Momentum Trajectory Suite📈 Momentum Trajectory Suite
🟢 Overview
Momentum Trajectory Suite is a multi-faceted indicator designed to help traders evaluate trend direction, volatility conditions, and behavioral sentiment in a single consolidated view.
By combining a customizable Trajectory EMA, adaptive Bollinger Bands, and a Greed vs. Fear heatmap, this tool empowers traders to identify directional bias, measure momentum strength, and spot potential reversals or continuation setups.
🧠 Concept
This indicator merges three classic techniques:
Trend Analysis: Trajectory EMA highlights the prevailing directional momentum by smoothing price action over a customizable period.
Volatility Envelopes: Bollinger Bands adapt to dynamic price swings, showing overbought/oversold extremes and periods of contraction or expansion.
Behavioral Sentiment: A Greed vs. Fear heatmap combines RSI and MACD Histogram readings to visualize when markets are dominated by buying enthusiasm or selling pressure.
The combination is designed to help traders interpret market context more effectively than using any single component alone.
🛠️ How to Use the Indicator
Trajectory EMA:
Use the blue EMA line to assess overall trend direction.
Price closing above the EMA may indicate bullish momentum; closing below may indicate bearish bias.
Buy/Sell Signals:
Green circles appear when price crosses above the EMA (potential long entry).
Red circles appear when price crosses below the EMA (potential exit or short entry).
Bollinger Bands:
Monitor upper/lower bands for overbought and oversold price extremes.
Narrowing bands may signal upcoming volatility expansion.
Greed vs. Fear Heatmap:
Green histogram bars indicate bullish sentiment when RSI exceeds 60 and MACD Histogram is positive.
Red histogram bars indicate bearish sentiment when RSI is below 40 and MACD Histogram is negative.
Gray bars indicate neutral or mixed conditions.
Background Color Zones:
The chart background shifts to green when EMA slope is positive and red when negative, providing quick directional cues.
All inputs are adjustable in settings, including EMA length, Bollinger Band parameters, and oscillator configurations.
📊 Interpretation
Bullish Conditions:
Price above the Trajectory EMA, background green, and Greed heatmap active.
May signal trend continuation and increased buying pressure.
Bearish Conditions:
Price below the Trajectory EMA, background red, and Fear heatmap active.
May signal momentum breakdown or potential continuation to the downside.
Volatility Clues:
Wide Bollinger Bands = trending, volatile market.
Narrow Bollinger Bands = low volatility and possible breakout setup.
Signal Confirmation:
Consider combining signals (e.g., EMA crossover + Greed/Fear heatmap + Bollinger Band touch) for higher-confidence entries.
📝 Notes
The script does not repaint or use future data.
Suitable for multiple timeframes (intraday to daily).
May be combined with other confirmation tools or price action analysis.
⚠️ Disclaimer
This script is for educational and informational purposes only and does not constitute financial advice. Trading carries risk and past performance is not indicative of future results. Always perform your own due diligence before making trading decisions.
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Squeeze & Breakout Confirmation StrategyThis strategy focuses on identifying periods of low volatility (Bollinger Band Squeeze) and then confirming the direction of the subsequent breakout with momentum, volume, and candle strength.
Concepts Applied: Bollinger Bands (Squeeze), RSI (Momentum), Market Volume (Conviction), Candle Size (Strength)
Buy Signal:
Bollinger Band Squeeze: Look for a period where the Bollinger Bands contract significantly, indicating low volatility and consolidation. The bands should be very close to the price action.
RSI Breakout: After the squeeze, wait for the price to break decisively above the upper Bollinger Band. Simultaneously, the RSI should break above 60 (or even 70), indicating strong bullish momentum.
Volume Surge: The breakout candle should be accompanied by a significant increase in trading volume, ideally above its recent average, confirming strong buying interest.
Strong Bullish Candle: The breakout candle itself should be a large, bullish candle (e.g., a strong green candle with a small upper wick or a bullish engulfing pattern), demonstrating buyer conviction.
Sell Signal (Short):
Bollinger Band Squeeze: Look for a period where the Bollinger Bands contract significantly.
RSI Breakdown: After the squeeze, wait for the price to break decisively below the lower Bollinger Band. Simultaneously, the RSI should break below 40 (or even 30), indicating strong bearish momentum.
Volume Surge: The breakdown candle should be accompanied by a significant increase in trading volume, ideally above its recent average, confirming strong selling interest.
Strong Bearish Candle: The breakdown candle itself should be a large, bearish candle (e.g., a strong red candle with a small lower wick or a bearish engulfing pattern), demonstrating seller conviction.
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.
Yelober - Intraday ETF Dashboard# How to Read the Yelober Intraday ETF Dashboard
The Intraday ETF Dashboard provides a powerful at-a-glance view of sector performance and trading opportunities. Here's how to interpret and use the information:
## Basic Dashboard Reading
### Color-Coding System
- **Green values**: Positive performance or bullish signals
- **Red values**: Negative performance or bearish signals
- **Symbol colors**: Green = buy signal, Red = sell signal, Gray = neutral
### Example 1: Identifying Strong Sectors
If you see XLF (Financials) with:
- Day % showing +2.65% (green background)
- Symbol in green color
- RSI of 58 (not overbought)
**Interpretation**: Financial sector is showing strength and momentum without being overextended. Consider long positions in top financial stocks like JPM or BAC.
### Example 2: Spotting Weakness
If you see XLK (Technology) with:
- Day % showing -1.20% (red background)
- Week % showing -3.50% (red background)
- Symbol in red color
- RSI of 35 (approaching oversold)
**Interpretation**: Technology sector is showing weakness across multiple timeframes. Consider avoiding tech stocks or taking short positions in names like MSFT or AAPL, but be cautious as the low RSI suggests a bounce may be coming.
## Advanced Interpretations
### Example 3: Sector Rotation Detection
If you observe:
- XLE (Energy) showing +2.10% while XLK (Technology) showing -1.50%
- Both sectors' Week % values showing the opposite trend
**Interpretation**: This suggests money is rotating out of technology into energy stocks. This rotation pattern is actionable - consider reducing tech exposure and increasing energy positions (look at XOM, CVX in the Top Stocks column).
### Example 4: RSI Divergences
If you see XLU (Utilities) with:
- Day % showing +0.50% (small positive)
- RSI showing 72 (overbought, red background)
**Interpretation**: Despite positive performance, the high RSI suggests the sector is overextended. This divergence between price and indicator suggests caution - the rally in utilities may be running out of steam.
### Example 5: Relative Strength in Weak Markets
If SPY shows -1.20% but XLP (Consumer Staples) shows +0.30%:
**Interpretation**: Consumer staples are showing defensive strength during market weakness. This is typical risk-off behavior. Consider defensive positions in stocks like PG, KO, or PEP for protection.
## Practical Application Scenarios
### Day Trading Setup
1. **Morning Market Assessment**:
- Check which sectors are green pre-market
- Focus on sectors with Day % > 1% and RSI between 40-70
- Identify 2-3 stocks from the Top Stocks column of the strongest sector
2. **Midday Reversal Hunting**:
- Look for sectors with symbol color changing from red to green
- Confirm with RSI moving away from extremes
- Trade stocks from that sector showing similar pattern changes
### Swing Trading Application
1. **Trend Following**:
- Identify sectors with positive Day % and Week %
- Look for RSI values in uptrend but not overbought (45-65)
- Enter positions in top stocks from these sectors, using daily charts for confirmation
2. **Contrarian Setups**:
- Find sectors with deeply negative Day % but RSI < 30
- Look for divergence (price making new lows but RSI rising)
- Consider counter-trend positions in the stronger stocks within these oversold sectors
## Reading Special Conditions
### Example 6: Risk-Off Environment
If you observe:
- XLP (Consumer Staples) and XLU (Utilities) both green
- XLK (Technology) and XLY (Consumer Disc) both red
- SPY slightly negative
**Interpretation**: Classic risk-off rotation. Investors are moving to safety. Consider defensive positioning and reducing exposure to growth sectors.
### Example 7: Market Breadth Analysis
Count the number of sectors in green vs. red:
- If 7+ sectors are green: Strong bullish breadth, consider aggressive long positioning
- If 7+ sectors are red: Weak market breadth, consider defensive positioning or shorts
- If evenly split: Market is indecisive, focus on specific sector strength instead of broad market exposure
Remember that this dashboard is most effective when combined with broader market analysis and appropriate risk management strategies.
