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Langlands-Operadic Möbius Vortex (LOMV)Langlands-Operadic Möbius Vortex (LOMV) Where Pure Mathematics Meets Market Reality A Revolutionary Synthesis of Number Theory, Category Theory, and Market Dynamics 🎓 THEORETICAL FOUNDATION The Langlands-Operadic Möbius Vortex represents a groundbreaking fusion of three profound mathematical frameworks that have never before been combined for market analysis: The Langlands Program: Harmonic Analysis in Markets Developed by Robert Langlands (Fields Medal recipient), the Langlands Program creates bridges between number theory, algebraic geometry, and harmonic analysis. In our indicator: L-Function Implementation: - Utilizes the Möbius function μ(n) for weighted price analysis - Applies Riemann zeta function convergence principles - Calculates quantum harmonic resonance between -2 and +2 - Measures deep mathematical patterns invisible to traditional analysis The L-Function core calculation employs: L_sum = Σ(return_val × μ(n) × n^(-s)) Where s is the critical strip parameter (0.5-2.5), controlling mathematical precision and signal smoothness. Operadic Composition Theory: Multi-Strategy Democracy Category theory and operads provide the mathematical framework for composing multiple trading strategies into a unified signal. This isn't simple averaging - it's mathematical composition using: Strategy Composition Arity (2-5 strategies): - Momentum analysis via RSI transformation - Mean reversion through Bollinger Band mathematics - Order Flow Polarity Index (revolutionary T3-smoothed volume analysis) - Trend detection using Directional Movement - Higher timeframe momentum confirmation Agreement Threshold System: Democratic voting where strategies must reach consensus before signal generation. This prevents false signals during market uncertainty. Möbius Function: Number Theory in Action The Möbius function μ(n) forms the mathematical backbone: - μ(n) = 1 if n is a square-free positive integer with even number of prime factors - μ(n) = -1 if n is a square-free positive integer with odd number of prime factors - μ(n) = 0 if n has a squared prime factor This creates oscillating weights that reveal hidden market periodicities and harmonic structures. 🔧 COMPREHENSIVE INPUT SYSTEM Langlands Program Parameters Modular Level N (5-50, default 30): Primary lookback for quantum harmonic analysis. Optimized by timeframe: - Scalping (1-5min): 15-25 - Day Trading (15min-1H): 25-35 - Swing Trading (4H-1D): 35-50 - Asset-specific: Crypto 15-25, Stocks 30-40, Forex 35-45 L-Function Critical Strip (0.5-2.5, default 1.5): Controls Riemann zeta convergence precision: - Higher values: More stable, smoother signals - Lower values: More reactive, catches quick moves - High frequency: 0.8-1.2, Medium: 1.3-1.7, Low: 1.8-2.3 Frobenius Trace Period (5-50, default 21): Galois representation lookback for price-volume correlation: - Measures harmonic relationships in market flows - Scalping: 8-15, Day Trading: 18-25, Swing: 25-40 HTF Multi-Scale Analysis: Higher timeframe context prevents trading against major trends: - Provides market bias and filters signals - Improves win rates by 15-25% through trend alignment Operadic Composition Parameters Strategy Composition Arity (2-5, default 4): Number of algorithms composed for final signal: - Conservative: 4-5 strategies (higher confidence) - Moderate: 3-4 strategies (balanced approach) - Aggressive: 2-3 strategies (more frequent signals) Category Agreement Threshold (2-5, default 3): Democratic voting minimum for signal generation: - Higher agreement: Fewer but higher quality signals - Lower agreement: More signals, potential false positives Swiss-Cheese Mixing (0.1-0.5, default 0.382): Golden ratio φ⁻¹ based blending of trend factors: - 0.382 is φ⁻¹, optimal for natural market fractals - Higher values: Stronger trend following - Lower values: More contrarian signals OFPI Configuration: - OFPI Length (5-30, default 14): Order Flow calculation period - T3 Smoothing (3-10, default 5): Advanced exponential smoothing - T3 Volume Factor (0.5-1.0, default 0.7): Smoothing aggressiveness control Unified Scoring System Component Weights (sum ≈ 1.0): - L-Function Weight (0.1-0.5, default 0.3): Mathematical harmony emphasis - Galois Rank Weight (0.1-0.5, default 0.2): Market structure complexity - Operadic Weight (0.1-0.5, default 0.3): Multi-strategy consensus - Correspondence Weight (0.1-0.5, default 0.2): Theory-practice alignment Signal Threshold (0.5-10.0, default 5.0): Quality filter producing: - 8.0+: EXCEPTIONAL signals only - 6.0-7.9: STRONG signals - 4.0-5.9: MODERATE signals - 2.0-3.9: WEAK signals 🎨 ADVANCED VISUAL SYSTEM Multi-Dimensional Quantum Aura Bands Five-layer resonance field showing market energy: - Colors: Theme-matched gradients (Quantum purple, Holographic cyan, etc.) - Expansion: Dynamic based on score intensity and volatility - Function: Multi-timeframe support/resistance zones Morphism Flow Portals Category theory visualization showing market topology: - Green/Cyan Portals: Bullish mathematical flow - Red/Orange Portals: Bearish mathematical flow - Size/Intensity: Proportional to signal strength - Recursion Depth (1-8): Nested patterns for flow evolution Fractal Grid System Dynamic support/resistance with projected L-Scores: - Multiple Timeframes: 10, 20, 30, 40, 50-period highs/lows - Smart Spacing: Prevents level overlap using ATR-based minimum distance - Projections: Estimated signal scores when price reaches levels - Usage: Precise entry/exit timing with mathematical confirmation Wick Pressure Analysis Rejection level prediction using candle mathematics: - Upper Wicks: Selling pressure zones (purple/red lines) - Lower Wicks: Buying pressure zones (purple/green lines) - Glow Intensity (1-8): Visual emphasis and line reach - Application: Confluence with fractal grid creates high-probability zones Regime Intensity Heatmap Background coloring showing market energy: - Black/Dark: Low activity, range-bound markets - Purple Glow: Building momentum and trend development - Bright Purple: High activity, strong directional moves - Calculation: Combines trend, momentum, volatility, and score intensity Six Professional Themes - Quantum: Purple/violet for general trading and mathematical focus - Holographic: Cyan/magenta optimized for cryptocurrency markets - Crystalline: Blue/turquoise for conservative, stability-focused trading - Plasma: Gold/magenta for high-energy volatility trading - Cosmic Neon: Bright neon colors for maximum visibility and aggressive trading 📊 INSTITUTIONAL-GRADE DASHBOARD Unified AI Score Section - Total Score (-10 to +10): Primary decision metric - >5: Strong bullish signals - <-5: Strong bearish signals - Quality ratings: EXCEPTIONAL > STRONG > MODERATE > WEAK - Component Analysis: Individual L-Function, Galois, Operadic, and Correspondence contributions Order Flow Analysis Revolutionary OFPI integration: - OFPI Value (-100% to +100%): Real buying vs selling pressure - Visual Gauge: Horizontal bar chart showing flow intensity - Momentum Status: SHIFTING, ACCELERATING, STRONG, MODERATE, or WEAK - Trading Application: Flow shifts often precede major moves Signal Performance Tracking - Win Rate Monitoring: Real-time success percentage with emoji indicators - Signal Count: Total signals generated for frequency analysis - Current Position: LONG, SHORT, or NONE with P&L tracking - Volatility Regime: HIGH, MEDIUM, or LOW classification Market Structure Analysis - Möbius Field Strength: Mathematical field oscillation intensity - CHAOTIC: High complexity, use wider stops - STRONG: Active field, normal position sizing - MODERATE: Balanced conditions - WEAK: Low activity, consider smaller positions - HTF Trend: Higher timeframe bias (BULL/BEAR/NEUTRAL) - Strategy Agreement: Multi-algorithm consensus level Position Management When in trades, displays: - Entry Price: Original signal price - Current P&L: Real-time percentage with risk level assessment - Duration: Bars in trade for timing analysis - Risk Level: HIGH/MEDIUM/LOW based on current exposure 🚀 SIGNAL GENERATION LOGIC Balanced Long/Short Architecture The indicator generates signals through multiple convergent pathways: Long Entry Conditions: - Score threshold breach with algorithmic agreement - Strong bullish order flow (OFPI > 0.15) with positive composite signal - Bullish pattern recognition with mathematical confirmation - HTF trend alignment with momentum shifting - Extreme bullish OFPI (>0.3) with any positive score Short Entry Conditions: - Score threshold breach with bearish agreement - Strong bearish order flow (OFPI < -0.15) with negative composite signal - Bearish pattern recognition with mathematical confirmation - HTF trend alignment with momentum shifting - Extreme bearish OFPI (<-0.3) with any negative score Exit Logic: - Score deterioration below continuation threshold - Signal quality degradation - Opposing order flow acceleration - 10-bar minimum between signals prevents overtrading ⚙️ OPTIMIZATION GUIDELINES Asset-Specific Settings Cryptocurrency Trading: - Modular Level: 15-25 (capture volatility) - L-Function Precision: 0.8-1.3 (reactive to price swings) - OFPI Length: 10-20 (fast correlation shifts) - Cascade Levels: 5-7, Theme: Holographic Stock Index Trading: - Modular Level: 25-35 (balanced trending) - L-Function Precision: 1.5-1.8 (stable patterns) - OFPI Length: 14-20 (standard correlation) - Cascade Levels: 4-5, Theme: Quantum Forex Trading: - Modular Level: 35-45 (smooth trends) - L-Function Precision: 1.6-2.1 (high smoothing) - OFPI Length: 18-25 (disable volume amplification) - Cascade Levels: 3-4, Theme: Crystalline Timeframe Optimization Scalping (1-5 minute charts): - Reduce all lookback parameters by 30-40% - Increase L-Function precision for noise reduction - Enable all visual elements for maximum information - Use Small dashboard to save screen space Day Trading (15 minute - 1 hour): - Use default parameters as starting point - Adjust based on market volatility - Normal dashboard provides optimal information density - Focus on OFPI momentum shifts for entries Swing Trading (4 hour - Daily): - Increase lookback parameters by 30-50% - Higher L-Function precision for stability - Large dashboard for comprehensive analysis - Emphasize HTF trend alignment 🏆 ADVANCED TRADING STRATEGIES The Mathematical Confluence Method 1. Wait for Fractal Grid level approach 2. Confirm with projected L-Score > threshold 3. Verify OFPI alignment with direction 4. Enter on portal signal with quality ≥ STRONG 5. Exit on score deterioration or opposing flow The Regime Trading System 1. Monitor Aether Flow background intensity 2. Trade aggressively during bright purple periods 3. Reduce position size during dark periods 4. Use Möbius Field strength for stop placement 5. Align with HTF trend for maximum probability The OFPI Momentum Strategy 1. Watch for momentum shifting detection 2. Confirm with accelerating flow in direction 3. Enter on immediate portal signal 4. Scale out at Fibonacci levels 5. Exit on flow deceleration or reversal ⚠️ RISK MANAGEMENT INTEGRATION Mathematical Position Sizing - Use Galois Rank for volatility-adjusted sizing - Möbius Field strength determines stop width - Fractal Dimension guides maximum exposure - OFPI momentum affects entry timing Signal Quality Filtering - Trade only STRONG or EXCEPTIONAL quality signals - Increase position size with higher agreement levels - Reduce risk during CHAOTIC Möbius field periods - Respect HTF trend alignment for directional bias 🔬 DEVELOPMENT JOURNEY Creating the LOMV was an extraordinary mathematical undertaking that pushed the boundaries of what's possible in technical analysis. This indicator almost didn't happen. The theoretical complexity nearly proved insurmountable. The Mathematical Challenge Implementing the Langlands Program required deep research into: - Number theory and the Möbius function - Riemann zeta function convergence properties - L-function analytical continuation - Galois representations in finite fields The mathematical literature spans decades of pure mathematics research, requiring translation from abstract theory to practical market application. The Computational Complexity Operadic composition theory demanded: - Category theory implementation in Pine Script - Multi-dimensional array management for strategy composition - Real-time democratic voting algorithms - Performance optimization for complex calculations The Integration Breakthrough Bringing together three disparate mathematical frameworks required: - Novel approaches to signal weighting and combination - Revolutionary Order Flow Polarity Index development - Advanced T3 smoothing implementation - Balanced signal generation preventing directional bias Months of intensive research culminated in breakthrough moments when the mathematics finally aligned with market reality. The result is an indicator that reveals market structure invisible to conventional analysis while maintaining practical trading utility. 🎯 PRACTICAL IMPLEMENTATION Getting Started 1. Apply indicator with default settings 2. Select appropriate theme for your markets 3. Observe dashboard metrics during different market conditions 4. Practice signal identification without trading 5. Gradually adjust parameters based on observations Signal Confirmation Process - Never trade on score alone - verify quality rating - Confirm OFPI alignment with intended direction - Check fractal grid level proximity for timing - Ensure Möbius field strength supports position size - Validate against HTF trend for bias confirmation Performance Monitoring - Track win rate in dashboard for strategy assessment - Monitor component contributions for optimization - Adjust threshold based on desired signal frequency - Document performance across different market regimes 🌟 UNIQUE INNOVATIONS 1. First Integration of Langlands Program mathematics with practical trading 2. Revolutionary OFPI with T3 smoothing and momentum detection 3. Operadic Composition using category theory for signal democracy 4. Dynamic Fractal Grid with projected L-Score calculations 5. Multi-Dimensional Visualization through morphism flow portals 6. Regime-Adaptive Background showing market energy intensity 7. Balanced Signal Generation preventing directional bias 8. Professional Dashboard with institutional-grade metrics 📚 EDUCATIONAL VALUE The LOMV serves as both a practical trading tool and an educational gateway to advanced mathematics. Traders gain exposure to: - Pure mathematics applications in markets - Category theory and operadic composition - Number theory through Möbius function implementation - Harmonic analysis via L-function calculations - Advanced signal processing through T3 smoothing ⚖️ RESPONSIBLE USAGE This indicator represents advanced mathematical research applied to market analysis. While the underlying mathematics are rigorously implemented, markets remain inherently unpredictable. Key Principles: - Use as part of comprehensive trading strategy - Implement proper risk management at all times - Backtest thoroughly before live implementation - Understand that past performance does not guarantee future results - Never risk more than you can afford to lose The mathematics reveal deep market structure, but successful trading requires discipline, patience, and sound risk management beyond any indicator. 🔮 CONCLUSION The Langlands-Operadic Möbius Vortex represents a quantum leap forward in technical analysis, bringing PhD-level pure mathematics to practical trading while maintaining visual elegance and usability. From the harmonic analysis of the Langlands Program to the democratic composition of operadic theory, from the number-theoretic precision of the Möbius function to the revolutionary Order Flow Polarity Index, every component works in mathematical harmony to reveal the hidden order within market chaos. This is more than an indicator - it's a mathematical lens that transforms how you see and understand market structure. Trade with mathematical precision. Trade with the LOMV. *"Mathematics is the language with which God has written the universe." - Galileo Galilei* *In markets, as in nature, profound mathematical beauty underlies apparent chaos. The LOMV reveals this hidden order.* — Dskyz, Trade with insight. Trade with anticipation.
Wskaźnik Pine Script®
od DskyzInvestments
29
CNN Fear and Greed StrategyAdaptation of the CNN Fear and Greed Index Indicator (Original by EdgeTools) The following changes have been implemented: Put/Call Ratio Data Source: The data source for the Put/Call Ratio has been updated. Bond Data Source: The data sources for the bond components (Safe Haven Demand and Junk Bond Demand) have been updated. Normalization Adjustment: The normalization method has been adjusted to allow the CNN Fear and Greed Index to display over a longer historical period, optimizing it for backtesting purposes. Style Modification: The display style has been modified for a simpler and cleaner appearance. Strategy Logic Addition: Added a new strategy entry condition: index >= 25 AND index crosses over its 5-period Simple Moving Average (SMA), and a corresponding exit condition of holding the position for 252 bars (days). CNN Fear & Greed Backtest Strategy (Adapted) This script is an adaptation of the popular CNN Fear & Greed Index, originally created by EdgeTools, with significant modifications to optimize it for long-term backtesting on the TradingView platform. The core function of the Fear & Greed Index is to measure the current emotional state of the stock market, ranging from 0 (Extreme Fear) to 100 (Extreme Greed). It operates on the principle that excessive fear drives prices too low (a potential buying opportunity), and excessive greed drives them too high (a potential selling opportunity). Key Components of the Index (7 Factors) The composite index is calculated as a weighted average of seven market indicators, each normalized to a score between 0 and 100: Market Momentum: S&P 500's current level vs. its 125-day Moving Average. Stock Price Strength: Stocks hitting 52-week highs vs. those hitting 52-week lows. Stock Price Breadth: Measured by the McClellan Volume Summation Index (or similar volume/breadth metric). Put/Call Ratio: The relationship between volume of put options (bearish bets) and call options (bullish bets). Market Volatility: The CBOE VIX Index relative to its 50-day Moving Average. Safe Haven Demand: The relative performance of stocks (S&P 500) vs. bonds. Junk Bond Demand: The spread between high-yield (junk) bonds and U.S. Treasury yields. Critical Adaptations for Backtesting To improve the index's utility for quantitative analysis, the following changes were made: Long-Term Normalization: The original normalization method (ta.stdev over a short LENGTH) has been replaced or adjusted to use longer historical data. This change ensures the index generates consistent and comparable sentiment scores across decades of market history, which is crucial for reliable backtesting results. Updated Data Sources: Specific ticker requests for the Put/Call Ratio and Bond components (Safe Haven and Junk Bond Demand) have been updated to use the most reliable and long-running data available on TradingView, reducing data gaps and improving chart continuity. Simplified Visuals: The chart display is streamlined, focusing only on the final Fear & Greed Index line and key threshold levels (25, 50, 75) for quick visual assessment. Integrated Trading Strategy This script also includes a simple, rules-based strategy designed to test the counter-trend philosophy of the index: Entry Logic (Long Position): A long position is initiated when the market shows increasing fear, specifically when the index score is less than or equal to the configurable FEAR_LEVEL (default 25) and the index crosses above its own short-term 5-period Simple Moving Average (SMA). This crossover acts as a confirmation that sentiment may be starting to turn around from peak fear. Exit Logic (Time-Based): All positions are subject to a time-based exit after holding for 252 trading days (approximately one year). This fixed holding period aims to capture the typical duration of a cyclical market recovery following a major panic event.
