BK AK-Zenith💥 Introducing BK AK-ZENITH — Adaptive Rhythm RSI for Peak/Valley Warfare 💥
This is not another generic RSI. This is ZENITH: it measures where momentum is on the scale, then tells you when it’s hitting extremes, when it’s turning, and when price is lying through its teeth with divergence.
At its core, ZENITH does one thing ruthlessly well:
it matches the oscillator’s period to the market’s current rhythm—adaptive when the market is fast, adaptive when the market is slow—so your signals stop being “late because the settings were wrong.”
🎖 Full Credit — Respect the Origin (AlgoAlpha)
The core RSI architecture in this form belongs to AlgoAlpha—one of the best introducers and coders on TradingView. They originated this adaptive/Rhythm-RSI framework and the way it’s presented and engineered.
BK AK-ZENITH is my enhancement layer on top of AlgoAlpha’s foundation.
I kept the spine intact, and I added tactical systems: clearer Peak/Valley warfare logic, pivot governance (anti-spam), divergence strike markers, momentum flip confirmation, and a war-room readout—so it trades like a weapon, not a toy.
Respect where it started: AlgoAlpha built the engine. I tuned it for battlefield use.
🧠 What Exactly is BK AK-ZENITH?
BK AK-ZENITH is an Adaptive Period RSI (or fixed if you choose), designed to read momentum like a range of intent rather than a single overbought/oversold gimmick.
Core Systems Inside ZENITH
✅ Adaptive Period RSI (Rhythm Engine)
Automatically adjusts its internal RSI length to match current market cadence.
(Optional fixed length mode if you want static.)
✅ Optional HMA Smoothing
Cleaner shape without turning it into a laggy moving average.
✅ Peak / Valley Zones (default 80/20)
Hard boundaries that define “true extremes” so you stop treating every wiggle like a signal.
✅ Pivot-Based BUY/SELL Triangles + Cooldown
Signals are governed by pivots and a cooldown so it doesn’t machine-gun trash.
✅ Momentum Flip Diamonds (◇)
Shows when the oscillator’s slope flips—clean confirmation for “engine change.”
✅ Divergence Lightning (⚡)
Exposes when price is performing confidence while momentum is quietly breaking.
✅ War-Room Table / Meter
Bias, zone, reading, and adaptive period printed so you don’t “interpret”—you execute.
✅ Alerts Suite
Pivots, divergences, zone entries—so the chart calls you, not your emotions.
🎯 How to use it (execution rules)
1) Zones = permission
Valley (≤ Valley level): demand territory. Stalk reversal structure; stop chasing breakdown candles.
Peak (≥ Peak level): supply territory. Harvest, tighten, stop adding risk at the top.
2) Pivot triangles = the shot clock
Your ▲/▼ signals are pivot-confirmed with a cooldown. That’s intentional.
This is designed to force patience and prevent overtrading.
3) Divergence = truth serum
When price makes the “confident” high/high or low/low but ZENITH disagrees, you’re seeing internal change before the crowd does.
Treat divergence as warning + timing context, not a gambling button.
4) Meter/Table = discipline
If you can’t summarize the state in one glance, you’ll overtrade. ZENITH prints the state so your brain stops inventing stories.
🔧 Settings that actually matter
Adaptive Period ON (default): the whole point of ZENITH
Peak/Valley levels: how strict extremes must be
Pivot strength + Cooldown: your anti-spam governor
Divergence pivot length: controls how “major” divergence must be
The “AK” in the name is an acknowledgment of my mentor A.K. His standards—patience, precision, clarity, emotional control—are why this tool is built with governors instead of hype.
And above all: all praise to Gd—the true source of wisdom, restraint, and right timing.
👑 King Solomon Lens — ZENITH Discernment
Solomon asked Gd for something most people never ask for: not wealth, not victory—discernment. The ability to separate what looks true from what is true.
That is exactly what momentum work is supposed to do.
1) Honest weights, honest measures.
In Solomon’s world, crooked scales were an abomination because they disguised reality. In trading, the crooked scale is your own excitement: you see one green candle and call it strength. ZENITH forces an honest measure—0 to 100—so you deal in degree, not drama. A Peak is not “bullish.” A Peak is “momentum priced in.” A Valley is not “bearish.” A Valley is “selling pressure reaching exhaustion.”
2) Wisdom adapts to seasons.
Solomon’s order wasn’t chaos—there was a time to build, a time to harvest, a time to wait. Markets have seasons too: trend seasons, chop seasons, compression seasons, expansion seasons. Fixed-length RSI pretends every season is the same. ZENITH does not. It listens for rhythm and adjusts its internal timing so your read stays relevant to today’s market tempo—not last month’s.
3) The sword test: revealing what’s hidden.
Solomon’s most famous judgment wasn’t about theatrics—it was about revealing the truth beneath appearances. Divergence is that same test in markets: price can perform strength while the engine quietly weakens, or perform weakness while momentum secretly repairs. The ⚡ is not a prophecy. It’s a revelation: “what you see on price is not the full story.”
That’s ZENITH discipline: measure → discern → execute.
And may Gd bless your judgment to act only when the measure is clean.
⚔️ Final
BK AK-ZENITH is a momentum fire-control system: adaptive rhythm + extreme zones + pivot timing + divergence truth.
Use it to stop feeling trades and start weighing them. Praise to Gd always. 🙏
M-oscillator
Kinetic Elasticity Reversion System - Adaptive Genesis Engine🧬 KERS-AGE - EVOLVED KINETIC ELASTICITY REVERSION SYSTEM
EDUCATIONAL GUIDE & THEORETICAL FOUNDATION
⚠️ IMPORTANT DISCLAIMER
This indicator and guide are provided for educational and informational purposes only. This is NOT financial advice, investment advice, or a recommendation to buy or sell any security.
Trading involves substantial risk of loss. Past performance does not guarantee future results. The performance metrics, win rates, and examples shown are from historical backtesting and do not represent actual trading results. Always conduct your own research, paper trade extensively, and never risk capital you cannot afford to lose.
The developers assume no responsibility for any trading losses incurred through use of this indicator.
INTRODUCTION
KERS-AGE (Kinetic Elasticity Reversion System - Adaptive Genetic Evolution) represents an educational exploration of adaptive trading systems. Unlike traditional indicators with fixed parameters, KERS-AGE demonstrates a dynamic, evolving approach that adjusts to market conditions through genetic algorithms and machine learning techniques.
This guide explains the theoretical concepts, technical implementation, and educational examples of how the system operates.
CONCEPTUAL FRAMEWORK
Traditional Indicators vs. Adaptive Systems:
Traditional Indicators:
Fixed parameters
Single strategy approach
Static behavior
Designed for specific conditions
Require manual optimization
Adaptive System Approach (KERS-AGE):
Dynamic parameters (adjust based on conditions)
Multiple strategies tested simultaneously
Pattern recognition (cluster analysis)
Regime-aware (speciation)
Automated optimization (genetic algorithms)
Transparent operation (detailed dashboard)
CORE CONCEPTS EXPLAINED
1. THE ELASTICITY ANALOGY 🎯
The indicator models price behavior as if connected to a moving average by an elastic band:
Price extends away → Elastic tension builds → Potential reversion point identified
Key Measurements:
STRETCH: Distance from price to equilibrium (MA)
TENSION: Normalized force calculation
THRESHOLD: Point where multiple factors align
Theoretical Foundation:
Markets have historically shown mean-reverting tendencies around fair value. This concept quantifies the deviation and identifies potential reversal zones based on multiple confluence factors.
Mathematical Approach:
text
Tension Score = (Price Distance from MA) / (Band Width) × Volatility Scaling
Signal Threshold = Multiple of ATR × Dynamic Volatility Ratio
Confluence = Tension Score + Additional Factors
2. THE 6 SIGNAL TYPES 📊
The system recognizes 6 distinct pattern categories:
A. ELASTIC SIGNALS
Pattern: Price reaches statistical band extremes
Theory: Maximum deviation from mean suggests potential reversion
Detection: Price touches outer zones (typically 2-3× ATR from MA)
Component: Mathematical band extension measurement
Historical Context: Often observed in markets with clear swing patterns
B. WICK SIGNALS
Pattern: Extended rejection wicks on candles
Theory: Failed breakout attempts may indicate directional exhaustion
Detection: Upper/lower wick exceeding 2× body size
Component: Real-time price rejection measurement
Historical Context: Common in volatile conditions with rapid reversals
C. EXHAUSTION SIGNALS
Pattern: Decelerating momentum despite price extension
Theory: Velocity and acceleration divergence may precede reversals
Detection: Decreasing velocity with negative acceleration
Component: Momentum derivative analysis
Historical Context: Often seen at trend maturity points
D. CLIMAX SIGNALS
Pattern: Volume spike at price extreme
Theory: Unusual volume at extremes historically correlates with turning points
Detection: Volume 1.5-2.5× average at band extreme
Component: Volume-price relationship analysis
Historical Context: Associated with institutional activity or capitulation
E. STRUCTURE SIGNALS
Pattern: Fractal pivot formations (swing highs/lows)
Theory: Market structure points have historically acted as support/resistance
Detection: 2-4 bar pivot patterns
Component: Classical technical analysis
Historical Context: Universal across timeframes and markets
F. DIVERGENCE SIGNALS
Pattern: RSI divergence versus price
Theory: Momentum divergence has historically preceded price reversals
Detection: Price makes new extreme but RSI does not
Component: Oscillator divergence detection
Historical Context: Considered a leading indicator in technical analysis
Pattern Confluence:
Historical testing suggests stronger signals when multiple types align:
Elastic + Wick + Volume = Higher confluence score
Elastic + Exhaustion + Divergence = Multiple confirmation factors
Any 3+ types = Increased pattern strength
Note: Past pattern performance does not guarantee future occurrence.
3. REGIME DETECTION 🌍
The system attempts to classify market conditions into three behavioral regimes:
📈 TREND REGIME
Detection Methodology:
text
Efficiency Ratio = Net Movement / Total Movement
Classification: Efficiency > 0.5 AND Volatility < 1.3 → TREND
Characteristics Observed:
Directional price movement
Relatively lower volatility
Defined higher highs/lower lows
Persistent directional momentum
System Response:
Reduces signal frequency
Prioritizes trend-specialist strategies
Applies additional filtering to counter-trend signals
Increases confluence requirements
Educational Note:
In trending conditions, counter-trend mean reversion signals historically have shown reduced reliability. Users may consider additional confirmation when trend regime is detected.
↔️ RANGE REGIME
Detection Methodology:
text
Classification: Efficiency < 0.5 AND Volatility 0.9-1.4 → RANGE
Characteristics Observed:
Oscillating price action
Defined support/resistance zones
Mean-reverting behavior patterns
Relatively balanced directional flow
System Response:
Increases signal frequency
Activates range-specialist strategies
Adjusts bands relative to volatility
Reduces confluence threshold
Educational Note:
Historical backtesting suggests mean reversion systems have performed better in ranging conditions. This does not guarantee future performance.
🌊 VOLATILE REGIME
Detection Methodology:
text
Classification: DVS (Dynamic Volatility Scaling) > 1.5 → VOLATILE
Characteristics Observed:
Erratic price swings
Expanded ranges
Elevated ATR readings
Often news or event-driven
System Response:
Activates volatility-specialist strategies
Widens bands automatically
Prioritizes wick rejection signals
Emphasizes volume confirmation
Educational Note:
Volatile conditions historically present both opportunity and increased risk. Wider stops may be appropriate for risk management.
4. GENETIC EVOLUTION EXPLAINED 🧬
The system employs genetic algorithms to optimize parameters - an approach used in computational finance research.
The Evolution Process:
STEP 1: INITIALIZATION
text
Initial State: System creates 4 starter strategies
- Strategy 0: Range-optimized parameters
- Strategy 1: Trend-optimized parameters
- Strategy 2: Volatility-optimized parameters
- Strategy 3: Balanced parameters
Each contains 14 adjustable parameters (genes):
- Band sensitivity
- Extension multiplier
- Wick threshold
- Momentum threshold
- Volume multiplier
- Component weights (elastic, wick, momentum, volume, fractal)
- Target percentage
STEP 2: COMPETITION (Shadow Trading)
text
Early Bars: All strategies generate signals in parallel
- Each tracks hypothetical performance independently
- Simulated P&L, win rate, Sharpe ratio calculated
- No actual trades executed (educational simulation)
- Performance metrics recorded for analysis
STEP 3: FITNESS EVALUATION
text
Fitness Calculation =
0.25 × Win Rate +
0.25 × PnL Score +
0.15 × Drawdown Score +
0.30 × Sharpe Ratio Score +
0.05 × Trade Count Score
With Walk-Forward enabled:
Fitness = 0.60 × Test Score + 0.40 × Train Score
With Speciation enabled:
Fitness adjusted by Diversity Penalty
STEP 4: SELECTION (Tournament)
text
Periodically (default every 50 bars):
- Randomly select 4 active strategies
- Compare fitness scores
- Top 2 selected as "parents"
STEP 5: CROSSOVER (Breeding)
text
Parent 1 Fitness: 0.65
Parent 2 Fitness: 0.55
Weight calculation: 0.65/(0.65+0.55) = 54%
For each parameter:
Child Parameter = (0.54 × Parent1) + (0.46 × Parent2)
Example:
Band Sensitivity: (0.54 × 1.5) + (0.46 × 2.0) = 1.73
STEP 6: MUTATION
text
For each parameter:
if random(0-1) < Mutation Rate (default 0.15):
Add random variation: -12% to +12%
Purpose: Prevents premature convergence
Enables: Discovery of novel parameter combinations
ADAPTIVE MUTATION:
If population fitness converges → Mutation rate × 1.5
(Encourages exploration when diversity decreases)
STEP 7: INSERTION
text
New strategy added to population:
- Assigned unique ID number
- Generation counter incremented
- Begins shadow trading
- Competes with existing strategies
STEP 8: CULLING (Selection Pressure)
text
Periodically (default every 100 bars):
- Identify lowest fitness strategy
- Verify not elite (protected top performers)
- Verify not last of species
- Remove from population
Result: Maintains selection pressure
Effect: Prevents weak strategies from diluting signals
STEP 9: SIGNAL GENERATION LOGIC
text
When determining signals to display:
If Ensemble enabled:
- All strategies cast weighted votes
- Weights based on fitness scores
- Specialists receive boost in matching regime
- Signal generated if consensus threshold reached
If Ensemble disabled:
- Single highest-fitness strategy used
STEP 10: ADAPTATION OBSERVATION
text
Over time: Population characteristics may shift
- Lower-performing strategies removed
- Higher-performing strategies replicated
- Parameters adjust toward observed optima
- Fitness scores generally trend upward
Long-term: Population reaches maturity
- Strategies become specialized
- Parameters optimized for recent conditions
- Performance stabilizes
Educational Context:
Genetic algorithms are a recognized computational method for optimization problems. This implementation applies those concepts to trading parameter optimization. Past optimization results do not guarantee future performance.
5. SPECIATION (Niche Specialization) 🐟🦎🦅
Inspired by biological speciation theory applied to algorithmic trading.
The Three Species:
RANGE SPECIALISTS 📊
text
Optimized for: Sideways market conditions
Parameter tendencies:
- Tighter bands (1.0-1.5× ATR)
- Higher sensitivity to elastic stretch
- Emphasis on fractal structure
- More frequent signal generation
Typically emerge when:
- Range regime detected
- Clear support/resistance present
- Mean reversion showing historical success
Historical backtesting observations:
- Win rates often in 55-65% range
- Smaller reward/risk ratios (0.5-1.5R)
- Higher trade frequency
TREND SPECIALISTS 📈
text
Optimized for: Directional market conditions
Parameter tendencies:
- Wider bands (2.0-2.5× ATR)
- Focus on momentum exhaustion
- Emphasis on divergence patterns
- More selective signal generation
Typically emerge when:
- Trend regime detected
- Strong directional movement observed
- Counter-trend exhaustion signals sought
Historical backtesting observations:
- Win rates often in 40-55% range
- Larger reward/risk ratios (1.5-3.0R)
- Lower trade frequency
VOLATILITY SPECIALISTS 🌊
text
Optimized for: High-volatility conditions
Parameter tendencies:
- Expanded bands (1.5-2.0× ATR)
- Priority on wick rejection patterns
- Strong volume confirmation requirement
- Very selective signals
Typically emerge when:
- Volatile regime detected
- High DVS ratio (>1.5)
- News-driven or event-driven conditions
Historical backtesting observations:
- Win rates often in 50-60% range
- Variable reward/risk ratios (1.0-2.5R)
- Opportunistic trade timing
Species Protection Mechanism:
text
Minimum Per Species: Configurable (default 2)
If Range specialists = 1:
→ Preferential spawning of Range type
→ Protection from culling process
Purpose: Ensures coverage across regime types
Theory: Markets cycle between behavioral states
Goal: Prevent extinction of specialized approaches
Fitness Sharing:
text
If Species has 4 members:
Individual Fitness × 1 / (4 ^ 0.3)
Individual Fitness × 0.72
Purpose: Creates pressure toward species diversity
Effect: Prevents single approach from dominating population
Educational Note: Speciation is a theoretical framework for maintaining strategy diversity. Past specialization performance does not guarantee future regime classification accuracy or signal quality.
6. WALK-FORWARD VALIDATION 📈
An out-of-sample testing methodology used in quantitative research to reduce overfitting risk.
The Overfitting Problem:
text
Hypothetical Example:
In-Sample Backtest: 85% win rate
Out-of-Sample Results: 35% win rate
Explanation: Strategy may have optimized to historical noise
rather than repeatable patterns
Walk-Forward Methodology:
Timeline Structure:
text
┌──────────────────────────────────────────────────────┐
│ Train Window │ Test Window │ Train │ Test │
│ (200 bars) │ (50 bars) │ (200) │ (50) │
└──────────────────────────────────────────────────────┘
In-Sample Out-of-Sample IS OOS
(Optimize) (Validate) Cycle 2...
TRAIN PHASE (In-Sample):
text
Example Bars 1-200: Strategies optimize parameters
- Performance tracked
- Not yet used for primary fitness
- Learning period
TEST PHASE (Out-of-Sample):
text
Example Bars 201-250: Strategies use optimized parameters
- Performance tracked separately
- Validation period
- Out-of-sample evaluation
FITNESS CALCULATION EXAMPLE:
text
Train Win Rate: 65%
Test Win Rate: 58%
Composite Fitness:
= (0.40 × 0.65) + (0.60 × 0.58)
= 0.26 + 0.35
= 0.61
Note: Test results weighted 60%, Train 40%
Theory: Out-of-sample may better indicate forward performance
OVERFIT DETECTION MECHANISM:
text
Gap = Train WR - Test WR = 65% - 58% = 7%
If Gap > Overfit Threshold (default 25%):
Fitness Penalty = Gap × 2
Example with 30% gap:
Strategy shows: Train 70%, Test 40%
Gap: 30% → Potential overfit flagged
Penalty: 30% × 2 = 60% fitness reduction
Result: Strategy likely to be culled
WINDOW ROLLING:
text
Example Bar 250: Test window complete
→ Reset both windows
→ Start new cycle
→ Previous results retained for analysis
Cycle Count increments
Historical performance tracked across multiple cycles
Educational Context:
Walk-forward analysis is a recognized approach in quantitative finance research for evaluating strategy robustness. However, past out-of-sample performance does not guarantee future results. Market conditions can change in ways not represented in historical data.
7. CLUSTER ANALYSIS 🔬
An unsupervised machine learning approach for pattern recognition.
The Concept:
text
Scenario: System identifies a price pivot that wasn't signaled
→ Extract pattern characteristics
→ Store features for analysis
→ Adjust detection for similar future patterns
Implementation:
STEP 1: FEATURE EXTRACTION
text
When significant move occurs without signal:
Extract 5-dimensional feature vector:
Feature Vector =
Example:
Observed Pattern:
STEP 2: CLUSTER ASSIGNMENT
text
Compare to existing cluster centroids using distance metric:
Cluster 0:
Cluster 1: ← Minimum distance
Cluster 2:
...
Assign to nearest cluster
STEP 3: CENTROID UPDATE
text
Old Centroid 1:
New Pattern:
Decay Rate: 0.95
Updated Centroid:
= 0.95 × Old + 0.05 × New
= Exponential moving average update
=
STEP 4: PROFIT TRACKING
text
Cluster Average Profit (hypothetical):
Old Average: 2.5R
New Observation: 3.2R
Updated: 0.95 × 2.5 + 0.05 × 3.2 = 2.535R
STEP 5: LEARNING ADJUSTMENT
text
If Cluster Average Profit > Threshold (e.g., 2.0R):
Cluster Learning Boost += increment (e.g., 0.1)
(Maximum cap: 2.0)
Effect: Future signals resembling this cluster receive adjustment
STEP 6: SCORE MODIFICATION
text
For signals matching cluster characteristics:
Base Score × Cluster Learning Boost
Example:
Base Score: 5.2
Cluster Boost: 1.3
Adjusted Score: 5.2 × 1.3 = 6.76
Result: Pattern more likely to generate signal
Cluster Interpretation Example:
text
CLUSTER 0: "High elastic, low volume"
Centroid:
Avg Profit: 3.5R (historical backtest)
Interpretation: Pure elastic signals in ranges historically favorable
CLUSTER 1: "Wick rejection, volatile"
Centroid:
Avg Profit: 2.8R (historical backtest)
Interpretation: Wick signals in volatility showed positive results
CLUSTER 2: "Exhaustion divergence"
Centroid:
Avg Profit: 4.2R (historical backtest)
Interpretation: Momentum exhaustion in trends performed well
Learning Progress Metrics:
text
Missed Total: 47
Clusters Updated: 142
Patterns Learned: 28
Interpretation:
- System identified 47 significant moves without signals
- Clusters updated 142 times (incremental refinement)
- Made 28 parameter adjustments
- Theoretically improving pattern recognition
Educational Note: Cluster analysis is a recognized machine learning technique. This implementation applies it to trading pattern recognition. Past cluster performance does not guarantee future pattern profitability or accurate classification.
8. ENSEMBLE VOTING 🗳️
A collective decision-making approach common in machine learning.
The Wisdom of Crowds Concept:
text
Single Model:
- May have blind spots
- Subject to individual bias
- Limited perspective
Ensemble of Models:
- Blind spots may offset
- Biases may average out
- Multiple perspectives considered
Implementation:
STEP 1: INDIVIDUAL VOTES
text
Example Bar 247:
Strategy 0 (Range): LONG (fitness: 0.65)
Strategy 1 (Trend): FLAT (fitness: 0.58)
Strategy 2 (Volatile): LONG (fitness: 0.52)
Strategy 3 (Balanced): SHORT (fitness: 0.48)
Strategy 4 (Range): LONG (fitness: 0.71)
Strategy 5 (Trend): FLAT (fitness: 0.55)
STEP 2: WEIGHT CALCULATION
text
Base Weight = Fitness Score
If strategy's species matches current regime:
Weight × Specialist Boost (configurable, default 1.5)
If strategy has recent positive performance:
Weight × Recent Performance Factor
Example for Strategy 0:
Base: 0.65
Range specialist in Range regime: 0.65 × 1.5 = 0.975
Recent performance adjustment: 0.975 × 1.13 = 1.10
STEP 3: WEIGHTED TALLYING
text
LONG votes:
S0: 1.10 + S2: 0.52 + S4: 0.71 = 2.33
SHORT votes:
S3: 0.48 = 0.48
FLAT votes:
S1: 0.58 + S5: 0.55 = 1.13
Total Weight: 2.33 + 0.48 + 1.13 = 3.94
STEP 4: CONSENSUS CALCULATION
text
LONG %: 2.33 / 3.94 = 59.1%
SHORT %: 0.48 / 3.94 = 12.2%
FLAT %: 1.13 / 3.94 = 28.7%
Minimum Consensus Setting: 60%
Result: NO SIGNAL (59.1% < 60%)
STEP 5: SIGNAL DETERMINATION
text
If LONG % >= Min Consensus:
→ Display LONG signal
→ Show consensus percentage in dashboard
If SHORT % >= Min Consensus:
→ Display SHORT signal
If neither threshold reached:
→ No signal displayed
Practical Examples:
text
Strong Consensus (85%):
5 strategies LONG, 0 SHORT, 1 FLAT
→ High agreement among models
Moderate Consensus (62%):
3 LONG, 2 SHORT, 1 FLAT
→ Borderline agreement
No Consensus (48%):
3 LONG, 2 SHORT, 1 FLAT
→ Insufficient agreement, no signal shown
Educational Note: Ensemble methods are widely used in machine learning to improve model robustness. This implementation applies ensemble concepts to trading signals. Past ensemble performance does not guarantee future signal quality or profitability.
9. THOMPSON SAMPLING 🎲
A Bayesian reinforcement learning technique for balancing exploration and exploitation.
The Exploration-Exploitation Dilemma:
text
EXPLOITATION: Use what appears to work
Benefit: Leverages observed success patterns
Risk: May miss better alternatives
EXPLORATION: Try less-tested approaches
Benefit: May discover superior methods
Risk: May waste resources on inferior options
Thompson Sampling Solution:
STEP 1: BETA DISTRIBUTIONS
text
For each signal type, maintain:
Alpha = Successes + 1
Beta = Failures + 1
Example for Elastic signals:
15 wins, 10 losses
Alpha = 16, Beta = 11
STEP 2: PROBABILITY SAMPLING
text
Rather than using simple Win Rate = 15/25 = 60%
Sample from Beta(16, 11) distribution:
Possible samples: 0.55, 0.62, 0.58, 0.64, 0.59...
