Meridian - Options Context

//@version=6
// SPDX-License-Identifier: MPL-2.0
// Meridian — Options Context · v0.3.0

indicator(
     title = "Meridian - Options Context",
     shorttitle = "Meridian Options Context",
     overlay = false,
     format = format.price,
     precision = 1,
     max_bars_back = 1000
)

import TradingView/ta/12 as tvta

string GROUP_SESSION = "01. Session"
string GROUP_NORMALIZATION = "02. Normalization"
string GROUP_TREND = "03. Trend"
string GROUP_STRUCTURE = "04. Structure"
string GROUP_MOMENTUM = "05. Momentum"
string GROUP_PARTICIPATION = "06. Participation"
string GROUP_PERSISTENCE = "07. Persistence"
string GROUP_VOLATILITY = "08. Volatility"
string GROUP_HTF = "09. Higher Timeframes"
string GROUP_VISUALS = "10. Visuals"
string GROUP_DASHBOARD = "11. Dashboard"
string GROUP_ALERTS = "12. Alerts"
string GROUP_COLORS = "13. Colors"
string GROUP_RESEARCH = "14. Research / Debug"

string sessionTimezone = input.string("America/New_York", "Session timezone", group = GROUP_SESSION)
string rthInput = input.session("0930-1600", "Regular session", group = GROUP_SESSION)
bool weekdaysOnly = input.bool(true, "Monday-Friday only", group = GROUP_SESSION)
int openingRangeMinutes = input.int(15, "Opening-range minutes", minval = 1, maxval = 120, group = GROUP_SESSION)

int atrLength = input.int(14, "ATR length", minval = 5, maxval = 100, group = GROUP_NORMALIZATION)

int fastEmaLength = input.int(9, "Fast EMA", minval = 1, maxval = 100, group = GROUP_TREND)
int slowEmaLength = input.int(21, "Slow EMA", minval = 2, maxval = 200, group = GROUP_TREND)
int trendSlopeLookback = input.int(5, "Slope lookback", minval = 1, maxval = 30, group = GROUP_TREND)
float trendSpreadScale = input.float(0.25, "EMA spread scale (ATR)", minval = 0.05, maxval = 2.0, step = 0.05, group = GROUP_TREND)
float trendSlopeScale = input.float(0.20, "EMA slope scale (ATR)", minval = 0.05, maxval = 2.0, step = 0.05, group = GROUP_TREND)

float vwapSource = input.source(hlc3, "RTH VWAP source", group = GROUP_STRUCTURE)
float vwapDistanceScale = input.float(0.35, "VWAP displacement scale (ATR)", minval = 0.05, maxval = 2.0, step = 0.05, group = GROUP_STRUCTURE)
float openingRangeBreakScale = input.float(0.25, "Opening-range break scale (ATR)", minval = 0.05, maxval = 2.0, step = 0.05, group = GROUP_STRUCTURE)
float sessionOpenScale = input.float(0.50, "RTH-open displacement scale (ATR)", minval = 0.05, maxval = 3.0, step = 0.05, group = GROUP_STRUCTURE)

float rsiSource = input.source(close, "RSI source", group = GROUP_MOMENTUM)
int rsiLength = input.int(14, "RSI length", minval = 2, maxval = 100, group = GROUP_MOMENTUM)
int momentumLookback = input.int(5, "Normalized-return lookback", minval = 1, maxval = 30, group = GROUP_MOMENTUM)
float momentumScale = input.float(1.0, "Normalized-return scale", minval = 0.10, maxval = 5.0, step = 0.10, group = GROUP_MOMENTUM)
int momentumDeltaLookback = input.int(3, "Momentum-change lookback", minval = 1, maxval = 20, group = GROUP_MOMENTUM)
float momentumDeltaScale = input.float(0.50, "Momentum-change scale", minval = 0.10, maxval = 2.0, step = 0.05, group = GROUP_MOMENTUM)

int rvolLookbackDays = input.int(20, "RVOL historical days", minval = 2, maxval = 100, group = GROUP_PARTICIPATION)
bool adjustRealtimeRvol = input.bool(true, "Estimate unclosed real-time bar", group = GROUP_PARTICIPATION)
float participationReturnScale = input.float(0.50, "Directional-return scale (ATR)", minval = 0.10, maxval = 3.0, step = 0.10, group = GROUP_PARTICIPATION)
float rvolLogScale = input.float(0.70, "RVOL intensity scale", minval = 0.10, maxval = 3.0, step = 0.05, group = GROUP_PARTICIPATION)
float highRvolThreshold = input.float(1.5, "High RVOL threshold", minval = 1.0, maxval = 10.0, step = 0.1, group = GROUP_PARTICIPATION)
float extremeRvolThreshold = input.float(2.0, "Extreme RVOL threshold", minval = 1.0, maxval = 20.0, step = 0.1, group = GROUP_PARTICIPATION)
float rvolDisplayCap = input.float(3.0, "RVOL display cap", minval = 1.0, maxval = 20.0, step = 0.25, group = GROUP_PARTICIPATION)

int persistenceLength = input.int(12, "Persistence bars", minval = 3, maxval = 60, group = GROUP_PERSISTENCE)

int volatilityShortLength = input.int(14, "Short ATR length", minval = 5, maxval = 50, group = GROUP_VOLATILITY)
int volatilityLongLength = input.int(50, "Long ATR length", minval = 20, maxval = 200, group = GROUP_VOLATILITY)
int volatilityZLookback = input.int(100, "Volatility history", minval = 30, maxval = 500, group = GROUP_VOLATILITY)
float volatilityZScale = input.float(1.5, "Volatility z-score scale", minval = 0.25, maxval = 5.0, step = 0.25, group = GROUP_VOLATILITY)

bool includeHigherTimeframes = input.bool(true, "Include HTF trend in Context", group = GROUP_HTF)
bool showHigherTimeframeMatrix = input.bool(true, "Show 1H / 4H / 1D / 1W states", group = GROUP_HTF)
int htfFastEmaLength = input.int(20, "HTF fast EMA", minval = 2, maxval = 100, group = GROUP_HTF)
int htfSlowEmaLength = input.int(50, "HTF slow EMA", minval = 5, maxval = 300, group = GROUP_HTF)
int htfAtrLength = input.int(14, "HTF ATR length", minval = 5, maxval = 100, group = GROUP_HTF)
int htfSlopeLookback = input.int(3, "HTF EMA slope bars", minval = 1, maxval = 20, group = GROUP_HTF)
int htfPathLookback = input.int(5, "HTF recent-path bars", minval = 2, maxval = 20, group = GROUP_HTF)
int htfPersistenceLength = input.int(6, "HTF persistence bars", minval = 3, maxval = 20, group = GROUP_HTF)
float htfSpreadScale = input.float(0.30, "HTF EMA spread scale (ATR)", minval = 0.05, maxval = 3.0, step = 0.05, group = GROUP_HTF)
float htfSlopeScale = input.float(0.12, "HTF EMA slope scale (ATR/bar)", minval = 0.02, maxval = 1.0, step = 0.01, group = GROUP_HTF)
float htfReturnScale = input.float(0.85, "HTF path-return scale", minval = 0.10, maxval = 3.0, step = 0.05, group = GROUP_HTF)
float htfDirectionalThreshold = input.float(20.0, "HTF directional threshold", minval = 5.0, maxval = 50.0, step = 5.0, group = GROUP_HTF)

