diff --git a/.gitattributes b/.gitattributes
index 67fba80a303a176f12f18c5fe0089488f200a63c..4226d012ebc36942259975efa21f20db4b9f6ab4 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -15,3 +15,49 @@ z.Others/z.Images[[:space:]]Folder/Screenshot[[:space:]]2025-11-15[[:space:]]184
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 Research[[:space:]]Papers/The[[:space:]]1CPMOTS.rp1_[[:space:]]Is[[:space:]]Treating[[:space:]]Trading[[:space:]]Worse[[:space:]]Than[[:space:]]a[[:space:]]Coin[[:space:]]Flip,[[:space:]]but[[:space:]]with[[:space:]]a[[:space:]]Slight[[:space:]]Edge,[[:space:]]Profitable_/1CPMOTS_rp1.pdf filter=lfs diff=lfs merge=lfs -text
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diff --git a/Pinescript Folder/Verttical Lines at Specific Time/Vertical lines at specific time.ipynb b/Pinescript Folder/Verttical Lines at Specific Time/Vertical lines at specific time.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..aedffcaeaa6da158d641918c9ae4d801f8015c00
--- /dev/null
+++ b/Pinescript Folder/Verttical Lines at Specific Time/Vertical lines at specific time.ipynb	
@@ -0,0 +1,116 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0a297891",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "//@version=5\n",
+    "indicator(\"vertical lines\", overlay=true)\n",
+    "\n",
+    "// Only show vertical lines if timeframe is intraday (less than daily)\n",
+    "showLines = timeframe.isintraday\n",
+    "\n",
+    "// Convert current bar time to Asia/Manila timezone\n",
+    "t = time(timeframe.period, \"Asia/Manila\")\n",
+    "\n",
+    "// Target hours excluding 0, 12, 20\n",
+    "var int[] targetHours = array.from(3, 6, 8, 15, 24) // 24 means midnight (0h)\n",
+    "\n",
+    "// Special hours separated\n",
+    "specialHour20 = 20\n",
+    "specialHour0 = 0  // midnight\n",
+    "specialHour12 = 12  // noon\n",
+    "\n",
+    "// Function to check if current bar time matches any target hour exactly at minute zero\n",
+    "isTargetTime() =>\n",
+    "    h = hour(t)\n",
+    "    m = minute(t)\n",
+    "    match = false\n",
+    "    for i = 0 to array.size(targetHours) - 1\n",
+    "        if (h == array.get(targetHours, i) or (h == 0 and array.get(targetHours, i) == 24)) and m == 0\n",
+    "            match := true\n",
+    "    match\n",
+    "\n",
+    "// Check if current time matches special hour 20 at minute zero\n",
+    "isSpecialTime20() =>\n",
+    "    h = hour(t)\n",
+    "    m = minute(t)\n",
+    "    (h == specialHour20) and (m == 0)\n",
+    "\n",
+    "// Check if current time matches special hour 0 at minute zero\n",
+    "isSpecialTime0() =>\n",
+    "    h = hour(t)\n",
+    "    m = minute(t)\n",
+    "    (h == specialHour0) and (m == 0)\n",
+    "\n",
+    "// Check if current time matches special hour 12 at minute zero\n",
+    "isSpecialTime12() =>\n",
+    "    h = hour(t)\n",
+    "    m = minute(t)\n",
+    "    (h == specialHour12) and (m == 0)\n",
+    "\n",
+    "// Detect new day (day break)\n",
+    "newDay = ta.change(time(\"D\"))\n",
+    "\n",
+    "// Only draw lines if timeframe is intraday\n",
+    "if showLines\n",
+    "    // Draw vertical line at day break (red, width 1)\n",
+    "    if newDay\n",
+    "        line.new(bar_index, low - 10 * syminfo.mintick, bar_index, high + 10 * syminfo.mintick, color=color.green, width=1, extend=extend.both)\n",
+    "\n",
+    "    // Draw vertical line at target times (blue, width 1)\n",
+    "    if isTargetTime()\n",
+    "        line.new(bar_index, low - 10 * syminfo.mintick, bar_index, high + 10 * syminfo.mintick, color=color.rgb(33, 149, 243, 90), width=1, extend=extend.both)\n",
+    "\n",
+    "    // Draw vertical line at special hour 20 (violet, width 1)\n",
+    "    if isSpecialTime20()\n",
+    "        line.new(bar_index, low - 10 * syminfo.mintick, bar_index, high + 10 * syminfo.mintick, color=color.new(color.teal, 0), width=1, extend=extend.both)\n",
+    "\n",
+    "    // Draw vertical line at special hour 0 (midnight) - orange, width 2, dotted\n",
+    "    if isSpecialTime0()\n",
+    "        var line midnightLine = na\n",
+    "        midnightLine := line.new(bar_index, low - 10 * syminfo.mintick, bar_index, high + 10 * syminfo.mintick, color=color.orange, width=1, extend=extend.both)\n",
+    "        line.set_style(midnightLine, line.style_dotted)\n",
+    "\n",
+    "    // Draw vertical line at special hour 12 (noon) - green, width 2, dotted \n",
+    "    if isSpecialTime12()\n",
+    "        var line noonLine = na\n",
+    "        noonLine := line.new(bar_index, low - 10 * syminfo.mintick, bar_index, high + 10 * syminfo.mintick, color=color.red, width=1, extend=extend.both)\n",
+    "        line.set_style(noonLine, line.style_dotted)\n"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "fff3f0fe",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d04214c",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Pinescript Folder/Vison Market Profile/vison market profile.ipynb b/Pinescript Folder/Vison Market Profile/vison market profile.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..d6499f4783e1ade111a8bc84fed98072bd300d57
--- /dev/null
+++ b/Pinescript Folder/Vison Market Profile/vison market profile.ipynb	
@@ -0,0 +1,452 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9d6c0e6d",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "//@version=5\n",
+    "indicator(\"Volume Profile and Indicator by Sunstoic\", \"v5 Vison Market Profile\", overlay = true, max_boxes_count = 500, max_lines_count = 500, max_bars_back = 5000)\n",
+    "\n",
+    "\n",
+    "// Inputs\n",
+    "disp = display.all - display.status_line\n",
+    "\n",
+    "vpGR = 'Volume & Sentiment Profile'\n",
+    "\n",
+    "vpTP   = 'displays total trading activity (both buying and selling trading activity) over a specified time period at specific price levels\\n\\n' +\n",
+    "          'row lengths, indicates the amount of the traded activity at specific price levels\\n\\n' +\n",
+    "          ' - high traded zones - usually represents consolidation zones (value areas)\\n' +\n",
+    "          ' - low traded zones - usually represents supply & demand or liquidity zones'\n",
+    " \n",
+    "vpSH = input.bool(true, 'Volume Profile', group = vpGR, tooltip = vpTP)\n",
+    "\n",
+    "vpUC = input.color(color.new(#000000, 50), '  Up Volume ', inline = 'VP', group = vpGR)\n",
+    "vpDC = input.color(color.new(#000000, 50), 'Down Volume ', inline = 'VP', group = vpGR)\n",
+    "\n",
+    "vaUC = input.color(color.new(#000000, 50), '  Value Area Up', inline = 'VA', group = vpGR)\n",
+    "vaDC = input.color(color.new(#000000, 50), 'Value Area Down', inline = 'VA', group = vpGR)\n",
+    "\n",
+    "\n",
+    "// User Inputs\n",
+    "pcSH = input.string('Developing POC', 'Point of Control', options = ['Developing POC', 'Last (Line)', 'None'], inline = 'POC', group = vpGR, display = disp)\n",
+    "pocC = input.color(color.rgb(0, 0, 0), '', inline = 'POC', group = vpGR)\n",
+    "pocW = input.int(2, '', minval = 1, inline = 'POC', group = vpGR, display = disp)\n",
+    "\n",
+    "// Mock Developing POC (replace with your own logic)\n",
+    "var float developingPOC = na\n",
+    "developingPOC := (high + low / 2)  // For demo purposes, this just uses the close\n",
+    "\n",
+    "// Plot the POC using stepline if enabled\n",
+    "plot(pcSH == 'Developing POC' ? developingPOC : na,title = 'Developing POC', color = pocC,linewidth = pocW,style = plot.style_stepline)\n",
+    "\n",
+    "vpVA = input.float(68, 'Value Area (%)', minval = 0, maxval = 100, group = vpGR, display = disp) / 100\n",
+    "\n",
+    "vahS = input.bool(true, 'Value Area High (VAH)', inline = 'VAH', group = vpGR)\n",
+    "vahC = input.color(#2962ff, '', inline = 'VAH', group = vpGR)\n",
+    "vahW = input.int(1, '', minval = 1, inline = 'VAH', group = vpGR, display = disp)\n",
+    "\n",
+    "vlSH = input.bool(true, 'Value Area Low (VAL)', inline = 'VAL', group = vpGR)\n",
+    "valC = input.color(#2962ff, '', inline = 'VAL', group = vpGR)\n",
+    "valW = input.int(1, '', minval = 1, inline = 'VAL', group = vpGR, display = disp)\n",
+    "\n",
+    "spPT = 'polarity methods is a measure used to divide the total volume into either up volume (trades that moved the price up) or ' + \n",
+    "         'down volume (trades that moved the price down), simply said conditions used to calculate up/down volume\\n\\n' +\n",
+    "         '* bar polarity\\n   up     => if close > open\\n   down => if close <= open\\n\\n' +\n",
+    "         '* bar buying/selling pressure\\n   up     => if (close - low) > (high - close)\\n   down => if (close - low) <= (high - close)'\n",
+    "spP1 = 'Bar Polarity'\n",
+    "spP2 = 'Bar Buying/Selling Pressure'\n",
+    "vpPT = input.string(spP1, 'Profile Polarity Method', options = [spP1, spP2], group = vpGR, tooltip = spPT, display = disp)\n",
+    "vsPT = vpPT == spP1\n",
+    "\n",
+    "vpLR = input.string('Visible Range', 'Profile Lookback Range', options = ['Fixed Range', 'Visible Range'], group = vpGR, display = disp)\n",
+    "vpRL = vpLR == 'Visible Range'\n",
+    "vpLT = 'applicable when \\'Lookback Range\\' is selected as \\'Fixed Range\\''\n",
+    "vpLN = input.int(360, 'Lookback Length / Fixed Range', minval = 10, maxval = 5000, step = 10, group = vpGR, tooltip = vpLT, display = disp)\n",
+    "vpLN:= last_bar_index < vpLN ? last_bar_index : vpLN - 1\n",
+    "\n",
+    "vpST = input.bool(true, 'Profile Stats', inline = 'STT', group = vpGR, display = disp)\n",
+    "ppLS = input.string('Small', \"\", options = ['Tiny', 'Small', 'Normal'], inline = 'STT', group = vpGR, display = disp)\n",
+    "lcDB = input.string('Top Right', '', options = ['Top Right', 'Middle Right', 'Bottom Left'], inline = 'STT', group = vpGR, display = disp)\n",
+    "\n",
+    "vpLV = input.bool(true, 'Profile Price Levels', inline = 'BBe', group = vpGR)\n",
+    "rpLS = input.string('Small', \"\", options=['Tiny', 'Small', 'Normal'], inline = 'BBe', group = vpGR, display = disp)\n",
+    "\n",
+    "vpPL = input.string('Right', 'Profile Placement', options = ['Right', 'Left'], group = vpGR, display = disp)\n",
+    "vpRT = vpPL == 'Right' \n",
+    "vpNR = input.int(250, 'Profile Number of Rows' , minval = 10, maxval = 500 , step = 10, group = vpGR, display = disp)\n",
+    "vpWD = input.float(31, 'Profile Width', minval = 0, maxval = 250, group = vpGR, display = disp) / 100\n",
+    "vpHO = input.int(0, 'Profile Horizontal Offset', maxval = 50, group = vpGR, display = disp)\n",
+    "\n",
+    "vaBG = input.bool(false, 'Value Area Background  ', inline = 'vBG', group = vpGR)\n",
+    "vBGC = input.color(color.new(#2962ff, 89), '', inline = 'vBG', group = vpGR)\n",
+    "\n",
+    "vpBG = input.bool(false, 'Profile Range Background ', inline = 'pBG', group = vpGR)\n",
+    "bgC  = input.color(color.new(#2962ff, 95), '', inline = 'pBG', group = vpGR)\n",
+    "\n",
+    "\n",
+    "cbGR = 'Volume Weighted Colored Bars'\n",
+    "\n",
+    "cbTT = 'Colors bars based on the bar\\'s volume relative to volume moving average\\n' +\n",
+    "         ' - Bold bars when bar\\'s volume is above volume moving average * upper threshold\\n' +\n",
+    "         ' - Light bars when bar\\'s volume is below volume moving average * lower threshold'\n",
+    "\n",
+    "vwcb = input.bool(false, 'Volume Weighted Colored Bars', group = cbGR, tooltip = cbTT)\n",
+    "upTH = input.float(1.618, '  Upper Threshold', minval=1., step=.1, group = cbGR, display = disp)\n",
+    "dnTH = input.float(0.618, '  Lower Threshold', minval=.1, step=.1, group = cbGR, display = disp) \n",
+    "\n",
+    "\n",
+    "// User Defined Types\n",
+    "type bar\n",
+    "    float o = open\n",
+    "    float h = high\n",
+    "    float l = low\n",
+    "    float c = close\n",
+    "    float v = volume\n",
+    "    int   i = bar_index\n",
+    "\n",
+    "type barData\n",
+    "    float [] bh\n",
+    "    float [] bl\n",
+    "    float [] bv\n",
+    "    bool  [] bp\n",
+    "    int   [] bn\n",
+    "\n",
+    "type volData\n",
+    "    float [] vt\n",
+    "    float [] vb\n",
+    "    float [] vd\n",
+    "\n",
+    "type tVP\n",
+    "    box         []  vp\n",
+    "    chart.point []  pPC\n",
+    "    polyline        dPC\n",
+    "    int             pcL\n",
+    "    int             laP\n",
+    "    int             lbP\n",
+    "    int             sI\n",
+    "\n",
+    "type tVH\n",
+    "    line        []  vh\n",
+    "    chart.point []  pMA\n",
+    "    polyline        vMA\n",
+    "\n",
+    "\n",
+    "// Variables\n",
+    "var barData bD = barData.new(\n",
+    "     array.new <float> (na), \n",
+    "     array.new <float> (na), \n",
+    "     array.new <float> (na), \n",
+    "     array.new <bool>  (na), \n",
+    "     array.new <int>   (na)\n",
+    " )\n",
+    "\n",
+    "volData vD = volData.new(\n",
+    "     array.new <float> (vpNR, 0.), \n",
+    "     array.new <float> (vpNR, 0.), \n",
+    "     array.new <float> (vpNR, 0.)\n",
+    " )\n",
+    "\n",
+    "var tVP VP = tVP.new(\n",
+    "     array.new<box>         (na),\n",
+    "     array.new<chart.point> (na),\n",
+    "     polyline.new           (na), na, na, na, na\n",
+    " )\n",
+    "\n",
+    "var tVH VH = tVH.new(\n",
+    "     array.new<line>        (na),\n",
+    "     array.new<chart.point> (na),\n",
+    "     polyline.new           (na)\n",
+    " )\n",
+    "\n",
+    "bar b = bar.new()\n",
+    "bar [] ltfBD = array.new<bar> (1, bar.new())\n",
+    "\n",
+    "var float pHST = na\n",
+    "var float pLST = na\n",
+    "var string ltf = na\n",
+    "\n",
+    "\n",
+    "// Functions / Methods\n",
+    "f_drawLineX(_x1, _y1, _x2, _y2, _xloc, _extend, _color, _style, _width) =>\n",
+    "    var id = line.new(_x1, _y1, _x2, _y2, _xloc, _extend, _color, _style, _width)\n",
+    "    line.set_xy1(id, _x1, _y1)\n",
+    "    line.set_xy2(id, _x2, _y2)\n",
+    "    line.set_color(id, _color)\n",
+    "\n",
+    "f_drawLabelX(_x, _y, _text, _color, _style, _textcolor, _size, _tooltip) =>\n",
+    "    var lb = label.new(_x, _y, _text, xloc.bar_index, yloc.price, _color, _style, _textcolor, _size, text.align_left, _tooltip)\n",
+    "    lb.set_xy(_x, _y)\n",
+    "    lb.set_text(_text)\n",
+    "    lb.set_tooltip(_tooltip)\n",
+    "    lb.set_textcolor(_textcolor)\n",
+    "\n",
+    "getData(_ltf) => request.security_lower_tf(syminfo.tickerid, _ltf, bar.new(), ignore_invalid_timeframe = true)\n",
+    "\n",
+    "f_gTF(_d) => \n",
+    "    int tfInMs = timeframe.in_seconds(timeframe.period)\n",
+    "    int  mInMS = 60\n",
+    "\n",
+    "    if _d == 2\n",
+    "        switch\n",
+    "            tfInMs <                 30  =>  '1S'\n",
+    "            tfInMs <          1 * mInMS  =>  '5S'\n",
+    "\n",
+    "            tfInMs <=        15 * mInMS  =>   '1'\n",
+    "            tfInMs <=        60 * mInMS  =>   '5'\n",
+    "            tfInMs <=       240 * mInMS  =>  '15'\n",
+    "            tfInMs <=      1440 * mInMS  =>  '60'\n",
+    "            => 'D'\n",
+    "\n",
+    "    else if _d == 1\n",
+    "        switch\n",
+    "            tfInMs <                 15  =>  '1S'\n",
+    "            tfInMs <                 30  =>  '5S'\n",
+    "            tfInMs <          1 * mInMS  => '15S'\n",
+    "\n",
+    "            tfInMs <=         5 * mInMS  =>   '1'\n",
+    "            tfInMs <=        15 * mInMS  =>   '5'\n",
+    "            tfInMs <=        60 * mInMS  =>  '15'\n",
+    "            tfInMs <=       240 * mInMS  =>  '60'\n",
+    "            tfInMs <=      1440 * mInMS  => '240'\n",
+    "            => 'D'\n",
+    "\n",
+    "f_gTS(_t) =>\n",
+    "    switch _t\n",
+    "        'Tiny'   => size.tiny\n",
+    "        'Small'  => size.small \n",
+    "        'Normal' => size.normal\n",
+    "        => size.auto\n",
+    "\n",
+    "f_crossingLevelX(_price, _level) =>\n",
+    "    (_level > _price and _level < _price[1]) or (_level < _price and _level > _price[1])\n",
+    "\n",
+    "alarm(_message) => \n",
+    "    alert(syminfo.ticker + ' ' + _message + ', price ' + str.tostring(b.c, format.mintick) + ', timeframe ' + timeframe.period, alert.freq_once_per_bar)\n",
+    "\n",
+    "\n",
+    "// Calculations Volume Profile\n",
+    "nzV  = nz(b.v)\n",
+    "\n",
+    "rpS  = f_gTS(rpLS)\n",
+    "ppLS:= f_gTS(ppLS)\n",
+    "\n",
+    "tPOS = lcDB == 'Top Right' ? position.top_right\n",
+    "     : lcDB == 'Middle Right' ? position.middle_right \n",
+    "     : position.bottom_left\n",
+    "\n",
+    "if time == chart.left_visible_bar_time\n",
+    "    VP.sI := b.i\n",
+    "\n",
+    "if vpRL\n",
+    "    vpLN := last_bar_index - VP.sI\n",
+    "\n",
+    "if b.i == last_bar_index - vpLN\n",
+    "    VP.sI   := b.i\n",
+    "    pLST := b.l \n",
+    "    pHST := b.h\n",
+    "else if b.i > last_bar_index - vpLN\n",
+    "    pLST := math.min(b.l, pLST)\n",
+    "    pHST := math.max(b.h, pHST)\n",
+    "\n",
+    "if vpLN <= 200\n",
+    "    ltf := f_gTF(2)\n",
+    "    ltfBD := getData(f_gTF(2))  \n",
+    "else if vpLN <= 700\n",
+    "    ltf := f_gTF(1)\n",
+    "    ltfBD := getData(f_gTF(1)) \n",
+    "else\n",
+    "    ltf := 'Chart'\n",
+    "    ltfBD := array.new<bar> (1, bar.new(b.o, b.h, b.l, b.c, b.v))\n",
+    "\n",
+    "if barstate.ishistory and (b.i >= last_bar_index - vpLN) and b.i < last_bar_index and ltfBD.size() > 0\n",
+    "\n",
+    "    if ltfBD.size() > 0 and not na(nz(ltfBD.get(0).v))\n",
+    "        for i = 0 to ltfBD.size() - 1\n",
+    "            bD.bh.push(ltfBD.get(i).h)\n",
+    "            bD.bl.push(ltfBD.get(i).l)\n",
+    "            bD.bv.push(ltfBD.get(i).v)\n",