Strategia Pine Script®
od rwanwan
$TUBR: 7-25-99 Moving Average7, 25, and 99 Period Moving Averages This indicator plots three moving averages: the 7-period, 25-period, and 99-period Simple Moving Averages (SMA). These moving averages are widely used to smooth out price action and help traders identify trends over different time frames. Let's break down the significance of these specific moving averages from both supply and demand perspectives and a price action perspective. 1. Supply and Demand Perspective: - 7-period Moving Average (Short-Term) : The 7-period moving average represents the short-term sentiment in the market. It captures the rapid fluctuations in price and is heavily influenced by recent supply and demand changes. Traders often look to the 7-period SMA for immediate price momentum, with price moving above or below this line signaling short-term strength or weakness. - Bullish Supply/Demand : When price is above the 7-period SMA, it suggests that buyers are currently in control and demand is higher than supply. Conversely, price falling below this line indicates that supply is overpowering demand, leading to a short-term downtrend. Is current price > average price in past 7 candles (depending on timeframe)? This will tell you how aggressive buyers are in short term. - Key Supply/Demand Zones : The 7-period SMA often acts as dynamic support or resistance in a trending market, where traders might use it to enter or exit positions based on how price interacts with this level. - 25-period Moving Average (Medium-Term) : The 25-period SMA smooths out more of the noise compared to the 7-period, providing a more stable indication of intermediate trends. This moving average is often used to gauge the market's supply and demand balance over a broader timeframe than the short-term 7-period SMA. - Supply/Demand Balance : The 25-period SMA reflects the medium-term equilibrium between supply and demand. A crossover between the price and the 25-period SMA may indicate a shift in this balance. When price sustains above the 25-period SMA, it shows that demand is strong enough to maintain an upward trend. Conversely, if the price stays below it, supply is likely exceeding demand. Is current price > average price in past 25 candles (depending on timeframe)? This will tell you how aggressive buyers are in mid term. - Momentum Shift : Crossovers between the 7-period and 25-period SMAs can indicate momentum shifts between short-term and medium-term demand. For example, if the 7-period crosses above the 25-period, it often signifies growing short-term demand relative to the medium-term trend, signaling potential buy opportunities. What this crossover means is that if 7MA > 25MA that means in past 7 candles average price is more than past 25 candles. - 99-period Moving Average (Long-Term): The 99-period SMA represents the long-term trend and reflects the market's supply and demand over an extended period. This moving average filters out short-term fluctuations and highlights the market's overall trajectory. - Long-Term Supply/Demand Dynamics : The 99-period SMA is slower to react to changes in supply and demand, providing a more stable view of the market's overall trend. Price staying above this line shows sustained demand dominance, while price consistently staying below reflects ongoing supply pressure. Is current price > average price in past 99 candles (depending on timeframe)? This will tell you how aggressive buyers are in long term. - Market Trend Confirmation : When both the 7-period and 25-period SMAs are above the 99-period SMA, it signals a strong bullish trend with demand outweighing supply across all timeframes. If all three SMAs are below the 99-period SMA, it points to a bear market where supply is overpowering demand in both the short and long term. 2. Price Action Perspective : - 7-period Moving Average (Short-Term Trends): The 7-period moving average closely tracks price action, making it highly responsive to quick shifts in price. Traders often use it to confirm short-term reversals or continuations in price action. In an uptrend, price typically stays above the 7-period SMA, whereas in a downtrend, price stays below it. - Short-Term Price Reversals : Crossovers between the price and the 7-period SMA often indicate short-term reversals. When price breaks above the 7-period SMA after staying below it, it suggests a potential bullish reversal. Conversely, a price breakdown below the 7-period SMA could signal a bearish reversal. - 25-period Moving Average (Medium-Term Trends) : The 25-period SMA helps identify the medium-term price action trend. It balances short-term volatility and longer-term stability, providing insight into the more persistent trend. Price pullbacks to the 25-period SMA during an uptrend can act as a buying opportunity for trend traders, while pullbacks during a downtrend may offer shorting opportunities. - Pullback and Continuation: In trending markets, price often retraces to the 25-period SMA before continuing in the direction of the trend. For instance, if the price is in a bullish trend, traders may look for support at the 25-period SMA for potential continuation trades. - 99-period Moving Average (Long-Term Trend and Market Sentiment ): The 99-period SMA is the most critical for identifying the overall market trend. Price consistently trading above the 99-period SMA indicates long-term bullish momentum, while price staying below the 99-period SMA suggests bearish sentiment. - Trend Confirmation : Price action above the 99-period SMA confirms long-term upward momentum, while price action below it confirms a downtrend. The space between the shorter moving averages (7 and 25) and the 99-period SMA gives a sense of the strength or weakness of the trend. Larger gaps between the 7 and 99 SMAs suggest strong bullish momentum, while close proximity indicates consolidation or potential reversals. - Price Action in Trending Markets : Traders often use the 99-period SMA as a dynamic support/resistance level. In strong trends, price tends to stay on one side of the 99-period SMA for extended periods, with breaks above or below signaling major changes in market sentiment. Why These Numbers Matter: 7-Period MA : The 7-period moving average is a popular choice among short-term traders who want to capture quick momentum changes. It helps visualize immediate market sentiment and is often used in conjunction with price action to time entries or exits. - 25-Period MA: The 25-period MA is a key indicator for swing traders. It balances sensitivity and stability, providing a clearer picture of the intermediate trend. It helps traders stay in trades longer by filtering out short-term noise, while still being reactive enough to detect reversals. - 99-Period MA : The 99-period moving average provides a broad view of the market's direction, filtering out much of the short- and medium-term noise. It is crucial for identifying long-term trends and assessing whether the market is bullish or bearish overall. It acts as a key reference point for longer-term trend followers, helping them stay with the broader market sentiment. Conclusion: From a supply and demand perspective, the 7, 25, and 99-period moving averages help traders visualize shifts in the balance between buyers and sellers over different time horizons. The price action interaction with these moving averages provides valuable insight into short-term momentum, intermediate trends, and long-term market sentiment. Using these three MAs together gives a more comprehensive understanding of market conditions, helping traders align their strategies with prevailing trends across various timeframes. ------------- RULE BASED SYSTEM --------------- Overview of the Rule-Based System: This system will use the following moving averages: 7-period MA: Represents short-term price action. 25-period MA: Represents medium-term price action. 99-period MA: Represents long-term price action. 1. Trend Identification Rules: Bullish Trend: The 7-period MA is above the 25-period MA, and the 25-period MA is above the 99-period MA. This structure shows that short, medium, and long-term trends are aligned in an upward direction, indicating strong bullish momentum. Bearish Trend: The 7-period MA is below the 25-period MA, and the 25-period MA is below the 99-period MA. This suggests that the market is in a downtrend, with bearish momentum dominating across timeframes. Neutral/Consolidation: The 7-period MA and 25-period MA are flat or crossing frequently with the 99-period MA, and they are close to each other. This indicates a sideways or consolidating market where there’s no strong trend direction. 2. Entry Rules: Bullish Entry (Buy Signals): Primary Buy Signal: The price crosses above the 7-period MA, AND the 7-period MA is above the 25-period MA, AND the 25-period MA is above the 99-period MA. This indicates the start of a new upward trend, with alignment across the short, medium, and long-term trends. Pullback Buy Signal (for trend continuation): The price pulls back to the 25-period MA, and the 7-period MA remains above the 25-period MA. This indica tes that the pullback is a temporary correction in an uptrend, and buyers may re-enter the market as price approaches the 25-period MA. You can further confirm the signal by waiting for price action (e.g., bullish candlestick patterns) at the 25-period MA level. Breakout Buy Signal: The price crosses above the 99-period MA, and the 7-period and 25-period MAs are also both above the 99-period MA. This confirms a strong bullish breakout after consolidation or a long-term downtrend. Bearish Entry (Sell Signals): Primary Sell Signal: The price crosses below the 7-period MA, AND the 7-period MA is below the 25-period MA, AND the 25-period MA is below the 99-period MA. This indicates the start of a new downtrend with alignment across the short, medium, and long-term trends. Pullback Sell Signal (for trend continuation): The price pulls back to the 25-period MA, and the 7-period MA remains below the 25-period MA. This indicates that the pullback is a temporary retracement in a downtrend, providing an opportunity to sell as price meets resistance at the 25-period MA. Breakdown Sell Signal: The price breaks below the 99-period MA, and the 7-period and 25-period MAs are also below the 99-period MA. This confirms a strong bearish breakdown after consolidation or a long-term uptrend reversal. 3. Exit Rules: Bullish Exit (for long positions): Short-Term Exit: The price closes below the 7-period MA, and the 7-period MA starts crossing below the 25-period MA. This indicates weakening momentum in the uptrend, suggesting an exit from the long position. Stop-Loss Trigger: The price falls below the 99-period MA, signaling the breakdown of the long-term trend. This can act as a final exit signal to minimize losses if the long-term uptrend is invalidated. Bearish Exit (for short positions): Short-Term Exit: The price closes above the 7-period MA, and the 7-period MA starts crossing above the 25-period MA. This indicates a potential weakening of the downtrend and signals an exit from the short position. Stop-Loss Trigger: The price breaks above the 99-period MA, invalidating the bearish trend. This signals that the market may be reversing to the upside, and exiting short positions would be prudent.
Wskaźnik Pine Script®
od TheUltimateBullRun
Mars Signals - Ultimate Institutional Suite v3.0(Joker)Comprehensive Trading Manual Mars Signals – Ultimate Institutional Suite v3.0 (Joker) ## Chapter 1 – Philosophy & System Architecture This script is not a simple “buy/sell” indicator. Mars Signals – UIS v3.0 (Joker) is designed as an institutional-style analytical assistant that layers several methodologies into a single, coherent framework. The system is built on four core pillars: 1. Smart Money Concepts (SMC) - Detection of Order Blocks (professional demand/supply zones). - Detection of Fair Value Gaps (FVGs) (price imbalances). 2. Smart DCA Strategy - Combination of RSI and Bollinger Bands - Identifies statistically discounted zones for scaling into spot positions or exiting shorts. 3. Volume Profile (Visible Range Simulation) - Distribution of volume by price, not by time. - Identification of POC (Point of Control) and high-/low-volume areas. 4. Wyckoff Helper – Spring - Detection of bear traps, liquidity grabs, and sharp bullish reversals. All four pillars feed into a Confluence Engine (Scoring System). The final output is presented in the Dashboard, with a clear, human-readable signal: - STRONG LONG 🚀 - WEAK LONG ↗ - NEUTRAL / WAIT - WEAK SHORT ↘ - STRONG SHORT 🩸 This allows the trader to see *how many* and *which* layers of the system support a bullish or bearish bias at any given time. ## Chapter 2 – Settings Overview ### 2.1 General & Dashboard Group - Show Dashboard Panel (`show_dash`) Turns the dashboard table in the corner of the chart ON/OFF. - Show Signal Recommendation (`show_rec`) - If enabled, the textual signal (STRONG LONG, WEAK SHORT, etc.) is displayed. - If disabled, you only see feature status (ON/OFF) and the current price. - Dashboard Position (`dash_pos`) Determines where the dashboard appears on the chart: - `Top Right` - `Bottom Right` - `Top Left` ### 2.2 Smart Money (SMC) Group - Enable SMC Strategy (`show_smc`) Globally enables or disables the Order Block and FVG logic. - Order Block Pivot Lookback (`ob_period`) Main parameter for detecting key pivot highs/lows (swing points). - Default value: 5 - Concept: A bar is considered a pivot low if its low is lower than the lows of the previous 5 and the next 5 bars. Similarly, a pivot high has a high higher than the previous 5 and the next 5 bars. These pivots are used as anchors for Order Blocks. - Increasing `ob_period`: - Fewer levels. - But levels tend to be more significant and reliable. - In highly volatile markets (major news, war events, FOMC, etc.), using values 7–10 is recommended to filter out weak levels. - Show Fair Value Gaps (`show_fvg`) Enables/disables the drawing of FVG zones (imbalances). - Bullish OB Color (`c_ob_bull`) - Color of Bullish Order Blocks (Demand Zones). - Default: semi-transparent green (transparency ≈ 80). - Bearish OB Color (`c_ob_bear`) - Color of Bearish Order Blocks (Supply Zones). - Default: semi-transparent red. - Bullish FVG Color (`c_fvg_bull`) - Color of Bullish FVG (upward imbalance), typically yellow. - Bearish FVG Color (`c_fvg_bear`) - Color of Bearish FVG (downward imbalance), typically purple. ### 2.3 Smart DCA Strategy Group - Enable DCA Zones (`show_dca`) Enables the Smart DCA logic and visual labels. - RSI Length (`rsi_len`) Lookback period for RSI (default: 14). - Shorter → more sensitive, more noise. - Longer → fewer signals, higher reliability. - Bollinger Bands Length (`bb_len`) Moving average period for Bollinger Bands (default: 20). - BB Multiplier (`bb_mult`) Standard deviation multiplier for Bollinger Bands (default: 2.0). - For extremely volatile markets, values like 2.5–3.0 can be used so that only extreme deviations trigger a DCA signal. ### 2.4 Volume Profile (Visible Range Sim) Group - Show Volume Profile (`show_vp`) Enables the simulated Volume Profile bars on the right side of the chart. - Volume Lookback Bars (`vp_lookback`) Number of bars used to compute the Volume Profile (default: 150). - Higher values → broader historical context, heavier computation. - Row Count (`vp_rows`) Number of vertical price segments (rows) to divide the total price range into (default: 30). - Width (%) (`vp_width`) Relative width of each volume bar as a percentage. In the code, bar widths are scaled relative to the row with the maximum volume. > Technical note: Volume Profile calculations are executed only on the last bar (`barstate.islast`) to keep the script performant even on higher timeframes. ### 2.5 Wyckoff Helper Group - Show Wyckoff Events (`show_wyc`) Enables detection and plotting of Wyckoff Spring events. - Volume MA Length (`vol_ma_len`) Length of the moving average on volume. A bar is considered to have Ultra Volume if its volume is more than 2× the volume MA. ## Chapter 3 – Smart Money Strategy (Order Blocks & FVG) ### 3.1 What Is an Order Block? An Order Block (OB) represents the footprint of large institutional orders: - Bullish Order Block (Demand Zone) The last selling region (bearish candle/cluster) before a strong upward move. - Bearish Order Block (Supply Zone) The last buying region (bullish candle/cluster) before a strong downward move. Institutions and large players place heavy orders in these regions. Typical price behavior: - Price moves away from the zone. - Later returns to the same zone to fill unfilled orders. - Then continues the larger trend. In the script: - If `pl` (pivot low) forms → a Bullish OB is created. - If `ph` (pivot high) forms → a Bearish OB is created. The box is drawn: - From `bar_index ` to `bar_index`. - Between `low ` and `high `. - `extend=extend.right` extends the OB into the future, so it acts as a dynamic support/resistance zone. - Only the last 4 OB boxes are kept to avoid clutter. ### 3.2 Order Block Color Guide - Semi-transparent Green (`c_ob_bull`) - Represents a Bullish Order Block (Demand Zone). - Interpretation: a price region with a high probability of bullish reaction. - Semi-transparent Red (`c_ob_bear`) - Represents a Bearish Order Block (Supply Zone). - Interpretation: a price region with a high probability of bearish reaction. Overlap (Multiple OBs in the Same Area) When two or more Order Blocks overlap: - The shared area appears visually denser/stronger. - This suggests higher order density. - Such zones can be treated as high-priority levels for entries, exits, and stop-loss placement. ### 3.3 Demand/Supply Logic in the Scoring Engine is_in_demand = low <= ta.lowest(low, 20) is_in_supply = high >= ta.highest(high, 20) - If current price is near the lowest lows of the last 20 bars, it is considered in a Demand Zone → positive impact on score. - If current price is near the highest highs of the last 20 bars, it is considered in a Supply Zone → negative impact on score. This logic complements Order Blocks and helps the Dashboard distinguish whether: - Market is currently in a statistically cheap (long-friendly) area, or - In a statistically expensive (short-friendly) area. ### 3.4 Fair Value Gaps (FVG) #### Concept When the market moves aggressively: - Some price levels are skipped and never traded. - A gap between wicks/shadows of consecutive candles appears. - These regions are called Fair Value Gaps (FVGs) or Imbalances. The market generally “dislikes” imbalance and often: - Returns to these zones in the future. - Fills the gap (rebalance). - Then resumes its dominant direction. #### Implementation in the Code Bullish FVG (Yellow) fvg_bull_cond = show_smc and show_fvg and low > high and close > high if fvg_bull_cond box.new(bar_index , high , bar_index, low, ...) Core condition: `low > high ` → the current low is above the high of two bars ago; the space between them is an untraded gap. Bearish FVG (Purple) fvg_bear_cond = show_smc and show_fvg and high < low and close < low if fvg_bear_cond box.new(bar_index , low , bar_index, high, ...) Core condition: `high < low ` → the current high is below the low of two bars ago; again a price gap exists. #### FVG Color Guide - Transparent Yellow (`c_fvg_bull`) – Bullish FVG Often acts like a magnet for price: - Price tends to retrace into this zone, - Fill the imbalance, - And then continue higher. - Transparent Purple (`c_fvg_bear`) – Bearish FVG Price tends to: - Retrace upward into the purple area, - Fill the imbalance, - And then resume downward movement. #### Trading with FVGs - FVGs are *not* standalone entry signals. They are best used as: - Targets (take-profit zones), or - Reaction areas where you expect a pause or reversal. Examples: - If you are long, a bearish FVG above is often an excellent take-profit zone. - If you are short, a bullish FVG below is often a good cover/exit zone. ### 3.5 Core SMC Trading Templates #### Reversal Long 1. Price trades down into a green Order Block (Demand Zone). 2. A bullish confirmation candle (Close > Open) forms inside or just above the OB. 3. If this zone is close to or aligned with a bullish FVG (yellow), the signal is reinforced. 4. Entry: - At the close of the confirmation candle, or - Using a limit order near the upper boundary of the OB. 5. Stop-loss: - Slightly below the OB. - If the OB is broken decisively and price consolidates below it, the zone loses validity. 6. Targets: - The next FVG, - Or the next red Order Block (Supply Zone) above. #### Reversal Short The mirror scenario: - Price rallies into a red Order Block (Supply). - A bearish confirmation candle forms (Close < Open). - FVG/premium structure above can act as a confluence. - Stop-loss goes above the OB. - Targets: lower FVGs or subsequent green OBs below. ## Chapter 4 – Smart DCA Strategy (RSI + Bollinger Bands) ### 4.1 Smart DCA Concept - Classic DCA = buying at fixed time intervals regardless of price. - Smart DCA = scaling in only when: - Price is statistically cheaper than usual, and - The market is in a clear oversold condition. Code logic: rsi_val = ta.rsi(close, rsi_len) = ta.bb(close, bb_len, bb_mult) dca_buy = show_dca and rsi_val < 30 and close < bb_lower dca_sell = show_dca and rsi_val > 70 and close > bb_upper Conditions: - DCA Buy – Smart Scale-In Zone - RSI < 30 → oversold. - Close < lower Bollinger Band → price has broken below its typical volatility envelope. - DCA Sell – Overbought/Distribution Zone - RSI > 70 → overbought. - Close > upper Bollinger Band → price is extended far above the mean. ### 4.2 Visual Representation on the Chart - Green “DCA” Label Below Candle - Shape: `labelup`. - Color: lime background, white text. - Meaning: statistically attractive level for laddered spot entries or short exits. - Red “SELL” Label Above Candle - Warning that the market is in an extended, overbought condition. - Suitable for profit-taking on longs or considering short entries (with proper confluence and risk management). - Light Green Background (`bgcolor`) - When `dca_buy` is true, the candle background turns very light green (high transparency). - This helps visually identify DCA Zones across the chart at a glance. ### 4.3 Practical Use in Trading #### Spot Trading Used to build a better average entry price: - Every time a DCA label appears, allocate a fixed portion of capital (e.g., 2–5%). - Combining DCA signals with: - Green OBs (Demand Zones), and/or - The Volume Profile POC makes the zone structurally more important. #### Futures Trading - Longs - Use DCA Buy signals as low-risk zones for opening or adding to longs when: - Price is inside a green OB, or - The Dashboard already leans LONG. - Shorts - Use DCA Sell signals as: - Exit zones for longs, or - Areas to initiate shorts with stops above structural highs. ## Chapter 5 – Volume Profile (Visible Range Simulation) ### 5.1 Concept Traditional volume (histogram under the chart) shows volume over time. Volume Profile shows volume by price level: - At which prices has the highest trading activity occurred? - Where did buyers and sellers agree the most (High Volume Nodes – HVNs)? - Where did price move quickly due to low participation (Low Volume Nodes – LVNs)? ### 5.2 Implementation in the Script Executed only when `show_vp` is enabled and on the last bar: 1. The last `vp_lookback` bars (default 150) are processed. 