UDVR + OBV Combo — MTF (v6)The UDVR + OBV Combo is a multi-timeframe volume analysis tool that blends the Up/Down Volume Ratio with a normalized On-Balance Volume signal. It highlights when accumulation or distribution truly supports price action, adds higher-timeframe context, and shades the background when both indicators align. Use it to confirm breakouts, spot divergences, and filter trades with the backing of real volume flows.
1.Up/Down Volume Ratio (UDVR)
•Compares the rolling sum of up-volume (bars where price closed higher) vs down-volume (bars where price closed lower).
•A ratio > 1.0 = more accumulation (bullish pressure).
•A ratio < 1.0 = more distribution (bearish pressure).
•Optional histogram shows deviations from the 1.0 baseline.
•Customizable handling of equal closes (count as up, down, split, or ignore).
•Configurable lookback length and optional EMA smoothing.
2. On-Balance Volume (OBV)
•Classic cumulative OBV implemented natively (adds volume on up-bars, subtracts on down-bars).
•Normalized with a z-score so it can be compared across different symbols/timeframes.
•Includes an EMA signal line for slope detection.
•Alignment of OBV vs its EMA highlights rising or waning participation.
3. Multi-Timeframe Support
•Both UDVR and OBV can be plotted from a higher timeframe (HTF) (e.g. Daily UDVR shown on a 1h chart).
•Lets you see big-money accumulation/distribution while trading intraday.
•Shaded background when current TF and HTF agree (both bullish or both bearish).
How to read it
• Bullish confirmation = UDVR > 1 (accumulation) and OBV above EMA (rising participation).
• Bearish confirmation = UDVR < 1 (distribution) and OBV below EMA (falling participation).
• Mixed signals (e.g. UDVR > 1 but OBV falling) = caution; price may lack conviction.
• Divergences : If price makes a new high but OBV or UDVR does not, it’s a warning of weakening trend.
• Higher timeframe context : set HTF = Daily or Weekly and watch how short-term signals align with institutional flows. A long trade on the 15m chart is stronger when Daily UDVR is also above 1.
Inputs
•UDVR Lookback: number of bars for rolling volume sums.
•Smoothing EMA: smooths UDVR for stability.
•Equal Close Handling: decide how equal closes affect UDVR.
•Signal Band: optional UDVR extreme thresholds.
•Show Histogram: toggle UDVR histogram around baseline.
•Higher Timeframe UDVR: overlay Daily/Weekly UDVR on lower timeframe charts.
•OBV EMA length: slope proxy for normalized OBV.
•OBV Normalization window: controls z-score sensitivity.
•Higher Timeframe OBV: overlay higher timeframe OBV.
Alerts
•UDVR Bullish/Bearish cross at the 1.0 baseline.
•OBV slope up/down when OBV crosses its EMA.
•Alignment signals when UDVR and OBV agree (both confirm bullish or bearish conditions).
Why it’s useful
•Combines trend, momentum, and participation in one place.
•Helps avoid false breakouts by checking if volume supports the move.
•Lets you spot accumulation/distribution shifts before they show up in price.
•Gives a higher timeframe context so you’re not trading against the “big picture.”
Once applied, the indicator creates a dedicated pane below price with the following components:
UDVR Line (green/red)
• Green when UDVR > 1.0 (more up-volume than down-volume → accumulation).
• Red when UDVR < 1.0 (more down-volume → distribution).
UDVR Baseline and Bands
• Grey baseline at 1.0 = balance between buying and selling volume.
• Optional upper/lower bands (default 1.5 and 0.67) highlight extreme imbalances.
• Shaded areas between baseline and bands provide visual context for strength/weakness.
UDVR Histogram (optional)
• Columns around the baseline showing (UDVR – 1.0).
• Quick way to gauge how far above/below balance the ratio is.
Higher-Timeframe UDVR (teal line)
• Overlays the UDVR from a higher timeframe (e.g. Daily) on your intraday chart.
• Lets you see whether institutional flows support your shorter-term signals.
OBV Normalized (blue/orange line)
• Classic OBV, but normalized with a z-score so it stays readable across assets.
• Blue when OBV is above its EMA (rising participation).
• Orange when below its EMA (waning participation).
OBV EMA (grey line)
• Signal line showing the slope of OBV.
• Crosses between OBV and this line mark shifts in participation.
Higher-Timeframe OBV (purple line, optional)
• Plots OBV from a higher timeframe for additional context.
Background Shading
• Light green = both UDVR > 1 and OBV > OBV-EMA (bullish alignment).
• Light red = both UDVR < 1 and OBV < OBV-EMA (bearish alignment).
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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.
Quarterly Theory ICT 01 [TradingFinder] XAMD + Q1-Q4 Sessions🔵 Introduction
The Quarterly Theory ICT indicator is an advanced analytical system based on the concepts of ICT (Inner Circle Trader) and fractal time. It divides time into quarterly periods and accurately determines entry and exit points for trades by using the True Open as the starting point of each cycle. This system is applicable across various time frames including annual, monthly, weekly, daily, and even 90-minute sessions.
Time is divided into four quarters: in the first quarter (Q1), which is dedicated to the Accumulation phase, the market is in a consolidation state, laying the groundwork for a new trend; in the second quarter (Q2), allocated to the Manipulation phase (also known as Judas Swing), sudden price changes and false moves occur, marking the true starting point of a trend change; the third quarter (Q3) is dedicated to the Distribution phase, during which prices are broadly distributed and price volatility peaks; and the fourth quarter (Q4), corresponding to the Continuation/Reversal phase, either continues or reverses the previous trend.
By leveraging smart algorithms and technical analysis, this system identifies optimal price patterns and trading positions through the precise detection of stop-run and liquidity zones.
With the division of time into Q1 through Q4 and by incorporating key terms such as Quarterly Theory ICT, True Open, Accumulation, Manipulation (Judas Swing), Distribution, Continuation/Reversal, ICT, fractal time, smart algorithms, technical analysis, price patterns, trading positions, stop-run, and liquidity, this system enables traders to identify market trends and make informed trading decisions using real data and precise analysis.
♦ Important Note :
This indicator and the "Quarterly Theory ICT" concept have been developed based on material published in primary sources, notably the articles on Daye( traderdaye ) and Joshuuu . All copyright rights are reserved.
🔵 How to Use
The Quarterly Theory ICT strategy is built on dividing time into four distinct periods across various time frames such as annual, monthly, weekly, daily, and even 90-minute sessions. In this approach, time is segmented into four quarters, during which the phases of Accumulation, Manipulation (Judas Swing), Distribution, and Continuation/Reversal appear in a systematic and recurring manner.
The first segment (Q1) functions as the Accumulation phase, where the market consolidates and lays the foundation for future movement; the second segment (Q2) represents the Manipulation phase, during which prices experience sudden initial changes, and with the aid of the True Open concept, the real starting point of the market’s movement is determined; in the third segment (Q3), the Distribution phase takes place, where prices are widely dispersed and price volatility reaches its peak; and finally, the fourth segment (Q4) is recognized as the Continuation/Reversal phase, in which the previous trend either continues or reverses.
This strategy, by harnessing the concepts of fractal time and smart algorithms, enables precise analysis of price patterns across multiple time frames and, through the identification of key points such as stop-run and liquidity zones, assists traders in optimizing their trading positions. Utilizing real market data and dividing time into Q1 through Q4 allows for a comprehensive and multi-level technical analysis in which optimal entry and exit points are identified by comparing prices to the True Open.
Thus, by focusing on keywords like Quarterly Theory ICT, True Open, Accumulation, Manipulation, Distribution, Continuation/Reversal, ICT, fractal time, smart algorithms, technical analysis, price patterns, trading positions, stop-run, and liquidity, the Quarterly Theory ICT strategy acts as a coherent framework for predicting market trends and developing trading strategies.
🔵b]Settings
Cycle Display Mode: Determines whether the cycle is displayed on the chart or on the indicator panel.
Show Cycle: Enables or disables the display of the ranges corresponding to each quarter within the micro cycles (e.g., Q1/1, Q1/2, Q1/3, Q1/4, etc.).
Show Cycle Label: Toggles the display of textual labels for identifying the micro cycle phases (for example, Q1/1 or Q2/2).
Table Display Mode: Enables or disables the ability to display cycle information in a tabular format.
Show Table: Determines whether the table—which summarizes the phases (Q1 to Q4)—is displayed.
Show More Info: Adds additional details to the table, such as the name of the phase (Accumulation, Manipulation, Distribution, or Continuation/Reversal) or further specifics about each cycle.
🔵 Conclusion
Quarterly Theory ICT provides a fractal and recurring approach to analyzing price behavior by dividing time into four quarters (Q1, Q2, Q3, and Q4) and defining the True Open at the beginning of the second phase.
The Accumulation, Manipulation (Judas Swing), Distribution, and Continuation/Reversal phases repeat in each cycle, allowing traders to identify price patterns with greater precision across annual, monthly, weekly, daily, and even micro-level time frames.
Focusing on the True Open as the primary reference point enables faster recognition of potential trend changes and facilitates optimal management of trading positions. In summary, this strategy, based on ICT principles and fractal time concepts, offers a powerful framework for predicting future market movements, identifying optimal entry and exit points, and managing risk in various trading conditions.
WMA Trend and Growth Rate IndicatorThe "WMA Trend and Growth Rate Indicator" is a powerful tool for analyzing market trends and momentum. By understanding its components and how to configure it, traders of all levels can leverage this indicator to enhance their trading strategies. Experiment with the settings and integrate it into your analysis to gain valuable insights into market movements.
Indicator Components
WMA Length : The length of the WMA. This controls how many periods are included in the calculation.
Start : The starting value for accumulation levels.
End : The ending value for accumulation levels.
Key Concepts
Weighted Moving Average (WMA): A type of moving average that gives more weight to recent price data, making it more responsive to recent price changes.
Growth Rate : Measures how much the WMA has increased or decreased over a specified period, expressed as a percentage.
Accumulation and Distribution Levels : Zones where buying (accumulation) or selling (distribution) pressure is expected.
Configuring the Inputs
WMA Length : Adjust this value to change the sensitivity of the WMA. A smaller value makes the WMA more sensitive to recent price changes, while a larger value smooths out the data more.
Start and End : Adjust these values to define the range for accumulation and distribution levels. The indicator will automatically adjust the colors based on whether the Start value is higher or lower than the End value.
Interpreting the Plots
WMAT Line : The main trend line that shows the direction and strength of the trend.
Growth Index : Shows the growth rate of the WMAT.
Accumulation Levels : Indicated by lines and fill colors, showing potential zones to increase positions.
Distribution Levels : Indicated by lines and fill colors, showing potential zones to decrease positions.
The indicator checks if "Start" is greater than "End". Based on this check, it assigns colors to the accumulation and distribution levels. This color scheme helps traders visually distinguish between areas of potential buying and selling zones.
Wyckoff Range StrategyThe Wyckoff Range Strategy is a trading strategy that aims to identify potential accumulation and distribution phases in the market using the principles of Wyckoff analysis. It also incorporates the detection of spring and upthrust patterns.
Here's a step-by-step explanation of how to use this strategy:
Understanding Accumulation and Distribution Phases:
Accumulation Phase: This is a period where smart money (large institutional traders) accumulates a particular asset at lower prices. It is characterized by a sideways or consolidating price action.
Distribution Phase: This is a period where smart money distributes or sells a particular asset at higher prices. It is also characterized by a sideways or consolidating price action.
Input Variables:
crossOverLength: This variable determines the length of the moving average crossover used to identify accumulation and distribution phases. You can adjust this value based on the market you are trading and the time frame you are analyzing.
stopPercentage: This variable determines the percentage used to calculate the stop loss level. It helps you define a predefined level at which you would exit a trade if the price moves against your position.
Strategy Conditions:
Enter Long: The strategy looks for a crossover of the close price above the SMA of the close price with a length of crossOverLength and a crossover of the low price above the SMA of the low price with a length of 20. This combination suggests the start of an accumulation phase and a potential buying opportunity.
Exit Long: The strategy looks for a crossunder of the close price below the SMA of the close price with a length of crossOverLength or a crossunder of the high price below the SMA of the high price with a length of 20. This combination suggests the end of an accumulation phase and a potential exit signal for long positions.
Enter Short: The strategy looks for a crossunder of the close price below the SMA of the close price with a length of crossOverLength and a crossunder of the high price below the SMA of the high price with a length of 20. This combination suggests the start of a distribution phase and a potential selling opportunity.
Exit Short: The strategy looks for a crossover of the close price above the SMA of the close price with a length of crossOverLength or a crossover of the low price above the SMA of the low price with a length of 20. This combination suggests the end of a distribution phase and a potential exit signal for short positions.
Stop Loss:
The strategy sets a stop loss level for both long and short positions. The stop loss level is calculated based on the stopPercentage variable, which represents the percentage of the current close price. If the price reaches the stop loss level, the strategy will automatically exit the position.