Rationale: Incorporates uncertainty
- Type with 5 trades: High uncertainty, wide sample variation
- Type with 50 trades: Lower uncertainty, narrow sample range
STEP 3: TYPE PRIORITIZATION
text
Example Bar 248:
Elastic sampled: 0.62
Wick sampled: 0.58
Exhaustion sampled: 0.71 ← Highest this sample
Climax sampled: 0.52
Structure sampled: 0.63
Divergence sampled: 0.45
Exhaustion type receives temporary boost
STEP 4: SIGNAL ADJUSTMENT
text
If current signal is Exhaustion type:
Score × (0.7 + 0.71 × 0.6)
Score × 1.126
If current signal is other type with lower sample:
Score × (0.7 + sample × 0.6)
(smaller adjustment)
STEP 5: OUTCOME FEEDBACK
text
When trade completes:
If WIN:
Alpha += 1
(Beta unchanged)
If LOSS:
Beta += 1
(Alpha unchanged)
Effect: Shifts probability distribution for future samples
Educational Context:
Thompson Sampling is a recognized Bayesian approach to the multi-armed bandit problem. This implementation applies it to signal type selection. The mathematical optimality assumes stationary distributions, which may not hold in financial markets. Past sampling performance does not guarantee future type selection accuracy.
10. DYNAMIC VOLATILITY SCALING (DVS) 📉
An adaptive approach where parameters adjust based on current vs. baseline volatility.
The Adaptation Problem:
text
Fixed bands (e.g., always 1.5 ATR):
In low volatility environment (vol = 0.5):
Bands may be too wide → fewer signals
In high volatility environment (vol = 2.0):
Bands may be too tight → excessive signals
The DVS Approach:
STEP 1: BASELINE ESTABLISHMENT
text
Calculate volatility over baseline period (default 100 bars):
Method options: ATR / Close, Parkinson, or Garman-Klass
Example average volatility = 1.2%
This represents "normal" for recent conditions
STEP 2: CURRENT VOLATILITY
text
Current bar volatility = 1.8%
STEP 3: DVS RATIO
text
DVS Ratio = Current / Baseline
= 1.8 / 1.2
= 1.5
Interpretation: Volatility currently 50% above baseline
STEP 4: BAND ADJUSTMENT
text
Base Band Width: 1.5 ATR
Adjusted Band Width:
Upper: 1.5 × DVS = 1.5 × 1.5 = 2.25 ATR
Lower: Same
Result: Bands expand 50% to accommodate higher volatility
STEP 5: THRESHOLD ADJUSTMENT
text
Base Thresholds:
Wick: 0.15
Momentum: 0.6
Adjusted:
Wick: 0.15 / DVS = 0.10 (easier to trigger in high vol)
Momentum: 0.6 × DVS = 0.90 (harder to trigger in high vol)
DVS Calculation Methods:
text
ATR RATIO (Simplest):
DVS = (ATR / Close) / SMA(ATR / Close, 100)
PARKINSON (Range-based):
σ = √(∑(ln(H/L))² / (4×n×ln(2)))
DVS = Current σ / Baseline σ
GARMAN-KLASS (Comprehensive):
σ = √(0.5×(ln(H/L))² - (2×ln(2)-1)×(ln(C/O))²)
DVS = Current σ / Baseline σ
ENSEMBLE (Robust):
DVS = Median(ATR_Ratio, Parkinson, Garman_Klass)
Educational Note: Dynamic volatility scaling is an approach to normalize indicators across varying market conditions. The effectiveness depends on the assumption that recent volatility patterns continue, which is not guaranteed. Past volatility adjustment performance does not guarantee future normalization accuracy.
11. PRESSURE KERNEL 💪
A composite measurement attempting to quantify directional force beyond simple price movement.
Components:
1. CLOSE LOCATION VALUE (CLV)
text
CLV = ((Close - Low) - (High - Close)) / Range
Examples:
Close at top of range: CLV = +1.0 (bullish position)
Close at midpoint: CLV = 0.0 (neutral)
Close at bottom: CLV = -1.0 (bearish position)
2. WICK ASYMMETRY
text
Wick Pressure = (Lower Wick - Upper Wick) / Range
Additional factors:
If Lower Wick > Body × 2: +0.3 (rejection boost)
If Upper Wick > Body × 2: -0.3 (rejection penalty)
3. BODY MOMENTUM
text
Body Ratio = Body Size / Range
Body Momentum = Close > Open ? +Body Ratio : -Body Ratio
Strong bullish candle: +0.9
Weak bullish candle: +0.2
Doji: 0.0
4. PATH ESTIMATE
text
Close Position = (Close - Low) / Range
Open Position = (Open - Low) / Range
Path = Close Position - Open Position
Additional adjustments:
If closed high with lower wick: +0.2
If closed low with upper wick: -0.2
5. MOMENTUM CONFIRMATION
text
Price Change / ATR
Examples:
+1.5 ATR move: +1.0 (capped)
+0.5 ATR move: +0.5
-0.8 ATR move: -0.8
COMPOSITE CALCULATION:
text
Pressure =
CLV × 0.25 +
Wick Pressure × 0.25 +
Body Momentum × 0.20 +
Path Estimate × 0.15 +
Momentum Confirm × 0.15
Volume context applied:
If Volume > 1.5× avg: × 1.3
If Volume < 0.5× avg: × 0.7
Final smoothing: 3-period EMA
Pressure Interpretation:
text
Pressure > 0.3: Suggests buying pressure
→ May support LONG signals
→ May reduce SHORT signal strength
Pressure < -0.3: Suggests selling pressure
→ May support SHORT signals
→ May reduce LONG signal strength
-0.3 to +0.3: Neutral range
→ Minimal directional bias
Educational Note: The Pressure Kernel is a custom composite indicator combining multiple price action metrics. These weightings are theoretical constructs. Past pressure readings do not guarantee future directional movement or signal quality.
USAGE GUIDE - EDUCATIONAL EXAMPLES
Getting Started:
STEP 1: Add Indicator
Open TradingView
Add KERS-AGE to chart
Allow minimum 100 bars for initialization
Verify dashboard displays Gen: 1+
STEP 2: Initial Observation Period
text
First 200 bars:
- System is in learning phase
- Signal frequency typically low
- Population evolution occurring
- Fitness scores generally increasing
Recommendation: Observe without trading during initialization
STEP 3: Signal Evaluation Criteria
text
Consider evaluating signals based on:
- Confidence percentage
- Grade assignment (A+, A, B+, B, C)
- Position within bands
- Historical win rate shown in dashboard
- Train vs. Test performance gap
Example Signal Evaluation Checklist:
Educational Criteria to Consider:
Signal appeared (⚡ arrow displayed)
Confidence level meets personal threshold
Grade meets personal quality standard
Ensemble consensus (if enabled) meets threshold
Historical win rate acceptable
Test performance reasonable vs. Train
Price location at band extreme
Regime classification appropriate for strategy
If trending: Signal direction aligns with personal analysis
Stop loss distance acceptable for risk tolerance
Position size appropriate (example: 1-2% account risk)
Note: This is an educational checklist, not trading advice. Users should develop their own criteria based on personal risk tolerance and strategy.
Risk Management Educational Examples:
POSITION SIZING EXAMPLE:
text
Hypothetical scenario:
Account: $10,000
Risk tolerance: 1.5% per trade = $150
Indicated stop distance: 1.5 ATR = $300 per contract
Calculation: $150 / $300 = 0.5 contracts
This is an educational example only, not a recommendation.
STOP LOSS EXAMPLES:
text
System provides stop level (red line)
Typically calculated as 1.5 ATR from entry
Alternative approaches users might consider:
LONG: Below recent swing low
SHORT: Above recent swing high
Users should determine stops based on personal risk management.
TAKE PROFIT EXAMPLES:
text
System provides target level (green line)
Typically calculated as price stretch × 60%
Alternative approaches users might consider:
Scale out: Partial exit at 1R, remainder at 2R
Trailing stop: Adjust stop after profit threshold
Users should determine targets based on personal strategy.
Educational Note: These are theoretical examples for educational purposes. Actual position sizing and risk management should be determined by each user based on their individual risk tolerance, account size, and trading plan.
OPTIMIZATION BY MARKET TYPE - EDUCATIONAL SUGGESTIONS
RANGE-BOUND MARKETS
Suggested Settings for Testing:
Population Size: 6-8
Min Confluence: 5.0-6.0
Min Consensus: 70%
Enable Speciation: Consider enabling
Min Per Species: 2
Theoretical Rationale:
More strategies may provide better coverage
Moderate confluence may generate more signals
Higher consensus may filter quality
Speciation may encourage range specialist emergence
Historical Backtest Observations:
Win rates in testing: Varied, often 50-65% range
Reward/risk ratios observed: 0.5-1.5R
Signal frequency: Relatively frequent
Disclaimer: Past backtesting results do not guarantee future performance.
TRENDING MARKETS
Suggested Settings for Testing:
Population Size: 4-5
Min Confluence: 6.0-7.0
Consider enabling MTF filter
MTF Timeframe: 3-5× current timeframe
Specialist Boost: 1.8-2.0
Theoretical Rationale:
Fewer strategies may adapt faster
Higher confluence may filter counter-trend noise
MTF may reduce counter-trend signals
Specialist boost may prioritize trend specialists
Historical Backtest Observations:
Win rates in testing: Varied, often 40-55% range
Reward/risk ratios observed: 1.5-3.0R
Signal frequency: Less frequent
Disclaimer: Past backtesting results do not guarantee future performance.
VOLATILE MARKETS (e.g., Cryptocurrency)
Suggested Settings for Testing:
Base Length: 25-30
Band Multiplier: 1.8-2.0
DVS: Consider enabling (Ensemble method)
Consider enabling Volume Filter
Volume Multiplier: 1.5-2.0
Theoretical Rationale:
Longer base may smooth noise
Wider bands may accommodate larger swings
DVS may be critical for adaptation
Volume filter may confirm genuine moves
Historical Backtest Observations:
Win rates in testing: Varied, often 45-60% range
Reward/risk ratios observed: 1.0-2.5R
Signal frequency: Moderate
Disclaimer: Cryptocurrency markets are highly volatile and risky. Past backtesting results do not guarantee future performance.
SCALPING (1-5min timeframes)
Suggested Settings for Testing:
Base Length: 15-20
Train Window: 150
Test Window: 30
Spawn Interval: 30
Min Confluence: 5.5-6.5
Consider enabling Ensemble
Min Consensus: 75%
Theoretical Rationale:
Shorter base may increase responsiveness
Shorter windows may speed evolution cycles
Quick spawning may enable rapid adaptation
Higher confluence may filter noise
Ensemble may reduce false signals
Historical Backtest Observations:
Win rates in testing: Varied, often 50-65% range
Reward/risk ratios observed: 0.5-1.0R
Signal frequency: Frequent but filtered
Disclaimer: Scalping involves high frequency trading with increased transaction costs and slippage risk. Past backtesting results do not guarantee future performance.
SWING TRADING (4H-Daily timeframes)
Suggested Settings for Testing:
Base Length: 25-35
Train Window: 300
Test Window: 100
Population Size: 7-8
Consider enabling Walk-Forward
Cooldown: 8-10 bars
Theoretical Rationale:
Longer timeframe may benefit from longer lookbacks
Larger windows may improve robustness testing
More population may increase stability
Walk-forward may be valuable for multi-day holds
Longer cooldown may reduce overtrading
Historical Backtest Observations:
Win rates in testing: Varied, often 45-60% range
Reward/risk ratios observed: 2.0-4.0R
Signal frequency: Infrequent but potentially higher quality
Disclaimer: Swing trading involves overnight and weekend risk. Past backtesting results do not guarantee future performance.
DASHBOARD GUIDE - INTERPRETATION EXAMPLES
Reading Each Section:
HEADER:
text
🧬 KERS-AGE EVOLVED 📈 TREND
Regime indication:
Color coding suggests current classification
(Green = Range, Orange = Trend, Purple = Volatile)
POPULATION:
text
Pop: 6/6
Gen: 42
Interpretation:
- Population at target size
- System at generation 42
- May indicate mature evolution
SPECIES (if enabled):
text
R:2 T:3 V:1
Interpretation:
- 2 Range specialists
- 3 Trend specialists
- 1 Volatility specialist
In TREND regime this distribution may be expected
WALK-FORWARD (if enabled):
text
Phase: 🧪 TEST
Cycles: 5
Train: 65%
Test: 58%
Considerations:
- Currently in test phase
- Completed 5 full cycles
- 7% performance gap between train and test
- Gap under default 25% overfit threshold
ENSEMBLE (if enabled):
text
Vote: 🟢 LONG
Consensus: 72%
Interpretation:
- Weighted majority voting LONG
- 72% agreement level
- Exceeds default 60% consensus threshold
SELECTED STRATEGY:
text
ID:23
Trades: 47
Win%: 58%
P&L: +8.3R
Fitness: 0.62
Information displayed:
- Strategy ID 23, Trend specialist
- 47 historical simulated trades
- 58% historical win rate
- +8.3R historical cumulative reward/risk
- 0.62 fitness score
Note: These are historical simulation metrics
SIGNAL QUALITY:
text
Conf: 78%
Grade: B+
Elastic: ████████░░
Wick: ██████░░░░
Momentum: ███████░░░
Pressure: ███████░░░
Information displayed:
- 78% confluence score
- B+ grade assignment
- Elastic component strongest
- Visual representation of component strengths
LEARNING (if enabled):
text
Missed: 47
Learned: 28
Interpretation:
- System identified 47 moves without signals
- 28 pattern adjustments made
- Suggests ongoing learning process
POSITION:
text
POS: 🟢 LONG
Score: 7.2
Current state:
- Simulated long position active
- 7.2 confluence score
- Monitor for potential exit signal
Educational Note: Dashboard displays are for informational and educational purposes. All performance metrics are historical simulations and do not represent actual trading results or future expectations.
FREQUENTLY ASKED QUESTIONS - EDUCATIONAL RESPONSES
Q: Why aren't signals showing?
A: Several factors may affect signal generation:
System may still be initializing (check Gen: counter)
Confluence score may be below threshold
Ensemble consensus (if enabled) may be below requirement
Current regime may naturally produce fewer signals
Filters may be active (volume, noise reduction)
Consider adjusting settings or allowing more time for evolution.
Q: The win rate seems low compared to backtesting?
A: Consider these factors:
First 200 bars typically represent learning period
Focus on TEST % rather than TRAIN % for realistic expectations
Trend regime historically shows 40-55% win rates in backtesting
Different market conditions may affect performance
System emphasizes reward/risk ratio alongside win rate
Past performance does not guarantee future results
Q: Should I take all signals?
A: This is a personal decision. Some users may consider:
Taking higher grades (A+, A) in any regime
Being more selective in trend regimes
Requiring higher ensemble consensus
Only trading during specific regimes
Paper trading extensively before live trading
Each user should develop their own signal selection criteria.
Q: Signals appear then disappear?
A: This may be expected behavior:
Default requires 2-bar persistence
Designed to filter brief spikes
Confirmation delay intended to reduce false signals
Wait for persistence requirement to be met
This is an intentional feature, not a malfunction.
Q: Test % much lower than Train %?
A: This may indicate:
Overfit detection system functioning
Gap exceeding threshold triggers penalty
Strategy may be optimizing to in-sample noise
System designed to cull such strategies
Walk-forward protection working as intended
This is a safety feature to reduce overfitting risk.
Q: The population keeps culling strategies?
A: This is part of normal evolution:
Lower-performing strategies removed periodically
Higher-performing strategies replicate
Population quality theoretically improves over time
Total culled count shows selection pressure
This is expected evolutionary behavior.
Q: Which timeframe works best?
A: Backtesting suggests 15min to 4H may be suitable ranges:
Lower timeframes may be noisier, may need more filtering
Higher timeframes may produce fewer signals
Extensive historical testing recommended for chosen asset
Each asset may behave differently
Consider paper trading across multiple timeframes
Personal testing is recommended for your specific use case.
Q: Does it work on all asset types?
A: Historical testing suggests:
Cryptocurrency: Consider longer Base Length (25-30) due to volatility
Forex: Standard settings may be appropriate starting point
Stocks: Standard settings, possibly smaller population (4-5)
Indices: Trend-focused settings may be worth testing
Each asset class has unique characteristics. Extensive testing recommended.
Q: Can settings be changed after initialization?
A: Yes, but considerations:
Population will reset
Strategies restart evolution
Learning progress resets
Consider testing new settings on separate chart first
May want to compare performance before committing
Settings changes restart the evolutionary process.
Q: Walk-Forward enabled or disabled?
A: Educational perspective:
Walk-Forward adds out-of-sample validation
May reduce overfitting risk
Results may be more conservative
Considered best practice in quantitative research
Requires more bars for meaningful data
Recommended for those concerned about robustness
Individual users should assess based on their needs.
Q: Ensemble mode or single strategy?
A: Trade-offs to consider:
Ensemble approach:
Requires consensus threshold
May have higher consistency
Typically fewer signals
Multiple perspectives considered
Single strategy approach:
More signals (varying quality)
Faster response to conditions
Higher variability
More active signal generation
Personal preference and risk tolerance should guide this choice.
ADVANCED CONSIDERATIONS
Evolution Time: Consider allowing 200+ bars for population maturity
Regime Awareness: Historical performance varies by regime classification
Confluence Range: Testing suggests 70-85% may be informative range
Ensemble Levels: 80%+ consensus historically associated with stronger agreement
Out-of-Sample Focus: Test performance may be more indicative than train performance
Learning Metrics: "Learned" count shows pattern adjustment over time
Pressure Levels: >0.4 pressure historically added confirmation
DVS Monitoring: >1.5 DVS typically widens bands and affects frequency
Species Balance: Healthy distribution might be 2-2-2 or 3-2-1, avoid 6-0-0
Timeframe Testing: Match to personal trading style, test thoroughly
Volume Importance: May be more critical for stocks/crypto than forex
MTF Utility: Historically more impactful in trending conditions
Grade Significance: A+ in trend regime historically rare and potentially significant
Risk Parameters: Standard risk management suggests 1-2% per trade maximum
Stop Levels: System stops are pre-calculated, widening may affect reward/risk
THEORETICAL FOUNDATIONS
Genetic Algorithms in Finance:
Traditional Optimization Approaches:
Grid search: Exhaustive but computationally expensive
Gradient descent: Efficient but prone to local optima
Random search: Simple but inefficient
Genetic Algorithm Characteristics:
Explores parameter space through evolutionary process
Balances exploration (mutation) and exploitation (selection)
Mitigates local optima through population diversity
Parallel evaluation via population approach
Inspired by biological evolution principles
Academic Context: Genetic algorithms are studied in computational finance literature for parameter optimization. Effectiveness varies based on problem characteristics and implementation.
Ensemble Methods in Machine Learning:
Single Model Limitations:
May overfit to specific patterns
Can have blind spots in certain conditions
May be brittle to distribution shifts
Ensemble Theoretical Benefits:
Variance reduction through averaging
Robustness through diversity
Improved generalization potential
Widely used (Random Forests, Gradient Boosting, etc.)
Academic Context: Ensemble methods are well-studied in machine learning literature. Performance benefits depend on base model diversity and correlation structure.
Walk-Forward Analysis:
Alternative Approaches:
Simple backtest: Risk of overfitting to full dataset
Single train/test split: Limited validation
Cross-validation: May violate time-series properties
Walk-Forward Characteristics:
Continuous out-of-sample validation
Respects temporal ordering
Attempts to detect strategy degradation
Used in quantitative trading research
Academic Context: Walk-forward analysis is discussed in quantitative finance literature as a robustness check. However, it assumes future regimes will resemble recent test periods, which is not guaranteed.
FINAL EDUCATIONAL SUMMARY
KERS-AGE demonstrates an adaptive systems approach to technical analysis. Rather than fixed rules, it implements:
✓ Evolutionary Optimization: Parameter adaptation through genetic algorithms
✓ Regime Classification: Attempted market condition categorization
✓ Out-of-Sample Testing: Walk-forward validation methodology
✓ Pattern Recognition: Cluster analysis and learning systems
✓ Ensemble Methodology: Collective decision-making framework
✓ Full Transparency: Comprehensive dashboard and metrics
This indicator is an educational tool demonstrating advanced algorithmic concepts.
Critical Reminders:
The system:
✓ Attempts to identify potential reversal patterns
✓ Adapts parameters to changing conditions
✓ Provides multiple filtering mechanisms
✓ Offers detailed performance metrics
Users must understand:
✓ No system guarantees profitable results
✓ Past performance does not predict future results
✓ Extensive testing and validation recommended
✓ Risk management is user's responsibility
✓ Market conditions can change unpredictably
✓ This is educational software, not financial advice
Success in trading requires: Proper education, risk management, discipline, realistic expectations, and personal responsibility for all trading decisions.
For Educational Use
🧬 KERS-AGE Development Team
⚠️ FINAL DISCLAIMER
This indicator and documentation are provided strictly for educational and informational purposes.
NOT FINANCIAL ADVICE: Nothing in this guide constitutes financial advice, investment advice, trading advice, or any recommendation to buy, sell, or hold any security or to engage in any trading strategy.
NO GUARANTEES: No representation is made that any account will or is likely to achieve profits or losses similar to those shown in backtests, examples, or historical data. Past performance is not indicative of future results.
SUBSTANTIAL RISK: Trading stocks, forex, futures, options, and cryptocurrencies involves substantial risk of loss and is not suitable for every investor. The high degree of leverage can work against you as well as for you.
YOUR RESPONSIBILITY: You are solely responsible for your own investment and trading decisions. You should conduct your own research, perform your own analysis, and consult with qualified financial advisors before making any trading decisions.
NO LIABILITY: The developers, contributors, and distributors of this indicator disclaim all liability for any losses or damages, direct or indirect, that may result from use of this indicator or reliance on any information provided.
PAPER TRADE FIRST: Users are strongly encouraged to thoroughly test this indicator in a paper trading environment before risking any real capital.
By using this indicator, you acknowledge that you have read this disclaimer, understand the risks involved in trading, and agree that you are solely responsible for your own trading decisions and their outcomes.
Educational Software Only | Trade at Your Own Risk | Not Financial Advice
Taking you to school. — Dskyz , Trade with insight. Trade with anticipation.
RunRox - Pairs Screener📊 Pairs Screener is part of our premium suite for pair trading.
This indicator is designed to scan and rank the most profitable and optimal pairs for the Pairs Strategy. The screener can backtest multiple metrics on deep historical data and display results for many pairs against one base asset at the same time.
This allows you to quickly detect market inefficiencies and select the most promising pairs for live trading.
HOW DOES THIS STRATEGY WORK⁉️
The core idea of the strategy is described in detail in our main indicator Pairs Strategy from the same product line.
There you can find a full explanation of the concept, the math behind pair trading, and the internal logic of the engine.
The Pairs Screener is built on top of the same core technology as the main indicator and uses the same internal logic and calculations.
It is designed as a key companion tool to the main strategy: it helps you find tradeable pairs, evaluate current deviations, sort and filter lists of candidates, and much more. All of these features will be described in this post.
✅ KEY FEATURES
More than 400+ assets available for scanning
Forex assets
Crypto assets
Lower Timeframe Backtester Strategy support
Invert signals mode
Hedge Coefficient (position size balancing between both legs)
6 hedge modes
Stop Loss support
Take Profit support
Whitelist with your own custom asset list
Blacklist to exclude unwanted assets
Custom filters
12 tracking metrics for pair evaluation
Customizable alerts
And many other tools for fine-tuning your search
The screener runs backtests simultaneously across a large number of assets and calculates metrics automatically.
This helps you very quickly find pairs with strong structural relationships or current inefficiencies that can be used as the basis for your pair trading strategies.
⚙️ MAIN SETTINGS
The first section controls the core parameters of the screener: Score, correlation, asset groups for scanning, and other base settings. All major crypto and forex symbols are embedded directly into the screener.
Since there are more than 400 assets, it is technically impossible to analyze everything at once, so we grouped them into batches of 40 assets per group.
The workflow is simple:
Open the chart of the asset you want to use as the base ticker.
In the screener settings choose the market (Crypto or Forex).
Select a Group (for example, Group 1) and the indicator will scan all assets inside that group against your base ticker.
Then you switch to Group 2, Group 3, etc., and repeat the scan.
Embedded universe:
400+ assets total
350+ Crypto – split into 10 groups
70+ Forex – split into 3 groups
Below is a description of each setting.
🔸 Exclude Dates
Allows you to specify a period that should be excluded from analysis.
Useful for removing abnormal spikes, news events, or any non-typical segments that distort the statistics for your pairs.
🔸 Market
Defines which universe will be used to build pairs with the current main asset:
Crypto – 350+ crypto symbols
Forex – 70+ FX symbols
Whitelist – your own custom list of assets
🔸 Group
Selects the asset group to scan.
As mentioned above, assets are split into groups of about 40 instruments:
350+ Crypto → 10 groups
70+ Forex → 3 groups
The screener will calculate all metrics only for the group you select.