Magnificent 7 OscillatorThe Magnificent 7 Oscillator is a sophisticated momentum-based technical indicator designed to analyze the collective performance of the seven largest technology companies in the U.S. stock market (Apple, Microsoft, Alphabet, Amazon, NVIDIA, Tesla, and Meta). This indicator incorporates established momentum factor research and provides three distinct analytical modes: absolute momentum tracking, equal-weighted market comparison, and relative performance analysis. The tool integrates five different oscillator methodologies and includes advanced breadth analysis capabilities.
Theoretical Foundation
Momentum Factor Research
The indicator's foundation rests on seminal momentum research in financial markets. Jegadeesh and Titman (1993) demonstrated that stocks with strong price performance over 3-12 month periods tend to continue outperforming in subsequent periods¹. This momentum effect was later incorporated into formal factor models by Carhart (1997), who extended the Fama-French three-factor model to include a momentum factor (UMD - Up Minus Down)².
The momentum calculation methodology follows the academic standard:
Momentum(t) = / P(t-n) × 100
Where P(t) is the current price and n is the lookback period.
The focus on the "Magnificent 7" stocks reflects the increasing market concentration observed in recent years. Fama and French (2015) noted that a small number of large-cap stocks can drive significant market movements due to their substantial index weights³. The combined market capitalization of these seven companies often exceeds 25% of the total S&P 500, making their collective momentum a critical market indicator.
Indicator Architecture
Core Components
1. Data Collection and Processing
The indicator employs robust data collection with error handling for missing or invalid security data. Each stock's momentum is calculated independently using the specified lookback period (default: 14 periods).
2. Composite Oscillator Calculation
Following Fama-French factor construction methodology, the indicator offers two weighting schemes:
- Equal Weight: Each active stock receives identical weighting (1/n)
- Market Cap Weight: Reserved for future enhancement
3. Oscillator Transformation Functions
The indicator provides five distinct oscillator types, each with established technical analysis foundations:
a) Momentum Oscillator (Default)
- Pure rate-of-change calculation
- Centered around zero
- Direct implementation of Jegadeesh & Titman methodology
b) RSI (Relative Strength Index)
- Wilder's (1978) relative strength methodology
- Transformed to center around zero for consistency
- Scale: -50 to +50
c) Stochastic Oscillator
- George Lane's %K methodology
- Measures current position within recent range
- Transformed to center around zero
d) Williams %R
- Larry Williams' range-based oscillator
- Inverse stochastic calculation
- Adjusted for zero-centered display
e) CCI (Commodity Channel Index)
- Donald Lambert's mean reversion indicator
- Measures deviation from moving average
- Scaled for optimal visualization
Operational Modes
Mode 1: Magnificent 7 Analysis
Tracks the collective momentum of the seven constituent stocks. This mode is optimal for:
- Technology sector analysis
- Growth stock momentum assessment
- Large-cap performance tracking
Mode 2: S&P 500 Equal Weight Comparison
Analyzes momentum using an equal-weighted S&P 500 reference (typically RSP ETF). This mode provides:
- Broader market momentum context
- Size-neutral market analysis
- Comparison baseline for relative performance
Mode 3: Relative Performance Analysis
Calculates the momentum differential between Magnificent 7 and S&P 500 Equal Weight. This mode enables:
- Sector rotation analysis
- Style factor assessment (Growth vs. Value)
- Relative strength identification
Formula: Relative Performance = MAG7_Momentum - SP500EW_Momentum
Signal Generation and Thresholds
Signal Classification
The indicator generates three signal states:
- Bullish: Oscillator > Upper Threshold (default: +2.0%)
- Bearish: Oscillator < Lower Threshold (default: -2.0%)
- Neutral: Oscillator between thresholds
Relative Performance Signals
In relative performance mode, specialized thresholds apply:
- Outperformance: Relative momentum > +1.0%
- Underperformance: Relative momentum < -1.0%
Alert System
Comprehensive alert conditions include:
- Threshold crossovers (bullish/bearish signals)
- Zero-line crosses (momentum direction changes)
- Relative performance shifts
- Breadth Analysis Component
The indicator incorporates market breadth analysis, calculating the percentage of constituent stocks with positive momentum. This feature provides insights into:
- Strong Breadth (>60%): Broad-based momentum
- Weak Breadth (<40%): Narrow momentum leadership
- Mixed Breadth (40-60%): Neutral momentum distribution
Visual Design and User Interface
Theme-Adaptive Display
The indicator automatically adjusts color schemes for dark and light chart themes, ensuring optimal visibility across different user preferences.
Professional Data Table
A comprehensive data table displays:
- Current oscillator value and percentage
- Active mode and oscillator type
- Signal status and strength
- Component breakdowns (in relative performance mode)
- Breadth percentage
- Active threshold levels
Custom Color Options
Users can override default colors with custom selections for:
- Neutral conditions (default: Material Blue)
- Bullish signals (default: Material Green)
- Bearish signals (default: Material Red)
Practical Applications
Portfolio Management
- Sector Allocation: Use relative performance mode to time technology sector exposure
- Risk Management: Monitor breadth deterioration as early warning signal
- Entry/Exit Timing: Utilize threshold crossovers for position sizing decisions
Market Analysis
- Trend Identification: Zero-line crosses indicate momentum regime changes
- Divergence Analysis: Compare MAG7 performance against broader market
- Volatility Assessment: Oscillator range and frequency provide volatility insights
Strategy Development
- Factor Timing: Implement growth factor timing strategies
- Momentum Strategies: Develop systematic momentum-based approaches
- Risk Parity: Use breadth metrics for risk-adjusted portfolio construction
Configuration Guidelines
Parameter Selection
- Momentum Period (5-100): Shorter periods (5-20) for tactical analysis, longer periods (50-100) for strategic assessment
- Smoothing Period (1-50): Higher values reduce noise but increase lag
- Thresholds: Adjust based on historical volatility and strategy requirements
Timeframe Considerations
- Daily Charts: Optimal for swing trading and medium-term analysis
- Weekly Charts: Suitable for long-term trend analysis
- Intraday Charts: Useful for short-term tactical decisions
Limitations and Considerations
Market Concentration Risk
The indicator's focus on seven stocks creates concentration risk. During periods of significant rotation away from large-cap technology stocks, the indicator may not represent broader market conditions.
Momentum Persistence
While momentum effects are well-documented, they are not permanent. Jegadeesh and Titman (1993) noted momentum reversal effects over longer time horizons (2-5 years).
Correlation Dynamics
During market stress, correlations among the constituent stocks may increase, reducing the diversification benefits and potentially amplifying signal intensity.
Performance Metrics and Backtesting
The indicator includes hidden plots for comprehensive backtesting:
- Individual stock momentum values
- Composite breadth percentage
- S&P 500 Equal Weight momentum
- Relative performance calculations
These metrics enable quantitative strategy development and historical performance analysis.
References
¹Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. Journal of Finance, 48(1), 65-91.
Carhart, M. M. (1997). On persistence in mutual fund performance. Journal of Finance, 52(1), 57-82.
Fama, E. F., & French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116(1), 1-22.
Wilder, J. W. (1978). New concepts in technical trading systems. Trend Research.
Risk-Adjusted Momentum Oscillator# Risk-Adjusted Momentum Oscillator (RAMO): Momentum Analysis with Integrated Risk Assessment
## 1. Introduction
Momentum indicators have been fundamental tools in technical analysis since the pioneering work of Wilder (1978) and continue to play crucial roles in systematic trading strategies (Jegadeesh & Titman, 1993). However, traditional momentum oscillators suffer from a critical limitation: they fail to account for the risk context in which momentum signals occur. This oversight can lead to significant drawdowns during periods of market stress, as documented extensively in the behavioral finance literature (Kahneman & Tversky, 1979; Shefrin & Statman, 1985).
The Risk-Adjusted Momentum Oscillator addresses this gap by incorporating real-time drawdown metrics into momentum calculations, creating a self-regulating system that automatically adjusts signal sensitivity based on current risk conditions. This approach aligns with modern portfolio theory's emphasis on risk-adjusted returns (Markowitz, 1952) and reflects the sophisticated risk management practices employed by institutional investors (Ang, 2014).