bool showContextLine = input.bool(true, "Show Context Direction", group = GROUP_VISUALS)
bool showConfidenceLine = input.bool(true, "Show Context Confidence", group = GROUP_VISUALS)
bool showRvol = input.bool(true, "Show RVOL columns", group = GROUP_VISUALS)
bool showRsiMomentum = input.bool(false, "Show RSI momentum score", group = GROUP_VISUALS)
bool showHtfComposite = input.bool(false, "Show HTF composite", group = GROUP_VISUALS)
bool showRegimeBackground = input.bool(true, "Subtle regime background", group = GROUP_VISUALS)
int regimeBackgroundTransparency = input.int(96, "Regime background transparency", minval = 85, maxval = 100, group = GROUP_VISUALS)

bool showDashboard = input.bool(true, "Show dashboard", group = GROUP_DASHBOARD)
string dashboardMode = input.string("Detailed", "Dashboard mode", options = ["Detailed", "Compact"], group = GROUP_DASHBOARD)
string dashboardSizeInput = input.string("Small", "Dashboard text size", options = ["Tiny", "Small", "Normal"], group = GROUP_DASHBOARD)

bool enableAlerts = input.bool(true, "Enable alert conditions", group = GROUP_ALERTS)
float highConfidenceDirectionThreshold = input.float(55.0, "High-confidence direction threshold", minval = 20.0, maxval = 100.0, step = 5.0, group = GROUP_ALERTS)
float highConfidenceThreshold = input.float(60.0, "High-confidence confidence threshold", minval = 20.0, maxval = 100.0, step = 5.0, group = GROUP_ALERTS)
int contextFailureWindow = input.int(12, "Strong-regime failure window (bars)", minval = 2, maxval = 50, group = GROUP_ALERTS)

color brandColor = input.color(#8B5CF6, "Brand / header", group = GROUP_COLORS)
color bullishColor = input.color(#2DD4BF, "Bullish / support", group = GROUP_COLORS)
color bearishColor = input.color(#F87171, "Bearish / resistance", group = GROUP_COLORS)
color informationColor = input.color(#38BDF8, "Information", group = GROUP_COLORS)
color neutralColor = input.color(#94A3B8, "Neutral", group = GROUP_COLORS)
color pendingColor = input.color(#A1A1AA, "Pending / developing", group = GROUP_COLORS)
color warningColor = input.color(#FBBF24, "Warning", group = GROUP_COLORS)
color secondaryColor = input.color(#C4B5FD, "Secondary / confidence", group = GROUP_COLORS)
color panelColor = input.color(#090E17, "Dashboard background", group = GROUP_COLORS)
color borderColor = input.color(#334155, "Dashboard border", group = GROUP_COLORS)
color textColor = input.color(#F8FAFC, "Dashboard text", group = GROUP_COLORS)

bool showResearchValues = input.bool(false, "Show research values in Data Window", group = GROUP_RESEARCH)
bool showLegacyComparison = input.bool(false, "Show v0.1 binary-score comparison", group = GROUP_RESEARCH)

int REGIME_STRONG_BEAR = -3
int REGIME_BEAR = -2
int REGIME_BEAR_LEAN = -1
int REGIME_BALANCED = 0
int REGIME_BULL_LEAN = 1
int REGIME_BULL = 2
int REGIME_STRONG_BULL = 3

f_clamp(float value, float lower, float upper) =>
    math.max(lower, math.min(upper, value))

f_sat(float value) =>
    value / (1.0 + math.abs(value))

f_bound(float value) =>
    100.0 * f_sat(value)

f_sign(float value) =>
    value > 0.0 ? 1.0 : value < 0.0 ? -1.0 : 0.0

f_weighted2(float v1, float w1, float v2, float w2) =>
    float numerator = 0.0
    float denominator = 0.0
    if not na(v1)
        numerator := numerator + v1 * w1
        denominator := denominator + w1
    if not na(v2)
        numerator := numerator + v2 * w2
        denominator := denominator + w2
    denominator > 0.0 ? numerator / denominator : na

f_weighted3(float v1, float w1, float v2, float w2, float v3, float w3) =>
    float numerator = 0.0
    float denominator = 0.0
    if not na(v1)
        numerator := numerator + v1 * w1
        denominator := denominator + w1
    if not na(v2)
        numerator := numerator + v2 * w2
        denominator := denominator + w2
    if not na(v3)
        numerator := numerator + v3 * w3
        denominator := denominator + w3
    denominator > 0.0 ? numerator / denominator : na

f_weighted4(float v1, float w1, float v2, float w2, float v3, float w3, float v4, float w4) =>
    float numerator = 0.0
    float denominator = 0.0
    if not na(v1)
        numerator := numerator + v1 * w1
        denominator := denominator + w1
    if not na(v2)
        numerator := numerator + v2 * w2
        denominator := denominator + w2
    if not na(v3)
        numerator := numerator + v3 * w3
        denominator := denominator + w3
    if not na(v4)
        numerator := numerator + v4 * w4
        denominator := denominator + w4
    denominator > 0.0 ? numerator / denominator : na

f_textSize(string sizeText) =>
    sizeText == "Normal" ? size.normal : sizeText == "Small" ? size.small : size.tiny

f_scoreText(float value) =>
    na(value) ? "n/a" : value > 0.0 ? "+" + str.tostring(math.round(value)) : str.tostring(math.round(value))

f_ratioText(float value) =>
    na(value) ? "n/a" : str.tostring(value, "#.00") + "x"

f_confidenceText(float value) =>
    na(value) ? "n/a" : value > 65.0 ? "HIGH" : value >= 35.0 ? "MOD" : "LOW"

f_volatilityText(float value) =>
    na(value) ? "n/a" : value >= 80.0 ? "EXTREME" : value >= 60.0 ? "EXPANDED" : value >= 40.0 ? "NORMAL" : value >= 20.0 ? "QUIET" : "CONTRACTED"