🔸 Lower Timeframe
This option enables deep history analysis.
Each TradingView plan has a limit on the number of visible bars (for example, 5,000 bars on the basic plan). In standard mode you would only get statistics for the last 5,000 bars of your current timeframe.
If you want a deeper backtest on a lower timeframe, you can do the following:
Suppose your target timeframe for analysis is 5 minutes.
Switch your chart to a 30-minute timeframe.
Enable Lower Timeframe in the indicator.
Select 5 minutes as the lower timeframe inside the screener.
In this mode the screener can reconstruct and analyze up to 99,000 bars of data for your assets. This allows you to evaluate pairs on a much deeper history and see whether the results are stable over a larger sample.
🔸 Method
Here you choose the deviation model:
preferred Z-Score or S-Score for your analysis,
plus you can enable Invert to search for negatively correlated pairs and calculate their profit correctly.
🔸 Period
This is the lookback period for Z/S Score.
It defines how many bars are used to calculate the deviation metric for each pair.
🔸 Correlation Period
This is the number of bars used to calculate correlation between the base asset and each candidate in the group.
The resulting correlation value is also displayed in the results table.
🔀 HEDGE COEFFICIENT
The next block of settings is related to the hedge coefficient.
This defines how much margin is allocated to each leg of the pair.
The classic approach in pair trading is to split the position equally between both assets.
For example, if you allocate 100 USD to a trade , the standard model would open 50 USD long on one asset and 50 USD short on the other.
This works well for pairs with similar volatility , such as BTCUSDT / ETHUSDT
However, if you use a pair like BTCUSDT / DOGEUSDT , the volatility of these assets is very different.
They can still be correlated, but their amplitude is not the same. While Bitcoin might move 2% , Dogecoin can move 10% over the same period.
Because of that, for pairs with strongly different volatility, we can use a hedge coefficient and, for example, enter with 30 USD on one leg and 70 USD on the other, taking the volatility difference into account.
This is the main idea behind the Hedge Coefficient section and its primary use.
The indicator includes 6 methods of calculating the coefficient:
Cumulative RMA
Beta OLS
Beta TLS
Beta EMA
RMA Range
RMA Delta
Each method uses a different formula to compute the hedge coefficient and to size the position based on different metrics of the assets.
We leave it to the trader to decide which algorithm works best for their specific pair and style.
Below are the settings inside this section:
🔹 Method
When Auto Hedge is enabled, you can select which method to use from the list above.
The chosen method will automatically calculate the hedge coefficient between the two legs.
🔹 Hedge Coefficient
This is the manual hedge ratio per trade when Auto Hedge is disabled.
By default it is set to 1, which means the position is opened 50/50 between the two assets.
🔹 Min Allowed Hedge Coef.
This is the minimum allowed hedge coefficient.
By default it is 0.2, which means the model will not go below a 20% / 80% split between the legs.
🔹 MA Length
For methods that use moving averages (for example Beta EMA), this parameter sets the period used to calculate the hedge coefficient.
💰 STRATEGY SETTINGS
This section defines the base backtesting settings for all assets in the screener.
Here you configure entries, exits, Stop Loss, and other parameters used to find the most optimal pairs for your strategy. 🔸 Commission %
In this field you set your broker’s fee percentage per trade.
The indicator automatically calculates the correct commission for each leg of every trade. You only need to input the real commission rate that your broker charges for volume. No additional manual calculations are required.
🔸 Qty $
The margin amount used for backtesting across all assets in the screener.
This margin is split between both legs of the pair either equally or according to the selected hedge coefficient.
🔸 Entry
The Z/S Score deviation level at which the backtest opens a trade for each pair.
🔸 Exit
The Z/S Score level at which the backtest closes trades for the tested assets.
🔸 Stop Loss
PnL threshold at which a trade is force-closed during the historical test.
🔸 Cooldown
Number of bars the strategy will wait after a Stop Loss before opening the next trade.
This block gives you flexible control over how your strategy is tested on 400+ assets, helping you standardize the rules and compare pairs under the exact same conditions.
🗒️ WHITELIST
In this section you can define your own custom list of assets for monitoring and backtesting.
This is useful if you want to work with symbols that are not included in the built-in lists, such as exotic crypto from smaller exchanges, specific stocks, or any custom universe 🔹 Exchange Prefix
Enter the exchange prefix used for your tickers.
Example: BINANCE, OANDA, etc.
🔹 Ticker Postfix
Enable this option if the tickers require a postfix.
Example 1: .P for Binance Futures perpetual contracts.
Example 2: USDT if you only provide the base asset in the ticker list.
🔹 Ticker List
Enter a comma-separated list of tickers to analyze.
Example 1: BTCUSDT, ETHUSDT, BNBUSDT (when the exchange prefix is set).
Example 2: BTC, ETH, BNB (when using postfix USDT).
Example 3: BINANCE:BTCUSDT.P, OANDA:EURUSD (when different exchanges are used and the prefix option is disabled).
This gives you full flexibility to build a screener universe that matches exactly the assets you trade.
⛔ BLACKLIST
In this section you can enable a blacklist of unwanted assets that should be skipped during analysis. Enter a comma-separated list of tickers to exclude from the screener:
Example 1: BTCUSDT, ETHUSDT
Example 2: BTC, ETH (all tickers that contain these symbols will be excluded)
This helps you quickly remove illiquid, noisy, or unwanted instruments from the results without changing your main groups or whitelist.
📈 DASHBOARD
This section controls the results dashboard: table position, style, and sorting logic.
Here is what you can configure:
Result Table – position of the results table on the chart.
Background / Text – colors and opacity for the table background and text.
Table Size – overall size of the results table (from 0 to 30).
Show Results – how many rows (pairs) to display in the table.
Sort by (stat) – which metric to use for sorting the results.
Available options: Profit Factor, Profit, Winrate, Correlation, Score.
This lets you quickly focus on the most interesting pairs according to the exact metric that matters most for your strategy.
📎 FILTER SETTINGS
This section lets you filter the results table by metric values.
For example, you can show only pairs with a minimum correlation of 0.8 to focus on more stable relationships. 🔸 Min Correlation
Minimum allowed correlation between the two assets over the selected lookback period.
🔸 Min Score
Minimum absolute Score (Z-Score or S-Score) required to include a pair in the results.
For example, 2.0 means only pairs with Score >= 2.0 or <= -2.0 will be displayed.
🔸 Min Winrate
Minimum win rate percentage for a pair to be included in the table.
🔸 Min Profit Factor
Minimum profit factor required for a pair to stay in the results. These filters help you quickly narrow the list down to pairs that meet your quality criteria and match your risk profile.
📌 COLUMN SELECTION
This section lets you fully customize which metrics are displayed in the results table.
You can enable or hide any column to focus only on the data you need to identify the best pairs for trading. The screener allows you to show up to 12 metrics at the same time, which gives a detailed view of pair quality. Available columns:
🔹 Exchange Prefix
Show the exchange prefix in the ticker.
🔹 Correlation
Correlation between the two assets’ prices over the lookback period.
🔹 Score
Current Score value (Z-Score or S-Score).
On lower timeframe research, Score is not displayed.
🔹 Spread
Shows spread as % change since entry.
Positive value = profit on the main position.
🔹 Unrealized PnL
Shows unrealized PnL as a $ value based on current prices.
🔹 Profit
Total profit from all trades: Gross Profit − Gross Loss.
🔹 Winrate
Percentage of profitable trades out of all executed trades.
🔹 Profit Factor
Gross Profit / Gross Loss.
🔹 Trades
Total number of trades.
🔹 Max Drawdown
Maximum observed loss from peak to trough before a new peak is made.
🔹 Max Loss
Largest loss recorded on a single trade.
🔹 Long/Short Profit
Separate profit/loss for long trades and short trades.
🔹 Avg. Trade Time
Average duration of trades.
All these metrics are designed to help you quickly identify the strongest pairs for your strategy.
You can change colors, opacity, and hide any columns that are not relevant to your workflow.
🔔 ALERT
The alert system in this screener works in a specific way.
Alerts are tied directly to the filters you set in the Filter Settings section:
Minimum Correlation
Minimum Score
Minimum Winrate
Minimum Profit Factor
You can configure alerts to trigger when a new pair appears that matches all your filter conditions. 💡 Example
You set:
Minimum Score = 3
Then you create an alert based on the screener.
When any pair reaches a Score greater than +3 or less than −3, you will receive a notification.
This is how alerts work in this screener.
The idea is to deliver the most relevant information about the current market situation without forcing you to watch the screener all the time.
Supported placeholders for alert messages: {{ticker_1}} – main ticker (the one on the chart).
{{ticker_2}} – the paired ticker listed in the table.
{{corr}} – correlation value.
{{score}} – Score value (Z-Score or S-Score).
{{time}} – bar open time (UTC).
{{timenow}} – alert trigger time (UTC). You can use these placeholders to build alert text or JSON payloads in any format required by your tools.
The screener is designed to significantly enhance your pair trading workflow: it helps you quickly identify working pairs and current market inefficiencies, and with the alert system you can react to opportunities without constantly sitting in front of the screen.
Always remember that past performance does not guarantee future results.
Use the screener data within a risk-controlled trading system and adjust position sizing according to your own risk management rules.
RunRox - Pairs Strategy🧬 Pairs Strategy is a new indicator by RunRox included in our premium subscription.
It is a specialized tool for trading pairs, built around working with two correlated instruments at the same time.
The indicator is designed specifically for pair trading logic: it helps track the relationship between two assets, identify statistical deviations, and generate signals for opening and managing long/short combinations on both legs of the pair.
Below in this description I will go through the core functions of the indicator and the main concepts behind the strategy so you can clearly understand how to apply it in your trading.
📌 CONCEPT
The core idea of pair trading is to find and trade correlated instruments that usually move in a similar way.
When these two assets temporarily diverge from each other, a trading opportunity appears.
In such moments, the relatively overvalued asset is sold (short leg), and the relatively undervalued asset is bought (long leg).
When the spread between them narrows and both instruments revert back toward their typical relationship (mean), the position is closed and the trader captures the profit from this convergence.
In practice, one leg of the pair can end up in a loss while the other generates a larger profit.
Due to the difference in performance between the two assets, the combined result of the pair trade can still be positive.
✅ KEY FEATURES:
2 deviation types (Z-Score and S-Score)
Invert signals mode
Hedge Coefficient (position size balancing between both legs)
6 hedge modes
Entries based on Score or RSI
Extra entries based on Score or Spread
Stop Loss
Take Profit
RSI Filter
RSI Pivot Mode
Built-in Backtester Strategy
Lower Timeframe Backtester Strategy
Live trade panel for current position
Equity curve chart
21 performance metrics in the backtester
2 alert types
*And many more fine-tuning options for pair trading
🔗 SCORE
Score is the core deviation metric between the two assets in the pair.
For example, if you are trading ETHUSDT/BTCUSDT, the indicator analyzes the relationship ETH/BTC, and when one leg temporarily diverges from the other, this difference is reflected in the Score value.
In other words, Score shows how much the current spread between the two instruments deviates from its typical state and is used as the main signal source for pair entries and exits.
In the screenshot above you can see how Score looks in our indicator.
Depending on how large the difference is between the two assets, the Score value can move in a range from −N to +N
When Score is in the −N zone, this is a 🟢 long zone for the first asset and a short zone for the second.
Using the ETH/BTC example: when Score is deeply negative, you open a long on ETH and a short on BTC at the same time, then close both legs when Score returns back to the 0 zone (balance between the two assets).
When Score is in the +N zone, this is a 🔴 short zone for the first asset and a long zone for the second.
In the same ETH/BTC example: when Score is strongly positive, you short ETH and long BTC, and again close both positions when Score comes back to the neutral 0 zone.
☯️ Z/S SCORE
Inside the indicator we added two different formulas for calculating the spread between the two legs of the pair: Z-Score and S-Score.
These approaches measure deviation in different ways and can produce slightly different signals depending on the chosen pair and its behavior.
This allows you to switch between Z-Score and S-Score and choose the method that gives more stable and cleaner signals for your specific instruments.
As you can see in the screenshot above, we used the same pair but applied different Score types to measure the spread and deviation from the norm.
🟣 Z-Score – generated 9 entry signals .
It reacts to price fluctuations more smoothly and usually stays within a range of approximately −8 to +8 .
🟠 S-Score – generated 5 entry signals .
It reacts to price changes more aggressively and produces wider deviations, often reaching −15 to +15 .
This gives traders the choice between a more sensitive but smoother model (Z-Score) and a more selective, stronger-deviation model (S-Score)
⁉️ HOW DOES THE STRATEGY WORK
Here is a basic example of how you can trade this pair trading strategy using our indicator and its signals.
In the classic approach the trade consists of one initial entry and several scale-ins (averaging) if the spread continues to move against the position.
The first entry is opened when Score reaches a standard deviation of −2 or +2.
If price does not revert to the mean and moves further against the position so that Score expands to −3 or +3, the strategy performs the first scale-in.
If Score extends to −4 or +4, a second scale-in is added.
If the spread grows even more and Score reaches −5 or +5, a third scale-in is executed.
In our indicator the number of averaging steps can be up to 4 scale-ins .
After that the position waits until Score returns back to the 0 level , where the whole pair position is closed.
This is the standard model of classical pair trading.
However there are many variations:
using Stop Loss and Take Profit,
exiting earlier or later than the 0 zone,
scaling in not by Score but by Spread, since Score is not linear while Spread is linear,
entering when RSI on both tickers shows opposite extremes, for example RSI 20 on one asset and RSI 80 on the other, and so on.
The number of possible trading styles for this strategy is very large.
We designed the indicator to cover as many of these variations as possible and added flexible tools so you can build your own pair trading logic on top of it.
Below is an example of a classic pair trade with two entries: one main entry and one extra entry (scale-in) .
The pair SUIUSDT / PENGUUSDT shows a high correlation, and on one of the trades the sequence looked like this:
A −2 Score deviation occurred into the long zone and triggered the Main Entry .
🔹 Main Entry
Long SUIUSDT – Margin: 5,000 USD, Entry price: 1.5708
Short PENGUUSDT – Margin: 5,000 USD, Entry price: 0.011793
Price then moved further against the position, Score went deeper into deviation, and the strategy added one extra entry.
🔸 Extra Entry
Long SUIUSDT – Margin: 5,000 USD, Entry price: 1.5938
Short PENGUUSDT – Margin: 5,000 USD, Entry price: 0.012173
The trade was closed when Score reverted back toward the 0 zone (mean reversion of the spread):
❎ Exit
SUIUSDT P&L: −403.34 USD, Exit price: 1.5184
PENGUUSDT P&L: +743.73 USD, Exit price: 0.011089
✅ Total P&L: +340.39 USD
With a total margin of 10,000 USD used per side (20,000 USD combined), this trade yielded around +1.7% on the deployed margin.
On different assets the size and speed of the spread movement will vary, but the principle remains the same.
This is just one example to illustrate how the strategy works in practice using simplified theoretical balances.
⚙️ MAIN SETTINGS
After explaining how the strategy works, we can move to the indicator settings and their logic.
The first block is Main Settings, which controls how the pair is built, how the spread is calculated, and how the backtest is performed.
The core idea of the indicator is to backtest historical data, generate entry signals, show open-position parameters, and provide all necessary metrics for both discretionary and algorithmic trading.
This is a complete framework for analyzing a pair of assets and building a trading system around them. Below I will go through the main parameters one by one.
🔹 Exclude Dates
Allows you to exclude abnormal periods in the pair’s history to remove outlier trades from the backtest.
This is useful when the market experienced extreme news events, listing spikes, or other non-typical situations that distort statistics.
🔹 Pair
Here you select the second asset for your pair.
For example, if your main chart is BTCUSDT, in this field you choose a correlated asset such as ETHUSDT, and the working pair becomes BTCUSDT / ETHUSDT.
The indicator then calculates spread, Score, and all related metrics based on this asset combination.
🔹 Lower Timeframe
This is a special mode for backtesting on a lower timeframe while using a higher timeframe chart to extend the history limit.
For example, if your TradingView plan provides only 5,000 bars of history on the current timeframe, you can switch your chart to a higher timeframe and select a lower timeframe in this setting.
The indicator will then reconstruct the pair logic using up to 99,000 bars of lower timeframe data for backtesting.
This allows you to test the pair on a much longer historical period and find more stable combinations of assets.
🔹 Method
Here you choose which deviation model you want to use: Z-Score or S-Score.
Both methods calculate spread deviation but use different formulas, which can give different signal behavior depending on the pair.
Examples of these two methods are shown earlier in this description.
🔹 Period
This parameter defines how many bars are used to calculate the average deviation for the pair.
If you set Period = 300, the indicator looks back 300 bars and calculates the typical spread deviation over that window.
For example, if the average deviation over 300 bars is around 1%, then a move to 2% or more will push Z/S Score closer to its boundary levels, since such a deviation is considered abnormal for that lookback period.
A larger Period means that only bigger deviations will be treated as anomalies.
A smaller Period makes the model more sensitive and treats smaller deviations as anomalies.
This allows you to tune how aggressive or conservative your pair trading signals should be.
🔹 Invert
This setting is used for negatively correlated pairs.
Some instruments have a positive correlation in the range from +0.8 to +1.0 (strong positive correlation), while others show a negative correlation from −0.8 to −1.0, meaning they usually move in opposite directions.
A classic example is the pair EURUSD and DXY.
As shown in the screenshot above, these instruments often have strong negative correlation due to macro factors and typically move in opposite directions: when EURUSD is rising, DXY is falling, and vice versa.
Such pairs can also be traded with our indicator.
To do this, we use the Invert option, which effectively flips one of the assets (as shown in the screenshot below). After inversion, both instruments are brought to a “same-direction” behavior from the model’s point of view.
From there, you trade the pair in the same way as a positively correlated one:
you open both legs in the same direction (both long or both short) depending on the spread and Score, and then wait for the spread between the inverted pair to converge back toward its mean.
🔀 HEDGE COEFFICIENT
The next block of settings is related to the hedge coefficient.
This defines how much margin is allocated to each leg of the pair.
The classic approach in pair trading is to split the position equally between both assets.
For example, if you allocate 100 USD to a trade , the standard model would open 50 USD long on one asset and 50 USD short on the other.
This works well for pairs with similar volatility , such as BTCUSDT / ETHUSDT
However, if you use a pair like BTCUSDT / DOGEUSDT , the volatility of these assets is very different.
They can still be correlated, but their amplitude is not the same. While Bitcoin might move 2% , Dogecoin can move 10% over the same period.
Because of that, for pairs with strongly different volatility, we can use a hedge coefficient and, for example, enter with 30 USD on one leg and 70 USD on the other, taking the volatility difference into account.
This is the main idea behind the Hedge Coefficient section and its primary use.
The indicator includes 6 methods of calculating the coefficient:
Cumulative RMA
Beta OLS
Beta TLS
Beta EMA
RMA Range
RMA Delta
Each method uses a different formula to compute the hedge coefficient and to size the position based on different metrics of the assets.
We leave it to the trader to decide which algorithm works best for their specific pair and style.
Below are the settings inside this section:
🔹 Method
When Auto Hedge is enabled, you can select which method to use from the list above.
The chosen method will automatically calculate the hedge coefficient between the two legs.
🔹 Hedge Coefficient
This is the manual hedge ratio per trade when Auto Hedge is disabled.
By default it is set to 1, which means the position is opened 50/50 between the two assets.
🔹 Min Allowed Hedge Coef.
This is the minimum allowed hedge coefficient.
By default it is 0.2, which means the model will not go below a 20% / 80% split between the legs.
🔹 MA Length
For methods that use moving averages (for example Beta EMA), this parameter sets the period used to calculate the hedge coefficient.
🛠️ STRATEGY SETTINGS
The next important block is Strategy Settings .
Here you define the core parameters used for backtesting: trading commission, position size, entry / exit logic, Stop Loss, Take Profit, and other rules that describe how you want the strategy to operate.
Below are all parameters with a detailed explanation.
🔸 Commission %
In this field you set your broker’s fee percentage per trade .
The indicator automatically calculates the correct commission for each leg of every trade. You only need to input the real commission rate that your broker charges for volume. No additional manual calculations are required.
🔸 Main Entry Mode
There are two options for the main entry:
Score - This is the primary entry method based on Z/S Score.
When Score reaches the deviation level defined in the settings below, the strategy opens the first position.
For example, if you set “Entry at 2 deviations”, the trade will be opened when Score hits ±2.
RSI Only - Alternative entry method based on RSI divergence between the two assets.
The exact RSI levels are defined in the RSI settings section below.
For example, if you set the entry threshold at 30, then when one asset has RSI below 30 and the second one has RSI above 70, the first entry will be triggered.
🔸 Extra Entries Mode
This defines how scale-ins (averaging) are executed. There are two modes:
Score - Works the same way as the main entry, but for additional entries.
For example, the main entry can be at 2 deviations, the first scale-in at 3, the second at 4, etc.
Spread - This mode uses the Spread (difference between the two assets) starting from the main entry moment.
As the spread continues to widen, the strategy can add extra entries based on spread growth rather than Score.
Since Score is a non-linear metric and Spread is linear, in some configurations averaging by Spread can produce better results than averaging by Score. This is pair- and strategy-dependent. 🔸 Entry parameters
Deviation / Spread threshold
Entry size
Main Entry – first field (deviation / spread), second field (position size)
Entry 2 – first field (deviation / spread), second field (position size)
Entry 3 – first field (deviation / spread), second field (position size)
Entry 4 – first field (deviation / spread), second field (position size)
This allows you to define up to four scaling steps with different triggers and different sizing.
🔸 Exit Level
This parameter defines at what Score level you want to exit the trade.
By default it is 0, which means the backtester closes the position when Score returns to the neutral (0) zone.
You can also use positive or negative values. Example:
Assume your main entry is configured at a 3 deviation.
You can exit at the 0 level, or you can set Exit Level = 2.
If your initial entry was at −3, the position will be closed when Score reaches +2.
If your initial entry was at +3, the position will be closed when Score reaches −2.
This approach can increase the profit per trade due to a larger captured spread, but it may also increase the holding time of the position.
🔸 Stop Loss
Here you define the maximum loss per trade in PnL units.
If a trade reaches the negative PnL value specified in this field and the Stop Loss option is enabled, the indicator will close the trade at a loss.
The Cooldown parameter sets a pause after a losing trade:
the strategy will wait a specified number of bars before opening the next trade.
🔸 Take Profit
Works similar to Stop Loss but for profit targets.
You set the desired PnL value you want to reach.
The trade will be closed when either the Take Profit target is hit or when Score reaches the exit level defined in the settings, whichever occurs first (depending on your configuration).
🔸 Show Qty in currency
When enabled, trade size is displayed in currency (USD) instead of token quantity.
This is useful for quickly understanding position size in monetary terms.
You will see this in the Current Trade panel, which is described later.
🔸 Size Rounding
Controls how many decimal places are used when rounding position size (from 0 to 10 digits after the decimal).
This is also used for the Current Trade panel so you can adjust how detailed or compact the size display should be.
📊 RSI FILTERS
This section is used for additional trade filtering.
RSI can be used in two ways:
as a primary entry signal,
or as an extra filter for entries based on Z/S Score.
If in the Strategy Settings the Main Entry Mode is set to RSI, then RSI becomes the main trigger for opening a position.
In this case a trade is opened when the RSI of the two assets reaches opposite zones.
Example:
If the threshold is set to 30, then:
when one asset has RSI below 30, and
the second asset has RSI above 70 (100 − 30),
the strategy opens the first entry.
All extra entries after that will be executed either by Spread or by Z/S Score, depending on your Extra Entries Mode.
Below are the parameters in this block:
RSI Length – standard RSI period setting.
RSI Pivot Mode – when enabled, RSI is used as an additional filter together with Z/S Score. The indicator looks for a reversal pattern on RSI (pivot behavior). If RSI forms a reversal structure, the trade is allowed to open. If not, the signal is skipped until a proper RSI pivot is formed.
Entry RSI Filter – here you define the RSI thresholds used for RSI-based entries. These are the same boundary levels described in the example above.
Overall, this section helps filter out lower-quality trades using additional RSI conditions or lets you build RSI-only entry logic based on extreme levels.
🎨 MAIN CHART STYLING
This section controls the visual appearance of trades on the main chart.
You can customize how the second asset line is drawn, as well as the icons for entries, scale-ins, and exits, including their size and style.
▫️ Price Line
This is the line that shows the price of the second asset and the relative difference between the two instruments.
You can adjust the line thickness and color to make it more readable on your chart.
▫️ Adjust Price Line by Hedge Coefficient
When this option is enabled, the second asset’s line is normalized by the hedge coefficient.
If you turn it off, the hedge coefficient will not be applied to the second asset’s line, and it will be displayed in raw form.
▫️ Entry Label
Here you can customize how the entry markers look:
choose the color, icon style, and size of the label that marks each trade entry and scale-in on the chart.
▫️ Exit Label
Similarly, you can define the color, icon style, and size of the label used for exits.
This helps visually separate entries and exits and makes it easier to read the trade history directly from the chart.
🎯 INDICATOR PANEL
This section controls the settings of the indicator panel, which works like an oscillator and allows you to visualize multiple metrics in one place.