Plotting Wyckoff Schematics:
The strategy plots different shapes on the chart to indicate the identified phases and patterns. Green and red labels indicate the accumulation and distribution phases, respectively. Blue triangles indicate spring patterns, and orange triangles indicate upthrust patterns.
To use this strategy, you can follow these steps:
Jim Forte — Anatomy of a Trading Range
robertbrain.com/Bull...+a+Trading+Range.pdf
Volume BubblesVolume Bubbles Indicator
Introduction
The Volume Bubbles indicator is a powerful tool designed to visually highlight significant volume spikes on your TradingView charts. It helps traders identify potential areas of whale accumulation (large buying activity) or dumping (large selling activity) by displaying colored bubbles on candles where volume exceeds a customizable threshold. Green bubbles indicate bullish (buy) volume on up candles, suggesting possible accumulation, while red bubbles signal bearish (sell) volume on down candles, indicating potential dumping. The bubble size scales with the volume magnitude, making it easy to spot major market moves at a glance.
This indicator is particularly useful for crypto, forex, and stock traders looking to gauge market sentiment and large player involvement without cluttering the chart. It's built in Pine Script v5 and overlays directly on your price action.
How It Works
The indicator calculates a moving average of volume (default: 20-period SMA) and detects spikes when current volume exceeds this average by a multiplier (default: 2x).
Buy Bubbles (Green): Appear on bullish candles (close >= open) at the low wick, representing potential whale buying or accumulation zones.
Sell Bubbles (Red): Appear on bearish candles (close < open) at the high wick, indicating potential whale selling or dumping zones.
Bubble Size: Dynamically sized based on volume thresholds – huge for >1M, large for 500K-1M, normal for <500K.
Transparency: Increases with volume ratio for better visibility on extreme spikes.
Tooltip:
Hover over a bubble to see detailed info like total volume, average volume, and ratio.
By focusing on these high-volume events, traders can spot key support/resistance levels where whales might be active.
How to Use for Whale Accumulation and Dumping
Whales (large holders) often move markets with high-volume trades. This indicator helps spot them:
Accumulation (Buying): Look for clusters of large green bubbles at price lows or during consolidations. This suggests whales are buying dips, potentially signaling a reversal or uptrend start. Combine with support levels for confirmation.
Dumping (Selling): Watch for big red bubbles at price highs or after rallies. This indicates whales unloading positions, which could lead to downtrends or corrections. Pair with resistance levels.
Tips:
Use on higher timeframes (e.g., 1H+) for reliable signals.
Confirm with other indicators like RSI or MACD to avoid false positives.
In trending markets, buy bubbles in uptrends confirm strength; sell bubbles in downtrends signal continuation.
Credits and Disclaimer
Inspired by volume analysis techniques. This is free to use; feedback welcome! Not financial advice – trade at your own risk.
Institutional Volume Profile# Institutional Volume Profile (IVP) - Advanced Volume Analysis Indicator
## Overview
The Institutional Volume Profile (IVP) is a sophisticated technical analysis tool that combines traditional volume profile analysis with institutional volume detection algorithms. This indicator helps traders identify key price levels where significant institutional activity has occurred, providing insights into market structure and potential support/resistance zones.
## Key Features
### 🎯 Volume Profile Analysis
- **Point of Control (POC)**: Identifies the price level with the highest volume activity
- **Value Area**: Highlights the price range containing a specified percentage (default 70%) of total volume
- **Multi-Row Distribution**: Displays volume distribution across 10-50 price levels for detailed analysis
- **Customizable Period**: Analyze volume profiles over 10-500 bars
### 🏛️ Institutional Volume Detection
- **Pocket Pivot Volume (PPV)**: Detects bullish institutional buying when up-volume exceeds recent down-volume peaks
- **Pivot Negative Volume (PNV)**: Identifies bearish institutional selling when down-volume exceeds recent up-volume peaks
- **Accumulation Detection**: Spots potential accumulation phases with high volume and narrow price ranges
- **Distribution Analysis**: Identifies distribution patterns with high volume but minimal price movement
### 🎨 Visual Customization Options
- **Multiple Color Schemes**: Heat Map, Institutional, Monochrome, and Rainbow themes
- **Bar Styles**: Solid, Gradient, Outlined, and 3D Effect rendering
- **Volume Intensity Display**: Visual intensity based on volume magnitude
- **Flexible Positioning**: Left or right side profile placement
- **Current Price Highlighting**: Real-time price level indication
### 📊 Advanced Visual Features
- **Volume Labels**: Display volume amounts at key price levels
- **Gradient Effects**: Multi-step gradient rendering for enhanced visibility
- **3D Styling**: Shadow effects for professional appearance
- **Opacity Control**: Adjustable transparency (10-100%)
- **Border Customization**: Configurable border width and styling
## How It Works
### Volume Distribution Algorithm
The indicator analyzes each bar within the specified period and distributes its volume proportionally across the price levels it touches. This creates an accurate representation of where trading activity has been concentrated.
### Institutional Detection Logic
- **PPV Trigger**: Current up-bar volume > highest down-volume in lookback period + above volume MA
- **PNV Trigger**: Current down-bar volume > highest up-volume in lookback period + above volume MA
- **Accumulation**: High volume + narrow range + bullish close
- **Distribution**: Very high volume + minimal price movement
### Value Area Calculation
Starting from the POC, the algorithm expands both upward and downward, adding volume until reaching the specified percentage of total volume (default 70%).
## Configuration Parameters
### Profile Settings
- **Profile Period**: 10-500 bars (default: 50)
- **Number of Rows**: 10-50 levels (default: 24)
- **Profile Width**: 10-100% of screen (default: 30%)
- **Value Area %**: 50-90% (default: 70%)
### Institutional Analysis
- **PPV Lookback Days**: 5-20 periods (default: 10)
- **Volume MA Length**: 10-200 periods (default: 50)
- **Institutional Threshold**: 1.0-2.0x multiplier (default: 1.2)
### Visual Controls
- **Bar Style**: Solid, Gradient, Outlined, 3D Effect
- **Color Scheme**: Heat Map, Institutional, Monochrome, Rainbow
- **Profile Position**: Left or Right side
- **Opacity**: 10-100%
- **Show Labels**: Volume amount display toggle
## Interpretation Guide
### Volume Profile Elements
- **Thick Horizontal Bars**: High volume nodes (strong support/resistance)
- **Thin Horizontal Bars**: Low volume nodes (weak levels)
- **White Line (POC)**: Strongest support/resistance level
- **Blue Highlighted Area**: Value Area (fair value zone)
### Institutional Signals
- **Blue Triangles (PPV)**: Bullish institutional buying detected
- **Orange Triangles (PNV)**: Bearish institutional selling detected
- **Color-Coded Bars**: Different colors indicate institutional activity types
### Color Scheme Meanings
- **Heat Map**: Red (high volume) → Orange → Yellow → Gray (low volume)
- **Institutional**: Blue (PPV), Orange (PNV), Aqua (Accumulation), Yellow (Distribution)
- **Monochrome**: Grayscale intensity based on volume
- **Rainbow**: Color-coded by price level position
## Trading Applications
### Support and Resistance
- POC acts as dynamic support/resistance
- High volume nodes indicate strong price levels
- Low volume areas suggest potential breakout zones
### Institutional Activity
- PPV above Value Area: Strong bullish signal
- PNV below Value Area: Strong bearish signal
- Accumulation patterns: Potential upward breakouts
- Distribution patterns: Potential downward pressure
### Market Structure Analysis
- Value Area defines fair value range
- Profile shape indicates market sentiment
- Volume gaps suggest potential price targets
## Alert Conditions
- PPV Detection at current price level
- PNV Detection at current price level
- PPV above Value Area (strong bullish)
- PNV below Value Area (strong bearish)
## Best Practices
1. Use multiple timeframes for confirmation
2. Combine with price action analysis
3. Pay attention to volume context (above/below average)
4. Monitor institutional signals near key levels
5. Consider overall market conditions
## Technical Notes
- Maximum 500 boxes and 100 labels for optimal performance
- Real-time calculations update on each bar close
- Historical analysis uses complete bar data
- Compatible with all TradingView chart types and timeframes
---
*This indicator is designed for educational and informational purposes. Always combine with other analysis methods and risk management strategies.*
Smart Money Concept [TradingFinder] Major OB + FVG + Liquidity🔵 Introduction
"Smart Money" refers to funds under the control of institutional investors, central banks, funds, market makers, and other financial entities. Ordinary people recognize investments made by those who have a deep understanding of market performance and possess information typically inaccessible to regular investors as "Smart Money".
Consequently, when market movements often diverge from expectations, traders identify the footprints of smart money. For example, when a classic pattern forms in the market, traders take short positions. However, the market might move upward instead. They attribute this contradiction to smart money and seek to capitalize on such inconsistencies in their trades.
The "Smart Money Concept" (SMC) is one of the primary styles of technical analysis that falls under the subset of "Price Action". Price action encompasses various subcategories, with one of the most significant being "Supply and Demand", in which SMC is categorized.
The SMC method aims to identify trading opportunities by emphasizing the impact of large traders (Smart Money) on the market, offering specific patterns, techniques, and trading strategies.
🟣 Key Terms of Smart Money Concept (SMC)
• Market Structure (Trend)
• Change of Character (ChoCh)
• Break of Structure (BoS)
• Order Blocks (Supply and Demand)
• Imbalance (IMB)
• Inefficiency (IFC)
• Fair Value Gap (FVG)
• Liquidity
• Premium and Discount
🔵 How Does the "Smart Money Concept Indicator" Work?
🟣 Market Structure
a. Accumulation
b. Market-Up
c. Distribution
d. Market-Down
a) Accumulation Phase : During the accumulation period, typically following a downtrend, smart money enters the market without significantly affecting the pricing trend.
b) Market-Up Phase : In this phase, the price of an asset moves upward from the accumulation range and begins to rise. Usually, the buying by retail investors is the main driver of this trend, and due to positive market sentiment, it continues.
c) Distribution Phase : The distribution phase, unlike the accumulation stage, occurs after an uptrend. In this phase, smart money attempts to exit the market without causing significant price fluctuations.
d) Market-Down Phase : In this stage, the price of an asset moves downward from the distribution phase, initiating a prolonged downtrend. Smart money liquidates all its positions by creating selling pressure, trapping latecomer investors.
The result of these four phases in the market becomes the market trend.
Types of Trends in Financial Markets :
a. Up-Trend
b. Down Trend
c. Range (No Trend)
a) Up-Trend : The market breaks consecutive highs.
b) Down Trend : The market breaks consecutive lows.
c) No Trend or Range : The market oscillates within a range without breaking either highs or lows.
🟣 Change of Character (ChoCh)
The "ChoCh" or "Change of Character" pattern indicates an initial change in order flow in financial markets. This structural change occurs when a major pivot in the opposite direction of the market trend fails. It signals a potential change in the market trend and can serve as a signal for short-term or long-term trend changes in a trading symbol.
🟣 Break of Structure (BoS)
The "BoS" or "Break of Structure" pattern indicates the continuation of the trend in financial markets. This structure forms when, in an uptrend, the price breaks its ceiling or, in a downtrend, the price breaks its floor.
🟣 Order Blocks (Supply and Demand)
Order blocks consist of supply and demand areas where the likelihood of price reversal is higher. There are six order blocks in this indicator, categorized based on their origin and formation reasons.
a. Demand Main Zone, "ChoCh" Origin.
b. Demand Sub Zone, "ChoCh" Origin.
c. Demand All Zone, "BoS" Origin.
d. Supply Main Zone, "ChoCh" Origin.
e. Supply Sub Zone, "ChoCh" Origin.
f. Supply All Zone, "BoS" Origin.
🟣 FVG | Inefficiency | Imbalance
These three terms are almost synonymous. They describe the presence of gaps between consecutive candle shadows. This inefficiency occurs when the market moves rapidly. Primarily, imbalances and these rapid movements stem from the entry of smart money and the imbalance between buyer and seller power. Therefore, identifying these movements is crucial for traders.
These areas are significant because prices often return to fill these gaps or even before they occur to fill price gaps.
🟣 Liquidity
Liquidity zones are areas where there is a likelihood of congestion of stop-loss orders. Liquidity is considered the driving force of the entire market, and market makers may manipulate the market using these zones. However, in many cases, this does not happen because there is insufficient liquidity in some areas.
Types of Liquidity in Financial Markets :
a. Trend Lines
b. Double Tops | Double Bottoms
c. Triple Tops | Triple Bottoms
d. Support Lines | Resistance Lines
All four types of liquidity in this indicator are automatically identified.
🟣 Premium and Discount
Premium and discount zones can assist traders in making better decisions. For instance, they may sell positions in expensive ranges and buy in cheaper ranges. The closer the price is to the major resistance, the more expensive it is, and the closer it is to the major support, the cheaper it is.
