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Kinetic Elasticity Reversion System - Adaptive Genesis Engine🧬 KERS-AGE - EVOLVED KINETIC ELASTICITY REVERSION SYSTEM EDUCATIONAL GUIDE & THEORETICAL FOUNDATION ⚠️ IMPORTANT DISCLAIMER This indicator and guide are provided for educational and informational purposes only. This is NOT financial advice, investment advice, or a recommendation to buy or sell any security. Trading involves substantial risk of loss. Past performance does not guarantee future results. The performance metrics, win rates, and examples shown are from historical backtesting and do not represent actual trading results. Always conduct your own research, paper trade extensively, and never risk capital you cannot afford to lose. The developers assume no responsibility for any trading losses incurred through use of this indicator. INTRODUCTION KERS-AGE (Kinetic Elasticity Reversion System - Adaptive Genetic Evolution) represents an educational exploration of adaptive trading systems. Unlike traditional indicators with fixed parameters, KERS-AGE demonstrates a dynamic, evolving approach that adjusts to market conditions through genetic algorithms and machine learning techniques. This guide explains the theoretical concepts, technical implementation, and educational examples of how the system operates. CONCEPTUAL FRAMEWORK Traditional Indicators vs. Adaptive Systems: Traditional Indicators: Fixed parameters Single strategy approach Static behavior Designed for specific conditions Require manual optimization Adaptive System Approach (KERS-AGE): Dynamic parameters (adjust based on conditions) Multiple strategies tested simultaneously Pattern recognition (cluster analysis) Regime-aware (speciation) Automated optimization (genetic algorithms) Transparent operation (detailed dashboard) CORE CONCEPTS EXPLAINED 1. THE ELASTICITY ANALOGY 🎯 The indicator models price behavior as if connected to a moving average by an elastic band: Price extends away → Elastic tension builds → Potential reversion point identified Key Measurements: STRETCH: Distance from price to equilibrium (MA) TENSION: Normalized force calculation THRESHOLD: Point where multiple factors align Theoretical Foundation: Markets have historically shown mean-reverting tendencies around fair value. This concept quantifies the deviation and identifies potential reversal zones based on multiple confluence factors. Mathematical Approach: text Tension Score = (Price Distance from MA) / (Band Width) × Volatility Scaling Signal Threshold = Multiple of ATR × Dynamic Volatility Ratio Confluence = Tension Score + Additional Factors 2. THE 6 SIGNAL TYPES 📊 The system recognizes 6 distinct pattern categories: A. ELASTIC SIGNALS Pattern: Price reaches statistical band extremes Theory: Maximum deviation from mean suggests potential reversion Detection: Price touches outer zones (typically 2-3× ATR from MA) Component: Mathematical band extension measurement Historical Context: Often observed in markets with clear swing patterns B. WICK SIGNALS Pattern: Extended rejection wicks on candles Theory: Failed breakout attempts may indicate directional exhaustion Detection: Upper/lower wick exceeding 2× body size Component: Real-time price rejection measurement Historical Context: Common in volatile conditions with rapid reversals C. EXHAUSTION SIGNALS Pattern: Decelerating momentum despite price extension Theory: Velocity and acceleration divergence may precede reversals Detection: Decreasing velocity with negative acceleration Component: Momentum derivative analysis Historical Context: Often seen at trend maturity points D. CLIMAX SIGNALS Pattern: Volume spike at price extreme Theory: Unusual volume at extremes historically correlates with turning points Detection: Volume 1.5-2.5× average at band extreme Component: Volume-price relationship analysis Historical Context: Associated with institutional activity or capitulation E. STRUCTURE SIGNALS Pattern: Fractal pivot formations (swing highs/lows) Theory: Market structure points have historically acted as support/resistance Detection: 2-4 bar pivot patterns Component: Classical technical analysis Historical Context: Universal across timeframes and markets F. DIVERGENCE SIGNALS Pattern: RSI divergence versus price Theory: Momentum divergence has historically preceded price reversals Detection: Price makes new extreme but RSI does not Component: Oscillator divergence detection Historical Context: Considered a leading indicator in technical analysis Pattern Confluence: Historical testing suggests stronger signals when multiple types align: Elastic + Wick + Volume = Higher confluence score Elastic + Exhaustion + Divergence = Multiple confirmation factors Any 3+ types = Increased pattern strength Note: Past pattern performance does not guarantee future occurrence. 3. REGIME DETECTION 🌍 The system attempts to classify market conditions into three behavioral regimes: 📈 TREND REGIME Detection Methodology: text Efficiency Ratio = Net Movement / Total Movement Classification: Efficiency > 0.5 AND Volatility < 1.3 → TREND Characteristics Observed: Directional price movement Relatively lower volatility Defined higher highs/lower lows Persistent directional momentum System Response: Reduces signal frequency Prioritizes trend-specialist strategies Applies additional filtering to counter-trend signals Increases confluence requirements Educational Note: In trending conditions, counter-trend mean reversion signals historically have shown reduced reliability. Users may consider additional confirmation when trend regime is detected. ↔️ RANGE REGIME Detection Methodology: text Classification: Efficiency < 0.5 AND Volatility 0.9-1.4 → RANGE Characteristics Observed: Oscillating price action Defined support/resistance zones Mean-reverting behavior patterns Relatively balanced directional flow System Response: Increases signal frequency Activates range-specialist strategies Adjusts bands relative to volatility Reduces confluence threshold Educational Note: Historical backtesting suggests mean reversion systems have performed better in ranging conditions. This does not guarantee future performance. 🌊 VOLATILE REGIME Detection Methodology: text Classification: DVS (Dynamic Volatility Scaling) > 1.5 → VOLATILE Characteristics Observed: Erratic price swings Expanded ranges Elevated ATR readings Often news or event-driven System Response: Activates volatility-specialist strategies Widens bands automatically Prioritizes wick rejection signals Emphasizes volume confirmation Educational Note: Volatile conditions historically present both opportunity and increased risk. Wider stops may be appropriate for risk management. 4. GENETIC EVOLUTION EXPLAINED 🧬 The system employs genetic algorithms to optimize parameters - an approach used in computational finance research. The Evolution Process: STEP 1: INITIALIZATION text Initial State: System creates 4 starter strategies - Strategy 0: Range-optimized parameters - Strategy 1: Trend-optimized parameters - Strategy 2: Volatility-optimized parameters - Strategy 3: Balanced parameters Each contains 14 adjustable parameters (genes): - Band sensitivity - Extension multiplier - Wick threshold - Momentum threshold - Volume multiplier - Component weights (elastic, wick, momentum, volume, fractal) - Target percentage STEP 2: COMPETITION (Shadow Trading) text Early Bars: All strategies generate signals in parallel - Each tracks hypothetical performance independently - Simulated P&L, win rate, Sharpe ratio calculated - No actual trades executed (educational simulation) - Performance metrics recorded for analysis STEP 3: FITNESS EVALUATION text Fitness Calculation = 0.25 × Win Rate + 0.25 × PnL Score + 0.15 × Drawdown Score + 0.30 × Sharpe Ratio Score + 0.05 × Trade Count Score With Walk-Forward enabled: Fitness = 0.60 × Test Score + 0.40 × Train Score With Speciation enabled: Fitness adjusted by Diversity Penalty STEP 4: SELECTION (Tournament) text Periodically (default every 50 bars): - Randomly select 4 active strategies - Compare fitness scores - Top 2 selected as "parents" STEP 5: CROSSOVER (Breeding) text Parent 1 Fitness: 0.65 Parent 2 Fitness: 0.55 Weight calculation: 0.65/(0.65+0.55) = 54% For each parameter: Child Parameter = (0.54 × Parent1) + (0.46 × Parent2) Example: Band Sensitivity: (0.54 × 1.5) + (0.46 × 2.0) = 1.73 STEP 6: MUTATION text For each parameter: if random(0-1) < Mutation Rate (default 0.15): Add random variation: -12% to +12% Purpose: Prevents premature convergence Enables: Discovery of novel parameter combinations ADAPTIVE MUTATION: If population fitness converges → Mutation rate × 1.5 (Encourages exploration when diversity decreases) STEP 7: INSERTION text New strategy added to population: - Assigned unique ID number - Generation counter incremented - Begins shadow trading - Competes with existing strategies STEP 8: CULLING (Selection Pressure) text Periodically (default every 100 bars): - Identify lowest fitness strategy - Verify not elite (protected top performers) - Verify not last of species - Remove from population Result: Maintains selection pressure Effect: Prevents weak strategies from diluting signals STEP 9: SIGNAL GENERATION LOGIC text When determining signals to display: If Ensemble enabled: - All strategies cast weighted votes - Weights based on fitness scores - Specialists receive boost in matching regime - Signal generated if consensus threshold reached If Ensemble disabled: - Single highest-fitness strategy used STEP 10: ADAPTATION OBSERVATION text Over time: Population characteristics may shift - Lower-performing strategies removed - Higher-performing strategies replicated - Parameters adjust toward observed optima - Fitness scores generally trend upward Long-term: Population reaches maturity - Strategies become specialized - Parameters optimized for recent conditions - Performance stabilizes Educational Context: Genetic algorithms are a recognized computational method for optimization problems. This implementation applies those concepts to trading parameter optimization. Past optimization results do not guarantee future performance. 5. SPECIATION (Niche Specialization) 🐟🦎🦅 Inspired by biological speciation theory applied to algorithmic trading. The Three Species: RANGE SPECIALISTS 📊 text Optimized for: Sideways market conditions Parameter tendencies: - Tighter bands (1.0-1.5× ATR) - Higher sensitivity to elastic stretch - Emphasis on fractal structure - More frequent signal generation Typically emerge when: - Range regime detected - Clear support/resistance present - Mean reversion showing historical success Historical backtesting observations: - Win rates often in 55-65% range - Smaller reward/risk ratios (0.5-1.5R) - Higher trade frequency TREND SPECIALISTS 📈 text Optimized for: Directional market conditions Parameter tendencies: - Wider bands (2.0-2.5× ATR) - Focus on momentum exhaustion - Emphasis on divergence patterns - More selective signal generation Typically emerge when: - Trend regime detected - Strong directional movement observed - Counter-trend exhaustion signals sought Historical backtesting observations: - Win rates often in 40-55% range - Larger reward/risk ratios (1.5-3.0R) - Lower trade frequency VOLATILITY SPECIALISTS 🌊 text Optimized for: High-volatility conditions Parameter tendencies: - Expanded bands (1.5-2.0× ATR) - Priority on wick rejection patterns - Strong volume confirmation requirement - Very selective signals Typically emerge when: - Volatile regime detected - High DVS ratio (>1.5) - News-driven or event-driven conditions Historical backtesting observations: - Win rates often in 50-60% range - Variable reward/risk ratios (1.0-2.5R) - Opportunistic trade timing Species Protection Mechanism: text Minimum Per Species: Configurable (default 2) If Range specialists = 1: → Preferential spawning of Range type → Protection from culling process Purpose: Ensures coverage across regime types Theory: Markets cycle between behavioral states Goal: Prevent extinction of specialized approaches Fitness Sharing: text If Species has 4 members: Individual Fitness × 1 / (4 ^ 0.3) Individual Fitness × 0.72 Purpose: Creates pressure toward species diversity Effect: Prevents single approach from dominating population Educational Note: Speciation is a theoretical framework for maintaining strategy diversity. Past specialization performance does not guarantee future regime classification accuracy or signal quality. 6. WALK-FORWARD VALIDATION 📈 An out-of-sample testing methodology used in quantitative research to reduce overfitting risk. The Overfitting Problem: text Hypothetical Example: In-Sample Backtest: 85% win rate Out-of-Sample Results: 35% win rate Explanation: Strategy may have optimized to historical noise rather than repeatable patterns Walk-Forward Methodology: Timeline Structure: text ┌──────────────────────────────────────────────────────┐ │ Train Window │ Test Window │ Train │ Test │ │ (200 bars) │ (50 bars) │ (200) │ (50) │ └──────────────────────────────────────────────────────┘ In-Sample Out-of-Sample IS OOS (Optimize) (Validate) Cycle 2... TRAIN PHASE (In-Sample): text Example Bars 1-200: Strategies optimize parameters - Performance tracked - Not yet used for primary fitness - Learning period TEST PHASE (Out-of-Sample): text Example Bars 201-250: Strategies use optimized parameters - Performance tracked separately - Validation period - Out-of-sample evaluation FITNESS CALCULATION EXAMPLE: text Train Win Rate: 65% Test Win Rate: 58% Composite Fitness: = (0.40 × 0.65) + (0.60 × 0.58) = 0.26 + 0.35 = 0.61 Note: Test results weighted 60%, Train 40% Theory: Out-of-sample may better indicate forward performance OVERFIT DETECTION MECHANISM: text Gap = Train WR - Test WR = 65% - 58% = 7% If Gap > Overfit Threshold (default 25%): Fitness Penalty = Gap × 2 Example with 30% gap: Strategy shows: Train 70%, Test 40% Gap: 30% → Potential overfit flagged Penalty: 30% × 2 = 60% fitness reduction Result: Strategy likely to be culled WINDOW ROLLING: text Example Bar 250: Test window complete → Reset both windows → Start new cycle → Previous results retained for analysis Cycle Count increments Historical performance tracked across multiple cycles Educational Context: Walk-forward analysis is a recognized approach in quantitative finance research for evaluating strategy robustness. However, past out-of-sample performance does not guarantee future results. Market conditions can change in ways not represented in historical data. 7. CLUSTER ANALYSIS 🔬 An unsupervised machine learning approach for pattern recognition. The Concept: text Scenario: System identifies a price pivot that wasn't signaled → Extract pattern characteristics → Store features for analysis → Adjust detection for similar future patterns Implementation: STEP 1: FEATURE EXTRACTION text When significant move occurs without signal: Extract 5-dimensional feature vector: Feature Vector = Example: Observed Pattern: STEP 2: CLUSTER ASSIGNMENT text Compare to existing cluster centroids using distance metric: Cluster 0: Cluster 1: ← Minimum distance Cluster 2: ... Assign to nearest cluster STEP 3: CENTROID UPDATE text Old Centroid 1: New Pattern: Decay Rate: 0.95 Updated Centroid: = 0.95 × Old + 0.05 × New = Exponential moving average update = STEP 4: PROFIT TRACKING text Cluster Average Profit (hypothetical): Old Average: 2.5R New Observation: 3.2R Updated: 0.95 × 2.5 + 0.05 × 3.2 = 2.535R STEP 5: LEARNING ADJUSTMENT text If Cluster Average Profit > Threshold (e.g., 2.0R): Cluster Learning Boost += increment (e.g., 0.1) (Maximum cap: 2.0) Effect: Future signals resembling this cluster receive adjustment STEP 6: SCORE MODIFICATION text For signals matching cluster characteristics: Base Score × Cluster Learning Boost Example: Base Score: 5.2 Cluster Boost: 1.3 Adjusted Score: 5.2 × 1.3 = 6.76 Result: Pattern more likely to generate signal Cluster Interpretation Example: text CLUSTER 0: "High elastic, low volume" Centroid: Avg Profit: 3.5R (historical backtest) Interpretation: Pure elastic signals in ranges historically favorable CLUSTER 1: "Wick rejection, volatile" Centroid: Avg Profit: 2.8R (historical backtest) Interpretation: Wick signals in volatility showed positive results CLUSTER 2: "Exhaustion divergence" Centroid: Avg Profit: 4.2R (historical backtest) Interpretation: Momentum exhaustion in trends performed well Learning Progress Metrics: text Missed Total: 47 Clusters Updated: 142 Patterns Learned: 28 Interpretation: - System identified 47 significant moves without signals - Clusters updated 142 times (incremental refinement) - Made 28 parameter adjustments - Theoretically improving pattern recognition Educational Note: Cluster analysis is a recognized machine learning technique. This implementation applies it to trading pattern recognition. Past cluster performance does not guarantee future pattern profitability or accurate classification. 8. ENSEMBLE VOTING 🗳️ A collective decision-making approach common in machine learning. The Wisdom of Crowds Concept: text Single Model: - May have blind spots - Subject to individual bias - Limited perspective Ensemble of Models: - Blind spots may offset - Biases may average out - Multiple perspectives considered Implementation: STEP 1: INDIVIDUAL VOTES text Example Bar 247: Strategy 0 (Range): LONG (fitness: 0.65) Strategy 1 (Trend): FLAT (fitness: 0.58) Strategy 2 (Volatile): LONG (fitness: 0.52) Strategy 3 (Balanced): SHORT (fitness: 0.48) Strategy 4 (Range): LONG (fitness: 0.71) Strategy 5 (Trend): FLAT (fitness: 0.55) STEP 2: WEIGHT CALCULATION text Base Weight = Fitness Score If strategy's species matches current regime: Weight × Specialist Boost (configurable, default 1.5) If strategy has recent positive performance: Weight × Recent Performance Factor Example for Strategy 0: Base: 0.65 Range specialist in Range regime: 0.65 × 1.5 = 0.975 Recent performance adjustment: 0.975 × 1.13 = 1.10 STEP 3: WEIGHTED TALLYING text LONG votes: S0: 1.10 + S2: 0.52 + S4: 0.71 = 2.33 SHORT votes: S3: 0.48 = 0.48 FLAT votes: S1: 0.58 + S5: 0.55 = 1.13 Total Weight: 2.33 + 0.48 + 1.13 = 3.94 STEP 4: CONSENSUS CALCULATION text LONG %: 2.33 / 3.94 = 59.1% SHORT %: 0.48 / 3.94 = 12.2% FLAT %: 1.13 / 3.94 = 28.7% Minimum Consensus Setting: 60% Result: NO SIGNAL (59.1% < 60%) STEP 5: SIGNAL DETERMINATION text If LONG % >= Min Consensus: → Display LONG signal → Show consensus percentage in dashboard If SHORT % >= Min Consensus: → Display SHORT signal If neither threshold reached: → No signal displayed Practical Examples: text Strong Consensus (85%): 5 strategies LONG, 0 SHORT, 1 FLAT → High agreement among models Moderate Consensus (62%): 3 LONG, 2 SHORT, 1 FLAT → Borderline agreement No Consensus (48%): 3 LONG, 2 SHORT, 1 FLAT → Insufficient agreement, no signal shown Educational Note: Ensemble methods are widely used in machine learning to improve model robustness. This implementation applies ensemble concepts to trading signals. Past ensemble performance does not guarantee future signal quality or profitability. 9. THOMPSON SAMPLING 🎲 A Bayesian reinforcement learning technique for balancing exploration and exploitation. The Exploration-Exploitation Dilemma: text EXPLOITATION: Use what appears to work Benefit: Leverages observed success patterns Risk: May miss better alternatives EXPLORATION: Try less-tested approaches Benefit: May discover superior methods Risk: May waste resources on inferior options Thompson Sampling Solution: STEP 1: BETA DISTRIBUTIONS text For each signal type, maintain: Alpha = Successes + 1 Beta = Failures + 1 Example for Elastic signals: 15 wins, 10 losses Alpha = 16, Beta = 11 STEP 2: PROBABILITY SAMPLING text Rather than using simple Win Rate = 15/25 = 60% Sample from Beta(16, 11) distribution: Possible samples: 0.55, 0.62, 0.58, 0.64, 0.59... Rationale: Incorporates uncertainty - Type with 5 trades: High uncertainty, wide sample variation - Type with 50 trades: Lower uncertainty, narrow sample range STEP 3: TYPE PRIORITIZATION text Example Bar 248: Elastic sampled: 0.62 Wick sampled: 0.58 Exhaustion sampled: 0.71 ← Highest this sample Climax sampled: 0.52 Structure sampled: 0.63 Divergence sampled: 0.45 Exhaustion type receives temporary boost STEP 4: SIGNAL ADJUSTMENT text If current signal is Exhaustion type: Score × (0.7 + 0.71 × 0.6) Score × 1.126 If current signal is other type with lower sample: Score × (0.7 + sample × 0.6) (smaller adjustment) STEP 5: OUTCOME FEEDBACK text When trade completes: If WIN: Alpha += 1 (Beta unchanged) If LOSS: Beta += 1 (Alpha unchanged) Effect: Shifts probability distribution for future samples Educational Context: Thompson Sampling is a recognized Bayesian approach to the multi-armed bandit problem. This implementation applies it to signal type selection. The mathematical optimality assumes stationary distributions, which may not hold in financial markets. Past sampling performance does not guarantee future type selection accuracy. 10. DYNAMIC VOLATILITY SCALING (DVS) 📉 An adaptive approach where parameters adjust based on current vs. baseline volatility. The Adaptation Problem: text Fixed bands (e.g., always 1.5 ATR): In low volatility environment (vol = 0.5): Bands may be too wide → fewer signals In high volatility environment (vol = 2.0): Bands may be too tight → excessive signals The DVS Approach: STEP 1: BASELINE ESTABLISHMENT text Calculate volatility over baseline period (default 100 bars): Method options: ATR / Close, Parkinson, or Garman-Klass Example average volatility = 1.2% This represents "normal" for recent conditions STEP 2: CURRENT VOLATILITY text Current bar volatility = 1.8% STEP 3: DVS RATIO text DVS Ratio = Current / Baseline = 1.8 / 1.2 = 1.5 Interpretation: Volatility currently 50% above baseline STEP 4: BAND ADJUSTMENT text Base Band Width: 1.5 ATR Adjusted Band Width: Upper: 1.5 × DVS = 1.5 × 1.5 = 2.25 ATR Lower: Same Result: Bands expand 50% to accommodate higher volatility STEP 5: THRESHOLD ADJUSTMENT text Base Thresholds: Wick: 0.15 Momentum: 0.6 Adjusted: Wick: 0.15 / DVS = 0.10 (easier to trigger in high vol) Momentum: 0.6 × DVS = 0.90 (harder to trigger in high vol) DVS Calculation Methods: text ATR RATIO (Simplest): DVS = (ATR / Close) / SMA(ATR / Close, 100) PARKINSON (Range-based): σ = √(∑(ln(H/L))² / (4×n×ln(2))) DVS = Current σ / Baseline σ GARMAN-KLASS (Comprehensive): σ = √(0.5×(ln(H/L))² - (2×ln(2)-1)×(ln(C/O))²) DVS = Current σ / Baseline σ ENSEMBLE (Robust): DVS = Median(ATR_Ratio, Parkinson, Garman_Klass) Educational Note: Dynamic volatility scaling is an approach to normalize indicators across varying market conditions. The effectiveness depends on the assumption that recent volatility patterns continue, which is not guaranteed. Past volatility adjustment performance does not guarantee future normalization accuracy. 11. PRESSURE KERNEL 💪 A composite measurement attempting to quantify directional force beyond simple price movement. Components: 1. CLOSE LOCATION VALUE (CLV) text CLV = ((Close - Low) - (High - Close)) / Range Examples: Close at top of range: CLV = +1.0 (bullish position) Close at midpoint: CLV = 0.0 (neutral) Close at bottom: CLV = -1.0 (bearish position) 2. WICK ASYMMETRY text Wick Pressure = (Lower Wick - Upper Wick) / Range Additional factors: If Lower Wick > Body × 2: +0.3 (rejection boost) If Upper Wick > Body × 2: -0.3 (rejection penalty) 3. BODY MOMENTUM text Body Ratio = Body Size / Range Body Momentum = Close > Open ? +Body Ratio : -Body Ratio Strong bullish candle: +0.9 Weak bullish candle: +0.2 Doji: 0.0 4. PATH ESTIMATE text Close Position = (Close - Low) / Range Open Position = (Open - Low) / Range Path = Close Position - Open Position Additional adjustments: If closed high with lower wick: +0.2 If closed low with upper wick: -0.2 5. MOMENTUM CONFIRMATION text Price Change / ATR Examples: +1.5 ATR move: +1.0 (capped) +0.5 ATR move: +0.5 -0.8 ATR move: -0.8 COMPOSITE CALCULATION: text Pressure = CLV × 0.25 + Wick Pressure × 0.25 + Body Momentum × 0.20 + Path Estimate × 0.15 + Momentum Confirm × 0.15 Volume context applied: If Volume > 1.5× avg: × 1.3 If Volume < 0.5× avg: × 0.7 Final smoothing: 3-period EMA Pressure Interpretation: text Pressure > 0.3: Suggests buying pressure → May support LONG signals → May reduce SHORT signal strength Pressure < -0.3: Suggests selling pressure → May support SHORT signals → May reduce LONG signal strength -0.3 to +0.3: Neutral range → Minimal directional bias Educational Note: The Pressure Kernel is a custom composite indicator combining multiple price action metrics. These weightings are theoretical constructs. Past pressure readings do not guarantee future directional movement or signal quality. USAGE GUIDE - EDUCATIONAL EXAMPLES Getting Started: STEP 1: Add Indicator Open TradingView Add KERS-AGE to chart Allow minimum 100 bars for initialization Verify dashboard displays Gen: 1+ STEP 2: Initial Observation Period text First 200 bars: - System is in learning phase - Signal frequency typically low - Population evolution occurring - Fitness scores generally increasing Recommendation: Observe without trading during initialization STEP 3: Signal Evaluation Criteria text Consider evaluating signals based on: - Confidence percentage - Grade assignment (A+, A, B+, B, C) - Position within bands - Historical win rate shown in dashboard - Train vs. Test performance gap Example Signal Evaluation Checklist: Educational Criteria to Consider: Signal appeared (⚡ arrow displayed) Confidence level meets personal threshold Grade meets personal quality standard Ensemble consensus (if enabled) meets threshold Historical win rate acceptable Test performance reasonable vs. Train Price location at band extreme Regime classification appropriate for strategy If trending: Signal direction aligns with personal analysis Stop loss distance acceptable for risk tolerance Position size appropriate (example: 1-2% account risk) Note: This is an educational checklist, not trading advice. Users should develop their own criteria based on personal risk tolerance and strategy. Risk Management Educational Examples: POSITION SIZING EXAMPLE: text Hypothetical scenario: Account: $10,000 Risk tolerance: 1.5% per trade = $150 Indicated stop distance: 1.5 ATR = $300 per contract Calculation: $150 / $300 = 0.5 contracts This is an educational example only, not a recommendation. STOP LOSS EXAMPLES: text System provides stop level (red line) Typically calculated as 1.5 ATR from entry Alternative approaches users might consider: LONG: Below recent swing low SHORT: Above recent swing high Users should determine stops based on personal risk management. TAKE PROFIT EXAMPLES: text System provides target level (green line) Typically calculated as price stretch × 60% Alternative approaches users might consider: Scale out: Partial exit at 1R, remainder at 2R Trailing stop: Adjust stop after profit threshold Users should determine targets based on personal strategy. Educational Note: These are theoretical examples for educational purposes. Actual position sizing and risk management should be determined by each user based on their individual risk tolerance, account size, and trading plan. OPTIMIZATION BY MARKET TYPE - EDUCATIONAL SUGGESTIONS RANGE-BOUND MARKETS Suggested Settings for Testing: Population Size: 6-8 Min Confluence: 5.0-6.0 Min Consensus: 70% Enable Speciation: Consider enabling Min Per Species: 2 Theoretical Rationale: More strategies may provide better coverage Moderate confluence may generate more signals Higher consensus may filter quality Speciation may encourage range specialist emergence Historical Backtest Observations: Win rates in testing: Varied, often 50-65% range Reward/risk ratios observed: 0.5-1.5R Signal frequency: Relatively frequent Disclaimer: Past backtesting results do not guarantee future performance. TRENDING MARKETS Suggested Settings for Testing: Population Size: 4-5 Min Confluence: 6.0-7.0 Consider enabling MTF filter MTF Timeframe: 3-5× current timeframe Specialist Boost: 1.8-2.0 Theoretical Rationale: Fewer strategies may adapt faster Higher confluence may filter counter-trend noise MTF may reduce counter-trend