Sniper V53 - Forex Reactive + DashboardRSI + OBV calculation on 4 time frames for trend changes.
The indicator warns of possible trend changes; use additional confirmations for areas of interest.
M-oscillator
Stochastic X-Score Signal📊 Stochastic X-Score Signal
This indicator is designed to analyze market momentum, direction, and strength in a single tool.
It combines Z-Score, Stochastic, Trend Filter, ADX/DI, and Volume to filter out high-quality trading signals.
🎯 Key Highlights
Measures price deviation using Z-Score
Converts data into Stochastic (0–100) to identify Overbought / Oversold
Uses HMA + ALMA to separate short-term momentum from long-term trend
Offers 4 signal sources, adjustable to different trading styles
Includes a Trend Filter to distinguish with-trend vs against-trend signals
Confirms real market strength with ADX/DI and Volume Gauge
⚙️ Signal System
🔺 BUY / 🔻 SELL from Reversal, Z-Score, ALMA, or MA Cross
With-trend signals = darker colors (stronger confirmation)
Against-trend signals = lighter colors (higher risk)
📊 Signal Quality Confirmation
ADX > 25 = strong trend
DI+ / DI- defines trend direction
Volume Candles clearly show buy vs sell pressure
🎨 Visualization
On-chart signals (Triangles + Bar Colors)
Indicator panel: Z-Score Histogram, Oscillator, ALMA, OB/OS zones
Gauge table for instant trend strength reading
🔔 Alerts Included
Bullish / Bearish (with-trend & against-trend)
MA Golden / Death Cross
Strong / Weak Trend alerts
High Buy / Sell Volume alerts
💡 Best For
Trend & Pullback traders
Traders who prefer one powerful indicator instead of many
Those who need signals with full market context
⚠️ This indicator is a market analysis tool and does not guarantee profits.
Always apply proper risk management when trading.
💬 Interested in our Indicator? Feel free to contact us via INBOX
📱 Facebook Page: Overdue Logic Indicator
www.facebook.com
ETHThe Indicator is using the combination of below indicators:
Relative Strength Index (RSI): A momentum oscillator used to identify overbought (above 70) or oversold (below 30) conditions, which can signal potential price reversals.
Moving Averages (MA & EMA): These smooth out price data to help identify the direction of the overall trend. Crossovers between different period MAs (e.g., a short-term MA crossing above a long-term MA) can generate buy or sell signals.
Moving Average Convergence Divergence (MACD): A trend-following momentum indicator that shows the relationship between two moving averages. A bullish crossover (MACD line above signal line) suggests upward momentum, while a bearish crossover (MACD line below signal line) indicates downward momentum.
Bollinger Bands: This volatility indicator consists of a middle band (moving average) and two outer bands based on standard deviation. Price touching the upper band may signal overbought conditions, while touching the lower band may signal oversold conditions or a potential bounce.
Volume Indicators (e.g., On-Balance Volume - OBV): Volume confirms the strength of a price movement. A price increase with high volume suggests strong buying pressure, validating the trend.
Ethereum Long/Short Ratio: This sentiment indicator compares the number of traders holding long positions versus short positions. A high ratio might indicate excessive bullish sentiment, potentially preceding a market correction.
Mystic Scales Dual Energy PRO [Destiny Quant]Mystic Scales Dual Energy PRO - Destiny Quant | 【天機衡】雙向能量
English Description
Balancing Momentum and Structure. Mystic Scales Dual Energy PRO utilizes a unique split-axis design to evaluate the balance between Market Momentum (WE2) and Market Health (WH1/WH2). It ensures you only execute trades when momentum is supported by a healthy market structure.
Custom Thresholds: Fully adjustable Entry/Exit score triggers with built-in hysteresis logic to prevent whipsaws.
Structural Health: Monitors DMI flows and Volume Ratios (VR) across Daily, Weekly, and Monthly timeframes.
Strategic Confluence: The perfect companion for the Celestial Mirror to confirm high-conviction entries.
中文說明
權衡動能與結構的平衡之衡 【天機衡】雙向能量 PRO 採用獨特的雙軸分離設計，同時權衡 「市場動能 (WE2)」 與 「市場健康度 (WH1/WH2)」。它確保您只在市場結構健康的前提下發動動能交易。
自訂門檻觸發：具備可調式進場/出場分數門檻，並內建遲滯邏輯 (Hysteresis) 有效過濾頻繁洗盤。
結構健康偵測：即時監控日、週、月線級別的 DMI 流向與成交量比率 (VR)。
策略共振：作為【天機鏡】的最佳拍檔，用來確認高勝率的共振進場時機。
🚀 Get Access / 獲取授權 This is an Invite-only script. To unlock the Celestial Mirror, please:
Visit the link in my profile.
