Grothendieck-Teichmüller Geometric SynthesisDskyz's Grothendieck-Teichmüller Geometric Synthesis (GTGS)
THEORETICAL FOUNDATION: A SYMPHONY OF GEOMETRIES
The 🎓 GTGS is built upon a revolutionary premise: that market dynamics can be modeled as geometric and topological structures. While not a literal academic implementation—such a task would demand computational power far beyond current trading platforms—it leverages core ideas from advanced mathematical theories as powerful analogies and frameworks for its algorithms. Each component translates an abstract concept into a practical market calculation, distinguishing GTGS by identifying deeper structural patterns rather than relying on standard statistical measures.
1. Grothendieck-Teichmüller Theory: Deforming Market Structure
The Theory : Studies symmetries and deformations of geometric objects, focusing on the "absolute" structure of mathematical spaces.
Indicator Analogy : The calculate_grothendieck_field function models price action as a "deformation" from its immediate state. Using the nth root of price ratios (math.pow(price_ratio, 1.0/prime)), it measures market "shape" stretching or compression, revealing underlying tensions and potential shifts.
2. Topos Theory & Sheaf Cohomology: From Local to Global Patterns
The Theory : A framework for assembling local properties into a global picture, with cohomology measuring "obstructions" to consistency.
Indicator Analogy : The calculate_topos_coherence function uses sine waves (math.sin) to represent local price "sections." Summing these yields a "cohomology" value, quantifying price action consistency. High values indicate coherent trends; low values signal conflict and uncertainty.
3. Tropical Geometry: Simplifying Complexity
The Theory : Transforms complex multiplicative problems into simpler, additive, piecewise-linear ones using min(a, b) for addition and a + b for multiplication.
Indicator Analogy : The calculate_tropical_metric function applies tropical_add(a, b) => math.min(a, b) to identify the "lowest energy" state among recent price points, pinpointing critical support levels non-linearly.
4. Motivic Cohomology & Non-Commutative Geometry
The Theory : Studies deep arithmetic and quantum-like properties of geometric spaces.
Indicator Analogy : The motivic_rank and spectral_triple functions compute weighted sums of historical prices to capture market "arithmetic complexity" and "spectral signature." Higher values reflect structured, harmonic price movements.
5. Perfectoid Spaces & Homotopy Type Theory
The Theory : Abstract fields dealing with p-adic numbers and logical foundations of mathematics.
Indicator Analogy : The perfectoid_conv and type_coherence functions analyze price convergence and path identity, assessing the "fractal dust" of price differences and price path cohesion, adding fractal and logical analysis.
The Combination is Key : No single theory dominates. GTGS ’s Unified Field synthesizes all seven perspectives into a comprehensive score, ensuring signals reflect deep structural alignment across mathematical domains.
🎛️ INPUTS: CONFIGURING THE GEOMETRIC ENGINE
The GTGS offers a suite of customizable inputs, allowing traders to tailor its behavior to specific timeframes, market sectors, and trading styles. Below is a detailed breakdown of key input groups, their functionality, and optimization strategies, leveraging provided tooltips for precision.
Grothendieck-Teichmüller Theory Inputs
🧬 Deformation Depth (Absolute Galois) :
What It Is : Controls the depth of Galois group deformations analyzed in market structure.
How It Works : Measures price action deformations under automorphisms of the absolute Galois group, capturing market symmetries.
Optimization :
Higher Values (15-20) : Captures deeper symmetries, ideal for major trends in swing trading (4H-1D).
Lower Values (3-8) : Responsive to local deformations, suited for scalping (1-5min).
Timeframes :
Scalping (1-5min) : 3-6 for quick local shifts.
Day Trading (15min-1H) : 8-12 for balanced analysis.
Swing Trading (4H-1D) : 12-20 for deep structural trends.
Sectors :
Stocks : Use 8-12 for stable trends.
Crypto : 3-8 for volatile, short-term moves.
Forex : 12-15 for smooth, cyclical patterns.
Pro Tip : Increase in trending markets to filter noise; decrease in choppy markets for sensitivity.
🗼 Teichmüller Tower Height :
What It Is : Determines the height of the Teichmüller modular tower for hierarchical pattern detection.
How It Works : Builds modular levels to identify nested market patterns.
Optimization :
Higher Values (6-8) : Detects complex fractals, ideal for swing trading.
Lower Values (2-4) : Focuses on primary patterns, faster for scalping.
Timeframes :
Scalping : 2-3 for speed.
Day Trading : 4-5 for balanced patterns.
Swing Trading : 5-8 for deep fractals.
Sectors :
Indices : 5-8 for robust, long-term patterns.
Crypto : 2-4 for rapid shifts.
Commodities : 4-6 for cyclical trends.
Pro Tip : Higher towers reveal hidden fractals but may slow computation; adjust based on hardware.
🔢 Galois Prime Base :
What It Is : Sets the prime base for Galois field computations.
How It Works : Defines the field extension characteristic for market analysis.
Optimization :
Prime Characteristics :
2 : Binary markets (up/down).
3 : Ternary states (bull/bear/neutral).
5 : Pentagonal symmetry (Elliott waves).
7 : Heptagonal cycles (weekly patterns).
11,13,17,19 : Higher-order patterns.
Timeframes :
Scalping/Day Trading : 2 or 3 for simplicity.
Swing Trading : 5 or 7 for wave or cycle detection.
Sectors :
Forex : 5 for Elliott wave alignment.
Stocks : 7 for weekly cycle consistency.
Crypto : 3 for volatile state shifts.
Pro Tip : Use 7 for most markets; 5 for Elliott wave traders.
Topos Theory & Sheaf Cohomology Inputs
🏛️ Temporal Site Size :
What It Is : Defines the number of time points in the topological site.
How It Works : Sets the local neighborhood for sheaf computations, affecting cohomology smoothness.
Optimization :
Higher Values (30-50) : Smoother cohomology, better for trends in swing trading.
Lower Values (5-15) : Responsive, ideal for reversals in scalping.
Timeframes :
Scalping : 5-10 for quick responses.
Day Trading : 15-25 for balanced analysis.
Swing Trading : 25-50 for smooth trends.
Sectors :
Stocks : 25-35 for stable trends.
Crypto : 5-15 for volatility.
Forex : 20-30 for smooth cycles.
Pro Tip : Match site size to your average holding period in bars for optimal coherence.
📐 Sheaf Cohomology Degree :
What It Is : Sets the maximum degree of cohomology groups computed.
How It Works : Higher degrees capture complex topological obstructions.
Optimization :
Degree Meanings :
1 : Simple obstructions (basic support/resistance).
2 : Cohomological pairs (double tops/bottoms).
3 : Triple intersections (complex patterns).
4-5 : Higher-order structures (rare events).
Timeframes :
Scalping/Day Trading : 1-2 for simplicity.
Swing Trading : 3 for complex patterns.
Sectors :
Indices : 2-3 for robust patterns.
Crypto : 1-2 for rapid shifts.
Commodities : 3-4 for cyclical events.
Pro Tip : Degree 3 is optimal for most trading; higher degrees for research or rare event detection.
🌐 Grothendieck Topology :
What It Is : Chooses the Grothendieck topology for the site.
How It Works : Affects how local data integrates into global patterns.
Optimization :
Topology Characteristics :
Étale : Finest topology, captures local-global principles.
Nisnevich : A1-invariant, good for trends.
Zariski : Coarse but robust, filters noise.
Fpqc : Faithfully flat, highly sensitive.
Sectors :
Stocks : Zariski for stability.
Crypto : Étale for sensitivity.
Forex : Nisnevich for smooth trends.
Indices : Zariski for robustness.
Timeframes :
Scalping : Étale for precision.
Swing Trading : Nisnevich or Zariski for reliability.
Pro Tip : Start with Étale for precision; switch to Zariski in noisy markets.
Unified Field Configuration Inputs
⚛️ Field Coupling Constant :
What It Is : Sets the interaction strength between geometric components.
How It Works : Controls signal amplification in the unified field equation.
Optimization :
Higher Values (0.5-1.0) : Strong coupling, amplified signals for ranging markets.
Lower Values (0.001-0.1) : Subtle signals for trending markets.
Timeframes :
Scalping : 0.5-0.8 for quick, strong signals.
Swing Trading : 0.1-0.3 for trend confirmation.
Sectors :
Crypto : 0.5-1.0 for volatility.
Stocks : 0.1-0.3 for stability.
Forex : 0.3-0.5 for balance.
Pro Tip : Default 0.137 (fine structure constant) is a balanced starting point; adjust up in choppy markets.
📐 Geometric Weighting Scheme :
What It Is : Determines the framework for combining geometric components.
How It Works : Adjusts emphasis on different mathematical structures.
Optimization :
Scheme Characteristics :
Canonical : Equal weighting, balanced.
Derived : Emphasizes higher-order structures.
Motivic : Prioritizes arithmetic properties.
Spectral : Focuses on frequency domain.
Sectors :
Stocks : Canonical for balance.
Crypto : Spectral for volatility.
Forex : Derived for structured moves.
Indices : Motivic for arithmetic cycles.
Timeframes :
Day Trading : Canonical or Derived for flexibility.
Swing Trading : Motivic for long-term cycles.
Pro Tip : Start with Canonical; experiment with Spectral in volatile markets.
Dashboard and Visual Configuration Inputs
📋 Show Enhanced Dashboard, 📏 Size, 📍 Position :
What They Are : Control dashboard visibility, size, and placement.
How They Work : Display key metrics like Unified Field , Resonance , and Signal Quality .
Optimization :
Scalping : Small size, Bottom Right for minimal chart obstruction.
Swing Trading : Large size, Top Right for detailed analysis.
