favorite momentum indicator for confirmation
The pulse animation can help draw attention to key levels
⚠️ Important Notes
This indicator identifies potential entries, not guaranteed profits
Always use proper risk management
Works best on liquid instruments with good volume
Backtest your settings before live trading
Not financial advice - use at your own risk
📊 Statistics Panel
The live statistics panel shows:
Current detection method
Support level value
Trend direction
Distance from support
Current signal status
🤝 Support
Created by AlphaNatt
For questions or suggestions, please comment below!
Happy dip hunting! 🎯
Not financial advice, always do your own research