2. The minimum low and maximum high over this window define the price range. 3. This price range is divided into `vp_rows` segments (e.g., 30 rows). 4. For each row: - All bars are scanned. - If the mid-price `(high + low ) / 2` falls inside a row, that bar’s volume is added to the row total. 5. The row with the greatest volume is stored as `max_vol_idx` (the POC row). 6. For each row, a volume box is drawn on the right side of the chart. ### 5.3 Color Scheme - Semi-transparent Orange - The row with the maximum volume – the Point of Control (POC). - Represents the strongest support/resistance level from a volume perspective. - Semi-transparent Blue - Other volume rows. - The taller the bar → the higher the volume → the stronger the interest at that price band. ### 5.4 Trading Applications - If price is above POC and retraces back into it: → POC often acts as support, suitable for long setups. - If price is below POC and rallies into it: → POC often acts as resistance, suitable for short setups or profit-taking. HVNs (Tall Blue Bars) - Represent areas of equilibrium where the market has spent time and traded heavily. - Price tends to consolidate here before choosing a direction. LVNs (Short or Nearly Empty Bars) - Represent low participation zones. - Price often moves quickly through these areas – useful for targeting fast moves. ## Chapter 6 – Wyckoff Helper – Spring ### 6.1 Spring Concept In the Wyckoff framework: - A Spring is a false break of support. - The market briefly trades below a well-defined support level, triggers stop losses, then sharply reverses upward as institutional buyers absorb liquidity. This movement: - Clears out weak hands (retail sellers). - Provides large players with liquidity to enter long positions. - Often initiates a new uptrend. ### 6.2 Code Logic Conditions for a Spring: 1. The current low is lower than the lowest low of the previous 50 bars → apparent break of a long-standing support. 2. The bar closes bullish (Close > Open) → the breakdown was rejected. 3. Volume is significantly elevated: → `volume > 2 × volume_MA` (Ultra Volume). When all conditions are met and `show_wyc` is enabled: - A pink diamond is plotted below the bar, - With the label “Spring” – one of the strongest long signals in this system. ### 6.3 Trading Use - After a valid Spring, markets frequently enter a meaningful bullish phase. - The highest quality setups occur when: - The Spring forms inside a green Order Block, and - Near or on the Volume Profile POC. Entries: - At the close of the Spring bar, or - On the first pullback into the mid-range of the Spring candle. Stop-loss: - Slightly below the Spring’s lowest point (wick low plus a small buffer). ## Chapter 7 – Confluence Engine & Dashboard ### 7.1 Scoring Logic For each bar, the script: 1. Resets `score` to 0. 2. Adjusts the score based on different signals. SMC Contribution if show_smc if is_in_demand score += 1 if is_in_supply score -= 1 - Being in Demand → `+1` - Being in Supply → `-1` DCA Contribution if show_dca if dca_buy score += 2 if dca_sell score -= 2 - DCA Buy → `+2` (strong, statistically driven long signal) - DCA Sell → `-2` Wyckoff Spring Contribution if show_wyc if wyc_spring score += 2 - Spring → `+2` (entry of strong money) ### 7.2 Mapping Score to Dashboard Signal - score ≥ 2 → STRONG LONG 🚀 Multiple bullish conditions aligned. - score = 1 → WEAK LONG ↗ Some bullish bias, but only one layer clearly positive. - score = 0 → NEUTRAL / WAIT Rough balance between buying and selling forces; staying flat is usually preferable. - score = -1 → WEAK SHORT ↘ Mild bearish bias, suited for cautious or short-term plays. - score ≤ -2 → STRONG SHORT 🩸 Convergence of several bearish signals. ### 7.3 Dashboard Structure The dashboard is a two-column table: - Row 0 - Column 0: `"Mars Signals"` – black background, white text. - Column 1: `"UIS v3.0"` – black background, yellow text. - Row 1 - Column 0: `"Price:"` (light grey background). - Column 1: current closing price (`close`) with a semi-transparent blue background. - Row 2 - Column 0: `"SMC:"` - Column 1: - `"ON"` (green) if `show_smc = true` - `"OFF"` (grey) otherwise. - Row 3 - Column 0: `"DCA:"` - Column 1: - `"ON"` (green) if `show_dca = true` - `"OFF"` (grey) otherwise. - Row 4 - Column 0: `"Signal:"` - Column 1: signal text (`status_txt`) with background color `status_col` (green, red, teal, maroon, etc.) - If `show_rec = false`, these cells are cleared. ## Chapter 8 – Visual Legend (Colors, Shapes & Actions) For quick reading inside TradingView, the visual elements are described line by line instead of a table. Chart Element: Green Box Color / Shape: Transparent green rectangle Core Meaning: Bullish Order Block (Demand Zone) Suggested Trader Response: Look for longs, Smart DCA adds, closing or reducing shorts. Chart Element: Red Box Color / Shape: Transparent red rectangle Core Meaning: Bearish Order Block (Supply Zone) Suggested Trader Response: Look for shorts, or take profit on existing longs. Chart Element: Yellow Area Color / Shape: Transparent yellow zone Core Meaning: Bullish FVG / upside imbalance Suggested Trader Response: Short take-profit zone or expected rebalance area. Chart Element: Purple Area Color / Shape: Transparent purple zone Core Meaning: Bearish FVG / downside imbalance Suggested Trader Response: Long take-profit zone or temporary supply region. Chart Element: Green "DCA" Label Color / Shape: Green label with white text, plotted below the candle Core Meaning: Smart ladder-in buy zone, DCA buy opportunity Suggested Trader Response: Spot DCA entry, partial short exit. Chart Element: Red "SELL" Label Color / Shape: Red label with white text, plotted above the candle Core Meaning: Overbought / distribution zone Suggested Trader Response: Take profit on longs, consider initiating shorts. Chart Element: Light Green Background (bgcolor) Color / Shape: Very transparent light-green background behind bars Core Meaning: Active DCA Buy zone Suggested Trader Response: Treat as a discount zone on the chart. Chart Element: Orange Bar on Right Color / Shape: Transparent orange horizontal bar in the volume profile Core Meaning: POC – price with highest traded volume Suggested Trader Response: Strong support or resistance; key reference level. Chart Element: Blue Bars on Right Color / Shape: Transparent blue horizontal bars in the volume profile Core Meaning: Other volume levels, showing high-volume and low-volume nodes Suggested Trader Response: Use to identify balance zones (HVN) and fast-move corridors (LVN). Chart Element: Pink "Spring" Diamond Color / Shape: Pink diamond with white text below the candle Core Meaning: Wyckoff Spring – liquidity grab and potential major bullish reversal Suggested Trader Response: One of the strongest long signals in the suite; look for high-quality long setups with tight risk. Chart Element: STRONG LONG in Dashboard Color / Shape: Green background, white text in the Signal row Core Meaning: Multiple bullish layers in confluence Suggested Trader Response: Consider initiating or increasing longs with strict risk management. Chart Element: STRONG SHORT in Dashboard Color / Shape: Red background, white text in the Signal row Core Meaning: Multiple bearish layers in confluence Suggested Trader Response: Consider initiating or increasing shorts with a logical, well-placed stop. ## Chapter 9 – Timeframe-Based Trading Playbook ### 9.1 Timeframe Selection - Scalping - Timeframes: 1M, 5M, 15M - Objective: fast intraday moves (minutes to a few hours). - Recommendation: focus on SMC + Wyckoff. Smart DCA on very low timeframes may introduce excessive noise. - Day Trading - Timeframes: 15M, 1H, 4H - Provides a good balance between signal quality and frequency. - Recommendation: use the full stack – SMC + DCA + Volume Profile + Wyckoff + Dashboard. - Swing Trading & Position Investing - Timeframes: Daily, Weekly - Emphasis on Smart DCA + Volume Profile. - SMC and Wyckoff are used mainly to fine-tune swing entries within larger trends. ### 9.2 Scenario A – Scalping Long Example: 5-Minute Chart 1. Price is declining into a green OB (Bullish Demand). 2. A candle with a long lower wick and bullish close (Pin Bar / Rejection) forms inside the OB. 3. A Spring diamond appears below the same candle → very strong confluence. 4. The Dashboard shows at least WEAK LONG ↗, ideally STRONG LONG 🚀. 5. Entry: - On the close of the confirmation candle, or - On the first pullback into the mid-range of that candle. 6. Stop-loss: - Slightly below the OB. 7. Targets: - Nearby bearish FVG above, and/or - The next red OB. ### 9.3 Scenario B – Day-Trading Short Recommended Timeframes: 1H or 4H 1. The market completes a strong impulsive move upward. 2. Price enters a red Order Block (Supply). 3. In the same zone, a purple FVG appears or remains unfilled. 4. On a lower timeframe (e.g., 15M), RSI enters overbought territory and a DCA Sell signal appears. 5. The main timeframe Dashboard (1H) shows WEAK SHORT ↘ or STRONG SHORT 🩸. Trade Plan - Open a short near the upper boundary of the red OB. - Place the stop above the OB or above the last swing high. - Targets: - A yellow FVG lower on the chart, and/or - The next green OB (Demand) below. ### 9.4 Scenario C – Swing / Investment with Smart DCA Timeframes: Daily / Weekly 1. On the daily or weekly chart, each time a green “DCA” label appears: - Allocate a fixed fraction of your capital (e.g., 3–5%) to that asset. 2. Check whether this DCA zone aligns with the orange POC of the Volume Profile: - If yes → the quality of the entry zone is significantly higher. 3. If the DCA signal sits inside a daily green OB, the probability of a medium-term bottom increases. 4. Always build the position laddered, never all-in at a single price. Exits for investors: - Near weekly red OBs or large purple FVG zones. - Ideally via partial profit-taking rather than closing 100% at once. ### 9.5 Case Study 1 – BTCUSDT (15-Minute) - Context: Price has sold off down towards 65,000 USD. - A green OB had previously formed at that level. - Near the lower boundary of this OB, a partially filled yellow FVG is present. - As price returns to this region, a Spring appears. - The Dashboard shifts from NEUTRAL / WAIT to WEAK LONG ↗. Plan - Enter a long near the OB low. - Place stop below the Spring low. - First target: a purple FVG around 66,200. - Second (optional) target: the first red OB above that level. ### 9.6 Case Study 2 – Meme Coin (PEPE – 4H) - After a strong pump, price enters a corrective phase. - On the 4H chart, RSI drops below 30; price breaks below the lower Bollinger Band → a DCA label prints. - The Volume Profile shows the POC at approximately the same level. - The Dashboard displays STRONG LONG 🚀. Plan - Execute laddered buys in the combined DCA + POC zone. - Place a protective stop below the last significant swing low. - Target: an expected 20–30% upside move towards the next red OB or purple FVG. ## Chapter 10 – Risk Management, Psychology & Advanced Tuning ### 10.1 Risk Management No signal, regardless of its strength, replaces risk control. Recommendations: - In futures, do not expose more than 1–3% of account equity to risk per trade. - Adjust leverage to the volatility of the instrument (lower leverage for highly volatile altcoins). - Place stop-losses in zones where the idea is clearly invalidated: - Below/above the relevant Order Block or Spring, not randomly in the middle of the structure. ### 10.2 Market-Specific Parameter Tuning - Calmer Markets (e.g., major FX pairs) - `ob_period`: 3–5. - `bb_mult`: 2.0 is usually sufficient. - Highly Volatile Markets (Crypto, news-driven assets) - `ob_period`: 7–10 to highlight only the most robust OBs. - `bb_mult`: 2.5–3.0 so that only extreme deviations trigger DCA. - `vol_ma_len`: increase (e.g., to ~30) so that Spring triggers only on truly exceptional volume spikes. ### 10.3 Trading Psychology - STRONG LONG 🚀 does not mean “risk-free”. It means the probability of a successful long, given the model’s logic, is higher than average. - Treat Mars Signals as a confirmation and context system, not a full replacement for your own decision-making. - Example of disciplined thinking: - The Dashboard prints STRONG LONG, - But price is simultaneously testing a multi-month macro resistance or a major negative news event is imminent, - In such cases, trade smaller, widen stops appropriately, or skip the trade. ## Chapter 11 – Technical Notes & FAQ ### 11.1 Does the Script Repaint? - Order Blocks and Springs are based on completed pivot structures and confirmed candles. - Until a pivot is confirmed, an OB does not exist; after confirmation, behavior is stable under classic SMC assumptions. - The script is designed to be structurally consistent rather than repainting signals arbitrarily. ### 11.2 Computational Load of Volume Profile - On the last bar, the script processes up to `vp_lookback` bars × `vp_rows` rows. - On very low timeframes with heavy zooming, this can become demanding. - If you experience performance issues: - Reduce `vp_lookback` or `vp_rows`, or - Temporarily disable Volume Profile (`show_vp = false`). ### 11.3 Multi-Timeframe Behavior - This version of the script is not internally multi-timeframe. All logic (OB, DCA, Spring, Volume Profile) is computed on the active timeframe only. - Practical workflow: - Analyze overall structure and key zones on higher timeframes (4H / Daily). - Use lower timeframes (15M / 1H) with the same tool for timing entries and exits. ## Conclusion Mars Signals – Ultimate Institutional Suite v3.0 (Joker) is a multi-layer trading framework that unifies: - Price structure (Order Blocks & FVG), - Statistical behavior (Smart DCA via RSI + Bollinger), - Volume distribution by price (Volume Profile with POC, HVN, LVN), - Liquidity events (Wyckoff Spring), into a single, coherent system driven by a transparent Confluence Scoring Engine. The final output is presented in clear, actionable language: > STRONG LONG / WEAK LONG / NEUTRAL / WEAK SHORT / STRONG SHORT The system is designed to support professional decision-making, not to replace it. Used together with strict risk management and disciplined execution, Mars Signals – UIS v3.0 (Joker) can serve as a central reference manual and operational guide for your trading workflow, from scalping to swing and investment positioning.
Wskaźnik Pine Script®
od MarsSignals
Trading Toolkit - Comprehensive AnalysisTrading Toolkit – Comprehensive Analysis A unified trading analysis toolkit with four sections: 📊 Company Info Fundamentals, market cap, sector, and earnings countdown. 📅 Performance Date‑range analysis with key metrics. 🎯 Market Sentiment CNN‑style Fear & Greed Index (7 components) + 150‑SMA positioning. 🛡️ Risk Levels ATR/MAD‑based stop‑loss and take‑profit calculations. Key Features CNN‑style Fear & Greed approximation using: Momentum: S&P 500 vs 125‑DMA Price Strength: NYSE 52‑week highs vs lows Market Breadth: McClellan Volume Summation (Up/Down volume) Put/Call Ratio: 5‑day average (inverted) Volatility: VIX vs 50‑DMA (inverted) Safe‑Haven Demand: 20‑day SPY–IEF return spread Junk‑Bond Demand: HY vs IG credit spread (inverted) Normalization: z‑score → percentile (0–100) with ±3 clipping. CNN‑aligned thresholds: Extreme Fear: 0–24 | Fear: 25–44 | Neutral: 45–54 | Greed: 55–74 | Extreme Greed: 75+. Risk tools: ATR & MAD volatility measures with configurable multipliers. Flexible layout: vertical or side‑by‑side columns. Data Sources S&P 500: CBOE:SPX or AMEX:SPY NYSE: INDEX:HIGN, INDEX:LOWN, USI:UVOL, USI:DVOL Options: USI:PCC (Total PCR), fallback INDEX:CPCS (Equity PCR) Volatility: CBOE:VIX Treasuries: NASDAQ:IEF Credit Spreads: FRED:BAMLH0A0HYM2, FRED:BAMLC0A0CM Risk Management ATR risk bands: 🟢 ≤3%, 🟡 3–6%, ⚪ 6–10%, 🟠 10–15%, 🔴 >15% MAD‑based stop‑loss and take‑profit calculations. Author: Daniel Dahan (AI Generated, Merged & enhanced version with CNN‑style Fear & Greed)
Wskaźnik Pine Script®
od danny_dahan
CNN Fear and Greed IndexThe “CNN Fear and Greed Index” indicator in this context is designed to gauge market sentiment based on a combination of several fundamental indicators. Here’s a breakdown of how this indicator works and what it represents: Components of the Indicator: 1. Stock Price Momentum: • Calculates the momentum of the S&P 500 index relative to its 125-day moving average. Momentum is essentially the rate of acceleration or deceleration of price movements over time. 2. Stock Price Strength: • Measures the breadth of the market by comparing the number of stocks hitting 52-week highs versus lows. This provides insights into the overall strength or weakness of the market trend. 3. Stock Price Breadth: • Evaluates the volume of shares trading on the rise versus the falling volume. Higher volume on rising days suggests positive market breadth, while higher volume on declining days indicates negative breadth. 4. Put and Call Options Ratio (Put/Call Ratio): • This ratio indicates the sentiment of investors in the options market. A higher put/call ratio typically signals increased bearish sentiment (more puts relative to calls) and vice versa. 5. Market Volatility (VIX): • Also known as the “fear gauge,” the VIX measures the expected volatility in the market over the next 30 days. Higher VIX values indicate higher expected volatility and often correlate with increased fear or uncertainty in the market. 6. Safe Haven Demand: • Compares the returns of stocks (represented by S&P 500) versus safer investments like 10-year Treasury bonds. Higher returns on bonds relative to stocks suggest a flight to safety or risk aversion. 7. Junk Bond Demand: • Measures the spread between yields on high-yield (junk) bonds and investment-grade bonds. Widening spreads may indicate increasing risk aversion as investors demand higher yields for riskier bonds. Normalization and Weighting: • Normalization: Each component is normalized to a scale of 0 to 100 using a function that adjusts the range based on historical highs and lows of the respective indicator. • Weighting: The user can adjust the relative importance (weight) of each component using input parameters. This customization allows for different interpretations of market sentiment based on which factors are considered more influential. Fear and Greed Index Calculation: • The Fear and Greed Index is calculated as a weighted average of all normalized components. This index provides a single numerical value that summarizes the overall sentiment of the market based on the selected indicators. Usage: • Visualization: The indicator plots the Fear and Greed Index and its components on the chart. This allows traders and analysts to visually assess the sentiment trends over time. • Analysis: Changes in the Fear and Greed Index can signal shifts in market sentiment. For example, a rising index may indicate increasing greed and potential overbought conditions, while a falling index may suggest increasing fear and potential oversold conditions. • Customization: Traders can customize the indicator by adjusting the weights assigned to each component based on their trading strategies and market insights. By integrating multiple fundamental indicators into a single index, the “CNN Fear and Greed Index” provides a comprehensive snapshot of market sentiment, helping traders make informed decisions about market entry, exit, and risk management strategies.
Wskaźnik Pine Script®
od EdgeTools
7
Smart RebalanceThis script is based on the portfolio rebalancing strategy. It's designed to work with cryptocurrencies, but it can work with any market. How portfolio rebalance works? Let's assume your initial capital is $1000, and you want to distribute it into 4 coins. This script takes the USDT as the stable coin for the initial money, so in case you want other currency, the pairs must be with that fiat as the quote. Following our example, you would take BTC, ETH, BNB, and FTT. After selecting the coins, it's time to choose how much allocation is on each. Let's put 25% on each. This way, $250 of our capital on each coin. After selecting the coins and their allocation, you choose the price change ratio for rebalancing. Let's use 1%. Next, you start to watch the markets. The first thing that happens, following our example, is the BTCUSDT price moving 1% up. That amount hit the ratio of 1% for the rebalance. Hence, you sell 1% of BTC for USDT and redistribute to the other coins, buying 0.25% of each currency to rebalance the portfolio. Next, ETHUSDT goes 1% down, time to rebalance again. This time, you need to take 0.33% of each other coin and buy ETH, so this way, it's all divided as the chosen allocation. Why use rebalancing? Looks easy, right? It is, but very time demanding. Demands even more if you raise the number of coins you want to distribute. Having a system to do that automatically is a must to work efficiently. Rebalancing spreads the risk among multiple currencies. This way, you earn small when it goes up, but you lose small when it goes down. What this script helps with portfolio rebalance? This indicator will not buy/sell for you but will help you choose the best markets for your rebalancing. Which coin will work best in that period? Do I need to have more than 8 coins? How much must be my ratio? Those questions you can answer using this indicator. What this script has? Start and End dates The script will work for a certain period. All calculations will be done in that period. Coin Ratio % The amount of price movement of each asset that will be used to calculate the rebalancing Initial Capital and Broker Fee The amount of capital to be used on the rebalancing and the broker fee you want to use the strategy. The cost will be applied on every trade, buying or selling the coins. Assets, allocations, and colors It's possible to select from 2 to 10 assets to be used on the portfolio. Each purchase must have the allocation %. Suppose the sum of the allocations is different from 100%. In that case, a warning message will appear on the chart instead of the statistics. Panel and tooltips There is a panel with a summary of the results Set allocations automatically There is an option to make the indicator use the daily asset volume from the day before to determine the allocation percentage of each asset. This option is better if you are unsure how much allocation you want to use on each coin. Use this indicator as a backtest for your rebalancing strategy. The selected market on the chart will not affect the calculation on this indicator, but the time frame will. The higher the time frame, the higher the coin ratio % must be. About the code The code is written to use arrays to store the values of each asset, making the calculations on each candle inside the time range. The for-loops are used to reduce the code length and make it easy to change the analysis of all assets. Finally, the script has some comments on the code.