signals Specialist boost may prioritize trend specialists Historical Backtest Observations: Win rates in testing: Varied, often 40-55% range Reward/risk ratios observed: 1.5-3.0R Signal frequency: Less frequent Disclaimer: Past backtesting results do not guarantee future performance. VOLATILE MARKETS (e.g., Cryptocurrency) Suggested Settings for Testing: Base Length: 25-30 Band Multiplier: 1.8-2.0 DVS: Consider enabling (Ensemble method) Consider enabling Volume Filter Volume Multiplier: 1.5-2.0 Theoretical Rationale: Longer base may smooth noise Wider bands may accommodate larger swings DVS may be critical for adaptation Volume filter may confirm genuine moves Historical Backtest Observations: Win rates in testing: Varied, often 45-60% range Reward/risk ratios observed: 1.0-2.5R Signal frequency: Moderate Disclaimer: Cryptocurrency markets are highly volatile and risky. Past backtesting results do not guarantee future performance. SCALPING (1-5min timeframes) Suggested Settings for Testing: Base Length: 15-20 Train Window: 150 Test Window: 30 Spawn Interval: 30 Min Confluence: 5.5-6.5 Consider enabling Ensemble Min Consensus: 75% Theoretical Rationale: Shorter base may increase responsiveness Shorter windows may speed evolution cycles Quick spawning may enable rapid adaptation Higher confluence may filter noise Ensemble may reduce false signals Historical Backtest Observations: Win rates in testing: Varied, often 50-65% range Reward/risk ratios observed: 0.5-1.0R Signal frequency: Frequent but filtered Disclaimer: Scalping involves high frequency trading with increased transaction costs and slippage risk. Past backtesting results do not guarantee future performance. SWING TRADING (4H-Daily timeframes) Suggested Settings for Testing: Base Length: 25-35 Train Window: 300 Test Window: 100 Population Size: 7-8 Consider enabling Walk-Forward Cooldown: 8-10 bars Theoretical Rationale: Longer timeframe may benefit from longer lookbacks Larger windows may improve robustness testing More population may increase stability Walk-forward may be valuable for multi-day holds Longer cooldown may reduce overtrading Historical Backtest Observations: Win rates in testing: Varied, often 45-60% range Reward/risk ratios observed: 2.0-4.0R Signal frequency: Infrequent but potentially higher quality Disclaimer: Swing trading involves overnight and weekend risk. Past backtesting results do not guarantee future performance. DASHBOARD GUIDE - INTERPRETATION EXAMPLES Reading Each Section: HEADER: text 🧬 KERS-AGE EVOLVED 📈 TREND Regime indication: Color coding suggests current classification (Green = Range, Orange = Trend, Purple = Volatile) POPULATION: text Pop: 6/6 Gen: 42 Interpretation: - Population at target size - System at generation 42 - May indicate mature evolution SPECIES (if enabled): text R:2 T:3 V:1 Interpretation: - 2 Range specialists - 3 Trend specialists - 1 Volatility specialist In TREND regime this distribution may be expected WALK-FORWARD (if enabled): text Phase: 🧪 TEST Cycles: 5 Train: 65% Test: 58% Considerations: - Currently in test phase - Completed 5 full cycles - 7% performance gap between train and test - Gap under default 25% overfit threshold ENSEMBLE (if enabled): text Vote: 🟢 LONG Consensus: 72% Interpretation: - Weighted majority voting LONG - 72% agreement level - Exceeds default 60% consensus threshold SELECTED STRATEGY: text ID:23 Trades: 47 Win%: 58% P&L: +8.3R Fitness: 0.62 Information displayed: - Strategy ID 23, Trend specialist - 47 historical simulated trades - 58% historical win rate - +8.3R historical cumulative reward/risk - 0.62 fitness score Note: These are historical simulation metrics SIGNAL QUALITY: text Conf: 78% Grade: B+ Elastic: ████████░░ Wick: ██████░░░░ Momentum: ███████░░░ Pressure: ███████░░░ Information displayed: - 78% confluence score - B+ grade assignment - Elastic component strongest - Visual representation of component strengths LEARNING (if enabled): text Missed: 47 Learned: 28 Interpretation: - System identified 47 moves without signals - 28 pattern adjustments made - Suggests ongoing learning process POSITION: text POS: 🟢 LONG Score: 7.2 Current state: - Simulated long position active - 7.2 confluence score - Monitor for potential exit signal Educational Note: Dashboard displays are for informational and educational purposes. All performance metrics are historical simulations and do not represent actual trading results or future expectations. FREQUENTLY ASKED QUESTIONS - EDUCATIONAL RESPONSES Q: Why aren't signals showing? A: Several factors may affect signal generation: System may still be initializing (check Gen: counter) Confluence score may be below threshold Ensemble consensus (if enabled) may be below requirement Current regime may naturally produce fewer signals Filters may be active (volume, noise reduction) Consider adjusting settings or allowing more time for evolution. Q: The win rate seems low compared to backtesting? A: Consider these factors: First 200 bars typically represent learning period Focus on TEST % rather than TRAIN % for realistic expectations Trend regime historically shows 40-55% win rates in backtesting Different market conditions may affect performance System emphasizes reward/risk ratio alongside win rate Past performance does not guarantee future results Q: Should I take all signals? A: This is a personal decision. Some users may consider: Taking higher grades (A+, A) in any regime Being more selective in trend regimes Requiring higher ensemble consensus Only trading during specific regimes Paper trading extensively before live trading Each user should develop their own signal selection criteria. Q: Signals appear then disappear? A: This may be expected behavior: Default requires 2-bar persistence Designed to filter brief spikes Confirmation delay intended to reduce false signals Wait for persistence requirement to be met This is an intentional feature, not a malfunction. Q: Test % much lower than Train %? A: This may indicate: Overfit detection system functioning Gap exceeding threshold triggers penalty Strategy may be optimizing to in-sample noise System designed to cull such strategies Walk-forward protection working as intended This is a safety feature to reduce overfitting risk. Q: The population keeps culling strategies? A: This is part of normal evolution: Lower-performing strategies removed periodically Higher-performing strategies replicate Population quality theoretically improves over time Total culled count shows selection pressure This is expected evolutionary behavior. Q: Which timeframe works best? A: Backtesting suggests 15min to 4H may be suitable ranges: Lower timeframes may be noisier, may need more filtering Higher timeframes may produce fewer signals Extensive historical testing recommended for chosen asset Each asset may behave differently Consider paper trading across multiple timeframes Personal testing is recommended for your specific use case. Q: Does it work on all asset types? A: Historical testing suggests: Cryptocurrency: Consider longer Base Length (25-30) due to volatility Forex: Standard settings may be appropriate starting point Stocks: Standard settings, possibly smaller population (4-5) Indices: Trend-focused settings may be worth testing Each asset class has unique characteristics. Extensive testing recommended. Q: Can settings be changed after initialization? A: Yes, but considerations: Population will reset Strategies restart evolution Learning progress resets Consider testing new settings on separate chart first May want to compare performance before committing Settings changes restart the evolutionary process. Q: Walk-Forward enabled or disabled? A: Educational perspective: Walk-Forward adds out-of-sample validation May reduce overfitting risk Results may be more conservative Considered best practice in quantitative research Requires more bars for meaningful data Recommended for those concerned about robustness Individual users should assess based on their needs. Q: Ensemble mode or single strategy? A: Trade-offs to consider: Ensemble approach: Requires consensus threshold May have higher consistency Typically fewer signals Multiple perspectives considered Single strategy approach: More signals (varying quality) Faster response to conditions Higher variability More active signal generation Personal preference and risk tolerance should guide this choice. ADVANCED CONSIDERATIONS Evolution Time: Consider allowing 200+ bars for population maturity Regime Awareness: Historical performance varies by regime classification Confluence Range: Testing suggests 70-85% may be informative range Ensemble Levels: 80%+ consensus historically associated with stronger agreement Out-of-Sample Focus: Test performance may be more indicative than train performance Learning Metrics: "Learned" count shows pattern adjustment over time Pressure Levels: >0.4 pressure historically added confirmation DVS Monitoring: >1.5 DVS typically widens bands and affects frequency Species Balance: Healthy distribution might be 2-2-2 or 3-2-1, avoid 6-0-0 Timeframe Testing: Match to personal trading style, test thoroughly Volume Importance: May be more critical for stocks/crypto than forex MTF Utility: Historically more impactful in trending conditions Grade Significance: A+ in trend regime historically rare and potentially significant Risk Parameters: Standard risk management suggests 1-2% per trade maximum Stop Levels: System stops are pre-calculated, widening may affect reward/risk THEORETICAL FOUNDATIONS Genetic Algorithms in Finance: Traditional Optimization Approaches: Grid search: Exhaustive but computationally expensive Gradient descent: Efficient but prone to local optima Random search: Simple but inefficient Genetic Algorithm Characteristics: Explores parameter space through evolutionary process Balances exploration (mutation) and exploitation (selection) Mitigates local optima through population diversity Parallel evaluation via population approach Inspired by biological evolution principles Academic Context: Genetic algorithms are studied in computational finance literature for parameter optimization. Effectiveness varies based on problem characteristics and implementation. Ensemble Methods in Machine Learning: Single Model Limitations: May overfit to specific patterns Can have blind spots in certain conditions May be brittle to distribution shifts Ensemble Theoretical Benefits: Variance reduction through averaging Robustness through diversity Improved generalization potential Widely used (Random Forests, Gradient Boosting, etc.) Academic Context: Ensemble methods are well-studied in machine learning literature. Performance benefits depend on base model diversity and correlation structure. Walk-Forward Analysis: Alternative Approaches: Simple backtest: Risk of overfitting to full dataset Single train/test split: Limited validation Cross-validation: May violate time-series properties Walk-Forward Characteristics: Continuous out-of-sample validation Respects temporal ordering Attempts to detect strategy degradation Used in quantitative trading research Academic Context: Walk-forward analysis is discussed in quantitative finance literature as a robustness check. However, it assumes future regimes will resemble recent test periods, which is not guaranteed. FINAL EDUCATIONAL SUMMARY KERS-AGE demonstrates an adaptive systems approach to technical analysis. Rather than fixed rules, it implements: ✓ Evolutionary Optimization: Parameter adaptation through genetic algorithms ✓ Regime Classification: Attempted market condition categorization ✓ Out-of-Sample Testing: Walk-forward validation methodology ✓ Pattern Recognition: Cluster analysis and learning systems ✓ Ensemble Methodology: Collective decision-making framework ✓ Full Transparency: Comprehensive dashboard and metrics This indicator is an educational tool demonstrating advanced algorithmic concepts. Critical Reminders: The system: ✓ Attempts to identify potential reversal patterns ✓ Adapts parameters to changing conditions ✓ Provides multiple filtering mechanisms ✓ Offers detailed performance metrics Users must understand: ✓ No system guarantees profitable results ✓ Past performance does not predict future results ✓ Extensive testing and validation recommended ✓ Risk management is user's responsibility ✓ Market conditions can change unpredictably ✓ This is educational software, not financial advice Success in trading requires: Proper education, risk management, discipline, realistic expectations, and personal responsibility for all trading decisions. For Educational Use 🧬 KERS-AGE Development Team ⚠️ FINAL DISCLAIMER This indicator and documentation are provided strictly for educational and informational purposes. NOT FINANCIAL ADVICE: Nothing in this guide constitutes financial advice, investment advice, trading advice, or any recommendation to buy, sell, or hold any security or to engage in any trading strategy. NO GUARANTEES: No representation is made that any account will or is likely to achieve profits or losses similar to those shown in backtests, examples, or historical data. Past performance is not indicative of future results. SUBSTANTIAL RISK: Trading stocks, forex, futures, options, and cryptocurrencies involves substantial risk of loss and is not suitable for every investor. The high degree of leverage can work against you as well as for you. YOUR RESPONSIBILITY: You are solely responsible for your own investment and trading decisions. You should conduct your own research, perform your own analysis, and consult with qualified financial advisors before making any trading decisions. NO LIABILITY: The developers, contributors, and distributors of this indicator disclaim all liability for any losses or damages, direct or indirect, that may result from use of this indicator or reliance on any information provided. PAPER TRADE FIRST: Users are strongly encouraged to thoroughly test this indicator in a paper trading environment before risking any real capital. By using this indicator, you acknowledge that you have read this disclaimer, understand the risks involved in trading, and agree that you are solely responsible for your own trading decisions and their outcomes. Educational Software Only | Trade at Your Own Risk | Not Financial Advice Taking you to school. — Dskyz , Trade with insight. Trade with anticipation.
Pine Script® indicator
by DskyzInvestments
RunRox - Pairs Screener📊 Pairs Screener is part of our premium suite for pair trading. This indicator is designed to scan and rank the most profitable and optimal pairs for the Pairs Strategy. The screener can backtest multiple metrics on deep historical data and display results for many pairs against one base asset at the same time. This allows you to quickly detect market inefficiencies and select the most promising pairs for live trading. HOW DOES THIS STRATEGY WORK⁉️ The core idea of the strategy is described in detail in our main indicator Pairs Strategy from the same product line. There you can find a full explanation of the concept, the math behind pair trading, and the internal logic of the engine. The Pairs Screener is built on top of the same core technology as the main indicator and uses the same internal logic and calculations. It is designed as a key companion tool to the main strategy: it helps you find tradeable pairs, evaluate current deviations, sort and filter lists of candidates, and much more. All of these features will be described in this post. ✅ KEY FEATURES More than 400+ assets available for scanning Forex assets Crypto assets Lower Timeframe Backtester Strategy support Invert signals mode Hedge Coefficient (position size balancing between both legs) 6 hedge modes Stop Loss support Take Profit support Whitelist with your own custom asset list Blacklist to exclude unwanted assets Custom filters 12 tracking metrics for pair evaluation Customizable alerts And many other tools for fine-tuning your search The screener runs backtests simultaneously across a large number of assets and calculates metrics automatically. This helps you very quickly find pairs with strong structural relationships or current inefficiencies that can be used as the basis for your pair trading strategies. ⚙️ MAIN SETTINGS The first section controls the core parameters of the screener: Score, correlation, asset groups for scanning, and other base settings. All major crypto and forex symbols are embedded directly into the screener. Since there are more than 400 assets, it is technically impossible to analyze everything at once, so we grouped them into batches of 40 assets per group. The workflow is simple: Open the chart of the asset you want to use as the base ticker. In the screener settings choose the market (Crypto or Forex). Select a Group (for example, Group 1) and the indicator will scan all assets inside that group against your base ticker. Then you switch to Group 2, Group 3, etc., and repeat the scan. Embedded universe: 400+ assets total 350+ Crypto – split into 10 groups 70+ Forex – split into 3 groups Below is a description of each setting. 🔸 Exclude Dates Allows you to specify a period that should be excluded from analysis. Useful for removing abnormal spikes, news events, or any non-typical segments that distort the statistics for your pairs. 🔸 Market Defines which universe will be used to build pairs with the current main asset: Crypto – 350+ crypto symbols Forex – 70+ FX symbols Whitelist – your own custom list of assets 🔸 Group Selects the asset group to scan. As mentioned above, assets are split into groups of about 40 instruments: 350+ Crypto → 10 groups 70+ Forex → 3 groups The screener will calculate all metrics only for the group you select. 🔸 Lower Timeframe This option enables deep history analysis. Each TradingView plan has a limit on the number of visible bars (for example, 5,000 bars on the basic plan). In standard mode you would only get statistics for the last 5,000 bars of your current timeframe. If you want a deeper backtest on a lower timeframe, you can do the following: Suppose your target timeframe for analysis is 5 minutes. Switch your chart to a 30-minute timeframe. Enable Lower Timeframe in the indicator. Select 5 minutes as the lower timeframe inside the screener. In this mode the screener can reconstruct and analyze up to 99,000 bars of data for your assets. This allows you to evaluate pairs on a much deeper history and see whether the results are stable over a larger sample. 🔸 Method Here you choose the deviation model: preferred Z-Score or S-Score for your analysis, plus you can enable Invert to search for negatively correlated pairs and calculate their profit correctly. 🔸 Period This is the lookback period for Z/S Score. It defines how many bars are used to calculate the deviation metric for each pair. 🔸 Correlation Period This is the number of bars used to calculate correlation between the base asset and each candidate in the group. The resulting correlation value is also displayed in the results table. 🔀 HEDGE COEFFICIENT The next block of settings is related to the hedge coefficient. This defines how much margin is allocated to each leg of the pair. The classic approach in pair trading is to split the position equally between both assets. For example, if you allocate 100 USD to a trade , the standard model would open 50 USD long on one asset and 50 USD short on the other. This works well for pairs with similar volatility , such as BTCUSDT / ETHUSDT However, if you use a pair like BTCUSDT / DOGEUSDT , the volatility of these assets is very different. They can still be correlated, but their amplitude is not the same. While Bitcoin might move 2% , Dogecoin can move 10% over the same period. Because of that, for pairs with strongly different volatility, we can use a hedge coefficient and, for example, enter with 30 USD on one leg and 70 USD on the other, taking the volatility difference into account. This is the main idea behind the Hedge Coefficient section and its primary use. The indicator includes 6 methods of calculating the coefficient: Cumulative RMA Beta OLS Beta TLS Beta EMA RMA Range RMA Delta Each method uses a different formula to compute the hedge coefficient and to size the position based on different metrics of the assets. We leave it to the trader to decide which algorithm works best for their specific pair and style. Below are the settings inside this section: 🔹 Method When Auto Hedge is enabled, you can select which method to use from the list above. The chosen method will automatically calculate the hedge coefficient between the two legs. 🔹 Hedge Coefficient This is the manual hedge ratio per trade when Auto Hedge is disabled. By default it is set to 1, which means the position is opened 50/50 between the two assets. 🔹 Min Allowed Hedge Coef. This is the minimum allowed hedge coefficient. By default it is 0.2, which means the model will not go below a 20% / 80% split between the legs. 🔹 MA Length For methods that use moving averages (for example Beta EMA), this parameter sets the period used to calculate the hedge coefficient. 💰 STRATEGY SETTINGS This section defines the base backtesting settings for all assets in the screener. Here you configure entries, exits, Stop Loss, and other parameters used to find the most optimal pairs for your strategy. 🔸 Commission % In this field you set your broker’s fee percentage per trade. The indicator automatically calculates the correct commission for each leg of every trade. You only need to input the real commission rate that your broker charges for volume. No additional manual calculations are required. 🔸 Qty $ The margin amount used for backtesting across all assets in the screener. This margin is split between both legs of the pair either equally or according to the selected hedge coefficient. 🔸 Entry The Z/S Score deviation level at which the backtest opens a trade for each pair. 🔸 Exit The Z/S Score level at which the backtest closes trades for the tested assets. 🔸 Stop Loss PnL threshold at which a trade is force-closed during the historical test. 🔸 Cooldown Number of bars the strategy will wait after a Stop Loss before opening the next trade. This block gives you flexible control over how your strategy is tested on 400+ assets, helping you standardize the rules and compare pairs under the exact same conditions. 🗒️ WHITELIST In this section you can define your own custom list of assets for monitoring and backtesting. This is useful if you want to work with symbols that are not included in the built-in lists, such as exotic crypto from smaller exchanges, specific stocks, or any custom universe 🔹 Exchange Prefix Enter the exchange prefix used for your tickers. Example: BINANCE, OANDA, etc. 🔹 Ticker Postfix Enable this option if the tickers require a postfix. Example 1: .P for Binance Futures perpetual contracts. Example 2: USDT if you only provide the base asset in the ticker list. 🔹 Ticker List Enter a comma-separated list of tickers to analyze. Example 1: BTCUSDT, ETHUSDT, BNBUSDT (when the exchange prefix is set). Example 2: BTC, ETH, BNB (when using postfix USDT). Example 3: BINANCE:BTCUSDT.P, OANDA:EURUSD (when different exchanges are used and the prefix option is disabled). This gives you full flexibility to build a screener universe that matches exactly the assets you trade. ⛔ BLACKLIST In this section you can enable a blacklist of unwanted assets that should be skipped during analysis. Enter a comma-separated list of tickers to exclude from the screener: Example 1: BTCUSDT, ETHUSDT Example 2: BTC, ETH (all tickers that contain these symbols will be excluded) This helps you quickly remove illiquid, noisy, or unwanted instruments from the results without changing your main groups or whitelist. 📈 DASHBOARD This section controls the results dashboard: table position, style, and sorting logic. Here is what you can configure: Result Table – position of the results table on the chart. Background / Text – colors and opacity for the table background and text. Table Size – overall size of the results table (from 0 to 30). Show Results – how many rows (pairs) to display in the table. Sort by (stat) – which metric to use for sorting the results. Available options: Profit Factor, Profit, Winrate, Correlation, Score. This lets you quickly focus on the most interesting pairs according to the exact metric that matters most for your strategy. 📎 FILTER SETTINGS This section lets you filter the results table by metric values. For example, you can show only pairs with a minimum correlation of 0.8 to focus on more stable relationships. 🔸 Min Correlation Minimum allowed correlation between the two assets over the selected lookback period. 🔸 Min Score Minimum absolute Score (Z-Score or S-Score) required to include a pair in the results. For example, 2.0 means only pairs with Score >= 2.0 or <= -2.0 will be displayed. 🔸 Min Winrate Minimum win rate percentage for a pair to be included in the table. 🔸 Min Profit Factor Minimum profit factor required for a pair to stay in the results. These filters help you quickly narrow the list down to pairs that meet your quality criteria and match your risk profile. 📌 COLUMN SELECTION This section lets you fully customize which metrics are displayed in the results table. You can enable or hide any column to focus only on the data you need to identify the best pairs for trading. The screener allows you to show up to 12 metrics at the same time, which gives a detailed view of pair quality. Available columns: 🔹 Exchange Prefix Show the exchange prefix in the ticker. 🔹 Correlation Correlation between the two assets’ prices over the lookback period. 🔹 Score Current Score value (Z-Score or S-Score). On lower timeframe research, Score is not displayed. 🔹 Spread Shows spread as % change since entry. Positive value = profit on the main position. 🔹 Unrealized PnL Shows unrealized PnL as a $ value based on current prices. 🔹 Profit Total profit from all trades: Gross Profit − Gross Loss. 🔹 Winrate Percentage of profitable trades out of all executed trades. 🔹 Profit Factor Gross Profit / Gross Loss. 🔹 Trades Total number of trades. 🔹 Max Drawdown Maximum observed loss from peak to trough before a new peak is made. 🔹 Max Loss Largest loss recorded on a single trade. 🔹 Long/Short Profit Separate profit/loss for long trades and short trades. 🔹 Avg. Trade Time Average duration of trades. All these metrics are designed to help you quickly identify the strongest pairs for your strategy. You can change colors, opacity, and hide any columns that are not relevant to your workflow. 🔔 ALERT The alert system in this screener works in a specific way. Alerts are tied directly to the filters you set in the Filter Settings section: Minimum Correlation Minimum Score Minimum Winrate Minimum Profit Factor You can configure alerts to trigger when a new pair appears that matches all your filter conditions. 💡 Example You set: Minimum Score = 3 Then you create an alert based on the screener. When any pair reaches a Score greater than +3 or less than −3, you will receive a notification. This is how alerts work in this screener. The idea is to deliver the most relevant information about the current market situation without forcing you to watch the screener all the time. Supported placeholders for alert messages: {{ticker_1}} – main ticker (the one on the chart). {{ticker_2}} – the paired ticker listed in the table. {{corr}} – correlation value. {{score}} – Score value (Z-Score or S-Score). {{time}} – bar open time (UTC). {{timenow}} – alert trigger time (UTC). You can use these placeholders to build alert text or JSON payloads in any format required by your tools. The screener is designed to significantly enhance your pair trading workflow: it helps you quickly identify working pairs and current market inefficiencies, and with the alert system you can react to opportunities without constantly sitting in front of the screen. Always remember that past performance does not guarantee future results. Use the screener data within a risk-controlled trading system and adjust position sizing according to your own risk management rules.
