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Naveen Prabhu with EMA//@version=6 indicator('Naveen Prabhu with EMA', overlay = true, max_labels_count = 500, max_lines_count = 500, max_boxes_count = 500) a = input(2, title = 'Key Vaule. \'This changes the sensitivity\'') c = input(5, title = 'ATR Period') h = input(false, title = 'Signals from Heikin Ashi Candles') BULLISH_LEG = 1 BEARISH_LEG = 0 BULLISH = +1 BEARISH = -1 GREEN = #089981 RED = #F23645 BLUE = #2157f3 GRAY = #878b94 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( false, '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( false, '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( false, '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( true, '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(GREEN, 80), 'Internal Bullish OB', group = BLOCKS_GROUP) internalBearishOrderBlockColor = input.color(color.new(#f77c80, 80), 'Internal Bearish OB', group = BLOCKS_GROUP) swingBullishOrderBlockColor = input.color(color.new(GREEN, 80), 'Bullish OB', group = BLOCKS_GROUP) swingBearishOrderBlockColor = input.color(color.new(#b22833, 80), 'Bearish OB', group = BLOCKS_GROUP) showEqualHighsLowsInput = input( false, '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) 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 trailingExtremes float top float bottom int barTime int barIndex int lastTopTime int lastBottomTime type fairValueGap float top float bottom int bias box topBox box bottomBox type trend int bias type equalDisplay line l_ine = na label l_abel = na type pivot float currentLevel float lastLevel bool crossed int barTime = time int barIndex = bar_index 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) 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 startOfNewLeg(int leg) => ta.change(leg) != 0 startOfBearishLeg(int leg) => ta.change(leg) == -1 startOfBullishLeg(int leg) => ta.change(leg) == +1 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) 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) 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) 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) 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) 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) 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) 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) 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) 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))) getStyle(string style) => switch style SOLID => line.style_solid DASHED => line.style_dashed DOTTED => line.style_dotted 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)) higherTimeframe(string timeframe) => timeframe.in_seconds() > timeframe.in_seconds(timeframe) 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 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') 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) 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) xATR = ta.atr(c) nLoss = a * xATR src = h ? request.security(ticker.heikinashi(syminfo.tickerid), timeframe.period, close, lookahead = barmerge.lookahead_off) : close xATRTrailingStop = 0.0 iff_1 = src > nz(xATRTrailingStop , 0) ? src - nLoss : src + nLoss iff_2 = src < nz(xATRTrailingStop , 0) and src < nz(xATRTrailingStop , 0) ? math.min(nz(xATRTrailingStop ), src + nLoss) : iff_1 xATRTrailingStop := src > nz(xATRTrailingStop , 0) and src > nz(xATRTrailingStop , 0) ? math.max(nz(xATRTrailingStop ), src - nLoss) : iff_2 pos = 0 iff_3 = src > nz(xATRTrailingStop , 0) and src < nz(xATRTrailingStop , 0) ? -1 : nz(pos , 0) pos := src < nz(xATRTrailingStop , 0) and src > nz(xATRTrailingStop , 0) ? 1 : iff_3 xcolor = pos == -1 ? color.red : pos == 1 ? color.green : color.blue ema = ta.ema(src, 1) above = ta.crossover(ema, xATRTrailingStop) below = ta.crossover(xATRTrailingStop, ema) buy = src > xATRTrailingStop and above sell = src < xATRTrailingStop and below barbuy = src > xATRTrailingStop barsell = src < xATRTrailingStop //---------------------------------------------------------------------------------------------------------------------} //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') alertcondition(buy, 'UT Long', 'UT Long') alertcondition(sell, 'UT Short', 'UT Short') plotshape(buy, title = 'Buy', text = 'Buy', style = shape.labelup, location = location.belowbar, color = color.new(color.green, 0), textcolor = color.new(color.white, 0), size = size.tiny) plotshape(sell, title = 'Sell', text = 'Sell', style = shape.labeldown, location = location.abovebar, color = color.new(color.red, 0), textcolor = color.new(color.white, 0), size = size.tiny) //-------------------------------------------------------------------------------------- // EMA ADDITIONS (Editable) //-------------------------------------------------------------------------------------- ema5Len = input.int(5, "5 EMA Length", minval = 1) ema9Len = input.int(9, "9 EMA Length", minval = 1) ema5 = ta.ema(src, ema5Len) ema9 = ta.ema(src, ema9Len) plot(ema5, "EMA 5", color = color.red, linewidth = 2) plot(ema9, "EMA 9", color = color.blue, linewidth = 2) barcolor(barbuy ? color.green : na) barcolor(barsell ? color.red : na)
Pine Script®指标
由naveenprabhu636提供
1
Tensor Market Analysis Engine (TMAE)# Tensor Market Analysis Engine (TMAE) ## Advanced Multi-Dimensional Mathematical Analysis System *Where Quantum Mathematics Meets Market Structure* --- ## 🎓 THEORETICAL FOUNDATION The Tensor Market Analysis Engine represents a revolutionary synthesis of three cutting-edge mathematical frameworks that have never before been combined for comprehensive market analysis. This indicator transcends traditional technical analysis by implementing advanced mathematical concepts from quantum mechanics, information theory, and fractal geometry. ### 🌊 Multi-Dimensional Volatility with Jump Detection **Hawkes Process Implementation:** The TMAE employs a sophisticated Hawkes process approximation for detecting self-exciting market jumps. Unlike traditional volatility measures that treat price movements as independent events, the Hawkes process recognizes that market shocks cluster and exhibit memory effects. **Mathematical Foundation:** ``` Intensity λ(t) = μ + Σ α(t - Tᵢ) ``` Where market jumps at times Tᵢ increase the probability of future jumps through the decay function α, controlled by the Hawkes Decay parameter (0.5-0.99). **Mahalanobis Distance Calculation:** The engine calculates volatility jumps using multi-dimensional Mahalanobis distance across up to 5 volatility dimensions: - **Dimension 1:** Price volatility (standard deviation of returns) - **Dimension 2:** Volume volatility (normalized volume fluctuations) - **Dimension 3:** Range volatility (high-low spread variations) - **Dimension 4:** Correlation volatility (price-volume relationship changes) - **Dimension 5:** Microstructure volatility (intrabar positioning analysis) This creates a volatility state vector that captures market behavior impossible to detect with traditional single-dimensional approaches. ### 📐 Hurst Exponent Regime Detection **Fractal Market Hypothesis Integration:** The TMAE implements advanced Rescaled Range (R/S) analysis to calculate the Hurst exponent in real-time, providing dynamic regime classification: - **H > 0.6:** Trending (persistent) markets - momentum strategies optimal - **H < 0.4:** Mean-reverting (anti-persistent) markets - contrarian strategies optimal - **H ≈ 0.5:** Random walk markets - breakout strategies preferred **Adaptive R/S Analysis:** Unlike static implementations, the TMAE uses adaptive windowing that adjusts to market conditions: ``` H = log(R/S) / log(n) ``` Where R is the range of cumulative deviations and S is the standard deviation over period n. **Dynamic Regime Classification:** The system employs hysteresis to prevent regime flipping, requiring sustained Hurst values before regime changes are confirmed. This prevents false signals during transitional periods. ### 🔄 Transfer Entropy Analysis **Information Flow Quantification:** Transfer entropy measures the directional flow of information between price and volume, revealing lead-lag relationships that indicate future price movements: ``` TE(X→Y) = Σ p(yₜ₊₁, yₜ, xₜ) log ``` **Causality Detection:** - **Volume → Price:** Indicates accumulation/distribution phases - **Price → Volume:** Suggests retail participation or momentum chasing - **Balanced Flow:** Market equilibrium or transition periods The system analyzes multiple lag periods (2-20 bars) to capture both immediate and structural information flows. --- ## 🔧 COMPREHENSIVE INPUT SYSTEM ### Core Parameters Group **Primary Analysis Window (10-100, Default: 50)** The fundamental lookback period affecting all calculations. Optimization by timeframe: - **1-5 minute charts:** 20-30 (rapid adaptation to micro-movements) - **15 minute-1 hour:** 30-50 (balanced responsiveness and stability) - **4 hour-daily:** 50-100 (smooth signals, reduced noise) - **Asset-specific:** Cryptocurrency 20-35, Stocks 35-50, Forex 40-60 **Signal Sensitivity (0.1-2.0, Default: 0.7)** Master control affecting all threshold calculations: - **Conservative (0.3-0.6):** High-quality signals only, fewer false positives - **Balanced (0.7-1.0):** Optimal risk-reward ratio for most trading styles - **Aggressive (1.1-2.0):** Maximum signal frequency, requires careful filtering **Signal Generation Mode:** - **Aggressive:** Any component signals (highest frequency) - **Confluence:** 2+ components agree (balanced approach) - **Conservative:** All 3 components align (highest quality) ### Volatility Jump Detection Group **Volatility Dimensions (2-5, Default: 3)** Determines the mathematical space complexity: - **2D:** Price + Volume volatility (suitable for clean markets) - **3D:** + Range volatility (optimal for most conditions) - **4D:** + Correlation volatility (advanced multi-asset analysis) - **5D:** + Microstructure volatility (maximum sensitivity) **Jump Detection Threshold (1.5-4.0σ, Default: 3.0σ)** Standard deviations required for volatility jump classification: - **Cryptocurrency:** 2.0-2.5σ (naturally volatile) - **Stock Indices:** 2.5-3.0σ (moderate volatility) - **Forex Major Pairs:** 3.0-3.5σ (typically stable) - **Commodities:** 2.0-3.0σ (varies by commodity) **Jump Clustering Decay (0.5-0.99, Default: 0.85)** Hawkes process memory parameter: - **0.5-0.7:** Fast decay (jumps treated as independent) - **0.8-0.9:** Moderate clustering (realistic market behavior) - **0.95-0.99:** Strong clustering (crisis/event-driven markets) ### Hurst Exponent Analysis Group **Calculation Method Options:** - **Classic R/S:** Original Rescaled Range (fast, simple) - **Adaptive R/S:** Dynamic windowing (recommended for trading) - **DFA:** Detrended Fluctuation Analysis (best for noisy data) **Trending Threshold (0.55-0.8, Default: 0.60)** Hurst value defining persistent market behavior: - **0.55-0.60:** Weak trend persistence - **0.65-0.70:** Clear trending behavior - **0.75-0.80:** Strong momentum regimes **Mean Reversion Threshold (0.2-0.45, Default: 0.40)** Hurst value defining anti-persistent behavior: - **0.35-0.45:** Weak mean reversion - **0.25-0.35:** Clear ranging behavior - **0.15-0.25:** Strong reversion tendency ### Transfer Entropy Parameters Group **Information Flow Analysis:** - **Price-Volume:** Classic flow analysis for accumulation/distribution - **Price-Volatility:** Risk flow analysis for sentiment shifts - **Multi-Timeframe:** Cross-timeframe causality detection **Maximum Lag (2-20, Default: 5)** Causality detection window: - **2-5 bars:** Immediate causality (scalping) - **5-10 bars:** Short-term flow (day trading) - **10-20 bars:** Structural flow (swing trading) **Significance Threshold (0.05-0.3, Default: 0.15)** Minimum entropy for signal generation: - **0.05-0.10:** Detect subtle information flows - **0.10-0.20:** Clear causality only - **0.20-0.30:** Very strong flows only --- ## 🎨 ADVANCED VISUAL SYSTEM ### Tensor Volatility Field Visualization **Five-Layer Resonance Bands:** The tensor field creates dynamic support/resistance zones that expand and contract based on mathematical field strength: - **Core Layer (Purple):** Primary tensor field with highest intensity - **Layer 2 (Neutral):** Secondary mathematical resonance - **Layer 3 (Info Blue):** Tertiary harmonic frequencies - **Layer 4 (Warning Gold):** Outer field boundaries - **Layer 5 (Success Green):** Maximum field extension **Field Strength Calculation:** ``` Field Strength = min(3.0, Mahalanobis Distance × Tensor Intensity) ``` The field amplitude adjusts to ATR and mathematical distance, creating dynamic zones that respond to market volatility. **Radiation Line Network:** During active tensor states, the system projects directional radiation lines showing field energy distribution: - **8 Directional Rays:** Complete angular coverage - **Tapering Segments:** Progressive transparency for natural visual flow - **Pulse Effects:** Enhanced visualization during volatility jumps ### Dimensional Portal System **Portal Mathematics:** Dimensional portals visualize regime transitions using category theory principles: - **Green Portals (◉):** Trending regime detection (appear below price for support) - **Red Portals (◎):** Mean-reverting regime (appear above price for resistance) - **Yellow Portals (○):** Random walk regime (neutral positioning) **Tensor Trail Effects:** Each portal generates 8 trailing particles showing mathematical momentum: - **Large Particles (●):** Strong mathematical signal - **Medium Particles (◦):** Moderate signal strength - **Small Particles (·):** Weak signal continuation - **Micro Particles (˙):** Signal dissipation ### Information Flow Streams **Particle Stream Visualization:** Transfer entropy creates flowing particle streams indicating information direction: - **Upward Streams:** Volume leading price (accumulation phases) - **Downward Streams:** Price leading volume (distribution phases) - **Stream Density:** Proportional to information flow strength **15-Particle Evolution:** Each stream contains 15 particles with progressive sizing and transparency, creating natural flow visualization that makes information transfer immediately apparent. ### Fractal Matrix Grid System **Multi-Timeframe Fractal Levels:** The system calculates and displays fractal highs/lows across five Fibonacci periods: - **8-Period:** Short-term fractal structure - **13-Period:** Intermediate-term patterns - **21-Period:** Primary swing levels - **34-Period:** Major structural levels - **55-Period:** Long-term fractal boundaries **Triple-Layer Visualization:** Each fractal level uses three-layer rendering: - **Shadow Layer:** Widest, darkest foundation (width 5) - **Glow Layer:** Medium white core line (width 3) - **Tensor Layer:** Dotted mathematical overlay (width 1) **Intelligent Labeling System:** Smart spacing prevents label overlap using ATR-based minimum distances. Labels include: - **Fractal Period:** Time-based identification - **Topological Class:** Mathematical complexity rating (0, I, II, III) - **Price Level:** Exact fractal price - **Mahalanobis Distance:** Current mathematical field strength - **Hurst Exponent:** Current regime classification - **Anomaly Indicators:** Visual strength representations (○ ◐ ● ⚡) ### Wick Pressure Analysis **Rejection Level Mathematics:** The system analyzes candle wick patterns to project future pressure zones: - **Upper Wick Analysis:** Identifies selling pressure and resistance zones - **Lower Wick Analysis:** Identifies buying pressure and support zones - **Pressure Projection:** Extends lines forward based on mathematical probability **Multi-Layer Glow Effects:** Wick pressure lines use progressive transparency (1-8 layers) creating natural glow effects that make pressure zones immediately visible without cluttering the chart. ### Enhanced Regime Background **Dynamic Intensity Mapping:** Background colors reflect mathematical regime strength: - **Deep Transparency (98% alpha):** Subtle regime indication - **Pulse Intensity:** Based on regime strength calculation - **Color Coding:** Green (trending), Red (mean-reverting), Neutral (random) **Smoothing Integration:** Regime changes incorporate 10-bar smoothing to prevent background flicker while maintaining responsiveness to genuine regime shifts. ### Color Scheme System **Six Professional Themes:** - **Dark (Default):** Professional trading environment optimization - **Light:** High ambient light conditions - **Classic:** Traditional technical analysis appearance - **Neon:** High-contrast visibility for active trading - **Neutral:** Minimal distraction focus - **Bright:** Maximum visibility for complex setups Each theme maintains mathematical accuracy while optimizing visual clarity for different trading environments and personal preferences. --- ## 📊 INSTITUTIONAL-GRADE DASHBOARD ### Tensor Field Status Section **Field Strength Display:** Real-time Mahalanobis distance calculation with dynamic emoji indicators: - **⚡ (Lightning):** Extreme field strength (>1.5× threshold) - **● (Solid Circle):** Strong field activity (>1.0× threshold) - **○ (Open Circle):** Normal field state **Signal Quality Rating:** Democratic algorithm assessment: - **ELITE:** All 3 components aligned (highest probability) - **STRONG:** 2 components aligned (good probability) - **GOOD:** 1 component active (moderate probability) - **WEAK:** No clear component signals **Threshold and Anomaly Monitoring:** - **Threshold Display:** Current mathematical threshold setting - **Anomaly Level (0-100%):** Combined volatility and volume spike measurement - **>70%:** High anomaly (red warning) - **30-70%:** Moderate anomaly (orange caution) - **<30%:** Normal conditions (green confirmation) ### Tensor State Analysis Section **Mathematical State Classification:** - **↑ BULL (Tensor State +1):** Trending regime with bullish bias - **↓ BEAR (Tensor State -1):** Mean-reverting regime with bearish bias - **◈ SUPER (Tensor State 0):** Random walk regime (neutral) **Visual State Gauge:** Five-circle progression showing tensor field polarity: - **🟢🟢🟢⚪⚪:** Strong bullish mathematical alignment - **⚪⚪🟡⚪⚪:** Neutral/transitional state - **⚪⚪🔴🔴🔴:** Strong bearish mathematical alignment **Trend Direction and Phase Analysis:** - **📈 BULL / 📉 BEAR / ➡️ NEUTRAL:** Primary trend classification - **🌪️ CHAOS:** Extreme information flow (>2.0 flow strength) - **⚡ ACTIVE:** Strong information flow (1.0-2.0 flow strength) - **😴 CALM:** Low information flow (<1.0 flow strength) ### Trading Signals Section **Real-Time Signal Status:** - **🟢 ACTIVE / ⚪ INACTIVE:** Long signal availability - **🔴 ACTIVE / ⚪ INACTIVE:** Short signal availability - **Components (X/3):** Active algorithmic components - **Mode Display:** Current signal generation mode **Signal Strength Visualization:** Color-coded component count: - **Green:** 3/3 components (maximum confidence) - **Aqua:** 2/3 components (good confidence) - **Orange:** 1/3 components (moderate confidence) - **Gray:** 0/3 components (no signals) ### Performance Metrics Section **Win Rate Monitoring:** Estimated win rates based on signal quality with emoji indicators: - **🔥 (Fire):** ≥60% estimated win rate - **👍 (Thumbs Up):** 45-59% estimated win rate - **⚠️ (Warning):** <45% estimated win rate **Mathematical Metrics:** - **Hurst Exponent:** Real-time fractal dimension (0.000-1.000) - **Information Flow:** Volume/price leading indicators - **📊 VOL:** Volume leading price (accumulation/distribution) - **💰 PRICE:** Price leading volume (momentum/speculation) - **➖ NONE:** Balanced information flow - **Volatility Classification:** - **🔥 HIGH:** Above 1.5× jump threshold - **📊 NORM:** Normal volatility range - **😴 LOW:** Below 0.5× jump threshold ### Market Structure Section (Large Dashboard) **Regime Classification:** - **📈 TREND:** Hurst >0.6, momentum strategies optimal - **🔄 REVERT:** Hurst <0.4, contrarian strategies optimal - **🎲 RANDOM:** Hurst ≈0.5, breakout strategies preferred **Mathematical Field Analysis:** - **Dimensions:** Current volatility space complexity (2D-5D) - **Hawkes λ (Lambda):** Self-exciting jump intensity (0.00-1.00) - **Jump Status:** 🚨 JUMP (active) / ✅ NORM (normal) ### Settings Summary Section (Large Dashboard) **Active Configuration Display:** - **Sensitivity:** Current master sensitivity setting - **Lookback:** Primary analysis window - **Theme:** Active color scheme - **Method:** Hurst calculation method (Classic R/S, Adaptive R/S, DFA) **Dashboard Sizing Options:** - **Small:** Essential metrics only (mobile/small screens) - **Normal:** Balanced information density (standard desktop) - **Large:** Maximum detail (multi-monitor setups) **Position Options:** - **Top Right:** Standard placement (avoids price action) - **Top Left:** Wide chart optimization - **Bottom Right:** Recent price focus (scalping) - **Bottom Left:** Maximum price visibility (swing trading) --- ## 🎯 SIGNAL GENERATION LOGIC ### Multi-Component Convergence System **Component Signal Architecture:** The TMAE generates signals through sophisticated component analysis rather than simple threshold crossing: **Volatility Component:** - **Jump Detection:** Mahalanobis distance threshold breach - **Hawkes Intensity:** Self-exciting process activation (>0.2) - **Multi-dimensional:** Considers all volatility dimensions simultaneously **Hurst Regime Component:** - **Trending Markets:** Price above SMA-20 with positive momentum - **Mean-Reverting Markets:** Price at Bollinger Band extremes - **Random Markets:** Bollinger squeeze breakouts with directional confirmation **Transfer Entropy Component:** - **Volume Leadership:** Information flow from volume to price - **Volume Spike:** Volume 110%+ above 20-period average - **Flow Significance:** Above entropy threshold with directional bias ### Democratic Signal Weighting **Signal Mode Implementation:** - **Aggressive Mode:** Any single component triggers signal - **Confluence Mode:** Minimum 2 components must agree - **Conservative Mode:** All 3 components must align **Momentum Confirmation:** All signals require momentum confirmation: - **Long Signals:** RSI >50 AND price >EMA-9 - **Short Signals:** RSI <50 AND price 0.6):** - **Increase Sensitivity:** Catch momentum continuation - **Lower Mean Reversion Threshold:** Avoid counter-trend signals - **Emphasize Volume Leadership:** Institutional accumulation/distribution - **Tensor Field Focus:** Use expansion for trend continuation - **Signal Mode:** Aggressive or Confluence for trend following **Range-Bound Markets (Hurst <0.4):** - **Decrease Sensitivity:** Avoid false breakouts - **Lower Trending Threshold:** Quick regime recognition - **Focus on Price Leadership:** Retail sentiment extremes - **Fractal Grid Emphasis:** Support/resistance trading - **Signal Mode:** Conservative for high-probability reversals **Volatile Markets (High Jump Frequency):** - **Increase Hawkes Decay:** Recognize event clustering - **Higher Jump Threshold:** Avoid noise signals - **Maximum Dimensions:** Capture full volatility complexity - **Reduce Position Sizing:** Risk management adaptation - **Enhanced Visuals:** Maximum information for rapid decisions **Low Volatility Markets (Low Jump Frequency):** - **Decrease Jump Threshold:** Capture subtle movements - **Lower Hawkes Decay:** Treat moves as independent - **Reduce Dimensions:** Simplify analysis - **Increase Position Sizing:** Capitalize on compressed volatility - **Minimal Visuals:** Reduce distraction in quiet markets --- ## 🚀 ADVANCED TRADING STRATEGIES ### The Mathematical Convergence Method **Entry Protocol:** 1. **Fractal Grid Approach:** Monitor price approaching significant fractal levels 2. **Tensor Field Confirmation:** Verify field expansion supporting direction 3. **Portal Signal:** Wait for dimensional portal appearance 4. **ELITE/STRONG Quality:** Only trade highest quality mathematical signals 5. **Component Consensus:** Confirm 2+ components agree in Confluence mode **Example Implementation:** - Price approaching 21-period fractal high - Tensor field expanding upward (bullish mathematical alignment) - Green portal appears below price (trending regime confirmation) - ELITE quality signal with 3/3 components active - Enter long position with stop below fractal level **Risk Management:** - **Stop Placement:** Below/above fractal level that generated signal - **Position Sizing:** Based on Mahalanobis distance (higher distance = smaller size) - **Profit Targets:** Next fractal level or tensor field resistance ### The Regime Transition Strategy **Regime Change Detection:** 1. **Monitor Hurst Exponent:** Watch for persistent moves above/below thresholds 2. **Portal Color Change:** Regime transitions show different portal colors 3. **Background Intensity:** Increasing regime background intensity 4. **Mathematical Confirmation:** Wait for regime confirmation (hysteresis) **Trading Implementation:** - **Trending Transitions:** Trade momentum breakouts, follow trend - **Mean Reversion Transitions:** Trade range boundaries, fade extremes - **Random Transitions:** Trade breakouts with tight stops **Advanced Techniques:** - **Multi-Timeframe:** Confirm regime on higher timeframe - **Early Entry:** Enter on regime transition rather than confirmation - **Regime Strength:** Larger positions during strong regime signals ### The Information Flow Momentum Strategy **Flow Detection Protocol:** 1. **Monitor Transfer Entropy:** Watch for significant information flow shifts 2. **Volume Leadership:** Strong edge when volume leads price 3. **Flow Acceleration:** Increasing flow strength indicates momentum 4. **Directional Confirmation:** Ensure flow aligns with intended trade direction **Entry Signals:** - **Volume → Price Flow:** Enter during accumulation/distribution phases - **Price → Volume Flow:** Enter on momentum confirmation breaks - **Flow Reversal:** Counter-trend entries when flow reverses **Optimization:** - **Scalping:** Use immediate flow detection (2-5 bar lag) - **Swing Trading:** Use structural flow (10-20 bar lag) - **Multi-Asset:** Compare flow between correlated assets ### The Tensor Field Expansion Strategy **Field Mathematics:** The tensor field expansion indicates mathematical pressure building in market structure: **Expansion Phases:** 1. **Compression:** Field contracts, volatility decreases 2. **Tension Building:** Mathematical pressure accumulates 3. **Expansion:** Field expands rapidly with directional movement 4. **Resolution:** Field stabilizes at new equilibrium **Trading Applications:** - **Compression Trading:** Prepare for breakout during field contraction - **Expansion Following:** Trade direction of field expansion - **Reversion Trading:** Fade extreme field expansion - **Multi-Dimensional:** Consider all field layers for confirmation ### The Hawkes Process Event Strategy **Self-Exciting Jump Trading:** Understanding that market shocks cluster and create follow-on opportunities: **Jump Sequence Analysis:** 1. **Initial Jump:** First volatility jump detected 2. **Clustering Phase:** Hawkes intensity remains elevated 3. **Follow-On Opportunities:** Additional jumps more likely 4. **Decay Period:** Intensity gradually decreases **Implementation:** - **Jump Confirmation:** Wait for mathematical jump confirmation - **Direction Assessment:** Use other components for direction - **Clustering Trades:** Trade subsequent moves during high intensity - **Decay Exit:** Exit positions as Hawkes intensity decays ### The Fractal Confluence System **Multi-Timeframe Fractal Analysis:** Combining fractal levels across different periods for high-probability zones: **Confluence Zones:** - **Double Confluence:** 2 fractal levels align - **Triple Confluence:** 3+ fractal levels cluster - **Mathematical Confirmation:** Tensor field supports the level - **Information Flow:** Transfer entropy confirms direction **Trading Protocol:** 1. **Identify Confluence:** Find 2+ fractal levels within 1 ATR 2. **Mathematical Support:** Verify tensor field alignment 3. **Signal Quality:** Wait for STRONG or ELITE signal 4. **Risk Definition:** Use fractal level for stop placement 5. **Profit Targeting:** Next major fractal confluence zone --- ## ⚠️ COMPREHENSIVE RISK MANAGEMENT ### Mathematical Position Sizing **Mahalanobis Distance Integration:** Position size should inversely correlate with mathematical field strength: ``` Position Size = Base Size × (Threshold / Mahalanobis Distance) ``` **Risk Scaling Matrix:** - **Low Field Strength (<2.0):** Standard position sizing - **Moderate Field Strength (2.0-3.0):** 75% position sizing - **High Field Strength (3.0-4.0):** 50% position sizing - **Extreme Field Strength (>4.0):** 25% position sizing or no trade ### Signal Quality Risk Adjustment **Quality-Based Position Sizing:** - **ELITE Signals:** 100% of planned position size - **STRONG Signals:** 75% of planned position size - **GOOD Signals:** 50% of planned position size - **WEAK Signals:** No position or paper trading only **Component Agreement Scaling:** - **3/3 Components:** Full position size - **2/3 Components:** 75% position size - **1/3 Components:** 50% position size or skip trade ### Regime-Adaptive Risk Management **Trending Market Risk:** - **Wider Stops:** Allow for trend continuation - **Trend Following:** Trade with regime direction - **Higher Position Size:** Trend probability advantage - **Momentum Stops:** Trail stops based on momentum indicators **Mean-Reverting Market Risk:** - **Tighter Stops:** Quick exits on trend continuation - **Contrarian Positioning:** Trade against extremes - **Smaller Position Size:** Higher reversal failure rate - **Level-Based Stops:** Use fractal levels for stops **Random Market Risk:** - **Breakout Focus:** Trade only clear breakouts - **Tight Initial Stops:** Quick exit if breakout fails - **Reduced Frequency:** Skip marginal setups - **Range-Based Targets:** Profit targets at range boundaries ### Volatility-Adaptive Risk Controls **High Volatility Periods:** - **Reduced Position Size:** Account for wider price swings - **Wider Stops:** Avoid noise-based exits - **Lower Frequency:** Skip marginal setups - **Faster Exits:** Take profits more quickly **Low Volatility Periods:** - **Standard Position Size:** Normal risk parameters - **Tighter Stops:** Take advantage of compressed ranges - **Higher Frequency:** Trade more setups - **Extended Targets:** Allow for compressed volatility expansion ### Multi-Timeframe Risk Alignment **Higher Timeframe Trend:** - **With Trend:** Standard or increased position size - **Against Trend:** Reduced position size or skip - **Neutral Trend:** Standard position size with tight management **Risk Hierarchy:** 1. **Primary:** Current timeframe signal quality 2. **Secondary:** Higher timeframe trend alignment 3. **Tertiary:** Mathematical field strength 4. **Quaternary:** Market regime classification --- ## 📚 EDUCATIONAL VALUE AND MATHEMATICAL CONCEPTS ### Advanced Mathematical Concepts **Tensor Analysis in Markets:** The TMAE introduces traders to tensor analysis, a branch of mathematics typically reserved for physics and advanced engineering. Tensors provide a framework for understanding multi-dimensional market relationships that scalar and vector analysis cannot capture. **Information Theory Applications:** Transfer entropy implementation teaches traders about information flow in markets, a concept from information theory that quantifies directional causality between variables. This provides intuition about market microstructure and participant behavior. **Fractal Geometry in Trading:** The Hurst exponent calculation exposes traders to fractal geometry concepts, helping understand that markets exhibit self-similar patterns across multiple timeframes. This mathematical insight transforms how traders view market structure. **Stochastic Process Theory:** The Hawkes process implementation introduces concepts from stochastic process theory, specifically self-exciting point processes. This provides mathematical framework for understanding why market events cluster and exhibit memory effects. ### Learning Progressive Complexity **Beginner Mathematical Concepts:** - **Volatility Dimensions:** Understanding multi-dimensional analysis - **Regime Classification:** Learning market personality types - **Signal Democracy:** Algorithmic consensus building - **Visual Mathematics:** Interpreting mathematical concepts visually **Intermediate Mathematical Applications:** - **Mahalanobis Distance:** Statistical distance in multi-dimensional space - **Rescaled Range Analysis:** Fractal dimension measurement - **Information Entropy:** Quantifying uncertainty and causality - **Field Theory:** Understanding mathematical fields in market context **Advanced Mathematical Integration:** - **Tensor Field Dynamics:** Multi-dimensional market force analysis - **Stochastic Self-Excitation:** Event clustering and memory effects - **Categorical Composition:** Mathematical signal combination theory - **Topological Market Analysis:** Understanding market shape and connectivity ### Practical Mathematical Intuition **Developing Market Mathematics Intuition:** The TMAE serves as a bridge between abstract mathematical concepts and practical trading applications. Traders develop intuitive understanding of: - **How markets exhibit mathematical structure beneath apparent randomness** - **Why multi-dimensional analysis reveals patterns invisible to single-variable approaches** - **How information flows through markets in measurable, predictable ways** - **Why mathematical models provide probabilistic edges rather than certainties** --- ## 🔬 IMPLEMENTATION AND OPTIMIZATION ### Getting Started Protocol **Phase 1: Observation (Week 1)** 1. **Apply with defaults:** Use standard settings on your primary trading timeframe 2. **Study visual elements:** Learn to interpret tensor fields, portals, and streams 3. **Monitor dashboard:** Observe how metrics change with market conditions 4. **No trading:** Focus entirely on pattern recognition and understanding **Phase 2: Pattern Recognition (Week 2-3)** 1. **Identify signal patterns:** Note what market conditions produce different signal qualities 2. **Regime correlation:** Observe how Hurst regimes affect signal performance 3. **Visual confirmation:** Learn to read tensor field expansion and portal signals 4. **Component analysis:** Understand which components drive signals in different markets **Phase 3: Parameter Optimization (Week 4-5)** 1. **Asset-specific tuning:** Adjust parameters for your specific trading instrument 2. **Timeframe optimization:** Fine-tune for your preferred trading timeframe 3. **Sensitivity adjustment:** Balance signal frequency with quality 4. **Visual customization:** Optimize colors and intensity for your trading environment **Phase 4: Live Implementation (Week 6+)** 1. **Paper trading:** Test signals with hypothetical trades 2. **Small position sizing:** Begin with minimal risk during learning phase 3. **Performance tracking:** Monitor actual vs. expected signal performance 4. **Continuous optimization:** Refine settings based on real performance data ### Performance Monitoring System **Signal Quality Tracking:** - **ELITE Signal Win Rate:** Track highest quality signals separately - **Component Performance:** Monitor which components provide best signals - **Regime Performance:** Analyze performance across different market regimes - **Timeframe Analysis:** Compare performance across different session times **Mathematical Metric Correlation:** - **Field Strength vs. Performance:** Higher field strength should correlate with better performance - **Component Agreement vs. Win Rate:** More component agreement should improve win rates - **Regime Alignment vs. Success:** Trading with mathematical regime should outperform ### Continuous Optimization Process **Monthly Review Protocol:** 1. **Performance Analysis:** Review win rates, profit factors, and maximum drawdown 2. **Parameter Assessment:** Evaluate if current settings remain optimal 3. **Market Adaptation:** Adjust for changes in market character or volatility 4. **Component Weighting:** Consider if certain components should receive more/less emphasis **Quarterly Deep Analysis:** 1. **Mathematical Model Validation:** Verify that mathematical relationships remain valid 2. **Regime Distribution:** Analyze time spent in different market regimes 3. **Signal Evolution:** Track how signal characteristics change over time 4. **Correlation Analysis:** Monitor correlations between different mathematical components --- ## 🌟 UNIQUE INNOVATIONS AND CONTRIBUTIONS ### Revolutionary Mathematical Integration **First-Ever Implementations:** 1. **Multi-Dimensional Volatility Tensor:** First indicator to implement true tensor analysis for market volatility 2. **Real-Time Hawkes Process:** First trading implementation of self-exciting point processes 3. **Transfer Entropy Trading Signals:** First practical application of information theory for trade generation 4. **Democratic Component Voting:** First algorithmic consensus system for signal generation 5. **Fractal-Projected Signal Quality:** First system to predict signal quality at future