Sectors : Universal across markets; adjust size based on screen setup.
Pro Tip : Use Large for analysis, Small for live trading.
📐 Show Motivic Cohomology Bands, 🌊 Morphism Flow, 🔮 Future Projection, 🔷 Holographic Mesh, ⚛️ Spectral Flow :
What They Are : Toggle visual elements representing mathematical calculations.
How They Work : Provide intuitive representations of market dynamics.
Optimization :
Timeframes :
Scalping : Enable Morphism Flow and Spectral Flow for momentum.
Swing Trading : Enable all for comprehensive analysis.
Sectors :
Crypto : Emphasize Morphism Flow and Future Projection for volatility.
Stocks : Focus on Cohomology Bands for stable trends.
Pro Tip : Disable non-essential visuals in fast markets to reduce clutter.
🌫️ Field Transparency, 🔄 Web Recursion Depth, 🎨 Mesh Color Scheme :
What They Are : Adjust visual clarity, complexity, and color.
How They Work : Enhance interpretability of visual elements.
Optimization :
Transparency : 30-50 for balanced visibility; lower for analysis.
Recursion Depth : 6-8 for balanced detail; lower for older hardware.
Color Scheme :
Purple/Blue : Analytical focus.
Green/Orange : Trading momentum.
Pro Tip : Use Neon Purple for deep analysis; Neon Green for active trading.
⏱️ Minimum Bars Between Signals :
What It Is : Minimum number of bars required between consecutive signals.
How It Works : Prevents signal clustering by enforcing a cooldown period.
Optimization :
Higher Values (10-20) : Fewer signals, avoids whipsaws, suited for swing trading.
Lower Values (0-5) : More responsive, allows quick reversals, ideal for scalping.
Timeframes :
Scalping : 0-2 bars for rapid signals.
Day Trading : 3-5 bars for balance.
Swing Trading : 5-10 bars for stability.
Sectors :
Crypto : 0-3 for volatility.
Stocks : 5-10 for trend clarity.
Forex : 3-7 for cyclical moves.
Pro Tip : Increase in choppy markets to filter noise.
Hardcoded Parameters
Tropical, Motivic, Spectral, Perfectoid, Homotopy Inputs : Fixed to optimize performance but influence calculations (e.g., tropical_degree=4 for support levels, perfectoid_prime=5 for convergence).
Optimization : Experiment with codebase modifications if advanced customization is needed, but defaults are robust across markets.
🎨 ADVANCED VISUAL SYSTEM: TRADING IN A GEOMETRIC UNIVERSE
The GTTMTSF ’s visuals are direct representations of its mathematics, designed for intuitive and precise trading decisions.
Motivic Cohomology Bands :
What They Are : Dynamic bands ( H⁰ , H¹ , H² ) representing cohomological support/resistance.
Color & Meaning : Colors reflect energy levels ( H⁰ tightest, H² widest). Breaks into H¹ signal momentum; H² touches suggest reversals.
How to Trade : Use for stop-loss/profit-taking. Band bounces with Dashboard confirmation are high-probability setups.
Morphism Flow (Webbing) :
What It Is : White particle streams visualizing market momentum.
Interpretation : Dense flows indicate strong trends; sparse flows signal consolidation.
How to Trade : Follow dominant flow direction; new flows post-consolidation signal trend starts.
Future Projection Web (Fractal Grid) :
What It Is : Fibonacci-period fractal projections of support/resistance.
Color & Meaning : Three-layer lines (white shadow, glow, colored quantum) with labels showing price, topological class, anomaly strength (φ), resonance (ρ), and obstruction ( H¹ ). ⚡ marks extreme anomalies.
How to Trade : Target ⚡/● levels for entries/exits. High-anomaly levels with weakening Unified Field are reversal setups.
Holographic Mesh & Spectral Flow :
What They Are : Visuals of harmonic interference and spectral energy.
How to Trade : Bright mesh nodes or strong Spectral Flow warn of building pressure before price movement.
📊 THE GEOMETRIC DASHBOARD: YOUR MISSION CONTROL
The Dashboard translates complex mathematics into actionable intelligence.
Unified Field & Signals :
FIELD : Master value (-10 to +10), synthesizing all geometric components. Extreme readings (>5 or <-5) signal structural limits, often preceding reversals or continuations.
RESONANCE : Measures harmony between geometric field and price-volume momentum. Positive amplifies bullish moves; negative amplifies bearish moves.
SIGNAL QUALITY : Confidence meter rating alignment. Trade only STRONG or EXCEPTIONAL signals for high-probability setups.
Geometric Components :
What They Are : Breakdown of seven mathematical engines.
How to Use : Watch for convergence. A strong Unified Field is reliable when components (e.g., Grothendieck , Topos , Motivic ) align. Divergence warns of trend weakening.
Signal Performance :
What It Is : Tracks indicator signal performance.
How to Use : Assesses real-time performance to build confidence and understand system behavior.
🚀 DEVELOPMENT & UNIQUENESS: BEYOND CONVENTIONAL ANALYSIS
The GTTMTSF was developed to analyze markets as evolving geometric objects, not statistical time-series.
Why This Is Unlike Anything Else :
Theoretical Depth : Uses geometry and topology, identifying patterns invisible to statistical tools.
Holistic Synthesis : Integrates seven deep mathematical frameworks into a cohesive Unified Field .
Creative Implementation : Translates PhD-level mathematics into functional Pine Script , blending theory and practice.
Immersive Visualization : Transforms charts into dynamic geometric landscapes for intuitive market understanding.
The GTTMTSF is more than an indicator; it’s a new lens for viewing markets, for traders seeking deeper insight into hidden order within chaos.
" Where there is matter, there is geometry. " - Johannes Kepler
— Dskyz , Trade with insight. Trade with anticipation.
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PriceLevels GBGoldbach Price Levels – Identify Algorithmic Key Zones
This open-source indicator is designed to help traders identify potential algorithmic key zones by highlighting price levels ending with specific numbers such as 03, 11, 29, 35, 65, and 71. These levels may act as inflection points or hesitation areas based on observed behavioral patterns in price movement.
What It Does:
📌 Scans and plots horizontal price levels where the price ends with one of the selected number combinations
🎯 Toggle on/off visibility for each number ending
🎨 Customize color and thickness for each level
🏷️ Shows price labels at the end of each line
🌗 Label styles (color/transparency) are adjustable for both dark and light chart themes
🧠 Why Use It:
This tool is ideal for discretionary traders who study market structure through static price anchors. It provides a visual reference for recurring numerical levels that may be used in algorithmic trading models or serve as psychological price zones.
⚠️ Disclaimer:
This script is open-source and intended for educational and analytical purposes only. No trading signals or performance guarantees are provided. Please use your own judgment when applying this tool in a trading context.
LRHA Trend Shift DetectorLRHA Trend Shift Detector (TSD)
The LRHA Trend Shift Detector is an advanced momentum exhaustion indicator that identifies potential trend reversals and changes by analyzing Linear Regression Heikin Ashi (LRHA) candle patterns. TSD focuses on detecting when strong directional moves begin to lose momentum.
🔬 Methodology
The indicator employs a three-stage detection process:
LRHA Calculation: Applies linear regression smoothing to Heikin Ashi candles, creating ultra-smooth trend-following candles that filter out market noise
Extended Move Detection: Identifies sustained directional moves by counting consecutive bullish or bearish LRHA candles
Momentum Exhaustion Analysis: Monitors for significant changes in candle size compared to recent averages
When an extended move shows clear signs of momentum exhaustion, the indicator signals a potential trend shift with red dots plotted above or below your candlesticks.
⚙️ Parameters
Core Settings
LRHA Length (11): Linear regression period for smoothing calculations. Lower values = more responsive, higher values = smoother trends.
Minimum Trend Bars (4): Consecutive candles required to establish an "extended move." Higher number detects longer term trend changes.
Exhaustion Bars (3): Number of consecutively smaller candles needed to signal exhaustion. Lower is more sensitive.
Size Reduction Threshold (40%): Percentage decrease in candle size to qualify as "exhaustion." Lower is more sensitive.
Trend Trading
Pullback Entries: Identify exhaustion in counter-trend moves for trend continuation
Exit Strategy: Recognize when main trend momentum is fading
Position Sizing: Reduce size when seeing exhaustion in your direction
🎛️ Optimization Tips
For More Signals (Aggressive)
- Decrease LRHA Length (7-9)
- Reduce Minimum Trend Bars (2-3)
- Lower Size Reduction Threshold (25-35%)
For Higher Quality (Conservative)
- Increase LRHA Length (13-18)
- Raise Minimum Trend Bars (5-6)
- Higher Size Reduction Threshold (45-55%)
⚠️ Important Notes⚠️
- **Not a Complete Strategy**: Use as confluence with other analysis methods
- **Market Context Matters**: Consider overall trend direction and key support/resistance levels
- **Risk Management Essential**: Always use proper position sizing and stop losses
- **Backtest First**: Optimize parameters for your specific trading style and instruments
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).
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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.