Pine Script® indicator
by RunRox
RunRox - Pairs Strategy🧬 Pairs Strategy is a new indicator by RunRox included in our premium subscription. It is a specialized tool for trading pairs, built around working with two correlated instruments at the same time. The indicator is designed specifically for pair trading logic: it helps track the relationship between two assets, identify statistical deviations, and generate signals for opening and managing long/short combinations on both legs of the pair. Below in this description I will go through the core functions of the indicator and the main concepts behind the strategy so you can clearly understand how to apply it in your trading. 📌 CONCEPT The core idea of pair trading is to find and trade correlated instruments that usually move in a similar way. When these two assets temporarily diverge from each other, a trading opportunity appears. In such moments, the relatively overvalued asset is sold (short leg), and the relatively undervalued asset is bought (long leg). When the spread between them narrows and both instruments revert back toward their typical relationship (mean), the position is closed and the trader captures the profit from this convergence. In practice, one leg of the pair can end up in a loss while the other generates a larger profit. Due to the difference in performance between the two assets, the combined result of the pair trade can still be positive. ✅ KEY FEATURES: 2 deviation types (Z-Score and S-Score) Invert signals mode Hedge Coefficient (position size balancing between both legs) 6 hedge modes Entries based on Score or RSI Extra entries based on Score or Spread Stop Loss Take Profit RSI Filter RSI Pivot Mode Built-in Backtester Strategy Lower Timeframe Backtester Strategy Live trade panel for current position Equity curve chart 21 performance metrics in the backtester 2 alert types *And many more fine-tuning options for pair trading 🔗 SCORE Score is the core deviation metric between the two assets in the pair. For example, if you are trading ETHUSDT/BTCUSDT, the indicator analyzes the relationship ETH/BTC, and when one leg temporarily diverges from the other, this difference is reflected in the Score value. In other words, Score shows how much the current spread between the two instruments deviates from its typical state and is used as the main signal source for pair entries and exits. In the screenshot above you can see how Score looks in our indicator. Depending on how large the difference is between the two assets, the Score value can move in a range from −N to +N When Score is in the −N zone, this is a 🟢 long zone for the first asset and a short zone for the second. Using the ETH/BTC example: when Score is deeply negative, you open a long on ETH and a short on BTC at the same time, then close both legs when Score returns back to the 0 zone (balance between the two assets). When Score is in the +N zone, this is a 🔴 short zone for the first asset and a long zone for the second. In the same ETH/BTC example: when Score is strongly positive, you short ETH and long BTC, and again close both positions when Score comes back to the neutral 0 zone. ☯️ Z/S SCORE Inside the indicator we added two different formulas for calculating the spread between the two legs of the pair: Z-Score and S-Score. These approaches measure deviation in different ways and can produce slightly different signals depending on the chosen pair and its behavior. This allows you to switch between Z-Score and S-Score and choose the method that gives more stable and cleaner signals for your specific instruments. As you can see in the screenshot above, we used the same pair but applied different Score types to measure the spread and deviation from the norm. 🟣 Z-Score – generated 9 entry signals . It reacts to price fluctuations more smoothly and usually stays within a range of approximately −8 to +8 . 🟠 S-Score – generated 5 entry signals . It reacts to price changes more aggressively and produces wider deviations, often reaching −15 to +15 . This gives traders the choice between a more sensitive but smoother model (Z-Score) and a more selective, stronger-deviation model (S-Score) ⁉️ HOW DOES THE STRATEGY WORK Here is a basic example of how you can trade this pair trading strategy using our indicator and its signals. In the classic approach the trade consists of one initial entry and several scale-ins (averaging) if the spread continues to move against the position. The first entry is opened when Score reaches a standard deviation of −2 or +2. If price does not revert to the mean and moves further against the position so that Score expands to −3 or +3, the strategy performs the first scale-in. If Score extends to −4 or +4, a second scale-in is added. If the spread grows even more and Score reaches −5 or +5, a third scale-in is executed. In our indicator the number of averaging steps can be up to 4 scale-ins . After that the position waits until Score returns back to the 0 level , where the whole pair position is closed. This is the standard model of classical pair trading. However there are many variations: using Stop Loss and Take Profit, exiting earlier or later than the 0 zone, scaling in not by Score but by Spread, since Score is not linear while Spread is linear, entering when RSI on both tickers shows opposite extremes, for example RSI 20 on one asset and RSI 80 on the other, and so on. The number of possible trading styles for this strategy is very large. We designed the indicator to cover as many of these variations as possible and added flexible tools so you can build your own pair trading logic on top of it. Below is an example of a classic pair trade with two entries: one main entry and one extra entry (scale-in) . The pair SUIUSDT / PENGUUSDT shows a high correlation, and on one of the trades the sequence looked like this: A −2 Score deviation occurred into the long zone and triggered the Main Entry . 🔹 Main Entry Long SUIUSDT – Margin: 5,000 USD, Entry price: 1.5708 Short PENGUUSDT – Margin: 5,000 USD, Entry price: 0.011793 Price then moved further against the position, Score went deeper into deviation, and the strategy added one extra entry. 🔸 Extra Entry Long SUIUSDT – Margin: 5,000 USD, Entry price: 1.5938 Short PENGUUSDT – Margin: 5,000 USD, Entry price: 0.012173 The trade was closed when Score reverted back toward the 0 zone (mean reversion of the spread): ❎ Exit SUIUSDT P&L: −403.34 USD, Exit price: 1.5184 PENGUUSDT P&L: +743.73 USD, Exit price: 0.011089 ✅ Total P&L: +340.39 USD With a total margin of 10,000 USD used per side (20,000 USD combined), this trade yielded around +1.7% on the deployed margin. On different assets the size and speed of the spread movement will vary, but the principle remains the same. This is just one example to illustrate how the strategy works in practice using simplified theoretical balances. ⚙️ MAIN SETTINGS After explaining how the strategy works, we can move to the indicator settings and their logic. The first block is Main Settings, which controls how the pair is built, how the spread is calculated, and how the backtest is performed. The core idea of the indicator is to backtest historical data, generate entry signals, show open-position parameters, and provide all necessary metrics for both discretionary and algorithmic trading. This is a complete framework for analyzing a pair of assets and building a trading system around them. Below I will go through the main parameters one by one. 🔹 Exclude Dates Allows you to exclude abnormal periods in the pair’s history to remove outlier trades from the backtest. This is useful when the market experienced extreme news events, listing spikes, or other non-typical situations that distort statistics. 🔹 Pair Here you select the second asset for your pair. For example, if your main chart is BTCUSDT, in this field you choose a correlated asset such as ETHUSDT, and the working pair becomes BTCUSDT / ETHUSDT. The indicator then calculates spread, Score, and all related metrics based on this asset combination. 🔹 Lower Timeframe This is a special mode for backtesting on a lower timeframe while using a higher timeframe chart to extend the history limit. For example, if your TradingView plan provides only 5,000 bars of history on the current timeframe, you can switch your chart to a higher timeframe and select a lower timeframe in this setting. The indicator will then reconstruct the pair logic using up to 99,000 bars of lower timeframe data for backtesting. This allows you to test the pair on a much longer historical period and find more stable combinations of assets. 🔹 Method Here you choose which deviation model you want to use: Z-Score or S-Score. Both methods calculate spread deviation but use different formulas, which can give different signal behavior depending on the pair. Examples of these two methods are shown earlier in this description. 🔹 Period This parameter defines how many bars are used to calculate the average deviation for the pair. If you set Period = 300, the indicator looks back 300 bars and calculates the typical spread deviation over that window. For example, if the average deviation over 300 bars is around 1%, then a move to 2% or more will push Z/S Score closer to its boundary levels, since such a deviation is considered abnormal for that lookback period. A larger Period means that only bigger deviations will be treated as anomalies. A smaller Period makes the model more sensitive and treats smaller deviations as anomalies. This allows you to tune how aggressive or conservative your pair trading signals should be. 🔹 Invert This setting is used for negatively correlated pairs. Some instruments have a positive correlation in the range from +0.8 to +1.0 (strong positive correlation), while others show a negative correlation from −0.8 to −1.0, meaning they usually move in opposite directions. A classic example is the pair EURUSD and DXY. As shown in the screenshot above, these instruments often have strong negative correlation due to macro factors and typically move in opposite directions: when EURUSD is rising, DXY is falling, and vice versa. Such pairs can also be traded with our indicator. To do this, we use the Invert option, which effectively flips one of the assets (as shown in the screenshot below). After inversion, both instruments are brought to a “same-direction” behavior from the model’s point of view. From there, you trade the pair in the same way as a positively correlated one: you open both legs in the same direction (both long or both short) depending on the spread and Score, and then wait for the spread between the inverted pair to converge back toward its mean. 🔀 HEDGE COEFFICIENT The next block of settings is related to the hedge coefficient. This defines how much margin is allocated to each leg of the pair. The classic approach in pair trading is to split the position equally between both assets. For example, if you allocate 100 USD to a trade , the standard model would open 50 USD long on one asset and 50 USD short on the other. This works well for pairs with similar volatility , such as BTCUSDT / ETHUSDT However, if you use a pair like BTCUSDT / DOGEUSDT , the volatility of these assets is very different. They can still be correlated, but their amplitude is not the same. While Bitcoin might move 2% , Dogecoin can move 10% over the same period. Because of that, for pairs with strongly different volatility, we can use a hedge coefficient and, for example, enter with 30 USD on one leg and 70 USD on the other, taking the volatility difference into account. This is the main idea behind the Hedge Coefficient section and its primary use. The indicator includes 6 methods of calculating the coefficient: Cumulative RMA Beta OLS Beta TLS Beta EMA RMA Range RMA Delta Each method uses a different formula to compute the hedge coefficient and to size the position based on different metrics of the assets. We leave it to the trader to decide which algorithm works best for their specific pair and style. Below are the settings inside this section: 🔹 Method When Auto Hedge is enabled, you can select which method to use from the list above. The chosen method will automatically calculate the hedge coefficient between the two legs. 🔹 Hedge Coefficient This is the manual hedge ratio per trade when Auto Hedge is disabled. By default it is set to 1, which means the position is opened 50/50 between the two assets. 🔹 Min Allowed Hedge Coef. This is the minimum allowed hedge coefficient. By default it is 0.2, which means the model will not go below a 20% / 80% split between the legs. 🔹 MA Length For methods that use moving averages (for example Beta EMA), this parameter sets the period used to calculate the hedge coefficient. 🛠️ STRATEGY SETTINGS The next important block is Strategy Settings . Here you define the core parameters used for backtesting: trading commission, position size, entry / exit logic, Stop Loss, Take Profit, and other rules that describe how you want the strategy to operate. Below are all parameters with a detailed explanation. 🔸 Commission % In this field you set your broker’s fee percentage per trade . The indicator automatically calculates the correct commission for each leg of every trade. You only need to input the real commission rate that your broker charges for volume. No additional manual calculations are required. 🔸 Main Entry Mode There are two options for the main entry: Score - This is the primary entry method based on Z/S Score. When Score reaches the deviation level defined in the settings below, the strategy opens the first position. For example, if you set “Entry at 2 deviations”, the trade will be opened when Score hits ±2. RSI Only - Alternative entry method based on RSI divergence between the two assets. The exact RSI levels are defined in the RSI settings section below. For example, if you set the entry threshold at 30, then when one asset has RSI below 30 and the second one has RSI above 70, the first entry will be triggered. 🔸 Extra Entries Mode This defines how scale-ins (averaging) are executed. There are two modes: Score - Works the same way as the main entry, but for additional entries. For example, the main entry can be at 2 deviations, the first scale-in at 3, the second at 4, etc. Spread - This mode uses the Spread (difference between the two assets) starting from the main entry moment. As the spread continues to widen, the strategy can add extra entries based on spread growth rather than Score. Since Score is a non-linear metric and Spread is linear, in some configurations averaging by Spread can produce better results than averaging by Score. This is pair- and strategy-dependent. 🔸 Entry parameters Deviation / Spread threshold Entry size Main Entry – first field (deviation / spread), second field (position size) Entry 2 – first field (deviation / spread), second field (position size) Entry 3 – first field (deviation / spread), second field (position size) Entry 4 – first field (deviation / spread), second field (position size) This allows you to define up to four scaling steps with different triggers and different sizing. 🔸 Exit Level This parameter defines at what Score level you want to exit the trade. By default it is 0, which means the backtester closes the position when Score returns to the neutral (0) zone. You can also use positive or negative values. Example: Assume your main entry is configured at a 3 deviation. You can exit at the 0 level, or you can set Exit Level = 2. If your initial entry was at −3, the position will be closed when Score reaches +2. If your initial entry was at +3, the position will be closed when Score reaches −2. This approach can increase the profit per trade due to a larger captured spread, but it may also increase the holding time of the position. 🔸 Stop Loss Here you define the maximum loss per trade in PnL units. If a trade reaches the negative PnL value specified in this field and the Stop Loss option is enabled, the indicator will close the trade at a loss. The Cooldown parameter sets a pause after a losing trade: the strategy will wait a specified number of bars before opening the next trade. 🔸 Take Profit Works similar to Stop Loss but for profit targets. You set the desired PnL value you want to reach. The trade will be closed when either the Take Profit target is hit or when Score reaches the exit level defined in the settings, whichever occurs first (depending on your configuration). 🔸 Show Qty in currency When enabled, trade size is displayed in currency (USD) instead of token quantity. This is useful for quickly understanding position size in monetary terms. You will see this in the Current Trade panel, which is described later. 🔸 Size Rounding Controls how many decimal places are used when rounding position size (from 0 to 10 digits after the decimal). This is also used for the Current Trade panel so you can adjust how detailed or compact the size display should be. 📊 RSI FILTERS This section is used for additional trade filtering. RSI can be used in two ways: as a primary entry signal, or as an extra filter for entries based on Z/S Score. If in the Strategy Settings the Main Entry Mode is set to RSI, then RSI becomes the main trigger for opening a position. In this case a trade is opened when the RSI of the two assets reaches opposite zones. Example: If the threshold is set to 30, then: when one asset has RSI below 30, and the second asset has RSI above 70 (100 − 30), the strategy opens the first entry. All extra entries after that will be executed either by Spread or by Z/S Score, depending on your Extra Entries Mode. Below are the parameters in this block: RSI Length – standard RSI period setting. RSI Pivot Mode – when enabled, RSI is used as an additional filter together with Z/S Score. The indicator looks for a reversal pattern on RSI (pivot behavior). If RSI forms a reversal structure, the trade is allowed to open. If not, the signal is skipped until a proper RSI pivot is formed. Entry RSI Filter – here you define the RSI thresholds used for RSI-based entries. These are the same boundary levels described in the example above. Overall, this section helps filter out lower-quality trades using additional RSI conditions or lets you build RSI-only entry logic based on extreme levels. 🎨 MAIN CHART STYLING This section controls the visual appearance of trades on the main chart. You can customize how the second asset line is drawn, as well as the icons for entries, scale-ins, and exits, including their size and style. ▫️ Price Line This is the line that shows the price of the second asset and the relative difference between the two instruments. You can adjust the line thickness and color to make it more readable on your chart. ▫️ Adjust Price Line by Hedge Coefficient When this option is enabled, the second asset’s line is normalized by the hedge coefficient. If you turn it off, the hedge coefficient will not be applied to the second asset’s line, and it will be displayed in raw form. ▫️ Entry Label Here you can customize how the entry markers look: choose the color, icon style, and size of the label that marks each trade entry and scale-in on the chart. ▫️ Exit Label Similarly, you can define the color, icon style, and size of the label used for exits. This helps visually separate entries and exits and makes it easier to read the trade history directly from the chart. 🎯 INDICATOR PANEL This section controls the settings of the indicator panel, which works like an oscillator and allows you to visualize multiple metrics in one place. You can flexibly enable, style, and scale each parameter. 🔹 Score Displays the main deviation metric between the two assets. You can customize the color and line thickness of the Score plot. 🔹 Spread Shows the spread between the two assets. It starts calculating from the moment the trade is opened. You can adjust its color and thickness for better visibility. 🔹 Total Profit Displays the cumulative profit for this pair and strategy as a line that grows (or falls) over time. Color, opacity, and line thickness can be customized. 🔹 Unrealized PNL Once a trade is opened, this line shows the current PnL of the active position. It also lets you see historical drawdowns on the pair. Color and thickness can be adjusted. 🔹 Released PNL Shows the realized PnL of each closed trade as bars. Useful for quickly evaluating the result of every individual trade in the backtest. 🔹 Correlation Plots the correlation coefficient between the two assets as a graph, so you can visually track how stable or unstable the relationship between them is over time. 🔹 Hedge Coefficient Shows the hedge coefficient as a line, which helps understand how the model is rebalancing exposure between the two legs depending on their behavior. For each metric there is also a 📎 Stretch option. Stretch allows you to compress or expand the scale of a specific line to visually align metrics with different ranges on the same panel and make the chart easier to read. 📈 PROFIT CHART Since TradingView does not natively support proper backtesting for pair trading, this indicator includes its own profit curve for the pair. You can visually see how the strategy performed over historical data: whether there were deep drawdowns, abnormal profit spikes, or stable equity growth over time. This makes it much easier to evaluate the quality of the pair and the strategy on history. In the settings of this section you can flexibly customize how the profit chart is displayed: labels, position of the panel, padding, and other visual details. Everything depends on your personal preferences, so we give full control over styling: you can adjust the look of the profit chart to match your layout or completely hide it from the chart if you do not need it. 📌 CURRENT TRADE This section controls the current trade table. When there is an active trade on the chart, the panel displays all key information for the open position: direction for each ticker (long or short), required position size for each leg, entry price for both assets, and real-time PnL for each leg separately, so you always have a clear view of the current situation. The main thing you can do with this table is customize its appearance: you can change the size, position on the chart, background and text colors, as well as separate coloring for positive / negative PnL and different colors for long and short positions. 📅 BACKTEST RESULTS The next key block is Backtest Results. This results table with detailed metrics gives you an extended view of how the pair and strategy perform: win rate, profit factor, long/short breakdown, and more than 20 additional stats that help you evaluate the potential of your setup. ⚠️ First of all, it is important to note ⚠️ past performance does not guarantee future results. Every trader must keep this in mind and factor these risks into their strategy. The table shows metrics in three cuts: All Entries Main Entries Extra Entries (scale-ins) Core metrics: Profit – total profit for each entry type. Winrate – win rate for this pair. Profit Factor – ratio of gross profit to gross loss for the strategy. Trades – number of trades in the backtest. Wins – number of winning trades. Losses – number of losing trades. Long Profit – profit generated by long positions. Short Profit – profit generated by short positions. Longs – total number of long trades. Shorts – total number of short trades. Avg. Time – average time spent in a trade. Additional metrics for a deeper evaluation of the pair: Correlation – current correlation between the two assets in the pair. Bars Processed – number of bars used in the analysis. Max Drawdown – maximum historical drawdown of the strategy. Biggest Loss – the largest single losing trade in the backtest. Recommended Hedge – recommended hedge coefficient based on historical behavior. Max Spread – maximum positive spread observed in history. Min Spread – maximum negative spread observed in history. Avg. Max Spread – average of positive extreme spread values (above 0). Avg. Min Spread – average of negative extreme spread values (below 0). Avg Positive Spread – average positive spread across all trades (only values above 0). Avg Negative Spread – average negative spread across all trades (only values below 0). Current Spread – current spread between the assets when a trade is open. These metrics together allow you to quickly assess how stable the pair is, how the risk/return profile looks, and whether the strategy parameters are suitable for live trading. You can fully customize this results table to fit your workflow: hide metrics you don’t need, change colors, opacity, and other visual styles, and reorder the focus of the stats according to your trading style. This way the backtest block can show only the metrics that matter to you most and remain clean and readable during analysis. 📣 ALERTS The next section is dedicated to alerts. Here you can configure all signals you need, both for manual trading and for full automation of this pair trading strategy. This block is designed to cover most practical use cases. The indicator supports two alert modes: Single Alert – one universal custom alert for all events. Two Alerts – separate alerts for each ticker so you can receive different messages per asset. Available alert events: Main Entry – when the main entry is triggered. Entry 2 – when the first scale-in is executed. Entry 3 – when the second scale-in is executed. Entry 4 – when the third scale-in is executed. Exit Alert – when the position is closed. StopLoss Alert – when Stop Loss is hit. TakeProfit Alert – when Take Profit is hit. All alerts are fully customizable and support a set of placeholders for building structured messages or JSON payloads. 🔹1 Alert Type List of supported placeholders: {{event}} – trigger name ('Entry 1', 'Exit'). {{dir_1}} – 'Long' or 'Short' for the main ticker. {{dir_2}} – 'Long' or 'Short' for the other ticker. {{action_1}} – 'Buy', 'Sell' or 'Close' for the main ticker. {{action_2}} – 'Buy', 'Sell' or 'Close' for the other ticker. {{price_1}} – price for the main ticker. {{price_2}} – price for the other ticker. {{qty_1}} – order size for the main ticker. {{qty_2}} – order size for the other ticker. {{ticker_1}} – main ticker (e.g. 'BTCUSD'). {{ticker_2}} – other ticker (e.g. 'ETHUSD'). {{time}} – candle open time in UTC. {{timenow}} – signal time in UTC. 🔹2 Alert Type List of supported placeholders: {{event}} – trigger name ('Entry 1', 'Exit', 'SL', 'TP'). {{action}} – 'Buy', 'Sell' or 'Close'. {{price}} – order price. {{qty}} – order size. {{ticker}} – ticker (e.g. 'BTCUSD'). {{time}} – candle open time in UTC. {{timenow}} – signal time in UTC. You can use these placeholders to build any JSON structure or custom alert text required by your trading bot, exchange API, or automation service. In this post I’ve explained how the indicator works, the core concept behind this pair trading strategy, and shown practical examples of trades together with a detailed breakdown of each unique feature inside the tool. We have invested a lot of work into building this indicator and we truly hope it will help you trade pair strategies more efficiently and more profitably by giving you structured, strategy-specific information that is difficult to obtain in any other way. ⚠️ Please also remember that past performance does not guarantee future results. Always evaluate the risks, the robustness of your setup, and your own risk tolerance before entering any position, and make independent, well-considered decisions when using this or any other strategy.
