📊 52-Week Price % Distance (Advanced Table)This TradingView Pine Script displays a compact, informative table showing how far the current price is from the 52-week high and low, expressed as percentages.
Sentiment
Top Crypto Above 28-Day AverageDescription
The “Top Crypto Above 28-Day Average” (CRYPTOTW) script scans a selectable universe of up to 120 top-capitalization cryptocurrencies (divided into customizable 40-symbol batches), then plots the count of those trading above their own 28-period simple moving average. It helps you gauge broad market strength and identify which tokens are showing momentum relative to their recent trend.
Key Features
• Batch Selection: Choose among “Top40,” “Mid40,” or “Low40” market-cap groups, or set a custom batch size (up to 40 symbols) to keep within the API limit.
• Dynamic Plot: Displays a live line chart of how many cryptos are above their 28-day MA on each bar.
• Reference Lines: Automatic horizontal lines at 25%, 50%, and 75% of your batch to provide quick visual thresholds.
• Background Coloration: The chart background shifts green/yellow/red based on whether more than 70%, 50–70%, or under 50% of the batch is above the MA.
• Optional Table: On the final bar, show a sortable table of up to 28 tickers currently above their 28-day MA, including current price, percent above MA, and “Above” status color-coding.
• Alerts:
• Strong Batch Performance: Fires when >70% of the batch is above the MA.
• Weak Batch Performance: Fires when <10 cryptos (i.e. <25%) are above the MA.
Inputs
• Show Results Table (show_table): Toggle the detailed table on/off.
• Table Position (table_position): Select one of the four corners for your table overlay.
• Max Cryptos to Display (max_display): Limit the number of rows in the results table.
• Current Batch (current_batch): Pick “Top40,” “Mid40,” or “Low40.”
• Batch Size (batch_size): Define the number of symbols (1–40) you want to include from the chosen batch.
How to Use
1. Add the CRYPTOTW indicator to any chart.
2. Select your batch and size to focus on the segment of the crypto market you follow.
3. Watch the plotted line to see the proportion of tokens with bullish momentum.
4. (Optional) Enable the results table to see exactly which tokens are outperforming their 28-day average.
5. Set alerts to be notified when the batch either overheats (strong performance) or cools off significantly.
Why It Matters
By tracking the share of assets riding their 28-day trend, you gain a macro-level view of market breadth—crucial for spotting emerging rallies or early signs of broad weakness. Whether you’re swing-trading individual altcoins or assessing overall market mood, this tool distills complex data into an intuitive, actionable signal.
SNIPERKILLS NQ JULY 18 2025, GAMEPLANNQ GAME PLAN JULY 18, 2025!
✅ Bullish Scenario
Condition: Price breaks and holds above 23,279.75
Targets:
🎯 Target 1: 23,320 — minor imbalance / reaction zone
🎯 Target 2: 23,375 — potential liquidity sweep
🎯 Target 3: 23,420 — psychological level / extended move
Stop Loss: Below 23,234.25 (Short Trigger / invalidation)
❌ Bearish Scenario
Condition: Price breaks and holds below 23,234.25
Targets:
🎯 Target 1: 23,200 — FVG or intraday demand
🎯 Target 2: 23,150 — mid-range flush target
🎯 Target 3: 23,017 — prior day’s low & major liquidity zone
Stop Loss: Above 23,279.75 (Long Trigger / invalidation)
RSI with 2-Pole FilterA momentum indicator that tells you if a stock is overbought or oversold.
RSI goes between 0 and 100.
70 = overbought (might fall)
<30 = oversold (might rise)
It often looks jagged or choppy on volatile days.
Think of this filter like a momentum smoother:
It still follows RSI closely,
But it doesn’t react to every little jiggle in price,
Which helps avoid false signals.
it keeps track of:
The current RSI,
The last 2 RSI values (inputs), and
The last 2 outputs (filtered RSIs).
It uses feedback to shape the output based on previous values, making it smoother than a simple moving average.
Ultra BUY SELL//@version=5
indicator("Ultra BUY SELL", overlay = false)
// Inputs
src = input(close, "Source", group = "Main settings")
p = input.int(180, "Trend period", group = "Main settings", tooltip = "Changes STRONG signals' sensitivity.", minval = 1)
atr_p = input.int(155, "ATR Period", group = "Main settings", minval = 1)
mult = input.float(2.1, "ATR Multiplier", step = 0.1, group = "Main settings", tooltip = "Changes sensitivity: higher period = higher sensitivty.")
mode = input.string("Type A", "Signal mode", options = , group = "Mode")
use_ema_smoother = input.string("No", "Smooth source with EMA?", options = , group = "Source")
src_ema_period = input(3, "EMA Smoother period", group = "Source")
color_bars = input(true, "Color bars?", group = "Addons")
signals_view = input.string("All", "Signals to show", options = , group = "Signal's Addon")
signals_shape = input.string("Labels", "Signal's shape", options = , group = "Signal's Addon")
buy_col = input(color.rgb(0, 255, 8), "Buy colour", group = "Signal's Addon", inline = "BS")
sell_col = input(color.rgb(255, 0, 0), "Sell colour", group = "Signal's Addon", inline = "BS")
// Calculations
src := use_ema_smoother == "Yes" ? ta.ema(src, src_ema_period) : src
// Source;
h = ta.highest(src, p)
// Highest of src p-bars back;
l = ta.lowest(src, p)
// Lowest of src p-bars back.
d = h - l
ls = ""
// Tracker of last signal
m = (h + l) / 2
// Initial trend line;
m := bar_index > p ? m : m
atr = ta.atr(atr_p)
// ATR;
epsilon = mult * atr
// Epsilon is a mathematical variable used in many different theorems in order to simplify work with mathematical object. Here it used as sensitivity measure.
change_up = (mode == "Type B" ? ta.cross(src, m + epsilon) : ta.crossover(src, m + epsilon)) or src > m + epsilon
// If price breaks trend line + epsilon (so called higher band), then it is time to update the value of a trend line;
change_down = (mode == "Type B" ? ta.cross(src, m - epsilon) : ta.crossunder(src, m - epsilon)) or src < m - epsilon
// If price breaks trend line - epsilon (so called higher band), then it is time to update the value of a trend line.
sb = open < l + d / 8 and open >= l
ss = open > h - d / 8 and open <= h
strong_buy = sb or sb or sb or sb or sb
strong_sell = ss or ss or ss or ss or ss
m := (change_up or change_down) and m != m ? m : change_up ? m + epsilon : change_down ? m - epsilon : nz(m , m)
// Updating the trend line.
ls := change_up ? "B" : change_down ? "S" : ls
// Last signal. Helps avoid multiple labels in a row with the same signal;
colour = ls == "B" ? buy_col : sell_col
// Colour of the trend line.
buy_shape = signals_shape == "Labels" ? shape.labelup : shape.triangleup
sell_shape = signals_shape == "Labels" ? shape.labeldown : shape.triangledown
// Plottings
// Signals with label shape
plotshape(signals_shape == "Labels" and (signals_view == "All" or signals_view == "Buy/Sell") and change_up and ls != "B" and not strong_buy, "Buy signal" , color = colour, style = buy_shape , location = location.belowbar, size = size.normal, text = "BUY", textcolor = color.white, force_overlay=true)
// Plotting the BUY signal;
plotshape(signals_shape == "Labels" and (signals_view == "All" or signals_view == "Buy/Sell") and change_down and ls != "S" and not strong_sell, "Sell signal" , color = colour, style = sell_shape, size = size.normal, text = "SELL", textcolor = color.white, force_overlay=true)
// Plotting the SELL signal.
plotshape(signals_shape == "Labels" and (signals_view == "All" or signals_view == "Strong") and change_up and ls != "B" and strong_buy, "Strong Buy signal" , color = colour, style = buy_shape , location = location.belowbar, size = size.normal, text = "STRONG", textcolor = color.white, force_overlay=true)
// Plotting the STRONG BUY signal;
plotshape(signals_shape == "Labels" and (signals_view == "All" or signals_view == "Strong") and change_down and ls != "S" and strong_sell, "Strong Sell signal" , color = colour, style = sell_shape, size = size.normal, text = "STRONG", textcolor = color.white, force_overlay=true)
// Plotting the STRONG SELL signal.