## 2. Theoretical Foundation
### 2.1 Momentum Theory and Market Anomalies
The momentum effect, first systematically documented by Jegadeesh & Titman (1993), represents one of the most robust anomalies in financial markets. Subsequent research has confirmed momentum's persistence across various asset classes, time horizons, and geographic markets (Fama & French, 1996; Asness, Moskowitz & Pedersen, 2013). However, momentum strategies are characterized by significant time-varying risk, with particularly severe drawdowns during market reversals (Barroso & Santa-Clara, 2015).
### 2.2 Drawdown Analysis and Risk Management
Maximum drawdown, defined as the peak-to-trough decline in portfolio value, serves as a critical risk metric in professional portfolio management (Calmar, 1991). Research by Chekhlov, Uryasev & Zabarankin (2005) demonstrates that drawdown-based risk measures provide superior downside protection compared to traditional volatility metrics. The integration of drawdown analysis into momentum calculations represents a natural evolution toward more sophisticated risk-aware indicators.
### 2.3 Adaptive Smoothing and Market Regimes
The concept of adaptive smoothing in technical analysis draws from the broader literature on regime-switching models in finance (Hamilton, 1989). Perry Kaufman's Adaptive Moving Average (1995) pioneered the application of efficiency ratios to adjust indicator responsiveness based on market conditions. RAMO extends this concept by incorporating volatility-based adaptive smoothing, allowing the indicator to respond more quickly during high-volatility periods while maintaining stability during quiet markets.
## 3. Methodology
### 3.1 Core Algorithm Design
The RAMO algorithm consists of several interconnected components:
#### 3.1.1 Risk-Adjusted Momentum Calculation
The fundamental innovation of RAMO lies in its risk adjustment mechanism:
Risk_Factor = 1 - (Current_Drawdown / Maximum_Drawdown × Scaling_Factor)
Risk_Adjusted_Momentum = Raw_Momentum × max(Risk_Factor, 0.05)
This formulation ensures that momentum signals are dampened during periods of high drawdown relative to historical maximums, implementing an automatic risk management overlay as advocated by modern portfolio theory (Markowitz, 1952).
#### 3.1.2 Multi-Algorithm Momentum Framework
RAMO supports three distinct momentum calculation methods:
1. Rate of Change: Traditional percentage-based momentum (Pring, 2002)
2. Price Momentum: Absolute price differences
3. Log Returns: Logarithmic returns preferred for volatile assets (Campbell, Lo & MacKinlay, 1997)
This multi-algorithm approach accommodates different asset characteristics and volatility profiles, addressing the heterogeneity documented in cross-sectional momentum studies (Asness et al., 2013).
### 3.2 Leading Indicator Components
#### 3.2.1 Momentum Acceleration Analysis
The momentum acceleration component calculates the second derivative of momentum, providing early signals of trend changes:
Momentum_Acceleration = EMA(Momentum_t - Momentum_{t-n}, n)
This approach draws from the physics concept of acceleration and has been applied successfully in financial time series analysis (Treadway, 1969).
#### 3.2.2 Linear Regression Prediction
RAMO incorporates linear regression-based prediction to project momentum values forward:
Predicted_Momentum = LinReg_Value + (LinReg_Slope × Forward_Offset)
This predictive component aligns with the literature on technical analysis forecasting (Lo, Mamaysky & Wang, 2000) and provides leading signals for trend changes.
#### 3.2.3 Volume-Based Exhaustion Detection
The exhaustion detection algorithm identifies potential reversal points by analyzing the relationship between momentum extremes and volume patterns:
Exhaustion = |Momentum| > Threshold AND Volume < SMA(Volume, 20)
This approach reflects the established principle that sustainable price movements require volume confirmation (Granville, 1963; Arms, 1989).
### 3.3 Statistical Normalization and Robustness
RAMO employs Z-score normalization with outlier protection to ensure statistical robustness:
Z_Score = (Value - Mean) / Standard_Deviation
Normalized_Value = max(-3.5, min(3.5, Z_Score))
This normalization approach follows best practices in quantitative finance for handling extreme observations (Taleb, 2007) and ensures consistent signal interpretation across different market conditions.
### 3.4 Adaptive Threshold Calculation
Dynamic thresholds are calculated using Bollinger Band methodology (Bollinger, 1992):
Upper_Threshold = Mean + (Multiplier × Standard_Deviation)
Lower_Threshold = Mean - (Multiplier × Standard_Deviation)
This adaptive approach ensures that signal thresholds adjust to changing market volatility, addressing the critique of fixed thresholds in technical analysis (Taylor & Allen, 1992).
## 4. Implementation Details
### 4.1 Adaptive Smoothing Algorithm
The adaptive smoothing mechanism adjusts the exponential moving average alpha parameter based on market volatility:
Volatility_Percentile = Percentrank(Volatility, 100)
Adaptive_Alpha = Min_Alpha + ((Max_Alpha - Min_Alpha) × Volatility_Percentile / 100)
This approach ensures faster response during volatile periods while maintaining smoothness during stable conditions, implementing the adaptive efficiency concept pioneered by Kaufman (1995).
### 4.2 Risk Environment Classification
RAMO classifies market conditions into three risk environments:
- Low Risk: Current_DD < 30% × Max_DD
- Medium Risk: 30% × Max_DD ≤ Current_DD < 70% × Max_DD
- High Risk: Current_DD ≥ 70% × Max_DD
This classification system enables conditional signal generation, with long signals filtered during high-risk periods—a approach consistent with institutional risk management practices (Ang, 2014).
## 5. Signal Generation and Interpretation
### 5.1 Entry Signal Logic
RAMO generates enhanced entry signals through multiple confirmation layers:
1. Primary Signal: Crossover between indicator and signal line
2. Risk Filter: Confirmation of favorable risk environment for long positions
3. Leading Component: Early warning signals via acceleration analysis
4. Exhaustion Filter: Volume-based reversal detection
This multi-layered approach addresses the false signal problem common in traditional technical indicators (Brock, Lakonishok & LeBaron, 1992).
### 5.2 Divergence Analysis
RAMO incorporates both traditional and leading divergence detection:
- Traditional Divergence: Price and indicator divergence over 3-5 periods
- Slope Divergence: Momentum slope versus price direction
- Acceleration Divergence: Changes in momentum acceleration
This comprehensive divergence analysis framework draws from Elliott Wave theory (Prechter & Frost, 1978) and momentum divergence literature (Murphy, 1999).
## 6. Empirical Advantages and Applications
### 6.1 Risk-Adjusted Performance
The risk adjustment mechanism addresses the fundamental criticism of momentum strategies: their tendency to experience severe drawdowns during market reversals (Daniel & Moskowitz, 2016). By automatically reducing position sizing during high-drawdown periods, RAMO implements a form of dynamic hedging consistent with portfolio insurance concepts (Leland, 1980).
### 6.2 Regime Awareness
RAMO's adaptive components enable regime-aware signal generation, addressing the regime-switching behavior documented in financial markets (Hamilton, 1989; Guidolin, 2011). The indicator automatically adjusts its parameters based on market volatility and risk conditions, providing more reliable signals across different market environments.
### 6.3 Institutional Applications
The sophisticated risk management overlay makes RAMO particularly suitable for institutional applications where drawdown control is paramount. The indicator's design philosophy aligns with the risk budgeting approaches used by hedge funds and institutional investors (Roncalli, 2013).
## 7. Limitations and Future Research
### 7.1 Parameter Sensitivity
Like all technical indicators, RAMO's performance depends on parameter selection. While default parameters are optimized for broad market applications, asset-specific calibration may enhance performance. Future research should examine optimal parameter selection across different asset classes and market conditions.
### 7.2 Market Microstructure Considerations
RAMO's effectiveness may vary across different market microstructure environments. High-frequency trading and algorithmic market making have fundamentally altered market dynamics (Aldridge, 2013), potentially affecting momentum indicator performance.
### 7.3 Transaction Cost Integration
Future enhancements could incorporate transaction cost analysis to provide net-return-based signals, addressing the implementation shortfall documented in practical momentum strategy applications (Korajczyk & Sadka, 2004).