Pine Script® indicator
by RunRox
RSI Distribution [Kodexius]RSI Distribution is a statistics driven visualization companion for the classic RSI oscillator. In addition to plotting RSI itself, it continuously builds a rolling sample of recent RSI values and projects their distribution as a forward drawn histogram, so you can see where RSI has spent most of its time over the selected lookback window. The indicator is designed to add context to oscillator readings. Instead of only treating RSI as a single point estimate that is either “high” or “low”, you can evaluate the current RSI level relative to its own recent history. This makes it easier to recognize when the market is operating inside a familiar regime, and when RSI is pushing into rarer tail conditions that tend to appear during momentum bursts, exhaustion, or volatility expansion. To complement the histogram, the script can optionally overlay a Gaussian curve fitted to the sample mean and standard deviation. It also runs a Jarque Bera normality check, based on skewness and excess kurtosis, and surfaces the result both visually and in a compact dashboard. On the oscillator panel itself, RSI is presented with a clean gradient line and standard overbought and oversold references, with fills that become more visible when RSI meaningfully extends beyond key thresholds. 🔹 Features 1. Distribution Histogram of Recent RSI Values The script stores the last N RSI values in an internal sample and uses that rolling window to compute a frequency distribution across a user selected number of bins. The histogram is drawn into the future by a configurable width in bars, which keeps it readable and prevents it from colliding with the active RSI plot. The result is a compact visual summary of where RSI clusters most often, whether it is spending more time near the center, or shifting toward higher or lower regimes. 2. Gaussian Overlay for Shape Intuition If enabled, a fitted bell curve is drawn on top of the histogram using the sample mean and standard deviation. This overlay is not intended as a direct trading signal. Its purpose is to provide a fast visual comparator between the empirical RSI distribution and a theoretical normal shape. When the histogram diverges strongly from the curve, you can quickly spot skew, heavy tails, or regime changes that often occur when market structure or volatility conditions shift. 3. Jarque Bera Normality Check With Clear PASS/FAIL Feedback The script computes skewness and excess kurtosis from the RSI sample, then forms the Jarque Bera statistic and compares it to a fixed 95% critical value. When the distribution is closer to normal under this test, the status is marked as PASS, otherwise it is marked as FAIL. This result is displayed in the dashboard and can also influence the histogram styling, giving immediate feedback about whether the recent RSI behavior resembles a bell shaped distribution or a more distorted, regime driven profile. Jarque Bera is a goodness of fit test that evaluates whether a dataset looks consistent with a normal distribution by checking two shape properties: skewness (asymmetry) and kurtosis (tail heaviness, expressed here as excess kurtosis where a perfect normal has 0). Under the null hypothesis of normality, skewness should be near 0 and excess kurtosis should be near 0. The test combines deviations in both into a single statistic, which is then compared to a chi square threshold. A PASS in this script means the sample does not show strong evidence against normality at the chosen threshold, while a FAIL means the sample is meaningfully skewed, heavy tailed, or both. In practical trading terms, a FAIL often suggests RSI is behaving in a regime where extremes and asymmetry are more common, which is typical during strong trends, volatility expansions, or one sided market pressure. It is still a statistical diagnostic, not a prediction tool, and results can vary with lookback length and market conditions. 4. Integrated Stats Dashboard A compact table in the top right summarizes key distribution moments and the normality result: Mean, StdDev, Skewness, Kurtosis, and the JB statistic with PASS/FAIL text. Skewness is color coded by sign to quickly distinguish right skew (more time at higher RSI) versus left skew (more time at lower RSI), which can be helpful when diagnosing trend bias and momentum persistence. 5. RSI Visual Quality and Context Zones RSI is plotted with a gradient color scheme and standard overbought and oversold reference lines. The overbought and oversold areas are filled with a smart gradient so visual emphasis increases when RSI meaningfully extends beyond the 70 and 30 regions, improving readability without overwhelming the panel. 🔹 Calculations This section summarizes the main calculations and transformations used internally. 1. RSI Series RSI is computed from the selected source and length using the standard RSI function: rsi_val = ta.rsi(rsi_src, rsi_len) 2. Rolling Sample Collection A float array stores recent RSI values. Each bar appends the newest RSI, and if the array exceeds the configured lookback, the oldest value is removed. Conceptually: rsi_history.push(rsi_val) if rsi_history.size() > lookback rsi_history.shift() This maintains a fixed size window that represents the most recent RSI behavior. 3. Mean, Variance, and Standard Deviation The script computes the sample mean across the array. Variance is computed as sample variance using (n - 1) in the denominator, and standard deviation is the square root of that variance. These values serve both the dashboard display and the Gaussian overlay parameters. 4. Skewness and Excess Kurtosis Skewness is calculated from the standardized third central moment with a small sample correction. Kurtosis is computed as excess kurtosis (kurtosis minus 3), so the normal baseline is 0. These two metrics summarize asymmetry and tail heaviness, which are the core ingredients for the Jarque Bera statistic. 5. Jarque Bera Statistic and Decision Rule Using skewness S and excess kurtosis K, the Jarque Bera statistic is computed as: JB = (n / 6.0) * (S^2 + 0.25 * K^2) Normality is flagged using a fixed critical value: is_normal = JB < 5.991 This produces a simple PASS/FAIL classification suitable for fast chart interpretation. 6. Histogram Binning and Scaling The RSI domain is treated as 0 to 100 and divided into a configurable number of bins. Bin size is: bin_size = 100.0 / bins Each RSI sample maps to a bin index via floor(rsi / bin_size), with clamping to ensure the index stays within valid bounds. The script counts occurrences per bin, tracks the maximum frequency, and normalizes each bar height by freq/max_freq so the histogram remains visually stable and comparable as the window updates. 7. Gaussian Curve Overlay (Optional) The Gaussian overlay uses the normal probability density function with mu as the sample mean and sigma as the sample standard deviation: normal_pdf(x) = (1 / (sigma * sqrt(2*pi))) * exp(-0.5 * ((x - mu)/sigma)^2) For drawing, the script samples x across the histogram width, evaluates the PDF, and normalizes it relative to its peak so the curve fits within the same visual height scale as the histogram.
Pine Script® indicator
by Kodexius
NeuralFlow Forecast Engine | NIFTY WeeklyAI-adaptive market equilibrium & expansion mapping. NeuralFlow doesn’t forecast by direction — it forecasts by where markets prefer to stabilize. NeuralFlow Forecast Engine™ is a proprietary Artificial Intelligence framework trained to identify where price is statistically inclined to rebalance and where expansion zones historically exhaust rather than extend. What the Bands Represent Band Layer Meaning AI Equilibrium (white core) Primary weekly balance zone where price is most likely to mean-revert Predictive Rails (aqua / purple) High-confidence corridor of institutional flow containment Outer Zones (green / red) Expansion limits where continuation historically decays Extreme Zones (top/bottom) Rare deviation envelope where auction completion is statistically favored NeuralFlow operates on proprietary, institution-grade Artificial Intelligence models trained specifically to map statistical rebalancing behavior, not trader predictions or sentiment. No discretionary drawing. No correlations. No lagging overlays. This engine updates only when underlying structure changes — not when candles fluctuate intraday. ⚠ Risk & Use Notice NeuralFlow Forecast Engine™ provides AI-derived structural zones, not trade signals or financial advice. Markets can behave outside modeled distributions, especially during macro catalysts, thin liquidity, or surprise volatility events. By loading or using this indicator, the user acknowledges full responsibility for any trades or outcomes based on its interpretation. Educational & analytical use only. Not financial advice
Pine Script® indicator
by NeuralMarkets
Sector Performance ProSector Performance Pro is a quantitative mean-reversion indicator designed to compare the relative performance of major U.S. equity sectors in a standardized and objective way. The indicator analyzes a set of sector ETFs (XLP, XLU, XLRE, XLV, XLE, XLB, XLF, XLC, XLI, XLY, XLK) and converts their historical behavior into z-scores. For each sector, logarithmic returns and volatility are calculated over a user-defined lookback period (default: 252 bars, approximately one trading year on a daily chart). These values are then normalized using a normal distribution, allowing all sectors to be compared on the same statistical scale. The plotted lines represent the log return z-scores of each sector. Positive values indicate above-average relative performance, while negative values indicate underperformance relative to the sector’s own historical distribution. Dashed volatility z-scores are calculated as well and can be enabled if additional risk context is desired. Horizontal reference lines at ±1, ±2, and ±3 standard deviations (sigma levels) help identify statistically significant deviations. Extreme z-scores may highlight potential overbought or oversold conditions and possible mean-reversion opportunities. This indicator is intended for market regime analysis, sector rotation strategies, and relative strength comparison, rather than precise entry or exit timing. It provides a high-level statistical view of how sectors are positioned relative to their historical norms.
Pine Script® indicator
by bronko791
ORB Fusion🎯 CORE INNOVATION: INSTITUTIONAL ORB FRAMEWORK WITH FAILED BREAKOUT INTELLIGENCE ORB Fusion represents a complete institutional-grade Opening Range Breakout system combining classic Market Profile concepts (Initial Balance, day type classification) with modern algorithmic breakout detection, failed breakout reversal logic, and comprehensive statistical tracking. Rather than simply drawing lines at opening range extremes, this system implements the full trading methodology used by professional floor traders and market makers—including the critical concept that failed breakouts are often higher-probability setups than successful breakouts . The Opening Range Hypothesis: The first 30-60 minutes of trading establishes the day's value area —the price range where the majority of participants agree on fair value. This range is formed during peak information flow (overnight news digestion, gap reactions, early institutional positioning). Breakouts from this range signal directional conviction; failures to hold breakouts signal trapped participants and create exploitable reversals. Why Opening Range Matters: 1. Information Aggregation : Opening range reflects overnight news, pre-market sentiment, and early institutional orders. It's the market's initial "consensus" on value. 2. Liquidity Concentration : Stop losses cluster just outside opening range. Breakouts trigger these stops, creating momentum. Failed breakouts trap traders, forcing reversals. 3. Statistical Persistence : Markets exhibit range expansion tendency —when price accepts above/below opening range with volume, it often extends 1.0-2.0x the opening range size before mean reversion. 4. Institutional Behavior : Large players (market makers, institutions) use opening range as reference for the day's trading plan. They fade extremes in rotation days and follow breakouts in trend days. Historical Context: Opening Range Breakout methodology originated in commodity futures pits (1970s-80s) where floor traders noticed consistent patterns: the first 30-60 minutes established a "fair value zone," and directional moves occurred when this zone was violated with conviction. J. Peter Steidlmayer formalized this observation in Market Profile theory, introducing the "Initial Balance" concept—the first hour (two 30-minute periods) defining market structure. 📊 OPENING RANGE CONSTRUCTION Four ORB Timeframe Options: 1. 5-Minute ORB (0930-0935 ET): Captures immediate market direction during "opening drive"—the explosive first few minutes when overnight orders hit the tape. Use Case: • Scalping strategies • High-frequency breakout trading • Extremely liquid instruments (ES, NQ, SPY) Characteristics: • Very tight range (often 0.2-0.5% of price) • Early breakouts common (7 of 10 days break within first hour) • Higher false breakout rate (50-60%) • Requires sub-minute chart monitoring Psychology: Captures panic buyers/sellers reacting to overnight news. Range is small because sample size is minimal—only 5 minutes of price discovery. Early breakouts often fail because they're driven by retail FOMO rather than institutional conviction. 2. 15-Minute ORB (0930-0945 ET): Balances responsiveness with statistical validity. Captures opening drive plus initial reaction to that drive. Use Case: • Day trading strategies • Balanced scalping/swing hybrid • Most liquid instruments Characteristics: • Moderate range (0.4-0.8% of price typically) • Breakout rate ~60% of days • False breakout rate ~40-45% • Good balance of opportunity and reliability Psychology: Includes opening panic AND the first retest/consolidation. Sophisticated traders (institutions, algos) start expressing directional bias. This is the "Goldilocks" timeframe—not too reactive, not too slow. 3. 30-Minute ORB (0930-1000 ET): Classic ORB timeframe. Default for most professional implementations. Use Case: • Standard intraday trading • Position sizing for full-day trades • All liquid instruments (equities, indices, futures) Characteristics: • Substantial range (0.6-1.2% of price) • Breakout rate ~55% of days • False breakout rate ~35-40% • Statistical sweet spot for extensions Psychology: Full opening auction + first institutional repositioning complete. By 10:00 AM ET, headlines are digested, early stops are hit, and "real" directional players reveal themselves. This is when institutional programs typically finish their opening positioning. Statistical Advantage: 30-minute ORB shows highest correlation with daily range. When price breaks and holds outside 30m ORB, probability of reaching 1.0x extension (doubling the opening range) exceeds 60% historically. 4. 60-Minute ORB (0930-1030 ET) - Initial Balance: Steidlmayer's "Initial Balance"—the foundation of Market Profile theory. Use Case: • Swing trading entries • Day type classification • Low-frequency institutional setups Characteristics: • Wide range (0.8-1.5% of price) • Breakout rate ~45% of days • False breakout rate ~25-30% (lowest) • Best for trend day identification Psychology: Full first hour captures A-period (0930-1000) and B-period (1000-1030). By 10:30 AM ET, all early positioning is complete. Market has "voted" on value. Subsequent price action confirms (trend day) or rejects (rotation day) this value assessment. Initial Balance Theory: IB represents the market's accepted value area . When price extends significantly beyond IB (>1.5x IB range), it signals a Trend Day —strong directional conviction. When price remains within 1.0x IB, it signals a Rotation Day —mean reversion environment. This classification completely changes trading strategy. 🔬 LTF PRECISION TECHNOLOGY The Chart Timeframe Problem: Traditional ORB indicators calculate range using the chart's current timeframe. This creates critical inaccuracies: Example: • You're on a 5-minute chart • ORB period is 30 minutes (0930-1000 ET) • Indicator sees only 6 bars (30min ÷ 5min/bar = 6 bars) • If any 5-minute bar has extreme wick, entire ORB is distorted The Problem Amplifies: • On 15-minute chart with 30-minute ORB: Only 2 bars sampled • On 30-minute chart with 30-minute ORB: Only 1 bar sampled • Opening spike or single large wick defines entire range (invalid) Solution: Lower Timeframe (LTF) Precision: ORB Fusion uses `request.security_lower_tf()` to sample 1-minute bars regardless of chart timeframe: ``` For 30-minute ORB on 15-minute chart: - Traditional method: Uses 2 bars (15min × 2 = 30min) - LTF Precision: Requests thirty 1-minute bars, calculates true high/low ``` Why This Matters: Scenario: ES futures, 15-minute chart, 30-minute ORB • Traditional ORB: High = 5850.00, Low = 5842.00 (range = 8 points) • LTF Precision ORB: High = 5848.50, Low = 5843.25 (range = 5.25 points) Difference: 2.75 points distortion from single 15-minute wick hitting 5850.00 at 9:31 AM then immediately reversing. LTF precision filters this out by seeing it was a fleeting wick, not a sustained high. Impact on Extensions: With inflated range (8 points vs 5.25 points): • 1.5x extension projects +12 points instead of +7.875 points • Difference: 4.125 points (nearly $200 per ES contract) • Breakout signals trigger late; extension targets unreachable Implementation: ```pinescript getLtfHighLow() => float ha = request.security_lower_tf(syminfo.tickerid, "1", high) float la = request.security_lower_tf(syminfo.tickerid, "1", low) ``` Function returns arrays of 1-minute high/low values, then finds true maximum and minimum across all samples. When LTF Precision Activates: Only when chart timeframe exceeds ORB session window: • 5-minute chart + 30-minute ORB: LTF used (chart TF > session bars needed) • 1-minute chart + 30-minute ORB: LTF not needed (direct sampling sufficient) Recommendation: Always enable LTF Precision unless you're on 1-minute charts. The computational overhead is negligible, and accuracy improvement is substantial. ⚖️ INITIAL BALANCE (IB) FRAMEWORK Steidlmayer's Market Profile Innovation: J. Peter Steidlmayer developed Market Profile in the 1980s for the Chicago Board of Trade. His key insight: market structure is best understood through time-at-price (value area) rather than just price-over-time (traditional charts). Initial Balance Definition: IB is the price range established during the first hour of trading, subdivided into: • A-Period : First 30 minutes (0930-1000 ET for US equities) • B-Period : Second 30 minutes (1000-1030 ET) A-Period vs B-Period Comparison: The relationship between A and B periods forecasts the day: B-Period Expansion (Bullish): • B-period high > A-period high • B-period low ≥ A-period low • Interpretation: Buyers stepping in after opening assessed • Implication: Bullish continuation likely • Strategy: Buy pullbacks to A-period high (now support) B-Period Expansion (Bearish): • B-period low < A-period low • B-period high ≤ A-period high • Interpretation: Sellers stepping in after opening assessed • Implication: Bearish continuation likely • Strategy: Sell rallies to A-period low (now resistance) B-Period Contraction: • B-period stays within A-period range • Interpretation: Market indecisive, digesting A-period information • Implication: Rotation day likely, stay range-bound • Strategy: Fade extremes, sell high/buy low within IB IB Extensions: Professional traders use IB as a ruler to project price targets: Extension Levels: • 0.5x IB : Initial probe outside value (minor target) • 1.0x IB : Full extension (major target for normal days) • 1.5x IB : Trend day threshold (classifies as trending) • 2.0x IB : Strong trend day (rare, ~10-15% of days) Calculation: ``` IB Range = IB High - IB Low Bull Extension 1.0x = IB High + (IB Range × 1.0) Bear Extension 1.0x = IB Low - (IB Range × 1.0) ``` Example: ES futures: • IB High: 5850.00 • IB Low: 5842.00 • IB Range: 8.00 points Extensions: • 1.0x Bull Target: 5850 + 8 = 5858.00 • 1.5x Bull Target: 5850 + 12 = 5862.00 • 2.0x Bull Target: 5850 + 16 = 5866.00 If price reaches 5862.00 (1.5x), day is classified as Trend Day —strategy shifts from mean reversion to trend following. 📈 DAY TYPE CLASSIFICATION SYSTEM Four Day Types (Market Profile Framework): 1. TREND DAY: Definition: Price extends ≥1.5x IB range in one direction and stays there. Characteristics: • Opens and never returns to IB • Persistent directional movement • Volume increases as day progresses (conviction building) • News-driven or strong institutional flow Frequency: ~20-25% of trading days Trading Strategy: • DO: Follow the trend, trail stops, let winners run • DON'T: Fade extremes, take early profits • Key: Add to position on pullbacks to previous extension level • Risk: Getting chopped in false trend (see Failed Breakout section) Example: FOMC decision, payroll report, earnings surprise—anything creating one-sided conviction. 2. NORMAL DAY: Definition: Price extends 0.5-1.5x IB, tests both sides, returns to IB. Characteristics: • Two-sided trading • Extensions occur but don't persist • Volume balanced throughout day • Most common day type Frequency: ~45-50% of trading days Trading Strategy: • DO: Take profits at extension levels, expect reversals • DON'T: Hold for massive moves • Key: Treat each extension as a profit-taking opportunity • Risk: Holding too long when momentum shifts Example: Typical day with no major catalysts—market balancing supply and demand. 3. ROTATION DAY: Definition: Price stays within IB all day, rotating between high and low. Characteristics: • Never accepts outside IB • Multiple tests of IB high/low • Decreasing volume (no conviction) • Classic range-bound action Frequency: ~25-30% of trading days Trading Strategy: • DO: Fade extremes (sell IB high, buy IB low) • DON'T: Chase breakouts • Key: Enter at extremes with tight stops just outside IB • Risk: Breakout finally occurs after multiple failures Example: [/b> Pre-holiday trading, summer doldrums, consolidation after big move. 4. DEVELOPING: Definition: Day type not yet determined (early in session). Usage: Classification before 12:00 PM ET when IB extension pattern unclear. ORB Fusion's Classification Algorithm: ```pinescript if close > ibHigh: ibExtension = (close - ibHigh) / ibRange direction = "BULLISH" else if close < ibLow: ibExtension = (ibLow - close) / ibRange direction = "BEARISH" if ibExtension >= 1.5: dayType = "TREND DAY" else if ibExtension >= 0.5: dayType = "NORMAL DAY" else if close within IB: dayType = "ROTATION DAY" ``` Why Classification Matters: Same setup (bullish ORB breakout) has opposite implications: • Trend Day : Hold for 2.0x extension, trail stops aggressively • Normal Day : Take profits at 1.0x extension, watch for reversal • Rotation Day : Fade the breakout immediately (likely false) Knowing day type prevents catastrophic errors like fading a trend day or holding through rotation. 🚀 BREAKOUT DETECTION & CONFIRMATION Three Confirmation Methods: 1. Close Beyond Level (Recommended): Logic: Candle must close above ORB high (bull) or below ORB low (bear). Why: • Filters out wicks (temporary liquidity grabs) • Ensures sustained acceptance above/below range • Reduces false breakout rate by ~20-30% Example: • ORB High: 5850.00 • Bar high touches 5850.50 (wick above) • Bar closes at 5848.00 (inside range) • Result: NO breakout signal vs. • Bar high touches 5850.50 • Bar closes at 5851.00 (outside range) • Result: BREAKOUT signal confirmed Trade-off: Slightly delayed entry (wait for close) but much higher reliability. 2. Wick Beyond Level: Logic: [/b> Any touch of ORB high/low triggers breakout. Why: • Earliest possible entry • Captures aggressive momentum moves Risk: • High false breakout rate (60-70%) • Stop runs trigger signals • Requires very tight stops (difficult to manage) Use Case: Scalping with 1-2 point profit targets where any penetration = trade. 3. Body Beyond Level: Logic: [/b> Candle body (close vs open) must be entirely outside range. Why: • Strictest confirmation • Ensures directional conviction (not just momentum) • Lowest false breakout rate Example: Trade-off: [/b> Very conservative—misses some valid breakouts but rarely triggers on false ones. Volume Confirmation Layer: All confirmation methods can require volume validation: Volume Multiplier Logic: Rationale: [/b> True breakouts are driven by institutional activity (large size). Volume spike confirms real conviction vs. stop-run manipulation. Statistical Impact: [/b> • Breakouts with volume confirmation: ~65% success rate • Breakouts without volume: ~45% success rate • Difference: 20 percentage points edge Implementation Note: [/b> Volume confirmation adds complexity—you'll miss breakouts that work but lack volume. However, when targeting 1.5x+ extensions (ambitious goals), volume confirmation becomes critical because those moves require sustained institutional participation. Recommended Settings by Strategy: [/b> Scalping (1-2 point targets): [/b> • Method: Close • Volume: OFF • Rationale: Quick in/out doesn't need perfection Intraday Swing (5-10 point targets): [/b> • Method: Close • Volume: ON (1.5x multiplier) • Rationale: Balance reliability and opportunity Position Trading (full-day holds): [/b> • Method: Body • Volume: ON (2.0x multiplier) • Rationale: Must be certain—large stops require high win rate 🔥 FAILED BREAKOUT SYSTEM The Core Insight: [/b> Failed breakouts are often more profitable [/b> than successful breakouts because they create trapped traders with predictable behavior. Failed Breakout Definition: [/b> A breakout that: 1. Initially penetrates ORB level with confirmation 2. Attracts participants (volume spike, momentum) 3. Fails to extend (stalls or immediately reverses) 4. Returns inside ORB range within N bars Psychology of Failure: [/b> When breakout fails: • Breakout buyers are trapped [/b>: Bought at ORB high, now underwater • Early longs reduce: Take profit, fearful of reversal • Shorts smell blood: See failed breakout as reversal signal • Result: Cascade of selling as trapped bulls exit + new shorts enter Mirror image for failed bearish breakouts (trapped shorts cover + new longs enter). Failure Detection Parameters: [/b> 1. Failure Confirmation Bars (default: 3): [/b> How many bars after breakout to confirm failure? Logic: Settings: [/b> • 2 bars: Aggressive failure detection (more signals, more false failures) • 3 bars Balanced (default) • 5-10 bars: Conservative (wait for clear reversal) Why This Matters: Too few bars: You call "failed breakout" when price is just consolidating before next leg. Too many bars: You miss the reversal entry (price already back in range). 