EMA Cross By Crypto collective Xეს ინდიკატორი გაძლევთ საშუალებას ნახოთ ყველაზე პოპულარული EMA წყვილები ერთ გრაფიკზე:
EMA 9/21
EMA 20/50
EMA 50/200
EMA 100/200
და საკუთარი, მომხმარებლის მიერ შერჩეული Custom წყვილი.
👉 თითოეულ წყვილს შეგიძლია ჩართო/გამორთო ინდიკატორის პარამეტრებიდან.
👉 შესაძლებელია ფერების შეცვლა, ასევე სურვილის შემთხვევაში EMA-ების higher timeframe-ზე გამოტანა (მაგ. 1D EMA 4H გრაფიკზე).
ეს ინსტრუმენტი განსაკუთრებით გამოსადეგია:
ტრენდების დადგენისთვის
გრძელვადიანი და მოკლევადიანი გადაკვეთების შესადარებლად
საკუთარი სტრატეგიის ტესტირებისთვის
This indicator lets you plot and compare the most commonly used EMA pairs on a single chart:
EMA 9/21
EMA 20/50
EMA 50/200
EMA 100/200
plus a fully customizable user-defined EMA pair.
👉 Each pair can be toggled on/off from the settings.
👉 Colors are customizable, and you can optionally display EMAs from a higher timeframe (e.g., show Daily EMAs on a 4H chart).
This tool is especially useful for:
Trend confirmation
Comparing short-term vs. long-term crosses
Backtesting your own strategies
Pesquisar nos scripts por "100年黄金价格走势"
Mayfair FX Scalper V-10 Price Action + SMC//@version=5
indicator("Mayfair FX Scalper V-10 Price Action + SMC", overlay=true)
// === INPUTS ===
rsiLength = input.int(14, title="RSI Length")
overbought = input.float(73, title="SELL Level")
oversold = input.float(31, title="BUY Level")
rsiSrc = input.source(open, title="RSI Source")
// === Color Inputs ===
entryLineColor = input.color(color.white, title="entry Label Color")
entryLabelColor = input.color(color.white, title="entry Lable Color")
slLineColor = input.color(color.red, title="Stop Loss Line Color")
slLabelColor = input.color(color.red, title="Stop Loss Label Color")
tpLineColor = input.color(color.blue, title="Take Profit Line Color")
tpLabelColor = input.color(color.blue, title="Take Profit Color")
entryTextColor = input.color(color.rgb(0, 0, 0) , title="entry Text Color")
slTextColor = input.color(color.white, title="Stop Lose Color")
tpTextColor = input.color(color.white, title="Take Profit Text Color")
//indicator("Author Info Display"
// Create table
var table infoTable = table.new(position.top_right, 2, 6, bgcolor=color.new(#000000, 1), border_width=1)
if barstate.islast
table.cell(infoTable, 0, 0, "Author:", text_color=color.white, text_size=size.small)
table.cell(infoTable, 1, 0, "MR WOW", text_color=color.rgb(255, 251, 0), text_size=size.large)
table.cell(infoTable, 0, 1, "YouTube:", text_color=color.white, text_size=size.small)
table.cell(infoTable, 1, 1, "www.youtube.com/@iammrwow", text_color=color.rgb(255, 251, 0), text_size=size.small)
table.cell(infoTable, 0, 3, "Website:", text_color=color.white, text_size=size.small)
table.cell(infoTable, 1, 3, "www.mrwowea.com", text_color=color.rgb(255, 251, 0), text_size=size.small)
// === RSI CALCULATION ===
rsi = ta.rsi(rsiSrc, rsiLength)
rawBuySignal = rsi < oversold
rawSellSignal = rsi > overbought
// === Confirmed Signals ===
isBullish = close > open
isBearish = close < open
newBuy = rawBuySignal and isBullish and close > open == false
newSell = rawSellSignal and isBearish and close < open == false
// === Trade State Variables ===
var bool inPosition = false
var bool isBuy = false
var float entryPrice = na
var float slPrice = na
var float tp1Price = na
var float tp2Price = na
var float tp3Price = na
var int entryBarIndex = na
var label labels = array.new()
var line lines = array.new()
// === Instrument & Timeframe SL/TP Setup ===
isGold = str.contains(syminfo.ticker, "XAU") or str.contains(syminfo.ticker, "GOLD")
instrumentType = syminfo.type == "crypto" ? "Crypto" : isGold ? "Gold" : syminfo.currency == "JPY" ? "JPY" : "Forex"
tf = timeframe.period
slPipsGold = tf == "1" ? 30 : tf == "3" ? 45 : tf == "5" ? 50 : tf == "15" ? 60 : 70
slPipsCrypto = tf == "1" ? 5 : tf == "3" ? 8 : tf == "5" ? 12 : tf == "15" ? 15 : 10
slPipsForex = tf == "1" ? 6 : tf == "3" ? 9 : tf == "5" ? 11 : tf == "15" ? 15 : 15
gold_slDist = 0.1 * slPipsGold
gold_tp1Dist = gold_slDist
gold_tp2Dist = gold_slDist * 2
gold_tp3Dist = gold_slDist * 3
pipSize = instrumentType == "Crypto" ? 1.0 : instrumentType == "Gold" or instrumentType == "JPY" ? 0.01 : 0.0001
slPips = instrumentType == "Crypto" ? slPipsCrypto : instrumentType == "Gold" ? slPipsGold : slPipsForex
slDist = slPips * pipSize
tp1Dist = slDist
tp2Dist = slDist * 2
tp3Dist = slDist * 3
// === Draw Line & Label ===
drawLine(y, txt, col, lblCol, extendToCurrent) =>
int lineEnd = extendToCurrent ? bar_index : entryBarIndex + 2
array.push(lines, line.new(entryBarIndex, y, lineEnd, y, color=col, width=2, extend=extend.none))
textCol = str.contains(txt, "Entry") ? entryTextColor : str.contains(txt, "Stop") ? slTextColor : tpTextColor
array.push(labels, label.new(lineEnd, y, txt, style=label.style_label_left, color=color.new(lblCol, 0), textcolor=textCol, size=size.small))
// === Check Exit ===
slHit = inPosition and ((isBuy and low <= slPrice) or (not isBuy and high >= slPrice))
tp3Hit = inPosition and ((isBuy and high >= tp3Price) or (not isBuy and low <= tp3Price))
shouldExit = slHit or tp3Hit
if shouldExit
for l in labels
label.delete(l)
array.clear(labels)
for ln in lines
line.delete(ln)
array.clear(lines)
inPosition := false
entryPrice := na
slPrice := na
tp1Price := na
tp2Price := na
tp3Price := na
entryBarIndex := na
// === Confirmed Signal with No Position ===
confirmedBuy = not inPosition and newBuy
confirmedSell = not inPosition and newSell
// === Signal Markers ===
plotshape(series=confirmedBuy, location=location.belowbar, color=color.rgb(33, 150, 243), style=shape.triangleup, text="BUY", textcolor=color.rgb(33, 150, 243))
plotshape(series=confirmedSell, location=location.abovebar, color=color.rgb(254, 254, 255), style=shape.triangledown, text="SELL", textcolor=color.rgb(239, 238, 247))
// === Entry Execution ===
if confirmedBuy or confirmedSell
entryPrice := close
entryBarIndex := bar_index
isBuy := confirmedBuy
inPosition := true
if isGold
slPrice := isBuy ? entryPrice - gold_slDist : entryPrice + gold_slDist
tp1Price := isBuy ? entryPrice + gold_tp1Dist : entryPrice - gold_tp1Dist
tp2Price := isBuy ? entryPrice + gold_tp2Dist : entryPrice - gold_tp2Dist
tp3Price := isBuy ? entryPrice + gold_tp3Dist : entryPrice - gold_tp3Dist
else
slPrice := isBuy ? entryPrice - slDist : entryPrice + slDist
tp1Price := isBuy ? entryPrice + tp1Dist : entryPrice - tp1Dist
tp2Price := isBuy ? entryPrice + tp2Dist : entryPrice - tp2Dist
tp3Price := isBuy ? entryPrice + tp3Dist : entryPrice - tp3Dist
drawLine(entryPrice, "Entry Price - After Candle Above Entry Price Then Place Trade: " + str.tostring(entryPrice), entryLineColor, entryLabelColor, false)
drawLine(slPrice, "Stop Loss: " + str.tostring(slPrice), slLineColor, slLabelColor, false)
drawLine(tp1Price, "(1:1) Take Profit: " + str.tostring(tp1Price), tpLineColor, tpLabelColor, false)
drawLine(tp2Price, "(2:1) Take Profit: " + str.tostring(tp2Price), tpLineColor, tpLabelColor, false)
drawLine(tp3Price, "(3:1) Take Profit: " + str.tostring(tp3Price), tpLineColor, tpLabelColor, false)
// === Update TP/SL Lines if Still in Trade ===
if inPosition and not (confirmedBuy or confirmedSell)
for ln in lines
line.delete(ln)
array.clear(lines)
for l in labels
label.delete(l)
array.clear(labels)
drawLine(entryPrice, "After Candle Closed Above Entry Line Buy & Below Sell :Entry Price-" + str.tostring(entryPrice), entryLineColor, entryLabelColor, true)
drawLine(slPrice, "Stop Loss: " + str.tostring(slPrice), slLineColor, slLabelColor, true)
drawLine(tp1Price, "(1:1) Take Profit: " + str.tostring(tp1Price), tpLineColor, tpLabelColor, true)
drawLine(tp2Price, "(2:1) Take Profit: " + str.tostring(tp2Price), tpLineColor, tpLabelColor, true)
drawLine(tp3Price, "(3:1) Take Profit: " + str.tostring(tp3Price), tpLineColor, tpLabelColor, true)
// === Bollinger Bands Inputs ===
bb_length = input.int(20, title="SMA & StdDev Length")
src = input.source(close, title="Source")
// === Bollinger Band Colors ===
color_upper_2_3 = input.color(color.new(#0db107, 64), title="Upper Band 2–3 Color")
color_upper_3_4 = input.color(color.new(#05c41f, 58), title="Upper Band 3–4 Color")
color_lower_2_3 = input.color(color.new(#bdbc9d, 80), title="Lower Band 2–3 Color")
color_lower_3_4 = input.color(color.new(#e9e6bf, 63), title="Lower Band 3–4 Color")
// === Bollinger Band Calculations ===
sma = ta.sma(src, bb_length)
stdev = ta.stdev(src, bb_length)
bb2_upper = sma + 2 * stdev
bb2_lower = sma - 2 * stdev
bb3_upper = sma + 3 * stdev
bb3_lower = sma - 3 * stdev
bb4_upper = sma + 4 * stdev
bb4_lower = sma - 4 * stdev
// === Hidden Plots for Fill ===
p_bb2_upper = plot(bb2_upper, color=na)
p_bb3_upper = plot(bb3_upper, color=na)
p_bb4_upper = plot(bb4_upper, color=na)
p_bb2_lower = plot(bb2_lower, color=na)
p_bb3_lower = plot(bb3_lower, color=na)
p_bb4_lower = plot(bb4_lower, color=na)
// === Band Zone Fills ===
fill(p_bb2_upper, p_bb3_upper, color=color_upper_2_3)
fill(p_bb3_upper, p_bb4_upper, color=color_upper_3_4)
fill(p_bb2_lower, p_bb3_lower, color=color_lower_2_3)
fill(p_bb3_lower, p_bb4_lower, color=color_lower_3_4)
//SMc
BULLISH_LEG = 1
BEARISH_LEG = 0
BULLISH = +1
BEARISH = -1
GREEN = #9c9c9c
RED = #9c9c9c
BLUE = #9c9c9c
GRAY = #ffffff
MONO_BULLISH = #b2b5be
MONO_BEARISH = #5d606b
HISTORICAL = 'Historical'
PRESENT = 'Present'
COLORED = 'Colored'
MONOCHROME = 'Monochrome'
ALL = 'All'
BOS = 'BOS'
CHOCH = 'CHoCH'
TINY = size.tiny
SMALL = size.small
NORMAL = size.normal
ATR = 'Atr'
RANGE = 'Cumulative Mean Range'
CLOSE = 'Close'
HIGHLOW = 'High/Low'
SOLID = '⎯⎯⎯'
DASHED = '----'
DOTTED = '····'
SMART_GROUP = 'Smart Money Concepts'
INTERNAL_GROUP = 'Real Time Internal Structure'
SWING_GROUP = 'Real Time Swing Structure'
BLOCKS_GROUP = 'Order Blocks'
EQUAL_GROUP = 'EQH/EQL'
GAPS_GROUP = 'Fair Value Gaps'
LEVELS_GROUP = 'Highs & Lows MTF'
ZONES_GROUP = 'Premium & Discount Zones'
modeTooltip = 'Allows to display historical Structure or only the recent ones'
styleTooltip = 'Indicator color theme'
showTrendTooltip = 'Display additional candles with a color reflecting the current trend detected by structure'
showInternalsTooltip = 'Display internal market structure'
internalFilterConfluenceTooltip = 'Filter non significant internal structure breakouts'
showStructureTooltip = 'Display swing market Structure'
showSwingsTooltip = 'Display swing point as labels on the chart'
showHighLowSwingsTooltip = 'Highlight most recent strong and weak high/low points on the chart'
showInternalOrderBlocksTooltip = 'Display internal order blocks on the chart\n\nNumber of internal order blocks to display on the chart'
showSwingOrderBlocksTooltip = 'Display swing order blocks on the chart\n\nNumber of internal swing blocks to display on the chart'
orderBlockFilterTooltip = 'Method used to filter out volatile order blocks \n\nIt is recommended to use the cumulative mean range method when a low amount of data is available'
orderBlockMitigationTooltip = 'Select what values to use for order block mitigation'
showEqualHighsLowsTooltip = 'Display equal highs and equal lows on the chart'
equalHighsLowsLengthTooltip = 'Number of bars used to confirm equal highs and equal lows'
equalHighsLowsThresholdTooltip = 'Sensitivity threshold in a range (0, 1) used for the detection of equal highs & lows\n\nLower values will return fewer but more pertinent results'
showFairValueGapsTooltip = 'Display fair values gaps on the chart'
fairValueGapsThresholdTooltip = 'Filter out non significant fair value gaps'
fairValueGapsTimeframeTooltip = 'Fair value gaps timeframe'
fairValueGapsExtendTooltip = 'Determine how many bars to extend the Fair Value Gap boxes on chart'
showPremiumDiscountZonesTooltip = 'Display premium, discount, and equilibrium zones on chart'
modeInput = input.string( HISTORICAL, 'Mode', group = SMART_GROUP, tooltip = modeTooltip, options = )
styleInput = input.string( COLORED, 'Style', group = SMART_GROUP, tooltip = styleTooltip,options = )
showTrendInput = input( false, 'Color Candles', group = SMART_GROUP, tooltip = showTrendTooltip)
showInternalsInput = input( true, 'Show Internal Structure', group = INTERNAL_GROUP, tooltip = showInternalsTooltip)
showInternalBullInput = input.string( ALL, 'Bullish Structure', group = INTERNAL_GROUP, inline = 'ibull', options = )
internalBullColorInput = input( GREEN, '', group = INTERNAL_GROUP, inline = 'ibull')
showInternalBearInput = input.string( ALL, 'Bearish Structure' , group = INTERNAL_GROUP, inline = 'ibear', options = )
internalBearColorInput = input( RED, '', group = INTERNAL_GROUP, inline = 'ibear')
internalFilterConfluenceInput = input( false, 'Confluence Filter', group = INTERNAL_GROUP, tooltip = internalFilterConfluenceTooltip)
internalStructureSize = input.string( TINY, 'Internal Label Size', group = INTERNAL_GROUP, options = )
showStructureInput = input( true, 'Show Swing Structure', group = SWING_GROUP, tooltip = showStructureTooltip)
showSwingBullInput = input.string( ALL, 'Bullish Structure', group = SWING_GROUP, inline = 'bull', options = )
swingBullColorInput = input( GREEN, '', group = SWING_GROUP, inline = 'bull')
showSwingBearInput = input.string( ALL, 'Bearish Structure', group = SWING_GROUP, inline = 'bear', options = )
swingBearColorInput = input( RED, '', group = SWING_GROUP, inline = 'bear')
swingStructureSize = input.string( SMALL, 'Swing Label Size', group = SWING_GROUP, options = )
showSwingsInput = input( false, 'Show Swings Points', group = SWING_GROUP, tooltip = showSwingsTooltip,inline = 'swings')
swingsLengthInput = input.int( 50, '', group = SWING_GROUP, minval = 10, inline = 'swings')
showHighLowSwingsInput = input( true, 'Show Strong/Weak High/Low',group = SWING_GROUP, tooltip = showHighLowSwingsTooltip)
showInternalOrderBlocksInput = input( true, 'Internal Order Blocks' , group = BLOCKS_GROUP, tooltip = showInternalOrderBlocksTooltip, inline = 'iob')
internalOrderBlocksSizeInput = input.int( 5, '', group = BLOCKS_GROUP, minval = 1, maxval = 20, inline = 'iob')
showSwingOrderBlocksInput = input( false, 'Swing Order Blocks', group = BLOCKS_GROUP, tooltip = showSwingOrderBlocksTooltip, inline = 'ob')
swingOrderBlocksSizeInput = input.int( 5, '', group = BLOCKS_GROUP, minval = 1, maxval = 20, inline = 'ob')
orderBlockFilterInput = input.string( 'Atr', 'Order Block Filter', group = BLOCKS_GROUP, tooltip = orderBlockFilterTooltip, options = )
orderBlockMitigationInput = input.string( HIGHLOW, 'Order Block Mitigation', group = BLOCKS_GROUP, tooltip = orderBlockMitigationTooltip, options = )
internalBullishOrderBlockColor = input.color(color.new(#808080, 80), 'Internal Bullish OB', group = BLOCKS_GROUP)
internalBearishOrderBlockColor = input.color(color.new(#808080, 80), 'Internal Bearish OB', group = BLOCKS_GROUP)
swingBullishOrderBlockColor = input.color(color.new(#808080, 80), 'Bullish OB', group = BLOCKS_GROUP)
swingBearishOrderBlockColor = input.color(color.new(#808080, 80), 'Bearish OB', group = BLOCKS_GROUP)
showEqualHighsLowsInput = input( true, 'Equal High/Low', group = EQUAL_GROUP, tooltip = showEqualHighsLowsTooltip)
equalHighsLowsLengthInput = input.int( 3, 'Bars Confirmation', group = EQUAL_GROUP, tooltip = equalHighsLowsLengthTooltip, minval = 1)
equalHighsLowsThresholdInput = input.float( 0.1, 'Threshold', group = EQUAL_GROUP, tooltip = equalHighsLowsThresholdTooltip, minval = 0, maxval = 0.5, step = 0.1)
equalHighsLowsSizeInput = input.string( TINY, 'Label Size', group = EQUAL_GROUP, options = )
showFairValueGapsInput = input( false, 'Fair Value Gaps', group = GAPS_GROUP, tooltip = showFairValueGapsTooltip)
fairValueGapsThresholdInput = input( true, 'Auto Threshold', group = GAPS_GROUP, tooltip = fairValueGapsThresholdTooltip)
fairValueGapsTimeframeInput = input.timeframe('', 'Timeframe', group = GAPS_GROUP, tooltip = fairValueGapsTimeframeTooltip)
fairValueGapsBullColorInput = input.color(color.new(#00ff68, 70), 'Bullish FVG' , group = GAPS_GROUP)
fairValueGapsBearColorInput = input.color(color.new(#ff0008, 70), 'Bearish FVG' , group = GAPS_GROUP)
fairValueGapsExtendInput = input.int( 1, 'Extend FVG', group = GAPS_GROUP, tooltip = fairValueGapsExtendTooltip, minval = 0)
showDailyLevelsInput = input( false, 'Daily', group = LEVELS_GROUP, inline = 'daily')
dailyLevelsStyleInput = input.string( SOLID, '', group = LEVELS_GROUP, inline = 'daily', options = )
dailyLevelsColorInput = input( BLUE, '', group = LEVELS_GROUP, inline = 'daily')
showWeeklyLevelsInput = input( false, 'Weekly', group = LEVELS_GROUP, inline = 'weekly')
weeklyLevelsStyleInput = input.string( SOLID, '', group = LEVELS_GROUP, inline = 'weekly', options = )
weeklyLevelsColorInput = input( BLUE, '', group = LEVELS_GROUP, inline = 'weekly')
showMonthlyLevelsInput = input( false, 'Monthly', group = LEVELS_GROUP, inline = 'monthly')
monthlyLevelsStyleInput = input.string( SOLID, '', group = LEVELS_GROUP, inline = 'monthly', options = )
monthlyLevelsColorInput = input( BLUE, '', group = LEVELS_GROUP, inline = 'monthly')
showPremiumDiscountZonesInput = input( false, 'Premium/Discount Zones', group = ZONES_GROUP , tooltip = showPremiumDiscountZonesTooltip)
premiumZoneColorInput = input.color( RED, 'Premium Zone', group = ZONES_GROUP)
equilibriumZoneColorInput = input.color( GRAY, 'Equilibrium Zone', group = ZONES_GROUP)
discountZoneColorInput = input.color( GREEN, 'Discount Zone', group = ZONES_GROUP)
//---------------------------------------------------------------------------------------------------------------------}
//DATA STRUCTURES & VARIABLES
//---------------------------------------------------------------------------------------------------------------------{
// @type UDT representing alerts as bool fields
// @field internalBullishBOS internal structure custom alert
// @field internalBearishBOS internal structure custom alert
// @field internalBullishCHoCH internal structure custom alert
// @field internalBearishCHoCH internal structure custom alert
// @field swingBullishBOS swing structure custom alert
// @field swingBearishBOS swing structure custom alert
// @field swingBullishCHoCH swing structure custom alert
// @field swingBearishCHoCH swing structure custom alert
// @field internalBullishOrderBlock internal order block custom alert