You can flexibly enable, style, and scale each parameter.
🔹 Score
Displays the main deviation metric between the two assets.
You can customize the color and line thickness of the Score plot.
🔹 Spread
Shows the spread between the two assets.
It starts calculating from the moment the trade is opened.
You can adjust its color and thickness for better visibility.
🔹 Total Profit
Displays the cumulative profit for this pair and strategy as a line that grows (or falls) over time.
Color, opacity, and line thickness can be customized.
🔹 Unrealized PNL
Once a trade is opened, this line shows the current PnL of the active position.
It also lets you see historical drawdowns on the pair.
Color and thickness can be adjusted.
🔹 Released PNL
Shows the realized PnL of each closed trade as bars.
Useful for quickly evaluating the result of every individual trade in the backtest.
🔹 Correlation
Plots the correlation coefficient between the two assets as a graph, so you can visually track how stable or unstable the relationship between them is over time.
🔹 Hedge Coefficient
Shows the hedge coefficient as a line, which helps understand how the model is rebalancing exposure between the two legs depending on their behavior.
For each metric there is also a 📎 Stretch option.
Stretch allows you to compress or expand the scale of a specific line to visually align metrics with different ranges on the same panel and make the chart easier to read.
📈 PROFIT CHART
Since TradingView does not natively support proper backtesting for pair trading, this indicator includes its own profit curve for the pair.
You can visually see how the strategy performed over historical data: whether there were deep drawdowns, abnormal profit spikes, or stable equity growth over time. This makes it much easier to evaluate the quality of the pair and the strategy on history.
In the settings of this section you can flexibly customize how the profit chart is displayed:
labels, position of the panel, padding, and other visual details.
Everything depends on your personal preferences, so we give full control over styling:
you can adjust the look of the profit chart to match your layout or completely hide it from the chart if you do not need it.
📌 CURRENT TRADE
This section controls the current trade table.
When there is an active trade on the chart, the panel displays all key information for the open position:
direction for each ticker (long or short),
required position size for each leg,
entry price for both assets,
and real-time PnL for each leg separately,
so you always have a clear view of the current situation.
The main thing you can do with this table is customize its appearance:
you can change the size, position on the chart, background and text colors, as well as separate coloring for positive / negative PnL and different colors for long and short positions.
📅 BACKTEST RESULTS
The next key block is Backtest Results.
This results table with detailed metrics gives you an extended view of how the pair and strategy perform: win rate, profit factor, long/short breakdown, and more than 20 additional stats that help you evaluate the potential of your setup.
⚠️ First of all, it is important to note ⚠️
past performance does not guarantee future results.
Every trader must keep this in mind and factor these risks into their strategy.
The table shows metrics in three cuts:
All Entries
Main Entries
Extra Entries (scale-ins)
Core metrics:
Profit – total profit for each entry type.
Winrate – win rate for this pair.
Profit Factor – ratio of gross profit to gross loss for the strategy.
Trades – number of trades in the backtest.
Wins – number of winning trades.
Losses – number of losing trades.
Long Profit – profit generated by long positions.
Short Profit – profit generated by short positions.
Longs – total number of long trades.
Shorts – total number of short trades.
Avg. Time – average time spent in a trade.
Additional metrics for a deeper evaluation of the pair:
Correlation – current correlation between the two assets in the pair.
Bars Processed – number of bars used in the analysis.
Max Drawdown – maximum historical drawdown of the strategy.
Biggest Loss – the largest single losing trade in the backtest.
Recommended Hedge – recommended hedge coefficient based on historical behavior.
Max Spread – maximum positive spread observed in history.
Min Spread – maximum negative spread observed in history.
Avg. Max Spread – average of positive extreme spread values (above 0).
Avg. Min Spread – average of negative extreme spread values (below 0).
Avg Positive Spread – average positive spread across all trades (only values above 0).
Avg Negative Spread – average negative spread across all trades (only values below 0).
Current Spread – current spread between the assets when a trade is open.
These metrics together allow you to quickly assess how stable the pair is, how the risk/return profile looks, and whether the strategy parameters are suitable for live trading. You can fully customize this results table to fit your workflow:
hide metrics you don’t need, change colors, opacity, and other visual styles, and reorder the focus of the stats according to your trading style.
This way the backtest block can show only the metrics that matter to you most and remain clean and readable during analysis.
📣 ALERTS
The next section is dedicated to alerts.
Here you can configure all signals you need, both for manual trading and for full automation of this pair trading strategy. This block is designed to cover most practical use cases. The indicator supports two alert modes:
Single Alert – one universal custom alert for all events.
Two Alerts – separate alerts for each ticker so you can receive different messages per asset.
Available alert events:
Main Entry – when the main entry is triggered.
Entry 2 – when the first scale-in is executed.
Entry 3 – when the second scale-in is executed.
Entry 4 – when the third scale-in is executed.
Exit Alert – when the position is closed.
StopLoss Alert – when Stop Loss is hit.
TakeProfit Alert – when Take Profit is hit.
All alerts are fully customizable and support a set of placeholders for building structured messages or JSON payloads.
🔹1 Alert Type
List of supported placeholders: {{event}} – trigger name ('Entry 1', 'Exit').
{{dir_1}} – 'Long' or 'Short' for the main ticker.
{{dir_2}} – 'Long' or 'Short' for the other ticker.
{{action_1}} – 'Buy', 'Sell' or 'Close' for the main ticker.
{{action_2}} – 'Buy', 'Sell' or 'Close' for the other ticker.
{{price_1}} – price for the main ticker.
{{price_2}} – price for the other ticker.
{{qty_1}} – order size for the main ticker.
{{qty_2}} – order size for the other ticker.
{{ticker_1}} – main ticker (e.g. 'BTCUSD').
{{ticker_2}} – other ticker (e.g. 'ETHUSD').
{{time}} – candle open time in UTC.
{{timenow}} – signal time in UTC.
🔹2 Alert Type
List of supported placeholders: {{event}} – trigger name ('Entry 1', 'Exit', 'SL', 'TP').
{{action}} – 'Buy', 'Sell' or 'Close'.
{{price}} – order price.
{{qty}} – order size.
{{ticker}} – ticker (e.g. 'BTCUSD').
{{time}} – candle open time in UTC.
{{timenow}} – signal time in UTC. You can use these placeholders to build any JSON structure or custom alert text required by your trading bot, exchange API, or automation service.
In this post I’ve explained how the indicator works, the core concept behind this pair trading strategy, and shown practical examples of trades together with a detailed breakdown of each unique feature inside the tool.
We have invested a lot of work into building this indicator and we truly hope it will help you trade pair strategies more efficiently and more profitably by giving you structured, strategy-specific information that is difficult to obtain in any other way.
⚠️ Please also remember that past performance does not guarantee future results.
Always evaluate the risks, the robustness of your setup, and your own risk tolerance before entering any position, and make independent, well-considered decisions when using this or any other strategy.
MACD + Divergence Indicator [Dynamic Filter]Title: MACD + Divergence
Description: This is an enhanced momentum analysis suite based on the classic Moving Average Convergence Divergence (MACD). It addresses the common weakness of the standard MACD—false signals during low-volatility consolidation—by integrating a Dynamic Volatility Filter and a Multi-Timeframe (MTF) Dashboard.
The Problem It Solves: Standard MACD indicators often generate "whipsaw" crossovers when the market is ranging (moving sideways). Traders often struggle to identify these consolidation zones until it is too late. This script solves this by calculating a dynamic "Consolidation Zone" based on Standard Deviation, visually warning traders when momentum is too weak to be reliable.
Key Features:
1. Dynamic Consolidation Filter (The Grey Zone)
The script calculates Upper and Lower bands around the MACD line using Standard Deviation (Volatility).
Grey Fill: When the MACD line is inside the grey bands, the market is in a "Squeeze" or low-volatility consolidation. Crossovers in this zone are often lower probability.
Breakout: When the MACD line exits the bands, it indicates a volatility expansion and a potentially stronger trend.
2. Automated Divergence Detection
Automatically scans for both Regular (Reversal) and Hidden (Continuation) divergences between Price and Momentum.
Bullish: Marked with Green lines/labels.
Bearish: Marked with Red lines/labels.
Customization: You can choose to calculate divergence based on the MACD Line or the Histogram via settings.
3. Multi-Timeframe (MTF) Dashboard
A customizable information table (optional) displays the MACD state across 4 different timeframes (e.g., 15m, 1H, 4H, Daily).
It checks for Trend Alignment (e.g., are all timeframes Bullish?) to help you trade in the direction of the higher timeframes.
4. Enhanced Visuals
4-Color Histogram: Visualizes momentum growing (bright) vs. momentum fading (pale) for both bullish and bearish phases.
Line Highlights: The MACD and Signal lines are clearly distinct, with configurable smoothing options (EMA/SMA).
Settings Guide:
Consolidation Filter: Increase the Dynamic Filter Multiplier (Default: 0.5) to widen the grey zone if you want to filter out more noise.
Oscillator Source: Switch between "MACD Line" or "Histogram" for divergence detection depending on your strategy.
Table: You can toggle the dashboard on/off or change its position to fit your chart layout.
Credits: Base MACD logic derived from standard technical analysis concepts. Dynamic filtering logic adapted from volatility band theories.
Amihud Illiquidity Ratio [MarkitTick]💡This indicator implements the Amihud Illiquidity Ratio, a financial metric designed to measure the price impact of trading volume. It assesses the relationship between absolute price returns and the volume required to generate that return, providing traders with insight into the "stress" levels of the market liquidity.
Concept and Originality
Standard volume indicators often look at volume in isolation. This script differentiates itself by contextualizing volume against price movement. It answers the question: "How much did the price move per unit of volume?" Furthermore, unlike static indicators, this implementation utilizes dynamic percentile zones (Linear Interpolation) to adapt to the changing volatility profile of the specific asset you are viewing.
Methodology
The calculation proceeds in three distinct steps:
1. Daily Return: The script calculates the absolute percentage change of the closing price relative to the previous close.
2. Raw Ratio: The absolute return is divided by the volume. I have introduced a standard scaling factor (1,000,000) to the calculation. This resolves the issue of the values being astronomically small (displayed as roughly 0) without altering the fundamental logic of the Amihud ratio (Absolute Return / Volume).
- High Ratio: Indicates that price is moving significantly on low volume (Illiquid/Thin Order Book).
- Low Ratio: Indicates that price requires massive volume to move (Liquid/Deep Order Book).
3. Dynamic Regimes: The script calculates the 75th and 25th percentiles of the ratio over a lookback period. This creates adaptive bands that define "High Stress" and "Liquid" zones relative to recent history.
How to Use
Traders can use this tool to identify market fragility:
- High Stress Zone (Red Background): When the indicator crosses above the 75th percentile, the market is in a High Illiquidity Regime. Price is slipping easily. This is often observed during panic selling or volatile tops where the order book is thin.
- Liquid Zone (Green Background): When the indicator drops below the 25th percentile, the market is in a Liquid Regime. The market is absorbing volume well, which is often characteristic of stable trends or accumulation phases.
- Dashboard: A visual table on the chart displays the current Amihud Ratio and the active Market Regime (High Stress, Normal, or Liquid).
Inputs
- Calculation Period: The lookback length for the average illiquidity (Default: 20).
- Smoothing Period: The length of the additional moving average to smooth out noise (Default: 5).
- Show Quant Dashboard: Toggles the visibility of the on-screen information table.
● How to read this chart
• Spike in Illiquidity (Red Zones)
Price is moving on "thin air." Expect high volatility or potential reversals.
• Low Illiquidity (Green/Stable Zones)
The market is deep and liquid. Trends here are more sustainable and reliable.
• Divergence
Watch for price making new highs while liquidity is drying up—a classic sign of an exhausted trend.
Example:
● Chart Overview
The chart displays the Amihud Illiquidity indicator applied to a Gold (XAUUSD) 4-hour timeframe.
Top Pane: Price action with manual text annotations highlighting market reversals relative to liquidity zones.
Bottom Pane: The specific technical indicator defined in the logic. It features a Blue Line (Raw Illiquidity), a Red Line (Signal/Smoothed), and dynamic background coloring (Red and Green vertical strips).
● Deep Visual Analysis
• High Stress Regime (Red Zones)
Visual Event: In the bottom pane, the background periodically shifts to a translucent red.
Technical Logic: This event is triggered when the amihudAvg (the smoothed illiquidity ratio) exceeds the 75th percentile ( hZone ) of the lookback period.
Forensic Interpretation: The logic calculates the absolute price change relative to volume. A spike into the red zone indicates that price is moving significantly on relatively lower volume (high price impact). Visually, the chart shows these red zones aligning with local price peaks (volatility expansion), leading to the bearish reversal marked by the red box in the top pane.
• Liquid Regime (Green Zones)
Visual Event: The background shifts to a translucent green in the bottom pane.
Technical Logic: This triggers when the amihudAvg falls below the 25th percentile ( lZone ).
Forensic Interpretation: This state represents a period where large volumes are absorbed with minimal price impact (efficiency). On the chart, this green zone corresponds to the consolidation trough (green box, top pane), validating the annotated accumulation phase before the bullish breakout.
• Indicator Lines
Blue Line: This is the illiquidityRaw value. It represents the raw daily return divided by volume.
Red Line: This is the smoothedVal , a Simple Moving Average (SMA) of the raw data, used to filter out noise and define the trend of liquidity stress.
● Anomalies & Critical Data
• The Reversal Pivot
The transition from the "High Stress" (Red) background to the "Liquid" (Green) background serves as a visual proxy for market regime change. The chart shows that as the Red zones dissipate (volatility contraction), the market enters a Green zone (efficient liquidity), which acted as the precursor to the sustained upward trend on the right side of the chart.
● About Yakov Amihud
Yakov Amihud is a leading researcher in market liquidity and asset pricing.
• Brief Background
Professor of Finance, affiliated with New York University (NYU).
Specializes in market microstructure, liquidity, and quantitative finance.
His work has had a major impact on both academic research and practical investment models.
● The Amihud (2002) Paper
In 2002, he published his influential paper: “Illiquidity and Stock Returns: Cross-Section and Time-Series Effects” .
• Key Contributions
Introduced the Amihud Illiquidity Measure, a simple yet powerful proxy for market liquidity.
Demonstrated that less liquid stocks tend to earn higher expected returns as compensation for liquidity risk.
The measure became one of the most widely used liquidity metrics in finance research.
● Why It Matters in Practice
Used in quantitative trading models.
Applied in portfolio construction and risk management.
Helpful as a liquidity filter to avoid assets with excessive price impact.
In short: Yakov Amihud established a practical and robust link between liquidity and returns, making his 2002 work a cornerstone in modern financial economics.
Disclaimer: All provided scripts and indicators are strictly for educational exploration and must not be interpreted as financial advice or a recommendation to execute trades. I expressly disclaim all liability for any financial losses or damages that may result, directly or indirectly, from the reliance on or application of these tools. Market participation carries inherent risk where past performance never guarantees future returns, leaving all investment decisions and due diligence solely at your own discretion.
FVG MTF Consensus OscillatorFVG MTF Consensus Oscillator
A multi-timeframe, multi-component oscillator that combines momentum, deviation, and slope analysis across multiple timeframes using Zeiierman's Chebyshev-filtered trend calculation. This indicator identifies potential turning points with zone-based signal classification and timeframe consensus filtering.
Backed by ML/Deep Learning evaluation on ES Futures data from 2015-2024.
🎯 Concept
Traditional oscillators suffer from two major weaknesses:
Single measurement - relying on one metric makes them susceptible to noise
Single timeframe - missing the bigger picture leads to fighting the trend
The FVG MTF Consensus Oscillator addresses both issues by combining three independent measurements across three timeframes into a weighted consensus signal.
The Three Components
Momentum - How fast is the trend moving?
Deviation - How far has price stretched from the trend?
Slope - What is the short-term directional bias?
The Three Timeframes
TF1 (Chart) - Your current chart timeframe (lowest weight)
TF2 (Medium) - Typically 1H or 4H (medium weight)
TF3 (High) - Typically 4H or Daily (highest weight)
By requiring agreement across multiple components AND multiple timeframes, the oscillator filters out noise while capturing meaningful, high-probability market movements.
🔧 How It Works
The Core: Chebyshev Type 1 Filter
At its heart, this indicator uses a Chebyshev Type 1 low-pass filter (inspired by Zeiierman's FVG Trend) to extract a clean trend line from price action. Unlike simple moving averages, the Chebyshev filter offers:
Sharper cutoff between trend and noise
Minimal lag for a given smoothness level
Controlled overshoot via the ripple parameter
Three Oscillator Components
1. Momentum Component
Momentum = Current Trend Value - Previous Trend Value
Measures the velocity of the trend. High positive values indicate strong upward acceleration, while high negative values show downward acceleration.
2. Deviation Component
Deviation = Close Price - Trend Value
Measures how far price has stretched away from the trend line. Useful for identifying overextended conditions and mean reversion opportunities.
3. Slope Component
Slope = Change in Trend over 3 bars
Captures the short-term directional bias of the trend itself, helping confirm trend changes.
Normalization & Component Consensus
Each component is individually normalized to a -100 to +100 scale using adaptive scaling. The oscillator output is a weighted average of all three components, allowing you to emphasize different aspects based on your trading style.
Multi-Timeframe Weighting
The final oscillator value combines all three timeframes using configurable weights:
Combined = (TF1 × Weight1 + TF2 × Weight2 + TF3 × Weight3) / Total Weight
Default weights (1, 2, 3) ensure higher timeframes have more influence, keeping you aligned with the dominant trend while timing entries on lower timeframes.
📊 Zone System
The oscillator uses a fuzzy zone system to classify market conditions:
ZoneRangeInterpretationSignal ColorNeutral-5 to +5No clear bias, avoid tradingGrayContinuation±5 to ±25Trend pullback, continuation setupsAquaDeep Swing±25 to ±50Extended move, stronger setupsGreenReversalBeyond ±50Extreme extension, reversal potentialOrange
When "Show Zone Background" is enabled, the background shading darkens as the oscillator moves into more extreme zones, providing instant visual feedback.
📈 Signal Interpretation
Turn Signals
The indicator plots triangular markers when the oscillator changes direction:
▲ Triangle Up (bottom): Oscillator turning up from a low
▼ Triangle Down (top): Oscillator turning down from a high
Signal Quality by Zone
Not all signals are equal. The signal color indicates which zone the turn occurred in:
ColorZoneProbabilityBest UseGrayNeutralLowAvoid or use very tight stopsAquaContinuationModerateTrend continuation entriesGreenDeep SwingHigherSwing trade entriesOrangeReversalHighestCounter-trend with caution
Timeframe Consensus Filter
Signals only fire when the required number of timeframes agree on direction. With default settings (TF Consensus = 2), at least 2 of 3 timeframes must be moving in the same direction for a signal to trigger.
This prevents:
Taking longs when higher timeframes are bearish
Taking shorts when higher timeframes are bullish
Whipsaws during timeframe disagreement
Trend Coloring
The combined oscillator line changes color based on trend direction:
Light purple (RGB 240, 174, 252): Majority of timeframes trending up
Dark purple (RGB 84, 19, 95): Majority of timeframes trending down
Info Table
When MTF is enabled, a table in the top-right corner displays:
Current oscillator values for each timeframe (TF1, TF2, TF3)
Combined value (CMB)
Color coding: Green = rising, Red = falling
⚙️ Settings Guide
Timeframe Settings
SettingDefaultDescriptionEnable Multi-TimeframeOnMaster switch for MTF functionalityTF1 (Chart)"" (current)First timeframe, typically your chart TFTF2 (Medium)60Second timeframe, typically 1HTF3 (High)240Third timeframe, typically 4HTF1/TF2/TF3 Weight1 / 2 / 3Influence of each TF on combined signal
Timeframe Tips:
Keep TF1 ≤ TF2 ≤ TF3 (ascending order)
For day trading: 5m / 15m / 1H
For swing trading: 1H / 4H / Daily
For position trading: 4H / Daily / Weekly
Display Settings
SettingDefaultDescriptionShow All TimeframesOffDisplay individual TF oscillator linesShow Combined LineOnDisplay the weighted combined oscillatorShow Zone BackgroundOffShade background based on current zone
Trend Filter Settings
SettingDefaultDescriptionTrend Ripple4.0Filter responsiveness (1-10). Higher = faster but more overshootTrend Cutoff0.1Cutoff frequency (0.01-0.5). Lower = smoother trendNormalization Length50Lookback for scaling. Longer = more stable
Component Weights
SettingDefaultDescriptionMomentum Weight1.0Emphasis on trend speedDeviation Weight1.0Emphasis on price stretch from trendSlope Weight1.0Emphasis on short-term trend direction
Component Tips:
For trend-following: Increase Momentum and Slope weights
For mean reversion: Increase Deviation weight
Set any weight to 0 to disable that component
Zone Thresholds
SettingDefaultDescriptionNeutral Zone5Inner boundary (±5 = neutral)Continuation Zone25Middle boundary for continuation setupsDeep Swing Zone50Outer boundary for reversal zone
Adjust based on instrument volatility. More volatile instruments may need wider zones.
Signal Filters
SettingDefaultDescriptionSignal Cooldown3Minimum bars between signalsMin Turn Size2.0Minimum oscillator change for valid turnTF Consensus Required2Minimum TFs agreeing for signal (1-3)
💡 Usage Examples
Example 1: Trend Continuation (Dip Buying)
Setup: Uptrend confirmed by higher timeframes
Check the info table - TF2 and TF3 should show green (rising)
Wait for TF1 to pull back, oscillator enters Continuation zone
Enter on Aqua ▲ signal (turn up with TF consensus)
Stop below recent swing low
Target: Previous high or next resistance
Why it works: You're buying a dip in an established uptrend with multi-timeframe confirmation.
Example 2: Deep Swing Entry
Setup: Extended move showing exhaustion
Oscillator reaches Deep Swing zone (±25 to ±50)
At least 2 TFs start showing the same direction
Enter on Green signal indicating momentum exhaustion
Use tighter stop as the move is already extended
Target: Return to Continuation zone or trend line
Why it works: Extended moves tend to mean-revert. The zone system identifies these opportunities.
Example 3: Reversal Setup (Advanced)
Setup: Extreme extension with diverging timeframes
Oscillator reaches Reversal zone (beyond ±50)
Watch for TF1 to turn while TF3 is still extended
Enter on Orange signal - this is counter-trend!
Use smaller position size and wider stops
Target: Return to Deep Swing or Continuation zone
Why it works: Extreme extensions eventually correct. The orange signal marks high-probability reversal points.
Example 4: Avoiding Bad Trades
What to avoid:
Gray signals in Neutral zone - No edge, random noise
Signals against TF3 direction - Fighting the dominant trend
Signals without TF consensus - Timeframe disagreement = choppy market
Multiple signals in quick succession - Let cooldown filter work
🔬 Multi-Timeframe Analysis Tips
Reading the Info Table
The info table shows real-time oscillator values:
| TF1 | TF2 | TF3 | CMB |
| 23.5 | 45.2 | 67.8 | 52.1 |
All green: Strong uptrend across all timeframes
All red: Strong downtrend across all timeframes
Mixed colors: Potential transition or consolidation
Timeframe Alignment States
TF1TF2TF3Interpretation↑↑↑Strong bull - look for long entries↓↓↓Strong bear - look for short entries↑↑↓Pullback in downtrend - caution on longs↓↓↑Pullback in uptrend - caution on shorts↑↓↑Choppy - reduce position size↓↑↓Choppy - reduce position size
The Power of Consensus
With TF Consensus = 2, signals only fire when 2+ timeframes agree. This single filter eliminates most whipsaws and keeps you aligned with the dominant trend.
For more conservative trading, set TF Consensus = 3 (all timeframes must agree).
⚠️ Important Notes
This indicator does not predict the future. It measures current market conditions and momentum across multiple timeframes.
Always use proper risk management. No indicator is 100% accurate.
Combine with price action. The oscillator works best when confirmed by support/resistance, candlestick patterns, or other confluence factors.
Respect the higher timeframe. When TF3 disagrees, trade smaller or sit out.
Zone signals are probabilistic. Orange (reversal) signals have higher probability but aren't guaranteed reversals.
Adjust settings per instrument. Default settings are optimized for ES Futures but may need tuning for other markets.
🧪 ML/Deep Learning Background
The default parameters and zone thresholds were evaluated using machine learning techniques on ES Futures data spanning 2015-2024. This included:
Optimization of component weights
Zone threshold calibration
Timeframe weight balancing
Signal filter tuning
While past performance doesn't guarantee future results, the parameters represent a data-driven starting point rather than arbitrary defaults.
🙏 Credits
This indicator is inspired by Zeiierman's Multitimeframe Fair Value Gap (FVG) indicator, specifically utilizing concepts from his Chebyshev Type 1 filter implementation for trend calculation.
