Alt Szn Oracle - Institutional GradeThe Alt Szn Oracle is a macro-level indicator built to help traders front-run altseason by tracking liquidity, dominance rotation, sentiment, and capital flows—all in one signal. It’s designed for those who don’t just chase pumps, but want to understand when the tide is turning and why. This tool doesn't predict specific coin breakouts—it tells you when the market as a whole is gearing up to rotate into higher beta assets like altcoins, including memes and microcaps.
The index consolidates ten macro inputs into a normalized, smoothed score from 0–100. These include Bitcoin and Ethereum dominance, ETH/BTC, altcoin market cap (Total3), relative volume flows, and stablecoin supply (USDT, USDC, DAI)—which act as proxies for risk-on appetite and dry powder entering the system. It also incorporates manually updated sentiment metrics from Google Trends and the Fear & Greed Index, giving it a behavioral edge that most indicators lack.
The logic is simple but powerful: when BTC dominance is falling, ETH/BTC is rising, altcoin volume increases relative to BTC/ETH, and stablecoins start moving—you're likely in the early innings of rotation. The index is also filtered through a volatility threshold and smoothed with an EMA to eliminate chop and fakeouts.
Use this indicator on macro charts like TOTAL3, TOTAL2, or ETHBTC to gauge market health, or overlay it on specific coins like PEPE, DOGE, or SOL to confirm if the tide is in your favor. Interpreting the score is straightforward: readings above 80 suggest euphoria and signal it’s time to de-risk, 60–80 indicates expansion and confirms altseason is underway, 40–60 is neutral, and 20–40 is a capitulation zone where smart money accumulates.
What sets this apart is that it doesn’t just track price—it reflects the flow of capital, the positioning of liquidity, and the sentiment of the crowd. Most altseason indicators are lagging, overfitted, or too simplistic. This one is modular, forward-looking, and grounded in real capital rotation theory.
If you're a trader who wants to time the cycle, not guess it, this is your tool. Refine it, fork it, or expand it to your niche—DeFi, NFTs, meme coins, or L1s. It’s a framework for reading the macro winds, not a signal service. Use it with discipline, and you’ll catch the wave while others drown in noise.
Cerca negli script per "市值60亿的股票"
Non-Lagging Longevity Zones [BigBeluga]🔵 OVERVIEW
A clean, non-lagging system for identifying price zones that persist over time—ranking them visually based on how long they survive without being invalidated.
Non-Lagging Longevity Zones uses non-lagging pivots to automatically build upper and lower zones that reflect key resistance and support. These zones are kept alive as long as price respects them and are instantly removed when invalidated. The indicator assigns a unique lifespan label to each zone in Days (D), Months (M), or Years (Y), providing instant context for historical relevance.
🔵 CONCEPTS
Non-Lag Pivot Detection: Detects upper and lower pivots using non-lagging swing identification (highest/lowest over length period).
h = ta.highest(len)
l = ta.lowest(len)
high_pivot = high == h and high < h
low_pivot = low == l and low > l
Longevity Ranking: Zones are preserved as long as price doesn't breach them. Levels that remain intact grow in visual intensity.
Time-Based Weighting: Each zone is labeled with its lifespan in days , emphasizing how long it has survived.
duration = last_bar_index - start
days_ = int(duration*(timeframe.in_seconds("")/60/60/24))
days = days_ >= 365 ? int(days_ / 365) : days_ >= 30 ? int(days_ / 30) : days_
marker = days_ >= 365 ? " Y" : days_ >= 30 ? " M" : " D"
Dynamic Coloring: Older zones are drawn with stronger fill, while newer ones appear fainter—making it easy to assess significance.
Self-Cleaning Logic: If price invalidates a zone, it’s instantly removed, keeping the chart clean and focused.
🔵 FEATURES
Upper and Lower Zones: Auto-detects valid high/low pivots and plots horizontal zones with ATR-based thickness.
Real-Time Validation: Zones are extended only if price stays outside them—giving precise control zones.
Gradient Fill Intensity: The longer a level survives, the more opaque the fill becomes.
Duration-Based Labeling: Time alive is shown at the root of each zone:
• D – short-term zones
• M – medium-term structure
• Y – long-term legacy levels
Smart Zone Clearing: Zones are deleted automatically once invalidated by price, keeping the display accurate.
Efficient Memory Handling: Keeps only the 10 most recent valid levels per side for optimal performance.
🔵 HOW TO USE
Track durable S/R zones that survived price tests without being breached.
Use longer-lived zones as high-confidence confluence areas for entries or targets.
Observe fill intensity to judge structural importance at a glance .
Layer with volume or momentum tools to confirm bounce or breakout probability.
Ideal for swing traders, structure-based traders, or macro analysis.
🔵 CONCLUSION
Non-Lagging Longevity Zones lets the market speak for itself—by spotlighting levels with proven survival over time. Whether you're trading trend continuation, mean reversion, or structure-based reversals, this tool equips you with an immediate read on what price zones truly matter—and how long they've stood the test of time.
Candle Emotion Oscillator [CEO]Candle Emotion Oscillator (CEO) - Revolutionary User Guide
🧠 World's First Market Psychology Oscillator
The Candle Emotion Oscillator (CEO) is a groundbreaking indicator that measures market emotions through pure candle price action analysis. This is the first oscillator ever created that translates candle patterns into psychological states, giving you unprecedented insight into market sentiment.
🚀 Revolutionary Concept
What Makes CEO Unique
100% Pure Price Action: No volume, no external data - just candle analysis
Market Psychology: Measures actual emotions: Fear, Greed, Panic, Euphoria
Never Been Done Before: First oscillator to analyze market emotions
Exhaustion Prediction: Detects emotional fatigue before reversals
Fast Response: Perfect for your 2-5 minute scalping setup
The Four Core Emotions
🟢 GREED (Positive Values)
What it measures: Market conviction and decisiveness
Candle Pattern: Large bodies, small wicks
Psychology: Traders are confident and decisive
Oscillator: Positive values (0 to +100)
Trading Implication: Trend continuation likely
🔴 FEAR (Negative Values)
What it measures: Market uncertainty and indecision
Candle Pattern: Small bodies, large wicks
Psychology: Traders are uncertain and hesitant
Oscillator: Negative values (0 to -100)
Trading Implication: Consolidation or reversal likely
🚀 EUPHORIA (Extreme Positive)
What it measures: Excessive optimism and buying pressure
Candle Pattern: Large green bodies with upper wicks
Psychology: Extreme bullish sentiment
Oscillator: Values above +60
Trading Implication: Overbought, reversal warning
💥 PANIC (Extreme Negative)
What it measures: Capitulation and selling pressure
Candle Pattern: Large red bodies with lower wicks
Psychology: Extreme bearish sentiment
Oscillator: Values below -60
Trading Implication: Oversold, reversal opportunity
📊 Visual Elements Explained
Main Components
Thick Colored Line: Primary emotion oscillator
Green: Greed (positive emotions)
Red: Fear (negative emotions)
Bright Green: Euphoria (extreme positive)
Dark Red: Panic (extreme negative)
Thin Blue Line: Emotion trend (longer-term context)
Background Gradient: Emotional intensity
Darker = stronger emotions
Lighter = weaker emotions
Diamond Signals: 🔶 Emotional exhaustion detected
Rocket Signals: 🚀 Extreme euphoria warning
Explosion Signals: 💥 Extreme panic warning
Information Table (Top Right)
The Sequences of FibonacciThe Sequences of Fibonacci - Advanced Multi-Timeframe Confluence Analysis System
THEORETICAL FOUNDATION & MATHEMATICAL INNOVATION
The Sequences of Fibonacci represents a revolutionary approach to market analysis that synthesizes classical Fibonacci mathematics with modern adaptive signal processing. This indicator transcends traditional Fibonacci retracement tools by implementing a sophisticated multi-dimensional confluence detection system that reveals hidden market structure through mathematical precision.
Core Mathematical Framework
Dynamic Fibonacci Grid System:
Unlike static Fibonacci tools, this system calculates highest highs and lowest lows across true Fibonacci sequence periods (8, 13, 21, 34, 55 bars) creating a dynamic grid of mathematical support and resistance levels that adapt to market structure in real-time.
Multi-Dimensional Confluence Detection:
The engine employs advanced mathematical clustering algorithms to identify areas where multiple derived Fibonacci retracement levels (0.382, 0.500, 0.618) from different timeframe perspectives converge. These "Confluence Zones" are mathematically classified by strength:
- CRITICAL Zones: 8+ converging Fibonacci levels
- HIGH Zones: 6-7 converging levels
- MEDIUM Zones: 4-5 converging levels
- LOW Zones: 3+ converging levels
Adaptive Signal Processing Architecture:
The system implements adaptive Stochastic RSI calculations with dynamic overbought/oversold levels that adjust to recent market volatility rather than using fixed thresholds. This prevents false signals during changing market conditions.
COMPREHENSIVE FEATURE ARCHITECTURE
Quantum Field Visualization System
Dynamic Price Field Mathematics:
The Quantum Field creates adaptive price channels based on EMA center points and ATR-based amplitude calculations, influenced by the Unified Field metric. This visualization system helps traders understand:
- Expected price volatility ranges
- Potential overextension zones
- Mathematical pressure points in market structure
- Dynamic support/resistance boundaries
Field Amplitude Calculation:
Field Amplitude = ATR × (1 + |Unified Field| / 10)
The system generates three quantum levels:
- Q⁰ Level: 0.618 × Field Amplitude (Primary channel)
- Q¹ Level: 1.0 × Field Amplitude (Secondary boundary)
- Q² Level: 1.618 × Field Amplitude (Extreme extension)
Advanced Market Analysis Dashboard
Unified Field Analysis:
A composite metric combining:
- Price momentum (40% weighting)
- Volume momentum (30% weighting)
- Trend strength (30% weighting)
Market Resonance Calculation:
Measures price-volume correlation over 14 periods to identify harmony between price action and volume participation.
Signal Quality Assessment:
Synthesizes Unified Field, Market Resonance, and RSI positioning to provide real-time evaluation of setup potential.
Tiered Signal Generation Logic
Tier 1 Signals (Highest Conviction):
Require ALL conditions:
- Adaptive StochRSI setup (exiting dynamic OB/OS levels)
- Classic StochRSI divergence confirmation
- Strong reversal bar pattern (adaptive ATR-based sizing)
- Level rejection from Confluence Zone or Fibonacci level
- Supportive Unified Field context
Tier 2 Signals (Enhanced Opportunity Detection):
Generated when Tier 1 conditions aren't met but exceptional circumstances exist:
- Divergence candidate patterns (relaxed divergence requirements)
- Exceptionally strong reversal bars at critical levels
- Enhanced level rejection criteria
- Maintained context filtering
Intelligent Visualization Features
Fractal Matrix Grid:
Multi-layer visualization system displaying:
- Shadow Layer: Foundational support (width 5)
- Glow Layer: Core identification (width 3, white)
- Quantum Layer: Mathematical overlay (width 1, dotted)
Smart Labeling System:
Prevents overlap using ATR-based minimum spacing while providing:
- Fibonacci period identification
- Topological complexity classification (0, I, II, III)
- Exact price levels
- Strength indicators (○ ◐ ● ⚡)
Wick Pressure Analysis:
Dynamic visualization showing momentum direction through:
- Multi-beam projection lines
- Particle density effects
- Progressive transparency for natural flow
- Strength-based sizing adaptation
PRACTICAL TRADING IMPLEMENTATION
Signal Interpretation Framework
Entry Protocol:
1. Confluence Zone Approach: Monitor price approaching High/Critical confluence zones
2. Adaptive Setup Confirmation: Wait for StochRSI to exit adaptive OB/OS levels
3. Divergence Verification: Confirm classic or candidate divergence patterns
4. Reversal Bar Assessment: Validate strong rejection using adaptive ATR criteria
5. Context Evaluation: Ensure Unified Field provides supportive environment
Risk Management Integration:
- Stop Placement: Beyond rejected confluence zone or Fibonacci level
- Position Sizing: Based on signal tier and confluence strength
- Profit Targets: Next significant confluence zone or quantum field boundary
Adaptive Parameter System
Dynamic StochRSI Levels:
Unlike fixed 80/20 levels, the system calculates adaptive OB/OS based on recent StochRSI range:
- Adaptive OB: Recent minimum + (range × OB percentile)
- Adaptive OS: Recent minimum + (range × OS percentile)
- Lookback Period: Configurable 20-100 bars for range calculation
Intelligent ATR Adaptation:
Bar size requirements adjust to market volatility:
- High Volatility: Reduced multiplier (bars naturally larger)
- Low Volatility: Increased multiplier (ensuring significance)
- Base Multiplier: 0.6× ATR with adaptive scaling
Optimization Guidelines
Timeframe-Specific Settings:
Scalping (1-5 minutes):
- Fibonacci Rejection Sensitivity: 0.3-0.8
- Confluence Threshold: 2-3 levels
- StochRSI Lookback: 20-30 bars
Day Trading (15min-1H):
- Fibonacci Rejection Sensitivity: 0.5-1.2
- Confluence Threshold: 3-4 levels
- StochRSI Lookback: 40-60 bars
Swing Trading (4H-1D):
- Fibonacci Rejection Sensitivity: 1.0-2.0
- Confluence Threshold: 4-5 levels
- StochRSI Lookback: 60-80 bars
Asset-Specific Optimization:
Cryptocurrency:
- Higher rejection sensitivity (1.0-2.5) for volatile conditions
- Enable Tier 2 signals for increased opportunity detection
- Shorter adaptive lookbacks for rapid market changes
Forex Major Pairs:
- Moderate sensitivity (0.8-1.5) for stable trending
- Focus on Higher/Critical confluence zones
- Longer lookbacks for institutional flow detection
Stock Indices:
- Conservative sensitivity (0.5-1.0) for institutional participation
- Standard confluence thresholds
- Balanced adaptive parameters
IMPORTANT USAGE CONSIDERATIONS
Realistic Performance Expectations
This indicator provides probabilistic advantages based on mathematical confluence analysis, not guaranteed outcomes. Signal quality varies with market conditions, and proper risk management remains essential regardless of signal tier.
Understanding Adaptive Features:
- Adaptive parameters react to historical data, not future market conditions
- Dynamic levels adjust to past volatility patterns
- Signal quality reflects mathematical alignment probability, not certainty
Market Context Awareness:
- Strong trending markets may produce fewer reversal signals
- Range-bound conditions typically generate more confluence opportunities
- News events and fundamental factors can override technical analysis
Educational Value
Mathematical Concepts Introduced:
- Multi-dimensional confluence analysis
- Adaptive signal processing techniques
- Dynamic parameter optimization
- Mathematical field theory applications in trading
- Advanced Fibonacci sequence applications
Skill Development Benefits:
- Understanding market structure through mathematical lens
- Recognition of multi-timeframe confluence principles
- Appreciation for adaptive vs. static analysis methods
- Integration of classical Fibonacci with modern signal processing
UNIQUE INNOVATIONS
First-Ever Implementations
1. True Fibonacci Sequence Periods: First indicator using authentic Fibonacci numbers (8,13,21,34,55) for timeframe analysis
2. Mathematical Confluence Clustering: Advanced algorithm identifying true Fibonacci level convergence
3. Adaptive StochRSI Boundaries: Dynamic OB/OS levels replacing fixed thresholds
4. Tiered Signal Architecture: Democratic signal weighting with quality classification
5. Quantum Field Price Visualization: Mathematical field representation of price dynamics
Visualization Breakthroughs
- Multi-Layer Fibonacci Grid: Three-layer rendering with intelligent spacing
- Dynamic Confluence Zones: Strength-based color coding and sizing
- Adaptive Parameter Display: Real-time visualization of dynamic calculations
- Mathematical Field Effects: Quantum-inspired price channel visualization
- Progressive Transparency Systems: Natural visual flow without chart clutter
COMPREHENSIVE DASHBOARD SYSTEM
Multi-Size Display Options
Small Dashboard: Core metrics for mobile/limited screen space
Normal Dashboard: Balanced information density for standard desktop use
Large Dashboard: Complete analysis suite including adaptive parameter values
Real-Time Metrics Tracking
Market Analysis Section:
- Unified Field strength with visual meter
- Market Resonance percentage
- Signal Quality assessment with emoji indicators
- Market Bias classification (Bullish/Bearish/Neutral)
Confluence Intelligence:
- Total active zones count
- High/Critical zone identification
- Nearest zone distance and strength
- Price-to-zone ATR measurement
Adaptive Parameters (Large Dashboard):
- Current StochRSI OB/OS levels
- Active ATR multiplier for bar sizing
- Volatility ratio for adaptive scaling
- Real-time StochRSI positioning
TECHNICAL SPECIFICATIONS
Pine Script Version: v5 (Latest)
Calculation Method: Real-time with confirmed bar processing
Maximum Objects: 500 boxes, 500 lines, 500 labels
Dashboard Positions: 4 corner options with size selection
Visual Themes: Quantum, Holographic, Crystalline, Plasma
Alert Integration: Complete alert system for all signal types
Performance Optimizations:
- Efficient confluence zone calculation using advanced clustering
- Smart label spacing prevents overlap
- Progressive transparency for visual clarity
- Memory-optimized array management
EDUCATIONAL FRAMEWORK
Learning Progression
Beginner Level:
- Understanding Fibonacci sequence applications
- Recognition of confluence zone concepts
- Basic signal interpretation
- Dashboard metric comprehension
Intermediate Level:
- Adaptive parameter optimization
- Multi-timeframe confluence analysis
- Signal quality assessment techniques
- Risk management integration
Advanced Level:
- Mathematical field theory applications
- Custom parameter optimization strategies
- Market regime adaptation techniques
- Professional trading system integration
DEVELOPMENT ACKNOWLEDGMENT
Special acknowledgment to @AlgoTrader90 - the foundational concepts of this system came from him and we developed it through a collaborative discussions about multi-timeframe Fibonacci analysis. While the original framework came from AlgoTrader90's innovative approach, this implementation represents a complete evolution of the logic with enhanced mathematical precision, adaptive parameters, and sophisticated signal filtering to deliver meaningful, actionable trading signals.
CONCLUSION
The Sequences of Fibonacci represents a quantum leap in technical analysis, successfully merging classical Fibonacci mathematics with cutting-edge adaptive signal processing. Through sophisticated confluence detection, intelligent parameter adaptation, and comprehensive market analysis, this system provides traders with unprecedented insight into market structure and potential reversal points.
The mathematical foundation ensures lasting relevance while the adaptive features maintain effectiveness across changing market conditions. From the dynamic Fibonacci grid to the quantum field visualization, every component reflects a commitment to mathematical precision, visual elegance, and practical utility.
Whether you're a beginner seeking to understand market confluence or an advanced trader requiring sophisticated analytical tools, this system provides the mathematical framework for informed decision-making based on time-tested Fibonacci principles enhanced with modern computational techniques.
Trade with mathematical precision. Trade with the power of confluence. Trade with The Sequences of Fibonacci.
"Mathematics is the language with which God has written the universe. In markets, Fibonacci sequences reveal the hidden harmonies that govern price movement, and those who understand these mathematical relationships hold the key to anticipating market behavior."
* Galileo Galilei (adapted for modern markets)
— Dskyz, Trade with insight. Trade with anticipation.
Intraday & Annual CAPM AlphaIntraday & Annual CAPM Alpha
This TradingView™ Pine v6 indicator computes and plots a stock’s CAPM α (alpha) on both intraday and daily/annualized timeframes, allowing you to monitor relative performance against a chosen benchmark (e.g. SPX, NDX).
⸻
Key Outputs
1. Intraday α per Bar (blue line)
• Calculates α from a rolling-window linear regression of the last N bars’ returns (default 60).
• Expressed as “extra return per bar” vs. the benchmark.
2. Intraday α Daily-Equivalent (stepped blue line)
• Scales the per-bar α to a full trading day (390 minutes), showing “if this pace held all day, outperformance (%)”.
3. Annualized α (yellow line)
• Performs the same CAPM regression on daily returns over a D-day lookback (default 252), then annualizes α by multiplying by 252.
• Indicates longer-term relative strength/weakness vs. the benchmark.
⸻
Inputs
• Benchmark Symbol: Choose any index or ETF (e.g. “SPX”, “NDX”).
• Intraday Lookback Bars: Number of bars for intraday α regression (default 60).
• Daily Lookback Days: Number of trading days for daily CAPM regression (default 252).
• Use Log Returns?: Toggle between arithmetic vs. log returns.
⸻
How to Use
• Short-Term Signals:
• Watch the blue α/bar line on 1–15 min charts. A cross from negative to positive suggests intraday outperformance; a reversal warns of weakening momentum.
• The blue daily-equivalent α gives a smoother view—e.g. > +1% signals strong intraday bias, < –1% signals underperformance.
• Long-Term Trends:
• On daily charts, focus on the yellow annualized α. A sustained positive α implies this stock has historically beaten the benchmark; sustained negative α implies the opposite.
• Combining Timeframes:
• Use intraday α for timing entries/exits within the session, and annualized α to confirm whether you want a bullish or bearish bias over days to weeks.
⸻
Install & Configure
1. Copy the Pine v6 script into the TradingView Pine Editor.
2. Set your favorite benchmark, lookback periods, and returns type.
3. Add to your chart to start visualizing real-time CAPM α signals!
Feel free to adjust the lookback windows and threshold levels to suit your trading style.
Tensor Market Analysis Engine (TMAE)# Tensor Market Analysis Engine (TMAE)
## Advanced Multi-Dimensional Mathematical Analysis System
*Where Quantum Mathematics Meets Market Structure*
---
## 🎓 THEORETICAL FOUNDATION
The Tensor Market Analysis Engine represents a revolutionary synthesis of three cutting-edge mathematical frameworks that have never before been combined for comprehensive market analysis. This indicator transcends traditional technical analysis by implementing advanced mathematical concepts from quantum mechanics, information theory, and fractal geometry.
