🎁

//@version=6
indicator("Ocs Ai Trader", shorttitle = "Ocs Ai Trader", overlay = true, max_bars_back = 500, max_lines_count = 500, max_labels_count = 500, max_boxes_count = 500)

const string GRP_GEN = "General Settings"
const string GRP_TSP = "Time Series Processing Layer"
const string GRP_SPL = "Signal Processing Layer"
const string GRP_FLT = "Filters"
const string GRP_ML  = "Machine Learning (KNN)"
const string GRP_GAU = "Gaussian Structure"
const string GRP_VTE = "Virtual Trade Emulator"
const string GRP_SR  = "Support & Resistance"
const string GRP_DSP = "Display"
const string GRP_TBL = "Backtesting Compiler (Stats Table)"
const string GRP_COL = "Colors"

const string TXT_TINY   = "Tiny"
const string TXT_SMALL  = "Small"
const string TXT_NORMAL = "Normal"
const string TXT_LARGE  = "Large"
const string HIT_BOOKED = "✓Booked"
const string HIT_PLAIN  = "Hit"

i_confirmBar  = input.bool(true, "Show Trade Signal only when bar is confirmed", group = GRP_GEN, tooltip = "When enabled, signals are produced on bar close only (no repaint).")
i_isPremium   = input.bool(true, "Is your TradingView Subscription Premium and above ?", group = GRP_GEN, tooltip = "Premium plans can read second based intrabar data for a precise Volume Delta. If your plan is not Premium, UNCHECK this option.")
i_ltf         = input.timeframe("1", "Lower Timeframe to use if TradingView Subscription is not premium !", group = GRP_GEN, tooltip = "Lower timeframe used for the Volume Delta when the Premium option is unchecked. Ignored if it is not lower than the chart timeframe.")
i_textSize    = input.string(TXT_SMALL, "Text Size :", options = [TXT_TINY, TXT_SMALL, TXT_NORMAL, TXT_LARGE], group = GRP_GEN)
i_sensitivity = input.float(1.0, "Signal Sensitivity", minval = 0.1, maxval = 5.0, step = 0.1, group = GRP_GEN, tooltip = "Exponential Probability Squeeze factor. Higher values produce more decisive probabilities and more signals.")
i_alertPass   = input.string("", "Alert Passcode", group = GRP_GEN, tooltip = "Optional passcode appended to every alert() message (useful for webhook authentication).")

i_dcMin       = input.int(8, "Dominant Cycle Period  Min", minval = 4, maxval = 50, group = GRP_TSP, inline = "dc")
i_dcMax       = input.int(48, "Max", minval = 10, maxval = 100, group = GRP_TSP, inline = "dc", tooltip = "Bounds of the Ehlers Homodyne Dominant Cycle. The detected cycle is used as the dynamic length of the adaptive calculations.")
i_amaFast     = input.int(2, "Adaptive Moving Average  Fast", minval = 1, group = GRP_TSP, inline = "ama")
i_amaSlow     = input.int(30, "Slow", minval = 2, group = GRP_TSP, inline = "ama")
i_stFactor    = input.float(3.0, "Supertrend  Factor", minval = 0.5, step = 0.1, group = GRP_TSP, inline = "st")
i_stAtrLen    = input.int(10, "ATR", minval = 1, group = GRP_TSP, inline = "st")
i_stochLen    = input.int(14, "Relative Strength Stochastics  Length", minval = 2, group = GRP_TSP, inline = "sto")
i_stochSmooth = input.int(3, "Smooth", minval = 1, group = GRP_TSP, inline = "sto")
i_cvdLen      = input.int(21, "Cumulative Volume Delta Length", minval = 2, group = GRP_TSP)
i_trixLen     = input.int(14, "TRIX  Length", minval = 2, group = GRP_TSP, inline = "trix")
i_trixSig     = input.int(9, "Signal", minval = 1, group = GRP_TSP, inline = "trix")

i_conf        = input.float(0.60, "Probability Distribution Confidence", minval = 0.50, maxval = 0.95, step = 0.01, group = GRP_SPL, tooltip = "Minimum bullish probability for long intents (and maximum for short intents = 1 - value).")
i_probAvg     = input.int(3, "Probability Average Length", minval = 1, maxval = 20, group = GRP_SPL)
i_probVarMax  = input.float(0.30, "Probability Variable Filter (Max StDev)", minval = 0.01, maxval = 1.0, step = 0.01, group = GRP_SPL)
i_lmsLen      = input.int(16, "Adaptive LMS Filter  Order", minval = 2, maxval = 64, group = GRP_SPL, inline = "lms")
i_lmsMu       = input.float(0.05, "Step (mu)", minval = 0.001, maxval = 1.0, step = 0.001, group = GRP_SPL, inline = "lms")
i_fisherTol   = input.float(0.25, "Fisher Transform Tolerance", minval = 0.0, maxval = 3.0, step = 0.05, group = GRP_SPL)
i_qrLen       = input.int(20, "Quadratic Regression / Trimean Length", minval = 5, maxval = 200, group = GRP_SPL)
i_useCvdDiv   = input.bool(true, "CVD Divergence Signal Structure", group = GRP_SPL, inline = "div")
i_divLen      = input.int(14, "Lookback", minval = 3, group = GRP_SPL, inline = "div")

i_useRegime     = input.bool(true, "Regime Filter", group = GRP_FLT, inline = "reg")
i_regimeThr     = input.float(-0.1, "Threshold", minval = -10.0, maxval = 10.0, step = 0.1, group = GRP_FLT, inline = "reg")
i_useVolFilter  = input.bool(true, "Volatility Probability Filter", group = GRP_FLT)
i_useAtrImpulse = input.bool(true, "Impulsive ATR Filter", group = GRP_FLT, inline = "imp")
i_atrImpMult    = input.float(0.75, "Multiplier", minval = 0.1, step = 0.05, group = GRP_FLT, inline = "imp")
i_useVolDeriv   = input.bool(true, "Volatility Derivative Filter (Elder)", group = GRP_FLT, inline = "vd")
i_volDerivTol   = input.float(0.15, "Tolerance", minval = 0.0, step = 0.01, group = GRP_FLT, inline = "vd")
i_useTrix       = input.bool(true, "TRIX Strength / Weakness Analyser", group = GRP_FLT)
i_useCvd        = input.bool(true, "Cumulative Volume Delta Regime Filter", group = GRP_FLT)
i_useMarubozu   = input.bool(true, "Marubozu Abnormality Detection", group = GRP_FLT, inline = "maru")
i_maruBody      = input.float(0.85, "Body Ratio", minval = 0.5, maxval = 1.0, step = 0.01, group = GRP_FLT, inline = "maru")

i_useKnn      = input.bool(true, "K-Nearest Neighbour Classification", group = GRP_ML)
i_knnK        = input.int(8, "Neighbours (K)", minval = 1, maxval = 50, group = GRP_ML)
i_knnWindow   = input.int(1000, "Training Window (Samples)", minval = 50, maxval = 5000, group = GRP_ML)
i_knnStride   = input.int(4, "Sampling Stride", minval = 1, maxval = 20, group = GRP_ML)
i_knnHorizon  = input.int(4, "Prediction Horizon (Bars)", minval = 1, maxval = 50, group = GRP_ML)
i_knnWeight   = input.float(0.50, "KNN Weight in Final Probability", minval = 0.0, maxval = 1.0, step = 0.05, group = GRP_ML)