## References
Aldridge, I. (2013). *High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems*. 2nd ed. Hoboken, NJ: John Wiley & Sons.
Ang, A. (2014). *Asset Management: A Systematic Approach to Factor Investing*. New York: Oxford University Press.
Arms, R. W. (1989). *The Arms Index (TRIN): An Introduction to the Volume Analysis of Stock and Bond Markets*. Homewood, IL: Dow Jones-Irwin.
Asness, C. S., Moskowitz, T. J., & Pedersen, L. H. (2013). Value and momentum everywhere. *Journal of Finance*, 68(3), 929-985.
Barroso, P., & Santa-Clara, P. (2015). Momentum has its moments. *Journal of Financial Economics*, 116(1), 111-120.
Bollinger, J. (1992). *Bollinger on Bollinger Bands*. New York: McGraw-Hill.
Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. *Journal of Finance*, 47(5), 1731-1764.
Calmar, T. (1991). The Calmar ratio: A smoother tool. *Futures*, 20(1), 40.
Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). *The Econometrics of Financial Markets*. Princeton, NJ: Princeton University Press.
Chekhlov, A., Uryasev, S., & Zabarankin, M. (2005). Drawdown measure in portfolio optimization. *International Journal of Theoretical and Applied Finance*, 8(1), 13-58.
Daniel, K., & Moskowitz, T. J. (2016). Momentum crashes. *Journal of Financial Economics*, 122(2), 221-247.
Fama, E. F., & French, K. R. (1996). Multifactor explanations of asset pricing anomalies. *Journal of Finance*, 51(1), 55-84.
Granville, J. E. (1963). *Granville's New Key to Stock Market Profits*. Englewood Cliffs, NJ: Prentice-Hall.
Guidolin, M. (2011). Markov switching models in empirical finance. In D. N. Drukker (Ed.), *Missing Data Methods: Time-Series Methods and Applications* (pp. 1-86). Bingley: Emerald Group Publishing.
Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. *Econometrica*, 57(2), 357-384.
Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. *Journal of Finance*, 48(1), 65-91.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. *Econometrica*, 47(2), 263-291.
Kaufman, P. J. (1995). *Smarter Trading: Improving Performance in Changing Markets*. New York: McGraw-Hill.
Korajczyk, R. A., & Sadka, R. (2004). Are momentum profits robust to trading costs? *Journal of Finance*, 59(3), 1039-1082.
Leland, H. E. (1980). Who should buy portfolio insurance? *Journal of Finance*, 35(2), 581-594.
Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. *Journal of Finance*, 55(4), 1705-1765.
Markowitz, H. (1952). Portfolio selection. *Journal of Finance*, 7(1), 77-91.
Murphy, J. J. (1999). *Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications*. New York: New York Institute of Finance.
Prechter, R. R., & Frost, A. J. (1978). *Elliott Wave Principle: Key to Market Behavior*. Gainesville, GA: New Classics Library.
Pring, M. J. (2002). *Technical Analysis Explained: The Successful Investor's Guide to Spotting Investment Trends and Turning Points*. 4th ed. New York: McGraw-Hill.
Roncalli, T. (2013). *Introduction to Risk Parity and Budgeting*. Boca Raton, FL: CRC Press.
Shefrin, H., & Statman, M. (1985). The disposition to sell winners too early and ride losers too long: Theory and evidence. *Journal of Finance*, 40(3), 777-790.
Taleb, N. N. (2007). *The Black Swan: The Impact of the Highly Improbable*. New York: Random House.
Taylor, M. P., & Allen, H. (1992). The use of technical analysis in the foreign exchange market. *Journal of International Money and Finance*, 11(3), 304-314.
Treadway, A. B. (1969). On rational entrepreneurial behavior and the demand for investment. *Review of Economic Studies*, 36(2), 227-239.
Wilder, J. W. (1978). *New Concepts in Technical Trading Systems*. Greensboro, NC: Trend Research.
Kaufman Trend Strategy# ✅ Kaufman Trend Strategy – Full Description (Script Publishing Version)
**Kaufman Trend Strategy** is a dynamic trend-following strategy based on Kaufman Filter theory.
It detects real-time trend momentum, reduces noise, and aims to enhance entry accuracy while optimizing risk.
⚠️ _For educational and research purposes only. Past performance does not guarantee future results._
---
## 🎯 Strategy Objective
- Smooth price noise using Kaufman Filter smoothing
- Detect the strength and direction of trends with a normalized oscillator
- Manage profits using multi-stage take-profits and adaptive ATR stop-loss logic
---
## ✨ Key Features
- **Kaufman Filter Trend Detection**
Extracts directional signal using a state space model.
- **Multi-Stage Profit-Taking**
Automatically takes partial profits based on color changes and zero-cross events.
- **ATR-Based Volatility Stops**
Stops adjust based on swing highs/lows and current market volatility.
---
## 📊 Entry & Exit Logic
**Long Entry**
- `trend_strength ≥ 60`
- Green trend signal
- Price above the Kaufman average
**Short Entry**
- `trend_strength ≤ -60`
- Red trend signal
- Price below the Kaufman average
**Exit (Long/Short)**
- Blue trend color → TP1 (50%)
- Oscillator crosses 0 → TP2 (25%)
- Trend weakens → Final exit (25%)
- ATR + swing-based stop loss
---
## 💰 Risk Management
- Initial capital: `$3,000`
- Order size: `$100` per trade (realistic, low-risk sizing)
- Commission: `0.002%`
- Slippage: `2 ticks`
- Pyramiding: `1` max position
- Estimated risk/trade: `~0.1–0.5%` of equity
> ⚠️ _No trade risks more than 5% of equity. This strategy follows TradingView script publishing rules._
---
## ⚙️ Default Parameters
- **1st Take Profit**: 50%
- **2nd Take Profit**: 25%
- **Final Exit**: 25%
- **ATR Period**: 14
- **Swing Lookback**: 10
- **Entry Threshold**: ±60
- **Exit Threshold**: ±40
---
## 📅 Backtest Summary
- **Symbol**: USD/JPY
- **Timeframe**: 1H
- **Date Range**: Jan 3, 2022 – Jun 4, 2025
- **Trades**: 924
- **Win Rate**: 41.67%
- **Profit Factor**: 1.108
- **Net Profit**: +$1,659.29 (+54.56%)
- **Max Drawdown**: -$1,419.73 (-31.87%)
---
## ✅ Summary
This strategy uses Kaufman filtering to detect market direction with reduced lag and increased smoothness.
It’s built with visual clarity and strong trade management, making it practical for both beginners and advanced users.
---
## 📌 Disclaimer
This script is for educational and informational purposes only and should not be considered financial advice.
Use with proper risk controls and always test in a demo environment before live trading.
System 0530 - Stoch RSI Strategy with ATR filterStrategy Description: System 0530 - Multi-Timeframe Stochastic RSI with ATR Filter
Overview:
This strategy, "System 0530," is designed to identify trading opportunities by leveraging the Stochastic RSI indicator across two different timeframes: a shorter timeframe for initial signal triggers (assumed to be the chart's current timeframe, e.g., 5-minute) and a longer timeframe (15-minute) for signal confirmation. It incorporates an ATR (Average True Range) filter to help ensure trades are taken during periods of adequate market volatility and includes a cooldown mechanism to prevent rapid, successive signals in the same direction. Trade exits are primarily handled by reversing signals.
How It Works:
1. Signal Initiation (e.g., 5-Minute Timeframe):
Long Signal Wait: A potential long entry is considered when the 5-minute Stochastic RSI %K line crosses above its %D line, AND the %K value at the time of the cross is at or below a user-defined oversold level (default: 30).
Short Signal Wait: A potential short entry is considered when the 5-minute Stochastic RSI %K line crosses below its %D line, AND the %K value at the time of the cross is at or above a user-defined overbought level (default: 70). When these conditions are met, the strategy enters a "waiting state" for confirmation from the 15-minute timeframe.
2. Signal Confirmation (15-Minute Timeframe):
Once in a waiting state, the strategy looks for confirmation on the 15-minute Stochastic RSI within a user-defined number of 5-minute bars (wait_window_5min_bars, default: 5 bars).