### 🌊 Multi-Dimensional Volatility with Jump Detection
**Hawkes Process Implementation:**
The TMAE employs a sophisticated Hawkes process approximation for detecting self-exciting market jumps. Unlike traditional volatility measures that treat price movements as independent events, the Hawkes process recognizes that market shocks cluster and exhibit memory effects.
**Mathematical Foundation:**
```
Intensity λ(t) = μ + Σ α(t - Tᵢ)
```
Where market jumps at times Tᵢ increase the probability of future jumps through the decay function α, controlled by the Hawkes Decay parameter (0.5-0.99).
**Mahalanobis Distance Calculation:**
The engine calculates volatility jumps using multi-dimensional Mahalanobis distance across up to 5 volatility dimensions:
- **Dimension 1:** Price volatility (standard deviation of returns)
- **Dimension 2:** Volume volatility (normalized volume fluctuations)
- **Dimension 3:** Range volatility (high-low spread variations)
- **Dimension 4:** Correlation volatility (price-volume relationship changes)
- **Dimension 5:** Microstructure volatility (intrabar positioning analysis)
This creates a volatility state vector that captures market behavior impossible to detect with traditional single-dimensional approaches.
### 📐 Hurst Exponent Regime Detection
**Fractal Market Hypothesis Integration:**
The TMAE implements advanced Rescaled Range (R/S) analysis to calculate the Hurst exponent in real-time, providing dynamic regime classification:
- **H > 0.6:** Trending (persistent) markets - momentum strategies optimal
- **H < 0.4:** Mean-reverting (anti-persistent) markets - contrarian strategies optimal
- **H ≈ 0.5:** Random walk markets - breakout strategies preferred
**Adaptive R/S Analysis:**
Unlike static implementations, the TMAE uses adaptive windowing that adjusts to market conditions:
```
H = log(R/S) / log(n)
```
Where R is the range of cumulative deviations and S is the standard deviation over period n.
**Dynamic Regime Classification:**
The system employs hysteresis to prevent regime flipping, requiring sustained Hurst values before regime changes are confirmed. This prevents false signals during transitional periods.
### 🔄 Transfer Entropy Analysis
**Information Flow Quantification:**
Transfer entropy measures the directional flow of information between price and volume, revealing lead-lag relationships that indicate future price movements:
```
TE(X→Y) = Σ p(yₜ₊₁, yₜ, xₜ) log
```
**Causality Detection:**
- **Volume → Price:** Indicates accumulation/distribution phases
- **Price → Volume:** Suggests retail participation or momentum chasing
- **Balanced Flow:** Market equilibrium or transition periods
The system analyzes multiple lag periods (2-20 bars) to capture both immediate and structural information flows.
---
## 🔧 COMPREHENSIVE INPUT SYSTEM
### Core Parameters Group
**Primary Analysis Window (10-100, Default: 50)**
The fundamental lookback period affecting all calculations. Optimization by timeframe:
- **1-5 minute charts:** 20-30 (rapid adaptation to micro-movements)
- **15 minute-1 hour:** 30-50 (balanced responsiveness and stability)
- **4 hour-daily:** 50-100 (smooth signals, reduced noise)
- **Asset-specific:** Cryptocurrency 20-35, Stocks 35-50, Forex 40-60
**Signal Sensitivity (0.1-2.0, Default: 0.7)**
Master control affecting all threshold calculations:
- **Conservative (0.3-0.6):** High-quality signals only, fewer false positives
- **Balanced (0.7-1.0):** Optimal risk-reward ratio for most trading styles
- **Aggressive (1.1-2.0):** Maximum signal frequency, requires careful filtering
**Signal Generation Mode:**
- **Aggressive:** Any component signals (highest frequency)
- **Confluence:** 2+ components agree (balanced approach)
- **Conservative:** All 3 components align (highest quality)
### Volatility Jump Detection Group
**Volatility Dimensions (2-5, Default: 3)**
Determines the mathematical space complexity:
- **2D:** Price + Volume volatility (suitable for clean markets)
- **3D:** + Range volatility (optimal for most conditions)
- **4D:** + Correlation volatility (advanced multi-asset analysis)
- **5D:** + Microstructure volatility (maximum sensitivity)
**Jump Detection Threshold (1.5-4.0σ, Default: 3.0σ)**
Standard deviations required for volatility jump classification:
- **Cryptocurrency:** 2.0-2.5σ (naturally volatile)
- **Stock Indices:** 2.5-3.0σ (moderate volatility)
- **Forex Major Pairs:** 3.0-3.5σ (typically stable)
- **Commodities:** 2.0-3.0σ (varies by commodity)
**Jump Clustering Decay (0.5-0.99, Default: 0.85)**
Hawkes process memory parameter:
- **0.5-0.7:** Fast decay (jumps treated as independent)
- **0.8-0.9:** Moderate clustering (realistic market behavior)
- **0.95-0.99:** Strong clustering (crisis/event-driven markets)
### Hurst Exponent Analysis Group
**Calculation Method Options:**
- **Classic R/S:** Original Rescaled Range (fast, simple)
- **Adaptive R/S:** Dynamic windowing (recommended for trading)
- **DFA:** Detrended Fluctuation Analysis (best for noisy data)
**Trending Threshold (0.55-0.8, Default: 0.60)**
Hurst value defining persistent market behavior:
- **0.55-0.60:** Weak trend persistence
- **0.65-0.70:** Clear trending behavior
- **0.75-0.80:** Strong momentum regimes
**Mean Reversion Threshold (0.2-0.45, Default: 0.40)**
Hurst value defining anti-persistent behavior:
- **0.35-0.45:** Weak mean reversion
- **0.25-0.35:** Clear ranging behavior
- **0.15-0.25:** Strong reversion tendency
### Transfer Entropy Parameters Group
**Information Flow Analysis:**
- **Price-Volume:** Classic flow analysis for accumulation/distribution
- **Price-Volatility:** Risk flow analysis for sentiment shifts
- **Multi-Timeframe:** Cross-timeframe causality detection
**Maximum Lag (2-20, Default: 5)**
Causality detection window:
- **2-5 bars:** Immediate causality (scalping)
- **5-10 bars:** Short-term flow (day trading)
- **10-20 bars:** Structural flow (swing trading)
**Significance Threshold (0.05-0.3, Default: 0.15)**
Minimum entropy for signal generation:
- **0.05-0.10:** Detect subtle information flows
- **0.10-0.20:** Clear causality only
- **0.20-0.30:** Very strong flows only
---
## 🎨 ADVANCED VISUAL SYSTEM
### Tensor Volatility Field Visualization
**Five-Layer Resonance Bands:**
The tensor field creates dynamic support/resistance zones that expand and contract based on mathematical field strength:
- **Core Layer (Purple):** Primary tensor field with highest intensity
- **Layer 2 (Neutral):** Secondary mathematical resonance
- **Layer 3 (Info Blue):** Tertiary harmonic frequencies
- **Layer 4 (Warning Gold):** Outer field boundaries
- **Layer 5 (Success Green):** Maximum field extension
**Field Strength Calculation:**
```
Field Strength = min(3.0, Mahalanobis Distance × Tensor Intensity)
```
The field amplitude adjusts to ATR and mathematical distance, creating dynamic zones that respond to market volatility.
**Radiation Line Network:**
During active tensor states, the system projects directional radiation lines showing field energy distribution:
- **8 Directional Rays:** Complete angular coverage
- **Tapering Segments:** Progressive transparency for natural visual flow
- **Pulse Effects:** Enhanced visualization during volatility jumps
### Dimensional Portal System
**Portal Mathematics:**
Dimensional portals visualize regime transitions using category theory principles:
- **Green Portals (◉):** Trending regime detection (appear below price for support)
- **Red Portals (◎):** Mean-reverting regime (appear above price for resistance)
- **Yellow Portals (○):** Random walk regime (neutral positioning)
**Tensor Trail Effects:**
Each portal generates 8 trailing particles showing mathematical momentum:
- **Large Particles (●):** Strong mathematical signal
- **Medium Particles (◦):** Moderate signal strength
- **Small Particles (·):** Weak signal continuation
- **Micro Particles (˙):** Signal dissipation
### Information Flow Streams
**Particle Stream Visualization:**
Transfer entropy creates flowing particle streams indicating information direction:
- **Upward Streams:** Volume leading price (accumulation phases)
- **Downward Streams:** Price leading volume (distribution phases)
- **Stream Density:** Proportional to information flow strength
**15-Particle Evolution:**
Each stream contains 15 particles with progressive sizing and transparency, creating natural flow visualization that makes information transfer immediately apparent.
### Fractal Matrix Grid System
**Multi-Timeframe Fractal Levels:**
The system calculates and displays fractal highs/lows across five Fibonacci periods:
- **8-Period:** Short-term fractal structure
- **13-Period:** Intermediate-term patterns
- **21-Period:** Primary swing levels
- **34-Period:** Major structural levels
- **55-Period:** Long-term fractal boundaries
**Triple-Layer Visualization:**
Each fractal level uses three-layer rendering:
- **Shadow Layer:** Widest, darkest foundation (width 5)
- **Glow Layer:** Medium white core line (width 3)
- **Tensor Layer:** Dotted mathematical overlay (width 1)
**Intelligent Labeling System:**
Smart spacing prevents label overlap using ATR-based minimum distances. Labels include:
- **Fractal Period:** Time-based identification
- **Topological Class:** Mathematical complexity rating (0, I, II, III)
- **Price Level:** Exact fractal price
- **Mahalanobis Distance:** Current mathematical field strength
- **Hurst Exponent:** Current regime classification
- **Anomaly Indicators:** Visual strength representations (○ ◐ ● ⚡)
### Wick Pressure Analysis
**Rejection Level Mathematics:**
The system analyzes candle wick patterns to project future pressure zones:
- **Upper Wick Analysis:** Identifies selling pressure and resistance zones
- **Lower Wick Analysis:** Identifies buying pressure and support zones
- **Pressure Projection:** Extends lines forward based on mathematical probability
**Multi-Layer Glow Effects:**
Wick pressure lines use progressive transparency (1-8 layers) creating natural glow effects that make pressure zones immediately visible without cluttering the chart.
### Enhanced Regime Background
**Dynamic Intensity Mapping:**
Background colors reflect mathematical regime strength:
- **Deep Transparency (98% alpha):** Subtle regime indication
- **Pulse Intensity:** Based on regime strength calculation
- **Color Coding:** Green (trending), Red (mean-reverting), Neutral (random)
**Smoothing Integration:**
Regime changes incorporate 10-bar smoothing to prevent background flicker while maintaining responsiveness to genuine regime shifts.
### Color Scheme System
**Six Professional Themes:**
- **Dark (Default):** Professional trading environment optimization
- **Light:** High ambient light conditions
- **Classic:** Traditional technical analysis appearance
- **Neon:** High-contrast visibility for active trading
- **Neutral:** Minimal distraction focus
- **Bright:** Maximum visibility for complex setups
Each theme maintains mathematical accuracy while optimizing visual clarity for different trading environments and personal preferences.
---
## 📊 INSTITUTIONAL-GRADE DASHBOARD
### Tensor Field Status Section
**Field Strength Display:**
Real-time Mahalanobis distance calculation with dynamic emoji indicators:
- **⚡ (Lightning):** Extreme field strength (>1.5× threshold)
- **● (Solid Circle):** Strong field activity (>1.0× threshold)
- **○ (Open Circle):** Normal field state
**Signal Quality Rating:**
Democratic algorithm assessment:
- **ELITE:** All 3 components aligned (highest probability)
- **STRONG:** 2 components aligned (good probability)
- **GOOD:** 1 component active (moderate probability)
- **WEAK:** No clear component signals
**Threshold and Anomaly Monitoring:**
- **Threshold Display:** Current mathematical threshold setting
- **Anomaly Level (0-100%):** Combined volatility and volume spike measurement
- **>70%:** High anomaly (red warning)
- **30-70%:** Moderate anomaly (orange caution)
- **<30%:** Normal conditions (green confirmation)
### Tensor State Analysis Section
**Mathematical State Classification:**
- **↑ BULL (Tensor State +1):** Trending regime with bullish bias
- **↓ BEAR (Tensor State -1):** Mean-reverting regime with bearish bias
- **◈ SUPER (Tensor State 0):** Random walk regime (neutral)
**Visual State Gauge:**
Five-circle progression showing tensor field polarity:
- **🟢🟢🟢⚪⚪:** Strong bullish mathematical alignment
- **⚪⚪🟡⚪⚪:** Neutral/transitional state
- **⚪⚪🔴🔴🔴:** Strong bearish mathematical alignment
**Trend Direction and Phase Analysis:**
- **📈 BULL / 📉 BEAR / ➡️ NEUTRAL:** Primary trend classification
- **🌪️ CHAOS:** Extreme information flow (>2.0 flow strength)
- **⚡ ACTIVE:** Strong information flow (1.0-2.0 flow strength)
- **😴 CALM:** Low information flow (<1.0 flow strength)
### Trading Signals Section
**Real-Time Signal Status:**
- **🟢 ACTIVE / ⚪ INACTIVE:** Long signal availability
- **🔴 ACTIVE / ⚪ INACTIVE:** Short signal availability
- **Components (X/3):** Active algorithmic components
- **Mode Display:** Current signal generation mode
**Signal Strength Visualization:**
Color-coded component count:
- **Green:** 3/3 components (maximum confidence)
- **Aqua:** 2/3 components (good confidence)
- **Orange:** 1/3 components (moderate confidence)
- **Gray:** 0/3 components (no signals)
### Performance Metrics Section
**Win Rate Monitoring:**
Estimated win rates based on signal quality with emoji indicators:
- **🔥 (Fire):** ≥60% estimated win rate
- **👍 (Thumbs Up):** 45-59% estimated win rate
- **⚠️ (Warning):** <45% estimated win rate
**Mathematical Metrics:**
- **Hurst Exponent:** Real-time fractal dimension (0.000-1.000)
- **Information Flow:** Volume/price leading indicators
- **📊 VOL:** Volume leading price (accumulation/distribution)
- **💰 PRICE:** Price leading volume (momentum/speculation)
- **➖ NONE:** Balanced information flow
- **Volatility Classification:**
- **🔥 HIGH:** Above 1.5× jump threshold
- **📊 NORM:** Normal volatility range
- **😴 LOW:** Below 0.5× jump threshold
### Market Structure Section (Large Dashboard)
**Regime Classification:**
- **📈 TREND:** Hurst >0.6, momentum strategies optimal
- **🔄 REVERT:** Hurst <0.4, contrarian strategies optimal
- **🎲 RANDOM:** Hurst ≈0.5, breakout strategies preferred
**Mathematical Field Analysis:**
- **Dimensions:** Current volatility space complexity (2D-5D)
- **Hawkes λ (Lambda):** Self-exciting jump intensity (0.00-1.00)
- **Jump Status:** 🚨 JUMP (active) / ✅ NORM (normal)
### Settings Summary Section (Large Dashboard)
**Active Configuration Display:**
- **Sensitivity:** Current master sensitivity setting
- **Lookback:** Primary analysis window
- **Theme:** Active color scheme
- **Method:** Hurst calculation method (Classic R/S, Adaptive R/S, DFA)
**Dashboard Sizing Options:**
- **Small:** Essential metrics only (mobile/small screens)
- **Normal:** Balanced information density (standard desktop)
- **Large:** Maximum detail (multi-monitor setups)
**Position Options:**
- **Top Right:** Standard placement (avoids price action)
- **Top Left:** Wide chart optimization
- **Bottom Right:** Recent price focus (scalping)
- **Bottom Left:** Maximum price visibility (swing trading)
---
## 🎯 SIGNAL GENERATION LOGIC
### Multi-Component Convergence System
**Component Signal Architecture:**
The TMAE generates signals through sophisticated component analysis rather than simple threshold crossing:
**Volatility Component:**
- **Jump Detection:** Mahalanobis distance threshold breach
- **Hawkes Intensity:** Self-exciting process activation (>0.2)
- **Multi-dimensional:** Considers all volatility dimensions simultaneously
**Hurst Regime Component:**
- **Trending Markets:** Price above SMA-20 with positive momentum
- **Mean-Reverting Markets:** Price at Bollinger Band extremes
- **Random Markets:** Bollinger squeeze breakouts with directional confirmation
**Transfer Entropy Component:**
- **Volume Leadership:** Information flow from volume to price
- **Volume Spike:** Volume 110%+ above 20-period average
- **Flow Significance:** Above entropy threshold with directional bias
### Democratic Signal Weighting
**Signal Mode Implementation:**
- **Aggressive Mode:** Any single component triggers signal
- **Confluence Mode:** Minimum 2 components must agree
- **Conservative Mode:** All 3 components must align
**Momentum Confirmation:**
All signals require momentum confirmation:
- **Long Signals:** RSI >50 AND price >EMA-9
- **Short Signals:** RSI <50 AND price 0.6):**
- **Increase Sensitivity:** Catch momentum continuation
- **Lower Mean Reversion Threshold:** Avoid counter-trend signals
- **Emphasize Volume Leadership:** Institutional accumulation/distribution
- **Tensor Field Focus:** Use expansion for trend continuation
- **Signal Mode:** Aggressive or Confluence for trend following
**Range-Bound Markets (Hurst <0.4):**
- **Decrease Sensitivity:** Avoid false breakouts
- **Lower Trending Threshold:** Quick regime recognition
- **Focus on Price Leadership:** Retail sentiment extremes
- **Fractal Grid Emphasis:** Support/resistance trading
- **Signal Mode:** Conservative for high-probability reversals
**Volatile Markets (High Jump Frequency):**
- **Increase Hawkes Decay:** Recognize event clustering
- **Higher Jump Threshold:** Avoid noise signals
- **Maximum Dimensions:** Capture full volatility complexity
- **Reduce Position Sizing:** Risk management adaptation
- **Enhanced Visuals:** Maximum information for rapid decisions
**Low Volatility Markets (Low Jump Frequency):**
- **Decrease Jump Threshold:** Capture subtle movements
- **Lower Hawkes Decay:** Treat moves as independent
- **Reduce Dimensions:** Simplify analysis
- **Increase Position Sizing:** Capitalize on compressed volatility
- **Minimal Visuals:** Reduce distraction in quiet markets
---
## 🚀 ADVANCED TRADING STRATEGIES
### The Mathematical Convergence Method
**Entry Protocol:**
1. **Fractal Grid Approach:** Monitor price approaching significant fractal levels
2. **Tensor Field Confirmation:** Verify field expansion supporting direction
3. **Portal Signal:** Wait for dimensional portal appearance
4. **ELITE/STRONG Quality:** Only trade highest quality mathematical signals
5. **Component Consensus:** Confirm 2+ components agree in Confluence mode
**Example Implementation:**
- Price approaching 21-period fractal high
- Tensor field expanding upward (bullish mathematical alignment)
- Green portal appears below price (trending regime confirmation)
- ELITE quality signal with 3/3 components active
- Enter long position with stop below fractal level
**Risk Management:**
- **Stop Placement:** Below/above fractal level that generated signal
- **Position Sizing:** Based on Mahalanobis distance (higher distance = smaller size)
- **Profit Targets:** Next fractal level or tensor field resistance
### The Regime Transition Strategy
**Regime Change Detection:**
1. **Monitor Hurst Exponent:** Watch for persistent moves above/below thresholds
2. **Portal Color Change:** Regime transitions show different portal colors
3. **Background Intensity:** Increasing regime background intensity
4. **Mathematical Confirmation:** Wait for regime confirmation (hysteresis)
**Trading Implementation:**
- **Trending Transitions:** Trade momentum breakouts, follow trend
- **Mean Reversion Transitions:** Trade range boundaries, fade extremes
- **Random Transitions:** Trade breakouts with tight stops
**Advanced Techniques:**
- **Multi-Timeframe:** Confirm regime on higher timeframe
- **Early Entry:** Enter on regime transition rather than confirmation
- **Regime Strength:** Larger positions during strong regime signals
### The Information Flow Momentum Strategy
**Flow Detection Protocol:**
1. **Monitor Transfer Entropy:** Watch for significant information flow shifts
2. **Volume Leadership:** Strong edge when volume leads price
3. **Flow Acceleration:** Increasing flow strength indicates momentum
4. **Directional Confirmation:** Ensure flow aligns with intended trade direction
**Entry Signals:**
- **Volume → Price Flow:** Enter during accumulation/distribution phases
- **Price → Volume Flow:** Enter on momentum confirmation breaks
- **Flow Reversal:** Counter-trend entries when flow reverses
**Optimization:**
- **Scalping:** Use immediate flow detection (2-5 bar lag)
- **Swing Trading:** Use structural flow (10-20 bar lag)
- **Multi-Asset:** Compare flow between correlated assets
### The Tensor Field Expansion Strategy
**Field Mathematics:**
The tensor field expansion indicates mathematical pressure building in market structure:
**Expansion Phases:**
1. **Compression:** Field contracts, volatility decreases
2. **Tension Building:** Mathematical pressure accumulates
3. **Expansion:** Field expands rapidly with directional movement
4. **Resolution:** Field stabilizes at new equilibrium
**Trading Applications:**
- **Compression Trading:** Prepare for breakout during field contraction
- **Expansion Following:** Trade direction of field expansion
- **Reversion Trading:** Fade extreme field expansion
- **Multi-Dimensional:** Consider all field layers for confirmation
### The Hawkes Process Event Strategy
**Self-Exciting Jump Trading:**
Understanding that market shocks cluster and create follow-on opportunities:
**Jump Sequence Analysis:**
1. **Initial Jump:** First volatility jump detected
2. **Clustering Phase:** Hawkes intensity remains elevated
3. **Follow-On Opportunities:** Additional jumps more likely
4. **Decay Period:** Intensity gradually decreases
**Implementation:**
- **Jump Confirmation:** Wait for mathematical jump confirmation
- **Direction Assessment:** Use other components for direction
- **Clustering Trades:** Trade subsequent moves during high intensity
- **Decay Exit:** Exit positions as Hawkes intensity decays
### The Fractal Confluence System
**Multi-Timeframe Fractal Analysis:**
Combining fractal levels across different periods for high-probability zones:
**Confluence Zones:**
- **Double Confluence:** 2 fractal levels align
- **Triple Confluence:** 3+ fractal levels cluster
- **Mathematical Confirmation:** Tensor field supports the level
- **Information Flow:** Transfer entropy confirms direction
**Trading Protocol:**
1. **Identify Confluence:** Find 2+ fractal levels within 1 ATR
2. **Mathematical Support:** Verify tensor field alignment
3. **Signal Quality:** Wait for STRONG or ELITE signal
4. **Risk Definition:** Use fractal level for stop placement
5. **Profit Targeting:** Next major fractal confluence zone
---
## ⚠️ COMPREHENSIVE RISK MANAGEMENT
### Mathematical Position Sizing
**Mahalanobis Distance Integration:**
Position size should inversely correlate with mathematical field strength:
```
Position Size = Base Size × (Threshold / Mahalanobis Distance)
```
**Risk Scaling Matrix:**
- **Low Field Strength (<2.0):** Standard position sizing
- **Moderate Field Strength (2.0-3.0):** 75% position sizing
- **High Field Strength (3.0-4.0):** 50% position sizing
- **Extreme Field Strength (>4.0):** 25% position sizing or no trade
### Signal Quality Risk Adjustment
**Quality-Based Position Sizing:**
- **ELITE Signals:** 100% of planned position size
- **STRONG Signals:** 75% of planned position size
- **GOOD Signals:** 50% of planned position size
- **WEAK Signals:** No position or paper trading only
**Component Agreement Scaling:**
- **3/3 Components:** Full position size
- **2/3 Components:** 75% position size
- **1/3 Components:** 50% position size or skip trade
### Regime-Adaptive Risk Management
**Trending Market Risk:**
- **Wider Stops:** Allow for trend continuation
- **Trend Following:** Trade with regime direction
- **Higher Position Size:** Trend probability advantage
- **Momentum Stops:** Trail stops based on momentum indicators
**Mean-Reverting Market Risk:**
- **Tighter Stops:** Quick exits on trend continuation
- **Contrarian Positioning:** Trade against extremes
- **Smaller Position Size:** Higher reversal failure rate
- **Level-Based Stops:** Use fractal levels for stops
**Random Market Risk:**
- **Breakout Focus:** Trade only clear breakouts
- **Tight Initial Stops:** Quick exit if breakout fails
- **Reduced Frequency:** Skip marginal setups
- **Range-Based Targets:** Profit targets at range boundaries
### Volatility-Adaptive Risk Controls
**High Volatility Periods:**
- **Reduced Position Size:** Account for wider price swings
- **Wider Stops:** Avoid noise-based exits
- **Lower Frequency:** Skip marginal setups
- **Faster Exits:** Take profits more quickly
**Low Volatility Periods:**
- **Standard Position Size:** Normal risk parameters
- **Tighter Stops:** Take advantage of compressed ranges
- **Higher Frequency:** Trade more setups
- **Extended Targets:** Allow for compressed volatility expansion
### Multi-Timeframe Risk Alignment
**Higher Timeframe Trend:**
- **With Trend:** Standard or increased position size
- **Against Trend:** Reduced position size or skip
- **Neutral Trend:** Standard position size with tight management
**Risk Hierarchy:**
1. **Primary:** Current timeframe signal quality
2. **Secondary:** Higher timeframe trend alignment
3. **Tertiary:** Mathematical field strength
4. **Quaternary:** Market regime classification
---
## 📚 EDUCATIONAL VALUE AND MATHEMATICAL CONCEPTS
### Advanced Mathematical Concepts
**Tensor Analysis in Markets:**
The TMAE introduces traders to tensor analysis, a branch of mathematics typically reserved for physics and advanced engineering. Tensors provide a framework for understanding multi-dimensional market relationships that scalar and vector analysis cannot capture.