i_gaussAdaptive = input.bool(true, "Adaptive Length (Dominant Cycle)", group = GRP_GAU)
i_gaussLen      = input.int(21, "Gaussian Length", minval = 2, group = GRP_GAU, inline = "gau")
i_gaussPoles    = input.int(4, "Poles", minval = 1, maxval = 4, group = GRP_GAU, inline = "gau")
i_gaussMult     = input.float(1.5, "Band ATR Multiplier", minval = 0.1, step = 0.1, group = GRP_GAU)
i_baseLen       = input.int(144, "Gaussian Baseline Length", minval = 10, group = GRP_GAU)

i_atrLenTrade  = input.int(14, "ATR Length", minval = 1, group = GRP_VTE)
i_tMult        = input.float(1.0, "Target Step (ATR)", minval = 0.1, step = 0.05, group = GRP_VTE)
i_stopMult     = input.float(2.25, "Stop Distance (ATR)", minval = 0.1, step = 0.05, group = GRP_VTE)
i_showT45      = input.bool(false, "Dynamic Target 4 & Target 5", group = GRP_VTE)
i_recoveryBars = input.int(10, "Recovery Window (Bars)", minval = 0, maxval = 100, group = GRP_VTE, tooltip = "After the stop is touched, a trade that still reaches T1 within this window is counted as a T1 Win (With Recoveries).")
i_labelOffset  = input.int(4, "Levels Label Offset (Bars)", minval = 1, maxval = 100, group = GRP_VTE, tooltip = "Distance, in bars, between the last bar and the level labels.")
i_hitStyle     = input.string(HIT_BOOKED, "Target Hit Text", options = [HIT_BOOKED, HIT_PLAIN], group = GRP_VTE)

i_showVolSR   = input.bool(true, "Volume Float Support & Resistance", group = GRP_SR)
i_srPivotLen  = input.int(10, "Pivot Length", minval = 2, group = GRP_SR, inline = "vsr")
i_srVolMult   = input.float(1.2, "Volume Multiplier", minval = 0.1, step = 0.1, group = GRP_SR, inline = "vsr")
i_srMaxBars   = input.int(60, "Max Extension (Bars)", minval = 5, maxval = 500, group = GRP_SR)
i_showPivots  = input.bool(true, "Pivotal Points & Lines", group = GRP_SR)
i_pivLen      = input.int(20, "Pivot Length", minval = 2, group = GRP_SR, inline = "piv")
i_pivMaxBars  = input.int(50, "Max Extension (Bars)", minval = 5, maxval = 500, group = GRP_SR, inline = "piv")
i_maxZones    = input.int(20, "Max Zones Kept On Chart", minval = 2, maxval = 100, group = GRP_SR)

i_showBand     = input.bool(true, "Gaussian Trend Band", group = GRP_DSP, inline = "band")
i_bandWidth    = input.int(10, "Thickness", minval = 1, maxval = 30, group = GRP_DSP, inline = "band")
i_showBaseline = input.bool(true, "Gaussian Baseline", group = GRP_DSP)
i_showDots     = input.bool(true, "Probability Triangles", group = GRP_DSP)
i_showBarColor = input.bool(true, "Colour Signal Candles", group = GRP_DSP)
i_showHistory  = input.bool(false, "Historical Buy Sell Positions by Algo", group = GRP_DSP)
i_showLevels   = input.bool(true, "Targets, Strength & Stop Levels", group = GRP_DSP)

i_showTable = input.bool(true, "Show Stats Table", group = GRP_TBL)
i_tablePos  = input.string("Bottom Left", "Position", options = ["Top Left", "Top Center", "Top Right", "Middle Left", "Middle Right", "Bottom Left", "Bottom Center", "Bottom Right"], group = GRP_TBL, inline = "tbl")
i_tableSize = input.string(TXT_TINY, "Size", options = [TXT_TINY, TXT_SMALL, TXT_NORMAL, TXT_LARGE], group = GRP_TBL, inline = "tbl")

i_colBandBull  = input.color(color.new(#4caf50, 82), "Trend Band  Bull", group = GRP_COL, inline = "c1")
i_colBandBear  = input.color(color.new(#f23645, 82), "Bear", group = GRP_COL, inline = "c1")
i_colDotBull   = input.color(color.new(#81c784, 30), "Triangles  Bull", group = GRP_COL, inline = "c2")
i_colDotBear   = input.color(color.new(#ef9a9a, 20), "Bear", group = GRP_COL, inline = "c2")
i_colDotNeut   = input.color(color.new(#9598a1, 25), "Triangles  Neutral", group = GRP_COL, inline = "c2b")
i_colDotWarn   = input.color(color.new(#ffb74d, 10), "Caution", group = GRP_COL, inline = "c2b")
i_colBaseline  = input.color(color.new(#a5d6a7, 40), "Gaussian Baseline", group = GRP_COL)
i_colBuyBar    = input.color(#7ee8c4, "Signal Candle  Buy", group = GRP_COL, inline = "c3")
i_colSellBar   = input.color(#f4a6d7, "Sell", group = GRP_COL, inline = "c3")
i_colBuyTgt    = input.color(#2962ff, "Buy Targets  Text", group = GRP_COL, inline = "b1")
i_colBuyTgtBg  = input.color(color.new(#2962ff, 90), "Bg", group = GRP_COL, inline = "b1")
i_colBuyTgtLn  = input.color(color.new(#4caf50, 30), "Line", group = GRP_COL, inline = "b1")
i_colBuyEnt    = input.color(#089981, "Buy Strength  Text", group = GRP_COL, inline = "b2")
i_colBuyEntBg  = input.color(color.new(#4caf50, 88), "Bg", group = GRP_COL, inline = "b2")
i_colBuyEntLn  = input.color(#2e9d4a, "Line", group = GRP_COL, inline = "b2")
i_colBuyStp    = input.color(#f23645, "Buy Stop  Text", group = GRP_COL, inline = "b3")
i_colBuyStpBg  = input.color(color.new(#f7525f, 88), "Bg", group = GRP_COL, inline = "b3")
i_colBuyStpLn  = input.color(color.new(#f23645, 30), "Line", group = GRP_COL, inline = "b3")
i_colSellTgt   = input.color(#f23645, "Sell Targets  Text", group = GRP_COL, inline = "s1")
i_colSellTgtBg = input.color(color.new(#f23645, 90), "Bg", group = GRP_COL, inline = "s1")
i_colSellTgtLn = input.color(color.new(#f23645, 30), "Line", group = GRP_COL, inline = "s1")
i_colSellEnt   = input.color(#f23645, "Sell Weakness  Text", group = GRP_COL, inline = "s2")
i_colSellEntBg = input.color(color.new(#ff9800, 85), "Bg", group = GRP_COL, inline = "s2")
i_colSellEntLn = input.color(#f23645, "Line", group = GRP_COL, inline = "s2")
i_colSellStp   = input.color(#2962ff, "Sell Stop  Text", group = GRP_COL, inline = "s3")
i_colSellStpBg = input.color(color.new(#4caf50, 88), "Bg", group = GRP_COL, inline = "s3")
i_colSellStpLn = input.color(color.new(#2962ff, 30), "Line", group = GRP_COL, inline = "s3")
i_colBrand     = input.color(#f7525f, "Brand  Text", group = GRP_COL, inline = "c7")
i_colBrandBg   = input.color(color.new(#ff9800, 88), "Background", group = GRP_COL, inline = "c7")
i_colVolZone   = input.color(color.new(#787b86, 85), "Volume Float Zones", group = GRP_COL)
i_colPivZone   = input.color(color.new(#2196f3, 85), "Pivotal Zones", group = GRP_COL)
i_colLong      = input.color(#089981, "Stats  Long", group = GRP_COL, inline = "c8")
i_colShort     = input.color(#f23645, "Short", group = GRP_COL, inline = "c8")
i_colTblBg     = input.color(#ffffff, "Table  Background", group = GRP_COL, inline = "c9")
i_colTblText   = input.color(#1e3a8a, "Text", group = GRP_COL, inline = "c9")
i_colTblBorder = input.color(#b2b5be, "Border", group = GRP_COL, inline = "c9")

