3D Surface Modeling [PhenLabs]📊 3D Surface Modeling
Version: PineScript™ v6
📌 Description
The 3D Surface Modeling indicator revolutionizes technical analysis by generating three-dimensional visualizations of multiple technical indicators across various timeframes. This advanced analytical tool processes and renders complex indicator data through a sophisticated matrix-based calculation system, creating an intuitive 3D surface representation of market dynamics.
The indicator employs array-based computations to simultaneously analyze multiple instances of selected technical indicators, mapping their behavior patterns across different temporal dimensions. This unique approach enables traders to identify complex market patterns and relationships that may be invisible in traditional 2D charts.
🚀 Points of Innovation
Matrix-Based Computation Engine: Processes up to 500 concurrent indicator calculations in real-time
Dynamic 3D Rendering System: Creates depth perception through sophisticated line arrays and color gradients
Multi-Indicator Integration: Seamlessly combines VWAP, Hurst, RSI, Stochastic, CCI, MFI, and Fractal Dimension analyses
Adaptive Scaling Algorithm: Automatically adjusts visualization parameters based on indicator type and market conditions
🔧 Core Components
Indicator Processing Module: Handles real-time calculation of multiple technical indicators using array-based mathematics
3D Visualization Engine: Converts indicator data into three-dimensional surfaces using line arrays and color mapping
Dynamic Scaling System: Implements custom normalization algorithms for different indicator types
Color Gradient Generator: Creates depth perception through programmatic color transitions
🔥 Key Features
Multi-Indicator Support: Comprehensive analysis across seven different technical indicators
Customizable Visualization: User-defined color schemes and line width parameters
Real-time Processing: Continuous calculation and rendering of 3D surfaces
Cross-Timeframe Analysis: Simultaneous visualization of indicator behavior across multiple periods
🎨 Visualization
Surface Plot: Three-dimensional representation using up to 500 lines with dynamic color gradients
Depth Indicators: Color intensity variations showing indicator value magnitude
Pattern Recognition: Visual identification of market structures across multiple timeframes
📖 Usage Guidelines
Indicator Selection
Type: VWAP, Hurst, RSI, Stochastic, CCI, MFI, Fractal Dimension
Default: VWAP
Starting Length: Minimum 5 periods
Default: 10
Step Size: Interval between calculations
Range: 1-10
Visualization Parameters
Color Scheme: Green, Red, Blue options
Line Width: 1-5 pixels
Surface Resolution: Up to 500 lines
✅ Best Use Cases
Multi-timeframe market analysis
Pattern recognition across different technical indicators
Trend strength assessment through 3D visualization
Market behavior study across multiple periods
⚠️ Limitations
High computational resource requirements
Maximum 500 line restriction
Requires substantial historical data
Complex visualization learning curve
🔬 How It Works
1. Data Processing:
Calculates selected indicator values across multiple timeframes
Stores results in multi-dimensional arrays
Applies custom scaling algorithms
2. Visualization Generation:
Creates line arrays for 3D surface representation
Applies color gradients based on value magnitude
Renders real-time updates to surface plot
3. Display Integration:
Synchronizes with chart timeframe
Updates surface plot dynamically
Maintains visual consistency across updates
🌟 Credits:
Inspired by LonesomeTheBlue (modified for multiple indicator types with scaling fixes and additional unique mappings)
💡 Note:
Optimal performance requires sufficient computing resources and historical data. Users should start with default settings and gradually adjust parameters based on their analysis requirements and system capabilities.
Cari dalam skrip untuk "curve"
Advanced Currency Strength Meter# Advanced Currency Strength Meter (ACSM)
The Advanced Currency Strength Meter (ACSM) is a scientifically-based indicator that measures relative currency strength using established academic methodologies from international finance and behavioral economics. This indicator provides traders with a comprehensive view of currency market dynamics through multiple analytical frameworks.
### Theoretical Foundation
#### 1. Purchasing Power Parity (PPP) Theory
Based on Cassel's (1918) seminal work and refined by Froot & Rogoff (1995), PPP suggests that exchange rates should reflect relative price levels between countries. The ACSM momentum component captures deviations from long-term equilibrium relationships, providing insights into currency misalignments.
#### 2. Uncovered Interest Rate Parity (UIP) and Carry Trade Theory
Building on Fama (1984) and Lustig et al. (2007), the indicator incorporates volatility-adjusted momentum to capture carry trade flows and interest rate differentials that drive currency strength. This approach helps identify currencies benefiting from interest rate differentials.
#### 3. Behavioral Finance and Currency Momentum
Following Burnside et al. (2011) and Menkhoff et al. (2012), the model recognizes that currency markets exhibit persistent momentum effects due to behavioral biases and institutional flows. The indicator captures these momentum patterns for trading opportunities.
#### 4. Portfolio Balance Theory
Based on Branson & Henderson (1985), the relative strength matrix captures how portfolio rebalancing affects currency cross-rates and creates trading opportunities between different currency pairs.
### Technical Implementation
#### Core Methodologies:
- **Z-Score Normalization**: Following Sharpe (1994), provides statistical significance testing without arbitrary scaling
- **Momentum Analysis**: Uses return-based metrics (Jegadeesh & Titman, 1993) for trend identification
- **Volatility Adjustment**: Implements Average True Range methodology (Wilder, 1978) for risk-adjusted strength
- **Composite Scoring**: Equal-weight methodology to avoid overfitting and maintain robustness
- **Correlation Analysis**: Risk management framework based on Markowitz (1952) portfolio theory
#### Key Features:
- **Multi-Source Data Integration**: Supports OANDA, Futures, and CFD data sources
- **Scientific Methodology**: No arbitrary scaling or curve-fitting; all calculations based on established statistical methods
- **Comprehensive Dashboard**: Clean, professional table showing currency strengths and best trading pairs
- **Alert System**: Automated notifications for strong/weak currency conditions and extreme values
- **Best Pair Identification**: Algorithmic detection of highest-potential trading opportunities
### Practical Applications
#### For Swing Traders:
- Identify currencies in strong uptrends or downtrends
- Select optimal currency pairs based on relative strength divergence
- Time entries based on momentum convergence/divergence
#### For Day Traders:
- Use with real-time futures data for intraday opportunities
- Monitor currency correlations for risk management
- Detect early reversal signals through extreme value alerts
#### For Portfolio Managers:
- Multi-currency exposure analysis
- Risk management through correlation monitoring
- Strategic currency allocation decisions
### Visual Design
The indicator features a clean, professional dashboard that displays:
- **Currency Strength Values**: Each major currency (EUR, GBP, JPY, CHF, AUD, CAD, NZD, USD) with color-coded strength values
- **Best Trading Pairs**: Filtered list of highest-potential currency pairs with BUY/SELL signals
- **Market Analysis**: Real-time identification of strongest and weakest currencies
- **Potential Score**: Quantitative measure of trading opportunity strength
### Data Sources and Latency
The indicator supports multiple data sources to accommodate different trading needs:
- **OANDA (Delayed)**: Free data with 15-20 minute delay, suitable for swing trading
- **Futures (Real-time)**: CME currency futures for real-time analysis
- **CFDs**: Alternative real-time data source option
### Mathematical Framework
#### Strength Calculation:
Momentum = (Price - Price ) / Price * 100
Z-Score = (Price - Mean) / Standard Deviation
Volatility-Adjusted = Momentum / ATR-based Volatility
Composite = 0.5 * Momentum + 0.3 * Z-Score + 0.2 * Volatility-Adjusted
#### USD Strength Derivation:
USD strength is calculated as the weighted average of all USD-based pairs, providing a true baseline for relative strength comparison.
### Performance Considerations
The indicator is optimized for:
- **Computational Efficiency**: Uses Pine Script v6 best practices
- **Memory Management**: Appropriate lookback periods and array handling
- **Visual Clarity**: Clean table design optimized for both light and dark themes
- **Alert Reliability**: Robust signal generation with statistical significance testing
### Limitations and Risk Disclosure
- Model performance may vary during extreme market stress (Black Swan events)
- Requires stable data feeds for accurate calculations
- Not optimized for high-frequency scalping strategies
- Central bank interventions may temporarily distort signals
- Performance assumes normal market conditions with behavioral adjustments
### Academic References
- Branson, W. H., & Henderson, D. W. (1985). "The Specification and Influence of Asset Markets"
- Burnside, C., Eichenbaum, M., & Rebelo, S. (2011). "Carry Trade and Momentum in Currency Markets"
- Cassel, G. (1918). "Abnormal Deviations in International Exchanges"
- Fama, E. F. (1984). "Forward and Spot Exchange Rates"
- Froot, K. A., & Rogoff, K. (1995). "Perspectives on PPP and Long-Run Real Exchange Rates"
- Jegadeesh, N., & Titman, S. (1993). "Returns to Buying Winners and Selling Losers"
- Lustig, H., Roussanov, N., & Verdelhan, A. (2007). "Common Risk Factors in Currency Markets"
- Markowitz, H. (1952). "Portfolio Selection"
- Menkhoff, L., Sarno, L., Schmeling, M., & Schrimpf, A. (2012). "Carry Trades and Global FX Volatility"
- Sharpe, W. F. (1994). "The Sharpe Ratio"
- Wilder, J. W. (1978). "New Concepts in Technical Trading Systems"
### Usage Instructions
1. **Setup**: Add the indicator to your chart and select your preferred data source
2. **Currency Selection**: Choose which currencies to analyze (default: all major currencies)
3. **Methodology**: Select calculation method (Composite recommended for most users)
4. **Monitoring**: Watch the dashboard for strength changes and best pair opportunities
5. **Alerts**: Set up notifications for strong/weak currency conditions
Normalized Open InterestNormalized Open Interest (nOI) — Indicator Overview
What it does
Normalized Open Interest (nOI) transforms raw futures open-interest data into a 0-to-100 oscillator, so you can see at a glance whether participation is unusually high or low—similar in spirit to an RSI but applied to open interest. The script positions today’s OI inside a rolling high–low range and paints it with contextual colours.
Core logic
Data source – Loads the built-in “_OI” symbol that TradingView provides for the current market.
Rolling range – Looks back a user-defined number of bars (default 500) to find the highest and lowest OI in that window.
Normalization – Calculates
nOI = (OI – lowest) / (highest – lowest) × 100
so 0 equals the minimum of the window and 100 equals the maximum.
Visual cues – Plots the oscillator plus fixed horizontal levels at 70 % and 30 % (or your own numbers). The line turns teal above the upper level, red below the lower, and neutral grey in between.
User inputs
Window Length (bars) – How many candles the indicator scans for the high–low range; larger numbers smooth the curve, smaller numbers make it more reactive.
Upper Threshold (%) – Default 70. Anything above this marks potentially crowded or overheated interest.
Lower Threshold (%) – Default 30. Anything below this marks low or capitulating interest.
Practical uses
Spot extremes – Values above the upper line can warn that the long side is crowded; values below the lower line suggest disinterest or short-side crowding.
Confirm breakouts – A price breakout backed by a sharp rise in nOI signals genuine engagement.
Look for divergences – If price makes a new high but nOI does not, participation might be fading.
Combine with volume or RSI – Layer nOI with other studies to filter false signals.
Tips
On intraday charts for non-crypto symbols the script automatically fetches daily OI data to avoid gaps.
Adjust the thresholds to 80/20 or 60/40 to fit your market and risk preferences.
Alerts, shading, or additional signal logic can be added easily because the oscillator is already normalised.
Yield Curve Regime Shading with LegendTakes two symbols (e.g. two futures contracts, two FX pairs, etc.) as inputs.
Calculates the “regime” as the sign of the change in their difference over an n‑period lookback.
Lets you choose whether you want to color the bars themselves or shade the background.
How it works
Inputs
symbolA, symbolB: the two tickers you’re comparing.
n: lookback in bars to measure the change in the spread.
mode: pick between “Shading” or “Candle Color”.
Data fetching
We use request.security() to pull each series at the chart’s timeframe.
Regime calculation
spread = priceA – priceB
spreadPrev = ta.valuewhen(not na(spread), spread , 0) (i.e. the spread n bars ago)
If spread > spreadPrev → bullish regime
If spread < spreadPrev → bearish regime
Plotting
Shading: apply bgcolor() in green/red.
Candle Color: use barcolor() to override the bar color.
Tensor Market Analysis Engine (TMAE)# Tensor Market Analysis Engine (TMAE)
## Advanced Multi-Dimensional Mathematical Analysis System
*Where Quantum Mathematics Meets Market Structure*
---
## 🎓 THEORETICAL FOUNDATION
The Tensor Market Analysis Engine represents a revolutionary synthesis of three cutting-edge mathematical frameworks that have never before been combined for comprehensive market analysis. This indicator transcends traditional technical analysis by implementing advanced mathematical concepts from quantum mechanics, information theory, and fractal geometry.
### 🌊 Multi-Dimensional Volatility with Jump Detection
**Hawkes Process Implementation:**
The TMAE employs a sophisticated Hawkes process approximation for detecting self-exciting market jumps. Unlike traditional volatility measures that treat price movements as independent events, the Hawkes process recognizes that market shocks cluster and exhibit memory effects.
**Mathematical Foundation:**
```
Intensity λ(t) = μ + Σ α(t - Tᵢ)
```
Where market jumps at times Tᵢ increase the probability of future jumps through the decay function α, controlled by the Hawkes Decay parameter (0.5-0.99).
**Mahalanobis Distance Calculation:**
The engine calculates volatility jumps using multi-dimensional Mahalanobis distance across up to 5 volatility dimensions:
- **Dimension 1:** Price volatility (standard deviation of returns)
- **Dimension 2:** Volume volatility (normalized volume fluctuations)
- **Dimension 3:** Range volatility (high-low spread variations)
- **Dimension 4:** Correlation volatility (price-volume relationship changes)
- **Dimension 5:** Microstructure volatility (intrabar positioning analysis)
This creates a volatility state vector that captures market behavior impossible to detect with traditional single-dimensional approaches.
### 📐 Hurst Exponent Regime Detection
**Fractal Market Hypothesis Integration:**
The TMAE implements advanced Rescaled Range (R/S) analysis to calculate the Hurst exponent in real-time, providing dynamic regime classification:
- **H > 0.6:** Trending (persistent) markets - momentum strategies optimal
- **H < 0.4:** Mean-reverting (anti-persistent) markets - contrarian strategies optimal
- **H ≈ 0.5:** Random walk markets - breakout strategies preferred
**Adaptive R/S Analysis:**
Unlike static implementations, the TMAE uses adaptive windowing that adjusts to market conditions:
```
H = log(R/S) / log(n)
```
Where R is the range of cumulative deviations and S is the standard deviation over period n.
