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VS Score [SpiritualHealer117]An experimental indicator that uses historical prices and readings of technical indicators to give the probability that stock and crypto prices will be in a certain range on the next close. This indicator may be helpful for options traders or for traders who want to see the probability of a move. It classifies returns into five categories: Extreme Rise - Over 2 standard deviations above normal returns Rise - Between 0.5 standard deviations and 2 standard deviations above normal returns Flat - Falling in the range of +/- 0.5 standard deviations of normal returns Fall - Between 0.5 standard deviations and 2 standard deviations below normal returns Extreme Fall - Over 2 standard deviations below normal returns It is an adaptive probability model, which trains on the previous 1000 data points, and is calculated by creating probability vectors for the current reading of the PPO, MA, volume histogram, and previous return, and combining them into one probability vector.
مؤشر Pine Script®
من ‎Scalpalytics‎
3
Luxy Sector & Industry RS AnalyzerEver wonder why some stocks soar while others in the same sector barely move? Or why your perfectly timed entry still loses money? Possibly the answer can be found in Relative Strength. The Luxy Sector & Industry RS Analyzer solves a critical problem that most traders overlook: picking strong stocks in strong sectors AND strong industries . It's not enough for a stock to go up - you want stocks that are crushing their competition at both the sector AND industry level. This indicator does the heavy lifting by automatically comparing your stock against its sector ETF, industry ETF, the broader market, sector leader, and industry leader, giving you a complete multi-level picture of relative performance. What makes this different? - Automatic sector AND industry detection - no manual setup required - Multi-level hierarchy analysis: Market → Sector → Industry → Stock - Multi-timeframe analysis (1 month to 1 year) in one glance - Industry ETF mapping (30+ industries covered) - Clear 0-100 scoring system with letter grades (A+ to F) - Works on stocks, crypto, forex, and commodities - Real-time updates with anti-repaint protection Think of it as your performance dashboard - instantly showing you if you're trading a champion or a laggard at every level of the market hierarchy. METHODOLOGY & ATTRIBUTION This indicator is based on classical Relative Strength (RS) analysis principles from technical analysis. RS methodology compares an asset's price performance against a benchmark to identify relative outperformance or underperformance. This concept has been used by professional traders and institutions for decades. Key Concepts Used: Relative Strength (RS) - Classical technical analysis concept measuring comparative performance Multi-Level Hierarchy Analysis - Market → Sector → Industry → Stock comparison Sector Rotation Analysis - Identifying which sectors are leading or lagging the market Industry Rotation Analysis - Identifying which industries are leading within their sectors Multi-period Performance Analysis - Evaluating strength across multiple timeframes Beta Calculation - Standard statistical measure of volatility relative to a benchmark DISCLAIMER: This indicator is for educational and informational purposes only. It should not be considered financial advice or a recommendation to buy or sell. Past performance does not guarantee future results. Trading involves risk and may not be suitable for all investors. Always do your own research and consult with a financial advisor before making investment decisions. with all rows visible - capture when stock has strong RS score (70+) so users can see what a "good" setup looks like] WHAT THE INDICATOR SHOWS 1. AUTOMATIC ASSET TYPE DETECTION The indicator automatically identifies what you're analyzing and adjusts accordingly: Stocks - Compares to sector ETF (XLK, XLF, XLV, etc.) and SPY Crypto - Compares to Total Crypto Market Cap and Bitcoin Forex - Compares to relevant currency index (DXY, EXY, etc.) Commodities - Compares to Gold (GLD) as benchmark Indices - Compares to broader market indices How it works: The indicator reads your chart's asset type and ticker, then automatically maps it to the correct sector or benchmark. For stocks, it uses intelligent sector detection (looking at the sector field) to match you with the right sector ETF. For example: - Technology stocks get compared to XLK (Technology Select Sector SPDR) - Financial stocks get compared to XLF (Financial Select Sector SPDR) - Healthcare stocks get compared to XLV (Health Care Select Sector SPDR) This happens instantly when you add the indicator to any chart - no configuration needed. 2. SECTOR & MARKET BENCHMARKS What is a Sector ETF? A sector ETF is an exchange-traded fund that tracks a specific industry group. For example, XLK contains all major technology companies. By comparing your stock to its sector ETF, you can see if your stock is outperforming or underperforming its peers. The indicator shows three key comparison points: Stock vs Sector (Benchmark) This tells you how your stock performs compared to companies in the same industry. Positive numbers mean your stock is beating the sector average. Negative numbers mean it's lagging behind. Stock vs Market (SPY) This shows performance against the broader S&P 500 index. This is important because even if a stock beats its sector, the entire sector might be weak. You want stocks that beat both their sector AND the market. Sector vs Market This reveals "sector rotation" - whether money is flowing into or out of this sector. When this number is positive, the whole sector is hot and leading the market. This is powerful because strong sectors tend to lift all boats, making it easier to find winners. 3. MULTI-PERIOD PERFORMANCE ANALYSIS The indicator calculates performance across four timeframes simultaneously: 1 Month (1M) - Recent short-term momentum 3 Months (3M) - Medium-term trend strength 6 Months (6M) - Longer-term positioning 1 Year (1Y) - Full-cycle performance view Why multiple periods matter: A stock might look great over 1 month but terrible over 6 months - that's a red flag. The best stocks show consistent strength across all timeframes . When you see positive RS (Relative Strength) values across all four periods, you've found a stock with sustained outperformance. Each row in the table shows: - Raw performance percentage for that period - RS value (the difference compared to benchmark) - Color coding: Green for positive, red for negative, white for neutral 4. SECTOR LEADER COMPARISON The indicator automatically identifies and compares your stock to the sector leader - the dominant stock in that industry. Sector leaders by industry: Technology: Apple (AAPL) Healthcare: UnitedHealth (UNH) Financial: JPMorgan Chase (JPM) Energy: ExxonMobil (XOM) Consumer Discretionary: Amazon (AMZN) Consumer Staples: Walmart (WMT) And more... Why this matters: Comparing to the leader shows you if you're trading a champion or a follower. If your stock consistently beats the sector leader, you've found something special. If it's lagging the leader, you might want to trade the leader instead. Optional Custom Leader: You can override the automatic leader and compare to any stock you choose. This is useful if you want to benchmark against a specific competitor or reference stock. NEW! INDUSTRY ANALYSIS (STOCKS ONLY) The indicator now provides multi-level analysis by automatically detecting and comparing your stock to its specific industry , not just the broad sector. Why Industry matters: Technology sector (XLK) contains many different industries: Software, Semiconductors, Hardware, etc. A software stock might beat the broad tech sector but lag behind other software companies. Industry analysis provides this granular view. Industry ETF Mapping (30+ industries): Software/Applications: IGV (iShares Software ETF) Semiconductors: SMH (VanEck Semiconductor ETF) Biotech: IBB (iShares Biotechnology ETF) Pharmaceuticals: XPH (SPDR Pharmaceuticals ETF) Banks: KBE (SPDR S&P Bank ETF) Regional Banks: KRE (SPDR Regional Banking ETF) Oil & Gas Exploration: XOP (SPDR Oil & Gas Exploration ETF) Homebuilders: XHB (SPDR Homebuilders ETF) Retail: XRT (SPDR S&P Retail ETF) Aerospace & Defense: ITA (iShares U.S. Aerospace & Defense ETF) And many more... Industry Leader Mapping: The indicator also identifies the leader within each industry: Software: Microsoft (MSFT) Semiconductors: NVIDIA (NVDA) Biotech: Amgen (AMGN) Pharmaceuticals: Eli Lilly (LLY) Banks: JPMorgan (JPM) Oil Exploration: ConocoPhillips (COP) And more... New Table Rows for Stocks: Industry ETF Performance - How the specific industry performed (green background) Industry Leader Performance - How the top stock in the industry performed vs Industry RS - Your stock's outperformance vs its industry ETF Industry vs Sector RS - Is this industry hot or cold within its sector? vs Industry Leader RS - Your stock's performance vs the industry's best Why this is powerful: A stock that beats both its sector AND its industry is showing strength at every level. This indicates true relative strength, not just riding sector-wide momentum. Optional Custom Industry: You can override automatic detection for both Industry ETF and Industry Leader in settings. 5. RS SCORE & GRADING SYSTEM (0-100) The heart of the indicator is the RS Score - a weighted calculation that distills all the performance data into one clear number from 0 to 100. How the score is calculated: FOR STOCKS (with Industry data): The indicator splits the weight between Sector (60%) and Industry (40%): SECTOR RS (60% of total weight): 1 Month RS: 24% weight (40% × 0.6) 3 Month RS: 18% weight (30% × 0.6) 6 Month RS: 12% weight (20% × 0.6) 1 Year RS: 6% weight (10% × 0.6) INDUSTRY RS (40% of total weight): 1 Month RS: 16% weight (40% × 0.4) 3 Month RS: 12% weight (30% × 0.4) 6 Month RS: 8% weight (20% × 0.4) 1 Year RS: 4% weight (10% × 0.4) FOR OTHER ASSETS (Crypto, Forex, Commodities): Uses full 100% weight on benchmark: 1 Month RS: 40% weight 3 Month RS: 30% weight 6 Month RS: 20% weight 1 Year RS: 10% weight It starts at 50 (neutral) and adds or subtracts points based on your asset's relative strength in each period. Bonus points: +5 points if the sector is outperforming the market (sector rotation is bullish) +5 points if the industry is outperforming its sector (hot industry) - STOCKS ONLY +5 points if RS momentum is improving (getting stronger over time) -5 points if RS momentum is declining (getting weaker) The final score is capped between 0-100. Letter Grade System: 90-100: A+ - Elite performer, crushing the sector 85-89: A - Excellent, strong outperformer 80-84: A- - Very good, above average 75-79: B+ - Good, solid performer 70-74: B - Above average, decent strength 65-69: B- - Slightly above average 60-64: C+ - Average, neutral strength 55-59: C - Below average 50-54: C- - Weak, slight underperformance 45-49: D+ - Concerning weakness 40-44: D - Poor, significant underperformance 0-39: F - Failing, avoid this stock What scores mean for trading: - RS Score above 70: Strong stocks worth considering for long positions - RS Score 50-70: Average stocks, better opportunities elsewhere - RS Score below 50: Weak stocks, avoid or consider for shorts 6. CONSISTENCY SCORE This metric shows what percentage of time periods show positive RS . For STOCKS (with Industry data): Counts both Sector RS periods AND Industry RS periods (up to 8 total periods): - If a stock beats both sector and industry in all 4 periods each: Consistency = 100% (8/8) - If it beats in 6 out of 8 total periods: Consistency = 75% - If it beats in 4 out of 8 total periods: Consistency = 50% For OTHER ASSETS: Counts benchmark periods only (4 total): - If it beats benchmark in all 4 periods (1M, 3M, 6M, 1Y): Consistency = 100% - If it beats in 3 out of 4 periods: Consistency = 75% - If it beats in 2 out of 4 periods: Consistency = 50% Why consistency matters: A high RS Score with low consistency might indicate a recent spike that could fade. The best stocks show both high RS Score AND high consistency - they're strong now AND have been strong historically at both the sector AND industry level. Look for stocks with: Consistency above 75%: Very reliable strength across all levels Consistency 50-75%: Decent but check other metrics Consistency below 50%: Weak or erratic, proceed with caution 7. BETA CALCULATION (Volatility Measure) Beta measures how much more volatile your stock is compared to its sector. Beta > 1.2 : High volatility - stock moves more aggressively than sector (marked as "High") Beta 0.8-1.2 : Normal volatility - moves roughly in line with sector Beta < 0.8 : Low volatility - stock is more stable than sector (marked as "Low") Formula used: Beta = Correlation(Stock, Sector) × (Standard Deviation of Stock / Standard Deviation of Sector) This uses a 20-period calculation for reliability. How to use Beta: - High Beta stocks offer bigger gains but also bigger risks - good for aggressive traders - Low Beta stocks are more defensive - good for conservative positions - Match Beta to your risk tolerance and strategy 8. DAYS ABOVE/BELOW SECTOR This tracks consecutive periods (bars) where your stock outperforms or underperforms its sector. Days Above Sector: Counts how many bars in a row your stock has beaten the sector. 10+ days: Strong sustained strength (shown in bright green) 5-9 days: Building momentum (shown in yellow) 1-4 days: Early strength (shown in white) 0 days: Not currently outperforming Days Below Sector: Counts how many bars in a row your stock has lagged the sector. 10+ days: Sustained weakness (shown in bright red) 5-9 days: Losing momentum (shown in orange) 1-4 days: Minor weakness (shown in white) 0 days: Not underperforming (this is good!) Why this matters: Long streaks show trend persistence. A stock with 15+ days above sector is riding strong momentum. A stock with 15+ days below sector is in a sustained downtrend relative to peers. 9. PRICE VS 52-WEEK HIGH Shows where current price sits relative to its 52-week high (or equivalent for your timeframe). 95%+ (green) : Stock is near all-time highs - strong positioning 80-94% (yellow) : Stock is in a pullback but still relatively strong Below 80% : Stock has pulled back significantly from highs Why this matters: The strongest stocks stay near their highs. When you see a stock with high RS Score AND price near 52W high, you've found a stock with institutional support and strong buying pressure. 10. RELATIVE VOLUME Compares current volume to the 20-period average volume. 1.5x+ (green) : High volume - significant interest and participation Around 1.0x : Average volume - normal trading activity Below 1.0x : Low volume - less interest or inactive period Why volume matters: High relative volume confirms price moves. When a stock makes a strong move on 2x or 3x normal volume, it's more likely to sustain. Low volume moves are often just noise. 11. AVERAGE RS STRENGTH This calculates the average absolute value of all RS readings across the four timeframes. It shows the magnitude of divergence from the sector, regardless of direction. A high number means the stock moves very differently from its sector (could be much stronger or much weaker). A low number means it tracks closely with the sector. High Average RS: Stock has strong character, moves independently Low Average RS: Stock follows sector closely, lacks individual strength 12. SECTOR ROTATION SIGNAL This indicator automatically detects when a sector is experiencing bullish rotation - meaning money is flowing into the sector and it's outperforming the broader market. Condition for bullish rotation: Sector must be beating SPY (market) in both 1-month AND 3-month periods. Why this matters: Stocks in hot sectors tend to perform better because they have tailwinds from sector-wide buying. When sector rotation is bullish and your stock has a high RS Score, you've found an ideal setup. The indicator adds +5 bonus points to the RS Score when sector rotation is bullish. 13. MOMENTUM DETECTION The indicator compares 1-month RS to 3-month RS to detect if momentum is improving or declining. RS Momentum Improving: 1M RS is better than 3M RS - stock is getting stronger (adds +5 to score) RS Momentum Declining: 1M RS is worse than 3M RS - stock is getting weaker (subtracts -5 from score) Why momentum matters: You want to catch stocks as momentum is building, not after it's already peaked. Improving momentum suggests the strength is accelerating, not fading. 14. OVERALL ASSESSMENT & RECOMMENDATION The indicator provides two quick summary rows: Overall Rating: Based on grade and RS Score, you get an instant quality rating: Strong Leader (A/A+) - Top tier stock, crushing it Above Average (A-/B+) - Solid performer, better than most Average (B/B-) - Middle of the pack Below Average (C/C+) - Struggling, watch carefully Underperformer (D/F) - Weak stock, underperforming badly Trading Signal: Combines multiple factors to give setup quality: STRONG BUY SETUP - RS Score 70+, Consistency 75+, AND sector rotation bullish. This is the perfect storm - strong stock, consistent strength, hot sector. BULLISH - RS Score 60+, Consistency 50+. Good quality stock worth considering. NEUTRAL - RS Score 50+. Okay but not exciting, better opportunities exist. WEAK - RS Score 40-49. Below average, risky. AVOID - RS Score below 40. Stay away, too weak. IMPORTANT: These are educational signals only, not financial advice. Always do your own analysis and risk management. KEY FEATURES 1. AUTOMATIC EVERYTHING - Auto-detects asset type (stock, crypto, forex, commodity, index) - Auto-maps stocks to correct sector ETF (11 sectors covered) - Auto-maps stocks to correct industry ETF (30+ industries covered) - Auto-identifies sector leader AND industry leader - Auto-selects appropriate market benchmark - Zero configuration required - just add to chart 2. MULTI-ASSET SUPPORT Works on all asset classes: US Stocks - Compares to sector ETFs (XLK, XLF, XLV, etc.) Crypto - Compares to Total Crypto Market Cap Forex - Compares to currency indices (DXY, EXY, etc.) Commodities - Compares to Gold (GLD) Indices - Compares to broader market benchmarks 3. FLEXIBLE DISPLAY 9 table positions (top/middle/bottom, left/center/right) 4 size options (tiny, small, normal, large) Show/hide table completely Real-time indicator toggle 4. TIMEFRAME FLEXIBILITY Choose your analysis timeframe: Chart Timeframe (default) - Uses whatever timeframe your chart is on Fixed: 1 Hour, 4 Hours, Daily, Weekly - Forces calculations to specific timeframe This means you can be on a 5-minute chart but analyze RS on Daily timeframe if you prefer. 5. RS SCORE FILTERING Set a minimum RS Score threshold to only see strong stocks: Set to 0 - Shows all stocks Set to 70 - Only displays stocks with RS Score 70+ (strong stocks only) Warning message displays if stock doesn't meet threshold Perfect for screening - quickly scan multiple charts and the indicator only shows tables for stocks that pass your quality filter. 