const string VERSION_TXT  = "Ocs AI ML V.312"
const string BRAND_TXT    = "Ocs Ai Trader"
const string RECOVERY_TXT = "(With Recoveries)"
const float  EPS          = 1e-10
const int    WARMUP_MULT  = 2
const float  ZONE_MIN_ATR = 0.25
const float  PIV_ZONE_ATR = 0.50
const float  BRAND_GAP    = 0.60
const string ARROW_UP     = "▲ "
const string ARROW_DN     = "▼ "
const string WARN_ICON    = "⚠ "
const color  TXT_DARK     = #131722

type TradeState
    int   dir       = 0
    int   sigBar    = 0
    float trigger   = na
    float stop      = na
    float t1        = na
    float t2        = na
    float t3        = na
    float t4        = na
    float t5        = na
    bool  triggered = false
    bool  t1Hit     = false
    bool  t2Hit     = false
    bool  t3Hit     = false
    bool  t4Hit     = false
    bool  t5Hit     = false
    bool  stopHit   = false
    int   stopBar   = na
    bool  closed    = false

type Stats
    int longTotal  = 0
    int shortTotal = 0
    int longWins   = 0
    int shortWins  = 0

type Zone
    box   bx
    float top
    float bottom
    int   dir
    int   startBar
    bool  active = true

f_tanh(float x) =>
    float xc = math.max(math.min(nz(x), 20.0), -20.0)
    float e2 = math.exp(2.0 * xc)
    (e2 - 1.0) / (e2 + 1.0)

f_smooth(float src, float alpha) =>
    float s = na
    s := na(s[1]) ? src : alpha * src + (1.0 - alpha) * s[1]
    s

f_rma(float src, float len) =>
    f_smooth(src, 1.0 / math.max(len, 1.0))

f_rsi(float src, int len) =>
    float chg = src - nz(src[1], src)
    float up  = f_rma(math.max(chg, 0.0), len)
    float dn  = f_rma(math.max(-chg, 0.0), len)
    dn <= EPS ? 100.0 : up <= EPS ? 0.0 : 100.0 - 100.0 / (1.0 + up / dn)

f_normalize(float src, float lo, float hi) =>
    var float hMin = 1e10
    var float hMax = -1e10
    hMin := math.min(nz(src, hMin), hMin)
    hMax := math.max(nz(src, hMax), hMax)
    lo + (hi - lo) * (nz(src) - hMin) / math.max(hMax - hMin, EPS)

f_vwma(float src, float vol, int len) =>
    float den = math.sum(vol, len)
    den > EPS ? math.sum(src * vol, len) / den : src

f_kama(float src, int len, int fastLen, int slowLen) =>
    float change = math.abs(src - nz(src[len], src))
    float noise  = math.sum(math.abs(src - nz(src[1], src)), len)
    float er     = noise > EPS ? change / noise : 0.0
    float fastSc = 2.0 / (fastLen + 1.0)
    float slowSc = 2.0 / (slowLen + 1.0)
    float sc     = math.pow(er * (fastSc - slowSc) + slowSc, 2)
    float k = na
    k := na(k[1]) ? src : k[1] + sc * (src - k[1])
    k

f_dominantCycle(float src, int minP, int maxP) =>
    float period  = 0.0
    float smoothP = 0.0
    float i2      = 0.0
    float q2      = 0.0
    float re      = 0.0
    float im      = 0.0
    float prevPeriod = nz(period[1], 15.0)
    float smooth  = (4.0 * src + 3.0 * nz(src[1], src) + 2.0 * nz(src[2], src) + nz(src[3], src)) / 10.0
    float adj     = 0.075 * prevPeriod + 0.54
    float detrender = (0.0962 * smooth + 0.5769 * nz(smooth[2]) - 0.5769 * nz(smooth[4]) - 0.0962 * nz(smooth[6])) * adj
    float q1      = (0.0962 * detrender + 0.5769 * nz(detrender[2]) - 0.5769 * nz(detrender[4]) - 0.0962 * nz(detrender[6])) * adj
    float i1      = nz(detrender[3])
    float jI      = (0.0962 * i1 + 0.5769 * nz(i1[2]) - 0.5769 * nz(i1[4]) - 0.0962 * nz(i1[6])) * adj
    float jQ      = (0.0962 * q1 + 0.5769 * nz(q1[2]) - 0.5769 * nz(q1[4]) - 0.0962 * nz(q1[6])) * adj
    i2 := 0.2 * (i1 - jQ) + 0.8 * nz(i2[1])
    q2 := 0.2 * (q1 + jI) + 0.8 * nz(q2[1])
    re := 0.2 * (i2 * nz(i2[1]) + q2 * nz(q2[1])) + 0.8 * nz(re[1])
    im := 0.2 * (i2 * nz(q2[1]) - q2 * nz(i2[1])) + 0.8 * nz(im[1])
    float p = im != 0.0 and re != 0.0 ? 2.0 * math.pi / math.atan(im / re) : prevPeriod
    p := math.min(p, 1.5 * prevPeriod)
    p := math.max(p, 0.67 * prevPeriod)
    p := math.max(math.min(p, maxP), minP)
    period  := 0.2 * p + 0.8 * prevPeriod
    smoothP := 0.33 * period + 0.67 * nz(smoothP[1], period)
    smoothP

f_gaussian(float src, float len, int poles) =>
    float beta  = (1.0 - math.cos(2.0 * math.pi / math.max(len, 2.0))) / (math.pow(2.0, 1.0 / poles) - 1.0)
    float alpha = -beta + math.sqrt(beta * beta + 2.0 * beta)
    float x     = 1.0 - alpha
    float f  = na
    float f1 = nz(f[1], src)
    float f2 = nz(f[2], f1)
    float f3 = nz(f[3], f2)
    float f4 = nz(f[4], f3)
    f := switch poles
        1 => alpha * src + x * f1
        2 => math.pow(alpha, 2) * src + 2.0 * x * f1 - math.pow(x, 2) * f2
        3 => math.pow(alpha, 3) * src + 3.0 * x * f1 - 3.0 * math.pow(x, 2) * f2 + math.pow(x, 3) * f3
        => math.pow(alpha, 4) * src + 4.0 * x * f1 - 6.0 * math.pow(x, 2) * f2 + 4.0 * math.pow(x, 3) * f3 - math.pow(x, 4) * f4
    f