Long Confirmation:
The 15-minute Stochastic RSI %K must be greater than or equal to its %D line.
The 15-minute Stochastic RSI %K value must be below a user-defined threshold (stoch_15min_long_entry_level, default: 40).
Short Confirmation:
The 15-minute Stochastic RSI %K must be less than or equal to its %D line.
The 15-minute Stochastic RSI %K value must be above a user-defined threshold (stoch_15min_short_entry_level, default: 60).
3. Filters:
ATR Volatility Filter: If enabled, trades are only confirmed if the current ATR value (converted to ticks) is above a user-defined minimum threshold (min_atr_value_ticks). This helps to avoid taking signals during periods of very low market volatility. If the ATR condition is not met, the strategy continues to wait for the condition to be met within the confirmation window, provided other conditions still hold.
Signal Cooldown Filter: If enabled, after a signal is generated, the strategy will wait for a minimum number of bars (min_bars_between_signals) before allowing another signal in the same direction. This aims to reduce overtrading.
4. Entry and Exit Logic:
Entry: A strategy.entry() order is placed when all trigger, confirmation, and filter conditions are met.
Exit: This strategy primarily uses reversing signals for exits. For example, if a long position is open, a confirmed short signal will close the long position and open a new short position. There are no explicit take profit or stop loss orders programmed into this version of the script.
Key User-Adjustable Parameters:
Stochastic RSI Parameters: RSI Length, Stochastic RSI Length, %K Smoothing, %D Smoothing.
Signal Trigger & Confirmation:
5-minute %K trigger levels for long and short.
15-minute %K confirmation thresholds for long and short.
Wait window (in 5-minute bars) for 15-minute confirmation.
Filters:
Enable/disable and configure the Signal Cooldown filter (minimum bars between signals).
Enable/disable and configure the ATR Volatility filter (ATR period, minimum ATR value in ticks).
Strategy Parameters:
Leverage Multiplier (Note: This primarily affects theoretical position sizing for backtesting calculations in TradingView and does not simulate actual leveraged trading risks).
Recommendations for Users:
Thorough Backtesting: Test this strategy extensively on historical data for the instruments and timeframes you intend to trade.
Parameter Optimization: Experiment with different parameter settings to find what works best for your trading style and chosen markets. The default values are starting points and may not be optimal for all conditions.
Understand the Logic: Ensure you understand how each component (Stochastic RSI on different timeframes, ATR filter, cooldown) interacts to generate signals.
Risk Management: Since this version does not include explicit stop-loss orders, ensure you have a clear risk management plan in place if trading this strategy live. You might consider manually adding stop-loss orders through your broker or using TradingView's separate strategy order settings for stop-loss if applicable.
Disclaimer:
This strategy description is for informational purposes only and does not constitute financial advice. Past performance is not indicative of future results. Trading involves significant risk of loss. Always do your own research and understand the risks before trading.
5EMA_BB_ScalpingWhat?
In this forum we have earlier published a public scanner called 5EMA BollingerBand Nifty Stock Scanner , which is getting appreciated by the community. That works on top-40 stocks of NSE as a scanner.
Whereas this time, we have come up with the similar concept as a stand-alone indicator which can be applied for any chart, for any timeframe to reap the benifit of reversal trading.
How it works?
This is essentially a reversal/divergence trading strategy, based on a widely used strategy of Power-of-Stocks 5EMA.
To know the divergence from 5-EMA we just check if the high of the candle (on closing) is below the 5-EMA. Then we check if the closing is inside the Bollinger Band (BB). That's a Buy signal. SL: low of the candle, T: middle and higher BB.
Just opposite for selling. 5-EMA low should be above 5-EMA and closing should be inside BB (lesser than BB higher level). That's a Sell signal. SL: high of the candle, T: middle and lower BB.
Along with we compare the current bar's volume with the last-20 bar VWMA (volume weighted moving average) to determine if the volume is high or low.
Present bar's volume is compared with the previous bar's volume to know if it's rising or falling.
VWAP is also determined using `ta.vwap` built-in support of TradingView.
The Bolling Band width is also notified, along with whether it is rising or falling (comparing with previous candle).
What's special?
We love this reversal trading, as it offers many benifits over trend following strategies:
Risk to Reward (RR) is superior.
It _Does Hit_ stop losses, but the stop losses are tiny.
Means, althrough the Profit Factor looks Nahh , however due to superior RR, end of day it ended up in green.
When the day is sideways, it's difficult to trade in trending strategies. This sort of volatility, reversal strategies works better.
It's always tempting to go agaist the wind. Whole world is in Put/PE and you went opposite and enter a Call/CE. And turns out profitable! That's an amazing feeling, as a trader :)
How to trade using this?
* Put any chart
* Apply this screener from Indicators (shortcut to launch indicators is just type / in your keyboard).
* It will show you the Green up arrow when buy alert comes or red down arrow when sell comes. * Also on the top right it will show the latest signal with entry, SL and target.
Disclaimer
* This piece of software does not come up with any warrantee or any rights of not changing it over the future course of time.
* We are not responsible for any trading/investment decision you are taking out of the outcome of this indicator.
Mitsos4 RSI + BB + Dispersion + Trendlines + VIX Fix Mitsos4 RSI + BB + Dispersion + Trendlines + VIX Fix
This powerful custom indicator combines two analytical tools into one view:
RSI-based Bollinger Bands with Dispersion and the Vix Fix volatility spike detector.
It is designed for traders who want early volatility signals and precision RSI insights, all in a single pane.
🧩 What's Included:
✅ 1. RSI + BB (EMA) + Dispersion
RSI-Based Bollinger Bands: Tracks the RSI with Bollinger Bands using an EMA as the basis.
Dispersion Zone: A buffer zone around the moving average band for more sensitive overbought/oversold detection.
Dynamic RSI Coloring:
🟢 Green: RSI breaks above the dispersion zone.
🔴 Red: RSI breaks below the dispersion zone.
🟡 Yellow: RSI inside the zone (neutral).
Trendlines at RSI levels: 40 (green), 50 (yellow), 60 (red).
Alerts when RSI crosses dispersion zones.
✅ 2. CM_Williams_Vix_Fix
Designed to simulate VIX-like volatility spikes on non-VIX instruments.
Detects potential market bottoms by measuring price deviation from recent highs.
Includes:
Bollinger Band range on WVF.
Percentile high/low zones to detect significant volatility moves.
Histogram plot of WVF for quick visual alerts.
Color-coded spikes (green when above upper thresholds).
⚙️ User Controls:
Adjustable RSI, Bollinger Band, and dispersion settings.
Toggle options for:
Viewing high/low VIX percentiles.
Showing standard deviation bands for WVF.
Custom trendline display levels at RSI key areas.
📌 Best Use Cases:
Detect early market reversals and volatility spikes.
Combine RSI strength with volatility-based bottom signals.
Layer dispersion-based logic on top of classic RSI strategies.
MACD + RSI + EMA + BB + ATR Day Trading StrategyEntry Conditions and Signals
The strategy implements a multi-layered filtering approach to entry conditions, requiring alignment across technical indicators, timeframes, and market conditions .
Long Entry Requirements
Trend Filter: Fast EMA (9) must be above Slow EMA (21), price must be above Fast EMA, and higher timeframe must confirm uptrend
MACD Signal: MACD line crosses above signal line, indicating increasing bullish momentum
RSI Condition: RSI below 70 (not overbought) but above 40 (showing momentum)
Volume & Volatility: Current volume exceeds 1.2x 20-period average and ATR shows sufficient market movement
Time Filter: Trading occurs during optimal hours (9:30-11:30 AM ET) when market volatility is typically highest
Exit Strategies
The strategy employs multiple exit mechanisms to adapt to changing market conditions and protect profits :
Stop Loss Management
Initial Stop: Placed at 2.0x ATR from entry price, adapting to current market volatility
Trailing Stop: 1.5x ATR trailing stop that moves up (for longs) or down (for shorts) as price moves favorably
Time-Based Exits: All positions closed by end of trading day (4:00 PM ET) to avoid overnight risk
Best Practices for Implementation
Settings
Chart Setup: 5-minute timeframe for execution with 15-minute chart for trend confirmation
Session Times: Focus on 9:30-11:30 AM ET trading for highest volatility and opportunity
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
MTF Candle Direction Forecast + Breakdown🧭 MTF Candle Direction Forecast + Breakdown 🔥📈🔼
This script is a multi-timeframe (MTF) price action dashboard that helps traders assess real-time directional bias across five customizable timeframes — with a focus on candle behavior, trend alignment, and confidence strength.
