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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.
Pine Script® indicator
by Tin2100
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)
Pine Script® indicator
by ictsniperkills
Tension Squeeze Clock v1.0🔥 Tension Squeeze Clock v1.0 Forecast explosive market moves before they happen. The Tension Squeeze Clock is a cyclical compression detector that identifies when the market is storing energy across multiple dimensions — and signals when that energy is about to uncoil. This indicator combines three critical components: 🔹 RSI Contraction – Detects when momentum is balanced and compressed 🔹 Volatility Squeeze – Measures low standard deviation in price movement 🔹 Range Tension – Flags tight candle ranges relative to average volatility When all three compressions align, the indicator prints a clear “Squeeze Ready” signal. When the pressure breaks, it signals “Squeeze Uncoiling” — a prime moment to watch for volatility surges or directional breakouts. 📈 Recommended Usage 🔍 This tool works especially well on the Daily timeframe, where coiled conditions often lead to significant price expansions. Use it to: Anticipate breakout setups Confirm coiled consolidation zones Add timing precision to your volume or divergence-based strategies 📊 Display Options Panel view with bar colors to reflect compression strength On-chart labels for squeeze signals Optional alerts when a squeeze begins or breaks Whether you're swing trading, trend riding, or timing reversals, the Tension Squeeze Clock helps you see what most indicators miss: the calm before the storm.
Pine Script® indicator
by JasonHyde
2
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.
Pine Script® indicator
by scrittyrow
2
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 )
Pine Script® indicator
by brunoscarabelli82
1
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.
Pine Script® indicator
by charleyRT
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.
Pine Script® indicator
by frankvest
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.
Pine Script® indicator
by The_Forex_Steward
3
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."
Pine Script® indicator
by cariste
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!
Pine Script® indicator
by Melatue
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.
Pine Script® indicator
by cariste
[Enhanced] L1 Banker Move🧠 L1 Banker Move This is a multi-layered momentum signal tool designed to reveal institutional activity before major price moves. It combines deep liquidity detection, price pressure dynamics, and short-term investor alignment to deliver actionable signals with clarity and precision. Key Features: 🔴 Institutional Signal Detects potential Level 1 banker moves based on deep price compression and long-term sweep logic (Lowest Low 90 + smoothed momentum spikes). 🔵 Institutional Build Phase Shows stealth accumulation/distribution zones using low volatility buildup and compression-based ratios over the past 30 bars. 🟢 Short-Term Investor Signal Confirms price shifts with VWAP cross, SMA structure, and fast/slow EMA delta acceleration. Useful for timing precision entries after institutional setups. 💜 Combined Strength Histogram A composite momentum bar that blends all three layers to visually rank the power of each setup. 🎯 Smart Highlighting & Alerts Background turns red when an institutional signal appears without retail confirmation—flagging early entry traps or front-running zones. Includes alert conditions to notify you of optimal entry moments. Customization: Adjust the EMA delta sensitivity Choose your preferred institutional timeframe (default: Daily)
Pine Script® indicator
by JasonHyde
[Enhanced] L1 Banker MoveThis is a multi-layered momentum signal tool designed to reveal institutional activity before major price moves. It combines deep liquidity detection, price pressure dynamics, and short-term investor alignment to deliver actionable signals with clarity and precision. Key Features: 🔴 Institutional Signal Detects potential Level 1 banker moves based on deep price compression and long-term sweep logic (Lowest Low 90 + smoothed momentum spikes). 🔵 Institutional Build Phase Shows stealth accumulation/distribution zones using low volatility buildup and compression-based ratios over the past 30 bars. 🟢 Short-Term Investor Signal Confirms price shifts with VWAP cross, SMA structure, and fast/slow EMA delta acceleration. Useful for timing precision entries after institutional setups. 💜 Combined Strength Histogram A composite momentum bar that blends all three layers to visually rank the power of each setup. 🎯 Smart Highlighting & Alerts Background turns red when an institutional signal appears without retail confirmation—flagging early entry traps or front-running zones. Includes alert conditions to notify you of optimal entry moments. Customization: Adjust the EMA delta sensitivity Choose your preferred institutional timeframe (default: Daily)
Pine Script® indicator
by JasonHyde
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.
Pine Script® indicator
by prowelltraders
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.
Pine Script® indicator
by Fullymobile
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.
Pine Script® indicator
by profitgang
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
Pine Script® indicator
by prowelltraders
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.
Pine Script® indicator
by BackQuant
3
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 :)
Pine Script® indicator
by HenriqueCentieiro