Daily Levels & Time MarkersKey Features:
Price Level Tracking:
Previous Day High/Low (PDH/PDL) - Shows yesterday's highest and lowest prices as horizontal lines
Overnight High/Low (ONH/ONL) - Tracks the highest and lowest prices during overnight sessions (4:00 PM to 9:30 AM ET)
Opening Range High/Low (ORH/ORL) - Captures the price range during the first 30 minutes of regular trading (9:30-10:00 AM ET)
Visual Elements:
Draws horizontal lines for previous day levels that extend across the chart
Creates rays (extending lines) for overnight and opening range levels that project forward from when they were established
Uses different colors and line styles for each level type (solid lines for daily levels, dashed for opening range)
Adds text labels showing the exact price values (PDH, PDL, ONH, ONL, ORH, ORL)
Time Markers:
Draws vertical dashed lines at key trading times: 10:00 AM, 11:30 AM, 1:00 PM, 2:30 PM, and 4:00 PM ET
Uses Eastern Time zone by default but allows customization
Customization Options:
Toggle each feature on/off independently
Customize colors for all line types
Adjust timezone settings
ICT TIME ELEMENTS [KaninFX]## Overview
The ICT Time Elements indicator is a comprehensive trading tool designed to visualize the most critical market sessions and timeframes according to Inner Circle Trader (ICT) methodology. This indicator helps traders identify high-probability trading opportunities by highlighting key market sessions, killzones, and liquidity periods throughout the trading day.
## Key Features
### 🕐 Complete ICT Time Framework
- **Asian Range**: 8:00 PM - 12:00 AM (NY Time) - Evening consolidation period
- **London Killzone**: 2:00 AM - 5:00 AM (NY Time) - European market opening liquidity
- **NY Killzone**: 7:00 AM - 10:00 AM (NY Time) - US market opening with high volatility
- **Silver Bullet Sessions**:
- London Silver Bullet: 3:00 AM - 4:00 AM
- AM Silver Bullet: 10:00 AM - 11:00 AM
- PM Silver Bullet: 2:00 PM - 3:00 PM
- **Lunch Hours**: 5:00 AM - 7:00 AM & 12:00 PM - 1:00 PM (Lower volatility periods)
- **News Embargo**: 8:30 AM - 9:30 AM (High impact news release window)
- **20-Minute Macros**: :50 to :10 minutes of each hour (Short-term reversal periods)
- **True Day Close**: 4:00 PM - 4:30 PM (Official market close)
### 🎨 Visual Customization
- **Multiple Themes**: Dark, Light, and Custom color schemes
- **Adjustable Opacity**: Control zone transparency (0-100%)
- **Font Customization**: Tiny, Small, Normal, Large text sizes
- **Custom Colors**: Personalize each zone with your preferred colors
- **Professional Display**: Clean histogram visualization with zone labels
### 🌍 Multi-Timezone Support
Built-in support for major trading centers:
- America/New_York (Default)
- America/Chicago
- America/Los_Angeles
- Europe/London
- Asia/Tokyo
- Asia/Shanghai
- Australia/Sydney
### 📊 Smart Information Display
- **Real-time Zone Detection**: Automatically identifies current active session
- **Zone Labels**: Clear labeling at the center of each time period
- **Current Zone Indicator**: Arrow pointer showing the active session
- **Comprehensive Info Table**: Quick reference for all time zones and their schedules
- **Flexible Table Positioning**: Place info table in any corner of your chart
### ⚡ Performance Optimized
- **Memory Management**: Automatic cleanup of old labels to maintain performance
- **Efficient Processing**: Optimized time calculations for smooth operation
- **Resource Control**: Limited label generation to prevent system overload
## How It Works
The indicator continuously monitors the current time against predefined ICT session schedules. When price action enters a recognized time zone, the indicator:
1. **Highlights the Period**: Colors the histogram bar according to the active session
2. **Labels the Zone**: Places descriptive text identifying the current market condition
3. **Updates Info Table**: Shows current session status and complete schedule
4. **Tracks Macro Periods**: Identifies 20-minute reversal windows within major sessions
### Special Features
- **Macro Detection**: Automatically identifies when current time falls within a 20-minute macro period
- **Session Overlap Handling**: Properly manages overlapping time zones with priority logic
- **Dynamic Color Adjustment**: Theme-aware color selection for optimal visibility
## Best Use Cases
### For ICT Traders
- Identify optimal entry times during killzone sessions
- Recognize silver bullet opportunities for quick scalps
- Avoid trading during lunch hour consolidations
- Prepare for news embargo volatility
### For Session Traders
- Track major market session transitions
- Plan trading strategy around high-liquidity periods
- Understand global market flow and timing
### For Swing Traders
- Identify macro trend continuation points
- Time position entries during optimal sessions
- Understand market structure changes across sessions
## Installation & Setup
1. Add the indicator to your TradingView chart
2. Select your preferred timezone from the dropdown
3. Choose theme (Dark/Light) or customize colors
4. Adjust font size and table position to your preference
5. Enable/disable features as needed for your trading style
## Pro Tips
- **Combine with Price Action**: Use time zones alongside support/resistance levels
- **Focus on Killzones**: Highest probability setups occur during London and NY killzones
- **Watch Silver Bullets**: These 1-hour windows often provide excellent reversal opportunities
- **Respect Lunch Hours**: Lower volatility periods - consider smaller position sizes
- **News Embargo Awareness**: Prepare for potential whipsaws during 8:30-9:30 AM
## Conclusion
The ICT Time Elements indicator transforms complex ICT timing concepts into an easy-to-read visual tool. Whether you're a beginner learning ICT methodology or an experienced trader looking to optimize your timing, this indicator provides the essential market session awareness needed for successful trading.
*Compatible with all TradingView plans and timeframes. Works best on 1-minute to 1-hour charts for optimal session visualization.*
Multi-Session ORBThe Multi-Session ORB Indicator is a customizable Pine Script (version 6) tool designed for TradingView to plot Opening Range Breakout (ORB) levels across four major trading sessions: Sydney, Tokyo, London, and New York. It allows traders to define specific ORB durations and session times in Central Daylight Time (CDT), making it adaptable to various trading strategies.
Key Features:
1. Customizable ORB Duration: Users can set the ORB duration (default: 15 minutes) via the inputMax parameter, determining the time window for calculating the high and low of each session’s opening range.
2. Flexible Session Times: The indicator supports user-defined session and ORB times for:
◦ Sydney: Default ORB (17:00–17:15 CDT), Session (17:00–01:00 CDT)
◦ Tokyo: Default ORB (19:00–19:15 CDT), Session (19:00–04:00 CDT)
◦ London: Default ORB (02:00–02:15 CDT), Session (02:00–11:00 CDT)
◦ New York: Default ORB (08:30–08:45 CDT), Session (08:30–16:00 CDT)
3. Session-Specific ORB Levels: For each session, the indicator calculates and tracks the high and low prices during the specified ORB period. These levels are updated dynamically if new highs or lows occur within the ORB timeframe.
4. Visual Representation:
◦ ORB high and low lines are plotted only during their respective session times, ensuring clarity.
◦ Each session’s lines are color-coded for easy identification:
▪ Sydney: Light Yellow (high), Dark Yellow (low)
▪ Tokyo: Light Pink (high), Dark Pink (low)
▪ London: Light Blue (high), Dark Blue (low)
▪ New York: Light Purple (high), Dark Purple (low)
◦ Lines are drawn with a linewidth of 2 and disappear when the session ends or if the timeframe is not intraday (or exceeds the ORB duration).
5. Intraday Compatibility: The indicator is optimized for intraday timeframes (e.g., 1-minute to 15-minute charts) and only displays when the chart’s timeframe multiplier is less than or equal to the ORB duration.
How It Works:
• Session Detection: The script uses the time() function to check if the current bar falls within the user-defined ORB or session time windows, accounting for all days of the week.
• ORB Logic: At the start of each session’s ORB period, the script initializes the high and low based on the first bar’s prices. It then updates these levels if subsequent bars within the ORB period exceed the current high or fall below the current low.
• Plotting: ORB levels are plotted as horizontal lines during the respective session, with visibility controlled to avoid clutter outside session times or on incompatible timeframes.
Use Case:
Traders can use this indicator to identify key breakout levels for each trading session, facilitating strategies based on price action around the opening range. The flexibility to adjust ORB and session times makes it suitable for various markets (e.g., forex, stocks, or futures) and time zones.
Limitations:
• The indicator is designed for intraday timeframes and may not display on higher timeframes (e.g., daily or weekly) or if the timeframe multiplier exceeds the ORB duration.
• Time inputs are in CDT, requiring users to adjust for their local timezone or market requirements.
• If you need to use this for GC/CL/SPY/QQQ you have to adjust the times by one hour.
This indicator is ideal for traders focusing on session-based breakout strategies, offering clear visualization and customization for global market sessions.
The ICT Ultimate Grid | MarketMaverisk GroupThe ICT Ultimate Grid | MarketMaverisk Group
This script is a fully customizable checklist based on ICT (Inner Circle Trader) concepts. It helps traders validate entry conditions across three timeframes:
LTP (Long-Term), ITP (Intermediate-Term), and STP (Short-Term).
⸻
✅ Purpose & Utility:
Instead of generating simple buy/sell signals, this tool assists traders in making structured, confirmation-based decisions. It presents a visual checklist with 11 customizable columns—each can be individually toggled for each timeframe and displays ✅ or ❌ confirmation status.
⸻
🧠 Confirmation Structure:
The checklist covers the following core elements from the ICT methodology:
• ERL⇔IRL and IRL⇔ERL (presented as special confirmations below the table)
• DOL – Drow On liqudity Level
• PD – permium or discuant
• SMT – Smart Money Trap / Inter-market Divergence
• CSD – Change in State of dlivery
• MSS – Market Structure Shift
• MMXM – Market maker (buy or sell) model
• FVG – Fair Value Gap
• OB – Order Block
• BRK.B – breker Block
Each item can be enabled or disabled for LTP, ITP, and STP individually.
⸻
📊 Visual Design:
• Clean, compact table displayed in the top-right corner of the chart.
• Clear color scheme (✅ Green = Confirmed, ❌ Red = Not Confirmed, Grey = Hidden/Disabled).
• Timeframes are stacked row-wise (LTP, ITP, STP).
• Inputs allow fine-grained control over what elements are shown in each timeframe.