f_trailBand(float base, float atrv, float mult, float src) =>
    float a      = nz(atrv, high - low)
    float upRaw  = base - mult * a
    float dnRaw  = base + mult * a
    float upT    = na
    float dnT    = na
    int   dir    = 1
    float upPrev = nz(upT[1], upRaw)
    float dnPrev = nz(dnT[1], dnRaw)
    upT := nz(src[1], src) > upPrev ? math.max(upRaw, upPrev) : upRaw
    dnT := nz(src[1], src) < dnPrev ? math.min(dnRaw, dnPrev) : dnRaw
    int prevDir = nz(dir[1], 1)
    dir := prevDir == -1 and src > dnPrev ? 1 : prevDir == 1 and src < upPrev ? -1 : prevDir
    float trail = dir == 1 ? upT : dnT
    [trail, dir]

f_fisher(float src, int len) =>
    float hh  = ta.highest(src, len)
    float ll  = ta.lowest(src, len)
    float rng = hh - ll
    float v   = 0.0
    v := 0.66 * (rng > EPS ? (src - ll) / rng - 0.5 : 0.0) + 0.67 * nz(v[1])
    v := math.max(math.min(v, 0.999), -0.999)
    float fish = 0.0
    fish := 0.5 * math.log((1.0 + v) / (1.0 - v)) + 0.5 * nz(fish[1])
    fish

f_lmsPredict(float src, simple int len, float mu) =>
    var array<float> w = array.new_float(len, 0.0)
    float pred   = 0.0
    float energy = EPS
    for i = 0 to len - 1
        float xi = nz(src[i + 1])
        pred   += w.get(i) * xi
        energy += xi * xi
    float err = nz(src) - pred
    for i = 0 to len - 1
        w.set(i, w.get(i) + mu * err * nz(src[i + 1]) / energy)
    float nxt = 0.0
    for i = 0 to len - 1
        nxt += w.get(i) * nz(src[i])
    nxt

f_quadReg(float src, simple int n) =>
    float sx   = 0.0
    float sx2  = 0.0
    float sx3  = 0.0
    float sx4  = 0.0
    float sy   = 0.0
    float sxy  = 0.0
    float sx2y = 0.0
    for i = 0 to n - 1
        float x = n - 1.0 - i
        float y = nz(src[i], src)
        sx   += x
        sx2  += x * x
        sx3  += x * x * x
        sx4  += x * x * x * x
        sy   += y
        sxy  += x * y
        sx2y += x * x * y
    float det = n * (sx2 * sx4 - sx3 * sx3) - sx * (sx * sx4 - sx3 * sx2) + sx2 * (sx * sx3 - sx2 * sx2)
    float a = src
    float b = 0.0
    float c = 0.0
    if math.abs(det) > EPS
        a := (sy * (sx2 * sx4 - sx3 * sx3) - sx * (sxy * sx4 - sx3 * sx2y) + sx2 * (sxy * sx3 - sx2 * sx2y)) / det
        b := (n * (sxy * sx4 - sx3 * sx2y) - sy * (sx * sx4 - sx3 * sx2) + sx2 * (sx * sx2y - sxy * sx2)) / det
        c := (n * (sx2 * sx2y - sxy * sx3) - sx * (sx * sx2y - sxy * sx2) + sy * (sx * sx3 - sx2 * sx2)) / det
    float xn = n - 1.0
    [a + b * xn + c * xn * xn, b + 2.0 * c * xn]

f_trimean(float src, int len) =>
    float q1 = ta.percentile_linear_interpolation(src, len, 25)
    float q2 = ta.median(src, len)
    float q3 = ta.percentile_linear_interpolation(src, len, 75)
    (q1 + 2.0 * q2 + q3) / 4.0

f_regimeFilter(float src, float threshold) =>
    float value1 = 0.0
    float value2 = 0.0
    float klmf   = 0.0
    value1 := 0.2 * (src - nz(src[1], src)) + 0.8 * nz(value1[1])
    value2 := 0.1 * (high - low) + 0.8 * nz(value2[1])
    float omega = value2 != 0.0 ? math.abs(value1 / value2) : 0.0
    float alpha = (-omega * omega + math.sqrt(math.pow(omega, 4) + 16.0 * math.pow(omega, 2))) / 8.0
    klmf := alpha * src + (1.0 - alpha) * nz(klmf[1], src)
    float absSlope = math.abs(klmf - nz(klmf[1], klmf))
    float avgSlope = nz(ta.ema(absSlope, 200))
    float normDecline = avgSlope != 0.0 ? (absSlope - avgSlope) / avgSlope : 0.0
    normDecline >= threshold

f_knnPredict(array<float> a1, array<float> a2, array<float> a3, array<float> a4, array<float> a5, array<int> lbl, float q1, float q2, float q3, float q4, float q5, int k, int stride) =>
    array<float> dists = array.new_float()
    array<int>   votes = array.new_int()
    int n = lbl.size()
    if n > 0
        for i = 0 to n - 1 by stride
            float d = math.log(1.0 + math.abs(a1.get(i) - q1)) + math.log(1.0 + math.abs(a2.get(i) - q2)) + math.log(1.0 + math.abs(a3.get(i) - q3)) + math.log(1.0 + math.abs(a4.get(i) - q4)) + math.log(1.0 + math.abs(a5.get(i) - q5))
            int  sz     = dists.size()
            bool accept = sz < k
            if not accept
                accept := d < dists.get(sz - 1)
            if accept
                int pos = 0
                while pos < sz
                    if dists.get(pos) > d
                        break
                    pos += 1
                dists.insert(pos, d)
                votes.insert(pos, lbl.get(i))
                if dists.size() > k
                    dists.pop()
                    votes.pop()
    float s = 0.0
    for v in votes
        s += v
    votes.size() > 0 ? s / votes.size() : 0.0

f_reached(int dir, float lvl) =>
    dir == 1 ? high >= lvl : low <= lvl

f_stopped(int dir, float lvl) =>
    dir == 1 ? low <= lvl : high >= lvl

f_updateZones(array<Zone> zones, int maxBars) =>
    for z in zones
        if z.active
            z.bx.set_right(bar_index)
            bool broken = z.dir == 1 ? close < z.bottom : close > z.top
            if broken or bar_index - z.startBar >= maxBars
                z.active := false
    zones.size()

f_addZone(array<Zone> zones, Zone z, int maxKeep) =>
    zones.push(z)
    if zones.size() > maxKeep
        Zone old = zones.shift()
        old.bx.delete()
    zones.size()

f_drawLevel(array<line> lns, array<label> lbs, int x1, int x2, float y, color lineCol, string lineStyle, int width, string txt, color txtCol, color bgCol, string txtSize, bool bold) =>
    lns.push(line.new(x1, y, x2, y, xloc = xloc.bar_index, color = lineCol, style = lineStyle, width = width))
    label lb = label.new(x2, y, txt, xloc = xloc.bar_index, color = bgCol, style = label.style_label_left, textcolor = txtCol, size = txtSize, textalign = text.align_left)
    if bold
        lb.set_text_formatting(text.format_bold)
    lbs.push(lb)
    lns.size()