**Information Theory Applications:**
Transfer entropy implementation teaches traders about information flow in markets, a concept from information theory that quantifies directional causality between variables. This provides intuition about market microstructure and participant behavior.
**Fractal Geometry in Trading:**
The Hurst exponent calculation exposes traders to fractal geometry concepts, helping understand that markets exhibit self-similar patterns across multiple timeframes. This mathematical insight transforms how traders view market structure.
**Stochastic Process Theory:**
The Hawkes process implementation introduces concepts from stochastic process theory, specifically self-exciting point processes. This provides mathematical framework for understanding why market events cluster and exhibit memory effects.
### Learning Progressive Complexity
**Beginner Mathematical Concepts:**
- **Volatility Dimensions:** Understanding multi-dimensional analysis
- **Regime Classification:** Learning market personality types
- **Signal Democracy:** Algorithmic consensus building
- **Visual Mathematics:** Interpreting mathematical concepts visually
**Intermediate Mathematical Applications:**
- **Mahalanobis Distance:** Statistical distance in multi-dimensional space
- **Rescaled Range Analysis:** Fractal dimension measurement
- **Information Entropy:** Quantifying uncertainty and causality
- **Field Theory:** Understanding mathematical fields in market context
**Advanced Mathematical Integration:**
- **Tensor Field Dynamics:** Multi-dimensional market force analysis
- **Stochastic Self-Excitation:** Event clustering and memory effects
- **Categorical Composition:** Mathematical signal combination theory
- **Topological Market Analysis:** Understanding market shape and connectivity
### Practical Mathematical Intuition
**Developing Market Mathematics Intuition:**
The TMAE serves as a bridge between abstract mathematical concepts and practical trading applications. Traders develop intuitive understanding of:
- **How markets exhibit mathematical structure beneath apparent randomness**
- **Why multi-dimensional analysis reveals patterns invisible to single-variable approaches**
- **How information flows through markets in measurable, predictable ways**
- **Why mathematical models provide probabilistic edges rather than certainties**
---
## 🔬 IMPLEMENTATION AND OPTIMIZATION
### Getting Started Protocol
**Phase 1: Observation (Week 1)**
1. **Apply with defaults:** Use standard settings on your primary trading timeframe
2. **Study visual elements:** Learn to interpret tensor fields, portals, and streams
3. **Monitor dashboard:** Observe how metrics change with market conditions
4. **No trading:** Focus entirely on pattern recognition and understanding
**Phase 2: Pattern Recognition (Week 2-3)**
1. **Identify signal patterns:** Note what market conditions produce different signal qualities
2. **Regime correlation:** Observe how Hurst regimes affect signal performance
3. **Visual confirmation:** Learn to read tensor field expansion and portal signals
4. **Component analysis:** Understand which components drive signals in different markets
**Phase 3: Parameter Optimization (Week 4-5)**
1. **Asset-specific tuning:** Adjust parameters for your specific trading instrument
2. **Timeframe optimization:** Fine-tune for your preferred trading timeframe
3. **Sensitivity adjustment:** Balance signal frequency with quality
4. **Visual customization:** Optimize colors and intensity for your trading environment
**Phase 4: Live Implementation (Week 6+)**
1. **Paper trading:** Test signals with hypothetical trades
2. **Small position sizing:** Begin with minimal risk during learning phase
3. **Performance tracking:** Monitor actual vs. expected signal performance
4. **Continuous optimization:** Refine settings based on real performance data
### Performance Monitoring System
**Signal Quality Tracking:**
- **ELITE Signal Win Rate:** Track highest quality signals separately
- **Component Performance:** Monitor which components provide best signals
- **Regime Performance:** Analyze performance across different market regimes
- **Timeframe Analysis:** Compare performance across different session times
**Mathematical Metric Correlation:**
- **Field Strength vs. Performance:** Higher field strength should correlate with better performance
- **Component Agreement vs. Win Rate:** More component agreement should improve win rates
- **Regime Alignment vs. Success:** Trading with mathematical regime should outperform
### Continuous Optimization Process
**Monthly Review Protocol:**
1. **Performance Analysis:** Review win rates, profit factors, and maximum drawdown
2. **Parameter Assessment:** Evaluate if current settings remain optimal
3. **Market Adaptation:** Adjust for changes in market character or volatility
4. **Component Weighting:** Consider if certain components should receive more/less emphasis
**Quarterly Deep Analysis:**
1. **Mathematical Model Validation:** Verify that mathematical relationships remain valid
2. **Regime Distribution:** Analyze time spent in different market regimes
3. **Signal Evolution:** Track how signal characteristics change over time
4. **Correlation Analysis:** Monitor correlations between different mathematical components
---
## 🌟 UNIQUE INNOVATIONS AND CONTRIBUTIONS
### Revolutionary Mathematical Integration
**First-Ever Implementations:**
1. **Multi-Dimensional Volatility Tensor:** First indicator to implement true tensor analysis for market volatility
2. **Real-Time Hawkes Process:** First trading implementation of self-exciting point processes
3. **Transfer Entropy Trading Signals:** First practical application of information theory for trade generation
4. **Democratic Component Voting:** First algorithmic consensus system for signal generation
5. **Fractal-Projected Signal Quality:** First system to predict signal quality at future price levels
### Advanced Visualization Innovations
**Mathematical Visualization Breakthroughs:**
- **Tensor Field Radiation:** Visual representation of mathematical field energy
- **Dimensional Portal System:** Category theory visualization for regime transitions
- **Information Flow Streams:** Real-time visual display of market information transfer
- **Multi-Layer Fractal Grid:** Intelligent spacing and projection system
- **Regime Intensity Mapping:** Dynamic background showing mathematical regime strength
### Practical Trading Innovations
**Trading System Advances:**
- **Quality-Weighted Signal Generation:** Signals rated by mathematical confidence
- **Regime-Adaptive Strategy Selection:** Automatic strategy optimization based on market personality
- **Anti-Spam Signal Protection:** Mathematical prevention of signal clustering
- **Component Performance Tracking:** Real-time monitoring of algorithmic component success
- **Field-Strength Position Sizing:** Mathematical volatility integration for risk management
---
## ⚖️ RESPONSIBLE USAGE AND LIMITATIONS
### Mathematical Model Limitations
**Understanding Model Boundaries:**
While the TMAE implements sophisticated mathematical concepts, traders must understand fundamental limitations:
- **Markets Are Not Purely Mathematical:** Human psychology, news events, and fundamental factors create unpredictable elements
- **Past Performance Limitations:** Mathematical relationships that worked historically may not persist indefinitely
- **Model Risk:** Complex models can fail during unprecedented market conditions
- **Overfitting Potential:** Highly optimized parameters may not generalize to future market conditions
### Proper Implementation Guidelines
**Risk Management Requirements:**
- **Never Risk More Than 2% Per Trade:** Regardless of signal quality
- **Diversification Mandatory:** Don't rely solely on mathematical signals
- **Position Sizing Discipline:** Use mathematical field strength for sizing, not confidence
- **Stop Loss Non-Negotiable:** Every trade must have predefined risk parameters
**Realistic Expectations:**
- **Mathematical Edge, Not Certainty:** The indicator provides probabilistic advantages, not guaranteed outcomes
- **Learning Curve Required:** Complex mathematical concepts require time to master
- **Market Adaptation Necessary:** Parameters must evolve with changing market conditions
- **Continuous Education Important:** Understanding underlying mathematics improves application
### Ethical Trading Considerations
**Market Impact Awareness:**
- **Information Asymmetry:** Advanced mathematical analysis may provide advantages over other market participants
- **Position Size Responsibility:** Large positions based on mathematical signals can impact market structure
- **Sharing Knowledge:** Consider educational contributions to trading community
- **Fair Market Participation:** Use mathematical advantages responsibly within market framework
### Professional Development Path
**Skill Development Sequence:**
1. **Basic Mathematical Literacy:** Understand fundamental concepts before advanced application
2. **Risk Management Mastery:** Develop disciplined risk control before relying on complex signals
3. **Market Psychology Understanding:** Combine mathematical analysis with behavioral market insights
4. **Continuous Learning:** Stay updated on mathematical finance developments and market evolution
---
## 🔮 CONCLUSION
The Tensor Market Analysis Engine represents a quantum leap forward in technical analysis, successfully bridging the gap between advanced pure mathematics and practical trading applications. By integrating multi-dimensional volatility analysis, fractal market theory, and information flow dynamics, the TMAE reveals market structure invisible to conventional analysis while maintaining visual clarity and practical usability.
### Mathematical Innovation Legacy
This indicator establishes new paradigms in technical analysis:
- **Tensor analysis for market volatility understanding**
- **Stochastic self-excitation for event clustering prediction**
- **Information theory for causality-based trade generation**
- **Democratic algorithmic consensus for signal quality enhancement**
- **Mathematical field visualization for intuitive market understanding**
### Practical Trading Revolution
Beyond mathematical innovation, the TMAE transforms practical trading:
- **Quality-rated signals replace binary buy/sell decisions**
- **Regime-adaptive strategies automatically optimize for market personality**
- **Multi-dimensional risk management integrates mathematical volatility measures**
- **Visual mathematical concepts make complex analysis immediately interpretable**
- **Educational value creates lasting improvement in trading understanding**
### Future-Proof Design
The mathematical foundations ensure lasting relevance:
- **Universal mathematical principles transcend market evolution**
- **Multi-dimensional analysis adapts to new market structures**
- **Regime detection automatically adjusts to changing market personalities**
- **Component democracy allows for future algorithmic additions**
- **Mathematical visualization scales with increasing market complexity**
### Commitment to Excellence
The TMAE represents more than an indicator—it embodies a philosophy of bringing rigorous mathematical analysis to trading while maintaining practical utility and visual elegance. Every component, from the multi-dimensional tensor fields to the democratic signal generation, reflects a commitment to mathematical accuracy, trading practicality, and educational value.
### Trading with Mathematical Precision
In an era where markets grow increasingly complex and computational, the TMAE provides traders with mathematical tools previously available only to institutional quantitative research teams. Yet unlike academic mathematical models, the TMAE translates complex concepts into intuitive visual representations and practical trading signals.
By combining the mathematical rigor of tensor analysis, the statistical power of multi-dimensional volatility modeling, and the information-theoretic insights of transfer entropy, traders gain unprecedented insight into market structure and dynamics.
### Final Perspective
Markets, like nature, exhibit profound mathematical beauty beneath apparent chaos. The Tensor Market Analysis Engine serves as a mathematical lens that reveals this hidden order, transforming how traders perceive and interact with market structure.
Through mathematical precision, visual elegance, and practical utility, the TMAE empowers traders to see beyond the noise and trade with the confidence that comes from understanding the mathematical principles governing market behavior.
Trade with mathematical insight. Trade with the power of tensors. Trade with the TMAE.
*"In mathematics, you don't understand things. You just get used to them." - John von Neumann*
*With the TMAE, mathematical market understanding becomes not just possible, but intuitive.*
— Dskyz, Trade with insight. Trade with anticipation.
Trend Gauge [BullByte]Trend Gauge
Summary
A multi-factor trend detection indicator that aggregates EMA alignment, VWMA momentum scaling, volume spikes, ATR breakout strength, higher-timeframe confirmation, ADX-based regime filtering, and RSI pivot-divergence penalty into one normalized trend score. It also provides a confidence meter, a Δ Score momentum histogram, divergence highlights, and a compact, scalable dashboard for at-a-glance status.
________________________________________
## 1. Purpose of the Indicator
Why this was built
Traders often monitor several indicators in parallel - EMAs, volume signals, volatility breakouts, higher-timeframe trends, ADX readings, divergence alerts, etc., which can be cumbersome and sometimes contradictory. The “Trend Gauge” indicator was created to consolidate these complementary checks into a single, normalized score that reflects the prevailing market bias (bullish, bearish, or neutral) and its strength. By combining multiple inputs with an adaptive regime filter, scaling contributions by magnitude, and penalizing weakening signals (divergence), this tool aims to reduce noise, highlight genuine trend opportunities, and warn when momentum fades.
Key Design Goals
Signal Aggregation
Merged trend-following signals (EMA crossover, ATR breakout, higher-timeframe confirmation) and momentum signals (VWMA thrust, volume spikes) into a unified score that reflects directional bias more holistically.
Market Regime Awareness
Implemented an ADX-style filter to distinguish between trending and ranging markets, reducing the influence of trend signals during sideways phases to avoid false breakouts.
Magnitude-Based Scaling
Replaced binary contributions with scaled inputs: VWMA thrust and ATR breakout are weighted relative to recent averages, allowing for more nuanced score adjustments based on signal strength.
Momentum Divergence Penalty
Integrated pivot-based RSI divergence detection to slightly reduce the overall score when early signs of momentum weakening are detected, improving risk-awareness in entries.
Confidence Transparency
Added a live confidence metric that shows what percentage of enabled sub-indicators currently agree with the overall bias, making the scoring system more interpretable.
Momentum Acceleration Visualization
Plotted the change in score (Δ Score) as a histogram bar-to-bar, highlighting whether momentum is increasing, flattening, or reversing, aiding in more timely decision-making.
Compact Informational Dashboard
Presented a clean, scalable dashboard that displays each component’s status, the final score, confidence %, detected regime (Trending/Ranging), and a labeled strength gauge for quick visual assessment.
________________________________________
## 2. Why a Trader Should Use It
Main benefits and use cases
1. Unified View: Rather than juggling multiple windows or panels, this indicator delivers a single score synthesizing diverse signals.
2. Regime Filtering: In ranging markets, trend signals often generate false entries. The ADX-based regime filter automatically down-weights trend-following components, helping you avoid chasing false breakouts.
3. Nuanced Momentum & Volatility: VWMA and ATR breakout contributions are normalized by recent averages, so strong moves register strongly while smaller fluctuations are de-emphasized.
4. Early Warning of Weakening: Pivot-based RSI divergence is detected and used to slightly reduce the score when price/momentum diverges, giving a cautionary signal before a full reversal.
5. Confidence Meter: See at a glance how many sub-indicators align with the aggregated bias (e.g., “80% confidence” means 4 out of 5 components agree ). This transparency avoids black-box decisions.
6. Trend Acceleration/Deceleration View: The Δ Score histogram visualizes whether the aggregated score is rising (accelerating trend) or falling (momentum fading), supplementing the main oscillator.
7. Compact Dashboard: A corner table lists each check’s status (“Bull”, “Bear”, “Flat” or “Disabled”), plus overall Score, Confidence %, Regime, Trend Strength label, and a gauge bar. Users can scale text size (Normal, Small, Tiny) without removing elements, so the full picture remains visible even in compact layouts.
8. Customizable & Transparent: All components can be enabled/disabled and parameterized (lengths, thresholds, weights). The full Pine code is open and well-commented, letting users inspect or adapt the logic.
9. Alert-ready: Built-in alert conditions fire when the score crosses weak thresholds to bullish/bearish or returns to neutral, enabling timely notifications.
________________________________________
## 3. Component Rationale (“Why These Specific Indicators?”)
Each sub-component was chosen because it adds complementary information about trend or momentum:
1. EMA Cross
o Basic trend measure: compares a faster EMA vs. a slower EMA. Quickly reflects trend shifts but by itself can whipsaw in sideways markets.
2. VWMA Momentum
o Volume-weighted moving average change indicates momentum with volume context. By normalizing (dividing by a recent average absolute change), we capture the strength of momentum relative to recent history. This scaling prevents tiny moves from dominating and highlights genuinely strong momentum.
3. Volume Spikes
o Sudden jumps in volume combined with price movement often accompany stronger moves or reversals. A binary detection (+1 for bullish spike, -1 for bearish spike) flags high-conviction bars.
4. ATR Breakout
o Detects price breaking beyond recent highs/lows by a multiple of ATR. Measures breakout strength by how far beyond the threshold price moves relative to ATR, capped to avoid extreme outliers. This gives a volatility-contextual trend signal.
5. Higher-Timeframe EMA Alignment
o Confirms whether the shorter-term trend aligns with a higher timeframe trend. Uses request.security with lookahead_off to avoid future data. When multiple timeframes agree, confidence in direction increases.
6. ADX Regime Filter (Manual Calculation)
o Computes directional movement (+DM/–DM), smoothes via RMA, computes DI+ and DI–, then a DX and ADX-like value. If ADX ≥ threshold, market is “Trending” and trend components carry full weight; if ADX < threshold, “Ranging” mode applies a configurable weight multiplier (e.g., 0.5) to trend-based contributions, reducing false signals in sideways conditions. Volume spikes remain binary (optional behavior; can be adjusted if desired).
7. RSI Pivot-Divergence Penalty
o Uses ta.pivothigh / ta.pivotlow with a lookback to detect pivot highs/lows on price and corresponding RSI values. When price makes a higher high but RSI makes a lower high (bearish divergence), or price makes a lower low but RSI makes a higher low (bullish divergence), a divergence signal is set. Rather than flipping the trend outright, the indicator subtracts (or adds) a small penalty (configurable) from the aggregated score if it would weaken the current bias. This subtle adjustment warns of weakening momentum without overreacting to noise.
8. Confidence Meter
o Counts how many enabled components currently agree in direction with the aggregated score (i.e., component sign × score sign > 0). Displays this as a percentage. A high percentage indicates strong corroboration; a low percentage warns of mixed signals.
9. Δ Score Momentum View
o Plots the bar-to-bar change in the aggregated score (delta_score = score - score ) as a histogram. When positive, bars are drawn in green above zero; when negative, bars are drawn in red below zero. This reveals acceleration (rising Δ) or deceleration (falling Δ), supplementing the main oscillator.