f_rawRegime(float score) =>
    score >= 75.0 ? REGIME_STRONG_BULL :
     score >= 45.0 ? REGIME_BULL :
     score >= 20.0 ? REGIME_BULL_LEAN :
     score <= -75.0 ? REGIME_STRONG_BEAR :
     score <= -45.0 ? REGIME_BEAR :
     score <= -20.0 ? REGIME_BEAR_LEAN :
     REGIME_BALANCED

f_updateRegime(int previousState, float score) =>
    int rawState = f_rawRegime(score)
    int nextState = previousState
    if previousState == REGIME_STRONG_BULL
        nextState := score >= 68.0 ? REGIME_STRONG_BULL : rawState
    else if previousState == REGIME_BULL
        nextState := score >= 75.0 ? REGIME_STRONG_BULL : score >= 38.0 ? REGIME_BULL : rawState
    else if previousState == REGIME_BULL_LEAN
        nextState := score >= 45.0 ? rawState : score >= 14.0 ? REGIME_BULL_LEAN : rawState
    else if previousState == REGIME_STRONG_BEAR
        nextState := score <= -68.0 ? REGIME_STRONG_BEAR : rawState
    else if previousState == REGIME_BEAR
        nextState := score <= -75.0 ? REGIME_STRONG_BEAR : score <= -38.0 ? REGIME_BEAR : rawState
    else if previousState == REGIME_BEAR_LEAN
        nextState := score <= -45.0 ? rawState : score <= -14.0 ? REGIME_BEAR_LEAN : rawState
    else
        nextState := rawState
    nextState

f_regimeText(int state) =>
    state == REGIME_STRONG_BULL ? "STRONG BULL" :
     state == REGIME_BULL ? "BULL" :
     state == REGIME_BULL_LEAN ? "BULL LEAN" :
     state == REGIME_STRONG_BEAR ? "STRONG BEAR" :
     state == REGIME_BEAR ? "BEAR" :
     state == REGIME_BEAR_LEAN ? "BEAR LEAN" :
     "BALANCED"

f_componentText(float score) =>
    na(score) ? "n/a" : score >= 75.0 ? "STRONG BULL" : score >= 45.0 ? "BULL" : score >= 20.0 ? "BULL LEAN" : score <= -75.0 ? "STRONG BEAR" : score <= -45.0 ? "BEAR" : score <= -20.0 ? "BEAR LEAN" : "BALANCED"

f_regimeColor(int state) =>
    state == REGIME_STRONG_BULL ? bullishColor :
     state == REGIME_BULL or state == REGIME_BULL_LEAN ? bullishColor :
     state == REGIME_STRONG_BEAR ? bearishColor :
     state == REGIME_BEAR or state == REGIME_BEAR_LEAN ? bearishColor :
     neutralColor

f_scoreColor(float score) =>
    na(score) ? neutralColor : score >= 75.0 ? bullishColor : score >= 20.0 ? bullishColor : score <= -75.0 ? bearishColor : score <= -20.0 ? bearishColor : neutralColor

f_levelsBias(float direction, float confidence) =>
    na(direction) or na(confidence) ? "n/a" : confidence < 35.0 ? "LOW CONVICTION" : direction >= 45.0 ? "PREFER SUPPORT" : direction <= -45.0 ? "PREFER RESISTANCE" : direction >= 20.0 ? "SUPPORT LEAN" : direction <= -20.0 ? "RESISTANCE LEAN" : "TWO-SIDED"

f_zonePreference(float direction, float confidence) =>
    na(direction) or na(confidence) or confidence < 35.0 or math.abs(direction) < 20.0 ? 0.0 : direction > 0.0 ? 1.0 : -1.0

f_tableCell(table tableId, int column, int row, string cellText, color textColor, color backgroundColor, string horizontalAlignment) =>
    table.cell(
         tableId,
         column,
         row,
         cellText,
         text_color = textColor,
         bgcolor = backgroundColor,
         text_size = f_textSize(dashboardSizeInput),
         text_halign = horizontalAlignment == "Left" ? text.align_left : text.align_right
    )

f_htfMetricsConfirmed() =>
    float htfAtr = ta.atr(htfAtrLength)
    float htfFast = ta.ema(close, htfFastEmaLength)
    float htfSlow = ta.ema(close, htfSlowEmaLength)
    float htfRsi = ta.rsi(close, rsiLength)
    float atrSafeHtf = math.max(nz(htfAtr, syminfo.mintick), syminfo.mintick)
    float spreadScoreRaw = f_bound(((htfFast - htfSlow) / atrSafeHtf) / htfSpreadScale)
    float slowSlopePerBar = ((htfSlow - htfSlow[htfSlopeLookback]) / math.max(float(htfSlopeLookback), 1.0)) / atrSafeHtf
    float fastSlopePerBar = ((htfFast - htfFast[htfSlopeLookback]) / math.max(float(htfSlopeLookback), 1.0)) / atrSafeHtf
    float slowSlopeScore = f_bound(slowSlopePerBar / htfSlopeScale)
    float fastSlopeScore = f_bound(fastSlopePerBar / htfSlopeScale)
    float slopeScoreRaw = f_weighted2(slowSlopeScore, 0.65, fastSlopeScore, 0.35)
    float pathMove = close - close[htfPathLookback]
    float pathReturnAtr = pathMove / (atrSafeHtf * math.sqrt(float(htfPathLookback)))
    float returnScoreRaw = f_bound(pathReturnAtr / htfReturnScale)
    float pathLength = ta.sma(math.abs(ta.change(close)), htfPathLookback) * htfPathLookback
    float efficiencyScoreRaw = not na(pathLength) and pathLength > syminfo.mintick ? f_clamp(100.0 * pathMove / pathLength, -100.0, 100.0) : na
    float persistenceScoreRaw = 100.0 * ta.sma(f_sign(close - htfFast), htfPersistenceLength)
    float rsiScoreRaw = f_bound((htfRsi - 50.0) / 20.0)
    float directionRaw = 0.28 * spreadScoreRaw + 0.22 * slopeScoreRaw + 0.22 * returnScoreRaw + 0.13 * efficiencyScoreRaw + 0.10 * persistenceScoreRaw + 0.05 * rsiScoreRaw
    float magnitudeRaw = 0.28 * math.abs(spreadScoreRaw) + 0.22 * math.abs(slopeScoreRaw) + 0.22 * math.abs(returnScoreRaw) + 0.13 * math.abs(efficiencyScoreRaw) + 0.10 * math.abs(persistenceScoreRaw) + 0.05 * math.abs(rsiScoreRaw)
    float coherenceRaw = magnitudeRaw > 0.0 ? f_clamp(math.abs(directionRaw) / magnitudeRaw, 0.0, 1.0) : 0.0
    float qualityRaw = f_clamp(magnitudeRaw * (0.60 + 0.40 * coherenceRaw), 0.0, 100.0)
    [f_clamp(directionRaw[1], -100.0, 100.0), qualityRaw[1]]

