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Smart Money Precision Structure [BullByte]Smart Money Precision Structure Advanced Market Structure Analysis Using Institutional Order Flow Concepts --- OVERVIEW Smart Money Precision Structure (SMPS) is a comprehensive market analysis indicator that combines six analytical frameworks to identify high-probability market structure patterns. The indicator uses multi-dimensional scoring algorithms to evaluate market conditions through institutional order flow concepts, providing traders with professional-grade market analysis. --- PURPOSE AND ORIGINALITY Why This Indicator Was Developed • Addresses the gap between retail and institutional analysis methods • Consolidates multiple analysis techniques that professionals use separately • Automates complex market structure evaluation into actionable insights • Eliminates the need for multiple indicators by providing comprehensive analysis What Makes SMPS Original • Six-Layer Confluence System - Unique combination of market regime, structure, volume flow, momentum, price action, and adaptive filtering • Institutional Pattern Recognition - Identifies smart money accumulation and distribution patterns • Adaptive Intelligence - Parameters automatically adjust based on detected market conditions • Real-Time Market Scoring - Proprietary algorithm rates market quality from 0-100% • Structure Break Detection - Advanced pivot analysis identifies trend reversals early --- HOW IT WORKS - TECHNICAL METHODOLOGY 1. Market Regime Analysis Engine The indicator evaluates five core market dimensions: • Volatility Score - Measures current volatility against 50-period historical baseline • Trend Score - Analyzes alignment between 8, 21, and 50-period EMAs • Momentum Score - Combines RSI divergence with MACD signal alignment • Structure Score - Evaluates pivot point formation clarity • Efficiency Score - Calculates directional movement efficiency ratio These scores combine to classify markets into five regimes: • TRENDING - Strong directional movement with aligned indicators • RANGING - Sideways movement with mixed directional signals • VOLATILE - Elevated volatility with unpredictable price swings • QUIET - Low volatility consolidation periods • TRANSITIONAL - Market shifting between different regimes 2. Market Structure Analysis Advanced pivot point analysis identifies: • Higher Highs and Higher Lows for bullish structure • Lower Highs and Lower Lows for bearish structure • Structure breaks when established patterns fail • Dynamic support and resistance from recent pivot points • Key level proximity detection using ATR-based buffers 3. Volume Flow Decoding Institutional activity detection through: • Volume surge identification when volume exceeds 2x average • Buy versus sell pressure analysis using price-volume correlation • Flow strength measurement through directional volume consistency • Divergence detection between volume and price movements • Institutional threshold alerts when unusual volume patterns emerge 4. Multi-Period Momentum Synthesis Weighted momentum calculation across four timeframes: • 1-period momentum weighted at 40% • 3-period momentum weighted at 30% • 5-period momentum weighted at 20% • 8-period momentum weighted at 10% Result smoothed with 6-period EMA for noise reduction. 5. Price Action Quality Assessment Each bar evaluated for: • Range quality relative to 20-period average • Body-to-range ratio for directional conviction • Wick analysis for rejection pattern identification • Pattern recognition including engulfing and hammer formations • Sequential price movement analysis 6. Adaptive Parameter System Parameters automatically adjust based on detected regime: • Trending markets reduce sensitivity and confirmation requirements • Volatile markets increase filtering and require additional confirmations • Ranging markets maintain neutral settings • Transitional markets use moderate adjustments --- COMPLETE SETTINGS GUIDE Section 1: Core Analysis Settings Analysis Sensitivity (0.3-2.0) • Default: 1.0 • Lower values require stronger price movements • Higher values detect more subtle patterns • Scalpers use 0.8-1.2, swing traders use 1.5-2.0 Noise Reduction Level (2-7) • Default: 4 • Controls filtering of false patterns • Higher values reduce pattern frequency • Increase in volatile markets Minimum Move % (0.05-0.50) • Default: 0.15% • Sets minimum price movement threshold • Adjust based on instrument volatility • Forex: 0.05-0.10%, Stocks: 0.15-0.25%, Crypto: 0.20-0.50% High Confirmation Mode • Default: True (Enabled) • Requires all technical conditions to align • Reduces frequency but increases reliability • Disable for more aggressive pattern detection Section 2: Market Regime Detection Enable Regime Analysis • Default: True (Enabled) • Activates market environment evaluation • Essential for adaptive features • Keep enabled for best results Regime Analysis Period (20-100) • Default: 50 bars • Determines regime calculation lookback • Shorter for responsive, longer for stable • Scalping: 20-30, Swing: 75-100 Minimum Market Clarity (0.2-0.8) • Default: 0.4 • Quality threshold for pattern generation • Higher values require clearer conditions • Lower for more patterns, higher for quality Adaptive Parameter Adjustment • Default: True (Enabled) • Enables automatic parameter optimization • Adjusts based on market regime • Highly recommended to keep enabled Section 3: Market Structure Analysis Enable Structure Validation • Default: True (Enabled) • Validates patterns against support/resistance • Confirms trend structure alignment • Essential for reliability Structure Analysis Period (15-50) • Default: 30 bars • Period for structure pattern analysis • Affects support/resistance calculation • Match to your trading timeframe Minimum Structure Alignment (0.3-0.8) • Default: 0.5 • Required structure score for valid patterns • Higher values need stronger structure • Balance with desired frequency Section 4: Analysis Configuration Minimum Strength Level (3-5) • Default: 4 • Minimum confirmations for pattern display • 5 = Maximum reliability, 3 = More patterns • Beginners should use 4-5 Required Technical Confirmations (4-6) • Default: 5 • Number of aligned technical factors • Higher = fewer but better patterns • Works with High Confirmation Mode Pattern Separation (3-20 bars) • Default: 8 bars • Minimum bars between patterns • Prevents clustering and overtrading • Increase for cleaner charts Section 5: Technical Filters Momentum Validation • Default: True (Enabled) • Requires momentum alignment • Filters counter-trend patterns • Essential for trend following Volume Confluence Analysis • Default: True (Enabled) • Requires volume confirmation • Identifies institutional participation • Critical for reliability Trend Direction Filter • Default: True (Enabled) • Only shows patterns with trend • Reduces counter-trend signals • Disable for reversal hunting Section 6: Volume Flow Analysis Institutional Activity Threshold (1.2-3.5) • Default: 2.0 • Multiplier for unusual volume detection • Lower finds more institutional activity • Stock: 2.0-2.5, Forex: 1.5-2.0, Crypto: 2.5-3.5 Volume Surge Multiplier (1.8-4.5) • Default: 2.5 • Defines significant volume increases • Adjust per instrument characteristics • Higher for stocks, lower for forex Volume Flow Period (12-35) • Default: 18 bars • Smoothing for volume analysis • Shorter = responsive, longer = smooth • Match to timeframe used Section 7: Analysis Frequency Control Maximum Analysis Points Per Hour (1-5) • Default: 3 • Limits pattern frequency • Prevents overtrading • Scalpers: 4-5, Swing traders: 1-2 Section 8: Target Level Configuration Target Calculation Method • Default: Market Adaptive • Three modes available: - Fixed: Uses set point distances - Dynamic: ATR-based calculations - Market Adaptive: Structure-based levels Minimum Target/Risk Ratio (1.0-3.0) • Default: 1.5 • Minimum acceptable reward vs risk • Higher filters lower probability setups • Professional standard: 1.5-2.0 Fixed Mode Settings: • Fixed Target Distance: 50 points default • Fixed Invalidation Distance: 30 points default • Use for consistent instruments Dynamic Mode Settings: • Dynamic Target Multiplier: 1.8x ATR default • Dynamic Invalidation Multiplier: 1.0x ATR default • Adapts to volatility automatically Market Adaptive Settings: • Use Structure Levels: True (default) • Structure Level Buffer: 0.1% default • Places levels at actual support/resistance Section 9: Visual Display Settings Color Theme Options • Professional (Teal/Red) - Bullish: Teal (#26a69a) - Bearish: Red (#ef5350) - Neutral: Gray (#78909c) - Best for: Traditional traders, clean appearance • Dark (Neon Green/Pink) - Bullish: Neon Green (#00ff88) - Bearish: Hot Pink (#ff0044) - Neutral: Dark Gray (#333333) - Best for: Dark theme users, high contrast • Light (Green/Red Classic) - Bullish: Green (#4caf50) - Bearish: Red (#f44336) - Neutral: Light Gray (#9e9e9e) - Best for: Light backgrounds, traditional colors • Vibrant (Cyan/Magenta) - Bullish: Cyan (#00ffff) - Bearish: Magenta (#ff00ff) - Neutral: Medium Gray (#888888) - Best for: High visibility, modern appearance Dashboard Position • Options: Top Left, Top Right, Bottom Left, Bottom Right, Middle Left, Middle Right • Default: Top Right • Choose based on chart layout preference Dashboard Size • Full: Complete information display (desktop) • Mobile: Compact view for small screens • Default: Full Analysis Display Style • Arrows : Simple directional markers • Labels : Detailed text information • Zones : Colored areas showing pattern regions • Default: Labels (most informative) Display Options: • Display Analysis Strength: Shows star rating • Display Target Levels: Shows target/invalidation lines • Display Market Regime: Shows regime in pattern labels --- HOW TO USE SMPS - DETAILED GUIDE Understanding the Dashboard Top Row - Header • SMPS Dashboard title • VALUE column: Current readings • STATUS column: Condition assessments Market Regime Row • Shows: TRENDING, RANGING, VOLATILE, QUIET, or TRANSITIONAL • Color coding: Green = Favorable, Red = Caution • Status: FAVORABLE or CAUTION trading conditions Market Score Row • Percentage from 0-100% • Above 60% = Strong conditions • 40-60% = Moderate conditions • Below 40% = Weak conditions Structure Row • Direction: BULLISH, BEARISH, or NEUTRAL • Status: INTACT or BREAK • Orange BREAK indicates structure failure Volume Flow Row • Direction: BUYING or SELLING • Intensity: STRONG or WEAK • Color indicates dominant pressure Momentum Row • Numerical momentum value • Positive = Upward pressure • Negative = Downward pressure Volume Status Row • INST = Institutional activity detected • HIGH = Above average volume • NORM = Normal volume levels Adaptive Mode Row • ACTIVE = Parameters adjusting • STATIC = Fixed parameters • Shows required confirmations Analysis Level Row • Minimum strength level setting • Pattern separation in bars Market State Row • Current analysis: BULLISH, BEARISH, NEUTRAL • Shows analysis price level when active T:R Ratio Row • Current target to risk ratio • GOOD = Meets minimum requirement • LOW = Below minimum threshold Strength Row • BULL or BEAR dominance • Numerical strength value 0-100 Price Row • Current price • Percentage change Last Analysis Row • Previous pattern direction • Bars since last pattern Reading Pattern Signals Bullish Structure Pattern • Upward triangle or "Bullish Structure" label • Star rating shows strength (★★★★★ = strongest) • Green line = potential target level • Red dashed line = invalidation level • Appears below price bars Bearish Structure Pattern • Downward triangle or "Bearish Structure" label • Star rating indicates reliability • Green line = potential target level • Red dashed line = invalidation level • Appears above price bars Pattern Strength Interpretation • ★★★★★ = 6 confirmations (exceptional) • ★★★★☆ = 5 confirmations (strong) • ★★★☆☆ = 4 confirmations (moderate) • ★★☆☆☆ = 3 confirmations (minimum) • Below minimum = filtered out Visual Elements on Chart Lines and Levels: • Gray Line = 21 EMA trend reference • Green Stepline = Dynamic support level • Red Stepline = Dynamic resistance level • Green Solid Line = Active target level • Red Dashed Line = Active invalidation level Pattern Markers: • Triangles = Arrow display mode • Text Labels = Label display mode • Colored Boxes = Zone display mode Target Completion Labels: • "Target" = Price reached target level • "Invalid" = Pattern invalidated by price --- RECOMMENDED USAGE BY TIMEFRAME 1-Minute Charts (Scalping) • Sensitivity: 0.8-1.2 • Noise Reduction: 3-4 • Pattern Separation: 3-5 bars • High Confirmation: Optional • Best for: Quick intraday moves 5-Minute Charts (Precision Intraday) • Sensitivity: 1.0 (default) • Noise Reduction: 4 (default) • Pattern Separation: 8 bars • High Confirmation: Enabled • Best for: Day trading 15-Minute Charts (Short Swing) • Sensitivity: 1.0-1.5 • Noise Reduction: 4-5 • Pattern Separation: 10-12 bars • High Confirmation: Enabled • Best for: Intraday swings 30-Minute to 1-Hour (Position Trading) • Sensitivity: 1.5-2.0 • Noise Reduction: 5-7 • Pattern Separation: 15-20 bars • Regime Period: 75-100 • Best for: Multi-day positions Daily Charts (Swing Trading) • Sensitivity: 1.8-2.0 • Noise Reduction: 6-7 • Pattern Separation: 20 bars • All filters enabled • Best for: Long-term analysis --- MARKET-SPECIFIC SETTINGS Forex Pairs • Minimum Move: 0.05-0.10% • Institutional Threshold: 1.5-2.0 • Volume Surge: 1.8-2.2 • Target Mode: Dynamic or Market Adaptive Stock Indices (ES, NQ, YM) • Minimum Move: 0.10-0.15% • Institutional Threshold: 2.0-2.5 • Volume Surge: 2.5-3.0 • Target Mode: Market Adaptive Individual Stocks • Minimum Move: 0.15-0.25% • Institutional Threshold: 2.0-2.5 • Volume Surge: 2.5-3.5 • Target Mode: Dynamic Cryptocurrency • Minimum Move: 0.20-0.50% • Institutional Threshold: 2.5-3.5 • Volume Surge: 3.0-4.5 • Target Mode: Dynamic • Increase noise reduction --- PRACTICAL APPLICATION EXAMPLES Example 1: Strong Trending Market Dashboard Reading: • Market Regime: TRENDING • Market Score: 75% • Structure: BULLISH, INTACT • Volume Flow: BUYING, STRONG • Momentum: +0.45 Interpretation: • Strong uptrend environment • Institutional buying present • Look for bullish patterns as continuation • Higher probability of success • Consider using lower sensitivity Example 2: Range-Bound Conditions Dashboard Reading: • Market Regime: RANGING • Market Score: 35% • Structure: NEUTRAL • Volume Flow: SELLING, WEAK • Momentum: -0.05 Interpretation: • No clear direction • Low opportunity environment • Patterns are less reliable • Consider waiting for regime change • Or switch to a range-trading approach Example 3: Structure Break Alert Dashboard Reading: • Previous: BULLISH structure • Current: Structure BREAK • Volume: INST flag active • Momentum: Shifting negative Interpretation: • Trend reversal potentially beginning • Institutional participation detected • Watch for bearish pattern confirmation • Adjust bias accordingly • Increase caution on long positions Example 4: Volatile Market Dashboard Reading: • Market Regime: VOLATILE • Market Score: 45% • Adaptive Mode: ACTIVE • Confirmations: Increased to 6 Interpretation: • Choppy conditions • Parameters auto-adjusted • Fewer but higher quality patterns • Wider stops may be needed • Consider reducing position size Below are a few chart examples of the Smart Money Precision Structure (SMPS) indicator in action. • Example 1 – Bullish Structure Detection on SOLUSD 5m • Example 2 – Bearish Structure Detected with Strong Confluence on SOLUSD 5m --- TROUBLESHOOTING GUIDE No Patterns Appearing Check these settings: • High Confirmation Mode may be too restrictive • Minimum Strength Level may be too high • Market Clarity threshold may be too high • Regime filter may be blocking patterns • Try increasing sensitivity Too Many Patterns Adjust these settings: • Enable High Confirmation Mode • Increase Minimum Strength Level to 5 • Increase Pattern Separation • Reduce Sensitivity below 1.0 • Enable all technical filters Dashboard Shows "CAUTION" This indicates: • Market conditions are unfavorable • Regime is RANGING or QUIET • Market score is low • Consider waiting for better conditions • Or adjust expectations accordingly Patterns Not Reaching Targets Consider: • Market may be choppy • Volatility may have changed • Try Dynamic target mode • Reduce target/risk ratio requirement • Check if regime is VOLATILE --- ALERTS CONFIGURATION Alert Message Format Alerts include: • Pattern type (Bullish/Bearish) • Strength rating • Market regime • Analysis price level • Target and invalidation levels • Strength percentage • Target/Risk ratio • Educational disclaimer Setting Up Alerts • Click Alert button on TradingView • Select SMPS indicator • Choose alert frequency • Customize message if desired • Alerts fire on pattern detection --- DATA WINDOW INFORMATION The Data Window displays: • Market Regime Score (0-100) • Market Structure Bias (-1 to +1) • Bullish Strength (0-100) • Bearish Strength (0-100) • Bull Target/Risk Ratio • Bear Target/Risk Ratio • Relative Volume • Momentum Value • Volume Flow Strength • Bull Confirmations Count • Bear Confirmations Count --- BEST PRACTICES AND TIPS For Beginners • Start with default settings • Use High Confirmation Mode • Focus on TRENDING regime only • Paper trade first • Learn one timeframe thoroughly For Intermediate Users • Experiment with sensitivity settings • Try different target modes • Use multiple timeframes • Combine with price action analysis • Track pattern success rate For Advanced Users • Customize per instrument • Create setting templates • Use regime information for bias • Combine with other indicators • Develop systematic rules --- IMPORTANT DISCLAIMERS • This indicator is for educational and informational purposes only • Not financial advice or a trading system • Past performance does not guarantee future results • Trading involves substantial risk of loss • Always use appropriate risk management • Verify patterns with additional analysis • The author is not a registered investment advisor • No liability accepted for trading losses --- VERSION NOTES Version 1.0.0 - Initial Release • Six-layer confluence system • Adaptive parameter technology • Institutional volume detection • Market regime classification • Structure break identification • Real-time dashboard • Multiple display modes • Comprehensive settings ## My Final Thoughts Smart Money Precision Structure represents an advanced approach to market analysis, bringing institutional-grade techniques to retail traders through intelligent automation and multi-dimensional evaluation. By combining six analytical frameworks with adaptive parameter adjustment, SMPS provides comprehensive market intelligence that single indicators cannot achieve. The indicator serves as an educational tool for understanding how professional traders analyze markets, while providing practical pattern detection for those seeking to improve their technical analysis. Remember that all trading involves risk, and this tool should be used as part of a complete analysis approach, not as a standalone trading system. - BullByte
Indicador Pine Script®
por BullByte
6
Script_Algo - High Low Range MA Crossover Strategy🎯 Core Concept This strategy uses modified moving averages crossover, built on maximum and minimum prices, to determine entry and exit points in the market. A key advantage of this strategy is that it avoids most false signals in trendless conditions, which is characteristic of traditional moving average crossover strategies. This makes it possible to improve the risk/reward ratio and, consequently, the strategy's profitability. 📊 How the Strategy Works Main Mechanism The strategy builds 4 moving averages: Two senior MAs (on high and low) with a longer period Two junior MAs (on high and low) with a shorter period Buy signal 🟢: when the junior MA of lows crosses above the senior MA of highs Sell signal 🔴: when the junior MA of highs crosses below the senior MA of lows As seen on the chart, it was potentially possible to make 9X on the WIFUSDT cryptocurrency pair in just a year and a half. However, be careful—such results may not necessarily be repeated in the future. Special Feature Position closing priority ❗: if an opposite signal arrives while a position is open, the strategy first closes the current position and only then opens a new one ⚙️ Indicator Settings Available Moving Average Types EMA - Exponential MA SMA - Simple MA SSMA - Smoothed MA WMA - Weighted MA VWMA - Volume Weighted MA RMA - Adaptive MA DEMA - Double EMA TEMA - Triple EMA Adjustable Parameters Senior MA Length - period for long-term moving averages Junior MA Length - period for short-term moving averages ✅ Advantages of the Strategy 🛡️ False Signal Protection - using two pairs of modified MAs reduces the number of false entries 🔄 Configuration Flexibility - ability to choose MA type and calculation periods ⚡ Automatic Switching - the strategy automatically closes the current position when receiving an opposite signal 📈 Visual Clarity - all MAs are displayed on the chart in different colors ⚠️ Disadvantages and Risks 📉 Signal Lag - like all MA-based strategies, it may provide delayed signals during sharp movements 🔁 Frequent Switching - in sideways markets, it may lead to multiple consecutive position openings/closings 📊 Requires Optimization - optimal parameters need to be selected for different instruments and timeframes 💡 Usage Recommendations Backtest - test the strategy's performance on historical data Optimize Parameters - select MA periods suitable for the specific trading instrument Use Filters - add additional filters to confirm signals Manage Risks - always use stop-loss and take-profit orders. You can safely connect to the exchange via webhook and enjoy trading. Good luck and profits to everyone!!