// Signal with arrow shape
plotshape(signals_shape == "Arrows" and (signals_view == "All" or signals_view == "Buy/Sell") and change_up and ls != "B" and not strong_buy, "Buy signal" , color = colour, style = buy_shape , location = location.belowbar, size = size.tiny, force_overlay=true)
// Plotting the BUY signal;
plotshape(signals_shape == "Arrows" and (signals_view == "All" or signals_view == "Buy/Sell") and change_down and ls != "S" and not strong_sell, "Sell signal" , color = colour, style = sell_shape, size = size.tiny, force_overlay=true)
// Plotting the SELL signal.
plotshape(signals_shape == "Arrows" and (signals_view == "All" or signals_view == "Strong") and change_up and ls != "B" and strong_buy, "Strong Buy signal" , color = colour, style = buy_shape , location = location.belowbar, size = size.tiny, force_overlay=true)
// Plotting the STRONG BUY signal;
plotshape(signals_shape == "Arrows" and (signals_view == "All" or signals_view == "Strong") and change_down and ls != "S" and strong_sell, "Strong Sell signal" , color = colour, style = sell_shape, size = size.tiny, force_overlay=true)
// Plotting the STRONG SELL signal.
barcolor(color_bars ? colour : na)
// Bar coloring
// Alerts
matype = input.string(title='MA Type', defval='EMA', options= )
ma_len1 = input(title='Short EMA1 Length', defval=5)
ma_len2 = input(title='Long EMA1 Length', defval=7)
ma_len3 = input(title='Short EMA2 Length', defval=5)
ma_len4 = input(title='Long EMA2 Length', defval=34)
ma_len5 = input(title='Short EMA3 Length', defval=98)
ma_len6 = input(title='Long EMA3 Length', defval=45)
ma_len7 = input(title='Short EMA4 Length', defval=7)
ma_len8 = input(title='Long EMA4 Length', defval=11)
ma_len9 = input(title='Short EMA5 Length', defval=11)
ma_len10 = input(title='Long EMA5 Length', defval=15)
ma_offset = input(title='Offset', defval=0)
//res = input(title="Resolution", type=resolution, defval="240")
f_ma(malen) =>
float result = 0
if matype == 'EMA'
result := ta.ema(src, malen)
result
if matype == 'SMA'
result := ta.sma(src, malen)
result
result
htf_ma1 = f_ma(ma_len1)
htf_ma2 = f_ma(ma_len2)
htf_ma3 = f_ma(ma_len3)
htf_ma4 = f_ma(ma_len4)
htf_ma5 = f_ma(ma_len5)
htf_ma6 = f_ma(ma_len6)
htf_ma7 = f_ma(ma_len7)
htf_ma8 = f_ma(ma_len8)
htf_ma9 = f_ma(ma_len9)
htf_ma10 = f_ma(ma_len10)
//plot(out1, color=green, offset=ma_offset)
//plot(out2, color=red, offset=ma_offset)
//lengthshort = input(8, minval = 1, title = "Short EMA Length")
//lengthlong = input(200, minval = 2, title = "Long EMA Length")
//emacloudleading = input(50, minval = 0, title = "Leading Period For EMA Cloud")
//src = input(hl2, title = "Source")
showlong = input(false, title='Show Long Alerts')
showshort = input(false, title='Show Short Alerts')
showLine = input(false, title='Display EMA Line')
ema1 = input(true, title='Show EMA Cloud-1')
ema2 = input(true, title='Show EMA Cloud-2')
ema3 = input(true, title='Show EMA Cloud-3')
ema4 = input(true, title='Show EMA Cloud-4')
ema5 = input(true, title='Show EMA Cloud-5')
emacloudleading = input.int(0, minval=0, title='Leading Period For EMA Cloud')
mashort1 = htf_ma1
malong1 = htf_ma2
mashort2 = htf_ma3
malong2 = htf_ma4
mashort3 = htf_ma5
malong3 = htf_ma6
mashort4 = htf_ma7
malong4 = htf_ma8
mashort5 = htf_ma9
malong5 = htf_ma10
cloudcolour1 = mashort1 >= malong1 ? color.rgb(0, 255, 0) : color.rgb(255, 0, 0)
cloudcolour2 = mashort2 >= malong2 ? #4caf4f47 : #ff110047
cloudcolour4 = mashort4 >= malong4 ? #4caf4f52 : #f2364652
cloudcolour5 = mashort5 >= malong5 ? #33ff0026 : #ff000026
//03abc1
mashortcolor1 = mashort1 >= mashort1 ? color.olive : color.maroon
mashortcolor2 = mashort2 >= mashort2 ? color.olive : color.maroon
mashortcolor3 = mashort3 >= mashort3 ? color.olive : color.maroon
mashortcolor4 = mashort4 >= mashort4 ? color.olive : color.maroon
mashortcolor5 = mashort5 >= mashort5 ? color.olive : color.maroon
mashortline1 = plot(ema1 ? mashort1 : na, color=showLine ? mashortcolor1 : na, linewidth=1, offset=emacloudleading, title='Short Leading EMA1', force_overlay=true)
mashortline2 = plot(ema2 ? mashort2 : na, color=showLine ? mashortcolor2 : na, linewidth=1, offset=emacloudleading, title='Short Leading EMA2', force_overlay=true)
mashortline3 = plot(ema3 ? mashort3 : na, color=showLine ? mashortcolor3 : na, linewidth=1, offset=emacloudleading, title='Short Leading EMA3', force_overlay=true)
mashortline4 = plot(ema4 ? mashort4 : na, color=showLine ? mashortcolor4 : na, linewidth=1, offset=emacloudleading, title='Short Leading EMA4', force_overlay=true)
mashortline5 = plot(ema5 ? mashort5 : na, color=showLine ? mashortcolor5 : na, linewidth=1, offset=emacloudleading, title='Short Leading EMA5', force_overlay=true)
malongcolor1 = malong1 >= malong1 ? color.green : color.red
malongcolor2 = malong2 >= malong2 ? color.green : color.red
malongcolor3 = malong3 >= malong3 ? color.green : color.red
malongcolor4 = malong4 >= malong4 ? color.green : color.red
malongcolor5 = malong5 >= malong5 ? color.green : color.red
malongline1 = plot(ema1 ? malong1 : na, color=showLine ? malongcolor1 : na, linewidth=3, offset=emacloudleading, title='Long Leading EMA1', force_overlay=true)
malongline2 = plot(ema2 ? malong2 : na, color=showLine ? malongcolor2 : na, linewidth=3, offset=emacloudleading, title='Long Leading EMA2', force_overlay=true)
malongline3 = plot(ema3 ? malong3 : na, color=showLine ? malongcolor3 : na, linewidth=3, offset=emacloudleading, title='Long Leading EMA3', force_overlay=true)
malongline4 = plot(ema4 ? malong4 : na, color=showLine ? malongcolor4 : na, linewidth=3, offset=emacloudleading, title='Long Leading EMA4', force_overlay=true)
malongline5 = plot(ema5 ? malong5 : na, color=showLine ? malongcolor5 : na, linewidth=3, offset=emacloudleading, title='Long Leading EMA5', force_overlay=true)
fill(mashortline1, malongline1, color=cloudcolour1, title='MA Cloud1', transp=45)
fill(mashortline2, malongline2, color=cloudcolour2, title='MA Cloud2', transp=65)
fill(mashortline4, malongline4, color=cloudcolour4, title='MA Cloud4', transp=65)
fill(mashortline5, malongline5, color=cloudcolour5, title='MA Cloud5', transp=65)
leftBars = input(15, title='Left Bars ')
rightBars = input(15, title='Right Bars')
volumeThresh = input(20, title='Volume Threshold')
//
highUsePivot = fixnan(ta.pivothigh(leftBars, rightBars) )
lowUsePivot = fixnan(ta.pivotlow(leftBars, rightBars) )
r1 = plot(highUsePivot, color=ta.change(highUsePivot) ? na : #FF0000, linewidth=3, offset=-(rightBars + 1), title='Resistance', force_overlay=true)
s1 = plot(lowUsePivot, color=ta.change(lowUsePivot) ? na : #00ff0d, linewidth=3, offset=-(rightBars + 1), title='Support', force_overlay=true)