price levels ### Advanced Visualization Innovations **Mathematical Visualization Breakthroughs:** - **Tensor Field Radiation:** Visual representation of mathematical field energy - **Dimensional Portal System:** Category theory visualization for regime transitions - **Information Flow Streams:** Real-time visual display of market information transfer - **Multi-Layer Fractal Grid:** Intelligent spacing and projection system - **Regime Intensity Mapping:** Dynamic background showing mathematical regime strength ### Practical Trading Innovations **Trading System Advances:** - **Quality-Weighted Signal Generation:** Signals rated by mathematical confidence - **Regime-Adaptive Strategy Selection:** Automatic strategy optimization based on market personality - **Anti-Spam Signal Protection:** Mathematical prevention of signal clustering - **Component Performance Tracking:** Real-time monitoring of algorithmic component success - **Field-Strength Position Sizing:** Mathematical volatility integration for risk management --- ## ⚖️ RESPONSIBLE USAGE AND LIMITATIONS ### Mathematical Model Limitations **Understanding Model Boundaries:** While the TMAE implements sophisticated mathematical concepts, traders must understand fundamental limitations: - **Markets Are Not Purely Mathematical:** Human psychology, news events, and fundamental factors create unpredictable elements - **Past Performance Limitations:** Mathematical relationships that worked historically may not persist indefinitely - **Model Risk:** Complex models can fail during unprecedented market conditions - **Overfitting Potential:** Highly optimized parameters may not generalize to future market conditions ### Proper Implementation Guidelines **Risk Management Requirements:** - **Never Risk More Than 2% Per Trade:** Regardless of signal quality - **Diversification Mandatory:** Don't rely solely on mathematical signals - **Position Sizing Discipline:** Use mathematical field strength for sizing, not confidence - **Stop Loss Non-Negotiable:** Every trade must have predefined risk parameters **Realistic Expectations:** - **Mathematical Edge, Not Certainty:** The indicator provides probabilistic advantages, not guaranteed outcomes - **Learning Curve Required:** Complex mathematical concepts require time to master - **Market Adaptation Necessary:** Parameters must evolve with changing market conditions - **Continuous Education Important:** Understanding underlying mathematics improves application ### Ethical Trading Considerations **Market Impact Awareness:** - **Information Asymmetry:** Advanced mathematical analysis may provide advantages over other market participants - **Position Size Responsibility:** Large positions based on mathematical signals can impact market structure - **Sharing Knowledge:** Consider educational contributions to trading community - **Fair Market Participation:** Use mathematical advantages responsibly within market framework ### Professional Development Path **Skill Development Sequence:** 1. **Basic Mathematical Literacy:** Understand fundamental concepts before advanced application 2. **Risk Management Mastery:** Develop disciplined risk control before relying on complex signals 3. **Market Psychology Understanding:** Combine mathematical analysis with behavioral market insights 4. **Continuous Learning:** Stay updated on mathematical finance developments and market evolution --- ## 🔮 CONCLUSION The Tensor Market Analysis Engine represents a quantum leap forward in technical analysis, successfully bridging the gap between advanced pure mathematics and practical trading applications. By integrating multi-dimensional volatility analysis, fractal market theory, and information flow dynamics, the TMAE reveals market structure invisible to conventional analysis while maintaining visual clarity and practical usability. ### Mathematical Innovation Legacy This indicator establishes new paradigms in technical analysis: - **Tensor analysis for market volatility understanding** - **Stochastic self-excitation for event clustering prediction** - **Information theory for causality-based trade generation** - **Democratic algorithmic consensus for signal quality enhancement** - **Mathematical field visualization for intuitive market understanding** ### Practical Trading Revolution Beyond mathematical innovation, the TMAE transforms practical trading: - **Quality-rated signals replace binary buy/sell decisions** - **Regime-adaptive strategies automatically optimize for market personality** - **Multi-dimensional risk management integrates mathematical volatility measures** - **Visual mathematical concepts make complex analysis immediately interpretable** - **Educational value creates lasting improvement in trading understanding** ### Future-Proof Design The mathematical foundations ensure lasting relevance: - **Universal mathematical principles transcend market evolution** - **Multi-dimensional analysis adapts to new market structures** - **Regime detection automatically adjusts to changing market personalities** - **Component democracy allows for future algorithmic additions** - **Mathematical visualization scales with increasing market complexity** ### Commitment to Excellence The TMAE represents more than an indicator—it embodies a philosophy of bringing rigorous mathematical analysis to trading while maintaining practical utility and visual elegance. Every component, from the multi-dimensional tensor fields to the democratic signal generation, reflects a commitment to mathematical accuracy, trading practicality, and educational value. ### Trading with Mathematical Precision In an era where markets grow increasingly complex and computational, the TMAE provides traders with mathematical tools previously available only to institutional quantitative research teams. Yet unlike academic mathematical models, the TMAE translates complex concepts into intuitive visual representations and practical trading signals. By combining the mathematical rigor of tensor analysis, the statistical power of multi-dimensional volatility modeling, and the information-theoretic insights of transfer entropy, traders gain unprecedented insight into market structure and dynamics. ### Final Perspective Markets, like nature, exhibit profound mathematical beauty beneath apparent chaos. The Tensor Market Analysis Engine serves as a mathematical lens that reveals this hidden order, transforming how traders perceive and interact with market structure. Through mathematical precision, visual elegance, and practical utility, the TMAE empowers traders to see beyond the noise and trade with the confidence that comes from understanding the mathematical principles governing market behavior. Trade with mathematical insight. Trade with the power of tensors. Trade with the TMAE. *"In mathematics, you don't understand things. You just get used to them." - John von Neumann* *With the TMAE, mathematical market understanding becomes not just possible, but intuitive.* — Dskyz, Trade with insight. Trade with anticipation.
Pine Script®指标
由DskyzInvestments提供
17
CoffeeShopCrypto Supply Demand PPO AdvancedCoffeeShopCrypto PPO Advanced is a structure-aware momentum oscillator and price-trend overlay designed to help traders interpret momentum strength, exhaustion, and continuation across evolving market conditions. It’s not a “buy/sell” signal tool — it's a momentum context tool that helps confirm trend intent. Original Code derived from the Price Oscillator Indicators (PPO) found in the TradingView Technical Indicators categories. You can view the info and calculation for the original PPO here www.tradingview.com Much like the MACD, the PPO uses a couple lagging indicators to present Momentum as a percentage. But it lacks context to market structure. What It’s Based On This tool is based on a dual-moving-average PPO oscillator structure (Percentage Price Oscillator) enhanced by: Oscillator pivot structure: detection of Lower Highs (LH) and Higher Lows (HL) inside the oscillator. Detection of Supply and Demand Trends via Market Absorption Ability to transfer its average plots to price action Detection of Trend Exhaustion Real-time price-based exhaustion levels: projecting potential future supply and demand using trendlines from weakening momentum. Integrated fast and slow Moving Averages on price using the same inputs as the oscillator, to visualize alignment between short- and long-term trends. These elements combine momentum context with price action in a visual, intuitive system. How It Works 1. Oscillator Structure LHs (above zero): momentum weakening in uptrends. HLs (below zero): momentum strengthening in downtrends. Only valid pivots are shown (e.g., an LH must be preceded by a valid LL). 2. Exhaustion Levels Green demand lines: price is making new lows, but oscillator prints HL → potential exhaustion. Red supply lines: price is making new highs, but oscillator prints LH → potential exhaustion. These lines are future-facing, projecting likely reaction zones based on momentum weakening. 3. Moving Averages on Price Two MAs are drawn on the price chart: Fast MA (same length as PPO short input) Slow MA (same length as PPO long input) These are not signal lines — they're visual guides for trend alignment. MA crossover = PO crosses zero. This indicates short- and long-term momentum are syncing — a powerful signal of trend conviction. When price is above both MAs, and the PO is rising above zero, bullish momentum is dominant. When price is below both MAs, and the PO is falling below zero, bearish momentum dominates. How Traders Can Use It ✅ Spot Trend Initiation Wait for clear trend confirmation in price. Use PPO Momentum+ to confirm momentum structure is aligned (e.g., HH/HL in oscillator + price above both MAs). 🔁 Track Continuations In uptrends, look for oscillator HH and HL sequences with price holding above both MAs. In downtrends, seek LL and LH sequences with price below both MAs. ⚠️ Watch for Exhaustion Price breaking below red (supply) lines after oscillator LH = bearish exhaustion signal. Price breaking above green (demand) lines after oscillator HL = bullish exhaustion signal. These levels act like pre-mapped S/R zones, showing where momentum previously failed and price may react. Why This Is Different Momentum tools often lag or mislead when used blindly. This tool visualizes structural failure in momentum and maps potential outcomes. The integration of oscillator and price-based tools ensures traders are always reading context, not just raw signals. Demand Trendlines Demand trendlines show us Wykoff's law of "Absorbed Supply Reversal" In real time. When aggressive selling pressure is persistently absorbed by passive buying interest without significant downward price continuation, and supply becomes exhausted, the market structure shifts as demand regains control—resulting in a directional reversal to the upside. This commonly happens in a 3 phase interaction of price. 1. Selling pressure is absorbed quickly by buyers. This PPO tool will calculate the trend of this absorption process 2. After there is a notable Bearish Exhaustion of price action, the PPO tool will draw a trendline of this absorption showing us the potential future prices where aggressive buyers will want to step in at lower prices. 3. After higher lows are defined in the oscillator, you'll see prices react in a strong bullish pattern at this trendline where aggressive buyers stepped in to reverse price action to the upside. Supply Trendlines Supply trendlines show us Wykoff's law of "Absorbed Demand Reversal" In real time. When aggressive buying pressure is persistently absorbed by passive selling interest without significant downward price continuation, and demand becomes exhausted, the market structure shifts as supply regains control—resulting in a directional reversal to the downside. This commonly happens in a 3 phase interaction of price. 1. Buying pressure is absorbed quickly by sellers. This PPO tool will calculate the trend of this absorption process. 2. After there is a notable Bullish Exhaustion of price action, the PPO tool will draw a trendline of this absorption showing us the potential future prices where aggressive sellers will want to step in at higher prices. 3. After lower highs are defined in the oscillator, you'll see prices react in a strong bearish pattern at this trendline where aggressive sellers stepped in to reverse price action to the downside. Lower High and Higher Low Signals When the oscillator signals Lower Highs or High Lows its only noting that momentum in that trend direction is slowing. THis indicates a coming pause in the market and the proceeding longs of an uptrend or shorts of a downtrend should be taken with caution. **These LH and HL markers are not reading as divergences in price vs momentum.** They are simply registering against the highs and lows of itself.. Moving Averages on Price Action The Oscillator will cross over its ZERO level the same time your Short and Long MAs cross each other. This will indicate that the short term average trend is moving ahead of the long term. Crossovers are not an entry signal. It's a method in determining you current timeframe trend strength. Always observe price action as it passes through each of your moving averages and compare it to the positioning and direction of the oscillator. If price dips in between the moving averages while the oscillator still shows a strong trend strength, you can wait for price to move ahead of your fast moving average. Bar Colors and Signal Line for Trend Strength Good Bullish Trend = Oscillator above zero + Signal rising below Oscillator Weak Bullish Trend = Oscillator above zero + Signal above Oscillator Good Bearish Trend = Oscillator below zero + Signal falling above Oscillator Weak Bearish Trend = Oscillator below zero + Signal below Oscillator Bar Colors Bars are colored to match Oscillator Momentum Strength. Colors are set by user. Why alter the known PPO (Percentage Price Oscillator) in this manner? The PPO tool is great for measuring the strength as percentage of price action over and average amount of candles however, with these changes, you know have the ability to correlate: Wycoff theory of supply and demand, Measure the depth of reversals and pullback by price positioning against moving averages, Project potential reversal and exhaustion pricing, Visibly note the structure of momentum much like you would note market structure, Its not enough to know there is momentum. Its better to know A) Is it enough B) Is there something in the way which will cause price to push back C) Does this momentum correlate to the prevailing trend
Pine Script®指标
由CoffeeshopCrypto提供
Fibonacci Circle Zones🟩 The Fibonacci Circle Zones indicator is a technical visualization tool, building upon the concept of traditional Fibonacci circles. It provides configurable options for analyzing geometric relationships between price and time, used to identify potential support and resistance zones derived from circle-based projections. The indicator constructs these Fibonacci circles based on two user-selected anchor points (Point A and Point B), which define the foundational price range and time duration for the geometric analysis. Key features include multiple mathematical Circle Formulas for radius scaling and several options for defining the circle's center point, enabling exploration of complex, non-linear geometric relationships between price and time distinct from traditional linear Fibonacci analysis. Available formulas incorporate various mathematical constants (π, e, φ variants, Silver Ratio) alongside traditional Fibonacci ratios, facilitating investigation into different scaling hypotheses. Furthermore, selecting the Center point relative to the A-B anchors allows these circular time-price patterns to be constructed and analyzed from different geometric perspectives. Analysis can be further tailored through detailed customization of up to 12 Fibonacci levels, including their mathematical values, colors, and visibility.. 📚 THEORY and CONCEPT 📚 Fibonacci circles represent an application of Fibonacci principles within technical analysis, extending beyond typical horizontal price levels by incorporating the dimension of time. These geometric constructions traditionally use numerical proportions, often derived from the Fibonacci sequence, to project potential zones of price-time interaction, such as support or resistance. A theoretical understanding of such geometric tools involves considering several core components: the significance of the chosen geometric origin or center point , the mathematical principles governing the proportional scaling of successive radii, and the fundamental calculation considerations (like chart scale adjustments and base radius definitions) that influence the resulting geometry and ensure its accurate representation. ⨀ Circle Center ⨀ The traditional construction methodology for Fibonacci circles begins with the selection of two significant anchor points on the chart, usually representing a key price swing, such as a swing low (Point A) and a subsequent swing high (Point B), or vice versa. This defined segment establishes the primary vector—representing both the price range and the time duration of that specific market move. From these two points, a base distance or radius is derived (this calculation can vary, sometimes using the vertical price distance, the time duration, or the diagonal distance). A center point for the circles is then typically established, often at the midpoint (time and price) between points A and B, or sometimes anchored directly at point B. Concentric circles are then projected outwards from this center point. The radii of these successive circles are calculated by multiplying the base distance by key Fibonacci ratios and other standard proportions. The underlying concept posits that markets may exhibit harmonic relationships or cyclical behavior that adheres to these proportions, suggesting these expanding geometric zones could highlight areas where future price movements might decelerate, reverse, or find equilibrium, reflecting a potential proportional resonance with the initial defining swing in both price and time. The Fibonacci Circle Zones indicator enhances traditional Fibonacci circle construction by offering greater analytical depth and flexibility: it addresses the origin point of the circles: instead of being limited to common definitions like the midpoint or endpoint B, this indicator provides a selection of distinct center point calculations relative to the initial A-B swing. The underlying idea is that the geometric source from which harmonic projections emanate might vary depending on the market structure being analyzed. This flexibility allows for experimentation with different center points (derived algorithmically from the A, B, and midpoint coordinates), facilitating exploration of how price interacts with circular zones anchored from various perspectives within the defining swing. Potential Center Points Setup : This view shows the anchor points A and B , defined by the user, which form the basis of the calculations. The indicator dynamically calculates various potential Center points ( C through N , and X ) based on the A-B structure, representing different geometric origins available for selection in the settings. Point X holds particular significance as it represents the calculated midpoint (in both time and price) between A and B. This 'X' point corresponds to the default 'Auto' center setting upon initial application of the indicator and aligns with the centering logic used in TradingView's standard Fibonacci Circle tool, offering a familiar starting point. The other potential center points allow for exploring circles originating from different geometric anchors relative to the A-B structure. While detailing the precise calculation for each is beyond the scope of this overview, they can be broadly categorized: points C through H are derived from relationships primarily within the A-B time/price range, whereas points I through N represent centers projected beyond point B, extrapolating the A-B geometry. Point J, for example, is calculated as a reflection of the A-X midpoint projected beyond B. This variety provides a rich set of options for analyzing circle patterns originating from historical, midpoint, and extrapolated future anchor perspectives. Default Settings (Center X, FibCircle) : Using the default Center X (calculated midpoint) with the default FibCircle . Although circles begin plotting only after Point B is established, their curvature shows they are geometrically centered on X. This configuration matches the standard TradingView Fib Circle tool, providing a baseline. Centering on Endpoint B : Using Point B, the user-defined end of the swing, as the Center . This anchors the circular projections directly to the swing's termination point. Unlike centering on the midpoint (X) or start point (A), this focuses the analysis on geometric expansion originating precisely from the conclusion of the measured A-B move. Projected Center J : Using the projected Point J as the Center . Its position is calculated based on the A-B swing (conceptually, it represents a forward projection related to the A-X midpoint relationship) and is located chronologically beyond Point B. This type of forward projection often allows complete circles to be visualized as price develops into the corresponding time zone. Time Symmetry Projection (Center L) : Uses the projected Point L as the Center . It is located at the price level of the start point (A), projected forward in time from B by the full duration of the A-B swing . This perspective focuses analysis on temporal symmetry , exploring geometric expansions from a point representing a full time cycle completion anchored back at the swing's origin price level. ⭕ Circle Formula Beyond the center point , the expansion of the projected circles is determined by the selected Circle Formula . This setting provides different mathematical methods, or scaling options , for scaling the circle radii. Each option applies a distinct mathematical constant or relationship to the base radius derived from the A-B swing, allowing for exploration of various geometric proportions. eScaled Mathematical Basis: Scales the radius by Euler's number ( e ≈ 2.718), the base of natural logarithms. This constant appears frequently in processes involving continuous growth or decay. Enables investigation of market geometry scaled by e , exploring relationships potentially based on natural exponential growth applied to time-price circles, potentially relevant for analyzing phases of accelerating momentum or volatility expansion. FibCircle Mathematical Basis: Scales the radius to align with TradingView’s built-in Fibonacci Circle Tool. Provides a baseline circle size, potentially emulating scaling used in standard drawing tools, serving as a reference point for comparison with other options. GoldenFib Mathematical Basis: Scales the radius by the Golden Ratio (φ ≈ 1.618). Explores the fundamental Golden Ratio proportion, central to Fibonacci analysis, applied directly to circular time-price geometry, potentially highlighting zones reflecting harmonic expansion or retracement patterns often associated with φ. GoldenContour Mathematical Basis: Scales the radius by a factor derived from Golden Ratio geometry (√(1 + φ²) / 2 ≈ 0.951). It represents a specific geometric relationship derived from φ. Allows analysis using proportions linked to the geometry of the Golden Rectangle, scaled to produce circles very close to the initial base radius. This explores structural relationships often associated with natural balance or proportionality observed in Golden Ratio constructions. SilverRatio Mathematical Basis: Scales the radius by the Silver Ratio (1 + √2 ≈ 2.414). The Silver Ratio governs relationships in specific regular polygons and recursive sequences. Allows exploration using the proportions of the Silver Ratio, offering a significant expansion factor based on another fundamental metallic mean for comparison with φ-based methods. PhiDecay Mathematical Basis: Scales the radius by φ raised to the power of -φ (φ⁻ᵠ ≈ 0.53). This unique exponentiation explores a less common, non-linear transformation involving φ. Explores market geometry scaled by this specific phi-derived factor which is significantly less than 1.0, offering a distinct contractile proportion for analysis, potentially relevant for identifying zones related to consolidation phases or decaying momentum. PhiSquared Mathematical Basis: Scales the radius by φ squared, normalized by dividing by 3 (φ² / 3 ≈ 0.873). Enables investigation of patterns related to the φ² relationship (a key Fibonacci extension concept), visualized at a scale just below 1.0 due to normalization. This scaling explores projections commonly associated with significant trend extension targets in linear Fibonacci analysis, adapted here for circular geometry. PiScaled Mathematical Basis: Scales the radius by Pi (π ≈ 3.141). Explores direct scaling by the fundamental circle constant (π), investigating proportions inherent to circular geometry within the market's time-price structure, potentially highlighting areas related to natural market cycles, rotational symmetry, or full-cycle completions. PlasticNumber Mathematical Basis: Scales the radius by the Plastic Number (approx 1.3247), the third metallic mean. Like φ and the Silver Ratio, it is the solution to a specific cubic equation and relates to certain geometric forms. Introduces another distinct fundamental mathematical constant for geometric exploration, comparing market proportions to those potentially governed by the Plastic Number. SilverFib Mathematical Basis: Scales the radius by the reciprocal Golden Ratio (1/φ ≈ 0.618). Explores proportions directly related to the core 0.618 Fibonacci ratio, fundamental within Fibonacci-based geometric analysis, often significant for identifying primary retracement levels or corrective wave structures within a trend. Unscaled Mathematical Basis: No scaling applied. Provides the base circle defined by points A/B and the Center setting without any additional mathematical scaling, serving as a pure geometric reference based on the A-B structure. 🧪 Advanced Calculation Settings Two advanced settings allow further refinement of the circle calculations: matching the chart's scale and defining how the base radius is calculated from the A-B swing. The Chart Scale setting ensures geometric accuracy by aligning circle calculations with the chart's vertical axis display. Price charts can use either a standard (linear) or logarithmic scale, where vertical distances represent price changes differently. The setting offers two options: Standard : Select this option when the price chart's vertical axis is set to a standard linear scale. Logarithmic : It is necessary to select this option if the price chart's vertical axis is set to a logarithmic scale. Doing so ensures the indicator adjusts its calculations to maintain correct geometric proportions relative to the visual price action on the log-scaled chart. The Radius Calc setting determines how the fundamental base radius is derived from the A-B swing, offering two primary options: Auto : This is the default setting and represents the traditional method for radius calculation. This method bases the radius calculation on the vertical price range of the A-B swing, focusing the geometry on the price amplitude. Geometric : This setting provides an alternative calculation method, determining the base radius from the diagonal distance between Point A and Point B. It considers both the price change and the time duration relative to the chart's aspect ratio, defining the radius based on the overall magnitude of the A-B price-time vector. This choice allows the resulting circle geometry to be based either purely on the swing's vertical price range ( Auto ) or on its combined price-time movement ( Geometric ). 🖼️ CHART EXAMPLES 🖼️ Default Behavior (X Center, FibCircle Formula) : This configuration uses the midpoint ( Center X) and the FibCircle scaling Formula , representing the indicator's effective default setup when 'Auto' is selected for both options initially. This is designed to match the output of the standard TradingView Fibonacci Circle drawing tool. Center B with Unscaled Formula : This example shows the indicator applied to an uptrend with the Center set to Point B and the Circle Formula set to Unscaled . This configuration projects the defined levels (0.236, 0.382, etc.) as arcs originating directly from the swing's termination point (B) without applying any additional mathematical scaling from the formulas. Visualization with Projected Center J : Here, circles are centered on the projected point J, calculated from the A-B structure but located forward in time from point B. Notice how using this forward-projected origin allows complete inner circles to be drawn once price action develops into that zone, providing a distinct visual representation of the expanding geometric field compared to using earlier anchor points. ( Unscaled formula used in this example). PhiSquared Scaling from Endpoint B : The PhiSquared scaling Formula applied from the user-defined swing endpoint (Point B). Radii expand based on a normalized relationship with φ² (the square of the Golden Ratio), creating a unique geometric structure and spacing between the circle levels compared to other formulas like Unscaled or GoldenFib . Centering on Swing Origin (Point A) : Illustrates using Point A, the user-defined start of the swing, as the circle Center . Note the significantly larger scale and wider spacing of the resulting circles. This difference occurs because centering on the swing's origin (A) typically leads to a larger base radius calculation compared to using the midpoint (X) or endpoint (B). ( Unscaled formula used). Center Point D : Point D, dynamically calculated from the A-B swing, is used as the origin ( Center =D). It is specifically located at the price level of the swing's start point (A) occurring precisely at the time coordinate of the swing's end point (B). This offers a unique perspective, anchoring the geometric expansion to the initial price level at the exact moment the defining swing concludes. ( Unscaled formula shown). Center Point G : Point G, also dynamically calculated from the A-B swing, is used as the origin ( Center =G). It is located at the price level of the swing's endpoint (B) occurring at the time coordinate of the start point (A). This provides the complementary perspective to Point D, anchoring the geometric expansion to the final price level achieved but originating from the moment the swing began . As observed in the example, using Point G typically results in very wide circle projections due to its position relative to the core A-B action. ( Unscaled formula shown). Center Point I: Half-Duration Projection : Using the dynamically calculated Point I as the Center . Located at Point B's price level but projected forward in time by half the A-B swing duration , Point I's calculated time coordinate often falls outside the initially visible chart area. As the chart progresses, this origin point will appear, revealing large, sweeping arcs representing geometric expansions based on a half-cycle temporal projection from the swing's endpoint price. ( Unscaled formula shown). Center Point M : Point M, also dynamically calculated from the A-B swing, serves as the origin ( Center =M). It combines the midpoint price level (derived from X) with a time coordinate projected forward from Point B by the full duration of the A-B swing . This perspective anchors the geometric expansion to the swing's balance price level but originates from the completion point of a full temporal cycle relative to the A-B move. Like other projected centers, using M allows for complete circles to be visualized as price progresses into its time zone. ( SilverFib formula shown). Geometric Validation & Functionality : Comparing the indicator (red lines), using its default settings ( Center X, FibCircle Formula ), against TradingView's standard Fib Circle tool (green lines/white background). The precise alignment, particularly visible at the 1.50 and 2.00 levels shown, validates the core geometry calculation. 🛠️ CONFIGURATION AND SETTINGS 🛠️ The Fibonacci Circle Zones indicator offers a range of configurable settings to tailor its functionality and visual representation. These options allow customization of the circle origin, scaling method, level visibility, visual appearance, and input points. Center and Formula Settings for selecting the circle origin and scaling method. Center : Dropdown menu to select the origin point for the circles. Auto : Automatically uses point X (the calculated midpoint between A and B). Selectable points including start/end (A, B), midpoint (X), plus various points derived from or projected beyond the A-B swing (C-N). Circle Formula : Dropdown menu to select the mathematical method for scaling circle radii. Auto : Automatically selects a default formula ('FibCircle' if Center is 'X', 'Unscaled' otherwise). Includes standard Fibonacci scaling ( FibCircle, GoldenFib ), other mathematical constants ( PiScaled, eScaled ), metallic means ( SilverRatio ), phi transformations ( PhiDecay, PhiSquared ), and others. Fib Levels Configuration options for the 12 individual Fibonacci levels. Advanced Settings Settings related to core calculation methods. Radius Calc : Defines how the base radius is calculated (e.g., 'Auto' for vertical price range, 'Geometric' for diagonal price-time distance). Chart Scale : Aligns circle calculations with the chart's vertical axis setting ('Standard' or 'Logarithmic') for accurate visual proportions. Visual Settings Settings controlling the visual display of the indicator elements. Plots : Dropdown controlling which parts of the calculated circles are displayed ( Upper , All , or Lower ). Labels : Dropdown controlling the display of the numerical level value labels ( All , Left , Right , or None ). Setup : Dropdown controlling the visibility of the initial setup graphics ( Show or Hide ). Info : Dropdown controlling the visibility of the small information table ( Show or Hide ). Text Size : Adjusts the font size for all text elements displayed by the indicator (Value ranges from 0 to 36). Line Width : Adjusts the width of the circle plots (1-10). Time/Price Inputs for the anchor points defining the base swing. These settings define the start (Point A) and end (Point B) of the price swing used for all calculations. Point A (Time, Price) : Input fields for the exact time coordinate and price level of the swing's starting point (A). Point B (Time, Price) : Input fields for the exact time coordinate and price level of the swing's ending point (B). Interactive Adjustment : Points A and B can typically be adjusted directly by clicking and dragging their markers on the chart (if 'Setup' is set to 'Show'). Changes update settings automatically. 📝 NOTES 📝 Fibonacci circles begin plotting only once the time corresponding to Point B has passed and is confirmed on the chart. While potential center locations might be visible earlier (as shown in the setup graphic), the final circle calculations require the complete geometry of the A-B swing. This approach ensures that as new price bars form, the circles are accurately rendered based on the finalized A-B relationship and the chosen center and scaling. The indicator's calculations are anchored to user-defined start (A) and end (B) points on the chart. When switching between charts with significantly different price scales (e.g., from an index at 5,000 to a crypto asset at $0.50), it is typically necessary to adjust these anchor points to ensure the circle elements are correctly positioned and scaled. ⚠️ DISCLAIMER ⚠️ The Fibonacci Circle Zones indicator is a visual analysis tool designed to illustrate Fibonacci relationships through geometric constructions incorporating curved lines, providing a structured framework for identifying potential areas of price interaction. Like all technical and visual indicators, these visual representations may visually align with key price zones in hindsight, reflecting observed price dynamics. It is not intended as a predictive or standalone trading signal indicator. The indicator calculates levels and projections using user-defined anchor points and Fibonacci ratios. While it aims to align with TradingView’s standard Fibonacci circle tool by employing mathematical and geometric formulas, no guarantee is made that its calculations are identical to TradingView's proprietary methods. 🧠 BEYOND THE CODE 🧠 The Fibonacci Circle Zones indicator, like other xxattaxx indicators , is designed with education and community collaboration in mind. Its open-source nature encourages exploration, experimentation, and the development of new Fibonacci and grid calculation indicators and tools. We hope this indicator serves as a framework and a starting point for future Innovation and discussions.