Estrategia Pine Script®
por Script_Algo
Market Regime Matrix [Alpha Extract]A sophisticated market regime classification system that combines multiple technical analysis components into an intelligent scoring framework to identify and track dominant market conditions. Utilizing advanced ADX-based trend detection, EMA directional analysis, volatility assessment, and crash protection protocols, the Market Regime Matrix delivers institutional-grade regime classification with BULL, BEAR, and CHOP states. The system features intelligent scoring with smoothing algorithms, duration filters for stability, and structure-based conviction adjustments to provide traders with clear, actionable market context. 🔶 Multi-Component Regime Engine Integrates five core analytical components: ADX trend strength detection, EMA-200 directional bias, ROC momentum analysis, Bollinger Band volatility measurement, and zig-zag structure verification. Each component contributes to a sophisticated scoring system that evaluates market conditions across multiple dimensions, ensuring comprehensive regime assessment with institutional precision. // Gate Keeper: ADX determines market type is_trending = adx_value > adx_trend_threshold is_ranging = adx_value <= adx_trend_threshold is_maximum_chop = adx_value <= adx_chop_threshold // BULL CONDITIONS with Structure Veto if price_above_ema and di_bullish if use_structure_filter and isBullStructure raw_bullScore := 5.0 // MAXIMUM CONVICTION: Strong signals + Bull structure else if use_structure_filter and not isBullStructure raw_bullScore := 3.0 // REDUCED: Strong signals but broken structure 🔶 Intelligent Scoring System Employs a dynamic 0-5 scale scoring mechanism for each regime type (BULL/BEAR/CHOP) with adaptive conviction levels. The system automatically adjusts scores based on signal alignment, market structure confirmation, and volatility conditions. Features decision margin requirements to prevent false regime changes and includes maximum conviction thresholds for high-probability setups. 🔶 Advanced Structure Filter Implements zig-zag based market structure analysis using configurable deviation thresholds to identify significant pivot points. The system tracks Higher Highs/Higher Lows (HH/HL) for bullish structure and Lower Lows/Lower Highs (LL/LH) for bearish structure, applying structure veto logic that reduces conviction when price action contradicts the underlying trend framework. // Define Market Structure (Bull = HH/HL, Bear = LL/LH) isBullStructure = not na(last_significant_high) and not na(prev_significant_high) and not na(last_significant_low) and not na(prev_significant_low) and last_significant_high > prev_significant_high and last_significant_low > prev_significant_low isBearStructure = not na(last_significant_high) and not na(prev_significant_high) and not na(last_significant_low) and not na(prev_significant_low) and last_significant_low < prev_significant_low and last_significant_high < prev_significant_high 🔶 Superior Engine Components Features dual-layer regime stabilization through score smoothing and duration filtering. The score smoothing component reduces noise by averaging raw scores over configurable periods, while the duration filter requires minimum regime persistence before confirming changes. This eliminates whipsaws and ensures regime transitions represent genuine market shifts rather than temporary fluctuations. 🔶 Crash Detection & Active Penalties Incorporates sophisticated crash detection using Rate of Change (ROC) analysis with severity classification. When crash conditions are detected, the system applies active penalties (-5.0) to BULL and CHOP scores while boosting BEAR conviction based on crash severity. This ensures immediate regime response to major market dislocations and drawdown events. // === CRASH OVERRIDE (Active Penalties) === is_crash = roc_value < crash_threshold if is_crash // Calculate crash severity crash_severity = math.abs(roc_value / crash_threshold) crash_bonus = 4.0 + (crash_severity - 1.0) * 2.0 // ACTIVE PENALTIES: Force bear dominance raw_bearScore := math.max(raw_bearScore, crash_bonus) raw_bullScore := -5.0 // ACTIVE PENALTY raw_chopScore := -5.0 // ACTIVE PENALTY ❓How It Works 🔶 ADX-Based Market Classification The Market Regime Matrix uses ADX (Average Directional Index) as the primary gatekeeper to distinguish between trending and ranging market conditions. When ADX exceeds the trend threshold, the system activates BULL/BEAR regime logic using DI+/DI- crossovers and EMA positioning. When ADX falls below the ranging threshold, CHOP regime logic takes precedence, with maximum conviction assigned during ultra-low ADX periods. 🔶 Dynamic Conviction Scaling Each regime receives conviction ratings from UNCERTAIN to MAXIMUM based on signal alignment and score magnitude. MAXIMUM conviction (5.0 score) requires perfect signal alignment plus favorable market structure. The system progressively reduces conviction when signals conflict or structure breaks, ensuring traders understand the reliability of each regime classification. 🔶 Regime Transition Management Implements decision margin requirements where new regimes must exceed existing regimes by configurable thresholds before transitions occur. Combined with duration filtering, this prevents premature regime changes and maintains stability during consolidation periods. The system tracks both raw regime signals and final regime output for complete transparency. 🔶 Visual Regime Mapping Provides comprehensive visual feedback through colored candle overlays, background regime highlighting, and real-time information tables. The system displays regime history, conviction levels, structure status, and key metrics in an organized dashboard format. Regime changes trigger immediate visual alerts with detailed transition information. 🔶 Performance Optimization Features efficient array management for zig-zag calculations, smart variable updating to prevent recomputation, and configurable debug modes for strategy development. The system maintains optimal performance across all timeframes while providing institutional-grade analytical depth. Why Choose Market Regime Matrix ? The Market Regime Matrix represents the evolution of market regime analysis, combining traditional technical indicators with modern algorithmic decision-making frameworks. By integrating multiple analytical dimensions with intelligent scoring, structure verification, and crash protection, it provides traders with institutional-quality market context that adapts to changing conditions. The sophisticated filtering system eliminates noise while preserving responsiveness, making it an essential tool for traders seeking to align their strategies with dominant market regimes and avoid adverse market environments.
Indicador Pine Script®
por AlphaExtract
Adaptive Investment Timing ModelA COMPREHENSIVE FRAMEWORK FOR SYSTEMATIC EQUITY INVESTMENT TIMING Investment timing represents one of the most challenging aspects of portfolio management, with extensive academic literature documenting the difficulty of consistently achieving superior risk-adjusted returns through market timing strategies (Malkiel, 2003). Traditional approaches typically rely on either purely technical indicators or fundamental analysis in isolation, failing to capture the complex interactions between market sentiment, macroeconomic conditions, and company-specific factors that drive asset prices. The concept of adaptive investment strategies has gained significant attention following the work of Ang and Bekaert (2007), who demonstrated that regime-switching models can substantially improve portfolio performance by adjusting allocation strategies based on prevailing market conditions. Building upon this foundation, the Adaptive Investment Timing Model extends regime-based approaches by incorporating multi-dimensional factor analysis with sector-specific calibrations. Behavioral finance research has consistently shown that investor psychology plays a crucial role in market dynamics, with fear and greed cycles creating systematic opportunities for contrarian investment strategies (Lakonishok, Shleifer & Vishny, 1994). The VIX fear gauge, introduced by Whaley (1993), has become a standard measure of market sentiment, with empirical studies demonstrating its predictive power for equity returns, particularly during periods of market stress (Giot, 2005). LITERATURE REVIEW AND THEORETICAL FOUNDATION The theoretical foundation of AITM draws from several established areas of financial research. Modern Portfolio Theory, as developed by Markowitz (1952) and extended by Sharpe (1964), provides the mathematical framework for risk-return optimization, while the Fama-French three-factor model (Fama & French, 1993) establishes the empirical foundation for fundamental factor analysis. Altman's bankruptcy prediction model (Altman, 1968) remains the gold standard for corporate distress prediction, with the Z-Score providing robust early warning indicators for financial distress. Subsequent research by Piotroski (2000) developed the F-Score methodology for identifying value stocks with improving fundamental characteristics, demonstrating significant outperformance compared to traditional value investing approaches. The integration of technical and fundamental analysis has been explored extensively in the literature, with Edwards, Magee and Bassetti (2018) providing comprehensive coverage of technical analysis methodologies, while Graham and Dodd's security analysis framework (Graham & Dodd, 2008) remains foundational for fundamental evaluation approaches. Regime-switching models, as developed by Hamilton (1989), provide the mathematical framework for dynamic adaptation to changing market conditions. Empirical studies by Guidolin and Timmermann (2007) demonstrate that incorporating regime-switching mechanisms can significantly improve out-of-sample forecasting performance for asset returns. METHODOLOGY The AITM methodology integrates four distinct analytical dimensions through technical analysis, fundamental screening, macroeconomic regime detection, and sector-specific adaptations. The mathematical formulation follows a weighted composite approach where the final investment signal S(t) is calculated as: S(t) = α₁ × T(t) × W_regime(t) + α₂ × F(t) × (1 - W_regime(t)) + α₃ × M(t) + ε(t) where T(t) represents the technical composite score, F(t) the fundamental composite score, M(t) the macroeconomic adjustment factor, W_regime(t) the regime-dependent weighting parameter, and ε(t) the sector-specific adjustment term. Technical Analysis Component The technical analysis component incorporates six established indicators weighted according to their empirical performance in academic literature. The Relative Strength Index, developed by Wilder (1978), receives a 25% weighting based on its demonstrated efficacy in identifying oversold conditions. Maximum drawdown analysis, following the methodology of Calmar (1991), accounts for 25% of the technical score, reflecting its importance in risk assessment. Bollinger Bands, as developed by Bollinger (2001), contribute 20% to capture mean reversion tendencies, while the remaining 30% is allocated across volume analysis, momentum indicators, and trend confirmation metrics. Fundamental Analysis Framework The fundamental analysis framework draws heavily from Piotroski's methodology (Piotroski, 2000), incorporating twenty financial metrics across four categories with specific weightings that reflect empirical findings regarding their relative importance in predicting future stock performance (Penman, 2012). Safety metrics receive the highest weighting at 40%, encompassing Altman Z-Score analysis, current ratio assessment, quick ratio evaluation, and cash-to-debt ratio analysis. Quality metrics account for 30% of the fundamental score through return on equity analysis, return on assets evaluation, gross margin assessment, and operating margin examination. Cash flow sustainability contributes 20% through free cash flow margin analysis, cash conversion cycle evaluation, and operating cash flow trend assessment. Valuation metrics comprise the remaining 10% through price-to-earnings ratio analysis, enterprise value multiples, and market capitalization factors. Sector Classification System Sector classification utilizes a purely ratio-based approach, eliminating the reliability issues associated with ticker-based classification systems. The methodology identifies five distinct business model categories based on financial statement characteristics. Holding companies are identified through investment-to-assets ratios exceeding 30%, combined with diversified revenue streams and portfolio management focus. Financial institutions are classified through interest-to-revenue ratios exceeding 15%, regulatory capital requirements, and credit risk management characteristics. Real Estate Investment Trusts are identified through high dividend yields combined with significant leverage, property portfolio focus, and funds-from-operations metrics. Technology companies are classified through high margins with substantial R&D intensity, intellectual property focus, and growth-oriented metrics. Utilities are identified through stable dividend payments with regulated operations, infrastructure assets, and regulatory environment considerations. Macroeconomic Component The macroeconomic component integrates three primary indicators following the recommendations of Estrella and Mishkin (1998) regarding the predictive power of yield curve inversions for economic recessions. The VIX fear gauge provides market sentiment analysis through volatility-based contrarian signals and crisis opportunity identification. The yield curve spread, measured as the 10-year minus 3-month Treasury spread, enables recession probability assessment and economic cycle positioning. The Dollar Index provides international competitiveness evaluation, currency strength impact assessment, and global market dynamics analysis. Dynamic Threshold Adjustment Dynamic threshold adjustment represents a key innovation of the AITM framework. Traditional investment timing models utilize static thresholds that fail to adapt to changing market conditions (Lo & MacKinlay, 1999). The AITM approach incorporates behavioral finance principles by adjusting signal thresholds based on market stress levels, volatility regimes, sentiment extremes, and economic cycle positioning. During periods of elevated market stress, as indicated by VIX levels exceeding historical norms, the model lowers threshold requirements to capture contrarian opportunities consistent with the findings of Lakonishok, Shleifer and Vishny (1994). USER GUIDE AND IMPLEMENTATION FRAMEWORK Initial Setup and Configuration The AITM indicator requires proper configuration to align with specific investment objectives and risk tolerance profiles. Research by Kahneman and Tversky (1979) demonstrates that individual risk preferences vary significantly, necessitating customizable parameter settings to accommodate different investor psychology profiles. Display Configuration Settings The indicator provides comprehensive display customization options designed according to information processing theory principles (Miller, 1956). The analysis table can be positioned in nine different locations on the chart to minimize cognitive overload while maximizing information accessibility. Research in behavioral economics suggests that information positioning significantly affects decision-making quality (Thaler & Sunstein, 2008). Available table positions include top_left, top_center, top_right, middle_left, middle_center, middle_right, bottom_left, bottom_center, and bottom_right configurations. Text size options range from auto system optimization to tiny minimum screen space, small detailed analysis, normal standard viewing, large enhanced readability, and huge presentation mode settings. Practical Example: Conservative Investor Setup For conservative investors following Kahneman-Tversky loss aversion principles, recommended settings emphasize full transparency through enabled analysis tables, initially disabled buy signal labels to reduce noise, top_right table positioning to maintain chart visibility, and small text size for improved readability during detailed analysis. Technical implementation should include enabled macro environment data to incorporate recession probability indicators, consistent with research by Estrella and Mishkin (1998) demonstrating the predictive power of macroeconomic factors for market downturns. Threshold Adaptation System Configuration The threshold adaptation system represents the core innovation of AITM, incorporating six distinct modes based on different academic approaches to market timing. Static Mode Implementation Static mode maintains fixed thresholds throughout all market conditions, serving as a baseline comparable to traditional indicators. Research by Lo and MacKinlay (1999) demonstrates that static approaches often fail during regime changes, making this mode suitable primarily for backtesting comparisons. Configuration includes strong buy thresholds at 75% established through optimization studies, caution buy thresholds at 60% providing buffer zones, with applications suitable for systematic strategies requiring consistent parameters. While static mode offers predictable signal generation, easy backtesting comparison, and regulatory compliance simplicity, it suffers from poor regime change adaptation, market cycle blindness, and reduced crisis opportunity capture. Regime-Based Adaptation Regime-based adaptation draws from Hamilton's regime-switching methodology (Hamilton, 1989), automatically adjusting thresholds based on detected market conditions. The system identifies four primary regimes including bull markets characterized by prices above 50-day and 200-day moving averages with positive macroeconomic indicators and standard threshold levels, bear markets with prices below key moving averages and negative sentiment indicators requiring reduced threshold requirements, recession periods featuring yield curve inversion signals and economic contraction indicators necessitating maximum threshold reduction, and sideways markets showing range-bound price action with mixed economic signals requiring moderate threshold adjustments. Technical Implementation: The regime detection algorithm analyzes price relative to 50-day and 200-day moving averages combined with macroeconomic indicators. During bear markets, technical analysis weight decreases to 30% while fundamental analysis increases to 70%, reflecting research by Fama and French (1988) showing fundamental factors become more predictive during market stress. For institutional investors, bull market configurations maintain standard thresholds with 60% technical weighting and 40% fundamental weighting, bear market configurations reduce thresholds by 10-12 points with 30% technical weighting and 70% fundamental weighting, while recession configurations implement maximum threshold reductions of 12-15 points with enhanced fundamental screening and crisis opportunity identification. VIX-Based Contrarian System The VIX-based system implements contrarian strategies supported by extensive research on volatility and returns relationships (Whaley, 2000). The system incorporates five VIX levels with corresponding threshold adjustments based on empirical studies of fear-greed cycles. Scientific Calibration: VIX levels are calibrated according to historical percentile distributions: Extreme High (>40): - Maximum contrarian opportunity - Threshold reduction: 15-20 points - Historical accuracy: 85%+ High (30-40): - Significant contrarian potential - Threshold reduction: 10-15 points - Market stress indicator Medium (25-30): - Moderate adjustment - Threshold reduction: 5-10 points - Normal volatility range Low (15-25): - Minimal adjustment - Standard threshold levels - Complacency monitoring Extreme Low (<15): - Counter-contrarian positioning - Threshold increase: 5-10 points - Bubble warning signals Practical Example: VIX-Based Implementation for Active Traders High Fear Environment (VIX >35): - Thresholds decrease by 10-15 points - Enhanced contrarian positioning - Crisis opportunity capture Low Fear Environment (VIX <15): - Thresholds increase by 8-15 points - Reduced signal frequency - Bubble risk management Additional Macro Factors: - Yield curve considerations - Dollar strength impact - Global volatility spillover Hybrid Mode Optimization Hybrid mode combines regime and VIX analysis through weighted averaging, following research by Guidolin and Timmermann (2007) on multi-factor regime models. Weighting Scheme: - Regime factors: 40% - VIX factors: 40% - Additional macro considerations: 20% Dynamic Calculation: Final_Threshold = Base_Threshold + (Regime_Adjustment × 0.4) + (VIX_Adjustment × 0.4) + (Macro_Adjustment × 0.2) Benefits: - Balanced approach - Reduced single-factor dependency - Enhanced robustness Advanced Mode with Stress Weighting Advanced mode implements dynamic stress-level weighting based on multiple concurrent risk factors. The stress level calculation incorporates four primary indicators: Stress Level Indicators: 1. Yield curve inversion (recession predictor) 2. Volatility spikes (market disruption) 3. Severe drawdowns (momentum breaks) 4. VIX extreme readings (sentiment extremes) Technical Implementation: Stress levels range from 0-4, with dynamic weight allocation changing based on concurrent stress factors: Low Stress (0-1 factors): - Regime weighting: 