f_fmt(float v) =>
    str.tostring(v, format.mintick)

f_targetText(int dir, string name, float lvl, bool hit) =>
    string arrow  = hit or dir == 1 ? ARROW_UP : ARROW_DN
    string suffix = hit ? (i_hitStyle == HIT_BOOKED ? " " + HIT_BOOKED : ", " + HIT_PLAIN) : ""
    arrow + name + " at " + f_fmt(lvl) + suffix

f_pct(int wins, int total) =>
    total > 0 ? str.tostring(wins * 100.0 / total, "#.##") + " %" : "0 %"

f_tablePosition(string s) =>
    switch s
        "Top Left"      => position.top_left
        "Top Center"    => position.top_center
        "Top Right"     => position.top_right
        "Middle Left"   => position.middle_left
        "Middle Right"  => position.middle_right
        "Bottom Center" => position.bottom_center
        "Bottom Right"  => position.bottom_right
        => position.bottom_left

f_textSize(string s) =>
    switch s
        TXT_SMALL  => size.small
        TXT_NORMAL => size.normal
        TXT_LARGE  => size.large
        => size.tiny

float volSafe  = na(volume) or volume <= 0 ? 1.0 : volume
float barRange = high - low
float atr14    = ta.atr(14)
float atrTrade = ta.atr(i_atrLenTrade)
float atrSafe  = math.max(nz(atr14, barRange), EPS)

float dcPeriod = f_dominantCycle(hl2, i_dcMin, i_dcMax)
int   dynLen   = math.max(i_dcMin, math.min(i_dcMax, math.round(nz(dcPeriod, i_gaussLen))))

int    chartSec   = timeframe.in_seconds(timeframe.period)
string premiumLtf = chartSec <= 3600 ? "1" : chartSec <= 14400 ? "5" : chartSec <= 86400 ? "15" : "60"
string wantedLtf  = i_isPremium ? premiumLtf : i_ltf
string ltfTf      = timeframe.in_seconds(wantedLtf) < chartSec ? wantedLtf : timeframe.period
[ltfUpArr, ltfDnArr] = request.security_lower_tf(syminfo.tickerid, ltfTf, [close > open ? nz(volume) : 0.0, close < open ? nz(volume) : 0.0], ignore_invalid_symbol = true)
bool  useLtf   = ltfTf != timeframe.period and ltfUpArr.size() > 0
float ltfUp    = useLtf ? nz(ltfUpArr.sum()) : 0.0
float ltfDn    = useLtf ? nz(ltfDnArr.sum()) : 0.0
float estBuy   = barRange > 0 ? volSafe * (close - low) / barRange : volSafe * 0.5
float estSell  = volSafe - estBuy
float barDelta = useLtf ? ltfUp - ltfDn : estBuy - estSell
float cvd      = ta.cum(barDelta)
float cvdEma   = ta.ema(cvd, i_cvdLen)
float cvdOsc   = (cvd - nz(cvdEma, cvd)) / math.max(nz(ta.stdev(barDelta, i_cvdLen)), EPS)
float cvdSlope = nz(ta.change(cvdEma))

float obvV       = ta.cum(math.sign(nz(ta.change(close))) * volSafe)
float obvFast    = ta.ema(obvV, 5)
float obvMid     = ta.ema(obvV, 13)
float obvSlow    = ta.ema(obvV, 34)
float obvStd     = math.max(nz(ta.stdev(obvV, 34)), EPS)
float obvImpulse = (0.2 * (obvV - nz(obvFast, obvV)) + 0.5 * (nz(obvFast, obvV) - nz(obvMid, obvV)) + 0.3 * (nz(obvMid, obvV) - nz(obvSlow, obvV))) / obvStd

float trixE      = ta.ema(ta.ema(ta.ema(math.log(close), i_trixLen), i_trixLen), i_trixLen)
float trix       = 10000.0 * nz(ta.change(trixE))
float trixSignal = ta.ema(trix, i_trixSig)
float trixNorm   = trix / math.max(nz(ta.stdev(trix, 50)), EPS)

float vwmaV     = f_vwma(close, volSafe, dynLen)
float kamaV     = f_kama(close, dynLen, i_amaFast, i_amaSlow)
float rsiV      = f_rsi(close, dynLen)
float stochRsiK = ta.sma(ta.stoch(rsiV, rsiV, rsiV, i_stochLen), i_stochSmooth)
[stLine, stDir] = ta.supertrend(i_stFactor, i_stAtrLen)
float fisherV   = f_fisher(hl2, dynLen)

float gaussLenF = i_gaussAdaptive ? float(dynLen) : float(i_gaussLen)
float gaussBase = f_gaussian(hlc3, gaussLenF, i_gaussPoles)
[bandTrail, bandDir] = f_trailBand(gaussBase, atr14, i_gaussMult, close)
float baseline  = f_gaussian(close, i_baseLen, 4)

float lmsSrc   = close - gaussBase
float lmsNext  = f_lmsPredict(lmsSrc, i_lmsLen, i_lmsMu)
float lmsStd   = math.max(nz(ta.stdev(lmsSrc, 50)), EPS)
[qrFit, qrSlope] = f_quadReg(close, i_qrLen)
float trimeanV = f_trimean(close, i_qrLen)
float hyperV   = f_tanh((close - nz(close[dynLen], close)) / math.max(atrSafe * math.sqrt(dynLen), EPS))

float c1  = f_tanh((close - vwmaV) / atrSafe)
float c2  = f_tanh((close - kamaV) / atrSafe)
float c3  = (rsiV - 50.0) / 50.0
float c4  = (nz(stochRsiK, 50.0) - 50.0) / 50.0
float c5  = stDir < 0 ? 1.0 : -1.0
float c6  = math.abs(fisherV) > i_fisherTol ? f_tanh(fisherV) : 0.0
float c7  = f_tanh(trixNorm)
float c8  = f_tanh(cvdOsc)
float c9  = f_tanh(obvImpulse)
float c10 = f_tanh(nz(lmsNext) / lmsStd)
float c11 = f_tanh(nz(qrSlope) * 5.0 / atrSafe)
float c12 = hyperV
float c13 = f_tanh((close - nz(trimeanV, close)) / atrSafe)
float tsScore = (c1 + c2 + c3 + c4 + c5 + c6 + c7 + c8 + c9 + c10 + c11 + c12 + c13) / 13.0

float probRaw = 1.0 / (1.0 + math.exp(-4.0 * i_sensitivity * tsScore))
float probAvg = nz(ta.sma(probRaw, i_probAvg), probRaw)
float probStd = nz(ta.stdev(probRaw, 5))

float feat1 = c3
float feat2 = f_tanh(fisherV / 2.0)
float feat3 = f_normalize(trixNorm, -1.0, 1.0)
float feat4 = f_normalize(cvdOsc, -1.0, 1.0)
float feat5 = c4