2. Failure Buffer (default: 0.1 ATR): [/b> How far inside ORB must price return to confirm failure? Formula: Why Buffer Matters: clear rejection [/b> (not just hovering at level). Settings: [/b> • 0.0 ATR: No buffer, immediate failure signal • 0.1 ATR: Small buffer (default) - filters noise • [b>0.2-0.3 ATR: Large buffer - only dramatic failures count Example: Reversal Entry System: [/b> When failure confirmed, system generates complete reversal trade: For Failed Bull Breakout (Short Reversal): [/b> Entry: [/b> Current close when failure confirmed Stop Loss: [/b> Extreme high since breakout + 0.10 ATR padding Target 1: [/b> ORB High - (ORB Range × 0.5) Target 2: Target 3: [/b> ORB High - (ORB Range × 1.5) Example: • ORB High: 5850, ORB Low: 5842, Range: 8 points • Breakout to 5853, fails, reverses to 5848 (entry) • Stop: 5853 + 1 = 5854 (6 point risk) • T1: 5850 - 4 = 5846 (-2 points, 1:3 R:R) • T2: 5850 - 8 = 5842 (-6 points, 1:1 R:R) • T3: 5850 - 12 = 5838 (-10 points, 1.67:1 R:R) [b>Why These Targets? [/b> • T1 (0.5x ORB below high): Trapped bulls start panic • T2 (1.0x ORB = ORB Mid): Major retracement, momentum fully reversed • T3 (1.5x ORB): Reversal extended, now targeting opposite side Historical Performance: [/b> Failed breakout reversals in ORB Fusion's tracking system show: • Win Rate: 65-75% (significantly higher than initial breakouts) • Average Winner: 1.2x ORB range • Average Loser: 0.5x ORB range (protected by stop at extreme) • Expectancy: Strongly positive even with <70% win rate Why Failed Breakouts Outperform: [/b> 1. Information Advantage: You now know what price did (failed to extend). Initial breakout trades are speculative; reversal trades are reactive to confirmed failure. 2. Trapped Participant Pressure: Every trapped bull becomes a seller. This creates sustained pressure. 3. Stop Loss Clarity: Extreme high is obvious stop (just beyond recent high). Breakout trades have ambiguous stops (ORB mid? Recent low? Too wide or too tight). 4. Mean Reversion Edge: Failed breakouts return to value (ORB mid). Initial breakouts try to escape value (harder to sustain). Critical Insight: [/b> "The best trade is often the one that trapped everyone else." Failed breakouts create asymmetric opportunity because you're trading against [/b> trapped participants rather than with [/b> them. When you see a failed breakout signal, you're seeing real-time evidence that the market rejected directional conviction—that's exploitable. 📐 FIBONACCI EXTENSION SYSTEM Six Extension Levels: [/b> Extensions project how far price will travel after ORB breakout. Based on Fibonacci ratios + empirical market behavior. 1. 1.272x (27.2% Extension): [/b> Formula: [/b> ORB High/Low + (ORB Range × 0.272) Psychology: [/b> Initial probe beyond ORB. Early momentum + trapped shorts (on bull side) covering. Probability of Reach: [/b> ~75-80% after confirmed breakout Trading: [/b> • First resistance/support after breakout • Partial profit target (take 30-50% off) • Watch for rejection here (could signal failure in progress) Why 1.272? [/b> Related to harmonic patterns (1.272 is √1.618). Empirically, markets often stall at 25-30% extension before deciding whether to continue or fail. 2. 1.5x (50% Extension): Formula: [/b> ORB High/Low + (ORB Range × 0.5) Psychology: [/b> Breakout gaining conviction. Requires sustained buying/selling (not just momentum spike). Probability of Reach: [/b> ~60-65% after confirmed breakout Trading: [/b> • Major partial profit (take 50-70% off) • Move stops to breakeven • Trail remaining position Why 1.5x? [/b> Classic halfway point to 2.0x. Markets often consolidate here before final push. If day type is "Normal," this is likely the high/low for the day. 3. 1.618x (Golden Ratio Extension): [/b> Formula: [/b> ORB High/Low + (ORB Range × 0.618) Psychology: [/b> Strong directional day. Institutional conviction + retail FOMO. Probability of Reach: [/b> ~45-50% after confirmed breakout Trading: [/b> • Final partial profit (close 80-90%) • Trail remainder with wide stop (allow breathing room) Why 1.618? [/b> Fibonacci golden ratio. Appears consistently in market geometry. When price reaches 1.618x extension, move is "mature" and reversal risk increases. 4. 2.0x (100% Extension): [/b> Formula: ORB High/Low + (ORB Range × 1.0) Psychology: [/b> Trend day confirmed. Opening range completely duplicated. Probability of Reach: [/b> ~30-35% after confirmed breakout Trading: Why 2.0x? [/b> Psychological level—range doubled. Also corresponds to typical daily ATR in many instruments (opening range ~ 0.5 ATR, daily range ~ 1.0 ATR). 5. 2.618x (Super Extension): Formula: [/b> ORB High/Low + (ORB Range × 1.618) Psychology: [/b> Parabolic move. News-driven or squeeze. Probability of Reach: [/b> ~10-15% after confirmed breakout [b>Trading: Why 2.618? [/b> Fibonacci ratio (1.618²). Rare to reach—when it does, move is extreme. Often precedes multi-day consolidation or reversal. 6. 3.0x (Extreme Extension): [/b> Formula: [/b> ORB High/Low + (ORB Range × 2.0) Psychology: [/b> Market melt-up/crash. Only in extreme events. [b>Probability of Reach: [/b> <5% after confirmed breakout Trading: [/b> • Close immediately if reached • These are outlier events (black swans, flash crashes, squeeze-outs) • Holding for more is greed—take windfall profit Why 3.0x? [/b> Triple opening range. So rare it's statistical noise. When it happens, it's headline news. Visual Example: ES futures, ORB 5842-5850 (8 point range), Bullish breakout: • ORB High : 5850.00 (entry zone) • 1.272x : 5850 + 2.18 = 5852.18 (first resistance) • 1.5x : 5850 + 4.00 = 5854.00 (major target) • 1.618x : 5850 + 4.94 = 5854.94 (strong target) • 2.0x : 5850 + 8.00 = 5858.00 (trend day) • 2.618x : 5850 + 12.94 = 5862.94 (extreme) • 3.0x : 5850 + 16.00 = 5866.00 (parabolic) Profit-Taking Strategy: Optimal scaling out at extensions: • Breakout entry at 5850.50 • 30% off at 1.272x (5852.18) → +1.68 points • 40% off at 1.5x (5854.00) → +3.50 points • 20% off at 1.618x (5854.94) → +4.44 points • 10% off at 2.0x (5858.00) → +7.50 points [b>Average Exit: Conclusion: [/b> Scaling out at extensions produces 40% higher expectancy than holding for home runs. 📊 GAP ANALYSIS & FILL PSYCHOLOGY [b>Gap Definition: [/b> Price discontinuity between previous close and current open: • Gap Up : Open > Previous Close + noise threshold (0.1 ATR) • Gap Down : Open < Previous Close - noise threshold Why Gaps Matter: [/b> Gaps represent unfilled orders [/b>. When market gaps up, all limit buy orders between yesterday's close and today's open are never filled. Those buyers are "left behind." Psychology: they wait for price to return ("fill the gap") so they can enter. This creates magnetic pull [/b> toward gap level. Gap Fill Statistics (Empirical): [/b> • Gaps <0.5% [/b>: 85-90% fill within same day • Gaps 0.5-1.0% [/b>: 70-75% fill within same day, 90%+ within week • Gaps >1.0% [/b>: 50-60% fill within same day (major news often prevents fill) Gap Fill Strategy: [/b> Setup 1: Gap-and-Go Gap opens, extends away from gap (doesn't fill). • ORB confirms direction away from gap • Trade WITH ORB breakout direction • Expectation: Gap won't fill today (momentum too strong) Setup 2: Gap-Fill Fade Gap opens, but fails to extend. Price drifts back toward gap. • ORB breakout TOWARD gap (not away) • Trade toward gap fill level • Target: Previous close (gap fill complete) Setup 3: Gap-Fill Rejection Gap fills (touches previous close) then rejects. • ORB breakout AWAY from gap after fill • Trade away from gap direction • Thesis: Gap filled (orders executed), now resume original direction [b>Example: Scenario A (Gap-and-Go): • ORB breaks upward to $454 (away from gap) • Trade: LONG breakout, expect continued rally • Gap becomes support ($452) Scenario B (Gap-Fill): • ORB breaks downward through $452.50 (toward gap) • Trade: SHORT toward gap fill at $450.00 • Target: $450.00 (gap filled), close position Scenario C (Gap-Fill Rejection): • Price drifts to $450.00 (gap filled) early in session • ORB establishes $450-$451 after gap fill • ORB breaks upward to $451.50 • Trade: LONG breakout (gap is filled, now resume rally) ORB Fusion Integration: [/b> Dashboard shows: • Gap type (Up/Down/None) • Gap size (percentage) • Gap fill status (Filled ✓ / Open) This informs setup confidence: • ORB breakout AWAY from unfilled gap: +10% confidence (gap becomes support/resistance) • ORB breakout TOWARD unfilled gap: -10% confidence (gap fill may override ORB) [b>📈 VWAP & INSTITUTIONAL BIAS [/b> [b>Volume-Weighted Average Price (VWAP): [/b> Average price weighted by volume at each price level. Represents true "average" cost for the day. [b>Calculation: Institutional Benchmark [/b>: Institutions (mutual funds, pension funds) use VWAP as performance benchmark. If they buy above VWAP, they underperformed; below VWAP, they outperformed. 2. [b>Algorithmic Target [/b>: Many algos are programmed to buy below VWAP and sell above VWAP to achieve "fair" execution. 3. [b>Support/Resistance [/b>: VWAP acts as dynamic support (price above) or resistance (price below). [b>VWAP Bands (Standard Deviations): [/b> • [b>1σ Band [/b>: VWAP ± 1 standard deviation - Contains ~68% of volume - Normal trading range - Bounces common • [b>2σ Band [/b>: VWAP ± 2 standard deviations - Contains ~95% of volume - Extreme extension - Mean reversion likely ORB + VWAP Confluence: [/b> Highest-probability setups occur when ORB and VWAP align: Bullish Confluence: [/b> • ORB breakout upward (bullish signal) • Price above VWAP (institutional buying) • Confidence boost: +15% Bearish Confluence: [/b> • ORB breakout downward (bearish signal) • Price below VWAP (institutional selling) • Confidence boost: +15% [b>Divergence Warning: • ORB breakout upward BUT price below VWAP • Conflict: Breakout says "buy," VWAP says "sell" • Confidence penalty: -10% • Interpretation: Retail buying but institutions not participating (lower quality breakout) 📊 MOMENTUM CONTEXT SYSTEM [b>Innovation: Candle Coloring by Position Rather than fixed support/resistance lines, ORB Fusion colors candles based on their [b>relationship to ORB : [b>Three Zones: [/b> 1. Inside ORB (Blue Boxes): [/b> [b>Calculation: • Darker blue: Near extremes of ORB (potential breakout imminent) • Lighter blue: Near ORB mid (consolidation) [b>Trading: [/b> Coiled spring—await breakout. [b>2. Above ORB (Green Boxes): [b>Calculation: 3. Below ORB (Red Boxes): Mirror of above ORB logic. [b>Special Contexts: [/b> [b>Breakout Bar (Darkest Green/Red): [/b> The specific bar where breakout occurs gets maximum color intensity regardless of distance. This highlights the pivotal moment. [b>Failed Breakout Bar (Orange/Warning): [/b> When failed breakout is confirmed, that bar gets orange/warning color. Visual alert: "reversal opportunity here." [b>Near Extension (Cyan/Magenta Tint): [/b> When price is within 0.5 ATR of an extension level, candle gets tinted cyan (bull) or magenta (bear). Indicates "target approaching—prepare to take profit." [b>Why Visual Context? [/b> Traditional indicators show lines. ORB Fusion shows [b>context-aware momentum [/b>. Glance at chart: • Lots of blue? Consolidation day (fade extremes). • Progressive green? Trend day (follow). • Green then orange? Failed breakout (reversal setup). This visual language communicates market state instantly—no interpretation needed. 🎯 TRADE SETUP GENERATION & GRADING [/b> [b>Algorithmic Setup Detection: [/b> ORB Fusion continuously evaluates market state and generates current best trade setup with: • Action (LONG / SHORT / FADE HIGH / FADE LOW / WAIT) • Entry price • Stop loss • Three targets • Risk:Reward ratio • Confidence score (0-100) • Grade (A+ to D) [b>Setup Types: [/b> [b>1. ORB LONG (Bullish Breakout): [/b> [b>Trigger: [/b> • Bullish ORB breakout confirmed • Not failed [b>Parameters: • Entry: Current close • Stop: ORB mid (protects against failure) • T1: ORB High + 0.5x range (1.5x extension) • T2: ORB High + 1.0x range (2.0x extension) • T3: ORB High + 1.618x range (2.618x extension) [b>Confidence Scoring: [b>Trigger: [/b> • Bearish breakout occurred • Failed (returned inside ORB) [b>Parameters: [/b> • Entry: Close when failure confirmed • Stop: Extreme low since breakout + 0.10 ATR • T1: ORB Low + 0.5x range • T2: ORB Low + 1.0x range (ORB mid) • T3: ORB Low + 1.5x range [b>Confidence Scoring: [b>Trigger: • Inside ORB • Close > ORB mid (near high) [b>Parameters: [/b> • Entry: ORB High (limit order) • Stop: ORB High + 0.2x range • T1: ORB Mid • T2: ORB Low [b>Confidence Scoring: [/b> Base: 40 points (lower base—range fading is lower probability than breakout/reversal) [b>Use Case: [/b> Rotation days. Not recommended on normal/trend days. [b>6. FADE LOW (Range Trade): Mirror of FADE HIGH. [b>7. WAIT: [b>Trigger: [/b> • ORB not complete yet OR • No clear setup (price in no-man's-land) [b>Action: [/b> Observe, don't trade. [b>Confidence: [/b> 0 points [b>Grading System: ``` Confidence → Grade 85-100 → A+ 75-84 → A 65-74 → B+ 55-64 → B 45-54 → C 0-44 → D ``` [b>Grade Interpretation: [/b> • [b>A+ / A: High probability setup. Take these trades. • [b>B+ / B [/b>: Decent setup. Trade if fits system rules. • [b>C [/b>: Marginal setup. Only if very experienced. • [b>D [/b>: Poor setup or no setup. Don't trade. [b>Example Scenario: [/b> ES futures: • ORB: 5842-5850 (8 point range) • Bullish breakout to 5851 confirmed • Volume: 2.0x average (confirmed) • VWAP: 5845 (price above VWAP ✓) • Day type: Developing (too early, no bonus) • Gap: None [b>Setup: [/b> • Action: LONG • Entry: 5851 • Stop: 5846 (ORB mid, -5 point risk) • T1: 5854 (+3 points, 1:0.6 R:R) • T2: 5858 (+7 points, 1:1.4 R:R) • T3: 5862.94 (+11.94 points, 1:2.4 R:R) [b>Confidence: LONG with 55% confidence. Interpretation: Solid setup, not perfect. Trade it if your system allows B-grade signals. [b>📊 STATISTICS TRACKING & PERFORMANCE ANALYSIS [/b> [b>Real-Time Performance Metrics: [/b> ORB Fusion tracks comprehensive statistics over user-defined lookback (default 50 days): [b>Breakout Performance: [/b> • [b>Bull Breakouts: [/b> Total count, wins, losses, win rate • [b>Bear Breakouts: [/b> Total count, wins, losses, win rate [b>Win Definition: [/b> Breakout reaches ≥1.0x extension (doubles the opening range) before end of day. [b>Example: [/b> • ORB: 5842-5850 (8 points) • Bull breakout at 5851 • Reaches 5858 (1.0x extension) by close • Result: WIN [b>Failed Breakout Performance: [/b> • [b>Total Failed Breakouts [/b>: Count of breakouts that failed • [b>Reversal Wins [/b>: Count where reversal trade reached target • [b>Failed Reversal Win Rate [/b>: Wins / Total Failed [b>Win Definition for Reversals: [/b> • Failed bull → reversal short reaches ORB mid • Failed bear → reversal long reaches ORB mid [b>Extension Tracking: [/b> • [b>Average Extension Reached [/b>: Mean of maximum extension achieved across all breakout days • [b>Max Extension Overall [/b>: Largest extension ever achieved in lookback period [b>Example: 🎨 THREE DISPLAY MODES [b>Design Philosophy: [/b> Not all traders need all features. Beginners want simplicity. Professionals want everything. ORB Fusion adapts. [b>SIMPLE MODE: [/b> [b>Shows: [/b> • Primary ORB levels (High, Mid, Low) • ORB box • Breakout signals (triangles) • Failed breakout signals (crosses) • Basic dashboard (ORB status, breakout status, setup) • VWAP [b>Hides: [/b> • Session ORBs (Asian, London, NY) • IB levels and extensions • ORB extensions beyond basic levels • Gap analysis visuals • Statistics dashboard • Momentum candle coloring • Narrative dashboard [b>Use Case: [/b> • Traders who want clean chart • Focus on core ORB concept only • Mobile trading (less screen space) [b>STANDARD MODE: [b>Shows Everything in Simple Plus: [/b> • Session ORBs (Asian, London, NY) • IB levels (high, low, mid) • IB extensions • ORB extensions (1.272x, 1.5x, 1.618x, 2.0x) • Gap analysis and fill targets • VWAP bands (1σ and 2σ) • Momentum candle coloring • Context section in dashboard • Narrative dashboard [b>Hides: [/b> • Advanced extensions (2.618x, 3.0x) • Detailed statistics dashboard [b>Use Case: [/b> • Most traders • Balance between information and clarity • Covers 90% of use cases [b>ADVANCED MODE: [b>Shows Everything: • All session ORBs • All IB levels and extensions • All ORB extensions (including 2.618x and 3.0x) • Full gap analysis • VWAP with both 1σ and 2σ bands • Momentum candle coloring • Complete statistics dashboard • Narrative dashboard • All context metrics [b>Use Case: [/b> • Professional traders • System developers • Those who want maximum information density [b>Switching Modes: [/b> Single dropdown input: "Display Mode" → Simple / Standard / Advanced Entire indicator adapts instantly. No need to toggle 20 individual settings. 📖 NARRATIVE DASHBOARD [b>Innovation: Plain-English Market State [/b> Most indicators show data. ORB Fusion explains what the data [b>means [/b>. [b>Narrative Components: [/b> [b>1. Phase: [/b> • "📍 Building ORB..." (during ORB session) • "📊 Trading Phase" (after ORB complete) • "⏳ Pre-Market" (before ORB session) [b>2. Status (Current Observation): [/b> • "⚠️ Failed breakout - reversal likely" • "🚀 Bullish momentum in play" • "📉 Bearish momentum in play" • "⚖️ Consolidating in range" • "👀 Monitoring for setup" [b>3. Next Level: Tells you what to watch for: • "🎯 1.5x @ 5854.00" (next extension target) • "Watch ORB levels" (inside range, await breakout) [b>4. Setup: [/b> Current trade setup + grade: • "LONG " (bullish breakout, A-grade) • "🔥 SHORT REVERSAL " (failed bull breakout, A+-grade) • "WAIT " (no setup) [b>5. Reason: [/b> Why this setup exists: • "ORB Bullish Breakout" • "Failed Bear Breakout - High Probability Reversal" • "Range Fade - Near High" [b>6. Tip (Market Insight): Contextual advice: • "🔥 TREND DAY - Trail stops" (day type is trending) • "🔄 ROTATION - Fade extremes" (day type is rotating) • "📊 Gap unfilled - magnet level" (gap creates target) • "📈 Normal conditions" (no special context) [b>Example Narrative: ``` 📖 ORB Narrative ━━━━━━━━━━━━━━━━ Phase | 📊 Trading Phase Status | 🚀 Bullish momentum in play Next | 🎯 1.5x @ 5854.00 📈 Setup | LONG Reason | ORB Bullish Breakout 💡 Tip | 🔥 TREND DAY - Trail stops ``` [b>Glance Interpretation: [/b> "We're in trading phase. Bullish breakout happened (momentum in play). Next target is 1.5x extension at 5854. Current setup is LONG with A-grade. It's a trend day, so trail stops (don't take early profits)." Complete market state communicated in 6 lines. No interpretation needed. [b>Why This Matters: Beginner traders struggle with "So what?" question. Indicators show lines and signals, but what does it mean [/b>? Narrative dashboard bridges this gap. Professional traders benefit too—rapid context assessment during fast-moving markets. No time to analyze; glance at narrative, get action plan. 🔔 INTELLIGENT ALERT SYSTEM [b>Four Alert Types: [/b> [b>1. Breakout Alert: [/b> [b>Trigger: [/b> ORB breakout confirmed (bull or bear) [b>Message: [/b> ``` 🚀 ORB BULLISH BREAKOUT Price: 5851.00 Volume Confirmed Grade: A ``` [b>Frequency: [/b> Once per bar (prevents spam) [b>2. Failed Breakout Alert: [/b> [b>Trigger: [/b> Breakout fails, reversal setup generated [b>Message: [/b> ``` 🔥 FAILED BULLISH BREAKOUT! HIGH PROBABILITY SHORT REVERSAL Entry: 5848.00 Stop: 5854.00 T1: 5846.00 T2: 5842.00 Historical Win Rate: 73% ``` [b>Why Comprehensive? [/b> Failed breakout alerts include complete trade plan. You can execute immediately from alert—no need to check chart. [b>3. Extension Alert: [b>Trigger: [/b> Price reaches extension level for first time [b>Message: [/b> ``` 🎯 Bull Extension 1.5x reached @ 5854.00 ``` [b>Use: [/b> Profit-taking reminder. When extension hit, consider scaling out. [b>4. IB Break Alert: [/b> [b>Trigger: [/b> Price breaks above IB high or below IB low [b>Message: [/b> ``` 📊 IB HIGH BROKEN - Potential Trend Day ``` [b>Use: [/b> Day type classification. IB break suggests trend day developing—adjust strategy to trend-following mode. [b>Alert Management: [/b> Each alert type can be enabled/disabled independently. Prevents notification overload. [b>Cooldown Logic: [/b> Alerts won't fire if same alert type triggered within last bar. Prevents: • "Breakout" alert every tick during choppy breakout • Multiple "extension" alerts if price oscillates at level Ensures: One clean alert per event. ⚙️ KEY PARAMETERS EXPLAINED [b>Opening Range Settings: [/b> • [b>ORB Timeframe [/b> (5/15/30/60 min): Duration of opening range window - 30 min recommended for most traders • [b>Use RTH Only [/b> (ON/OFF): Only trade during regular trading hours - ON recommended (avoids thin overnight markets) • [b>Use LTF Precision [/b> (ON/OFF): Sample 1-minute bars for accuracy - ON recommended (critical for charts >1 minute) • [b>Precision TF [/b> (1/5 min): Timeframe for LTF sampling - 1 min recommended (most accurate) [b>Session ORBs: [/b> • [b>Show Asian/London/NY ORB [/b> (ON/OFF): Display multi-session ranges - OFF in Simple mode - ON in Standard/Advanced if trading 24hr markets • [b>Session Windows [/b>: Time ranges for each session ORB - Defaults align with major session opens [b>Initial Balance: [/b> • [b>Show IB [/b> (ON/OFF): Display Initial Balance levels - ON recommended for day type classification • [b>IB Session Window [/b> (0930-1030): First hour of trading - Default is standard for US equities • [b>Show IB Extensions [/b> (ON/OFF): Project IB extension targets - ON recommended (identifies trend days) • [b>IB Extensions 1-4 [/b> (0.5x, 1.0x, 1.5x, 2.0x): Extension multipliers - Defaults are Market Profile standard [b>ORB Extensions: [/b> • [b>Show Extensions [/b> (ON/OFF): Project ORB extension targets - ON recommended (defines profit targets) • [b>Enable Individual Extensions [/b> (1.272x, 1.5x, 1.618x, 2.0x, 2.618x, 3.0x) - Enable 1.272x, 1.5x, 1.618x, 2.0x minimum - Disable 2.618x and 3.0x unless trading very volatile instruments [b>Breakout Detection: • [b>Confirmation Method [/b> (Close/Wick/Body): - Close recommended (best balance) - Wick for scalping - Body for conservative • [b>Require Volume Confirmation [/b> (ON/OFF): - ON recommended (increases reliability) • [b>Volume Multiplier [/b> (1.0-3.0): - 1.5x recommended - Lower for thin instruments - Higher for heavy volume instruments [b>Failed Breakout System: [/b> • [b>Enable Failed Breakouts [/b> (ON/OFF): - ON strongly recommended (highest edge) • [b>Bars to Confirm Failure [/b> (2-10): - 3 bars recommended - 2 for aggressive (more signals, more false failures) - 5+ for conservative (fewer signals, higher quality) • [b>Failure Buffer [/b> (0.0-0.5 ATR): - 0.1 ATR recommended - Filters noise during consolidation near ORB level • [b>Show Reversal Targets [/b> (ON/OFF): - ON recommended (visualizes trade plan) • [b>Reversal Target Mults [/b> (0.5x, 1.0x, 1.5x): - Defaults are tested values - Adjust based on average daily range [b>Gap Analysis: • [b>Show Gap Analysis [/b> (ON/OFF): - ON if trading instruments that gap frequently - OFF for 24hr markets (forex, crypto—no gaps) • [b>Gap Fill Target [/b> (ON/OFF): - ON to visualize previous close (gap fill level) [b>VWAP: • [b>Show VWAP [/b> (ON/OFF): - ON recommended (key institutional level) • [b>Show VWAP Bands [/b> (ON/OFF): - ON in Standard/Advanced - OFF in Simple • [b>Band Multipliers (1.0σ, 2.0σ): - Defaults are standard - 1σ = normal range, 2σ = extreme [b>Day Type: [/b> • [b>Show Day Type Analysis [/b> (ON/OFF): - ON recommended (critical for strategy adaptation) • [b>Trend Day Threshold [/b> (1.0-2.5 IB mult): - 1.5x recommended - When price extends >1.5x IB, classifies as Trend Day [b>Enhanced Visuals: • [b>Show Momentum Candles [/b> (ON/OFF): - ON for visual context - OFF if chart gets too colorful • [b>Show Gradient Zone Fills [/b> (ON/OFF): - ON for professional look - OFF for minimalist chart • [b>Label Display Mode [/b> (All/Adaptive/Minimal): - Adaptive recommended (shows nearby labels only) - All for information density - Minimal for clean chart • [b>Label Proximity [/b> (1.0-5.0 ATR): - 3.0 ATR recommended - Labels beyond this distance are hidden (Adaptive mode) [b>🎓 PROFESSIONAL USAGE PROTOCOL [/b> [b>Phase 1: Learning the System (Week 1) [/b> [b>Goal: [/b> Understand ORB concepts and dashboard interpretation [b>Setup: [/b> • Display Mode: STANDARD • ORB Timeframe: 30 minutes • Enable ALL features (IB, extensions, failed breakouts, VWAP, gap analysis) • Enable statistics tracking [b>Actions: [/b> • Paper trade ONLY—no real money • Observe ORB formation every day (9:30-10:00 AM ET for US markets) • Note when ORB breakouts occur and if they extend • Note when breakouts fail and reversals happen • Watch day type classification evolve during session • Track statistics—which setups are working? [b>Key Learning: [/b> • How often do breakouts reach 1.5x extension? (typically 50-60% of confirmed breakouts) • How often do breakouts fail? (typically 