// @field internalBearishOrderBlock internal order block custom alert
// @field swingBullishOrderBlock swing order block custom alert
// @field swingBearishOrderBlock swing order block custom alert
// @field equalHighs equal high low custom alert
// @field equalLows equal high low custom alert
// @field bullishFairValueGap fair value gap custom alert
// @field bearishFairValueGap fair value gap custom alert
type alerts
bool internalBullishBOS = false
bool internalBearishBOS = false
bool internalBullishCHoCH = false
bool internalBearishCHoCH = false
bool swingBullishBOS = false
bool swingBearishBOS = false
bool swingBullishCHoCH = false
bool swingBearishCHoCH = false
bool internalBullishOrderBlock = false
bool internalBearishOrderBlock = false
bool swingBullishOrderBlock = false
bool swingBearishOrderBlock = false
bool equalHighs = false
bool equalLows = false
bool bullishFairValueGap = false
bool bearishFairValueGap = false
// @type UDT representing last swing extremes (top & bottom)
// @field top last top swing price
// @field bottom last bottom swing price
// @field barTime last swing bar time
// @field barIndex last swing bar index
// @field lastTopTime last top swing time
// @field lastBottomTime last bottom swing time
type trailingExtremes
float top
float bottom
int barTime
int barIndex
int lastTopTime
int lastBottomTime
// @type UDT representing Fair Value Gaps
// @field top top price
// @field bottom bottom price
// @field bias bias (BULLISH or BEARISH)
// @field topBox top box
// @field bottomBox bottom box
type fairValueGap
float top
float bottom
int bias
box topBox
box bottomBox
// @type UDT representing trend bias
// @field bias BULLISH or BEARISH
type trend
int bias
// @type UDT representing Equal Highs Lows display
// @field l_ine displayed line
// @field l_abel displayed label
type equalDisplay
line l_ine = na
label l_abel = na
// @type UDT representing a pivot point (swing point)
// @field currentLevel current price level
// @field lastLevel last price level
// @field crossed true if price level is crossed
// @field barTime bar time
// @field barIndex bar index
type pivot
float currentLevel
float lastLevel
bool crossed
int barTime = time
int barIndex = bar_index
// @type UDT representing an order block
// @field barHigh bar high
// @field barLow bar low
// @field barTime bar time
// @field bias BULLISH or BEARISH
type orderBlock
float barHigh
float barLow
int barTime
int bias
// @variable current swing pivot high
var pivot swingHigh = pivot.new(na,na,false)
// @variable current swing pivot low
var pivot swingLow = pivot.new(na,na,false)
// @variable current internal pivot high
var pivot internalHigh = pivot.new(na,na,false)
// @variable current internal pivot low
var pivot internalLow = pivot.new(na,na,false)
// @variable current equal high pivot
var pivot equalHigh = pivot.new(na,na,false)
// @variable current equal low pivot
var pivot equalLow = pivot.new(na,na,false)
// @variable swing trend bias
var trend swingTrend = trend.new(0)
// @variable internal trend bias
var trend internalTrend = trend.new(0)
// @variable equal high display
var equalDisplay equalHighDisplay = equalDisplay.new()
// @variable equal low display
var equalDisplay equalLowDisplay = equalDisplay.new()
// @variable storage for fairValueGap UDTs
var array fairValueGaps = array.new()
// @variable storage for parsed highs
var array parsedHighs = array.new()
// @variable storage for parsed lows
var array parsedLows = array.new()
// @variable storage for raw highs
var array highs = array.new()
// @variable storage for raw lows
var array lows = array.new()
// @variable storage for bar time values
var array times = array.new()
// @variable last trailing swing high and low
var trailingExtremes trailing = trailingExtremes.new()
// @variable storage for orderBlock UDTs (swing order blocks)
var array swingOrderBlocks = array.new()
// @variable storage for orderBlock UDTs (internal order blocks)
var array internalOrderBlocks = array.new()
// @variable storage for swing order blocks boxes
var array swingOrderBlocksBoxes = array.new()
// @variable storage for internal order blocks boxes
var array internalOrderBlocksBoxes = array.new()
// @variable color for swing bullish structures
var swingBullishColor = styleInput == MONOCHROME ? MONO_BULLISH : swingBullColorInput
// @variable color for swing bearish structures
var swingBearishColor = styleInput == MONOCHROME ? MONO_BEARISH : swingBearColorInput
// @variable color for bullish fair value gaps
var fairValueGapBullishColor = styleInput == MONOCHROME ? color.new(MONO_BULLISH,70) : fairValueGapsBullColorInput
// @variable color for bearish fair value gaps
var fairValueGapBearishColor = styleInput == MONOCHROME ? color.new(MONO_BEARISH,70) : fairValueGapsBearColorInput
// @variable color for premium zone
var premiumZoneColor = styleInput == MONOCHROME ? MONO_BEARISH : premiumZoneColorInput
// @variable color for discount zone
var discountZoneColor = styleInput == MONOCHROME ? MONO_BULLISH : discountZoneColorInput
// @variable bar index on current script iteration
varip int currentBarIndex = bar_index
// @variable bar index on last script iteration
varip int lastBarIndex = bar_index
// @variable alerts in current bar
alerts currentAlerts = alerts.new()
// @variable time at start of chart
var initialTime = time
// we create the needed boxes for displaying order blocks at the first execution
if barstate.isfirst
if showSwingOrderBlocksInput
for index = 1 to swingOrderBlocksSizeInput
swingOrderBlocksBoxes.push(box.new(na,na,na,na,xloc = xloc.bar_time,extend = extend.right))
if showInternalOrderBlocksInput
for index = 1 to internalOrderBlocksSizeInput
internalOrderBlocksBoxes.push(box.new(na,na,na,na,xloc = xloc.bar_time,extend = extend.right))
// @variable source to use in bearish order blocks mitigation
bearishOrderBlockMitigationSource = orderBlockMitigationInput == CLOSE ? close : high
// @variable source to use in bullish order blocks mitigation
bullishOrderBlockMitigationSource = orderBlockMitigationInput == CLOSE ? close : low
// @variable default volatility measure
atrMeasure = ta.atr(200)
// @variable parsed volatility measure by user settings
volatilityMeasure = orderBlockFilterInput == ATR ? atrMeasure : ta.cum(ta.tr)/bar_index
// @variable true if current bar is a high volatility bar
highVolatilityBar = (high - low) >= (2 * volatilityMeasure)
// @variable parsed high
parsedHigh = highVolatilityBar ? low : high
// @variable parsed low
parsedLow = highVolatilityBar ? high : low
// we store current values into the arrays at each bar
parsedHighs.push(parsedHigh)
parsedLows.push(parsedLow)
highs.push(high)
lows.push(low)
times.push(time)
//---------------------------------------------------------------------------------------------------------------------}
//USER-DEFINED FUNCTIONS
//---------------------------------------------------------------------------------------------------------------------{
// @function Get the value of the current leg, it can be 0 (bearish) or 1 (bullish)
// @returns int
leg(int size) =>
var leg = 0
newLegHigh = high > ta.highest( size)
newLegLow = low < ta.lowest( size)
if newLegHigh
leg := BEARISH_LEG
else if newLegLow
leg := BULLISH_LEG
leg
// @function Identify whether the current value is the start of a new leg (swing)
// @param leg (int) Current leg value
// @returns bool
startOfNewLeg(int leg) => ta.change(leg) != 0
// @function Identify whether the current level is the start of a new bearish leg (swing)
// @param leg (int) Current leg value
// @returns bool
startOfBearishLeg(int leg) => ta.change(leg) == -1
// @function Identify whether the current level is the start of a new bullish leg (swing)
// @param leg (int) Current leg value
// @returns bool
startOfBullishLeg(int leg) => ta.change(leg) == +1
// @function create a new label
// @param labelTime bar time coordinate
// @param labelPrice price coordinate
// @param tag text to display
// @param labelColor text color
// @param labelStyle label style
// @returns label ID
drawLabel(int labelTime, float labelPrice, string tag, color labelColor, string labelStyle) =>
var label l_abel = na
if modeInput == PRESENT
l_abel.delete()
l_abel := label.new(chart.point.new(labelTime,na,labelPrice),tag,xloc.bar_time,color=color(na),textcolor=labelColor,style = labelStyle,size = size.small)
// @function create a new line and label representing an EQH or EQL
// @param p_ivot starting pivot
// @param level price level of current pivot
// @param size how many bars ago was the current pivot detected
// @param equalHigh true for EQH, false for EQL
// @returns label ID
drawEqualHighLow(pivot p_ivot, float level, int size, bool equalHigh) =>
equalDisplay e_qualDisplay = equalHigh ? equalHighDisplay : equalLowDisplay
string tag = 'EQL'
color equalColor = swingBullishColor
string labelStyle = label.style_label_up
if equalHigh
tag := 'EQH'
equalColor := swingBearishColor
labelStyle := label.style_label_down
if modeInput == PRESENT
line.delete( e_qualDisplay.l_ine)
label.delete( e_qualDisplay.l_abel)
e_qualDisplay.l_ine := line.new(chart.point.new(p_ivot.barTime,na,p_ivot.currentLevel), chart.point.new(time ,na,level), xloc = xloc.bar_time, color = equalColor, style = line.style_dotted)
labelPosition = math.round(0.5*(p_ivot.barIndex + bar_index - size))
e_qualDisplay.l_abel := label.new(chart.point.new(na,labelPosition,level), tag, xloc.bar_index, color = color(na), textcolor = equalColor, style = labelStyle, size = equalHighsLowsSizeInput)
// @function store current structure and trailing swing points, and also display swing points and equal highs/lows
// @param size (int) structure size
// @param equalHighLow (bool) true for displaying current highs/lows
// @param internal (bool) true for getting internal structures
// @returns label ID
getCurrentStructure(int size,bool equalHighLow = false, bool internal = false) =>
currentLeg = leg(size)
newPivot = startOfNewLeg(currentLeg)
pivotLow = startOfBullishLeg(currentLeg)
pivotHigh = startOfBearishLeg(currentLeg)
if newPivot
if pivotLow
pivot p_ivot = equalHighLow ? equalLow : internal ? internalLow : swingLow
if equalHighLow and math.abs(p_ivot.currentLevel - low ) < equalHighsLowsThresholdInput * atrMeasure
drawEqualHighLow(p_ivot, low , size, false)
p_ivot.lastLevel := p_ivot.currentLevel
p_ivot.currentLevel := low
p_ivot.crossed := false
p_ivot.barTime := time
p_ivot.barIndex := bar_index
if not equalHighLow and not internal
trailing.bottom := p_ivot.currentLevel
trailing.barTime := p_ivot.barTime
trailing.barIndex := p_ivot.barIndex
trailing.lastBottomTime := p_ivot.barTime
if showSwingsInput and not internal and not equalHighLow
drawLabel(time , p_ivot.currentLevel, p_ivot.currentLevel < p_ivot.lastLevel ? 'LL' : 'HL', swingBullishColor, label.style_label_up)
else
pivot p_ivot = equalHighLow ? equalHigh : internal ? internalHigh : swingHigh
if equalHighLow and math.abs(p_ivot.currentLevel - high ) < equalHighsLowsThresholdInput * atrMeasure
drawEqualHighLow(p_ivot,high ,size,true)
p_ivot.lastLevel := p_ivot.currentLevel
p_ivot.currentLevel := high
p_ivot.crossed := false
p_ivot.barTime := time
p_ivot.barIndex := bar_index
if not equalHighLow and not internal
trailing.top := p_ivot.currentLevel
trailing.barTime := p_ivot.barTime
trailing.barIndex := p_ivot.barIndex
trailing.lastTopTime := p_ivot.barTime
if showSwingsInput and not internal and not equalHighLow
drawLabel(time , p_ivot.currentLevel, p_ivot.currentLevel > p_ivot.lastLevel ? 'HH' : 'LH', swingBearishColor, label.style_label_down)
// @function draw line and label representing a structure
// @param p_ivot base pivot point
// @param tag test to display
// @param structureColor base color
// @param lineStyle line style
// @param labelStyle label style
// @param labelSize text size
// @returns label ID
drawStructure(pivot p_ivot, string tag, color structureColor, string lineStyle, string labelStyle, string labelSize) =>
var line l_ine = line.new(na,na,na,na,xloc = xloc.bar_time)
var label l_abel = label.new(na,na)
if modeInput == PRESENT
l_ine.delete()
l_abel.delete()
l_ine := line.new(chart.point.new(p_ivot.barTime,na,p_ivot.currentLevel), chart.point.new(time,na,p_ivot.currentLevel), xloc.bar_time, color=structureColor, style=lineStyle)
l_abel := label.new(chart.point.new(na,math.round(0.5*(p_ivot.barIndex+bar_index)),p_ivot.currentLevel), tag, xloc.bar_index, color=color(na), textcolor=structureColor, style=labelStyle, size = labelSize)
// @function delete order blocks
// @param internal true for internal order blocks
// @returns orderBlock ID
deleteOrderBlocks(bool internal = false) =>
array orderBlocks = internal ? internalOrderBlocks : swingOrderBlocks
for in orderBlocks
bool crossedOderBlock = false
if bearishOrderBlockMitigationSource > eachOrderBlock.barHigh and eachOrderBlock.bias == BEARISH
crossedOderBlock := true
if internal
currentAlerts.internalBearishOrderBlock := true
else
currentAlerts.swingBearishOrderBlock := true
else if bullishOrderBlockMitigationSource < eachOrderBlock.barLow and eachOrderBlock.bias == BULLISH
crossedOderBlock := true
if internal
currentAlerts.internalBullishOrderBlock := true
else
currentAlerts.swingBullishOrderBlock := true
if crossedOderBlock
orderBlocks.remove(index)
// @function fetch and store order blocks
// @param p_ivot base pivot point
// @param internal true for internal order blocks
// @param bias BULLISH or BEARISH
// @returns void
storeOrdeBlock(pivot p_ivot,bool internal = false,int bias) =>
if (not internal and showSwingOrderBlocksInput) or (internal and showInternalOrderBlocksInput)
array a_rray = na
int parsedIndex = na
if bias == BEARISH
a_rray := parsedHighs.slice(p_ivot.barIndex,bar_index)
parsedIndex := p_ivot.barIndex + a_rray.indexof(a_rray.max())
else
a_rray := parsedLows.slice(p_ivot.barIndex,bar_index)
parsedIndex := p_ivot.barIndex + a_rray.indexof(a_rray.min())
orderBlock o_rderBlock = orderBlock.new(parsedHighs.get(parsedIndex), parsedLows.get(parsedIndex), times.get(parsedIndex),bias)
array orderBlocks = internal ? internalOrderBlocks : swingOrderBlocks
if orderBlocks.size() >= 100
orderBlocks.pop()
orderBlocks.unshift(o_rderBlock)
// @function draw order blocks as boxes
// @param internal true for internal order blocks
// @returns void
drawOrderBlocks(bool internal = false) =>
array orderBlocks = internal ? internalOrderBlocks : swingOrderBlocks
orderBlocksSize = orderBlocks.size()
if orderBlocksSize > 0
maxOrderBlocks = internal ? internalOrderBlocksSizeInput : swingOrderBlocksSizeInput
array parsedOrdeBlocks = orderBlocks.slice(0, math.min(maxOrderBlocks,orderBlocksSize))
array b_oxes = internal ? internalOrderBlocksBoxes : swingOrderBlocksBoxes
for in parsedOrdeBlocks
orderBlockColor = styleInput == MONOCHROME ? (eachOrderBlock.bias == BEARISH ? color.new(MONO_BEARISH,80) : color.new(MONO_BULLISH,80)) : internal ? (eachOrderBlock.bias == BEARISH ? internalBearishOrderBlockColor : internalBullishOrderBlockColor) : (eachOrderBlock.bias == BEARISH ? swingBearishOrderBlockColor : swingBullishOrderBlockColor)
box b_ox = b_oxes.get(index)
b_ox.set_top_left_point( chart.point.new(eachOrderBlock.barTime,na,eachOrderBlock.barHigh))
b_ox.set_bottom_right_point(chart.point.new(last_bar_time,na,eachOrderBlock.barLow))
b_ox.set_border_color( internal ? na : orderBlockColor)
b_ox.set_bgcolor( orderBlockColor)
// @function detect and draw structures, also detect and store order blocks
// @param internal true for internal structures or order blocks
// @returns void
displayStructure(bool internal = false) =>
var bullishBar = true
var bearishBar = true
if internalFilterConfluenceInput
bullishBar := high - math.max(close, open) > math.min(close, open - low)
bearishBar := high - math.max(close, open) < math.min(close, open - low)
pivot p_ivot = internal ? internalHigh : swingHigh
trend t_rend = internal ? internalTrend : swingTrend
lineStyle = internal ? line.style_dashed : line.style_solid
labelSize = internal ? internalStructureSize : swingStructureSize
extraCondition = internal ? internalHigh.currentLevel != swingHigh.currentLevel and bullishBar : true
bullishColor = styleInput == MONOCHROME ? MONO_BULLISH : internal ? internalBullColorInput : swingBullColorInput
if ta.crossover(close,p_ivot.currentLevel) and not p_ivot.crossed and extraCondition
string tag = t_rend.bias == BEARISH ? CHOCH : BOS
if internal
currentAlerts.internalBullishCHoCH := tag == CHOCH
currentAlerts.internalBullishBOS := tag == BOS
else
currentAlerts.swingBullishCHoCH := tag == CHOCH
currentAlerts.swingBullishBOS := tag == BOS
p_ivot.crossed := true
t_rend.bias := BULLISH
displayCondition = internal ? showInternalsInput and (showInternalBullInput == ALL or (showInternalBullInput == BOS and tag != CHOCH) or (showInternalBullInput == CHOCH and tag == CHOCH)) : showStructureInput and (showSwingBullInput == ALL or (showSwingBullInput == BOS and tag != CHOCH) or (showSwingBullInput == CHOCH and tag == CHOCH))
if displayCondition
drawStructure(p_ivot,tag,bullishColor,lineStyle,label.style_label_down,labelSize)
if (internal and showInternalOrderBlocksInput) or (not internal and showSwingOrderBlocksInput)
storeOrdeBlock(p_ivot,internal,BULLISH)
p_ivot := internal ? internalLow : swingLow
extraCondition := internal ? internalLow.currentLevel != swingLow.currentLevel and bearishBar : true
bearishColor = styleInput == MONOCHROME ? MONO_BEARISH : internal ? internalBearColorInput : swingBearColorInput
if ta.crossunder(close,p_ivot.currentLevel) and not p_ivot.crossed and extraCondition
string tag = t_rend.bias == BULLISH ? CHOCH : BOS
if internal
currentAlerts.internalBearishCHoCH := tag == CHOCH
currentAlerts.internalBearishBOS := tag == BOS
else
currentAlerts.swingBearishCHoCH := tag == CHOCH
currentAlerts.swingBearishBOS := tag == BOS
p_ivot.crossed := true
t_rend.bias := BEARISH
displayCondition = internal ? showInternalsInput and (showInternalBearInput == ALL or (showInternalBearInput == BOS and tag != CHOCH) or (showInternalBearInput == CHOCH and tag == CHOCH)) : showStructureInput and (showSwingBearInput == ALL or (showSwingBearInput == BOS and tag != CHOCH) or (showSwingBearInput == CHOCH and tag == CHOCH))
if displayCondition
