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Indicatori e strategie

Orb DivergenceOrb Divergence is a market reversal indicator that visually highlights moments when price momentum is ready to shift direction. It detects hidden energy imbalances within price movements and identifies potential trend turning points formed by these accumulations. The indicator displays colored orbs and clear “UP / DOWN” signals to mark upcoming reversals in a simple and intuitive way. Rather than focusing on short-term reactions, it emphasizes key zones where market momentum may truly change. Designed as a visual compass, Orb Divergence helps you spot the moments when the market takes a breath — and prepares to turn. For a deeper and more data-driven approach to market structure and reversal dynamics, you may also want to explore Teometric Demand Model V3;
Indicatore Pine Script®
di SignalSuiteDaphne
Ultimate Parabolic SAR with Advanced Signal# 📊 Ultimate Parabolic SAR with Advanced Signal ## Overview The **Ultimate Parabolic SAR** is a comprehensive trading indicator that enhances the classic Parabolic SAR with advanced multi-indicator filtering, customizable signal generation, and multi-timeframe analysis. Perfect for traders of all styles - from scalping to swing trading. ## 🎯 Key Features ### 1. **Smart Trading Modes** Pre-configured PSAR settings optimized for different trading styles: - **Futures 1m** (0.04/0.03/0.4) - Ultra-fast scalping - **Scalping/Futures 15m** (0.03/0.02/0.25) - Quick intraday trades - **Day Trade 1h-4h** (0.02/0.02/0.2) - Standard intraday trading - **Swing Trade 1D** (0.01/0.01/0.15) - Position trading - **Custom** - Full manual control ### 2. **Advanced Signal Filtering System** Combine up to 6 technical indicators to filter entry signals: - ✅ **RSI Filter** - Avoid overbought/oversold zones - ✅ **MACD Filter** - Confirm momentum direction - ✅ **Stochastic RSI** - Additional momentum validation - ✅ **MA Filter** - 20/50 Moving Average trend confirmation - ✅ **EMA Filter** - 20/50 Exponential Moving Average trend - ✅ **SMA Filter** - 200 Simple Moving Average long-term trend *Enable filters individually or combine them for higher-quality signals* ### 3. **Multi-Timeframe (MTF) Dashboard** Real-time percentage change analysis across 6 timeframes: - 5m, 15m, 1h, 4h, 1D, 1W - Color-coded arrows (▲ Green = Up, ▼ Red = Down) - Fully customizable: toggle timeframes, adjust size & position - Instant market sentiment overview ### 4. **Highly Customizable Visuals** - **Buy/Sell Signals**: Adjustable size (Tiny/Small/Normal/Large) - **Signal Offset**: Fine-tune label positioning - **Color Customization**: Separate colors for Buy/Sell labels - **Start Points**: Optional small dots at PSAR reversals - **State Highlighting**: Visual background fill for trend direction ### 5. **Multiple Moving Averages Display** Independent visualization of trend indicators: - MA 20 & MA 50 (Simple Moving Averages) - EMA 20 & EMA 50 (Exponential Moving Averages) - SMA 200 (Long-term trend) - Custom colors for each line - Show/hide independently from filter usage ## 📈 How to Use ### Basic Setup 1. Add indicator to chart 2. Choose a **Trading Mode** that matches your timeframe 3. Optionally enable **Signal Filters** for cleaner entries 4. Watch for **Buy/Sell** labels on PSAR reversals ### Conservative Approach Enable multiple filters for higher-quality signals: ``` ✅ RSI Filter ✅ MACD Filter ✅ EMA Trend Filter ✅ SMA Filter ``` *Result: Fewer but more reliable signals* ### Aggressive Approach Use PSAR alone or with minimal filtering: ``` ❌ All filters OFF ``` *Result: More frequent trading opportunities* ### Multi-Timeframe Confirmation 1. Enable MTF Table (top-right by default) 2. Check if multiple timeframes align 3. Enter trades when 3+ timeframes show same direction 4. Example: 15m ▲, 1h ▲, 4h ▲ = Strong uptrend ## 🎨 Customization Options ### Entry Signal Settings - Show/Hide Buy/Sell signals - Adjust label size for visibility - Offset labels to avoid chart clutter - Custom colors for Buy (default: green) and Sell (default: orange) ### MTF Table Settings - Position: Top/Bottom, Left/Right corners - Text size: Tiny to Large - Toggle individual timeframes (5m to 1W) - Custom background and text colors ### Visual Elements - PSAR point width adjustment - Highlight PSAR reversal points - Trend state background highlighting - Independent MA/EMA/SMA line display ## 💡 Trading Strategies ### Strategy 1: Trend Following ``` Mode: Day Trade 1h-4h Filters: EMA + SMA enabled MTF: Check 4h and 1D alignment Entry: Buy when all confirm uptrend ``` ### Strategy 2: Momentum Scalping ``` Mode: Scalping/Futures 15m Filters: RSI + MACD enabled MTF: Focus on 5m and 15m Entry: Quick in/out on momentum shifts ``` ### Strategy 3: Swing Trading ``` Mode: Swing Trade 1D Filters: All enabled for quality MTF: Check 1D and 1W trends Entry: Patient entries at major reversals ``` ## ⚠️ Important Notes - **Not Financial Advice**: This indicator is for educational purposes only - **Backtest First**: Test settings on your preferred assets before live trading - **Combine with Risk Management**: Always use stop-losses and position sizing - **Market Conditions**: Indicator performs best in trending markets - **False Signals**: All indicators can produce false signals - no system is perfect ## 🔧 Technical Details - **Pine Script Version**: v6 - **Overlay**: Yes (plots on main chart) - **Repainting**: No - signals appear in real-time and don't repaint - **Calculations**: Uses request.security for MTF data - **Performance**: Optimized for fast loading and minimal lag ## 📊 Best Practices 1. **Start Simple**: Use one trading mode without filters initially 2. **Add Filters Gradually**: Enable one filter at a time to understand impact 3. **Match Timeframes**: Use timeframe-appropriate trading mode 4. **Monitor MTF Dashboard**: Confirm trade direction across timeframes 5. **Adjust to Market**: Change modes for ranging vs. trending markets ## 🎓 Educational Value Perfect for traders learning: - Parabolic SAR indicator mechanics - Multi-indicator confirmation strategies - Timeframe analysis techniques - Risk management through filtered signals - Visual chart analysis and pattern recognition ## 📝 Version History **Current Version**: 1.0 - Initial release with full feature set - 5 trading modes - 6 indicator filters - MTF dashboard with 6 timeframes - Comprehensive customization options --- ## 🙏 Credits Parabolic SAR original concept by J. Welles Wilder Jr. ## 📧 Support For questions, suggestions, or bug reports, please comment below or message directly. --- **Happy Trading! 🚀** Tony Pham 2025 *Remember: The best indicator is the one you understand and use consistently.*
Indicatore Pine Script®
di pkt2204
✝️📈📉☢️BANG is a comprehensive multi-timeframe indicator for TradingView, designed for intraday trading of futures and stocks. To use: Add to a low timeframe chart (e.g., 1-5 minutes), configure anchor mode (daily/weekly/monthly/manual) for session resets, and select timeframe for signals (e.g., 5m). Monitor the mini-charts for HTF overview (candles, VWEMA/VWAP, FVG), RSI/MACD trends, market data table (VIX/VXN/etc.), and ICT structure (pivots, BOS/CHoCH). For trading: Enter LONG/SHORT on signal bars with strength ☢️ (1-6, higher = stronger confluence), confirmed by multi-timeframe alignment (e.g., anchor daily while trading minutes). Use ATR-based risk management; backtest in demo mode.
