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Pinescript Folder/0SCT2025/SCT2025indicator1-pinescript.js

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+//@version=6
+//indicator("v5", overlay = true, max_labels_count = 300, max_lines_count = 300, max_boxes_count = 300, max_bars_back = 300)
+indicator("Sunstoic", "v6 Sunstoic", true, max_bars_back = 5000, max_boxes_count = 500, max_lines_count = 500, max_labels_count = 500)
+
+
+//candle logic
+// === Inputs ===
+bCol = input.color(#008080, title="Bull Border")
+rCol = input.color(#e20000, title="Bear Border")
+bgB = input.color(color.new(#008080, 20), title="Bull Body")
+bgR = input.color(color.new(#FF0000, 20), title="Bear Body")
+
+
+    // === Input your time zone (Manila = GMT+8) 
+timeSessionStart = timestamp("GMT+8", year, month, dayofmonth, 6, 0)  // Start of day
+isNewDay = ta.change(time("D"))  // Detect new day
+
+// Track the current day's developing close on each bar
+var float devClose = na
+if bool(isNewDay)
+    devClose := close  // reset on new day
+else
+    devClose := close  // update each bar
+
+
+// === Default Daily Candle (6:00 AM) ===
+dO = request.security(syminfo.tickerid, "D", open)
+dH = request.security(syminfo.tickerid, "D", high)
+dL = request.security(syminfo.tickerid, "D", low)
+dC = request.security(syminfo.tickerid, "D", close)
+
+
+// Calculate daily high and low using 'day' timeframe
+var float dailyHigh = na
+var float dailyLow = na
+newDay = ta.change(time("D"))
+
+
+// On new day, reset high/low
+if bool(newDay)
+    dailyHigh := close
+    dailyLow := open
+else
+    dailyHigh := math.max(dailyHigh, close)
+    dailyLow := math.min(dailyLow, open)
+
+
+// Midpoint of the daily candle 
+midPrice = (dailyHigh + dailyLow) / 2
+
+
+dBull = dC >= dO
+dCol = dBull ? bCol : rCol
+dBg = dBull ? bgB : bgR
+
+
+// Offset for visuals
+ofs = 10
+bw = 2
+rIdx = bar_index + ofs + bw / 2
+lIdx = bar_index + ofs - bw / 2
+xMid = int(bar_index + ofs)
+
+
+// Body of daily candle box
+var box dBx = na
+box.delete(dBx)
+tB = math.max(dO, devClose) //y=dC
+bB = math.min(dO, devClose)
+dBx := box.new(left=int(lIdx), right=int(rIdx), top=tB, bottom=bB, border_color=dCol, bgcolor=dBg)
+
+
+// Wicks for daily candle
+var line dW1 = na
+var line dW2 = na
+line.delete(dW1)
+line.delete(dW2)
+dW1 := line.new(x1=xMid, y1=dH, x2=xMid, y2=tB, color=dCol)
+dW2 := line.new(x1=xMid, y1=bB, x2=xMid, y2=dL, color=dCol)
+
+
+
+// Labels for daily candle
+var label lblO = na
+var label lblH = na
+var label lblL = na
+var label lblC = na
+label.delete(lblO)
+label.delete(lblH)
+label.delete(lblL)
+label.delete(lblC)
+
+
+lblStyle = label.style_label_right
+lblSize = size.tiny
+lblOfs = -2
+
+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)
+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)
+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)
+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)
+
+// === Custom Daily Candle (8:00 AM Manila) ===
+sh = 24
+sm = 0
+stt = timestamp("UTC", year, month, dayofmonth, sh, sm)
+
+
+var float cO = na
+var float cH = na
+var float cL = na
+var float cC = na
+
+
+if (time == stt)
+    cO := open
+    cH := high
+    cL := low
+    cC := close
+
+
+if (not na(cO))
+    cH := math.max(cH, high)
+    cL := math.min(cL, low)
+    cC := close
+
+
+cBull = cC >= cO
+cCol = cBull ? bCol : rCol
+cBg = cBull ? bgB : bgR
+
+
+cOfs = 12
+cbw = 2
+crIdx = bar_index + cOfs + cbw / 2
+clIdx = bar_index + cOfs - cbw / 2
+cX = int(bar_index + cOfs)
+
+
+// Custom body box
+var box cBx = na
+box.delete(cBx)
+ctB = math.max(cO, devClose) //y=dC
+cbB = math.min(cO, devClose)
+cBx := box.new(left=int(clIdx), right=int(crIdx), top=ctB, bottom=cbB, border_color=cCol, bgcolor=cBg)
+
+
+// Custom wicks
+var line cW1 = na
+var line cW2 = na
+line.delete(cW1)
+line.delete(cW2)
+cW1 := line.new(x1=cX, y1=cH, x2=cX, y2=ctB, color=cCol)
+cW2 := line.new(x1=cX, y1=cbB, x2=cX, y2=cL, color=cCol)
+
+
+// Custom labels
+var label clO = na
+var label clH = na
+var label clL = na
+var label clC = na
+label.delete(clO)
+label.delete(clH)
+label.delete(clL)
+label.delete(clC)
+
+
+clStyle = label.style_label_left
+clSize = size.tiny
+clOfs = 2
+
+
+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)
+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)
+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)
+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)
+
+// === High and Low Lines (using bar_index for x coords) ===
+// Track bar_index of high and low bars within current day
+var int highBarIndex = na
+var int lowBarIndex = na
+var float dayHigh = na
+var float dayLow = na
+
+
+startOfDay = timestamp(year, month, dayofmonth, 0, 0)
+t6amManila = timestamp("Asia/Manila", year, month, dayofmonth, 6, 0)
+
+
+issNewDay = ta.change(time("D"))
+
+
+if (time >= startOfDay)
+    if na(dayHigh) or high > dayHigh
+        dayHigh := high
+        highBarIndex := bar_index
+    if na(dayLow) or low < dayLow
+        dayLow := low
+        lowBarIndex := bar_index
+
+
+if bool(issNewDay)
+    dayHigh := na
+    dayLow := na
+    highBarIndex := na
+    lowBarIndex := na
+
+
+// Draw high line from high bar index to current xMid
+var line highLine = na
+if not na(dayHigh) and not na(highBarIndex)
+    if na(highLine)
+        highLine := line.new(x1=highBarIndex, y1=dayHigh, x2=xMid, y2=dayHigh, color=dCol, width=2, style = line.style_solid)
+    else
+        line.set_xy1(highLine, highBarIndex, dayHigh)
+        line.set_xy2(highLine, xMid, dayHigh)
+
+
+// Draw low line from low bar index to current xMid
+var line lowLine = na
+if not na(dayLow) and not na(lowBarIndex)
+    if na(lowLine)
+        lowLine := line.new(x1=lowBarIndex, y1=dayLow, x2=xMid, y2=dayLow, color=dCol, width=2, style = line.style_solid)
+    else
+        line.set_xy1(lowLine, lowBarIndex, dayLow)
+        line.set_xy2(lowLine, xMid, dayLow)
+
+
+
+
+
+// --- 6:00 AM Asia/Manila horizontal line ---
+// Manila 6:00 AM timestamp today
+manila_6am = timestamp("GMT+8", year, month, dayofmonth, 30, 0)
+
+
+// Detect bar where time crosses 6:00 AM Manila
+isStartBar6am = (time >= manila_6am) and (time[1] < manila_6am)
+
+
+// Store 6:00 AM bar open price and index
+var float price6am = na
+var int bar6am_index = na
+
+
+if isStartBar6am
+    price6am := open
+    bar6am_index := bar_index
+
+
+// Draw horizontal line from 6AM bar index to xMid at price6am
+var line hLine6am = na
+
+
+if not na(price6am) and not na(bar6am_index)
+    line.delete(hLine6am)
+    hLine6am := line.new(x1=bar6am_index, y1=price6am, x2=xMid, y2=price6am, color=color.new(#000000, 0), width=1, style=line.style_solid)
+
+
+// --- 8:00 AM Asia/Manila horizontal line ---
+// Detect bar where time crosses 8:00 AM Manila
+isStartBar8am = (time >= stt) and (time[1] < stt)
+
+
+// Store 8:00 AM bar open price and index
+var float price8am = na
+var int bar8am_index = na
+
+
+if isStartBar8am
+    price8am := open
+    bar8am_index := bar_index
+
+
+// Draw horizontal line from 8AM bar index to cX at price8am
+var line hLine8am = na
+var line hhLine8am = na
+
+
+if not na(price8am) and not na(bar8am_index)
+    line.delete(hLine8am)
+    hLine8am := line.new(x1=bar8am_index, y1=price8am, x2=cX, y2=price8am, color=color.new(#000000, 0), width=1, style=line.style_solid)
+
+
+
+// Draw horizontal line that updates000000000000000000000000
+var line devCloseLine = na
+var line ddevCloseLine = na
+if bar_index > 0
+    if na(devCloseLine)
+        devCloseLine := line.new(x1=bar_index, y1=devClose , x2=xMid , y2=devClose, style = line.style_solid, color=color.new(#ecc900, 0), width=1)
+    else
+        line.set_xy1(devCloseLine, bar_index, devClose)
+        line.set_xy2(devCloseLine, bar_index + 13, devClose)
+    if na(ddevCloseLine) 
+        //ddevCloseLine := line.new(x1=xMid, y1=devClose, x2=xMid, y2=devClose, extend=extend.right, color=dCol, width=2)
+        ddevCloseLine := line.new(x1=xMid, y1=devClose, x2=xMid , y2=devClose, style = line.style_dotted, extend=extend.right, color=color.black, width=1)
+    else
+        line.set_xy1(ddevCloseLine, bar_index + 19,  devClose)
+        line.set_xy2(ddevCloseLine, bar_index + 20, devClose)
+
+
+
+//zone logic
+// Disable visuals if timeframe is higher than 1 hour
+isValidTF = timeframe.isminutes and timeframe.multiplier <= 60
+
+
+// Current time components
+currentTime = timestamp(year, month, dayofmonth, hour, minute)
+
+// Define 12PM to 12AM session
+start12pm = timestamp(year, month, dayofmonth, 12, 0)
+end12am = timestamp(year, month, dayofmonth, 23, 59)
+in12pmTo12am = currentTime >= start12pm and currentTime <= end12am
+
+// Define 8AM to 8PM session
+start8am = timestamp(year, month, dayofmonth, 8, 0)
+end8pm = timestamp(year, month, dayofmonth, 20, 0)
+in8amTo8pm = currentTime >= start8am and currentTime <= end8pm
+
+// Define 6PM to 8PM session
+start6pm = timestamp(year, month, dayofmonth, 6, 0)
+eend8pm = timestamp(year, month, dayofmonth, 8, 0)
+in6pmTo8pm = currentTime >= start6pm and currentTime <= eend8pm
+
+start6am = timestamp(year, month, dayofmonth, 18, 0)
+eend8am = timestamp(year, month, dayofmonth, 20, 0)
+in6amTo8am = currentTime >= start6am and currentTime <= eend8am
+
+// Apply background highlights
+bgcolor(isValidTF and in12pmTo12am ? color.new(color.black, 99) : na)
+bgcolor(isValidTF and in8amTo8pm ? color.new(color.black, 99) : na)
+bgcolor(isValidTF and in6pmTo8pm ? color.new(color.black, 97) : na)
+bgcolor(isValidTF and in6amTo8am ? color.new(color.black, 95) : na)
+
+// Get the current day of the week and the current time
+isMonday = dayofweek == dayofweek.sunday
+ccurrentTime = timestamp(year, month, dayofmonth, hour, minute)
+
+// Define the time range for 6:00 AM to 8:00 AM
+startTime = timestamp(year, month, dayofmonth, 18, 6) // 6:00 AM
+endTime = timestamp(year, month, dayofmonth, 20, 0) // 8:00 AM
+
+// Check if it's Monday and the current time is within the range of 6:00 AM to 8:00 AM
+isInTimeRange = isMonday and ccurrentTime >= startTime and ccurrentTime <= endTime
+
+// Highlight the area with a background color
+bgcolor(isValidTF and isInTimeRange ? color.new(color.blue, 90) : na)
+
+// Vertical lines logic
+// Only show vertical lines if timeframe is intraday and valid
+showLines = timeframe.isintraday and isValidTF
+
+// Convert current bar time to Asia/Manila timezone
+t = time(timeframe.period, 'Asia/Manila')
+
+// Target hours excluding 0, 12, 20
+var array<int> targetHours = array.from(3, 6, 8, 15, 24) // 24 means midnight (0h)
+
+// Special hours separated
+specialHour20 = 20
+specialHour0 = 0 // midnight
+specialHour12 = 12 // noon
+
+// Function to check if current bar time matches any target hour exactly at minute zero
+isTargetTime() =>
+    h = hour(t)
+    m = minute(t)
+    match = false
+    for i = 0 to array.size(targetHours) - 1 by 1
+        if (h == array.get(targetHours, i) or h == 0 and array.get(targetHours, i) == 24) and m == 0
+            match := true
+            match
+    match
+
+// Check if current time matches special hour 20 at minute zero
+isSpecialTime20() =>
+    h = hour(t)
+    m = minute(t)
+    h == specialHour20 and m == 0
+
+// Check if current time matches special hour 0 at minute zero
+isSpecialTime0() =>
+    h = hour(t)
+    m = minute(t)
+    h == specialHour0 and m == 0
+
+// Check if current time matches special hour 12 at minute zero
+isSpecialTime12() =>
+    h = hour(t)
+    m = minute(t)
+    h == specialHour12 and m == 0
+
+// Detect new day (day break)
+nnewDay = ta.change(time('D'))
+
+// Only draw lines if timeframe is intraday and valid
+if showLines
+    // Draw vertical line at day break (green, width 1)
+    if bool(nnewDay)
+        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)
+
+    // Draw vertical line at target times (blue, width 1)
+    if isTargetTime()
+        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)
+
+    // Draw vertical line at special hour 20 (teal, width 1)
+    if isSpecialTime20()
+        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)
+
+    // Draw vertical line at special hour 0 (midnight) - orange, dotted
+    if isSpecialTime0()
+        var line midnightLine = na
+        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)
+        line.set_style(midnightLine, line.style_dotted)
+
+    // Draw vertical line at special hour 12 (noon) - red, dotted
+    if isSpecialTime12()
+        var line noonLine = na
+        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)
+        line.set_style(noonLine, line.style_dotted)
+
+//hourly logic
+// === UTILITY FUNCTION ===
+f_show(tf) =>
+    timeframe.period != tf
+
+// === HOURLY ===
+hH = request.security(syminfo.tickerid, '60', high, lookahead = barmerge.lookahead_on)
+hL = request.security(syminfo.tickerid, '60', low, lookahead = barmerge.lookahead_on)
+hIdx = request.security(syminfo.tickerid, '60', bar_index, lookahead = barmerge.lookahead_on)
+
+var int hPrev = na
+var float hHi = na
+var int hHiBar = na
+var line hHiLine = na
+var bool hHiOn = false
+var float hLo = na
+var int hLoBar = na
+var line hLoLine = na
+var bool hLoOn = false
+
+if hIdx != hPrev
+    hPrev := hIdx
+    hHi := na
+    hHiBar := na
+    line.delete(hHiLine)
+    hHiLine := na
+    hHiOn := false
+    hLo := na
+    hLoBar := na
+    line.delete(hLoLine)
+    hLoLine := na
+    hLoOn := false
+    hLoOn
+
+if hHiOn and not na(hHiLine)
+    bMin = math.min(open, close)
+    bMax = math.max(open, close)
+    if hHi <= bMax and hHi >= bMin
+        line.set_x2(hHiLine, bar_index)
+        line.set_y2(hHiLine, hHi)
+        hHiOn := false
+        hHiOn
+    else
+        line.set_x2(hHiLine, bar_index)
+        line.set_y2(hHiLine, hHi)
+
+if hLoOn and not na(hLoLine)
+    bMin = math.min(open, close)
+    bMax = math.max(open, close)
+    if hLo <= bMax and hLo >= bMin
+        line.set_x2(hLoLine, bar_index)
+        line.set_y2(hLoLine, hLo)
+        hLoOn := false
+        hLoOn
+    else
+        line.set_x2(hLoLine, bar_index)
+        line.set_y2(hLoLine, hLo)
+
+
+// === D/W/M High-Low Steplines ===
+ddH = request.security(syminfo.tickerid, 'D', high, lookahead = barmerge.lookahead_on)
+ddL = request.security(syminfo.tickerid, 'D', low, lookahead = barmerge.lookahead_on)
+wH = request.security(syminfo.tickerid, 'W', high, lookahead = barmerge.lookahead_on)
+wL = request.security(syminfo.tickerid, 'W', low, lookahead = barmerge.lookahead_on)
+mH = request.security(syminfo.tickerid, 'M', high, lookahead = barmerge.lookahead_on)
+mL = request.security(syminfo.tickerid, 'M', low, lookahead = barmerge.lookahead_on)
+
+plot(f_show('60') ? hH : na, title = 'H High', color = color.new(color.black, 90), style = plot.style_stepline, linewidth = 2)
+plot(f_show('60') ? hL : na, title = 'H Low', color = color.new(color.black, 90), style = plot.style_stepline, linewidth = 2)
+plot(f_show('D') ? ddH : na, title = 'D High', color = color.new(color.green, 50), style = plot.style_stepline)
+plot(f_show('D') ? ddL : na, title = 'D Low', color = color.new(color.green, 70), style = plot.style_stepline)
+plot(f_show('W') ? wH : na, title = 'W High', color = color.new(color.blue, 90), style = plot.style_stepline, linewidth = 2)
+plot(f_show('W') ? wL : na, title = 'W Low', color = color.new(color.blue, 90), style = plot.style_stepline, linewidth = 2)
+plot(f_show('M') ? mH : na, title = 'M High', color = color.new(color.purple, 90), style = plot.style_stepline, linewidth = 3)
+plot(f_show('M') ? mL : na, title = 'M Low', color = color.new(color.purple, 90), style = plot.style_stepline, linewidth = 3)
+
+
+//vwap logic
+// VWAP calculation from 1-minute data
+f_vwap_calc() =>
+    var float cumPV = 0.0
+    var float cumVol = 0.0
+    var float cumPV_buy = 0.0
+    var float cumVol_buy = 0.0
+    var float cumPV_sell = 0.0
+    var float cumVol_sell = 0.0
+
+    // Reset on new day
+    if bool(ta.change(time('D')))
+        cumPV := 0.0
+        cumVol := 0.0
+        cumPV_buy := 0.0
+        cumVol_buy := 0.0
+        cumPV_sell := 0.0
+        cumVol_sell := 0.0
+        cumVol_sell
+
+    buyVol = close > open ? volume : 0.0
+    sellVol = close < open ? volume : 0.0
+
+    cumPV := cumPV + close * volume
+    cumVol := cumVol + volume
+
+    cumPV_buy := cumPV_buy + close * buyVol
+    cumVol_buy := cumVol_buy + buyVol
+
+    cumPV_sell := cumPV_sell + close * sellVol
+    cumVol_sell := cumVol_sell + sellVol
+
+    vwap = cumVol != 0 ? cumPV / cumVol : na
+    buyVWAP = cumVol_buy != 0 ? cumPV_buy / cumVol_buy : na
+    sellVWAP = cumVol_sell != 0 ? cumPV_sell / cumVol_sell : na
+
+    [vwap, buyVWAP, sellVWAP]
+
+// Pull 1-minute VWAP values
+[vwap_1m, buyVWAP_1m, sellVWAP_1m] = request.security(syminfo.tickerid, '1', f_vwap_calc())
+
+// Only show on intraday charts
+showVWAP = not timeframe.isdaily and not timeframe.isweekly and not timeframe.ismonthly
+  
+// Plot VWAPs with conditional display
+plot_vwap = plot(showVWAP ? vwap_1m : na, color = color.rgb(238, 218, 90), linewidth = 1, title = 'VWAP (1m)')
+//plot_buy_vwap = plot(showVWAP ? buyVWAP_1m : na, color = color.rgb(0, 137, 123, 80), linewidth = 1, title = 'Buy Delta VWAP (1m)')
+//plot_sell_vwap = plot(showVWAP ? sellVWAP_1m : na, color = color.rgb(255, 82, 82, 80), linewidth = 1, title = 'Sell Delta VWAP (1m)')
+
+// Fill areas
+//fill(plot_vwap, plot_buy_vwap, color = showVWAP ? color.new(color.teal, 95) : na, title = 'Buy VWAP Fill')
+//fill(plot_vwap, plot_sell_vwap, color = showVWAP ? color.new(color.red, 95) : na, title = 'Sell VWAP Fill')
+
+//table logic
+// === Function to get OHLC of specified candle ===
+get_prev_ohlc(tf, shift) =>
+    o = request.security(syminfo.tickerid, tf, open[shift])
+    h = request.security(syminfo.tickerid, tf, high[shift])
+    l = request.security(syminfo.tickerid, tf, low[shift])
+    c = request.security(syminfo.tickerid, tf, close[shift])
+    [o, h, l, c]
+
+// === Function to calculate ranges and colors ===
+get_data(tf, shift) =>
+    [o, h, l, c] = get_prev_ohlc(tf, shift)
+    full_range = h - l
+    oc_range = math.abs(c - o)
+    hl_range = h - l
+    is_bull = c > o
+    bg_color = is_bull ? color.new(color.teal, 90) : color.new(color.red, 90)
+    [full_range, oc_range, hl_range, bg_color]
+
+// === Daily Candle Data ===
+[d1_fr, d1_oc, d1_hl, d1_bg] = get_data("D", 1)
+[d2_fr, d2_oc, d2_hl, d2_bg] = get_data("D", 2)
+
+// === Weekly Candle Data ===
+[w0_fr, w0_oc, w0_hl, w0_bg] = get_data("W", 0) // Developing Week
+[w1_fr, w1_oc, w1_hl, w1_bg] = get_data("W", 1)
+[w2_fr, w2_oc, w2_hl, w2_bg] = get_data("W", 2)
+
+
+// Daily candle (6:00 AM)
+sixambgcolor = dO < dC ?  color.rgb(0, 137, 123, 90) : color.rgb(255, 82, 82, 90)
+
+// Custom candle (8:00 AM Manila)
+eightambgcolor = cO < cC ?  color.rgb(0, 137, 123, 90) : color.rgb(255, 82, 82, 90) 
+
+// Adjust UTC time to Manila (UTC+8) qewrqwerwerwerwerwerew
+manilaTime = time + 8 * 60 * 60 * 1000  // Shift by 8 hours in milliseconds
+
+// Extract date components from Manila time
+manilaYear  = year(manilaTime)
+manilaMonth = month(manilaTime)
+manilaDay   = dayofmonth(manilaTime)
+
+// Format date as "DD/MM/YYYY"
+formattedDate = str.tostring(manilaDay, "00") + "/" + str.tostring(manilaMonth, "00") + "/" + str.tostring(manilaYear)
+
+// Track day changes in Manila time
+var int prevManilaDay = na
+newManilaDay = manilaDay != prevManilaDay
+
+// Update stored day
+if newManilaDay
+    prevManilaDay := manilaDay
+//qweqweqweqweqeqweqweqwewqeqwewqeqe
+
+// UTC/UTC+8 Time
+var label utcLbl = na
+var label utc8Lbl = na
+label.delete(utcLbl)
+label.delete(utc8Lbl)
+
+//utcStr = "UTC: " + str.tostring(hour, "00") + ":" + str.tostring(minute, "00")
+uutcStr = str.tostring(hour, "00") + ":" + str.tostring(minute, "00") + " 𝘜𝘛𝘊+8"
+utc8H = (hour + 12) % 24
+//utc8Str = "UTC: " + str.tostring(utc8H, "00") + ":" + str.tostring(minute, "00")
+uutc8Str =str.tostring(utc8H, "00") + ":" + str.tostring(minute, "00") + " 𝘜𝘛𝘊+8"
+
+// TF Label
+var label tfLbl = na
+label.delete(tfLbl)
+var label tfLbl2 = na
+label.delete(tfLbl2)
+
+tfS = ""
+if (str.length(timeframe.period) > 1 and str.substring(timeframe.period, str.length(timeframe.period)-1, str.length(timeframe.period)) == "m")
+    tfS := "m"
+else if (str.length(timeframe.period) > 1 and str.substring(timeframe.period, str.length(timeframe.period)-1, str.length(timeframe.period)) == "h")
+    tfS := "h"
+tfP = str.replace(timeframe.period, tfS, "")
+tfText = tfP + tfS + "m 𝘪𝘯𝘵𝘰 1D"
+
+// Range Labels
+dHL = math.abs(dH - dL)
+dOC = math.abs(dO - dC)
+cHL = math.abs(cH - cL)
+cOC = math.abs(cO - cC)
+
+// === Create and update table ===
+var table tt = table.new(position.top_right, 80, 80, border_width=1)
+
+if bar_index == 1
+    table.cell(tt, 0, 0, "", text_color=color.rgb(255, 255, 255, 100), text_size = size.tiny, bgcolor=color.rgb(120, 123, 134, 100))
+    table.cell(tt, 1, 0, "", text_color=color.rgb(255, 255, 255, 100), text_size = size.tiny,bgcolor=color.rgb(120, 123, 134, 100))
+
+    table.cell(tt, 0, 1, "", text_color=color.rgb(255, 255, 255, 100), text_size = size.tiny, bgcolor=color.rgb(120, 123, 134, 100))
+    table.cell(tt, 1, 1, "", text_color=color.rgb(255, 255, 255, 100), text_size = size.tiny,bgcolor=color.rgb(120, 123, 134, 100))
+ 
+// === Daily Rows ===
+table.cell(tt, 1, 2, "𝑺𝒑𝒆𝒄𝒊𝒇𝒊𝒄𝒂𝒕𝒊𝒐𝒏", text_color = color.white, text_size = size.tiny, bgcolor = color.navy)
+
+table.cell(tt, 0, 3, "Date", text_size = size.tiny, text_color = color.white)
+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))
+ 
+table.cell(tt, 0, 4, "Time", text_size = size.tiny, text_color = color.white)
+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))
+ 
+table.cell(tt, 0, 5, "Time", text_size = size.tiny, text_color = color.white)
+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))
+
+table.cell(tt, 0, 6, "Type", text_size = size.tiny, text_color = color.white)
+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))
+
+table.cell(tt, 1, 7, "𝑯𝒊𝒈𝒉𝒆𝒓 𝑻𝑭", text_color = color.white, text_size = size.tiny, bgcolor = color.navy)
+
+table.cell(tt, 0, 8, "6:00\nяαηgє", text_size = size.tiny, text_color = color.white)
+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))
+
+table.cell(tt, 0, 9, "8:00\nяαηgє", text_size = size.tiny, text_color = color.white)
+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))
+
+table.cell(tt, 1, 11, "1D 𝑪𝒂𝒏𝒅𝒍𝒆", text_color = color.white, text_size = size.tiny, bgcolor = color.navy)
+
+table.cell(tt, 0, 12, "-1D \nяαηgє", text_size = size.tiny, text_color = color.white)
+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))
+
+table.cell(tt, 0, 13, "-2D \nяαηgє", text_size = size.tiny, text_color = color.white)
+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))
+
+table.cell(tt, 1, 14, "1W 𝑪𝒂𝒏𝒅𝒍𝒆", text_color = color.white, text_size = size.tiny, bgcolor = color.navy)
+
+table.cell(tt, 0, 15, "0W \nяαηgє", text_size = size.tiny, text_color = color.white)
+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))
+
+table.cell(tt, 0, 16, "-1W \nяαηgє", text_size = size.tiny, text_color = color.white)
+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))
+
+table.cell(tt, 0, 17, "-2W \nяαηgє", text_size = size.tiny, text_color = color.white)
+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))
+
+table.cell(tt, 1, 18, "© 2025 𝚆𝚀𝚜", text_color = color.white, text_size = size.tiny, bgcolor = color.black)
+
+//developinf day 
+// === INPUTS ===
+startSession = input.session("0000-2359", title="Session Time")
+sshowLines = input.bool(true, title="Show Lines")
+showLabels = input.bool(true, title="Show Labels")
+
+// === TIME LOGIC ===
+isNNewDay = ta.change(time("D"))
+
+// === DEVELOPING HIGH/LOW TRACKING ===
+var float devHigh = na
+var float devLow  = na
+
+// Reset at new day
+if bool(isNNewDay)
+    devHigh := high
+    devLow  := low
+else
+    devHigh := math.max(devHigh, high)
+    devLow  := math.min(devLow, low)
+
+// === PLOTTING ===
+plot(sshowLines ? devHigh : na, title="Developing High", color=color.rgb(76, 175, 79, 50), linewidth=2, style=plot.style_stepline)
+plot(sshowLines ? devLow : na, title="Developing Low", color=color.rgb(255, 82, 82, 50), linewidth=2, style=plot.style_stepline)
+
+
+// Input only controls how many 1-minute candles are used
+llen = input.int(24, "VWMA Length (1m)")
+
+// VWMA calculation inside 1-minute context
+vwma_1m = request.security(syminfo.tickerid, "1", math.sum(close * volume, llen) / math.sum(volume, llen))
+
+// Only plot if current timeframe is 5 minutes
+is_5min = timeframe.period == "5"
+
+plot(is_5min ? vwma_1m : na, title="1-Minute VWMA", color=color.rgb(117, 117, 117), linewidth=2)
+
+//Profile Settings
+tf = input.timeframe("D", title = "Timeframe", inline = "0", group = "PROFILE SETTINGS")
+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.")
+vap = input.float(0, title = "Value Area %", inline = "1_1", group = "PROFILE SETTINGS")
+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.")
+//Display Settings
+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")
+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")
+//Additional Data Displays
+extend_day = input.bool(false, title = "Extend POC/VAH/VAL", inline = "1", group = "Additional Data Displays")
+lab_tog = input.bool(true, title = "Label POC/VAH/VAL", inline = "2", group = "Additional Data Displays") 
+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!")
+dev_va = input.bool(false, title = "Rolling VAH/VAL", inline = "4", group = "Additional Data Displays")
+//High/Low Volume Nodes
+node_tog = input.bool(true, title = "Highlight Nodes", group = "High/Low Volume Nodes")
+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
+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
+//Colors
+poc_color = input.color(#d3be00, title = "POC/TPO Color", group = "Colors")
+var_color = input.color(#886000, title = "Value High/Low Color", group = "Colors")
+vaz_color = input.color(color.new(#886000, 0), title = "Value Zone Color", group = "Colors")
+ov_color = input.color(#886000, title = "Profile Color", group = "Colors")
+lv_color = input.color(#886000, title  = "Low Volume Color", group = "Colors")
+hv_color = input.color(#886000, title = "High Volume Color", group = "Colors")
+//⭕Manual Overrides⭕
+lb_override = input.bool(false, title = ">", group = "⭕Manual Overrides⭕[CHECK TO ENABLE]", inline = "1")
+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.")
+row_size_override = input.bool(false, title = ">", group = "⭕Manual Overrides⭕[CHECK TO ENABLE]", inline = "2")
+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.")
+///_________________________________________
+///Misc Stuff
+///‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾
+ 
+round_to(_round,_to) =>
+    math.round(_round/_to)*_to
+
+///_________________________________________
+///Setup for Length
+///‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾
+tf_change = timeframe.change(tf)
+var int bi_nd = na // BI_ND = Bar Index _ New Day
+var int bi_lnd = na //BI_LND = Bar Index Last New Day
+
+bi_lnd := tf_change?bi_nd:bi_lnd
+bi_nd := tf_change?bar_index:bi_nd
+
+tf_len = (bi_nd - bi_lnd)+1
+bs_newtf = (bar_index - bi_nd)+1
+id_lb_bars = timeframe.in_seconds(tf)/timeframe.in_seconds(timeframe.period)
+dwm_lb_bars = (math.max(tf_len,bs_newtf)>1?math.max(tf_len,bs_newtf)-1:1)
+auto_lb_bars = timeframe.in_seconds(tf) >= timeframe.in_seconds("D")?dwm_lb_bars:id_lb_bars
+lb_bars = (lb_calc?(na(tf_len)?bar_index:(lb_override?lb_over_bars:auto_lb_bars)):na(bs_newtf)?bar_index:bs_newtf)
+///_________________________________________
+///Warning for Large Timeframe
+///‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾
+var d = dayofmonth
+var m = switch
+    month == 1 => "Jan"
+    month == 2 => "Feb"
+    month == 3 => "Mar"
+    month == 4 => "Apr"
+    month == 5 => "May"
+    month == 6 => "Jun"
+    month == 7 => "Jul"
+    month == 8 => "Aug"
+    month == 9 => "Sep"
+    month == 10 => "Oct"
+    month == 11 => "Nov"
+    month == 12 => "Dec"
+var y = year - (int(year/100)*100)
+send_warning = (lb_calc and (na(tf_len) or bar_index<lb_bars)) or (not lb_calc and na(bs_newtf))
+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
+if na(volume) and (mp == false)
+    runtime.error("No Volume Data. Please Use a Ticker with Volume Data or Switch to Market Profile Calculation.")
+
+///_________________________________________
+///Start Data Accumulation
+///‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾
+//By storing the data, I can use it later without the need for historical reference. 
+//Additionally, I can control the amount of data kept in memory to only hold the lookback amount of bars.
+//This is all of the advantages of lookback calcs, with all the advantages of progressive calculations.
+
+src_high = math.round_to_mintick(high)
+src_low = math.round_to_mintick(low)
+
+var highs = array.new_float(na)
+var lows = array.new_float(na)
+var vol = array.new_float(na)
+
+highs.push(src_high)
+lows.push(src_low)
+if not mp
+    vol.push(volume)
+
+if highs.size() >= lb_bars
+    for i = 0 to bar_index
+        if highs.size() == lb_bars
+            break
+        else
+            highs.shift()
+            lows.shift()
+            if not mp
+                vol.shift()
+    
+//Granularity Gets more corse farther from current bar for speed. Replay mode can get a full gran profile if needed.
+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
+
+
+
+full_hist_calc = (dev_poc or dev_va)
+tick_size = full_hist_calc or barstate.islast?math.ceil(((highs.max()-lows.min())/v_gran)/syminfo.mintick)*syminfo.mintick:na
+
+
+///_________________________________________
+///Profile Calcs
+///‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾
+data_map = map.new<float,float>()
+
+if full_hist_calc and highs.size() > 0
+    highs_size = array.size(highs)-1
+    for i = 0 to highs_size
+        hi = round_to(highs.get(i),tick_size)
+        lo = round_to(lows.get(i),tick_size)
+        candle_index = ((hi-lo)/tick_size)
+        tick_vol = mp?1:math.round((vol.get(i)/(candle_index+1)),3)
+        for e = 0 to candle_index
+            val = round_to(lo+(e*tick_size),tick_size)
+            data_map.put(val, math.round(nz(data_map.get(val)),3)+tick_vol)
+
+if (full_hist_calc == false) and barstate.islast
+    highs_size = array.size(highs)-1
+    for i = 0 to highs_size
+        hi = round_to(highs.get(i),tick_size)
+        lo = round_to(lows.get(i),tick_size)
+        candle_index = ((hi-lo)/tick_size)
+        tick_vol = mp?1:math.round((vol.get(i)/(candle_index+1)),3)
+        for e = 0 to candle_index
+            val = round_to(lo+(e*tick_size),tick_size)
+            data_map.put(val,nz(data_map.get(val))+tick_vol)
+
+///_________________________________________
+////Other Profile Values
+///‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾
+keys = map.keys(data_map)
+values = map.values(data_map)
+array.sort(keys, order.ascending)
+///_________________________________________
+///AQUIRE POC
+///‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾
+//POC = Largest Volume Closest to Price Average of Profile
+//
+float poc = 0
+float poc_vol = values.max()
+prof_avg = keys.avg()
+if barstate.islast or dev_poc
+    for [key, value] in data_map
+        if (value == poc_vol and math.abs(key-prof_avg)<math.abs(poc-prof_avg))
+            poc := key
+
+///_________________________________________
+///VALUE ZONES
+///‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾
+max_vol = array.sum(values)*(vap/100)
+max_index = poc
+vol_count = poc_vol
+up_count = max_index
+down_count = max_index
+keys_size = array.size(keys)
+if barstate.islast or dev_va
+    for i = 0 to keys_size 
+        if vol_count >= max_vol
+            break
+        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)))
+        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)))
+        if (((upper_vol >= lower_vol) and upper_vol > 0) or (lower_vol>0 == false)) and up_count < array.max(keys)-tick_size
+            vol_count += upper_vol                                                    
+            up_count := round_to(up_count+(tick_size*2),tick_size)                                                       
+        else if down_count > array.min(keys)+tick_size
+            vol_count += lower_vol
+            down_count := round_to(down_count-(tick_size*2),tick_size)  
+val = down_count
+vah = up_count
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+//END PROFILE CALCs//
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+
+
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+//Cluster ID for Volume Nodes//
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+//   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.
+//   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.
+
+vgroup_pull(_var,_array,_num1,_num2) =>
+    _var == 1 and _num1>=_num2 ? data_map.get(array.get(_array,_num1-_num2)):                               //Pulls Index Value from Below
+     _var == 2 and array.size(_array)-1 >= (_num1 + _num2) ? data_map.get(array.get(_array,_num1+_num2))    //Pulls Index Value from Above 
+     :0
+hvn_points = array.new_int(na)  
+if array.size(keys) > 0 and barstate.islast and node_tog
+    array.clear(hvn_points)
+    for [i,v] in keys    
+        _val = data_map.get(v)
+        ary = array.new_float(na)                               
+        for e = 0 to int(keys.size()*hi_width) 
+            array.push(ary,vgroup_pull(1,keys,i,e))
+            array.push(ary,vgroup_pull(2,keys,i,e))       
+        max = array.max(ary)  
+        avg = array.avg(ary)
+        if _val >= math.avg(max,avg)
+            array.push(hvn_points,i)
+
+lvn_points = array.new_int(na)
+if array.size(keys) > 0 and barstate.islast and node_tog
+    array.clear(lvn_points)
+    for [i,v] in keys
+        _val = data_map.get(v) 
+        ary = array.new_float(na) 
+        for e = 0 to int(array.size(keys)*lo_width)  
+            array.push(ary,vgroup_pull(1,keys,i,e))
+            array.push(ary,vgroup_pull(2,keys,i,e))
+        min = array.min(ary)
+        avg = array.avg(ary)
+        if _val <= math.avg(min,avg)  
+            array.push(lvn_points,i)
+
+///_________________________________________
+///Cluster Merging
+///‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾
+
+merge_clusters(_array) =>
+    if array.size(_array)>0
+        ary_size = array.size(_array)-1
+        for i = 0 to ary_size
+            merge_found = false
+            _val = array.get(_array,i)
+            prof_merge_percent = int(array.size(keys)*0.02)
+            for e = prof_merge_percent to 0
+                if merge_found
+                    array.push(_array,_val+e)
+                else if array.includes(_array,_val+e)
+                    merge_found := true            
+                dummy = "This is here to make the loop return 'void'. " //Delete This line to Break the Indicator.
+
+// run it
+if barstate.islast and node_tog
+    merge_clusters(hvn_points)
+    merge_clusters(lvn_points)
+
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+//END Cluster ID Calcs//
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+
+
+/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+//Display//
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+
+var profile = array.new_line(na)	
+var box_profile = array.new_box(na)	 
+if barstate.islast
+    if array.size(profile) > 0
+        for [index,line] in profile         
+            line.delete(line)       
+        array.clear(profile)
+    if array.size(box_profile) > 0        
+        for [index,box] in box_profile      
+            box.delete(box)     
+        array.clear(box_profile)
+    if polyline.all.size() > 0        
+        for pl in polyline.all      
+            pl.delete() 
+    
+prof_color(_num,_key) => //Function for determining what color each profile index gets
+    switch
+        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.
+        array.includes(lvn_points,_num) => lv_color     //If it is < lv% of the max volume, give it the low volume color 
+        array.includes(hvn_points,_num) => hv_color     //If its > hv%  of the max volume, give it the high volume color 
+        (_key>up_count or _key<down_count) => ov_color                       //If its above or below our value zone, give it the out of value color
+        => vaz_color // Everything else is inside the value zone so it gets the value zone color
+
+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.
+
+///_________________________________________
+///Drawing the Profile
+///‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾
+scale = disp_size/array.max(values)
+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)
+if barstate.islast
+    if extend_day
+        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)
+        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)
+        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)
+    for [i,get_key] in keys
+        get_vol = data_map.get(get_key)
+        scaled = math.round(get_vol*scale)
+        p1 = extend_day and disp_size > 0 ? bar_index : (bar_index+prof_offset)
+        p2 = extend_day and disp_size < 0 ? bar_index : ((bar_index+scaled)+prof_offset)
+        if get_key == poc
+            array.push(box_profile,box.new(p1,poc,p2,poc, border_color = poc_color, border_style = line.style_solid, border_width = 1))
+        else if get_key == vah
+            array.push(box_profile,box.new(p1,vah,p2,vah, border_color = var_color, border_style = line.style_solid, border_width = 1))
+        else if get_key == val
+            array.push(box_profile,box.new(p1,val,p2,val, border_color = var_color, border_style = line.style_solid, border_width = 1))
+        else if dash(i) and (array.size(profile) <= 499) and (math.abs(scaled)>=1)
+            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))
+        else if (array.size(box_profile) <= 499) and (math.abs(scaled)>=1)
+            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))
+
+//Drawing labels for the profile
+    lab.set_xy(bar_index+prof_offset, keys.max())
+    lab.set_textcolor(send_warning?color.rgb(255, 136, 0):chart.fg_color)
+    if lab_tog 
+        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)
+        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)
+        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)
+        poc_lab.set_xy(bar_index+prof_offset,poc)
+        vah_lab.set_xy(bar_index+prof_offset,vah)
+        val_lab.set_xy(bar_index+prof_offset,val)
+// Alerts
+alertcondition(ta.crossover(close,poc), "Cross-Over Point of Control")
+alertcondition(ta.crossunder(close,poc), "Cross-Under Point of Control")
+alertcondition(ta.crossover(close,vah), "Cross-Over Value High")
+alertcondition(ta.crossunder(close,vah), "Cross-Under Value High")
+alertcondition(ta.crossover(close,val), "Cross-Over Value Low")
+alertcondition(ta.crossunder(close,val),"Cross-Under Value Low")
+//Plotting Developing Lines
+plot(dev_poc?(poc==0?na:poc):na, color = poc_color, title = "Developing POC/TOP")
+plot(dev_va?vah:na, color = var_color, title = "Developing VAH")
+plot(dev_va?val:na, color = var_color, title = "Developing VAL")
+
+

Pinescript Folder/0SCT2025/SCT2025indicator2-pinescript.js

@@ -0,0 +1,1055 @@
+//@version=5
+indicator("v6 Sunstoic VP&TPO", shorttitle = "v6 Sunstoic VP&TPO", overlay = true, max_lines_count = 500, max_boxes_count = 500, max_labels_count = 500, max_bars_back = 2000)
+
+
+ti                                     = input.string(defval = "Regular", title = "Calculation Type", options = ["Regular", "Fixed Range", "Fixed Interval"], group = "Calculation Type")
+
+
+auto                                   = input.string(defval = "Custom", options = ["Auto", "Custom"], title = "Auto Calculate Tick Levels? Custom?", inline = "1", group = "Current Session Configurations")
+tickzz                                 = input.float(defval = 2000 ,title = "Ticks", inline = "1", group = "Current Session Configurations")
+tickLevels                             = input.bool(false, title = "Show Tick Levels?", group = "Current Session Configurations")
+textSize                               = input.string(defval = "Tiny", options = ["Auto","Tiny", "Small", "Normal", "Large", "Huge"], group = "Current Session Configurations")
+showIb                                 = input.bool(defval = false, title = "Show Initial Balance Range?", group = "Current Session Configurations")
+sess                                   = input.string(defval = "D", title = "Recalculate After How Much Time?", tooltip = "from 1 to 1440 for minutes \nfrom 1D to 365D for days \nfrom 1W to 52W for weeks \nfrom 1M to 12M for months", group = 'If "Regular" is Selected')
+st                                     = input.time(defval = timestamp("19 Sep 2022 00:00 +0300"), title = "Fixed Range Start", group = 'If "Fixed Range" Is Selected')
+timE                                   = input.session(defval = "1300-1700", title = "Time Range", group = 'If "Fixed Interval" is Selected', tooltip = 'Select "Fixed Interval" For The "Calculation Type" Setting To Activate This Feature')
+showPre                                 = input.bool(defval = true, title = "Show Previous Sessions TPO?", group = "Previous Session Settings")
+blackBox                                = input.bool(defval = false, title = "Segment Previous Sessions With Black Box?", group = "Previous Session Settings")
+rang                                    = input.bool(defval = true, title = "Show Previous Sessions Ranges?", group = "Previous Session Settings")
+
+
+distCalc                                = input.float(defval = 5.0, title = "% Distance to Hide Old SP Lines", tooltip = "If Price Exceeds The % Threshold Defined For This
+                                             Setting (Price Distance From An Existing Sp Line - The Sp Line Will Dissapear Until Price Is Within Proximity Once More",
+                                              group = "Previous Session Settings")
+distCalc2                               = input.float(defval = 5.0, title = "% Distance to Hide Old VA Lines", tooltip = "If Price Exceeds The % Threshold Defined For This
+                                             Setting (Price Distance From An Existing Va Line - The Va Line Will Dissapear Until Price Is Within Proximity Once More",
+                                              group = "Previous Session Settings")
+distCalc3                               = input.float(defval = 5.0, title = "% Distance to Hide Old POC Lines", tooltip = "If Price Exceeds The % Threshold Defined For This
+                                             Setting (Price Distance From An Existing Poc Line - The Poc Line Will Dissapear Until Price Is Within Proximity Once More",
+                                              group = "Previous Session Settings")
+
+
+spShw                                  = input.bool(defval = true, title = "Show SP Lines and Labels", group = "Display Options", tooltip = "If Deselected, TPO Letters Will Only Turn Red When a SP Forms. No Other Identifying Features are Displayed")
+fr                                     = input.bool(defval = true, title = "Show Fixed Range Label and Line?" , group ="Display Options")
+warn                                   = input.bool(defval = true, title = "Show Warning", group = "Display Options")
+col                                    = input.color(defval = color.gray, title = "Main Character Color (Gray Default)", group = "Colors")
+col1                                   = input.color(defval = color.red  , title = "SP Character Color   (Red Default)", group = "Colors")
+col2                                   = input.color(defval = color.yellow, title = "POC Character Color (Yellow Default)", group = "Colors")
+col3                                   = input.color(defval = color.blue, title = "IB Character Color (Blue Default)", group = "Colors")
+col4                                   = input.color(defval = color.rgb(0, 114, 59), title = "Value Area Color (Lime Default)", group = "Colors")
+col5                                   = input.color(defval = color.rgb(163, 147, 0), title = "Value Area Letter Color (White Default)", group = "Colors")
+fnt                                    = input.string(defval = "Default", title = "Font Type", options = ["Default", "Monospace"], group = "Colors")
+
+if timeframe.isdwm
+    ti := "Fixed Range"
+
+
+if fr == true and barstate.islast
+    line.new(math.round(st), close, math.round(st), close + 0.001, extend = extend.both, color = color.white, width = 4, xloc = xloc.bar_time)
+    if ti != "Fixed Range"
+        var box frStart = box.new(math.round(st), high + ta.tr, math.round(st), low - ta.tr,
+     bgcolor = color.new(color.white, 100), border_color = na, text_size = size.normal, text_color = color.white, text_wrap = text.wrap_none,  text = "If Selected in Settings, \nFixed Range Begins Here", xloc = xloc.bar_time)
+
+
+fixTime = time(timeframe.period, timE)
+
+
+fonT = switch fnt
+   
+    "Default"   => font.family_default
+    "Monospace" => font.family_monospace
+
+finTim = switch ti
+   
+    "Regular" => timeframe.change(sess)
+    "Fixed Range" => time[1] < st and time >= st
+    "Fixed Interval" => na(fixTime[1]) and not na(fixTime)
+
+sz = switch textSize
+   
+    "Auto"             => size.auto
+    "Tiny"             => size.tiny
+    "Small"            => size.small
+    "Normal"           => size.normal
+    "Large"            => size.large
+    "Huge"             => size.huge
+
+var string [] str = array.from(
+   
+     " A",
+     " B",
+     " C",
+     " D",
+     " E",
+     " F",
+     " G",
+     " H",
+     " I",
+     " J",
+     " K",
+     " L",
+     " M",
+     " N",
+     " O",
+     " P",
+     " Q",
+     " R",
+     " S",
+     " T",
+     " U",
+     " V",
+     " W",
+     " X",
+     " Y",
+     " Z",
+     " a",
+     " b",
+     " c",
+     " d",
+     " e",
+     " f",
+     " g",
+     " h",
+     " i",
+     " j",
+     " k",
+     " l",  
+     " m",
+     " n",
+     " o",
+     " p",
+     " q",
+     " r",
+     " s",
+     " t",
+     " u",
+     " v",
+     " w",
+     " x",
+     " y",
+     " z"
+
+
+     )
+
+
+if barstate.isfirst
+
+    sX = ""
+
+    for i = 0 to 51
+        sX := array.get(str, i) +  "1 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "2 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "3 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "4 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "5 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "6 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "7 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "8 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "9 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "10 "             // => Loops are run sequentially, not simultaneously, so string characters populate in order
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "11 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "12 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "13 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "14 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "15 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "16 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "17 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "18 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "19 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "20 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "21 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "22 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "23 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "24 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "25 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "26 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "27 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "28 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "29 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "30 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "31 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "32 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "33 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "34 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "35 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "36 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "37 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "38 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "39 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "39 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "40 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "41 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "42 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "43 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "44 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "45 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "46 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "47 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "48 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "49 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "50 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "51 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "52 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "53 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "54 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "55 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "56 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "57 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "58 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "59 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "60 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "61 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "62 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "63 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "64 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "65 "
+        array.push(str, sX)
+    for i = 0 to 51
+        sX := array.get(str, i) +  "66 "
+        array.push(str, sX)
+
+cond(y, x) =>    
+    not na(str.match(label.get_text(array.get(y, x)), "[1-9]"))
+
+cond2(y, x) =>
+    not na(str.match(label.get_text(array.get(y, x)), "[10-66]"))
+
+atr                                      = ta.atr(14)
+var float tickz                          = 0.0
+ticks2 = array.new_float()
+
+
+if ti == "Regular" or ti == "Fixed Interval"
+    if last_bar_index - bar_index == 1601
+        if syminfo.mintick >= 0.01
+            tickz := auto == "Custom" ? tickzz :
+             auto == "Auto" and timeframe.period == "1" ? atr * 50 :
+             auto == "Auto" and timeframe.period == "5" ? atr * 40 :
+                                                          atr * 30
+           
+        else
+            tickz := auto == "Custom" ? tickzz : atr * 100000
+else
+    if time < st
+        if syminfo.mintick >= 0.01
+            tickz := auto == "Custom" ? tickzz :
+             auto == "Auto" and timeframe.period == "1" ? atr * 50 :
+             auto == "Auto" and timeframe.period == "5" ? atr * 40 :
+                                                          atr * 30        
+        else
+            tickz := auto == "Custom" ? tickzz : atr * 100000        
+
+
+var line [] tpoLines                = array.new_line()
+ticks                               = array.new_float()
+var float max                       = 0.0
+var float min                       = 10000000
+var float [] track                  = array.new_float()
+var label [] pocL                   = array.new_label()
+var float [] finChe                 = array.new_float()
+var line j                          = line.new(time, high, time, low, color = color.aqua, width = 4, xloc = xloc.bar_time)
+var int first                       = 0
+var int firstBar                    = 0
+max                                 := math.max(high, max)
+min                                 := math.min(low, min)
+var float ibF                       = 0.0
+var line [] ib                      = array.new_line()
+var label [] tpoLabels              = array.new_label()
+var label [] SP                     = array.new_label()
+var line  [] val                    = array.new_line()
+var label [] VA                     = array.new_label()
+var line ibBar                      = na
+var linefill fil                    = na
+var label op                        = label.new(time, open, xloc = xloc.bar_time, size = size.large, text_font_family = fonT, color = color.new(color.white, 100), text = "●", style = label.style_label_right, textcolor = color.blue)
+var int timRound = 0
+if session.isfirstbar_regular[4] and timRound == 0
+    timRound := math.round(time - time[4])
+
+
+timeCond = switch ti
+   
+    "Regular"        => last_bar_index - bar_index <= 1600
+    "Fixed Range"    => time >= st
+    "Fixed Interval" => last_bar_index - bar_index <= 1600
+
+
+if timeCond
+   
+    if showIb == true and ti == "Regular"
+        if time == ibF
+           
+            array.push(ib, line.new(first, max, time, max, color = color.new(col3, 50), xloc = xloc.bar_time))
+            array.push(ib, line.new(first, min, time, min, color = color.new(col3, 50), xloc = xloc.bar_time))
+       
+        if array.size(ib) > 1
+           
+            linefill.new(array.get(ib, 0), array.get(ib, 1), color.new(col3, 95))
+   
+    if time == ibF and ti == "Regular"
+        line.delete(ibBar)
+        ibBar := line.new(first, max, first, min, color = color.blue, xloc =xloc.bar_time, width = 4)
+
+    if finTim
+       
+        if array.size(val) > 0
+            for i = 0 to array.size(val) - 1
+                line.delete(array.shift(val))
+       
+        if array.size(VA) > 0
+            for i = 0 to array.size(VA) - 1
+                label.delete(array.shift(VA))
+       
+        if array.size(track) > 0
+            array.clear(track)
+       
+        if array.size(finChe) > 0
+            array.clear(finChe)
+       
+        if array.size(ib) > 0
+            for i = 0 to array.size(ib) - 1
+                line.delete(array.shift(ib))
+       
+        if array.size(tpoLines) > 0
+            for i = 0 to array.size(tpoLines) - 1
+                line.delete(array.shift(tpoLines))
+       
+        if array.size(tpoLabels) > 0
+            for i = 0 to array.size(tpoLabels) - 1
+                label.delete(array.shift(tpoLabels))
+       
+        if array.size(SP) > 0
+            for i = 0 to array.size(SP) - 1
+                label.delete(array.shift(SP))
+       
+        if array.size(pocL) > 0
+            for i = 0 to array.size(pocL) - 1
+                label.delete(array.shift(pocL))
+       
+        max := high
+        min := low
+       
+        first := math.round(time)
+        ibF   := math.round(timestamp(year, month, dayofmonth, hour + 1, minute, second))
+       
+        label.set_x(op, first), label.set_y(op, open)
+
+
+        firstBar := bar_index
+        array.push(ticks, low)
+        array.push(track, low)
+        for i = 1 to 500
+            if array.get(ticks, i - 1) + (tickz * syminfo.mintick) <= high
+                array.push(ticks, array.get(ticks, i - 1) + (tickz * syminfo.mintick))
+            else
+                break
+
+        for i = 0 to array.size(ticks) - 1
+            array.push(tpoLines, line.new(bar_index, array.get(ticks, i) ,
+                                          bar_index + 1,  array.get(ticks, i),
+                                          color = tickLevels == true ? color.new(color.lime, 75) : na, xloc = xloc.bar_index))
+            array.push(tpoLabels, label.new(first,  array.get(ticks, i) , text = "   A", xloc = xloc.bar_time,
+                                          color = color.new(col, 100), textcolor = col,  text_font_family = fonT, style = label.style_label_left))
+        
+if timeCond and not finTim and ti == "Regular"
+  or barstate.islast and ti == "Fixed Range"
+  or timeCond and not finTim and ti == "Fixed Interval" and fixTime
+
+
+    calc = max - min
+    var label ibav = label.new(bar_index, close, color = na, style = label.style_label_left,  text_font_family = fonT)
+    if array.size(ib) > 1
+        for i = 0 to array.size(ib) - 1
+            line.set_x2(array.get(ib, i), time)
+        label.set_y(ibav, math.avg(line.get_y1(array.get(ib, 0)), line.get_y1(array.get(ib, 1))))
+        label.set_x(ibav, bar_index + 2)
+        label.set_text(ibav, "Initial Balance Range")
+        label.set_textcolor(ibav, col3)
+        label.set_color(ibav,color.new(color.white, 100))
+    else
+        label.set_textcolor(ibav, na)
+   
+    if array.size(VA) > 0
+        for i = 0 to array.size(VA) - 1
+            label.delete(array.shift(VA))
+
+    if array.size(val) > 0
+        for i = 0 to array.size(val) - 1
+            line.delete(array.shift(val))
+       
+    if array.size(tpoLines) > 0
+        for i = 0 to array.size(tpoLines) - 1
+            line.delete(array.shift(tpoLines))
+   
+    if array.size(tpoLabels) > 0
+        for i = 0 to array.size(tpoLabels) - 1
+            label.delete(array.shift(tpoLabels))
+    if array.size(SP) > 0
+        for i = 0 to array.size(SP) - 1
+            label.delete(array.shift(SP))
+   
+    if array.size(pocL) > 0
+        for i = 0 to array.size(pocL) - 1
+            label.delete(array.shift(pocL))
+   
+    if array.size(finChe) > 0
+        array.clear(finChe)
+
+
+    if array.size(track) > 0
+        array.push(ticks, array.get(track, array.size(track) - 1))
+        for i = 1 to 500
+            if array.get(ticks, i - 1) + (tickz * syminfo.mintick) <= max
+                array.push(ticks, array.get(ticks, i - 1) + (tickz * syminfo.mintick))
+            else
+                break
+        array.push(ticks2, array.get(track, array.size(track) - 1))
+        for i = 1 to 500
+            if array.get(ticks2, i - 1) - (tickz * syminfo.mintick) >= min
+                array.push(ticks2, array.get(ticks2, i - 1) - (tickz * syminfo.mintick))
+            else
+                break
+        for i = array.size(ticks2) - 1 to 0
+
+
+            array.push(tpoLines, line.new( first, array.get(ticks2, i),
+
+
+                                                 last_bar_time,  
+                                                 array.get(ticks2, i),
+                                                 color = tickLevels == true ? color.new(color.lime, 75) : na,
+                                                 xloc = xloc.bar_time
+
+
+                                                 ))
+
+
+            array.push(tpoLabels, label.new( first, array.get(ticks2, i),
+
+
+                                                 color = color.new(color.white, 100),
+                                                 textcolor = col,
+                                                 size = sz,
+                                                 style = label.style_label_left,
+                                                 xloc = xloc.bar_time ,
+                                                 text_font_family = fonT
+                                                 ))                
+        for i = 0 to array.size(ticks) - 1
+            array.push(tpoLines, line.new( first, array.get(ticks, i),
+
+
+                                                 last_bar_time,  
+                                                 array.get(ticks, i),
+                                                 color = tickLevels == true ? color.new(color.lime, 75) : na,
+                                                 xloc = xloc.bar_time
+
+
+                                                 ))
+
+
+            array.push(tpoLabels, label.new( first, array.get(ticks, i),
+
+
+                                                 color = color.new(color.white, 100),
+                                                 textcolor = col,
+                                                 size = sz,
+                                                 style = label.style_label_left,
+                                                 xloc = xloc.bar_time,
+                                                 text_font_family = fonT
+                                                 ))
+
+
+    if array.size(tpoLines) > 1 and bar_index - firstBar < array.size(str)
+       
+        levels = array.new_float()
+        che = array.new_float(array.size(tpoLines), 0)
+        for i = bar_index - firstBar to 0
+            for x = 0 to array.size(tpoLines) - 1
+                if line.get_y1(array.get(tpoLines, x)) <= high[i] and line.get_y1(array.get(tpoLines, x)) >= low[i]
+                    label.set_text(array.get(tpoLabels, x),
+                     text = label.get_text(array.get(tpoLabels, x)) + array.get(str, math.abs(bar_index - firstBar - i)))
+                    array.set(che, x, array.get(che, x) + 1)
+                   
+       
+        len = 0.0
+        for x = 0 to array.size(tpoLabels) - 1
+            len := math.max(len, array.get(che, x))
+       
+        lenTrack = 0
+       
+        for x = 0 to array.size(tpoLabels) - 1
+           
+            if array.get(che, x) == len
+                label.set_textcolor(array.get(tpoLabels, x), col2)
+                lenTrack := x
+                if bar_index - firstBar >= 4
+                   
+                    line.set_color(array.get(tpoLines, x), color.new(col2, 75))
+                    line.set_width(array.get(tpoLines, x), 2)
+                    array.push(finChe, line.get_y1(array.get(tpoLines, x)))
+                    if array.size(finChe) == 1
+                        array.push(pocL, label.new(first, line.get_y1(array.get(tpoLines, x)), xloc = xloc.bar_time,
+                         color = color.new(col, 100), textcolor = col2, style = label.style_label_right,  text_font_family = fonT, text = "POC", size = sz))
+                       
+                       
+                        break
+ 
+        sum = array.new_float()
+        sum1 = array.new_float()
+        lin = array.new_float()
+        lin1 = array.new_float()
+        cheX = array.new_float()
+        cheX1 = array.new_float()
+           
+        if lenTrack > 0
+            for x = lenTrack - 1 to 0
+                array.push(sum , array.size(sum) == 0 ? array.get(che, x) : array.get(sum, array.size(sum) - 1) + array.get(che, x))
+                array.push(lin, label.get_y(array.get(tpoLabels, x)))
+                array.push(cheX, array.get(che, x))
+            for x = lenTrack to array.size(che) - 1
+                array.push(sum1, array.size(sum1) == 0 ? array.get(che, x) : array.get(sum1, array.size(sum1) - 1) + array.get(che, x))
+                array.push(lin1, label.get_y(array.get(tpoLabels, x)))
+                array.push(cheX1, array.get(che, x))
+               
+               
+            miN = math.min(array.size(sum), array.size(sum1))
+           
+           
+            for n = 0 to miN - 1
+                if array.get(sum, n) + array.get(sum1, n) >= array.sum(che) * .7
+                    array.push(val,line.new(first , array.get(lin, n), time,
+                     array.get(lin, n), xloc = xloc.bar_time, color = color.new(col4, 75)))
+                     
+                    array.push(val,line.new(first, array.get(lin1, n), time,
+                     array.get(lin1, n), xloc = xloc.bar_time, color = color.new(col4, 75)))
+                   
+                    array.push(VA, label.new(first, line.get_y1(array.get(val, 0)), text = line.get_y1(array.get(val, 0)) > line.get_y1(array.get(val, 1)) ? "VAH" : "VAL",
+                     textcolor = col4, size = sz, color = color.new(color.white, 100), style = label.style_label_right, text_font_family = fonT, xloc = xloc.bar_time))
+                   
+                    array.push(VA, label.new(first, line.get_y1(array.get(val, 1)), text = line.get_y1(array.get(val, 0)) > line.get_y1(array.get(val, 1)) ? "VAL" : "VAH",
+                     textcolor = col4, size = sz, color = color.new(color.white, 100), style = label.style_label_right, text_font_family = fonT, xloc = xloc.bar_time))
+                                               
+                   
+                    break
+                         
+            if array.size(val) < 2
+           
+                stop = 0
+           
+                if miN == array.size(sum1)
+               
+               
+                    for n = 0 to array.size(cheX1) - 1
+                        if array.get(cheX1, n) >= math.round(len * .7)
+                            stop := n
+                    for n = 0 to array.size(sum) - 1    
+                   
+                        if array.get(sum, n) + array.get(sum1, stop) >= array.sum(che) * .7
+                           
+                            array.push(val,line.new(first, array.get(lin, n), time,
+                             array.get(lin, n), xloc = xloc.bar_time, color = color.new(col4, 75)))
+                           
+                            array.push(val,line.new(first, array.get(lin1, stop), time,
+                             array.get(lin1, stop), xloc = xloc.bar_time, color = color.new(col4, 75)))
+                                   
+                            array.push(VA, label.new(first, line.get_y1(array.get(val, 0)), text = line.get_y1(array.get(val, 0)) > line.get_y1(array.get(val, 1)) ? "VAH" : "VAL",
+                             textcolor = col4, size = sz, color = color.new(color.white, 100),  text_font_family = fonT, style = label.style_label_right, xloc = xloc.bar_time))
+                           
+                            array.push(VA, label.new(first, line.get_y1(array.get(val, 1)), text = line.get_y1(array.get(val, 0)) > line.get_y1(array.get(val, 1)) ? "VAL" : "VAH",
+                             textcolor = col4, size = sz, color = color.new(color.white, 100),  text_font_family = fonT, style = label.style_label_right, xloc = xloc.bar_time))
+                                               
+                            break
+               
+                else
+               
+                    for n = 0 to array.size(cheX) - 1
+                        if array.get(cheX, n) >= math.round(len * .7)
+                            stop := n
+                    for n = 0 to array.size(sum1) - 1    
+                   
+                        if array.get(sum, stop) + array.get(sum1, n) >= array.sum(che) * .7
+                   
+                            array.push(val,line.new(first, array.get(lin1, n), time,
+                             array.get(lin1, n), xloc = xloc.bar_time, color = color.new(col4, 75)))
+                       
+                            array.push(val,line.new(first, array.get(lin, stop), time,
+                             array.get(lin, stop), xloc = xloc.bar_time, color = color.new(col4, 75)))
+                                   
+                            array.push(VA, label.new(first, line.get_y1(array.get(val, 0)), text = line.get_y1(array.get(val, 0)) > line.get_y1(array.get(val, 1)) ? "VAH" : "VAL",
+                             textcolor = col4, size = sz, color = color.new(color.white, 100),  text_font_family = fonT, style = label.style_label_right, xloc = xloc.bar_time))
+                           
+                            array.push(VA, label.new(first, line.get_y1(array.get(val, 1)), text = line.get_y1(array.get(val, 0)) > line.get_y1(array.get(val, 1)) ? "VAL" : "VAH",
+                             textcolor = col4, size = sz, color = color.new(color.white, 100),  text_font_family = fonT, style = label.style_label_right, xloc = xloc.bar_time))
+                                                     
+                            break                
+         
+        if array.size(val) == 2 and array.size(pocL) > 0 and array.size(tpoLabels) > 0
+            fil := linefill.new(array.get(val, 0), array.get(val, 1), color = color.new(col4, 90))
+           
+            for i = 0 to array.size(tpoLabels) - 1
+                if line.get_y1(array.get(val, 0)) > line.get_y2(array.get(val, 1))
+                    if label.get_y(array.get(tpoLabels, i)) <= line.get_y1(array.get(val, 0))
+                      and label.get_y(array.get(tpoLabels, i)) >= line.get_y1(array.get(val, 1))
+                      and label.get_y(array.get(tpoLabels, i)) != label.get_y(array.get(pocL, 0))
+                        label.set_textcolor(array.get(tpoLabels, i), col5)
+                   
+                    else if label.get_y(array.get(tpoLabels, i)) == label.get_y(array.get(pocL, 0))
+                        label.set_textcolor(array.get(tpoLabels, i), col2)
+           
+           
+                else
+                   
+                    if label.get_y(array.get(tpoLabels, i)) >= line.get_y1(array.get(val, 0) )
+                      and label.get_y(array.get(tpoLabels, i)) <= line.get_y1(array.get(val, 1))
+                      and label.get_y(array.get(tpoLabels, i)) != label.get_y(array.get(pocL, 0))
+                        label.set_textcolor(array.get(tpoLabels, i), col5)                          
+                   
+                    else if label.get_y(array.get(tpoLabels, i)) == label.get_y(array.get(pocL, 0))
+                        label.set_textcolor(array.get(tpoLabels, i), col2)
+         
+         
+        for x = 0 to array.size(tpoLabels) - 1
+            if str.length(label.get_text(array.get(tpoLabels, x))) == 2
+              or str.length(label.get_text(array.get(tpoLabels, x))) == 5 and cond2(tpoLabels, x) == true
+              or str.length(label.get_text(array.get(tpoLabels, x))) == 4 and cond(tpoLabels, x) == true
+                label.set_textcolor(array.get(tpoLabels, x), col1)
+                if bar_index - firstBar >= 4 and spShw == true
+                    line.set_color(array.get(tpoLines, x), color.new(col1, 75))
+                    array.push(SP, label.new(time, line.get_y1(array.get(tpoLines, x)), xloc = xloc.bar_time, color = color.new(col, 100),
+                     style = label.style_label_left, text = "SP", textcolor = col1,  text_font_family = fonT, size = size.tiny))
+               
+        if array.size(VA) == 2 and array.size(pocL) > 0
+            if label.get_y(array.get(VA, 0)) == label.get_y(array.get(pocL, 0))
+                label.set_x(array.get(VA, 0), first - timRound)
+            if label.get_y(array.get(VA, 1)) == label.get_y(array.get(pocL, 0))
+                label.set_x(array.get(VA, 1), first - timRound)
+if ti == "Regular" or ti == "Fixed Range"  
+    line.set_x1(j, first)
+    line.set_x2(j, first)
+    line.set_y1(j, max)
+    line.set_y2(j, min)    
+else
+    if fixTime  
+        line.set_x1(j, first)
+        line.set_x2(j, first)
+        line.set_y1(j, max)
+        line.set_y2(j, min)    
+
+var line  [] SPCopy        = array.new_line()
+var line  [] valCopy       = array.new_line()
+var label [] tpoLabelsCopy = array.new_label()
+var line  [] pocCopy       = array.new_line()
+var line  [] jCopy         = array.new_line()
+var line  [] ibBarCopy     = array.new_line()
+var box   [] bBox          = array.new_box()
+
+
+tCnd = hour == str.tonumber(str.substring(timE, str.pos(timE, "-") + 1, str.length(timE) - 2))
+     and minute == str.tonumber(str.substring(timE, str.pos(timE, "-") + 3))
+
+
+if session.islastbar and barstate.isconfirmed
+   and timeCond and ti == "Regular" and array.size(tpoLabels) > 0
+   and showPre == true
+   or tCnd and barstate.isconfirmed and timeCond and ti == "Fixed Interval"
+   and array.size(tpoLabels) > 0 and showPre == true
+   
+    if blackBox == true  
+        array.push(bBox, box.new(first, max, time, min, xloc = xloc.bar_time, bgcolor = #000000, border_color = na))
+    if rang == true
+
+
+        array.push(jCopy, line.copy(j))
+        array.push(ibBarCopy, line.copy(ibBar))
+    if array.size(val) > 0 and distCalc2 != 0
+        for i = 0 to array.size(val) - 1
+            array.push(valCopy, line.copy(array.get(val, i)))
+
+
+    if array.size(tpoLabels) > 0
+        for i = 0 to array.size(tpoLabels) - 1
+            array.push(tpoLabelsCopy, label.copy(array.get(tpoLabels, i)))
+            if label.get_y(array.get(tpoLabels, i)) == label.get_y(array.get(pocL, 0))
+                array.push(pocCopy, line.copy(array.get(tpoLines, i)))
+   
+    if array.size(SP) > 0 and distCalc != 0
+        for i = 0 to array.size(SP) - 1
+            array.push(SPCopy, line.new(first, label.get_y(array.get(SP, i)), time, label.get_y(array.get(SP, i)), xloc = xloc.bar_time, color = color.new(col1, 80)))
+   
+
+
+if array.size(SPCopy) > 0
+    for i = 0 to array.size(SPCopy) - 1
+        if line.get_y1(array.get(SPCopy, i)) <= high and line.get_y1(array.get(SPCopy, i)) >= low  
+            line.delete(array.get(SPCopy, i))
+        else
+            if math.abs((close / line.get_y1(array.get(SPCopy, i))- 1)* 100) <= distCalc
+                line.set_x2(array.get(SPCopy, i), time)
+            else if math.abs((close / line.get_y1(array.get(SPCopy, i)) - 1)* 100) >= distCalc
+                line.set_x2(array.get(SPCopy, i), line.get_x1(array.get(SPCopy, i)))
+               
+if array.size(valCopy) > 0
+    for i = 0 to array.size(valCopy) - 1
+        if line.get_y1(array.get(valCopy, i)) <= high and line.get_y1(array.get(valCopy, i)) >= low  
+            line.delete(array.get(valCopy, i))
+        else if math.abs((close / line.get_y1(array.get(valCopy, i))- 1)* 100) <= distCalc2
+            line.set_x2(array.get(valCopy, i), time)
+        else if math.abs((close / line.get_y1(array.get(valCopy, i)) - 1)* 100) >= distCalc2
+            line.set_x2(array.get(valCopy, i), line.get_x1(array.get(valCopy, i)))
+
+
+if array.size(pocCopy) > 0
+    for i = 0 to array.size(pocCopy) - 1
+        if line.get_y1(array.get(pocCopy, i)) <= high and line.get_y1(array.get(pocCopy, i)) >= low  
+            line.delete(array.get(pocCopy, i))
+        else if math.abs((close / line.get_y1(array.get(pocCopy, i))- 1)* 100) <= distCalc3
+            line.set_x2(array.get(pocCopy, i), time)
+        else if math.abs((close / line.get_y1(array.get(pocCopy, i)) - 1)* 100) >= distCalc3
+            line.set_x2(array.get(pocCopy, i), line.get_x1(array.get(pocCopy, i)))
+
+
+if array.size(tpoLabelsCopy) > 500
+    for i = 0 to array.size(tpoLabelsCopy) - 500
+        label.delete(array.shift(tpoLabelsCopy))
+    if array.size(ibBarCopy) > 1
+        line.delete(array.shift(ibBarCopy))
+        line.delete(array.shift(jCopy))
+    if array.size(bBox) > 1
+        box.delete(array.shift(bBox))
+
+
+tf = input.timeframe("D", title = "Timeframe", inline = "0", group = "PROFILE SETTINGS")
+
+
+vap = input.float(70, title = "Value Area %", group = "PROFILE SETTINGS")/100
+lb_days = input.int(5, title = "# of Profiles", maxval = 20, minval = 1, tooltip = "Max: 20 \nLarge Display = Less Granular Profiles\nSmall Display = More Granular Profiles")
+mp = input.bool(false, title = "Calculate As Market Profile", group = "PROFILE SETTINGS", tooltip = "Calculations will distribue a 1 instead of the candle's volume.")
+
+
+disp_size = input.int(-10, 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.\n When Positive, the profile will display from the start of the day. \n When Negative, the profile will display from the end of the day.\nNotes:\n-This value is # bars away from your profile's Axis.\n-The farther from 0 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 AUTO SCALE - Fits the profile within the lookback period.")
+auto_size = input.bool(true, title = "Auto-Scale", inline = "3", group = "DISPLAY SETTINGS")
+
+
+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
+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
+
+
+poc_color = input.color(#d3be00, title = "POC Color", group = "Colors")
+var_color = input.color(#886000, title = "Value High/Low Color", group = "Colors")
+vaz_color = input.color(color.new(#886000,0), title = "Value Zone Color", group = "Colors")
+ov_color = input.color(#886000, title = "Profile Color", group = "Colors")
+lv_color = input.color(#886000, title  = "Low Volume Color", group = "Colors")
+hv_color = input.color(#886000, title = "High Volume Color", group = "Colors")
+
+
+fix_z(_val) => _val>0?_val:1
+
+
+round_to(_round,_to) =>
+    math.round(_round/_to)*_to
+
+
+vgroup_pull(_var,_array,_num1,_num2) =>
+    _var == 1 and _num1>=_num2?array.get(_array,_num1-_num2):
+      _var == 2 and array.size(_array)-1 >= (_num1 + _num2)?array.get(_array,_num1+_num2)
+      :0
+
+
+prof_color(_num,_mv,_a1,_a2,_a3,_a4) =>
+    _num==0?na:
+     _num==_mv?poc_color:
+     (_num==_a3 or _num==_a4)?var_color:
+     array.includes(_a1,_num) and array.includes(_a2,_num)?((_num>_a3 or _num<_a4)?ov_color:vaz_color):
+     array.includes(_a2,_num)?lv_color:
+     array.includes(_a1,_num)?hv_color:
+     (_num>_a3 or _num<_a4)?ov_color:
+     vaz_color  
+
+
+var line_array = array.new_line(na)
+var box_array = array.new_box(na)
+
+
+kill_bar = ta.valuewhen(timeframe.change(tf),bar_index,lb_days+1)+1
+if array.size(line_array) > 0
+    for i = array.size(line_array)-1 to 0
+        ln = array.get(line_array,i)
+        lx = line.get_x1(ln)
+        if lx <= kill_bar
+            line.delete(ln)
+            array.remove(line_array,i)
+if array.size(box_array) > 0
+    for i = array.size(box_array)-1 to 0
+        bx = array.get(box_array,i)
+        bl = box.get_left(bx)
+        if bl <= kill_bar or na(bl)
+            box.delete(bx)
+            array.remove(box_array,i)
+if array.size(label.all) > 0
+    for i = array.size(label.all)-1 to 0
+        lb = array.get(label.all,i)
+        lx = label.get_x(lb)
+        if lx <= kill_bar
+            label.delete(lb)
+            array.remove(label.all,i)
+
+
+get_prof(_mp,_tf) =>
+    new_calc = timeframe.change(_tf)
+    last_new_calc = ta.valuewhen(new_calc,bar_index,1)
+    index_num = math.floor(1000/lb_days)-1
+    start_time = request.security("",_tf,ta.valuewhen(bar_index == last_bar_index-lb_days,time,0))
+    calc_bars = (bar_index - ta.valuewhen(new_calc,bar_index,0))[1]+1
+    base = ta.lowest(low,fix_z(calc_bars)+1)[1]
+    roof = ta.highest(high,fix_z(calc_bars)+1)[1]
+    tick_size = round_to(math.max(((roof - base)/index_num),syminfo.mintick),(syminfo.mintick/100))
+    c_hi = round_to(high,tick_size)
+    c_lo = round_to(low,tick_size)
+    candle_range = c_hi - c_lo
+    candle_index = (candle_range/tick_size)+1
+    tick_vol = _mp?1:volume/candle_index
+    main = array.new_float(na)
+    hvn_points = array.new_int(na)
+    lvn_points = array.new_int(na)
+    if new_calc and time >= start_time
+        for i = 0 to index_num
+            index_price = base + (i*tick_size)
+            if index_price >= roof
+                break
+            float index_sum = 0
+            for e = 1 to calc_bars
+                if index_price <= c_hi[e] and index_price >= c_lo[e]
+                    index_sum := index_sum + tick_vol[e]
+            array.push(main,index_sum)
+        max_index = math.round(math.avg(array.indexof(main,array.max(main)),array.lastindexof(main,array.max(main))))
+        poc = base + (tick_size*max_index)
+        max_vol = array.sum(main)*vap
+        vol_count = max_index >=0?array.get(main, max_index):0.0
+        up_count = max_index
+        down_count = max_index
+        if array.size(main) > 0
+            for x = 0 to array.size(main)-1
+                if vol_count >= max_vol
+                    break                                                                  
+                uppervol = up_count<array.size(main)-1?array.get(main, up_count + 1):na
+                lowervol = down_count>0?array.get(main, down_count - 1):na
+                if ((uppervol >= lowervol) and not na(uppervol)) or na(lowervol)
+                    vol_count += uppervol
+                    up_count += 1
+                else                                                                        
+                    vol_count += lowervol
+                    down_count -= 1
+        val = base + (tick_size*down_count)
+        vah = base + (tick_size*up_count)
+        uc = up_count
+        dc = down_count
+       
+
+        ///////////////////////////////
+        //Cluster ID for Volume Nodes//
+        ///////////////////////////////
+ 
+        if array.size(main) > 0
+            for i = 0 to array.size(main)-1                    
+                _val = array.get(main,i)                        
+                ary = array.new_float(na)                              
+                for e = 0 to int(array.size(main)*hi_width)                          
+                    array.push(ary,vgroup_pull(1,main,i,e))        
+                    array.push(ary,vgroup_pull(2,main,i,e))      
+                max = array.max(ary)                      
+                if _val >= math.avg(max,array.avg(ary))  
+                    array.push(hvn_points,i)          
+
+
+        if array.size(main) > 0    
+            for i = 0 to array.size(main)-1
+                _val = array.get(main,i)
+                ary = array.new_float(na)
+                for e = 0 to int(array.size(main)*lo_width)  
+                    array.push(ary,vgroup_pull(1,main,i,e))
+                    array.push(ary,vgroup_pull(2,main,i,e))
+                min = array.min(ary)
+                if _val <= math.avg(min,array.avg(ary))  
+                    array.push(lvn_points,i)
+        ///_________________________________________
+        ///Cluster Merging
+        ///‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾
+        merge_per = 0.02
+        if array.size(hvn_points)>0
+            for i = 0 to array.size(hvn_points)-1
+                hi_found = false
+                lo_found = false
+                _val = array.get(hvn_points,i)
+                for e = int(array.size(main)*merge_per) to 0
+                    if array.includes(hvn_points,_val+e)
+                        hi_found := true
+                    if hi_found
+                        array.push(hvn_points,_val+e)
+                    if array.includes(hvn_points,_val-e)
+                        lo_found := true
+                    if lo_found
+                        array.push(hvn_points,_val-e)
+
+
+        if array.size(lvn_points)>0
+            for i = 0 to array.size(lvn_points)-1
+                hi_found = false
+                lo_found = false
+                _val = array.get(lvn_points,i)
+                for e = int(array.size(main)*merge_per) to 0
+                    if array.includes(lvn_points,_val+e)
+                        hi_found := true
+                    if hi_found
+                        array.push(lvn_points,_val+e)
+                    if array.includes(lvn_points,_val-e)
+                        lo_found := true
+                    if lo_found
+                        array.push(lvn_points,_val-e)
+        prof_axis = disp_size>0?last_new_calc:bar_index[1]
+        display_size = auto_size and disp_size!=0?(disp_size/math.abs(disp_size))*(calc_bars-1):disp_size
+        
+
+        if array.size(main) > 0
+            for i = 0 to array.size(main) - 1
+                scale = display_size/array.max(main)
+                scaled = math.round(array.get(main,i)*scale)
+                if ((i>uc) or (i<dc)) and (array.size(line_array) <= 499)
+                    array.push(line_array,line.new(prof_axis,base+(i*tick_size[1]),(prof_axis+scaled),base+(i*tick_size[1]), color = prof_color(i,max_index,hvn_points,lvn_points,uc,dc), style = (i<dc or i>uc?line.style_dotted:line.style_solid)))
+                else if ((i<=uc) or (i>=dc)) and (array.size(box_array) <= 499)
+                    array.push(box_array,box.new(prof_axis,base+(i*tick_size[1]),(prof_axis+scaled),base+(i*tick_size[1]), border_color = prof_color(i,max_index,hvn_points,lvn_points,uc,dc), border_style = (i<dc or i>uc?line.style_dotted:line.style_solid), border_width = 1))
+                else if (array.size(line_array) <= 499)
+                    array.push(line_array,line.new(prof_axis,base+(i*tick_size[1]),(prof_axis+scaled),base+(i*tick_size[1]), color = prof_color(i,max_index,hvn_points,lvn_points,uc,dc), style = (i<dc or i>uc?line.style_dotted:line.style_solid)))
+                else if (array.size(box_array) <= 499)
+                    array.push(box_array,box.new(prof_axis,base+(i*tick_size[1]),(prof_axis+scaled),base+(i*tick_size[1]), border_color = prof_color(i,max_index,hvn_points,lvn_points,uc,dc), border_style = (i<dc or i>uc?line.style_dotted:line.style_solid), border_width = 1))
+             
+get_prof(mp,tf)