Send a direct message for subscription details.
本指標為 僅限邀請 (Invite-only)。欲獲取授權，請：
點擊我個人主頁的連結（官網/商店）。
透過 TradingView 私訊聯繫我了解訂閱詳情。
Celestial Mirror AI Score PRO - Destiny QuantCelestial Mirror AI Score PRO - Destiny Quant | 【天機鏡】AI 評分系統
English Description
The Strategic Brain of Quantitative Trading. The Celestial Mirror AI Score PRO is a multi-factor weighting engine designed by Destiny Quant Lab. It acts as a digital "Mirror," revealing the hidden truth of market quality. By integrating over 10+ quantitative factors, including the proprietary Zanger Explosion Algorithm, it provides a real-time AI Score (0-99).
Institutional Detection: Uses advanced VSA logic to track "Smart Money" footprints.
Dual Engine: Switch between "Factor Analysis" (Swing) and "Explosion" (Momentum) modes.
Quant Dashboard: Real-time monitoring of momentum, volume structure, and pivot hierarchy.
中文說明
量化交易的策略大腦 【天機鏡】AI 評分系統 PRO 是由 天機量化實驗室 開發的多因子加權引擎。它如同數位之鏡，照見市場體質的虛實。本指標結合了 10 多項量化因子與獨家 Zanger 爆發演算法，將複雜盤面轉化為 0-99 的即時評分。
機構追蹤：透過進階量價分析 (VSA) 偵測大戶資金流向。
雙模式引擎：提供適合波段的「因子分析」與捕捉飆股噴發的「爆發預測」模式。
天機數據面板：即時監測動能、量能與樞軸位置，讓數據一目了然。
🚀 Get Access / 獲取授權 This is an Invite-only script. To unlock the Celestial Mirror, please:
Visit the link in my profile.
Send a direct message for subscription details.
本指標為 僅限邀請 (Invite-only)。欲獲取授權，請：
點擊我個人主頁的連結（官網/商店）。
透過 TradingView 私訊聯繫我了解訂閱詳情。
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.
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.
Reversal Score System v3 [Rulph]RSS3 - Reversal Score System v3
RSS3 is a multi-component reversal detection system that quantifies momentum exhaustion and trend weakness through a normalized Score from -1 (maximum bullish pressure) to +1 (maximum bearish pressure). It is designed to work across crypto, stocks, forex and futures, from intraday to 4H/D timeframes.
A full article with real trade examples (BTC, NVDA, GBP/USD, E-mini S&P) is available here:
How to Make 18% in a Week: RSS3 Reversal Trading Across 4 Markets
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CORE COMPONENTS
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1. Volatility Pressure Index (VPI)
VPI measures market stress using a composite of:
• RSI deviation from the neutral 50 level (directional momentum bias)
• Annualized volatility (VIX-style) to detect volatility expansion
• Normalized candle range vs recent history
• Price position relative to Bollinger Bands (statistical extension)
These inputs are weighted and normalized into a single pressure reading:
• High VPI → overbought stress zone
• Low VPI → oversold compression zone
Concept: VPI answers " Is the market stretched beyond sustainable levels? "
Example: BTC 15m bearish with high VPI before the drop
2. Trend Direction Force Index (TDFI)
TDFI measures directional trend strength using:
• Spread between a fast MMA and a slower SMMA (trend acceleration/deceleration)
• Average impulse between the two MAs (momentum persistence)
• Normalized trend strength with a weighting scheme
• Positive TDFI → bullish directional pressure
• Negative TDFI → bearish directional pressure
• Extreme values (> 0.7 or < -0.7) highlight overextended trends
Concept: TDFI answers " How strong is the current directional move, and is it overextended? "
Example: ES 4H showing strong TDFI before reversal
3. Final Score
The final Score combines VPI and TDFI with divergence bonuses:
Score = (VPI_weight × VPI) + (TDFI_weight × TDFI) - Bull_Div_Bonus + Bear_Div_Bonus
Key ideas:
• VPI and TDFI are first normalized, then combined
• Divergences modulate Score via bonuses/penalties
• Recent and stronger divergences have more influence (decaying over time)
This produces a single, continuous measure of reversal pressure from -1 to +1.