+    "\n",
+    "            if vsPT\n",
+    "                bD.bp.push(ltfBD.get(i).c > ltfBD.get(i).o)\n",
+    "            else\n",
+    "                bD.bp.push(ltfBD.get(i).c - ltfBD.get(i).l > ltfBD.get(i).h - ltfBD.get(i).c)\n",
+    "\n",
+    "        bD.bn.push(ltfBD.size())\n",
+    "\n",
+    "pSTP = (pHST - pLST) / vpNR\n",
+    "\n",
+    "if barstate.islast and ltfBD.size() > 0\n",
+    "\n",
+    "    if VP.vp.size() > 0\n",
+    "        for i = 0 to VP.vp.size() - 1\n",
+    "            box.delete(VP.vp.shift())\n",
+    "\n",
+    "    if bD.bn.size() > vpLN\n",
+    "        qt = bD.bn.shift()\n",
+    "        for i = 0 to qt - 1\n",
+    "            bD.bh.shift()\n",
+    "            bD.bl.shift()\n",
+    "            bD.bv.shift()\n",
+    "            bD.bp.shift()\n",
+    "\n",
+    "    VP.pPC.clear()\n",
+    "    VP.dPC.delete()\n",
+    "\n",
+    "    if ltfBD.size() > 0 and not na(nz(ltfBD.get(0).v))\n",
+    "        for i = 0 to ltfBD.size() - 1\n",
+    "            bD.bh.push(ltfBD.get(i).h)\n",
+    "            bD.bl.push(ltfBD.get(i).l)\n",
+    "            bD.bv.push(ltfBD.get(i).v)\n",
+    "\n",
+    "            if vsPT\n",
+    "                bD.bp.push(ltfBD.get(i).c > ltfBD.get(i).o)\n",
+    "            else\n",
+    "                bD.bp.push(ltfBD.get(i).c - ltfBD.get(i).l > ltfBD.get(i).h - ltfBD.get(i).c)\n",
+    "\n",
+    "        bD.bn.push(ltfBD.size())\n",
+    "\n",
+    "    bI  = vpLN\n",
+    "    bSZ = 0\n",
+    "    aSZ = bD.bv.size()\n",
+    "\n",
+    "    for aI = 0 to aSZ - 1\n",
+    "\n",
+    "        i = 0\n",
+    "        for pLL = pLST to pHST - pSTP by pSTP\n",
+    "\n",
+    "            lH = bD.bh.get(aI)\n",
+    "            lL = bD.bl.get(aI)\n",
+    "            lV = bD.bv.get(aI)\n",
+    "\n",
+    "            if lH >= pLL and lL < pLL + pSTP\n",
+    "\n",
+    "                vPOR = if lL >= pLL and lH > pLL + pSTP\n",
+    "                    (pLL + pSTP - lL) / (lH - lL)\n",
+    "                else if lH <= pLL + pSTP and lL < pLL\n",
+    "                    (lH - pLL) / (lH - lL)\n",
+    "                else if lL >= pLL and lH <= pLL + pSTP\n",
+    "                    1\n",
+    "                else\n",
+    "                    pSTP / (lH - lL)\n",
+    "\n",
+    "                vD.vt.set(i, vD.vt.get(i) + lV * vPOR)\n",
+    "\n",
+    "                if bD.bp.get(aI)\n",
+    "                    vD.vb.set(i, vD.vb.get(i) + lV * vPOR)\n",
+    "            i += 1\n",
+    "\n",
+    "        if pcSH == 'Developing POC'\n",
+    "            if aI == bD.bn.get(vpLN - bI)\n",
+    "                VP.pPC.push(chart.point.from_index(b.i[bI], math.avg(b.h[bI], b.l[bI])))\n",
+    "                VP.pPC.push(chart.point.from_index(b.i[bI] + 1, pLST + (vD.vt.indexof(vD.vt.max()) + .5) * pSTP))\n",
+    "                bSZ += bD.bn.get(vpLN - bI)\n",
+    "                bI  -= 1\n",
+    "            else if aI == (bSZ + bD.bn.get(vpLN - bI)) and bSZ != 0\n",
+    "                VP.pPC.push(chart.point.from_index(b.i[bI] + 1, pLST + (vD.vt.indexof(vD.vt.max()) + .5) * pSTP))\n",
+    "                bSZ += bD.bn.get(vpLN - bI)\n",
+    "                bI  -= 1\n",
+    "            else if bI == 0\n",
+    "                VP.pPC.push(chart.point.from_index(b.i[bI] + 1, pLST + (vD.vt.indexof(vD.vt.max()) + .5) * pSTP))\n",
+    "                bSZ += bD.bn.get(vpLN - bI)\n",
+    "\n",
+    "    VP.dPC := polyline.new(VP.pPC, false, false, xloc.bar_index, pocC, color(na), line.style_solid, pocW)\n",
+    "\n",
+    "    for i = 0 to vpNR - 1\n",
+    "        bbp = 2 * vD.vb.get(i) - vD.vt.get(i)\n",
+    "        vD.vd.set(i, vD.vd.get(i) + bbp * (bbp > 0 ? 1 : -1) )\n",
+    "\n",
+    "    VP.pcL := vD.vt.indexof(vD.vt.max())\n",
+    "    ttV     = vD.vt.sum() * vpVA\n",
+    "    va      = vD.vt.get(VP.pcL)\n",
+    "    VP.laP := VP.pcL\n",
+    "    VP.lbP := VP.pcL\n",
+    "    \n",
+    "    while va < ttV\n",
+    "        if VP.lbP == 0 and VP.laP == vpNR - 1\n",
+    "            break\n",
+    "\n",
+    "        vaP = 0.\n",
+    "        if VP.laP < vpNR - 1 \n",
+    "            vaP := vD.vt.get(VP.laP + 1)\n",
+    "\n",
+    "        vbP = 0.\n",
+    "        if VP.lbP > 0\n",
+    "            vbP := vD.vt.get(VP.lbP - 1)\n",
+    "        \n",
+    "        if vaP >= vbP\n",
+    "            va  += vaP\n",
+    "            VP.laP += 1\n",
+    "        else\n",
+    "            va  += vbP\n",
+    "            VP.lbP -= 1\n",
+    "\n",
+    "    vaH = pLST + (VP.laP + 1.) * pSTP\n",
+    "    poc = pLST + (VP.pcL + .5) * pSTP\n",
+    "    vaL = pLST + (VP.lbP + .0) * pSTP\n",
+    "    pLN = vpLN > 360 ? 360 : vpLN\n",
+    "\n",
+    "    \n",
+    "    if pcSH == 'Last (Line)'\n",
+    "        f_drawLineX(VP.sI, poc, last_bar_index, poc, xloc.bar_index, extend.none, pocC, line.style_solid, pocW)\n",
+    "\n",
+    "    \n",
+    "    if vaBG\n",
+    "        VP.vp.push(box.new(VP.sI, vaL, last_bar_index, vaH, vBGC, 1, line.style_dotted, bgcolor = vBGC))\n",
+    "\n",
+    "    \n",
+    "    for i = 0 to vpNR - 1\n",
+    "    \n",
+    "        if vpSH\n",
+    "            sBI = vpRT ? int((pLN * vpWD + vpHO)) + int(last_bar_index - vD.vb.get(i) / vD.vt.max() * pLN * vpWD) : VP.sI\n",
+    "            eBI = vpRT ? int((pLN * vpWD + vpHO)) + last_bar_index : int(sBI + vD.vb.get(i) / vD.vt.max() * pLN * vpWD)\n",
+    "            VP.vp.push(box.new(sBI, pLST + (i + .1) * pSTP, eBI, pLST + (i + .9) * pSTP, color(na), bgcolor = i >= VP.lbP and i <= VP.laP ? vaUC : vpUC))\n",
+    "\n",
+    "            sBI := vpRT ? sBI : eBI\n",
+    "            eBI := vpRT ? sBI - int( (vD.vt.get(i) - vD.vb.get(i)) / vD.vt.max() * pLN * vpWD) : sBI + int( (vD.vt.get(i) - vD.vb.get(i)) / vD.vt.max() * pLN * vpWD)\n",
+    "            VP.vp.push(box.new(sBI, pLST + (i + .1) * pSTP, eBI, pLST + (i + .9) * pSTP, color(na), bgcolor = i >= VP.lbP and i <= VP.laP ? vaDC : vpDC))\n",
+    "\n",
+    "\n",
+    "// Calculations Volume Histogram\n",
+    "pHSTv = ta.highest(b.h, vpLN < 497 ? vpLN : 496)\n",
+    "pLSTv = ta.lowest (b.l, vpLN < 497 ? vpLN : 496)\n",
+    "vHST  = ta.highest(nzV, vpLN < 497 ? vpLN : 496)\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "c4630aae",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7209f45a",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Pinescript Folder/v5 Variant 2/fuhfuhlogic.ipynb b/Pinescript Folder/v5 Variant 2/fuhfuhlogic.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..765d24a3ae19a5e9914af72d9ca4a141be663281
--- /dev/null
+++ b/Pinescript Folder/v5 Variant 2/fuhfuhlogic.ipynb	
@@ -0,0 +1,1193 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9132c720",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "//@version=6\n",
+    "//indicator(\"v5\", overlay = true, max_labels_count = 300, max_lines_count = 300, max_boxes_count = 300, max_bars_back = 300)\n",
+    "indicator(\"Sunstoic\", \"v5\", true, max_bars_back = 5000, max_boxes_count = 500, max_lines_count = 500, max_labels_count = 500)\n",
+    "\n",
+    "\n",
+    "//candle logic\n",
+    "// === Inputs ===\n",
+    "bCol = input.color(#008080, title=\"Bull Border\")\n",
+    "rCol = input.color(#e20000, title=\"Bear Border\")\n",
+    "bgB = input.color(color.new(#008080, 20), title=\"Bull Body\")\n",
+    "bgR = input.color(color.new(#FF0000, 20), title=\"Bear Body\")\n",
+    "\n",
+    "\n",
+    "    // === Input your time zone (Manila = GMT+8) \n",
+    "timeSessionStart = timestamp(\"GMT+8\", year, month, dayofmonth, 6, 0)  // Start of day\n",
+    "isNewDay = ta.change(time(\"D\"))  // Detect new day\n",
+    "\n",
+    "// Track the current day's developing close on each bar\n",
+    "var float devClose = na\n",
+    "if bool(isNewDay)\n",
+    "    devClose := close  // reset on new day\n",
+    "else\n",
+    "    devClose := close  // update each bar\n",
+    "\n",
+    "\n",
+    "// === Default Daily Candle (6:00 AM) ===\n",
+    "dO = request.security(syminfo.tickerid, \"D\", open)\n",
+    "dH = request.security(syminfo.tickerid, \"D\", high)\n",
+    "dL = request.security(syminfo.tickerid, \"D\", low)\n",
+    "dC = request.security(syminfo.tickerid, \"D\", close)\n",
+    "\n",
+    "\n",
+    "// Calculate daily high and low using 'day' timeframe\n",
+    "var float dailyHigh = na\n",
+    "var float dailyLow = na\n",
+    "newDay = ta.change(time(\"D\"))\n",
+    "\n",
+    "\n",
+    "// On new day, reset high/low\n",
+    "if bool(newDay)\n",
+    "    dailyHigh := close\n",
+    "    dailyLow := open\n",
+    "else\n",
+    "    dailyHigh := math.max(dailyHigh, close)\n",
+    "    dailyLow := math.min(dailyLow, open)\n",
+    "\n",
+    "\n",
+    "// Midpoint of the daily candle\n",
+    "midPrice = (dailyHigh + dailyLow) / 2\n",
+    "\n",
+    "\n",
+    "dBull = dC >= dO\n",
+    "dCol = dBull ? bCol : rCol\n",
+    "dBg = dBull ? bgB : bgR\n",
+    "\n",
+    "\n",
+    "// Offset for visuals\n",
+    "ofs = 10\n",
+    "bw = 2\n",
+    "rIdx = bar_index + ofs + bw / 2\n",
+    "lIdx = bar_index + ofs - bw / 2\n",
+    "xMid = int(bar_index + ofs)\n",
+    "\n",
+    "\n",
+    "// Body of daily candle box\n",
+    "var box dBx = na\n",
+    "box.delete(dBx)\n",
+    "tB = math.max(dO, devClose) //y=dC\n",
+    "bB = math.min(dO, devClose)\n",
+    "dBx := box.new(left=int(lIdx), right=int(rIdx), top=tB, bottom=bB, border_color=dCol, bgcolor=dBg)\n",
+    "\n",
+    "\n",
+    "// Wicks for daily candle\n",
+    "var line dW1 = na\n",
+    "var line dW2 = na\n",
+    "line.delete(dW1)\n",
+    "line.delete(dW2)\n",
+    "dW1 := line.new(x1=xMid, y1=dH, x2=xMid, y2=tB, color=dCol)\n",
+    "dW2 := line.new(x1=xMid, y1=bB, x2=xMid, y2=dL, color=dCol)\n",
+    "\n",
+    "\n",
+    "\n",
+    "// Labels for daily candle\n",
+    "var label lblO = na\n",
+    "var label lblH = na\n",
+    "var label lblL = na\n",
+    "var label lblC = na\n",
+    "label.delete(lblO)\n",
+    "label.delete(lblH)\n",
+    "label.delete(lblL)\n",
+    "label.delete(lblC)\n",
+    "\n",
+    "\n",
+    "lblStyle = label.style_label_right\n",
+    "lblSize = size.tiny\n",
+    "lblOfs = -2\n",
+    "\n",
+    "lblO := label.new(x=xMid + lblOfs, y=dO, text=\"6O \" + str.tostring(dO, \"#\"), style=lblStyle, color=color.new(#2195f3, 100), textcolor=color.black, size=lblSize)\n",
+    "lblH := label.new(x=xMid + lblOfs, y=dH, text=\"6H \" + str.tostring(dH, \"#\"), style=lblStyle, color=color.new(#2195f3, 100), textcolor=color.black, size=lblSize)\n",
+    "lblL := label.new(x=xMid + lblOfs, y=dL, text=\"6L \" + str.tostring(dL, \"#\"), style=lblStyle, color=color.new(#2195f3, 100), textcolor=color.black, size=lblSize)\n",
+    "lblC := label.new(x=xMid + lblOfs, y=devClose, text=\"6C \" + str.tostring(dC, \"#\"), style=lblStyle, color=color.new(#2195f3, 100), textcolor=color.black, size=lblSize)\n",
+    "\n",
+    "// === Custom Daily Candle (8:00 AM Manila) ===\n",
+    "sh = 24\n",
+    "sm = 0\n",
+    "st = timestamp(\"UTC\", year, month, dayofmonth, sh, sm)\n",
+    "\n",
+    "\n",
+    "var float cO = na\n",
+    "var float cH = na\n",
+    "var float cL = na\n",
+    "var float cC = na\n",
+    "\n",
+    "\n",
+    "if (time == st)\n",
+    "    cO := open\n",
+    "    cH := high\n",
+    "    cL := low\n",
+    "    cC := close\n",
+    "\n",
+    "\n",
+    "if (not na(cO))\n",
+    "    cH := math.max(cH, high)\n",
+    "    cL := math.min(cL, low)\n",
+    "    cC := close\n",
+    "\n",
+    "\n",
+    "cBull = cC >= cO\n",
+    "cCol = cBull ? bCol : rCol\n",
+    "cBg = cBull ? bgB : bgR\n",
+    "\n",
+    "\n",
+    "cOfs = 12\n",
+    "cbw = 2\n",
+    "crIdx = bar_index + cOfs + cbw / 2\n",
+    "clIdx = bar_index + cOfs - cbw / 2\n",
+    "cX = int(bar_index + cOfs)\n",
+    "\n",
+    "\n",
+    "// Custom body box\n",
+    "var box cBx = na\n",
+    "box.delete(cBx)\n",
+    "ctB = math.max(cO, devClose) //y=dC\n",
+    "cbB = math.min(cO, devClose)\n",
+    "cBx := box.new(left=int(clIdx), right=int(crIdx), top=ctB, bottom=cbB, border_color=cCol, bgcolor=cBg)\n",
+    "\n",
+    "\n",
+    "// Custom wicks\n",
+    "var line cW1 = na\n",
+    "var line cW2 = na\n",
+    "line.delete(cW1)\n",
+    "line.delete(cW2)\n",
+    "cW1 := line.new(x1=cX, y1=cH, x2=cX, y2=ctB, color=cCol)\n",
+    "cW2 := line.new(x1=cX, y1=cbB, x2=cX, y2=cL, color=cCol)\n",
+    "\n",
+    "\n",
+    "// Custom labels\n",
+    "var label clO = na\n",
+    "var label clH = na\n",
+    "var label clL = na\n",
+    "var label clC = na\n",
+    "label.delete(clO)\n",
+    "label.delete(clH)\n",
+    "label.delete(clL)\n",
+    "label.delete(clC)\n",
+    "\n",
+    "\n",
+    "clStyle = label.style_label_left\n",
+    "clSize = size.tiny\n",
+    "clOfs = 2\n",
+    "\n",
+    "\n",
+    "clO := label.new(x=cX + clOfs, y=cO, text=\"8O \" + str.tostring(cO, \"#\"), style=clStyle, color=color.new(#2195f3, 100), textcolor=color.black, size=clSize)\n",
+    "clH := label.new(x=cX + clOfs, y=cH, text=\"8H \" + str.tostring(cH, \"#\"), style=clStyle, color=color.new(#2195f3, 100), textcolor=color.black, size=clSize)\n",
+    "clL := label.new(x=cX + clOfs, y=cL, text=\"8L \" + str.tostring(cL, \"#\"), style=clStyle, color=color.new(#2195f3, 100), textcolor=color.black, size=clSize)\n",
+    "clC := label.new(x=cX + clOfs, y=devClose, text=\"8C \" + str.tostring(dC, \"#\"), style=clStyle, color=color.new(#2195f3, 100), textcolor=color.black, size=clSize)\n",
+    "\n",
+    "// === High and Low Lines (using bar_index for x coords) ===\n",
+    "// Track bar_index of high and low bars within current day\n",
+    "var int highBarIndex = na\n",
+    "var int lowBarIndex = na\n",
+    "var float dayHigh = na\n",
+    "var float dayLow = na\n",
+    "\n",
+    "\n",
+    "startOfDay = timestamp(year, month, dayofmonth, 0, 0)\n",
+    "t6amManila = timestamp(\"Asia/Manila\", year, month, dayofmonth, 6, 0)\n",
+    "\n",
+    "\n",
+    "issNewDay = ta.change(time(\"D\"))\n",
+    "\n",
+    "\n",
+    "if (time >= startOfDay)\n",
+    "    if na(dayHigh) or high > dayHigh\n",
+    "        dayHigh := high\n",
+    "        highBarIndex := bar_index\n",
+    "    if na(dayLow) or low < dayLow\n",
+    "        dayLow := low\n",
+    "        lowBarIndex := bar_index\n",
+    "\n",
+    "\n",
+    "if bool(issNewDay)\n",
+    "    dayHigh := na\n",
+    "    dayLow := na\n",
+    "    highBarIndex := na\n",
+    "    lowBarIndex := na\n",
+    "\n",
+    "\n",
+    "// Draw high line from high bar index to current xMid\n",
+    "var line highLine = na\n",
+    "if not na(dayHigh) and not na(highBarIndex)\n",
+    "    if na(highLine)\n",
+    "        highLine := line.new(x1=highBarIndex, y1=dayHigh, x2=xMid, y2=dayHigh, color=dCol, width=2, style = line.style_solid)\n",
+    "    else\n",
+    "        line.set_xy1(highLine, highBarIndex, dayHigh)\n",
+    "        line.set_xy2(highLine, xMid, dayHigh)\n",
+    "\n",
+    "\n",
+    "// Draw low line from low bar index to current xMid\n",
+    "var line lowLine = na\n",
+    "if not na(dayLow) and not na(lowBarIndex)\n",
+    "    if na(lowLine)\n",
+    "        lowLine := line.new(x1=lowBarIndex, y1=dayLow, x2=xMid, y2=dayLow, color=dCol, width=2, style = line.style_solid)\n",
+    "    else\n",
+    "        line.set_xy1(lowLine, lowBarIndex, dayLow)\n",
+    "        line.set_xy2(lowLine, xMid, dayLow)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "// --- 6:00 AM Asia/Manila horizontal line ---\n",
+    "// Manila 6:00 AM timestamp today\n",
+    "manila_6am = timestamp(\"GMT+8\", year, month, dayofmonth, 30, 0)\n",
+    "\n",
+    "\n",
+    "// Detect bar where time crosses 6:00 AM Manila\n",
+    "isStartBar6am = (time >= manila_6am) and (time[1] < manila_6am)\n",
+    "\n",
+    "\n",
+    "// Store 6:00 AM bar open price and index\n",
+    "var float price6am = na\n",
+    "var int bar6am_index = na\n",
+    "\n",
+    "\n",
+    "if isStartBar6am\n",
+    "    price6am := open\n",
+    "    bar6am_index := bar_index\n",
+    "\n",
+    "\n",
+    "// Draw horizontal line from 6AM bar index to xMid at price6am\n",
+    "var line hLine6am = na\n",
+    "\n",
+    "\n",
+    "if not na(price6am) and not na(bar6am_index)\n",
+    "    line.delete(hLine6am)\n",
+    "    hLine6am := line.new(x1=bar6am_index, y1=price6am, x2=xMid, y2=price6am, color=color.new(#ecc900, 0), width=1, style=line.style_solid)\n",
+    "\n",
+    "\n",
+    "// --- 8:00 AM Asia/Manila horizontal line ---\n",
+    "// Detect bar where time crosses 8:00 AM Manila\n",
+    "isStartBar8am = (time >= st) and (time[1] < st)\n",
+    "\n",
+    "\n",
+    "// Store 8:00 AM bar open price and index\n",
+    "var float price8am = na\n",
+    "var int bar8am_index = na\n",
+    "\n",
+    "\n",
+    "if isStartBar8am\n",
+    "    price8am := open\n",
+    "    bar8am_index := bar_index\n",
+    "\n",
+    "\n",
+    "// Draw horizontal line from 8AM bar index to cX at price8am\n",
+    "var line hLine8am = na\n",
+    "var line hhLine8am = na\n",
+    "\n",
+    "\n",
+    "if not na(price8am) and not na(bar8am_index)\n",
+    "    line.delete(hLine8am)\n",
+    "    hLine8am := line.new(x1=bar8am_index, y1=price8am, x2=cX, y2=price8am, color=color.new(#fad400, 0), width=1, style=line.style_solid)\n",
+    "\n",
+    "\n",
+    "\n",
+    "// Draw horizontal line that updates000000000000000000000000\n",
+    "var line devCloseLine = na\n",
+    "var line ddevCloseLine = na\n",
+    "if bar_index > 0\n",
+    "    if na(devCloseLine)\n",
+    "        devCloseLine := line.new(x1=bar_index, y1=devClose , x2=xMid , y2=devClose, style = line.style_solid, color=color.new(#ecc900, 0), width=1)\n",