Pinescript Folder/12pm Key Level/4_00 utc key.ipynb

@@ -0,0 +1,90 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bf1a901b",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "//@version=5\n",
+    "indicator(\"4:00 UTC Box with Midline (3min/5min)\", overlay=true)\n",
+    "\n",
+    "/// Adjustable Target Time Setting\n",
+    "targetHour = input.int(0, title=\"Target Hour (UTC)\", minval=0, maxval=23)\n",
+    "targetMinute = input.int(0, title=\"Target Minute (UTC)\", minval=0, maxval=59)\n",
+    "\n",
+    "// Timeframe detection\n",
+    "iiis3min = timeframe.period == \"3\"\n",
+    "iiis5min = timeframe.period == \"5\"\n",
+    "showIndicator = iiis3min or iiis5min\n",
+    "\n",
+    "// Extension length based on timeframe\n",
+    "boxLength = iiis3min ? 379 : iiis5min ? 228 : na\n",
+    "\n",
+    "// Detect target time candle\n",
+    "isTargetTime = (hour == targetHour) and (minute == targetMinute)\n",
+    "\n",
+    "// Get UTC date\n",
+    "getUTCDate(t) =>\n",
+    "    year(t) * 10000 + month(t) * 100 + dayofmonth(t)\n",
+    "\n",
+    "currentDate = getUTCDate(time)\n",
+    "todayDate = getUTCDate(timenow)\n",
+    "isToday = currentDate == todayDate\n",
+    "\n",
+    "// Persistent variables\n",
+    "var float boxHigh = na\n",
+    "var float boxLow = na\n",
+    "var float boxMid = na\n",
+    "var int   barStart = na\n",
+    "var box   myBox = na\n",
+    "var line  midLine = na\n",
+    "\n",
+    "// Draw only previous day's box at today's target time (8:00), skip drawing if it's today's box\n",
+    "if showIndicator and isTargetTime and not isToday\n",
+    "    boxHigh := high\n",
+    "    boxLow := low\n",
+    "    boxMid := (high + low) / 2\n",
+    "    barStart := bar_index\n",
+    "    myBox := box.new(left=barStart, top=boxHigh, right=barStart + boxLength, bottom=boxLow, border_color=color.orange, bgcolor=color.new(color.orange, 85))\n",
+    "    midLine := line.new(x1=barStart, y1=boxMid, x2=barStart + boxLength, y2=boxMid, color=color.rgb(0, 0, 0, 90), style=line.style_solid)\n",
+    "\n",
+    "// Remove after extension length\n",
+    "if not na(barStart) and bar_index > barStart + boxLength\n",
+    "    myBox := na\n",
+    "    midLine := na\n"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "51731e1f",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4663f21f",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Pinescript Folder/1D Candle Preview/daily candle preview.ipynb

@@ -0,0 +1,258 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6a054be2",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "//@version=5\n",
+    "indicator(\"D1 Candle Preview\", overlay=true)\n",
+    "\n",
+    "// === Inputs ===\n",
+    "bCol = input.color(#089981, title=\"Bull Border\")\n",
+    "rCol = input.color(color.red, title=\"Bear Border\")\n",
+    "bgB = input.color(color.new(#089981, 20), title=\"Bull Body\")\n",
+    "bgR = input.color(color.new(#ff5252, 20), title=\"Bear Body\")\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",
+    "dBull = dC >= dO\n",
+    "dCol = dBull ? bCol : rCol\n",
+    "dBg = dBull ? bgB : bgR\n",
+    "\n",
+    "ofs = 30\n",
+    "bw = 6\n",
+    "rIdx = bar_index + ofs + bw / 2\n",
+    "lIdx = bar_index + ofs - bw / 2\n",
+    "xMid = int(bar_index + ofs)\n",
+    "\n",
+    "// Body\n",
+    "var box dBx = na\n",
+    "box.delete(dBx)\n",
+    "tB = math.max(dO, dC)\n",
+    "bB = math.min(dO, dC)\n",
+    "dBx := box.new(left=int(lIdx), right=int(rIdx), top=tB, bottom=bB, border_color=dCol, bgcolor=dBg)\n",
+    "\n",
+    "// Wicks\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",
+    "// Labels\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",
+    "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, format.mintick), 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, format.mintick), 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, format.mintick), style=lblStyle, color=color.new(#2195f3, 100), textcolor=color.black, size=lblSize)\n",
+    "lblC := label.new(x=xMid + lblOfs, y=dC, text=\"6C \" + str.tostring(dC, format.mintick), style=lblStyle, color=color.new(#2195f3, 100), textcolor=color.black, size=lblSize)\n",
+    "\n",
+    "// Open lines\n",
+    "var line moL = na\n",
+    "line.delete(moL)\n",
+    "var line moH = na\n",
+    "line.delete(moH)\n",
+    "var line moLw = na\n",
+    "line.delete(moLw)\n",
+    "\n",
+    "moL := line.new(x1=bar_index - 500, y1=dO, x2=xMid, y2=dO, color=color.rgb(0, 0, 0, 80), style=line.style_dotted, width=1)\n",
+    "moH := line.new(x1=bar_index - 500, y1=dL, x2=xMid, y2=dL, color=color.rgb(0, 0, 0, 50), style=line.style_dotted, width=1)\n",
+    "moLw := line.new(x1=bar_index - 500, y1=dH, x2=xMid, y2=dH, color=color.rgb(0, 0, 0, 50), style=line.style_dotted, width=1)\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",
+    "var float cO = na\n",
+    "var float cH = na\n",
+    "var float cL = na\n",
+    "var float cC = na\n",
+    "\n",
+    "if (time == st)\n",
+    "    cO := open\n",
+    "    cH := high\n",
+    "    cL := low\n",
+    "    cC := close\n",
+    "\n",
+    "if (not na(cO))\n",
+    "    cH := math.max(cH, high)\n",
+    "    cL := math.min(cL, low)\n",
+    "    cC := close\n",
+    "\n",
+    "cBull = cC >= cO\n",
+    "cCol = cBull ? bCol : rCol\n",
+    "cBg = cBull ? bgB : bgR\n",
+    "\n",
+    "cOfs = 37\n",
+    "cbw = 6\n",
+    "crIdx = bar_index + cOfs + cbw / 2\n",
+    "clIdx = bar_index + cOfs - cbw / 2\n",
+    "cX = int(bar_index + cOfs)\n",
+    "\n",
+    "// Custom body\n",
+    "var box cBx = na\n",
+    "box.delete(cBx)\n",
+    "ctB = math.max(cO, cC)\n",
+    "cbB = math.min(cO, cC)\n",
+    "cBx := box.new(left=int(clIdx), right=int(crIdx), top=ctB, bottom=cbB, border_color=cCol, bgcolor=cBg)\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",
+    "// 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",
+    "clStyle = label.style_label_left\n",
+    "clSize = size.tiny\n",
+    "clOfs = 2\n",
+    "\n",
+    "clO := label.new(x=cX + clOfs, y=cO, text=\"8O \" + str.tostring(cO, format.mintick), 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, format.mintick), 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, format.mintick), style=clStyle, color=color.new(#2195f3, 100), textcolor=color.black, size=clSize)\n",
+    "clC := label.new(x=cX + clOfs, y=cC, text=\"8C \" + str.tostring(cC, format.mintick), style=clStyle, color=color.new(#2195f3, 100), textcolor=color.black, size=clSize)\n",
+    "\n",
+    "// Custom open lines\n",
+    "var line cMoL = na\n",
+    "line.delete(cMoL)\n",
+    "var line cMoH = na\n",
+    "line.delete(cMoH)\n",
+    "var line cMoLw = na\n",
+    "line.delete(cMoLw)\n",
+    "\n",
+    "cMoL := line.new(x1=bar_index - 500, y1=cO, x2=cX, y2=cO, color=color.rgb(255, 0, 0, 80), style=line.style_dotted, width=1)\n",
+    "cMoH := line.new(x1=bar_index - 500, y1=cH, x2=cX, y2=cH, color=color.rgb(255, 0, 0, 50), style=line.style_dotted, width=1)\n",
+    "cMoLw := line.new(x1=bar_index - 500, y1=cL, x2=cX, y2=cL, color=color.rgb(255, 0, 0, 50), style=line.style_dotted, width=1)\n",
+    "\n",
+    "// Date Label\n",
+    "var label cDt = na\n",
+    "label.delete(cDt)\n",
+    "\n",
+    "cYOfs = (cH - cL) * 0.05\n",
+    "cLblY = cH + cYOfs\n",
+    "\n",
+    "cDt := label.new(x=cX + clOfs, y=cC + 6, text=str.tostring(dayofmonth) + \"/\" + str.tostring(month) + \"/\" + str.tostring(year), style=clStyle, color=color.new(#2195f3, 100), textcolor=color.black, size=clSize)\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",
+    "\n",
+    "tfP = str.replace(timeframe.period, tfS, \"\")\n",
+    "tfText = tfP + tfS + \"m to 1D\"\n",
+    "\n",
+    "tfLbl := label.new(x=cX + clOfs, y=cC - 3, text=tfText, style=clStyle, color=color.new(#2195f3, 100), textcolor=color.black, size=clSize)\n",
+    "tfLbl2 := label.new(x=cX + clOfs, y=cC - 2, text=\"Candle Type:\", style=clStyle, color=color.new(#2195f3, 100), textcolor=color.black, size=clSize)\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",
+    "var label dHLlbl = na\n",
+    "var label dOClbl = na\n",
+    "label.delete(dHLlbl)\n",
+    "label.delete(dOClbl)\n",
+    "\n",
+    "var label cHLlbl = na\n",
+    "var label cOClbl = na\n",
+    "label.delete(cHLlbl)\n",
+    "label.delete(cOClbl)\n",
+    "\n",
+    "var label dRange = na\n",
+    "var label cRange = na\n",
+    "label.delete(dRange)\n",
+    "label.delete(cRange)\n",
+    "\n",
+    "dRange := label.new(x=cX + clOfs, y=cC + 10, text=\"6am Range\", style=clStyle, color=color.new(#2195f3, 100), textcolor=color.black, size=clSize)\n",
+    "dHLlbl := label.new(x=cX + clOfs, y=cC + 9, text=\"6HL: $\" + str.tostring(dHL, format.mintick), style=clStyle, color=color.new(#2195f3, 100), textcolor=color.black, size=clSize)\n",
+    "dOClbl := label.new(x=cX + clOfs, y=cC + 8, text=\"6OC: $\" + str.tostring(dOC, format.mintick), style=clStyle, color=color.new(#2195f3, 100), textcolor=color.black, size=clSize)\n",
+    "cRange := label.new(x=cX + clOfs, y=cC + 4, text=\"8am Range\", style=clStyle, color=color.new(#2195f3, 100), textcolor=color.black, size=clSize)\n",
+    "cHLlbl := label.new(x=cX + clOfs, y=cC + 3, text=\"8HL: $\" + str.tostring(cHL, format.mintick), style=clStyle, color=color.new(#2195f3, 100), textcolor=color.black, size=clSize)\n",
+    "cOClbl := label.new(x=cX + clOfs, y=cC + 2, text=\"8OC: $\" + str.tostring(cOC, format.mintick), style=clStyle, color=color.new(#2195f3, 100), textcolor=color.black, size=clSize)\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",
+    "utc8H = (hour + 12) % 24\n",
+    "utc8Str = \"UTC: \" + str.tostring(utc8H, \"00\") + \":\" + str.tostring(minute, \"00\")\n",
+    "\n",
+    "utc8Lbl := label.new(x=cX + clOfs, y=cC + 13, text=utc8Str, style=clStyle, color=color.new(#2195f3, 100), textcolor=color.black, size=clSize)\n",
+    "utcLbl := label.new(x=cX + clOfs, y=cC + 12, text=utcStr, style=clStyle, color=color.new(#2195f3, 100), textcolor=color.black, size=clSize)\n"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "87444566",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "496baee5",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Pinescript Folder/3-Candle Pattern Highlighter (1-Minute Only) General engulfing/3-Candle Pattern Highlighter (1-Minute Only).ipynb

@@ -0,0 +1,93 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aebaab2e",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "//@version=5\n",
+    "indicator(\"3-Candle Pattern Highlighter (1-Minute Only)\", overlay=true, max_boxes_count=500, max_lines_count=500, max_labels_count=500)\n",
+    "\n",
+    "// ─────────── Restrict to 1-Minute Timeframe\n",
+    "is_1m = timeframe.period == \"1\"\n",
+    "\n",
+    "// ─────────── Candle Data\n",
+    "o = open\n",
+    "h = high\n",
+    "l = low\n",
+    "c = close\n",
+    "\n",
+    "// ─────────── Utility Functions\n",
+    "isBull(c_, o_) => c_ > o_\n",
+    "isBear(c_, o_) => c_ < o_\n",
+    "\n",
+    "// ─────────── Pattern Logic\n",
+    "bear3cp_t1 = isBull(c[2], o[2]) and isBear(c[1], o[1]) and c[1] > o[2] and isBear(c, o) and c < o[2] and c < c[1]\n",
+    "bear3cp_t2 = isBull(c[2], o[2]) and isBear(c[1], o[1]) and c[1] < o[2] and isBear(c, o) and c < o[2] and c < c[1]\n",
+    "bull3cp_t1 = isBear(c[2], o[2]) and isBull(c[1], o[1]) and c[1] < o[2] and isBull(c, o) and c > o[2] and c > c[1]\n",
+    "bull3cp_t2 = isBear(c[2], o[2]) and isBull(c[1], o[1]) and c[1] > o[2] and isBull(c, o) and c > o[2] and c > c[1]\n",
+    "\n",
+    "// ─────────── Persistent Arrays\n",
+    "var box[] boxes = array.new_box()\n",
+    "var line[] lines = array.new_line()\n",
+    "\n",
+    "// ─────────── Draw Helper Function\n",
+    "drawBoxAndLine(cond, baseOpen, baseClose, baseBar, extendBars, colorFill) =>\n",
+    "    if is_1m and cond\n",
+    "        top = math.max(baseOpen, baseClose)\n",
+    "        bottom = math.min(baseOpen, baseClose)\n",
+    "        mid = (top + bottom) / 2\n",
+    "        x1 = baseBar\n",
+    "        x2 = baseBar + extendBars\n",
+    "        bx = box.new(left=x1, right=x2, top=top, bottom=bottom, bgcolor=color.new(colorFill, 95), border_color=colorFill)\n",
+    "        ln = line.new(x1=x1, x2=x2, y1=mid, y2=mid, color=colorFill, width=1, style=line.style_dotted)\n",
+    "        array.push(boxes, bx)\n",
+    "        array.push(lines, ln)\n",
+    "\n",
+    "// ─────────── Draw for Each Pattern (only on 1-minute)\n",
+    "drawBoxAndLine(bear3cp_t1, o[1], c[1], bar_index[1], 5, color.rgb(0, 0, 0, 95))\n",
+    "drawBoxAndLine(bear3cp_t2, o[2], c[2], bar_index[2], 6, color.rgb(0, 0, 0, 95))\n",
+    "drawBoxAndLine(bull3cp_t1, o[1], c[1], bar_index[1], 5, color.rgb(0, 0, 0, 95))\n",
+    "drawBoxAndLine(bull3cp_t2, o[2], c[2], bar_index[2], 6, color.rgb(0, 0, 0, 95))\n",
+    "\n",
+    "//// ─────────── Plot Shapes (only on 1-minute)\n",
+    "//plotshape(is_1m and bear3cp_t1, title=\"↓1\", text=\"↓1\", style=shape.triangledown, location=location.abovebar, color=color.rgb(0, 0, 0, 80), size=size.tiny)\n",
+    "//plotshape(is_1m and bear3cp_t2, title=\"↓2\", text=\"↓2\", style=shape.triangledown, location=location.abovebar, color=color.rgb(0, 0, 0, 80), size=size.tiny)\n",
+    "//plotshape(is_1m and bull3cp_t1, title=\"↑1\", text=\"↑1\", style=shape.triangleup, location=location.belowbar, color=color.rgb(0, 0, 0, 80), size=size.tiny)\n",
+    "//plotshape(is_1m and bull3cp_t2, title=\"↑2\", text=\"↑2\", style=shape.triangleup, location=location.belowbar, color=color.rgb(0, 0, 0, 80), size=size.tiny)\n"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "8e44ceb8",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "978dadc8",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Pinescript Folder/3-Minute Engulfing Pattern (3mEP) - 1m Visible/3-Minute Engulfing Pattern (3mEP) - 1m Visible.ipynb