10. Dashboard
• A table in the indicator pane’s top-right with 11 rows:
1. EMA Cross status
2. VWMA Momentum status
3. Volume Spike status
4. ATR Breakout status
5. Higher-Timeframe Trend status
6. Score (numeric)
7. Confidence %
8. Regime (“Trending” or “Ranging”)
9. Trend Strength label (e.g., “Weak Bullish Trend”, “Strong Bearish Trend”)
10. Gauge bar visually representing score magnitude
• All rows always present; size_opt (Normal, Small, Tiny) only changes text size via text_size, not which elements appear. This ensures full transparency.
________________________________________
## 4. What Makes This Indicator Stand Out
• Regime-Weighted Multi-Factor Score: Trend and momentum signals are adaptively weighted by market regime (trending vs. ranging) , reducing false signals.
• Magnitude Scaling: VWMA and ATR breakout contributions are normalized by recent average momentum or ATR, giving finer gradation compared to simple ±1.
• Integrated Divergence Penalty: Divergence directly adjusts the aggregated score rather than appearing as a separate subplot; this influences alerts and trend labeling in real time.
• Confidence Meter: Shows the percentage of sub-signals in agreement, providing transparency and preventing blind trust in a single metric.
• Δ Score Histogram Momentum View: A histogram highlights acceleration or deceleration of the aggregated trend score, helping detect shifts early.
• Flexible Dashboard: Always-visible component statuses and summary metrics in one place; text size scaling keeps the full picture available in cramped layouts.
• Lookahead-Safe HTF Confirmation: Uses lookahead_off so no future data is accessed from higher timeframes, avoiding repaint bias.
• Repaint Transparency: Divergence detection uses pivot functions that inherently confirm only after lookback bars; description documents this lag so users understand how and when divergence labels appear.
• Open-Source & Educational: Full, well-commented Pine v6 code is provided; users can learn from its structure: manual ADX computation, conditional plotting with series = show ? value : na, efficient use of table.new in barstate.islast, and grouped inputs with tooltips.
• Compliance-Conscious: All plots have descriptive titles; inputs use clear names; no unnamed generic “Plot” entries; manual ADX uses RMA; all request.security calls use lookahead_off. Code comments mention repaint behavior and limitations.
________________________________________
## 5. Recommended Timeframes & Tuning
• Any Timeframe: The indicator works on small (e.g., 1m) to large (daily, weekly) timeframes. However:
o On very low timeframes (<1m or tick charts), noise may produce frequent whipsaws. Consider increasing smoothing lengths, disabling certain components (e.g., volume spike if volume data noisy), or using a larger pivot lookback for divergence.
o On higher timeframes (daily, weekly), consider longer lookbacks for ATR breakout or divergence, and set Higher-Timeframe trend appropriately (e.g., 4H HTF when on 5 Min chart).
• Defaults & Experimentation: Default input values are chosen to be balanced for many liquid markets. Users should test with replay or historical analysis on their symbol/timeframe and adjust:
o ADX threshold (e.g., 20–30) based on instrument volatility.
o VWMA and ATR scaling lengths to match average volatility cycles.
o Pivot lookback for divergence: shorter for faster markets, longer for slower ones.
• Combining with Other Analysis: Use in conjunction with price action, support/resistance, candlestick patterns, order flow, or other tools as desired. The aggregated score and alerts can guide attention but should not be the sole decision-factor.
________________________________________
## 6. How Scoring and Logic Works (Step-by-Step)
1. Compute Sub-Scores
o EMA Cross: Evaluate fast EMA > slow EMA ? +1 : fast EMA < slow EMA ? -1 : 0.
o VWMA Momentum: Calculate vwma = ta.vwma(close, length), then vwma_mom = vwma - vwma . Normalize: divide by recent average absolute momentum (e.g., ta.sma(abs(vwma_mom), lookback)), clip to .
o Volume Spike: Compute vol_SMA = ta.sma(volume, len). If volume > vol_SMA * multiplier AND price moved up ≥ threshold%, assign +1; if moved down ≥ threshold%, assign -1; else 0.
o ATR Breakout: Determine recent high/low over lookback. If close > high + ATR*mult, compute distance = close - (high + ATR*mult), normalize by ATR, cap at a configured maximum. Assign positive contribution. Similarly for bearish breakout below low.
o Higher-Timeframe Trend: Use request.security(..., lookahead=barmerge.lookahead_off) to fetch HTF EMAs; assign +1 or -1 based on alignment.
2. ADX Regime Weighting
o Compute manual ADX: directional movements (+DM, –DM), smoothed via RMA, DI+ and DI–, then DX and ADX via RMA. If ADX ≥ threshold, market is considered “Trending”; otherwise “Ranging.”
o If trending, trend-based contributions (EMA, VWMA, ATR, HTF) use full weight = 1.0. If ranging, use weight = ranging_weight (e.g., 0.5) to down-weight them. Volume spike stays binary ±1 (optional to change if desired).
3. Aggregate Raw Score
o Sum weighted contributions of all enabled components. Count the number of enabled components; if zero, default count = 1 to avoid division by zero.
4. Divergence Penalty
o Detect pivot highs/lows on price and corresponding RSI values, using a lookback. When price and RSI diverge (bearish or bullish divergence), check if current raw score is in the opposing direction:
If bearish divergence (price higher high, RSI lower high) and raw score currently positive, subtract a penalty (e.g., 0.5).
If bullish divergence (price lower low, RSI higher low) and raw score currently negative, add a penalty.
o This reduces score magnitude to reflect weakening momentum, without flipping the trend outright.
5. Normalize and Smooth
o Normalized score = (raw_score / number_of_enabled_components) * 100. This yields a roughly range.
o Optional EMA smoothing of this normalized score to reduce noise.
6. Interpretation
o Sign: >0 = net bullish bias; <0 = net bearish bias; near zero = neutral.
o Magnitude Zones: Compare |score| to thresholds (Weak, Medium, Strong) to label trend strength (e.g., “Weak Bullish Trend”, “Medium Bearish Trend”, “Strong Bullish Trend”).
o Δ Score Histogram: The histogram bars from zero show change from previous bar’s score; positive bars indicate acceleration, negative bars indicate deceleration.
o Confidence: Percentage of sub-indicators aligned with the score’s sign.
o Regime: Indicates whether trend-based signals are fully weighted or down-weighted.
________________________________________
## 7. Oscillator Plot & Visualization: How to Read It
Main Score Line & Area
The oscillator plots the aggregated score as a line, with colored fill: green above zero for bullish area, red below zero for bearish area. Horizontal reference lines at ±Weak, ±Medium, and ±Strong thresholds mark zones: crossing above +Weak suggests beginning of bullish bias, above +Medium for moderate strength, above +Strong for strong trend; similarly for bearish below negative thresholds.
Δ Score Histogram
If enabled, a histogram shows score - score . When positive, bars appear in green above zero, indicating accelerating bullish momentum; when negative, bars appear in red below zero, indicating decelerating or reversing momentum. The height of each bar reflects the magnitude of change in the aggregated score from the prior bar.
Divergence Highlight Fill
If enabled, when a pivot-based divergence is confirmed:
• Bullish Divergence : fill the area below zero down to –Weak threshold in green, signaling potential reversal from bearish to bullish.
• Bearish Divergence : fill the area above zero up to +Weak threshold in red, signaling potential reversal from bullish to bearish.
These fills appear with a lag equal to pivot lookback (the number of bars needed to confirm the pivot). They do not repaint after confirmation, but users must understand this lag.
Trend Direction Label
When score crosses above or below the Weak threshold, a small label appears near the score line reading “Bullish” or “Bearish.” If the score returns within ±Weak, the label “Neutral” appears. This helps quickly identify shifts at the moment they occur.
Dashboard Panel
In the indicator pane’s top-right, a table shows:
1. EMA Cross status: “Bull”, “Bear”, “Flat”, or “Disabled”
2. VWMA Momentum status: similarly
3. Volume Spike status: “Bull”, “Bear”, “No”, or “Disabled”
4. ATR Breakout status: “Bull”, “Bear”, “No”, or “Disabled”
5. Higher-Timeframe Trend status: “Bull”, “Bear”, “Flat”, or “Disabled”
6. Score: numeric value (rounded)
7. Confidence: e.g., “80%” (colored: green for high, amber for medium, red for low)
8. Regime: “Trending” or “Ranging” (colored accordingly)
9. Trend Strength: textual label based on magnitude (e.g., “Medium Bullish Trend”)
10. Gauge: a bar of blocks representing |score|/100
All rows remain visible at all times; changing Dashboard Size only scales text size (Normal, Small, Tiny).
________________________________________
## 8. Example Usage (Illustrative Scenario)
Example: BTCUSD 5 Min
1. Setup: Add “Trend Gauge ” to your BTCUSD 5 Min chart. Defaults: EMAs (8/21), VWMA 14 with lookback 3, volume spike settings, ATR breakout 14/5, HTF = 5m (or adjust to 4H if preferred), ADX threshold 25, ranging weight 0.5, divergence RSI length 14 pivot lookback 5, penalty 0.5, smoothing length 3, thresholds Weak=20, Medium=50, Strong=80. Dashboard Size = Small.
2. Trend Onset: At some point, price breaks above recent high by ATR multiple, volume spikes upward, faster EMA crosses above slower EMA, HTF EMA also bullish, and ADX (manual) ≥ threshold → aggregated score rises above +20 (Weak threshold) into +Medium zone. Dashboard shows “Bull” for EMA, VWMA, Vol Spike, ATR, HTF; Score ~+60–+70; Confidence ~100%; Regime “Trending”; Trend Strength “Medium Bullish Trend”; Gauge ~6–7 blocks. Δ Score histogram bars are green and rising, indicating accelerating bullish momentum. Trader notes the alignment.
3. Divergence Warning: Later, price makes a slightly higher high but RSI fails to confirm (lower RSI high). Pivot lookback completes; the indicator highlights a bearish divergence fill above zero and subtracts a small penalty from the score, causing score to stall or retrace slightly. Dashboard still bullish but score dips toward +Weak. This warns the trader to tighten stops or take partial profits.
4. Trend Weakens: Score eventually crosses below +Weak back into neutral; a “Neutral” label appears, and a “Neutral Trend” alert fires if enabled. Trader exits or avoids new long entries. If score subsequently crosses below –Weak, a “Bearish” label and alert occur.
5. Customization: If the trader finds VWMA noise too frequent on this instrument, they may disable VWMA or increase lookback. If ATR breakouts are too rare, adjust ATR length or multiplier. If ADX threshold seems off, tune threshold. All these adjustments are explained in Inputs section.
6. Visualization: The screenshot shows the main score oscillator with colored areas, reference lines at ±20/50/80, Δ Score histogram bars below/above zero, divergence fill highlighting potential reversal, and the dashboard table in the top-right.
________________________________________
## 9. Inputs Explanation
A concise yet clear summary of inputs helps users understand and adjust:
1. General Settings
• Theme (Dark/Light): Choose background-appropriate colors for the indicator pane.
• Dashboard Size (Normal/Small/Tiny): Scales text size only; all dashboard elements remain visible.
2. Indicator Settings
• Enable EMA Cross: Toggle on/off basic EMA alignment check.
o Fast EMA Length and Slow EMA Length: Periods for EMAs.
• Enable VWMA Momentum: Toggle VWMA momentum check.
o VWMA Length: Period for VWMA.
o VWMA Momentum Lookback: Bars to compare VWMA to measure momentum.
• Enable Volume Spike: Toggle volume spike detection.
o Volume SMA Length: Period to compute average volume.
o Volume Spike Multiplier: How many times above average volume qualifies as spike.
o Min Price Move (%): Minimum percent change in price during spike to qualify as bullish or bearish.
• Enable ATR Breakout: Toggle ATR breakout detection.
o ATR Length: Period for ATR.
o Breakout Lookback: Bars to look back for recent highs/lows.
o ATR Multiplier: Multiplier for breakout threshold.
• Enable Higher Timeframe Trend: Toggle HTF EMA alignment.
o Higher Timeframe: E.g., “5” for 5-minute when on 1-minute chart, or “60” for 5 Min when on 15m, etc. Uses lookahead_off.
• Enable ADX Regime Filter: Toggles regime-based weighting.
o ADX Length: Period for manual ADX calculation.
o ADX Threshold: Value above which market considered trending.
o Ranging Weight Multiplier: Weight applied to trend components when ADX < threshold (e.g., 0.5).
• Scale VWMA Momentum: Toggle normalization of VWMA momentum magnitude.
o VWMA Mom Scale Lookback: Period for average absolute VWMA momentum.
• Scale ATR Breakout Strength: Toggle normalization of breakout distance by ATR.
o ATR Scale Cap: Maximum multiple of ATR used for breakout strength.
• Enable Price-RSI Divergence: Toggle divergence detection.
o RSI Length for Divergence: Period for RSI.
o Pivot Lookback for Divergence: Bars on each side to identify pivot high/low.
o Divergence Penalty: Amount to subtract/add to score when divergence detected (e.g., 0.5).
3. Score Settings
• Smooth Score: Toggle EMA smoothing of normalized score.
• Score Smoothing Length: Period for smoothing EMA.
• Weak Threshold: Absolute score value under which trend is considered weak or neutral.
• Medium Threshold: Score above Weak but below Medium is moderate.
• Strong Threshold: Score above this indicates strong trend.
4. Visualization Settings
• Show Δ Score Histogram: Toggle display of the bar-to-bar change in score as a histogram. Default true.
• Show Divergence Fill: Toggle background fill highlighting confirmed divergences. Default true.
Each input has a tooltip in the code.
________________________________________
## 10. Limitations, Repaint Notes, and Disclaimers
10.1. Repaint & Lag Considerations
• Pivot-Based Divergence Lag: The divergence detection uses ta.pivothigh / ta.pivotlow with a specified lookback. By design, a pivot is only confirmed after the lookback number of bars. As a result:
o Divergence labels or fills appear with a delay equal to the pivot lookback.
o Once the pivot is confirmed and the divergence is detected, the fill/label does not repaint thereafter, but you must understand and accept this lag.
o Users should not treat divergence highlights as predictive signals without additional confirmation, because they appear after the pivot has fully formed.
• Higher-Timeframe EMA Alignment: Uses request.security(..., lookahead=barmerge.lookahead_off), so no future data from the higher timeframe is used. This avoids lookahead bias and ensures signals are based only on completed higher-timeframe bars.
• No Future Data: All calculations are designed to avoid using future information. For example, manual ADX uses RMA on past data; security calls use lookahead_off.
10.2. Market & Noise Considerations
• In very choppy or low-liquidity markets, some components (e.g., volume spikes or VWMA momentum) may be noisy. Users can disable or adjust those components’ parameters.
• On extremely low timeframes, noise may dominate; consider smoothing lengths or disabling certain features.
• On very high timeframes, pivots and breakouts occur less frequently; adjust lookbacks accordingly to avoid sparse signals.
10.3. Not a Standalone Trading System
• This is an indicator, not a complete trading strategy. It provides signals and context but does not manage entries, exits, position sizing, or risk management.
• Users must combine it with their own analysis, money management, and confirmations (e.g., price patterns, support/resistance, fundamental context).
• No guarantees: past behavior does not guarantee future performance.
10.4. Disclaimers
• Educational Purposes Only: The script is provided as-is for educational and informational purposes. It does not constitute financial, investment, or trading advice.
• Use at Your Own Risk: Trading involves risk of loss. Users should thoroughly test and use proper risk management.
• No Guarantees: The author is not responsible for trading outcomes based on this indicator.
• License: Published under Mozilla Public License 2.0; code is open for viewing and modification under MPL terms.
________________________________________
## 11. Alerts
• The indicator defines three alert conditions:
1. Bullish Trend: when the aggregated score crosses above the Weak threshold.
2. Bearish Trend: when the score crosses below the negative Weak threshold.
3. Neutral Trend: when the score returns within ±Weak after being outside.
Good luck
– BullByte
MTF RSI MA System + Adaptive BandsMTF RSI MA System + Adaptive Bands
Overview
MTF RSI MA System + Adaptive Bands is a highly customizable Pine Script indicator for traders seeking a versatile tool for multi-timeframe (MTF) analysis. Unlike traditional RSI, it focuses on the Moving Average of RSI (RSI MA), delivering smoother and more flexible trading signals. The main screenshot displays the indicator in two panels to showcase its diverse capabilities.
Important: Timeframes do not adjust automatically – users must manually set them to match the chart’s timeframe.
Features
Core Component: Built around RSI MA, not raw RSI, for smoother trend signals.
Multi-Timeframe: Analyze RSI MA across three customizable timeframes (default: 4H, 8H, 12H).
Adaptive Bands: Three band calculation methods (Fixed, Percent, StdDev) for dynamic signals.
Flexible Signals: Generated via RSI MA crossovers, band interactions, or directional alignment across timeframes.
Background Coloring: Highlights when RSI MAs across timeframes move in the same direction, aiding trend confirmation.
Screenshot Panels Configuration
Upper Panel: Shows RSI, RSI MA, and fixed bands for reversal strategies (RSI crossing bands).
Lower Panel: Displays three RSI MAs (Alligator-style) for trend-following, with background coloring for directional alignment.
Band Calculation Methods
The indicator offers three ways to calculate bands around RSI MA, each with unique characteristics:
Fixed Bands
Set at a fixed point value (default: 10) above and below RSI MA.
Example: If RSI MA = 50, band value = 10 → upper band = 60, lower = 40.
Use Case: Best for stable markets or fixed-range preferences.
Tip: Adjust the band value to widen or narrow the range based on asset volatility.
Percent Bands
Calculated as a percentage of RSI MA (default: 10%).
Example: If RSI MA = 50, band value = 10% → upper band = 55, lower = 45.
Use Case: Ideal for assets with varying volatility, as bands scale with RSI MA.
Tip: Experiment with percentage values to match typical price swings.
Standard Deviation Bands (StdDev)
Based on RSI’s standard deviation over the MA period, multiplied by a user-defined factor (default: 10).
Example: If RSI MA = 50, standard deviation = 5, factor = 2 → upper band = 60, lower = 40.
Important: The default value (10) may produce wide bands. Reduce to 1–2 for tighter, practical bands.
Use Case: Best for dynamic markets with fluctuating volatility.
Configuration Options
RSI Length: Set RSI calculation period (default: 20).
MA Length: Set RSI MA period (default: 20).
MA Type: Choose SMA or EMA for RSI MA (default: EMA).
Timeframes: Configure three timeframes (default: 4H, 8H, 12H) for MTF analysis.
Overbought/Oversold Levels: Optionally display fixed levels (default: 70/30).
Background Coloring: Enable/disable for each timeframe to highlight directional alignment.
How to Use
Add Indicator: Load it onto your TradingView chart.
Setup:
Reversals: Configure like the upper panel (RSI, RSI MA, bands) and watch for RSI crossing bands.
Trends: Configure like the lower panel (three RSI MAs) and look for fastest MA crossovers and background coloring.
Adjust Timeframes: Manually set tf1, tf2, tf3 (e.g., 1H, 2H, 4H on a 1H chart) to suit your strategy.
Adjust Bands: Choose band type (Fixed, Percent, StdDev) and value. For StdDev, reduce to 1–2 for tighter bands.
Experiment: Test settings to match your trading style, whether scalping, swing trading, or long-term.
Notes
Timeframes: Always match tf1, tf2, tf3 to your chart’s needs, as they don’t auto-adjust.
StdDev Bands: Lower the default value (10) to avoid overly wide bands.
Versatility: Works across markets (stocks, forex, crypto).
Crypto Cycle Projection📈 Crypto Cycle Projection – Indicator Description
This indicator is designed to visually track and forecast repeating price cycles in the crypto market. It highlights a defined time-based cycle starting from a chosen date or the latest bar on the chart. By identifying cycle Start, Midpoint, and End zones, traders can gain insights into timing-based market structure and possible pivot periods.
⚙️ User Settings Explained
Start Point
Start from Last Candle (useLastCandle) – When enabled, the cycle begins from the most recent candle on the chart.
Manual Date (Year / Month / Day) – If Start from Last Candle is disabled, you can manually set a specific start date for the cycle.
Display Options
- Show Projection (showZone) – Toggles the display of the main cycle projection.
- Show Outer Bars (showOuter) – Adds faded edge bars around the key cycle zones for better visual emphasis.
- Show Previous Cycle (showPreviousCycle) – Adds the prior cycle to the chart, going one full cycle period back from the main start point.
Show Next Cycle (showNextCycle) – Projects one additional cycle forward beyond the current.
Cycle Parameters
Cycle Period (cyclePeriod) – Defines the number of bars in a full cycle (e.g., 60 = 60 bars). This sets the spacing between Start → Midpoint → End.
Each cycle section is color-coded:
Start = White
Midpoint = Yellow
End = Green
These reference lines and zones help you align trades with cycle timing for potential reversals, continuations, or volatility expansions.
Co-author Credit:
Matthew Hyland @ParabolicMatt
Market Pulse ProMarket Pulse Pro (Pulse‑X) — User Guide
Market Pulse Pro, also known as Pulse‑X, is an advanced momentum indicator that combines SMI, Stochastic RSI, and a smoothed signal line to identify zones of buying and selling strength in the market. It is designed to assess the balance of power between bulls and bears with clear visualizations.
How It Works
The indicator calculates three main components:
SMI (Stochastic Momentum Index) – measures price position relative to its recent range.
Stochastic RSI – captures overbought/oversold extremes of the RSI.
Smoothed Signal Line – based on closing price, smoothed using various methods (such as HMA, EMA, etc.).
Each component is normalized to create two final values:
Bull Herd (Buying Strength) – green line.
Bear Winter (Selling Strength) – red line.
Interpretation
Bull Herd (high green values): Bulls dominate the market. May indicate the start or continuation of an uptrend.