drawStructure(p_ivot,tag,bearishColor,lineStyle,label.style_label_up,labelSize)
if (internal and showInternalOrderBlocksInput) or (not internal and showSwingOrderBlocksInput)
storeOrdeBlock(p_ivot,internal,BEARISH)
// @function draw one fair value gap box (each fair value gap has two boxes)
// @param leftTime left time coordinate
// @param rightTime right time coordinate
// @param topPrice top price level
// @param bottomPrice bottom price level
// @param boxColor box color
// @returns box ID
fairValueGapBox(leftTime,rightTime,topPrice,bottomPrice,boxColor) => box.new(chart.point.new(leftTime,na,topPrice),chart.point.new(rightTime + fairValueGapsExtendInput * (time-time ),na,bottomPrice), xloc=xloc.bar_time, border_color = boxColor, bgcolor = boxColor)
// @function delete fair value gaps
// @returns fairValueGap ID
deleteFairValueGaps() =>
for in fairValueGaps
if (low < eachFairValueGap.bottom and eachFairValueGap.bias == BULLISH) or (high > eachFairValueGap.top and eachFairValueGap.bias == BEARISH)
eachFairValueGap.topBox.delete()
eachFairValueGap.bottomBox.delete()
fairValueGaps.remove(index)
// @function draw fair value gaps
// @returns fairValueGap ID
drawFairValueGaps() =>
= request.security(syminfo.tickerid, fairValueGapsTimeframeInput, [close , open , time , high , low , time , high , low ],lookahead = barmerge.lookahead_on)
barDeltaPercent = (lastClose - lastOpen) / (lastOpen * 100)
newTimeframe = timeframe.change(fairValueGapsTimeframeInput)
threshold = fairValueGapsThresholdInput ? ta.cum(math.abs(newTimeframe ? barDeltaPercent : 0)) / bar_index * 2 : 0
bullishFairValueGap = currentLow > last2High and lastClose > last2High and barDeltaPercent > threshold and newTimeframe
bearishFairValueGap = currentHigh < last2Low and lastClose < last2Low and -barDeltaPercent > threshold and newTimeframe
if bullishFairValueGap
currentAlerts.bullishFairValueGap := true
fairValueGaps.unshift(fairValueGap.new(currentLow,last2High,BULLISH,fairValueGapBox(lastTime,currentTime,currentLow,math.avg(currentLow,last2High),fairValueGapBullishColor),fairValueGapBox(lastTime,currentTime,math.avg(currentLow,last2High),last2High,fairValueGapBullishColor)))
if bearishFairValueGap
currentAlerts.bearishFairValueGap := true
fairValueGaps.unshift(fairValueGap.new(currentHigh,last2Low,BEARISH,fairValueGapBox(lastTime,currentTime,currentHigh,math.avg(currentHigh,last2Low),fairValueGapBearishColor),fairValueGapBox(lastTime,currentTime,math.avg(currentHigh,last2Low),last2Low,fairValueGapBearishColor)))
// @function get line style from string
// @param style line style
// @returns string
getStyle(string style) =>
switch style
SOLID => line.style_solid
DASHED => line.style_dashed
DOTTED => line.style_dotted
// @function draw MultiTimeFrame levels
// @param timeframe base timeframe
// @param sameTimeframe true if chart timeframe is same as base timeframe
// @param style line style
// @param levelColor line and text color
// @returns void
drawLevels(string timeframe, bool sameTimeframe, string style, color levelColor) =>
= request.security(syminfo.tickerid, timeframe, [high , low , time , time],lookahead = barmerge.lookahead_on)
float parsedTop = sameTimeframe ? high : topLevel
float parsedBottom = sameTimeframe ? low : bottomLevel
int parsedLeftTime = sameTimeframe ? time : leftTime
int parsedRightTime = sameTimeframe ? time : rightTime
int parsedTopTime = time
int parsedBottomTime = time
if not sameTimeframe
int leftIndex = times.binary_search_rightmost(parsedLeftTime)
int rightIndex = times.binary_search_rightmost(parsedRightTime)
array timeArray = times.slice(leftIndex,rightIndex)
array topArray = highs.slice(leftIndex,rightIndex)
array bottomArray = lows.slice(leftIndex,rightIndex)
parsedTopTime := timeArray.size() > 0 ? timeArray.get(topArray.indexof(topArray.max())) : initialTime
parsedBottomTime := timeArray.size() > 0 ? timeArray.get(bottomArray.indexof(bottomArray.min())) : initialTime
var line topLine = line.new(na, na, na, na, xloc = xloc.bar_time, color = levelColor, style = getStyle(style))
var line bottomLine = line.new(na, na, na, na, xloc = xloc.bar_time, color = levelColor, style = getStyle(style))
var label topLabel = label.new(na, na, xloc = xloc.bar_time, text = str.format('P{0}H',timeframe), color=color(na), textcolor = levelColor, size = size.small, style = label.style_label_left)
var label bottomLabel = label.new(na, na, xloc = xloc.bar_time, text = str.format('P{0}L',timeframe), color=color(na), textcolor = levelColor, size = size.small, style = label.style_label_left)
topLine.set_first_point( chart.point.new(parsedTopTime,na,parsedTop))
topLine.set_second_point( chart.point.new(last_bar_time + 20 * (time-time ),na,parsedTop))
topLabel.set_point( chart.point.new(last_bar_time + 20 * (time-time ),na,parsedTop))
bottomLine.set_first_point( chart.point.new(parsedBottomTime,na,parsedBottom))
bottomLine.set_second_point(chart.point.new(last_bar_time + 20 * (time-time ),na,parsedBottom))
bottomLabel.set_point( chart.point.new(last_bar_time + 20 * (time-time ),na,parsedBottom))
// @function true if chart timeframe is higher than provided timeframe
// @param timeframe timeframe to check
// @returns bool
higherTimeframe(string timeframe) => timeframe.in_seconds() > timeframe.in_seconds(timeframe)
// @function update trailing swing points
// @returns int
updateTrailingExtremes() =>
trailing.top := math.max(high,trailing.top)
trailing.lastTopTime := trailing.top == high ? time : trailing.lastTopTime
trailing.bottom := math.min(low,trailing.bottom)
trailing.lastBottomTime := trailing.bottom == low ? time : trailing.lastBottomTime
// @function draw trailing swing points
// @returns void
drawHighLowSwings() =>
var line topLine = line.new(na, na, na, na, color = swingBearishColor, xloc = xloc.bar_time)
var line bottomLine = line.new(na, na, na, na, color = swingBullishColor, xloc = xloc.bar_time)
var label topLabel = label.new(na, na, color=color(na), textcolor = swingBearishColor, xloc = xloc.bar_time, style = label.style_label_down, size = size.tiny)
var label bottomLabel = label.new(na, na, color=color(na), textcolor = swingBullishColor, xloc = xloc.bar_time, style = label.style_label_up, size = size.tiny)
rightTimeBar = last_bar_time + 20 * (time - time )
topLine.set_first_point( chart.point.new(trailing.lastTopTime, na, trailing.top))
topLine.set_second_point( chart.point.new(rightTimeBar, na, trailing.top))
topLabel.set_point( chart.point.new(rightTimeBar, na, trailing.top))
topLabel.set_text( swingTrend.bias == BEARISH ? 'Strong High' : 'Weak High')
bottomLine.set_first_point( chart.point.new(trailing.lastBottomTime, na, trailing.bottom))
bottomLine.set_second_point(chart.point.new(rightTimeBar, na, trailing.bottom))
bottomLabel.set_point( chart.point.new(rightTimeBar, na, trailing.bottom))
bottomLabel.set_text( swingTrend.bias == BULLISH ? 'Strong Low' : 'Weak Low')
// @function draw a zone with a label and a box
// @param labelLevel price level for label
// @param labelIndex bar index for label
// @param top top price level for box
// @param bottom bottom price level for box
// @param tag text to display
// @param zoneColor base color
// @param style label style
// @returns void
drawZone(float labelLevel, int labelIndex, float top, float bottom, string tag, color zoneColor, string style) =>
var label l_abel = label.new(na,na,text = tag, color=color(na),textcolor = zoneColor, style = style, size = size.small)
var box b_ox = box.new(na,na,na,na,bgcolor = color.new(zoneColor,80),border_color = color(na), xloc = xloc.bar_time)
b_ox.set_top_left_point( chart.point.new(trailing.barTime,na,top))
b_ox.set_bottom_right_point(chart.point.new(last_bar_time,na,bottom))
l_abel.set_point( chart.point.new(na,labelIndex,labelLevel))
// @function draw premium/discount zones
// @returns void
drawPremiumDiscountZones() =>
drawZone(trailing.top, math.round(0.5*(trailing.barIndex + last_bar_index)), trailing.top, 0.95*trailing.top + 0.05*trailing.bottom, 'Premium', premiumZoneColor, label.style_label_down)
equilibriumLevel = math.avg(trailing.top, trailing.bottom)
drawZone(equilibriumLevel, last_bar_index, 0.525*trailing.top + 0.475*trailing.bottom, 0.525*trailing.bottom + 0.475*trailing.top, 'Equilibrium', equilibriumZoneColorInput, label.style_label_left)
drawZone(trailing.bottom, math.round(0.5*(trailing.barIndex + last_bar_index)), 0.95*trailing.bottom + 0.05*trailing.top, trailing.bottom, 'Discount', discountZoneColor, label.style_label_up)
//---------------------------------------------------------------------------------------------------------------------}
//MUTABLE VARIABLES & EXECUTION
//---------------------------------------------------------------------------------------------------------------------{
parsedOpen = showTrendInput ? open : na
candleColor = internalTrend.bias == BULLISH ? swingBullishColor : swingBearishColor
plotcandle(parsedOpen,high,low,close,color = candleColor, wickcolor = candleColor, bordercolor = candleColor)
if showHighLowSwingsInput or showPremiumDiscountZonesInput
updateTrailingExtremes()
if showHighLowSwingsInput
drawHighLowSwings()
if showPremiumDiscountZonesInput
drawPremiumDiscountZones()
if showFairValueGapsInput
deleteFairValueGaps()
getCurrentStructure(swingsLengthInput,false)
getCurrentStructure(5,false,true)
if showEqualHighsLowsInput
getCurrentStructure(equalHighsLowsLengthInput,true)
if showInternalsInput or showInternalOrderBlocksInput or showTrendInput
displayStructure(true)
if showStructureInput or showSwingOrderBlocksInput or showHighLowSwingsInput
displayStructure()
if showInternalOrderBlocksInput
deleteOrderBlocks(true)
if showSwingOrderBlocksInput
deleteOrderBlocks()
if showFairValueGapsInput
drawFairValueGaps()
if barstate.islastconfirmedhistory or barstate.islast
if showInternalOrderBlocksInput
drawOrderBlocks(true)
if showSwingOrderBlocksInput
drawOrderBlocks()
lastBarIndex := currentBarIndex
currentBarIndex := bar_index
newBar = currentBarIndex != lastBarIndex
if barstate.islastconfirmedhistory or (barstate.isrealtime and newBar)
if showDailyLevelsInput and not higherTimeframe('D')
drawLevels('D',timeframe.isdaily,dailyLevelsStyleInput,dailyLevelsColorInput)
if showWeeklyLevelsInput and not higherTimeframe('W')
drawLevels('W',timeframe.isweekly,weeklyLevelsStyleInput,weeklyLevelsColorInput)
if showMonthlyLevelsInput and not higherTimeframe('M')
drawLevels('M',timeframe.ismonthly,monthlyLevelsStyleInput,monthlyLevelsColorInput)
//---------------------------------------------------------------------------------------------------------------------}
//ALERTS
//---------------------------------------------------------------------------------------------------------------------{
alertcondition(currentAlerts.internalBullishBOS, 'Internal Bullish BOS', 'Internal Bullish BOS formed')
alertcondition(currentAlerts.internalBullishCHoCH, 'Internal Bullish CHoCH', 'Internal Bullish CHoCH formed')
alertcondition(currentAlerts.internalBearishBOS, 'Internal Bearish BOS', 'Internal Bearish BOS formed')
alertcondition(currentAlerts.internalBearishCHoCH, 'Internal Bearish CHoCH', 'Internal Bearish CHoCH formed')
alertcondition(currentAlerts.swingBullishBOS, 'Bullish BOS', 'Internal Bullish BOS formed')
alertcondition(currentAlerts.swingBullishCHoCH, 'Bullish CHoCH', 'Internal Bullish CHoCH formed')
alertcondition(currentAlerts.swingBearishBOS, 'Bearish BOS', 'Bearish BOS formed')
alertcondition(currentAlerts.swingBearishCHoCH, 'Bearish CHoCH', 'Bearish CHoCH formed')
alertcondition(currentAlerts.internalBullishOrderBlock, 'Bullish Internal OB Breakout', 'Price broke bullish internal OB')
alertcondition(currentAlerts.internalBearishOrderBlock, 'Bearish Internal OB Breakout', 'Price broke bearish internal OB')
alertcondition(currentAlerts.swingBullishOrderBlock, 'Bullish Swing OB Breakout', 'Price broke bullish swing OB')
alertcondition(currentAlerts.swingBearishOrderBlock, 'Bearish Swing OB Breakout', 'Price broke bearish swing OB')
alertcondition(currentAlerts.equalHighs, 'Equal Highs', 'Equal highs detected')
alertcondition(currentAlerts.equalLows, 'Equal Lows', 'Equal lows detected')
alertcondition(currentAlerts.bullishFairValueGap, 'Bullish FVG', 'Bullish FVG formed')
alertcondition(currentAlerts.bearishFairValueGap, 'Bearish FVG', 'Bearish FVG formed')
//---------------------------------------------------------------------------------------------------------------------}
Percentage Change per 5 Candles
🔎 What this indicator does
This indicator calculates and displays the percentage change of each candlestick directly on the chart.
• If a candle closed higher than it opened (bullish candle), it shows a positive % change (green).
• If a candle closed lower than it opened (bearish candle), it shows a negative % change (red).
• Small moves below your chosen threshold (e.g., 0.1%) are ignored to avoid clutter.
• The labels are placed above, below, or in the center of the candle (you choose).
So essentially, every candle “tells you in numbers” exactly how much it changed relative to its opening price.
________________________________________
⚙️ How it operates (the logic inside)
1. Calculate the change
o Formula:
\text{% Change} = \frac{(\text{Close} - \text{Open})}{\text{Open}} \times 100
o Example: If a candle opens at 100 and closes at 105, that’s a +5% change.
2. Round it nicely
o You can control decimals (e.g., show 2 decimals → +5.23%).
3. Filter out noise
o If a candle barely moved (say 0.02%), the label won’t appear unless you reduce the threshold.
4. Style the labels
o Bullish = green text, slightly transparent green background.
o Bearish = red text, slightly transparent red background.
o Neutral (0%) = gray.
5. Place the labels
o Options: above the candle, below the candle, or centered.
o Small vertical offset is applied so labels don’t overlap the candle itself.
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📊 How this helps traders
This indicator turns visual candles into quantifiable numbers at a glance. Instead of guessing whether a move was “big” or “small,” you see it clearly.
Key Benefits:
1. Quick volatility analysis
o You can instantly see if candles are making big % swings or just small moves.
o This is especially useful on higher timeframes (daily/weekly) where moves can be large.
2. Pattern confirmation
o For example, you might spot a strong bullish engulfing candle — the % change label helps confirm whether it was truly significant (e.g., +4.5%) or just modest (+0.7%).
3. Noise filtering
o By setting a minimum % threshold, you only see labels when moves are meaningful (say > 0.5%). This keeps focus on important candles.
4. Backtesting & comparison
o You can compare moves across time:
“How strong was this breakout candle compared to the last one?”
“Are today’s bearish candles weaker or stronger than yesterday’s bullish candles?”
5. Better decision-making
o If you’re trading breakouts, reversals, or trend-following, knowing the % size of each candle helps confirm if the move has enough momentum.
________________________________________
✅ In short:
This indicator quantifies price action. Instead of just seeing “green” or “red” candles, you now know exactly how much the price changed in percentage terms, directly on the chart, in real time. It helps you distinguish between strong and weak moves and makes your analysis more precise.
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VIX Stoch RSI Oscillator [HUD Box + Compression]vix stoch rsi Oscillator
watch volatility without switching charts,
gives signal based off fib levels 0-100 / volatility,
emoji box to show signal,
HUD Box: emoji-coded tactical feedback
bounce 100 "💥 Expansion" :
bounce 0.8 "🔴 Overbought" :
bounce 0.618 "📉 Distribution" :
bounce 0.5 "🧠 Midline" :
bounce 0.382 "📈 Accumulation" :
bounce 0.2 "🟢 Oversold" :
bounce0.0 "💣 Expansion" : "⚪ Neutral"
Tiger EMA/STOCH
This logic checks if the oscillator is trending above or below its 48-period EMA,
If above, it paints the line GREEN🟢 (bullish),
If below, it paints it RED🔴 (bearish),
If compression is active, it overrides both with purple🟣 to highlight tactical squeeze conditions,
⚠️WARNING⚠️
ALWAYS REMEMBER THIS CHART IS VIX/USD
IN MOST CASES SPY MOVES VICE VERSA
I AM NOT RESPOSIBLE FOR YOUR OWN ACTIONS/TRADE IDEAS
AMEX:USD
TVC:VIX
SP:SPX
Chimera [theUltimator5]In myth, the chimera is an “impossible” hybrid—lion, goat, and serpent fused into one—striking to look at and formidable in presence. The word has come to mean a beautiful, improbable union of parts that shouldn’t work together, yet do.
Chimera is a dual-mode market context tool that blends a multi-input oscillator with classic ADX/DI trend strength, plus optional multi-timeframe “gap-line” tracking. Use it to visualize regime (trend vs. range), momentum swings around an adaptive midline, and higher timeframe (HTF) reference levels that auto-terminate on touch/cross.
Modes
1) Oscillator view
A smoothed composite of five common inputs—RSI, MACD (oscillator), Bollinger position, Stochastic, and an ATR/DI-weighted bias. Each is normalized to a comparable 0–100 style scale, averaged, and plotted as a candle-style oscillator (short vs. long smoothing, wickless for clarity). A dynamic midline with standard-deviation bands frames neutral → bearish/bullish zones. Colors ramp from neutral to your chosen Oversold/Overbought endpoints; consolidation can override to white.
Here is a description of the (5) signals used to calculate the sentiment oscillator:
RSI (14): Measures recent momentum by comparing average gains vs. losses. High = strength after advances; low = weakness after declines. (Z-score normalized to 0–100.)
MACD oscillator (12/26/9): Uses the difference between MACD and its signal (histogram) to gauge momentum shifts. Positive = bullish tilt; negative = bearish. (Z-score normalized.)
Bollinger Bands position (20, 2): Locates price within the bands (0–100 from lower → upper). Near upper suggests strength/expansion; near lower suggests weakness/contraction. (Then normalized.)
Stochastic (14, 3, 3): Shows where the close sits within the recent high-low range, smoothed via %D. Higher values = closes near highs; lower = near lows. (Scaled 0–100.)