+    "    else\n",
+    "        line.set_xy1(devCloseLine, bar_index, devClose)\n",
+    "        line.set_xy2(devCloseLine, bar_index + 13, devClose)\n",
+    "    if na(ddevCloseLine) \n",
+    "        //ddevCloseLine := line.new(x1=xMid, y1=devClose, x2=xMid, y2=devClose, extend=extend.right, color=dCol, width=2)\n",
+    "        ddevCloseLine := line.new(x1=xMid, y1=devClose, x2=xMid , y2=devClose, style = line.style_dotted, extend=extend.right, color=color.black, width=1)\n",
+    "    else\n",
+    "        line.set_xy1(ddevCloseLine, bar_index + 19,  devClose)\n",
+    "        line.set_xy2(ddevCloseLine, bar_index + 20, devClose)\n",
+    "\n",
+    "\n",
+    "\n",
+    "//zone logic\n",
+    "// Disable visuals if timeframe is higher than 1 hour\n",
+    "isValidTF = timeframe.isminutes and timeframe.multiplier <= 60\n",
+    "\n",
+    "\n",
+    "// Current time components\n",
+    "currentTime = timestamp(year, month, dayofmonth, hour, minute)\n",
+    "\n",
+    "// Define 12PM to 12AM session\n",
+    "start12pm = timestamp(year, month, dayofmonth, 12, 0)\n",
+    "end12am = timestamp(year, month, dayofmonth, 23, 59)\n",
+    "in12pmTo12am = currentTime >= start12pm and currentTime <= end12am\n",
+    "\n",
+    "// Define 8AM to 8PM session\n",
+    "start8am = timestamp(year, month, dayofmonth, 8, 0)\n",
+    "end8pm = timestamp(year, month, dayofmonth, 20, 0)\n",
+    "in8amTo8pm = currentTime >= start8am and currentTime <= end8pm\n",
+    "\n",
+    "// Define 6PM to 8PM session\n",
+    "start6pm = timestamp(year, month, dayofmonth, 6, 0)\n",
+    "eend8pm = timestamp(year, month, dayofmonth, 8, 0)\n",
+    "in6pmTo8pm = currentTime >= start6pm and currentTime <= eend8pm\n",
+    "\n",
+    "start6am = timestamp(year, month, dayofmonth, 18, 0)\n",
+    "eend8am = timestamp(year, month, dayofmonth, 20, 0)\n",
+    "in6amTo8am = currentTime >= start6am and currentTime <= eend8am\n",
+    "\n",
+    "// Apply background highlights\n",
+    "bgcolor(isValidTF and in12pmTo12am ? color.new(color.black, 99) : na)\n",
+    "bgcolor(isValidTF and in8amTo8pm ? color.new(color.black, 99) : na)\n",
+    "bgcolor(isValidTF and in6pmTo8pm ? color.new(color.black, 97) : na)\n",
+    "bgcolor(isValidTF and in6amTo8am ? color.new(color.black, 95) : na)\n",
+    "\n",
+    "// Get the current day of the week and the current time\n",
+    "isMonday = dayofweek == dayofweek.sunday\n",
+    "ccurrentTime = timestamp(year, month, dayofmonth, hour, minute)\n",
+    "\n",
+    "// Define the time range for 6:00 AM to 8:00 AM\n",
+    "startTime = timestamp(year, month, dayofmonth, 18, 6) // 6:00 AM\n",
+    "endTime = timestamp(year, month, dayofmonth, 20, 0) // 8:00 AM\n",
+    "\n",
+    "// Check if it's Monday and the current time is within the range of 6:00 AM to 8:00 AM\n",
+    "isInTimeRange = isMonday and ccurrentTime >= startTime and ccurrentTime <= endTime\n",
+    "\n",
+    "// Highlight the area with a background color\n",
+    "bgcolor(isValidTF and isInTimeRange ? color.new(color.blue, 90) : na)\n",
+    "\n",
+    "// Vertical lines logic\n",
+    "// Only show vertical lines if timeframe is intraday and valid\n",
+    "showLines = timeframe.isintraday and isValidTF\n",
+    "\n",
+    "// Convert current bar time to Asia/Manila timezone\n",
+    "t = time(timeframe.period, 'Asia/Manila')\n",
+    "\n",
+    "// Target hours excluding 0, 12, 20\n",
+    "var array<int> targetHours = array.from(3, 6, 8, 15, 24) // 24 means midnight (0h)\n",
+    "\n",
+    "// Special hours separated\n",
+    "specialHour20 = 20\n",
+    "specialHour0 = 0 // midnight\n",
+    "specialHour12 = 12 // noon\n",
+    "\n",
+    "// Function to check if current bar time matches any target hour exactly at minute zero\n",
+    "isTargetTime() =>\n",
+    "    h = hour(t)\n",
+    "    m = minute(t)\n",
+    "    match = false\n",
+    "    for i = 0 to array.size(targetHours) - 1 by 1\n",
+    "        if (h == array.get(targetHours, i) or h == 0 and array.get(targetHours, i) == 24) and m == 0\n",
+    "            match := true\n",
+    "            match\n",
+    "    match\n",
+    "\n",
+    "// Check if current time matches special hour 20 at minute zero\n",
+    "isSpecialTime20() =>\n",
+    "    h = hour(t)\n",
+    "    m = minute(t)\n",
+    "    h == specialHour20 and m == 0\n",
+    "\n",
+    "// Check if current time matches special hour 0 at minute zero\n",
+    "isSpecialTime0() =>\n",
+    "    h = hour(t)\n",
+    "    m = minute(t)\n",
+    "    h == specialHour0 and m == 0\n",
+    "\n",
+    "// Check if current time matches special hour 12 at minute zero\n",
+    "isSpecialTime12() =>\n",
+    "    h = hour(t)\n",
+    "    m = minute(t)\n",
+    "    h == specialHour12 and m == 0\n",
+    "\n",
+    "// Detect new day (day break)\n",
+    "nnewDay = ta.change(time('D'))\n",
+    "\n",
+    "// Only draw lines if timeframe is intraday and valid\n",
+    "if showLines\n",
+    "    // Draw vertical line at day break (green, width 1)\n",
+    "    if bool(nnewDay)\n",
+    "        line.new(bar_index, low - 10 * syminfo.mintick, bar_index, high + 10 * syminfo.mintick, color = color.rgb(76, 175, 79, 30), width = 1, extend = extend.both)\n",
+    "\n",
+    "    // Draw vertical line at target times (blue, width 1)\n",
+    "    if isTargetTime()\n",
+    "        line.new(bar_index, low - 10 * syminfo.mintick, bar_index, high + 10 * syminfo.mintick, color = color.rgb(33, 149, 243, 90), width = 1, extend = extend.both)\n",
+    "\n",
+    "    // Draw vertical line at special hour 20 (teal, width 1)\n",
+    "    if isSpecialTime20()\n",
+    "        line.new(bar_index, low - 10 * syminfo.mintick, bar_index, high + 10 * syminfo.mintick, color = color.new(color.teal, 50), width = 1, extend = extend.both)\n",
+    "\n",
+    "    // Draw vertical line at special hour 0 (midnight) - orange, dotted\n",
+    "    if isSpecialTime0()\n",
+    "        var line midnightLine = na\n",
+    "        midnightLine := line.new(bar_index, low - 10 * syminfo.mintick, bar_index, high + 10 * syminfo.mintick, color = color.orange, width = 1, extend = extend.both)\n",
+    "        line.set_style(midnightLine, line.style_dotted)\n",
+    "\n",
+    "    // Draw vertical line at special hour 12 (noon) - red, dotted\n",
+    "    if isSpecialTime12()\n",
+    "        var line noonLine = na\n",
+    "        noonLine := line.new(bar_index, low - 10 * syminfo.mintick, bar_index, high + 10 * syminfo.mintick, color = color.red, width = 1, extend = extend.both)\n",
+    "        line.set_style(noonLine, line.style_dotted)\n",
+    "\n",
+    "//hourly logic\n",
+    "// === UTILITY FUNCTION ===\n",
+    "f_show(tf) =>\n",
+    "    timeframe.period != tf\n",
+    "\n",
+    "// === HOURLY ===\n",
+    "hH = request.security(syminfo.tickerid, '60', high, lookahead = barmerge.lookahead_on)\n",
+    "hL = request.security(syminfo.tickerid, '60', low, lookahead = barmerge.lookahead_on)\n",
+    "hIdx = request.security(syminfo.tickerid, '60', bar_index, lookahead = barmerge.lookahead_on)\n",
+    "\n",
+    "var int hPrev = na\n",
+    "var float hHi = na\n",
+    "var int hHiBar = na\n",
+    "var line hHiLine = na\n",
+    "var bool hHiOn = false\n",
+    "var float hLo = na\n",
+    "var int hLoBar = na\n",
+    "var line hLoLine = na\n",
+    "var bool hLoOn = false\n",
+    "\n",
+    "if hIdx != hPrev\n",
+    "    hPrev := hIdx\n",
+    "    hHi := na\n",
+    "    hHiBar := na\n",
+    "    line.delete(hHiLine)\n",
+    "    hHiLine := na\n",
+    "    hHiOn := false\n",
+    "    hLo := na\n",
+    "    hLoBar := na\n",
+    "    line.delete(hLoLine)\n",
+    "    hLoLine := na\n",
+    "    hLoOn := false\n",
+    "    hLoOn\n",
+    "\n",
+    "if hHiOn and not na(hHiLine)\n",
+    "    bMin = math.min(open, close)\n",
+    "    bMax = math.max(open, close)\n",
+    "    if hHi <= bMax and hHi >= bMin\n",
+    "        line.set_x2(hHiLine, bar_index)\n",
+    "        line.set_y2(hHiLine, hHi)\n",
+    "        hHiOn := false\n",
+    "        hHiOn\n",
+    "    else\n",
+    "        line.set_x2(hHiLine, bar_index)\n",
+    "        line.set_y2(hHiLine, hHi)\n",
+    "\n",
+    "if hLoOn and not na(hLoLine)\n",
+    "    bMin = math.min(open, close)\n",
+    "    bMax = math.max(open, close)\n",
+    "    if hLo <= bMax and hLo >= bMin\n",
+    "        line.set_x2(hLoLine, bar_index)\n",
+    "        line.set_y2(hLoLine, hLo)\n",
+    "        hLoOn := false\n",
+    "        hLoOn\n",
+    "    else\n",
+    "        line.set_x2(hLoLine, bar_index)\n",
+    "        line.set_y2(hLoLine, hLo)\n",
+    "\n",
+    "// === 30M ===\n",
+    "tH = request.security(syminfo.tickerid, '30', high, lookahead = barmerge.lookahead_on)\n",
+    "tL = request.security(syminfo.tickerid, '30', low, lookahead = barmerge.lookahead_on)\n",
+    "tO = request.security(syminfo.tickerid, '30', open, lookahead = barmerge.lookahead_on)\n",
+    "tC = request.security(syminfo.tickerid, '30', close, lookahead = barmerge.lookahead_on)\n",
+    "tIdx = request.security(syminfo.tickerid, '30', bar_index, lookahead = barmerge.lookahead_on)\n",
+    "\n",
+    "plot(tO, title = '30M Open', color = color.rgb(0, 20, 133, 80), style = plot.style_stepline_diamond, linewidth = 1)\n",
+    "\n",
+    "isBull = tC > tO\n",
+    "isBear = tC < tO\n",
+    "\n",
+    "phb = plot(isBull ? tH : na, color = color.new(#4caf4f, 100), style = plot.style_steplinebr)\n",
+    "plb = plot(isBull ? tL : na, color = color.new(#4caf4f, 100), style = plot.style_steplinebr)\n",
+    "phr = plot(isBear ? tH : na, color = color.new(#ff5252, 100), style = plot.style_steplinebr)\n",
+    "plr = plot(isBear ? tL : na, color = color.new(#ff5252, 100), style = plot.style_steplinebr)\n",
+    "\n",
+    "fill(phb, plb, color = color.new(color.blue, 95))\n",
+    "fill(phr, plr, color = color.new(color.red, 95))\n",
+    "\n",
+    "var int tPrev = na\n",
+    "var float tHi = na\n",
+    "var int tHiBar = na\n",
+    "var line tHiLine = na\n",
+    "var bool tHiOn = false\n",
+    "var float tLo = na\n",
+    "var int tLoBar = na\n",
+    "var line tLoLine = na\n",
+    "var bool tLoOn = false\n",
+    "\n",
+    "if tIdx != tPrev\n",
+    "    tPrev := tIdx\n",
+    "    tHi := na\n",
+    "    tHiBar := na\n",
+    "    line.delete(tHiLine)\n",
+    "    tHiLine := na\n",
+    "    tHiOn := false\n",
+    "    tLo := na\n",
+    "    tLoBar := na\n",
+    "    line.delete(tLoLine)\n",
+    "    tLoLine := na\n",
+    "    tLoOn := false\n",
+    "    tLoOn\n",
+    "\n",
+    "if tHiOn and not na(tHiLine)\n",
+    "    bMin = math.min(open, close)\n",
+    "    bMax = math.max(open, close)\n",
+    "    if tHi <= bMax and tHi >= bMin\n",
+    "        line.set_x2(tHiLine, bar_index)\n",
+    "        line.set_y2(tHiLine, tHi)\n",
+    "        tHiOn := false\n",
+    "        tHiOn\n",
+    "    else\n",
+    "        line.set_x2(tHiLine, bar_index)\n",
+    "        line.set_y2(tHiLine, tHi)\n",
+    "\n",
+    "if tLoOn and not na(tLoLine)\n",
+    "    bMin = math.min(open, close)\n",
+    "    bMax = math.max(open, close)\n",
+    "    if tLo <= bMax and tLo >= bMin\n",
+    "        line.set_x2(tLoLine, bar_index)\n",
+    "        line.set_y2(tLoLine, tLo)\n",
+    "        tLoOn := false\n",
+    "        tLoOn\n",
+    "    else\n",
+    "        line.set_x2(tLoLine, bar_index)\n",
+    "        line.set_y2(tLoLine, tLo)\n",
+    "\n",
+    "// === D/W/M High-Low Steplines ===\n",
+    "ddH = request.security(syminfo.tickerid, 'D', high, lookahead = barmerge.lookahead_on)\n",
+    "ddL = request.security(syminfo.tickerid, 'D', low, lookahead = barmerge.lookahead_on)\n",
+    "wH = request.security(syminfo.tickerid, 'W', high, lookahead = barmerge.lookahead_on)\n",
+    "wL = request.security(syminfo.tickerid, 'W', low, lookahead = barmerge.lookahead_on)\n",
+    "mH = request.security(syminfo.tickerid, 'M', high, lookahead = barmerge.lookahead_on)\n",
+    "mL = request.security(syminfo.tickerid, 'M', low, lookahead = barmerge.lookahead_on)\n",
+    "\n",
+    "plot(f_show('60') ? hH : na, title = 'H High', color = color.new(color.black, 90), style = plot.style_stepline, linewidth = 2)\n",
+    "plot(f_show('60') ? hL : na, title = 'H Low', color = color.new(color.black, 90), style = plot.style_stepline, linewidth = 2)\n",
+    "plot(f_show('D') ? ddH : na, title = 'D High', color = color.new(color.green, 50), style = plot.style_stepline)\n",
+    "plot(f_show('D') ? ddL : na, title = 'D Low', color = color.new(color.green, 70), style = plot.style_stepline)\n",
+    "plot(f_show('W') ? wH : na, title = 'W High', color = color.new(color.blue, 50), style = plot.style_stepline)\n",
+    "plot(f_show('W') ? wL : na, title = 'W Low', color = color.new(color.blue, 70), style = plot.style_stepline)\n",
+    "plot(f_show('M') ? mH : na, title = 'M High', color = color.new(color.purple, 40), style = plot.style_stepline)\n",
+    "plot(f_show('M') ? mL : na, title = 'M Low', color = color.new(color.purple, 60), style = plot.style_stepline)\n",
+    "\n",
+    "\n",
+    "//vwap logic\n",
+    "// VWAP calculation from 1-minute data\n",
+    "f_vwap_calc() =>\n",
+    "    var float cumPV = 0.0\n",
+    "    var float cumVol = 0.0\n",
+    "    var float cumPV_buy = 0.0\n",
+    "    var float cumVol_buy = 0.0\n",
+    "    var float cumPV_sell = 0.0\n",
+    "    var float cumVol_sell = 0.0\n",
+    "\n",
+    "    // Reset on new day\n",
+    "    if bool(ta.change(time('D')))\n",
+    "        cumPV := 0.0\n",
+    "        cumVol := 0.0\n",
+    "        cumPV_buy := 0.0\n",
+    "        cumVol_buy := 0.0\n",
+    "        cumPV_sell := 0.0\n",
+    "        cumVol_sell := 0.0\n",
+    "        cumVol_sell\n",
+    "\n",
+    "    buyVol = close > open ? volume : 0.0\n",
+    "    sellVol = close < open ? volume : 0.0\n",
+    "\n",
+    "    cumPV := cumPV + close * volume\n",
+    "    cumVol := cumVol + volume\n",
+    "\n",
+    "    cumPV_buy := cumPV_buy + close * buyVol\n",
+    "    cumVol_buy := cumVol_buy + buyVol\n",
+    "\n",
+    "    cumPV_sell := cumPV_sell + close * sellVol\n",
+    "    cumVol_sell := cumVol_sell + sellVol\n",
+    "\n",
+    "    vwap = cumVol != 0 ? cumPV / cumVol : na\n",
+    "    buyVWAP = cumVol_buy != 0 ? cumPV_buy / cumVol_buy : na\n",
+    "    sellVWAP = cumVol_sell != 0 ? cumPV_sell / cumVol_sell : na\n",
+    "\n",
+    "    [vwap, buyVWAP, sellVWAP]\n",
+    "\n",
+    "// Pull 1-minute VWAP values\n",
+    "[vwap_1m, buyVWAP_1m, sellVWAP_1m] = request.security(syminfo.tickerid, '1', f_vwap_calc())\n",
+    "\n",
+    "// Only show on intraday charts\n",
+    "showVWAP = not timeframe.isdaily and not timeframe.isweekly and not timeframe.ismonthly\n",
+    " \n",
+    "// Plot VWAPs with conditional display\n",
+    "plot_vwap = plot(showVWAP ? vwap_1m : na, color = color.rgb(255, 153, 0, 0), linewidth = 1, title = 'VWAP (1m)')\n",
+    "plot_buy_vwap = plot(showVWAP ? buyVWAP_1m : na, color = color.rgb(0, 137, 123, 80), linewidth = 1, title = 'Buy Delta VWAP (1m)')\n",
+    "plot_sell_vwap = plot(showVWAP ? sellVWAP_1m : na, color = color.rgb(255, 82, 82, 80), linewidth = 1, title = 'Sell Delta VWAP (1m)')\n",
+    "\n",
+    "// Fill areas\n",
+    "fill(plot_vwap, plot_buy_vwap, color = showVWAP ? color.new(color.teal, 95) : na, title = 'Buy VWAP Fill')\n",
+    "fill(plot_vwap, plot_sell_vwap, color = showVWAP ? color.new(color.red, 95) : na, title = 'Sell VWAP Fill')\n",
+    "\n",
+    "//table logic\n",
+    "// === Function to get OHLC of specified candle ===\n",
+    "get_prev_ohlc(tf, shift) =>\n",
+    "    o = request.security(syminfo.tickerid, tf, open[shift])\n",
+    "    h = request.security(syminfo.tickerid, tf, high[shift])\n",
+    "    l = request.security(syminfo.tickerid, tf, low[shift])\n",
+    "    c = request.security(syminfo.tickerid, tf, close[shift])\n",
+    "    [o, h, l, c]\n",
+    "\n",
+    "// === Function to calculate ranges and colors ===\n",
+    "get_data(tf, shift) =>\n",
+    "    [o, h, l, c] = get_prev_ohlc(tf, shift)\n",
+    "    full_range = h - l\n",
+    "    oc_range = math.abs(c - o)\n",
+    "    hl_range = h - l\n",
+    "    is_bull = c > o\n",
+    "    bg_color = is_bull ? color.new(color.green, 90) : color.new(color.red, 90)\n",
+    "    [full_range, oc_range, hl_range, bg_color]\n",
+    "\n",
+    "// === Daily Candle Data ===\n",
+    "[d1_fr, d1_oc, d1_hl, d1_bg] = get_data(\"D\", 1)\n",
+    "[d2_fr, d2_oc, d2_hl, d2_bg] = get_data(\"D\", 2)\n",
+    "\n",
+    "// === Weekly Candle Data ===\n",
+    "[w0_fr, w0_oc, w0_hl, w0_bg] = get_data(\"W\", 0) // Developing Week\n",
+    "[w1_fr, w1_oc, w1_hl, w1_bg] = get_data(\"W\", 1)\n",
+    "[w2_fr, w2_oc, w2_hl, w2_bg] = get_data(\"W\", 2)\n",
+    "\n",
+    "\n",
+    "// Daily candle (6:00 AM)\n",
+    "sixambgcolor = dO < dC ?  color.rgb(76, 175, 79, 90) : color.rgb(255, 82, 82, 90)\n",
+    "\n",
+    "// Custom candle (8:00 AM Manila)\n",
+    "eightambgcolor = cO < cC ?  color.rgb(76, 175, 79, 90) : color.rgb(255, 82, 82, 90) \n",
+    "\n",
+    "// Adjust UTC time to Manila (UTC+8) qewrqwerwerwerwerwerew\n",
+    "manilaTime = time + 8 * 60 * 60 * 1000  // Shift by 8 hours in milliseconds\n",
+    "\n",
+    "// Extract date components from Manila time\n",
+    "manilaYear  = year(manilaTime)\n",
+    "manilaMonth = month(manilaTime)\n",
+    "manilaDay   = dayofmonth(manilaTime)\n",
+    "\n",
+    "// Format date as \"DD/MM/YYYY\"\n",
+    "formattedDate = str.tostring(manilaDay, \"00\") + \"/\" + str.tostring(manilaMonth, \"00\") + \"/\" + str.tostring(manilaYear)\n",
+    "\n",