Pine Script®指标
由xxattaxx提供
3
BTC – VERI - Valuation & Entity Ratio IndexVERI: Valuation & Entity Ratio IndexObservation-only. Data: IntoTheBlock. Overview & Philosophy The name VERI is derived from the Latin Veritas (Truth). In a crypto market often driven by deceptive speculative noise, this indicator seeks to establish the "On-Chain Truth" of a price trend. It operates on the thesis that price action is only sustainable when verified by high-conviction capital flows.VERI is a fundamental composite oscillator that fuses Entity Behavior (Who is holding?) with Network Valuation (Is the price fair?) to identify Bitcoin market cycle extremes. The "Alpha" Why this Composite stands out: on-chain metrics often tell only half the story. MVRV tells you if the price is cheap, but not if anyone is actually buying. Whale Activity tells you if large players are moving, but not if they are accumulating at a value discount. VERI fuses these two dimensions into a single Z-Score. It identifies the rare, high-probability moments where Smart Money Conviction intersects with Deep Value. Methodology The Mathematics of VERI: The indicator constructs a composite index using three fundamental metrics from IntoTheBlock: The "Who" (Entity Ratio) : We calculate the flow ratio between Whales (>1% supply holders) and Retail (<0.1% supply holders). A rising ratio indicates supply is transferring from weak hands to strong hands. The "Why" (Valuation Multiplier) : We utilize the MVRV (Market Value to Realized Value) ratio. To isolate value opportunities, we use the inverse (1 / MVRV). The Fusion : These factors are multiplied to create the raw VERI index. Normalization & Inversion We apply a rolling Z-Score (standard deviation from the mean) and invert the result. How to Interpret the Indicator Because the output is inverted, the visual logic matches price action intuitively: 🟥 Distribution Zone (High Values > 1.5): The Signal: "Low Conviction Overvaluation." Context: The price is historically expensive relative to the cost basis (High MVRV), and Whales are distributing coins to Retail.Implication: Historically precedes macro tops or deep corrections. 🟩 Accumulation Zone (Low Values < -1.5): The Signal: "High Conviction Undervaluation."Context: The price is historically cheap (Low MVRV), and Whales are aggressively accumulating relative to Retail. Implication: Historically precedes macro bottoms and generational entry points. Zero Line : Represents the historical baseline. A crossover of the zero line often confirms a regime shift (e.g., from Bear to Bull). Visual Guide & Features Dynamic Coloring: The line turns Red in the Distribution Zone, Blue in the Accumulation Zone, and Orange during neutral trends. Zone Labels: Static labels are pinned to the left side of the chart for immediate context. The "Data Check" Monitor (Status Table): Since this indicator relies on third-party fundamental data, we have included a diagnostic table in the bottom-right corner. Data Check Monitor Guide STATUS: LIVE (Green): The indicator is functioning correctly. All data feeds (Whales, Retail, MVRV) are being retrieved successfully. STATUS: WAIT (Red): The indicator cannot retrieve data. This might happen for some reasons, e.g. your TradingView plan may not support IntoTheBlock integration. Settings Lookback Period (Default: 365): The window used for Z-Score normalization. We use a full year to smooth out seasonal volatility. Smoothing (Default: 7): A 7-day smoothing is applied to the signal to filter out daily noise. Zone Thresholds: Users can customize the specific Z-Score levels for the Distribution and Accumulation bands. Disclaimer This script is for research and educational purposes only. It uses historical on-chain data to visualize market structure and does not constitute financial advice. Past performance of whale entities does not guarantee future results. Tags bitcoin, btc, on-chain, mvrv, whales, valuation, fundamentals, cycle, oscillator, veri
Pine Script®指标
由Rob_Maths提供
Bollinger Bands Delta Matrix Analytics [BDMA] Bollinger Bands Delta Matrix Analytics (BDMA) v7.0 Deep Kinetic Engine – 5x8 Volatility & Delta Decision Matrix 1. Introduction & Concept Bollinger Bands Delta Matrix Analytics (BDMA) v7.0 is an analytical framework that merges: - Spatial analysis via Bollinger Bands (%B location), - with a 4-factor Deep Kinetic Engine based on: • Total Volume • Buy Volume • Sell Volume • Delta (Buy – Sell) Z-Scores and converts them into an expanded 5×8 decision matrix that continuously tracks where price is trading and how the underlying orderflow is behaving. BDMA is not a trading system or strategy. It does not generate entry/exit signals. Instead, it provides a structured contextual map of volatility, volume, and delta so traders can: - identify climactic extensions vs. fakeouts, - distinguish strong initiative moves vs. passive absorption, - and detect squeezes, traps, and liquidity voids with a unified visual dashboard. 2. Spatial Engine – Bollinger S-States (S1–S5) The spatial dimension of BDMA comes from classic Bollinger Bands. Price location is expressed as Percent B (%B) and mapped into 5 spatial states (S-States): S1 – Hyper Extension (Above Upper Band) Price has pushed beyond the upper Bollinger Band. Often associated with parabolic or blow-off behavior, late-stage momentum, and elevated reversal risk. S2 – Resistance Test (Upper Zone) Price trades in the upper Bollinger region but remains inside the bands. Represents a sustained test of resistance, typically within an established or emerging uptrend. S3 – Neutral Zone (Middle) Price hovers around the mid-band. This is the mean reversion gravity field where the market often consolidates or transitions between regimes. S4 – Support Test (Lower Zone) Price trades in the lower Bollinger region but inside the bands. Represents a sustained test of support within range or downtrend structures. S5 – Hyper Drop (Below Lower Band) Price extends below the lower Bollinger Band. Often aligned with panic, forced liquidations, or capitulation-type behavior, with increased snap-back risk. These 5 S-States define the vertical axis (rows) of the BDMA matrix. 3. Deep Kinetic Engine – 4-Factor Z-Score & D-States (D1–D8) The Deep Kinetic Engine transforms raw volume and delta into standardized Z-Scores to measure how abnormal current activity is relative to its recent history. For each bar: - Raw Buy Volume is estimated from the candle’s position within its range - Raw Sell Volume is complementary to buy volume - Raw Delta = Buy Volume – Sell Volume - Total Volume = Buy Volume + Sell Volume These 4 series are then normalized using a unified Z-Score lookback to produce: 1. Z_Vol_Total – overall activity and liquidity intensity 2. Z_Vol_Buy – aggression from buyers (attack) 3. Z_Vol_Sell – aggression from sellers (defense or attack) 4. Z_Delta – net victory of one side over the other Thresholds for Extreme, Significant, and Neutral Z-Score levels are fully configurable, allowing you to tune the sensitivity of the kinetic states. Using Z_Vol_Total and Z_Delta (plus threshold logic), BDMA assigns one of 8 Deep Kinetic states (D-States): D1 – Climax Buy Extreme Total Volume + Extreme Positive Delta → Buying climax or blow-off behavior. D2 – Strong Buy High Volume + High Positive Delta → Confirmed bullish initiative activity. D3 – Weak Buy / Fakeout Low Volume + High Positive Delta → Bullish delta without commitment, low-liquidity breakout risk. D4 – Absorption / Conflict High Volume + Neutral Delta → Aggressive two-way trade, strong absorption, war zone behavior. D5 – Neutral Low Volume + Neutral Delta → Low-energy environment with low conviction. D6 – Weak Sell / Fakeout Low Volume + High Negative Delta → Bearish delta without commitment, low-liquidity breakdown risk. D7 – Strong Sell High Volume + High Negative Delta → Confirmed bearish initiative activity. D8 – Capitulation Extreme Volume + Extreme Negative Delta → Panic selling or capitulation regime. These 8 D-States define the horizontal axis (columns) of the BDMA matrix. 4. The 5×8 BDMA Decision Matrix The core of BDMA is a 5×8 matrix where: - Rows (1–5) = Spatial S-States (S1…S5) - Columns (1–8) = Kinetic D-States (D1…D8) Each of the 40 possible combinations (SxDy) is pre-computed and mapped to: - a Status or Regime Title (for example: Climax Breakout, Bear Trap Spring, Capitulation Breakdown), - a Bias (Climactic Bull, Neutral, Strong Bear, Conflict or Reversal Risk, and similar labels), - and a Strategic Signal or Consideration (for example: High reversal risk, Wait for confirmation, Low probability zone – avoid). Internally, BDMA resolves all 40 regimes so the current state can be displayed on the dashboard without performance overhead. 5. Key Regime Families (How to Read the Matrix) 5.1. Breakouts and Breakdowns Climax Breakout (Top-side) Spatial S1 with Kinetic D1 or D2 Bias: Explosive or Extreme Bull Signal: - Strong or climactic upside extension with abnormal bullish orderflow. - Trend continuation is possible, but reversal risk is extremely high after blow-off phases. Low-Conviction Breakout (Fakeout Risk) S1 with D3 (Weak Buy, low liquidity) Bias: Weak Bull – Caution Signal: - Breakout not supported by volume. - Elevated risk of failed auction or bull trap. Capitulation Breakdown (Bottom-side) Spatial S5 with Kinetic D8 Bias: Climactic Bear (panic) Signal: - Capitulation-type selling or forced liquidations. - Trend can still proceed, but snap-back or violent short-covering risk is high. Initiative Breakdown vs. Weak Breakdown - Strong, high-volume breakdown typically corresponds to D7 (Strong Sell). - Low-volume breakdown often corresponds to D6 (Weak Sell or Fakeout) with potential for failure. 5.2. Absorption, Traps and Springs Absorption at Resistance (Top-side conflict) S1 or S2 with D4 (Absorption or Conflict) Bias: Conflict – Extreme Tension Signal: - Heavy two-way trade near resistance. - Potential distribution or reversal if sellers begin to dominate. Bull Trap or Failed Auction Typically S1 with D6 (Weak Sell breakdown behavior after a top-side attempt) Indicates a breakout attempt that fails and reverses, often after poor liquidity structure. Absorption at Support and Bear Trap (Spring) S4 or S5 with D4 or D3 Bias: Conflict or Weak Bear – Reversal Risk Signal: - Aggressive buying into lows (spring or shakeout behavior). - Potential bear trap if price reclaims lost territory. 5.3. Trend Phases Strong Uptrend Phases Typically seen when S2–S3 combine with strong bullish kinetic behavior. Bias: Strong or Extreme Bull Signal: - Pullbacks into S3 or S4 with supportive kinetic states often act as trend continuation zones. Strong Downtrend Phases Typically seen when S3–S4 combine with strong bearish kinetic behavior. Bias: Strong or Extreme Bear Signal: - Rallies into resistance with strong bearish kinetic backing may act as continuation sell zones. 5.4. Neutral, Exhaustion and Squeeze Exhaustion or Liquidity Void S1 or S5 with D5 (Neutral kinetics) Bias: Neutral or Exhaustion Signal: - Spatial extremes without kinetic confirmation. - Often marks the end of a move, with poor follow-through. Choppy, Low-Activity Range S3 with D5 Bias: Neutral Signal: - Low volume, low conviction market. - Typically a low-probability environment where standing aside can be logical. Squeeze or High-Tension Zone S3 with D4 or tightly clustered kinetic values Bias: Conflict or High Tension Signal: - Hidden battle inside a volatility contraction. - Often precedes large directionally-biased moves. 6. Dashboard Layout & Reading Guide When Show Dashboard is enabled, BDMA displays: 1. Title and Status Line Name of the current regime (for example: Climax Breakout, Bear Trap Spring, Mean Reversion). 2. Bias Line Plain-language summary of directional context such as Climactic Bull, Strong Bear, Neutral, or Conflict and Reversal Risk. 3. Signal or Strategic Notes Concise guidance focused on risk and context, not entries. For example: - High reversal risk – aggressive traders only - Wait for confirmation (break or rejection) - Low probability zone – avoid taking new positions 4. Kinetic Profile (4-Factor Z-Score) Shows the current Z-Scores for Total Volume (Activity), Buy Volume (Attack), Sell Volume (Defense), and Delta (Net Result). 5. Matrix Heatmap (5×8) Visual representation of S-State vs. D-State with color coding: - Bullish clusters in a green spectrum - Bearish clusters in a red spectrum - Conflict or exhaustion zones in yellow, amber, or neutral tones The dashboard can be repositioned (top right, middle right, or bottom right) and its size can be adjusted (Tiny, Small, Normal, or Large) to fit different layouts. 7. Inputs & Customization 7.1. Core Parameters (Bollinger and Z-Score) - Bollinger Length and Standard Deviation define the spatial engine. - Z-Score Lookback (All Factors) defines how many bars are used to normalize volume and delta. 7.2. Deep Kinetic Thresholds - Extreme Threshold defines what is considered climactic (D1 or D8). - Significant Threshold distinguishes strong initiative vs. weak or fakeout behavior. - Neutral Threshold is the band within which delta is treated as neutral. These thresholds allow you to tune the sensitivity of the kinetic classification to fit different timeframes or instruments. 7.3. Calculation Method (Volume Delta) Geometry (Approx) - Fast, non-repainting approach based on candle geometry. - Suitable for most users and real-time decision-making. Intrabar (Precise) - Uses lower-timeframe data for more precise volume delta estimation. - Intrabar mode can repaint and requires compatible data and plan support on the platform. - Best used for post-analysis or research, not blind automation. 7.4. Visuals and Interface - Toggle Bollinger Bands visibility on or off. - Switch between Dark and Light color themes. - Configure dashboard visibility, matrix heatmap display, position, and size. 8. Multi-Language Semantic Engine (Asia and Middle East Focus) BDMA v7.0 includes a fully integrated multi-language layer, targeting a wide geographic user base. Supported Languages: English, Türkçe, Русский, 简体中文, हिन्दी, العربية, فارسی, עברית All dashboard labels, regime titles, bias descriptions, and signal texts are dynamically translated via an internal dictionary, while semantic meaning is kept consistent across languages. This makes BDMA suitable for multi-language communities, study groups, and educational content across different regions. However, due to the heavy computational load of the Deep Kinetic Engine and TradingView’s strict Pine Script execution limits, it was not possible to expand support to additional languages. Adding more translation layers would significantly increase memory usage and exceed runtime constraints. For this reason, the current language set represents the maximum optimized configuration achievable without compromising performance or stability. 9. Practical Usage Notes BDMA is most powerful when used as a contextual overlay on top of market structure (HH, HL, LH, LL), higher-timeframe trend, key levels, and your own execution framework. Recommended usage: - Identify the current regime (Status and Bias). - Check whether price location (S-State) and kinetic behavior (D-State) agree with your trade idea. - Be especially cautious in climactic and absorption or conflict zones, where volatility and risk can be elevated. Avoid treating BDMA as an automatic green equals buy, red equals sell tool. The real edge comes from understanding where you are in the volatility or kinetic spectrum, not from forcing signals out of the matrix. 10. Limitations & Important Warnings BDMA does not predict the future. It organizes current and recent data into a structured context. Volume data quality depends on the underlying symbol, exchange, and broker feed. Forex, crypto, indices, and stocks may all behave differently. Intrabar mode can repaint and is sensitive to lower-timeframe data availability and your plan type. Use it with extra caution and primarily for research. No indicator can remove the need for clear trading rules, disciplined risk management, and psychological control. 11. Disclaimer This script is provided strictly for educational and analytical purposes. It is not a trading system, signal service, financial product, or investment advice. Nothing in this indicator or its description should be interpreted as a recommendation to buy or sell any asset. Past behavior of any indicator or market pattern does not guarantee future results. Trading and investing involve significant risk, including the risk of losing more than your initial capital in leveraged products. You are solely responsible for your own decisions, risk management, and results. By using this script, you acknowledge that you understand these risks and agree that the author or authors and publisher or publishers are not liable for any loss or damage arising from its use.
Pine Script®指标
由ata_sabanci提供
SMC Structure IndicatorTitle: SMC Structures Indicator Description: The SMC Structures indicator is a powerful tool designed to identify and visualize key structural elements in price action, based on the principles of Smart Money Concepts (SMC). This indicator helps traders identify potential areas of support, resistance, and price reversals by highlighting significant market structures. Key Features: Structure Identification: The indicator automatically detects and marks important high and low structures in the market. Break of Structure (BOS) Detection: It identifies and labels instances where previous structures are broken, indicating potential trend changes or continuations. Change of Character (CHoCH) Detection: The indicator recognizes and marks Changes of Character, which are significant shifts in market behavior. Customizable Visuals: Users can personalize the appearance of BOS and CHoCH markings, including colors, line styles, and widths. Current Structure Display: The indicator can optionally show the current active structure, helping traders understand the immediate market context. Historical Structure Tracking: Users can specify the number of historical structure breaks to display, allowing for a cleaner chart while maintaining relevant information. Flexible Break Confirmation: The indicator offers the option to confirm structure breaks using either the candle body or wick, accommodating different trading styles. Technical Details: The indicator uses advanced algorithms to identify significant price structures based on local highs and lows. It employs a lookback period of 10 bars for structure detection, ensuring relevance to current market conditions. The code includes safeguards to handle different market phases and avoid false signals during ranging periods. Customization Options: Colors for Bullish and Bearish BOS and CHoCH markings Line styles and widths for all structure markings Number of historical breaks to display Option to show or hide the current active structure Choice between candle body or wick for structure break confirmation Use Cases: Trend Analysis: Identify the start of new trends or potential trend reversals. Support and Resistance: Pinpoint key levels where price may react. Trade Entry and Exit: Use structure breaks as potential entry or exit signals. Market Context: Understand the broader market structure to make informed trading decisions.
Pine Script®指标
由kylemorton494提供
1
SuperSmoother MA OscillatorSuperSmoother MA Oscillator - Ehlers-Inspired Lag-Minimized Signal Framework Overview The SuperSmoother MA Oscillator is a crossover and momentum detection framework built on the pioneering work of John F. Ehlers, who introduced digital signal processing (DSP) concepts into technical analysis. Traditional moving averages such as SMA and EMA are prone to two persistent flaws: excessive lag, which delays recognition of trend shifts, and high-frequency noise, which produces unreliable whipsaw signals. Ehlers’ SuperSmoother filter was designed to specifically address these flaws by creating a low-pass filter with minimal lag and superior noise suppression, inspired by engineering methods used in communications and radar systems. This oscillator extends Ehlers’ foundation by combining the SuperSmoother filter with multi-length moving average oscillation, ATR-based normalization, and dynamic color coding. The result is a tool that helps traders identify market momentum, detect reliable crossovers earlier than conventional methods, and contextualize volatility and phase shifts without being distracted by transient price noise. Unlike conventional oscillators, which either oversimplify price structure or overload the chart with reactive signals, the SuperSmoother MA Oscillator is designed to balance responsiveness and stability. By preprocessing price data with the SuperSmoother filter, traders gain a signal framework that is clean, robust, and adaptable across assets and timeframes. Theoretical Foundation Traditional MA oscillators such as MACD or dual-EMA systems react to raw or lightly smoothed price inputs. While effective in some conditions, these signals are often distorted by high-frequency oscillations inherent in market data, leading to false crossovers and poor timing. The SuperSmoother approach modifies this dynamic: by attenuating unwanted frequencies, it preserves structural price movements while eliminating meaningless noise. This is particularly useful for traders who need to distinguish between genuine market cycles and random short-term price flickers. In practical terms, the oscillator helps identify: Early trend continuations (when fast averages break cleanly above/below slower averages). Preemptive breakout setups (when compressed oscillator ranges expand). Exhaustion phases (when oscillator swings flatten despite continued price movement). Its multi-purpose design allows traders to apply it flexibly across scalping, day trading, swing setups, and longer-term trend positioning, without needing separate tools for each. The oscillator’s visual system - fast/slow lines, dynamic coloration, and zero-line crossovers - is structured to provide trend clarity without hiding nuance. Strong green/red momentum confirms directional conviction, while neutral gray phases emphasize uncertainty or low conviction. This ensures traders can quickly gauge the market state without losing access to subtle structural signals. How It Works The SuperSmoother MA Oscillator builds signals through a layered process: SuperSmoother Filtering (Ehlers’ Method) At its core lies Ehlers’ two-pole recursive filter, mathematically engineered to suppress high-frequency components while introducing minimal lag. Compared to traditional EMA smoothing, the SuperSmoother achieves better spectral separation - it allows meaningful cyclical market structures to pass through, while eliminating erratic spikes and aliasing. This makes it a superior preprocessing stage for oscillator inputs. Fast and Slow Line Construction Within the oscillator framework, the filtered price series is used to build two internal moving averages: a fast line (short-term momentum) and a slow line (longer-term directional bias). These are not plotted directly on the chart - instead, their relationship is transformed into the oscillator values you see. The interaction between these two internal averages - crossovers, separation, and compression - forms the backbone of trend detection: Uptrend Signal : Fast MA rises above the slow MA with expanding distance, generating a positive oscillator swing. Downtrend Signal : Fast MA falls below the slow MA with widening divergence, producing a negative oscillator swing. Neutral/Transition : Lines compress, flattening the oscillator near zero and often preceding volatility expansion. This design ensures traders receive the information content of dual-MA crossovers while keeping the chart visually clean and focused on the oscillator’s dynamics. ATR-Based Normalization Markets vary in volatility. To ensure the oscillator behaves consistently across assets, ATR (Average True Range) normalization scales outputs relative to prevailing volatility conditions. This prevents the oscillator from appearing overly sensitive in calm markets or too flat during high-volatility regimes. Dynamic Color Coding Color transitions reflect underlying market states: Strong Green : Bullish alignment, momentum expanding. Strong Red : Bearish alignment, momentum expanding. These visual cues allow traders to quickly gauge trend direction and strength at a glance, with expanding colors indicating increasing conviction in the underlying momentum. Interpretation The oscillator offers a multi-dimensional view of price dynamics: Trend Analysis : Fast/slow line alignment and zero-line interactions reveal trend direction and strength. Expansions indicate momentum building; contractions flag weakening conditions or potential reversals. Momentum & Volatility : Rapid divergence between lines reflects increasing momentum. Compression highlights periods of reduced volatility and possible upcoming expansion. Cycle Awareness : Because of Ehlers’ DSP foundation, the oscillator captures market cycles more cleanly than conventional MA systems, allowing traders to anticipate turning points before raw price action confirms them. Divergence Detection : When oscillator momentum fades while price continues in the same direction, it signals exhaustion - a cue to tighten stops or anticipate reversals. By focusing on filtered, volatility-adjusted signals, traders avoid overreacting to noise while gaining early access to structural changes in momentum. Strategy Integration The SuperSmoother MA Oscillator adapts across multiple trading approaches: Trend Following Enter when fast/slow alignment is strong and expanding: A fast line crossing above the slow line with expanding green signals confirms bullish continuation. Use ATR-normalized expansion to filter entries in line with prevailing volatility. Breakout Trading Periods of compression often precede breakouts: A breakout occurs when fast lines diverge decisively from slow lines with renewed green/red strength. Exhaustion and Reversals Oscillator divergence signals weakening trends: Flattening momentum while price continues trending may indicate overextension. Traders can exit or hedge positions in anticipation of corrective phases. Multi-Timeframe Confluence Apply the oscillator on higher timeframes to confirm the directional bias. Use lower timeframes for refined entries during compression → expansion transitions. Technical Implementation Details SuperSmoother Algorithm (Ehlers) : Recursive two-pole filter minimizes lag while removing high-frequency noise. Oscillator Framework : Fast/slow MAs derived from filtered prices. ATR Normalization : Ensures consistent amplitude across market regimes. Dynamic Color Engine : Aligns visual cues with structural states (expansion and contraction). Multi-Factor Analysis : Combines crossover logic, volatility context, and cycle detection for robust outputs. This layered approach ensures the oscillator is highly responsive without overloading charts with noise. Optimal Application Parameters Asset-Specific Guidance: Forex : Normalize with moderate ATR scaling; focus on slow-line confirmation. Equities : Balance responsiveness with smoothing; useful for capturing sector rotations. Cryptocurrency : Higher ATR multipliers recommended due to volatility. Futures/Indices : Lower frequency settings highlight structural trends. Timeframe Optimization: Scalping (1-5min) : Higher sensitivity, prioritize fast-line signals. Intraday (15m-1h) : Balance between fast/slow expansions. Swing (4h-Daily) : Focus on slow-line momentum with fast-line timing. Position (Daily-Weekly) : Slow lines dominate; fast lines highlight cycle shifts. Performance Characteristics High Effectiveness: Trending environments with moderate-to-high volatility. Assets with steady liquidity and clear cyclical structures. Reduced Effectiveness: Flat/choppy conditions with little directional bias. Ultra-short timeframes (<1m), where noise dominates. Integration Guidelines Confluence : Combine with liquidity zones, order blocks, and volume-based indicators for confirmation. Risk Management : Place stops beyond slow-line thresholds or ATR-defined zones. Dynamic Trade Management : Use expansions/contractions to scale position sizes or tighten stops. Multi-Timeframe Confirmation : Filter lower-timeframe entries with higher-timeframe momentum states. Disclaimer The SuperSmoother MA Oscillator is an advanced trend and momentum analysis tool, not a guaranteed profit system. Its effectiveness depends on proper parameter settings per asset and disciplined risk management. Traders should use it as part of a broader technical framework and not in isolation.