🔵 How to Use
🟣 Change of Character (ChoCh) and Break of Structure (BoS)
This indicator detects "ChoCh" and "BoS" in both Minor and Major states. You can turn on the display of these lines by referring to the last part of the settings.
🟣 Order Blocks (Supply and Demand)
Order blocks are Zones where the probability of price reversal is higher. In demand Zones you can buy opportunities and in supply Zones you can check sell opportunities.
The "Refinement" feature allows you to adjust the width of the order block according to your strategy. There are two modes, "Aggressive" and "Defensive," in the "Order Block Refine". The difference between "Aggressive" and "Defensive" lies in the width of the order block.
For risk-averse traders, the "Defensive" mode is suitable as it provides a lower loss limit and a greater reward-to-risk ratio. For risk-taking traders, the "Aggressive" mode is more appropriate. These traders prefer to enter trades at higher prices, and this mode, which has a wider order block width, is more suitable for this group of individuals.
🟣 Fair Value Gap (FVG) | Imbalance (IMB) | Inefficiency (IFC)
In order to identify the "fair value gap" on the chart, it must be analyzed candle by candle. In this process, it is important to pay attention to candles with a large size, and a candle and a candle should be examined before that.
Candles before and after this central candle should have long shadows and their bodies should not overlap with the central candle body. The distance between the shadows of the first and third candles is known as the FVG range.
These areas work in two ways :
• Supply and demand area : In this case, the price reacts to these areas and the trend is reversed.
• Liquidity zone : In this scenario, the price "fills" the zone and then reaches the order block.
Important note : In most cases, the FVG zone of very small width acts as a supply and demand zone, while the zone of significant width acts as a liquidity zone and absorbs price.
When the FVG filter is activated, the FVG regions are filtered based on the specified algorithm.
FVG filter types include the following :
1. Very Aggressive Mode : In addition to the initial condition, an additional condition is considered. For bullish FVG, the maximum price of the last candle must be greater than the maximum price of the middle candle.
Similarly, for a bearish FVG, the minimum price of the last candle must be lower than the minimum price of the middle candle. This mode removes the minimum number of FVGs.
2. Aggressive : In addition to the very aggressive condition, the size of the middle candle is also considered. The size of the center candle should not be small and therefore more FVGs are removed in this case.
3. Defensive : In addition to the conditions of the very aggressive mode, this mode also considers the size of the middle pile, which should be relatively large and make up the majority of the body.
Also, to identify bullish FVGs, the second and third candles must be positive, while for bearish FVGs, the second and third candles must be negative. This mode filters out a significant number of FVGs and keeps only those of good quality.
4. Very Defensive : In addition to the conditions of the defensive mode, in this mode the first and third candles should not be very small-bodied doji candles. This mode filters out most FVGs and only the best quality ones remain.
🟣 Liquidity
These levels are where traders intend to exit their trades. "Market makers" or smart money usually accumulate or distribute their trading positions near these levels, where many retail traders have placed their "stop loss" orders. When liquidity is collected from these losses, the price often reverses.
A "Stop hunt" is a move designed to offset liquidity generated by established stop losses. Banks often use major news events to trigger stop hunts and capture liquidity released into the market. For example, if they intend to execute heavy buy orders, they encourage others to sell through stop-hots.
Consequently, if there is liquidity in the market before reaching the order block area, the validity of that order block is higher. Conversely, if the liquidity is close to the order block, that is, the price reaches the order block before reaching the liquidity limit, the validity of that order block is lower.
🟣 Alert
With the new alert functionality in this indicator, you won't miss any important trading signals. Alerts are activated when the price hits the last order block.
1. It is possible to set alerts for each "symbol" and "time frame". The system will automatically detect both and include them in the warning message.
2. Each alert provides the exact date and time it was triggered. This helps you measure the timeliness of the signal and evaluate its relevance.
3. Alerts include target order block price ranges. The "Proximal" level represents the initial price level strike, while the "Distal" level represents the maximum price gap in the block. These details are included in the warning message.
4. You can customize the alert name through the "Alert Name" entry.
5. Create custom messages for "long" and "short" alerts to be sent with notifications.
🔵 Setting
a. Pivot Period of Order Blocks Detector :
Using this parameter, you can set the zigzag period that is formed based on the pivots.
b. Order Blocks Validity Period (Bar) :
You can set the validity period of each Order Block based on the number of candles that have passed since the origin of the Order Block.
c. Demand Main Zone, "ChoCh" Origin :
You can control the display or not display as well as the color of Demand Main Zone, "ChoCh" Origin.
d. Demand Sub Zone, "ChoCh" Origin :
You can control the display or not display as well as the color of Demand Sub Zone, "ChoCh" Origin.
e. Demand All Zone, "BoS" Origin :
You can control the display or not display as well as the color of Demand All Zone, "BoS" Origin.
f. Supply Main Zone, "ChoCh" Origin :
You can control the display or not display as well as the color of Supply Main Zone, "ChoCh" Origin.
g. Supply Sub Zone, "ChoCh" Origin :
You can control the display or not display as well as the color of Supply Sub Zone, "ChoCh" Origin.
h. Supply All Zone, "BoS" Origin :
You can control the display or not display as well as the color of Supply All Zone, "BoS" Origin.
i. Refine Demand Main : You can choose to be refined or not and also the type of refining.
j. Refine Demand Sub : You can choose to be refined or not and also the type of refining.
k. Refine Demand BoS : You can choose to be refined or not and also the type of refining.
l. Refine Supply Main : You can choose to be refined or not and also the type of refining.
m. Refine Supply Sub : You can choose to be refined or not and also the type of refining.
n. Refine Supply BoS : You can choose to be refined or not and also the type of refining.
o. Show Demand FVG : You can choose to show or not show Demand FVG.
p. Show Supply FVG : You can choose to show or not show Supply FVG
q. FVG Filter : You can choose whether FVG is filtered or not. Also specify the type of filter you want to use.
r. Show Statics High Liquidity Line : Show or not show Statics High Liquidity Line.
s. Show Statics Low Liquidity Line : Show or not show Statics Low Liquidity Line.
t. Show Dynamics High Liquidity Line : Show or not show Dynamics High Liquidity Line.
u. Show Dynamics Low Liquidity Line : Show or not show Dynamics Low Liquidity Line.
v. Statics Period Pivot :
Using this parameter, you can set the Swing period that is formed based on Static Liquidity Lines.
w. Dynamics Period Pivot :
Using this parameter, you can set the Swing period that is formed based Dynamics Liquidity Lines.
x. Statics Liquidity Line Sensitivity :
is a number between 0 and 0.4. Increasing this number decreases the sensitivity of the "Statics Liquidity Line Detection" function and increases the number of lines identified. The default value is 0.3.
y. Dynamics Liquidity Line Sensitivity :
is a number between 0.4 and 1.95. Increasing this number increases the sensitivity of the "Dynamics Liquidity Line Detection" function and decreases the number of lines identified. The default value is 1.
z. Alerts Name : You can customize the alert name using this input and set it to your desired name.
aa. Alert Demand Main Mitigation :
If you want to receive the alert about Demand Main 's mitigation after setting the alerts, leave this tick on. Otherwise, turn it off.
bb. Alert Demand Sub Mitigation :
If you want to receive the alert about Demand Sub's mitigation after setting the alerts, leave this tick on. Otherwise, turn it off.
cc. Alert Demand BoS Mitigation :
If you want to receive the alert about Demand BoS's mitigation after setting the alerts, leave this tick on. Otherwise, turn it off.
dd. Alert Supply Main Mitigation :
If you want to receive the alert about Supply Main's mitigation after setting the alerts, leave this tick on. Otherwise, turn it off.
ee. Alert Supply Sub Mitigation :
If you want to receive the alert about Supply Sub's mitigation after setting the alerts, leave this tick on. Otherwise, turn it off.
ff. Alert Supply BoS Mitigation :
If you want to receive the alert about Supply BoS's mitigation after setting the alerts, leave this tick on. Otherwise, turn it off.
gg. Message Frequency :
This parameter, represented as a string, determines the frequency of announcements. Options include: 'All' (triggers the alert every time the function is called), 'Once Per Bar' (triggers the alert only on the first call within the bar), and 'Once Per Bar Close' (activates the alert only during the final script execution of the real-time bar upon closure). The default setting is 'Once per Bar'.
hh. Show Alert time by Time Zone :
The date, hour, and minute displayed in alert messages can be configured to reflect any chosen time zone. For instance, if you prefer London time, you should input 'UTC+1'. By default, this input is configured to the 'UTC' time zone.
ii. Display More Info : The 'Display More Info' option provides details regarding the price range of the order blocks (Zone Price), along with the date, hour, and minute. If you prefer not to include this information in the alert message, you should set it to 'Off'.
You also have access to display or not to display, choose the Style and Color of all the lines below :
a. Major Bullish "BoS" Lines
b. Major Bearish "BoS" Lines
c. Minor Bullish "BoS" Lines
d. Minor Bearish "BoS" Lines
e. Major Bullish "ChoCh" Lines
f. Major Bearish "ChoCh" Lines
g. Minor Bullish "ChoCh" Lines
h. Minor Bearish "ChoCh" Lines
i. Last Major Support Line
j. Last Major Resistance Line
k. Last Minor Support Line
l. Last Minor Resistance Line
Up Down Volume Ratio by 3iauThis script considers the total volume within a user specified time frame, and whether price closed higher or lower at the end of each period within that time frame.
EXAMPLE:
* If the time period of interest is 50-periods, the script considers the volume within each of those 50 periods beginning with the most recent closed period.
* SumUpVol = the sum of all volume occurring within only those periods where price closed higher than that of the previous period.
* SumDnVol = the sum of all volume occurring within only those periods where price closed lower than that of the previous period.
* Difference = the difference between SumUpVol and SumDnVol = SumUpVol - SumDnVol
* Total = the sum of SumUpVol and SumDnVol = SumUpVol + SumDnVol
* The plot will present the change in Difference divided by Total = Difference/Total = (SumUpVol - SumDnVol)/(SumUpVol + SumDnVol) occurring within those 50 periods. What will be plotted is the moving average of this value. The user can specify the moving average type and the number of period for which the average is calculated.
* The plot needs to be fitted into a range, for example, +/- 50 (default) or +/-100, by multiplying the result of Difference/Total by a user specified constant. The constant will contain the majority (not all) of the values within +/- the specified value.
* Range = the user specified constant. If Range = 50, the majority of values plotted will be fall within the range +/- 50.
* Therefore, what is plotted is the moving average of Range * Difference / Total.
* When the value = 0, accumulation = distribution over the user specified 50-periods time frame.
* When the value is positive, accumulation > distribution over the user specified 50-periods time frame.
* When the value is negative, distribution > accumulation over the user specified 50-periods time frame.
This plot allows one to see possible accumulation and distribution occurring within a particular stock. The slope of this plot must be considered, and not any single value. The selected constant (“Range” in the example above) does not have an effect on the slope of the plot.
Three values may be plotted at once, for comparison of accumulation or distribution occurring over different time frames. For example, compare Difference / Total calculated over a 50-periods timeframe with 10-periods timeframe, both time frames beginning with the most recent closed period.
In addition to the above, J. Welles Wilder’s Relative Strength Index (RSI) can be plotted over the Difference / Total.
NOTE: this script is not the same as the more commonly used Up/Down Volume Ratio defined as SumUpVol / SumDnVol over a 50-periods time frame, where SumUpVol = the sum of all volume occurring within only those periods where price closed higher than that of the previous period, and SumDnVol = the sum of all volume occurring within only those periods where price closed lower than that of the previous period.
Compare...
Up Down Volume Ratio = SumUpVol / SumDnVol
Up Down Volume Ratio by 3iau = the moving average of Range * (SumUpVol - SumDnVol) / (SumUpVol + SumDnVol)
TARVIS Labs - Bitcoin Macro Bottom/Top SignalsSCRIPT DESCRIPTION
This is a script specifically written to help provide indicators from a macro view. This script is best run on the 1 day interval on Bitstamp's $BTCUSD chart. It helps indicate when to accumulate bitcoin, and when its in a bull run when there are local tops, strong top warnings, and a signal to exit a bull run. This is described further below.
If you don't have interest in trading on the way to the top I suggest turning off the following indicators in the settings of the indicator:
- Opportunity To Buy Back In Indicator
- Local Top Near Bull Run Top Indicator
ACCUMULATION ZONE INDICATOR - LIGHT GREEN
Description
When we look at the history of Bitcoin every bottom has crossed below the 100 week EMA. Once it does its accompanied by hash ribbon cross with miner capitulation. After that is the prime time to accumulate as theres a clearer signal the bottom is in. Specifically, a signal to look for is the 14 day MACD/signal cross and the 14 day MACD continuing to stay above the signal until the price returns above the 100 week EMA. This is prime accumulation territory.
