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Daily Floor PivotsDaily Floor Pivots with Comprehensive Statistical Analysis Overview This indicator combines traditional floor pivot levels with golden zone analysis and comprehensive statistical insights derived from 15 years of historical NQ futures data. While the pivot levels and golden zones can be applied to any instrument, the statistical tables are specifically calibrated for NQ/MNQ futures based on analysis of 2,482 NY Regular Trading Hours (RTH) sessions from 2010-2025. What Makes This Indicator Original Unlike standard pivot indicators that merely plot levels, this tool provides: Enhanced Golden Zone Analysis: Calculates not only the main golden zone (0.5-0.618 retracement of previous day's range) but also golden zones between each pivot pair (PP-R1, R1-R2, R2-R3, PP-S1, S1-S2, S2-S3) Data-Driven Statistical Tables: Two comprehensive tables displaying real statistics from 2,482 trading days of NQ analysis, including: Probability-based touch rates and continuation patterns Context-aware statistics based on opening position Gap analysis and behavioral patterns First touch dynamics and time-to-reach averages Granular Customization: Every visual element and statistical section can be independently toggled, allowing traders to focus on what matters most to their strategy How It Works Pivot Calculation Methodology The indicator uses the standard floor pivot formula based on the previous day's price action: Pivot Point (PP) = (Previous High + Previous Low + Previous Close) / 3 Resistance Levels: R1, R2, R3 calculated from PP and previous range Support Levels: S1, S2, S3 calculated from PP and previous range Golden Zone Calculations Main Golden Zone: The 0.5 to 0.618 Fibonacci retracement of the previous day's range, representing a key reversal and continuation area. Inter-Pivot Golden Zones: For each adjacent pivot pair, golden zones are calculated as: Resistance pairs (PP→R1, R1→R2, R2→R3): 0.5-0.618 range from the lower pivot Support pairs (PP→S1, S1→S2, S2→S3): 0.382-0.5 range from the upper pivot These zones represent high-probability areas where price tends to react when moving between pivot levels. Statistical Analysis Source All statistics displayed in the tables are derived from external Python analysis of 15 years of 1-minute NQ futures data (2010-2025), specifically analyzing NY RTH sessions (9:30 AM - 4:00 PM EST). The analysis tracked: 2,482 complete trading days Intraday pivot touches and closes Opening position context Gap behavior relative to previous day Time-of-day patterns Sequential pivot interactions IMPORTANT: While the pivot levels and golden zones are universally applicable mathematical calculations that work on any instrument, the statistical percentages shown in the tables are specific to NQ/MNQ behavior only. Do not assume these statistics transfer to other instruments. Configuration Guide Basic Settings Number of Periods Back (1-20, default: 3) Controls how many historical pivot periods are displayed on the chart Setting to 1 shows only current day's pivots Higher values show more historical context Labels Position (Left/Right) Choose whether pivot labels appear on the left or right side of each level line Line Width (1-5, default: 2) Adjust the thickness of all pivot and golden zone lines Golden Zone Customization Show Daily Golden Zone (0.5-0.618) Toggle the main golden zone on/off When enabled, displays a shaded box between the 0.5 and 0.618 retracement levels Line Color / Fill Color Customize the appearance of the main golden zone Fill color determines the shaded box transparency Show Labels / Show Prices Control whether "0.5" and "0.618" labels appear Control whether price values are displayed on labels Inter-Pivot Golden Zones Six toggle options allow you to show/hide individual golden zones: PP to R1 / PP to S1: Most frequently touched (60.8% / 50.9%) R1 to R2 / S1 to S2: Moderately touched (25.2% / 24.0%) R2 to R3 / S2 to S3: Rarely touched (9.4% / 10.5%) Line Color / Fill Color: Customize appearance of all inter-pivot zones Show Labels / Show Prices: Control labeling for inter-pivot zones Usage Tip: Disable outer zones (R2-R3, S2-S3) on lower volatility days to reduce chart clutter. Pivot Display Show Support/Resistance Levels: Master toggle for all pivot lines Show SR Labels / Show SR Prices: Control labeling on pivot levels Individual level toggles and colors: PP (Pivot Point): The central reference point R1/S1: Primary resistance/support (38.9% / 35.4% touch rate) R2/S2: Secondary levels (15.6% / 16.1% touch rate) R3/S3: Extended levels (5.1% / 7.3% touch rate) Color Customization: Each level's color can be independently set Overall Statistics Table Show Overall Statistics Table: Master toggle Table Size: tiny/small/normal/large/huge/auto Table Position: Top Left/Top Right/Bottom Left/Bottom Right Section Toggles (enable/disable individual sections): Current Session Info Touch & Close Rates Continue & Reject Rates First Touch Statistics Golden Zone Statistics Daily Close Distribution Highest/Lowest Levels Reached Context Statistics Table Show Context Statistics Table: Master toggle Table Size: tiny/small/normal/large/huge/auto Table Position: Top Left/Top Right/Bottom Left/Bottom Right Section Toggles: Current Opening Zone Opening Zone Statistics Previous Day Gap Context Understanding the Statistical Tables TABLE 1: OVERALL STATISTICS This table presents universal statistics from 2,482 days of NQ analysis. Current Session Info Displays real-time context for the active session: Open: Where the current RTH session opened relative to pivots (e.g., "GZ_TO_R1" means opened between the PP-R1 golden zone and R1) Now: Current price position relative to pivots Direction: Bull (close > open), Bear (close < open), or Flat How to use: This section helps you quickly understand where price opened and where it currently is, providing immediate context for the day's action. Touch & Close Rates Shows probability that each pivot level will be reached during RTH: Touch %: Percentage of days where price touched this level at any point Example: R1 touched 38.9% of days, PP touched 57.5% of days Close %: Percentage of days where price closed beyond this level Example: R1 close beyond happened 39.8% of days How to interpret: Higher touch rates indicate more reliable levels for intraday targeting The difference between touch and close rates shows rejection frequency PP has the highest touch rate (57.5%), making it the most magnetic level Outer levels (R3/S3) have low touch rates (5.1%/7.3%), indicating rare extension days Continue & Reject Rates When a level is touched, these statistics show what happens next: Continue %: Probability price continues through the level Example: When PP is touched, price continues 88.1% of the time Reject %: Probability price rejects from the level and reverses Example: When R1 is touched, price rejects 50.9% of the time How to interpret: PP shows highest continuation (88.1%), confirming it's a poor reversal level Support levels (S1/S2/S3) show strong rejection rates (62.5%/60.7%/56.1%), making them better reversal candidates Continuation rates above 80% suggest the level is better as a target than an entry First Touch Statistics Analyzes which pivot is typically touched first during RTH: 1st Touch %: Probability this level is the first pivot encountered PP is first touched 37.1% of days (most common) R1 is first touched 26.0% of days S1 is first touched 10.9% of days 1st→Continue: If this level is touched first, probability of continuation S1-S3 show 95.6%-100% continuation when touched first This means when price reaches support first, it usually continues lower Avg Time: Minutes after 9:30 AM EST before first touch PP: 1h 6m average S3: 19m average (when bearish) R3: 3h 19m average (when bullish) How to interpret: Opening away from PP means higher probability of reaching extremes (R2/R3 or S2/S3) When support is touched first (within first 2 hours), expect continuation lower Late-day first touches (after 2 PM) often indicate strong trending days Multi-Touch: Shows how often levels are tested multiple times (92.8%-95.0% across all levels) Golden Zone Statistics Main GZ: 58.5% touch rate for the 0.5-0.618 zone Inter-Pivot zones: PP-R1: 60.8% (highest probability) PP-S1: 50.9% R1-R2: 25.2% S1-S2: 24.0% R2-R3: 9.4% S2-S3: 10.5% How to interpret: Main GZ is touched more often than any individual resistance level PP-R1 and PP-S1 golden zones are high-probability mean reversion areas Outer golden zones (R2-R3, S2-S3) are only relevant on high volatility days Daily Close Distribution Shows where RTH sessions typically close: Above/Below PP: 58.5% close above, 41.5% below (slight bullish bias) Above R1: 24.5% of days Below S1: 18.7% of days In GZ: Only 6.3% close in the golden zone (typically transits through it) How to interpret: Most days (58.5%) have bullish bias (close above PP) Less than 25% of days are strong trending days (beyond R1/S1) Golden zone is an action area, not a resting area Highest/Lowest Levels Reached Distribution of the most extreme level reached: High Resist: R1 (26.0%), R2 (10.8%), R3 (5.1%) Low Support: S1 (35.4%), S2 (1.9%), S3 (0.6%) How to interpret: Most days don't reach beyond R1 or S1 R3/S3 are rare events (5.1%/0.6%), indicating major trending days S1 is reached as lowest level more often than R1 as highest, suggesting downside is more frequently tested TABLE 2: CONTEXT STATISTICS This table provides conditional statistics based on how the session opened. Current Opening Zone Displays which of 13 possible zones the RTH session opened in: ABOVE_R3, R2_TO_R3, R1_TO_R2, GZ_TO_R1, IN_GZ, PP_TO_GZ, AT_PP, GZ_TO_PP, S1_TO_GZ, S2_TO_S1, S3_TO_S2, BELOW_S3 How to use: This immediately tells you the market structure and what type of day to expect. Opening Zone Statistics Detailed statistics for the current opening zone (only shows for 6 major zones): For each zone, you see: Occurs: How often this opening scenario happens GZ_TO_R1: 38.4% (most common) AT_PP: 12.8% S1_TO_GZ: 24.2% R1_TO_R2: 9.4% S2_TO_S1: 6.3% IN_GZ: 3.8% Bull/Bear %: Close direction probability Example: GZ_TO_R1 is perfectly balanced (50.0% bull / 49.6% bear) R1_TO_R2 is bullish (58.1% bull / 41.0% bear) Levels Hit: Probability of reaching each pivot level from this opening Helps identify high-probability