f_htfBreadthText(float score1, float score2, float score3, float score4, float threshold) =>
    int bullCount = (not na(score1) and score1 >= threshold ? 1 : 0) + (not na(score2) and score2 >= threshold ? 1 : 0) + (not na(score3) and score3 >= threshold ? 1 : 0) + (not na(score4) and score4 >= threshold ? 1 : 0)
    int bearCount = (not na(score1) and score1 <= -threshold ? 1 : 0) + (not na(score2) and score2 <= -threshold ? 1 : 0) + (not na(score3) and score3 <= -threshold ? 1 : 0) + (not na(score4) and score4 <= -threshold ? 1 : 0)
    int availableCount = (not na(score1) ? 1 : 0) + (not na(score2) ? 1 : 0) + (not na(score3) ? 1 : 0) + (not na(score4) ? 1 : 0)
    bullCount > bearCount ? str.tostring(bullCount) + "/" + str.tostring(availableCount) + " BULL" : bearCount > bullCount ? str.tostring(bearCount) + "/" + str.tostring(availableCount) + " BEAR" : availableCount > 0 ? "MIXED" : "n/a"

string sessionDays = weekdaysOnly ? ":23456" : ":1234567"
string rthSession = rthInput + sessionDays
bool inRth = not na(time(timeframe.period, rthSession, sessionTimezone))
int calendarDayKey = year(time, sessionTimezone) * 10000 + month(time, sessionTimezone) * 100 + dayofmonth(time, sessionTimezone)
bool newCalendarDay = na(calendarDayKey[1]) or calendarDayKey != calendarDayKey[1]
bool rthStart = inRth and (session.isfirstbar_regular or newCalendarDay or not inRth[1])
int staleThresholdMs = int(math.max(timeframe.in_seconds() * 1000.0 * 3.0, 30.0 * 60.0 * 1000.0))
bool staleData = barstate.islast and timenow > time_close + staleThresholdMs
string dataState = staleData ? "STALE / CLOSED" : barstate.isrealtime and not barstate.isconfirmed ? "LIVE" : "CONFIRMED"

if barstate.isfirst and (not timeframe.isintraday or timeframe.in_seconds() >= 3600)
    runtime.error("Meridian Options Context is designed for intraday charts below 60 minutes.")

float atr = ta.atr(atrLength)
float atrSafe = math.max(nz(atr, syminfo.mintick), syminfo.mintick)
float atrPct = atrSafe / math.max(close, syminfo.mintick)

var float currentSessionOpen = na
if rthStart
    currentSessionOpen := open

bool dailyVwapAnchor = rthStart
float sessionVwap = ta.vwap(vwapSource, dailyVwapAnchor)

var int openingRangeEndTime = na
var float openingRangeHigh = na
var float openingRangeLow = na
var bool openingRangeFinalized = false
if rthStart
    openingRangeEndTime := time + openingRangeMinutes * 60 * 1000
    openingRangeHigh := high
    openingRangeLow := low
    openingRangeFinalized := false
bool inOpeningRange = inRth and not na(openingRangeEndTime) and time < openingRangeEndTime
if inOpeningRange and not rthStart
    openingRangeHigh := math.max(openingRangeHigh, high)
    openingRangeLow := math.min(openingRangeLow, low)
if inRth and not inOpeningRange and not openingRangeFinalized and not na(openingRangeEndTime)
    openingRangeFinalized := true
bool openingRangeTimeframeWarning = timeframe.in_seconds() > openingRangeMinutes * 60

float fastEma = ta.ema(close, fastEmaLength)
float slowEma = ta.ema(close, slowEmaLength)
float spreadNorm = (fastEma - slowEma) / atrSafe
float slowSlopeNorm = ((slowEma - slowEma[trendSlopeLookback]) / math.max(float(trendSlopeLookback), 1.0)) / atrSafe
float fastSlopeNorm = ((fastEma - fastEma[trendSlopeLookback]) / math.max(float(trendSlopeLookback), 1.0)) / atrSafe
float trendSpreadScore = f_bound(spreadNorm / trendSpreadScale)
float trendSlowSlopeScore = f_bound(slowSlopeNorm / trendSlopeScale)
float trendFastSlopeScore = f_bound(fastSlopeNorm / trendSlopeScale)
float trendScore = f_clamp(0.55 * trendSpreadScore + 0.30 * trendSlowSlopeScore + 0.15 * trendFastSlopeScore, -100.0, 100.0)

float vwapStructureScore = inRth and not na(sessionVwap) ? f_bound(((close - sessionVwap) / atrSafe) / vwapDistanceScale) : na
float openingRangeScore = na
if inRth and openingRangeFinalized and not openingRangeTimeframeWarning and not na(openingRangeHigh) and not na(openingRangeLow)
    float openingRangeWidth = math.max(openingRangeHigh - openingRangeLow, syminfo.mintick)
    if close > openingRangeHigh
        openingRangeScore := 30.0 + 70.0 * f_sat(((close - openingRangeHigh) / atrSafe) / openingRangeBreakScale)
    else if close < openingRangeLow
        openingRangeScore := -30.0 - 70.0 * f_sat(((openingRangeLow - close) / atrSafe) / openingRangeBreakScale)
    else
        float position = 2.0 * (close - openingRangeLow) / openingRangeWidth - 1.0
        openingRangeScore := 30.0 * f_clamp(position, -1.0, 1.0)
float sessionOpenScore = inRth and not na(currentSessionOpen) ? f_bound(((close - currentSessionOpen) / atrSafe) / sessionOpenScale) : na
float structureScore = f_weighted3(vwapStructureScore, 0.60, openingRangeScore, 0.30, sessionOpenScore, 0.10)

float rsiValue = ta.rsi(rsiSource, rsiLength)
float rsiMomentumScore = not na(rsiValue) ? f_bound((rsiValue - 50.0) / 20.0) : na
float momentumReturn = close[momentumLookback] > 0.0 ? math.log(close / close[momentumLookback]) : na
float expectedMove = atrPct * math.sqrt(momentumLookback)
float momentumReturnNorm = not na(momentumReturn) and expectedMove > 0.0 ? momentumReturn / expectedMove : na
float returnMomentumScore = not na(momentumReturnNorm) ? f_bound(momentumReturnNorm / momentumScale) : na
float momentumDeltaRaw = not na(returnMomentumScore) and not na(returnMomentumScore[momentumDeltaLookback]) ? (returnMomentumScore - returnMomentumScore[momentumDeltaLookback]) / 100.0 : na
float momentumDeltaScore = not na(momentumDeltaRaw) ? f_bound(momentumDeltaRaw / momentumDeltaScale) : na
float momentumScore = f_weighted3(returnMomentumScore, 0.55, rsiMomentumScore, 0.35, momentumDeltaScore, 0.10)