30-40%) • Which setup grade (A/B/C) actually performs best? (should see A-grade outperforming) • What day type produces best results? (trend days favor breakouts, rotation days favor fades) [b>Phase 2: Parameter Optimization (Week 2) [/b> [b>Goal: [/b> Tune system to your instrument and timeframe [b>ORB Timeframe Selection: • Run 5 days with 15-minute ORB • Run 5 days with 30-minute ORB • Compare: Which captures better breakouts on your instrument? • Typically: 30-minute optimal for most, 15-minute for very liquid (ES, SPY) [b>Volume Confirmation Testing: • Run 5 days WITH volume confirmation • Run 5 days WITHOUT volume confirmation • Compare: Does volume confirmation increase win rate? • If win rate improves by >5%: Keep volume confirmation ON • If no improvement: Turn OFF (avoid missing valid breakouts) [b>Failed Breakout Bars: [b>Goal: [/b> Develop personal trading rules based on system signals [b>Setup Selection Rules: [/b> Define which setups you'll trade: • [b>Conservative: [/b> Only A+ and A grades • [b>Balanced: [/b> A+, A, B+ grades • [b>Aggressive: [/b> All grades B and above Test each approach for 5-10 trades, compare results. [b>Position Sizing by Grade: [/b> Consider risk-weighting by setup quality: • A+ grade: 100% position size • A grade: 75% position size • B+ grade: 50% position size • B grade: 25% position size Example: If max risk is $1000/trade: • A+ setup: Risk $1000 • A setup: Risk $750 • B+ setup: Risk $500 This matches bet sizing to edge. [b>Day Type Adaptation: [/b> Create rules for different day types: Trend Days: • Take ALL breakout signals (A/B/C grades) • Hold for 2.0x extension minimum • Trail stops aggressively (1.0 ATR trail) • DON'T fade—reversals unlikely Rotation Days: • ONLY take failed breakout reversals • Ignore initial breakout signals (likely to fail) • Take profits quickly (0.5x extension) • Focus on fade setups (Fade High/Fade Low) Normal Days: • Take A/A+ breakout signals only • Take ALL failed breakout reversals (high probability) • Target 1.0-1.5x extensions • Partial profit-taking at extensions Time-of-Day Rules: [/b> Breakouts at different times have different probabilities: 10:00-10:30 AM (Early Breakout): • ORB just completed • Fresh breakout • Probability: Moderate (50-55% reach 1.0x) • Strategy: Conservative position sizing 10:30-12:00 PM (Mid-Morning): • Momentum established • Volume still healthy • Probability: High (60-65% reach 1.0x) • Strategy: Standard position sizing 12:00-2:00 PM (Lunch Doldrums): • Volume dries up • Whipsaw risk increases • Probability: Low (40-45% reach 1.0x) • Strategy: Avoid new entries OR reduce size 50% 2:00-4:00 PM (Afternoon Session): • Late-day positioning • EOD squeezes possible • Probability: Moderate-High (55-60%) • Strategy: Watch for IB break—if trending all day, follow [b>Phase 4: Live Micro-Sizing (Month 2) [/b> [b>Goal: [/b> Validate paper trading results with minimal risk [b>Setup: [/b> • 10-20% of intended full position size • Take ONLY A+ and A grade setups • Follow stop loss and targets religiously [b>Execution: [/b> • Execute from alerts OR from dashboard setup box • Entry: Close of signal bar OR next bar market order • Stop: Use exact stop from setup (don't widen) • Targets: Scale out at T1/T2/T3 as indicated [b>Tracking: [/b> • Log every trade: Entry, Exit, Grade, Outcome, Day Type • Calculate: Win rate, Average R-multiple, Max consecutive losses • Compare to paper trading results (should be within 15%) [b>Red Flags: [/b> • Win rate <45%: System not suitable for this instrument/timeframe • Major divergence from paper trading: Execution issues (slippage, late entries, emotional exits) • Max consecutive losses >8: Hitting rough patch OR market regime changed [b>Phase 5: Scaling Up (Months 3-6) [b>Goal: [/b> Gradually increase to full position size [b>Progression: [/b> • Month 3: 25-40% size (if micro-sizing profitable) • Month 4: 40-60% size • Month 5: 60-80% size • Month 6: 80-100% size [b>Milestones Required to Scale Up: [/b> • Minimum 30 trades at current size • Win rate ≥48% • Profit factor ≥1.2 • Max drawdown <20% • Emotional control (no revenge trading, no FOMO) [b>Advanced Techniques: [b>Multi-Timeframe ORB: Assumes first 30-60 minutes establish value. Violation: Market opens after major news, price discovery continues for hours (opening range meaningless). 2. [b>Volume Indicates Conviction: ES, NQ, RTY, SPY, QQQ—high liquidity, clean ORB formation, reliable extensions • [b>Large-Cap Stocks: AAPL, MSFT, TSLA, NVDA (>$5B market cap, >5M daily volume) • [b>Liquid Futures: CL (crude oil), GC (gold), 6E (EUR/USD), ZB (bonds)—24hr markets benefit from session ORBs • [b>Major Forex Pairs: [/b> EUR/USD, GBP/USD, USD/JPY—London/NY session ORBs work well [b>Performs Poorly On: [/b> • [b>Illiquid Stocks: <$1M daily volume, wide spreads, gappy price action • [b>Penny Stocks: [/b> Manipulated, pump-and-dump, no real price discovery • [b>Low-Volume ETFs: Exotic sector ETFs, leveraged products with thin volume • [b>Crypto on Sketchy Exchanges: Wash trading, spoofing invalidates volume analysis • [b>Earnings Days: [/b> ORB completes before earnings release, then completely resets (useless) • Binary Event Days: FDA approvals, court rulings—discontinuous price action [b>Known Weaknesses: [/b> • [b>Slow Starts: ORB doesn't complete until 10:00 AM (30-min ORB). Early morning traders have no signals for 30 minutes. Consider using 15-minute ORB if this is problematic. • [b>Failure Detection Lag: [/b> Failed breakout requires 3+ bars to confirm. By the time system signals reversal, price may have already moved significantly back inside range. Manual traders watching in real-time can enter earlier. • [b>Extension Overshoot: [/b> System projects extensions mathematically (1.5x, 2.0x, etc.). Actual moves may stop short (1.3x) or overshoot (2.2x). Extensions are targets, not magnets. • [b>Day Type Misclassification: [/b> Early in session, day type is "Developing." By the time it's classified definitively (often 11:00 AM+), half the day is over. Strategy adjustments happen late. • [b>Gap Assumptions: [/b> System assumes gaps want to fill. Strong trend days never fill gaps (gap becomes support/resistance forever). Blindly trading toward gaps can backfire on trend days. • [b>Volume Data Quality: Forex doesn't have centralized volume (uses tick volume as proxy—less reliable). Crypto volume is often fake (wash trading). Volume confirmation less effective on these instruments. • [b>Multi-Session Complexity: [/b> When using Asian/London/NY ORBs simultaneously, chart becomes cluttered. Requires discipline to focus on relevant session for current time. [b>Risk Factors: [/b> • [b>Opening Gaps: Large gaps (>2%) can create distorted ORBs. Opening range might be unusually wide or narrow, making extensions unreliable. • [b>Low Volatility Environments:[/b> When VIX <12, opening ranges can be tiny (0.2-0.3%). Extensions are equally tiny. Profit targets don't justify commission/slippage. • [b>High Volatility Environments:[/b> When VIX >30, opening ranges are huge (2-3%+). Extensions project unrealistic targets. Failed breakouts happen faster (volatility whipsaw). • [b>Algorithm Dominance:[/b> In heavily algorithmic markets (ES during overnight session), ORB levels can be manipulated—algos pin price to ORB high/low intentionally. Breakouts become stop-runs rather than genuine directional moves. [b>⚠️ RISK DISCLOSURE[/b> Trading futures, stocks, options, forex, and cryptocurrencies involves substantial risk of loss and is not suitable for all investors. Opening Range Breakout strategies, while based on sound market structure principles, do not guarantee profits and can result in significant losses. The ORB Fusion indicator implements professional trading concepts including Opening Range theory, Market Profile Initial Balance analysis, Fibonacci extensions, and failed breakout reversal logic. These methodologies have theoretical foundations but past performance—whether backtested or live—is not indicative of future results. Opening Range theory assumes the first 30-60 minutes of trading establish a meaningful value area and that breakouts from this range signal directional conviction. This assumption may not hold during: • Major news events (FOMC, NFP, earnings surprises) • Market structure changes (circuit breakers, trading halts) • Low liquidity periods (holidays, early closures) • Algorithmic manipulation or spoofing Failed breakout detection relies on patterns of trapped participant behavior. While historically these patterns have shown statistical edges, market conditions change. Institutional algorithms, changing market structure, or regime shifts can reduce or eliminate edges that existed historically. Initial Balance classification (trend day vs rotation day vs normal day) is a heuristic framework, not a deterministic prediction. Day type can change mid-session. Early classification may prove incorrect as the day develops. Extension projections (1.272x, 1.5x, 1.618x, 2.0x, etc.) are probabilistic targets derived from Fibonacci ratios and empirical market behavior. They are not "support and resistance levels" that price must reach or respect. Markets can stop short of extensions, overshoot them, or ignore them entirely. Volume confirmation assumes high volume indicates institutional participation and conviction. In algorithmic markets, volume can be artificially high (HFT activity) or artificially low (dark pools, internalization). Volume is a proxy, not a guarantee of conviction. LTF precision sampling improves ORB accuracy by using 1-minute bars but introduces additional data dependencies. If 1-minute data is unavailable, inaccurate, or delayed, ORB calculations will be incorrect. The grading system (A+/A/B+/B/C/D) and confidence scores aggregate multiple factors (volume, VWAP, day type, IB expansion, gap context) into a single assessment. This is a mechanical calculation, not artificial intelligence. The system cannot adapt to unprecedented market conditions or events outside its programmed logic. Real trading involves slippage, commissions, latency, partial fills, and rejected orders not present in indicator calculations. ORB Fusion generates signals at bar close; actual fills occur with delay. Opening range forms during highest volatility (first 30 minutes)—spreads widen, slippage increases. Execution quality significantly impacts realized results. Statistics tracking (win rates, extension levels reached, day type distribution) is based on historical bars in your lookback window. If lookback is small (<50 bars) or market regime changed, statistics may not represent future probabilities. Users must independently validate system performance on their specific instruments, timeframes, and broker execution environment. Paper trade extensively (100+ trades minimum) before risking capital. Start with micro position sizing (5-10% of intended size) for 50+ trades to validate execution quality matches expectations. Never risk more than you can afford to lose completely. Use proper position sizing (0.5-2% risk per trade maximum). Implement stop losses on every single trade without exception. Understand that most retail traders lose money—sophisticated indicators do not change this fundamental reality. They systematize analysis but cannot eliminate risk. The developer makes no warranties regarding profitability, suitability, accuracy, reliability, or fitness for any purpose. Users assume full responsibility for all trading decisions, parameter selections, risk management, and outcomes. By using this indicator, you acknowledge that you have read, understood, and accepted these risk disclosures and limitations, and you accept full responsibility for all trading activity and potential losses. [b>═══════════════════════════════════════════════════════════════════════════════[/b> [b>CLOSING STATEMENT[/b> [b>═══════════════════════════════════════════════════════════════════════════════[/b> Opening Range Breakout is not a trick. It's a framework. The first 30-60 minutes reveal where participants believe value lies. Breakouts signal directional conviction. Failures signal trapped participants. Extensions define profit targets. Day types dictate strategy. Failed breakouts create the highest-probability reversals. ORB Fusion doesn't predict the future—it identifies [b>structure[/b>, detects [b>breakouts[/b>, recognizes [b>failures[/b>, and generates [b>probabilistic trade plans[/b> with defined risk and reward. The edge is not in the opening range itself. The edge is in recognizing when the market respects structure (follow breakouts) versus when it violates structure (fade breakouts). The edge is in detecting failures faster than discretionary traders. The edge is in systematic classification that prevents catastrophic errors—like fading a trend day or holding through rotation. Most indicators draw lines. ORB Fusion implements a complete institutional trading methodology: Opening Range theory, Market Profile classification, failed breakout intelligence, Fibonacci projections, volume confirmation, gap psychology, and real-time performance tracking. Whether you're a beginner learning market structure or a professional seeking systematic ORB implementation, this system provides the framework. "The market's first word is its opening range. Everything after is commentary." — ORB Fusion
Pine Script® indicator
by DskyzInvestments
10
Implicit Dolar MEPWhich stock or CEDEAR offers the best implied MEP dollar rate? This indicator displays labels positioned at the level of the implied MEP dollar rate for the 10 equity instruments (stocks, CEDEARs and ETFs) with the highest trading volume in MEP dollars over the last month on the BYMA market. The implied rate for each asset is calculated as the ratio between its price in ARS and its price in MEP dollars, for example: GGAL / GGALD. As a reference (benchmark), a white line is plotted representing the implied MEP dollar rate of the AL30 bond, calculated as AL30 / AL30D, which is the most liquid government bond in the BYMA market. Settings • The user may enter the ticker of any bi-currency instrument (fixed income or equity) to add its label to the chart. Key information An information box highlights: • The asset with the most expensive implied dollar (Best SELL). • The asset with the cheapest implied dollar (Best BUY). Not an investment recommendation. This information is provided for informational purposes only and does not constitute an offer, solicitation, or investment advice. Investment decisions are the sole responsibility of the investor.
Pine Script® indicator
by Eco Valores
Strategy Validator - Backtest & Live TradeStrategy Validator is an analytical indicator for TradingView designed to evaluate trade setups, risk structure, and price behavior in real time. The indicator builds trade scenarios, visualizes key trade levels, and provides statistical context based on historical data. Its purpose is to help traders better understand current trade conditions and make decisions based on market structure rather than isolated signals. 🔶 Conceptual Approach Many tools focus on a single question: “Is there a signal right now?” Strategy Validator approaches the market differently by analyzing a trade as a structured scenario that includes: • current market state • price position relative to key levels • acceptable risk boundaries • potential price expansion range The indicator evaluates how well the active trade configuration aligns with historically observed market conditions and visualizes its boundaries directly on the chart. 🔶 Trade Condition Analysis For each active configuration, the indicator evaluates: • alignment of the entry with market structure • market phase (directional movement, range, or elevated activity) • risk-to-outcome relationship • historical behavior of similar setups The goal is to highlight trades with a clear structure and controlled risk , while filtering out weak or unstable conditions. 🎯 Trade Structure on Chart Each trade is displayed directly on the chart with clearly defined elements: • Entry — trade entry level • Take Profit — projected target level • Stop Loss — risk limitation level based on market structure and volatility This allows immediate understanding of trade boundaries without manual calculations. 🧪 Statistical Context & Backtest The indicator includes an integrated statistical panel showing: • Win Rate • Profit Factor • Maximum Drawdown • Net Result • Total Trades Two information blocks are available: • BACKTEST — historical behavior of similar trade conditions • POSITION — parameters and real-time state of the current trade Historical data is used to provide analytical context, not to predict future performance. 🔶 Markets & Timeframes 🌍 Compatible with all TradingView markets: • Crypto • Forex • Stocks & ETFs • Indices • Commodities • Futures ⏱ Works on all timeframes — from intraday analysis to long-term charts. 🔶 Intended Use Strategy Validator is designed for traders who: • focus on risk-aware decision making • prefer structured market analysis • evaluate trades in context rather than isolation • use statistics as a supporting decision layer Important Notice This indicator is an analytical tool and does not guarantee profitability. It is not a trading robot or an automated execution system. Past results do not guarantee future outcomes. Always apply proper risk management.
Pine Script® indicator
by RoboMarket
RSI For Loop | PWRSI For Loop – True Dominance Oscillator RSI For Loop – True Momentum Dominance Through Historical Comparison The Relative Strength Index (RSI) is excellent at measuring recent price change intensity, but a reading of 70 or 30 has completely different implications depending on the market regime. RSI For Loop removes this ambiguity by transforming RSI into a clean, zero-centered dominance / percentile-rank oscillator that always tells you exactly how strong or weak the current momentum is compared to recent history. How it works - Standard RSI is calculated normally (default length 46). - A simple for-loop compares the current RSI value against the actual RSI value of every previous bar inside the user-defined lookback window (default 1 to 99 bars ≈ one full quarter on daily charts). - Current RSI higher → +1 point - Current RSI lower → –1 point The resulting score ranges from –99 to +99 and is naturally centered around zero: 1. +40 = current momentum beats ~70 % of the last 99 bars (approximation) 2. –60 = current momentum is weaker than ~80 % of the last 99 bars (approximation) 3. Near zero = balanced or ranging market Additional statistical layers - A very long rolling median of the score (default 240 periods) serves as a slow, robust dynamic centerline - Upper and lower 3σ bands are calculated from the standard deviation of the underlying RSI median (default length 60) to highlight truly rare extreme-dominance phases - Asymmetric trend thresholds (default Long +15 / Short –28) reflect the empirical observation that downside momentum is usually sharper and faster Origin and development The core idea of using a for-loop on RSI was originally introduced by @viResearch in his invite-only “RSI For Loop” script. While studying that concept I realised I needed an even more regime-robust strength gauge that looks back far enough to capture full market cycles (2–4 months). Therefore I completely rewrote the loop to compare against actual historical RSI values instead of fixed levels, added a 240-period median centerline, 3σ extreme bands, asymmetric thresholds, and visual signals. All parameters were extensively tested across dozens of major assets (BTC, ETH, SOL, SUI, BNB, XRP, TRX, DOGE, LINK, PAXG, CVX, HYPE, VIRTUAL + 20+ more cryptos; Magnificent 7 stocks, QQQ, SPX, XAUUSD) with the goal of achieving consistent profitability, high Sortino ratio and low drawdown in simple trend-following setups. The final defaults represent the most robust compromise found — they keep you in real trends for dozens or hundreds of bars while staying almost silent in choppy, ranging markets. Important Note The optimization process is tailored to MY needs and have to be adjusted to you prefered timeframe! I was mainly looking for an indicator that shows the underlying strength of an asset, the trend componant was only a bonus in my eyes. How to use it 1. Green triangle below bar → score crosses above +15 → new bullish regime confirmed → enter or add to longs 2. Magenta triangle above bar → score crosses below –28 → exit longs or go cash/short While score stays clearly positive → bullish bias hold 3. Score touching or breaking the 3σ bands → extreme conviction zone (add to winners or prepare for exhaustion) Strength Recommended defaults (My preference) RSI length 46 Loop range 1–99 Long threshold +15 Short threshold –28 Median length 240 SD length 60 Recommended Universal Settings (Tested for low Max-Drawdown, high Sortino) RSI length 44 Loop range 1–60 Long threshold +14 Short threshold –10 Median length 180 SD length 28 Works on every asset class, but the current settings are tuned for major liquid markets. Disclaimer: This is not financial advice. Backtests are based on past results and are not indicative of future performance.
Pine Script® indicator
by PaulWegelin
4
SMC Academy [PhenLabs]📊 SMC Academy Version: PineScript™ v6 📌 Description The SMC Academy indicator is a comprehensive educational tool designed to demystify Smart Money Concepts (SMC) for traders of all levels. Unlike standard indicators that simply print signals, this script uses a “Learning Phase” system that allows users to toggle between individual concepts—such as Market Structure, Liquidity, Imbalances, and Order Blocks—or view them all simultaneously. It lets you focus on one piece of the puzzle at a time. 🚀 Points of Innovation Progressive Learning Modes: Toggle between 5 distinct phases to master concepts individually before using the Full Strategy Mode. Educational Tooltips: Hover over labels to read detailed explanations of why a BOS, MSS, or Liquidity zone was identified. Smart Filtering: Uses ATR and Volume integration to filter out low-quality Fair Value Gaps and weak Order Blocks. HTF Dashboard: A built-in panel analyzes Higher Timeframe (4H) data to ensure you are trading in alignment with the broader trend. 🔧 Core Components Market Structure Engine: Automatically detects Swing Highs and Lows to map out market direction using configurable swing lengths. Liquidity Manager: Identifies unmitigated swing points that serve as Buy-Side (BSL) and Sell-Side (SSL) liquidity magnets. Imbalance Detector: Highlights Fair Value Gaps (FVG) where price inefficiencies exist, using ATR thresholds to ignore noise. Order Block Identifier: Locates the specific candles responsible for structure breaks, validated by volume analysis. 🔥 Key Features Break of Structure (BOS): Automatically marks trend continuation signals with solid lines and color-coded labels. Market Structure Shift (MSS): Identifies potential trend reversals when significant swing points are breached. Dashboard Context: Displays the current trend direction and the 4H context directly on your chart. Custom Alerts: Built-in alert conditions for structure breaks and new Order Blocks allow for automated tracking. 🎨 Visualization Structure Lines: Solid lines indicate confirmed breaks (Green for Bullish, Red for Bearish). Liquidity Zones: Dotted lines extending rightward indicate resting liquidity levels that price may target. FVG Boxes: Shaded boxes highlight imbalance zones, automatically extending for a user-defined number of bars. Dashboard: A clean, non-intrusive table in the top-right corner displays trend status and active mode. 📖 Usage Guidelines Setting Categories Learning Mode: Select from ‘1. Market Structure’ through ‘5. Full Strategy Mode’ to filter what appears on the chart. Swing Detection Length: Default (5). Determines the sensitivity of the swing high/low detection. Structure Break Type: Options (Close/Wick). Choose whether a candle close or just a wick is required to confirm a break. Min FVG Size: Default (0.5 ATR). Filters out gaps smaller than this multiplier to reduce noise. Filter Weak OBs by Volume: Default (True). Only highlights Order Blocks where volume exceeds the 20-period average. ✅ Best Use Cases Educational Study: Isolate “Phase 1: Market Structure” to practice identifying trend changes without distraction. Trend Following: Use “Phase 3: Imbalances” to find entry points within an established trend. Reversal Trading: Combine “Phase 2: Liquidity” and “Phase 4: Order Blocks” to catch reversals at key levels. ⚠️ Limitations Subjectivity: Market structure can be interpreted differently depending on the swing length settings used. Ranging Markets: Like all trend-following concepts, false BOS/MSS signals may generate during choppy, sideways price action. Repainting: While the signals are non-repainting once confirmed, the live candle may flash a signal before the close if “Close” mode is selected. 💡 What Makes This Unique Interactive Learning: The inclusion of tooltip explanations transforms this from a simple tool into an active mentor. Phase-Based Workflow: The ability to strip the chart back to basics at the click of a button is unique to the PhenLabs ecosystem. 🔬 How It Works Swing Analysis: The script calculates pivot highs and lows based on your length input to define the structural landscape. Break Validation: It checks if price crosses these pivot points to trigger BOS (Continuation) or MSS (Reversal) logic. Volume Confirmation: For Order Blocks, it looks back inside the swing leg to find the specific candle responsible for the move, verifying it has significant volume. 💡 Note: For the best experience, start in Phase 1 to calibrate your Swing Detection Length to the specific volatility of the asset you are trading before enabling Full Strategy Mode.