//Volume %
short = ta.ema(volume, 5)
long = ta.ema(volume, 10)
osc = 100 * (short - long) / long
//For bull / bear wicks
// This Pine Script™ code is subject to the terms of the Mozilla Public License 2.0 at mozilla.org
// © divudivu600
// Developer By ALCON ALGO
//telegram : @harmonicryptosignals
//@version = 5
//indicator(shorttitle='Oscillator Vision', title='Alcon Oscillator Vision', overlay=false)
n1 = input(10, 'Channel length')
n2 = input(21, 'Average length')
reaction_wt = input.int(defval=1, title='Reaction in change of direction', minval=1)
nsc = input.float(53, 'Levels About Buys', minval=0.0)
nsv = input.float(-53, 'Levels About Sells', maxval=-0.0)
Buy_sales = input(true, title='Only Smart Buy Reversal')
Sell_sales = input(true, title='Only Smart Sell Reversal')
Histogram = input(true, title='Show Histogarm')
//Trendx = input(false, title='Show Trendx')
barras = input(true, title='Divergence on chart(Bars)')
divregbull = input(true, title='Regular Divergence Bullish')
divregbear = input(true, title='Regular Divergence Bearish')
divhidbull = input(true, title='Show Divergence Hidden Bullish')
divhidbear = input(true, title='Show Divergence Hidden Bearish')
Tags = input(true, title='Show Divergence Lable')
amme = input(false, title='Activar media movil Extra para WT')
White = #FDFEFE
Black = #000000
Bearish = #e91e62
Bullish = #18e0ff
Strong_Bullish = #2962ff
Bullish2 = #00bedc
Blue1 = #00D4FF
Blue2 = #009BBA
orange = #FF8B00
yellow = #FFFB00
LEZ = #0066FF
purp = #FF33CC
// Colouring
tf(_res, _exp, gaps_on) =>
gaps_on == 0 ? request.security(syminfo.tickerid, _res, _exp) : gaps_on == true ? request.security(syminfo.tickerid, _res, _exp, barmerge.gaps_on, barmerge.lookahead_off) : request.security(syminfo.tickerid, _res, _exp, barmerge.gaps_off, barmerge.lookahead_off)
ha_htf = ''
show_ha = input.bool(true, "Show HA Plot/ Market Bias", group="HA Market Bias")
ha_len = input(7, 'Period', group="HA Market Bias")
ha_len2 = input(10, 'Smoothing', group="HA Market Bias")
// Calculations {
o = ta.ema(open, ha_len)
c = ta.ema(close, ha_len)
h1 = ta.ema(high, ha_len)
l1 = ta.ema(low, ha_len)
haclose = tf(ha_htf, (o + h1 + l1 + c) / 4, 0)
xhaopen = tf(ha_htf, (o + c) / 2, 0)
haopen = na(xhaopen ) ? (o + c) / 2 : (xhaopen + haclose ) / 2
hahigh = math.max(h1, math.max(haopen, haclose))
halow = math.min(l1, math.min(haopen, haclose))
o2 = tf(ha_htf, ta.ema(haopen, ha_len2), 0)
c2 = tf(ha_htf, ta.ema(haclose, ha_len2), 0)
h2 = tf(ha_htf, ta.ema(hahigh, ha_len2), 0)
l2 = tf(ha_htf, ta.ema(halow, ha_len2), 0)
ha_avg = (h2 + l2) / 2
// }
osc_len = 8
osc_bias = 100 *(c2 - o2)
osc_smooth = ta.ema(osc_bias, osc_len)
sigcolor =
(osc_bias > 0) and (osc_bias >= osc_smooth) ? color.new(Bullish, 35) :
(osc_bias > 0) and (osc_bias < osc_smooth) ? color.new(Bullish2, 75) :
(osc_bias < 0) and (osc_bias <= osc_smooth) ? color.new(Bearish, 35) :
(osc_bias < 0) and (osc_bias > osc_smooth) ? color.new(Bearish, 75) :
na
// }
nsc1 = nsc
nsc2 = nsc + 5
nsc3 = nsc + 10
nsc4 = nsc + 15
nsc5 = nsc + 20
nsc6 = nsc + 25
nsc7 = nsc + 30
nsc8 = nsc + 35
nsv1 = nsv - 5
nsv2 = nsv - 10
nsv3 = nsv - 15
nsv4 = nsv - 20
nsv5 = nsv - 25
nsv6 = nsv - 30
nsv7 = nsv - 35
nsv8 = nsv - 40
ap = hlc3
esa = ta.ema(ap, n1)
di = ta.ema(math.abs(ap - esa), n1)
ci = (ap - esa) / (0.015 * di)
tci = ta.ema(ci, n2)
wt1 = tci
wt2 = ta.sma(wt1, 4)
direction = 0
direction := ta.rising(wt1, reaction_wt) ? 1 : ta.falling(wt1, reaction_wt) ? -1 : nz(direction )
Change_of_direction = ta.change(direction, 1)
pcol = direction > 0 ? Strong_Bullish : direction < 0 ? Bearish : na
obLevel1 = input(60, 'Over Bought Level 1')
obLevel2 = input(53, 'Over Bought Level 2')
osLevel1 = input(-60, 'Over Sold Level 1')
osLevel2 = input(-53, 'Over Sold Level 2')
rsi = ta.rsi(close,14)
color greengrad = color.from_gradient(rsi, 10, 90, #00ddff, #007d91)
color redgrad = color.from_gradient(rsi, 10, 90, #8b002e, #e91e62)
ob1 = plot(obLevel1, color=#e91e6301)
os1 = plot(osLevel1, color=#00dbff01)
ob2 = plot(obLevel2, color=#e91e6301)
os2 = plot(osLevel2, color=#00dbff01)
p1 = plot(wt1, color=#00dbff01)
p2 = plot(wt2, color=#e91e6301)
plot(wt1 - wt2, color=wt2 - wt1 > 0 ? redgrad : greengrad, style=plot.style_columns)
// fill(p1,p2,color = wt2 - wt1 > 0 ? redgrad: greengrad) // old
fill(p1,p2,color = sigcolor)
// new
fill(ob1,ob2,color = #e91e6350)
fill(os1,os2,color = #00dbff50)
midpoint = (nsc + nsv) / 2
ploff = (nsc - midpoint) / 8
BullSale = ta.crossunder(wt1, wt2) and wt1 >= nsc and Buy_sales == true
BearSale = ta.crossunder(wt1, wt2) and Buy_sales == false
Bullishh = ta.crossover(wt1, wt2) and wt1 <= nsv and Sell_sales == true
Bearishh = ta.crossover(wt1, wt2) and Sell_sales == false
plot(BullSale ? wt2 + ploff : na, style=plot.style_circles, color=color.new(Bearish, 0), linewidth=6, title='BuysG')
plot(BearSale ? wt2 + ploff : na, style=plot.style_circles, color=color.new(Bearish, 0), linewidth=6, title='SellsG')
plot(Bullishh ? wt2 - ploff : na, style=plot.style_circles, color=color.new(Strong_Bullish, 0), linewidth=6, title='Buys On Sale')
plot(Bearishh ? wt2 - ploff : na, style=plot.style_circles, color=color.new(Strong_Bullish, 0), linewidth=6, title='Sells on Sale')
//plot(Histogram ? wt1 - wt2 : na, style=plot.style_area, color=color.new(Blue2, 80), linewidth=1, title='Histograma')
//barcolor(barras == true and Bullishh == true or barras == true and Bearishh == true ? Bullish2 : na)
//barcolor(barras == true and BullSale == true or barras == true and BearSale == true ? Bearish : na)
/////// Divergence ///////
f_top_fractal(_src) =>
_src < _src and _src < _src and _src > _src and _src > _src
f_bot_fractal(_src) =>
_src > _src and _src > _src and _src < _src and _src < _src
f_fractalize(_src) =>
f_top_fractal(_src) ? 1 : f_bot_fractal(_src) ? -1 : 0
fractal_top1 = f_fractalize(wt1) > 0 ? wt1 : na
fractal_bot1 = f_fractalize(wt1) < 0 ? wt1 : na
high_prev1 = ta.valuewhen(fractal_top1, wt1 , 0)
high_price1 = ta.valuewhen(fractal_top1, high , 0)
low_prev1 = ta.valuewhen(fractal_bot1, wt1 , 0)
low_price1 = ta.valuewhen(fractal_bot1, low , 0)
regular_bearish_div1 = fractal_top1 and high > high_price1 and wt1 < high_prev1 and divregbear == true
hidden_bearish_div1 = fractal_top1 and high < high_price1 and wt1 > high_prev1 and divhidbear == true
regular_bullish_div1 = fractal_bot1 and low < low_price1 and wt1 > low_prev1 and divregbull == true
hidden_bullish_div1 = fractal_bot1 and low > low_price1 and wt1 < low_prev1 and divhidbull == true
col1 = regular_bearish_div1 ? Bearish : hidden_bearish_div1 ? Bearish : na