targets Example: From GZ_TO_R1, PP is hit 52.9%, R1 is hit 49.0%, S1 is hit 21.6% How to interpret: GZ_TO_R1 (most common): Balanced day, watch PP and GZ for direction clues AT_PP: Slight bullish bias (56.9%), high chance of touching both PP (92.8%) and GZ (90.3%) R1_TO_R2: Bullish bias (58.1%), expect continuation to R2 (58.1% chance) S2_TO_S1: Bullish reversal setup (59.9%), very high chance of S1 touch (82.8%) IN_GZ: Rare opening (3.8%), bullish bias, virtually guaranteed GZ touch (100%) Previous Day Gap Context Shows current gap scenario and typical behavior: Three scenarios: GAP UP: Opened Above Yesterday's High (20.5% of days) R1 Touch: 65.9% (high probability) R2 Touch: 42.1% S1 Touch: 15.0% (low probability) Bias: Bullish continuation GAP DOWN: Opened Below Yesterday's Low (11.3% of days) S1 Touch: 71.5% (high probability) S2 Touch: 55.2% R1 Touch: 12.1% (low probability) Bias: Bearish continuation NO GAP: Opened Within Yesterday's Range (68.2% of days) PP Touch: 69.5% GZ Touch: 71.7% R1 Touch: 35.2% Bias: Balanced (watch for direction at PP/GZ) How to interpret: Gap days (up or down) tend to continue in the gap direction When gapping, fade trades are low probability (15.0% and 12.1%) Most days (68.2%) open within previous range, making PP and GZ critical decision zones The "bias" line provides clear directional guidance for trade selection Practical Application Examples Example 1: Standard Day Setup Scenario: RTH opens at 20,450 PP: 20,400 GZ: 20,390-20,395 R1: 20,425 Previous day high: 20,460 What the tables tell you: Opening Zone: "GZ_TO_R1" (38.4% occurrence) Gap Context: "NO GAP" (68.2% occurrence) Expected behavior: Balanced (50/50 bull/bear) High probability: PP touch (52.9%), GZ touch (56.8%) Moderate probability: R1 touch (49.0%), S1 touch (21.6%) Trade plan: Wait for price to reach PP (52.9% chance) or GZ (56.8% chance) Look for directional confirmation at these levels First target R1 if bullish, S1 if bearish Avoid assuming direction without confirmation (perfectly balanced opening) Example 2: Gap Up Day Scenario: RTH opens at 20,510 Previous day high: 20,460 R1: 20,425 R2: 20,475 What the tables tell you: Gap Context: "GAP UP" (20.5% occurrence) R1 touch: 65.9% probability R2 touch: 42.1% probability S1 touch: Only 15.0% probability Bias: Bullish continuation Trade plan: Favor long setups Target R1 first (65.9% chance), then R2 (42.1%) If R1 breaks, R2 becomes likely target Shorting is low probability (only 15.0% reach S1) Example 3: Opening in Golden Zone Scenario: RTH opens at 20,393 PP: 20,400 GZ: 20,390-20,395 What the tables tell you: Opening Zone: "IN_GZ" (rare, only 3.8% occurrence) Bullish bias: 58.1% GZ touch: 100% (guaranteed - already there) PP touch: 75.3% R1 touch: 41.9% Trade plan: Expect price to test PP (75.3% chance) Slight bullish bias suggests long setups better than shorts Watch how price reacts at PP - likely to continue to R1 (41.9%) This is an uncommon opening, suggesting potential for larger moves Best Practices Match Your Instrument: Remember, statistics are NQ-specific. If trading other instruments, use the levels but disregard the statistical percentages. Combine with Price Action: Use the statistics for probability context, not as standalone signals. Always confirm with price action, volume, and your trading methodology. Adapt Table Display: Don't display all sections all the time. Toggle based on your trading phase: Pre-market: Focus on "Gap Context" to understand the setup Market open: Watch "Opening Zone Statistics" for directional bias Intraday: Monitor "Current Session Info" for position tracking Understand Context: A 60% touch rate doesn't mean guaranteed—it means 40% of days don't touch. Use these probabilities to size positions and manage expectations. Inter-Pivot Golden Zones: These are most useful when price is already in motion toward a level. For example, if price breaks above PP heading to R1, the PP-R1 golden zone (60.8% touch rate) becomes a high-probability pullback area. Time Awareness: The "Avg Time" statistics help you understand urgency. If it's 10:30 AM and S1 hasn't been touched (average is 55 minutes), the window for bearish moves is closing. Technical Notes Time Zone: All times referenced are NY/EST Session Definition: RTH is 9:30 AM - 4:00 PM EST Calculation Period: Pivots update daily based on previous 24-hour period (18:00 previous day to 17:00 current day) Data Source: Statistics derived from 12 years of NQ 1-minute futures data (2013-2025) Sample Size: 2,482 complete RTH trading sessions Disclaimer This indicator provides statistical probabilities based on historical NQ futures data. Past performance does not guarantee future results. The statistical tables are educational tools and should not be the sole basis for trading decisions. Always: Use proper risk management Combine with your own analysis Understand that probabilities are not certainties Remember that statistics are instrument-specific (NQ/MNQ only) Credits Statistical analysis performed using Python analysis of 12 years of historical NQ futures data. All pivot and golden zone calculations use standard mathematical formulas applicable to any instrument.