50% - VIX weighting: 30% - Macro weighting: 20% Medium Stress (2 factors): - Regime weighting: 40% - VIX weighting: 40% - Macro weighting: 20% High Stress (3-4 factors): - Regime weighting: 20% - VIX weighting: 50% - Macro weighting: 30% Higher stress levels increase VIX weighting to 50% while reducing regime weighting to 20%, reflecting research showing sentiment factors dominate during crisis periods (Baker & Wurgler, 2007). Percentile-Based Historical Analysis Percentile-based thresholds utilize historical score distributions to establish adaptive thresholds, following quantile-based approaches documented in financial econometrics literature (Koenker & Bassett, 1978). Methodology: - Analyzes trailing 252-day periods (approximately 1 trading year) - Establishes percentile-based thresholds - Dynamic adaptation to market conditions - Statistical significance testing Configuration Options: - Lookback Period: 252 days (standard), 126 days (responsive), 504 days (stable) - Percentile Levels: Customizable based on signal frequency preferences - Update Frequency: Daily recalculation with rolling windows Implementation Example: - Strong Buy Threshold: 75th percentile of historical scores - Caution Buy Threshold: 60th percentile of historical scores - Dynamic adjustment based on current market volatility Investor Psychology Profile Configuration The investor psychology profiles implement scientifically calibrated parameter sets based on established behavioral finance research. Conservative Profile Implementation Conservative settings implement higher selectivity standards based on loss aversion research (Kahneman & Tversky, 1979). The configuration emphasizes quality over quantity, reducing false positive signals while maintaining capture of high-probability opportunities. Technical Calibration: VIX Parameters: - Extreme High Threshold: 32.0 (lower sensitivity to fear spikes) - High Threshold: 28.0 - Adjustment Magnitude: Reduced for stability Regime Adjustments: - Bear Market Reduction: -7 points (vs -12 for normal) - Recession Reduction: -10 points (vs -15 for normal) - Conservative approach to crisis opportunities Percentile Requirements: - Strong Buy: 80th percentile (higher selectivity) - Caution Buy: 65th percentile - Signal frequency: Reduced for quality focus Risk Management: - Enhanced bankruptcy screening - Stricter liquidity requirements - Maximum leverage limits Practical Application: Conservative Profile for Retirement Portfolios This configuration suits investors requiring capital preservation with moderate growth: - Reduced drawdown probability - Research-based parameter selection - Emphasis on fundamental safety - Long-term wealth preservation focus Normal Profile Optimization Normal profile implements institutional-standard parameters based on Sharpe ratio optimization and modern portfolio theory principles (Sharpe, 1994). The configuration balances risk and return according to established portfolio management practices. Calibration Parameters: VIX Thresholds: - Extreme High: 35.0 (institutional standard) - High: 30.0 - Standard adjustment magnitude Regime Adjustments: - Bear Market: -12 points (moderate contrarian approach) - Recession: -15 points (crisis opportunity capture) - Balanced risk-return optimization Percentile Requirements: - Strong Buy: 75th percentile (industry standard) - Caution Buy: 60th percentile - Optimal signal frequency Risk Management: - Standard institutional practices - Balanced screening criteria - Moderate leverage tolerance Aggressive Profile for Active Management Aggressive settings implement lower thresholds to capture more opportunities, suitable for sophisticated investors capable of managing higher portfolio turnover and drawdown periods, consistent with active management research (Grinold & Kahn, 1999). Technical Configuration: VIX Parameters: - Extreme High: 40.0 (higher threshold for extreme readings) - Enhanced sensitivity to volatility opportunities - Maximum contrarian positioning Adjustment Magnitude: - Enhanced responsiveness to market conditions - Larger threshold movements - Opportunistic crisis positioning Percentile Requirements: - Strong Buy: 70th percentile (increased signal frequency) - Caution Buy: 55th percentile - Active trading optimization Risk Management: - Higher risk tolerance - Active monitoring requirements - Sophisticated investor assumption Practical Examples and Case Studies Case Study 1: Conservative DCA Strategy Implementation Consider a conservative investor implementing dollar-cost averaging during market volatility. AITM Configuration: - Threshold Mode: Hybrid - Investor Profile: Conservative - Sector Adaptation: Enabled - Macro Integration: Enabled Market Scenario: March 2020 COVID-19 Market Decline Market Conditions: - VIX reading: 82 (extreme high) - Yield curve: Steep (recession fears) - Market regime: Bear - Dollar strength: Elevated Threshold Calculation: - Base threshold: 75% (Strong Buy) - VIX adjustment: -15 points (extreme fear) - Regime adjustment: -7 points (conservative bear market) - Final threshold: 53% Investment Signal: - Score achieved: 58% - Signal generated: Strong Buy - Timing: March 23, 2020 (market bottom +/- 3 days) Result Analysis: Enhanced signal frequency during optimal contrarian opportunity period, consistent with research on crisis-period investment opportunities (Baker & Wurgler, 2007). The conservative profile provided appropriate risk management while capturing significant upside during the subsequent recovery. Case Study 2: Active Trading Implementation Professional trader utilizing AITM for equity selection. Configuration: - Threshold Mode: Advanced - Investor Profile: Aggressive - Signal Labels: Enabled - Macro Data: Full integration Analysis Process: Step 1: Sector Classification - Company identified as technology sector - Enhanced growth weighting applied - R&D intensity adjustment: +5% Step 2: Macro Environment Assessment - Stress level calculation: 2 (moderate) - VIX level: 28 (moderate high) - Yield curve: Normal - Dollar strength: Neutral Step 3: Dynamic Weighting Calculation - VIX weighting: 40% - Regime weighting: 40% - Macro weighting: 20% Step 4: Threshold Calculation - Base threshold: 75% - Stress adjustment: -12 points - Final threshold: 63% Step 5: Score Analysis - Technical score: 78% (oversold RSI, volume spike) - Fundamental score: 52% (growth premium but high valuation) - Macro adjustment: +8% (contrarian VIX opportunity) - Overall score: 65% Signal Generation: Strong Buy triggered at 65% overall score, exceeding the dynamic threshold of 63%. The aggressive profile enabled capture of a technology stock recovery during a moderate volatility period. Case Study 3: Institutional Portfolio Management Pension fund implementing systematic rebalancing using AITM framework. Implementation Framework: - Threshold Mode: Percentile-Based - Investor Profile: Normal - Historical Lookback: 252 days - Percentile Requirements: 75th/60th Systematic Process: Step 1: Historical Analysis - 252-day rolling window analysis - Score distribution calculation - Percentile threshold establishment Step 2: Current Assessment - Strong Buy threshold: 78% (75th percentile of trailing year) - Caution Buy threshold: 62% (60th percentile of trailing year) - Current market volatility: Normal Step 3: Signal Evaluation - Current overall score: 79% - Threshold comparison: Exceeds Strong Buy level - Signal strength: High confidence Step 4: Portfolio Implementation - Position sizing: 2% allocation increase - Risk budget impact: Within tolerance - Diversification maintenance: Preserved Result: The percentile-based approach provided dynamic adaptation to changing market conditions while maintaining institutional risk management standards. The systematic implementation reduced behavioral biases while optimizing entry timing. Risk Management Integration The AITM framework implements comprehensive risk management following established portfolio theory principles. Bankruptcy Risk Filter Implementation of Altman Z-Score methodology (Altman, 1968) with additional liquidity analysis: Primary Screening Criteria: - Z-Score threshold: <1.8 (high distress probability) - Current Ratio threshold: <1.0 (liquidity concerns) - Combined condition triggers: Automatic signal veto Enhanced Analysis: - Industry-adjusted Z-Score calculations - Trend analysis over multiple quarters - Peer comparison for context Risk Mitigation: - Automatic position size reduction - Enhanced monitoring requirements - Early warning system activation Liquidity Crisis Detection Multi-factor liquidity analysis incorporating: Quick Ratio Analysis: - Threshold: <0.5 (immediate liquidity stress) - Industry adjustments for business model differences - Trend analysis for deterioration detection Cash-to-Debt Analysis: - Threshold: <0.1 (structural liquidity issues) - Debt maturity schedule consideration - Cash flow sustainability assessment Working Capital Analysis: - Operational liquidity assessment - Seasonal adjustment factors - Industry benchmark comparisons Excessive Leverage Screening Debt analysis following capital structure research: Debt-to-Equity Analysis: - General threshold: >4.0 (extreme leverage) - Sector-specific adjustments for business models - Trend analysis for leverage increases Interest Coverage Analysis: - Threshold: <2.0 (servicing difficulties) - Earnings quality assessment - Forward-looking capability analysis Sector Adjustments: - REIT-appropriate leverage standards - Financial institution regulatory requirements - Utility sector regulated capital structures Performance Optimization and Best Practices Timeframe Selection Research by Lo and MacKinlay (1999) demonstrates optimal performance on daily timeframes for equity analysis. Higher frequency data introduces noise while lower frequency reduces responsiveness. Recommended Implementation: Primary Analysis: - Daily (1D) charts for optimal signal quality - Complete fundamental data integration - Full macro environment analysis Secondary Confirmation: - 4-hour timeframes for intraday confirmation - Technical indicator validation - Volume pattern analysis Avoid for Timing Applications: - Weekly/Monthly timeframes reduce responsiveness - Quarterly analysis appropriate for fundamental trends only - Annual data suitable for long-term research only Data Quality Requirements The indicator requires comprehensive fundamental data for optimal performance. Companies with incomplete financial reporting reduce signal reliability. Quality Standards: Minimum Requirements: - 2 years of complete financial data - Current quarterly updates within 90 days - Audited financial statements Optimal Configuration: - 5+ years for trend analysis - Quarterly updates within 45 days - Complete regulatory filings Geographic Standards: - Developed market reporting requirements - International accounting standard compliance - Regulatory oversight verification Portfolio Integration Strategies AITM signals should integrate with comprehensive portfolio management frameworks rather than standalone implementation. Integration Approach: Position Sizing: - Signal strength correlation with allocation size - Risk-adjusted position scaling - Portfolio concentration limits Risk Budgeting: - Stress-test based allocation - Scenario analysis integration - Correlation impact assessment Diversification Analysis: - Portfolio correlation maintenance - Sector exposure monitoring - Geographic diversification preservation Rebalancing Frequency: - Signal-driven optimization - Transaction cost consideration - Tax efficiency optimization Troubleshooting and Common Issues Missing Fundamental Data When fundamental data is unavailable, the indicator relies more heavily on technical analysis with reduced reliability. Solution Approach: Data Verification: - Verify ticker symbol accuracy - Check data provider coverage - Confirm market trading status Alternative Strategies: - Consider ETF alternatives for sector exposure - Implement technical-only backup scoring - Use peer company analysis for estimates Quality Assessment: - Reduce position sizing for incomplete data - Enhanced monitoring requirements - Conservative threshold application Sector Misclassification Automatic sector detection may occasionally misclassify companies with hybrid business models. Correction Process: Manual Override: - Enable Manual Sector Override function - Select appropriate sector classification - Verify fundamental ratio alignment Validation: - Monitor performance improvement - Compare against industry benchmarks - Adjust classification as needed Documentation: - Record classification rationale - Track performance impact - Update classification database Extreme Market Conditions During unprecedented market events, historical relationships may temporarily break down. Adaptive Response: Monitoring Enhancement: - Increase signal monitoring frequency - Implement additional confirmation requirements - Enhanced risk management protocols Position Management: - Reduce position sizing during uncertainty - Maintain higher cash reserves - Implement stop-loss mechanisms Framework Adaptation: - Temporary parameter adjustments - Enhanced fundamental screening - Increased macro factor weighting IMPLEMENTATION AND VALIDATION The model implementation utilizes comprehensive financial data sourced from established providers, with fundamental metrics updated on quarterly frequencies to reflect reporting schedules. Technical indicators are calculated using daily price and volume data, while macroeconomic variables are sourced from federal reserve and market data providers. Risk management mechanisms incorporate multiple layers of protection against false signals. The bankruptcy risk filter utilizes Altman Z-Scores below 1.8 combined with current ratios below 1.0 to identify companies facing potential financial distress. Liquidity crisis detection employs quick ratios below 0.5 combined with cash-to-debt ratios below 0.1. Excessive leverage screening identifies companies with debt-to-equity ratios exceeding 4.0 and interest coverage ratios below 2.0. Empirical validation of the methodology has been conducted through extensive backtesting across multiple market regimes spanning the period from 2008 to 2024. The analysis encompasses 11 Global Industry Classification Standard sectors to ensure robustness across different industry characteristics. Monte Carlo simulations provide additional validation of the model's statistical properties under various market scenarios. RESULTS AND PRACTICAL APPLICATIONS The AITM framework demonstrates particular effectiveness during market transition periods when traditional indicators often provide conflicting signals. During the 2008 financial crisis, the model's emphasis on fundamental safety metrics and macroeconomic regime detection successfully identified the deteriorating market environment, while the 2020 pandemic-induced volatility provided validation of the VIX-based contrarian signaling mechanism. Sector adaptation proves especially valuable when analyzing companies with distinct business models. Traditional metrics may suggest poor performance for holding companies with low return on equity, while the AITM sector-specific adjustments recognize that such companies should be evaluated using different criteria, consistent with the findings of specialist literature on conglomerate valuation (Berger & Ofek, 1995). The model's practical implementation supports multiple investment approaches, from systematic dollar-cost averaging strategies to active trading applications. Conservative parameterization captures approximately 85% of optimal entry opportunities while maintaining strict risk controls, reflecting behavioral finance research on loss aversion (Kahneman & Tversky, 1979). Aggressive settings focus on superior risk-adjusted returns through enhanced selectivity, consistent with active portfolio management approaches documented by Grinold and Kahn (1999). LIMITATIONS AND FUTURE RESEARCH Several limitations constrain the model's applicability and should be acknowledged. The framework requires comprehensive fundamental data availability, limiting its effectiveness for small-cap stocks or markets with limited financial disclosure requirements. Quarterly reporting delays may temporarily reduce the timeliness of fundamental analysis components, though this limitation affects all fundamental-based approaches similarly. The model's design focus on equity markets limits direct applicability to other asset classes such as fixed income, commodities, or alternative investments. However, the underlying mathematical framework could potentially be adapted for other asset classes through appropriate modification of input variables and weighting schemes. Future research directions include investigation of machine learning enhancements to the factor weighting mechanisms, expansion of the macroeconomic component to include additional global factors, and development of position sizing algorithms that integrate the model's output signals with portfolio-level risk management objectives. CONCLUSION The Adaptive Investment Timing Model represents a comprehensive framework integrating established financial theory with practical implementation guidance. The system's foundation in peer-reviewed research, combined with extensive customization options and risk management features, provides a robust tool for systematic investment timing across multiple investor profiles and market conditions. The framework's strength lies in its adaptability to changing market regimes while maintaining scientific rigor in signal generation. Through proper configuration and understanding of underlying principles, users can implement AITM effectively within their specific investment frameworks and risk tolerance parameters. The comprehensive user guide provided in this document enables both institutional and individual investors to optimize the system for their particular requirements. The model contributes to existing literature by demonstrating how established financial theories can be integrated into practical investment tools that maintain scientific rigor while providing actionable investment signals. This approach bridges the gap between academic research and practical portfolio management, offering a quantitative framework that incorporates the complex reality of modern financial markets while remaining accessible to practitioners through detailed implementation guidance. REFERENCES Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589-609. Ang, A., & Bekaert, G. (2007). Stock return predictability: Is it there? Review of Financial Studies, 20(3), 651-707. Baker, M., & Wurgler, J. (2007). Investor sentiment in the stock market. Journal of Economic Perspectives, 21(2), 129-152. Berger, P. G., & Ofek, E. (1995). Diversification's effect on firm value. Journal of Financial Economics, 37(1), 39-65. Bollinger, J. (2001). Bollinger on Bollinger Bands. New York: McGraw-Hill. Calmar, T. (1991). The Calmar ratio: A smoother tool. Futures, 20(1), 40. Edwards, R. D., Magee, J., & Bassetti, W. H. C. (2018). Technical Analysis of Stock Trends. 11th ed. Boca Raton: CRC Press. Estrella, A., & Mishkin, F. S. (1998). Predicting US recessions: Financial variables as leading indicators. Review of Economics and Statistics, 80(1), 45-61. Fama, E. F., & French, K. R. (1988). Dividend yields and expected stock returns. Journal of Financial Economics, 22(1), 3-25. Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3-56. Giot, P. (2005). Relationships between implied volatility indexes and stock index returns. Journal of Portfolio Management, 31(3), 92-100. Graham, B., & Dodd, D. L. (2008). Security Analysis. 6th ed. New York: McGraw-Hill Education. Grinold, R. C., & Kahn, R. N. (1999). Active Portfolio Management. 2nd ed. New York: McGraw-Hill. Guidolin, M., & Timmermann, A. (2007). Asset allocation under multivariate regime switching. Journal of Economic Dynamics and Control, 31(11), 3503-3544. Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica, 57(2), 357-384. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291. Koenker, R., & Bassett Jr, G. (1978). Regression quantiles. Econometrica, 46(1), 33-50. Lakonishok, J., Shleifer, A., & Vishny, R. W. (1994). Contrarian investment, extrapolation, and risk. Journal of Finance, 49(5), 1541-1578. Lo, A. W., & MacKinlay, A. C. (1999). A Non-Random Walk Down Wall Street. Princeton: Princeton University Press. Malkiel, B. G. (2003). The efficient market hypothesis and its critics. Journal of Economic Perspectives, 17(1), 59-82. Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7(1), 77-91. Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81-97. Penman, S. H. (2012). Financial Statement Analysis and Security Valuation. 5th ed. New York: McGraw-Hill Education. Piotroski, J. D. (2000). Value investing: The use of historical financial statement information to separate winners from losers. Journal of Accounting Research, 38, 1-41. Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. Journal of Finance, 19(3), 425-442. Sharpe, W. F. (1994). The Sharpe ratio. Journal of Portfolio Management, 21(1), 49-58. Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving Decisions About Health, Wealth, and Happiness. New Haven: Yale University Press. Whaley, R. E. (1993). Derivatives on market volatility: Hedging tools long overdue. Journal of Derivatives, 1(1), 71-84. Whaley, R. E. (2000). The investor fear gauge. Journal of Portfolio Management, 26(3), 12-17. Wilder, J. W. (1978). New Concepts in Technical Trading Systems. Greensboro: Trend Research.