ATR/DI composite (14): Volatility-weighted directional bias: (+DI − −DI) amplified by ATR as a % of price and its relative average. Positive = bullish pressure with volatility; negative = bearish. (Rank/scale normalized.)
All five are normalized and averaged into one composite, then smoothed (short/long) and compared to an adaptive midline with bands.
2) ADX view
Shows ADX, +DI, –DI with user-defined High Threshold. Transparency and color shift with regime. When ADX is strong, a directional “fire/ice” gradient fills the area between ADX and the high threshold, biased toward the dominant DI; when ADX is weak, a soft white fade highlights low-trend conditions.
HTF gap-line tracking (optional; both modes)
Detects “gap-like” reference levels after weak-trend consolidation flips into a sudden DI jump.
Anchors a line at the event bar’s open and auto-terminates upon first touch/cross (tick-size tolerance).
Auto-selects up to three higher timeframes suited to your chart resolution and prints non-overlapping lines with labels like 1H / 4H / 1D. Lower-priority duplicates are suppressed to reduce clutter.
Confirmation / repaint notes
Signals and lines finalize on bar close of the relevant timeframe.
HTF elements update only on the HTF bar close. During a forming bar they may appear transiently.
Line removal finalizes after the bar that produced the touch/cross closes.
Visual cues & effects
Oscillator candles: Open/High = long smoothing; Low/Close = short smoothing (no wicks).
Adaptive bands: Midline ± StdDev Multiplier × stdev of the blended series.
Consolidation tint: Optional white backdrop/candles when the consolidation condition is true (balance + low ADX).
Breakout VFX (optional): With strong DI/ADX and Bollinger breaks, renders a subtle “fire” flare above upper-band thrusts or “ice” shelf below lower-band thrusts.
Inputs (high-level)
Visual Style: Oscillator or ADX.
General (Oscillator): Lookback Period, Short/Long Smoothing, Standard Deviation Multiplier.
Color (Oscillator): Oversold/Overbought colors for gradient endpoints.
Plot (Oscillator): Show Candles, Show Slow MA Line, Show Individual Component (RSI/MACD/BB/Stoch/ATR).
Table (Oscillator): Show Information Table & position (compact dashboard of component values + status).
ADX / Gaps / VFX (both modes): ADX High Threshold, Highlight Backgrounds, Show Gap Labels, Visual Overlay Effects, and color choices for current-TF & HTF lines.
HTF selection: Automatic ladder (3 tiers) based on your chart timeframe.
Alerts (built-in)
Buy Signal – Primary: Oscillator exits oversold.
Sell Signal – Primary: Oscillator exits overbought.
Gap Fill Line Created (Any TF)
Gap Fill Line Terminated (Any TF)
ADX Crossed ABOVE/BELOW Low Threshold
ADX Crossed ABOVE/BELOW High Threshold
Consolidation Started
Alerts evaluate on the close of the relevant timeframe.
How to read it (quick guide)
Pick your lens: Oscillator for blended momentum around an adaptive midline; ADX for trend strength and DI skew.
Watch extremes & mean re-entries (Oscillator): Approaches to the top/bottom band show persistent momentum; returns toward the midline show normalization.
Check regime (ADX): Below Low = low-trend; above High = strong trend, with “fire/ice” bias toward +DI/–DI.
Track gap lines: Fresh labels mark new reference levels; lines auto-remove on first interaction. HTF lines add context but finalize only on HTF close.
The uniqueness from this indicator comes from multiple areas:
1. A unique multi-timeframe algorithm detects gap fill zones and plots them on the chart.
2. Visual effects for both visual modes were hand crafted to provide a visually stunning and intuitive interface.
3. The algorithm to determine sentiment uses a unique blend of weight and sensitivity adjustment to create a plot with elastic upper and lower bounds based off historical volatility and price action.
Information Flow Analysis[b🔄 Information Flow Analysis: Systematic Multi-Component Market Analysis Framework
SYSTEM OVERVIEW AND ANALYTICAL FOUNDATION
The Information Flow Kernel - Hybrid combines established technical analysis methods into a unified analytical framework. This indicator systematically processes three distinct data streams - directional price momentum, volume-weighted pressure dynamics, and intrabar development patterns - integrating them through weighted mathematical fusion to produce statistically normalized market flow measurements.
COMPREHENSIVE MATHEMATICAL FRAMEWORK
Component 1: Directional Flow Analysis
The directional component analyzes price momentum through three mathematical vectors:
Price Vector: p = C - O (intrabar directional bias)
Momentum Vector: m = C_t - C_{t-1} (bar-to-bar velocity)
Acceleration Vector: a = m_t - m_{t-1} (momentum rate of change)
Directional Signal Integration:
S_d = \text{sgn}(p) \cdot |p| + \text{sgn}(m) \cdot |m| \cdot 0.6 + \text{sgn}(a) \cdot |a| \cdot 0.3
The signum function preserves directional information while absolute values provide magnitude weighting. Coefficients create a hierarchy emphasizing intrabar movement (100%), momentum (60%), and acceleration (30%).
Final Directional Output: K_1 = S_d \cdot w_d where w_d is the directional weight parameter.
Component 2: Volume-Weighted Pressure Analysis
Volume Normalization: r_v = \frac{V_t}{\overline{V_n}} where \overline{V_n} represents the n-period simple moving average of volume.
Base Pressure Calculation: P_{base} = \Delta C \cdot r_v \cdot w_v where \Delta C = C_t - C_{t-1} and w_v is the velocity weighting factor.
Volume Confirmation Function:
f(r_v) = \begin{cases}
1.4 & \text{if } r_v > 1.2 \
0.7 & \text{if } r_v < 0.8 \
1.0 & \text{otherwise}
\end{cases}
Final Pressure Output: K_2 = P_{base} \cdot f(r_v)
Component 3: Intrabar Development Analysis
Bar Position Calculation: B = \frac{C - L}{H - L} when H - L > 0 , else B = 0.5
Development Signal Function:
S_{dev} = \begin{cases}
2(B - 0.5) & \text{if } B > 0.6 \text{ or } B < 0.4 \
0 & \text{if } 0.4 \leq B \leq 0.6
\end{cases}
Final Development Output: K_3 = S_{dev} \cdot 0.4
Master Integration and Statistical Normalization
Weighted Component Fusion: F_{raw} = 0.5K_1 + 0.35K_2 + 0.15K_3
Sensitivity Scaling: F_{master} = F_{raw} \cdot s where s is the sensitivity parameter.
Statistical Normalization Process:
Rolling Mean: \mu_F = \frac{1}{n}\sum_{i=0}^{n-1} F_{master,t-i}
Rolling Standard Deviation: \sigma_F = \sqrt{\frac{1}{n}\sum_{i=0}^{n-1} (F_{master,t-i} - \mu_F)^2}
Z-Score Computation: z = \frac{F_{master} - \mu_F}{\sigma_F}
Boundary Enforcement: z_{bounded} = \max(-3, \min(3, z))
Final Normalization: N = \frac{z_{bounded}}{3}
Flow Metrics Calculation:
Intensity: I = |z|
Strength Percentage: S = \min(100, I \times 33.33)
Extreme Detection: \text{Extreme} = I > 2.0
DETAILED INPUT PARAMETER SPECIFICATIONS
Sensitivity (0.1 - 3.0, Default: 1.0)
Global amplification multiplier applied to the master flow calculation. Functions as: F_{master} = F_{raw} \cdot s
Low Settings (0.1 - 0.5): Enhanced precision for subtle market movements. Optimal for low-volatility environments, scalping strategies, and early detection of minor directional shifts. Increases responsiveness but may amplify noise.
Moderate Settings (0.6 - 1.2): Balanced sensitivity for standard market conditions across multiple timeframes.
High Settings (1.3 - 3.0): Reduced sensitivity to minor fluctuations while emphasizing significant flow changes. Ideal for high-volatility assets, trending markets, and longer timeframes.
Directional Weighting (0.1 - 1.0, Default: 0.7)
Controls emphasis on price direction versus volume and positioning factors. Applied as: K_{1,weighted} = K_1 \times w_d
Lower Values (0.1 - 0.4): Reduces directional bias, favoring volume-confirmed moves. Optimal for ranging markets where momentum may generate false signals.
Higher Values (0.7 - 1.0): Amplifies directional signals from price vectors and acceleration. Ideal for trending conditions where directional momentum drives price action.
Velocity Weighting (0.1 - 1.0, Default: 0.6)
Scales volume-confirmed price change impact. Applied in: P_{base} = \Delta C \times r_v \times w_v
Lower Values (0.1 - 0.4): Dampens volume spike influence, focusing on sustained pressure patterns. Suitable for illiquid assets or news-sensitive markets.
Higher Values (0.8 - 1.0): Amplifies high-volume directional moves. Optimal for liquid markets where volume provides reliable confirmation.
Volume Length (3 - 20, Default: 5)
Defines lookback period for volume averaging: \overline{V_n} = \frac{1}{n}\sum_{i=0}^{n-1} V_{t-i}
Short Periods (3 - 7): Responsive to recent volume shifts, excellent for intraday analysis.
Long Periods (13 - 20): Smoother averaging, better for swing trading and higher timeframes.
DASHBOARD SYSTEM
Primary Flow Gauge
Bilaterally symmetric visualization displaying normalized flow direction and intensity:
Segment Calculation: n_{active} = \lfloor |N| \times 15 \rfloor
Left Fill: Bearish flow when N < -0.01
Right Fill: Bullish flow when N > 0.01
Neutral Display: Empty segments when |N| \leq 0.01
Visual Style Options:
Matrix: Digital blocks (▰/▱) for quantitative precision
Wave: Progressive patterns (▁▂▃▄▅▆▇█) showing flow buildup
Dots: LED-style indicators (●/○) with intensity scaling
Blocks: Modern squares (■/□) for professional appearance
Pulse: Progressive markers (⎯ to █) emphasizing intensity buildup
Flow Intensity Visualization
30-segment horizontal bar graph with mathematical fill logic:
Segment Fill: For i \in : filled if \frac{i}{29} \leq \frac{S}{100}
Color Coding System:
Orange (S > 66%): High intensity, strong directional conviction
Cyan (33% ≤ S ≤ 66%): Moderate intensity, developing bias
White (S < 33%): Low intensity, neutral conditions
Extreme Detection Indicators
Circular markers flanking the gauge with state-dependent illumination:
Activation: I > 2.0 \land |N| > 0.3
Bright Yellow: Active extreme conditions
Dim Yellow: Normal conditions
Metrics Display
Balance Value: Raw master flow output ( F_{master} ) showing absolute directional pressure
Z-Score Value: Statistical deviation ( z_{bounded} ) indicating historical context
Dynamic Narrative System
Context-sensitive interpretation based on mathematical thresholds:
Extreme Flow: I > 2.0 \land |N| > 0.6
Moderate Flow: 0.3 < |N| \leq 0.6
High Volatility: S > 50 \land |N| \leq 0.3
Neutral State: S \leq 50 \land |N| \leq 0.3
ALERT SYSTEM SPECIFICATIONS
Mathematical Trigger Conditions:
Extreme Bullish: I > 2.0 \land N > 0.6
Extreme Bearish: I > 2.0 \land N < -0.6
High Intensity: S > 80
Bullish Shift: N_t > 0.3 \land N_{t-1} \leq 0.3
Bearish Shift: N_t < -0.3 \land N_{t-1} \geq -0.3
TECHNICAL IMPLEMENTATION AND PERFORMANCE
Computational Architecture
The system employs efficient calculation methods minimizing processing overhead:
Single-pass mathematical operations for all components
Conditional visual rendering (executed only on final bar)
Optimized array operations using direct calculations
Real-Time Processing
The indicator updates continuously during bar formation, providing immediate feedback on changing market conditions. Statistical normalization ensures consistent interpretation across varying market regimes.
Market Applicability
Optimal performance in liquid markets with consistent volume patterns. May require parameter adjustment for:
Low-volume or after-hours sessions
News-driven market conditions
Highly volatile cryptocurrency markets
Ranging versus trending market environments
PRACTICAL APPLICATION FRAMEWORK
Market State Classification
This indicator functions as a comprehensive market condition assessment tool providing:
Trend Analysis: High intensity readings ( S > 66% ) with sustained directional bias indicate strong trending conditions suitable for momentum strategies.
Reversal Detection: Extreme readings ( I > 2.0 ) at key technical levels may signal potential trend exhaustion or reversal points.
Range Identification: Low intensity with neutral flow ( S < 33%, |N| < 0.3 ) suggests ranging market conditions suitable for mean reversion strategies.
Volatility Assessment: High intensity without clear directional bias indicates elevated volatility with conflicting pressures.
Integration with Trading Systems
The normalized output range facilitates integration with automated trading systems and position sizing algorithms. The statistical basis provides consistent interpretation across different market conditions and asset classes.
LIMITATIONS AND CONSIDERATIONS
This indicator combines established technical analysis methods and processes historical data without predicting future price movements. The system performs optimally in liquid markets with consistent volume patterns and may produce false signals in thin trading conditions or during news-driven market events. This indicator is provided for educational and analytical purposes only and does not constitute financial advice. Users should combine this analysis with proper risk management, position sizing, and additional confirmation methods before making any trading decisions. Past performance does not guarantee future results.
Note: The term "kernel" in this context refers to modular calculation components rather than mathematical kernel functions in the formal computational sense.
As quantitative analyst Ralph Vince noted: "The essence of successful trading lies not in predicting market direction, but in the systematic processing of market information and the disciplined management of probability distributions."
— Dskyz, Trade with insight. Trade with anticipation.
ORB + SMA + EMA + BUY/SELL by yuvaraj ORB (Opening Range Breakout)
Meaning:
ORB stands for Opening Range Breakout.
It is a trading strategy where you watch the price movement for the first few minutes after the market opens (for example, 9:15 – 9:30 AM in India).
You mark the high and low during this period.
If price goes above the high, it signals a possible buy (long trade).
If price goes below the low, it signals a possible sell (short trade).
Why traders use it:
First few minutes decide the market direction.
Helps catch early momentum trades.
Very popular for intraday traders (Nifty, BankNifty, Crude Oil, etc.).
Example:
Market opens at 9:15.
First 5 minutes: High = 100, Low = 95.
If price moves above 100 → Buy.
If price moves below 95 → Sell.
📌 SMA (Simple Moving Average)
Meaning:
SMA stands for Simple Moving Average.
It is the average closing price of a stock over a certain number of candles.
Example:
SMA 9 → Average price of last 9 candles.
SMA 50 → Average price of last 50 candles.
Why traders use it:
Shows trend direction.
SMA going up → Uptrend, SMA going down → Downtrend.
You can use multiple SMAs (for example SMA 9 and SMA 50):
If SMA 9 crosses above SMA 50 → Buy signal.
If SMA 9 crosses below SMA 50 → Sell signal.
🔑 Key Difference:
Feature ORB SMA
Type Strategy (price breakout) Indicator (average price)
Use Entry trigger for trades Identifies trend direction
Works Best Intraday (first minutes) Any timeframe (intraday or swing)
Plots ORB High/Low lines for the first few minutes
Plots SMA 9/50/180 & EMA 20
Plots trailing stopline + Buy/Sell arrows
Optional bar color / background color toggle
Alert conditions for Buy/Sell
ORB high/low lines
SMA 9/50/180 + EMA 20
Buy/Sell arrows + trailing stopline
Triple Confirmation StrategyTriple Confirmation Strategy (TCS)
This indicator combines three different technical tools to provide more reliable entry signals:
RSI + Moving Average crossover → momentum confirmation
MACD line & signal crossover → trend direction signal
OBV + EMA crossover → volume-based confirmation
A signal is valid only if all three conditions occur within a given number of bars (default: 5). Optionally, it can be set to trigger only when the third confirmation happens at the current bar.
✨ Features
BUY / SELL markers on the chart
Alertcondition support → alerts can be set instantly
Grouped settings (RSI, MACD, OBV, Logic)
Diagnostic overlay (WSCD-style): RSI, MACD, and OBV visualized on a normalized –100…100 scale for easier monitoring
🎯 Usage
Suitable for both intraday and swing trading with default settings.
Parameters are fully customizable (lookback periods, bar window, diagnostic overlay).
Signals should not be used as a standalone trading system but are most effective when combined with broader context and other forms of analysis.
Confluence Engine Confluence Engine is a practical, non-repainting decision aid that scores market conditions from −100…+100 by combining six proven modules: Trend, Momentum, Volatility, Volume, Structure, and an HTF confirmation. It’s designed for crypto, forex, indices, and stocks, and it fires entries only on confirmed bar closes.
What’s inside
Trend: EMA 20/50/200 alignment plus a Supertrend/KAMA toggle (you choose the baseline).
Momentum: RSI + MACD with confirmed-pivot divergence detection.
Volatility: ATR% and Bollinger Band width vs its average to favor expansion over chop.
Volume: OBV-style cumulative flow slope + volume surge vs SMA×multiplier.
Market Structure: Confirmed pivots, BOS (break of structure) and CHOCH (change of character).
HTF Filter: Closed higher-timeframe context via request.security(..., barmerge.gaps_on, barmerge.lookahead_off).
Why it does not repaint
Signals are computed and plotted on closed bars only.
Pivots/divergences use confirmed pivot points (no forward look).
HTF series are fetched with lookahead_off and use the last closed HTF bar in realtime.
No future bar references are used for entries or alerts.
How to use (3 steps)
Pick a timeframe pair: use a 4–6× HTF multiplier (5m→30m, 15m→1h, 1h→4h, 4h→1D, 1D→1W).
Trade with the HTF: take longs only when the HTF filter is bullish; shorts only when bearish.
Prefer expansion: act when BB width > its average and ATR% is elevated; skip most signals in compression.
Suggested presets (start here)
Crypto (BTC/ETH): 15m→1h, 1h→4h. stLen=10, stMult=3.0, bbLen=20, surgeMul=1.8–2.2, thresholds +40 / −40 (intraday can try +35 / −35).
Forex majors: 15m→1h, 1h→4h. stLen=10–14, stMult=2.5–3.0, surgeMul=1.5–1.8, thresholds +35 / −35 (swing: +45 / −45).
US equities (liquid): 5m→30m/1h, 15m→1h/2h. stMult=3.0–3.5, surgeMul=1.6–2.0, thresholds +45 / −45 to reduce chop.
Indices (ES/NQ): 5m→30m, 15m→1h. Defaults are fine; start at +40 / −40.
Gold/Oil: 15m→1h, 1h→4h. Thresholds +35 / −35, surgeMul=1.6–1.9.
Inputs (plain English)
Use Supertrend (off = KAMA): choose the trend baseline.
EMA Fast/Mid/Slow: 20/50/200 by default for classic stack.
RSI/MACD + divergence pivots: momentum and exhaustion context.
ATR Length & BB Length: volatility regime detection.
Volume SMA & Surge Multiplier: defines “meaningful” volume spikes.
Pivot left/right & “Confirm BOS/CHOCH on Close”: structure strictness.
Enable HTF & Higher Timeframe: confirms the lower timeframe direction.
Thresholds (+long / −short): when the score crosses these, you get signals.
Signals & alerts (IDs preserved)
Entry shapes plot at bar close when the score crosses thresholds.
Alerts you can enable:
CONFLUENCE LONG — long entry signal
CONFLUENCE SHORT — short entry signal
BULLISH BIAS — score turned positive
BEARISH BIAS — score turned negative
Best practices
Focus on signals with HTF agreement and volatility expansion; require volume participation (surge or rising OBV slope) for higher quality.
Raise thresholds (+45/−45 or +50/−50) to reduce whipsaws in choppy sessions.
Lower thresholds (+35/−35) only if you also require volatility/volume filters.
Performance & scope
Works across crypto/FX/equities/indices; no broker data or special feeds required.
No repainting by design; signals/alerts are computed on closed bars.
As with any tool, results vary by regime; always combine with risk management.
Disclosure
This script is for educational purposes only and is not financial advice. Trading involves risk. Test on historical data and paper trade before using live.