**Dynamic Regime Classification:**
The system employs hysteresis to prevent regime flipping, requiring sustained Hurst values before regime changes are confirmed. This prevents false signals during transitional periods.
### 🔄 Transfer Entropy Analysis
**Information Flow Quantification:**
Transfer entropy measures the directional flow of information between price and volume, revealing lead-lag relationships that indicate future price movements:
```
TE(X→Y) = Σ p(yₜ₊₁, yₜ, xₜ) log
```
**Causality Detection:**
- **Volume → Price:** Indicates accumulation/distribution phases
- **Price → Volume:** Suggests retail participation or momentum chasing
- **Balanced Flow:** Market equilibrium or transition periods
The system analyzes multiple lag periods (2-20 bars) to capture both immediate and structural information flows.
---
## 🔧 COMPREHENSIVE INPUT SYSTEM
### Core Parameters Group
**Primary Analysis Window (10-100, Default: 50)**
The fundamental lookback period affecting all calculations. Optimization by timeframe:
- **1-5 minute charts:** 20-30 (rapid adaptation to micro-movements)
- **15 minute-1 hour:** 30-50 (balanced responsiveness and stability)
- **4 hour-daily:** 50-100 (smooth signals, reduced noise)
- **Asset-specific:** Cryptocurrency 20-35, Stocks 35-50, Forex 40-60
**Signal Sensitivity (0.1-2.0, Default: 0.7)**
Master control affecting all threshold calculations:
- **Conservative (0.3-0.6):** High-quality signals only, fewer false positives
- **Balanced (0.7-1.0):** Optimal risk-reward ratio for most trading styles
- **Aggressive (1.1-2.0):** Maximum signal frequency, requires careful filtering
**Signal Generation Mode:**
- **Aggressive:** Any component signals (highest frequency)
- **Confluence:** 2+ components agree (balanced approach)
- **Conservative:** All 3 components align (highest quality)
### Volatility Jump Detection Group
**Volatility Dimensions (2-5, Default: 3)**
Determines the mathematical space complexity:
- **2D:** Price + Volume volatility (suitable for clean markets)
- **3D:** + Range volatility (optimal for most conditions)
- **4D:** + Correlation volatility (advanced multi-asset analysis)
- **5D:** + Microstructure volatility (maximum sensitivity)
**Jump Detection Threshold (1.5-4.0σ, Default: 3.0σ)**
Standard deviations required for volatility jump classification:
- **Cryptocurrency:** 2.0-2.5σ (naturally volatile)
- **Stock Indices:** 2.5-3.0σ (moderate volatility)
- **Forex Major Pairs:** 3.0-3.5σ (typically stable)
- **Commodities:** 2.0-3.0σ (varies by commodity)
**Jump Clustering Decay (0.5-0.99, Default: 0.85)**
Hawkes process memory parameter:
- **0.5-0.7:** Fast decay (jumps treated as independent)
- **0.8-0.9:** Moderate clustering (realistic market behavior)
- **0.95-0.99:** Strong clustering (crisis/event-driven markets)
### Hurst Exponent Analysis Group
**Calculation Method Options:**
- **Classic R/S:** Original Rescaled Range (fast, simple)
- **Adaptive R/S:** Dynamic windowing (recommended for trading)
- **DFA:** Detrended Fluctuation Analysis (best for noisy data)
**Trending Threshold (0.55-0.8, Default: 0.60)**
Hurst value defining persistent market behavior:
- **0.55-0.60:** Weak trend persistence
- **0.65-0.70:** Clear trending behavior
- **0.75-0.80:** Strong momentum regimes
**Mean Reversion Threshold (0.2-0.45, Default: 0.40)**
Hurst value defining anti-persistent behavior:
- **0.35-0.45:** Weak mean reversion
- **0.25-0.35:** Clear ranging behavior
- **0.15-0.25:** Strong reversion tendency
### Transfer Entropy Parameters Group
**Information Flow Analysis:**
- **Price-Volume:** Classic flow analysis for accumulation/distribution
- **Price-Volatility:** Risk flow analysis for sentiment shifts
- **Multi-Timeframe:** Cross-timeframe causality detection
**Maximum Lag (2-20, Default: 5)**
Causality detection window:
- **2-5 bars:** Immediate causality (scalping)
- **5-10 bars:** Short-term flow (day trading)
- **10-20 bars:** Structural flow (swing trading)
**Significance Threshold (0.05-0.3, Default: 0.15)**
Minimum entropy for signal generation:
- **0.05-0.10:** Detect subtle information flows
- **0.10-0.20:** Clear causality only
- **0.20-0.30:** Very strong flows only
---
## 🎨 ADVANCED VISUAL SYSTEM
### Tensor Volatility Field Visualization
**Five-Layer Resonance Bands:**
The tensor field creates dynamic support/resistance zones that expand and contract based on mathematical field strength:
- **Core Layer (Purple):** Primary tensor field with highest intensity
- **Layer 2 (Neutral):** Secondary mathematical resonance
- **Layer 3 (Info Blue):** Tertiary harmonic frequencies
- **Layer 4 (Warning Gold):** Outer field boundaries
- **Layer 5 (Success Green):** Maximum field extension
**Field Strength Calculation:**
```
Field Strength = min(3.0, Mahalanobis Distance × Tensor Intensity)
```
The field amplitude adjusts to ATR and mathematical distance, creating dynamic zones that respond to market volatility.
**Radiation Line Network:**
During active tensor states, the system projects directional radiation lines showing field energy distribution:
- **8 Directional Rays:** Complete angular coverage
- **Tapering Segments:** Progressive transparency for natural visual flow
- **Pulse Effects:** Enhanced visualization during volatility jumps
### Dimensional Portal System
**Portal Mathematics:**
Dimensional portals visualize regime transitions using category theory principles:
- **Green Portals (◉):** Trending regime detection (appear below price for support)
- **Red Portals (◎):** Mean-reverting regime (appear above price for resistance)
- **Yellow Portals (○):** Random walk regime (neutral positioning)
**Tensor Trail Effects:**
Each portal generates 8 trailing particles showing mathematical momentum:
- **Large Particles (●):** Strong mathematical signal
- **Medium Particles (◦):** Moderate signal strength
- **Small Particles (·):** Weak signal continuation
- **Micro Particles (˙):** Signal dissipation
### Information Flow Streams
**Particle Stream Visualization:**
Transfer entropy creates flowing particle streams indicating information direction:
- **Upward Streams:** Volume leading price (accumulation phases)
- **Downward Streams:** Price leading volume (distribution phases)
- **Stream Density:** Proportional to information flow strength
**15-Particle Evolution:**
Each stream contains 15 particles with progressive sizing and transparency, creating natural flow visualization that makes information transfer immediately apparent.
### Fractal Matrix Grid System
**Multi-Timeframe Fractal Levels:**
The system calculates and displays fractal highs/lows across five Fibonacci periods:
- **8-Period:** Short-term fractal structure
- **13-Period:** Intermediate-term patterns
- **21-Period:** Primary swing levels
- **34-Period:** Major structural levels
- **55-Period:** Long-term fractal boundaries
**Triple-Layer Visualization:**
Each fractal level uses three-layer rendering:
- **Shadow Layer:** Widest, darkest foundation (width 5)
- **Glow Layer:** Medium white core line (width 3)
- **Tensor Layer:** Dotted mathematical overlay (width 1)
**Intelligent Labeling System:**
Smart spacing prevents label overlap using ATR-based minimum distances. Labels include:
- **Fractal Period:** Time-based identification
- **Topological Class:** Mathematical complexity rating (0, I, II, III)
- **Price Level:** Exact fractal price
- **Mahalanobis Distance:** Current mathematical field strength
- **Hurst Exponent:** Current regime classification
- **Anomaly Indicators:** Visual strength representations (○ ◐ ● ⚡)
### Wick Pressure Analysis
**Rejection Level Mathematics:**
The system analyzes candle wick patterns to project future pressure zones:
- **Upper Wick Analysis:** Identifies selling pressure and resistance zones
- **Lower Wick Analysis:** Identifies buying pressure and support zones
- **Pressure Projection:** Extends lines forward based on mathematical probability
**Multi-Layer Glow Effects:**
Wick pressure lines use progressive transparency (1-8 layers) creating natural glow effects that make pressure zones immediately visible without cluttering the chart.
### Enhanced Regime Background
**Dynamic Intensity Mapping:**
Background colors reflect mathematical regime strength:
- **Deep Transparency (98% alpha):** Subtle regime indication
- **Pulse Intensity:** Based on regime strength calculation
- **Color Coding:** Green (trending), Red (mean-reverting), Neutral (random)
**Smoothing Integration:**
Regime changes incorporate 10-bar smoothing to prevent background flicker while maintaining responsiveness to genuine regime shifts.
### Color Scheme System
**Six Professional Themes:**
- **Dark (Default):** Professional trading environment optimization
- **Light:** High ambient light conditions
- **Classic:** Traditional technical analysis appearance
- **Neon:** High-contrast visibility for active trading
- **Neutral:** Minimal distraction focus
- **Bright:** Maximum visibility for complex setups
Each theme maintains mathematical accuracy while optimizing visual clarity for different trading environments and personal preferences.
---
## 📊 INSTITUTIONAL-GRADE DASHBOARD
### Tensor Field Status Section
**Field Strength Display:**
Real-time Mahalanobis distance calculation with dynamic emoji indicators:
- **⚡ (Lightning):** Extreme field strength (>1.5× threshold)
- **● (Solid Circle):** Strong field activity (>1.0× threshold)
- **○ (Open Circle):** Normal field state
**Signal Quality Rating:**
Democratic algorithm assessment:
- **ELITE:** All 3 components aligned (highest probability)
- **STRONG:** 2 components aligned (good probability)
- **GOOD:** 1 component active (moderate probability)
- **WEAK:** No clear component signals
**Threshold and Anomaly Monitoring:**
- **Threshold Display:** Current mathematical threshold setting
- **Anomaly Level (0-100%):** Combined volatility and volume spike measurement
- **>70%:** High anomaly (red warning)
- **30-70%:** Moderate anomaly (orange caution)
- **<30%:** Normal conditions (green confirmation)
### Tensor State Analysis Section
**Mathematical State Classification:**
- **↑ BULL (Tensor State +1):** Trending regime with bullish bias
- **↓ BEAR (Tensor State -1):** Mean-reverting regime with bearish bias
- **◈ SUPER (Tensor State 0):** Random walk regime (neutral)
**Visual State Gauge:**
Five-circle progression showing tensor field polarity:
- **🟢🟢🟢⚪⚪:** Strong bullish mathematical alignment
- **⚪⚪🟡⚪⚪:** Neutral/transitional state
- **⚪⚪🔴🔴🔴:** Strong bearish mathematical alignment
**Trend Direction and Phase Analysis:**
- **📈 BULL / 📉 BEAR / ➡️ NEUTRAL:** Primary trend classification
- **🌪️ CHAOS:** Extreme information flow (>2.0 flow strength)
- **⚡ ACTIVE:** Strong information flow (1.0-2.0 flow strength)
- **😴 CALM:** Low information flow (<1.0 flow strength)
### Trading Signals Section
**Real-Time Signal Status:**
- **🟢 ACTIVE / ⚪ INACTIVE:** Long signal availability
- **🔴 ACTIVE / ⚪ INACTIVE:** Short signal availability
- **Components (X/3):** Active algorithmic components
- **Mode Display:** Current signal generation mode
**Signal Strength Visualization:**
Color-coded component count:
- **Green:** 3/3 components (maximum confidence)
- **Aqua:** 2/3 components (good confidence)
- **Orange:** 1/3 components (moderate confidence)
- **Gray:** 0/3 components (no signals)
### Performance Metrics Section
**Win Rate Monitoring:**
Estimated win rates based on signal quality with emoji indicators:
- **🔥 (Fire):** ≥60% estimated win rate
- **👍 (Thumbs Up):** 45-59% estimated win rate
- **⚠️ (Warning):** <45% estimated win rate
**Mathematical Metrics:**
- **Hurst Exponent:** Real-time fractal dimension (0.000-1.000)
- **Information Flow:** Volume/price leading indicators
- **📊 VOL:** Volume leading price (accumulation/distribution)
- **💰 PRICE:** Price leading volume (momentum/speculation)
- **➖ NONE:** Balanced information flow
- **Volatility Classification:**
- **🔥 HIGH:** Above 1.5× jump threshold
- **📊 NORM:** Normal volatility range
- **😴 LOW:** Below 0.5× jump threshold
### Market Structure Section (Large Dashboard)
**Regime Classification:**
- **📈 TREND:** Hurst >0.6, momentum strategies optimal
- **🔄 REVERT:** Hurst <0.4, contrarian strategies optimal
- **🎲 RANDOM:** Hurst ≈0.5, breakout strategies preferred
**Mathematical Field Analysis:**
- **Dimensions:** Current volatility space complexity (2D-5D)
- **Hawkes λ (Lambda):** Self-exciting jump intensity (0.00-1.00)
- **Jump Status:** 🚨 JUMP (active) / ✅ NORM (normal)
### Settings Summary Section (Large Dashboard)
**Active Configuration Display:**
- **Sensitivity:** Current master sensitivity setting
- **Lookback:** Primary analysis window
- **Theme:** Active color scheme
- **Method:** Hurst calculation method (Classic R/S, Adaptive R/S, DFA)
**Dashboard Sizing Options:**
- **Small:** Essential metrics only (mobile/small screens)
- **Normal:** Balanced information density (standard desktop)
- **Large:** Maximum detail (multi-monitor setups)
**Position Options:**
- **Top Right:** Standard placement (avoids price action)
- **Top Left:** Wide chart optimization
- **Bottom Right:** Recent price focus (scalping)
- **Bottom Left:** Maximum price visibility (swing trading)
---
## 🎯 SIGNAL GENERATION LOGIC
### Multi-Component Convergence System
**Component Signal Architecture:**
The TMAE generates signals through sophisticated component analysis rather than simple threshold crossing:
**Volatility Component:**
- **Jump Detection:** Mahalanobis distance threshold breach
- **Hawkes Intensity:** Self-exciting process activation (>0.2)
- **Multi-dimensional:** Considers all volatility dimensions simultaneously
**Hurst Regime Component:**
- **Trending Markets:** Price above SMA-20 with positive momentum
- **Mean-Reverting Markets:** Price at Bollinger Band extremes
- **Random Markets:** Bollinger squeeze breakouts with directional confirmation
**Transfer Entropy Component:**
- **Volume Leadership:** Information flow from volume to price
- **Volume Spike:** Volume 110%+ above 20-period average
- **Flow Significance:** Above entropy threshold with directional bias
### Democratic Signal Weighting
**Signal Mode Implementation:**
- **Aggressive Mode:** Any single component triggers signal
- **Confluence Mode:** Minimum 2 components must agree
- **Conservative Mode:** All 3 components must align
**Momentum Confirmation:**
All signals require momentum confirmation:
- **Long Signals:** RSI >50 AND price >EMA-9
- **Short Signals:** RSI <50 AND price 0.6):**
- **Increase Sensitivity:** Catch momentum continuation
- **Lower Mean Reversion Threshold:** Avoid counter-trend signals
- **Emphasize Volume Leadership:** Institutional accumulation/distribution
- **Tensor Field Focus:** Use expansion for trend continuation
- **Signal Mode:** Aggressive or Confluence for trend following
**Range-Bound Markets (Hurst <0.4):**
- **Decrease Sensitivity:** Avoid false breakouts
- **Lower Trending Threshold:** Quick regime recognition
- **Focus on Price Leadership:** Retail sentiment extremes
- **Fractal Grid Emphasis:** Support/resistance trading
- **Signal Mode:** Conservative for high-probability reversals
**Volatile Markets (High Jump Frequency):**
- **Increase Hawkes Decay:** Recognize event clustering
- **Higher Jump Threshold:** Avoid noise signals
- **Maximum Dimensions:** Capture full volatility complexity
- **Reduce Position Sizing:** Risk management adaptation
- **Enhanced Visuals:** Maximum information for rapid decisions
**Low Volatility Markets (Low Jump Frequency):**
- **Decrease Jump Threshold:** Capture subtle movements
- **Lower Hawkes Decay:** Treat moves as independent
- **Reduce Dimensions:** Simplify analysis
- **Increase Position Sizing:** Capitalize on compressed volatility
- **Minimal Visuals:** Reduce distraction in quiet markets
---
## 🚀 ADVANCED TRADING STRATEGIES
### The Mathematical Convergence Method
**Entry Protocol:**
1. **Fractal Grid Approach:** Monitor price approaching significant fractal levels
2. **Tensor Field Confirmation:** Verify field expansion supporting direction
3. **Portal Signal:** Wait for dimensional portal appearance
4. **ELITE/STRONG Quality:** Only trade highest quality mathematical signals
5. **Component Consensus:** Confirm 2+ components agree in Confluence mode
**Example Implementation:**
- Price approaching 21-period fractal high
- Tensor field expanding upward (bullish mathematical alignment)
- Green portal appears below price (trending regime confirmation)
- ELITE quality signal with 3/3 components active
- Enter long position with stop below fractal level
**Risk Management:**
- **Stop Placement:** Below/above fractal level that generated signal