Pine Script® indicator
by PhenLabs
Strategy Validator PRO - Backtest & Alerts📊 Strategy Validator PRO is a professional analytical indicator for TradingView designed to evaluate trade configurations, risk structure, and potential outcomes under current market conditions. The indicator generates trade signals, visualizes entry and exit levels, and provides statistical context to support data-driven trading decisions rather than assumptions. ⚠️ PRO version is available by invitation only. A simplified FREE version is publicly available for concept evaluation. 🔶 Core Concept Most indicators answer the question: “Where to enter?” Strategy Validator PRO focuses on a more important one: “How justified is this trade under current market conditions?” The indicator applies a predefined analytical logic and evaluates how well the current trade setup aligns with statistically stable market conditions identified on an extended historical dataset. 🔶 Key Features 📈 Trade Configuration Analysis The indicator evaluates: • signal quality • current market regime (trend, consolidation, volatility) • risk-to-outcome structure • historical behavior of similar setups 🎯 The goal is to filter weak or unfavorable trade conditions rather than simply generate signals. 🎯 Trade Structure on Chart Each trade is displayed directly on the chart with a clear structure: • Entry — trade entry level • Take Profit — projected target level • Stop Loss — risk limitation level based on market structure and volatility This allows immediate understanding of trade boundaries without manual calculations. 🧪 Integrated Backtest Dashboard The indicator displays performance statistics for the active logic: • Win Rate • Profit Factor • Maximum Drawdown • Net Profit • Total Trades Two panels are available: • BACKTEST — historical performance overview • POSITION — details of the current open trade 📌 Historical data is used to provide statistical context, not to predict future performance. 🔔 TradingView Alerts (PRO) In the PRO version, alerts can be configured using TradingView’s native alert system. Alerts may be created for: • trade configuration formation • target level reached • risk limitation triggered Available alert formats: • Simple — plain text • With levels — including price levels • JSON — structured format for external analysis 🔶 PRO vs FREE 🟢 FREE • Base analytical logic • Limited historical depth • Core trade structure • Backtest dashboard • Trade history 🔵 PRO • Extended historical analysis • Higher statistical sampling depth • Trade condition filtering • TradingView alerts FREE version is intended for concept evaluation. PRO version is designed for systematic and disciplined trading. 🔶 Markets & Timeframes 🌍 Compatible with all TradingView markets: • Crypto • Forex • Stocks & ETFs • Indices • Commodities • Futures ⏱ Works on all timeframes — from intraday to long-term. 🔶 Access 🔓 FREE version Publicly available. 🔐 PRO version Available by invitation. Access can be requested via the author’s profile links or by contacting the author directly. ⚠️ Important Notice This indicator is an analytical tool and does not guarantee profitability. It is not a trading robot or automated execution system. Past performance does not guarantee future results. Always apply proper risk management.
Pine Script® indicator
by RoboMarket
ES1! H1 Stats+ES1! H1 Stats - Detailed Prob & Excursion Indicator Overview ES1! H1 Stats - Detailed Prob & Excursion is a specialized statistical overlay indicator for TradingView, tailored for E-mini S&P 500 futures (ES1!) on a 1-hour framework. It provides real-time insights into the probability of price returning to the hourly open after sweeping the previous hour’s high (PHH) or previous hour’s low (PHL), based on historical data segmented by hour (0–23) and 20-minute intervals. The indicator visualizes these sweeps with lines, labels, circles, background fills, and “excursion zones” (also called “Magic Boxes”) that highlight median/mean extensions post-sweep, along with percentile lines (75th / 90th / 95th) for gauging potential “pain” or extreme moves. This tool is designed for intraday S&P 500 traders focusing on liquidity sweeps and mean-reversion behavior, helping to quantify edge using empirical probabilities and excursion statistics. The data is hardcoded from extensive historical analysis of ES1! behavior (e.g., probabilities ranging roughly from ~7% to ~91%, with sample sizes up to 2000+ per segment), making it a backtested reference rather than a dynamic learning model. It emphasizes visual clarity during active hours, with options to filter for Regular Trading Hours (RTH: 09:00–15:59 ET) or high-probability (>70%) events only. Note: This is an educational tool for analyzing market structure; it does not predict future performance or provide trading signals/advice. Past data does not guarantee future results, and users should backtest on current conditions (as of December 2025 data availability) and use at their own risk, in compliance with TradingView’s house rules. Key Features • Sweep Detection & Probability Labels: Identifies when price breaks PHH (upside) or PHL (downside), displaying a centered label with probability of returning to the hourly open, sample size (N), time of sweep, and a checkmark (✅) if the open is retested post-sweep. • Visual Lines & Markers: Draws hourly open (h.o.), PHH, and PHL lines with customizable styles/colors; adds small circles on sweep bars for quick spotting. • Breakout→Open Background Fill: Shaded zone from sweep bar until price returns to open, visualizing extension duration and retracement. • Excursion (Pain) Zone - “Magic Box”: Post-sweep box showing median/mean extension percentages, colored dynamically by probability (green high, orange mid, red low); includes dashed lines for 75th/90th/95th percentiles to mark statistical extremes. • Time-Segmented Data: Probabilities and excursions vary by hour (0–23) and 20-min segments (0–19 min: _0, 20–39: _1, 40–59: _2), capturing intraday nuances (e.g., higher probs in early/late hours). • Filters for Focus: RTH-only mode hides non-session elements; high-prob-only shows >70% events to reduce noise. • Alerts: Triggers on PHH/PHL sweeps with messages for chart checks. How It Works • Data Foundation: Uses pre-computed maps for probabilities (prob_high_taken/prob_low_taken), sample sizes, and excursions (mean, median, p75/p90/p95 as percentages of open). Data is initialized on the first bar via f_init_high_data() and f_init_low_data(), covering 24 hours with 3 segments each (e.g., key "9_1" for 09:20–09:39). Probabilities represent historical likelihood of price returning to open after sweep; excursions quantify average/rare extensions (e.g., 0.156% mean = 0.156% of open price). • Period Detection: On new 1H bars (new_period_bar), resets visuals, draws lines for open/PHH/PHL extending 1 hour forward, and labels if enabled. Uses request.security on standard ticker for real OHLC, bypassing chart transformations (e.g., Heikin Ashi). • Sweep Logic: On each bar, checks if real high > PHH or real low < PHL. If so, fetches segment-specific data (hour + floor(minute/20)), displays probability label centered mid-hour. Skips if filtered (RTH-only or <70% prob). • Excursion Visualization: If enabled, draws “Magic Box” from 1-min to 58-min into the hour, bounded by mean/median levels (top/bottom adjusted for high/low sweep). Adds percentile lines with labels (e.g., “75%”) at right end. Box color reflects prob strength for quick bias assessment. • Retest Check: Monitors for open retest post-sweep (high/low cross open, or gap scenarios from prev bar). Adds ✅ to label if hit on subsequent bars (skips sweep bar to avoid false positives). Stops background fill on retest or at 58-min mark. • Background Fill: Activates on sweep, shades until retest, using user color. • Cleanup & Performance: Manages labels in arrays, clears on new periods; no excess drawing beyond max counts (500 lines/labels/boxes). This setup blends statistical backtesting with real-time visualization: hardcoded data provides empirical probabilities/excursions (reducing subjectivity in breakouts), while dynamic elements (lines, fills, boxes) overlay structure on the chart. It helps ES traders assess if a sweep is “high-edge” (e.g., >70% probability of reverting) or likely to run (low probability, high excursion), pairing historical context with current price action. Settings and Customization Inputs are grouped for ease: Settings: o Show RTH Only (9:00–15:59): Restricts to main session (default: false; tooltip: for RTH-focused stats). o Show High Prob Only (>70%): Filters low-prob sweeps visually (default: false; tooltip: highlights confidence). Visuals: o Show Line Labels: Toggle “h.o.” / “phh” / “phl” (default: true). o Period Open Line Color: Gray 50% (default). o Previous High/Low Line Colors: Gray 100% (default). o Open Line Style/Width: Dotted/1 (default; options: Solid/Dotted/Dashed). Breakout→Open Background: o Show Breakout→Open Background: Toggle fill (default: true). o Fill Color: Teal 85% (default). Breakout Circles: o Show Breakout Circles: Toggle (default: true). o PHH/PHL Break Circle Colors: White 20% (default). Info Label Style: o Text Size: Small (default; options: Auto/Tiny/Normal/Large/Huge). o Label Text Color: White (default). o Low/Mid/High Probability Colors: Red 20% / Orange 20% / Green 20% (default). Excursion (Pain) Zone: o Show Excursion Zone: Toggle Magic Box (default: true). o Excursion Box Color: Gray 75% (default; dynamic overrides). o 75th/90th/95th Percentile Lines: Orange 30% / Red 30% / Dark Red 100% (default). No additional tables/plots; all elements are lines/labels/boxes for overlay focus. Usage Tips • Breakout Trading: Watch for sweeps with high probability (>70%, green label) as potential fades back to open; low probability (red) may signal runs—use the excursion box for targets (e.g., exit at 90th percentile for extremes). • Time Awareness: Probabilities often peak in key liquidity windows and drop in quieter hours; segments capture momentum shifts (e.g., _2 often lower prob). • RTH Focus: Enable for cleaner stats during high-liquidity session hours; disable for a 24-hour view. • Visual Filtering: Use high-prob-only in volatile conditions to reduce noise; combine with volume or other confluence tools for confirmation. • Alerts Integration: Set TradingView alerts on sweeps; check label for probability/N before acting. • Chart Setup: Best on 1H or lower ES1! charts; adjust text size for readability on smaller screens. • Backtesting: Manually review historical sweeps against data maps to validate; update hardcoded values if new data emerges (as of 2025). Limitations • Fixed Data: Hardcoded stats may not reflect recent market changes (e.g., post-2025 regime shifts); not adaptive. • Reactive Only: Detects sweeps after they occur; no predictive signals. • Timeframe Specific: Locked to 1H logic; may not translate to other assets/timeframes without recoding data. • Visual Clutter: On busy charts, labels/boxes may overlap—toggle selectively. • No Live Stats: Sample sizes are historical; real-time N/prob not updated. • Gaps & Extremes: Handles gaps in retest logic, but rare events (e.g., macro news) may exceed the 95th percentile. Disclaimer This indicator is for informational and educational purposes only. Trading involves significant risk of loss and is not suitable for all investors. The hardcoded data represents past E-mini S&P 500 futures (ES1!) performance and does not guarantee future outcomes. No claims of profitability are made—results depend on market conditions, user strategy, and risk management. Consult a financial advisor before trading, and backtest extensively. Abiding by TradingView rules, this tool provides no investment recommendations.
Pine Script® indicator
by tncylyv
6B1! H1 Stats+6B1! H1 Stats - Detailed Prob & Excursion Indicator Overview 6B1! H1 Stats - Detailed Prob & Excursion is a specialized statistical overlay indicator for TradingView, tailored for British Pound futures (6B1!) on a 1-hour framework. It provides real-time insights into the probability of price returning to the hourly open after sweeping the previous hour’s high (PHH) or previous hour’s low (PHL), based on historical data segmented by hour (0–23) and 20-minute intervals. The indicator visualizes these sweeps with lines, labels, circles, background fills, and “excursion zones” (also called “Magic Boxes”) that highlight median/mean extensions post-sweep, along with percentile lines (75th / 90th / 95th) for gauging potential “pain” or extreme moves. This tool is designed for intraday British Pound traders focusing on liquidity sweeps and mean-reversion behavior, helping to quantify edge using empirical probabilities and excursion statistics. The data is hardcoded from extensive historical analysis of 6B1! behavior (e.g., probabilities ranging roughly from ~7% to ~91%, with sample sizes up to 2000+ per segment), making it a backtested reference rather than a dynamic learning model. It emphasizes visual clarity during active hours, with options to filter for Regular Trading Hours (RTH: 09:00–15:59 ET) or high-probability (>70%) events only. Note: This is an educational tool for analyzing market structure; it does not predict future performance or provide trading signals/advice. Past data does not guarantee future results, and users should backtest on current conditions (as of December 2025 data availability) and use at their own risk, in compliance with TradingView’s house rules. ________________________________________ Key Features • Sweep Detection & Probability Labels: Identifies when price breaks PHH (upside) or PHL (downside), displaying a centered label with probability of returning to the hourly open, sample size (N), time of sweep, and a checkmark (✅) if the open is retested post-sweep. • Visual Lines & Markers: Draws hourly open (h.o.), PHH, and PHL lines with customizable styles/colors; adds small circles on sweep bars for quick spotting. • Breakout→Open Background Fill: Shaded zone from sweep bar until price returns to open, visualizing extension duration and retracement. • Excursion (Pain) Zone - “Magic Box”: Post-sweep box showing median/mean extension percentages, colored dynamically by probability (green high, orange mid, red low); includes dashed lines for 75th/90th/95th percentiles to mark statistical extremes. • Time-Segmented Data: Probabilities and excursions vary by hour (0–23) and 20-min segments (0–19 min: _0, 20–39: _1, 40–59: _2), capturing intraday nuances (e.g., higher probs in early/late hours). • Filters for Focus: RTH-only mode hides non-session elements; high-prob-only shows >70% events to reduce noise. • Alerts: Triggers on PHH/PHL sweeps with messages for chart checks. ________________________________________ How It Works • Data Foundation: Uses pre-computed maps for probabilities (prob_high_taken/prob_low_taken), sample sizes, and excursions (mean, median, p75/p90/p95 as percentages of open). Data is initialized on the first bar via f_init_high_data() and f_init_low_data(), covering 24 hours with 3 segments each (e.g., key "9_1" for 09:20–09:39). Probabilities represent historical likelihood of price returning to open after sweep; excursions quantify average/rare extensions (e.g., 0.156% mean = 0.156% of open price). • Period Detection: On new 1H bars (new_period_bar), resets visuals, draws lines for open/PHH/PHL extending 1 hour forward, and labels if enabled. Uses request.security on standard ticker for real OHLC, bypassing chart transformations (e.g., Heikin Ashi). • Sweep Logic: On each bar, checks if real high > PHH or real low < PHL. If so, fetches segment-specific data (hour + floor(minute/20)), displays probability label centered mid-hour. Skips if filtered (RTH-only or <70% prob). • Excursion Visualization: If enabled, draws “Magic Box” from 1-min to 58-min into the hour, bounded by mean/median levels (top/bottom adjusted for high/low sweep). Adds percentile lines with labels (e.g., “75%”) at right end. Box color reflects prob strength for quick bias assessment. • Retest Check: Monitors for open retest post-sweep (high/low cross open, or gap scenarios from prev bar). Adds ✅ to label if hit on subsequent bars (skips sweep bar to avoid false positives). Stops background fill on retest or at 58-min mark. • Background Fill: Activates on sweep, shades until retest, using user color. • Cleanup & Performance: Manages labels in arrays, clears on new periods; no excess drawing beyond max counts (500 lines/labels/boxes). This setup blends statistical backtesting with real-time visualization: hardcoded data provides empirical probabilities/excursions (reducing subjectivity in breakouts), while dynamic elements (lines, fills, boxes) overlay structure on the chart. It helps British Pound traders assess if a sweep is “high-edge” (e.g., >70% probability of reverting) or likely to run (low probability, high excursion), pairing historical context with current price action. ________________________________________ Settings and Customization Inputs are grouped for ease: 1. Settings: o Show RTH Only (9:00–15:59): Restricts to main session (default: false; tooltip: for RTH-focused stats). o Show High Prob Only (>70%): Filters low-prob sweeps visually (default: false; tooltip: highlights confidence). 2. Visuals: o Show Line Labels: Toggle “h.o.” / “phh” / “phl” (default: true). o Period Open Line Color: Gray 50% (default). o Previous High/Low Line Colors: Gray 100% (default). o Open Line Style/Width: Dotted/1 (default; options: Solid/Dotted/Dashed). 3. Breakout→Open Background: o Show Breakout→Open Background: Toggle fill (default: true). o Fill Color: Teal 85% (default). 4. Breakout Circles: o Show Breakout Circles: Toggle (default: true). o PHH/PHL Break Circle Colors: White 20% (default). 5. Info Label Style: o Text Size: Small (default; options: Auto/Tiny/Normal/Large/Huge). o Label Text Color: White (default). o Low/Mid/High Probability Colors: Red 20% / Orange 20% / Green 20% (default). 6. Excursion (Pain) Zone: o Show Excursion Zone: Toggle Magic Box (default: true). o Excursion Box Color: Gray 75% (default; dynamic overrides). o 75th/90th/95th Percentile Lines: Orange 30% / Red 30% / Dark Red 100% (default). No additional tables/plots; all elements are lines/labels/boxes for overlay focus. ________________________________________ Usage Tips • Breakout Trading: Watch for sweeps with high probability (>70%, green label) as potential fades back to open; low probability (red) may signal runs—use the excursion box for targets (e.g., exit at 90th percentile for extremes). • Time Awareness: Probabilities often peak in key liquidity windows and drop in quieter hours; segments capture momentum shifts (e.g., _2 often lower prob). • RTH Focus: Enable for cleaner stats during high-liquidity session hours; disable for a 24-hour view. • Visual Filtering: Use high-prob-only in volatile conditions to reduce noise; combine with volume or other confluence tools for confirmation. • Alerts Integration: Set TradingView alerts on sweeps; check label for probability/N before acting. • Chart Setup: Best on 1H or lower 6B1! charts; adjust text size for readability on smaller screens. • Backtesting: Manually review historical sweeps against data maps to validate; update hardcoded values if new data emerges (as of 2025). ________________________________________ Limitations • Fixed Data: Hardcoded stats may not reflect recent market changes (e.g., post-2025 regime shifts); not adaptive. • Reactive Only: Detects sweeps after they occur; no predictive signals. • Timeframe Specific: Locked to 1H logic; may not translate to other assets/timeframes without recoding data. • Visual Clutter: On busy charts, labels/boxes may overlap—toggle selectively. • No Live Stats: Sample sizes are historical; real-time N/prob not updated. • Gaps & Extremes: Handles gaps in retest logic, but rare events (e.g., macro news) may exceed the 95th percentile. ________________________________________ Disclaimer This indicator is for informational and educational purposes only. Trading involves significant risk of loss and is not suitable for all investors. The hardcoded data represents past British Pound futures (6B1!) performance and does not guarantee future outcomes. No claims of profitability are made—results depend on market conditions, user strategy, and risk management. Consult a financial advisor before trading, and backtest extensively. Abiding by TradingView rules, this tool provides no investment recommendations.
Pine Script® indicator
by tncylyv
HMA & RSI Delta Hybrid SignalsA lag-free trend follower combining Hull Moving Average (HMA) with RSI Momentum Delta to filter false signals and catch high-probability reversals. # 🚀 HMA & RSI Delta Hybrid Signals This indicator represents a hybrid approach to trend trading by combining the smoothness of the **Hull Moving Average (HMA)** with the explosive detection capabilities of **RSI Momentum Delta**. Unlike standard indicators that rely solely on price crossovers, this tool confirms the trend direction with the *velocity* of the price change (Momentum Delta), reducing false signals in choppy markets. ### 🧠 How It Works? **1. Trend Detection (HMA):** The script uses the **Hull Moving Average**, known for being extremely fast and lag-free, to determine the overall market direction. * **Orange Line:** Represents the HMA Trend. The slope determines if we are in an Uptrend or Downtrend. **2. Momentum Confirmation (RSI Delta):** Instead of looking at raw RSI levels (like 70 or 30), this algorithm calculates the **"Delta"** (Absolute change from the previous bar). * It asks: *"Is the price moving in the trend direction with enough speed?"* * If the RSI jumps significantly (determined by the `Delta Threshold`), it confirms a strong entry. ### 🎯 Signal Modes (Sensitivity) You can choose between two modes depending on your trading style: * **🛡️ Conservative Mode (Default):** * Strict filtering. * Requires the Trend to match the HMA direction AND the RSI Delta to exceed the specific threshold (e.g., 0.8). * *Best for:* Avoiding false signals in sideways markets. * **⚔️ Aggressive Mode:** * Faster entries. * Requires the Trend to match the HMA direction AND any positive momentum change in RSI. * *Best for:* Scalping or catching the very beginning of a move. ### ✨ Key Features * **Non-Repainting Signals:** Once a bar closes, the signal is fixed. * **Non-Repeating:** It will not spam multiple "BUY" signals in a row; it waits for a trend change or reset. * **Visual Trend:** Background color changes based on the HMA slope (Green for Bullish, Purple for Bearish). * **Fully Customizable:** Adjust HMA length, RSI period, and Delta sensitivity. --- **⚠️ DISCLAIMER:** This tool is for educational and analytical purposes only. Always manage your risk.
Pine Script® indicator
by OsilatorindicatorPro
Custom ORBIT GSK-VIZAG-AP-INDIA🚀 Custom ORBIT — Opening Range Breakout & Reversal Indicator This indicator automatically calculates and plots the Opening Range (OR) high and low levels for a user-defined session and duration. It is designed to assist intraday traders by providing immediate visual signals for both price breakouts and subsequent reversals from these key levels. The indicator is particularly suitable for markets with defined trading hours, such as the Indian indices (Nifty, Bank Nifty), given its default time settings are based on GMT+5:30. ⚙️ How It Works (Indicator Logic) The indicator operates based on three main logical components: time definition, level calculation, and signal generation. 1. Time Session and Range Definition: All time calculations are based on GMT+5:30 (Indian Standard Time/IST). The script defines a specific trading session from a customizable start time (default 9:15 AM) to a session end time (default 3:30 PM). The Opening Range (OR) is established during the initial duration, which is set by the rangeMinutes input (default 15 minutes, meaning the OR is calculated from 9:15 AM to 9:30 AM). 2. Level Calculation and Plotting: During the initial range duration, the script captures the absolute highest price (OR High) and the absolute lowest price (OR Low). Once this period ends, two horizontal lines—a green line for the OR High and a red line for the OR Low—are drawn and automatically extended across the chart for the remainder of the active trading session. The visual style of these lines can be customized to Dotted, Dashed, or Solid. 3. Breakout and Reversal Logic: The indicator actively tracks the market's state relative to the OR levels to generate four distinct signals: Break Up: A signal is generated when the closing price crosses over the OR High, indicating potential upward momentum. Break Down: A signal is generated when the closing price crosses under the OR Low, indicating potential downward momentum. Reversal Down: This yellow signal occurs only after a price has already broken above the OR High (Break Up state), and then the price moves back into the range (closing below the ORH), suggesting a failed breakout. Reversal Up: This yellow signal occurs only after a price has already broken below the OR Low (Break Down state), and then the price moves back into the range (closing above the ORL), suggesting a failed breakdown. 💡 Suggested Use Cases The signals generated by this indicator can be used in two primary ways: Breakout Trading: A trader may enter a long position on a "Break Up" signal or a short position on a "Break Down" signal. A common risk management practice is to use the opposite OR level (ORL for long trades, ORH for short trades) as a stop-loss reference. Faded Breakout / Reversal Trading: Look for the yellow "Reversal Up" or "Reversal Down" signals. These signals indicate a rejection of the OR level, and a trader may take a counter-trend position with the expectation that the price will return to the consolidation range or move toward the opposite OR level. ⚠️ Educational Disclaimer This indicator is for educational and illustrative purposes only. It provides technical signals based on mathematical calculation of price action and should not be construed as financial advice, trading advice, or a solicitation to buy or sell any financial instrument. Trading carries a high level of risk, and you may lose more than your initial deposit. Past performance is not indicative of future results. Always consult with a qualified financial professional before making any investment decisions.