Stocks Multi-Indicator Alerts (cryptodaddy)//@version=6
// Multi-Indicator Alerts
// --------------------------------------------
// This script combines technical indicators and basic analyst data
// to produce composite buy and sell signals. Each block is heavily
// commented so future modifications are straightforward.
indicator("Multi-Indicator Alerts", overlay=true, max_labels_count=500)
//// === Daily momentum indicators ===
// Relative Strength Index measures price momentum.
rsiLength = input.int(14, "RSI Length")
rsi = ta.rsi(close, rsiLength)
// Money Flow Index incorporates volume to track capital movement.
// In Pine Script v6 the function only requires a price source and length;
// volume is taken from the built-in `volume` series automatically.
mfLength = input.int(14, "Money Flow Length")
mf = ta.mfi(hlc3, mfLength)
// `mfUp`/`mfDown` flag a turn in money flow over the last two bars.
mfUp = ta.rising(mf, 2)
mfDown = ta.falling(mf, 2)
//// === WaveTrend oscillator ===
// A simplified WaveTrend model produces "dots" indicating potential
// exhaustion points. Values beyond +/-53 are treated as oversold/overbought.
n1 = input.int(10, "WT Channel Length")
n2 = input.int(21, "WT Average Length")
ap = hlc3 // typical price
esa = ta.ema(ap, n1) // smoothed price
d = ta.ema(math.abs(ap - esa), n1) // smoothed deviation
ci = (ap - esa) / (0.015 * d) // channel index
tci = ta.ema(ci, n2) // trend channel index
wt1 = tci // main line
wt2 = ta.sma(wt1, 4) // signal line
greenDot = ta.crossover(wt1, wt2) and wt1 < -53
redDot = ta.crossunder(wt1, wt2) and wt1 > 53
plotshape(greenDot, title="Green Dot", style=shape.circle, color=color.green, location=location.belowbar, size=size.tiny)
plotshape(redDot, title="Red Dot", style=shape.circle, color=color.red, location=location.abovebar, size=size.tiny)
//// === Analyst fundamentals ===
// Fundamental values from TradingView's database. If a ticker lacks data
// these will return `na` and the related conditions simply evaluate false.
rating = request.financial(syminfo.tickerid, "rating", period="FY")
targetHigh = request.financial(syminfo.tickerid, "target_high_price", period="FY")
targetLow = request.financial(syminfo.tickerid, "target_low_price", period="FY")
upsidePct = (targetHigh - close) / close * 100
downsidePct = (close - targetLow) / close * 100
// `rating` comes back as a numeric value (1 strong sell -> 5 strong buy). Use
// thresholds instead of string comparisons so the script compiles even when
// the broker only supplies numeric ratings.
ratingBuy = rating >= 4 // buy or strong buy
ratingNeutralOrBuy = rating >= 3 // neutral or better
upsideCondition = upsidePct >= 2 * downsidePct // upside at least twice downside
downsideCondition = downsidePct >= upsidePct // downside greater or equal
//// === Daily moving-average context ===
// 50 EMA represents short-term trend; 200 EMA long-term bias.
ema50 = ta.ema(close, 50)
ema200 = ta.ema(close, 200)
longBias = close > ema200 // price above 200-day = long bias
momentumFavorable = close > ema50 // price above 50-day = positive momentum
//// === Weekly trend filter ===
// Higher timeframe confirmation to reduce noise.
weeklyClose = request.security(syminfo.tickerid, "W", close)
weeklyEMA20 = request.security(syminfo.tickerid, "W", ta.ema(close, 20))
weeklyRSI = request.security(syminfo.tickerid, "W", ta.rsi(close, rsiLength))
// Weekly Money Flow uses the same two-argument `ta.mfi()` inside `request.security`.
weeklyMF = request.security(syminfo.tickerid, "W", ta.mfi(hlc3, mfLength))
weeklyFilter = weeklyClose > weeklyEMA20
//// === Buy evaluation ===
// Each true condition contributes one point to `buyScore`.
c1_buy = rsi < 50 // RSI below midpoint
c2_buy = mfUp // Money Flow turning up
c3_buy = greenDot // WaveTrend oversold bounce
c4_buy = ratingBuy // Analyst rating Buy/Strong Buy
c5_buy = upsideCondition // Forecast upside twice downside
buyScore = (c1_buy?1:0) + (c2_buy?1:0) + (c3_buy?1:0) + (c4_buy?1:0) + (c5_buy?1:0)
// Require all five conditions plus trend filters and persistence for two bars.
buyCond = c1_buy and c2_buy and c3_buy and c4_buy and c5_buy and longBias and momentumFavorable and weeklyFilter and weeklyRSI > 50 and weeklyMF > 50
buySignal = buyCond and buyCond
//// === Sell evaluation ===
// Similar logic as buy side but inverted.
c1_sell = rsi > 70 // RSI above overbought threshold
c2_sell = mfDown // Money Flow turning down
c3_sell = redDot // WaveTrend overbought reversal
c4_sell = ratingNeutralOrBuy // Analysts neutral or still buy
c5_sell = downsideCondition // Downside at least equal to upside
sellScore = (c1_sell?1:0) + (c2_sell?1:0) + (c3_sell?1:0) + (c4_sell?1:0) + (c5_sell?1:0)
// For exits require weekly filters to fail or long bias lost.
sellCond = c1_sell and c2_sell and c3_sell and c4_sell and c5_sell and (not longBias or not weeklyFilter or weeklyRSI < 50)
sellSignal = sellCond and sellCond
// Plot composite scores for quick reference.
plot(buyScore, "Buy Score", color=color.green)
plot(sellScore, "Sell Score", color=color.red)
//// === Confidence table ===
// Shows which of the five buy/sell checks are currently met.
var table status = table.new(position.top_right, 5, 2, border_width=1)
if barstate.islast
table.cell(status, 0, 0, "RSI", bgcolor=c1_buy?color.new(color.green,0):color.new(color.red,0))
table.cell(status, 1, 0, "MF", bgcolor=c2_buy?color.new(color.green,0):color.new(color.red,0))
table.cell(status, 2, 0, "Dot", bgcolor=c3_buy?color.new(color.green,0):color.new(color.red,0))
table.cell(status, 3, 0, "Rating", bgcolor=c4_buy?color.new(color.green,0):color.new(color.red,0))
table.cell(status, 4, 0, "Target", bgcolor=c5_buy?color.new(color.green,0):color.new(color.red,0))
table.cell(status, 0, 1, "RSI>70", bgcolor=c1_sell?color.new(color.red,0):color.new(color.green,0))
table.cell(status, 1, 1, "MF down",bgcolor=c2_sell?color.new(color.red,0):color.new(color.green,0))
table.cell(status, 2, 1, "Red dot", bgcolor=c3_sell?color.new(color.red,0):color.new(color.green,0))
table.cell(status, 3, 1, "Rating", bgcolor=c4_sell?color.new(color.red,0):color.new(color.green,0))
table.cell(status, 4, 1, "Target", bgcolor=c5_sell?color.new(color.red,0):color.new(color.green,0))
//// === Alert text ===
// Include key metrics in alerts so the chart doesn't need to be opened.
buyMsg = "BUY: RSI " + str.tostring(rsi, "#.##") +
", MF " + str.tostring(mf, "#.##") +
", Upside " + str.tostring(upsidePct, "#.##") + "%" +
", Downside " + str.tostring(downsidePct, "#.##") + "%" +
", Rating " + str.tostring(rating, "#.##")
sellMsg = "SELL: RSI " + str.tostring(rsi, "#.##") +
", MF " + str.tostring(mf, "#.##") +
", Upside " + str.tostring(upsidePct, "#.##") + "%" +
", Downside " + str.tostring(downsidePct, "#.##") + "%" +
", Rating " + str.tostring(rating, "#.##")
// Alert conditions use static messages; dynamic data is sent via `alert()`
alertcondition(buySignal, title="Buy Signal", message="Buy conditions met")
alertcondition(sellSignal, title="Sell Signal", message="Sell conditions met")
if buySignal
alert(buyMsg, alert.freq_once_per_bar_close)
if sellSignal
alert(sellMsg, alert.freq_once_per_bar_close)
//// === Watch-out flags ===
// Gentle warnings when trends weaken but before full sell signals.
warnRSI = rsi > 65 and rsi <= 65
warnAnalyst = upsidePct < 2 * downsidePct and upsidePct > downsidePct
alertcondition(warnRSI, title="RSI Watch", message="RSI creeping above 65")
alertcondition(warnAnalyst, title="Analyst Watch", message="Analyst upside shrinking")
if warnRSI
alert("RSI creeping above 65: " + str.tostring(rsi, "#.##"), alert.freq_once_per_bar_close)
if warnAnalyst
alert("Analyst upside shrinking: up " + str.tostring(upsidePct, "#.##") + "% vs down " + str.tostring(downsidePct, "#.##") + "%", alert.freq_once_per_bar_close)
//// === Plot bias moving averages ===
plot(ema50, color=color.orange, title="EMA50")
plot(ema200, color=color.blue, title="EMA200")
//// === Cross alerts for context ===
goldenCross = ta.crossover(ema50, ema200)
deathCross = ta.crossunder(ema50, ema200)
alertcondition(goldenCross, title="Golden Cross", message="50 EMA crossed above 200 EMA")
alertcondition(deathCross, title="Death Cross", message="50 EMA crossed below 200 EMA")
All-In-One MA Stack ScalperWhat is this Indicator?
This tool is an advanced, multi-layered breakout and trend-following indicator designed for lower timeframes. It identifies high-conviction buy and sell signals by combining moving average stacking with a suite of professional-grade filters.
How Does It Work?
A signal is generated only when ALL of the following conditions are met:
Moving Average Stack (5M Chart):
Buy: The close price is above all five moving averages (MAs: 100, 48, 36, 24, 12).
Sell: The close price is below all five MAs.
Volatility Filter (ATR):
Signals only print when the current ATR (14) is at least 80% of its 100-period average, ensuring you only trade in actively moving markets.
Candle Structure Filter:
The current candle must have a real body that is at least 35% of the candle’s total range, filtering out dojis and indecision bars.
Big Candle Filter:
The candle’s total range must be at least 40% of the current ATR, avoiding signals on minor, insignificant moves.
Volume Filter:
The current volume must be at least 80% of its 50-period average, filtering out signals during illiquid or quiet market conditions.
Minimum Distance from All MAs:
Price must be a minimum distance (20% ATR) away from each MA, confirming a clean breakout and avoiding signals in tight MA clusters or ranging markets.
RSI Momentum Filter:
Buy: RSI(14) must be greater than 55.
Sell: RSI(14) must be less than 45.
This ensures trades are only taken in the direction of momentum.
ADX Trend Filter:
ADX(14,14) must be above 20, ensuring signals only print in trending conditions (not in chop/range).
Minimum Bars Between Signals:
Only one signal per direction is allowed every 10 bars to avoid overtrading and signal clustering.
What Does This Achieve?
Reduces noise and false signals common in basic MA cross or stack systems.
Captures only strong, high-momentum, and high-conviction moves.
Helps you avoid chop, range, and news whipsaws by combining multiple market filters.
Perfect for advanced scalpers, intraday trend followers, or as a trade filter for algos/EAs.
How to Use It:
Apply to your 5-minute chart.
Green BUY signals: Only when all bullish conditions align.
Red SELL signals: Only when all bearish conditions align.
Use as a stand-alone system or as a filter for your own entries.
Recommended For:
Scalpers & intraday traders who want only the best opportunities.
EA and bot builders seeking reliable signal logic.
Manual traders seeking confirmation of high-probability breakouts.
Tip:
Adjust any of the filters (e.g., RSI/ADX thresholds, minBars, minDist) to make it more/less selective for your style or market.
Stock Scoring SystemThe EMA Scoring System is designed to help traders quickly assess market trend strength and decide portfolio allocation. It compares price vs. key EMAs (21, 50, 100) and also checks the relative strength between EMAs. Based on these conditions, it assigns a score (-6 to +6) and a corresponding allocation percentage.
+6 Score = 100% allocation (strong bullish trend)
-6 Score = 10% allocation (strong bearish trend)
Scores in between represent intermediate trend strength.
📌 Key Features
✅ Scoring Model: Evaluates price vs. EMA alignment and EMA cross relationships.
✅ Allocation % Display: Converts score into suggested portfolio allocation.
✅ Background Highlighting: Green shades for bullish conditions, red shades for bearish.
✅ Customizable Table Position: Choose between Top Right, Top Center, Bottom Right, or Bottom Center.
✅ Toggleable EMAs: Show/Hide 21 EMA, 50 EMA, and 100 EMA directly from indicator settings.
✅ Simple & Intuitive: One glance at the chart tells you trend strength and suggested allocation.
📈 How It Works
Score Calculation:
Price above an EMA = +1, below = -1
Faster EMA above slower EMA = +1, else -1
Maximum score = +6, minimum = -6
Allocation Mapping:
+6 → 100% allocation
+4 to +5 → 100% allocation
+2 to +3 → 75% allocation
0 to +1 → 50% allocation
-1 to -2 → 30% allocation
-3 to -4 → 20% allocation
-5 to -6 → 10% allocation
Visual Output:
Table shows SCORE + Allocation %
Background color shifts with score (green for bullish, red for bearish)
⚠️ Disclaimer
This indicator is for educational purposes only. It does not constitute financial advice. Always backtest and combine with your own analysis before making trading decisions.
Universal Stochastic Fusion (Simplified) — v6What this indicator is
This indicator is called Universal Stochastic Fusion (USF).
It’s a tool that helps traders see when the market might be too high (overbought) or too low (oversold), and when it might be a good time to buy or sell.
________________________________________
How it works
Think of the market like a rubber band.
• If the band stretches too far up, it usually snaps back down.
• If it stretches too far down, it usually bounces back up.
The USF indicator measures this stretch using something called the Stochastic Oscillator (just a fancy way of saying it looks at where the current price sits compared to recent highs and lows).
It shows this on a scale from 0 to 100:
• Near 100 → market is stretched upward (too hot).
• Near 0 → market is stretched downward (too cold).
• Around 50 → normal, middle ground.
________________________________________
What’s special about USF
1. Two views at once
o It lets you see the market’s stretch on your current chart and on another timeframe (like a daily view).
o This way, you can see the short-term and the bigger picture together.
2. Smart levels
o Instead of always using the same “too high/too low” levels (like 80 and 20), it can adjust these lines automatically depending on how wild or calm the market is.
3. Buy and Sell signals
o When the market looks too low and starts turning up, it can mark a BUY.
o When the market looks too high and starts turning down, it can mark a SELL.
4. Extra filter (optional)
o It can also use another tool (RSI) to double-check signals, so you don’t get as many false alerts.
________________________________________
How this helps traders
• It helps traders avoid buying when prices are already too high.
• It helps them spot possible bottoms where prices may bounce back.
• It combines short-term and long-term signals so traders don’t get tricked by quick moves.
________________________________________
Where it works
This indicator is universal — meaning it works on almost any market:
• Stocks (like Apple, Tesla, etc.)
• Forex (currencies like EUR/USD)
• Crypto (Bitcoin, Ethereum, etc.)
• Commodities (Gold, Oil, etc.)
• Futures and Indices (S&P 500, Nasdaq, etc.)
Because all these markets share the same pattern of prices going up and down too much and then pulling back, the USF can be applied everywhere.
________________________________________
👉 In short:
The Universal Stochastic Fusion is like a heat meter for the market.
It tells you when prices might be too hot (good chance to sell) or too cold (good chance to buy), and it works in all markets and timeframes.
________________________________________
Retail Sentiment Indicator - Multi-Asset CFD & Fear/Greed IndexRetail Sentiment Indicator - Multi-Asset CFD & Fear/Greed Index
Overview
The Retail Sentiment Indicator provides real-time sentiment data for major financial instruments including stocks, forex, commodities, and cryptocurrencies. This indicator displays retail trader positioning and market sentiment using CFD data and fear/greed indices.
Methodology and Scale Calculation
This indicator operates on a **-50 to +50 scale** with zero representing perfect market equilibrium.
Scale Interpretation:
- **Zero (0)**: Market balance - exactly 50% of investors buying, 50% selling
- **Positive values**: Majority buying pressure
- Example: If 63% of investors are buying, the indicator shows +13 (63 - 50 = +13)
- **Negative values**: Majority selling pressure
- Example: If 92% of investors are selling, the indicator shows -42 (50 - 92 = -42)
BTC Fear & Greed Index Scaling:
The original `BTC FEAR&GREED` index is natively scaled from 0-100 by its creator. In our indicator, this data has been rescaled to also fit the -50 to +50 range for consistency with other sentiment data sources.
This unified scaling approach allows for direct comparison across all instruments and data sources within the indicator.
-Important Data Source Selection-
Bitcoin (BTC) Data Sources
When viewing Bitcoin charts, the indicator offers **two different data sources**:
1. **Default Auto-Mode**: `BTCUSD Retail CFD` - Retail CFD traders sentiment data (automatically loaded).
2. **Manual Selection**: `BTC FEAR&GREED` - Fear & Greed Index from website: alternative dot me
**To access BTC Fear & Greed Index**: Input settings -> disable checkbox "Auto-load Sentiment Data" -> manually select "BTC FEAR&GREED" from the dropdown menu.
US Stock Market Data Sources
For US stocks and indices (S&P 500, NASDAQ, Dow Jones), there are **two data source options**:
1. **Default Auto-Mode**: Individual retail CFD sentiment data for each instrument
2. **Manual Selection**: `SNN FEAR&GREED` - SNN's Fear & Greed Index covering the overall US market sentiment. SNN was used as the name to avoid any potential trademark infringement.
**To access SNN Fear & Greed Index**: When viewing US market charts, disable in input settings checkbox "Auto-load Sentiment Data" and manually select "SNN FEAR&GREED" from the dropdown menu.
This distinction allows traders to choose between **instrument-specific retail sentiment** (auto-mode) or **broader market sentiment indices** (manual selection).
Features
- **Auto-Detection**: Automatically loads sentiment data based on the current chart symbol
- **Manual Selection**: Choose from 40+ supported instruments when auto-detection is unavailable
- **Multiple Data Sources**: Combines retail CFD sentiment with Fear & Greed indices
- **Visual Zones**: Clear greed/fear zones with color-coded backgrounds
- **Real-time Updates**: Live sentiment data from merged data sources
Supported Instruments
Major Indices
- S&P 500, NASDAQ, Dow Jones 30, DAX
Forex Pairs
- Major pairs: EURUSD, GBPUSD, USDJPY, USDCHF, USDCAD
- Cross pairs: EURJPY, GBPJPY, AUDUSD, NZDUSD, and 20+ others
Commodities
- Precious metals: Gold (XAUUSD), Silver (XAGUSD)
- Energy: WTI Oil
- Agricultural: Wheat, Coffee
- Industrial: Copper
Cryptocurrencies
- Bitcoin (BTC) sentiment data
- BTC & SNN Fear & Greed indices
How to Use
1. **Auto Mode** (Default): Enable "Auto-load Sentiment Data" to automatically display sentiment for the current chart symbol
2. **Manual Mode**: Disable auto-load and select from the dropdown menu for specific instruments
3. **Interpretation**:
- Values above 0 (green) indicate retail greed/bullish sentiment
- Values below 0 (red) indicate retail fear/bearish sentiment
- Fear & Greed indices use 0-100 scale (50 is neutral)
Data Sources
This indicator uses curated sentiment data from retail CFD providers and established fear/greed indices. Data is updated regularly and sourced from reputable financial data providers.
Trading Strategy & Market Philosophy
Contrarian Trading Approach
The primary purpose of this indicator is based on the fundamental market principle that **the majority of retail investors are often wrong**, and markets typically move opposite to the positions held by the majority of market participants.