- **Position Sizing:** Based on Mahalanobis distance (higher distance = smaller size)
- **Profit Targets:** Next fractal level or tensor field resistance
### The Regime Transition Strategy
**Regime Change Detection:**
1. **Monitor Hurst Exponent:** Watch for persistent moves above/below thresholds
2. **Portal Color Change:** Regime transitions show different portal colors
3. **Background Intensity:** Increasing regime background intensity
4. **Mathematical Confirmation:** Wait for regime confirmation (hysteresis)
**Trading Implementation:**
- **Trending Transitions:** Trade momentum breakouts, follow trend
- **Mean Reversion Transitions:** Trade range boundaries, fade extremes
- **Random Transitions:** Trade breakouts with tight stops
**Advanced Techniques:**
- **Multi-Timeframe:** Confirm regime on higher timeframe
- **Early Entry:** Enter on regime transition rather than confirmation
- **Regime Strength:** Larger positions during strong regime signals
### The Information Flow Momentum Strategy
**Flow Detection Protocol:**
1. **Monitor Transfer Entropy:** Watch for significant information flow shifts
2. **Volume Leadership:** Strong edge when volume leads price
3. **Flow Acceleration:** Increasing flow strength indicates momentum
4. **Directional Confirmation:** Ensure flow aligns with intended trade direction
**Entry Signals:**
- **Volume → Price Flow:** Enter during accumulation/distribution phases
- **Price → Volume Flow:** Enter on momentum confirmation breaks
- **Flow Reversal:** Counter-trend entries when flow reverses
**Optimization:**
- **Scalping:** Use immediate flow detection (2-5 bar lag)
- **Swing Trading:** Use structural flow (10-20 bar lag)
- **Multi-Asset:** Compare flow between correlated assets
### The Tensor Field Expansion Strategy
**Field Mathematics:**
The tensor field expansion indicates mathematical pressure building in market structure:
**Expansion Phases:**
1. **Compression:** Field contracts, volatility decreases
2. **Tension Building:** Mathematical pressure accumulates
3. **Expansion:** Field expands rapidly with directional movement
4. **Resolution:** Field stabilizes at new equilibrium
**Trading Applications:**
- **Compression Trading:** Prepare for breakout during field contraction
- **Expansion Following:** Trade direction of field expansion
- **Reversion Trading:** Fade extreme field expansion
- **Multi-Dimensional:** Consider all field layers for confirmation
### The Hawkes Process Event Strategy
**Self-Exciting Jump Trading:**
Understanding that market shocks cluster and create follow-on opportunities:
**Jump Sequence Analysis:**
1. **Initial Jump:** First volatility jump detected
2. **Clustering Phase:** Hawkes intensity remains elevated
3. **Follow-On Opportunities:** Additional jumps more likely
4. **Decay Period:** Intensity gradually decreases
**Implementation:**
- **Jump Confirmation:** Wait for mathematical jump confirmation
- **Direction Assessment:** Use other components for direction
- **Clustering Trades:** Trade subsequent moves during high intensity
- **Decay Exit:** Exit positions as Hawkes intensity decays
### The Fractal Confluence System
**Multi-Timeframe Fractal Analysis:**
Combining fractal levels across different periods for high-probability zones:
**Confluence Zones:**
- **Double Confluence:** 2 fractal levels align
- **Triple Confluence:** 3+ fractal levels cluster
- **Mathematical Confirmation:** Tensor field supports the level
- **Information Flow:** Transfer entropy confirms direction
**Trading Protocol:**
1. **Identify Confluence:** Find 2+ fractal levels within 1 ATR
2. **Mathematical Support:** Verify tensor field alignment
3. **Signal Quality:** Wait for STRONG or ELITE signal
4. **Risk Definition:** Use fractal level for stop placement
5. **Profit Targeting:** Next major fractal confluence zone
---
## ⚠️ COMPREHENSIVE RISK MANAGEMENT
### Mathematical Position Sizing
**Mahalanobis Distance Integration:**
Position size should inversely correlate with mathematical field strength:
```
Position Size = Base Size × (Threshold / Mahalanobis Distance)
```
**Risk Scaling Matrix:**
- **Low Field Strength (<2.0):** Standard position sizing
- **Moderate Field Strength (2.0-3.0):** 75% position sizing
- **High Field Strength (3.0-4.0):** 50% position sizing
- **Extreme Field Strength (>4.0):** 25% position sizing or no trade
### Signal Quality Risk Adjustment
**Quality-Based Position Sizing:**
- **ELITE Signals:** 100% of planned position size
- **STRONG Signals:** 75% of planned position size
- **GOOD Signals:** 50% of planned position size
- **WEAK Signals:** No position or paper trading only
**Component Agreement Scaling:**
- **3/3 Components:** Full position size
- **2/3 Components:** 75% position size
- **1/3 Components:** 50% position size or skip trade
### Regime-Adaptive Risk Management
**Trending Market Risk:**
- **Wider Stops:** Allow for trend continuation
- **Trend Following:** Trade with regime direction
- **Higher Position Size:** Trend probability advantage
- **Momentum Stops:** Trail stops based on momentum indicators
**Mean-Reverting Market Risk:**
- **Tighter Stops:** Quick exits on trend continuation
- **Contrarian Positioning:** Trade against extremes
- **Smaller Position Size:** Higher reversal failure rate
- **Level-Based Stops:** Use fractal levels for stops
**Random Market Risk:**
- **Breakout Focus:** Trade only clear breakouts
- **Tight Initial Stops:** Quick exit if breakout fails
- **Reduced Frequency:** Skip marginal setups
- **Range-Based Targets:** Profit targets at range boundaries
### Volatility-Adaptive Risk Controls
**High Volatility Periods:**
- **Reduced Position Size:** Account for wider price swings
- **Wider Stops:** Avoid noise-based exits
- **Lower Frequency:** Skip marginal setups
- **Faster Exits:** Take profits more quickly
**Low Volatility Periods:**
- **Standard Position Size:** Normal risk parameters
- **Tighter Stops:** Take advantage of compressed ranges
- **Higher Frequency:** Trade more setups
- **Extended Targets:** Allow for compressed volatility expansion
### Multi-Timeframe Risk Alignment
**Higher Timeframe Trend:**
- **With Trend:** Standard or increased position size
- **Against Trend:** Reduced position size or skip
- **Neutral Trend:** Standard position size with tight management
**Risk Hierarchy:**
1. **Primary:** Current timeframe signal quality
2. **Secondary:** Higher timeframe trend alignment
3. **Tertiary:** Mathematical field strength
4. **Quaternary:** Market regime classification
---
## 📚 EDUCATIONAL VALUE AND MATHEMATICAL CONCEPTS
### Advanced Mathematical Concepts
**Tensor Analysis in Markets:**
The TMAE introduces traders to tensor analysis, a branch of mathematics typically reserved for physics and advanced engineering. Tensors provide a framework for understanding multi-dimensional market relationships that scalar and vector analysis cannot capture.
**Information Theory Applications:**
Transfer entropy implementation teaches traders about information flow in markets, a concept from information theory that quantifies directional causality between variables. This provides intuition about market microstructure and participant behavior.
**Fractal Geometry in Trading:**
The Hurst exponent calculation exposes traders to fractal geometry concepts, helping understand that markets exhibit self-similar patterns across multiple timeframes. This mathematical insight transforms how traders view market structure.
**Stochastic Process Theory:**
The Hawkes process implementation introduces concepts from stochastic process theory, specifically self-exciting point processes. This provides mathematical framework for understanding why market events cluster and exhibit memory effects.
### Learning Progressive Complexity
**Beginner Mathematical Concepts:**
- **Volatility Dimensions:** Understanding multi-dimensional analysis
- **Regime Classification:** Learning market personality types
- **Signal Democracy:** Algorithmic consensus building
- **Visual Mathematics:** Interpreting mathematical concepts visually
**Intermediate Mathematical Applications:**
- **Mahalanobis Distance:** Statistical distance in multi-dimensional space
- **Rescaled Range Analysis:** Fractal dimension measurement
- **Information Entropy:** Quantifying uncertainty and causality
- **Field Theory:** Understanding mathematical fields in market context
**Advanced Mathematical Integration:**
- **Tensor Field Dynamics:** Multi-dimensional market force analysis
- **Stochastic Self-Excitation:** Event clustering and memory effects
- **Categorical Composition:** Mathematical signal combination theory
- **Topological Market Analysis:** Understanding market shape and connectivity
### Practical Mathematical Intuition
**Developing Market Mathematics Intuition:**
The TMAE serves as a bridge between abstract mathematical concepts and practical trading applications. Traders develop intuitive understanding of:
- **How markets exhibit mathematical structure beneath apparent randomness**
- **Why multi-dimensional analysis reveals patterns invisible to single-variable approaches**
- **How information flows through markets in measurable, predictable ways**
- **Why mathematical models provide probabilistic edges rather than certainties**
---
## 🔬 IMPLEMENTATION AND OPTIMIZATION
### Getting Started Protocol
**Phase 1: Observation (Week 1)**
1. **Apply with defaults:** Use standard settings on your primary trading timeframe
2. **Study visual elements:** Learn to interpret tensor fields, portals, and streams
3. **Monitor dashboard:** Observe how metrics change with market conditions
4. **No trading:** Focus entirely on pattern recognition and understanding
**Phase 2: Pattern Recognition (Week 2-3)**
1. **Identify signal patterns:** Note what market conditions produce different signal qualities
2. **Regime correlation:** Observe how Hurst regimes affect signal performance
3. **Visual confirmation:** Learn to read tensor field expansion and portal signals
4. **Component analysis:** Understand which components drive signals in different markets
**Phase 3: Parameter Optimization (Week 4-5)**
1. **Asset-specific tuning:** Adjust parameters for your specific trading instrument
2. **Timeframe optimization:** Fine-tune for your preferred trading timeframe
3. **Sensitivity adjustment:** Balance signal frequency with quality
4. **Visual customization:** Optimize colors and intensity for your trading environment
**Phase 4: Live Implementation (Week 6+)**
1. **Paper trading:** Test signals with hypothetical trades
2. **Small position sizing:** Begin with minimal risk during learning phase
3. **Performance tracking:** Monitor actual vs. expected signal performance
4. **Continuous optimization:** Refine settings based on real performance data
### Performance Monitoring System
**Signal Quality Tracking:**
- **ELITE Signal Win Rate:** Track highest quality signals separately
- **Component Performance:** Monitor which components provide best signals
- **Regime Performance:** Analyze performance across different market regimes
- **Timeframe Analysis:** Compare performance across different session times
**Mathematical Metric Correlation:**
- **Field Strength vs. Performance:** Higher field strength should correlate with better performance
- **Component Agreement vs. Win Rate:** More component agreement should improve win rates
- **Regime Alignment vs. Success:** Trading with mathematical regime should outperform
### Continuous Optimization Process
**Monthly Review Protocol:**
1. **Performance Analysis:** Review win rates, profit factors, and maximum drawdown
2. **Parameter Assessment:** Evaluate if current settings remain optimal
3. **Market Adaptation:** Adjust for changes in market character or volatility
4. **Component Weighting:** Consider if certain components should receive more/less emphasis
**Quarterly Deep Analysis:**
1. **Mathematical Model Validation:** Verify that mathematical relationships remain valid
2. **Regime Distribution:** Analyze time spent in different market regimes
3. **Signal Evolution:** Track how signal characteristics change over time
4. **Correlation Analysis:** Monitor correlations between different mathematical components
---
## 🌟 UNIQUE INNOVATIONS AND CONTRIBUTIONS
### Revolutionary Mathematical Integration
**First-Ever Implementations:**
1. **Multi-Dimensional Volatility Tensor:** First indicator to implement true tensor analysis for market volatility
2. **Real-Time Hawkes Process:** First trading implementation of self-exciting point processes
3. **Transfer Entropy Trading Signals:** First practical application of information theory for trade generation
4. **Democratic Component Voting:** First algorithmic consensus system for signal generation
5. **Fractal-Projected Signal Quality:** First system to predict signal quality at future price levels
### Advanced Visualization Innovations
**Mathematical Visualization Breakthroughs:**
- **Tensor Field Radiation:** Visual representation of mathematical field energy
- **Dimensional Portal System:** Category theory visualization for regime transitions
- **Information Flow Streams:** Real-time visual display of market information transfer
- **Multi-Layer Fractal Grid:** Intelligent spacing and projection system
- **Regime Intensity Mapping:** Dynamic background showing mathematical regime strength
### Practical Trading Innovations
**Trading System Advances:**
- **Quality-Weighted Signal Generation:** Signals rated by mathematical confidence
- **Regime-Adaptive Strategy Selection:** Automatic strategy optimization based on market personality
- **Anti-Spam Signal Protection:** Mathematical prevention of signal clustering
- **Component Performance Tracking:** Real-time monitoring of algorithmic component success
- **Field-Strength Position Sizing:** Mathematical volatility integration for risk management
---
## ⚖️ RESPONSIBLE USAGE AND LIMITATIONS
### Mathematical Model Limitations
**Understanding Model Boundaries:**
While the TMAE implements sophisticated mathematical concepts, traders must understand fundamental limitations:
- **Markets Are Not Purely Mathematical:** Human psychology, news events, and fundamental factors create unpredictable elements
- **Past Performance Limitations:** Mathematical relationships that worked historically may not persist indefinitely
- **Model Risk:** Complex models can fail during unprecedented market conditions
- **Overfitting Potential:** Highly optimized parameters may not generalize to future market conditions
### Proper Implementation Guidelines
**Risk Management Requirements:**
- **Never Risk More Than 2% Per Trade:** Regardless of signal quality
- **Diversification Mandatory:** Don't rely solely on mathematical signals
- **Position Sizing Discipline:** Use mathematical field strength for sizing, not confidence
- **Stop Loss Non-Negotiable:** Every trade must have predefined risk parameters
**Realistic Expectations:**
- **Mathematical Edge, Not Certainty:** The indicator provides probabilistic advantages, not guaranteed outcomes
- **Learning Curve Required:** Complex mathematical concepts require time to master
- **Market Adaptation Necessary:** Parameters must evolve with changing market conditions
- **Continuous Education Important:** Understanding underlying mathematics improves application
### Ethical Trading Considerations
**Market Impact Awareness:**
- **Information Asymmetry:** Advanced mathematical analysis may provide advantages over other market participants
- **Position Size Responsibility:** Large positions based on mathematical signals can impact market structure
- **Sharing Knowledge:** Consider educational contributions to trading community
- **Fair Market Participation:** Use mathematical advantages responsibly within market framework
### Professional Development Path
**Skill Development Sequence:**
1. **Basic Mathematical Literacy:** Understand fundamental concepts before advanced application
2. **Risk Management Mastery:** Develop disciplined risk control before relying on complex signals
3. **Market Psychology Understanding:** Combine mathematical analysis with behavioral market insights
4. **Continuous Learning:** Stay updated on mathematical finance developments and market evolution
---
## 🔮 CONCLUSION
The Tensor Market Analysis Engine represents a quantum leap forward in technical analysis, successfully bridging the gap between advanced pure mathematics and practical trading applications. By integrating multi-dimensional volatility analysis, fractal market theory, and information flow dynamics, the TMAE reveals market structure invisible to conventional analysis while maintaining visual clarity and practical usability.