+    "// Track day changes in Manila time\n",
+    "var int prevManilaDay = na\n",
+    "newManilaDay = manilaDay != prevManilaDay\n",
+    "\n",
+    "// Update stored day\n",
+    "if newManilaDay\n",
+    "    prevManilaDay := manilaDay\n",
+    "//qweqweqweqweqeqweqweqwewqeqwewqeqe\n",
+    "\n",
+    "// UTC/UTC+8 Time\n",
+    "var label utcLbl = na\n",
+    "var label utc8Lbl = na\n",
+    "label.delete(utcLbl)\n",
+    "label.delete(utc8Lbl)\n",
+    "\n",
+    "//utcStr = \"UTC: \" + str.tostring(hour, \"00\") + \":\" + str.tostring(minute, \"00\")\n",
+    "uutcStr = str.tostring(hour, \"00\") + \":\" + str.tostring(minute, \"00\") + \" 𝘜𝘛𝘊+8\"\n",
+    "utc8H = (hour + 12) % 24\n",
+    "//utc8Str = \"UTC: \" + str.tostring(utc8H, \"00\") + \":\" + str.tostring(minute, \"00\")\n",
+    "uutc8Str =str.tostring(utc8H, \"00\") + \":\" + str.tostring(minute, \"00\") + \" 𝘜𝘛𝘊+8\"\n",
+    "\n",
+    "// TF Label\n",
+    "var label tfLbl = na\n",
+    "label.delete(tfLbl)\n",
+    "var label tfLbl2 = na\n",
+    "label.delete(tfLbl2)\n",
+    "\n",
+    "tfS = \"\"\n",
+    "if (str.length(timeframe.period) > 1 and str.substring(timeframe.period, str.length(timeframe.period)-1, str.length(timeframe.period)) == \"m\")\n",
+    "    tfS := \"m\"\n",
+    "else if (str.length(timeframe.period) > 1 and str.substring(timeframe.period, str.length(timeframe.period)-1, str.length(timeframe.period)) == \"h\")\n",
+    "    tfS := \"h\"\n",
+    "tfP = str.replace(timeframe.period, tfS, \"\")\n",
+    "tfText = tfP + tfS + \"m 𝘪𝘯𝘵𝘰 1D\"\n",
+    "\n",
+    "// Range Labels\n",
+    "dHL = math.abs(dH - dL)\n",
+    "dOC = math.abs(dO - dC)\n",
+    "cHL = math.abs(cH - cL)\n",
+    "cOC = math.abs(cO - cC)\n",
+    "\n",
+    "// === Create and update table ===\n",
+    "var table tt = table.new(position.top_right, 80, 80, border_width=1)\n",
+    "\n",
+    "if bar_index == 1\n",
+    "    table.cell(tt, 0, 0, \"\", text_color=color.rgb(255, 255, 255, 100), text_size = size.tiny, bgcolor=color.rgb(120, 123, 134, 100))\n",
+    "    table.cell(tt, 1, 0, \"\", text_color=color.rgb(255, 255, 255, 100), text_size = size.tiny,bgcolor=color.rgb(120, 123, 134, 100))\n",
+    "\n",
+    "    table.cell(tt, 0, 1, \"\", text_color=color.rgb(255, 255, 255, 100), text_size = size.tiny, bgcolor=color.rgb(120, 123, 134, 100))\n",
+    "    table.cell(tt, 1, 1, \"\", text_color=color.rgb(255, 255, 255, 100), text_size = size.tiny,bgcolor=color.rgb(120, 123, 134, 100))\n",
+    "\n",
+    "// === Daily Rows ===\n",
+    "table.cell(tt, 1, 2, \"𝑺𝒑𝒆𝒄𝒊𝒇𝒊𝒄𝒂𝒕𝒊𝒐𝒏\", text_color = color.white, text_size = size.tiny, bgcolor = color.navy)\n",
+    "\n",
+    "table.cell(tt, 0, 3, \"Date\", text_size = size.tiny)\n",
+    "table.cell(tt, 1, 3, formattedDate, text_size = size.tiny, bgcolor = color.rgb(120, 123, 134, 90))\n",
+    " \n",
+    "table.cell(tt, 0, 4, \"Time\", text_size = size.tiny)\n",
+    "table.cell(tt, 1, 4, text = uutc8Str, text_size = size.tiny, bgcolor = color.rgb(120, 123, 134, 90))\n",
+    " \n",
+    "table.cell(tt, 0, 5, \"Time\", text_size = size.tiny)\n",
+    "table.cell(tt, 1, 5, text=uutcStr, text_size = size.tiny, bgcolor = color.rgb(120, 123, 134, 90))\n",
+    "\n",
+    "table.cell(tt, 0, 6, \"Type\", text_size = size.tiny)\n",
+    "table.cell(tt, 1, 6, text=tfText, text_size = size.tiny, bgcolor = color.rgb(120, 123, 134, 90))\n",
+    "\n",
+    "table.cell(tt, 1, 7, \"𝑯𝒊𝒈𝒉𝒆𝒓 𝑻𝑭\", text_color = color.white, text_size = size.tiny, bgcolor = color.navy)\n",
+    "\n",
+    "table.cell(tt, 0, 8, \"6:00\\nяαηgє\", text_size = size.tiny)\n",
+    "table.cell(tt, 1, 8, \"HL | $\" + str.tostring(dHL, \"#\") + \"\\nOC | $\" + str.tostring(dOC, \"#\"), text_size = size.tiny, bgcolor = sixambgcolor)\n",
+    "\n",
+    "table.cell(tt, 0, 9, \"8:00\\nяαηgє\", text_size = size.tiny)\n",
+    "table.cell(tt, 1, 9, \"HL | $\" + str.tostring(cHL, \"#\") + \"\\nOC | $\" + str.tostring(cOC, \"#\"), text_size = size.tiny, bgcolor = eightambgcolor)\n",
+    "\n",
+    "table.cell(tt, 1, 11, \"1D 𝑪𝒂𝒏𝒅𝒍𝒆\", text_color = color.white, text_size = size.tiny, bgcolor = color.navy)\n",
+    "\n",
+    "table.cell(tt, 0, 12, \"-1D \\nяαηgє\", text_size = size.tiny)\n",
+    "table.cell(tt, 1, 12, \"HL | $\" + str.tostring(d1_hl, \"#\") + \"\\nOC | $\" + str.tostring(d1_oc, \"#\"), text_size = size.tiny, bgcolor=d1_bg)\n",
+    "\n",
+    "table.cell(tt, 0, 13, \"-2D \\nяαηgє\", text_size = size.tiny)\n",
+    "table.cell(tt, 1, 13, \"HL | $\" + str.tostring(d2_hl, \"#\") + \"\\nOC | $\" + str.tostring(d2_oc, \"#\"), text_size = size.tiny, bgcolor=d2_bg)\n",
+    "\n",
+    "table.cell(tt, 1, 14, \"1W 𝑪𝒂𝒏𝒅𝒍𝒆\", text_color = color.white, text_size = size.tiny, bgcolor = color.navy)\n",
+    "\n",
+    "table.cell(tt, 0, 15, \"0W \\nяαηgє\", text_size = size.tiny)\n",
+    "table.cell(tt, 1, 15, \"HL | $\" + str.tostring(w0_hl, \"#\") + \"\\nOC | $\" + str.tostring(w0_oc, \"#\"), text_size = size.tiny, bgcolor=w0_bg)\n",
+    "\n",
+    "table.cell(tt, 0, 16, \"-1W \\nяαηgє\", text_size = size.tiny)\n",
+    "table.cell(tt, 1, 16, \"HL | $\" + str.tostring(w1_hl, \"#\") + \"\\nOC | $\" + str.tostring(w1_oc, \"#\"), text_size = size.tiny, bgcolor=w1_bg)\n",
+    "\n",
+    "table.cell(tt, 0, 17, \"-2W \\nяαηgє\", text_size = size.tiny)\n",
+    "table.cell(tt, 1, 17, \"HL | $\" + str.tostring(w2_hl, \"#\") + \"\\nOC | $\" + str.tostring(w2_oc, \"#\"), text_size = size.tiny, bgcolor=w2_bg)\n",
+    "\n",
+    "table.cell(tt, 1, 18, \"© 2025 𝚆𝚀𝚜\", text_color = color.white, text_size = size.tiny, bgcolor = color.black)\n",
+    "\n",
+    "\n",
+    "//@version=5\n",
+    "\n",
+    "\n",
+    "// Functions  ----------------------------------------------------------------------------------- //\n",
+    "\n",
+    "\n",
+    "f_drawOnlyLineX(_x1, _y1, _x2, _y2, _xloc, _extend, _color, _style, _width) =>\n",
+    "    id = line.new(_x1, _y1, _x2, _y2, _xloc, _extend, _color, _style, _width)\n",
+    "\n",
+    "\n",
+    "f_drawLineX(_x1, _y1, _x2, _y2, _xloc, _extend, _color, _style, _width) =>\n",
+    "    var id = line.new(_x1, _y1, _x2, _y2, _xloc, _extend, _color, _style, _width)\n",
+    "    line.set_xy1(id, _x1, _y1)\n",
+    "    line.set_xy2(id, _x2, _y2)\n",
+    "    line.set_color(id, _color)\n",
+    "    id\n",
+    "\n",
+    "\n",
+    "f_drawOnlyBoxX(_left, _top, _right, _bottom, _border_color, _border_width, _border_style) =>\n",
+    "    box.new(_left, _top, _right, _bottom, _border_color, _border_width, _border_style, bgcolor=_border_color)\n",
+    "\n",
+    "\n",
+    "f_drawOnlyLabelX(_x, _y, _text, _xloc, _yloc, _color, _style, _textcolor, _size, _textalign, _tooltip) =>\n",
+    "    label.new(_x, _y, _text, _xloc, _yloc, _color, _style, _textcolor, _size, _textalign, _tooltip)\n",
+    "\n",
+    "\n",
+    "f_drawLabelX(_x, _y, _text, _xloc, _yloc, _color, _style, _textcolor, _size, _textalign, _tooltip) =>\n",
+    "    var id = label.new(_x, _y, _text, _xloc, _yloc, _color, _style, _textcolor, _size, _textalign, _tooltip)\n",
+    "    label.set_xy(id, _x, _y)\n",
+    "    label.set_text(id, _text)\n",
+    "    label.set_tooltip(id, _tooltip)\n",
+    "\n",
+    "\n",
+    "f_getHighLow(_len, _calc, _offset) =>\n",
+    "    if _calc\n",
+    "        htf_l = low [_offset]\n",
+    "        htf_h = high[_offset]\n",
+    "        vol   = 0.\n",
+    "       \n",
+    "        for x = 0 to _len - 1\n",
+    "            htf_l := math.min(low [_offset + x], htf_l)\n",
+    "            htf_h := math.max(high[_offset + x], htf_h)\n",
+    "            vol += volume[_offset + x]\n",
+    "\n",
+    "\n",
+    "        htf_l := math.min(low [_offset + _len], htf_l)\n",
+    "        htf_h := math.max(high[_offset + _len], htf_h)\n",
+    "       \n",
+    "        [htf_h, htf_l, vol]\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "// check for box breaches - code snippet from pine user guide\n",
+    "f_checkBreaches(arrayOfBoxes, extend) =>\n",
+    "    int qtyOfBoxes = array.size(arrayOfBoxes)\n",
+    "    for boxNo = 0 to (qtyOfBoxes > 0 ? qtyOfBoxes - 1 : na)\n",
+    "        if boxNo < array.size(arrayOfBoxes)\n",
+    "            box   currentBox    = array.get(arrayOfBoxes, boxNo)\n",
+    "            float boxMidLevel   = math.avg(box.get_bottom(currentBox), box.get_top(currentBox))\n",
+    "            bool  boxWasCrossed = math.sign(close[1] - boxMidLevel) != math.sign(close - boxMidLevel)\n",
+    "            bool  boxWasTouched = math.sign(close[1] - boxMidLevel) != math.sign(low - boxMidLevel) or math.sign(close[1] - boxMidLevel) != math.sign(high - boxMidLevel)\n",
+    "\n",
+    "\n",
+    "            if boxWasCrossed and extend == 'Until Bar Cross'\n",
+    "                array.remove(arrayOfBoxes, boxNo)\n",
+    "                int(na)\n",
+    "            else if boxWasTouched and extend == 'Until Bar Touch'\n",
+    "                array.remove(arrayOfBoxes, boxNo)\n",
+    "                int(na)\n",
+    "            else\n",
+    "                box.set_right(currentBox, bar_index)\n",
+    "                int(na)\n",
+    "\n",
+    "\n",
+    "// Functions  ----------------------------------------------------------------------------------- //\n",
+    "\n",
+    "// Inputs --------------------------------------------------------------------------------------- //\n",
+    "\n",
+    "\n",
+    "group_volume_profile    = 'Pivot Points Volume Profile'\n",
+    "\n",
+    "\n",
+    "tooltip_pvt       = 'The Pivot Points High Low indicator is used to determine and anticipate potential changes in market price and reversals\\n' +\n",
+    "                     '\\'Volume Profile, Pivot Points Anchored\\' Custom indicator addtionally calculates the trading activity between two Pivot Points'\n",
+    "pvtLength         = input.int(20, \"Pivot Points Left/Right Length\", minval=1, group = group_volume_profile, tooltip = tooltip_pvt)\n",
+    "\n",
+    "\n",
+    "tooltip_vp        = 'Common Interest Profile (Total Volume) - displays total trading activity over a specified time period at specific price levels'\n",
+    "volumeProfile     = input.bool(true, 'Volume Profile (Common Interest)'             , inline='BB3', group = group_volume_profile, tooltip = tooltip_vp)\n",
+    "totalVolumeColor  = input.color(color.new(#fbc02d, 70), ''                          , inline='BB3', group = group_volume_profile)\n",
+    "vaVolumeColor     = input.color(color.new(#434651, 70), ''                          , inline='BB3', group = group_volume_profile)\n",
+    "\n",
+    "\n",
+    "tooltip_va        = 'Value Area (VA) – The range of price levels in which a specified percentage of all volume was traded during the time period'\n",
+    "isValueArea       = input.float(68, \"Value Area Volume %\", minval = 0, maxval = 100               , group = group_volume_profile, tooltip = tooltip_va) / 100\n",
+    "\n",
+    "\n",
+    "tooltip_poc       = 'Point of Control (POC) - The price level for the time period with the highest traded volume'\n",
+    "pointOfControl    = input.bool(false, 'Point of Control (PoC)'                       , inline='PoC', group = group_volume_profile, tooltip = tooltip_poc)\n",
+    "pocColor          = input.color(color.new(#ff0000, 0), ''                           , inline='PoC', group = group_volume_profile)\n",
+    "pocExtend         = input.string('None', 'Extend Point of Control (PoC)', options=['Until Last Bar', 'Until Bar Cross', 'Until Bar Touch', 'None'], group = group_volume_profile)\n",
+    "\n",
+    "\n",
+    "tooltip_vah       = 'Value Area High (VAH) - The highest price level within the value area'\n",
+    "valueAreaHigh     = input.bool(true, 'Value Area High (VAH)'                        , inline='VAH', group = group_volume_profile, tooltip = tooltip_vah)\n",
+    "vahColor          = input.color(color.new(#2962ff, 100), ''                           , inline='VAH', group = group_volume_profile)\n",
+    "\n",
+    "\n",
+    "tooltip_val       = 'Value Area Low (VAL) - The lowest price level within the value area'\n",
+    "valueAreaLow      = input.bool(true, 'Value Area Low (VAL) '                        , inline='VAL', group = group_volume_profile, tooltip = tooltip_val)\n",
+    "valColor          = input.color(color.new(#2962ff, 100), ''                           , inline='VAL', group = group_volume_profile)\n",
+    "\n",
+    "\n",
+    "vaBackground      = input.bool(true, 'Background Fill of Value Area (VA)'           , inline='vBG', group = group_volume_profile)\n",
+    "vaBackgroundColor = input.color(color.new(#886000, 100), ''                          , inline='vBG', group = group_volume_profile)\n",
+    "\n",
+    "\n",
+    "levels            = input.string('Pivot Points', 'Level Labels', options = ['Pivot Points', 'Profile High/Low', 'Value Area High/Low'], group = group_volume_profile)\n",
+    "pvtPrice          = input(false, \"Price\", inline = 'Levels', group=group_volume_profile)\n",
+    "pvtChange         = input(false, \"Price Change\", inline = 'Levels', group=group_volume_profile)\n",
+    "pvtVolume         = input(false, \"Cumulative Volume\", inline = 'Levels', group=group_volume_profile)\n",
+    "\n",
+    "\n",
+    "profileLevels     = input.int(34, 'Number of Rows' , minval = 10, maxval = 100 , step = 1         , group = group_volume_profile)\n",
+    "profilePlacement  = input.string('Left', 'Placment', options = ['Right', 'Left']                  , group = group_volume_profile)\n",
+    "profileWidth      = input.int(30, 'Profile Width %', minval = 0, maxval = 100                     , group = group_volume_profile) / 100\n",
+    "backgroundFill    = input.bool(true, 'Background Fill of Profile Range'             , inline ='BG', group = group_volume_profile)\n",
+    "backgroundColor   = input.color(color.new(#2962ff, 95), ''                          , inline ='BG', group = group_volume_profile)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "// Definitions ---------------------------------------------------------------------------------- //\n",
+    "\n",
+    "\n",
+    "barPriceLow       = low\n",
+    "barPriceHigh      = high\n",
+    "bullCandle        = close > open\n",
+    "nzVolume          = nz(volume)\n",
+    "\n",
+    "\n",
+    "volumeStorageT    = array.new_float(profileLevels + 1, 0.)\n",
+    "\n",
+    "\n",
+    "var a_poc         = array.new_box()\n",
+    "\n",
+    "\n",
+    "var x1            = 0\n",
+    "var x2            = 0\n",
+    "var levelAbovePoc = 0\n",
+    "var levelBelowPoc = 0\n",
+    "var pvtHigh1      = 0.\n",
+    "var pvtLow1       = 0.\n",
+    "var pvtLast       = ''\n",
+    "\n",
+    "\n",
+    "// Calculations --------------------------------------------------------------------------------- //\n",
+    "\n",
+    "\n",
+    "pvtHigh  = ta.pivothigh(pvtLength, pvtLength)\n",
+    "pvtLow   = ta.pivotlow (pvtLength, pvtLength)\n",
+    "proceed  = not na(pvtHigh) or not na(pvtLow)\n",
+    "\n",
+    "\n",
+    "if proceed\n",
+    "    x1 := x2\n",
+    "    x2 := bar_index\n",
+    "\n",
+    "\n",
+    "if not na(pvtHigh)\n",
+    "    pvtHigh1 := pvtHigh\n",
+    "    pvtLast  := 'H'\n",
+    "\n",
+    "\n",
+    "if not na(pvtLow)\n",
+    "    pvtLow1  := pvtLow\n",
+    "    pvtLast  := 'L'\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "profileLength = x2 - x1\n",
+    "\n",
+    "\n",
+    "[priceHighest, priceLowest, tradedVolume] = f_getHighLow(profileLength, proceed, pvtLength)\n",
+    "priceStep = (priceHighest - priceLowest) / profileLevels\n",
+    "\n",
+    "\n",
+    "if proceed and bool(nzVolume) and priceStep > 0 and bar_index > profileLength and profileLength > 0\n",
+    "\n",
+    "\n",
+    "    for barIndexx = 1 to profileLength\n",
+    "        level = 0\n",
+    "        barIndex = barIndexx + pvtLength\n",
+    "       \n",
+    "        for priceLevel = priceLowest to priceHighest by priceStep\n",
+    "            if barPriceHigh[barIndex] >= priceLevel and barPriceLow[barIndex] < priceLevel + priceStep\n",
+    "                array.set(volumeStorageT, level, array.get(volumeStorageT, level) + nzVolume[barIndex] * ((barPriceHigh[barIndex] - barPriceLow[barIndex]) == 0 ? 1 : priceStep / (barPriceHigh[barIndex] - barPriceLow[barIndex])) )\n",
+    "            level += 1\n",
+    "\n",
+    "\n",
+    "    pocLevel          = array.indexof(volumeStorageT, array.max(volumeStorageT))\n",
+    "    totalVolumeTraded = array.sum(volumeStorageT) * isValueArea\n",
+    "    valueArea         = array.get(volumeStorageT, pocLevel)\n",
+    "    levelAbovePoc    := pocLevel\n",
+    "    levelBelowPoc    := pocLevel\n",
+    "   \n",
+    "    while valueArea < totalVolumeTraded\n",
+    "        if levelBelowPoc == 0 and levelAbovePoc == profileLevels - 1\n",
+    "            break\n",
+    "\n",
+    "\n",
+    "        volumeAbovePoc = 0.\n",
+    "        if levelAbovePoc < profileLevels - 1\n",
+    "            volumeAbovePoc := array.get(volumeStorageT, levelAbovePoc + 1)\n",
+    "\n",
+    "\n",
+    "        volumeBelowPoc = 0.\n",
+    "        if levelBelowPoc > 0\n",
+    "            volumeBelowPoc := array.get(volumeStorageT, levelBelowPoc - 1)\n",
+    "       \n",
+    "        if volumeBelowPoc == 0 and volumeAbovePoc == 0\n",
+    "            break\n",
+    "       \n",
+    "        if volumeAbovePoc >= volumeBelowPoc\n",
+    "            valueArea     += volumeAbovePoc\n",
+    "            levelAbovePoc += 1\n",
+    "        else\n",
+    "            valueArea     += volumeBelowPoc\n",
+    "            levelBelowPoc -= 1\n",
+    "\n",
+    "\n",
+    "    for level = 0 to profileLevels - 1\n",
+    "        if volumeProfile\n",