Indicatore Pine Script®
di nepolix
NSE Pairs Screener-20 pair This advanced Pine Script screener is designed for pairs trading on the National Stock Exchange (NSE) of India. It simultaneously monitors up to 20 stock pairs, calculates key statistical metrics, and provides real-time trading signals based on mean reversion strategies. Key Features 1. Multi-Pair Analysis Monitor up to 20 stock pairs simultaneously Customizable number of pairs to display (1-20) Pre-configured with popular NSE stock pairs across various sectors 2. Statistical Calculations Correlation Analysis: Measures the strength of relationship between paired stocks Z-Score Calculation: Identifies extreme deviations from the mean spread Cointegration Score: Validates long-term equilibrium relationships Dynamic Hedge Ratio: Calculates optimal position sizing between pairs 3. Trading Signals Long Signal: When spread is oversold (Z-score ≤ -2.0) Short Signal: When spread is overbought (Z-score ≥ 2.0) Exit Signal: When spread returns to mean (Z-score ≤ 0.5) Watch Status: Pairs requiring monitoring 4. Automated Alert System Single comprehensive alert for all qualifying pairs Customizable alert thresholds for correlation, Z-score, and cointegration On-chart visual alerts with detailed information Notification support via TradingView's alert system 5. Visual Display Clean, color-coded table interface Adjustable table position (9 positions available) Highlighted trading opportunities Real-time metric updates Configuration Parameters Screener Settings Number of Pairs to Display: 1-20 pairs (default: 20) Calculation Parameters Parameter Default Range Description Correlation Lookback Period25220-500Historical period for correlation calculation Z-Score SMA Length205-100Moving average length for spread calculation Hedge Ratio Length205-100Period for hedge ratio smoothing Minimum Correlation0.70.5-1.0Threshold for pair validation Alert Settings Parameter Default Range Description Alert Correlation Threshold0.70.5-1.0Minimum correlation for alerts Alert Z-Score Threshold2.01.0-3.0Z-score trigger level Alert Cointegration Threshold90%80-99%Minimum cointegration percentage Display Settings Table Position: 9 position options (default: middle_center) Table Background Color: Customizable Highlight Opportunities: Toggle visual highlighting of trading signals Pre-Configured Stock Pairs The script includes 20 carefully selected NSE pairs across various sectors: Financial Services RELIANCE / ONGC HDFCBANK / ICICIBANK SBIN / PNB KOTAKBANK / AXISBANK BAJFINANCE / BAJAJFINSV Information Technology TCS / INFY WIPRO / HCLTECH TECHM / LTIM Consumer Goods ITC / HINDUNILVR TITAN / TANLA ASIANPAINT / BERGEPAINT Telecommunications BHARTIARTL / IDEA Automotive MARUTI / TATAMOTORS Infrastructure & Industrials LT / UBL POWERGRID / NTPC Pharmaceuticals SUNPHARMA / CIPLA DIVISLAB / DRREDDY Materials ULTRACEMCO / ACC UPL / JSWSTEEL Energy ADANIENT / ADANIPOWER 🎨 Color-Coded Metrics Correlation 🟢 Green: ≥ Minimum threshold (strong relationship) 🔴 Red: < Minimum threshold (weak relationship) Z-Score 🔴 Red: |Z| ≥ 2.0 (extreme deviation - trading opportunity) 🟡 Yellow: 0.5 < |Z| < 2.0 (normal range - watch) 🟢 Green: |Z| ≤ 0.5 (mean reversion - exit signal) Cointegration 🟢 Green: ≥ 70% (strong cointegration) 🟡 Yellow: 50-70% (moderate cointegration) 🔴 Red: < 50% (weak cointegration) Status 🟢 Green: Long (buy spread) 🔴 Red: Short (sell spread) 🔵 Blue: Exit (close positions) ⚪ Gray: Watch (monitor) Validation 🟢 Green: Pass (meets all criteria) 🔴 Red: Fail (doesn't meet criteria) How It Works 1. Data Collection The script fetches real-time closing prices for all 20 stock pairs from NSE. 2. Statistical Analysis For each pair, the script calculates: Log Returns: Natural logarithm of price changes Correlation: Pearson correlation coefficient between returns Hedge Ratio: Price ratio smoothed over specified period Spread: Price difference adjusted by hedge ratio Z-Score: Standardized spread deviation 3. Signal Generation Based on Z-score thresholds: Z ≥ 2.0: Short spread (short overvalued, long undervalued) Z ≤ -2.0: Long spread (long overvalued, short undervalued) |Z| ≤ 0.5: Exit positions (spread reverted to mean) 4. Validation Pairs must meet criteria: Correlation ≥ minimum threshold Valid trading signal (entry or exit) 5. Alert Triggering Alerts fire when pairs simultaneously meet: Correlation ≥ alert threshold |Z-score| ≥ alert threshold Cointegration ≥ alert threshold Alert System The script features a single comprehensive alert that monitors all pairs: Consolidated Notifications: One alert for all qualifying pairs Detailed Information: Includes pair names, signal type, and key metrics Visual Indicators: Red label on chart with complete details Customizable Thresholds: Adjust sensitivity based on trading style Alert Message Format PAIR TRADING OPPORTUNITIES Pair X: STOCK1/STOCK2 Signal: LONG/SHORT Spread Z-Score: X.XX Correlation: X.XXX Cointegration: XX.X% Trading Strategy Guide Entry Rules Long Spread (Z-score ≤ -2.0): Buy Stock Y Sell Stock X (in ratio of hedge ratio) Short Spread (Z-score ≥ 2.0): Sell Stock Y Buy Stock X (in ratio of hedge ratio) Exit Rules Close positions when Z-score returns to ±0.5 Set stop-loss at Z-score ±3.0 (extreme deviations) Risk Management Only trade pairs with correlation ≥ 0.7 Prefer cointegration scores ≥ 90% Monitor hedge ratio changes Diversify across multiple pairs Customization Options Adding New Pairs Simply modify the stock symbol inputs in the respective pair groups (Pair 1 through Pair 20). Adjusting Sensitivity Conservative: Increase Z-score threshold to 2.5-3.0 Aggressive: Decrease Z-score threshold to 1.5-2.0 Long-term: Increase lookback period to 500 Short-term: Decrease lookback period to 50-100 Visual Preferences Change table position to suit your layout Adjust background colors for better contrast Toggle opportunity highlighting on/off Technical Notes Calculation Method Uses logarithmic returns for correlation (better statistical properties) Z-score normalized by standard deviation Cointegration approximated using correlation strength Hedge ratio smoothed using simple moving average Performance Considerations Calculations update on every bar close Table displays only on the last bar Alert checks occur at bar close Maximum 500 labels supported (more than sufficient) Limitations Does not account for transaction costs Assumes linear relationships between pairs Historical correlation doesn't guarantee future behaviour Requires sufficient liquidity in both stocks Best Practices Back test Thoroughly: Test parameters on historical data before live trading Monitor Regularly: Check pairs daily for validation changes Diversify: Trade multiple pairs to reduce risk Stay Informed: Be aware of corporate actions, news affecting pairs Adjust Parameters: Optimize for current market conditions Use Stop-Losses: Protect against extreme divergences Track Performance: Maintain trading journal for continuous improvement Indicator Information Version: Pine Script v5 Overlay: False (separate pane) Max Labels: 500 Update Frequency: Every bar close Compatible Timeframes: All (works best on daily or higher) Getting Started Add to Chart: Apply indicator to any NSE stock Configure Pairs: Adjust stock symbols as needed Set Parameters: Customize calculation and alert settings Create Alert: Set up Trading View alert for notifications Monitor: Watch the table for trading opportunities Execute: Trade based on validated signals 📞Support & Updates This script is designed for educational and research purposes. Always: Conduct thorough back testing Use proper risk management Consider transaction costs Consult with financial advisors Trade responsibly Disclaimer: This indicator is for educational purposes only. Past performance does not guarantee future results. Always conduct your own research and risk assessment before trading.
Indicatore Pine Script®
di kingshukghosh71
✝️📈📉☢️🔱NUKE is a multi-ticker signal indicator, optimized for intraday futures and stocks trading. To use: Add to a multi-timeframe charts (e.g., 1m, 5m, 15m), select up to 5 tickers (e.g., MNQ, MES), and set an anchor mode (daily, weekly, monthly, or manual) for session resets. Enable/disable signal components like AVWAP, Price Stoch, VWEMA crosses, ADX, and BB in settings. For trading: Monitor the dynamic table for recent LONG (L↑) or SHORT (S↓) entries with strength ☢️ (1 to 6, higher indicates stronger confluence). Enter positions in the signal direction on the chart ticker or selected ones, using multi-timeframe confirmation (e.g., anchor to daily while trading on seconds/minutes). Apply proper risk management, such as ATR-based stops, and test in demo mode.
Indicatore Pine Script®
di nepolix
Custom Checklist# Custom Checklist - Trading Preparation & Reminders A fully customizable checklist overlay indicator for TradingView that helps traders maintain discipline and follow their trading routine systematically. ## 🎯 Purpose This indicator serves as a visual reminder system on your charts to ensure you complete all necessary analysis steps before entering a trade. Perfect for traders who want to maintain consistency and avoid emotional or rushed trading decisions. ## ✨ Key Features - **20 Customizable Lines**: Create your own checklist items with any text you need - **Flexible Display Options**: - Show/hide title header - Toggle entire checklist on/off - Position anywhere on chart (9 positions available) - Adjustable text size (tiny to huge) - **Symbol Filtering**: Option to show checklist only on specific symbols (BTC/USD, GOLD, SPX500, USOIL) - **Customizable Appearance**: - Background color - Text color - Border color - Transparency controls - **Clean Interface**: Empty by default - add only the items you need ## 📋 Use Cases - **Morning Routine**: Daily market preparation checklist - **Trade Entry Rules**: Verify all setup conditions are met - **Risk Management**: Confirm stop-loss, position size, and exit strategy - **Multi-Timeframe Analysis**: Ensure you checked all required timeframes - **Technical Analysis**: Track which indicators and patterns you've reviewed - **News & Events**: Remember to check economic calendar and news - **Personal Rules**: Your custom trading rules and reminders ## 🎨 Customization Every aspect is customizable: - All 20 lines can be edited to your needs - Only non-empty lines are displayed - Table position adjustable to any corner or middle position - Color scheme fully customizable to match your chart theme - Text size scalable for different screen sizes ## 💡 How to Use 1. Add indicator to your chart 2. Open Settings > Checklist Items 3. Fill in your checklist items (Line 1, Line 2, etc.) 4. Customize colors and position in Display Settings 5. Optional: Enable "Show Only on Specific Symbols" to show on select instruments ## 🔧 Display Settings - **Checklist Title**: Custom header for your checklist - **Show Title Header**: Toggle title display - **Show Checklist**: Master on/off switch - **Symbol Filter**: Restrict display to specific trading instruments - **Position**: 9 placement options (corners and middle positions) - **Text Size**: 5 size options (tiny, small, normal, large, huge) - **Colors**: Background, text, and border fully customizable ## 📝 Example Checklist Ideas **Swing Trading:** - Support/Resistance levels identified - Trend direction confirmed - Volume analysis completed - RSI/MACD signals checked - Risk/Reward ratio calculated **Day Trading:** - Pre-market review done - Key levels marked - Economic calendar checked - Trading plan written - Position size calculated **Technical Analysis:** - Multiple timeframe alignment - Chart patterns identified - Moving averages reviewed - Fibonacci levels drawn - Volume profile analyzed ## ⚙️ Technical Details - Pine Script v6 - Overlay indicator (displays on main chart) - Lightweight - no complex calculations - No repainting - Works on all timeframes and instruments ## 🎓 Perfect For - Beginner traders learning systematic analysis - Experienced traders maintaining discipline - Anyone who wants visual trading reminders - Traders following multi-step strategies - Those prone to FOMO or emotional trading --- **Note**: This is a visual tool only. It does not generate trading signals or perform analysis. It serves as a reminder checklist to help you follow your own trading process consistently.