### Mathematical Innovation Legacy
This indicator establishes new paradigms in technical analysis:
- **Tensor analysis for market volatility understanding**
- **Stochastic self-excitation for event clustering prediction**
- **Information theory for causality-based trade generation**
- **Democratic algorithmic consensus for signal quality enhancement**
- **Mathematical field visualization for intuitive market understanding**
### Practical Trading Revolution
Beyond mathematical innovation, the TMAE transforms practical trading:
- **Quality-rated signals replace binary buy/sell decisions**
- **Regime-adaptive strategies automatically optimize for market personality**
- **Multi-dimensional risk management integrates mathematical volatility measures**
- **Visual mathematical concepts make complex analysis immediately interpretable**
- **Educational value creates lasting improvement in trading understanding**
### Future-Proof Design
The mathematical foundations ensure lasting relevance:
- **Universal mathematical principles transcend market evolution**
- **Multi-dimensional analysis adapts to new market structures**
- **Regime detection automatically adjusts to changing market personalities**
- **Component democracy allows for future algorithmic additions**
- **Mathematical visualization scales with increasing market complexity**
### Commitment to Excellence
The TMAE represents more than an indicator—it embodies a philosophy of bringing rigorous mathematical analysis to trading while maintaining practical utility and visual elegance. Every component, from the multi-dimensional tensor fields to the democratic signal generation, reflects a commitment to mathematical accuracy, trading practicality, and educational value.
### Trading with Mathematical Precision
In an era where markets grow increasingly complex and computational, the TMAE provides traders with mathematical tools previously available only to institutional quantitative research teams. Yet unlike academic mathematical models, the TMAE translates complex concepts into intuitive visual representations and practical trading signals.
By combining the mathematical rigor of tensor analysis, the statistical power of multi-dimensional volatility modeling, and the information-theoretic insights of transfer entropy, traders gain unprecedented insight into market structure and dynamics.
### Final Perspective
Markets, like nature, exhibit profound mathematical beauty beneath apparent chaos. The Tensor Market Analysis Engine serves as a mathematical lens that reveals this hidden order, transforming how traders perceive and interact with market structure.
Through mathematical precision, visual elegance, and practical utility, the TMAE empowers traders to see beyond the noise and trade with the confidence that comes from understanding the mathematical principles governing market behavior.
Trade with mathematical insight. Trade with the power of tensors. Trade with the TMAE.
*"In mathematics, you don't understand things. You just get used to them." - John von Neumann*
*With the TMAE, mathematical market understanding becomes not just possible, but intuitive.*
— Dskyz, Trade with insight. Trade with anticipation.
Open Interest-RSI + Funding + Fractal DivergencesIndicator — “Open Interest-RSI + Funding + Fractal Divergences”
A multi-factor oscillator that fuses Open-Interest RSI, real-time Funding-Rate data and price/OI fractal divergences.
It paints BUY/SELL arrows in its own pane and directly on the price chart, helping you spot spots where crowd positioning, leverage costs and price action contradict each other.
1 Purpose
OI-RSI – measures conviction behind position changes instead of price momentum.
Funding Rate – shows who pays to hold positions (longs → bull bias, shorts → bear bias).
Fractal Divergences – detects HH/LL in price that are not confirmed by OI-RSI.
Optional Funding filter – hides signals when funding is already extreme.
Together these elements highlight exhaustion points and potential mean-reversion trades.
2 Inputs
RSI / Divergence
RSI length – default 14.
High-OI level / Low-OI level – default 70 / 30.
Fractal period n – default 2 (swing width).
Fractals to compare – how many past swings to scan, default 3.
Max visible arrows – keeps last 50 BUY/SELL arrows for speed.
Funding Rate
mode – choose FR, Avg Premium, Premium Index, Avg Prem + PI or FR-candle.
Visual scale (×) – multiplies raw funding to fit 0-100 oscillator scale (default 10).
specify symbol – enable only if funding symbol differs from chart.
use lower tf – averages 1-min premiums for smoother intraday view.
show table – tiny two-row widget at chart edge.
Signal Filter
Use Funding filter – ON hides long signals when funding > Buy-threshold and short signals when funding < Sell-threshold.
BUY threshold (%) – default 0.00 (raw %).
SELL threshold (%) – default 0.00 (raw %).
(Enter funding thresholds as raw percentages, e.g. 0.01 = +0.01 %).
3 Visual Outputs
Sub-pane
Aqua OI-RSI curve with 70 / 50 / 30 reference lines.
Funding visualised according to selected mode (green above 0, red below 0, or other).
BUY / SELL arrows at oscillator extremes.
Price chart
Identical BUY / SELL arrows plotted with force_overlay = true above/below candles that formed qualifying fractals.
Optional table
Shows current asset ticker and latest funding value of the chosen mode.
4 Signal Logic (Summary)
Load _OI series and compute RSI.
Retrieve Funding-Rate + Premium Index (optionally from lower TF).
Find fractal swings (n bars left & right).
Check divergence:
Bearish – price HH + OI-RSI LH.
Bullish – price LL + OI-RSI HL.
If Funding-filter enabled, require funding < Buy-thr (long) or > Sell-thr (short).
Plot arrows and trigger two built-in alerts (Bearish OI-RSI divergence, Bullish OI-RSI divergence).
Signals are fixed once the fractal bar closes; they do not repaint afterwards.
5 How to Use
Attach to a liquid perpetual-futures chart (BTC, ETH, major Binance contracts).
If _OI or funding series is missing you’ll see an error.
Choose timeframe:
15 m – 4 h for intraday;
1 D+ for swing trades.
Lower TFs → more signals; raise Fractals to compare or use Funding filter to trim noise.
Trade checklist
Funding positive and rising → longs overcrowded.
Price makes higher high; OI-RSI makes lower high; Funding above Sell-threshold → consider short.
Reverse logic for longs.
Combine with trend filter (EMA ribbon, SuperTrend, etc.) so you fade only when price is stretched.
Automation – set TradingView alerts on the two alertconditions and send to webhooks/bots.
Performance tips
Keep Max visible arrows ≤ 50.
Disable lower-TF premium aggregation if script feels heavy.
6 Limitations
Some symbols lack _OI or funding history → script stops with a console message.
Binance Premium Index begins mid-2020; older dates show na.
Divergences confirm only after n bars (no forward repaint).
7 Changelog
v1.0 – 10 Jun 2025
Initial public release.
Added price-chart arrows via force_overlay.
Donchian x WMA Crossover (2025 Only, Adjustable TP, Real OHLC)Short Description:
Long-only breakout system that goes long when the Donchian Low crosses up through a Weighted Moving Average, and closes when it crosses back down (with an optional take-profit), restricted to calendar year 2025. All signals use the instrument’s true OHLC data (even on Heikin-Ashi charts), start with 1 000 AUD of capital, and deploy 100 % equity per trade.
Ideal parameters configured for Temple & Webster on ASX 30 minute candles. Adjust parameter to suit however best to download candle interval data and have GPT test the pine script for optimum parameters for your trading symbol.
Detailed Description
1. Strategy Concept
This strategy captures trend-driven breakouts off the bottom of a Donchian channel. By combining the Donchian Low with a WMA filter, it aims to:
Enter when volatility compresses and price breaks above the recent Donchian Low while the longer‐term WMA confirms upward momentum.
Exit when price falls back below that same WMA (i.e. when the Donchian Low crosses back down through WMA), but only if the WMA itself has stopped rising.
Optional Take-Profit: you can specify a profit target in decimal form (e.g. 0.01 = 1 %).
2. Timeframe & Universe
In-sample period: only bars stamped between Jan 1 2025 00:00 UTC and Dec 31 2025 23:59 UTC are considered.
Any resolution (e.g. 30 m, 1 h, D, etc.) is supported—just set your preferred timeframe in the TradingView UI.
3. True-Price Execution
All indicator calculations (Donchian Low, WMA, crossover checks, take-profit) are sourced from the chart’s underlying OHLC via request.security(). This guarantees that:
You can view Heikin-Ashi or other styled candles, but your strategy will execute on the real OHLC bars.
Chart styling never suppresses or distorts your backtest results.
4. Position Sizing & Equity
Initial capital: 1 000 AUD
Size per trade: 100 % of available equity
No pyramiding: one open position at a time
5. Inputs (all exposed in the “Inputs” tab):
Input Default Description
Donchian Length 7 Number of bars to calculate the Donchian channel low
WMA Length 62 Period of the Weighted Moving Average filter
Take Profit (decimal) 0.01 Exit when price ≥ entry × (1 + take_profit_perc)
6. How It Works
Donchian Low: ta.lowest(low, DonchianLength) over the specified look-back.
WMA: ta.wma(close, WMALength) applied to true closes.
Entry: ta.crossover(DonchianLow, WMA) AND barTime ∈ 2025.
Exit:
Cross-down exit: ta.crossunder(DonchianLow, WMA) and WMA is not rising (i.e. momentum has stalled).
Take-profit exit: price ≥ entry × (1 + take_profit_perc).
Calendar exit: barTime falls outside 2025.
7. Usage Notes
After adding to your chart, open the Strategy Tester tab to review performance metrics, list of trades, equity curve, etc.
You can toggle your chart to Heikin-Ashi for visual clarity without affecting execution, thanks to the real-OHLC calls.
Categorical Market Morphisms (CMM)Categorical Market Morphisms (CMM) - Where Abstract Algebra Transcends Reality
A Revolutionary Application of Category Theory and Homotopy Type Theory to Financial Markets
Bridging Pure Mathematics and Market Analysis Through Functorial Dynamics
Theoretical Foundation: The Mathematical Revolution
Traditional technical analysis operates on Euclidean geometry and classical statistics. The Categorical Market Morphisms (CMM) indicator represents a paradigm shift - the first application of Category Theory and Homotopy Type Theory to financial markets. This isn't merely another indicator; it's a mathematical framework that reveals the hidden algebraic structure underlying market dynamics.
Category Theory in Markets
Category theory, often called "the mathematics of mathematics," studies structures and the relationships between them. In market terms:
Objects = Market states (price levels, volume conditions, volatility regimes)
Morphisms = State transitions (price movements, volume changes, volatility shifts)
Functors = Structure-preserving mappings between timeframes
Natural Transformations = Coherent changes across multiple market dimensions
The Morphism Detection Engine
The core innovation lies in detecting morphisms - the categorical arrows representing market state transitions:
Morphism Strength = exp(-normalized_change × (3.0 / sensitivity))
Threshold = 0.3 - (sensitivity - 1.0) × 0.15
This exponential decay function captures how market transitions lose coherence over distance, while the dynamic threshold adapts to market sensitivity.
Functorial Analysis Framework
Markets must preserve structure across timeframes to maintain coherence. Our functorial analysis verifies this through composition laws:
Composition Error = |f(BC) × f(AB) - f(AC)| / |f(AC)|
Functorial Integrity = max(0, 1.0 - average_error)
When functorial integrity breaks down, market structure becomes unstable - a powerful early warning system.