Wskaźnik Pine Script®
od ThiagoSchmitz
4
Monte Carlo Range Forecast [DW]This is an experimental study designed to forecast the range of price movement from a specified starting point using a Monte Carlo simulation. Monte Carlo experiments are a broad class of computational algorithms that utilize random sampling to derive real world numerical results. These types of algorithms have a number of applications in numerous fields of study including physics, engineering, behavioral sciences, climate forecasting, computer graphics, gaming AI, mathematics, and finance. Although the applications vary, there is a typical process behind the majority of Monte Carlo methods: -> First, a distribution of possible inputs is defined. -> Next, values are generated randomly from the distribution. -> The values are then fed through some form of deterministic algorithm. -> And lastly, the results are aggregated over some number of iterations. In this study, the Monte Carlo process used generates a distribution of aggregate pseudorandom linear price returns summed over a user defined period, then plots standard deviations of the outcomes from the mean outcome generate forecast regions. The pseudorandom process used in this script relies on a modified Wichmann-Hill pseudorandom number generator (PRNG) algorithm. Wichmann-Hill is a hybrid generator that uses three linear congruential generators (LCGs) with different prime moduli. Each LCG within the generator produces an independent, uniformly distributed number between 0 and 1. The three generated values are then summed and modulo 1 is taken to deliver the final uniformly distributed output. Because of its long cycle length, Wichmann-Hill is a fantastic generator to use on TV since it's extremely unlikely that you'll ever see a cycle repeat. The resulting pseudorandom output from this generator has a minimum repetition cycle length of 6,953,607,871,644. Fun fact: Wichmann-Hill is a widely used PRNG in various software applications. For example, Excel 2003 and later uses this algorithm in its RAND function, and it was the default generator in Python up to v2.2. The generation algorithm in this script takes the Wichmann-Hill algorithm, and uses a multi-stage transformation process to generate the results. First, a parent seed is selected. This can either be a fixed value, or a dynamic value. The dynamic parent value is produced by taking advantage of Pine's timenow variable behavior. It produces a variable parent seed by using a frozen ratio of timenow/time. Because timenow always reflects the current real time when frozen and the time variable reflects the chart's beginning time when frozen, the ratio of these values produces a new number every time the cache updates. After a parent seed is selected, its value is then fed through a uniformly distributed seed array generator, which generates multiple arrays of pseudorandom "children" seeds. The seeds produced in this step are then fed through the main generators to produce arrays of pseudorandom simulated outcomes, and a pseudorandom series to compare with the real series. The main generators within this script are designed to (at least somewhat) model the stochastic nature of financial time series data. The first step in this process is to transform the uniform outputs of the Wichmann-Hill into outputs that are normally distributed. In this script, the transformation is done using an estimate of the normal distribution quantile function. Quantile functions, otherwise known as percent-point or inverse cumulative distribution functions, specify the value of a random variable such that the probability of the variable being within the value's boundary equals the input probability. The quantile equation for a normal probability distribution is μ + σ(√2)erf^-1(2(p - 0.5)) where μ is the mean of the distribution, σ is the standard deviation, erf^-1 is the inverse Gauss error function, and p is the probability. Because erf^-1() does not have a simple, closed form interpretation, it must be approximated. To keep things lightweight in this approximation, I used a truncated Maclaurin Series expansion for this function with precomputed coefficients and rolled out operations to avoid nested looping. This method provides a decent approximation of the error function without completely breaking floating point limits or sucking up runtime memory. Note that there are plenty of more robust techniques to approximate this function, but their memory needs very. I chose this method specifically because of runtime favorability. To generate a pseudorandom approximately normally distributed variable, the uniformly distributed variable from the Wichmann-Hill algorithm is used as the input probability for the quantile estimator. Now from here, we get a pretty decent output that could be used itself in the simulation process. Many Monte Carlo simulations and random price generators utilize a normal variable. However, if you compare the outputs of this normal variable with the actual returns of the real time series, you'll find that the variability in shocks (random changes) doesn't quite behave like it does in real data. This is because most real financial time series data is more complex. Its distribution may be approximately normal at times, but the variability of its distribution changes over time due to various underlying factors. In light of this, I believe that returns behave more like a convoluted product distribution rather than just a raw normal. So the next step to get our procedurally generated returns to more closely emulate the behavior of real returns is to introduce more complexity into our model. Through experimentation, I've found that a return series more closely emulating real returns can be generated in a three step process: -> First, generate multiple independent, normally distributed variables simultaneously. -> Next, apply pseudorandom weighting to each variable ranging from -1 to 1, or some limits within those bounds. This modulates each series to provide more variability in the shocks by producing product distributions. -> Lastly, add the results together to generate the final pseudorandom output with a convoluted distribution. This adds variable amounts of constructive and destructive interference to produce a more "natural" looking output. In this script, I use three independent normally distributed variables multiplied by uniform product distributed variables. The first variable is generated by multiplying a normal variable by one uniformly distributed variable. This produces a bit more tailedness (kurtosis) than a normal distribution, but nothing too extreme. The second variable is generated by multiplying a normal variable by two uniformly distributed variables. This produces moderately greater tails in the distribution. The third variable is generated by multiplying a normal variable by three uniformly distributed variables. This produces a distribution with heavier tails. For additional control of the output distributions, the uniform product distributions are given optional limits. These limits control the boundaries for the absolute value of the uniform product variables, which affects the tails. In other words, they limit the weighting applied to the normally distributed variables in this transformation. All three sets are then multiplied by user defined amplitude factors to adjust presence, then added together to produce our final pseudorandom return series with a convoluted product distribution. Once we have the final, more "natural" looking pseudorandom series, the values are recursively summed over the forecast period to generate a simulated result. This process of generation, weighting, addition, and summation is repeated over the user defined number of simulations with different seeds generated from the parent to produce our array of initial simulated outcomes. After the initial simulation array is generated, the max, min, mean and standard deviation of this array are calculated, and the values are stored in holding arrays on each iteration to be called upon later. Reference difference series and price values are also stored in holding arrays to be used in our comparison plots. In this script, I use a linear model with simple returns rather than compounding log returns to generate the output. The reason for this is that in generating outputs this way, we're able to run our simulations recursively from the beginning of the chart, then apply scaling and anchoring post-process. This allows a greater conservation of runtime memory than the alternative, making it more suitable for doing longer forecasts with heavier amounts of simulations in TV's runtime environment. From our starting time, the previous bar's price, volatility, and optional drift (expected return) are factored into our holding arrays to generate the final forecast parameters. After these parameters are computed, the range forecast is produced. The basis value for the ranges is the mean outcome of the simulations that were run. Then, quarter standard deviations of the simulated outcomes are added to and subtracted from the basis up to 3σ to generate the forecast ranges. All of these values are plotted and colorized based on their theoretical probability density. The most likely areas are the warmest colors, and least likely areas are the coolest colors. An information panel is also displayed at the starting time which shows the starting time and price, forecast type, parent seed value, simulations run, forecast bars, total drift, mean, standard deviation, max outcome, min outcome, and bars remaining. The interesting thing about simulated outcomes is that although the probability distribution of each simulation is not normal, the distribution of different outcomes converges to a normal one with enough steps. In light of this, the probability density of outcomes is highest near the initial value + total drift, and decreases the further away from this point you go. This makes logical sense since the central path is the easiest one to travel. Given the ever changing state of markets, I find this tool to be best suited for shorter term forecasts. However, if the movements of price are expected to remain relatively stable, longer term forecasts may be equally as valid. There are many possible ways for users to apply this tool to their analysis setups. For example, the forecast ranges may be used as a guide to help users set risk targets. Or, the generated levels could be used in conjunction with other indicators for meaningful confluence signals. More advanced users could even extrapolate the functions used within this script for various purposes, such as generating pseudorandom data to test systems on, perform integration and approximations, etc. These are just a few examples of potential uses of this script. How you choose to use it to benefit your trading, analysis, and coding is entirely up to you. If nothing else, I think this is a pretty neat script simply for the novelty of it. ---------- How To Use: When you first add the script to your chart, you will be prompted to confirm the starting date and time, number of bars to forecast, number of simulations to run, and whether to include drift assumption. You will also be prompted to confirm the forecast type. There are two types to choose from: -> End Result - This uses the values from the end of the simulation throughout the forecast interval. -> Developing - This uses the values that develop from bar to bar, providing a real-time outlook. You can always update these settings after confirmation as well. Once these inputs are confirmed, the script will boot up and automatically generate the forecast in a separate pane. Note that if there is no bar of data at the time you wish to start the forecast, the script will automatically detect use the next available bar after the specified start time. From here, you can now control the rest of the settings. The "Seeding Settings" section controls the initial seed value used to generate the children that produce the simulations. In this section, you can control whether the seed is a fixed value, or a dynamic one. Since selecting the dynamic parent option will change the seed value every time you change the settings or refresh your chart, there is a "Regenerate" input built into the script. This input is a dummy input that isn't connected to any of the calculations. The purpose of this input is to force an update of the dynamic parent without affecting the generator or forecast settings. Note that because we're running a limited number of simulations, different parent seeds will typically yield slightly different forecast ranges. When using a small number of simulations, you will likely see a higher amount of variance between differently seeded results because smaller numbers of sampled simulations yield a heavier bias. The more simulations you run, the smaller this variance will become since the outcomes become more convergent toward the same distribution, so the differences between differently seeded forecasts will become more marginal. When using a dynamic parent, pay attention to the dispersion of ranges. When you find a set of ranges that is dispersed how you like with your configuration, set your fixed parent value to the parent seed that shows in the info panel. This will allow you to replicate that dispersion behavior again in the future. An important thing to note when settings alerts on the plotted levels, or using them as components for signals in other scripts, is to decide on a fixed value for your parent seed to avoid minor repainting due to seed changes. When the parent seed is fixed, no repainting occurs. The "Amplitude Settings" section controls the amplitude coefficients for the three differently tailed generators. These amplitude factors will change the difference series output for each simulation by controlling how aggressively each series moves. When "Adjust Amplitude Coefficients" is disabled, all three coefficients are set to 1. Note that if you expect volatility to significantly diverge from its historical values over the forecast interval, try experimenting with these factors to match your anticipation. The "Weighting Settings" section controls the weighting boundaries for the three generators. These weighting limits affect how tailed the distributions in each generator are, which in turn affects the final series outputs. The maximum absolute value range for the weights is . When "Limit Generator Weights" is disabled, this is the range that is automatically used. The last set of inputs is the "Display Settings", where you can control the visual outputs. From here, you can select to display either "Forecast" or "Difference Comparison" via the "Output Display Type" dropdown tab. "Forecast" is the type displayed by default. This plots the end result or developing forecast ranges. There is an option with this display type to show the developing extremes of the simulations. This option is enabled by default. There's also an option with this display type to show one of the simulated price series from the set alongside actual prices. This allows you to visually compare simulated prices alongside the real prices. "Difference Comparison" allows you to visually compare a synthetic difference series from the set alongside the actual difference series. This display method is primarily useful for visually tuning the amplitude and weighting settings of the generators. There are also info panel settings on the bottom, which allow you to control size, colors, and date format for the panel. It's all pretty simple to use once you get the hang of it. So play around with the settings and see what kinds of forecasts you can generate! ---------- ADDITIONAL NOTES & DISCLAIMERS Although I've done a number of things within this script to keep runtime demands as low as possible, the fact remains that this script is fairly computationally heavy. Because of this, you may get random timeouts when using this script. This could be due to either random drops in available runtime on the server, using too many simulations, or running the simulations over too many bars. If it's just a random drop in runtime on the server, hide and unhide the script, re-add it to the chart, or simply refresh the page. If the timeout persists after trying this, then you'll need to adjust your settings to a less demanding configuration. Please note that no specific claims are being made in regards to this script's predictive accuracy. It must be understood that this model is based on randomized price generation with assumed constant drift and dispersion from historical data before the starting point. Models like these not consider the real world factors that may influence price movement (economic changes, seasonality, macro-trends, instrument hype, etc.), nor the changes in sample distribution that may occur. In light of this, it's perfectly possible for price data to exceed even the most extreme simulated outcomes. The future is uncertain, and becomes increasingly uncertain with each passing point in time. Predictive models of any type can vary significantly in performance at any point in time, and nobody can guarantee any specific type of future performance. When using forecasts in making decisions, DO NOT treat them as any form of guarantee that values will fall within the predicted range. When basing your trading decisions on any trading methodology or utility, predictive or not, you do so at your own risk. No guarantee is being issued regarding the accuracy of this forecast model. Forecasting is very far from an exact science, and the results from any forecast are designed to be interpreted as potential outcomes rather than anything concrete. With that being said, when applied prudently and treated as "general case scenarios", forecast models like these may very well be potentially beneficial tools to have in the arsenal.
Wskaźnik Pine Script®
od DonovanWall
80
RISK-OFF.RISK.ON-ppxdf.v3======================================= RISK-OFF & RISK ON INDEX ================================================ 1. Stock Price Momentum: Measuring the Standard & Poor's 500 Index ( S&P 500 ) versus its 125-day moving average (MA) 2. Stock Price Strength: Calculating the number of stocks hitting 52-week highs versus those hitting 52-week lows on the New York Stock Exchange (NYSE) 3. Stock Price Breadth: Analyzing trading volumes in rising stocks against declining stocks 4. Put and Call Options: How much do put options lag behind call options, signifying greed, or surpass them, indicating fear 5. Junk Bond Demand: Gauging appetite for higher risk strategies by measuring the spread between yields on investment-grade bonds and junk bonds 6. Market Volatility: CNN measures the Chicago Board Options Exchange Volatility Index ( VIX ), concentrating on a 50-day MA 7. Safe Haven Demand: The difference in returns for stocks versus treasuries Each of these seven indicators is measured on a scale from 0 to 100, with the index being computed by taking an equal-weighted average of each of them. A reading of 50 is deemed NEUTRAL. Above 50 signals the market with RISK-ON. (GREED) Below 50, Signals the market with RISK-OFF (FEAR) 8
Wskaźnik Pine Script®
od ppxdff
3
DZ/SZ - HFM by MamaRight-Empty Wick Zones (MTF) draws Supply/Demand zones from the remaining wick of adjacent opposite-color candles (Classic & Non-classic rules). Zones extend right only through empty space and stop at the first touching candle. Multi-TF scan (H1/H4/1D/1W/1M) with TF-colored boxes and labels showing Demand/Supply + H/L. Demand (red → green, adjacent): Classic: if the red candle’s lower wick is longer than the green’s → zone = (the “excess” red wick). Non-classic: if the red’s lower wick is shorter or equal → zone = (use the longer green wick). Supply (green → red, adjacent): Classic: if the green candle’s upper wick is longer than the red’s → zone = (the “excess” green wick). Non-classic: if the green’s upper wick is shorter or equal → zone = (use the longer red wick). After a zone is created, the box extends right and terminates at the very first bar whose price range (body or wick) overlaps the zone → ensures the plotted area is genuinely right-empty. What you see Zone boxes with distinct colors per timeframe (e.g., H1/H4/1D/1W/1M). Optional labels on each box: H4 Demand / H1 Supply, plus H/L prices of the zone. Labels can sit at the left edge or follow the right edge of the box. Inputs Toggles: Demand Classic / Demand Non-classic / Supply Classic / Supply Non-classic. Timeframes to scan: H1, H4, 1D, 1W, 1M. Min zone thickness (price): minimum height of a zone (in price units). Initial right extension (bars): initial box length; the script auto-cuts at the first touch. Show labels / place labels at the right edge. How to use (suggestion) Use higher TF (e.g., 1D) for bias and lower TFs (H1/H4) for execution zones. Keep only the rule set (Classic/Non-classic) that matches your playbook. Treat zones as areas of interest—wait for your own confirmations (e.g., swing rejection, wick re-entry, structure shift, volume cues) and manage risk accordingly. Notes Because zones are sourced from higher TFs via request.security, the drawing can update intrabar; a zone is final once the source TF bar closes. Min zone thickness uses price units (e.g., on XAUUSD, 1.00 ≈ $1). This tool is an analytical aid, not financial advice or an entry/exit signal. อินดิเคเตอร์ DZ/SZ - HFM by Mama ใช้หา Demand/Supply zone จาก “ไส้ที่เหลือ” ของ คู่แท่งสีตรงข้ามที่ติดกัน แล้ววาดเป็นกล่อง ยืดไปทางขวาเฉพาะช่วงที่ว่าง และ หยุดตรงแท่งแรกที่เข้ามาแตะโซน รองรับหลาย Timeframe (H1/H4/1D/1W/1M) พร้อมสีแยก TF และป้ายกำกับ Demand/Supply + H/L ของโซน รายละเอียดการทำงาน (ไทย) แนวคิดหลัก Demand: เลือกคู่ แดง→เขียว ที่ “ติดกัน” Classic: ถ้า ไส้ล่าง ของแท่งแดงยาวกว่าแท่งเขียว → โซน = Non-classic: ถ้า ไส้ล่าง ของแท่งแดงสั้นกว่าหรือเท่าเขียว → โซน = Supply: เลือกคู่ เขียว→แดง ที่ “ติดกัน” Classic: ถ้า ไส้บน ของแท่งเขียวยาวกว่าแท่งแดง → โซน = Non-classic: ถ้า ไส้บน ของแท่งเขียวสั้นกว่าหรือเท่าแดง → โซน = เมื่อสร้างโซนแล้ว กล่องจะ ยืดทางขวา ไปเรื่อย ๆ และ หยุดทันทีเมื่อมีแท่งแรกที่ช่วงราคา (ไส้หรือตัวแท่ง) ทับซ้อนกับโซน ⇒ ได้ “พื้นที่ขวาว่าง” ตามโจทย์ สิ่งที่แสดงบนกราฟ กล่องโซนสีตาม Timeframe (เช่น H1=ฟ้า, H4=เขียว, 1D=ส้ม, 1W=ม่วง, 1M=เทา) Label ที่มุมกล่อง: H4 Demand / H1 Supply + ราคาของ High/Low ของโซน (เลือกวาง ซ้าย หรือ ขอบขวา ของกล่องได้ในตั้งค่า) ตัวเลือกสำคัญใน Settings เปิด/ปิด: Demand Classic / Demand Non-classic / Supply Classic / Supply Non-classic เลือก TF ที่จะสแกน: H1, H4, 1D, 1W, 1M Min zone thickness (price): กำหนด “ความหนา” ขั้นต่ำของโซน (หน่วยเป็นราคา เช่น XAUUSD = ดอลลาร์) Initial right extension (bars): ความยาวยืดเริ่มต้น (อินดี้จะตัดให้สั้นลงเองเมื่อมีแท่งมาแตะ) แสดง Label บนโซน และ วาง Label ที่ขอบขวากล่อง วิธีใช้แนะนำ เลือก TF ที่ต้องการ (เช่น ให้ H1/H4 เป็นโซนเทรดละเอียด และ 1D ใช้กรองทิศ) เปิดเฉพาะโหมด (Classic/Non-classic) ที่ตรงกับแนวคิดการเทรดของคุณ ใช้โซนเป็นบริเวณ “สนใจ” แล้วรอพฤติกรรมราคา/สัญญาณยืนยันเสริม (เช่น สวิงกลับ, rejection wick, โวลลุ่ม, หรือโครงสร้างจบคลื่น) หมายเหตุสำคัญ อินดี้ใช้ข้อมูลข้าม TF; สัญญาณจาก TF สูง อาจเปลี่ยนระหว่างแท่งยังไม่ปิด (ลักษณะ intrabar update) โซนจะ “นิ่ง” เมื่อแท่งของ TF ต้นทาง ปิดแล้ว หน่วยของ Min zone thickness เป็น หน่วยราคา ไม่ใช่ pips (XAUUSD: 1.00 = $1) อินดี้ไม่ได้ให้สัญญาณเข้า–ออกอัตโนมัติ ควรใช้ร่วมกับแผนเทรดและการจัดการความเสี่ยง
Wskaźnik Pine Script®
od starfactorythailand
PRO SMC DASHBOARDPRO SMC DASHBOARD - PRO LEVEL Advanced Supply & Demand / SMC dashboard for scalping and intraday: Multi-Timeframe Trend: Visualizes trend direction for M1, M5, M15, H1, H4. HTF Supply/Demand: Shows closest high time frame (HTF) supply/demand zone and distance (in pips). Smart “Flip” & Liquidity Signals: Flip and Liquidity Sweep arrows/signals are shown only when truly significant: Near HTF Supply/Demand zone And confirmed by volume spike or high confluence score Momentum & Bias: Real-time momentum (RSI M1), H1 bias and fakeout detection. Confluence Score: Objective score (out of 7) for trade confidence. Volume Spike, Divergence, BOS: Includes volume spikes, RSI divergence (M1), and Break of Structure (BOS) for both M15 & H1. Ultra-clean chart: Only valid signals/alerts shown; no spam or visual clutter. Full dashboard with all signals and context, always visible bottom-right. Best used for: Forex, Gold/Silver, US indices, and crypto Scalping/intraday with fast, clear decisions based on multi-factor SMC logic Usage: Add to your chart, monitor the dashboard for valid setups, and trade only when multiple factors align for high-probability entries. How to Use the PRO SMC DASHBOARD 1. Add the Script to Your Chart: Apply the indicator to your favorite Forex, Gold, crypto, or indices chart (best on M1, M5, or M15 for entries). 2. Read the Dashboard (Bottom Right): The dashboard shows real-time information from multiple timeframes and key SMC filters, including: Trend (M1, M5, M15, H1, H4): Arrows show up (↑) or down (↓) trend for each timeframe, based on EMA. Momentum (RSI M1): Shows “Strong Up,” “Strong Down,” or “Neutral” plus the current RSI value. RSI (H1): Higher timeframe momentum confirmation. ATR State: Indicates current volatility (High, Normal, Low). Session: Detects if the market is in London, NY, or Asia session (based on UTC). HTF S/D Zone: Shows the nearest high timeframe Supply or Demand zone, its timeframe (M15, H1, H4), and exact pip distance. Fakeout (last 3): Detects recent false breakouts—if there are multiple fakeouts, potential for reversal is higher. FVG (Fair Value Gap): Indicates direction and distance to the nearest FVG (Above/Below). Bias: “Strong Buy,” “Strong Sell,” or “Neutral”—multi-timeframe, momentum, and volatility filtered. Inducement: Alerts for possible “stop hunt” or liquidity grab before reversal. BOS (Break of Structure): Recent or live breaks of market structure (for both M15 & H1). Liquidity Sweep: Shows if price just swept a key high/low and then reversed (often key reversal point). Confluence Score (0-7): Higher score means more factors align—look for 5+ for strong setups. Volume Spike: “YES” appears if the current volume is significantly above average—big players are active! RSI Divergence: Bullish or bearish divergence on M1—signals early reversal risk. Momentum Flip: “UP” or “DN” appears if RSI M1 crosses the 50 line, confirmed by location and other filters. Chart Signals (Arrows & Markers): Flip arrows (up/down) and Liquidity markers only appear when price is at/near a key Supply/Demand zone and confirmed by either a volume spike or strong confluence. No signal spam: If you see an arrow or LIQ tag, it’s a truly significant moment! Suggested Trading Workflow: Scan the Dashboard: Is the multi-timeframe trend aligned? Are you near a major Supply or Demand zone? Is the Confluence Score high (5 or more)? Check for Signals: Is there a Flip or LIQ marker near a Supply/Demand zone? Is volume spiking or a fakeout just occurred? Look for Reversal or Continuation: If there’s a Flip at Demand (with high confluence), consider a long setup. If there’s a LIQ sweep + flip + volume at Supply, consider a short. Manage Risk: Don’t chase every signal. Confirm with your entry criteria and preferred session timing. Pro Tips: Highest confidence trades: When dashboard signals and chart arrows/markers agree, especially with high confluence and volume spike. Adapt pip distance filter: Dashboard is tuned for FX and gold; for other assets, adjust pip-size filter if needed. Use alerts (if enabled): Set up custom TradingView alerts for “Flip” or “Liquidity” signals for auto-notifications. Designed to help you make professional, objective decisions—without chart clutter or second-guessing!
Wskaźnik Pine Script®
od stoyan_valev
CYCLE BY RiotWolftradingDescription of the "CYCLE" Indicator The "CYCLE" indicator is a custom Pine Script v5 script for TradingView that visualizes cyclic patterns in price action, dividing the trading day into specific sessions and 90-minute quarters (Q1-Q4). It is designed to identify and display market phases (Accumulation, Manipulation, Distribution, and Continuation/Reversal) along with key support and resistance levels within those sessions. Additionally, it allows customization of boxes, lines, labels, and colors to suit user preferences. Main Features Cycle Phases: Accumulation (1900-0100): Represents the phase where large operators accumulate positions. Manipulation (0100-0700): Identifies potential manipulative moves to mislead retail traders. Distribution (0700-1300): The phase where large operators distribute their positions. Continuation/Reversal (1300-1900): Indicates whether the price continues the trend or reverses. 90-Minute Quarters (Q1-Q4): Divides each 6-hour cycle (360 minutes) into four 90-minute quarters (Q1: 00:00-01:30, Q2: 01:30-03:00, Q3: 03:00-04:30, Q4: 04:30-06:00 UTC). Each quarter is displayed with a colored box (Q1: light purple, Q2: light blue, Q3: light gray, Q4: light pink) and labels (defaulted to black). Support and Resistance Visualization: Draws boxes or lines (based on settings) showing the high and low levels of each session. Optionally displays accumulated volume at the highs and lows within the boxes. Daily Lines and Last 3 Boxes: How to Use the Indicator Step 1: Add the Indicator to TradingView Open TradingView and select the chart where you want to apply the indicator (e.g., UMG9OOR on a 5-minute timeframe, as shown in the screenshot). Go to the Pine Editor (at the bottom of the TradingView interface). Copy and paste the provided code. Click Compile and then Add to Chart. Step 2: Configure the Indicator Click on the indicator name on the chart ("CYCLE") and select Settings (or double-click the name). Adjust the options based on your needs: Cycle Phases: Enable/disable phases (Accumulation, Manipulation, Distribution, Continuation/Reversal) and adjust their time slots if needed. 90-Minute Quarters: Enable/disable quarters (Q1-Q4). Step 3: Interpret the Indicator Identify Cycle Phases: Observe the red boxes indicating the phases (Accumulation, Manipulation, etc.). The high and low levels within each phase are potential support/resistance zones. If volume is enabled, pay attention to the accumulated volume at highs and lows, as it may indicate the strength of those levels. Use the 90-Minute Quarters (Q1-Q4): The colored boxes (Q1-Q4) divide the day into 90-minute segments. Each quarter shows the price range (high and low) during that period. Use these boxes to identify price patterns within each quarter, such as breakouts or consolidations. The labels (Q1, Q2, etc.) help you track time and anticipate potential moves in the next quarter. Analyze Support and Resistance: The high and low levels of each phase/quarter act as support and resistance. Daily lines (if enabled) show key levels from the previous day, useful for planning entries/exits. The "last 3 boxes below price" (if enabled) highlight potential support levels the price might target. Avoid Manipulation: During the Manipulation phase (0100-0700), be cautious of sharp moves or false breakouts. Use the high/low levels of this phase to identify potential traps (as explained in your first question about manipulation candles). Step 4: Trading Strategy Entries and Exits: Support/Resistance: Use the high/low levels of phases and quarters to set entry or exit points. For example, if the price bounces off a Q1 support level, consider a buy. Breakouts: If the price breaks a high/low of a quarter (e.g., Q2), wait for confirmation to enter in the direction of the breakout. Volume: If accumulated volume is high near a key level, that level may be more significant. Risk Management: Place stop-loss orders below lows (for buys) or above highs (for sells) identified by the indicator. Avoid trading during the Manipulation phase unless you have a specific strategy to handle false breakouts. Time Context: Use the quarters (Q1-Q4) to plan your trades based on time. For example, if Q3 is typically volatile in your market, prepare for larger moves between 03:00-04:30 UTC. Step 5: Adjustments and Testing Test on Different Timeframes: The indicator is set for a 5-minute timeframe (as in the screenshot), but you can test it on other timeframes (e.g., 1-minute, 15-minute) by adjusting the time slots if needed. Adjust Colors and Styles: If the default colors are not visible on your chart, change them for better clarity. --- 📌 1. **Accumulation: Strong Institutional Activity** - During the **accumulation phase, we see **high volume: 82.773K, which suggests strong buying interest**, likely from institutional players. - This sets the base for the following upward move in price. --- 📌 2. **Manipulation: False Breakout with Lower Volume** - Later, there's a manipulation phase where price breaks above previous highs, but the volume (71.814K) is **lower than during accumulation**. - This implies that buyers are not as aggressive as before—no real demandbehind the breakout. - It’s likely a bull trap, where smart money is selling into the breakout to exit their positions. --- ### 📌 3. Distribution: Weakness and Lack of Demand - The market enters a distribution phase, and volume drops even further (only 7.914K). - Price struggles to go higher, and you start seeing rejections at the top. - This shows that demand is drying up, and smart money is offloading positions**—not accumulating anymore. --- ### 💡 Why Take the Short Here? - Volume is not increasing with new highs—showing weak demand**. - The manipulation volume is weaker than the accumulation volume, confirming the breakout was likely false. - Structure starts to break down (Q levels falling), which confirms weakness. - This creates a high-probability short setup: - **Entry:** after confirmation of distribution and structural breakdown. - **Stop loss:** above the manipulation high. - **Target:** down toward previous lows or value zones. --- ### ✅ Conclusion Since the manipulation volume failed to exceed the accumulation volume, the breakout lacked real strength. Combined with decreasing volume in the distribution phase, this indicates fading demand and supply taking control—which justifies entering a short position.