+    "            startBoxIndex = profilePlacement == 'Right' ? bar_index - int(array.get(volumeStorageT, level) / array.max(volumeStorageT) * profileLength * profileWidth)  : bar_index - profileLength\n",
+    "            endBoxIndex   = profilePlacement == 'Right' ? bar_index  :  startBoxIndex + int( array.get(volumeStorageT, level) / array.max(volumeStorageT) * profileLength * profileWidth)\n",
+    "            f_drawOnlyBoxX(startBoxIndex - pvtLength, priceLowest + (level + 0.1) * priceStep, endBoxIndex - pvtLength, priceLowest + (level + 0.9) * priceStep, level >= levelBelowPoc and level <= levelAbovePoc ? totalVolumeColor : vaVolumeColor, 1, line.style_solid)\n",
+    "\n",
+    "\n",
+    "    if backgroundFill\n",
+    "        f_drawOnlyBoxX(bar_index[pvtLength] - profileLength, priceHighest, bar_index[pvtLength], priceLowest, backgroundColor, 1, line.style_dotted)\n",
+    "\n",
+    "\n",
+    "    if pointOfControl\n",
+    "        array.push(a_poc, box.new(bar_index[pvtLength] - profileLength, priceLowest + (array.indexof(volumeStorageT, array.max(volumeStorageT)) + .40) * priceStep, bar_index[pvtLength], priceLowest + (array.indexof(volumeStorageT, array.max(volumeStorageT)) + .60) * priceStep, pocColor, bgcolor = pocColor ))\n",
+    "\n",
+    "\n",
+    "    vah = f_drawOnlyLineX(bar_index[pvtLength] - profileLength, priceLowest + (levelAbovePoc + 1.00) * priceStep, bar_index[pvtLength], priceLowest + (levelAbovePoc + 1.00) * priceStep, xloc.bar_index, extend.none, valueAreaHigh ? vahColor : #00000000, line.style_solid, 2)\n",
+    "    val = f_drawOnlyLineX(bar_index[pvtLength] - profileLength, priceLowest + (levelBelowPoc + 0.00) * priceStep, bar_index[pvtLength], priceLowest + (levelBelowPoc + 0.00) * priceStep, xloc.bar_index, extend.none, valueAreaLow  ? valColor : #00000000, line.style_solid, 2)\n",
+    "\n",
+    "\n",
+    "    if vaBackground\n",
+    "        linefill.new(vah, val, vaBackgroundColor)\n",
+    "\n",
+    "\n",
+    "    statTip = '\\n -Traded Volume : ' + str.tostring(tradedVolume, format.volume) + ' (' + str.tostring(profileLength - 1) + ' bars)' +\n",
+    "                   '\\n  *Average Volume/Bar : ' + str.tostring(tradedVolume / (profileLength - 1), format.volume) +\n",
+    "                   '\\n\\nProfile High : ' + str.tostring(priceHighest, format.mintick) + ' ↑ %' + str.tostring((priceHighest - priceLowest) / priceLowest  * 100, '#.##') +\n",
+    "                   '\\nProfile Low : '  + str.tostring(priceLowest, format.mintick) + ' ↓ %' + str.tostring((priceHighest - priceLowest) / priceHighest  * 100, '#.##') +\n",
+    "                   '\\n -Point Of Control : ' + str.tostring(priceLowest + (array.indexof(volumeStorageT, array.max(volumeStorageT)) + .50) * priceStep, format.mintick) +\n",
+    "                   '\\n\\nValue Area High : ' + str.tostring(priceLowest + (levelAbovePoc + 1.00) * priceStep, format.mintick) +\n",
+    "                   '\\nValue Area Low : '  + str.tostring(priceLowest + (levelBelowPoc + 0.00) * priceStep, format.mintick) +\n",
+    "                   '\\n -Value Area Width : %' + str.tostring(((priceLowest + (levelAbovePoc + 1.00) * priceStep) - (priceLowest + (levelBelowPoc + 0.00) * priceStep)) / (priceHighest - priceLowest) * 100, '#.##') +\n",
+    "                   '\\n\\nNumber of Bars (Profile) : ' + str.tostring(profileLength)\n",
+    "   \n",
+    "    if levels != 'Pivot Points'\n",
+    "        upperPriceLevel = levels == 'Value Area High/Low' ? priceLowest + (levelAbovePoc + 1.00) * priceStep : priceHighest\n",
+    "        lowerPriceLevel = levels == 'Value Area High/Low' ? priceLowest + (levelBelowPoc + 0.00) * priceStep : priceLowest\n",
+    "\n",
+    "\n",
+    "        upperText = (pvtPrice ? str.tostring(upperPriceLevel, format.mintick) : '') + (not na(pvtHigh) ? (pvtChange ? (pvtPrice ? ' ↑ %' : '↑ %') + str.tostring((pvtHigh - pvtLow1) * 100 / pvtLow1 , '#.##') : '') + (pvtVolume ? (pvtPrice or pvtChange ? '\\n' : '') + str.tostring(tradedVolume, format.volume) : '') : '')\n",
+    "        lowerText = (pvtPrice ? str.tostring(lowerPriceLevel, format.mintick) : '') + (not na(pvtLow) ? (pvtChange ? (pvtPrice ? ' ↓ %' : '↓ %') + str.tostring((pvtHigh1 - pvtLow) * 100 / pvtHigh1, '#.##') : '') + (pvtVolume ? (pvtPrice or pvtChange ? '\\n' : '') + str.tostring(tradedVolume, format.volume) : '') : '')\n",
+    "       \n",
+    "        f_drawOnlyLabelX(bar_index[pvtLength] - profileLength / 2, upperPriceLevel, upperText, xloc.bar_index, yloc.price, (upperText != '' ? chart.fg_color : #00000000), label.style_circle, chart.bg_color, size.tiny, text.align_center, 'Profile High : ' + str.tostring(priceHighest, format.mintick) + '\\n %' + str.tostring((priceHighest - priceLowest) / priceLowest  * 100, '#.##') + ' higher than the Profile Low' + statTip )\n",
+    "        f_drawOnlyLabelX(bar_index[pvtLength] - profileLength / 2, lowerPriceLevel, lowerText, xloc.bar_index, yloc.price, (lowerText != '' ? chart.fg_color : #00000000), label.style_circle  , chart.bg_color , size.tiny, text.align_center, 'Profile Low : '  + str.tostring(priceLowest, format.mintick)  + '\\n %' + str.tostring((priceHighest - priceLowest) / priceHighest * 100, '#.##') + ' lower than the Profile High' + statTip )\n",
+    "    else\n",
+    "        if not na(pvtHigh)\n",
+    "            f_drawOnlyLabelX(bar_index[pvtLength], pvtHigh, (pvtPrice ? str.tostring(pvtHigh, format.mintick) : '') + (pvtChange ? (pvtPrice ? ' ↑ %' : '↑ %') + str.tostring((pvtHigh - pvtLow1) * 100 / pvtLow1 , '#.##') : '') + (pvtVolume ? (pvtPrice or pvtChange ? '\\n' : '') + str.tostring(tradedVolume, format.volume) : ''), xloc.bar_index, yloc.price, chart.fg_color, label.style_diamond, chart.bg_color, (not pvtPrice and not pvtChange and not pvtVolume ? size.tiny : size.tiny), text.align_center, 'Pivot High : ' + str.tostring(pvtHigh, format.mintick) + '\\n -Price Change : %' + str.tostring((pvtHigh - pvtLow1) * 100 / pvtLow1 , '#.##') + statTip)\n",
+    "        if not na(pvtLow)\n",
+    "            f_drawOnlyLabelX(bar_index[pvtLength], pvtLow , (pvtPrice ? str.tostring(pvtLow , format.mintick) : '') + (pvtChange ? (pvtPrice ? ' ↓ %' : '↓ %') + str.tostring((pvtHigh1 - pvtLow) * 100 / pvtHigh1, '#.##') : '') + (pvtVolume ? (pvtPrice or pvtChange ? '\\n' : '') + str.tostring(tradedVolume, format.volume) : ''), xloc.bar_index, yloc.price, chart.fg_color, label.style_diamond  , chart.bg_color, (not pvtPrice and not pvtChange and not pvtVolume ? size.tiny : size.tiny), text.align_center, 'Pivot Low : ' + str.tostring(pvtLow, format.mintick) + '\\n -Price Change : %' + str.tostring((pvtHigh1 - pvtLow) * 100 / pvtHigh1, '#.##') + statTip)\n",
+    "\n",
+    "\n",
+    "if pointOfControl and pocExtend != 'None'\n",
+    "    f_checkBreaches(a_poc, pocExtend)\n",
+    "\n",
+    "\n",
+    "var a_profileD    = array.new_box()\n",
+    "profileLength    := barstate.islast ? last_bar_index - x2 + pvtLength  : 1\n",
+    "priceHighest     := ta.highest(high, profileLength > 0 ? profileLength + 1 : 1)\n",
+    "priceLowest      := ta.lowest (low , profileLength > 0 ? profileLength + 1 : 1)\n",
+    "priceStep        := (priceHighest - priceLowest) / profileLevels\n",
+    "var pocLevel      = 0\n",
+    "\n",
+    "\n",
+    "[_, _, tradedVolume1] = f_getHighLow(profileLength, true, 0)\n",
+    "\n",
+    "\n",
+    "   \n",
+    "if barstate.islast and bool(nzVolume) and profileLength > 0 and priceStep > 0\n",
+    "   \n",
+    "    if array.size(a_profileD) > 0\n",
+    "        for i = 0 to array.size(a_profileD) - 1\n",
+    "            box.delete(array.shift(a_profileD))\n",
+    "\n",
+    "\n",
+    "    for barIndex = 1 to profileLength\n",
+    "        level = 0\n",
+    "\n",
+    "\n",
+    "        for priceLevel = priceLowest to priceHighest by priceStep\n",
+    "            if barPriceHigh[barIndex] >= priceLevel and barPriceLow[barIndex] < priceLevel + priceStep\n",
+    "                array.set(volumeStorageT, level, array.get(volumeStorageT, level) + nzVolume[barIndex] * ((barPriceHigh[barIndex] - barPriceLow[barIndex]) == 0 ? 1 : priceStep / (barPriceHigh[barIndex] - barPriceLow[barIndex])) )\n",
+    "            level += 1\n",
+    "\n",
+    "\n",
+    "    pocLevel          := array.indexof(volumeStorageT, array.max(volumeStorageT))\n",
+    "    totalVolumeTraded = array.sum(volumeStorageT) * isValueArea\n",
+    "    valueArea         = array.get(volumeStorageT, pocLevel)\n",
+    "    levelAbovePoc    := pocLevel\n",
+    "    levelBelowPoc    := pocLevel\n",
+    "   \n",
+    "    while valueArea < totalVolumeTraded\n",
+    "        if levelBelowPoc == 0 and levelAbovePoc == profileLevels - 1\n",
+    "            break\n",
+    "\n",
+    "\n",
+    "        volumeAbovePoc = 0.\n",
+    "        if levelAbovePoc < profileLevels - 1\n",
+    "            volumeAbovePoc := array.get(volumeStorageT, levelAbovePoc + 1)\n",
+    "\n",
+    "\n",
+    "        volumeBelowPoc = 0.\n",
+    "        if levelBelowPoc > 0\n",
+    "            volumeBelowPoc := array.get(volumeStorageT, levelBelowPoc - 1)\n",
+    "           \n",
+    "        if volumeBelowPoc == 0 and volumeAbovePoc == 0\n",
+    "            break\n",
+    "       \n",
+    "        if volumeAbovePoc >= volumeBelowPoc\n",
+    "            valueArea     += volumeAbovePoc\n",
+    "            levelAbovePoc += 1\n",
+    "        else\n",
+    "            valueArea     += volumeBelowPoc\n",
+    "            levelBelowPoc -= 1\n",
+    "\n",
+    "\n",
+    "    for level = 0 to profileLevels - 1\n",
+    "        if volumeProfile\n",
+    "            startBoxIndex = profilePlacement == 'Right' ? bar_index - int(array.get(volumeStorageT, level) / array.max(volumeStorageT) * profileLength * profileWidth)  : bar_index - profileLength\n",
+    "            endBoxIndex   = profilePlacement == 'Right' ? bar_index  :  startBoxIndex + int( array.get(volumeStorageT, level) / array.max(volumeStorageT) * profileLength * profileWidth)\n",
+    "            array.push(a_profileD, box.new(startBoxIndex, priceLowest + (level + 0.1) * priceStep, endBoxIndex, priceLowest + (level + 0.9) * priceStep, level >= levelBelowPoc and level <= levelAbovePoc ? totalVolumeColor : vaVolumeColor, bgcolor = level >= levelBelowPoc and level <= levelAbovePoc ? totalVolumeColor : vaVolumeColor ))\n",
+    "\n",
+    "\n",
+    "    if backgroundFill\n",
+    "        array.push(a_profileD, box.new(bar_index - profileLength, priceHighest, bar_index, priceLowest, backgroundColor, bgcolor = backgroundColor ))\n",
+    "\n",
+    "\n",
+    "    if pointOfControl\n",
+    "        array.push(a_profileD, box.new(bar_index - profileLength, priceLowest + (pocLevel + .40) * priceStep, bar_index, priceLowest + (pocLevel + .60) * priceStep, pocColor, bgcolor = pocColor ))\n",
+    "\n",
+    "\n",
+    "    vah = f_drawLineX(bar_index - profileLength, priceLowest + (levelAbovePoc + 1.00) * priceStep, bar_index, priceLowest + (levelAbovePoc + 1.00) * priceStep, xloc.bar_index, extend.none, valueAreaHigh ? vahColor : #00000000, line.style_solid, 2)\n",
+    "    val = f_drawLineX(bar_index - profileLength, priceLowest + (levelBelowPoc + 0.00) * priceStep, bar_index, priceLowest + (levelBelowPoc + 0.00) * priceStep, xloc.bar_index, extend.none, valueAreaLow  ? valColor : #00000000, line.style_solid, 2)\n",
+    "\n",
+    "\n",
+    "    if vaBackground\n",
+    "        linefill.new(vah, val, vaBackgroundColor)\n",
+    "\n",
+    "\n",
+    "    if levels != 'Pivot Points'\n",
+    "        statTip = '\\n -Traded Volume : ' + str.tostring(tradedVolume1, format.volume) + ' (' + str.tostring(profileLength - 1) + ' bars)' +\n",
+    "                   '\\n  *Average Volume/Bar : ' + str.tostring(tradedVolume1 / (profileLength - 1), format.volume) +\n",
+    "                   '\\n\\nProfile High : ' + str.tostring(priceHighest, format.mintick) + ' ↑ %' + str.tostring((priceHighest - priceLowest) / priceLowest  * 100, '#.##') +\n",
+    "                   '\\nProfile Low : '  + str.tostring(priceLowest, format.mintick) + ' ↓ %' + str.tostring((priceHighest - priceLowest) / priceHighest  * 100, '#.##') +\n",
+    "                   '\\n -Point Of Control : ' + str.tostring(priceLowest + (array.indexof(volumeStorageT, array.max(volumeStorageT)) + .50) * priceStep, format.mintick) +\n",
+    "                   '\\n\\nValue Area High : ' + str.tostring(priceLowest + (levelAbovePoc + 1.00) * priceStep, format.mintick) +\n",
+    "                   '\\nValue Area Low : '  + str.tostring(priceLowest + (levelBelowPoc + 0.00) * priceStep, format.mintick) +\n",
+    "                   '\\n -Value Area Width : %' + str.tostring(((priceLowest + (levelAbovePoc + 1.00) * priceStep) - (priceLowest + (levelBelowPoc + 0.00) * priceStep)) / (priceHighest - priceLowest) * 100, '#.##') +\n",
+    "                   '\\n\\nNumber of Bars (Profile) : ' + str.tostring(profileLength) +\n",
+    "                   (pvtChange ? '\\n\\n*price change caculated based on last pivot high/low and last price' : '')\n",
+    "\n",
+    "\n",
+    "       \n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "9c8c1f22",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4b13eedd",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Pinescript Folder/v6 Sunstoic/part1.ipynb b/Pinescript Folder/v6 Sunstoic/part1.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..4ca19804155433121fc468055868bf049b851c82
--- /dev/null
+++ b/Pinescript Folder/v6 Sunstoic/part1.ipynb	
@@ -0,0 +1,1105 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "707005e1",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "//@version=6\n",
+    "//indicator(\"v5\", overlay = true, max_labels_count = 300, max_lines_count = 300, max_boxes_count = 300, max_bars_back = 300)\n",
+    "indicator(\"Sunstoic\", \"v6 Sunstoic\", true, max_bars_back = 5000, max_boxes_count = 500, max_lines_count = 500, max_labels_count = 500)\n",
+    "\n",
+    "\n",
+    "//candle logic\n",
+    "// === Inputs ===\n",
+    "bCol = input.color(#008080, title=\"Bull Border\")\n",
+    "rCol = input.color(#e20000, title=\"Bear Border\")\n",
+    "bgB = input.color(color.new(#008080, 20), title=\"Bull Body\")\n",
+    "bgR = input.color(color.new(#FF0000, 20), title=\"Bear Body\")\n",
+    "\n",
+    "\n",
+    "    // === Input your time zone (Manila = GMT+8) \n",
+    "timeSessionStart = timestamp(\"GMT+8\", year, month, dayofmonth, 6, 0)  // Start of day\n",
+    "isNewDay = ta.change(time(\"D\"))  // Detect new day\n",
+    "\n",
+    "// Track the current day's developing close on each bar\n",
+    "var float devClose = na\n",
+    "if bool(isNewDay)\n",
+    "    devClose := close  // reset on new day\n",
+    "else\n",
+    "    devClose := close  // update each bar\n",
+    "\n",
+    "\n",
+    "// === Default Daily Candle (6:00 AM) ===\n",
+    "dO = request.security(syminfo.tickerid, \"D\", open)\n",
+    "dH = request.security(syminfo.tickerid, \"D\", high)\n",
+    "dL = request.security(syminfo.tickerid, \"D\", low)\n",
+    "dC = request.security(syminfo.tickerid, \"D\", close)\n",
+    "\n",
+    "\n",
+    "// Calculate daily high and low using 'day' timeframe\n",
+    "var float dailyHigh = na\n",
+    "var float dailyLow = na\n",
+    "newDay = ta.change(time(\"D\"))\n",
+    "\n",
+    "\n",
+    "// On new day, reset high/low\n",
+    "if bool(newDay)\n",
+    "    dailyHigh := close\n",
+    "    dailyLow := open\n",
+    "else\n",
+    "    dailyHigh := math.max(dailyHigh, close)\n",
+    "    dailyLow := math.min(dailyLow, open)\n",
+    "\n",
+    "\n",
+    "// Midpoint of the daily candle\n",
+    "midPrice = (dailyHigh + dailyLow) / 2\n",
+    "\n",
+    "\n",
+    "dBull = dC >= dO\n",
+    "dCol = dBull ? bCol : rCol\n",
+    "dBg = dBull ? bgB : bgR\n",
+    "\n",
+    "\n",
+    "// Offset for visuals\n",
+    "ofs = 10\n",
+    "bw = 2\n",
+    "rIdx = bar_index + ofs + bw / 2\n",
+    "lIdx = bar_index + ofs - bw / 2\n",
+    "xMid = int(bar_index + ofs)\n",
+    "\n",
+    "\n",
+    "// Body of daily candle box\n",
+    "var box dBx = na\n",
+    "box.delete(dBx)\n",
+    "tB = math.max(dO, devClose) //y=dC\n",
+    "bB = math.min(dO, devClose)\n",
+    "dBx := box.new(left=int(lIdx), right=int(rIdx), top=tB, bottom=bB, border_color=dCol, bgcolor=dBg)\n",
+    "\n",
+    "\n",
+    "// Wicks for daily candle\n",
+    "var line dW1 = na\n",
+    "var line dW2 = na\n",
+    "line.delete(dW1)\n",
+    "line.delete(dW2)\n",
+    "dW1 := line.new(x1=xMid, y1=dH, x2=xMid, y2=tB, color=dCol)\n",
+    "dW2 := line.new(x1=xMid, y1=bB, x2=xMid, y2=dL, color=dCol)\n",
+    "\n",
+    "\n",
+    "\n",
+    "// Labels for daily candle\n",
+    "var label lblO = na\n",
+    "var label lblH = na\n",
+    "var label lblL = na\n",
+    "var label lblC = na\n",
+    "label.delete(lblO)\n",
+    "label.delete(lblH)\n",
+    "label.delete(lblL)\n",
+    "label.delete(lblC)\n",
+    "\n",
+    "\n",
+    "lblStyle = label.style_label_right\n",
+    "lblSize = size.tiny\n",
+    "lblOfs = -2\n",
+    "\n",
+    "lblO := label.new(x=xMid + lblOfs, y=dO, text=\"6O \" + str.tostring(dO, \"#\"), style=lblStyle, color=color.new(#2195f3, 100), textcolor=color.black, size=lblSize)\n",
+    "lblH := label.new(x=xMid + lblOfs, y=dH, text=\"6H \" + str.tostring(dH, \"#\"), style=lblStyle, color=color.new(#2195f3, 100), textcolor=color.black, size=lblSize)\n",
+    "lblL := label.new(x=xMid + lblOfs, y=dL, text=\"6L \" + str.tostring(dL, \"#\"), style=lblStyle, color=color.new(#2195f3, 100), textcolor=color.black, size=lblSize)\n",