Pine Script®指标
由BOSWaves提供
7
Fibonacci Time-Price Zones🟩 Fibonacci Time-Price Zones is a chart visualization tool that combines Fibonacci ratios with time-based and price-based geometry to analyze market behavior. Unlike typical Fibonacci indicators that focus solely on horizontal price levels, this indicator incorporates time into the analysis, providing a more dynamic perspective on price action. The indicator offers multiple ways to visualize Fibonacci relationships. Drawing segmented circles creates a unique perspective on price action by incorporating time into the analysis. These segmented circles, similar to TradingView's built-in Fibonacci Circles, are derived from Fibonacci time and price levels, allowing traders to identify potential turning points based on the dynamic interaction between price and time. As another distinct visualization method, the indicator incorporates orthogonal patterns, created by the intersection of horizontal and vertical Fibonacci levels. These intersections form L-shaped connections on the chart, derived from key Fibonacci price and time intervals, highlighting potential areas of support or resistance at specific points in time. In addition to these geometric approaches, another option is sloped lines, which project Fibonacci levels that account for both time and price along the trendline. These projections derive their angles from the interplay between Fibonacci price levels and Fibonacci time intervals, creating dynamic zones on the chart. The slope of these lines reflects the direction and angle of the trend, providing a visual representation of price alignment with market direction, while maintaining the time-price relationship unique to this indicator The indicator also includes horizontal Fibonacci levels similar to traditional retracement and extension tools. However, unlike standard tools, traders can display retracement levels, extension levels, or both simultaneously from a single instance of the indicator. These horizontal levels maintain consistency with the chosen visualization method, automatically scaling and adapting whether used with circles, orthogonal patterns, or slope-based analysis. By combining these distinct methods—circles, orthogonal patterns, sloped projections, and horizontal levels—the indicator provides a comprehensive approach to Fibonacci analysis based on both time and price relationships. Each visualization method offers a unique perspective on market structure while maintaining the core principle of time-price interaction. ⭕ THEORY AND CONCEPT ⭕ While traditional Fibonacci tools excel at identifying potential support and resistance levels through price-based ratios (0.236, 0.382, 0.618), they do not incorporate the dimension of time in market analysis. Extensions and retracements effectively measure price relationships within trends, yet markets move through both price and time dimensions simultaneously. Fibonacci circles represent an evolution in technical analysis by incorporating time intervals alongside price levels. Based on the mathematical principle that markets often move in circular patterns proportional to Fibonacci ratios, these circles project potential support and resistance zones as partial circles radiating from significant price points. However, traditional circle-based tools can create visual complexity that obscures key market relationships. The integration of time into Fibonacci analysis reveals how price movements often respect both temporal and price-based ratios, suggesting a deeper geometric structure to market behavior. The Fibonacci Time-Price Zones indicator advances these concepts by providing multiple geometric approaches to visualize time-price relationships. Each shape option—circles, orthogonal patterns, slopes, and horizontal levels—represents a different mathematical perspective on how Fibonacci ratios manifest across both dimensions. This multi-faceted approach allows traders to observe how price responds to Fibonacci-based zones that account for both time and price movements, potentially revealing market structure that purely price-based tools might miss. Shape Options The indicator employs four distinct geometric approaches to analyze Fibonacci relationships across time and price dimensions: Circular : Represents the cyclical nature of market movements through partial circles, where each radius is scaled by Fibonacci ratios incorporating both time and price components. This geometry suggests market movements may follow proportional circular paths from significant pivot points, reflecting the harmonic relationship between time and price. Orthogonal : Constructs L-shaped patterns that separate the time and price components of Fibonacci relationships. The horizontal component represents price levels, while the vertical component measures time intervals, allowing analysis of how these dimensions interact independently at key market points. Sloped : Projects Fibonacci levels along the prevailing trend, incorporating both time and price in the angle of projection. This approach suggests that support and resistance levels may maintain their relationship to price while adjusting to the temporal flow of the market. Horizontal : Provides traditional static Fibonacci levels that serve as a reference point for comparing price-only analysis with the dynamic time-price relationships shown in the other three shapes. This baseline approach allows traders to evaluate how the incorporation of time dimension enhances or modifies traditional Fibonacci analysis. By combining these geometric approaches, the Fibonacci Time-Price Zones indicator creates a comprehensive analytical framework that bridges traditional and advanced Fibonacci analysis. The horizontal levels serve as familiar reference points, while the dynamic elements—circular, orthogonal, and sloped projections—reveal how price action responds to temporal relationships. This multi-dimensional approach enables traders to study market structure through various geometric lenses, providing deeper insights into time-price symmetry within technical analysis. Whether applied to retracements, extensions, or trend analysis, the indicator offers a structured methodology for understanding how markets move through both price and time dimensions. 🛠️ CONFIGURATION AND SETTINGS 🛠️ The Fibonacci Time-Price Zones indicator offers a range of configurable settings to tailor its functionality and visual representation to your specific analysis needs. These options allow you to customize zone visibility, structures, horizontal lines, and other features. Important Note: The indicator's calculations are anchored to user-defined start and end points on the chart. When switching between charts with significantly different price scales (e.g., from Bitcoin at $100,000 to Silver at $30), adjustment of these anchor points is required to ensure correct positioning of the Fibonacci elements. Fibonacci Levels The indicator allows users to customize Fibonacci levels for both retracement and extension analysis. Each level can be individually configured with the following options: Visibility : Toggle the visibility of each level to focus on specific areas of interest. Level Value : Set the Fibonacci ratio for the level, such as 0.618 or 1.000, to align with your analysis needs. Color : Customize the color of each level for better visual clarity. Line Thickness : Adjust the line thickness to emphasize critical levels or maintain a cleaner chart. Setup Zone Type : Select which Fibonacci zones to display: - Retracement : Shows potential pull back levels within the trend - Extension : Projects levels beyond the trend for potential continuation targets - Both : Displays both retracement and extension zones simultaneously Shape : Choose from four visualization methods: - Circular : Time-price based semicircles centered on point B - Orthogonal : L-shaped patterns combining time and price levels - Sloped : Trend-aligned projections of Fibonacci levels - Horizontal : Traditional horizontal Fibonacci levels Visual Settings Fill % : Adjusts the fill intensity of zones: 0% : No fill between levels 100% : Maximum fill between levels Lines : Trendline : The base A-B trend with customizable color Extension : B-C projection line Retracement : B-D pullback line Labels : Points : Show/hide A, B, C, D markers Levels : Show/hide Fibonacci percentages Time-Price Points Set the time and price for the points that define the Fibonacci zones and horizontal levels. These points are defined upon loading the chart. These points can be configured directly in the settings or adjusted interactively on the live chart. A and B Points : These user-defined time and price points determine the basis for calculating the semicircles and Fibonacci levels. While the settings panel displays their exact values for fine-tuning, the easiest way to modify these points is by dragging them directly on the chart for quick adjustments. Interactive Adjustments : Any changes made to the points on the chart will automatically synchronize with the settings panel, ensuring consistency and precision. 🖼️ CHART EXAMPLES 🖼️ Fibonacci Time-Price Zones using the 'Circular' Shape option. Note the price interaction at the 0.786 level, which acts as a support zone. Additional points of interest include resistance near the 0.618 level and consolidation around the 0.5 level, highlighting the utility of both horizontal and semicircular Fibonacci projections in identifying key price areas. Fibonacci Time-Price Zones using the 'Sloped' Shape option. The chart displays price retracing along the sloped Fibonacci levels, with blue arrows highlighting potential support zones at 0.618 and 0.786, and a red arrow indicating potential resistance at the 1.0 level. This visual representation aligns with the prevailing downtrend, suggesting potential selling pressure at the 1.0 Fibonacci level. Fibonacci Time-Price Zones using the 'Orthogonal' Shape option. The chart demonstrates price action interacting with vertical zones created by the orthogonal lines at the 0.618, 0.786, and 1.0 Fibonacci levels. Blue arrows highlight potential support areas, while red arrows indicate potential resistance areas, revealing how the orthogonal lines can identify distinct points of price interaction. Fibonacci Time-Price Zones using the 'Circular' Shape option. The chart displays price action in relation to segmented circles emanating from the starting point (point A). The circles represent different Fibonacci ratios (0.382, 0.5, 0.618, 0.786) and their intersections with the price axis create potential zones of support and resistance. This approach offers a visually distinct way to analyze potential turning points based on both price and time. Fibonacci Time-Price Zones using the 'Sloped' Shape option. The sloped Fibonacci levels (0.786, 0.618, 0.5) create zones of potential support and resistance, with price finding clear interaction within these areas. The ellipses highlight this price action, particularly the support between 0.786 and 0.618, which aligns closely with the trend. Fibonacci Time-Price Zones using the 'Circular' Shape option. The price action appears to be ‘hugging’ the 0.5 Fibonacci level, suggesting potential resistance. This demonstrates how the circular zones can identify potential turning points and areas of consolidation which might not be seen with linear analysis. Fibonacci Time-Price Zones using the 'Sloped' Shape option with Point D marker enabled. The chart demonstrates clear price action closely following along the sloped Retracement line until the orthogonal intersection at the 0.618 levels where the trend is broken and price dips throughout the 0.618 to 0.786 horizontal zone. Price jumps back to the retracement slope at the start of the 0.786 horizontal zone and continues to the 1.0 horizontal zone. The aqua-colored retracement line is enabled to further emphasize this retracement slope . Geometric validation using TradingView's built-in Fibonacci Circle tool (overlaid). The alignment at the 0.5 and 1.0 levels demonstrates the indicator's consistent approximation of Fibonacci Circles. Comparison of Fibonacci Time-Price Zones (Shape: Horizontal) with TradingView's Built-in Retracement and Extension Tools (overlaid): This example demonstrates how the Horizontal structure aligns with TradingView’s retracement and extension levels, allowing users to integrate multiple tools seamlessly. The Fibonacci circle connects retracement and extension zones, highlighting the potential relationship between past retracements and future extensions. 📐 GEOMETRIC FOUNDATIONS 📐 This indicator integrates circular and straight representations of Fibonacci levels, specifically the Circular , Orthogonal , Sloped , and Horizontal shape options. The geometric principles behind these shapes differ significantly, requiring distinct scaling methods for accurate representation. The Circular shape employs logarithmic scaling with radial expansion, where the distance from a central point determines the level's position, creating partial circles that align with TradingView's built-in Fibonacci Circle tool. The other three shapes utilize geometric progression scaling for linear extension from a starting point, resulting in straight lines that align with TradingView's built-in Fibonacci retracement and extension tools. Due to these distinct geometric foundations and scaling methods, perfectly aligning both the partial circles and straight lines simultaneously is mathematically constrained, though any differences are typically visually imperceptible. The Circular shape's partial circles are calculated and scaled to align with TradingView's built-in Fibonacci Circles. These circles are plotted from the second swing point onward. This approach ensures consistent and accurate visualization across all market types, including those with gaps or closed sessions, which unlike 24/7 markets, do not have a direct one-to-one correspondence between bar indices and time. To maintain accurate geometric proportions across varying chart scales, the indicator calculates an aspect ratio by normalizing the proportional difference between vertical (price) and horizontal (time) distances of the swing points. This normalization factor ensures geometric shapes maintain their mathematical properties regardless of price scale magnitude or time period span, while maintaining the correct proportions of the geometric constructions at any chart zoom level. The indicator automatically applies the appropriate scaling factor based on the selected shape option, optimizing either circular proportions and proper radius calculations for each Fibonacci level, or straight-line relationships between Fibonacci levels. These distinct scaling approaches maintain mathematical integrity while preserving the essential characteristics of each geometric representation, ensuring optimal visualization accuracy whether using circular or linear shapes. ⚠️ DISCLAIMER ⚠️ The Fibonacci Time-Price Zones indicator is a visual analysis tool designed to illustrate Fibonacci relationships through geometric constructions incorporating both curved and straight lines, providing a structured framework for identifying potential areas of price interaction. It is not intended as a predictive or standalone trading signal indicator. The indicator calculates levels and projections using user-defined anchor points and Fibonacci ratios. While it aims to align with TradingView’s Fibonacci extension, retracement, and circle tools by employing mathematical and geometric formulas, no guarantee is made that its calculations are identical to TradingView's proprietary methods. Like all technical and visual indicators, these visual representations may visually align with key price zones in hindsight, reflecting observed price dynamics. However, these visualizations are not standalone signals for trading decisions and should be interpreted as part of a broader analytical approach. This indicator is intended for educational and analytical purposes, complementing other tools and methods of market analysis. Users are encouraged to integrate it into a comprehensive trading strategy, customizing its settings to suit their specific needs and market conditions. 🧠 BEYOND THE CODE 🧠 The Fibonacci Time-Price Zones indicator is designed to encourage both education and community engagement. By integrating time-sensitive geometry with Fibonacci-based frameworks, it bridges traditional grid-based analysis with dynamic time-price relationships. The inclusion of semicircles, horizontal levels, orthogonal structures, and sloped trends provides users with versatile tools to explore the interaction between price movements and temporal intervals while maintaining clarity and adaptability. As an open-source tool, the indicator invites exploration, experimentation, and customization. Whether used as a standalone resource or alongside other technical strategies, it serves as a practical and educational framework for understanding market structure and Fibonacci relationships in greater depth. Your feedback and contributions are essential to refining and enhancing the Fibonacci Time-Price Zones indicator. We look forward to the creative applications, adaptations, and insights this tool inspires within the trading community.
Pine Script®指标
由xxattaxx提供
39
Triple Gaussian Smoothed Ribbon [BOSWaves]Triple Gaussian Smoothed Ribbon – Adaptive Gaussian Framework Overview The Triple Gaussian Smoothed Ribbon is a next-generation market visualization framework built on the principles of Gaussian filtering - a mathematical model from digital signal processing designed to remove noise while preserving the integrity of the underlying trend. Unlike conventional moving averages that suffer from phase lag and overreaction to volatility spikes, Gaussian smoothing produces a symmetrical, low-lag curve that isolates meaningful directional shifts with exceptional clarity. Developed under the Adaptive Gaussian Framework, this indicator extends the classical Gaussian model into a multi-stage smoothing and visualization system. By layering three progressive Gaussian filters and rendering their interactions as a gradient-based ribbon field, it translates market energy into a coherent, visually structured trend environment. Each ribbon layer represents a progressively smoothed component of price motion, producing a high-fidelity gradient field that evolves in sync with real-time trend strength and momentum. The result is a uniquely fluid trend and reversal detection system - one that feels organic, adapts seamlessly across timeframes, and reveals hidden transitions in market structure long before traditional indicators confirm them. Theoretical Foundation The Gaussian filter, derived from the Gaussian function developed by Carl Friedrich Gauss in 1809, operates on the principle of weighted symmetry, assigning higher importance to central price data while tapering influence toward historical extremes following a bell-curve distribution. This symmetrical design minimizes phase distortion and smooths without introducing lag spikes — a stark contrast to exponential or linear filters that sacrifice temporal accuracy for responsiveness. By cascading three Gaussian stages in sequence, the indicator creates a multi-frequency decomposition of price action: The first stage captures immediate trend transitions. The second absorbs mid-term volatility ripples. The third stabilizes structural directionality. The final composite ribbon reflects the market’s dominant frequency - a smoothed yet reactive trend spine - while an independent, heavier Gaussian smoothing serves as a reference layer to gauge whether the primary motion leads or lags relative to broader market structure. This multi-layered Gaussian framework effectively replicates the behavior of a signal-processing filter bank: isolating meaningful cyclical movements, suppressing random noise, and revealing phase shifts with minimal delay. How It Works Triple Gaussian Core Price data is passed through three successive Gaussian smoothing stages, each refining the trend further and removing higher-frequency distortions. The result is a fluid, continuously adaptive baseline that responds naturally to directional changes without overshooting or flattening key inflection points. Adaptive Ribbon Architecture The indicator visualizes its internal dynamics through a five-layer gradient ribbon. Each layer represents a progressively delayed Gaussian curve, creating a color field that dynamically shifts between bullish and bearish tones. Expanding ribbons indicate accelerating momentum and trend conviction. Compressing ribbons reflect consolidation and volatility contraction. The smooth color gradient provides a real-time depiction of energy buildup or dissipation within the trend, making it visually clear when the market is entering a state of expansion, transition, or exhaustion. Momentum-Weighted Opacity Ribbon transparency adjusts according to normalized momentum strength. As trend force builds, colors intensify and layers become more opaque, signifying conviction. When momentum wanes, ribbons fade - an early visual cue for potential reversals or pauses in trend continuation. Candle Gradient Integration Optional candle coloring ties the chart’s candles to the prevailing Gaussian gradient, allowing traders to view raw price action and smoothed wave dynamics as a unified system. This integration produces a visually coherent chart environment that communicates directional intent instantly. Signal Detection Logic Directional cues emerge when the smoother, broader Gaussian curve crosses the faster-reacting Gaussian line, marking structural inflection points in the filtered trend. Bullish shifts : short-term momentum transitions upward through the long-term baseline after a localized trough. Bearish shifts : momentum declines through the baseline following a local peak. To maintain integrity in choppy markets, the framework applies a trend-strength and separation filter, which blocks weak or overlapping conditions where movement lacks conviction. Interpretation The Triple Gaussian Smoothed Ribbon provides a layered, intuitive read on market structure: Trend Continuation : Expanding ribbons with deep color intensity confirm directional strength. Reversal Phases : Color gradients flip direction, indicating a phase shift or exhaustion point. Compression Zones : Tight, pale ribbons reveal equilibrium phases often preceding breakouts. Momentum Divergence : Fading color intensity despite continued price movement signals weakening conviction. These transitions mirror the natural ebb and flow of market energy - captured through the Gaussian filter’s ability to represent smooth curvature without distortion. Strategy Integration Trend Following Engage during strong directional expansions. When ribbons widen and color gradients intensify, the trend is accelerating with high confidence. Reversal Identification Monitor for full gradient inversion and fading momentum opacity. These conditions often precede transitional phases and early reversals. Breakout Anticipation Flat, compressed ribbons signal low volatility and energy buildup. A sudden gradient expansion with renewed opacity confirms breakout initiation. Multi-Timeframe Alignment Use higher timeframes to establish directional bias and lower timeframes for entry during compression-to-expansion transitions. Technical Implementation Details Triple Gaussian Stack : Sequential smoothing stages produce low-lag, high-purity signals. Adaptive Ribbon Rendering : Five-layer Gaussian visualization for gradient-based trend depth. Momentum Normalization : Opacity dynamically tied to trend strength and volatility context. Consolidation Filter : Suppresses false signals in low-energy or range-bound conditions. Integrated Candle Mode : Optional color synchronization with underlying gradient flow. Alert System : Built-in notifications for bullish and bearish transitions. This structure blends the precision of digital signal processing with the readability of visual market analysis, creating a clean but information-rich framework. Optimal Application Parameters Asset Recommendations Cryptocurrency : Higher smoothing and sigma for stability under volatility. Forex : Balanced parameters for cycle identification and reduced noise. Equities : Moderate Gaussian length for responsive yet stable trend reads. Indices & Futures : Longer smoothing periods for structural confirmation. Timeframe Recommendations Scalping (1 - 5m) : Use shorter smoothing for fast reactivity. Intraday (15m - 1h) : Mid-length Gaussian chain for balance. Swing (4h - 1D) : Prioritize clarity and opacity-driven trend phases. Position (Daily - Weekly) : Longer smoothing to capture macro rhythm. Performance Characteristics Most Effective In : Trending markets with recurring volatility cycles. Transitional phases where early directional confirmation is crucial. Less Effective In: Ultra-low volume markets with erratic tick data. Random, micro-chop conditions with no structural flow. Integration Guidelines Pair with volatility or volume expansion tools for enhanced breakout confirmation. Use ribbon compression to anticipate volatility shifts. Align entries with gradient expansion in the dominant color direction. Scale position size relative to opacity strength and ribbon width. Disclaimer The Triple Gaussian Smoothed Ribbon – Adaptive Gaussian Framework is designed as a signal visualization and trend interpretation tool, not a standalone trading system. Its accuracy depends on appropriate parameter tuning, contextual confirmation, and disciplined risk management. It should be applied as part of a comprehensive technical or algorithmic trading strategy.
Pine Script®指标
由BOSWaves提供
3
EBL - Enhanced BOS LogicEBL - Enhanced BOS Logic The EBL (Enhanced Break of Structure Logic) script is a powerful tool for traders who want to identify and act on key structural shifts in the market. By combining visual cues, such as horizontal lines and dynamic arrows, the script highlights critical points of interest where market behavior may indicate significant bullish or bearish momentum. What Makes EBL Unique? Break of Structure (BOS) Identification: The script dynamically detects when price breaks above or below significant highs and lows, marking these levels as key BOS points. Once a BOS level is confirmed, it is displayed on the chart as a horizontal line, allowing traders to easily identify areas of potential support and resistance. Real-Time Validation and Invalidations: Bullish BOS levels remain active until a bearish candle closes below the initiating bullish candle. Similarly, bearish BOS levels remain active until a bullish candle closes above the initiating bearish candle. If a BOS level is invalidated, both the corresponding line and its arrow are automatically removed to maintain chart clarity. Visual Clarity with Arrows and Lines: Customizable triangle arrows (green for bullish and red for bearish) appear alongside lines to signal entry opportunities. Traders can adjust line length, colors, and visibility of arrows to fit their charting style. Alerts for Confirmation: Receive alerts when bullish or bearish structures are confirmed, ensuring you never miss a signal even when away from your chart. How the Script Works Detection of Bullish and Bearish Structures: The script identifies a "Bullish Break" when the price closes above the high of a bullish candle followed by a bearish one. A "Bearish Break" is detected when the price closes below the low of a bearish candle followed by a bullish one. Line and Arrow Placement: Horizontal lines are drawn at the high or low of the respective BOS level. Triangular arrows are plotted just below or above the respective levels to indicate potential trade opportunities. Automatic Cleanup: When a line is invalidated by opposing market movement, both the line and its connected arrow are automatically removed from the chart. How to Use EBL Settings: Adjust line colors (green for bullish, red for bearish) to suit your charting theme. Customize arrow visibility or hide lines if you prefer a less cluttered chart. Set the horizontal line length to match your desired timeframe and analysis depth. Trading Concepts: Trend Reversal Zones: Use invalidated BOS levels as signals for possible trend reversals. Momentum Trading: Follow confirmed BOS levels to identify areas where price momentum is likely to continue. Dynamic Support and Resistance: Leverage the lines to identify evolving support and resistance zones. Alerts: Enable alerts to receive notifications when bullish or bearish trends are confirmed, allowing you to stay informed without constant monitoring. Conceptual Basis This script is based on the widely used market structure concept, which is fundamental to price action trading. By tracking the highs and lows created by bullish and bearish movements, the EBL script provides an objective and systematic approach to identifying and trading key structural points in the market. With the EBL - Enhanced BOS Logic, traders can visually and systematically track market structure, identify potential trade setups, and maintain a cleaner chart with automated line and arrow management. This script is ideal for trend-following, scalping, and swing trading strategies across all markets and timeframes.
Pine Script®指标
由Niko-picasso369提供
3
Price Action Analyst [OmegaTools]Price Action Analyst (PAA) is an advanced trading tool designed to assist traders in identifying key price action structures such as order blocks, market structure shifts, liquidity grabs, and imbalances. With its fully customizable settings, the script offers both novice and experienced traders insights into potential market movements by visually highlighting premium/discount zones, breakout signals, and significant price levels. This script utilizes complex logic to determine significant price action patterns and provides dynamic tools to spot strong market trends, liquidity pools, and imbalances across different timeframes. It also integrates an internal backtesting function to evaluate win rates based on price interactions with supply and demand zones. The script combines multiple analysis techniques, including market structure shifts, order block detection, fair value gaps (FVG), and ICT bias detection, to provide a comprehensive and holistic market view. Key Features: Order Block Detection: Automatically detects order blocks based on price action and strength analysis, highlighting potential support/resistance zones. Market Structure Analysis: Tracks internal and external market structure changes with gradient color-coded visuals. Liquidity Grabs & Breakouts: Detects potential liquidity grab and breakout areas with volume confirmation. Fair Value Gaps (FVG): Identifies bullish and bearish FVGs based on historical price action and threshold calculations. ICT Bias: Integrates ICT bias analysis, dynamically adjusting based on higher-timeframe analysis. Supply and Demand Zones: Highlights supply and demand zones using customizable colors and thresholds, adjusting dynamically based on market conditions. Trend Lines: Automatically draws trend lines based on significant price pivots, extending them dynamically over time. Backtesting: Internal backtesting engine to calculate the win rate of signals generated within supply and demand zones. Percentile-Based Pricing: Plots key percentile price levels to visualize premium, fair, and discount pricing zones. High Customizability: Offers extensive user input options for adjusting zone detection, color schemes, and structure analysis. User Guide: Order Blocks: Order blocks are significant support or resistance zones where strong buyers or sellers previously entered the market. These zones are detected based on pivot points and engulfing price action. The strength of each block is determined by momentum, volume, and liquidity confirmations. Demand Zones: Displayed in shades of blue based on their strength. The darker the color, the stronger the zone. Supply Zones: Displayed in shades of red based on their strength. These zones highlight potential resistance areas. The zones will dynamically extend as long as they remain valid. Users can set a maximum number of order blocks to be displayed. Market Structure: Market structure is classified into internal and external shifts. A bullish or bearish market structure break (MSB) occurs when the price moves past a previous high or low. This script tracks these breaks and plots them using a gradient color scheme: Internal Structure: Short-term market structure, highlighting smaller movements. External Structure: Long-term market shifts, typically more significant. Users can choose how they want the structure to be visualized through the "Market Structure" setting, choosing from different visual methods. Liquidity Grabs: The script identifies liquidity grabs (false breakouts designed to trap traders) by monitoring price action around highs and lows of previous bars. These are represented by diamond shapes: Liquidity Buy: Displayed below bars when a liquidity grab occurs near a low. Liquidity Sell: Displayed above bars when a liquidity grab occurs near a high. Breakouts: Breakouts are detected based on strong price momentum beyond key levels: Breakout Buy: Triggered when the price closes above the highest point of the past 20 bars with confirmation from volume and range expansion. Breakout Sell: Triggered when the price closes below the lowest point of the past 20 bars, again with volume and range confirmation. Fair Value Gaps (FVG): Fair value gaps (FVGs) are periods where the price moves too quickly, leaving an unbalanced market condition. The script identifies these gaps: Bullish FVG: When there is a gap between the low of two previous bars and the high of a recent bar. Bearish FVG: When a gap occurs between the high of two previous bars and the low of the recent bar. FVGs are color-coded and can be filtered by their size to focus on more significant gaps. ICT Bias: The script integrates the ICT methodology by offering an auto-calculated higher-timeframe bias: Long Bias: Suggests the market is in an uptrend based on higher timeframe analysis. Short Bias: Indicates a downtrend. Neutral Bias: Suggests no clear directional bias. Trend Lines: Automatic trend lines are drawn based on significant pivot highs and lows. These lines will dynamically adjust based on price movement. Users can control the number of trend lines displayed and extend them over time to track developing trends. Percentile Pricing: The script also plots the 25th percentile (discount zone), 75th percentile (premium zone), and a fair value price. This helps identify whether the current price is overbought (premium) or oversold (discount). Customization: Zone Strength Filter: Users can set a minimum strength threshold for order blocks to be displayed. Color Customization: Users can choose colors for demand and supply zones, market structure, breakouts, and FVGs. Dynamic Zone Management: The script allows zones to be deleted after a certain number of bars or dynamically adjusts zones based on recent price action. Max Zone Count: Limits the number of supply and demand zones shown on the chart to maintain clarity. Backtesting & Win Rate: The script includes a backtesting engine to calculate the percentage of respect on the interaction between price and demand/supply zones. Results are displayed in a table at the bottom of the chart, showing the percentage rating for both long and short zones. Please note that this is not a win rate of a simulated strategy, it simply is a measure to understand if the current assets tends to respect more supply or demand zones. How to Use: Load the script onto your chart. The default settings are optimized for identifying key price action zones and structure on intraday charts of liquid assets. Customize the settings according to your strategy. For example, adjust the "Max Orderblocks" and "Strength Filter" to focus on more significant price action areas. Monitor the liquidity grabs, breakouts, and FVGs for potential trade opportunities. Use the bias and market structure analysis to align your trades with the prevailing market trend. Refer to the backtesting win rates to evaluate the effectiveness of the zones in your trading. Terms & Conditions: By using this script, you agree to the following terms: Educational Purposes Only: This script is provided for informational and educational purposes and does not constitute financial advice. Use at your own risk. No Warranty: The script is provided "as-is" without any guarantees or warranties regarding its accuracy or completeness. The creator is not responsible for any losses incurred from the use of this tool. Open-Source License: This script is open-source and may be modified or redistributed in accordance with the TradingView open-source license. Proper credit to the original creator, OmegaTools, must be maintained in any derivative works.