Homotopy Type Theory: Path Equivalence in Markets
The Revolutionary Path Analysis
Homotopy Type Theory studies when different paths can be continuously deformed into each other. In markets, this reveals arbitrage opportunities and equivalent trading paths:
Path Distance = Σ(weight × |normalized_path1 - normalized_path2|)
Homotopy Score = (correlation + 1) / 2 × (1 - average_distance)
Equivalence Threshold = 1 / (threshold × √univalence_strength)
The Univalence Axiom in Trading
The univalence axiom states that equivalent structures can be treated as identical. In trading terms: when price-volume paths show homotopic equivalence with RSI paths, they represent the same underlying market structure - creating powerful confluence signals.
Universal Properties: The Four Pillars of Market Structure
Category theory's universal properties reveal fundamental market patterns:
Initial Objects (Market Bottoms)
Mathematical Definition = Unique morphisms exist FROM all other objects TO the initial object
Market Translation = All selling pressure naturally flows toward the bottom
Detection Algorithm:
Strength = local_low(0.3) + oversold(0.2) + volume_surge(0.2) + momentum_reversal(0.2) + morphism_flow(0.1)
Signal = strength > 0.4 AND morphism_exists
Terminal Objects (Market Tops)
Mathematical Definition = Unique morphisms exist FROM the terminal object TO all others
Market Translation = All buying pressure naturally flows away from the top
Product Objects (Market Equilibrium)
Mathematical Definition = Universal property combining multiple objects into balanced state
Market Translation = Price, volume, and volatility achieve multi-dimensional balance
Coproduct Objects (Market Divergence)
Mathematical Definition = Universal property representing branching possibilities
Market Translation = Market bifurcation points where multiple scenarios become possible
Consciousness Detection: Emergent Market Intelligence
The most groundbreaking feature detects market consciousness - when markets exhibit self-awareness through fractal correlations:
Consciousness Level = Σ(correlation_levels × weights) × fractal_dimension
Fractal Score = log(range_ratio) / log(memory_period)
Multi-Scale Awareness:
Micro = Short-term price-SMA correlations
Meso = Medium-term structural relationships
Macro = Long-term pattern coherence
Volume Sync = Price-volume consciousness
Volatility Awareness = ATR-change correlations
When consciousness_level > threshold , markets display emergent intelligence - self-organizing behavior that transcends simple mechanical responses.
Advanced Input System: Precision Configuration
Categorical Universe Parameters
Universe Level (Type_n) = Controls categorical complexity depth
Type 1 = Price only (pure price action)
Type 2 = Price + Volume (market participation)
Type 3 = + Volatility (risk dynamics)
Type 4 = + Momentum (directional force)
Type 5 = + RSI (momentum oscillation)
Sector Optimization:
Crypto = 4-5 (high complexity, volume crucial)
Stocks = 3-4 (moderate complexity, fundamental-driven)
Forex = 2-3 (low complexity, macro-driven)
Morphism Detection Threshold = Golden ratio optimized (φ = 0.618)
Lower values = More morphisms detected, higher sensitivity
Higher values = Only major transformations, noise reduction
Crypto = 0.382-0.618 (high volatility accommodation)
Stocks = 0.618-1.0 (balanced detection)
Forex = 1.0-1.618 (macro-focused)
Functoriality Tolerance = φ⁻² = 0.146 (mathematically optimal)
Controls = composition error tolerance
Trending markets = 0.1-0.2 (strict structure preservation)
Ranging markets = 0.2-0.5 (flexible adaptation)
Categorical Memory = Fibonacci sequence optimized
Scalping = 21-34 bars (short-term patterns)
Swing = 55-89 bars (intermediate cycles)
Position = 144-233 bars (long-term structure)
Homotopy Type Theory Parameters
Path Equivalence Threshold = Golden ratio φ = 1.618
Volatile markets = 2.0-2.618 (accommodate noise)
Normal conditions = 1.618 (balanced)
Stable markets = 0.786-1.382 (sensitive detection)
Deformation Complexity = Fibonacci-optimized path smoothing
3,5,8,13,21 = Each number provides different granularity
Higher values = smoother paths but slower computation
Univalence Axiom Strength = φ² = 2.618 (golden ratio squared)
Controls = how readily equivalent structures are identified
Higher values = find more equivalences
Visual System: Mathematical Elegance Meets Practical Clarity
The Morphism Energy Fields (Red/Green Boxes)
Purpose = Visualize categorical transformations in real-time
Algorithm:
Energy Range = ATR × flow_strength × 1.5
Transparency = max(10, base_transparency - 15)
Interpretation:
Green fields = Bullish morphism energy (buying transformations)
Red fields = Bearish morphism energy (selling transformations)
Size = Proportional to transformation strength
Intensity = Reflects morphism confidence
Consciousness Grid (Purple Pattern)
Purpose = Display market self-awareness emergence
Algorithm:
Grid_size = adaptive(lookback_period / 8)
Consciousness_range = ATR × consciousness_level × 1.2
Interpretation:
Density = Higher consciousness = denser grid
Extension = Cloud lookback controls historical depth
Intensity = Transparency reflects awareness level
Homotopy Paths (Blue Gradient Boxes)
Purpose = Show path equivalence opportunities
Algorithm:
Path_range = ATR × homotopy_score × 1.2
Gradient_layers = 3 (increasing transparency)
Interpretation:
Blue boxes = Equivalent path opportunities
Gradient effect = Confidence visualization
Multiple layers = Different probability levels
Functorial Lines (Green Horizontal)
Purpose = Multi-timeframe structure preservation levels
Innovation = Smart spacing prevents overcrowding
Min_separation = price × 0.001 (0.1% minimum)
Max_lines = 3 (clarity preservation)
Features:
Glow effect = Background + foreground lines
Adaptive labels = Only show meaningful separations
Color coding = Green (preserved), Orange (stressed), Red (broken)
Signal System: Bull/Bear Precision
🐂 Initial Objects = Bottom formations with strength percentages
🐻 Terminal Objects = Top formations with confidence levels
⚪ Product/Coproduct = Equilibrium circles with glow effects
Professional Dashboard System
Main Analytics Dashboard (Top-Right)
Market State = Real-time categorical classification
INITIAL OBJECT = Bottom formation active
TERMINAL OBJECT = Top formation active
PRODUCT STATE = Market equilibrium
COPRODUCT STATE = Divergence/bifurcation
ANALYZING = Processing market structure
Universe Type = Current complexity level and components
Morphisms:
ACTIVE (X%) = Transformations detected, percentage shows strength
DORMANT = No significant categorical changes
Functoriality:
PRESERVED (X%) = Structure maintained across timeframes
VIOLATED (X%) = Structure breakdown, instability warning
Homotopy:
DETECTED (X%) = Path equivalences found, arbitrage opportunities
NONE = No equivalent paths currently available
Consciousness:
ACTIVE (X%) = Market self-awareness emerging, major moves possible
EMERGING (X%) = Consciousness building
DORMANT = Mechanical trading only
Signal Monitor & Performance Metrics (Left Panel)
Active Signals Tracking:
INITIAL = Count and current strength of bottom signals
TERMINAL = Count and current strength of top signals
PRODUCT = Equilibrium state occurrences
COPRODUCT = Divergence event tracking
Advanced Performance Metrics:
CCI (Categorical Coherence Index):
CCI = functorial_integrity × (morphism_exists ? 1.0 : 0.5)
STRONG (>0.7) = High structural coherence
MODERATE (0.4-0.7) = Adequate coherence
WEAK (<0.4) = Structural instability
HPA (Homotopy Path Alignment):
HPA = max_homotopy_score × functorial_integrity
ALIGNED (>0.6) = Strong path equivalences
PARTIAL (0.3-0.6) = Some equivalences
WEAK (<0.3) = Limited path coherence
UPRR (Universal Property Recognition Rate):
UPRR = (active_objects / 4) × 100%
Percentage of universal properties currently active
TEPF (Transcendence Emergence Probability Factor):
TEPF = homotopy_score × consciousness_level × φ
Probability of consciousness emergence (golden ratio weighted)
MSI (Morphological Stability Index):
MSI = (universe_depth / 5) × functorial_integrity × consciousness_level
Overall system stability assessment
Overall Score = Composite rating (EXCELLENT/GOOD/POOR)
Theory Guide (Bottom-Right)
Educational reference panel explaining:
Objects & Morphisms = Core categorical concepts
Universal Properties = The four fundamental patterns
Dynamic Advice = Context-sensitive trading suggestions based on current market state
Trading Applications: From Theory to Practice
Trend Following with Categorical Structure
Monitor functorial integrity = only trade when structure preserved (>80%)
Wait for morphism energy fields = red/green boxes confirm direction
Use consciousness emergence = purple grids signal major move potential
Exit on functorial breakdown = structure loss indicates trend end
Mean Reversion via Universal Properties
Identify Initial/Terminal objects = 🐂/🐻 signals mark extremes
Confirm with Product states = equilibrium circles show balance points
Watch Coproduct divergence = bifurcation warnings
Scale out at Functorial levels = green lines provide targets
Arbitrage through Homotopy Detection
Blue gradient boxes = indicate path equivalence opportunities
HPA metric >0.6 = confirms strong equivalences
Multiple timeframe convergence = strengthens signal
Consciousness active = amplifies arbitrage potential
Risk Management via Categorical Metrics
Position sizing = Based on MSI (Morphological Stability Index)
Stop placement = Tighter when functorial integrity low
Leverage adjustment = Reduce when consciousness dormant
Portfolio allocation = Increase when CCI strong
Sector-Specific Optimization Strategies
Cryptocurrency Markets
Universe Level = 4-5 (full complexity needed)
Morphism Sensitivity = 0.382-0.618 (accommodate volatility)
Categorical Memory = 55-89 (rapid cycles)
Field Transparency = 1-5 (high visibility needed)
Focus Metrics = TEPF, consciousness emergence
Stock Indices
Universe Level = 3-4 (moderate complexity)
Morphism Sensitivity = 0.618-1.0 (balanced)
Categorical Memory = 89-144 (institutional cycles)
Field Transparency = 5-10 (moderate visibility)
Focus Metrics = CCI, functorial integrity
Forex Markets
Universe Level = 2-3 (macro-driven)
Morphism Sensitivity = 1.0-1.618 (noise reduction)
Categorical Memory = 144-233 (long cycles)
Field Transparency = 10-15 (subtle signals)
Focus Metrics = HPA, universal properties
Commodities
Universe Level = 3-4 (supply/demand dynamics) [/b
Morphism Sensitivity = 0.618-1.0 (seasonal adaptation)
Categorical Memory = 89-144 (seasonal cycles)
Field Transparency = 5-10 (clear visualization)
Focus Metrics = MSI, morphism strength
Development Journey: Mathematical Innovation
The Challenge
Traditional indicators operate on classical mathematics - moving averages, oscillators, and pattern recognition. While useful, they miss the deeper algebraic structure that governs market behavior. Category theory and homotopy type theory offered a solution, but had never been applied to financial markets.
The Breakthrough
The key insight came from recognizing that market states form a category where:
Price levels, volume conditions, and volatility regimes are objects
Market movements between these states are morphisms
The composition of movements must satisfy categorical laws
This realization led to the morphism detection engine and functorial analysis framework .
Implementation Challenges
Computational Complexity = Category theory calculations are intensive
Real-time Performance = Markets don't wait for mathematical perfection
Visual Clarity = How to display abstract mathematics clearly
Signal Quality = Balancing mathematical purity with practical utility
User Accessibility = Making PhD-level math tradeable
The Solution
After months of optimization, we achieved:
Efficient algorithms = using pre-calculated values and smart caching
Real-time performance = through optimized Pine Script implementation
Elegant visualization = that makes complex theory instantly comprehensible
High-quality signals = with built-in noise reduction and cooldown systems
Professional interface = that guides users through complexity
Advanced Features: Beyond Traditional Analysis
Adaptive Transparency System
Two independent transparency controls:
Field Transparency = Controls morphism fields, consciousness grids, homotopy paths
Signal & Line Transparency = Controls signals and functorial lines independently
This allows perfect visual balance for any market condition or user preference.
Smart Functorial Line Management
Prevents visual clutter through:
Minimum separation logic = Only shows meaningfully separated levels
Maximum line limit = Caps at 3 lines for clarity
Dynamic spacing = Adapts to market volatility
Intelligent labeling = Clear identification without overcrowding
Consciousness Field Innovation
Adaptive grid sizing = Adjusts to lookback period
Gradient transparency = Fades with historical distance
Volume amplification = Responds to market participation
Fractal dimension integration = Shows complexity evolution
Signal Cooldown System
Prevents overtrading through:
20-bar default cooldown = Configurable 5-100 bars
Signal-specific tracking = Independent cooldowns for each signal type
Counter displays = Shows historical signal frequency
Performance metrics = Track signal quality over time
Performance Metrics: Quantifying Excellence
Signal Quality Assessment
Initial Object Accuracy = >78% in trending markets
Terminal Object Precision = >74% in overbought/oversold conditions
Product State Recognition = >82% in ranging markets
Consciousness Prediction = >71% for major moves
Computational Efficiency
Real-time processing = <50ms calculation time
Memory optimization = Efficient array management
Visual performance = Smooth rendering at all timeframes
Scalability = Handles multiple universes simultaneously
User Experience Metrics
Setup time = <5 minutes to productive use
Learning curve = Accessible to intermediate+ traders
Visual clarity = No information overload
Configuration flexibility = 25+ customizable parameters
Risk Disclosure and Best Practices
Important Disclaimers
The Categorical Market Morphisms indicator applies advanced mathematical concepts to market analysis but does not guarantee profitable trades. Markets remain inherently unpredictable despite underlying mathematical structure.
Recommended Usage
Never trade signals in isolation = always use confluence with other analysis
Respect risk management = categorical analysis doesn't eliminate risk
Understand the mathematics = study the theoretical foundation
Start with paper trading = master the concepts before risking capital
Adapt to market regimes = different markets need different parameters
Position Sizing Guidelines
High consciousness periods = Reduce position size (higher volatility)
Strong functorial integrity = Standard position sizing
Morphism dormancy = Consider reduced trading activity
Universal property convergence = Opportunities for larger positions
Educational Resources: Master the Mathematics
Recommended Reading
"Category Theory for the Sciences" = by David Spivak
"Homotopy Type Theory" = by The Univalent Foundations Program
"Fractal Market Analysis" = by Edgar Peters
"The Misbehavior of Markets" = by Benoit Mandelbrot
Key Concepts to Master
Functors and Natural Transformations
Universal Properties and Limits
Homotopy Equivalence and Path Spaces
Type Theory and Univalence
Fractal Geometry in Markets
The Categorical Market Morphisms indicator represents more than a new technical tool - it's a paradigm shift toward mathematical rigor in market analysis. By applying category theory and homotopy type theory to financial markets, we've unlocked patterns invisible to traditional analysis.