Original indicator: Multitimeframe Fair Value Gap – FVG (Zeiierman)
📝 Changelog
v1.0
Initial release
Three-component consensus oscillator (Momentum, Deviation, Slope)
Multi-timeframe support with weighted combination
Fuzzy zone classification system
Configurable component and timeframe weights
TF consensus filter for signal quality
Signal cooldown and minimum turn size filters
Real-time info table with TF values
Optional zone background shading
Adaptive Z-Score Oscillator [QuantAlgo]🟢 Overview
The Adaptive Z-Score Oscillator transforms price action into statistical significance measurements by calculating how many standard deviations the current price deviates from its moving average baseline, then dynamically adjusting threshold levels based on historical distribution patterns. Unlike traditional oscillators that rely on fixed overbought/oversold levels, this indicator employs percentile-based adaptive thresholds that automatically calibrate to changing market volatility regimes and statistical characteristics. By offering both adaptive and fixed threshold modes alongside multiple moving average types and customizable smoothing, the indicator provides traders and investors with a robust framework for identifying extreme price deviations, mean reversion opportunities, and underlying trend conditions through the visualization of price behavior within a statistical distribution context.
🟢 How It Works
The indicator begins by establishing a dynamic baseline using a user-selected moving average type applied to closing prices over the specified length period, then calculates the standard deviation to measure price dispersion:
basis = ma(close, length, maType)
stdev = ta.stdev(close, length)
The core Z-Score calculation quantifies how many standard deviations the current price sits above or below the moving average basis, creating a normalized oscillator that facilitates cross-asset and cross-timeframe comparisons:
zScore = stdev != 0 ? (close - basis) / stdev : 0
smoothedZ = ma(zScore, smooth, maType)
The adaptive threshold mechanism employs percentile calculations over a historical lookback period to determine statistically significant extreme zones. Rather than using fixed levels like ±2.0, the indicator identifies where a specified percentage of historical Z-Score readings have fallen, automatically adjusting to market regime changes:
upperThreshold = adaptive ? ta.percentile_linear_interpolation(smoothedZ, percentilePeriod, upperPercentile) : fixedUpper
lowerThreshold = adaptive ? ta.percentile_linear_interpolation(smoothedZ, percentilePeriod, lowerPercentile) : fixedLower
The visualization architecture creates a four-tier coloring system that distinguishes between extreme conditions (beyond the adaptive thresholds) and moderate conditions (between the midpoint and threshold levels), providing visual gradation of statistical significance through opacity variations and immediate recognition of distribution extremes.
🟢 How to Use This Indicator
▶ Overbought and Oversold Identification:
The indicator identifies potential overbought conditions when the smoothed Z-Score crosses above the upper threshold, indicating that price has deviated to a statistically extreme level above its mean. Conversely, oversold conditions emerge when the Z-Score crosses below the lower threshold, signaling statistically significant downward deviation. In adaptive mode (default), these thresholds automatically adjust to the asset's historical behavior, i.e., during high volatility periods, the thresholds expand to accommodate wider price swings, while during low volatility regimes, they contract to capture smaller deviations as significant. This dynamic calibration reduce false signals that plague fixed-level oscillators when market character shifts between volatile and ranging conditions.
▶ Mean Reversion Trading Applications:
The Z-Score framework excels at identifying mean reversion opportunities by highlighting when price has stretched too far from its statistical equilibrium. When the oscillator reaches extreme bearish levels (below the lower threshold with deep red coloring), it suggests price has become statistically oversold and may snap back toward the mean, presenting potential long entry opportunities for mean reversion traders. Symmetrically, extreme bullish readings (above the upper threshold with bright green coloring) indicate potential short opportunities or long exit points as price becomes statistically overbought. The moderate zones (lighter colors between midpoint and threshold) serve as early warning areas where traders can prepare for potential reversals, while exits from extreme zones (crossing back inside the thresholds) often provide confirmation that mean reversion is underway.
▶ Trend and Distribution Analysis:
Beyond discrete overbought/oversold signals, the histogram's color pattern and shape reveal the underlying trend structure and distribution characteristics. Sustained periods where the Z-Score oscillates primarily in positive territory (green bars) indicate a bullish trend where price consistently trades above its moving average baseline, even if not reaching extreme levels. Conversely, predominant negative readings (red bars) suggest bearish trend conditions. The distribution shape itself provides insight into market behavior, e.g., a narrow, centered distribution clustering near zero indicates tight ranging conditions with price respecting the mean, while a wide distribution with frequent extreme readings reveals volatile trending or choppy conditions. Asymmetric distributions skewed heavily toward one side demonstrate persistent directional bias, whereas balanced distributions suggest equilibrium between bulls and bears.
▶ Built-in Alerts:
Seven alert conditions enable automated monitoring of statistical extremes and trend transitions. Enter Overbought and Enter Oversold alerts trigger when the Z-Score crosses into extreme zones, providing early warnings of potential reversal setups. Exit Overbought and Exit Oversold alerts signal when price begins reverting from extremes, offering confirmation that mean reversion has initiated. Zero Cross Up and Zero Cross Down alerts identify transitions through the neutral line, indicating shifts between above-mean and below-mean price action that can signal trend changes. The Extreme Zone Entry alert fires on any extreme threshold penetration regardless of direction, allowing unified monitoring of both overbought and oversold opportunities.
▶ Color Customization:
Six visual themes (Classic, Aqua, Cosmic, Ember, Neon, plus Custom) accommodate different chart backgrounds and aesthetic preferences, ensuring optimal contrast and readability across trading platforms. The bar transparency control (0-90%) allows fine-tuning of visual prominence, with minimal transparency creating bold, attention-grabbing bars for primary analysis, while higher transparency values produce subtle background context when using the oscillator alongside other indicators. The extreme and moderate zone coloring system uses automatic opacity variation to create instant visual hierarchy, with darkest colors highlight the most statistically significant deviations demanding immediate attention, while lighter shades mark developing conditions that warrant monitoring but may not yet justify action. Optional candle coloring extends the Z-Score color scheme directly to the price candles on the main chart, enabling traders to instantly recognize statistical extremes and trend conditions without needing to reference the oscillator panel, creating a unified visual experience where both price action and statistical analysis share the same color language.
USD Liquidity Regime for BTC Perps (Dual) V1USD Liquidity Regime for BTC Perps (Dual)
This intents to be a BTC Perps USD Liquidity Regime macro indicator.
As it names states it is designed for BTCUSDT perpetual futures traders.
It attempts to tracks USD strength (DXY, UUP, yields, VIX composite) as liquidity proxy:
Lower index = weak USD = Risk-On (green background/histogram = long tailwind for BTC).
Higher = strong USD = Risk-Off (red = caution longs, shorts favor).
How to use:
Green background/histogram: Favor longs — rallies likely, dips bought.
Red: Caution longs — corrections hurt, short bias possible.
Blue line (index) vs red SMA: Crosses signal regime shifts.
Histogram strength: Bigger bars = stronger bias.
This is not intended as financial advise or trigger signal tool.
This is a work in progress
Its value is limited, if you do not understand any or some of the words above please do not use this indicator. If you did, then you understand you are not supposed to use this alone to make decisions.
Feel free to ask any questions, this is a work in progress.
Feel free to suggest improvements.
Educational macro context tool — not signals/advice.
Ok for avoiding going against the USD trend dominance by following liquidity.
By @frank_vergaram
Buying Opportunity Score V2.1Overview
A composite scoring system (0-100) that identifies high-probability buying opportunities during market pullbacks. Validated through backtesting on SPY from 2010-2024.
How It Works
The indicator combines multiple fear and oversold signals into a single actionable score. When fear is elevated and the market is oversold, the score rises. Higher scores historically correlate with better forward returns.
Scoring Components
VIX Level (30 pts) - Market fear gauge
Drawdown (30 pts) - Distance from 52-week high
RSI 14 (12 pts) - Oversold confirmation
Bollinger Band (13 pts) - Statistical extreme
VIX Timing (15 pts) - Bonus when VIX declining from peak
Signal Levels
80+ = STRONG BUY (high conviction)
70-79 = BUY (consider entry)
60-69 = WATCH (monitor closely)
Below 60 = No signal
Backtest Results (SPY, 2010-2024)
70+ Signals: 85% win rate, 7.5% average 20-day return
80+ Signals: 100% win rate, 14% average 20-day return
Features
Statistics table showing 1Y, 3Y, 5Y rolling performance
Signal markers (green triangles) on buy signals
Outcome labels showing WIN/LOSS after measurement period
Multiple alert options
Works on SPY, QQQ, IWM (use VIX for all)
How To Use
Add to SPY, QQQ, or IWM (daily timeframe)
Wait for score to reach 70+ or 80+
Green triangle marks signal day
Check statistics table for recent performance
Set alerts for notifications
Alerts Available
STRONG BUY Signal (80+)
BUY Signal (70+)
Moderate Signal (60+)
Score Crossed 80/70
Score Dropped Below 70
Important Notes
Designed for daily timeframe on broad market ETFs
Signals confirm at end of day (bar close)
Statistics table shows rolling windows based on loaded data
Past performance does not guarantee future results
Microstructure Participation & Acceptance Indicator📊 Microstructure Participation & Acceptance Indicator
An advanced participation-based filter combining VWAP distance analysis, volume delta detection, and real-time acceptance/rejection state identification—designed for smaller timeframe trading.
📊 FEATURES
VWAP Distance Normalization
Context-aware fair value measurement:
Automatically resets based on selected anchor (Session/Week/Month)
ATR-normalized distance calculation for universal application
Identifies when price is extended or compressed relative to equilibrium
Configurable extreme distance threshold (default: 1.5 ATR)
Adjustable source input (default: HLC3)
Volume Delta Proxy
Bull vs Bear participation tracking:
Calculates volume imbalance between bullish and bearish candles
EMA smoothing for cleaner signal generation (default: 9 periods)
Delta ratio measurement to identify dominant side
Expansion/compression detection to gauge momentum commitment
Configurable expansion threshold (default: 1.3x)
Acceptance/Rejection State Machine
Real-time market regime identification with six distinct states:
🟢 Accepted Long
Price moving away from VWAP with expanding bullish delta
Distance from VWAP increasing
Volume confirming the move
Indicates real buying pressure—trade WITH the move
🟢 Accepted Short
Price moving away from VWAP with expanding bearish delta
Distance from VWAP increasing
Volume confirming the move
Indicates real selling pressure—trade WITH the move
🟠 Fade Long
Price extended beyond threshold (>1.5 ATR above VWAP)
Delta not supporting the extension
Volume participation absent or diminishing
Potential mean-reversion short setup
🟠 Fade Short
Price extended beyond threshold (>1.5 ATR below VWAP)
Delta not supporting the extension
Volume participation absent or diminishing
Potential mean-reversion long setup
⚪ Chop
Price compressed near VWAP
Bollinger Bands tight (width compressed)
Delta neutral—no clear commitment
NO TRADE ZONE—wait for expansion
⚪ Neutral
Transitional state between regimes
Momentum shifting but not yet confirmed
Monitor for next acceptance signal
Bollinger Bands
Standard volatility measurement with TradingView default styling:
Adjustable period length (default: 20)
Configurable standard deviation multiplier (default: 2.0)
Visual fill between bands for volatility context
Used internally for chop/compression detection
Live Dashboard
Real-time metrics display (top-right corner):
Current market state with color coding
VWAP distance in ATR units
Delta ratio (bull/bear volume balance)
Delta state (Expanding/Compressing)
High-contrast design for instant readability
🎯 HOW TO USE
For Trend Trading:
Accepted Long/Short backgrounds indicate confirmed participation—stay with the trend
Strong moves typically travel 1-1.5 ATR from VWAP with delta support
Use VWAP as dynamic support/resistance
Combine with momentum indicators (MACD, RSI) for confluence
Price above VWAP + Accepted Long state = bullish bias
Price below VWAP + Accepted Short state = bearish bias
For Mean Reversion:
Fade Long/Short states signal overextension without participation
Price beyond 1.5 ATR from VWAP with weak delta = potential reversal
Look for price return to VWAP when extended
Bollinger Band extremes + Fade state = high-probability mean reversion setup
VWAP acts as mean reversion anchor during range-bound sessions
For Risk Management:
Chop state = avoid new entries
Bollinger Band compression + Chop = pre-expansion zone (wait for breakout)
Delta compression after strong move = early exhaustion warning
State transitions (Accepted → Neutral → Fade) = tighten stops
Signal Confirmation:
Strongest setups occur when multiple factors align:
BB breakout + Accepted state + price above/below VWAP
Price rejection at BB bands + Fade state
VWAP support/resistance hold + state transition
Delta expansion + distance increasing + trend direction
⚙️ SETTINGS
All components are fully customizable through organized input groups:
VWAP Distance Group:
VWAP source (default: HLC3)
Anchor period (Session/Week/Month)
ATR length for normalization (default: 14)
Extreme distance threshold in ATR multiples (default: 1.5)
Volume Delta Group:
Delta EMA length (default: 9)
Delta expansion threshold (default: 1.3)
Acceptance Logic Group:
Acceptance lookback period (default: 5)
Chop threshold in VWAP/ATR units (default: 0.3)
Bollinger Bands Group:
BB length (default: 20)
Standard deviation multiplier (default: 2.0)
Display Group:
Toggle state backgrounds
Toggle state change labels
Toggle VWAP line
Toggle Bollinger Bands
💡 EDUCATIONAL VALUE
This indicator teaches important concepts:
How institutional money identifies fair value (VWAP)
The difference between price movement and market acceptance
Why volume participation matters more than price action alone
How to distinguish between noise and committed directional moves
The relationship between volatility compression and expansion cycles
Why distance from equilibrium predicts mean reversion probability
⚠️ IMPORTANT NOTES
This indicator is for educational and informational purposes only
This is a filter, not a standalone trading system
No indicator is perfect—always use proper risk management
Past performance does not guarantee future results
Combine with your own analysis and risk tolerance
Test thoroughly on historical data before live trading
This is not financial advice—use at your own risk
🔧 TECHNICAL DETAILS
Pine Script Version 6
Overlay indicator (displays on price chart)
All calculations use standard, well-documented formulas
No repainting—all signals are confirmed on bar close
Compatible with all timeframes and instruments
Optimized for smaller timeframes (1-5 minute charts)
Minimal computational overhead
📝 CHANGELOG
Version 1.0
Initial release
VWAP distance normalization with ATR scaling
Volume delta proxy system (bull/bear EMA)
6-state acceptance/rejection state machine
Bollinger Bands integration
Real-time dashboard with live metrics
State change labels and background coloring
Full customization options
Developed for traders who need objective participation filters to distinguish high-probability setups from low-quality noise—without cluttering their charts with multiple indicator panels.
Quantum Algo Matrix Quantum Algo Matrix
Multi-Layer Market Intelligence
🔹 Overview
Quantum Algo Matrix is a multi-dimensional market analysis system designed to identify high-probability reversal and continuation zones by combining momentum, volatility, trend structure, multi-timeframe correlation, and AI-based confirmation into a single, coherent framework.
Instead of relying on a single indicator, this script cross-validates signals across independent methodologies, significantly reducing noise and false positives.
It is best suited for active traders, swing traders, and systematic traders who value confirmation, structure, and context over single-trigger signals.
🧠 Core Components & How They Work Together
1️⃣ WaveTrend Engine (Market Structure & Extremes)
At the heart of the system lies a WaveTrend oscillator, which identifies overbought and oversold market conditions with multiple graded levels:
Level 1 (L1) → Primary extreme zones
Level 2 (L2) → Secondary confirmation zones
Level 0 (L0) → Extended exhaustion zones beyond normal extremes
Signals are only considered when WaveTrend momentum confirms a structural extreme, ensuring trades are taken where risk-reward is asymmetric, not mid-range.
Visual differentiation (lines, dots, and crosses) clearly communicates signal strength and hierarchy.
2️⃣ WVF – Volatility Reversal Detection
The WVF module tracks volatility expansion and contraction relative to historical extremes:
Identifies panic selling and emotional spikes
Uses percentile-based thresholds, not fixed values
Optional standard deviation & range filters reduce noise
WVF reversal signals are gated by WaveTrend zones, meaning volatility alone is never enough — price must also be in a statistically significant location.
This avoids the common pitfall of chasing volatility in trending or neutral conditions.
3️⃣ Squeeze Momentum (SQZ) – Pressure & Energy Release
The Squeeze Momentum module measures volatility compression vs expansion, highlighting when the market is:
Building pressure (compression)
Releasing energy (expansion)
Unlike traditional implementations, SQZ is scaled to the WaveTrend range, allowing it to visually integrate with the rest of the system.
The result is a clear momentum context that confirms whether a signal occurs:
Into expansion (higher probability)
Or during decay (lower probability)
4️⃣ Multi-Timeframe Correlation (MTF Filter)
One of the most powerful features of Quantum Algo Matrix is its Multi-Timeframe WaveTrend Correlation Filter.
When enabled, the script checks WaveTrend conditions across multiple higher timeframes (user-selectable):
45m
60m
120m
(optional lower / higher frames)
A signal is only validated when current timeframe conditions align with higher-timeframe momentum, ensuring:
Trades are with the broader market context
Lower-timeframe noise is filtered out
Counter-trend signals are reduced
This is especially effective in volatile or choppy markets.
5️⃣ AI SuperTrend Clustering (Advanced Confirmation Layer)
The AI module introduces a machine-learning-inspired clustering approach:
Multiple SuperTrend variations are generated
Their behavior is clustered using K-means logic
Bullish, bearish, and neutral consensus streams are extracted
Output is normalized and scaled to the WaveTrend environment
Rather than predicting price, the AI acts as a confidence validator:
Confirms strength
Filters weak setups
Prevents entries during indecision
This layer dramatically improves signal quality consistency, especially during transitions and regime changes.
🎯 Final Signal Logic (Why It’s Accurate)
A final LONG or SHORT signal is only produced when:
✔ WaveTrend confirms a valid extreme
✔ Volatility (WVF) shows a qualified reversal or memory condition
✔ Momentum (SQZ) supports expansion or pressure release
✔ Multi-Timeframe structure is aligned (optional)
✔ AI consensus confirms directional confidence (optional)
Because each component is independent, the probability of random alignment is low — this is what makes the system robust and statistically sound.
🧩 Customization & Flexibility
Every module can be enabled or disabled
Visuals are clean and user-controlled
Works on all markets (crypto, forex, indices, stocks)
Optimized for intraday to swing timeframes
No repainting logic in signal generation
⚠️ Important Notes
This script is a decision-support system, not a prediction tool.
It is designed to help traders identify high-quality opportunities, manage risk more effectively, and avoid emotional trading.
Always combine with:
Proper risk management
Market structure awareness
Personal trading rules
⭐ Summary
Quantum Algo Matrix is not a single indicator —
it is a complete market intelligence framework.
By blending structure, volatility, momentum, correlation, and AI-based confirmation, it delivers clearer signals, fewer false positives, and stronger contextual awareness across all timeframes.
All-in-One Momentum Composite The Four Components (and Why They're Chosen)
RSI (Relative Strength Index) – Classic overbought/oversold oscillator (14-period default). Measures speed and change of price movements.
Stochastic (%D line) – Smoothened momentum indicator that compares closing price to the price range over a period. Excellent at spotting reversals in ranging markets.
WaveTrend – Very popular in crypto and forex communities (originally by LazyBear). It’s essentially a momentum oscillator based on overbought/oversold channels, similar to a faster, smoother RSI/Stochastic hybrid. Known for early divergence signals and clean crossovers.
MACD Histogram – Captures momentum changes and trend strength via the difference between fast and slow EMAs. The histogram shows acceleration/deceleration.
JK Scalp - Nishith RajwarJK Scalp Nishith Rajwar
Multi-Stochastic Rotation & Momentum Scalping Framework
JK Scalp is a rule-based momentum and rotation oscillator designed for short-term scalping and intraday execution.
It focuses on how momentum rotates across multiple stochastic speeds, instead of relying on a single oscillator or lagging averages.
This is an execution aid, not a predictive indicator.
🧠 Concept & Originality
Unlike standard stochastic tools, JK Scalp uses four synchronized stochastic layers:
• Fast (9,3) → execution timing
• Medium (14,3) → structure confirmation
• Slow (44,3) → swing context
• Trend (60,10,10) → dominant momentum regime
The core idea is quad-rotation:
High-probability trades occur when all momentum layers rotate together after reaching an extreme.
This script combines:
• Momentum rotation
• Divergence logic
• Flag continuation logic
• Trend-state filtering
into a single cohesive framework, not a simple indicator mashup.
📊 How to Use (Step-by-Step)
1️⃣ Best Timeframes
• Scalping: 1m – 3m
• Intraday: 5m – 15m
• Avoid higher timeframes (not designed for swing holding)
Works best on:
• Index options
• Index futures
• Highly liquid stocks
• Crypto majors
2️⃣ Understanding the Signals
🔁 Quad Rotation (Core Signal)
A valid rotation requires:
• Fast, Medium, Slow, and Trend stochastic moving in the same direction
• Momentum exiting Overbought / Oversold zones
• Trend stochastic supporting the move
This filters out random oscillator noise.
3️⃣ Entry Conditions
🟢 LONG Setup
• Bullish quad rotation
• Either:
– Bullish divergence OR
– Bullish flag pullback
• Fast stochastic turning up
🔴 SHORT Setup
• Bearish quad rotation
• Either:
– Bearish divergence OR
– Bearish flag pullback
• Fast stochastic turning down
⚠️ Signals are confirmation-based, not anticipatory.
4️⃣ SUPER LONG / SUPER SHORT
These appear only when:
• Quad rotation
• Divergence confirmation
They represent high-confidence momentum inflection zones, not guaranteed reversals.
5️⃣ Stop-Loss Visualization
Optional SL zones are plotted using:
• Recent swing high / low
• ATR-based buffer (configurable)
This helps traders visualize risk, not automate exits.
🎨 Visual System (Why It Looks Different)
• Multi-layer glow effects → momentum strength
• Dynamic cloud → fast vs trend dominance
• Color-shifting fast line → acceleration vs decay
• Chart overlays → execution clarity without clutter
Everything is designed for speed and readability during live trading.
⭐ Unique Selling Points (USP)
✅ Multi-speed stochastic rotation (not single-line signals)
✅ Context-first, not signal spam
✅ Built-in divergence + continuation logic
✅ Non-repainting logic
✅ Designed for scalpers, not hindsight analysis
✅ Works across indices, options, crypto, and futures
⚠️ Important Notes
• Not a standalone trading system
• Best combined with:
– Market structure
– Key levels
– Session timing
• Avoid low-liquidity or news-spike candles
This indicator guides execution, it does not replace discretion.
👤 Who This Is For
• Scalpers & intraday traders
• Options traders needing precise timing
• Traders who understand momentum & structure
• Users who want fewer but higher-quality signals
🏁 Summary
JK Scalp helps you trade momentum rotation, not overbought/oversold myths.
Wait for alignment. Execute with discipline.
USD Liquidity Regime IndexTrack global risk regimes with this USD Liquidity Composite —
It aims to be a daily macro indicator blending normalised DXY (50%), UUP (20%), 10Y Treasury yields (20%), and VIX (10%).
How to read:
When the blue index is above its red SMA: Strong USD, tightening liquidity → Risk-Off mode (often bearish for Nasdaq, BTC, and risk assets).
When the line is below: Weak USD, abundant liquidity → Risk-On (bullish environment).
Example: In 2022's bear market, the index stayed high above SMA most of the year, signalling persistent Risk-Off as USD surged.
Features on-chart table, regime background colors, and crossover alerts.
Great contextual tool for macro traders IMO.
Educational only — not financial advice.
Use at your own risk.
By @frank_vergaram
SMI Trigger System - Lower - NPR21/ChatGPTSMI Trigger System (Lower) — Buy Low / Hrugu (Modified)
This indicator is a modified version of the original SMI Trigger System created by Buy Low, with later enhancements by Hrugu, published with permission.
The script is a lower-pane Smoothed Stochastic Momentum Index (SMI) designed to deliver clear, visually intuitive momentum signals without unnecessary clutter. This version focuses exclusively on SMI behavior and removes auxiliary indicators to keep signals clean, readable, and consistent across timeframes.
Key Features
Smoothed SMI line with dynamic color changes based on momentum direction
Raw SMI line for additional reference
Zero-line split cloud shading for quick bullish/bearish momentum identification
Upper and lower SMI reference levels for overbought/oversold context
Exact-bar SMI color-flip triangle markers for immediate visual confirmation
Adjustable triangle size and offset so markers do not overlap the SMI line
Fully customizable colors for:
Zero line
Smoothed SMI (up/down)
Raw SMI
Cloud above and below zero
Upper and lower SMI levels
How to Use
This indicator is designed to highlight momentum shifts, not to predict price. It works best when combined with price structure, trend context, or higher-timeframe bias.
1. SMI Line & Color Changes
The smoothed SMI line changes color based on momentum direction:
Up color → momentum strengthening
Down color → momentum weakening
A color change often signals a potential momentum shift.
2. SMI Color-Flip Triangles
Green ▲ triangle below the SMI
Appears when the smoothed SMI turns upward (bearish → bullish momentum).
Red ▼ triangle above the SMI
Appears when the smoothed SMI turns downward (bullish → bearish momentum).
Triangles are plotted on the same bar the SMI changes color and are offset so they do not overlap the SMI line.
These markers are intended as visual confirmations, not standalone trade signals.
3. Zero Line & Cloud
The zero line separates bullish and bearish momentum regimes.
Cloud above zero → bullish momentum bias
Cloud below zero → bearish momentum bias
Stronger signals often occur when SMI flips in the direction of the cloud.