col2 = regular_bullish_div1 ? Strong_Bullish : hidden_bullish_div1 ? Strong_Bullish : na
//plot(title='Divergence Bearish', series=fractal_top1 ? wt1 : na, color=col1, linewidth=2, transp=0)
//plot(title='Divergence Bullish', series=fractal_bot1 ? wt1 : na, color=col2, linewidth=2, transp=0)
plotshape(regular_bearish_div1 and divregbear and Tags ? wt1 + ploff * 1 : na, title='Divergence Regular Bearish', text='Bear', location=location.absolute, style=shape.labeldown, size=size.tiny, color=color.new(Bearish, 0), textcolor=color.new(White, 0))
plotshape(hidden_bearish_div1 and divhidbear and Tags ? wt1 + ploff * 1 : na, title='Divergence Hidden Bearish', text='H Bear', location=location.absolute, style=shape.labeldown, size=size.tiny, color=color.new(Bearish, 0), textcolor=color.new(White, 0))
plotshape(regular_bullish_div1 and divregbull and Tags ? wt1 - ploff * 1 : na, title='Divergence Regular Bullish', text='Bull', location=location.absolute, style=shape.labelup, size=size.tiny, color=color.new(Strong_Bullish, 0), textcolor=color.new(White, 0))
plotshape(hidden_bullish_div1 and divhidbull and Tags ? wt1 - ploff * 1 : na, title='Divergence Hidden Bullish', text='H Bull', location=location.absolute, style=shape.labelup, size=size.tiny, color=color.new(Strong_Bullish, 0), textcolor=color.new(White, 0))
/////// Unfazed Alerts //////
////////////////////////////////////////////////-MISTERMOTA MOMENTUM-/////////////////////////////////////
source = input(close)
responsiveness = math.max(0.00001, input.float(0.9, minval=0.0, maxval=1.0))
periodd = input(50)
sd = ta.stdev(source, 50) * responsiveness
var worm = source
diff = source - worm
delta = math.abs(diff) > sd ? math.sign(diff) * sd : diff
worm += delta
ma = ta.sma(source, periodd)
raw_momentum = (worm - ma) / worm
current_med = raw_momentum
min_med = ta.lowest(current_med, periodd)
max_med = ta.highest(current_med, periodd)
temp = (current_med - min_med) / (max_med - min_med)
value = 0.5 * 2
value *= (temp - .5 + .5 * nz(value ))
value := value > .9999 ? .9999 : value
value := value < -0.9999 ? -0.9999 : value
temp2 = (1 + value) / (1 - value)
momentum = .25 * math.log(temp2)
momentum += .5 * nz(momentum )
//momentum := raw_momentum
signal = nz(momentum )
trend = math.abs(momentum) <= math.abs(momentum )
////////////////////////////////////////////////-GROWING/FAILING-//////////////////////////////////////////
length = input.int(title="MOM Period", minval=1, defval=14, group="MOM Settings")
srcc = input(title="MOM Source", defval=hlc3, group="MOM Settings")
txtcol_grow_above = input(#1a7b24, "Above Grow", group="MOM Settings", inline="Above")
txtcol_fall_above = input(#672ec5, "Fall", group="MOM Settings", inline="Above")
txtcol_grow_below = input(#F37121, "Below Grow", group="MOM Settings", inline="Below")
txtcol_fall_below = input(#be0606, "Fall", group="MOM Settings", inline="Below")
ma(source, length, type) =>
switch type
"SMA" => ta.sma(source, length)
"EMA" => ta.ema(source, length)
"SMMA (RMA)" => ta.rma(source, length)
"WMA" => ta.wma(source, length)
"VWMA" => ta.vwma(source, length)
typeMA = input.string(title = "Method", defval = "SMA", options= , group="MA Settings")
smoothingLength = input.int(title = "Length", defval = 5, minval = 1, maxval = 100, group="MA Settings")
smoothingLine = ma(delta, smoothingLength, typeMA)
deltaText=(delta > 0 ? (delta > delta ? " MOM > 0 and ▲ Growing, MOM = " + str.tostring(delta , "#.##") :" MOM > 0 and ▼ Falling, MOM = " + str.tostring(delta , "#.##") ) : (delta > delta ? "MOM < 0 and ▲ Growing, MOM = " + str.tostring(delta , "#.##"): " MOM < 0 and ▼ Falling, MOM = " + str.tostring(delta , "#.##")))
oneDay = 24 * 60 * 60 * 1000
barsAhead = 3
tmf = if timeframe.ismonthly
barsAhead * oneDay * 30
else if timeframe.isweekly
barsAhead * oneDay * 7
else if timeframe.isdaily
barsAhead * oneDay
else if timeframe.isminutes
barsAhead * oneDay * timeframe.multiplier / 1440
else if timeframe.isseconds
barsAhead * oneDay * timeframe.multiplier / 86400
else
0
angle(_src) =>
rad2degree = 180 / 3.14159265359
//pi
ang = rad2degree * math.atan((_src - _src ) / ta.atr(14))
ang
emae = angle(smoothingLine)
emaanglestat = emae > emae ? "▲ Growing": "▼ Falling"
deltaTextxxx = "MOM MA/ATR angle value is " + str.tostring(emae, "#.##") + "° and is " + emaanglestat
deltacolorxxx = emae >0 and emae >=emae ? txtcol_grow_above : txtcol_fall_below
// Label
label lpt1 = label.new(time, -30, text=deltaTextxxx , color=deltacolorxxx, xloc=xloc.bar_time, style=label.style_label_left, textcolor=color.white, textalign=text.align_left, size=size.normal)
label.set_x(lpt1, label.get_x(lpt1) + tmf)
label.delete(lpt1 )
txtdeltaColors = (delta > 50 ? (delta < delta ? txtcol_grow_above : txtcol_fall_above) : (delta < delta ? txtcol_grow_below : txtcol_fall_below))
label ldelta1 = label.new(time, 30, text=deltaText , color=txtdeltaColors, xloc=xloc.bar_time, style=label.style_label_left, textcolor=color.white, textalign=text.align_left, size=size.normal)
label.set_x(ldelta1, label.get_x(ldelta1) + tmf)
label.delete(ldelta1 )
Info TableOverview
The Info Table V1 is a versatile TradingView indicator tailored for intraday futures traders, particularly those focusing on MESM2 (Micro E-mini S&P 500 futures) on 1-minute charts. It presents essential market insights through two customizable tables: the Main Table for predictive and macro metrics, and the New Metrics Table for momentum and volatility indicators. Designed for high-activity sessions like 9:30 AM–11:00 AM CDT, this tool helps traders assess price alignment, sentiment, and risk in real-time. Metrics update dynamically (except weekly COT data), with optional alerts for key conditions like volatility spikes or momentum shifts.
This indicator builds on foundational concepts like linear regression for predictions and adapts open-source elements for enhanced functionality. Gradient code is adapted from TradingView's Color Library. QQE logic is adapted from LuxAlgo's QQE Weighted Oscillator, licensed under CC BY-NC-SA 4.0. The script is released under the Mozilla Public License 2.0.
Key Features
Two Customizable Tables: Positioned independently (e.g., top-right for Main, bottom-right for New Metrics) with toggle options to show/hide for a clutter-free chart.
Gradient Coloring: User-defined high/low colors (default green/red) for quick visual interpretation of extremes, such as overbought/oversold or high volatility.
Arrows for Directional Bias: In the New Metrics Table, up (↑) or down (↓) arrows appear in value cells based on metric thresholds (top/bottom 25% of range), indicating bullish/high or bearish/low conditions.