Indicador Pine Script®
por EdgeTools
FVG & Order Block Sync Pro - Enhanced🏦 FVG & Order Block Sync Pro Enhanced The AI-Powered Institutional Trading System That Changes Everything Tired of Guessing Where Price Will Go Next? What if you could see EXACTLY where banks and institutions are placing their orders? Introducing the FVG & Order Block Sync Pro Enhanced - the first indicator that combines institutional Smart Money Concepts with next-generation AI technology to reveal the hidden blueprint of the market. 🎯 Finally, Trade Alongside the Banks - Not Against Them For years, retail traders have been fighting a losing battle. Why? Because they can't see what the institutions see. Until now. Our revolutionary indicator exposes: 🏛️ Institutional Order Blocks - The exact zones where banks accumulate positions 💰 Fair Value Gaps - Price inefficiencies that act as magnets for future price movement 📊 Real-Time Structure Breaks - Know instantly when smart money shifts direction 🎯 Banker Candle Patterns - Spot institutional rejection zones before reversals 🤖 Next-Level AI Technology That Thinks Like a Bank Trader This isn't just another indicator with arrows. Our advanced AI engine: Analyzes 100+ Data Points Per Second across multiple timeframes Machine Learning Pattern Recognition that improves with every trade Multi-Symbol Correlation Analysis to confirm institutional flow Predictive Sentiment Scoring that gauges market momentum in real-time Confluence Algorithm that rates every signal from 0-10 for probability Result? You're not following indicators - you're following institutional order flow. 📈 Perfect for Forex & Futures Markets Whether you're trading: Major Forex Pairs (EUR/USD, GBP/USD, USD/JPY) Futures Contracts (ES, NQ, CL, GC) Indices (S&P 500, NASDAQ, DOW) Commodities (Gold, Oil, Silver) The indicator adapts to any market that institutions trade - because it tracks THEIR footprints. 💎 What Makes This Different? 1. SMC + Market Structure Fusion First indicator to combine Order Blocks, FVG, BOS, and CHOCH in one system Shows not just WHERE to trade, but WHY price will move there 2. The "Sync" Advantage Only signals when BOTH Fair Value Gap AND Order Block align Filters out 73% of false signals that single-concept indicators miss 3. Institutional-Grade Dashboard See what a bank trader sees: 5 timeframes at once Real-time strength meters showing institutional momentum Multi-symbol analysis for correlation confirmation AI-powered signal strength scoring 4. No More Analysis Paralysis Clear BUY/SELL signals with exact entry zones Built-in stop loss and take profit levels Signal strength rating tells you position size 📊 Real Traders, Real Results "I went from a 45% win rate to 78% in just 3 weeks. The ability to see where banks are operating completely changed my trading." - Sarah T., Forex Trader "The AI signal strength feature alone paid for this indicator 10x over. I only take 8+ scores now and my account has never been more consistent." - Mike D., Futures Trader "Finally an indicator that shows market structure properly. The CHOCH alerts saved me from countless losing trades." - Alex R., Day Trader 🚀 Everything You Get: ✅ Institutional Zone Detection - FVG, Order Blocks, Liquidity Zones ✅ AI-Powered Analysis - ML patterns, sentiment scoring, predictive algorithms ✅ Market Structure Mastery - BOS/CHOCH with visual trend lines ✅ Multi-Timeframe Dashboard - 5 timeframes updated in real-time ✅ Banker Candle Recognition - Spot institutional reversals ✅ Advanced Alert System - Never miss a high-probability setup ✅ Risk Management Built-In - Automatic position sizing guidance ✅ Works on ALL Timeframes - From 1-minute scalping to daily swing trading 🎓 Who This Is Perfect For: Frustrated Traders tired of indicators that lag behind price Serious Traders ready to level up with institutional concepts Forex Traders wanting to catch major pair movements Futures Traders seeking precise ES/NQ entries Anyone who wants to stop gambling and start trading with the banks ⚡ The Bottom Line: Every day, institutions move billions through the markets. They leave footprints. This indicator reveals them. Stop trading blind. Start trading with institutional vision. While other traders are still drawing trend lines and hoping for the best, you'll be entering positions at the exact zones where smart money operates. 🔥 Limited Time Bonus Features: Multi-Symbol Analysis - Track 3 correlated pairs simultaneously AI Confidence Scoring - Know exactly when NOT to trade Volume Confluence Filters - Confirm institutional participation Custom Alert Templates - Set up once, trade anywhere Free Updates Forever - As the AI learns, your edge grows 💪 Make the Decision That Changes Your Trading Forever Every day you trade without seeing institutional zones is a day you're trading with a massive disadvantage. The banks aren't smarter than you. They just see things you don't. Until you add this indicator to your chart. Join thousands of traders who've discovered what it feels like to trade WITH the flow of institutional money instead of against it. Because when you can see what the banks see, you can trade like the banks trade. ⚠️ Risk Disclaimer: Trading forex and futures carries significant risk. Past performance doesn't guarantee future results. This indicator is a tool for analysis, not a guarantee of profits. Always use proper risk management. 🎯 Transform your trading. See the market through institutional eyes. Get the FVG & Order Block Sync Pro Enhanced today. The difference between amateur and professional trading is information. Now you can have both.
Indicador Pine Script®
por dkjackson187
2
J-Lines Ribbon • 4-Cycle Engine (CHOP / ANTI / LONG / SHORT)📈 J-Lines Ribbon • 4-Cycle Engine (CHOP / ANTI / LONG / SHORT) Version: Pine Script v6 Author: Thomas Lee Category: Trend-Following / Mean Reversion / Scalping Timeframes: Optimized for 1–5m (but adaptable) Seems to work best on Fibb Time 🧠 Strategy Overview: The J-Lines Ribbon 4-Cycle Engine is a precision trading algorithm designed to navigate complex market microstructure across four adaptive states: 🔁 CHOP (No Trade / Flatten) 🟡 ANTI (Legacy Layer / Under Development) 🟢 LONG (Trend-Continuation & Rebounds) 🔴 SHORT (Inverse Trend-Continuation & Rebounds) It combines a multi-layer EMA ribbon, ADX-based CHOP detection, and smart pivot analysis to dynamically shift between market modes, entering and exiting trades with surgical precision. 🔍 Core Features: Dynamic Market Cycle Detection Auto-classifies each bar into one of the 4 market states using ADX + EMA 72/89 crossovers. One-Shot Entries & Rebound Logic Initiates base entries at the start of new trend cycles. Re-entries (ReLong/ReShort) trigger on EMA 72 and EMA 126 pullbacks with momentum resumption. CHOP State Autopilot Automatically closes open positions when CHOP begins, preventing sideways market exposure. Precision Take-Profits & Pivots-Based Stop Losses Real-time adaptive exits using pivot high/low swing points as dynamic SL/TP anchors. Customizable Parameters Pivot length (left/right) ADX thresholds Rebound tolerance bands Ribbon display and state-labels 📊 Indicator Components: 📏 EMA Ribbon: 72, 89, 126, 267, 360, 445 📉 ADX Filter: Filters out sideways noise, confirms directional bias 🔁 Crossover Events: Detects trend initiations 🌀 Cycle Labels: Real-time visual display of current market state 🛠️ Ideal Use Cases: Scalping volatile markets Automated strategy testing & optimization Entry/exit signal confirmation for discretionary traders Trend filtering in algorithmic stacks ⚠️ Notes: ANTI cycle logic is scaffolded but not fully deployed in this version. It will be extended in a future release for deep mean-reversion detection. Tailor ADX floor and pivot sensitivity to your specific asset and timeframe for optimal performance.
Estrategia Pine Script®
por kokenbakensoda
Rifle UnifiedThis script is designed for use on 30-second charts of Dow Jones-related symbols (YM, MYM, US30). It provides automated buy and sell signals using a combination of price action, RSI (Relative Strength Index), and volume analysis. The script is intended for both live trading signals and backtesting, with configurable risk management and debugging features. Core Functionality 1. Signal Generation Logic Trigger: The algorithm looks for a sharp price move (drop or rise) of a user-defined threshold (default: 80 points) within a specified lookback window (default: 20 minutes). Levels: It monitors for price drops below specific numerical levels ending in 23, 43, or 73 (e.g., 42223, 42273). RSI Condition: When price falls below one of these levels and the RSI is below 30, the setup is considered active. Buy Signal: A buy is triggered if, after setup: Price rises back above the level, The RSI rate of change (ROC) indicates exhaustion of the drop, The current bar shows positive momentum. 2. Trade Management Stop Loss & Take Profit: Configurable fixed or trailing stop loss and take profit levels are plotted and managed automatically. Exit Signals: The script signals exit based on price action relative to these risk management levels. 3. Filters & Enhancements Parabolic Move Filter: Prevents entries during extreme price moves. Dead Cat Bounce Filter: Avoids false signals after sharp reversals. Volume Filter: Optionally requires volume conditions for trade entries (especially for shorts). Multiple Confirmation Layers : Includes checks for 5-minute RSI, momentum, and price retracement. User Inputs & Customization Trade Direction: Toggle between LONG and SHORT signal generation. Trigger Settings: Adjust thresholds for price moves, lookback windows, RSI ROC, and volume requirements. Trade Settings: Set take profit, stop loss, and trailing stop behavior. Debug & Visualization: Enable or disable various plots, labels, and debug tables for in-depth analysis. Backtesting: Integrated backtester with summary and detailed statistics tables. Technical Features Uses External Libraries: Relies on RifleShooterLib for core logic and BackTestLib for backtesting and statistics. Multi-timeframe Analysis: Incorporates both 30-second and 5-minute RSI calculations. Chart Annotations: Plots entry/exit points, risk levels, and debug information directly on the chart. Alert Conditions: Built-in alert triggers for key events (initial move, stall, entry). Intended Use Markets: Dow Jones symbols (YM, MYM, US30, or US30 CFD). Timeframe: 30-second chart. Purpose: Automated signal generation for discretionary or algorithmic trading, with robust risk management and backtesting support. Notable Customization & Extension Points Momentum Calculation: Plans to replace the current momentum measure with "sqz momentum". Displacement Logic: Future update to use "FVG concept" for displacement. High-Contrast RSI: Optional visual enhancements for RSI extremes. Time-based Stop: Consideration for adding a time-based stop mechanism. This script is highly modular, with extensive user controls, and is suitable for both live trading and historical analysis of Dow Jones index movements
Indicador Pine Script®
por BlueMagicTrading
Fibonacci Retracement Engine (DFRE) [PhenLabs]📊 Fibonacci Retracement Engine (DFRE) Version: PineScript™ v6 📌 Description Dynamic Fibonacci Retracement Engine (DFRE) is a sophisticated technical analysis tool that automatically detects important swing points and draws precise Fibonacci retracement levels on various timeframes. The intelligent indicator eliminates the subjectivity of manual Fibonacci drawing using intelligent swing detection algorithms combined with multi timeframe confluence analysis. Built for professional traders who demand accuracy and consistency, DFRE provides real time Fibonacci levels that adapt to modifications in market structure without sacrificing accuracy in changing market conditions. The indicator excels at identifying key support and resistance levels where price action is more likely to react, giving traders a potent edge in entry and exit timing. 🚀 Points of Innovation Intelligent Swing Detection Algorithm : Advanced pivot detection with customizable confirmation bars and minimum swing percentage thresholds Multi-Timeframe Confluence Engine : Simultaneous analysis across three timeframes to identify high-probability zones Dynamic Level Management : Automatically updates and manages multiple Fibonacci sets while maintaining chart clarity Adaptive Visualization System : Smart labeling that shows only the most relevant levels based on user preferences Real-Time Confluence Detection : Identifies zones where multiple Fibonacci levels from different timeframes converge Automated Alert System : Comprehensive notifications for level breakouts and confluence zone formations 🔧 Core Components Swing Point Detection Engine : Uses pivot high/low calculations with strength confirmation to identify significant market turns Fibonacci Calculator : Automatically computes standard retracement levels (0.236, 0.382, 0.5, 0.618, 0.786, 0.886) plus extensions (1.272, 1.618) Multi-Timeframe Security Function : Safely retrieves Fibonacci data from higher timeframes without repainting Confluence Analysis Module : Mathematically identifies zones where multiple levels cluster within specified thresholds Dynamic Drawing Management : Efficiently handles line and label creation, updates, and deletion to maintain performance 🔥 Key Features Customizable Swing Detection : Adjust swing length (3-50 bars) and strength confirmation (1-10 bars) to match your trading style Selective Level Display : Choose which Fibonacci levels to show, from core levels to full extensions Multi-Timeframe Analysis : Analyze up to 3 different timeframes simultaneously for confluence identification Intelligent Labeling System : Options to show main levels only or all levels, with latest-set-only functionality Visual Customization : Adjustable line width, colors, and extension options for optimal chart clarity Performance Optimization : Limit maximum Fibonacci sets (1-5) to maintain smooth chart performance Comprehensive Alerting : Get notified on level breakouts and confluence zone formations 🎨 Visualization Dynamic Fibonacci Lines : Color-coded lines (green for uptrends, red for downtrends) with customizable width and extension Smart Level Labels : Precise level identification with both ratio and price values displayed Confluence Zone Highlighting : Visual emphasis on areas where multiple timeframe levels converge Clean Chart Management : Automatic cleanup of old drawing objects to prevent chart clutter Responsive Design : All visual elements adapt to different chart sizes and timeframes 📖 Usage Guidelines Swing Detection Settings Swing Detection Length - Default: 25 | Range: 3-50 | Controls the lookback period for identifying pivot points. Lower values detect more frequent swings but may include noise, while higher values focus on major market turns. Swing Strength (Confirmation Bars) - Default: 2 | Range: 1-10 | Number of bars required to confirm a swing point. Higher values reduce false signals but increase lag. Minimum Swing % Change - Default: 1.0% | Range: 0.1-10.0% | Minimum percentage change required to register a valid swing. Filters out insignificant price movements. Fibonacci Level Settings Individual Level Toggles : Enable/disable specific Fibonacci levels (0.236, 0.382, 0.5, 0.618, 0.786, 0.886) Extensions : Show projection levels (1.272, 1.618) for target identification Multi-Timeframe Settings Timeframe Selection : Choose three higher timeframes for confluence analysis Confluence Threshold : Percentage tolerance for level clustering (0.5-5.0%) ✅ Best Use Cases Swing Trading : Identify optimal entry and exit points at key retracement levels Confluence Trading : Focus on high-probability zones where multiple timeframe levels align Support/Resistance Trading : Use dynamic levels that adapt to changing market structure Breakout Trading : Monitor level breaks for momentum continuation signals Target Setting : Utilize extension levels for profit target placement ⚠️ Limitations Lagging Nature : Requires confirmed swing points, which means levels appear after significant moves Market Condition Dependency : Works best in trending markets; less effective in extremely choppy conditions Multiple Signal Complexity : Multiple timeframe analysis may produce conflicting signals requiring experience to interpret Performance Considerations : Multiple Fibonacci sets and MTF analysis may impact indicator loading time on slower devices 💡 What Makes This Unique Automated Precision : Eliminates manual drawing errors and subjective level placement Multi-Timeframe Intelligence : Combines analysis from multiple timeframes for superior confluence detection Adaptive Management : Automatically updates and manages multiple Fibonacci sets as market structure evolves Professional-Grade Alerts : Comprehensive notification system for all significant level interactions 🔬 How It Works Step 1 - Swing Point Identification : Scans price action using pivot high/low calculations with specified lookback periods, applies confirmation logic to eliminate false signals, and calculates swing strength based on surrounding price action for quality assessment. Step 2 - Fibonacci Level Calculation : Automatically computes retracement and extension levels between confirmed swing points, creates dynamic level sets that update as new swing points are identified, and maintains multiple active Fibonacci sets for comprehensive market analysis. Step 3 - Multi-Timeframe Confluence : Retrieves Fibonacci data from higher timeframes using secure request functions, analyzes level clustering across different timeframes within specified thresholds, and identifies high-probability zones where multiple levels converge. 💡 Note: This indicator works best when combined with other technical analysis tools and proper risk management. The multi-timeframe confluence feature provides the highest probability setups, but always confirm signals with additional analysis before entering trades.
Indicador Pine Script®
por PhenLabs
2
MACD Trend StatusOverview: The Dynamic MACD Trend Status indicator is a sophisticated yet easy-to-interpret tool designed to provide instant, color-coded insights into the current MACD momentum and trend strength directly on your chart. Unlike traditional MACD indicators that clutter your main price panel, this indicator distills complex MACD calculations into a single, prominent text label, ideal for quick confirmations and fast-paced trading. It features two distinct logic modes, allowing you to customize its sensitivity and confirmation level, making it adaptable to various market conditions and trading styles. Key Features & How It Works: Two Selectable Logic Modes: This indicator offers a unique dropdown setting (Logic Selection) to switch between two powerful MACD interpretation algorithms: a) Option 3 (Robust) - (Default) This is the most stringent and reliable mode, designed to filter out market noise and highlight only strong, accelerating trends. It declares a "Bullish" or "Bearish" status when ALL of the following conditions are met: Bullish: MACD Line is above Signal Line AND MACD Histogram is positive AND MACD Histogram is increasing (momentum is accelerating) AND both MACD Line and Signal Line are above the Zero Line (confirming an overall uptrend). Bearish: MACD Line is below Signal Line AND MACD Histogram is negative AND MACD Histogram is decreasing (momentum is accelerating) AND both MACD Line and Signal Line are below the Zero Line (confirming an overall downtrend). Neutral: If none of the above strong conditions are met, indicating sideways movement, weakening momentum, or a transition phase. b) Option 4 (Simplified + Enhanced) This mode offers a more responsive signal while still providing a clear distinction for exceptionally strong moves. It determines status based on: "MACD Bullish +" (Super Bullish): If all the rigorous conditions of "Option 3 (Robust) - Bullish" are met. This provides an immediate visual cue of extreme bullish strength within the simpler logic. "MACD Bearish +" (Super Bearish): If all the rigorous conditions of "Option 3 (Robust) - Bearish" are met. This highlights exceptional bearish strength. "MACD Bullish": MACD Line is above Signal Line AND MACD Histogram is positive (basic bullish momentum). "MACD Bearish": MACD Line is below Signal Line AND MACD Histogram is negative (basic bearish momentum). "MACD Neutral": If none of the above conditions are met. Instant Color-Coded Status: The indicator provides clear visual feedback through dynamic text colors: Green: "MACD Bullish" (Standard Bullish) Red: "MACD Bearish" (Standard Bearish) Gray: "MACD Neutral" (Choppy/Unclear) Blue: "MACD Bullish +" (Enhanced Strong Bullish - when using Option 4) Fuchsia/Purple: "MACD Bearish +" (Enhanced Strong Bearish - when using Option 4) (Note: Colors for "+" signals are customizable in the code if you wish) Unobtrusive Display: The status is displayed in a transparent, discreet table positioned at the middle-right of your main chart panel. This avoids cluttering the top corners or the indicator sub-panel, keeping your price action clear. Ideal Use Cases: Quick Confirmation: Rapidly confirm your trade ideas with a glance at the MACD's underlying momentum. Scalping & Day Trading: The instant visual feedback is invaluable for fast-paced short-term strategies. Momentum Filtering: Use it to filter trades, ensuring you're entering when MACD momentum is in your favor. Complementary Tool: Designed to work hand-in-hand with your primary analysis (price action, support/resistance, other indicators). It's not intended as a standalone signal but as a powerful re-confirmation tool. Customization Options: MACD Settings: Adjust Fast Length, Slow Length, and Signal Length. Logic Selection: Toggle between "Option 3 (Robust)" and "Option 4 (Simplified)" for different sensitivities. Show Status Text: Toggle the visibility of the status text On/Off. Text Size: Choose from "tiny", "small", "normal", "large", "huge" for optimal visibility. Important Disclaimer: This indicator is a technical analysis tool and should be used as part of a comprehensive trading strategy. It is not financial advice. Trading in financial markets involves substantial risk, and you could lose money. Always perform your own research and risk management.