+    "lblC := label.new(x=xMid + lblOfs, y=devClose, text=\"6C \" + str.tostring(dC, \"#\"), style=lblStyle, color=color.new(#2195f3, 100), textcolor=color.black, size=lblSize)\n",
+    "\n",
+    "// === Custom Daily Candle (8:00 AM Manila) ===\n",
+    "sh = 24\n",
+    "sm = 0\n",
+    "stt = timestamp(\"UTC\", year, month, dayofmonth, sh, sm)\n",
+    "\n",
+    "\n",
+    "var float cO = na\n",
+    "var float cH = na\n",
+    "var float cL = na\n",
+    "var float cC = na\n",
+    "\n",
+    "\n",
+    "if (time == stt)\n",
+    "    cO := open\n",
+    "    cH := high\n",
+    "    cL := low\n",
+    "    cC := close\n",
+    "\n",
+    "\n",
+    "if (not na(cO))\n",
+    "    cH := math.max(cH, high)\n",
+    "    cL := math.min(cL, low)\n",
+    "    cC := close\n",
+    "\n",
+    "\n",
+    "cBull = cC >= cO\n",
+    "cCol = cBull ? bCol : rCol\n",
+    "cBg = cBull ? bgB : bgR\n",
+    "\n",
+    "\n",
+    "cOfs = 12\n",
+    "cbw = 2\n",
+    "crIdx = bar_index + cOfs + cbw / 2\n",
+    "clIdx = bar_index + cOfs - cbw / 2\n",
+    "cX = int(bar_index + cOfs)\n",
+    "\n",
+    "\n",
+    "// Custom body box\n",
+    "var box cBx = na\n",
+    "box.delete(cBx)\n",
+    "ctB = math.max(cO, devClose) //y=dC\n",
+    "cbB = math.min(cO, devClose)\n",
+    "cBx := box.new(left=int(clIdx), right=int(crIdx), top=ctB, bottom=cbB, border_color=cCol, bgcolor=cBg)\n",
+    "\n",
+    "\n",
+    "// Custom wicks\n",
+    "var line cW1 = na\n",
+    "var line cW2 = na\n",
+    "line.delete(cW1)\n",
+    "line.delete(cW2)\n",
+    "cW1 := line.new(x1=cX, y1=cH, x2=cX, y2=ctB, color=cCol)\n",
+    "cW2 := line.new(x1=cX, y1=cbB, x2=cX, y2=cL, color=cCol)\n",
+    "\n",
+    "\n",
+    "// Custom labels\n",
+    "var label clO = na\n",
+    "var label clH = na\n",
+    "var label clL = na\n",
+    "var label clC = na\n",
+    "label.delete(clO)\n",
+    "label.delete(clH)\n",
+    "label.delete(clL)\n",
+    "label.delete(clC)\n",
+    "\n",
+    "\n",
+    "clStyle = label.style_label_left\n",
+    "clSize = size.tiny\n",
+    "clOfs = 2\n",
+    "\n",
+    "\n",
+    "clO := label.new(x=cX + clOfs, y=cO, text=\"8O \" + str.tostring(cO, \"#\"), style=clStyle, color=color.new(#2195f3, 100), textcolor=color.black, size=clSize)\n",
+    "clH := label.new(x=cX + clOfs, y=cH, text=\"8H \" + str.tostring(cH, \"#\"), style=clStyle, color=color.new(#2195f3, 100), textcolor=color.black, size=clSize)\n",
+    "clL := label.new(x=cX + clOfs, y=cL, text=\"8L \" + str.tostring(cL, \"#\"), style=clStyle, color=color.new(#2195f3, 100), textcolor=color.black, size=clSize)\n",
+    "clC := label.new(x=cX + clOfs, y=devClose, text=\"8C \" + str.tostring(dC, \"#\"), style=clStyle, color=color.new(#2195f3, 100), textcolor=color.black, size=clSize)\n",
+    "\n",
+    "// === High and Low Lines (using bar_index for x coords) ===\n",
+    "// Track bar_index of high and low bars within current day\n",
+    "var int highBarIndex = na\n",
+    "var int lowBarIndex = na\n",
+    "var float dayHigh = na\n",
+    "var float dayLow = na\n",
+    "\n",
+    "\n",
+    "startOfDay = timestamp(year, month, dayofmonth, 0, 0)\n",
+    "t6amManila = timestamp(\"Asia/Manila\", year, month, dayofmonth, 6, 0)\n",
+    "\n",
+    "\n",
+    "issNewDay = ta.change(time(\"D\"))\n",
+    "\n",
+    "\n",
+    "if (time >= startOfDay)\n",
+    "    if na(dayHigh) or high > dayHigh\n",
+    "        dayHigh := high\n",
+    "        highBarIndex := bar_index\n",
+    "    if na(dayLow) or low < dayLow\n",
+    "        dayLow := low\n",
+    "        lowBarIndex := bar_index\n",
+    "\n",
+    "\n",
+    "if bool(issNewDay)\n",
+    "    dayHigh := na\n",
+    "    dayLow := na\n",
+    "    highBarIndex := na\n",
+    "    lowBarIndex := na\n",
+    "\n",
+    "\n",
+    "// Draw high line from high bar index to current xMid\n",
+    "var line highLine = na\n",
+    "if not na(dayHigh) and not na(highBarIndex)\n",
+    "    if na(highLine)\n",
+    "        highLine := line.new(x1=highBarIndex, y1=dayHigh, x2=xMid, y2=dayHigh, color=dCol, width=2, style = line.style_solid)\n",
+    "    else\n",
+    "        line.set_xy1(highLine, highBarIndex, dayHigh)\n",
+    "        line.set_xy2(highLine, xMid, dayHigh)\n",
+    "\n",
+    "\n",
+    "// Draw low line from low bar index to current xMid\n",
+    "var line lowLine = na\n",
+    "if not na(dayLow) and not na(lowBarIndex)\n",
+    "    if na(lowLine)\n",
+    "        lowLine := line.new(x1=lowBarIndex, y1=dayLow, x2=xMid, y2=dayLow, color=dCol, width=2, style = line.style_solid)\n",
+    "    else\n",
+    "        line.set_xy1(lowLine, lowBarIndex, dayLow)\n",
+    "        line.set_xy2(lowLine, xMid, dayLow)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "// --- 6:00 AM Asia/Manila horizontal line ---\n",
+    "// Manila 6:00 AM timestamp today\n",
+    "manila_6am = timestamp(\"GMT+8\", year, month, dayofmonth, 30, 0)\n",
+    "\n",
+    "\n",
+    "// Detect bar where time crosses 6:00 AM Manila\n",
+    "isStartBar6am = (time >= manila_6am) and (time[1] < manila_6am)\n",
+    "\n",
+    "\n",
+    "// Store 6:00 AM bar open price and index\n",
+    "var float price6am = na\n",
+    "var int bar6am_index = na\n",
+    "\n",
+    "\n",
+    "if isStartBar6am\n",
+    "    price6am := open\n",
+    "    bar6am_index := bar_index\n",
+    "\n",
+    "\n",
+    "// Draw horizontal line from 6AM bar index to xMid at price6am\n",
+    "var line hLine6am = na\n",
+    "\n",
+    "\n",
+    "if not na(price6am) and not na(bar6am_index)\n",
+    "    line.delete(hLine6am)\n",
+    "    hLine6am := line.new(x1=bar6am_index, y1=price6am, x2=xMid, y2=price6am, color=color.new(#000000, 0), width=1, style=line.style_solid)\n",
+    "\n",
+    "\n",
+    "// --- 8:00 AM Asia/Manila horizontal line ---\n",
+    "// Detect bar where time crosses 8:00 AM Manila\n",
+    "isStartBar8am = (time >= stt) and (time[1] < stt)\n",
+    "\n",
+    "\n",
+    "// Store 8:00 AM bar open price and index\n",
+    "var float price8am = na\n",
+    "var int bar8am_index = na\n",
+    "\n",
+    "\n",
+    "if isStartBar8am\n",
+    "    price8am := open\n",
+    "    bar8am_index := bar_index\n",
+    "\n",
+    "\n",
+    "// Draw horizontal line from 8AM bar index to cX at price8am\n",
+    "var line hLine8am = na\n",
+    "var line hhLine8am = na\n",
+    "\n",
+    "\n",
+    "if not na(price8am) and not na(bar8am_index)\n",
+    "    line.delete(hLine8am)\n",
+    "    hLine8am := line.new(x1=bar8am_index, y1=price8am, x2=cX, y2=price8am, color=color.new(#000000, 0), width=1, style=line.style_solid)\n",
+    "\n",
+    "\n",
+    "\n",
+    "// Draw horizontal line that updates000000000000000000000000\n",
+    "var line devCloseLine = na\n",
+    "var line ddevCloseLine = na\n",
+    "if bar_index > 0\n",
+    "    if na(devCloseLine)\n",
+    "        devCloseLine := line.new(x1=bar_index, y1=devClose , x2=xMid , y2=devClose, style = line.style_solid, color=color.new(#ecc900, 0), width=1)\n",
+    "    else\n",
+    "        line.set_xy1(devCloseLine, bar_index, devClose)\n",
+    "        line.set_xy2(devCloseLine, bar_index + 13, devClose)\n",
+    "    if na(ddevCloseLine) \n",
+    "        //ddevCloseLine := line.new(x1=xMid, y1=devClose, x2=xMid, y2=devClose, extend=extend.right, color=dCol, width=2)\n",
+    "        ddevCloseLine := line.new(x1=xMid, y1=devClose, x2=xMid , y2=devClose, style = line.style_dotted, extend=extend.right, color=color.black, width=1)\n",
+    "    else\n",
+    "        line.set_xy1(ddevCloseLine, bar_index + 19,  devClose)\n",
+    "        line.set_xy2(ddevCloseLine, bar_index + 20, devClose)\n",
+    "\n",
+    "\n",
+    "\n",
+    "//zone logic\n",
+    "// Disable visuals if timeframe is higher than 1 hour\n",
+    "isValidTF = timeframe.isminutes and timeframe.multiplier <= 60\n",
+    "\n",
+    "\n",
+    "// Current time components\n",
+    "currentTime = timestamp(year, month, dayofmonth, hour, minute)\n",
+    "\n",
+    "// Define 12PM to 12AM session\n",
+    "start12pm = timestamp(year, month, dayofmonth, 12, 0)\n",
+    "end12am = timestamp(year, month, dayofmonth, 23, 59)\n",
+    "in12pmTo12am = currentTime >= start12pm and currentTime <= end12am\n",
+    "\n",
+    "// Define 8AM to 8PM session\n",
+    "start8am = timestamp(year, month, dayofmonth, 8, 0)\n",
+    "end8pm = timestamp(year, month, dayofmonth, 20, 0)\n",
+    "in8amTo8pm = currentTime >= start8am and currentTime <= end8pm\n",
+    "\n",
+    "// Define 6PM to 8PM session\n",
+    "start6pm = timestamp(year, month, dayofmonth, 6, 0)\n",
+    "eend8pm = timestamp(year, month, dayofmonth, 8, 0)\n",
+    "in6pmTo8pm = currentTime >= start6pm and currentTime <= eend8pm\n",
+    "\n",
+    "start6am = timestamp(year, month, dayofmonth, 18, 0)\n",
+    "eend8am = timestamp(year, month, dayofmonth, 20, 0)\n",
+    "in6amTo8am = currentTime >= start6am and currentTime <= eend8am\n",
+    "\n",
+    "// Apply background highlights\n",
+    "bgcolor(isValidTF and in12pmTo12am ? color.new(color.black, 99) : na)\n",
+    "bgcolor(isValidTF and in8amTo8pm ? color.new(color.black, 99) : na)\n",
+    "bgcolor(isValidTF and in6pmTo8pm ? color.new(color.black, 97) : na)\n",
+    "bgcolor(isValidTF and in6amTo8am ? color.new(color.black, 95) : na)\n",
+    "\n",
+    "// Get the current day of the week and the current time\n",
+    "isMonday = dayofweek == dayofweek.sunday\n",
+    "ccurrentTime = timestamp(year, month, dayofmonth, hour, minute)\n",
+    "\n",
+    "// Define the time range for 6:00 AM to 8:00 AM\n",
+    "startTime = timestamp(year, month, dayofmonth, 18, 6) // 6:00 AM\n",
+    "endTime = timestamp(year, month, dayofmonth, 20, 0) // 8:00 AM\n",
+    "\n",
+    "// Check if it's Monday and the current time is within the range of 6:00 AM to 8:00 AM\n",
+    "isInTimeRange = isMonday and ccurrentTime >= startTime and ccurrentTime <= endTime\n",
+    "\n",
+    "// Highlight the area with a background color\n",
+    "bgcolor(isValidTF and isInTimeRange ? color.new(color.blue, 90) : na)\n",
+    "\n",
+    "// Vertical lines logic\n",
+    "// Only show vertical lines if timeframe is intraday and valid\n",
+    "showLines = timeframe.isintraday and isValidTF\n",
+    "\n",
+    "// Convert current bar time to Asia/Manila timezone\n",
+    "t = time(timeframe.period, 'Asia/Manila')\n",
+    "\n",
+    "// Target hours excluding 0, 12, 20\n",
+    "var array<int> targetHours = array.from(3, 6, 8, 15, 24) // 24 means midnight (0h)\n",
+    "\n",
+    "// Special hours separated\n",
+    "specialHour20 = 20\n",
+    "specialHour0 = 0 // midnight\n",
+    "specialHour12 = 12 // noon\n",
+    "\n",
+    "// Function to check if current bar time matches any target hour exactly at minute zero\n",
+    "isTargetTime() =>\n",
+    "    h = hour(t)\n",
+    "    m = minute(t)\n",
+    "    match = false\n",
+    "    for i = 0 to array.size(targetHours) - 1 by 1\n",
+    "        if (h == array.get(targetHours, i) or h == 0 and array.get(targetHours, i) == 24) and m == 0\n",
+    "            match := true\n",
+    "            match\n",
+    "    match\n",
+    "\n",
+    "// Check if current time matches special hour 20 at minute zero\n",
+    "isSpecialTime20() =>\n",
+    "    h = hour(t)\n",
+    "    m = minute(t)\n",
+    "    h == specialHour20 and m == 0\n",
+    "\n",
+    "// Check if current time matches special hour 0 at minute zero\n",
+    "isSpecialTime0() =>\n",
+    "    h = hour(t)\n",
+    "    m = minute(t)\n",
+    "    h == specialHour0 and m == 0\n",
+    "\n",
+    "// Check if current time matches special hour 12 at minute zero\n",
+    "isSpecialTime12() =>\n",
+    "    h = hour(t)\n",
+    "    m = minute(t)\n",
+    "    h == specialHour12 and m == 0\n",
+    "\n",
+    "// Detect new day (day break)\n",
+    "nnewDay = ta.change(time('D'))\n",
+    "\n",
+    "// Only draw lines if timeframe is intraday and valid\n",
+    "if showLines\n",
+    "    // Draw vertical line at day break (green, width 1)\n",
+    "    if bool(nnewDay)\n",
+    "        line.new(bar_index, low - 10 * syminfo.mintick, bar_index, high + 10 * syminfo.mintick, color = color.rgb(76, 175, 79, 90), width = 1, extend = extend.both)\n",
+    "\n",
+    "    // Draw vertical line at target times (blue, width 1)\n",
+    "    if isTargetTime()\n",
+    "        line.new(bar_index, low - 10 * syminfo.mintick, bar_index, high + 10 * syminfo.mintick, color = color.rgb(33, 149, 243, 95), width = 1, extend = extend.both)\n",
+    "\n",
+    "    // Draw vertical line at special hour 20 (teal, width 1)\n",
+    "    if isSpecialTime20()\n",
+    "        line.new(bar_index, low - 10 * syminfo.mintick, bar_index, high + 10 * syminfo.mintick, color = color.new(color.teal, 50), width = 1, extend = extend.both)\n",
+    "\n",
+    "    // Draw vertical line at special hour 0 (midnight) - orange, dotted\n",
+    "    if isSpecialTime0()\n",
+    "        var line midnightLine = na\n",
+    "        midnightLine := line.new(bar_index, low - 10 * syminfo.mintick, bar_index, high + 10 * syminfo.mintick, color = color.rgb(255, 153, 0, 50), width = 1, extend = extend.both)\n",
+    "        line.set_style(midnightLine, line.style_dotted)\n",
+    "\n",
+    "    // Draw vertical line at special hour 12 (noon) - red, dotted\n",
+    "    if isSpecialTime12()\n",
+    "        var line noonLine = na\n",
+    "        noonLine := line.new(bar_index, low - 10 * syminfo.mintick, bar_index, high + 10 * syminfo.mintick, color = color.rgb(255, 82, 82, 50), width = 1, extend = extend.both)\n",
+    "        line.set_style(noonLine, line.style_dotted)\n",
+    "\n",
+    "//hourly logic\n",
+    "// === UTILITY FUNCTION ===\n",
+    "f_show(tf) =>\n",
+    "    timeframe.period != tf\n",
+    "\n",
+    "// === HOURLY ===\n",
+    "hH = request.security(syminfo.tickerid, '60', high, lookahead = barmerge.lookahead_on)\n",
+    "hL = request.security(syminfo.tickerid, '60', low, lookahead = barmerge.lookahead_on)\n",
+    "hIdx = request.security(syminfo.tickerid, '60', bar_index, lookahead = barmerge.lookahead_on)\n",
+    "\n",
+    "var int hPrev = na\n",
+    "var float hHi = na\n",
+    "var int hHiBar = na\n",
+    "var line hHiLine = na\n",
+    "var bool hHiOn = false\n",
+    "var float hLo = na\n",
+    "var int hLoBar = na\n",
+    "var line hLoLine = na\n",
+    "var bool hLoOn = false\n",
+    "\n",
+    "if hIdx != hPrev\n",
+    "    hPrev := hIdx\n",
+    "    hHi := na\n",
+    "    hHiBar := na\n",
+    "    line.delete(hHiLine)\n",
+    "    hHiLine := na\n",
+    "    hHiOn := false\n",
+    "    hLo := na\n",
+    "    hLoBar := na\n",
+    "    line.delete(hLoLine)\n",
+    "    hLoLine := na\n",
+    "    hLoOn := false\n",
+    "    hLoOn\n",
+    "\n",
+    "if hHiOn and not na(hHiLine)\n",
+    "    bMin = math.min(open, close)\n",
+    "    bMax = math.max(open, close)\n",
+    "    if hHi <= bMax and hHi >= bMin\n",
+    "        line.set_x2(hHiLine, bar_index)\n",
+    "        line.set_y2(hHiLine, hHi)\n",
+    "        hHiOn := false\n",
+    "        hHiOn\n",
+    "    else\n",
+    "        line.set_x2(hHiLine, bar_index)\n",
+    "        line.set_y2(hHiLine, hHi)\n",
+    "\n",
+    "if hLoOn and not na(hLoLine)\n",
+    "    bMin = math.min(open, close)\n",
+    "    bMax = math.max(open, close)\n",
+    "    if hLo <= bMax and hLo >= bMin\n",
+    "        line.set_x2(hLoLine, bar_index)\n",
+    "        line.set_y2(hLoLine, hLo)\n",
+    "        hLoOn := false\n",
+    "        hLoOn\n",
+    "    else\n",
+    "        line.set_x2(hLoLine, bar_index)\n",
+    "        line.set_y2(hLoLine, hLo)\n",
+    "\n",
+    "\n",
+    "// === D/W/M High-Low Steplines ===\n",
+    "ddH = request.security(syminfo.tickerid, 'D', high, lookahead = barmerge.lookahead_on)\n",
+    "ddL = request.security(syminfo.tickerid, 'D', low, lookahead = barmerge.lookahead_on)\n",
+    "wH = request.security(syminfo.tickerid, 'W', high, lookahead = barmerge.lookahead_on)\n",
+    "wL = request.security(syminfo.tickerid, 'W', low, lookahead = barmerge.lookahead_on)\n",
+    "mH = request.security(syminfo.tickerid, 'M', high, lookahead = barmerge.lookahead_on)\n",
+    "mL = request.security(syminfo.tickerid, 'M', low, lookahead = barmerge.lookahead_on)\n",
+    "\n",
+    "plot(f_show('60') ? hH : na, title = 'H High', color = color.new(color.black, 90), style = plot.style_stepline, linewidth = 2)\n",
+    "plot(f_show('60') ? hL : na, title = 'H Low', color = color.new(color.black, 90), style = plot.style_stepline, linewidth = 2)\n",
+    "plot(f_show('D') ? ddH : na, title = 'D High', color = color.new(color.green, 50), style = plot.style_stepline)\n",
+    "plot(f_show('D') ? ddL : na, title = 'D Low', color = color.new(color.green, 70), style = plot.style_stepline)\n",
+    "plot(f_show('W') ? wH : na, title = 'W High', color = color.new(color.blue, 90), style = plot.style_stepline, linewidth = 2)\n",
+    "plot(f_show('W') ? wL : na, title = 'W Low', color = color.new(color.blue, 90), style = plot.style_stepline, linewidth = 2)\n",
+    "plot(f_show('M') ? mH : na, title = 'M High', color = color.new(color.purple, 90), style = plot.style_stepline, linewidth = 3)\n",
+    "plot(f_show('M') ? mL : na, title = 'M Low', color = color.new(color.purple, 90), style = plot.style_stepline, linewidth = 3)\n",
+    "\n",
+    "\n",
+    "//vwap logic\n",
+    "// VWAP calculation from 1-minute data\n",
+    "f_vwap_calc() =>\n",
+    "    var float cumPV = 0.0\n",
+    "    var float cumVol = 0.0\n",
+    "    var float cumPV_buy = 0.0\n",
+    "    var float cumVol_buy = 0.0\n",
+    "    var float cumPV_sell = 0.0\n",
+    "    var float cumVol_sell = 0.0\n",
+    "\n",
+    "    // Reset on new day\n",
+    "    if bool(ta.change(time('D')))\n",