Pine Script®指标
由OmegaTools提供
13
Fresh Algo | Signals & Overlays™ v25//@version=5 indicator("Fresh Algo | Signals & Overlays™ v25", overlay=true, max_lines_count=500, max_labels_count=500, max_boxes_count=350) // Telegram Join Us >> t.me //...................../´¯¯/) //...................,/¯.../ //.................../..../ //.............../´¯/'..'/´¯¯`·¸ //.........../'/.../..../....../¨¯\ //..........('(....´...´... ¯~/'..') //...........\..............'...../ //............\....\.........._.·´ //.............\..............( //..............\..............\ //---- //--------- // Telegram Join Us >> t.me bullcolor = #00dbff bearcolor = #b2b5be ema150 = ta.ema(close, 150) ema250 = ta.ema(close, 250) gr_customalert = "Custom Alerts" gr_signal = "General Configurations" gr_PullBacksignal = "Trading Assistants" gr_RiskManage = "Risk Management" gr_dash = "Dashboard Configurations" //symbol info symInfoCheck = false symInfo = syminfo.ticker + ' | ' + timeframe.period + (timeframe.isminutes ? 'M' : na) date = str.tostring(dayofmonth(time_close)) + '/' + str.tostring(month(time_close)) + '/' + str.tostring(year(time_close)) //text positioning textVPosition = 'middle' textHPosition = 'center' //symbol info positioning symVPosition = 'top' symHPosition = 'left' //cell size width = 0 height = 0 //title settings c_title = #b2b5be80 s_title = 'large' a_title = 'center' //subtitle settings c_subtitle = #b2b5be80 s_subtitle = 'normal' a_subtitle = 'center' c_bg = color.new(color.blue, 100) // Get user input showSignals = textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' ? input(true, "Show Signal's", group=gr_signal) : na //showSignals = true sensitivity = textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' ? input.float(2.4, "Sensitivity", 0.1, step=0.1, group=gr_signal): na STuner = textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' ? input.int(10, "Signal Tuner(1-25)", minval = 1, maxval = 25, group=gr_signal): na Presets = "All Signals" //Presets = input.string("All Signals", "Presets", , group=gr_signal) filterstyle = textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' ? input.string("Trending Signals ", "Signal Mode / Filters", ["Trending Signals ", "Contrarian Signals ", "High Volume ", "Strong ", "Swing ", "Smooth ", "Scalping ", "Scalping+ "], group=gr_signal): na //TextStyle = input.string("Minimal", "Signal Style", , group=gr_signal) //periodTrendCloud = input.string("Smooth", "Trend Cloud Style", , group=gr_Other_Settings) TextStyle = "Minimal" consSignalsFilter = filterstyle == "Trending Signals " ? true : false StrongSignalsOnly = filterstyle == "Strong " ? true : false highVolSignals = filterstyle == "High Volume " ? true : false signalsTrendCloud = (filterstyle == "Smooth ") ? true : (filterstyle == "Scalping ") ? true : (filterstyle == "Scalping+ ") ? true : (filterstyle == "Swing ") ? true : false ContrarianOnly = filterstyle == "Contrarian Signals " ? true : false TrendMap = 'Trend Gradient' momentumCandles = false assistantenable = textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' ? input(true,'', group=gr_PullBacksignal, inline = 'sexyshit'): na assistantmode = textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' ? input.string('Trend Assistant', 'Assistant | Mode', , group = gr_PullBacksignal, inline = 'sexyshit'): na Show_PR = textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' ? input.bool(true, title="", group = gr_PullBacksignal , inline = "Features1"): na MSTuner = textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' ? input.int(8, "Reversal Dot | Tuner(2-30)", minval = 2, maxval = 30, group=gr_PullBacksignal, inline = "Features1"): na LongTrendAverage = assistantmode == 'Trend Tracker' and assistantenable == true ? true : false analyscloud = assistantmode == 'Trend Assistant' and assistantenable == true ? true : false showTrendCloud = (filterstyle == "Smooth ") ? true : (filterstyle == "Scalping ") ? true : (filterstyle == "Scalping+ ") ? true : (filterstyle == "Swing ") ? true : false periodTrendCloud = (filterstyle == "Smooth ") ? "Smooth" : (filterstyle == "Scalping ") ? "Scalping" : (filterstyle == "Scalping+ ") ? "Scalping+" : (filterstyle == "Swing ") ? "Swing" : na //ScalpingPlus = input(false, "Fast trend cloud", group=gr_Other_Settings) //fastTrendCloudLen = input.int(55, "Fast trend cloud", 2, group=gr_Other_Settings) fill(plot(showTrendCloud and periodTrendCloud == "Smooth" ? na : assistantenable == true and assistantmode == 'Trend Tracker' ? ema150 : na, "", na, editable=false), plot(showTrendCloud and periodTrendCloud == "Smooth" ? na : assistantenable == true and assistantmode == 'Trend Tracker' ? ema250 : na, "", na, editable=false), ema150 > ema250 ? color.new(bullcolor, 70) : ema150 < ema250 ? color.new(bearcolor, 70) : na) showDashboard = textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' ? input(true, "Smart Panel", group = gr_dash , inline = "Features1"): na locationDashboard = textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' ? input.string("Bottom Right", "Dashboard Location", , group = gr_dash , tooltip="Smart Panel"): na sizeDashboard = textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' ? input.string("Small", "Dashboard Size", , group = gr_dash , tooltip="Smart Panel"): na tpLabels = textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' ? input(true, "Dynamic Take Profit Lables", group=gr_RiskManage): na ShowTpSlAreas = textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' ? input(true, "Show take Profit/Stop-loss Area", group=gr_RiskManage): na ShowTrailingSL = textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' ? input(false, "Show trailing Stop-loss", group=gr_RiskManage): na usePercSL = textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' ? input(false, "SL/TRAILING", inline="1", group=gr_RiskManage): na percTrailingSL = textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' ? input.float(1, "", 0, step=0.1, inline="1", group=gr_RiskManage): na useTP1 = textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' ? input(true, "", inline="1", group=gr_RiskManage): na multTP1 = textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' ? input.float(1, "TP 1", 0, inline="1", group=gr_RiskManage): na useTP2 = textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' ? input(true, "", inline="4", group=gr_RiskManage): na multTP2 = textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' ? input.float(2, "TP 2           ", 0, inline="4", group=gr_RiskManage): na useTP3 = textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' ? input(true, "", inline="4", group=gr_RiskManage): na multTP3 = textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' ? input.float(3, "TP 3", 0, inline="4", group=gr_RiskManage): na ShowSwings = textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' ? input(false, "Show Market Structure      ", inline="3", group=gr_RiskManage): na periodSwings = textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' ? input.int(10, "        ", 2, inline="3", group=gr_RiskManage): na //showTS = input(title='Show Trend Shifter', defval=false, group='Contrarian SIGNALS') // showsignals = input(title='Show Signals', defval=false, group='Contrarian SIGNALS') // Alerts Managemnt Normalbuy_alert = textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' ? input.bool(title='Buy Signal   ', defval=false, inline = "NB", group=gr_customalert): na Strongbuy_alert = textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' ? input.bool(title='Strong Buy', defval=false, inline = "NB", group=gr_customalert): na Normalsell_alert = textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' ? input.bool(title='Sell Signal   ', defval=false , inline = "NS", group=gr_customalert): na Strongsell_alert = textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' ? input.bool(title='Strong Sell', defval=false , inline = "NS", group=gr_customalert): na slalert = textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' ? input.bool(title='Stop-Loss     ', defval=false , inline = "SLTP1", group=gr_customalert): na tp1alert = textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' ? input.bool(title='Target 1', defval=false , inline = "SLTP1", group=gr_customalert): na tp2alert = textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' ? input.bool(title='Target 2       ', defval=false , inline = "TP2TP3", group=gr_customalert): na tp3alert = textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' ? input.bool(title='Target 3', defval=false , inline = "TP2TP3", group=gr_customalert): na bullcrosscloud_alert = textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' ? input.bool(title='Bullish Cloud', defval=false, inline = "CD", group=gr_customalert): na bearcrosscloud_alert = textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' ? input.bool(title='Bearish Cloud', defval=false, inline = "CD", group=gr_customalert): na showCons = false paintCons = false // Signal Text SimpleBuy = "Buy" StrongB = "Strong\nBuy" SimpleSell = "Sell" StrongS = "Strong\nSell" if TextStyle == "Normal" SimpleBuy := "Buy" StrongB := "Strong\nBuy" SimpleSell:= "Sell" StrongS := "Strong\nSell" if TextStyle == "Minimal" SimpleBuy := "▲" StrongB := "▲+" SimpleSell:= "▼" StrongS := "▼+" // Signal Text Color // bullsignalcolor = #000000 // bearsignalcolor = color.rgb(0, 0, 0) // if TextStyle == "Normal" // bullsignalcolor := color.rgb(0, 0, 0) // bearsignalcolor := color.rgb(0, 0, 0) // if TextStyle == "Minimal" // bullsignalcolor := color.rgb(0, 0, 0) // bearsignalcolor := color.rgb(0, 0, 0) src = close RSII = ta.ema(ta.rsi(src, 50), 30) TR = math.abs(RSII - RSII ) wwalpha = 1 / 50 WWMA = 0.0 WWMA := wwalpha * TR + (1 - wwalpha) * nz(WWMA ) ATRRSI = 0.0 ATRRSI := wwalpha * WWMA + (1 - wwalpha) * nz(ATRRSI ) TsFast = ta.ema(ta.rsi(src, 50), 30) TsUP = TsFast + ATRRSI * 4.236 TsDN = TsFast - ATRRSI * 4.236 textWatermark = table.new(textVPosition + '_' + textHPosition, 1, 3) TsSlow = 0.0 TsSlow := TsUP < nz(TsSlow ) ? TsUP : TsFast > nz(TsSlow ) and TsFast < nz(TsSlow ) ? TsDN : TsDN > nz(TsSlow ) ? TsDN : TsFast < nz(TsSlow ) and TsFast > nz(TsSlow ) ? TsUP : nz(TsSlow ) Colorh = TsFast > 55 ? color.rgb(255, 0, 0) : TsFast < 45 ? color.rgb(0, 255, 8) : #ffffff //QQF = plot(TsFast, 'TS FAST', color=color.new(color.maroon, 100), linewidth=2, display=display.none, editable = false) //QQS = plot(TsSlow, 'TS SLOW', color=color.new(color.white, 100), linewidth=2, display=display.none , editable = false) //plot(TsFast, color=Colorh, linewidth=2, style=plot.style_area, histbase=50) //BearLimit = hline(60, color=color.gray, linestyle=hline.style_dashed) //BullLimt = hline(40, color=color.gray, linestyle=hline.style_dashed) bulllim = 45 bearlim = 55 BullSignalr = ta.crossover(TsFast, TsSlow) and TsFast < bulllim BearSignallr = ta.crossunder(TsFast, TsSlow) and TsFast > bearlim ///////////////////////////////////////////////////////// // Trap Detector //////////////////////////////////////////////////////// // Functions wavetrend(src, chlLen, avgLen) => esa = ta.ema(src, chlLen) d = ta.ema(math.abs(src - esa), chlLen) ci = (src - esa) / (0.015 * d) wt1 = ta.ema(ci, avgLen) wt2 = ta.sma(wt1, 3) f_top_fractal(src) => src < src and src < src and src > src and src > src f_bot_fractal(src) => src > src and src > src and src < src and src < src f_fractalize (src) => f_top_fractal(src) ? 1 : f_bot_fractal(src) ? -1 : 0 f_findDivs(src, topLimit, botLimit) => fractalTop = f_fractalize(src) > 0 and src >= topLimit ? src : na fractalBot = f_fractalize(src) < 0 and src <= botLimit ? src : na highPrev = ta.valuewhen(fractalTop, src , 0) highPrice = ta.valuewhen(fractalTop, high , 0) lowPrev = ta.valuewhen(fractalBot, src , 0) lowPrice = ta.valuewhen(fractalBot, low , 0) bearSignal = fractalTop and high > highPrice and src < highPrev bullSignal = fractalBot and low < lowPrice and src > lowPrev // Get components = wavetrend(close, 5*MSTuner, 10*MSTuner) = f_findDivs(wt2, 10, -35) = f_findDivs(wt2, 40, -70) wtDivBull = wtDivBull1 or wtDivBull2 wtDivBear = wtDivBear1 or wtDivBear2 plotshape(ta.crossover(wt1, wt2) and Show_PR and wt2 <= -60 and textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center', "Reversal Dot Buy" , shape.circle, location.belowbar, color.new(bullcolor,60), size=size.tiny) plotshape(ta.crossunder(wt1, wt2) and Show_PR and wt2 >= 60 and textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center', "Reversal Dot Sell", shape.circle, location.abovebar, color.new(bearcolor,60), size=size.tiny) rsi = ta.rsi(close ,14) // Functions f_chartTfInMinutes() => float _resInMinutes = timeframe.multiplier * ( timeframe.isseconds ? 1. / 60 : timeframe.isminutes ? 1. : timeframe.isdaily ? 60. * 24 : timeframe.isweekly ? 60. * 24 * 7 : timeframe.ismonthly ? 60. * 24 * 30.4375 : na) atr(len) => tr = ta.tr atr = 0.0 atr := nz(atr + (tr - atr ) / len, tr) supertrend(src, factor, len) => atr = ta.atr(len) upperBand = src + factor * atr lowerBand = src - factor * atr prevLowerBand = nz(lowerBand ) prevUpperBand = nz(upperBand ) lowerBand := lowerBand > prevLowerBand or close < prevLowerBand ? lowerBand : prevLowerBand upperBand := upperBand < prevUpperBand or close > prevUpperBand ? upperBand : prevUpperBand int direction = na float superTrend = na prevSuperTrend = superTrend if prevSuperTrend == prevUpperBand direction := close > upperBand ? 1 : -1 else direction := close < lowerBand ? -1 : 1 superTrend := direction == 1 ? lowerBand : direction == -1 ? upperBand : na dchannel(len)=> hh = ta.highest(len) ll = ta.lowest (len) trend = 0 trend := close > hh ? 1 : close < ll ? -1 : nz(trend ) trendScalper(show, len1, len2, len3, colorBull, colorBear, colorBarBull, colorBarBear) => avgOC = math.avg(open, close) ha_o = 0.0, ha_o := na(ha_o ) ? avgOC : (ha_o + ohlc4 ) / 2 ema1 = ta.ema(ha_o, len1), ema2 = ta.ema(ha_o, len2), ema3 = ta.ema(ha_o, len3) ris1 = ema1 > ema1 , ris2 = ema2 > ema2 , ris3 = ema3 > ema3 fal1 = ema1 < ema1 , fal2 = ema2 < ema2 , fal3 = ema3 < ema3 colorEma1 = ris1 ? colorBull : fal1 ? colorBear : na, colorEma2 = ris2 ? colorBull : fal2 ? colorBear : na, colorEma3 = ris3 ? colorBull : fal3 ? colorBear : na fillEma1 = avgOC > ema1 ? colorBull : avgOC < ema1 ? colorBear : na, fillEma2 = ema1 > ema2 ? colorBull : ema1 < ema2 ? colorBear : na, fillEma3 = ema2 > ema3 ? colorBull : ema2 < ema3 ? colorBear : na colorBar = close < ema1 and close < ema2 ? colorBarBear : colorBarBull candlesMom() => = ta.macd(close, 2, 4, 3) (macd > 10 and macd > macd ) or (macd < 10 and macd < macd ) trailingSL(buy, sell, factor, len, usePerc, perc) => atr = atr(len) upperBand = high + (usePerc ? high * (perc / 100) : factor * atr) lowerBand = low - (usePerc ? low * (perc / 100) : factor * atr) prevLowerBand = nz(lowerBand ) prevUpperBand = nz(upperBand ) lowerBand := lowerBand > prevLowerBand or buy ? lowerBand : prevLowerBand upperBand := upperBand < prevUpperBand or sell ? upperBand : prevUpperBand int direction = na float stop = na prevSuperTrend = stop if prevSuperTrend == prevUpperBand direction := buy ? 1 : -1 else direction := sell ? -1 : 1 stop := direction == 1 ? lowerBand : direction == -1 ? upperBand : na add_to_zz(zz, val, bi) => array.unshift(zz, bi) array.unshift(zz, val) if array.size(zz) > 12 array.pop(zz) update_zz(zz, val, bi, dir) => if array.size(zz) == 0 add_to_zz(zz, val, bi) else if dir == 1 and val > array.get(zz, 0) or dir == -1 and val < array.get(zz, 0) array.set(zz, 0, val) array.set(zz, 1, bi) 0 // Get components vosc = ta.obv - ta.ema(ta.obv, 20) bs = ta.ema(nz(math.abs((open - close) / (high - low) * 100)), 3) ema = ta.ema(close, 200) emaBull = close > ema equal_tf(res) => str.tonumber(res) == f_chartTfInMinutes() higher_tf(res) => str.tonumber(res) > f_chartTfInMinutes() too_small_tf(res) => (timeframe.isweekly and res=="1") or (timeframe.ismonthly and str.tonumber(res) < 10) securityNoRep(sym, res, src) => bool bull = na bull := equal_tf(res) ? src : bull bull := higher_tf(res) ? request.security(sym, res, src, barmerge.gaps_off, barmerge.lookahead_on) : bull bull_array = request.security_lower_tf(syminfo.tickerid, higher_tf(res) ? str.tostring(f_chartTfInMinutes()) : too_small_tf(res) ? (timeframe.isweekly ? "3" : "10") : res, src) if array.size(bull_array) > 1 and not equal_tf(res) and not higher_tf(res) bull := array.pop(bull_array) array.clear(bull_array) bull //TF1Bull = securityNoRep(syminfo.tickerid, "1" , emaBull) //TF3Bull = securityNoRep(syminfo.tickerid, "3" , emaBull) TF5Bull = securityNoRep(syminfo.tickerid, "5" , emaBull) //TF10Bull = securityNoRep(syminfo.tickerid, "10" , emaBull) TF15Bull = securityNoRep(syminfo.tickerid, "15" , emaBull) TF30Bull = securityNoRep(syminfo.tickerid, "30" , emaBull) TF60Bull = securityNoRep(syminfo.tickerid, "60" , emaBull) //TF120Bull = securityNoRep(syminfo.tickerid, "120" , emaBull) TF240Bull = securityNoRep(syminfo.tickerid, "240" , emaBull) //TF720Bull = securityNoRep(syminfo.tickerid, "720" , emaBull) //TFDBull = securityNoRep(syminfo.tickerid, "1440", emaBull) hma55 = ta.hma(close, 55 ) = ta.macd(close, 12, 26, 9) supertrend = supertrend(close, sensitivity, STuner) maintrend = dchannel(30) confBull = (ta.crossover (close, supertrend) or (ta.crossover (close, supertrend) and maintrend < 0)) and macd > 0 and macd > macd and ema150 > ema250 and hma55 > hma55 and maintrend > 0 and textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' confBear = (ta.crossunder(close, supertrend) or (ta.crossunder(close, supertrend) and maintrend > 0)) and macd < 0 and macd < macd and ema150 < ema250 and hma55 < hma55 and maintrend < 0 and textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' trendcloud = supertrend(ohlc4, periodTrendCloud == "Swing" ? 7 : 4, 10) hma = periodTrendCloud == "Scalping+" ? ta.hma(close, 55) : na none = close > 0 = ta.dmi(14, 14) consFilter = adx > 20 ContBear = TsFast > 65 ContBull = TsFast < 35 StrongFilter = ta.ema(close, 200) //volFilter = (ta.ema(volume, 25) - ta.ema(volume, 26)) / ta.ema(volume, 26) > 0 volFilter = (ta.ema(volume, 15) - ta.ema(volume, 20)) / ta.ema(volume, 25) > 0 trendFilter = trendcloud bull = (Presets == "All Signals" ? ta.crossover (close, supertrend) : confBull and not confBull ) and Presets != "Trend Scalper" and (StrongSignalsOnly ? close > StrongFilter : none) and (ContrarianOnly ? ContBull : none) and (consSignalsFilter ? consFilter : none) and (highVolSignals ? volFilter : none) and (signalsTrendCloud ? (periodTrendCloud == "Smooth" ? ema150 > ema250 : close > trendFilter) : none) bear = (Presets == "All Signals" ? ta.crossunder(close, supertrend) : confBear and not confBear ) and Presets != "Trend Scalper" and (StrongSignalsOnly ? close < StrongFilter : none) and (ContrarianOnly ? ContBear : none) and (consSignalsFilter ? consFilter : none) and (highVolSignals ? volFilter : none) and (signalsTrendCloud ? (periodTrendCloud == "Smooth" ? ema150 < ema250 : close < trendFilter) : none) countBull = ta.barssince(bull) countBear = ta.barssince(bear) trigger = nz(countBull, bar_index) < nz(countBear, bar_index) ? 1 : 0 = trendScalper(Presets == "Trend Scalper" ? true : false, 5, 9, 21, bullcolor, bearcolor, bullcolor, bearcolor) trailingStop = trailingSL(bull, bear, 2.2, 14, usePercSL, percTrailingSL) float _ph = ta.highestbars(high, periodSwings) == 0 ? high : na float _pl = ta.lowestbars (low, periodSwings) == 0 ? low : na var _dir = 0, dir_ = _pl and na(_ph) ? -1 : _dir, _dir := _ph and na(_pl) ? 1 : dir_, dirChg = ta.change(_dir) var zz = array.new_float(0), zzOld = array.copy(zz) float zzLive = _ph or _pl ? (dirChg ? add_to_zz(zz, _dir == 1 ? _ph : _pl, bar_index) : update_zz(zz, _dir == 1 ? _ph : _pl, bar_index, _dir)) : na float hb_ = ta.highestbars(10) == 0 ? high : na float lb_ = ta.lowestbars (10) == 0 ? low : na var int dir = 0 float zz_ = na float pp = na var int consCnt = 0 var float condHi = na var float condLo = na float H_ = ta.highest(5) float L_ = ta.lowest (5) var line lineUp = na var line lineDn = na bool breakUp = false bool breakDn = false var float pvh1_price = array.new_float(1000, na) var int pvh1_time = array.new_int (1000, na) var float pvl1_price = array.new_float(1000, na) var int pvl1_time = array.new_int (1000, na) var float pvh2_price = array.new_float(1000, na) var int pvh2_time = array.new_int (1000, na) var float pvl2_price = array.new_float(1000, na) var int pvl2_time = array.new_int (1000, na) var float htcmrll_price = na var int htcmrll_time = na var float ltcmrhh_price = na var int ltcmrhh_time = na var box long_boxes = array.new_box() var box short_boxes = array.new_box() var float temp_pv_0 = na var float temp_pv_1 = na var float temp_pv_2 = na bool pvh = high < high and high > high bool pvl = low > low and low < low int pv1_time = bar_index float pv1_high = high float pv1_low = low var buyBars = array.new_box(365, na) for i = 0 to 364 box.delete(array.get(buyBars, i)) var sellBars = array.new_box(365, na) for i = 0 to 364 box.delete(array.get(sellBars, i)) // Colors green = bullcolor, green50 = color.new(green, 50), green20 = color.new(green, 80) red = bearcolor, red50 = color.new(red, 50), red20 = color.new(red, 80) silver = #B2B5BE, silver50 = color.new(silver, 50), silver20 = color.new(silver, 80) // Plots atrBand = usePercSL ? (trigger ? low : high) * (percTrailingSL / 100) : ta.atr(14) * 2.2 atrStop = trigger ? low - atrBand : high + atrBand lastTrade(src) => ta.valuewhen(bull or bear, src, 0) entry_y = lastTrade(close) stop_y = lastTrade(atrStop) tp1_y = (entry_y-lastTrade(atrStop))*multTP1 + entry_y tp2_y = (entry_y-lastTrade(atrStop))*multTP2 + entry_y tp3_y = (entry_y-lastTrade(atrStop))*multTP3 + entry_y labelTpSl(cond, y, txt, color) => label labelTpSl = ShowTpSlAreas and cond ? label.new(bar_index + 1, y, txt, xloc.bar_index, yloc.price, color, label.style_label_left, color.white, size.normal) : na label.delete(labelTpSl ) labelTpSl(none, entry_y, "Entry : " + str.tostring(math.round_to_mintick(entry_y)), color.orange) labelTpSl(none, stop_y , "Stop loss : " + str.tostring(math.round_to_mintick(atrStop)), bearcolor) labelTpSl(useTP1 and multTP1 != 0, tp1_y, "TP 1 : " + str.tostring(math.round_to_mintick(tp1_y)), bullcolor) labelTpSl(useTP2 and multTP2 != 0, tp2_y, "TP 2 : " + str.tostring(math.round_to_mintick(tp2_y)), bullcolor) labelTpSl(useTP3 and multTP3 != 0, tp3_y, "TP 3 : " + str.tostring(math.round_to_mintick(tp3_y)), bullcolor) lineTpSl(cond, y, color, style) => line lineTpSl = ShowTpSlAreas and cond ? line.new(bar_index - (trigger ? countBull : countBear), y, bar_index + 1, y, xloc.bar_index, extend.none, color, style) : na line.delete(lineTpSl ) lineTpSl(none, entry_y, color.orange, line.style_dashed) lineTpSl(none, stop_y , bearcolor , line.style_solid ) lineTpSl(useTP1 and multTP1 != 0, tp1_y, bullcolor, line.style_dotted) lineTpSl(useTP2 and multTP2 != 0, tp2_y, bullcolor, line.style_dotted) lineTpSl(useTP3 and multTP3 != 0, tp3_y, bullcolor, line.style_dotted) buy = showSignals and bull ? label.new(bar_index, low , close > StrongFilter ? StrongB : SimpleBuy , xloc.bar_index, yloc.belowbar, bullcolor, label.style_label_up , #000000, size.normal) : na sell = showSignals and bear ? label.new(bar_index, high, close < StrongFilter ? StrongS : SimpleSell , xloc.bar_index, yloc.abovebar, bearcolor , label.style_label_down, #000000, size.normal) : na tpLabels(tp) => tp1Bull = ta.crossover (rsi, 70), tp2Bull = ta.crossover (rsi, 75), tp3Bull = ta.crossover (rsi, 80) tp1Bear = ta.crossunder(rsi, 30), tp2Bear = ta.crossunder(rsi, 25), tp3Bear = ta.crossunder(rsi, 20) tp1Bull := tp1Bull and (nz(ta.barssince(tp1Bull) , 9999) > countBull), tp2Bull := tp2Bull and (ta.barssince(tp1Bull) <= countBull), tp2Bull := tp2Bull and (nz(ta.barssince(tp2Bull) , 9999) > countBull), tp3Bull := tp3Bull and (ta.barssince(tp2Bull) <= countBull), tp3Bull := tp3Bull and (nz(ta.barssince(tp3Bull) , 9999) > countBull) tp1Bear := tp1Bear and (nz(ta.barssince(tp1Bear) , 9999) > countBear), tp2Bear := tp2Bear and (ta.barssince(tp1Bear) <= countBear), tp2Bear := tp2Bear and (nz(ta.barssince(tp2Bear) , 9999) > countBear), tp3Bear := tp3Bear and (ta.barssince(tp2Bear) <= countBear), tp3Bear := tp3Bear and (nz(ta.barssince(tp3Bear) , 9999) > countBear) if Presets != "Trend Scalper" and tpLabels trigger ? (tp == 1 ? tp1Bull : tp == 2 ? tp2Bull : tp3Bull) : (tp == 1 ? tp1Bear : tp == 2 ? tp2Bear : tp3Bear) plotshape(tpLabels(1), "", shape.xcross, location.abovebar, trigger ? green : na , 0, "TP 1", trigger ? green : na , false) plotshape(tpLabels(2), "", shape.xcross, location.abovebar, trigger ? green : na , 0, "TP 2", trigger ? green : na , false) plotshape(tpLabels(3), "", shape.xcross, location.abovebar, trigger ? green : na , 0, "TP 3", trigger ? green : na , false) plotshape(tpLabels(1), "", shape.xcross, location.belowbar, trigger ? na : red, 0, "TP 1", trigger ? na : red, false) plotshape(tpLabels(2), "", shape.xcross, location.belowbar, trigger ? na : red, 0, "TP 2", trigger ? na : red, false) plotshape(tpLabels(3), "", shape.xcross, location.belowbar, trigger ? na : red, 0, "TP 3", trigger ? na : red, false) var label zzLabel = na if array.size(zz) > 12 and ShowSwings if array.get(zz, 0) != array.get(zzOld, 0) or array.get(zz, 1) != array.get(zzOld, 1) if array.get(zz, 2) == array.get(zzOld, 2) and array.get(zz, 3) == array.get(zzOld, 3) label.delete(zzLabel) zzLabel := label.new(math.round(array.get(zz, 1)), array.get(zz, 0), _dir == 1 ? array.get(zz, 0) > array.get(zz, 4) ? ((array.get(zz, 4) < array.get(zz, 8)) ? "High" : "HH") : "LH" : array.get(zz, 0) < array.get(zz, 4) ? ((array.get(zz, 4) > array.get(zz, 8)) ? "Low" : "LL") : "HL", xloc.bar_index, yloc.price, color.new(color.white, 100), _dir == 1 ? label.style_label_down : label.style_label_up, _dir == 1 ? bullcolor : bearcolor) if showCons and barstate.isconfirmed dir := hb_ and na(lb_) ? 1 : lb_ and na(hb_) ? -1 : dir if hb_ and lb_ if dir == 1 zz_ := hb_ else zz_ := lb_ else zz_ := hb_ ? hb_ : lb_ ? lb_ : na for x = 0 to 1000 if na(close) or dir != dir break if zz_ if na(pp) pp := zz_ else if dir == 1 and zz_ > pp pp := zz_ if dir == -1 and zz_ < pp pp := zz_ if pp != pp if consCnt > 5 if pp > condHi breakUp := true if pp < condLo breakDn := true if consCnt > 0 and pp <= condHi and pp >= condLo consCnt += 1 else consCnt := 0 else consCnt += 1 if consCnt >= 5 if consCnt == 5 condHi := H_ condLo := L_ else line.delete(lineUp) line.delete(lineDn) condHi := math.max(condHi, high) condLo := math.min(condLo, low ) lineUp := line.new(bar_index, condHi , bar_index - consCnt, condHi , color=bearcolor , style=line.style_dashed) lineDn := line.new(bar_index, condLo , bar_index - consCnt, condLo , color=color.lime, style=line.style_dashed) fill(plot(condHi, "", na, 1, plot.style_stepline, editable=false), plot(condLo, "", na, 1, plot.style_stepline, editable=false), paintCons and consCnt > 5 ? color.white : na, "", false) //buy_col = color.new(#0ac20a,0) //sell_col = color.new(#fd1605,0) //text_col = color.new(#FFFFFF,0) // -------- Bearish trend (blue) color selection -------- // getSellColor(count) => // if count == 1 // color.new(#11e7f2,0) // else // if count == 2 // color.new(#11d9f2,0) // else // if count == 3 // color.new(#11cbf2,0) // else // if count == 4 // color.new(#11aff2,0) // else // if count == 5 // color.new(#1193f2,0) // else // if count == 6 // color.new(#1176f2,0) // else // if count == 7 // color.new(#105df4,0) // else // if count == 8 // color.new(#1051f5,0) // else // if count == 9 // color.new(#0f44f5,0) // else // if count == 10 // color.new(#0c3de0,0) // else // if count == 11 // color.new(#0935ca,0) // else // if count == 12 // color.new(#062eb4,0) // else // if count == 13 // color.new(#02269e,0) // -------- Bullish trend (blue) color selection -------- // getBuyColor(count) => // if count == 1 // color.new(#eef211,0) // else // if count == 2 // color.new(#efdc11,0) // else // if count == 3 // color.new(#f0c511,0) // else // if count == 4 // color.new(#f1af11,0) // else // if count == 5 // color.new(#f29811,0) // else // if count == 6 // color.new(#f28811,0) // else // if count == 7 // color.new(#f27811,0) // else // if count == 8 // color.new(#f26811,0) // else // if count == 9 // color.new(#f25811,0) // else // if count == 10 // color.new(#ea420d,0) // else // if count == 11 // color.new(#e12c09,0) // else // if count == 12 // color.new(#d81605,0) // else // if count == 13 // color.new(#cf0000,0) // -------- Calculate bearish trend sequence -------- buySetup = 0 buySetup := close < close ? buySetup == 13 ? 1 : buySetup + 1 : 0 // -------- Calculate bullish trend sequence -------- sellSetup = 0 sellSetup := close > close ? sellSetup == 13 ? 1 : sellSetup + 1 : 0 // -------- Paint bars -------- //barColour = buySetup >= 1 ? getBuyColor(buySetup) : sellSetup >= 1 ? getSellColor(sellSetup) : na // Candle Coloring // Input FastteyLength = 12 SjlowLeyLength = 26 srrrc = close signalXLength = 9 // Data reference = ta.macd(srrrc, FastteyLength, SjlowLeyLength, signalXLength) // 4 level of green // greenHigh = #eeff00 // greenMidHigh = #c7ca00 // greenMidLow = #ddb500 // greenLow = #8635ff // // Yellow // yellowLow = #8635ff // // 4 level of red // redHigh = #ffffff // redMidHigh = #cecece // redMidLow = #dbdbdb // redLow = #8635ff // // Default color // candleBody = yellowLow // // Ranging trend // if histX > 0 // if histX > histX and histX > 0 // candleBody := greenLow // if histX < 0 // if histX < histX and histX < 0 // candleBody := redLow // // Bullish trend // if MacdX > 0 and histX > 0 // candleBody := greenMidLow // if histX > histX and MacdX > 0 and histX > 0 // candleBody := greenMidHigh // if histX > histX and MacdX > 0 and histX > 0 // candleBody := greenHigh // // Bearish trend // if MacdX < 0 and histX < 0 // candleBody := redMidLow // if histX < histX and MacdX < 0 and histX < 0 // candleBody := redMidHigh // if histX < histX and MacdX < 0 and histX < 0 // candleBody := redHigh //barcolor(candleBody) //barcolor(TrendMap == 'RSI Gradient' ? barColour : na, title='Bar colors (heatmap)',editable=false) //barcolor(momentumCandles and candlesMom() ? color.rgb(187, 187, 187) : TrendMap == 'Signal Based' ? (Presets == "Trend Scalper" ? colorBar : na(countBull) and na(countBear) ? color.gray : trigger ? bullcolor : bearcolor) : TrendMap == 'RSI Gradient' ? barColour : TrendMap == 'Trend Gradient' ? candleBody : na , editable=false) //plotcandle(open, high, low, close , color = momentumCandles and candlesMom() ? color.rgb(187, 187, 187) : TrendMap == 'Signal Based' ? (Presets == "Trend Scalper" ? colorBar : na(countBull) and na(countBear) ? color.gray : trigger ? bullcolor : bearcolor) : TrendMap == 'RSI Gradient' ? barColour : TrendMap == 'Trend Gradient' ? candleBody : na , editable=false , wickcolor = momentumCandles and candlesMom() ? color.rgb(187, 187, 187) : TrendMap == 'Signal Based' ? (Presets == "Trend Scalper" ? colorBar : na(countBull) and na(countBear) ? color.gray : trigger ? bullcolor : bearcolor) : TrendMap == 'RSI Gradient' ? barColour : TrendMap == 'Trend Gradient' ? candleBody : na , editable=false , bordercolor = momentumCandles and candlesMom() ? color.rgb(187, 187, 187) : TrendMap == 'Signal Based' ? (Presets == "Trend Scalper" ? colorBar : na(countBull) and na(countBear) ? color.gray : trigger ? bullcolor : bearcolor) : TrendMap == 'RSI Gradient' ? barColour : TrendMap == 'Trend Gradient' ? candleBody : na , editable=false , editable = false) fill(plot(showTrendCloud and periodTrendCloud == "Smooth" ? ema150 : na, "", na, editable=false), plot(showTrendCloud and periodTrendCloud == "Smooth" ? ema250 : na, "", na, editable=false), ema150 > ema250 ? color.new(bullcolor, 70) : ema150 < ema250 ? color.new(bearcolor, 70) : na) plot(ShowTrailingSL and trigger and nz(ta.barssince(low < trailingStop), bar_index) > countBull ? trailingStop : na, "", green, 1, plot.style_linebr, editable=false) plot(ShowTrailingSL and not trigger and nz(ta.barssince(high > trailingStop), bar_index) > countBear ? trailingStop : na, "", red , 1, plot.style_linebr, editable=false) p0 = plot(avgOC, "", na , editable=false) p1 = plot(ema5 , "", colorEma5 , editable=false) p2 = plot(ema9 , "", colorEma9 , editable=false) p3 = plot(ema21, "", colorEma21, editable=false) plot(LongTrendAverage ? ta.ema(close, 250) : na, 'Trend Tracer', linewidth=2, color=close > ta.ema(close, 250) ? color.new(bullcolor, 45) : color.new(bearcolor, 45)) fill(p0, p1, fillEma5 ) fill(p1, p2, fillEma9 ) fill(p2, p3, fillEma21) fill(plot(showTrendCloud and periodTrendCloud != "Smooth" and periodTrendCloud != "Scalping+" and trendcloud != 0 and close > trendcloud ? trendcloud : na, "", bullcolor, 1, plot.style_linebr, editable=false), p0, color.new(bullcolor, 90)) fill(plot(showTrendCloud and periodTrendCloud != "Smooth" and periodTrendCloud != "Scalping+" and trendcloud != 0 and close < trendcloud ? trendcloud : na, "", bearcolor , 1, plot.style_linebr, editable=false), p0, color.new(bearcolor , 90)) //fill(plot(hma, "", hma > hma ? green : hma < hma ? red : na, editable=false), plot(hma , "", hma > hma ? green : hma < hma ? red : na, editable=false), hma > hma ? green : hma < hma ? red : na) //////////////////////////////////////////////////////////////////////////////////////////////// // Get user input indicatorTF = "Chart" // Functions sqz(bbLen, bbMult, kcLen, kcMult, source) => upperBB = ta.sma(source, bbLen) + ta.stdev(source, bbLen) * bbMult lowerBB = ta.sma(source, bbLen) - ta.stdev(source, bbLen) * bbMult upperKC = ta.sma(source, kcLen) + ta.sma(ta.tr, kcLen) * kcMult lowerKC = ta.sma(source, kcLen) - ta.sma(ta.tr, kcLen) * kcMult sqzOn = lowerBB > lowerKC and upperBB < upperKC sqzOff = lowerBB < lowerKC and upperBB > upperKC qqe(rsiLen, rsiSmooth, factor, source, bbLen, bbMult) => rsiMa = ta.ema(ta.rsi(source, rsiLen), rsiSmooth) delta = ta.ema(ta.ema(math.abs(ta.mom(rsiMa, 1)), rsiLen * 2 - 1), rsiLen * 2 - 1) * factor longBand = 0.0, longBand := rsiMa > longBand and rsiMa > longBand ? math.max(longBand , rsiMa - delta) : rsiMa - delta shortBand = 0.0, shortBand := rsiMa < shortBand and rsiMa < shortBand ? math.min(shortBand , rsiMa + delta) : rsiMa + delta cross1 = ta.cross(rsiMa, shortBand ) cross2 = ta.cross(rsiMa, longBand ) trend = 0.0, trend := cross1 ? 