Strategy for Usage
A good strategy to use when accumulating the bottom is dollar-cost averaging over a 30 day period. The accumulation zone can last longer than 30 days but 30 days is a good range of time to DCA.
STRONG BUY IN ACCUMULATION ZONE INDICATOR - DARK GREEN
Description
We can add to the bottoming signal by looking for post-downtrend reversals inside the bottoming signal. We do this by using a 9/19 daily cross.
Strategy for Usage
These post-downtrend reversals can potentially provide better targeted days for accumulation than the broader bottoming signal and can be used to add more on that day than on an average day for the dollar cost average strategy. Say for example, use 1/3 of funds on these days rather than 1/30th.
OPPORTUNITY TO BUY BACK IN INDICATOR - BLUE
Description
When the 1d 18 EMA > 1d 63 EMA and the 12/52 1d crosses. These together provide good buy opportunities to buy bitcoin.
Strategy for Usage
If you happen to find yourself out of the market from your own TA or a trade, this signal can provide a buy opportunity to reenter the market if you're out of it.
BULL RUN LOCAL TOP INDICATOR - ORANGE
Description
We will similarly use the 100 week EMA to determine trend reversal into a bull run. When we see the 100 week EMA uptrending, we can begin to look for local tops using the 9/19 daily MACD/signal bearish cross along with the 12 EMA having a negative slope, which could be the beginning signal for a local top.
Strategy for Usage
This is a rather light indicator, but can be used in tandem with your own technical analysis to determine if you want to reenter after you exit from its signal.
LOCAL TOP NEAR BULL RUN TOP INDICATOR - RED
Description
When the 100 week EMA is in an uptrend we can look for significant loss of momentum in order to determine if a local top is in near a bull run top. Similar to the Bull Run Local Top Indicator, this strategy uses a MACD/signal cross but instead uses the 30/65 day EMAs.
Strategy for Usage
Ideally the right strategy to use here is to exit the market when this indicator starts. When the indicator ends if the "End of Bull Run Indicator" is not showing on the chart you can buy back into the market.
TOP IS LIKELY IN INDICATOR
Description
When the 100 week EMA is in a very strong uptrend and the 9/19 weekly MACD/signal bearish cross occurs, and the 63 EMA begins to downtrend.
Strategy for Usage
This signal typically accompanies the "Local Top Near Bull Run Top Indicator" therefore if you're following the strategy you would likely already be out of the market, but if you're not and this signal fires its a strong signal the top is in and we're likely going to start seeing a strong retrace. This is typically right before we see the "End of Bull Run Indicator". There is only one occurrence where it wasn't followed by a large drop & the "End of Bull Run Indicator" and that was in the 2017 bull run where there were many strong retracements post local top. The likelihood we see that again is low, but if it were to happen you can buy back into the market when the "Top is Likely In Indicator" and the "Local Top Near Bull Run Top Indicator" are not firing.
TOP IS LIKELY IN INDICATOR
Description
When the 100 week EMA is in a strong uptrend and the 9/19 weekly MACD/signal bearish cross occurs, and the 63 EMA begins to downtrend.
Strategy for Usage
This signal typically accompanies the "Local Top Near Bull Run Top Indicator" therefore if you're following the strategy you would likely already be out of the market, but if you're not and this signal fires its a strong signal the top is in and we're likely going to start seeing a strong retrace. This is typically right before we see the "End of Bull Run Indicator". There is only one occurrence where it wasn't followed by a large drop & the "End of Bull Run Indicator" and that was in the 2017 bull run where there were many strong retracements post local top. The likelihood we see that again is low, but if it were to happen you can buy back into the market when the "Top is Likely In Indicator" and the "Local Top Near Bull Run Top Indicator" are not firing.
END OF BULL RUN INDICATOR
Description
When the 100 week EMA is in an uptrend and the 1d 18 EMA crosses the 1d 63 EMA.
Strategy for Usage
When the 100 week EMA is a strong uptrend and the 18/63 cross occurs the top is very likely in. It has occurred in every bull run top leading to the bear market.
PA-Adaptive, Stepped-MA of Composite RSI [Loxx]PA-Adaptive, Stepped-MA of Composite RSI is an RSI indicator using a different kind of RSI called Composite RSI. This indicator is Phase Accumulation Cycle Adaptive and uses a stepped moving average.
What is Composite RSI?
The name of the composite RSI might mislead a bit.
Composite RSI is not "compositing" RSIs but is a rather new way of calculating the RSI. Unlike the RSI that is a sort of a momentum indicators, composite RSI is more a trending indicator. It tends to filter out insignificant price changes and seems to be good in identifying the underlying trends.
What is the Phase Accumulation Cycle?
The phase accumulation method of computing the dominant cycle is perhaps the easiest to comprehend. In this technique, we measure the phase at each sample by taking the arctangent of the ratio of the quadrature component to the in-phase component. A delta phase is generated by taking the difference of the phase between successive samples. At each sample we can then look backwards, adding up the delta phases.When the sum of the delta phases reaches 360 degrees, we must have passed through one full cycle, on average.The process is repeated for each new sample.
The phase accumulation method of cycle measurement always uses one full cycle’s worth of historical data.This is both an advantage and a disadvantage.The advantage is the lag in obtaining the answer scales directly with the cycle period.That is, the measurement of a short cycle period has less lag than the measurement of a longer cycle period. However, the number of samples used in making the measurement means the averaging period is variable with cycle period. longer averaging reduces the noise level compared to the signal.Therefore, shorter cycle periods necessarily have a higher out- put signal-to-noise ratio.
Included
Bar coloring
Signals
Alerts
Loxx's Expanded Source Types
Loxx's Special Phase Accumulation Cycle
PA-Adaptive MACD w/ Variety Levels [Loxx]PA-Adaptive MACD w/ Variety Levels is a Phase Accumulation Adaptive MACD with both floating and quantile levels. This is tuned for Forex. You'll have to adjust the Phase Accumulation Cycle settings to work for crypto and stock markets.
What is MACD?
Moving average convergence divergence ( MACD ) is a trend-following momentum indicator that shows the relationship between two moving averages of a security’s price. The MACD is calculated by subtracting the 26-period exponential moving average ( EMA ) from the 12-period EMA .
What is the Phase Accumulation Cycle?
The phase accumulation method of computing the dominant cycle is perhaps the easiest to comprehend. In this technique, we measure the phase at each sample by taking the arctangent of the ratio of the quadrature component to the in-phase component. A delta phase is generated by taking the difference of the phase between successive samples. At each sample we can then look backwards, adding up the delta phases.When the sum of the delta phases reaches 360 degrees, we must have passed through one full cycle, on average.The process is repeated for each new sample.
The phase accumulation method of cycle measurement always uses one full cycle’s worth of historical data.This is both an advantage and a disadvantage.The advantage is the lag in obtaining the answer scales directly with the cycle period.That is, the measurement of a short cycle period has less lag than the measurement of a longer cycle period. However, the number of samples used in making the measurement means the averaging period is variable with cycle period. longer averaging reduces the noise level compared to the signal.Therefore, shorter cycle periods necessarily have a higher out- put signal-to-noise ratio.
Included:
Zero-line and signal cross options for bar coloring, signals, and alerts
Alerts
Signals
Loxx's Expanded Source Types
4 moving average types
Adaptivity: Measures of Dominant Cycles and Price Trend [Loxx]Adaptivity: Measures of Dominant Cycles and Price Trend is an indicator that outputs adaptive lengths using various methods for dominant cycle and price trend timeframe adaptivity. While the information output from this indicator might be useful for the average trader in one off circumstances, this indicator is really meant for those need a quick comparison of dynamic length outputs who wish to fine turn algorithms and/or create adaptive indicators.
This indicator compares adaptive output lengths of all publicly known adaptive measures. Additional adaptive measures will be added as they are discovered and made public.
The first released of this indicator includes 6 measures. An additional three measures will be added with updates. Please check back regularly for new measures.
Ehers:
Autocorrelation Periodogram
Band-pass
Instantaneous Cycle
Hilbert Transformer
Dual Differentiator
Phase Accumulation (future release)
Homodyne (future release)
Jurik:
Composite Fractal Behavior (CFB)
Adam White:
Veritical Horizontal Filter (VHF) (future release)
What is an adaptive cycle, and what is Ehlers Autocorrelation Periodogram Algorithm?
From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, page 135:
"Adaptive filters can have several different meanings. For example, Perry Kaufman's adaptive moving average (KAMA) and Tushar Chande's variable index dynamic average (VIDYA) adapt to changes in volatility . By definition, these filters are reactive to price changes, and therefore they close the barn door after the horse is gone.The adaptive filters discussed in this chapter are the familiar Stochastic , relative strength index (RSI), commodity channel index (CCI), and band-pass filter.The key parameter in each case is the look-back period used to calculate the indicator. This look-back period is commonly a fixed value. However, since the measured cycle period is changing, it makes sense to adapt these indicators to the measured cycle period. When tradable market cycles are observed, they tend to persist for a short while.Therefore, by tuning the indicators to the measure cycle period they are optimized for current conditions and can even have predictive characteristics.
The dominant cycle period is measured using the Autocorrelation Periodogram Algorithm. That dominant cycle dynamically sets the look-back period for the indicators. I employ my own streamlined computation for the indicators that provide smoother and easier to interpret outputs than traditional methods. Further, the indicator codes have been modified to remove the effects of spectral dilation.This basically creates a whole new set of indicators for your trading arsenal."
What is this Hilbert Transformer?
An analytic signal allows for time-variable parameters and is a generalization of the phasor concept, which is restricted to time-invariant amplitude, phase, and frequency. The analytic representation of a real-valued function or signal facilitates many mathematical manipulations of the signal. For example, computing the phase of a signal or the power in the wave is much simpler using analytic signals.
The Hilbert transformer is the technique to create an analytic signal from a real one. The conventional Hilbert transformer is theoretically an infinite-length FIR filter. Even when the filter length is truncated to a useful but finite length, the induced lag is far too large to make the transformer useful for trading.
From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, pages 186-187:
"I want to emphasize that the only reason for including this section is for completeness. Unless you are interested in research, I suggest you skip this section entirely. To further emphasize my point, do not use the code for trading. A vastly superior approach to compute the dominant cycle in the price data is the autocorrelation periodogram. The code is included because the reader may be able to capitalize on the algorithms in a way that I do not see. All the algorithms encapsulated in the code operate reasonably well on theoretical waveforms that have no noise component. My conjecture at this time is that the sample-to-sample noise simply swamps the computation of the rate change of phase, and therefore the resulting calculations to find the dominant cycle are basically worthless.The imaginary component of the Hilbert transformer cannot be smoothed as was done in the Hilbert transformer indicator because the smoothing destroys the orthogonality of the imaginary component."
What is the Dual Differentiator, a subset of Hilbert Transformer?
From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, page 187:
"The first algorithm to compute the dominant cycle is called the dual differentiator. In this case, the phase angle is computed from the analytic signal as the arctangent of the ratio of the imaginary component to the real component. Further, the angular frequency is defined as the rate change of phase. We can use these facts to derive the cycle period."
What is the Phase Accumulation, a subset of Hilbert Transformer?
From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, page 189:
"The next algorithm to compute the dominant cycle is the phase accumulation method. The phase accumulation method of computing the dominant cycle is perhaps the easiest to comprehend. In this technique, we measure the phase at each sample by taking the arctangent of the ratio of the quadrature component to the in-phase component. A delta phase is generated by taking the difference of the phase between successive samples. At each sample we can then look backwards, adding up the delta phases.When the sum of the delta phases reaches 360 degrees, we must have passed through one full cycle, on average.The process is repeated for each new sample.
The phase accumulation method of cycle measurement always uses one full cycle's worth of historical data.This is both an advantage and a disadvantage.The advantage is the lag in obtaining the answer scales directly with the cycle period.That is, the measurement of a short cycle period has less lag than the measurement of a longer cycle period. However, the number of samples used in making the measurement means the averaging period is variable with cycle period. longer averaging reduces the noise level compared to the signal.Therefore, shorter cycle periods necessarily have a higher out- put signal-to-noise ratio."
What is the Homodyne, a subset of Hilbert Transformer?
From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, page 192:
"The third algorithm for computing the dominant cycle is the homodyne approach. Homodyne means the signal is multiplied by itself. More precisely, we want to multiply the signal of the current bar with the complex value of the signal one bar ago. The complex conjugate is, by definition, a complex number whose sign of the imaginary component has been reversed."
What is the Instantaneous Cycle?
The Instantaneous Cycle Period Measurement was authored by John Ehlers; it is built upon his Hilbert Transform Indicator.
From his Ehlers' book Cybernetic Analysis for Stocks and Futures: Cutting-Edge DSP Technology to Improve Your Trading by John F. Ehlers, 2004, page 107:
"It is obvious that cycles exist in the market. They can be found on any chart by the most casual observer. What is not so clear is how to identify those cycles in real time and how to take advantage of their existence. When Welles Wilder first introduced the relative strength index (rsi), I was curious as to why he selected 14 bars as the basis of his calculations. I reasoned that if i knew the correct market conditions, then i could make indicators such as the rsi adaptive to those conditions. Cycles were the answer. I knew cycles could be measured. Once i had the cyclic measurement, a host of automatically adaptive indicators could follow.