Key Strategy Guidelines:
- **Contrarian Signal**: When the majority of users are positioned on one side of the market, there is statistically greater market advantage in taking positions in the opposite direction
- **Trend Exhaustion Signal**: An interesting observed phenomenon occurs when, during a long-lasting trend where the majority of investors have consistently been on the wrong side, the Sentiment indicator suddenly shows that the majority has flipped and opened positions in the direction of that long-running trend. This is often a signal of fuel exhaustion for further movement in that direction
Interpretation Examples
- High greed readings (majority bullish) → Consider bearish opportunities
- High fear readings (majority bearish) → Consider bullish opportunities
- Sudden sentiment flip during established trends → Potential trend reversal signal
Technical Notes
- Built with PineScript v6
- Dynamic symbol detection with fallback options
- Optimized for performance with minimal resource usage
- Color-coded visualization with customizable zones
Data Sources & Expansion
Acknowledgments
We extend our gratitude to **TradingView** for enabling the use of custom data feeds based on GitHub repositories, making this comprehensive sentiment analysis possible.
Data Expansion Opportunities
As the operator of this indicator, I am **open to suggestions for new data sources** that could be integrated and published. If you have ideas for additional instruments or sentiment data:
How to Submit Suggestions:
1. Send a **private message** with your proposal
2. Include: **instrument/data type**, **source**, and **brief description**
3. If technically feasible, we will work to import and publish the data
Data Infrastructure Status
Current Data Upload Process:
Please note that sentiment data uploads may occasionally experience minor interruptions. However, this should not pose significant issues as sentiment data typically changes gradually rather than rapidly.
Infrastructure Development:
We are actively working on establishing permanent cloud-based infrastructure to ensure continuous, automated data collection and upload processes. This will provide more reliable and consistent data availability in the future.
Disclaimer
This indicator is for educational and informational purposes only. Sentiment data should be used as part of a comprehensive trading strategy and not as the sole basis for trading decisions. Past performance does not guarantee future results. The contrarian approach described is a market theory and may not always produce profitable results.
Perp Imbalance Zones • Pro (clean)USD Premium (perp vs spot) → (Perp − Spot) / Spot.
Imbalance (z-score of that premium) → how extreme the current premium is relative to its own history over lenPrem bars.
Hysteresis state machine → flips to a SHORT bias when perp-long pressure is extreme; flips to LONG bias when perp-short pressure is extreme. It exits only after the imbalance cools (prevents whipsaw).
Price stretch filter (±σ) → optional Bollinger check so signals only fire when price is already stretched.
HTF confirmation (optional) → require higher-timeframe imbalance to agree with the current-TF bias.
Gradient visuals → line + background tint deepen as |z| grows (more extreme pressure).
What you see on the pane
A single line (z):
Above 0 = perp richer than spot (perp longs pressing).
Below 0 = perp cheaper than spot (perp shorts pressing).
Guides: dotted levels at ±enterZ (entry) and ±exitZ (cool-off/exit).
Background tint:
Red when state = SHORT bias (perp longs heavy).
Blue when state = LONG bias (perp shorts heavy).
Tint intensity scales with |z| (via hotZ).
Labels (optional): prints when bias flips.
Alerts (optional): “Enter SHORT/LONG bias” and “Exit bias”.
How to use it (playbook)
Attach & set symbols
Put the script on your chart.
Set Spot symbol and Perp symbol to the venue you trade (e.g., BINANCE:BTCUSDT + BINANCE:BTCUSDTPERP).
Read the bias
SHORT bias (red background): perp longs over-extended. Look for short entries if price is at resistance, σ-stretched, or your PA system agrees.
LONG bias (blue background): perp shorts over-extended. Look for long entries at support/σ-stretched down.
Entries
Use the bias flip as a context/confirm. Combine with your structure trigger (OB/level sweep, rejection wick, micro-break in market structure, etc.).
If useSigma=true, only trade when price is already ≥ upper band (shorts) or ≤ lower band (longs).
Exits
Bias auto-exits when |z| falls below exitZ.
You can also take profits at your levels or when the line fades back toward 0 while price mean-reverts to the middle band.
Tuning (what each knob does)
enterZ / exitZ (signal strictness + hysteresis)
Higher enterZ → fewer, cleaner signals (e.g., 1.8–2.2).
exitZ should be lower than enterZ (e.g., 0.6–1.0) to prevent flicker.
lenPrem (context window for z)
Larger (50–100) = steadier baseline, fewer signals.
Smaller (20–30) = more reactive, more signals.
smoothLen (EMA on z)
2–3 = snappier; 5–7 = smoother/laggier but cleaner.
useSigma, bbLen, bbK (price-stretch filter)
On filters chop. Try bbLen=100, bbK=1.0–1.5.
Off if you want more frequent signals or you already gate with your own σ/Keltner.
useHTF, htfTF, htfZmin (trend/confirmation)
Turn on to require higher-TF imbalance agreement (e.g., trading 1H → confirm with 4H htfTF=240, htfZmin≈0.6–1.0).
hotZ (visual intensity)
Lower (2.0–2.5) heats up faster; higher (4.0) is more subtle.
Ready-made presets
Conservative swing (fewer, higher-conviction):
enterZ=2.0, exitZ=1.0, lenPrem=60–80, smoothLen=5, useSigma=true, bbK=1.5, useHTF=true (240/0.8).
Balanced intraday (default feel):
enterZ=1.6–1.8, exitZ=0.8–1.0, lenPrem=50, smoothLen=3–4, useSigma=true, bbK=1.0–1.25, useHTF=false/true depending on trendiness.
Aggressive scalping (more signals):
enterZ=1.2–1.4, exitZ=0.6–0.8, lenPrem=20–30, smoothLen=2–3, useSigma=false, useHTF=false.
Practical tips
Don’t trade the line in isolation. Use it to time trades into your levels: VWAP bands, Monday high/low, prior POC/VAH/VAL, order blocks, etc.
Perp-led reversals often snap—be ready to scale out quickly back to mid-bands.
Venue matters. Keep spot & perp from the same exchange family to avoid cross-venue quirks.
Alerts: enable after you’ve tuned thresholds for your timeframe so you only get high-quality pings.
Optimized ADX DI CCI Strategy### Key Features:
- Combines ADX, DI+/-, CCI, and RSI for signal generation.
- Supports customizable timeframes for indicators.
- Offers multiple exit conditions (Moving Average cross, ADX change, performance-based stop-loss).
- Tracks and displays trade statistics (e.g., win rate, capital growth, profit factor).
- Visualizes trades with labels and optional background coloring.
- Allows countertrading (opening an opposite trade after closing one).
1. **Indicator Calculation**:
- **ADX and DI+/-**: Calculated using the `ta.dmi` function with user-defined lengths for DI and ADX smoothing.
- **CCI**: Computed using the `ta.cci` function with a configurable source (default: `hlc3`) and length.
- **RSI (optional)**: Calculated using the `ta.rsi` function to filter overbought/oversold conditions.
- **Moving Averages**: Used for CCI signal smoothing and trade exits, with support for SMA, EMA, SMMA (RMA), WMA, and VWMA.
2. **Signal Generation**:
- **Buy Signal**: Triggered when DI+ > DI- (or DI+ crosses over DI-), CCI > MA (or CCI crosses over MA), and optional ADX/RSI filters are satisfied.
- **Sell Signal**: Triggered when DI+ < DI- (or DI- crosses over DI+), CCI < MA (or CCI crosses under MA), and optional ADX/RSI filters are satisfied.
3. **Trade Execution**:
- **Entry**: Long or short trades are opened using `strategy.entry` when signals are detected, provided trading is allowed (`allow_long`/`allow_short`) and equity is positive.
- **Exit**: Trades can be closed based on:
- Opposite signal (if no other exit conditions are used).
- MA cross (price crossing below/above the exit MA for long/short trades).
- ADX percentage change exceeding a threshold.
- Performance-based stop-loss (trade loss exceeding a percentage).
- **Countertrading**: If enabled, closing a trade triggers an opposite trade (e.g., closing a long opens a short).
4. **Visualization**:
- Labels are plotted at trade entries/exits (e.g., "BUY," "SELL," arrows).
- Optional background coloring highlights open trades (green for long, red for short).
- A statistics table displays real-time metrics (e.g., capital, win rates).
5. **Trade Tracking**:
- Tracks the number of long/short trades, wins, and overall performance.
- Monitors equity to prevent trading if it falls to zero.
### 2.3 Key Components
- **Indicator Calculations**: Uses `request.security` to fetch indicator data for the specified timeframe.
- **MA Function**: A custom `ma_func` handles different MA types for CCI and exit conditions.
- **Signal Logic**: Combines crossover/under checks with recent bar windows for flexibility.
- **Exit Conditions**: Multiple configurable exit strategies for risk management.
- **Statistics Table**: Updates dynamically with trade and capital metrics.
## 3. Configuration Options
The script provides extensive customization through input parameters, grouped for clarity in the TradingView settings panel. Below is a detailed breakdown of each setting and its impact.
### 3.1 Strategy Settings (Global)
- **Initial Capital**: Default `10000`. Sets the starting capital for backtesting.
- **Effect**: Determines the base equity for calculating position sizes and performance metrics.
- **Default Quantity Type**: `strategy.percent_of_equity` (50% of equity).
- **Effect**: Controls the size of each trade as a percentage of available equity.
- **Pyramiding**: Default `2`. Allows up to 2 simultaneous trades in the same direction.
- **Effect**: Enables multiple entries if conditions are met, increasing exposure.
- **Commission**: 0.2% per trade.
- **Effect**: Simulates trading fees, reducing net profit in backtesting.
- **Margin**: 100% for long and short trades.
- **Effect**: Assumes no leverage; adjust for margin trading simulations.
- **Calc on Every Tick**: `true`.
- **Effect**: Ensures real-time signal updates for precise execution.
### 3.2 Indicator Settings
- **Indicator Timeframe** (`indicator_timeframe`):
- **Options**: `""` (chart timeframe), `1`, `5`, `15`, `30`, `60`, `240`, `D`, `W`.
- **Default**: `""` (uses chart timeframe).
- **Effect**: Determines the timeframe for ADX, DI, CCI, and RSI calculations. A higher timeframe reduces noise but may delay signals.
### 3.3 ADX & DI Settings
- **DI Length** (`adx_di_len`):
- **Default**: `30`.
- **Range**: Minimum `1`.
- **Effect**: Sets the period for calculating DI+ and DI-. Longer periods smooth trends but reduce sensitivity.
- **ADX Smoothing Length** (`adx_smooth_len`):
- **Default**: `14`.
- **Range**: Minimum `1`.
- **Effect**: Smooths the ADX calculation. Longer periods produce smoother ADX values.
- **Use ADX Filter** (`use_adx_filter`):
- **Default**: `false`.
- **Effect**: If `true`, requires ADX to exceed the threshold for signals to be valid, filtering out weak trends.
- **ADX Threshold** (`adx_threshold`):
- **Default**: `25`.
- **Range**: Minimum `0`.
- **Effect**: Sets the minimum ADX value for valid signals when the filter is enabled. Higher values restrict trades to stronger trends.
### 3.4 CCI Settings
- **CCI Length** (`cci_length`):
- **Default**: `20`.
- **Range**: Minimum `1`.
- **Effect**: Sets the period for CCI calculation. Longer periods reduce noise but may lag.
- **CCI Source** (`cci_src`):
- **Default**: `hlc3` (average of high, low, close).
- **Effect**: Defines the price data for CCI. `hlc3` is standard, but users can choose other sources (e.g., `close`).
- **CCI MA Type** (`ma_type`):
- **Options**: `SMA`, `EMA`, `SMMA (RMA)`, `WMA`, `VWMA`.
- **Default**: `SMA`.
- **Effect**: Determines the moving average type for CCI signal smoothing. EMA is more responsive; VWMA weights by volume.
- **CCI MA Length** (`ma_length`):
- **Default**: `14`.
- **Range**: Minimum `1`.
- **Effect**: Sets the period for the CCI MA. Longer periods smooth the MA but may delay signals.
### 3.5 RSI Filter Settings
- **Use RSI Filter** (`use_rsi_filter`):
- **Default**: `false`.
- **Effect**: If `true`, applies RSI-based overbought/oversold filters to signals.
- **RSI Length** (`rsi_length`):
- **Default**: `14`.
- **Range**: Minimum `1`.
- **Effect**: Sets the period for RSI calculation. Longer periods reduce sensitivity.
- **RSI Lower Limit** (`rsi_lower_limit`):
- **Default**: `30`.
- **Range**: `0` to `100`.
- **Effect**: Defines the oversold threshold for buy signals. Lower values allow trades in more extreme conditions.
- **RSI Upper Limit** (`rsi_upper_limit`):
- **Default**: `70`.
- **Range**: `0` to `100`.
- **Effect**: Defines the overbought threshold for sell signals. Higher values allow trades in more extreme conditions.
### 3.6 Signal Settings
- **Cross Window** (`cross_window`):
- **Default**: `0`.
- **Range**: `0` to `5` bars.
- **Effect**: Specifies the lookback period for detecting DI+/- or CCI crosses. `0` requires crosses on the current bar; higher values allow recent crosses, increasing signal frequency.
- **Allow Long Trades** (`allow_long`):
- **Default**: `true`.
- **Effect**: Enables/disables new long trades. If `false`, only closing existing longs is allowed.
- **Allow Short Trades** (`allow_short`):
- **Default**: `true`.
- **Effect**: Enables/disables new short trades. If `false`, only closing existing shorts is allowed.
- **Require DI+/DI- Cross for Buy** (`buy_di_cross`):
- **Default**: `true`.
- **Effect**: If `true`, requires a DI+ crossover DI- for buy signals; if `false`, DI+ > DI- is sufficient.
- **Require CCI Cross for Buy** (`buy_cci_cross`):
- **Default**: `true`.
- **Effect**: If `true`, requires a CCI crossover MA for buy signals; if `false`, CCI > MA is sufficient.
- **Require DI+/DI- Cross for Sell** (`sell_di_cross`):
- **Default**: `true`.
- **Effect**: If `true`, requires a DI- crossover DI+ for sell signals; if `false`, DI+ < DI- is sufficient.
- **Require CCI Cross for Sell** (`sell_cci_cross`):
- **Default**: `true`.
- **Effect**: If `true`, requires a CCI crossunder MA for sell signals; if `false`, CCI < MA is sufficient.
- **Countertrade** (`countertrade`):
- **Default**: `true`.
- **Effect**: If `true`, closing a trade triggers an opposite trade (e.g., close long, open short) if allowed.
- **Color Background for Open Trades** (`color_background`):
- **Default**: `true`.
- **Effect**: If `true`, colors the chart background green for long trades and red for short trades.
### 3.7 Exit Settings
- **Use MA Cross for Exit** (`use_ma_exit`):
- **Default**: `true`.
- **Effect**: If `true`, closes trades when the price crosses the exit MA (below for long, above for short).
- **MA Length for Exit** (`ma_exit_length`):
- **Default**: `20`.
- **Range**: Minimum `1`.
- **Effect**: Sets the period for the exit MA. Longer periods delay exits.
- **MA Type for Exit** (`ma_exit_type`):
- **Options**: `SMA`, `EMA`, `SMMA (RMA)`, `WMA`, `VWMA`.
- **Default**: `SMA`.
- **Effect**: Determines the MA type for exit signals. EMA is more responsive; VWMA weights by volume.
- **Use ADX Change Stop-Loss** (`use_adx_stop`):
- **Default**: `false`.
- **Effect**: If `true`, closes trades when the ADX changes by a specified percentage.
- **ADX % Change for Stop-Loss** (`adx_change_percent`):
- **Default**: `5.0`.
- **Range**: Minimum `0.0`, step `0.1`.
- **Effect**: Specifies the percentage change in ADX (vs. previous bar) that triggers a stop-loss. Higher values reduce premature exits.
- **Use Performance Stop-Loss** (`use_perf_stop`):
- **Default**: `false`.
- **Effect**: If `true`, closes trades when the loss exceeds a percentage threshold.
- **Performance Stop-Loss (%)** (`perf_stop_percent`):
- **Default**: `-10.0`.
- **Range**: `-100.0` to `0.0`, step `0.1`.
- **Effect**: Specifies the loss percentage that triggers a stop-loss. More negative values allow larger losses before exiting.
## 4. Visual and Statistical Output
- **Labels**: Displayed at trade entries/exits with arrows (↑ for buy, ↓ for sell) and text ("BUY," "SELL"). A "No Equity" label appears if equity is zero.
- **Background Coloring**: Optionally colors the chart background (green for long, red for short) to indicate open trades.
- **Statistics Table**: Displayed at the top center of the chart, updated on timeframe changes or trade events. Includes:
- **Capital Metrics**: Initial capital, current capital, capital growth (%).
- **Trade Metrics**: Total trades, long/short trades, win rate, long/short win rates, profit factor.
- **Open Trade Status**: Indicates if a long, short, or no trade is open.
## 5. Alerts
- **Buy Signal Alert**: Triggered when `buy_signal` is true ("Cross Buy Signal").
- **Sell Signal Alert**: Triggered when `sell_signal` is true ("Cross Sell Signal").
- **Usage**: Users can set up TradingView alerts to receive notifications for trade signals.
Smart Breadth [smartcanvas]Overview
This indicator is a market breadth analysis tool focused on the S&P 500 index. It visualizes the percentage of S&P 500 constituents trading above their 50-day and 200-day moving averages, integrates the McClellan Oscillator for advance-decline analysis, and detects various breadth-based signals such as thrusts, divergences, and trend changes. The indicator is displayed in a separate pane and provides visual cues, a summary label with tooltip, and alert conditions to highlight potential market conditions.
The tool uses data symbols like S5FI (percentage above 50-day MA), S5TH (percentage above 200-day MA), ADVN/DECN (S&P advances/declines), and optionally NYSE advances/declines for certain calculations. If primary data is unavailable, it falls back to calculated breadth from advance-decline ratios.
This indicator is intended for educational and analytical purposes to help users observe market internals. My intention was to pack in one indicator things you will only find in a few. It does not provide trading signals as financial advice, and users are encouraged to use it in conjunction with their own research and risk management strategies. No performance guarantees are implied, and historical patterns may not predict future market behavior.
Key Components and Visuals
Plotted Lines:
Aqua line: Percentage of S&P 500 stocks above their 50-day MA.
Purple line: Percentage of S&P 500 stocks above their 200-day MA.
Optional orange line (enabled via "Show Momentum Line"): 10-day momentum of the 50-day MA breadth, shifted by +50 for scaling.
Optional line plot (enabled via "Show McClellan Oscillator"): McClellan Oscillator, colored green when positive and red when negative. Can use actual scale or normalized to fit breadth percentages (0-100).
Horizontal Levels:
Dotted green at 70%: "Strong" level.
Dashed green at user-defined green threshold (default 60%): "Buy Zone".
Dashed yellow at user-defined yellow threshold (default 50%): "Neutral".
Dotted red at 30%: "Oversold" level.
Optional dotted lines for McClellan (when shown and not using actual scale): Overbought (red), Oversold (green), and Zero (gray), scaled to fit.
Background Coloring:
Green shades for bullish/strong bullish states.
Yellow for neutral.
Orange for caution.
Red for bearish.
Signal Shapes:
Rocket emoji (🚀) at bottom for Zweig Breadth Thrust trigger.
Green circle at bottom for recovery signal.
Red triangle down at top for negative divergence warning.
Green triangle up at bottom for positive divergence.
Light green triangle up at bottom for McClellan oversold bounce.
Green diamond at bottom for capitulation signal.
Summary Label (Right Side):
Displays current action (e.g., "BUY", "HOLD") with emoji, breadth percentages with colored circles, McClellan value with emoji, market state, risk/reward stars, and active signals.
Hover tooltip provides detailed breakdown: action priority, breadth metrics, McClellan status, momentum/trend, market state, active signals, data quality, thresholds, recent changes, and a general recommendation category.
Calculations and Logic
Breadth Percentages: Derived from S5FI/S5TH or calculated from advances/(advances + declines) * 100, with fallback adjustments.
McClellan Oscillator: Difference between fast (default 19) and slow (default 39) EMAs of net advances (advances - declines).
Momentum: 10-day change in 50-day MA breadth percentage.
Trend Analysis: Counts consecutive rising days in breadth to detect upward trends.
Breadth Thrust (Zweig): 10-day EMA of advances/total issues crossing from below a bottom level (default 40) to above a top level (default 61.5). Can use S&P or NYSE data.