Indicatore Pine Script®
di cfircoo
QV ATR Active Range ValuesQuantVault ### Description for Presentation The "QV ATR Active Range Values" indicator is a forward-looking tool designed for traders to estimate potential price ranges over 1, 2, or 3 months based on historical volatility and momentum. It leverages the Average True Range (ATR) to measure volatility and incorporates a "win rate" derived from recent candle colors to bias projections toward upside or downside potential. This creates asymmetric range forecasts that reflect market directionality, helping users anticipate breakout levels, set targets, or manage risk. The indicator overlays projected high/low lines on the chart and displays a compact table summarizing days to key percentage targets (e.g., +30% or -20%) alongside projected prices and percentage changes. Ideal for swing traders or investors seeking data-driven price projections without relying on complex models. ### Detailed Explanation of How It Works This indicator uses Pine Script v5 on TradingView to compute and visualize price projections. Below, I'll break it down step by step, including the key calculations, logic, and outputs. Note that it assumes a trading month has 21 days (a common approximation for business days), and all projections are based on daily timeframes derived from weekly data. #### 1. **User Inputs** - **ATR Length (Lookback)**: Default 25. This is the period used to calculate the ATR and count candle colors. - **Show Projections**: Boolean toggles to display 1-month (yellow), 2-month (orange), or 3-month (green/red) lines. By default, only 1-month is shown. #### 2. **Period Definitions** - Months are converted to days assuming 21 trading days per month: - 1 month: 21 days - 2 months: 42 days - 3 months: 63 days - These periods represent the forward-looking horizons for projections. #### 3. **Volatility Calculation (ATR)** - **Weekly ATR**: Fetched using `request.security` on the weekly timeframe with the specified ATR length (e.g., average true range over the last 25 weeks). - **Daily ATR**: Derived by dividing the weekly ATR by 5 (approximating 5 trading days per week). This scales volatility to a daily basis. - **Base Projections**: For each period, multiply daily ATR by the number of days in that period. This estimates the total expected range if volatility persists: - 3 months: `daily_atr * 63` - 2 months: `daily_atr * 42` - 1 month: `daily_atr * 21` #### 4. **Momentum Bias (Win Rate)** - Counts the number of "green" (close > open, bullish) and "red" (close < open, bearish) candles over the ATR lookback period. - **Win Rate**: Fraction of green candles out of total colored candles (green + red). Defaults to 0.5 (50%) if no colored candles exist. - This win rate introduces asymmetry: In bullish periods (high win rate), upside projections are larger; in bearish periods (low win rate), downside projections dominate. #### 5. **Adjusted Projections** - **Upside Projection**: Base projection multiplied by win rate (e.g., for 3 months: `base_projection_3 * win_rate`). - **Downside Projection**: Base projection multiplied by (1 - win rate). - **Projected Prices**: - High: Current close + upside projection - Low: Current close - downside projection - This creates realistic, direction-biased ranges rather than symmetric ones. #### 6. **Chart Overlays (Plots)** - Lines are plotted only if the corresponding toggle is enabled, with 50% transparency for a dimmed effect: - 3-month high: Solid green line - 3-month low: Solid red line - 2-month high/low: Dashed orange lines - 1-month high/low: Dashed yellow lines (#f6e122) - These lines extend horizontally from the current bar, visualizing potential future highs/lows. #### 7. **Daily Rates and Days to Targets** - **Up Rate**: `daily_atr * win_rate` (expected daily upward movement). - **Down Rate**: `daily_atr * (1 - win_rate)` (expected daily downward movement). - **Days to Targets**: Calculates approximate trading days to reach fixed percentage moves from the current close, using the rates: - +30%: `(close * 0.30) / up_rate` (rounded) - +20%: `(close * 0.20) / up_rate` - +10%: `(close * 0.10) / up_rate` - -10%: `(close * 0.10) / down_rate` - -20%: `(close * 0.20) / down_rate` - -30%: `(close * 0.30) / down_rate` - If a rate is zero, days are set to `na` (not applicable). #### 8. **Table Display** - A single combined table is created at the top-center of the chart with a semi-transparent black background (80% opacity) and white borders. - **Structure** (6 columns x 7 rows): - **Left Section (Days to Targets, Columns 0-1)**: - Lists percentage targets (+30% to -30%) with corresponding days, colored green for upside and red for downside. - **Separation (Column 2)**: Empty for visual spacing. - **Right Section (Projections, Columns 3-5)**: - Shows 1M/2M/3M highs and lows with: - Projected price (formatted to 2 decimals). - Percentage change from close (e.g., `((projected_high - close) / close) * 100`). - Colors match the plot lines: Yellow for 1M, orange for 2M, green for 3M high, red for 3M low. - The table updates dynamically with each bar, providing at-a-glance insights. #### Key Assumptions and Limitations - **Volatility Persistence**: Assumes future ATR matches historical levels; actual volatility can fluctuate. - **Linear Projection**: Treats price movement as additive daily increments, ignoring compounding or non-linear effects. - **Candle Count**: Only considers colored candles (ignores doji where open = close), and uses a simple win rate without weighting by size. - **Timeframe**: Best on daily charts; weekly ATR scaling assumes consistent weekly-to-daily ratios. - **No Backtesting**: This is a visualization tool, not a strategy with entry/exit signals. Test projections against historical data for accuracy. This indicator combines volatility forecasting with basic sentiment analysis for practical, visual projections. If you're presenting it, emphasize how the win rate adds a directional edge over plain ATR-based ranges, making it more adaptive to trending markets. If you need modifications or examples on specific tickers, let me know!
Indicatore Pine Script®
di QuantVault
580TrendTrades - Ride the Wave, Master the Trend580TrendTrades by 580TradingLab is a precision-built trend-following indicator designed to help traders identify and stay aligned with major market moves. Using multi-EMAs alignment, momentum confirmation and adaptive filters, it detects strong directional shifts while avoiding choppy noise. It highlights clear BUY and SELL zones, helping you capture the middle of big trends-not just the tops or bottoms. Focus on momentum. Trade in sync with the trend.
Indicatore Pine Script®
di jlwt225
Launchpad & SlingshotOverview and Originality: This indicator combines two complementary trading concepts—Launchpad (LP) and Slingshot (SS)—into a single, cohesive tool designed to identify potential trend continuations and reversals in trending markets. Launchpads provide context on overall trend alignment via stacked moving averages, acting as a filter for higher-probability setups, while Slingshot pinpoints precise entry timing during short-term pullbacks or bounces within those trends. This synergy reduces false signals by requiring both trend confirmation (LP) and momentum shift (SS), making it more robust than using either in isolation. Unlike simple merges, this script adds original enhancements such as a "curling" filter on the shortest Launchpad MA to ensure directional momentum, separate configurable MAs for bullish/bearish Slingshot thresholds, and combined LP/SS alerts for chained patterns (e.g., LP following SS). These improvements aim to enhance usability for trend-following strategies, particularly in volatile stocks or forex pairs, by providing visual labels, alerts, and multi-timeframe support without overcomplicating the core logic. Underlying Concepts: Launchpad (LP): Based on the idea of moving average "stacking," where shorter-period MAs align above longer ones in uptrends (bullish stack) or below in downtrends (bearish stack). This detects when price is in a strong, aligned trend phase, similar to how Guppy Multiple Moving Averages identify trend strength through ribbon compression/expansion. The script uses up to four customizable MAs (default: 8/21/50/200 EMAs of close), calculating the highest/lowest among included ones as the key crossover level. A signal triggers when the stack forms from a non-stacked state and price crosses the extreme MA, indicating potential trend acceleration. Slingshot (SS): Draws from Scot1and's bullish pattern, which looks for price to remain below a 4-period EMA of highs for three consecutive bars (signaling a controlled pullback), then close above it (indicating rebound momentum). This script symmetrizes it for bearish cases using a separate 4-period EMA of lows, allowing detection of breakdowns after temporary bounces in downtrends. The separation of bull/bear sources is an original adaptation to better capture market structure asymmetry—highs for resistance in uptrends, lows for support in downtrends—reducing noise compared to a single-source approach. The components work together by allowing users to spot "LP after SS" patterns: a Slingshot pullback/rebound followed by a Launchpad stack crossover, which often signals stronger continuations. This chained logic is grounded in momentum trading principles, where short-term mean reversion (SS) aligns with longer-term trend bias (LP) for improved risk-reward entries. How It Works: The script calculates signals on each bar as follows: Launchpad Calculations: Build an array of included MAs (users can exclude any via inputs). Check for stacking: For bull LP, shorter MAs > longer ones; for bear, shorter < longer. Require a transition from non-stacked to stacked state. Price must cross above the highest MA (bull) or below the lowest (bear). Original filter: The shortest MA must be "curling" up (current > previous for bull) or down (current < previous for bear) to confirm recent momentum, preventing signals in counter-trend flattenings. Slingshot Calculations: Use separate MAs: Bull SS uses EMA of highs (default); Bear SS uses EMA of lows. For bull SS: Close below bull MA for the prior N bars (default 3), then close above it. For bear SS: Close above bear MA for prior N bars, then close below it. No additional filters like volume or momentum jumps are applied, staying true to the pattern's simplicity. Combined and Additional Signals: "LP after SS": Triggers if LP occurs immediately after an SS, highlighting high-conviction setups. Stack alerts: Pure stack with price above/below extremes, for trend monitoring. All MAs can use multi-timeframe data via the timeframe input. Alerts are set for each condition, and labels appear on the chart (configurable visibility, size, colors). Labels combine (e.g., "Bull LP & SS") if both trigger simultaneously. How to Use It: Add the script to your chart via TradingView's indicator menu. Default settings suit daily/intraday charts for trending assets like stocks in bull markets (e.g., tech sector during rallies). Interpretation: Bull SS: Look for labels during uptrends; enter long on close above the blue Bull SS MA line after a 3-bar pullback. Use as a dip-buy signal. Bear SS: In downtrends, enter short on close below the purple Bear SS MA after a 3-bar bounce. Bull LP: Confirms trend strength; enter long on crossover if shortest MA is rising (green label). Bear LP: Short entry on downside crossover with falling shortest MA (red label). Prioritize "LP after SS" for layered confirmation—e.g., SS rebound leading into LP acceleration. Monitor stack alerts for overall bias; avoid trading against the stack. Customization: Launchpad Group: Adjust lengths/sources/types; exclude MAs for simpler stacks (e.g., just 50/200 for long-term). Slingshot Group: Change length (4 default), type (EMA), sources (high/low defaults), or preceding bars (3 default). Display: Toggle labels, set timeframe (e.g., "D" for daily MAs on hourly chart), adjust offset for label positioning. Test on historical data: Apply to strong trenders like AAPL or BTC; backtest entries with stops below recent lows. For best results, combine with volume confirmation or broader market context—e.g., above 200-day MA for longs. This is not financial advice; always use risk management.