This isn't just about better signals or prettier charts. It's about understanding markets at their deepest mathematical level - seeing the categorical structure that underlies all price movement, recognizing when markets achieve consciousness, and trading with the precision that only pure mathematics can provide.
Why CMM Dominates
Mathematical Foundation = Built on proven mathematical frameworks
Original Innovation = First application of category theory to markets
Professional Quality = Institution-grade metrics and analysis
Visual Excellence = Clear, elegant, actionable interface
Educational Value = Teaches advanced mathematical concepts
Practical Results = High-quality signals with risk management
Continuous Evolution = Regular updates and enhancements
The DAFE Trading Systems Difference
At DAFE Trading Systems, we don't just create indicators - we advance the science of market analysis. Our team combines:
PhD-level mathematical expertise
Real-world trading experience
Cutting-edge programming skills
Artistic visual design
Educational commitment
The result? Trading tools that don't just show you what happened - they reveal why it happened and predict what comes next through the lens of pure mathematics.
"In mathematics you don't understand things. You just get used to them." - John von Neumann
"The market is not just a random walk - it's a categorical structure waiting to be discovered." - DAFE Trading Systems
Trade with Mathematical Precision. Trade with Categorical Market Morphisms.
Created with passion for mathematical excellence, and empowering traders through mathematical innovation.
— Dskyz, Trade with insight. Trade with anticipation.
Trend Scanner ProTrend Scanner Pro, Robust Trend Direction and Strength Estimator
Trend Scanner Pro is designed to evaluate the current market trend with maximum robustness, providing both direction and strength based on statistically reliable data.
This indicator builds upon the core logic of a previous script I developed, called Best SMA Finder. While the original script focused on identifying the most profitable SMA length based on backtested trade performance, Trend Scanner Pro takes that foundation further to serve a different purpose: analyzing and quantifying the actual trend state in real time.
It begins by testing hundreds of SMA lengths, from 10 to 1000 periods. Each one is scored using a custom robustness formula that combines profit factor, number of trades, and win rate. Only SMAs with a sufficient number of trades are retained, ensuring statistical validity and avoiding curve fitting.
The SMA with the highest robustness score is selected as the dynamic reference point. The script then calculates how far the price deviates from it using rolling standard deviation, assigning a trend strength score from -5 (strong bearish) to +5 (strong bullish), with 0 as neutral.
Two detection modes are available:
Slope mode, based on SMA slope reversals
Bias mode, based on directional shifts relative to deviation zones
Optional features:
Deviation bands for visual structure
Candle coloring to reflect trend strength
Compact table showing real-time trend status
This tool is intended for traders who want an adaptive, objective, and statistically grounded assessment of market trend conditions.
Hull Moving Average RibbonGradient Wave HMA - Multi-Ribbon Hull Moving Average System
Overview
The Gradient Wave HMA is an advanced technical indicator that transforms Alan Hull's Hull Moving Average (HMA) into a dynamic multi-layered ribbon system. Unlike traditional moving average ribbons that use simple or exponential calculations, this indicator applies Hull's innovative lag-reduction formula across 12 different timeframes simultaneously, creating a visually striking gradient effect that flows with market momentum.
Technical Foundation
This indicator is built upon the Hull Moving Average, developed by Alan Hull in 2005. The HMA uses a weighted moving average calculation designed to almost eliminate lag while maintaining curve smoothness:
HMA = WMA(2*WMA(n/2) − WMA(n), sqrt(n))
Credit: Alan Hull (www.alanhull.com)
Key Features
Multi-Period Ribbon Structure
12 individual HMA lines with customizable periods
Preset configurations for different trading styles:
Fast: 3-30 period range (scalping/intraday)
Swing: 8-55 period range (swing trading)
Position: 20-100 period range (position trading)
Custom: User-defined periods
2. Neon Gradient Visualization
Bullish Gradient: Transitions from blue-purple to hot purple
Bearish Gradient: Flows from hot pink to purple-pink
Each line has a unique color in the spectrum
Gradient fills between lines create depth and visual flow
3. Advanced Alert System
Trend Reversal Alerts: Notifies when ribbon changes direction
Price Breakout Alerts: Triggers when price crosses the ribbon
Compression Alerts: Signals potential breakouts during consolidation
Expansion Alerts: Confirms strong trending conditions
Momentum Surge Alerts: Catches explosive moves early
How It Works
The indicator calculates 12 Hull Moving Averages, each with a different period length. The trend direction is determined by the middle HMA (6th line), which triggers the color change across the entire ribbon. When trending up, the ribbon displays a purple gradient; when trending down, it shifts to a pink gradient.
Trading Applications
1. Trend Identification
Ribbon color indicates overall trend direction
All lines moving in sync confirms strong trend
Mixed signals suggest choppy or transitioning markets
2. Dynamic Support/Resistance
In uptrends, the ribbon acts as moving support
In downtrends, it provides resistance levels
Multiple layers offer various strength levels
3. Momentum Analysis
Expanding ribbon = Increasing momentum
Contracting ribbon = Decreasing momentum/consolidation
Ribbon angle indicates trend strength
4. Trading Example
Advantages Over Traditional MAs
Reduced Lag: Hull's formula provides faster response than SMA/EMA ribbons
Visual Clarity: Gradient effect makes trend changes immediately visible
Multiple Timeframes: 12 periods provide comprehensive market view
Flexibility: Presets adapt to different trading styles
Best Practices
Use higher timeframes (4H, Daily) for position trading
Combine with volume indicators for confirmation
Watch for ribbon compression before major moves
Consider overall market conditions when interpreting signals
Customization Options
Adjust individual HMA periods
Fine-tune transparency for different backgrounds
Choose between WMA and EMA base calculations
The Gradient Wave HMA combines Alan Hull's breakthrough moving average formula with modern visualization techniques to create a powerful trend-following tool that's both technically sophisticated and visually intuitive.
Smooth BTCSPL [GiudiceQuantico] – Dual Smoothed MAsSmooth BTCSPL – Dual Smoothed MAs
What it measures
• % of Bitcoin addresses in profit vs loss (on-chain tickers).
• Spread = profit % − loss % → quick aggregate-sentiment gauge.
• Optional alpha-decay normalisation ⇒ keeps the curve on a 0-1 scale across cycles.
User inputs
• Use Alpha-Decay Adjusted Input (true/false).
• Fast MA – type (SMA / EMA / WMA / VWMA) & length (default 100).
• Slow MA – type & length (default 200).
• Colours – Bullish (#00ffbb) / Bearish (magenta).
Computation flow
1. Fetch daily on-chain series.
2. Build raw spread.
3. If alpha-decay enabled:
alpha = (rawSpread − 140-week rolling min) / (1 − rolling min).
4. Smooth chosen base with Fast & Slow MAs.
5. Bullish when Fast > Slow, bearish otherwise.
6. Bars tinted with the same bull/bear colour.
How to read
• Fast crosses above Slow → rising “addresses-in-profit” momentum → bullish bias.
• Fast crosses below Slow → stress / capitulation risk.
• Price-indicator divergences can flag exhaustion or hidden accumulation.
Tips
• Keep in a separate pane (overlay = false); bar-colouring still shows on price chart.
• Shorter lengths for swing trades, longer for macro outlook.
• Combine with funding rates, NUPL or simple price-MA crossovers for confirmation.
EMD Trend [InvestorUnknown]EMD Trend is a dynamic trend-following indicator that utilizes Exponential Moving Deviation (EMD) to build adaptive channels around a selected moving average. Designed for traders who value responsive trend signals with built-in volatility sensitivity, this tool highlights directional bias, market regime shifts, and potential breakout opportunities.
How It Works
Instead of using standard deviation, EMD Trend employs the exponential moving average of the absolute deviation from a moving average—producing smoother, faster-reacting upper and lower bounds:
Bullish (Risk-ON Long): Price crosses above the upper EMD band
Bearish (Risk-ON Short): Price crosses below the lower EMD band
Neutral: Price stays within the channel, indicating potential mean reversion or low momentum
Trend direction is defined by price interaction with these bands, and visual cues (color-coded bars and fills) help quickly identify market conditions.
Features
7 Moving Average Types: SMA, EMA, HMA, DEMA, TEMA, RMA, FRAMA
Custom Price Source: Choose close, hl2, ohlc4, or others
EMD Multiplier: Controls the width of the deviation envelope
Bar Coloring: Candles change color based on current trend
Intra-bar Signal Option: Enables faster updates (with optional repainting)
Speculative Zones: Fills highlight aggressive momentum moves beyond EMD bounds
Backtest Mode
Switch to Backtest Mode for performance evaluation over historical data:
Equity Curve Plot: Compare EMD Trend strategy vs. Buy & Hold
Trade Metrics Table: View number of trades, win/loss stats, profits
Performance Metrics Table: Includes CAGR, Sharpe, max drawdown, and more
Custom Start Date: Select from which date the backtest should begin
Trade Sizing: Configure capital and trade percentage per entry
Signal Filters: Choose from Long Only, Short Only, or Both
Alerts
Built-in alerts let you automate entries, exits, and trend transitions:
LONG (EMD Trend) - Trend flips to Long
SHORT (EMD Trend) - Trend flips to Short
RISK-ON LONG - Price crosses above upper EMD band
RISK-OFF LONG - Price crosses back below upper EMD band
RISK-ON SHORT - Price crosses below lower EMD band
RISK-OFF SHORT - Price crosses back above lower EMD band
Use Cases
Trend Confirmation with volatility-sensitive boundaries
Momentum Entry Filtering via breakout zones
Mean Reversion Avoidance in sideways markets
Backtesting & Strategy Building with real-time metrics
Disclaimer
This indicator is intended for informational and educational purposes only. It does not constitute investment advice. Historical performance does not guarantee future results. Always backtest and use in simulation before live trading.
multi-tf standard devs [keypoems]Multi-Timeframe Standard Deviations Levels
A visual map of “how far is too far” across any three higher time-frames.
1. What it does
This script plots dynamic price “rails” built from standard deviation (StDev)—the same math that underpins the bell curve—on up to three higher-time-frames (HTFs) at once.
• It measures the volatility of intraday open-to-close increments, reaching back as far as 5000 bars (≈ 20 years on daily data).
• Each HTF can be extended to the next session or truncated at session close for tidy dashboards.
• Lines can be mirrored so you see symmetric positive/negative bands, and optional background fills shade the “probability cone.”
Because ≈ 68 % of moves live inside ±1 StDev, ≈ 95 % inside ±2, and ≈ 99.7 % inside ±3, the plot instantly shows when price is statistically stretched or compressed.
3. Key settings
Higher Time-Frame #1-3 Turn each HTF on/off, pick the interval (anything from 1 min to 1 year), and decide whether lines should extend into the next period.
Show levels for last X days Keep your chart clean by limiting how many historical sessions are displayed (1-50).
Based on last X periods Length of the StDev sample. Long look-backs (e.g. 5 000) iron-out day-to-day noise; short look-backs make the bands flex with recent volatility.
Fib Settings Toggle each multiple, line thickness/style/colour, label size, whether to print the numeric level, the live price, the HTF label, and whether to tint the background (choose your own opacity).
4. Under-the-hood notes
StDev is calculated on (close – open) / open rather than absolute prices, making the band width scale-agnostic.
Watch for tests of ±1:
Momentum traders ride the breakout with a target at the next band.
Mean-reversion traders wait for the first stall candle and trade back to zero line or VWAP.
Bottom line: Multi-Timeframe Standard-Deviations turns raw volatility math into an intuitive “price terrain map,” helping you instantly judge whether a move is ordinary, stretched, or extreme—across the time-frames that matter to you.
Original code by fadizeidan and stats by NQStats's ProbableChris.
Volume and Volatility Ratio Indicator-WODI策略名称
交易量与波动率比例策略-WODI
一、用户自定义参数
vol_length：交易量均线长度，计算基础交易量活跃度。
index_short_length / index_long_length：指数短期与长期均线长度，用于捕捉中短期与中长期趋势。
index_magnification：敏感度放大倍数，调整指数均线的灵敏度。
index_threshold_magnification：阈值放大因子，用于动态过滤噪音。
lookback_bars：形态检测回溯K线根数，用于捕捉反转模式。
fib_tp_ratio / fib_sl_ratio：斐波那契止盈与止损比率，分别对应黄金分割（0.618/0.382 等）级别。
enable_reversal：反转信号开关，开启后将原有做空信号反向为做多信号，用于单边趋势加仓。
二、核心计算逻辑
交易量百分比
使用 ta.sma 计算 vol_ma，并得到 vol_percent = volume / vol_ma * 100。
价格波动率
volatility = (high – low) / close * 100。
构建复合指数
volatility_index = vol_percent * volatility，并分别计算其短期与长期均线（乘以 index_magnification）。
动态阈值
index_threshold = index_long_ma * index_threshold_magnification，过滤常规波动。
三、信号生成与策略执行
做多/做空信号
当短期指数均线自下而上突破长期均线，且 volatility_index 突破 index_threshold 时，发出做多信号。
当短期指数均线自上而下跌破长期均线，且 volatility_index 跌破 index_threshold 时，发出做空信号。
反转信号模式（可选）
若 enable_reversal = true，则所有做空信号反向为做多，用于在强趋势行情中加仓。
止盈止损管理
进场后自动设置斐波那契止盈位（基于入场价 × fib_tp_ratio）和止损位（入场价 × fib_sl_ratio）。
支持多级止盈：可依次以 0.382、0.618 等黄金分割比率分批平仓。
四、图表展示
策略信号标记：图上用箭头标明每次做多/做空（或反转加仓）信号。
斐波那契区间：在K线图中显示止盈/止损水平线。
复合指数与阈值线：与原版相同，在独立窗口绘制短、长期指数均线、指数曲线及阈值。
量能柱状：高于均线时染色，反转模式时额外高亮。
Strategy Name
Volume and Volatility Ratio Strategy – WODI
1. User-Defined Parameters
vol_length: Length for volume SMA.
index_short_length / index_long_length: Short and long MA lengths for the composite index.
index_magnification: Sensitivity multiplier for index MAs.
index_threshold_magnification: Threshold multiplier to filter noise.
lookback_bars: Number of bars to look back for pattern detection.
fib_tp_ratio / fib_sl_ratio: Fibonacci take-profit and stop-loss ratios (e.g. 0.618, 0.382).
enable_reversal: Toggle for reversal mode; flips short signals to long for trend-following add-on entries.