Wskaźnik Pine Script®
od lucymatos
Long Only - Double EMA + SessionOverview This is a high-probability Long-Only trend-following strategy designed primarily for the 65-minute and 4-hour timeframes. It utilizes a dual-layered filter system to align trades with both macro and mid-term market momentum, ensuring entries only occur during healthy uptrends. The strategy is optimized for volatile, high-growth assets like TSLA and MSFT. How It Works The strategy relies on three primary pillars of technical analysis to confirm an "A+" setup: Macro Trend Filter (200 EMA): We only look for long opportunities when the price is above the 200-period Exponential Moving Average. This keeps the strategy on the right side of the long-term trend and avoids "buying the dip" during major bear markets. Momentum Filter (50 EMA): The 50 EMA acts as a local trend filter. By requiring price to be above both EMAs, we ensure the medium-term momentum is also bullish. The Trigger (Stochastic RSI): We enter when the Stochastic RSI K-line crosses above the 20 level (Oversold). This identifies local "oversold" pullbacks within a larger uptrend. Risk Management & Exit Plan This strategy is built with professional-grade capital preservation in mind: Trailing Stop-Loss: A 5% trailing stop follows the price as it moves in our favor. This protects unrealized profits and helps mitigate the drawdown during sudden reversals. Dynamic Profit Target: The strategy exits automatically if the Stochastic RSI K-line reaches the 97 level, capturing gains at the peak of momentum. Session Filter: To avoid the "noise" of pre-market and low-volume afternoon trading, the strategy is restricted to the Market Open (9:30 AM EST) window where institutional volume is highest. Backtesting Notes Realistic Simulation: This strategy includes a 0.05% commission and 2 ticks of slippage to reflect real-world execution costs. Recommended Assets: Optimized for Nasdaq-100 components and high-volume growth stocks. Timeframe: Best performance found on 65m or 4h intervals.
Strategia Pine Script®
od suddenSeahorse88500
BUY Sell Signal (Kewme)//@version=6 indicator("EMA Cross RR Box (1:4 TP Green / SL Red)", overlay=true, max_lines_count=500, max_boxes_count=500) // ===== INPUTS ===== emaFastLen = input.int(9, "Fast EMA") emaSlowLen = input.int(15, "Slow EMA") atrLen = input.int(14, "ATR Length") slMult = input.float(1.0, "SL ATR Multiplier") rr = input.float(4.0, "Risk Reward (1:4)") // 🔥 1:4 RR // ===== EMA ===== emaFast = ta.ema(close, emaFastLen) emaSlow = ta.ema(close, emaSlowLen) plot(emaFast, color=color.green, title="EMA Fast") plot(emaSlow, color=color.red, title="EMA Slow") // ===== ATR ===== atr = ta.atr(atrLen) // ===== EMA CROSS ===== buySignal = ta.crossover(emaFast, emaSlow) sellSignal = ta.crossunder(emaFast, emaSlow) // ===== VARIABLES ===== var box tpBox = na var box slBox = na var line tpLine = na var line slLine = na // ===== BUY ===== if buySignal if not na(tpBox) box.delete(tpBox) if not na(slBox) box.delete(slBox) if not na(tpLine) line.delete(tpLine) if not na(slLine) line.delete(slLine) entry = close sl = entry - atr * slMult tp = entry + atr * slMult * rr // ✅ 1:4 TP // TP ZONE (GREEN) tpBox := box.new( left=bar_index, top=tp, right=bar_index + 20, bottom=entry, bgcolor=color.new(color.green, 80), border_color=color.green ) // SL ZONE (RED) slBox := box.new( left=bar_index, top=entry, right=bar_index + 20, bottom=sl, bgcolor=color.new(color.red, 80), border_color=color.red ) tpLine := line.new(bar_index, tp, bar_index + 20, tp, color=color.green, width=2) slLine := line.new(bar_index, sl, bar_index + 20, sl, color=color.red, width=2) label.new(bar_index, low, "BUY", style=label.style_label_up, color=color.green, textcolor=color.white) // ===== SELL ===== if sellSignal if not na(tpBox) box.delete(tpBox) if not na(slBox) box.delete(slBox) if not na(tpLine) line.delete(tpLine) if not na(slLine) line.delete(slLine) entry = close sl = entry + atr * slMult tp = entry - atr * slMult * rr // ✅ 1:4 TP // TP ZONE (GREEN) tpBox := box.new( left=bar_index, top=entry, right=bar_index + 20, bottom=tp, bgcolor=color.new(color.green, 80), border_color=color.green ) // SL ZONE (RED) slBox := box.new( left=bar_index, top=sl, right=bar_index + 20, bottom=entry, bgcolor=color.new(color.red, 80), border_color=color.red ) tpLine := line.new(bar_index, tp, bar_index + 20, tp, color=color.green, width=2) slLine := line.new(bar_index, sl, bar_index + 20, sl, color=color.red, width=2) label.new(bar_index, high, "SELL", style=label.style_label_down, color=color.red, textcolor=color.white)
Wskaźnik Pine Script®
od arup19901
TWR of Bill WilliamsThis indicator was taken from the book “Trading Chaos Pt 1” by Bill Williams. TWR contains 3 Moving Averages Ripple - MA with 5 bars length Wave - MA with 13 bars length Tide - MA with 34 bars length According to Bill Williams, you should take only a long position if the Ripple(5 bars length) is higher than Wave(13) and Tide(34). Also, you should take only a short position, if the Ripple (the fastest MA) is lower than Wave MA and Tide MA(slowest MA). This indicator is also used if you want to fill in the Profitunity Trading Partner table.