Measurement of market cycles is not easy. The signal-to-noise ratio is often very low, making measurement difficult even using a good measurement technique. Additionally, the measurements theoretically involve simultaneously solving a triple infinity of parameter values. The parameters required for the general solutions were frequency, amplitude, and phase. Some standard engineering tools, like fast fourier transforms (ffs), are simply not appropriate for measuring market cycles because ffts cannot simultaneously meet the stationarity constraints and produce results with reasonable resolution. Therefore i introduced maximum entropy spectral analysis (mesa) for the measurement of market cycles. This approach, originally developed to interpret seismographic information for oil exploration, produces high-resolution outputs with an exceptionally short amount of information. A short data length improves the probability of having nearly stationary data. Stationary data means that frequency and amplitude are constant over the length of the data. I noticed over the years that the cycles were ephemeral. Their periods would be continuously increasing and decreasing. Their amplitudes also were changing, giving variable signal-to-noise ratio conditions. Although all this is going on with the cyclic components, the enduring characteristic is that generally only one tradable cycle at a time is present for the data set being used. I prefer the term dominant cycle to denote that one component. The assumption that there is only one cycle in the data collapses the difficulty of the measurement process dramatically."
What is the Band-pass Cycle?
From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, page 47:
"Perhaps the least appreciated and most underutilized filter in technical analysis is the band-pass filter. The band-pass filter simultaneously diminishes the amplitude at low frequencies, qualifying it as a detrender, and diminishes the amplitude at high frequencies, qualifying it as a data smoother. It passes only those frequency components from input to output in which the trader is interested. The filtering produced by a band-pass filter is superior because the rejection in the stop bands is related to its bandwidth. The degree of rejection of undesired frequency components is called selectivity. The band-stop filter is the dual of the band-pass filter. It rejects a band of frequency components as a notch at the output and passes all other frequency components virtually unattenuated. Since the bandwidth of the deep rejection in the notch is relatively narrow and since the spectrum of market cycles is relatively broad due to systemic noise, the band-stop filter has little application in trading."
From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, page 59:
"The band-pass filter can be used as a relatively simple measurement of the dominant cycle. A cycle is complete when the waveform crosses zero two times from the last zero crossing. Therefore, each successive zero crossing of the indicator marks a half cycle period. We can establish the dominant cycle period as twice the spacing between successive zero crossings."
What is Composite Fractal Behavior (CFB)?
All around you mechanisms adjust themselves to their environment. From simple thermostats that react to air temperature to computer chips in modern cars that respond to changes in engine temperature, r.p.m.'s, torque, and throttle position. It was only a matter of time before fast desktop computers applied the mathematics of self-adjustment to systems that trade the financial markets.
Unlike basic systems with fixed formulas, an adaptive system adjusts its own equations. For example, start with a basic channel breakout system that uses the highest closing price of the last N bars as a threshold for detecting breakouts on the up side. An adaptive and improved version of this system would adjust N according to market conditions, such as momentum, price volatility or acceleration.
Since many systems are based directly or indirectly on cycles, another useful measure of market condition is the periodic length of a price chart's dominant cycle, (DC), that cycle with the greatest influence on price action.
The utility of this new DC measure was noted by author Murray Ruggiero in the January '96 issue of Futures Magazine. In it. Mr. Ruggiero used it to adaptive adjust the value of N in a channel breakout system. He then simulated trading 15 years of D-Mark futures in order to compare its performance to a similar system that had a fixed optimal value of N. The adaptive version produced 20% more profit!
This DC index utilized the popular MESA algorithm (a formulation by John Ehlers adapted from Burg's maximum entropy algorithm, MEM). Unfortunately, the DC approach is problematic when the market has no real dominant cycle momentum, because the mathematics will produce a value whether or not one actually exists! Therefore, we developed a proprietary indicator that does not presuppose the presence of market cycles. It's called CFB (Composite Fractal Behavior) and it works well whether or not the market is cyclic.
CFB examines price action for a particular fractal pattern, categorizes them by size, and then outputs a composite fractal size index. This index is smooth, timely and accurate
Essentially, CFB reveals the length of the market's trending action time frame. Long trending activity produces a large CFB index and short choppy action produces a small index value. Investors have found many applications for CFB which involve scaling other existing technical indicators adaptively, on a bar-to-bar basis.
What is VHF Adaptive Cycle?
Vertical Horizontal Filter (VHF) was created by Adam White to identify trending and ranging markets. VHF measures the level of trend activity, similar to ADX DI. Vertical Horizontal Filter does not, itself, generate trading signals, but determines whether signals are taken from trend or momentum indicators. Using this trend information, one is then able to derive an average cycle length.
Market PulseBINANCE:BTCUSDT
This is the "Market Pulse" indicator from TOS Indicators.
The scope of this indicator is to identify which one of the four market stages we're in
█ WHAT ARE THE 4 STAGES?
ACCELERATION (or uptrend)
DECELERATION (or downtrend)
ACCUMULATION (occurs after the market has presumably found a bottom and buyers are coming in)
DISTRIBUTION (occurs after the market has presumably found a top and sellers are coming in)
█ WHAT ARE THE TOOLS THAT IT USES TO IDENTIFY THEM?
3 VWMA (Volume Weighted Moving Average)
1 VMA (Variable Moving Average)
VWMA = is a moving average which takes volume into account, and gives closes with higher volume an higher weight
vwma(src, len) => ta.sma(src * volume, len) / ta.sma(volume, len)
VMA = is a moving average which automatically adjusts the smoothing constant using Market Volatility
vma(src, len) =>
vi = ta.cmo(src, len) / 100
alpha = 2 / (len + 1) * math.abs(vi)
vma = 0.0
vma := alpha * src + nz(vma ) * (1 - alpha)
█ HOW CAN I INTERPRET THE INDICATOR?
1) On the top right you can see a box which tells you the Market Stage of the chart you are currently using:
If VWMA8 > VWMA21 > VWMA34 it signals ACCELERATION, color coded in green
If VWMA8 < VWMA21 < VWMA34 it signals DECELERATION, color coded in red
If neither of the previous two conditions are met it signals ACCUMULATION (yellow) if price closes above the VMA and DISTRIBUTION (orange) if price closes below the VMA
2) Next you have the actual VMA which is the line plotted on the chart and color coded in green, red or gray accordingly to the Market Stage with a filter applied:
for a bullish signal (green label) the market needs to be in ACCELERATION and price must be above the VMA
for a bearish signal (red label) the market needs to be in DECELERATION and price must be below the VMA
This characteristic makes it sometimes slower at giving direction indications, but also makes it more suitable to be considered as actual signals for buying and selling
ACCUMULATION and DISTRIBUTION are both rapresented with color gray, if you want you can consider:
the line going from green to gray as ACCUMULATION, your bias is bullish until the line turns red
the line going from red to gray as DISTRIBUTION, your bias is bearish until the line turns green
3) Then you can choose to plot the 3 VWMA to indentify pullbacks and entries for your trades
4) Finally you have the Market Screener, which you can choose to plot and gives a fast look to the markets you are interested on
It basically gives you the Market Stage for every Symbol you choose using the timeframes you input
The maximum number of Symbols you can set is 20, and for all of them you have 2 different timeframes you can choose to analyse.
By default the Symbols are set to the top 20 Cryptocurrency by Market Cap, and the timeframes to 4h and D
There is an option which is on by default and color codes ACCUMULATION and DISTRIBUTION the same as the box on the top right, you can turn it off to make them gray
As I've written in the tooltip inside the indicator you should only use the screener to analyse timeframes which are equal or higher than the one you are currently on your chart.
If you don't plan to use the screener you can delete every symbol from the input boxes to make the indicator update faster when changing timeframe or market.
Be aware that the screener is on BETA and may give repainting signals!
Buying Climax + Spring [Darwinian]Buying Climax + Spring Indicator
Overview
Advanced Wyckoff-based indicator that identifies potential market reversals through **Buying Climax** patterns (exhaustion tops) and **Spring** patterns (accumulation bottoms). Designed for traders seeking high-probability reversal signals with strict uptrend validation.
---
Method
🔴 Buying Climax Detection
Identifies exhaustion patterns at market tops using multi-condition analysis:
**Base Buying Climax (Red Triangle)**
- Volume spike > 1.8x average
- Range expansion > 1.8x average
- New 20-bar high reached
- Close finishes in lower 30% of bar range
- **Strict uptrend validation**: Price must be 30%+ above 20-day low
**Enhanced Buying Climax (Maroon Triangle)**
- All Base BC conditions PLUS:
- Gap up from previous high
- Intraday fade (close < open and below midpoint)
- **Higher confidence reversal signal**
🟢 Wyckoff Spring Detection
Identifies accumulation patterns at support levels:
- Price breaks below recent pivot low (false breakdown)
- Close recovers above pivot level (rejection)
- Occurs at trading range low
- Optional volume confirmation (1.5x+ average)
- Limited to 3 attempts per pivot (prevents over-signaling)
✅ Uptrend Validation Filter
**Four-condition composite filter** prevents false signals in sideways/downtrending markets:
1. Close-to-close rise ≥ 5% over lookback period
2. Price structure: Close > MA(10) > MA(20)
3. Swing low significantly below current price
4. **Primary requirement**: Current high ≥ 30% above 20-day low
---
Input Tuning Guide
Buying Climax Settings:
**Volume & Range Thresholds**
- `Volume Spike Threshold`: Default 1.8x
- Lower (1.5x) = More signals, more noise
- Higher (2.0-2.5x) = Fewer but stronger exhaustion signals
- `Range Spike Threshold`: Default 1.8x
- Adjust parallel to volume threshold
- Higher values = extreme volatility required
**Pattern Detection**
- `New High Lookback`: Default 20 bars
- Shorter (10-15) = Recent highs only
- Longer (30-50) = Major breakout detection
- `Close Off High Fraction`: Default 0.3 (30%)
- Lower (0.2) = Stricter rejection requirement
- Higher (0.4-0.5) = Allow weaker intraday fades
- `Gap Threshold`: Default 0.002 (0.2%)
- Increase (0.005-0.01) for stocks with wider spreads
- Decrease (0.001) for tight-spread instruments
- `Confirmation Window`: Default 5 bars
- Shorter (3) = Faster confirmation, more false positives
- Longer (7-10) = Wait for deeper automatic reaction
Uptrend Filter Settings
**Critical for Signal Quality**
- `Minimum Rise from 20-day Low`: Default 0.30 (30%)
- **Most important parameter**
- Lower (0.20-0.25) = More signals in moderate uptrends
- Higher (0.40-0.50) = Only extreme parabolic moves
- `Pole Lookback`: Default 30 bars
- Shorter (20) = Recent momentum focus
- Longer (40-50) = Longer-term trend validation
- `Minimum Rise % for Pole`: Default 0.05 (5%)
- Adjust based on market volatility
- Higher in strong bull markets (7-10%)
Wyckoff Spring Settings
- `Pivot Length`: Default 6 bars
- Shorter (3-4) = More frequent pivots, more signals
- Longer (8-10) = Major support/resistance only
- `Volume Threshold`: Default 1.5x
- Higher (1.8-2.0x) = Stronger conviction required
- Disable volume requirement for low-volume stocks
- `Trading Range Period`: Default 20 bars
- Match to consolidation timeframe being traded
- Shorter (10-15) for intraday patterns
- Longer (30-40) for weekly consolidations
---
Recommended Workflow
1. **Start with defaults** on daily timeframe
2. **Adjust uptrend filter** first (30% rise parameter)
- Too many signals? Increase to 35-40%
- Too few? Decrease to 25%
3. **Fine-tune volume/range multipliers** based on instrument volatility
4. **Enable alerts** for real-time monitoring:
- Base BC → Initial warning
- Enhanced BC → High-priority reversal
- Confirmed BC (AR) → Strong follow-through
- Spring → Accumulation opportunity
---
Alert System
- **Base Buying Climax**: Standard exhaustion pattern detected
- **Enhanced BC (Gap+Fade)**: Higher confidence reversal setup
- **Confirmed BC (AR)**: Automatic reaction validated (price drops below BC midline)
- **Wyckoff Spring**: Accumulation pattern at support
---
Best Practices
- Combine with support/resistance analysis
- Watch for BC clusters (multiple timeframes)
- Spring patterns work best after Buying Climax distribution
- Backtest parameters on your specific instruments
- Higher timeframes (daily/weekly) = higher reliability
---
Technical Notes
- Built with Pine Script v6
- No repainting (signals finalize on bar close)
- Minimal CPU usage (optimized calculations)
- Works on all timeframes and instruments
- Overlay indicator (displays on price chart)
---
*Indicator follows classical Wyckoff methodology with modern volatility filters*
MFx Radar (Money Flow x-Radar)Description:
MFx Radar is a precision-built multi-timeframe analysis tool designed to identify high-probability trend shifts and accumulation/distribution events using a combination of WaveTrend dynamics, normalized money flow, RSI, BBWP, and OBV-based trend biasing.