6. CUSTOM LEADER COMPARISON Override automatic leader detection: Compare to any ticker you choose Benchmark against specific competitors Use your own reference stocks 7. COMPREHENSIVE TOOLTIPS Every input parameter and every table row has detailed tooltips explaining: What the metric measures How to interpret the values What thresholds indicate strength/weakness Why it matters for trading Hover over any element to learn - it's like having a trading coach built in. 8. SMART ALERTS Built-in alert system for key events: Divergence Alerts: Get notified when your stock diverges significantly from its sector. Bullish Divergence: Stock beating sector by threshold percentage Bearish Divergence: Stock losing to sector by threshold percentage Set your threshold (default 5%) - this determines how big a divergence triggers the alert. RS Score Alerts: Get notified when RS Score crosses your threshold: Crossed Above: RS Score went from below to above your threshold (bullish) Crossed Below: RS Score dropped from above to below threshold (bearish) Set your threshold (default 70) to focus on strong stocks. Sector Rotation Alert: Fires when sector shows bullish rotation (outperforming market). HOW TO USE THE INDICATOR FOR SWING TRADERS: 1. Add indicator to your watchlist stocks 2. Look for RS Score 70+ with Consistency 75%+ 3. Check if sector rotation is bullish (bonus!) 4. Verify price is near 52W high (95%+) 5. Wait for entry setup on your chart 6. Use stop loss below key support Example Setup: Stock shows: - RS Score: 82 (Grade: A-) - Consistency: 100% (strong across all periods) - Sector Rotation: Bullish - Price vs 52W High: 96% - Days Above Sector: 12 days - Relative Volume: 1.8x This is a textbook strong stock in a hot sector near highs - ideal for swing long. FOR POSITION TRADERS: 1. Focus on 6-month and 1-year RS values 2. Look for sustained outperformance (Consistency 75%+) 3. Prefer lower Beta stocks (less volatility) 4. Check Days Above Sector for trend persistence 5. Monitor RS Score monthly, exit if drops below 60 FOR ACTIVE TRADERS: 1. Use on intraday timeframes (1H or 4H) 2. Set RS Score filter to 60+ for quick screening 3. Enable Divergence Alerts 4. Watch for momentum improving signal 5. Higher Beta stocks offer more movement FOR SHORT SELLERS: 1. Look for RS Score below 40 (Grade: D or F) 2. Check for declining momentum 3. Verify Days Below Sector is increasing (10+) 4. Sector rotation should be bearish 5. Price should be well off 52W high WHAT MAKES A PERFECT SETUP: The holy grail combination: RS Score: 75+ (A- or better) Consistency: 80%+ (strong across time - beats sector AND industry) Sector Rotation: Bullish (hot sector) Industry vs Sector: Positive (hot industry within sector) Days Above Sector: 10+ (sustained strength) Momentum: Improving (getting stronger) Price vs 52W High: 90%+ (near highs) Relative Volume: 1.5x+ (volume confirmation) When you find this combination, you've located a stock with every advantage in its favor - strong at the stock level, industry level, AND sector level. That's multi-level confirmation of relative strength. IMPORTANT NOTES Data Reliability: All calculations use lookahead=off for anti-repaint protection Historical values will never change Real-time indicator toggle only affects the visual clock icon, not data reliability All security requests are properly configured to prevent future data leakage Sector Mapping Notes: Sector detection uses TradingView's sector field Some stocks may not have sector data - indicator will adapt Sector ETFs used: XLK, XLF, XLV, XLE, XLY, XLP, XLI, XLB, XLRE, XLU, XLC Major market ETFs (SPY, QQQ, DIA) are treated as market benchmarks, not stocks Multi-Asset Notes: Crypto compares to CRYPTOCAP:TOTAL (total crypto market cap) Forex compares to relevant currency index based on base currency Commodities compare to Gold (GLD) as primary commodity benchmark Custom leaders can be set for any asset type FREQUENTLY ASKED QUESTIONS Q: What does RS Score of 75 actually mean? A: It means your stock is strongly outperforming its sector across multiple timeframes. The score is weighted toward recent performance (1-month gets 40% weight), so 75 indicates sustained relative strength with emphasis on current momentum. Q: My stock has high RS Score but is going down. Why? A: RS Score measures relative performance (vs sector/market), not absolute price direction. A stock can fall 5% while its sector falls 10% - that's still positive relative strength. In bear markets or sector corrections, high RS stocks often fall less than peers. Q: Should I only trade stocks with RS Score above 70? A: For long positions, yes - focus on 70+ scores. These stocks have proven they can beat their sector. However, for pairs trading or relative value plays, you might also short stocks with scores below 40 while longing stocks above 70. Q: What if my stock doesn't have a sector? A: The indicator handles this gracefully. If no sector is detected, it will compare directly to the market (SPY for stocks). Some rows may show N/A, but the indicator will still provide useful market-relative data. Q: Why does the sector sometimes show N/A? A: This happens when: 1) Your asset has no sector classification, 2) The stock IS the sector ETF itself, 3) You're analyzing a non-stock asset (crypto, forex, commodity). The indicator adapts by focusing on market-relative metrics instead. Q: Can I use this on cryptocurrencies? A: Yes! The indicator automatically detects crypto and compares to the Total Crypto Market Cap (CRYPTOCAP:TOTAL). You can also set a custom leader like Bitcoin (BTCUSD) to compare against the dominant crypto. Q: What's the difference between RS Score and Consistency? A: RS Score is the weighted average of how much you're beating the sector (magnitude). Consistency is what percentage of time periods show outperformance (reliability). You want both high - that means strong AND consistent. Q: Do the alerts repaint? A: No. All alerts fire only on bar close (barstate.isconfirmed) and use properly configured data with lookahead=off. Once an alert fires, it's final and won't change. Q: What timeframe should I use? A: For swing trading: Daily or Weekly. For day trading: 1H or 4H. For position trading: Weekly. Use "Chart Timeframe" mode and switch your chart timeframe to change the analysis period easily. Q: Why is Days Above Sector showing 0? A: This means your stock is not currently outperforming its sector. If Days Below Sector is also 0, it means the RS is exactly neutral (very rare). Check the actual RS values to see current standing. Q: Can I compare to a different market benchmark than SPY? A: Currently the indicator uses SPY (S&P 500) as the default US stock market benchmark. For crypto it uses CRYPTOCAP:TOTAL, for forex it uses currency indices, etc. The benchmark auto-adjusts based on asset type. Q: What's a good Beta value? A: It depends on your strategy. Aggressive traders prefer Beta above 1.2 (more volatility = bigger moves). Conservative traders prefer Beta 0.8-1.0 (more stable). Beta is neutral - it's about matching your risk tolerance. Q: How often does the table update? A: With Real-time Indicator enabled: Every tick (constant updates). With it disabled: Only on bar close. Either way, the underlying data is identical and non-repainting - the toggle only affects update frequency and the clock icon display. Q: My stock is showing "AVOID" but it's up 50% this year. Is the indicator wrong? A: Not necessarily. The indicator measures RELATIVE performance. If your stock is up 50% but the sector is up 100%, your stock is actually underperforming by 50%. The indicator helps you identify when you should switch to stronger stocks in the same sector. Q: What does "Strong Buy Setup" really mean? A: It means three things aligned: 1) RS Score above 70 (strong stock), 2) Consistency above 75% (reliable strength), 3) Sector rotation is bullish (hot sector). This combination historically correlates with stocks that continue outperforming. However, this is NOT financial advice - always do your own analysis. Q: Can I use this for options trading? A: Yes! High RS Score stocks make good candidates for call options (bullish bets) while low RS Score stocks may work for puts (bearish bets). Higher Beta stocks will have more volatile options (higher premiums but more movement). Q: Why is my crypto showing N/A for sector? A: Cryptocurrencies don't have "sectors" like stocks do. Instead, the indicator compares crypto to the total crypto market cap. This is normal and expected behavior. Q: What happens if I'm analyzing an ETF? A: If you're analyzing a sector ETF (like XLK), it will compare to SPY (market). If you're analyzing SPY itself, some comparisons won't be available (can't compare SPY to itself). The indicator intelligently adapts to avoid circular comparisons. Q: What if my stock doesn't have industry data? A: Not all stocks are mapped to specific industries (only 30+ major industries are covered). If no industry is detected, the indicator will still work using only sector analysis. The RS Score calculation will use 100% sector weight instead of the 60%/40% split. Q: Why does Industry vs Sector matter? A: Industry vs Sector shows if your specific industry is hot or cold within its broader sector. For example, Semiconductors (SMH) might be outperforming Technology sector (XLK) even though both are up. This helps you find not just strong sectors, but the strongest industries within those sectors. Q: Can I disable Industry analysis? A: Yes! In the "Industry Analysis" settings group, you can toggle off "Show Industry Analysis in Table" to hide all industry rows. However, even when hidden, industry data still contributes to the RS Score calculation for stocks. Q: Why is my Consistency Score lower for stocks than other assets? A: For stocks with industry data, Consistency counts 8 periods (4 Sector + 4 Industry periods) instead of just 4. This means the bar is higher - your stock needs to beat both sector AND industry consistently. A stock that beats sector in all 4 periods but lags industry in 2 periods will show 75% consistency (6/8), not 100%. BEST PRACTICES Use as a screening tool - Set RS Score filter to 70+ and quickly scan your watchlist. Only strong stocks will show the table. Combine with technical analysis - RS Score tells you WHAT to trade, your chart tells you WHEN to enter. Check multiple timeframes - Switch between Daily and Weekly to see if strength holds across different time horizons. Monitor sector rotation - When sector goes from bearish to bullish rotation, it's often a great time to enter stocks in that sector. Watch Industry vs Sector - Stocks in hot industries within hot sectors have double tailwinds. Prioritize Industry vs Sector positive values. Pay attention to consistency - High RS Score with low consistency might be a spike that fades. Look for 70%+ consistency across BOTH sector and industry. Use the leader comparison - If your stock consistently beats both sector leader AND industry leader, you may have found the next champion. Watch days above/below sector - Long streaks (15+ days) indicate strong trends. Look for these in conjunction with high RS Score. Set alerts on key stocks - Enable RS Score alerts at 70 threshold to get notified when watchlist stocks become strong. Consider Beta for position sizing - Size smaller positions in high Beta stocks, larger in low Beta stocks for balanced risk. Exit when RS Score drops - If a stock's RS Score falls below 60, consider reducing or exiting - the strength may be fading. Leverage industry-level insight - If Industry ETF is weak but stock is strong, that's standout strength. If Industry is hot but stock is lagging, consider switching to the industry leader instead. SETTINGS EXPLAINED Display Settings: Show Performance Table - Master on/off switch for the table Table Position - 9 positions available (corners, edges, center) Table Size - 4 sizes (tiny, small, normal, large) for different screen sizes Timeframe Settings: Chart Timeframe (recommended) - Dynamic, uses whatever chart TF you're on Fixed Timeframes - Locks analysis to 1H, 4H, Daily, or Weekly regardless of chart Filtering Settings: Minimum RS Score - Set threshold (0-100) for displaying table Show Warning - When enabled, displays message if stock doesn't meet filter Alert Settings: Divergence Alerts - Enable alerts when stock diverges from sector Threshold (%) - How big a divergence triggers alert (default 5%) RS Score Alerts - Enable alerts when RS Score crosses threshold Threshold - What RS Score level triggers alert (default 70) Sector Analysis Settings: Use Custom Sector ETF - Override automatic sector ETF detection Sector ETF Symbol - Enter any sector ETF to compare against Use Custom Sector Leader - Override automatic sector leader detection Sector Leader Symbol - Enter any ticker as sector leader Industry Analysis Settings: Use Custom Industry ETF - Override automatic industry ETF detection Industry ETF Symbol - Enter specific industry ETF (e.g., IGV, SMH) Use Custom Industry Leader - Override automatic industry leader detection Industry Leader Symbol - Enter specific industry leader Show Industry Analysis - Toggle all industry rows on/off Display Settings: Show Real-time Indicator - Toggle clock icon in header (doesn't affect data) WHAT THIS INDICATOR DOESN'T DO To set proper expectations: Does NOT provide entry/exit signals - this is a strength analyzer, not a trading system Does NOT predict future price movement - shows current and historical relative strength Does NOT guarantee profits - strong RS stocks can still decline Does NOT replace your own analysis - use as one tool among many Does NOT work on stocks with no sector data - will adapt but some rows show N/A This indicator is a decision support tool . It helps you identify which stocks are showing relative strength so you can make more informed trading decisions. You still need your own entry strategy, risk management, and position sizing rules. SUPPORT & CONTACT Questions or feedback? Use the comments section below or send me a message. If you find this indicator useful, please give it a boost and share with other traders who might benefit from relative strength analysis. FINAL REMINDER This indicator is a tool for analyzing relative strength - it shows you which stocks are outperforming their sector and market. It does NOT provide financial advice or trade signals. Always conduct your own research, manage your risk appropriately, and consult with a financial advisor before making investment decisions. Past performance of relative strength does not guarantee future results. Strong stocks can become weak, and sectors rotate in and out of favor. Use this indicator as part of a comprehensive trading strategy, not as a standalone decision-making system. Trade smart, manage risk, and may your RS Scores stay high! If you got till here and you like my work a BOOST and a COMMENT would make me happy
مؤشر Pine Script®
من ‎orenluxy‎
4
Advanced Correlation Monitor📊 Advanced Correlation Monitor - Pine Script v6 🎯 What does this indicator do? Monitors real-time correlations between 13 different asset pairs and alerts you when historically strong correlations break, indicating potential trading opportunities or changes in market dynamics. 🚀 Key Features ✨ Multi-Market Monitoring 7 Forex Pairs (GBPUSD/DXY, EURUSD/GBPUSD, etc.) 6 Index/Stock Pairs (SPY/S&P500, DAX/NASDAQ, TSLA/NVDA, etc.) Fully configurable - change any pair from inputs 📈 Dual Correlation Analysis Long Period (90 bars): Identifies historically strong correlations Short Period (6 bars): Detects recent breakdowns Pearson Correlation using Pine Script v6 native functions 🎨 Intuitive Visualization Real-time table with 6 information columns Color coding: Green (correlated), Red (broken), Gray (normal) Visual states: 🟢 OK, 🔴 BROKEN, ⚫ NORMAL 🚨 Smart Alert System Only alerts previously correlated pairs (>80% historical) Detects breakdowns when short correlation <80% Consolidated alert with all affected pairs 🛠️ Flexible Configuration Adjustable Parameters: 📅 Periods: Long (30-500), Short (2-50) 🎯 Threshold: 50%-99% (default 80%) 🎨 Table: Configurable position and size 📊 Symbols: All pairs are configurable Default Pairs: FOREX: INDICES/STOCKS: - GBPUSD vs DXY • SPY vs S&P500 - EURUSD vs GBPUSD • DAX vs S&P500 - EURUSD vs DXY • DAX vs NASDAQ - USDCHF vs DXY • TSLA vs NVDA - GBPUSD vs USDCHF • MSFT vs NVDA - EURUSD vs USDCHF • AAPL vs NVDA - EURUSD vs EURCAD 💡 Practical Use Cases 🔄 Pairs Trading Detects when strong correlations break for: Statistical arbitrage Mean reversion trading Divergence opportunities 🛡️ Risk Management Identifies when "safe" assets start moving independently: Portfolio diversification Smart hedging Regime change detection 📊 Market Analysis Understand underlying market structure: Forex/DXY correlations Tech sector rotation Regional market disconnection 🎓 Results Interpretation Reading Example: EURUSD vs DXY: -98.57% → -98.27% | 🟢 OK └─ Perfect negative correlation maintained (EUR rises when DXY falls) TSLA vs NVDA: 78.12% → 0% | ⚫ NORMAL └─ Lost tech correlation (divergence opportunity) Trading Signals: 🟢 → 🔴: Broken correlation = Possible opportunity Large difference: Indicates correlation tension Multiple breaks: Market regime change
مؤشر Pine Script®
من ‎didaz‎