Example: Score swinging from extreme bearish to extreme bullish zones
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DIVERGENCES AND SEQUENTIAL LABELS
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RSS3 automatically detects classic divergences separately for VPI and TDFI:
• Bullish divergence: price makes a lower low, indicator makes a higher low
• Bearish divergence: price makes a higher high, indicator makes a lower high
Each divergence is tracked for:
• VPI (v-series)
• TDFI (t-series)
Sequential labeling:
• v1, v2, v3... = 1st, 2nd, 3rd VPI divergence in the current direction
• t1, t2, t3... = 1st, 2nd, 3rd TDFI divergence in the current direction
• v2t1 = double divergence (2nd VPI + 1st TDFI on the same pivot)
The sequence resets when direction changes (bullish → bearish or vice versa).
This allows you to distinguish:
• early warnings (v1/t1)
• reinforced late-stage signals (v3, v4, …)
• strong confluence (vXtY double divergences)
Example: Sequential v/t labels building up before a major reversal
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MULTI‑TIMEFRAME FILTER (MTF)
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The MTF filter uses a higher timeframe to control counter-trend entries:
Modes:
• Off – no filtering
• Reduce – divergence bonus is reduced when signal is against HTF trend
• Block – counter-trend divergences are completely hidden
Use cases:
• On intraday charts, use 4H/D as HTF to avoid shorting strong uptrends
• On 4H, use Daily/Weekly as HTF context for swing trades
This protects capital by avoiding low-probability mean-reversion attempts in strong trends.
Example: BTC 1h counter-trend signals filtered by MTF (grayed out)
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HOW TO USE RSS3
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Typical entry logic:
1. Wait for a divergence (green = bullish, red = bearish)
2. Check Score:
• |Score| > 0.5 → stronger, higher-confidence signal
• Score near ±1.0 → extreme exhaustion zone
3. Optionally wait +2 bars after divergence to confirm the pivot
Typical exits:
• Conservative: opposite divergence appears
• More aggressive: Score crosses through 0 or reaches the opposite ±0.5 zone
• Always combine with a volatility-based stop (e.g., 2–3 × ATR)
Recommended timeframes:
• 5–15m: active intraday/swing setups
• 1–4h: swing trading
• D/W: position trading
RSS3 is not a complete trading strategy. It is an advanced reversal and exhaustion engine intended to be combined with:
• support/resistance
• volume/flow tools
• existing trend or breakout systems
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WHAT MAKES RSS3 ORIGINAL
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RSS3 is not a simple mashup of standard indicators. It introduces:
• Composite volatility index (VPI) combining RSI deviation, volatility, range and Bollinger position
• Composite trend force index (TDFI) based on MA spread and impulse
• Unified Score from -1 to +1 for reversal strength
• Decay-weighted divergence bonuses with amplitude sensitivity
• Dual-source divergences (VPI + TDFI) with sequential v/t labeling
• MTF-aware filtering that can reduce or block counter-trend signals
Real trade examples and detailed commentary:
English article with 4 markets
Disclaimer: All trading involves risk. This tool does not guarantee profits. Always backtest and manage risk according to your rules.
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RSS3 - Reversal Score System v3 (Система оценки разворотов)
RSS3 — это многокомпонентная система поиска разворотов, которая количественно оценивает истощение импульса и ослабление тренда через нормализованный Score от -1 (максимальное бычье давление) до +1 (максимальное медвежье давление). Индикатор рассчитан на работу с криптовалютами, акциями, форексом и фьючерсами на разных таймфреймах — от интрадей до 4H/D.
Подробная статья с реальными примерами сделок на NVTK, BTCUSDT и CNY/RUB доступна здесь:
Как заработать 18% за неделю на разворотах: система RSS3
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КЛЮЧЕВЫЕ КОМПОНЕНТЫ
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1. Volatility Pressure Index (VPI)
VPI измеряет "напряжение" рынка через комбинацию:
• отклонения RSI от нейтрального уровня 50 (направленный моментум)
• годовой волатильности (по типу VIX) — фиксирует расширение волатильности
• нормализованного диапазона свечей относительно недавней истории
• положения цены относительно полос Боллинджера (статистическое перерастяжение)
Все компоненты взвешиваются и нормализуются в единый индекс давления:
• высокий VPI → зона перекупленности и стресса
• низкий VPI → зона перепроданности и сжатия
Идея: VPI отвечает на вопрос " насколько рынок перерастянут относительно нормального состояния? ".