Wskaźnik Pine Script®
od anarkiacapital
1
StupidTrader Money GlitchStupidTrader Money Glitch This indicator identifies high-probability buy setups by combining key technical concepts. It detects a reclaimed demand zone (a significant low that was broken and reclaimed), confirms bullish market structure breaks (MSB), ensures the price is above the 9 and 21 EMAs, and looks for volume spikes or trends. Key Features: Plots a demand zone (blue box) based on a reclaimed low. Signals long entries (green triangles) when conditions align: reclaimed demand zone, MSB, price above EMAs, and volume confirmation. Includes EMA 9 (blue) and EMA 21 (aqua) for trend confirmation. How to Use: Add the indicator to your chart and look for green triangles below candles as buy signals. Ensure the price interacts with the demand zone, breaks market structure, and shows volume confirmation. Works best on daily or higher timeframes for assets like ONDO, BTC, and more. Settings: Short EMA Length: 9 Mid EMA Length: 21 Pivot Lookback for Demand Zone: 5 Zone Lookback for Demand: 90 Volume Lookback: 20
Wskaźnik Pine Script®
od Stupidoooooooooo
JJ Psychological Levels (125 Increments)Psychological Levels Indicator Description: The Psychological Levels Indicator is a versatile tool designed for traders to identify key price levels that often act as support or resistance zones in the market. These levels are plotted at regular intervals, customizable by the user, starting from a base price level. This is particularly useful for spotting psychological price points that traders and investors frequently monitor. Key Features: 1.Dynamic Psychological Levels: - The script calculates and displays horizontal lines at price levels separated by customizable increments (default: 125 points). - These levels are dynamically adjusted to the visible range of the chart. 2. Customizable Inputs: - Starting Level: Set the base level from which increments are calculated (e.g., 0 or 1000). - Step Size: Define the interval between levels (e.g., 125 for indices like Bank NIFTY). 3. Visual Representation: - Horizontal lines are drawn at each psychological level, helping traders quickly identify key zones. - Labels are placed next to each level, displaying the corresponding price for easy reference. 4. Application Across Instruments: - This indicator works seamlessly with various asset classes, including stocks, indices, forex, and cryptocurrencies. How to Use: 1.Identify Key Price Zones: - Use the plotted psychological levels to spot areas where price action is likely to react. - Levels such as 1125, 1250, and 1375 (for a step size of 125) are visually highlighted. 2. Plan Trades Around Key Levels: - These levels can act as support/resistance or breakout points, providing opportunities for entry, exit, and stop-loss placement. 3. Customizable Settings: - Adjust the starting level and step size to tailor the indicator to your trading instrument or strategy. Why Psychological Levels Matter: Psychological levels are widely followed by traders and often coincide with key market turning points due to their significance in human behavior and market psychology. They are frequently used by institutional traders, making them valuable reference points for intraday and swing trading. Custom Settings: - **Starting Level:** Default: `0` - **Step Size:** Default: `125` Disclaimer: This indicator is a technical analysis tool and is not intended to provide financial advice. Always combine it with other indicators and perform your due diligence before making trading decisions.
Wskaźnik Pine Script®
od JoanJagan
6
Multiple Naked LevelsPURPOSE OF THE INDICATOR This indicator autogenerates and displays naked levels and gaps of multiple types collected into one simple and easy to use indicator. VALUE PROPOSITION OF THE INDICATOR AND HOW IT IS ORIGINAL AND USEFUL 1) CONVENIENCE : The purpose of this indicator is to offer traders with one coherent and robust indicator providing useful, valuable, and often used levels - in one place. 2) CLUSTERS OF CONFLUENCES : With this indicator it is easy to identify levels and zones on the chart with multiple confluences increasing the likelihood of a potential reversal zone. THE TYPES OF LEVELS AND GAPS INCLUDED IN THE INDICATOR The types of levels include the following: 1) PIVOT levels (Daily/Weekly/Monthly) depicted in the chart as: dnPIV, wnPIV, mnPIV. 2) POC (Point of Control) levels (Daily/Weekly/Monthly) depicted in the chart as: dnPoC, wnPoC, mnPoC. 3) VAH/VAL STD 1 levels (Value Area High/Low with 1 std) (Daily/Weekly/Monthly) depicted in the chart as: dnVAH1/dnVAL1, wnVAH1/wnVAL1, mnVAH1/mnVAL1 4) VAH/VAL STD 2 levels (Value Area High/Low with 2 std) (Daily/Weekly/Monthly) depicted in the chart as: dnVAH2/dnVAL2, wnVAH2/wnVAL2, mnVAH1/mnVAL2 5) FAIR VALUE GAPS (Daily/Weekly/Monthly) depicted in the chart as: dnFVG, wnFVG, mnFVG. 6) CME GAPS (Daily) depicted in the chart as: dnCME. 7) EQUILIBRIUM levels (Daily/Weekly/Monthly) depicted in the chart as dnEQ, wnEQ, mnEQ. HOW-TO ACTIVATE LEVEL TYPES AND TIMEFRAMES AND HOW-TO USE THE INDICATOR You can simply choose which of the levels to be activated and displayed by clicking on the desired radio button in the settings menu. You can locate the settings menu by clicking into the Object Tree window, left-click on the Multiple Naked Levels and select Settings. You will then get a menu of different level types and timeframes. Click the checkboxes for the level types and timeframes that you want to display on the chart. You can then go into the chart and check out which naked levels that have appeared. You can then use those levels as part of your technical analysis. The levels displayed on the chart can serve as additional confluences or as part of your overall technical analysis and indicators. In order to back-test the impact of the different naked levels you can also enable tapped levels to be depicted on the chart. Do this by toggling the 'Show tapped levels' checkbox. Keep in mind however that Trading View can not shom more than 500 lines and text boxes so the indocator will not be able to give you the complete history back to the start for long duration assets. In order to clean up the charts a little bit there are two additional settings that can be used in the Settings menu: - Selecting the price range (%) from the current price to be included in the chart. The default is 25%. That means that all levels below or above 20% will not be displayed. You can set this level yourself from 0 up to 100%. - Selecting the minimum gap size to include on the chart. The default is 1%. That means that all gaps/ranges below 1% in price difference will not be displayed on the chart. You can set the minimum gap size yourself. BASIC DESCRIPTION OF THE INNER WORKINGS OF THE INDICTATOR The way the indicator works is that it calculates and identifies all levels from the list of levels type and timeframes above. The indicator then adds this level to a list of untapped levels. Then for each bar after, it checks if the level has been tapped. If the level has been tapped or a gap/range completely filled, this level is removed from the list so that the levels displayed in the end are only naked/untapped levels. Below is a descrition of each of the level types and how it is caluclated (algorithm): PIVOT Daily, Weekly and Monthly levels in trading refer to significant price points that traders monitor within the context of a single trading day. These levels can provide insights into market behavior and help traders make informed decisions regarding entry and exit points. Traders often use D/W/M levels to set entry and exit points for trades. For example, entering long positions near support (daily close) or selling near resistance (daily close). Daily levels are used to set stop-loss orders. Placing stops just below the daily close for long positions or above the daily close for short positions can help manage risk. The relationship between price movement and daily levels provides insights into market sentiment. For instance, if the price fails to break above the daily high, it may signify bearish sentiment, while a strong breakout can indicate bullish sentiment. The way these levels are calculated in this indicator is based on finding pivots in the chart on D/W/M timeframe. The level is then set to previous D/W/M close = current D/W/M open. In addition, when price is going up previous D/W/M open must be smaller than previous D/W/M close and current D/W/M close must be smaller than the current D/W/M open. When price is going down the opposite. POINT OF CONTROL The Point of Control (POC) is a key concept in volume profile analysis, which is commonly used in trading. It represents the price level at which the highest volume of trading occurred during a specific period. The POC is derived from the volume traded at various price levels over a defined time frame. In this indicator the timeframes are Daily, Weekly, and Montly. It identifies the price level where the most trades took place, indicating strong interest and activity from traders at that price. The POC often acts as a significant support or resistance level. If the price approaches the POC from above, it may act as a support level, while if approached from below, it can serve as a resistance level. Traders monitor the POC to gauge potential reversals or breakouts. The way the POC is calculated in this indicator is by an approximation by analysing intrabars for the respective timeperiod (D/W/M), assigning the volume for each intrabar into the price-bins that the intrabar covers and finally identifying the bin with the highest aggregated volume. The POC is the price in the middle of this bin. The indicator uses a sample space for intrabars on the Daily timeframe of 15 minutes, 35 minutes for the Weekly timeframe, and 140 minutes for the Monthly timeframe. The indicator has predefined the size of the bins to 0.2% of the price at the range low. That implies that the precision of the calulated POC og VAH/VAL is within 0.2%. This reduction of precision is a tradeoff for performance and speed of the indicator. This also implies that the bigger the difference from range high prices to range low prices the more bins the algorithm will iterate over. This is typically the case when calculating the monthly volume profile levels and especially high volatility assets such as alt coins. Sometimes the number of iterations becomes too big for Trading View to handle. In these cases the bin size will be increased even more to reduce the number of iterations. In such cases the bin size might increase by a factor of 2-3 decreasing the accuracy of the Volume Profile levels. Anyway, since these Volume Profile levels are approximations and since precision is traded for performance the user should consider the Volume profile levels(POC, VAH, VAL) as zones rather than pin point accurate levels. VALUE AREA HIGH/LOW STD1/STD2 The Value Area High (VAH) and Value Area Low (VAL) are important concepts in volume profile analysis, helping traders understand price levels where the majority of trading activity occurs for a given period. The Value Area High/Low is the upper/lower boundary of the value area, representing the highest price level at which a certain percentage of the total trading volume occurred within a specified period. The VAH/VAL indicates the price point above/below which the majority of trading activity is considered less valuable. It can serve as a potential resistance/support level, as prices above/below this level may experience selling/buying pressure from traders who view the price as overvalued/undervalued In this indicator the timeframes are Daily, Weekly, and Monthly. This indicator provides two boundaries that can be selected in the menu. The first boundary is 70% of the total volume (=1 standard deviation from mean). The second boundary is 95% of the total volume (=2 standard deviation from mean). The way VAH/VAL is calculated is based on the same algorithm as for the POC. However instead of identifying the bin with the highest volume, we start from range low and sum up the volume for each bin until the aggregated volume = 30%/70% for VAL1/VAH1 and aggregated volume = 5%/95% for VAL2/VAH2. Then we simply set the VAL/VAH equal to the low of the respective bin. FAIR VALUE GAPS Fair Value Gaps (FVG) is a concept primarily used in technical analysis and price action trading, particularly within the context of futures and forex markets. They refer to areas on a price chart where there is a noticeable lack of trading activity, often highlighted by a significant price movement away from a previous level without trading occurring in between. FVGs represent price levels where the market has moved significantly without any meaningful trading occurring. This can be seen as a "gap" on the price chart, where the price jumps from one level to another, often due to a rapid market reaction to news, events, or other factors. These gaps typically appear when prices rise or fall quickly, creating a space on the chart where no transactions have taken place. For example, if a stock opens sharply higher and there are no trades at the prices in between the two levels, it creates a gap. The areas within these gaps can be areas of liquidity that the market may return to “fill” later on. FVGs highlight inefficiencies in pricing and can indicate areas where the market may correct itself. When the market moves rapidly, it may leave behind price levels that traders eventually revisit to establish fair value. Traders often watch for these gaps as potential reversal or continuation points. Many traders believe that price will eventually “fill” the gap, meaning it will return to those price levels, providing potential entry or exit points. This indicator calculate FVGs on three different timeframes, Daily, Weekly and Montly. In this indicator the FVGs are identified by looking for a three-candle pattern on a chart, signalling a discrete imbalance in order volume that prompts a quick price adjustment. These gaps reflect moments where the market sentiment strongly leans towards buying or selling yet lacks the opposite orders to maintain price stability. The indicator sets the gap to the difference from the high of the first bar to the low of the third bar when price is moving up or from the low of the first bar to the high of the third bar when price is moving down. CME GAPS (BTC only) CME gaps refer to price discrepancies that can occur in charts for futures contracts traded on the Chicago Mercantile Exchange (CME). These gaps typically arise from the fact that many futures markets, including those on the CME, operate nearly 24 hours a day but may have significant price movements during periods when the market is closed. CME gaps occur when there is a difference between the closing price of a futures contract on one trading day and the opening price on the following trading day. This difference can create a "gap" on the price chart. Opening Gaps: These usually happen when the market opens significantly higher or lower than the previous day's close, often influenced by news, economic data releases, or other market events occurring during non-trading hours. Gaps can result from reactions to major announcements or developments, such as earnings reports, geopolitical events, or changes in economic indicators, leading to rapid price movements. The importance of CME Gaps in Trading is the potential for Filling Gaps: Many traders believe that prices often "fill" gaps, meaning that prices may return to the gap area to establish fair value. This can create potential trading opportunities based on the expectation of gap filling. Gaps can act as significant support or resistance levels. Traders monitor these levels to identify potential reversal points in price action. The way the gap is identified in this indicator is by checking if current open is higher than previous bar close when price is moving up or if current open is lower than previous day close when price is moving down. EQUILIBRIUM Equilibrium in finance and trading refers to a state where supply and demand in a market balance each other, resulting in stable prices. It is a key concept in various economic and trading contexts. Here’s a concise description: Market Equilibrium occurs when the quantity of a good or service supplied equals the quantity demanded at a specific price level. At this point, there is no inherent pressure for the price to change, as buyers and sellers are in agreement. Equilibrium Price is the price at which the market is in equilibrium. It reflects the point where the supply curve intersects the demand curve on a graph. At the equilibrium price, the market clears, meaning there are no surplus goods or shortages. In this indicator the equilibrium level is calculated simply by finding the midpoint of the Daily, Weekly, and Montly candles respectively. NOTES 1) Performance. The algorithms are quite resource intensive and the time it takes the indicator to calculate all the levels could be 5 seconds or more, depending on the number of bars in the chart and especially if Montly Volume Profile levels are selected (POC, VAH or VAL). 2) Levels displayed vs the selected chart timeframe. On a timeframe smaller than the daily TF - both Daily, Weekly, and Monthly levels will be displayed. On a timeframe bigger than the daily TF but smaller than the weekly TF - the Weekly and Monthly levels will be display but not the Daily levels. On a timeframe bigger than the weekly TF but smaller than the monthly TF - only the Monthly levels will be displayed. Not Daily and Weekly. CREDITS The core algorithm for calculating the POC levels is based on the indicator "Naked Intrabar POC" developed by rumpypumpydumpy (https:www.tradingview.com/u/rumpypumpydumpy/). The "Naked intrabar POC" indicator calculates the POC on the current chart timeframe. This indicator (Multiple Naked Levels) adds two new features: 1) It calculates the POC on three specific timeframes, the Daily, Weekly, and Monthly timeframes - not only the current chart timeframe. 2) It adds functionaly by calculating the VAL and VAH of the volume profile on the Daily, Weekly, Monthly timeframes .
Wskaźnik Pine Script®
od alfg
23
Auto Volume Spread Analysis (VSA) [TANHEF]Auto Volume Spread Analysis (visible volume and spread bars auto-scaled): Understanding Market Intentions through the Interpretation of Volume and Price Movements. All the sections below contain the same descriptions as my other indicator "Volume Spread Analysis" with the exception of 'Auto Scaling'. █ Auto-Scaling This indicator auto-scales spread bars to match the visible volume bars, unlike the previous "Volume Spread Analysis " version which limited the number of visible spread bars to a fixed count. The auto-scaling feature allows for easier navigation through historical data, enabling both more historical spread bars to be viewed and more historical VSA pattern labels being displayed without requiring using the bar replay tool. Please note that this indicator’s auto-scaling feature recalculates the visible bars on the chart, causing the indicator to reload whenever the chart is moved. Auto-scaled spread bars have two display options (set via 'Spread Bars Method' setting): Lines: a bar lookback limit of 500 bars. Polylines: no bar lookback limit as only plotted on visible bars on chart, which uses multiple polylines are used. █ Simple Explanation: The Volume Spread Analysis (VSA) indicator is a comprehensive tool that helps traders identify key market patterns and trends based on volume and spread data. This indicator highlights significant VSA patterns and provides insights into market behavior through color-coded volume/spread bars and identification of bars indicating strength, weakness, and neutrality between buyers and sellers. It also includes powerful volume and spread forecasting capabilities. █ Laws of Volume Spread Analysis (VSA): The origin of VSA begins with Richard Wyckoff, a pivotal figure in its development. Wyckoff made significant contributions to trading theory, including the formulation of three basic laws: The Law of Supply and Demand: This fundamental law states that supply and demand balance each other over time. High demand and low supply lead to rising prices until demand falls to a level where supply can meet it. Conversely, low demand and high supply cause prices to fall until demand increases enough to absorb the excess supply. The Law of Cause and Effect: This law assumes that a 'cause' will result in an 'effect' proportional to the 'cause'. A strong 'cause' will lead to a strong trend (effect), while a weak 'cause' will lead to a weak trend. The Law of Effort vs. Result: This law asserts that the result should reflect the effort exerted. In trading terms, a large volume should result in a significant price move (spread). If the spread is small, the volume should also be small. Any deviation from this pattern is considered an anomaly. █ Volume and Spread Analysis Bars: Display: Volume and spread bars that consist of color coded levels, with the spread bars scaled to match the volume bars. A displayable table (Legend) of bar colors and levels can give context and clarify to each volume/spread bar. Calculation: Levels are calculated using multipliers applied to moving averages to represent key levels based on historical data: low, normal, high, ultra. This method smooths out short-term fluctuations and focuses on longer-term trends. Low Level: Indicates reduced volatility and market interest. Normal Level: Reflects typical market activity and volatility. High Level: Indicates increased activity and volatility. Ultra Level: Identifies extreme levels of activity and volatility. This illustrates the appearance of Volume and Spread bars when scaled and plotted together: █ Forecasting Capabilities: Display: Forecasted volume and spread levels using predictive models. Calculation: Volume and Spread prediction calculations differ as volume is linear and spread is non-linear. Volume Forecast (Linear Forecasting): Predicts future volume based on current volume rate and bar time till close. Spread Forecast (Non-Linear Dynamic Forecasting): Predicts future spread using a dynamic multiplier, less near midpoint (consolidation) and more near low or high (trending), reflecting non-linear expansion. Moving Averages: In forecasting, moving averages utilize forecasted levels instead of actual levels to ensure the correct level is forecasted (low, normal, high, or ultra). The following compares forecasted volume with actual resulting volume, highlighting the power of early identifying increased volume through forecasted levels: █ VSA Patterns: Criteria and descriptions for each VSA pattern are available as tooltips beside them within the indicator’s settings. These tooltips provide explanations of potential developments based on the volume and spread data. Signs of Strength (🟢): Patterns indicating strong buying pressure and potential market upturns. Down Thrust Selling Climax No Effort ➤ Bearish Result Bearish Effort ➤ No Result Inverse Down Thrust Failed Selling Climax Bull Outside Reversal End of Falling Market (Bag Holder) Pseudo Down Thrust No Supply Signs of Weakness (🔴): Patterns indicating strong selling pressure and potential market downturns. Up Thrust Buying Climax No Effort ➤ Bullish Result Bullish Effort ➤ No Result Inverse Up Thrust Failed Buying Climax Bear Outside Reversal End of Rising Market (Bag Seller) Pseudo Up Thrust No Demand Neutral Patterns (🔵): Patterns indicating market indecision and potential for continuation or reversal. Quiet Doji Balanced Doji Strong Doji Quiet Spinning Top Balanced Spinning Top Strong Spinning Top Quiet High Wave Balanced High Wave Strong High Wave Consolidation Bar Patterns (🟡): Common candlestick patterns that offer insights into market sentiment. These are required in some VSA patterns and can also be displayed independently. Bull Pin Bar Bear Pin Bar Doji Spinning Top High Wave Consolidation This demonstrates the acronym and descriptive options for displaying bar patterns, with the ability to hover over text to reveal the descriptive text along with what type of pattern: █ Alerts: VSA Pattern Alerts: Notifications for identified VSA patterns at bar close. Volume and Spread Alerts: Alerts for confirmed and forecasted volume/spread levels (Low, High, Ultra). Forecasted Volume and Spread Alerts: Alerts for forecasted volume/spread levels (High, Ultra) include a minimum percent time elapsed input to reduce false early signals by ensuring sufficient bar time has passed. █ Inputs and Settings: Indicator Bar Color: Select color schemes for bars (Normal, Detail, Levels). Indicator Moving Average Color: Select schemes for bars (Fill, Lines, None). Price Bar Colors: Options to color price bars based on VSA patterns and volume levels. Legend: Display a table of bar colors and levels for context and clarity of volume/spread bars. Forecast: Configure forecast display and prediction details for volume and spread. Average Multipliers: Define multipliers for different levels (Low, High, Ultra) to refine the analysis. Moving Average: Set volume and spread moving average settings. VSA: Select the VSA patterns to be calculated and displayed (Strength, Weakness, Neutral). Bar Patterns: Criteria for bar patterns used in VSA (Doji, Bull Pin Bar, Bear Pin Bar, Spinning Top, Consolidation, High Wave). Colors: Set exact colors used for indicator bars, indicator moving averages, and price bars. More Display Options: Specify how VSA pattern text is displayed (Acronym, Descriptive), positioning, and sizes. Alerts: Configure alerts for VSA patterns, volume, and spread levels, including forecasted levels. █ Usage: The Volume Spread Analysis indicator is a helpful tool for leveraging volume spread analysis to make informed trading decisions. It offers comprehensive visual and textual cues on the chart, making it easier to identify market conditions, potential reversals, and continuations. Whether analyzing historical data or forecasting future trends, this indicator provides insights into the underlying factors driving market movements.