Consensus Highlighting: The New Metrics Table's title cells ("Metric" and "Value") turn green if all arrows are ↑ (strong bullish consensus), red if all are ↓ (strong bearish consensus), or gray otherwise.
Predicted Price Plot: Optional line (default blue) overlaying the ML-predicted price for visual comparison with actual price action.
Alerts: Notifications for high/low Frahm Volatility (≥8 or ≤3) and QQE Bias crosses (bullish/bearish momentum shifts).
Main Table Metrics
This table focuses on predictive, positional, and macro insights:
ML-Predicted Price: A linear regression forecast using normalized price, volume, and RSI over a customizable lookback (default 500 bars). Gradient scales from low (red) to high (green) relative to the current price ± threshold (default 100 points).
Deviation %: Percentage difference between current price and predicted price. Gradient highlights extremes (±0.5% default threshold), signaling potential overextensions.
VWAP Deviation %: Percentage difference from Volume Weighted Average Price (VWAP). Gradient indicates if price is above (green) or below (red) fair value (±0.5% default).
FRED UNRATE % Change: Percentage change in U.S. unemployment rate (via FRED data). Cell turns red for increases (economic weakness), green for decreases (strength), gray if zero or disabled.
Open Interest: Total open MESM2 futures contracts. Gradient scales from low (red) to high (green) up to a hardcoded 300,000 threshold, reflecting market participation.
COT Commercial Long/Short: Weekly Commitment of Traders data for commercial positions. Long cell green if longs > shorts (bullish institutional sentiment); Short cell red if shorts > longs (bearish); gray otherwise.
New Metrics Table Metrics
This table emphasizes technical momentum and volatility, with arrows for quick bias assessment:
QQE Bias: Smoothed RSI vs. trailing stop (default length 14, factor 4.236, smooth 5). Green for bullish (RSI > stop, ↑ arrow), red for bearish (RSI < stop, ↓ arrow), gray for neutral.
RSI: Relative Strength Index (default period 14). Gradient from oversold (red, <30 + threshold offset, ↓ arrow if ≤40) to overbought (green, >70 - offset, ↑ arrow if ≥60).
ATR Volatility: Score (1–20) based on Average True Range (default period 14, lookback 50). High scores (green, ↑ if ≥15) signal swings; low (red, ↓ if ≤5) indicate calm.
ADX Trend: Average Directional Index (default period 14). Gradient from weak (red, ↓ if ≤0.25×25 threshold) to strong trends (green, ↑ if ≥0.75×25).
Volume Momentum: Score (1–20) comparing current to historical volume (lookback 50). High (green, ↑ if ≥15) suggests pressure; low (red, ↓ if ≤5) implies weakness.
Frahm Volatility: Score (1–20) from true range over a window (default 24 hours, multiplier 9). Dynamic gradient (green/red/yellow); ↑ if ≥7.5, ↓ if ≤2.5.
Frahm Avg Candle (Ticks): Average candle size in ticks over the window. Blue gradient (or dynamic green/red/yellow); ↑ if ≥0.75 percentile, ↓ if ≤0.25.
Arrows trigger on metric-specific logic (e.g., RSI ≥60 for ↑), providing directional cues without strict color ties.
Customization Options
Adapt the indicator to your strategy:
ML Inputs: Lookback (10–5000 bars) and RSI period (2+) for prediction sensitivity—shorter for volatility, longer for trends.
Timeframes: Individual per metric (e.g., 1H for QQE Bias to match higher frames; blank for chart timeframe).
Thresholds: Adjust gradients and arrows (e.g., Deviation 0.1–5%, ADX 0–100, RSI overbought/oversold).
QQE Settings: Length, factor, and smooth for fine-tuned momentum.
Data Toggles: Enable/disable FRED, Open Interest, COT for focus (e.g., disable macro for pure intraday).
Frahm Options: Window hours (1+), scale multiplier (1–10), dynamic colors for avg candle.
Plot/Table: Line color, positions, gradients, and visibility.
Ideal Use Case
Perfect for MESM2 scalpers and trend traders. Use the Main Table for entry confirmation via predicted deviations and institutional positioning. Leverage the New Metrics Table arrows for short-term signals—enter bullish on green consensus (all ↑), avoid chop on low volatility. Set alerts to catch shifts without constant monitoring.
Why It's Valuable
Info Table V1 consolidates diverse metrics into actionable visuals, answering critical questions: Is price mispriced? Is momentum aligning? Is volatility manageable? With real-time updates, consensus highlights, and extensive customization, it enhances precision in fast markets, reducing guesswork for confident trades.
Note: Optimized for futures; some metrics (OI, COT) unavailable on non-futures symbols. Test on demo accounts. No financial advice—use at your own risk.
The provided script reuses open-source elements from TradingView's Color Library and LuxAlgo's QQE Weighted Oscillator, as noted in the script comments and description. Credits are appropriately given in both the description and code comments, satisfying the requirement for attribution.
Regarding significant improvements and proportion:
The QQE logic comprises approximately 15 lines of code in a script exceeding 400 lines, representing a small proportion (<5%).
Adaptations include integration with multi-timeframe support via request.security, user-customizable inputs for length, factor, and smooth, and application within a broader table-based indicator for momentum bias display (with color gradients, arrows, and alerts). This extends the original QQE beyond standalone oscillator use, incorporating it as one of seven metrics in the New Metrics Table for confluence analysis (e.g., consensus highlighting when all metrics align). These are functional enhancements, not mere stylistic or variable changes.
The Color Library usage is via official import (import TradingView/Color/1 as Color), leveraging built-in gradient functions without copying code, and applied to enhance visual interpretation across multiple metrics.
The script complies with the rules: reused code is minimal, significantly improved through integration and expansion, and properly credited. It qualifies for open-source publication under the Mozilla Public License 2.0, as stated.
Glamour ETF Index vs. QQQ mit MA10, MA20 & MA50Stan Weinstein uses the term "Glamour Index" as a sentiment indicator to assess how speculative or overheated the stock market is. The Glamour Index measures the relationship between so-called "glamour stocks" (trendy stocks, hyped stocks with high media attention and sometimes extreme price increases) and solid, more conservative stocks. Weinstein uses this index to: 1) Analyze market sentiment – particularly whether the market is in a speculative euphoria phase.
2) Identify warning signs of a potential top formation or an impending downturn.
My basket compares performance against the QQQ (alternatively, SPY or any other benchmark is also possible).
My basket consists of the ETFs in the ARK universe, as well as other growth ETFs such as IPO, FFTY, and QQQJ.
Multi-TF Z-Score IndicatorIndicator to find the Z score for the daily 4h, 1h, 15m and 5 min time frames with 20 previous samples.
Fractal Pullback Market StructureFractal Pullback Market Structure
Author: The_Forex_Steward
License: Mozilla Public License 2.0
The Fractal Pullback Market Structure indicator is a sophisticated price action tool designed to visualize internal structure shifts and break-of-structure (BoS) events with high accuracy. It leverages fractal pullback logic to identify market swing points and confirm whether a directional change has occurred.
This indicator detects swing highs and lows based on fractal behavior, drawing zigzag lines to connect these key pivot points. It classifies and labels each structural point as either a Higher High (HH), Higher Low (HL), Lower High (LH), or Lower Low (LL). Internal shifts are marked using triangle symbols on the chart, distinguishing bullish from bearish developments.
Break of Structure events are confirmed when price closes beyond the most recent swing high or low, and a horizontal line is drawn at the breakout level. This helps traders validate when a structural trend change is underway.
Users can configure the lookback period that defines the sensitivity of the pullback detection, as well as a timeframe multiplier to align the logic with higher timeframes such as 4H or Daily. There are visual customization settings for the zigzag lines and BoS markers, including color, width, and style (solid, dotted, or dashed).
Alerts are available for each key structural label—HH, HL, LH, LL—as well as for BoS events. These alerts are filtered through a selectable alert mode that separates signals by timeframe category: Low Timeframe (LTF), Medium Timeframe (MTF), and High Timeframe (HTF). Each mode allows the user to receive alerts only when relevant to their strategy.