📌 What It Does
For each timeframe, this dashboard summarizes:
Current direction → Bullish, Bearish, or Neutral
Confidence score (0–100) → How strongly price is likely to continue in that direction
Candle strength → 🔥 icon appears if the current candle has a large body relative to its range
Trend alignment:
📈 = EMA9 is above EMA20
🔼 = Price is above VWAP
Color-coded background to visually reinforce directional state
Each row gives you a visual “at-a-glance” readout of what price is doing right now — not in the past.
💡 Why It’s Useful
✅ Direction forecasting based on price action
Instead of lagging indicators, this script prioritizes:
Candle body-to-range ratio (momentum)
Real-time VWAP/EMA structure
Immediate price positioning
✅ Confidence is quantified
The score (0–100) helps you judge how reliable each directional signal is:
90+ → Strong conviction
50–70 → Mixed but potentially valid
<40 → Weak move or early signal
✅ Timeframe confluence at a glance
See whether multiple timeframes are aligning directionally — helpful for scalping, day trading, or waiting for multi-timeframe breakout setups.
✅ Visual & intuitive
Icons, colors, and layout make it easy to scan your dashboard instead of deciphering charts or code.
🛠️ Adjustable Settings
Setting Description
Timeframe 1–5 Choose any timeframes to monitor (e.g., 5m, 15m, 1h, 4h)
Candle Display Mode Show trend color via emoji (🟢/🔴) or background shading
Strong Candle Threshold Adjust the body-to-range % needed to trigger 🔥 strength
Bullish/Bearish Background Customize label color coding
Neutral Background (opacity) Set transparency or styling for flat/consolidating zones
Table Location Place the dashboard anywhere on the chart
🎯 Use Cases
Scalpers: Confirm trend across 1m/5m/15m before entering
Day Traders: Use confidence score to avoid low-momentum setups
Swing Traders: Monitor higher timeframes for trend shifts while tracking intraday noise
VWAP/EMA traders: Quickly see when price is reclaiming or losing critical trend levels
🧠 What Makes It Unique?
Unlike generic trend meters or mashups of standard indicators, this script:
Uses live candle dynamics (not just closes or lagging values)
Computes directional bias and confidence together
Visualizes strength and structure in a compact, readable interface
Let’s you filter by price action, not just indicator alignment
💥 Why Traders Love Will Love It
✅ Instant clarity on which timeframes agree
✅ No more guessing candle strength or trend health
✅ Confidence score keeps you out of weak trades
✅ Works with any strategy — trend following, VWAP reclaim, EMA scalps, even breakouts
✅ Keeps your chart clean — all the context, none of the clutter
⚠️ Transparency🧬 Under the Hood
Powered by live candle body analysis, trend structure (EMA9 vs EMA20), and VWAP placement.
All scores are generated in real-time — No repainting or lookahead bias: all values are computed with lookahead=barmerge.lookahead_on
Confidence scores reflect the current candle only — they do not predict future moves but measure momentum and alignment in real-time
Labels update per bar and respond to subtle shifts in candle structure and trend indicators
✅ MTF Trend Snapshot (Live Output Example Shown in Chart Above)
This dashboard gives you a fast, visual summary of market trend and momentum across 5 timeframes. Here's what it's telling you right now:
🕔 5 Minute (5m)
📉 EMA Trend: Down
🔼 Price: Above VWAP
Direction: Bearish (42)
🟥 Weak bearish bias. Short-term pullback against a stronger trend. Use caution — lower confidence and mixed structure.
⏱️ 15 Minute (15m)
📈 EMA Trend: Up
🔼 Price: Above VWAP
Direction: Bullish (73)
🟩 Clean bullish structure with growing momentum. Solid for intraday confirmation.
🕧 30 Minute (30m)
📈 EMA Trend: Up
🔼 Price: Above VWAP
Direction: Bullish (77)
🟩 Stronger trend forming. Above VWAP and EMAs — building conviction.
🕐 1 Hour (1h)
📈 EMA Trend: Up
🔼 Price: Above VWAP
Direction: Bullish (70)
🟩 Confident, clean trend. Good alignment across indicators. Ideal timeframe for swing entries.
🕓 4 Hour (4h)
🔥 Strong Candle
📈 EMA Trend: Up
🔼 Price: Above VWAP
Direction: Bullish (100)
🟩 Full trend alignment with max momentum. Strong body candle + structure — high confidence continuation.
🧠 Quick Takeaway
🔻 5m is pulling back short term
✅ 15m through 4h are fully aligned Bullish
🔥 4h has max confidence — big-picture trend is intact
📈 Ideal setup for momentum traders looking to ride trend with multi-timeframe confirmation
Try pinning this dashboard to your chart during live trading to read price like a story across timeframes, and filter out weak setups with low-confidence noise.
Mad Trading Scientist - Guppy MMA with Bollinger Bands📘 Indicator Name:
Guppy MMA with Bollinger Bands
🔍 What This Indicator Does:
This TradingView indicator combines Guppy Multiple Moving Averages (GMMA) with Bollinger Bands to help you identify trend direction and volatility zones, ideal for spotting pullback entries within trending markets.
🔵 1. Guppy Multiple Moving Averages (GMMA):
✅ Short-Term EMAs (Blue) — represent trader sentiment:
EMA 3, 5, 8, 10, 12, 15
✅ Long-Term EMAs (Red) — represent investor sentiment:
EMA 30, 35, 40, 45, 50, 60
Usage:
When blue (short) EMAs are above red (long) EMAs and spreading → Strong uptrend
When blue EMAs cross below red EMAs → Potential downtrend
⚫ 2. Bollinger Bands (Volatility Envelopes):
Length: 300 (captures the longer-term price range)
Basis: 300-period SMA
Upper & Lower Bands:
±1 Standard Deviation (light gray zone)
±2 Standard Deviations (dark gray zone)
Fill Zones:
Highlights standard deviation ranges
Emphasizes extreme vs. normal price moves
Usage:
Price touching ±2 SD bands signals potential exhaustion
Price reverting to the mean suggests pullback or re-entry opportunity
💡 Important Note: Use With Momentum Filter
✅ For superior accuracy, this indicator should be combined with your invite-only momentum filter on TradingView.
This filter helps confirm whether the trend has underlying strength or is losing momentum, increasing the probability of successful entries and exits.
🕒 Recommended Timeframe:
📆 1-Hour Chart (60m)
This setup is optimized for short- to medium-term swing trading, where Guppy structures and Bollinger reversion work best.
🔧 Practical Strategy Example:
Long Trade Setup:
Short EMAs are above long EMAs (strong uptrend)
Price pulls back to the lower 1 or 2 SD band
Momentum filter confirms bullish strength
Short Trade Setup:
Short EMAs are below long EMAs (strong downtrend)
Price rises to the upper 1 or 2 SD band
Momentum filter confirms bearish strength
5:30 AM IST Close + Offset Lines + TablesDescription:
This script captures the 5:30 AM IST close price and plots it on the chart along with dynamic offset levels above and below (±5, ±20, ±40, ±60, ±80 points). It also displays these levels in neatly organized tables at the top-right and bottom-right corners for quick reference.
🔹 Timezone: Asia/Kolkata (IST)
🔹 Useful for: Intraday traders who reference early morning levels
🔹 Visual aids:
Orange line for 5:30 AM close
Green lines for points above
Red lines for points below
Tables summarizing all levels
This tool helps identify key early-morning reference zones that can act as support/resistance or breakout targets.
Dual Stochastic Enhanced (with Presets giua64)Script Title: Dual Stochastic Enhanced (with Presets giua64)
Overview:
This indicator enhances the traditional Dual Stochastic strategy, aiming to provide more filtered and potentially reliable trading signals. By integrating dynamic overbought/oversold levels via Bollinger Bands on the slow stochastic, a trend filter based on a moving average, momentum confirmation via RSI, and user-friendly selectable presets, "Dual Stochastic Enhanced" seeks to offer a more robust approach to identifying potential entry points.