1 : cross2 ? -1 : nz(trend , 1) fastDelta = trend == 1 ? longBand : shortBand _hist = rsiMa - 50 _line = fastDelta - 50 = ta.bb(_line, bbLen, bbMult) // Get components cond(_offset) => top = ta.highest(high, 10) bot = ta.lowest(low, 10) osc = ta.ema(hlc3, 5) - ta.ema(ohlc4, 20) oscRis = osc > osc oscFal = osc < osc oscA0 = osc > 0 oscB0 = osc < 0 oscTop = oscFal and oscRis oscBot = oscRis and oscFal bullR = oscB0 and oscBot and ((osc > ta.valuewhen(oscB0 and oscBot, osc, 1) and bot < ta.valuewhen(oscB0 and oscBot, bot, 1))) bearR = oscA0 and oscTop and ((osc < ta.valuewhen(oscA0 and oscTop, osc, 1) and top > ta.valuewhen(oscA0 and oscTop, top, 1))) bullH = oscB0 and oscBot and ((osc < ta.valuewhen(oscB0 and oscBot, osc, 1) and bot > ta.valuewhen(oscB0 and oscBot, bot, 1))) bearH = oscA0 and oscTop and ((osc > ta.valuewhen(oscA0 and oscTop, osc, 1) and top < ta.valuewhen(oscA0 and oscTop, top, 1))) = sqz(20, 2, 20, 2, close) = qqe(6, 6, 3, close, 50, 0.001) = qqe(6, 5, 1.618, close, 50, 1) = ta.dmi(14, 14) [osc , oscRis , oscFal , oscA0 , oscB0 , oscTop , oscBot , bullR , bearR , bullH , bearH , sqzOn , sqzOff , _hist1 , upper1 , lower1 , _hist2 , _line2 , tvr ] tf = indicatorTF == "Chart" ? timeframe.period : indicatorTF == "1 minute" ? "1" : indicatorTF == "3 minutes" ? "3" : indicatorTF == "5 minutes" ? "5" : indicatorTF == "10 minutes" ? "10" : indicatorTF == "15 minutes" ? "15" : indicatorTF == "30 minutes" ? "30" : indicatorTF == "45 minutes" ? "45" : indicatorTF == "1 hour" ? "60" : indicatorTF == "2 hours" ? "120" : indicatorTF == "3 hours" ? "180" : indicatorTF == "4 hours" ? "240" : indicatorTF == "12 hours" ? "720" : indicatorTF == "1 day" ? "1D" : indicatorTF == "1 week" ? "1W" : indicatorTF == "1 month" ? "1M" : na = request.security(syminfo.tickerid, tf, cond(indicatorTF != "Chart" and barstate.isrealtime ? 1 : 0)) //colorTVR = tvr < 15 ? #F6525F : tvr > 15 and tvr < 25 ? #B2B5BE : #66BB6A // Plots //plot(Presets == "Money Moves TrendVR" ? tvr : na, "", colorTVR, editable=false) TrendText = "Trending" if tvr < 15 and tvr < 25 TrendText := "No trend" if tvr > 15 and tvr < 25 TrendText := "Ranging" //------------------------------------------------------------------------------------------------------- Volatitiry //Calculates Volatility for Dashboard atrr = 3 * ta.atr(10) stdAtr = 2 * ta.stdev(atrr, 20) smaAtr = ta.sma(atrr, 20) topAtrDev = smaAtr + stdAtr bottomAtrDev = smaAtr - stdAtr calcDev = (atrr - bottomAtrDev) / (topAtrDev - bottomAtrDev) percentVol = 40 * calcDev + 30 AvrLength = 21 PercentFilter = 144 xAavrVolume = ta.rma(volume, AvrLength) nResLess = volume * 100 / xAavrVolume < PercentFilter ? 0 : volume nRes = nResLess clr = close < open ? #b2b5be : #00dbff //plot(nRes, color=clr, style=plot.style_columns, title='Volume Filter', transp=20) VolitiText = "Inactive" if nRes VolitiText := "Active" ////////////////////////////////////////// ema69 = ta.ema(close, 9) totalSentTxt = ema69 > ema69 ? 'Bullish' : ema69 < ema69 ? 'Bearish' : 'Flat' // INputs //Timezones tz_incr = 0 use_exchange = false //------------------------------------------------------------------------------ //Settings //-----------------------------------------------------------------------------{ //Session A NYSes = true NYTxt = 'New York' NYTime = '1300-2200' //Session B LDSes = true sesb_txt = 'London' sesb_ses = '0700-1600' //Session C show_sesc = true sesc_txt = 'Tokyo' sesc_ses = '0000-0900' //Session D show_sesd = true sesd_txt = 'Sydney' sesd_ses = '2100-0600' //-----------------------------------------------------------------------------} //Sessions //-----------------------------------------------------------------------------{ tff = timeframe.period var tz = use_exchange ? syminfo.timezone : str.format('UTC{0}{1}', tz_incr >= 0 ? '+' : '-', math.abs(tz_incr)) is_sesa = math.sign(nz(time(tff, NYTime, tz))) is_sesb = math.sign(nz(time(tff, sesb_ses, tz))) is_sesc = math.sign(nz(time(tff, sesc_ses, tz))) is_sesd = math.sign(nz(time(tff, sesd_ses, tz))) //////////////////////////////////////////// SessionText = "Default" if is_sesd SessionText := sesd_txt if is_sesc SessionText := sesc_txt if is_sesb SessionText := sesb_txt if is_sesa SessionText := NYTxt if is_sesd and is_sesc SessionText := "Sydney/Tokyo" if is_sesb and is_sesc SessionText := "Tokyo/London" if is_sesb and is_sesa SessionText := "London/Newyork" if is_sesa and is_sesd SessionText := "Newyork/Sydney" //-----------------------------------------------------------------------------} //Overlays color.green : color.red // var dashboard_loc = locationDashboard == "Top Right" ? position.top_right : locationDashboard == "Middle Right" ? position.middle_right : locationDashboard == "Bottom Right" ? position.bottom_right : locationDashboard == "Top Center" ? position.top_center : locationDashboard == "Middle Center" ? position.middle_center : locationDashboard == "Bottom Center" ? position.bottom_center : locationDashboard == "Top Left" ? position.top_left : locationDashboard == "Middle Left" ? position.middle_left : position.bottom_left var dashboard_size = sizeDashboard == "Large" ? size.large : sizeDashboard == "Normal" ? size.normal : sizeDashboard == "Small" ? size.small : size.tiny var dashboard = showDashboard ? table.new(dashboard_loc, 3, 7, color.rgb(30, 34, 45 , 60), #3d384300, 2, color.rgb(30, 34, 45 , 60), 1) : na dashboard_cell(column, row, txt, signal=false) => table.cell(dashboard, column, row, txt, 0, 0, signal ? #000000 : color.white, text_size=dashboard_size) dashboard_cell_bg(column, row, col) => table.cell_set_bgcolor(dashboard, column, row, col) if barstate.islast and showDashboard // MTF Trend dashboard_cell(0, 0 , "MTF") dashboard_cell(0, 2 , "M5") , dashboard_cell_bg(0, 2 , TF5Bull ? #00dbff : #b2b5be) dashboard_cell(0, 3 , "M15") , dashboard_cell_bg(0, 3 , TF15Bull ? #00dbff : #b2b5be) dashboard_cell(0, 4 , "M30") , dashboard_cell_bg(0, 4 , TF30Bull ? #00dbff : #b2b5be) dashboard_cell(0, 5 , "1H") , dashboard_cell_bg(0, 5 , TF60Bull ? #00dbff : #b2b5be) dashboard_cell(0, 6 , "4H") , dashboard_cell_bg(0, 6 , TF240Bull ? #00dbff : #b2b5be) // Middel part dashboard_cell(1, 0 , "Fresh Algo | Signals & Overlays™") dashboard_cell(1, 2 , "🔥 Market State               ") dashboard_cell(1, 3 , "⚠️ Volatility                    ") dashboard_cell(1, 4 , "🏦 Institutional Activity    ") dashboard_cell(1, 5 , "🕒 Current Session (UTC)  ") dashboard_cell(1, 6 , "🌊 Trend Pressure           ") // End part dashboard_cell(2, 0 , "") dashboard_cell(2, 2 , TrendText) dashboard_cell(2, 3 , str.tostring(percentVol, '##.##') + '%') dashboard_cell(2, 4 , VolitiText) dashboard_cell(2, 5 , SessionText) dashboard_cell(2, 6 , totalSentTxt) // Alerts f_sl_crossed() => ret = false stop = ShowTrailingSL ? trailingStop : stop_y crossBull = low >= stop and low < stop and ta.barssince(low >= stop and low < stop ) >= countBull - 1 crossBear = high <= stop and high > stop and ta.barssince(high <= stop and high > stop ) >= countBear - 1 ret := trigger ? crossBull : crossBear f_tp_crossed(tp) => ret = false profit = tp crossBull = high <= profit and high > profit and ta.barssince(high <= profit and high > profit ) >= countBull - 1 crossBear = low >= profit and low < profit and ta.barssince(low >= profit and low < profit ) >= countBear - 1 ret := trigger ? crossBull : crossBear alert01 = (bull and close <= StrongFilter) or (bear and close >= StrongFilter) alert02 = bull or bear alert03 = (bull and close > StrongFilter) or (bear and close < StrongFilter) alert04 = bull and close <= StrongFilter alert06 = bear and close >= StrongFilter alert07 = bull and close > StrongFilter alert08 = bear and close < StrongFilter alert09 = f_sl_crossed() alert11 = f_tp_crossed(tp1_y) alert12 = f_tp_crossed(tp2_y) alert13 = f_tp_crossed(tp3_y) alert14 = periodTrendCloud == "Smooth" ? ta.crossunder(ema150, ema250) : (close < trendcloud) and (close > trendcloud) alert15 = periodTrendCloud == "Smooth" ? ta.crossover (ema150, ema250) : (close > trendcloud) and (close < trendcloud) // Signal Alerts if alert04 and Normalbuy_alert alert('Buy Signal Alert !!!' , alert.freq_once_per_bar_close) alert(syminfo.tickerid) if alert07 and Strongbuy_alert alert('Strong Buy Signal Alert !!!', alert.freq_once_per_bar_close) alert(syminfo.tickerid) if (bear and close >= StrongFilter) and Normalsell_alert alert('Sell Signal Alert !!!', alert.freq_once_per_bar_close) alert(syminfo.tickerid) if (bear and close < StrongFilter) and Strongsell_alert alert('Strong Sell Signal Alert !!!', alert.freq_once_per_bar_close) alert(syminfo.tickerid) // Risk Management Alerts if alert09 and slalert alert('SL Alert !!!', alert.freq_once_per_bar_close) alert(syminfo.tickerid) if alert11 and tp1alert alert('Target 1 Alert !!!', alert.freq_once_per_bar_close) alert(syminfo.tickerid) if alert12 and tp2alert alert('Target 2 Alert !!!', alert.freq_once_per_bar_close) alert(syminfo.tickerid) if alert13 and tp3alert alert('Target 3 Alert !!!', alert.freq_once_per_bar_close) alert(syminfo.tickerid) // Cloud Alert if alert15 and bullcrosscloud_alert alert('Cloud Turned Bullish !!!', alert.freq_once_per_bar_close) alert(syminfo.tickerid) if alert14 and bearcrosscloud_alert alert('Cloud Turned Bearish !!!', alert.freq_once_per_bar_close) alert(syminfo.tickerid) alertcondition(alert01, "Any Signal Contrarian Buy / Contrarian sell", "Buy or Sell") alertcondition(alert04, "Contrarian Buy alert", "Buy") alertcondition(alert06, "Contrarian Sell alert", "Sell") // Bar Coloring // Input fastLength = 12 slowLength = 26 srcceed = close signalLength = 9 // Data reference = ta.macd(srcceed, fastLength, slowLength, signalLength) // 4 level of green greenHighh = #00dbff greenMidHighh = #61eaff greenMidLowh = #40e6ff greenLowh = #61eaff // Yellow yellowLowh = #80eeff // 4 level of red redHighh = #a1f3ff redMidHighh = #c0f7ff redMidLowh = #e0fbff redLowh = #FFFFFF // Default color candleBodyd = yellowLowh // Ranging trend if hist > 0 if hist > hist and hist > 0 candleBodyd := greenLowh if hist < 0 if hist < hist and hist < 0 candleBodyd := redLowh // Bullish trend if macdda > 0 and hist > 0 candleBodyd := greenMidLowh if hist > hist and macd > 0 and hist > 0 candleBodyd := greenMidHighh if hist > hist and macd > 0 and hist > 0 candleBodyd := greenHighh // Bearish trend if macdda < 0 and hist < 0 candleBodyd := redMidLowh if hist < hist and macd < 0 and hist < 0 candleBodyd := redMidHighh if hist < hist and macd < 0 and hist < 0 candleBodyd := redHighh barcolor(candleBodyd) // Include suggestion by Shaheen204 // tenkan_len = 365 tenkan_mult = 3 kijun_len = 365 kijun_mult = 7 spanB_len = 365 spanB_mult = 10 offset = 1 //------------------------------------------------------------------------------ avg(src,length,mult)=> atr = ta.atr(50)*mult up = hl2 + atr dn = hl2 - atr upper = 0.,lower = 0. upper := src < upper ? math.min(up,upper ) : up lower := src > lower ? math.max(dn,lower ) : dn os = 0,max = 0.,min = 0. os := src > upper ? 1 : src < lower ? 0 : os spt = os == 1 ? lower : upper max := ta.cross(src,spt) ? math.max(src,max ) : os == 1 ? math.max(src,max ) : spt min := ta.cross(src,spt) ? math.min(src,min ) : os == 0 ? math.min(src,min ) : spt math.avg(max,min) //------------------------------------------------------------------------------ tenkan = avg(close,tenkan_len,tenkan_mult) kijun = avg(close,kijun_len,kijun_mult) senkouA = math.avg(kijun,tenkan) senkouB = avg(close,spanB_len,spanB_mult) //------------------------------------------------------------------------------ cloud_a = color.new(#00dbff, 80) cloud_b = color.new(#b2b5be, 80) A = plot(senkouA,'Senkou Span A',na,offset=offset-1, editable = false) B = plot(senkouB,'Senkou Span B',na,offset=offset-1, editable = false) fill(A,B,senkouA > senkouB and textVPosition == 'middle' and textHPosition == 'center' and c_title == #b2b5be80 and s_title == 'large' and a_title == 'center' and c_subtitle == #b2b5be80 and s_subtitle == 'normal' and a_subtitle == 'center' and analyscloud == true ? cloud_a : analyscloud == true ? cloud_b : na) // Telegram Join Us >> t.me //...................../´¯¯/) //...................,/¯.../ //.................../..../ //.............../´¯/'..'/´¯¯`·¸ //.........../'/.../..../....../¨¯\ //..........('(....´...´... ¯~/'..') //...........\..............'...../ //............\....\.........._.·´ //.............\..............( //..............\..............\ //---- //--------- // Telegram Join Us >> t.me
Pine Script®指标
由Damnonlyhere提供
Equal Highs/Lows Multi-Pivot [Julio]Equal Highs/Lows Multi-Pivot Description A sophisticated multi-timeframe pivot analysis tool that detects and highlights equal highs and equal lows across four different pivot lengths simultaneously. This indicator identifies price levels where the market creates identical extremes, a powerful signal of institutional support/resistance and potential reversal or breakout zones. How It Works Four Independent Pivot Streams Pivot 1 (Intraday - 2 bars): Ultra-fast level detection for scalpers Pivot 2 (Session - 4 bars): Short-term swing levels Pivot 3 (Daily - 6 bars): Medium-term structural levels Pivot 4 (Weekly - 9 bars): Long-term institutional levels Equal High (EQH) Detection Compares consecutive swing highs and draws a line when two highs are nearly identical within a defined threshold. The indicator uses ATR-based confluence to determine "equality," filtering out noise while catching true market structure. Equal Low (EQL) Detection Same logic applied to swing lows, identifying support zones where price repeatedly fails to break below previous lows. Key Features Four Simultaneous Timeframes: Analyze intraday, session, daily, and weekly structures all on one chart ATR-Based Confluence Threshold: Automatically adjusts sensitivity based on current volatility (no fake signals) Color-Coded Levels: Each pivot length has distinct colors for instant visual identification Highs: Red, Orange, Yellow, Fuchsia Lows: Green, Blue, Aqua, Purple Confirmation Mode: Optional setting to wait for full pivot confirmation before marking levels Customizable Alert Zones: Toggle individual pivot lengths on/off to reduce clutter Smart Label Positioning: Labels auto-center between the two equal pivots for clarity Ideal For Swing traders tracking support/resistance across multiple timeframes Scalpers identifying micro-structure for quick entries and exits Market structure analysts studying institutional price action patterns Multi-timeframe traders needing confluence from intraday to weekly levels Anyone trading 1-minute to 4-hour charts Trading Applications Identify strong support/resistance zones: Equal levels = confirmed institutional levels Confirm trend reversals: Multiple equal lows = strong accumulation zone; multiple equal highs = distribution Plan entries with precision: Enter near equal levels for higher probability setups Detect liquidity concentration: Where price repeatedly tests the same level Multi-timeframe confluence: Look for equal levels across multiple pivot lengths for ultra-strong zones How to Use Identify the equal levels: Color-coded lines instantly show where price creates matching extremes Check for confluence: Strong setups occur where multiple pivot lengths align Wait for price action: Watch for breakouts through equal levels or reversals at these zones Enter with structure: Use equal levels as entry/exit triggers combined with your trading methodology Manage with confidence: These levels mark institutional decision points Customization Options Adjust pivot lengths to match your preferred timeframe structure Set ATR threshold sensitivity (lower = stricter equality, higher = more signals) Toggle confirmation mode for additional filter Enable/disable individual pivot streams to reduce visual clutter Customize colors to match your chart theme Default Settings Optimized For NASDAQ futures and liquid forex pairs Intraday and swing trading (1-minute to 4-hour charts) Smart Money / ICT trading methodologies Volatility-adjusted confluence detection
Pine Script®指标
由julioperez75955提供
Structure Pilot - Z&Z [Wang Indicators]Structure Pilot Zone & Zil is a complete suite of structure driven features that's build around pattern that can be visible around any timeframe. Built in collaboration with Dave Teaches, All these tools were shaped and combined together as the only toolkit Structure & DTFX traders want to have ! ▫️ Structures & Zones ▫️ Zones are drawn when a break of structure (new high or low being created) or a market reversal happens. It will highlight the last valid down move before a new high for bullish zones and the last valid up move before a new low for bearish zones. These zones are used to analyze the market trend and to make entries into the market trend once the price retraces into these zones. For example, with the latest bullish zones drawn in green for LTF zones and in blue for HTF zones, when the price retraces into this zone, there is a strong probability that the price will turn around to provide a buying opportunity all the way to the top of the zone or even higher. These buying opportunities generally occur at specific retracement levels in the 30%, 50% and 70% zones, automatically represented by broken lines in the zones when they are created. Example with bullish zones : The aim with these zones is to find places on the chart where it's best to buy or sell, in order to take the biggest possible move while minimizing your risk. Indeed, if the price is rising and a bullish zone has been created, I don't want to buy on the highs, preferring to wait for a retracement in my bullish zone to buy lower and reduce my risk, as the invalidation of the current trend will be found below the last protected low under the bullish zone drawn in blue for the HTF and in green for the LTF. Conversely, if the price is falling and a bearish zone has been created, I don't want to sell at the bottom. I'd rather wait for a retracement in the bearish zone to sell higher and reduce my risk, as the invalidation of the current trend will this time be above the last protected high above the bearish zone drawn in orange for the HTF and red for the LTF. Example with bearish zones : When it comes to market structure, it's good to know that zones recur within the same trend at a frequency of between 3 and 6 before there's a trend reversal. So, after a certain number of successive zones, you can expect a reversal or the last protected high or low to be breached. The indicator automatically counts the number of successive zones, so you can keep track of the market and avoid surprises. The zones are generated through the structure length. It can be increased to display larger (and more important) zones. As we recommend keeping the default value (20) for new traders, experienced traders will find some success with other settings depending on their strategies. Structure Pilot also provides auto HTF Zones, which is particularly useful to have a macro vision of the market. Settings: Swing types: Bullish only, Bearish only, both, or none Structure length Swing count: useful when it comes to tracking Trend strenght in any given time frame Show Zones: Display boxes with 30%, 50%, and 70% fibs Show HTF Zones: Display HTF zones with the same retracement configuration as the regular zones Show 30%, 50% and 70%: Enable/disable these options to show or hide the corresponding fibs. Box visibility, Line width & Line style: Style configuration for the zone All settings can be activated or deactivated in the indicator parameters to suit individual needs and preferences. 30% Level : This is often considered a shallow retracement. If prices pull back to this level after an uptrend and flip in a lower timeframe, traders might view it as a strong sign of continued bullish momentum. Conversely, after a downtrend, this level could act as a temporary resistance where sellers might re-enter after a flip in a lower timeframe. 50% Level : This level is seen as a balance point or midpoint in the price move. A retracement to 50% can indicate a strong trend change or continuation. 70% Level : A retracement this deep can signal that the market might be losing steam or that the previous trend could be weakening. If the price bounces off this level, it might suggest that the trend is still in control but needed a more significant correction before moving further in its original direction. We as structure traders prefer to take entry out of The 50% or when price retrace past it there will be something at the level i'm looking for price to reverse from either some specific candles or imbalances. Advanced traders might combine these levels with other tools or chart patterns that we bundle in this indicator. ▫️ ZIL ▫️ The ZIL Indicator is designed to automate the process of identifying key structural levels in the market and applying Fibonacci retracements when a significant price break occurs. The indicator detects when a market structure (high or low) is broken and a candle closes below the previous low or above the previous high, indicating a potential trend shift or continuation. • Tracks the break of structural lows or highs and waits for a confirmation candle that closes above or bellow the candle that set the new low. Automated Fibonacci Retracement: • Once the structure break is confirmed, the indicator automatically plots a Fibonacci retracement between: • The high of the last bullish move (before the new low is set) or the low of the last bearish move (before the new high is set) • The newly formed low after the structure break or the newly formed high after the structure break Fibonacci levels plotted with colors : • -0.27 : Dark red - Stop loss • 0 : white - The new high/low - Potential entry • 0.3, Orange 0.5, Light green 0.7: Green : Levels - Partial and take profit zones • 1.15 pale blue - for your runner We may long the retracement when the price is comming from a bearish zone using the ZIL to manage Example : Multi-Timeframe Support: • Using the option "HTF ZIL" will display ZIL on higher timeframe (corresponding to the HTF Zones) on your charts to help traders find structural breaks and Fibonacci setups in both short-term and long-term markets. HTF ZIL is really usefull to manage trades if the regular ZIL target get ran through Wang use case : HTF zill level are used when the small zill get ran through ▫️ Opening Range Tracker ▫️ The Opening Range Tracker is designed to help traders identify and track the opening range of a specified time period, specifically starting with the 144-minute candle between 8:24 AM and 10:48 AM. (default value) The indicator highlights this range and automatically plots key levels (30%, 50%, 70%) to provide potential strong reaction areas for trading. The time period for the opening range is fully customizable, allowing users to adjust it according to their strategy. Opening range should be seen and used as a classic zone. If we trade above or below it price tend to come back into it and bounce of of the One or multiple level... classic 30/50/70. • Customizable Opening Range: Adapt the indicator to any market or session by changing the opening range time window. • Precise Levels for Trading: The 30%, 50%, and 70% levels provide key zones where price may react, helping traders define entries, exits, or stop loss placements. • Visual Clarity: The range box and levels make it easy to see the important price areas during the opening range and the rest of the trading session. If we range a lot in the opening range, we may range for the rest of the day. We should keep that in mind to avoid taking wrong decisions. its basically a large zone that's we have seen often time price rejects from the level in it Daily Reset: Each trading day resets the opening range, giving traders fresh data and new opportunities to capitalize on market movements. Structure Pilot is built for beginner and experienced. It provides the tools to the traders that want to learn, understand, and trade efficiently within the principles of structure trading. ▫️ Alerts▫️ Alerts can be configured to these events : New Swing / HTF Swing Trend Change Zil attached to a zone/HTF zone Price cross 30/50/70 zones levels Trend change and align the HTF/LTF trend On cross partial (50%) and take profit (70%) ZIL and HTF ZIL On cross Zil can now be configured for Bull or Bear zone On HTF ZIL when 30% is crossed
Pine Script®指标
由MrWangTactics提供
Smart Money Trades Pro [BOSWaves]Smart Money Trades Pro – Advanced Market Structure & Liquidity Visualizer Overview Smart Money Trades Pro is a comprehensive trading tool designed for traders seeking an in-depth understanding of market structure, liquidity dynamics, and institutional flow. The indicator systematically identifies key market turning points, including break of structure (BOS) and change of character (CHoCH) events, and overlays these with adaptive visualizations to highlight high-probability trade setups. By integrating ATR-based risk zones, progressive take-profit levels, and real-time trade analytics, Smart Money Trades Pro transforms complex price action into an interpretable framework suitable for multiple trading styles, including scalping, intraday, and swing trading. Unlike traditional static indicators, Smart Money Trades Pro adapts continuously to market conditions. It evaluates swing highs and lows over a configurable lookback period, then determines structural breaks using customizable confirmation methods (candle body or wick). The resulting signals are augmented with dynamic entry, stop-loss, and target levels, allowing traders to analyze potential trade opportunities with both precision and context. The indicator’s design ensures that each visual element—trend-colored candles, signal markers, and risk/reward boxes—reflects real-time market conditions, offering an actionable interpretation of institutional activity. How It Works The indicator’s foundation is built upon market structure analysis. By calculating pivot highs and lows over a specified period, Smart Money Trades Pro identifies potential points of liquidity accumulation and exhaustion. When price breaks a pivot high or low, the indicator evaluates whether this constitutes a BOS or a CHoCH, signaling trend continuation or reversal. These events are marked on the chart with distinct visual cues, allowing traders to quickly discern shifts in market sentiment without manually analyzing historical price action. Once a structural break is confirmed, the indicator automatically determines entry levels, stop-loss placements, and progressive take-profit zones (TP1, TP2, TP3). These calculations are based on ATR-derived volatility, ensuring that targets scale with current market conditions. Risk and reward zones are plotted as shaded boxes, providing a clear visual representation of potential profit relative to risk for each trade setup. This system allows traders to maintain discipline and consistency, with dynamic trade management baked directly into the visualization. Trend direction is further reinforced by color-coded candles, which reflect the prevailing market bias. Bullish trends are represented by one color, bearish trends by another, and neutral conditions are displayed in muted tones. This continuous visual feedback simplifies the process of trend assessment and helps confirm the validity of trade setups alongside BOS and CHoCH markers. Signals and Breakouts Smart Money Trades Pro includes structured visual signals to indicate actionable price movements: Bullish Break Signals – Triangular markers below the candle appear when a swing high is broken, suggesting potential long opportunities. Bearish Break Signals – Triangular markers above the candle appear when a swing low is broken, indicating potential short setups. Change of Character (CHoCH) – Special markers highlight trend reversals, showing where momentum shifts from bullish to bearish or vice versa. These markers are strategically spaced to prevent overlap and remain clear during high-volatility periods. Traders can use them in combination with trend-colored candles, risk/reward zones, and ATR-based targets to assess the strength and reliability of each setup. The integrated table provides live trade information, including entry price, stop-loss level, take-profit levels, risk/reward ratio, and trade direction, ensuring that trade decisions are informed and data-driven. Interpretation Trend Analysis : The indicator’s trend coloring, combined with BOS and CHoCH detection, provides an immediate view of market direction. Rising structures indicate bullish momentum, while falling structures signal bearish momentum. CHoCH markers highlight potential trend reversals or significant liquidity sweeps. Volatility and Risk Assessment : ATR-based calculations determine stop-loss distances and target levels, giving a quantitative measure of risk relative to market volatility. Wide ATR readings indicate periods of high price fluctuation, whereas narrow readings suggest consolidation and reduced risk exposure. Market Structure Insights : By monitoring swing highs and lows alongside break confirmations, traders can identify where institutional players are likely active. Areas with multiple structural breaks or overlapping targets can indicate liquidity hotspots, potential reversal zones, or areas of market congestion. Trade Management : The built-in trade zones allow traders to visualize entry, risk, and reward simultaneously. Progressive targets (TP1, TP2, TP3) reflect incremental profit-taking strategies, while dynamic stop-loss levels help preserve capital during adverse moves. Strategy Integration Smart Money Trades Pro supports a range of trading approaches: Trend Following : Enter trades in the direction of confirmed BOS while using CHoCH markers and trend-colored candles to validate momentum. Pullback Entries : Use failed breakout retests or minor reversals toward broken structure levels for lower-risk entries. Mean Reversion : In consolidated zones with narrow ATR and repeated BOS/CHoCH activity, anticipate reversals or short-term corrective moves. Multi-Timeframe Confirmation : Overlay signals on higher or lower timeframes to filter noise and improve trade accuracy. Stop-loss levels should be placed just beyond the opposing structural point, while take-profit targets can be scaled using the ATR-based zones. Progressive targets allow for partial exits or scaling out of trades while maintaining exposure to larger moves. Advanced Techniques Traders seeking greater precision can combine Smart Money Trades Pro with volume, momentum, or volatility indicators to validate signals. Observing sequences of BOS and CHoCH markers across multiple timeframes provides insight into liquidity accumulation and depletion trends. Tracking the expansion or contraction of ATR-based zones helps anticipate shifts in volatility, enabling better timing for entries and exits. Customizing the structure period and confirmation type allows the indicator to adapt to different asset classes and timeframes. Shorter periods increase sensitivity to smaller swings, while longer periods filter noise and emphasize higher-probability structural breaks. By integrating these features, the indicator offers a robust statistical framework for disciplined, data-driven trading decisions. Inputs and Customization Structure Detection Period : Defines the lookback window for pivot high and low calculation. Break Confirmation : Choose whether to confirm breaks using candle body or wick. Display CHoCH : Toggle visibility of change-of-character markers. Color Trend Bars : Enable color-coding of candles based on market structure direction. Show Info Table : Display trade dashboard showing entry, stop-loss, take-profits, risk/reward, and bias. Table Position : Choose from top-left, top-right, bottom-left, or bottom-right placement. Color Customization : Configure bullish, bearish, neutral, risk, reward, and text colors for enhanced visual clarity. Why Use Smart Money Trades Pro Smart Money Trades Pro transforms complex market behavior into an actionable visual framework. By combining market structure analysis, liquidity tracking, ATR-based risk/reward mapping, and a dynamic trade dashboard, it provides a multidimensional view of the market. Traders can focus on execution, interpret trends, and evaluate overextensions or reversals without relying on guesswork. The indicator is suitable for scalping, intraday, and swing strategies, offering a comprehensive system for understanding and trading alongside institutional participants.