Wskaźnik Pine Script®
od vildstein
ORB | Feng FuturesThe ORB | Feng Futures indicator automatically detects the Opening Range Breakout (ORB) for each trading session, plotting the High, Low, and Midline in real time. This tool is built for futures traders who rely on ORB structure to confirm trends, identify breakout zones, and recognize reversal areas early in the session. Features: • Auto-calculated ORB High, Low, and Midline • Multi-timezone session support (NY, Chicago, London, Tokyo, etc.) • Customize ORB time range and time window for display • Real-time updating lines that freeze at session close • Optional labels with customizable size, color, and offset • Save and view multiple previous ORB sessions • Full color customization for all levels • Automatically hides on higher timeframes (Daily+) to reduce clutter • Works on ES, NQ, and all intraday futures charts • Works on stocks, crypto, forex, and other tradeable assets where ORB is applicable Disclaimer: This indicator is for educational purposes only and does not constitute financial advice. Trading futures involves significant risk and may not be suitable for all investors. Always do your own research and use proper risk management.
Wskaźnik Pine Script®
od ericfengg
Risk Size Calculator - Indices/Metals This indicator is a universal position sizing tool that automatically calculates how many contracts or units to trade based on your defined dollar risk and stop size, while intelligently adapting to the asset you’re trading. Key Features Works on any asset: indices, metals, futures, stocks, crypto, etc. Auto stop interpretation: Metals (GC, MGC, SI, SIL, etc.) → Ticks Everything else → Points Single stop input (no switching between points/ticks manually) Auto preset stops per asset class (optional) Uses TradingView’s native contract data (pointvalue, mintick) for accuracy Clean, readable top-right panel with: Risk ($) Stop (Points or Ticks, auto-labeled) Contracts / Units Actual Risk ($) Optional manual $-per-point override for edge cases or custom instruments Designed for fast execution with zero mental math and minimal chart disruption.
Wskaźnik Pine Script®
od marcusblairbrand
Live PDH/PDL Dashboard - Exact Time Fix saleem shaikh//@version=5 indicator("Live PDH/PDL Dashboard - Exact Time Fix", overlay=true) // --- 1. Stocks ki List --- s1 = "NSE:RELIANCE", s2 = "NSE:HDFCBANK", s3 = "NSE:ICICIBANK" s4 = "NSE:INFY", s5 = "NSE:TCS", s6 = "NSE:SBIN" s7 = "NSE:BHARTIARTL", s8 = "NSE:AXISBANK", s9 = "NSE:ITC", s10 = "NSE:KOTAKBANK" // --- 2. Function: Har stock ke andar jaakar breakout time check karna --- get_data(ticker) => // Kal ka High/Low (Daily timeframe se) pdh_val = request.security(ticker, "D", high , lookahead=barmerge.lookahead_on) pdl_val = request.security(ticker, "D", low , lookahead=barmerge.lookahead_on) // Aaj ka breakout check karna (Current timeframe par) curr_close = close is_pdh_break = curr_close > pdh_val is_pdl_break = curr_close < pdl_val // Breakout kab hua uska time pakadna (ta.valuewhen use karke) var float break_t = na if (is_pdh_break or is_pdl_break) and na(break_t) // Sirf pehla breakout time capture karega break_t := time // --- 3. Sabhi stocks ka Data fetch karna --- = request.security(s1, timeframe.period, get_data(s1)) = request.security(s2, timeframe.period, get_data(s2)) = request.security(s3, timeframe.period, get_data(s3)) = request.security(s4, timeframe.period, get_data(s4)) = request.security(s5, timeframe.period, get_data(s5)) = request.security(s6, timeframe.period, get_data(s6)) = request.security(s7, timeframe.period, get_data(s7)) = request.security(s8, timeframe.period, get_data(s8)) = request.security(s9, timeframe.period, get_data(s9)) = request.security(s10, timeframe.period, get_data(s10)) // --- 4. Table UI Setup --- var tbl = table.new(position.top_right, 3, 11, bgcolor=color.rgb(33, 37, 41), border_width=1, border_color=color.gray) // Row update karne ka logic updateRow(row, name, price, hi, lo, breakT) => table.cell(tbl, 0, row, name, text_color=color.white, text_size=size.small) string timeDisplay = na(breakT) ? "-" : str.format("{0,time,HH:mm}", breakT) if price > hi table.cell(tbl, 1, row, "PDH BREAK", bgcolor=color.new(color.green, 20), text_color=color.white, text_size=size.small) table.cell(tbl, 2, row, timeDisplay, text_color=color.white, text_size=size.small) else if price < lo table.cell(tbl, 1, row, "PDL BREAK", bgcolor=color.new(color.red, 20), text_color=color.white, text_size=size.small) table.cell(tbl, 2, row, timeDisplay, text_color=color.white, text_size=size.small) else table.cell(tbl, 1, row, "Normal", text_color=color.gray, text_size=size.small) table.cell(tbl, 2, row, "-", text_color=color.gray, text_size=size.small) // --- 5. Table Draw Karna --- if barstate.islast table.cell(tbl, 0, 0, "Stock", text_color=color.white, bgcolor=color.gray) table.cell(tbl, 1, 0, "Signal", text_color=color.white, bgcolor=color.gray) table.cell(tbl, 2, 0, "Time", text_color=color.white, bgcolor=color.gray) updateRow(1, "RELIANCE", c1, h1, l1, t1) updateRow(2, "HDFC BANK", c2, h2, l2, t2) updateRow(3, "ICICI BANK", c3, h3, l3, t3) updateRow(4, "INFY", c4, h4, l4, t4) updateRow(5, "TCS", c5, h5, l5, t5) updateRow(6, "SBI", c6, h6, l6, t6) updateRow(7, "BHARTI", c7, h7, l7, t7) updateRow(8, "AXIS", c8, h8, l8, t8) updateRow(9, "ITC", c9, h9, l9, t9) updateRow(10, "KOTAK", c10, h10, l10, t10)
Wskaźnik Pine Script®
od hekagamers