4. Upper & Lower SMI Levels
Upper and lower reference levels help identify extended momentum.
Momentum flips near or beyond these levels may indicate:
Exhaustion
Potential pullbacks
Trend continuation setups when aligned with higher-timeframe direction
5. Best Practices
Use this indicator as a confirmation tool, not a prediction tool.
Combine with:
Market structure
Support and resistance
Trend direction
Volume or price action
Works well on tick charts, intraday timeframes, and higher-timeframe analysis.
Additional Notes
Triangles do not repaint
All visual elements are user-configurable
No ADX or Awesome Oscillator components
Designed for clarity, speed, and ease of interpretation
This script is intended for analytical and educational purposes only and does not constitute trading advice.
SMI Trigger System SMI Trigger System (Lower) — Buy Low / Hrugu (Modified)
This indicator is a modified version of the original SMI Trigger System created by Buy Low, with later enhancements by Hrugu, published with permission.
The script is a lower-pane Smoothed Stochastic Momentum Index (SMI) designed to deliver clear, visually intuitive momentum signals without unnecessary clutter. This version focuses exclusively on SMI behavior and removes auxiliary indicators to keep signals clean, readable, and consistent across timeframes.
Key Features
Smoothed SMI line with dynamic color changes based on momentum direction
Raw SMI line for additional reference
Zero-line split cloud shading for quick bullish/bearish momentum identification
Upper and lower SMI reference levels for overbought/oversold context
Exact-bar SMI color-flip triangle markers for immediate visual confirmation
Adjustable triangle size and offset so markers do not overlap the SMI line
Fully customizable colors for:
Zero line
Smoothed SMI (up/down)
Raw SMI
Cloud above and below zero
Upper and lower SMI levels
How to Use
This indicator is designed to highlight momentum shifts, not to predict price. It works best when combined with price structure, trend context, or higher-timeframe bias.
1. SMI Line & Color Changes
The smoothed SMI line changes color based on momentum direction:
Up color → momentum strengthening
Down color → momentum weakening
A color change often signals a potential momentum shift.
2. SMI Color-Flip Triangles
Green ▲ triangle below the SMI
Appears when the smoothed SMI turns upward (bearish → bullish momentum).
Red ▼ triangle above the SMI
Appears when the smoothed SMI turns downward (bullish → bearish momentum).
Triangles are plotted on the same bar the SMI changes color and are offset so they do not overlap the SMI line.
These markers are intended as visual confirmations, not standalone trade signals.
3. Zero Line & Cloud
The zero line separates bullish and bearish momentum regimes.
Cloud above zero → bullish momentum bias
Cloud below zero → bearish momentum bias
Stronger signals often occur when SMI flips in the direction of the cloud.
4. Upper & Lower SMI Levels
Upper and lower reference levels help identify extended momentum.
Momentum flips near or beyond these levels may indicate:
Exhaustion
Potential pullbacks
Trend continuation setups when aligned with higher-timeframe direction
5. Best Practices
Use this indicator as a confirmation tool, not a prediction tool.
Combine with:
Market structure
Support and resistance
Trend direction
Volume or price action
Works well on tick charts, intraday timeframes, and higher-timeframe analysis.
Additional Notes
Triangles do not repaint
All visual elements are user-configurable
No ADX or Awesome Oscillator components
Designed for clarity, speed, and ease of interpretation
This script is intended for analytical and educational purposes only and does not constitute trading advice.
SMI Trigger System The SMI Trigger System is a lower-pane momentum indicator based on a Hull-smoothed Stochastic Momentum Index (SMI). It is designed to assist in identifying potential momentum shifts by highlighting signal alignment and level interactions.
This indicator is intended to be used as part of a broader analysis framework. Confluence between trend, structure, and higher-timeframe context defines the setup, while SMI signal behavior may be used for confirmation.
The script can be applied across multiple timeframes and markets. It does not generate trade signals on its own and should be used alongside additional analysis and risk management techniques.
For educational purposes only. Not financial advice.
SMI Trigger System This is a great lower indicator that’s incredibly accurate on any timeframe. I’ve been using it for years on TOS, and now with the help of ChatGPT — since I don’t code — I can now use it on TradingView. Originally written by "Buy Low" and further modified by "Hguru" in TOS. It helps confirm confluence and improves trade confidence.
Kinetic Scalper [BULLBYTE]KINETIC SCALPER - ADVANCED MOMENTUM & CONFLUENCE TRADING SYSTEM
A SOPHISTICATED MULTI-FACTOR ANALYSIS INDICATOR FOR PRECISION ENTRIES
The Kinetic Scalper is a comprehensive trading analysis tool that combines volume-weighted momentum calculations, multi-oscillator divergence detection, and a proprietary 15-factor confluence scoring system to identify high-probability reversal setups across all timeframes.
WHAT MAKES THIS INDICATOR ORIGINAL
This is NOT a simple mashup of existing indicators.
The Kinetic Scalper features a completely custom momentum engine called the "Kinetic Pulse" - a volume-weighted momentum oscillator with Fisher Transform normalization that fundamentally differs from standard RSI or other momentum indicators. Every component feeds into a unified algorithmic framework designed specifically for this system.
KEY INNOVATIONS:
KINETIC PULSE ENGINE
Unlike standard RSI which uses simple price changes, the Kinetic Pulse applies:
→ Volume weighting to price movements (high-volume moves carry more weight)
→ EMA smoothing instead of traditional SMA (faster response to changes)
→ Fisher Transform normalization for improved signal clarity
→ Adaptive period adjustment based on current volatility regime
→ Result: A momentum oscillator that responds to conviction, not just price noise
15-FACTOR CONFLUENCE SCORING SYSTEM
Every signal is graded based on the number of confirming factors present:
→ Momentum position (oversold/overbought extremes)
→ Momentum velocity (direction change confirmation)
→ Momentum acceleration (strength of reversal)
→ Multi-oscillator divergence (price vs. 3 oscillators)
→ Volume confirmation (above-average participation)
→ Volume delta analysis (buying vs. selling pressure)
→ Higher timeframe alignment (trend confirmation from larger timeframe)
→ Session timing (major forex session awareness)
→ Structure clearance (clear path to profit targets)
→ Support/resistance proximity (confluence with key levels)
→ Market regime filtering (trending vs. choppy conditions)
Signals are graded A+, A, or B based on how many factors align:
• CONSERVATIVE MODE: A+ requires 12+ factors, A requires 9+, B requires 7+
• BALANCED MODE: A+ requires 10+ factors, A requires 7+, B requires 5+
• AGGRESSIVE MODE: A+ requires 8+ factors, A requires 5+, B requires 3+
TRADE ANALYSIS STATE MACHINE
A sophisticated monitoring system that tracks trade conditions in real-time using:
→ 5-state analysis framework (Factors Aligned / Positive Bias / Mixed Signals / Factors Weakening / Negative Bias)
→ Hysteresis-based transitions (different thresholds to enter vs. exit states)
→ Confidence smoothing with EMA (reduces noise, prevents flip-flopping)
→ Minimum commitment periods before state changes
→ Override logic for significant events (near TP/SL, momentum reversals)
→ Result: Stable, actionable guidance that doesn't change on every bar
INSTRUMENT-AWARE CALIBRATION
Automatically detects what you're trading and applies optimized parameters:
→ Forex Majors: Standard ATR, high session weight
→ Forex Crosses: Tighter stops, moderate session weight
→ Crypto: Wider stops (1.8x multiplier), reduced session weight (24/7 markets)
→ Indices: Moderate-wide stops, high session weight
→ Commodities: Moderate stops, moderate session weight
WHAT THIS INDICATOR DOES
The Kinetic Scalper is designed to identify high-confluence reversal opportunities by analyzing multiple dimensions of market behavior simultaneously.
CORE FUNCTIONS:
1. SIGNAL GENERATION
→ Identifies potential reversal points at oversold/overbought extremes
→ Confirms with multi-oscillator divergence detection
→ Validates with volume, higher timeframe, and structural analysis
→ Filters out low-probability setups automatically
→ Grades signals based on total confluence factors present
2. AUTOMATED TRADE TRACKING
→ Calculates structure-based or ATR-based stop loss levels
→ Projects take profit targets using risk-to-reward ratios
→ Monitors live position status (P/L, distance to targets, R-multiple)
→ Tracks TP1 and TP2 hits automatically
→ Displays outcome markers (TP HIT, PARTIAL WIN, STOPPED)
3. REAL-TIME CONDITION MONITORING
→ Analyzes 6 factor categories during active trades
→ Provides confidence scoring (0-100 scale)
→ Generates actionable guidance based on current market state
→ Alerts when conditions deteriorate or improve
→ Helps with trade management decisions
4. COMPREHENSIVE MARKET ANALYSIS
→ Session detection (Asian, London, New York, Overlap)
→ Volatility regime identification (Low, Normal, High, Extreme)
→ Trend state classification (Trending Up/Down, Ranging, Transitioning)
→ Volume analysis (relative volume and delta approximation)
→ Choppiness filtering (blocks signals in ranging markets)
WHY USE THIS INDICATOR
PROBLEM: Most momentum indicators generate too many false signals at extremes.
SOLUTION: The Kinetic Scalper requires MULTIPLE confirming factors before generating a signal, dramatically reducing noise and focusing on high-confluence setups.
ADVANTAGES:
✓ QUALITY OVER QUANTITY
→ Signal grading ensures you can filter for only the highest-quality setups
→ A+ signals have 10-12+ confirming factors aligned
→ Cooldown periods prevent over-trading the same move
✓ COMPLETE TRADE FRAMEWORK
→ Entry signals with confluence justification
→ Calculated stop loss based on market structure or ATR
→ Two profit targets with clear risk-to-reward ratios
→ Live trade monitoring with factor analysis
→ Outcome tracking and visual markers
✓ ADAPTIVE TO MARKET CONDITIONS
→ Volatility-based period adjustment for momentum calculations
→ Instrument-specific ATR multipliers
→ Session awareness for forex traders
→ Higher timeframe trend filtering
→ Automatic regime detection (trending vs. choppy)
✓ TRANSPARENT METHODOLOGY
→ Every input has detailed tooltips explaining its purpose
→ Signal tooltips show exactly why a signal was generated
→ Dashboard displays all relevant market conditions
→ Factor scores are visible during trades
→ No "black box" mystery calculations
✓ NON-REPAINTING & RELIABLE
→ All signals use barstate.isconfirmed (only on closed bars)
→ Higher timeframe data uses lookahead_off with historical offset
→ No future data access or repainting behavior
→ What you see is what you get - signals don't disappear or move
HOW THE INDICATOR WORKS
SIGNAL GENERATION PROCESS:
STEP 1: MOMENTUM ANALYSIS
The Kinetic Pulse engine calculates volume-weighted momentum:
→ Price changes are weighted by volume ratio vs. 20-bar average
→ High-volume moves have more influence on the oscillator
→ Gains and losses are smoothed using EMA (not SMA like RSI)
→ Fisher Transform is applied for normalization to 0-100 scale
→ Result: Momentum reading that emphasizes conviction, not noise
STEP 2: REVERSAL DETECTION
The indicator looks for potential reversal conditions:
→ Kinetic Pulse reaching oversold zone (below dynamic lower threshold)
→ Momentum velocity turning positive after being negative (for longs)
→ OR bullish divergence detected on multiple oscillators
→ Price making lower lows while oscillators make higher lows = divergence
STEP 3: MULTI-OSCILLATOR DIVERGENCE CONFIRMATION
Divergence is validated across three sources:
→ Kinetic Pulse divergence
→ CCI divergence
→ Stochastic divergence
→ Multiple oscillators confirming divergence increases signal reliability
STEP 4: CONFLUENCE FACTOR SCORING
The system evaluates all 15 possible confirming factors:
→ Momentum position: Is pulse oversold/overbought? (+0 to +2 points)
→ Momentum direction: Is velocity reversing? (+0 to +2 points)
→ Momentum acceleration: Is reversal strengthening? (+0 to +1 point)
→ Divergence count: How many oscillators show divergence? (+0 to +2 points)
→ Volume strength: Is volume above 1.3x average? (+0 to +1 point)
→ Volume delta: Is cumulative delta positive/negative? (+0 to +1 point)
→ HTF alignment: Does higher timeframe support direction? (+0 to +2 points)
→ Session timing: Is it a prime trading session? (+0 to +1 point)
→ Clear air: Is path to targets clear of obstacles? (+0 to +1 point)
→ Structure confluence: Are we near support/resistance? (+0 to +1 point)
→ Market regime: Is market trending, not choppy? (+0 to +1 point)
Total possible score: 15 points
Minimum for signal: 3-12 points depending on sensitivity mode
STEP 5: FILTER VALIDATION
Before generating a signal, additional checks are performed:
→ Volume must be above minimum threshold (if filter enabled)
→ Higher timeframe must not oppose the signal direction (if filter enabled)
→ Target path must be clear of major resistance/support (if filter enabled)
→ Volatility must not be EXTREME (blocks signals in chaos)
→ Risk-to-reward ratio must meet minimum requirement
→ Cooldown period must have elapsed since last signal
STEP 6: SIGNAL GRADING
If all filters pass, the signal is graded based on score:
→ A+ Grade: Highest confluence (8-12+ factors depending on sensitivity)
→ A Grade: High confluence (5-9+ factors)
→ B Grade: Moderate confluence (3-7+ factors)
Only graded signals (A+, A, or B) are displayed.
STEP 7: TRADE LEVEL CALCULATION
Stop loss and targets are calculated automatically:
STOP LOSS METHODS:
• Structure-Based: Uses recent swing low/high with ATR buffer, constrained by min/max ATR limits
• ATR-Based: Pure ATR multiplier with min/max constraints
• Fixed ATR: Simple ATR multiplier, no adjustments
TARGET CALCULATION:
• TP1: Entry ± (Stop Distance × Target 1 R:R)
• TP2: Entry ± (Stop Distance × Target 2 R:R)
• Default: TP1 at 1.0 R:R (1:1), TP2 at 2.0 R:R (1:2)
STEP 8: TRADE MONITORING
Once a signal is taken, the indicator tracks:
→ Current P/L in ticks and R-multiples
→ Distance to each target in ATR units
→ Distance to stop loss in ATR units
→ TP1 hit detection (marks with label, updates lines)
→ TP2 hit detection (closes trade, marks outcome)
→ Stop loss hit detection (closes trade, differentiates partial vs. full loss)
STEP 9: FACTOR ANALYSIS (DURING TRADES)
The Trade Analysis Panel monitors 6 key factor categories:
→ Momentum: Is momentum still aligned with trade direction? (-15 to +15 pts)
→ Position: Current R-multiple position (-12 to +12 pts)
→ Volume: Is volume still supportive? (-6 to +6 pts)
→ HTF Alignment: Does HTF still support trade? (-6 to +8 pts)
→ Target Proximity: How close are we to targets? (0 to +10 pts)
→ Stop Proximity: Are we dangerously close to stop? (-15 to +3 pts)
Raw scores are summed and smoothed using 5-bar EMA to create Confidence Score (0-100).
STEP 10: STATE MACHINE TRANSITIONS
Based on smoothed confidence, the system transitions between 5 states:
→ FACTORS ALIGNED (72+): Everything looks good
→ POSITIVE BIAS (58-72): Conditions favorable
→ MIXED SIGNALS (48-58): Neutral conditions
→ FACTORS WEAKENING (22-48): Concerning signals
→ NEGATIVE BIAS (<22): Poor conditions
Hysteresis prevents rapid flipping between states (different entry/exit thresholds).
RECOMMENDED TIMEFRAMES & INSTRUMENTS
TIMEFRAME VERSATILITY:
Despite the name "Scalper," this indicator works on ALL timeframes:
✓ LOWER TIMEFRAMES (1m - 15m)
→ Ideal for: Scalping and very short-term trades
→ Expect: More signals, faster trades, requires active monitoring
→ Best for: Forex majors, liquid crypto pairs
→ Tip: Use Conservative sensitivity to reduce noise
✓ MID TIMEFRAMES (15m - 1H)
→ Ideal for: Intraday trading and day trading
→ Expect: Moderate signal frequency, 1-4 hour trade duration
→ Best for: Forex, indices, major crypto
→ Tip: Balanced sensitivity works well here
✓ HIGHER TIMEFRAMES (4H - Daily)
→ Ideal for: Swing trading and position trading
→ Expect: Fewer signals, higher-quality setups, multi-day trades
→ Best for: All instruments
→ Tip: Can use Aggressive sensitivity for more opportunities
INSTRUMENT COMPATIBILITY:
✓ FOREX MAJORS (EUR/USD, GBP/USD, USD/JPY, etc.)
→ Auto-detected or manually select "Forex Major"
→ Session filtering is highly valuable here
→ London/NY overlap generates best signals
✓ FOREX CROSSES (EUR/GBP, AUD/NZD, etc.)
→ Auto-detected or manually select "Forex Cross"
→ Slightly tighter stops applied automatically
→ Session weight reduced vs. majors
✓ CRYPTOCURRENCIES (BTC, ETH, SOL, etc.)
→ Auto-detected or manually select "Crypto"
→ Wider stops (1.8x multiplier) due to volatility
→ Session filtering less relevant (24/7 markets)
→ Works well on both spot and perpetual futures
✓ INDICES (S&P 500, NASDAQ, DAX, etc.)
→ Auto-detected or manually select "Index"
→ Session opens (NY, London) are important
→ Moderate stop widths applied
✓ COMMODITIES (Gold, Silver, Oil, etc.)
→ Auto-detected or manually select "Commodity"
→ Moderate stops and session awareness
→ Works well on both spot and futures
VISUAL ELEMENTS EXPLAINED
SIGNAL MARKERS:
The indicator offers 3 display styles (choose in settings):
• PREMIUM STYLE (Default)
→ Signal appears below/above candles with connecting line
→ Background panel with grade badge (LONG , SHORT , etc.)
→ Entry price displayed
→ Direction arrow pointing to entry candle
→ Most informative, best for detailed analysis
• MINIMAL STYLE
→ Simple dot marker with grade text next to it
→ Clean, unobtrusive design
→ Best for mobile devices or cluttered charts
→ Less visual noise
• CLASSIC STYLE
→ Diamond marker with grade badge below/above
→ Traditional indicator aesthetic
→ Good balance between info and simplicity
ALL STYLES INCLUDE:
→ Signal tooltips with complete trade plan details
→ Grade display (A+, A, or B)
→ Color coding (bright colors for A+, standard for A/B)
SIGNAL TOOLTIP CONTENTS:
When you hover over any signal marker, you'll see:
→ Signal direction and grade
→ Confluence score (actual points vs. required)
→ Reason for signal (divergence type, reversal pattern)
→ Complete trade plan (Entry, Stop, TP1, TP2)
→ Risk in ticks
→ Risk-to-reward ratios
→ Market conditions at signal (Pulse value, HTF status, Volume, Session)
TRADE LEVEL LINES:
When Trade Tracking is enabled:
• ENTRY LINE (Yellow/Gold)
→ Solid horizontal line at entry price
→ Shaded zone around entry (±ATR buffer)
→ Label showing entry price
→ Extends 20-25 bars into future
• STOP LOSS LINE (Orange/Red)
→ Dashed line at stop level
→ Label showing stop price and distance in ticks
→ Turns dotted and changes color after TP1 hit (breakeven implied)
→ Deleted when trade closes
• TAKE PROFIT 1 LINE (Blue)
→ Dotted line at TP1 level
→ Label showing price and R:R ratio (e.g., "1:1.0")
→ Turns solid and changes to green when hit
→ Deleted after TP1 hit
• TAKE PROFIT 2 LINE (Blue)
→ Solid line at TP2 level
→ Label showing price and R:R ratio (e.g., "1:2.0")
→ This is the "full win" target
→ Deleted when trade closes
OUTCOME MARKERS:
When trade milestones are reached:
• - Green label appears when first target is touched
• - Green label when second target is touched (trade complete)
• - Red label if stop loss hit before any target
• - Orange label if TP1 hit but then stopped out
PREVIOUS DAY LEVELS:
If enabled (Show Previous Day Levels):
• PDH (Previous Day High) - Solid red/orange line
→ Label shows "PDH: "
→ Useful resistance reference for intraday trading
• PDL (Previous Day Low) - Solid green line
→ Label shows "PDL: "
→ Useful support reference for intraday trading
BACKGROUND TINTS:
Subtle background colors indicate states:
→ Light green tint: Active long position being tracked
→ Light red tint: Active short position being tracked
→ Light orange tint: Extreme volatility warning (signals blocked)
DASHBOARD GUIDE
The indicator features TWO dashboard panels:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
MAIN DASHBOARD (Top Right by default)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
WHEN NO TRADE IS ACTIVE:
→ Bias: Current market bias (BULLISH, BEARISH, NEUTRAL, LEAN LONG/SHORT)
→ Based on Kinetic Pulse position and velocity
→ Helps you understand overall momentum direction
→ Pulse: Current Kinetic Pulse value (0-100 scale)
→ <30 = Oversold (potential long setups developing)
→ >70 = Overbought (potential short setups developing)
→ 40-60 = Neutral zone
→ Volatility: Current volatility regime (LOW, NORMAL, HIGH, EXTREME)
→ Calculated from ATR ratio vs. 100-period average
→ EXTREME volatility blocks all signals (too chaotic)
→ Trend: Market state classification
→ TREND UP / TREND DOWN: ADX > 25, directional movement clear
→ RANGING: ADX < 20, choppy conditions
→ TRANSITIONING: ADX 20-25, developing conditions
→ VOLATILE: Extreme ATR regime
→ Session: Current forex session
→ ASIAN (00:00-08:00 UTC)
→ LONDON (07:00-16:00 UTC)
→ NEW YORK (13:00-22:00 UTC)
→ LDN/NY (13:00-16:00 UTC) - Overlap period, highest volatility
→ OFF-HOURS: Outside major sessions
→ Volume: Current volume vs. 20-bar average
→ Displayed as multiplier (e.g., "1.45x" = 45% above average)
→ Green if >1.3x (high volume, bullish for signal quality)
→ Red if <0.8x (low volume, bearish for signal quality)
→ HTF: Higher timeframe analysis status
→ BULLISH: HTF momentum supports longs
→ BEARISH: HTF momentum supports shorts
→ NEUTRAL: No clear HTF direction
→ Best Score: Highest confluence score currently available
→ Shows both long and short scores
→ Format: " / "
→ Example: "8/7 " means long score is 8, threshold is 7, long is leading
→ Helps you anticipate which direction might signal next
→ PDH/PDL: Previous day high and low prices
→ Quick reference for intraday support/resistance
WHEN TRADE IS ACTIVE:
→ Trade: Direction and grade (e.g., "LONG ")
→ Entry: Entry price of current trade
→ P/L: Current profit/loss
→ Shown in ticks and R-multiples
→ Format: "+45 | +0.75R" or "-20 | -0.35R"
→ Green when positive, red when negative
→ TP1: First target status
→ Shows price and distance if not hit
→ Shows "HIT" in green if reached
→ TP2: Second target price and distance
→ Stop: Stop loss price and current distance from stop
→ Bars: Number of bars since entry (trade duration)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
TRADE ANALYSIS PANEL (Bottom Left by default)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
This panel provides algorithmic analysis of market conditions. It does NOT provide investment advice or recommendations.
WHEN NO TRADE IS ACTIVE:
Shows scanning status and signal readiness:
→ Long/Short Readiness Gauges
→ Visual bar showing proximity to signal threshold
→ Score display (e.g., "8/7" means 8 points scored, 7 needed)
→ "RDY" indicator when threshold reached
→ Status Messages
→ "Scanning for setups..." - Normal scanning mode
→ "Long setup ready - cooldown: X bars" - Signal qualified but in cooldown
→ "Oversold conditions - watch for reversal" - Setup developing
→ "Choppy conditions detected" - Warning about market state
→ "Extreme volatility - signals blocked" - Safety filter active
WHEN TRADE IS ACTIVE:
Header shows current analysis state:
→ FACTORS ALIGNED (Green) - Everything looks good, confidence 72+
→ POSITIVE BIAS (Light Green) - Conditions favorable, confidence 58-72
→ MIXED SIGNALS (Blue) - Neutral conditions, confidence 48-58
→ FACTORS WEAKENING (Orange) - Concerning signals, confidence 22-48
→ NEGATIVE BIAS (Red) - Poor conditions, confidence <22
Confidence Score:
→ Displayed as percentage (0-100%)
→ Visual gauge (|||||.....)