+    "        cumPV := 0.0\n",
+    "        cumVol := 0.0\n",
+    "        cumPV_buy := 0.0\n",
+    "        cumVol_buy := 0.0\n",
+    "        cumPV_sell := 0.0\n",
+    "        cumVol_sell := 0.0\n",
+    "        cumVol_sell\n",
+    "\n",
+    "    buyVol = close > open ? volume : 0.0\n",
+    "    sellVol = close < open ? volume : 0.0\n",
+    "\n",
+    "    cumPV := cumPV + close * volume\n",
+    "    cumVol := cumVol + volume\n",
+    "\n",
+    "    cumPV_buy := cumPV_buy + close * buyVol\n",
+    "    cumVol_buy := cumVol_buy + buyVol\n",
+    "\n",
+    "    cumPV_sell := cumPV_sell + close * sellVol\n",
+    "    cumVol_sell := cumVol_sell + sellVol\n",
+    "\n",
+    "    vwap = cumVol != 0 ? cumPV / cumVol : na\n",
+    "    buyVWAP = cumVol_buy != 0 ? cumPV_buy / cumVol_buy : na\n",
+    "    sellVWAP = cumVol_sell != 0 ? cumPV_sell / cumVol_sell : na\n",
+    "\n",
+    "    [vwap, buyVWAP, sellVWAP]\n",
+    "\n",
+    "// Pull 1-minute VWAP values\n",
+    "[vwap_1m, buyVWAP_1m, sellVWAP_1m] = request.security(syminfo.tickerid, '1', f_vwap_calc())\n",
+    "\n",
+    "// Only show on intraday charts\n",
+    "showVWAP = not timeframe.isdaily and not timeframe.isweekly and not timeframe.ismonthly\n",
+    "  \n",
+    "// Plot VWAPs with conditional display\n",
+    "plot_vwap = plot(showVWAP ? vwap_1m : na, color = color.rgb(238, 218, 90), linewidth = 1, title = 'VWAP (1m)')\n",
+    "//plot_buy_vwap = plot(showVWAP ? buyVWAP_1m : na, color = color.rgb(0, 137, 123, 80), linewidth = 1, title = 'Buy Delta VWAP (1m)')\n",
+    "//plot_sell_vwap = plot(showVWAP ? sellVWAP_1m : na, color = color.rgb(255, 82, 82, 80), linewidth = 1, title = 'Sell Delta VWAP (1m)')\n",
+    "\n",
+    "// Fill areas\n",
+    "//fill(plot_vwap, plot_buy_vwap, color = showVWAP ? color.new(color.teal, 95) : na, title = 'Buy VWAP Fill')\n",
+    "//fill(plot_vwap, plot_sell_vwap, color = showVWAP ? color.new(color.red, 95) : na, title = 'Sell VWAP Fill')\n",
+    "\n",
+    "//table logic\n",
+    "// === Function to get OHLC of specified candle ===\n",
+    "get_prev_ohlc(tf, shift) =>\n",
+    "    o = request.security(syminfo.tickerid, tf, open[shift])\n",
+    "    h = request.security(syminfo.tickerid, tf, high[shift])\n",
+    "    l = request.security(syminfo.tickerid, tf, low[shift])\n",
+    "    c = request.security(syminfo.tickerid, tf, close[shift])\n",
+    "    [o, h, l, c]\n",
+    "\n",
+    "// === Function to calculate ranges and colors ===\n",
+    "get_data(tf, shift) =>\n",
+    "    [o, h, l, c] = get_prev_ohlc(tf, shift)\n",
+    "    full_range = h - l\n",
+    "    oc_range = math.abs(c - o)\n",
+    "    hl_range = h - l\n",
+    "    is_bull = c > o\n",
+    "    bg_color = is_bull ? color.new(color.teal, 90) : color.new(color.red, 90)\n",
+    "    [full_range, oc_range, hl_range, bg_color]\n",
+    "\n",
+    "// === Daily Candle Data ===\n",
+    "[d1_fr, d1_oc, d1_hl, d1_bg] = get_data(\"D\", 1)\n",
+    "[d2_fr, d2_oc, d2_hl, d2_bg] = get_data(\"D\", 2)\n",
+    "\n",
+    "// === Weekly Candle Data ===\n",
+    "[w0_fr, w0_oc, w0_hl, w0_bg] = get_data(\"W\", 0) // Developing Week\n",
+    "[w1_fr, w1_oc, w1_hl, w1_bg] = get_data(\"W\", 1)\n",
+    "[w2_fr, w2_oc, w2_hl, w2_bg] = get_data(\"W\", 2)\n",
+    "\n",
+    "\n",
+    "// Daily candle (6:00 AM)\n",
+    "sixambgcolor = dO < dC ?  color.rgb(0, 137, 123, 90) : color.rgb(255, 82, 82, 90)\n",
+    "\n",
+    "// Custom candle (8:00 AM Manila)\n",
+    "eightambgcolor = cO < cC ?  color.rgb(0, 137, 123, 90) : color.rgb(255, 82, 82, 90) \n",
+    "\n",
+    "// Adjust UTC time to Manila (UTC+8) qewrqwerwerwerwerwerew\n",
+    "manilaTime = time + 8 * 60 * 60 * 1000  // Shift by 8 hours in milliseconds\n",
+    "\n",
+    "// Extract date components from Manila time\n",
+    "manilaYear  = year(manilaTime)\n",
+    "manilaMonth = month(manilaTime)\n",
+    "manilaDay   = dayofmonth(manilaTime)\n",
+    "\n",
+    "// Format date as \"DD/MM/YYYY\"\n",
+    "formattedDate = str.tostring(manilaDay, \"00\") + \"/\" + str.tostring(manilaMonth, \"00\") + \"/\" + str.tostring(manilaYear)\n",
+    "\n",
+    "// Track day changes in Manila time\n",
+    "var int prevManilaDay = na\n",
+    "newManilaDay = manilaDay != prevManilaDay\n",
+    "\n",
+    "// Update stored day\n",
+    "if newManilaDay\n",
+    "    prevManilaDay := manilaDay\n",
+    "//qweqweqweqweqeqweqweqwewqeqwewqeqe\n",
+    "\n",
+    "// UTC/UTC+8 Time\n",
+    "var label utcLbl = na\n",
+    "var label utc8Lbl = na\n",
+    "label.delete(utcLbl)\n",
+    "label.delete(utc8Lbl)\n",
+    "\n",
+    "//utcStr = \"UTC: \" + str.tostring(hour, \"00\") + \":\" + str.tostring(minute, \"00\")\n",
+    "uutcStr = str.tostring(hour, \"00\") + \":\" + str.tostring(minute, \"00\") + \" 𝘜𝘛𝘊+8\"\n",
+    "utc8H = (hour + 12) % 24\n",
+    "//utc8Str = \"UTC: \" + str.tostring(utc8H, \"00\") + \":\" + str.tostring(minute, \"00\")\n",
+    "uutc8Str =str.tostring(utc8H, \"00\") + \":\" + str.tostring(minute, \"00\") + \" 𝘜𝘛𝘊+8\"\n",
+    "\n",
+    "// TF Label\n",
+    "var label tfLbl = na\n",
+    "label.delete(tfLbl)\n",
+    "var label tfLbl2 = na\n",
+    "label.delete(tfLbl2)\n",
+    "\n",
+    "tfS = \"\"\n",
+    "if (str.length(timeframe.period) > 1 and str.substring(timeframe.period, str.length(timeframe.period)-1, str.length(timeframe.period)) == \"m\")\n",
+    "    tfS := \"m\"\n",
+    "else if (str.length(timeframe.period) > 1 and str.substring(timeframe.period, str.length(timeframe.period)-1, str.length(timeframe.period)) == \"h\")\n",
+    "    tfS := \"h\"\n",
+    "tfP = str.replace(timeframe.period, tfS, \"\")\n",
+    "tfText = tfP + tfS + \"m 𝘪𝘯𝘵𝘰 1D\"\n",
+    "\n",
+    "// Range Labels\n",
+    "dHL = math.abs(dH - dL)\n",
+    "dOC = math.abs(dO - dC)\n",
+    "cHL = math.abs(cH - cL)\n",
+    "cOC = math.abs(cO - cC)\n",
+    "\n",
+    "// === Create and update table ===\n",
+    "var table tt = table.new(position.top_right, 80, 80, border_width=1)\n",
+    "\n",
+    "if bar_index == 1\n",
+    "    table.cell(tt, 0, 0, \"\", text_color=color.rgb(255, 255, 255, 100), text_size = size.tiny, bgcolor=color.rgb(120, 123, 134, 100))\n",
+    "    table.cell(tt, 1, 0, \"\", text_color=color.rgb(255, 255, 255, 100), text_size = size.tiny,bgcolor=color.rgb(120, 123, 134, 100))\n",
+    "\n",
+    "    table.cell(tt, 0, 1, \"\", text_color=color.rgb(255, 255, 255, 100), text_size = size.tiny, bgcolor=color.rgb(120, 123, 134, 100))\n",
+    "    table.cell(tt, 1, 1, \"\", text_color=color.rgb(255, 255, 255, 100), text_size = size.tiny,bgcolor=color.rgb(120, 123, 134, 100))\n",
+    " \n",
+    "// === Daily Rows ===\n",
+    "table.cell(tt, 1, 2, \"𝑺𝒑𝒆𝒄𝒊𝒇𝒊𝒄𝒂𝒕𝒊𝒐𝒏\", text_color = color.white, text_size = size.tiny, bgcolor = color.navy)\n",
+    "\n",
+    "table.cell(tt, 0, 3, \"Date\", text_size = size.tiny, text_color = color.white)\n",
+    "table.cell(tt, 1, 3, formattedDate, text_size = size.tiny, bgcolor = color.rgb(120, 123, 134, 90), text_color = color.rgb(255, 255, 255, 30))\n",
+    " \n",
+    "table.cell(tt, 0, 4, \"Time\", text_size = size.tiny, text_color = color.white)\n",
+    "table.cell(tt, 1, 4, text = uutc8Str, text_size = size.tiny, bgcolor = color.rgb(120, 123, 134, 90), text_color = color.rgb(255, 255, 255, 30))\n",
+    " \n",
+    "table.cell(tt, 0, 5, \"Time\", text_size = size.tiny, text_color = color.white)\n",
+    "table.cell(tt, 1, 5, text=uutcStr, text_size = size.tiny, bgcolor = color.rgb(120, 123, 134, 90), text_color = color.rgb(255, 255, 255, 30))\n",
+    "\n",
+    "table.cell(tt, 0, 6, \"Type\", text_size = size.tiny, text_color = color.white)\n",
+    "table.cell(tt, 1, 6, text=tfText, text_size = size.tiny, bgcolor = color.rgb(120, 123, 134, 90), text_color = color.rgb(255, 255, 255, 30))\n",
+    "\n",
+    "table.cell(tt, 1, 7, \"𝑯𝒊𝒈𝒉𝒆𝒓 𝑻𝑭\", text_color = color.white, text_size = size.tiny, bgcolor = color.navy)\n",
+    "\n",
+    "table.cell(tt, 0, 8, \"6:00\\nяαηgє\", text_size = size.tiny, text_color = color.white)\n",
+    "table.cell(tt, 1, 8, \"HL | $\" + str.tostring(dHL, \"#\") + \"\\nOC | $\" + str.tostring(dOC, \"#\"), text_size = size.tiny, bgcolor = sixambgcolor, text_color = color.rgb(255, 255, 255, 30))\n",
+    "\n",
+    "table.cell(tt, 0, 9, \"8:00\\nяαηgє\", text_size = size.tiny, text_color = color.white)\n",
+    "table.cell(tt, 1, 9, \"HL | $\" + str.tostring(cHL, \"#\") + \"\\nOC | $\" + str.tostring(cOC, \"#\"), text_size = size.tiny, bgcolor = eightambgcolor, text_color = color.rgb(255, 255, 255, 30))\n",
+    "\n",
+    "table.cell(tt, 1, 11, \"1D 𝑪𝒂𝒏𝒅𝒍𝒆\", text_color = color.white, text_size = size.tiny, bgcolor = color.navy)\n",
+    "\n",
+    "table.cell(tt, 0, 12, \"-1D \\nяαηgє\", text_size = size.tiny, text_color = color.white)\n",
+    "table.cell(tt, 1, 12, \"HL | $\" + str.tostring(d1_hl, \"#\") + \"\\nOC | $\" + str.tostring(d1_oc, \"#\"), text_size = size.tiny, bgcolor=d1_bg, text_color = color.rgb(255, 255, 255, 30))\n",
+    "\n",
+    "table.cell(tt, 0, 13, \"-2D \\nяαηgє\", text_size = size.tiny, text_color = color.white)\n",
+    "table.cell(tt, 1, 13, \"HL | $\" + str.tostring(d2_hl, \"#\") + \"\\nOC | $\" + str.tostring(d2_oc, \"#\"), text_size = size.tiny, bgcolor=d2_bg, text_color = color.rgb(255, 255, 255, 30))\n",
+    "\n",
+    "table.cell(tt, 1, 14, \"1W 𝑪𝒂𝒏𝒅𝒍𝒆\", text_color = color.white, text_size = size.tiny, bgcolor = color.navy)\n",
+    "\n",
+    "table.cell(tt, 0, 15, \"0W \\nяαηgє\", text_size = size.tiny, text_color = color.white)\n",
+    "table.cell(tt, 1, 15, \"HL | $\" + str.tostring(w0_hl, \"#\") + \"\\nOC | $\" + str.tostring(w0_oc, \"#\"), text_size = size.tiny, bgcolor=w0_bg, text_color = color.rgb(255, 255, 255, 30))\n",
+    "\n",
+    "table.cell(tt, 0, 16, \"-1W \\nяαηgє\", text_size = size.tiny, text_color = color.white)\n",
+    "table.cell(tt, 1, 16, \"HL | $\" + str.tostring(w1_hl, \"#\") + \"\\nOC | $\" + str.tostring(w1_oc, \"#\"), text_size = size.tiny, bgcolor=w1_bg, text_color = color.rgb(255, 255, 255, 30))\n",
+    "\n",
+    "table.cell(tt, 0, 17, \"-2W \\nяαηgє\", text_size = size.tiny, text_color = color.white)\n",
+    "table.cell(tt, 1, 17, \"HL | $\" + str.tostring(w2_hl, \"#\") + \"\\nOC | $\" + str.tostring(w2_oc, \"#\"), text_size = size.tiny, bgcolor=w2_bg, text_color = color.rgb(255, 255, 255, 30))\n",
+    "\n",
+    "table.cell(tt, 1, 18, \"© 2025 𝚆𝚀𝚜\", text_color = color.white, text_size = size.tiny, bgcolor = color.black)\n",
+    "\n",
+    "//developinf day \n",
+    "// === INPUTS ===\n",
+    "startSession = input.session(\"0000-2359\", title=\"Session Time\")\n",
+    "sshowLines = input.bool(true, title=\"Show Lines\")\n",
+    "showLabels = input.bool(true, title=\"Show Labels\")\n",
+    "\n",
+    "// === TIME LOGIC ===\n",
+    "isNNewDay = ta.change(time(\"D\"))\n",
+    "\n",
+    "// === DEVELOPING HIGH/LOW TRACKING ===\n",
+    "var float devHigh = na\n",
+    "var float devLow  = na\n",
+    "\n",
+    "// Reset at new day\n",
+    "if bool(isNNewDay)\n",
+    "    devHigh := high\n",
+    "    devLow  := low\n",
+    "else\n",
+    "    devHigh := math.max(devHigh, high)\n",
+    "    devLow  := math.min(devLow, low)\n",
+    "\n",
+    "// === PLOTTING ===\n",
+    "plot(sshowLines ? devHigh : na, title=\"Developing High\", color=color.rgb(76, 175, 79, 50), linewidth=2, style=plot.style_stepline)\n",
+    "plot(sshowLines ? devLow : na, title=\"Developing Low\", color=color.rgb(255, 82, 82, 50), linewidth=2, style=plot.style_stepline)\n",
+    "\n",
+    "\n",
+    "// Input only controls how many 1-minute candles are used\n",
+    "llen = input.int(24, \"VWMA Length (1m)\")\n",
+    "\n",
+    "// VWMA calculation inside 1-minute context\n",
+    "vwma_1m = request.security(syminfo.tickerid, \"1\", math.sum(close * volume, llen) / math.sum(volume, llen))\n",
+    "\n",
+    "// Only plot if current timeframe is 5 minutes\n",
+    "is_5min = timeframe.period == \"5\"\n",
+    "\n",
+    "plot(is_5min ? vwma_1m : na, title=\"1-Minute VWMA\", color=color.rgb(117, 117, 117), linewidth=2)\n",
+    "\n",
+    "//Profile Settings\n",
+    "tf = input.timeframe(\"D\", title = \"Timeframe\", inline = \"0\", group = \"PROFILE SETTINGS\")\n",
+    "lb_calc = input.bool(false, title = \"Use Lookback Calc?\", inline = \"0\", group = \"PROFILE SETTINGS\", tooltip = \"Lookback Calculation Historically compiles a profile to display the data from the requested timeframe amount of time.\\nProgressive Calculation (Default) restarts the profile calculation on each change of the requested timeframe(New Bar).\\n\\nExamples:\\nLookback = '1D' will display a profile based on 1 Day of historical data.\\nProgressive = '1D' will display a profile base on data since the start of the Day.\\n\\nTip: There is a manual control for lookback bars at the bottom if you prefer to see a specific number.\")\n",
+    "vap = input.float(0, title = \"Value Area %\", inline = \"1_1\", group = \"PROFILE SETTINGS\")\n",
+    "mp = input.bool(false, title = \"Calculate As Market Profile\", inline = \"2\", group = \"PROFILE SETTINGS\", tooltip = \"Calculations will distribue a 1 instead of the candle's volume.\")\n",
+    "//Display Settings\n",
+    "disp_size = input.int(-30, minval = -500,maxval = 500,title  = \"Display Size   \", inline = \"3\", group = \"DISPLAY SETTINGS\", tooltip = \"The entire range of your profile will scale to fit inside this range.\\nNotes:\\n-This value is # bars away from your profile's axis.\\n-The larger this value is, the more granular your (horizontal) view will be. This does not change the Profiles' value; because of this, sometimes the POC looks tied with other values widely different. The POC CAN be tied to values close to it, but if the value is far away it is likely to just be a visual constraint.\\n-This Value CAN be negative\")\n",
+    "prof_offset = input.int(0, minval = -500,maxval = 500, title  = \"Display Offset\", inline = \"4\", group = \"DISPLAY SETTINGS\", tooltip = \"Offset your profile's axis (Left/Right) to customize your display to fit your style.\\nNotes:\\n-Zero = Current Bar\\n-This Value CAN be negative\")\n",
+    "//Additional Data Displays\n",
+    "extend_day = input.bool(false, title = \"Extend POC/VAH/VAL\", inline = \"1\", group = \"Additional Data Displays\")\n",
+    "lab_tog = input.bool(true, title = \"Label POC/VAH/VAL\", inline = \"2\", group = \"Additional Data Displays\") \n",
+    "dev_poc = input.bool(false, title = \"Rolling POC/TPO\", inline = \"3\", group = \"Additional Data Displays\", tooltip = \"Displays Value as it Develops!\\nNote: Will Cause Longer Load Time. If not needed, it is reccomended to turn these off.\\n\\n> Consider manually overriding the Row Size Below!\")\n",
+    "dev_va = input.bool(false, title = \"Rolling VAH/VAL\", inline = \"4\", group = \"Additional Data Displays\")\n",
+    "//High/Low Volume Nodes\n",
+    "node_tog = input.bool(true, title = \"Highlight Nodes\", group = \"High/Low Volume Nodes\")\n",
+    "hi_width = input.int(10, maxval = 100, minval = 1,title = \"[HVN] Analysis Width %      ↕\", group = \"High/Low Volume Nodes\", tooltip = \"[HVN] = High Volume Node\\nAnalysis Width % = % of profile to take into account when determining what is a High Volume Node and what is Not.\")*0.01\n",
+    "lo_width = input.int(10, maxval = 100, minval = 1, title = \"[LVN]  Analysis Width %      ↕\", group = \"High/Low Volume Nodes\", tooltip = \"[LVN] = Low Volume Node\\nAnalysis Width % = % of profile to take into account when determining what is a Low Volume Node and what is Not.\")*0.01\n",
+    "//Colors\n",
+    "poc_color = input.color(#d3be00, title = \"POC/TPO Color\", group = \"Colors\")\n",
+    "var_color = input.color(#886000, title = \"Value High/Low Color\", group = \"Colors\")\n",
+    "vaz_color = input.color(color.new(#886000, 0), title = \"Value Zone Color\", group = \"Colors\")\n",
+    "ov_color = input.color(#886000, title = \"Profile Color\", group = \"Colors\")\n",
+    "lv_color = input.color(#886000, title  = \"Low Volume Color\", group = \"Colors\")\n",
+    "hv_color = input.color(#886000, title = \"High Volume Color\", group = \"Colors\")\n",
+    "//⭕Manual Overrides⭕\n",
+    "lb_override = input.bool(false, title = \">\", group = \"⭕Manual Overrides⭕[CHECK TO ENABLE]\", inline = \"1\")\n",
+    "lb_over_bars = input.int(500, title = \"Lookback\", group = \"⭕Manual Overrides⭕[CHECK TO ENABLE]\", inline = \"1\", tooltip = \"Lookback Calc must be enabled for this feature.\")\n",
+    "row_size_override = input.bool(false, title = \">\", group = \"⭕Manual Overrides⭕[CHECK TO ENABLE]\", inline = \"2\")\n",
+    "row_size_input = input.int(100, minval= 2, maxval = 999, title = \"Profile Rows\", group = \"⭕Manual Overrides⭕[CHECK TO ENABLE]\", inline = \"2\", tooltip = \"Manually set the Number of Rows to Use.\\nReccomended when faster calculations are neccesary, like replay mode or Developing POC/VAH/VAL.\")\n",
+    "///_________________________________________\n",
+    "///Misc Stuff\n",
+    "///‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾\n",
+    " \n",
+    "round_to(_round,_to) =>\n",
+    "    math.round(_round/_to)*_to\n",
+    "\n",
+    "///_________________________________________\n",
+    "///Setup for Length\n",
+    "///‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾\n",
+    "tf_change = timeframe.change(tf)\n",
+    "var int bi_nd = na // BI_ND = Bar Index _ New Day\n",
+    "var int bi_lnd = na //BI_LND = Bar Index Last New Day\n",
+    "\n",
+    "bi_lnd := tf_change?bi_nd:bi_lnd\n",
+    "bi_nd := tf_change?bar_index:bi_nd\n",
+    "\n",
+    "tf_len = (bi_nd - bi_lnd)+1\n",
+    "bs_newtf = (bar_index - bi_nd)+1\n",