Indicatore Pine Script®
di BigGuavaTrading
Malama's Heat MapOverview Malama's Heat Map is an overlay indicator that visualizes historical liquidity as a dynamic heatmap aligned with the price chart, using volume as a proxy to map activity across time (X-axis) and price levels (Y-axis). It constructs a grid of up to 5000 cells via a matrix, distributing bar volume into discrete price bins to highlight concentration zones, creating a color-graded visualization from cool (low activity) to hot (high liquidity). This aids in identifying "Type II" fair value areas, support/resistance from past volume clusters, or potential imbalances without order book access. Built for v6 compatibility with efficiency in mind—computations run solely on the last bar, includes object limit enforcement, and offers two intra-bar volume distribution methods for flexible approximation. Core Mechanics The indicator generates a trailing heatmap through binning, accumulation, and box-based rendering: Grid Setup: Configurable lookback (bars back, default 100) sets horizontal time span; bins (price divisions, default 50) define vertical resolution, limited to 5000 total cells to prevent errors. Bin height dynamically = max(mintick, (lookback high - low) / bins). Y-Axis Stabilization: Anchors boundaries to the prior bar's high/low (if available) for a flicker-free view during live bar updates. All historical bar data (high/low/close/volume) is clipped to these bounds. Volume Distribution Proxy: Even: Divides bar volume equally across spanned bins (straightforward uniform spread). POC Weighted (Inverse): Treats bar close as POC proxy; applies inverse distance weighting (1/(|bin - POC bin| + 1), normalized) to emphasize volume near the estimated control point, simulating clustered intra-bar trading. Matrix Building: On last bar only, loops backward over lookback bars (newest right-aligned). For each, computes low/high bin indices, distributes volume per selected method into the matrix (columns=time, rows=price bins from low to high). Scaling & Palette: Extracts max matrix value for relative normalization (0-1); maps to a 5-tier stepped color scheme (user-customizable: blue 90% transp. low → red 50% transp. high) for non-linear intensity. Rendering: Clears old boxes, then iterates matrix to draw only non-zero cells as thin boxes: X spans one bar width (left=historical index from bar_index, right=next bar), Y fills bin height. Borderless for seamless heatmap effect. The result is a right-leaning, chart-scrolling visualization emphasizing recent liquidity buildup. Why This Adds Value & Originality While session-based volume profiles exist, this heatmap captures ongoing multi-bar liquidity evolution ("Type II" style), revealing horizontal value areas or gaps dynamically. Originality shines in the custom inverse-weighting for POC realism (no ta.* dependencies), matrix-driven persistence for quick redraws, and stabilization to eliminate repaints—issues plaguing similar scripts. v6 adaptations (e.g., custom clamp, matrix recreation on input change) ensure broad compatibility without bloat. It condenses complex liquidity scanning into one tool: spot red "hot" bands as magnets, blue voids as FVGs. Unlike generic heatmaps, the proxy options and limit-aware design scale across timeframes/assets (e.g., forex vs. crypto), reducing the need for layered indicators. How to Use Setup: Apply as overlay. Defaults suit ~4-day 1H view; tune lookback/bins (e.g., 50x100 for intraday fine-detail, but watch 5000 cap—errors auto-flag excesses). Select "POC Weighted" for nuanced clustering, "Even" for simplicity. Customize palette (e.g., desaturate for dark themes). Reading the Heatmap: X-Axis (Time): Left=older (fainter context), right=recent focus; tracks evolving liquidity trails. Y-Axis (Price): Bottom=range low, top=high; vertical density shows price-level attraction. Colors: Faint blue (sparse volume, possible inefficiencies) → vivid red (dense activity, likely SR). Horizontal streaks = sustained value zones. Trading Insights: Price wicking into red? Anticipate fills/reversals. Blue gaps post-break? Targets for retraces. Ideal on 5M–Daily; layer with candlesticks off for purity. Example: In BTCUSD 4H, a yellow-red band at $60K from prior consolidation → treat as dynamic support for longs on dips. Tips Balance settings: High bins = sharper verticals but cap lookback (e.g., 80x60=4800 cells). Test on volatile pairs first. "POC Weighted" excels in ranging markets; switch to "Even" for trending (avoids close-bias skew). For deeper analysis, screenshot/export or pair with divergence tools; add manual alerts via box counts if extended. Efficiency: Last-bar only keeps it snappy; refresh on input tweaks. Limitations & Disclaimer Visualization is historical/proxy-based—lagging by one bar, no forward projection or tick-level precision (close-as-POC is estimate). Clipping may trim outlier wicks; low-volume bars dilute globally. Stepped colors are relative (max scales per redraw), potentially compressing extremes. Exceeds 5000 cells? Runtime error halts—no fallback resize. Not real liquidity (volume ≠ depth); best as visual aid, not quantitative. Updates post-close only. Backtest zones on specific symbols—correlation ≠ causation. Not advice; trade responsibly. Ideas in comments!
Indicatore Pine Script®
di makenlegacy
580TL — NovaSenseNovaSense by 580TradingLab combines multi-EMA structure, price actions, momentum confirmation, and volatility logic to detect trend strength and early reversals with high accuracy. It filters out market noise, identifies "location zones" for optional entries, and sends timely Buy/Sell alerts when institutional momentum shifts. Designed for traders who value clarity, discipline, and precision. Trade with clarity. Sense the trend before it flips.
Indicatore Pine Script®
di jlwt225
Manifold Singularity EngineManifold Singularity Engine: Catastrophe Theory Detection Through Multi-Dimensional Topology Analysis The Manifold Singularity Engine applies catastrophe theory from mathematical topology to multi-dimensional price space analysis, identifying potential reversal conditions by measuring manifold curvature, topological complexity, and fractal regime states. Unlike traditional reversal indicators that rely on price pattern recognition or momentum oscillators, this system reconstructs the underlying geometric surface (manifold) that price evolves upon and detects points where this topology undergoes catastrophic folding—mathematical singularities that correspond to forced directional changes in price dynamics. The indicator combines three analytical frameworks: phase space reconstruction that embeds price data into a multi-dimensional coordinate system, catastrophe detection that measures when this embedded manifold reaches critical curvature thresholds indicating topology breaks, and Hurst exponent calculation that classifies the current fractal regime to adaptively weight detection sensitivity. This creates a geometry-based reversal detection system with visual feedback showing topology state, manifold distortion fields, and directional probability projections. What Makes This Approach Different Phase Space Embedding Construction The core analytical method reconstructs price evolution as movement through a three-dimensional coordinate system rather than analyzing price as a one-dimensional time series. The system calculates normalized embedding coordinates: X = normalize(price_velocity, window) , Y = normalize(momentum_acceleration, window) , and Z = normalize(volume_weighted_returns, window) . These coordinates create a trajectory through phase space where price movement traces a path across a geometric surface—the market manifold. This embedding approach differs fundamentally from traditional technical analysis by treating price not as a sequential data stream but as a dynamical system evolving on a curved surface in multi-dimensional space. The trajectory's geometric properties (curvature, complexity, folding) contain information about impending directional changes that single-dimension analysis cannot capture. When this manifold undergoes rapid topological deformation, price must respond with directional change—this is the mathematical basis for catastrophe detection. Statistical normalization using z-score transformation (subtracting mean, dividing by standard deviation over a rolling window) ensures the coordinate system remains scale-invariant across different instruments and volatility regimes, allowing identical detection logic to function on forex, crypto, stocks, or indices without recalibration. Catastrophe Score Calculation The catastrophe detection formula implements a composite anomaly measurement combining multiple topology metrics: Catastrophe_Score = 0.45×Curvature_Percentile + 0.25×Complexity_Ratio + 0.20×Condition_Percentile + 0.10×Gradient_Percentile . Each component measures a distinct aspect of manifold distortion: Curvature (κ) is computed using the discrete Laplacian operator: κ = √ , which measures how sharply the manifold surface bends at the current point. High curvature values indicate the surface is folding or developing a sharp corner—geometric precursors to catastrophic topology breaks. The Laplacian measures second derivatives (rate of change of rate of change), capturing acceleration in the trajectory's path through phase space. Topological Complexity counts sign changes in the curvature field over the embedding window, measuring how chaotically the manifold twists and oscillates. A smooth, stable surface produces low complexity; a highly contorted, unstable surface produces high complexity. This metric detects when the geometric structure becomes informationally dense with multiple local extrema, suggesting an imminent topology simplification event (catastrophe). Condition Number measures the Jacobian matrix's sensitivity: Condition = |Trace| / |Determinant|, where the Jacobian describes how small changes in price produce changes in the embedding coordinates. High condition numbers indicate numerical instability—points where the coordinate transformation becomes ill-conditioned, suggesting the manifold mapping is approaching a singularity. Each metric is converted to percentile rank within a rolling window, then combined using weighted sum. The percentile transformation creates adaptive thresholds that automatically adjust to each instrument's characteristic topology without manual recalibration. The resulting 0-100% catastrophe score represents the current bar's position in the distribution of historical manifold distortion—values above the threshold (default 65%) indicate statistically extreme topology states where reversals become geometrically probable. This multi-metric ensemble approach prevents false signals from isolated anomalies: all four geometric features must simultaneously indicate distortion for a high catastrophe score, ensuring only true manifold breaks trigger detection. Hurst Exponent Regime Classification The Hurst exponent calculation implements rescaled range (R/S) analysis to measure the fractal dimension of price returns: H = log(R/S) / log(n) , where R is the range of cumulative deviations from mean and S is the standard deviation. The resulting value classifies market behavior into three fractal regimes: Trending Regime (H > 