Пример: NVTK 1H — медвежья дивергенция VPI перед падением
2. Trend Direction Force Index (TDFI)
TDFI оценивает силу направленного движения через:
• расхождение между быстрой MMA и более медленной SMMA (ускорение/замедление тренда)
• средний импульс между двумя скользящими (устойчивость импульса)
• нормализованную силу тренда с весовой схемой
• положительный TDFI → бычье направленное давление
• отрицательный TDFI → медвежье направленное давление
• экстремальные значения (> 0.7 или < -0.7) показывают чрезмерно растянутый тренд
Идея: TDFI отвечает на вопрос " насколько силён текущий тренд и не заходит ли он слишком далеко? ".
Пример: NVTK 1h — сильный TDFI
3. Финальный Score
Финальный Score объединяет VPI и TDFI с учётом бонусов за дивергенции:
Score = (VPI_weight × VPI) + (TDFI_weight × TDFI) - Bull_Div_Bonus + Bear_Div_Bonus
Основные идеи:
• VPI и TDFI предварительно нормализуются
• дивергенции корректируют Score через бонусы/штрафы
• более свежие и сильные дивергенции дают больший вклад (с затуханием во времени)
Результат — единый непрерывный индикатор давления на разворот в диапазоне от -1 до +1.
Пример: BTCUSDT 2H — переход Score из медвежьей зоны в бычью
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ДИВЕРГЕНЦИИ И ПОСЛЕДОВАТЕЛЬНЫЕ МЕТКИ v/t
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RSS3 автоматически определяет классические дивергенции отдельно для VPI и TDFI:
• бычья дивергенция — цена делает более низкий минимум, индикатор — более высокий минимум
• медвежья дивергенция — цена делает более высокий максимум, индикатор — более низкий максимум
Для каждой дивергенции ведутся отдельные счётчики:
• для VPI — последовательность v1, v2, v3…
• для TDFI — последовательность t1, t2, t3…
Примеры маркировки:
• v1 — первая дивергенция VPI в текущем направлении
• t2 — вторая дивергенция TDFI
• v2t1 — двойная дивергенция (2‑я VPI + 1‑я TDFI на одном пивоте)
Счётчики сбрасываются при смене направления (бычья → медвежья и наоборот).
Это позволяет отличать:
• ранние сигналы-предупреждения (v1/t1)
• поздние, усиленные сигналы (v3, v4 и далее)
• зоны сильной конфлюенции (vXtY двойные дивергенции)
Пример: CNY/RUB 15m — накопление v/t меток перед разворотом
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МУЛЬТИ-ТАЙМФРЕЙМОВЫЙ ФИЛЬТР (MTF)
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MTF-фильтр использует старший таймфрейм, чтобы уменьшать или блокировать контртрендовые сигналы.
Режимы:
• Off — фильтрация отключена
• Reduce — сила дивергенции против старшего тренда уменьшается
• Block — контртрендовые дивергенции полностью скрываются
Примеры:
• на 15m/30m — использовать 4H/D как старший ТФ
• на 1H/4H — использовать Daily/Weekly для свинг-позиций
Это помогает не лезть против сильного тренда только потому, что локально появилась дивергенция.
Пример: NVTK 1H — MTF-фильтр блокирует контртрендовые сигналы (серые маркеры)
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КАК ИСПОЛЬЗОВАТЬ RSS3
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Вход:
1) дождаться дивергенции (зелёный треугольник — бычья, красный — медвежья)
2) проверить Score:
• |Score| > 0.5 усиливает значимость сигнала
• значения около ±1.0 указывают на экстремальные зоны истощения
3) при необходимости подождать +2 бара после дивергенции для подтверждения пивота
Выход:
• консервативно — по дивергенции в обратную сторону
• агрессивнее — при пересечении Score через 0 или выходе в противоположную зону ±0.5
• стоп-лосс — от волатильности (например, 2–3 × ATR)
Рекомендуемые таймфреймы:
• 5–15m — активный интрадей/свинг (подходит для CNY/RUB и других ликвидных инструментов)
• 1H — акции типа NVTK, связка с MTF-фильтром по 2H/4H
• 2H–4H — BTCUSDT и фьючерсы для свинг-позиций
RSS3 — это не готовая стратегия, а продвинутый модуль поиска разворотов и зон истощения, который лучше всего работает в связке:
• с уровнями поддержки/сопротивления,
• объёмными/ордерфлоу-индикаторами,
• трендовыми и пробойными системами.