• Additional rows are used to confirm:
• HTF Key Level
• Direction: Reversal ↩️ or Continuation 🔂
• Bias: Bullish 🔼 or Bearish 🔽
⸻
📈 Use Case:
This tool is ideal for traders who follow:
• ICT-based trading approaches
• Market structure + Liquidity analysis
• Day trading, scalping, or swing setups
• Confirmation-based entries after higher-timeframe alignment
⸻
⚙️ Recommended Timeframe Settings:
• LTP = D1 or 4H
• ITP = 1H or 15min
• STP = 5min or 3min or 1min
• Session time: Best used between 02:00 and 05:00 on london killzone & 08:00 and 12:00 on New york killzone in New York timezone (UTC -5)
(you can customize this in strategy version)
⸻
🛠 Technical Note:
This version is an indicator and does not generate signals or alerts by itself. For full automation, a strategy version is also available upon request.
⸻
Let me know if you’d like me to also write a “strategy description” or help you prepare the public chart layout 📊 to make your publish clean and attractivE
JPMorgan G7 Volatility IndexThe JPMorgan G7 Volatility Index: Scientific Analysis and Professional Applications
Introduction
The JPMorgan G7 Volatility Index (G7VOL) represents a sophisticated metric for monitoring currency market volatility across major developed economies. This indicator functions as an approximation of JPMorgan's proprietary volatility indices, providing traders and investors with a normalized measurement of cross-currency volatility conditions (Clark, 2019).
Theoretical Foundation
Currency volatility is fundamentally defined as "the statistical measure of the dispersion of returns for a given security or market index" (Hull, 2018, p.127). In the context of G7 currencies, this volatility measurement becomes particularly significant due to the economic importance of these nations, which collectively represent more than 50% of global nominal GDP (IMF, 2022).
According to Menkhoff et al. (2012, p.685), "currency volatility serves as a global risk factor that affects expected returns across different asset classes." This finding underscores the importance of monitoring G7 currency volatility as a proxy for global financial conditions.
Methodology
The G7VOL indicator employs a multi-step calculation process:
Individual volatility calculation for seven major currency pairs using standard deviation normalized by price (Lo, 2002)
- Weighted-average combination of these volatilities to form a composite index
- Normalization against historical bands to create a standardized scale
- Visual representation through dynamic coloring that reflects current market conditions
The mathematical foundation follows the volatility calculation methodology proposed by Bollerslev et al. (2018):
Volatility = σ(returns) / price × 100
Where σ represents standard deviation calculated over a specified timeframe, typically 20 periods as recommended by the Bank for International Settlements (BIS, 2020).
Professional Applications
Professional traders and institutional investors employ the G7VOL indicator in several key ways:
1. Risk Management Signaling
According to research by Adrian and Brunnermeier (2016), elevated currency volatility often precedes broader market stress. When the G7VOL breaches its high volatility threshold (typically 1.5 times the 100-period average), portfolio managers frequently reduce risk exposure across asset classes. As noted by Borio (2019, p.17), "currency volatility spikes have historically preceded equity market corrections by 2-7 trading days."
2. Counter-Cyclical Investment Strategy
Low G7 volatility periods (readings below the lower band) tend to coincide with what Shin (2017) describes as "risk-on" environments. Professional investors often use these signals to increase allocations to higher-beta assets and emerging markets. Campbell et al. (2021) found that G7 volatility in the lowest quintile historically preceded emerging market outperformance by an average of 3.7% over subsequent quarters.
3. Regime Identification
The normalized volatility framework enables identification of distinct market regimes:
- Readings above 1.0: Crisis/high volatility regime
- Readings between -0.5 and 0.5: Normal volatility regime
- Readings below -1.0: Unusually calm markets
According to Rey (2015), these regimes have significant implications for global monetary policy transmission mechanisms and cross-border capital flows.
Interpretation and Trading Applications
G7 currency volatility serves as a barometer for global financial conditions due to these currencies' centrality in international trade and reserve status. As noted by Gagnon and Ihrig (2021, p.423), "G7 currency volatility captures both trade-related uncertainty and broader financial market risk appetites."
Professional traders apply this indicator in multiple contexts:
- Leading indicator: Research from the Federal Reserve Board (Powell, 2020) suggests G7 volatility often leads VIX movements by 1-3 days, providing advance warning of broader market volatility.
- Correlation shifts: During periods of elevated G7 volatility, cross-asset correlations typically increase what Brunnermeier and Pedersen (2009) term "correlation breakdown during stress periods." This phenomenon informs portfolio diversification strategies.
- Carry trade timing: Currency carry strategies perform best during low volatility regimes as documented by Lustig et al. (2011). The G7VOL indicator provides objective thresholds for initiating or exiting such positions.
References
Adrian, T. and Brunnermeier, M.K. (2016) 'CoVaR', American Economic Review, 106(7), pp.1705-1741.
Bank for International Settlements (2020) Monitoring Volatility in Foreign Exchange Markets. BIS Quarterly Review, December 2020.
Bollerslev, T., Patton, A.J. and Quaedvlieg, R. (2018) 'Modeling and forecasting (un)reliable realized volatilities', Journal of Econometrics, 204(1), pp.112-130.
Borio, C. (2019) 'Monetary policy in the grip of a pincer movement', BIS Working Papers, No. 706.
Brunnermeier, M.K. and Pedersen, L.H. (2009) 'Market liquidity and funding liquidity', Review of Financial Studies, 22(6), pp.2201-2238.
Campbell, J.Y., Sunderam, A. and Viceira, L.M. (2021) 'Inflation Bets or Deflation Hedges? The Changing Risks of Nominal Bonds', Critical Finance Review, 10(2), pp.303-336.
Clark, J. (2019) 'Currency Volatility and Macro Fundamentals', JPMorgan Global FX Research Quarterly, Fall 2019.
Gagnon, J.E. and Ihrig, J. (2021) 'What drives foreign exchange markets?', International Finance, 24(3), pp.414-428.
Hull, J.C. (2018) Options, Futures, and Other Derivatives. 10th edn. London: Pearson.
International Monetary Fund (2022) World Economic Outlook Database. Washington, DC: IMF.
Lo, A.W. (2002) 'The statistics of Sharpe ratios', Financial Analysts Journal, 58(4), pp.36-52.
Lustig, H., Roussanov, N. and Verdelhan, A. (2011) 'Common risk factors in currency markets', Review of Financial Studies, 24(11), pp.3731-3777.
Menkhoff, L., Sarno, L., Schmeling, M. and Schrimpf, A. (2012) 'Carry trades and global foreign exchange volatility', Journal of Finance, 67(2), pp.681-718.
Powell, J. (2020) Monetary Policy and Price Stability. Speech at Jackson Hole Economic Symposium, August 27, 2020.
Rey, H. (2015) 'Dilemma not trilemma: The global financial cycle and monetary policy independence', NBER Working Paper No. 21162.
Shin, H.S. (2017) 'The bank/capital markets nexus goes global', Bank for International Settlements Speech, January 15, 2017.
(MVD) Meta-Volatility Divergence (DAFE) Meta-Volatility Divergence (MVD)
Reveal the Hidden Tension in Volatility.
The Meta-Volatility Divergence (MVD) indicator is a next-generation tool designed to expose the disagreement between multiple volatility measures—helping you spot when the market’s “volatility engines” are out of sync, and a regime shift or volatility event may be brewing.
What Makes MVD Unique?
Multi-Source Volatility Analysis:
Unlike traditional volatility indicators that rely on a single measure, MVD fuses four distinct volatility signals:
ATR (Average True Range): Captures the average range of price movement.
Stdev (Standard Deviation): Measures the dispersion of closing prices.
Range: The average difference between high and low.
VoVix: A proprietary “volatility of volatility” metric, quantifying the difference between fast and slow ATR, normalized by ATR’s own volatility.
Divergence Engine:
The core MVD line (yellow) represents the mean absolute deviation (MAD) of these volatility measures from their average. When the line is flat, all volatility measures are in agreement. When the line rises, it means the market’s volatility signals are diverging—often a precursor to regime shifts, volatility expansions, or hidden stress.
Dynamic Z-Score Normalization:
The MVD line is normalized as a Z-score, so you can easily spot when current divergence is rare or extreme compared to recent history.
Visual Clarity:
Yellow center line: Tracks the real-time divergence of volatility measures.
Green dashed thresholds: Mark the ±2.00 Z-score levels, highlighting when divergence is unusually high and action may be warranted.
Dashboard: Toggleable panel shows all key metrics (ATR, Stdev, VoVix, MVD Z) and your custom branding.
Compact Info Label : For mobile or minimalist users, a single-line summary keeps you informed without clutter.
What Makes The MVD line move?
- The MVD line rises when the included volatility measures (ATR, Stdev, Range, VoVix) are moving in different directions or at different magnitudes. For example, if ATR is rising but Stdev is falling, the line will move up, signaling disagreement.
- The line falls or flattens when all volatility measures are in sync, indicating a consensus in the market’s volatility regime.
- VoVix adds a unique dimension, making the indicator especially sensitive to sudden changes in volatility structure that most tools miss.
Inputs & Settings
ATR Length: Sets the lookback for ATR calculation. Shorter = more sensitive, longer = smoother.
Stdev Length: Sets the lookback for standard deviation. Adjust for your asset’s volatility.
Range Length: Sets the lookback for the average high-low range.
MVD Lookback: Controls the window for Z-score normalization. Higher values = more historical context, lower = more responsive.
Show Dashboard: Toggle the full dashboard panel on/off.
Show Compact Info Label: Toggle the mobile-friendly info line on/off.
Tip:
Adjust these settings to match your asset’s volatility and your trading timeframe. There is no “one size fits all”—tuning is key to extracting the most value from MVD.