string regularSessionTicker = ticker.new(syminfo.prefix, syminfo.ticker, session.regular)
[_, _, barRvolRaw] = request.security(
     regularSessionTicker,
     timeframe.period,
     tvta.relativeVolume(rvolLookbackDays, "D", false, adjustRealtimeRvol),
     gaps = barmerge.gaps_on,
     lookahead = barmerge.lookahead_off
)
[_, _, cumulativeRvolRaw] = request.security(
     regularSessionTicker,
     timeframe.period,
     tvta.relativeVolume(rvolLookbackDays, "D", true, adjustRealtimeRvol),
     gaps = barmerge.gaps_on,
     lookahead = barmerge.lookahead_off
)
float barRvol = inRth ? barRvolRaw : na
float cumulativeRvol = inRth ? cumulativeRvolRaw : na
float barRange = math.max(high - low, math.max(atrSafe * 0.02, syminfo.mintick))
float closeLocationValue = f_clamp((2.0 * close - high - low) / barRange, -1.0, 1.0)
float oneBarReturnNorm = close[1] > 0.0 ? math.log(close / close[1]) / atrPct : na
float returnDirection = not na(oneBarReturnNorm) ? f_sat(oneBarReturnNorm / participationReturnScale) : 0.0
float directionalProxy = f_clamp(0.65 * closeLocationValue + 0.35 * returnDirection, -1.0, 1.0)
float barIntensity = not na(barRvol) and barRvol > 0.0 ? f_sat(math.max(math.log(barRvol), 0.0) / rvolLogScale) : na
float cumulativeIntensity = not na(cumulativeRvol) and cumulativeRvol > 0.0 ? f_sat(math.max(math.log(cumulativeRvol), 0.0) / rvolLogScale) : na
float participationIntensity = f_weighted2(barIntensity, 0.65, cumulativeIntensity, 0.35)
float participationScore = not na(participationIntensity) ? 100.0 * directionalProxy * participationIntensity : na
float participationQuality = not na(participationIntensity) ? 100.0 * participationIntensity : na

var int rthBarCount = 0
if rthStart
    rthBarCount := 1
else if inRth
    rthBarCount := rthBarCount + 1
else
    rthBarCount := 0
float persistenceDivisor = math.max(math.min(persistenceLength, rthBarCount), 1)
float vwapPersistenceInput = inRth and not na(sessionVwap) ? f_sign(close - sessionVwap) : 0.0
float emaPersistenceInput = inRth ? f_sign(fastEma - slowEma) : 0.0
float returnPersistenceInput = inRth and close[1] > 0.0 ? f_sign(math.log(close / close[1])) : 0.0
float vwapPersistenceAverage = ta.sma(vwapPersistenceInput, persistenceLength)
float emaPersistenceAverage = ta.sma(emaPersistenceInput, persistenceLength)
float returnPersistenceAverage = ta.sma(returnPersistenceInput, persistenceLength)
float vwapPersistence = inRth ? vwapPersistenceAverage * persistenceLength / persistenceDivisor : na
float emaPersistence = inRth ? emaPersistenceAverage * persistenceLength / persistenceDivisor : na
float returnPersistence = inRth ? returnPersistenceAverage * persistenceLength / persistenceDivisor : na
float persistenceScore = inRth ? 100.0 * f_weighted3(vwapPersistence, 0.40, emaPersistence, 0.35, returnPersistence, 0.25) : na
persistenceScore := not na(persistenceScore) ? f_clamp(persistenceScore, -100.0, 100.0) : na

float volatilityShortAtr = ta.atr(volatilityShortLength)
float volatilityLongAtr = ta.atr(volatilityLongLength)
float volatilityRatio = not na(volatilityLongAtr) and volatilityLongAtr > 0.0 ? volatilityShortAtr / volatilityLongAtr : na
float volatilityMean = ta.sma(volatilityRatio, volatilityZLookback)
float volatilityDeviation = ta.stdev(volatilityRatio, volatilityZLookback)
float volatilityZ = not na(volatilityDeviation) and volatilityDeviation > 0.0 ? (volatilityRatio - volatilityMean) / volatilityDeviation : na
float volatilityScore = not na(volatilityZ) ? f_clamp(50.0 + 50.0 * f_sat(volatilityZ / volatilityZScale), 0.0, 100.0) : na
float volatilityRelevance = not na(volatilityScore) ? f_clamp(100.0 - 1.5 * math.abs(volatilityScore - 65.0), 0.0, 100.0) : na