Global Sovereign Spread MonitorIn the summer of 2011, the yield on Italian government bonds rose dramatically while German Bund yields fell to historic lows. This divergence, measured as the BTP-Bund spread, reached nearly 550 basis points in November of that year, signaling what would become the most severe test of the European monetary union since its inception. Portfolio managers who monitored this spread had days, sometimes weeks, of advance warning before equity markets crashed. Those who ignored it suffered significant losses. The Global Sovereign Spread Monitor is built on a simple but powerful observation that has been validated repeatedly in academic literature: sovereign bond spreads contain forward-looking information about systemic risk that is not fully reflected in equity prices (Longstaff et al., 2011). When investors demand higher yields to hold peripheral government debt relative to safe-haven bonds, they are expressing a view about credit risk, liquidity conditions, and the probability of systemic stress. This information, when properly analyzed, provides actionable signals for traders across all asset classes. The Science of Sovereign Spreads The academic study of government bond yield differentials began in earnest following the creation of the European Monetary Union. Codogno, Favero and Missale (2003) published what remains one of the foundational papers in this field, examining why yields on government bonds within a currency union should differ at all. Their analysis, published in Economic Policy, identified two primary drivers: credit risk and liquidity. Countries with higher debt-to-GDP ratios and weaker fiscal positions commanded higher yields, but importantly, these spreads widened dramatically during periods of market stress even when fundamentals had not changed significantly. This observation led to a crucial insight that Favero, Pagano and von Thadden (2010) explored in depth in the Journal of Financial and Quantitative Analysis. They found that liquidity effects can amplify credit risk during stress periods, creating a feedback loop where rising spreads reduce liquidity, which in turn pushes spreads even higher. This dynamic explains why sovereign spreads often move in non-linear fashion, remaining stable for extended periods before suddenly widening rapidly. Longstaff, Pan, Pedersen and Singleton (2011) extended this research in their American Economic Review paper by examining the relationship between sovereign credit default swap spreads and bond spreads across multiple countries. Their key finding was that a significant portion of sovereign credit risk is driven by global factors rather than country-specific fundamentals. This means that when spreads widen in Italy, it often reflects broader risk aversion that will eventually affect other asset classes including equities and corporate bonds. The practical implication of this research is clear: sovereign spreads function as a leading indicator for systemic risk. Aizenman, Hutchison and Jinjarak (2013) confirmed this in their analysis of European sovereign debt default probabilities, finding that spread movements preceded rating downgrades and provided earlier warning signals than traditional fundamental analysis. How the Indicator Works The Global Sovereign Spread Monitor translates these academic findings into a systematic framework for monitoring credit conditions. The indicator calculates yield differentials between peripheral government bonds and German Bunds, which serve as the benchmark safe-haven asset in European markets. Italian ten-year yields minus German ten-year yields produce the BTP-Bund spread, the single most important metric for Eurozone stress. Spanish yields minus German yields produce the Bonos-Bund spread, providing a secondary confirmation signal. The transatlantic US-Bund spread captures divergence between the two major safe-haven markets. Raw spreads are converted to Z-scores, which measure how many standard deviations the current spread is from its historical average over the lookback period. This normalization is essential because absolute spread levels vary over time with interest rate cycles and structural changes in sovereign debt markets. A spread of 150 basis points might have been concerning in 2007 but entirely normal in 2023 following the European debt crisis and subsequent ECB interventions. The composite index combines these individual Z-scores using weights that reflect the relative importance of each spread for global risk assessment. Italy receives the highest weight because it represents the third-largest sovereign bond market globally and any Italian debt crisis would have systemic implications for the entire Eurozone. Spain provides confirmation of peripheral stress, while the US-Bund spread captures flight-to-quality dynamics between the two primary safe-haven markets. Regime classification transforms the continuous Z-score into discrete states that correspond to different market environments. The Stress regime indicates that spreads have widened to levels historically associated with crisis periods. The Elevated regime signals rising risk aversion that warrants increased attention. Normal conditions represent typical spread behavior, while the Calm regime may actually signal complacency and potential mean-reversion opportunities. Retail Trader Applications For individual traders without access to institutional research teams, the Global Sovereign Spread Monitor provides a window into the macro environment that typically remains opaque. The most immediate application is risk management for equity positions. Consider a trader holding a diversified portfolio of European stocks. When the composite Z-score rises above 1.0 and enters the Elevated regime, historical data suggests an increased probability of equity market drawdowns in the coming days to weeks. This does not mean the trader must immediately liquidate all positions, but it does suggest reducing position sizes, tightening stop-losses, or adding hedges such as put options or inverse ETFs. The BTP-Bund spread specifically provides actionable information for anyone trading EUR/USD or European equity indices. Research by De Grauwe and Ji (2013) demonstrated that sovereign spreads and currency movements are closely linked during stress periods. When the BTP-Bund spread widens sharply, the Euro typically weakens against the Dollar as investors question the sustainability of the monetary union. A retail forex trader can use the indicator to time entries into EUR/USD short positions or to exit long positions before spread-driven selloffs occur. The regime classification system simplifies decision-making for traders who cannot constantly monitor multiple data feeds. When the dashboard displays Stress, it is time to adopt a defensive posture regardless of what individual stock charts might suggest. When it displays Calm, the trader knows that risk appetite is elevated across institutional markets, which typically supports equity prices but also means that any negative catalyst could trigger a sharp reversal. Mean-reversion signals provide opportunities for