@@ -0,0 +1,85 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c7ab3c10",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "//@version=5   \n",
+    "indicator(\"3-Minute Engulfing Pattern (3mEP) - 1m Visible\", overlay=true, max_boxes_count=500, max_lines_count=500, max_labels_count=500)\n",
+    "\n",
+    "// === Retrieve 3-minute candle data ===\n",
+    "[o3, h3, l3, c3, t3] = request.security(syminfo.tickerid, \"3\", [open, high, low, close, time])\n",
+    "\n",
+    "// === Detect Bearish Engulfing (Bear-3mEP) ===\n",
+    "bear1 = c3[1] > o3[1]                      // 1st candle bullish\n",
+    "bear2 = c3 < o3                            // 2nd candle bearish\n",
+    "bearEngulf = bear1 and bear2 and c3 < o3[1] and o3 > c3[1]\n",
+    "\n",
+    "// === Detect Bullish Engulfing (Bull-3mEP) ===\n",
+    "bull1 = c3[1] < o3[1]                      // 1st candle bearish\n",
+    "bull2 = c3 > o3                            // 2nd candle bullish\n",
+    "bullEngulf = bull1 and bull2 and c3 > o3[1] and o3 < c3[1]\n",
+    "\n",
+    "// === Convert the 3-minute pattern to 1-minute bar timing ===\n",
+    "// If on lower timeframe (like 1-minute), we’ll find the first bar >= 3-minute candle time\n",
+    "patternTime = bearEngulf or bullEngulf ? t3 : na\n",
+    "patternTimePlot = request.security(syminfo.tickerid, \"3\", patternTime[0], lookahead=barmerge.lookahead_on)\n",
+    "patternOccurred = ta.change(patternTimePlot)\n",
+    "\n",
+    "// === When a new 3m pattern occurs, find its matching 1m bar ===\n",
+    "if bearEngulf\n",
+    "    // find the first 1-minute bar matching that 3-minute candle time\n",
+    "    bi = ta.valuewhen(time >= t3, bar_index, 0)\n",
+    "    if not na(bi)\n",
+    "        // Draw rectangle for 1st candle’s body (from 3m data)\n",
+    "        box.new(left=bi,  right=bi + 6,  top=math.max(o3[1], c3[1]),  bottom=math.min(o3[1], c3[1]), bgcolor=color.new(#ceaf00, 55),  border_color=color.new(#ceaf00, 0))\n",
+    "        // Mid-body line\n",
+    "        mid_bear = (o3[1] + c3[1]) / 2\n",
+    "        line.new( x1=bi, y1=mid_bear, x2=bi + 6, y2=mid_bear, color=color.new(color.red, 0), style=line.style_dotted, width=1 )\n",
+    "        // Label\n",
+    "        label.new(bi, h3, \"\", style=label.style_circle, color=color.new(color.red, 0), textcolor=color.white)\n",
+    "\n",
+    "if bullEngulf\n",
+    "    bi = ta.valuewhen(time >= t3, bar_index, 0)\n",
+    "    if not na(bi)\n",
+    "        box.new( left=bi,   right=bi + 6,  top=math.max(o3[1], c3[1]),  bottom=math.min(o3[1], c3[1]),  bgcolor=color.new(#ceaf00, 55),  border_color=color.new(#ceaf00, 0))\n",
+    "        mid_bull = (o3[1] + c3[1]) / 2\n",
+    "        line.new(x1=bi, y1=mid_bull,x2=bi + 6, y2=mid_bull,color=color.new(color.green, 0), style=line.style_dotted, width=1 )\n",
+    "        label.new(bi, l3, \"\", style=label.style_circle, color=color.new(color.green, 0), textcolor=color.white)\n"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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tQw89pClOmuVouhrKkelcGnVmKSJTBUW8TTm6AJdXUN67rQrapMXV1nQVP/nkk9UWCsW1nPasbajtbarj5GhUOcq0BHW1f5XSTswU1yqKmGmipitfa2kV5TvT1bVR3bTrwlD5Ar5z5059LUBFZJqlmpqutkz5KqKgtZVvpuUUkSlBpoZMDZkaqmCrqK25quA9yz/ivUNV1nRn4nBBj0dT9DULZ1calWnIyso7yymuZGdFJ66aSpM5EWV6mxK0tEVUQcS0K+uaV3DhwoXy1jXvvQcVVxHFNdEekLahfG1bi2rUTENKUJqSLaIi6vqb0rQNVbAhbc/y1bCIJjrVLCLvPUttJShNcX9TZe3QamqH2qeToyk+EY0qYsnalT5LOMneDU20HK2r1b2HaEMAAhCAAAQgAAEIQCApBEbXwEnZlhbV5ZvuSIPfRykNiwkBXULqBlL3dbpX9P6m7pgUj20RXWY2NjZeccUVsS0bVjV9cH7k8J+wZsUgmRIQgAAEIAABCEAABjpT0wAAEABJREFUAhCAQHQERtHAurrRJY8uiKJbZXi2boR0caTro+FOCP9pUd1KhTUlhKqkBCagu0S7r5NXO3CSO6L6mLz77rtD/0CKx671wamPTPl4FKdmYAJEIQABCEAAAhCAAAQgEAsCo2jgWCxBDQhAAAIQiIIAUyEAAQhAAAIQgAAEYkcADRw7llSCAAQgAIHYEqAaBCAAAQhAAAIQiDUBNHCsiVIPAhCAAAQgED0BKkAAAhCAAAQgEB8CaOD4cKUqBCAAAQhAAAKREWAWBCAAAQhAIJ4EgmngPfyBAAQgAAEIQAACEEgYARaCAAQgAIEYEQgiooNp4CDTGIIABCAAAQhAAAIQgEDMCFAIAhCAQKIIjK6Bx/MHAhCAAAQgAAEIQAACEIgTAcpCAAIxJTCqlB5dA49aggQIQAACEIAABCAAAQhAAAJhE2ACBJJBAA2cDOqsCQEIQAACEIAABCAAAQhkMgHOnjwCaODksWdlCEAAAhCAAAQgAAEIQAACmUYg2edFAyf7CbA+BCAAAQhAAAIQgAAEIAABCCSKQDI1cKLOyDoQgAAEIAABCEAAAhCAAAQgAIFhAmjgYQqJ/48VIQABCEAAAhCAAAQgAAEIQCDxBNDAiWee6StyfghAAAIQgAAEIAABCEAAAskigAZOFnnWzUQCnBkCEIAABCAAAQhAAAIQSC4BNHBy+bM6BDKFAOeEAAQgAAEIQAACEEgPAu3t7fX19T5n6e/vr6ysLCoqKi8v7+3t9R5VsqY4CcrZsGGDd4La3qNTp05tb29XsKmpSckyNdSV1dTUqCvzr6DREA0NHCIo0iAAAQhESIBpEIAABCAAAQhAIG0ISN8uXrx4165dPidatWpVbW1tX1/fnDlzWlpanFHJ15/+9Kfq7tix48wzz1RCa2vrypUrVUdBH9NEJfT09FRUVEgGt7W1dXd3d3Z2rlmzRl3TvUoIUsGnYMAuGjggFoIQgAAEIBADApSAAAQgAAEIQCDNCDQ0NEjl+h+qrq5uwYIFPnEJ102bNp177rmKS9Y2NjaqEaJJ6M6cObOgoGDKlCl2t7x27dqqqipNLy0tnTZtWkAVrdFRDQ08KiISIAABCEAAAmETYAIEIAABCEAg/QjoUresrMyEqMfvj0SpxKpG6+rqNKju0qVLv/WtbxUWFqrrmMSt0qRsnYjTWLRoUVFRUWVlZX9/v65/nbga27dv7+rqUsNs586dqm/tcD0aOFxi5EMAAhCAAAQgEIwAYxCAAAQgEA8Cu2ddHI+yodfUpW5bW1t1dfVIUyRrOzo6dFsrqawc3RjX1tYqqLZjKrJmzRoNORGnUVJSIt3b3d2tyJYtW+TjZGjgOIGlLAQgAAEIQAACGUeAA0MAAhBIVwK6mNWl7vr164uLi3Vb29LSYkLX/7w1NTWSylLCylGm8jVr8eLFura1IsuWLfMRxipSUFDQ3NysuBomhnXhrLhjM2bMUNzp6m5ZyU43rAYaOCxcJEMAAhCAAAQgAAEIBCBACAIQSG8Ckqbr1q3rO/zHxG1dXZ1kbXl5uYlh++HPgtDa2jpx4sSqqqrDuX2617300kvvuusuSdZVq1YpYe7cufIySeLKykppZrUdU7Crq0sCWLZp0yZ1tYoiFRUVikhaK3PHjh3ypaWl8hEYGjgCaEyBAAQgAAEIQAACEIDAqwT4CwIZS0CydsmSJZ2dnSKwYsUKXRQXFRWtXr064Fud29vbb7311gceeEA3w0pramrSLMekdaWHFdeo7nsXHP6jhrqSvvae6urqaolh5SxcuFCSW7LcmR5WAw0cFi6SIQABCEAAAhCAAAQgAIFXCfBXZhKQPm1ubrazS6DqyldtKVK7KO7o6JA2VsRM8bvvvlu3uLKenh67HJbXNbKGNMVKWVtxmUU0XQ11ZVpRXSdHdVRNkcgMDRwZN2ZBAAIQgAAEIAABCEAAAplLgJOLgK52N2/ebAJV3VQxNHCqPCn2CQEIQAACEIAABCAAAQhAIPkEnB3oMraxsdHppkoDDZwqT4p9QgACEIAABCAAAQhAAAIQgEC0BKLRwNGuzXwIQAACEIAABCAAAQhAAAIQgEAiCaCBI6PNLAhAAAIQgAAEIAABCEAAAhBIPQJo4NR7ZsneMetDAAIQgAAEIAABCEAAAhBIVQJo4FR9cuw7GQRYEwIQgAAEIAABCEAAAhBIbQJo4NR+fuweAokiwDoQgAAEIAABCEAAAhBIBwJo4HR4ipwBAhCIJwFqQwACEIAABCAAAQikDwE0cPo8S04CAQhAINYEqAcBCEAAAhCAAATSjQAaON2eKOeBAAQgAIFYEKAGBCAAAQhAAALpSQANnJ7PlVNBAAIQgAAEIiXAPAhAAAIQgEA6E0ADp/PT5WwQgAAEIAABCIRDgFwIQAACEEh/Amjg9H/GnBACEIAABCAAAQiMRoBxCEAAAplCAA2cKU+ac0IAAhCAAAQgAAEIBCJADALuJZB/x03u3VzK7gwNnLKPjo1DAAIQgAAEIAABCEAgKgJMThkCu2ddrL16e3WxyAiggSPjxiwIQAACEIAABCAAAQhAIHUJpMDO7RLYfApsN3W2iAZOnWfFTiEAAQhAAAIQgAAEIAABCERLINPno4Ez/SOA80MAAhCAAAQgAAEIQAACqUKAt0NH96SGZ6OBhynwHwQgAAEIQAACEIAABCAAAdcSmLB1o5lrd5hCG8tUDZxCj4itQgACEIAABCAAAQhAAAIQgECMCKCBYwQyhcqwVQhAAAIQgAAEIAABCEAAAiMQ2D3r4hSzOZX7Vv90hNMECKOBA0AhlLYEOBgEIAABCEAAAhCAAATcQSDID3wOMuSOvbtsFwcO7P9xS+h7QgOHzopMCKQyAfYOAQhAAAIQgAAEIACBdCVw4EDoJ0MDh86KTAhAIDUJsGsIQAACEIAABCAAAQi8RgAN/BoJ/oYABCCQfgQ4EQQgAAEIQAACEIg1gbHXfXj8T77rUzXnTRVH/O7uCVs3HvHHX+ctnK9RpU24f70iBS2r1JWN//at6srUUNfbnGSNylRqXEO9TXcKjlv6GQ2ZOTW9i4TYRgOHCIo0CEAAAhBIKQJsFgIQgAAEIACBOBCQvs37wHuzxuT51B579QcO/vlvu2ddPPTc83mVF0kS514wZ+/NX9/b0Jg1qUgSV5ZderK6ez5Rn3XcFHW9K+y7487dcy7VdI0OvfCiSmUffdSBe+5V5NBDnWM+fIUla0gJCvYvqrZIBB4NHAE0pkAAAhCAAARcTICtQQACEIAABOJGQHJUKte//J5P3iDF68QH/9YumSoR60SyjjnKc+DAoRdeHPxb+9CzvTkzznCGvBt5l1yg7oHfb/YpqGCsDA0cK5LUgQAEIAABCEAg2QRYHwIQgIDLCOjG0mU7imo7479969CLLw8++K+AVXRFfMQff61RyVdLGLf0M2Pra/c1fV/XvEMvvOTJy8s+5mhdEWefWGIJPl5Duee/SZfA0sk2pIiSVdO6WcccPf47jRO2blRli0TgR9HA+1e9+nuWnIdHQ5SBAAQRkPGRAAQRkPGRAAQRkPGRAAQRkCXnI2HWxVpaxupAEAEZHwlpA+H5H9yvs8icZ6q2mROJecPq+/ix130467gp+378c5+409Wt7yvveHfOWadLKltQN8P7Gm8fW/dRzZUMPrTj8XEN9RKxkrKHep6zHG+fe+7ZQ/sP6BLYCY69+gNDr/SrjkUG/7pNhz3wmw2SypLHFgzXj6KBx1RfZRUltWkAQR8DQACCCMj4SACCCMj4SACCCMj4SACCCMiS85GwdaOWlrE6EERAlrofCfl33OS/+ckfmaNDyWzI3ztDMWyolI9JcOa965Ls4qlSsHnvWpB90gmO0PXJPLj5fkll5VtcwljS1975rPthU7BDewYG/7HdErx97gVzhp7tdS6BpZyzS0/ef+cvLEdKWBXUdq6U1Y7ARtHAEVRkCgQgAAEIQAACEIAABCCQCAKskY4EpIR1LPmB65abqZt0ky595bIrpWBluoY99MRTkqP25mcTw+N/8l1JVu1zWO7u3599zNEFLaukhGXZJ5Y4t77jln5GEvrgvfcduOdeDR3xu7uVplkyVcuaVDS4/Z9qyzQq1S39rEx1vS3rtW8t9g6G3kYDh86KTAhAAAIQgAAEIAABCEDAHQTYhQsISJ0eeqgz6+gjtZd937pDklXX1NlnlunmVkO6EB7f9GXdGztvZpbKzZ3/dklo3ehqio/lnDtj6N99++640+Jjr/5A1jFH57z5HNWccP96CWzp5+H21o2qc+A3v5cst8xwPRo4XGLkQwACEIAABCAAAQhAAAIQiA0BXauahVEu2alSsP2v/WoiXfDaD6ySIrWL4lfe8W4JYO1RatZ+3ZGTrLhGNV2jMpvijCrutDWqe2YjM+znXKpqShhuz7pYZdVVTmSGBo6MG7MgAAEIQAACEIAABCAAAQhESCD/jps007waKWq6m80+bbqkaeL2H4uV0MCxoEgNCEAAAhCAAAQgAAEIQAACGUZAl7F7PvTxlDt0qmrglAPNhiEAAQhAAAIQgAAEIAABCERPYMJrPws9+lKZWQENnHrPnR1DAAIQgAAEIAABCEAAAi4kEM17m22ueRceLZ22hAZOp6eZ/mfhhBCAAAQgAAEIQAACEIBAsgiM/c+Pjv/eV2Vq2B7GLb9eXdmYxf9hER+vBGdIDWXKnOk+yYnpooETw5lVIBAtAeZDAAIQgAAEIAABCGQCgYHrluuY5tVwj+W985Kso47c1/T9/T9uyT5+qrrStNreno997sD6e3POPjN31nnqepsEcPbUYy2i0eyTT9B0JWu6uhZPvEcDJ545K0IAAuERIBsCEIAABCAAAQhAIOkEsk8+ceillwcffvTg1r8P7X4lq3CiNO2hx5/Sxg498tjQwL6syUdLFY+//SuSxwrqsvdQd89Q3y61ZZq196bbNF3t5BoaOLn8WR0CEIBAMAKMQQACEIAABCCQigR2z7o4FbcdfM9ZhRO8E7Knn5yVn+9EsvJypYqdrhr7vvn9wfu3quFtEsa5b525f/VdksTe8US20cCJpM1aEIAABCAQKgHyIAABCEAAAhlLwIVvhA7xWey/61d7aj9/4Le/Hylfwvjgn9rGLFnMe6FHQkQcAhCAAAQgkHEEODAEIAABCEDAhQSGdu723tWhRx8fGhhwIkMHDg7tfPVtz04wYEMKeWj3K9mlJwccTUCQe+AEQGYJCEAAAhCAAARCIkASBCAAAQi4lsDQSy9nT5mcc9p0XeFm5ecf2vH40M7d2SefoA1nn3qK/KFHHvP+fmBFvC3vnZeMu7FOkeHpE44YCk0wKz/mhgaOOVIKQgACEIAABCAAgbAJMAECEICAywnsv+tXuvgdW/fRMVcvGnyo8+DWvx/c8AeJ4fHf+2rexe8Y/Os/gv+8K13/HurqVrKmH3qmR91knRcNnCzyrAsBCEAAAsJ2gBQAABAASURBVBCAAAQgMEyA/yCQZgTsB2KZD/1oA9ctNwt9SuIz9950256PfU4mPazVJXoHbvySus73ACvutJ0EBdWWqTGc/LHP7fvm99VNlqGBk0WedSEAAQhAAAIQgAAEMp0A508nAvl33KTjmFdDZjLYvLpm3gmKTNi60fFqYAkggAZOAGSWgAAEIAABCEAAAhCAAATeQICOQ8BksNOlEW8CaOB4E6Y+BCAAAQhAAAIQgAAEIJBBBEzTmg94bILJJYAGTi5/VocABCAAAQhAAAIQgAAEoiXg8wbjaMsxP24E4lR4TPVVoVdGA4fOikwIQAACEIAABCAAAQhAAAKjE/C/BLbvCjatLj9w3XJVMa+Gq0ybD26Pfb/xS2+v8MnJ3vw/i47OK3m47fz+Z/t//RONKu3Upx9U5HPnnOzdVUSmZE1R3ExtRRSXaZbmKv6DD1yqrkwNdWUqpa7sgS9+Rl3Hxi5JAQ3sqqfMZiAAAQhAAAIQgAAEIAABCEBgdAK9vb2LFy/etWuXT+qqVatqa2v7+vrmzJnT0tLS39/f3Ny8bdu2zs7Ojo6O9vb2ioqKnp4eJXR3d8+ePVvJBQUFPkU0UQlKU7KmtLW1KVkV1qxZo+6GDRuUr4TW1taVK1dqJ+pGYNwDRwAtyilMhwAEIAABCEAAAhCAAAQgkJIEGhoapHL9t15XV7dgwQInLn0rDTxlyhQn4jS2bNmi9ty5c+WDmITuzJkzVUdFysvLpXjXrl1bVVWlKaWlpdOmTVNE7QgMDRwBNKZETICJEIAABCAAAQhAAAIQyDgC9kZo8yl9+KamprKyMhOiHr8/EqUSqxqtq6uzQd3czp8/X7e4ute1iO6Hb7/99oCXwEpYtGhRUVFRZWWl0nT9q4hj27dv7+rqcro7d+7Uck43rAYaOCxcJEMgYgJMhAAEIAABCEAAAhCAQAoTaG9vb2trq66uHukMurDt6OjQba2ksuXoZvjee++9/PLLNdciO3bsKCwsDHgJXFJSIt3b3d2tTLsrViMehgaOB1VqQgAC3gRoQwACEIAABCAAgUwkMGHrRh3bvBopbbqYXbp06fr164uLi3Vb29LS4ghdn3PV1NRIKivf4hLG0sCtra3WbW5utnc4W9fxBQUFGlKyGiaGdeHsjKoxY8YMxdUwk5BWsrXD9WjgcImRDwEIQCB0AmRCAAIQgAAEIACBdCAgabpu3bq+w38kgCWD6+rqent7y8vLTQzX19fbZa/k7sSJE3fv3i0xLCUs27Rpkwla5euieN68eUZEQ5WVlUqzrnkFu7q6lC/TRHU1S5GKigpFdMmsNF0my5eWlspHYGjgCKAxBQIQgAAERiVAAgQgAAEIQAACaU5AN7FLlizp7OzUOVesWKGL4qKiotWrVzc0NGhI4nb69Om6N9b97YLDPy5LIlmaWWpW+T4mrSs9rOlOvqZoorqSvrW1tSpYXV0tMaychQsXSnJLlvsUCbGLBg4RFGkQgAAEIACBEAmQBgEIQAACEHiVQHq8EfrVw7z2l9Rpc3Oz9SRQq6qq1JYitYti3fRKryoirdtz+JchOcneE5VgU2zU2oevmfssogQ1LKKJ6jo5KqviikRmaODIuDELAhCAAAQgAAF/AkQgAAEIQCCDCOhed/PmzSZQU+jYaOAUelhsFQIQgAAEIAAB1xJgYxCAAARGJGC3wfID1y03U6oa8iltuoxtbGxMuSOggVPukbFhCEAAAhCAAAQg4CoCbAYCLiKQf8dNLtoNW3ElATSwKx8Lm4IABCAAAQhAAAIQSAECbBECEEg9Amjg1Htm7BgCEIAABCAAAQhAAALJJsD6EEhVAmjgVH1y7BsCEIAABCAAAQhAAAIQSAYB1kxtAmjg1H5+7B4CEIAABCAAAQhAAAIQSCSBzP6W40SSjtdaaOB4kaUuBCAAAQhAAAIQgAAEIAABCLiNQKQa2G3nYD8QgAAEIAABCEAAAhCAAAQSSGD3rIttNW6GjUOqeDRw+E+KGRCAAAQgAAEIQAACEICAuwk4AjW22zS5a94qW9u8RfAuJ4AGdvkDctn22A4EIAABCEAAAhCAAAQg4PGYxjZvPAauW25mXbxrCaCBXfto2JjLCLAdCEAAAhCAAAQgAAEIREpgwtaNkU5lXowJoIFjDJRyEEhDAhwJAhCAAAQgAAEIuJiAvQ9ZXqZtytv1rLdXHIOAEUADGwc8BCAAgUAEiEEAAhCAAAQgkDoE7K3I8qmzZXaaBAJo4CRAZ0kIQAACKUCALUIAAhCAAAQgAIF0JIAGTsenypkgAAEIQCAaAsyFAAQgAAEIQCB9CaCB0/fZcjIIQAACEIBAuATIhwAEIAABCKQ7ATRwuj9hzgcBCEAAAhCAQCgEyIEABCAAgcwggAbOjOfMKSEAAQhAAAIQgMBIBIhDAAIQyCQCaOBMetqcFQIQgAAEIAABCEDAmwBtCEAg8wiggTPvmXNiCEAAAhCAAAQgAAEIQCA6AhO2blQB82pgKUQADZxCD4utQgACEIAABCAAAQhAAAKjEzBpKr971sXKNq/GqxbTvwauW6565tXIBGtvb6+vr/c5aX9/f2VlZVFRUXl5eW9vr0aVNnXqVEVqamrUdRIUUVyjCnqbIoprVHWUrKGmpiZ1ZWqoK1MpdWUbNmxQNzJDA0fGjVkQgAAEIAABCEAAAhCAQEYTkMZOvfNHvWPp28WLF+/atcun0qpVq2pra/v6+ubMmdPS0iIR29zcvG3bts7Ozo6Ojvb2dsvXkHJ6enoqKiosYl75jY2N99xzj0ZLSkpUTVPa2tq6u7tVYc2aNeqa7lVCa2vrypUrtRObG65HA4dLjHwIQAACEIAABCAAAQhAAAIZSqChoUEq1//wdXV1CxYscOIFBQXSwFOmTHEiwRs7duxQQmlpqXxVVZV0r4TuzJkzVUdF7G557dq1GlKC0qZNmxa2BtbMw4YGPowBBwEIQAACEIAABCAAAQhAAAJBCTQ1NZWVlZkQ9fj9kSiVWNVoXV2dDermdv78+brFdW59Fy1aVFRU5Lzb2dLkNXfnzp1qmHV1den22Nrmt2/frqC15ZWsKWpEYJmogSPAxBQIQAACEIAABCAAAQhAwG0E8u+4yW1bSuP9tLe3t7W1VVdXj3RGXdhKuOq2VlLZcnQzfO+9915++eWaq0hJSYkueLu7u9XesmWLfFIMDZwU7ElblIUhAAEIQAACEIAABCCQHgRMAJv3P1FmfrOuP4dYRfr7+5cuXbp+/fri4mLd5ba0tDhC12eJmpoaSWXlW1zCWBq4tbW14LV3R6thYtgSzCutsLDQ2vJK0JWyGo7NmDFDQaerZE1xumE10MBh4SI5hQmwdQhAAAIQgAAEIACBtCRgP5PZfFoe0A2HknBdt25d3+E/EsCSwXV1db29vVKqJobr6+vtsldyd+LEibt375YYlhKWbdq0qayszDmFIl1dXYqoUVlZqTQNSdA+/fTT9l3BukmuqqpSgiYqR6sov6KiQhENKdnSSg9/87C64RoaOFxi5EMg9QiwYwhAAAIQgAAEIACB+BHIWPkt4bpkyZLOzk6xXbFihS6Ki4qKVq9e3dDQoCGJ2+nTp+veWPe3CxYskJqV4lWCE9Esx5S/bNmyefPmKUFB5cs0UcmSvrW1tUqorq6WGFbCwoULJbkly5UZgaGBI4DGFAhAIGUIsFEIQAACEIAABDKBwKgqdPesi80ygUa8zyh12tzcbKtIoOrOVm0pUrso7ujokF5VRDe3PT09ujm2ZCfBJ2KjyldZDcmciBrqyjSkBKeCyqq4IpEZGjgybsyCAAQg4HYC7A8CEIAABCAAAQjElUB7e/vmzZtNoMZ1odgWRwPHlifVIAABCEAg+QTYAQQgAAEIQMAI2E/GMm+R4F53xcETGPUmoMvYxsZG70hKtNHAKfGY2CQEIAABCEAgJAIkQQACEHAJgZF+XLNLtsc2MpkAGjiTnz5nhwAEIAABCKQPAU4CAQhkDgEEduY863icFA0cD6rUhAAEIAABCEAAAokjwEoQgAAEIBA6ATRw6KzIhAAEIAABCEAAAhBwFwF2AwEIQCBcAmjgcImRDwEIQAACEIAABCAAgeQTyNgd2BuhzYcCIfQfiBVKNXLSgAAaOA0eIkeAAAQgAAEIQAACEIBABhGwow5ct1wN82pgEAiRABo4RFCkQQACEIAABCAAAQhAAALpSSD0W+Wkn58NRE8ADRw9QypAAAIQgAAEIAABCEAAArEhYHJ096yLzWJTNBZVtJ9YlKFG5ARiNRMNHCuS1IEABCAAAQhAAAIQgAAEoiJgAth8VIWYDIGRCaSiBh75NIxAAAIQgAAEIAABCEAAAmlKwF8bx/abge2m13yaIuRYwwTQwMMUUuc/dgoBCEAAAhCAAAQgAAEIxJKASWvzsaxLLbcSQAO79cmwL18C9CEAAQhAAAIQgAAEIJAgAtwGJwh0MpZBAyeDOmtCIDwCZEMAAhCAAAQgAAEIvErA3v9s/tVQPP/ifjiedJNTGw2cHO6sCgEIhEaALAhAAAIQgAAEIDBMwLSo+eF+FP+Ffsdry8nbFPNRrMxUVxBAA7viMbAJCEAAAn4ECEAAAhCAAATSgYAEZBocY8LWjTqFvO6fzdTFUpQAGjhFHxzbhgAEIJDGBDgaBCAAAQikIYEgl6hu08m2VfNp+CQy/kho4Iz/EAAABCAAAQi4iABbgQAEIJBWBEzcypueNB/9CXUTG24RXeGaBZmoBI2aVwNLVwJo4HR9spwLAhCAAAQgkFoE2C0EIAABj4lb83HCYTrcfJyWoKzLCaCBXf6A2B4EIAABCEAAAmlPgANCIKMJ6JZY55d/8MWOvz+3Te2u3V3yjmnIaSexwf1wEuHHdmk0cGx5Ug0CEIAABCAAAQhAIHQCZKYJgchk6ksDL/36sd86CG7e+pWv/u1r6n7mvnp5s+f3vGANPARiRQANHCuS1IEABCAAAQhAAAIQgEDoBDI3U4JZyvYb//jW1b9fcudDP3VA9B/ol6m79+BeebPqTTVq9PT3yGMuIdDe3l5f//rXKWxX/f39lZWVRUVF5eXlvb29Cipt6tSpitTUDD9EJ0GRDRs2KMHbvEc1q729XaNNTU1Klqmhrkyl1JX5V9BoiIYGDhEUaRCAAAQgAAEIQAACEIBATAh4au79xOan/6haBw4dkA9iG587W6Mn/2zVL+efp0Zcv1VY9XnDsyAEN+nbxYsX79q1yydt1apVtbW1fX19c+bMaWlpkaZtbm7etm1bZ2dnR0eHNO2OHTvOPPNMJbS2tq5cuVJ1fCqoq4lK6Onpqaio0JS2trbu7m5VWLNmjbqme5UQpIKKjGpo4FERkQABCEAAAhCAAAQgAAEIhEFAN73KNq+Gt31723fV9Za+8ycPfw+weQ05DbW97Sfbv6futrVZI5lGU8FSfo8NDQ1Suf7HqKurW7BggRMvKCiQBp4yZYoTkazXnQ68AAAQAElEQVRtbGx0uqM2JHRnzpypOipid8tr166tqqrSxNLS0mnTpgVU0Rod1dDAoyIiAQIQgAAEIAABCEAAAhAIiYCP7rVrW/M2f0v3A9a49/lzzKw7qt/ryVPO/5twbXH5bf6mISwBBJqamsrKykyIevz+SJRKrGq0rq7OBnVzO3/+fN3iSgB7PBbzSNwqTcr21b7XX4sWLSoqKqqsrNQ1sq5/vUY827dv7+p6/Yel7dy5U8t5J4TeRgOHzopMCEAAAhCAAAQgAAEIQCAqAnsO7ol4vq6IH+nfeeezj/6w48dWxGlYFx9XAu3t7W1tbdXV1SOtIlnb0dGh21pJZcvRzfC99957+eWXa65F1JAk1mWydb19SUmJdG93d7eCW7ZskY+TJUMDx+kolIUABCAAAQhAAAIQgAAEkkfALoHNx2kXA4cG/9r3Qtnxl1r9a8uvtgY+3gR0Mbt06dL169cXFxfrtralpcURuj5L19TUSCor3+ISxtLAra2t6iqoIsuWLVNQXW8reO2902qYGNaFs3fCjBkzFHcihYWF/kWc0eANNHBwPrEepR4EIAABCEAAAhCAAATSnYC9+dm8zjo4NChvdu/z56hhXo0ITDL4Z8889HjH57s7rncsgjpMCYuApOm6dev6Dv+RAJYMrqur6+3tLS8vNzFcX1+vO17VlNydOHHi7t27JYYlemWbNm0yQbtq1SolzJ07V16mocrKSqWp7ZiCXV1dypdporpaRZGKigpFdMmszB07dsiXlpbKR2Bo4AigMSUiAkyCAAQgAAEIQAACEMhIAr957HfOuee/9hOwnIYz5N248sYCdeUDpg1mj3/stJ+cUzXkbcoP13bPujj4FJPx5oNnZuaobmKXLFnS2dmp469YsUJ3vEVFRatXr25oaNCQxO306dN1b6z72wULFkgh33rrrQ888IAiSmtqatIsx6R1pYcV16jla4oa6kr61tbWqmB1dbXEsHIWLlwoyS1Z7kwPq4EGDgsXyRCIiACTIAABCEAAAhCAQAYT+O8da6VmBcC8GmGZyWDvKQcOHfjt46/rau+h0NsmgM37zLLfkGTeZ4iuCEidNjc3qyGTQK2qqlJDitQuijs6OqRXFdHNbU9Pj26OLdnpKiLTNbJNsVFrKy6ziCqooa5MK6rr5KisqikSmaGBI+PGLAhAIDQCZEEAAhCAAAQgkPEE9g3uizmDQ0NDW3vaIihrytZ8BNOZ4k1AV7ubN282geodd3kbDezyB8T2IACBlCXAxiEAAQhAAAIZTMD7/cMDBweCk/C/6Q2er1HV3Nr7VzWwJBLQZWxYv/I3iVv1XhoN7E2DNgQgAAEIxIIANSAAAQhAAAJ+BCJ7I7RfmdcD/3rpX6934tbyFvNxW4TCCSWABk4obhaDAAQgAIE0J8DxIAABCEAgUQReHHgpUUuxTloRQAOn1ePkMBCAAAQgAIGkEWBhCEAAAoEILJjyYKDw6LFR7433Du7dN7h/9EJkQOCNBNDAb+RBDwIQgAAEIAABCIRLgHwIQGBkAkNDh0YajODbgL1LZWdlHxoa9I7QhkAoBNDAoVAiBwIQgAAEIAABCEAgEAFiriGQf8dNydqLLS1v3zpr3tlMXs4Yp22NKKWvFZHP8mSNyx2nBgaBsAiggcPCRTIEIAABCEAAAhCAAAQOE8CFRqBwTGFoiWFnTRwzQTI47GlMyHgCaOCM/xAAAAQgAAEIQAACEIAABMIiEE7ySYUnhpMeRu60idPCyCYVAq8RQAO/RoK/IQABCEAAAhCAAAQgAIFYE3jTseeNzRkb66qevOy8mVPeHPOyoxckI/UJoIFT/xlyAghAAAIQgAAEIAABCLiVwJuOPffAoQMx392QZ0iVIys7YevGUSeGkjNqkXRLSJfzoIHT5UlyDghAAAIQgAAEIAABCIRGID+BP0DrqPyjzplcEdq+wsg6bdKpxx1xXBgTSIXAawQi0cCvzeVvCEAAAhCAAAQgAAEIQCAMAokUn2FsK86pV5z2H2P8fjp0NGuOyxm7qOz90VRgbiYTQAOH9/TJhgAEIAABCEAAAhCAgJsJ7J51sdu2d/qRZXOKZ+dl58VkY9lZ2ecee27FMWfHpBpFMpAAGjgDH3qER2YaBCAAAQhAAAIQgEAKEXDVnXPNjOqisYUxoTdxzMSPn10TfSn7jl/5geuWm0VfkwopQQANnBKPiU0mmQDLQwACEIAABCAAAXcS8BG6dgls3tmwT9eJx6MhMRmwbH5u/g0zPxf9O6J1CXzDm68vHDsx4CoEIRAKATRwKJTIgUDmEuDkEIAABCAAAQi4n4ApYfOu3e1pk05d8ZZl43LHRbzDvOy85W9ZWn70mRFXYCIERAANLAgYBCAAgQAECEEAAhCAAATSnkAir4gFc8YxZzXOveX4CcePywlPCY/NGTul4NivzL35vGPPVR0MAtEQQANHQ4+5EIAABNKTAKeCAAQgAIGUIGAXv/ID4XxHq/J1OnkTwOYVSYydVHji9y68/V2nXJblycrOCkmMKHPhSZc0z//OqZNKE7NJVklvAiF92KU3Ak4HAQhAAAIQcAjQgAAEIJAeBCRxdRB5k7jmFXGJXXXGB1df3PzuU945YcyE/Nz83Oxcn43lZOcoXpBXcNnJl0r9fqT8mpysHJ8cuhCIjAAaODJuzIIABCAAAQikGwHOAwEIQCCRBCaPn3xt+dV3X/qTL751+YfPuOptx88946jTpxedIj+3eM6HTl+8/C1LWyrv+uiM6uOOmBqnjU3YujFOlSnrZgJoYDc/HfYGAQhAAAIQgEAiCLAGBFxOQNe5Pjv0j/gkxKMbp0VPP7Ksavq7Pvemz9w698vfmHebfP2bP/ve0ip+9lU8HmJMara3t9fX1/uU6u/vr6ysLCoqKi8v7+3t1ajSpk6dqkhNzau/y8qJKFP5yvE2/9GmpiZNl6lhmSqlrmzDhg0WicCjgSOAxhQIQAACEIAABCCQJgQ4RmoRGPUtzXaxad7/aKNO95/iREwAm4+mjlOQRooSkL5dvHjxrl27fPa/atWq2travr6+OXPmtLS0SOI2Nzdv27ats7Ozo6Ojvb1dkcbGxnvuuUc5JSUlyveu4D+qKW1tbd3d3aqwZs0adU33anpra+vKlSu1E+8KobfRwKGzIhMCEIAABCAAAQhAIH0IJP4kJiDlpSHN4reHgeuWq7h5NWRa0fFqRGyxqhPxBpiYXAINDQ1Suf57qKurW7BggRMvKCiQBp4yZYoT2bFjh9qlpcM/2KyqqkrKVl3H/EcldGfOnKk6KmJ3y2vXrtVETVGRadOmoYGFAoMABCAAAQhAAAIQgAAEXiVgAtjuhM2/OuDx0IBAZASamprKyspMiHr8/kiUSqxqtK6uzgZ1czt//nzd4lZUVGh0586dFpfv6urS3a8aZv6juj22IfPbt2/XFGvLq5SmqBGBcQ8cATSmQAACEIAABCAAAQhAIEEEdG+slcyr8ci/d1jbvCLxtoQtFO+DWH18xATa29vb2tqqq6tHqqALWwlX3dZKKluObobvvffeyy+/XHMt4gaPBnbDU2APEIAABCAAAQhAAAJpQiB+itHehDx1wSccUhdNblf7+3NOfmLnE2qYceVrHPD+BKKJ6M526dKl69evLy4uXrRoUUtLiyN0fcrW1NRIKivf4hLG0sCtra1qFBYWWlC+pKSkoKBADTP/UV0p25D5GTNmaIq15VVKU9SIwNDAEUBjCgQgAAEIQAACEIAABBJB4H8eXRtkmSHPkEZ/9cj/u/5/P3/j/cv+9dK/1MUgEA8C0qvr1q3rO/xHAlgyuK6urre3V0rVxHB9fb1d9kruTpw4cffu3RLDUsKyTZs2lZWVSbI+/fTT9n2/uiuuqqrSUGVlpdK0Yf9RTdFE5WiVrq6uiooKRTRRyVak9PC3FqsbinnnoIG9adCGAAQgAAEIQAACEIBAjAlEdjO8/cUHqzfW3N35i1B2s39wf8eLDy19YMWqB38QSj45EIgJAQnXJUuW2E+3WrFihS6Ki4qKVq9e3dDQoCGJ2+nTpxcXF+v+dsGCBYosW7Zs3rx5ytHqisg75j+qBE3UdEnf2tpaJVRXV0sMa/rChQsluSXLnelhNTJNA4cFh2QIQAACEIAABCAAAQgkiIC91dkWu+eJ3y9/4Iu9e57be3CvRbz9/Mnb1PX26soOHDqw8al7P9P6uZf3/ltdH5uwdaOZT5wuBMIlIHXa3NxssyRQdaOrthSpXRR3dHRIryqim9uenh7dHDvJmqiuzCI2xdrK9xlVRENKlmlIXctXV2VVXJHIDA0cGbdUnMWeIQABCEAAAhCAAAQSTcAugc2HuPZvHvvtjx66c3Bo0Cf/yhuHv3nSvM+Q05Vm3tH36I1blv47kAy2tIHrlptZFw+BiAm0t7dv3rzZBGrERRI/EQ2ceOasmHgCrAgBCEAAAhCAAASST0DKU5swr4aZjzz+32e2/OSfPxs4OKDRe58/Rz4Ce7a/50t/+bIzUde/TpsGBGJIQJexjY2NMSyYmFJo4MRwZhUIJIsA60IAAhCAAAQgkBwCPuI24CYsR97eCC3/zW3f/tHKlwMmhxV8qq/z5rVnb1ub5WNhFRk1WTsfNYcECLiNABrYbU+E/UAAArEiQB0IQAACEIBA6hHYP7g/lE3bNwMHydzvyf171vQ9Jy8vLr/NsSD5/kPoW38mRNKDABo4PZ4jp4AABCDgTYA2BCAAAQi4lMDAdcuD7+zuW/qVYF4NHwv+zcA+yQeGDv33c0/6BOlCAAJoYD4GIAABCEAgnQhwFghAAAKuIGCXqPImes0H2Zl9y25YEjdINWfoyYFXnhp45YcdP3YiARvap9nuWRebOWmKq21eDQwCaUAADZwGD5EjQAACEIAABDweDxAgAIHUI2DaWN5+CFbMD7D/0KG2nS9cW351zCt7F9T+1TWvBgYBlxNAA7v8AbE9CEAAAhCAAARGJUACBFKMgN36mretP/hihzVi7rftisFP2NKuTOKaV1dml8Pm1cUgkCoE0MCp8qTYJwQgAAEIQAACEPAnQCRNCDzy7x0+J7FfjGTeZyis7nP7B57o+Hx3x/Wy0CeasjUf+iwyIZASBNDAKfGY2CQEIAABCEAAAhCAgA8BV3ftvtR8KBt9YucTI6XZj4A2P1JOkHh+bv7kd+w4p2rILEhmwCE7gvmACQQhkHIE0MAp98jYMAQgAAEIQAACEICASwnYxal5Z4uhCMi+fTvtB2J5e6eCXyOMQHZW9u79u8OY4Jfq7N9p+KUQgEAqEUADp9LTYq8QgAAEIAABCEAAAmlJYHBo0Odcuvg1U1wN+YjNv3jEpVwwkS1AIFoCaOBoCTIfAhCAAAQgAAEIQAAC4RLw/oFYmpufky8fHxsalzsuhpVt5/K6FjZTcTXksTgToHxsCKCBY8ORKhCAAAQgAAEIQAACEIiYwLEFxzpz7e3QTjfKxoFDT718hgAAEABJREFUB48ad2TwIqZgzQfPZBQCSSIQy2XRwLGkSS0IQAACEIAABCAAAQiERcCU56mTSsfkjAlrYojJOVk5k8dPDpisu1yzgKMEIZCuBFJNA6frc+BcEIAABCAAAQhAAAIZTOD0I0/L9mTFA8BpR54aellJYiXLmzI3rwgGgXQikJ1Oh0nzs3A8CEAAAhCAAAQgAAG3EvD5WdDaZlgC8qTCkyaOnahZsbX83HHvKJkXSs2wdhtKQXIg4FoCaGDXPho25kWAJgQgAAEIQAACEHATAX/FG/3u3nnyZWNzxkZf540Vsi4ITQO/cdYoPV0Uj5LBMARcTAAN7OKHw9Yg4PF4gAABCEAAAhCAQGYQWHjSJTnZOTE8qxT15aXvDbcg+jZcYuSnHAE0cMo9MjYMgYwhwEEhAAEIQAACmURAkvWaMz80Pm98rA49YcyE/zj1fRFX493REaNjossJoIFd/oDYHgQgkJEEODQEIAABCKQUAXtrtLx0o1lk219w4iWnH1mWm50b2XTvWSryqXM+kZUVl5+z5b0QbQikHAE0cMo9MjYMAQhAIK0JcDgIQAACmU2g/k2fnTL+2CgZjM/Nv7b86nMmV0RZh+kQCEigvb29vr7eZ6i/v7+ysrKoqKi8vLy3t1ejNTU16sqamprU1aypU6eqa6ZkTVHcTG1FbEhpSlZcEy2ihroyp+aGDRvUjczQwJFxYxYEIACB+BLYPevi+C5AdRcSYEsQgEAKEtDdb2x3PT5v/E2zVxx/RLEuciOrPC533BWnvf+dJ1dGNp1ZEAhOQPp28eLFu3bt8klbtWpVbW1tX1/fnDlzWlpalKaE7u7uzs7ONWvWtLe3V1RU9PT0KEHB2bNnK7mgoEA53qaJSlCakjWlra1NyU4F071KaG1tXblypS3hPT3ENho4RFCkQQACEIAABCAQNwIUhkBqEjABLD9w3XKdwLwaUdox+cfc9vbGs4+ZMT437O8NlnL++NkffW9pVZR7YDoERiLQ0NAgles/WldXt2DBAic+ZcqU5uZmf5WrhC1btsjPnTtXPohJ6M6cOVMVVMrulteuXVtVVaUppaWl06ZNQwMLBQYBCEAAAhCAAARSjQD7hUAgAgV54xvO/8LVh39E1picMYFSfGO6/q045uzbL2h6Rxx+GZLvYvQzlUBTU1NZWZkJUY/fH4lSiVWN1tXVOYO6zi0sLJRqtUh/f//tt98e8BJYCYsWLSoqKqqsrFSarn8VcWz79u1dXV1Od+fOnVrO6YbV4B44LFwkQwACEIAABCAAAQjEiABlRiOw8KRL7rzkB4tPv/Lo/KMlccdkBxDD+bn5WVlZbzr2vGWzbvzS7IbjjygerSrjEIiQgNRsW1tbdXX1SPN1YdvR0aHbWklly5FMXbly5c0336zrXIvs2LFDkjjgJXBJSYl0b3d3tzLtrliNeBgaOB5UqQkBCEAAAhCAAAQgAIGRCYQ8Iun7nunv/tElq26Zs/Ka8g/NO/5tpx9ZduLEE04pOvncyee8r/Q9nz7vUz+/9Kcrzl929jEzQq5KIgTCJqCL2aVLl65fv764uFi3tS0tLY7Q9alVU1Mjqax8xRsaGi6//PKKigq1zZqbm+0dztZ1vESyhqSi1TAxrAtnZ1SNGTNmKK6GmYS0kq0drkcDh0uMfAhAAAIQgAAEIAABCCSawPSiUy47ufKzb/p049tuuf2CpqZ5X/viW5dffeaHzp/6loI83x8slOjNhbUeyalJQNJ03bp1fYf/SABLBtfV1emat7y83MRwfX29Lop1uNbW1okTJyp/w4YN999/vzIVNFO+LornzZtnXenkyspKaWbrmlewq6tLAli2adMmdTVLEQlpRXTJrDRdJss7769WOyxDA4eFi2QIQAACSSOQf8dNSVubhSEAAQhAAAIQiJ5A2lXQTeySJUs6Ozt1shUrVuiiuKioaPXq1br+lXC9/vrrn3nmGQlXBU3oSiRLM0vNKt/HpHWlh5Wpe2bd9y44/EcNdVWhtrZWa1VXV0sMK2fhwoWS3JLZPkVC7KKBQwRFGgQgAAEIQAACEIAABCAAAQh4pE+bm5sNhARqVVWV2lKkdlGsm17pVZkah6+Nh13z4Xzvic4UK+VMV7ZFlKCGujJNVNfJsV+epEhkhgaOjBuzIAABCEAAAhCAAAQgAAEIZDQB3etu3rzZBGoKgUi0Bk4hNGwVAhCAAAQgAAEIQAACIRKI1S8HDnE50iDgBgIVFRWNjY1u2ElYe0ADh4UrqmQmQwACEIAABCAAAQhAAAIQgEByCaCBk8s/U1bnnBCAAAQgAAEIQCBtCNgPKZS3u1/zaXM6DgKBtCeABk77R8wBk0yA5SEAAQhAAAIQgAAEIAAB9xBAA7vnWbATCKQbAc4TAQHdKvjM2j3rYp8IXQhAAAIQgAAEIACBiAmggSNGx0QIQAACIxKIcsB0r/koSzEdAhCAAATiSoA3QscVL8UhEA8CaOB4UKUmBCAAgUgI2CWw/IStGzVf3mSweUVSwtgkBCAAgTQmoE/RaXw6jgaBDCGABs6QB80xIQCB5BNAyib/GcR5B5SHAAQyhIDd/ZrPkCNzTAikEwE0cDo9Tc4CAQhkBAG0dEY85lQ7JPuFQCYQsEtg85lwXs4IgXQlgAZO1yfLuSAAAQhAAAIQSAQB1hiFwDPPeH77W88Xv+iprvYsWuS5/HLPFVd4rrnGc+ONnpYWT2fnKNPdOswlsFufDPuCwOgE0MCjMyIDAhCAAAQgAAEIQMCfwCiRv/7Vc8MNnuuv99x1l2f7ds/LL3sOHPAMDXkOHvTs2uV55BHPr37l+dKXPB//uOfee0cpxTAEIACB2BFAA8eOJZUgAAEIREpg1HfWWYL5SBdhHgQgAIFEEejpGb74/eY3PY8+Oqx7JXpHWnnvXs9zz3l+/GNPba2no2OkLBfG2RIEIJC6BNDAqfvs2DkEIJCqBKKRsvbNwOZT9fzsGwIQSG8CW7d6PvvZ4YvfgYFQD6rMZ58dvhP+zW9CnUJe8giwMgRSnQAaONWfIPuHAARSnoBJYvMpfxgOAAEIuIfA4497JCm//OXhK9Zrr/XIPvYxz/Llnrvv9vzf/8Vwm2/49NXa6vn61z379kVSX9fFv/qV52c/i2QucyCQEAIskh4E0MDp8Rw5BQQgkA4EBq5bbpYOh+EMEIBAEgls2uT5xCc8y5YN68m//92jK9adOz2y55/3PPSQ5//9P89Xv+r50IeGv023ry+W2/zrXz3f/e7wt/tGXHTPHs/vf+9ZuzbiAkyEAATiRCCdyqKB0+lpchYIQMCNBN5wQ+LGDbInCEAgjQj87W+emprhb6/t7R2+jNXNasDDDQx4+vuHf1yzkn/3u4ApYQdffNHzzW96BgfDnugzQRtrafH84x8+YboQgAAEYkUgXA0cq3WpAwEIQCA9CYSlePm23vT8IOBUEEgWgbvuGn4f8gsvePbuDWkLUsgHDnh+8Yvhn1/1yishTQmStGqVR9I6SELoQ9rV978fejqZEIAABMIigAYOHReZEIAABCAAAQhAwK0Evv51z8aNw3e/4W5wz57hN0jfeKNHF7nhznXyt2+P8U91lib/7W+d8jQgAAEIxJAAGjiGMNO4FEeDAARiRsAuis0HKTph68YgowxBAAIQeAOB73zHs23b8Nub3xANuTM4OPw9wytXRiKhbZH//u9QL58tf1Svq2zVHDWNBAhAAALhE0ADh8+MGZlFgNOmHgGXvMHYZxvWNW9MrW3eIkG8/aws+SA5DEEAAhlKYP16z1/+4tF1bpTn7+0d/obeyIo89FBk84LNOnDA87e/BUsIPjYw4HnppWFTI3gmoxCAQIYRQANn2APnuBAIg0Cmp456VesPyKbIm7KVV1tp5tUI1+w22Hy4c8mHAAQygsCzz3p+9KMYCGDBkubcvt1z331qhm15eZqy+5nd8jEzadc//zm8apLxv/ud5wtf8Hz4w56rrvJ88pPDpoa6y5d71q3zPP98eAXJhgAEAhFob2+vr6/3Genv76+srCwqKiovL+/VP0aPp6amRl1ZU1OTkp0ERaZOnaoiCnqbIoprVHWUrCFNVFemhroyp+aGDRvUjczQwJFxYxYEIJDeBDgdBCAAgRQhcNddnkOHYrZXyc6f/CSsavY1vvx5ZSaAzYdVIVjyP/8ZbNR77JFHPF/6kudTnxr+hU+a9cornqEhz/79w6aGurqp/ulPhyXxzTd7Hn3UeyptCEAgLALSt4sXL961a5fPrFWrVtXW1vb19c2ZM6elpUVpSuju7u7s7FyzZk17e7u6Mg0pp6enp6KiQl3HJHobGxvvuecejZaUlKiaprS1tXlXMN2rhNbW1pUrV9oSToXQG2jg0FmRCQEIZCgBXedm0sk5KwQgkDoEnnnG8/e/x3i7e/d6/vCHGNeMuNxLL42u8PUlgDvu8Oiat719+Dcz6TZ7pOU0NDg4/FuXli71/OAHI2URhwAEghNoaGiQyvXPqaurW7BggROfMmVKc3NzQUGBEwne2LFjhxJKS0vlq6qqpJwldGfOnKkKKmV3y2vXrtWQEpQ2bdo0NLBQYBCAAASSRgCdnDT0sVyYWhBIEIGYfca4775h1RfbXUsDr18f25KRV8vO9vT1BZv+7LOeT3/a09rqkb4NlvfGsYMHh3W+Jh5+u+Ybx+hBAALBCDQ1NZWVlVVVVXkC/ZEolVjVaF1dnTOu69zCwkKpVossWrSoqKjIebezBeU1d+fOnWqYdXV1dXR0WNv89u3bFbS2vJI1RY0IjHvgCKAxBQIQcCOBmL2mfOPhrKz5N47Qg0A6EeAsqUnggQeG3/Eb8713dQ3/KKmYl42goDRwEHH71FOeZcs8ugzfty/s2pry9NOvTg97MhMgkKEEpGbb2tqqq6tHOr8ubCVcdVsrqWw5kqkrV668+eabdZ2rSElJiS54u7u71d6yZYt8UgwNnBTsLAoBCERFwL79TCXiJ01tCfNaCIMABNKXQMqeTBcmL74Yl92PGeN55JFYVY7qO4SHhjyHf9pWgM3o+F/+skc+wFjIoX//23PzzZ5XXgl5AokQyFwC/f39S5cuXb9+fXFxse5yW1paHKHrA6WmpkZSWfmKNzQ0XH755fatv5LBzc3N0slqmBhWgmOK67rY6SpBV8pOV40ZM2YoqIaZkjXF2uF6NHC4xMiHAASSTMB0qbwJYPPJ2pO2oaVtD+bVldlPcpYfuG65mYIYBCAAgVgS0P3n2LHRFRxh9t69w5erIwwmNHzwoKewMPCK3/qW5+WXAw+FFX3pJY9KhTWFZAhkJAEJ13Xr1vUd/iMBLBlcV1ena14pVRPD9fX1uigWm9bW1okTJyp/w4YN999/vzIV9DbJ466urrKyMjUqKyulmTUqQfv000/bdwXrJrmqqkoJmzZtUo5WUb6EtCIaUrKlOe+vViQsQwOHhYtkCEAAAgEISOsqal6NgKZRM8a0V4oAABAASURBVEliJZhXA4MABDKHgH3VLGbn1R1mzGq9sZBuX3t73xgKr6e7X7PwpvlnH3WUJyfHP+z54x+Hb6oPHQowFGrotbzBQc8//+n53/99rc/fEIBAGAQkXJcsWdLZ2ak5K1as0EVxUVHR6tWrdf0r4Xr99dc/88wzEq4KSuhKzUrxqq2bZN3oev8ALU1XqWXLls2bN08J6mpUpjQlq0Jtba0SqqurJYaVsHDhQkluyWxlRmBo4AigMQUCEEgfAt6Xt+Geyl7Omre5pmzNWwQPAQhAIF4E9u8f/WcmR7z2wEC4UyccP0FTzKsRGzvjjMB17rorNr8S2arrsCpo7YzwHBIC0RKQOm1ubrYqEqi6s1VbitQuijs6OqRXZWocvjYedsp3EtRX15libXVVVkMyJ6KGujINKcGp4P+rlTQauqGBQ2dFJgQgkGIEguhbb+Ga+FMhkhPPnBUhkIYEcnODHCr/krNtVPex1gjPj/RduOFViS47P9/zlrcEKHHffZ594f8QrACFvEJ79njuv9+rTzNdCXCuGBNob2/fvHmzCdQYl45nOTRwPOlSGwIQgAAEIAABCBwmYF96k7cvz5k/PBKpmzjRkx23F3JHHx3ptmI3TyJ/5swA5X7/e49ubgMMRBFSQZWNogBTIeB6AnHZYEVFRWNjY1xKx7No3D51xnPT1IYABCDgQ8BeTcrLNCQvs4Z8KGb5oWQqR69i5TEIQAACySQwdarn4EGfDUR46+tTZdw4z/HH+8RG72qWX5Ltx7zfYNDA2LGe97wnQIbEaux+ZvUb6nd2evbvf0OEDgQgkCYEfI+BBvYlQh8CEEgVAhO2btRWfbx3V6NpYHaiNDgIR4AABGJM4NhjPYFkp1axN0LLm/g0r3ioNjTkOeWUUJOdvNNPd5pOw7492Lz/NrRDJ9OncSCnb9uh92xbm+VtwzmPPebJzx9uxPw/wXz00ZhXpSAEIOBCApmkgV2Iny1BAAIQgAAEIJDWBPLvuMksLqesqIhL2SOO8EybFnbl6mpniole804w9MZQXvae959bfNZXi8tvc+zV6c8+6xkcfLUd278OHfL09MS2JNUgAAF3EkADu/O5xHpX1IMABBJFgJ93lSjSrAOBFCNgnxzMx2zrF1440lVw5Evk5nrmz49kuu6lNW2EH6Zll8DmlTWSKWFoXO6eC47f1/abV3PuvPPVhv21c6fzA7GUbLHY+L17PSoem1pUgQAEXE0ADezqx8PmYkCAEhBwK4EJWzea6TWxmVt3yr4gAIHYENC/9JgUev3nF5SXe6ZOVc0gbyrWaHg2NOS56KJQpry+jdey9TnNs2SJZ8yY1wKh/m37N037yqP/7r/geM+HP/zqZKdh/QMH7O+A3ioEHAop6Pf91SHNIgkCEEg1AmjgVHti7BcCoRMgEwIQgAAE0ohA/h03BTjN4sUmIM0HSAgrNG6c533v80yaFNakNyTrDvmTn/SMcBv8hszXOrZz86/FRv577NhQM0euMeJI+Op9xFIMQAACLiaABnbxw2FrEIBAxAQSPnH49iPhi7IgBCAAAU9FhQPBbkHNO0GnMVLcSRhuHHWU54orhhsj/CcdbmaXwOZ9c2fP9tx223Dw8A+viuC7grs7rve3tz7/RdXMf+mhYX/J2XYc84rEwKT/oxH/MdgBJSAAgQQRQAMnCDTLQAACEEgQAZaBAAQyhoDkaCzPqsvburoQC9oX/uQHrltu9oaJ9quVPvEJz4knesaO9eTkvGH0cMeuc80fDrzuzqka8rfXh+PUysryFBfHqTZlIQABVxFAA7vqcbAZCEAgPAJ6+RXehARmu3lvCcSQ2KVYDQIQOExg4Pf/p7/NqxGSZWd7brjBc/LJISWHmHT++Z6vfc1z882eRYten5GVNax7x493IpLQaptXIwIbLhjBNJ8phw55pk/3idGFAATSkgAaOC0fK4eCAARCImDv4jPvTPDpOnE3N1Jxz27mmZJ7Y9MJJBDj29d47tzehyxv7xk277/gwbH7H73wvm1PnbttbZa/WX64p37964AnneR573utiHz+xTOG/dxS+ZFMn9PMnARb3bwTtIYJYHk7nXkbCsvvnPyk99nDmksyBCCQWgTQwKn1vNgtBCAwOoHXX3iNkGuvosyPkJIaYb1GdMlG3bMTlwBhGwklkLzF9JFvFtctjPTJyuLmbQP22W/CCUXWNS8BbI2Afmhszv7TJvV99q0T599QXH6bv9ksW0XebmvN21DEXkXMnAqv7n/rRicScSNcJXwo7+CLpz4e8XJMhAAEUosAGji1nhe7hQAEMp2A8xpRr7zFwrwaGAQgkBgC0oFayLwaSbbDy0tJ6m/zanhuvdXz5jcPN/Lyhv0I/w2NyT44tWDXFaV9155+qHDMCFnBwvbpKFhGHMbsmAMPvRSktglg80HSvIeyjzvllI/3en8TsvcobQhAIM0IoIHT7IFyHAhAIBICpiTNRzLfNXPS4AiuYclGIOAuAqa65e2fufnAWzzpJM/nPz88dNVVntNP94wf7/0jqYZysw9OGb9n3vF915W/XHf2vjOPHM4M5z9ToeHMGD037Jof/vCEU47WFbfMvu1ZXm2tZF6NMGzsWM+HPxxGPqkQgECKE0ADp/gDZPsQgEDGE7CXwuYzHgYAIJDpBN5wN1tZ6Vm50vPTn3p+8AOHyws3v+XlT1e8snDagZIjnKB/44TN/f5BF0XOPdfz9rd7Dv/uJe9dRSKAVeTCCz1nn+1dx+1t9gcBCERHAA0cHT9mQwACqUBANycBt2kvFs0HTHBz0LZt3s37ZG8QgEAoBOL7ZawJE96whzvvfLUbpOHx9B3c/8ze/mc9Rc/sfubV/Pj/FcbntI9+1DNtmic3N6pN5eV5dG3+kY9EVYTJiSTAWhCIBQE0cCwoUgMCEEgFAlLCZgMBf5tlKhzBbXsUT7dtif1AILUIjPqPyBLMBzmaPq0FGfUdct7369fou/IDf3ip5yuPb9eUGx7+2y2Pb3/bLW/+zH2fU9fsLz1breEKv3Sp54QTPGMi+Wbm4f2PHes58USPigx3+A8CrifABmNHAA0cO5ZUggAEUpBAGHcOr53OXoyafy2WiX8bAXm7vzKfiSA4MwQSQsBUrvlQFgw906o9v39g1TMPf/bhtl/1PrFjzy4FDw4d2ntoUI2Bg3vnT96mhvzX//7Nxfdc/ZvHfqdu8q2gwPPlL3sqKiLZyfjxnnPOGf7dxePGhTJdn+uUJqpmamMQgEDiCMR6JTRwrIlSDwIQSEECUsJm/nu3uLw0npmTo1dCaptXA4MABCAQFgGTVfL63KKJ5tUYyeL32Wbji93/9cg//tL3wr3Pn3Ng6JCzgbtvGf6uYPMWHDg4sHPfzrv+9bPrNn2848WHLJhMn5vrueGGgRNmeXSpq3YoW8nL80j3fuhDns99zvunhQWZqmekUfNqYBCAQKoTSCUNnOqs2T8EIOB+AvYa1LxrdytBHnBvtm3zARPSI5j2B0yPx8QpoicQP8Xl8/Ouvv30v37zQteQZ8jZs5Sw2vJX3lighnk1HNPNcE9/z9IHlt/zxO+dYPCGPnGZScmbBc8Pb/SSSzzf/76nqspzxBGe/PyAc4d/VZKGJkzwvOc9njvu8Fx0UcA0ghCAQCYQQAOnxlNmlxCAQGII2Es0eb1E04rm1fA2Bc28g0lsa7da3bwaGAQgAIEgBLo7rteovNmXOn7dsev5gcGDCkr0Ol6NUe3Q0KEfPXTn2kd/M2pmuAn2Cc18qF/2krj9wAc8d97puX74gK+uOH788K+GOvJIz2mnDUd08fvjH3uuuMJTUDDc5T8IQCBTCaCBM/XJp8652SkEXELAXo2Zd8mW2AYEIJCKBOzTiLy+mqb9m1fDbNTrX000s4nmbW4o/q3Pf1Fp8tNvuUj25VvGHfDkKDKqzT/8LcE+aQMHB372r5//ses+n3isuiaAzTs1RzlyRYWT6fnpT/PnlnpWrRr+tmFF+QVIgoBBIBYE2tvb6+vrfSr19/dXVlYWFRWVl5f39vZqtKamRl1ZU1OTuk6CIhs2bFDE27xHp06d2t7erlFNVLJMDXVlTk3/ChoN0dDAIYIiDQJJIMCSEEgtAnqdahbzbUsVmKm+iptXA4NAhhMYRQ0GomNTzAcaH45J7poNd0b7b+/g3m/+/esb107ctjbLx0abGmxcOj/YMGMQgEDyCEjfLl68eNeu4R+e572LVatW1dbW9vX1zZkzp6WlRWka7e7u7uzsXLNmjTTtjh07zjzzTCW0trauXLnSEpTjbZqohJ6enoqKCk1pa2vzrmC6VwlBKnhXG6mNBh6JDHEIQCCZBCJYOykvmIK/jozgFKk4xcibT8X9s2cIJJ5A4v+92Irmdd6e/h75WNmgJ3tTwfuKy2/ztlgVj6aOvnam6fL2hTPzimAQgEA0BBoaGqRy/SvU1dUtWLDAiU+ZMqW5ubnA61sPJGsbGxudhFEbErozZ85UBZWyu+W1a9dWVVVpYmlp6bRp0wKqaI2OamjgURGRAAEIZCiBZOnbVMTtvLBOxc2zZwjEg0DX7q4/Pfvn3z2+3in+8L8fGTz8q4acSJCGZJtGzasRE/P+d3p3Z4t3TftOYO9IuO0nBnZ3vPLvH3b82CY6DetG7G3P5iMuwkQIQCCGBJqamsrKykyIevz+SJRKrGq0rq7OGdR1bmFhoVSrE2ltbVWalK0TcRqLFi0qKiqqrKzs7+/XBbITV2P79u1dXV1qmO3cuVPLWTtcjwYOlxj5EIAABCAQLwLUhUCqE/jnS/9q+se3rlz/oc+0fu6b2779o4fudE70hQcaLv/doqUPLN/S/YATTErjmQ99srXrf2O79L5Dg/e88My15VdbWadh3ei9fVHS/KjVTDObHzWZBAhAIHQCUrNtbW3V1dUjTZGs7ejo0G2tpLLlSKauXLny5ptv1nWuRVRkzZo1uky2rrcvKSmR7u3u7lZwy5Yt8nEyNHCcwFIWAhCAAAQgEAYBUlOdgK55b9jyXyv+dNMfnv7j7v279w7uW33TC/sH9zvnGjg4cPDQwe0vPHj7tu9c8/slAX+OlF38mreJpvrMWyR6/+dn/zwmZ4xTxy6BzTvBCBoP9/d1diyzHzctH0GFmE8xbuZjXpyCEMg0ArqYXbp06fr164uLi3Vb29LS4ghdHxQ1NTWSyspXXFr38ssvr6ioUFumoIosW7ZMalldb5NIbm5uVlwNE8O6cPZOmDFjhuJORHfLSna6YTXQwGHhIhkCEEg9AvZq0nzq7Z4dQyADCKTBEX/x8K+OW/BJXQLvHdw76nEkhl8ceKn5/+5o+POXej/8+tsFNdHUmrzM6aoRW9vS/YC3OJ9/+Ac+m49moXG5+YfO23BO1ZBj0VRjLgQg4DYCkqbr1q3rO/xHAlgyuK6uTte85eXlJobr6+t1x6ttt7a2Tpw4UfkbNmy4//77lamg2apVq9SYO3c7Hv7rAAAQAElEQVSuvEySuLKyUppZbccU7OrqkgCWbdq0SV2tooiEtCK6ZFbmjh075L3fX61u6IYGDp0VmRCAAARSj0DwnwHjfGkgeFrqHZsdQyCxBP7n0bXOgnahevct/YqYV8Pf9hzcs+359rrWzz6580n/0bhGntj5hOpfeWOB49WQSQabqR2BDRzc2/HSQxFMDD7FeUuzfV0geDKjEIBAggnoJnbJkiWdnZ1ad8WKFbrjLSoqWr16ta5/JVyvv/76Z555RsJVQQldKeRbb731gQce0E2yIk1NTZrlmLSu9LDiGtV974LDf9RQVxVqa2u1VnV1tcSwchYuXCjJLZntTA+rgQYOCxfJEIBAMgk4gi2yTdjrJ/ORVUihWVGySqGTslUIJIxAkH9W/Qf22DZMAJu3SBB/aOjQiwMvLn1gRd+9P5DSkwVJjtXQS3tfPhjyj+YKd9Gndj0d+pTEZAb5nB/kgSZmb6wCgdQlIH3a3Nxs+5dAraqqUluK1C6KOzo6pFdlahy+Nh52ytdFbk9Pz3Dn8H+6RrYpGvKerkGLKKiGujKtqK7lq6s6qqZIZIYGjowbsyAAgUQTsBcr5hO9NuuFSUC3ymZhziMdAm4kEO9PO7v277p561cOHDqQmMPv2rcrLzsvTmvt3LczTpXDLRv6FxTsk5V8uEtEk89cCKQNAV3tbt682QRqCh0KDZxCD4utQgACwwTsi/rmh/v8NzIBe1VnfuSsBI2YkNBmzBK0KstAIG4E7u9+/cc723uezfssaO8ulveJe3dfGHjxu+3f9454t2P76W5waNCT5V1+lLa9ZXqUpNeGh4u/1nbt3/a5yNu7dqtpuTEOlWYEdBnb2NiYcodCA6fcI2PDEIBA0giEfrGQ4C36bCzer+2svv8ZbRvmfUZtinmfIboQSFECdzz4A+lDM/8jKK6geTWC2/7B/Vu6tzz4YofSHMXrNBSMoY3NGTs0dCiGBb1Lqbh3lzYEIOBNgLZ7CKCB3fMs2AkEIBCYQJTCyV5Hmg+8ANFkE4jyESd7+6yfoQQGDg7E8OT7Bvf/7F8/j2HBkUodOW6S1hppNMr40flHRVmB6RCAQFoScNuh0MBueyLsBwIQgECoBALeuIY6OdI806vmI63BPAi4jsBIH9IWl5dp0+bVkO09OMqvQQrxElilzB5+6cENawu3rc1yzOKx9QV5BUVjC2Nb06l22qTTnLY7G/YEzbtzh+wKAhBIAIFEauAEHIclIAABCLxKQPrQ7NU+f8WagK7WzWJdmHoQSCaB3bMuDri8fbSbf/D9V1523L8CpkUTPOTJ7Z589e895cXlt8miKRV87hlHnRE8IbLR/Nz8M446PbK5iZ+lR6lFzavhYyN9GPik0YUABFKUABo4MQ+OVSAAAVcQGOnljis2F/UmpPlVw7waGAQgEC4BUz7mg8x98MWHDh46GCQhsqFDnqF/7Hrp2vKrI5se+qy3HT9XejX0/NAzz5lcEXoymRCAAASSRQANnCzymbMuJ4UABBJNwKS++SBr27sBzQdJS+TQqNojkZthrUwgYB9y9q/A/Ein/uKfVy69f/lNf1nZ/H93/PHp1nhoYC397N5d3R3Xm6kbJzt/6qwxsf71SFmerAunvSNOG46+rH1xUN4+MZqPviwVIACBFCWABk7RB8e2U4IAm4RAognoFV4ES5oMMB/6dOdbFr0bmq6uvJnaZtbFQyApBIKL24BbemngpZ/+82cfuucaZ/Rvz/19+4sP/rX37+ueuOfJXU85cWvYt/7K2+9AMm9DYfns7HHTLnnxnKohs7DmhpX8/tP+Iz93XFhTRk1+9ynvGjWHBAhAAAJuIIAGdsNTYA8QSD8CnChVCYSuhIsPf8uijx/42SWKOIdX28yJeDdCX8t7Fm0IREwgdDF87cbr/t+O//n3vr6Aaw15hgLGow/mZOf2H+yPvs6oFd51ymVFY4uctIhFu1WQnL7itP+YUnCsdd3pI/sSoTvPwq4gAIEoCaCBowTIdAhAAAI+BFKgG7oSSIHDsEUIxIHAoaFDg0ODYRXWJbDlR6kn46avbXev+9pzPvF6J7rWlIIpHzz9A9HVYDYEIACBxBFAAyeONStBAAKxIsC3co1KMggiE8DmferYxax5n6HQumFk2Srmw5hGKgTSmoCE9/i8gsQc8ayjy7e0fDo/Nz/K5SaMmXDDmz8XZRGmQwACEEgkATRwImmzFgQgAAE3ErC3CJofaX/SzGYjJYwUP2HzG97YaauYH2lKasbZdSoRcO0XX4aGho7OPyphKC89aeGVZVeMi+IbgyWAG87/QvERxyVsz9Ev5HyJ0GmMVNM+6ckr02ykTOIQgEBqEUADp9bzYrcQgAAEYkZAL+lUy3zootTyn7ogQVdV2iHmegLpucF7nz8n8Qc7LuFismr6uz929kdzs3PCPWx+7rgTC0/86ttuOXVSabhzXZvv2i+OuJYYG4NAihJAA6fog2PbEIAABBJH4JF/73AW+9gfatVufOLBu3sea9v5wq6D+9WNxnTHouny9urTvCIYBFKBQIz3OHPKm2JcMYRyF5TM++Y7vnHW0eWStSGkv5ry7lPedfs7vlF8RPGrff6CAAQgkDoE0MCp86zYKQQgECkBu7qMdHY6zwtCRlp0z4E9a3b8txpLH1juUHhm9zNqP9y/8w8v9azu+tdnOtukh7/W8UsFZT/s+LG8Y/ZGaPNOMPQ7Z2cKDQjEg4A+tlXWvBpJt7E5Y9963PlJ2ca0CSVfnvOlG978OSnh7Kzskd4dPT43Pz83v/KkS1dd1PzB0/khWEl5ViwKAQjEgAAaOAYQKQEBCEAgzQiYKhicU/Xzzl/oaHsP7pX3t0FP9pDHIz38eHbJVx7f/vTe/mvLr/ZOs7dMm/eOh97W/XDoyWRCIAgB+1iStw9veftyzJe/NtN+krN5q2BvhDZvkQR4CdHpRackYKGRljjv2HOlhH90yaqPn11z8YkXnXbkqVMLphyTf/TxRxRXHHP2Faf9xw0zP/fLy+6uObva5b8GaaQDBozrw8BMHxJKkJdZQ34EIwwBCKQ2ATRwaj8/dg8BCEAghgT0QlDVsu//H3mz/YMB3ursLRUsbe+hwR17dt306Lb7Xu5VZOBnl5ipbSYZ/KfJK4JcO1ua4yVUnDYNCMSWgH2cm1flh19+WH4ks4928yPlxCSuq1eX/HqhI8cd+Y6St9dWfPy2t916x0Xf++Elq743/9tfmt2w+PQrz52chO+RjgneUIrYh4T5UPIzNYdzQyAdCKCB0+EpcgYIQAACMSTw2ftuiKya7oR/2fvEb55/OqzpEsZmYc0iGQKxI5DllLLf8WveCSagkZOVI3mpa9gErMUSQQj4CGD71CQfZApDmUSAs6YPATRw+jxLTgIBCEAgSgJ7Du5Rhe5XuuUjM10Ib3yp+54Xh79nOHgFe61pfqRMuw02P1IOcQhESeDAoQOq4HPTa13zGo23FY0t/GTFx+K9CvVDJKDPS2Yh5pMGgQwgkG5HRAOn2xPlPBBIMwKmf8yn2dFceJzb/vr16Hc1MDioq+D2XS+PWkqvMkfNsQTdw5hZFw+BCAjYh5B8BHPjOmVsztgbZ90wYcyEuK5CcQhAAAIQcAiEo4GdSTQgAAEIJJyAvXI1n/DFM2LB3z+56aGX/xWTo+4/dOjOZ3fIx6QaRSAQDQH7Cpp5nzqDQ4M+EXX93wgd19vgI/KO+OJbl5826VQtjUEAAhBIFQLt7e319fU+u+3v76+srCwqKiovL+/tHf75IDU1NerKmpqaLFkTp06dqogylW9Bx/uPaqKSZWpYmlNzw4YNFonAo4FDg0YWBCAAgbQmsH9w/48eunPPgeH3QsfkoHsO7v35P3/c3XH9nyavkJfFpCxFIBA6gYC616bbexAGDu696r8mWiTxfnxu/qmTSr8+r/HMo85I/OqsCAEIQCBiAtK3ixcv3rVrl0+FVatW1dbW9vX1zZkzp6WlRWlK6O7u7uzsXLNmTXt7u0RvY2PjPffco5ySkhLlK8Ex/1FNaWtr865gulfTW1tbV65caUs4FUJvoIFDZ5WpmZw7LQgEeS2YFufjENESuOfJ3x88dNCpYldh8nff0q+geTVCt4OenL/klJddtuecqiHHQp9OZgQE+Gdu0ELnkJ2VPeQZsln+Pn7Xv+Nyxo7PG7/4jA9+7e2NUwum+i9NBAIQgICbCTQ0NEjl+u+wrq5uwYIFTnzKlCnNzc0FBQVOZMeOHWqXlpbKV1VVSRur4Zj/qITuzJkzVUGl7G557dq1mqgpKjJt2jQ0sFBgEIg1AepBIGMI/PaxdfsG9/kfVzJYQXnTA+YVCcWys7K2dN8fSmYEObtnXWwWwVymQMAI6CY2JyvH2jH3WZ6ssTljnbJ52XnSvVruhIknXFN+9U8X/OidJ1c6ozQgAAEIpAqBpqamsrIyE6Ievz8SpRKrGq2rq3MGdZ1bWFgo1arRnTt3OvGuri7d/Tpd/9GOjg5nVI3t27drihpmKqUp1g7Xcw8cLjHyIZAZBDhlJhF4atfTffv67MSSu9ZwvH/EGQre2Htw3x+fvi94TsBRu8cz75MwYetGM584XQj4ELCPH/M+Q97do/KP9O7GsF18xHEfOeuaRae9/12nXPbe0qoPn7H4+vM+/aNLVn37gqZLT1owJmdMDNeiFAQgAIHEEJCabWtrq66uHmk5XdhKuOq2VlLZciRTV65cefPNN+s61yJu8GhgNzwF9gCBBBGwezP5BK2Xostk3rY7X+6M06F39D0aTWX7EWjmo6nD3EwmoI8fs4AQTpt0WsB49ME3T3nTwhMv+eDpH6g+6yPXnPnhd09/lyKTxk2KvjIVIAABCCSFgO5sly5dun79+uLi4kWLFrW0tDQ1NQXcSU1NjaSy8jXa0NBw+eWXV1RUqC2FrAthNcxKSkq8hbH/qK6ULdP8jBkzNMXa8iqlKWpEYGjgCKAxBQIpRsBuQsyn2NbZbkII7Oh77NU3QnstF/H1r1eN4eazr/QM/8V/EHAlgbdMnZmfmx/zranmeceeG/OyFIQABCCQRALSq+vWres7/EcCWDK4rq5O17xSqiaG6+vrdVGsHba2tk6cOFH5GzZsuP/++5WpoEyS9emnn7bv+9VdcVVVlXRyZWWlNHPA0bKysk2bNilHq3R1dUlIK6KJSrYipYe/tVjdcA0NHC4x8iEAAQikG4Hn+nvjdKTc7NyX974cp+IxLku5jCQwa+rMQ0OHYn70sTljzz5mRszLUhACEICA2whI1i5ZssR+utWKFSt0UVxUVLR69Wpd/0q4Xn/99c8884yEq4ISukpetmzZvHnz1NVBvH+Alrr+o0rQxa+unVWhtrZWCdXV1RLDmr5w4UJJbslsTYzA0MARQGMKBCAAgZQkMNLb4AcO7o3fefYOxrF4rLY9EplY1U9wnSDHCfx+kATvz03L5WXnXXLiRTkx/clYEsDvmf5uqa62kQAAEABJREFUN52SvUAAAhCIMQGp0+bmZisqgaobXbWlSO2iuKOjQ3pVpsbha+NhZ/maONzp67OuTbG2KviMKqIhy9eQupavSE9Pj66FFYnM0MCRcWMWBCAAgfQhkJMd0o/GDeuHQjt0srPC/h/NwHXLNd28GvEwSUGzeBRPSk0dR+vKmwA2rwg2CgGP5/LS94b4T2DUUpYwLnfsu065zNp4CEAAAulNoL29ffPmzSZQU+ikYb80SaGzsVUIQCAyAi559axX85Htn1nhEpg0tijcKSHmDw0NTRwzMcTkCVs3KtO8I4CdhobiZPYBbz5OS7itrA5r5oaNRfkvPcrpIjBp3KSPlF9TkDde7egtPzf/urOqc7Nzoy8V9wosAAEIQCBqArqMbWxsjLpMoguggRNNnPUgAIFQCNjrWvOh5JMzKgFpHuWYV8PbTi46OYLbWu8KI7UHBgemFkwZadQ/bgLYP04kJgTsH5T5mBRMmyKXnrTgnMnnRP/7isbnjb9g2jvedvyctCGTtgfhYBCAQGYTQANn9vPn9BBwPQG7BjTv+s26boMmKc0H2dz0olPG5YwNkhDx0LHjJxfkFUQ8nYmZQ0BfnTFL1pFvePP1pUXTx0bxD2F8bv6Mo8+qmVGdrCOwLgRCIkASBCDg8aCB+SiAAARShgBKOJpHZUpY3jCat4J64X7g0EFrx9bPmjIztgWpBoGRCNj9tuPtI9z8SFN84l+Ze/NZx5QXRPSmaM2aNXXm0lmf96lJFwIQcBEBtgKB1wiggV8jwd8QgMBhArqK0d/m1XCDhfUq1g0bTrk95GbnvuW4WTHftq7U3l7ytpiXpaAPAVN9PsFU6drmzbthzyvesuy9pe8J91sDlH9l2Qc+c16dG47AHiAAAQgEIEDojQTQwG/kQQ8CmUpAr0HNMhVApp+76pR3Rv/NkD4QTy46qbRouk+QbmwJ6J+tCppXYyRz1Ve1RtpkNHEjYD6aOpr7/lMv/8a828479ty87LwsT5YiI1lOVo7srced/90Lv8UPgh6JEnEIQAACSSYQaHk0cCAqxCAAAQhkGIFTJ5365mPfFMNDS1F/6PTFMSxIqeAE7O0S5oNnpveoEfD2EZz35MKTGs7/gpTw+0rfM7Vgiq55x+eOL8grKMgb9mqr5vETjr/itPdL/d44s774iGJFMAhAAAIQSBUCmaKBU+V5sE8IxJZATG5FYrslqrmWQPVZ19qL++h3OC533EUnzC8/+szoS1Ehkwk4n8GSco99wsRpHz7zqjsu+t7PLr3zS7NXfOa8//xkxSfkV87+4i8u+9n3Lrz9A2XvP+6I4zL5AXF2CEAAAilKAA2cog8ujG2TmrEE7OWj+YyFwMFDJ3BU/lF1530qLzsv9CkjZR5/RHGK/nTcpGitkTBGH0+Pf/72UMxHzySCCkfkHXHqpFNnTnnznOK3ypdOmh6rrxZFsBmmQAACEIBA9ATQwNEzpIJ7CbAzIxDl2wKtCD4TCJw/dVb1WR8ZG8WvhxGl4wqmfuEt/6VGwixW6sjqmE/Y5lloJAIm4M2PlBMwbp/xAg4RhAAEIAABCIgAGlgQMAikIQGOBAEjMGHrRmuE6BeedEnN2deFmOyTlp+bXzqp9Ctzbz5y3JE+Q27uCpGZmzfJ3gISMIVsPmACQQhAAAIQgIA/ATSwPxMiEIBAahNg91ESmD/tgq++7SslE46Xpg29VF523sUnzP/62xsnjZsU+iwyIQABCEAAAhCAQIIJoIETDJzlIAABCMSRQIilR32zaNmRp333wts/dMbiorGFwZVwdla21O+MY8768pwvLTnr2hA3EKc03sYcAdhkQYvm8vaRf+/Y8OTGHz50ZwTnZQoEIAABCEAADczHAAQygsComsc9FKJ5ZeyeU6THTi47+dKfLvzxp8/91FuPO79wbGFOds743OHfDVOQVzAud1xOVs60idOuOO0/vjHvqzfPvkmyOSmnNgm3e9bFSVndzYvaPyXzbt6n/97smfrHH9/5+Le2fWfRusXLHlix+sEf/s+OtU7OpVMeUvvSqQ99ue3WB1/sUBuDAAQgAAEIjEQADTwSGeIQyBQCI73czJTzc87RCJx/3FtunFl/18If33nJD2+Zu3L5W5Y2nP+Fpnlf+3/v/MV3LvjmlWWLTph4wmg1Yj8+4fD3OZuPffVUq2j7HelLXfZv3LxlJt2bMjcf+mauv+/zm566t/9A/8DBgX2D+7wn7j+0X939g/v//OxfbvrLzTfev+yxvscVwSAAAQhAAAL+BNDA/kyIQAACEEhPAlEqxsKxE08uPOmMo07XlW/xEcfpWjgVMYWru1LxjG7eczT8Dxw64H80JzJ/8jZr7z24t+PFhz7dev1vHvutRfAQgAAEIAABbwJoYG8atCEAgTcQcNXF0Rt2RgcCEBiBgH2lQ96uhc2PkJvosAlgeduVvGMhbuXe588JMXPIM3R3Z8v3/u/7IeanYhp7hgAEIACByAiggSPjxiwIQAACEIAABEYhEMHX0WyK+VGqjzbcf2DP5id++6W1Z29bm+Vvo81m3NUE2BwEIACBaAiggaOhx1wIpDABXcW4cPe2K/Mu3B5bShUCMVFQqXJY9hmEwF5P3oM5p/5r6meKy2/ztiBTGIKAywmwPQhAIHoCaODoGVIBAhCIAQF0bwwgUgIC7iAQwT/nCXH7IWcDgwd/3vN4195+d7BhFxCAQOQEmAmBWBFAA8eKJHUgAIFYEhi4brnKmVcDgwAE4kHALszNx7y+lTUf8+J33xKepm3p4cdEx/whUBACEEgcAbet1N7eXl9f77Or/v7+ysrKoqKi8vLy3t5eZ7SmpmbDhg3qtre3T506VQlmStYUxc3UVsSGlKZkxZuamiyihroyVbOI1VQkAkMDRwCNKRBIeQJ2SyNvL0/Np/ypOAAEIBAagfhdump9fWJxvBqhm+0q9PywMp8YeOWR/l0/7PhxWLNIhgAEIAABfwLSt4sXL961a5fP0KpVq2pra/v6+ubMmdPS0mKjkqxOu6KioqenRwnd3d2zZ89WckFBgaU5XslKUJqSJYPb2tqU3NnZuWbNGnVN9yqhtbV15cqV2okzMawGGjgsXCRDwNUE7KWnq7cYzua4BA6HFrkQCI9AXAVneFsJOdt+KLS8XQKbD3H2vkODm19+9tryq0PMJw0CEIAABEYi0NDQIJXrP1pXV7dgwQLvuC5vL7zwQsld76DaW7ZskZ87d658EJPQnTlzpnTylClT7G557dq1VVVVmlJaWjpt2rS018A6KQYBCLiIQJrpbReRZSsQSFkCQT4tBBlK2HG37/53wtZiIQhAAALpSkCytqyszISox++PRKnEqkbr6uo0KH/ppZeq4W39/f233357wEtgpS1atKioqKiyslJpuv5VxLHt27d3dXU53Z07d2o5pxtWg3vgsHAlJZlFIZB8ArxZOvnPgB2MRsA+Ss2PlptZ43bla979Jx/8319pk1feWDB/8jYzdWNiWR7PY3t2x6QURSAAAQhkJoH29va2trbq6uqRjq8L246ODt3WSiqPlLNjx47CwsKAl8AlJSXSvd3d3Zprd8VqxMPQwPGgSs1YEaBOjAnoRbCZvc3YfARr2JWOvPSGWQRFmAKBmBDQh7TqmFcjuOmDVgny9sFvXpF0NZ8DWte8a4/81K6n4rS3wUP7Ox6/s7vjelmclqAsBCAAgTQmoIvZpUuXrl+/vri4WLe1LS0tIwndmpoaSWXlB6TR3Nxs73D2GS0oKNCQVLQaJoZ14eydM2PGDMWdiIS0kp1uWA00cFi4SIZAwgiwEAQgMAqBEHXvKFUybNjlAlhP48WBF+W9TXfCZroWVlxeXTXk1TZTd1Q74MkZe+qXzqkaMhs1nwQIQAACEPAmIGm6bt26vsN/JIAlg+vq6np7e8vLy00M19fX66JYU1pbWydOnKh8tX1M+boonjdvnsWlkysrK6WZrWtewa6uLglg2aZNm9TVLEUqKioU0SWz0nSZLF9aWiofgaGBI4DGFAi4nYCuuVy+xaA7dPne2R4EIBAeAfv3Lm8K3Nv7F9pzYI9/MFaRXft5L3SsWFIHAhCAwDAB3cQuWbKks7NTnRUrVuiiuKioaPXq1Q0NDYr4m0SyNLPUrP+QtK70sKbrnln3vQsO/1FDXUnf2tparVVdXS0xrJyFCxdKcgeU2f6V/SNoYH8mRCAAAQgkiwDrRkIgsgthU2Ijrbd71sUjDREPSEASV3HzakRuWVkfXlY00nTd+tqQLoGtEZbPCiubZAhAAAIQGIGA9Glzc7MNSqBWVVWpLUVqF8W66ZVeVURmQeWrLVPDmaiujVrE2oevmfssogQ1LKKJ6jo59suTFInM0MCRcWMWBBJKIAYvK0Per8kJedMA8jLNNq9GPMwOaD4e9amZCgSi3aM+aFVC3j5WzSuSgSYISTy1fXHBfATbmJB3RG5WbgQTQ5kyadykUNLIgQAEIACBEAnoXnfz5s0mUEOc4oY0NLAbngJ7gIC7CNgLaG8fv/2Z7rWXy/IyrWVeDQwCYRGwD9qAU+wjLeCQC4LptoUg/4SDDBmFYwuOzcqKy33tuNxxx44/1lbBQwACEIBATAhUVFQ0NjbGpFQii6CBE0mbtSDgOgKmGeTthal5Z5eKO+14NEyWmI9HfWpCAAKuJ+C7wZMmnjhwYMA3GqP+yYUnxagSZSAAAQhAIIUJoIFT+OGxdQhAAAIQCIWAfZ3FfCj53jmZ/J5qbw4RtO2LaPL2xTXzo9bRbe30SdNHTYsgYWzO2JMKT4xgYtymUBgCEIAABJJDAA2cHO6sCoH4EbAX+vL2itN8/JajMgTSj4DpXvP+pxsp7p9JRAI4AgiXnDB/XM64CCYGn/K24jnBExhNIAGWggAEIJBMAmjgZNJnbQgkkQDaOInwWTpZBOzD3rz/Hkywycs0Km9y17wiqWs6S3I3PxLzgLu6YNo7smP9LcE5WTkLT1oQcDmCEEgsAVaDAASSTwANnPxnwA4g4BICYb1Ijd+eXbKN+B2QyhCAQHACedl5i8quyM/ND54W+qgE8LySt5dMOD70KWRCAAJxIEBJCLiFABrYLU+CfUAgSgL5d9ykCvKmIc0rEkPTbZhZDGtSCgKpQkD/uFJlq2mwz/dMf/cx+UfH6iD5ueOuOfNDsapGHQhAAALhE2CGuwiggd31PNgNBFxIwN5Fad6F22NLEEgAARPA8voykJYzr0YSTZtJ4ureS8dpJ58+7z9zs2Pwi4LH5Iz5z3NrC8cWeu+ZNgQgAAEIJIqAG9dBA7vxqbCnjCLghhfTGQWcw2YUAf9/XyG+RSKUL/rESfuF8oBsafOh5KdizvSiU+rf9NnsrKheqOTn5usG+C1TZ6UiAfYMAQhAAAJxIhDV/1rC2BOpEICAWwmE8lo/lBx/seF/4vR+ye5/XiLJJWAfk+Yj24l95JsPUiGaJYKUDWVIkt4slOSUyzn/uPiGp+gAABAASURBVLfcOLM+4ttgTbz6zKsuO7ky5Q7OhiEAAQhAIK4E0MBxxXu4OA4CySOgF8fJW3zElU0Jmx8xiQEIJIPASHLX/imZt33ZB7B5i+DjQeAtU2d97e23njDxBN3ohl5/TM6YI8dNWjbrvy49aWHos8iEAAQgAIEMIYAGzpAHnaRjsmzGELB7MPMjHdrUgnnLue9dwy9P/1z17q7dXd7qwkbxEIiMgOlY85FVCGvW7lkXK9+8GljMCZxcePK3L2j64OkfKMgrGJc7Lnh9qV9d/77z5MtWXdR83rHnBE9mFAIQgAAEMpMAGjgznzunjj+B0VbwloKj5YY97nI9ecUJT+tI7yp++Ot//6YaX/nrVz9zX/17f/P++v+9ccOTG/cc3KMgBoFoCCRMAEezyVSfa5/EvH1cT/TuU97ZUnnXR89acvqRZTlZOeNz88fnjh+bM1Y23M4br9VPKjzx6jOuunPBD68+8yqJYUUwCEAAAhCAgD8BNLA/EyIQcDUBV103mdIw71DbPWv4WszpWuMfz2+zhvxLAy/Nn7xtz4E9/Qf61ZDfe3DvgUMH/vVy5+oHf3jV+qt/8s+79g/uVyYGgRQlYMowRTcf2bb1pTezyKaHOGv+CRc2vu2WX72zZeXsm2rP+UT1WdcuOeuaT1R8rOH8L/zisp996x3feOcpl00cMyHEaqRBAAIQgEBmEkADZ+Zz59SpQcBeRnt7E8DmLe7+kxw8dLDpH9+6ZeutoWx13+C+/YcO/Pqx335k40fbev8ayhR35bAbCEAg/gTysnNLJ02fU/zWS068eMGJl7zt+Lm6HB6fO3wVHP/FWQECEIAABFKeABo45R8hB0hXAiZxzafuGXv6ez/1x09v6X5g7+C+0E+hS+C+fX23/vW2/3n016HPIjPJBDJjeXvXg/nMODGnhAAEIAABCKQbATRwuj1RzgMB9xDo7e/9r/uXde1+Rpo2gl1pVsvDv/zlI2simMsUCMSbwMB1y7WEeY9ao5m9fWO0LMYhAAEIQAACEIg7ATRw3BGzAASCELCXxeaDpKXi0ODQoS9tveXFgZei2fyeA3vWPPLfm576QzRFmAuBaAgEufINXQBHs4GEzY3wE1HC9sdCEIAABCAAgRgRQAPHCCRlIBAOAXuHs/lw5rkxdySF8N327z3X/5zt+N7nI/8NJQMHB77T/r2ndw3/KGmrhocABGJOwASw+ZgXT9uCHAwCEIAABFKTABo4NZ8bu84kAnbXZD5Vzv1/L2y/75ktP1r5ckw2PDg0+P3tq2NSiiIQiJhAZP8GXf6lLn0NyyxiLEzMUAIcGwIQgEAqE0ADp/LTS/beuTFI2BOI7MV3wrbnv9BP//mzfYNh/BAs/wo+kUf6drS/8H8+QboQSAoBicYQ1zUBLG+fLc2HONflaTqUdmheDQwCGUSAo0IAAqlPAA2c+s+QE0DAfQSe3PVUbDe19+De/9nBz4iOLVSqxYuAKUPz8VrDNXXtK3TmXbMpNgIBCMSHAFUh8BqB9vb2+vr613qv/t3f319ZWVlUVFReXt7b2/tq1OOpqanZsGGDuk6CcqZOndre3q6gtymiuEZVR8kaampqUlemhroyVVNXZjUVicDQwBFAYwoEIDAiAbvp8n4XtH0zsPkRp4UwsO35bX9aO2bb2izZQ5umhzCDFAgkmYApQ/NJ3grLQwACEIBANASY60VA+nbx4sW7du3yig03V61aVVtb29fXN2fOnJaWluHQYQHstC2irnJ6enoqKiosYl6it7Gx8Z577tFoSUmJqkkSt7W1dXd3d3Z2rlmzRl3TvUpobW1duXKldmJzw/Vo4HCJkQ+BuBNIxesje3eo+ZEAzZ+8TUPm1QjXxuXmZ83633OqhmRnXvRouNPJhwAEIAABCEAAAhAIm4DfhIaGBqlcv7Cnrq5uwYIF3nFd3l544YWzZ8/2Do7U3rFjh4ZKS0vlq6qqpHsldGfOnFlQUDBlyhS7W167dq2GlKC0adOmoYGFAoMABFxE4O5b+rUb82pEbwMHBx55efiTY/SlqAABCEAAAhCAAAQgEAEBydqysjIToh6/PxKlEqsaraur06D8pZdeqoa3LVq0qKioyHm3szOkuTt37nS6XV1dHR0dTleN7du3K6iGmZI1xdrh+lDvgcOtS34aE0jFW8o0fhw6mr39WA032Isb79A2rryxQD7m9uSuJ2Nek4IQSC0CCfgMHHyJ4KOpBZPdQgACEIBAWATa29vb2tqqq6tHmqULWwlX3dZKKgfMKSkp0QVvd3e3Rrds2SKfFEMDj46dDG8C9urHvHecdqwIGFt5+x5C87EqnoA6uw/sjt8qO/ftil9xKqcfAXtzvvnEnE7/chOzEKtAAAIQgAAEEkygv79/6dKl69evLy4u1l1uS0vLSEK3pqZGUln5PjssKChobm6WTlbDxLB3guKFhYVORAm6Una6asyYMUNBNcyUrCnWDtejgcMllln5QU5rd4/ePkgyQ5lAwJTGoUOHghw24m8GtpqDQ8GKWw4eAv4E7GtJ5v1HYxUxAWw+VjXdVsdOJy+YZm7bIfuBAAQgAIE4EZBwXbduXd/hPxLAksF1dXW9vb1SqiaG6+vrdVGs1VtbWydOnKh8tQOa5HFXV1dZWZkalZWV0sxKk6B9+umn7buCdZNcVVWlhE2bNilHqyi/oqJCEQ0p2dJKD3/zsLrhGho4XGLk+xJIVxnse06X9fUaVDsyr4Z7bFzuuPhtZnxefvyKUzktCdiXZhJ8NIlDrWheDQwCEIAABCCQrgQkXJcsWdLZ2akDrlixQhfFRUVFq1evbmhoUMTHpGaleJWgm2Td6Pr8AC2VWrZs2bx585SgiRqVKU3Jkr61tbVKqK6ulhhWwsKFCyW5g8hsVQhiaOAgcDJxKHRNpVeWZpmIKT5ntq8mhFvbZpkPd+5I+dHEj84/KprpwedOLZgSPIFRCEAghgT0icUshjUpBQEIQAACaUBA6rS5udkOIoGqO1u1pUjtorijo0N6VRGZBZXvtA9fJPfZdBu1thKU5j2qiIYsoiF1LV8R/1+tpNHQDQ0cOqsMzXRUsdMIAsJy7DWTfJDMmAwlYImY7DOjihydf3Sczjs2Z0zppOEflx+n+lYWD4EMJ2Cfxs1nOAqODwEIQAACoxJob2/fvHmzCdRRk92TgAZ2z7NI5k78xaRF7GWQvEz7M69GJlsmQ9DNvx69/MB1y83UTaBlnXFkWQKXy7ilODAERED/tB2vRihmU0LJJAcCEIAABNKMQEVFRWNjY8odCg2cco8slhsOXc7ZSxx5mXZgXg1vs2rmveO004mABHAox8nPjf037k4aN+mEiSeEsjo5EAiXQMzzA36SjPkqFIQABCAAAQhAIAICaOAIoDElMAF7zWc+cEYaRXVPbpZGZ4rZUaSTc7JzYlbucKFxOWPffcplh5s4CKQSgdh+SrQvMpqPFQWro3+2aphXA4MABCAAAQikMQE0cBo/3AQdTa+ZzLRebF/tqaB7TIpXm7GXnubVxUYicMWp/zHSUGTxMTljFp64ILK5zIJA+hHQJ1uz9DtaIk/EWhCAAAQgkJkE0MCZ+dyHT21Czvxw3+OxtrzpPfM25O31wsu7m8ZtoTAzFOb9zztS3D8zcyJV098Vw8OOzx1/bfnVMb9bjuEOKQUBCEAgtQiwWwhAAAKZTAANnMlP/9Wzm4Qz/2qIvyAQNYGn1zct/q8JV95YMH/yNhUzr0a4NjZnbMXksy+cdkG4E8mHQIIJ2NcHzSd46ZgsN2HrxpjUoQgEXE6A7UEAAhBAA/MxEF8C8ZDWupuN76ZDqG4vFuXtgOZtnrZnpq4TV0TdcM3/xbRWVBF5GzKviExtM7XdYGcedcYnKmrG5Y6LZjM5WTnFRxx3/Zs+HU0R5kJABPSvQz4epn+PZvEoTk0IQAACMSRAKQhAwAiggY1DRnu9etP55e01onlFfGykuE+ad9cUoHnveIa07eDm43dkPTiz+C0RceWLT7jo6jOuGpMzJrIKugE+sfCEL81uyMvOi6wCsyAAgVgRiOwLebFanToQgAAEoiTAdAh4E0ADe9PIiLZex5iZpjWfiic3bWk+5vsXolFrSnn65Iy0Gatm3mdKBF3/dSMokrAplSdf+tnz6iJYbnxu/swpb/7a2xsnjpkYwXSmQAACEIAABCAAAQh4PB4g+BNAA/szIRItARNp5qOt5TfflKR5v8F4Bews5n3WsKC8CWDzPjn2hQZ5jZr5JKR3963Hna8Dzj7urSFeCI/LHVc0tuhjZ3+0/s2fzcmK8e9Y0k4wCCSXgD4JJHcDrA4BCEAAAhDIEAIjHRMNPBKZjIhLlaXrOaN5lWkC27wPHxEz845LAHt3nbbFzTvBjG18fubnbpmz8m3Hz5US1h2vv7iV9B2TnXf8hOM/Un71Txf+aF7J2zOWFQdPYwL2qcl8Ch3TPh+aT6Fts1UIQAACEIBAQAKZoIEDHpxgoglE+ZpPr73MAu47yuIBayoouet4Ndxmtj237SrIfk6dVPq5N33ml5fdfeOsG64644NvP37ujGPOOvOoM9405bx3nXLZx2Zcd/sF3/zehbcvOPGSIEUYggAEIAABCEAAAhCAQDQE0MDR0HP/3FB36NxVppysCvWEkeY5QBxEo1ayKeZDnzVq2bRJ0A1wxTFnv6/0Pde/6TM3z77pK3NvXvGWZdVnfeSCae847oipaXNMDpKZBOwfvv/Z9anAzH/IOxKnL+d5LxFlWwc0i7IO0yEAAQhAAAJJJIAGTiL8dF5ar/bidDx7+SWvJczsVaN5r0UT3dRmtKR5bU9tb/OPeI/ShgAEIOBCAvYJTd4+g5l34T7ZEgQgAAEIQCAsAmjgsHCRHCEBE6jmIyzBtDAIkAoBCEAAAhCAAAQgAAEIBCaABg7MhWhMCOj2wCwm1UIpkn/HTUozsW1eXSyTCHBWCKQegVA+cSXrE5o+h6ceUHYMAQhAAAIQCEoADRwUT6YO2ose+YHrlpulBAl7HWk+JTbsvUl7gWveO04bAiETIDElCdinLPMuOYA+7btkJ2wDAhCAAAQgEA8CaOB4UE1cTedlE9ppJOhhIYo3Rn1ZQfuUt5eY5hVJe8uck6b9o3TrAVNyX/H+hJOSUNg0BCAAAQhAIP4E0MDxZ5ykFfxfXTlqMEk7StyyEplmdmTzQZa3BCNmPkhylEPamFUwWWhd895xG7UIHgIQyFgCIXxGylg2HBwCEIAABCAQIQE0cITgXDvN5Jxrt5esjdnrSPM+ezBi5m1I+tPMuonxJoPNJ2ZFVoEABCCQ4gTYPgQgAAEIQCASAmjgSKi5do4JOXkTe+Zdu9vINrb+iQ0bn9q0taeta3eXVZBuNJNwVcS8GjJrm1c3uKmIEsyrgUEAAhBIFoG0/OydLJhpui7HggAEIJBMAu3t7fX19T476O/vr6ysLCoqKi8v7+3tdUZramo2bNigrpPq92BCAAAQAElEQVSgHIso6Jj36NSpU9vb2zXU1NSkZJka6spUTV2ZfwWNhmho4BBBkZYcAo/vfFyS/uoNS5zlf9jx4zu2/+Ab//jmZ1rrF6374FfaGv/x/DZn1F/u+kdU0MmnAQEIQCApBEzlmk/KBlgUAilLgI1DAAJJJiB9u3jx4l27dvnsY9WqVbW1tX19fXPmzGlpabFRSVanvWPHjjPPPFMJra2tK1euVB3L8fZKVkJPT09FRYVkcFtbW3d3d2dn55o1a9Q13auEIBW8q43URgOPRCYF4qbl5O1VlLzpPfM+B1CaIubVcL89sfPJFX+66cxf3KWt/uLpafJm+wb3yfoP7Nk7uFf+gWf//JW2r37iD59q6/2bJaScd26enUbKHYENQwACIRKwf+bmR5qiT+YaMq8GBgEIQOA1AvwNAVcQaGhokMr130pdXd2CBQu847q8vfDCC2fPnm1BydrGxkZrh+IldGfOnFlQUDBlyhS7W167dm1VVZXmlpaWTps2LaCK1uiohgYeFZEbE3ykrL2cMu+zXWWamTCWlynHvBojWcBqIyXHPP7fO9b+5x8/433BG2SJgYMDT+/u+urfvvb1v3/z4KGDQTI1JBqOVwODAAQgAIFQCIz6f41QipADAQhAIFICzHMLAcnasrIyE6Ievz8SpRKrGq2rq9Og/KWXXqqGj0ncKk3K1ieu7qJFi4qKiiorK/v7+3X9q4hj27dv7+p69XshFdy5c6eWUyMCQwNHAM2NU5IrWUMnopsNsyBTvvrXr/3ykV8NeYa8c+ZP3mbmHfRu7z2490/P/qmu9bPP73nBO25t8TGzl3HmtRMbjZXXErEqRR0IQCDTCNgnEPN2dvscJS9TxLwaGAQgAAEIZBIBF521vb29ra2turp6pD1J1nZ0dOi2VlJ5pBwVWbNmjS6T/RNKSkqke7u7uzW0ZcsW+TgZGjhOYN1Y1oSfduY01E6M2as688FXXLn1lr8+9/f+A3uCpwUc3Te4/6ldT//X/V94ae9LARO8g/Zq0rwTD2WHTjINCEAAAhEQGPXzjCWYj6A+UyAAAQhAAALxIKCL2aVLl65fv764uFi3tS0tLSMJ3ZqaGkll5ftvQ0EVWbZsmdSyz2hBQUFzc7PiapgYLisr886ZMWOG4k6ksLBQyU43rAYaOCxcqZdscte8+3d/x/bVD77YMXBwIJqtPrfnuZu3fiWaCsyFAAQg4AYCJoPNu2E/7AECEIAABDKZgKTpunXr+g7/kQCWDK6rq+vt7S0vLzcxXF9frzteIWptbZ04caLy1faxVatWKTJ37lx5mSRxZWWlNLPajinY1dVVdvjPpk2b1NUqilRUVCimS2Zl7tixQ760tFQ+AksFDRzBsTJ+ir1mMu9OGPl33GSm7dll7APP/uk///LsnohugFXE257Z3f2jh+70jtCGAAQg4H4CI33SVtxMX9A0c/9Z2CEEIAABCGQCAd3ELlmypLOzU4ddsWKF7niLiopWr14d8K3OUsi33nrrAw88oJtkpTU1NWmWY9K60sOKa1T3vQsO/1FDXUnf2tparVVdXS0xrJyFCxdKcgeU2U7BIA00cBA4Lhh64xYkGt8YSKWeXsAF3K4JYPn5v7tXCfc+f4687O5b+uUjM90kr93x3+vWTtq2NsvbIqvGLAhAAALJIjDSZ85k7Yd1IQABCEAAAiIgfdrc3KyGTAK1qqpKDSlSuyju6OiQXlVEZkHlq62L3J6ensMXycNO18g2aqWsPTzQ12cRTVHDIlbByVEdVVNCZIYGjoxbMmeZEpa32wD5ZO4mPmsPC2CPx3xkKxzyZP95wnuLy29zbKQ6Amg2UgJxCEAAAhCAAAQgAAEIQMCfgK52N2/ebALVf9S1ETSwax8NG4uWwIOv/PvF/Xt/2PFjn0JSvIrY7Yp5dd1l7AYCEIAABCAAAQhAAAKuJ6DL2LB+5a9LDoQGdsmDGH0buvhVknk1RrVUUXe2z2u/cNSoJwo3IceTtW33y9eWXx3uRPKTSYC1IQABCEAAAhCAAAQgEE8CaOB40o1PbbvGNO+/wkhx/0xXRQ4MHpg/eZu2ZF6N6O3A0KG/73wx+jpUgECCCLAMBCAAAQhAAAIQgED8CaCB4884DiuEJXTDSo7DZkMqOTg0qLwYCmBVk3Xteam743ozdX3MrqB9gnQhAIEkEGBJCEAAAhCAAAQgkCgCaOBEkWadcAhceWOBWTiTAuQeyBpXeunuc6qGzAJkEIIABCCQXAKsDgEIQAACEIBAYgmggRPLm9USSyAvO/eVA5H/jqXEbpbVIAABCGQYAY4LAQhAAAIQSAYBNHAyqLPmGwnUrJj6xsDrvfmTt5m9HgqrlZU15DkU1gySIQABCCSAgH0vhrx9u4r5BKzLEm4hwD4gAAEIQCB5BNDAyWPPyq8RyM8d91ozxn8fPHQwP3d8jItSDgIQgEBMCSCAY4qTYq4nwAYhAAEIJJsAGjjZTyCE9UP/fUghFHNjyjH5x8RpWzlZOUVjCz2p9ocXxKn2xNgvBCAAAQhAIAQCpEAAAu4ggAZ2x3MYeRcmgM2PnJUyIxO2bjQzmWf+9KPK4nSA4iOOi1NlykIAAhCIkoA+GUZZgekQgAAEUoYAG4WAmwiggd30NILuxeSi+aCJKTPonOWso8vH5+bHfN9ZnqxZU2fGvCwFIQABCEAAAhCAAAQgECoB8txHAA3svmeSeTs6Z3JFdlbsPxTH5IxBA2feRxMnhkA6EOCKOB2eImeAAAQgAAG3Eoi98HDrSd2+r7R5t3NkoBecdEludm5kc0eaNW1iySmFJ480ShwCEIAABCAAAQhAAAIQyEACidDAGYiVI4dLoOqUd8X2Knhc7rgPln0g3G2QDwEIQAACEIAABCAAAQikNwE0cHyfb2yrO99AG9uybqhWOLbw6jOuGp8Xm99jJDl9zjEV5x17rhuOxh4gAAEIQAACEIAABCAAAfcQQAO751m8upPdsy5+tZXif4W7/XeeclnZpNNi8o5oKepPVHws3A2QDwEIQAACEIAABCAAAQikPQE0sHsfcQZ+h3D9m6+fWjAly5MVzVPJy867cWZ94diJ0RSJci7TIQABCEAAAhCAAAQgAAF3EkADu/O5ZOiuCvLGr5z9xRMmThubMzYyBPm5+Q3nf+H0I+P1C4cj21VGzeKwEIAABCAAAQhAAAIQcDMBNHCSn47PZa+9Edp8kneWpOWPHHfk1+d9deaUN0nNhrUF5Z9UeOJtb791xjFnhTWRZAjEigB1IAABCEAAAhCAAATcTwANnMxnZAJYXqZ9yNvvhJRX2yID1y03UzdDLC87r/7N13+y4uNH5x8lZTvqqcfkjJFdcdp/fOsd35g2oWTUfBIgAIGYE6AgBCAAAQhAAAIQSBUCaGBXPClTufLObtQ2cyKZ1njb8XN+dMnqj5193amTSrOzssfnjc/JynEgZHmyJI/HZOdNLZiy+PQr71zwg/eVvscZpQEBCEAgYQRYCAIQgAAEIJBpBNrb2+vr631O3d/fX1lZWVRUVF5e3tvb64zW1NRs2LDBuu3t7VOnTlWOMpVvQcf7jzY1NSlZpoalqZq6MqemxcPyaOCwcCUiWZfAiVgmRdZ4R8m8r7298ScLfvjZ8+quOuODC09aMK/k7RedMP/K0xfpovjbF37rjou+957p7z4i74gUORDbhAAEIJA+BDgJBCAAAQhkIAHp28WLF+/atcvn7KtWraqtre3r65szZ05LS4uNSrI6bYnexsbGe+65RzklJSXKtxzz/qOSxG1tbd3d3Z2dnWvWrFHXdK+mt7a2rly5UjuxueF6NHC4xGKQn3/HTWYxqJUZJQrHFs6c8mbd9H787I9KDH/qnE8sOu39uijWJXBmAOCUEIAABCDgLgLsBgIQgEDGEmhoaJDK9T9+XV3dggULvOO6vL3wwgtnz55twR07dqhRWloqX1VVJWWrhmP+oxK6M2fOLCgomDJlit0tr127VhM1RUWmTZuGBhYKDAIQgAAEIAABCEAgvgSoDgEIZDIBydqysjIToh6/PxKlEqsaraur06D8pZdeqoaZRnfu3Glt+a6uLt39qmHmP9rR0WFD5rdv364p1pZXKU1RIwLjHjgCaDGbYt/xKx+zihSCAAQgAAEIQAACEIgPAapCIMMJtLe3t7W1VVdXj8RBF7YSrrqtlVQeKccNcTSwG54Ce4AABCAAAQhAAAIQgIB7CbAzCOjOdunSpevXry8uLl60aFFLS8tIQrempkZSWfk+0KSQCwsLnWBJSUlBQYHT9R/VlbIzqsaMGTM0RQ0zldIUa4fr0cDhEos8P/+OmyKfzEwIQAACEIAABCAAAQhAIOEEWNAISK+uW7eu7/AfCWDJ4Lq6ut7eXilVE8P19fW6KFZya2vrxIkTla+2t0myPv300/Z9v7orrqqqkk6urKyUZlaa/2hZWdmmTZuUo1W6uroqKioU0UQlW5HSw99arG64hgYOl1gM8u3Nz+ZjUI4SEIAABCAAAQhAAAIQgAAEYk1g1HoSrkuWLLGfbrVixQpdFBcVFa1evbqhocF/rpKXLVs2b9485WjU5wdo+Y8qQRe/unaW9K2trVVCdXW1xLCmL1y4UJLbX2arbCiGBg6FUgxy7BLYvMohgAUBgwAEIAABCEAAAhCAAARSjoDUaXNzs21bAlU3umpLkdpFcUdHh/SqIjILKl9tmRqHL5L7bLqNWtt/VBENWb4mqmv5ivT09OhaWJHILBQNHFllZkEAAhCAAAQgAAEIQAACEIBA2hJob2/fvHmzCdQUOiQaOPjDYhQCEIAABCAAAQhAAAIQgAAEAhDQZWxjY2OAAXeH0MDufj7J3B1rQwACEIAABCAAAQhAAAIQSDcCaOB0e6KcJxYEqAEBCEAAAhCAAAQgAAEIpCcBNHB6PldOBYFICTAPAhCAAAQgAAEIQAAC6UwADRz3p+v8LOi4r8QCEIBAVASYDAEIQAACEIAABCCQ/gQySwP7y9Hdsy5O/4fMCSEAAQiMQoBhCEAAAhCAAAQgkCkE0lAD+wtd/4cZSo7/rAgitpD5CKYzBQIQgAAE4kyA8hCAAAQgAAEIZBaBNNTAoz5AU6TydglsftRZUSYMXLfcLMo6TIcABCAAAQjEiABlIAABCEAAAplIIIM0sESvnrB5NTAIQAACEIAABDKVAOeGAAQgAIHMJZBBGjj4Q3Zugx2R7ESCT2QUAhCAAAQgAAEIpA4BdgoBCEAg0wmkvAZGqWb6hzDnhwAEIAABCEAAAiERIAkCEIDAMIGU18DDhwjzP/u+XPkg80xamw+SxhAEIAABCEAAAhCAAARcT4ANQgACrxNIVQ3svGP59aP4tSJQsFbWvF89T1gFRyriX5YIBCAAAQhAAAIQgAAEIBAfAlSFgC+BlNTAJi/lTZSat5MpqIa8Bc0rEo3FpEg0G2AuBCAAAQhAAAIQgAAEIACBMAmQHphASmrgBa1rvwAAEABJREFUwEeJImoq13wUZZgKAQhAAAIQgAAEIAABCEAAAkknEGwDGa2BJ2zdKDbyMmsMXLfcTF0zE8bmLRKK10W00syrgUEAAhCAAAQgAAEIQAACEICAGwikuwYejbGp3yBZliBvMth8kHyfIVPU8j5xuhCAAAQgAAEIQAACEIAABCCQeAIZoYEjuI+V6DXTI1FD3tvCVcLecxPXZiUIQAACEIAABCAAAQhAAAIQeCOBjNDAbzxy4J6/0PXJU4KZCWDzTo7JbHkzJ04jOQRYFQIQgAAEIAABCEAAAhCAQCAC6aOBfURpoMPGKybdq9Lm1ZDZm5/Nq4tBIHEEWAkCEIAABCAAAQhAAAIQGJlAamtgXczqaPImgM0rgkEAAplIgDNDAAIQgAAEIAABCMSfQHt7e319vc86/f39lZWVRUVF5eXlvb29GpVX24lo1tSpU9U1U7KmKM1MbUVsSGlKVrypqckiaqgrq6mpsciGDRvUjcxSWwOHcma7njUfSn5YOT6q2y5+zYdVh2QIQAACURFgMgQgAAEIQAACEEgIASnbxYsX79q1y2e1VatW1dbW9vX1zZkzp6WlRaMNDQ233XabIkuWLFG7oqKip6dH3e7u7tmzZyu5oKBAad6miUpQmpLb29vb2tqU3NnZuWbNGnVN9yqhtbV15cqV2on33NDb6a+BHRaSpmZOJMqGCWDzUZZiOgQgAAEIREiAaRCAAAQgAAEIJJCA1KxUrv+CdXV1CxYscOISqF1dXZKyisybN0+aWTe9asu2bNkiP3fuXPkgJqE7c+ZM6eQpU6boPlkF165dW1VVpSmlpaXTpk1TRO0ILIM0cAR0Ak6Z8NpvFQ44KpkdME4QAhCAAAQgEGMClIMABCAAAQgklkBTU1NZWZkJUY/fH4lSiVWN1tXVqb1z504n5emnn969e7e6UsK33357wEtgjS5atKioqKiyslJpuv5VxLHt27dLVDtdFdcSTjesRppo4OC6NCwioSTbcsEzEcPB+TAKAQhAAAIQiJwAMyEAAQhAIOEE2g+/Obm6unqklXVh29HRodtaSeWRcnbs2FFYWBjwErikpES6t7u7W3PtrliNeJh7NXD+HTeZ7Z51sVnA8zty1BpSnmYBk2Mb1IpmsS1LNQhAAAIQgAAEIDAiAQYgAAEIJIOALmaXLl26fv364uJi3da2tLSMJHRramra2tokdGXOTqdNmzZhwgR1m5ub7R3OantbQUGBhqSi1TAxrAtn74QZM2Yo7kRUXMlON6yGezWw/zFMEsv7DxGBAAQgAAEIQAACEEhzAhwPAhBIHgFJ03Xr1vUd/iMBLBlsb3guLy83MVxfX6+LYm2wtbV14sSJkydPVtuJmO7t7e3VRfG8efM0JJOurqyslGZW2zEFu7q6JIBlmzZtUlezFKmoqFBEl8zK1GWyfGlpqXwElkoa2DmebnrVNq9GQNMNreLylmZeEQwCEIAABCAAAQhAAAIpRoDtQsCVBHQTu2TJks7OTu1uxYoVuiguKipavXp1Q0ODNPPNN998zTXXKCIpa++gliSWZpaaVb6PSetKDytZ98y6711w+I8a6kr61tbWai0VkRhWzsKFCyW5tYRPkRC7KamBQzxbwtIksM0StiILQQACEIAABCAAAQhAICMIcEj3EZA+bW5utn1JoFZVVaktRWoXxbrplV5VRFq35/AvQ1Jco4p4T1RXQQ1ZKWsfvmbus4gS1LCIJqrr5KisiisSmblOA+e/9m3A/ueRzlTQvBqjmi6BR80hAQIQgAAEIAABCEAAAhCAgBsJuH5PutfdvHmzCVTXb/b1DbpOA7++taCt0JWwyoSVrHwMAhCAAAQgAAEIQAACEICAewg894+Xt97S8ev33feT89b9oOzX8mq33frQ/lcOJnGTuoxtbGyMywbiWTRaDaxr2zhtz4SrvF3nmo/TWpSFAAQgAAEIQAACEIAABCDgQgIvP7xrY81ffnfllofuenzckWNO+48Tzv1kmbzaRxw/fswRuYcef2rg00tfeeeVe790m+dAMiWxC+kF3FK0GtgpunvWxU47Vo2B65ZbKQSwccBDAAIQgAAEIAABCEAAAplDYMfarv959x+f/8fLsz5f/qG/VV6y6nw1zvnkafJqn/HBk4Rib8OtB//016HnXzzwu417v9KkCBacQMw0cPBlGI2AAFMgAAEIQAACEIAABCAAgYwlIAH8v5//x/FzJ7/3dxeUX31Kbn6OP4qhF14afPhRJ36w7R9Om8ZIBKLSwPZGaHm7BDY/0krEIRA6ATIhAAEIQAACEIAABCCQyQRefniXCWDd946fPG4kFFnHHJV11JHOaM704Zthp0sjIIGQNLBUbsDJsQ0mZpXY7plqEIg5AQpCAAIQgAAEIAABCEDgb9/459iJeXNvPmckFFtv6ZBO1ui4z33SZHDOadPH1l6nCBacQEgaOHiJUEb9r4hN8SpuZl35geuWm4VSlhwIQCCdCHAWCEAAAhCAAAQgAAEReO4fL3e1Plfx8dMC3gAf3HPwD5/6a8edj/U9vlvJue+Yc8T6liN+9/PxP/lu9sknJPcnRWs/7rcEaWAfENK6iphXA4MABCCQ4QQ4PgQgAAEIQAACEHAIPPn7Z7NyssquONGJOI1dT72y/sN/enLjs+d9quzkhcVOPOuYo9T+58+eaP/Ow2pgQQjEWAPbpa68Lek0rIuHAAQgAAEI+BCgCwEIQAACEICAD4Hev71U/NZj/H8IVu9fX/zdB+9/4cF/n1xZrFtin1nqdrX29rS9qAYWhEB4GjgsTWvJ8jLtwLwaGAQgAAEIQAACHo8HCBCAAAQgAIGABHY++UrRKRN8hh5f98y6qx4YeHHfpFMnvuNrb/IZta5maa618SMRCE8Dj1RlwtaNZiMlEIcABCAAAQhAAAIOARoQgAAEIPD/2TsTIKvKa1Hri/c9FVSCBBSHoMYneYCFgCEyRC6ITCokDjGaPMWpiEMVlhYOYIVUIFFLzcvFxAnFGKMiYEEFccBZpmcJqNEUOAAqiFFRAkG9Jre832Xd/HfffZru092nOef0+bpW/qx/7fX/+1/fXuffe53dtPUQ+Nu2v//Ptv+UdXh1xptPX7Y8LJ+8vuXOrvNCsIcxWkYxNnTbHREoTQ2cZqcSRo8WpVD8N8CFTLRIQAISkIAEJCABCUhAAhJIBP6pzW5f/PVvqYvSfew3zl01OoT3wFiQf76xN3aUJIxibOpWrFLehTVcA0fVShu/zBwt3QbXTSUc0qCnDhKQgAQkIAEJSEACEpCABCQQBPbp0nbzW//xN5+jm22fvuxF3gPv0eF/jfpd/0NHHZg9hM4oxqIo9RBouAauZ3Cdh6h767TXZdwlnGnjv4dEW6ebRglIQAISkIAEJCABCUhAAjVCYL8++25Y8uHfP/u3XLwv/Wb1moc3fK3HV0/4/YD9ju6QO4o/oxibs9vNEWigBp6y/NncALrxEjhausVLDKGl1g1hLAUwbe2JEUtAAhKQgAQkIAEJSEACEqiDQJdhnb/8ty9XzVyXPbbqwXXL/2VVl+M7j/xtv72/3jZ7KHT8GcXY6NruiEADNXCdw6J8zbZ1uiVjVLm0DMEYLYpSqwSMWwISkIAEJCABCUhAAhLYIYFOvdofNKgTb30//eDz5LRl3bbuZx025F+O3m3P3ZIxKXjizyjGJqNKnQQaroEpWUMoYpkiWpRGSdNGNeoUOkugGgi4RglIQAISkIAEJCABCTRMoM+l/+dft/zt+Ykrk+u3JnTre1X31M0peOLPqJzdbiGBhmvg7Jjml7KU09kJ1SUggZohYKASkIAEJCABCUhAAsUSaH/E3t+5ttf65z947PylvOOtZxhH8cETf0bV4+mhINC4GjjGlLG1hC4jfE8tAQk0lYDjJCABCUhAAhKQQKMJHD7mIMpaituHTnjq1bvf+nvBn8jCgp2j+OCJf6PPUZMDGqiBJ/U+tiaxGLQEJCABCZSEgJNIQAISkIAEJNB0ApS13533zx17tf//1756T5+Hed+LsvLm1bToWFA4ig+eTT9NjY1soAauMRqGKwEJSEACEigVAeeRgAQkIAEJlIBA+yP2Pv7Wb59w38BuPzz084+/WD3r7RU3r6JFx4Kdo/iU4Ew1M4U1cM1cagOVgAQkIAEJ7CQCnkYCEpCABEpMoFOv9n2v6j56zrH/d/moc1eNpkXHgr3EZ6qB6ayBa+AiG6IEJCABCUhAAjuJgKeRgAQkIIFKJ2ANXOlXyPVJQAISkIAEJCCBaiDgGiUgAQlUBwFr4Oq4Tq5SAhKQgAQkIAEJSKBSCbguCUigmghYA1fT1XKtEpCABCQgAQlIQAISqCQCrkUC1UfAGrj6rpkrloAEJCABCUhAAhKQgATKTcDzVysBa+BqvXKuWwISkIAEJCABCUhAAhKQQDkIVPc5rYGr+/q5eglIQAISkIAEJCABCUhAAhIonkDzauDiz6OnBCQgAQlIQAISkIAEJCABCUig3ASsgZt6BRwnAQlIQAISkIAEJCABCUhAAtVGwBq42q5YJazXNUhAAhKQgAQkIAEJSEACEqhOAg3UwNdee2277T+PPvpoNsBt27aNGjWKI927d3///fc5RIuetWDMyv/b/pMsd91110svvRRdjuTmD3u52hQd4aSFsUi6yLhx4woXVucQxuKPwAqHwlHMiSQ7DJk8PNEZRZuOll1hqcSCoLAY1skK6SJEiiUrXNz999+fQ0QU9mRhFGPDmG2ZFkmWCs2Q7etj/URBdEjEzpWK/E+W7Y7/1aTwccM5DgAHfyQbeByKFoeYP7oMxMLZ6aKzBlr0ChGiIBYEhSWxNoKlS4uOJSsJCBGFPVmIK2IMe2rxzNKYOnVqmnbChAkMT54VorDgQBHroQsNJGuMQ7SsPz4yWVz1D2EUDlkmAMES9NAhSYtb5QirZYWxHtZGsAChRQ9jahOQ5J8sxBUxJudQ8GT+0GmrLkNYMzEOGDCgkEYcan6GwCelHzrEmLmipHBVLBJj4SIxkjxIigiFLoJS6I8lN1XlZwgxpmwnK/ikEB1pQJ4QTk4wcggH3HCOo4SMBckx4RPEzNhDOFH445bV6Ya9clqWR1CxHpRYf53rTEBwC/9kIXYIhJEWHUtMlejBkAzhKILOJLihV4ikWNKCY2HYS7KHEGxiAhmwx/ygzup0w14hLZeJ1SJpkSwMY7aLJQQjnkiKAoUughI+0WZpJOCg5kk1HNB5DgndtloINFADX3nllQ888ED//v0HDhyYDemOO+645JJLNm/ezCcNBw5Nnjz5hhtuwHLeeeehY8kK2bNw+w9K2F944YVnnnkm9FXbf0KvhPayyy476KCDiGXy5MmXX345ex/JTcgsGJk3bx4fm9w66xyCEX+Ce/vttxmeG8K006dPh0piguX5559/44038OSMK1aswIJeCcJ6uGQbNmwgnNmzZ9NlqSwMCwkwbdq0FKYnL6cAABAASURBVAVG9FtvvXXlypU4v/rqqzhjuf766x955BGowraQBg6gQFCYAeF00ENBmAdBqRDhyhIFsaQMQfn6178eNCJnskvlOv7whz+cMWMGDrjhzFF22HfffRcLAK+77jooYcwKFpJt7ty5ycg8YC9bhqR11KWwWi4Z4XClIkMIk90ASuwM6NlBXOXGZgixL1q0KNFghiVLlnBSpkV/7bXXcECvEGFJPD1wZdN66r/cLL7VZwgo2DlPP/10lBCygtwgQ8gT9DBGC8BayxACh8+gQYMAgp6TUmUIH8+0zfJpYgsCde5cZexCIJshrISH1+znCEsIQFg/Gw63CTYcuuwGuS0oPFOLTxXtISybTSNLg88InxTS44orrmC7IBx8ktDF2Ni7DHMyIe3YsWMByGxkCGBRSAxShS565Ug2QwiZhZEDLJIciPVjCWH9jdpD9tlnH3IJGmxK5557LsOZn4cWWiak5WFm69at6JUgrKfwcrMw+JR2DyE3YEJbFRlC+EBgwVzKKVOmQIluqfaQOjNkzpw5pApn4Vw8h4ROV6kKAg3UwF988QXlDeVumzZtuMB8+cGmTGDjx48fPnw4SgiHuJX27NmTLh+/LVu25PKAp3ZqBhIIBR+O4sO2hcJYbkvo2CtBYj1jxoxhMcSyadOm2Fg//vhjPlQcxb7ffvvR8rniO1eOYiSE3BCcu3Tpcvjhh+PMNwXcSwiWUUkY2K5du7/85S9UNWFkCAqlL+17773Xvn17ZkavBGFt3/rWt0gDwiENWBgKi49bAheXQ+w+AQSdew8OaeVx3aGBBVCFlxsHJqnSDFm2bBn5P3ToUAIn6sgZEAGKQojrzrUmcD4gOOBGtqxZs4aUQMeCfd9994UwPlnB0qtXL+67MZxDWGirIkPWrVsHk65du7Lgnj17ogOkORkChJEjR8Y8zBmJ99RTT6GTPDApTCoOlUwaOdHMmTPZNg888MAYRw4UXm6A1FSGcAW5XjxLBRPC52qSG3TZabkjQIn7C0w4xOeisXsI81dvhgCBqJcvX574hAUaJdxDIEweMjMfGXSUtIejl124gtkMYT3kw5AhQ/gWHj0EC0xgxU5LhpAnYadle8zdpDBmhfmrKENY7d57752tgYl3/Pjx2YjgAI3mZEjMxgcQhdkQ7jh8MFFij40PJkcrQWCSzZDCHGCRzbnLMByJTYnwgfDll19yUoxk11tvvYUFvRIkVsVS+QjwIMFDBWtDSr6HRLDVkiF8fcMTJmvmafPggw8GCDtGCfcQZkbATkuGxFMH2yldTs3rLozoSrUQaKAGfuedd4gk9xIYC0JusfmSbWzK6NxKMYYwKpcHbB94cn9CwYejbO4opA6fZG5L6HFLRimvEAu7Hnsry6ClOEEh4/nmlacTbkgodDEiHMWnziF8Nijq2J5wi0oAJSt8YE7Z/oMSdoZwM+ObbCakRccSh8re5lZCFwiTJk0iNF57QqZwhdyKjjvuOL6dxZOIshnCLTZ3uUmM6s2Q3XffnWvNwyVBEelnn32WaGAnB8DF69+99toLO8Ro8WS7DB07RzFmBQcmpI7iUwacOMQ8ZAW5wVlo0bHEobK3uZVQAxN7GPmME2zhChuVITz6nHHGGczJbEwFAXSez1Dgc9FFF/G1EfYKkXPOOYe0T4thx4NAnZebKCo9Q1IYzVMAcv3116c5uHDZPaHwroFn7WQIwXIrmThxIkpOSpgh5CHMjznmGD4y3Hw5UTwmolSC5DKEJfF0EY8H6HUKD7hAe+yxx6AXu01yy3WxV9ceAg22EZadEz4406dP54t1Qo5DTc6QGE5LPtAyIZOjdOvWjW0WQSFhSBuMlSAwye4haUkslS/QKXuSJSmN2kNiVPzeAfdlUmjEiBGkDbdj9B/96EfBJ9zK27IeHhtYJMuIOwsKV5CPA0pOaiRDuEw8W6bYSV2uV2n3kJg8ZQhPHbGdciIO8akMBV2pCgIN1MB8zHgK5xGNYPh08QUh+YSORJf6jZsQ3XqEnOBpns2Lb5KoiOgi1MDUPCjk0EknncTwytlnWUxO2GGvu+46Kj1qYBS6OPClLEDggN5YYQZe/wIEYRI4xKd38ODB7FYchQ86/Bs7807z59Yybdq0DRs28LGHDOcdPnz4xo0budDoCN0nnniCMp9w6NYjhF/tGQIBipwDDjiADNljjz3ICoQrS5LUE3g9h0gPJgQmHxPgkB4I+ztZUS0ZAhOe1dq1aweTuFuTEk3LEFJo/fr1POJAg6wDBTnTtWtXvlZDoXviiSdSD6PUg7TSDpkhhVeE+wufGsjEIRKmyD2kpTIk1lGmFg7QKOEewoeFUPiw8Emh7OHTxI0mjNirQnIZQnfq1KnDhg1rMIpWkyHsq5s2bRo3bhzXq5kZwgzMxhZNO2PGDG43YKSY5JGMBzNqPxS6GPGsWGF5U6ZMIQ3iSZVNo2l3GUomnsegwW3rzjvvZDYgYInPCLddbr6V/EhW5wUyQwqxsGmwr0ImDtEleYrZQ3IZwnCeOmI7JQl5GuGZBAW7Ui0EGqiBP/jgA7bFeoJhI+Z9FLskktz4ajm+nQoL957FixeTHOwmZB5dsqRz587MzCbLLnPUUUfhiZ22EuTjjz9mhayENt4J87jAK18e5W+88cZevXoVPpQUDmE4FQuPGijsm2ygbKnoIUxI9QgQBCZ8q8RXAHzA+FjixjcL7LZ4phnQyytcvuwC6LLI+H6E2ych1Hn5CIcamGBRshmSo8HYas8QAgQC17R///6jR48mt7O40CloucQoJAMFYdu2bUOnxc5RkKInAS9VH0ayDjiUxLhVV4ZkmQzd/oviKbqk4FNMhpBCCxYsiK8YVqxYwSssMAKHD+P8+fOZ7Rvf+AZ3I+zoFSusmbVxHQsvN3aMHELBrVVmCKFlhUuf3RNyd43kiVvtZEiKuk6l+RnCHQ3mnTp14s5CmsU2hVLn6XapEitRdO/enZsIG0J2yblu69hD+OqZp4XsL6NlQ25shjCW2/fm7T+UjnRJBu7ObKd890QXhZsOaYNesUIIbBGkwY5WWOQewkeDJAEGt3KG8PDGPQUCPKzef//9vXv3xsjjLvYdnWgn23OXO/vUvaOV5IY0+BzCPOCFCVL5GcLDM9nLmkO4oFyy0OtvSZ5i9hAmzGZI3K/JECbnOYSnEfYcntzoKtVC4H/Uv9COHTumHGIfJEvire+Ef/wVVhKCL8lwYx5uQrRY+DqEXEQPISfYtfkIISh0EXKFmdli+BDiTJfNN/zL2/KuiVRmhSyDWOLf9KLzzSsESHo2EVaLhfo//vlrnUP4ij0e1hm1aNEiugwJYQ/lzV7aWVDorl279pNPPsGBqpiCBzgItx/OiLHsQsgskpUTDs9PbBksKShhQWe13J4DCBbg4IwwirEcfeedd6JEYVSWBmOxkBikB4JCF4lRVZQhRN23b1/SgwtKUHT5vIwaNQoIXFPyhw8IOkAoCA855BDen6NjwY5/IEVBGEvOgAIgCGUwQDBWXYYQ3WmnnUZE559/Pm3TMoRJePLgwwgKhOe/xx9/HAtJxUdv6dKlPJ2wh7ARgYizVKCw1MLLzWprKkNy1yWe2CL5ubhx1+D+AhPIIMXvIa0gQ3JwogsEaJRwD4mbLOTZXdnGQwljnLHy25QhZA7PISwYSlG0EFTsqFiILrujNjpDmLfyhLguv/xyvmaN7ZQFYmlyhjC8UCIZ2K/YTrn5olBUhLHQuRIs3FO4V3KLTIvB0rTnkDRDKDyuQIDPCA+EzEk6QaNyHskKHyq4CcbKs22tZQibAA9LEODy0fKEQJuTpu0huUnosgXRgp0nEJ5DOBcZQs6w22BXqoJAAzVwnz596gzjJz/5ycSJE+OXRnjiJwmmTp06duxYLNyE0gbNWLKE+xPbBzqC8sorr3B/IlfoUguRsii0FbLPEsudd97Jrkos1113HXFhGT9+PC/32HRYJ69oUoC8HCYQHAqH8J0ZD+sxhMdfuoQZQolLecyh6KLQ5ZX4rrvuymzIwIED+ThxlJqHLQyl7ML6uRfyIg4CvP5lkVz3oMT6J02ahCUtEp3nV74ew59RjMWCD55QxQ0LbUjryBBiAQgtX58TLEoI4ZMepP2MGTO4TwMEe+TPjTfeiI4F+w033JAdRfXLIUbRInxM5s2bt2TJkurKEFYefwD8wQcfBALdJARbfIbweeHJIz4RzEAWPfTQQx9++CGTMO3JJ5+Mgp3MrJA9hMUUyo4ud+1kSI4J147dtc67Bp5c01rLEKKuU0qYITyoxWx8WPgmjkvAGbkdV+NDG9vj4MGDuaEQS7wG5LZCdOyoWOImRXQhrWMP4Razfv36xYsXEyOB8ygf0RE1lxIgjbrLxNhsSxqQDF23/yFDGHLfiaMVu6/ydMSXAjBhtQBhx4gFR9uoPSSGZFsm54sAwHLrIbUohhHeBGLPupVLr/Ny72gxtZMhPFyRwyTDiBEj+I6My7cjJthhWPwegn9O+FyQeBiZp1+/fpyLlONZpUJeXLEwpUECDdTAXNE0BTrVLNUgFi72ww8/zDsZLNixkAQbN27Egp2jWEKwUymFDxYUHuXxwU6XnQUJpfAXjLGXRVgkcRELEcU6WQbLw4Kw+AgQC244c5QWnaPZIbDCguCJTxJCzrpxCrp8p8AMzIPgzymwv/baa7RpYHkVVkUsCOtnJayTBdNFwkJLILFgWnQOMQpnhKN0kWTBiODZajIEIGAhKASFbgo2hZ/yh0uMDhCEowxJQuYwlhnCwlFgXnjhhWHEzrQMB10lZwiLJxBiZKnoSATCstFpCYrYiYUuwlG6SLJgRLBnJ2HgsmXL+K4NDhw95x9/fYpzIVgqRFge1ystCQhEQXQIEbHIcEjBYuQQghvOONCiY0E4iiUJ0zI5M4SFo8Cs/Axhtaw8hcylZNlER5gEG0dTXOlo8idMnJFkYQiCPc1Al4HVmCGsHIFPIsD1RU/BEiaxIylYoKFjQTjK8CTZeTByFNQzZ86M2ejigJ2WGZgHvUKEJcUiYz2sjRWy4OhyFCaQoYuRwBGMdBEG0kU4RDcJXSZhqrBUS4awWiJKK0cntCREDQdoYMcTIcw4moYQMnoYOYpPkjjEJHVasIc/kyPJpxIU1hZLivAjOtowsmxSnUvMUmnR0yEsHKWLhDOWEGjcd999PXv2jC4tnuHDIb7Bp0VyPriVUVghgSBcYtaWVgIfsgI4WGjRIxC6hUMYyHAmQTiKT5I4xGx1WrCHP5Mjyae8SqyZWLjuXP1YTBhjtVhYOUwgg44RZwQjXYRY6CIcopuESXJXnyEIDkzFA3wowKSLrlQFgQZq4KqIwUVKQAISkIAEJCABCUhAAk0h4BgJ1B4Ba+Dau+ZGLAEJSEACEpCABCQgAQlIoFYJWAPX6pU3bglIQAISkIAEJCABCUigNgnUdtTWwLV9/Y1eAhKQgAQkIAEJSEACEpBA7RDYZRdr4Fq62sb/ek3sAAAQAElEQVQqAQlIQAISkIAEJCABCUigtgnUbg1c29fd6CUgAQlIQAISkIAEJCABCdQiAWvgWrzqxiwBCUhAAhKQgAQkIAEJSKA2CVgD1+Z1r92ojVwCEpCABCQgAQlIQAISqGUC1sC1fPWNvbYIGK0EJCABCUhAAhKQgAQk0HAN/Kk/EpCABKqcgMuXgAQkIAEJSEACEqgRAg0W+Q3XwA1OoYMEJCABCVQsARcmAQlIQAISkIAEJJAlUF8NvKc/EpCABCQggaol4MIlIAEJSEACEqhZAtmiN6fXVwPnXO1KQAISkIAEJFAVBFykBCQgAQlIQAI7ImANvCMy2iUgAQlIQAISqD4CrlgCEpCABCRQPwFr4Pr5eFQCEpCABCQgAQlUBwFXKQEJSEACxRCwBi6Gkj4SkIAEJCABCUhAApVLwJVJQAISKJ6ANXDxrPSUgAQkIAEJSEACEpBAZRFwNRKQQGMJWAM3lpj+EpCABCQgAQlIQAISkED5CbgCCTSNgDVw07g5SgISkIAEJCABCUhAAhKQQHkIeNbmELAGbg49x0pAAhKQgAQkIAEJSEACEpDAziPQ/DNZAzefoTNIQAISkIAEJCABCUhAAhKQQHUQqN4auDr4ukoJSEACEpCABCQgAQlIQAISqBwC1sCVcy2KX4meEpCABCQgAQlIQAISkIAEJNAUAtbATaHmmPIR8MwSkIAEJCABCUhAAhKQgASaTsAauOnsHCmBnUvAs0lAAhKQgAQkIAEJSEACzSVgDdxcgo6XgARanoBnkIAEJCABCUhAAhKQQGkIWAOXhqOzSEACEmgZAs4qAQlIQAISkIAEJFBKAtbApaTpXBKQgAQkUDoCziQBCUhAAhKQgARKT8AauPRMnVECEpCABCTQPAKOloAEJCABCUigpQhYA7cUWeeVgAQkIAEJSKDxBBwhAQlIQAISaFkC1sAty9fZJSABCUhAAhKQQHEE9JKABCQggZ1BwBq4bsrvvvvu7bff/tZbb9V9eKdY165dO3369HXr1u2Us3kSCUhAAhKQgAQkUC4CnlcCEpDAziNQVA3croifWPKf//zncePG/e/tPyh0w/7yyy8XzjF48OA4mmsLPXOWnH9JuukUzLZ+/foePXpMmDChd+/eq1atwtJ8eeqpp9IpVqxYUeeEZ5xxRvhA5vXXXz/qqKMuv/zynj17UgzX6a9RAhKQgAQkIAEJSKDKCbh8CUhgZxMoqgYuflFXX331Aw888MH2HxS6Mfbuu+8OJdueffbZ2W7l6NTAaTGrV69OenMUylq+GYgZeMMcSrb96KOPFixYEBbI8CI6dFrqYVpFAhKQgAQkIAEJSEACrYiAoUigPARKXAM/+eST2Tiefvpputu2baMeRslKmzZtTjnllKylcvRvf/vbQ4cOZT3HHHPM6NGjUUoiF1xwQcwDDb4lCD21GEMPMkOGDBk4cCCWQYMGDRs2DEWRgAQkIAEJSEACEpCABFoFAYMoJ4ES18Bf+cpXstHsuuuudGfNmvXZZ5+hZOX73//+nnvumbUkfXPmp35jOppV3n///UWLFr3yyitZY6Eebum3tXMOrJlVPPLIIzk73U8//XTFihX1/I70hx9+yALw+fzzz/FPcuaZZ7Zt2za6M2bMCCW19957b+iJzB/+8AfWMHfu3LBnW3guW7ZsyZIlW7ZsydrVJSABCUhAAhKQgAQkIAEJVDCB8i+txDUwLy2zMUW3sN7DZ+zYsbRNk/hHs7R//etff/3rX/M+OV6iUnNOmjSpa9euJ5xwwne+850DDjhg8eLFuIWkc2XdjjjiCN76Ukymo6HEENpc9+abb+7cufPgwYMZddBBB6XfXg43yuMpU6b06NGDBeDTpUuXZ555hklC9thjj3POOSc877rrrlCiXblyZSqqE5kYRRs+0T788MPHHXfc/vvvP3z48JEjRx588MHXXHMN5+UoS8IZyb6NZxlYRowYgUPIo48+igXBPyy2EpCABCQgAQlIQAISkIAEaoRAiWvgn//857zG7NChQ8eOHX/wgx/84he/eHn7T45mr169evTokTM2oXv66adPnDjxiSeeiH89S5caNc2zbdu2k08+OXWTknOj+Pze976XjtavUGMnh61bt55xxhkMDwvvZgcOHHjDDTdQY4cFhXOFHu35558fCu+fedUcOu3vfvc7WqR+MhdddBEvk1988UU8k0ybNg0j3ZNOOokWWbhwIS2yceNGXkejLF269KOPPkJBHn/8cVok+aMrEpCABCQgAQlIQAISkIAEaoFAUTXw5oKfHaHp1KnTbbfd9uabb77++uu33HILlXCL/jWsRYsW8T7zyCOP3G233aZOncpL11hYv379LrnkkiFDhlCFhiW1hW68rC50S/45Za+99po8eTJvcQ899NA4dNNNN4VC9Zv+W0q9e/e++OKLjz322NzMvDrmFfEu2wfccccd2/9/F3xmz54d+tlnnx1KYfvYY4/9/ve/x77PPvv86le/mj9/Pu/A9957byxPP/00h9I/XU6/ws1LY46G8Po3FOYJJflH11YCEpCABCQgAQlIQAISkECrJ1BUDdxkCryJnTlzZm5427ZtTynRX8PiVfO6deuee+65Sy+9NJWUFKULFiz42c9+NmfOnMJfwy50mzt3bjLmllrYvf3228ePH897Y+rPOJreyqZJKJKffPLJKVOmzJs3r3ABF154YQx84YUXVq5ciU6lGv+st34yVO84I7zIPeusswYMGMDr3wcffBALct9993Xr1q1Lly7ob7/99htvvIGSimF0amZa3lpv2LABBU/8UXa2eD4JSEACEpCABCQgAQlIQALlI9CyNfCsWbPin6pmAzzttNN29Newsm7F6D/96U/D7U9/+hPvqtHbt2+f/s0t3e9+97uHHHIISsiO3E499VTe0IZP/W3//v3DoXv37qGsWbMGZcWKFVHHdujQgSIZSwgLSJ5h4R11stx6660Y77nnHlqkfjLpr3z17duXt98hw4cPZyDy0ksv0aZXuwsXLuQLCEpxjCG8B+ZaUD9HN3lG13ZnEPAcEpCABCQgAQlIQAISkEC5CbRsDVz4FpR40998Qm+mdOzYMWbYunVrKAceeGAoqT388MOTXo/bN7/5zeRWjxK/e4zDXnvtRZvkiy++CL1z586hpPaII45Ieig//vGPQ+ElOa+Cn3322ejWQyb7nywO51xLxfv555+nf+JLxZvK3SuuuCKcKYnTL0InzzhkK4GWJeDsEpCABCQgAQlIQAISqAwCLVgDb/9jWC/nwqz/bz7lnIvv8lI0nOOPY4Uebfo3unSLdMOzsbLffvvFkLVr13723/9DUK+99locSu2ZZ5657777Rhc9lPrJtGvXLtwYOH8HP7vvvnvv3r0POOAAPJ977jkKbJSvfvWrV111Fa/H0e+///7Fixej4IMniiIBCbQ4AU8gAQlIQAISkIAEJFBJBFqwBm7Rv4aVY8i71qgSP/nkk+zL53nz5mVr4CLdcpMX0+3Spcthhx2GJ6+ax40bhxJy9dVXr1q1KvRse+6550b3vffeC6Wev4aFQ9u2bfv06YOyafvPgH/8HHzwwVOmTMGOgRY58cQTaRFeBdOOGjWKNowLFixAR6KLokhAAhJoQQJOLQEJSEACEpCABCqPQEvVwNu2bYv3kNmQKeRK9dewstOGft5554Vy6aWXjhgx4pprrhkzZsxZZ50VxtQW6Zb8i1euvPLKcKbw7ty58/HHH09h/Jvf/CaMuTbVwGEvhsz48ePDmaCGDBnC292TTz75yCOPXLZs2QknnHDvvffG0dwvOUcXhzgabRhDt5WABCQggdITcEYJSEACEpCABCqVQEvVwLNnz/70009zUdf/N59yzo3tTpo0qW/fvjFq6dKl06ZNS/+ppDBGW6RbODeqPfXUUydMmBBDiP2FF17YvHkzZXBYcm2nTp2yXwcUQ4Y6llPEPMuXL7/llluefPLJ6B599NHDhg0LvV+/fh06dAi9TZs2lOLoQ4cOTf+AmaP4YFQkIAEJSEACpSfgjBKQgAQkIIHKJtBSNfBdd91VGHg9f/Op0LkJloceeuiCCy6IX4pmeNeuXefPn4+SkyLdcqOK6V599dW8/R49ejR1Zvv27XlbO3fu3B0NTH8ZC4ciydxxxx333HNP9s93devW7Yorrli4cOHXvvY15glJv+rM+/Cw0I4cOZIW8SUwEBQJSEACEpBA6Qk4owQkIAEJVAOBJtbAvOHMSS7YZ599NudAt0ePHjm3BruMCsl6hoU2a0Tntef111+/bt26ZcuWrV69mnbAgAG4heAQUoxbDKGNISgh0Y02LLTRpUXnfexvf/vbN998c82aNXPmzNmwYQN2pPAPVvfu3Rv/kDrJxCFahiehguUt9xNPPEF5T4yLFy++6qqr0tFQfvnLXzIKmT59elhob7vtNizITTfdRFeRgAQkIAEJSEACJSPgRBKQgASqh0ATa+AKD5A3wJ06dWpwkUW6NThPcjjllFN49bp8+fKwUADzZjj0QYMGhVKStk+fPpT3xcRYktM5iQQkIAEJSEACEpBA3QS0SkAC1UagddbAZbkKkydPfvHFF3lJO2TIkMMOO2zgwIHdunV7+eX//K9DXXzxxWVZlSeVgAQkIAEJSEACEpBAixBwUglUJwFr4JJdt/79+6dfeN60adMf//jHNPXdd9/NO+fUVZGABCQgAQlIQAISkIAEqpiAS69mAtbAJbt6Q4cOffXVV2fNmnXaaadRDyPHHXfclVdeuWjRojFjxpTsNE4kAQlIQAISkIAEJCABCUigXASq/7zWwCW+hlTCt99++8Pbf2bPnk0N3L179xKfw+kkIAEJSEACEpCABCQgAQlIoEkEml4DN+l0DpKABCQgAQlIQAISkIAEJCABCZSNgDVwU9A7RgISkIAEJCABCUhAAhKQgASqkYA1cDVetXKu2XNLQAISkIAEJCABCUhAAhKoXgLWwNV77Vz5zibg+SQgAQlIQAISkIAEJCCBaidgDVztV9D1S2BnEPAcEpCABCQgAQlIQAISaB0ErIFbx3U0CglIoKUIOK8EJCABCUhAAhKQQGsiYA3cmq6msUhAAhIoJQHnkoAEJCABCUhAAq2PgDVw67umRiQBCUhAAs0l4HgJSEACEpCABForAWvg1npljUsCEpCABCTQFAKOkYAEJCABCbRuAtbArfv6Gp0EJCABCUhAAsUS0E8CEpCABGqBgDVwLVxlY5SABCQgAQlIQAL1EfCYBCQggdohYA1cO9faSCUgAQlIQAISkIAE8gTsS0ACtUbAGrjWrrjxSkACEpCABCQgAQlI4D8I+D8J1CYBa+DavO5GLQEJSEACEpCABCQggdolYOS1TMAauJavvrFLQAISkIAEJCABCUhAArVFwGitgc0BCUhAAhKQgAQkIAEJSEACEmj9BCJCa+DgYCsBCUhAAhKQgAQkIAEJZOFZlAAAAAxJREFUSEACrZ/AvwMAAP//kyJ7MQAAAAZJREFUAwDrsjJTbtTTFgAAAABJRU5ErkJggg=="
+    }
+   },
+   "cell_type": "markdown",
+   "id": "8fcd9481",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6512af2c",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Pinescript Folder/30m high & low/30m highlighter in lower timeframe.ipynb