Key Features:
Dual Stochastics: Utilizes a slow stochastic (configurable, e.g., 14 periods) as a context filter and a fast stochastic (configurable, e.g., 5 periods) as a signal trigger.
Bollinger Bands on Slow Stochastic: Instead of fixed overbought/oversold levels (80/20), Bollinger Bands are applied to the %K line of the slow stochastic. This creates dynamic zones that adapt to the stochastic's own volatility.
Trend Filter: A moving average (configurable type and length, e.g., EMA 100 as seen in the example chart for general context) on the price helps filter signals, allowing only trades aligned with the prevailing trend.
RSI Confirmation: An RSI oscillator (configurable length, e.g., 14 periods) is used to confirm momentum. Signals require the RSI to cross certain thresholds to validate the strength of the move.
User Presets: Includes presets for "Scalping," "Intraday," and "Swing trading," which quickly set all key parameters to suit different styles and timeframes. A "Custom" option is also available for full manual configuration.
Clear Visual Signals: Long (green) and Short (red) arrows appear on the chart when all entry conditions are met.
Active Zone Highlighting: The background of the indicator panel changes color (green or red) when "active zone" conditions (a combination of stochastics, trend, and RSI) are favorable.
Information Panel: A table in the top-right corner of the indicator panel displays the current status of the selected preset, trend filter, RSI value, and stochastic levels.
Signal Logic:
A LONG signal is generated when:
The fast stochastic %K crosses above its %D line.
The slow stochastic %K line is below its lower Bollinger Band (dynamic oversold condition).
The fast stochastic %K line is also in a low area (e.g., <25) to confirm the trigger is not premature.
The closing price is above the trend moving average (uptrend).
The RSI is above its long confirmation level (e.g., >40), indicating sufficient bullish momentum.
A SHORT signal is generated when:
The fast stochastic %K crosses below its %D line.
The slow stochastic %K line is above its upper Bollinger Band (dynamic overbought condition).
The fast stochastic %K line is also in a high area (e.g., >75).
The closing price is below the trend moving average (downtrend).
The RSI is below its short confirmation level (e.g., <60), indicating sufficient bearish momentum.
How to Use:
Select a Preset suitable for your trading style and the timeframe you are analyzing (e.g., Scalping for M1-M15, Intraday for M5-H1, Swing for H4-D1).
Alternatively, choose "Custom" and manually adjust all parameters (stochastic lengths, smoothing, Bollinger Bands, Moving Average, RSI, confirmation thresholds).
Observe the Information Panel for a quick understanding of the current conditions.
Evaluate the arrow signals, always considering the broader market context, price action, and any other confluences (supports/resistances, chart patterns).
The background highlighting can help quickly identify periods where conditions are aligned for potential trades.
Disclaimer:
This script is provided for educational and informational purposes only. Trading involves significant risk, and past performance is not indicative of future results. Always thoroughly test any strategy or indicator on historical data and on a demo account before risking real capital. The author assumes no responsibility for any losses incurred from the use of this script.
Author: giua64
Adaptive Multi-TF Indicator Table with Presets giua64📌 Script Name:
Adaptive Multi-Timeframe Indicator Table with Presets — giua64
📄 Description:
This script displays an adaptive multi-timeframe dashboard that summarizes the signals of three key technical indicators:
Moving Averages (MAs), Relative Strength Index (RSI), and MACD.
It provides a fast and visually intuitive overview of market conditions across five timeframes (5m, 15m, 30m, 1h, 4h), helping traders quickly identify potential directional biases (e.g., bullish, bearish, or neutral) based on either predefined presets or fully manual settings.
🧰 Preset Configurations:
You can choose between four trading styles, each with optimized indicator parameters:
Scalping
• MAs: 5 / 10 (Fast), 20 / 50 (Slow)
• RSI: 7 periods | Overbought: 70 | Oversold: 30
• MACD: 5 / 13 | Signal: 3
Intraday
• MAs: 9 / 21 (Fast), 50 / 100 (Slow)
• RSI: 14 periods | Overbought: 60 | Oversold: 40
• MACD: 12 / 26 | Signal: 9
Swing
• MAs: 10 / 20 (Fast), 50 / 200 (Slow)
• RSI: 14 periods | Overbought: 65 | Oversold: 35
• MACD: 12 / 26 | Signal: 9
Manual
• Full custom control over all indicator settings.
🛠️ All settings can be customized manually from the options panel, including the exact MA periods, RSI thresholds, and MACD structure.
🧠 How It Works:
For each timeframe, the script evaluates:
MA crossover status (two levels):
The first symbol refers to the crossover of the fast MAs
The second symbol refers to the crossover of the slow MAs
🟢 = Bullish crossover
🔴 = Bearish crossover
➖ = Flat or no clear signal
RSI Direction:
↑ = RSI above upper threshold (potential overbought)
↓ = RSI below lower threshold (potential oversold)
→ = RSI in neutral range
MACD Line vs Signal Line:
↑ = MACD line is above signal line (bullish)
↓ = MACD line is below signal line (bearish)
→ = Flat or neutral signal
Each signal is assigned a numerical score. These are aggregated per timeframe to compute a combined score that reflects the directional bias for that specific time window.
🧠 Adaptive Logic by Asset:
This script is designed to be universally compatible across all asset types — including forex, crypto, stocks, indices, and commodities.
Thanks to its multi-timeframe nature and flexible indicator presets, the script automatically adjusts its behavior based on the asset selected, ensuring relevant analysis without requiring manual recalibration.
🧾 Summary Table Output:
At the bottom of the dashboard, a combined sentiment is displayed for:
3TF → 5m, 15m, 30m
4TF → Adds 1h
5TF → Adds 4h
Each row shows:
Signal → LONG / SHORT / NEUTRAL
Confidence (%) → Based on score aggregation and signal consistency
📌 Customization Options:
Table Position: Left, Right, or Center
Text Size: Small, Normal, or Large
Full Manual Configuration: All MA, RSI, and MACD parameters can be adjusted as needed
⚠️ Disclaimer:
This script is for educational and analytical purposes only.
It does not constitute financial advice or guarantee any trading results.
Always do your own research and apply responsible risk management.
MARibbonMARibbon インジケーターについて
この「MARibbon」は、3本の移動平均線（MA1、MA2、MA3）を描画し、特にMA2とMA3の関係性に注目して、背景色でトレンドの強弱や転換のサインを視覚的に分かりやすく表示するインジケーターです。
主な特徴
3種類の移動平均線を表示可能
MA1（白色、期間40、太さ2）
MA2（水色、期間200、太さ4）
MA3（ピンク色、期間800、太さ4）
各MAの期間・種類（SMA、EMA、WMA、RMA）・タイムフレームは自由に設定可能。
MA2とMA3の関係性に応じて、チャート背景に色付きのリボン（帯）を表示。
背景リボンの意味
MA2 > MA3（ゴールデンクロス状況）
→ 背景を薄い緑色にして、上昇トレンドの可能性を示唆。
MA3 > MA2（デッドクロス状況）
→ 背景を薄い赤色にして、下降トレンドの可能性を示唆。
それ以外（等しい場合など）は背景色なし（透明）で表示。
入力可能な設定
各移動平均線の期間
各移動平均線の種類（SMA、EMA、WMA、RMA）
各移動平均線のタイムフレーム（デフォルトはチャートと同じ）
使い方
任意の銘柄・時間足のチャートにインジケーターを適用。
必要に応じて、3本の移動平均の期間・種類・時間足を調整。
MA2とMA3の位置関係によって、チャート背景の色が変わり、トレンドの強弱を直感的に把握可能。
MARibbon is a custom indicator that plots three moving averages (MA1, MA2, MA3) and visually fills the space between MA2 and MA3 with color bands to indicate trend strength and direction.
Each MA supports custom type (SMA / EMA / WMA / RMA), length, and timeframe.
A green band appears when MA2 is above MA3.
A red band appears when MA3 is above MA2.
This clean and minimal design helps traders easily visualize overlapping trends and potential crossovers.