Bear Winter (high red values): Bears dominate. May indicate reversal or continuation of a downtrend.
Convergence around 50%: Market is balanced. Signals are weaker or indecisive.
Tip: Combine with price action analysis or support/resistance levels to confirm entries.
Customizable Settings
You can adjust:
SMI Period, Smooth K, and D – control the sensitivity of the SMI.
RSI Period – sets the RSI calculation window.
Signal Period – period for the price-based signal line.
Smoothing Methods – choose between HMA, EMA, WMA, JMA, SMMA, etc.
Line Width – thickness of the plotted lines.
Note: The JMA (Jurik Moving Average) used in this script is not the original proprietary version.
It is a custom public version, based on open-source code shared by the TradingView community.
The original JMA is copyrighted and owned by Jurik Research.
How to Use It in Practice
Buy Entries
When the green Bull Herd line crosses above 60 and the red Bear Winter line falls below 40.
Entry is more reliable if the green line is rising steadily.
Sell Entries
When the red Bear Winter line crosses above 60 and the green Bull Herd line falls.
Signals are stronger when there is a clear crossover and divergence between the two lines.
Avoid trading near the neutral zone (~50%), where the market shows indecision.
Additional Tips
Combine with volume analysis or reversal candlestick patterns for higher accuracy.
Test different smoothing methods: HMA is more responsive, SMMA is smoother and slower.
CCO_LibraryLibrary "CCO_Library"
Contrarian Crowd Oscillator (CCO) Library - Multi-oscillator consensus indicator for contrarian trading signals
@author B3AR_Trades
calculate_oscillators(rsi_length, stoch_length, cci_length, williams_length, roc_length, mfi_length, percentile_lookback, use_rsi, use_stochastic, use_williams, use_cci, use_roc, use_mfi)
Calculate normalized oscillator values
Parameters:
rsi_length (simple int) : (int) RSI calculation period
stoch_length (int) : (int) Stochastic calculation period
cci_length (int) : (int) CCI calculation period
williams_length (int) : (int) Williams %R calculation period
roc_length (int) : (int) ROC calculation period
mfi_length (int) : (int) MFI calculation period
percentile_lookback (int) : (int) Lookback period for CCI/ROC percentile ranking
use_rsi (bool) : (bool) Include RSI in calculations
use_stochastic (bool) : (bool) Include Stochastic in calculations
use_williams (bool) : (bool) Include Williams %R in calculations
use_cci (bool) : (bool) Include CCI in calculations
use_roc (bool) : (bool) Include ROC in calculations
use_mfi (bool) : (bool) Include MFI in calculations
Returns: (OscillatorValues) Normalized oscillator values
calculate_consensus_score(oscillators, use_rsi, use_stochastic, use_williams, use_cci, use_roc, use_mfi, weight_by_reliability, consensus_smoothing)
Calculate weighted consensus score
Parameters:
oscillators (OscillatorValues) : (OscillatorValues) Individual oscillator values
use_rsi (bool) : (bool) Include RSI in consensus
use_stochastic (bool) : (bool) Include Stochastic in consensus
use_williams (bool) : (bool) Include Williams %R in consensus
use_cci (bool) : (bool) Include CCI in consensus
use_roc (bool) : (bool) Include ROC in consensus
use_mfi (bool) : (bool) Include MFI in consensus
weight_by_reliability (bool) : (bool) Apply reliability-based weights
consensus_smoothing (int) : (int) Smoothing period for consensus
Returns: (float) Weighted consensus score (0-100)
calculate_consensus_strength(oscillators, consensus_score, use_rsi, use_stochastic, use_williams, use_cci, use_roc, use_mfi)
Calculate consensus strength (agreement between oscillators)
Parameters:
oscillators (OscillatorValues) : (OscillatorValues) Individual oscillator values
consensus_score (float) : (float) Current consensus score
use_rsi (bool) : (bool) Include RSI in strength calculation
use_stochastic (bool) : (bool) Include Stochastic in strength calculation
use_williams (bool) : (bool) Include Williams %R in strength calculation
use_cci (bool) : (bool) Include CCI in strength calculation
use_roc (bool) : (bool) Include ROC in strength calculation
use_mfi (bool) : (bool) Include MFI in strength calculation
Returns: (float) Consensus strength (0-100)
classify_regime(consensus_score)
Classify consensus regime
Parameters:
consensus_score (float) : (float) Current consensus score
Returns: (ConsensusRegime) Regime classification
detect_signals(consensus_score, consensus_strength, consensus_momentum, regime)
Detect trading signals
Parameters:
consensus_score (float) : (float) Current consensus score
consensus_strength (float) : (float) Current consensus strength
consensus_momentum (float) : (float) Consensus momentum
regime (ConsensusRegime) : (ConsensusRegime) Current regime classification
Returns: (TradingSignals) Trading signal conditions
calculate_cco(rsi_length, stoch_length, cci_length, williams_length, roc_length, mfi_length, consensus_smoothing, percentile_lookback, use_rsi, use_stochastic, use_williams, use_cci, use_roc, use_mfi, weight_by_reliability, detect_momentum)
Calculate complete CCO analysis
Parameters:
rsi_length (simple int) : (int) RSI calculation period
stoch_length (int) : (int) Stochastic calculation period
cci_length (int) : (int) CCI calculation period
williams_length (int) : (int) Williams %R calculation period
roc_length (int) : (int) ROC calculation period
mfi_length (int) : (int) MFI calculation period
consensus_smoothing (int) : (int) Consensus smoothing period
percentile_lookback (int) : (int) Percentile ranking lookback
use_rsi (bool) : (bool) Include RSI
use_stochastic (bool) : (bool) Include Stochastic
use_williams (bool) : (bool) Include Williams %R
use_cci (bool) : (bool) Include CCI
use_roc (bool) : (bool) Include ROC
use_mfi (bool) : (bool) Include MFI
weight_by_reliability (bool) : (bool) Apply reliability weights
detect_momentum (bool) : (bool) Calculate momentum and acceleration
Returns: (CCOResult) Complete CCO analysis results
calculate_cco_default()
Calculate CCO with default parameters
Returns: (CCOResult) CCO result with standard settings
cco_consensus_score()
Get just the consensus score with default parameters
Returns: (float) Consensus score (0-100)
cco_consensus_strength()
Get just the consensus strength with default parameters
Returns: (float) Consensus strength (0-100)
is_panic_bottom()
Check if in panic bottom condition
Returns: (bool) True if panic bottom signal active
is_euphoric_top()
Check if in euphoric top condition
Returns: (bool) True if euphoric top signal active
bullish_consensus_reversal()
Check for bullish consensus reversal
Returns: (bool) True if bullish reversal detected
bearish_consensus_reversal()
Check for bearish consensus reversal
Returns: (bool) True if bearish reversal detected
bearish_divergence()
Check for bearish divergence
Returns: (bool) True if bearish divergence detected
bullish_divergence()
Check for bullish divergence
Returns: (bool) True if bullish divergence detected
get_regime_name()
Get current regime name
Returns: (string) Current consensus regime name
get_contrarian_signal()
Get contrarian signal
Returns: (string) Current contrarian trading signal
get_position_multiplier()
Get position size multiplier
Returns: (float) Recommended position sizing multiplier
OscillatorValues
Individual oscillator values
Fields:
rsi (series float) : RSI value (0-100)
stochastic (series float) : Stochastic value (0-100)
williams (series float) : Williams %R value (0-100, normalized)
cci (series float) : CCI percentile value (0-100)
roc (series float) : ROC percentile value (0-100)
mfi (series float) : Money Flow Index value (0-100)
ConsensusRegime
Consensus regime classification
Fields:
extreme_bearish (series bool) : Extreme bearish consensus (<= 20)
moderate_bearish (series bool) : Moderate bearish consensus (20-40)
mixed (series bool) : Mixed consensus (40-60)
moderate_bullish (series bool) : Moderate bullish consensus (60-80)
extreme_bullish (series bool) : Extreme bullish consensus (>= 80)
regime_name (series string) : Text description of current regime
contrarian_signal (series string) : Contrarian trading signal
TradingSignals
Trading signals
Fields:
panic_bottom_signal (series bool) : Extreme bearish consensus with high strength
euphoric_top_signal (series bool) : Extreme bullish consensus with high strength
consensus_reversal_bullish (series bool) : Bullish consensus reversal
consensus_reversal_bearish (series bool) : Bearish consensus reversal
bearish_divergence (series bool) : Bearish price-consensus divergence
bullish_divergence (series bool) : Bullish price-consensus divergence
strong_consensus (series bool) : High consensus strength signal
CCOResult
Complete CCO calculation results
Fields:
consensus_score (series float) : Main consensus score (0-100)
consensus_strength (series float) : Consensus strength (0-100)
consensus_momentum (series float) : Rate of consensus change
consensus_acceleration (series float) : Rate of momentum change
oscillators (OscillatorValues) : Individual oscillator values
regime (ConsensusRegime) : Regime classification
signals (TradingSignals) : Trading signals
position_multiplier (series float) : Recommended position sizing multiplier
Mariam Market DashboardMariam Market Dashboard – A Quick Guide
Purpose:
Shows if the market is trending, volatile, or stuck so you can decide when to trade or wait.
How to Use
Add the indicator to your chart. Adjust basic settings like EMA, RSI, ATR lengths, and timezone if needed. Use it before entering any trade to confirm market conditions.
What Each Metric Means (with general ranges)
Session: Identifies which market session is active (New York, London, Tokyo).
Trend: Shows current market direction. “Up” means price above EMA and VWAP, “Down” means price below. Use this to confirm bullish or bearish bias.
HTF Trend: Confirms trend on a higher timeframe for stronger signals.
ATR (Average True Range): Measures market volatility or price movement speed.
Low ATR (e.g., below 0.5% of price) means quiet or slow market; high ATR (above 1% of price) means volatile or fast-moving market, good for active trades.
Strong Bar: A candlestick closing near its high (above 75% of range) indicates strong buying momentum; closing near its low indicates strong selling momentum.
Higher Volume: Volume higher than average (typically 10-20% above normal) means more market activity and stronger moves.
Volume / Avg Volume: Ratio above 1.2 (120%) shows volume is significantly higher than usual, signaling strong interest.
RVol % (Relative Volume %): Above 100% means volume is hotter than normal, increasing chances of strong moves; below 50% means low activity and possible indecision.
Delta: Difference between buying and selling volume (if available). A positive delta means buyers dominate; negative means sellers dominate.
ADX (Average Directional Index): Measures trend strength:
Below 20 means weak or no trend;
Above 25 means strong trend;
Between 20-25 is moderate trend.
RSI (Relative Strength Index): Momentum oscillator:
Below 30 = oversold (potential buy);
Above 70 = overbought (potential sell);
Between 40-60 means neutral momentum.
MACD: Confirms momentum direction:
Positive MACD histogram bars indicate bullish momentum;
Negative bars indicate bearish momentum.
Choppiness Index: Measures how much the market is ranging versus trending:
Above 60 = very choppy/sideways market;
Below 40 = trending market.
Consolidation: When true, price is stuck in a narrow range, signaling indecision. Avoid breakout trades during this.
Quick Trading Reminder
Trade only when the trend is clear and volume is above average. Avoid trading in low volume or choppy markets.
Kaufman Trend Strength Signal█ Overview
Kaufman Trend Strength Signal is an advanced trend detection tool that decomposes price action into its underlying directional trend and localized oscillation using a vector-based Kalman Filter.
By integrating adaptive smoothing and dynamic weighting via a weighted moving average (WMA), this indicator provides real-time insight into both trend direction and trend strength — something standard moving averages often fail to capture.
The core model assumes that observed price consists of two components:
(1) a directional trend, and
(2) localized noise or oscillation.
Using a two-step Predict & Update cycle, the filter continuously refines its trend estimate as new market data becomes available.
█ How It Works
This indicator employs a Kalman Filter model that separates the trend from short-term fluctuations in a price series.
Predict & Update Cycle : With each new bar, the filter predicts the price state and updates that prediction using the latest observed price, producing a smooth but adaptive trend line.
Trend Strength Normalization : Internally, the oscillator component is normalized against recent values (N periods) to calculate a trend strength score between -100 and +100.
(Note: The oscillator is not plotted on the chart but is used for signal generation.)
Filtered MA Line : The trend component is plotted as a smooth Kalman Filter-based moving average (MA) line on the main chart.
Threshold Cross Signals : When the internal trend strength crosses a user-defined threshold (default: ±60), visual entry arrows are displayed to signal momentum shifts.
█ Key Features
Adaptive Trend Estimation : Real-time filtering that adjusts dynamically to market changes.
Visual Buy/Sell Signals : Entry arrows appear when the trend strength crosses above or below the configured threshold.
Built-in Range Filter : The MA line turns blue when trend strength is weak (|value| < 10), helping you filter out choppy, sideways conditions.
█ How to Use
Trend Detection :
• Green MA = bullish trend
• Red MA = bearish trend
• Blue MA = no trend / ranging market
Entry Signals :
• Green triangle = trend strength crossed above +Threshold → potential bullish entry
• Red triangle = trend strength crossed below -Threshold → potential bearish entry
█ Settings
Entry Threshold : Level at which the trend strength triggers entry signals (default: 60)
Process Noise 1 & 2 : Control the filter’s responsiveness to recent price action. Higher = more reactive; lower = smoother.
Measurement Noise : Sets how much the filter "trusts" price data. High = smoother MA, low = faster response but more noise.
Trend Lookback (N2) : Number of bars used to normalize trend strength. Lower = more sensitive; higher = more stable.
Trend Smoothness (R2) : WMA smoothing applied to the trend strength calculation.
█ Visual Guide
Green MA Line → Bullish trend
Red MA Line → Bearish trend
Blue MA Line → Sideways/range
Green Triangle → Entry signal (trend strengthening)
Red Triangle → Entry signal (trend weakening)
█ Best Practices
In high-volatility conditions, increase Measurement Noise to reduce false signals.
Combine with other indicators (e.g., RSI, MACD, EMA) for confirmation and filtering.
Adjust "Entry Threshold" and noise settings depending on your timeframe and trading style.
❗ Disclaimer
This script is provided for educational purposes only and should not be considered financial advice or a recommendation to buy/sell any asset.
Trading involves risk. Past performance does not guarantee future results.
Always perform your own analysis and use proper risk management when trading.
CommonUtils█ OVERVIEW
This library is a utility tool for Pine Script™ developers. It provides a collection of helper functions designed to simplify common tasks such as mapping user-friendly string inputs to Pine Script™ constants and formatting timeframe strings for display. The primary goal is to make main scripts cleaner, more readable, and reduce repetitive boilerplate code. It is envisioned as an evolving resource, with potential for new utilities to be added over time based on community needs and feedback.
█ CONCEPTS
The library primarily focuses on two main concepts:
Input Mapping
Pine Script™ often requires specific constants for function parameters (e.g., `line.style_dashed` for line styles, `position.top_center` for table positions). However, presenting these technical constants directly to users in script inputs can be confusing. Input mapping involves:
Allowing users to select options from more descriptive, human-readable strings (e.g., "Dashed", "Top Center") in the script's settings.
Providing functions within this library (e.g., `mapLineStyle`, `mapTablePosition`) that take these user-friendly strings as input.
Internally, these functions use switch statements or similar logic to convert (map) the input string to the corresponding Pine Script™ constant required by built-in functions.
This approach enhances user experience and simplifies the main script's logic by centralizing the mapping process.
Timeframe Formatting
Raw timeframe strings obtained from variables like `timeframe.period` (e.g., "1", "60", "D", "W") or user inputs are not always ideal for direct display in labels or panels. The `formatTimeframe` function addresses this by:
Taking a raw timeframe string as input.
Parsing this string to identify its numerical part and unit (e.g., minutes, hours, days, weeks, months, seconds, milliseconds).
Converting it into a more standardized and readable format (e.g., "1min", "60min", "Daily", "Weekly", "1s", "10M").
Offering an optional `customSuffix` parameter (e.g., " FVG", " Period") to append to the formatted string, making labels more descriptive, especially in multi-timeframe contexts.
The function is designed to correctly interpret various common timeframe notations used in TradingView.
█ NOTES
Ease of Use: The library functions are designed with simple and understandable signatures. They typically take a string input and return the corresponding Pine Script™ constant or a formatted string.
Default Behaviors: Mapping functions (`mapLineStyle`, `mapTablePosition`, `mapTextSize`) generally return a sensible default value (e.g., `line.style_solid` for `mapLineStyle`) in case of a non-matching input. This helps prevent errors in the main script.
Extensibility of Formatting: The `formatTimeframe` function, with its `customSuffix` parameter, allows for flexible customization of timeframe labels to suit the specific descriptive needs of different indicators or contexts.
Performance Considerations: These utility functions primarily use basic string operations and switch statements. For typical use cases, their impact on overall script performance is negligible. However, if a function like `formatTimeframe` were to be called excessively in a loop with dynamic inputs (which is generally not its intended use), performance should be monitored.
No Dependencies: This library is self-contained and does not depend on any other external Pine Script™ libraries.
█ EXPORTED FUNCTIONS
mapLineStyle(styleString)
Maps a user-provided line style string to its corresponding Pine Script™ line style constant.
Parameters:
styleString (simple string) : The input string representing the desired line style (e.g., "Solid", "Dashed", "Dotted" - typically from constants like LS1, LS2, LS3).
Returns: The Pine Script™ constant for the line style (e.g., line.style_solid). Defaults to line.style_solid if no match.
mapTablePosition(positionString)
Maps a user-provided table position string to its corresponding Pine Script™ position constant.
Parameters:
positionString (simple string) : The input string representing the desired table position (e.g., "Top Right", "Top Center" - typically from constants like PP1, PP2).
Returns: The Pine Script™ constant for the table position (e.g., position.top_right). Defaults to position.top_right if no match.
mapTextSize(sizeString)
Maps a user-provided text size string to its corresponding Pine Script™ size constant.
Parameters:
sizeString (simple string) : The input string representing the desired text size (e.g., "Tiny", "Small" - typically from constants like PTS1, PTS2).
Returns: The Pine Script™ constant for the text size (e.g., size.tiny). Defaults to size.small if no match.
formatTimeframe(tfInput, customSuffix)
Formats a raw timeframe string into a more display-friendly string, optionally appending a custom suffix.
Parameters:
tfInput (simple string) : The raw timeframe string from user input or timeframe.period (e.g., "1", "60", "D", "W", "1S", "10M", "2H").
customSuffix (simple string) : An optional suffix to append to the formatted timeframe string (e.g., " FVG", " Period"). Defaults to an empty string.
Returns: The formatted timeframe string (e.g., "1min", "60min", "Daily", "Weekly", "1s", "10min", "2h") with the custom suffix appended.
FvgCalculations█ OVERVIEW
This library provides the core calculation engine for identifying Fair Value Gaps (FVGs) across different timeframes and for processing their interaction with price. It includes functions to detect FVGs on both the current chart and higher timeframes, as well as to check for their full or partial mitigation.
█ CONCEPTS
The library's primary functions revolve around the concept of Fair Value Gaps and their lifecycle.
Fair Value Gap (FVG) Identification
An FVG, or imbalance, represents a price range where buying or selling pressure was significant enough to cause a rapid price movement, leaving an "inefficiency" in the market. This library identifies FVGs based on three-bar patterns:
Bullish FVG: Forms when the low of the current bar (bar 3) is higher than the high of the bar two periods prior (bar 1). The FVG is the space between the high of bar 1 and the low of bar 3.
Bearish FVG: Forms when the high of the current bar (bar 3) is lower than the low of the bar two periods prior (bar 1). The FVG is the space between the low of bar 1 and the high of bar 3.
The library provides distinct functions for detecting FVGs on the current (Low Timeframe - LTF) and specified higher timeframes (Medium Timeframe - MTF / High Timeframe - HTF).
FVG Mitigation
Mitigation refers to price revisiting an FVG.
Full Mitigation: An FVG is considered fully mitigated when price completely closes the gap. For a bullish FVG, this occurs if the current low price moves below or touches the FVG's bottom. For a bearish FVG, it occurs if the current high price moves above or touches the FVG's top.
Partial Mitigation (Entry/Fill): An FVG is partially mitigated when price enters the FVG's range but does not fully close it. The library tracks the extent of this fill. For a bullish FVG, if the current low price enters the FVG from above, that low becomes the new effective top of the remaining FVG. For a bearish FVG, if the current high price enters the FVG from below, that high becomes the new effective bottom of the remaining FVG.
FVG Interaction
This refers to any instance where the current bar's price range (high to low) touches or crosses into the currently unfilled portion of an active (visible and not fully mitigated) FVG.
Multi-Timeframe Data Acquisition
To detect FVGs on higher timeframes, specific historical bar data (high, low, and time of bars at indices and relative to the higher timeframe's last completed bar) is required. The requestMultiTFBarData function is designed to fetch this data efficiently.
█ CALCULATIONS AND USE
The functions in this library are typically used in a sequence to manage FVGs:
1. Data Retrieval (for MTF/HTF FVGs):
Call requestMultiTFBarData() with the desired higher timeframe string (e.g., "60", "D").
This returns a tuple of htfHigh1, htfLow1, htfTime1, htfHigh3, htfLow3, htfTime3.
2. FVG Detection:
For LTF FVGs: Call detectFvg() on each confirmed bar. It uses high , low, low , and high along with barstate.isconfirmed.
For MTF/HTF FVGs: Call detectMultiTFFvg() using the data obtained from requestMultiTFBarData().
Both detection functions return an fvgObject (defined in FvgTypes) if an FVG is found, otherwise na. They also can classify FVGs as "Large Volume" (LV) if classifyLV is true and the FVG size (top - bottom) relative to the tfAtr (Average True Range of the respective timeframe) meets the lvAtrMultiplier.