@@ -0,0 +1,125 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "74c3e3dd",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "//@version=5\n",
+    "indicator(\"30-Min High/Low Area Color + Line Until Body Overlap\", overlay=true)\n",
+    "\n",
+    "// Get 30-minute values\n",
+    "thirtyMinHigh = request.security(syminfo.tickerid, \"30\", high, lookahead=barmerge.lookahead_on)\n",
+    "thirtyMinLow  = request.security(syminfo.tickerid, \"30\", low, lookahead=barmerge.lookahead_on)\n",
+    "thirtyMinOpen = request.security(syminfo.tickerid, \"30\", open, lookahead=barmerge.lookahead_on)\n",
+    "thirtyMinClose = request.security(syminfo.tickerid, \"30\", close, lookahead=barmerge.lookahead_on)\n",
+    "thirtyMinBarIndex = request.security(syminfo.tickerid, \"30\", bar_index, lookahead=barmerge.lookahead_on)\n",
+    "\n",
+    "// Determine candle direction\n",
+    "isBullish = thirtyMinClose > thirtyMinOpen\n",
+    "isBearish = thirtyMinClose < thirtyMinOpen\n",
+    "\n",
+    "// Plot invisible outlines for fill\n",
+    "plotHighGreen = plot(isBullish ? thirtyMinHigh : na, title=\"High Bullish\", color=color.new(#4caf4f, 100))\n",
+    "plotLowGreen = plot(isBullish ? thirtyMinLow : na, title=\"Low Bullish\", color=color.new(#4caf4f, 100))\n",
+    "\n",
+    "plotHighRed = plot(isBearish ? thirtyMinHigh : na, title=\"High Bearish\", color=color.new(#ff5252, 100))\n",
+    "plotLowRed = plot(isBearish ? thirtyMinLow : na, title=\"Low Bearish\", color=color.new(#ff5252, 100))\n",
+    "\n",
+    "// Fill between high and low based on candle direction\n",
+    "fill(plotHighGreen, plotLowGreen, color=color.new(#008080, 93), title=\"Bullish Area\")\n",
+    "fill(plotHighRed, plotLowRed, color=color.new(#FF0000, 93), title=\"Bearish Area\")\n",
+    "\n",
+    "\n",
+    "// State tracking\n",
+    "var int prevThirtyMinBarIndex = na\n",
+    "\n",
+    "var float highPrice = na\n",
+    "var int highStartBar = na\n",
+    "var line highLine = na\n",
+    "var bool highActive = false\n",
+    "\n",
+    "var float lowPrice = na\n",
+    "var int lowStartBar = na\n",
+    "var line lowLine = na\n",
+    "var bool lowActive = false\n",
+    "\n",
+    "// Detect new 30-minute period\n",
+    "if thirtyMinBarIndex != prevThirtyMinBarIndex\n",
+    "    prevThirtyMinBarIndex := thirtyMinBarIndex\n",
+    "\n",
+    "    // Reset high\n",
+    "    highPrice := na\n",
+    "    highStartBar := na\n",
+    "    if not na(highLine)\n",
+    "        line.delete(highLine)\n",
+    "    highLine := na\n",
+    "    highActive := false\n",
+    "\n",
+    "    // Reset low\n",
+    "    lowPrice := na\n",
+    "    lowStartBar := na\n",
+    "    if not na(lowLine)\n",
+    "        line.delete(lowLine)\n",
+    "    lowLine := na\n",
+    "    lowActive := false\n",
+    "\n",
+    "// Extend high line until body overlaps\n",
+    "if highActive and not na(highLine)\n",
+    "    bodyMin = math.min(open, close)\n",
+    "    bodyMax = math.max(open, close)\n",
+    "    if highPrice <= bodyMax and highPrice >= bodyMin\n",
+    "        line.set_x2(highLine, bar_index)\n",
+    "        line.set_y2(highLine, highPrice)\n",
+    "        highActive := false\n",
+    "    else\n",
+    "        line.set_x2(highLine, bar_index)\n",
+    "        line.set_y2(highLine, highPrice)\n",
+    "\n",
+    "// Extend low line until body overlaps\n",
+    "if lowActive and not na(lowLine)\n",
+    "    bodyMin = math.min(open, close)\n",
+    "    bodyMax = math.max(open, close)\n",
+    "    if lowPrice <= bodyMax and lowPrice >= bodyMin\n",
+    "        line.set_x2(lowLine, bar_index)\n",
+    "        line.set_y2(lowLine, lowPrice)\n",
+    "        lowActive := false\n",
+    "    else\n",
+    "        line.set_x2(lowLine, bar_index)\n",
+    "        line.set_y2(lowLine, lowPrice)\n"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "3eb02f43",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3931b081",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Pinescript Folder/3am to close & 9am to 11am Candle/limited candles.ipynb

@@ -0,0 +1,194 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c62e0188",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "//@version=5\n",
+    "indicator(\"Custom Candle with Matching Wicks (Adjustable)\", overlay=true)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "//━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n",
+    "// FIRST CANDLE\n",
+    "//━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n",
+    "\n",
+    "\n",
+    "// === Adjustable Session Settings ===\n",
+    "startH1 = input.int(20, \"First Candle Start Hour\", minval=0, maxval=23)\n",
+    "startM1 = input.int(0, \"First Candle Start Minute\", minval=0, maxval=59)\n",
+    "endH1   = input.int(22, \"First Candle End Hour\", minval=0, maxval=23)\n",
+    "endM1   = input.int(15, \"First Candle End Minute\", minval=0, maxval=59)\n",
+    "\n",
+    "\n",
+    "// === Detect session start and end ===\n",
+    "isStart1 = (hour == startH1 and minute == startM1)\n",
+    "isEnd1   = (hour == endH1 and minute == endM1)\n",
+    "\n",
+    "\n",
+    "// === Declare variables ===\n",
+    "var float o1 = na\n",
+    "var float h1 = na\n",
+    "var float l1 = na\n",
+    "var float c1 = na\n",
+    "var int x1_start = na\n",
+    "var int x1_end   = na\n",
+    "\n",
+    "\n",
+    "// === Start of session ===\n",
+    "if isStart1\n",
+    "    o1 := open\n",
+    "    h1 := high\n",
+    "    l1 := low\n",
+    "    x1_start := bar_index\n",
+    "\n",
+    "\n",
+    "// === During session ===\n",
+    "if (hour > startH1 or (hour == startH1 and minute > startM1)) and\n",
+    "   (hour < endH1 or (hour == endH1 and minute <= endM1))\n",
+    "    h1 := math.max(h1, high)\n",
+    "    l1 := math.min(l1, low)\n",
+    "\n",
+    "\n",
+    "// === End of session ===\n",
+    "if isEnd1\n",
+    "    c1 := close\n",
+    "    x1_end := bar_index\n",
+    "\n",
+    "\n",
+    "    candleColor1 = c1 >= o1 ? color.green : color.red\n",
+    "    bodyTop1     = math.max(o1, c1)\n",
+    "    bodyBottom1  = math.min(o1, c1)\n",
+    "    x1_mid       = math.floor((x1_start + x1_end) / 2)\n",
+    "\n",
+    "\n",
+    "    // Draw candle body\n",
+    "    box.new(left = x1_start, right = x1_end, top = bodyTop1, bottom = bodyBottom1, border_color = candleColor1, bgcolor = color.new(candleColor1, 70))\n",
+    "\n",
+    "\n",
+    "    // Draw wick top\n",
+    "    line.new(x1_mid, bodyTop1, x1_mid, h1, color=candleColor1)\n",
+    "\n",
+    "\n",
+    "    // Draw wick bottom\n",
+    "    line.new(x1_mid, bodyBottom1, x1_mid, l1, color=candleColor1)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "//━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n",
+    "// SECOND CANDLE\n",
+    "//━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n",
+    "\n",
+    "\n",
+    "// === Adjustable Settings ===\n",
+    "startH2 = input.int(15, \"Second Candle Start Hour\", minval=0, maxval=23)\n",
+    "startM2 = input.int(0, \"Second Candle Start Minute\", minval=0, maxval=59)\n",
+    "endH2   = input.int(16, \"Second Candle End Hour\", minval=0, maxval=23)\n",
+    "\n",
+    "\n",
+    "// === Dynamic endMinute based on timeframe ===\n",
+    "var int endM2 = na\n",
+    "if timeframe.isminutes\n",
+    "    tfMins = timeframe.multiplier\n",
+    "    if tfMins == 1\n",
+    "        endM2 := 59\n",
+    "    else if tfMins == 3\n",
+    "        endM2 := 57\n",
+    "    else if tfMins == 5\n",
+    "        endM2 := 55\n",
+    "    else if tfMins == 15\n",
+    "        endM2 := 45\n",
+    "    else\n",
+    "        endM2 := 59  // Default fallback\n",
+    "\n",
+    "\n",
+    "// === Detect session start and end ===\n",
+    "isStart2 = (hour == startH2 and minute == startM2)\n",
+    "isEnd2   = (hour == endH2 and minute == endM2)\n",
+    "\n",
+    "\n",
+    "// === Declare session variables ===\n",
+    "var float o2 = na\n",
+    "var float h2 = na\n",
+    "var float l2 = na\n",
+    "var float c2 = na\n",
+    "var int x2_start = na\n",
+    "var int x2_end   = na\n",
+    "\n",
+    "\n",
+    "// === Start of session ===\n",
+    "if isStart2\n",
+    "    o2 := open\n",
+    "    h2 := high\n",
+    "    l2 := low\n",
+    "    x2_start := bar_index\n",
+    "\n",
+    "\n",
+    "// === During session ===\n",
+    "if (hour > startH2 or (hour == startH2 and minute > startM2)) and\n",
+    "   (hour < endH2 or (hour == endH2 and minute <= endM2))\n",
+    "    h2 := math.max(h2, high)\n",
+    "    l2 := math.min(l2, low)\n",
+    "\n",
+    "\n",
+    "// === End of session ===\n",
+    "if isEnd2\n",
+    "    c2 := close\n",
+    "    x2_end := bar_index\n",
+    "\n",
+    "\n",
+    "    candleColor2 = c2 >= o2 ? color.green : color.red\n",
+    "    bodyTop2     = math.max(o2, c2)\n",
+    "    bodyBottom2  = math.min(o2, c2)\n",
+    "    x2_mid       = math.floor((x2_start + x2_end) / 2)\n",
+    "\n",
+    "\n",
+    "    // Draw candle body\n",
+    "    box.new(left = x2_start, right = x2_end, top = bodyTop2, bottom = bodyBottom2, border_color = candleColor2, bgcolor = color.new(candleColor2, 70))\n",
+    "\n",
+    "\n",
+    "    // Draw wick top\n",
+    "    line.new(x2_mid, bodyTop2, x2_mid, h2, color=candleColor2)\n",
+    "\n",
+    "\n",
+    "    // Draw wick bottom\n",
+    "    line.new(x2_mid, bodyBottom2, x2_mid, l2, color=candleColor2)\n"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "33b64283",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1634ab33",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Pinescript Folder/4CCP + 4CRP Patterns/4CCP + 4CRP Patterns.ipynb

@@ -0,0 +1,131 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c9d0bb40",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "//@version=5\n",
+    "indicator(\"4CCP + 4CRP Patterns\", overlay=true, max_boxes_count=500, max_lines_count=500, max_labels_count=500)\n",
+    "\n",
+    "// === Restrict to 1-minute timeframe ===\n",
+    "is_1m = timeframe.period == \"1\"\n",
+    "\n",
+    "// === Candle Data ===\n",
+    "o = open\n",
+    "c = close\n",
+    "\n",
+    "// === Helper: body top, bottom, mid ===\n",
+    "getBodyCoords(_o, _c) =>\n",
+    "    top = math.max(_o, _c)\n",
+    "    bot = math.min(_o, _c)\n",
+    "    mid = (top + bot) / 2\n",
+    "    [top, bot, mid]\n",
+    "\n",
+    "// === Persistent arrays for drawing objects ===\n",
+    "var box[] boxes = array.new_box()\n",
+    "var line[] lines = array.new_line()\n",
+    "var label[] labels = array.new_label()\n",
+    "\n",
+    "// === Draw helper function ===\n",
+    "drawBoxAndLine(idx, top, bot, mid, colorFill, labelText) =>\n",
+    "    x1 = idx\n",
+    "    x2 = x1 + 5\n",
+    "    bx = box.new(left=x1, right=x2, top=top, bottom=bot, bgcolor=color.new(colorFill, 70), border_color=colorFill)\n",
+    "    ln = line.new(x1=x1, y1=mid, x2=x2, y2=mid, color=colorFill, width=1, style=line.style_dotted)\n",
+    "    array.push(boxes, bx)\n",
+    "    array.push(lines, ln)\n",
+    "    txt = label.new(x=x1+2, y=mid+syminfo.mintick*20, text=labelText, style=label.style_none, textcolor=color.white, size=size.tiny)\n",
+    "    array.push(labels, txt)\n",
+    "\n",
+    "// === === 4CCP Patterns ===\n",
+    "// --- T1.1 ---\n",
+    "t1_1 = c[3] > o[3] and c[2] < o[3] and c[1] > o[2] and c[1] < c[3] and c > c[1] and c > o[2] and c > c[3]\n",
+    "if is_1m and t1_1\n",
+    "    [top, bot, mid] = getBodyCoords(o[2], c[2])\n",
+    "    drawBoxAndLine(bar_index-2, top, bot, mid, color.rgb(0,192,255), \"4CCP-T1.1\")\n",
+    "\n",
+    "// --- T1.2 ---\n",
+    "t1_2 = c[3] > o[3] and c[2] < o[3] and c[1] > c[2] and c[1] < o[2] and c > c[1] and c > o[2] and c > c[3]\n",
+    "if is_1m and t1_2\n",
+    "    [top, bot, mid] = getBodyCoords(o[2], c[2])\n",
+    "    drawBoxAndLine(bar_index-2, top, bot, mid, color.rgb(255,192,0), \"4CCP-T1.2\")\n",
+    "\n",
+    "// --- T2.1 ---\n",
+    "t2_1 = c[3] < o[3] and c[2] > o[3] and c[1] < o[2] and c[1] < c[3] and c < c[1] and c < o[2] and c < c[3]\n",
+    "if is_1m and t2_1\n",
+    "    [top, bot, mid] = getBodyCoords(o[2], c[2])\n",
+    "    drawBoxAndLine(bar_index-2, top, bot, mid, color.rgb(255,64,64), \"4CCP-T2.1\")\n",
+    "\n",
+    "// --- T2.2 ---\n",
+    "t2_2 = c[3] < o[3] and c[2] > o[3] and c[1] < c[2] and c[1] > o[2] and c < c[1] and c < o[2] and c < c[3]\n",
+    "if is_1m and t2_2\n",
+    "    [top, bot, mid] = getBodyCoords(o[2], c[2])\n",
+    "    drawBoxAndLine(bar_index-2, top, bot, mid, color.rgb(255,128,0), \"4CCP-T2.2\")\n",
+    "\n",
+    "// === === 4CRP Patterns (rectangle/line from 1st candle) ===\n",
+    "// --- 4CRP-T1.1 (bearish first candle) ---\n",
+    "c1_bear = c[3] < o[3]\n",
+    "in_range2 = math.max(o[2], c[2]) <= math.max(o[3], c[3]) and math.min(o[2], c[2]) >= math.min(o[3], c[3])\n",
+    "in_range3 = math.max(o[1], c[1]) <= math.max(o[3], c[3]) and math.min(o[1], c[1]) >= math.min(o[3], c[3])\n",
+    "in_range4 = math.max(o, c) <= math.max(o[3], c[3]) and math.min(o, c) >= math.min(o[3], c[3])\n",
+    "cond_4CRP_T1_1 = c1_bear and in_range2 and in_range3 and in_range4\n",
+    "\n",
+    "if is_1m and cond_4CRP_T1_1\n",
+    "    [top, bot, mid] = getBodyCoords(o[3], c[3])  // 1st candle range\n",
+    "    drawBoxAndLine(bar_index-3, top, bot, mid, color.purple, \"4CRP-T1.1\")\n",
+    "\n",
+    "// --- 4CRP-T2.1 (bullish first candle) ---\n",
+    "c1_bull = c[3] > o[3]\n",
+    "in_range2b = math.max(o[2], c[2]) <= math.max(o[3], c[3]) and math.min(o[2], c[2]) >= math.min(o[3], c[3])\n",
+    "in_range3b = math.max(o[1], c[1]) <= math.max(o[3], c[3]) and math.min(o[1], c[1]) >= math.min(o[3], c[3])\n",
+    "in_range4b = math.max(o, c) <= math.max(o[3], c[3]) and math.min(o, c) >= math.min(o[3], c[3])\n",
+    "cond_4CRP_T2_1 = c1_bull and in_range2b and in_range3b and in_range4b\n",
+    "\n",
+    "if is_1m and cond_4CRP_T2_1\n",
+    "    [top, bot, mid] = getBodyCoords(o[3], c[3])  // 1st candle range\n",
+    "    drawBoxAndLine(bar_index-3, top, bot, mid, color.green, \"4CRP-T2.1\")\n",
+    "\n",
+    "// === Optional plotshapes for reference ===\n",
+    "plotshape(is_1m and t1_1, text=\"↑T1.1\", style=shape.triangleup, location=location.belowbar, color=color.rgb(0,192,255), size=size.tiny)\n",
+    "plotshape(is_1m and t1_2, text=\"↑T1.2\", style=shape.triangleup, location=location.belowbar, color=color.rgb(255,192,0), size=size.tiny)\n",
+    "plotshape(is_1m and t2_1, text=\"↓T2.1\", style=shape.triangledown, location=location.abovebar, color=color.rgb(255,64,64), size=size.tiny)\n",
+    "plotshape(is_1m and t2_2, text=\"↓T2.2\", style=shape.triangledown, location=location.abovebar, color=color.rgb(255,128,0), size=size.tiny)\n",
+    "plotshape(is_1m and cond_4CRP_T1_1, text=\"4CRP-T1\", style=shape.triangleup, location=location.belowbar, color=color.purple, size=size.tiny)\n",
+    "plotshape(is_1m and cond_4CRP_T2_1, text=\"4CRP-T2\", style=shape.triangleup, location=location.belowbar, color=color.green, size=size.tiny)\n"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "b5980db4",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4eabbd09",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Pinescript Folder/4CCP + 6CBP Pattern Detector + 30minute bars (AsiaManila)/4CCP + 6CBP Pattern Detector + 30minute bars (AsiaManila).ipynb

@@ -0,0 +1,417 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "92b75b14",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "//@version=5\n",
+    "indicator(\"4CCP + 6CBP Pattern Detector + 30minute bars (Asia/Manila)\", overlay=true, max_boxes_count=500, max_lines_count=500, max_labels_count=500)\n",
+    "\n",
+    "// ────────────── Restrict to 1-Minute Timeframe\n",
+    "is_1m = timeframe.period == \"1\"\n",
+    "\n",
+    "// ────────────── Candle Data\n",
+    "o = open\n",
+    "h = high\n",
+    "l = low\n",
+    "c = close\n",
+    "\n",
+    "// ────────────── Helpers for Manila time formatting\n",
+    "f_two(n) => n < 10 ? \"0\" + str.tostring(n) : str.tostring(n)\n",
+    "\n",
+    "// return \"HH:MM\" for a given bar offset (use offset = 0 for current bar, 2 for bar_index[2], etc.)\n",
+    "manilaTimeStr(offset) =>\n",
+    "    // use built-in time[] timestamp and extract hour/minute in specified timezone\n",
+    "    hh = hour(time[offset], \"Asia/Manila\")\n",
+    "    mm = minute(time[offset], \"Asia/Manila\")\n",
+    "    f_two(hh) + \":\" + f_two(mm)\n",
+    "\n",
+    "// If you prefer HH:MM:SS, replace the return with:\n",
+    "// ss = second(time[offset], \"Asia/Manila\")\n",
+    "// f_two(hh) + \":\" + f_two(mm) + \":\" + f_two(ss)\n",
+    "\n",
+    "// ────────────── Utility\n",
+    "isBull(c_, o_) => c_ > o_\n",
+    "isBear(c_, o_) => c_ < o_\n",
+    "\n",
+    "// ────────────── Body coords (top, bottom, mid)\n",
+    "getBodyCoords(_o, _c) =>\n",
+    "    _top = math.max(_o, _c)\n",
+    "    _bot = math.min(_o, _c)\n",
+    "    _mid = (_top + _bot) / 2\n",
+    "    [_top, _bot, _mid]\n",
+    "\n",
+    "// ────────────── 4CCP Patterns\n",
+    "\n",
+    "// 4CCP-T1.1 Bullish\n",
+    "t1_1 = isBull(c[3], o[3]) and\n",
+    "       isBear(c[2], o[2]) and c[2] < o[3] and\n",
+    "       isBull(c[1], o[1]) and c[1] > o[2] and c[1] > c[3] and\n",
+    "       isBull(c, o) and c > c[1] and c > o[2] and c > c[3]\n",
+    "\n",
+    "// 4CCP-T1.2 Bullish Type 2\n",
+    "t1_2 = isBull(c[3], o[3]) and\n",
+    "       isBear(c[2], o[2]) and c[2] < o[3] and\n",
+    "       isBull(c[1], o[1]) and c[1] > c[2] and c[1] < o[2] and\n",
+    "       isBull(c, o) and c > c[1] and c > o[2] and c > c[3]\n",
+    "\n",
+    "// 4CCP-T2.1 Bearish\n",
+    "t2_1 = isBear(c[3], o[3]) and\n",
+    "       isBull(c[2], o[2]) and c[2] > o[3] and\n",
+    "       isBear(c[1], o[1]) and c[1] < o[2] and c[1] < c[3] and\n",
+    "       isBear(c, o) and c < c[1] and c < o[2] and c < c[3]\n",
+    "\n",
+    "// 4CCP-T2.2 Bearish Type 2\n",
+    "t2_2 = isBear(c[3], o[3]) and\n",
+    "       isBull(c[2], o[2]) and c[2] > o[3] and\n",
+    "       isBear(c[1], o[1]) and c[1] < c[2] and c[1] > o[2] and\n",
+    "       isBear(c, o) and c < c[1] and c < o[2] and c < c[3]\n",
+    "\n",
+    "// ────────────── Persistent arrays\n",
+    "var box[] boxes = array.new_box()\n",
+    "var line[] lines = array.new_line()\n",
+    "var label[] labs = array.new_label()\n",
+    "\n",
+    "// ────────────── Draw helper (WITH Manila time stamp)\n",
+    "drawBoxAndLine(cond, baseBarOffset, extendBars, colorFill) =>\n",
+    "    if is_1m and cond\n",
+    "        [top, bot, mid] = getBodyCoords(open[baseBarOffset], close[baseBarOffset])\n",
+    "        x1 = bar_index - baseBarOffset\n",
+    "        x2 = x1 + extendBars\n",
+    "\n",
+    "        bx = box.new(left = x1, right = x2, top = top, bottom = bot,\n",
+    "                     bgcolor = color.new(colorFill, 70), border_color = colorFill)\n",
+    "        ln = line.new(x1 = x1, y1 = mid, x2 = x2, y2 = mid,\n",
+    "                      color = colorFill, width = 1, style = line.style_dotted)\n",
+    "\n",
+    "        array.push(boxes, bx)\n",
+    "        array.push(lines, ln)  \n",
+    "\n",
+    "        // Manila timestamp for the pattern bar\n",
+    "        ts = manilaTimeStr(baseBarOffset)\n",
+    "        tLabel = label.new( x1, top,ts, style = label.style_label_down,textcolor = color.rgb(0, 0, 0),  color = color.new(colorFill, 100),size = size.small )\n",
+    "        array.push(labs, tLabel)\n",
+    "\n",
+    "// ────────────── Draw all patterns\n",
+    "drawBoxAndLine(t1_1, 2, 5, color.rgb(0, 192, 255))\n",
+    "drawBoxAndLine(t1_2, 2, 5, color.rgb(0, 38, 255))\n",
+    "drawBoxAndLine(t2_1, 2, 5, color.rgb(255, 64, 64))\n",
+    "drawBoxAndLine(t2_2, 2, 5, color.rgb(255, 128, 0))\n",
+    "\n",
+    "// ────────────────────────────\n",
+    "// Helper Function: Midpoint\n",
+    "get_mid(_o, _c) => (_o + _c) / 2\n",
+    "\n",
+    "// ─────────────────────────��──\n",
+    "// 6CBP Logic\n",
+    "bear_bull1 = c[5] > o[5]\n",
+    "bear_bull2 = c[4] > o[4] and c[4] > c[5]\n",
+    "bear_bear3 = c[3] < o[3] and c[3] < o[4]\n",
+    "bear_bull4 = c[2] > c[3] and c[2] < o[3]\n",
+    "bear_bear5 = c[1] < o[2] and c[1] < c[3] and c[1] < o[4]\n",
+    "bear_bear6 = c < o and c < o[5] and c < c[1]\n",
+    "\n",
+    "bearish_6CBP = bear_bull1 and bear_bull2 and bear_bear3 and bear_bull4 and bear_bear5 and bear_bear6\n",
+    "\n",
+    "o4_bear = o[2]\n",
+    "c4_bear = c[2]\n",
+    "h4_bear = h[2]\n",
+    "l4_bear = l[2]\n",
+    "mid4_bear = get_mid(o4_bear, c4_bear)\n",
+    "\n",
+    "// Bullish inverse\n",
+    "bull_bear1 = c[5] < o[5]\n",
+    "bull_bear2 = c[4] < o[4] and c[4] < c[5]\n",
+    "bull_bull3 = c[3] > o[3] and c[3] > o[4]\n",
+    "bull_bear4 = c[2] < c[3] and c[2] > o[3]\n",
+    "bull_bull5 = c[1] > o[2] and c[1] > c[3] and c[1] > o[4]\n",
+    "bull_bull6 = c > o and c > o[5] and c > c[1]\n",
+    "\n",
+    "bullish_6CBP = bull_bear1 and bull_bear2 and bull_bull3 and bull_bear4 and bull_bull5 and bull_bull6\n",
+    "\n",
+    "o4_bull = o[2]\n",
+    "c4_bull = c[2]\n",
+    "h4_bull = h[2]\n",
+    "l4_bull = l[2]\n",
+    "mid4_bull = get_mid(o4_bull, c4_bull)\n",
+    "\n",
+    "// ────────────────────────────\n",
+    "// 🟥 Bearish 6CBP\n",
+    "if bearish_6CBP\n",
+    "    // Tiny diamond on top wick of 4th candle\n",
+    "    label.new(bar_index[2], h4_bear, \"6CBP↓\",  style=label.style_diamond, color=color.new(color.red, 0), textcolor=color.new(#000000, 0), size=size.tiny)\n",
+    "\n",
+    "    // Rectangle for 4th candle’s body, extended 10 bars right\n",
+    "    box.new(left=bar_index[2], right=bar_index[2] + 10, top=math.max(o4_bear, c4_bear), bottom=math.min(o4_bear, c4_bear),bgcolor=color.new(color.red, 85),border_color=color.new(color.red, 0))\n",
+    "\n",
+    "    // Midline for reference\n",
+    "    line.new(bar_index[2], mid4_bear, bar_index[2] + 10, mid4_bear,  color=color.new(color.red, 0), width=1 )\n",
+    "\n",
+    "    // ✅ Manila timestamp for this 4th-bar (offset = 2)\n",
+    "    label.new(bar_index[2], h4_bear, manilaTimeStr(2), style = label.style_label_down,  textcolor = color.rgb(0, 0, 0), color = color.new(color.red, 100),  size = size.small)\n",
+    "\n",
+    "// ────────────────────────────\n",
+    "// 🟩 Bullish 6CBP\n",
+    "if bullish_6CBP\n",
+    "    // Tiny diamond on bottom wick of 4th candle\n",
+    "    label.new(bar_index[2], l4_bull, text=\"6CBP↑\",style=label.style_diamond,color=color.new(color.lime, 0),textcolor=color.new(#000000, 0), size=size.tiny )\n",
+    "\n",
+    "    // Rectangle for 4th candle’s body, extended 10 bars right\n",
+    "    box.new(left=bar_index[2], right=bar_index[2] + 10, top=math.max(o4_bull, c4_bull),bottom=math.min(o4_bull, c4_bull), bgcolor=color.new(color.lime, 85),    border_color=color.new(color.lime, 0)  )\n",
+    "\n",
+    "    // Midline for reference\n",
+    "    line.new(  bar_index[2],  mid4_bull,  bar_index[2] + 10,  mid4_bull, color=color.new(color.lime, 0), width=1  )\n",
+    "\n",
+    "    // ✅ Manila timestamp for this 4th-bar (offset = 2)\n",
+    "    label.new(  bar_index[2], l4_bull, manilaTimeStr(2),style = label.style_label_up, textcolor = color.rgb(0, 0, 0),color = color.new(color.lime, 100),  size = size.small)\n",
+    "\n",
+    "///////////////////////////////\n",
+    "\n",
+    "// === INPUTS ===\n",
+    "showSeconds = input.bool(false, \"Show seconds (HH:MM:SS)\", inline=\"s\")\n",
+    "labelBg = input.color(color.new(#363a45, 100), \"Label background\")\n",
+    "labelTxtColor = input.color(color.rgb(0, 0, 0), \"Label text color\")\n",
+    "\n",
+    "// === HELPERS: lowercase day and month names ===\n",
+    "f_dayname(_d) =>_d == dayofweek.monday ? \"mon\" : _d == dayofweek.tuesday ? \"tue\" :_d == dayofweek.wednesday ? \"wed\" :_d == dayofweek.thursday ? \"thu\" :_d == dayofweek.friday ? \"fri\" : _d == dayofweek.saturday ? \"sat\" : \"sun\"\n",
+    "\n",
+    "f_monthname(_m) => _m == 1 ? \"jan\" : _m == 2 ? \"feb\" :_m == 3 ? \"mar\" :_m == 4 ? \"apr\" : _m == 5 ? \"may\" : _m == 6 ? \"jun\" : _m == 7 ? \"jul\" :_m == 8 ? \"aug\" : _m == 9 ? \"sep\" : _m == 10 ? \"oct\" : _m == 11 ? \"nov\" :  \"dec\"\n",
+    "\n",
+    "// === TIME CONVERSION TO ASIA/MANILA (UTC+8) ===\n",
+    "t_now_manila = timenow + (13 * 60 * 60 * 1000)  // correct offset is +8 hours\n",
+    "\n",
+    "// Extract components in Asia/Manila timezone\n",
+    "hh = hour(t_now_manila)\n",
+    "min_ = minute(t_now_manila)\n",
+    "sec_ = second(t_now_manila)\n",
+    "dofw = dayofweek(t_now_manila)\n",
+    "mon = month(t_now_manila)\n",
+    "dy = dayofmonth(t_now_manila)\n",
+    "yr = year(t_now_manila)\n",
+    "\n",
+    "// Format helpers\n",
+    "ff_two(n) => n < 10 ? \"0\" + str.tostring(n) : str.tostring(n)\n",
+    "\n",
+    "// Format \"HH:MM\" or \"HH:MM:SS\"\n",
+    "timeStr = showSeconds ? ff_two(hh) + \":\" + ff_two(min_) + \":\" + ff_two(sec_) : ff_two(hh) + \":\" + ff_two(min_) + \" utc8\"\n",
+    "// Format \"mon/oct 27/2025\"\n",
+    "dateStr = f_dayname(dofw) + \"/\" + f_monthname(mon) + \" \" + ff_two(dy) + \"/\" + str.tostring(yr)\n",
+    "\n",
+    "lblText = timeStr + \"   \" + \"\\n          \" + dateStr\n",
+    "\n",
+    "// === DRAW LABEL ON MOST RECENT CLOSE ===\n",
+    "var label lbl = na\n",
+    "\n",
+    "if barstate.islast\n",
+    "    if na(lbl)\n",
+    "        lbl := label.new( bar_index - 5 , close, lblText,   xloc=xloc.bar_index, yloc=yloc.price, style=label.style_label_left,   color=labelBg, textcolor=labelTxtColor, size=size.small )\n",
+    "    else\n",
+    "        label.set_xy(lbl, bar_index, close)\n",
+    "        label.set_text(lbl, lblText)\n",
+    "        label.set_color(lbl, labelBg)\n",
+    "        label.set_textcolor(lbl, labelTxtColor)\n",
+    "        label.set_size(lbl, size.small)\n",
+    "\n",
+    " //30minute bar candles Logic\\\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "// ---- SETTINGS ----\n",
+    "len    = input.int(30, \"Number of CLOSED 30m candles\", minval=1)\n",
+    "offset = input.int(5, \"Offset from right edge (bars)\")\n",
+    "spacing = 3    // horizontal footprint per 30m candle (2 body + 1 gap)\n",
+    "\n",
+    "// ---- COLOR SETTINGS ----\n",
+    "bullColor = input.color(color.new(#dbdbdb, 0), \"Bull Candle Color\")\n",
+    "bearColor = input.color(color.new(#2e2e2e, 0), \"Bear Candle Color\")\n",
+    "wickColor = input.color(#2e2e2e, \"Wick Color\")\n",
+    "borderCol = input.color(#2e2e2e, \"Border Color\")\n",
+    "\n",
+    "// ---- GET 30m CLOSED series ----   \n",
+    "[o30, h30, l30, c30] = request.security(syminfo.tickerid, \"30\", [open, high, low, close], barmerge.gaps_on)\n",
+    "\n",
+    "// ---- detect 30m boundary ----\n",
+    "isNew30Boundary = ta.change(time(\"30\"))\n",
+    "\n",
+    "// ---- Buffers for closed candles ----\n",
+    "var float[] bufO = array.new_float(len, na)\n",
+    "var float[] bufH = array.new_float(len, na)\n",
+    "var float[] bufL = array.new_float(len, na)\n",
+    "var float[] bufC = array.new_float(len, na)\n",
+    "\n",
+    "// Maintain buffer if len changed\n",
+    "if array.size(bufO) != len\n",
+    "    bufO := array.new_float(len, na)\n",
+    "    bufH := array.new_float(len, na)\n",
+    "    bufL := array.new_float(len, na)\n",
+    "    bufC := array.new_float(len, na)\n",
+    "\n",
+    "// ---- Store CLOSED 30m candle on new boundary ----\n",
+    "if isNew30Boundary\n",
+    "    closedO = o30[1]\n",
+    "    closedH = h30[1]\n",
+    "    closedL = l30[1]\n",
+    "    closedC = c30[1]\n",
+    "\n",
+    "    if not na(closedO) and not na(closedC)\n",
+    "        array.unshift(bufO, closedO)\n",
+    "        array.unshift(bufH, closedH)\n",
+    "        array.unshift(bufL, closedL)\n",
+    "        array.unshift(bufC, closedC)\n",
+    "\n",
+    "        if array.size(bufO) > len\n",
+    "            array.pop(bufO)\n",
+    "            array.pop(bufH)\n",
+    "            array.pop(bufL)\n",
+    "            array.pop(bufC)\n",
+    "\n",
+    "// ---- Historical Prefill ----\n",
+    "var bool didPrefill = false\n",
+    "if not didPrefill\n",
+    "    maxScanBars = 2000\n",
+    "    var float[] tO = array.new_float()\n",
+    "    var float[] tH = array.new_float()\n",
+    "    var float[] tL = array.new_float()\n",
+    "    var float[] tC = array.new_float()\n",
+    "\n",
+    "    for i = 1 to maxScanBars\n",
+    "        if bar_index - i < 0\n",
+    "            break\n",
+    "        if time(\"30\")[i] != time(\"30\")[i+1]\n",
+    "            co = o30[i+1]\n",
+    "            ch = h30[i+1]\n",
+    "            cl = l30[i+1]\n",
+    "            cc = c30[i+1]\n",
+    "            if not na(co) and not na(cc)\n",
+    "                array.push(tO, co)\n",
+    "                array.push(tH, ch)\n",
+    "                array.push(tL, cl)\n",
+    "                array.push(tC, cc)\n",
+    "        if array.size(tO) >= len\n",
+    "            break\n",
+    "\n",
+    "    if array.size(tO) > 0\n",
+    "        bufO := array.new_float()\n",
+    "        bufH := array.new_float()\n",
+    "        bufL := array.new_float()\n",
+    "        bufC := array.new_float()\n",
+    "        for j = array.size(tO) - 1 to 0 by -1\n",
+    "            array.push(bufO, array.get(tO, j))\n",
+    "            array.push(bufH, array.get(tH, j))\n",
+    "            array.push(bufL, array.get(tL, j))\n",
+    "            array.push(bufC, array.get(tC, j))\n",
+    "        while array.size(bufO) < len\n",
+    "            array.push(bufO, na)\n",
+    "            array.push(bufH, na)\n",
+    "            array.push(bufL, na)\n",
+    "            array.push(bufC, na)\n",
+    "    didPrefill := true\n",
+    "\n",
+    "// ---- LIVE 30m candle on lower TF (manual accumulation) ----\n",
+    "tf_sec = 30 * 60  // 30 minutes in seconds\n",
+    "curr30_open_time = math.floor(time / 1000 / tf_sec) * tf_sec * 1000  // start time of current 30m candle in ms\n",
+    "\n",
+    "var float liveO = na\n",
+    "var float liveH = na\n",
+    "var float liveL = na\n",
+    "var float liveC = na\n",
+    "\n",
+    "if na(liveO) or time == curr30_open_time\n",
+    "    liveO := open\n",
+    "    liveH := high\n",
+    "    liveL := low\n",
+    "\n",
+    "liveH := math.max(liveH, high)\n",
+    "liveL := math.min(liveL, low)\n",
+    "liveC := close\n",
+    "\n",
+    "// ---- DRAW ----\n",
+    "var box[] bodies = array.new_box()\n",
+    "var line[] wicks = array.new_line()\n",
+    "\n",
+    "if barstate.isfirst\n",
+    "    // +1 slot for LIVE candle\n",
+    "    for i = 0 to len\n",
+    "        array.push(bodies, na)\n",
+    "        array.push(wicks, na)\n",
+    "\n",
+    "// ---- RENDER ----\n",
+    "anchor = bar_index\n",
+    "for i = 0 to len\n",
+    "    _o = i == 0 ? liveO : array.get(bufO, i - 1)\n",
+    "    _h = i == 0 ? liveH : array.get(bufH, i - 1)\n",
+    "    _l = i == 0 ? liveL : array.get(bufL, i - 1)\n",
+    "    _c = i == 0 ? liveC : array.get(bufC, i - 1)\n",
+    "\n",
+    "    if na(_o) or na(_c)\n",
+    "        continue\n",
+    "\n",
+    "    hi = math.max(_o, _c)\n",
+    "    lo = math.min(_o, _c)\n",
+    "\n",
+    "    xL = anchor + offset + (len - i + 1) * spacing\n",
+    "    xR = xL + 2\n",
+    "    xM = xL + 1\n",
+    "\n",
+    "    _bg = _c >= _o ? bullColor : bearColor\n",
+    "\n",
+    "    // body\n",
+    "    body = array.get(bodies, i)\n",
+    "    if na(body)\n",
+    "        body := box.new(xL, hi, xR, lo, border_color=borderCol, bgcolor=_bg)\n",
+    "        array.set(bodies, i, body)\n",
+    "    else\n",
+    "        box.set_left(body, xL)\n",
+    "        box.set_right(body, xR)\n",
+    "        box.set_top(body, hi)\n",
+    "        box.set_bottom(body, lo)\n",
+    "        box.set_bgcolor(body, _bg)\n",
+    "\n",
+    "    // wick\n",
+    "    wick = array.get(wicks, i)\n",
+    "    if na(wick)\n",
+    "        wick := line.new(xM, _h, xM, _l, width=1, color=wickColor)\n",
+    "        array.set(wicks, i, wick)\n",
+    "    else\n",
+    "        line.set_x1(wick, xM)\n",
+    "        line.set_x2(wick, xM)\n",
+    "        line.set_y1(wick, _h)\n",
+    "        line.set_y2(wick, _l)\n"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "e967ece1",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ea257dc",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Pinescript Folder/4CCP Pattern detector/4CCP Pattern detector-pinescript.js