How to make MVD work for you:
Threshold Crosses: When the MVD line crosses above or below the green dashed thresholds (±2.00), it signals that volatility measures are diverging more than usual. This is a heads-up that a volatility event, regime shift, or hidden market stress may be developing.
Not a Buy/Sell Signal: A threshold cross is not a direct buy or sell signal. It is an indication that the market’s volatility structure is changing. Use it as a filter, confirmation, or alert in combination with your own strategy and risk management.
Dashboard & Info Line: Use the dashboard for a full view of all metrics, or the info label for a quick glance—especially useful on mobile.
Chart: MNQ! on 5min frames
ATR: 14
StDev L: 11
Range L: 13
MDV LB: 13
Important Note
MVD is a market structure and volatility regime tool.
It is designed to alert you to potential changes in market conditions, not to provide direct trade entries or exits. Always combine with your own analysis and risk management.
Meta-Volatility Divergence:
See the market’s hidden tension. Anticipate the next wave.
For educational purposes only. Not financial advice. Always use proper risk management.
Use with discipline. Trade your edge.
— Dskyz, for DAFE Trading Systems
Multi VWAPsMulti VWAPs Inspired by Biran Shannon and his book:
"MAXIMUM TRADING GAINS WITH ANCHORED VWAP . The Perfect Combination of Price, Time & Volume."
(ISBN 9798986868004)
A comprehensive VWAP (Volume Weighted Average Price) indicator that combines multiple timeframes and sessions in one view. Perfect for day trading and swing trading across different markets.
Features:
• Multiple VWAP Timeframes:
- Daily VWAP
- Weekly VWAP
- Monthly VWAP
- Quarterly VWAP
- Yearly VWAP
• Session-specific VWAPs:
- London Session (3:00 AM - 11:30 AM NY time)
- New York Session (9:30 AM - 4:00 PM NY time)
• Additional Indicators:
- Midnight Price Line (Previous day's closing price)
- 5-Day Moving Average
- 50-Day Moving Average
• Customization Options:
- Toggle individual VWAPs and indicators
- Customize colors for each component
- Adjustable label positioning
- MA smoothing settings
- Option to show/hide previous day's midnight price
• Smart Features:
- Auto-adjusting calculations based on timeframe
- Clear session boundaries
- Optimized for all chart timeframes
- Clean label system
Perfect for:
• Day traders tracking multiple timeframe momentum
• Swing traders using longer-term VWAPs
• Session traders focusing on London/NY hours
• Multi-timeframe analysis
• Price action trading with VWAP support/resistance
This indicator combines essential trading tools in one clean interface, helping you make informed decisions without cluttering your chart.
EMA SuiteFor strategies with moving averages, of course. My preference is to use Fibonacci values, but it can be configured with any setup. When working on a single timeframe, it allows adding averages or groups of averages from other timeframes, I’ve used this for scalping. The indicator is designed to be dynamic and adaptable. By editing the script, it’s easy to add or remove averages.
Larger averages might slow down loading, and a color palette selector could be added since manually setting 11 values is tedious.
I’m open to any suggestions
Liquid Pulse Liquid Pulse by Dskyz (DAFE) Trading Systems
Liquid Pulse is a trading algo built by Dskyz (DAFE) Trading Systems for futures markets like NQ1!, designed to snag high-probability trades with tight risk control. it fuses a confluence system—VWAP, MACD, ADX, volume, and liquidity sweeps—with a trade scoring setup, daily limits, and VIX pauses to dodge wild volatility. visuals include simple signals, VWAP bands, and a dashboard with stats.
Core Components for Liquid Pulse
Volume Sensitivity (volumeSensitivity) controls how much volume spikes matter for entries. options: 'Low', 'Medium', 'High' default: 'High' (catches small spikes, good for active markets) tweak it: 'Low' for calm markets, 'High' for chaos.
MACD Speed (macdSpeed) sets the MACD’s pace for momentum. options: 'Fast', 'Medium', 'Slow' default: 'Medium' (solid balance) tweak it: 'Fast' for scalping, 'Slow' for swings.
Daily Trade Limit (dailyTradeLimit) caps trades per day to keep risk in check. range: 1 to 30 default: 20 tweak it: 5-10 for safety, 20-30 for action.
Number of Contracts (numContracts) sets position size. range: 1 to 20 default: 4 tweak it: up for big accounts, down for small.
VIX Pause Level (vixPauseLevel) stops trading if VIX gets too hot. range: 10 to 80 default: 39.0 tweak it: 30 to avoid volatility, 50 to ride it.
Min Confluence Conditions (minConditions) sets how many signals must align. range: 1 to 5 default: 2 tweak it: 3-4 for strict, 1-2 for more trades.
Min Trade Score (Longs/Shorts) (minTradeScoreLongs/minTradeScoreShorts) filters trade quality. longs range: 0 to 100 default: 73 shorts range: 0 to 100 default: 75 tweak it: 80-90 for quality, 60-70 for volume.
Liquidity Sweep Strength (sweepStrength) gauges breakouts. range: 0.1 to 1.0 default: 0.5 tweak it: 0.7-1.0 for strong moves, 0.3-0.5 for small.
ADX Trend Threshold (adxTrendThreshold) confirms trends. range: 10 to 100 default: 41 tweak it: 40-50 for trends, 30-35 for weak ones.
ADX Chop Threshold (adxChopThreshold) avoids chop. range: 5 to 50 default: 20 tweak it: 15-20 to dodge chop, 25-30 to loosen.
VWAP Timeframe (vwapTimeframe) sets VWAP period. options: '15', '30', '60', '240', 'D' default: '60' (1-hour) tweak it: 60 for day, 240 for swing, D for long.
Take Profit Ticks (Longs/Shorts) (takeProfitTicksLongs/takeProfitTicksShorts) sets profit targets. longs range: 5 to 100 default: 25.0 shorts range: 5 to 100 default: 20.0 tweak it: 30-50 for trends, 10-20 for chop.
Max Profit Ticks (maxProfitTicks) caps max gain. range: 10 to 200 default: 60.0 tweak it: 80-100 for big moves, 40-60 for tight.
Min Profit Ticks to Trail (minProfitTicksTrail) triggers trailing. range: 1 to 50 default: 7.0 tweak it: 10-15 for big gains, 5-7 for quick locks.
Trailing Stop Ticks (trailTicks) sets trail distance. range: 1 to 50 default: 5.0 tweak it: 8-10 for room, 3-5 for fast locks.
Trailing Offset Ticks (trailOffsetTicks) sets trail offset. range: 1 to 20 default: 2.0 tweak it: 1-2 for tight, 5-10 for loose.
ATR Period (atrPeriod) measures volatility. range: 5 to 50 default: 9 tweak it: 14-20 for smooth, 5-9 for reactive.
Hardcoded Settings volLookback: 30 ('Low'), 20 ('Medium'), 11 ('High') volThreshold: 1.5 ('Low'), 1.8 ('Medium'), 2 ('High') swingLen: 5
Execution Logic Overview trades trigger when confluence conditions align, entering long or short with set position sizes. exits use dynamic take-profits, trailing stops after a profit threshold, hard stops via ATR, and a time stop after 100 bars.
Features Multi-Signal Confluence: needs VWAP, MACD, volume, sweeps, and ADX to line up.
Risk Control: ATR-based stops (capped 15 ticks), take-profits (scaled by volatility), and trails.
Market Filters: VIX pause, ADX trend/chop checks, volatility gates. Dashboard: shows scores, VIX, ADX, P/L, win %, streak.
Visuals Simple signals (green up triangles for longs, red down for shorts) and VWAP bands with glow. info table (bottom right) with MACD momentum. dashboard (top right) with stats.
Chart and Backtest:
NQ1! futures, 5-minute chart. works best in trending, volatile conditions. tweak inputs for other markets—test thoroughly.
Backtesting: NQ1! Frame: Jan 19, 2025, 09:00 — May 02, 2025, 16:00 Slippage: 3 Commission: $4.60
Fee Typical Range (per side, per contract)
CME Exchange $1.14 – $1.20
Clearing $0.10 – $0.30
NFA Regulatory $0.02
Firm/Broker Commis. $0.25 – $0.80 (retail prop)
TOTAL $1.60 – $2.30 per side
Round Turn: (enter+exit) = $3.20 – $4.60 per contract
Disclaimer this is for education only. past results don’t predict future wins. trading’s risky—only use money you can lose. backtest and validate before going live. (expect moderators to nitpick some random chart symbol rule—i’ll fix and repost if they pull it.)
About the Author Dskyz (DAFE) Trading Systems crafts killer trading algos. Liquid Pulse is pure research and grit, built for smart, bold trading. Use it with discipline. Use it with clarity. Trade smarter. I’ll keep dropping badass strategies ‘til i build a brand or someone signs me up.
2025 Created by Dskyz, powered by DAFE Trading Systems. Trade smart, trade bold.
Hippo Battlefield - Bulls VS Bears 20 bars## Hippo Battlefield – Bulls VS Bears (20 Bars)
**What it is**
A multi-dimensional momentum-and-sentiment oscillator that combines classic Bull/Bear Power with ATR- or peak-normalization, then layers on RSI and MACD-derived metrics into:
1. **A colored bar series** showing net Bull+Bear Power strength over the last 20 bars,
2. **A dynamic table** of each of those 20 BBP values (grouped into four 5-bar “quartals”), with symbols, per-bar change, and rolling averages, and
3. **A composite “Weighted BBP” histogram** blending normalized RSI, MACD, and BBP into a single view.