+    "id_lb_bars = timeframe.in_seconds(tf)/timeframe.in_seconds(timeframe.period)\n",
+    "dwm_lb_bars = (math.max(tf_len,bs_newtf)>1?math.max(tf_len,bs_newtf)-1:1)\n",
+    "auto_lb_bars = timeframe.in_seconds(tf) >= timeframe.in_seconds(\"D\")?dwm_lb_bars:id_lb_bars\n",
+    "lb_bars = (lb_calc?(na(tf_len)?bar_index:(lb_override?lb_over_bars:auto_lb_bars)):na(bs_newtf)?bar_index:bs_newtf)\n",
+    "///_________________________________________\n",
+    "///Warning for Large Timeframe\n",
+    "///‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾\n",
+    "var d = dayofmonth\n",
+    "var m = switch\n",
+    "    month == 1 => \"Jan\"\n",
+    "    month == 2 => \"Feb\"\n",
+    "    month == 3 => \"Mar\"\n",
+    "    month == 4 => \"Apr\"\n",
+    "    month == 5 => \"May\"\n",
+    "    month == 6 => \"Jun\"\n",
+    "    month == 7 => \"Jul\"\n",
+    "    month == 8 => \"Aug\"\n",
+    "    month == 9 => \"Sep\"\n",
+    "    month == 10 => \"Oct\"\n",
+    "    month == 11 => \"Nov\"\n",
+    "    month == 12 => \"Dec\"\n",
+    "var y = year - (int(year/100)*100)\n",
+    "send_warning = (lb_calc and (na(tf_len) or bar_index<lb_bars)) or (not lb_calc and na(bs_newtf))\n",
+    "warning = send_warning?\"[WARNING ⚠]\"+\" [\"+(lb_override?str.tostring(lb_over_bars) + \" Bars\": tf)+\"]\"+\"\\nData Start: \" + str.tostring(d,\"00\")+\" \"+m + \" '\" + str.tostring(y) + \"\\n\\n\":na\n",
+    "if na(volume) and (mp == false)\n",
+    "    runtime.error(\"No Volume Data. Please Use a Ticker with Volume Data or Switch to Market Profile Calculation.\")\n",
+    "\n",
+    "///_________________________________________\n",
+    "///Start Data Accumulation\n",
+    "///‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾\n",
+    "//By storing the data, I can use it later without the need for historical reference. \n",
+    "//Additionally, I can control the amount of data kept in memory to only hold the lookback amount of bars.\n",
+    "//This is all of the advantages of lookback calcs, with all the advantages of progressive calculations.\n",
+    "\n",
+    "src_high = math.round_to_mintick(high)\n",
+    "src_low = math.round_to_mintick(low)\n",
+    "\n",
+    "var highs = array.new_float(na)\n",
+    "var lows = array.new_float(na)\n",
+    "var vol = array.new_float(na)\n",
+    "\n",
+    "highs.push(src_high)\n",
+    "lows.push(src_low)\n",
+    "if not mp\n",
+    "    vol.push(volume)\n",
+    "\n",
+    "if highs.size() >= lb_bars\n",
+    "    for i = 0 to bar_index\n",
+    "        if highs.size() == lb_bars\n",
+    "            break\n",
+    "        else\n",
+    "            highs.shift()\n",
+    "            lows.shift()\n",
+    "            if not mp\n",
+    "                vol.shift()\n",
+    "    \n",
+    "//Granularity Gets more corse farther from current bar for speed. Replay mode can get a full gran profile if needed.\n",
+    "v_gran = row_size_override?row_size_input:bar_index>=(last_bar_index-1000)?999: bar_index>=(last_bar_index-2000)?249: bar_index>=(last_bar_index-4000)?124:64\n",
+    "\n",
+    "\n",
+    "\n",
+    "full_hist_calc = (dev_poc or dev_va)\n",
+    "tick_size = full_hist_calc or barstate.islast?math.ceil(((highs.max()-lows.min())/v_gran)/syminfo.mintick)*syminfo.mintick:na\n",
+    "\n",
+    "\n",
+    "///_________________________________________\n",
+    "///Profile Calcs\n",
+    "///‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾\n",
+    "data_map = map.new<float,float>()\n",
+    "\n",
+    "if full_hist_calc and highs.size() > 0\n",
+    "    highs_size = array.size(highs)-1\n",
+    "    for i = 0 to highs_size\n",
+    "        hi = round_to(highs.get(i),tick_size)\n",
+    "        lo = round_to(lows.get(i),tick_size)\n",
+    "        candle_index = ((hi-lo)/tick_size)\n",
+    "        tick_vol = mp?1:math.round((vol.get(i)/(candle_index+1)),3)\n",
+    "        for e = 0 to candle_index\n",
+    "            val = round_to(lo+(e*tick_size),tick_size)\n",
+    "            data_map.put(val, math.round(nz(data_map.get(val)),3)+tick_vol)\n",
+    "\n",
+    "if (full_hist_calc == false) and barstate.islast\n",
+    "    highs_size = array.size(highs)-1\n",
+    "    for i = 0 to highs_size\n",
+    "        hi = round_to(highs.get(i),tick_size)\n",
+    "        lo = round_to(lows.get(i),tick_size)\n",
+    "        candle_index = ((hi-lo)/tick_size)\n",
+    "        tick_vol = mp?1:math.round((vol.get(i)/(candle_index+1)),3)\n",
+    "        for e = 0 to candle_index\n",
+    "            val = round_to(lo+(e*tick_size),tick_size)\n",
+    "            data_map.put(val,nz(data_map.get(val))+tick_vol)\n",
+    "\n",
+    "///_________________________________________\n",
+    "////Other Profile Values\n",
+    "///‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾\n",
+    "keys = map.keys(data_map)\n",
+    "values = map.values(data_map)\n",
+    "array.sort(keys, order.ascending)\n",
+    "///_________________________________________\n",
+    "///AQUIRE POC\n",
+    "///‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾\n",
+    "//POC = Largest Volume Closest to Price Average of Profile\n",
+    "//\n",
+    "float poc = 0\n",
+    "float poc_vol = values.max()\n",
+    "prof_avg = keys.avg()\n",
+    "if barstate.islast or dev_poc\n",
+    "    for [key, value] in data_map\n",
+    "        if (value == poc_vol and math.abs(key-prof_avg)<math.abs(poc-prof_avg))\n",
+    "            poc := key\n",
+    "\n",
+    "///_________________________________________\n",
+    "///VALUE ZONES\n",
+    "///‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾\n",
+    "max_vol = array.sum(values)*(vap/100)\n",
+    "max_index = poc\n",
+    "vol_count = poc_vol\n",
+    "up_count = max_index\n",
+    "down_count = max_index\n",
+    "keys_size = array.size(keys)\n",
+    "if barstate.islast or dev_va\n",
+    "    for i = 0 to keys_size \n",
+    "        if vol_count >= max_vol\n",
+    "            break\n",
+    "        upper_vol = nz(data_map.get(round_to(up_count+tick_size,tick_size))) + nz(data_map.get(round_to(up_count+(tick_size*2),tick_size)))\n",
+    "        lower_vol = nz(data_map.get(round_to(down_count-tick_size,tick_size))) + nz(data_map.get(round_to(down_count-(tick_size*2),tick_size)))\n",
+    "        if (((upper_vol >= lower_vol) and upper_vol > 0) or (lower_vol>0 == false)) and up_count < array.max(keys)-tick_size\n",
+    "            vol_count += upper_vol                                                    \n",
+    "            up_count := round_to(up_count+(tick_size*2),tick_size)                                                       \n",
+    "        else if down_count > array.min(keys)+tick_size\n",
+    "            vol_count += lower_vol\n",
+    "            down_count := round_to(down_count-(tick_size*2),tick_size)  \n",
+    "val = down_count\n",
+    "vah = up_count\n",
+    "////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////\n",
+    "//END PROFILE CALCs//\n",
+    "////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////\n",
+    "\n",
+    "\n",
+    "////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////\n",
+    "//Cluster ID for Volume Nodes//\n",
+    "////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////\n",
+    "//   Cluster ID analyzes a local group (cluster) of volume profile indices (rows) to determine if the current index is higher(or lower) than those around it.\n",
+    "//   The Analysis width is a % of the entire # of rows in the profile to pull into a cluster, by increasing or decreasing this value, we can tune the profile to our individual preference.\n",
+    "\n",
+    "vgroup_pull(_var,_array,_num1,_num2) =>\n",
+    "    _var == 1 and _num1>=_num2 ? data_map.get(array.get(_array,_num1-_num2)):                               //Pulls Index Value from Below\n",
+    "     _var == 2 and array.size(_array)-1 >= (_num1 + _num2) ? data_map.get(array.get(_array,_num1+_num2))    //Pulls Index Value from Above \n",
+    "     :0\n",
+    "hvn_points = array.new_int(na)  \n",
+    "if array.size(keys) > 0 and barstate.islast and node_tog\n",
+    "    array.clear(hvn_points)\n",
+    "    for [i,v] in keys    \n",
+    "        _val = data_map.get(v)\n",
+    "        ary = array.new_float(na)                               \n",
+    "        for e = 0 to int(keys.size()*hi_width) \n",
+    "            array.push(ary,vgroup_pull(1,keys,i,e))\n",
+    "            array.push(ary,vgroup_pull(2,keys,i,e))       \n",
+    "        max = array.max(ary)  \n",
+    "        avg = array.avg(ary)\n",
+    "        if _val >= math.avg(max,avg)\n",
+    "            array.push(hvn_points,i)\n",
+    "\n",
+    "lvn_points = array.new_int(na)\n",
+    "if array.size(keys) > 0 and barstate.islast and node_tog\n",
+    "    array.clear(lvn_points)\n",
+    "    for [i,v] in keys\n",
+    "        _val = data_map.get(v) \n",
+    "        ary = array.new_float(na) \n",
+    "        for e = 0 to int(array.size(keys)*lo_width)  \n",
+    "            array.push(ary,vgroup_pull(1,keys,i,e))\n",
+    "            array.push(ary,vgroup_pull(2,keys,i,e))\n",
+    "        min = array.min(ary)\n",
+    "        avg = array.avg(ary)\n",
+    "        if _val <= math.avg(min,avg)  \n",
+    "            array.push(lvn_points,i)\n",
+    "\n",
+    "///_________________________________________\n",
+    "///Cluster Merging\n",
+    "///‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾\n",
+    "\n",
+    "merge_clusters(_array) =>\n",
+    "    if array.size(_array)>0\n",
+    "        ary_size = array.size(_array)-1\n",
+    "        for i = 0 to ary_size\n",
+    "            merge_found = false\n",
+    "            _val = array.get(_array,i)\n",
+    "            prof_merge_percent = int(array.size(keys)*0.02)\n",
+    "            for e = prof_merge_percent to 0\n",
+    "                if merge_found\n",
+    "                    array.push(_array,_val+e)\n",
+    "                else if array.includes(_array,_val+e)\n",
+    "                    merge_found := true            \n",
+    "                dummy = \"This is here to make the loop return 'void'. \" //Delete This line to Break the Indicator.\n",
+    "\n",
+    "// run it\n",
+    "if barstate.islast and node_tog\n",
+    "    merge_clusters(hvn_points)\n",
+    "    merge_clusters(lvn_points)\n",
+    "\n",
+    "////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////\n",
+    "//END Cluster ID Calcs//\n",
+    "////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////\n",
+    "\n",
+    "\n",
+    "/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////\n",
+    "//Display//\n",
+    "////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////\n",
+    "\n",
+    "var profile = array.new_line(na)\t\n",
+    "var box_profile = array.new_box(na)\t \n",
+    "if barstate.islast\n",
+    "    if array.size(profile) > 0\n",
+    "        for [index,line] in profile         \n",
+    "            line.delete(line)       \n",
+    "        array.clear(profile)\n",
+    "    if array.size(box_profile) > 0        \n",
+    "        for [index,box] in box_profile      \n",
+    "            box.delete(box)     \n",
+    "        array.clear(box_profile)\n",
+    "    if polyline.all.size() > 0        \n",
+    "        for pl in polyline.all      \n",
+    "            pl.delete() \n",
+    "    \n",
+    "prof_color(_num,_key) => //Function for determining what color each profile index gets\n",
+    "    switch\n",
+    "        array.includes(hvn_points,_num) and array.includes(lvn_points,_num) => ((_key>up_count or _key<down_count)?ov_color:vaz_color) /// If its BOTH a HVN and LVN only display color based on valuezone (in value or out) I plan to imporove this in the future.\n",
+    "        array.includes(lvn_points,_num) => lv_color     //If it is < lv% of the max volume, give it the low volume color \n",
+    "        array.includes(hvn_points,_num) => hv_color     //If its > hv%  of the max volume, give it the high volume color \n",
+    "        (_key>up_count or _key<down_count) => ov_color                       //If its above or below our value zone, give it the out of value color\n",
+    "        => vaz_color // Everything else is inside the value zone so it gets the value zone color\n",
+    "\n",
+    "dash(_i) => (_i/2 - math.floor(_i/2)) > 0 //only true when the number is odd, Making it oscillate on/off, I can make use of this to evenly distribute boxes and lines throughout the display to make it cleaner.\n",
+    "\n",
+    "///_________________________________________\n",
+    "///Drawing the Profile\n",
+    "///‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾\n",
+    "scale = disp_size/array.max(values)\n",
+    "var lab = label.new(na,na, style = (disp_size<0?label.style_label_lower_right:label.style_label_lower_left), color = color.rgb(0,0,0,100), textalign = text.align_left, text_font_family = font.family_monospace)\n",
+    "if barstate.islast\n",
+    "    if extend_day\n",
+    "        polyline.new(array.from(chart.point.from_time(time[lb_bars],poc),chart.point.from_time(time,poc)), line_color = poc_color, xloc = xloc.bar_time, line_width = 2)\n",
+    "        polyline.new(array.from(chart.point.from_time(time[lb_bars],vah),chart.point.from_time(time,vah)), line_color = var_color, xloc = xloc.bar_time, line_width = 2)\n",
+    "        polyline.new(array.from(chart.point.from_time(time[lb_bars],val),chart.point.from_time(time,val)), line_color = var_color, xloc = xloc.bar_time, line_width = 2)\n",
+    "    for [i,get_key] in keys\n",
+    "        get_vol = data_map.get(get_key)\n",
+    "        scaled = math.round(get_vol*scale)\n",
+    "        p1 = extend_day and disp_size > 0 ? bar_index : (bar_index+prof_offset)\n",
+    "        p2 = extend_day and disp_size < 0 ? bar_index : ((bar_index+scaled)+prof_offset)\n",
+    "        if get_key == poc\n",
+    "            array.push(box_profile,box.new(p1,poc,p2,poc, border_color = poc_color, border_style = line.style_solid, border_width = 1))\n",
+    "        else if get_key == vah\n",
+    "            array.push(box_profile,box.new(p1,vah,p2,vah, border_color = var_color, border_style = line.style_solid, border_width = 1))\n",
+    "        else if get_key == val\n",
+    "            array.push(box_profile,box.new(p1,val,p2,val, border_color = var_color, border_style = line.style_solid, border_width = 1))\n",
+    "        else if dash(i) and (array.size(profile) <= 499) and (math.abs(scaled)>=1)\n",
+    "            array.push(profile,line.new(bar_index+prof_offset,get_key,(bar_index+scaled)+prof_offset,get_key, color = prof_color(i,get_key), style = (get_key<down_count or get_key>up_count?line.style_dotted:line.style_solid),width = 1))\n",
+    "        else if (array.size(box_profile) <= 499) and (math.abs(scaled)>=1)\n",
+    "            array.push(box_profile,box.new(bar_index+prof_offset,get_key,(bar_index+scaled)+prof_offset,get_key, border_color = prof_color(i,get_key), border_style = (get_key<down_count or get_key>up_count?line.style_dotted:line.style_solid), border_width = 1))\n",
+    "\n",
+    "//Drawing labels for the profile\n",
+    "    lab.set_xy(bar_index+prof_offset, keys.max())\n",
+    "    lab.set_textcolor(send_warning?color.rgb(255, 136, 0):chart.fg_color)\n",
+    "    if lab_tog \n",
+    "        var poc_lab = label.new(bar_index+prof_offset,poc, text = mp?\"TPO\":\"POC\", tooltip = (mp?\"TPO: $\":\"POC: $\") + str.tostring(poc,format.mintick),size = size.small, style = (disp_size<0?label.style_label_left:extend_day?label.style_label_lower_right:label.style_label_right), color = color.rgb(0,0,0,100), textcolor = poc_color, textalign = (disp_size<0?text.align_right:text.align_left), text_font_family = font.family_monospace)\n",
+    "        var vah_lab = label.new(bar_index+prof_offset,vah, text = \"VAH\",size = size.small,tooltip = \"VAH: \" + str.tostring(vah,format.mintick), style = (disp_size<0?label.style_label_left:extend_day?label.style_label_lower_right:label.style_label_right), color = color.rgb(0,0,0,100), textcolor = var_color, textalign = (disp_size<0?text.align_right:text.align_left), text_font_family = font.family_monospace)\n",
+    "        var val_lab = label.new(bar_index+prof_offset,val, text = \"VAL\",size = size.small,tooltip = \"VAL: \" + str.tostring(val,format.mintick), style = (disp_size<0?label.style_label_left:extend_day?label.style_label_lower_right:label.style_label_right), color = color.rgb(0,0,0,100), textcolor = var_color, textalign = (disp_size<0?text.align_right:text.align_left), text_font_family = font.family_monospace)\n",
+    "        poc_lab.set_xy(bar_index+prof_offset,poc)\n",
+    "        vah_lab.set_xy(bar_index+prof_offset,vah)\n",
+    "        val_lab.set_xy(bar_index+prof_offset,val)\n",
+    "// Alerts\n",
+    "alertcondition(ta.crossover(close,poc), \"Cross-Over Point of Control\")\n",
+    "alertcondition(ta.crossunder(close,poc), \"Cross-Under Point of Control\")\n",
+    "alertcondition(ta.crossover(close,vah), \"Cross-Over Value High\")\n",
+    "alertcondition(ta.crossunder(close,vah), \"Cross-Under Value High\")\n",
+    "alertcondition(ta.crossover(close,val), \"Cross-Over Value Low\")\n",
+    "alertcondition(ta.crossunder(close,val),\"Cross-Under Value Low\")\n",
+    "//Plotting Developing Lines\n",
+    "plot(dev_poc?(poc==0?na:poc):na, color = poc_color, title = \"Developing POC/TOP\")\n",
+    "plot(dev_va?vah:na, color = var_color, title = \"Developing VAH\")\n",
+    "plot(dev_va?val:na, color = var_color, title = \"Developing VAL\")\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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