@@ -0,0 +1,374 @@
+//@version=5
+indicator("4CCP + 6CBP Pattern Detector + 30minute bars (Asia/Manila)", overlay=true, max_boxes_count=500, max_lines_count=500, max_labels_count=500)
+
+// ────────────── Restrict to 1-Minute Timeframe
+is_1m = timeframe.period == "1"
+
+// ────────────── Candle Data
+o = open
+h = high
+l = low
+c = close
+
+// ────────────── Helpers for Manila time formatting
+f_two(n) => n < 10 ? "0" + str.tostring(n) : str.tostring(n)
+
+// return "HH:MM" for a given bar offset (use offset = 0 for current bar, 2 for bar_index[2], etc.)
+manilaTimeStr(offset) =>
+    // use built-in time[] timestamp and extract hour/minute in specified timezone
+    hh = hour(time[offset], "Asia/Manila")
+    mm = minute(time[offset], "Asia/Manila")
+    f_two(hh) + ":" + f_two(mm)
+
+// If you prefer HH:MM:SS, replace the return with:
+// ss = second(time[offset], "Asia/Manila")
+// f_two(hh) + ":" + f_two(mm) + ":" + f_two(ss)
+
+// ────────────── Utility
+isBull(c_, o_) => c_ > o_
+isBear(c_, o_) => c_ < o_
+
+// ────────────── Body coords (top, bottom, mid)
+getBodyCoords(_o, _c) =>
+    _top = math.max(_o, _c)
+    _bot = math.min(_o, _c)
+    _mid = (_top + _bot) / 2
+    [_top, _bot, _mid]
+
+// ────────────── 4CCP Patterns
+
+// 4CCP-T1.1 Bullish
+t1_1 = isBull(c[3], o[3]) and
+       isBear(c[2], o[2]) and c[2] < o[3] and
+       isBull(c[1], o[1]) and c[1] > o[2] and c[1] > c[3] and
+       isBull(c, o) and c > c[1] and c > o[2] and c > c[3]
+
+// 4CCP-T1.2 Bullish Type 2
+t1_2 = isBull(c[3], o[3]) and
+       isBear(c[2], o[2]) and c[2] < o[3] and
+       isBull(c[1], o[1]) and c[1] > c[2] and c[1] < o[2] and
+       isBull(c, o) and c > c[1] and c > o[2] and c > c[3]
+
+// 4CCP-T2.1 Bearish
+t2_1 = isBear(c[3], o[3]) and
+       isBull(c[2], o[2]) and c[2] > o[3] and
+       isBear(c[1], o[1]) and c[1] < o[2] and c[1] < c[3] and
+       isBear(c, o) and c < c[1] and c < o[2] and c < c[3]
+
+// 4CCP-T2.2 Bearish Type 2
+t2_2 = isBear(c[3], o[3]) and
+       isBull(c[2], o[2]) and c[2] > o[3] and
+       isBear(c[1], o[1]) and c[1] < c[2] and c[1] > o[2] and
+       isBear(c, o) and c < c[1] and c < o[2] and c < c[3]
+
+// ────────────── Persistent arrays
+var box[] boxes = array.new_box()
+var line[] lines = array.new_line()
+var label[] labs = array.new_label()
+
+// ────────────── Draw helper (WITH Manila time stamp)
+drawBoxAndLine(cond, baseBarOffset, extendBars, colorFill) =>
+    if is_1m and cond
+        [top, bot, mid] = getBodyCoords(open[baseBarOffset], close[baseBarOffset])
+        x1 = bar_index - baseBarOffset
+        x2 = x1 + extendBars
+
+        bx = box.new(left = x1, right = x2, top = top, bottom = bot,
+                     bgcolor = color.new(colorFill, 70), border_color = colorFill)
+        ln = line.new(x1 = x1, y1 = mid, x2 = x2, y2 = mid,
+                      color = colorFill, width = 1, style = line.style_dotted)
+
+        array.push(boxes, bx)
+        array.push(lines, ln)  
+
+        // Manila timestamp for the pattern bar
+        ts = manilaTimeStr(baseBarOffset)
+        tLabel = label.new( x1, top,ts, style = label.style_label_down,textcolor = color.rgb(0, 0, 0),  color = color.new(colorFill, 100),size = size.small )
+        array.push(labs, tLabel)
+
+// ────────────── Draw all patterns
+drawBoxAndLine(t1_1, 2, 5, color.rgb(0, 192, 255))
+drawBoxAndLine(t1_2, 2, 5, color.rgb(0, 38, 255))
+drawBoxAndLine(t2_1, 2, 5, color.rgb(255, 64, 64))
+drawBoxAndLine(t2_2, 2, 5, color.rgb(255, 128, 0))
+
+// ────────────────────────────
+// Helper Function: Midpoint
+get_mid(_o, _c) => (_o + _c) / 2
+
+// ────────────────────────────
+// 6CBP Logic
+bear_bull1 = c[5] > o[5]
+bear_bull2 = c[4] > o[4] and c[4] > c[5]
+bear_bear3 = c[3] < o[3] and c[3] < o[4]
+bear_bull4 = c[2] > c[3] and c[2] < o[3]
+bear_bear5 = c[1] < o[2] and c[1] < c[3] and c[1] < o[4]
+bear_bear6 = c < o and c < o[5] and c < c[1]
+
+bearish_6CBP = bear_bull1 and bear_bull2 and bear_bear3 and bear_bull4 and bear_bear5 and bear_bear6
+
+o4_bear = o[2]
+c4_bear = c[2]
+h4_bear = h[2]
+l4_bear = l[2]
+mid4_bear = get_mid(o4_bear, c4_bear)
+
+// Bullish inverse
+bull_bear1 = c[5] < o[5]
+bull_bear2 = c[4] < o[4] and c[4] < c[5]
+bull_bull3 = c[3] > o[3] and c[3] > o[4]
+bull_bear4 = c[2] < c[3] and c[2] > o[3]
+bull_bull5 = c[1] > o[2] and c[1] > c[3] and c[1] > o[4]
+bull_bull6 = c > o and c > o[5] and c > c[1]
+
+bullish_6CBP = bull_bear1 and bull_bear2 and bull_bull3 and bull_bear4 and bull_bull5 and bull_bull6
+
+o4_bull = o[2]
+c4_bull = c[2]
+h4_bull = h[2]
+l4_bull = l[2]
+mid4_bull = get_mid(o4_bull, c4_bull)
+
+// ──��─────────────────────────
+// 🟥 Bearish 6CBP
+if bearish_6CBP
+    // Tiny diamond on top wick of 4th candle
+    label.new(bar_index[2], h4_bear, "6CBP↓",  style=label.style_diamond, color=color.new(color.red, 0), textcolor=color.new(#000000, 0), size=size.tiny)
+
+    // Rectangle for 4th candle’s body, extended 10 bars right
+    box.new(left=bar_index[2], right=bar_index[2] + 10, top=math.max(o4_bear, c4_bear), bottom=math.min(o4_bear, c4_bear),bgcolor=color.new(color.red, 85),border_color=color.new(color.red, 0))
+
+    // Midline for reference
+    line.new(bar_index[2], mid4_bear, bar_index[2] + 10, mid4_bear,  color=color.new(color.red, 0), width=1 )
+
+    // ✅ Manila timestamp for this 4th-bar (offset = 2)
+    label.new(bar_index[2], h4_bear, manilaTimeStr(2), style = label.style_label_down,  textcolor = color.rgb(0, 0, 0), color = color.new(color.red, 100),  size = size.small)
+
+// ────────────────────────────
+// 🟩 Bullish 6CBP
+if bullish_6CBP
+    // Tiny diamond on bottom wick of 4th candle
+    label.new(bar_index[2], l4_bull, text="6CBP↑",style=label.style_diamond,color=color.new(color.lime, 0),textcolor=color.new(#000000, 0), size=size.tiny )
+
+    // Rectangle for 4th candle’s body, extended 10 bars right
+    box.new(left=bar_index[2], right=bar_index[2] + 10, top=math.max(o4_bull, c4_bull),bottom=math.min(o4_bull, c4_bull), bgcolor=color.new(color.lime, 85),    border_color=color.new(color.lime, 0)  )
+
+    // Midline for reference
+    line.new(  bar_index[2],  mid4_bull,  bar_index[2] + 10,  mid4_bull, color=color.new(color.lime, 0), width=1  )
+
+    // ✅ Manila timestamp for this 4th-bar (offset = 2)
+    label.new(  bar_index[2], l4_bull, manilaTimeStr(2),style = label.style_label_up, textcolor = color.rgb(0, 0, 0),color = color.new(color.lime, 100),  size = size.small)
+
+///////////////////////////////
+
+// === INPUTS ===
+showSeconds = input.bool(false, "Show seconds (HH:MM:SS)", inline="s")
+labelBg = input.color(color.new(#363a45, 100), "Label background")
+labelTxtColor = input.color(color.rgb(0, 0, 0), "Label text color")
+
+// === HELPERS: lowercase day and month names ===
+f_dayname(_d) =>_d == dayofweek.monday ? "mon" : _d == dayofweek.tuesday ? "tue" :_d == dayofweek.wednesday ? "wed" :_d == dayofweek.thursday ? "thu" :_d == dayofweek.friday ? "fri" : _d == dayofweek.saturday ? "sat" : "sun"
+
+f_monthname(_m) => _m == 1 ? "jan" : _m == 2 ? "feb" :_m == 3 ? "mar" :_m == 4 ? "apr" : _m == 5 ? "may" : _m == 6 ? "jun" : _m == 7 ? "jul" :_m == 8 ? "aug" : _m == 9 ? "sep" : _m == 10 ? "oct" : _m == 11 ? "nov" :  "dec"
+
+// === TIME CONVERSION TO ASIA/MANILA (UTC+8) ===
+t_now_manila = timenow + (13 * 60 * 60 * 1000)  // correct offset is +8 hours
+
+// Extract components in Asia/Manila timezone
+hh = hour(t_now_manila)
+min_ = minute(t_now_manila)
+sec_ = second(t_now_manila)
+dofw = dayofweek(t_now_manila)
+mon = month(t_now_manila)
+dy = dayofmonth(t_now_manila)
+yr = year(t_now_manila)
+
+// Format helpers
+ff_two(n) => n < 10 ? "0" + str.tostring(n) : str.tostring(n)
+
+// Format "HH:MM" or "HH:MM:SS"
+timeStr = showSeconds ? ff_two(hh) + ":" + ff_two(min_) + ":" + ff_two(sec_) : ff_two(hh) + ":" + ff_two(min_) + " utc8"
+// Format "mon/oct 27/2025"
+dateStr = f_dayname(dofw) + "/" + f_monthname(mon) + " " + ff_two(dy) + "/" + str.tostring(yr)
+
+lblText = timeStr + "   " + "\n          " + dateStr
+
+// === DRAW LABEL ON MOST RECENT CLOSE ===
+var label lbl = na
+
+if barstate.islast
+    if na(lbl)
+        lbl := label.new( bar_index - 5 , close, lblText,   xloc=xloc.bar_index, yloc=yloc.price, style=label.style_label_left,   color=labelBg, textcolor=labelTxtColor, size=size.small )
+    else
+        label.set_xy(lbl, bar_index, close)
+        label.set_text(lbl, lblText)
+        label.set_color(lbl, labelBg)
+        label.set_textcolor(lbl, labelTxtColor)
+        label.set_size(lbl, size.small)
+
+ //30minute bar candles Logic\
+
+
+
+
+// ---- SETTINGS ----
+len    = input.int(30, "Number of CLOSED 30m candles", minval=1)
+offset = input.int(5, "Offset from right edge (bars)")
+spacing = 3    // horizontal footprint per 30m candle (2 body + 1 gap)
+
+// ---- COLOR SETTINGS ----
+bullColor = input.color(color.new(#dbdbdb, 0), "Bull Candle Color")
+bearColor = input.color(color.new(#2e2e2e, 0), "Bear Candle Color")
+wickColor = input.color(#2e2e2e, "Wick Color")
+borderCol = input.color(#2e2e2e, "Border Color")
+
+// ---- GET 30m CLOSED series ----   
+[o30, h30, l30, c30] = request.security(syminfo.tickerid, "30", [open, high, low, close], barmerge.gaps_on)
+
+// ---- detect 30m boundary ----
+isNew30Boundary = ta.change(time("30"))
+
+// ---- Buffers for closed candles ----
+var float[] bufO = array.new_float(len, na)
+var float[] bufH = array.new_float(len, na)
+var float[] bufL = array.new_float(len, na)
+var float[] bufC = array.new_float(len, na)
+
+// Maintain buffer if len changed
+if array.size(bufO) != len
+    bufO := array.new_float(len, na)
+    bufH := array.new_float(len, na)
+    bufL := array.new_float(len, na)
+    bufC := array.new_float(len, na)
+
+// ---- Store CLOSED 30m candle on new boundary ----
+if isNew30Boundary
+    closedO = o30[1]
+    closedH = h30[1]
+    closedL = l30[1]
+    closedC = c30[1]
+
+    if not na(closedO) and not na(closedC)
+        array.unshift(bufO, closedO)
+        array.unshift(bufH, closedH)
+        array.unshift(bufL, closedL)
+        array.unshift(bufC, closedC)
+
+        if array.size(bufO) > len
+            array.pop(bufO)
+            array.pop(bufH)
+            array.pop(bufL)
+            array.pop(bufC)
+
+// ---- Historical Prefill ----
+var bool didPrefill = false
+if not didPrefill
+    maxScanBars = 2000
+    var float[] tO = array.new_float()
+    var float[] tH = array.new_float()
+    var float[] tL = array.new_float()
+    var float[] tC = array.new_float()
+
+    for i = 1 to maxScanBars
+        if bar_index - i < 0
+            break
+        if time("30")[i] != time("30")[i+1]
+            co = o30[i+1]
+            ch = h30[i+1]
+            cl = l30[i+1]
+            cc = c30[i+1]
+            if not na(co) and not na(cc)
+                array.push(tO, co)
+                array.push(tH, ch)
+                array.push(tL, cl)
+                array.push(tC, cc)
+        if array.size(tO) >= len
+            break
+
+    if array.size(tO) > 0
+        bufO := array.new_float()
+        bufH := array.new_float()
+        bufL := array.new_float()
+        bufC := array.new_float()
+        for j = array.size(tO) - 1 to 0 by -1
+            array.push(bufO, array.get(tO, j))
+            array.push(bufH, array.get(tH, j))
+            array.push(bufL, array.get(tL, j))
+            array.push(bufC, array.get(tC, j))
+        while array.size(bufO) < len
+            array.push(bufO, na)
+            array.push(bufH, na)
+            array.push(bufL, na)
+            array.push(bufC, na)
+    didPrefill := true
+
+// ---- LIVE 30m candle on lower TF (manual accumulation) ----
+tf_sec = 30 * 60  // 30 minutes in seconds
+curr30_open_time = math.floor(time / 1000 / tf_sec) * tf_sec * 1000  // start time of current 30m candle in ms
+
+var float liveO = na
+var float liveH = na
+var float liveL = na
+var float liveC = na
+
+if na(liveO) or time == curr30_open_time
+    liveO := open
+    liveH := high
+    liveL := low
+
+liveH := math.max(liveH, high)
+liveL := math.min(liveL, low)
+liveC := close
+
+// ---- DRAW ----
+var box[] bodies = array.new_box()
+var line[] wicks = array.new_line()
+
+if barstate.isfirst
+    // +1 slot for LIVE candle
+    for i = 0 to len
+        array.push(bodies, na)
+        array.push(wicks, na)
+
+// ---- RENDER ----
+anchor = bar_index
+for i = 0 to len
+    _o = i == 0 ? liveO : array.get(bufO, i - 1)
+    _h = i == 0 ? liveH : array.get(bufH, i - 1)
+    _l = i == 0 ? liveL : array.get(bufL, i - 1)
+    _c = i == 0 ? liveC : array.get(bufC, i - 1)
+
+    if na(_o) or na(_c)
+        continue
+
+    hi = math.max(_o, _c)
+    lo = math.min(_o, _c)
+
+    xL = anchor + offset + (len - i + 1) * spacing
+    xR = xL + 2
+    xM = xL + 1
+
+    _bg = _c >= _o ? bullColor : bearColor
+
+    // body
+    body = array.get(bodies, i)
+    if na(body)
+        body := box.new(xL, hi, xR, lo, border_color=borderCol, bgcolor=_bg)
+        array.set(bodies, i, body)
+    else
+        box.set_left(body, xL)
+        box.set_right(body, xR)
+        box.set_top(body, hi)
+        box.set_bottom(body, lo)
+        box.set_bgcolor(body, _bg)
+
+    // wick
+    wick = array.get(wicks, i)
+    if na(wick)
+        wick := line.new(xM, _h, xM, _l, width=1, color=wickColor)
+        array.set(wicks, i, wick)
+    else
+        line.set_x1(wick, xM)
+        line.set_x2(wick, xM)
+        line.set_y1(wick, _h)
+        line.set_y2(wick, _l)

Pinescript Folder/4CEP + 10CRP Patterns/4CEP + 10CRP Patterns.ipynb

@@ -0,0 +1,111 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a6891e41",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "//@version=5\n",
+    "indicator(\"4CEP + 10CRP Patterns\", overlay=true, max_boxes_count=500, max_lines_count=500, max_labels_count=500)\n",
+    "\n",
+    "// ────────── Candle OHLC\n",
+    "o = open\n",
+    "h = high\n",
+    "l = low\n",
+    "c = close\n",
+    "\n",
+    "// ────────── Bullish/Bearish Functions (body only)\n",
+    "bullish(candle_open, candle_close) => candle_close > candle_open\n",
+    "bearish(candle_open, candle_close) => candle_close < candle_open\n",
+    "\n",
+    "// ────────── 4CEP-T1 & 4CEP-T2 (as before)\n",
+    "cond1_T1 = bullish(o[3], c[3])\n",
+    "cond2_T1 = bearish(o[2], c[2])\n",
+    "cond3_T1 = bearish(o[1], c[1]) and c[1] < c[2]\n",
+    "cond4_T1 = bullish(o[0], c[0]) and c[0] > o[1] and c[0] > o[2] and c[0] > c[3]\n",
+    "pattern_T1 = cond1_T1 and cond2_T1 and cond3_T1 and cond4_T1\n",
+    "\n",
+    "cond1_T2 = bearish(o[3], c[3])\n",
+    "cond2_T2 = bullish(o[2], c[2])\n",
+    "cond3_T2 = bullish(o[1], c[1]) and c[1] > c[2]\n",
+    "cond4_T2 = bearish(o[0], c[0]) and c[0] < o[1] and c[0] < o[2] and c[0] < c[3]\n",
+    "pattern_T2 = cond1_T2 and cond2_T2 and cond3_T2 and cond4_T2\n",
+    "\n",
+    "// ────────── Function to draw rectangle + horizontal line + tiny circle label\n",
+    "draw_rect_line_circle(y_top, y_bottom, y_mid, bar_idx, box_color, label_y, label_color) =>\n",
+    "    box.new( left = bar_idx, right = bar_idx + 5, top = y_top, bottom = y_bottom, border_color = box_color, bgcolor = color.new(box_color, 80))\n",
+    "    line.new(  x1 = bar_idx,  y1 = y_mid, x2 = bar_idx + 5,  y2 = y_mid, color = color.red, width = 2, style = line.style_solid )\n",
+    "    label.new(bar_idx, label_y, \"\", style=label.style_circle, color=label_color, textcolor=color.white, size=size.tiny)\n",
+    "\n",
+    "// ────────── Draw rectangles for 4CEP-T1 (Green)\n",
+    "if pattern_T1\n",
+    "    y_top3 = math.max(o[1], c[1])\n",
+    "    y_bottom3 = math.min(o[1], c[1])\n",
+    "    y_mid3 = (y_top3 + y_bottom3)/2\n",
+    "    label_y_T1 = y_bottom3 - (y_top3 - y_bottom3) * 0.3\n",
+    "    draw_rect_line_circle(y_top3, y_bottom3, y_mid3, bar_index[1], color.new(color.green, 60), label_y_T1, color.green)\n",
+    "\n",
+    "// ────────── Draw rectangles for 4CEP-T2 (Red)\n",
+    "if pattern_T2\n",
+    "    y_top3 = math.max(o[1], c[1])\n",
+    "    y_bottom3 = math.min(o[1], c[1])\n",
+    "    y_mid3 = (y_top3 + y_bottom3)/2\n",
+    "    label_y_T2 = y_top3 + (y_top3 - y_bottom3) * 0.3\n",
+    "    draw_rect_line_circle(y_top3, y_bottom3, y_mid3, bar_index[1], color.new(color.red, 60), label_y_T2, color.red)\n",
+    "\n",
+    "// ────────── 10CRP-T1 Pattern\n",
+    "range_top = h[9]  // 1st candle high (10 bars ago)\n",
+    "range_bottom = l[9] // 1st candle low\n",
+    "range_mid = (range_top + range_bottom)/2\n",
+    "\n",
+    "// Check if candles 2–10 are inside 1st candle's range\n",
+    "in_range = true\n",
+    "for i = 8 to 0\n",
+    "    in_range := in_range and c[i] <= range_top and c[i] >= range_bottom\n",
+    "\n",
+    "pattern_10CRP = in_range\n",
+    "\n",
+    "// ────────── Draw rectangle for 10CRP-T1\n",
+    "if pattern_10CRP\n",
+    "    // Rectangle: 1st candle range, extend 10 bars\n",
+    "    box.new( left = bar_index[9],right = bar_index[9] + 10, top = range_top, bottom = range_bottom,border_color = color.rgb(33, 149, 243, 80),  bgcolor = color.new(color.blue, 90))\n",
+    "    // Horizontal line: center of 1st candle range\n",
+    "    line.new(x1 = bar_index[9], y1 = range_mid, x2 = bar_index[9] + 10, y2 = range_mid,color = color.rgb(33, 149, 243, 80), width = 2, style = line.style_solid )\n",
+    "    // Tiny circle label at center of 1st candle\n",
+    "    //label.new(bar_index[9], range_mid, \"\", style=label.style_circle, color=color.blue, textcolor=color.white, size=size.tiny)\n"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "99653056",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9f850c29",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Pinescript Folder/4CEP-T1 & T2 Patterns - Tiny Circle Labels on 3rd Candle/4CEP-T1 & T2 Patterns - Tiny Circle Labels on 3rd Candle.ipynb

@@ -0,0 +1,91 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c8bb7010",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "//@version=5\n",
+    "indicator(\"4CEP-T1 & T2 Patterns - Tiny Circle Labels on 3rd Candle\", overlay=true, max_boxes_count=500, max_lines_count=500, max_labels_count=500)\n",
+    "\n",
+    "// ────────── Candle OHLC\n",
+    "o = open\n",
+    "c = close\n",
+    "\n",
+    "// ────────── Bullish/Bearish Functions (body only)\n",
+    "bullish(candle_open, candle_close) => candle_close > candle_open\n",
+    "bearish(candle_open, candle_close) => candle_close < candle_open\n",
+    "\n",
+    "// ────────── 4CEP-T1 Pattern Conditions\n",
+    "cond1_T1 = bullish(o[3], c[3])\n",
+    "cond2_T1 = bearish(o[2], c[2])\n",
+    "cond3_T1 = bearish(o[1], c[1]) and c[1] < c[2]\n",
+    "cond4_T1 = bullish(o[0], c[0]) and c[0] > o[1] and c[0] > o[2] and c[0] > c[3]\n",
+    "pattern_T1 = cond1_T1 and cond2_T1 and cond3_T1 and cond4_T1\n",
+    "\n",
+    "// ────────── 4CEP-T2 Pattern Conditions\n",
+    "cond1_T2 = bearish(o[3], c[3])\n",
+    "cond2_T2 = bullish(o[2], c[2])\n",
+    "cond3_T2 = bullish(o[1], c[1]) and c[1] > c[2]\n",
+    "cond4_T2 = bearish(o[0], c[0]) and c[0] < o[1] and c[0] < o[2] and c[0] < c[3]\n",
+    "pattern_T2 = cond1_T2 and cond2_T2 and cond3_T2 and cond4_T2\n",
+    "\n",
+    "// ────────── Function to draw rectangle + horizontal line + tiny circle label for 3rd candle\n",
+    "draw_rect_line_circle(y_top, y_bottom, y_mid, bar_idx, box_color, label_y, label_color) =>\n",
+    "    box.new( left = bar_idx, right = bar_idx + 5, top = y_top,  bottom = y_bottom, border_color = box_color,  bgcolor = color.new(box_color, 80) )\n",
+    "    line.new( x1 = bar_idx,  y1 = y_mid, x2 = bar_idx + 5, y2 = y_mid, color = color.red,  width = 2, style = line.style_solid )\n",
+    "    label.new(bar_idx, label_y, \"\", style=label.style_circle, color=label_color, textcolor=color.white, size=size.tiny)\n",
+    "\n",
+    "// ────────── Draw rectangles for 4CEP-T1 (Dark Green)\n",
+    "if pattern_T1\n",
+    "    y_top3 = math.max(o[1], c[1])\n",
+    "    y_bottom3 = math.min(o[1], c[1])\n",
+    "    y_mid3 = (y_top3 + y_bottom3)/2\n",
+    "    // Tiny circle label below 3rd candle\n",
+    "    label_y_T1 = y_bottom3 - (y_top3 - y_bottom3)\n",
+    "    draw_rect_line_circle(y_top3, y_bottom3, y_mid3, bar_index[1], color.new(color.green, 60), label_y_T1, color.green)\n",
+    "\n",
+    "// ────────── Draw rectangles for 4CEP-T2 (Dark Red)\n",
+    "if pattern_T2\n",
+    "    y_top3 = math.max(o[1], c[1])\n",
+    "    y_bottom3 = math.min(o[1], c[1])\n",
+    "    y_mid3 = (y_top3 + y_bottom3)/2\n",
+    "    // Tiny circle label above 3rd candle\n",
+    "    label_y_T2 = y_top3 + (y_top3 - y_bottom3)\n",
+    "    draw_rect_line_circle(y_top3, y_bottom3, y_mid3, bar_index[1], color.new(color.red, 60), label_y_T2, color.red)\n"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "d5eba9ee",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e550cdc",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Pinescript Folder/5 Candle Pattern Highlighter (1-Minute Only)/5 Candle Pattern Highlighter (1-Minute Only).ipynb

@@ -0,0 +1,105 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8d34b7c5",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "//@version=5\n",
+    "indicator(\"5 Candle Pattern Highlighter (1-Minute Only)\", overlay=true, max_boxes_count=500, max_lines_count=500, max_labels_count=500)\n",
+    "\n",
+    "// ─────────── Restrict to 1-Minute Timeframe\n",
+    "is_1m = timeframe.period == \"1\"\n",
+    "\n",
+    "// ─────────── Candle Data\n",
+    "o = open\n",
+    "h = high\n",
+    "l = low\n",
+    "c = close\n",
+    "\n",
+    "// ─────────── Utility Functions\n",
+    "isBull(c_, o_) => c_ > o_\n",
+    "isBear(c_, o_) => c_ < o_\n",
+    "\n",
+    "// ─────────── 5-Candle Pattern Logic (from image)\n",
+    "\n",
+    "// 5CP-T1 (Bearish)\n",
+    "bear5cp_t1 = isBear(c[4], o[4]) and                                    // 1st bearish\n",
+    "              isBear(c[3], o[3]) and c[3] < c[4] and                   // 2nd bearish, close < 1st close\n",
+    "              isBull(c[2], o[2]) and c[2] > c[3] and c[2] < o[3] and   // 3rd bullish, close > 2nd close but < 2nd open\n",
+    "              isBear(c[1], o[1]) and c[1] < c[2] and c[1] < o[2] and   // 4th bearish, close < 3rd close and < 2nd open\n",
+    "              isBear(c, o) and c < c[1] and c < c[3]                   // 5th bearish, close < 4th close and < 3rd close\n",
+    "\n",
+    "// 5CP-T2 (Bullish)\n",
+    "bull5cp_t2 = isBull(c[4], o[4]) and                                    // 1st bullish\n",
+    "              isBull(c[3], o[3]) and c[3] > c[4] and                   // 2nd bullish, close > 1st close\n",
+    "              isBear(c[2], o[2]) and c[2] < c[3] and c[2] > o[3] and   // 3rd bearish, close < 2nd close but > 2nd open\n",
+    "              isBull(c[1], o[1]) and c[1] > c[2] and c[1] > o[2] and   // 4th bullish, close > 3rd close and > 2nd open\n",
+    "              isBull(c, o) and c > c[1] and c > c[2]                   // 5th bullish, close > 4th close and > 3rd open\n",
+    "\n",
+    "// ─────────── Persistent Arrays\n",
+    "var box[] boxes = array.new_box()\n",
+    "var line[] lines = array.new_line()\n",
+    "\n",
+    "// ─────────── Draw Helper Function (fixed to lock to price)\n",
+    "drawBoxAndLine(cond, baseBarOffset, extendBars, colorFill) =>\n",
+    "    if is_1m and cond\n",
+    "        baseOpen = open[baseBarOffset]\n",
+    "        baseClose = close[baseBarOffset]\n",
+    "        top = math.max(baseOpen, baseClose)\n",
+    "        bottom = math.min(baseOpen, baseClose)\n",
+    "        mid = (top + bottom) / 2\n",
+    "\n",
+    "        x1 = bar_index - baseBarOffset\n",
+    "        x2 = x1 + extendBars\n",
+    "\n",
+    "        bx = box.new(left=x1, right=x2, top=top, bottom=bottom, bgcolor=color.new(colorFill, 70), border_color=colorFill)\n",
+    "        ln = line.new(x1=x1, x2=x2, y1=mid, y2=mid, color=colorFill, width=1, style=line.style_dotted)\n",
+    "        array.push(boxes, bx)\n",
+    "        array.push(lines, ln)\n",
+    "\n",
+    "// ─────────── Draw for 5CP Patterns\n",
+    "drawBoxAndLine(bear5cp_t1, 2, 5, color.rgb(255, 128, 0))\n",
+    "drawBoxAndLine(bull5cp_t2, 2, 5, color.rgb(0, 255, 128))\n",
+    "\n",
+    "// ─────────── Plot Shapes (only on 1-minute)\n",
+    "plotshape(is_1m and bear5cp_t1, title=\"↓5CP-T1\", text=\"↓5T1\", style=shape.triangledown,\n",
+    "     location=location.abovebar, color=color.rgb(255, 128, 0), size=size.tiny)\n",
+    "plotshape(is_1m and bull5cp_t2, title=\"↑5CP-T2\", text=\"↑5T2\", style=shape.triangleup,\n",
+    "     location=location.belowbar, color=color.rgb(0, 255, 128), size=size.tiny)\n"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "dca6d5bd",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30da667c",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Pinescript Folder/6CBP Pattern Detector (Bearish & Bullish)/6CBP Pattern Detector (Bearish & Bullish).ipynb

@@ -0,0 +1,122 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4da63e10",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "//@version=5\n",
+    "indicator(\"6CBP Pattern Detector (Bearish & Bullish)\", overlay=true, max_labels_count=500, max_lines_count=500, max_boxes_count=500)\n",
+    "\n",
+    "// ────────────────────────────\n",
+    "// Candle Data\n",
+    "// ────────────────────────────\n",
+    "o = open\n",
+    "h = high\n",
+    "l = low\n",
+    "c = close\n",
+    "\n",
+    "// ────────────────────────────\n",
+    "// Helper Function: Midpoint of Candle Body\n",
+    "// ────────────────────────────\n",
+    "get_mid(_o, _c) => (_o + _c) / 2\n",
+    "\n",
+    "// ────────────────────────────\n",
+    "// 🟥 Bearish 6CBP Pattern Logic\n",
+    "// ────────────────────────────\n",
+    "bear_bull1 = c[5] > o[5]\n",
+    "bear_bull2 = c[4] > o[4] and c[4] > c[5]\n",
+    "bear_bear3 = c[3] < o[3] and c[3] < o[4]\n",
+    "bear_bull4 = c[2] > c[3] and c[2] < o[3]\n",
+    "bear_bear5 = c[1] < o[2] and c[1] < c[3] and c[1] < o[4]\n",
+    "bear_bear6 = c < o and c < o[5] and c < c[1]\n",
+    "\n",
+    "bearish_6CBP = bear_bull1 and bear_bull2 and bear_bear3 and bear_bull4 and bear_bear5 and bear_bear6\n",
+    "\n",
+    "// 4th candle coordinates (bearish reference)\n",
+    "o4_bear = o[2]\n",
+    "c4_bear = c[2]\n",
+    "h4_bear = h[2]\n",
+    "l4_bear = l[2]\n",
+    "mid4_bear = get_mid(o4_bear, c4_bear)\n",
+    "\n",
+    "// ────────────────────────────\n",
+    "// 🟩 Bullish 6CBP Pattern Logic (Inverse)\n",
+    "// ────────────────────────────\n",
+    "bull_bear1 = c[5] < o[5]\n",
+    "bull_bear2 = c[4] < o[4] and c[4] < c[5]\n",
+    "bull_bull3 = c[3] > o[3] and c[3] > o[4]\n",
+    "bull_bear4 = c[2] < c[3] and c[2] > o[3]\n",
+    "bull_bull5 = c[1] > o[2] and c[1] > c[3] and c[1] > o[4]\n",
+    "bull_bull6 = c > o and c > o[5] and c > c[1]\n",
+    "\n",
+    "bullish_6CBP = bull_bear1 and bull_bear2 and bull_bull3 and bull_bear4 and bull_bull5 and bull_bull6\n",
+    "\n",
+    "// 4th candle coordinates (bullish reference)\n",
+    "o4_bull = o[2]\n",
+    "c4_bull = c[2]\n",
+    "h4_bull = h[2]\n",
+    "l4_bull = l[2]\n",
+    "mid4_bull = get_mid(o4_bull, c4_bull)\n",
+    "\n",
+    "// ────────────────────────────\n",
+    "// 🟥 Draw Bearish Signal Elements\n",
+    "// ────────────────────────────\n",
+    "if bearish_6CBP\n",
+    "    // Tiny diamond on top wick of 4th candle\n",
+    "    label.new( bar_index[2], h4_bear,text=\"6CBP↓\",  style=label.style_diamond,color=color.new(color.red, 0), textcolor=color.new(#000000, 0),  size=size.tiny)\n",
+    "\n",
+    "    // Rectangle for 4th candle’s body, extended 10 bars right\n",
+    "    box.new(  left=bar_index[2], right=bar_index[2] + 10,  top=math.max(o4_bear, c4_bear), bottom=math.min(o4_bear, c4_bear),bgcolor=color.new(color.red, 85),border_color=color.new(color.red, 0))\n",
+    "\n",
+    "    // Midline for reference\n",
+    "    line.new( bar_index[2], mid4_bear, bar_index[2] + 10, mid4_bear,  color=color.new(color.red, 0), width=1 )\n",
+    "\n",
+    "// ────────────────────────────\n",
+    "// 🟩 Draw Bullish Signal Elements\n",
+    "// ────────────────────────────\n",
+    "if bullish_6CBP\n",
+    "    // Tiny diamond on bottom wick of 4th candle\n",
+    "    label.new(bar_index[2], l4_bull, text=\"6CBP↑\",style=label.style_diamond,color=color.new(color.lime, 0),textcolor=color.new(#000000, 0), size=size.tiny )\n",
+    "\n",
+    "    // Rectangle for 4th candle’s body, extended 10 bars right\n",
+    "    box.new(left=bar_index[2], right=bar_index[2] + 10, top=math.max(o4_bull, c4_bull),bottom=math.min(o4_bull, c4_bull), bgcolor=color.new(color.lime, 85),    border_color=color.new(color.lime, 0)  )\n",
+    "\n",
+    "    // Midline for reference\n",
+    "    line.new(  bar_index[2],  mid4_bull,  bar_index[2] + 10,  mid4_bull, color=color.new(color.lime, 0), width=1  )\n"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "923618c9",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4238b4f9",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Pinescript Folder/6CBP Pattern Detector (Bearish & Bullish)/with timestamp version.ipynb

@@ -0,0 +1,196 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8a15e001",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "//@version=5\n",
+    "indicator(\"4CCP + 6CBP Pattern Detector (Asia/Manila)\", overlay=true, max_boxes_count=500, max_lines_count=500, max_labels_count=500)\n",
+    "\n",
+    "// ────────────── Restrict to 1-Minute Timeframe\n",
+    "is_1m = timeframe.period == \"1\"\n",
+    "\n",
+    "// ────────────── Candle Data\n",
+    "o = open\n",
+    "h = high\n",
+    "l = low\n",
+    "c = close\n",
+    "\n",
+    "// ────────────── Helpers for Manila time formatting\n",
+    "f_two(n) => n < 10 ? \"0\" + str.tostring(n) : str.tostring(n)\n",
+    "\n",
+    "// return \"HH:MM\" for a given bar offset (use offset = 0 for current bar, 2 for bar_index[2], etc.)\n",
+    "manilaTimeStr(offset) =>\n",
+    "    // use built-in time[] timestamp and extract hour/minute in specified timezone\n",
+    "    hh = hour(time[offset], \"Asia/Manila\")\n",
+    "    mm = minute(time[offset], \"Asia/Manila\")\n",
+    "    f_two(hh) + \":\" + f_two(mm)\n",
+    "\n",
+    "// If you prefer HH:MM:SS, replace the return with:\n",
+    "// ss = second(time[offset], \"Asia/Manila\")\n",
+    "// f_two(hh) + \":\" + f_two(mm) + \":\" + f_two(ss)\n",
+    "\n",
+    "// ────────────── Utility\n",
+    "isBull(c_, o_) => c_ > o_\n",
+    "isBear(c_, o_) => c_ < o_\n",
+    "\n",
+    "// ────────────── Body coords (top, bottom, mid)\n",
+    "getBodyCoords(_o, _c) =>\n",
+    "    _top = math.max(_o, _c)\n",
+    "    _bot = math.min(_o, _c)\n",
+    "    _mid = (_top + _bot) / 2\n",
+    "    [_top, _bot, _mid]\n",
+    "\n",
+    "// ────────────── 4CCP Patterns\n",
+    "\n",
+    "// 4CCP-T1.1 Bullish\n",
+    "t1_1 = isBull(c[3], o[3]) and\n",
+    "       isBear(c[2], o[2]) and c[2] < o[3] and\n",
+    "       isBull(c[1], o[1]) and c[1] > o[2] and c[1] > c[3] and\n",
+    "       isBull(c, o) and c > c[1] and c > o[2] and c > c[3]\n",
+    "\n",
+    "// 4CCP-T1.2 Bullish Type 2\n",
+    "t1_2 = isBull(c[3], o[3]) and\n",
+    "       isBear(c[2], o[2]) and c[2] < o[3] and\n",
+    "       isBull(c[1], o[1]) and c[1] > c[2] and c[1] < o[2] and\n",
+    "       isBull(c, o) and c > c[1] and c > o[2] and c > c[3]\n",
+    "\n",
+    "// 4CCP-T2.1 Bearish\n",
+    "t2_1 = isBear(c[3], o[3]) and\n",
+    "       isBull(c[2], o[2]) and c[2] > o[3] and\n",
+    "       isBear(c[1], o[1]) and c[1] < o[2] and c[1] < c[3] and\n",
+    "       isBear(c, o) and c < c[1] and c < o[2] and c < c[3]\n",
+    "\n",
+    "// 4CCP-T2.2 Bearish Type 2\n",
+    "t2_2 = isBear(c[3], o[3]) and\n",
+    "       isBull(c[2], o[2]) and c[2] > o[3] and\n",
+    "       isBear(c[1], o[1]) and c[1] < c[2] and c[1] > o[2] and\n",
+    "       isBear(c, o) and c < c[1] and c < o[2] and c < c[3]\n",
+    "\n",
+    "// ────────────── Persistent arrays\n",
+    "var box[] boxes = array.new_box()\n",
+    "var line[] lines = array.new_line()\n",
+    "var label[] labs = array.new_label()\n",
+    "\n",
+    "// ────────────── Draw helper (WITH Manila time stamp)\n",
+    "drawBoxAndLine(cond, baseBarOffset, extendBars, colorFill) =>\n",
+    "    if is_1m and cond\n",
+    "        [top, bot, mid] = getBodyCoords(open[baseBarOffset], close[baseBarOffset])\n",
+    "        x1 = bar_index - baseBarOffset\n",
+    "        x2 = x1 + extendBars\n",
+    "\n",
+    "        bx = box.new(left = x1, right = x2, top = top, bottom = bot,\n",
+    "                     bgcolor = color.new(colorFill, 70), border_color = colorFill)\n",
+    "        ln = line.new(x1 = x1, y1 = mid, x2 = x2, y2 = mid,\n",
+    "                      color = colorFill, width = 1, style = line.style_dotted)\n",
+    "\n",
+    "        array.push(boxes, bx)\n",
+    "        array.push(lines, ln)  \n",
+    "\n",
+    "        // Manila timestamp for the pattern bar\n",
+    "        ts = manilaTimeStr(baseBarOffset)\n",
+    "        tLabel = label.new( x1, top,ts, style = label.style_label_down,textcolor = color.rgb(0, 0, 0),  color = color.new(colorFill, 100),size = size.small )\n",
+    "        array.push(labs, tLabel)\n",
+    "\n",
+    "// ────────────── Draw all patterns\n",
+    "drawBoxAndLine(t1_1, 2, 5, color.rgb(0, 192, 255))\n",
+    "drawBoxAndLine(t1_2, 2, 5, color.rgb(0, 38, 255))\n",
+    "drawBoxAndLine(t2_1, 2, 5, color.rgb(255, 64, 64))\n",
+    "drawBoxAndLine(t2_2, 2, 5, color.rgb(255, 128, 0))\n",
+    "\n",
+    "// ────────────────────────────\n",
+    "// Helper Function: Midpoint\n",
+    "get_mid(_o, _c) => (_o + _c) / 2\n",
+    "\n",
+    "// ────────────────────────────\n",
+    "// 6CBP Logic\n",
+    "bear_bull1 = c[5] > o[5]\n",
+    "bear_bull2 = c[4] > o[4] and c[4] > c[5]\n",
+    "bear_bear3 = c[3] < o[3] and c[3] < o[4]\n",
+    "bear_bull4 = c[2] > c[3] and c[2] < o[3]\n",
+    "bear_bear5 = c[1] < o[2] and c[1] < c[3] and c[1] < o[4]\n",
+    "bear_bear6 = c < o and c < o[5] and c < c[1]\n",
+    "\n",
+    "bearish_6CBP = bear_bull1 and bear_bull2 and bear_bear3 and bear_bull4 and bear_bear5 and bear_bear6\n",
+    "\n",
+    "o4_bear = o[2]\n",
+    "c4_bear = c[2]\n",
+    "h4_bear = h[2]\n",
+    "l4_bear = l[2]\n",
+    "mid4_bear = get_mid(o4_bear, c4_bear)\n",
+    "\n",
+    "// Bullish inverse\n",
+    "bull_bear1 = c[5] < o[5]\n",
+    "bull_bear2 = c[4] < o[4] and c[4] < c[5]\n",
+    "bull_bull3 = c[3] > o[3] and c[3] > o[4]\n",
+    "bull_bear4 = c[2] < c[3] and c[2] > o[3]\n",
+    "bull_bull5 = c[1] > o[2] and c[1] > c[3] and c[1] > o[4]\n",
+    "bull_bull6 = c > o and c > o[5] and c > c[1]\n",
+    "\n",
+    "bullish_6CBP = bull_bear1 and bull_bear2 and bull_bull3 and bull_bear4 and bull_bull5 and bull_bull6\n",
+    "\n",
+    "o4_bull = o[2]\n",
+    "c4_bull = c[2]\n",
+    "h4_bull = h[2]\n",
+    "l4_bull = l[2]\n",
+    "mid4_bull = get_mid(o4_bull, c4_bull)\n",
+    "\n",
+    "// ────────────────────────────\n",
+    "// 🟥 Bearish 6CBP\n",
+    "if bearish_6CBP\n",
+    "    // Tiny diamond on top wick of 4th candle\n",
+    "    label.new(bar_index[2], h4_bear, \"6CBP↓\",  style=label.style_diamond, color=color.new(color.red, 0), textcolor=color.new(#000000, 0), size=size.tiny)\n",
+    "\n",
+    "    // Rectangle for 4th candle’s body, extended 10 bars right\n",
+    "    box.new(left=bar_index[2], right=bar_index[2] + 10, top=math.max(o4_bear, c4_bear), bottom=math.min(o4_bear, c4_bear),bgcolor=color.new(color.red, 85),border_color=color.new(color.red, 0))\n",
+    "\n",
+    "    // Midline for reference\n",
+    "    line.new(bar_index[2], mid4_bear, bar_index[2] + 10, mid4_bear,  color=color.new(color.red, 0), width=1 )\n",
+    "\n",
+    "    // ✅ Manila timestamp for this 4th-bar (offset = 2)\n",
+    "    label.new(bar_index[2], h4_bear, manilaTimeStr(2), style = label.style_label_down,  textcolor = color.rgb(0, 0, 0), color = color.new(color.red, 100),  size = size.small)\n",
+    "\n",
+    "// ────────────────────────────\n",
+    "// 🟩 Bullish 6CBP\n",
+    "if bullish_6CBP\n",
+    "    // Tiny diamond on bottom wick of 4th candle\n",
+    "    label.new(bar_index[2], l4_bull, text=\"6CBP↑\",style=label.style_diamond,color=color.new(color.lime, 0),textcolor=color.new(#000000, 0), size=size.tiny )\n",
+    "\n",
+    "    // Rectangle for 4th candle’s body, extended 10 bars right\n",
+    "    box.new(left=bar_index[2], right=bar_index[2] + 10, top=math.max(o4_bull, c4_bull),bottom=math.min(o4_bull, c4_bull), bgcolor=color.new(color.lime, 85),    border_color=color.new(color.lime, 0)  )\n",
+    "\n",
+    "    // Midline for reference\n",
+    "    line.new(  bar_index[2],  mid4_bull,  bar_index[2] + 10,  mid4_bull, color=color.new(color.lime, 0), width=1  )\n",
+    "\n",
+    "    // ✅ Manila timestamp for this 4th-bar (offset = 2)\n",
+    "    label.new(  bar_index[2], l4_bull, manilaTimeStr(2),style = label.style_label_up, textcolor = color.rgb(0, 0, 0),color = color.new(color.lime, 100),  size = size.small)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8ecebab8",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Pinescript Folder/89RS/89RS Rangebreakout Stoporder Model.ipynb

@@ -0,0 +1,999 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "825e20b5",
+   "metadata": {},
+   "source": [
+    "#   The 8-9pm Rangebreakout Stoporder Method \n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e1f6d78",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "source": [
+    "##  Flow\n",
+    "\n",
+    "\n",
+    "![Test 1 Result](https://github.com/algorembrant/Pinescript.TradingView-Indicators.and.Strategies/blob/main/Images/Screenshot%202025-11-15%20135957.png?raw=true)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "95539c46",
+   "metadata": {},
+   "source": [
+    "##  About\n",
+    "\n",
+    "| Property             | Details                                    |\n",
+    "|---------------------|-------------------------------------------|\n",
+    "| **Ticker/Instrument** | XAUUSD                                     |\n",
+    "| **Broker**            | OANDA (TradingView only)                  |\n",
+    "| **Timeframe**         | Non-specific                               |\n",
+    "| **Timezone**          | Asia/Manila (UTC+8)                        |\n",
+    "| **Session**           | New York (customed 1h)                    |\n",
+    "| **Method**            | Range Breakout                             |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6abff555",
+   "metadata": {},
+   "source": [
+    "##  Details\n",
+    "\n",
+    "Locate the x-y coordinate of candles formed from 8pm to 9pm (20 oclock in asianmanila timeframe).\n",
+    "\n",
+    "Among these candles, find the highest point and lowest point, and make vertical lines until it makes a rectangle from those vertices. This will serve as range.\n",
+    "   \n",
+    "    ┌──────────────  sessionHigh\n",
+    "    │\n",
+    "    │   ████  ← 8PM–9PM candles\n",
+    "    │\n",
+    "    └──────────────  sessionLow\n",
+    "\n", 
+    "Locate the y-coordinate and mark the high, low, and midpoit of the range.\n",
+    "\n",
+    "    ┌────────────── HIGH   (green)\n",
+    "    │   ████\n",
+    "    │   ████\n",
+    "     ────────────── MID    (orange)\n",
+    "    │   ████\n",
+    "    │   ████\n",
+    "    └────────────── LOW    (red)\n",
+    "\n",
+    "The high will serve as buystop and to calculate/locate it's TP y-coordinate, absolute value of high minus mid and times it by two, then add it to the high(buystop price) to project the y-coordinate of it's TP. \n",
+    "\n",
+    "Inverse the logic or mirror it for the sellstop.\n",
+    "\n",
+    "The midpoint will serve as SL for both orders.\n",
+    "\n",
+    "**One range one setup two orders four outcomes** \n",
+    "\n",
+    "    ─────────────── TP\n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "     \n",
+    "     \n",
+    "    ┌─────────────► buystop\n",
+    "    │    ████    │\n",
+    "    │    ████    │\n",
+    "     ────────────── SL\n",
+    "    │    ████    │\n",
+    "    │    ████    │\n",
+    "    └─────────────► sellstop\n",
+    "    \n",
+    "       \n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    ─────────────── TP\n",
+    "\n",
+    "locate and highlight the candle at 4am. This will serve as the validation for the setup."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "73e27daa",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "//Pinescript Indicator\n",
+    "//@version=5\n",
+    "indicator(\"8PM–9PM Range Strategy (Midpoint as SL) + 4AM Candle\", overlay=true, max_lines_count=500, max_labels_count=500, max_boxes_count=200)\n",
+    "\n",
+    "// SETTINGS\n",
+    "tz = \"Asia/Manila\"\n",
+    "startHour = 20\n",
+    "endHour   = 21\n",
+    "\n",
+    "// TIME DETECTION\n",
+    "barHour = hour(time, tz)\n",
+    "barMin  = minute(time, tz)\n",
+    "inSession    = barHour >= startHour and barHour < endHour\n",
+    "sessionOpen  = barHour == startHour and barMin == 0\n",
+    "sessionClose = barHour == endHour and barMin == 0\n",
+    "\n",
+    "// TRACK RANGE DURING SESSION\n",
+    "var float sHigh = na\n",
+    "var float sLow  = na\n",
+    "var int   sStart = na\n",
+    "var int   sEnd   = na\n",
+    "var box   sBox = na\n",
+    "\n",
+    "if sessionOpen\n",
+    "    sHigh := high\n",
+    "    sLow  := low\n",
+    "    sStart := bar_index\n",
+    "    sEnd := na\n",
+    "\n",
+    "if inSession\n",
+    "    sHigh := math.max(sHigh, high)\n",
+    "    sLow  := math.min(sLow, low)\n",
+    "\n",
+    "if sessionClose and not na(sHigh)\n",
+    "    sEnd := bar_index\n",
+    "\n",
+    "    // RANGE BOX\n",
+    "    if not na(sBox)\n",
+    "        box.delete(sBox)\n",
+    "    sBox := box.new(\n",
+    "         left = sStart,\n",
+    "         right = sEnd,\n",
+    "         top = sHigh,\n",
+    "         bottom = sLow,\n",
+    "         bgcolor = color.new(#000000, 85),\n",
+    "         border_color = color.new(#000000, 0))\n",
+    "\n",
+    "    // CORE LEVELS\n",
+    "    mid = (sHigh + sLow) / 2\n",
+    "\n",
+    "    // BUY & SELL SETUPS\n",
+    "    buyStop = sHigh\n",
+    "    buySL   = mid\n",
+    "    buyTP   = sHigh + (math.abs(sHigh - mid) * 2)\n",
+    "\n",
+    "    sellStop = sLow\n",
+    "    sellSL   = mid\n",
+    "    sellTP   = sLow - (math.abs(mid - sLow) * 2)\n",
+    "\n",
+    "    // DRAW RANGE LINES\n",
+    "    line.new(sStart, sHigh, sEnd, sHigh, color=color.new(color.green, 0), style=line.style_dotted)\n",
+    "    line.new(sStart, sLow,  sEnd, sLow,  color=color.new(color.red, 0),   style=line.style_dotted)\n",
+    "    line.new(sStart, mid,   sEnd, mid,   color=color.new(color.orange, 0), style=line.style_dotted)\n",
+    "\n",
+    "    // DRAW BUY SETUP\n",
+    "    line.new(sEnd, buyStop, sEnd + 5, buyStop, color=color.new(color.lime, 0), width=2)\n",
+    "    line.new(sEnd, buySL,   sEnd + 5, buySL,   color=color.new(color.orange, 0), width=2, style=line.style_dashed)\n",
+    "    line.new(sEnd, buyTP,   sEnd + 5, buyTP,   color=color.new(color.lime, 0), width=2, style=line.style_dotted)\n",
+    "\n",
+    "    label.new(sEnd + 5, buyStop, text=str.format(\"BUY STOP\\n{0}\", str.tostring(buyStop, format.mintick)), style=label.style_label_left, color=color.new(color.lime, 0), textcolor=color.white, size=size.tiny)\n",
+    "    label.new(sEnd + 5, buySL,   text=str.format(\"SL (MID)\\n{0}\",   str.tostring(buySL,   format.mintick)), style=label.style_label_left, color=color.new(color.orange, 0), textcolor=color.white, size=size.tiny)\n",
+    "    label.new(sEnd + 5, buyTP,   text=str.format(\"BUY TP\\n{0}\",     str.tostring(buyTP,   format.mintick)), style=label.style_label_left, color=color.new(color.lime, 0), textcolor=color.white, size=size.tiny)\n",
+    "\n",
+    "    // DRAW SELL SETUP\n",
+    "    line.new(sEnd, sellStop, sEnd + 5, sellStop, color=color.new(color.red, 0), width=2)\n",
+    "    line.new(sEnd, sellSL,   sEnd + 5, sellSL,   color=color.new(color.orange, 0), width=2, style=line.style_dashed)\n",
+    "    line.new(sEnd, sellTP,   sEnd + 5, sellTP,   color=color.new(color.red, 0), width=2, style=line.style_dotted)\n",
+    "\n",
+    "    label.new(sEnd + 5, sellStop, text=str.format(\"SELL STOP\\n{0}\", str.tostring(sellStop, format.mintick)), style=label.style_label_left, color=color.new(color.red, 0), textcolor=color.white, size=size.tiny)\n",
+    "    label.new(sEnd + 5, sellSL,   text=str.format(\"SL (MID)\\n{0}\",  str.tostring(sellSL,   format.mintick)), style=label.style_label_left, color=color.new(color.orange, 0), textcolor=color.white, size=size.tiny)\n",
+    "    label.new(sEnd + 5, sellTP,   text=str.format(\"SELL TP\\n{0}\",   str.tostring(sellTP,   format.mintick)), style=label.style_label_left, color=color.new(color.red, 0), textcolor=color.white, size=size.tiny)\n",
+    "\n",
+    "// 4AM CANDLE HIGHLIGHT\n",
+    "is4am = barHour == 4 and barMin == 0\n",
+    "var box box4am = na\n",
+    "if is4am\n",
+    "    if not na(box4am)\n",
+    "        box.delete(box4am)\n",
+    "    box4am := box.new(left = bar_index, right = bar_index + 1, top = high, bottom = low, bgcolor = color.new(color.blue, 75), border_color = color.new(color.blue, 0))\n",
+    "    label.new(bar_index, high, \"4 AM\", style=label.style_label_down, color=color.new(color.blue, 0), textcolor=color.white, size=size.tiny)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4cc0c07",
+   "metadata": {},
+   "source": [
+    "##  **Setup Overview:**  \n",
+    "This framework defines the conditions for executing the trading strategy.\n",
+    "\n",
+    "![Template](https://github.com/algorembrant/Pinescript.TradingView-Indicators.and.Strategies/blob/main/Images/Screenshot%202025-11-15%20153904.png?raw=true)\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67e8fa53",
+   "metadata": {},
+   "source": [
+    "## Rules\n",
+    "\n",
+    "1. Use **Asia/Manila timezone (UTC+8)**.  \n",
+    "2. Identify the **8 PM – 9 PM price range**.  \n",
+    "3. Candles outside this range will guide trading decisions.  \n",
+    "4. The developing price (including wicks) **must revisit the mid-price** of the range before proceeding.  \n",
+    "5. Once the mid-price is revisited:  \n",
+    "   - Place a **Buy Stop** at the range **high**.  \n",
+    "   - Place a **Sell Stop** at the range **low**.  \n",
+    "6. **Take Profit (TP):** 2R multiple from the entry.  \n",
+    "7. **Stop Loss (SL):** mid-price for both Buy Stop and Sell Stop.  \n",
+    "8. Both Buy Stop and Sell Stop **may trigger** in the same setup, valid until the **4 AM candle**.  \n",
+    "9. Maximum outcomes per setup: **2 wins, 2 losses, or a mix**.  \n",
+    "10. If the trade hasn’t hit any target by 4 AM, **cancel the orders**.  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6a00c10a",
+   "metadata": {},
+   "source": [
+    "## Risk Management\n",
+    "\n",
+    "- Starting capital: **$2,000**  \n",
+    "- Risk per trade: **3% of current capital** (applies separately to Buy Stop and Sell Stop).  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "962a30ba",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "//Pinescript Strategy\n",
+    "//@version=5\n",
+    "strategy(\"8PM–9PM Range Strategy + 4AM Candle\", overlay=true, initial_capital=2000)\n",
+    "\n",
+    "// TIME SETTINGS\n",
+    "tz = \"Asia/Manila\"\n",
+    "startHour = 20\n",
+    "endHour   = 21\n",
+    "riskPerc = 3.0\n",
+    "\n",
+    "barHour = hour(time, tz)\n",
+    "barMin  = minute(time, tz)\n",
+    "inSession = barHour >= startHour and barHour < endHour\n",
+    "sessionOpen = barHour == startHour and barMin == 0\n",
+    "sessionClose = barHour == endHour and barMin == 0\n",
+    "is4am = barHour == 4 and barMin == 0\n",
+    "\n",
+    "// RANGE VARIABLES (from indicator)\n",
+    "var float sHigh = na\n",
+    "var float sLow  = na\n",
+    "var int sStart = na\n",
+    "var int sEnd   = na\n",
+    "var bool touchedMid = false\n",
+    "var bool ordersPlaced = false\n",
+    "\n",
+    "if sessionOpen\n",
+    "    sHigh := high\n",
+    "    sLow  := low\n",
+    "    sStart := bar_index\n",
+    "    sEnd := na\n",
+    "    touchedMid := false\n",
+    "    ordersPlaced := false\n",
+    "\n",
+    "if inSession\n",
+    "    sHigh := math.max(sHigh, high)\n",
+    "    sLow  := math.min(sLow, low)\n",
+    "\n",
+    "if sessionClose and not na(sHigh)\n",
+    "    sEnd := bar_index\n",
+    "\n",
+    "// USE INDICATOR COORDINATES\n",
+    "mid = (sHigh + sLow)/2\n",
+    "buyStop  = sHigh\n",
+    "buySL    = mid\n",
+    "buyTP    = sHigh + 2*(sHigh - mid)\n",
+    "sellStop = sLow\n",
+    "sellSL   = mid\n",
+    "sellTP   = sLow - 2*(mid - sLow)\n",
+    "\n",
+    "// DETECT MID TOUCH (entry condition)\n",
+    "if not na(mid) and not ordersPlaced\n",
+    "    if high >= mid and low <= mid\n",
+    "        touchedMid := true\n",
+    "\n",
+    "// PLACE ORDERS BASED ON INDICATOR COORDINATES\n",
+    "if touchedMid and not ordersPlaced\n",
+    "    riskAmount = strategy.equity * riskPerc / 100.0\n",
+    "\n",
+    "    stopDistBuy = math.abs(buyStop - buySL)\n",
+    "    stopDistSell = math.abs(sellStop - sellSL)\n",
+    "    qtyBuy = stopDistBuy > 0 ? math.max(1, math.round(riskAmount / stopDistBuy)) : 0\n",
+    "    qtySell = stopDistSell > 0 ? math.max(1, math.round(riskAmount / stopDistSell)) : 0\n",
+    "\n",
+    "    if qtyBuy > 0\n",
+    "        strategy.entry(\"BUY_STOP\", strategy.long, qty=qtyBuy, stop=buyStop)\n",
+    "        strategy.exit(\"EXIT_BUY\", from_entry=\"BUY_STOP\", stop=buySL, limit=buyTP)\n",
+    "\n",
+    "    if qtySell > 0\n",
+    "        strategy.entry(\"SELL_STOP\", strategy.short, qty=qtySell, stop=sellStop)\n",
+    "        strategy.exit(\"EXIT_SELL\", from_entry=\"SELL_STOP\", stop=sellSL, limit=sellTP)\n",
+    "\n",
+    "    ordersPlaced := true\n",
+    "\n",
+    "// CANCEL / CLOSE ORDERS AT 4AM\n",
+    "if is4am\n",
+    "    strategy.cancel(\"BUY_STOP\")\n",
+    "    strategy.cancel(\"SELL_STOP\")\n",
+    "    if strategy.position_size > 0\n",
+    "        strategy.close(\"BUY_STOP\")\n",
+    "    if strategy.position_size < 0\n",
+    "        strategy.close(\"SELL_STOP\")\n",
+    "    touchedMid := false\n",
+    "    ordersPlaced := false\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d86ec81b",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "source": [
+    "## Implementation Note\n",
+    "\n",
+    "Use the **y-coordinates from the indicator** to calculate the range, entries, SL, and TP targets.\n",
+    "\n",
+    "![Strategy Example](https://github.com/algorembrant/Pinescript.TradingView-Indicators.and.Strategies/blob/main/Images/Screenshot%202025-11-15%20131419.png?raw=true)\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ecfc7d56",
+   "metadata": {},
+   "source": [
+    "##  Result\n",
+    "\n",
+    "![Test 1 Result](https://github.com/algorembrant/Pinescript.TradingView-Indicators.and.Strategies/blob/main/Images/Screenshot%202025-11-15%20132923.png?raw=true)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8f46fccc",
+   "metadata": {},
+   "source": [
+    "## Test 1: Notice \n",
+    "\n",
+    "| Topic / Flaws       | Details                                                                                                             | Status       |\n",
+    "|--------------------|---------------------------------------------------------------------------------------------------------------------|-------------|\n",
+    "| **Position sizing** | The bot does not fully account for P&L when sizing positions. For example, it risks 3% per trade, but the loss can exceed this and gains may exceed 6%. | Unresolved  |\n",
+    "| **R-multiple**      | Trades indicate the possibility of reaching a 4R multiple upon review.                                             | Unresolved  |\n",
+    "| **Win rate**        | The current win rate is 40–43%, which is lower than ideal.                                                          | Unresolved  |\n",
+    "\n",
+    "This strategy is naturally profitable, but I aim to **increase the R-multiples and win rate** through additional testing.\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "723406d3",
+   "metadata": {},
+   "source": [
+    "## Adjustments\n",
+    "\n",
+    "For getting higher 4R-multiple instead of 2R.\n",
+    "Same range, same TP price but different SL size.\n",
+    "Stoploss are is tighter.\n",
+    "\n",
+    "stoploss calculation:\n",
+    "\n",
+    "$ buystop.stoploss   =   (ABS(buystop.entryprice - sellstop.entryprice)/4)*1 - buystop.entryprice $\n",
+    "\n",
+    "$ sellstop.stoploss   =   (ABS(sellstop.entryprice - buystop.entryprice)/4)*1 + sellstop.entryprice $\n",
+    "\n",
+    "takeprofit calculation:\n",
+    "\n",
+    "$ buystop.takeprofit   =   (ABS(buystop.entryprice - sellstop.entryprice)/4)*4 + buystop.entryprice $\n",
+    "\n",
+    "$ sellstop.takeprofit   =   (ABS(sellstop.entryprice - buystop.entryprice)/4)*4 - sellstop.entryprice $\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89759e91",
+   "metadata": {},
+   "source": [
+    "##  Pinescript"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "15216c7f",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "//@version=5\n",
+    "indicator(\"8PM–9PM Range Strategy (Tighter SL for 4R) + 4AM Candle\", overlay=true, max_lines_count=500, max_labels_count=500, max_boxes_count=200)\n",
+    "\n",
+    "// SETTINGS\n",
+    "tz = \"Asia/Manila\"\n",
+    "startHour = 20\n",
+    "endHour   = 21\n",
+    "\n",
+    "// TIME DETECTION\n",
+    "barHour = hour(time, tz)\n",
+    "barMin  = minute(time, tz)\n",
+    "inSession    = barHour >= startHour and barHour < endHour\n",
+    "sessionOpen  = barHour == startHour and barMin == 0\n",
+    "sessionClose = barHour == endHour and barMin == 0\n",
+    "\n",
+    "// TRACK RANGE DURING SESSION\n",
+    "var float sHigh = na\n",
+    "var float sLow  = na\n",
+    "var int   sStart = na\n",
+    "var int   sEnd   = na\n",
+    "var box   sBox = na\n",
+    "\n",
+    "if sessionOpen\n",
+    "    sHigh := high\n",
+    "    sLow  := low\n",
+    "    sStart := bar_index\n",
+    "    sEnd := na\n",
+    "\n",
+    "if inSession\n",
+    "    sHigh := math.max(sHigh, high)\n",
+    "    sLow  := math.min(sLow, low)\n",
+    "\n",
+    "if sessionClose and not na(sHigh)\n",
+    "    sEnd := bar_index\n",
+    "\n",
+    "    // RANGE BOX\n",
+    "    if not na(sBox)\n",
+    "        box.delete(sBox)\n",
+    "    sBox := box.new(\n",
+    "         left = sStart,\n",
+    "         right = sEnd,\n",
+    "         top = sHigh,\n",
+    "         bottom = sLow,\n",
+    "         bgcolor = color.new(#000000, 85),\n",
+    "         border_color = color.new(#000000, 0))\n",
+    "\n",
+    "    // CORE LEVELS\n",
+    "    buyStop  = sHigh\n",
+    "    sellStop = sLow\n",
+    "\n",
+    "    // use 'rng' instead of reserved word 'range'\n",
+    "    rng   = math.abs(buyStop - sellStop)\n",
+    "    quarter = rng / 4\n",
+    "\n",
+    "    // NEW STOPLOSS CALC (TIGHTER)\n",
+    "    buySL  = buyStop  - quarter\n",
+    "    sellSL = sellStop + quarter\n",
+    "\n",
+    "    // NEW TAKEPROFIT CALC (4R)\n",
+    "    buyTP  = buyStop  + rng\n",
+    "    sellTP = sellStop - rng\n",
+    "\n",
+    "    // Mid is kept for drawing reference\n",
+    "    mid = (sHigh + sLow) / 2\n",
+    "\n",
+    "    // DRAW RANGE LINES\n",
+    "    line.new(sStart, sHigh, sEnd, sHigh, color=color.new(color.green, 0), style=line.style_dotted)\n",
+    "    line.new(sStart, sLow,  sEnd, sLow,  color=color.new(color.red, 0),   style=line.style_dotted)\n",
+    "    line.new(sStart, mid,   sEnd, mid,   color=color.new(color.orange, 0), style=line.style_dotted)\n",
+    "\n",
+    "    // DRAW BUY SETUP\n",
+    "    line.new(sEnd, buyStop, sEnd + 5, buyStop, color=color.new(color.lime, 0), width=2)\n",
+    "    line.new(sEnd, buySL,   sEnd + 5, buySL,   color=color.new(color.orange, 0), width=2, style=line.style_dashed)\n",
+    "    line.new(sEnd, buyTP,   sEnd + 5, buyTP,   color=color.new(color.lime, 0), width=2, style=line.style_dotted)\n",
+    "\n",
+    "    label.new(sEnd + 5, buyStop, text=str.format(\"BUY STOP\\n{0}\", str.tostring(buyStop, format.mintick)), style=label.style_label_left, color=color.new(color.lime, 0), textcolor=color.white, size=size.tiny)\n",
+    "    label.new(sEnd + 5, buySL,   text=str.format(\"SL (Quarter)\\n{0}\", str.tostring(buySL, format.mintick)), style=label.style_label_left, color=color.new(color.orange, 0), textcolor=color.white, size=size.tiny)\n",
+    "    label.new(sEnd + 5, buyTP,   text=str.format(\"BUY TP\\n{0}\",     str.tostring(buyTP, format.mintick)), style=label.style_label_left, color=color.new(color.lime, 0), textcolor=color.white, size=size.tiny)\n",
+    "\n",
+    "    // DRAW SELL SETUP\n",
+    "    line.new(sEnd, sellStop, sEnd + 5, sellStop, color=color.new(color.red, 0), width=2)\n",
+    "    line.new(sEnd, sellSL,   sEnd + 5, sellSL,   color=color.new(color.orange, 0), width=2, style=line.style_dashed)\n",
+    "    line.new(sEnd, sellTP,   sEnd + 5, sellTP,   color=color.new(color.red, 0), width=2, style=line.style_dotted)\n",
+    "\n",
+    "    label.new(sEnd + 5, sellStop, text=str.format(\"SELL STOP\\n{0}\", str.tostring(sellStop, format.mintick)), style=label.style_label_left, color=color.new(color.red, 0), textcolor=color.white, size=size.tiny)\n",
+    "    label.new(sEnd + 5, sellSL,   text=str.format(\"SL (Quarter)\\n{0}\",  str.tostring(sellSL,   format.mintick)), style=label.style_label_left, color=color.new(color.orange, 0), textcolor=color.white, size=size.tiny)\n",
+    "    label.new(sEnd + 5, sellTP,   text=str.format(\"SELL TP\\n{0}\",   str.tostring(sellTP,   format.mintick)), style=label.style_label_left, color=color.new(color.red, 0), textcolor=color.white, size=size.tiny)\n",
+    "\n",
+    "// 4AM CANDLE HIGHLIGHT\n",
+    "is4am = barHour == 4 and barMin == 0\n",
+    "var box box4am = na\n",
+    "if is4am\n",
+    "    if not na(box4am)\n",
+    "        box.delete(box4am)\n",
+    "    box4am := box.new(left = bar_index, right = bar_index + 1, top = high, bottom = low, bgcolor = color.new(color.blue, 75), border_color = color.new(color.blue, 0))\n",
+    "    label.new(bar_index, high, \"4 AM\", style=label.style_label_down, color=color.new(color.blue, 0), textcolor=color.white, size=size.tiny)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c2c2c63b",
+   "metadata": {},
+   "source": [
+    "##  Setup\n",
+    "\n",
+    "![image](https://github.com/algorembrant/Pinescript.TradingView-Indicators.and.Strategies/blob/main/Images/Screenshot%202025-11-15%20160104.png?raw=true)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25b60c8b",
+   "metadata": {},
+   "source": [
+    "## Rules\n",
+    "\n",
+    "1. Use **Asia/Manila timezone (UTC+8)**.  \n",
+    "2. Identify the **8 PM – 9 PM price range**.  \n",
+    "3. Candles outside this range will guide trading decisions.  \n",
+    "4. The developing price (including wicks) **must revisit the mid-price** of the range before proceeding.  \n",
+    "5. Once the mid-price is revisited:  \n",
+    "   - Place a **Buy Stop** at the range **high**.  \n",
+    "   - Place a **Sell Stop** at the range **low**.  \n",
+    "6. **Take Profit (TP):** 4R multiple from the entry.  \n",
+    "7. **Stop Loss (SL):** quarter-price for both respective Buy Stop and Sell Stop.  \n",
+    "8. Both Buy Stop and Sell Stop **may trigger** in the same setup, valid until the **4 AM candle**.  \n",
+    "9. Maximum outcomes per setup: **2 wins, 2 losses, or a mix**.  \n",
+    "10. If the trade hasn’t hit any target by 4 AM, **cancel the orders**.  \n",
+    "\n",
+    "## Concept:\n",
+    "\n",
+    "Starting capital: $2000\n",
+    "\n",
+    "Risk per trade: 3% of current capital (applies independently to Buy Stop and Sell Stop)\n",
+    "\n",
+    "Stop loss: quarter of the 8–9 PM range (SL = quarter)\n",
+    "\n",
+    "Calculate position size per trade so that risk in USD = 3% of account\n",
+    "\n",
+    "$  Position Size (units)   =  (Account Risk in USD / SL in price units) $\n",
+    "\t​\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "caebac0c",
+   "metadata": {},
+   "source": [
+    "##  Pinescript Strategy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "318ec1fd",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "//@version=5\n",
+    "strategy(\"8PM–9PM Range Strategy + 4AM + Risk Mgmt\", overlay=true,\n",
+    "  initial_capital=2000, \n",
+    "  default_qty_type=strategy.fixed, \n",
+    "  default_qty_value=1, \n",
+    "  max_bars_back=500)\n",
+    "\n",
+    "// ========== INPUTS ==========\n",
+    "tz = \"Asia/Manila\"\n",
+    "startHour = input.int(20, \"Range Start Hour (24h)\", minval=0, maxval=23)\n",
+    "endHour   = input.int(21, \"Range End Hour (24h)\", minval=0, maxval=23)\n",
+    "riskPerc  = input.float(3.0, \"Risk % per trade\", step=0.1)\n",
+    "\n",
+    "// ========== TIME ==========\n",
+    "barHour = hour(time, tz)\n",
+    "barMin  = minute(time, tz)\n",
+    "inSession    = barHour >= startHour and barHour < endHour\n",
+    "sessionOpen  = barHour == startHour and barMin == 0\n",
+    "sessionClose = barHour == endHour and barMin == 0\n",
+    "is4am = barHour == 4 and barMin == 0\n",
+    "\n",
+    "// ========== PERSISTENT RANGE VARIABLES ==========\n",
+    "var float sHigh = na\n",
+    "var float sLow  = na\n",
+    "var int   sStart = na\n",
+    "var int   sEnd   = na\n",
+    "var box   sBox = na\n",
+    "\n",
+    "// ========== DECLARE PRICE LEVEL VARIABLES ==========\n",
+    "var float buyStop = na\n",
+    "var float sellStop = na\n",
+    "var float rng = na\n",
+    "var float quarter = na\n",
+    "var float buySL = na\n",
+    "var float sellSL = na\n",
+    "var float buyTP = na\n",
+    "var float sellTP = na\n",
+    "var float mid = na\n",
+    "\n",
+    "// ========== SESSION RANGE TRACKING ==========\n",
+    "if sessionOpen\n",
+    "    sHigh := high\n",
+    "    sLow  := low\n",
+    "    sStart := bar_index\n",
+    "    sEnd := na\n",
+    "\n",
+    "if inSession and not na(sStart)\n",
+    "    sHigh := math.max(sHigh, high)\n",
+    "    sLow  := math.min(sLow, low)\n",
+    "\n",
+    "if sessionClose and not na(sHigh)\n",
+    "    sEnd := bar_index\n",
+    "\n",
+    "    if not na(sBox)\n",
+    "        box.delete(sBox)\n",
+    "    sBox := box.new(left = sStart, right = sEnd, top = sHigh, bottom = sLow, bgcolor = color.new(color.black, 85), border_color = color.new(color.black, 0))\n",
+    "\n",
+    "    // reset session flags\n",
+    "    var bool setup_active = false\n",
+    "    var bool revisited_mid = false\n",
+    "    var bool orders_sent = false\n",
+    "    setup_active := true\n",
+    "    revisited_mid := false\n",
+    "    orders_sent := false\n",
+    "\n",
+    "// Persistent flags\n",
+    "var bool _setup_active = false\n",
+    "var bool _revisited_mid = false\n",
+    "var bool _orders_sent = false\n",
+    "\n",
+    "if sessionClose and not na(sHigh)\n",
+    "    _setup_active := true\n",
+    "    _revisited_mid := false\n",
+    "    _orders_sent := false\n",
+    "\n",
+    "if na(sHigh)\n",
+    "    _setup_active := false\n",
+    "\n",
+    "// ========== CALCULATE PRICE LEVELS ==========\n",
+    "if not na(sHigh)\n",
+    "    buyStop  := sHigh\n",
+    "    sellStop := sLow\n",
+    "    rng := math.abs(buyStop - sellStop)\n",
+    "    quarter := rng / 4\n",
+    "    buySL := buyStop - quarter\n",
+    "    sellSL := sellStop + quarter\n",
+    "    buyTP := buyStop + rng\n",
+    "    sellTP := sellStop - rng\n",
+    "    mid := (sHigh + sLow) / 2\n",
+    "\n",
+    "// Draw range lines\n",
+    "if sessionClose and not na(sHigh)\n",
+    "    line.new(sStart, sHigh, sEnd, sHigh, color=color.new(color.green, 0), style=line.style_dotted)\n",
+    "    line.new(sStart, sLow,  sEnd, sLow,  color=color.new(color.red, 0),   style=line.style_dotted)\n",
+    "    line.new(sStart, mid,   sEnd, mid,   color=color.new(color.orange, 0), style=line.style_dotted)\n",
+    "\n",
+    "// ========== MID REVISIT ==========\n",
+    "if _setup_active and not _revisited_mid and not na(mid)\n",
+    "    if (high >= mid) and (low <= mid)\n",
+    "        _revisited_mid := true\n",
+    "\n",
+    "// ========== RISK MANAGEMENT: POSITION SIZE ==========\n",
+    "calc_qty(sl_price) =>\n",
+    "    // risk in USD\n",
+    "    acct_risk = strategy.equity * riskPerc / 100\n",
+    "    // position size in units = account risk / SL distance\n",
+    "    qty = acct_risk / math.abs(sl_price - close)\n",
+    "    qty\n",
+    "\n",
+    "// ========== PLACE STOP ORDERS ==========\n",
+    "if _setup_active and _revisited_mid and not _orders_sent and not na(buyStop)\n",
+    "    buy_qty  = calc_qty(buySL)\n",
+    "    sell_qty = calc_qty(sellSL)\n",
+    "\n",
+    "    strategy.entry(\"BuyStop\", strategy.long, stop=buyStop, qty=buy_qty)\n",
+    "    strategy.exit(\"BuyExit\", from_entry=\"BuyStop\", stop=buySL, limit=buyTP)\n",
+    "\n",
+    "    strategy.entry(\"SellStop\", strategy.short, stop=sellStop, qty=sell_qty)\n",
+    "    strategy.exit(\"SellExit\", from_entry=\"SellStop\", stop=sellSL, limit=sellTP)\n",
+    "\n",
+    "    _orders_sent := true\n",
+    "\n",
+    "// ========== CANCEL ORDERS AT 4AM ==========\n",
+    "if is4am\n",
+    "    strategy.cancel(\"BuyStop\")\n",
+    "    strategy.cancel(\"SellStop\")\n",
+    "    _setup_active := false\n",
+    "    _revisited_mid := false\n",
+    "    _orders_sent := false\n",
+    "\n",
+    "// ========== 4AM HIGHLIGHT ==========\n",
+    "var box box4am = na\n",
+    "if is4am\n",
+    "    if not na(box4am)\n",
+    "        box.delete(box4am)\n",
+    "    box4am := box.new(left = bar_index, right = bar_index + 1, top = high, bottom = low,\n",
+    "                       bgcolor = color.new(color.blue, 75), border_color = color.new(color.blue, 0))\n",
+    "    label.new(bar_index, high, \"4 AM\", style=label.style_label_down,\n",
+    "              color=color.new(color.blue, 0), textcolor=color.white, size=size.tiny)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbe0a41d",
+   "metadata": {},
+   "source": [
+    "##  Test 2 Results\n",
+    "\n",
+    "![image](https://github.com/algorembrant/Pinescript.TradingView-Indicators.and.Strategies/blob/main/Images/Screenshot%202025-11-15%20162112.png?raw=true)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e0f58655",
+   "metadata": {},
+   "source": [
+    "##  Test 2 Results\n",
+    "![image](https://github.com/algorembrant/Pinescript.TradingView-Indicators.and.Strategies/blob/main/Images/Screenshot%202025-11-15%20162726.png?raw=true)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef340795",
+   "metadata": {},
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "Its not the best to tighten the SL area.\n",
+    "\n",
+    "Its not the best to put a 4am cut off limit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "60104f44",
+   "metadata": {},
+   "source": [
+    "#   If we remove the 4am cutoff Logic\n",
+    "\n",
+    "If we remove the 4 AM cutoff logic, the strategy will no longer cancel pending orders at 4 AM — orders will stay active until they hit SL or TP. Everything else (mid-price revisit, Buy/Sell Stop, 4R TP, quarter SL, risk management) stays intact."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "192593b0",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "//@version=5\n",
+    "strategy(\"8PM–9PM Range Strategy + Risk Mgmt (No 4AM cutoff)\", overlay=true,\n",
+    "  initial_capital=2000, \n",
+    "  default_qty_type=strategy.fixed, \n",
+    "  default_qty_value=1, \n",
+    "  max_bars_back=500)\n",
+    "\n",
+    "// ========== INPUTS ==========\n",
+    "tz = \"Asia/Manila\"\n",
+    "startHour = input.int(20, \"Range Start Hour (24h)\", minval=0, maxval=23)\n",
+    "endHour   = input.int(21, \"Range End Hour (24h)\", minval=0, maxval=23)\n",
+    "riskPerc  = input.float(3.0, \"Risk % per trade\", step=0.1)\n",
+    "\n",
+    "// ========== TIME ==========\n",
+    "barHour = hour(time, tz)\n",
+    "barMin  = minute(time, tz)\n",
+    "inSession    = barHour >= startHour and barHour < endHour\n",
+    "sessionOpen  = barHour == startHour and barMin == 0\n",
+    "sessionClose = barHour == endHour and barMin == 0\n",
+    "\n",
+    "// ========== PERSISTENT RANGE VARIABLES ==========\n",
+    "var float sHigh = na\n",
+    "var float sLow  = na\n",
+    "var int   sStart = na\n",
+    "var int   sEnd   = na\n",
+    "var box   sBox = na\n",
+    "\n",
+    "// ========== DECLARE PRICE LEVEL VARIABLES ==========\n",
+    "var float buyStop = na\n",
+    "var float sellStop = na\n",
+    "var float rng = na\n",
+    "var float quarter = na\n",
+    "var float buySL = na\n",
+    "var float sellSL = na\n",
+    "var float buyTP = na\n",
+    "var float sellTP = na\n",
+    "var float mid = na\n",
+    "\n",
+    "// ========== SESSION RANGE TRACKING ==========\n",
+    "if sessionOpen\n",
+    "    sHigh := high\n",
+    "    sLow  := low\n",
+    "    sStart := bar_index\n",
+    "    sEnd := na\n",
+    "\n",
+    "if inSession and not na(sStart)\n",
+    "    sHigh := math.max(sHigh, high)\n",
+    "    sLow  := math.min(sLow, low)\n",
+    "\n",
+    "if sessionClose and not na(sHigh)\n",
+    "    sEnd := bar_index\n",
+    "\n",
+    "    if not na(sBox)\n",
+    "        box.delete(sBox)\n",
+    "    sBox := box.new(left = sStart, right = sEnd, top = sHigh, bottom = sLow,bgcolor = color.new(color.black, 85), border_color = color.new(color.black, 0))\n",
+    "\n",
+    "    // reset session flags\n",
+    "    var bool setup_active = false\n",
+    "    var bool revisited_mid = false\n",
+    "    var bool orders_sent = false\n",
+    "    setup_active := true\n",
+    "    revisited_mid := false\n",
+    "    orders_sent := false\n",
+    "\n",
+    "// Persistent flags\n",
+    "var bool _setup_active = false\n",
+    "var bool _revisited_mid = false\n",
+    "var bool _orders_sent = false\n",
+    "\n",
+    "if sessionClose and not na(sHigh)\n",
+    "    _setup_active := true\n",
+    "    _revisited_mid := false\n",
+    "    _orders_sent := false\n",
+    "\n",
+    "if na(sHigh)\n",
+    "    _setup_active := false\n",
+    "\n",
+    "// ========== CALCULATE PRICE LEVELS ==========\n",
+    "if not na(sHigh)\n",
+    "    buyStop  := sHigh\n",
+    "    sellStop := sLow\n",
+    "    rng := math.abs(buyStop - sellStop)\n",
+    "    quarter := rng / 4\n",
+    "    buySL := buyStop - quarter\n",
+    "    sellSL := sellStop + quarter\n",
+    "    buyTP := buyStop + rng\n",
+    "    sellTP := sellStop - rng\n",
+    "    mid := (sHigh + sLow) / 2\n",
+    "\n",
+    "// Draw range lines\n",
+    "if sessionClose and not na(sHigh)\n",
+    "    line.new(sStart, sHigh, sEnd, sHigh, color=color.new(color.green, 0), style=line.style_dotted)\n",
+    "    line.new(sStart, sLow,  sEnd, sLow,  color=color.new(color.red, 0),   style=line.style_dotted)\n",
+    "    line.new(sStart, mid,   sEnd, mid,   color=color.new(color.orange, 0), style=line.style_dotted)\n",
+    "\n",
+    "// ========== MID REVISIT ==========\n",
+    "if _setup_active and not _revisited_mid and not na(mid)\n",
+    "    if (high >= mid) and (low <= mid)\n",
+    "        _revisited_mid := true\n",
+    "\n",
+    "// ========== RISK MANAGEMENT: POSITION SIZE ==========\n",
+    "calc_qty(sl_price) =>\n",
+    "    acct_risk = strategy.equity * riskPerc / 100\n",
+    "    distance = math.abs(sl_price - close)\n",
+    "    distance := distance < syminfo.mintick ? syminfo.mintick : distance\n",
+    "    qty = acct_risk / distance\n",
+    "    qty := math.max(qty, 1)   // minimum 1 unit\n",
+    "    qty\n",
+    "\n",
+    "// ========== PLACE STOP ORDERS ==========\n",
+    "if _setup_active and _revisited_mid and not _orders_sent and not na(buyStop)\n",
+    "    buy_qty  = calc_qty(buySL)\n",
+    "    sell_qty = calc_qty(sellSL)\n",
+    "\n",
+    "    strategy.entry(\"BuyStop\", strategy.long, stop=buyStop, qty=buy_qty)\n",
+    "    strategy.exit(\"BuyExit\", from_entry=\"BuyStop\", stop=buySL, limit=buyTP)\n",
+    "\n",
+    "    strategy.entry(\"SellStop\", strategy.short, stop=sellStop, qty=sell_qty)\n",
+    "    strategy.exit(\"SellExit\", from_entry=\"SellStop\", stop=sellSL, limit=sellTP)\n",
+    "\n",
+    "    _orders_sent := true\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c99c924a",
+   "metadata": {},
+   "source": [
+    "#   Test 3 Results\n",
+    "\n",
+    "![image](https://github.com/algorembrant/Pinescript.TradingView-Indicators.and.Strategies/blob/main/Images/Screenshot%202025-11-15%20164303.png?raw=true)\n",
+    "\n",
+    "at this point, i am conviced that i should work on the riskmanagement and position sizing logic first."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b5e8959",
+   "metadata": {},
+   "source": [
+    "# 📈 Position Lot Size Formula for Risk Management\n",
+    "\n",
+    "This formula helps traders determine the appropriate size of a trading position based on their risk tolerance, capital, and trade setup.\n",
+    "\n",
+    "## Formula\n",
+    "\n",
+    "$$\n",
+    "\\text{position_lotsize} = \\frac{(\\text{risk%_in_decimal}) \\cdot \\text{Capital}}{|\\text{Entry_price} - \\text{Stoploss_price}|} \\times 0.01\\_\\text{standard}\n",
+    "$$\n",
+    "\n",
+    "##  Explanation\n",
+    "\n",
+    "- **risk%_in_decimal**: The percentage of capital you're willing to risk, expressed as a decimal (e.g., 2% → 0.02).\n",
+    "- **Capital**: Your total trading capital.\n",
+    "- **Entry_price**: The price at which you enter the trade.\n",
+    "- **Stoploss_price**: The price at which you exit the trade to limit losses.\n",
+    "- **0.01_standard**: A scaling factor, likely referring to standard lot size (e.g., 1 standard lot = 100,000 units).\n",
+    "\n",
+    "## Use Case\n",
+    "\n",
+    "This formula ensures that each trade risks only a fixed percentage of your capital, helping maintain consistent risk management and avoid oversized positions.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## Formula \n",
+    "\n",
+    "$\tbuy = buystop, buylimit, buymarket\t$\n",
+    "\n",
+    "$\tbuy_stoploss_price =(entry_price - ticks)\t$\n",
+    "\n",
+    "$\tbuy_takeprofit_price =( entry_price + ticks)\t$\n",
+    "\n",
+    "$\tsell = sellstop, selllimit, sellmarket\t$\n",
+    "\n",
+    "$\tsell_stoploss_price =( entry_price + ticks)\t$\n",
+    "\n",
+    "$\tsell_takeprofit_price =( entry_price - ticks)\t$\n",
+    "\n",
+    "\n",
+    "The `buy` is a type of order.The `buy_stoploss_price` is the stop loss for buy order.The `buy_takeprofit_price` is the take profit for buy order.The `sell` is a type of order.The `sell_stoploss_price` is the stop loss for sell order.The `sell_takeprofit_price` is the take profit for sell order.\n",
+    "\n",
+    "# 📊 Trading Order Price Calculations\n",
+    "\n",
+    "This table summarizes how to calculate stop loss and take profit prices for different types of buy and sell orders.\n",
+    "\n",
+    "| Order Type | Entry Type                         | Stop Loss Formula                     | Take Profit Formula                   |\n",
+    "|------------|------------------------------------|---------------------------------------|---------------------------------------|\n",
+    "| Buy        | `buystop`, `buylimit`, `buymarket` | `buy_stoploss_price = entry_price - ticks` | `buy_takeprofit_price = entry_price + ticks` |\n",
+    "| Sell       | `sellstop`, `selllimit`, `sellmarket` | `sell_stoploss_price = entry_price + ticks` | `sell_takeprofit_price = entry_price - ticks` |\n",
+    "\n",
+    "## 🧾 Notes\n",
+    "\n",
+    "- **Buy Orders**: Expect price to rise. Stop loss is below entry; take profit is above.\n",
+    "- **Sell Orders**: Expect price to fall. Stop loss is above entry; take profit is below.\n",
+    "- **ticks**: Represents the price movement unit used to define risk and reward thresholds.\n",
+    "\n",
+    "Use this table to quickly reference how to set your stop loss and take profit levels based on order direction.\n",
+    "\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Pinescript Folder/89RS/89RSindicator-pinescript.js