[htf1hScore, htf1hQuality] = request.security(regularSessionTicker, "60", f_htfMetricsConfirmed(), gaps = barmerge.gaps_off, lookahead = barmerge.lookahead_on)
[htf4hScore, htf4hQuality] = request.security(regularSessionTicker, "240", f_htfMetricsConfirmed(), gaps = barmerge.gaps_off, lookahead = barmerge.lookahead_on)
[htf1dScore, htf1dQuality] = request.security(regularSessionTicker, "1D", f_htfMetricsConfirmed(), gaps = barmerge.gaps_off, lookahead = barmerge.lookahead_on)
[htf1wScore, htf1wQuality] = request.security(regularSessionTicker, "1W", f_htfMetricsConfirmed(), gaps = barmerge.gaps_off, lookahead = barmerge.lookahead_on)
float htfNumerator = 0.0
float htfDenominator = 0.0
float htfMagnitude = 0.0
float htfQualityNumerator = 0.0
float htfQualityDenominator = 0.0
if not na(htf1hScore)
    htfNumerator := htfNumerator + 0.35 * htf1hScore
    htfDenominator := htfDenominator + 0.35
    htfMagnitude := htfMagnitude + 0.35 * math.abs(htf1hScore)
if not na(htf1hQuality)
    htfQualityNumerator := htfQualityNumerator + 0.35 * htf1hQuality
    htfQualityDenominator := htfQualityDenominator + 0.35
if not na(htf4hScore)
    htfNumerator := htfNumerator + 0.30 * htf4hScore
    htfDenominator := htfDenominator + 0.30
    htfMagnitude := htfMagnitude + 0.30 * math.abs(htf4hScore)
if not na(htf4hQuality)
    htfQualityNumerator := htfQualityNumerator + 0.30 * htf4hQuality
    htfQualityDenominator := htfQualityDenominator + 0.30
if not na(htf1dScore)
    htfNumerator := htfNumerator + 0.22 * htf1dScore
    htfDenominator := htfDenominator + 0.22
    htfMagnitude := htfMagnitude + 0.22 * math.abs(htf1dScore)
if not na(htf1dQuality)
    htfQualityNumerator := htfQualityNumerator + 0.22 * htf1dQuality
    htfQualityDenominator := htfQualityDenominator + 0.22
if not na(htf1wScore)
    htfNumerator := htfNumerator + 0.13 * htf1wScore
    htfDenominator := htfDenominator + 0.13
    htfMagnitude := htfMagnitude + 0.13 * math.abs(htf1wScore)
if not na(htf1wQuality)
    htfQualityNumerator := htfQualityNumerator + 0.13 * htf1wQuality
    htfQualityDenominator := htfQualityDenominator + 0.13
float htfScore = htfDenominator > 0.0 ? htfNumerator / htfDenominator : na
float htfCoherence = htfMagnitude > 0.0 ? f_clamp(math.abs(htfNumerator) / htfMagnitude, 0.0, 1.0) : na
float htfAverageMagnitude = htfDenominator > 0.0 ? htfMagnitude / htfDenominator : na
float htfAverageQuality = htfQualityDenominator > 0.0 ? htfQualityNumerator / htfQualityDenominator : na
float htfConvictionBase = f_weighted2(htfAverageMagnitude, 0.45, htfAverageQuality, 0.55)
float htfConviction = not na(htfConvictionBase) and not na(htfCoherence) ? f_clamp(htfConvictionBase * htfCoherence, 0.0, 100.0) : na
float htfTacticalScore = f_weighted2(htf1hScore, 0.55, htf4hScore, 0.45)
float htfMacroScore = f_weighted2(htf1dScore, 0.65, htf1wScore, 0.35)
string htfBreadthText = f_htfBreadthText(htf1hScore, htf4hScore, htf1dScore, htf1wScore, htfDirectionalThreshold)

float localWeightScale = includeHigherTimeframes ? 0.85 : 1.0
float trendWeight = 0.26 * localWeightScale
float structureWeight = 0.24 * localWeightScale
float momentumWeight = 0.21 * localWeightScale
float participationWeight = 0.16 * localWeightScale
float persistenceWeight = 0.13 * localWeightScale
float htfWeight = includeHigherTimeframes ? 0.15 : 0.0
float contextNumerator = 0.0
float contextDenominator = 0.0
float contextMagnitude = 0.0
if not na(trendScore)
    contextNumerator := contextNumerator + trendWeight * trendScore
    contextDenominator := contextDenominator + trendWeight
    contextMagnitude := contextMagnitude + trendWeight * math.abs(trendScore)
if not na(structureScore)
    contextNumerator := contextNumerator + structureWeight * structureScore
    contextDenominator := contextDenominator + structureWeight
    contextMagnitude := contextMagnitude + structureWeight * math.abs(structureScore)
if not na(momentumScore)
    contextNumerator := contextNumerator + momentumWeight * momentumScore
    contextDenominator := contextDenominator + momentumWeight
    contextMagnitude := contextMagnitude + momentumWeight * math.abs(momentumScore)
if not na(participationScore)
    contextNumerator := contextNumerator + participationWeight * participationScore
    contextDenominator := contextDenominator + participationWeight
    contextMagnitude := contextMagnitude + participationWeight * math.abs(participationScore)
if not na(persistenceScore)
    contextNumerator := contextNumerator + persistenceWeight * persistenceScore
    contextDenominator := contextDenominator + persistenceWeight
    contextMagnitude := contextMagnitude + persistenceWeight * math.abs(persistenceScore)
if htfWeight > 0.0 and not na(htfScore)
    contextNumerator := contextNumerator + htfWeight * htfScore
    contextDenominator := contextDenominator + htfWeight
    contextMagnitude := contextMagnitude + htfWeight * math.abs(htfScore)
float contextDirection = contextDenominator > 0.0 ? f_clamp(contextNumerator / contextDenominator, -100.0, 100.0) : na
float contextCoherence = contextMagnitude > 0.0 ? f_clamp(math.abs(contextNumerator) / contextMagnitude, 0.0, 1.0) : 0.0
float contextAverageMagnitude = contextDenominator > 0.0 ? contextMagnitude / contextDenominator : 0.0
float agreementScore = f_clamp(contextAverageMagnitude * contextCoherence, 0.0, 100.0)
float contextConfidence = f_weighted4(agreementScore, 0.45, participationQuality, 0.30, volatilityRelevance, 0.15, not na(persistenceScore) ? math.abs(persistenceScore) : na, 0.10)
contextConfidence := not na(contextConfidence) ? f_clamp(contextConfidence, 0.0, 100.0) : na

var int regimeState = REGIME_BALANCED
if barstate.isconfirmed and not na(contextDirection)
    regimeState := f_updateRegime(regimeState, contextDirection)
string regimeText = f_regimeText(regimeState)
color regimeColor = f_regimeColor(regimeState)
string levelsBiasText = f_levelsBias(contextDirection, contextConfidence)
float zonePreference = f_zonePreference(contextDirection, contextConfidence)

int legacyVwapVote = not inRth or na(sessionVwap) ? 0 : close > sessionVwap ? 1 : close < sessionVwap ? -1 : 0
int legacyRsiVote = na(rsiValue) ? 0 : rsiValue >= 55.0 ? 1 : rsiValue <= 45.0 ? -1 : 0
int legacyEmaVote = fastEma > slowEma ? 1 : fastEma < slowEma ? -1 : 0
int legacyOrVote = not openingRangeFinalized or openingRangeTimeframeWarning ? 0 : close > openingRangeHigh ? 1 : close < openingRangeLow ? -1 : 0
int legacyParticipationVote = na(barRvol) or barRvol < highRvolThreshold ? 0 : close > open ? 1 : close < open ? -1 : 0
int legacyScore = legacyVwapVote + legacyRsiVote + legacyEmaVote + legacyOrVote + legacyParticipationVote