0.55) : Persistent price movement where future changes are positively correlated with past changes. The manifold exhibits directional momentum with smooth topology evolution. In this regime, catastrophe signals receive 1.2× confidence multiplier because manifold breaks in trending conditions produce high-magnitude directional changes. Mean-Reverting Regime (H < 0.45) : Anti-persistent price movement where future changes tend to oppose past changes. The manifold exhibits oscillatory topology with frequent small-scale distortions. Catastrophe signals receive 0.8× confidence multiplier because reversal significance is diminished in choppy conditions where the manifold constantly folds at minor scales. Random Walk Regime (H ≈ 0.50) : No statistical correlation in returns. The manifold evolution is geometrically neutral with moderate topology stability. Standard 1.0× confidence multiplier applies. This adaptive weighting system solves a critical problem in reversal detection: the same geometric catastrophe has different trading implications depending on the fractal regime. A manifold fold in a strong trend suggests a significant reversal opportunity; the same fold in mean-reversion suggests a minor oscillation. The Hurst-based regime filter ensures detection sensitivity automatically adjusts to market character without requiring trader intervention. The implementation uses logarithmic price returns rather than raw prices to ensure stationarity, and applies the calculation over a configurable window (default 5 bars) to balance responsiveness with statistical validity. The Hurst value is then smoothed using exponential moving average to reduce noise while maintaining regime transition detection. Multi-Layer Confirmation Architecture The system implements five independent confirmation filters that must simultaneously validate before any singularity signal generates: 1. Catastrophe Threshold : The composite anomaly score must exceed the configured threshold (default 0.65 on 0-1 scale), ensuring the manifold distortion is statistically extreme relative to recent history. 2. Pivot Structure Confirmation : Traditional swing high/low patterns (using ta.pivothigh and ta.pivotlow with configurable lookback) must form at the catastrophe bar. This ensures the geometric singularity coincides with observable price structure rather than occurring mid-swing where interpretation is ambiguous. 3. Swing Size Validation : The pivot magnitude must exceed a minimum threshold measured in ATR units (default 1.5× Average True Range). This filter prevents signals on insignificant price jiggles that lack meaningful reversal potential, ensuring only substantial swings with adequate risk/reward ratios generate signals. 4. Volume Confirmation : Current volume must exceed 1.3× the 20-period moving average, confirming genuine market participation rather than low-liquidity price noise. Manifold catastrophes without volume support often represent false topology breaks that don't translate to sustained directional change. 5. Regime Validity : The market must be classified as either trending (ADX > configured threshold, default 30) or volatile (ATR expansion > configured threshold, default 40% above 30-bar average), and must NOT be in choppy/ranging state. This critical filter prevents trading during geometrically unfavorable conditions where edge deteriorates. All five conditions must evaluate true simultaneously for a signal to generate. This conjunction-based logic (AND not OR) dramatically reduces false positives while preserving true reversal detection. The architecture recognizes that geometric catastrophes occur frequently in noisy data, but only those catastrophes that align with confirming evidence across price structure, participation, and regime characteristics represent tradable opportunities. A cooldown mechanism (default 8 bars between signals) prevents signal clustering at extended pivot zones where the manifold may undergo multiple small catastrophes during a single reversal process. Direction Classification System Unlike binary bull/bear systems, the indicator implements a voting mechanism combining four directional indicators to classify each catastrophe: Pivot Vote : +1 if pivot low, -1 if pivot high, 0 otherwise Trend Vote : Based on slow frequency (55-period EMA) slope—+1 if rising, -1 if falling, 0 if flat Flow Vote : Based on Y-gradient (momentum acceleration)—+1 if positive, -1 if negative, 0 if neutral Mid-Band Vote : Based on price position relative to medium frequency (21-period EMA)—+1 if above, -1 if below, 0 if at The total vote sum classifies the singularity: ≥2 votes = Bullish , ≤-2 votes = Bearish , -1 to +1 votes = Neutral (skip) . This majority-consensus approach ensures directional classification requires alignment across multiple timeframes and analysis dimensions rather than relying on a single indicator. Neutral signals (mixed voting) are displayed but should not be traded, as they represent geometric catastrophes without clear directional resolution. Core Calculation Methodology Embedding Coordinate Generation Three normalized phase space coordinates are constructed from price data: X-Dimension (Velocity Space): price_velocity = close - close X = (price_velocity - mean) / stdev over hurstWindow Y-Dimension (Acceleration Space): momentum = close - close momentum_accel = momentum - momentum Y = (momentum_accel - mean) / stdev over hurstWindow Z-Dimension (Volume-Weighted Space): vol_normalized = (volume - mean) / stdev over embedLength roc = (close - close ) / close Z = (roc × vol_normalized - mean) / stdev over hurstWindow These coordinates define a point in 3D phase space for each bar. The trajectory connecting these points is the reconstructed manifold. Gradient Field Calculation First derivatives measure local manifold slope: dX/dt = X - X dY/dt = Y - Y Gradient_Magnitude = √ The gradient direction indicates where the manifold is "pushing" price. Positive Y-gradient suggests upward topological pressure; negative Y-gradient suggests downward pressure. Curvature Tensor Components Second derivatives measure manifold bending using discrete Laplacian: Laplacian_X = X - 2×X + X Laplacian_Y = Y - 2×Y + Y Laplacian_Magnitude = √ This is then normalized: Curvature_Normalized = (Laplacian_Magnitude - mean) / stdev over embedLength High normalized curvature (>1.5) indicates sharp manifold folding. Complexity Accumulation Sign changes in curvature field are counted: Sign_Flip = 1 if sign(Curvature ) ≠ sign(Curvature ), else 0 Topological_Complexity = sum(Sign_Flip) over embedLength window This measures oscillation frequency in the geometry. Complexity >5 indicates chaotic topology. Condition Number Stability Analysis Jacobian matrix sensitivity is approximated: dX/dp = dX/dt / (price_change + epsilon) dY/dp = dY/dt / (price_change + epsilon) Jacobian_Determinant = (dX/dt × dY/dp) - (dX/dp × dY/dt) Jacobian_Trace = dX/dt + dY/dp Condition_Number = |Trace| / (|Determinant| + epsilon) High condition numbers indicate numerical instability near singularities. Catastrophe Score Assembly Each metric is converted to percentile rank over embedLength window, then combined: Curvature_Percentile = percentrank(abs(Curvature_Normalized), embedLength) Gradient_Percentile = percentrank(Gradient_Magnitude, embedLength) Condition_Percentile = percentrank(abs(Condition_Z_Score), embedLength) Complexity_Ratio = clamp(Topological_Complexity / embedLength, 0, 1) Final score: Raw_Anomaly = 0.45×Curvature_P + 0.25×Complexity_R + 0.20×Condition_P + 0.10×Gradient_P Catastrophe_Score = Raw_Anomaly × Hurst_Multiplier Values are clamped to range. Hurst Exponent Calculation Rescaled range analysis on log returns: Calculate log returns: r = log(close) - log(close ) Compute cumulative deviations from mean Find range: R = max(cumulative_dev) - min(cumulative_dev) Calculate standard deviation: S = stdev(r, hurstWindow) Compute R/S ratio Hurst = log(R/S) / log(hurstWindow) Clamp to and smooth with 5-period EMA Regime Classification Logic Volatility Regime: ATR_MA = SMA(ATR(14), 30) Vol_Expansion = ATR / ATR_MA Is_Volatile = Vol_Expansion > (1.0 + minVolExpansion) Trend Regime (Corrected ADX): Calculate directional movement (DM+, DM-) Smooth with Wilder's RMA(14) Compute DI+ and DI- as percentages Calculate DX = |DI+ - DI-| / (DI+ + DI-) × 100 ADX = RMA(DX, 14) Is_Trending = ADX > (trendStrength × 100) Chop Detection: Is_Chopping = NOT Is_Trending AND NOT Is_Volatile Regime Validity: Regime_Valid = (Is_Trending OR Is_Volatile) AND NOT Is_Chopping Signal Generation Logic For each bar: Check if catastrophe score > topologyStrength threshold Verify regime is valid Confirm Hurst alignment (trending or mean-reverting with pivot) Validate pivot quality (price extended outside spectral bands then re-entered) Confirm volume/volatility participation Check cooldown period has elapsed If all true: compute directional vote If vote ≥2: Bullish Singularity If vote ≤-2: Bearish Singularity If -1 to +1: Neutral (display but skip) All conditions must be true for signal generation. Visual System Architecture Spectral Decomposition Layers Three harmonic frequency bands visualize entropy state: Layer 1 (Surface Frequency): Center: EMA(8) Width: ±0.3 × 0.5 × ATR Transparency: 75% (most visible) Represents fast oscillations Layer 2 (Mid Frequency): Center: EMA(21) Width: ±0.5 × 0.5 × ATR Transparency: 85% Represents medium cycles Layer 3 (Deep Frequency): Center: EMA(55) Width: ±0.7 × 0.5 × ATR Transparency: 92% (most transparent) Represents slow baseline Convergence of layers indicates low entropy (stable topology). Divergence indicates high entropy (catastrophe building). This decomposition reveals how different frequency components of price movement interact—when all three align, the manifold is in equilibrium; when they separate, topology is unstable. Energy Radiance Fields Concentric boxes emanate from each singularity bar: For each singularity, 5 layers are generated: Layer n: bar_index ± (n × 1.5 bars), close ± (n × 0.4 × ATR) Transparency gradient: inner 75% → outer 95% Color matches signal direction These fields visualize the "energy well" of the catastrophe—wider fields indicate stronger topology distortion. The exponential expansion creates a natural radiance effect. Singularity Node Geometry N-sided polygon (default hexagon) at each signal bar: Vertices calculated using polar coordinates Rotation angle: bar_index × 0.1 (creates animation) Radius: ATR × singularity_strength × 2 Connects vertices with colored lines The rotating geometric primitive marks the exact catastrophe bar with visual prominence. Gradient Flow Field Directional arrows display manifold slope: Spawns every 3 bars when gradient_magnitude > 0.1 Symbol: "↗" if dY/dt > 0.1, "↘" if dY/dt < -0.1, "→" if neutral Color: Bull/bear/neutral based on direction Density limited to flowDensity parameter Arrows cluster when gradient is strong, creating intuitive topology visualization. Probability Projection Cones Forward trajectory from each singularity: Projects 10 bars forward Direction based on vote classification Center line: close + (direction × ATR × 3) Uncertainty width: ATR × singularity_strength × 2 Dashed boundaries, solid center These are mathematical projections based on current gradient, not price targets. They visualize expected manifold evolution