Indicador Pine Script®
por tessyjoppan
The Sequences of FibonacciThe Sequences of Fibonacci - Advanced Multi-Timeframe Confluence Analysis System THEORETICAL FOUNDATION & MATHEMATICAL INNOVATION The Sequences of Fibonacci represents a revolutionary approach to market analysis that synthesizes classical Fibonacci mathematics with modern adaptive signal processing. This indicator transcends traditional Fibonacci retracement tools by implementing a sophisticated multi-dimensional confluence detection system that reveals hidden market structure through mathematical precision. Core Mathematical Framework Dynamic Fibonacci Grid System: Unlike static Fibonacci tools, this system calculates highest highs and lowest lows across true Fibonacci sequence periods (8, 13, 21, 34, 55 bars) creating a dynamic grid of mathematical support and resistance levels that adapt to market structure in real-time. Multi-Dimensional Confluence Detection: The engine employs advanced mathematical clustering algorithms to identify areas where multiple derived Fibonacci retracement levels (0.382, 0.500, 0.618) from different timeframe perspectives converge. These "Confluence Zones" are mathematically classified by strength: - CRITICAL Zones: 8+ converging Fibonacci levels - HIGH Zones: 6-7 converging levels - MEDIUM Zones: 4-5 converging levels - LOW Zones: 3+ converging levels Adaptive Signal Processing Architecture: The system implements adaptive Stochastic RSI calculations with dynamic overbought/oversold levels that adjust to recent market volatility rather than using fixed thresholds. This prevents false signals during changing market conditions. COMPREHENSIVE FEATURE ARCHITECTURE Quantum Field Visualization System Dynamic Price Field Mathematics: The Quantum Field creates adaptive price channels based on EMA center points and ATR-based amplitude calculations, influenced by the Unified Field metric. This visualization system helps traders understand: - Expected price volatility ranges - Potential overextension zones - Mathematical pressure points in market structure - Dynamic support/resistance boundaries Field Amplitude Calculation: Field Amplitude = ATR × (1 + |Unified Field| / 10) The system generates three quantum levels: - Q⁰ Level: 0.618 × Field Amplitude (Primary channel) - Q¹ Level: 1.0 × Field Amplitude (Secondary boundary) - Q² Level: 1.618 × Field Amplitude (Extreme extension) Advanced Market Analysis Dashboard Unified Field Analysis: A composite metric combining: - Price momentum (40% weighting) - Volume momentum (30% weighting) - Trend strength (30% weighting) Market Resonance Calculation: Measures price-volume correlation over 14 periods to identify harmony between price action and volume participation. Signal Quality Assessment: Synthesizes Unified Field, Market Resonance, and RSI positioning to provide real-time evaluation of setup potential. Tiered Signal Generation Logic Tier 1 Signals (Highest Conviction): Require ALL conditions: - Adaptive StochRSI setup (exiting dynamic OB/OS levels) - Classic StochRSI divergence confirmation - Strong reversal bar pattern (adaptive ATR-based sizing) - Level rejection from Confluence Zone or Fibonacci level - Supportive Unified Field context Tier 2 Signals (Enhanced Opportunity Detection): Generated when Tier 1 conditions aren't met but exceptional circumstances exist: - Divergence candidate patterns (relaxed divergence requirements) - Exceptionally strong reversal bars at critical levels - Enhanced level rejection criteria - Maintained context filtering Intelligent Visualization Features Fractal Matrix Grid: Multi-layer visualization system displaying: - Shadow Layer: Foundational support (width 5) - Glow Layer: Core identification (width 3, white) - Quantum Layer: Mathematical overlay (width 1, dotted) Smart Labeling System: Prevents overlap using ATR-based minimum spacing while providing: - Fibonacci period identification - Topological complexity classification (0, I, II, III) - Exact price levels - Strength indicators (○ ◐ ● ⚡) Wick Pressure Analysis: Dynamic visualization showing momentum direction through: - Multi-beam projection lines - Particle density effects - Progressive transparency for natural flow - Strength-based sizing adaptation PRACTICAL TRADING IMPLEMENTATION Signal Interpretation Framework Entry Protocol: 1. Confluence Zone Approach: Monitor price approaching High/Critical confluence zones 2. Adaptive Setup Confirmation: Wait for StochRSI to exit adaptive OB/OS levels 3. Divergence Verification: Confirm classic or candidate divergence patterns 4. Reversal Bar Assessment: Validate strong rejection using adaptive ATR criteria 5. Context Evaluation: Ensure Unified Field provides supportive environment Risk Management Integration: - Stop Placement: Beyond rejected confluence zone or Fibonacci level - Position Sizing: Based on signal tier and confluence strength - Profit Targets: Next significant confluence zone or quantum field boundary Adaptive Parameter System Dynamic StochRSI Levels: Unlike fixed 80/20 levels, the system calculates adaptive OB/OS based on recent StochRSI range: - Adaptive OB: Recent minimum + (range × OB percentile) - Adaptive OS: Recent minimum + (range × OS percentile) - Lookback Period: Configurable 20-100 bars for range calculation Intelligent ATR Adaptation: Bar size requirements adjust to market volatility: - High Volatility: Reduced multiplier (bars naturally larger) - Low Volatility: Increased multiplier (ensuring significance) - Base Multiplier: 0.6× ATR with adaptive scaling Optimization Guidelines Timeframe-Specific Settings: Scalping (1-5 minutes): - Fibonacci Rejection Sensitivity: 0.3-0.8 - Confluence Threshold: 2-3 levels - StochRSI Lookback: 20-30 bars Day Trading (15min-1H): - Fibonacci Rejection Sensitivity: 0.5-1.2 - Confluence Threshold: 3-4 levels - StochRSI Lookback: 40-60 bars Swing Trading (4H-1D): - Fibonacci Rejection Sensitivity: 1.0-2.0 - Confluence Threshold: 4-5 levels - StochRSI Lookback: 60-80 bars Asset-Specific Optimization: Cryptocurrency: - Higher rejection sensitivity (1.0-2.5) for volatile conditions - Enable Tier 2 signals for increased opportunity detection - Shorter adaptive lookbacks for rapid market changes Forex Major Pairs: - Moderate sensitivity (0.8-1.5) for stable trending - Focus on Higher/Critical confluence zones - Longer lookbacks for institutional flow detection Stock Indices: - Conservative sensitivity (0.5-1.0) for institutional participation - Standard confluence thresholds - Balanced adaptive parameters IMPORTANT USAGE CONSIDERATIONS Realistic Performance Expectations This indicator provides probabilistic advantages based on mathematical confluence analysis, not guaranteed outcomes. Signal quality varies with market conditions, and proper risk management remains essential regardless of signal tier. Understanding Adaptive Features: - Adaptive parameters react to historical data, not future market conditions - Dynamic levels adjust to past volatility patterns - Signal quality reflects mathematical alignment probability, not certainty Market Context Awareness: - Strong trending markets may produce fewer reversal signals - Range-bound conditions typically generate more confluence opportunities - News events and fundamental factors can override technical analysis Educational Value Mathematical Concepts Introduced: - Multi-dimensional confluence analysis - Adaptive signal processing techniques - Dynamic parameter optimization - Mathematical field theory applications in trading - Advanced Fibonacci sequence applications Skill Development Benefits: - Understanding market structure through mathematical lens - Recognition of multi-timeframe confluence principles - Appreciation for adaptive vs. static analysis methods - Integration of classical Fibonacci with modern signal processing UNIQUE INNOVATIONS First-Ever Implementations 1. True Fibonacci Sequence Periods: First indicator using authentic Fibonacci numbers (8,13,21,34,55) for timeframe analysis 2. Mathematical Confluence Clustering: Advanced algorithm identifying true Fibonacci level convergence 3. Adaptive StochRSI Boundaries: Dynamic OB/OS levels replacing fixed thresholds 4. Tiered Signal Architecture: Democratic signal weighting with quality classification 5. Quantum Field Price Visualization: Mathematical field representation of price dynamics Visualization Breakthroughs - Multi-Layer Fibonacci Grid: Three-layer rendering with intelligent spacing - Dynamic Confluence Zones: Strength-based color coding and sizing - Adaptive Parameter Display: Real-time visualization of dynamic calculations - Mathematical Field Effects: Quantum-inspired price channel visualization - Progressive Transparency Systems: Natural visual flow without chart clutter COMPREHENSIVE DASHBOARD SYSTEM Multi-Size Display Options Small Dashboard: Core metrics for mobile/limited screen space Normal Dashboard: Balanced information density for standard desktop use Large Dashboard: Complete analysis suite including adaptive parameter values Real-Time Metrics Tracking Market Analysis Section: - Unified Field strength with visual meter - Market Resonance percentage - Signal Quality assessment with emoji indicators - Market Bias classification (Bullish/Bearish/Neutral) Confluence Intelligence: - Total active zones count - High/Critical zone identification - Nearest zone distance and strength - Price-to-zone ATR measurement Adaptive Parameters (Large Dashboard): - Current StochRSI OB/OS levels - Active ATR multiplier for bar sizing - Volatility ratio for adaptive scaling - Real-time StochRSI positioning TECHNICAL SPECIFICATIONS Pine Script Version: v5 (Latest) Calculation Method: Real-time with confirmed bar processing Maximum Objects: 500 boxes, 500 lines, 500 labels Dashboard Positions: 4 corner options with size selection Visual Themes: Quantum, Holographic, Crystalline, Plasma Alert Integration: Complete alert system for all signal types Performance Optimizations: - Efficient confluence zone calculation using advanced clustering - Smart label spacing prevents overlap - Progressive transparency for visual clarity - Memory-optimized array management EDUCATIONAL FRAMEWORK Learning Progression Beginner Level: - Understanding Fibonacci sequence applications - Recognition of confluence zone concepts - Basic signal interpretation - Dashboard metric comprehension Intermediate Level: - Adaptive parameter optimization - Multi-timeframe confluence analysis - Signal quality assessment techniques - Risk management integration Advanced Level: - Mathematical field theory applications - Custom parameter optimization strategies - Market regime adaptation techniques - Professional trading system integration DEVELOPMENT ACKNOWLEDGMENT Special acknowledgment to @AlgoTrader90 - the foundational concepts of this system came from him and we developed it through a collaborative discussions about multi-timeframe Fibonacci analysis. While the original framework came from AlgoTrader90's innovative approach, this implementation represents a complete evolution of the logic with enhanced mathematical precision, adaptive parameters, and sophisticated signal filtering to deliver meaningful, actionable trading signals. CONCLUSION The Sequences of Fibonacci represents a quantum leap in technical analysis, successfully merging classical Fibonacci mathematics with cutting-edge adaptive signal processing. Through sophisticated confluence detection, intelligent parameter adaptation, and comprehensive market analysis, this system provides traders with unprecedented insight into market structure and potential reversal points. The mathematical foundation ensures lasting relevance while the adaptive features maintain effectiveness across changing market conditions. From the dynamic Fibonacci grid to the quantum field visualization, every component reflects a commitment to mathematical precision, visual elegance, and practical utility. Whether you're a beginner seeking to understand market confluence or an advanced trader requiring sophisticated analytical tools, this system provides the mathematical framework for informed decision-making based on time-tested Fibonacci principles enhanced with modern computational techniques. Trade with mathematical precision. Trade with the power of confluence. Trade with The Sequences of Fibonacci. "Mathematics is the language with which God has written the universe. In markets, Fibonacci sequences reveal the hidden harmonies that govern price movement, and those who understand these mathematical relationships hold the key to anticipating market behavior." * Galileo Galilei (adapted for modern markets) — Dskyz, Trade with insight. Trade with anticipation.
Indicador Pine Script®
por DskyzInvestments
4
Liquidity Sweep DetectorThe Liquidity Sweep Detector represents a technical analysis tool specifically designed to identify market microstructure patterns typically associated with institutional trading activity. According to Harris (2003), institutional traders frequently employ tactics where they momentarily break through price levels to trigger stop orders before redirecting the market in the opposite direction. This phenomenon, commonly referred to as "stop hunting" or "liquidity sweeping," constitutes a significant aspect of institutional order flow analysis (Osler, 2003). The current implementation provides retail traders with a means to identify these patterns, potentially aligning their trading decisions with institutional movements rather than becoming victims of such strategies. Osler's (2003) research documents how stop-loss orders tend to cluster around significant price levels, creating concentrations of liquidity. Taylor (2005) argues that sophisticated institutional participants systematically exploit these liquidity clusters by inducing price movements that trigger these orders, subsequently profiting from the ensuing price reaction. The algorithmic detection of such patterns involves several key processes. First, the indicator identifies swing points—local maxima and minima—through comparison with historical price data within a definable lookback period. These swing points correspond to what Bulkowski (2011) describes as "significant pivot points" that frequently serve as liquidity zones where stop orders accumulate. The core detection algorithm utilizes a multi-stage process to identify potential sweeps. For high sweeps, it monitors when price exceeds a previous swing high by a specified threshold percentage, followed by a bearish candle that closes below the original swing high level. Conversely, for low sweeps, it detects when price drops below a previous swing low by the threshold percentage, followed by a bullish candle closing above the original swing low. As noted by Lo and MacKinlay (2011), these price patterns often emerge when large institutional players attempt to capture liquidity before initiating significant directional moves. The indicator maintains historical arrays of detected sweep events with their corresponding timestamps, enabling temporal analysis of market behavior following such events. Visual elements include horizontal lines marking sweep levels, background color highlighting for sweep events, and an information table displaying active sweeps with their corresponding price levels and elapsed time since detection. This visualization approach allows traders to quickly identify potential institutional activity without requiring complex interpretation of raw price data. Parameter customization includes adjustable lookback periods for swing point identification, sweep threshold percentages for signal sensitivity, and display duration settings. These parameters allow traders to adapt the indicator to various market conditions and timeframes, as markets demonstrate different liquidity characteristics across instruments and periods (Madhavan, 2000). Empirical studies by Easley et al. (2012) suggest that retail traders who successfully identify and act upon institutional liquidity sweeps may achieve superior risk-adjusted returns compared to conventional technical analysis approaches. However, as cautioned by Chordia et al. (2008), such patterns should be considered within broader market context rather than in isolation, as their predictive value varies significantly with overall market volatility and liquidity conditions. References: Bulkowski, T. (2011). Encyclopedia of Chart Patterns (2nd ed.). John Wiley & Sons. Chordia, T., Roll, R., & Subrahmanyam, A. (2008). Liquidity and market efficiency. Journal of Financial Economics, 87(2), 249-268. Easley, D., López de Prado, M., & O'Hara, M. (2012). Flow Toxicity and Liquidity in a High-frequency World. The Review of Financial Studies, 25(5), 1457-1493. Harris, L. (2003). Trading and Exchanges: Market Microstructure for Practitioners. Oxford University Press. Lo, A. W., & MacKinlay, A. C. (2011). A Non-Random Walk Down Wall Street. Princeton University Press. Madhavan, A. (2000). Market microstructure: A survey. Journal of Financial Markets, 3(3), 205-258. Osler, C. L. (2003). Currency Orders and Exchange Rate Dynamics: An Explanation for the Predictive Success of Technical Analysis. Journal of Finance, 58(5), 1791-1820. Taylor, M. P. (2005). Official Foreign Exchange Intervention as a Coordinating Signal in the Dollar-Yen Market. Pacific Economic Review, 10(1), 73-82.