→ Trend indicator (Rising, Falling, Stable)
→ Shows momentum of confidence change
Factor Breakdown (if enabled):
Shows 6 factor categories with individual scores:
→ Momentum: Is momentum aligned with trade? (-15 to +15 points)
→ Positive if velocity matches trade direction
→ Negative if momentum opposes trade
→ Position: Current R-multiple analysis (-12 to +12 points)
→ Positive if trade is in profit
→ Negative if underwater
→ Score increases as profit grows
→ Volume: Is volume supportive? (-6 to +6 points)
→ Positive if volume above average
→ Negative if volume weak
→ HTF Align: Higher timeframe status (-6 to +8 points)
→ Positive if HTF still supports trade direction
→ Negative if HTF turned against trade
→ Target: Proximity to profit targets (0 to +10 points)
→ Higher score when approaching targets
→ Bonus if TP1 already hit and near TP2
→ Stop Dist: Distance from stop loss (-15 to +3 points)
→ Negative if dangerously close to stop (<0.3 ATR)
→ Positive if well away from stop (>1.5 ATR)
Each factor shows:
• Score value with +/- indicator
• Trend symbol: + (improving), - (deteriorating), = (stable)
• Visual gauge
Guidance Messages:
→ "TARGET 2 APPROACHING" - TP2 within 0.3 ATR
→ "TARGET 1 APPROACHING" - TP1 within 0.3 ATR
→ "STOP PROXIMITY WARNING" - Stop within 0.3 ATR
→ "Factors aligned - Holding" - Positive state, stay in trade
→ "Conditions favorable" - Still looking good
→ "Conditions mixed - " - Neutral assessment
→ "Factors deteriorating" - Warning of weakening setup
→ "Confluence weakening - secure gains" - Consider exit if profitable
COMPACT MODE (Mobile-Friendly):
→ Reduces panel size by showing only essential info
→ Factor icons instead of full breakdowns
→ Simplified guidance messages
→ Perfect for smaller screens
SETTINGS GUIDE
MASTER SETTINGS:
Instrument Type
→ Purpose: Optimizes ATR multipliers and session weights for your asset
→ Options: Auto-Detect (recommended), Forex Major, Forex Cross, Crypto, Index, Commodity
→ Default: Auto-Detect
→ When to change: If auto-detection is incorrect for your symbol
Signal Sensitivity
→ Purpose: Controls how many factors required before generating signals
→ Options:
• Conservative: Requires 12+ for A+, 9+ for A, 7+ for B (fewer, highest quality)
• Balanced: Requires 10+ for A+, 7+ for A, 5+ for B (recommended)
• Aggressive: Requires 8+ for A+, 5+ for A, 3+ for B (more frequent)
→ Default: Balanced
→ When to change: If you want fewer signals (Conservative) or more opportunities (Aggressive)
Enable Trade Signals
→ Purpose: Master on/off switch for signal generation
→ Default: ON
→ When to disable: If you only want to use the analysis dashboards without signals
Enable Trade Tracking
→ Purpose: Tracks active trades and monitors conditions until TP/SL hit
→ Default: ON
→ When to disable: If you manage trades manually and don't want automatic tracking
Show Entry/Stop/Target Levels
→ Purpose: Displays trade plan lines and labels on chart
→ Default: ON
→ When to disable: If you prefer clean charts or manage levels yourself
DISPLAY SETTINGS:
Color Theme
→ Purpose: Optimizes colors for your chart background
→ Options: Dark (for dark charts), Light (for light charts)
→ Default: Dark
Signal Display Style
→ Purpose: Visual style of signal markers
→ Options:
• Premium: Badge with line and background panel (most detailed)
• Minimal: Simple dot with grade text (cleanest)
• Classic: Diamond marker with badge (traditional)
→ Default: Premium
Signal Distance
→ Purpose: How far signal labels appear from price bars (in ATR units)
→ Range: 0.5 to 10.0
→ Default: 2.0
→ When to adjust: Increase to 3.0-4.0 if signals hide behind candle wicks
TP/SL Label Distance
→ Purpose: Spacing of price labels to prevent overlap
→ Range: 0.5 to 5.0
→ Default: 1.5
Show Previous Day Levels
→ Purpose: Display PDH/PDL reference lines
→ Default: ON
→ Best for: Intraday traders who respect previous day levels
MAIN DASHBOARD:
Show Main Dashboard
→ Purpose: Toggle visibility of market conditions table
→ Default: ON
Main Dashboard Position
→ Options: Top Right, Top Left, Bottom Right, Bottom Left
→ Default: Top Right
→ When to change: To avoid overlap with TradingView's built-in panels
TRADE ANALYSIS PANEL:
Show Trade Analysis Panel
→ Purpose: Toggle factor analysis dashboard
→ Default: ON
Analysis Panel Position
→ Options: Top Right, Top Left, Bottom Right, Bottom Left, Middle Right, Middle Left
→ Default: Bottom Left
→ Recommended: Bottom Right or Middle Right to avoid overlap with Main Dashboard
Compact Mode
→ Purpose: Reduces panel size for mobile or smaller screens
→ Default: OFF
→ When to enable: Mobile trading, small screens, or minimalist preference
Show Factor Details
→ Purpose: Displays individual factor scores vs. overall confidence only
→ Default: ON
→ When to disable: For more compact view showing only state and confidence
RISK MANAGEMENT:
Stop Loss Method
→ Purpose: How stop loss distance is calculated
→ Options:
• Structure-Based: Uses swing highs/lows with ATR buffer (recommended)
• ATR-Based: Pure ATR multiplier with min/max constraints
• Fixed ATR: Simple multiplier, no adjustments
→ Default: Structure-Based
→ Impact: Structure-Based respects market geometry but constrains within safe limits
ATR Stop Multiplier
→ Purpose: Multiplier for ATR-based stop calculation
→ Range: 0.5 to 3.0
→ Default: 1.5
→ When to adjust:
• Increase to 2.0-2.5 for more breathing room (fewer false stops)
• Decrease to 1.0-1.2 for tighter stops (but more stop-outs)
Maximum Stop Distance (ATR)
→ Purpose: Cap on stop width to prevent excessive risk
→ Range: 1.0 to 5.0
→ Default: 2.5
→ Impact: If structure-based stop exceeds this, ATR-based stop is used instead
Minimum Stop Distance (ATR)
→ Purpose: Floor on stop width to avoid noise-induced stops
→ Range: 0.2 to 1.0
→ Default: 0.5
→ Impact: Prevents stops too tight to survive normal volatility
Target 1 Risk/Reward Ratio
→ Purpose: R:R for first profit target
→ Range: 0.5 to 2.0
→ Default: 1.0 (1:1 ratio)
→ Common values: 1.0 for quick profit taking, 1.5 for patient trading
Target 2 Risk/Reward Ratio
→ Purpose: R:R for second profit target (full win)
→ Range: 1.0 to 4.0
→ Default: 2.0 (1:2 ratio)
→ Common values: 2.0-3.0 for balanced risk/reward
Minimum R:R Required
→ Purpose: Filters out signals with poor risk/reward
→ Range: 0.5 to 2.0
→ Default: 1.0
→ Impact: Signals where potential reward doesn't meet this ratio are rejected
→ WARNING: Always ensure your position sizing means a stop loss = no more than 1-2% of your account, regardless of R:R ratio
SIGNAL FILTERS:
Session Awareness
→ Purpose: Weights signals higher during major forex sessions
→ Default: ON
→ Impact: Doesn't block signals, but session quality factors into scoring
→ Best for: Forex traders
Session Timezone
→ Purpose: Timezone for session calculations
→ Options: UTC, America/New_York, Europe/London, Asia/Tokyo, Asia/Hong_Kong
→ Default: UTC
→ When to change: Match your broker's server time
Higher Timeframe Alignment
→ Purpose: Checks HTF momentum before generating signals
→ Default: ON
→ Impact: Filters counter-trend signals, improves quality
→ Recommended: Keep enabled
HTF Timeframe
→ Purpose: Which higher timeframe to check
→ Default: Auto (blank field)
→ Auto selection:
• 1m chart → 5m HTF
• 5m chart → 15m HTF
• 15m chart → 1H HTF
• 1H chart → 4H HTF
• 4H+ chart → Daily HTF
→ Manual override: Enter any timeframe (e.g., "60" for 1-hour)
Volume Confirmation
→ Purpose: Requires above-average volume for signals
→ Default: ON
→ Impact: Filters low-liquidity false signals
→ Recommended: Keep enabled
Minimum Volume Ratio
→ Purpose: Volume threshold vs. 20-bar average
→ Range: 0.3 to 2.0
→ Default: 0.8 (80% of average)
→ When to adjust:
• Increase to 1.2-1.5 for only high-volume signals
• Decrease to 0.5-0.7 for more permissive filtering
Structure Clearance Check
→ Purpose: Ensures clear path to targets (no nearby resistance/support)
→ Default: ON
→ Impact: Prevents trades with immediate obstacles
→ Recommended: Keep enabled
Minimum Bars Between Signals
→ Purpose: Cooldown period after each signal
→ Range: 1 to 10
→ Default: 3
→ Impact: After a signal, this many bars must pass before another in same direction
→ When to adjust:
• Increase to 5-7 to prevent over-trading
• Decrease to 1-2 for faster re-entries
ADVANCED TUNING:
Momentum Period
→ Purpose: Base period for Kinetic Pulse calculation
→ Range: 5 to 30
→ Default: 14
→ When to adjust:
• Lower (8-10): More responsive, noisier
• Higher (18-21): Smoother, slower to react
→ Note: If Adaptive Period enabled, this is adjusted automatically
Adaptive Period
→ Purpose: Auto-adjusts momentum period based on volatility
→ Default: ON
→ Impact: Shortens period in high volatility, lengthens in low volatility
→ Recommended: Keep enabled for automatic optimization
Divergence Lookback
→ Purpose: How far back to search for divergence patterns
→ Range: 10 to 60
→ Default: 30
→ When to adjust:
• Shorter (15-20): Only recent divergences
• Longer (40-50): Catches older divergences (may be less relevant)
Swing Detection Bars
→ Purpose: Bars required on each side to confirm swing high/low
→ Range: 2 to 7
→ Default: 3
→ Impact on stops:
• Lower (2-3): More swing points, potentially tighter stops
• Higher (5-7): Only major swings, wider stops
Choppiness Index Threshold
→ Purpose: Threshold above which market considered choppy
→ Range: 38.2 to 80.0
→ Default: 61.8
→ Impact:
• Lower (50-55): Stricter quality filter (fewer signals in ranging markets)
• Higher (65-70): More permissive (allows signals in choppier conditions)
HOW TO READ SIGNALS
SIGNAL ANATOMY:
When a signal appears, you'll see:
1. DIRECTIONAL MARKER
→ Arrow, dot, or diamond pointing to entry candle (depends on style)
→ Positioned below price for LONG, above price for SHORT
→ Connected to price with line (Premium style)
2. GRADE BADGE
→ Displays signal quality: LONG , SHORT , etc.
→ Color coding:
• Bright green/cyan for A+ longs
• Standard green for A/B longs
• Bright pink/magenta for A+ shorts
• Standard red for A/B shorts
3. ENTRY PRICE (Premium style only)
→ Shows exact entry price at signal generation
4. TOOLTIP (all styles)
→ Hover over signal to see complete trade plan
→ Includes: Entry, Stop, TP1, TP2, Risk, R:R ratios, market conditions, signal reason, confluence score
INTERPRETING GRADES:
→ A+ SIGNALS (Highest Quality)
• 8-12+ confirming factors aligned
• Multiple divergences OR strong momentum reversal
• HTF alignment + volume + session timing + clear structure
• These are your highest-probability setups
• Recommended action: Give these priority, consider larger position size
→ A SIGNALS (High Quality)
• 5-9+ confirming factors aligned
• Good confluence, most key factors present
• Missing 1-2 optimal conditions
• These are still quality trades
• Recommended action: Standard position size, solid setups
→ B SIGNALS (Moderate Quality)
• 3-7+ confirming factors aligned
• Minimum viable confluence
• May be missing HTF alignment, volume, or session timing
• Higher variance outcomes
• Recommended action: Smaller position size or skip if conservative
SIGNAL NARRATIVE:
Each signal tooltip includes a narrative explaining WHY it was generated:
→ "Multi-divergence at oversold extreme"
• Multiple oscillators showing bullish divergence
• Kinetic Pulse in oversold zone
• High-quality reversal setup
→ "Bullish divergence near support"
• Divergence detected
• Price near key support level (swing low or PDL)
• Structure confluence
→ "Momentum reversal with HTF alignment"
• Kinetic Pulse velocity reversing
• Higher timeframe supports direction
• Strong trend-following setup
→ "Oversold momentum reversal"
• Extreme Kinetic Pulse reading reversing
• May not have divergence but strong momentum shift
READING THE TRADE PLAN:
Every signal comes with a complete trade plan:
→ ENTRY: The close price of the signal candle
• This is where the signal triggered
• If using limit orders, you might improve on this price
→ STOP: Calculated stop loss level
• Based on your Stop Loss Method setting
• Distance shown in ticks
• Risk tolerance: Ensure this represents ≤1-2% of your account
→ TP1: First profit target
• Default: 1:1 risk-reward
• This is your partial profit or first exit
• Consider taking 50% off at TP1
→ TP2: Second profit target
• Default: 1:2 risk-reward
• This is your "full win" target
• Hold remaining position for this level
SIGNAL FREQUENCY EXPECTATIONS:
Frequency varies by timeframe, sensitivity, and market conditions:
→ AGGRESSIVE MODE
• Lower timeframes (1m-5m): 5-15 signals per day
• Mid timeframes (15m-1H): 2-5 signals per day
• Higher timeframes (4H-D): 1-3 signals per week
→ BALANCED MODE (Default)
• Lower timeframes: 3-8 signals per day
• Mid timeframes: 1-3 signals per day
• Higher timeframes: 2-5 signals per week
→ CONSERVATIVE MODE
• Lower timeframes: 1-4 signals per day
• Mid timeframes: 0-2 signals per day
• Higher timeframes: 1-3 signals per week
Note: Frequency also depends on market volatility and trending vs. ranging conditions.
Example - Kinetic Scalper Trade Sequence
Here's an example showing the complete trade lifecycle with all dashboard transitions, annotations, and descriptions.
INSTRUMENT & TIMEFRAME DETAILS
Symbol: Nifty 50 Index (NSE)
Date: December 15, 2025
Session: London session (active trading hours)
Instrument Type: Index (auto-detected)
TRADE SEQUENCE BREAKDOWN
SCREENSHOT 1: Pre-Signal Setup Building (Image 1)
Time: ~12:00-14:30 UTC+5:30(approx.)
Price Action: Uptrend showing signs of exhaustion near 26,200
Market State: Price at session highs
Main Dashboard (Top Right):
- Bias: LEAN SHORT
- Pulse: 58.9 (approaching overbought)
- Volatility: NORMAL
- Trend: TRANSITIONING
- Session: LONDON (favorable timing)
- Volume: 0.98x (slightly below average)
- HTF: BULLISH (caution for counter-trend)
- Best Score: 9/5 (Short score building)
- PDH/PDL: 26098.25 / 25938.95
Trade Analysis Panel (Bottom Left):
- Status: NO ACTIVE TRADE
- Long Score: 5/5 (RDY)
- Short Score: 9/5 (RDY)
- Panel Message: "Short pattern developing - score: 9"
Description :
Setup Development Phase: The indicator identifies a potential short opportunity as price reaches the previous day's high. The short confluence score has climbed to 9/15 points, meeting the 'Balanced' sensitivity threshold for a Grade B signal. Notice the 'LEAN SHORT' bias and the Kinetic Pulse reading of 58.9 approaching overbought territory. The Trade Analysis panel shows 'Short pattern developing' with 9/5 factors aligned. Key factors: momentum approaching reversal zone, price at resistance (PDH), and London session providing favorable conditions.
SCREENSHOT 2: Signal Generated & Trade Entered (Image 2)
Time: ~13:00 UTC+5:30 (signal bar)
Entry Price: 26,184.65
Signal Grade: Grade
Main Dashboard (Top Right):
- Trade: SHORT
- Entry: 26184.65
- P/L: 5.95 pts | +0.2R (early positive movement)
- TP1: 26157.00 (33.2 pts away)
- TP2: 26129.35 (60.84 pts away)
- Stop: 26212.30 (22.1 pts away)
- Bars: 1 (just entered)
Trade Analysis Panel (Bottom Left):
- Header: TRADE ANALYSIS
- Status Bar: "Conditions mixed - improving 57%"
- Confidence: 57% RISING
- Factor Breakdown:
- Momentum: -4 (velocity not yet aligned)
- Position: +4 (slight profit)
- Volume: +2 = (volume present)
- HTF Align: +2 = (not strongly aligned)
- Target: +0 - (far from TP)
- Stop Dist: +3 - (good distance)
- Bottom Status: "Conditions mixed - Monitoring"
- Disclaimer: "Analysis only - Not financial advice"
Description:
Signal Activation: A Grade A short signal triggers at 26,184.65 after the short confluence score reached qualifying levels. The indicator places a structure-based stop loss at 26,212.30 (27.65 points risk) with dual targets at 1:1 and 1:2 risk-reward ratios.
The Trade Analysis Panel immediately begins monitoring with an initial confidence score of 57% - classified as 'MIXED SIGNALS' but showing a 'RISING' trend. Factor analysis reveals: momentum not yet aligned (-4 points as price just reversed), position slightly favorable (+4 points already +0.2R), volume adequate (+2), HTF showing weak alignment (+2 as we're counter-trend), stop well-placed (+3), but targets still distant (0 points).
Notice how the Main Dashboard switches from market scanning mode to active trade tracking, now displaying entry price, live P/L in both points (5.95 pts) and R-multiples (+0.2R), and distances to all key levels. The analysis panel provides real-time factor scoring to help monitor trade health.
SCREENSHOT 3: TP1 Hit - Trade Performing Well (Image 3)
Time: ~14:20 UTC+5:30(approx)
Price: ~26,154 (TP1 zone)
Bars in Trade: 29
Main Dashboard (Top Right):
- Trade: SHORT
- Entry: 26184.65
- P/L: 30.85 pts | +1.12R (excellent progress)
- TP1: HIT (displayed in green)
- TP2: 26129.35 (24.44 pts away)
- Stop: 26212.30 (58.5 pts away - well protected)
- Bars: 29
Trade Analysis Panel (Bottom Left):
- Header: TRADE ANALYSIS
- Status Bar: "Multiple factors positive"
- Confidence: 78% RISING
- Factor Breakdown:
- Momentum: +8 = (ALIGNED)
- Position: +8 + (strong profit zone)
- Volume: +2 + (continued support)
- HTF Align: +8 = (now strongly aligned)
- Target: +10 + (TP1 achieved, approaching TP2)
- Stop Dist: +3 + (excellent cushion)
- Bottom Status: "Multiple factors positive"
- Visual State: Green background (FACTORS ALIGNED state)
Description:
Trade Execution Phase - First Target Achieved: After 29 bars , price reaches the first take-profit target at 26,157.00. The ' ' marker confirms partial profit taking. Current P/L shows +30.85 points (+1.12R), exceeding the initial 1:1 risk-reward.
The Trade Analysis Panel shows dramatic improvement - confidence has surged to 78% (FACTORS ALIGNED state) with most factors now positive:
- Momentum factor improved to +8 (velocity aligned with trade direction)
- Position factor at +8 (over +1R profit zone)
- HTF Align jumped to +8 (higher timeframe now confirming the move)
- Target factor maxed at +10 (TP1 achieved, TP2 within reach)
- Stop Distance at +3 (58.5 points cushion providing safety)
Notice the panel status displays 'Multiple factors positive' with a green-tinted background, indicating optimal trade conditions. The confidence trend shows 'RISING' suggesting continued momentum. With TP1 secured and only 24.44 points to TP2, the trade is well-positioned for a full 1:2R win.
SCREENSHOT 4: TP2 Reached - Trade Complete (Image 4)
Time: ~15:00+ UTC+5:30
Final Exit: 26,129.35 (TP2)
Final Result: Full TP2 win
Main Dashboard (Top Right):
- Bias: NEUTRAL (reverted to scanning mode)
- Pulse: 45.2 (returned to neutral zone)
- Volatility: NORMAL
- Trend: TREND DOWN (confirmed the move)
- Session: LONDON
- Volume: 1.26x (increased as move developed)
- HTF: BEARISH (fully aligned post-trade)
- Best Score: 5/5 (neutral after completion)
Trade Analysis Panel (Bottom Left):
- Status: NO ACTIVE TRADE (reverted)
- Long Score: 5/5 (RDY)
- Short Score: 5/5 (RDY)
- Panel Message: "Scanning - prime session active"
- Light blue/cyan background (back to scanning mode)
Description:
Trade Completion - Full Target Achieved: The short trade reaches its second take-profit target at 26,129.35, securing a complete 1:2 risk-reward win. The ' ' marker confirms the exit. Final results:
- Entry: 26,184.65
- Exit: 26,129.35
- Profit: 55.30 points (approximately +2.0R)
- Outcome: Full TP2 success
Post-Trade Analysis: After trade closure, the indicator automatically returns to market scanning mode. The Main Dashboard reverts to showing market conditions rather than trade metrics. Notice how the 'Trend' now displays 'TREND DOWN' - confirming the move we captured. Volume increased to 1.26x during the winning move, validating the signal quality.
The Trade Analysis Panel switches back to 'NO ACTIVE TRADE' status and resumes displaying long/short setup scores. The confidence-based factor monitoring was instrumental throughout the trade:
- Initial entry at 57% confidence (MIXED SIGNALS)
- Peak confidence of 78% at TP1 (FACTORS ALIGNED)
- Real-time factor updates helped confirm trade validity
This example demonstrates the indicator's complete workflow: setup identification → signal generation → entry execution → live trade monitoring → systematic exit at targets.
KEY FEATURES DEMONSTRATED
1. Dual Dashboard System
- Main Dashboard: Market conditions (scanning) → Trade metrics (active position)
- Analysis Panel: Setup scores (scanning) → Factor-based confidence (in-trade)
2. Visual Trade Management
- Color-coded entry zones (yellow)
- Risk levels clearly marked (red dashed stop)
- Profit targets with R:R ratios labeled
- Achievement markers ( , )
3. Real-Time Factor Analysis
- 6-factor scoring system (Momentum, Position, Volume, HTF, Target, Stop Dist)
- Confidence percentage with trend indicators
- State machine (MIXED → FACTORS ALIGNED)
- Hysteresis prevents false state changes
4. Risk Management
- Structure-based stop placement (respects swing highs)
- Multiple take-profit levels (1:1 and 1:2 R:R)
- Live P/L tracking in points and R-multiples
- Distance monitoring to all key levels
This complete example showcases the indicator's progression from setup identification through trade completion, demonstrating how the dual-dashboard system and factor-based analysis provide continuous trade guidance. The structured stop-loss and dual-target approach delivered the planned 1:2 risk-reward ratio with systematic, rule-based execution.