@@ -0,0 +1,112 @@
+//@version=5
+indicator("8PM–9PM Range Strategy (Midpoint as SL) + 4AM Candle", overlay=true, max_lines_count=500, max_labels_count=500, max_boxes_count=200)
+
+//──────────────────────────────
+// SETTINGS
+//──────────────────────────────
+tz = "Asia/Manila"
+startHour = 20
+endHour   = 21
+
+//──────────────────────────────
+// TIME DETECTION
+//──────────────────────────────
+barHour = hour(time, tz)
+barMin  = minute(time, tz)
+inSession    = barHour >= startHour and barHour < endHour
+sessionOpen  = barHour == startHour and barMin == 0
+sessionClose = barHour == endHour and barMin == 0
+
+//──────────────────────────────
+// TRACK RANGE DURING SESSION
+//──────────────────────────────
+var float sHigh = na
+var float sLow  = na
+var int   sStart = na
+var int   sEnd   = na
+var box   sBox = na
+
+if sessionOpen
+    sHigh := high
+    sLow  := low
+    sStart := bar_index
+    sEnd := na
+
+if inSession
+    sHigh := math.max(sHigh, high)
+    sLow  := math.min(sLow, low)
+
+if sessionClose and not na(sHigh)
+    sEnd := bar_index
+
+    //──────────────────────────────
+    // RANGE BOX
+    //──────────────────────────────
+    if not na(sBox)
+        box.delete(sBox)
+    sBox := box.new(
+         left = sStart,
+         right = sEnd,
+         top = sHigh,
+         bottom = sLow,
+         bgcolor = color.new(#000000, 85),
+         border_color = color.new(#000000, 0))
+
+    //──────────────────────────────
+    // CORE LEVELS
+    //──────────────────────────────
+    mid = (sHigh + sLow) / 2
+
+    // BUY setup
+    buyStop = sHigh
+    buySL   = mid
+    buyTP   = sHigh + (math.abs(sHigh - mid) * 2)
+
+    // SELL setup (mirror)
+    sellStop = sLow
+    sellSL   = mid
+    sellTP   = sLow - (math.abs(mid - sLow) * 2)
+
+    //──────────────────────────────
+    // DRAW RANGE LINES
+    //──────────────────────────────
+    line.new(sStart, sHigh, sEnd, sHigh, color=color.new(color.green, 0), style=line.style_dotted)
+    line.new(sStart, sLow, sEnd, sLow, color=color.new(color.red, 0), style=line.style_dotted)
+    line.new(sStart, mid, sEnd, mid, color=color.new(color.orange, 0), style=line.style_dotted)
+
+    //──────────────────────────────
+    // DRAW BUY SETUP
+    //──────────────────────────────
+    line.new(sEnd, buyStop, sEnd + 5, buyStop, color=color.new(color.lime, 0), width=2)
+    line.new(sEnd, buySL, sEnd + 5, buySL, color=color.new(color.orange, 0), width=2, style=line.style_dashed)
+    line.new(sEnd, buyTP, sEnd + 5, buyTP, color=color.new(color.lime, 0), width=2, style=line.style_dotted)
+
+    label.new(sEnd + 5, buyStop,text=str.format("BUY STOP\n{0}", str.tostring(buyStop, format.mintick)),style=label.style_label_left, color=color.new(color.lime, 0), textcolor=color.white, size=size.tiny)
+
+    label.new(sEnd + 5, buySL,text=str.format("SL (MID)\n{0}", str.tostring(buySL, format.mintick)), style=label.style_label_left, color=color.new(color.orange, 0), textcolor=color.white, size=size.tiny)
+
+    label.new(sEnd + 5, buyTP,text=str.format("BUY TP\n{0}", str.tostring(buyTP, format.mintick)),style=label.style_label_left, color=color.new(color.lime, 0), textcolor=color.white, size=size.tiny)
+
+    //──────────────────────────────
+    // DRAW SELL SETUP
+    //──────────────────────────────
+    line.new(sEnd, sellStop, sEnd + 5, sellStop, color=color.new(color.red, 0), width=2)
+    line.new(sEnd, sellSL, sEnd + 5, sellSL, color=color.new(color.orange, 0), width=2, style=line.style_dashed)
+    line.new(sEnd, sellTP, sEnd + 5, sellTP, color=color.new(color.red, 0), width=2, style=line.style_dotted)
+
+    label.new(sEnd + 5, sellStop, text=str.format("SELL STOP\n{0}", str.tostring(sellStop, format.mintick)), style=label.style_label_left, color=color.new(color.red, 0), textcolor=color.white, size=size.tiny)
+
+    label.new(sEnd + 5, sellSL,text=str.format("SL (MID)\n{0}", str.tostring(sellSL, format.mintick)), style=label.style_label_left, color=color.new(color.orange, 0), textcolor=color.white, size=size.tiny)
+
+    label.new(sEnd + 5, sellTP,  text=str.format("SELL TP\n{0}", str.tostring(sellTP, format.mintick)), style=label.style_label_left, color=color.new(color.red, 0),textcolor=color.white, size=size.tiny)
+
+//──────────────────────────────
+// 4:00 AM CANDLE HIGHLIGHT
+//──────────────────────────────
+is4am = barHour == 4 and barMin == 0
+var box box4am = na
+if is4am
+    if not na(box4am)
+        box.delete(box4am)
+    box4am := box.new( left = bar_index, right = bar_index + 1, top = high,  bottom = low,  bgcolor = color.new(color.blue, 75), border_color = color.new(color.blue, 0))
+    label.new(bar_index, high, "4 AM", style=label.style_label_down, color=color.new(color.blue, 0),textcolor=color.white,size=size.tiny)

Pinescript Folder/89RS/89RSstrategy-pinescript.js

@@ -0,0 +1,95 @@
+//@version=5
+strategy("8PM–9PM Range Strategy + 4AM Candle", overlay=true, initial_capital=2000)
+
+// ----------------------
+// TIME SETTINGS
+// ----------------------
+tz = "Asia/Manila"
+startHour = 20
+endHour   = 21
+riskPerc = 3.0
+
+barHour = hour(time, tz)
+barMin  = minute(time, tz)
+inSession = barHour >= startHour and barHour < endHour
+sessionOpen = barHour == startHour and barMin == 0
+sessionClose = barHour == endHour and barMin == 0
+is4am = barHour == 4 and barMin == 0
+
+// ----------------------
+// RANGE VARIABLES (from indicator)
+// ----------------------
+var float sHigh = na
+var float sLow  = na
+var int sStart = na
+var int sEnd   = na
+var bool touchedMid = false
+var bool ordersPlaced = false
+
+// capture range during session
+if sessionOpen
+    sHigh := high
+    sLow  := low
+    sStart := bar_index
+    sEnd := na
+    touchedMid := false
+    ordersPlaced := false
+
+if inSession
+    sHigh := math.max(sHigh, high)
+    sLow  := math.min(sLow, low)
+
+if sessionClose and not na(sHigh)
+    sEnd := bar_index
+
+// ----------------------
+// USE INDICATOR COORDINATES
+// ----------------------
+mid = (sHigh + sLow)/2
+buyStop  = sHigh
+buySL    = mid
+buyTP    = sHigh + 2*(sHigh - mid)
+sellStop = sLow
+sellSL   = mid
+sellTP   = sLow - 2*(mid - sLow)
+
+// ----------------------
+// DETECT MID TOUCH (entry condition)
+// ----------------------
+if not na(mid) and not ordersPlaced
+    if high >= mid and low <= mid
+        touchedMid := true
+
+// ----------------------
+// PLACE ORDERS BASED ON INDICATOR COORDINATES
+// ----------------------
+if touchedMid and not ordersPlaced
+    riskAmount = strategy.equity * riskPerc / 100.0
+
+    stopDistBuy = math.abs(buyStop - buySL)
+    stopDistSell = math.abs(sellStop - sellSL)
+    qtyBuy = stopDistBuy > 0 ? math.max(1, math.round(riskAmount / stopDistBuy)) : 0
+    qtySell = stopDistSell > 0 ? math.max(1, math.round(riskAmount / stopDistSell)) : 0
+
+    if qtyBuy > 0
+        strategy.entry("BUY_STOP", strategy.long, qty=qtyBuy, stop=buyStop)
+        strategy.exit("EXIT_BUY", from_entry="BUY_STOP", stop=buySL, limit=buyTP)
+
+    if qtySell > 0
+        strategy.entry("SELL_STOP", strategy.short, qty=qtySell, stop=sellStop)
+        strategy.exit("EXIT_SELL", from_entry="SELL_STOP", stop=sellSL, limit=sellTP)
+
+    ordersPlaced := true
+
+// ----------------------
+// CANCEL / CLOSE ORDERS AT 4AM
+// ----------------------
+if is4am
+    strategy.cancel("BUY_STOP")
+    strategy.cancel("SELL_STOP")
+    if strategy.position_size > 0
+        strategy.close("BUY_STOP")
+    if strategy.position_size < 0
+        strategy.close("SELL_STOP")
+    touchedMid := false
+    ordersPlaced := false

Pinescript Folder/AMDX/quarterly.ipynb

@@ -0,0 +1,106 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "29eb0119",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "//@version=5\n",
+    "indicator(\"Candle-Overlay Boxes for A/M/D/X Intervals (3-Minute Only)\", overlay=true)\n",
+    "\n",
+    "is3min = timeframe.period == \"3\"\n",
+    "\n",
+    "// Helper function to check time ranges\n",
+    "inTimeRange(startHour, startMin, endHour, endMin) =>\n",
+    "    t = timestamp(\"Asia/Manila\", year, month, dayofmonth, hour, minute)\n",
+    "    startT = timestamp(\"Asia/Manila\", year, month, dayofmonth, startHour, startMin)\n",
+    "    endT = timestamp(\"Asia/Manila\", year, month, dayofmonth, endHour, endMin)\n",
+    "    t >= startT and t < endT\n",
+    "\n",
+    "// Intervals\n",
+    "inA = inTimeRange(5, 0, 6, 30)\n",
+    "inM = inTimeRange(6, 30, 8, 0)\n",
+    "inD = inTimeRange(8, 0, 9, 30)\n",
+    "inX = inTimeRange(9, 30, 11, 0)\n",
+    "\n",
+    "// Create and update boxes based on intervals\n",
+    "var box boxA = na\n",
+    "var box boxM = na\n",
+    "var box boxD = na\n",
+    "var box boxX = na\n",
+    "\n",
+    "if is3min and inA\n",
+    "    if na(boxA)\n",
+    "        boxA := box.new(left=bar_index, right=bar_index, top=high, bottom=low, bgcolor=color.new(color.blue, 85), border_color=color.rgb(255, 255, 255, 100), text=\"A\", text_color=color.white, text_size=size.large)\n",
+    "    else\n",
+    "        box.set_right(boxA, bar_index)\n",
+    "        box.set_top(boxA, math.max(box.get_top(boxA), high))\n",
+    "        box.set_bottom(boxA, math.min(box.get_bottom(boxA), low))\n",
+    "else\n",
+    "    boxA := na\n",
+    "\n",
+    "if is3min and inM\n",
+    "    if na(boxM)\n",
+    "        boxM := box.new(left=bar_index, right=bar_index, top=high, bottom=low, bgcolor=color.new(color.green, 85), border_color=color.rgb(255, 255, 255, 100), text=\"M\", text_color=color.white, text_size=size.large)\n",
+    "    else\n",
+    "        box.set_right(boxM, bar_index)\n",
+    "        box.set_top(boxM, math.max(box.get_top(boxM), high))\n",
+    "        box.set_bottom(boxM, math.min(box.get_bottom(boxM), low))\n",
+    "else\n",
+    "    boxM := na\n",
+    "\n",
+    "if is3min and inD\n",
+    "    if na(boxD)\n",
+    "        boxD := box.new(left=bar_index, right=bar_index, top=high, bottom=low, bgcolor=color.new(color.orange, 85), border_color=color.rgb(255, 255, 255, 100), text=\"D\", text_color=color.white, text_size=size.large)\n",
+    "    else\n",
+    "        box.set_right(boxD, bar_index)\n",
+    "        box.set_top(boxD, math.max(box.get_top(boxD), high))\n",
+    "        box.set_bottom(boxD, math.min(box.get_bottom(boxD), low))\n",
+    "else\n",
+    "    boxD := na\n",
+    "\n",
+    "if is3min and inX\n",
+    "    if na(boxX)\n",
+    "        boxX := box.new(left=bar_index, right=bar_index, top=high, bottom=low, bgcolor=color.new(color.purple, 85), border_color=color.rgb(255, 255, 255, 100), text=\"X\", text_color=color.white, text_size=size.large)\n",
+    "    else\n",
+    "        box.set_right(boxX, bar_index)\n",
+    "        box.set_top(boxX, math.max(box.get_top(boxX), high))\n",
+    "        box.set_bottom(boxX, math.min(box.get_bottom(boxX), low))\n",
+    "else\n",
+    "    boxX := na\n"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "93d479e2",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00591a49",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Pinescript Folder/Daily Candle in Lower Timeframes/daily candle convertor.ipynb

@@ -0,0 +1,343 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "53ebed60",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "//@version=5\n",
+    "indicator(\"fucking candles\", overlay = true, max_labels_count = 300, max_lines_count = 300, max_boxes_count = 300, max_bars_back = 300)\n",
+    "\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) =000000000000000000\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 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 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",
+    "\n",
+    "lblO := label.new(x=xMid + lblOfs, y=dO, text=\"6O \" + str.tostring(dO, format.mintick), 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, format.mintick), 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, format.mintick), 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, format.mintick), 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, format.mintick), 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, format.mintick), 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, format.mintick), 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, format.mintick), 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 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=color.black, width=1, style = line.style_dotted)\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=color.black, width=1, style = line.style_dotted)\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=2, 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=2, 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_dotted, color=dCol, 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"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "296d3cbd",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cde1a799",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Pinescript Folder/Exact 30m Candles in any timeframe/live 30m candles.ipynb