---
### Key Inputs
- **Length (EMA)** – look-back for the underlying EMA (default 60)
- **Normalization Length** – look-back window for peak-normalization (default 60)
- **Use ATR for Norm.** – toggle ATR-based normalization vs. highest-abs(BBP)
- **Show Tables** – toggle the bottom-right 21×11 grid of raw and average BBP values
---
### What You See
#### 1. Colored Bars (Overlay = false)
- Bars are colored by normalized BBP intensity:
- Extreme Bull (≥+10): deep blue
- Strong Bull (+5 to +10): green/yellow
- Weak Bull (+0 to +5): dark green
- Weak Bear (–0 to –5): dark red
- Strong Bear (–5 to –10): pink/red
- Extreme Bear (<–10): magenta
#### 2. Bottom-Right Table (20 Bars of Data)
- Divided into four columns (0–4, 5–9, 10–14, 15–19 bars ago) and one “average” row.
- Each cell shows:
1. Bar index (1–20),
2. Normalized BBP value (to four decimals),
3. Direction symbol (↑/↓/=),
4. Bar-to-bar change (± value),
5. A separator “|”.
- At the very bottom, each column’s 5-bar average is displayed as “Avg: X.XXXX” with a dot marker.
#### 3. Top-Center Mini-Table
- When ≥20 bars have elapsed, shows the date at 20 bars ago and the average BBP across the full 20-bar window.
#### 4. Normalized RSI Line
- Rescales the classic 14-period RSI into a –20…+20 band to align with BBP.
#### 5. MACD Lines (Hidden) & Composite Histogram
- MACD and signal lines are calculated but not plotted by default.
- A “Weighted BBP” histogram combines:
- 20% normalized RSI,
- 20% average of (MACD + signal + normalized BBP),
- 60% normalized BBP
- Plotted as columns, color-coded by strength using the same palette as the main bars.
#### 6. Middle Reference Line
- A horizontal zero line to anchor over/under-zero readings.
---
### How to Use It
- **Trend confirmation**: Strong blue/green bars alongside a rising histogram suggest bull conviction; strong reds/magentas signal bear dominance.
- **Divergence spotting**: Watch for price making new highs/lows while BBP or the histogram fails to follow.
- **Quartal analysis**: The 5-bar group averages can reveal whether recent momentum is accelerating or waning.
- **Cross-indicator weighting**: Because RSI, MACD, and raw BBP all feed into the final histogram, you get a smoothed, blended view of momentum shifts.
---
**Tip:** Tweak the EMA and normalization length to suit your preferred timeframe (e.g. shorter for intraday scalps, longer for swing trades). Enable/disable the table if you prefer a cleaner pane.
Clenow MomentumClenow Momentum Method
The Clenow Momentum Method, developed by Andreas Clenow, is a systematic, quantitative trading strategy focused on capturing medium- to long-term price trends in financial markets. Popularized through Clenow’s book, Stocks on the Move: Beating the Market with Hedge Fund Momentum Strategies, the method leverages momentum—an empirically observed phenomenon where assets that have performed well in the recent past tend to continue performing well in the near future.
Theoretical Foundation
Momentum investing is grounded in behavioral finance and market inefficiencies. Investors often exhibit herding behavior, underreact to new information, or chase trends, causing prices to trend beyond fundamental values. Clenow’s method builds on academic research, such as Jegadeesh and Titman (1993), which demonstrated that stocks with high returns over 3–12 months outperform those with low returns over similar periods.
Clenow’s approach specifically uses **annualized momentum**, calculated as the rate of return over a lookback period (typically 90 days), annualized to reflect a yearly percentage. The formula is:
Momentum=(((Close N periods agoCurrent Close)^N252)−1)×100
- Current Close: The most recent closing price.
- Close N periods ago: The closing price N periods back (e.g., 90 days).
- N: Lookback period (commonly 90 days).
- 252: Approximate trading days in a year for annualization.
This metric ranks stocks by their momentum, prioritizing those with the strongest upward trends. Clenow’s method also incorporates risk management, diversification, and volatility adjustments to enhance robustness.
Methodology
The Clenow Momentum Method involves the following steps:
1. Universe Selection:
- A broad universe of liquid stocks is chosen, often from major indices (e.g., S&P 500, Nasdaq 100) or global exchanges.
- Filters should exclude illiquid stocks (e.g., low average daily volume) or those with extreme volatility.
2. Momentum Calculation:
- Stocks are ranked based on their annualized momentum over a lookback period (typically 90 days, though 60–120 days can be common tests).
- The top-ranked stocks (e.g., top 10–20%) are selected for the portfolio.
3. Volatility Adjustment (Optional):
- Clenow sometimes adjusts momentum scores by volatility (e.g., dividing by the standard deviation of returns) to favor stocks with smoother trends.
- This reduces exposure to erratic price movements.
4. Portfolio Construction:
- A diversified portfolio of 10–25 stocks is constructed, with equal or volatility-weighted allocations.
- Position sizes are often adjusted based on risk (e.g., 1% of capital per position).
5. Rebalancing:
- The portfolio is rebalanced periodically (e.g., weekly or monthly) to maintain exposure to high-momentum stocks.
- Stocks falling below a momentum threshold are replaced with higher-ranked candidates.
6. Risk Management:
- Stop-losses or trailing stops may be applied to limit downside risk.
- Diversification across sectors reduces concentration risk.
Implementation in TradingView
Key features include:
- Customizable Lookback: Users can adjust the lookback period in pinescript (e.g., 90 days) to align with Clenow’s methodology.
- Visual Cues: Background colors (green for positive, red for negative momentum) and a zero line help identify trend strength.
- Integration with Screeners: TradingView’s stock screener can filter high-momentum stocks, which can then be analyzed with the custom indicator.
Strengths
1. Simplicity: The method is straightforward, relying on a single metric (momentum) that’s easy to calculate and interpret.
2. Empirical Support: Backed by decades of academic research and real-world hedge fund performance.
3. Adaptability: Applicable to stocks, ETFs, or other asset classes, with flexible lookback periods.
4. Risk Management: Diversification and periodic rebalancing reduce idiosyncratic risk.
5. TradingView Integration: Pine Script implementation enables real-time visualization, enhancing decision-making for stocks like NVDA or SPY.
Limitations
1. Mean Reversion Risk: Momentum can reverse sharply in bear markets or during sector rotations, leading to drawdowns.
2. Transaction Costs: Frequent rebalancing increases trading costs, especially for retail traders with high commissions. This is not as prevalent with commission free trading becoming more available.
3. Overfitting Risk: Over-optimizing lookback periods or filters can reduce out-of-sample performance.
4. Market Conditions: Underperforms in low-momentum or highly volatile markets.
Practical Applications
The Clenow Momentum Method is ideal for:
Retail Traders: Use TradingView’s screener to identify high-momentum stocks, then apply the Pine Script indicator to confirm trends.
Portfolio Managers: Build diversified momentum portfolios, rebalancing monthly to capture trends.
Swing Traders: Combine with volume filters to target short-term breakouts in high-momentum stocks.
Cross-Platform Workflow: Integrate with Python scanners to rank stocks, then visualize on TradingView for trade execution.
Comparison to Other Strategies
Vs. Minervini’s VCP: Clenow’s method is purely quantitative, while Minervini’s Volatility Contraction Pattern (your April 11, 2025 query) combines momentum with chart patterns. Clenow is more systematic but less discretionary.
Vs. Mean Reversion: Momentum bets on trend continuation, unlike mean reversion strategies that target oversold conditions.
Vs. Value Investing: Momentum outperforms in bull markets but may lag value strategies in recovery phases.
Conclusion
The Clenow Momentum Method is a robust, evidence-based strategy that capitalizes on price trends while managing risk through diversification and rebalancing. Its simplicity and adaptability make it accessible to retail traders, especially when implemented on platforms like TradingView with custom Pine Script indicators. Traders must be mindful of transaction costs, mean reversion risks, and market conditions. By combining Clenow’s momentum with volume filters and alerts, you can optimize its application for swing or position trading.
SMT SwiftEdge PowerhouseSMT SwiftEdge Powerhouse: Precision Trading with Divergence, Liquidity Grabs, and OTE Zones
The SMT SwiftEdge Powerhouse is a powerful trading tool designed to help traders identify high-probability entry points during the most active market sessions—London and New York. By combining Smart Money Technique (SMT) Divergence, Liquidity Grabs, and Optimal Trade Entry (OTE) Zones, this script provides a unique and cohesive strategy for capturing market reversals with precision. Whether you're a scalper or a swing trader, this indicator offers clear visual signals to enhance your trading decisions on any timeframe.
What Does This Script Do?
This script integrates three key concepts to identify potential trading opportunities:
SMT Divergence:
SMT Divergence compares the price action of two correlated assets (e.g., Nasdaq and S&P 500 futures) to detect hidden market reversals. When one asset makes a higher high while the other makes a lower high (bearish divergence), or one makes a lower low while the other makes a higher low (bullish divergence), it signals a potential reversal. This technique leverages institutional "smart money" behavior to anticipate market shifts.
Liquidity Grabs:
Liquidity Grabs occur when price breaks above recent highs or below recent lows on higher timeframes (5m and 15m), often triggering stop-loss orders from retail traders. These breakouts are identified using pivot points and confirm institutional activity, setting the stage for a reversal. The script focuses on liquidity grabs during the London and New York sessions for maximum market activity.
Optimal Trade Entry (OTE) Zones:
OTE Zones are Fibonacci-based retracement areas (e.g., 61.8%) calculated after a liquidity grab. These zones highlight where price is likely to retrace before continuing in the direction of the reversal, offering a high-probability entry point. The script adjusts the width of these zones using the Average True Range (ATR) to adapt to market volatility.