Pine Script® indicator
by prowelltraders
Market Session Terrain Monitor vs 1.0 (UTC)Summary Market Session Terrain Monitor helps traders understand where the market is within its normal intraday behavior, not where it should go. It is a decision-support tool designed to reduce late entries, over-trading, and narrative bias by grounding intraday analysis in historical session statistics. Purpose Market Session Terrain Monitor provides statistical context for intraday market movement by analyzing how much each major trading session typically moves, how much it has moved so far, and what market state the current session inherits from previous sessions. The indicator is designed to answer one core question: Is the current session early, normal, or already expanded relative to its historical behavior? This indicator does not predict direction and does not generate buy or sell signals. It is intended as a context and state-awareness tool to support independent, structure-based decision making. Sessions Analyzed The trading day is divided into three independent sessions, defined in UTC time: • Asia • London • New York Each session is analyzed separately using its own historical data. No session is assumed to control or predict the behavior of another. Session Range For each session, the indicator measures the session range, defined as the session high minus the session low. This captures how much the market actually moved during that session, regardless of direction. P90 Expansion Benchmark For each session, the indicator calculates a P90 expansion benchmark. • P90 represents the range that only about ten percent of historical sessions exceed • It reflects a large but repeatable expansion, not an extreme outlier • It is used as a normalization reference so sessions with different volatility characteristics can be compared on equal terms The P90 values are displayed in the table header in price units, such as USD, as a reference for scale. Percent of P90 Current and previous session ranges are expressed as a percentage of that session’s own P90. This shows: • How much of a statistically large session has already been used • Whether the session is still early, behaving normally, or approaching expansion Rolling Comparative Table The table displays three rows, ordered by time and anchored to the current active session: • Current · Session • Previous · Session • Previous-2 · Session Each row shows: • Session name • Session range in price units • Session range as a percentage of that session’s P90 This rolling layout provides context about the market state inherited by the current session without implying causality. How to Use the Indicator The indicator helps with: • Identifying whether a session is early or late in its statistical range • Avoiding entries when a session is already stretched • Recognizing compression versus expansion regimes • Understanding the market state the current session inherits The indicator does not: • Predict direction • Forecast highs or lows • Assume that one session determines the next Directional decisions should come from price structure, execution rules, and risk management. Design Philosophy • Range first, direction second • State awareness over narrative • Statistical normalization instead of absolute numbers • Comparative, not predictive The indicator intentionally avoids estimating remaining range or subtracting previous session movement, as those approaches introduce bias and false causality. Suitable Markets • Gold and silver • Forex pairs • Indices • Other liquid instruments with clear session behavior
Pine Script® indicator
by HHH70
Intraday Volume Pulse GSK-VIZAG-AP-INDIA📊 Intraday Volume Pulse — by GSK-VIZAG-AP-INDIA Overview: This indicator displays a simple and effective intraday volume summary in table format, starting from a user-defined session time. It provides an approximate breakdown of buy volume, sell volume, cumulative delta, and total volume — all updated in real-time. 🧠 Key Features ✅ Session Start Control Choose the session start hour and minute (default is 09:15 for NSE). 🌐 Timezone Selector View volume data in your preferred timezone: IST, GMT, EST, CST, etc. 📈 Buy/Sell Volume Estimation Logic Buy Volume: When candle closes above open Sell Volume: When candle closes below open Equal: Volume split equally if Open == Close 🔄 Daily Auto-Reset All volume metrics reset at the start of a new trading day. 🎨 Color-Coded Volume Insights Buy Volume: Green shade if positive Sell Volume: Red shade if positive Cumulative Delta: Dynamic red/green based on net pressure Total Volume: Neutral gray with emphasis text 🧾 Readable Number Formatting Volumes are displayed in "K", "L", and "Cr" units for easier readability. 📌 Table Positioning Choose from top/bottom corners to best fit your layout. ⚠️ Note All data shown is approximate and based on candle structure — it does not reflect actual order book or tick-level data. This is a visual estimation tool to guide real-time intraday decisions. ✍️ Signature GSK-VIZAG-AP-INDIA Creator of practical TradingView tools focused on volume dynamics and trader psychology.
Pine Script® indicator
by prowelltraders
Market Session Terrain Monitor v1.0Summary Market Session Terrain Monitor helps traders understand where the market is within its normal intraday behavior, not where it should go. It is a decision-support tool designed to reduce late entries, over-trading, and narrative bias by grounding intraday analysis in historical session statistics. Purpose Market Session Terrain Monitor provides statistical context for intraday market movement by analyzing how much each major trading session typically moves, how much it has moved so far, and what market state the current session inherits from previous sessions. The indicator is designed to answer one core question: Is the current session early, normal, or already expanded relative to its historical behavior? This indicator does not predict direction and does not generate buy or sell signals. It is intended as a context and state-awareness tool to support independent, structure-based decision making. Sessions Analyzed The trading day is divided into three independent sessions, defined in UTC time: • Asia • London • New York Each session is analyzed separately using its own historical data. No session is assumed to control or predict the behavior of another. Session Range For each session, the indicator measures the session range, defined as the session high minus the session low. This captures how much the market actually moved during that session, regardless of direction. P90 Expansion Benchmark For each session, the indicator calculates a P90 expansion benchmark. • P90 represents the range that only about ten percent of historical sessions exceed • It reflects a large but repeatable expansion, not an extreme outlier • It is used as a normalization reference so sessions with different volatility characteristics can be compared on equal terms The P90 values are displayed in the table header in price units, such as USD, as a reference for scale. Percent of P90 Current and previous session ranges are expressed as a percentage of that session’s own P90. This shows: • How much of a statistically large session has already been used • Whether the session is still early, behaving normally, or approaching expansion Rolling Comparative Table The table displays three rows, ordered by time and anchored to the current active session: • Current · Session • Previous · Session • Previous-2 · Session Each row shows: • Session name • Session range in price units • Session range as a percentage of that session’s P90 This rolling layout provides context about the market state inherited by the current session without implying causality. How to Use the Indicator The indicator helps with: • Identifying whether a session is early or late in its statistical range • Avoiding entries when a session is already stretched • Recognizing compression versus expansion regimes • Understanding the market state the current session inherits The indicator does not: • Predict direction • Forecast highs or lows • Assume that one session determines the next Directional decisions should come from price structure, execution rules, and risk management. Design Philosophy • Range first, direction second • State awareness over narrative • Statistical normalization instead of absolute numbers • Comparative, not predictive The indicator intentionally avoids estimating remaining range or subtracting previous session movement, as those approaches introduce bias and false causality. Suitable Markets • Gold and silver • Forex pairs • Indices • Other liquid instruments with clear session behavior
Pine Script® indicator
by HHH70
Option Chain Pro+ [Max Pain + PCR] # 📊 Option Chain Pro+ - Complete Options Trading System ## 🎯 Overview **Option Chain Pro+** is the most comprehensive options analysis indicator for Indian indices (NIFTY, BANKNIFTY, FINNIFTY, MIDCAP, SENSEX, BANKEX). This professional-grade tool combines real-time option chain data, Greeks calculation, Max Pain analysis, Put-Call Ratio (PCR), and intelligent trading signals - all in one powerful indicator. Perfect for both **premium sellers** and **directional option buyers**, this indicator provides actionable trading signals with specific strike recommendations and entry prices. --- ## ✨ KEY FEATURES ### 📈 **Complete Option Chain Display** - **Real-time option prices** for Calls and Puts across multiple strikes - **All 5 Greeks**: Delta (Δ), Gamma (Γ), Theta (θ), Vega (ν), Rho (ρ) - **Implied Volatility (IV)** for each strike - **Put-Call Ratio (PCR)** column showing sentiment at each strike level - **Configurable strikes** (5-15 strikes, default: 9) - **Color-coded highlighting** for easy identification: - 🟠 Orange: ATM (At-The-Money) strike - 🔴 Red: Max Pain strike (💀MP) - 🟢 Green: Recommended Call buy (🚀) - 🟣 Magenta: Recommended Put buy (🔻) ### 💀 **Max Pain Analysis** - **Automatic calculation** of Max Pain point (where option buyers lose most) - **Visual highlighting** in option chain table - **Chart level** plotting (red dashed line) - **Trading signals** based on distance from Max Pain - **Most effective** in expiry week (last 3-5 days) ### 📊 **Put-Call Ratio (PCR) Analysis** - **Overall PCR**: Total Put premium / Total Call premium - **Strike-wise PCR**: Individual PCR at each strike level - **Color-coded signals**: - 🔴 Red (PCR > 1.5): Bearish - Heavy put buying - 🟠 Orange (PCR 0.7-1.5): Neutral - Balanced - 🟢 Green (PCR < 0.7): Bullish - Heavy call buying - **Support/Resistance identification** from PCR levels ### 🎯 **Intelligent Trading Signals** #### **Greek-Based Analysis (7 Indicators)** 1. **DELTA**: Direction bias (Bullish/Bearish/Neutral) 2. **GAMMA**: Risk assessment (High/Moderate/Low) 3. **THETA**: Time decay speed (Fast/Moderate/Slow) 4. **VEGA**: Volatility environment (High/Moderate/Low) 5. **VIX**: Fear gauge (High/Moderate/Low fear) 6. **PCR**: Market sentiment (Bearish/Neutral/Bullish) 7. **MAX PAIN**: Price magnet effect (Below/At/Above) #### **💰 Premium Selling Signals** - **Automated recommendations** for credit strategies - Signals: SELL PREMIUM / HEDGE/PROTECT / NEUTRAL STRATEGY - Perfect for Iron Condors, Credit Spreads, and premium collection #### **🚀 Option Buying Signals** - **Specific strike recommendations** for directional trades - **Entry prices** displayed in real-time - **Risk/Reward assessment**: FAVORABLE / MODERATE / UNFAVORABLE - **Visual highlighting** in option chain for recommended strikes - Separate signals for Calls (🚀) and Puts (🔻) ### 📐 **Advanced Greeks Calculation** - **Black-Scholes model** implementation in Pine Script - **Real-time calculation** for all strikes - **Accurate pricing** using current market data - **Configurable risk-free rate** (default: 6.5%) - **IV estimation** from India VIX with multiplier option --- ## 🔧 HOW IT WORKS ### **Data Collection** 1. Fetches real-time spot/futures price 2. Calculates ATM (At-The-Money) strike automatically 3. Retrieves option prices for configured number of strikes 4. Pulls India VIX for volatility estimation ### **Greeks Calculation** - Implements Black-Scholes model for European options - Calculates Delta, Gamma, Theta, Vega, Rho for each strike - Uses 3 days to expiry (configurable via expiry date input) - Adjusts for Indian market conventions ### **Max Pain Calculation** - Simulates price settlement at each strike - Calculates total option buyer losses (Calls + Puts) - Identifies strike with maximum buyer loss - Updates in real-time as prices change ### **PCR Analysis** - Computes Put/Call premium ratio at each strike - Aggregates overall PCR across all strikes - Color-codes based on sentiment thresholds - Identifies support/resistance from extreme PCR values ### **Signal Generation** Combines multiple factors: - Greek values (especially Delta, Gamma, Theta) - VIX level (volatility environment) - PCR sentiment (fear/greed gauge) - Max Pain distance (price magnet) - Generates BUY or SELL recommendations with specific strikes --- ## 🎨 VISUAL COMPONENTS ### **Main Option Chain Table (17 Columns)** Left to Right: 1. **Call Greeks**: Rho, Gamma, Theta, Vega, Delta 2. **Call IV**: Implied Volatility 3. **Call Price**: Premium 4. **Strike**: Strike price with markers (*ATM, 💀MP, 🚀, 🔻) 5. **PCR**: Put-Call Ratio (color-coded) 6. **Put Price**: Premium 7. **Put IV**: Implied Volatility 8. **Put Greeks**: Delta, Vega, Theta, Gamma, Rho **Footer**: ATM IV | Overall PCR | Max Pain | VIX | VWAP ### **Trading Signals Table (16 Rows)** 1. **Header**: Indicator | Value | Signal | Action 2. **7 Analysis Rows**: Delta, Gamma, Theta, Vega, VIX, PCR, Max Pain 3. **Sell Strategy**: Recommendation for premium selling 4. **Buy Opportunity**: Recommendation for directional buying 5. **Buy Details**: Specific strike + Entry price 6. **Risk/Reward**: Assessment of buy opportunity ### **Chart Elements** - **Price plot**: Underlying price (white line) - **ATM line**: Orange dashed horizontal line - **Max Pain line**: Red dashed horizontal line --- ## ⚙️ SETTINGS & CUSTOMIZATION ### **Plot Settings** - **Spot Symbol**: NIFTY, BANKNIFTY, MIDCAP, FINNIFTY, SENSEX, BANKEX - **Ref Strike**: Manual strike reference (used when Auto Tracking = NONE) - **Expiry Date**: Format YYYY-MM-DD (e.g., 2025-12-19) - **Auto Tracking**: SPOT / FUTURES / NONE - FUTURES (recommended): Uses futures price for ATM calculation - SPOT: Uses spot index price - NONE: Uses manual Ref Strike - **Dashboard Location**: Position of option chain table (9 positions) - **Signals Location**: Position of trading signals table (9 positions) ### **Display Settings** - **Number of Strikes**: 5-15 (default: 9) - More strikes = Better Max Pain accuracy - Fewer strikes = Faster loading - **Color Scheme**: Dark / Light - **Show Trading Signals**: Toggle signals table ON/OFF - **Show Symbols (Debug)**: Display option symbols instead of prices ### **Strike Difference** Configure strike intervals for each index: - NIFTY: 50 (default) - BANKNIFTY: 100 (default) - MIDCAP: 25 (default) - FINNIFTY: 50 (default) - SENSEX: 100 (default) - BANKEX: 100 (default) ### **Advanced Settings** - **Risk Free Rate**: 6.5% (default) - Used in Greeks calculation - **IV Multiplier**: 1.0 (default) - Adjust VIX-based IV estimation ### **Buy Strategy** - **Buy Strike Distance (OTM)**: 1-5 strikes (default: 2) - 1 = Closer to ATM (higher probability, lower leverage) - 2 = Balanced (recommended) - 3-5 = Further OTM (lower probability, higher leverage) --- ## 📚 TRADING STRATEGIES SUPPORTED ### **1. Premium Selling Strategies** **When to use**: High Theta + Low VIX + High IV Rank - Iron Condors - Credit Spreads (Bull/Bear) - Naked Put selling (cash-secured) - Ratio spreads **Signals to watch**: - SELL STRATEGY = "SELL PREMIUM" - Theta > -15 (fast decay) - VIX > 15 (high premiums) - Gamma < 0.002 (low risk) ### **2. Directional Buying** **When to use**: Low VIX + High Gamma + Strong trend - ATM/OTM Call buying (bullish) - ATM/OTM Put buying (bearish) - Debit spreads **Signals to watch**: - BUY OPPORTUNITY = "🚀 BUY CALL" or "🔻 BUY PUT" - RISK/REWARD = "FAVORABLE" - VIX < 13 (cheap options) - Clear directional bias from Delta ### **3. Max Pain Trading (Expiry Week)** **When to use**: Last 3 days before expiry - Price gravitates toward Max Pain - Fade extremes, buy toward Max Pain **Example**: - Max Pain: 26000 - Current: 25850 (below) - Action: Buy 25900 CE, target 26000 ### **4. PCR Contrarian** **When to use**: Extreme PCR readings - PCR > 1.5: Excessive fear → Sell Puts - PCR < 0.7: Excessive greed → Sell Calls ### **5. Support/Resistance from PCR** **When to use**: Identify key levels - High PCR at strike = Strong support (Put wall) - Low PCR at strike = Strong resistance (Call wall) --- ## 💡 HOW TO USE ### **Step 1: Setup** 1. Add indicator to NIFTY/BANKNIFTY chart 2. Set expiry date (Thursday for weekly, last Thursday for monthly) 3. Choose number of strikes (9 recommended) 4. Select Auto Tracking = FUTURES 5. Position tables (Option Chain: top_right, Signals: bottom_right) ### **Step 2: Analyze Greeks** Check the **Trading Signals Table**: - **Delta**: Market direction bias - **Gamma**: Risk of sudden moves - **Theta**: Speed of time decay - **Vega**: Volatility environment - **VIX**: Overall fear/greed - **PCR**: Put/Call sentiment - **Max Pain**: Price magnet ### **Step 3: Identify Opportunities** **For Premium Selling**: - Check "💰 SELL STRATEGY" row - If "SELL PREMIUM" → Look for credit spread setups - High Theta + Low Gamma = Ideal for selling **For Option Buying**: - Check "🎯 BUY OPPORTUNITY" row - If "🚀 BUY CALL" or "🔻 BUY PUT" appears - Note the recommended STRIKE and PRICE - Check RISK/REWARD assessment - FAVORABLE = Full position size - MODERATE = Half position size - UNFAVORABLE = Wait ### **Step 4: Execute** 1. Locate highlighted strike in option chain (🚀 green or 🔻 magenta) 2. Verify price matches recommendation 3. Execute trade with proper position sizing 4. Set stop loss: 50% of premium paid for buyers 5. Target: 100-150% profit (2-2.5x) ### **Step 5: Monitor** - **Max Pain line**: Price tends to gravitate here near expiry - **PCR values**: Watch for shifts in sentiment - **Greeks changes**: Delta/Gamma shifts indicate trend changes - **VIX spikes**: Exit short premium positions if VIX > 20 --- ## 🎓 INTERPRETATION GUIDE ### **Delta Signals** - **> 0.6**: Bullish bias → Sell Puts / Buy Calls - **0.4-0.6**: Neutral → Iron Condor / Range strategies - **< 0.4**: Bearish bias → Sell Calls / Buy Puts ### **Gamma Signals** - **> 0.002**: High risk → Avoid selling, spreads only - **0.001-0.002**: Moderate risk → Use defined risk strategies - **< 0.001**: Low risk → Safe to sell premium ### **Theta Signals** - **|θ| > 20**: Fast decay → Aggressive premium selling - **|θ| 10-20**: Moderate decay → Credit spreads - **|θ| < 10**: Slow decay → Buy options (cheaper) ### **Vega Signals** - **> 12**: High volatility → Sell volatility (straddles/strangles) - **8-12**: Moderate → Neutral strategies - **< 8**: Low volatility → Buy options (underpriced) ### **VIX Signals** - **> 15**: High fear → Sell premium (expensive options) - **12-15**: Moderate → Neutral - **< 12**: Low fear → Buy protection / Long options ### **PCR Signals** - **> 1.5**: Bearish (Put heavy) → Contrarian: Sell Puts - **0.7-1.5**: Neutral (Balanced) → Range strategies - **< 0.7**: Bullish (Call heavy) → Contrarian: Sell Calls ### **Max Pain Signals** - **Below Max Pain**: Upside bias → Buy Calls / Sell Puts - **At Max Pain**: Consolidation → Iron Condor - **Above Max Pain**: Downside bias → Buy Puts / Sell Calls --- ## 📊 EXAMPLE SCENARIOS ### **Scenario 1: Premium Selling Setup** ``` Greeks Analysis: - Delta: 0.52 (Neutral) - Gamma: 0.0010 (Low Risk) - Theta: -18 (Fast Decay) - Vega: 13.5 (High Vol) - VIX: 16.5 (High Fear) - PCR: 1.4 (Neutral) Signal: SELL PREMIUM ✅ Action: Sell Iron Condor Setup: Sell 26050 CE + 25850 PE, Buy wings ``` ### **Scenario 2: Bullish Buy Setup** ``` Greeks Analysis: - Delta: 0.58 (Bullish) - Gamma: 0.0018 (High - Big moves expected) - Theta: -12 (Moderate) - Vega: 8.5 (Moderate) - VIX: 11.2 (Low - Cheap options) - PCR: 1.6 (Bearish - Contrarian opportunity) - Max Pain: 26000, Current: 25850 Signal: 🚀 BUY CALL Strike: 26050 CE Price: 12.50 Risk/Reward: FAVORABLE ✅ Action: Buy 26050 CE at ₹12.50 Target: ₹25-30 (2x) Stop: ₹6 (50% loss) ``` ### **Scenario 3: Max Pain Trade** ``` Max Pain: 26000 Current Price: 25850 (150 points below) Days to Expiry: 2 PCR: 1.2 (Neutral) Signal: BELOW MAX PAIN → Upside Likely Action: Buy 25900 CE Reason: Price likely to move toward Max Pain Target: 26000 (Max Pain level) ``` --- ## ⚠️ IMPORTANT NOTES ### **Data Limitations** - Uses **simplified Greeks** calculation (assumes 3 DTE by default) - Option prices may have slight delays (TradingView data refresh) - Max Pain calculation is **approximation** based on current premiums - Not all option symbols may be available on TradingView ### **Best Practices** 1. **Verify prices** on your broker platform before trading 2. **Use during market hours** (9:15 AM - 3:30 PM IST) for accurate data 3. **Most effective** 3-5 days before expiry 4. **Combine with price action** and trend analysis 5. **Risk management**: Never risk more than 2% per trade ### **Optimization Tips** - **Increase strikes** to 9-11 for better Max Pain accuracy - **Use FUTURES** tracking for liquid indices (NIFTY, BANKNIFTY) - **Enable debug mode** initially to verify symbols are correct - **Adjust IV Multiplier** if VIX seems over/underestimated --- ## 🔄 UPDATES & SUPPORT ### **Version 1.0 Features** ✅ Complete option chain display (17 columns) ✅ All 5 Greeks calculation ✅ Max Pain analysis ✅ Put-Call Ratio (PCR) - Overall + Strike-wise ✅ Trading signals (Buy + Sell) ✅ Specific strike recommendations ✅ Risk/Reward assessment ✅ Support for 6 Indian indices ✅ Configurable strikes (5-15) ✅ Dark/Light color schemes ✅ Auto ATM tracking ### **Planned Updates** 🔜 OI (Open Interest) data integration 🔜 Historical Max Pain tracking 🔜 PCR trends and momentum 🔜 Custom alerts for signals 🔜 Multi-expiry analysis 🔜 Volatility smile/skew display --- ## 📖 EDUCATIONAL RESOURCES ### **Understanding Greeks** - **Delta**: Rate of change in option price vs underlying (0-1 for calls, -1-0 for puts) - **Gamma**: Rate of change of Delta (highest at ATM) - **Theta**: Time decay per day (always negative for buyers) - **Vega**: Sensitivity to volatility changes - **Rho**: Sensitivity to interest rate changes (less important for short-term) ### **Max Pain Theory** Max Pain suggests that market makers manipulate prices toward the strike where option buyers lose the most money. While controversial, it has statistical validity in expiry week when: 1. Volume is high 2. Market makers hedge positions 3. Pin risk causes clustering at certain strikes ### **PCR as Sentiment Indicator** - PCR > 1: More put buying than call buying (bearish) - PCR < 1: More call buying than put buying (bullish) - **Contrarian use**: Extreme readings often precede reversals - **Confirmation use**: With trend for continuation trades --- ## 🎯 WHO IS THIS FOR? ### ✅ **Perfect For:** - Options traders (all experience levels) - Premium sellers (credit strategies) - Directional option buyers - Intraday option traders - Swing traders in options - Risk managers - Market makers - Professional traders ### ✅ **Use Cases:** - Daily options trading on NIFTY/BANKNIFTY - Weekly expiry strategies - Monthly expiry positioning - Volatility trading - Hedging portfolios - Greeks-based strategies - Statistical arbitrage --- ## ⚖️ DISCLAIMER **This indicator is for educational and informational purposes only.** - NOT financial advice or recommendation to buy/sell - Past performance does not guarantee future results - Options trading involves substantial risk of loss - Greeks calculations are theoretical models - Max Pain is not guaranteed to be reached - Always verify data with your broker - Use proper risk management and position sizing - Consult a financial advisor before trading **The author is not responsible for any trading losses.** --- ## 📞 SUPPORT For questions, issues, or feature requests: - Comment below this indicator - Check TradingView documentation for Pine Script basics - Review NSE option chain for symbol verification --- ## 🏆 WHY CHOOSE THIS INDICATOR? ### **Comprehensive** - Most complete options analysis tool on TradingView - Combines Greeks + Max Pain + PCR + Signals in one ### **Professional** - Used by professional traders - Based on proven Black-Scholes model - Real-time calculations ### **Actionable** - Specific strike recommendations - Entry prices displayed - Clear Buy/Sell signals - Risk/Reward assessment ### **Customizable** - Multiple indices supported - Configurable strikes - Adjustable parameters - Flexible positioning ### **Visual** - Color-coded for easy reading - Highlighted opportunities - Chart levels for reference - Professional table layouts --- ## 🚀 GET STARTED 1. **Add to chart**: Click "Add to favorites" ⭐ 2. **Apply to NIFTY or BANKNIFTY** chart 3. **Set expiry date** in settings 4. **Configure strikes** (9 recommended) 5. **Start trading** with professional insights! --- **Happy Trading! 📊💰** *If you find this indicator useful, please like, comment, and share!* *Your feedback helps improve future versions.* --- **Tags**: #options #greeks #nifty #banknifty #maxpain #pcr #delta #gamma #theta #vega #optionchain #india #nse #trading #signals
Pine Script® indicator
by shivkumar503244
SCOTTGO - RSI Divergence IndicatorRSI Divergence Indicator This indicator combines the Relative Strength Index (RSI) with an automatic divergence detection system. It is designed to help traders spot potential trend changes by: Color-Coded RSI: The main RSI line dynamically changes color (e.g., green/red) above and below a user-defined threshold (default 50) to highlight strong or weak momentum instantly. Divergence Signals: It automatically identifies and plots four types of RSI divergences (Regular Bullish, Hidden Bullish, Regular Bearish, and Hidden Bearish) between the price and the oscillator. Custom Alerts: Includes alerts for all divergence types so you can be notified when a new signal is found. This tool helps visualize momentum shifts and potential reversals in the market.
Pine Script® indicator
by SCOTTGO