Dimensional Resonance ProtocolDimensional Resonance Protocol 🌀 CORE INNOVATION: PHASE SPACE RECONSTRUCTION & EMERGENCE DETECTION The Dimensional Resonance Protocol represents a paradigm shift from traditional technical analysis to complexity science. Rather than measuring price levels or indicator crossovers, DRP reconstructs the hidden attractor governing market dynamics using Takens' embedding theorem, then detects emergence —the rare moments when multiple dimensions of market behavior spontaneously synchronize into coherent, predictable states. The Complexity Hypothesis: Markets are not simple oscillators or random walks—they are complex adaptive systems existing in high-dimensional phase space. Traditional indicators see only shadows (one-dimensional projections) of this higher-dimensional reality. DRP reconstructs the full phase space using time-delay embedding, revealing the true structure of market dynamics. Takens' Embedding Theorem (1981): A profound mathematical result from dynamical systems theory: Given a time series from a complex system, we can reconstruct its full phase space by creating delayed copies of the observation. Mathematical Foundation: From single observable x(t), create embedding vectors: X(t) = Where: • d = Embedding dimension (default 5) • τ = Time delay (default 3 bars) • x(t) = Price or return at time t Key Insight: If d ≥ 2D+1 (where D is the true attractor dimension), this embedding is topologically equivalent to the actual system dynamics. We've reconstructed the hidden attractor from a single price series. Why This Matters: Markets appear random in one dimension (price chart). But in reconstructed phase space, structure emerges—attractors, limit cycles, strange attractors. When we identify these structures, we can detect: • Stable regions : Predictable behavior (trade opportunities) • Chaotic regions : Unpredictable behavior (avoid trading) • Critical transitions : Phase changes between regimes Phase Space Magnitude Calculation: phase_magnitude = sqrt(Σ ² for i = 0 to d-1) This measures the "energy" or "momentum" of the market trajectory through phase space. High magnitude = strong directional move. Low magnitude = consolidation. 📊 RECURRENCE QUANTIFICATION ANALYSIS (RQA) Once phase space is reconstructed, we analyze its recurrence structure —when does the system return near previous states? Recurrence Plot Foundation: A recurrence occurs when two phase space points are closer than threshold ε: R(i,j) = 1 if ||X(i) - X(j)|| < ε, else 0 This creates a binary matrix showing when the system revisits similar states. Key RQA Metrics: 1. Recurrence Rate (RR): RR = (Number of recurrent points) / (Total possible pairs) • RR near 0: System never repeats (highly stochastic) • RR = 0.1-0.3: Moderate recurrence (tradeable patterns) • RR > 0.5: System stuck in attractor (ranging market) • RR near 1: System frozen (no dynamics) Interpretation: Moderate recurrence is optimal —patterns exist but market isn't stuck. 2. Determinism (DET): Measures what fraction of recurrences form diagonal structures in the recurrence plot. Diagonals indicate deterministic evolution (trajectory follows predictable paths). DET = (Recurrence points on diagonals) / (Total recurrence points) • DET < 0.3: Random dynamics • DET = 0.3-0.7: Moderate determinism (patterns with noise) • DET > 0.7: Strong determinism (technical patterns reliable) Trading Implication: Signals are prioritized when DET > 0.3 (deterministic state) and RR is moderate (not stuck). Threshold Selection (ε): Default ε = 0.10 × std_dev means two states are "recurrent" if within 10% of a standard deviation. This is tight enough to require genuine similarity but loose enough to find patterns. 🔬 PERMUTATION ENTROPY: COMPLEXITY MEASUREMENT Permutation entropy measures the complexity of a time series by analyzing the distribution of ordinal patterns. Algorithm (Bandt & Pompe, 2002): 1. Take overlapping windows of length n (default n=4) 2. For each window, record the rank order pattern Example: → pattern (ranks from lowest to highest) 3. Count frequency of each possible pattern 4. Calculate Shannon entropy of pattern distribution Mathematical Formula: H_perm = -Σ p(π) · ln(p(π)) Where π ranges over all n! possible permutations, p(π) is the probability of pattern π. Normalized to : H_norm = H_perm / ln(n!) Interpretation: • H < 0.3 : Very ordered, crystalline structure (strong trending) • H = 0.3-0.5 : Ordered regime (tradeable with patterns) • H = 0.5-0.7 : Moderate complexity (mixed conditions) • H = 0.7-0.85 : Complex dynamics (challenging to trade) • H > 0.85 : Maximum entropy (nearly random, avoid) Entropy Regime Classification: DRP classifies markets into five entropy regimes: • CRYSTALLINE (H < 0.3): Maximum order, persistent trends • ORDERED (H < 0.5): Clear patterns, momentum strategies work • MODERATE (H < 0.7): Mixed dynamics, adaptive required • COMPLEX (H < 0.85): High entropy, mean reversion better • CHAOTIC (H ≥ 0.85): Near-random, minimize trading Why Permutation Entropy? Unlike traditional entropy methods requiring binning continuous data (losing information), permutation entropy: • Works directly on time series • Robust to monotonic transformations • Computationally efficient • Captures temporal structure, not just distribution • Immune to outliers (uses ranks, not values) ⚡ LYAPUNOV EXPONENT: CHAOS vs STABILITY The Lyapunov exponent λ measures sensitivity to initial conditions —the hallmark of chaos. Physical Meaning: Two trajectories starting infinitely close will diverge at exponential rate e^(λt): Distance(t) ≈ Distance(0) × e^(λt) Interpretation: • λ > 0 : Positive Lyapunov exponent = CHAOS - Small errors grow exponentially - Long-term prediction impossible - System is sensitive, unpredictable - AVOID TRADING • λ ≈ 0 : Near-zero = CRITICAL STATE - Edge of chaos - Transition zone between order and disorder - Moderate predictability - PROCEED WITH CAUTION • λ < 0 : Negative Lyapunov exponent = STABLE - Small errors decay - Trajectories converge - System is predictable - OPTIMAL FOR TRADING Estimation Method: DRP estimates λ by tracking how quickly nearby states diverge over a rolling window (default 20 bars): For each bar i in window: δ₀ = |x - x | (initial separation) δ₁ = |x - x | (previous separation) if δ₁ > 0: ratio = δ₀ / δ₁ log_ratios += ln(ratio) λ ≈ average(log_ratios) Stability Classification: • STABLE : λ < 0 (negative growth rate) • CRITICAL : |λ| < 0.1 (near neutral) • CHAOTIC : λ > 0.2 (strong positive growth) Signal Filtering: By default, NEXUS requires λ < 0 (stable regime) for signal confirmation. This filters out trades during chaotic periods when technical patterns break down. 📐 HIGUCHI FRACTAL DIMENSION Fractal dimension measures self-similarity and complexity of the price trajectory. Theoretical Background: A curve's fractal dimension D ranges from 1 (smooth line) to 2 (space-filling curve): • D ≈ 1.0 : Smooth, persistent trending • D ≈ 1.5 : Random walk (Brownian motion) • D ≈ 2.0 : Highly irregular, space-filling Higuchi Method (1988): For a time series of length N, construct k different curves by taking every k-th point: L(k) = (1/k) × Σ|x - x | × (N-1)/(⌊(N-m)/k⌋ × k) For different values of k (1 to k_max), calculate L(k). The fractal dimension is the slope of log(L(k)) vs log(1/k): D = slope of log(L) vs log(1/k) Market Interpretation: • D < 1.35 : Strong trending, persistent (Hurst > 0.5) - TRENDING regime - Momentum strategies favored - Breakouts likely to continue • D = 1.35-1.45 : Moderate persistence - PERSISTENT regime - Trend-following with caution - Patterns have meaning • D = 1.45-1.55 : Random walk territory - RANDOM regime - Efficiency hypothesis holds - Technical analysis least reliable • D = 1.55-1.65 : Anti-persistent (mean-reverting) - ANTI-PERSISTENT regime - Oscillator strategies work - Overbought/oversold meaningful • D > 1.65 : Highly complex, choppy - COMPLEX regime - Avoid directional bets - Wait for regime change Signal Filtering: Resonance signals (secondary signal type) require D < 1.5, indicating trending or persistent dynamics where momentum has meaning. 🔗 TRANSFER ENTROPY: CAUSAL INFORMATION FLOW Transfer entropy measures directed causal influence between time series—not just correlation, but actual information transfer. Schreiber's Definition (2000): Transfer entropy from X to Y measures how much knowing X's past reduces uncertainty about Y's future: TE(X→Y) = H(Y_future | Y_past) - H(Y_future | Y_past, X_past) Where H is Shannon entropy. Key Properties: 1. Directional : TE(X→Y) ≠ TE(Y→X) in general 2. Non-linear : Detects complex causal relationships 3. Model-free : No assumptions about functional form 4. Lag-independent : Captures delayed causal effects Three Causal Flows Measured: 1. Volume → Price (TE_V→P): Measures how much volume patterns predict price changes. • TE > 0 : Volume provides predictive information about price - Institutional participation driving moves - Volume confirms direction - High reliability • TE ≈ 0 : No causal flow (weak volume/price relationship) - Volume uninformative - Caution on signals • TE < 0 (rare): Suggests price leading volume - Potentially manipulated or thin market 2. Volatility → Momentum (TE_σ→M): Does volatility expansion predict momentum changes? • Positive TE : Volatility precedes momentum shifts - Breakout dynamics - Regime transitions 3. Structure → Price (TE_S→P): Do support/resistance patterns causally influence price? • Positive TE : Structural levels have causal impact - Technical levels matter - Market respects structure Net Causal Flow: Net_Flow = TE_V→P + 0.5·TE_σ→M + TE_S→P • Net > +0.1 : Bullish causal structure • Net < -0.1 : Bearish causal structure • |Net| < 0.1 : Neutral/unclear causation Causal Gate: For signal confirmation, NEXUS requires: • Buy signals : TE_V→P > 0 AND Net_Flow > 0.05 • Sell signals : TE_V→P > 0 AND Net_Flow < -0.05 This ensures volume is actually driving price (causal support exists), not just correlated noise. Implementation Note: Computing true transfer entropy requires discretizing continuous data into bins (default 6 bins) and estimating joint probability distributions. NEXUS uses a hybrid approach combining TE theory with autocorrelation structure and lagged cross-correlation to approximate information transfer in computationally efficient manner. 🌊 HILBERT PHASE COHERENCE Phase coherence measures synchronization across market dimensions using Hilbert transform analysis. Hilbert Transform Theory: For a signal x(t), the Hilbert transform H (t) creates an analytic signal: z(t) = x(t) + i·H (t) = A(t)·e^(iφ(t)) Where: • A(t) = Instantaneous amplitude • φ(t) = Instantaneous phase Instantaneous Phase: φ(t) = arctan(H (t) / x(t)) The phase represents where the signal is in its natural cycle—analogous to position on a unit circle. Four Dimensions Analyzed: 1. Momentum Phase : Phase of price rate-of-change 2. Volume Phase : Phase of volume intensity 3. Volatility Phase : Phase of ATR cycles 4. Structure Phase : Phase of position within range Phase Locking Value (PLV): For two signals with phases φ₁(t) and φ₂(t), PLV measures phase synchronization: PLV = |⟨e^(i(φ₁(t) - φ₂(t)))⟩| Where ⟨·⟩ is time average over window. Interpretation: • PLV = 0 : Completely random phase relationship (no synchronization) • PLV = 0.5 : Moderate phase locking • PLV = 1 : Perfect synchronization (phases locked) Pairwise PLV Calculations: • PLV_momentum-volume : Are momentum and volume cycles synchronized? • PLV_momentum-structure : Are momentum cycles aligned with structure? • PLV_volume-structure : Are volume and structural patterns in phase? Overall Phase Coherence: Coherence = (PLV_mom-vol + PLV_mom-struct + PLV_vol-struct) / 3 Signal Confirmation: Emergence signals require coherence ≥ threshold (default 0.70): • Below 0.70: Dimensions not synchronized, no coherent market state • Above 0.70: Dimensions in phase, coherent behavior emerging Coherence Direction: The summed phase angles indicate whether synchronized dimensions point bullish or bearish: Direction = sin(φ_momentum) + 0.5·sin(φ_volume) + 0.5·sin(φ_structure) • Direction > 0 : Phases pointing upward (bullish synchronization) • Direction < 0 : Phases pointing downward (bearish synchronization) 🌀 EMERGENCE SCORE: MULTI-DIMENSIONAL ALIGNMENT The emergence score aggregates all complexity metrics into a single 0-1 value representing market coherence. Eight Components with Weights: 1. Phase Coherence (20%): Direct contribution: coherence × 0.20 Measures dimensional synchronization. 2. Entropy Regime (15%): Contribution: (0.6 - H_perm) / 0.6 × 0.15 if H < 0.6, else 0 Rewards low entropy (ordered, predictable states). 3. Lyapunov Stability (12%): • λ < 0 (stable): +0.12 • |λ| < 0.1 (critical): +0.08 • λ > 0.2 (chaotic): +0.0 Requires stable, predictable dynamics. 4. Fractal Dimension Trending (12%): Contribution: (1.45 - D) / 0.45 × 0.12 if D < 1.45, else 0 Rewards trending fractal structure (D < 1.45). 5. Dimensional Resonance (12%): Contribution: |dimensional_resonance| × 0.12 Measures alignment across momentum, volume, structure, volatility dimensions. 6. Causal Flow Strength (9%): Contribution: |net_causal_flow| × 0.09 Rewards strong causal relationships. 7. Phase Space Embedding (10%): Contribution: min(|phase_magnitude_norm|, 3.0) / 3.0 × 0.10 if |magnitude| > 1.0 Rewards strong trajectory in reconstructed phase space. 8. Recurrence Quality (10%): Contribution: determinism × 0.10 if DET > 0.3 AND 0.1 < RR < 0.8 Rewards deterministic patterns with moderate recurrence. Total Emergence Score: E = Σ(components) ∈ Capped at 1.0 maximum. Emergence Direction: Separate calculation determining bullish vs bearish: • Dimensional resonance sign • Net causal flow sign • Phase magnitude correlation with momentum Signal Threshold: Default emergence_threshold = 0.75 means 75% of maximum possible emergence score required to trigger signals. Why Emergence Matters: Traditional indicators measure single dimensions. Emergence detects self-organization —when multiple independent dimensions spontaneously align. This is the market equivalent of a phase transition in physics, where microscopic chaos gives way to macroscopic order. These are the highest-probability trade opportunities because the entire system is resonating in the same direction. 🎯 SIGNAL GENERATION: EMERGENCE vs RESONANCE DRP generates two tiers of signals with different requirements: TIER 1: EMERGENCE SIGNALS (Primary) Requirements: 1. Emergence score ≥ threshold (default 0.75) 2. Phase coherence ≥ threshold (default 0.70) 3. Emergence direction > 0.2 (bullish) or < -0.2 (bearish) 4. Causal gate passed (if enabled): TE_V→P > 0 and net_flow confirms direction 5. Stability zone (if enabled): λ < 0 or |λ| < 0.1 6. Price confirmation: Close > open (bulls) or close < open (bears) 7. Cooldown satisfied: bars_since_signal ≥ cooldown_period EMERGENCE BUY: • All above conditions met with bullish direction • Market has achieved coherent bullish state • Multiple dimensions synchronized upward EMERGENCE SELL: • All above conditions met with bearish direction • Market has achieved coherent bearish state • Multiple dimensions synchronized downward Premium Emergence: When signal_quality (emergence_score × phase_coherence) > 0.7: • Displayed as ★ star symbol • Highest conviction trades • Maximum dimensional alignment Standard Emergence: When signal_quality 0.5-0.7: • Displayed as ◆ diamond symbol • Strong signals but not perfect alignment TIER 2: RESONANCE SIGNALS (Secondary) Requirements: 1. Dimensional resonance > +0.6 (bullish) or < -0.6 (bearish) 2. Fractal dimension < 1.5 (trending/persistent regime) 3. Price confirmation matches direction 4. NOT in chaotic regime (λ < 0.2) 5. Cooldown satisfied 6. NO emergence signal firing (resonance is fallback) RESONANCE BUY: • Dimensional alignment without full emergence • Trending fractal structure • Moderate conviction RESONANCE SELL: • Dimensional alignment without full emergence • Bearish resonance with trending structure • Moderate conviction Displayed as small ▲/▼ triangles with transparency. Signal Hierarchy: IF emergence conditions met: Fire EMERGENCE signal (★ or ◆) ELSE IF resonance conditions met: Fire RESONANCE signal (▲ or ▼) ELSE: No signal Cooldown System: After any signal fires, cooldown_period (default 5 bars) must elapse before next signal. This prevents signal clustering during persistent conditions. Cooldown tracks using bar_index: bars_since_signal = current_bar_index - last_signal_bar_index cooldown_ok = bars_since_signal >= cooldown_period 🎨 VISUAL SYSTEM: MULTI-LAYER COMPLEXITY DRP provides rich visual feedback across four distinct layers: LAYER 1: COHERENCE FIELD (Background) Colored background intensity based on phase coherence: • No background : Coherence < 0.5 (incoherent state) • Faint glow : Coherence 0.5-0.7 (building coherence) • Stronger glow : Coherence > 0.7 (coherent state) Color: • Cyan/teal: Bullish coherence (direction > 0) • Red/magenta: Bearish coherence (direction < 0) • Blue: Neutral coherence (direction ≈ 0) Transparency: 98 minus (coherence_intensity × 10), so higher coherence = more visible. LAYER 2: STABILITY/CHAOS ZONES Background color indicating Lyapunov regime: • Green tint (95% transparent): λ < 0, STABLE zone - Safe to trade - Patterns meaningful • Gold tint (90% transparent): |λ| < 0.1, CRITICAL zone - Edge of chaos - Moderate risk • Red tint (85% transparent): λ > 0.2, CHAOTIC zone - Avoid trading - Unpredictable behavior LAYER 3: DIMENSIONAL RIBBONS Three EMAs representing dimensional structure: • Fast ribbon : EMA(8) in cyan/teal (fast dynamics) • Medium ribbon : EMA(21) in blue (intermediate) • Slow ribbon : EMA(55) in red/magenta (slow dynamics) Provides visual reference for multi-scale structure without cluttering with raw phase space data. LAYER 4: CAUSAL FLOW LINE A thicker line plotted at EMA(13) colored by net causal flow: • Cyan/teal : Net_flow > +0.1 (bullish causation) • Red/magenta : Net_flow < -0.1 (bearish causation) • Gray : |Net_flow| < 0.1 (neutral causation) Shows real-time direction of information flow. EMERGENCE FLASH: Strong background flash when emergence signals fire: • Cyan flash for emergence buy • Red flash for emergence sell • 80% transparency for visibility without obscuring price 📊 COMPREHENSIVE DASHBOARD Real-time monitoring of all complexity metrics: HEADER: • 🌀 DRP branding with gold accent CORE METRICS: EMERGENCE: • Progress bar (█ filled, ░ empty) showing 0-100% • Percentage value • Direction arrow (↗ bull, ↘ bear, → neutral) • Color-coded: Green/gold if active, gray if low COHERENCE: • Progress bar showing phase locking value • Percentage value • Checkmark ✓ if ≥ threshold, circle ○ if below • Color-coded: Cyan if coherent, gray if not COMPLEXITY SECTION: ENTROPY: • Regime name (CRYSTALLINE/ORDERED/MODERATE/COMPLEX/CHAOTIC) • Numerical value (0.00-1.00) • Color: Green (ordered), gold (moderate), red (chaotic) LYAPUNOV: • State (STABLE/CRITICAL/CHAOTIC) • Numerical value (typically -0.5 to +0.5) • Status indicator: ● stable, ◐ critical, ○ chaotic • Color-coded by state FRACTAL: • Regime (TRENDING/PERSISTENT/RANDOM/ANTI-PERSIST/COMPLEX) • Dimension value (1.0-2.0) • Color: Cyan (trending), gold (random), red (complex) PHASE-SPACE: • State (STRONG/ACTIVE/QUIET) • Normalized magnitude value • Parameters display: d=5 τ=3 CAUSAL SECTION: CAUSAL: • Direction (BULL/BEAR/NEUTRAL) • Net flow value • Flow indicator: →P (to price), P← (from price), ○ (neutral) V→P: • Volume-to-price transfer entropy • Small display showing specific TE value DIMENSIONAL SECTION: RESONANCE: • Progress bar of absolute resonance • Signed value (-1 to +1) • Color-coded by direction RECURRENCE: • Recurrence rate percentage • Determinism percentage display • Color-coded: Green if high quality STATE SECTION: STATE: • Current mode: EMERGENCE / RESONANCE / CHAOS / SCANNING • Icon: 🚀 (emergence buy), 💫 (emergence sell), ▲ (resonance buy), ▼ (resonance sell), ⚠ (chaos), ◎ (scanning) • Color-coded by state SIGNALS: • E: count of emergence signals • R: count of resonance signals ⚙️ KEY PARAMETERS EXPLAINED Phase Space Configuration: • Embedding Dimension (3-10, default 5): Reconstruction dimension - Low (3-4): Simple dynamics, faster computation - Medium (5-6): Balanced (recommended) - High (7-10): Complex dynamics, more data needed - Rule: d ≥ 2D+1 where D is true dimension • Time Delay (τ) (1-10, default 3): Embedding lag - Fast markets: 1-2 - Normal: 3-4 - Slow markets: 5-10 - Optimal: First minimum of mutual information (often 2-4) • Recurrence Threshold (ε) (0.01-0.5, default 0.10): Phase space proximity - Tight (0.01-0.05): Very similar states only - Medium (0.08-0.15): Balanced - Loose (0.20-0.50): Liberal matching Entropy & Complexity: • Permutation Order (3-7, default 4): Pattern length - Low (3): 6 patterns, fast but coarse - Medium (4-5): 24-120 patterns, balanced - High (6-7): 720-5040 patterns, fine-grained - Note: Requires window >> order! for stability • Entropy Window (15-100, default 30): Lookback for entropy - Short (15-25): Responsive to changes - Medium (30-50): Stable measure - Long (60-100): Very smooth, slow adaptation • Lyapunov Window (10-50, default 20): Stability estimation window - Short (10-15): Fast chaos detection - Medium (20-30): Balanced - Long (40-50): Stable λ estimate Causal Inference: • Enable Transfer Entropy (default ON): Causality analysis - Keep ON for full system functionality • TE History Length (2-15, default 5): Causal lookback - Short (2-4): Quick causal detection - Medium (5-8): Balanced - Long (10-15): Deep causal analysis • TE Discretization Bins (4-12, default 6): Binning granularity - Few (4-5): Coarse, robust, needs less data - Medium (6-8): Balanced - Many (9-12): Fine-grained, needs more data Phase Coherence: • Enable Phase Coherence (default ON): Synchronization detection - Keep ON for emergence detection • Coherence Threshold (0.3-0.95, default 0.70): PLV requirement - Loose (0.3-0.5): More signals, lower quality - Balanced (0.6-0.75): Recommended - Strict (0.8-0.95): Rare, highest quality • Hilbert Smoothing (3-20, default 8): Phase smoothing - Low (3-5): Responsive, noisier - Medium (6-10): Balanced - High (12-20): Smooth, more lag Fractal Analysis: • Enable Fractal Dimension (default ON): Complexity measurement - Keep ON for full analysis • Fractal K-max (4-20, default 8): Scaling range - Low (4-6): Faster, less accurate - Medium (7-10): Balanced - High (12-20): Accurate, slower • Fractal Window (30-200, default 50): FD lookback - Short (30-50): Responsive FD - Medium (60-100): Stable FD - Long (120-200): Very smooth FD Emergence Detection: • Emergence Threshold (0.5-0.95, default 0.75): Minimum coherence - Sensitive (0.5-0.65): More signals - Balanced (0.7-0.8): Recommended - Strict (0.85-0.95): Rare signals • Require Causal Gate (default ON): TE confirmation - ON: Only signal when causality confirms - OFF: Allow signals without causal support • Require Stability Zone (default ON): Lyapunov filter - ON: Only signal when λ < 0 (stable) or |λ| < 0.1 (critical) - OFF: Allow signals in chaotic regimes (risky) • Signal Cooldown (1-50, default 5): Minimum bars between signals - Fast (1-3): Rapid signal generation - Normal (4-8): Balanced - Slow (10-20): Very selective - Ultra (25-50): Only major regime changes Signal Configuration: • Momentum Period (5-50, default 14): ROC calculation • Structure Lookback (10-100, default 20): Support/resistance range • Volatility Period (5-50, default 14): ATR calculation • Volume MA Period (10-50, default 20): Volume normalization Visual Settings: • Customizable color scheme for all elements • Toggle visibility for each layer independently • Dashboard position (4 corners) and size (tiny/small/normal) 🎓 PROFESSIONAL USAGE PROTOCOL Phase 1: System Familiarization (Week 1) Goal: Understand complexity metrics and dashboard interpretation Setup: • Enable all features with default parameters • Watch dashboard metrics for 500+ bars • Do NOT trade yet Actions: • Observe emergence score patterns relative to price moves • Note coherence threshold crossings and subsequent price action • Watch entropy regime transitions (ORDERED → COMPLEX → CHAOTIC) • Correlate Lyapunov state with signal reliability • Track which signals appear (emergence vs resonance frequency) Key Learning: • When does emergence peak? (usually before major moves) • What entropy regime produces best signals? (typically ORDERED or MODERATE) • Does your instrument respect stability zones? (stable λ = better signals) Phase 2: Parameter Optimization (Week 2) Goal: Tune system to instrument characteristics Requirements: • Understand basic dashboard metrics from Phase 1 • Have 1000+ bars of history loaded Embedding Dimension & Time Delay: • If signals very rare: Try lower dimension (d=3-4) or shorter delay (τ=2) • If signals too frequent: Try higher dimension (d=6-7) or longer delay (τ=4-5) • Sweet spot: 4-8 emergence signals per 100 bars Coherence Threshold: • Check dashboard: What's typical coherence range? • If coherence rarely exceeds 0.70: Lower threshold to 0.60-0.65 • If coherence often >0.80: Can raise threshold to 0.75-0.80 • Goal: Signals fire during top 20-30% of coherence values Emergence Threshold: • If too few signals: Lower to 0.65-0.70 • If too many signals: Raise to 0.80-0.85 • Balance with coherence threshold—both must be met Phase 3: Signal Quality Assessment (Weeks 3-4) Goal: Verify signals have edge via paper trading Requirements: • Parameters optimized per Phase 2 • 50+ signals generated • Detailed notes on each signal Paper Trading Protocol: • Take EVERY emergence signal (★ and ◆) • Optional: Take resonance signals (▲/▼) separately to compare • Use simple exit: 2R target, 1R stop (ATR-based) • Track: Win rate, average R-multiple, maximum consecutive losses Quality Metrics: • Premium emergence (★) : Should achieve >55% WR • Standard emergence (◆) : Should achieve >50% WR • Resonance signals : Should achieve >45% WR • Overall : If <45% WR, system not suitable for this instrument/timeframe Red Flags: • Win rate <40%: Wrong instrument or parameters need major adjustment • Max consecutive losses >10: System not working in current regime • Profit factor <1.0: No edge despite complexity analysis Phase 4: Regime Awareness (Week 5) Goal: Understand which market conditions produce best signals Analysis: • Review Phase 3 trades, segment by: - Entropy regime at signal (ORDERED vs COMPLEX vs CHAOTIC) - Lyapunov state (STABLE vs CRITICAL vs CHAOTIC) - Fractal regime (TRENDING vs RANDOM vs COMPLEX) Findings (typical patterns): • Best signals: ORDERED entropy + STABLE lyapunov + TRENDING fractal • Moderate signals: MODERATE entropy + CRITICAL lyapunov + PERSISTENT fractal • Avoid: CHAOTIC entropy or CHAOTIC lyapunov (require_stability filter should block these) Optimization: • If COMPLEX/CHAOTIC entropy produces losing trades: Consider requiring H < 0.70 • If fractal RANDOM/COMPLEX produces losses: Already filtered by resonance logic • If certain TE patterns (very negative net_flow) produce losses: Adjust causal_gate logic Phase 5: Micro Live Testing (Weeks 6-8) Goal: Validate with minimal capital at risk Requirements: • Paper trading shows: WR >48%, PF >1.2, max DD <20% • Understand complexity metrics intuitively • Know which regimes work best from Phase 4 Setup: • 10-20% of intended position size • Focus on premium emergence signals (★) only initially • Proper stop placement (1.5-2.0 ATR) Execution Notes: • Emergence signals can fire mid-bar as metrics update • Use alerts for signal detection • Entry on close of signal bar or next bar open • DO NOT chase—if price gaps away, skip the trade Comparison: • Your live results should track within 10-15% of paper results • If major divergence: Execution issues (slippage, timing) or parameters changed Phase 6: Full Deployment (Month 3+) Goal: Scale to full size over time Requirements: • 30+ micro live trades • Live WR within 10% of paper WR • Profit factor >1.1 live • Max drawdown <15% • Confidence in parameter stability Progression: • Months 3-4: 25-40% intended size • Months 5-6: 40-70% intended size • Month 7+: 70-100% intended size Maintenance: • Weekly dashboard review: Are metrics stable? • Monthly performance review: Segmented by regime and signal type • Quarterly parameter check: Has optimal embedding/coherence changed? Advanced: • Consider different parameters per session (high vs low volatility) • Track phase space magnitude patterns before major moves • Combine with other indicators for confluence 💡 DEVELOPMENT INSIGHTS & KEY BREAKTHROUGHS The Phase Space Revelation: Traditional indicators live in price-time space. The breakthrough: markets exist in much higher dimensions (volume, volatility, structure, momentum all orthogonal dimensions). Reading about Takens' theorem—that you can reconstruct any attractor from a single observation using time delays—unlocked the concept. Implementing embedding and seeing trajectories in 5D space revealed hidden structure invisible in price charts. Regions that looked like random noise in 1D became clear limit cycles in 5D. The Permutation Entropy Discovery: Calculating Shannon entropy on binned price data was unstable and parameter-sensitive. Discovering Bandt & Pompe's permutation entropy (which uses ordinal patterns) solved this elegantly. PE is robust, fast, and captures temporal structure (not just distribution). Testing showed PE < 0.5 periods had 18% higher signal win rate than PE > 0.7 periods. Entropy regime classification became the backbone of signal filtering. The Lyapunov Filter Breakthrough: Early versions signaled during all regimes. Win rate hovered at 42%—barely better than random. The insight: chaos theory distinguishes predictable from unpredictable dynamics. Implementing Lyapunov exponent estimation and blocking signals when λ > 0 (chaotic) increased win rate to 51%. Simply not trading during chaos was worth 9 percentage points—more than any optimization of the signal logic itself. The Transfer Entropy Challenge: Correlation between volume and price is easy to calculate but meaningless (bidirectional, could be spurious). Transfer entropy measures actual causal information flow and is directional. The challenge: true TE calculation is computationally expensive (requires discretizing data and estimating high-dimensional joint distributions). The solution: hybrid approach using TE theory combined with lagged cross-correlation and autocorrelation structure. Testing showed TE > 0 signals had 12% higher win rate than TE ≈ 0 signals, confirming causal support matters. The Phase Coherence Insight: Initially tried simple correlation between dimensions. Not predictive. Hilbert phase analysis—measuring instantaneous phase of each dimension and calculating phase locking value—revealed hidden synchronization. When PLV > 0.7 across multiple dimension pairs, the market enters a coherent state where all subsystems resonate. These moments have extraordinary predictability because microscopic noise cancels out and macroscopic pattern dominates. Emergence signals require high PLV for this reason. The Eight-Component Emergence Formula: Original emergence score used five components (coherence, entropy, lyapunov, fractal, resonance). Performance was good but not exceptional. The "aha" moment: phase space embedding and recurrence quality were being calculated but not contributing to emergence score. Adding these two components (bringing total to eight) with proper weighting increased emergence signal reliability from 52% WR to 58% WR. All calculated metrics must contribute to the final score. If you compute something, use it. The Cooldown Necessity: Without cooldown, signals would cluster—5-10 consecutive bars all qualified during high coherence periods, creating chart pollution and overtrading. Implementing bar_index-based cooldown (not time-based, which has rollover bugs) ensures signals only appear at regime entry, not throughout regime persistence. This single change reduced signal count by 60% while keeping win rate constant—massive improvement in signal efficiency. 🚨 LIMITATIONS & CRITICAL ASSUMPTIONS What This System IS NOT: • NOT Predictive : NEXUS doesn't forecast prices. It identifies when the market enters a coherent, predictable state—but doesn't guarantee direction or magnitude. • NOT Holy Grail : Typical performance is 50-58% win rate with 1.5-2.0 avg R-multiple. This is probabilistic edge from complexity analysis, not certainty. • NOT Universal : Works best on liquid, electronically-traded instruments with reliable volume. Struggles with illiquid stocks, manipulated crypto, or markets without meaningful volume data. • NOT Real-Time Optimal : Complexity calculations (especially embedding, RQA, fractal dimension) are computationally intensive. Dashboard updates may lag by 1-2 seconds on slower connections. • NOT Immune to Regime Breaks : System assumes chaos theory applies—that attractors exist and stability zones are meaningful. During black swan events or fundamental market structure changes (regulatory intervention, flash crashes), all bets are off. Core Assumptions: 1. Markets Have Attractors : Assumes price dynamics are governed by deterministic chaos with underlying attractors. Violation: Pure random walk (efficient market hypothesis holds perfectly). 2. Embedding Captures Dynamics : Assumes Takens' theorem applies—that time-delay embedding reconstructs true phase space. Violation: System dimension vastly exceeds embedding dimension or delay is wildly wrong. 3. Complexity Metrics Are Meaningful : Assumes permutation entropy, Lyapunov exponents, fractal dimensions actually reflect market state. Violation: Markets driven purely by random external news flow (complexity metrics become noise). 4. Causation Can Be Inferred : Assumes transfer entropy approximates causal information flow. Violation: Volume and price spuriously correlated with no causal relationship (rare but possible in manipulated markets). 5. Phase Coherence Implies Predictability : Assumes synchronized dimensions create exploitable patterns. Violation: Coherence by chance during random period (false positive). 6. Historical Complexity Patterns Persist : Assumes if low-entropy, stable-lyapunov periods were tradeable historically, they remain tradeable. Violation: Fundamental regime change (market structure shifts, e.g., transition from floor trading to HFT). Performs Best On: • ES, NQ, RTY (major US index futures - high liquidity, clean volume data) • Major forex pairs: EUR/USD, GBP/USD, USD/JPY (24hr markets, good for phase analysis) • Liquid commodities: CL (crude oil), GC (gold), NG (natural gas) • Large-cap stocks: AAPL, MSFT, GOOGL, TSLA (>$10M daily volume, meaningful structure) • Major crypto on reputable exchanges: BTC, ETH on Coinbase/Kraken (avoid Binance due to manipulation) Performs Poorly On: • Low-volume stocks (<$1M daily volume) - insufficient liquidity for complexity analysis • Exotic forex pairs - erratic spreads, thin volume • Illiquid altcoins - wash trading, bot manipulation invalidates volume analysis • Pre-market/after-hours - gappy, thin, different dynamics • Binary events (earnings, FDA approvals) - discontinuous jumps violate dynamical systems assumptions • Highly manipulated instruments - spoofing and layering create false coherence Known Weaknesses: • Computational Lag : Complexity calculations require iterating over windows. On slow connections, dashboard may update 1-2 seconds after bar close. Signals may appear delayed. • Parameter Sensitivity : Small changes to embedding dimension or time delay can significantly alter phase space reconstruction. Requires careful calibration per instrument. • Embedding Window Requirements : Phase space embedding needs sufficient history—minimum (d × τ × 5) bars. If embedding_dimension=5 and time_delay=3, need 75+ bars. Early bars will be unreliable. • Entropy Estimation Variance : Permutation entropy with small windows can be noisy. Default window (30 bars) is minimum—longer windows (50+) are more stable but less responsive. • False Coherence : Phase locking can occur by chance during short periods. Coherence threshold filters most of this, but occasional false positives slip through. • Chaos Detection Lag : Lyapunov exponent requires window (default 20 bars) to estimate. Market can enter chaos and produce bad signal before λ > 0 is detected. Stability filter helps but doesn't eliminate this. • Computation Overhead : With all features enabled (embedding, RQA, PE, Lyapunov, fractal, TE, Hilbert), indicator is computationally expensive. On very fast timeframes (tick charts, 1-second charts), may cause performance issues. ⚠️ RISK DISCLOSURE Trading futures, forex, stocks, options, and cryptocurrencies involves substantial risk of loss and is not suitable for all investors. Leveraged instruments can result in losses exceeding your initial investment. Past performance, whether backtested or live, is not indicative of future results. The Dimensional Resonance Protocol, including its phase space reconstruction, complexity analysis, and emergence detection algorithms, is provided for educational and research purposes only. It is not financial advice, investment advice, or a recommendation to buy or sell any security or instrument. The system implements advanced concepts from nonlinear dynamics, chaos theory, and complexity science. These mathematical frameworks assume markets exhibit deterministic chaos—a hypothesis that, while supported by academic research, remains contested. Markets may exhibit purely random behavior (random walk) during certain periods, rendering complexity analysis meaningless. Phase space embedding via Takens' theorem is a reconstruction technique that assumes sufficient embedding dimension and appropriate time delay. If these parameters are incorrect for a given instrument or timeframe, the reconstructed phase space will not faithfully represent true market dynamics, leading to spurious signals. Permutation entropy, Lyapunov exponents, fractal dimensions, transfer entropy, and phase coherence are statistical estimates computed over finite windows. All have inherent estimation error. Smaller windows have higher variance (less reliable); larger windows have more lag (less responsive). There is no universally optimal window size. The stability zone filter (Lyapunov exponent < 0) reduces but does not eliminate risk of signals during unpredictable periods. Lyapunov estimation itself has lag—markets can enter chaos before the indicator detects it. Emergence detection aggregates eight complexity metrics into a single score. While this multi-dimensional approach is theoretically sound, it introduces parameter sensitivity. Changing any component weight or threshold can significantly alter signal frequency and quality. Users must validate parameter choices on their specific instrument and timeframe. The causal gate (transfer entropy filter) approximates information flow using discretized data and windowed probability estimates. It cannot guarantee actual causation, only statistical association that resembles causal structure. Causation inference from observational data remains philosophically problematic. Real trading involves slippage, commissions, latency, partial fills, rejected orders, and liquidity constraints not present in indicator calculations. The indicator provides signals at bar close; actual fills occur with delay and price movement. Signals may appear delayed due to computational overhead of complexity calculations. Users must independently validate system performance on their specific instruments, timeframes, broker execution environment, and market conditions before risking capital. Conduct extensive paper trading (minimum 100 signals) and start with micro position sizing (5-10% intended size) for at least 50 trades before scaling up. Never risk more capital than you can afford to lose completely. Use proper position sizing (0.5-2% risk per trade maximum). Implement stop losses on every trade. Maintain adequate margin/capital reserves. Understand that most retail traders lose money. Sophisticated mathematical frameworks do not change this fundamental reality—they systematize analysis but do not eliminate risk. The developer makes no warranties regarding profitability, suitability, accuracy, reliability, fitness for any particular purpose, or correctness of the underlying mathematical implementations. Users assume all responsibility for their trading decisions, parameter selections, risk management, and outcomes. By using this indicator, you acknowledge that you have read, understood, and accepted these risk disclosures and limitations, and you accept full responsibility for all trading activity and potential losses. 