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ЧТО ДЕЛАЕТ RSS3 ОРИГИНАЛЬНЫМ
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RSS3 — это не просто "микс" стандартных индикаторов. В нём реализованы:
• составной волатильностный индекс VPI (RSI-отклонение, волатильность, диапазон, полосы Боллинджера)
• составной трендовый индекс TDFI (спред и импульс между скользящими средними)
• единый Score от -1 до +1 для оценки силы разворота
• бонусы за дивергенции с учётом амплитуды и затухания во времени
• двойные дивергенции (VPI + TDFI) с последовательной маркировкой v/t
• MTF-фильтр, который может ослаблять или полностью блокировать контртрендовые сигналы
Детальные примеры сделок на NVTK, BTCUSDT и CNY/RUB:
Русская статья по RSS3
Дисклеймер: Торговля на финансовых рынках связана с риском. Индикатор не гарантирует доходности. Всегда тестируйте и соблюдайте собственные правила риск-менеджмента.
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
EMA13-EMA21 Difference Indicator# EMA13-EMA21 Difference Indicator
## Description
This indicator calculates the difference between the 13-period Exponential Moving Average (EMA13) and the 21-period Exponential Moving Average (EMA21), helping traders visually assess short-term market momentum.
**Core Logic:**
- When the difference is positive (green), the short-term EMA is above the long-term EMA, indicating a bullish trend
- When the difference is negative (red), the short-term EMA is below the long-term EMA, indicating a bearish trend
- Crossovers of the zero line can serve as potential trend reversal signals
**Use Cases:**
- Trend direction identification
- Momentum strength analysis
- Entry and exit timing assistance
**Disclaimer:**
This indicator is for reference only. It is recommended to combine it with other technical analysis tools for comprehensive judgment. This does not constitute investment advice.
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.
SVE Compression Mirror (Companion)Why This Tool Exists
Intraday markets are driven not only by direction, but by volatility state and energy dynamics. Periods of compression, expansion, and transition often determine whether price behavior favors patience, rotation, or acceleration.
The SVE Compression Mirror (Companion) was created to make volatility compression and release conditions visible in real time, helping traders understand what type of market environment is currently present before forming directional conviction.
This indicator displays a two-state compression condition consistent with that referenced by the SVE Volatility Engine, exposed here as a standalone lower-pane context display.
________________________________________
How the Indicator Is Intended to Be Used
This indicator is designed strictly as a context layer, independent of trade direction or bias.
It highlights:
• Volatility compression versus expansion
• Transitions between compressed and released states
• Momentum behavior as energy builds or dissipates
The purpose is to support environment awareness, not to predict outcomes or generate signals.
________________________________________
What Appears on the Chart
When applied, the indicator displays:
• A lower-pane histogram representing momentum behavior
• Visual markers indicating whether volatility is compressed or released
• A clean, uncluttered presentation optimized for intraday use
The display is intentionally minimal and designed to pair with other structural or decision-support tools.
________________________________________
Intended Users
This indicator is designed for:
• Intraday traders seeking clearer volatility context
• Discretionary traders who value regime awareness
• Professionals and advanced retail traders who prioritize environment over prediction
________________________________________
Disclaimer
This indicator is provided for informational and analytical purposes only and does not constitute investment advice.
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.
Dipy the MFT Super OscillatorDipy the MFT Super Oscillator
A multi-timeframe bandpass oscillator for mean-reversion and "buy the dip" strategies.
🎯 What It Does
Isolates market cycles within a specific frequency range to identify overbought/oversold conditions and reversal points.
⏱️ Multi-Timeframe
Set Signal Timeframe to calculate signals on higher TF while viewing lower TF chart. Example: 5min chart + 1H signals = noise reduction with precise timing.
⚙️ Key Settings
Bandwidth/BandEdge: Define the cycle range to capture
Cloud Type: None for thresholds, others for consensus cloud
Thresholds: Overbought/oversold levels for signals
💡 Best Use
Combine with trend indicator (only buy dips in uptrend)
Higher Signal Timeframe = cleaner signals
Cloud mode = more conservative entries
🔔 Alerts
Create ONE alert for all signals.
Derived from TASC 2025.04 Ultimate Oscillator by John Ehlers.
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.