if topology continues current trajectory. Dashboard Metrics Explanation The real-time control panel displays six core metrics plus regime status: H (Hurst Exponent): Value: Current Hurst (0-1 scale) Label: TREND (>0.55), REVERT (<0.45), or RANDOM (0.45-0.55) Icon: Direction arrow based on regime Purpose: Shows fractal character—only trade when favorable Σ (Catastrophe Score): Value: Current composite anomaly (0-100%) Bar gauge shows relative strength Icon: ◆ if above threshold, ○ if below Purpose: Primary signal strength indicator κ (Curvature): Value: Normalized Laplacian magnitude Direction arrow shows sign Color codes severity (green<0.8, yellow<1.5, red≥1.5) Purpose: Shows manifold bending intensity ⟳ (Topology Complexity): Value: Count of sign flips in curvature Icon: ◆ if >3, ○ otherwise Color codes chaos level Purpose: Indicates geometric instability V (Volatility Expansion): Value: ATR expansion percentage above 30-bar average Icon: ● if volatile, ○ otherwise Purpose: Confirms energy present for reversal T (Trend Strength): Value: ADX reading (0-100) Icon: ● if trending, ○ otherwise Purpose: Shows directional bias strength R (Regime): Label: EXPLOSIVE / TREND / VOLATILE / CHOP / NEUTRAL Icon: ✓ if valid, ✗ if invalid Purpose: Go/no-go filter for trading STATE (Bottom Display): Shows: "◆ BULL SINGULARITY" (green), "◆ BEAR SINGULARITY" (red), "◆ WEAK/NEUTRAL" (orange), or "— Monitoring —" (gray) Purpose: Current signal status at a glance How to Use This Indicator Initial Setup and Configuration Apply the indicator to your chart with default settings as a starting point. The default parameters (21-bar embedding, 5-bar Hurst window, 2.5σ singularity threshold, 0.65 topology confirmation) are optimized for balanced detection across most instruments and timeframes. For very fast markets (scalping crypto, 1-5min charts), consider reducing embedding depth to 13-15 bars and Hurst window to 3 bars for more responsive detection. For slower markets (swing trading stocks, 4H-Daily charts), increase embedding depth to 34-55 bars and Hurst window to 8-10 bars for more stable topology measurement. Enable the dashboard (top right recommended) to monitor real-time metrics. The control panel is your primary decision interface—glancing at the dashboard should instantly communicate whether conditions favor trading and what the current topology state is. Position and size the dashboard to remain visible but not obscure price action. Enable regime filtering (strongly recommended) to prevent trading during choppy/ranging conditions where geometric edge deteriorates. This single setting can dramatically improve overall performance by eliminating low-probability environments. Reading Dashboard Metrics for Trade Readiness Before considering any trade, verify the dashboard shows favorable conditions: Hurst (H) Check: The Hurst Exponent reading is your first filter. Only consider trades when H > 0.50 . Ideal conditions show H > 0.60 with "TREND" label—this indicates persistent directional price movement where manifold catastrophes produce significant reversals. When H < 0.45 (REVERT label), the market is mean-reverting and catastrophes represent minor oscillations rather than substantial pivots. Do not trade in mean-reverting regimes unless you're explicitly using range-bound strategies (which this indicator is not optimized for). When H ≈ 0.50 (RANDOM label), edge is neutral—acceptable but not ideal. Catastrophe (Σ) Monitoring: Watch the Σ percentage build over time. Readings consistently below 50% indicate stable topology with no imminent reversals. When Σ rises above 60-65%, manifold distortion is approaching critical levels. Signals only fire when Σ exceeds the configured threshold (default 65%), so this metric pre-warns you of potential upcoming catastrophes. High-conviction setups show Σ > 75%. Regime (R) Validation: The regime classification must read TREND, VOLATILE, or EXPLOSIVE—never trade when it reads CHOP or NEUTRAL. The checkmark (✓) must be present in the regime cell for trading conditions to be valid. If you see an X (✗), skip all signals until regime improves. This filter alone eliminates most losing trades by avoiding geometrically unfavorable environments. Combined High-Conviction Profile: The strongest trading opportunities show simultaneously: H > 0.60 (strong trending regime) Σ > 75% (extreme topology distortion) R = EXPLOSIVE or TREND with ✓ κ (Curvature) > 1.5 (sharp manifold fold) ⟳ (Complexity) > 4 (chaotic geometry) V (Volatility) showing elevated ATR expansion When all metrics align in this configuration, the manifold is undergoing severe distortion in a favorable fractal regime—these represent maximum-conviction reversal opportunities. Signal Interpretation and Entry Logic Bullish Singularity (▲ Green Triangle Below Bar): This marker appears when the system detects a manifold catastrophe at a price low with bullish directional consensus. All five confirmation filters have aligned: topology score exceeded threshold, pivot low structure formed, swing size was significant, volume/volatility confirmed participation, and regime was valid. The green color indicates the directional vote totaled +2 or higher (majority bullish). Trading Approach: Consider long entry on the bar immediately following the signal (bar after the triangle). The singularity bar itself is where the geometric catastrophe occurred—entering after allows you to see if price confirms the reversal. Place stop loss below the singularity bar's low (with buffer of 0.5-1.0 ATR for volatility). Initial target can be the previous swing high, or use the probability cone projection as a guide (though not a guarantee). Monitor the dashboard STATE—if it flips to "◆ BEAR SINGULARITY" or Hurst drops significantly, consider exiting even if target not reached. Bearish Singularity (▼ Red Triangle Above Bar): This marker appears when the system detects a manifold catastrophe at a price high with bearish directional consensus. Same five-filter confirmation process as bullish signals. The red color indicates directional vote totaled -2 or lower (majority bearish). Trading Approach: Consider short entry on the bar following the signal. Place stop loss above the singularity bar's high (with buffer). Target previous swing low or use cone projection as reference. Exit if opposite signal fires or Hurst deteriorates. Neutral Signal (● Orange Circle at Price Level): This marker indicates the catastrophe detection system identified a topology break that passed catastrophe threshold and regime filters, but the directional voting system produced a mixed result (vote between -1 and +1). This means the four directional components (pivot, trend, flow, mid-band) are not in agreement about which way the reversal should resolve. Trading Approach: Skip these signals. Neutral markers are displayed for analytical completeness but should not be traded. They represent geometric catastrophes without clear directional resolution—essentially, the manifold is breaking but the direction of the break is ambiguous. Trading neutral signals dramatically increases false signal rate. Only trade green (bullish) or red (bearish) singularities. Visual Confirmation Using Spectral Layers The three colored ribbons (spectral decomposition layers) provide entropy visualization that helps confirm signal quality: Divergent Layers (High Entropy State): When the three frequency bands (fast 8-period, medium 21-period, slow 55-period) are separated with significant gaps between them, the manifold is in high entropy state—different frequency components of price movement are pulling in different directions. This geometric tension precedes catastrophes. Strong signals often occur when layers are divergent before the signal, then begin reconverging immediately after. Convergent Layers (Low Entropy State): When all three ribbons are tightly clustered or overlapping, the manifold is in equilibrium—all frequency components agree. This stable geometry makes catastrophe detection more reliable because topology breaks clearly stand out against the baseline stability. If you see layers converge, then a singularity fires, then layers diverge, this pattern suggests a genuine regime transition. Signal Quality Assessment: High-quality singularity signals should show: Divergent layers (high entropy) in the 5-10 bars before signal Singularity bar occurs when price has extended outside at least one of the spectral bands (shows pivot extended beyond equilibrium) Close of singularity bar re-enters the spectral band zone (shows mean reversion starting) Layers begin reconverging in 3-5 bars after signal (shows new equilibrium forming) This pattern visually confirms the geometric narrative: manifold became unstable (divergence), reached critical distortion (extended outside equilibrium), broke catastrophically (singularity), and is now stabilizing in new direction (reconvergence). Using Energy Fields for Trade Management The concentric glowing boxes around each singularity visualize the topology distortion magnitude: Wide Energy Fields (5+ Layers Visible): Large radiance indicates strong catastrophe with high manifold curvature. These represent significant topology breaks and typically precede larger price moves. Wide fields justify wider profit targets and longer hold times. The outer edge of the largest box can serve as a dynamic support/resistance zone—price often respects these geometric boundaries. Narrow Energy Fields (2-3 Layers): Smaller radiance indicates moderate catastrophe. While still valid signals (all filters passed), expect smaller follow-through. Use tighter profit targets and be prepared for quicker exit if momentum doesn't develop. These are valid but lower-conviction trades. Field Interaction Zones: When energy fields from consecutive signals overlap or touch, this indicates a prolonged topology distortion region—often corresponds to consolidation zones or complex reversal patterns (head-and-shoulders, double tops/bottoms). Be more cautious in these areas as the manifold is undergoing extended restructuring rather than a clean catastrophe. Probability Cone Projections The dashed cone extending forward from each singularity is a mathematical projection, not a price target: Cone Direction: The center line direction (upward for bullish, downward for bearish, flat for neutral) shows the expected trajectory based on current manifold gradient and singularity direction. This is where the topology suggests price "should" go if the catastrophe completes normally. Cone Width: The uncertainty band (upper and lower dashed boundaries) represents the range of outcomes given current volatility (ATR-based). Wider cones indicate higher uncertainty—expect more price volatility even if direction is correct. Narrower cones suggest more constrained movement. Price-Cone Interaction: Price following near the center line = catastrophe resolving as expected, geometric projection accurate Price breaking above upper cone = stronger-than-expected reversal, consider holding for larger targets Price breaking below lower cone (for bullish signal) = catastrophe failing, manifold may be re-folding in opposite direction, consider exit Price oscillating within cone = normal reversal process, hold position The 10-bar projection length means cones show expected behavior over the next ~10 bars. Don't confuse this with longer-term price targets. Gradient Flow Field Interpretation The directional arrows (↗, ↘, →) scattered across the chart show the manifold's Y-gradient (vertical acceleration dimension): Upward Arrows (↗): Positive Y-gradient indicates the momentum acceleration dimension is pushing upward—the manifold topology has upward "slope" at this location. Clusters of upward arrows suggest bullish topological pressure building. These often appear before bullish singularities fire. Downward Arrows (↘): Negative Y-gradient indicates downward topological pressure. Clusters precede bearish singularities. Horizontal Arrows (→): Neutral gradient indicates balanced topology with no strong directional pressure. Using Flow Field: The arrows provide real-time topology state information even between singularity signals. If you're in a long position from a bullish singularity and begin seeing increasing downward arrows appearing, this suggests manifold gradient is shifting—consider tightening stops. Conversely, if arrows remain upward or neutral, topology supports continuation. Don't confuse arrow direction with immediate price direction—arrows show geometric slope, not price prediction. They're confirmatory context, not entry signals themselves. Parameter Optimization for Your Trading Style For Scalping / Fast Trading (1m-15m charts): Embedding Depth: 13-15 bars (faster topology reconstruction) Hurst Window: 3 bars (responsive fractal detection) Singularity Threshold: 2.0-2.3σ (more sensitive) Topology Confirmation: 0.55-0.60 (lower barrier) Min Swing Size: 0.8-1.2 ATR (accepts smaller moves) Pivot Lookback: 3-4 bars (quick pivot detection) This configuration increases signal frequency for active trading but requires diligent monitoring as false signal rate increases. Use tighter stops. For Day Trading / Standard Approach (15m-4H charts): Keep default settings (21 embed, 5 Hurst, 2.5σ, 0.65 confirmation, 1.5 ATR, 5 pivot) These are balanced for quality over quantity Best win rate and risk/reward ratio Recommended for most traders For Swing Trading / Position Trading (4H-Daily charts): Embedding Depth: 34-55 bars (stable long-term topology) Hurst Window: 8-10 bars (smooth fractal measurement) Singularity Threshold: 3.0-3.5σ (only extreme catastrophes) Topology Confirmation: 0.75-0.85 (high conviction only) Min Swing Size: 2.5-4.0 ATR (major moves only) Pivot Lookback: 8-13 bars (confirmed swings) This configuration produces infrequent but highly reliable signals suitable for position sizing and longer hold times. Volatility Adaptation: In extremely volatile instruments (crypto, penny stocks), increase Min Volatility Expansion to 0.6-0.8 to avoid over-signaling during "always volatile" conditions. In stable instruments (major forex pairs, blue-chip stocks), decrease to 0.3 to allow signals during moderate volatility spikes. Trend vs Range Preference: If you prefer trading only strong trends, increase Min Trend Strength to 0.5-0.6 (ADX > 50-60). If you're comfortable with volatility-based trading in weaker trends, decrease to 0.2 (ADX > 20). The default 0.3 balances both approaches. Complete Trading Workflow Example Step 1 - Pre-Session Setup: Load chart with MSE indicator. Check dashboard position is visible. Verify regime filter is enabled. Review recent signals to gauge current instrument behavior. Step 2 - Market Assessment: Observe dashboard Hurst reading. If H < 0.45 (mean-reverting), consider skipping this session or using other strategies. If H > 0.50, proceed. Check regime shows TREND, VOLATILE, or EXPLOSIVE with checkmark—if CHOP, wait for regime shift alert. Step 3 - Signal Wait: Monitor catastrophe score (Σ). Watch for it climbing above 60%. Observe spectral layers—look for divergence building. If you see curvature (κ) rising above 1.0 and complexity (⟳) increasing, catastrophe is building. Do not anticipate—wait for the actual signal marker. Step 4 - Signal Recognition: ▲ Bullish or ▼ Bearish triangle appears at a bar. Dashboard STATE changes to "◆ BULL/BEAR SINGULARITY". Energy field appears around the signal bar. Check signal quality: Was Σ > 70% at signal? (Higher quality) Are energy fields wide? (Stronger catastrophe) Did layers diverge before and reconverge after? (Clean break) Is Hurst still > 0.55? (Good regime) Step 5 - Entry Decision: If signal is green/red (not orange neutral), all confirmations look strong, and no immediate contradicting factors appear, prepare entry on next bar open. Wait for confirmation bar to form—ideally it should close in the signal direction (bullish signal → bar closes higher, bearish signal → bar closes lower). Step 6 - Position Entry: Enter at open or shortly after open of bar following signal bar. Set stop loss: for bullish signals, place stop at singularity_bar_low - (0.75 × ATR); for bearish signals, place stop at singularity_bar_high + (0.75 × ATR). The buffer accommodates volatility while protecting against catastrophe failure. Step 7 - Trade Management: Monitor dashboard continuously: If Hurst drops below 0.45, consider reducing position If opposite singularity fires, exit immediately (manifold has re-folded) If catastrophe score drops below 40% and stays there, topology has stabilized—consider partial profit taking Watch gradient flow arrows—if they shift to opposite direction persistently, tighten stops Step 8 - Profit Taking: Use probability cone as a guide—if price reaches outer cone boundary, consider taking partial profits. If price follows center line cleanly, hold for larger target. Traditional technical targets work well: previous swing high/low, round numbers, Fibonacci extensions. Don't expect precision—manifold projections give direction and magnitude estimates, not exact prices. Step 9 - Exit: Exit on: (a) opposite signal appears, (b) dashboard shows regime became invalid (checkmark changes to X), (c) technical target reached, (d) Hurst deteriorates significantly, (e) stop loss hit, or (f) time-based exit if using session limits. Never hold through opposite singularity signals—the manifold has broken in the other direction and your trade thesis is invalidated. Step 10 - Post-Trade Review: After exit, review: Did the probability cone projection align with actual price movement? Were the energy fields proportional to move size? Did spectral layers show expected reconvergence? Use these observations to calibrate your interpretation of signal quality over time. Best Performance Conditions This topology-based approach performs optimally in specific market environments: Favorable Conditions: Well-Developed Swing Structure: Markets with clear rhythm of advances and declines where pivots form at regular intervals. The manifold reconstruction depends on swing formation, so instruments that trend in clear waves work best. Stocks, major forex pairs during active sessions, and established crypto assets typically exhibit this characteristic. Sufficient Volatility for Topology Development: The embedding process requires meaningful price movement to construct multi-dimensional coordinates. Extremely quiet markets (tight consolidations, holiday trading, after-hours) lack the volatility needed for manifold differentiation. Look for ATR expansion above average—when volatility is present, geometry becomes meaningful. Trending with Periodic Reversals: The ideal environment is not pure trend (which rarely reverses) nor pure range (which reverses constantly at small scale), but rather trending behavior punctuated by occasional significant counter-trend reversals. This creates the catastrophe conditions the system is designed to detect: manifold building directional momentum, then undergoing sharp topology break at extremes. Liquid Instruments Where EMAs Reflect True Flow: The spectral layers and frequency decomposition require that moving averages genuinely represent market consensus. Thinly traded instruments with sporadic orders don't create smooth manifold topology. Prefer instruments with consistent volume where EMA calculations reflect actual capital flow rather than random tick sequences. Challenging Conditions: Extremely Choppy / Whipsaw Markets: When price oscillates rapidly with no directional persistence (Hurst < 0.40), the manifold undergoes constant micro-catastrophes that don't translate to tradable reversals. The regime filter helps avoid these, but awareness is important. If you see multiple neutral signals clustering with no follow-through, market is too chaotic for this approach. Very Low Volatility Consolidation: Tight ranges with ATR below average cause the embedding coordinates to compress into a small region of phase space, reducing geometric differentiation. The manifold becomes nearly flat, and catastrophe detection loses sensitivity. The regime filter's volatility component addresses this, but manually avoiding dead markets improves results. Gap-Heavy Instruments: Stocks that gap frequently (opening outside previous close) create discontinuities in the manifold trajectory. The embedding process assumes continuous evolution, so gaps introduce artifacts. Most gaps don't invalidate the approach, but instruments with daily gaps >2% regularly may show degraded performance. Consider using higher timeframes (4H, Daily) where gaps are less proportionally significant. Parabolic Moves / Blowoff Tops: When price enters an exponential acceleration phase (vertical rally or crash), the manifold evolves too rapidly for the standard embedding window to track. Catastrophe detection may lag or produce false signals mid-move. These conditions are rare but identifiable by Hurst > 0.75 combined with ATR expansion >2.0× average. If detected, consider sitting out or using very tight stops as geometry is in extreme distortion. The system adapts by reducing signal frequency in poor conditions—if you notice long periods with no signals, the topology likely lacks the geometric structure needed for reliable catastrophe detection. This is a feature, not a bug: it prevents forced trading during unfavorable environments. Theoretical Justification for Approach Why Manifold Embedding? Traditional technical analysis treats price as a one-dimensional time series: current price is predicted from past prices in sequential order. This approach ignores the structure of price dynamics—the relationships between velocity, acceleration, and participation that govern how price actually evolves. Dynamical systems theory (from physics and mathematics) provides an alternative framework: treat price as a state variable in a multi-dimensional phase space. In this view, each market condition corresponds to a point in N-dimensional space, and market evolution is a trajectory through this space. The geometry of this space (its topology) constrains what trajectories are possible. Manifold embedding reconstructs this hidden geometric structure from observable price data. By creating coordinates from velocity, momentum acceleration, and volume-weighted returns, we map price evolution onto a 3D surface. This surface—the manifold—reveals geometric relationships that aren't visible in price charts alone. The mathematical theorem underlying this approach (Takens' Embedding Theorem from dynamical systems theory) proves that for deterministic or weakly stochastic systems, a state space reconstruction from time-delayed observations