@@ -0,0 +1,208 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "307d38bf",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "//@version=5\n",
+    "indicator(\"Exact 30m Panel on Any TF (Closed + LIVE 30m Candle)\", overlay=true, max_labels_count=500, max_boxes_count=500, max_lines_count=500)\n",
+    "\n",
+    "// ---- SETTINGS ----\n",
+    "len    = input.int(30, \"Number of CLOSED 30m candles\", minval=1)\n",
+    "offset = input.int(5, \"Offset from right edge (bars)\")\n",
+    "spacing = 3    // horizontal footprint per 30m candle (2 body + 1 gap)\n",
+    "\n",
+    "// ---- COLOR SETTINGS ----\n",
+    "bullColor = input.color(color.new(color.green, 0), \"Bull Candle Color\")\n",
+    "bearColor = input.color(color.new(color.red, 0), \"Bear Candle Color\")\n",
+    "wickColor = input.color(color.black, \"Wick Color\")\n",
+    "borderCol = color.black \n",
+    "\n",
+    "// ---- GET 30m CLOSED series ----   \n",
+    "[o30, h30, l30, c30] = request.security(syminfo.tickerid, \"30\", [open, high, low, close], barmerge.gaps_on)\n",
+    "\n",
+    "// ---- detect 30m boundary ----\n",
+    "isNew30Boundary = ta.change(time(\"30\"))\n",
+    "\n",
+    "// ---- Buffers for closed candles ----\n",
+    "var float[] bufO = array.new_float(len, na)\n",
+    "var float[] bufH = array.new_float(len, na)\n",
+    "var float[] bufL = array.new_float(len, na)\n",
+    "var float[] bufC = array.new_float(len, na)\n",
+    "\n",
+    "// Maintain buffer if len changed\n",
+    "if array.size(bufO) != len\n",
+    "    bufO := array.new_float(len, na)\n",
+    "    bufH := array.new_float(len, na)\n",
+    "    bufL := array.new_float(len, na)\n",
+    "    bufC := array.new_float(len, na)\n",
+    "\n",
+    "// ---- Store CLOSED 30m candle on new boundary ----\n",
+    "if isNew30Boundary\n",
+    "    closedO = o30[1]\n",
+    "    closedH = h30[1]\n",
+    "    closedL = l30[1]\n",
+    "    closedC = c30[1]\n",
+    "\n",
+    "    if not na(closedO) and not na(closedC)\n",
+    "        array.unshift(bufO, closedO)\n",
+    "        array.unshift(bufH, closedH)\n",
+    "        array.unshift(bufL, closedL)\n",
+    "        array.unshift(bufC, closedC)\n",
+    "\n",
+    "        if array.size(bufO) > len\n",
+    "            array.pop(bufO)\n",
+    "            array.pop(bufH)\n",
+    "            array.pop(bufL)\n",
+    "            array.pop(bufC)\n",
+    "\n",
+    "// ---- Historical Prefill ----\n",
+    "var bool didPrefill = false\n",
+    "if not didPrefill\n",
+    "    maxScanBars = 2000\n",
+    "    var float[] tO = array.new_float()\n",
+    "    var float[] tH = array.new_float()\n",
+    "    var float[] tL = array.new_float()\n",
+    "    var float[] tC = array.new_float()\n",
+    "\n",
+    "    for i = 1 to maxScanBars\n",
+    "        if bar_index - i < 0\n",
+    "            break\n",
+    "        if time(\"30\")[i] != time(\"30\")[i+1]\n",
+    "            co = o30[i+1]\n",
+    "            ch = h30[i+1]\n",
+    "            cl = l30[i+1]\n",
+    "            cc = c30[i+1]\n",
+    "            if not na(co) and not na(cc)\n",
+    "                array.push(tO, co)\n",
+    "                array.push(tH, ch)\n",
+    "                array.push(tL, cl)\n",
+    "                array.push(tC, cc)\n",
+    "        if array.size(tO) >= len\n",
+    "            break\n",
+    "\n",
+    "    if array.size(tO) > 0\n",
+    "        bufO := array.new_float()\n",
+    "        bufH := array.new_float()\n",
+    "        bufL := array.new_float()\n",
+    "        bufC := array.new_float()\n",
+    "        for j = array.size(tO) - 1 to 0 by -1\n",
+    "            array.push(bufO, array.get(tO, j))\n",
+    "            array.push(bufH, array.get(tH, j))\n",
+    "            array.push(bufL, array.get(tL, j))\n",
+    "            array.push(bufC, array.get(tC, j))\n",
+    "        while array.size(bufO) < len\n",
+    "            array.push(bufO, na)\n",
+    "            array.push(bufH, na)\n",
+    "            array.push(bufL, na)\n",
+    "            array.push(bufC, na)\n",
+    "    didPrefill := true\n",
+    "\n",
+    "// ---- LIVE 30m candle on lower TF (manual accumulation) ----\n",
+    "tf_sec = 30 * 60  // 30 minutes in seconds\n",
+    "curr30_open_time = math.floor(time / 1000 / tf_sec) * tf_sec * 1000  // start time of current 30m candle in ms\n",
+    "\n",
+    "var float liveO = na\n",
+    "var float liveH = na\n",
+    "var float liveL = na\n",
+    "var float liveC = na\n",
+    "\n",
+    "if na(liveO) or time == curr30_open_time\n",
+    "    liveO := open\n",
+    "    liveH := high\n",
+    "    liveL := low\n",
+    "\n",
+    "liveH := math.max(liveH, high)\n",
+    "liveL := math.min(liveL, low)\n",
+    "liveC := close\n",
+    "\n",
+    "// ---- DRAW ----\n",
+    "var box[] bodies = array.new_box()\n",
+    "var line[] wicks = array.new_line()\n",
+    "\n",
+    "if barstate.isfirst\n",
+    "    // +1 slot for LIVE candle\n",
+    "    for i = 0 to len\n",
+    "        array.push(bodies, na)\n",
+    "        array.push(wicks, na)\n",
+    "\n",
+    "// ---- RENDER ----\n",
+    "anchor = bar_index\n",
+    "for i = 0 to len\n",
+    "    _o = i == 0 ? liveO : array.get(bufO, i - 1)\n",
+    "    _h = i == 0 ? liveH : array.get(bufH, i - 1)\n",
+    "    _l = i == 0 ? liveL : array.get(bufL, i - 1)\n",
+    "    _c = i == 0 ? liveC : array.get(bufC, i - 1)\n",
+    "\n",
+    "    if na(_o) or na(_c)\n",
+    "        continue\n",
+    "\n",
+    "    hi = math.max(_o, _c)\n",
+    "    lo = math.min(_o, _c)\n",
+    "\n",
+    "    xL = anchor + offset + (len - i + 1) * spacing\n",
+    "    xR = xL + 2\n",
+    "    xM = xL + 1\n",
+    "\n",
+    "    _bg = _c >= _o ? bullColor : bearColor\n",
+    "\n",
+    "    // body\n",
+    "    body = array.get(bodies, i)\n",
+    "    if na(body)\n",
+    "        body := box.new(xL, hi, xR, lo, border_color=borderCol, bgcolor=_bg)\n",
+    "        array.set(bodies, i, body)\n",
+    "    else\n",
+    "        box.set_left(body, xL)\n",
+    "        box.set_right(body, xR)\n",
+    "        box.set_top(body, hi)\n",
+    "        box.set_bottom(body, lo)\n",
+    "        box.set_bgcolor(body, _bg)\n",
+    "\n",
+    "    // wick\n",
+    "    wick = array.get(wicks, i)\n",
+    "    if na(wick)\n",
+    "        wick := line.new(xM, _h, xM, _l, width=1, color=wickColor)\n",
+    "        array.set(wicks, i, wick)\n",
+    "    else\n",
+    "        line.set_x1(wick, xM)\n",
+    "        line.set_x2(wick, xM)\n",
+    "        line.set_y1(wick, _h)\n",
+    "        line.set_y2(wick, _l)\n",
+    "\n"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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xZy1WwT9NpxmjnLRzSok/wK9evcpnAQTBmEVMplPH8CcInVHHP4zl6MFwwKhgVDAqRIhVqDOXCPmzinvyMyQy09PEJwLecANT+MwiZcUoxlCEpUzL0Y9zWjH1ExM3LPXgmW84sHT0EAFiZBXNKOk8cOAAZTSjzM+B4AShn4lxgUgDf9JmUUbDGMIBN5yjhyA0iw030iBCDJFe+FOJHiamaPQUB0w9JEAa+OQbkckwYhY4ELmgh+n0hDNZ8SqRHyrVmRg++YmlUSsSkIAEJCABCUhAAhJoPoGNNXDzc4oVOXzjjLT8eVR4Wm6eAIeQHBhyXsc5Yf4vdW8+ct0j5HK5qampn/iJn6h75MoDcnO+6fpX5VPq42kUCUhAAhKQgAQkIAEJSGBzBDbQwAVHT5tZixMhDo44PqowSJxKVTWlwsi6FRPgLDHO6yipFzu0Tw/35Pvf//7Kb6RGZM7NyZ1J2YjgxixNwF4JSEACEpCABCQgAQnUg8AGGrgeSxhDAhKQgAQ2RkxOTwAAEABJREFUQcCpEpCABCQgAQlIQAL1I6AGrh9LI0lAAhKQQH0JGE0CEpCABCQgAQnUm4AauN5EjScBCUhAAhLYPAEjSEACEpCABCTQGAJq4MZwNaoEJCABCUhAArURcJYEJCABCUigkQTKaeAv+SUBCUhAAhKQgAQk0DQCLiQBCUhAAnUiUEZEl9PAZaY5JAEJSEACEpCABCQggboRMJAEJCCBZhHYWAM/wy8JSEACEpCABCQgAQlIoEEEDCsBCdSVwIZSemMNvGEIHSQgAQlIQAISkIAEJCABCVRNwAkSaAUBNXArqLumBCQgAQlIQAISkIAEJNDNBNx76wiogVvH3pUlIAEJSEACEpCABCQgAQl0G4FW71cN3Oor4PoSkIAEJCABCUhAAhKQgAQk0CwCrdTAzdqj60hAAhKQgAQkIAEJSEACEpCABJ4goAZ+gkLz/3NFCUhAAhKQgAQkIAEJSEACEmg+ATVw85l3+4ruXwISkIAEJCABCUhAAhKQQKsIqIFbRd51u5GAe5aABCQgAQlIQAISkIAEWktADdxa/q4ugW4h4D4lIAEJSEACEpCABCTQDgTUwO1wFcxBAhLoZALuTQISkIAEJCABCUigfQiogdvnWpiJBCQggU4j4H4kIAEJSEACEug8ArlcbmJiomBfa2trQ0NDvb29u3btWllZYXR0dJRm2Llz55IDPdu2bSMIPvlGD/2MEgdnhqanp2liVGhiKSYBadZmauDauDlLAhKQgAQkUI6AYxKQgAQkIIGOJIC+PXTo0LVr1wp2d/z48bGxsdXV1b17987Ozsbo5OQkPdj+/fujhyGaV65cGRwcjJ4oEb1TU1Nnz55ltL+/n2hI4vn5+eXl5cXFxZMnT9IM3YvD3NzcsWPHyCTmVluqgaslpr8EJCABCUhAAuUIOCYBCUhAAh1MAFmLyi3e4Pj4eBK6xaMb9ly6dAmfnTt3Ug4PD6N7Ebp79uzp6enp6+uLs+VTp04xhANuO3bsUAODQpOABCQgAQlIQAKtJODaEpCABDqbwPT09MDAQAjRG4q+EKWIVUbRwzGIYO7N++loOg8ePEhP+mlnesKYe/Xq1ahTLi0tLSwsUEl28eJFOlMTZ6akZlUVz4GrwqWzBCQgAQlIQAISkEAJAnZJQAIdTyCXy83Pz4+MjKy3Uw5sEa6c1iKV8dmyZQtnuat5Px3d39/PAe/y8jKj999/P2VLTA3cEuwuKgEJSEACEpCABCTQIQTchgS6gcDa2tqRI0fOnDmzfft2znJnZ2dD6BbvfXR0FKmM/9TUVPzSL0fHSN+enp6ZmRl0MpUQw/lz6d+6dWvqwYEj5dSksnv3bjqphOHMlKhXW6qBqyWmvwQkIAEJSEACEpCABCTwBAH/6x4CCNfTp09zqIshgJHB4+PjKysrKNUQwxMTExwUA4SzX06A8acehgBGBkedEnm8tLRED5WhoSE0M50I2suXL8dvBXOSPDw8jMP58+fxYRX8kdP0MIRzuO28/svDNKs1NXC1xPSXgAQkIAEJSEACEpCABLqdgPuHAML18OHDSFzqd999NwfFvb29J06cmJycpAdxSxNDwY6MjKBmUbw0OUnmRLfgD2gR6ujRo/v27cOBuYxiuOGM9B0bG8OBIITC4cCBA0jufJnNlMpNDVw5Kz0lIAEJSEACEpCABCQgAQl0OwHU6czMTFBAoHJmSx1FGgfFCwsL6FV68OHQGKOfUYwKTYwhHKIn6jQJyxCWeqjQxBjCIfxpFv/TSoxWbmrgylnpKQEJSEACEpCABCQgAQlIQAJPEsjlchcuXAiB+mRXFv63GQ2chf2ZowQkIAEJSEACEpCABCQgAQk0gMDg4ODU1FQDAjc2pBq4Nr7OkoAEJCABCUhAAhKQgAQkIIHsEVADZ++atTpj15eABCQgAQlIQAISkIAEJJBVAmrgrF45824FAdeUgAQkIAEJSEACEpCABLJNQA2c7etn9hJoFgHXkYAEJCABCUhAAhKQQCcQUAN3wlV0DxKQQCMJGFsCEpCABCQgAQlIoHMIqIE751q6EwlIQAL1JmA8CUhAAhKQgAQk0GkE1MCddkXdjwQkIAEJ1IOAMSQgAQlIQAIS6EwCauDOvK7uSgISkIAEJFArAedJQAISkIAEOpmAGriTr657k4AEJCABCUigGgL6SkACEpBA5xNQA3f+NXaHEpCABCQgAQlIYCMCjktAAhLoFgJq4G650u5TAhKQgAQkIAEJSKAUAfskIIHuIqAG7q7r7W4lIAEJSEACEpCABCTwdQL+XwLdSEAN3I1X3T1LQAISkIAEJCABCUiguwm4++4loAbu3mvvziUgAQlIQAISkIAEJCCB7iPQ7TtWA3f7HeD+JSABCUhAAhKQgAQkIAEJdAeBJ3apBn6Cgv9JQAISkIAEJCABCUhAAhKQQDcQ6FYN3A3X1j1KQAISkIAEJCABCUhAAhKQwFMJqIGfyqMbWu5RAhKQgAQkIAEJSEACEpBAtxJQA3frle/OfbtrCUhAAhKQgAQkIAEJSGBzBHK53MTEREGMtbW1oaGh3t7eXbt2raysMDo6Okoz7Ny5c/Tkcrlt27bRgyf+9ORb8ej09DTOGJXwTDEjYHRWW6qBqyWmvwSyScCsJSABCUhAAhKQgAQksGkC6NtDhw5du3atINLx48fHxsZWV1f37t07Ozsbo5OTk/Rg+/fvR/ROTU2dPXuWZn9/P/7hE2XxKJJ4fn5+eXl5cXHx5MmTNEP3Mn1ubu7YsWNkEnOrLdXA1RLTXwISyBoB85WABCQgAQlIQAISqBMBZC0qtzjY+Pg4Qre4P/VcunSJ+s6dOymHh4dRtlSSFY8idPfs2dPT09PX1xdny6dOnWIiUwiyY8cONTAoNAlIQAISeCoBWxKQgAQkIAEJSKB+BKanpwcGBkKI3lD0hShFrDKKHo5BBHPv1386mtGrV69GP+XS0hJnv1TCikcXFhZiKMqLFy8yJeqUhGIKlRrMc+AaoDlFAhKQgATanoAJSkACEpCABCRQVwK5XG5+fn5kZGS9qBzYIlw5rUUq47NlyxbOclef+tPR9Lfc1MAtvwQmIAEJSEACEqgrAYNJQAISkIAE6k2AM9sjR46cOXNm+/btBw8enJ2dDaFbvM7o6ChSGf+pqanBwUEcODpeXFxEIW/dupVmWH9/f09PT9Qpi0c5UqY/2e7du5mSmoRiSmpWVVEDV4VLZwlIQAISkIAE2piAqUlAAhKQQGMIoFdPnz7NoS6GAEYGj4+Pr6ysoFRDDE9MTHBQzOKc/XICjD/1MAQwMhjJevny5fi9X86Kh4eH0clDQ0NoZtyKR5ly/vx5fFhlaWkJOU0PE3GOIDuv/2oxzWpNDVwtMf0lIAEJSEACEpBA+xEwIwlIQAJNJ4BwPXz4MBKXle+++24Oint7e0+cODE5OUkP4pYmhoIdGRnB+ejRo/v27aOH0YI/oFU8igMHvxw7I33HxsZwIAihmH7gwAEkd77MJmDlpgaunJWeEpCABCQgAQlIQALtR8CMJCCB5hJAnc7MzMSaCFROdKmjSOOgeGFhAb1KDz4cGmP0M0oPE2liDNGkk6Go0ywYpYchnDGGaIY/zStXrnAsTE9tpgaujZuzJCABCUhAAhKQgAQk0GoCri+BlhLI5XIXLlwIgdrSRKpbXA1cHS+9JSABCUhAAhKQgAQkIIHWEzCDNiDAYezU1FQbJFJdCmrg6njpLQEJSEACEpCABCQgAQlIoJUEXHtzBNTAm+PnbAlIQAISkIAEJCABCUhAAhJoDoF6rKIGrgdFY0hAAhKQgAQkIAEJSEACEpBAFghkVQNnga05SkACEpCABCQgAQlIQAISkEB7EVADt9f1qCQbfSQgAQlIQAISkIAEJCABCUigNgJq4Nq4Oas1BFxVAhKQgAQkIAEJSEACEpDAZgiogTdDz7kSaB4BV5KABCQgAQlIQAISkIAENk9ADbx5hkaQgAQaS8DoEpCABCQgAQlIQAISqBcBNXC9SBpHAhKQQP0JGFECEpCABCQgAQlIoL4E1MD15Wk0CUhAAhKoDwGjSEACEpCABCQggUYQUAM3gqoxJSABCUhAArUTcKYEJCABCUhAAo0joAZuHFsjS0ACEpCABCRQHQG9JSABCUhAAo0moAZuNGHjS0ACEpCABCQggY0J6CEBCUhAAs0hoAZuDmdXkYAEJCABCUhAAhIoTcBeCUhAAs0koAZuJm3XkoAEJCABCUhAAhKQwDcIWJOABJpPQA3cfOauKAEJSEACEpCABCQggW4n4P4l0CoCauBWkXddCUhAAhKQgAQkIAEJSKAbCbjn1hJQA7eWv6tLQAISkIAEJCABCUhAAhLIEoFcLjcxMVGQ8dra2tDQUG9v765du1ZWVvJHcWZKOOCAbdu2jZ58H+r00M8ocXCmZ3p6miZGhSY2OjpKEzt37hzN2kwNXBs3Z0lAAhKQgAQkIAEJSEACEug6AujbQ4cOXbt2rWDnx48fHxsbW11d3bt37+zsbBpFvr73ve9NTYbwuXLlyuDgYOqkguidmpo6e/Yso/39/UTL5XLz8/PLy8uLi4snT56kGboXh7m5uWPHjpEJE2uwVmngGlJ1igQkIAEJSEACEpCABCQgAQm0ksDk5CQqtziD8fHx/fv3F/QjXM+fP3/LLbcU9Bc3L126ROfOnTsph4eH0b0I3T179vT09PT19cXZ8qlTpxjCAbcdO3aogUGRFTNPCUhAAhKQgAQkIAEJSEAC2SPAoe7AwEAI0RuKvhCliFVGx8fHGaR55MiRd77znVu3bqUZdvDgwd7e3vTTztFJifPVq1ephC0tLS0sLEQ9yosXL9IZdUqcmUKlBvMcuAZoTqmZgBMlIAEJSEACEpCABCQggUwS4FB3fn5+ZGRkvew5sEW4clqLVMaHE+OxsTE6qYf19/dzwLu8vEzz/vvvp2yJqYFbgt1Fu5CAW5aABCQgAQlIQAISkEBWCaytrXGoe+bMme3bt3OWOzs7G0K3eD+jo6NIZZQwPnjiz6xDhw499thjMzMzSOKenp4Qw/lz6c8/LsaBI+V8h927d9OZenBmSmpWVVEDV4VLZwlIoAYCTpGABCQgAQlIQAISyDYBhOvp06dXr3+FuB0fH19ZWUGphhiOP/7MJufm5rZs2TI8PHzdd5VT39e85jXve9/7kmRFTi8tLQ0MDFAZGhpCMzOL0cuXL8dvBaOfmY7D+fPn8WEV/AcHB+lhCOdw23n9l4dpVmtq4GqJ6S8BCUigcgJ6SkACEpCABCQggY4lgHA9fPjw4uIiO7z77rs5KO7t7T1x4sTk5CQ9BYaaRfHiwMkwJ7oFf0CLUEePHt23bx8OTGQUww1npG/8TPXIyAhiGIcDBw4guZHleNZgauAaoDlFAhKQgAQ2JKCDBCQgAQlIQAKdSQB1OjMzE3tDoHJmSx1FGgfFCwsLCFp6wuh///vfzykuld3ef9wAABAASURBVHDgfDimR0/UcSYsQ1jqoUITYwiH8KdZ/E8rMVq5qYErZ6WnBCQgAQlIoBIC+khAAhKQgAS6gkAul7tw4UII1AxtWA2coYtlqhKQgAQkIIE2J2B6EpCABCTQRQQ43Z2amsrchtXAmbtkJiwBCUhAAhKQQBsSMCUJSEACEsgGATVwNq6TWUpAAhKQgAQkIIF2JWBeEpCABLJEQA2cpatlrhKQgAQkIAEJSEAC7UTAXCQggewRUANn75qZsQQkIAEJSEACEpCABFpNwPUlkFUCauCsXjnzloAEJCABCUhAAhKQgARaQcA1s01ADZzt62f2EpCABCQgAQlIQAISkIAEmkWgE9ZRA3fCVXQPEpCABCQgAQlIQAISkIAEJFAJgVo1cCWx9ZGABCQgAQlIQAISkIAEJCABCbQTATVw9VfDGRKQgAQkIAEJSEACEpCABCSQTQJq4Gxet1Zl7boSkIAEJCABCUhAAhKQgASyTEANnOWrZ+7NJOBaEpCABCQgAQlIQAISkED2CaiBs38N3YEEGk3A+BKQgAQkIAEJSEACEugUAmrgTrmS7kMCEmgEAWNKQAISkIAEJCABCXQWATVwZ11PdyMBCUigXgSMIwEJSEACEpCABDqRgBq4E6+qe5KABCQggc0QcK4EJCABCUhAAp1LQA3cudfWnUlAAhKQgASqJaC/BCQgAQlIoNMJqIE7/Qq7PwlIQAISkIAEKiGgjwQkIAEJdAcBNXB3XGd3KQEJSEACEpCABNYjYL8EJCCBbiKgBu6mq+1eJSABCUhAAhKQgATyCViXgASqJ5DL5SYmJgrmra2tDQ0N9fb27tq1a2VlJX8UZ6bQQ7lt2zZ88MSfnnwrHp2ensYZoxKeo6OjNLFz585FTw2lGrgGaE6RgAQkIAEJSEACEpBAxgmYvgRqIoC+PXTo0LVr1wpmHz9+fGxsbHV1de/evbOzs2kU+fre976XJqJ3amrq7Nmz+PT39+NPZ7Li0VwuNz8/v7y8vLi4ePLkSZqhe5k+Nzd37NgxMknTq6qogavCpbMEJCABCUhAAhKQgAQkkHECpr8JApOTk6jc4gDj4+P79+8v6Ee4nj9//pZbbqH/0qVLlDt37qQcHh5G2VJJVjyK0N2zZ09PT09fX1+cLZ86dYqJTCHIjh071MCg0CQgAQlIQAISkIAEJCABCUhgfQKbG+FQd2BgIIToDUVfiFLEKqPj4+MM0jxy5Mg73/nOrVu3RvPq1atUwpaWljj7jTolzgWjCwsL9Ce7ePEiU1ITZ6akZlUVz4GrwqWzBCQgAQlIQAISkIAEJCCBbiTAoe78/PzIyMh6m+fAFuHKaS1SGR9OjMfGxuik3hb29STUwF8n4f8lIAEJSEACEpCABCQgAQl0PYGnv/vtJe0H5v+//3XfwHf93q/9i8vz//mW55X0oZOhX3rGtajgiT+zmEv9wmtvpclQfp0mlt8T9d/bsyPiMErMu3u/zHSGaBIk1Wlufc89x/7ijyu/bt2ogSuno6cEJCABCUhAAhKQgAQkIAEJtDOBr3zta+/IPVB5hmrgyll1gqd7kIAEJCABCUhAAhKQgAQk0GEEkMGV70gNXDkrPbNNwOwlIAEJSEACEpCABCQgAQmogb0HJND5BNyhBCQgAQlIQAISkIAE6kXgba/Y96f/svAvY+173os+8ZM///ib3/boG3/x9Tt3sxZuVw8foecj//pnaWK/9c9+jCaGJ/70FBhTmE4Z/UzEGTsz9AZ60nR6GKKnNlMD18bNWRKQQDYImKUEJCABCTSNwNPf/famreVCEsg6gVtvuz2jW0DfvuX7vv/bv/lbCvKfeNne+5Y+zuvAI1+8eui7/zck7vCL/snP/vF/e9OHT9309B5kLT2399/8nxY/euCD72XuT9y8izLfIjLT3/7nc/QzhYlM/+WP/I/d3/lcRun8zNpjTGeVW/7wt2jWZmrg2rg5SwISkEC7EzA/CUhAAhKQgAQkUHcCdw7ehkwtDvua0+/92f/x31L/3Gc+hUx9/6WL+T1Xv/LlF9zYS8/ff+2rV770GJV8I/KfP7rMrOj8+NXPU7l567Mpr33l71G/VOpiauC6YDSIBCQgAQm0EQFTkYAEJCABCUigEQTODL1h5UtffOizj5QMzlHto2/8RUbRw+HwW//sx6b3vmbiT++Lo93/54PnXtr7XWd/9A0PP7YaPeFGydznP3Prq7a/6PE3vy1+Uhr9/O6/+fNfGLwNm/34XyGqcXtez41Mx4fINGuzDTRw+neWOG6OBazAQQhCgADmnSAECGDeCUKAANYOd4JpQADzWjQfQjCPsvmrsyLm6kKAANbOd8Ktt92ORYZRoWzDnEmp2N72in0vvLF36qPr/itEqNabfudXbn3u85HKMZ2T4fEHzkz9wO3M3fe8F73nVcP3LX38lz/yP15x03Z6widKznu/8rWvvunDpw5c/0npiZftxeHN3/OKn/3j//bglcv/9qUvYzqeH17+FPT+0+JHb++/OXrorNY20MBHX/7PIyJS24oQuAeEIAQIYN4JQoAA5p0gBAhg7XAnmAYEsNZeCxIIa20ars5VEELbQrjprT2vvumjlBgV8sTa7XqRUoEhOBGiL9ryLI5hf3rgZf/kWc9JQrfA89Sn/m+kMv7RjzC++LnPfv9z+//Z817w91/76v/58QVOgP/80eXhF/2TcEhl/MAz570fW/07OpnCRKaH6v6Jm3ehqOOE+cqXHvv2b/5mzoRxq8E20MA1RHSKBCQgAQlIQAISkEAzCbiWBCQggUYTQJq+5Pf+A2ewGMew//cX/hY5+vqdux994y+GGP7TfznCyS1poF2//LWvIlA/8q9/FiWMvbT3uz792CpD3/X0HpQwlb5nPHPlS19k6BM/+fO40fPxq5/f8m3fziid+NODUaFJKBQvupeesG3PuPHvv/a1mn9DWA0cGC0lIAEJSEACEpCABLJHwIwlIIEWEuCQlhNdBC05/NKffYiDYg60X3HT9ntzDzLEgfAHXvN6zo2vfuXLHOHG8e8v3fLP8Nn6bd8Rp7tMDMP/vz38MUbDH4EdDjTf86phDoSZ/ltf/6eVXv/du//zxz6KLI+51ZZq4GqJ6S8BCUhAAhKQgAS6iABnPl2020xt1WQzQSB+3TcTqVaeJII2/fVmDngfuv4nslCkcVB80+/8CoKWaKjWrSfu4TUkOaNsaWJ44o9RSaOEZQiLnhiliTGRgMmBsASnpzZTA9fGzVkSkIAEJCABCUhAAhKQQGsIuGqbEHjbK/YNflcf0rRN8qkwDTVwhaB0k4AEJCABCUhAAhKQgAQk0GICbbU8h7E/8H8db6uUKklGDVwJJX0kIAEJSEACEpCABCQgAQlIoJUE6rW2GrheJI0jAQlIQAISkIAEJCABCUhAAu1OIIsauN2Zmp8EJCABCUhAAhLoPAKP3Xp7523KHUlAAlUR+JXvv/2PfvSnMCox8fi+19LEfv5/+6fRk8ofev6LTx14PUOU1OnHhyaWptPZfFMDN5/5ZlZ0rgQkIAEJSEACEpCABCQggRYQ+KmXDvY945l3/dl9U7kHXrzlWTTRtOTxIx/83d+7dPEHntsfQpeesIM3f98ff+ZhRhe/8HfUGf2eZz2H6TgznWa4Nb9UAzefuSvWRsBZEpCABCQgAQm0I4Gnv/vt7ZiWOUlAAvUm8D3PumnlS1/M/d3Kf3/kk1e/8uXv/I5noGn/5gt/yzoXP7fypa/+w/aeLaji00OHkMdI3Gd8y7fSz+jffOHRb/2mb/7cl780MvcBptPTWlMDt5a/q0ugEgL6SEACEpCABNqagDK4rS+PyUmgTgS+8zuenh/p+579XFRu6vnWb/omVHFqooe/9Zu+OTXxjNFf+f7bf6T/5v/PR/4YIZ1Gm1xRAzcZuMtJQAJVEdBZAhKQgAQkIAEJSCAzBP7DX/7J0On3/e7Hcutl/It/dt8fLX38/3XLP+egeD2fRvergRtN2PgSkIAEaiPgLAlIQALtQoBj3viDWFGWTOvxN7+N/iipaBKQQOcR+NyXH8/f1F99/rNf+uo/pJ5/+F//63Nf/lJqLq9d+4f/9bXUxDONopCvfuXLu7+zL402uaIGbjJwl5OABCQggQ0J6CABCUhAAhKQQNsR+OzjX+x/5pbB7+rjCPcZ13/X93Nffvx7nvUcEg1Be/FzK+n3gT93XQ9Hf/wiMfXfeOWP4sz0rd/2HeFAs/mmBm4+c1eUgAQkIAEJrEfAfglIQAISkECbEvgPf/knHOf+6vffPjG498//9jP//ZFPzn78ImL4j370p37i5l0Xlj+Z//euqP/R0sdf/fwXM4pynv34RY5/P37t8zSZ/slrX6DZqn2qgVtF3nUlIAEJSEACEsgnYF0CEpCABNqdwMjcB37kg7+LoYfJFaH7kx86STP9DjD9qY7Kpc4oPnjizyhN7Bf/7D6arTI1cKvIu64EJCABCUhAAhIIApYSkIAEJNA8Amrg5rF2JQlIQAISkIAEJCCBpxKwJQEJSKDZBNTAzSa+yfWe/u63bzKC0yUgAQlIQAISkED3ELj1ttvbdbPmJQEJtIaAGrg13F1VAhKQgAQkIAEJdBgB/2GkDrugjdyOsSVQZwJHXv7PK4+oBq6clZ4SkIAEJCABCUig2wk8duvtYd0Owv1LoEYCGZjG51nl7b/u2PNvP/IZfD71429+yTv/4Je/tIX62Me/+Kd7fpwKTUYZovy7n/wF7Ac/8BBTGApLPVHBjX78X/6790UEevDHmIgPQ1QoIzLOuP2LuUWGqIcdVQNn4M4yRQlIQAISkIAEJNAGBCr8NasbH3rij7hGmbKucG7ytyIBCXQYgb6+vsOHDy8uLrKvu++++8iRI729vSdOnJicnGRodHT05ptv3r59e39///79+9fW1oaGhnBIPcxKhv/Ro0f37duHA534Y0zEeWBgYGxsDIeRkZGlpSUcDhw4MDEx0dPTg2cN5jlwDdBaP4XPX1ufhBlIQAISkIAEJCABCUhAAt1HAHU6MzMT+0agDg8PU0eRnj59enV1dWFhAb1Kz+Dg4JUrV+gJ5+RQ0BOj+BOWISz1UKGJMYRDikBYgtNTm7VCA9eWqbMkIAEJSEACEpCABFpHoOAQuHWJuLIEJNAuBHK53IULF0KgtktOFeShBq4AUh1dDCUBCUhAAhKQgAQkIAEJSKAjCHAYOzU1lbmtqIEzd8kym7CJS0ACEpCABCTQ9gSe/u63hz3m375q+4tlghKQQG0E1MC1cXOWBKohoK8EJCABCUhAAhKQgAQk0B4E1MDtcR0qy4LPZXGMkk9nqWsSaHcC5icBCUhAAhKQgAQkIIF2IqAGbqerYS4SkEAnEXAvEpCABDJFoODj9fgLWFE+/ua3ZWorJisBCUigHAE1cDk6jklAAhKQQC0EnCMBCXQQAQVwB11MtyIBCTxBQA38BAX/k4AEJCABCdSbfOgyAAAQAElEQVSHgFEkIAEJSEACEmhvAmrg9r4+ZicBCUhAAhLICgHzlIAEJCABCWSBgBo4C1fJHCUgAQlIQAISaGcCGc/t6e9+e/wycJQZ343pS0ACEtiAgBp4A0AOS0ACEpCABCQgAQmsS8ABCUhAAlkjoAbO2hV7ar5+XvtUHrYkIAEJSEACEmgSAU6PWSlKKt1o7lkCEsgmATVwW1w3pWxbXAaTkIAEJCABCXQrgfg3kCjjr0BHWTmMav0rj1yh56233V6hp271IWAUCWSZgBq4/levaR+IhnKOsv7bMKIEJCABCUhAAhKogEDLBXAFOeoigfoRMFL2CaiBs3oN+aQ2q6mbtwQkIAEJSEACWSDgw0YWrpI5SqCJBDplKTVwp1xJ9yEBCUhAAhKQgATyCFT7g2lJ8Xqum0fRqgQqJeAP5FdKqg38atHAbZB2h6RQ7ZtT2nZ6l0o9ViQgAQlIQAISkIAEJCABCUhgQwJq4A0RPcXBhgQkIAEJSEACEsgiAf+ASBavmjlLQAKNIKAGbgTV2mOudzK8Xn/tK1U/0xkSkIAEJCABCbQhAcVtG14UU5LArbfdHgaKqFBSxx59x1q+0aM1mYAauMnAK1qu4M0sBHCUFc3Xqd4EjCcBCUhAAhLIKIGCh4rN7yJ+IYvy8Te/LWzzMY0ggY4kcNNbe/It9vjQg/cVWwxlqMzlchMTEwUJr62tDQ0N9fb27tq1a2VlhVHctm3bRs/o6ChNLPXgiT89+VY8Oj09zXSMSngSiiZ27ty56KmhVAPXAK1lU3inYe0oqWgSaAIBl5CABCQgAQlIQAISkEAigL49dOjQtWvXUk9Ujh8/PjY2trq6unfv3tnZWSTuzMzMRz/60cXFxYWFhVwuR8/U1NTZs2fx6e/vxz8mRlk8ypT5+fnl5WUinDx5kmboXqbPzc0dO3aMTGJutaUauFpi3/Cv+wer3whtTQISaAMCpiABCUhAAiUJcAhcsr8JnemnSZuwlktIQAIlCUxOTqJyi4fGx8f379+f+nt6etDAfX19qefSpUvUd+7cSTk8PIyypZKseBShu2fPHuIQJM6WT506xUSmEGTHjh1qYFA0yer+M8mhpaNs0h5cRgISkEBZAg5KQALZJRAPKvnles8Y6/Vnd+9mLgEJNJrA9PT0wMBACNEbir4QpYhVRsfHx2OQk9tXv/rVnOIODg4yevXq1einXFpa4uyXSljxKKfHMRTlxYsXmRJ1SkIxhUoN5jlwDdCqnlLyPSb/zanqiJueUDKlTUc1gAQkIIHME3ADEpBAlxDwSLlLLrTbrCOBXC43Pz8/MjKyXkwObBGunNYilcOHk+EPfehDd9xxB3Ojpx1KNXA7XAVzkIAEJCABCbSegBlki0B8mF6Qc3RGWTCUmuVHk5sVCUhAAvkEOLM9cuTImTNntm/ffvDgwdnZ2SR0892oj46OIpXxp44hjNHAc3NzVLZu3UpPWH9/f09PT9Qpi0c5UqY/2e7du5mSmoRiSmpWVVEDV4WrOmfPWqvjpbcEJCABCUigRQQ6b9n4C5pRdt7u3JEEJNB8AujV06dPr17/QgAjg8fHx1dWVlCqIYYnJibisBe5u2XLlsceewwxjBLGzp8/PzAwgGS9fPly/N4vZ8XDw8MMDQ0N4cZ2ikeZwkR8WGVpaWlwcJAeJuIcQXZe/9VimtWaGrhaYhX58wlrCOAoK5qjkwQkIAEJSEACEqiSQDxpUGIFU/MFcIxGWeBmUwIS2AyBW2+7PYwgqUIde/Qda/lGT+cZwvXw4cPx163uvvtuDop7e3tPnDgxOTnJEOL25ptv5tyY89v9+/fTc/To0X379uEDCnookxWP4sBEpiN9x8bGcBgZGUEMM/3AgQNIbmR5ml5VRQ1cFa4nnVsocfPfz57MpsH/8/2ywYANLwEJSEACEqiaAI8izKGMP9EcZcm3bIYwnLViAvZIoC4EbnprT7IU8KEH7wujJyqU1DvDUKczMzOxFwQqJ7rUUaRxULywsIBepYeT2ytXrnBynJyZSBOLnpgSdfwLRulhCGeMIZrhT5OwBKenNlMD18bNWRKQgAQkIAEJSEACEsgqAfOWQF0I5HK5CxcuhECtS8DmBFEDN4fzU1bhU1vsKV3XG9EZZcmPcq97WUhAAhKQgAQkIIEaCcRjBmU8aURZYyynSSCbBMy6jgQ4jJ2amqpjwOaEUgM3h3OJVeKnmqMsMWyXBCQgAQlIQAISqBOB+HFoynjwoKRO7ChpUtckIIGOJ+AGg4AaODhYSkACEpCABCQggWwTQNCGmo0y25sxewlIQAJ1JZAfTA2cT8N6OQJPf/fbyw07JgEJSEACEpBA6wgggGPxagVwtf6xiqUEJCCB7BLoNg3c2CvVJSrR3x1q7G1kdAlIQAISkEA1BJL6rWaSvhKQgAS6l4AauHuu/ZM77RKh/uRu/Z8EJCABCUigswjU/D5eLJWLezoLlbuRgAQkUJqAGrg0l5K9Nb/rlIzW5p0Fm40mZRwCR9nmW8hLz6oEJCABCUhAAk8S8IefnwTh/yTQFAKd9M8CNwVYMxZRA9eBcoYEISKWDUeZobTJWauVgPMkIAEJSKBzCMQ7OCU6Nqxz9uZOJCABCTSLgBq4WaRdRwISaDYB15OABDYg4IehGwByWAISyBSBW2+7PVP5mmzLCKiBW4behSUgAQk0jICBJSABCUhAAhKQgARKE1ADl+bSJr1+Qt8mF8I0JCCB7BAwUwlIQAISkIAEJFCOgBq4HJ0uH3v6u98eIjzKLqfh9iUgAQm0PQETlIAEJCABCUhgYwJq4I0ZhQeCkEqUVDQJSEACEpCABNqGgIlIQAISkIAEKiWgBq6UVL7f429+W36zhvrmI9SwqFMkIAEJSEACEug4Am5IAhKQgASqI6AGro4X3iFfb3zovrD4OeEoGS1vHiOX5+OoBCQgAQk0h0B6P6rw/as5WbmKBKokoHuXEij++8/0YOCgDKOuSWA9Amrg9chU3R/PE1FWODnkdIXOuklAAhKQgAQ2SSDepPLLTQZ0etMIxFVr2nIu1PYEujHBELeUbJ4yjHrYQ303JoseSwmsR0ANvB6Z2vtRtljx/Hj3ijJ/lPNkmpQxK0p6mmPlTwDIijQoI6so6dEkIAEJSKCTCJR/L+iknWZ6LzxC8EYclumNmLwEaiZw01t7bnprTyprjuPELiegBu7qGyAeeqLsahBuXgISkIAEJCABCUhAAu1OwPzqQ0ANXB+Om4zCQesmI6hjNwnQ6RKQgAS6kwBvQPEOEmV3QmjCrjnCbcIqLiEBCTSawK233d7oJYy/DoF6dquB60kzW7F47slWwmYrAQlIQAISkIAEJCABCbScQC6Xm5iYKEhjbW1taGiot7d3165dKysrjI6OjtLEpqenaSYHerZt20YQOvONHvoZJQ7ODDGRJkaFJpZinjt3jmZtljUNXNsunSUBCUhAAhLoYgIeQnbxxe+QrXP4FsZ+okJJHaOCpUrUaWoSkECDCKBvDx06dO3atYL4x48fHxsbW11d3bt37+zsLG44LC8vLy4unjx5MpfL0cQYwufKlSuDg4M0kyF6p6amzp49y2h/fz/RmDI/P58fIXQvDnNzc8eOHYslUoTKK2rgylmV9ozTVMrHr/+jwVGWdt1kb12nk3BBPJ+QCoDYlIAEJND+BKr6AeZ4nY+y/bdmhhIoIJD+DFJU8kfTXwOmkt9vXQISaASByclJVG5x5PHx8f3796f+vr6+mZmZnp6e1FO+cunSJRx27txJOTw8jHJG6O7Zs4cIhIqz5VOnTjGEA247duxQA4NCawiBYrXckGXKB3VUAhKQgASqIVBe6MbHtVFWE1VfCUhAAhLodgLT09MDAwMhRG8o+kKUIlYZHR8fT4Mc527duhXVGj0HDx7s7e1NP+0cnZTMvXr1KpWwpaWlhYWFqEd58eJFOqNOiTNTqNRgngNvDK38k8TG86vx8ImkGlrd4esuJSABCUig1QSqOm9vdbKuLwEJ1IGAP1RfEiJqdn5+fmRkpOQonRzYIlw5rUUq08SQqceOHbvnnns4zqXZ39/PAe/y8jL1+++/n7IlpgZuCXYXlYAEKiCgiwQkUIoAn8yGhTCLspTjE314PvG/G25IlWhatopA+evVqqxcVwISkMCGBNbW1o4cOXLmzJnt27dzljs7O5uEbsHc0dFRpDL+9E9OTt5xxx3xq7/I4JmZGXQylRDDOCSjn+Pi1MSBI+XUpLJ79246qYThzJSoV1uqgasl1r3+nlF377V3580n4IoSqIlAgb4K3Rslr+HJaortJAlIQAIS6GoCCNfTp0+vXv9CACODx8fHOeZFqYYYnpiY4KAYRnNzc1u2bMH/3LlzDzzwAJ505hvyeGlpaWBggMrQ0BCamVEE7eXLl+O3gjlJHh4exuH8+fP4sAr+CGl6GMI53NLPV9NTlamBq8Kl8zcIFDxpfWPAmgQkIIHNEHBu9QR8Qa6eWZ1neAnqDNRwEpBARgggXA8fPry4uEi+d999NwfFvb29J06c4PgX4XrnnXc+8sgjCFc6EbqoWRQvdU6SOdHN/wNaTCfU0aNH9+3bhwNNRjHccCbC2NgYDiMjI4hhHA4cOIDkRmbjWYOpgWuAtu4UPmJfd6z6gXhDjbL62c6QgAQkIIGsEdhEvrW9WfhXDzeB/Mmpccz+ZKPs//CMy5Rflp3hYKUEbl15LFmac+ttt4fRkyrUNQlIYPMEUKczMzMRB4HKmS11FGkcFC8sLKBXMSrXj42fKPBPDrRppilRp0lYhrDUQ4UmxhAOKULxP63EaOWmBq6cVVM9fS5pKm4Xk4AEJJBBAvFOEWW16df3Q9tqVy/h35ZdqNYwsgvhSqVaI0IlUyp0qyRUt/k89OB9GLumDKMe9lDfjcmix1ICEqgvgVwud+HChRCo9Y3c0Ghq4IbibVLweGOOsklLuowEJCABCUigiwnUU7I+FaPv5k/lYUsCEmhrAoODg1NTU22dYqnk1MClqDS4z0/fGwzY8BKQgAQk8BQCvu88BUdjGiW1a0jlKNe7CiUnNibH9otqRhLIDoH0E/VRocxO7mZaSEANXEik7u14b4uyquDx421RlpwYQ1GWdMjvjDfg/B7rEpCABCQgAQmUJFDhu3a8t0ZZMk7qLBDAEZ8SwydK4kQlSvq1Tibg3tqSAMo2jOyiQkk9LP1oPZXoscwoATVwHS5cSRUanVFucg3eOMM2GafC6b71VghKNwlIQAKdR2DDtwB0Wuftumk7iqcCSoxFKbGoUPJeT6lJoPMJtPcOb3prT761d7JmVyMBNXCN4JyWCGz4wJQ8rUhAAhKQQOMIxKtxlI1bpcsjV453ww8LQv12Oc/23H7+uV97ZmhWGSZg6u1BQA3cHtfBLCQgAQlIQAKbI4CmCgv1FeXmQjr7KQRCAEf5lAEbEpCABCSwIYF2clADN/Bq8CxSW/TKJ1buWVsm8eXBeQAAEABJREFUzpKABCQggUwQyH87iB+ppcRIPkoqG1qouyg3dF7PYZPT1wublf7YfnwAQRnwo4wt5F+p6FmvjFDrjdovAQlURcDj/apwdbxzszVw5oDyBkbOUVLpVIsNRtmpe3RfEpCABCRQkkCosihLOtgJgXw+8XYZJUPlLV8Al/Gs0K1MBIckIAEJSKBCAmrgCkHdEG9OUVY656l+zWylN+ZqP0UO/yibmbBrSUACEpBADQTyhVkN052ySQI8FYRFnHj3jDJ6ai4jSJQ1B3GiBCRQXwK3rjyWLD/yo+9YS5bfb71tCaiB2/bSbDYxZHC8d0a52XCl5lceudRs+yQgAQlIoFEEEGaNCm3cUgT4MAIrNVJdXwSJsrqZektAAg0m8NCD92EsQhlGHYs6ZapT0dqcgBq4igu0yUcKpodVsaSu2SfgDiQgAQlIIOsEQpSmkndzdhQlFU0CEpCABLJFQA3c8OuV3jIbvlJTFmA7YU1ZzUWyTcDsJSCB7BJ4+rvfTvJlft6nzBATM2qx65qT5/2RuZQhj6Okp7zhn+9Q0GSoI1GzL00CEpBAqwiogVtFviHr8jYZ1pDoBpWABComoKME2oRAhTJsw2xDHKaS9xqmUIZR7xJLAnVDsBs6VEgMwnhGSUWTgAQkIIHNE1ADb57hxhHSW+bGrjV5RPwoawrwjUkRhJI3b+wbA9YkIAEJVEZArywS4GW/TNpJ+iaf8I8ydWa3EgozbTOaUbZqU8E2ylblkK1147cxs5Wz2UpAAq0ioAZuFfkOXDeeHjpwY25JAhKQQGUEOt4rPhuNsuM36wYlIAEJSKBTCaiBO+TK1vZRccyijAeaKDuEiNuQgAQkIIFaCcRnmlFWeBxaoVutGTVqXuyxUdGNKwEJSEACbUlADdyWl8WkJCABCUhAAm1JoBs+LeWj4TA2G1b+UuSPMjG/aV0CEugwAv7UfWdcUDVwZ1zHDXYRn3NHuYGrwxKQgAQkIAEJtAGBTMjpNuBULoVbVx5Llvxuve32MHqiQkldk4AEuoeAGrh7rvUTO+XzbP4XJZV8K9mZ72BdAhKQgAQkUJJA/CB0lCUd2r8z08m3P96WZMh5XRirpwp17KG+G/ONnhrMKRKQQHYJqIHb+to14jPgDbVuIxZta8omJwEJSCDjBFr7ut3a1Tdz6eLHo6LM7i42Q8C5EqiNgLMkkMvlJiYmCjisra0NDQ319vbu2rVrZWWF0dHRUZrY9PQ0TYyJ27ZtowdP/OnJt+JRJuKMUQnPFPPcuXPRU0OpBq4BmlMkIAEJSEACEpBAUwnwETbW1CVdTAKZIpB+7p1K4xI3MgTQt4cOHbp27Rr1fDt+/PjY2Njq6urevXtnZ2dxY3R5eXlxcfHkyZO5XA7ROzU1dfbsWXz6+/vxxyFZ8ShT5ufn8yOE7mX63NzcsWPHYokUofKKGnhdVv5Y1LpoHJCABCQgAQlcJxDHp6mMt84or49nqQiFGWX75A3bsPZJyUwk0IYE0o+7R4Uykry16Ne/o78RZVq0EcHbIWbKYXJyEpWbmqkyPj6+f//+1Ozr65uZmenp6Uk9ly5dor5z507K4eFhtDGVZMWjCN09e/YQgVBxtnzq1CkmMoUgO3bsUAODQpOABCQgAQlkiUBGtWIgRphFpdFl/KxyfVcpiJn20m4CuL67NpoEMkcABbv5nMv/7nddlth8khmKMD09PTAwEEL0hqIvRClildHx8fE0yHHu1q1bUa2MXr16NfUvLS1x9puaxaMLCwtplMrFixeZQiWMUEyJerVltefA1cbPgH/BG2EGMjZFCUhAAhKQQLMIoA9DGUbZrGUbuE6870fZwGUMLQEJSKDjCKBm5+fnR0ZG1tsZB7YIV05rkcrhg0w9duzYPffcw3Fu9LRDqQau/CroKQEJSEACEuguAgjgDt4wqj6sg/fo1iQgAQnUiwBntkeOHDlz5sz27dsPHjw4OzubhG7BEqOjo0hl/OmfnJy84447BgcHqaOQORCmEtbf358vjItHOVIOzyh3797NlKhTEoopVGowNXAN0DpqSmXPN+tuOX6QL8rkVNBM/VYkIAEJSCCjBNCKm8x8k28NTA8jDSqUmgRqJnBr3r8bTD3i3Hqb/25wkLCUQGkC6NXTp0+vXv9CACODx8fHOeZFqYYYnpiY4KCYyXNzc1u2bMH/3LlzDzzwAJ50YkjWy5cvx+/9clY8PDyMTh4aGkIzlxwdGBg4f/48PqyytLSEkKaHiThHkJ3Xf7WYZrWmBi4kFj8cFe+vURZ62P46gU3q56+HafP/m54EJCCBxhKo8L2Gt6ewxmbTltHZeG15FbCtOU5tqzurPQk89OB9YaQXFUrqWP4vjlKnR5OABMoTQNYePnw4/rrV3XffzUFxb2/viRMnOP5FuN55552PPPIIwpVOhC7OR48e3bdvH03C5v8BLZrFozhw8MuxMxHGxsZwGBkZQQwz/cCBA0huZDYTazA18DegpXdK3iND3UX5DQ9rEuguAu5WAhKQQKUEfMeslJR+EpCABLJPAHU6MzMT+0CgcqJLHUUaB8ULCwvoVYzK9WPjJ4rwZ+ITjdXVaMaUqBOhYJQehsKfIZrhT8+VK1c4FqanNlMDb8Atfvoryg1cu2y44HGHDw4AQBkfJURJjyaBbBIwawm0LwHeksLqnmK8sFMSn+BRUtEk0JEE0vFvR+7OTUmgOQRyudyFCxdCoDZnxbqsogauC0aDSEACEugYAm6k4QT4uLDha7hAHgE/ls2DYVUCEpBAPQlwGDs1NVXPiE2JpQZuCubOXYTjAjZHGccFUdKjSUACEsgggWakHAKYMoRZlM1Y2DUkIAEJSEACErhOQA18HYOFBCQgAQlIoKsJPLl5PtN8srbp/7WbvOdzB/YUJRVNAhLoZgL+GfBuvvrsXQ0MBE0CEpCABCQggXoSCAEcZT3jNiSWQSUggW4kcNNbe/ItIXj0HWv5lvqtdBIBNXAnXU33IgEJSEACEqgPgdp+tyWOkaOMPJp/7sqKYaHAo4xkomRrYdHs7tLdS6CjCGz+75wRodg6ipGbuU6g2zUwb5NwoIz3yCjpwdJbOO+UNLNosYUom5B/dkE1AY5LSEACEtiQQP570IbOOlRFQLZV4eoOZ3cpAQl0L4Fu18BdcuVDnUbZJVt2mxKQgAQk0G0E+EQ7f8vxEXCU+f3WJdD1BAQggW4noAbu9jugcfv3Q/fGsTWyBCTQYQR8wdz8BQ0BTBkf+Ea5+bBGkIAEOouAu5HAEwTUwE9Q8L+GEuCJpKHxDS4BCUggiwTifDLKgvx92SwAYlMCEpCABDZNwADfIKAG/gYLaxKQgAQkIAEJ1J0Akj4OuqOsLX58WEAZQaKsLZSzJCABCUigywgUblcNXEjEdh0J+IxSR5iGkoAEupYAGjJDe0emls+2mW8NkQwl9vib3xZWPj1HJSABCUig4wl0kwbu+IvpBiUgAQlIoP0IVKhgQxlG2X6bMCMJSEACbUEg/1/upd4WOZlEBgmogTN40apJmU++n3C/4QY+/I5Ko8tYMcpGr2V8CUhAAp1BYMPXTIR0vIxH2Rm7dhcSkIAEShK49bbbwxiNCiX19C/3FtRpahKoioAa+Elc8fwRpU8YT0LpjP+5CwlIQALtSiDedNo1u4bkFQfd+WXly1SOy/fxyqnqKYH2JHDTW3vyrT2TNKvsEuheDcxn6tm9bGYugYoI6CQBCUigPQiEfI2yPTIyCwlIQAIS6F4CXaqBQwBH2b0X351LoIMJuDUJtAGBOO3cMJHuVIYBJ8oNEekgAQlIQAISqCOBLtXAiWD8uFSUqdOKBCQggQwTMHUJtCWBkPr5ZVumaVISkIAEJND5BLpdA+df4Xhjzu+xXgkBuVVCSR8JSKAZBNp7jRrOPAumZOgTW94awrgm5dPe/M9kbT4CSWoSkECGCDz04H0ZytZU25CAGrgNL4opSUACGxMo0AYbT9BDAq0jgEiLOzbKhiRiUAlIoCsJxB9M7sqtu+lWEsjlchMTEwUZrK2tDQ0N9fb27tq1a2VlJY2Ojo6eO3eOZnLAZ9u2bblcjs58o4d+RomDM0PT09M0MSo0MaLRxCImPTWYGrgGaE7ZgACf/W/g4bAEJCABCUjgqQRq+IDgySlPjWNLAhKQgAQaSgB9e+jQoWvXrhWscvz48bGxsdXV1b17987OzsYokjXVo4cmPleuXBkcHIyeKBG9U1NTZ8+eZbS/v59oSOL5+fnl5eXFxcWTJ0/SDN2Lw9zc3LFjx8gk5lZbqoGrJaZ/dQQ4/WACJU8qYTQ1CUhAAhLoWgL1+pyU9xQYRkmlS81tS0ACEmg6gcnJSVRu8bLj4+P79+/P7+fw9od+6Iduu+22/M716pcuXWJo586dlMPDw+hehO6ePXt6enr6+vribPnUqVMM4YDbjh071MCgqI+V/7Wl+qxhFAlIQAISaDUBtVOrr8A31o/PSWlXclGQ0GH4JyNCqlvpFgLuM2sE/LHtrF2x0vkiawcGBkKI3lD0hShFrDI6Pj7OIOVrXvMaKvl28ODB3t7e9NPOaYi5V69eTc2lpaWFhYXUpHLx4kU6qYThzJSoV1t6DlwtMf0lIIEWE6jkKbnFKbq8BDZBIOQcZdzqUW4iXrdMjY+wo+yWPbvPriXgxiXQIgK5XG5+fn5kZGS99TmwRbhyWotULunT39/PAe/y8jKj999/P2VLTA3cEuwuKgEJSEACEtiAAKedeETZndKODwIgoElAAhL4BgFrrSOwtrZ25MiRM2fObN++nbPc2dnZ9YTu6OgoUhn/gmR7enpmZmbQyVRCDOc70L9169bUgwNHyqlJZffu3XRSCcOZKVGvtlQDV0tMfwlIoDUE4mk4zsSibE0eriqBphPoTgHcdMwuKAEJSKC9CbQ6O4Tr6dOnV69/IYCRwePj4ysrKyjVEMMTExMcFJPm3Nzcli1b8Kde0pDHS0tLAwMDVIaGhtDMuCFoL1++HL8VzEny8PAwDufPn8eHVfAfHBykhyGcw23n9V8eplmtqYFv8Nmi2pumcn+OLwJvlJVP1FMCBQRCAEdZMGRTApUQSDdPqsQsP08JDpYSkIAEJCCBGgggXA8fPry4uMjcu+++m4Pi3t7eEydOTE5O0lNgqFkULw6cJHOiW/AHtAh19OjRffv24cBERjHccB4YGBgbG8NhZGQEMYzDgQMHkNxlZDYRypgauAyc7A2hOUmaEs2JUdckIAFFjvdAIhACmDLuiijTaMnKhj4bOpQMa2cjCPjG1wiqxswogWz9AapsZZvRW6K+aaNOZ2ZmIiZns5zZUkeRxkHxwsICepUeLDrxT/XrB8mrMT1Go44Dbvmj9DAUPQzRDH96iv9pJUYrtyxp4Mp3pacEJCABCUigDAE+K2Q0yjYRsSQThpALI8NuNj6qYPtRUtEkIAEJ1Ebg1pXHkuVHePQda/mWP2S9cgK5XO7ChQshUFtI058AABAASURBVCuf1XJPNXDLL0FFCegkAQkUEwjpEmXxqD0SSARCR0WZOhGcqV5DhWhhzI2bkCZ1re4E/ESg7kgNKIHuIfDQg/eFseVUSXV6Cuo0taoIDA4OTk1NVTWlHZzVwO1wFcyhHAHHJJBPIHRLlPn91iVQCQHUFG5RUilvlWtabshwpkQPh60XnNXD1nPosH42W3JHQAtjdD0fhjQJSEACEpBA3QmogeuO1IASqBsBA21IALGBT5RUyluFbuWDOJotAohSLCRWlBXmzyw8o6zkzongUTKx2NB7dEZJRZOABCQgAQlIoFUE1MCtIu+6EpBAOQKVjCknKqGkT4UENhS6GzpsuJB37IaIwiE+eoi6pQQkIAEJSKDuBNTAdUdqQAl0AoEOeAbN7hY64QZqvz2UOaHdZLIq200CrPnSBPkoN5mD0yXQJgT888htciFMo+MJdJ0G9rG44+9pN9glBHzw7cgL3apNxRlvlCVziKEoSzrY2RIC6XWgZiHdkrRdtBICqsFKKGXFh6uJkS1lGHUs6pSpTkWTQBMIdJ0GbgJTlwgC6dEkmpaZINAZHxKlXVAJ0RJlJi6BSW6SABc9IlR40XmlwmLKeuWGDiUnRgJRlnQo6CzZrG3pkqHslIAEJNBCAje9tSdZJWnk/6tF1CuZoo8EKifQjRo4PSFVjknPzRDY8ON5r8hm8Hbh3KpUQThThhSJsguhueV6EeD1Kixe2aLMD87Nlox+6pTFbnRqiYAVCSQCHAmG0RMVSuoYQijf6NGyTuAh/+GirF/CzObfjRqYixWPI1HS1NqEgPqkTS4Ej/hkQskVwah3mLG1DtuR2wkCcWUp476NMoZqLkPE1jzdiRIoT8DRYgLptDAq4VAsluiJIUsJSEAC1RLIjAbmmYa91eWBhjgdbD6udfDFdWsSkMDmCTTu08/iyL4g13C94u0+ygqnF5OvcKJurSXQtNVVy01DncWFvD2yeNU2n3NmNPDmt2oECUhAAhKQQG0EULNhIbfyy9oCOmtDAkLeEJEO2SVg5hKQQGsJqIFby9/Vb4gP+ynjkD9KubQbAa9LhVeEO7lCT90aSgC9SvwoQ0rRrMS41cMqcdan0QSqunaNTsb4EugMAun3q1u1HdeVQDsQ6CIN7LNpO9xw5iCBlhOIlwJKpU7Lr0X7JBBqOcr2ycpMJCABCUigkwi4l/Yh0C0amOddoEfp58qg0CTQPQRqEDbxWtE9iDpypzVc947k4KYkIAEJSEACLSfQbglkXgP7qNput5T5dDYBzk47e4PsLl5VKNksRs+GhvOGPjq0hEAjPvRsRMyWwHFRCUggKwT8AeasXCnzzAqBZmrgrDAxTwlI4CkEQgdG+ZSBrml089675iI/uVFOj8OebPs/CUhAAhKQgAQ6jkBWNfDmT12a+0F+x904bqgTCcS3VZRpf+iBVA8pGGXqbJ9K+cRiI5TxvR9l+yRvJpshwE0btpkgBXO5Q8IK+mtoEqeGWd02he9NtkwZuKKkR5NAAYFO/ZdsOOnF2CxlGHUs1aNCSWf3WKde7u65gm2706xq4LYFamJFBOzIHgGePsMidZ5KqURJpQOM3ZXcBTqqZL+dWSEQVzbKanPupDu82r23ib+XoE0uhGm0isBNb+1Jlp/DQ303Jsvv76S6WreTrmYm9qIGzsRlMsk6E2iW1Klz2oYrJsBDc1gcAkdZ7BY9eEbFsmMI+L3cMZcyfyO1fYqRH8G6BJpMoNuOZ5uMty7LKbPrgrFjgqiBO+ZSuhEJbExgPYnYACGxcTJZ8QhoUZbPOTBS4hxW3t/RRhBQPjWCqjElIIGsEECNh5FwVCjL1BnSJFADgVwuNzExUTBxbW1taGiot7d3165dKysraXR0dPTcuXPRzOVy27ZtwwdP/KMzlcWj09PTOGNUwo1oNLEUM/qrKrOhgXmmZFdR8mRJXZOABOpFIL6zKEM8RFmv4M2Pw0ZYlJKNYNQrNKbgGSWVsPWOjgvcwvnrpf+XgAQkIAEJNJYAyjaMZVKFehlLP1AdlTKeDkmgPAH07aFDh65du1bgdvz48bGxsdXV1b17987OzsYokjXVEb1TU1Nnz57Fp7+/H//wibJ4FEk8Pz+/vLy8uLh48uRJmqF7mT43N3fs2DEyibnVltnQwNXuSn8JSKA2AgWicT0FWFvwhs6KVCljC1HWtmLMpSRabRG6eFYLtu7nES2A7pISkEAbEEi/PEwlpfPQg/eF0RMVSuqaBOpIYHJyEpVbHHB8fHz//v35/Rze/tAP/dBtt90WnZcuXaKyc+dOyuHhYZQtlWTFowjdPXv29PT09PX1xdnyqVOnmMgUguzYsUMNfIPnw9wNmgQqJBCyofLvGtVghWB1awkBPrAIa8nqX1/U/0tAAhKQgAQ6nwCydmBgIIToDUVfiFLEKqPj4+MMUr7mNa+hEsbo1atXo065tLTE2S+VsOLRhYWFGIry4sWLTIk6JaGYQqUG8xy4BmhOqQ+BkGH1iWUUCeQRQA7ltTao8kFAWL7fehHwzHezLgEJXCdgIQEJSEACXUEgl8vNz8+PjIyst1sObBGunNYildfzaYd+NXA7XAVzkEBTCcSnD1E2TdTFcjXss+aJlazl+XYllPSRgATWJ+CIBCQggW4hwJntkSNHzpw5s3379oMHD87Ozq4ndEdHR5HK+BegQSFv3bo1dfb39/f09KRm8ShHymmUyu7du5lCJYxQTIl6tWXXaeD1jneqBae/BCRQG4Gmqe4N01MAb4iobR34ZCSsbTM0MQl0AQG3KAEJdBcB9Orp06dXr38hgJHB4+PjKysrKNUQwxMTExwUA2Vubm7Lli34U883JOvly5fj9345Kx4eHkYnDw0NoZlxKx4dGBg4f/48PqyytLQ0ODhIDxNxjiA7r/9qMc1qres0cLWA9JeABFpIAJ3D6lFS0SRQQICPNbGCTpsSkIAEGkzA8BKQwJMEEK6HDx+Ov2519913c1Dc29t74sSJycnJJz3y/ofz0aNH9+3bhw/dBX9Aq3gUBw5+OXZG+o6NjeEwMjKCGGb6gQMHkNzFMpuwlZgauBJK+tSTgHqmnjS7JhY6J6xrduxGJSABCUhAAm1IwJQk8AQB1OnMzMwTtRtuQKByoksdRRoHxQsLC319ffRg0Yk/dYzK9YPk1Zgeo1EvHqWHofBnIs3wp+fKlSscC9NTm6mBa+PmrE4g8Nitt4d1wmayvweuRfY34Q4kIAEJSEACEuhUAu6rBIFcLnfhwoUQqCWG27VLDdyuV6bT84rT4ChbstcWLt38/cZmQ2RG2fwcSq5IYmGRVZQlPSvpbPTv90Z6UUY+UWcLNCk5qaYSJRWtBgJgjFmpEk1LCUhAAhLICoFbb7s9K6maZ8UE1nXkMHZqamrd4XYdUAO365XpmrwQDGEZ3XF6Ug851Ia7iAyjbMP0Wp5SKGdK7kOSiZJKMoaoR0lFk4AEJCABCVRIQDVYISjdJNBkAtVo4Can5nISkEBmCSTJHZVUxicFUZbfXEwp79O00QIBHLlFWbCXYgndtCQzvVDALN5CAd5iB3skIAEJSEACEpBAtQTUwJUR00sCpQjEgztlPKlHWcqxfftQdyHboqww0SzutJKtVQgh3KIEYJnIgMLKODhUIQExVghKNwlIQAISkIAENiSgBt4QUdc7CEACpQhUokn4dICpUVKp0CqJXGGo+rqF3I0yBHDEj3qU0RM+UUaPZVUE4p6Jsm3vh6p2pLMEJCABCUhAAu1DQA3cPtfCTNqPgBk9lUCIuiifOrJBK/RhlOVdQ/BEWd6zzUdroNTmO2pVepCM+yHKVqXhuhKQgAQkIAEJdAwBNXDHXMosbSSOd7KUcafnWkKdrrNlBMk6I5vqjrBRbipQiyZXDrBFCWZ12YJbgpeOsKzux7wlIAEJdC6BW2+7PYwtRoWSetij71hLFj2WEmghgWxrYB6GYEcZ5wNR0qO1P4HQDFG2f7ZmuHkC7XytC4TWDRXvtuaJFa/QXY68mLPhKKmUsbidoizj5pAEJCCBriXw0IP3NX/vN721J99SAiSD0aQMo65JoIUEsq2BWwjOpaslEGohyvJz+SwDK+/jaPsTqETJlN8FEULkRFneuTmjldzAzcmkUau0Ii4XmmWjpIJxxcOoaxKQgASaRuDWlcfyLa3LeWaBpSErEpBAFgmogbN41TKfM0+3xXsIdRFl8ag9mycQbCmDPyX1zYctE4ElGI2SiiaBAgL5urdgKL/JjRoW91J+me+22brzJSCB7iYQ55OUYKAMox72UN+NyaLHsloCIK12iv4SaBABNXCDwBq2wwlU+OzeWgqZSLK1iFy95QTy79JKxG34tDztTkvA/UhAAhKQgAS6iUAmNXA8M0XZTRfLvbYXAe/A/Oux3o+vq1jyKXVqvdrvBfzDAkjcJPll9FtKoBkEXEMCEqiVwK233V7rVOdJoMUEMqmBq2LGk1ZV/jq3kMCNDzX87zfU937wqb2+PFt477l0IwhUcnv4TdQI8saUQEUEdJKABCTQrQSyrYE3fHiKJ7Aou/USu28JSKD1BLr2VSg2vt6PCbT+wpiBBCTQnQTc9SYIcPwbRoyoUFLXJJAhAtnWwGVAx4NXcthQLSdPK40j0IRj3vLJx10RZXnPjh+N74g23GbcJFG2bZJtyK1NUkpaN/+7LDqjzO9vk5xNQwISkEB3EajHbvP/ASTqKeSj71jLt9RvRQLtRqBjNXC7gTafTBDgAT2M53WsLjnXK05dkgltWZdQBIFVlLHHKOnRuopA5bdB8uwqPm5WAhKQQJcQeOjB+8LYb1QoqbeRmYoEvk5ADfx1Ev6/cwnw5B2WtsgBI5aaZSp11HV1DFUm4YIhNh47jbJg1KYEWkIg7sYoW5KAi0pAAhKQgAS6i4C7fSqBTtPALZEZT0Vqq30JxDN3lNVmWe2thfjEYlaU1a5YR//atlxbAuy6tonrzWpm8uvlYH9VBOIeiDJ/YlzKKPP7rUtAAhKQgAQkIIFGESgVN/MauOTjVDx7RVlq1/ZJoG4Eiu/Akoo3OqOMtfPr0VNQpht4Q8+CiTYl0BwC6RYtsxzfIDc+dF9Yvhv9+U3rEpCABLqKgH9Eqqsut5ttQwKZ18DlmcZj1uNvflt5N0cl0CCdyaM/bKOk0vEWoijKjt+sG4SA1xoImgQkIAEJSKDbCORyuYmJiYJdr62tDQ0N9fb27tq1a2VlhVFK6qknOdCzbds2guCTb/TQzyhxcGZoenqaJkaFJjY6OkoTO3fuHM3arMM1cG1QOmyW28k0gRDnUWZrI/EJ1Ho5o52w9Ubt7wwCxfdAQU9BszN27S4kIAEJ1JfArSuPJcuP3LZ/gdk/hZV/mTqyjrI9dOjQtWvXCnZ3/PjxsbGx1dXVvXv3zs7OMjo5OXnvvffSc/jwYer0YAzRc+XKlcHBQZrJEL1TU1Nnz55ltL+/n2i5XG5+fn55eXlxcfFnOz7zAAAQAElEQVTkyZM0Q/fiMDc3d+zYMTJJ06uqqIGrwqVzHQg086m3Duk2IETl2q9yzwak2YyQcTNEWbBeyc4Cn0w3s/i5RkngXKmwkqN2SkACEpDAZgigJzEiUIZRx6JOmepUNmP+ePZm6HXbXNQsKrd41+Pj4/v370/9CNSlpaUQuvv27UMzo3LTaHHl0qVLdO7cuZNyeHgY3YvQ3bNnT09PT19fH+fJBDx16hRDOOC2Y8cOeqjXYF2kgXlKqwGQUyRQFYEQNlFWNbHuzq0NuJlvt+750fHWXiNXl4AEJCABCUhAAtUSmJ6eHhgYCCF6Q9EXohSxyuj4+Dj1q1evJpfLly9/8YtfpHnw4MHe3t700870hBX4o58XFhZiKMqLFy/SGXVKgjOFSg3W+Rp4M8/iNQB1SicRCCkbZfl9hWyLsrxnmdF06lvJimXiONTOBNJVbuckzU0CEpCABCQgAQkUE8hd/+HkkZGR4qHo4cAW4cppLVI5egrK/v5+DniXl5fpv//++ylbYhnTwGgMrCWkXLQzCJT8TCQ0Z5Rpmw2600ICRVmwYlq6OysNAt5tMIv3Gzdbcb89EpCABCQggUoI+GPSlVDqBp+1tbUjR46cOXNm+/btnOXOzs6uJ3RHR0fn5+e3Xv9KZHbs2PHc5z53ZmYGndzT0xNiOI1SoZ8ZVMJw4Eg56lHu3r2bzqhT4swUKjVYxjRw7DBkTJTRYymBGgggusJqmOuUfAJgpEkZ35VR0lOJVeVcSUB9GkQALR3GZzdhBQtxA4QV9Det6UISkIAEupBA/EpwF27cLTefAML19OnTq9e/EMDI4PiBZ5RqiOGJiQkOiklsbm5uy5YtN910E/XUE7/ZSw+GnF5aWhoYGKAyNDSEZqYTQXv58uX4rWBOkoeHh3E4f/48PisrK/gPDg7SwxDO4bbz+i8P06zWMqmB2aTPzUDQmkaAJ37WipJKSePpv2R/p3ay3/g2jLJTt1nffaEhCUgZ91KU9GTLuPQkHCWVYuOWCCsesqdBBAwrAQlIYEMC6Y9LRyX5c8yL0aQMo65JoEICCNfDhw8vLi7if/fdd3NQ3Nvbe+LEicnJSTTzPffc88Y3vpEepOzIyAhqFsVLk5NkTnTz/4AW0wl19OjRffv24UCTUQw3nJG+Y2NjOBAEMYzDgQMHkNwsgWcNllUNXGarGX2sLLMjh5pPoMzzfYOSQRcROUoqbW6JD1InUk090bTMJxCXNcr8/qzXufpY1ndh/lknYP4SkEAlBDguDsM5KpTUwx7quzFZ9FhKoDwB1OnMzEz4IFA5s6WOIo2D4oWFBfQqPZzcXrlyhZNj+hnFqNDEYnr0RB1/wjKEpR4qNDGGcAh/moQlOD21WQdq4NpAOKubCYR+o4wH+igBQk8Y9ZqNCPlzI3iU0V8gjeJDnCjDobgsP1rsb0+bEIjrHmWbpGQaEpBAdgmYuQQkIIGWE8jlchcuXAiB2vJkKk+gQzRwyIwoK9+8nhJoGoG4OaNs2qIu1P4EWvJxRsHHLu1PyQwlIAEJFBCwKQEJtAkBDmOnpqbaJJnK0+gQDVz5hvWUQDcQaImyCrAecgYHSwlIQAISkEAjCBhTAhLYPAE18OYZGqFGAm0lltIJbZOzihO5KGvkmDetXnHyQlrtZALlPytJo8X3Vfp+6WQ67k0CEuhWAvm/KFsbg/jLUpRMpwwrU2dIq4SAPhKoF4EO1MA8nIWMibJepIwjgU0S4M4sEyFu1yjLuDnUeQSS1GzO1kLQUsa6Ua63dPnR9WbZLwEJSEACN721J98SkPSnp6KS+rNVefQda/mWreQzmq1p15dAB2rg+gIymgQkIIHNECj/2cdmIlc7F91bcopCtyQWOyUgAQlIoCQBzsnDGI0KJXVNAg0i0IiwauBGUDVmJxNYT0jUa88hmaLMPxZm3bBQLFHWa1HjNJRAXMcoY6G4vlHSw9UMo94SY/WWrOuiEpCABCQgAQlIoPkEsqKBm0/GFTuBAKKxvtuIgFGWjIyqCUPwYCV97OxCAulmSJUEgRsm1ZtZYd2wZi7qWhKQgAQ6iUD8oi8lm6LEqITl/6gw9ei0lIAE2oRA52hgHubahGm90zBemxIoFjMVJlrzxArj69YZBAo+ailoVrLHNIUKxpQoqWgSkIAEJFAXAqV/6ffB+x66biwRFUrqmgQk0CYEOkcDtwlQ06grgQ4Pxgc3oYej7PDdduX2uMTN33dJoRv3GCVGSlFS2dAiWpQbOusgAQlIQAJtTkA13uYXyPSaQ0AN3BzOriKBagno3+EEQh5Hmb/VArVZ/Ju6xT3501M94kQZnUn3pkr0pzJFLpiFP5bcrEhAAhKQgAQkIIFME1ADZ/rymXwhgfQQXzhQ73Y7SIKmbbbe8DaMp8MNoUKjLH+hwwdk67lxr2I4VGvocyzNIkhY6rEiAQlIQALdTMAj5W6++lnfuxo461ewk/Nf75m++XtGCYShAVg9Siott3xEoYUoozPKlmdoAlUSqMW9Lhc9bhhKopFElFQ0CUhAAhLoeAK3rjyWLG32Z1914NbbbsfooaRJRZNAZxBoUw2cHr94IOsM0O5CAhKQwIYE4qUvynzn6KGMl8T8Mt+t5jof8dQ8t34TjSQBCUig8wkkqRmVdtgwx7kYmVCGUcd+68NnU5MKTTo1CXQGgTbVwJ0B111IoAyBmlVHTIyyZHyGwkqO2pkJAo+/+W1hBdlyZemhXE8Go5NxiJJKJUa0Stz0aSQBY0tAAl1BACUZxm6jQkldk4AEmkygHTVwPL1RrveQ12RGLieBlhBAArVkXRdtcwIVSlbun7AKt0PYsJhFWeFE3SSwOQLOloAEniTw6DvWkj3Z5f8kIIHGEGhHDdyYnRpVAt1IQCWTiauO+CRPSq5XGM1k0U+TIUpNAhLoFALuozSBpAOjUtqp43o5EMbYFmUYdU0CEmgQgfbSwJz95u+TJz+alDz5hdHUJFBMIO6crP/gADd58dZK9vBNUbK/oLPygAUTbbYhgfJXk1siLNyibMNdmJIEJCCB6wTWLUL+UeJBGUa9S4z9dslO3aYEWkugvTRwa1m4ekYJhACOMqNbMG0JrEcAZbve0Hr9CuD1yNgvAQlIoA0IZC8FlXn2rpkZb0RADbwRIccl0FICSKCwEDZRtjQjF28IAa5yQ+IaVAISkIAEJNAuBMyjcwjkcrmJiYmC/aytrQ0NDfX29u7atWtlZYVRSur5PUzctm0bPXjij0++FY9OT0/jjFEJz9HRUZrYuXPnoqeGsu00sKd5NVxFpxQQiB+KLujs1CabDevUDbqv8gT4WAQr7+OoBCQgAQlIQAItJdBRi6NsDx06dO3atYJdHT9+fGxsbHV1de/evbOzs4xOTk7ee++99Bw+fJg6ondqaurs2bP09Pf3449PsuLRXC43Pz+/vLy8uLh48uRJmqF7mT43N3fs2DEySdOrqrSdBib7eJ6LkqYmgRoIIAvj85Qoa4jQtCmbudU5PMRINUoqWpcQ4IqHdcl+3aYEJCCB1hKIf843yvxM4g93RZnfH/UM/RRxhlINtpYtJICaReUWJzA+Pr5///7Uj0BdWloaHBykZ9++fWjmixcvUt+5cyfl8PAwypZKskuXLlHPH0Xo7tmzp6enp6+vj/NkAp46dYqJ4bZjxw56qNdg7aiBa9hG8ZTN6IriaPY0nwAilkWjpFKJhSSgLHDmZggr6K+tSajaJjZ6FomFNXoh40tAAhKQgAS6igD6EGPLlGHUsajnl3RqEuhsAtPT0wMDAyFEbyj6QpQiVhkdHx+nfvXq1eRy+fLlhx9+OL8HhczZb3Io8Gd0YWEhjVJBQtNJJYxQTIl6tWWzNHCVefEoHzOK9Uz0W3YwAS46lr/BqpRw/sSM1gu2n9FdlEw7tkbJ9zhW0sdOCUhAAhKQgAQkIIE2JJC7/sPJIyMj6+XGgS3CldNapPJ6Pu3Q36YauB3Q1C0HA0lAAhKQgAQkIAEJSEACEsgyAc5sjxw5cubMme3btx88eHB2dnY9oTs6Ojo/P7/1+lfa8Y4dO174whfSl3r6+/t7enpSE/1cMMqRchqlsnv3bqZQCcOZKVGvtuw0Dcz5UrUI9G8ggZaG5pgxrKVZNHBx7/YGwjW0BCQgAQm0lMBDD97X0vVdXAISKCSAXj19+vTq9S8EMDI4fuAZpRpieGJigoNips3NzW3ZsuWmm26innr27Nnzohe96PLly/F7v5wVDw8Po6uHhobQzHgiaAtGBwYGzp8/j8/KysrS9V8tpoeJOEeQ+OVhmtVap2ngavevf6cSaL0+bABZJH0DohpSAhKQgAQkIAEJSEACtRBAuB4+fDj+utXdd9/NQXFvb++JEycmJyfRzPfcc88b3/hGepCyIyMjOB89enTfvn30sFj+H9CiWTyKAwe/HDsjfcfGxnAgCGKY6QcOHEByswQTazA1cA3QnCIBCZQl4KAEJCABCUhAAhKQQOcSQJ3OzMzE/hConOhSR5HGQfHCwgJ6lZ7BwcErV65wckw/o/QwkSYW0+lkKOrFo/QwhDPGRJrhT5OwBKenNlMD18bNWQ0nwEFuWPxBrCgbvqoLSGCTBJwuAQlIQAISkIAEuoZALpe7cOFCCNQMbVoNnKGLZaoSkIAE2piAqUlAAhKQgAQk0GUEOIydmprK3KbVwJm7ZCb8DQJPf/fbv9GwJgEJSKBVBFxXAhKQgAQkIIHsEFADZ+damelTCYQAjvKpI7YkIAEJSKBZBFxHAhKQgAQkkDUC7aKBVTJZu3PaKN/4a8lRRlo3PuQ/qBAkLCUgAQlIoGEEDCwBCUhAAtkk0C4aOJv0zFoCEpCABCQgAQl0HwF3LAEJSCDLBNTAWb565i6B6wQ8976OwUICEpCABCTQeAL1WOGhB/2BtXpwNIYEaiXQFho4fhA6ylo34jwJSEACEpCABCQgAQlIoGEEDCyBTiHQFho4H2b+b3Xm91uXQBkC3jZl4DgkAQlIQAISkIAEJLApAk7uLALN1sBx2PvYrbeXxKiSKYnFTglIQAISkIAEJCABCUhAAi0g0IlLNlsDdyJD9yQBCUhAAhKQgAQkIAEJSEAC2SBQqQauy27iEDjK9Y6C67KQQSQgAQlIQAISkIAEJCABCUhAAsUEmqqBi5fPRI9JSkACEpCABCQgAQlIQAISkEBnEFADd8Z1bNQujCsBCUhAAhKQgAQkIAEJSKCTCKiBO+lqupd6EjCWBCQgAQlIQAISkIAEJNB5BNTAnXdNO3lH/hp5c65uQ1e58aH7Ghrf4BKQgAQkIIEOI/DQg751dtgldTstJqAGbvEFcHkJSKCtCJiMBCQgAQlIQAISkEBnE2iSBo6/Bd3ZKN1downEIXCUjV7L+BLoQgJuWQISkIAEJCABCXQDgSZp4PVQqmfWI2N/IhA/Ohtl6rQiAQlIoI4EDCUBCUhAAhKQQPcQaIYGjkPgKPPJhgCOMr/foLSu+QAAEABJREFUugQkIAEJSEACzSHgKhKQgAQkIIFuI9AMDZyYPv7mt1GP0mM9UGi1ESj+PKW2OM6SgAQkIIFuJuDeJSABCUigNgK5XG5iYqJg7rlz53qvfw0NDa2trTGaekZHR2nSydB1l95t27YRhM58o4d+HHDDmaHp6WmaGBWaGKFoYgSnWZs1VQPXlqKzJCABCUhAAhKQgATqSMBQEpCABGomsLKycujQoWvXrhVEuHDhwtzc3Orqan9///Hjx3E7duxY9OCZJOvs7Cw+V65cGRwcpD8Zondqaurs2bOMRoRcLjc/P7+8vLy4uHjy5EmaEQQHwhKcJdL0qirN1sBxCFxVijpLQAISkIAEJCABCUigLgQMIgEJbJLA5OTk3r17i4OgYPNlbQjUvr4+PIeHh0+dOkWljF26dInRnTt3UuKP7kXo7tmzp6enhyC7du0iIEEYwgG3HTt20EO9BmuIBt7wV3z9QegaLpVTJCABCUhAAhKQgAQkUDMBJ0pg8wSmp6cHBgZCiN5Q6gtdurCwsG/fPoQr4zQp0a6UYQcPHuzt7U0/7RydlHhevXqVStjS0hJxoh7lxYsX6Yw6Jc5MoVKDNUQD15CHUyQgAQlIQAISkIAEJCABCTSCgDHrQiB3/YeTR0ZGykTjlPiOO+7gQBgNfPToUcQwind2dhblzKz+/n4OeJeXl6nff//9lC2x+mhg/0ZRSy6ei0pAAhKQgAQkIAEJSEACEihDoF5Da2trR44cOXPmzPbt2znLRdZyJlwQ/Ny5cxzVJpG8f//++N1dpiCGe3p6ZmZm0MZUQgznT6d/69atqQeHXbt2pSaV3bt300klDGemRL3asjoNvOEPOVe7vP4SqI2AH7vUxs1ZEpCABCQgAQlIQAISqIEAwvX06dNoWgwBjAweHx9fWVlBqYYYpn7nnXeOjY3hmeJzdHzgwAFUMSfDqRM5jVTmZJjK0NDQ6PW/Go2gvXz5cvxW8KlTp4aHh3E4f/48PkTGnwj0MESccNt5/ZeHaa5n6/VXp4HXi5L6VSYJhZU6Eij5C+Tx99WirONahpKABCQgAQlIQAISkIAEKiGAcD18+PDi4iLOk5OTjzzyCNq4t7cXYYxwRb5ydHzXXXehlnGgieJllGNhTnQ5JaYzGaHSz07TySiGG85IX6Q1DmhpxDAR0NUTExP5YpsplVs9NXAI4CgrOTEOYUMZMibKylOv2FNHCUhAAhKQgAQkIAEJSEACEqgPAdTpzMxMxEKgcmZLnR6OiMMWFhaQrGhUjo5DAOMQzXDAOfVEnSZh80fpYSh6GKKZIhT/00qMVm61aOBQuZWvUYmnArgSStX7OEMCEpCABCQgAQlIQAISkEBDCORyuQsXLoRAbcgCjQlaiwZuTCZGlUB9CRhNAhKQgAQkIAEJSEACEmgggcHBwampqQYu0JjQauDGcDWqBFpJwLUlIAEJSEACEpCABCQggdIE1MCludgrAQlkk4BZS0ACEpCABCQgAQlIoByBijRwI34BOJK68aH7omIpgRoI+GvkNUBzSucScGcSkIAEJCABCUhAAhsT2FgDhwCmjD/1HGUEzq9HT0HJrIIemxKQgAQkIIF6EzCeBCQgAQlIQAISqJTAxhq40kj6SUACEpCABCTQbAKuJwEJSEACEpBAdQTqoIHjsDfK6hbXWwISkIAEJCABCdRIwGkSkIAEJCCBWgjUQQOnZeOXM6OkM1RxlDQ1CUhAAhKQgAQkIIF6EDCGBCQgAQnUTqBGDbyesk0COD8jOsPyO61LoFoC8RfUouSOqna6/hKQgAQkIAEJZJ+AO5CABCSwWQJVaGC0RxjyA4s/iBXlZrNwvgQkIAEJSEACEpCABCRQjoBjEpBAfQhUoYELFkQP00OJHqYSJRVNAhKQgAQkIAEJSEACEpBA/QgYSQL1JFC7BiYLBDClJgEJSEACEpCABCQgAQlIQAINIGDI+hPYlAZO6XgInFBYkYAEJCABCUhAAhKQgAQkIIFNE2hUgPpo4OLsOCIOYRxlsYM9EqiZgDdVzeicKAEJSEACEpCABCQggS4n0CgNXE+sxpKABCQgAQlIQAISkIAEJCABCdSDwMYaOM7coqzHisaohoC+EpCABCQgAQlIQAISkIAEJFA/Ahtr4PqtZSQJVEOglO+ND91Xqts+CUhAAhKQgAQk0FgCDz3oQ0hjCRtdAk0jUJEG9hC4adfDhSTwBAH/k4AEJCABCUhAAhKQgAQaQ6AiDVzt0umwTvFcLTr9JdDtBNy/BCQgAQlIQAISkIAEGkmgIRq4kQkbWwISkECHEnBbEpCABCQgAQlIIAsEcrncxMREQabnzp3rvf41NDS0trbGaOoZHR2liTFx27ZteCUfOpMVj05PT+OMUQk3QtHECB49NZRq4BqgOUUCEpCABOpKwGASkIAEJCABCWSEwMrKyqFDh65du1aQ74ULF+bm5lZXV/v7+48fP47bsWPHogdPJCvCeGpq6uzZs8mH/mTFo7lcbn5+fnl5eXFx8eTJkzQJgj/TCUtwlqBZg6mBa4DmFAlIQAISkECdCBhGAhKQgAQkkCkCk5OTe/fuLU4ZfTs4OJj6Q6D29fXRMzw8fOrUqUuXLlHfuXMnJT0oWyrJikcRunv27Onp6SHIrl27CEgQJjKFIDt27KCHeg2mBq4BmlMkIAEJSEACEtg0AQNIQAISkEDWCExPTw8MDIQQvaHUF7p0YWFh3759CFfGaVKiXSmpX716lUrY0tISZ79RpyweJQ79yS5evMiU1CQUU1KzqooauCpcOktAAhKQgAQkIIFNEzCABCQggQwSyF3/4eSRkZEyuXNKfMcdd3AgjAY+evQoYri3t3d2dhblXGZWk4fUwE0G7nISkIAEJCABCUigiwm4dQlIIJsEOLM9cuTImTNntm/ffvDgQWQtZ8IFWzl37hxHtUkk79+/P353lymIYVTx1q1b05T+/v6enp7ULB7dtWtXGqWye/duplAJIxRTol5tqQaulpj+EpCABCQgAQlIQAISqJ6AMySQZQLo1dOnT6NpMQQwMnh8fHxlZQWlGmKY+p133jk2NoZn2ihHxwcOHEAVx8nw5cuX4/d+T506NTw8jK4eGhoavf5XoxG0BaMcHZ8/fx4fIiOtiUAPEwkeQXZe/9VimtWaGrhaYvpLQAISkIAEJCABCUhAAtUQ0LdDCSBcDx8+HH/danJy8pFHHkEb9/b2IowRrshXjo7vuusu1DIAcE4/HU2TU2LKZMWjOHDwyxky0hdpjQNaGjFMfHT1xMREvthOcSqpqIEroaSPBCQgAQlIQAISkIAEJCCB6gl04gzU6czMTOwMgcqJLnV6OCIOW1hYQLKiUTk6DgGMA8bEcMCZZjhEnWbBKD0MhT9DNMOfnitXrnAsTE9tpgaujZuzJCABCUhAAhKQgAQkIAEJdDWBXC534cKFEKglQLRrlxq4Xa+MeUlAAhKQgAQkIAEJSEACEmhjAhzGTk1NtXGCpVNrhgYuvbK9EpCABCQgAQlIQAISkIAEJCCB5hJQAzeWt9ElIAEJSEACEpCABCQgAQlIoH0IqIHb51p0WibuRwISkIAEJCABCUhAAhKQQLsRUAO32xUxn04g4B4kIAEJSEACEpCABCQggfYkoAZuz+tiVhLIKgHzloAEJCABCUhAAhKQQDsTUAO389UxNwlIIEsEzFUCEpCABCQgAQlIoP0JqIHb/xqZoQQkIIF2J2B+EpCABCQgAQlIICsE1MBZuVLmKQEJSEAC7UjAnCQgAQlIQAISyBYBNXC2rpfZSkACEpCABNqFgHlIQAISkIAEskhADZzFq2bOEpCABCQgAQm0koBrS0ACEpBAdgmogbN77cxcAhKQgAQkIAEJNJuA60lAAhLIOgE1cNavoPlLQAISkIAEJCABCTSDgGtIQAKdQUAN3BnX0V1IQAISkIAEJCABCUigUQSMK4FOIqAG7qSr6V4kIAEJSEACEpCABCQggXoSMFbnEVADd941dUcSkIAEJCABCUhAAhKQgAQ2S6BT56uBO/XKui8JSEACEpCABCQgAQlIQAISKCRQiQYunGNbAhKQgAQkIAEJSEACEpCABCSQRQJq4PJXzVEJSEACEpCABCQgAQlIQAIS6BwCauDOuZb13onxJCABCUhAAhKQgAQkIAEJFBLI5XITExMFvefOneu9/jU0NLS2tsYobtu2baNv165dKysrdDJEE6OfUXzyjR76GcUNZ4amp6dpYlRoYqOjozQxlqNZm6mBa+PmrM4m4O4kIAEJSEACEpCABCQggRIEULOHDh26du1awdiFCxfm5uZWV1f7+/uPHz/O6MzMzF133UXP3r17Z2dn6cGo0HPlypXBwUGayRC9U1NTZ8+eZTQi5HK5+fn55eXlxcXFkydP0gzdiwMLHTt2jEzS9KoqauCqcOksgY4n4AYlIAEJSEACEpCABCSwLoHJyUk0bfEwCrZA1g4MDCBfw5N6VNYrL126xNDOnTsph4eHmYjQ3bNnT09PT19fX5wknzp1iiEccNuxY4caGBSaBCQggc0QcK4EJCABCUhAAhKQQDkC09PTAwMDIURvKPWFLl1YWNi3bx+D4+PjlL29vZT79++nxA4ePEhP+mlnesKYePXq1ahTLi0tEYdKsosXL9KZmjgzJTWrqngOXBUunSUgAQl0JAE3JQEJSEACEpCABDYgkLv+w8kjIyNl/DglvuOOO+JAeHR0FMHMie4DDzwQP8bc399Pc3l5mQj3338/ZUtMDdwS7C4qAQlIQAJtQsA0JCABCUhAAhLYmMDa2tqRI0fOnDmzfft2znJnZ2c5Ey6YhtDlqDZEMoe01PHs6+u799573/Wud+E8MzNDs6enJ8QwPcno37p1a2risGvXrtSksnv3bjqphOHMlKhXW6qBqyWmvwQkIAEJSKAzCLgLCUhAAhKQQKUEEK6nT59evf6FAEbcjo+PI3RRqiGGqd95551jY2N4RtBPf/rTHB1T5+wX+Zr6kdPIY46IqQwNDXFcjA+C9vLly/FbwfF7vzicP38eHyLjz9kyPQzhHG47r//yMM1qTQ1cLTH9JSABCUhAAhLIOgHzl4AEJCCBOhBAuB4+fBiJS6zJyclHHnkEbdzb24swpofj32ieOHGCUdQsipdRTpKRxOk3hPHECHX06NF9+/bhQJNRDDeckb5Iaxw4YUYM43DgwIGJiYkkqvGvytTAVeHSWQISkIAEJCABCWSagMlLQAIS2CwB1OnMzExEQaDGn8ii5/oh8RPFwsICkhW3Jxqrq9FEsqaTZJyZHj1Rp5n8Uw+ViMAQDuFPT/E/rcRo5aYGrpyVnhKQgAQkIAEJSEAC2SVg5hKQQJ0J5HK5CxcuhECtc+hGhlMDN5KusSUgAQlIQAISkIAEJNB6AmYggYYQGBwcnJqaakjoRgZVAzeSrrElIAEJSEACEpCABCQggVYScG0JFBJQAxcSsSKzzTgAABAASURBVC0BCUhAAhKQgAQkIAEJSCD7BNxBaQJq4NJc7JWABCQgAQlIQAISkIAEJCCBbBIol7UauBwdxyQgAQlIQAISkIAEJCABCUigkwh0ugbupGvlXiQgAQlIQAISkIAEJCABCUhgcwTUwJvj186zzU0CEpCABCQgAQlIQAISkIAEnkpADfxUHrY6g4C7kIAEJCABCUhAAhKQgAQkUIqAGrgUFfskkF0CZi4BCUhAAhKQgAQkIAEJrE9ADbw+G0ckIIFsETBbCUhAAhKQgAQkIAEJbERADbwRIcclIAEJtD8BM5SABCQgAQlIQAISqIyAGrgyTnpJQAISkEB7EjArCUhAAhKQgAQkUA0BNXA1tPSVgAQkIAEJtA8BM5GABCQgAQlIoHoCauDqmTlDAhKQgAQkIIHWEnB1CUhAAhKQQK0E1MC1knOeBCQgAQlIQAISaD4BV5SABCQggc0RUANvjp+zJSABCUhAAhKQgASaQ8BVJCABCdSDgBq4HhSNIQEJSEACEpCABCQggcYRMLIEJFA/Amrg+rE0kgQkIAEJSEACEpCABCRQXwJGk0C9CaiB603UeBKQgAQkIAEJSEACEpCABDZPoF0j5HK5iYmJguzOnTvXe/1raGhobW2NUdy2bdtG365du1ZWVvJ7kg+dyZJ/Gp2enmY6RiXcRkdHaWIsFz01lGrgGqA5RQISkIAEJCABCUjgCQKf/cjnH/rlhQ/8qz/+3Zeffs/AByipz//qX3/li199Ytj/JCCBjiOAmj106NC1a9cKdnbhwoW5ubnV1dX+/v7jx48zOjMzc9ddd9Gzd+/e2dlZhPHU1NTZs2fpST64hRWOHj+ey+Xm5+eXl5cXFxdPnjxJM3Qv01no2LFjZBJzqy3VwNUS018CEpCABCQgAQlI4IbPf+zafaN/9sHX3//X7/vkdzz72176r19wy1sGKKk/8/nP+LZnfsv/+uSnH//5I1/8sdd/+f997w3/oCT2npFAhxCYnJxE0xZvBn07ODiY3z8wMIB8jR7qly5dor5z507K4eHhNEQTKx5F6O7Zs6enp6evry9Okk+dOsVEnAmyY8eOjtXA7FCTgAQkIAEJSEACEmgrApdOLf3X13740Y98/tZf3PVTfz70I8d/gMrL3vJSSurf85MvItsvT/7qV//kf/7jo3/3Dx+878u/Mk2PJgEJZJ3A9PT0wMBACNEbSn2hSxcWFvbt28fg+Pg4ZW9vL+X+/fsZunr1KvWwpaUlzn6jTlk8Shz6k128eJEpqUkopqRmVRXPgavC1VRnF5OABCQgAQlIQAJtSAAB/D9+8SPPf+VN//KDP7jrp1/yLU//5uIk//FvP/e1j3089X91/iOpbkUCEsgogdz1H04eGRkpkz+nxHfccUccCI+Ojg4MDHDe+8ADD8SPMZeZ2MwhNXAzabtWpQT0k4AEJCABCUigPQl8/mPXQgBz3vuMm75jvSSf9pzvfNp3PjuNfvPNT5wMp6YVCUggcwQ4sz1y5MiZM2e2b99+8ODB2dlZzoQLdoHQ5ag2RDKHtNTx7Ovru/fee9/1rndtvf6VpvT39/f09KQmboynJqO7du1KTSq7d++mk0oYzkyJerWlGrhaYvpLoLEEjC4BCUhAAhJoZwJ//h/+5tu3fOsr73nZekk+9MsL6GRGv+OtbwkZ/M0vvfnbx95MjyYBCWSXAHr19OnTq9e/EMCI2/HxcYQuSjXEMPU777xzbGwMz9jmpz/9aY6OqXMUjHx90YtedPny5UvXfys4frMXXT00NMRxMT4I2oJRzpDPnz+PD5GR05wt08NEnCPIzp1P/GoxzWpNDVwtMf0lIIFGETCuBCQgAQm0OYHPfuTzS3OfHfx/vLTkCfBXv/TV//5//M+F//yJ1U8+xka+5VV7n3lm9pkf/P1n/O5vftOLX+BfioaJJoEOI4BwPXz4MBKXfU1OTj7yyCNo497eXoQxPRz/RvPEiROM4nz06NF9+/bhwOj+/fspkxWP4oBy5tgZ6Yu0xoETZsQw0w8cODAxMZHEdgpSYUUNXCEo3SQgAQk0kIChJSABCWSCwMN/9JmnffPTBn7ihcXZXvv0F8/82z95+L7PvPz/GHjxge3J4WnP+U7qf/N7n8r9xseoaBKQQAcQQJ3OzMzERhCo8Sey6Ll+SPxEsbCwgGTF7YnG6mo08U89ONNExHK2HHWaBaP0MBQRGKIZ/vRcuXKFY2F6ajM1cG3cnCUBCUhAAvUhYBQJSCBDBFb+/HPb/+lziv8I1sr//LsP/uQDf/tXX3jx0HZOiYt3tDS3cmX+74r77ZGABDJNIJfLXbhwIQRqhjaiBs7QxTJVCUhAAhLoKAJuRgKZI3D14S/2vuTGgrQ/efqR02948PG/+/tnffeWV/3aKwpGo8ks5kbdUgIS6BgCHMZOTU1lbjtq4MxdMhOWgAQkIAEJZJ6AG8gogX9Y++q3PfNb85Nf+J2Pf/gX/iJ6vvD/u/aegQ+E0R+dUTKLuVG3lIAEJNBaAmrg1vJ3dQlIQAISkIAEuotApnf7rT3f8pUv/kP+Fna98eY3Lb42jHPgGHrVr72c/qhHySzmRt1SAhKQQGsJqIFby9/VJSABCUhAAhKQQGYIbH3hM1c/8cTffC7O+MO/8OecAz/9u7596L23vXjo+QUOzNr6wmcWdNqUgAQk0BICauCWYHdRCUhAAhKQgAQkkD0Cfa/4zuU/+duvPv61gtRzv/mxT55efs73PetHf29v3//+XQWj+DOLuQX9XdV0sxKQQPsQUAO3z7UwEwlIQAISkIAEJNDWBF74I8/7x6/94+L/+XB+lot/8PBfvHPxhbc/7zX/+Z9uecEz84eijj+zmBtNy24j4H4l0G4E1MDtdkXMRwISkIAEJCABCbQpgefe8uz+fc/l1PdLj345pXjt4bVd//YlP/TO//1bnvEtqTNV8MSfWcxNnVYk0CUE3GZ7ElADt+d1MSsJSEACEpCABCTQjgRe8fPf8/fX/uH+Ix9Nye2Z+N5bf2lXahZU8MSfWQX9NiUggc4m0M67UwO389UxNwlIQAISkIAEJNBeBJ790i3/7FdueeT+R/9o5E854y2THKP44Ik/s8p4OiQBCUigmQQarYGbuRfXkoAEJCABCUhAAhJoOIGdw/3IWsTt//WjFxb+0ye+WvQnsuihn1F88MS/4Tm5gAQkIIGKCaiBK0ZVtaMTJCABCUhAAhKQQGcSQNb+iw+86qZbnv3Qryz87itOc95L5aO//jFK6vRQYRQfPDsTgbuSgAQyS0ANnNlL19aJm5wEJCABCUhAAh1O4Nkv3XL7zPf/6Ptf+b2HXvzlz3/lY3/46Y/8+iIldXroZxSfDqfg9iQggQwSUANn8KKZclsTMDkJSEACEpBAFxF47i3PvvWXdr32v/zzn/qLoTctvpaSOj30dxEFtyoBCWSKgBo4U5fLZCXQ1gRMTgISkIAEJCABCUhAAu1OQA3c7lfI/CQggSwQMEcJSEACEpCABCQggWwQUANn4zqZpQQkIIF2JWBeEpCABCQgAQlIIEsE1MBZulrmKgEJSEAC7UTAXCQgAQlIQAISyB4BNXD2rpkZS0ACEpCABFpNwPUlIAEJSEACWSWgBs7qlTNvCUhAAhKQgARaQcA1JSABCUgg2wTUwNm+fmYvAQlIQAISkIAEmkXAdSQgAQl0AgE1cCdcRfcgAQlIQAISkIAEJNBIAsaWgAQ6h4AauHOupTuRgAQkIAEJSEACEpBAvQkYTwKdRkAN3GlX1P1IQAISkIAEJCABCUhAAvUgYIzOJLCBBv6zP/sz9r2ysrJr167e3l5K6vQU2OjoKKPYuXPnGMIHT5qU1OlJljy3bduWy+Wi/5577kluExMTqT9Gy5R4vv71r19bWwufFDzSiM4oiU8y+SkxlxzoGRoaShHCOcXBATc6mU6SVDDqOBRMoX89I0J+kriRHhGoFBiRK0yS1Umb5DGmMJFQlDUnyfSqjE3t3buXFZnFdkgjWYLGUL2MhdgmS1BSj7BAACyZRDO/pJM08IcSbgxNT0/TxKjQLDBi4kkZ/VRqIMks0mMJSuqsS0yaGIgicn5JJgxhVKKfu4ImVtKfmASkDGcqm0+SUKzFilhKg85kdDKEUYnO8klCnovC3sOZ5m//9m+nOt/dUS9fMqvg8oU//SUjkBsZYlTCs/lJklvkTBpcpkQg8qEsSInLx32CMyV1HAqMgHUnCR9WTEazYNGCJMkhNkV/gWc0iYBFnZJrzRQqGP3cWlQybWwnCMQ15bJSASCdDBVvDVCMYrF3rizXlyYl9WJ/gmz+KhOWxIhDNOoY8FkUo0KzwKpNkiBYClLzVSY9kiTVFApKJJOaqQIriJE/JXX6Kann99CZb2SIpZ6akyQ9kiRVQlFyocssSvKMYmwE/zJJJk8CEhZnjBdwplDBeGVL/TTLGNtkRYxKciMBlkjNVCF+AbfinuRMBMJiKUmcSTIcqOMAn2iWL8mNOBgVPEmPOsa3T3EEIhckCQpyKOlPDvRjOOBGcKbXJUkSIz0ikwwxiZxv9NCfP8rq5EAPs5ibnKnTQz/GFCYyRFlbkmm/MCROspI3THFKxT0RoWSSOPONEw7UWSLqG5YFSaYmBArSJhSRC7hxk+CJUcEhWR2ThD/XgiUoqccSVGgWZ8ho5UkmT4KzceZidNZAMvIhDllRJ0iAogfjpgIIwZOBi36MSnRSoYlRiZ4o80OlJMmQ/nDAvySH66OdXGyggf/mb/4GRpOTk4cPH15dXb333nupF/AIcIzOzc0dO3aMK4cPnvQwi3qB/+zsLENnz559y1vegjMX9U/+5E9YBTfqf/3Xf00n9Q2NKQcOHLh69Wp4FqcR/VGSRn5KLDQ1NUUOZNLf33/8+PFwS2VBkqSEMyUOlAsLC4899hj1Da0gSfzJ8+DBg1SKraokt27dCnDyZ19vetOb2BGJ1ZZkcSble9jCvn37WDrc9u/fTx1bXl6+7bbbXvva1w4ODsZQvcoCMoRlv6973es+/OEPUy8whgouLldhfn6e9BYXF0+ePEmzYAo9Dz74IGX010ayIMlLly597/d+L1i4TPF9EcGjZK2ClKDK0Hr+DDGl7kmy01OnTkGGJCFDk4WSsWK1SRKHi8LeIwgB/8t/+S9cEZrU+e6OOs31DIeCyxeeTD906NC1a9eimcr2SfIlL3kJNxhX8PTp0z09PSlDKsUXt+BuwafAGkFyfHyc9DCC8y5b8EJUkOQnP/nJmZmZj370o2yKVzw4F2TIlTp//YtKDHG3EDnqzMKintGSfRXcirxT8H4BQF5pGcIhf2sFALljm3CVSYA08l8MuVJcCL6p4c83NU18klWbJMGvX+TzVCIIwWu4yqSR/35NKDIpuAPpDCvmVtwTnlGSW12SJE4+Sbb5O7/zO1zukk8y5M/qjOIWr/Dlkyx4qGCtGp58wAj/gotLJvUiWZAk9zDyxcRVAAAQAElEQVS3OiU7peR1oJInn+IkL1y4ACVY8e3DNxHR8q2AG2T45mLd9fwblCSJjY2NsSif77NEfobUq0qyXo9nXFmWJiXoxT1GE0OxvPe976WSb8Xcinvy/UsmWe37NQFLJgku0sZ4PsQnWXFKxXdLcqZSryTJh0dl8knfy2TCk/MLXvCCV77ylSyUbwwV3IHlk3zVq17FtyTBCcKloeSbpQaSBUnyIH3lyhXCEpxHa27O/OeK4pSKe8gkWXGSvJJwX4UDbxZY1Luq3EAD33TTTQ8//PDS0tLAwABcBgcHqXN1uen5fALidPIMPTw8TGXnzp07duzgMRcfPOlBKfHMyv1EvcDCmVDxkspLJA48On/kIx+p5Eowkeezd77zncwKK0gDh5Qk9YKULl68yCxyoCT59VbEgR0xHfvHf/zH2C83zSc+8Ql6mFve8ClIkgjslBs9Tdx8koEajCxXQ5IpkworrPIXf/EX+VtIE++//34uX/qQKfVvssKKBZePO+rf//t/zysCqiOC08OHZLE0dxGdXDvKuLhcsj179vDy0dfXx6M/ARnKNy7KnXfeyS0UnThUS5IpBUmSAC+jETDK/CSLU2J1ssWTiXHXUc+3RiR54403cotCJi20mSSZy7fSG97wBnYXAWlSiSvCBj/96U9zo9JTxsIZCPgAJCJQ55bj0YQKBm2uY7zZsFbBxWUhJuJGkGKSDU2SRZPlJ1mQ0oYvko1Okot+xx138O1QJkleunHDJ+2ooMKV4omWZxQqDJEzU7heVAj7wAMPUKc/uxb74i5iC9xRbAeLt8LoxIGdpluxJVeZ3ApeDIu/IzaTJHvc/FUmAe6l/Pfrqt4KP/WpTxW8unKPsfFkdUmSaAUk+cyo4AmejdR8uYmPcefEi1K8EvKqTif589bJ3UW9vBVf3PqSjNVTkuy32rdCIhQnyVthPKgwGrb5J5+6J1l8uTefZOyaa10byYKXFIJAjyvOJz633HILdYz3Qe5JhriLaIKFMl6vinsYKraUZNyBMYulK3m/JhqeLEeFpePepl5gZZIsvlsK5kZzM0kCp/gFhI88eGXL/8B680nCIRhGWRXJkknG3nm0poJW53UvPegWcyvuYVaxRZKE6rD36+KdVtKzgQYmBBqYGyWuKN97fFfQmQyO3Fupyansxz72McrUc/nyZb7/UzNVuKgIUR6wuPDE52JQ4RL+3M/93Gc+85nktl6Fibyn8gQfDsVpEC2GKKkXpMSm8nvYAhHwfIrdcENKku3zGTZvV7hR5ymfmAXOxc2CJHHg25g3AyrFRsD8lOBWYZLxgSUoSKyGJIszKd/Dpo4cOVLsA5l3vetdjTgELibDHQVGYBanQU+BPxeXT6/pTwaoVKeCP7ffv/k3/wZP6vTgUC1JJhZcPpIkFMZdzVsU3KgnY4lUp8L3AqtTCSMUAaMeJc3GJclLP9f0j/7ojzaZJK/4W7Zsef3rX8/ni9wPZM738g/8wA9AgPxpImKjQn09w4Htp1GwEIoM0R68dqf+VKmWZIOSfPzxx/lojCSLfwyJ/NlFSpjdfWyjF8kGJUkmpMHLON8RBQdHDBUkyYXAmafAV7/61RwnFn+7cVm5InwAQQVPbniuPhWSZ4nXvOY11AlLmVGDABcrJQ+fF7/4xXG/sce//Mu/TENU2CkOVMKY2JyrzHIFL4aRIf1h+c0akuTibv4q88KS/35NYtxOpE2l2Aqw81b4yU9+Ep7Jkx5uttSkUpckiUNKJEYl34CG5ABCQWe1lzump4cKtlnDk0/+1SQgTRImberFxhIF3DYkGUFSksSv9q2QCMyiTJaa5MMrD0cjaYgKnQVJVvjk04gkSYY3a641epjcktFfW5KbeTzjxiu4x0gD482aj5P48DGlFxWG8pNkboUkU5I1vF+XTJJ8+Myat0JgkhXNZDQLkuSWSKNU0t1CPd82k2TBoryAfPazn+Wbmph1TBIUPAPzDEDaNZAsTjJe5SIsRz75ZxUsUQCKJkZ/soJm9Ec0kiR4h71fxwarLTfWwETkbj5x4gT3Co9NL3jBC5BbfDjKGT0vvoxWawQh1Bvf+Mb3ve99vDVy4bkePEhR4fL82I/9GM/6VKoNW+xfxyS5oXnh5o4hSd63fvAHf7Dk7VWcw4Y9NSfJ6wgpQZJL8573vIdvj3omuWHeRQ68IfFJdpzEFg02vIPt83kej1k1rMTrIHcgR8pcWepEqCNJxAD6ge8gwrZtkrzf33PPPT/yIz/C7b2ZJOHPtwYfBnNzcj/wXcz3Mt/RVIjM9zicqYCiKuMDAkT1yMhImsXrBm+cpJ16Kq80KEkS4KOx1dVVHse54myzPZMkTzKMQ2DqGybJC9SHPvQh/LmT8U/GBnmG4C2AV6HYLz28QvL4SIXvoB//8R/HmTdayo6xn/7pn+bJkldddv20pz0Nelgb3ooFwGtOkkvZtKvMnVbbQ0Wjk4zzItKDas0kmeuTD++DvJLwogENeNZ2uZlbQJJXG74fefHhTuBNnPegmh/P4vpysMmnrixUc5K8A5ISLxR1fzyDIXKIPEkvjPdBXoLye6J/w7IgSfzr8n5NHK4F7zK8G/KpdzxTtVuSPJOw/ZQkVEm75iQ//OEPb7/+xe1HEILXiyRZ8Xkrn3dwCEx9M49nBUnyzcJl6uz3a4htaBtrYB5b+e7ieyx+JP2Hf/iHuQwpLnWuempyqV760pdSpp4dO3agmVOTCt8SfG+klz9erVjilltu+eAHP8jozTffzN3DVadeuRWnQc5pOvWClF74whfm97AFIiR/KvlJxg1NYs973vN+//d//+UvfzkBeSinH896GTHzU4Jb+SRxjm9gLg1zSQZujU5yvc2yOh+AcRHRP+v51NzP7thsmg6ZgjsqDUWlwJ+Ly4eRMRQl91tUKMmchzxeAXm74qJTf/TRR2sgWbBoJElwPrI9evQoo6yVb/k50L97927ypBLGfvOnEIfEGpRkrMhzCZQKdE5VSfKS+sADD/B0wnvBgw8+yGNE6B/uSZbgu5vbg4D00yxjbJztJ4dt27YdO3bszJkzhCU41yieTpIDMVOdSnmSDUqSa8dxd5zGkD9psBBlGK8tOESdkt2Vf5FkbiNIkgOZEBzJyvMZmeQbQzikHpKMjdBDhSdXXm2oJ+NW4SoDn1C8BNEkMq+Q3EgXLlzgqfRlL3sZzvRTZtTYOBxS8vC56aab+KyN9y9uRR7vcEijxQCbc5VTAqnCRUl1KvnNapPk8jX/KkM1HzuvpRy/F/TkvwU0NEmC8/3Cay8k861akszltYs7Z5NPPvlXk5gFTXryrVqSzM1PkvedGt4KCVKQVTTPnTvH50f5H2XiiRUnWf7JhymNS5LgGB/lFzzgVZsktysvmFxuXhuZWxvJ4nuMY3z2zosP74a8Jx46dIhXXRIOYyHWjTolr1flSeKcn2Rt79fFSZIGb4W8EZADl54nfCrJGGXd1CRJHjxSkwpTKJPhvPkkCxblJeWZz3wmkelnIXQgZb7Rz2jq2TDJ9Ku2fMLOrNpIFixKkvEqR0zOD+BM5HwrAEUTK+NQkCR3Toe9X+fvvfL6BhqYT0riVuZ7+HWvex1x4yWMlzOeTXl7oAfu8VwbwvX7vu/76Iwh7t2SFw+HMMLyWsO1Rzv96Z/+KfKSK82HE1yecKi8LEiDgCnJuJPyU3rRi150+fLlSJjki78H8tfFjXcCgvAQT0yAkDBk4kbP96yhTsAgSXymt2eSJFbeQMQhMB9PcvnKe9YwWkymeBVupPRrElydgovLvYGGxIf7irdhrmBKg8x5sePzHd6uKKlz09ZwuUsmyQECC8UHeFRIICVZnBI93Iq4kRIlNzBlGD0kRnp1T5LI8bcfIfPEu3VfX81JcuuiCsgQ420PznzuSP5cLL6v+e5mR1wa2LIE/esZPvmXD/UVqoOw8fbP56xkyxtniGG4FVxcetYj2aAk819AyI2tsQsq+Unmp1T+RbKhSRKce4lrQZJYmSRx41mQi4VBGKr4J2M7XA4uCkaFJoYPG+cS84TBdafJnZCmZK7CXvJvxXSVwcKHFKGLygBszlUupgp2rhdXjdz4NuTljkpttyLXlIvLJcao0MSID5m6X+Uyb4V89MA2uXUpeX0ueKggJXIjQ4wKTawuScKQDzF5CWK/LI3VTJK5+Ubk2p582FfBxc0P+/9n70xjpKq2BXxv9IcDziiKQ1DjU4MYFQ2KoAZEAWfB+QfOcUwwMQ6oUSNqNA4xqMgQREBUBgMBcQBECFMIg+AQQASM4PDEaESQ+Ot9l/Xuvueeoqurq6u7q7u/zmJn7bXXXnudb58aVu/qIvSyScb01PLqwC7z0lbXdz6FScLt/vvvz75DKJJk47w9K0ySl8J0j/EulGewakiSm5kdYS9oBwwYwE2O8Gagb9++8VFKXgd5dEOYuzT3fFVoIUhNEtfOVdfp9ZpokMwmmV5cGOL5n1GUIkniUOstTYSQ8pLkHmZ6zI0nkOxTSiSPQ7UlyV6wrbwx4xfNpIfwvFH8PWTpJLlqyHOH8BhvGa/X8ClDaqmBu3TpAiPixrv5CRMmsCt0s0JVzAstx2h9+vThSYR76+mnn77pppuwsB+MZp1zOmUkxSRLELZfv34oOLAxPHJQ6iQslE2DgGk6ei4lFuJ0jhuLJHHr3bs3bU3CXRhPiDy28eThhPAGEXtNU8qwN4ska7ounlYOOugg3mzV5FAfeyGZ4tEKN5dd4zd5/OqUW4uXYRxShOzv2FiId1djx44tY7uZm7vHeMJ97rnnOEVhXW4znl7ToiiFKRW5gRsuSbasR48epAcZ3urRJbckpSfJU/OQIUNSkQBhgE+ePJmwRCNs165dQYSdxzuPeow1CT6lPzYJUiVJst38MguSPKuQP1dBbklym1vkSbKhSfLKx0OAvUi5JSWXJO9EKfY4xucGZjfhnDy5WF6Y2dawoKxatYqn37hqboPYd9oynskjZjW0XA5byYayreQTBHhbTAE8a9YsRjFmJQewcXY5m0Do5Ml+sWvwZ69zeZaeZFPtMjdn7rm00BJXStugSfK2hydwftnBDcCjm7VYMUnpJNOUpPAcyDMhW8Ol1emdT/HNTfFDIXjpJGNKtuUdThkvhUQoTBKGmzZt4gATklGw4RZSmCRYCh934VzYpiTr+vasMMnHH3+cX3mQ4ciRI0k4u1ZTJZm7x0gjm1VOL+RWaMlNyXZ5ruYZAwvP56W/XuNfmCQvHGBEeFFgFJ8khSkVbkRyLlTKSxJuRR4IJPniiy9m16qSJEmJ5xweL+wIek4KuRVaclNSl7cZlL5cJpYW83rNtZQhtdTAvAGKoAMHDuQ0hjspurBOH+nByBC/mkoWNgwdC3ZGY0q0vJtnbui07AE+tOg333wzE1FYC0GpVQjF9FiCFp1FWTriMJp0LOiM4oMnkRmli5AS3axgYTRZ0LHQZSJPzbTI+PHjiYmxVmF62dCf6QAAEABJREFUWjScuboISJdREotQtOiklPwZpYskf6YghQngGT4MlZEkMeskXAJvgmPjmJjrYqmsFJIhPqvzZpQhdK4aaEGALjSAhiQLCl2EIRySYCf51EWfNm0aRizErBNJMsluX+qyKEJkAmaTZBXsSKQUo3QJwlwSSIIn01MXvVJJEpPVWRQhLN1IgxXRERSGENzoxijdXJJhDx/cECZOmjQpYrJTvLfAiAIBWvQiQhyWQAiSdcMeFiJw+0VwHDDijOBAN5Kh25hJsmUsx6JIpJFNsjCl5A8QRkk7BB1LRAgLV1dBkkTLBi8xSWZFMtGSPA895kYXZeHChaSNHQvxkVByEzE2L+FC2FAkXQgW7j0uOS4EhW7cirF3OHMnBApadCzAYTSm0KJjIRR6CPHL3mUikAY7wnLoCNFYFIklGC0jSaIRk7kERFDqucskw1Vz7UQLgRuphs4orFiULi06+Sf/QgtuCPbKJsllEpCwBCc9cgghH4yMlkGSUFwmF4gSQhwujZZuXd/5ECpSygYkVexEQ7BHtujkjI4/ywX5QgtuIURgbui06FhQmFinl0KmMJFFEYJku1gAyIVjJzGSYZQWnaGUJKN0EeLgkBUsjCYLOha69U+SCCTAopEhMQlOYqSHTovOKD54YmGULhIJYAlhNPf+EM/wYahOJPFnOZZgaRKI+LTY0xIDBw7MJowzEsvhydJ0kWTBiGQj0EW4hRAUdqdOr9eEyiXJWqyIYGeUmEQukmTyJ1uckzA3XWYYiYOg1zVJ6MEwm1KhhchlJEkckiRVsgohDoJe/yQBAhxChbAKSJMFhStCcAuHQkvYc0kSh5wxMspcJBSmo7QqqaUGblUsvFgJSEACEpCABCQgAQlIoPoJmKEE6kPAGrg+9JwrAQlIQAISkIAEJCABCUig8Qi4Uv0JWAPXn6ERJCABCUhAAhKQgAQkIAEJSKBhCVQqujVwpUgaRwISkIAEJCABCUhAAhKQgASqnUBzrIGrnan5SUACEpCABCQgAQlIQAISkEB1ErAGrs59qSkr7RKQgAQkIAEJSEACEpCABCRQPgFr4PLZObNxCbiaBCQgAQlIQAISkIAEJCCB+hKwBq4vQedLoOEJuIIEJCABCUhAAhKQgAQkUBkC1sCV4WgUCUigYQgYVQISkIAEJCABCUhAApUkUHsNvN0fCUhAAhJoAgIuKQEJSEACEpCABCRQZwK1lsu118C1htBBAhKQgAQkUFECBpOABCQgAQlIQAINRaBYDbyXPxKQgAQkIAEJNCoBF5OABCQgAQlIoAIEihTQxWrgItMckoAEJCABCUhAAhUlYDAJSEACEpBAYxCwBm4Myq4hAQlIQAISkIAEaibgiAQkIAEJNB4Ba+DGY+1KEpCABCQgAQlIQAL/TcCeBCQggcYmYA3c2MRdTwISkIAEJCABCUhAAv/4hwwkIIGmIWAN3DTcXVUCEpCABCQgAQlIQAKtlYDXLYGmJGAN3JT0XVsCEpCABCQgAQlIQAISaE0EvNamJ2AN3PR7YAYSkIAEJCABCUhAAhKQgARaOoFquT5r4GrZCfOQgAQkIAEJSEACEpCABCQggYYm0BQ1cENfk/ElIAEJSEACEpCABCQgAQlIQAK7ImANvCsqDWczsgQkIAEJSEACEpCABCQgAQk0HQFr4KZj39pW9nolIAEJSEACEpCABCQgAQk0NQFr4KbeAddvDQS8RglIQAISkIAEJCABCUigOghYA1fHPpiFBFoqAa9LAhKQgAQkIAEJSEAC1UTAGriadsNcJCCBlkTAa5GABCQgAQlIQAISqD4C1sDVtydmJAEJSKC5EzB/CUhAAhKQgAQkUK0ErIGrdWfMSwISkIAEmiMBc5aABCQgAQlIoLoJWANX9/6YnQQkIAEJSKC5EDBPCUhAAhKQQHMgYA3cHHbJHCUgAQlIQAISqGYC5iYBCUhAAs2HgDVw89krM5WABCQgAQlIQALVRsB8JCABCTQ3AtbAu96x77//fvjw4d9+++2uhxvFumHDhpEjR27cuLFRVnMRCUhAAhKQgAQkIIG6ENBXAhJongRKqoH3L+EnLv/nn3++4447/mfnDwrdsK9cubIwRo8ePWI01xZ65iw5/4p00xJE27RpU6dOnR544IHOnTuvXr0aS/3l008/TUssX758lwGvv/768IHM2rVrTz311Pvvv/+UU06hGN6lv0YJSEACEpCABCQgAQk0AQGXlEBzJlBSDVz6BQ4aNOjdd9/9350/KHRj7ujRo0PJtjfeeGO2Wz06NXBKZs2aNUmvj0JZy28GIgInzKFk2y1btsyYMSMskOEgOnRa6mFaRQISkIAEJCABCUhAAhJoYgIu3/wJVLgGnj17dpbJnDlz6G7bto16GCUre++9d//+/bOW6tHPPPPMXr16kc9ZZ5112WWXoVREbr/99ogDDX5LEHpqMYYeZHr27Nm9e3cs55133oUXXoiiSEACEpCABCQgAQlIQAISaDICLWXhCtfAu+22W5bMP//5T7oTJ07866+/ULJyzTXX7LXXXllL0n/P/BQ3ptGs8tNPP82fP3/VqlVZY6EebunT2jkHciaLDz/8MGenu3379uXLlxf5jPQvv/xCAvjs2LED/yQ33HBDmzZtovvmm2+Gktpx48aFnshMmzaNHKZMmRL2bAvPxYsXL1y48I8//sja1SUgAQlIQAISkIAEJCABCUigCIFyauAi4Ti0zI5Gt7Dew+emm26iLU/ij2Zp//zzz9dee43z5DhEpeZ89NFHTzjhhIsvvvicc845/PDDFyxYgFtIWivrdvzxx3PqSzGZRkOJKbS57quvvtq+ffsePXow68gjj0yfXg43yuPBgwd36tSJBPDp0KHDZ599RpCQPffc8+abbw7PUaNGhRLtihUrUlGdyMQs2vCJ9oMPPjj//PMPO+yw3r179+3b96ijjnrsscdYl1FSwhnJnsaTBpY+ffrgEPLRRx9hQfAPi60EJCABCUhAAhKQgAQkIIFWQqDCNfAzzzzDMWbbtm0POeSQ66677tlnn1258ydH87TTTqNQzBnL6F577bWPPPLIrFmz4q9n6VKjpjjbtm3r169f6iYl50bxeeWVV6bR4go1dnLYunXr9ddfz/SwcDbbvXv3F154gRo7LCisFXq0t912WyicP3PUHDrt2LFjaZHiZO6++24Ok5cuXYpnkiFDhmCke+mll9IiM2fOpEV+/PFHjqNRFi1atGXLFhTkk08+oUWSP7oiAQlIQAISkIAEJCABCUigNRAoqQb+veCnJjTt2rUbNmzYunXr1q5dO3ToUCrhBv02rPnz53OeefLJJ+++++5PP/00h66RWNeuXe+9996ePXtShYYltYVuHFYXuiX/nLLPPvs88cQTnOIec8wxMfTSSy+FQvWb/i+lzp0733PPPeeee24uMkfHHBGH/4gRI0LBZ9KkSaHfeOONoRS2H3/88dtvv419v/32e+WVV6ZPn84Z+L777otlzpw5DKU/XU4f4ebQmNEQjn9DIU4oyT+6tbY6SEACEpCABCQgAQlIQAISaO4ESqqBy75ITmLfe++93PQ2bdr0r9C3YXHUvHHjxnnz5t13332ppKQonTFjxlNPPTV58uTCj2EXuk2ZMiUZc6kWdocPHz5w4EDOjak/YzSdyqYgFMmzZ88ePHjw1KlTCxO46667YuKSJUtWrFiBTqUaf9ZbnAzVO84IB7kDBgzo1q0bx78TJkzAgowfP75jx44dOnRA/+6777755huUVAyjUzPTcmq9efNmFDzxR1FKIaCPBCQgAQlIQAISkIAEJNAyCDRsDTxx4sT4U9UsrKuvvrqmb8PKupWiP/nkk+H29ddfc1aNfuCBB6a/uaV7xRVXHH300SghNbldddVVnNCGT/H27LPPDoeTTjoplPXr16MsX7486ti2bdtSJGMJIYHkGRbOqJPljTfewDhmzBhapDiZ9C1fXbp04fQ7pHfv3kxEPv/8c9p0tDtz5kx+AUEpjjGEc2D2gvo5uskzurYSqImAdglIQAISkIAEJCABCbQkAg1bAxeegsIufecTej3lkEMOiQhbt24N5Ygjjggltccdd1zSi7ideOKJya2IEp89xmGfffahTfL333+H3r59+1BSe/zxxyc9lDvvvDMUDsk5Cp47d250i5DJ/pfF4ZxrqXh37NiR/sSXijeVuw8++GA4UxKnD0InzxiylYAECglokYAEJCABCUhAAhJoeQQasAbe+WVYK3PIin/nU8659C6HouEcX44VerTpb3TpluiGZ13l0EMPjSkbNmz467//I6ivvvoqhlJ7ww03HHTQQdFFD6U4mf333z/cmDi9hp899tijc+fOhx9+OJ7z5s2jwEY54IADHn74YY7H0d95550FCxag4IMniiIBCUhglwQ0SkACEpCABCQggZZKoAFr4Ab9NqzcfnDWGlXib7/9lj18njp1arYGLtEtF7yUbocOHY499lg8OWq+4447UEIGDRq0evXq0LPtLbfcEt0ffvghlCLfhoVDmzZtTj/9dJRfd/50+/fPUUcdNXjwYOwYaJFLLrmEFuEomPaiiy6iDeOMGTPQkeiiKBKQgAQkkCNgVwISkIAEJCCBlk2goWrgbdu2xTlkFh+FXKW+DSsbNvRbb701lPvuu69Pnz6PPfbY5ZdfPmDAgDCmtkS35F+68tBDD4UzhXf79u0vuOACCuPXX389jLk21cBhL4XMwIEDw5mL6tmzJ6e7/fr1O/nkkxcvXnzxxRePGzcuRnMfco4uDjEabRhDt5WABCQgAQkEAVsJSEACEpBAayDQUDXwpEmTtm/fniNY/Dufcs517T766KNdunSJWYsWLRoyZEj6r5LCGG2JbuFcp/aqq6564IEHYgrXvmTJkt9//50yOCy5tl27dtlfB5RChjqWJSLOsmXLhg4dOnv27OieccYZF154Yehdu3Zt27Zt6HvvvTelOHqvXr3SHzAzig9GRQISkIAEJCCBIGArAQlIQAKth0BD1cCjRo0qhFjkO58KncuwvP/++7fffnt8KJrpJ5xwwvTp01FyUqJbblYp3UGDBnH6fdlll1FnHnjggZzWTpkypaaJ6ZuxcCiRzIgRI8aMGZP9+q6OHTs++OCDM2fOPPjgg4kTkj7qzHl4WGj79u1Li3gIDARFAhKQgAQkIIEgYCsBCUigtREoswbmhDMnOXBz587NOdDt1KlTzq3WLrNCsp5hoc0a0Tn2fP755zdu3Lh48eI1a9bQduvWDbcQHEJKcYsptDEFJSS60YaFNrq06JzHvvXWW+vWrVu/fv3kyZM3b96MHSn8wurOnTvjH7JLMjFEy/QkVLCccs+aNYvynmtcsGDBww8/nEZDefnll5mFjBw5Miy0w4YNw4K89NJLdBUJSEACEpCABCTQygl4+RKQQOskUGYNXOWwOAFu165drUmW6FZrnOTQv39/jl6XLVsWFtHJgCUAAALeSURBVApgToZDP++880KpSHv66adT3pdyjRVZziASkIAEJCABCUhAAi2JgNcigdZMoGXWwE2yo0888cTSpUs5pO3Zs+exxx7bvXv3jh07rlz5//871D333NMkWbmoBCQgAQlIQAISkIAEJJAIqEjAGrhi98DZZ5+dPvD866+/fvHFFyn06NGjOXNOXRUJSEACEpCABCQgAQlIQAKNTMDlgoA1cHCoQNurV68vv/xy4sSJV199NfUwcv755z/00EPz58+//PLLK7CAISQgAQlIQAISkIAEJCABCUig7gSyM6yBszQqoFMJDx8+/IOdP5MmTaIGPumkkyoQ1xASkIAEJCABCUhAAhKQgAQkUG8Cra0GrjcwA0hAAhKQgAQkIAEJSEACEpBAsyVgDdxst67OiTtBAhKQgAQkIAEJSEACEpBAaydgDdza74DWcf1epQQkIAEJSEACEpCABCQggX8RsAb+FwX/SaDlEvDKJCABCUhAAhKQgAQkIIH/ELAG/g8LNQlIoGUR8GokIAEJSEACEpCABCSQJ2ANnCdiXwISkEDzJ+AVSEACEpCABCQgAQnsmoA18K65aJWABCQggeZJwKwlIAEJSEACEpBAMQLWwMXoOCYBCUhAAhJoPgTMVAISkIAEJCCB2glYA9fOSA8JSEACEpCABKqbgNlJQAISkIAESiVgDVwqKf0kIAEJSEACEpBA9REwIwlIQAISqBsBa+C68dJbAhKQgAQkIAEJSKA6CJiFBCQggXIIWAOXQ805EpCABCQgAQlIQAISaDoCriwBCZRPwBq4fHbOlIAEJCABCUhAAhKQgAQal4CrSaC+BKyB60vQ+RKQgAQkIAEJSEACEpCABBqegCtUhoA1cGU4GkUCEpCABCQgAQlIQAISkIAEGoZAJaNaA1eSprEkIAEJSEACEpCABCQgAQlIoJoJNLcauJpZmpsEJCABCUhAAhKQgAQkIAEJVDeB/wMAAP//3jByPgAAAAZJREFUAwATqURxkXA+WwAAAABJRU5ErkJggg=="
+    }
+   },
+   "cell_type": "markdown",
+   "id": "b556b59d",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "538cfd6b",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Pinescript Folder/Hihglight Key Hourly Candles/highlight.ipynb