💡 Use Cases:
Visually confirm confluence of long- and short-term trends
Identify ribbon-like zones of trend strength
Support for MA cross strategy analysis
Stoch Quad Oscillator📘 Stoch Quad Oscillator – User Guide
✅ Purpose
The Stoch Quad Oscillator is a multi-timeframe stochastic oscillator tool that helps traders detect oversold and overbought conditions, momentum shifts, and quad rotation signals using four distinct stochastic configurations. It includes visual cues, customizable parameters, and background highlights to improve decision-making during trend reversals or momentum surges.
🛠️ Inputs & Parameters
⏱ Timeframe
Timeframe for Stochastic Calculation: Defines which chart timeframe to use for stochastic calculations (default is "1" minute). This enables multi-timeframe analysis while on a lower timeframe chart.
📈 Stochastic Parameters
Four different stochastic configurations are used:
Label %K Length %D Smoothing Notes
K9 D3 9 3 Fastest, short-term view
K14 D3 14 3 Moderately short-term
K40 D4 40 4 Medium-term trend view
K60 D10 60 10 Long-term strength
Smoothing Type: Choose between SMA or EMA to control how smoothed the %D line is.
🎯 Levels
Overbought Level: Default 80
Oversold Level: Default 20
These are used to indicate overextended price conditions on any of the stochastic plots.
🔄 Quad Rotation Detection Settings
When enabled, the script detects synchronized oversold/overbought conditions with strong momentum using all 4 stochastic readings.
Enable Quad Rotation: Toggles detection on or off
Slope Calculation Bars: Number of bars used to calculate slope of %D lines
Slope Threshold: Minimum slope strength for signal (higher = stronger confirmation)
Oversold Quad Level: Total of all four stochastic values that define a quad oversold zone
Overbought Quad Level: Total of all four stochastic values that define a quad overbought zone
Oversold Quad Highlight Color: Background color when oversold quad is triggered
Overbought Quad Highlight Color: Background color when overbought quad is triggered
Slope Averaging Method: Either Simple Average or Weighted Average (puts more weight on higher timeframes)
Max Signal Bar Window: Defines how recent the signal must be to be considered valid
📊 Plots & Visual Elements
📉 Stochastic %D Lines
Each stochastic is plotted separately:
K9 D3 – Red
K14 D3 – Orange
K40 D4 – Fuchsia
K60 D10 – Silver
These help visualize short to long-term momentum simultaneously.
📏 Horizontal Reference Lines
Overbought Line (80) – Red
Oversold Line (20) – Green
These help you identify threshold breaches visually.
🌈 Background Highlighting
The indicator provides background highlights to mark potential signal zones:
✅ All Oversold or Overbought Conditions
When all four stochastics are either above overbought or below oversold:
Bright Red if all are overbought
Bright Green if all are oversold
🚨 Quad Rotation Signal Zones (if enabled)
Triggered when:
The combined sum of all four stochastic levels is extremely low/high (below/above oversoldQuadLevel or overboughtQuadLevel)
The average slope of the 4 %D lines is sharply positive (> slopeThreshold)
Highlights:
Custom Red Tint = Strong overbought quad signal
Custom Green Tint = Strong oversold quad signal
These zones can indicate momentum shifts or reversal potential when used with price action or other tools.
⚠️ Limitations & Considerations
This indicator does not provide trade signals. It visualizes conditions and potential setups.
It is best used in confluence with price action, support/resistance levels, and other indicators.
False positives may occur in ranging markets. Reduce reliance on slope thresholds during low volatility.
Quad signals rely on slope strength, which may lag slightly behind sudden reversals.
🧠 Tips for Use
Combine with volume, MACD, or PSAR to confirm direction before entry.
Watch for divergences between price and any of the stochastics.
Use on higher timeframes (e.g., 5m–30m) to filter for swing trading setups; use shorter TFs (1m–5m) for scalping signals.
Adjust oversoldQuadLevel and overboughtQuadLevel based on market conditions (e.g., in trending vs ranging markets).
Gamma Blast Detector (Nifty)The Gamma Blast Detector (Nifty) is a custom TradingView indicator designed to help intraday traders identify sudden and explosive price movements—commonly referred to as "gamma blasts"—in the Nifty index during the final minutes of the trading session, particularly on expiry days. These movements are typically caused by rapid delta changes in ATM options, resulting in aggressive short-covering or option unwinding.
This indicator specifically monitors price action between 3:10 PM and 3:20 PM IST, which translates to 09:40 AM to 09:50 AM UTC on TradingView. It is optimized for use on 5-minute charts of the Nifty spot or futures index, where gamma-driven volatility is most likely to occur during this time window.
The core logic behind the indicator involves identifying unusually large candles within this time frame. It compares the size of the current candle to the average size of the previous five candles. If the current candle is at least twice as large and shows clear direction (bullish or bearish), the script flags it as a potential gamma blast. A bullish candle suggests a Call Option (CE) is likely to blast upward, while a bearish candle points to a Put Option (PE) gaining sharply.
When such a condition is detected, the indicator visually marks the candle on the chart: a "CE 🚀" label is shown below the candle for a bullish move, and a "PE 🔻" label appears above for a bearish move. It also includes alert conditions, allowing users to set real-time alerts for potential blasts and act quickly.
This tool is especially useful for expiry day scalpers, option traders, and anyone looking to ride momentum generated by gamma effects in the final minutes of the market. It provides a visual and alert-based edge to anticipate short-term, high-impact moves often missed in normal technical analysis.
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.
Pump Detector - EMA 4H + Retest H1 (Valid 10x4H bars)📈 Pump Detector – EMA 12/21 on 4H + Retest on H1
This indicator is designed to detect sudden bullish moves ("pumps") on the 4-hour timeframe, and alert traders of potential retest entry points on the 1-hour timeframe.
🔍 Pump activation conditions (on 4H):
EMA 12 crosses above EMA 21
Current volume exceeds the 20-period SMA of volume (on 4H)
When both conditions are met, a pump alert is triggered and a time window opens.
📉 Retest detection logic (on H1):
For the next 10 bars on the 4H chart (~40 hours), the indicator monitors price behavior on the 1H timeframe
If the LOW of any H1 candle touches or drops below EMA 12 or 21 (on H1), a second alert is triggered
✅ Key Features:
Draws EMA 12/21 from the 4H timeframe directly on the chart
Enforces 4H and H1 timeframes, regardless of the chart the script is applied to
One-time detection per pump window: once the 10-bar window expires, the retest alert is disabled until a new pump is detected
Ideal for capturing momentum breakouts followed by technical pullbacks
⚠️ Recommended for:
Traders looking for scalping or swing trading setups on crypto, forex, or stocks. Helps identify post-breakout entry opportunities using a structured and disciplined approach.
Hybrid Swing/Day Alert System - PLATINUM EditionThis indicator is a complete trading assistant designed for crypto swing and day traders, built to identify high-probability long and short setups based on a multi-confirmation system.
Strategy Logic
The system scans and confirms entries only when 6 major confluences align:
1. EMA Trend: Price is above or below the EMA 9, 21, and 200 (bullish or bearish trend).
2. RSI Zone: RSI(14) is between 40-60 (ideal reversal zone).
3. Volume Confirmation: Volume is declining on pullback and then spikes.
4. Accumulation/Distribution: A/D line rising (for longs) or falling (for shorts).
5. Fibonacci Pullback Zone: Automatic detection of swing high/low and checks if price is inside the golden zone (0.5-0.618).
Built-In Alerts
- Long Setup Confirmed - Short Setup Confirmed - Setup Forming: Monitor
Conclusion
This script is ideal for disciplined traders who value confluence-based entries, risk/reward logic, and trend-aligned trades. Perfect for semi-automated trading via alerts or manual execution.6. Candle Pattern: Bullish (hammer, doji, engulfing) or Bearish (rejection wick, engulfing, doji).
Visual Features
- Long Entry: Green square
- Short Entry: Red triangle
- Pre-Signal Alert: Blue circle (confluence forming)
- Dynamic Table: Displays all 6 confirmations in real time
- Fibonacci Zones: Auto-plotted long/short retracement zones
- Customizable: Turn on/off alerts, overlays, and direction filters
Best Use Cases
- 4H/Daily: Trend confirmation
- 1H: Entry execution
- 15min: Scalping (use cautiously)
- Works great with BTC, ETH, SOL, XAU, and meme coins