This indicator excels in trend confirmation and reversal detection. Traders can use it to identify developing structure, validate internal shifts, and anticipate breakout continuation or rejection. It is particularly useful for Smart Money Concept (SMC) traders, swing traders, and those looking to refine entries and exits based on price structure rather than lagging indicators.
Visual clarity, adaptable timeframe logic, and precise structural event detection make this tool a valuable addition to any price action trader’s toolkit.
USDT + USDC DominanceUSDT and USDC Dominance: This refers to the combined market capitalization of Tether (USDT) and USD Coin (USDC) as a percentage of the total cryptocurrency market capitalization. It measures the proportion of the crypto market held by these stablecoins, which are pegged to the US dollar. High dominance indicates a "risk-off" sentiment, where investors hold stablecoins for safety during market uncertainty. A drop in dominance suggests capital is flowing into riskier assets like altcoins, often signaling a bullish market or the start of an "alt season."
Economy RadarEconomy Radar — Key US Macro Indicators Visualized
A handy tool for traders and investors to monitor major US economic data in one chart.
Includes:
Inflation: CPI, PCE, yearly %, expectations
Monetary policy: Fed funds rate, M2 money supply
Labor market: Unemployment, jobless claims, consumer sentiment
Economy & markets: GDP, 10Y yield, US Dollar Index (DXY)
Options:
Toggle indicators on/off
Customizable colors
Tooltips explain each metric (in Russian & English)
Perfect for spotting economic cycles and supporting trading decisions.
Add to your chart and get a clear macro picture instantly!
USDT + USDC Dominance USDT + USDC Dominance: This refers to the combined market capitalization of Tether (USDT) and USD Coin (USDC) as a percentage of the total cryptocurrency market capitalization. It measures the proportion of the crypto market held by these stablecoins, which are pegged to the US dollar. High dominance indicates a "risk-off" sentiment, where investors hold stablecoins for safety during market uncertainty. A drop in dominance suggests capital is flowing into riskier assets like altcoins, often signaling a bullish market or the start of an "alt season.
RSI(14) Custom by ChadRSI 14 : this indicator works in low time frame like 1h and 4h, for entry long position and short position. when the line touch 70 mean the price is overbought, when the line touch 50 it"s neutral, and when the line touch 30 mean price is oversold.
Delta Spike Detector [GSK-VIZAG-AP-INDIA]📌 Delta Spike Detector – Volume Imbalance Ratio
By GSK-VIZAG-AP-INDIA
📘 Overview
This indicator highlights aggressive buying or selling activity by analyzing the imbalance between estimated Buy and Sell volume per candle. It flags moments when one side dominates the other significantly — defined by user-selectable volume ratio thresholds (10x, 15x, 20x, 25x).
📊 How It Works
Buy/Sell Volume Estimation
Approximates buyer and seller participation using candle structure:
Buy Volume = Proximity of close to low
Sell Volume = Proximity of close to high
Delta & Delta Ratio
Delta = Buy Volume − Sell Volume
Delta Ratio = Ratio of dominant volume side to the weaker side
When this ratio exceeds a threshold, it’s classified as a spike.
Spike Labels
Labels are plotted on the chart:
10x B, 15x B, 20x B, 25x B → Buy Spike Labels (below candles)
10x S, 15x S, 20x S, 25x S → Sell Spike Labels (above candles)
The color of each label reflects the spike strength.
⚙️ User Inputs
Enable/Disable Buy or Sell Spikes
Set custom delta ratio thresholds (default: 10x, 15x, 20x, 25x)
🎯 Use Cases
Spotting sudden aggressive activity (e.g. smart money moves, traps, breakouts)
Identifying short-term market exhaustion or momentum bursts
Complementing other trend or volume-based tools
⚠️ Important Notes
The script uses approximated Buy/Sell Volume based on price position, not actual order flow.
This is not a buy/sell signal generator. It should be used in context with other confirmation indicators or market structure.
✍️ Credits
Developed by GSK-VIZAG-AP-INDIA
For educational and research use only.
Multi SMA AnalyzerMulti SMA Analyzer with Custom SMA Table & Advanced Session Logic
A feature-rich SMA analysis suite for traders, offering up to 7 configurable SMAs, in-depth trend detection, real-time table, and true session-aware calculations.
Ideal for those who want to combine intraday, swing, and higher-timeframe trend analysis with maximum chart flexibility.
Key Features
📊 Multi-SMA Overlay
- 7 SMAs (default: 5, 20, 50, 100, 200, 21, 34)—individually configurable (period, source, color, line style)
- Show/hide each SMA, custom line style (solid, stepline, circles), and color logic
- Dynamic color: full opacity above SMA, reduced when below
⏰ Session-Aware SMAs
- Each SMA can be calculated using only user-defined session hours/days/timezone
- “Ignore extended hours” option for accurate intraday trend
📋 Smart Data Table
- Live SMA values, % distance from price, and directional arrows (↑/↓/→)
- Bull/Bear/Sideways trend classification
- Custom table position, size, colors, transparency
- Table can run on chart or custom (higher) timeframe for multi-TF analysis
🎯 Golden/Death Cross Detection
- Flexible crossover engine: select any two from (5, 10, 20, 50, 100, 200) for fast/slow SMA cross signals
- Plots icons (★ Golden, 💀 Death), optional crossover labels with custom size/colors
🏷️ SMA Labels
- Optional on-chart SMA period labels
- Custom placement (above/below/on line), size, color, offset
🚨 Signal & Trend Engine
- Bull/Bear/Sideways logic: price vs. multiple SMAs (not just one pair)
- Volume spike detection (2x 20-period SMA)
- Bullish engulfing candlestick detection
- All signals can use chart or custom table timeframe
🎨 Visual Customization
- Dynamic background color (Bull: green, Bear: red, Neutral: gray)
- Every visual aspect is customizable: label/table colors, transparency, size, position
🔔 Built-in Alerts
- Crossovers (SMA20/50, Golden/Death)
- Bull trend, volume spikes, engulfing pattern—all alert-ready
How It Works
- Session Filtering:
- SMAs can be set to count only bars from your chosen market session, for true intraday/trading-hour signals
Dynamic Table & Signals:
- Table and all signal logic run on your selected chart or custom timeframe
Flexible Crossover:
- Choose any pair (5, 10, 20, 50, 100, 200) for cross detection—SMA 10 is available for crossover even if not shown as an SMA line
Everything is modular:
- Toggle features, set visuals, and alerts to your workflow
🚨 How to Use Alerts
- All key signals (crossovers, trend shifts, volume spikes, engulfing patterns) are available as alert conditions.
To enable:
- Click the “Alerts” (clock) icon at the top of TradingView.
- Select your desired signal (e.g., “Golden Cross”) from the condition dropdown.
- Set your alert preferences and create the alert.
- Now, you’ll get notified automatically whenever a signal occurs!
Perfect For
- Multi-timeframe and swing traders seeking higher timeframe SMA confirmation
- Intraday traders who want to ignore pre/post-market data
- Anyone wanting a modern, powerful, fully customizable multi-SMA overlay
// P.S: Experiment with Golden Cross where Fast SMA is 5 and Slow SMA is 20.
// Set custom timeframe for 4 hr while monitoring your chart on 15 min time frame.
// Enable Background Color and Use Table Timeframe for Background.
// Uncheck Pine labels in Style tab.
Clean, open-source, and loaded with pro features—enjoy!
Like, share, and let me know if you'd like any new features added.
Floor and Roof Indicator with SignalsFloor and Roof Indicator with Trading Signals
A comprehensive support and resistance indicator that identifies premium and discount zones with automated signal generation.
Key Features:
Dynamic Support/Resistance Zones: Calculates floor (support) and roof (resistance) levels using price action and volatility
Premium/Discount Zone Identification: Highlights areas where price may find resistance or support
Customizable Signal Frequency: Control how often signals are displayed (every Nth occurrence)
Visual Signal Table: Optional table showing the last 5 long and short signal prices
Multiple Timeframe Compatibility: Works across all timeframes
Technical Details:
Uses ATR-based calculations for dynamic zone width adjustment
Combines Bollinger Bands with highest/lowest price analysis
Smoothing options for cleaner signal generation
Fully customizable colors and display options
How to Use:
Floor Zones (Blue): Potential support areas where long positions may be considered
Roof Zones (Pink): Potential resistance areas where short positions may be considered
Signal Crosses: Visual markers when price interacts with key levels
Signal Table: Track recent signal prices for analysis
Settings:
Length: Period for calculations (default: 200)
Smooth: Smoothing factor for cleaner signals
Zone Width: Adjust the thickness of support/resistance zones
Signal Frequency: Control signal display frequency
Visual Options: Customize colors and table position
Alerts Available:
Long signal alerts when price touches discount zones
Short signal alerts when price reaches premium zones
Educational Purpose: This indicator is designed to help traders identify potential support and resistance areas. Always combine with proper risk management and additional analysis.