ALERT SYSTEM
The indicator includes 9 built-in alert conditions:
SIGNAL ALERTS:
→ High-Grade Long Signal (A+)
• Triggers only on A+ long signals
• For traders who want only the highest-quality longs
• Message: "KINETIC SCALPER: LONG @ "
→ High-Grade Short Signal (A+)
• Triggers only on A+ short signals
• For traders who want only the highest-quality shorts
• Message: "KINETIC SCALPER: SHORT @ "
→ Long Signal
• Triggers on ANY qualified long signal (A+, A, or B)
• For traders who want all long opportunities
• Message: "KINETIC SCALPER: LONG @ "
→ Short Signal
• Triggers on ANY qualified short signal
• For traders who want all short opportunities
• Message: "KINETIC SCALPER: SHORT @ "
TRADE MANAGEMENT ALERTS:
→ TP1 Hit
• Triggers when first profit target is reached
• Useful for partial profit taking notifications
• Message: "KINETIC SCALPER: TP1 REACHED"
→ TP2 Reached
• Triggers when second profit target is reached
• Trade is complete, full win achieved
• Message: "KINETIC SCALPER: TP2 REACHED"
→ Stop Loss Hit
• Triggers when stop loss is reached
• Important for trade management and risk tracking
• Message: "KINETIC SCALPER: STOP LOSS"
ANALYSIS STATE ALERTS:
→ Analysis State: Negative Bias
• Triggers when factor analysis enters "Negative Bias" state
• Warning that trade conditions are deteriorating
• Consider reducing position or preparing to exit
• Message: "KINETIC SCALPER: Analysis state changed to NEGATIVE BIAS"
→ Analysis State: Factors Weakening
• Triggers when factor analysis enters "Factors Weakening" state
• Caution that confluence is diminishing
• Monitor trade closely
• Message: "KINETIC SCALPER: Analysis state changed to FACTORS WEAKENING"
HOW TO SET UP ALERTS:
1. Click the "Create Alert" button in TradingView
2. Condition: Select "Kinetic Scalper "
3. Choose your desired alert from the dropdown
4. Configure your alert options:
→ Once Per Bar Close (recommended for non-repainting)
→ Frequency: Once Per Bar Close or Only Once
5. Set expiration and notification methods (popup, email, webhook, etc.)
6. Create alert
RECOMMENDED ALERT STRATEGY:
For active traders:
→ Set "Long Signal" and "Short Signal" alerts for all opportunities
→ Set "TP1 Hit", "TP2 Reached", and "Stop Loss Hit" for trade management
→ Consider "Analysis State: Negative Bias" for trade monitoring
For selective traders:
→ Set only "High-Grade Long Signal (A+)" and "High-Grade Short Signal (A+)"
→ Focus on the absolute highest-quality setups
→ Set TP/SL alerts for position management
USAGE TIPS & BEST PRACTICES
SIGNAL SELECTION:
✓ GRADE MATTERS
→ A+ signals have statistically more confluence factors
→ If you're conservative, trade only A+ signals
→ B signals can work but require more discretion
✓ CONFLUENCE WITH YOUR ANALYSIS
→ Use this indicator as CONFIRMATION, not sole decision criteria
→ Combine with your own support/resistance analysis
→ Check for fundamental events (news, economic data)
→ Respect major round numbers and psychological levels
✓ SESSION TIMING (Forex)
→ Best signals often occur during London/NY overlap
→ Avoid signals 10 minutes before major news releases
→ Asian session signals can be valid but lower liquidity
✓ TIMEFRAME CONFLUENCE
→ If you get an A+ signal on 15m, check if 1H chart agrees
→ Higher timeframe confirmation adds conviction
→ Avoid signals that oppose the daily/4H trend
TRADE MANAGEMENT:
✓ POSITION SIZING
→ ALWAYS size positions so stop loss = 1-2% of account
→ Never risk more than you can afford to lose
→ Smaller position on B signals, standard on A, larger on A+ (within limits)
✓ PARTIAL PROFIT TAKING
→ Consider taking 50% off at TP1
→ Move stop to breakeven after TP1 hit
→ Let remaining position run to TP2
✓ TRAILING STOPS
→ The indicator doesn't auto-trail stops (manual decision)
→ After TP1, you might manually move stop to entry (breakeven)
→ Consider ATR-based trailing stop for runners
✓ WATCH THE ANALYSIS PANEL
→ If state changes to "Factors Weakening" while in profit, consider exit
→ "Negative Bias" during a trade is a strong warning
→ "Factors Aligned" confirms your trade thesis is still valid
RISK MANAGEMENT:
✓ NEVER IGNORE STOPS
→ The calculated stop is there for a reason
→ Moving stop further away increases risk exponentially
→ If stopped out, accept it and wait for next setup
✓ AVOID REVENGE TRADING
→ If you get stopped out, resist urge to immediately re-enter
→ Signal cooldown helps with this
→ Wait for next qualified signal
✓ RESPECT VOLATILITY WARNINGS
→ If indicator shows "EXTREME" volatility, signals are blocked for a reason
→ Don't force trades in chaotic conditions
→ Wait for regime to normalize
✓ CORRELATION RISK
→ Be aware of correlation if trading multiple pairs
→ EUR/USD and GBP/USD are highly correlated
→ Don't stack risk on correlated instruments
OPTIMIZATION:
✓ START WITH DEFAULTS
→ Default settings are well-tested
→ Don't over-optimize for recent market behavior
→ Give settings at least 20-30 trades before judging
✓ TIMEFRAME-SPECIFIC ADJUSTMENTS
→ Lower timeframes: Consider increasing Signal Distance to 3.0-4.0
→ Higher timeframes: ATR Stop Multiplier might go to 2.0-2.5
→ Crypto: Ensure Instrument Type is set to "Crypto" for proper stops
✓ SENSITIVITY CALIBRATION
→ Too many signals? Switch to Conservative
→ Missing good setups? Try Balanced or Aggressive
→ Quality > Quantity always
✓ KEEP A JOURNAL
→ Track which signal grades work best for you
→ Note which sessions produce best results
→ Review stopped trades for patterns
THINGS TO AVOID:
✗ DON'T chase signals after several bars have passed
✗ DON'T ignore the stop loss or move it further away
✗ DON'T overtrade by taking every B-grade signal
✗ DON'T trade during major news if you're not experienced
✗ DON'T use this as your only analysis tool
✗ DON'T expect 100% win rate (no indicator has this)
✗ DON'T risk more than 1-2% per trade regardless of signal grade
UNDERSTANDING THE METHODOLOGY
WHY VOLUME WEIGHTING?
Traditional momentum oscillators treat all price moves equally. A 10-point move on low volume is weighted the same as a 10-point move on high volume.
The Kinetic Pulse corrects this by:
→ Calculating volume ratio vs. 20-bar average
→ Applying square root transformation to volume ratio (prevents extreme weights)
→ Multiplying price changes by volume weight
→ Result: High-volume moves influence the oscillator more than low-volume noise
This helps filter false breakouts and emphasizes moves with participation.
WHY FISHER TRANSFORM?
Fisher Transform is a mathematical transformation that:
→ Normalizes probability distributions
→ Creates sharper turning points
→ Amplifies extremes while compressing the middle
→ Makes overbought/oversold levels more distinct
Applied to the Kinetic Pulse, it helps identify genuine extremes vs. noise.
WHY MULTI-OSCILLATOR DIVERGENCE?
Single-source divergence can give false signals. By requiring divergence confirmation across multiple oscillators (Kinetic Pulse, CCI, Stochastic), the system filters out:
→ Divergences caused by calculation quirks in one oscillator
→ Temporary momentum anomalies
→ False divergence on noisy, low-timeframe charts
Multiple sources confirming the same pattern increases reliability.
WHY ADAPTIVE PERIODS?
Fixed periods can be:
→ Too slow during high volatility (miss fast reversals)
→ Too fast during low volatility (generate noise)
The adaptive system:
→ Shortens period when ATR ratio > 1.3 (high volatility = need faster response)
→ Lengthens period when ATR ratio < 0.7 (low volatility = need noise filtering)
→ Keeps period in reasonable range (60% to 140% of base period)
→ Result: Oscillator adjusts to current market pace automatically
WHY HYSTERESIS IN STATE MACHINE?
Without hysteresis, the analysis state would flip-flop on every bar, creating:
→ Confusing, contradictory guidance
→ Analysis paralysis
→ Lack of actionable information
Hysteresis solves this by:
→ Using different thresholds to ENTER vs. EXIT a state
→ Example: Enter "Factors Aligned" at 72+ confidence, but don't exit until <62
→ This creates stable states that persist through minor fluctuations
→ Requires minimum commitment period (3 bars) before state changes
→ Overrides commitment for significant events (near TP/SL)
→ Result: Stable, trustworthy analysis that changes only when truly warranted
WHY CONFIDENCE SMOOTHING?
Raw factor scores fluctuate bar-by-bar based on momentary conditions. Smoothing:
→ Uses 5-period EMA on raw confidence scores
→ Filters out single-bar anomalies
→ Preserves genuine trends in confidence
→ Prevents false state transitions
→ Result: More reliable assessment of actual trade health
WHY INSTRUMENT-SPECIFIC PARAMETERS?
Different instruments have different characteristics:
→ Forex is highly liquid, respects technical levels well, standard ATR works
→ Crypto is extremely volatile, needs wider stops (1.8x) to avoid false stops
→ Indices respect session opens strongly, session weighting is important
→ Commodities fall in between
Auto-detection applies research-based multipliers automatically.
WHY STRUCTURE-BASED STOPS?
ATR-based stops can:
→ Place stop in middle of consolidation (easily hit)
→ Ignore obvious invalidation levels
→ Be too tight during expansion or too wide during contraction
Structure-based stops:
→ Use actual swing highs/lows (where traders actually place stops)
→ Add small ATR buffer to avoid stop hunting
→ Constrain within min/max ATR limits for safety
→ Result: Stops that respect market geometry while managing risk
DISCLAIMER & RISK WARNING
READ THIS CAREFULLY BEFORE USING THIS INDICATOR
This indicator is provided for EDUCATIONAL and INFORMATIONAL purposes only.
❌ NOT FINANCIAL ADVICE
This indicator does NOT constitute financial advice, investment recommendations, or solicitation to buy or sell any financial instrument. All information is for educational purposes only.
❌ NO GUARANTEES
→ Past performance does NOT guarantee future results
→ No indicator can predict future price movements with certainty
→ Signal grades represent confluence, NOT win probability
→ A+ signals can lose, B signals can win - markets are probabilistic
❌ SUBSTANTIAL RISK
Trading financial instruments involves SUBSTANTIAL RISK of loss:
→ You can lose your entire investment
→ Leveraged trading amplifies both gains AND losses
→ Never trade with money you cannot afford to lose
→ Never risk more than 1-2% of your account per trade
❌ YOUR RESPONSIBILITY
→ All trading decisions are YOUR responsibility
→ You must conduct your own analysis before entering trades
→ Consult a licensed financial advisor before trading
→ Understand the risks specific to your jurisdiction and situation
→ Only trade with capital you can afford to lose completely
❌ NO HOLY GRAIL
→ This indicator is a TOOL, not a complete trading system
→ It should be used as part of a broader analysis framework
→ Combine with your own technical analysis, risk management, and judgment
→ No indicator works 100% of the time in all market conditions
❌ ANALYSIS PANEL DISCLAIMER
The "Trade Analysis Panel" provides ALGORITHMIC ANALYSIS of market factors.
→ It does NOT provide investment advice or recommendations
→ Factor scores are mathematical calculations, not predictions
→ Guidance messages are informational, not directives
→ All trading decisions remain your responsibility
❌ BACKTESTING LIMITATIONS
→ This is an indicator, not a strategy, so no backtesting results are provided
→ Any backtesting you perform includes hindsight bias and optimization bias
→ Historical performance does not indicate future performance
→ Slippage, commissions, and real-world execution differ from backtests
❌ MARKET CONDITIONS
→ This indicator performs differently in trending vs. ranging markets
→ Extreme volatility can produce false signals or whipsaws
→ Low liquidity periods increase execution risk
→ Major news events can invalidate technical analysis
BY USING THIS INDICATOR, YOU ACKNOWLEDGE:
→ You have read and understood this disclaimer
→ You accept full responsibility for your trading decisions
→ You understand the substantial risks involved in trading
→ You will not hold the author liable for any losses incurred
→ You are using this tool as part of your own due diligence process
KEY FEATURES SUMMARY
✅ Volume-Weighted Kinetic Pulse Engine (proprietary momentum calculation)
✅ 15-Factor Confluence Scoring System (graded signals: A+, A, B)
✅ Multi-Oscillator Divergence Detection (Pulse + CCI + Stochastic)
✅ Higher Timeframe Trend Alignment Filter
✅ Adaptive Period Adjustment (volatility-responsive)
✅ Instrument-Aware Calibration (Forex, Crypto, Indices, Commodities)
✅ Structure-Based Stop Loss Calculation (respects swing highs/lows)
✅ Automated Trade Tracking (entry, stop, TP1, TP2, P/L)
✅ Real-Time Factor Analysis State Machine (5-state system with hysteresis)
✅ Session Awareness (Asian, London, New York, Overlap)
✅ Volatility Regime Detection (blocks signals in extreme conditions)
✅ Choppiness Filter (reduces signals in ranging markets)
✅ Volume Confirmation (relative volume and delta analysis)
✅ Clean Air Check (validates clear path to targets)
✅ Comprehensive Dashboards (market conditions + trade analysis)
✅ Customizable Display (3 signal styles, color themes, positioning)
✅ 9 Built-In Alert Conditions (signals, TP/SL hits, state changes)
✅ Fully Non-Repainting (barstate.isconfirmed, lookahead_off)
✅ Previous Day Levels (PDH/PDL reference lines)
✅ Mobile-Friendly Compact Mode (for smaller screens)
TECHNICAL SPECIFICATIONS
→ Pine Script Version: v6
→ Indicator Type: Overlay (displays on price chart)
→ License: Mozilla Public License 2.0
→ Copyright: BULLBYTE
→ Object Limits: 300 labels, 100 lines, 50 boxes
→ Memory Management: Automatic cleanup system (FIFO queue)
→ Repainting: Non-repainting (signals confirmed on bar close)
→ Timeframe Support: All timeframes (1s to Monthly)
→ Instrument Support: Forex, Crypto, Indices, Commodities, Stocks
→ HTF Data Handling: lookahead_off with historical offset
VERSION HISTORY
v1.0 - Initial Release
→ Kinetic Pulse engine with volume weighting and Fisher Transform
→ 15-factor confluence scoring system
→ Trade analysis state machine with hysteresis
→ Automated trade tracking and monitoring
→ Dual dashboard system (market conditions + factor analysis)
→ 9 alert conditions
→ 3 signal display styles
→ Instrument-aware calibration
→ Full risk management framework
WHO IS THIS INDICATOR FOR?
IDEAL FOR:
✓ Scalpers and day traders seeking high-confluence reversal entries
✓ Swing traders who want quality over quantity
✓ Traders who appreciate systematic, rules-based analysis
✓ Multi-timeframe traders who value HTF confirmation
✓ Forex traders who respect session timing
✓ Crypto traders needing volatility-adjusted parameters
✓ Traders who want complete trade management (entry, stop, targets)
✓ Analytical traders who want transparency in signal generation
NOT IDEAL FOR:
✗ Traders seeking a "set and forget" holy grail system
✗ Traders who don't want to learn the methodology
✗ Traders unwilling to accept losing trades as part of the process
✗ Traders who need constant signals (this is a quality-focused system)
✗ Traders who ignore risk management
FINAL THOUGHTS
The Kinetic Scalper is the result of extensive research into momentum behavior, volume confirmation, and multi-factor confluence analysis. It's designed to identify high-probability reversal setups while maintaining strict risk management and providing complete transparency.
This is NOT a magic solution. It's a sophisticated TOOL that requires:
→ Understanding of the methodology
→ Proper risk management discipline
→ Patience to wait for quality setups
→ Willingness to accept losses as part of trading
→ Integration with your own analysis and judgment
Used properly as part of a complete trading plan, the Kinetic Scalper can help you identify high-confluence opportunities and manage trades systematically.
Remember: Quality over quantity. Discipline over emotion. Risk management over everything.
Trade smart. Trade safe.
© 2025 BULLBYTE | Kinetic Scalper v1.0 | For Educational Purposes Only
VR Volume Ratio + Divergence (Pro)成交量比率 (Volume Ratio, VR) 是一項通過分析股價上漲與下跌日的成交量，來研判市場資金氣氛的技術指標。本腳本基於傳統 VR 公式進行了優化，增加了**「趨勢變色」與「自動背離偵測」**功能，幫助交易者更精準地捕捉量價轉折點。
Introduction
Volume Ratio (VR) is a technical indicator that measures the strength of a trend by comparing the volume on up-days versus down-days. This script enhances the classic VR formula with "Trend Color Coding" and "Auto-Divergence Detection", helping traders identify volume-price reversals more accurately.
核心功能與參數
公式原理： VR = (Qu + Qf/2) / (Qd + Qf/2) * 100
Qu: 上漲日成交量 (Up volume)
Qd: 下跌日成交量 (Down volume)
Qf: 平盤日成交量 (Flat volume)
參數 (Length)：預設為 26 日，這是市場公認最有效的短中線參數。
關鍵水位線 (Key Levels)：
< 40% (底部區)：量縮極致，市場情緒冰點，常對應股價底部，適合尋找買點。
100% (中軸)：多空分界線。
> 260% (多頭警戒)：進入強勢多頭行情，但需注意過熱。
> 450% (頭部區)：成交量過大，市場情緒亢奮，通常為頭部訊號。
視覺優化 (Visuals)：
紅漲綠跌：當 VR 數值大於前一日顯示為紅色（動能增強）；小於前一日顯示為綠色（動能退潮）。
背離訊號 (Divergence)：自動標記量價背離。
▲ 底背離 (Bullish)：股價創新低，但 VR 指標墊高（主力吸籌）。
▼ 頂背離 (Bearish)：股價創新高，但 VR 指標走弱（買氣衰竭）。
Features & Settings
Formula Logic: Calculated as VR = (Qu + Qf/2) / (Qd + Qf/2) * 100.
Default Length: 26, widely regarded as the optimal setting for short-to-medium term analysis.
Key Zones:
< 40% (Oversold/Bottom): Extreme low volume, often indicating a market bottom and potential buying opportunity.
100% (Neutral): The balance point between bulls and bears.
> 260% (Bullish Zone): Strong uptrend, volume is expanding.
> 450% (Overbought/Top): Extreme high volume, often indicating a market top and potential reversal.
Visual Enhancements:
Color Coding: Line turns Red when VR rises (Momentum Up) and Green when VR falls (Momentum Down).
Divergence Signals: Automatically marks divergence points on the chart.
▲ Bullish Divergence: Price makes a lower low, but VR makes a higher low (Accumulation).
▼ Bearish Divergence: Price makes a higher high, but VR makes a lower high (Distribution).
應用策略建議
抄底策略：當 VR 跌破 40% 後，指標線由綠翻紅，或出現「▲底背離」訊號時，為極佳的波段進場點。
逃頂策略：當 VR 衝過 450% 進入高檔區，一旦指標線由紅翻綠，或出現「▼頂背離」訊號時，建議分批獲利了結。
Strategy Guide
Bottom Fishing: Look for entries when VR drops below 40% and turns red, or when a "▲ Bullish Divergence" label appears.
Taking Profit: Consider selling when VR exceeds 450% and turns green, or when a "▼ Bearish Divergence" label appears.
Disclaimer: This tool is for informational purposes only and does not constitute financial advice. / 本腳本僅供參考，不構成投資建議。
ULTIMATE AI SYSTEM [PRO MAX 2025 - NEUTRAL FIXED]🚀 Overview
The ULTIMATE AI SYSTEM is a next-generation trading toolkit designed to identify high-probability reversal setups within established trends. This Parallel Edition introduces a dynamic Linear Regression Channel combined with a powerful Multi-Divergence Engine capable of scanning 14 different oscillators simultaneously.
🔥 Key Features
1. Parallel Linear Regression Channel
This is the core of the trend filtering system. Unlike standard regression channels:
• Parallel Bands: The upper and lower bands are perfectly parallel to the baseline, calculated using Standard Deviation (Deviation 2.0 default).
• Dynamic Trend Coloring: The channel automatically changes color based on the slope:
• 🟢 Green: Uptrend (Bullish Bias)
• 🔴 Red: Downtrend (Bearish Bias)
• Mean Reversion Logic: It helps filter out trades that are over-extended (e.g., buying at the top of the channel).
2. AI Multi-Divergence Engine
The script scans 14 Indicators simultaneously across Fibonacci Lookback Periods (5, 8, 13, 21 candles) to find hidden momentum shifts.
• Indicators Scanned: RSI, CCI, MACD, OBV, MFI, MOM, STOCH, CMF, ADX, ATR, WPR, ROC, TSI.
• Algorithm: A signal is only triggered if a user-defined minimum number of indicators (default: 4) show divergence at the same time.
3. Smart Filtering Layer
To reduce false signals, every trade must pass 5 layers of confirmation:
1. Trend Filter: Price must be above/below the EMA 200 (optional).
2. Regression Filter: Buy signals are validated only if price is within the safe zone of the Linear Regression Channel.
3. RSI Guard: Prevents buying in Overbought or selling in Oversold conditions (optimized 75/25 levels).
4. Candle Validation: Buys require a Green candle; Sells require a Red candle.
5. Lookback Confirmation: Uses Fibonacci sequences to validate divergence strength.
4. Pro Dashboard
A real-time table on the chart displays:
• Current value of all oscillators.
• Live Divergence status (▲ for Bullish, ▼ for Bearish).
• Overall Market Sentiment score.
🛠 How It Works
• LONG (BUY) Signal:
1. Market Sentiment is Bullish (Minimum 4 indicators showing bullish divergence).
2. Price is above EMA 200 (Trend Confirmation).
3. Linear Regression Slope is Positive (or price is at the channel bottom).
4. RSI is below 75 (Not Overbought).
5. Candle closes Green.
• SHORT (SELL) Signal:
1. Market Sentiment is Bearish (Minimum 4 indicators showing bearish divergence).
2. Price is below EMA 200 (Trend Confirmation).
3. Linear Regression Slope is Negative (or price is at the channel top).
4. RSI is above 25 (Not Oversold).
5. Candle closes Red.
⚙️ Settings
• Min Confirmations: Number of indicators required to trigger a signal (Default: 4).
• Regression Length: Length for the Linear Regression Channel (Default: 100).
• Channel Deviation: Width of the parallel channel (Default: 2.0).
• EMA Filter: Toggle EMA 200 filtering On/Off.
• Lookback Periods: Customizable Fibonacci sequence for divergence detection.
⚠️ Disclaimer
This script is for educational and analytical purposes only. No indicator guarantees 100% accuracy. Always use proper risk management and combine signals with your own price action analysis.
Code Author:
Version: PRO MAX 2025 - PARALLEL EDITION
BTC - ALSI: Altcoin Season Index (Dynamic Eras)Title: BTC - ALSI: Altcoin Season Index (Dynamic Eras)
Overview & Philosophy
The Altcoin Season Index (ALSI) is a quantitative tool designed to answer the most critical question in crypto capital rotation: "Is it time to hold Bitcoin, or is it time to take risks on Altcoins?"
Most "Altseason" indicators suffer from Survivor Bias or Obsolescence. They either track a static list of coins that includes "dead" assets from previous cycles (ghosts of 2017), or they break completely when major tokens collapse (like LUNA or FTT).
This indicator solves this by using a Time-Varying Basket. The indicator automatically adjusts its reference list of Top 20 coins based on historical eras. This ensures the index tracks the winners of the moment—capturing the DeFi summer of 2020, the NFT craze of 2021, and the AI/Meme narratives of 2024/2025.
Methodology
The indicator calculates the percentage of the Top 20 Altcoins that are outperforming Bitcoin over a rolling window (Default: 90 Days).
The "Win" Count: For every major Altcoin performing better than BTC, the index adds a point.
Dynamic Eras: The basket of coins changes depending on the date:
2020 Era (DeFi Summer): Tracks the "Blue Chips" of the DeFi revolution like UNI, LINK, DOT, and early movers like VET and FIL.
2021 Era (Layer 1 Wars): Tracks the explosion of alternative smart contract platforms, adding winners like SOL, AVAX, MATIC, and ALGO.
2022 Era (The Survivors): Filters for resilience during the Bear Market, solidifying the status of established assets like SHIB and ATOM.
2023 Era (Infrastructure & Scale): Captures the rise of "Next-Gen" tech leading into the pre-halving year, introducing TON, APT (Aptos), and ARB (Arbitrum).
2024/25 Era (AI & Speed): Tracks the current Super-Cycle leaders, focusing on the AI narrative (TAO, RNDR), High-Performance L1s (SUI), and modern Memes (PEPE).
Chart Analysis & Strategy ( The "Alpha" )
As seen in the chart above, there is a strong correlation between ALSI Peaks and local tops in TOTAL3 (The Crypto Market Cap excluding BTC & ETH).
The Entry (Rotation): When the indicator rises above the neutral 50 line, it signals that capital is beginning to rotate out of Bitcoin and into Altcoins. This has historically been a strong confirmation signal to increase exposure to high-beta assets.
The Exit (Saturation): When the indicator hits 100 (or sustains in the Red Zone > 75), it means every single Altcoin is beating Bitcoin. Historically, this extreme exuberance often marks a local top in the TOTAL3 chart. This is the zone where smart money typically sells into strength, rather than opening new positions.
How to Read the Visuals
🚀 Altcoin Season (Red Zone > 75): Strong Altcoin dominance. The market is "Risk On."
🛡️ Bitcoin Season (Blue Zone < 25): Bitcoin dominance. Alts are bleeding against BTC. Historically, this is a defensive zone to hold BTC or Stablecoins.
Data Dashboard: A status table in the bottom-right corner displays the live Index Value, current Regime, and a System Check to ensure all 20 data feeds are active.
Settings
Lookback Period: Default 90 Days. Lowering this (e.g., to 30) makes the index faster but noisier.
Thresholds: Adjustable zones for Altcoin Season (Default: 75) and Bitcoin Season (Default: 25).
Credits & Attribution
This open-source indicator is built on the shoulders of giants. I acknowledge the original creators of the concept and the pioneers of its implementation on TradingView:
Original Concept: BlockchainCenter.net. - They established the industry standard definition: 75% of the Top 50 coins outperforming Bitcoin over 90 days = Altseason..
TradingView Implementation: Adam_Nguyen - He implemented the "Dynamic Era" logic (updating the coin list annually) on TradingView. Our code structure for the time-based switching is inspired by his methodology. See also his implementation in the chart. ( Altcoin Season Index - Adam) .
Comparison: Why use ALSI | RM?
While inspired by the above, ALSI introduces three key improvements:
Open Source: Unlike other popular TradingView versions (which are closed-source), this script is fully transparent. You can see exactly which coins are triggering the signal.
Sanitized History (Anti-Fragile): Historical Top 20 snapshots are not blindly used. "Dead" coins (like LUNA and FTT) from previous eras are manually filtered out. A raw index would crash during the Terra/FTX collapses, giving a false "Bitcoin Season" signal purely due to bad actors. The curated list preserves the integrity of the market structure signal.
Narrative Relevance: The 2024/25 basket was updated to include TAO (Bittensor) and RNDR, ensuring the index captures the dominant AI narrative, rather than tracking fading assets from the previous cycle.
You can compare the ALSI indicator with other available tradingview indicators in the chart: Different indicators for the same idea are shown in the 3 Pane window below the BTC and Total3 chart, whereas ALSI is the top pane indicator.
Important Note on Coin Selection Baskets are highly curated: Dead/irrelevant coins (FTT, LUNA, BSV) are excluded for clean signals. This prevents historical breaks and ensures Era T5 captures current narratives (AI, Memes) via TAO/RNDR. See above. Users are free to adjust the source code to test their own baskets.
Disclaimer
This script is for research and educational purposes only. Past correlations between ALSI and TOTAL3 do not guarantee future results. Market regimes can change, and "Altseasons" can be cut short by macro events.
Tags
bitcoin, btc, altseason, dominance, total3, rotation, cycle, index, alsi, Rob Maths