Multi-Timeframe Trend Scanner
Analyze trend direction across 5 customizable timeframes using WaveTrend logic to produce a clear trend consensus.
Smart Money Flow Detection
Adaptive hybrid money flow combines CMF and MFI, normalized across lookback periods, to pinpoint shifts in accumulation or distribution with high sensitivity.
Event-Based Labels & Alerts
Minimalist "Accum" and "Distr" text labels appear at key inflection points, based on hybrid flow flips — designed to highlight smart money moves without clutter.
Trigger & Pattern Recognition
Built-in logic detects anchor points, trigger confirmations, and rare "Snake Eye" formations directly on WaveTrend, enhancing trade timing accuracy.
Visual Dashboard Table
A real-time table provides score-based insight into signal quality, trend direction, and volume behavior, giving you a full picture at a glance.
MFx Radar helps streamline discretionary and system-based trading decisions by surfacing key confluences across price, volume, and momentum all while staying out of your way visually.
How to Use MFx Radar
MFx Radar is a multi-timeframe market intelligence tool designed to help you spot trend direction, momentum shifts, volume strength, and high-probability trade setups using confluence across price, flow, and timeframes.
Where to find settings To see the full visual setup:
After adding the script, open the Settings gear. Go to the Inputs tab and enable:
Show Trigger Diamonds
Show WT Cross Circles
Show Anchor/Trigger/Snake Eye Labels
Show Table
Show OBV Divergence
Show Multi-TF Confluence
Show Signal Score
Then, go to the Style tab to adjust colors and fills for the wave plots and hybrid money flow. (Use published chart as a reference.)
What the Waves and Colors Mean
Blue WaveTrend (WT1 / WT2). These are the main momentum waves.
WT1 > WT2 = bullish momentum
WT1 < WT2 = bearish momentum
Above zero = bullish bias
Below zero = bearish bias
When WT1 crosses above WT2, it often marks the beginning of a move — these are shown as green trigger diamonds.
VWAP-MACD Line
The yellow fill helps spot volume-based momentum.
Rising = trend acceleration
Use together with BBWP (bollinger band width percentile) and hybrid money flow for confirmation.
Hybrid Money Flow
Combines CMF and MFI, normalized and smoothed.
Green = accumulation
Red = distribution
Transitions are key — especially when price moves up, but money flow stays red (a divergence warning).
This is useful for spotting fakeouts or confirming smart money shifts.
Orange Vertical Highlights
Shows when price is rising, but money flow is still red.
Often a sign of hidden distribution or "exit pump" behavior.
Table Dashboard (Bottom-Right)
BBWP (Volatility Pulse)
When BBWP is low (<20), it signals consolidation — a breakout is likely to follow.
Use this with ADX and WaveTrend position to anticipate directional breakouts.
Trend by ADX
Shows whether the market is trending and in which direction.
Combined with money flow and RSI, this gives strong confirmation on breakouts.
OBV HTF Bias
Gives higher timeframe pressure (bullish/bearish/neutral).
Helps avoid taking counter-trend trades.
Pattern Labels (WT-Based)
A = Anchor Wave — WT hitting oversold
T = Trigger Wave — WT turning back up after anchor
👀 = Snake Eyes — Rare pattern, usually signaling strong reversal potential
These help in timing entries, especially when they align with other signals like BBWP breakouts, confluence, or smart money flow flips.
Multi-Timeframe (MTF) Consensus
The system checks WaveTrend on 5 different timeframes and gives:
Color-coded signals on each TF
A final score: “Mostly Up,” “Mostly Down,” or “Mixed”
When MTFs align with wave crosses, BBWP expansion, and hybrid money flow shifts, the probability of sustained move is higher.
Divergence Spotting (Advanced Tip)
Watch for:Price rising while money flow is red → Possible trap / early exit
Price dropping while money flow is green → Early accumulation
Combine this with anchor-trigger patterns and MTF trend support for spotting bottoms or tops early.
Final Tips
Use WT trigger crosses as initial signal. Confirm with money flow direction + color flip
Look at BBWP for breakout timing. Use table as your decision dashboard
Favor trades that align with MTF consensus
DYNAMIC TRADING DASHBOARDStudy Material for the "Dynamic Trading Dashboard"
This Dynamic Trading Dashboard is designed as an educational tool within the TradingView environment. It compiles commonly used market indicators and analytical methods into one visual interface so that traders and learners can see relationships between indicators and price action. Understanding these indicators, step by step, can help traders develop discipline, improve technical analysis skills, and build strategies. Below is a detailed explanation of each module.
________________________________________
1. Price and Daily Reference Points
The dashboard displays the current price, along with percentage change compared to the day’s opening price. It also highlights whether the price is moving upward or downward using directional symbols. Alongside, it tracks daily high, low, open, and daily range.
For traders, daily levels provide valuable reference points. The daily high and low are considered intraday support and resistance, while the median price of the day often acts as a pivot level for mean reversion traders. Monitoring these helps learners see how price oscillates within daily ranges.
________________________________________
2. VWAP (Volume Weighted Average Price)
VWAP is calculated as a cumulative average price weighted by volume. The dashboard compares the current price with VWAP, showing whether the market is trading above or below it.
For traders, VWAP is often a guide for institutional order flow. Price trading above VWAP suggests bullish sentiment, while trading below VWAP indicates bearish sentiment. Learners can use VWAP as a training tool to recognize trend-following vs. mean reversion setups.
________________________________________
3. Volume Analysis
The system distinguishes between buy volume (when the closing price is higher than the open) and sell volume (when the closing price is lower than the open). A progress bar highlights the ratio of buying vs. selling activity in percentage.
This is useful because volume confirms price action. For instance, if prices rise but sell volume dominates, it can signal weakness. New traders learning with this tool should focus on how volume often precedes price reversals and trends.
________________________________________
4. RSI (Relative Strength Index)
RSI is a momentum oscillator that measures price strength on a scale from 0 to 100. The dashboard classifies RSI readings into overbought (>70), oversold (<30), or neutral zones and adds visual progress bars.
RSI helps learners understand momentum shifts. During training, one should notice how trending markets can keep RSI extended for longer periods (not immediate reversal signals), while range-bound markets react more sharply to RSI extremes. It is an excellent tool for practicing trend vs. range identification.
________________________________________
5. MACD (Moving Average Convergence Divergence)
The MACD indicator involves a fast EMA, slow EMA, and signal line, with focus on crossovers. The dashboard shows whether a “bullish cross” (MACD above signal line) or “bearish cross” (MACD below signal line) has occurred.
MACD teaches traders to identify trend momentum shifts and divergence. During practice, traders can explore how MACD signals align with VWAP trends or RSI levels, which helps in building a structured multi-indicator analysis.
________________________________________
6. Stochastic Oscillator
This indicator compares the current close relative to a range of highs and lows over a period. Displayed values oscillate between 0 and 100, marking zones of overbought (>80) and oversold (<20).
Stochastics are useful for students of trading to recognize short-term momentum changes. Unlike RSI, it reacts faster to price volatility, so false signals are common. Part of the training exercise can be to observe how stochastic “flips” can align with volume surges or daily range endpoints.
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7. Trend & Momentum Classification
The dashboard adds simple labels for trend (uptrend, downtrend, neutral) based on RSI thresholds. Additionally, it provides quick momentum classification (“bullish hold”, “bearish hold”, or neutral).
This is beneficial for beginners as it introduces structured thinking: differentiating long-term market bias (trend) from short-term directional momentum. By combining both, traders can practice filtering signals instead of trading randomly.
________________________________________
8. Accumulation / Distribution Bias
Based on RSI levels, the script generates simplified tags such as “Accumulate Long”, “Accumulate Short”, or “Wait”.
This is purely an interpretive guide, helping learners think in terms of accumulation phases (when markets are low) and distribution phases (when markets are high). It reinforces the concept that trading is not only directional but also involves timing.
________________________________________
9. Overall Market Status and Score
Finally, the dashboard compiles multiple indicators (VWAP position, RSI, MACD, Stochastics, and price vs. median levels) into a Market Score expressed as a percentage. It also labels the market as Overbought, Oversold, or Normal.
This scoring system isn’t a recommendation but a learning framework. Students can analyze how combining different indicators improves decision-making. The key training focus here is confluence: not depending on one indicator but observing when several conditions align.
Extended Study Material with Formulas
________________________________________
1. Daily Reference Levels (High, Low, Open, Median, Range)
• Day High (H): Maximum price of the session.
DayHigh=max(Hightoday)DayHigh=max(Hightoday)
• Day Low (L): Minimum price of the session.
DayLow=min(Lowtoday)DayLow=min(Lowtoday)
• Day Open (O): Opening price of the session.
DayOpen=OpentodayDayOpen=Opentoday
• Day Range:
Range=DayHigh−DayLowRange=DayHigh−DayLow
• Median: Mid-point between high and low.
Median=DayHigh+DayLow2Median=2DayHigh+DayLow
These act as intraday guideposts for seeing how far the price has stretched from its key reference levels.
________________________________________
2. VWAP (Volume Weighted Average Price)
VWAP considers both price and volume for a weighted average:
VWAPt=∑i=1t(Pricei×Volumei)∑i=1tVolumeiVWAPt=∑i=1tVolumei∑i=1t(Pricei×Volumei)
Here, Price_i can be the average price (High + Low + Close) ÷ 3, also known as hlc3.
• Interpretation: Price above VWAP = bullish bias; Price below = bearish bias.
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3. Volume Buy/Sell Analysis
The dashboard splits total volume into buy volume and sell volume based on candle type.
• Buy Volume:
BuyVol=Volumeif Close > Open, else 0BuyVol=Volumeif Close > Open, else 0
• Sell Volume:
SellVol=Volumeif Close < Open, else 0SellVol=Volumeif Close < Open, else 0
• Buy Ratio (%):
VolumeRatio=BuyVolBuyVol+SellVol×100VolumeRatio=BuyVol+SellVolBuyVol×100
This helps traders gauge who is in control during a session—buyers or sellers.
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4. RSI (Relative Strength Index)
RSI measures strength of momentum by comparing gains vs. losses.
Step 1: Compute average gains (AG) and losses (AL).
AG=Average of Upward Closes over N periodsAG=Average of Upward Closes over N periodsAL=Average of Downward Closes over N periodsAL=Average of Downward Closes over N periods
Step 2: Calculate relative strength (RS).
RS=AGALRS=ALAG
Step 3: RSI formula.
RSI=100−1001+RSRSI=100−1+RS100
• Used to detect overbought (>70), oversold (<30), or neutral momentum zones.
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5. MACD (Moving Average Convergence Divergence)
• Fast EMA:
EMAfast=EMA(Close,length=fast)EMAfast=EMA(Close,length=fast)
• Slow EMA:
EMAslow=EMA(Close,length=slow)EMAslow=EMA(Close,length=slow)
• MACD Line:
MACD=EMAfast−EMAslowMACD=EMAfast−EMAslow
• Signal Line:
Signal=EMA(MACD,length=signal)Signal=EMA(MACD,length=signal)
• Histogram:
Histogram=MACD−SignalHistogram=MACD−Signal
Crossovers between MACD and Signal are used in studying bullish/bearish phases.
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6. Stochastic Oscillator
Stochastic compares the current close against a range of highs and lows.
%K=Close−LowestLowHighestHigh−LowestLow×100%K=HighestHigh−LowestLowClose−LowestLow×100
Where LowestLow and HighestHigh are the lowest and highest values over N periods.
The %D line is a smooth version of %K (using a moving average).
%D=SMA(%K,smooth)%D=SMA(%K,smooth)
• Values above 80 = overbought; below 20 = oversold.
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7. Trend and Momentum Classification
This dashboard generates simplified trend/momentum logic using RSI.
• Trend:
• RSI < 40 → Downtrend
• RSI > 60 → Uptrend
• In Between → Neutral
• Momentum Bias:
• RSI > 70 → Bullish Hold
• RSI < 30 → Bearish Hold
• Otherwise Neutral
This is not predictive, only a classification framework for educational use.
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8. Accumulation/Distribution Bias
Based on extreme RSI values:
• RSI < 25 → Accumulate Long Bias
• RSI > 80 → Accumulate Short Bias
• Else → Wait/No Action
This helps learners understand the idea of accumulation at lows (strength building) and distribution at highs (profit booking).