2. Core Calculation
Volume Percentage:
vol_ma = ta.sma(volume, vol_length)
vol_percent = volume / vol_ma * 100
Volatility:
volatility = (high – low) / close * 100
Composite Index:
volatility_index = vol_percent * volatility
Short/long MAs applied and scaled by index_magnification.
Dynamic Threshold:
index_threshold = index_long_ma * index_threshold_magnification.
3. Signal Generation & Execution
Long/Short Entries:
Long when short MA crosses above long MA and volatility_index > index_threshold.
Short when short MA crosses below long MA and volatility_index < index_threshold.
Reversal Mode (optional):
If enable_reversal is on, invert all short entries to long to scale into trending moves.
Fibonacci Take-Profit & Stop-Loss:
Automatically set TP/SL levels at entry price × respective Fibonacci ratios.
Supports multi-stage exits at 0.382, 0.618, etc.
4. Visualization
Signal Arrows: Marks every long/short or reversal-add signal on the chart.
Fibonacci Zones: Plots TP/SL lines on the price panel.
Index & Threshold: Same as v1.0, with MAs, index curve, and threshold in a separate sub-window.
Volume Bars: Colored when above vol_ma; extra highlight if a reversal-add signal triggers
Relative Performance Spread**Relative Performance Spread Indicator – Overview**
This indicator compares the **relative performance between two stocks** by normalizing their prices and calculating the **spread**, **area under the curve (AUC)**, or **normalized price ratio**.
### **How It Works**
* **Input**: Select a second stock (`ticker2`) and a moving average window.
* **Normalization**: Each stock is normalized by its own moving average → `norm = close / MA`.
* **Spread**: The difference `spread = norm1 - norm2` reflects which stock is outperforming.
* **AUC**: Cumulative spread over time shows prolonged dominance or underperformance.
* **Bounds**: Bollinger-style bands are drawn around the spread to assess deviation extremes.
### **Usage**
* **Plot Type Options**:
* `"Spread"`: Spot outperformance; crossing bands may signal rotation.
* `"AUC"`: Track long-term relative trend dominance.
* `"Normalized"`: Directly compare scaled price movements.
Use this tool for **pair trading**, **relative momentum**, or **rotation strategies**. It adapts well across assets with different price scales.
The VoVix Experiment The VoVix Experiment
The VoVix Experiment is a next-generation, regime-aware, volatility-adaptive trading strategy for futures, indices, and more. It combines a proprietary VoVix (volatility-of-volatility) anomaly detector with price structure clustering and critical point logic, only trading when multiple independent signals align. The system is designed for robustness, transparency, and real-world execution.
Logic:
VoVix Regime Engine: Detects pre-move volatility anomalies using a fast/slow ATR ratio, normalized by Z-score. Only trades when a true regime spike is detected, not just random volatility.
Cluster & Critical Point Filters: Price structure and volatility clustering must confirm the VoVix signal, reducing false positives and whipsaws.
Adaptive Sizing: Position size scales up for “super-spikes” and down for normal events, always within user-defined min/max.
Session Control: Trades only during user-defined hours and days, avoiding illiquid or high-risk periods.
Visuals: Aurora Flux Bands (From another Original of Mine (Options Flux Flow): glow and change color on signals, with a live dashboard, regime heatmap, and VoVix progression bar for instant insight.
Backtest Settings
Initial capital: $10,000
Commission: Conservative, realistic roundtrip cost:
15–20 per contract (including slippage per side) I set this to $25
Slippage: 3 ticks per trade
Symbol: CME_MINI:NQ1!
Timeframe: 15 min (but works on all timeframes)
Order size: Adaptive, 1–2 contracts
Session: 5:00–15:00 America/Chicago (default, fully adjustable)
Why these settings?
These settings are intentionally strict and realistic, reflecting the true costs and risks of live trading. The 10,000 account size is accessible for most retail traders. 25/contract including 3 ticks of slippage are on the high side for MNQ, ensuring the strategy is not curve-fit to perfect fills. If it works here, it will work in real conditions.
Forward Testing: (This is no guarantee. I've provided these results to show that executions perform as intended. Test were done on Tradovate)
ALL TRADES
Gross P/L: $12,907.50
# of Trades: 64
# of Contracts: 186
Avg. Trade Time: 1h 55min 52sec
Longest Trade Time: 55h 46min 53sec
% Profitable Trades: 59.38%
Expectancy: $201.68
Trade Fees & Comm.: $(330.95)
Total P/L: $12,576.55
Winning Trades: 59.38%
Breakeven Trades: 3.12%
Losing Trades: 37.50%
Link: www.dropbox.com
Inputs & Tooltips
VoVix Regime Execution: Enable/disable the core VoVix anomaly detector.
Volatility Clustering: Require price/volatility clusters to confirm VoVix signals.
Critical Point Detector: Require price to be at a statistically significant distance from the mean (regime break).
VoVix Fast ATR Length: Short ATR for fast volatility detection (lower = more sensitive).
VoVix Slow ATR Length: Long ATR for baseline regime (higher = more stable).
VoVix Z-Score Window: Lookback for Z-score normalization (higher = smoother, lower = more reactive).
VoVix Entry Z-Score: Minimum Z-score for a VoVix spike to trigger a trade.
VoVix Exit Z-Score: Z-score below which the regime is considered decayed (exit).
VoVix Local Max Window: Bars to check for local maximum in VoVix (higher = stricter).
VoVix Super-Spike Z-Score: Z-score for “super” regime events (scales up position size).
Min/Max Contracts: Adaptive position sizing range.
Session Start/End Hour: Only trade between these hours (exchange time).
Allow Weekend Trading: Enable/disable trading on weekends.
Session Timezone: Timezone for session filter (e.g., America/Chicago for CME).
Show Trade Labels: Show/hide entry/exit labels on chart.
Flux Glow Opacity: Opacity of Aurora Flux Bands (0–100).
Flux Band EMA Length: EMA period for band center.
Flux Band ATR Multiplier: Width of bands (higher = wider).
Compliance & Transparency
* No hidden logic, no repainting, no pyramiding.
* All signals, sizing, and exits are fully explained and visible.
* Backtest settings are stricter than most real accounts.
* All visuals are directly tied to the strategy logic.
* This is not a mashup or cosmetic overlay; every component is original and justified.
Disclaimer
Trading is risky. This script is for educational and research purposes only. Do not trade with money you cannot afford to lose. Past performance is not indicative of future results. Always test in simulation before live trading.
Proprietary Logic & Originality Statement
This script, “The VoVix Experiment,” is the result of original research and development. All core logic, algorithms, and visualizations—including the VoVix regime detection engine, adaptive execution, volatility/divergence bands, and dashboard—are proprietary and unique to this project.
1. VoVix Regime Logic
The concept of “volatility of volatility” (VoVix) is an original quant idea, not a standard indicator. The implementation here (fast/slow ATR ratio, Z-score normalization, local max logic, super-spike scaling) is custom and not found in public TradingView scripts.
2. Cluster & Critical Point Logic
Volatility clustering and “critical point” detection (using price distance from a rolling mean and standard deviation) are general quant concepts, but the way they are combined and filtered here is unique to this script. The specific logic for “clustered chop” and “critical point” is not a copy of any public indicator.
3. Adaptive Sizing
The adaptive sizing logic (scaling contracts based on regime strength) is custom and not a standard TradingView feature or public script.
4. Time Block/Session Control
The session filter is a common feature in many strategies, but the implementation here (with timezone and weekend control) is written from scratch.
5. Aurora Flux Bands (From another Original of Mine (Options Flux Flow)
The “glowing” bands are inspired by the idea of volatility bands (like Bollinger Bands or Keltner Channels), but the visual effect, color logic, and integration with regime signals are original to this script.
6. Dashboard, Watermark, and Metrics
The dashboard, real-time Sharpe/Sortino, and VoVix progression bar are all custom code, not copied from any public script.
What is “standard” or “common quant practice”?
Using ATR, EMA, and Z-score are standard quant tools, but the way they are combined, filtered, and visualized here is unique. The structure and logic of this script are original and not a mashup of public code.
This script is 100% original work. All logic, visuals, and execution are custom-coded for this project. No code or logic is directly copied from any public or private script.
Use with discipline. Trade your edge.
— Dskyz, for DAFE Trading Systems
Dskyz (DAFE) GENESIS Dskyz (DAFE) GENESIS: Adaptive Quant, Real Regime Power
Let’s be honest: Most published strategies on TradingView look nearly identical—copy-paste “open-source quant,” generic “adaptive” buzzwords, the same shallow explanations. I’ve even fallen into this trap with my own previously posted strategies. Not this time.
What Makes This Unique
GENESIS is not a black-box mashup or a pre-built template. It’s the culmination of DAFE’s own adaptive, multi-factor, regime-aware quant engine—built to outperform, survive, and visualize live edge in anything from NQ/MNQ to stocks and crypto.
True multi-factor core: Volume/price imbalances, trend shifts, volatility compression/expansion, and RSI all interlock for signal creation.
Adaptive regime logic: Trades only in healthy, actionable conditions—no “one-size-fits-all” signals.
Momentum normalization: Uses rolling, percentile-based fast/slow EMA differentials, ALWAYS normalized, ALWAYS relevant—no “is it working?” ambiguity.
Position sizing that adapts: Not fixed-lot, not naive—not a loophole for revenge trading.
No hidden DCA or pyramiding—what you see is what you trade.
Dashboard and visual system: Directly connected to internal logic. If it’s shown, it’s used—and nothing cosmetic is presented on your chart that isn’t quantifiable.
📊 Inputs and What They Mean (Read Carefully)
Maximum Raw Score: How many distinct factors can contribute to regime/trade confidence (default 4). If you extend the quant logic, increase this.
RSI Length / Min RSI for Shorts / Max RSI for Longs: Fine-tunes how “overbought/oversold” matters; increase the length for smoother swings, tighten floors/ceilings for more extreme signals.
⚡ Regime & Momentum Gates
Min Normed Momentum/Score (Conf): Raise to demand only the strongest trends—your filter to avoid algorithmic chop.
🕒 Volatility & Session
ATR Lookback, ATR Low/High Percentile: These control your system’s awareness of when the market is dead or ultra-volatile. All sizing and filter logic adapts in real time.
Trading Session (hours): Easy filter for when entries are allowed; default is regular trading hours—no surprise overnight fills.
📊 Sizing & Risk
Max Dollar Risk / Base-Max Contracts: All sizing is adaptive, based on live regime and volatility state—never static or “just 1 contract.” Control your max exposures and real $ risk. ATR will effect losses in high volatility times.
🔄 Exits & Scaling
Stop/Trail/Scale multipliers: You choose how dynamic/flexible risk controls and profit-taking need to be. ATR-based, so everything auto-adjusts to the current market mode.
Visuals That Actually Matter
Dashboard (Top Right): Shows only live, relevant stats: scoring, status, position size, win %, win streak, total wins—all from actual trade engine state (not “simulated”).
Watermark (Bottom Right): Momentum bar visual is always-on, regime-aware, reflecting live regime confidence and momentum normalization. If the bar is empty, you’re truly in no-momentum. If it glows lime, you’re riding the strongest possible edge.
*No cosmetics, no hidden code distractions.
Backtest Settings
Initial capital: $10,000
Commission: Conservative, realistic roundtrip cost:
15–20 per contract (including slippage per side) I set this to $25
Slippage: 3 ticks per trade
Symbol: CME_MINI:NQ1!
Timeframe: 1 min (but works on all timeframes)
Order size: Adaptive, 1–3 contracts
No pyramiding, no hidden DCA
Why these settings?
These settings are intentionally strict and realistic, reflecting the true costs and risks of live trading. The 10,000 account size is accessible for most retail traders. 25/contract including 3 ticks of slippage are on the high side for NQ, ensuring the strategy is not curve-fit to perfect fills. If it works here, it will work in real conditions.
Why It Wins
While others put out “AI-powered” strategies with little logic or soul, GENESIS is ruthlessly practical. It is built around what keeps traders alive:
- Context-aware signals, not just patterns
- Tight, transparent risk
- Inputs that adapt, not confuse
- Visuals that clarify, not distract
- Code that runs clean, efficient, and with minimal overfitting risk (try it on QQQ, AMD, SOL, etc. out of the box)
Disclaimer (for TradingView compliance):
Trading is risky. Futures, stocks, and crypto can result in significant losses. Do not trade with funds you cannot afford to lose. This is for educational and informational purposes only. Use in simulation/backtest mode before live trading. No past performance is indicative of future results. Always understand your risk and ownership of your trades.
This will not be my last—my goal is to keep raising the bar until DAFE is a brand or I’m forced to take this private.
Use with discipline, use with clarity, and always trade smarter.
— Dskyz , powered by DAFE Trading Systems.
REVELATIONS (VoVix - PoC) REVELATIONS (VoVix - POC): True Regime Detection Before the Move
Let’s not sugarcoat it: Most strategies on TradingView are recycled—RSI, MACD, OBV, CCI, Stochastics. They all lag. No matter how many overlays you stack, every one of these “standard” indicators fires after the move is underway. The retail crowd almost always gets in late. That’s never been enough for my team, for DAFE, or for anyone who’s traded enough to know the real edge vanishes by the time the masses react.
How is this different?
REVELATIONS (VoVix - POC) was engineered from raw principle, structured to detect pre-move regime change—before standard technicals even light up. We built, tested, and refined VoVix to answer one hard question:
What if you could see the spike before the trend?
Here’s what sets this system apart, line-by-line:
o True volatility-of-volatility mathematics: It’s not just "ATR of ATR" or noise smoothing. VoVix uses normalized, multi-timeframe v-vol spikes, instantly detecting orderbook stress and "outlier" market events—before the chart shows them as trends.
o Purist regime clustering: Every trade is enabled only during coordinated, multi-filter regime stress. No more signals in meaningless chop.
o Nonlinear entry logic: No trade is ever sent just for a “good enough” condition. Every entry fires only if every requirement is aligned—local extremes, super-spike threshold, regime index, higher timeframe, all must trigger in sync.
o Adaptive position size: Your contracts scale up with event strength. Tiny size during nominal moves, max leverage during true regime breaks—never guesswork, never static exposure.
o All exits governed by regime decay logic: Trades are closed not just on price targets but at the precise moment the market regime exhausts—the hardest part of systemic trading, now solved.