of a single variable captures the essential dynamics of the full system. We apply this principle: even though we only observe price, the embedded coordinates (derivatives of price) reconstruct the underlying dynamical structure. Why Catastrophe Theory? Catastrophe theory, developed by mathematician René Thom (Fields Medal 1958), describes how continuous systems can undergo sudden discontinuous changes when control parameters reach critical values. A classic example: gradually increasing force on a beam causes smooth bending, then sudden catastrophic buckling. The beam's geometry reaches a critical curvature where topology must break. Markets exhibit analogous behavior: gradual price changes build tension in the manifold topology until critical distortion is reached, then abrupt directional change occurs (reversal). Catastrophes aren't random—they're mathematically necessary when geometric constraints are violated. The indicator detects these geometric precursors: high curvature (manifold bending sharply), high complexity (topology oscillating chaotically), high condition number (coordinate mapping becoming singular). These metrics quantify how close the manifold is to a catastrophic fold. When all simultaneously reach extreme values, topology break is imminent. This provides a logical foundation for reversal detection that doesn't rely on pattern recognition or historical correlation. We're measuring geometric properties that mathematically must change when systems reach critical states. This is why the approach works across different instruments and timeframes—the underlying geometry is universal. Why Hurst Exponent? Markets exhibit fractal behavior: patterns at different time scales show statistical self-similarity. The Hurst exponent quantifies this fractal structure by measuring long-range dependence in returns. Critically for trading, Hurst determines whether recent price movement predicts future direction (H > 0.5) or predicts the opposite (H < 0.5). This is regime detection: trending vs mean-reverting behavior. The same manifold catastrophe has different trading implications depending on regime. In trending regime (high Hurst), catastrophes represent significant reversal opportunities because the manifold has been building directional momentum that suddenly breaks. In mean-reverting regime (low Hurst), catastrophes represent minor oscillations because the manifold constantly folds at small scales. By weighting catastrophe signals based on Hurst, the system adapts detection sensitivity to the current fractal regime. This is a form of meta-analysis: not just detecting geometric breaks, but evaluating whether those breaks are meaningful in the current fractal context. Why Multi-Layer Confirmation? Geometric anomalies occur frequently in noisy market data. Not every high-curvature point represents a tradable reversal—many are artifacts of microstructure noise, order flow imbalances, or low-liquidity ticks. The five-filter confirmation system (catastrophe threshold, pivot structure, swing size, volume, regime) addresses this by requiring geometric anomalies to align with observable market evidence. This conjunction-based logic implements the principle: extraordinary claims require extraordinary evidence . A manifold catastrophe (extraordinary geometric event) alone is not sufficient. We additionally require: price formed a pivot (visible structure), swing was significant (adequate magnitude), volume confirmed participation (capital backed the move), and regime was favorable (trending or volatile, not chopping). Only when all five dimensions agree do we have sufficient evidence that the geometric anomaly represents a genuine reversal opportunity rather than noise. This multi-dimensional approach is analogous to medical diagnosis: no single test is conclusive, but when multiple independent tests all suggest the same condition, confidence increases dramatically. Each filter removes a different category of false signals, and their combination creates a robust detection system. The result is a signal set with dramatically improved reliability compared to any single metric alone. This is the power of ensemble methods applied to geometric analysis. Important Disclaimers This indicator applies mathematical topology and catastrophe theory to multi-dimensional price space reconstruction. It identifies geometric conditions where manifold curvature, topological complexity, and coordinate singularities suggest potential reversal zones based on phase space analysis. It should not be used as a standalone trading system. The embedding coordinates, catastrophe scores, and Hurst calculations are deterministic mathematical formulas applied to historical price data. These measurements describe current and recent geometric relationships in the reconstructed manifold but do not predict future price movements. Past geometric patterns and singularity markers do not guarantee future market behavior will follow similar topology evolution. The manifold reconstruction assumes certain mathematical properties (sufficient embedding dimension, quasi-stationarity, continuous dynamics) that may not hold in all market conditions. Gaps, flash crashes, circuit breakers, news events, and other discontinuities can violate these assumptions. The system attempts to filter problematic conditions through regime classification, but cannot eliminate all edge cases. The spectral decomposition, energy fields, and probability cones are visualization aids that represent mathematical constructs, not price predictions. The probability cone projects current gradient forward assuming topology continues current trajectory—this is a mathematical "if-then" statement, not a forecast. Market topology can and does change unexpectedly. All trading involves substantial risk. The singularity markers represent analytical conditions where geometric mathematics align with threshold criteria, not certainty of directional change. Use appropriate risk management for every trade: position sizing based on account risk tolerance (typically 1-2% maximum risk per trade), stop losses placed beyond recent structure plus volatility buffer, and never risk capital needed for living expenses. The confirmation filters (pivot, swing size, volume, regime) are designed to reduce false signals but cannot eliminate them entirely. Markets can produce geometric anomalies that pass all filters yet fail to develop into sustained reversals. This is inherent to probabilistic systems operating on noisy real-world data. No indicator can guarantee profitable trades or eliminate losses. The catastrophe detection provides an analytical framework for identifying potential reversal conditions, but actual trading outcomes depend on numerous factors including execution, slippage, spreads, position sizing, risk management, psychological discipline, and market conditions that may change after signal generation. Use this tool as one component of a comprehensive trading plan that includes multiple forms of analysis, proper risk management, emotional discipline, and realistic expectations about win rates and drawdowns. Combine catastrophe signals with additional confirmation methods such as support/resistance analysis, volume patterns, multi-timeframe alignment, and broader market context. The spacing filter, cooldown mechanism, and regime validation are designed to reduce noise and over-signaling, but market conditions can change rapidly and render any analytical signal invalid. Always use stop losses and never risk capital you cannot afford to lose. Past performance of detection accuracy does not guarantee future results. Technical Implementation Notes All calculations execute on closed bars only—signals and metric values do not repaint after bar close. The indicator does not use any lookahead bias in its calculations. However, the pivot detection mechanism (ta.pivothigh and ta.pivotlow) inherently identifies pivots with a lag equal to the lookback parameter, meaning the actual pivot occurred at bar but is recognized at bar . This is standard behavior for pivot functions and is not repainting—once recognized, the pivot bar never changes. The normalization system (z-score transformation over rolling windows) requires approximately 30-50 bars of historical data to establish stable statistics. Values in the first 30-50 bars after adding the indicator may show instability as the rolling means and standard deviations converge. Allow adequate warmup period before relying on signals. The spectral layer arrays, energy field boxes, gradient flow labels, and node geometry lines are subject to TradingView drawing object limits (500 lines, 500 boxes, 500 labels per indicator as specified in settings). The system implements automatic cleanup by deleting oldest objects when limits approach, but on very long charts with many signals, some historical visual elements may be removed to stay within limits. This does not affect signal generation or dashboard metrics—only historical visual artifacts. Dashboard and visual rendering update only on the last bar to minimize computational overhead. The catastrophe detection logic executes on every bar, but table cells and drawing objects refresh conditionally to optimize performance. If experiencing chart lag, reduce visual complexity: disable spectral layers, energy fields, or flow field to improve rendering speed. Core signal detection continues to function with all visual elements disabled. The Hurst calculation uses logarithmic returns rather than raw price to ensure stationarity, and implements clipping to range to handle edge cases where R/S analysis produces invalid values (which can occur during extended periods of identical prices or numerical overflow). The 5-period EMA smoothing reduces noise while maintaining responsiveness to regime transitions. The condition number calculation adds epsilon (1e-10) to denominators to prevent division by zero when Jacobian determinant approaches zero—which is precisely the singularity condition we're detecting. This numerical stability measure ensures the indicator doesn't crash when detecting the very phenomena it's designed to identify. The indicator has been tested across multiple timeframes (5-minute through daily) and multiple asset classes (forex majors, stock indices, individual equities, cryptocurrencies, commodities, futures). It functions identically across all instruments due to the adaptive normalization approach and percentage-based metrics. No instrument-specific code or parameter sets are required. The color scheme system implements seven preset themes plus custom mode. Color assignments are applied globally and affect all visual elements simultaneously. The opacity calculation system multiplies component-specific transparency with master opacity to create hierarchical control—adjusting master opacity affects all visuals proportionally while maintaining their relative transparency relationships. All alert conditions trigger only on bar close to prevent false alerts from intrabar fluctuations. The regime transition alerts (VALID/INVALID) are particularly useful for knowing when trading edge appears or disappears, allowing traders to adjust activity levels accordingly. — Dskyz, Trade with insight. Trade with anticipation.
Indicatore Pine Script®
di DskyzInvestments