This description focuses on the technical aspects and educational value while avoiding any language that could be interpreted as financial advice or guaranteed profits.
BANKNIFTY Contribution Table [GSK-VIZAG-AP-INDIA]1. Overview
This indicator provides a real-time visual contribution table of the 12 constituent stocks in the BANKNIFTY index. It displays key metrics for each stock that help traders quickly understand how each component is impacting the index at any given moment.
2. Purpose / Trading Use Case
The tool is designed for intraday and short-term traders who rely on index movement and its internal strength or weakness. By seeing which stocks are contributing positively or negatively, traders can:
Confirm trend strength or divergence within the index.
Identify whether a BANKNIFTY move is broad-based or driven by a few heavyweights.
Detect reversals when individual components decouple from index direction.
3. Key Features and Logic
Live LTP: Current price of each BANKNIFTY stock.
Price Change: Difference between current LTP and previous day’s close.
% Change: Percentage move from previous close.
Weight %: Static weight of each stock within the BANKNIFTY index (user-defined).
This estimates how much each stock contributes to the BANKNIFTY’s point change.
Sorted View: The stocks are sorted by their weight (descending), so high-impact movers are always at the top.
4. User Inputs / Settings
Table Position (tableLocationOpt):
Choose where the table appears on the chart:
top_left, top_right, bottom_left, or bottom_right.
This helps position the table away from your price action or indicators.
5. Visual and Plotting Elements
Table Layout: 6 columns
Stock | Contribution | Weight % | LTP | Change | % Change
Color Coding:
Green/red for positive/negative price changes and contributions.
Alternating background rows for better visibility.
BANKNIFTY row is highlighted separately at the top.
Text & Background Colors are chosen for both readability and direction indication.
6. Tips for Effective Use
Use this table on 1-minute or 5-minute intraday charts to see near real-time market structure.
Watch for:
A few heavyweight stocks pulling the index alone (can signal weak internal breadth).
Broad green/red across all rows (signals strong directional momentum).
Combine this with price action or volume-based strategies for confirmation.
Best used during market hours for live updates.
7. What Makes It Unique
Unlike other contribution tables that show only static data or require paid feeds, this script:
Updates in real time.
Uses dynamic calculated contributions.
Places BANKNIFTY at the top and presents the entire internal structure clearly.
Doesn’t repaint or rely on lagging indicators.
8. Alerts / Additional Features
No alerts are added in this version.
(Optional: Alerts can be added to notify when a certain stock contributes above/below a threshold.)
9. Technical Concepts Used
request.security() to pull both 1-minute and daily close data.
Conditional color formatting based on price change direction.
Dynamic table rendering using table.new() and table.cell().
Static weights assigned manually for BANKNIFTY stocks (can be updated if index weights change).
10. Disclaimer
This script is intended for educational and informational purposes only. It does not constitute financial advice or a buy/sell recommendation.
Users should test and validate the tool on paper or demo accounts before applying it to live trading.
📌 Note: Due to internet connectivity, data delays, or broker feeds, real-time values (LTP, change, contribution, etc.) may slightly differ from other platforms or terminals. Use this indicator as a supportive visual tool, not a sole decision-maker.
Script Title: BANKNIFTY Contribution Table -
Author: GSK-VIZAG-AP-INDIA
Version: Final Public Release
Momentum Regression [BackQuant]Momentum Regression
The Momentum Regression is an advanced statistical indicator built to empower quants, strategists, and technically inclined traders with a robust visual and quantitative framework for analyzing momentum effects in financial markets. Unlike traditional momentum indicators that rely on raw price movements or moving averages, this tool leverages a volatility-adjusted linear regression model (y ~ x) to uncover and validate momentum behavior over a user-defined lookback window.
Purpose & Design Philosophy
Momentum is a core anomaly in quantitative finance — an effect where assets that have performed well (or poorly) continue to do so over short to medium-term horizons. However, this effect can be noisy, regime-dependent, and sometimes spurious.
The Momentum Regression is designed as a pre-strategy analytical tool to help you filter and verify whether statistically meaningful and tradable momentum exists in a given asset. Its architecture includes:
Volatility normalization to account for differences in scale and distribution.
Regression analysis to model the relationship between past and present standardized returns.
Deviation bands to highlight overbought/oversold zones around the predicted trendline.
Statistical summary tables to assess the reliability of the detected momentum.
Core Concepts and Calculations
The model uses the following:
Independent variable (x): The volatility-adjusted return over the chosen momentum period.
Dependent variable (y): The 1-bar lagged log return, also adjusted for volatility.
A simple linear regression is performed over a large lookback window (default: 1000 bars), which reveals the slope and intercept of the momentum line. These values are then used to construct:
A predicted momentum trendline across time.
Upper and lower deviation bands , representing ±n standard deviations of the regression residuals (errors).
These visual elements help traders judge how far current returns deviate from the modeled momentum trend, similar to Bollinger Bands but derived from a regression model rather than a moving average.
Key Metrics Provided
On each update, the indicator dynamically displays:
Momentum Slope (β₁): Indicates trend direction and strength. A higher absolute value implies a stronger effect.
Intercept (β₀): The predicted return when x = 0.
Pearson’s R: Correlation coefficient between x and y.
R² (Coefficient of Determination): Indicates how well the regression line explains the variance in y.
Standard Error of Residuals: Measures dispersion around the trendline.
t-Statistic of β₁: Used to evaluate statistical significance of the momentum slope.
These statistics are presented in a top-right summary table for immediate interpretation. A bottom-right signal table also summarizes key takeaways with visual indicators.
Features and Inputs
✅ Volatility-Adjusted Momentum : Reduces distortions from noisy price spikes.
✅ Custom Lookback Control : Set the number of bars to analyze regression.
✅ Extendable Trendlines : For continuous visualization into the future.
✅ Deviation Bands : Optional ±σ multipliers to detect abnormal price action.
✅ Contextual Tables : Help determine strength, direction, and significance of momentum.
✅ Separate Pane Design : Cleanly isolates statistical momentum from price chart.
How It Helps Traders
📉 Quantitative Strategy Validation:
Use the regression results to confirm whether a momentum-based strategy is worth pursuing on a specific asset or timeframe.
🔍 Regime Detection:
Track when momentum breaks down or reverses. Slope changes, drops in R², or weak t-stats can signal regime shifts.
📊 Trade Filtering:
Avoid false positives by entering trades only when momentum is both statistically significant and directionally favorable.
📈 Backtest Preparation:
Before running costly simulations, use this tool to pre-screen assets for exploitable return structures.
When to Use It
Before building or deploying a momentum strategy : Test if momentum exists and is statistically reliable.
During market transitions : Detect early signs of fading strength or reversal.
As part of an edge-stacking framework : Combine with other filters such as volatility compression, volume surges, or macro filters.
Conclusion
The Momentum Regression indicator offers a powerful fusion of statistical analysis and visual interpretation. By combining volatility-adjusted returns with real-time linear regression modeling, it helps quantify and qualify one of the most studied and traded anomalies in finance: momentum.
Tsallis Entropy Market RiskTsallis Entropy Market Risk Indicator
What Is It?
The Tsallis Entropy Market Risk Indicator is a market analysis tool that measures the degree of randomness or disorder in price movements. Unlike traditional technical indicators that focus on price patterns or momentum, this indicator takes a statistical physics approach to market analysis.
Scientific Foundation
The indicator is based on Tsallis entropy, a generalization of traditional Shannon entropy developed by physicist Constantino Tsallis. The Tsallis entropy is particularly effective at analyzing complex systems with long-range correlations and memory effects—precisely the characteristics found in crypto and stock markets.