By combining these components, the script identifies when institutional activity (liquidity grabs) aligns with market reversals (SMT divergence) and pinpoints precise entry points (OTE zones) during high-liquidity sessions.
Why Combine These Components?
The integration of SMT Divergence, Liquidity Grabs, and OTE Zones creates a robust trading system for several reasons:
Synergy of Institutional Signals: SMT Divergence and Liquidity Grabs both reflect "smart money" behavior—divergence shows hidden reversals, while liquidity grabs confirm institutional intent to trap retail traders. Together, they provide a strong foundation for identifying high-probability setups.
Session-Based Precision: Focusing on the London and New York sessions ensures signals occur during periods of high volatility and liquidity, increasing their reliability.
Precision Entries with OTE: After confirming a setup with divergence and liquidity grabs, OTE zones provide a clear entry area, reducing guesswork and improving trade accuracy.
Adaptability: The script works on any timeframe, with adjustable settings for signal sensitivity, session times, and Fibonacci levels, making it versatile for different trading styles.
This combination makes the script unique by aligning institutional insights with actionable entry points, tailored to the most active market hours.
How to Use the Script
Setup:
Add the script to your chart (works on any timeframe, e.g., 1m, 5m, 15m).
Configure the settings in the indicator's inputs:
Session Settings: Adjust the start/end times for London and New York sessions (default: London 8-11 UTC, New York 13-16 UTC). You can disable session restrictions if desired.
Asset Settings: Set the primary and secondary assets for SMT Divergence (default: NQ1! and ES1!). Ensure the assets are correlated.
Signal Settings: Adjust the lookback period, ATR period, and signal sensitivity (Low/Medium/High) to control the frequency of signals.
OTE Settings: Choose the Fibonacci level for OTE zones (default: 61.8%).
Visual Settings: Enable/disable OTE zones, SMT labels, and debug labels for troubleshooting.
Interpreting Signals:
Blue Circles: Indicate a liquidity grab (price breaking a 5m or 15m pivot high/low), marking the start of a potential setup.
Blue OTE Zones: Appear after a liquidity grab, showing the retracement area (e.g., 61.8% Fibonacci level) where price is likely to enter for a reversal trade. The label "OTE Trigger 5m/15m" confirms the direction (Short/Long) and session.
Green/Red Entry Boxes: Mark precise entry points when price enters the OTE zone and confirms the SMT Divergence. Green boxes indicate a long entry, red boxes a short entry.
Trading Example:
On a 1m chart, a blue circle appears when price breaks a 5m pivot high during the London session.
A blue OTE zone forms, showing a retracement area (e.g., 61.8% Fibonacci level) with the label "OTE Trigger 5m/15m (Short, London)".
Price retraces into the OTE zone, and a red "Short Entry" box appears, confirming a bearish SMT Divergence.
Enter a short trade at the red box, with a stop-loss above the OTE zone and a take-profit at the next support level.
Originality and Utility
The SMT SwiftEdge Powerhouse stands out by merging SMT Divergence, Liquidity Grabs, and OTE Zones into a single, session-focused indicator. Unlike traditional indicators that focus on one aspect of price action, this script combines institutional reversal signals with precise entry zones, tailored to the most active market hours. Its adaptability across timeframes, customizable settings, and clear visual cues make it a versatile tool for traders seeking to capitalize on smart money movements with confidence.
Tips for Best Results
Use on correlated assets like NQ1! (Nasdaq futures) and ES1! (S&P 500 futures) for accurate SMT Divergence.
Test on lower timeframes (1m, 5m) for scalping or higher timeframes (15m, 1H) for swing trading.
Adjust the "Signal Sensitivity" to "High" for more signals or "Low" for fewer, high-quality setups.
Enable "Show Debug Labels" if signals are not appearing as expected, to troubleshoot pivot points and liquidity grabs.
The Mayan CalendarThis indicator displays the current date in the Mayan Calendar, based on real-time UTC time. It calculates and presents:
🌀 Long Count (Baktun.Katun.Tun.Uinal.Kin) – A linear count of days since the Mayan epoch (August 11, 3114 BCE).
🔮 Tzolk'in Date – A 260-day sacred cycle combining a number (1–13) and one of 20 day names (e.g., 4 Ajaw).
🌾 Haab' Date – A 365-day civil cycle divided into 18 months of 20 days + 5 "nameless" days (Wayeb').
The calculations follow Smithsonian standards and align with the Maya Calendar Converter from the National Museum of the American Indian:
👉 maya.nmai.si.edu
The results are shown in a table overlay on your chart's top-right corner. This indicator is great for symbolic traders, astro enthusiasts, or anyone interested in ancient timekeeping systems woven into financial timeframes. Enjoy, time travelers! ⌛
MACD [AlchimistOfCrypto]🌠 MACD Optimized with Python – Decoding the Chaos of Markets 🌠
Category: Trend Analysis 📈
"Like the dynamic systems studied in chaos theory, financial markets appear unpredictable at first glance. Yet, as Edward Lorenz demonstrated, even in apparent chaos reside harmonious mathematical structures. The MACD (Moving Average Convergence Divergence) represents this quest for order within disorder—a mathematical formulation that extracts coherent signals from price noise. By combining moving averages of different periods, this indicator reveals hidden cycles and precise moments when market energy shifts, like a pendulum obeying the immutable laws of physics."
📊 Technical Overview
The MACD Optimized with Python is a revolutionary take on the classic Moving Average Convergence Divergence indicator. Powered by Python-driven optimizations 🐍, it adapts to specific timeframes, delivering razor-sharp signals for traders seeking to navigate the market’s chaos with precision.
⚙️ How It Works
- Python-Optimized Parameters 🔧: Unlike the standard MACD (12,26,9), our version uses mathematically tailored parameters for each timeframe:
- 1H: 11/38/27
- 4H: 9/98/27
- 1D: 45/90/29
- 1W: 9/16/3
- 2W: 5/20/5
- Intuitive Visuals 🎨:
- Crossovers marked by colored dots 🟢🔴 for clear entry/exit signals.
- Histogram with a color gradient 🌈 to show direction and momentum intensity.
- Customizable Signals 🎯: Choose to display long, short, or both signals to match your trading style.
🚀 How to Use This Indicator
1. Select Your Timeframe ⏰: Choose the timeframe aligned with your trading horizon (1H, 4H, 1D, 1W, or 2W).
2. Spot Crossovers 🔍: Watch for the MACD line (green) crossing the signal line (red) to identify potential trend changes.
3. Confirm with Divergence ✅: Combine crossovers with price-MACD divergence for high-probability trend reversal signals.
📅 Release Notes
Unlock the hidden order of markets with this Python-optimized MACD. Stay tuned for future enhancements! ✨
🏷️ Tags
#Trading #TechnicalAnalysis #MACD #TrendAnalysis #Python #MultiTimeframe #Divergence #Momentum #TradingStrategy #RiskManagement #Forex #Stocks #Crypto #ChaosTheory #OptimizedTrading
Collatz Conjecture - DolphinTradeBot1️⃣ Overview
Every positive number follows its own unique path to reach 1 according to the Collatz rule.
Some numbers reach the end quickly and directly.
Others rise significantly before crashing down sharply.
Some get stuck within a certain range for a while before finally reaching 1.
Each number follows a different pattern — the number of steps it takes, how high it climbs, or which values it passes through cannot be predicted in advance.
This is a structure that appears chaotic but ultimately leads to order:
Every number reaches 1, but the way it gets there is entirely uncertain.
2️⃣ How Is It Work?
The rule is simple:
▪️ If the number is even → divide it by two.
▪️ If it’s odd → multiply it by three and add one.
Repeat this process at each step.
Example :
Let’s say the starting number is 7:
7 → 22 → 11 → 34 → 17 → 52 → 26 → 13 → 40 → 20 → 10 → 5 → 16 → 8 → 4 → 2 → 1
It reaches 1 in 17 steps.
And from there, it always enters the same cycle:
4 → 2 → 1 → 4 → 2 → 1...
3️⃣ Why Is It Worth Learning?
🎯 This indicator isn’t just mathematical fun—it’s a thought experiment for those who dare to question market behavior.
▪️ It’s fun.
Watching numbers behave in unpredictable ways from a simple rule set is surprisingly enjoyable.
▪️ It shows how hard it is to teach a computer what randomness really is .
The Collatz process can be used to simulate chaotic behavior and may even inspire creative ways to introduce complexity into your code.
▪️ It makes you think — especially in financial markets.
The patternless, yet rule-based structure of Collatz can help train your mind to recognize that not all unpredictability is random. It’s a great mental model for navigating complex systems like price action.
▪️ Just like price movements in financial markets, this ancient problem remains unsolved.
Despite its simplicity, the Collatz conjecture has resisted proof for decades — a reminder that even the most basic-looking systems can hide deep complexity.
4️⃣ How To Use?
Super easy — in the indicator’s settings, there’s just one input field.
Enter any positive number, and you’ll see the pattern it follows on its way to 1.
You can also observe how many steps it takes and which values it visits in the info box at the top center of the chart.
5️⃣ Some Examples
You Can Observe the Chaos in the Following Examples⤵️
For Input Number → 12
For Input Number → 13
For Input Number → 14
For Input Number → 32768
For Input Number → 47
MA Deviation// -----------------------------------------------------------------------------
// MA Deviation Marking & Alert (MA Divergence)
// -----------------------------------------------------------------------------
// Short Title: MA Deviation Radar
// Author: zhipeng luo
// Version: 1.0
// Date: 2025-04-11
// -----------------------------------------------------------------------------
// Overview:
// This indicator identifies and highlights price bars where the closing price
// deviates significantly from its Simple Moving Average (SMA) by a user-defined
// percentage. It visually marks these bars on the chart and provides
// configurable alert conditions for threshold breaches.