float scaledRvol = na(barRvol) ? na : math.min(barRvol / rvolDisplayCap, 1.0) * 100.0
color rvolColumnColor = na(participationScore) ? color.new(neutralColor, 88) : participationScore >= 0.0 ? color.new(bullishColor, 82) : color.new(bearishColor, 82)
plot(showRvol ? scaledRvol : na, "RVOL normalized", color = rvolColumnColor, style = plot.style_columns, linewidth = 1, histbase = 0)
zeroPlot = plot(0.0, "Context Fill Baseline", color = color.new(neutralColor, 100), linewidth = 1)
contextPlot = plot(showContextLine ? contextDirection : na, "Context Direction", color = regimeColor, linewidth = 3)
fill(contextPlot, zeroPlot, color = showContextLine ? color.new(regimeColor, 91) : na, title = "Context fill")
plot(showConfidenceLine ? contextConfidence : na, "Confidence Line", color = color.new(secondaryColor, 15), linewidth = 1)
plot(showRsiMomentum ? rsiMomentumScore : na, "RSI Momentum", color = color.new(brandColor, 25), linewidth = 1)
plot(showHtfComposite ? htfScore : na, "HTF Composite", color = color.new(#FBBF24, 20), linewidth = 2)
plot(showLegacyComparison ? legacyScore * 20.0 : na, "Legacy v0.1 Score x20", color = color.new(warningColor, 30), linewidth = 1)

hline(100.0, "Maximum", color = color.new(neutralColor, 100))
hline(75.0, "+75 Strong Bull", color = color.new(bullishColor, 70), linestyle = hline.style_dotted)
hline(45.0, "+45 Bull", color = color.new(bullishColor, 78), linestyle = hline.style_dotted)
hline(20.0, "+20 Bull Lean", color = color.new(bullishColor, 88), linestyle = hline.style_dotted)
hline(0.0, "Balanced", color = color.new(neutralColor, 55), linestyle = hline.style_dashed)
hline(-20.0, "-20 Bear Lean", color = color.new(bearishColor, 88), linestyle = hline.style_dotted)
hline(-45.0, "-45 Bear", color = color.new(bearishColor, 78), linestyle = hline.style_dotted)
hline(-75.0, "-75 Strong Bear", color = color.new(bearishColor, 70), linestyle = hline.style_dotted)
hline(-100.0, "Minimum", color = color.new(neutralColor, 100))

bgcolor(showRegimeBackground ? color.new(regimeColor, regimeBackgroundTransparency) : na, title = "Context regime background")

plot(contextDirection, "Context Score", display = showResearchValues ? display.data_window : display.none)
plot(contextConfidence, "Context Confidence", display = showResearchValues ? display.data_window : display.none)
plot(agreementScore, "Agreement Score", display = showResearchValues ? display.data_window : display.none)
plot(contextCoherence * 100.0, "Directional Coherence", display = showResearchValues ? display.data_window : display.none)
plot(trendScore, "Trend Score", display = showResearchValues ? display.data_window : display.none)
plot(structureScore, "Structure Score", display = showResearchValues ? display.data_window : display.none)
plot(momentumScore, "Momentum Score", display = showResearchValues ? display.data_window : display.none)
plot(participationScore, "Participation Score", display = showResearchValues ? display.data_window : display.none)
plot(persistenceScore, "Persistence Score", display = showResearchValues ? display.data_window : display.none)
plot(volatilityScore, "Volatility Score", display = showResearchValues ? display.data_window : display.none)
plot(barRvol, "Bar RVOL", display = showResearchValues ? display.data_window : display.none)
plot(cumulativeRvol, "Cumulative RVOL", display = showResearchValues ? display.data_window : display.none)
plot(htfScore, "HTF Score", display = showResearchValues ? display.data_window : display.none)
plot(htfConviction, "HTF Conviction", display = showResearchValues ? display.data_window : display.none)
plot(htfCoherence * 100.0, "HTF Directional Coherence", display = showResearchValues ? display.data_window : display.none)
plot(htfTacticalScore, "HTF Tactical Score", display = showResearchValues ? display.data_window : display.none)
plot(htfMacroScore, "HTF Macro Score", display = showResearchValues ? display.data_window : display.none)
plot(htf1hScore, "1H Trend Score", display = showResearchValues ? display.data_window : display.none)
plot(htf4hScore, "4H Trend Score", display = showResearchValues ? display.data_window : display.none)
plot(htf1dScore, "1D Trend Score", display = showResearchValues ? display.data_window : display.none)
plot(htf1wScore, "1W Trend Score", display = showResearchValues ? display.data_window : display.none)
plot(htf1hQuality, "1H Trend Quality", display = showResearchValues ? display.data_window : display.none)
plot(htf4hQuality, "4H Trend Quality", display = showResearchValues ? display.data_window : display.none)
plot(htf1dQuality, "1D Trend Quality", display = showResearchValues ? display.data_window : display.none)
plot(htf1wQuality, "1W Trend Quality", display = showResearchValues ? display.data_window : display.none)
plot(zonePreference, "Zone Preference", display = showResearchValues ? display.data_window : display.none)
plot(legacyScore, "Legacy Context Score", display = showResearchValues ? display.data_window : display.none)

var table contextTable = table.new(
     position.top_right,
     3,
     10,
     bgcolor = color.new(panelColor, 10),
     frame_color = color.new(borderColor, 35),
     frame_width = 1,
     border_color = color.new(borderColor, 72),
     border_width = 1)

if barstate.isfirst
    table.merge_cells(contextTable, 0, 0, 2, 0)