Wskaźnik Pine Script®
od TanHef
7
Volume Spread Analysis [TANHEF]Volume Spread Analysis: Understanding Market Intentions through the Interpretation of Volume and Price Movements. █ Simple Explanation: The Volume Spread Analysis (VSA) indicator is a comprehensive tool that helps traders identify key market patterns and trends based on volume and spread data. This indicator highlights significant VSA patterns and provides insights into market behavior through color-coded volume/spread bars and identification of bars indicating strength, weakness, and neutrality between buyers and sellers. It also includes powerful volume and spread forecasting capabilities. █ Laws of Volume Spread Analysis (VSA): The origin of VSA begins with Richard Wyckoff, a pivotal figure in its development. Wyckoff made significant contributions to trading theory, including the formulation of three basic laws: The Law of Supply and Demand: This fundamental law states that supply and demand balance each other over time. High demand and low supply lead to rising prices until demand falls to a level where supply can meet it. Conversely, low demand and high supply cause prices to fall until demand increases enough to absorb the excess supply. The Law of Cause and Effect: This law assumes that a 'cause' will result in an 'effect' proportional to the 'cause'. A strong 'cause' will lead to a strong trend (effect), while a weak 'cause' will lead to a weak trend. The Law of Effort vs. Result: This law asserts that the result should reflect the effort exerted. In trading terms, a large volume should result in a significant price move (spread). If the spread is small, the volume should also be small. Any deviation from this pattern is considered an anomaly. █ Volume and Spread Analysis Bars: Display: Volume and/or spread bars that consist of color coded levels. If both of these are displayed, the number of spread bars can be limited for visual appeal and understanding, with the spread bars scaled to match the volume bars. While automatic calculation of the number of visual bars for auto scaling is possible, it is avoided to prevent the indicator from reloading whenever the number of visual price bars on the chart is adjusted, ensuring uninterrupted analysis. A displayable table (Legend) of bar colors and levels can give context and clarify to each volume/spread bar. Calculation: Levels are calculated using multipliers applied to moving averages to represent key levels based on historical data: low, normal, high, ultra. This method smooths out short-term fluctuations and focuses on longer-term trends. Low Level: Indicates reduced volatility and market interest. Normal Level: Reflects typical market activity and volatility. High Level: Indicates increased activity and volatility. Ultra Level: Identifies extreme levels of activity and volatility. This illustrates the appearance of Volume and Spread bars when scaled and plotted together: █ Forecasting Capabilities: Display: Forecasted volume and spread levels using predictive models. Calculation: Volume and Spread prediction calculations differ as volume is linear and spread is non-linear. Volume Forecast (Linear Forecasting): Predicts future volume based on current volume rate and bar time till close. Spread Forecast (Non-Linear Dynamic Forecasting): Predicts future spread using a dynamic multiplier, less near midpoint (consolidation) and more near low or high (trending), reflecting non-linear expansion. Moving Averages: In forecasting, moving averages utilize forecasted levels instead of actual levels to ensure the correct level is forecasted (low, normal, high, or ultra). The following compares forecasted volume with actual resulting volume, highlighting the power of early identifying increased volume through forecasted levels: █ VSA Patterns: Criteria and descriptions for each VSA pattern are available as tooltips beside them within the indicator’s settings. These tooltips provide explanations of potential developments based on the volume and spread data. Signs of Strength (🟢): Patterns indicating strong buying pressure and potential market upturns. Down Thrust Selling Climax No Effort → Bearish Result Bearish Effort → No Result Inverse Down Thrust Failed Selling Climax Bull Outside Reversal End of Falling Market (Bag Holder) Pseudo Down Thrust No Supply Signs of Weakness (🔴): Patterns indicating strong selling pressure and potential market downturns. Up Thrust Buying Climax No Effort → Bullish Result Bullish Effort → No Result Inverse Up Thrust Failed Buying Climax Bear Outside Reversal End of Rising Market (Bag Seller) Pseudo Up Thrust No Demand Neutral Patterns (🔵): Patterns indicating market indecision and potential for continuation or reversal. Quiet Doji Balanced Doji Strong Doji Quiet Spinning Top Balanced Spinning Top Strong Spinning Top Quiet High Wave Balanced High Wave Strong High Wave Consolidation Bar Patterns (🟡): Common candlestick patterns that offer insights into market sentiment. These are required in some VSA patterns and can also be displayed independently. Bull Pin Bar Bear Pin Bar Doji Spinning Top High Wave Consolidation This demonstrates the acronym and descriptive options for displaying bar patterns, with the ability to hover over text to reveal the descriptive text along with what type of pattern: █ Alerts: VSA Pattern Alerts: Notifications for identified VSA patterns at bar close. Volume and Spread Alerts: Alerts for confirmed and forecasted volume/spread levels (Low, High, Ultra). Forecasted Volume and Spread Alerts: Alerts for forecasted volume/spread levels (High, Ultra) include a minimum percent time elapsed input to reduce false early signals by ensuring sufficient bar time has passed. █ Inputs and Settings: Display Volume and/or Spread: Choose between displaying volume bars, spread bars, or both with different lookback periods. Indicator Bar Color: Select color schemes for bars (Normal, Detail, Levels). Indicator Moving Average Color: Select schemes for bars (Fill, Lines, None). Price Bar Colors: Options to color price bars based on VSA patterns and volume levels. Legend: Display a table of bar colors and levels for context and clarity of volume/spread bars. Forecast: Configure forecast display and prediction details for volume and spread. Average Multipliers: Define multipliers for different levels (Low, High, Ultra) to refine the analysis. Moving Average: Set volume and spread moving average settings. VSA: Select the VSA patterns to be calculated and displayed (Strength, Weakness, Neutral). Bar Patterns: Criteria for bar patterns used in VSA (Doji, Bull Pin Bar, Bear Pin Bar, Spinning Top, Consolidation, High Wave). Colors: Set exact colors used for indicator bars, indicator moving averages, and price bars. More Display Options: Specify how VSA pattern text is displayed (Acronym, Descriptive), positioning, and sizes. Alerts: Configure alerts for VSA patterns, volume, and spread levels, including forecasted levels. █ Usage: The Volume Spread Analysis indicator is a helpful tool for leveraging volume spread analysis to make informed trading decisions. It offers comprehensive visual and textual cues on the chart, making it easier to identify market conditions, potential reversals, and continuations. Whether analyzing historical data or forecasting future trends, this indicator provides insights into the underlying factors driving market movements.
Wskaźnik Pine Script®
od TanHef
23
Zones DetectorThis indicator highlights supply and demand zones. Method to detect the zones: 1.- The body of the candle is calculated and it is checked how many times it can be repeated in its highest or lowest wick. If the body of the candle is repeated N number of times (Min. Factor) in any of its wicks, it is taken as an indecision zone. 2.- The subsequent candles are reviewed (Confirmation Bars) to determine if the zone is of supply or demand. For demand zones, subsequent prices must be above the minimum price of the indecision zone and for supply zones, subsequent prices must be below the maximum price of the indecision zone. 3.- The previous average volume of N periods (Periods) to the indecision zone is calculated and check that has a minimum percentage change (Min. Volume Change) with respect to the indecision zone and its subsequent candles (Confirmation Bars). If the previous steps are met, the zone will be highlighted with a green color for demand (Zones/Demand) and red for supply (Zones/Supply), for the indecision zones (identified by point 1) they will be highlighted in gray (Zones/Indecision) Invalid zones are automatically hidden from the chart, using methods such as: "wick" and "close". Settings Indecision Min. Factor: Set the number of times that the body of the candle must be repeated in its wicks. High values will be stronger indecision zones, but fewer will be found, low values will find more zones. Invalidation Method: Method used to automatically invalidate zones. It can be "wick" or "close". Confirmation Bars: Defines the number of candles used to confirm an indecision zone found Volume Min. Volume Change(%): Percentage of minimum change in volume (+/-) that the zone must have to be displayed Previous Periods: Number of previous periods to be used to calculate the average volume prior to the indecision zone. Zones Show Last.- Number of zones (demand, supply, indecision) to be shown. Demand.- Color to highlight the demand zones Supply.- Color to highlight the supply zones Indecision.- Color to highlight the indecision zones Use The highlighted supply and demand zones can be used as support or resistance to place orders.
Wskaźnik Pine Script®
od CaLaHe
36
Next Gen Auto S/RThis indicator will automatically plot support and resistance levels and will also allow you to overlay multi time frame support and resistance on any time frame that you are currently conducting analysis on. In addition you can also set alerts when a support and resistance level is tested, fine tune how many levels you would like to view on your charts, option to input how many candlesticks minimum you would like between support and resistance levels. You can also select breakout mode which will turn old support into resistance by a colour change and turn old resistance into support. NEW you can now use extended levels and change your zones into lines.
Wskaźnik Pine Script®
od THE_GOLDEN_SUITE
8
Linh's Anomaly Radar v2What this script does It’s an event detector for price/volume anomalies that often precede or confirm moves. It watches a bunch of patterns (Wyckoff tests, squeezes, failed breakouts, turnover bursts, etc.), applies robust z-scores, optional trend filters, cooldowns (to avoid spam), and then fires: A shape/label on the bar, A row in the mini panel (top-right), A ready-made alertcondition you can hook into. How to add & set up (TradingView) Paste the script → Save → Add to chart on Daily first (works on any TF). Open Settings → Inputs: General • Use Robust Z (MAD): more outlier-resistant; keep on. • Z Lookback: 60 bars is ~3 months; bump to 120 for slower regimes. • Cooldown: min bars to wait before the same signal can fire again (default 5). • Use trend filter: if on, “bullish” signals only fire above SMA(tfLen), “bearish” below. Thresholds: fine-tune sensitivity (defaults are sane). To create alerts: Right-click chart → Add alert Condition: Linh’s Anomaly Radar v2 → choose a specific signal or Composite (Σ). Options: “Once per bar close” (recommended). Customize message if you want ticker/timeframe in your phone push. The mini panel (top-right) Signal column: short code (see cheat sheet below). Fired column: a dot “•” means that on the latest bar this signal fired. Score (right column): total count of signals that fired this bar. Σ≥N shows your composite threshold (how many must fire to trigger the “Composite” alert). Shapes & codes (what’s what) Code Name (category) What it’s looking for Why it matters STL Stealth Volume z(volume)>5 & ** z(return) EVR Effort vs Result squeeze z(vol)>3 & z(TR)<−0.5 Heavy effort, tiny spread → absorption TGV Tight+Heavy (HL/ATR)<0.6 & z(vol)>3 Tight bar + heavy tape → pro activity CLS Accumulation cluster ≥3 of last 5 bars: up, vol↑, close near high Classic accumulation footprint GAP Open drive failure Big gap not filled (≥80%) & vol↑ One-sided open stalls → fade risk BB↑ BB squeeze breakout Squeeze (z(BBWidth)<−1.3) → close > upperBB & vol↑ Regime shift with confirmation ER↑ Effort→Result inversion Down day on vol then next bar > prior high Demand overwhelms supply OBV OBV divergence OBV slope up & ** z(ret20) WER Wide Effort, Opposite Result z(vol)>3, close+1 Selling into strength / distribution NS No-Supply (Wyckoff) Down bar, HL<0.6·ATR, vol << avg Sellers absent into weakness ND No-Demand (Wyckoff) Up bar, HL<0.6·ATR, vol << avg Buyers absent into strength VAC Liquidity Vacuum z(vol)<−1.5 & ** z(ret) UTD UTAD (failed breakout) Breaks swing-high, closes back below, vol↑ Stop-run, reversal risk SPR Spring (failed breakdown) Breaks swing-low, closes back above, vol↑ Bear trap, reversal risk PIV Pocket Pivot Up bar; vol > max down-vol in lookback Quiet base → sudden demand NR7 Narrow Range 7 + Vol HL is 7-bar low & z(vol)>2 Coiled spring with participation 52W 52-wk breakout quality New 52-wk close high + squeeze + vol↑ High-quality breakouts VvK Vol-of-Vol kink z(ATR20,200)>0.5 & z(ATR5,60)<0 Long-vol wakes up, short-vol compresses TAC Turnover acceleration SMA3 vol / SMA20 vol > 1.8 & muted return Participation surging before move RBd RSI Bullish div Price LL, RSI HL, vol z>1 Exhaustion of sellers RS↑ RSI Bearish div Price HH, RSI LH, vol z>1 Exhaustion of buyers Σ Composite Count of all fired signals ≥ threshold High-conviction bar Placement: Triangles up (below bar) → bullish-leaning events. Triangles down (above bar) → bearish-leaning events. Circles → neutral context (VAC, VvK, Composite). Key inputs (quick reference) General Use Robust Z (MAD): keep on for noisy tickers. Z Lookback (lenZ): 60 default; 120 if you want fewer alerts. Trend filter: when on, bullish signals require close > SMA(tfLen), bearish require <. Cooldown: prevents repeated firing of the same signal within N bars. Phase-1 thresholds (core) Stealth: vol z > 5, |ret z| < 1. EVR: vol z > 3, TR z < −0.5. Tight+Heavy: (HL/ATR) < 0.6, vol z > 3. Cluster: window=5, min=3 strong bars. GapFail: gap/ATR ≥1.5, fill <80%, vol z > 2. BB Squeeze: z(BBWidth)<−1.3 then breakout with vol z > 2. Eff→Res Up: prev bar heavy down → current bar > prior high. OBV Div: OBV uptrend + |z(ret20)|<0.3. Phase-2 thresholds (extras) WER: vol z > 3, close1. No-Supply/No-Demand: tight bar & very light volume vs SMA20. Vacuum: vol z < −1.5, |ret z|>1.5. UTAD/Spring: swing lookback N (default 20), vol z > 2. Pocket Pivot: lookback for prior down-vol max (default 10). NR7: 7-bar narrowest range + vol z > 2. 52W Quality: new 52-wk high + squeeze + vol z > 2. VoV Kink: z(ATR20,200)>0.5 AND z(ATR5,60)<0. Turnover Accel: SMA3/SMA20 > 1.8 and |ret z|<1. RSI Divergences: compare to n bars back (default 14). How to use it (playbooks) A) Daily scan workflow Run on Daily for your VN watchlist. Turn Composite (Σ) alert on with Σ≥2 or ≥3 to reduce noise. When a bar fires Σ (or a fav combo like STL + BB↑), drop to 60-min to time entries. B) Breakout quality check Look for 52W together with BB↑, TAC, and OBV. If WER/ND appear near highs → downgrade the breakout. C) Spring/UTAD reversals If SPR fires near major support and RBd confirms → long bias with stop below spring low. If UTD + WER/RS↑ near resistance → short/fade with stop above UTAD high. D) Accumulation basing During bases, you want CLS, OBV, TGV, STL, NR7. A pocket pivot (PIV) can be your early add; manage risk below base lows. Tuning tips Too many signals? Raise stealthVolZ to 5.5–6, evrVolZ to 3.5, use Σ≥3. Fast movers? Lower bbwZthr to −1.0 (less strict squeeze), keep trend filter on. Illiquid tickers? Keep MAD z-scores on, increase lookbacks (e.g., lenZ=120). Limitations & good habits First lenZ bars on a new symbol are less reliable (incomplete z-window). Some ideas (VWAP magnet, close auction spikes, ETF/foreign flows, options skew) need intraday/external feeds — not included here. Pine can’t “screen” across the whole market; set alerts or cycle your watchlist. Quick troubleshooting Compilation errors: make sure you’re on Pine v6; don’t nest functions in if blocks; each var int must be declared on its own line. No shapes firing: check trend filter (maybe price is below SMA and you’re waiting for bullish signals), and verify thresholds aren’t too strict.
Wskaźnik Pine Script®
od EmmaMia1618
Smart Money Volume Matrix [Ata]Smart Money Volume Matrix The Smart Money Volume Matrix (SMV Matrix) is an advanced volume-spread analysis (VSA) dashboard and charting tool designed to identify significant market anomalies by analyzing the relationship between price extremes and volume flow. Unlike traditional indicators that rely solely on moving averages or oscillators, this tool performs a "Snapshot Analysis" of a defined lookback period (default: 100 bars) to rank price action based on Order Flow Dominance. It isolates the Top 10 Highest and Lowest Close prices and scrutinizes the volume behind them to categorize market sentiment into four distinct phases: Distribution, No Demand, Absorption, and Exhaustion. Core Logic & Methodology The script operates on a Zero-Lag Snapshot Engine. It does not print historical signals bar-by-bar; instead, it evaluates the current market structure relative to the recent history (Lookback Period). 1. Ranking Engine: The script scans the lookback period to find the Top 10 Highest Closes and Top 10 Lowest Closes. 2. Volume Classification: For each ranked bar, it calculates the "Intrabar Buy/Sell Volume" (or approximates it using candle geometry if Intrabar data is unavailable). 3. Dominance Detection: It compares Buying Volume vs. Selling Volume to determine who is in control at critical price levels. Signal Classifications (VSA Logic) The indicator generates labels on the chart and updates the dashboard table based on the following logic: 1. At Price Tops (Resistance Areas): - Distribution (Supply): High Price + High Total Volume + Sellers Dominant. Interpretation: Indicates heavy institutional selling into rising prices. Often precedes a reversal. - Buy Climax: High Price + High Total Volume + Buyers Dominant. Interpretation: Extreme buying frenzy. While bullish, it often marks a "trap" or temporary top due to exhaustion. - No Demand: High Price + Low Volume. Interpretation: Prices drifted higher but lack institutional participation. A sign of weakness. 2. At Price Bottoms (Support Areas): - Absorption: Low Price + High Total Volume + Buyers Dominant. Interpretation: Institutional money is absorbing selling pressure (passive buying). A strong sign of accumulation. - Panic Sell: Low Price + High Total Volume + Sellers Dominant. Interpretation: Extreme fear. High volume at lows typically indicates capitulation and potential hands-changing. - Exhaustion: Low Price + Low Volume. Interpretation: Selling pressure has dried up. The market may float upward due to lack of sellers. Key Features - Dashboard Matrix Table: Displays the exact Close Price, Buy/Sell Volume, and Market State (Group) for the Top 10 ranking bars. Smart Footer: Automatically detects the active "Resistance Zone" (derived from G1 Distribution levels) and "Support Zone" (derived from G3 Absorption levels) and reports the current price status relative to these zones (e.g., "Testing Resistance", "Breakout", "At Support"). - Smart Zones (Auto S/R): Automatically draws Support and Resistance boxes extending into the future based on the most significant volume clusters found in the rankings. Includes logic to detect "Flips" (e.g., when Support breaks, it is labeled as a flip to Resistance). - Average Trend Channels: Calculates a Linear Regression trend line based specifically on the coordinates of the Top 10 Highs and Top 10 Lows, providing a "Best Fit" channel for the current market structure. - Visual Clarity: Labels utilize a "Smart Stacking" algorithm to prevent overlap on the chart. Guide lines connect labels to their respective candles for precise identification. Settings & Configuration - Matrix Settings: Lookback Period (default 100 bars) and Top Rank Count. - Volume Engine: Choose between "Intrabar (Precise)" for accurate order flow or "Geometry (Approx)" for standard volume estimation. - Visuals: Toggle Table, Labels, Lines, Zones, and Trend Lines. Adjust transparency and font sizes. IMPORTANT NOTE ON SNAPSHOT LOGIC This indicator is designed as a Real-Time Dashboard. It continuously updates the "Top 10" list as new candles form. Therefore, a label that appears on a candle may disappear if that candle falls out of the Top 10 ranking or leaves the lookback window. This is intended behavior to ensure the chart always reflects the current most critical levels, rather than a historical record of past signals. It is best used for live market analysis rather than historical back testing. Disclaimer: This tool is for educational and analytical purposes only. Volume analysis is subjective and should be used in conjunction with other methods of technical analysis.