var array<float> knnA1  = array.new_float()
var array<float> knnA2  = array.new_float()
var array<float> knnA3  = array.new_float()
var array<float> knnA4  = array.new_float()
var array<float> knnA5  = array.new_float()
var array<int>   knnLbl = array.new_int()

if i_useKnn and bar_index > i_knnHorizon
    int lbl = close > close[i_knnHorizon] ? 1 : close < close[i_knnHorizon] ? -1 : 0
    knnA1.push(feat1[i_knnHorizon])
    knnA2.push(feat2[i_knnHorizon])
    knnA3.push(feat3[i_knnHorizon])
    knnA4.push(feat4[i_knnHorizon])
    knnA5.push(feat5[i_knnHorizon])
    knnLbl.push(lbl)
    if knnLbl.size() > i_knnWindow
        knnA1.shift()
        knnA2.shift()
        knnA3.shift()
        knnA4.shift()
        knnA5.shift()
        knnLbl.shift()

float knnScore  = i_useKnn ? f_knnPredict(knnA1, knnA2, knnA3, knnA4, knnA5, knnLbl, feat1, feat2, feat3, feat4, feat5, i_knnK, i_knnStride) : 0.0
float knnProb   = (knnScore + 1.0) / 2.0
float knnW      = i_useKnn ? i_knnWeight : 0.0
float finalProb = probAvg * (1.0 - knnW) + knnProb * knnW

bool  regimeOk     = f_regimeFilter(ohlc4, i_regimeThr)
bool  volOk        = nz(ta.atr(1)) > nz(ta.atr(10))
bool  atrImpulseOk = nz(ta.atr(5)) >= nz(ta.sma(ta.tr(true), 30)) * i_atrImpMult
float volDeriv     = (atrSafe - nz(atr14[3], atrSafe)) / atrSafe
bool  volDerivOk   = nz(ta.ema(volDeriv, 3)) >= -i_volDerivTol
bool  trixLongOk   = trix >= nz(trixSignal, trix) or trix > 0
bool  trixShortOk  = trix <= nz(trixSignal, trix) or trix < 0
bool  cvdLongOk    = cvdSlope >= 0
bool  cvdShortOk   = cvdSlope <= 0
float bodySize     = math.abs(close - open)
bool  isMarubozu   = barRange > 0 and bodySize / barRange >= i_maruBody and barRange >= atrSafe * 1.2
bool  bullMarubozu = isMarubozu and close > open
bool  bearMarubozu = isMarubozu and close < open
bool  probVarOk    = probStd <= i_probVarMax

float pLow     = ta.lowest(low, i_divLen)
float pHigh    = ta.highest(high, i_divLen)
float cLow     = ta.lowest(cvd, i_divLen)
float cHigh    = ta.highest(cvd, i_divLen)
bool  bullDiv  = pLow < nz(pLow[i_divLen], pLow) and cLow > nz(cLow[i_divLen], cLow)
bool  bearDiv  = pHigh > nz(pHigh[i_divLen], pHigh) and cHigh < nz(cHigh[i_divLen], cHigh)

bool barOk = not i_confirmBar or barstate.isconfirmed

bool filtersLong  = (not i_useRegime or regimeOk) and (not i_useVolFilter or volOk) and (not i_useAtrImpulse or atrImpulseOk) and (not i_useVolDeriv or volDerivOk) and (not i_useTrix or trixLongOk) and (not i_useCvd or cvdLongOk or bullDiv) and (not i_useMarubozu or not bearMarubozu) and probVarOk
bool filtersShort = (not i_useRegime or regimeOk) and (not i_useVolFilter or volOk) and (not i_useAtrImpulse or atrImpulseOk) and (not i_useVolDeriv or volDerivOk) and (not i_useTrix or trixShortOk) and (not i_useCvd or cvdShortOk or bearDiv) and (not i_useMarubozu or not bullMarubozu) and probVarOk

bool rawLong  = bandDir == 1 and (finalProb >= i_conf or (i_useCvdDiv and bullDiv and finalProb > 0.5))
bool rawShort = bandDir == -1 and (finalProb <= 1.0 - i_conf or (i_useCvdDiv and bearDiv and finalProb < 0.5))

var int lastDir = 0
bool buySignal  = false
bool sellSignal = false

if barOk and bar_index > i_dcMax * WARMUP_MULT
    if rawLong and filtersLong and lastDir != 1
        buySignal := true
        lastDir   := 1
    else if rawShort and filtersShort and lastDir != -1
        sellSignal := true
        lastDir    := -1

var TradeState cur   = na
var Stats      stats = Stats.new()

bool evTriggered = false
bool evT1        = false
bool evT2        = false
bool evT3        = false
bool evT4        = false
bool evT5        = false
bool evStop      = false
int  evDir       = 0

if not na(cur)
    if not cur.closed and not cur.triggered and bar_index > cur.sigBar
        if f_reached(cur.dir, cur.trigger)
            cur.triggered := true
            evTriggered   := true
            evDir         := cur.dir
            if cur.dir == 1
                stats.longTotal := stats.longTotal + 1
            else
                stats.shortTotal := stats.shortTotal + 1
    if not cur.closed and cur.triggered
        evDir := cur.dir
        if not cur.stopHit and f_stopped(cur.dir, cur.stop)
            cur.stopHit := true
            cur.stopBar := bar_index
            evStop      := true
        if not cur.t1Hit and f_reached(cur.dir, cur.t1)
            cur.t1Hit := true
            evT1      := true
            if cur.dir == 1
                stats.longWins := stats.longWins + 1
            else
                stats.shortWins := stats.shortWins + 1
        if not cur.stopHit
            if cur.t1Hit and not cur.t2Hit and f_reached(cur.dir, cur.t2)
                cur.t2Hit := true
                evT2      := true
            if cur.t2Hit and not cur.t3Hit and f_reached(cur.dir, cur.t3)
                cur.t3Hit := true
                evT3      := true
            if i_showT45 and cur.t3Hit and not cur.t4Hit and f_reached(cur.dir, cur.t4)
                cur.t4Hit := true
                evT4      := true
            if i_showT45 and cur.t4Hit and not cur.t5Hit and f_reached(cur.dir, cur.t5)
                cur.t5Hit := true
                evT5      := true
        bool finalTargetHit = i_showT45 ? cur.t5Hit : cur.t3Hit
        if finalTargetHit
            cur.closed := true
        else if cur.stopHit
            if cur.t1Hit or bar_index - cur.stopBar >= i_recoveryBars
                cur.closed := true

if buySignal or sellSignal
    int   d   = buySignal ? 1 : -1
    float a   = nz(atrTrade, barRange)
    float trg = d == 1 ? high : low
    if not na(cur) and not cur.closed
        cur.closed := true
    cur := TradeState.new(dir = d, sigBar = bar_index, trigger = trg, stop = trg - d * i_stopMult * a, t1 = trg + d * 1.0 * i_tMult * a, t2 = trg + d * 2.0 * i_tMult * a, t3 = trg + d * 3.0 * i_tMult * a, t4 = trg + d * 4.0 * i_tMult * a, t5 = trg + d * 5.0 * i_tMult * a)