Pine Script®指标
由BOSWaves提供
31
Advanced Market TheoryADVANCED MARKET THEORY (AMT) This is not an indicator. It is a lens through which to see the true nature of the market. Welcome to the definitive application of Auction Market Theory. What you have before you is the culmination of decades of market theory, fused with state-of-the-art data analysis and visual engineering. It is an institutional-grade intelligence engine designed for the serious trader who seeks to move beyond simplistic indicators and understand the fundamental forces that drive price. This guide is your complete reference. Read it. Study it. Internalize it. The market is a complex story, and this tool is the language with which to read it. PART I: THE GRAND THEORY - A UNIVERSE IN AN AUCTION To understand the market, you must first understand its purpose. The market is a mechanism of discovery, organized by a continuous, two-way auction. This foundational concept was pioneered by the legendary trader J. Peter Steidlmayer at the Chicago Board of Trade in the 1980s. He observed that beneath the chaotic facade of ticking prices lies a beautifully organized structure. The market's primary function is not to go up or down, but to facilitate trade by seeking a price level that encourages the maximum amount of interaction between buyers and sellers. This price is "value." The Organizing Principle: The Normal Distribution Over any given period, the market's activity will naturally form a bell curve (a normal distribution) turned on its side. This is the blueprint of the auction. The Point of Control (POC): This is the peak of the bell curve—the single price level where the most trade occurred. It represents the point of maximum consensus, the "fairest price" as determined by the market participants. It is the gravitational center of the session. The Value Area (VA): This is the heart of the bell curve, typically containing 70% of the session's activity (one standard deviation). This is the zone of "accepted value." Prices within this area are considered fair and are where the market is most comfortable conducting business. The Extremes: The thin areas at the top and bottom of the curve are the "unfair" prices. These are levels where one side of the auction (buyers at the top, sellers at the bottom) was shut off, and trade was quickly rejected. These are areas of emotional trading and excess. The Narrative of the Day: Balance vs. Imbalance Every trading session is a story of the market's search for value. Balance: When the market rotates and builds a symmetrical, bell-shaped profile, it is in a state of balance . Buyers and sellers are in agreement, and the market is range-bound. Imbalance: When the market moves decisively away from a balanced area, it is in a state of imbalance . This is a trend. The market is actively seeking new information and a new area of value because the old one was rejected. Your Purpose as a Trader Your job is to read this story in real-time. Are we in balance or imbalance? Is the auction succeeding or failing at these new prices? The Advanced Market Theory engine is your Rosetta Stone to translate this complex narrative into actionable intelligence. PART II: THE AMT ENGINE - AN EVOLUTION IN MARKET VISION A standard market profile tool shows you a picture. The AMT Engine gives you the architect's full schematics, the engineer's stress tests, and the psychologist's behavioral analysis, all at once. This is what makes it the Advanced Market Theory. We have fused the timeless principles with layers of modern intelligence: TRINITY ANALYSIS: You can view the market through three distinct lenses. A Volume Profile shows where the money traded. A TPO (Time) Profile shows where the market spent its time. The revolutionary Hybrid Profile fuses both, giving you a complete picture of market conviction—marrying volume with duration. AUTOMATED STRUCTURAL DECODING: The engine acts as your automated analyst, identifying critical structural phenomena in real-time: Poor Highs/Lows: Weak auction points that signal a high probability of reversal. Single Prints & Ledges: Footprints of rapid, aggressive market moves and areas of strong institutional acceptance. Day Type Classification: The engine analyzes the session's personality as it develops ("Trend Day," "Normal Day," etc.), allowing you to adapt your strategy to the market's current character. MACRO & MICRO FUSION: Via the Composite Profile , the engine merges weeks of data to reveal the major institutional battlegrounds that govern long-term price action. You can see the daily skirmish and the multi-month war on a single chart. ORDER FLOW INTELLIGENCE: The ultimate advancement is the integrated Cumulative Volume Delta (CVD) engine. This moves beyond structure to analyze the raw aggression of buyers versus sellers. It is your window into the market's soul, automatically detecting critical Divergences that often precede major trend shifts. ADAPTIVE SIGNALING: The engine's signal generation is not static; it is a thinking system. It evaluates setups based on a multi-factor Confluence Score , understands the market Regime (e.g., High Volatility), and adjusts its own confidence ( Probability % ) based on the complete context. This is not a tool that gives you signals. This is a tool that gives you understanding . PART III: THE VISUAL KEY - A LEXICON OF MARKET STRUCTURE Every element on your chart is a piece of information. This is your guide to reading it fluently. --- THE CORE ARCHITECTURE --- The Profile Histogram: The primary visual on the left of each session. Its shape is the story. A thin profile is a trend; a fat, symmetrical profile is balance. Blue Box : The zone of accepted, "fair" value. The heart of the session's business. Bright Orange Line & Label : The Point of Control. The gravitational center. The price of maximum consensus. The most significant intraday level. Dashed Blue Lines & Labels : The boundaries of value. Critical inflection points where the market decides to either remain in balance or seek value elsewhere. Dashed Cyan Lines & Labels : The major, long-term structural levels derived from weeks of data. These are institutional reference points and carry immense weight. Treat them as primary support and resistance. Dashed Orange Lines & Labels : Marks a Poor or Unfinished Auction . These represent emotional, weak extremes and are high-probability targets for future price action. Diamond Markers : Mark Single Prints , which are footprints of aggressive, one-sided moves that left a "liquidity vacuum." Price is often drawn back to these levels to "repair" the poor structure. Arrow Markers : Mark Ledges , which are areas of strong horizontal acceptance. They often act as powerful support/resistance in the future. Dotted Gray Lines & Labels : The projected daily range based on multiples of the Initial Balance . Use them to set realistic profit targets and gauge the day's potential. --- THE SIGNAL SUITE --- Colored Triangles : These are your high-probability entry signals. The color is a strategic playbook: Gold Triangle : ELITE Signal. An A+ setup with overwhelming confluence. This is the highest quality signal the engine can produce. Yellow Triangle : FADE Signal. A counter-trend setup against an exhausted move at a structural extreme. Cyan Triangle : BREAKOUT Signal. A momentum setup attempting to capitalize on a breakout from the value area. Purple Triangle : ROTATION Signal. A mean-reversion setup within the value area, typically from one edge towards the POC. Magenta Triangle : LIQUIDITY Signal. A sophisticated setup that identifies a "stop run" or liquidity sweep. Percentage Number: The engine's calculated probability of success . This is not a guarantee, but a data-driven confidence score. Dotted Gray Line: The signal's Entry Price . Dashed Green Lines: The calculated Take Profit Targets . Dashed Red Line: The calculated Stop Loss level. PART IV: THE DASHBOARD - YOUR STRATEGIC COMMAND CENTER The dashboard is your real-time intelligence briefing. It synthesizes all the engine's analysis into a clear, concise, and constantly updating summary. --- CURRENT SESSION --- POC, VAH, VAL: The live values for the core structure. Profile Shape: Is the current auction top-heavy ( b-shaped ), bottom-heavy ( P-shaped ), or balanced ( D-shaped )? VA Width: Is the value area expanding (trending) or contracting (balancing)? Day Type: The engine's judgment on the day's personality. Use this to select the right strategy. IB Range & POC Trend: Key metrics for understanding the opening sentiment and its evolution. --- CVD ANALYSIS --- Session CVD: The raw order flow. Is there more net buying or selling pressure in this session? CVD Trend & DIVERGENCE: This is your order flow intelligence. Is the order flow confirming the price action? If "DIVERGENCE" flashes, it is a critical, high-alert warning of a potential reversal. --- MARKET METRICS --- Volume, ATR, RSI: Your standard contextual metrics, providing a quick read on activity, volatility, and momentum. Regime: The engine's assessment of the broad market environment: High Volatility (favor breakouts), Low Volatility (favor mean reversion), or Normal . --- PROFILE STATS, COMPOSITE, & STRUCTURE --- These sections give you a quick quantitative summary of the profile structure, the major long-term Composite levels, and any active Poor Structures. --- SIGNAL TYPES & ACTIVE SIGNAL --- A permanent key to the signal colors and their meanings, along with the full details of the most recent active signal: its Type , Probability , Entry , Stop , and Target . PART V: THE INPUTS MENU - CALIBRATING YOUR LENS This engine is designed to be calibrated to your specific needs as a trader. Every input is a lever. This is not a "one size fits all" tool. The extensive tooltips are your built-in user manual, but here are the key areas of focus: --- MARKET PROFILE ENGINE --- Profile Mode: This is the most fundamental choice. Volume is the standard for price-based support and resistance. TPO is for analyzing time-based acceptance. Hybrid is the professional's choice, fusing both for a complete picture. Profile Resolution: This is your zoom lens. Lower values for scalping and intraday precision. Higher values for a cleaner, big-picture view suitable for swing trading. Composite Sessions: Your timeframe for macro analysis. 5-10 sessions for a weekly view; 20-30 sessions for a monthly, structural view. --- SESSION & VALUE AREA --- These settings must be configured correctly for your specific asset. The Session times are critical. The Initial Balance should reflect the key opening period for your market (60 minutes is standard for equities). --- SIGNAL ENGINE & RISK MANAGEMENT --- Signal Mode: THIS IS YOUR PERSONAL RISK PROFILE. Set it to Conservative to see only the absolute best A+ setups. Use Elite or Balanced for a standard approach. Use Aggressive only if you are an experienced scalper comfortable with managing more frequent, lower-probability setups. ATR Multipliers: This suite gives you full, dynamic control over your risk/reward parameters. You can precisely define your initial stop loss distance and profit targets based on the market's current volatility. A FINAL WORD FROM THE ARCHITECT The creation of this engine was a journey into the very heart of market dynamics. It was born from a frustrating truth: that the most profound market theories were often confined to books and expensive institutional platforms, inaccessible to the modern retail trader. The goal was to bridge that gap. The challenge was monumental. Making each discrete system—the volume profile, the TPO counter, the composite engine, the CVD tracker, the signal generator, the dynamic dashboard—work was a task in itself. But the true struggle, the frustrating, painstaking process that consumed countless hours, was making them work in unison . It was about ensuring the CVD analysis could intelligently inform the signal engine, that the day type classification could adjust the probability scores, and that the composite levels could provide context to the intraday structure, all in a seamless, real-time dance of data. This engine is the result of that relentless pursuit of integration. It is built on the belief that a trader's greatest asset is not a signal, but clarity . It was designed to clear the noise, to organize the chaos, and to present the elegant, underlying logic of the market auction so that you can make better, more informed, and more confident decisions. It is now in your hands. Use it not as a crutch, but as a lens. See the market for what it truly is. "The market can remain irrational longer than you can remain solvent." - John Maynard Keynes DISCLAIMER This script is an advanced analytical tool provided for informational and educational purposes only. It is not financial advice. All trading involves substantial risk, and past performance is not indicative of future results. The signals, probabilities, and metrics generated by this indicator do not constitute a recommendation to buy or sell any financial instrument. You, the user, are solely responsible for all trading decisions, risk management, and outcomes. Use this tool to supplement your own analysis and trading strategy. PUBLISHING CATEGORIES Volume Profile Market Profile Order Flow
Pine Script®指标
由DskyzInvestments提供
31
Categorical Market Morphisms (CMM)Categorical Market Morphisms (CMM) - Where Abstract Algebra Transcends Reality A Revolutionary Application of Category Theory and Homotopy Type Theory to Financial Markets Bridging Pure Mathematics and Market Analysis Through Functorial Dynamics Theoretical Foundation: The Mathematical Revolution Traditional technical analysis operates on Euclidean geometry and classical statistics. The Categorical Market Morphisms (CMM) indicator represents a paradigm shift - the first application of Category Theory and Homotopy Type Theory to financial markets. This isn't merely another indicator; it's a mathematical framework that reveals the hidden algebraic structure underlying market dynamics. Category Theory in Markets Category theory, often called "the mathematics of mathematics," studies structures and the relationships between them. In market terms: Objects = Market states (price levels, volume conditions, volatility regimes) Morphisms = State transitions (price movements, volume changes, volatility shifts) Functors = Structure-preserving mappings between timeframes Natural Transformations = Coherent changes across multiple market dimensions The Morphism Detection Engine The core innovation lies in detecting morphisms - the categorical arrows representing market state transitions: Morphism Strength = exp(-normalized_change × (3.0 / sensitivity)) Threshold = 0.3 - (sensitivity - 1.0) × 0.15 This exponential decay function captures how market transitions lose coherence over distance, while the dynamic threshold adapts to market sensitivity. Functorial Analysis Framework Markets must preserve structure across timeframes to maintain coherence. Our functorial analysis verifies this through composition laws: Composition Error = |f(BC) × f(AB) - f(AC)| / |f(AC)| Functorial Integrity = max(0, 1.0 - average_error) When functorial integrity breaks down, market structure becomes unstable - a powerful early warning system. Homotopy Type Theory: Path Equivalence in Markets The Revolutionary Path Analysis Homotopy Type Theory studies when different paths can be continuously deformed into each other. In markets, this reveals arbitrage opportunities and equivalent trading paths: Path Distance = Σ(weight × |normalized_path1 - normalized_path2|) Homotopy Score = (correlation + 1) / 2 × (1 - average_distance) Equivalence Threshold = 1 / (threshold × √univalence_strength) The Univalence Axiom in Trading The univalence axiom states that equivalent structures can be treated as identical. In trading terms: when price-volume paths show homotopic equivalence with RSI paths, they represent the same underlying market structure - creating powerful confluence signals. Universal Properties: The Four Pillars of Market Structure Category theory's universal properties reveal fundamental market patterns: Initial Objects (Market Bottoms) Mathematical Definition = Unique morphisms exist FROM all other objects TO the initial object Market Translation = All selling pressure naturally flows toward the bottom Detection Algorithm: Strength = local_low(0.3) + oversold(0.2) + volume_surge(0.2) + momentum_reversal(0.2) + morphism_flow(0.1) Signal = strength > 0.4 AND morphism_exists Terminal Objects (Market Tops) Mathematical Definition = Unique morphisms exist FROM the terminal object TO all others Market Translation = All buying pressure naturally flows away from the top Product Objects (Market Equilibrium) Mathematical Definition = Universal property combining multiple objects into balanced state Market Translation = Price, volume, and volatility achieve multi-dimensional balance Coproduct Objects (Market Divergence) Mathematical Definition = Universal property representing branching possibilities Market Translation = Market bifurcation points where multiple scenarios become possible Consciousness Detection: Emergent Market Intelligence The most groundbreaking feature detects market consciousness - when markets exhibit self-awareness through fractal correlations: Consciousness Level = Σ(correlation_levels × weights) × fractal_dimension Fractal Score = log(range_ratio) / log(memory_period) Multi-Scale Awareness: Micro = Short-term price-SMA correlations Meso = Medium-term structural relationships Macro = Long-term pattern coherence Volume Sync = Price-volume consciousness Volatility Awareness = ATR-change correlations When consciousness_level > threshold , markets display emergent intelligence - self-organizing behavior that transcends simple mechanical responses. Advanced Input System: Precision Configuration Categorical Universe Parameters Universe Level (Type_n) = Controls categorical complexity depth Type 1 = Price only (pure price action) Type 2 = Price + Volume (market participation) Type 3 = + Volatility (risk dynamics) Type 4 = + Momentum (directional force) Type 5 = + RSI (momentum oscillation) Sector Optimization: Crypto = 4-5 (high complexity, volume crucial) Stocks = 3-4 (moderate complexity, fundamental-driven) Forex = 2-3 (low complexity, macro-driven) Morphism Detection Threshold = Golden ratio optimized (φ = 0.618) Lower values = More morphisms detected, higher sensitivity Higher values = Only major transformations, noise reduction Crypto = 0.382-0.618 (high volatility accommodation) Stocks = 0.618-1.0 (balanced detection) Forex = 1.0-1.618 (macro-focused) Functoriality Tolerance = φ⁻² = 0.146 (mathematically optimal) Controls = composition error tolerance Trending markets = 0.1-0.2 (strict structure preservation) Ranging markets = 0.2-0.5 (flexible adaptation) Categorical Memory = Fibonacci sequence optimized Scalping = 21-34 bars (short-term patterns) Swing = 55-89 bars (intermediate cycles) Position = 144-233 bars (long-term structure) Homotopy Type Theory Parameters Path Equivalence Threshold = Golden ratio φ = 1.618 Volatile markets = 2.0-2.618 (accommodate noise) Normal conditions = 1.618 (balanced) Stable markets = 0.786-1.382 (sensitive detection) Deformation Complexity = Fibonacci-optimized path smoothing 3,5,8,13,21 = Each number provides different granularity Higher values = smoother paths but slower computation Univalence Axiom Strength = φ² = 2.618 (golden ratio squared) Controls = how readily equivalent structures are identified Higher values = find more equivalences Visual System: Mathematical Elegance Meets Practical Clarity The Morphism Energy Fields (Red/Green Boxes) Purpose = Visualize categorical transformations in real-time Algorithm: Energy Range = ATR × flow_strength × 1.5 Transparency = max(10, base_transparency - 15) Interpretation: Green fields = Bullish morphism energy (buying transformations) Red fields = Bearish morphism energy (selling transformations) Size = Proportional to transformation strength Intensity = Reflects morphism confidence Consciousness Grid (Purple Pattern) Purpose = Display market self-awareness emergence Algorithm: Grid_size = adaptive(lookback_period / 8) Consciousness_range = ATR × consciousness_level × 1.2 Interpretation: Density = Higher consciousness = denser grid Extension = Cloud lookback controls historical depth Intensity = Transparency reflects awareness level Homotopy Paths (Blue Gradient Boxes) Purpose = Show path equivalence opportunities Algorithm: Path_range = ATR × homotopy_score × 1.2 Gradient_layers = 3 (increasing transparency) Interpretation: Blue boxes = Equivalent path opportunities Gradient effect = Confidence visualization Multiple layers = Different probability levels Functorial Lines (Green Horizontal) Purpose = Multi-timeframe structure preservation levels Innovation = Smart spacing prevents overcrowding Min_separation = price × 0.001 (0.1% minimum) Max_lines = 3 (clarity preservation) Features: Glow effect = Background + foreground lines Adaptive labels = Only show meaningful separations Color coding = Green (preserved), Orange (stressed), Red (broken) Signal System: Bull/Bear Precision 🐂 Initial Objects = Bottom formations with strength percentages 🐻 Terminal Objects = Top formations with confidence levels ⚪ Product/Coproduct = Equilibrium circles with glow effects Professional Dashboard System Main Analytics Dashboard (Top-Right) Market State = Real-time categorical classification INITIAL OBJECT = Bottom formation active TERMINAL OBJECT = Top formation active PRODUCT STATE = Market equilibrium COPRODUCT STATE = Divergence/bifurcation ANALYZING = Processing market structure Universe Type = Current complexity level and components Morphisms: ACTIVE (X%) = Transformations detected, percentage shows strength DORMANT = No significant categorical changes Functoriality: PRESERVED (X%) = Structure maintained across timeframes VIOLATED (X%) = Structure breakdown, instability warning Homotopy: DETECTED (X%) = Path equivalences found, arbitrage opportunities NONE = No equivalent paths currently available Consciousness: ACTIVE (X%) = Market self-awareness emerging, major moves possible EMERGING (X%) = Consciousness building DORMANT = Mechanical trading only Signal Monitor & Performance Metrics (Left Panel) Active Signals Tracking: INITIAL = Count and current strength of bottom signals TERMINAL = Count and current strength of top signals PRODUCT = Equilibrium state occurrences COPRODUCT = Divergence event tracking Advanced Performance Metrics: CCI (Categorical Coherence Index): CCI = functorial_integrity × (morphism_exists ? 1.0 : 0.5) STRONG (>0.7) = High structural coherence MODERATE (0.4-0.7) = Adequate coherence WEAK (<0.4) = Structural instability HPA (Homotopy Path Alignment): HPA = max_homotopy_score × functorial_integrity ALIGNED (>0.6) = Strong path equivalences PARTIAL (0.3-0.6) = Some equivalences WEAK (<0.3) = Limited path coherence UPRR (Universal Property Recognition Rate): UPRR = (active_objects / 4) × 100% Percentage of universal properties currently active TEPF (Transcendence Emergence Probability Factor): TEPF = homotopy_score × consciousness_level × φ Probability of consciousness emergence (golden ratio weighted) MSI (Morphological Stability Index): MSI = (universe_depth / 5) × functorial_integrity × consciousness_level Overall system stability assessment Overall Score = Composite rating (EXCELLENT/GOOD/POOR) Theory Guide (Bottom-Right) Educational reference panel explaining: Objects & Morphisms = Core categorical concepts Universal Properties = The four fundamental patterns Dynamic Advice = Context-sensitive trading suggestions based on current market state Trading Applications: From Theory to Practice Trend Following with Categorical Structure Monitor functorial integrity = only trade when structure preserved (>80%) Wait for morphism energy fields = red/green boxes confirm direction Use consciousness emergence = purple grids signal major move potential Exit on functorial breakdown = structure loss indicates trend end Mean Reversion via Universal Properties Identify Initial/Terminal objects = 🐂/🐻 signals mark extremes Confirm with Product states = equilibrium circles show balance points Watch Coproduct divergence = bifurcation warnings Scale out at Functorial levels = green lines provide targets Arbitrage through Homotopy Detection Blue gradient boxes = indicate path equivalence opportunities HPA metric >0.6 = confirms strong equivalences Multiple timeframe convergence = strengthens signal Consciousness active = amplifies arbitrage potential Risk Management via Categorical Metrics Position sizing = Based on MSI (Morphological Stability Index) Stop placement = Tighter when functorial integrity low Leverage adjustment = Reduce when consciousness dormant Portfolio allocation = Increase when CCI strong Sector-Specific Optimization Strategies Cryptocurrency Markets Universe Level = 4-5 (full complexity needed) Morphism Sensitivity = 0.382-0.618 (accommodate volatility) Categorical Memory = 55-89 (rapid cycles) Field Transparency = 1-5 (high visibility needed) Focus Metrics = TEPF, consciousness emergence Stock Indices Universe Level = 3-4 (moderate complexity) Morphism Sensitivity = 0.618-1.0 (balanced) Categorical Memory = 89-144 (institutional cycles) Field Transparency = 5-10 (moderate visibility) Focus Metrics = CCI, functorial integrity Forex Markets Universe Level = 2-3 (macro-driven) Morphism Sensitivity = 1.0-1.618 (noise reduction) Categorical Memory = 144-233 (long cycles) Field Transparency = 10-15 (subtle signals) Focus Metrics = HPA, universal properties Commodities Universe Level = 3-4 (supply/demand dynamics) [/b Morphism Sensitivity = 0.618-1.0 (seasonal adaptation) Categorical Memory = 89-144 (seasonal cycles) Field Transparency = 5-10 (clear visualization) Focus Metrics = MSI, morphism strength Development Journey: Mathematical Innovation The Challenge Traditional indicators operate on classical mathematics - moving averages, oscillators, and pattern recognition. While useful, they miss the deeper algebraic structure that governs market behavior. Category theory and homotopy type theory offered a solution, but had never been applied to financial markets. The Breakthrough The key insight came from recognizing that market states form a category where: Price levels, volume conditions, and volatility regimes are objects Market movements between these states are morphisms The composition of movements must satisfy categorical laws This realization led to the morphism detection engine and functorial analysis framework . Implementation Challenges Computational Complexity = Category theory calculations are intensive Real-time Performance = Markets don't wait for mathematical perfection Visual Clarity = How to display abstract mathematics clearly Signal Quality = Balancing mathematical purity with practical utility User Accessibility = Making PhD-level math tradeable The Solution After months of optimization, we achieved: Efficient algorithms = using pre-calculated values and smart caching Real-time performance = through optimized Pine Script implementation Elegant visualization = that makes complex theory instantly comprehensible High-quality signals = with built-in noise reduction and cooldown systems Professional interface = that guides users through complexity Advanced Features: Beyond Traditional Analysis Adaptive Transparency System Two independent transparency controls: Field Transparency = Controls morphism fields, consciousness grids, homotopy paths Signal & Line Transparency = Controls signals and functorial lines independently This allows perfect visual balance for any market condition or user preference. Smart Functorial Line Management Prevents visual clutter through: Minimum separation logic = Only shows meaningfully separated levels Maximum line limit = Caps at 3 lines for clarity Dynamic spacing = Adapts to market volatility Intelligent labeling = Clear identification without overcrowding Consciousness Field Innovation Adaptive grid sizing = Adjusts to lookback period Gradient transparency = Fades with historical distance Volume amplification = Responds to market participation Fractal dimension integration = Shows complexity evolution Signal Cooldown System Prevents overtrading through: 20-bar default cooldown = Configurable 5-100 bars Signal-specific tracking = Independent cooldowns for each signal type Counter displays = Shows historical signal frequency Performance metrics = Track signal quality over time Performance Metrics: Quantifying Excellence Signal Quality Assessment Initial Object Accuracy = >78% in trending markets Terminal Object Precision = >74% in overbought/oversold conditions Product State Recognition = >82% in ranging markets Consciousness Prediction = >71% for major moves Computational Efficiency Real-time processing = <50ms calculation time Memory optimization = Efficient array management Visual performance = Smooth rendering at all timeframes Scalability = Handles multiple universes simultaneously User Experience Metrics Setup time = <5 minutes to productive use Learning curve = Accessible to intermediate+ traders Visual clarity = No information overload Configuration flexibility = 25+ customizable parameters Risk Disclosure and Best Practices Important Disclaimers The Categorical Market Morphisms indicator applies advanced mathematical concepts to market analysis but does not guarantee profitable trades. Markets remain inherently unpredictable despite underlying mathematical structure. Recommended Usage Never trade signals in isolation = always use confluence with other analysis Respect risk management = categorical analysis doesn't eliminate risk Understand the mathematics = study the theoretical foundation Start with paper trading = master the concepts before risking capital Adapt to market regimes = different markets need different parameters Position Sizing Guidelines High consciousness periods = Reduce position size (higher volatility) Strong functorial integrity = Standard position sizing Morphism dormancy = Consider reduced trading activity Universal property convergence = Opportunities for larger positions Educational Resources: Master the Mathematics Recommended Reading "Category Theory for the Sciences" = by David Spivak "Homotopy Type Theory" = by The Univalent Foundations Program "Fractal Market Analysis" = by Edgar Peters "The Misbehavior of Markets" = by Benoit Mandelbrot Key Concepts to Master Functors and Natural Transformations Universal Properties and Limits Homotopy Equivalence and Path Spaces Type Theory and Univalence Fractal Geometry in Markets The Categorical Market Morphisms indicator represents more than a new technical tool - it's a paradigm shift toward mathematical rigor in market analysis. By applying category theory and homotopy type theory to financial markets, we've unlocked patterns invisible to traditional analysis. This isn't just about better signals or prettier charts. It's about understanding markets at their deepest mathematical level - seeing the categorical structure that underlies all price movement, recognizing when markets achieve consciousness, and trading with the precision that only pure mathematics can provide. Why CMM Dominates Mathematical Foundation = Built on proven mathematical frameworks Original Innovation = First application of category theory to markets Professional Quality = Institution-grade metrics and analysis Visual Excellence = Clear, elegant, actionable interface Educational Value = Teaches advanced mathematical concepts Practical Results = High-quality signals with risk management Continuous Evolution = Regular updates and enhancements The DAFE Trading Systems Difference At DAFE Trading Systems, we don't just create indicators - we advance the science of market analysis. Our team combines: PhD-level mathematical expertise Real-world trading experience Cutting-edge programming skills Artistic visual design Educational commitment The result? Trading tools that don't just show you what happened - they reveal why it happened and predict what comes next through the lens of pure mathematics. "In mathematics you don't understand things. You just get used to them." - John von Neumann "The market is not just a random walk - it's a categorical structure waiting to be discovered." - DAFE Trading Systems Trade with Mathematical Precision. Trade with Categorical Market Morphisms. Created with passion for mathematical excellence, and empowering traders through mathematical innovation. — Dskyz, Trade with insight. Trade with anticipation.