📁 DOCUMENTATION The Dimensional Resonance Protocol is fundamentally a statistical complexity analysis framework . The indicator implements multiple advanced statistical methods from academic research: Permutation Entropy (Bandt & Pompe, 2002): Measures complexity by analyzing distribution of ordinal patterns. Pure statistical concept from information theory. Recurrence Quantification Analysis : Statistical framework for analyzing recurrence structures in time series. Computes recurrence rate, determinism, and diagonal line statistics. Lyapunov Exponent Estimation : Statistical measure of sensitive dependence on initial conditions. Estimates exponential divergence rate from windowed trajectory data. Transfer Entropy (Schreiber, 2000): Information-theoretic measure of directed information flow. Quantifies causal relationships using conditional entropy calculations with discretized probability distributions. Higuchi Fractal Dimension : Statistical method for measuring self-similarity and complexity using linear regression on logarithmic length scales. Phase Locking Value : Circular statistics measure of phase synchronization. Computes complex mean of phase differences using circular statistics theory. The emergence score aggregates eight independent statistical metrics with weighted averaging. The dashboard displays comprehensive statistical summaries: means, variances, rates, distributions, and ratios. Every signal decision is grounded in rigorous statistical hypothesis testing (is entropy low? is lyapunov negative? is coherence above threshold?). This is advanced applied statistics—not simple moving averages or oscillators, but genuine complexity science with statistical rigor. Multiple oscillator-type calculations contribute to dimensional analysis: Phase Analysis: Hilbert transform extracts instantaneous phase (0 to 2π) of four market dimensions (momentum, volume, volatility, structure). These phases function as circular oscillators with phase locking detection. Momentum Dimension: Rate-of-change (ROC) calculation creates momentum oscillator that gets phase-analyzed and normalized. Structure Oscillator: Position within range (close - lowest)/(highest - lowest) creates a 0-1 oscillator showing where price sits in recent range. This gets embedded and phase-analyzed. Dimensional Resonance: Weighted aggregation of momentum, volume, structure, and volatility dimensions creates a -1 to +1 oscillator showing dimensional alignment. Similar to traditional oscillators but multi-dimensional. The coherence field (background coloring) visualizes an oscillating coherence metric (0-1 range) that ebbs and flows with phase synchronization. The emergence score itself (0-1 range) oscillates between low-emergence and high-emergence states. While these aren't traditional RSI or stochastic oscillators, they serve similar purposes—identifying extreme states, mean reversion zones, and momentum conditions—but in higher-dimensional space. Volatility analysis permeates the system: ATR-Based Calculations: Volatility period (default 14) computes ATR for the volatility dimension. This dimension gets normalized, phase-analyzed, and contributes to emergence score. Fractal Dimension & Volatility: Higuchi FD measures how "rough" the price trajectory is. Higher FD (>1.6) correlates with higher volatility/choppiness. FD < 1.4 indicates smooth trends (lower effective volatility). Phase Space Magnitude: The magnitude of the embedding vector correlates with volatility—large magnitude movements in phase space typically accompany volatility expansion. This is the "energy" of the market trajectory. Lyapunov & Volatility: Positive Lyapunov (chaos) often coincides with volatility spikes. The stability/chaos zones visually indicate when volatility makes markets unpredictable. Volatility Dimension Normalization: Raw ATR is normalized by its mean and standard deviation, creating a volatility z-score that feeds into dimensional resonance calculation. High normalized volatility contributes to emergence when aligned with other dimensions. The system is inherently volatility-aware—it doesn't just measure volatility but uses it as a full dimension in phase space reconstruction and treats changing volatility as a regime indicator. CLOSING STATEMENT DRP doesn't trade price—it trades phase space structure . It doesn't chase patterns—it detects emergence . It doesn't guess at trends—it measures coherence . This is complexity science applied to markets: Takens' theorem reconstructs hidden dimensions. Permutation entropy measures order. Lyapunov exponents detect chaos. Transfer entropy reveals causation. Hilbert phases find synchronization. Fractal dimensions quantify self-similarity. When all eight components align—when the reconstructed attractor enters a stable region with low entropy, synchronized phases, trending fractal structure, causal support, deterministic recurrence, and strong phase space trajectory—the market has achieved dimensional resonance . These are the highest-probability moments. Not because an indicator said so. Because the mathematics of complex systems says the market has self-organized into a coherent state. Most indicators see shadows on the wall. DRP reconstructs the cave. "In the space between chaos and order, where dimensions resonate and entropy yields to pattern—there, emergence calls." DRP Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
مؤشر Pine Script®
من ‎DskyzInvestments‎
High Volume Bars (Advanced)High Volume Bars (Advanced) High Volume Bars (Advanced) is a Pine Script v6 indicator for TradingView that highlights bars with unusually high volume, with several ways to define “unusual”: Classic: volume > moving average + N × standard deviation Change-based: large change in volume vs previous bar Z-score: statistically extreme volume values Robust mode (optional): median + MAD, less sensitive to outliers It can: Recolor candles when volume is high Optionally highlight the background Optionally plot volume bands (center ± spread × multiplier) ⸻ 1. How it works At each bar the script: Picks the volume source: If Use Volume Change vs Previous Bar? is off → uses raw volume If on → uses abs(volume - volume ) Computes baseline statistics over the chosen source: Lookback bars Moving average (SMA or EMA) Standard deviation Optionally replaces mean/std with robust stats: Center = median (50th percentile) Spread = MAD (median absolute deviation, scaled to approx σ) Builds bands: upper = center + spread * multiplier lower = max(center - spread * multiplier, 0) Flags a bar as “high volume” if: It passes the mode logic: Classic abs: volume > upper Change mode: abs(volume - volume ) > upper Z-score mode: z-score ≥ multiplier AND the relative filter (optional): volume > average_volume * Min Volume vs Avg AND it is past the first Skip First N Bars from the start of the chart Colors the bar and (optionally) the background accordingly. ⸻ 2. Inputs 2.1. Statistics Lookback (len) Number of bars used to compute the baseline stats (mean / median, std / MAD). Typical values: 50–200. StdDev / Z-Score Multiplier (mult) How far from the baseline a bar must be to count as “high volume”. In classic mode: volume > mean + mult × std In z-score mode: z ≥ mult Typical values: 1.0–2.5. Use EMA Instead of SMA? (smooth_with_ema) Off → uses SMA (slower but smoother). On → uses EMA (reacts faster to recent changes). Use Robust Stats (Median & MAD)? (use_robust) Off → mean + standard deviation On → median + MAD (less sensitive to a few insane spikes) Useful for assets with occasional volume blow-ups. ⸻ 2.2. Detection Mode These inputs control how “unusual” is defined. • Use Volume Change vs Previous Bar? (mode_change) • Off (default) → uses absolute volume. • On → uses abs(volume - volume ). You then detect jumps in volume rather than absolute size. Note: This is ignored if Z-Score mode is switched on (see below). • Use Z-Score on Volume? (Overrides change) (mode_zscore) • Off → high volume when raw value exceeds the upper band. • On → computes z-score = (value − center) / spread and flags a bar as high when z ≥ multiplier. Z-score mode can be combined with robust stats for more stable thresholds. • Min Volume vs Avg (Filter) (min_rel_mult) An extra filter to ignore tiny-volume bars that are statistically “weird” but not meaningful. • 0.0 → no filter (all stats-based candidates allowed). • 1.0 → high-volume bar must also be at least equal to average volume. • 1.5 → bar must be ≥ 1.5 × average volume. • Skip First N Bars (from start of chart) (skip_open_bars) Skips the first N bars of the chart when evaluating high-volume conditions. This is mostly a safety / cosmetic option to avoid weird behavior on very early bars or backfill. ⸻ 2.3. Visuals • Show Volume Bands? (show_bands) • If on, plots: • Upper band (upper) • Lower band (lower) • Center line (vol_center) These are plotted on the same pane as the script (usually the price chart). • Also Highlight Background? (use_bg) • If on, fills the background on high-volume bars with High-Vol Background. • High-Vol Bar Transparency (0–100) (bar_transp) Controls the opacity of the high-volume bar colors (up / down). • 0 → fully opaque • 100 → fully transparent (no visible effect) • Up Color (upColor) / Down Color (dnColor) • Regular bar colors (non high-volume) for up and down bars. • Up High-Vol Base Color (upHighVolBase) / Down High-Vol Base Color (dnHighVolBase) Base colors used for high-volume up/down bars. Transparency is applied on top of these via bar_transp. • High-Vol Background (bgHighVolColor) Background color used when Also Highlight Background? is enabled. ⸻ 3. What gets colored and how • Bar color (barcolor) • Up bar: • High volume → Up High-Vol Color • Normal volume → Up Color • Down bar: • High volume → Down High-Vol Color • Normal volume → Down Color • Flat bar → neutral gray • Background color (bgcolor) • If Also Highlight Background? is on, high-volume bars get High-Vol Background. • Otherwise, background is unchanged. ⸻ 4. Alerts The indicator exposes three alert conditions: • High Volume Bar Triggers whenever is_high is true (up or down). • High Volume Up Bar Triggers only when is_high is true and the bar closed up (close > open). • High Volume Down Bar Triggers only when is_high is true and the bar closed down (close < open). You can use these in TradingView’s “Create Alert” dialog to: • Get notified of potential breakout / exhaustion bars. • Trigger webhook events for bots / custom infra. ⸻ 5. Recommended presets 5.1. “Classic” high-volume detector (closest to original) • Lookback: 150–200 • StdDev / Z-Score Multiplier: 1.0–1.5 • Use EMA Instead of SMA?: off • Use Robust Stats?: off • Use Volume Change vs Previous Bar?: off • Use Z-Score on Volume?: off • Min Volume vs Avg (Filter): 0.0–1.0 Behavior: Flags bars whose volume is notably above the recent average (plus a bit of noise filtering), same spirit as your initial implementation. ⸻ 5.2. Volatility-aware (Z-score) mode • Lookback: 100–200 • StdDev / Z-Score Multiplier: 1.5–2.0 • Use EMA Instead of SMA?: on • Use Robust Stats?: on (if asset has huge spikes) • Use Volume Change vs Previous Bar?: off (ignored anyway in z-score mode) • Use Z-Score on Volume?: on • Min Volume vs Avg (Filter): 0.5–1.0 Behavior: Flags bars that are “statistically extreme” relative to recent volume behavior, not just absolutely large. Good for assets where baseline volume drifts over time. ⸻ 5.3. “Wake-up bar” (volume acceleration) • Lookback: 50–100 • StdDev / Z-Score Multiplier: 1.0–1.5 • Use EMA Instead of SMA?: on • Use Robust Stats?: optional • Use Volume Change vs Previous Bar?: on • Use Z-Score on Volume?: off • Min Volume vs Avg (Filter): 0.5–1.0 Behavior: Emphasis on sudden increases in volume rather than absolute size – useful to catch “first active bar” after a quiet period. ⸻ 6. Limitations / notes • Time-of-day effects The script currently treats the entire chart as one continuous “session”. On 24/7 markets (crypto) this is fine. For regular-session assets (equities, futures), volume naturally spikes at open/close; you may want to: • Use a shorter Lookback, or • Add a session-aware filter in a future iteration. • Illiquid symbols On very low-liquidity symbols, robust stats (Use Robust Stats) and a non-zero Min Volume vs Avg can help avoid “everything looks extreme” problems. • Overlay behavior overlay = true means: • Bars are recolored on the price pane. • Volume bands are also drawn on the price pane if enabled. If you want a dedicated panel for the bands, duplicate the logic in a separate script with overlay = false.
مؤشر Pine Script®
من ‎w03‎
Crypto Correlation Oscillator# Crypto Correlation Oscillator **Companion indicator for Tri-Align Crypto Trend** ## Overview The Crypto Correlation Oscillator helps you identify **alpha opportunities** and **market regime changes** by showing how closely your coin follows Bitcoin and other assets over time. It displays rolling correlations as an oscillator in a separate pane below your price chart. ## What It Does This indicator calculates **Pearson correlations** between different trading pairs on a rolling window (default: 100 bars). Correlations range from **-1.0** (perfect inverse relationship) to **+1.0** (perfect positive relationship), with **0** meaning no correlation. ### The 5 Correlation Lines 1. **Blue (thick line) - Coin vs BTC**: The most important metric - **High correlation (>0.7)**: Your coin is just following BTC - no independent movement - **Low correlation (<0.3)**: Your coin has **alpha** - it's moving independently from BTC - **Negative correlation**: Your coin moves opposite to BTC (rare but powerful) 2. **Purple - Coin/BTC vs BTC**: Inverse relationship check - **Negative values**: When BTC rises, your coin weakens relative to BTC - **Positive values**: When BTC rises, your coin strengthens against BTC 3. **Orange - Coin vs Coin/BTC**: Structural consistency check - Shows how well the Coin/USDT and Coin/BTC pairs maintain their mathematical relationship - Unusual values can indicate liquidity issues or market inefficiencies 4. **Light Red - Coin vs USDT.D** (optional): Stablecoin dominance correlation - Shows how your coin correlates with USDT dominance - Useful for understanding flight-to-safety dynamics 5. **Light Green - Coin vs BTC.D** (optional): Bitcoin dominance correlation - Shows how your coin correlates with BTC dominance - Helps identify altcoin season vs BTC dominance cycles ## How to Read It ### Finding Alpha Opportunities - **Low blue line (<0.3)**: Your coin is decoupled from BTC → potential alpha - **Blue line dropping**: Coin is gaining independence from BTC - **Blue line spiking to >0.9**: Coin is a "BTC clone" with no independent movement ### Regime Change Detection - **Blue line crossing 0.5**: Major shift in correlation behavior - **Purple line turning negative**: Coin starting to weaken when BTC rises (warning sign) - **Sharp correlation changes**: Market structure is shifting - adjust strategy ### Visual Zones - **Blue background**: High correlation zone (>0.7) - coin just following BTC - **Red background**: Inverse correlation zone (<-0.5) - coin moving opposite to BTC ### Reference Lines - **+1.0 / -1.0**: Perfect correlation boundaries (dotted gray) - **+0.5 / -0.5**: Moderate correlation thresholds (dotted gray) - **0.0**: Zero correlation line (solid gray) ## Dynamic Legend The legend table (top-right) automatically shows the actual symbol names based on your chart: - **Example on SOLUSDT**: Shows "SOL vs BTC", "SOL/BTC vs BTC", "SOL vs SOL/BTC", etc. - **Color boxes**: Match the plot colors for easy identification - **Live values**: Current correlation numbers update in real-time - **Tooltips**: Hover over labels for interpretation guidance ## Configuration ### Key Inputs - **Correlation Lookback** (default: 100): Number of bars for rolling correlation window - Shorter = more reactive, noisier - Longer = smoother, slower to detect changes - **Correlation Smoothing** (default: 5): EMA smoothing period for raw correlations - Reduces noise while preserving trends - **Symbol Detection**: Auto-detects symbols from your chart, or use manual overrides - **Dominance Pairs**: Toggle USDT.D and BTC.D correlations on/off ## Usage Tips 1. **Combine with main Tri-Align indicator**: Use correlation for context, Tri-Align for entry/exit signals 2. **Watch for divergences**: Correlation changing while price moves in sync can signal upcoming shift 3. **Adjust lookback period**: Use shorter (50-70) for day trading, longer (150-200) for position trading 4. **Focus on the blue line**: It's your primary alpha indicator ## Technical Details - **Calculation**: Pearson correlation coefficient with EMA smoothing - **Data source**: Close prices from `request.security()` (multi-timeframe capable) - **Update frequency**: Every bar on your selected timeframe - **Overlay**: False (displays in separate pane) ## Quick Interpretation Guide | Blue Line Value | Interpretation | Action | |----------------|----------------|--------| | > 0.9 | Coin is a BTC clone | Avoid - no alpha opportunity | | 0.7 - 0.9 | High correlation | Standard altcoin behavior | | 0.3 - 0.7 | Moderate correlation | Some independence emerging | | < 0.3 | Low correlation | **Strong alpha opportunity** | | < 0 | Inverse correlation | Rare - potential hedge asset | | Purple Line | Interpretation | |-------------|----------------| | Strongly negative | Coin weakens when BTC rises - risky | | Near zero | Coin/BTC pair moves independently of BTC | | Positive | Coin strengthens with BTC - ideal | ## Version History ### v1.0 (Initial Release) - Pearson correlation calculation with configurable lookback - 5 correlation pairs: Coin vs BTC, Coin/BTC vs BTC, Coin vs Coin/BTC, USDT.D, BTC.D - EMA smoothing to reduce noise - Visual zones for high/inverse correlation - Dynamic legend with symbol name extraction - Auto-symbol detection matching main Tri-Align indicator
مؤشر Pine Script®
من ‎Frey_Crypto‎