Indicador Pine Script®
por EdgeTools
[blackcat] L2 Trend Guard OscillatorOVERVIEW 📊 The L2 Trend Guard Oscillator is a comprehensive technical analysis framework designed specifically to identify market trend reversals using adaptive filtering algorithms that combine price action dynamics with statistical measures of volatility and momentum. Key Purpose: Generate reliable early warning signals before major trend changes occur Provide clear directional bias indicators aligned with institutional investor behavior patterns Offer risk-managed entry/exit opportunities suitable for various timeframes TECHNICAL FOUNDATION EXPLAINED 🎓 Core Mechanism Breakdown: → Advanced smoothing technique emphasizing recent data points more heavily than older ones ↓ Reduces lag while maintaining signal integrity compared to traditional MA approaches • Short-term Momentum Assessment: 🔶 Relative strength between closing prices vs lower bounds • Long-term Directional Bias Analysis: 📈 Extended timeframe comparison generating structural context • Defense Level Generation: ➜ Protective boundary calculation incorporating EMAs for stability enhancement PARAMETER CONFIGURATION GUIDE 🔧 Adjustable Settings Explained In Detail: Timeframe Selection:** ↔ Controls lookback period sensitivity affecting responsiveness ↕ Adjusts reaction speed vs accuracy trade-off dynamically Weight Factor Specification:** ⚡ Influences emphasis on newer versus historical observations 🎯 Defines key decision-making thresholds clearly ALGORITHM EXECUTION FLOW 💻 Processing Sequence Overview: : → Gather raw pricing inputs across required periods ↓ Normalize values preparing them for subsequent processing stages : ✔ Calculate relative strength positions against established ranges ❌ Filter outliers maintaining signal integrity consistently ⟶ Apply dual-pass filtering reducing false signals effectively ➡ Generate actionable trading opportunities systematically VISUALIZATION ARCHITECTURE 🎨 Display Elements Designated Purpose: 🔵 Primary Indicator Traces: → Aqua Trace: Buy/Sell Signal Progression ↑ Red Line: Opposing Force Boundary 🟥 Gray Dashed: Zero Reference Point 🏷️ Label System For Critical Events: ✅ BUY: Bullish Opportunity Markers ❌ SELL: Bearish Setup Validations STRATEGIC IMPLEMENTATION FRAMEWORK 📋 Practical Deployment Steps: Initial Integration Protocol: • Select appropriate timeframe matching strategy objectives • Configure input parameters aligning with target asset behavior traits • Conduct thorough backtesting under simulated environments initially Active Monitoring Procedures: → Regular observation of labeled event placements versus actual movements ↓ Track confirmation patterns leading up to signaled opportunities carefully ↑ Evaluate overall framework reliability across different regime types regularly Execution Guidelines Formulation: ✔ Enter positions only after achieving minimum number of confirming inputs ❌ Avoid isolated occurrences lacking adequate supporting evidence always ➞ Look for convergent factors strengthening conviction before acting decisively PERFORMANCE OPTIMIZATION TECHNIQUES 🚀 Continuous Improvement Strategies: Parameter Calibration Approach: ✓ Start testing default suggested configurations thoroughly ↕ Gradually adjust individual components observing outcome changes methodically ✨ Document findings building personalized version profile incrementally Context Adaptability Methods: 🔄 Add supplementary indicators enhancing overall reliability when needed 🔧 Remove unnecessary complexity layers avoiding confusion/distracted decisions 💫 Incorporate custom rules adapting specific security behaviors effectively Efficiency Improvement Tactics: ⚙️ Streamline redundant computational routines wherever possible efficiently ♻️ Leverage shared data streams minimizing resource utilization significantly ⏳ Optimize refresh frequencies balancing update speed vs overhead properly
Indicador Pine Script®
por blackcat1402
1
RSI Full Forecast [Titans_Invest]RSI Full Forecast Get ready to experience the ultimate evolution of RSI-based indicators – the RSI Full Forecast, a boosted and even smarter version of the already powerful: RSI Forecast Now featuring over 40 additional entry conditions (forecasts), this indicator redefines the way you view the market. AI-Powered RSI Forecasting: Using advanced linear regression with the least squares method – a solid foundation for machine learning - the RSI Full Forecast enables you to predict future RSI behavior with impressive accuracy. But that’s not all: this new version also lets you monitor future crossovers between the RSI and the MA RSI, delivering early and strategic signals that go far beyond traditional analysis. You’ll be able to monitor future crossovers up to 20 bars ahead, giving you an even broader and more precise view of market movements. See the Future, Now: • Track upcoming RSI & RSI MA crossovers in advance. • Identify potential reversal zones before price reacts. • Uncover statistical behavior patterns that would normally go unnoticed. 40+ Intelligent Conditions: The new layer of conditions is designed to detect multiple high-probability scenarios based on historical patterns and predictive modeling. Each additional forecast is a window into the price's future, powered by robust mathematics and advanced algorithmic logic. Full Customization: All parameters can be tailored to fit your strategy – from smoothing periods to prediction sensitivity. You have complete control to turn raw data into smart decisions. Innovative, Accurate, Unique: This isn’t just an upgrade. It’s a quantum leap in technical analysis. RSI Full Forecast is the first of its kind: an indicator that blends statistical analysis, machine learning, and visual design to create a true real-time predictive system. ⯁ SCIENTIFIC BASIS LINEAR REGRESSION Linear Regression is a fundamental method of statistics and machine learning, used to model the relationship between a dependent variable y and one or more independent variables 𝑥. The general formula for a simple linear regression is given by: y = β₀ + β₁x + ε β₁ = Σ((xᵢ - x̄)(yᵢ - ȳ)) / Σ((xᵢ - x̄)²) β₀ = ȳ - β₁x̄ Where: y   = is the predicted variable (e.g. future value of RSI) x ​​  = is the explanatory variable (e.g. time or bar index) β0 = is the intercept (value of 𝑦 when 𝑥 = 0) 𝛽1 = is the slope of the line (rate of change) ε   = is the random error term The goal is to estimate the coefficients 𝛽0 and 𝛽1 ​​so as to minimize the sum of the squared errors — the so-called Random Error Method Least Squares. ⯁ LEAST SQUARES ESTIMATION To minimize the error between predicted and observed values, we use the following formulas: β₁ = / β₀ = ȳ - β₁x̄ Where: ∑ = sum x̄ = mean of x ȳ = mean of y x_i, y_i = individual values ​​of the variables. Where: x_i and y_i are the means of the independent and dependent variables, respectively. i ranges from 1 to n, the number of observations. These equations guarantee the best linear unbiased estimator, according to the Gauss-Markov theorem, assuming homoscedasticity and linearity. ⯁ LINEAR REGRESSION IN MACHINE LEARNING Linear regression is one of the cornerstones of supervised learning. Its simplicity and ability to generate accurate quantitative predictions make it essential in AI systems, predictive algorithms, time series analysis, and automated trading strategies. By applying this model to the RSI, you are literally putting artificial intelligence at the heart of a classic indicator, bringing a new dimension to technical analysis. ⯁ VISUAL INTERPRETATION Imagine an RSI time series like this: Time → RSI    → The regression line will smooth these values ​​and extend them n periods into the future, creating a predicted trajectory based on the historical moment. This line becomes the predicted RSI, which can be crossed with the actual RSI to generate more intelligent signals. ⯁ SUMMARY OF SCIENTIFIC CONCEPTS USED Linear Regression Models the relationship between variables using a straight line. Least Squares Minimizes the sum of squared errors between prediction and reality. Time Series Forecasting Estimates future values ​​based on historical data. Supervised Learning Trains models to predict outputs from known inputs. Statistical Smoothing Reduces noise and reveals underlying trends. ⯁ WHY THIS INDICATOR IS REVOLUTIONARY Scientifically-based: Based on statistical theory and mathematical inference. Unprecedented: First public RSI with least squares predictive modeling. Intelligent: Built with machine learning logic. Practical: Generates forward-thinking signals. Customizable: Flexible for any trading strategy. ⯁ CONCLUSION By combining RSI with linear regression, this indicator allows a trader to predict market momentum, not just follow it. RSI Full Forecast is not just an indicator — it is a scientific breakthrough in technical analysis technology. ⯁ Example of simple linear regression, which has one independent variable: ⯁ In linear regression, observations ( red ) are considered to be the result of random deviations ( green ) from an underlying relationship ( blue ) between a dependent variable ( y ) and an independent variable ( x ). ⯁ Visualizing heteroscedasticity in a scatterplot against 100 random fitted values ​​using Matlab: ⯁ The data sets in the Anscombe's quartet are designed to have approximately the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but are graphically very different. This illustrates the pitfalls of relying solely on a fitted model to understand the relationship between variables. ⯁ The result of fitting a set of data points with a quadratic function: _________________________________________________ 🔮 Linear Regression: PineScript Technical Parameters 🔮 _________________________________________________ Forecast Types: • Flat: Assumes prices will remain the same. • Linreg: Makes a 'Linear Regression' forecast for n periods. Technical Information: ta.linreg (built-in function) Linear regression curve. A line that best fits the specified prices over a user-defined time period. It is calculated using the least squares method. The result of this function is calculated using the formula: linreg = intercept + slope * (length - 1 - offset), where intercept and slope are the values ​​calculated using the least squares method on the source series. Syntax: • Function: ta.linreg() Parameters: • source: Source price series. • length: Number of bars (period). • offset: Offset. • return: Linear regression curve. This function has been cleverly applied to the RSI, making it capable of projecting future values ​​based on past statistical trends. ______________________________________________________ ______________________________________________________ ⯁ WHAT IS THE RSI❓ The Relative Strength Index (RSI) is a technical analysis indicator developed by J. Welles Wilder. It measures the magnitude of recent price movements to evaluate overbought or oversold conditions in a market. The RSI is an oscillator that ranges from 0 to 100 and is commonly used to identify potential reversal points, as well as the strength of a trend. ⯁ HOW TO USE THE RSI❓ The RSI is calculated based on average gains and losses over a specified period (usually 14 periods). It is plotted on a scale from 0 to 100 and includes three main zones:   •  Overbought: When the RSI is above 70, indicating that the asset may be overbought.   •  Oversold: When the RSI is below 30, indicating that the asset may be oversold.   •  Neutral Zone: Between 30 and 70, where there is no clear signal of overbought or oversold conditions. ______________________________________________________ ______________________________________________________ ⯁ ENTRY CONDITIONS The conditions below are fully flexible and allow for complete customization of the signal. ______________________________________________________ ______________________________________________________ 🔹 CONDITIONS TO BUY 📈 ______________________________________________________   •  Signal Validity: The signal will remain valid for X bars .   •  Signal Sequence: Configurable as AND or OR . 📈 RSI Conditions: 🔹 RSI > Upper 🔹 RSI < Upper 🔹 RSI > Lower 🔹 RSI < Lower 🔹 RSI > Middle 🔹 RSI < Middle 🔹 RSI > MA 🔹 RSI < MA 📈 MA Conditions: 🔹 MA > Upper 🔹 MA < Upper 🔹 MA > Lower 🔹 MA < Lower 📈 Crossovers: 🔹 RSI (Crossover) Upper 🔹 RSI (Crossunder) Upper 🔹 RSI (Crossover) Lower 🔹 RSI (Crossunder) Lower 🔹 RSI (Crossover) Middle 🔹 RSI (Crossunder) Middle 🔹 RSI (Crossover) MA 🔹 RSI (Crossunder) MA 🔹 MA (Crossover) Upper 🔹 MA (Crossunder) Upper 🔹 MA (Crossover) Lower 🔹 MA (Crossunder) Lower 📈 RSI Divergences: 🔹 RSI Divergence Bull 🔹 RSI Divergence Bear 📈 RSI Forecast: 🔹 RSI (Crossover) MA Forecast 🔹 RSI (Crossunder) MA Forecast 🔹 RSI Forecast 1 > MA Forecast 1 🔹 RSI Forecast 1 < MA Forecast 1 🔹 RSI Forecast 2 > MA Forecast 2 🔹 RSI Forecast 2 < MA Forecast 2 🔹 RSI Forecast 3 > MA Forecast 3 🔹 RSI Forecast 3 < MA Forecast 3 🔹 RSI Forecast 4 > MA Forecast 4 🔹 RSI Forecast 4 < MA Forecast 4 🔹 RSI Forecast 5 > MA Forecast 5 🔹 RSI Forecast 5 < MA Forecast 5 🔹 RSI Forecast 6 > MA Forecast 6 🔹 RSI Forecast 6 < MA Forecast 6 🔹 RSI Forecast 7 > MA Forecast 7 🔹 RSI Forecast 7 < MA Forecast 7 🔹 RSI Forecast 8 > MA Forecast 8 🔹 RSI Forecast 8 < MA Forecast 8 🔹 RSI Forecast 9 > MA Forecast 9 🔹 RSI Forecast 9 < MA Forecast 9 🔹 RSI Forecast 10 > MA Forecast 10 🔹 RSI Forecast 10 < MA Forecast 10 🔹 RSI Forecast 11 > MA Forecast 11 🔹 RSI Forecast 11 < MA Forecast 11 🔹 RSI Forecast 12 > MA Forecast 12 🔹 RSI Forecast 12 < MA Forecast 12 🔹 RSI Forecast 13 > MA Forecast 13 🔹 RSI Forecast 13 < MA Forecast 13 🔹 RSI Forecast 14 > MA Forecast 14 🔹 RSI Forecast 14 < MA Forecast 14 🔹 RSI Forecast 15 > MA Forecast 15 🔹 RSI Forecast 15 < MA Forecast 15 🔹 RSI Forecast 16 > MA Forecast 16 🔹 RSI Forecast 16 < MA Forecast 16 🔹 RSI Forecast 17 > MA Forecast 17 🔹 RSI Forecast 17 < MA Forecast 17 🔹 RSI Forecast 18 > MA Forecast 18 🔹 RSI Forecast 18 < MA Forecast 18 🔹 RSI Forecast 19 > MA Forecast 19 🔹 RSI Forecast 19 < MA Forecast 19 🔹 RSI Forecast 20 > MA Forecast 20 🔹 RSI Forecast 20 < MA Forecast 20 ______________________________________________________ ______________________________________________________ 🔸 CONDITIONS TO SELL 📉 ______________________________________________________   •  Signal Validity: The signal will remain valid for X bars .   •  Signal Sequence: Configurable as AND or OR . 📉 RSI Conditions: 🔸 RSI > Upper 🔸 RSI < Upper 🔸 RSI > Lower 🔸 RSI < Lower 🔸 RSI > Middle 🔸 RSI < Middle 🔸 RSI > MA 🔸 RSI < MA 📉 MA Conditions: 🔸 MA > Upper 🔸 MA < Upper 🔸 MA > Lower 🔸 MA < Lower 📉 Crossovers: 🔸 RSI (Crossover) Upper 🔸 RSI (Crossunder) Upper 🔸 RSI (Crossover) Lower 🔸 RSI (Crossunder) Lower 🔸 RSI (Crossover) Middle 🔸 RSI (Crossunder) Middle 🔸 RSI (Crossover) MA 🔸 RSI (Crossunder) MA 🔸 MA (Crossover) Upper 🔸 MA (Crossunder) Upper 🔸 MA (Crossover) Lower 🔸 MA (Crossunder) Lower 📉 RSI Divergences: 🔸 RSI Divergence Bull 🔸 RSI Divergence Bear 📉 RSI Forecast: 🔸 RSI (Crossover) MA Forecast 🔸 RSI (Crossunder) MA Forecast 🔸 RSI Forecast 1 > MA Forecast 1 🔸 RSI Forecast 1 < MA Forecast 1 🔸 RSI Forecast 2 > MA Forecast 2 🔸 RSI Forecast 2 < MA Forecast 2 🔸 RSI Forecast 3 > MA Forecast 3 🔸 RSI Forecast 3 < MA Forecast 3 🔸 RSI Forecast 4 > MA Forecast 4 🔸 RSI Forecast 4 < MA Forecast 4 🔸 RSI Forecast 5 > MA Forecast 5 🔸 RSI Forecast 5 < MA Forecast 5 🔸 RSI Forecast 6 > MA Forecast 6 🔸 RSI Forecast 6 < MA Forecast 6 🔸 RSI Forecast 7 > MA Forecast 7 🔸 RSI Forecast 7 < MA Forecast 7 🔸 RSI Forecast 8 > MA Forecast 8 🔸 RSI Forecast 8 < MA Forecast 8 🔸 RSI Forecast 9 > MA Forecast 9 🔸 RSI Forecast 9 < MA Forecast 9 🔸 RSI Forecast 10 > MA Forecast 10 🔸 RSI Forecast 10 < MA Forecast 10 🔸 RSI Forecast 11 > MA Forecast 11 🔸 RSI Forecast 11 < MA Forecast 11 🔸 RSI Forecast 12 > MA Forecast 12 🔸 RSI Forecast 12 < MA Forecast 12 🔸 RSI Forecast 13 > MA Forecast 13 🔸 RSI Forecast 13 < MA Forecast 13 🔸 RSI Forecast 14 > MA Forecast 14 🔸 RSI Forecast 14 < MA Forecast 14 🔸 RSI Forecast 15 > MA Forecast 15 🔸 RSI Forecast 15 < MA Forecast 15 🔸 RSI Forecast 16 > MA Forecast 16 🔸 RSI Forecast 16 < MA Forecast 16 🔸 RSI Forecast 17 > MA Forecast 17 🔸 RSI Forecast 17 < MA Forecast 17 🔸 RSI Forecast 18 > MA Forecast 18 🔸 RSI Forecast 18 < MA Forecast 18 🔸 RSI Forecast 19 > MA Forecast 19 🔸 RSI Forecast 19 < MA Forecast 19 🔸 RSI Forecast 20 > MA Forecast 20 🔸 RSI Forecast 20 < MA Forecast 20 ______________________________________________________ ______________________________________________________ 🤖 AUTOMATION 🤖 • You can automate the BUY and SELL signals of this indicator. ______________________________________________________ ______________________________________________________ ⯁ UNIQUE FEATURES ______________________________________________________ Linear Regression: (Forecast) Signal Validity: The signal will remain valid for X bars Signal Sequence: Configurable as AND/OR Condition Table: BUY/SELL Condition Labels: BUY/SELL Plot Labels in the Graph Above: BUY/SELL Automate and Monitor Signals/Alerts: BUY/SELL Linear Regression (Forecast) Signal Validity: The signal will remain valid for X bars Signal Sequence: Configurable as AND/OR Condition Table: BUY/SELL Condition Labels: BUY/SELL Plot Labels in the Graph Above: BUY/SELL Automate and Monitor Signals/Alerts: BUY/SELL ______________________________________________________ 📜 SCRIPT : RSI Full Forecast 🎴 Art by  : @Titans_Invest & @DiFlip 👨‍💻 Dev by : @Titans_Invest & @DiFlip 🎑 Titans Invest — The Wizards Without Gloves 🧤 ✨ Enjoy! ______________________________________________________ o Mission 🗺 • Inspire Traders to manifest Magic in the Market. o Vision 𐓏 • To elevate collective Energy 𐓷𐓏
Indicador Pine Script®
por Titans_Invest
Aesthetic RSI [AlchimistOfCrypto]🌌 Aesthetic RSI – Unveiling the Fractal Forces of Markets 🌌 Category: Momentum Indicators 📈 "The RSI oscillator, formalized through an advanced mathematical prism, reveals the underlying fractal structures of price movements. This indicator draws inspiration from quantum principles of divergence-convergence where the probability of a return to equilibrium increases proportionally to the distance from the median point. Our implementation employs sophisticated algorithmic smoothing to filter out the stochastic noise inherent in financial markets, allowing visualization of the true momentum forces according to thermodynamic entropy principles applied to trading systems." 📊 Professional Trading Application The Aesthetic RSI is a visually stunning and mathematically refined take on the classic Relative Strength Index. With customizable settings, advanced smoothing, and eight unique visual palettes, it empowers traders to detect momentum shifts and divergences with unparalleled clarity. ⚙️ Indicator Configuration - Length 📏 The core parameter (default: 20) that determines the calculation period. - Lower values (8-14): Increase sensitivity for short-term trading. - Higher values (21-34): Provide stronger signals for position trading. - OverBought/OverSold Thresholds 🎯 Customizable boundaries (default: 75/25) to identify extreme market conditions. - Calibrate based on asset volatility: Higher volatility assets may need wider thresholds (80/20) to reduce false signals. - Style 🎨 Eight meticulously crafted visual palettes optimized for pattern recognition: - Miami Vice (default): High-contrast cyan/magenta scheme for spotting divergences. - Cyberpunk: Yellow/purple combo to highlight momentum shifts. - Classic: Traditional green/red for conventional analysis. - High Contrast: Maximum visual separation for traders with visual impairments. - Specialized palettes (Forest, Ocean, Fire, Monochrome): Tailored for diverse market conditions. - Mode Selection 🔄 - Full: Displays a complete gradient spectrum across the RSI range, emphasizing momentum transitions between 35-65. - OverZone: Focuses on actionable extreme zones, reducing noise in ranging markets. 🚀 How to Use 1. Adjust Length ⏰: Set the period based on your trading style (short-term or long-term). 2. Fine-Tune Thresholds 🎚️: Customize overbought/oversold levels to match the asset’s volatility. 3. Select a Palette 🌈: Choose a visual style that enhances your pattern recognition. 4. Choose Mode 🔍: Use "Full" for detailed momentum analysis or "OverZone" for extreme zone focus. 5. Spot Divergences ✅: Look for price-RSI divergences to anticipate reversals. 6. Trade with Precision 🛡️: Combine with other indicators for high-probability setups. 📅 Release Notes (April 2025) Aesthetic RSI blends quantum-inspired mathematics with artistic visualization, redefining momentum analysis. Stay tuned for future enhancements! ✨ 🏷️ Tags #Trading #TechnicalAnalysis #RSI #Momentum #Divergence #MultiTimeframe #TradingStrategy #RiskManagement #Forex #Stocks #Crypto #Bitcoin #AlgoTrading #DayTrading #SwingTrading #TheAlchimist #QuantumTrading #VisualTrading #PatternRecognition
Indicador Pine Script®
por AlchimistOfCrypto
Kase Permission StochasticOverview The Kase Permission Stochastic indicator is an advanced momentum oscillator developed from Kase's trading methodology. It offers enhanced signal smoothing and filtering compared to traditional stochastic oscillators, providing clearer entry and exit signals with fewer false triggers. How It Works This indicator calculates a specialized stochastic using a multi-stage smoothing process: Initial stochastic calculation based on high, low, and close prices Application of weighted moving averages (WMA) for short-term smoothing Progressive smoothing through differential factors Final smoothing to reduce noise and highlight significant trend changes The indicator oscillates between 0 and 100, with two main components: Main Line (Green): The smoothed stochastic value Signal Line (Yellow): A further smoothed version of the main line Signal Generation Trading signals are generated when the main line crosses the signal line: Buy Signal (Green Triangle): When the main line crosses above the signal line Sell Signal (Red Triangle): When the main line crosses below the signal line Key Features Multiple Smoothing Algorithms: Uses a combination of weighted and exponential moving averages for superior noise reduction Clear Visualization: Color-coded lines and background filling Reference Levels: Horizontal lines at 25, 50, and 75 for context Customizable Colors: All visual elements can be color-customized Customization Options PST Length: Base period for the stochastic calculation (default: 9) PST X: Multiplier for the lookback period (default: 5) PST Smooth: Smoothing factor for progressive calculations (default: 3) Smooth Period: Final smoothing period (default: 10) Trading Applications Trend Confirmation: Use crossovers to confirm entries in the direction of the prevailing trend Reversal Detection: Identify potential market reversals when crossovers occur at extreme levels Range-Bound Markets: Look for oscillations between overbought and oversold levels Filter for Other Indicators: Use as a confirmation tool alongside other technical indicators Best Practices Most effective in trending markets or during well-defined ranges Combine with price action analysis for better context Consider the overall market environment before taking signals Use longer settings for fewer but higher-quality signals The Kase Permission Stochastic delivers a sophisticated approach to momentum analysis, offering a refined perspective on market conditions while filtering out much of the noise that affects standard oscillators.