Pine Script®指标
由DskyzInvestments提供
9
Topological Market Stress (TMS) - Quantum FabricTopological Market Stress (TMS) - Quantum Fabric What Stresses The Market? Topological Market Stress (TMS) represents a revolutionary fusion of algebraic topology and quantum field theory applied to financial markets. Unlike traditional indicators that analyze price movements linearly, TMS examines the underlying topological structure of market data—detecting when the very fabric of market relationships begins to tear, warp, or collapse. Drawing inspiration from the ethereal beauty of quantum field visualizations and the mathematical elegance of topological spaces, this indicator transforms complex mathematical concepts into an intuitive, visually stunning interface that reveals hidden market dynamics invisible to conventional analysis. Theoretical Foundation: Topology Meets Markets Topological Holes in Market Structure In algebraic topology, a "hole" represents a fundamental structural break—a place where the normal connectivity of space fails. In markets, these topological holes manifest as: Correlation Breakdown: When traditional price-volume relationships collapse Volatility Clustering Failure: When volatility patterns lose their predictive power Microstructure Stress: When market efficiency mechanisms begin to fail The Mathematics of Market Topology TMS constructs a topological space from market data using three key components: 1. Correlation Topology ρ(P,V) = correlation(price, volume, period) Hole Formation = 1 - |ρ(P,V)| When price and volume decorrelate, topological holes begin forming. 2. Volatility Clustering Topology σ(t) = volatility at time t Clustering = correlation(σ(t), σ(t-1), period) Breakdown = 1 - |Clustering| Volatility clustering breakdown indicates structural instability. 3. Market Efficiency Topology Efficiency = |price - EMA(price)| / ATR Measures how far price deviates from its efficient trajectory. Multi-Scale Topological Analysis Markets exist across multiple temporal scales simultaneously. TMS analyzes topology at three distinct scales: Micro Scale (3-15 periods): Immediate structural changes, market microstructure stress Meso Scale (10-50 periods): Trend-level topology, medium-term structural shifts Macro Scale (50-200 periods): Long-term structural topology, regime-level changes The final stress metric combines all scales: Combined Stress = 0.3×Micro + 0.4×Meso + 0.3×Macro How TMS Works 1. Topological Space Construction Each market moment is embedded in a multi-dimensional topological space where: - Price efficiency forms one dimension - Correlation breakdown forms another - Volatility clustering breakdown forms the third 2. Hole Detection Algorithm The indicator continuously scans this topological space for: Hole Formation: When stress exceeds the formation threshold Hole Persistence: How long structural breaks maintain Hole Collapse: Sudden topology restoration (regime shifts) 3. Quantum Visualization Engine The visualization system translates topological mathematics into intuitive quantum field representations: Stress Waves: Main line showing topological stress intensity Quantum Glow: Surrounding field indicating stress energy Fabric Integrity: Background showing structural health Multi-Scale Rings: Orbital representations of different timeframes 4. Signal Generation Stable Topology (✨): Normal market structure, standard trading conditions Stressed Topology (⚡): Increased structural tension, heightened volatility expected Topological Collapse (🕳️): Major structural break, regime shift in progress Critical Stress (🌋): Extreme conditions, maximum caution required Inputs & Parameters 🕳️ Topological Parameters Analysis Window (20-200, default: 50) Primary period for topological analysis 20-30: High-frequency scalping, rapid structure detection 50: Balanced approach, recommended for most markets 100-200: Long-term position trading, major structural shifts only Hole Formation Threshold (0.1-0.9, default: 0.3) Sensitivity for detecting topological holes 0.1-0.2: Very sensitive, detects minor structural stress 0.3: Balanced, optimal for most market conditions 0.5-0.9: Conservative, only major structural breaks Density Calculation Radius (0.1-2.0, default: 0.5) Radius for local density estimation in topological space 0.1-0.3: Fine-grained analysis, sensitive to local changes 0.5: Standard approach, balanced sensitivity 1.0-2.0: Broad analysis, focuses on major structural features Collapse Detection (0.5-0.95, default: 0.7) Threshold for detecting sudden topology restoration 0.5-0.6: Very sensitive to regime changes 0.7: Balanced, reliable collapse detection 0.8-0.95: Conservative, only major regime shifts 📊 Multi-Scale Analysis Enable Multi-Scale (default: true) - Analyzes topology across multiple timeframes simultaneously - Provides deeper insight into market structure at different scales - Essential for understanding cross-timeframe topology interactions Micro Scale Period (3-15, default: 5) Fast scale for immediate topology changes 3-5: Ultra-fast, tick/minute data analysis 5-8: Fast, 5m-15m chart optimization 10-15: Medium-fast, 30m-1H chart focus Meso Scale Period (10-50, default: 20) Medium scale for trend topology analysis 10-15: Short trend structures 20-25: Medium trend structures (recommended) 30-50: Long trend structures Macro Scale Period (50-200, default: 100) Slow scale for structural topology 50-75: Medium-term structural analysis 100: Long-term structure (recommended) 150-200: Very long-term structural patterns ⚙️ Signal Processing Smoothing Method (SMA/EMA/RMA/WMA, default: EMA) Method for smoothing stress signals SMA: Simple average, stable but slower EMA: Exponential, responsive and recommended RMA: Running average, very smooth WMA: Weighted average, balanced approach Smoothing Period (1-10, default: 3) Period for signal smoothing 1-2: Minimal smoothing, noisy but fast 3-5: Balanced, recommended for most applications 6-10: Heavy smoothing, slow but very stable Normalization (Fixed/Adaptive/Rolling, default: Adaptive) Method for normalizing stress values Fixed: Static 0-1 range normalization Adaptive: Dynamic range adjustment (recommended) Rolling: Rolling window normalization 🎨 Quantum Visualization Fabric Style Options: Quantum Field: Flowing energy visualization with smooth gradients Topological Mesh: Mathematical topology with stepped lines Phase Space: Dynamical systems view with circular markers Minimal: Clean, simple display with reduced visual elements Color Scheme Options: Quantum Gradient: Deep space blue → Quantum red progression Thermal: Black → Hot orange thermal imaging style Spectral: Purple → Gold full spectrum colors Monochrome: Dark gray → Light gray elegant simplicity Multi-Scale Rings (default: true) - Display orbital rings for different time scales - Visualizes how topology changes across timeframes - Provides immediate visual feedback on cross-scale dynamics Glow Intensity (0.0-1.0, default: 0.6) Controls the quantum glow effect intensity 0.0: No glow, pure line display 0.6: Balanced, recommended setting 1.0: Maximum glow, full quantum field effect 📋 Dashboard & Alerts Show Dashboard (default: true) Real-time topology status display Current market state and trading recommendations Stress level visualization and fabric integrity status Show Theory Guide (default: true) Educational panel explaining topological concepts Dashboard interpretation guide Trading strategy recommendations Enable Alerts (default: true) Extreme stress detection alerts Topological collapse notifications Hole formation and recovery signals Visual Logic & Interpretation Main Visualization Elements Quantum Stress Line Primary indicator showing topological stress intensity Color intensity reflects current market state Line style varies based on selected fabric style Glow effect indicates stress energy field Equilibrium Line Silver line showing average stress level Reference point for normal market conditions Helps identify when stress is elevated or suppressed Upper/Lower Bounds Red upper bound: High stress threshold Green lower bound: Low stress threshold Quantum fabric fill between bounds shows stress field Multi-Scale Rings Aqua circles : Micro-scale topology (immediate changes) Orange circles: Meso-scale topology (trend-level changes) Provides cross-timeframe topology visualization Dashboard Information Topology State Icons: ✨ STABLE: Normal market structure, standard trading conditions ⚡ STRESSED: Increased structural tension, monitor closely 🕳️ COLLAPSE: Major structural break, regime shift occurring 🌋 CRITICAL: Extreme conditions, reduce risk exposure Stress Bar Visualization: Visual representation of current stress level (0-100%) Color-coded based on current topology state Real-time percentage display Fabric Integrity Dots: ●●●●● Intact: Strong market structure (0-30% stress) ●●●○○ Stressed: Weakening structure (30-70% stress) ●○○○○ Fractured: Breaking down structure (70-100% stress) Action Recommendations: ✅ TRADE: Normal conditions, standard strategies apply ⚠️ WATCH: Monitor closely, increased vigilance required 🔄 ADAPT: Change strategy, regime shift in progress 🛑 REDUCE: Lower risk exposure, extreme conditions Trading Strategies In Stable Topology (✨ STABLE) - Normal trading conditions apply - Use standard technical analysis - Regular position sizing appropriate - Both trend-following and mean-reversion strategies viable In Stressed Topology (⚡ STRESSED) - Increased volatility expected - Widen stop losses to account for higher volatility - Reduce position sizes slightly - Focus on high-probability setups - Monitor for potential regime change During Topological Collapse (🕳️ COLLAPSE) - Major regime shift in progress - Adapt strategy immediately to new market character - Consider closing positions that rely on previous regime - Wait for new topology to stabilize before major trades - Opportunity for contrarian plays if collapse is extreme In Critical Stress (🌋 CRITICAL) - Extreme market conditions - Significantly reduce risk exposure - Avoid new positions until stress subsides - Focus on capital preservation - Consider hedging existing positions Advanced Techniques Multi-Timeframe Topology Analysis - Use higher timeframe TMS for regime context - Use lower timeframe TMS for precise entry timing - Alignment across timeframes = highest probability trades Topology Divergence Trading - Most powerful at regime boundaries - Price makes new high/low but topology stress decreases - Early warning of potential reversals - Combine with key support/resistance levels Stress Persistence Analysis - Long periods of stable topology often precede major moves - Extended stress periods often resolve in regime changes - Use persistence tracking for position sizing decisions Originality & Innovation TMS represents a genuine breakthrough in applying advanced mathematics to market analysis: True Topological Analysis: Not a simplified proxy but actual topological space construction and hole detection using correlation breakdown, volatility clustering analysis, and market efficiency measurement. Quantum Aesthetic: Transforms complex topology mathematics into an intuitive, visually stunning interface inspired by quantum field theory visualizations. Multi-Scale Architecture: Simultaneous analysis across micro, meso, and macro timeframes provides unprecedented insight into market structure dynamics. Regime Detection: Identifies fundamental market character changes before they become obvious in price action, providing early warning of structural shifts. Practical Application: Clear, actionable signals derived from advanced mathematical concepts, making theoretical topology accessible to practical traders. This is not a combination of existing indicators or a cosmetic enhancement of standard tools. It represents a fundamental reimagining of how we measure, visualize, and interpret market dynamics through the lens of algebraic topology and quantum field theory. Best Practices Start with defaults: Parameters are optimized for broad market applicability Match timeframe: Adjust scales based on your trading timeframe Confirm with price action: TMS shows market character, not direction Respect topology changes: Reduce risk during regime transitions Use appropriate strategies: Adapt approach based on current topology state Monitor persistence: Track how long topology states maintain Cross-timeframe analysis: Align multiple timeframes for highest probability trades Alerts Available Extreme Topological Stress: Market fabric under severe deformation Topological Collapse Detected: Regime shift in progress Topological Hole Forming: Market structure breakdown detected Topology Stabilizing: Market structure recovering to normal Chart Requirements Recommended Markets: All liquid markets (forex, stocks, crypto, futures) Optimal Timeframes: 5m to Daily (adaptable to any timeframe) Minimum History: 200 bars for proper topology construction Best Performance: Markets with clear regime characteristics Academic Foundation This indicator draws from cutting-edge research in: - Algebraic topology and persistent homology - Quantum field theory visualization techniques - Market microstructure analysis - Multi-scale dynamical systems theory - Correlation topology and network analysis Disclaimer This indicator is for educational and research purposes only. It does not constitute financial advice or provide direct buy/sell signals. Topological analysis reveals market structure characteristics, not future price direction. Always use proper risk management and combine with your own analysis. Past performance does not guarantee future results. See markets through the lens of topology. Trade the structure, not the noise. Bringing advanced mathematics to practical trading through quantum-inspired visualization. Trade with insight. Trade with structure. — Dskyz , for DAFE Trading Systems
Pine Script®指标
由DskyzInvestments提供
4
Rally Base Drop SND Pivots Strategy [LuxAlgo X PineIndicators]This strategy is based on the Rally Base Drop (RBD) SND Pivots indicator developed by LuxAlgo. Full credit for the concept and original indicator goes to LuxAlgo. The Rally Base Drop SND Pivots Strategy is a non-repainting supply and demand trading system that detects pivot points based on Rally, Base, and Drop (RBD) candles. This strategy automatically identifies key market structure levels, allowing traders to: Identify pivot-based supply and demand (SND) zones. Use fixed criteria for trend continuation or reversals. Filter out market noise by requiring structured price formations. Enter trades based on breakouts of key SND pivot levels. How the Rally Base Drop SND Pivots Strategy Works 1. Pivot Point Detection Using RBD Candles The strategy follows a rigid market structure methodology, where pivots are detected only when: A Rally (R) consists of multiple consecutive bullish candles. A Drop (D) consists of multiple consecutive bearish candles. A Base (B) is identified as a transition between Rallies and Drops, acting as a pivot point. The pivot level is confirmed when the formation is complete. Unlike traditional fractal-based pivots, RBD Pivots enforce stricter structural rules, ensuring that each pivot: Has a well-defined bullish or bearish price movement. Reduces false signals caused by single-bar fluctuations. Provides clear supply and demand levels based on structured price movements. These pivot levels are drawn on the chart using color-coded boxes: Green zones represent bullish pivot levels (Rally Base formations). Red zones represent bearish pivot levels (Drop Base formations). Once a pivot is confirmed, the high or low of the base candle is used as the reference level for future trades. 2. Trade Entry Conditions The strategy allows traders to select from three trading modes: Long Only – Only takes long trades when bullish pivot breakouts occur. Short Only – Only takes short trades when bearish pivot breakouts occur. Long & Short – Trades in both directions based on pivot breakouts. Trade entry signals are triggered when price breaks through a confirmed pivot level: Long Entry: A bullish pivot level is formed. Price breaks above the bullish pivot level. The strategy enters a long position. Short Entry: A bearish pivot level is formed. Price breaks below the bearish pivot level. The strategy enters a short position. The strategy includes an optional mode to reverse long and short conditions, allowing traders to experiment with contrarian entries. 3. Exit Conditions Using ATR-Based Risk Management This strategy uses the Average True Range (ATR) to calculate dynamic stop-loss and take-profit levels: Stop-Loss (SL): Placed 1 ATR below entry for long trades and 1 ATR above entry for short trades. Take-Profit (TP): Set using a Risk-Reward Ratio (RR) multiplier (default = 6x ATR). When a trade is opened: The entry price is recorded. ATR is calculated at the time of entry to determine stop-loss and take-profit levels. Trades exit automatically when either SL or TP is reached. If reverse conditions mode is enabled, stop-loss and take-profit placements are flipped. Visualization & Dynamic Support/Resistance Levels 1. Pivot Boxes for Market Structure Each pivot is marked with a colored box: Green boxes indicate bullish demand zones. Red boxes indicate bearish supply zones. These boxes remain on the chart to act as dynamic support and resistance levels, helping traders identify key price reaction zones. 2. Horizontal Entry, Stop-Loss, and Take-Profit Lines When a trade is active, the strategy plots: White line → Entry price. Red line → Stop-loss level. Green line → Take-profit level. Labels display the exact entry, SL, and TP values, updating dynamically as price moves. Customization Options This strategy offers multiple adjustable settings to optimize performance for different market conditions: Trade Mode Selection → Choose between Long Only, Short Only, or Long & Short. Pivot Length → Defines the number of required Rally & Drop candles for a pivot. ATR Exit Multiplier → Adjusts stop-loss distance based on ATR. Risk-Reward Ratio (RR) → Modifies take-profit level relative to risk. Historical Lookback → Limits how far back pivot zones are displayed. Color Settings → Customize pivot box colors for bullish and bearish setups. Considerations & Limitations Pivot Breakouts Do Not Guarantee Reversals. Some pivot breaks may lead to continuation moves instead of trend reversals. Not Optimized for Low Volatility Conditions. This strategy works best in trending markets with strong momentum. ATR-Based Stop-Loss & Take-Profit May Require Optimization. Different assets may require different ATR multipliers and RR settings. Market Noise May Still Influence Pivots. While this method filters some noise, fake breakouts can still occur. Conclusion The Rally Base Drop SND Pivots Strategy is a non-repainting supply and demand system that combines: Pivot-based market structure analysis (using Rally, Base, and Drop candles). Breakout-based trade entries at confirmed SND levels. ATR-based dynamic risk management for stop-loss and take-profit calculation. This strategy helps traders: Identify high-probability supply and demand levels. Trade based on structured market pivots. Use a systematic approach to price action analysis. Automatically manage risk with ATR-based exits. The strict pivot detection rules and built-in breakout validation make this strategy ideal for traders looking to: Trade based on market structure. Use defined support & resistance levels. Reduce noise compared to traditional fractals. Implement a structured supply & demand trading model. This strategy is fully customizable, allowing traders to adjust parameters to fit their market and trading style. Full credit for the original concept and indicator goes to LuxAlgo.
Pine Script®策略
由PineIndicators提供
Quantura - Liquidity Sweep & Run LevelsIntroduction “Quantura – Liquidity Sweep & Run Levels” is a structural price-action indicator designed to automatically detect swing-based liquidity zones and visualize potential sweep and run events. It helps traders identify areas where liquidity has likely been taken (sweep) or released (run), improving precision in market structure analysis and timing of entries or exits. Originality & Value This tool translates institutional liquidity concepts into an automated visual framework. Instead of simply marking highs and lows, it dynamically monitors swing points, tracks their breaches, and identifies subsequent reactions. The indicator is built to highlight the liquidity dynamics that often precede reversals or continuations. Its originality lies in: Automatic identification and tracking of swing highs and lows. Real-time detection of broken levels and liquidity sweeps. Distinction between “Run” and “Sweep” modes for different market behaviors. Persistent historical visualization of liquidity levels using clean line structures. Configurable signal markers for bullish and bearish sweep confirmations. Functionality & Core Logic Detects swing highs and lows using a user-defined Swing Length parameter. Stores and updates all swing levels dynamically with arrays for efficient memory handling. Draws horizontal lines from each detected swing point to visualize potential liquidity zones. Monitors when price breaks a swing level and marks that event as “broken.” Generates signals when the market either sweeps above/below or runs away from those levels, depending on the chosen mode. Provides optional visual signal markers (“▲” for bullish sweeps, “▼” for bearish sweeps). Parameters & Customization Mode: Choose between “Sweep” (detects liquidity grabs) or “Run” (detects breakout continuations). Swing Length: Sets the sensitivity for detecting swing highs/lows. A higher value focuses on larger structures, while smaller values detect micro liquidity points. Bullish Color / Bearish Color: Customize color themes for sweep/run lines and signal markers. Signals: Enables or disables visual up/down markers for confirmed events. Visualization & Display Horizontal lines represent potential liquidity levels (unbroken swing highs/lows). Once broken, lines automatically stop extending, marking the moment liquidity is taken. Depending on the selected mode: “Sweep” mode identifies false breaks or stop-hunt behavior. “Run” mode highlights breakouts that continue the trend. Colored arrows indicate the direction and type of liquidity reaction. Clean, non-intrusive visualization suitable for overlaying on price charts. Use Cases Detect liquidity sweeps before major reversals. Identify breakout continuations after liquidity runs. Combine with Supply/Demand or FVG indicators for multi-layered confirmation. Validate liquidity bias in algorithmic or discretionary strategies. Analyze market manipulation patterns and institutional stop-hunting behavior. Limitations & Recommendations This indicator identifies structural behavior but does not guarantee trade direction or profitability. Works best on liquid markets with clear swing structures (e.g., crypto, forex, indices). Signal interpretation should be combined with confluence tools such as volume, order flow, or structure-based filters. Excessively small swing settings may cause over-signaling in volatile markets. Markets & Timeframes Optimized for all major asset classes — including crypto, Forex, indices, and equities — and for intraday to higher-timeframe structural analysis (5-minute up to daily charts). Author & Access Developed 100% by Quantura. Published as a Open-source script indicator. Access is free. Compliance Note This description fully complies with TradingView’s Script Publishing Rules and House Rules . It avoids performance claims, provides transparency on methodology, and clearly describes indicator behavior and limitations.
Pine Script®指标
由Quantura提供
Ornstein-Uhlenbeck Trend Channel [BOSWaves]Ornstein-Uhlenbeck Trend Channel - Adaptive Mean Reversion with Dynamic Equilibrium Geometry Overview The Ornstein-Uhlenbeck Trend Channel introduces an advanced equilibrium-mapping framework that blends statistical mean reversion with adaptive trend geometry. Traditional channels and regression bands react linearly to volatility, often failing to capture the natural rhythm of price equilibrium. This model evolves that concept through a dynamic reversion engine, where equilibrium adapts continuously to volatility, trend slope, and structural bias - forming a living channel that bends, expands, and contracts in real time. The result is a smooth, equilibrium-driven representation of market balance - not just trend direction. Instead of static bands or abrupt slope shifts, traders see fluid, volatility-aware motion that mirrors the natural pull-and-release dynamic of market behavior. Each channel visualizes the probabilistic boundaries of fair value, showing where price tends to revert and where it accelerates away from its statistical mean. Unlike conventional envelopes or Bollinger-type constructs, the Ornstein-Uhlenbeck framework is volatility-reactive and equilibrium-sensitive, providing traders with a contextual map of where price is likely to stabilize, extend, or exhaust. Theoretical Foundation The Ornstein-Uhlenbeck Trend Channel is inspired by stochastic mean-reversion processes - mathematical models used to describe systems that oscillate around a drifting equilibrium. While linear regression channels assume constant variance, financial markets operate under variable volatility and shifting equilibrium points. The OU process accounts for this by treating price as a mean-seeking motion governed by volatility and trend persistence. At its core are three interacting components: Equilibrium Mean (μ) : Represents the evolving balance point of price, adjusting to directional bias and volatility. Reversion Rate (θ) : Defines how strongly price is pulled back toward equilibrium after deviation, capturing the self-correcting nature of market structure. Volatility Coefficient (σ) : Controls how far and how quickly price can diverge from equilibrium before mean reversion pressure increases. By embedding this stochastic model inside a volatility-adjusted framework, the system accurately scales across different markets and conditions - maintaining meaningful equilibrium geometry across crypto, forex, indices, or commodities. This design gives traders a mathematically grounded yet visually intuitive interpretation of dynamic balance in live market motion. How It Works The Ornstein-Uhlenbeck Trend Channel is constructed through a structured multi-stage process that merges stochastic logic with volatility mechanics: Equilibrium Estimation Core : The indicator begins by identifying the evolving mean using adaptive smoothing influenced by trend direction and volatility. This becomes the live centerline - the statistical anchor around which price naturally oscillates. Volatility Normalization Layer : ATR or rolling deviation is used to calculate volatility intensity. The output scales the channel width dynamically, ensuring that boundaries reflect current variance rather than static thresholds. Directional Bias Engine : EMA slope and trend confirmation logic determine whether equilibrium should tilt upward or downward. This creates asymmetrical channel motion that bends with the prevailing trend rather than staying horizontal. Channel Boundary Construction : Upper and lower bands are plotted at volatility-proportional distances from the mean. These envelopes form the “statistical pressure zones” that indicate where mean reversion or acceleration may occur. Signal and Lifecycle Control : Channel breaches, mean crossovers, and slope flips mark statistically significant events - exhaustion, continuation, or rebalancing. Older equilibrium zones gradually fade, ensuring a clear, context-aware visual field. Through these layers, the channel forms a continuously updating equilibrium corridor that adapts in real time - breathing with the market’s volatility and rhythm. Interpretation The Ornstein-Uhlenbeck Trend Channel reframes how traders interpret balance and momentum. Instead of viewing price as directional movement alone, it visualizes the constant tension between trending force and equilibrium pull. Uptrend Phases : The equilibrium mean tilts upward, with price oscillating around or slightly above the midline. Upper band touches signal momentum extension; lower touches reflect healthy reversion. Downtrend Phases : The mean slopes downward, with upper-band interactions marking resistance zones and lower bands acting as reversion boundaries. Equilibrium Transitions : Flat mean sections indicate balance or distribution phases. Breaks from these neutral zones often precede directional expansion. Overextension Events : When price closes beyond an outer boundary, it marks statistically significant disequilibrium - an early warning of exhaustion or volatility reset. Visually, the OU channel translates volatility and equilibrium into structured geometry, giving traders a statistical lens on trend quality, reversion probability, and volatility stress points. Strategy Integration The Ornstein-Uhlenbeck Trend Channel integrates seamlessly into both mean-reversion and trend-continuation systems: Trend Alignment : Use mean slope direction to confirm higher-timeframe bias before entering continuation setups. Reversion Entries : Target rejections from outer bands when supported by volume or divergence, capturing snapbacks toward equilibrium. Volatility Breakout Mapping : Monitor boundary expansions to identify transition from compression to expansion phases. Liquidity Zone Confirmation : Combine with BOS or order-block indicators to validate structural zones against equilibrium positioning. Momentum Filtering : Align with oscillators or volume profiles to isolate equilibrium-based pullbacks with statistical context. Technical Implementation Details Core Engine : Stochastic Ornstein-Uhlenbeck process for continuous mean recalibration. Volatility Framework : ATR- and deviation-based scaling for dynamic channel expansion. Directional Logic : EMA-slope driven bias for adaptive mean tilt. Channel Composition : Independent upper and lower envelopes with smoothing and transparency control. Signal Structure : Alerts for mean crossovers and boundary breaches. Performance Profile : Lightweight, multi-timeframe compatible implementation optimized for real-time responsiveness. Optimal Application Parameters Timeframe Guidance: 1 - 5 min : Reactive equilibrium tracking for short-term scalping and microstructure analysis. 15 - 60 min : Medium-range setups for volatility-phase transitions and intraday structure. 4H - Daily : Macro equilibrium mapping for identifying exhaustion, distribution, or reaccumulation zones. Suggested Configuration: Mean Length : 20 - 50 Volatility Multiplier : 1.5× - 2.5× Reversion Sensitivity : 0.4 - 0.8 Smoothing : 2 - 5 Parameter tuning should reflect asset liquidity, volatility, and desired reversion frequency. Performance Characteristics High Effectiveness: Trending environments with cyclical pullbacks and volatility oscillation. Markets exhibiting consistent equilibrium-return behavior (indices, majors, high-cap crypto). Reduced Effectiveness: Low-volatility consolidations with minimal variance. Random walk markets lacking definable equilibrium anchors. Integration Guidelines Confluence Framework : Pair with BOSWaves structural tools or momentum oscillators for context validation. Directional Control : Follow mean slope alignment for directional conviction before acting on channel extremes. Risk Calibration : Use outer band violations for controlled contrarian entries or trailing stop management. Multi-Timeframe Synergy : Derive macro equilibrium zones on higher timeframes and refine entries on lower levels. Disclaimer The Ornstein-Uhlenbeck Trend Channel is a professional-grade equilibrium and volatility framework. It is not predictive or profit-assured; performance depends on parameter calibration, volatility regime, and disciplined execution. BOSWaves recommends using it as part of a comprehensive analytical stack combining structure, liquidity, and momentum context.
Pine Script®指标
由BOSWaves提供
2
One Shot One Kill ICT [TradingFinder] Liquidity MMXM + CISD OTE🔵 Introduction The One Shot One Kill trading setup is one of the most advanced methods in the field of Smart Money Concept (SMC) and ICT. Designed with a focus on concepts such as Liquidity Hunt, Discount Market, and Premium Market, this strategy emphasizes precise Price Action analysis and market structure shifts. It enables traders to identify key entry and exit points using a structured Trading Model. The core process of this setup begins with a Liquidity Hunt. Initially, the price targets areas like the Previous Day High and Previous Day Low to absorb liquidity. Once the Change in State of Delivery(CISD)is broken, the market structure shifts, signaling readiness for trade entry. At this stage, Fibonacci retracement levels are drawn, and the trader enters a position as the price retraces to the 0.618 Fibonacci level. Part of the Smart Money approach, this setup combines liquidity analysis with technical tools, creating an opportunity for traders to enter high-accuracy trades. By following this setup, traders can identify critical market moves and capitalize on reversal points effectively. Bullish : Bearish : 🔵 How to Use The One Shot One Kill setup is a structured and advanced trading strategy based on Liquidity Hunt, Fibonacci retracement, and market structure shifts (CISD). With a focus on precise Price Action analysis, this setup helps traders identify key market movements and plan optimal trade entries and exits. It operates in two scenarios: Bullish and Bearish, each with distinct steps. 🟣 Bullish One Shot One Kill In the Bullish scenario, the process starts with the price moving toward the Previous Day Low, where liquidity is absorbed. At this stage, retail sellers are trapped as they enter short trades at lower levels. Following this, the market reverses upward and breaks the CISD, signaling a shift in market structure toward bullishness. Once this shift is identified, traders draw Fibonacci levels from the lowest point to the highest point of the move. When the price retraces to the 0.618 Fibonacci level, conditions for a buy position are met. The target for this trade is typically the Previous Day High or other significant liquidity zones where major buyers are positioned, offering a high probability of price reversal. 🟣 Bearish One Shot One Kill In the Bearish scenario, the price initially moves toward the Previous Day High to absorb liquidity. Retail buyers are trapped as they enter long trades near the highs. After the liquidity hunt, the market reverses downward, breaking the CISD, which signals a bearish shift in market structure. Following this confirmation, Fibonacci levels are drawn from the highest point to the lowest point of the move. When the price retraces to the 0.618 Fibonacci level, a sell position is initiated. The target for this trade is usually the Previous Day Low or other key liquidity zones where major sellers are active. This setup provides a precise and logical framework for traders to identify market movements and enter trades at critical reversal points. 🔵 Settings 🟣 CISD Logical settings Bar Back Check : Determining the return of candles to identify the CISD level. CISD Level Validity : CISD level validity period based on the number of candles. 🟣 LIQUIDITY Logical settings Swing period : You can set the swing detection period. Max Swing Back Method : It is in two modes "All" and "Custom". If it is in "All" mode, it will check all swings, and if it is in "Custom" mode, it will check the swings to the extent you determine. Max Swing Back : You can set the number of swings that will go back for checking. 🟣 CISD Display settings Displaying or not displaying swings and setting the color of labels and lines. 🟣 LIQUIDITY Display settings Displaying or not displaying swings and setting the color of labels and lines. 🔵 Conclusion The One Shot One Kill setup is one of the most effective and well-structured trading strategies for identifying and capitalizing on key market movements. By incorporating concepts such as Liquidity Hunt, CISD, and Fibonacci retracement, this setup allows traders to enter trades with high precision at optimal points. The strategy emphasizes detailed Price Action analysis and the identification of Smart Money behavior, helping traders to execute successful trades against the general market trend. With a focus on identifying liquidity in the Previous Day High and Low and aligning it with Fibonacci retracement levels, this setup provides a robust framework for entering both bullish and bearish trades. The combination of liquidity analysis and Fibonacci retracement at the 0.618 level enables traders to minimize risk and exploit major market moves effectively. Ultimately, success with the One Shot One Kill setup requires practice, patience, and strict adherence to its rules. By mastering its concepts and focusing on high-probability setups, traders can enhance their decision-making skills and build a sustainable and professional trading approach.
Pine Script®指标
由TFlab提供
17