if barstate.islast
    color invisible = color.new(panelColor, 100)
    color rowBackground = showDashboard ? color.new(panelColor, 12) : invisible
    color alternateBackground = showDashboard ? color.new(panelColor, 5) : invisible
    color headerBackground = showDashboard ? color.new(brandColor, 18) : invisible
    color hiddenText = color.new(textColor, 100)
    if showDashboard
        f_tableCell(contextTable, 0, 0, "Meridian -- Options Context", textColor, headerBackground, "Left")
        f_tableCell(contextTable, 0, 1, "Direction", neutralColor, rowBackground, "Left")
        f_tableCell(contextTable, 1, 1, f_scoreText(contextDirection), f_scoreColor(contextDirection), rowBackground, "Right")
        f_tableCell(contextTable, 2, 1, regimeText, regimeColor, rowBackground, "Right")
        f_tableCell(contextTable, 0, 2, "Confidence", neutralColor, alternateBackground, "Left")
        f_tableCell(contextTable, 1, 2, na(contextConfidence) ? "n/a" : str.tostring(math.round(contextConfidence)), secondaryColor, alternateBackground, "Right")
        f_tableCell(contextTable, 2, 2, f_confidenceText(contextConfidence), secondaryColor, alternateBackground, "Right")
        f_tableCell(contextTable, 0, 3, "Levels Bias", neutralColor, rowBackground, "Left")
        f_tableCell(contextTable, 1, 3, "", neutralColor, rowBackground, "Right")
        f_tableCell(contextTable, 2, 3, levelsBiasText, zonePreference > 0.0 ? bullishColor : zonePreference < 0.0 ? bearishColor : neutralColor, rowBackground, "Right")
        f_tableCell(contextTable, 0, 4, "HTF", neutralColor, alternateBackground, "Left")
        f_tableCell(contextTable, 1, 4, f_scoreText(htfScore), f_scoreColor(htfScore), alternateBackground, "Right")
        f_tableCell(contextTable, 2, 4, htfBreadthText, f_scoreColor(htfScore), alternateBackground, "Right")
        if dashboardMode == "Detailed"
            f_tableCell(contextTable, 0, 5, "Trend", neutralColor, rowBackground, "Left")
            f_tableCell(contextTable, 1, 5, f_scoreText(trendScore), f_scoreColor(trendScore), rowBackground, "Right")
            f_tableCell(contextTable, 2, 5, f_componentText(trendScore), f_scoreColor(trendScore), rowBackground, "Right")
            f_tableCell(contextTable, 0, 6, "Structure", neutralColor, alternateBackground, "Left")
            f_tableCell(contextTable, 1, 6, f_scoreText(structureScore), f_scoreColor(structureScore), alternateBackground, "Right")
            f_tableCell(contextTable, 2, 6, f_componentText(structureScore), f_scoreColor(structureScore), alternateBackground, "Right")
            f_tableCell(contextTable, 0, 7, "Participation", neutralColor, rowBackground, "Left")
            f_tableCell(contextTable, 1, 7, f_ratioText(barRvol), informationColor, rowBackground, "Right")
            f_tableCell(contextTable, 2, 7, f_componentText(participationScore), f_scoreColor(participationScore), rowBackground, "Right")
            f_tableCell(contextTable, 0, 8, "Volatility", neutralColor, alternateBackground, "Left")
            f_tableCell(contextTable, 1, 8, na(volatilityScore) ? "n/a" : str.tostring(math.round(volatilityScore)), warningColor, alternateBackground, "Right")
            f_tableCell(contextTable, 2, 8, f_volatilityText(volatilityScore), volatilityScore >= 60.0 ? warningColor : neutralColor, alternateBackground, "Right")
            f_tableCell(contextTable, 0, 9, "Data", neutralColor, rowBackground, "Left")
            f_tableCell(contextTable, 1, 9, inRth ? "RTH" : "OUTSIDE", inRth ? bullishColor : neutralColor, rowBackground, "Right")
            f_tableCell(contextTable, 2, 9, dataState, staleData ? warningColor : dataState == "LIVE" ? informationColor : neutralColor, rowBackground, "Right")
        else
            for row = 5 to 8
                f_tableCell(contextTable, 0, row, "", hiddenText, invisible, "Left")
                f_tableCell(contextTable, 1, row, "", hiddenText, invisible, "Right")
                f_tableCell(contextTable, 2, row, "", hiddenText, invisible, "Right")
            f_tableCell(contextTable, 0, 9, "Data", neutralColor, rowBackground, "Left")
            f_tableCell(contextTable, 1, 9, inRth ? "RTH" : "OUTSIDE", inRth ? bullishColor : neutralColor, rowBackground, "Right")
            f_tableCell(contextTable, 2, 9, dataState, staleData ? warningColor : dataState == "LIVE" ? informationColor : neutralColor, rowBackground, "Right")
    else
        for row = 0 to 9
            for col = 0 to 2
                table.cell(contextTable, col, row, "", bgcolor = invisible, text_color = hiddenText)

bool confirmedBar = barstate.isconfirmed
bool enteredStrongBull = enableAlerts and confirmedBar and regimeState == REGIME_STRONG_BULL and regimeState[1] != REGIME_STRONG_BULL
bool enteredStrongBear = enableAlerts and confirmedBar and regimeState == REGIME_STRONG_BEAR and regimeState[1] != REGIME_STRONG_BEAR
bool enteredBullish = enableAlerts and confirmedBar and regimeState > REGIME_BALANCED and regimeState[1] <= REGIME_BALANCED
bool enteredBearish = enableAlerts and confirmedBar and regimeState < REGIME_BALANCED and regimeState[1] >= REGIME_BALANCED
bool highConfidenceDirectionalRaw = not na(contextDirection) and not na(contextConfidence) and math.abs(contextDirection) >= highConfidenceDirectionThreshold and contextConfidence >= highConfidenceThreshold
bool highConfidenceDirectional = enableAlerts and confirmedBar and highConfidenceDirectionalRaw and not highConfidenceDirectionalRaw[1]
bool alignedParticipationRaw = not na(barRvol) and barRvol >= highRvolThreshold and not na(participationScore) and not na(contextDirection) and math.abs(contextDirection) >= 20.0 and f_sign(participationScore) == f_sign(contextDirection)
bool alignedParticipationSurge = enableAlerts and confirmedBar and alignedParticipationRaw and not alignedParticipationRaw[1]
var int lastStrongBullBar = na
var int lastStrongBearBar = na
if enteredStrongBull
    lastStrongBullBar := bar_index
if enteredStrongBear
    lastStrongBearBar := bar_index
bool bullContextFailure = enableAlerts and confirmedBar and not na(lastStrongBullBar) and bar_index - lastStrongBullBar <= contextFailureWindow and contextDirection < 20.0 and contextDirection[1] >= 20.0
bool bearContextFailure = enableAlerts and confirmedBar and not na(lastStrongBearBar) and bar_index - lastStrongBearBar <= contextFailureWindow and contextDirection > -20.0 and contextDirection[1] <= -20.0

alertcondition(enteredStrongBull, "Strong Bull Context", "{{ticker}} entered Strong Bull context. Context: {{plot(\"Context Score\")}}.")
alertcondition(enteredStrongBear, "Strong Bear Context", "{{ticker}} entered Strong Bear context. Context: {{plot(\"Context Score\")}}.")
alertcondition(enteredBullish, "Bullish Context Transition", "{{ticker}} transitioned into bullish context. Context: {{plot(\"Context Score\")}}.")
alertcondition(enteredBearish, "Bearish Context Transition", "{{ticker}} transitioned into bearish context. Context: {{plot(\"Context Score\")}}.")
alertcondition(highConfidenceDirectional, "High-Confidence Directional Context", "{{ticker}} reached a high-confidence directional context. Context: {{plot(\"Context Score\")}} | Confidence: {{plot(\"Context Confidence\")}}.")
alertcondition(alignedParticipationSurge, "Aligned RVOL Surge", "{{ticker}} printed a high-RVOL bar aligned with Context Direction. RVOL: {{plot(\"Bar RVOL\")}}.")
alertcondition(bullContextFailure, "Bull Context Failure", "{{ticker}} lost a recent Strong Bull context within the configured failure window.")
alertcondition(bearContextFailure, "Bear Context Failure", "{{ticker}} lost a recent Strong Bear context within the configured failure window.")