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9. Overall Market Status and Score
The tool adds up 5 bullish conditions:
1. Price above VWAP
2. RSI > 50
3. MACD > Signal
4. Stochastic > 50
5. Price above Daily Median
BullishScore=ConditionsMet5×100BullishScore=5ConditionsMet×100
Then it categorizes the market:
• RSI > 70 or Stoch > 80 → Overbought
• RSI < 30 or Stoch < 20 → Oversold
• Else → Normal
This encourages learners to think in terms of probabilistic conditions instead of single-indicator signals.
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⚠️ Warning:
• Trading financial markets involves substantial risk.
• You can lose more money than you invest.
• Past performance of indicators does not guarantee future results.
• This script must not be copied, resold, or republished without authorization from aiTrendview.
By using this material or the code, you agree to take full responsibility for your trading decisions and acknowledge that this is not financial advice.
________________________________________
⚠️ Disclaimer and Warning (From aiTrendview)
This Dynamic Trading Dashboard is created strictly for educational and research purposes on the TradingView platform. It does not provide financial advice, buy/sell recommendations, or guaranteed returns. Any use of this tool in live trading is completely at the user’s own risk. Markets are inherently risky; losses can exceed initial investment.
The intellectual property of this script and its methodology belongs to aiTrendview. Unauthorized reproduction, modification, or redistribution of this code is strictly prohibited. By using this study material or the script, you acknowledge personal responsibility for any trading outcomes. Always consult professional financial advisors before making investment decisions.
Advanced Volume Profile Pro Delta + POC + VAH/VAL# Advanced Volume Profile Pro - Delta + POC + VAH/VAL Analysis System
## WHAT THIS SCRIPT DOES
This script creates a comprehensive volume profile analysis system that combines traditional volume-at-price distribution with delta volume calculations, Point of Control (POC) identification, and Value Area (VAH/VAL) analysis. Unlike standard volume indicators that show only total volume over time, this script analyzes volume distribution across price levels and estimates buying vs selling pressure using multiple calculation methods to provide deeper market structure insights.
## WHY THIS COMBINATION IS ORIGINAL AND USEFUL
**The Problem Solved:** Traditional volume indicators show when volume occurs but not where price finds acceptance or rejection. Standalone volume profiles lack directional bias information, while basic delta calculations don't provide structural context. Traders need to understand both volume distribution AND directional sentiment at key price levels.
**The Solution:** This script implements an integrated approach that:
- Maps volume distribution across price levels using configurable row density
- Estimates delta (buying vs selling pressure) using three different methodologies
- Identifies Point of Control (highest volume price level) for key support/resistance
- Calculates Value Area boundaries where 70% of volume traded
- Provides real-time alerts for key level interactions and volume imbalances
**Unique Features:**
1. **Developing POC Visualization**: Real-time tracking of Point of Control migration throughout the session via blue dotted trail, revealing institutional accumulation/distribution patterns before they complete
2. **Multi-Method Delta Calculation**: Price Action-based, Bid/Ask estimation, and Cumulative methods for different market conditions
3. **Adaptive Timeframe System**: Auto-adjusts calculation parameters based on chart timeframe for optimal performance
4. **Flexible Profile Types**: N Bars Back (precise control), Days Back (calendar-based), and Session-based analysis modes
5. **Advanced Imbalance Detection**: Identifies and highlights significant buying/selling imbalances with configurable thresholds
6. **Comprehensive Alert System**: Monitors POC touches, Value Area entry/exit, and major volume imbalances
## HOW THE SCRIPT WORKS TECHNICALLY
### Core Volume Profile Methodology:
**1. Price Level Distribution:**
- Divides price range into user-defined rows (10-50 configurable)
- Calculates row height: `(Highest Price - Lowest Price) / Number of Rows`
- Distributes each bar's volume across price levels it touched proportionally
**2. Delta Volume Calculation Methods:**
**Price Action Method:**
```
Price Range = High - Low
Buy Pressure = (Close - Low) / Price Range
Sell Pressure = (High - Close) / Price Range
Buy Volume = Total Volume × Buy Pressure
Sell Volume = Total Volume × Sell Pressure
Delta = Buy Volume - Sell Volume
```
**Bid/Ask Estimation Method:**
```
Average Price = (High + Low + Close) / 3
Buy Volume = Close > Average ? Volume × 0.6 : Volume × 0.4
Sell Volume = Total Volume - Buy Volume
```
**Cumulative Method:**
```
Buy Volume = Close > Open ? Volume : Volume × 0.3
Sell Volume = Close ≤ Open ? Volume : Volume × 0.3
```
**3. Point of Control (POC) Identification:**
- Scans all price levels to find maximum volume concentration
- POC represents the price level with highest trading activity
- Acts as significant support/resistance level
- **Developing POC Feature**: Tracks POC evolution in real-time via blue dotted trail, showing how institutional interest migrates throughout the session. Upward POC migration indicates accumulation patterns, downward migration suggests distribution, providing early trend signals before price confirmation.
**4. Value Area Calculation:**
- Starts from POC and expands up/down to encompass 70% of total volume
- VAH (Value Area High): Upper boundary of value area
- VAL (Value Area Low): Lower boundary of value area
- Expansion algorithm prioritizes direction with higher volume
**5. Adaptive Range Selection:**
Based on profile type and timeframe optimization:
- **N Bars Back**: Fixed lookback period with performance optimization (20-500 bars)
- **Days Back**: Calendar-based analysis with automatic timeframe adjustment (1-365 days)
- **Session**: Current trading session or custom session times
### Performance Optimization Features:
- **Sampling Algorithm**: Reduces calculation load on large datasets while maintaining accuracy
- **Memory Management**: Clears previous drawings to prevent performance degradation
- **Safety Constraints**: Prevents excessive memory usage with configurable limits
## HOW TO USE THIS SCRIPT
### Initial Setup:
1. **Profile Configuration**: Select profile type based on trading style:
- N Bars Back: Precise control over data range
- Days Back: Intuitive calendar-based analysis
- Session: Real-time session development
2. **Row Density**: Set number of rows (30 default) - more rows = higher resolution, slower performance
3. **Delta Method**: Choose calculation method based on market type:
- Price Action: Best for trending markets
- Bid/Ask Estimate: Good for ranging markets
- Cumulative: Smoothed approach for volatile markets
4. **Visual Settings**: Configure colors, position (left/right), and display options
### Reading the Profile:
**Volume Bars:**
- **Length**: Represents relative volume at that price level
- **Color**: Green = net buying pressure, Red = net selling pressure
- **Intensity**: Darker colors indicate volume imbalances above threshold
**Key Levels:**
- **POC (Blue Line)**: Highest volume price - major support/resistance
- **VAH (Purple Dashed)**: Value Area High - upper boundary of fair value
- **VAL (Orange Dashed)**: Value Area Low - lower boundary of fair value
- **Value Area Fill**: Shaded region showing main trading range
**Developing POC Trail:**
- **Blue Dotted Lines**: Show real-time POC evolution throughout the session
- **Migration Patterns**: Upward trail indicates bullish accumulation, downward trail suggests bearish distribution
- **Early Signals**: POC movement often precedes price movement, providing advance warning of institutional activity
- **Institutional Footprints**: Reveals where smart money concentrated volume before final POC establishment
### Trading Applications:
**Support/Resistance Analysis:**
- POC acts as magnetic price level - expect reactions
- VAH/VAL provide intermediate support/resistance levels
- Profile edges show areas of low volume acceptance
**Developing POC Analysis:**
- **Upward Migration**: POC moving higher = institutional accumulation, bullish bias
- **Downward Migration**: POC moving lower = institutional distribution, bearish bias
- **Stable POC**: Tight clustering = balanced market, range-bound conditions
- **Early Trend Detection**: POC direction change often precedes price breakouts
**Entry Strategies:**
- Buy at VAL with POC as target (in uptrends)
- Sell at VAH with POC as target (in downtrends)
- Breakout plays above/below profile extremes
**Volume Imbalance Trading:**
- Strong buying imbalance (>60% threshold) suggests continued upward pressure
- Strong selling imbalance suggests continued downward pressure
- Imbalances near key levels provide high-probability setups
**Multi-Timeframe Context:**
- Use higher timeframe profiles for major levels
- Lower timeframe profiles for precise entries
- Session profiles for intraday trading structure
## SCRIPT SETTINGS EXPLANATION
### Volume Profile Settings:
- **Profile Type**: Determines data range for calculation
- N Bars Back: Exact number of bars (20-500 range)
- Days Back: Calendar days with timeframe adaptation (1-365 days)
- Session: Trading session-based (intraday focus)
- **Number of Rows**: Profile resolution (10-50 range)
- **Profile Width**: Visual width as chart percentage (10-50%)
- **Value Area %**: Volume percentage for VA calculation (50-90%, 70% standard)
- **Auto-Adjust**: Automatically optimizes for different timeframes
### Delta Volume Settings:
- **Show Delta Volume**: Enable/disable delta calculations
- **Delta Calculation Method**: Choose methodology based on market conditions
- **Highlight Imbalances**: Visual emphasis for significant volume imbalances
- **Imbalance Threshold**: Percentage for imbalance detection (50-90%)
### Session Settings:
- **Session Type**: Daily, Weekly, Monthly, or Custom periods
- **Custom Session Time**: Define specific trading hours
- **Previous Sessions**: Number of historical sessions to display
### Days Back Settings:
- **Lookback Days**: Number of calendar days to analyze (1-365)
- **Automatic Calculation**: Script automatically converts days to bars based on timeframe:
- Intraday: Accounts for 6.5 trading hours per day
- Daily: 1 bar per day
- Weekly/Monthly: Proportional adjustment
### N Bars Back Settings:
- **Lookback Bars**: Exact number of bars to analyze (20-500)
- **Precise Control**: Best for systematic analysis and backtesting
### Visual Customization:
- **Colors**: Bullish (green), Bearish (red), and level colors
- **Profile Position**: Left or Right side of chart
- **Profile Offset**: Distance from current price action
- **Labels**: Show/hide level labels and values
- **Smooth Profile Bars**: Enhanced visual appearance
### Alert Configuration:
- **POC Touch**: Alerts when price interacts with Point of Control
- **VA Entry/Exit**: Alerts for Value Area boundary interactions
- **Major Imbalance**: Alerts for significant volume imbalances
## VISUAL FEATURES
### Profile Display:
- **Horizontal Bars**: Volume distribution across price levels
- **Color Coding**: Delta-based coloring for directional bias
- **Smooth Rendering**: Optional smoothing for cleaner appearance
- **Transparency**: Configurable opacity for chart readability
### Level Lines:
- **POC**: Solid blue line with optional label
- **VAH/VAL**: Dashed colored lines with value displays
- **Extension**: Lines extend across relevant time periods
- **Value Area Fill**: Optional shaded region between VAH/VAL
### Information Table:
- **Current Values**: Real-time POC, VAH, VAL prices
- **VA Range**: Value Area width calculation
- **Positioning**: Multiple table positions available
- **Text Sizing**: Adjustable for different screen sizes
## IMPORTANT USAGE NOTES
**Realistic Expectations:**
- Volume profile analysis provides structural context, not trading signals
- Delta calculations are estimations based on price action, not actual order flow
- Past volume distribution does not guarantee future price behavior
- Combine with other analysis methods for comprehensive market view
**Best Practices:**
- Use appropriate profile types for your trading style:
- Day Trading: Session or Days Back (1-5 days)
- Swing Trading: Days Back (10-30 days) or N Bars Back
- Position Trading: Days Back (60-180 days)
- Consider market context (trending vs ranging conditions)
- Verify key levels with additional technical analysis
- Monitor profile development for changing market structure
**Performance Considerations:**
- Higher row counts increase calculation complexity
- Large lookback periods may affect chart performance
- Auto-adjust feature optimizes for most use cases
- Consider using session profiles for intraday efficiency
**Limitations:**
- Delta calculations are estimations, not actual transaction data
- Profile accuracy depends on available price/volume history
- Effectiveness varies across different instruments and market conditions
- Requires understanding of volume profile concepts for optimal use
**Data Requirements:**
- Requires volume data for accurate calculations
- Works best on liquid instruments with consistent volume
- May be less effective on very low volume or exotic instruments
This script serves as a comprehensive volume analysis tool for traders who need detailed market structure information with integrated directional bias analysis and real-time POC development tracking for informed trading decisions.
Dark Pool Block Trades - Institutional Volume📊 Dark Pool Block Trades - Institutional Volume
Visualize where institutional money positions before major price moves occur. This indicator reveals hidden dark pool block trades that often precede significant price movements - because when smart money deploys millions and billions in strategic accumulation or distribution, retail traders need to see where it's happening.
🎯 WHY DARK POOL DATA MATTERS:
Institutions don't move large capital randomly. Dark pool block trades represent strategic positioning by sophisticated money managers with superior research and conviction. These trades create hidden support/resistance levels that often predict future price action.
The key principle: Follow institutional flow, don't fight it. When institutions get involved, they create high-probability trading opportunities.