more active traders. When spreads reach extreme levels in either direction, they tend to revert toward their historical average. A Z-score above 2.0 that begins declining suggests professional investors are starting to buy peripheral debt again, which historically precedes broader risk-on behavior. A Z-score below minus 1.0 that starts rising may indicate that complacency is ending and risk-off positioning is beginning. The key for retail traders is to use the indicator as a filter rather than a primary signal generator. If technical analysis suggests a long entry in European stocks, check the sovereign spread regime first. If spreads are elevated or rising, the technical setup becomes higher risk. If spreads are stable or compressing, the technical signal has a higher probability of success. Professional Applications Institutional investors use sovereign spread analysis in more sophisticated ways that go beyond simple risk filtering. Systematic macro funds incorporate spread data into quantitative models that generate trading signals across multiple asset classes simultaneously. Portfolio managers at large asset allocators use sovereign spreads to make strategic allocation decisions. When the composite Z-score trends higher over several weeks, they reduce exposure to peripheral European equities and bonds while increasing allocations to German Bunds, US Treasuries, and other safe-haven assets. This rotation often happens before explicit risk-off signals appear in equity markets, giving these investors a performance advantage. Fixed income specialists at banks and hedge funds use sovereign spreads for relative value trades. When the BTP-Bund spread widens to historically elevated levels but fundamentals have not deteriorated proportionally, they may go long Italian government bonds and short German Bunds, betting on mean reversion. These trades require careful risk management because spreads can widen further before reversing, but when properly sized they offer attractive risk-adjusted returns. Risk managers at financial institutions use sovereign spread monitoring as an input to Value-at-Risk models and stress testing frameworks. Elevated spreads indicate higher correlation among risk assets, which means diversification benefits are reduced precisely when they are needed most. This information feeds into position sizing decisions across the entire trading book. Currency traders at proprietary trading firms incorporate sovereign spreads into their EUR/USD and EUR/CHF models. The relationship between the BTP-Bund spread and EUR weakness is well-documented in academic literature and provides a systematic edge when combined with other factors such as interest rate differentials and positioning data. Central bank watchers use sovereign spreads to anticipate policy responses. The European Central Bank has demonstrated repeatedly that it will intervene when spreads reach levels that threaten financial stability, most notably through the Outright Monetary Transactions program announced in 2012 and the Transmission Protection Instrument introduced in 2022. Understanding spread dynamics helps investors anticipate these interventions and position accordingly. Interpreting the Dashboard The statistics panel provides real-time information that supports both quick assessments and deeper analysis. The composite Z-score is the primary metric, representing the weighted average of all spread Z-scores. Values above zero indicate spreads are wider than their historical average, while values below zero indicate compression. The magnitude matters: a reading of 0.5 suggests modestly elevated stress, while 2.0 or higher indicates conditions similar to historical crisis periods. The regime classification translates the Z-score into actionable categories. Stress should trigger immediate review of risk exposure and consideration of hedges. Elevated warrants increased vigilance and potentially reduced position sizes. Normal indicates no immediate concerns from sovereign markets. Calm suggests risk appetite may be elevated, which supports risk assets but also creates potential for sharp reversals if sentiment changes. The percentile ranking provides historical context by showing where the current Z-score falls within its distribution over the lookback period. A reading of 90 percent means spreads are wider than they have been 90 percent of the time over the past year, which is significant even if the absolute Z-score is not extreme. This metric helps identify when spreads are creeping higher before they reach official stress thresholds. Momentum indicates whether spreads are widening or compressing. Rising momentum during elevated spread conditions is particularly concerning because it suggests stress is accelerating. Falling momentum during stress suggests the worst may be past and mean reversion could be beginning. Individual spread readings allow traders to identify which component is driving the composite signal. If the BTP-Bund spread is elevated but Bonos-Bund remains normal, the stress may be Italy-specific rather than systemic. If all spreads are widening together, the signal reflects broader flight-to-quality that affects all risk assets. The bias indicator provides a simple summary for traders who need quick guidance. Risk-Off means spreads indicate defensive positioning is appropriate. Risk-On means spread conditions support risk-taking. Neutral means spreads provide no clear directional signal. Limitations and Risk Factors No indicator provides perfect signals, and sovereign spread analysis has specific limitations that users must understand. The European Central Bank has demonstrated its willingness to intervene in sovereign bond markets when spreads threaten financial stability. The Transmission Protection Instrument announced in 2022 specifically targets situations where spreads widen beyond levels justified by fundamentals. This creates a floor under peripheral bond prices and means that extremely elevated spreads may not persist as long as historical patterns would suggest. Political events can cause sudden spread movements that are impossible to anticipate. Elections, government formation crises, and policy announcements can move spreads by 50 basis points or more in a single session. The