The indicator also borrows from Log-Periodic Power Law (LPPL).
Core Concepts
1. Entropy Deficit
The primary measurement is the "entropy deficit," which represents how far the market is from a state of maximum randomness:
Low Entropy Deficit (0-0.3): The market exhibits random, uncorrelated price movements typical of efficient markets
Medium Entropy Deficit (0.3-0.5): Some patterns emerging, moderate deviation from randomness
High Entropy Deficit (0.5-0.7): Strong correlation patterns, potentially indicating herding behavior
Extreme Entropy Deficit (0.7-1.0): Highly ordered price movements, often seen before significant market events
2. Multi-Scale Analysis
The indicator calculates entropy across different timeframes:
Short-term Entropy (blue line): Captures recent market behavior (20-day window)
Long-term Entropy (green line): Captures structural market behavior (120-day window)
Main Entropy (purple line): Primary measurement (60-day window)
3. Scale Ratio
This measures the relationship between long-term and short-term entropy. A healthy market typically has a scale ratio above 0.85. When this ratio drops below 0.85, it suggests abnormal relationships between timeframes that often precede market dislocations.
How It Works
Data Collection: The indicator samples price returns over specific lookback periods
Probability Distribution Estimation: It creates a histogram of these returns to estimate their probability distribution
Entropy Calculation: Using the Tsallis q-parameter (typically 1.5), it calculates how far this distribution is from maximum entropy
Normalization: Results are normalized against theoretical maximum entropy to create the entropy deficit measure
Risk Assessment: Multiple factors are combined to generate a composite risk score and classification
Market Interpretation
Low Risk Environments (Risk Score < 25)
Market is functioning efficiently with reasonable randomness
Price discovery is likely effective
Normal trading and investment approaches appropriate
Medium Risk Environments (Risk Score 25-50)
Increasing correlation in price movements
Beginning of trend formation or momentum
Time to monitor positions more closely
High Risk Environments (Risk Score 50-75)
Strong herding behavior present
Market potentially becoming one-sided
Consider reducing position sizes or implementing hedges
Extreme Risk Environments (Risk Score > 75)
Highly ordered market behavior
Significant imbalance between buyers and sellers
Heightened probability of sharp reversals or corrections
Practical Application Examples
Market Tops: Often characterized by gradually increasing entropy deficit as momentum builds, followed by extreme readings near the actual top
Market Bottoms: Can show high entropy deficit during capitulation, followed by normalization
Range-Bound Markets: Typically display low and stable entropy deficit measurements
Trending Markets: Often show moderate entropy deficit that remains relatively consistent
Advantages Over Traditional Indicators
Forward-Looking: Identifies changing market structure before price action confirms it
Statistical Foundation: Based on robust mathematical principles rather than empirical patterns
Adaptability: Functions across different market regimes and asset classes
Noise Filtering: Focuses on meaningful structural changes rather than price fluctuations
Limitations
Not a Timing Tool: Signals market risk conditions, not precise entry/exit points
Parameter Sensitivity: Results can vary based on the chosen parameters
Historical Context: Requires some historical perspective to interpret effectively
Complementary Tool: Works best alongside other analysis methods
Enjoy :)
Intra-bar Close/Open Gap [YuL]Just checking one idea: look at gaps between close and open bars on lower timeframe to try to estimate how much slippage exists there that may be a result of buying or selling pressure.
Perhaps it only useful in real time to see if situation of the current bar is changing.
Open to ideas and suggestions.
Better MACD📘 Better MACD – Adaptive Momentum & Divergence Suite
Better MACD is a comprehensive momentum-trend tool that evolves the traditional MACD into a multi-dimensional, divergence-aware oscillator. It leverages exponential smoothing across logarithmic rate-of-change of OHLC data, adaptive signal processing, and intelligent divergence detection logic to provide traders with earlier, smoother, and more reliable momentum signals.
This indicator is built for professional-level analysis, suitable for scalping, swing trading, and trend-following systems.
🧬 Core Concept
Unlike the classic MACD which subtracts two EMAs of price, Better MACD constructs a signal by:
Applying logarithmic transformation on the change between OHLC components (Close, High, Low, Open).
Using double EMA smoothing to filter noise and volatility, Triangular method. 1st to 2nd Smoothing.
Averaging and de-biasing the results through a custom linear regression model, 4th Smoothing.
Subtracting a fast SMA and slow SMA response to yield a dynamic MACD value, 3rd Smoothing.
The result is a smooth, adaptive, and high-resolution MACD-style oscillator that responds more naturally to trend conditions and price geometry.
🧠 Features Breakdown
1. 📈 Multi-Layer MACD Engine
Src1: Smoothed Log Rate-of-Change on Close
Src2: Smoothed Log Rate-of-Change on High
Src3: Smoothed Log Rate-of-Change on Low
Src4: Smoothed Log Rate-of-Change on Open
These are blended using highest high, lowest low, and average Close price over a configurable window for more complete trend detection. The open-based Src4 is subtracted using SMA.
2. 🧮 Signal Line
A fast EMA (signalLength) of the Better MACD value is used for crossover logic.
Crossovers of MACD and Signal line signal potential entries or exits.
3. 📊 MACD Histogram
Visualizes the difference between MACD and Signal line.
Dynamically color-coded:
Green/Light Green for bullish impulse
Red/Pink for bearish impulse
Width and color intensity reflect strength and momentum slope.
🎨 Visual Enhancements
Feature Description
✅ Ribbon Fill Optional fill between MACD and Signal line, colored by trend direction
✅ Zero-Line Background Background highlights above/below 0 to easily read bullish/bearish bias
✅ Crossover Highlights Tiny circles plotted when MACD crosses Signal line
🔍 Divergence Detection Suite
The script includes a full Divergence Engine to detect:
🔼 Bullish Regular Divergence (Price lower lows + Indicator higher lows)
🔽 Bearish Regular Divergence (Price higher highs + Indicator lower highs)
🟢 Bullish Hidden Divergence (Price higher lows + Indicator lower lows)
🔴 Bearish Hidden Divergence (Price lower highs + Indicator higher highs)
🧩 Divergence Modes:
Supports both Regular, Hidden, or Both simultaneously
Detects from either Close Price or Heikin Ashi-derived candles
Uses dynamic pivot tracking with configurable lookback and divergence sensitivity
Divergence lines are labeled, colored, and plotted in real-time
🔁 Styling & Customization:
Choose from Solid, Dashed, or Dotted line styles
Configure separate colors and widths for all divergence types
Control number of divergence lines visible or only show the most recent
Divergences update live without repainting
⚠️ Alerts
Alerts are built-in for real-time notification:
MACD Histogram reversals (rising → falling, or vice versa)
Divergence signals (all 4 types, grouped and individually)
Combines seamlessly with TradingView alerts for actionable triggers
🔧 Input Controls (Grouped by Purpose)
Better MACD Group
1st–4th Smoothing Lengths: Controls responsiveness of MACD core engine
Signal Length: Smoothness of signal line
Toggles for crossover highlights, zero cross fills, and ribbon fills
Divergence Settings
Enable/disable divergence lines
Choose divergence type (Regular, Hidden, Both)
Set confirmation requirements
Customize pivot detection and bar search depth
Styling Options
Colors, line widths, and line styles for each divergence type
Heikin Ashi Mode for smoother pivots and divergences
🧠 How to Use
✅ For Trend Traders:
Use MACD > Signal + Histogram > 0 → Bullish confirmation
MACD < Signal + Histogram < 0 → Bearish confirmation
Wait for pullbacks with hidden divergences to enter in trend direction
✅ For Reversal Traders:
Look for Regular Divergences at trend exhaustion points
Combine with price action (e.g., support/resistance or candle pattern)
✅ For Swing & Day Traders:
Enable Heikin Ashi Mode for smoother divergence pivots
Use zero line background + histogram color to time entries
📌 Summary
Feature Description
🚀 Advanced MACD Core Smoother, more reliable, multi-source-based MACD
🔍 Divergence Engine Detects 4 divergence types with pivot logic
🎯 Real-Time Alerts Alerts for histogram slope and divergences
🎛️ Deep Customization Full styling, smoothing, and detection controls
📉 Heikin Ashi Support Improved signal quality in trend-based markets