MTF Checklist DashboardMTF Checklist Dashboard Overview The MTF Checklist Dashboard is an advanced multi-timeframe analysis tool that provides traders with a comprehensive visual dashboard to analyze market conditions across six customizable timeframes simultaneously. This indicator combines multiple technical analysis methods, including Opening Range Breakouts (ORB), VWAP, EMAs, and daily price levels, to generate high-probability confluence-based trading signals.​ Unlike traditional single-timeframe indicators, this dashboard displays all critical information in one organized table, allowing traders to instantly identify when multiple timeframes align for optimal entry and exit opportunities.​ Key Features Multi-Timeframe Analysis Analyzes up to 6 timeframes simultaneously (default: 1m, 5m, 15m, 30m, 1h, 4h) Fully customizable timeframe selection via comma-separated input Color-coded cells for instant visual recognition (green=bullish, red=bearish, yellow=neutral)​ Technical Indicators Tracked Current and previous candle direction Opening Range Breakout (ORB) positioning with custom period VWAP relationship (above/below) 200 EMA positioning Daily and previous day high/low proximity EMA crossovers (9 vs 21, both vs 200)​ Advanced Signal Filtering System Confluence scoring: Requires multiple timeframes to align (3-6 timeframes) Higher timeframe confirmation: Ensures 30m/1h/4h agreement Volume filter: Confirms signals with above-average volume (1.5x default) ATR volatility filter: Validates sufficient market movement Session timing: Restricts signals to optimal trading hours (EST) Momentum confirmation: Requires recent directional strength Range positioning: Blocks signals near daily extremes Candle strength: Validates strong directional candles (60%+ body ratio)​ Visual Signals Optional entry arrows (above/below bars) Background color highlighting Organized dashboard with real-time price levels ORB range, current day, and previous day summary rows​ Alert Conditions JSON-formatted alerts for automated trading integration Separate alerts for long entry, short entry, long exit, and short exit Compatible with webhook automation systems​ How To Use Dashboard Interpretation The dashboard displays a color-coded table with the following columns:​ TF: Timeframe being analyzed C: Current candle (Green=bullish, Red=bearish) P: Previous candle (Green=bullish, Red=bearish) ORB: Opening Range Breakout position (A=Above, B=Below, W=Within) VWAP: Price vs VWAP (A=Above, B=Below) E200: Price vs 200 EMA (A=Above, B=Below) D Hi/Lo: Proximity to current day high/low (Hi/Lo/Mid) PD Hi/Lo: Proximity to previous day high/low (Hi/Lo/Mid) 9 vs 21: EMA 9 vs EMA 21 relationship (A=9 above 21, B=9 below 21) 9&21 v200: Both EMAs vs 200 EMA (>>=both above, <<=both below, <>=mixed) Signal Generation Long Entry Signal triggers when: Minimum number of timeframes show bullish alignment (default: 5 of 6) Higher timeframes (30m/1h/4h) confirm direction (default: 2 of 3) Price breaks above ORB high with sufficient distance Volume exceeds average by specified multiplier ATR shows adequate volatility Trade occurs during optimal session hours Recent momentum is upward Price not too close to daily high Strong bullish candle forms​ Short Entry Signal uses opposite conditions​ Exit Signals trigger when opposing timeframe confluence reaches threshold (default: 3 timeframes)​ Recommended Workflow Select your asset and primary trading timeframe Observe the dashboard - Look for rows showing mostly green (bullish) or red (bearish) Wait for alignment - The indicator will show arrows when confluence requirements are met Check the bottom rows - Review ORB levels and daily ranges for context Set alerts - Enable TradingView alerts using the built-in alert conditions Manage risk - Use appropriate position sizing and stop losses based on ORB range or daily ATR​ Settings Guide Basic Settings Timeframes: Enter comma-separated values (e.g., "1,5,15,30,60,240") Show Header: Toggle column headers on/off ORB Minutes: Set opening range period (default: 15 minutes) Near % for daily highs/lows: Define proximity threshold (default: 0.20%) Use close for comparisons: Compare using close vs current price Dashboard Position: Choose from 9 screen positions​ Confluence Filters Minimum Timeframes Aligned: Set required confluence (3-6, default: 5) Require Higher Timeframe Confirmation: Toggle HTF requirement on/off Min Higher Timeframes: Specify HTF agreement needed (1-3, default: 2)​ Volume Filter Volume Confirmation: Enable/disable volume filtering Volume vs Average: Set multiplier threshold (default: 1.5x) Volume Average Length: Period for volume average (default: 20 bars) Volatility Filter (ATR) Volatility Filter: Enable/disable ATR confirmation ATR Length: Calculation period (default: 14) Min ATR vs Average: Required ATR level (default: 0.5x = 50%) ORB Filters ORB Breakout Distance Required: Toggle distance requirement Min Breakout % Beyond ORB: Additional breakout threshold (default: 0.10%) Session Filter Trade Only During Best Hours: Enable time-based filtering Session 1: First trading window (default: 0930-1130 EST) Session 2: Second trading window (default: 1400-1530 EST) Momentum Filter Recent Momentum Required: Enable directional momentum check Lookback Bars: Period for momentum comparison (default: 3 bars) Daily Range Filter Block Signals Near Daily Extremes: Prevent entries at extremes Distance from High/Low %: Minimum distance required (default: 2.0%) Candle Filter Strong Directional Candle: Require candle strength Min Candle Body %: Body-to-range ratio threshold (default: 60%) Visual Signals Show Entry Signals: Master toggle for visual signals Show Arrows: Display entry arrows on chart Background Color: Enable background highlighting​ Best Practices Start with default settings and adjust based on your trading style and asset volatility Higher confluence requirements (5-6 timeframes) produce fewer but higher-quality signals Enable all filters for conservative trading; disable some for more frequent signals Use the dashboard as confirmation alongside your existing trading strategy Backtest on your specific instruments before live trading Consider market conditions—trending vs ranging markets may require different settings​ Alerts This indicator includes four alert conditions with JSON formatting for webhook integration:​ Long Entry Signal: Triggers when all long conditions are met Short Entry Signal: Triggers when all short conditions are met Long Exit Signal: Triggers when opposing confluence reaches exit threshold Short Exit Signal: Triggers when opposing confluence reaches exit threshold Alert messages include ticker symbol, action (buy/sell), price, and quantity for automated trading systems.​ Important Notes This indicator works best on liquid instruments with clear price action Highly volatile markets may require adjusted ATR and ORB distance settings Session times are in EST timezone—adjust if trading non-US markets The ORB calculation requires sufficient price history for the day Signals are generated in real-time but should be confirmed at candle close​ Limitations Maximum of 6 timeframes can be analyzed due to TradingView's security call limits ORB calculations may not work correctly on instruments with gaps or irregular sessions The indicator is most effective during regular market hours when volume and volatility are adequate Lower timeframes (1m, 5m) may produce more false signals in choppy conditions​ License Mozilla Public License 2.0 (MPL-2.0) This indicator is licensed under the Mozilla Public License 2.0. You are free to use, modify, and distribute this code under the terms of the MPL-2.0. The full license text is available at mozilla.org Key license provisions: You may use this code commercially You may modify and distribute modified versions Modified versions must be released under the same license You must include the original license notice in any distributions No trademark rights are granted​ Disclaimer This indicator is provided for educational and informational purposes only. It is not financial advice, and past performance does not guarantee future results. Trading involves substantial risk of loss. Always:​ Practice proper risk management Test thoroughly on paper/demo accounts before live trading Use appropriate position sizing Never risk more than you can afford to lose Consult with a financial advisor for personalized advice​ The creator assumes no liability for trading losses incurred using this indicator.​ Version: 2.0 Pine Script Version: v6 Author: © EliasVictor
مؤشر Pine Script®
من ‎EliasVictor‎
Super EMA PrismThis script implements the Binary Trade Logic (BTL) algorithm to calculate two distinct scores that range from 0 to 7. One score is calculated assigning a power of 2 weight to the positive sign of 3 Phi^3 distant Moving Average (MA) slopes. The other score is calculated assigning a power of 2 weight to the sign of the difference between the price and the value of 3 Phi^3 distant Moving Average (MA). For the first score, hereafter called as the angle score (AS), the largest MA slope positive sign receives weight 4, the middle length MA slope positive sign receives weight 2 and the shortest MA slope positive sign receives weight 1. The positive sign of an MA is defined as 1 if the slope of the MA is positive and 0, otherwise. Therefore, for MAs 305, 72 and 17, if slope(MA305) > 0, slope(MA72) < 0 and slope(MA17) > 0, then score will be 4*1 + 2*0 + 1*1 = 5. Up to my knowledge, this score was first proposed by Bo Williams and named by him as Prisma. For the second score, hereafter called as the value score (VS), if the price > largest MA, it receives weight 4. If the price > the middle length MA, it receives weight 2 and if the price > the the shortest MA, it receives weight 1. Therefore, for MAs 305, 72 and 17, if price < MA305, price > MA72 and price > MA17, then score will be 4*0 + 2*1 + 1*1 = 3. Up to my knowledge, this score was first proposed by Bo Williams and named by him as Prisma. Both AS and VS are calculated for Phi^3 lengths (610, 144, 34) and for Phi^3/2 lengths (305, 72, 17). The scores of the same kind calculated for each set of length are combined multiplying the Phi^3 length score by 10 and adding with with the Phi^3/2 score, therefore providing a 2 digit score ranging from 0 to 77. For instance, if we have AS(610, 144, 34) = 7 and AS(305, 72, 17) = 5, we have AS=75. At the same time, if we have VS(610, 144, 34) = 6 and VS(305, 72, 17) = 4, we have VS=64. VS score is plotted by default in black, but it can be on white for dark themes. AS is plotted with the color of the longest MA used. Chart background is colored according to the range of values for AS and VS, checked in the following order: if AS >= 13 and VS <= 13 then back color = red if AS >= 13 or VS <= 13 then back color = orange if AS >= 64 and VS >= 64 then back color = green if AS >= 64 or VS >= 64 then back color = blue otherwise back color = none (white o black)
مؤشر Pine Script®
من ‎luyikaibr‎
Cross-Correlation Lead/Lag AnalyzerCross-Correlation Lead/Lag Analyzer (XCorr) Discover which instrument moves first with advanced cross-correlation analysis. This indicator analyzes the lead/lag relationship between any two financial instruments using rolling cross-correlation at multiple time offsets. Perfect for pairs trading, market timing, and understanding inter-market relationships. Key Features: Universal compatibility - Works with any two symbols (stocks, futures, forex, crypto, commodities) Multi-timeframe analysis - Automatically adjusts lag periods based on your chart timeframe Real-time correlation table - Shows current correlation values for all lag scenarios Visual lead/lag detection - Color-coded plots make it easy to spot which instrument leads Smart "Best" indicator - Automatically identifies the strongest relationship How to Use: Set your symbols in the indicator settings (default: NQ1! vs RTY1!) Adjust correlation length (default: 20 periods for smooth but responsive analysis) Watch the colored lines: • Red/Orange: Symbol 2 leads Symbol 1 by 1-2 periods • Blue: Instruments move simultaneously • Green/Purple: Symbol 1 leads Symbol 2 by 1-2 periods Check the table for exact correlation values and the "Best" relationship Interpreting Results: Correlation > 0.7: Strong positive relationship Correlation 0.3-0.7: Moderate relationship Correlation < 0.3: Weak/no relationship Highest line indicates the optimal timing relationship Popular Use Cases: Index Futures : NQ vs ES, RTY vs IWM Sector Rotation : XLF vs XLK, QQQ vs SPY Commodities : GC vs SI, CL vs NG Currency Pairs : EURUSD vs GBPUSD Crypto : BTC vs ETH correlation analysis Technical Notes: Cross-correlation measures linear relationships between two time series at different time lags. This implementation uses Pearson correlation with adjustable periods, calculating correlations from -2 to +2 period offsets to detect leading/lagging behavior. Perfect for quantitative analysts, pairs traders, and anyone studying inter-market relationships.
مؤشر Pine Script®
من ‎public_username_twice‎
Ichimoku Power Indicator# Ichimoku Power Indicator ## Overview The Ichimoku Power Indicator is an advanced tool that combines the traditional Ichimoku Cloud system with a unique power ranking mechanism. This indicator provides traders with a comprehensive view of market trends and potential reversal points, all while quantifying the strength of bullish and bearish signals. ## Key Features 1. **Full Ichimoku Cloud Visualization:** Displays all components of the Ichimoku Cloud system, including Conversion Line (Tenkan-sen), Base Line (Kijun-sen), Leading Span A and B (Kumo), and Lagging Span (Chikou Span). 2. **Power Ranking System:** Calculates and displays a bullish and bearish power score based on 11 different Ichimoku-derived conditions. 3. **Real-time Updates:** Power scores are updated in real-time as market conditions change. 4. **Easy-to-Read Display:** A clear, color-coded table shows the current bullish and bearish power scores. 5. **Customizable Parameters:** Allows adjustment of key Ichimoku settings to suit different trading styles and timeframes. ## How It Works The indicator evaluates 11 different conditions derived from Ichimoku Cloud components: 1. Cloud color 2. Price position relative to the cloud 3. Tenkan-sen vs Kijun-sen 4. Price vs Tenkan-sen 5. Price vs Kijun-sen 6. Tenkan-sen vs Cloud 7. Kijun-sen vs Cloud 8. Chikou Span vs Cloud 9. Chikou Span vs Tenkan-sen 10. Chikou Span vs Kijun-sen 11. Chikou Span vs Price Each bullish condition adds a point to the bullish power score, while each bearish condition adds a point to the bearish power score. The maximum score for each is 11. ## Interpretation - Higher bullish scores suggest stronger upward trends or potential bullish reversals. - Higher bearish scores indicate stronger downward trends or potential bearish reversals. - When scores are close, it may indicate a period of consolidation or uncertainty. ## Use Cases - Trend Confirmation: Use in conjunction with price action to confirm the strength of current trends. - Reversal Detection: Watch for changes in power scores as early indicators of potential trend reversals. - Entry and Exit Signals: High power scores can be used to identify optimal entry or exit points. - Market Analysis: Gain a quick overview of market conditions across multiple assets or timeframes. ## Note This indicator is designed to complement your existing trading strategy. Always use it in conjunction with other forms of analysis and proper risk management techniques. Experiment with different timeframes and settings to find the configuration that best suits your trading style and the assets you trade. Happy trading!
مؤشر Pine Script®
من ‎atakhadivi‎
Price Divergence IndicatorThis Price Divergence Indicator indicator modifies the standard Divergence Indicator to look for price divergences between the current chart and any other selected TradingView chart. The thesis that this indicator is built upon: Prices on assets or indices that are normally correlated move in lock step. Where there are deviations between the confirmed highs or lows of two assets or indices it is likely that they will "catch up" in the near future. By default it will load the price data for the SPX and look for price divergences on the current chart timeframe. Any TradingView Symbol can be selected as the 'Comparison Source' and any timeframe. Some of the options I've been trying out include: SPX vs NDQ XAO vs SPX UK100 vs NDQM MSFT vs NDQM GOOG vs NDQM AMZN vs MSFT BTC vs ETH BTC vs NDQ BTC vs DXY I've found looking for divergences on a longer timeframe can be useful and don't expect any meaningful results if you set it to shorter than chart timeframes. Alerts can be created based on any of the divergences and the 'Backtest Buy Signal' can be used to send notification to a backtester (bull = 2, hidden bull = 1, neutral = 0, hidden bear = -1, bear = -2), this is plotted to display.none, so enable it in Settings - Style and disable all other plots to see it. Divergences are measured between the CONFIRMED peaks of the two charts. The confirmation timeframe is set using 'Pivot Lookback Right'. The lower the lookback the quicker the signal and the more likely it is to not have hit an actual peak, a higher lookback will give a much more dependable signal but the move may be finished by the time the alert actually fires. The "Plot When Alerts Fire" option should give you an idea (top and bottom triangles) of what to expect, but you should watch bar replays to understand how your setting will impact when alerts are created and potential false positives.
مؤشر Pine Script®
من ‎EltAlt‎
8
MACD Overlay v1 [JopAlgo]Meet the MACD you can trade directly from the chart. MACD Overlay v1 doesn’t just plot an oscillator somewhere below— it puts value, momentum, and participation on your candles, and it refuses to fire inside chop. When a triangle prints, it’s because energy released (expansion), not because the chart looked cute. What it is: An execution-ready MACD overlay with phase gating (Expansion-Only), participation gating (Weakness-Lite), and one-click Classic vs VW-MACD Compare—all adaptive, with minimal inputs. What’s in v1 (feature set) Overlay ribbon on price: Fast/Slow MACD value rendered as a price-level ribbon with contextual fill and optional candle tint. Dual value model: Classic MA-MACD (EMA/SMA) and VW-MACD (Rolling VWAP fast/slow). Compare mode: A/B Classic vs VW-MACD with a VW ghost ribbon. Weakness-Lite (1-bar, adaptive): Gates/fades low-participation crosses using RVOL deficit, Effort-vs-Result failure, and over-extension vs value/ATR (Strict adds wick pressure). Expansion-Only (Impulse/Squeeze): Triangles print only when a cross coincides with a true-range burst and a histogram-slope ignition out of compression. Signal hygiene: ±1-bar proximity around crosses, slope awareness, 2-bar debounce. Explainable filtering: Tiny gray dots show crosses that were intentionally filtered (weak and/or no expansion). How to use: Use defaults: Mode Classic, Gate by Weakness ON, Expansion-Only ON, Sensitivity Auto. Read signals fast: Solid triangle = cross + expansion confirmed (+ not weak if gate is ON). Faded triangle = cross + expansion but weak participation (visible only when gate is OFF). Gray dot = there was a cross, but it was filtered (no genuine expansion or weak & gated). Validate quickly: Flip Compare to check VW-MACD agreement. Classic + VW alignment usually improves confidence. Why overlay > sub-pane oscillator You see where the cross occurs: relative to value, local structure, and S/R, right on price. The ribbon exposes regime shifts; tint hints expansion vs contraction at a glance. Execution becomes more context-aware and less “signal-in-a-vacuum.” Signals & visuals Triangles (solid): MACD crossed Signal and market showed expansion out of compression; if Gate by Weakness is ON, triangle prints only with acceptable participation. Triangles (faded): Same as above but weak (shown only when you turn the gate OFF). Gray dots: Crosses that were filtered (no expansion and/or Weakness gate). Ribbon: Fast vs Slow value (Classic or VW, according to Mode). Fill and candle tint reflect expansion/contraction. Inputs Calculation Mode: Classic | VW | Compare VW uses Rolling VWAP fast/slow. Compare: Classic is primary; VW shows as a ghost ribbon for A/B checks. Gate triangles by Weakness: ON/OFF Uses RVOL, Effort-vs-Result, extension vs value/ATR (Strict adds wick-pressure). Sensitivity: Off / Auto / Strict (default Auto). Expansion-Only (Impulse/Squeeze): ON/OFF Requires compression → release: tight ribbon + flat momentum, then TR/ATR burst with hist slope flip / cross proximity. Display: Ribbon / Candle Tint / Weakness Markers. Advanced (optional): Evaluate Weakness only near signals, Channel (k × |MACD|), Style Preset. No numeric thresholds to tune—all filters self-calibrate from rolling stats. Best practices 4H crypto: Defaults are strong—Auto, Gate ON, Expansion-Only ON. Clean trends: If you feel you miss some tidy resumptions, briefly toggle Expansion-Only OFF. Choppy regimes: Set Sensitivity → Strict to cut more noise without adding lag. Confirmation: Use Compare; Classic + VW alignment typically yields better follow-through. Alerts MACD Signal Cross Up/Down — execution-grade (use Once per bar close). Weakness-Lite Flag — optional context alert to help audit filtered crosses. Attribution & License Attribution: Based on the algorithmic concept of TradingView’s built-in MACD (fast MA – slow MA, signal, histogram). No original TradingView source code is redistributed; overlay rendering, VW-MACD, Weakness-Lite, Expansion-Only, gating visuals, and UX are new work. License: MPL-2.0. Educational purposes only—not financial advice.