💰 HOW INSTITUTIONS INFLUENCE PRICE:
- Large block trades establish hidden accumulation/distribution zones
- Smart money builds positions BEFORE retail awareness increases
- Institutional activity creates "footprints" at key technical levels
- These trades often signal conviction plays ahead of major moves
- Institutions typically add to winning positions throughout trends
🔍 WHAT THIS INDICATOR SHOWS:
- Visual overlay of dark pool block trades directly on price charts
- Track institutional positioning across major stocks and ETFs
- Identify accumulation/distribution zones before they become obvious to retail
- Spot high-conviction institutional trades in real-time visualization
- Customizable block trade size filters and timeframe selection
- Historical institutional activity up to 5 years or custom ranges
💡 THE TRADING ADVANTAGE:
Instead of guessing price direction, see where institutions are already positioning. When large block trades appear in dark pools, you're witnessing strategic institutional commitment that frequently leads to significant price movements.
⚡ HOW IT WORKS:
This Pine Script displays institutional dark pool transactions as visual markers on your charts. The script comes with sample data for immediate use. For expanded ticker coverage and real-time updates, external data services are available.
🎯 IDEAL FOR:
- Swing traders following institutional footprints
- Traders seeking setups backed by smart money conviction
- Position traders looking for accumulation zones
- Anyone wanting to align with institutional flow rather than fight it
🔄 SAMPLE DATA INCLUDED:
Pre-loaded with institutional activity data across popular tickers, updated daily to demonstrate how dark pool activity correlates with future price movements.
The script initially covers these tickers going back 6 months showing the top 10 trades by volume over 400,000 shares: AAPL, AMD, AMZN, ARKK, ARKW, BAC, BITO, COIN, COST, DIA, ETHA, GLD, GOOGL, HD, HYG, IBB, IWM, JNJ, JPM, LQD, MA, META, MSFT, NVDA, PG, QQQ, RIOT, SLV, SMCI, SMH, SOXX, SPY, TLT, TSLA, UNH, USO, V, VEA, VNQ, VOO, VTI, VWO, WMT, XLE, XLF, XLK, XLU, XLV, XLY
Volume Range Profile with Fair Value (Zeiierman)█ Overview
The Volume Range Profile with Fair Value (Zeiierman) is a precision-built volume-mapping tool designed to help traders visualize where institutional-level activity is occurring within the price range — and how that volume behavior shifts over time.
Unlike traditional volume profiles that rely on fixed session boundaries or static anchors, this tool dynamically calculates and displays volume zones across both the upper and lower ends of a price range, revealing point-of-control (POC) levels, directional volume flow, and a fair value drift line that updates live with each candle.
You’re not just looking at volume anymore. You’re dissecting who’s in control — and at what price.
⚪ In simple terms:
Upper Zone = The upper portion of the price range, showing concentrated volume activity — typically where selling or distribution may occur
Lower Zone = The lower portion of the price range, highlighting areas of high volume — often associated with buying or accumulation
POC Bin = The bin (price level) with the highest traded volume in the zone — considered the most accepted price by the market
Fair Value Trend = A dynamic trend line tracking the average POC price over time — visualizing the evolving fair value
Zone Labels = Display real-time breakdown of buy/sell volume within each zone and inside the POC — revealing who’s in control
█ How It Works
⚪ Volume Zones
Upper Zone: Anchored at the highest high in the lookback period
Lower Zone: Anchored at the lowest low in the lookback period
Width is user-defined via % of range
Each zone is divided into a series of volume bins
⚪ Volume Bins (Histograms)
Each zone is split into N bins that show how much volume occurred at each level:
Taller = More volume
The POC bin (Point of Control) is highlighted
Labels show % of volume in the POC relative to the whole zone
⚪ Buy vs Sell Breakdown
Each volume bin is split by:
Buy Volume = Close ≥ Open
Sell Volume = Close < Open
The script accumulates these and displays total Buy/Sell volume per zone.
⚪ Fair Value Drift Line
A POC trend is plotted over time:
Represents where volume was most active across each range
Color changes dynamically — green for rising, red for falling
Serves as a real-time fair value anchor across changing market structure
█ How to Use
⚪ Identify Key Control Zones
Use Upper/Lower Zone structures to understand where supply and demand is building.
Zones automatically adapt to recent highs/lows and re-center volume accordingly.
⚪ Follow Institutional Activity
Watch for POC clustering near price tops or bottoms.
Large volumes near extremes may indicate accumulation or distribution.
⚪ Spot Fair Value Drift
The fair value trend line (average POC price) gives insight into market equilibrium.
One strategy can be to trade a re-test of the fair value trend, trades are taken in the direction of the current trend.
█ Understanding Buy & Sell Volume Labels (Zone Totals)
These labels show the total buy and sell volume accumulated within each zone over the selected lookback period:
Buy Vol (green label) → Total volume where candles closed bullish
Sell Vol (red label) → Total volume where candles closed bearish
Together, they tell you which side dominated:
Higher Buy Vol → Bullish accumulation zone
Higher Sell Vol → Bearish distribution zone
This gives a quick visual insight into who controlled the zone, helping you spot areas of demand or supply imbalance.
█ Understanding POC Volume Labels
The POC (Point of Control) represents the price level where the most volume occurred within the zone. These labels break down that volume into:
Buy % – How much of the volume was buying (price closed up)
Sell % – How much was selling (price closed down)
Total % – How much of the entire zone’s volume happened at the POC
Use it to spot strong demand or supply zones:
High Buy % + High Total % → Strong buying interest = likely support
High Sell % + High Total % → Strong selling pressure = likely resistance
It gives a deeper look into who was in control at the most important price level.
█ Why It’s Useful
Track where fair value is truly forming
Detect aggressive volume accumulation or dumping
Visually split buyer/seller control at the most relevant price levels
Adapt volume structures to current trend direction
█ Settings Explained
Lookback Period: Number of bars to scan for highs/lows. Higher = smoother zones, Lower = reactive.
Zone Width (% of Range): Controls how much of the range is used to define each zone. Higher = broader zones.
Bins per Zone: Number of volume slices per zone. Higher = more detail, but heavier on resources.
-----------------
Disclaimer
The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
Grid TraderGrid Trader Indicator ( GTx ):
Overview
The Grid Trader Indicator is a tool that helps traders visualize key levels within a specified trading range. The indicator plots accumulation and distribution levels, an entry level, an exit level, and a midpoint. This guide will help you understand how to use the indicator and its features for effective grid trading.
Basics of Trading Range, Grid Buy, and Grid Sell
Trading Range
A trading range is the horizontal price movement between a defined upper ( resistance ) and lower ( support ) level over a period of time. When a security trades within a range, it repeatedly moves between these two levels without trending upwards or downwards significantly. Traders often use the trading range to identify potential buy and sell points:
Upper Level (Resistance): This is the price level at which selling pressure overcomes buying pressure, preventing the price from rising further.
Lower Level (Support): This is the price level at which buying pressure overcomes selling pressure, preventing the price from falling further.
Grid Trading Strategy
Grid trading is a type of trading strategy that involves placing buy and sell orders at predefined intervals around a set price. It aims to profit from the natural market volatility by buying low and selling high in a range-bound market. The strategy divides the trading range into several grid levels where orders are placed.
Grid Buy
Grid buy orders are placed at intervals below the current price . When the price drops to these levels, buy orders are triggered . This strategy ensures that the trader buys more as the price falls, potentially lowering the average purchase price .
Grid Sell
Grid sell orders are placed at intervals above the current price . When the price rises to these levels, sell orders are triggered . This ensures that the trader sells portions of their holdings as the price increases, potentially securing profits at higher levels .
Key Points of Grid Trading
Grid Size : The interval between each buy and sell order. This can be constant (e.g., $2 intervals) or variable based on certain conditions.
Accumulation Range : The lower part of the trading range where buy orders are placed.
Distribution Range : The upper part of the trading range where sell orders are placed.
Midpoint : The average price of the entry and exit levels, often used as a reference point for balance.
As the price moves up and down within this range, your buy orders will be triggered as the price drops and your sell orders will be triggered as the price rises. This allows you to accumulate more of the asset at lower prices and sell portions at higher prices, profiting from the price oscillations within the defined range. Grid trading can be particularly effective in a sideways market where there is no clear long-term trend. However, it requires careful monitoring and adjustment of grid levels based on market conditions to minimize risks and maximize returns .
Configuring the Indicator :
Once the indicator is added, you will see a settings icon next to it. Click on it to open the settings menu.
Adjust the Upper Level , Lower Level , Entry Level , and Exit Level to match your trading strategy and market conditions.
Set the Levels Visibility to control how many bars back the levels will be plotted.
Interpreting the Levels :
Accumulation Levels : These are plotted below the entry level and are potential buy zones. They are labeled as Accumulation Level 1, 2, and 3.
Distribution Levels : These are plotted above the exit level and are potential sell zones. They are labeled as Distribution Level 1, 2, and 3.
Upper Level : Marked in fuchsia, indicating the top boundary of the trading range.
Exit Level : Marked in yellow, indicating the level at which you plan to exit trades.
Midpoint : Marked in white, indicating the average of the entry and exit levels.
Entry Level : Marked in yellow, indicating the level at which you plan to enter trades.
Lower Level : Marked in aqua, indicating the bottom boundary of the trading range.
By visualizing key levels, you can make informed decisions on where to place buy and sell orders, potentially maximizing your trading profits through systematic grid trading.
Sadgir Patterns with SL/TPThe "Sadgir Patterns with SL/TP" is a cutting-edge trading indicator designed for traders seeking to leverage the power of Hull Moving Averages in conjunction with phase accumulation analysis. This unique indicator, developed on the Pine Script platform, is ideal for various markets, including stocks, forex, cryptocurrencies, and commodities.
Key Features:
Adaptive Hull Moving Average: Utilizes an adaptive Hull Moving Average, which provides a smooth and responsive moving average line, aiding in identifying trend directions and potential market reversals.
Phase Accumulation Analysis: Integrates phase accumulation calculations to dynamically adjust the length of the Hull Moving Average, ensuring that the indicator stays in sync with market conditions.
Signal Generation: Generates clear "Long" and "Short" signals, which are visually represented on the chart, assisting traders in making informed decisions.
Dynamic Stop Loss and Take Profit Levels: Automatically calculates and plots dynamic stop loss (SL) and take profit (TP) levels as horizontal lines on the chart, based on user-defined percentage settings. These levels adjust in real-time with the price action, offering a systematic approach to risk management.
Customizable Settings: Provides users with the flexibility to adjust the source of the moving average, power settings for the Hull Moving Average, cycles, and powers for phase accumulation, as well as the percentage values for SL and TP levels.
Visual and Alert Features: Includes options for coloring the bars based on the trend direction and displays trade signals with distinct shapes. Additionally, alert conditions are set for both Long and Short signals, enabling traders to stay informed of potential trade opportunities.
Usage:
This indicator is designed for traders of all levels, from beginners to advanced. It can be used for trend following, catching reversals, or as part of a larger trading strategy. The dynamic SL and TP levels aid in managing trades effectively, providing both entry and exit points. However, traders are advised to use this indicator in conjunction with other analysis tools and consider the overall market context for the best results.
Disclaimer:
Trading involves risk, and it's important to do your own research and consider your risk tolerance before using this indicator. This tool is not intended as financial advice.
NSE:BANKNIFTY
NSE:NIFTY
MCX:CRUDEOIL1!
Market Phases NJRMarket Phases Indicator
Overview:
The Market Phases Indicator is a versatile tool designed for traders to identify key market phases, including accumulation, distribution, markup, and markdown. By analyzing the relationship between price and volume, this indicator aims to assist traders in recognizing potential shifts in market sentiment and trend direction.
Features:
1. **Moving Average Analysis:**
- Utilizes a customizable moving average length to assess the overall trend direction.
2. **Volume Confirmation:**
- Incorporates volume analysis to confirm the strength of identified market phases.
3. **Visualization:**
- Clearly visualizes accumulation, distribution, markup, and markdown phases on the price chart using intuitive shapes.
Input Parameters:
- **Moving Average Length (default: 20):**
- Adjusts the length of the moving average for trend analysis.
- **Volume Multiplier (default: 1.5):**
- Sets the multiplier to customize the volume threshold for identifying significant market phases.
How to Use:
1. **Accumulation and Distribution:**
- Green triangles indicate potential accumulation phases when the closing price is above the moving average, and volume is higher than the specified threshold. Red triangles indicate potential distribution phases.
2. **Markup and Markdown:**
- Blue triangles suggest potential markup phases when the closing price is above the moving average, and volume is below the specified threshold. Orange triangles indicate potential markdown phases.
Important Notes:
- This indicator is a tool for analysis and should be used in conjunction with other technical analysis methods.
- Parameters can be adjusted based on the specific characteristics of the asset being analyzed.
Disclaimer:
Trading involves risk, and no indicator can guarantee profits. Users should exercise caution, conduct thorough research, and consider risk management principles when making trading decisions.