3. FVG State Updates (on each new bar for existing FVGs):
First, check for overall price interaction using fvgInteractionCheck(). This function determines if the current bar's high/low has touched or entered the FVG's currentTop or currentBottom.
If interaction occurs and the FVG is not already mitigated:
Call checkMitigation() to determine if the FVG has been fully mitigated by the current bar's currentHigh and currentLow. If true, the FVG's isMitigated status is updated.
If not fully mitigated, call checkPartialMitigation() to see if the price has further entered the FVG. This function returns the newLevel to which the FVG has been filled (e.g., currentLow for a bullish FVG, currentHigh for bearish). This newLevel is then used to update the FVG's currentTop or currentBottom.
The calling script (e.g., fvgMain.c) is responsible for storing and managing the array of fvgObject instances and passing them to these update functions.
█ NOTES
Bar State for LTF Detection: The detectFvg() function relies on barstate.isconfirmed to ensure FVG detection is based on closed bars, preventing FVGs from being detected prematurely on the currently forming bar.
Higher Timeframe Data (lookahead): The requestMultiTFBarData() function uses lookahead = barmerge.lookahead_on. This means it can access historical data from the higher timeframe that corresponds to the current bar on the chart, even if the higher timeframe bar has not officially closed. This is standard for multi-timeframe analysis aiming to plot historical HTF data accurately on a lower timeframe chart.
Parameter Typing: Functions like detectMultiTFFvg and detectFvg infer the type for boolean (classifyLV) and numeric (lvAtrMultiplier) parameters passed from the main script, while explicitly typed series parameters (like htfHigh1, currentAtr) expect series data.
fvgObject Dependency: The FVG detection functions return fvgObject instances, and fvgInteractionCheck takes an fvgObject as a parameter. This UDT is defined in the FvgTypes library, making it a dependency for using FvgCalculations.
ATR for LV Classification: The tfAtr (for MTF/HTF) and currentAtr (for LTF) parameters are expected to be the Average True Range values for the respective timeframes. These are used, if classifyLV is enabled, to determine if an FVG's size qualifies it as a "Large Volume" FVG based on the lvAtrMultiplier.
MTF/HTF FVG Appearance Timing: When displaying FVGs from a higher timeframe (MTF/HTF) on a lower timeframe (LTF) chart, users might observe that the most recent MTF/HTF FVG appears one LTF bar later compared to its appearance on a native MTF/HTF chart. This is an expected behavior due to the detection mechanism in `detectMultiTFFvg`. This function uses historical bar data from the MTF/HTF (specifically, data equivalent to `HTF_bar ` and `HTF_bar `) to identify an FVG. Therefore, all three bars forming the FVG on the MTF/HTF must be fully closed and have shifted into these historical index positions relative to the `request.security` call from the LTF chart before the FVG can be detected and displayed on the LTF. This ensures that the MTF/HTF FVG is identified based on confirmed, closed bars from the higher timeframe.
█ EXPORTED FUNCTIONS
requestMultiTFBarData(timeframe)
Requests historical bar data for specific previous bars from a specified higher timeframe.
It fetches H , L , T (for the bar before last) and H , L , T (for the bar three periods prior)
from the requested timeframe.
This is typically used to identify FVG patterns on MTF/HTF.
Parameters:
timeframe (simple string) : The higher timeframe to request data from (e.g., "60" for 1-hour, "D" for Daily).
Returns: A tuple containing: .
- htfHigh1 (series float): High of the bar at index 1 (one bar before the last completed bar on timeframe).
- htfLow1 (series float): Low of the bar at index 1.
- htfTime1 (series int) : Time of the bar at index 1.
- htfHigh3 (series float): High of the bar at index 3 (three bars before the last completed bar on timeframe).
- htfLow3 (series float): Low of the bar at index 3.
- htfTime3 (series int) : Time of the bar at index 3.
detectMultiTFFvg(htfHigh1, htfLow1, htfTime1, htfHigh3, htfLow3, htfTime3, tfAtr, classifyLV, lvAtrMultiplier, tfType)
Detects a Fair Value Gap (FVG) on a higher timeframe (MTF/HTF) using pre-fetched bar data.
Parameters:
htfHigh1 (float) : High of the first relevant bar (typically high ) from the higher timeframe.
htfLow1 (float) : Low of the first relevant bar (typically low ) from the higher timeframe.
htfTime1 (int) : Time of the first relevant bar (typically time ) from the higher timeframe.
htfHigh3 (float) : High of the third relevant bar (typically high ) from the higher timeframe.
htfLow3 (float) : Low of the third relevant bar (typically low ) from the higher timeframe.
htfTime3 (int) : Time of the third relevant bar (typically time ) from the higher timeframe.
tfAtr (float) : ATR value for the higher timeframe, used for Large Volume (LV) FVG classification.
classifyLV (bool) : If true, FVGs will be assessed to see if they qualify as Large Volume.
lvAtrMultiplier (float) : The ATR multiplier used to define if an FVG is Large Volume.
tfType (series tfType enum from no1x/FvgTypes/1) : The timeframe type (e.g., types.tfType.MTF, types.tfType.HTF) of the FVG being detected.
Returns: An fvgObject instance if an FVG is detected, otherwise na.
detectFvg(classifyLV, lvAtrMultiplier, currentAtr)
Detects a Fair Value Gap (FVG) on the current (LTF - Low Timeframe) chart.
Parameters:
classifyLV (bool) : If true, FVGs will be assessed to see if they qualify as Large Volume.
lvAtrMultiplier (float) : The ATR multiplier used to define if an FVG is Large Volume.
currentAtr (float) : ATR value for the current timeframe, used for LV FVG classification.
Returns: An fvgObject instance if an FVG is detected, otherwise na.
checkMitigation(isBullish, fvgTop, fvgBottom, currentHigh, currentLow)
Checks if an FVG has been fully mitigated by the current bar's price action.
Parameters:
isBullish (bool) : True if the FVG being checked is bullish, false if bearish.
fvgTop (float) : The top price level of the FVG.
fvgBottom (float) : The bottom price level of the FVG.
currentHigh (float) : The high price of the current bar.
currentLow (float) : The low price of the current bar.
Returns: True if the FVG is considered fully mitigated, false otherwise.
checkPartialMitigation(isBullish, currentBoxTop, currentBoxBottom, currentHigh, currentLow)
Checks for partial mitigation of an FVG by the current bar's price action.
It determines if the price has entered the FVG and returns the new fill level.
Parameters:
isBullish (bool) : True if the FVG being checked is bullish, false if bearish.
currentBoxTop (float) : The current top of the FVG box (this might have been adjusted by previous partial fills).
currentBoxBottom (float) : The current bottom of the FVG box (similarly, might be adjusted).
currentHigh (float) : The high price of the current bar.
currentLow (float) : The low price of the current bar.
Returns: The new price level to which the FVG has been filled (e.g., currentLow for a bullish FVG).
Returns na if no new partial fill occurred on this bar.
fvgInteractionCheck(fvg, highVal, lowVal)
Checks if the current bar's price interacts with the given FVG.
Interaction means the price touches or crosses into the FVG's
current (possibly partially filled) range.
Parameters:
fvg (fvgObject type from no1x/FvgTypes/1) : The FVG object to check.
Its isMitigated, isVisible, isBullish, currentTop, and currentBottom fields are used.
highVal (float) : The high price of the current bar.
lowVal (float) : The low price of the current bar.
Returns: True if price interacts with the FVG, false otherwise.
Mandelbrot-Fibonacci Cascade Vortex (MFCV)Mandelbrot-Fibonacci Cascade Vortex (MFCV) - Where Chaos Theory Meets Sacred Geometry
A Revolutionary Synthesis of Fractal Mathematics and Golden Ratio Dynamics
What began as an exploration into Benoit Mandelbrot's fractal market hypothesis and the mysterious appearance of Fibonacci sequences in nature has culminated in a groundbreaking indicator that reveals the hidden mathematical structure underlying market movements. This indicator represents months of research into chaos theory, fractal geometry, and the golden ratio's manifestation in financial markets.
The Theoretical Foundation
Mandelbrot's Fractal Market Hypothesis Traditional efficient market theory assumes normal distributions and random walks. Mandelbrot proved markets are fractal - self-similar patterns repeating across all timeframes with power-law distributions. The MFCV implements this through:
Hurst Exponent Calculation: H = log(R/S) / log(n/2)
Where:
R = Range of cumulative deviations
S = Standard deviation
n = Period length
This measures market memory:
H > 0.5: Trending (persistent) behavior
H = 0.5: Random walk
H < 0.5: Mean-reverting (anti-persistent) behavior
Fractal Dimension: D = 2 - H
This quantifies market complexity, where higher dimensions indicate more chaotic behavior.
Fibonacci Vortex Theory Markets don't move linearly - they spiral. The MFCV reveals these spirals using Fibonacci sequences:
Vortex Calculation: Vortex(n) = Price + sin(bar_index × φ / Fn) × ATR(Fn) × Volume_Factor
Where:
φ = 0.618 (golden ratio)
Fn = Fibonacci number (8, 13, 21, 34, 55)
Volume_Factor = 1 + (Volume/SMA(Volume,50) - 1) × 0.5
This creates oscillating spirals that contract and expand with market energy.
The Volatility Cascade System
Markets exhibit volatility clustering - Mandelbrot's "Noah Effect." The MFCV captures this through cascading volatility bands:
Cascade Level Calculation: Level(i) = ATR(20) × φ^i
Each level represents a different fractal scale, creating a multi-dimensional view of market structure. The golden ratio spacing ensures harmonic resonance between levels.
Implementation Architecture
Core Components:
Fractal Analysis Engine
Calculates Hurst exponent over user-defined periods
Derives fractal dimension for complexity measurement
Identifies market regime (trending/ranging/chaotic)
Fibonacci Vortex Generator
Creates 5 independent spiral oscillators
Each spiral follows a Fibonacci period
Volume amplification creates dynamic response
Cascade Band System
Up to 8 volatility levels
Golden ratio expansion between levels
Dynamic coloring based on fractal state
Confluence Detection
Identifies convergence of vortex and cascade levels
Highlights high-probability reversal zones
Real-time confluence strength calculation
Signal Generation Logic
The MFCV generates two primary signal types:
Fractal Signals: Generated when:
Hurst > 0.65 (strong trend) AND volatility expanding
Hurst < 0.35 (mean reversion) AND RSI < 35
Trend strength > 0.4 AND vortex alignment
Cascade Signals: Triggered by:
RSI > 60 AND price > SMA(50) AND bearish vortex
RSI < 40 AND price < SMA(50) AND bullish vortex
Volatility expansion AND trend strength > 0.3
Both signals implement a 15-bar cooldown to prevent overtrading.
Advanced Input System
Mandelbrot Parameters:
Cascade Levels (3-8):
Controls number of volatility bands
Crypto: 5-7 (high volatility)
Indices: 4-5 (moderate volatility)
Forex: 3-4 (low volatility)
Hurst Period (20-200):
Lookback for fractal calculation
Scalping: 20-50
Day Trading: 50-100
Swing Trading: 100-150
Position Trading: 150-200
Cascade Ratio (1.0-3.0):
Band width multiplier
1.618: Golden ratio (default)
Higher values for trending markets
Lower values for ranging markets
Fractal Memory (21-233):
Fibonacci retracement lookback
Uses Fibonacci numbers for harmonic alignment
Fibonacci Vortex Settings:
Spiral Periods:
Comma-separated Fibonacci sequence
Fast: "5,8,13,21,34" (scalping)
Standard: "8,13,21,34,55" (balanced)
Extended: "13,21,34,55,89" (swing)
Rotation Speed (0.1-2.0):
Controls spiral oscillation frequency
0.618: Golden ratio (balanced)
Higher = more signals, more noise
Lower = smoother, fewer signals
Volume Amplification:
Enables dynamic spiral expansion
Essential for stocks and crypto
Disable for forex (no central volume)
Visual System Architecture
Cascade Bands:
Multi-level volatility envelopes
Gradient coloring from primary to secondary theme
Transparency increases with distance from price
Fill between bands shows fractal structure
Vortex Spirals:
5 Fibonacci-period oscillators
Blue above price (bullish pressure)
Red below price (bearish pressure)
Multiple display styles: Lines, Circles, Dots, Cross
Dynamic Fibonacci Levels:
Auto-updating retracement levels
Smart update logic prevents disruption near levels
Distance-based transparency (closer = more visible)
Updates every 50 bars or on volatility spikes
Confluence Zones:
Highlighted boxes where indicators converge
Stronger confluence = stronger support/resistance
Key areas for reversal trades
Professional Dashboard System
Main Fractal Dashboard: Displays real-time:
Hurst Exponent with market state
Fractal Dimension with complexity level
Volatility Cascade status
Vortex rotation impact
Market regime classification
Signal strength percentage
Active indicator levels
Vortex Metrics Panel: Shows:
Individual spiral deviations
Convergence/divergence metrics
Real-time vortex positioning
Fibonacci period performance
Fractal Metrics Display: Tracks:
Dimension D value
Market complexity rating
Self-similarity strength
Trend quality assessment
Theory Guide Panel: Educational reference showing:
Mandelbrot principles
Fibonacci vortex concepts
Dynamic trading suggestions
Trading Applications
Trend Following:
High Hurst (>0.65) indicates strong trends
Follow cascade band direction
Use vortex spirals for entry timing
Exit when Hurst drops below 0.5
Mean Reversion:
Low Hurst (<0.35) signals reversal potential
Trade toward vortex spiral convergence
Use Fibonacci levels as targets
Tighten stops in chaotic regimes
Breakout Trading:
Monitor cascade band compression
Watch for vortex spiral alignment
Volatility expansion confirms breakouts
Use confluence zones for targets
Risk Management:
Position size based on fractal dimension
Wider stops in high complexity markets
Tighter stops when Hurst is extreme
Scale out at Fibonacci levels
Market-Specific Optimization
Cryptocurrency:
Cascade Levels: 5-7
Hurst Period: 50-100
Rotation Speed: 0.786-1.2
Enable volume amplification
Stock Indices:
Cascade Levels: 4-5
Hurst Period: 80-120
Rotation Speed: 0.5-0.786
Moderate cascade ratio
Forex:
Cascade Levels: 3-4
Hurst Period: 100-150
Rotation Speed: 0.382-0.618
Disable volume amplification
Commodities:
Cascade Levels: 4-6
Hurst Period: 60-100
Rotation Speed: 0.5-1.0
Seasonal adjustment consideration
Innovation and Originality
The MFCV represents several breakthrough innovations:
First Integration of Mandelbrot Fractals with Fibonacci Vortex Theory
Unique synthesis of chaos theory and sacred geometry
Novel application of Hurst exponent to spiral dynamics
Dynamic Volatility Cascade System
Golden ratio-based band expansion
Multi-timeframe fractal analysis
Self-adjusting to market conditions
Volume-Amplified Vortex Spirals
Revolutionary spiral calculation method
Dynamic response to market participation
Multiple Fibonacci period integration
Intelligent Signal Generation
Cooldown system prevents overtrading
Multi-factor confirmation required
Regime-aware signal filtering
Professional Analytics Dashboard
Institutional-grade metrics display
Real-time fractal analysis
Educational integration
Development Journey
Creating the MFCV involved overcoming numerous challenges:
Mathematical Complexity: Implementing Hurst exponent calculations efficiently
Visual Clarity: Displaying multiple indicators without cluttering
Performance Optimization: Managing array operations and calculations
Signal Quality: Balancing sensitivity with reliability
User Experience: Making complex theory accessible
The result is an indicator that brings PhD-level mathematics to practical trading while maintaining visual elegance and usability.
Best Practices and Guidelines
Start Simple: Use default settings initially
Match Timeframe: Adjust parameters to your trading style
Confirm Signals: Never trade MFCV signals in isolation
Respect Regimes: Adapt strategy to market state
Manage Risk: Use fractal dimension for position sizing
Color Themes
Six professional themes included:
Fractal: Balanced blue/purple palette
Golden: Warm Fibonacci-inspired colors
Plasma: Vibrant modern aesthetics
Cosmic: Dark mode optimized
Matrix: Classic green terminal
Fire: Heat map visualization
Disclaimer
This indicator is for educational and research purposes only. It does not constitute financial advice. While the MFCV reveals deep market structure through advanced mathematics, markets remain inherently unpredictable. Past performance does not guarantee future results.
The integration of Mandelbrot's fractal theory with Fibonacci vortex dynamics provides unique market insights, but should be used as part of a comprehensive trading strategy. Always use proper risk management and never risk more than you can afford to lose.
Acknowledgments
Special thanks to Benoit Mandelbrot for revolutionizing our understanding of markets through fractal geometry, and to the ancient mathematicians who discovered the golden ratio's universal significance.
"The geometry of nature is fractal... Markets are fractal too." - Benoit Mandelbrot
Revealing the Hidden Order in Market Chaos Trade with Mathematical Precision. Trade with MFCV.
— Created with passion for the TradingView community
Trade with insight. Trade with anticipation.
— Dskyz , for DAFE Trading Systems
Average RSI (Daily + Weekly)📈 Average RSI (Relative Strength Index) – Beginner’s Guide
What it is:
The Average RSI is a technical indicator that combines multiple RSI values—such as daily and weekly RSI—into a single, smoothed line. This helps traders get a clearer picture of a stock’s momentum over both short- and medium-term timeframes.
Why it matters:
The RSI tells you whether a stock is potentially overbought (priced too high and due for a pullback) or oversold (priced too low and due for a bounce). Traditional RSI uses a scale from 0 to 100, with key levels at 70 (overbought) and 30 (oversold).
By averaging RSI across different timeframes, you reduce noise and get a better signal for trends and reversals.
How traders use it:
✅ Buy zone: When the average RSI dips below 40, it could signal a good entry point.
⚠️ Neutral zone: Between 40 and 60 means the trend isn’t strong—wait for more confirmation.
🚫 Sell zone: Above 60–70 may indicate the asset is overbought or due for a pullback.
Helpful for:
Spotting better entry/exit points
Filtering out false signals
Staying in trend-following trades longer
WaveTrend Filtered Signals (LazyBear Style)WaveTrend Filtered Signals (LazyBear Style)
This indicator is based on the popular WaveTrend oscillator (LazyBear) and adds several optional filters to improve signal quality:
✅ Available filters:
WT oversold/overbought zones – enabled by default. Signals are shown only if WT was previously in the specified zone (e.g., < -60 for longs, > 60 for shorts).
SMA trend filter – allows filtering signals in the direction of the moving average trend.
SMA position filter – signals appear only when price is above (for long) or below (for short) the moving average.
Consolidation filter – ignores signals during low-volatility sideways price movement.
💡 All filters are optional and can be enabled or disabled in the settings.
The default setup focuses on a clean approach: WaveTrend + oversold/overbought zones, with other filters left for customization.
Bober XM v2.0# ₿ober XM v2.0 Trading Bot Documentation
**Developer's Note**: While our previous Bot 1.3.1 was removed due to guideline violations, this setback only fueled our determination to create something even better. Rising from this challenge, Bober XM 2.0 emerges not just as an update, but as a complete reimagining with multi-timeframe analysis, enhanced filters, and superior adaptability. This adversity pushed us to innovate further and deliver a strategy that's smarter, more agile, and more powerful than ever before. Challenges create opportunity - welcome to Cryptobeat's finest work yet.
## !!!!You need to tune it for your own pair and timeframe and retune it periodicaly!!!!!
## Overview
The ₿ober XM v2.0 is an advanced dual-channel trading bot with multi-timeframe analysis capabilities. It integrates multiple technical indicators, customizable risk management, and advanced order execution via webhook for automated trading. The bot's distinctive feature is its separate channel systems for long and short positions, allowing for asymmetric trade strategies that adapt to different market conditions across multiple timeframes.
### Key Features
- **Multi-Timeframe Analysis**: Analyze price data across multiple timeframes simultaneously
- **Dual Channel System**: Separate parameter sets for long and short positions
- **Advanced Entry Filters**: RSI, Volatility, Volume, Bollinger Bands, and KEMAD filters
- **Machine Learning Moving Average**: Adaptive prediction-based channels
- **Multiple Entry Strategies**: Breakout, Pullback, and Mean Reversion modes
- **Risk Management**: Customizable stop-loss, take-profit, and trailing stop settings
- **Webhook Integration**: Compatible with external trading bots and platforms
### Strategy Components
| Component | Description |
|---------|-------------|
| **Dual Channel Trading** | Uses either Keltner Channels or Machine Learning Moving Average (MLMA) with separate settings for long and short positions |
| **MLMA Implementation** | Machine learning algorithm that predicts future price movements and creates adaptive bands |
| **Pivot Point SuperTrend** | Trend identification and confirmation system based on pivot points |
| **Three Entry Strategies** | Choose between Breakout, Pullback, or Mean Reversion approaches |
| **Advanced Filter System** | Multiple customizable filters with multi-timeframe support to avoid false signals |
| **Custom Exit Logic** | Exits based on OBV crossover of its moving average combined with pivot trend changes |
### Note for Novice Users
This is a fully featured real trading bot and can be tweaked for any ticker — SOL is just an example. It follows this structure:
1. **Indicator** – gives the initial signal
2. **Entry strategy** – decides when to open a trade
3. **Exit strategy** – defines when to close it
4. **Trend confirmation** – ensures the trade follows the market direction
5. **Filters** – cuts out noise and avoids weak setups
6. **Risk management** – controls losses and protects your capital
To tune it for a different pair, you'll need to start from scratch:
1. Select the timeframe (candle size)
2. Turn off all filters and trend entry/exit confirmations
3. Choose a channel type, channel source and entry strategy
4. Adjust risk parameters
5. Tune long and short settings for the channel
6. Fine-tune the Pivot Point Supertrend and Main Exit condition OBV
This will generate a lot of signals and activity on the chart. Your next task is to find the right combination of filters and settings to reduce noise and tune it for profitability.