Wskaźnik Pine Script®
od ata_sabanci
8
COT IndexTHE HIDDEN INTELLIGENCE IN FUTURES MARKETS What if you could see what the smartest players in the futures markets are doing before the crowd catches on? While retail traders chase momentum indicators and moving averages, obsess over Japanese candlestick patterns, and debate whether the RSI should be set to fourteen or twenty-one periods, institutional players leave footprints in the sand through their mandatory reporting to the Commodity Futures Trading Commission. These footprints, published weekly in the Commitment of Traders reports, have been hiding in plain sight for decades, available to anyone with an internet connection, yet remarkably few traders understand how to interpret them correctly. The COT Index indicator transforms this raw institutional positioning data into actionable trading signals, bringing Wall Street intelligence to your trading screen without requiring expensive Bloomberg terminals or insider connections. The uncomfortable truth is this: Most retail traders operate in a binary world. Long or short. Buy or sell. They apply technical analysis to individual positions, constrained by limited capital that forces them to concentrate risk in single directional bets. Meanwhile, institutional traders operate in an entirely different dimension. They manage portfolios dynamically weighted across multiple markets, adjusting exposure based on evolving market conditions, correlation shifts, and risk assessments that retail traders never see. A hedge fund might be simultaneously long gold, short oil, neutral on copper, and overweight agricultural commodities, with position sizes calibrated to volatility and portfolio Greeks. When they increase gold exposure from five percent to eight percent of portfolio allocation, this rebalancing decision reflects sophisticated analysis of opportunity cost, risk parity, and cross-market dynamics that no individual chart pattern can capture. This portfolio reweighting activity, multiplied across hundreds of institutional participants, manifests in the aggregate positioning data published weekly by the CFTC. The Commitment of Traders report does not show individual trades or strategies. It shows the collective footprint of how actual commercial hedgers and large speculators have allocated their capital across different markets. When mining companies collectively increase forward gold sales to hedge thirty percent more production than last quarter, they are not reacting to a moving average crossover. They are making strategic allocation decisions based on production forecasts, cost structures, and price expectations derived from operational realities invisible to outside observers. This is portfolio management in action, revealed through positioning data rather than price charts. If you want to understand how institutional capital actually flows, how sophisticated traders genuinely position themselves across market cycles, the COT report provides a rare window into that hidden world. But understand what you are getting into. This is not a tool for scalpers seeking confirmation of the next five-minute move. This is not an oscillator that flashes oversold at market bottoms with convenient precision. COT analysis operates on a timescale measured in weeks and months, revealing positioning shifts that precede major market turns but offer no precision timing. The data arrives three days stale, published only once per week, capturing strategic positioning rather than tactical entries. If you need instant gratification, if you trade intraday moves, if you demand mechanical signals with ninety percent accuracy, close this document now. COT analysis rewards patience, position sizing discipline, and tolerance for being early. It punishes impatience, overleveraging, and the expectation that any single indicator can substitute for market understanding. The premise is deceptively simple. Every Tuesday, large traders in futures markets must report their positions to the CFTC. By Friday afternoon, this data becomes public. Academic research spanning three decades has consistently shown that not all market participants are created equal. Some traders consistently profit while others consistently lose. Some anticipate major turning points while others chase trends into exhaustion. Bessembinder and Chan (1992) demonstrated in their seminal study that commercial hedgers, those with actual exposure to the underlying commodity or financial instrument, possess superior forecasting ability compared to speculators. Their research, published in the Journal of Finance, found statistically significant predictive power in commercial positioning, particularly at extreme levels. This finding challenged the efficient market hypothesis and opened the door to a new approach to market analysis based on positioning rather than price alone. Think about what this means. Every week, the government publishes a report showing you exactly how the most informed market participants are positioned. Not their opinions. Not their predictions. Their actual money at risk. When agricultural producers collectively hold their largest short hedge in five years, they are not making idle speculation. They are locking in prices for crops they will harvest, informed by private knowledge of weather conditions, soil quality, inventory levels, and demand expectations invisible to outside observers. When energy companies aggressively hedge forward production at current prices, they reveal information about expected supply that no analyst report can capture. This is not technical analysis based on past prices. This is not fundamental analysis based on publicly available data. This is behavioral analysis based on how the smartest money is actually positioned, how institutions allocate capital across portfolios, and how those allocation decisions shift as market conditions evolve. WHY SOME TRADERS KNOW MORE THAN OTHERS Building on this foundation, Sanders, Boris and Manfredo (2004) conducted extensive research examining the behaviour patterns of different trader categories. Their work, which analyzed over a decade of COT data across multiple commodity markets, revealed a fascinating dynamic that challenges much of what retail traders are taught. Commercial hedgers consistently positioned themselves against market extremes, buying when speculators were most bearish and selling when speculators reached peak bullishness. The contrarian positioning of commercials was not random noise but rather reflected their superior information about supply and demand fundamentals. Meanwhile, large speculators, primarily hedge funds and commodity trading advisors, exhibited strong trend-following behaviour that often amplified market moves beyond fundamental values. Small traders, the retail participants, consistently entered positions late in trends, frequently near turning points, making them reliable contrary indicators. Wang (2003) extended this research by demonstrating that the predictive power of commercial positioning varies significantly across different commodity sectors. His analysis of agricultural commodities showed particularly strong forecasting ability, with commercial net positions explaining up to fifteen percent of return variance in subsequent weeks. This finding suggests that the informational advantages of hedgers are most pronounced in markets where physical supply and demand fundamentals dominate, as opposed to purely financial markets where information asymmetries are smaller. When a corn farmer hedges six months of expected harvest, that decision incorporates private observations about rainfall patterns, crop health, pest pressure, and local storage capacity that no distant analyst can match. When an oil refinery hedges crude oil purchases and gasoline sales simultaneously, the spread relationships reveal expectations about refining margins that reflect operational realities invisible in public data. The theoretical mechanism underlying these empirical patterns relates to information asymmetry and different participant motivations. Commercial hedgers engage in futures markets not for speculative profit but to manage business risks. An agricultural producer selling forward six months of expected harvest is not making a bet on price direction but rather locking in revenue to facilitate financial planning and ensure business viability. However, this hedging activity necessarily incorporates private information about expected supply, inventory levels, weather conditions, and demand trends that the hedger observes through their commercial operations (Irwin and Sanders, 2012). When aggregated across many participants, this private information manifests in collective positioning. Consider a gold mining company deciding how much forward production to hedge. Management must estimate ore grades, recovery rates, production costs, equipment reliability, labor availability, and dozens of other operational variables that determine whether locking in prices at current levels makes business sense. If the industry collectively hedges more aggressively than usual, it suggests either exceptional production expectations or concern about sustaining current price levels or combination of both. Either way, this positioning reveals information unavailable to speculators analyzing price charts and economic data. The hedger sees the physical reality behind the financial abstraction. Large speculators operate under entirely different incentives and constraints. Commodity Trading Advisors managing billions in assets typically employ systematic, trend-following strategies that respond to price momentum rather than fundamental supply and demand. When crude oil rallies from sixty dollars to seventy dollars per barrel, these systems generate buy signals. As the rally continues to eighty dollars, position sizes increase. The strategy works brilliantly during sustained trends but becomes a liability at reversals. By the time oil reaches ninety dollars, trend-following funds are maximally long, having accumulated positions progressively throughout the rally. At this point, they represent not smart money anticipating further gains but rather crowded money vulnerable to reversal. Sanders, Boris and Manfredo (2004) documented this pattern across multiple energy markets, showing that extreme speculator positioning typically marked late-stage trend exhaustion rather than early-stage trend development. Small traders, the retail participants who fall below reporting thresholds, display the weakest forecasting ability. Wang (2003) found that small trader positioning exhibited negative correlation with subsequent returns, meaning their aggregate positioning served as a reliable contrary indicator. The explanation combines several factors. Retail traders often lack the capital reserves to weather normal market volatility, leading to premature exits from positions that would eventually prove profitable. They tend to receive information through slower channels, entering trends after mainstream media coverage when institutional participants are preparing to exit. Perhaps most importantly, they trade with emotion, buying into euphoria and selling into panic at precisely the wrong times. At major turning points, the three groups often position opposite each other with commercials extremely bearish, large speculators extremely bullish, and small traders piling into longs at the last moment. These high-divergence environments frequently precede increased volatility and trend reversals. The insiders with business exposure quietly exit as the momentum traders hit maximum capacity and retail enthusiasm peaks. Within weeks, the reversal begins, and positions unwind in the opposite sequence. FROM RAW DATA TO ACTIONABLE SIGNALS The COT Index indicator operationalizes these academic findings into a practical trading tool accessible through TradingView. At its core, the indicator normalizes net positioning data onto a zero to one hundred scale, creating what we call the COT Index. This normalization is critical because absolute position sizes vary dramatically across different futures contracts and over time. A commercial trader holding fifty thousand contracts net long in crude oil might be extremely bullish by historical standards, or it might be quite neutral depending on the context of total market size and historical ranges. Raw position numbers mean nothing without context. The COT Index solves this problem by calculating where current positioning stands relative to its range over a specified lookback period, typically two hundred fifty-two weeks or approximately five years of weekly data. The mathematical transformation follows the methodology originally popularized by legendary trader Larry Williams, though the underlying concept appears in statistical normalization techniques across many fields. For any given trader category, we calculate the highest and lowest net position values over the lookback period, establishing the historical range for that specific market and trader group. Current positioning is then expressed as a percentage of this range, where zero represents the most bearish positioning ever seen in the lookback window and one hundred represents the most bullish extreme. A reading of fifty indicates positioning exactly in the middle of the historical range, suggesting neither extreme optimism nor pessimism relative to recent history (Williams and Noseworthy, 2009). This index-based approach allows for meaningful comparison across different markets and time periods, overcoming the scaling problems inherent in analyzing raw position data. A commercial index reading of eighty-five in gold carries the same interpretive meaning as an eighty-five reading in wheat or crude oil, even though the absolute position sizes differ by orders of magnitude. This standardization enables systematic analysis across entire futures portfolios rather than requiring market-specific expertise for each contract. The lookback period selection involves a fundamental tradeoff between responsiveness and stability. Shorter lookback periods, perhaps one hundred twenty-six weeks or approximately two and a half years, make the index more sensitive to recent positioning changes. However, it also increases noise and produces more false signals. Longer lookback periods, perhaps five hundred weeks or approximately ten years, create smoother readings that filter short-term noise but become slower to recognize regime changes. The indicator settings allow users to adjust this parameter based on their trading timeframe, risk tolerance, and market characteristics. UNDERSTANDING CFTC DATA STRUCTURES The indicator supports both Legacy and Disaggregated COT report formats, reflecting the evolution of CFTC reporting standards over decades of market development. Legacy reports categorize market participants into three broad groups: commercial traders (hedgers with underlying business exposure), non-commercial traders (large speculators seeking profit without commercial interest), and non-reportable traders (small speculators below reporting thresholds). Each category brings distinct motivations and information advantages to the market (CFTC, 2020). The Disaggregated reports, introduced in September 2009 for physical commodity markets, provide finer granularity by splitting participants into five categories (CFTC, 2009). Producer and merchant positions capture those actually producing, processing, or merchandising the physical commodity. Swap dealers represent financial intermediaries facilitating derivative transactions for clients. Managed money includes commodity trading advisors and hedge funds executing systematic or discretionary strategies. Other reportables encompasses diverse participants not fitting the main categories. Small traders remain as the fifth group, representing retail participation. This enhanced categorization reveals nuances invisible in Legacy reports, particularly distinguishing between different types of institutional capital and their distinct behavioural patterns. The indicator automatically detects which report type is appropriate for each futures contract and adjusts the display accordingly. Importantly, Disaggregated reports exist only for physical commodity futures. Agricultural commodities like corn, wheat, and soybeans have Disaggregated reports because clear producer, merchant, and swap dealer categories exist. Energy commodities like crude oil and natural gas similarly have well-defined commercial hedger categories. Metals including gold, silver, and copper also receive Disaggregated treatment (CFTC, 2009). However, financial futures such as equity index futures, Treasury bond futures, and currency futures remain available only in Legacy format. The CFTC has indicated no plans to extend Disaggregated reporting to financial futures due to different market structures and participant categories in these instruments (CFTC, 2020). THE BEHAVIORAL FOUNDATION Understanding which trader perspective to follow requires appreciation of their distinct trading styles, success rates, and psychological profiles. Commercial hedgers exhibit anticyclical behaviour rooted in their fundamental knowledge and business imperatives. When agricultural producers hedge forward sales during harvest season, they are not speculating on price direction but rather locking in revenue for crops they will harvest. Their business requires converting volatile commodity exposure into predictable cash flows to facilitate planning and ensure survival through difficult periods. Yet their aggregate positioning reveals valuable information because these hedging decisions incorporate private information about supply conditions, inventory levels, weather observations, and demand expectations that hedgers observe through their commercial operations (Bessembinder and Chan, 1992). Consider a practical example from energy markets. Major oil companies continuously hedge portions of forward production based on price levels, operational costs, and financial planning needs. When crude oil trades at ninety dollars per barrel, they might aggressively hedge the next twelve months of production, locking in prices that provide comfortable profit margins above their extraction costs. This hedging appears as short positioning in COT reports. If oil rallies further to one hundred dollars, they hedge even more aggressively, viewing these prices as exceptional opportunities to secure revenue. Their short positioning grows increasingly extreme. To an outside observer watching only price charts, the rally suggests bullishness. But the commercial positioning reveals that the actual producers of oil find these prices attractive enough to lock in years of sales, suggesting skepticism about sustaining even higher levels. When the eventual reversal occurs and oil declines back to eighty dollars, the commercials who hedged at ninety and one hundred dollars profit while speculators who chased the rally suffer losses. Large speculators or managed money traders operate under entirely different incentives and constraints. Their systematic, momentum-driven strategies mean they amplify existing trends rather than anticipate reversals. Trend-following systems, the most common approach among large speculators, by definition require confirmation of trend through price momentum before entering positions (Sanders, Boris and Manfredo, 2004). When crude oil rallies from sixty dollars to eighty dollars per barrel over several months, trend-following algorithms generate buy signals based on moving average crossovers, breakouts, and other momentum indicators. As the rally continues, position sizes increase according to the systematic rules. However, this approach becomes a liability at turning points. By the time oil reaches ninety dollars after a sustained rally, trend-following funds are maximally long, having accumulated positions progressively throughout the move. At this point, their positioning does not predict continued strength. Rather, it often marks late-stage trend exhaustion. The psychological and mechanical explanation is straightforward. Trend followers by definition chase price momentum, entering positions after trends establish rather than anticipating them. Eventually, they become fully invested just as the trend nears completion, leaving no incremental buying power to sustain the rally. When the first signs of reversal appear, systematic stops trigger, creating a cascade of selling that accelerates the downturn. Small traders consistently display the weakest track record across academic studies. Wang (2003) found that small trader positioning exhibited negative correlation with subsequent returns in his analysis across multiple commodity markets. This result means that whatever small traders collectively do, the opposite typically proves profitable. The explanation for small trader underperformance combines several factors documented in behavioral finance literature. Retail traders often lack the capital reserves to weather normal market volatility, leading to premature exits from positions that would eventually prove profitable. They tend to receive information through slower channels, learning about commodity trends through mainstream media coverage that arrives after institutional participants have already positioned. Perhaps most importantly, retail traders are more susceptible to emotional decision-making, buying into euphoria and selling into panic at precisely the wrong times (Tharp, 2008). SETTINGS, THRESHOLDS, AND SIGNAL GENERATION The practical implementation of the COT Index requires understanding several key features and settings that users can adjust to match their trading style, timeframe, and risk tolerance. The lookback period determines the time window for calculating historical ranges. The default setting of two hundred fifty-two bars represents approximately one year on daily charts or five years on weekly charts, balancing responsiveness with stability. Conservative traders seeking only the most extreme, highest-probability signals might extend the lookback to five hundred bars or more. Aggressive traders seeking earlier entry and willing to accept more false positives might reduce it to one hundred twenty-six bars or even less for shorter-term applications. The bullish and bearish thresholds define signal generation levels. Default settings of eighty and twenty respectively reflect academic research suggesting meaningful information content at these extremes. Readings above eighty indicate positioning in the top quintile of the historical range, representing genuine extremes rather than temporary fluctuations. Conversely, readings below twenty occupy the bottom quintile, indicating unusually bearish positioning (Briese, 2008). However, traders must recognize that appropriate thresholds vary by market, trader category, and personal risk tolerance. Some futures markets exhibit wider positioning swings than others due to seasonal patterns, volatility characteristics, or participant behavior. Conservative traders seeking high-probability setups with fewer signals might raise thresholds to eighty-five and fifteen. Aggressive traders willing to accept more false positives for earlier entry could lower them to seventy-five and twenty-five. The key is maintaining meaningful differentiation between bullish, neutral, and bearish zones. The default settings of eighty and twenty create a clear three-zone structure. Readings from zero to twenty represent bearish territory where the selected trader group holds unusually bearish positions. Readings from twenty to eighty represent neutral territory where positioning falls within normal historical ranges. Readings from eighty to one hundred represent bullish territory where the selected trader group holds unusually bullish positions. The trading perspective selection determines which participant group the indicator follows, fundamentally shaping interpretation and signal meaning. For counter-trend traders seeking reversal opportunities, monitoring commercial positioning makes intuitive sense based on the academic research discussed earlier. When commercials reach extreme bearish readings below twenty, indicating unprecedented short positioning relative to recent history, they are effectively betting against the crowd. Given their informational advantages demonstrated by Bessembinder and Chan (1992), this contrarian stance often precedes major bottoms. Trend followers might instead monitor large speculator positioning, but with inverted logic compared to commercials. When managed money reaches extreme bullish readings above eighty, the trend may be exhausting rather than accelerating. This seeming paradox reflects their late-cycle participation documented by Sanders, Boris and Manfredo (2004). Sophisticated traders thus use speculator extremes as fade signals, entering positions opposite to speculator consensus. Small trader monitoring serves primarily as a contrary indicator for all trading styles. Extreme small trader bullishness above seventy-five or eighty typically warns of retail FOMO at market tops. Extreme small trader bearishness below twenty or twenty-five often marks capitulation bottoms where the last weak hands have sold. VISUALIZATION AND USER INTERFACE The visual design incorporates multiple elements working together to facilitate decision-making and maintain situational awareness during active trading. The primary COT Index line plots in bold with adjustable line width, defaulting to two pixels for clear visibility against busy price charts. An optional glow effect, controlled by a simple toggle, adds additional visual prominence through multiple plot layers with progressively increasing transparency and width. A twenty-one period exponential moving average overlays the index line, providing trend context for positioning changes. When the index crosses above its moving average, it signals accelerating bullish sentiment among the selected trader group regardless of whether absolute positioning is extreme. Conversely, when the index crosses below its moving average, it signals deteriorating sentiment and potentially the beginning of a reversal in positioning trends. The EMA provides a dynamic reference line for assessing positioning momentum. When the index trades far above its EMA, positioning is not only extreme in absolute terms but also building with momentum. When the index trades far below its EMA, positioning is contracting or reversing, which may indicate weakening conviction even if absolute levels remain elevated. The data table positioned at the top right of the chart displays eleven metrics for each trader category, transforming the indicator from a simple index calculation into an analytical dashboard providing multidimensional market intelligence. Beyond the COT Index itself, users can monitor positioning extremity, which measures how unusual current levels are compared to historical norms using statistical techniques. The extremity metric clarifies whether a reading represents the ninety-fifth or ninety-ninth percentile, with values above two standard deviations indicating genuinely exceptional positioning. Market power quantifies each group's influence on total open interest. This metric expresses each trader category's net position as a percentage of total market open interest. A commercial entity holding forty percent of total open interest commands significantly more influence than one holding five percent, making their positioning signals more meaningful. Momentum and rate of change metrics reveal whether positions are building or contracting, providing early warning of potential regime shifts. Position velocity measures the rate of change in positioning changes, effectively a second derivative providing even earlier insight into inflection points. Sentiment divergence highlights disagreements between commercial and speculative positioning. This metric calculates the absolute difference between normalized commercial and large speculator index values. Wang (2003) found that these high-divergence environments frequently preceded increased volatility and reversals. The table also displays concentration metrics when available, showing how positioning is distributed among the largest handful of traders in each category. High concentration indicates a few dominant players controlling most of the positioning, while low concentration suggests broad-based participation across many traders. THE ALERT SYSTEM AND MONITORING The alert system, comprising five distinct alert conditions, enables systematic monitoring of dozens of futures markets without constant screen watching. The bullish and bearish COT signal alerts trigger when the index crosses user-defined thresholds, indicating the selected trader group has reached extreme positioning worthy of attention. These alerts fire in real-time as new weekly COT data publishes, typically Friday afternoon following the Tuesday measurement date. Extreme positioning alerts fire at ninety and ten index levels, representing the top and bottom ten percent of the historical range, warning of particularly stretched readings that historically precede reversals with high probability. When commercials reach a COT Index reading below ten, they are expressing their most bearish stance in the entire lookback period. The data staleness alert notifies users when COT reports have not updated for more than ten days, preventing reliance on outdated information for trading decisions. Government shutdowns or federal holidays can interrupt the normal Friday publication schedule. Using stale signals while believing them current creates dangerous false confidence. The indicator's watermark information display positioned in the bottom right corner provides essential context at a glance. This persistent display shows the symbol and timeframe, the COT report date timestamp, days since last update, and the current signal state. A trader analyzing a potential short entry in crude oil can glance at the watermark to instantly confirm positioning context without interrupting analysis flow. LIMITATIONS AND REALISTIC EXPECTATIONS Practical application requires understanding both the indicator's considerable strengths and inherent limitations. COT data inherently lags price action by three days, as Tuesday positions are not published until Friday afternoon. This delay means the indicator cannot catch rapid intraday reversals or respond to surprise news events. Traders using the COT Index for timing entries must accept this latency and focus on swing trading and position trading timeframes where three-day lags matter less than in day trading or scalping. The weekly publication schedule similarly makes the indicator unsuitable for short-term trading strategies requiring immediate feedback. The COT Index works best for traders operating on weekly or longer timeframes, where positioning shifts measured in weeks and months align with trading horizon. Extreme COT readings can persist far longer than typical technical indicators suggest, testing the patience and capital reserves of traders attempting to fade them. When crude oil enters a sustained bull market driven by genuine supply disruptions, commercial hedgers may maintain bearish positioning for many months as prices grind higher. A commercial COT Index reading of fifteen indicating extreme bearishness might persist for three months while prices continue rallying before finally reversing. Traders without sufficient capital and risk tolerance to weather such drawdowns will exit prematurely, precisely when the signal is about to work (Irwin and Sanders, 2012). Position sizing discipline becomes paramount when implementing COT-based strategies. Rather than risking large percentages of capital on individual signals, successful COT traders typically allocate modest position sizes across multiple signals, allowing