plot(i_showBand and bandDir == 1 ? bandTrail : na, "Gaussian Band Bull", i_colBandBull, i_bandWidth, plot.style_linebr)
plot(i_showBand and bandDir == -1 ? bandTrail : na, "Gaussian Band Bear", i_colBandBear, i_bandWidth, plot.style_linebr)
plot(i_showBaseline ? baseline : na, "Gaussian Baseline", i_colBaseline, 1)

bool dotCaution   = bullMarubozu or bearMarubozu or not probVarOk
bool dotCautionDn = dotCaution and (bullMarubozu or (not bearMarubozu and finalProb >= 0.5))
bool dotCautionUp = dotCaution and not dotCautionDn
bool dotBull      = not dotCaution and finalProb >= i_conf
bool dotBear      = not dotCaution and finalProb <= 1.0 - i_conf
bool dotNeutUp    = not dotCaution and not dotBull and not dotBear and finalProb >= 0.5
bool dotNeutDn    = not dotCaution and not dotBull and not dotBear and finalProb < 0.5

plotshape(i_showDots and dotBull and not buySignal, "Bull Probability", shape.triangleup, location.belowbar, i_colDotBull, size = size.tiny)
plotshape(i_showDots and dotBear and not sellSignal, "Bear Probability", shape.triangledown, location.abovebar, i_colDotBear, size = size.tiny)
plotshape(i_showDots and dotNeutUp, "Neutral Up Probability", shape.triangleup, location.belowbar, i_colDotNeut, size = size.tiny)
plotshape(i_showDots and dotNeutDn, "Neutral Down Probability", shape.triangledown, location.abovebar, i_colDotNeut, size = size.tiny)
plotshape(i_showDots and dotCautionDn, "Caution Down", shape.triangledown, location.abovebar, i_colDotWarn, size = size.tiny)
plotshape(i_showDots and dotCautionUp, "Caution Up", shape.triangleup, location.belowbar, i_colDotWarn, size = size.tiny)

plotshape(i_showHistory and buySignal, "Buy Position", shape.labelup, location.belowbar, i_colLong, text = "Buy", textcolor = color.white, size = size.tiny)
plotshape(i_showHistory and sellSignal, "Sell Position", shape.labeldown, location.abovebar, i_colShort, text = "Sell", textcolor = color.white, size = size.tiny)

barcolor(i_showBarColor and buySignal ? i_colBuyBar : i_showBarColor and sellSignal ? i_colSellBar : na, title = "Signal Candles")

var array<Zone> volZones = array.new<Zone>()
var array<Zone> pivZones = array.new<Zone>()

float volAvg = ta.sma(volSafe, 20)
float srPH   = ta.pivothigh(high, i_srPivotLen, i_srPivotLen)
float srPL   = ta.pivotlow(low, i_srPivotLen, i_srPivotLen)
float pvPH   = ta.pivothigh(high, i_pivLen, i_pivLen)
float pvPL   = ta.pivotlow(low, i_pivLen, i_pivLen)

if i_showVolSR
    f_updateZones(volZones, i_srMaxBars)
    bool  volFloat = volSafe[i_srPivotLen] >= nz(volAvg[i_srPivotLen]) * i_srVolMult
    float minH     = nz(atr14[i_srPivotLen], atrSafe) * ZONE_MIN_ATR
    int   leftX    = bar_index - i_srPivotLen
    if not na(srPH) and volFloat
        float zt = srPH
        float zb = math.max(open[i_srPivotLen], close[i_srPivotLen])
        zb := zt - zb < minH ? zt - minH : zb
        f_addZone(volZones, Zone.new(box.new(leftX, zt, bar_index, zb, border_color = color(na), bgcolor = i_colVolZone), zt, zb, -1, leftX), i_maxZones)
    if not na(srPL) and volFloat
        float zb = srPL
        float zt = math.min(open[i_srPivotLen], close[i_srPivotLen])
        zt := zt - zb < minH ? zb + minH : zt
        f_addZone(volZones, Zone.new(box.new(leftX, zt, bar_index, zb, border_color = color(na), bgcolor = i_colVolZone), zt, zb, 1, leftX), i_maxZones)

if i_showPivots
    f_updateZones(pivZones, i_pivMaxBars)
    float pivH = nz(atr14[i_pivLen], atrSafe) * PIV_ZONE_ATR
    int   pivX = bar_index - i_pivLen
    if not na(pvPH)
        f_addZone(pivZones, Zone.new(box.new(pivX, pvPH, bar_index, pvPH - pivH, border_color = color(na), bgcolor = i_colPivZone), pvPH, pvPH - pivH, -1, pivX), i_maxZones)
    if not na(pvPL)
        f_addZone(pivZones, Zone.new(box.new(pivX, pvPL + pivH, bar_index, pvPL, border_color = color(na), bgcolor = i_colPivZone), pvPL + pivH, pvPL, 1, pivX), i_maxZones)

var array<line>  lvlLines  = array.new<line>()
var array<label> lvlLabels = array.new<label>()

if barstate.islast
    for ln in lvlLines
        ln.delete()
    for lb in lvlLabels
        lb.delete()
    lvlLines.clear()
    lvlLabels.clear()
    if i_showLevels and not na(cur)
        string lblSize = f_textSize(i_textSize)
        bool   isLong  = cur.dir == 1
        int    xr      = bar_index + i_labelOffset
        int    xl      = math.max(cur.sigBar - 1, 0)
        float  aGap    = nz(atrTrade, atrSafe)

        color  tgtTxt  = isLong ? i_colBuyTgt : i_colSellTgt
        color  tgtBg   = isLong ? i_colBuyTgtBg : i_colSellTgtBg
        color  tgtLn   = isLong ? i_colBuyTgtLn : i_colSellTgtLn
        color  entTxt  = isLong ? i_colBuyEnt : i_colSellEnt
        color  entBg   = isLong ? i_colBuyEntBg : i_colSellEntBg
        color  entLn   = isLong ? i_colBuyEntLn : i_colSellEntLn
        color  stpTxt  = isLong ? i_colBuyStp : i_colSellStp
        color  stpBg   = isLong ? i_colBuyStpBg : i_colSellStpBg
        color  stpLn   = isLong ? i_colBuyStpLn : i_colSellStpLn

        if i_showT45
            f_drawLevel(lvlLines, lvlLabels, xl, xr, cur.t5, tgtLn, line.style_dotted, 1, f_targetText(cur.dir, "T5", cur.t5, cur.t5Hit), tgtTxt, tgtBg, lblSize, false)
            f_drawLevel(lvlLines, lvlLabels, xl, xr, cur.t4, tgtLn, line.style_dotted, 1, f_targetText(cur.dir, "T4", cur.t4, cur.t4Hit), tgtTxt, tgtBg, lblSize, false)
        f_drawLevel(lvlLines, lvlLabels, xl, xr, cur.t3, tgtLn, line.style_dotted, 1, f_targetText(cur.dir, "T3", cur.t3, cur.t3Hit), tgtTxt, tgtBg, lblSize, false)
        f_drawLevel(lvlLines, lvlLabels, xl, xr, cur.t2, tgtLn, line.style_dotted, 1, f_targetText(cur.dir, "T2", cur.t2, cur.t2Hit), tgtTxt, tgtBg, lblSize, false)
        f_drawLevel(lvlLines, lvlLabels, xl, xr, cur.t1, tgtLn, line.style_dotted, 1, f_targetText(cur.dir, "T1", cur.t1, cur.t1Hit), tgtTxt, tgtBg, lblSize, false)