@@ -0,0 +1,77 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "81f3fe14",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "//@version=5\n",
+    "//xauusd OANDA\n",
+    "indicator(\"Highlight Key Hour Candles\", overlay = true, max_labels_count = 500, max_lines_count = 500, max_boxes_count = 500, max_bars_back = 500)\n",
+    "\n",
+    "\n",
+    "// Define the time conditions for specified key hours in UTC+8\n",
+    "A = (hour == 14 and minute == 0)         // 2:00 am\n",
+    "B = (hour == 7 and minute == 30)        // 3:30 PM\n",
+    "C = (hour == 18 and minute == 0)        // 6:00 AM next day\n",
+    "D = (hour == 20 and minute == 0)       // 8:00 AM next day\n",
+    "E = (hour == 8 and minute == 30)       // 8:00 PM next day\n",
+    "F = (hour == 5 and minute == 0)       // 5:00 PM \n",
+    "G = (hour == 6 and minute == 30)       // 6:30 PM \n",
+    "\n",
+    "Highlight = B or C or D or E or F or G\n",
+    "\n",
+    "// Highlight the key hour candles with a subtle color\n",
+    "bgcolor(Highlight ? color.new(color.gray, 90) : na)\n",
+    "bgcolor(D? color.new(#363a45, 75) : na)\n",
+    "\n",
+    "\n",
+    "// Get the current day of the week and the current time\n",
+    "isMonday = dayofweek == dayofweek.sunday\n",
+    "currentTime = 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, 0) // 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 currentTime >= startTime and currentTime <= endTime\n",
+    "\n",
+    "// Highlight the area with a background color\n",
+    "bgcolor(isInTimeRange ? color.new(color.blue, 90) : na)"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "bac965d2",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5ea763b1",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Pinescript Folder/Manual Key level/key levels input.ipynb

@@ -0,0 +1,150 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5e67c5b2",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "//@version=5\n",
+    "indicator(\"6 Custom Key Labels with Date\", overlay=true)\n",
+    "// === Date Formatter Function ===\n",
+    "f_format_date(ts) =>\n",
+    "    // Convert timestamp to UTC time\n",
+    "    ts_utc = timestamp(\"UTC\", year(ts), month(ts), dayofmonth(ts),16, 0)  // Set the time to 04:00 UTC\n",
+    "\n",
+    "    day_of_week = dayofweek(ts_utc) == 1 ? \"Sun\" : dayofweek(ts_utc) == 2 ? \"Mon\" : dayofweek(ts_utc) == 3 ? \"Tue\" : dayofweek(ts_utc) == 4 ? \"Wed\" : dayofweek(ts_utc) == 5 ? \"Thu\" : dayofweek(ts_utc) == 6 ? \"Fri\" : \"Sat\"\n",
+    "    day_of_month = month(ts_utc) == 12 ? \"Dec\" : month(ts_utc) == 1 ? \"Jan\" : month(ts_utc) == 2 ? \"Feb\" : month(ts_utc) == 3 ? \"Mar\" : month(ts_utc) == 4 ? \"Apr\" : month(ts_utc) == 5 ? \"May\" : month(ts_utc) == 6 ? \"Jun\" : month(ts_utc) == 7 ? \"Jul\" : month(ts_utc) == 8 ? \"Aug\" : month(ts_utc) == 9 ? \"Sep\" : month(ts_utc) == 10 ? \"Oct\" : \"Nov\"\n",
+    "    day_of_week + \"'\" + day_of_month + \"-\" + str.tostring(dayofmonth(ts_utc))\n",
+    "\n",
+    "_ = input.bool(true, title=\"\", group=\"YESTERDAY's YESTERDAY KEY LEVELS\")\n",
+    "\n",
+    "selected_date1 = input.time(timestamp(\"2024-04-29 04:00 +0000\"), title=\"Select Date\")\n",
+    "input_price_1 = input.float(0, \"-2YK High  -----------\", step=0.1)\n",
+    "input_color_1 = input.color(color.red, \"Color\")\n",
+    "\n",
+    "selected_date2 = input.time(timestamp(\"2024-04-29 04:00 +0000\"), title=\"Select Date\")\n",
+    "input_price_2 = input.float(0, \"-2YK Mid  -----------\", step=0.1)\n",
+    "input_color_2 = input.color(color.green, \"Color\")\n",
+    "\n",
+    "selected_date3 = input.time(timestamp(\"2024-04-29 04:00 +0000\"), title=\"Select Date\")\n",
+    "input_price_3 = input.float(0, \"-2YK Low  -----------\", step=0.1)\n",
+    "input_color_3 = input.color(color.red, \"Color\")\n",
+    "\n",
+    "_ = input.bool(true, title=\"\", group=\"YESTERDAY's KEY LEVELS\")\n",
+    "\n",
+    "selected_date4 = input.time(timestamp(\"2024-04-29 04:00 +0000\"), title=\"Select Date\")\n",
+    "input_price_4 = input.float(0, \"-1YK High  -----------\", step=0.1)\n",
+    "input_color_4 = input.color(color.red, \"Color\")\n",
+    "\n",
+    "selected_date5 = input.time(timestamp(\"2024-04-29 04:00 +0000\"), title=\"Select Date\")\n",
+    "input_price_5 = input.float(0, \"-1YK Mid  -----------\", step=0.1)\n",
+    "input_color_5 = input.color(color.green, \"Color\")\n",
+    "\n",
+    "selected_date6 = input.time(timestamp(\"2024-04-29 04:00 +0000\"), title=\"Select Date\")\n",
+    "input_price_6 = input.float(0, \"-1YK Low  -----------\", step=0.1)\n",
+    "input_color_6 = input.color(color.red, \"Color\")\n",
+    "\n",
+    "\n",
+    "// === Labels and Lines ===\n",
+    "\n",
+    "// K1\n",
+    "var label lb1 = na\n",
+    "var line ln1 = na\n",
+    "if input_price_1 != 0\n",
+    "    if na(lb1)\n",
+    "        lb1 := label.new(x=bar_index + 5,y=input_price_1,text=\"High \" + str.tostring(input_price_1) + \" \" + f_format_date(selected_date1),style=label.style_label_left,color=color.new(input_color_1, 100),textcolor=input_color_1,size=size.tiny,yloc=yloc.price)\n",
+    "        ln1 := line.new(x1=bar_index + 4, y1=input_price_1, x2=bar_index + 5, y2=input_price_1, color=input_color_1, width=1)\n",
+    "    label.set_xy(lb1, bar_index + 5, input_price_1)\n",
+    "    line.set_xy1(ln1, bar_index - 300, input_price_1)\n",
+    "    line.set_xy2(ln1, bar_index + 5, input_price_1)\n",
+    "\n",
+    "// K2\n",
+    "var label lb2 = na\n",
+    "var line ln2 = na\n",
+    "if input_price_2 != 0\n",
+    "    if na(lb2)\n",
+    "        lb2 := label.new(x=bar_index + 5,y=input_price_2,text=\"Mid \" + str.tostring(input_price_2) + \" \" + f_format_date(selected_date2),style=label.style_label_left,color=color.new(input_color_2, 100),textcolor=input_color_2,size=size.tiny,yloc=yloc.price)\n",
+    "        ln2 := line.new(x1=bar_index + 4, y1=input_price_2, x2=bar_index + 5, y2=input_price_2, color=input_color_2, width=1)\n",
+    "    label.set_xy(lb2, bar_index + 5, input_price_2)\n",
+    "    line.set_xy1(ln2, bar_index - 300, input_price_2)\n",
+    "    line.set_xy2(ln2, bar_index + 5, input_price_2)\n",
+    "\n",
+    "// K3\n",
+    "var label lb3 = na\n",
+    "var line ln3 = na\n",
+    "if input_price_3 != 0\n",
+    "    if na(lb3)\n",
+    "        lb3 := label.new(x=bar_index + 5,y=input_price_3,text=\"Low \" + str.tostring(input_price_3) + \" \" + f_format_date(selected_date3),style=label.style_label_left,color=color.new(input_color_3, 100),textcolor=input_color_3,size=size.tiny,yloc=yloc.price)\n",
+    "        ln3 := line.new(x1=bar_index + 4, y1=input_price_3, x2=bar_index + 5, y2=input_price_3, color=input_color_3, width=1)\n",
+    "    label.set_xy(lb3, bar_index + 5, input_price_3)\n",
+    "    line.set_xy1(ln3, bar_index - 300, input_price_3)\n",
+    "    line.set_xy2(ln3, bar_index + 5, input_price_3)\n",
+    "\n",
+    "// K4\n",
+    "var label lb4 = na\n",
+    "var line ln4 = na\n",
+    "if input_price_4 != 0\n",
+    "    if na(lb4)\n",
+    "        lb4 := label.new(x=bar_index + 5,y=input_price_4,text=\"High \" + str.tostring(input_price_4) + \" \" + f_format_date(selected_date4),style=label.style_label_left,color=color.new(input_color_4, 100),textcolor=input_color_4,size=size.tiny,yloc=yloc.price)\n",
+    "        ln4 := line.new(x1=bar_index + 4, y1=input_price_4, x2=bar_index + 5, y2=input_price_4, color=input_color_4, width=1)\n",
+    "    label.set_xy(lb4, bar_index + 5, input_price_4)\n",
+    "    line.set_xy1(ln4, bar_index - 300, input_price_4)\n",
+    "    line.set_xy2(ln4, bar_index + 5, input_price_4)\n",
+    "\n",
+    "// K5\n",
+    "var label lb5 = na\n",
+    "var line ln5 = na\n",
+    "if input_price_5 != 0\n",
+    "    if na(lb5)\n",
+    "        lb5 := label.new(x=bar_index + 5,y=input_price_5,text=\"Mid \" + str.tostring(input_price_5) + \" \" + f_format_date(selected_date5),style=label.style_label_left,color=color.new(input_color_5, 100),textcolor=input_color_5, size=size.tiny,yloc=yloc.price)\n",
+    "        ln5 := line.new(x1=bar_index + 4, y1=input_price_5, x2=bar_index + 5, y2=input_price_5, color=input_color_5, width=1)\n",
+    "    label.set_xy(lb5, bar_index + 5, input_price_5)\n",
+    "    line.set_xy1(ln5, bar_index - 300, input_price_5)\n",
+    "    line.set_xy2(ln5, bar_index + 5, input_price_5)\n",
+    "\n",
+    "// K6\n",
+    "var label lb6 = na\n",
+    "var line ln6 = na\n",
+    "if input_price_6 != 0\n",
+    "    if na(lb6)\n",
+    "        lb6 := label.new(x=bar_index + 5,y=input_price_6,text=\"Low \" + str.tostring(input_price_6) + \" \" + f_format_date(selected_date6),style=label.style_label_left,color=color.new(input_color_6, 100),textcolor=input_color_6,size=size.tiny,yloc=yloc.price)\n",
+    "        ln6 := line.new(x1=bar_index + 4, y1=input_price_6, x2=bar_index + 5, y2=input_price_6, color=input_color_6, width=1)\n",
+    "    label.set_xy(lb6, bar_index + 5, input_price_6)\n",
+    "    line.set_xy1(ln6, bar_index - 300, input_price_6)\n",
+    "    line.set_xy2(ln6, bar_index + 5, input_price_6)\n"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "830d31bc",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dc8f8264",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Pinescript Folder/Market Profile (Volume based)/market profile volume based.ipynb

@@ -0,0 +1,183 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5b2ea924",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "//@version=5\n",
+    "indicator('Market Profile', 'Market Profile', overlay=true, max_bars_back=5000, max_lines_count=500)\n",
+    "\n",
+    "// === Input Parameters ===\n",
+    "TimeframeU = input.string(defval='D', title='Higher Time Frame', options=['D'])\n",
+    "percent = input.float(70.0, title='Percent for Value Area %', minval=1, maxval=100) / 100\n",
+    "showpocline = input(true, title='Show POC Line')\n",
+    "offset_bars = input(true, title='Plot Profile From Start of Session (Not End)')\n",
+    "showwhat = input.string(defval='Show All Channels', title='Show Value Area', options=['Don\\'t Show Value Area', 'Show Value Area', 'Show All Channels'])\n",
+    "linewdth = input.int(defval=3, title='Line Width', minval=1, maxval=10)\n",
+    "srate = input.float(defval=100., title='Sizing Rate %', minval=10, maxval=500) / 100\n",
+    "poc_col = input(defval=color.new(color.yellow, 0), title='POC Line Color')\n",
+    "vah_col = input(defval=color.new(color.yellow, 50), title='Value Area Color')\n",
+    "nonva_col = input(defval=color.new(color.gray, 50), title='Non-Value Area Color')\n",
+    "\n",
+    "// === Configuration ===\n",
+    "rowCount = 50  // Number of TPO rows\n",
+    "\n",
+    "// === Variables ===\n",
+    "var float highesthtf = na\n",
+    "var float lowesthtf = na\n",
+    "var float highest = high\n",
+    "var float lowest = low\n",
+    "var int barnum = 0\n",
+    "var int len = 0\n",
+    "bool newbar = ta.change(time(TimeframeU)) != 0\n",
+    "\n",
+    "if newbar\n",
+    "    highesthtf := high\n",
+    "    lowesthtf := low\n",
+    "    barnum := 0\n",
+    "else\n",
+    "    highesthtf := math.max(highesthtf, high)\n",
+    "    lowesthtf := math.min(lowesthtf, low)\n",
+    "    barnum += 1\n",
+    "\n",
+    "if newbar\n",
+    "    highest := highesthtf[1]\n",
+    "    lowest := lowesthtf[1]\n",
+    "    len := barnum[1]\n",
+    "\n",
+    "float channel = (highest - lowest) / rowCount\n",
+    "\n",
+    "included(t1, t2, t3, t4) =>\n",
+    "    _ret = t3 >= t1 and t3 <= t2 or t4 >= t1 and t4 <= t2 or t3 <= t1 and t4 >= t2\n",
+    "    _ret\n",
+    "\n",
+    "get_tpo(lower, upper) =>\n",
+    "    float ret = 0.\n",
+    "    for x = 1 to len by 1\n",
+    "        if included(lower, upper, low[x], high[x])\n",
+    "            ret += 1\n",
+    "    ret\n",
+    "\n",
+    "ch = array.new_float(rowCount + 2, 0.)\n",
+    "if newbar\n",
+    "    for x = 1 to rowCount by 1\n",
+    "        array.set(ch, x, get_tpo(lowest + (x - 1) * channel, lowest + x * channel))\n",
+    "\n",
+    "get_index(up, down) =>\n",
+    "    float upval = array.get(ch, up)\n",
+    "    float downval = array.get(ch, down)\n",
+    "    [upval >= downval ? up : down, math.max(upval, downval)]\n",
+    "\n",
+    "float total = 0.\n",
+    "int poc_loc = 0\n",
+    "float poc_val = 0.\n",
+    "var int gl_poc_loc = 0\n",
+    "if newbar\n",
+    "    for x = 1 to rowCount by 1\n",
+    "        cval = array.get(ch, x)\n",
+    "        total += cval\n",
+    "        if cval >= poc_val\n",
+    "            poc_val := cval\n",
+    "            poc_loc := x\n",
+    "    gl_poc_loc := poc_loc\n",
+    "\n",
+    "float va_val = poc_val\n",
+    "int vahind = poc_loc\n",
+    "int valind = poc_loc\n",
+    "if newbar\n",
+    "    for x = 1 to rowCount by 1\n",
+    "        if va_val >= total * percent\n",
+    "            break\n",
+    "        [ind, chval] = get_index(vahind + 1, valind - 1)\n",
+    "        if chval == 0\n",
+    "            break\n",
+    "        if ind == vahind + 1\n",
+    "            vahind := ind\n",
+    "            va_val += chval\n",
+    "        else\n",
+    "            valind := ind\n",
+    "            va_val += chval\n",
+    "\n",
+    "get_middle(x) =>\n",
+    "    float ret = (lowest + (x - 1) * channel + lowest + x * channel) / 2\n",
+    "    ret\n",
+    "\n",
+    "get_base(x) =>\n",
+    "    float ret = lowest + (x - 1) * channel\n",
+    "    ret\n",
+    "\n",
+    "var int bartime = na\n",
+    "bartime := na(bartime) ? time - time[1] : math.min(bartime, time - time[1])\n",
+    "lastb = ta.valuewhen(newbar, time, 1)\n",
+    "\n",
+    "draw_mp(y, chval, is_va) =>\n",
+    "    rchval = math.round(srate * chval)\n",
+    "    linecol = is_va ? vah_col : nonva_col\n",
+    "    offset_line = line.new(x1=time - time + lastb, y1=y, x2=time - time + lastb + bartime * rchval, y2=y, color=linecol, xloc=xloc.bar_time, width=linewdth)\n",
+    "    offset_line\n",
+    "\n",
+    "var float midpoc = na\n",
+    "if newbar\n",
+    "    if showpocline\n",
+    "        var line poc_line = na\n",
+    "        midpoc := get_middle(poc_loc)\n",
+    "        poc_line := line.new(x1=time, y1=midpoc, x2=time - time + lastb, y2=midpoc, color=poc_col, xloc=xloc.bar_time, width=1)\n",
+    "\n",
+    "    if showwhat == 'Show Value Area' or showwhat == 'Show All Channels'\n",
+    "        var line[] lines = array.new_line(rowCount)\n",
+    "        for i = 0 to rowCount - 1\n",
+    "            array.set(lines, i, na)\n",
+    "\n",
+    "        str = showwhat == 'Show All Channels' ? 1 : valind\n",
+    "        end = showwhat == 'Show All Channels' ? rowCount : vahind\n",
+    "        for x = str to end by 1\n",
+    "            is_va = x >= valind and x <= vahind\n",
+    "            chval = array.get(ch, x)\n",
+    "            y = get_base(x)\n",
+    "            l = draw_mp(y, chval, is_va)\n",
+    "            array.set(lines, x - 1, l)\n",
+    "\n",
+    "price_in_poc = close >= get_base(gl_poc_loc) and close <= get_base(gl_poc_loc + 1)\n",
+    "price_above_poc = close > get_base(gl_poc_loc + 1)\n",
+    "price_below_poc = close < get_base(gl_poc_loc)\n",
+    "\n",
+    "alertcondition(price_in_poc, title='Price in POC', message='Price in POC')\n",
+    "alertcondition((price_in_poc[1] or price_below_poc[1]) and price_above_poc, title='Price went above POC', message='Price went above POC')\n",
+    "alertcondition((price_in_poc[1] or price_above_poc[1]) and price_below_poc, title='Price went below POC', message='Price went below POC')\n"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "07f52b35",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c88ce11",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Pinescript Folder/Risk Manager and Position Sizing/Risk Manager and Position Sizing-pinescript.js

@@ -0,0 +1,86 @@
+//@version=5
+indicator("GOLD  riskmanager by Sunstoic", overlay=true, max_labels_count=500, max_boxes_count=500)
+
+// ─────── Inputs ────────────────────────────────
+groupLevels = "Levels"
+entryPrice = input.price(0, title = "Entry Price:", confirm = true, group = groupLevels)
+stopPrice  = input.price(0, title = "Stop Price:", confirm = true, group = groupLevels)
+tpPrice1   = input.price(0, title = "Take-Profit Price:", confirm = true, group = groupLevels)
+
+// ─────── Account & Risk Settings ────────────────────────────────
+groupAccount = "Account Settings"
+capital = input.float(2000, "Account Capital", step = 1, group = groupAccount)
+riskPercent = input.float(3, "Risk %", step = 0.1, group = groupAccount)
+pipValue = input.float(0.01, "Tick Value (e.g. 0.01 for Gold)", step = 0.0001, group = groupAccount)
+
+// ─────── Calculations ────────────────────────────────
+slTicks = math.abs(entryPrice - stopPrice)
+tp1Ticks = math.abs(tpPrice1 - entryPrice)
+
+riskAmount = capital * (riskPercent / 100)
+positionSize = riskAmount / (slTicks / pipValue)
+positionSizeRounded = math.round(positionSize * 100) / 100  // 2 decimals
+
+gain1 = (tp1Ticks / slTicks) * riskPercent
+
+// format helper → show #.## consistently
+fmt(x) => str.format("{0,number,#.##}", x)
+
+// ─────── Draw Risk & Reward Boxes ────────────────────────────────
+var box riskBox = na
+var box rewardBox1 = na
+
+if barstate.islast
+    if not na(riskBox)
+        box.delete(riskBox)
+    if not na(rewardBox1)
+        box.delete(rewardBox1)
+
+    leftBar = bar_index + 2
+    rightBar = bar_index + 5
+
+    riskBox := box.new(left=leftBar, right=rightBar, top=math.max(entryPrice, stopPrice), bottom=math.min(entryPrice, stopPrice),bgcolor=color.new(color.red, 80), border_color=color.new(color.red, 0))
+
+    if tpPrice1 != 0
+        rewardBox1 := box.new(left=leftBar, right=rightBar, top=math.max(entryPrice, tpPrice1), bottom=math.min(entryPrice, tpPrice1),bgcolor=color.new(color.green, 80), border_color=color.new(color.green, 0))
+
+// ─────── Labels for Prices ────────────────────────────────
+var label lblEntry = na
+var label lblSL = na
+var label lblTP1 = na
+var label lblInfo = na
+
+if barstate.islast
+    if not na(lblEntry)
+        label.delete(lblEntry)
+    if not na(lblSL)
+        label.delete(lblSL)
+    if not na(lblTP1)
+        label.delete(lblTP1)
+    if not na(lblInfo)
+        label.delete(lblInfo)
+
+    textClr = color.new(color.black, 0)
+    smallFont = size.small
+
+    lblEntry := label.new(bar_index + 5, entryPrice, "E1 " + str.tostring(entryPrice, format.mintick),style=label.style_label_left, color=color.new(color.silver, 100), textcolor=textClr, size=smallFont)
+    lblSL := label.new(bar_index + 5, stopPrice, "S1 " + str.tostring(stopPrice, format.mintick),style=label.style_label_left, color=color.new(color.red, 100), textcolor=textClr, size=smallFont)
+    if tpPrice1 != 0 
+        lblTP1 := label.new(bar_index + 5, tpPrice1, "T1 " + str.tostring(tpPrice1, format.mintick),style=label.style_label_left, color=color.new(color.green, 100), textcolor=textClr, size=smallFont)
+
+    infoText = 
+       fmt(capital) + " Equity" + "\n" +
+       fmt(riskAmount) + " (" + fmt(riskPercent) + "%)" + " Risk\n" +
+       fmt(slTicks) + " SL ticks" + "\n" + 
+       fmt(tp1Ticks) + " TP ticks" + "\n" +
+       "1:" + fmt(tp1Ticks/slTicks) + " RRR" + "\n\n" +
+       "        -" + fmt(riskPercent) + "%" + " Potential Loss\n" +
+       "+" + fmt(gain1) + "%" + " Potential Gain\n" +
+       fmt(positionSizeRounded) + " lot Bet Size"
+
+    lblInfo := label.new(bar_index + 15, ((stopPrice + tpPrice1) / 2), infoText,style=label.style_label_left, color=color.new(#b3b6be, 100), textcolor=textClr, size=smallFont)
+
+// ─────── Plot guide lines ────────────────────────────────
+plot(entryPrice, "Entry", color=color.new(#919191, 0), style=plot.style_linebr )
+plot(stopPrice, "Stop", color=color.new(color.red, 0), style=plot.style_linebr)
+plot(tpPrice1, "TP1", color=color.new(color.green, 0), style=plot.style_linebr)

Pinescript Folder/Risk Manager and Position Sizing/Risk Manager and Position Sizing.ipynb

@@ -0,0 +1,135 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "70878bcb",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "132a53a8",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "//@version=5\n",
+    "indicator(\"GOLD  riskmanager by Sunstoic\", overlay=true, max_labels_count=500, max_boxes_count=500)\n",
+    "\n",
+    "// ─────── Inputs ────────────────────────────────\n",
+    "groupLevels = \"Levels\"\n",
+    "entryPrice = input.price(0, title = \"Entry Price:\", confirm = true, group = groupLevels)\n",
+    "stopPrice  = input.price(0, title = \"Stop Price:\", confirm = true, group = groupLevels)\n",
+    "tpPrice1   = input.price(0, title = \"Take-Profit Price:\", confirm = true, group = groupLevels)\n",
+    "\n",
+    "// ─────── Account & Risk Settings ────────────────────────────────\n",
+    "groupAccount = \"Account Settings\"\n",
+    "capital = input.float(2000, \"Account Capital\", step = 1, group = groupAccount)\n",
+    "riskPercent = input.float(3, \"Risk %\", step = 0.1, group = groupAccount)\n",
+    "pipValue = input.float(0.01, \"Tick Value (e.g. 0.01 for Gold)\", step = 0.0001, group = groupAccount)\n",
+    "\n",
+    "// ─────── Calculations ────────────────────────────────\n",
+    "slTicks = math.abs(entryPrice - stopPrice)\n",
+    "tp1Ticks = math.abs(tpPrice1 - entryPrice)\n",
+    "\n",
+    "riskAmount = capital * (riskPercent / 100)\n",
+    "positionSize = riskAmount / (slTicks / pipValue)\n",
+    "positionSizeRounded = math.round(positionSize * 100) / 100  // 2 decimals\n",
+    "\n",
+    "gain1 = (tp1Ticks / slTicks) * riskPercent\n",
+    "\n",
+    "// format helper → show #.## consistently\n",
+    "fmt(x) => str.format(\"{0,number,#.##}\", x)\n",
+    "\n",
+    "// ─────── Draw Risk & Reward Boxes ────────────────────────────────\n",
+    "var box riskBox = na\n",
+    "var box rewardBox1 = na\n",
+    "\n",
+    "if barstate.islast\n",
+    "    if not na(riskBox)\n",
+    "        box.delete(riskBox)\n",
+    "    if not na(rewardBox1)\n",
+    "        box.delete(rewardBox1)\n",
+    "\n",
+    "    leftBar = bar_index + 2\n",
+    "    rightBar = bar_index + 5\n",
+    "\n",
+    "    riskBox := box.new(left=leftBar, right=rightBar, top=math.max(entryPrice, stopPrice), bottom=math.min(entryPrice, stopPrice),bgcolor=color.new(color.red, 80), border_color=color.new(color.red, 0))\n",
+    "\n",
+    "    if tpPrice1 != 0\n",
+    "        rewardBox1 := box.new(left=leftBar, right=rightBar, top=math.max(entryPrice, tpPrice1), bottom=math.min(entryPrice, tpPrice1),bgcolor=color.new(color.green, 80), border_color=color.new(color.green, 0))\n",
+    "\n",
+    "// ─────── Labels for Prices ────────────────────────────────\n",
+    "var label lblEntry = na\n",
+    "var label lblSL = na\n",
+    "var label lblTP1 = na\n",
+    "var label lblInfo = na\n",
+    "\n",
+    "if barstate.islast\n",
+    "    if not na(lblEntry)\n",
+    "        label.delete(lblEntry)\n",
+    "    if not na(lblSL)\n",
+    "        label.delete(lblSL)\n",
+    "    if not na(lblTP1)\n",
+    "        label.delete(lblTP1)\n",
+    "    if not na(lblInfo)\n",
+    "        label.delete(lblInfo)\n",
+    "\n",
+    "    textClr = color.new(color.black, 0)\n",
+    "    smallFont = size.small\n",
+    "\n",
+    "    lblEntry := label.new(bar_index + 5, entryPrice, \"E1 \" + str.tostring(entryPrice, format.mintick),style=label.style_label_left, color=color.new(color.silver, 100), textcolor=textClr, size=smallFont)\n",
+    "    lblSL := label.new(bar_index + 5, stopPrice, \"S1 \" + str.tostring(stopPrice, format.mintick),style=label.style_label_left, color=color.new(color.red, 100), textcolor=textClr, size=smallFont)\n",
+    "    if tpPrice1 != 0 \n",
+    "        lblTP1 := label.new(bar_index + 5, tpPrice1, \"T1 \" + str.tostring(tpPrice1, format.mintick),style=label.style_label_left, color=color.new(color.green, 100), textcolor=textClr, size=smallFont)\n",
+    "\n",
+    "    infoText = \n",
+    "       fmt(capital) + \" Equity\" + \"\\n\" +\n",
+    "       fmt(riskAmount) + \" (\" + fmt(riskPercent) + \"%)\" + \" Risk\\n\" +\n",
+    "       fmt(slTicks) + \" SL ticks\" + \"\\n\" + \n",
+    "       fmt(tp1Ticks) + \" TP ticks\" + \"\\n\" +\n",
+    "       \"1:\" + fmt(tp1Ticks/slTicks) + \" RRR\" + \"\\n\\n\" +\n",
+    "       \"        -\" + fmt(riskPercent) + \"%\" + \" Potential Loss\\n\" +\n",
+    "       \"+\" + fmt(gain1) + \"%\" + \" Potential Gain\\n\" +\n",
+    "       fmt(positionSizeRounded) + \" lot Bet Size\"\n",
+    "\n",
+    "    lblInfo := label.new(bar_index + 15, ((stopPrice + tpPrice1) / 2), infoText,style=label.style_label_left, color=color.new(#b3b6be, 100), textcolor=textClr, size=smallFont)\n",
+    "\n",
+    "// ─────── Plot guide lines ────────────────────────────────\n",
+    "plot(entryPrice, \"Entry\", color=color.new(#919191, 0), style=plot.style_linebr )\n",
+    "plot(stopPrice, \"Stop\", color=color.new(color.red, 0), style=plot.style_linebr)\n",
+    "plot(tpPrice1, \"TP1\", color=color.new(color.green, 0), style=plot.style_linebr)\n"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "8f38d4d8",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4adbe7ec",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Pinescript Folder/Table Statistics/clean table.ipynb

@@ -0,0 +1,126 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4189b6d9",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "//@version=5\n",
+    "indicator(\"Previous 2 Daily and Weekly Candle Ranges\", overlay=true)\n",
+    "\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, 80) : color.new(color.red, 80)\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",
+    "// === Create and update table ===\n",
+    "var table t = table.new(position.top_right, 80, 80, border_width=1)\n",
+    "\n",
+    "if bar_index == 1\n",
+    "    table.cell(t, 0, 0, \"\", text_color=color.rgb(255, 255, 255, 100), text_size = size.tiny, bgcolor=color.rgb(120, 123, 134, 100))\n",
+    "    table.cell(t, 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(t, 0, 1, \"\", text_color=color.rgb(255, 255, 255, 100), text_size = size.tiny, bgcolor=color.rgb(120, 123, 134, 100))\n",
+    "    table.cell(t, 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(t, 1, 2, \"𝑫𝒂𝒕𝒂\", text_color = color.white, text_size = size.tiny, bgcolor = color.navy)\n",
+    "\n",
+    "table.cell(t, 0, 3, \"Date\", text_size = size.tiny)\n",
+    "table.cell(t, 1, 3, \"A5\", text_size = size.tiny)\n",
+    "\n",
+    "table.cell(t, 0, 4, \"Time\", text_size = size.tiny)\n",
+    "table.cell(t, 1, 4, \"B2 - Prev 1\", text_size = size.tiny)\n",
+    " \n",
+    "table.cell(t, 0, 5, \"Time\", text_size = size.tiny)\n",
+    "table.cell(t, 1, 5, \"B2 - Prev 1\", text_size = size.tiny)\n",
+    "\n",
+    "table.cell(t, 0, 6, \"Type\", text_size = size.tiny)\n",
+    "table.cell(t, 1, 6, \"B2 - Prev 1\", text_size = size.tiny)\n",
+    "\n",
+    "table.cell(t, 1, 7, \"𝑯𝒊𝒈𝒉𝒆𝒓 𝑻𝑭\", text_color = color.white, text_size = size.tiny, bgcolor = color.navy)\n",
+    "\n",
+    "table.cell(t, 0, 8, \"6:00\\nяαηgє\", text_size = size.tiny)\n",
+    "table.cell(t, 1, 8, \"HL:\\nOC:\", text_size = size.tiny)\n",
+    "\n",
+    "table.cell(t, 0, 9, \"8:00\\nяαηgє\", text_size = size.tiny)\n",
+    "table.cell(t, 1, 9, \"HL:\\nOC:\", text_size = size.tiny)\n",
+    "\n",
+    "table.cell(t, 1, 11, \"1D 𝑪𝒂𝒏𝒅𝒍𝒆\", text_color = color.white, text_size = size.tiny, bgcolor = color.navy)\n",
+    "\n",
+    "table.cell(t, 0, 12, \"-1D \\nяαηgє\", text_size = size.tiny)\n",
+    "table.cell(t, 1, 12, \"HL | $\" + str.tostring(d1_hl, \"#\") + \"\\nOC | $\" + str.tostring(d1_oc, \"#\"), text_size = size.tiny, bgcolor=d1_bg)\n",
+    "\n",
+    "table.cell(t, 0, 13, \"-2D \\nяαηgє\", text_size = size.tiny)\n",
+    "table.cell(t, 1, 13, \"HL | $\" + str.tostring(d2_hl, \"#\") + \"\\nOC | $\" + str.tostring(d2_oc, \"#\"), text_size = size.tiny, bgcolor=d2_bg)\n",
+    "\n",
+    "table.cell(t, 1, 14, \"1W 𝑪𝒂𝒏𝒅𝒍𝒆\", text_color = color.white, text_size = size.tiny, bgcolor = color.navy)\n",
+    "\n",
+    "table.cell(t, 0, 15, \"0W \\nяαηgє\", text_size = size.tiny)\n",
+    "table.cell(t, 1, 15, \"HL | $\" + str.tostring(w0_hl, \"#\") + \"\\nOC | $\" + str.tostring(w0_oc, \"#\"), text_size = size.tiny, bgcolor=w0_bg)\n",
+    "\n",
+    "table.cell(t, 0, 16, \"-1W \\nяαηgє\", text_size = size.tiny)\n",
+    "table.cell(t, 1, 16, \"HL | $\" + str.tostring(w1_hl, \"#\") + \"\\nOC | $\" + str.tostring(w1_oc, \"#\"), text_size = size.tiny, bgcolor=w1_bg)\n",
+    "\n",
+    "table.cell(t, 0, 17, \"-2W \\nяαηgє\", text_size = size.tiny)\n",
+    "table.cell(t, 1, 17, \"HL | $\" + str.tostring(w2_hl, \"#\") + \"\\nOC | $\" + str.tostring(w2_oc, \"#\"), text_size = size.tiny, bgcolor=w2_bg)\n",
+    "\n"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "3c843f9d",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d806c586",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Pinescript Folder/Time Logic Reference/Time Logic Reference.ipynb

@@ -0,0 +1,53 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7f48b5c6",
+   "metadata": {},
+   "source": [
+    "//Start of Time logic Reference\n",
+    "\n",
+    "\n",
+    "//@version=5\n",
+    "indicator(\"12-Hour True AM/PM Logic - Tiny Labels\", overlay=true)\n",
+    "\n",
+    "// SETTINGS\n",
+    "tz = \"Asia/Manila\"\n",
+    "\n",
+    "// TIME LOGIC\n",
+    "barHour = hour(time, tz)\n",
+    "barMin  = minute(time, tz)\n",
+    "\n",
+    "// CONVERT TO 12-HOUR FORMAT WITH AM/PM\n",
+    "hour12 = barHour % 12\n",
+    "hour12 := hour12 == 0 ? 12 : hour12\n",
+    "ampm = barHour < 12 ? \"AM\" : \"PM\"\n",
+    "\n",
+    "// STORE OPEN PRICE FOR EACH HOUR AND PERIOD\n",
+    "var float[] hourlyOpenAM = array.new_float(12, na)  // 12 AM hours\n",
+    "var float[] hourlyOpenPM = array.new_float(12, na)  // 12 PM hours\n",
+    "\n",
+    "if barMin == 0\n",
+    "    if barHour < 12\n",
+    "        array.set(hourlyOpenAM, hour12 - 1, open)\n",
+    "    else\n",
+    "        array.set(hourlyOpenPM, hour12 - 1, open)\n",
+    "\n",
+    "    // DRAW TINY LABELS\n",
+    "    label.new(bar_index, open, str.tostring(hour12) + \" \" + ampm + \" Open: \" + str.tostring(open),\n",
+    "              style=label.style_label_up, color=color.new(color.gray,0),\n",
+    "              textcolor=color.white, size=size.tiny)\n",
+    "\n",
+    "\n",
+    "//End of Time Logic Reference"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Pinescript Folder/v5 Cummulative Delta/cummulative deta.ipynb

@@ -0,0 +1,165 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3fc37695",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "//@version=5\n",
+    "indicator(\"v5 Cumulative Delta Logic\", overlay=false, format=format.volume)\n",
+    "\n",
+    "// === INPUTS ===\n",
+    "isSummer   = input.bool(true, \"Is it Summer? (Uncheck for Winter)\", group=\"Exness Schedule\")\n",
+    "customTF   = input.timeframe(\"1\", \"Lower Timeframe for Delta Calc\", group=\"Settings\")\n",
+    "barsBack   = input.int(300, \"Bars to Calculate\", group=\"Settings\")\n",
+    "\n",
+    "// === TIME SETTINGS ===\n",
+    "timeOffset     = 8 * 60 * 60 * 1000  // Manila is UTC+8\n",
+    "manilaTime     = time + timeOffset\n",
+    "manilaHour     = hour(manilaTime)\n",
+    "manilaMinute   = minute(manilaTime)\n",
+    "manilaSecond   = second(manilaTime)\n",
+    "\n",
+    "isSaturday     = dayofweek == dayofweek.saturday\n",
+    "\n",
+    "// === RESET SESSION (6AM Manila Time for Cumulative Delta) ===\n",
+    "sessStartHour = 2\n",
+    "sessStartMin  = switch timeframe.period\n",
+    "    \"1\" => 4\n",
+    "    \"3\" => 3\n",
+    "    \"5\" => 0\n",
+    "    \"15\" => 0\n",
+    "    \"60\" => 0\n",
+    "    \"240\" => 0\n",
+    "    => 4  // fallback\n",
+    "\n",
+    "isSessionStart = (manilaHour == sessStartHour and manilaMinute == sessStartMin and not isSaturday)\n",
+    "\n",
+    "// === DELTA CALCULATION ===\n",
+    "upAndDownVolume() =>\n",
+    "    pos = close > open or (close == open and close >= close[1]) ? volume : 0.0\n",
+    "    neg = close < open or (close == open and close < close[1]) ? -volume : 0.0\n",
+    "    [pos, neg]\n",
+    "\n",
+    "[upArr, downArr] = request.security_lower_tf(syminfo.tickerid, customTF, upAndDownVolume())\n",
+    "upVol   = array.sum(upArr)\n",
+    "downVol = array.sum(downArr)\n",
+    "delta   = upVol + downVol\n",
+    "\n",
+    "// === CUMULATIVE DELTA ===\n",
+    "var float cumDelta = na\n",
+    "cumDelta := isSessionStart ? delta : nz(cumDelta[1]) + delta\n",
+    "\n",
+    "// === DELTA CANDLES ===\n",
+    "var float o = na\n",
+    "var float h = na\n",
+    "var float l = na\n",
+    "var float c = na\n",
+    "\n",
+    "o := isSessionStart ? 0 : nz(cumDelta[1])\n",
+    "h := math.max(cumDelta, nz(cumDelta[1]))\n",
+    "l := math.min(cumDelta, nz(cumDelta[1]))\n",
+    "c := cumDelta\n",
+    "\n",
+    "plotcandle(o <= c ? o : na, h, l, c, title=\"Delta Green\", color=color.green, wickcolor=color.green, bordercolor=color.green)\n",
+    "plotcandle(o >= c ? o : na, h, l, c, title=\"Delta Red\", color=color.red, wickcolor=color.red, bordercolor=color.red)\n",
+    "\n",
+    "// === BAR EXTENSION LOGIC BASED ON TIMEFRAME ===\n",
+    "getExtensionBars() =>\n",
+    "    switch timeframe.period\n",
+    "        \"1\" => 180\n",
+    "        \"3\" => 60\n",
+    "        \"5\" => 36\n",
+    "        \"15\" => 12\n",
+    "        \"30\" => 6\n",
+    "        \"60\" => 3\n",
+    "        => 20  // default\n",
+    "\n",
+    "// === DRAW HORIZONTAL LINES AT SPECIFIC MANILA HOURS ===\n",
+    "var line[] deltaLines = array.new_line()\n",
+    "\n",
+    "drawDeltaLine(hour_, color_) =>\n",
+    "    isMatch = manilaHour == hour_ and manilaMinute == 0 and manilaSecond == 0\n",
+    "    if isMatch and not na(cumDelta)\n",
+    "        extendBars = getExtensionBars()\n",
+    "        l = line.new(x1 = bar_index,y1 = cumDelta,x2 = bar_index + extendBars,y2 = cumDelta,color = color_,width = 1,style = line.style_solid,extend = extend.none)\n",
+    "        array.push(deltaLines, l)\n",
+    "\n",
+    "// === CALL FUNCTION FOR MANILA TIMES ===\n",
+    "drawDeltaLine(5, color.orange)    // 9 AM\n",
+    "drawDeltaLine(8, color.orange)    // 12 PM\n",
+    "drawDeltaLine(11, color.black)    // 3 PM\n",
+    "drawDeltaLine(14, color.teal)     // 6 PM\n",
+    "drawDeltaLine(17, color.blue)     // 9 PM\n",
+    "drawDeltaLine(20, color.fuchsia)  // 12 AM\n",
+    "drawDeltaLine(23, color.red)      // 3 AM\n",
+    "\n",
+    "// === SAFETY CHECK ===\n",
+    "var float totalVol = 0.0\n",
+    "totalVol += nz(volume)\n",
+    "if barstate.islast and totalVol == 0\n",
+    "    runtime.error(\"No volume data. Try a different symbol or lower timeframe.\")\n",
+    "\n",
+    "\n",
+    "// === Zero Threshold Line for Cumulative Delta ===\n",
+    "hline(0, \"Zero Threshold\", color=color.gray, linestyle=hline.style_solid, linewidth=1)\n",
+    "\n",
+    "\n",
+    "\n",
+    "// Input: length for periods ago\n",
+    "length = input.int(7, minval=1, title=\"VROC Period\")\n",
+    "\n",
+    "// Calculate VROC\n",
+    "vroc_raw = (volume - volume[length]) / volume[length] * 1500\n",
+    "\n",
+    "// Determine direction: Buy Side if price closed higher, Sell Side if lower\n",
+    "priceUp = close > close[length]\n",
+    "priceDown = close < close[length]\n",
+    "\n",
+    "// Separate Buy Side and Sell Side VROC\n",
+    "vroc_buy = priceUp ? vroc_raw : na\n",
+    "vroc_sell = priceDown ? vroc_raw : na\n",
+    "\n",
+    "// Plot VROC as bar-style columns\n",
+    "plot(vroc_buy, color=color.green, title=\"Buy Side VROC\", style=plot.style_columns)\n",
+    "plot(vroc_sell, color=color.red, title=\"Sell Side VROC\", style=plot.style_columns)\n",
+    "\n",
+    "// Zero line for reference\n",
+    "hline(0, \"Zero Line\", color=color.gray, linestyle=hline.style_dotted)\n",
+    "\n"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "c308f27b",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42446ccf",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Python Folder/3 tick-volume pattern hedge/save.ipynb

@@ -0,0 +1,163 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8822ff95",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import MetaTrader5 as mt5\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
+    "from datetime import datetime, timedelta\n",
+    "\n",
+    "# ----------------------------\n",
+    "# PARAMETERS\n",
+    "# ----------------------------\n",
+    "SYMBOL = \"BTCUSDc\"\n",
+    "TIMEFRAME = mt5.TIMEFRAME_D1\n",
+    "LOOKBACK_DAYS = 365 * 7  # 7 years\n",
+    "SEQ_LENGTH = 60\n",
+    "FEATURES = ['Close', 'pctB', 'RSI', 'MACD', 'Signal_Line', 'Momentum', 'ATR']\n",
+    "\n",
+    "# ----------------------------\n",
+    "# HELPER FUNCTIONS\n",
+    "# ----------------------------\n",
+    "def initialize_mt5():\n",
+    "    if not mt5.initialize():\n",
+    "        raise RuntimeError(f\"Failed to initialize MT5: {mt5.last_error()}\")\n",
+    "\n",
+    "def fetch_data(symbol, timeframe, lookback_days):\n",
+    "    utc_now = datetime.utcnow()\n",
+    "    from_date = utc_now - timedelta(days=lookback_days)\n",
+    "    rates = mt5.copy_rates_range(symbol, timeframe, from_date, utc_now)\n",
+    "    if rates is None or len(rates) == 0:\n",
+    "        raise RuntimeError(f\"No data retrieved for {symbol}\")\n",
+    "    df = pd.DataFrame(rates)\n",
+    "    df['time'] = pd.to_datetime(df['time'], unit='s')\n",
+    "    df.set_index('time', inplace=True)\n",
+    "    return df\n",
+    "\n",
+    "def compute_indicators(df):\n",
+    "    df['MA20'] = df['close'].rolling(20).mean()\n",
+    "    df['MA50'] = df['close'].rolling(50).mean()\n",
+    "    df['STD'] = df['close'].rolling(20).std()\n",
+    "    df['Upper_Band'] = df['MA20'] + (df['STD'] * 2.5)\n",
+    "    df['Lower_Band'] = df['MA20'] - (df['STD'] * 2.5)\n",
+    "    df['pctB'] = (df['close'] - df['Lower_Band']) / (df['Upper_Band'] - df['Lower_Band'])\n",
+    "\n",
+    "    delta = df['close'].diff()\n",
+    "    up = delta.clip(lower=0)\n",
+    "    down = -delta.clip(upper=0)\n",
+    "    roll_up = up.rolling(14).mean()\n",
+    "    roll_down = down.rolling(14).mean()\n",
+    "    RS = roll_up / roll_down\n",
+    "    df['RSI'] = 100.0 - (100.0 / (1.0 + RS))\n",
+    "\n",
+    "    exp1 = df['close'].ewm(span=12, adjust=False).mean()\n",
+    "    exp2 = df['close'].ewm(span=26, adjust=False).mean()\n",
+    "    df['MACD'] = exp1 - exp2\n",
+    "    df['Signal_Line'] = df['MACD'].ewm(span=9, adjust=False).mean()\n",
+    "\n",
+    "    df['Momentum'] = df['close'] - df['close'].shift(10)\n",
+    "    df['TR'] = df[['high','close']].max(axis=1) - df[['low','close']].min(axis=1)\n",
+    "    df['ATR'] = df['TR'].rolling(14).mean()\n",
+    "\n",
+    "    df.dropna(inplace=True)\n",
+    "    return df\n",
+    "\n",
+    "def generate_signals(df):\n",
+    "    # Simple backtest logic: Buy/Sell signals based on predicted vs RSI thresholds\n",
+    "    df['Predicted_Change'] = df['close'].pct_change().shift(-1)  # naive predictor: next close % change\n",
+    "    rsi_buy = df['RSI'].quantile(0.4)\n",
+    "    rsi_sell = df['RSI'].quantile(0.6)\n",
+    "    pred_buy = df['Predicted_Change'].quantile(0.6)\n",
+    "    pred_sell = df['Predicted_Change'].quantile(0.4)\n",
+    "    df['Signal'] = 0\n",
+    "    df.loc[(df['Predicted_Change'] > pred_buy) & (df['RSI'] < rsi_buy), 'Signal'] = 1\n",
+    "    df.loc[(df['Predicted_Change'] < pred_sell) & (df['RSI'] > rsi_sell), 'Signal'] = -1\n",
+    "    return df\n",
+    "\n",
+    "def backtest(df, initial_capital=500.0, transaction_cost=0.0005, stop_loss_pct=0.1, take_profit_pct=0.2):\n",
+    "    cash = initial_capital\n",
+    "    holdings = 0\n",
+    "    entry_price = None\n",
+    "    positions = []\n",
+    "    portfolio_value = []\n",
+    "\n",
+    "    for idx, row in df.iterrows():\n",
+    "        price = row['close']\n",
+    "        signal = row['Signal']\n",
+    "\n",
+    "        # Enter long\n",
+    "        if signal == 1 and cash > 0:\n",
+    "            amount = cash * 0.5 * (1 - transaction_cost)\n",
+    "            holdings += amount / price\n",
+    "            cash -= amount\n",
+    "            entry_price = price\n",
+    "            positions.append({'Date': str(idx), 'Position': 'Buy', 'Price': price})\n",
+    "\n",
+    "        # Exit long\n",
+    "        elif signal == -1 and holdings > 0:\n",
+    "            cash += holdings * price * (1 - transaction_cost)\n",
+    "            holdings = 0\n",
+    "            entry_price = None\n",
+    "            positions.append({'Date': str(idx), 'Position': 'Sell', 'Price': price})\n",
+    "\n",
+    "        # Stop loss / take profit\n",
+    "        elif holdings > 0 and entry_price:\n",
+    "            if price <= entry_price * (1 - stop_loss_pct):\n",
+    "                cash += holdings * price * (1 - transaction_cost)\n",
+    "                holdings = 0\n",
+    "                entry_price = None\n",
+    "                positions.append({'Date': str(idx), 'Position': 'Stop Loss', 'Price': price})\n",
+    "            elif price >= entry_price * (1 + take_profit_pct):\n",
+    "                cash += holdings * price * (1 - transaction_cost)\n",
+    "                holdings = 0\n",
+    "                entry_price = None\n",
+    "                positions.append({'Date': str(idx), 'Position': 'Take Profit', 'Price': price})\n",
+    "\n",
+    "        total_val = cash + holdings * price\n",
+    "        portfolio_value.append(total_val)\n",
+    "\n",
+    "    df['Portfolio_Value'] = portfolio_value\n",
+    "    df['Daily_Return'] = df['Portfolio_Value'].pct_change()\n",
+    "    df['Cumulative_Return'] = (1 + df['Daily_Return'].fillna(0)).cumprod()\n",
+    "    total_return = (df['Portfolio_Value'].iloc[-1] - initial_capital) / initial_capital * 100\n",
+    "    return df, positions, total_return\n",
+    "\n",
+    "# ----------------------------\n",
+    "# MAIN EXECUTION\n",
+    "# ----------------------------\n",
+    "if __name__ == \"__main__\":\n",
+    "    initialize_mt5()\n",
+    "    df = fetch_data(SYMBOL, TIMEFRAME, LOOKBACK_DAYS)\n",
+    "    df = compute_indicators(df)\n",
+    "    df = generate_signals(df)\n",
+    "    df, positions, total_return = backtest(df)\n",
+    "\n",
+    "    print(\"Backtest complete!\")\n",
+    "    print(f\"Total return: {total_return:.2f}%\")\n",
+    "    print(\"Positions executed:\")\n",
+    "    for pos in positions[-5:]:  # last 5 positions\n",
+    "        print(pos)\n",
+    "\n",
+    "    mt5.shutdown()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Python Folder/All country's Total Debt/Part2/update 2.ipynb

@@ -0,0 +1,123 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f331d5f9",
+   "metadata": {},
+   "source": [
+    "import requests\n",
+    "from bs4 import BeautifulSoup\n",
+    "import pandas as pd\n",
+    "import plotly.graph_objects as go\n",
+    "import plotly.express as px\n",
+    "import re\n",
+    "\n",
+    "# =========================\n",
+    "# USER SETTINGS\n",
+    "# =========================\n",
+    "USER_COUNTRY = \"Philippines\"\n",
+    "USER_COLOR = \"teal\"\n",
+    "\n",
+    "HIGHLIGHT_COUNTRY = \"Japan\"\n",
+    "HIGHLIGHT_COLOR = \"orange\"\n",
+    "\n",
+    "# =========================\n",
+    "# FETCH NATIONAL DEBT DATA\n",
+    "# =========================\n",
+    "url = \"https://worldpopulationreview.com/country-rankings/countries-by-national-debt\"\n",
+    "html = requests.get(url).text\n",
+    "soup = BeautifulSoup(html, \"html.parser\")\n",
+    "table = soup.find(\"table\")\n",
+    "rows = table.find_all(\"tr\")[1:]\n",
+    "\n",
+    "DEBT_MULT = {\"T\": 1e12, \"B\": 1e9, \"M\": 1e6}\n",
+    "records = []\n",
+    "\n",
+    "for row in rows:\n",
+    "    cols = row.find_all(\"td\")\n",
+    "    if len(cols) < 5:\n",
+    "        continue\n",
+    "\n",
+    "    country = cols[1].text.strip()\n",
+    "    if country.upper() == \"TOTAL\":\n",
+    "        continue\n",
+    "\n",
+    "    debt_text = cols[2].text.strip()\n",
+    "    gdp_pct_text = cols[3].text.strip()\n",
+    "    per_capita_text = cols[4].text.strip()\n",
+    "\n",
+    "    m = re.match(r\"\\$(\\d+(\\.\\d+)?)([TBM])\", debt_text)\n",
+    "    if not m:\n",
+    "        continue\n",
+    "\n",
+    "    debt_val = float(m.group(1))\n",
+    "    debt_unit = m.group(3)\n",
+    "    debt_usd = int(debt_val * DEBT_MULT[debt_unit])\n",
+    "\n",
+    "    debt_pct_gdp = float(gdp_pct_text.replace(\"%\", \"\")) if \"%\" in gdp_pct_text else None\n",
+    "\n",
+    "    pc = None\n",
+    "    pc_text = per_capita_text.replace(\"$\", \"\").replace(\",\", \"\").strip()\n",
+    "    pc_match = re.match(r\"(\\d+(\\.\\d+)?)\\s*(Mn|Bn)?\", pc_text)\n",
+    "    if pc_match:\n",
+    "        v = float(pc_match.group(1))\n",
+    "        u = pc_match.group(3)\n",
+    "        if u == \"Mn\":\n",
+    "            pc = int(v * 1_000_000)\n",
+    "        elif u == \"Bn\":\n",
+    "            pc = int(v * 1_000_000_000)\n",
+    "        else:\n",
+    "            pc = int(v)\n",
+    "\n",
+    "    records.append({\n",
+    "        \"Country\": country,\n",
+    "        \"Debt Label\": debt_text,\n",
+    "        \"Debt USD\": debt_usd,\n",
+    "        \"Debt % GDP\": debt_pct_gdp,\n",
+    "        \"Debt Per Capita\": pc\n",
+    "    })\n",
+    "\n",
+    "df = pd.DataFrame(records)\n",
+    "\n",
+    "# =========================\n",
+    "# HELPERS\n",
+    "# =========================\n",
+    "def format_value_units(val, metric):\n",
+    "    if metric == \"Debt % GDP\":\n",
+    "        return f\"{val}%\"\n",
+    "    if val >= 1e12:\n",
+    "        return f\"${val/1e12:.2f} T\"\n",
+    "    if val >= 1e9:\n",
+    "        return f\"${val/1e9:.2f} B\"\n",
+    "    if val >= 1e6:\n",
+    "        return f\"${val/1e6:.2f} M\"\n",
+    "    return f\"${val:,}\"\n",
+    "\n",
+    "def create_rank_df(df, col):\n",
+    "    d = df.dropna(subset=[col]).sort_values(col, ascending=False).reset_index(drop=True)\n",
+    "    d[\"Rank\"] = d.index + 1\n",
+    "    return d.sort_values(col)\n",
+    "\n",
+    "def bar_colors(d):\n",
+    "    return [\n",
+    "        USER_COLOR if c == USER_COUNTRY\n",
+    "        else HIGHLIGHT_COLOR if c == HIGHLIGHT_COUNTRY\n",
+    "        else \"#1f77b4\"\n",
+    "        for c in d[\"Country\"]\n",
+    "    ]\n",
+    "\n",
+    "def text_colors(d):\n",
+    "    return [\n",
+    "        USER_COLOR if c == USER_COUNTRY\n",
+    "        else HIGHLIG\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Python Folder/Bubble Orders/xau_bubbles_zones.xls

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Python Folder/Candlestick, Tick Volume, Range, Body , Wick/promting attempts.ipynb

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