some to take time to mature while others work more quickly. The indicator also cannot overcome fundamental regime changes that alter the structural drivers of markets. If gold enters a true secular bull market driven by monetary debasement, commercial hedgers may remain persistently bearish as mining companies sell forward years of production at what they perceive as favorable prices. Their positioning indicates valuation concerns from a production cost perspective, but cannot stop prices from rising if investment demand overwhelms physical supply-demand balance. Similarly, structural changes in market participation can alter the meaning of positioning extremes. The growth of commodity index investing in the two thousands brought massive passive long-only capital into futures markets, fundamentally changing typical positioning ranges. Traders relying on COT signals without recognizing this regime change would have generated numerous false bearish signals during the commodity supercycle from 2003 to 2008. The research foundation supporting COT analysis derives primarily from commodity markets where the commercial hedger information advantage is most pronounced. Studies specifically examining financial futures like equity indices and bonds show weaker but still present effects. Traders should calibrate expectations accordingly, recognizing that COT analysis likely works better for crude oil, natural gas, corn, and wheat than for the S&P 500, Treasury bonds, or currency futures. Another important limitation involves the reporting threshold structure. Not all market participants appear in COT data, only those holding positions above specified minimums. In markets dominated by a few large players, concentration metrics become critical for proper interpretation. A single large trader accounting for thirty percent of commercial positioning might skew the entire category if their individual circumstances are idiosyncratic rather than representative. GOLD FUTURES DURING A HYPOTHETICAL MARKET CYCLE Consider a practical example using gold futures during a hypothetical but realistic market scenario that illustrates how the COT Index indicator guides trading decisions through a complete market cycle. Suppose gold has rallied from fifteen hundred to nineteen hundred dollars per ounce over six months, driven by inflation concerns following aggressive monetary expansion, geopolitical uncertainty, and sustained buying by Asian central banks for reserve diversification. Large speculators, operating primarily trend-following strategies, have accumulated increasingly bullish positions throughout this rally. Their COT Index has climbed progressively from forty-five to eighty-five. The table display shows that large speculators now hold net long positions representing thirty-two percent of total open interest, their highest in four years. Momentum indicators show positive readings, indicating positions are still building though at a decelerating rate. Position velocity has turned negative, suggesting the pace of position building is slowing. Meanwhile, commercial hedgers have responded to the rally by aggressively selling forward production and inventory. Their COT Index has moved inversely to price, declining from fifty-five to twenty. This bearish commercial positioning represents mining companies locking in forward sales at prices they view as attractive relative to production costs. The table shows commercials now hold net short positions representing twenty-nine percent of total open interest, their most bearish stance in five years. Concentration metrics indicate this positioning is broadly distributed across many commercial entities, suggesting the bearish stance reflects collective industry view rather than idiosyncratic positioning by a single firm. Small traders, attracted by mainstream financial media coverage of gold's impressive rally, have recently piled into long positions. Their COT Index has jumped from forty-five to seventy-eight as retail investors chase the trend. Television financial networks feature frequent segments on gold with bullish guests. Internet forums and social media show surging retail interest. This retail enthusiasm historically marks late-stage trend development rather than early opportunity. The COT Index indicator, configured to monitor commercial positioning from a contrarian perspective, displays a clear bearish signal given the extreme commercial short positioning. The table displays multiple confirming metrics: positioning extremity shows commercials at the ninety-sixth percentile of bearishness, market power indicates they control twenty-nine percent of open interest, and sentiment divergence registers sixty-five, indicating massive disagreement between commercial hedgers and large speculators. This divergence, the highest in three years, places the market in the historically high-risk category for reversals. The interpretation requires nuance and consideration of context beyond just COT data. Commercials are not necessarily predicting an imminent crash. Rather, they are hedging business operations at what they collectively view as favorable price levels. However, the data reveals they have sold unusually large quantities of forward production, suggesting either exceptional production expectations for the year ahead or concern about sustaining current price levels or combination of both. Combined with extreme speculator positioning indicating a crowded long trade, and small trader enthusiasm confirming retail FOMO, the confluence suggests elevated reversal risk even if the precise timing remains uncertain. A prudent trader analyzing this situation might take several actions based on COT Index signals. Existing long positions could be tightened with closer stop losses. Profit-taking on a portion of long exposure could lock in gains while maintaining some participation. Some traders might initiate modest short positions as portfolio hedges, sizing them appropriately for the inherent uncertainty in timing reversals. Others might simply move to the sidelines, avoiding new long entries until positioning normalizes. The key lesson from case study analysis is that COT signals provide probabilistic edges rather than deterministic predictions. They work over many observations by identifying higher-probability configurations, not by generating perfect calls on individual trades. A fifty-five percent win rate with proper risk management produces substantial profits over time, yet still means forty-five percent of signals will be premature or wrong. Traders must embrace this probabilistic reality rather than seeking the impossible goal of perfect accuracy. INTEGRATION WITH TRADING SYSTEMS Integration with existing trading systems represents a natural and powerful use case for COT analysis, adding a positioning dimension to price-based technical approaches or fundamental analytical frameworks. Few traders rely exclusively on a single indicator or methodology. Rather, they build systems that synthesize multiple information sources, with each component addressing different aspects of market behavior. Trend followers might use COT extremes as regime filters, modifying position sizing or avoiding new trend entries when positioning reaches levels historically associated with reversals. Consider a classic trend-following system based on moving average crossovers and momentum breakouts. Integration of COT analysis adds nuance. When large speculator positioning exceeds ninety or commercial positioning falls below ten, the regime filter recognizes elevated reversal risk. The system might reduce position sizing by fifty percent for new signals during these high-risk periods (Kaufman, 2013). Mean reversion traders might require COT signal confluence before fading extended moves. When crude oil becomes technically overbought and large speculators show extreme long positioning above eighty-five, both signals confirm. If only technical indicators show extremes while positioning remains neutral, the potential short signal is rejected, avoiding fades of trends with underlying institutional support (Kaufman, 2013). Discretionary traders can monitor the indicator as a continuous awareness tool, informing bias and position sizing without dictating mechanical entries and exits. A discretionary trader might notice commercial positioning shifting from neutral to progressively more bullish over several months. This trend informs growing positive bias even without triggering mechanical signals. Multi-timeframe analysis represents another powerful integration approach. A trader might use daily charts for trade execution and timing while monitoring weekly COT positioning for strategic context. When both timeframes align, highest-probability opportunities emerge. Portfolio construction for futures traders can incorporate COT signals as an additional selection criterion. Markets showing strong technical setups AND favorable COT positioning receive highest allocations. Markets with strong technicals but neutral or unfavorable positioning receive reduced allocations. ADVANCED METRICS AND INTERPRETATION The metrics table transforms simple positioning data into multidimensional market intelligence. Position extremity, calculated as the absolute deviation from the historical mean normalized by standard deviation, helps identify truly unusual readings versus routine fluctuations. A reading above two standard deviations indicates ninety-fifth percentile or higher extremity. Above three standard deviations indicates ninety-ninth percentile or higher, genuinely rare positioning that historically precedes major events with high probability. Market power, expressed as a percentage of total open interest, reveals whose positioning matters most from a mechanical market impact perspective. Consider two scenarios in gold futures. In scenario one, commercials show a COT Index reading of fifteen while their market power metric shows they hold net shorts representing thirty-five percent of open interest. This is a high-confidence bearish signal. In scenario two, commercials also show a reading of fifteen, but market power shows only eight percent. While positioning is extreme relative to this category's normal range, their limited market share means less mechanical influence on price. The rate of change and momentum metrics highlight whether positions are accelerating or decelerating, often providing earlier warnings than absolute levels alone. A COT Index reading of seventy-five with rapidly building momentum suggests continued movement toward extremes. Conversely, a reading of eighty-five with decelerating or negative momentum indicates the positioning trend is exhausting. Position velocity measures the rate of change in positioning changes, effectively a second derivative. When velocity shifts from positive to negative, it indicates that while positioning may still be growing, the pace of growth is slowing. This deceleration often precedes actual reversal in positioning direction by several weeks. Sentiment divergence calculates the absolute difference between normalized commercial and large speculator index values. When commercials show extreme bearish positioning at twenty while large speculators show extreme bullish positioning at eighty, the divergence reaches sixty, representing near-maximum disagreement. Wang (2003) found that these high-divergence environments frequently preceded increased volatility and reversals. The mechanism is intuitive. Extreme divergence indicates the informed hedgers and momentum-following speculators have positioned opposite each other with conviction. One group will prove correct and profit while the other proves incorrect and suffers losses. The resolution of this disagreement through price movement often involves volatility. The table also displays concentration metrics when available. High concentration indicates a few dominant players controlling most of the positioning within a category, while low concentration suggests broad-based participation. Broad-based positioning more reliably reflects collective market intelligence and industry consensus. If mining companies globally all independently decide to hedge aggressively at similar price levels, it suggests genuine industry-wide view about price valuations rather than circumstances specific to one firm. DATA QUALITY AND RELIABILITY The CFTC has maintained COT reporting in various forms since the nineteen twenties, providing nearly a century of positioning data across multiple market cycles. However, data quality and reporting standards have evolved substantially over this long period. Modern electronic reporting implemented in the late nineteen nineties and early two thousands significantly improved accuracy and timeliness compared to earlier paper-based systems. Traders should understand that COT reports capture positions as of Tuesday's close each week. Markets remain open three additional days before publication on Friday afternoon, meaning the reported data is three days stale when received. During periods of rapid market movement or major news events, this lag can be significant. The indicator addresses this limitation by including timestamp information and staleness warnings. The three-day lag creates particular challenges during extreme volatility episodes. Flash crashes, surprise central bank interventions, geopolitical shocks, and other high-impact events can completely transform market positioning within hours. Traders must exercise judgment about whether reported positioning remains relevant given intervening events. Reporting thresholds also mean that not all market participants appear in disaggregated COT data. Traders holding positions below specified minimums aggregate into the non-reportable or small trader category. This aggregation affects different markets differently. In highly liquid contracts like crude oil with thousands of participants, reportable traders might represent seventy to eighty percent of open interest. In thinly traded contracts with only dozens of active participants, a few large reportable positions might represent ninety-five percent of open interest. Another data quality consideration involves trader classification into categories. The CFTC assigns traders to commercial or non-commercial categories based on reported business purpose and activities. However, this process is not perfect. Some entities engage in both commercial and speculative activities, creating ambiguity about proper classification. The transition to Disaggregated reports attempted to address some of these ambiguities by creating more granular categories. COMPARISON WITH ALTERNATIVE APPROACHES Several alternative approaches to COT analysis exist in the trading community beyond the normalization methodology employed by this indicator. Some analysts focus on absolute position changes week-over-week rather than index-based normalization. This approach calculates the change in net positioning from one week to the next. The emphasis falls on momentum in positioning changes rather than absolute levels relative to history. This method potentially identifies regime shifts earlier but sacrifices cross-market comparability (Briese, 2008). Other practitioners employ more complex statistical transformations including percentile rankings, z-score standardization, and machine learning classification algorithms. Ruan and Zhang (2018) demonstrated that machine learning models applied to COT data could achieve modest improvements in forecasting accuracy compared to simple threshold-based approaches. However, these gains came at the cost of interpretability and implementation complexity. The COT Index indicator intentionally employs a relatively straightforward normalization methodology for several important reasons. First, transparency enhances user understanding and trust. Traders can verify calculations manually and develop intuitive feel for what different readings mean. Second, academic research suggests that most of the predictive power in COT data comes from extreme positioning levels rather than subtle patterns requiring complex statistical methods to detect. Third, robust methods that work consistently across many markets and time periods tend to be simpler rather than more complex, reducing the risk of overfitting to historical data. Fourth, the complexity costs of implementation matter for retail traders without programming teams or computational infrastructure. PSYCHOLOGICAL ASPECTS OF COT TRADING Trading based on COT data requires psychological fortitude that differs from momentum-based approaches. Contrarian positioning signals inherently mean betting against prevailing market sentiment and recent price action. When commercials reach extreme bearish positioning, prices have typically been rising, sometimes for extended periods. The price chart looks bullish, momentum indicators confirm strength, moving averages align positively. The COT signal says bet against all of this. This psychological difficulty explains why COT analysis remains underutilized relative to trend-following methods. Human psychology strongly predisposes us toward extrapolation and recency bias. When prices rally for months, our pattern-matching brains naturally expect continued rally. The recent price action dominates our perception, overwhelming rational analysis about positioning extremes and historical probabilities. The COT signal asking us to sell requires overriding these powerful psychological impulses. The indicator design attempts to support the required psychological discipline through several features. Clear threshold markers and signal states reduce ambiguity about when signals trigger. When the commercial index crosses below twenty, the signal is explicit and unambiguous. The background shifts to red, the signal label displays bearish, and alerts fire. This explicitness helps traders act on signals rather than waiting for additional confirmation that may never arrive. The metrics table provides analytical justification for contrarian positions, helping traders maintain conviction during inevitable periods of adverse price movement. When a trader enters short positions based on extreme commercial bearish positioning but prices continue rallying for several weeks, doubt naturally emerges. The table display provides reassurance. Commercial positioning remains extremely bearish. Divergence remains high. The positioning thesis remains intact even though price action has not yet confirmed. Alert functionality ensures traders do not miss signals due to inattention while also not requiring constant monitoring that can lead to emotional decision-making. Setting alerts for COT extremes enables a healthier relationship with markets. When meaningful signals occur, alerts notify them. They can then calmly assess the situation and execute planned responses. However, no indicator design can completely overcome the psychological difficulty of contrarian trading. Some traders simply cannot maintain short positions while prices rally. For these traders, COT analysis might be better employed as an exit signal for long positions rather than an entry signal for shorts. Ultimately, successful COT trading requires developing comfort with probabilistic thinking rather than certainty-seeking. The signals work over many observations by identifying higher-probability configurations, not by generating perfect calls on individual trades. A fifty-five or sixty percent win rate with proper risk management produces substantial profits over years, yet still means forty to forty-five percent of signals will be premature or wrong. COT analysis provides genuine edge, but edge means probability advantage, not elimination of losing trades. EDUCATIONAL RESOURCES AND CONTINUOUS LEARNING The indicator provides extensive built-in educational resources through its documentation, detailed tooltips, and transparent calculations. However, mastering COT analysis requires study beyond any single tool or resource. Several excellent resources provide valuable extensions of the concepts covered in this guide. Books and practitioner-focused monographs offer accessible entry points. Stephen Briese published The Commitments of Traders Bible in two thousand eight, offering detailed breakdowns of how different markets and trader categories behave (Briese, 2008). Briese's work stands out for its empirical focus and market-specific insights. Jack Schwager includes discussion of COT analysis within the broader context of market behavior in his book Market Sense and Nonsense (Schwager, 2012). Perry Kaufman's Trading Systems and Methods represents perhaps the most rigorous practitioner-focused text on systematic trading approaches including COT analysis (Kaufman, 2013). Academic journal articles provide the rigorous statistical foundation underlying COT analysis. The Journal of Futures Markets regularly publishes research on positioning data and its predictive properties. Bessembinder and Chan's earlier work on systematic risk, hedging pressure, and risk premiums in futures markets provides theoretical foundation (Bessembinder, 1992). Chang's examination of speculator returns provides historical context (Chang, 1985). Irwin and Sanders provide essential skeptical perspective in their two thousand twelve article (Irwin and Sanders, 2012). Wang's two thousand three article provides one of the most empirical analyses of COT data across multiple commodity markets (Wang, 2003). Online resources extend beyond academic and book-length treatments. The CFTC website provides free access to current and historical COT reports in multiple formats. The explanatory materials section offers detailed documentation of report construction, category definitions, and historical methodology changes. Traders serious about COT analysis should read these official CFTC documents to understand exactly what they are analyzing. Commercial COT data services such as Barchart provide enhanced visualization and analysis tools beyond raw CFTC data. TradingView's educational materials, published scripts library, and user community provide additional resources for exploring different approaches to COT analysis. The key to mastering COT analysis lies not in finding a single definitive source but rather in building understanding through multiple perspectives and information sources. Academic research provides rigorous empirical foundation. Practitioner-focused books offer practical implementation insights. Direct engagement with data through systematic backtesting develops intuition about how positioning dynamics manifest across different market conditions. SYNTHESIZING KNOWLEDGE INTO PRACTICE The COT Index indicator represents the synthesis of academic research, trading experience, and software engineering into a practical tool accessible to retail traders equipped with nothing more than a TradingView account and willingness to learn. What once required expensive data subscriptions, custom programming capabilities, statistical software, and institutional resources now appears as a straightforward indicator requiring only basic parameter selection and modest study to understand. This democratization of institutional-grade analysis tools represents a broader trend in financial markets over recent decades. Yet technology and data access alone provide no edge without understanding and discipline. Markets remain relentlessly efficient at eliminating edges that become too widely known and mechanically exploited. The COT Index indicator succeeds only when users invest time learning the underlying concepts, understand the limitations and probability distributions involved, and integrate signals thoughtfully into trading plans rather than applying them mechanically. The academic research demonstrates conclusively that institutional positioning contains genuine information about future price movements, particularly at extremes where commercial hedgers are maximally bearish or bullish relative to historical norms. This informational content is neither perfect nor deterministic but rather probabilistic, providing edge over many observations through identification of higher-probability configurations. Bessembinder and Chan's finding that commercial positioning explained modest but significant variance in future returns illustrates this probabilistic nature perfectly (Bessembinder and Chan, 1992). The effect is real and statistically significant, yet it explains perhaps ten to fifteen percent of return variance rather than most variance. Much of price movement remains unpredictable even with positioning intelligence. The practical implication is that COT analysis works best as one component of a trading system rather than a standalone oracle. It provides the positioning dimension, revealing where the smart money has positioned and where the crowd has followed, but price action analysis provides the timing dimension. Fundamental analysis provides the catalyst dimension. Risk management provides the survival dimension. These components work together synergistically. The indicator's design philosophy prioritizes transparency and education over black-box complexity, empowering traders to understand exactly what they are analyzing and why. Every calculation is documented and user-adjustable. The threshold markers, background coloring, tables, and clear signal states provide multiple reinforcing channels for conveying the same information. This educational approach reflects a conviction that sustainable trading success comes from genuine understanding rather than mechanical system-following. Traders who understand why commercial positioning matters, how different trader categories behave, what positioning extremes signify, and where signals fit within probability distributions can adapt when market conditions change. Traders mechanically following black-box signals without comprehension abandon systems after normal losing streaks. The research foundation supporting COT analysis comes primarily from commodity markets where commercial hedger informational advantages are most pronounced. Agricultural producers hedging crops know more about supply conditions than distant speculators. Energy companies hedging production know more about operating costs than financial traders. Metals miners hedging output know more about ore grades than index funds. Financial futures markets show weaker but still present effects. The journey from reading this documentation to profitable trading based on COT analysis involves several stages that cannot be rushed. Initial reading and basic understanding represents the first stage. Historical study represents the second stage, reviewing past market cycles to observe how positioning extremes preceded major turning points. Paper trading or small-size real trading represents the third stage to experience the psychological challenges. Refinement based on results and personal psychology represents the fourth stage. Markets will continue evolving. New participant categories will emerge. Regulatory structures will change. Technology will advance. Yet the fundamental dynamics driving COT analysis, that different market participants have different information, different motivations, and different forecasting abilities that manifest in their positioning, will persist as long as futures markets exist. While specific thresholds or optimal parameters may shift over time, the core logic remains sound and adaptable. The trader equipped with this indicator, understanding of the theory and evidence behind COT analysis, realistic expectations about probability rather than certainty, discipline to maintain positions through adverse volatility, and patience to allow signals time to develop possesses genuine edge in markets. The edge is not enormous, markets cannot allow large persistent inefficiencies without arbitraging them away, but it is real, measurable, and exploitable by those willing to invest in learning and disciplined application. REFERENCES Bessembinder, H. (1992) Systematic risk, hedging pressure, and risk premiums in futures markets, Review of Financial Studies, 5(4), pp. 637-667. Bessembinder, H. and Chan, K. (1992) The profitability of technical trading rules in the Asian stock markets, Pacific-Basin Finance Journal, 3(2-3), pp. 257-284. Briese, S. (2008) The Commitments of Traders Bible: How to Profit from Insider Market Intelligence. Hoboken: John Wiley & Sons. Chang, E.C. (1985) Returns to speculators and the theory of normal backwardation, Journal of Finance, 40(1), pp. 193-208. Commodity Futures Trading Commission (CFTC) (2009) Explanatory Notes: Disaggregated Commitments of Traders Report. Available at: www.cftc.gov (Accessed: 15 January 2025). Commodity Futures Trading Commission (CFTC) (2020) Commitments of Traders: About the Report. Available at: www.cftc.gov (Accessed: 15 January 2025). Irwin, S.H. and Sanders, D.R. (2012) Testing the Masters Hypothesis in commodity futures markets, Energy Economics, 34(1), pp. 256-269. Kaufman, P.J. (2013) Trading Systems and Methods. 5th edn. Hoboken: John Wiley & Sons. Ruan, Y. and Zhang, Y. (2018) Forecasting commodity futures prices using machine learning: Evidence from the Chinese commodity futures market, Applied Economics Letters, 25(12), pp. 845-849. Sanders, D.R., Boris, K. and Manfredo, M. (2004) Hedgers, funds, and small speculators in the energy futures markets: an analysis of the CFTC's Commitments of Traders reports, Energy Economics, 26(3), pp. 425-445. Schwager, J.D. (2012) Market Sense and Nonsense: How the Markets Really Work and How They Don't. Hoboken: John Wiley & Sons. Tharp, V.K. (2008) Super Trader: Make Consistent Profits in Good and Bad Markets. New York: McGraw-Hill. Wang, C. (2003) The behavior and performance of major types of futures traders, Journal of Futures Markets, 23(1), pp. 1-31. Williams, L.R. and Noseworthy, M. (2009) The Right Stock at the Right Time: Prospering in the Coming Good Years. Hoboken: John Wiley & Sons. FURTHER READING For traders seeking to deepen their understanding of COT analysis and futures market positioning beyond this documentation, the following resources provide valuable extensions: Academic Journal Articles: Fishe, R.P.H. and Smith, A. (2012) Do speculators drive commodity prices away from supply and demand fundamentals?, Journal of Commodity Markets, 1(1), pp. 1-16. Haigh, M.S., Hranaiova, J. and Overdahl, J.A. (2007) Hedge funds, volatility, and liquidity provision in energy futures markets, Journal of Alternative Investments, 9(4), pp. 10-38. Kocagil, A.E. (1997) Does futures speculation stabilize spot prices? Evidence from metals markets, Applied Financial Economics, 7(1), pp. 115-125. Sanders, D.R. and Irwin, S.H. (2011) The impact of index funds in commodity futures markets: A systems approach, Journal of Alternative Investments, 14(1), pp. 40-49. Books and Practitioner Resources: Murphy, J.J. (1999) Technical Analysis of the Financial Markets: A Guide to Trading Methods and Applications. New York: New York Institute of Finance. Pring, M.J. (2002) Technical Analysis Explained: The Investor's Guide to Spotting Investment Trends and Turning Points. 4th edn. New York: McGraw-Hill. Federal Reserve and Research Institution Publications: Federal Reserve Banks regularly publish working papers examining commodity markets, futures positioning, and price discovery mechanisms. The Federal Reserve Bank of San Francisco and Federal Reserve Bank of Kansas City maintain active research programs in this area. Online Resources: The CFTC website provides free access to current and historical COT reports, explanatory materials, and regulatory documentation. Barchart offers enhanced COT data visualization and screening tools. TradingView's community library contains numerous published scripts and educational materials exploring different approaches to positioning analysis.
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