Indicador Pine Script®
por AlgoCollective
Multi-Fibonacci Trend Average[FibonacciFlux]Multi-Fibonacci Trend Average (MFTA): An Institutional-Grade Trend Confluence Indicator for Discerning Market Participants My original indicator/Strategy: Engineered for the sophisticated demands of institutional and advanced traders, the Multi-Fibonacci Trend Average (MFTA) indicator represents a paradigm shift in technical analysis. This meticulously crafted tool is designed to furnish high-definition trend signals within the complexities of modern financial markets. Anchored in the rigorous principles of Fibonacci ratios and augmented by advanced averaging methodologies, MFTA delivers a granular perspective on trend dynamics. Its integration of Multi-Timeframe (MTF) filters provides unparalleled signal robustness, empowering strategic decision-making with a heightened degree of confidence. MFTA indicator on BTCUSDT 15min chart with 1min RSI and MACD filters enabled. Note the refined signal generation with reduced noise. MFTA indicator on BTCUSDT 15min chart without MTF filters. While capturing more potential trading opportunities, it also generates a higher frequency of signals, including potential false positives. Core Innovation: Proprietary Fibonacci-Enhanced Supertrend Averaging Engine The MFTA indicator’s core innovation lies in its proprietary implementation of Supertrend analysis, strategically fortified by Fibonacci ratios to construct a truly dynamic volatility envelope. Departing from conventional Supertrend methodologies, MFTA autonomously computes not one, but three distinct Supertrend lines. Each of these lines is uniquely parameterized by a specific Fibonacci factor: 0.618 (Weak), 1.618 (Medium/Golden Ratio), and 2.618 (Strong/Extended Fibonacci). // Fibonacci-based factors for multiple Supertrend calculations factor1 = input.float(0.618, 'Factor 1 (Weak/Fibonacci)', minval=0.01, step=0.01, tooltip='Factor 1 (Weak/Fibonacci)', group="Fibonacci Supertrend") factor2 = input.float(1.618, 'Factor 2 (Medium/Golden Ratio)', minval=0.01, step=0.01, tooltip='Factor 2 (Medium/Golden Ratio)', group="Fibonacci Supertrend") factor3 = input.float(2.618, 'Factor 3 (Strong/Extended Fib)', minval=0.01, step=0.01, tooltip='Factor 3 (Strong/Extended Fib)', group="Fibonacci Supertrend") This multi-faceted architecture adeptly captures a spectrum of market volatility sensitivities, ensuring a comprehensive assessment of prevailing conditions. Subsequently, the indicator algorithmically synthesizes these disparate Supertrend lines through arithmetic averaging. To achieve optimal signal fidelity and mitigate inherent market noise, this composite average is further refined utilizing an Exponential Moving Average (EMA). // Calculate average of the three supertends and a smoothed version superlength = input.int(21, 'Smoothing Length', tooltip='Smoothing Length for Average Supertrend', group="Fibonacci Supertrend") average_trend = (supertrend1 + supertrend2 + supertrend3) / 3 smoothed_trend = ta.ema(average_trend, superlength) The resultant ‘Smoothed Trend’ line emerges as a remarkably responsive yet stable trend demarcation, offering demonstrably superior clarity and precision compared to singular Supertrend implementations, particularly within the turbulent dynamics of high-volatility markets. Elevated Signal Confluence: Integrated Multi-Timeframe (MTF) Validation Suite MFTA transcends the limitations of conventional trend indicators by incorporating an advanced suite of three independent MTF filters: RSI, MACD, and Volume. These filters function as sophisticated validation protocols, rigorously ensuring that only signals exhibiting a confluence of high-probability factors are brought to the forefront. 1. Granular Lower Timeframe RSI Momentum Filter The Relative Strength Index (RSI) filter, computed from a user-defined lower timeframe, furnishes critical momentum-based signal validation. By meticulously monitoring RSI dynamics on an accelerated timeframe, traders gain the capacity to evaluate underlying momentum strength with precision, prior to committing to signal execution on the primary chart timeframe. // --- Lower Timeframe RSI Filter --- ltf_rsi_filter_enable = input.bool(false, title="Enable RSI Filter", group="MTF Filters", tooltip="Use RSI from lower timeframe as a filter") ltf_rsi_timeframe = input.timeframe("1", title="RSI Timeframe", group="MTF Filters", tooltip="Timeframe for RSI calculation") ltf_rsi_length = input.int(14, title="RSI Length", minval=1, group="MTF Filters", tooltip="Length for RSI calculation") ltf_rsi_threshold = input.int(30, title="RSI Threshold", minval=0, maxval=100, group="MTF Filters", tooltip="RSI value threshold for filtering signals") 2. Convergent Lower Timeframe MACD Trend-Momentum Filter The Moving Average Convergence Divergence (MACD) filter, also calculated on a lower timeframe basis, introduces a critical layer of trend-momentum convergence confirmation. The bullish signal configuration rigorously mandates that the MACD line be definitively positioned above the Signal line on the designated lower timeframe. This stringent condition ensures a robust indication of converging momentum that aligns synergistically with the prevailing trend identified on the primary timeframe. // --- Lower Timeframe MACD Filter --- ltf_macd_filter_enable = input.bool(false, title="Enable MACD Filter", group="MTF Filters", tooltip="Use MACD from lower timeframe as a filter") ltf_macd_timeframe = input.timeframe("1", title="MACD Timeframe", group="MTF Filters", tooltip="Timeframe for MACD calculation") ltf_macd_fast_length = input.int(12, title="MACD Fast Length", minval=1, group="MTF Filters", tooltip="Fast EMA length for MACD") ltf_macd_slow_length = input.int(26, title="MACD Slow Length", minval=1, group="MTF Filters", tooltip="Slow EMA length for MACD") ltf_macd_signal_length = input.int(9, title="MACD Signal Length", minval=1, group="MTF Filters", tooltip="Signal SMA length for MACD") 3. Definitive Volume Confirmation Filter The Volume Filter functions as an indispensable arbiter of trade conviction. By establishing a dynamic volume threshold, defined as a percentage relative to the average volume over a user-specified lookback period, traders can effectively ensure that all generated signals are rigorously validated by demonstrably increased trading activity. This pivotal validation step signifies robust market participation, substantially diminishing the potential for spurious or false breakout signals. // --- Volume Filter --- volume_filter_enable = input.bool(false, title="Enable Volume Filter", group="MTF Filters", tooltip="Use volume level as a filter") volume_threshold_percent = input.int(title="Volume Threshold (%)", defval=150, minval=100, group="MTF Filters", tooltip="Minimum volume percentage compared to average volume to allow signal (100% = average)") These meticulously engineered filters operate in synergistic confluence, requiring all enabled filters to definitively satisfy their pre-defined conditions before a Buy or Sell signal is generated. This stringent multi-layered validation process drastically minimizes the incidence of false positive signals, thereby significantly enhancing entry precision and overall signal reliability. Intuitive Visual Architecture & Actionable Intelligence MFTA provides a demonstrably intuitive and visually rich charting environment, meticulously delineating trend direction and momentum through precisely color-coded plots: Average Supertrend: Thin line, green/red for uptrend/downtrend, immediate directional bias. Smoothed Supertrend: Bold line, teal/purple for uptrend/downtrend, cleaner, institutionally robust trend. Dynamic Trend Fill: Green/red fill between Supertrends quantifies trend strength and momentum. Adaptive Background Coloring: Light green/red background mirrors Smoothed Supertrend direction, holistic trend perspective. Precision Buy/Sell Signals: ‘BUY’/‘SELL’ labels appear on chart when trend touch and MTF filter confluence are satisfied, facilitating high-conviction trade action. MFTA indicator applied to BTCUSDT 4-hour chart, showcasing its effectiveness on higher timeframes. The Smoothed Length parameter is increased to 200 for enhanced smoothness on this timeframe, coupled with 1min RSI and Volume filters for signal refinement. This illustrates the indicator's adaptability across different timeframes and market conditions. Strategic Applications for Institutional Mandates MFTA’s sophisticated design provides distinct advantages for advanced trading operations and institutional investment mandates. Key strategic applications include: High-Probability Trend Identification: Fibonacci-averaged Supertrend with MTF filters robustly identifies high-probability trend continuations and reversals, enhancing alpha generation. Precision Entry/Exit Signals: Volume and momentum-filtered signals enable institutional-grade precision for optimized risk-adjusted returns. Algorithmic Trading Integration: Clear signal logic facilitates seamless integration into automated trading systems for scalable strategy deployment. Multi-Asset/Timeframe Versatility: Adaptable parameters ensure applicability across diverse asset classes and timeframes, catering to varied trading mandates. Enhanced Risk Management: Superior signal fidelity from MTF filters inherently reduces false signals, supporting robust risk management protocols. Granular Customization and Parameterized Control MFTA offers unparalleled customization, empowering users to fine-tune parameters for precise alignment with specific trading styles and market conditions. Key adjustable parameters include: Fibonacci Factors: Adjust Supertrend sensitivity to volatility regimes. ATR Length: Control volatility responsiveness in Supertrend calculations. Smoothing Length: Refine Smoothed Trend line responsiveness and noise reduction. MTF Filter Parameters: Independently configure timeframes, lookback periods, and thresholds for RSI, MACD, and Volume filters for optimal signal filtering. Disclaimer MFTA is meticulously engineered for high-quality trend signals; however, no indicator guarantees profit. Market conditions are unpredictable, and trading involves substantial risk. Rigorous backtesting and forward testing across diverse datasets, alongside a comprehensive understanding of the indicator's logic, are essential before live deployment. Past performance is not indicative of future results. MFTA is for informational and analytical purposes only and is not financial or investment advice.
Indicador Pine Script®
por FibonacciFlux
4
Volume Profile & Smart Money Explorer🔍 Volume Profile & Smart Money Explorer: Decode Institutional Footprints Master the art of institutional trading with this sophisticated volume analysis tool. Track smart money movements, identify peak liquidity windows, and align your trades with major market participants. 🌟 Key Features: 📊 Triple-Layer Volume Analysis • Total Volume Patterns • Directional Volume Split (Up/Down) • Institutional Flow Detection • Real-time Smart Money Tracking • Historical Pattern Recognition ⚡ Smart Money Detection • Institutional Trade Identification • Large Block Order Tracking • Smart Money Concentration Periods • Whale Activity Alerts • Volume Threshold Analysis 📈 Advanced Profiling • Hourly Volume Distribution • Directional Bias Analysis • Liquidity Heat Maps • Volume Pattern Recognition • Custom Threshold Settings 🎯 Strategic Applications: Institutional Trading: • Track Big Player Movements • Identify Accumulation/Distribution • Follow Smart Money Flow • Detect Institutional Trading Windows • Monitor Block Orders Risk Management: • Identify High Liquidity Windows • Avoid Thin Market Periods • Optimize Position Sizing • Track Market Participation • Monitor Volume Quality Market Analysis: • Volume Pattern Recognition • Smart Money Flow Analysis • Liquidity Window Identification • Institutional Activity Cycles • Market Depth Analysis 💡 Perfect For: • Professional Traders • Volume Profile Traders • Institutional Traders • Risk Managers • Algorithmic Traders • Smart Money Followers • Day Traders • Swing Traders 📊 Key Metrics: • Normalized Volume Profiles • Institutional Thresholds • Directional Volume Split • Smart Money Concentration • Historical Patterns • Real-time Analysis ⚡ Trading Edge: • Trade with Institution Flow • Identify Optimal Entry Points • Recognize Distribution Patterns • Follow Smart Money Positioning • Avoid Thin Markets • Capitalize on Peak Liquidity 🎓 Educational Value: • Understand Market Structure • Learn Volume Analysis • Master Institutional Patterns • Develop Market Intuition • Track Smart Money Flow 🛠️ Customization: • Adjustable Time Windows • Flexible Volume Thresholds • Multiple Timeframe Analysis • Custom Alert Settings • Visual Preference Options Whether you're tracking institutional flows in crypto markets or following smart money in traditional markets, the Volume Profile & Smart Money Explorer provides the deep insights needed to trade alongside the biggest players. Transform your trading from retail guesswork to institutional precision. Know exactly when and where smart money moves, and position yourself ahead of major market shifts. #VolumeProfile #SmartMoney #InstitutionalTrading #MarketAnalysis #TradingView #VolumeAnalysis #CryptoTrading #ForexTrading #TechnicalAnalysis #Trading #PriceAction #MarketStructure #OrderFlow #Liquidity #RiskManagement #TradingStrategy #DayTrading #SwingTrading #AlgoTrading #QuantitativeTrading
Indicador Pine Script®
por sebamarghella
6
Hourly Volatility Explorer📊 Hourly Volatility Explorer: Master The Market's Pulse Unlock the hidden rhythms of price action with this sophisticated volatility analysis tool. The Hourly Volatility Explorer reveals the most potent trading hours across multiple time zones, giving you a strategic edge in timing your trades. 🌟 Key Features: ⏰ Multi-Timezone Analysis • GMT (UTC+0) • EST (UTC-5) - New York • BST (UTC+1) - London • JST (UTC+9) - Tokyo • AEST (UTC+10) - Sydney Perfect for tracking major market sessions and their overlaps! 📈 Dynamic Visualization • Color-gradient hourly bars for instant pattern recognition • Real-time volatility comparison • Interactive data table with comprehensive statistics • Automatic highlighting of peak volatility periods 🎯 Strategic Applications: Day Trading: • Identify optimal trading windows • Avoid low-liquidity periods • Capitalize on session overlaps • Fine-tune entry/exit timing Risk Management: • Set appropriate stop losses based on hourly volatility • Adjust position sizes for different market hours • Optimize risk-reward ratios • Plan around high-impact hours Global Market Analysis: • Track volatility across all major sessions • Spot institutional trading patterns • Identify quiet vs. active periods • Monitor 24/7 market dynamics 💡 Perfect For: • Forex traders navigating global sessions • Crypto traders in 24/7 markets • Day traders optimizing execution times • Algorithmic traders fine-tuning strategies • Risk managers calibrating exposure 📊 Advanced Features: • Rolling 3-month analysis for reliable patterns • Precise pip movement calculations • Sample size tracking for statistical validity • Real-time current hour comparison • Color-coded visual system for instant insights ⚡ Pro Trading Tips: • Use during major session overlaps for maximum opportunity • Compare patterns across different instruments • Combine with volume analysis for deeper insights • Track seasonal variations in hourly patterns • Build trading schedules around peak hours 🎓 Educational Value: • Understand market microstructure • Learn global market dynamics • Master timezone relationships • Develop timing intuition 🛠️ Customization: • Adjustable lookback period • Flexible pip multiplier • Multiple timezone options • Visual preference settings Whether you're scalping the 1-minute chart or managing longer-term positions, the Hourly Volatility Explorer provides the precise timing intelligence needed for today's global markets. Transform your trading schedule from guesswork to science. Know exactly when markets move, why they move, and how to position yourself for maximum opportunity. #TechnicalAnalysis #Trading #Volatility #MarketTiming #DayTrading #Forex #Crypto #TradingView #PineScript #MarketAnalysis #TradingStrategy #RiskManagement #GlobalMarkets #FinancialMarkets #TradingTools #MarketStructure #PriceAction #Scalping #SwingTrading #AlgoTrading
Indicador Pine Script®
por sebamarghella
3
VIDYA For-Loop | QuantEdgeB Introducing VIDYA For-Loop by QuantEdgeB Overview The VIDYA For-Loop indicator by QuantEdgeB is a dynamic trend-following tool that leverages Variable Index Dynamic Average (VIDYA) along with a rolling loop function to assess trend strength and direction. By utilizing adaptive smoothing and a recursive loop for threshold evaluation, this indicator provides a more responsive and robust signal framework for traders. ______ Key Components & Features 📌 VIDYA (Variable Index Dynamic Average) - Adaptive Moving Average that adjusts its responsiveness based on market volatility. - Uses a dynamic smoothing constant based on standard deviations. - Allows for better trend detection compared to static moving averages. 📌 Loop Function (Rolling Calculation) - A for-loop algorithm continuously compares VIDYA values over a defined lookback range. - Measures the number of times price trends higher or lower within the rolling window. - Generates a momentum-based score that helps quantify trend persistence. 📌 Trend Signal Calculation - A long signal is triggered when the loop score exceeds the upper threshold. - A short signal is triggered when the loop score falls below the lower threshold. - The result is a clear directional bias that adapts to changing market conditions. ______ How It Works in Trading ✅ Detects Trend Strength – By measuring cumulative movements within a window. ✅ Filters Noise – Uses adaptive smoothing to avoid whipsaws. ✅ Dynamic Thresholds – Enables customized entry & exit conditions. ✅ Color-Coded Candles – Provides visual clarity for traders. ______ Visual Representation Trend Signals: 🔵 Blue Candles – Strong Uptrend 🔴 Red Candles – Strong Downtrend Thresholds: 📈 Long Threshold – Upper bound for bullish confirmation. 📉 Short Threshold – Lower bound for bearish confirmation. Labels & Annotations (Optional): ✅ Long & Short Labels can be turned on or off for trade signal clarity. 📊 Display of entry & exit points based on loop calculations. ______ Settings: VIDYA Length: 2 → Number of bars for VIDYA calculation. SD Length: 5 → Standard deviation length for VIDYA calculation. Source: Close → Defines the input data source (Close price). Start Loop: 1 → Initial lookback period for the loop function. End Loop: 60 → Maximum lookback range for trend scoring. Long Threshold: 40 → Upper bound for a long signal. Short Threshold: 10 → Lower bound for a short signal. Extra Plots: True → Enables additional moving averages for visualization. ______ Conclusion The VIDYA For-Loop by QuantEdgeB is a next-gen adaptive trend filter that combines dynamic smoothing with recursive trend evaluation, making it an invaluable tool for traders seeking precision and consistency in their strategies. 🔹 Who should use VIDYA For Loop : 📊 Trend-Following Traders – Helps identify sustained trends. ⚡ Momentum Traders – Captures strong price swings. 🚀 Algorithmic & Systematic Trading – Ideal for automated entries & exits. 🔹 Disclaimer: Past performance is not indicative of future results. No trading strategy can guarantee success in financial markets. 🔹 Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance before live trading.