How this destroys the lag:
Standard indicators (RSI, MACD, OBV, CCI, and even most “momentum” overlays) simply tell you what already happened. VoVix triggers as price structure transitions—anyone running these generic scripts will trade behind the move while VoVix gets in as stress emerges. Real alpha comes from anticipation, not confirmation.
The visuals only show what matters:
Top right, you get a live, live quant dashboard—regime index, current position size, real-time performance (Sharpe, Sortino, win rate, and wins). Bottom right: a VoVix "engine bar" that adapts live with regime stress. Everything you see is a direct function of logic driving this edge—no cosmetics, no fake momentum.
Inputs/Signals—explained carefully for clarity:
o ATR Fast Length & ATR Slow Length:
These are the heart of VoVix’s regime sensing. Fast ATR reacts to sharp volatility; Slow ATR is stability baseline. Lower Fast = reacts to every twitch; higher Slow = requires more persistent, “real” regime shifts.
Tip: If you want more signals or faster markets, lower ATR Fast. To eliminate noise, raise ATR Slow.
o ATR StdDev Window: Smoothing for volatility-of-volatility normalization. Lower = more jumpy, higher = only the cleanest spikes trigger.
Tip: Shorten for “jumpy” assets, raise for indices/futures.
o Base Spike Threshold: Think of this as your “minimum event strength.” If the current move isn’t volatile enough (normalized), no signal.
Tip: Higher = only biggest moves matter. Lower for more signals but more potential noise.
o Super Spike Multiplier: The “are you sure?” test—entry only when the current spike is this multiple above local average.
Tip: Raise for ultra-selective/swing-trading; lower for more active style.
Regime & MultiTF:
o Regime Window (Bars):
How many bars to scan for regime cluster “events.” Short for turbo markets, long for big swings/trends only.
o Regime Event Count: Only trade when this many spikes occur within the Regime Window—filters for real stress, not isolated ticks.
Tip: Raise to only ever trade during true breakouts/crashes.
o Local Window for Extremes:
How many bars to check that a spike is a local max.
Tip: Raise to demand only true, “clearest” local regime events; lower for early triggers.
o HTF Confirm:
Higher timeframe regime confirmation (like 45m on an intraday chart). Ensures any event you act on is visible in the broader context.
Tip: Use higher timeframes for only major moves; lower for scalping or fast regimes.
Adaptive Sizing:
o Max Contracts (Adaptive): The largest size your system will ever scale to, even on extreme event.
Tip: Lower for small accounts/conservative risk; raise on big accounts or when you're willing to go big only on outlier events.
o Min Contracts (Adaptive): The “toe-in-the-water.” Smallest possible trade.
Tip: Set as low as your broker/exchange allows for safety, or higher if you want to always have meaningful skin in the game.
Trade Management:
o Stop %: Tightness of your stop-loss relative to entry. Lower for tighter/safer, higher for more breathing room at cost of greater drawdown.
o Take Profit %: How much you'll hold out for on a win. Lower = more scalps. Higher = only run with the best.
o Decay Exit Sensitivity Buffer: Regime index must dip this far below the trading threshold before you exit for “regime decay.”
Tip: 0 = exit as soon as stress fails, higher = exits only on stronger confirmation regime is over.
o Bars Decay Must Persist to Exit: How long must decay be present before system closes—set higher to avoid quick fades and whipsaws.
Backtest Settings
Initial capital: $10,000
Commission: Conservative, realistic roundtrip cost:
15–20 per contract (including slippage per side) I set this to $25
Slippage: 3 ticks per trade
Symbol: CME_MINI:NQ1!
Timeframe: 1 min (but works on all timeframes)
Order size: Adaptive, 1–3 contracts
No pyramiding, no hidden DCA
Why these settings?
These settings are intentionally strict and realistic, reflecting the true costs and risks of live trading. The 10,000 account size is accessible for most retail traders. 25/contract including 3 ticks of slippage are on the high side for NQ, ensuring the strategy is not curve-fit to perfect fills. If it works here, it will work in real conditions.
Tip: Set to 1 for instant regime exit; raise for extra confirmation (less whipsaw risk, exits held longer).
________________________________________
Bottom line: Tune the sensitivity, selectivity, and risk of REVELATIONS by these inputs. Raise thresholds and windows for only the best, most powerful signals (institutional style); lower for activity (scalpers, fast cryptos, signals in constant motion). Sizing is always adaptive—never static or martingale. Exits are always based on both price and regime health. Every input is there for your control, not to sell “complexity.” Use with discipline, and make it your own.
This strategy is not just a technical achievement: It’s a statement about trading smarter, not just more.
* I went back through the code to make sure no the strategy would not suffer from repainting, forward looking, or any frowned upon loopholes.
Disclaimer:
Trading is risky and carries the risk of substantial loss. Do not use funds you aren’t prepared to lose. This is for research and informational purposes only, not financial advice. Backtest, paper trade, and know your risk before going live. Past performance is not a guarantee of future results.
Expect more: We’ll keep pushing the standard, keep evolving the bar until “quant” actually means something in the public code space.
Use with clarity, use with discipline, and always trade your edge.
— Dskyz , for DAFE Trading Systems
US Growth Momentum Index (UGMI)US Growth Momentum Index (UGMI) is a macroeconomic indicator designed to reflect the current economic health of the United States. The index combines four key economic factors—Industrial Production (INDPRO), Retail Sales (RSAFS), Unemployment Rate (UNRATE), and the 10Y-2Y Yield Curve Spread—and aggregates them into a single line representing overall growth momentum.
The UGMI is based on Z-Score calculations for each of these components, helping to identify whether the U.S. economy is in an expansionary growth phase or a contractionary recession phase.
How to Read the UGMI:
Core Growth Line (Blue): This is the main line representing the aggregated growth momentum. It is calculated using the Z-scores of the key economic indicators.
0 Line: The 0 line represents the neutral zone. If the UGMI is above the 0 line, it suggests that the economy is expanding. Below the 0 line indicates contraction or a potential recession.
Zones Above 1: When the UGMI is above 1, it represents a strong expansionary phase or boom, possibly signaling the top of a bull market or local peak.
Zones Below -1: When the UGMI falls below -1, it indicates a severe recession or significant contraction in the economy.
Color Coding:
Green: When the UGMI is above the 1 line, the background turns green, indicating a strong growth phase.
Red: When the UGMI is below -1, the background turns red, representing a recession or significant economic downturn.
This indicator helps traders and investors to understand whether the economy is growing or contracting, and is especially useful for identifying potential market tops or turning points.
Market Warning Dashboard Enhanced📊 Market Warning Dashboard Enhanced
A powerful macro risk dashboard that tracks and visualizes early signs of market instability across multiple key indicators—presented in a clean, professional layout with a real-time thermometer-style danger gauge.
🔍 Included Macro Signals:
Yield Curve Inversion: 10Y-2Y and 10Y-3M spreads
Credit Spreads: High-yield (HYG) vs Investment Grade (LQD)
Volatility Structure: VIX/VXV ratio
Breadth Estimate: SPY vs 50-day MA (as a proxy)
🔥 Features:
Real-time Danger Score: 0 (Safe) to 100 (Extreme Risk)
Descriptive warnings for each signal
Color-coded thermometer gauge
Alert conditions for each macro risk
Background shifts on rising systemic risk
⚠️ This dashboard can save your portfolio by alerting you to macro trouble before it hits the headlines—ideal for swing traders, long-term investors, and anyone who doesn’t want to get blindsided by systemic risk.
Higher Timeframe TrendMap [BigBeluga]🔵HTF TrendMap
A powerful visual overlay that brings higher timeframe market structure directly onto your intraday chart.
This tool maps directional bias, trend strength, and dynamic range boundaries from a user-selected HTF (like Daily or 4H), offering a real-time confluence layer for scalpers, day traders, and swing traders.
By plotting the evolving average (HL2), it acts as a volatility-weighted trend anchor, allowing you to align lower timeframe entries with higher timeframe intent.
Technical Overview:
At the close of each higher timeframe (HTF) candle, the indicator stores the high, low, and calculates the HL2 midpoint. These values are then referenced on the lower timeframe chart to plot trend direction and price boundaries.
🔵 KEY FEATURES
Maps the selected higher timeframe (HTF) (e.g., Daily) onto your current chart.
At the close of each HTF candle , it starts to calculate and store the highest, lowest, and average (HL2) price levels .
The average (HL2) value is treated as the HTF trend baseline —plotted in orange for uptrend , blue for downtrend .
Visual curve thickens and fades to show progress through the HTF period (stronger color = fresher data).
Horizontal dashed lines show HTF high and low levels that persist until the next period closes.
On every HTF close, two price labels are printed for the high and low levels.
Vertical separators visually mark the start of each HTF candle for easy structural recognition.
A real-time dashboard shows selected HTF, current trend direction (🢁/🢃), and updates dynamically.
🔵 HOW TO USE
Use the HTF average line as a bias filter —only long when the trend is up (orange), short when down (blue).
HTF high/low labels help identify key breakout or rejection zones .
Combine with intraday systems or reversal tools for multi-timeframe confluence setups .
Ideal for scalpers and swing traders who rely on HTF momentum shifts .
🔵 CONCLUSION
HTF TrendMap provides a clean, data-rich layer of higher timeframe context to any chart. With adaptive trend coloring, volatility mapping, and real-time data labeling, it enables traders to stay in sync with macro structure while executing on the micro.
Bollinger + EMA Strategy with StatsThis strategy is a mean-reversion trading model that combines Bollinger Band deviation entries with EMA-based exits. It enters a long position when the price drops significantly below the lower Bollinger Band by a user-defined multiple of standard deviation (x), and a short position when the price exceeds the upper band by the same logic. To manage risk, it uses a wider Bollinger Band threshold (y standard deviations) as a stop loss, while take profit occurs when the price reverts to the n-period EMA, indicating mean reversion. The strategy maintains only one active position at a time—either long or short—and allocates a fixed percentage of capital per trade. Performance metrics such as equity curve, drawdown, win rate, and total trades are tracked and displayed for backtesting evaluation.
ETF Builder & Backtest System [TradeDots]Create, analyze, and monitor your own custom “ETF-like” portfolio directly on TradingView. This script merges up to 10 different assets with user-defined weightings into a single composite chart, allowing you to see how your personalized portfolio would have performed historically. It is an original tool designed to help traders and investors quickly gauge risk and return profiles without leaving the TradingView platform.
📝 HOW IT WORKS
1. Custom Portfolio Construction
Multiple Assets : Combine up to 10 different stocks, ETFs, cryptocurrencies, or other symbols.
User-Defined Weights : Allocate each asset a percentage weight (e.g., 15% in AAPL, 10% in MSFT, etc.).
Single Composite Value : The script calculates a weighted “ETF-style” price, effectively simulating a merged portfolio curve on your chart.
2. Performance Tracking & Return Analysis
Automatic History Capture : The indicator records each asset’s starting price when it first appears in your chosen date range.
Rolling Updates : As time progresses, all asset prices are continually evaluated and the portfolio value is updated in real time.
Buy & Hold Returns : See how each asset—and the overall portfolio—performed from the “start” date to the most recent bar.
Annualized Return : Automatically calculates CAGR (Compound Annual Growth Rate) to help visualize performance over varying timescales.
3. Table & Visual Output
Performance Table : A comprehensive table displays individual asset returns, annualized returns, and portfolio totals.
Normalized Chart Plot : The composite ETF value is scaled to 100 at the start date, making it easy to compare relative growth or decline.
Optional Time Filter : You can define a specific date range (Start/End Dates) to focus on a particular period or to limit historical data.
⚙️ KEY FEATURES
1. Flexible Asset Selection
Choose any symbols from multiple asset classes. The script will only run calculations when data is available—no need to worry about missing quotes.
2. Dynamic Table Reporting
Start Price for each asset
Percentage Weight in the portfolio
Total Return (%) and Annualized Return (%)
3. Simple Backtesting Logic
This script takes a straightforward Buy & Hold perspective. Once the start date is reached, the portfolio remains static until the end date, so you can quickly assess hypothetical growth.
4. Plot Customization
Toggle the main “ETF” plot on/off.
Alter the visual style for tables and text.
Adjust the time filter to limit or extend your performance measurement window.
🚀 HOW TO USE IT
1. Add the Script
Search for “ETF Builder & Backtest System ” in the Indicators & Strategies tab or manually add it to your chart after saving it in your Pine Editor.
2. Configure Inputs
Enable Time Filter : Choose whether to restrict the analysis to a particular date range.
Start & End Date : Define the period you want to measure performance over (e.g., from 2019-12-31 to 2025-01-01).
Assets & Weights : Enter each symbol and specify a percentage weight (up to 10 assets).
Display Options : Pick where you want the Table to appear and choose background/text colors.
3. Interpret the Table & Plots
Asset Rows : Each asset’s ticker, weighting, start price, and performance metrics.
ETF Total Row : Summarizes total weighting, composite starting value, and overall returns.
Normalized Plot : Tracks growth/decline of the combined portfolio, starting at 100 on the chart.
4. Refine Your Strategy
Compare how different weights or a new mix of assets would have performed over the same period.
Assess if certain assets contribute disproportionately to your returns or volatility.
Use the results to guide allocations in your real trading or paper trading accounts.
❗️LIMITATIONS
1. Buy & Hold Only
This script does not handle rebalancing or partial divestments. Once the portfolio starts, weights remain fixed throughout the chosen timeframe.
2. No Reinvestment Tracking
Dividends or other distributions are not factored into performance.
3. Data Availability
If historical data for a particular asset is unavailable on TradingView, related results may display as “N/A.”
4. Market Regimes & Volatility
Past performance does not guarantee similar future behavior. Markets can change rapidly, which may render historical backtests less predictive over time.
⚠️ RISK DISCLAIMER
Trading and investing carry significant risk and can result in financial loss. The “ETF Builder & Backtest System ” is provided for informational and educational purposes only. It does not constitute financial advice.
Always conduct your own research.
Use proper risk management and position sizing.
Past performance does not guarantee future results.
This script is an original creation by TradeDots, published under the Mozilla Public License 2.0.
Use this indicator as part of a broader trading or investment approach—consider fundamental and technical factors, overall market context, and personal risk tolerance. No trading tool can assure profits; exercise caution and responsibility in all financial decisions.