//
// How it Works:
// 1. Calculates the Simple Moving Average (SMA) based on the 'MA Period' input.
// 2. Computes the percentage deviation of the closing price from the SMA value.
// Formula: `((Close - SMA) / SMA) * 100`
// 3. Compares the calculated deviation percentage against the positive and
// negative 'Threshold (%)' input values.
// 4. Marks the background of the price bars when a threshold is exceeded:
// - Red Background: Price deviation is greater than the positive threshold.
// - Green Background: Price deviation is less than the negative threshold.
// 5. Includes an optional, non-visible plot of the MA line itself.
// 6. Offers three distinct alert conditions for automation and notifications.
//
// Features:
// - Customizable Simple Moving Average period.
// - Adjustable deviation threshold percentage.
// - Clear visual signals using background colors on the main chart.
// - Built-in Alert Conditions:
// - MA Positive Deviation Alert (Triggers when price > MA + Threshold %)
// - MA Negative Deviation Alert (Triggers when price < MA - Threshold %)
// - MA Deviation Alert - Any (Triggers on either positive or negative breach)
//
// How to Use:
// - Identify Potential Extremes: Useful for spotting potential overbought (large
// positive deviation) or oversold (large negative deviation) conditions
// which might precede price corrections or mean reversion.
// - Gauge Trend Extension: Extreme deviations can sometimes indicate that a
// trend is overextended and might be due for a pause or reversal.
// - Parameter Tuning: Adjust the 'MA Period' and '(Threshold %)' settings to
// suit the specific asset, timeframe, and volatility characteristics you
// are analyzing. Lower thresholds yield more signals; higher thresholds
// focus on more significant deviations.
// - Alerts: Set up alerts via the TradingView alert menu using the provided
// conditions ("MA Positive Deviation Alert", "MA Negative Deviation Alert",
// "MA Deviation Alert - Any") to get notified of potential setups.
//
// Parameters:
// - MA Period (Default: 200): The lookback period for the SMA calculation.
// - (Threshold %) (Default: 7.0): The percentage deviation (positive and
// negative) from the MA required to trigger a background signal and alert.
//
// Alerts & Important Note:
// Three alert conditions corresponding to the signals are available:
// 1. "MA Positive Deviation Alert"
// 2. "MA Negative Deviation Alert"
// 3. "MA Deviation Alert - Any"
//
// ***Please Note:*** The value shown after "( {{plot_0}}%)" or
// "( {{plot_0}}%)" in the default alert message refers to the
// **Moving Average value** (`plot_0`), not the actual deviation percentage.
// The alert *triggers correctly* based on the deviation percentage crossing
// the threshold, but the number displayed by the `{{plot_0}}` placeholder
// in the message is the MA's value at that time due to the script's
// internal plot order.
//
// Disclaimer: This indicator is provided for informational and analytical
// purposes only. It does not constitute financial advice or a recommendation
// to buy or sell any asset. Always conduct your own research and use proper
// risk management. Trading involves significant risk.
// -----------------------------------------------------------------------------
Multi-SMA Dashboard (10 SMAs)Description:
This script, "Multi-SMA Dashboard (10 SMAs)," creates a dashboard on a TradingView chart to analyze ten Simple Moving Averages (SMAs) of varying lengths. It overlays the chart and displays a table with each SMA’s direction, price position relative to the SMA, and angle of movement, providing a comprehensive trend overview.
How It Works:
1. **Inputs**: Users define lengths for 10 SMAs (default: 5, 10, 20, 50, 100, 150, 200, 250, 300, 350), select a price source (default: close), and customize table appearance and options like angle units (degrees/radians) and debug plots.
2. **SMA Calculation**: Computes 10 SMAs using the `ta.sma()` function with user-specified lengths and price source.
3. **Direction Determination**: The `sma_direction()` function checks each SMA’s trend:
- "Up" if current SMA > previous SMA.
- "Down" if current SMA < previous SMA.
- "Flat" if equal (no strength distinction).
4. **Price Position**: Compares the price source to each SMA, labeling it "Above" or "Below."
5. **Angle Calculation**: Tracks the most recent direction change point for each SMA and calculates its angle (atan of price change over time) in degrees or radians, based on the `showInRadians` toggle.
6. **Table Display**: A 12-column table shows:
- Columns 1-10: SMA name, direction (Up/Down/Flat), Above/Below status, and angle.
- Column 11: Summary of Up, Down, and Flat counts.
- Colors reflect direction (lime for Up/Above, red for Down/Below, white for Flat).
7. **Debug Option**: Optionally plots all SMAs and price for visual verification when `debug_plots_toggle` is enabled.
Indicators Used:
- Simple Moving Averages (SMAs): 10 user-configurable SMAs ranging from short-term (e.g., 5) to long-term (e.g., 350) periods.
The script runs continuously, updating the table on each bar, and overlays the chart to assist traders in assessing multi-timeframe trend direction and momentum without cluttering the view unless debug mode is active.
London Breakout Tracker - Box Style📊 London Breakout Tracker (Pine Script v6)
This script is designed to track the Asian session range and identify breakout opportunities when the London session begins. It highlights high-probability trade setups and helps avoid fakeouts or overly wide ranges.
🧱 1. Session Time Definitions (Adjusted for Kenyan Time)
The Asian session is defined as:
3:00 AM to 11:00 AM (Kenyan Time)
🔐 2. Asian Session High & Low
During the Asian session:
The script tracks the highest high and lowest low to define the range.
These are stored in variables: asianHigh and asianLow.
🧊 3. Box Drawing for the Asian Range
Once the Asian session ends:
A visual box is drawn around the session using box.new().
This box spans from the session start to end bars and from the high to low.
It helps visually see the range price must break out from.
🚨 4. Breakout Signals
After the Asian session:
A Long Breakout signal is generated if:
The candle closes above the Asian High.
A Short Breakout signal is generated if:
The candle closes below the Asian Low.
This corresponds to 00:00 to 08:00 UTC
These are shown with:
✅ Green up label for long breakouts
❌ Red down label for short breakouts
🧯 5. Fakeout Detection
If price breaks out but closes back inside the Asian range, it’s marked as a Fakeout:
Long Fakeout: Price breaks above high, then closes back below.
Short Fakeout: Price breaks below low, then closes back above.
These are marked with orange X-crosses above or below candles.
⚠️ 6. Wide Range Filter
If the Asian session range is too wide (e.g. > 40 pips), a gray background is drawn.
This warns you not to trade that day since breakouts from wide ranges are unreliable.
📣 7. Alert Conditions
The script can trigger alerts in TradingView when:
🔔 A Long or Short Breakout occurs
⚠️ A Fakeout is detected
You can set these up via the TradingView alert system.
🎯 Overall Purpose:
The script helps you:
Clearly see the Asian session range
Identify breakout opportunities at the London open
Avoid trading during fakeouts or wide-range sessions
Get alerted when breakout/fakeout conditions occur
2013-2025 Moon Phases & Mercury RetrogradesIndicator Description: 2013-2025 Moon Phases & Mercury Retrogrades
This Pine Script (version 5) indicator overlays key astrological events on a TradingView chart, specifically tracking full moons, new moons, and Mercury retrograde periods from 2013 to 2025. It is designed to help traders and astrology enthusiasts visualize these celestial events alongside price action, potentially identifying correlations or patterns.
Features:
New Moons:
Visualization: Plotted as small white circles above the price bars.
Data: Includes 156 specific new moon dates from January 11, 2013, to December 20, 2025.
Purpose: Marks the start of the lunar cycle, often associated with new beginnings or shifts in energy.
Full Moons:
Visualization: Plotted as small orange circles above the price bars.
Data: Includes 157 specific full moon dates from January 27, 2013, to December 15, 2025.
Purpose: Highlights the peak of the lunar cycle, often linked to heightened emotions or market volatility in astrological analysis.
Mercury Retrogrades:
Visualization: Displayed as a light red background highlight across the chart.
Data: Covers 39 Mercury retrograde periods, with precise start and end timestamps from February 23, 2013, to November 29, 2025.
Purpose: Indicates periods traditionally associated with communication issues, delays, or reversals, which some traders monitor for potential market impacts.
Technical Details:
Overlay: The indicator is set to overlay=true, meaning it displays directly on the price chart rather than in a separate pane.
Date Matching: Uses a helper function is_date(y, m, d) to check if the current chart date matches any of the predefined event dates, leveraging TradingView's year, month, and dayofmonth variables.
Visualization Methods:
plotshape: Used for new moons (white circles) and full moons (orange circles), positioned above bars for clear visibility.
bgcolor: Used for Mercury retrograde periods, applying a semi-transparent red highlight (transparency level 85) to the background during active retrograde periods.
Time Range: Spans from January 2013 to December 2025, providing a comprehensive 13-year view of these astrological events.
Usage:
Add the script to your TradingView chart to see new moons, full moons, and Mercury retrograde periods overlaid on your chosen symbol and timeframe.
The white and orange circles appear on specific dates, while the red background highlights extend across the duration of each Mercury retrograde period.
Useful for traders incorporating astrology into their analysis or anyone interested in tracking these celestial events alongside financial data.
Notes:
The script assumes accurate date data as provided; users should verify dates against astronomical sources if precision is critical.
The transparency of the Mercury retrograde background can be adjusted by modifying the value in color.new(color.red, 85) (0 = fully opaque, 100 = fully transparent).
Best viewed on daily or higher timeframes for clarity, though it works on any timeframe supported by TradingView.
This indicator provides a visual tool to explore the potential influence of lunar phases and Mercury retrograde periods on market behavior, blending astrology with technical analysis in a clear, customizable format.