Divergences: Compares S&P 500 price highs/lows with breadth or McClellan over a lookback period (default 20) to detect positive (bullish) or negative (bearish) divergences.
Market States: Determined by breadth levels relative to thresholds, trend direction, and McClellan conditions (e.g., strong bullish if above green threshold, rising, and McClellan supportive).
Actions: Prioritized logic (0-10) selects an action like "BUY" or "AVOID LONGS" based on signals, states, and conditions. Higher priority (e.g., capitulation at 10) overrides lower ones.
Alerts: Triggered on new occurrences of key conditions, such as breadth thrust, divergences, state changes, etc.
Input Parameters
The indicator offers customization through grouped inputs, but the use of defaults is encouraged.
Usage Notes
Add the indicator to a chart of any symbol (though designed around S&P 500 data; works best on daily or higher timeframes). Monitor the label and tooltip for a consolidated view of conditions. Set up alerts for specific events.
This script relies on external security requests, which may have data availability issues on certain exchanges or timeframes. The fallback mechanism ensures continuity but may differ slightly from primary sources.
Disclaimer
This indicator is provided for informational and educational purposes only. It does not constitute investment advice, financial recommendations, or an endorsement of any trading strategy. Market conditions can change rapidly, and users should not rely solely on this tool for decision-making. Always perform your own due diligence, consult with qualified professionals if needed, and be aware of the risks involved in trading. The author and TradingView are not responsible for any losses incurred from using this script.
Martingale Strategy Simulator [BackQuant]Martingale Strategy Simulator
Purpose
This indicator lets you study how a martingale-style position sizing rule interacts with a simple long or short trading signal. It computes an equity curve from bar-to-bar returns, adapts position size after losing streaks, caps exposure at a user limit, and summarizes risk with portfolio metrics. An optional Monte Carlo module projects possible future equity paths from your realized daily returns.
What a martingale is
A martingale sizing rule increases stake after losses and resets after a win. In its classical form from gambling, you double the bet after each loss so that a single win recovers all prior losses plus one unit of profit. In markets there is no fixed “even-money” payout and returns are multiplicative, so an exact recovery guarantee does not exist. The core idea is unchanged:
Lose one leg → increase next position size
Lose again → increase again
Win → reset to the base size
The expectation of your strategy still depends on the signal’s edge. Sizing does not create positive expectancy on its own. A martingale raises variance and tail risk by concentrating more capital as a losing streak develops.
What it plots
Equity – simulated portfolio equity including compounding
Buy & Hold – equity from holding the chart symbol for context
Optional helpers – last trade outcome, current streak length, current allocation fraction
Optional diagnostics – daily portfolio return, rolling drawdown, metrics table
Optional Monte Carlo probability cone – p5, p16, p50, p84, p95 aggregate bands
Model assumptions
Bar-close execution with no slippage or commissions
Shorting allowed and frictionless
No margin interest, borrow fees, or position limits
No intrabar moves or gaps within a bar (returns are close-to-close)
Sizing applies to equity fraction only and is capped by your setting
All results are hypothetical and for education only.
How the simulator applies it
1) Directional signal
You pick a simple directional rule that produces +1 for long or −1 for short each bar. Options include 100 HMA slope, RSI above or below 50, EMA or SMA crosses, CCI and other oscillators, ATR move, BB basis, and more. The stance is evaluated bar by bar. When the stance flips, the current trade ends and the next one starts.
2) Sizing after losses and wins
Position size is a fraction of equity:
Initial allocation – the starting fraction, for example 0.15 means 15 percent of equity
Increase after loss – multiply the next allocation by your factor after a losing leg, for example 2.00 to double
Reset after win – return to the initial allocation
Max allocation cap – hard ceiling to prevent runaway growth
At a high level the size after k consecutive losses is
alloc(k) = min( cap , base × factor^k ) .
In practice the simulator changes size only when a leg ends and its PnL is known.
3) Equity update
Let r_t = close_t / close_{t-1} − 1 be the symbol’s bar return, d_{t−1} ∈ {+1, −1} the prior bar stance, and a_{t−1} the prior bar allocation fraction. The simulator compounds:
eq_t = eq_{t−1} × (1 + a_{t−1} × d_{t−1} × r_t) .
This is bar-based and avoids intrabar lookahead. Costs, slippage, and borrowing costs are not modeled.
Why traders experiment with martingale sizing
Mean-reversion contexts – if the signal often snaps back after a string of losses, adding size near the tail of a move can pull the average entry closer to the turn
Behavioral or microstructure edges – some rules have modest edge but frequent small whipsaws; size escalation may shorten time-to-recovery when the edge manifests
Exploration and stress testing – studying the relationship between streaks, caps, and drawdowns is instructive even if you do not deploy martingale sizing live
Why martingale is dangerous
Martingale concentrates capital when the strategy is performing worst. The main risks are structural, not cosmetic:
Loss streaks are inevitable – even with a 55 percent win rate you should expect multi-loss runs. The probability of at least one k-loss streak in N trades rises quickly with N.
Size explodes geometrically – with factor 2.0 and base 10 percent, the sequence is 10, 20, 40, 80, 100 (capped) after five losses. Without a strict cap, required size becomes infeasible.
No fixed payout – in gambling, one win at even odds resets PnL. In markets, there is no guaranteed bounce nor fixed profit multiple. Trends can extend and gaps can skip levels.
Correlation of losses – losses cluster in trends and in volatility bursts. A martingale tends to be largest just when volatility is highest.
Margin and liquidity constraints – leverage limits, margin calls, position limits, and widening spreads can force liquidation before a mean reversion occurs.
Fat tails and regime shifts – assumptions of independent, Gaussian returns can understate tail risk. Structural breaks can keep the signal wrong for much longer than expected.
The simulator exposes these dynamics in the equity curve, Max Drawdown, VaR and CVaR, and via Monte Carlo sketches of forward uncertainty.
Interpreting losing streaks with numbers
A rough intuition: if your per-trade win probability is p and loss probability is q=1−p , the chance of a specific run of k consecutive losses is q^k . Over many trades, the chance that at least one k-loss run occurs grows with the number of opportunities. As a sanity check:
If p=0.55 , then q=0.45 . A 6-loss run has probability q^6 ≈ 0.008 on any six-trade window. Across hundreds of trades, a 6 to 8-loss run is not rare.
If your size factor is 1.5 and your base is 10 percent, after 8 losses the requested size is 10% × 1.5^8 ≈ 25.6% . With factor 2.0 it would try to be 10% × 2^8 = 256% but your cap will stop it. The equity curve will still wear the compounded drawdown from the sequence that led to the cap.
This is why the cap setting is central. It does not remove tail risk, but it prevents the sizing rule from demanding impossible positions
Note: The p and q math is illustrative. In live data the win rate and distribution can drift over time, so real streaks can be longer or shorter than the simple q^k intuition suggests..
Using the simulator productively
Parameter studies
Start with conservative settings. Increase one element at a time and watch how the equity, Max Drawdown, and CVaR respond.
Initial allocation – lower base reduces volatility and drawdowns across the board
Increase factor – set modestly above 1.0 if you want the effect at all; doubling is aggressive
Max cap – the most important brake; many users keep it between 20 and 50 percent
Signal selection
Keep sizing fixed and rotate signals to see how streak patterns differ. Trend-following signals tend to produce long wrong-way streaks in choppy ranges. Mean-reversion signals do the opposite. Martingale sizing interacts very differently with each.
Diagnostics to watch
Use the built-in metrics to quantify risk:
Max Drawdown – worst peak-to-trough equity loss
Sharpe and Sortino – volatility and downside-adjusted return
VaR 95 percent and CVaR – tail risk measures from the realized distribution
Alpha and Beta – relationship to your chosen benchmark
If you would like to check out the original performance metrics script with multiple assets with a better explanation on all metrics please see
Monte Carlo exploration
When enabled, the forecast draws many synthetic paths from your realized daily returns:
Choose a horizon and a number of runs
Review the bands: p5 to p95 for a wide risk envelope; p16 to p84 for a narrower range; p50 as the median path
Use the table to read the expected return over the horizon and the tail outcomes
Remember it is a sketch based on your recent distribution, not a predictor
Concrete examples
Example A: Modest martingale
Base 10 percent, factor 1.25, cap 40 percent, RSI>50 signal. You will see small escalations on 2 to 4 loss runs and frequent resets. The equity curve usually remains smooth unless the signal enters a prolonged wrong-way regime. Max DD may rise moderately versus fixed sizing.
Example B: Aggressive martingale
Base 15 percent, factor 2.0, cap 60 percent, EMA cross signal. The curve can look stellar during favorable regimes, then a single extended streak pushes allocation to the cap, and a few more losses drive deep drawdown. CVaR and Max DD jump sharply. This is a textbook case of high tail risk.
Strengths
Bar-by-bar, transparent computation of equity from stance and size
Explicit handling of wins, losses, streaks, and caps
Portable signal inputs so you can A–B test ideas quickly
Risk diagnostics and forward uncertainty visualization in one place
Example, Rolling Max Drawdown
Limitations and important notes
Martingale sizing can escalate drawdowns rapidly. The cap limits position size but not the possibility of extended adverse runs.
No commissions, slippage, margin interest, borrow costs, or liquidity limits are modeled.
Signals are evaluated on closes. Real execution and fills will differ.
Monte Carlo assumes independent draws from your recent return distribution. Markets often have serial correlation, fat tails, and regime changes.
All results are hypothetical. Use this as an educational tool, not a production risk engine.
Practical tips
Prefer gentle factors such as 1.1 to 1.3. Doubling is usually excessive outside of toy examples.
Keep a strict cap. Many users cap between 20 and 40 percent of equity per leg.
Stress test with different start dates and subperiods. Long flat or trending regimes are where martingale weaknesses appear.
Compare to an anti-martingale (increase after wins, cut after losses) to understand the other side of the trade-off.
If you deploy sizing live, add external guardrails such as a daily loss cut, volatility filters, and a global max drawdown stop.
Settings recap
Backtest start date and initial capital
Initial allocation, increase-after-loss factor, max allocation cap
Signal source selector
Trading days per year and risk-free rate
Benchmark symbol for Alpha and Beta
UI toggles for equity, buy and hold, labels, metrics, PnL, and drawdown
Monte Carlo controls for enable, runs, horizon, and result table
Final thoughts
A martingale is not a free lunch. It is a way to tilt capital allocation toward losing streaks. If the signal has a real edge and mean reversion is common, careful and capped escalation can reduce time-to-recovery. If the signal lacks edge or regimes shift, the same rule can magnify losses at the worst possible moment. This simulator makes those trade-offs visible so you can calibrate parameters, understand tail risk, and decide whether the approach belongs anywhere in your research workflow.
PnL Bubble [%] | Fractalyst1. What's the indicator purpose?
The PnL Bubble indicator transforms your strategy's trade PnL percentages into an interactive bubble chart with professional-grade statistics and performance analytics. It helps traders quickly assess system profitability, understand win/loss distribution patterns, identify outliers, and make data-driven strategy improvements.
How does it work?
Think of this indicator as a visual report card for your trading performance. Here's what it does:
What You See
Colorful Bubbles: Each bubble represents one of your trades
Blue/Cyan bubbles = Winning trades (you made money)
Red bubbles = Losing trades (you lost money)
Bigger bubbles = Bigger wins or losses
Smaller bubbles = Smaller wins or losses
How It Organizes Your Trades:
Like a Photo Album: Instead of showing all your trades at once (which would be messy), it shows them in "pages" of 500 trades each:
Page 1: Your first 500 trades
Page 2: Trades 501-1000
Page 3: Trades 1001-1500, etc.
What the Numbers Tell You:
Average Win: How much money you typically make on winning trades
Average Loss: How much money you typically lose on losing trades
Expected Value (EV): Whether your trading system makes money over time
Positive EV = Your system is profitable long-term
Negative EV = Your system loses money long-term
Payoff Ratio (R): How your average win compares to your average loss
R > 1 = Your wins are bigger than your losses
R < 1 = Your losses are bigger than your wins
Why This Matters:
At a Glance: You can instantly see if you're a profitable trader or not
Pattern Recognition: Spot if you have more big wins than big losses
Performance Tracking: Watch how your trading improves over time
Realistic Expectations: Understand what "average" performance looks like for your system
The Cool Visual Effects:
Animation: The bubbles glow and shimmer to make the chart more engaging
Highlighting: Your biggest wins and losses get extra attention with special effects
Tooltips: hover any bubble to see details about that specific trade.
What are the underlying calculations?
The indicator processes trade PnL data using a dual-matrix architecture for optimal performance:
Dual-Matrix System:
• Display Matrix (display_matrix): Bounded to 500 trades for rendering performance
• Statistics Matrix (stats_matrix): Unbounded storage for complete statistical accuracy
Trade Classification & Aggregation:
// Separate wins, losses, and break-even trades
if val > 0.0
pos_sum += val // Sum winning trades
pos_count += 1 // Count winning trades
else if val < 0.0
neg_sum += val // Sum losing trades
neg_count += 1 // Count losing trades
else
zero_count += 1 // Count break-even trades
Statistical Averages:
avg_win = pos_count > 0 ? pos_sum / pos_count : na
avg_loss = neg_count > 0 ? math.abs(neg_sum) / neg_count : na
Win/Loss Rates:
total_obs = pos_count + neg_count + zero_count
win_rate = pos_count / total_obs
loss_rate = neg_count / total_obs
Expected Value (EV):
ev_value = (avg_win × win_rate) - (avg_loss × loss_rate)
Payoff Ratio (R):
R = avg_win ÷ |avg_loss|
Contribution Analysis:
ev_pos_contrib = avg_win × win_rate // Positive EV contribution
ev_neg_contrib = avg_loss × loss_rate // Negative EV contribution
How to integrate with any trading strategy?
Equity Change Tracking Method:
//@version=6
strategy("Your Strategy with Equity Change Export", overlay=true)
float prev_trade_equity = na
float equity_change_pct = na
if barstate.isconfirmed and na(prev_trade_equity)
prev_trade_equity := strategy.equity
trade_just_closed = strategy.closedtrades != strategy.closedtrades
if trade_just_closed and not na(prev_trade_equity)
current_equity = strategy.equity
equity_change_pct := ((current_equity - prev_trade_equity) / prev_trade_equity) * 100
prev_trade_equity := current_equity
else
equity_change_pct := na
plot(equity_change_pct, "Equity Change %", display=display.data_window)
Integration Steps:
1. Add equity tracking code to your strategy
2. Load both strategy and PnL Bubble indicator on the same chart
3. In bubble indicator settings, select your strategy's equity tracking output as data source
4. Configure visualization preferences (colors, effects, page navigation)
How does the pagination system work?
The indicator uses an intelligent pagination system to handle large trade datasets efficiently:
Page Organization:
• Page 1: Trades 1-500 (most recent)
• Page 2: Trades 501-1000
• Page 3: Trades 1001-1500
• Page N: Trades to
Example: With 1,500 trades total (3 pages available):
• User selects Page 1: Shows trades 1-500
• User selects Page 4: Automatically falls back to Page 3 (trades 1001-1500)
5. Understanding the Visual Elements
Bubble Visualization:
• Color Coding: Cyan/blue gradients for wins, red gradients for losses
• Size Mapping: Bubble size proportional to trade magnitude (larger = bigger P&L)
• Priority Rendering: Largest trades displayed first to ensure visibility
• Gradient Effects: Color intensity increases with trade magnitude within each category
Interactive Tooltips:
Each bubble displays quantitative trade information:
tooltip_text = outcome + " | PnL: " + pnl_str +
"\nDate: " + date_str + " " + time_str +
"\nTrade #" + str.tostring(trade_number) + " (Page " + str.tostring(active_page) + ")" +
"\nRank: " + str.tostring(rank) + " of " + str.tostring(n_display_rows) +
"\nPercentile: " + str.tostring(percentile, "#.#") + "%" +
"\nMagnitude: " + str.tostring(magnitude_pct, "#.#") + "%"
Example Tooltip:
Win | PnL: +2.45%
Date: 2024.03.15 14:30
Trade #1,247 (Page 3)
Rank: 5 of 347
Percentile: 98.6%
Magnitude: 85.2%
Reference Lines & Statistics:
• Average Win Line: Horizontal reference showing typical winning trade size
• Average Loss Line: Horizontal reference showing typical losing trade size
• Zero Line: Threshold separating wins from losses
• Statistical Labels: EV, R-Ratio, and contribution analysis displayed on chart
What do the statistical metrics mean?
Expected Value (EV):
Represents the mathematical expectation per trade in percentage terms
EV = (Average Win × Win Rate) - (Average Loss × Loss Rate)
Interpretation:
• EV > 0: Profitable system with positive mathematical expectation
• EV = 0: Break-even system, profitability depends on execution
• EV < 0: Unprofitable system with negative mathematical expectation
Example: EV = +0.34% means you expect +0.34% profit per trade on average
Payoff Ratio (R):
Quantifies the risk-reward relationship of your trading system
R = Average Win ÷ |Average Loss|
Interpretation:
• R > 1.0: Wins are larger than losses on average (favorable risk-reward)
• R = 1.0: Wins and losses are equal in magnitude
• R < 1.0: Losses are larger than wins on average (unfavorable risk-reward)
Example: R = 1.5 means your average win is 50% larger than your average loss
Contribution Analysis (Σ):
Breaks down the components of expected value
Positive Contribution (Σ+) = Average Win × Win Rate
Negative Contribution (Σ-) = Average Loss × Loss Rate
Purpose:
• Shows how much wins contribute to overall expectancy
• Shows how much losses detract from overall expectancy
• Net EV = Σ+ - Σ- (Expected Value per trade)
Example: Σ+: 1.23% means wins contribute +1.23% to expectancy
Example: Σ-: -0.89% means losses drag expectancy by -0.89%
Win/Loss Rates:
Win Rate = Count(Wins) ÷ Total Trades
Loss Rate = Count(Losses) ÷ Total Trades
Shows the probability of winning vs losing trades
Higher win rates don't guarantee profitability if average losses exceed average wins
7. Demo Mode & Synthetic Data Generation
When using built-in sources (close, open, etc.), the indicator generates realistic demo trades for testing:
if isBuiltInSource(source_data)
// Generate random trade outcomes with realistic distribution
u_sign = prand(float(time), float(bar_index))
if u_sign < 0.5
v_push := -1.0 // Loss trade
else
// Skewed distribution favoring smaller wins (realistic)
u_mag = prand(float(time) + 9876.543, float(bar_index) + 321.0)
k = 8.0 // Skewness factor
t = math.pow(u_mag, k)
v_push := 2.5 + t * 8.0 // Win trade
Demo Characteristics:
• Realistic win/loss distribution mimicking actual trading patterns
• Skewed distribution favoring smaller wins over large wins
• Deterministic randomness for consistent demo results
• Includes jitter effects to prevent visual overlap
8. Performance Limitations & Optimizations
Display Constraints:
points_count = 500 // Maximum 500 dots per page for optimal performance
Pine Script v6 Limits:
• Label Count: Maximum 500 labels per indicator
• Line Count: Maximum 100 lines per indicator
• Box Count: Maximum 50 boxes per indicator
• Matrix Size: Efficient memory management with dual-matrix system
Optimization Strategies:
• Pagination System: Handle unlimited trades through 500-trade pages
• Priority Rendering: Largest trades displayed first for maximum visibility
• Dual-Matrix Architecture: Separate display (bounded) from statistics (unbounded)
• Smart Fallback: Automatic page clamping prevents empty displays
Impact & Workarounds:
• Visual Limitation: Only 500 trades visible per page
• Statistical Accuracy: Complete dataset used for all calculations
• Navigation: Use page input to browse through entire trade history
• Performance: Smooth operation even with thousands of trades
9. Statistical Accuracy Guarantees
Data Integrity:
• Complete Dataset: Statistics matrix stores ALL trades without limit
• Proper Aggregation: Separate tracking of wins, losses, and break-even trades
• Mathematical Precision: Pine Script v6's enhanced floating-point calculations
• Dual-Matrix System: Display limitations don't affect statistical accuracy
Calculation Validation:
// Verified formulas match standard trading mathematics
avg_win = pos_sum / pos_count // Standard average calculation
win_rate = pos_count / total_obs // Standard probability calculation
ev_value = (avg_win * win_rate) - (avg_loss * loss_rate) // Standard EV formula
Accuracy Features:
• Mathematical Correctness: Formulas follow established trading statistics
• Data Preservation: Complete dataset maintained for all calculations
• Precision Handling: Proper rounding and boundary condition management
• Real-Time Updates: Statistics recalculated on every new trade
10. Advanced Technical Features
Real-Time Animation Engine:
// Shimmer effects with sine wave modulation
offset = math.sin(shimmer_t + phase) * amp
// Dynamic transparency with organic flicker
new_transp = math.min(flicker_limit, math.max(-flicker_limit, cur_transp + dir * flicker_step))
• Sine Wave Shimmer: Dynamic glowing effects on bubbles
• Organic Flicker: Random transparency variations for natural feel
• Extreme Value Highlighting: Special visual treatment for outliers
• Smooth Animations: Tick-based updates for fluid motion
Magnitude-Based Priority Rendering:
// Sort trades by magnitude for optimal visual hierarchy
sort_indices_by_magnitude(values_mat)
• Largest First: Most important trades always visible
• Intelligent Sorting: Custom bubble sort algorithm for trade prioritization
• Performance Optimized: Efficient sorting for real-time updates
• Visual Hierarchy: Ensures critical trades never get hidden
Professional Tooltip System:
• Quantitative Data: Pure numerical information without interpretative language
• Contextual Ranking: Shows trade position within page dataset
• Percentile Analysis: Performance ranking as percentage
• Magnitude Scaling: Relative size compared to page maximum
• Professional Format: Clean, data-focused presentation
11. Quick Start Guide
Step 1: Add Indicator
• Search for "PnL Bubble | Fractalyst" in TradingView indicators
• Add to your chart (works on any timeframe)
Step 2: Configure Data Source
• Demo Mode: Leave source as "close" to see synthetic trading data
• Strategy Mode: Select your strategy's PnL% output as data source
Step 3: Customize Visualization
• Colors: Set positive (cyan), negative (red), and neutral colors
• Page Navigation: Use "Trade Page" input to browse trade history
• Visual Effects: Built-in shimmer and animation effects are enabled by default
Step 4: Analyze Performance
• Study bubble patterns for win/loss distribution
• Review statistical metrics: EV, R-Ratio, Win Rate
• Use tooltips for detailed trade analysis
• Navigate pages to explore full trade history
Step 5: Optimize Strategy
• Identify outlier trades (largest bubbles)