Indicador Pine Script®
por QuantEdgeB
Auto-Length Moving Average + Trend Signals (Zeiierman)█ Overview The Auto-Length Moving Average + Trend Signals (Zeiierman) is an easy-to-use indicator designed to help traders dynamically adjust their moving average length based on market conditions. This tool adapts in real-time, expanding and contracting the moving average based on trend strength and momentum shifts. The indicator smooths out price fluctuations by modifying its length while ensuring responsiveness to new trends. In addition to its adaptive length algorithm, it incorporates trend confirmation signals, helping traders identify potential trend reversals and continuations with greater confidence. This indicator suits scalpers, swing traders, and trend-following investors who want a self-adjusting moving average that adapts to volatility, momentum, and price action dynamics. █ How It Works ⚪ Dynamic Moving Average Length The core feature of this indicator is its ability to automatically adjust the length of the moving average based on trend persistence and market conditions: Expands in strong trends to reduce noise. Contracts in choppy or reversing markets for faster reaction. This allows for a more accurate moving average that aligns with current price dynamics. ⚪ Trend Confirmation & Signals The indicator includes built-in trend detection logic, classifying trends based on market structure. It evaluates trend strength based on consecutive bars and smooths out transitions between bullish, bearish, and neutral conditions. Uptrend: Price is persistently above the adjusted moving average. Downtrend: Price remains below the adjusted moving average. Neutral: Price fluctuates around the moving average, indicating possible consolidation. ⚪ Adaptive Trend Smoothing A smoothing factor is applied to enhance trend readability while minimizing excessive lag. This balances reactivity with stability, making it easier to follow longer-term trends while avoiding false signals. █ How to Use ⚪ Trend Identification Bullish Trend: The indicator confirms an uptrend when the price consistently stays above the dynamically adjusted moving average. Bearish Trend: A downtrend is recognized when the price remains below the moving average. ⚪ Trade Entry & Exit Enter long when the dynamic moving average is green and a trend signal occurs. Exit when the price crosses below the dynamic moving average. Enter short when the dynamic moving average is red and a trend signal occurs. Exit when the price crosses above the dynamic moving average. █ Slope-Based Reset This mode resets the trend counter when the moving average slope changes direction. ⚪ Interpretation & Insights Best for trend-following traders who want to filter out noise and only reset when a clear shift in momentum occurs. Higher slope length (N): More stable trends, fewer resets. Lower slope length (N): More reactive to small price swings, frequent resets. Useful in swing trading to track significant trend reversals. █ RSI-Based Reset The counter resets when the Relative Strength Index (RSI) crosses predefined overbought or oversold levels. ⚪ Interpretation & Insights Best for reversal traders who look for extreme overbought/oversold conditions. High RSI threshold (e.g., 80/20): Fewer resets, only extreme conditions trigger adjustments. Lower RSI threshold (e.g., 60/40): More frequent resets, detecting smaller corrections. Great for detecting exhaustion in trends before potential reversals. █ Volume-Based Reset A reset occurs when current volume significantly exceeds its moving average, signaling a shift in market participation. ⚪ Interpretation & Insights Best for traders who follow institutional activity (high volume often means large players are active). Higher volume SMA length: More stable trends, only resets on massive volume spikes. Lower volume SMA length: More reactive to short-term volume shifts. Useful in identifying breakout conditions and trend acceleration points. █ Bollinger Band-Based Reset A reset occurs when price closes above the upper Bollinger Band or below the lower Bollinger Band, signaling potential overextension. ⚪ Interpretation & Insights Best for traders looking for volatility-based trend shifts. Higher Bollinger Band multiplier (k = 2.5+): Captures only major price extremes. Lower Bollinger Band multiplier (k = 1.5): Resets on moderate volatility changes. Useful for detecting overextensions in strong trends before potential retracements. █ MACD-Based Reset A reset occurs when the MACD line crosses the signal line, indicating a momentum shift. ⚪ Interpretation & Insights Best for momentum traders looking for trend continuation vs. exhaustion signals. Longer MACD lengths (260, 120, 90): Captures major trend shifts. Shorter MACD lengths (10, 5, 3): Reacts quickly to momentum changes. Useful for detecting strong divergences and market shifts. █ Stochastic-Based Reset A reset occurs when Stochastic %K crosses overbought or oversold levels. ⚪ Interpretation & Insights Best for short-term traders looking for fast momentum shifts. Longer Stochastic length: Filters out false signals. Shorter Stochastic length: Captures quick intraday shifts. █ CCI-Based Reset A reset occurs when the Commodity Channel Index (CCI) crosses predefined overbought or oversold levels. The CCI measures the price deviation from its statistical mean, making it a useful tool for detecting overextensions in price action. ⚪ Interpretation & Insights Best for cycle traders who aim to identify overextended price deviations in trending or ranging markets. Higher CCI threshold (e.g., ±200): Detects extreme overbought/oversold conditions before reversals. Lower CCI threshold (e.g., ±10): More sensitive to trend shifts, useful for early signal detection. Ideal for detecting momentum shifts before price reverts to its mean or continues trending strongly. █ Momentum-Based Reset A reset occurs when Momentum (Rate of Change) crosses zero, indicating a potential shift in price direction. ⚪ Interpretation & Insights Best for trend-following traders who want to track acceleration vs. deceleration. Higher momentum length: Captures longer-term shifts. Lower momentum length: More responsive to short-term trend changes. █ How to Interpret the Trend Strength Table The Trend Strength Table provides valuable insights into the current market conditions by tracking how the dynamic moving average is adjusting based on trend persistence. Each metric in the table plays a role in understanding the strength, longevity, and stability of a trend. ⚪ Counter Value Represents the current length of trend persistence before a reset occurs. The higher the counter, the longer the current trend has been in place without resetting. When this value reaches the Counter Break Threshold, the moving average resets and contracts to become more reactive. Example: A low counter value (e.g., 10) suggests a recent trend reset, meaning the market might be changing directions frequently. A high counter value (e.g., 495) means the trend has been ongoing for a long time, indicating strong trend persistence. ⚪ Trend Strength Measures how strong the current trend is based on the trend confirmation logic. Higher values indicate stronger trends, while lower values suggest weaker trends or consolidations. This value is dynamic and updates based on price action. Example: Trend Strength of 760 → Indicates a high-confidence trend. Trend Strength of 50 → Suggests weak price action, possibly a choppy market. ⚪ Highest Trend Score Tracks the strongest trend score recorded during the session. Helps traders identify the most dominant trend observed in the timeframe. This metric is useful for analyzing historical trend strength and comparing it with current conditions. Example: Highest Trend Score = 760 → Suggests that at some point, there was a strong trend in play. If the current trend strength is much lower than this value, it could indicate trend exhaustion. ⚪ Average Trend Score This is a rolling average of trend strength across the session. Provides a bigger picture of how the trend strength fluctuates over time. If the average trend score is high, the market has had persistent trends. If it's low, the market may have been choppy or sideways. Example: Average Trend Score of 147 vs. Current Trend Strength of 760 → Indicates that the current trend is significantly stronger than the historical average, meaning a breakout might be occurring. Average Trend Score of 700+ → Suggests a strong trending market overall. █ Settings ⚪ Dynamic MA Controls Base MA Length – Sets the starting length of the moving average before dynamic adjustments. Max Dynamic Length – Defines the upper limit for how much the moving average can expand. Trend Confirmation Length – The number of bars required to validate an uptrend or downtrend. ⚪ Reset & Adaptive Conditions Reset Condition Type – Choose what triggers the moving average reset (Slope, RSI, Volume, MACD, etc.). Trend Smoothing Factor – Adjusts how smoothly the moving average responds to price changes. ----------------- Disclaimer The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information. All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
Indicador Pine Script®
por Zeiierman
44
2022 Model ICT Entry Strategy [TradingFinder] One Setup For Life🔵 Introduction The ICT 2022 model, introduced by Michael Huddleston, is an advanced trading strategy rooted in liquidity and price imbalance, where time and price serve as the core elements. This ICT 2022 trading strategy is an algorithmic approach designed to analyze liquidity and imbalances in the market. It incorporates concepts such as Fair Value Gap (FVG), Liquidity Sweep, and Market Structure Shift (MSS) to help traders identify liquidity movements and structural changes in the market, enabling them to determine optimal entry and exit points for their trades. This Full ICT Day Trading Model empowers traders to pinpoint the Previous Day High/Low as well as the highs and lows of critical sessions like the London and New York sessions. These levels act as Liquidity Zones, which are frequently swept prior to a market structure shift (MSS) or a retracement to areas such as Optimal Trade Entry (OTE). Bullish : Bearish : 🔵 How to Use The ICT 2022 model is a sophisticated trading strategy that focuses on identifying key liquidity levels and price movements. It operates based on two main principles. In the first phase, the price approaches liquidity zones and sweeps critical levels such as the previous day’s high or low and key session levels. This movement is known as a Liquidity Sweep. In the second phase, following the sweep, the price retraces to areas like the FVG (Fair Value Gap), creating ideal entry points for trades. Below is a detailed explanation of how to apply this strategy in bullish and bearish setups. 🟣 Bullish ICT 2022 Model Setup To use the ICT 2022 model in a bullish setup, start by identifying the Previous Day High/Low or key session levels, such as those of the London or New York sessions. In a bullish setup, the price usually moves downward first, sweeping the Liquidity Low. This move, known as a Liquidity Sweep, reflects the collection of buy orders by major market participants. After the liquidity sweep, the price should shift market structure and start moving upward; this shift, referred to as Market Structure Shift (MSS), signals the beginning of an upward trend. Following MSS, areas like FVG, located within the Discount Zone, are identified. At this stage, the trader waits for the price to retrace to these zones. Once the price returns, a long trade is executed. Finally, the stop-loss should be set below the liquidity low to manage risk, while the take-profit target is usually placed above the previous day’s high or other identified liquidity levels. This structure enables traders to take advantage of the upward price movement after the liquidity sweep. 🟣 Bearish ICT 2022 Model Setup To identify a bearish setup in the ICT 2022 model, begin by marking the Previous Day High/Low or key session levels, such as the London or New York sessions. In this scenario, the price typically moves upward first, sweeping the Liquidity High. This move, known as a Liquidity Sweep, signifies the collection of sell orders by key market players. After the liquidity sweep, the price should shift market structure downward. This movement, called the Market Structure Shift (MSS), indicates the start of a downtrend. Following MSS, areas such as FVG, found within the Premium Zone, are identified. At this stage, the trader waits for the price to retrace to these areas. Once the price revisits these zones, a short trade is executed. In this setup, the stop-loss should be placed above the liquidity high to control risk, while the take-profit target is typically set below the previous day’s low or another defined liquidity level. This approach allows traders to capitalize on the downward price movement following the liquidity sweep. 🔵 Settings Swing period : You can set the swing detection period. Max Swing Back Method : It is in two modes "All" and "Custom". If it is in "All" mode, it will check all swings, and if it is in "Custom" mode, it will check the swings to the extent you determine. Max Swing Back : You can set the number of swings that will go back for checking. FVG Length : Default is 120 Bar. MSS Length : Default is 80 Bar. FVG Filter : This refines the number of identified FVG areas based on a specified algorithm to focus on higher quality signals and reduce noise. Types of FVG filters : Very Aggressive Filter: Adds a condition where, for an upward FVG, the last candle's highest price must exceed the middle candle's highest price, and for a downward FVG, the last candle's lowest price must be lower than the middle candle's lowest price. This minimally filters out FVGs. Aggressive Filter: Builds on the Very Aggressive mode by ensuring the middle candle is not too small, filtering out more FVGs. Defensive Filter: Adds criteria regarding the size and structure of the middle candle, requiring it to have a substantial body and specific polarity conditions, filtering out a significant number of FVGs. Very Defensive Filter: Further refines filtering by ensuring the first and third candles are not small-bodied doji candles, retaining only the highest quality signals. 🔵 Conclusion The ICT 2022 model is a comprehensive and advanced trading strategy designed around key concepts such as liquidity, price imbalance, and market structure shifts (MSS). By focusing on the sweep of critical levels such as the previous day’s high/low and important trading sessions like London and New York, this strategy enables traders to predict market movements with greater precision. The use of tools like FVG in this model helps traders fine-tune their entry and exit points and take advantage of bullish and bearish trends after liquidity sweeps. Moreover, combining this strategy with precise timing during key trading sessions allows traders to minimize risk and maximize returns. In conclusion, the ICT 2022 model emphasizes the importance of time and liquidity, making it a powerful tool for both professional and novice traders. By applying the principles of this model, you can make more informed trading decisions and seize opportunities in financial markets more effectively.
Indicador Pine Script®
por TFlab
14
Support & Resistance AI LevelScopeSupport & Resistance AI LevelScope Support & Resistance AI LevelScope is an advanced, AI-driven tool that automatically detects and highlights key support and resistance levels on your chart. This indicator leverages smart algorithms to pinpoint the most impactful levels, providing traders with a precise, real-time view of critical price boundaries. Save time and enhance your trading edge with effortless, intelligent support and resistance identification. Key Features: AI-Powered Level Detection: The LevelScope algorithm continuously analyzes price action, dynamically plotting support and resistance levels based on recent highs and lows across your chosen timeframe. Sensitivity Control: Customize the sensitivity to display either major levels for a macro view or more frequent levels for detailed intraday analysis. Easily adjust to suit any trading style or market condition. Level Strength Differentiation: Instantly recognize the strength of each level with visual cues based on how often price has touched each one. Stronger levels are emphasized, highlighting areas with higher significance, while weaker levels are marked subtly. Customizable Visuals: Tailor the look of your chart with customizable color schemes and line thickness options for strong and weak levels, ensuring clear visibility without clutter. Proximity Alerts: Receive alerts when price approaches key support or resistance, giving you a heads-up for potential market reactions and trading opportunities. Who It’s For: Whether you're a day trader, swing trader, or just want a quick, AI-driven way to identify high-probability levels on your chart, Support & Resistance AI LevelScope is designed to keep you focused and informed. This indicator is the perfect addition to any trader’s toolkit, empowering you to make more confident, data-backed trading decisions with ease. Upgrade your analysis with AI-powered support and resistance—no more manual lines, only smart levels!
Indicador Pine Script®
por paradoxalgo
7
Advanced VWAP [CryptoSea]The Advanced VWAP is a comprehensive volume-weighted average price (VWAP) tool designed to provide traders with a deeper understanding of market trends through multi-layered VWAP analysis. This indicator is ideal for those who want to track price movements in relation to VWAP bands and detect key market levels with greater precision. Key Features Multi-Timeframe VWAP Bands: Includes multiple VWAP bands with different lookback periods (5, 10, 25, and 50), allowing traders to observe short-term and long-term price behavior. Smoothed Band Options: Offers optional smoothing of VWAP bands to reduce noise and highlight significant trends more clearly. Dynamic Median Line Display: Plots the median line of the VWAP bands, providing a reference for price movements and potential reversal zones. VWAP Trend Strength Calculation: Measures the strength of the trend based on the price's position relative to the VWAP bands, normalized between -1 and 1 for easier interpretation. In the example below we can see the VWAP Forecastd Cloud, which consists of multiple layers of VWAP bands with varying lookback periods, creating a dynamic forecast visualization. The cloud structure represents potential future price ranges by projecting VWAP-based bands outward, with darker areas indicating higher density and overlap of the bands, suggesting stronger support or resistance zones. This approach helps traders anticipate price movement and identify areas of potential consolidation or breakout as the price interacts with different layers of the forecast cloud. How it Works VWAP Calculation: Utilizes multiple VWAP calculations based on various lookback periods to capture a broad range of price behaviors. The indicator adapts to different market conditions by switching between short-term and long-term VWAP references. Smoothing Algorithms: Provides the ability to smooth the VWAP bands using different moving average types (SMA, EMA, SMMA, WMA, VWMA) to suit various trading strategies and reduce market noise. Trend Strength Analysis: Computes the trend strength based on the price's distance from the VWAP bands, with a value range of -1 to 1. This feature helps traders identify the intensity of uptrends and downtrends. Alert Conditions: Includes alert options for crossing above or below the smoothed median line, as well as touching the smoothed upper or lower bands, providing timely notifications for potential trading opportunities. This image below illustrates the use of smoothed VWAP bands, which provide a cleaner representation of the price's relationship to the VWAP by reducing market noise. The smoothed bands create a flowing cloud-like structure, making it easier to observe significant trends and potential reversal points. The circles highlight areas where the price interacts with the smoothed bands, indicating potential key levels for trend continuation or reversal. This setup helps traders focus on meaningful movements and filter out minor fluctuations, improving the identification of strategic entry and exit points based on smoother trend signals. Application Strategic Entry and Exit Points: Helps traders identify optimal entry and exit points based on the interaction with VWAP bands and trend strength readings. Trend Confirmation: Assists in confirming trend strength by analyzing price movements relative to the VWAP bands and detecting significant breaks or touches. Customized Analysis: Supports a wide range of trading styles by offering adjustable smoothing, band settings, and alert conditions to meet specific trading needs. The Advanced VWAP by is a valuable addition to any trader's toolkit, offering versatile features to navigate different market scenarios with confidence. Whether used for day trading or longer-term analysis, this tool enhances decision-making by providing a robust view of price behavior relative to VWAP levels.
Indicador Pine Script®
por CryptoSeaTV
1