        string entryTxt = (isLong ? ARROW_UP + "Strength Above " : ARROW_DN + "Weakness Below ") + f_fmt(cur.trigger) + (cur.triggered ? ",\nTriggered" : "")
        f_drawLevel(lvlLines, lvlLabels, xl, xr, cur.trigger, entLn, line.style_solid, 2, entryTxt, entTxt, entBg, lblSize, true)

        string stopTxt = WARN_ICON + "Stop at " + f_fmt(cur.stop) + (cur.stopHit ? ", " + HIT_PLAIN : "")
        f_drawLevel(lvlLines, lvlLabels, xl, xr, cur.stop, stpLn, line.style_dotted, 1, stopTxt, stpTxt, stpBg, lblSize, false)

        lvlLabels.push(label.new(xr, cur.stop - cur.dir * aGap * BRAND_GAP, BRAND_TXT, xloc = xloc.bar_index, color = i_colBrandBg, style = label.style_label_left, textcolor = i_colBrand, size = lblSize, textalign = text.align_center))

string statLongRate  = f_pct(stats.longWins, stats.longTotal)
string statShortRate = f_pct(stats.shortWins, stats.shortTotal)

var table statsTbl = table.new(f_tablePosition(i_tablePos), 4, 4, bgcolor = i_colTblBg, frame_color = i_colTblBorder, frame_width = 1, border_color = i_colTblBorder, border_width = 1)

if barstate.islast
    table.clear(statsTbl, 0, 0, 3, 3)
    if i_showTable
        string ts      = f_textSize(i_tableSize)
        color  longBg  = color.new(i_colLong, 88)
        color  shortBg = color.new(i_colShort, 88)
        table.cell(statsTbl, 0, 0, VERSION_TXT, text_color = i_colTblText, text_size = ts, text_halign = text.align_left)
        table.cell(statsTbl, 1, 0, "Stats", text_color = i_colTblText, text_size = ts, bgcolor = color.new(i_colTblText, 92))
        table.cell(statsTbl, 2, 0, "Long", text_color = i_colLong, text_size = ts, bgcolor = longBg)
        table.cell(statsTbl, 3, 0, "Short", text_color = i_colShort, text_size = ts, bgcolor = shortBg)
        table.cell(statsTbl, 0, 1, "", text_size = ts)
        table.cell(statsTbl, 1, 1, "Total Trades", text_color = i_colTblText, text_size = ts, text_halign = text.align_right)
        table.cell(statsTbl, 2, 1, str.tostring(stats.longTotal), text_color = TXT_DARK, text_size = ts)
        table.cell(statsTbl, 3, 1, str.tostring(stats.shortTotal), text_color = TXT_DARK, text_size = ts)
        table.cell(statsTbl, 0, 2, RECOVERY_TXT, text_color = i_colTblText, text_size = ts)
        table.cell(statsTbl, 1, 2, "T1 Wins", text_color = i_colTblText, text_size = ts, text_halign = text.align_right)
        table.cell(statsTbl, 2, 2, str.tostring(stats.longWins), text_color = TXT_DARK, text_size = ts)
        table.cell(statsTbl, 3, 2, str.tostring(stats.shortWins), text_color = TXT_DARK, text_size = ts)
        table.cell(statsTbl, 0, 3, "", text_color = i_colTblText, text_size = ts)
        table.cell(statsTbl, 1, 3, "T1 Win %", text_color = i_colTblText, text_size = ts, text_halign = text.align_right)
        table.cell(statsTbl, 2, 3, statLongRate, text_color = TXT_DARK, text_size = ts)
        table.cell(statsTbl, 3, 3, statShortRate, text_color = TXT_DARK, text_size = ts)

alertcondition(buySignal, "Ocs Ai Trader : Buy Signal", "Ocs Ai Trader : Buy Signal on {{ticker}} ({{interval}}) at {{close}}")
alertcondition(sellSignal, "Ocs Ai Trader : Sell Signal", "Ocs Ai Trader : Sell Signal on {{ticker}} ({{interval}}) at {{close}}")
alertcondition(buySignal or sellSignal, "Ocs Ai Trader : Any Signal", "Ocs Ai Trader : New Signal on {{ticker}} ({{interval}}) at {{close}}")
alertcondition(evTriggered, "Ocs Ai Trader : Strength / Weakness Triggered", "Ocs Ai Trader : Entry Triggered on {{ticker}} ({{interval}}) at {{close}}")
alertcondition(evT1, "Ocs Ai Trader : T1 Hit", "Ocs Ai Trader : T1 Hit on {{ticker}} ({{interval}})")
alertcondition(evT2, "Ocs Ai Trader : T2 Hit", "Ocs Ai Trader : T2 Hit on {{ticker}} ({{interval}})")
alertcondition(evT3, "Ocs Ai Trader : T3 Hit", "Ocs Ai Trader : T3 Hit on {{ticker}} ({{interval}})")
alertcondition(evT4, "Ocs Ai Trader : T4 Hit", "Ocs Ai Trader : T4 Hit on {{ticker}} ({{interval}})")
alertcondition(evT5, "Ocs Ai Trader : T5 Hit", "Ocs Ai Trader : T5 Hit on {{ticker}} ({{interval}})")
alertcondition(evStop, "Ocs Ai Trader : Stop Hit", "Ocs Ai Trader : Stop Hit on {{ticker}} ({{interval}})")

string passTxt = str.length(i_alertPass) > 0 ? " | Passcode: " + i_alertPass : ""
string side    = evDir == 1 ? "Long" : "Short"

if buySignal or sellSignal
    string sigTxt = BRAND_TXT + " : " + (buySignal ? "BUY " : "SELL ") + syminfo.ticker + " (" + timeframe.period + ")" + (buySignal ? " | Strength Above " : " | Weakness Below ") + f_fmt(cur.trigger) + " | T1 " + f_fmt(cur.t1) + " | T2 " + f_fmt(cur.t2) + " | T3 " + f_fmt(cur.t3) + (i_showT45 ? " | T4 " + f_fmt(cur.t4) + " | T5 " + f_fmt(cur.t5) : "") + " | Stop " + f_fmt(cur.stop) + passTxt
    alert(sigTxt, alert.freq_once_per_bar_close)
if evTriggered
    alert(BRAND_TXT + " : " + side + " Triggered on " + syminfo.ticker + " (" + timeframe.period + ") at " + f_fmt(close) + passTxt, alert.freq_once_per_bar)
if evT1 or evT2 or evT3 or evT4 or evT5
    string tgt = evT5 ? "T5" : evT4 ? "T4" : evT3 ? "T3" : evT2 ? "T2" : "T1"
    alert(BRAND_TXT + " : " + side + " " + tgt + " Hit on " + syminfo.ticker + " (" + timeframe.period + ") at " + f_fmt(close) + passTxt, alert.freq_once_per_bar)
if evStop
    alert(BRAND_TXT + " : " + side + " Stop Hit on " + syminfo.ticker + " (" + timeframe.period + ") at " + f_fmt(close) + passTxt, alert.freq_once_per_bar)