• Analyze risk-reward profile through R-Ratio
• Monitor Expected Value for system profitability
• Use contribution analysis to understand win/loss impact
12. Why Choose PnL Bubble Indicator?
Unique Advantages:
• Advanced Pagination: Handle unlimited trades with smart fallback system
• Dual-Matrix Architecture: Perfect balance of performance and accuracy
• Professional Statistics: Institution-grade metrics with complete data integrity
• Real-Time Animation: Dynamic visual effects for engaging analysis
• Quantitative Tooltips: Pure numerical data without subjective interpretations
• Priority Rendering: Intelligent magnitude-based display ensures critical trades are always visible
Technical Excellence:
• Built with Pine Script v6 for maximum performance and modern features
• Optimized algorithms for smooth operation with large datasets
• Complete statistical accuracy despite display optimizations
• Professional-grade calculations matching institutional trading analytics
Practical Benefits:
• Instantly identify system profitability through visual patterns
• Spot outlier trades and risk management issues
• Understand true risk-reward profile of your strategies
• Make data-driven decisions for strategy optimization
• Professional presentation suitable for performance reporting
Disclaimer & Risk Considerations:
Important: Historical performance metrics, including positive Expected Value (EV), do not guarantee future trading success. Statistical measures are derived from finite sample data and subject to inherent limitations:
• Sample Bias: Historical data may not represent future market conditions or regime changes
• Ergodicity Assumption: Markets are non-stationary; past statistical relationships may break down
• Survivorship Bias: Strategies showing positive historical EV may fail during different market cycles
• Parameter Instability: Optimal parameters identified in backtesting often degrade in forward testing
• Transaction Cost Evolution: Slippage, spreads, and commission structures change over time
• Behavioral Factors: Live trading introduces psychological elements absent in backtesting
• Black Swan Events: Extreme market events can invalidate statistical assumptions instantaneously
Commodity Channel Index DualThe CCI Dual is a custom TradingView indicator built in Pine Script v5, designed to help traders identify potential buy and sell signals using two Commodity Channel Index (CCI) oscillators. It combines a shorter-period CCI (default: 14) for quick momentum detection with a longer-period CCI (default: 50) for confirmation, focusing on mean-reversion opportunities in overbought or oversold conditions.
This setup is particularly suited for volatile markets like cryptocurrencies on higher timeframes (e.g., 3-day charts), where it highlights reversals by requiring both CCIs to cross out of extreme zones within a short window (default: 3 bars).
The indicator plots the CCIs, customizable bands (inner: 100, OB/OS: 175, outer: 200), dynamic fills for visual emphasis, background highlights for signals, and alert conditions for notifications.
How It Works
The indicator calculates two CCIs based on user-defined lengths and source (default: close price):
CCI Calculation: CCI measures price deviation from its average, using the formula: CCI = (Typical Price - Simple Moving Average) / (0.015 * Mean Deviation). The short CCI reacts faster to price changes, while the long CCI provides smoother, trend-aware confirmation.
Overbought/Oversold Levels: Customizable thresholds define extremes (Overbought at +175, Oversold at -175 by default). Bands are plotted at inner (±100), mid (±175 dashed), and outer (±200) levels, with gray fills for the outer zones.
Dynamic Fills: The longer CCI is used to shade areas beyond OB/OS levels in red (overbought) or green (oversold) for quick visual cues.
Signals:
Buy Signal: Triggers when both CCIs cross above the Oversold level (-175) within the signal window (3 bars). This suggests a potential upward reversal from an oversold state.
Sell Signal: Triggers when both cross below the Overbought level (+175) within the window, indicating a possible downward reversal.
Visuals and Alerts: Buy signals highlight the background green, sells red. Separate alertconditions allow setting TradingView alerts for buys or sells independently.
Customization: Adjust lengths, levels, and window via inputs to fit your timeframe or asset—e.g., higher OB/OS for crypto volatility.
This logic reduces noise by requiring dual confirmation, but like all oscillators, it can produce false signals in strong trends where prices stay extended.
To mitigate false signals (e.g., in trending markets), layer the CCI Dual with MACD (default: 12,26,9) and RSI (default: 14) for multi-indicator confirmation:
With MACD: Only take CCI buys if the MACD line is above the signal line (or histogram positive), confirming bullish momentum. For sells, require MACD bearish crossover. This filters counter-trend signals by aligning with trend strength—e.g., ignore CCI sells if MACD shows upward momentum.
With RSI: Confirm CCI oversold buys only if RSI is below 30 and rising (or shows bullish divergence). For overbought sells, RSI above 70 and falling. This adds overextension validation, reducing whipsaws in crypto trends.
I made this customizable for you to find what works best for your asset you are trading. I trade the 6 hour and 3 day timeframe mainly on major cryptocurrency pairs. I hope you enjoy this script and it serves you well.
Advanced Range Analyzer ProAdvanced Range Analyzer Pro – Adaptive Range Detection & Breakout Forecasting
Overview
Advanced Range Analyzer Pro is a comprehensive trading tool designed to help traders identify consolidations, evaluate their strength, and forecast potential breakout direction. By combining volatility-adjusted thresholds, volume distribution analysis, and historical breakout behavior, the indicator builds an adaptive framework for navigating sideways price action. Instead of treating ranges as noise, this system transforms them into opportunities for mean reversion or breakout trading.
How It Works
The indicator continuously scans price action to identify active range environments. Ranges are defined by volatility compression, repeated boundary interactions, and clustering of volume near equilibrium. Once detected, the indicator assigns a strength score (0–100), which quantifies how well-defined and compressed the consolidation is.
Breakout probabilities are then calculated by factoring in:
Relative time spent near the upper vs. lower range boundaries
Historical breakout tendencies for similar structures
Volume distribution inside the range
Momentum alignment using auxiliary filters (RSI/MACD)
This creates a live probability forecast that updates as price evolves. The tool also supports range memory, allowing traders to analyze the last completed range after a breakout has occurred. A dynamic strength meter is displayed directly above each consolidation range, providing real-time insight into range compression and breakout potential.
Signals and Breakouts
Advanced Range Analyzer Pro includes a structured set of visual tools to highlight actionable conditions:
Range Zones – Gradient-filled boxes highlight active consolidations.
Strength Meter – A live score displayed in the dashboard quantifies compression.
Breakout Labels – Probability percentages show bias toward bullish or bearish continuation.
Breakout Highlights – When a breakout occurs, the range is marked with directional confirmation.
Dashboard Table – Displays current status, strength, live/last range mode, and probabilities.
These elements update in real time, ensuring that traders always see the current state of consolidation and breakout risk.
Interpretation
Range Strength : High scores (70–100) indicate strong consolidations likely to resolve explosively, while low scores suggest weak or choppy ranges prone to false signals.
Breakout Probability : Directional bias greater than 60% suggests meaningful breakout pressure. Equal probabilities indicate balanced compression, favoring mean-reversion strategies.
Market Context : Ranges aligned with higher timeframe trends often resolve in the dominant direction, while counter-trend ranges may lead to reversals or liquidity sweeps.
Volatility Insight : Tight ranges with low ATR imply imminent expansion; wide ranges signal extended consolidation or distribution phases.
Strategy Integration
Advanced Range Analyzer Pro can be applied across multiple trading styles:
Breakout Trading : Enter on probability shifts above 60% with confirmation of volume or momentum.
Mean Reversion : Trade inside ranges with high strength scores by fading boundaries and targeting equilibrium.
Trend Continuation : Focus on ranges that form mid-trend, anticipating continuation after consolidation.
Liquidity Sweeps : Use failed breakouts at boundaries to capture reversals.
Multi-Timeframe : Apply on higher timeframes to frame market context, then execute on lower timeframes.
Advanced Techniques
Combine with volume profiles to identify areas of institutional positioning within ranges.
Track sequences of strong consolidations for trend development or exhaustion signals.
Use breakout probability shifts in conjunction with order flow or momentum indicators to refine entries.
Monitor expanding/contracting range widths to anticipate volatility cycles.
Custom parameters allow fine-tuning sensitivity for different assets (crypto, forex, equities) and trading styles (scalping, intraday, swing).
Inputs and Customization
Range Detection Sensitivity : Controls how strictly ranges are defined.
Strength Score Settings : Adjust weighting of compression, volume, and breakout memory.
Probability Forecasting : Enable/disable directional bias and thresholds.
Gradient & Fill Options : Customize range visualization colors and opacity.
Dashboard Display : Toggle live vs last range, info table size, and position.
Breakout Highlighting : Choose border/zone emphasis on breakout events.
Why Use Advanced Range Analyzer Pro
This indicator provides a data-driven approach to trading consolidation phases, one of the most common yet underutilized market states. By quantifying range strength, mapping probability forecasts, and visually presenting risk zones, it transforms uncertainty into clarity.
Whether you’re trading breakouts, fading ranges, or mapping higher timeframe context, Advanced Range Analyzer Pro delivers a structured, adaptive framework that integrates seamlessly into multiple strategies.
Volume Heat ZoneVolume Zones Indicator
This Pine Script creates a volume-based zone analysis tool for TradingView.
Function:
Divides the price range (high to low) into 20 levels over a 100-candle lookback period
Measures volume activity at each price level
Draws boxes at levels with above-average volume (1.5x threshold)
Key Settings:
Lookback Period (100): Number of candles analyzed
Price Levels (20): Price range subdivisions
Volume Threshold (1.5): Minimum volume multiplier for zones
Candle Offset (1): Excludes current candle from analysis
Projection Bars (10): Extends boxes 10 bars into the future
How it works:
The indicator identifies price levels where significant trading volume occurred historically, highlighting potential support/resistance zones. Boxes are redrawn on each confirmed candle, showing dynamic volume concentration areas that traders can use for entry/exit decisions.
Adaptive Rolling Quantile Bands [CHE] Adaptive Rolling Quantile Bands
Part 1 — Mathematics and Algorithmic Design
Purpose. The indicator estimates distribution‐aware price levels from a rolling window and turns them into dynamic “buy” and “sell” bands. It can work on raw price or on *residuals* around a baseline to better isolate deviations from trend. Optionally, the percentile parameter $q$ adapts to volatility via ATR so the bands widen in turbulent regimes and tighten in calm ones. A compact, latched state machine converts these statistical levels into high-quality discretionary signals.
Data pipeline.
1. Choose a source (default `close`; MTF optional via `request.security`).
2. Optionally compute a baseline (`SMA` or `EMA`) of length $L$.
3. Build the *working series*: raw price if residual mode is off; otherwise price minus baseline (if a baseline exists).
4. Maintain a FIFO buffer of the last $N$ values (window length). All quantiles are computed on this buffer.
5. Map the resulting levels back to price space if residual mode is on (i.e., add back the baseline).
6. Smooth levels with a short EMA for readability.
Rolling quantiles.
Given the buffer $X_{t-N+1..t}$ and a percentile $q\in $, the indicator sorts a copy of the buffer ascending and linearly interpolates between adjacent ranks to estimate:
* Buy band $\approx Q(q)$
* Sell band $\approx Q(1-q)$
* Median $Q(0.5)$, plus optional deciles $Q(0.10)$ and $Q(0.90)$
Quantiles are robust to outliers relative to means. The estimator uses only data up to the current bar’s value in the buffer; there is no look-ahead.
Residual transform (optional).
In residual mode, quantiles are computed on $X^{res}_t = \text{price}_t - \text{baseline}_t$. This centers the distribution and often yields more stationary tails. After computing $Q(\cdot)$ on residuals, levels are transformed back to price space by adding the baseline. If `Baseline = None`, residual mode simply falls back to raw price.
Volatility-adaptive percentile.
Let $\text{ATR}_{14}(t)$ be current ATR and $\overline{\text{ATR}}_{100}(t)$ its long SMA. Define a volatility ratio $r = \text{ATR}_{14}/\overline{\text{ATR}}_{100}$. The effective quantile is:
Smoothing.
Each level is optionally smoothed by an EMA of length $k$ for cleaner visuals. This smoothing does not change the underlying quantile logic; it only stabilizes plots and signals.
Latched state machines.
Two three-step processes convert levels into “latched” signals that only fire after confirmation and then reset:
* BUY latch:
(1) HLC3 crosses above the median →
(2) the median is rising →
(3) HLC3 prints above the upper (orange) band → BUY latched.
* SELL latch:
(1) HLC3 crosses below the median →
(2) the median is falling →
(3) HLC3 prints below the lower (teal) band → SELL latched.
Labels are drawn on the latch bar, with a FIFO cap to limit clutter. Alerts are available for both the simple band interactions and the latched events. Use “Once per bar close” to avoid intrabar churn.
MTF behavior and repainting.
MTF sourcing uses `lookahead_off`. Quantiles and baselines are computed from completed data only; however, any *intrabar* cross conditions naturally stabilize at close. As with all real-time indicators, values can update during a live bar; prefer bar-close alerts for reliability.
Complexity and parameters.
Each bar sorts a copy of the $N$-length window (practical $N$ values keep this inexpensive). Typical choices: $N=50$–$100$, $q_0=0.15$–$0.25$, $k=2$–$5$, baseline length $L=20$ (if used), adaptation strength $s=0.2$–$0.7$.
Part 2 — Practical Use for Discretionary/Active Traders
What the bands mean in practice.
The teal “buy” band marks the lower tail of the recent distribution; the orange “sell” band marks the upper tail. The median is your dynamic equilibrium. In residual mode, these tails are deviations around trend; in raw mode they are absolute price percentiles. When ATR adaptation is on, tails breathe with regime shifts.
Two core playbooks.
1. Mean-reversion around a stable median.
* Context: The median is flat or gently sloped; band width is relatively tight; instrument is ranging.
* Entry (long): Look for price to probe or close below the buy band and then reclaim it, especially after HLC3 recrosses the median and the median turns up.
* Stops: Place beyond the most recent swing low or $1.0–1.5\times$ ATR(14) below entry.
* Targets: First scale at the median; optional second scale near the opposite band. Trail with the median or an ATR stop.
* Symmetry: Mirror the rules for shorts near the sell band when the median is flat to down.
2. Continuation with latched confirmations.
* Context: A developing trend where you want fewer but cleaner signals.
* Entry (long): Take the latched BUY (3-step confirmation) on close, or on the next bar if you require bar-close validation.
* Invalidation: A close back below the median (or below the lower band in strong trends) negates momentum.
* Exits: Trail under the median for conservative exits or under the teal band for trend-following exits. Consider scaling at structure (prior swing highs) or at a fixed $R$ multiple.
Parameter guidance by timeframe.
* Scalping / LTF (1–5m): $N=30$–$60$, $q_0=0.20$, $k=2$–3, residual mode on, baseline EMA $L=20$, adaptation $s=0.5$–0.7 to handle micro-vol spikes. Expect more signals; rely on latched logic to filter noise.
* Intraday swing (15–60m): $N=60$–$100$, $q_0=0.15$–0.20, $k=3$–4. Residual mode helps but is optional if the instrument trends cleanly. $s=0.3$–0.6.
* Swing / HTF (4H–D): $N=80$–$150$, $q_0=0.10$–0.18, $k=3$–5. Consider `SMA` baseline for smoother residuals and moderate adaptation $s=0.2$–0.4.
Baseline choice.
Use EMA for responsiveness (fast trend shifts) and SMA for stability (smoother residuals). Turning residual mode on is advantageous when price exhibits persistent drift; turning it off is useful when you explicitly want absolute bands.
How to time entries.
Prefer bar-close validation for both band recaptures and latched signals. If you must act intrabar, accept that crosses can “un-cross” before close; compensate with tighter stops or reduced size.
Risk management.
Position size to a fixed fractional risk per trade (e.g., 0.5–1.0% of equity). Define invalidation using structure (swing points) plus ATR. Avoid chasing when distance to the opposite band is small; reward-to-risk degrades rapidly once you are deep inside the distribution.
Combos and filters.
* Pair with a higher-timeframe median slope as a regime filter (trade only in the direction of the HTF median).
* Use band width relative to ATR as a range/trend gauge: unusually narrow bands suggest compression (mean-reversion bias); expanding bands suggest breakout potential (favor latched continuation).
* Volume or session filters (e.g., avoid illiquid hours) can materially improve execution.
Alerts for discretion.
Enable “Cross above Buy Level” / “Cross below Sell Level” for early notices and “Latched BUY/SELL” for conviction entries. Set alerts to “Once per bar close” to avoid noise.
Common pitfalls.
Do not interpret band touches as automatic signals; context matters. A strong trend will often ride the far band (“band walking”) and punish counter-trend fades—use the median slope and latched logic to separate trend from range. Do not oversmooth levels; you will lag breaks. Do not set $q$ too small or too large; extremes reduce statistical meaning and practical distance for stops.
A concise checklist.
1. Is the median flat (range) or sloped (trend)?
2. Is band width expanding or contracting vs ATR?
3. Are we near the tail level aligned with the intended trade?
4. For continuation: did the 3 steps for a latched signal complete?
5. Do stops and targets produce acceptable $R$ (≥1.5–2.0)?
6. Are you trading during liquid hours for the instrument?
Summary. ARQB provides statistically grounded, regime-aware bands and a disciplined, latched confirmation engine. Use the bands as objective context, the median as your equilibrium line, ATR adaptation to stay calibrated across regimes, and the latched logic to time higher-quality discretionary entries.
Disclaimer
No indicator guarantees profits. Adaptive Rolling Quantile Bands is a decision aid; always combine with solid risk management and your own judgment. Backtest, forward test, and size responsibly.
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Enhance your trading precision and confidence 🚀
Best regards
Chervolino