### Default Strategy values
Default values are tuned for: Symbol BITGET:SOLUSDT.P 5min candle
Filters are off by default: Try to play with it to understand how it works
## Configuration Guide
### General Settings
| Setting | Description | Default Value |
|---------|-------------|---------------|
| **Long Positions** | Enable or disable long trades | Enabled |
| **Short Positions** | Enable or disable short trades | Enabled |
| **Risk/Reward Area** | Visual display of stop-loss and take-profit zones | Enabled |
| **Long Entry Source** | Price data used for long entry signals | hl2 (High+Low/2) |
| **Short Entry Source** | Price data used for short entry signals | hl2 (High+Low/2) |
The bot allows you to trade long positions, short positions, or both simultaneously. Each direction has its own set of parameters, allowing for fine-tuned strategies that recognize the asymmetric nature of market movements.
### Multi-Timeframe Settings
1. **Enable Multi-Timeframe Analysis**: Toggle 'Enable Multi-Timeframe Analysis' in the Multi-Timeframe Settings section
2. **Configure Timeframes**: Set appropriate higher timeframes based on your trading style:
- Timeframe 1: Default is now 15 minutes (intraday confirmation)
- Timeframe 2: Default is 4 hours (trend direction)
3. **Select Sources per Indicator**: For each indicator (RSI, KEMAD, Volume, etc.), choose:
- The desired timeframe (current, mtf1, or mtf2)
- The appropriate price type (open, high, low, close, hl2, hlc3, ohlc4)
### Entry Strategies
- **Breakout**: Enter when price breaks above/below the channel
- **Pullback**: Enter when price pulls back to the channel
- **Mean Reversion**: Enter when price is extended from the channel
You can enable different strategies for long and short positions.
### Core Components
### Risk Management
- **Position Size**: Control risk with percentage-based position sizing
- **Stop Loss Options**:
- Fixed: Set a specific price or percentage from entry
- ATR-based: Dynamic stop-loss based on market volatility
- Swing: Uses recent swing high/low points
- **Take Profit**: Multiple targets with percentage allocation
- **Trailing Stop**: Dynamic stop that follows price movement
## Advanced Usage Strategies
### Moving Average Type Selection Guide
- **SMA**: More stable in choppy markets, good for higher timeframes
- **EMA/WMA**: More responsive to recent price changes, better for entry signals
- **VWMA**: Adds volume weighting for stronger trends, use with Volume filter
- **HMA**: Balance between responsiveness and noise reduction, good for volatile markets
### Multi-Timeframe Strategy Approaches
- **Trend Confirmation**: Use higher timeframe RSI (mtf2) for overall trend, current timeframe for entries
- **Entry Precision**: Use KEMAD on current timeframe with volume filter on mtf1
- **False Signal Reduction**: Apply RSI filter on mtf1 with strict KEMAD settings
### Market Condition Optimization
| Market Condition | Recommended Settings |
|------------------|----------------------|
| **Trending** | Use Breakout strategy with KEMAD filter on higher timeframe |
| **Ranging** | Use Mean Reversion with strict RSI filter (mtf1) |
| **Volatile** | Increase ATR multipliers, use HMA for moving averages |
| **Low Volatility** | Decrease noise parameters, use pullback strategy |
## Webhook Integration
The strategy features a professional webhook system that allows direct connectivity to your exchange or trading platform of choice through third-party services like 3commas, Alertatron, or Autoview.
The webhook payload includes all necessary parameters for automated execution:
- Entry price and direction
- Stop loss and take profit levels
- Position size
- Custom identifier for webhook routing
## Performance Optimization Tips
1. **Start with Defaults**: Begin with the default settings for your timeframe before customizing
2. **Adjust One Component at a Time**: Make incremental changes and test the impact
3. **Match MA Types to Market Conditions**: Use appropriate moving average types based on the Market Condition Optimization table
4. **Timeframe Synergy**: Create logical relationships between timeframes (e.g., 5min chart with 15min and 4h higher timeframes)
5. **Periodic Retuning**: Markets evolve - regularly review and adjust parameters
## Common Setups
### Crypto Trend-Following
- MLMA with EMA or HMA
- Higher RSI thresholds (75/25)
- KEMAD filter on mtf1
- Breakout entry strategy
### Stock Swing Trading
- MLMA with SMA for stability
- Volume filter with higher threshold
- KEMAD with increased filter order
- Pullback entry strategy
### Forex Scalping
- MLMA with WMA and lower noise parameter
- RSI filter on current timeframe
- Use highest timeframe for trend direction only
- Mean Reversion strategy
## Webhook Configuration
- **Benefits**:
- Automated trade execution without manual intervention
- Immediate response to market conditions
- Consistent execution of your strategy
- **Implementation Notes**:
- Requires proper webhook configuration on your exchange or platform
- Test thoroughly with small position sizes before full deployment
- Consider latency between signal generation and execution
### Backtesting Period
Define a specific historical period to evaluate the bot's performance:
| Setting | Description | Default Value |
|---------|-------------|---------------|
| **Start Date** | Beginning of backtest period | January 1, 2025 |
| **End Date** | End of backtest period | December 31, 2026 |
- **Best Practice**: Test across different market conditions (bull markets, bear markets, sideways markets)
- **Limitation**: Past performance doesn't guarantee future results
## Entry and Exit Strategies
### Dual-Channel System
A key innovation of the Bober XM is its dual-channel approach:
- **Independent Parameters**: Each trade direction has its own channel settings
- **Asymmetric Trading**: Recognizes that markets often behave differently in uptrends versus downtrends
- **Optimized Performance**: Fine-tune settings for both bullish and bearish conditions
This approach allows the bot to adapt to the natural asymmetry of markets, where uptrends often develop gradually while downtrends can be sharp and sudden.
### Channel Types
#### 1. Keltner Channels
Traditional volatility-based channels using EMA and ATR:
| Setting | Long Default | Short Default |
|---------|--------------|---------------|
| **EMA Length** | 37 | 20 |
| **ATR Length** | 13 | 17 |
| **Multiplier** | 1.4 | 1.9 |
| **Source** | low | high |
- **Strengths**:
- Reliable in trending markets
- Less prone to whipsaws than Bollinger Bands
- Clear visual representation of volatility
- **Weaknesses**:
- Can lag during rapid market changes
- Less effective in choppy, non-trending markets
#### 2. Machine Learning Moving Average (MLMA)
Advanced predictive model using kernel regression (RBF kernel):
| Setting | Description | Options |
|---------|-------------|--------|
| **Source MA** | Price data used for MA calculations | Any price source (low/high/close/etc.) |
| **Moving Average Type** | Type of MA algorithm for calculations | SMA, EMA, WMA, VWMA, RMA, HMA |
| **Trend Source** | Price data used for trend determination | Any price source (close default) |
| **Window Size** | Historical window for MLMA calculations | 5+ (default: 16) |
| **Forecast Length** | Number of bars to forecast ahead | 1+ (default: 3) |
| **Noise Parameter** | Controls smoothness of prediction | 0.01+ (default: ~0.43) |
| **Band Multiplier** | Multiplier for channel width | 0.1+ (default: 0.5-0.6) |
- **Strengths**:
- Predictive rather than reactive
- Adapts quickly to changing market conditions
- Better at identifying trend reversals early
- **Weaknesses**:
- More computationally intensive
- Requires careful parameter tuning
- Can be sensitive to input data quality
### Entry Strategies
| Strategy | Description | Ideal Market Conditions |
|----------|-------------|-------------------------|
| **Breakout** | Enters when price breaks through channel bands, indicating strong momentum | High volatility, emerging trends |
| **Pullback** | Enters when price retraces to the middle band after testing extremes | Established trends with regular pullbacks |
| **Mean Reversion** | Enters at channel extremes, betting on a return to the mean | Range-bound or oscillating markets |
#### Breakout Strategy (Default)
- **Implementation**: Enters long when price crosses above the upper band, short when price crosses below the lower band
- **Strengths**: Captures strong momentum moves, performs well in trending markets
- **Weaknesses**: Can lead to late entries, higher risk of false breakouts
- **Optimization Tips**:
- Increase channel multiplier for fewer but more reliable signals
- Combine with volume confirmation for better accuracy
#### Pullback Strategy
- **Implementation**: Enters long when price pulls back to middle band during uptrend, short during downtrend pullbacks
- **Strengths**: Better entry prices, lower risk, higher probability setups
- **Weaknesses**: Misses some strong moves, requires clear trend identification
- **Optimization Tips**:
- Use with trend filters to confirm overall direction
- Adjust middle band calculation for market volatility
#### Mean Reversion Strategy
- **Implementation**: Enters long at lower band, short at upper band, expecting price to revert to the mean
- **Strengths**: Excellent entry prices, works well in ranging markets
- **Weaknesses**: Dangerous in strong trends, can lead to fighting the trend
- **Optimization Tips**:
- Implement strong trend filters to avoid counter-trend trades
- Use smaller position sizes due to higher risk nature
### Confirmation Indicators
#### Pivot Point SuperTrend
Combines pivot points with ATR-based SuperTrend for trend confirmation:
| Setting | Default Value |
|---------|---------------|
| **Pivot Period** | 25 |
| **ATR Factor** | 2.2 |
| **ATR Period** | 41 |
- **Function**: Identifies significant market turning points and confirms trend direction
- **Implementation**: Requires price to respect the SuperTrend line for trade confirmation
#### Weighted Moving Average (WMA)
Provides additional confirmation layer for entries:
| Setting | Default Value |
|---------|---------------|
| **Period** | 15 |
| **Source** | ohlc4 (average of Open, High, Low, Close) |
- **Function**: Confirms trend direction and filters out low-quality signals
- **Implementation**: Price must be above WMA for longs, below for shorts
### Exit Strategies
#### On-Balance Volume (OBV) Based Exits
Uses volume flow to identify potential reversals:
| Setting | Default Value |
|---------|---------------|
| **Source** | ohlc4 |
| **MA Type** | HMA (Options: SMA, EMA, WMA, RMA, VWMA, HMA) |
| **Period** | 22 |
- **Function**: Identifies divergences between price and volume to exit before reversals
- **Implementation**: Exits when OBV crosses its moving average in the opposite direction
- **Customizable MA Type**: Different MA types provide varying sensitivity to OBV changes:
- **SMA**: Traditional simple average, equal weight to all periods
- **EMA**: More weight to recent data, responds faster to price changes
- **WMA**: Weighted by recency, smoother than EMA
- **RMA**: Similar to EMA but smoother, reduces noise
- **VWMA**: Factors in volume, helpful for OBV confirmation
- **HMA**: Reduces lag while maintaining smoothness (default)
#### ADX Exit Confirmation
Uses Average Directional Index to confirm trend exhaustion:
| Setting | Default Value |
|---------|---------------|
| **ADX Threshold** | 35 |
| **ADX Smoothing** | 60 |
| **DI Length** | 60 |
- **Function**: Confirms trend weakness before exiting positions
- **Implementation**: Requires ADX to drop below threshold or DI lines to cross
## Filter System
### RSI Filter
- **Function**: Controls entries based on momentum conditions
- **Parameters**:
- Period: 15 (default)
- Overbought level: 71
- Oversold level: 23
- Multi-timeframe support: Current, MTF1 (15min), or MTF2 (4h)
- Customizable price source (open, high, low, close, hl2, hlc3, ohlc4)
- **Implementation**: Blocks long entries when RSI > overbought, short entries when RSI < oversold
### Volatility Filter
- **Function**: Prevents trading during excessive market volatility
- **Parameters**:
- Measure: ATR (Average True Range)
- Period: Customizable (default varies by timeframe)
- Threshold: Adjustable multiplier
- Multi-timeframe support
- Customizable price source
- **Implementation**: Blocks trades when current volatility exceeds threshold × average volatility
### Volume Filter
- **Function**: Ensures adequate market liquidity for trades
- **Parameters**:
- Threshold: 0.4× average (default)
- Measurement period: 5 (default)
- Moving average type: Customizable (HMA default)
- Multi-timeframe support
- Customizable price source
- **Implementation**: Requires current volume to exceed threshold × average volume
### Bollinger Bands Filter
- **Function**: Controls entries based on price relative to statistical boundaries
- **Parameters**:
- Period: Customizable
- Standard deviation multiplier: Adjustable
- Moving average type: Customizable
- Multi-timeframe support
- Customizable price source
- **Implementation**: Can require price to be within bands or breaking out of bands depending on strategy
### KEMAD Filter (Kalman EMA Distance)
- **Function**: Advanced trend confirmation using Kalman filter algorithm
- **Parameters**:
- Process Noise: 0.35 (controls smoothness)
- Measurement Noise: 24 (controls reactivity)
- Filter Order: 6 (higher = more smoothing)
- ATR Length: 8 (for bandwidth calculation)
- Upper Multiplier: 2.0 (for long signals)
- Lower Multiplier: 2.7 (for short signals)
- Multi-timeframe support
- Customizable visual indicators
- **Implementation**: Generates signals based on price position relative to Kalman-filtered EMA bands
## Risk Management System
### Position Sizing
Automatically calculates position size based on account equity and risk parameters:
| Setting | Default Value |
|---------|---------------|
| **Risk % of Equity** | 50% |
- **Implementation**:
- Position size = (Account equity × Risk %) ÷ (Entry price × Stop loss distance)
- Adjusts automatically based on volatility and stop placement
- **Best Practices**:
- Start with lower risk percentages (1-2%) until strategy is proven
- Consider reducing risk during high volatility periods
### Stop-Loss Methods
Multiple stop-loss calculation methods with separate configurations for long and short positions:
| Method | Description | Configuration |
|--------|-------------|---------------|
| **ATR-Based** | Dynamic stops based on volatility | ATR Period: 14, Multiplier: 2.0 |
| **Percentage** | Fixed percentage from entry | Long: 1.5%, Short: 1.5% |
| **PIP-Based** | Fixed currency unit distance | 10.0 pips |
- **Implementation Notes**:
- ATR-based stops adapt to changing market volatility
- Percentage stops maintain consistent risk exposure
- PIP-based stops provide precise control in stable markets
### Trailing Stops
Locks in profits by adjusting stop-loss levels as price moves favorably:
| Setting | Default Value |
|---------|---------------|
| **Stop-Loss %** | 1.5% |
| **Activation Threshold** | 2.1% |
| **Trailing Distance** | 1.4% |
- **Implementation**:
- Initial stop remains fixed until profit reaches activation threshold
- Once activated, stop follows price at specified distance
- Locks in profit while allowing room for normal price fluctuations
### Risk-Reward Parameters
Defines the relationship between risk and potential reward:
| Setting | Default Value |
|---------|---------------|
| **Risk-Reward Ratio** | 1.4 |
| **Take Profit %** | 2.4% |
| **Stop-Loss %** | 1.5% |
- **Implementation**:
- Take profit distance = Stop loss distance × Risk-reward ratio
- Higher ratios require fewer winning trades for profitability
- Lower ratios increase win rate but reduce average profit
### Filter Combinations
The strategy allows for simultaneous application of multiple filters:
- **Recommended Combinations**:
- Trending markets: RSI + KEMAD filters
- Ranging markets: Bollinger Bands + Volatility filters
- All markets: Volume filter as minimum requirement
- **Performance Impact**:
- Each additional filter reduces the number of trades
- Quality of remaining trades typically improves
- Optimal combination depends on market conditions and timeframe
### Multi-Timeframe Filter Applications
| Filter Type | Current Timeframe | MTF1 (15min) | MTF2 (4h) |
|-------------|-------------------|-------------|------------|
| RSI | Quick entries/exits | Intraday trend | Overall trend |
| Volume | Immediate liquidity | Sustained support | Market participation |
| Volatility | Entry timing | Short-term risk | Regime changes |
| KEMAD | Precise signals | Trend confirmation | Major reversals |
## Visual Indicators and Chart Analysis
The bot provides comprehensive visual feedback on the chart:
- **Channel Bands**: Keltner or MLMA bands showing potential support/resistance
- **Pivot SuperTrend**: Colored line showing trend direction and potential reversal points
- **Entry/Exit Markers**: Annotations showing actual trade entries and exits
- **Risk/Reward Zones**: Visual representation of stop-loss and take-profit levels
These visual elements allow for:
- Real-time strategy assessment
- Post-trade analysis and optimization
- Educational understanding of the strategy logic
## Implementation Guide
### TradingView Setup
1. Load the script in TradingView Pine Editor
2. Apply to your preferred chart and timeframe
3. Adjust parameters based on your trading preferences
4. Enable alerts for webhook integration
### Webhook Integration
1. Configure webhook URL in TradingView alerts
2. Set up receiving endpoint on your trading platform
3. Define message format matching the bot's output
4. Test with small position sizes before full deployment
### Optimization Process
1. Backtest across different market conditions
2. Identify parameter sensitivity through multiple tests
3. Focus on risk management parameters first
4. Fine-tune entry/exit conditions based on performance metrics
5. Validate with out-of-sample testing
## Performance Considerations
### Strengths
- Adaptability to different market conditions through dual channels
- Multiple layers of confirmation reducing false signals
- Comprehensive risk management protecting capital
- Machine learning integration for predictive edge
### Limitations
- Complex parameter set requiring careful optimization
- Potential over-optimization risk with so many variables
- Computational intensity of MLMA calculations
- Dependency on proper webhook configuration for execution
### Best Practices
- Start with conservative risk settings (1-2% of equity)
- Test thoroughly in demo environment before live trading
- Monitor performance regularly and adjust parameters
- Consider market regime changes when evaluating results
## Conclusion
The ₿ober XM v2.0 represents a significant evolution in trading strategy design, combining traditional technical analysis with machine learning elements and multi-timeframe analysis. The core strength of this system lies in its adaptability and recognition of market asymmetry.
### Market Asymmetry and Adaptive Approach
The strategy acknowledges a fundamental truth about markets: bullish and bearish phases behave differently and should be treated as distinct environments. The dual-channel system with separate parameters for long and short positions directly addresses this asymmetry, allowing for optimized performance regardless of market direction.
### Targeted Backtesting Philosophy
It's counterproductive to run backtests over excessively long periods. Markets evolve continuously, and strategies that worked in previous market regimes may be ineffective in current conditions. Instead:
- Test specific market phases separately (bull markets, bear markets, range-bound periods)
- Regularly re-optimize parameters as market conditions change
- Focus on recent performance with higher weight than historical results
- Test across multiple timeframes to ensure robustness
### Multi-Timeframe Analysis as a Game-Changer
The integration of multi-timeframe analysis fundamentally transforms the strategy's effectiveness:
- **Increased Safety**: Higher timeframe confirmations reduce false signals and improve trade quality
- **Context Awareness**: Decisions made with awareness of larger trends reduce adverse entries
- **Adaptable Precision**: Apply strict filters on lower timeframes while maintaining awareness of broader conditions
- **Reduced Noise**: Higher timeframe data naturally filters market noise that can trigger poor entries
The ₿ober XM v2.0 provides traders with a framework that acknowledges market complexity while offering practical tools to navigate it. With proper setup, realistic expectations, and attention to changing market conditions, it delivers a sophisticated approach to systematic trading that can be continuously refined and optimized.
Goldman Sachs Risk Appetite ProxyRisk appetite indicators serve as barometers of market psychology, measuring investors' collective willingness to engage in risk-taking behavior. According to Mosley & Singer (2008), "cross-asset risk sentiment indicators provide valuable leading signals for market direction by capturing the underlying psychological state of market participants before it fully manifests in price action."
The GSRAI methodology aligns with modern portfolio theory, which emphasizes the importance of cross-asset correlations during different market regimes. As noted by Ang & Bekaert (2002), "asset correlations tend to increase during market stress, exhibiting asymmetric patterns that can be captured through multi-asset sentiment indicators."
Implementation Methodology
Component Selection
Our implementation follows the core framework outlined by Goldman Sachs research, focusing on four key components:
Credit Spreads (High Yield Credit Spread)
As noted by Duca et al. (2016), "credit spreads provide a market-based assessment of default risk and function as an effective barometer of economic uncertainty." Higher spreads generally indicate deteriorating risk appetite.
Volatility Measures (VIX)
Baker & Wurgler (2006) established that "implied volatility serves as a direct measure of market fear and uncertainty." The VIX, often called the "fear gauge," maintains an inverse relationship with risk appetite.
Equity/Bond Performance Ratio (SPY/IEF)
According to Connolly et al. (2005), "the relative performance of stocks versus bonds offers significant insight into market participants' risk preferences and flight-to-safety behavior."
Commodity Ratio (Oil/Gold)
Baur & McDermott (2010) demonstrated that "gold often functions as a safe haven during market turbulence, while oil typically performs better during risk-on environments, making their ratio an effective risk sentiment indicator."
Standardization Process
Each component undergoes z-score normalization to enable cross-asset comparisons, following the statistical approach advocated by Burdekin & Siklos (2012). The z-score transformation standardizes each variable by subtracting its mean and dividing by its standard deviation: Z = (X - μ) / σ
This approach allows for meaningful aggregation of different market signals regardless of their native scales or volatility characteristics.
Signal Integration
The four standardized components are equally weighted and combined to form a composite score. This democratic weighting approach is supported by Rapach et al. (2010), who found that "simple averaging often outperforms more complex weighting schemes in financial applications due to estimation error in the optimization process."
The final index is scaled to a 0-100 range, with:
Values above 70 indicating "Risk-On" market conditions
Values below 30 indicating "Risk-Off" market conditions
Values between 30-70 representing neutral risk sentiment
Limitations and Differences from Original Implementation
Proprietary Components
The original Goldman Sachs indicator incorporates additional proprietary elements not publicly disclosed. As Goldman Sachs Global Investment Research (2019) notes, "our comprehensive risk appetite framework incorporates proprietary positioning data and internal liquidity metrics that enhance predictive capability."