indicator will reflect these moves but cannot predict them. Liquidity conditions in sovereign bond markets can temporarily distort spread readings, particularly around quarter-end and year-end when banks adjust their balance sheets. These technical factors can cause spread widening or compression that does not reflect fundamental credit risk. The relationship between sovereign spreads and other asset classes is not constant over time. During some periods, spread movements lead equity moves by several days. During others, both markets move simultaneously. The indicator provides valuable information about credit conditions, but users should not expect mechanical relationships between spread signals and subsequent price moves in other markets. Conclusion The Global Sovereign Spread Monitor represents a systematic application of academic research on sovereign credit risk to practical trading decisions. The indicator monitors yield differentials between peripheral and safe-haven government bonds, normalizes these spreads using statistical methods, and classifies market conditions into regimes that correspond to different risk environments. For retail traders, the indicator provides risk management information that was previously available only to institutional investors with access to Bloomberg terminals and dedicated research teams. By checking the sovereign spread regime before executing trades, individual investors can avoid taking excessive risk during periods of elevated credit stress. For professional investors, the indicator offers a standardized framework for monitoring sovereign credit conditions that can be integrated into broader macro models and risk management systems. The real-time calculation of Z-scores, regime classifications, and component spreads provides the inputs needed for systematic trading strategies. The academic foundation is robust, built on peer-reviewed research published in top finance and economics journals over the past two decades. The practical applications have been validated through multiple market cycles including the European debt crisis of 2011-2012, the COVID-19 shock of 2020, and the rate normalization stress of 2022. Sovereign spreads will continue to provide valuable forward-looking information about systemic risk for as long as credit conditions vary across countries and investors respond rationally to changes in default probabilities. The Global Sovereign Spread Monitor makes this information accessible and actionable for traders at all levels of sophistication. References Aizenman, J., Hutchison, M. and Jinjarak, Y. (2013) What is the Risk of European Sovereign Debt Defaults? Fiscal Space, CDS Spreads and Market Pricing of Risk. Journal of International Money and Finance, 34, pp. 37-59. Codogno, L., Favero, C. and Missale, A. (2003) Yield Spreads on EMU Government Bonds. Economic Policy, 18(37), pp. 503-532. De Grauwe, P. and Ji, Y. (2013) Self-Fulfilling Crises in the Eurozone: An Empirical Test. Journal of International Money and Finance, 34, pp. 15-36. Favero, C., Pagano, M. and von Thadden, E.L. (2010) How Does Liquidity Affect Government Bond Yields? Journal of Financial and Quantitative Analysis, 45(1), pp. 107-134. Longstaff, F.A., Pan, J., Pedersen, L.H. and Singleton, K.J. (2011) How Sovereign Is Sovereign Credit Risk? American Economic Review, 101(6), pp. 2191-2212. Manganelli, S. and Wolswijk, G. (2009) What Drives Spreads in the Euro Area Government Bond Market? Economic Policy, 24(58), pp. 191-240. Arghyrou, M.G. and Kontonikas, A. (2012) The EMU Sovereign-Debt Crisis: Fundamentals, Expectations and Contagion. Journal of International Financial Markets, Institutions and Money, 22(4), pp. 658-677.
Wskaźnik Pine Script®
od EdgeTools
Sesion Operativa - Codigo InstitucionalThis indicator is designed for institutional and precision traders who need to visualize market liquidity and key session operating ranges without visual clutter. Unlike standard session indicators, this tool focuses on clarity and the projection of key levels (Highs and Lows) to identify potential future reaction zones. Key Features: 4 Customizable Sessions: Pre-configured with key institutional times (Pre-NY, NY Open, London, and Asia). Each session is fully adjustable in time, color, and style. Minimalist Labeling: Displays the session name and operating range (in pips/points) in a clean, direct format (e.g., NY - 45), removing decimals and unnecessary text to keep the chart clean. Range Projections: Option to project the Highs and Lows of each session forward (N candles) to use them as dynamic support or resistance levels. Opening Highlight (NYSE): Special feature to highlight candle colors during specific high-volatility times (default 09:30 - 09:35 UTC-5), perfect for identifying manipulation or liquidity injections at the stock market open. Adjustable Time Zone: Default setting is UTC-5 (New York), but fully adaptable to any user time zone.
Wskaźnik Pine Script®
od sebaztiansaenzX
Discipline Sleeping TimeThe Sleeping Time indicator highlights a predefined time window on the chart that represents your sleeping hours. This will help doing backtest easily by filtering out unrealistic result of trades while we are still sleeping. During the selected period: - The chart background is softly shaded to visually mark your sleep window - The first candle of the range is labeled “Sleep” - The last candle of the range is labeled “Wake Up” You can also use it for other purpose. This makes it easy to: - Visually avoid trading during sleep hours - Identify when a trading session should be inactive - Maintain discipline and consistency across different markets and timezones Key Features: - Custom Time Range Define your sleeping hours using a start and end time. - UTC Offset Selector Adjust the time window using a UTC offset dropdown (−10 to +13), so the indicator aligns correctly with your local time. - Clear Visual Markers Background shading during sleep hours - Start label: Sleep - End label: Wake Up - Customizable Labels Change label text, size, and style to suit your chart layout. Best Use Case Use this indicator to lock in rest time, avoid emotional trades, and respect personal trading boundaries. Because good trades start with good sleep 😴
Wskaźnik Pine Script®
od LightNCI