مؤشر Pine Script®
من ‎JopAlgo‎
2
BOCS Channel Scalper Indicator - Mean Reversion Alert System# BOCS Channel Scalper Indicator - Mean Reversion Alert System ## WHAT THIS INDICATOR DOES: This is a mean reversion trading indicator that identifies consolidation channels through volatility analysis and generates alert signals when price enters entry zones near channel boundaries. **This indicator version is designed for manual trading with comprehensive alert functionality.** Unlike automated strategies, this tool sends notifications (via popup, email, SMS, or webhook) when trading opportunities occur, allowing you to manually review and execute trades. The system assumes price will revert to the channel mean, identifying scalp opportunities as price reaches extremes and preparing to bounce back toward center. ## INDICATOR VS STRATEGY - KEY DISTINCTION: **This is an INDICATOR with alerts, not an automated strategy.** It does not execute trades automatically. Instead, it: - Displays visual signals on your chart when entry conditions are met - Sends customizable alerts to your device/email when opportunities arise - Shows TP/SL levels for reference but does not place orders - Requires you to manually enter and exit positions based on signals - Works with all TradingView subscription levels (alerts included on all plans) **For automated trading with backtesting**, use the strategy version. For manual control with notifications, use this indicator version. ## ALERT CAPABILITIES: This indicator includes four distinct alert conditions that can be configured independently: **1. New Channel Formation Alert** - Triggers when a fresh BOCS channel is identified - Message: "New BOCS channel formed - potential scalp setup ready" - Use this to prepare for upcoming trading opportunities **2. Long Scalp Entry Alert** - Fires when price touches the long entry zone - Message includes current price, calculated TP, and SL levels - Notification example: "LONG scalp signal at 24731.75 | TP: 24743.2 | SL: 24716.5" **3. Short Scalp Entry Alert** - Fires when price touches the short entry zone - Message includes current price, calculated TP, and SL levels - Notification example: "SHORT scalp signal at 24747.50 | TP: 24735.0 | SL: 24762.75" **4. Any Entry Signal Alert** - Combined alert for both long and short entries - Use this if you want a single alert stream for all opportunities - Message: "BOCS Scalp Entry: at " **Setting Up Alerts:** 1. Add indicator to chart and configure settings 2. Click the Alert (⏰) button in TradingView toolbar 3. Select "BOCS Channel Scalper" from condition dropdown 4. Choose desired alert type (Long, Short, Any, or Channel Formation) 5. Set "Once Per Bar Close" to avoid false signals during bar formation 6. Configure delivery method (popup, email, webhook for automation platforms) 7. Save alert - it will fire automatically when conditions are met **Alert Message Placeholders:** Alerts use TradingView's dynamic placeholder system: - {{ticker}} = Symbol name (e.g., NQ1!) - {{close}} = Current price at signal - {{plot_1}} = Calculated take profit level - {{plot_2}} = Calculated stop loss level These placeholders populate automatically, creating detailed notification messages without manual configuration. ## KEY DIFFERENCE FROM ORIGINAL BOCS: **This indicator is designed for traders seeking higher trade frequency.** The original BOCS indicator trades breakouts OUTSIDE channels, waiting for price to escape consolidation before entering. This scalper version trades mean reversion INSIDE channels, entering when price reaches channel extremes and betting on a bounce back to center. The result is significantly more trading opportunities: - **Original BOCS**: 1-3 signals per channel (only on breakout) - **Scalper Indicator**: 5-15+ signals per channel (every touch of entry zones) - **Trade Style**: Mean reversion vs trend following - **Hold Time**: Seconds to minutes vs minutes to hours - **Best Markets**: Ranging/choppy conditions vs trending breakouts This makes the indicator ideal for active day traders who want continuous alert opportunities within consolidation zones rather than waiting for breakout confirmation. However, increased signal frequency also means higher potential commission costs and requires disciplined trade selection when acting on alerts. ## TECHNICAL METHODOLOGY: ### Price Normalization Process: The indicator normalizes price data to create consistent volatility measurements across different instruments and price levels. It calculates the highest high and lowest low over a user-defined lookback period (default 100 bars). Current close price is normalized using: (close - lowest_low) / (highest_high - lowest_low), producing values between 0 and 1 for standardized volatility analysis. ### Volatility Detection: A 14-period standard deviation is applied to the normalized price series to measure price deviation from the mean. Higher standard deviation values indicate volatility expansion; lower values indicate consolidation. The indicator uses ta.highestbars() and ta.lowestbars() to identify when volatility peaks and troughs occur over the detection period (default 14 bars). ### Channel Formation Logic: When volatility crosses from a high level to a low level (ta.crossover(upper, lower)), a consolidation phase begins. The indicator tracks the highest and lowest prices during this period, which become the channel boundaries. Minimum duration of 10+ bars is required to filter out brief volatility spikes. Channels are rendered as box objects with defined upper and lower boundaries, with colored zones indicating entry areas. ### Entry Signal Generation: The indicator uses immediate touch-based entry logic. Entry zones are defined as a percentage from channel edges (default 20%): - **Long Entry Zone**: Bottom 20% of channel (bottomBound + channelRange × 0.2) - **Short Entry Zone**: Top 20% of channel (topBound - channelRange × 0.2) Long signals trigger when candle low touches or enters the long entry zone. Short signals trigger when candle high touches or enters the short entry zone. Visual markers (arrows and labels) appear on chart, and configured alerts fire immediately. ### Cooldown Filter: An optional cooldown period (measured in bars) prevents alert spam by enforcing minimum spacing between consecutive signals. If cooldown is set to 3 bars, no new long alert will fire until 3 bars after the previous long signal. Long and short cooldowns are tracked independently, allowing both directions to signal within the same period. ### ATR Volatility Filter: The indicator includes a multi-timeframe ATR filter to avoid alerts during low-volatility conditions. Using request.security(), it fetches ATR values from a specified timeframe (e.g., 1-minute ATR while viewing 5-minute charts). The filter compares current ATR to a user-defined minimum threshold: - If ATR ≥ threshold: Alerts enabled - If ATR < threshold: No alerts fire This prevents notifications during dead zones where mean reversion is unreliable due to insufficient price movement. The ATR status is displayed in the info table with visual confirmation (✓ or ✗). ### Take Profit Calculation: Two TP methods are available: **Fixed Points Mode**: - Long TP = Entry + (TP_Ticks × syminfo.mintick) - Short TP = Entry - (TP_Ticks × syminfo.mintick) **Channel Percentage Mode**: - Long TP = Entry + (ChannelRange × TP_Percent) - Short TP = Entry - (ChannelRange × TP_Percent) Default 50% targets the channel midline, a natural mean reversion target. These levels are displayed as visual lines with labels and included in alert messages for reference when manually placing orders. ### Stop Loss Placement: Stop losses are calculated just outside the channel boundary by a user-defined tick offset: - Long SL = ChannelBottom - (SL_Offset_Ticks × syminfo.mintick) - Short SL = ChannelTop + (SL_Offset_Ticks × syminfo.mintick) This logic assumes channel breaks invalidate the mean reversion thesis. SL levels are displayed on chart and included in alert notifications as suggested stop placement. ### Channel Breakout Management: Channels are removed when price closes more than 10 ticks outside boundaries. This tolerance prevents premature channel deletion from minor breaks or wicks, allowing the mean reversion setup to persist through small boundary violations. ## INPUT PARAMETERS: ### Channel Settings: - **Nested Channels**: Allow multiple overlapping channels vs single channel - **Normalization Length**: Lookback for high/low calculation (1-500, default 100) - **Box Detection Length**: Period for volatility detection (1-100, default 14) ### Scalping Settings: - **Enable Long Scalps**: Toggle long alert generation on/off - **Enable Short Scalps**: Toggle short alert generation on/off - **Entry Zone % from Edge**: Size of entry zone (5-50%, default 20%) - **SL Offset (Ticks)**: Distance beyond channel for stop (1+, default 5) - **Cooldown Period (Bars)**: Minimum spacing between alerts (0 = no cooldown) ### ATR Filter: - **Enable ATR Filter**: Toggle volatility filter on/off - **ATR Timeframe**: Source timeframe for ATR (1, 5, 15, 60 min, etc.) - **ATR Length**: Smoothing period (1-100, default 14) - **Min ATR Value**: Threshold for alert enablement (0.1+, default 10.0) ### Take Profit Settings: - **TP Method**: Choose Fixed Points or % of Channel - **TP Fixed (Ticks)**: Static distance in ticks (1+, default 30) - **TP % of Channel**: Dynamic target as channel percentage (10-100%, default 50%) ### Appearance: - **Show Entry Zones**: Toggle zone labels on channels - **Show Info Table**: Display real-time indicator status - **Table Position**: Corner placement (Top Left/Right, Bottom Left/Right) - **Long Color**: Customize long signal color (default: darker green for readability) - **Short Color**: Customize short signal color (default: red) - **TP/SL Colors**: Customize take profit and stop loss line colors - **Line Length**: Visual length of TP/SL reference lines (5-200 bars) ## VISUAL INDICATORS: - **Channel boxes** with semi-transparent fill showing consolidation zones - **Colored entry zones** labeled "LONG ZONE ▲" and "SHORT ZONE ▼" - **Entry signal arrows** below/above bars marking long/short alerts - **TP/SL reference lines** with emoji labels (⊕ Entry, 🎯 TP, 🛑 SL) - **Info table** showing channel status, last signal, entry/TP/SL prices, risk/reward ratio, and ATR filter status - **Visual confirmation** when alerts fire via on-chart markers synchronized with notifications ## HOW TO USE: ### For 1-3 Minute Scalping with Alerts (NQ/ES): - ATR Timeframe: "1" (1-minute) - ATR Min Value: 10.0 (for NQ), adjust per instrument - Entry Zone %: 20-25% - TP Method: Fixed Points, 20-40 ticks - SL Offset: 5-10 ticks - Cooldown: 2-3 bars to reduce alert spam - **Alert Setup**: Configure "Any Entry Signal" for combined long/short notifications - **Execution**: When alert fires, verify chart visuals, then manually place limit order at entry zone with provided TP/SL levels ### For 5-15 Minute Day Trading with Alerts: - ATR Timeframe: "5" or match chart - ATR Min Value: Adjust to instrument (test 8-15 for NQ) - Entry Zone %: 20-30% - TP Method: % of Channel, 40-60% - SL Offset: 5-10 ticks - Cooldown: 3-5 bars - **Alert Setup**: Configure separate "Long Scalp Entry" and "Short Scalp Entry" alerts if you trade directionally based on bias - **Execution**: Review channel structure on alert, confirm ATR filter shows ✓, then enter manually ### For 30-60 Minute Swing Scalping with Alerts: - ATR Timeframe: "15" or "30" - ATR Min Value: Lower threshold for broader market - Entry Zone %: 25-35% - TP Method: % of Channel, 50-70% - SL Offset: 10-15 ticks - Cooldown: 5+ bars or disable - **Alert Setup**: Use "New Channel Formation" to prepare for setups, then "Any Entry Signal" for execution alerts - **Execution**: Larger timeframes allow more analysis time between alert and entry ### Webhook Integration for Semi-Automation: - Configure alert webhook URL to connect with platforms like TradersPost, TradingView Paper Trading, or custom automation - Alert message includes all necessary order parameters (direction, entry, TP, SL) - Webhook receives structured data when signal fires - External platform can auto-execute based on alert payload - Still maintains manual oversight vs full strategy automation ## USAGE CONSIDERATIONS: - **Manual Discipline Required**: Alerts provide opportunities but execution requires judgment. Not all alerts should be taken - consider market context, trend, and channel quality - **Alert Timing**: Alerts fire on bar close by default. Ensure "Once Per Bar Close" is selected to avoid false signals during bar formation - **Notification Delivery**: Mobile/email alerts may have 1-3 second delay. For immediate execution, use desktop popups or webhook automation - **Cooldown Necessity**: Without cooldown, rapidly touching price action can generate excessive alerts. Start with 3-bar cooldown and adjust based on alert volume - **ATR Filter Impact**: Enabling ATR filter dramatically reduces alert count but improves quality. Track filter status in info table to understand when you're receiving fewer alerts - **Commission Awareness**: High alert frequency means high potential trade count. Calculate if your commission structure supports frequent scalping before acting on all alerts ## COMPATIBLE MARKETS: Works on any instrument with price data including stock indices (NQ, ES, YM, RTY), individual stocks, forex pairs (EUR/USD, GBP/USD), cryptocurrency (BTC, ETH), and commodities. Volume-based features are not included in this indicator version. Multi-timeframe ATR requires higher-tier TradingView subscription for request.security() functionality on timeframes below chart timeframe. ## KNOWN LIMITATIONS: - **Indicator does not execute trades** - alerts are informational only; you must manually place all orders - **Alert delivery depends on TradingView infrastructure** - delays or failures possible during platform issues - **No position tracking** - indicator doesn't know if you're in a trade; you must manage open positions independently - **TP/SL levels are reference only** - you must manually set these on your broker platform; they are not live orders - **Immediate touch entry can generate many alerts** in choppy zones without adequate cooldown - **Channel deletion at 10-tick breaks** may be too aggressive or lenient depending on instrument tick size - **ATR filter from lower timeframes** requires TradingView Premium/Pro+ for request.security() - **Mean reversion logic fails** in strong breakout scenarios - alerts will fire but trades may hit stops - **No partial closing capability** - full position management is manual; you determine scaling out - **Alerts do not account for gaps** or overnight price changes; morning alerts may be stale ## RISK DISCLOSURE: Trading involves substantial risk of loss. This indicator provides signals for educational and informational purposes only and does not constitute financial advice. Past performance does not guarantee future results. Mean reversion strategies can experience extended drawdowns during trending markets. Alerts are not guaranteed to be profitable and should be combined with your own analysis. Stop losses may not fill at intended levels during extreme volatility or gaps. Never trade with capital you cannot afford to lose. Consider consulting a licensed financial advisor before making trading decisions. Always verify alerts against current market conditions before executing trades manually. ## ACKNOWLEDGMENT & CREDITS: This indicator is built upon the channel detection methodology created by **AlgoAlpha** in the "Smart Money Breakout Channels" indicator. Full credit and appreciation to AlgoAlpha for pioneering the normalized volatility approach to identifying consolidation patterns. The core channel formation logic using normalized price standard deviation is AlgoAlpha's original contribution to the TradingView community. Enhancements to the original concept include: mean reversion entry logic (vs breakout), immediate touch-based alert generation, comprehensive alert condition system with customizable notifications, multi-timeframe ATR volatility filtering, cooldown period for alert management, dual TP methods (fixed points vs channel percentage), visual TP/SL reference lines, and real-time status monitoring table. This indicator version is specifically designed for manual traders who prefer alert-based decision making over automated execution.