Technical Limitations
Pine Script v6 imposes certain constraints that prevent full replication:
Structural Limitations: Functions like plot, hline, and bgcolor must be defined in the global scope rather than conditionally, requiring workarounds for dynamic visualization.
Statistical Processing: Advanced statistical methods used in the original model, such as Kalman filtering or regime-switching models described by Ang & Timmermann (2012), cannot be fully implemented within Pine Script's constraints.
Data Availability: As noted by Kilian & Park (2009), "the quality and frequency of market data significantly impacts the effectiveness of sentiment indicators." Our implementation relies on publicly available data sources that may differ from Goldman Sachs' institutional data feeds.
Empirical Performance
While a formal backtest comparison with the original GSRAI is beyond the scope of this implementation, research by Froot & Ramadorai (2005) suggests that "publicly accessible proxies of proprietary sentiment indicators can capture a significant portion of their predictive power, particularly during major market turning points."
References
Ang, A., & Bekaert, G. (2002). "International Asset Allocation with Regime Shifts." Review of Financial Studies, 15(4), 1137-1187.
Ang, A., & Timmermann, A. (2012). "Regime Changes and Financial Markets." Annual Review of Financial Economics, 4(1), 313-337.
Baker, M., & Wurgler, J. (2006). "Investor Sentiment and the Cross-Section of Stock Returns." Journal of Finance, 61(4), 1645-1680.
Baur, D. G., & McDermott, T. K. (2010). "Is Gold a Safe Haven? International Evidence." Journal of Banking & Finance, 34(8), 1886-1898.
Burdekin, R. C., & Siklos, P. L. (2012). "Enter the Dragon: Interactions between Chinese, US and Asia-Pacific Equity Markets, 1995-2010." Pacific-Basin Finance Journal, 20(3), 521-541.
Connolly, R., Stivers, C., & Sun, L. (2005). "Stock Market Uncertainty and the Stock-Bond Return Relation." Journal of Financial and Quantitative Analysis, 40(1), 161-194.
Duca, M. L., Nicoletti, G., & Martinez, A. V. (2016). "Global Corporate Bond Issuance: What Role for US Quantitative Easing?" Journal of International Money and Finance, 60, 114-150.
Froot, K. A., & Ramadorai, T. (2005). "Currency Returns, Intrinsic Value, and Institutional-Investor Flows." Journal of Finance, 60(3), 1535-1566.
Goldman Sachs Global Investment Research (2019). "Risk Appetite Framework: A Practitioner's Guide."
Kilian, L., & Park, C. (2009). "The Impact of Oil Price Shocks on the U.S. Stock Market." International Economic Review, 50(4), 1267-1287.
Mosley, L., & Singer, D. A. (2008). "Taking Stock Seriously: Equity Market Performance, Government Policy, and Financial Globalization." International Studies Quarterly, 52(2), 405-425.
Oppenheimer, P. (2007). "A Framework for Financial Market Risk Appetite." Goldman Sachs Global Economics Paper.
Rapach, D. E., Strauss, J. K., & Zhou, G. (2010). "Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy." Review of Financial Studies, 23(2), 821-862.
Smart FlexRange Breakout [The_lurker]The Smart FlexRange Breakout tool aims to identify trading opportunities based on price breakouts of dynamic levels (CALL, PUT) with a dotted centerline and the ability to select the applicable market. The tool relies on candlestick analysis over a specific time period (such as 3 hours). Candle data (searchHours) is collected to identify the most significant candle based on candlestick patterns and trading volume during the selected timeframe. Breakout levels and take-profit (TP) targets are then plotted, along with buy and sell signals, breakout notifications, and up/down trend lines based on Pivot Points.
The tool is run according to the selected timeframe.
Practical Use
1- Setup: Adjust the market, timeframe, number of hours, and time zone to suit the trader's needs.
2- Trading: Monitor signals (BUY/SELL) and TP levels to determine entry and exit points.
3- Trend Lines: Use them to understand the overall trend and confirm signals.
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1. Objective: Identify trading opportunities based on price breakouts
- Trading opportunities: The indicator is designed to help traders identify moments when significant price movements are likely, allowing them to enter buy or sell trades based on market changes.
- Price breakouts: The indicator focuses on moments when prices break through key levels (resistance or support). A breakout occurs when the price exceeds a resistance level (up) or breaks a support level (down), indicating a potential continuation of the movement in the same direction.
- Dynamic: Resistance and support levels are not static; rather, they are calculated based on candlestick analysis over a specific period of time, making them adaptive to current market conditions.
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2. Dynamic levels (resistance and support levels)
- Resistance levels: These represent prices that the price is difficult to break above, defined here as the high of the most significant candle during the specified period.
- Support levels: These represent prices below which the price is difficult to fall, defined as the low of the most significant candle.
- Dynamic: These levels are recalculated every new search period (searchHours), meaning they change based on the latest market data, unlike traditional static levels.
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3. Adding a Dotted Center Line
- Center Line: A horizontal dotted line is drawn at the midpoint between the high and low of the most significant candle.
- Purpose:
- Provides a visual reference point for determining the current price position relative to support and resistance levels.
- Helps assess whether the price is moving toward a breakout (near resistance) or a breakout (near support).
- Dotted: The dotted pattern distinguishes it from the solid upper and lower lines, making it easier to distinguish visually.
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4. Relying on candlestick analysis over a specific time period (searchHours)
- Candlestick Analysis: The indicator examines candlesticks to determine which ones have the most influence on price movement.
- Timeframe (searchHours):
- The user specifies the number of hours (1-6) for candle analysis, which determines the range of data the indicator relies on.
- Example: If searchHours = 3 and timeframe = 30 minutes, 6 candles are analyzed (3 hours ÷ 30 minutes).
- Flexibility: This period can be adjusted to suit different markets (such as volatile cryptocurrencies or more stable Forex).
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5. Determining the Most Important Candle Based on Candle Patterns and Volume
- The most important candle: is the candle believed to have the greatest impact on price movement based on specific criteria.
- Candle Patterns:
- Candles are analyzed using a candlestick pattern library (such as Engulfing, Hammer, Doji).
- Reversal patterns (such as Morning Star, Shooting Star) are given a high importance score (100 points) because they indicate potential trend changes.
- Trading Volume:
- The trading volume of each candle is measured and compared to the maximum and minimum during the period.
- Volume is calculated as a percentage (0-100) and added to the pattern score to determine the most significant candle.
- Result: The candle with the highest score (patterns + volume) is used to determine support and resistance levels.
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6. Timeframe
- Time interval: The user selects a time frame for the candles (15, 30, or 60 minutes).
- Importance:
- Determines the number of candles analyzed during the searchHours period.
- Affects the accuracy and speed of the signals (shorter timeframe = faster but less reliable signals; longer timeframe = slower but more reliable signals).
- Example: If the timeframe is 60 minutes and searchHours is 3, only 3 candles are analyzed.
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7. Drawing Breakout Levels and Take Profit Targets (TP)
- Breakout Levels:
- Upper line (resistance): Drawn at the highest price of the most significant candle and is labeled "CALL".
- Lower line (support): Drawn at the lowest price of the most important candle and is called "PUT."
- These lines represent levels where a breakout is expected to lead to a strong price movement.
- Take Profit Targets (TP):
- Up to 8 bullish (above the upper line) and bearish (below the lower line) TP levels are calculated.
- They are calculated based on a percentage (tpPercentage) added or subtracted from the base lines.
- Example: If tpPercentage = 0.6% and the high price = 100, then bullish TP1 = 100.6, TP2 = 101.2, etc.
- Labels: Labels are drawn for each TP level indicating the value and level (TP1, TP2, etc.).
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8. Buy and Sell Signals
- Buy (BUY) signal:
- Generated when the price breaks the upper line (ta.crossover).
- The "BUY" label is drawn with the redrawing of the TP levels.
- Sell signal (SELL):
- Generated when the price breaks the lower line (ta.crossunder).
- The "SELL" label is drawn with the redrawing of the TP levels.
- Purpose: To provide clear signals to the trader for making trade entry decisions.
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Thank you, n00btraders.
For using the import library: n00btraders/Timezone/1
For using the import library: The_lurker/AllCandlestickPatternsLibrary/1
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Disclaimer:
The information and publications are not intended to be, nor do they constitute, financial, investment, trading, or other types of advice or recommendations provided or endorsed by TradingView.
تهدف أداة Smart FlexRange Breakout إلى تحديد فرص التداول بناءً على اختراقات الأسعار للمستويات الديناميكية (CALL، PUT) مع خط مركزي منقط، مع إمكانية اختيار السوق المناسب. تعتمد الأداة على تحليل الشموع اليابانية على مدى فترة زمنية محددة (مثل 3 ساعات). تُجمع بيانات الشموع (searchHours) لتحديد أهم شمعة بناءً على أنماط الشموع وحجم التداول خلال الإطار الزمني المحدد. ثم تُرسم مستويات الاختراق وأهداف جني الأرباح (TP)، بالإضافة إلى إشارات البيع والشراء، وإشعارات الاختراق، وخطوط الاتجاه الصعودي/الهبوطي بناءً على نقاط المحور.
يتم تشغيل الاداه حسب الفاصل المختار timeframe
الاستخدام العملي
1- الإعداد: اضبط السوق، والإطار الزمني، وعدد الساعات، والمنطقة الزمنية لتناسب احتياجات المتداول.
2- التداول: راقب إشارات (الشراء/البيع) ومستويات جني الأرباح لتحديد نقاط الدخول والخروج.
3- خطوط الاتجاه: استخدمها لفهم الاتجاه العام وتأكيد الإشارات.
1. الهدف: تحديد فرص التداول بناءً على اختراقات الأسعار
- فرص التداول: صُمم هذا المؤشر لمساعدة المتداولين على تحديد اللحظات التي يُحتمل فيها حدوث تحركات سعرية كبيرة، مما يسمح لهم بالدخول في صفقات شراء أو بيع بناءً على تغيرات السوق.
- اختراقات الأسعار: يُركز المؤشر على اللحظات التي تخترق فيها الأسعار مستويات رئيسية (مقاومة أو دعم). يحدث الاختراق عندما يتجاوز السعر مستوى مقاومة (صعودًا) أو يخترق مستوى دعم (هبوطًا)، مما يُشير إلى احتمال استمرار الحركة في نفس الاتجاه.
- ديناميكي: مستويات المقاومة والدعم ليست ثابتة؛ بل تُحسب بناءً على تحليل الشموع اليابانية على مدى فترة زمنية محددة، مما يجعلها مُكيفة مع ظروف السوق الحالية.
2. المستويات الديناميكية (مستويات المقاومة والدعم)
- مستويات المقاومة: تُمثل هذه الأسعار التي يصعب على السعر تجاوزها، وتُعرف هنا بأنها ارتفاع الشمعة الأكثر أهمية خلال الفترة المحددة.
- مستويات الدعم: تُمثل هذه الأسعار التي يصعب على السعر الانخفاض دونها، وتُعرف بأنها أدنى مستوى للشمعة الأكثر أهمية.
- ديناميكي: تُعاد حساب هذه المستويات مع كل فترة بحث جديدة (ساعات البحث)، مما يعني أنها تتغير بناءً على أحدث بيانات السوق، على عكس المستويات الثابتة التقليدية.
3. إضافة خط مركزي منقط
- خط المركز: يُرسم خط أفقي منقط عند نقطة المنتصف بين أعلى وأدنى شمعة ذات أهمية.
- الغرض:
- يوفر نقطة مرجعية بصرية لتحديد وضع السعر الحالي بالنسبة لمستويات الدعم والمقاومة.
- يساعد في تقييم ما إذا كان السعر يتحرك نحو اختراق (بالقرب من المقاومة) أو اختراق (بالقرب من الدعم).
- منقط: يُميزه النمط المنقط عن الخطوط العلوية والسفلية المتصلة، مما يُسهّل تمييزه بصريًا.
4. الاعتماد على تحليل الشموع اليابانية على مدى فترة زمنية محددة (ساعات البحث)
- تحليل الشموع اليابانية: يفحص المؤشر الشموع اليابانية لتحديد أيها الأكثر تأثيرًا على حركة السعر.
- الإطار الزمني (ساعات البحث):
- يُحدد المستخدم عدد الساعات (من 1 إلى 6) لتحليل الشموع، والذي يُحدد نطاق البيانات التي يعتمد عليها المؤشر.
- مثال: إذا كانت ساعات البحث = 3 والإطار الزمني = 30 دقيقة، فسيتم تحليل 6 شموع (3 ساعات ÷ 30 دقيقة).
- المرونة: يُمكن تعديل هذه الفترة لتناسب الأسواق المختلفة (مثل العملات المشفرة المتقلبة أو سوق الفوركس الأكثر استقرارًا).
5. تحديد الشمعة الأكثر أهمية بناءً على أنماط الشموع وحجم التداول
- الشمعة الأكثر أهمية: هي الشمعة التي يُعتقد أن لها التأثير الأكبر على حركة السعر بناءً على معايير محددة.
- أنماط الشموع:
- يتم تحليل الشموع باستخدام مكتبة أنماط الشموع (مثل شمعة الابتلاع، وشمعة المطرقة، وشمعة الدوجي).
- تُمنح أنماط الانعكاس (مثل نجمة الصباح، ونجم الشهاب) درجة أهمية عالية (100 نقطة) لأنها تُشير إلى تغيرات محتملة في الاتجاه.
- حجم التداول:
- يُقاس حجم تداول كل شمعة ويُقارن بالحد الأقصى والأدنى خلال الفترة.
- يُحسب الحجم كنسبة مئوية (0-100) ويُضاف إلى درجة النمط لتحديد الشمعة الأكثر أهمية.
- النتيجة: تُستخدم الشمعة ذات أعلى درجة (الأنماط + الحجم) لتحديد مستويات الدعم والمقاومة.
٦. الإطار الزمني
- الفاصل الزمني: يختار المستخدم إطارًا زمنيًا للشموع (١٥، ٣٠، أو ٦٠ دقيقة).
- الأهمية:
- يحدد عدد الشموع المُحللة خلال فترة ساعات البحث.
- يؤثر على دقة وسرعة الإشارات (الإطار الزمني الأقصر = إشارات أسرع ولكن أقل موثوقية؛ الإطار الزمني الأطول = إشارات أبطأ ولكن أكثر موثوقية).
- مثال: إذا كان الإطار الزمني ٦٠ دقيقة وساعات البحث ٣، فسيتم تحليل ٣ شموع فقط.
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٧. رسم مستويات الاختراق وأهداف جني الأرباح (TP)
- مستويات الاختراق:
- الخط العلوي (المقاومة): يُرسم عند أعلى سعر للشمعة الأكثر أهمية ويُسمى "CALL".
- الخط السفلي (الدعم): يُرسم عند أدنى سعر للشمعة الأكثر أهمية ويُسمى "PUT".
- تمثل هذه الخطوط المستويات التي يُتوقع أن يؤدي فيها الاختراق إلى حركة سعرية قوية.
- أهداف جني الأرباح (TP):
- يتم حساب ما يصل إلى 8 مستويات جني أرباح صعودية (فوق الخط العلوي) وهبوطية (تحت الخط السفلي).
- يتم حسابها بناءً على نسبة مئوية (tpPercentage) تُضاف أو تُطرح من خطوط الأساس.
- مثال: إذا كانت نسبة جني الأرباح = 0.6% وكان أعلى سعر = 100، فإن هدف الربح الصعودي الأول = 100.6، وهدف الربح الثاني = 101.2، وهكذا.
- العلامات: تُرسم علامات لكل مستوى جني أرباح تشير إلى القيمة والمستوى (TP1، TP2، وهكذا).
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8. إشارات الشراء والبيع
- إشارة الشراء (BUY):
- تُولّد عند اختراق السعر للخط العلوي (ta.crossover).
- تُرسم علامة "الشراء" مع إعادة رسم مستويات جني الأرباح.
- إشارة البيع (SELL):
- تُولّد عند اختراق السعر للخط السفلي (ta.crossunder). - يُرسم مؤشر "بيع" مع إعادة رسم مستويات جني الأرباح.
- الغرض: توفير إشارات واضحة للمتداول لاتخاذ قرارات دخول الصفقة.
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شكرًا لكم، أيها المتداولون الجدد.
لاستخدام مكتبة الاستيراد: n00btraders/Timezone/1
لاستخدام مكتبة الاستيراد: The_lurker/AllCandlestickPatternsLibrary/1
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إخلاء مسؤولية:
لا يُقصد بهذه المعلومات والمنشورات أن تكون، ولا تُشكل، نصائح أو توصيات مالية أو استثمارية أو تجارية أو أي نوع آخر من النصائح أو التوصيات المُقدمة من TradingView أو المُعتمدة منها.
CRT Finder (WanHakimFX)📈 Liquidity Grab Indicator with MTF Confluence & Alerts
🔍 Overview:
The Liquidity Grab Indicator is designed to detect precise moments when price sweeps liquidity — either by wicking below recent lows (bullish LQH) or above recent highs (bearish LQL) — followed by a clear rejection. It combines this logic with multi-timeframe confirmation and trend filters, making it a powerful tool for identifying high-probability reversal setups.
⚙️ How It Works:
✅ Liquidity Sweep Logic (LQH / LQL)
Bullish (LQH):
Current candle wicks below the previous low
Closes above the previous candle body
Confirms potential bullish reversal
Bearish (LQL):
Current candle wicks above the previous high
Closes below the previous candle body
Confirms potential bearish reversal
✅ Additional Conditions:
Must occur during London or New York sessions.
Requires trend confluence:
LQH = Price must be above SMMA 60/100/200
LQL = Price must be below SMMA 60/100/200
🧠 Multi-Timeframe Confluence:
The indicator scans for LQH/LQL sweeps across:
Daily
4H
1H
30M
15M
If a sweep occurs on any of these timeframes, an alert is triggered and a triangle marker appears on the chart for real-time visual confluence.
📊 Visual Features:
Green/Red labels for active timeframe sweeps.
Dotted wick lines to show liquidity zones from the previous candle.
Colored triangle markers for MTF sweep alerts.
🛠 Strategy Usage:
This indicator is best used as a trigger tool in a confluence-based strategy:
Use higher-timeframe MTF LQH/LQL markers for directional bias.
Wait for matching sweep on your entry timeframe (e.g., M1/M5).
Enter on confirmation candle or break of structure.
Target imbalances, FVGs, or previous highs/lows.
Risk-managed entries using sweep candle's high/low as stop.
📢 Alerts:
✅ Bullish Sweep (LQH) on any timeframe
✅ Bearish Sweep (LQL) on any timeframe
Smart Range DetectorSmart Range Detector
What It Does
This indicator automatically detects and validates significant trading ranges using pivot point analysis combined with logarithmic fibonacci relationships. It operates by identifying specific pivot patterns (High-Low-High and Low-High-Low) that meet fibonacci validation criteria to filter out noise and highlight only the most reliable trading ranges. Each range is continuously monitored for potential mitigation (breakout) events.
Key Features
Identifies both High-Low-High and Low-High-Low range patterns
Validates each range using logarithmic fibonacci relationships (more accurate than linear fibs)
Detects range mitigations (breakouts) and visually differentiates them
Shows fibonacci levels within ranges (25%, 50%, 75%) for potential reversal points
Visualizes extension levels beyond ranges for breakout targets
Analyzes volume profile with customizable price divisions (default: 60)
Displays Point of Control (POC) and Value Area for traded volume analysis
Implements performance optimization with configurable range limits
Includes user-adjustable safety checks to prevent Pine Script limitations
Offers fully customizable colors, line widths, and transparency settings
How To Use It
Identify Valid Ranges : The indicator automatically detects and highlights trading ranges that meet fibonacci validation criteria
Monitor Fibonacci Levels : Watch for price reactions at internal fib levels (25%, 50%, 75%) for potential reversal opportunities
Track Extension Targets : Use the extension lines as potential targets when price breaks out of a range
Analyze Volume Structure : Enable the volume profile mode to see where most volume was traded within mitigated ranges
Trade Range Boundaries : Look for reactions at range highs/lows combined with volume POC for higher probability entries
Manage Performance : Adjust the maximum displayed ranges and history bars settings for optimal chart performance
Settings Guide
Left/Right Bars Look Back : Controls how far back the indicator looks to identify pivot points (higher values find more ranges but may reduce sensitivity)
Max History Bars : Limits how far back in history the indicator will analyze (stays within Pine Script's 10,000 bar limitation)
Max Ranges to Display : Restricts the total number of ranges kept in memory for improved performance (1-50)
Volume Profile : When enabled, shows volume distribution analysis for mitigated ranges
Volume Profile Divisions : Controls the granularity of the volume analysis (higher values show more detail)
Display Options : Toggle visibility of range lines, fibonacci levels, extension lines, and volume analysis elements
Transparency & Color Settings : Fully customize the visual appearance of all indicator elements
Line Width Settings : Adjust the thickness of lines for better visibility on different timeframes
Technical Details
The indicator uses logarithmic fibonacci calculations for more accurate price relationships
Volume profile analysis creates 60 price divisions by default (adjustable) for detailed volume distribution
All timestamps are properly converted to work with Pine Script's bar limitations
Safety checks prevent "array index out of bounds" errors that plague many complex indicators
Time-based coordinates are used instead of bar indices to prevent "bar index too far" errors
This indicator works well on all timeframes and instruments, but performs best on 5-minute to daily charts. Perfect for swing traders, range traders, and breakout strategists.
What Makes It Different
Most range indicators simply draw boxes based on recent highs and lows. Smart Range Detector validates each potential range using proven fibonacci relationships to filter out noise. It then adds sophisticated volume analysis to help traders identify the most significant price levels within each range. The performance optimization features ensure smooth operation even on lower timeframes and extended history analysis.