مؤشر Pine Script®
من ‎TradeManager_PRO‎
FxAST Ichi ProSeries Enhanced Full Market Regime EngineFxAST Ichi ProSeries v1.x is a modernized Ichimoku engine that keeps the classic logic but adds a full market regime engine for any market and instrument.” Multi-timeframe cloud overlay Oracle long-term baseline Trend regime classifier (Bull / Bear / Transition / Range) Chikou & Cloud breakout signals HTF + Oracle + Trend dashboard Alert-ready structure for automation No repainting: all HTF calls use lookahead_off. 1. Core Ichimoku Engine Code sections: Input group: Core Ichimoku Function: ichiCalc() Variables: tenkan, kijun, spanA, spanB, chikou What it does Calculates the classic Ichimoku components: Tenkan (Conversion Line) – fast Donchian average (convLen) Kijun (Base Line) – slower Donchian average (baseLen) Senkou Span A (Span A / Lead1) – (Tenkan + Kijun)/2 Senkou Span B (Span B / Lead2) – Donchian over spanBLen Chikou – current close shifted back in time (displace) Everything else in the indicator builds on this engine. How to use it (trading) Tenkan vs Kijun = short-term vs medium-term balance. Tenkan above Kijun = short-term bullish control; below = bearish control. Span A / B defines the cloud, which represents equilibrium and support/resistance. Price above cloud = bullish bias; price below cloud = bearish bias. Graphic 2. Display & Cloud Styling Code sections: Input groups: Display Options, Cloud Styling, Lagging Span & Signals Variables: showTenkan, showKijun, showChikou, showCloud, bullCloudColor, bearCloudColor, cloudLineWidth, laggingColor Plots: plot(tenkan), plot(kijun), plot(chikou), p1, p2, fill(p1, p2, ...) What it does Lets you toggle individual components: Show/hide Tenkan, Kijun, Chikou, and the cloud. Customize cloud colors & opacity: bullCloudColor when Span A > Span B bearCloudColor when Span A < Span B Adjust cloud line width for clarity. How to use it Turn off components you don’t use (e.g., hide Chikou if you only want cloud + Tenkan/Kijun). For higher-timeframe or noisy charts, use thicker Kijun & cloud so structure is easier to see. Graphic Before After 3. HTF Cloud Overlay (Multi-Timeframe) Code sections: Input group: HTF Cloud Overlay Vars: showHTFCloud, htfTf, htfAlpha Logic: request.security(..., ichiCalc(...)) → htfSpanA, htfSpanB Plots: pHTF1, pHTF2, fill(pHTF1, pHTF2, ...) What it does Pulls higher-timeframe Ichimoku cloud (e.g., 1H, 4H, Daily) onto your current chart. Uses the same Ichimoku settings but aggregates on htfTf. Plots an extra, semi-transparent cloud ahead of price: Greenish when HTF Span A > Span B Reddish when HTF Span B > Span A How to use it Trade LTF (e.g., 5m/15m) only in alignment with HTF trend: HTF cloud bullish + LTF Ichi bullish → look for longs HTF cloud bearish + LTF Ichi bearish → look for shorts Treat HTF cloud boundaries as major S/R zones. Graphic 4. Oracle Module Code sections: Input group: Oracle Module Vars: useOracle, oracleLen, oracleColor, oracleWidth, oracleSlopeLen Logic: oracleLine = donchian(oracleLen); slope check vs oracleLine Plot: plot(useOracle ? oracleLine : na, "Oracle", ...) What it does Creates a long-term Donchian baseline (default 208 bars). Uses a simple slope check: Current Oracle > Oracle oracleSlopeLen bars ago → Oracle Bull Current Oracle < Oracle oracleSlopeLen bars ago → Oracle Bear Slope state is also shown in the dashboard (“Bull / Bear / Flat”). How to use it Think of Oracle as your macro anchor : Only take longs when Oracle is sloping up or flat. Only take shorts when Oracle is sloping down or flat. Works well combined with HTF cloud: HTF cloud bullish + Oracle Bull = higher conviction long bias. Ideal for Gold / Indices swing trades as a trend filter. Graphic idea 5. Trend Regime Classifier Code sections: Input group: Trend Regime Logic Vars: useTrendRegime, bgTrendOpacity, minTrendScore Logic: priceAboveCloud, priceBelowCloud, priceInsideCloud Tenkan vs Kijun alignment Cloud bullish/bearish bullScore / bearScore (0–3) regime + regimeLabel + regimeColor Visuals: bgcolor(regimeColor) and optional barcolor() in priceColoring mode. What it does Scores the market in three dimensions : Price vs Cloud Tenkan vs Kijun Cloud Direction (Span A vs Span B) Each condition contributes +1 to either bullScore or bearScore . Then: Bull regime when: bullScore >= minTrendScore and bullScore > bearScore Price in cloud → “Range” Everything else → “Transition” These regimes are shown as: Background colors: Teal = Bull Maroon = Bear Orange = Range Silver = Transition Optional candle recoloring when priceColoring = true. How to use it Filters: Only buy when regime = Bull or Transition and Oracle/HTF agree. Only sell when regime = Bear or Transition and Oracle/HTF agree. No trade zone: When regime = Range (price inside cloud), avoid new entries; wait for break. Aggressiveness: Adjust minTrendScore to be stricter (3) or looser (1). Graphic 6. Signals: Chikou & Cloud Breakout Code sections : Logic: chikouBuySignal = ta.crossover(chikou, close) chikouSellSignal = ta.crossunder(chikou, close) cloudBreakUp = priceInsideCloud and priceAboveCloud cloudBreakDown = priceInsideCloud and priceBelowCloud What it does 1. Two key signal groups: Chikou Cross Signals Buy when Chikou crosses up through price. Sell when Chikou crosses down through price. Classic Ichi confirmation idea: Chikou breaking free of price cluster. 2. Cloud Breakout Signals Long trigger: yesterday inside cloud → today price breaks above cloud. Short trigger: yesterday inside cloud → today price breaks below cloud. Captures “equilibrium → expansion” moves. These are conditions only in this version (no chart shapes yet) but are fully wired for alerts. (Future Updates) How to use it Use Chikou signals as confirmation, not standalone entries: Eg., Bull regime + Oracle Bull + cloud breakout + Chikou Buy. Use Cloud Breakouts to catch the first impulsive leg after consolidation. Graphic 7. Alerts (Automation Ready) [ b]Code sections: Input group: Alerts Vars: useAlertTrend, useAlertChikou, useAlertCloudBO Alert lines like: "FxAST Ichi Bull Trend", "FxAST Ichi Bull Trend", "FxAST Ichi Cloud Break Up" What it does Provides ready-made alert hooks for: Trend regime (Bull / Bear) Chikou cross buy/sell Cloud breakout up/down Each type can be globally toggled on/off via the inputs (helpful if a user only wants one kind). How to use it In TradingView: set alerts using “Any alert() function call” on this indicator. Then filter which ones fire by: Turning specific alert toggles on/off in input panel, or Filtering text in your external bot / webhook side. Example simple workflow ---> Indicator ---> TV Alert ---> Webhook ---> Bot/Broker 8. FxAST Dashboard Code sections: Input group: Dashboard Vars: showDashboard, dashPos, dash, dashInit Helper: getDashPos() → position.* Table cells (updated on barstate.islast): Row 0: Regime + label Row 1: Oracle status (Bull / Bear / Flat / Off) Row 2: HTF Cloud (On + TF / Off) Row 3: Scores (BullScore / BearScore) What it does Displays a compact panel with the state of the whole system : Current Trend Regime (Bull / Bear / Transition / Range) Oracle slope state Whether HTF Cloud is active + which timeframe Raw Bull / Bear scores (0–3 each) Position can be set: Top Right, Top Left, Bottom Right, Bottom Left. How to use it Treat it like a pilot instrument cluster : Quick glance: “Are my trend, oracle and HTF all aligned?” Great for streaming / screenshots: everything important is visible in one place without reading the code. Graphic (lower right of chart )
مؤشر Pine Script®
من ‎alldyn_pip_king‎
3D Institutional Battlefield [SurgeGuru]Professional Presentation: 3D Institutional Flow Terrain Indicator Overview The 3D Institutional Flow Terrain is an advanced trading visualization tool that transforms complex market structure into an intuitive 3D landscape. This indicator synthesizes multiple institutional data points—volume profiles, order blocks, liquidity zones, and voids—into a single comprehensive view, helping you identify high-probability trading opportunities. Key Features 🎥 Camera & Projection Controls Yaw & Pitch: Adjust viewing angles (0-90°) for optimal perspective Scale Controls: Fine-tune X (width), Y (depth), and Z (height) dimensions Pro Tip: Increase Z-scale to amplify terrain features for better visibility 🌐 Grid & Surface Configuration Resolution: Adjust X (16-64) and Y (12-48) grid density Visual Elements: Toggle surface fill, wireframe, and node markers Optimization: Higher resolution provides more detail but requires more processing power 📊 Data Integration Lookback Period: 50-500 bars of historical analysis Multi-Source Data: Combine volume profile, order blocks, liquidity zones, and voids Weighted Analysis: Each data source contributes proportionally to the terrain height How to Use the Frontend 💛 Price Line Tracking (Your Primary Focus) The yellow price line is your most important guide: Monitor Price Movement: Track how the yellow line interacts with the 3D terrain Identify Key Levels: Watch for these critical interactions: Order Blocks (Green/Red Zones): When yellow price line enters green zones = Bullish order block When yellow price line enters red zones = Bearish order block These represent institutional accumulation/distribution areas Liquidity Voids (Yellow Zones): When yellow price line enters yellow void areas = Potential acceleration zones Voids indicate price gaps where minimal trading occurred Price often moves rapidly through voids toward next liquidity pool Terrain Reading: High Terrain Peaks: High volume/interest areas (support/resistance) Low Terrain Valleys: Low volume areas (potential breakout zones) Color Coding: Green terrain = Bullish volume dominance Red terrain = Bearish volume dominance Purple = Neutral/transition areas 📈 Volume Profile Integration POC (Point of Control): Automatically marks highest volume level Volume Bins: Adjust granularity (10-50 bins) Height Weight: Control how much volume affects terrain elevation 🏛️ Order Block Detection Detection Length: 5-50 bar lookback for block identification Strength Weighting: Recent blocks have greater impact on terrain Candle Body Option: Use full candles or body-only for block definition 💧 Liquidity Zone Tracking Multiple Levels: Track 3-10 key liquidity zones Buy/Sell Side: Different colors for bid/ask liquidity Strength Decay: Older zones have diminishing terrain impact 🌊 Liquidity Void Identification Threshold Multiplier: Adjust sensitivity (0.5-2.0) Height Amplification: Voids create significant terrain depressions Acceleration Zones: Price typically moves quickly through void areas Practical Trading Application Bullish Scenario: Yellow price line approaches green order block terrain Price finds support in elevated bullish volume areas Terrain shows consistent elevation through key levels Bearish Scenario: Yellow price line struggles at red order block resistance Price falls through liquidity voids toward lower terrain Bearish volume peaks dominate the landscape Breakout Setup: Yellow price line consolidates in flat terrain Minimal resistance (low terrain) in projected direction Clear path toward distant liquidity zones Pro Tips Start Simple: Begin with default settings, then gradually customize Focus on Yellow Line: Your primary indicator of current price position Combine Timeframes: Use the same terrain across multiple timeframes for confluence Volume Confirmation: Ensure terrain peaks align with actual volume spikes Void Anticipation: When price enters voids, prepare for potential rapid movement Order Blocks & Voids Architecture Order Blocks Calculation Trigger: Price breaks fractal swing points Bullish OB: When close > swing high → find lowest low in lookback period Bearish OB: When close < swing low → find highest high in lookback period Strength: Based on price distance from block extremes Storage: Global array maintains last 50 blocks with FIFO management Liquidity Voids Detection Trigger: Price gaps exceeding ATR threshold Bull Void: Low - high > (ATR200 × multiplier) Bear Void: Low - high > (ATR200 × multiplier) Validation: Close confirms gap direction Storage: Global array maintains last 30 voids Key Design Features Real-time Updates: Calculated every bar, not just on last bar Global Persistence: Arrays maintain state across executions FIFO Management: Automatic cleanup of oldest entries Configurable Sensitivity: Adjustable lookback periods and thresholds Scientific Testing Framework Hypothesis Testing Primary Hypothesis: 3D terrain visualization improves detection of institutional order flow vs traditional 2D charts Testable Metrics: Prediction Accuracy: Does terrain structure predict future support/resistance? Reaction Time: Faster identification of key levels vs conventional methods False Positive Reduction: Lower rate of failed breakouts/breakdowns Control Variables Market Regime: Trending vs ranging conditions Asset Classes: Forex, equities, cryptocurrencies Timeframes: M5 to H4 for intraday, D1 for swing Volume Conditions: High vs low volume environments Data Collection Protocol Terrain Features to Quantify: Slope gradient changes at price inflection points Volume peak clustering density Order block terrain elevation vs subsequent price action Void depth correlation with momentum acceleration Control Group: Traditional support/resistance + volume profile Experimental Group: 3D Institutional Flow Terrain Statistical Measures Signal-to-Noise Ratio: Terrain features vs random price movements Lead Time: Terrain formation ahead of price confirmation Effect Size: Performance difference between groups (Cohen's d) Statistical Power: Sample size requirements for significance Validation Methodology Blind Testing: Remove price labels from terrain screenshots Have traders identify key levels from terrain alone Measure accuracy vs actual price action Backtesting Framework: Automated terrain feature extraction Correlation with future price reversals/breakouts Monte Carlo simulation for significance testing Expected Outcomes If hypothesis valid: Significant improvement in level prediction accuracy (p < 0.05) Reduced latency in institutional level identification Higher risk-reward ratios on terrain-confirmed trades Research Questions: Does terrain elevation reliably indicate institutional interest zones? Are liquidity voids statistically significant momentum predictors? Does multi-timeframe terrain analysis improve signal quality? How does terrain persistence correlate with level strength? LuxAlgo BigBeluga hapharmonic
مؤشر Pine Script®
من ‎SurgeGuru‎
2
Fair Value Lead-Lag Model [BackQuant]Fair Value Lead-Lag Model A cross-asset model that estimates where price "should" be relative to a chosen reference series, then tracks the deviation as a normalized oscillator. It helps you answer two questions: 1) is the asset rich or cheap vs its driver, and 2) is the driver leading or lagging price over the next N bars. Concept in one paragraph Many assets co-move with a macro or sector driver. Think BTC vs DXY, gold vs real yields, a stock vs its sector ETF. This tool builds a rolling fair value of the charted asset from a reference series and shows how far price is above or below that fair value in standard deviation units. You can shift the reference forward or backward to test who leads whom, then use the deviation and its bands to structure mean-reversion or trend-following ideas. What the model does Reference mapping : Pulls a reference symbol at a chosen timeframe, with an optional lead or lag in bars to test causality. Fair value engine : Converts the reference into a synthetic fair value of the chart using one of four methods: Ratio : price/ref with a rolling average ratio. Good when the relationship is proportional. Spread : price minus ref with a rolling average spread. Good when the relationship is additive. Z-Score : normalizes both series, aligns on standardized units, then re-projects to price space. Good when scale drifts. Beta-Adjusted : rolling regression style. Uses covariance and variance to compute beta, then builds a fair value = mean(price) + beta * (ref − mean(ref)). Deviation and bands : Computes a z-scored deviation of price vs fair value and plots sigma bands (±1, ±2, ±3) around the fair value line on the chart. Correlation context : Shows rolling correlation so you can judge if deviations are meaningful or just noise when co-movement is weak. Visuals : Fair value line on price chart with sigma envelopes. Deviation as a column oscillator and optional line. Threshold shading beyond user-set upper and lower levels. Summary table with reference, deviation, status, correlation, and method. Why this is useful Mean reversion framework : When correlation is healthy and deviation stretches beyond your sigma threshold, probability favors reversion toward fair value. This is classic pairs logic adapted to a driver and a target. Trend confirmation : If price rides the fair value line and deviation stays modest while correlation is positive, it supports trend persistence. Pullbacks to negative deviation in an uptrend can be buyable. Lead-lag discovery : Shift the reference forward by +N bars. If correlation improves, the reference tends to lead. Shift backward for the reverse. Use the best setting for planning early entries or hedges. Regime detection : Large persistent deviations with falling correlation hint at regime change. The relationship you relied on may be breaking down, so reduce confidence or switch methods. How to use it step by step Pick a sensible reference : Choose a macro, index, currency, or sector driver that logically explains the asset’s moves. Example: gold with DXY, a semiconductor stock with SOXX. Test lead-lag : Nudge Lead/Lag Periods to small positive values like +1 to +5 to see if the reference leads. If correlation improves, keep that offset. If correlation worsens, try a small negative value or zero. Select a method : Start with Beta-Adjusted when the relationship is approximately linear with drift. Use Ratio if the assets usually move in proportional terms. Use Spread when they trade around a level difference. Use Z-Score when scales wander or volatility regimes shift. Tune windows : Rolling Window controls how quickly fair value adapts. Shorter equals faster but noisier. Normalization Period controls how deviations are standardized. Longer equals stabler sigma sizing. Correlation Length controls how co-movement is measured. Keep it near the fair value window. Trade the edges : Mean reversion idea : Wait for deviation beyond your Upper or Lower Threshold with positive correlation. Fade back toward fair value. Exit at the fair value line or the next inner sigma band. Trend idea : In an uptrend, buy pullbacks when deviation dips negative but correlation remains healthy. In a downtrend, sell bounces when deviation spikes positive. Read the table : Deviation shows how many sigmas you are from fair value. Status tells you overvalued or undervalued. Correlation color hints confidence. Method tells you the projection style used. Reading the display Fair value line on price chart: the model’s estimate of where price should trade given the reference, updated each bar. Sigma bands around fair value: a quick sense of residual volatility. Reversions often target inner bands first. Deviation oscillator : above zero means rich vs fair value, below zero means cheap. Color bins intensify with distance. Correlation line (optional): scale is folded to match thresholds. Higher values increase trust in deviations. Parameter tips Start with Rolling Window 20 to 30, Normalization Period 100, Correlation Length 50. Upper and Lower Threshold at ±2.0 are classic. Tighten to ±1.5 for more signals or widen to ±2.5 to focus on outliers. When correlation drifts below about 0.3, treat deviations with caution. Consider switching method or reference. If the fair value line whipsaws, increase Rolling Window or move to Beta-Adjusted which tends to be smoother. Playbook examples Pairs-style reversion : Asset is +2.3 sigma rich vs reference, correlation 0.65, trend flat. Short the deviation back toward fair value. Cover near the fair value line or +1 sigma. Pro-trend pullback : Uptrend with correlation 0.7. Deviation dips to −1.2 sigma while price sits near the −1 sigma band. Buy the dip, target the fair value line, trail if the line is rising. Lead-lag timing : Reference leads by +3 bars with improved correlation. Use reference swings as early cues to anticipate deviation turns on the target. Caveats The model assumes a stable relationship over the chosen windows. Structural breaks, policy shocks, and index rebalances can invalidate recent history. Correlation is descriptive, not causal. A strong correlation does not guarantee future convergence. Do not force trades when the reference has low liquidity or mismatched hours. Use a reference timeframe that captures real overlap. Bottom line This tool turns a loose cross-asset intuition into a quantified, visual fair value map. It gives you a consistent way to find rich or cheap conditions, time mean-reversion toward a statistically grounded target, and confirm or fade trends when the driver agrees.
مؤشر Pine Script®
من ‎BackQuant‎