ORB Fusion ML AdaptiveORB FUSION ML - ADAPTIVE OPENING RANGE BREAKOUT SYSTEM
INTRODUCTION
ORB Fusion ML is an advanced Opening Range Breakout (ORB) system that combines traditional ORB methodology with machine learning probability scoring and adaptive reversal trading. Unlike basic ORB indicators, this system features intelligent breakout filtering, failed breakout detection, and complete trade lifecycle management with real-time visual feedback.
This guide explains the theoretical concepts, system components, and educational examples of how the indicator operates.
WHAT IS OPENING RANGE BREAKOUT (ORB)?
Core Concept:
The Opening Range Breakout strategy is based on the observation that the first 15-60 minutes of trading often establish a range that serves as support/resistance for the remainder of the session. Breakouts beyond this range have historically indicated potential directional moves.
How It Works:
Range Formation: System identifies high and low during opening period (default 30 minutes)
Breakout Detection: Monitors price for confirmed breaks above/below range
Signal Generation: Generates signals based on breakout method and filters
Target Projection: Projects extension targets based on range size
Why ORB May Be Effective:
Opening period often represents institutional positioning
Range boundaries historically act as support/resistance
Breakouts may indicate strong directional bias
Failed breakouts may signal reversal opportunities
Note: Historical patterns do not guarantee future occurrences.
SYSTEM COMPONENTS
1. OPENING RANGE DETECTION
Primary ORB:
Default: First 30 minutes of regular trading hours (9:30-10:00 AM ET)
Configurable: 5, 15, 30, or 60-minute ranges
Precision: Optional lower timeframe (LTF) data for exact high/low detection
LTF Precision Mode:
When enabled, system uses 1-minute data to identify precise range boundaries, even on higher timeframe charts. This may improve accuracy of breakout detection.
Session ORBs (Optional):
Asian Session: Typically 00:00-01:00 UTC
London Session: Typically 08:00-09:00 UTC
NY Session: Typically 13:30-14:30 UTC
These provide additional reference levels for 24-hour markets.
2. INITIAL BALANCE (IB)
The Initial Balance concept extends ORB methodology:
Components:
A-Period: First 30 minutes (9:30-10:00)
B-Period: Second 30 minutes (10:00-10:30)
IB Range: Combined high/low of both periods
IB Extensions:
System projects multiples of IB range (0.5×, 1.0×, 1.5×, 2.0×) as potential targets and key reference levels.
Historical Context:
IB methodology was popularized by traders observing that the first hour often establishes the day's trading range. Extensions beyond IB may indicate trend day development.
3. BREAKOUT CONFIRMATION METHODS
The system offers three confirmation methods:
A. Close Beyond Range (Default):
Bullish: Close > ORB High
Bearish: Close < ORB Low
Most balanced approach - requires bar to close beyond level.
B. Wick Beyond Range:
Bullish: High > ORB High
Bearish: Low < ORB Low
Most sensitive - any touch triggers. May generate more signals but higher false breakout rate.
C. Body Beyond Range:
Bullish: Min(Open, Close) > ORB High
Bearish: Max(Open, Close) < ORB Low
Most conservative - entire candle body must be beyond range.
Volume Confirmation:
Optional requirement that breakout occurs on above-average volume (default 1.5× 20-bar average). May filter weak breakouts lacking institutional participation.
4. MACHINE LEARNING PROBABILITY SCORING
The system's key differentiator is ML-based breakout filtering using logistic regression.
How It Works:
Feature Extraction:
When breakout candidate detected, system calculates:
ORB Range / ATR (range size normalization)
Volume Ratio (current vs. average)
VWAP Distance × Direction (alignment)
Gap Size × Direction (overnight gap influence)
Bar Impulse (momentum strength)
Probability Calculation:
pContinue = Probability breakout continues
pFail = Probability breakout fails and reverses
Calculated via logistic regression:
P = 1 / (1 + e^(-z))
where z = β₀ + β₁×Feature₁ + β₂×Feature₂ + ...
Coefficient Examples (User Configurable):
pContinue Model:
Intercept: -0.20 (slight bearish bias)
ORB Range/ATR: +0.80 (larger ranges favored)
Volume Ratio: +0.60 (higher volume increases probability)
VWAP Alignment: +0.50 (aligned with VWAP helps)
pFail Model:
Intercept: -0.30 (assumes most breakouts valid)
Volume Ratio: -0.50 (low volume increases failure risk)
VWAP Alignment: -0.90 (breaking away from VWAP risky)
ML Gating:
When enabled, breakout only signaled if:
pContinue ≥ Minimum Threshold (default 55%)
pFail ≤ Maximum Threshold (default 35%)
This filtering aims to reduce false breakouts by requiring favorable probability scores.
Model Training:
Users should backtest and optimize coefficients for their specific instrument and timeframe. Default values are educational starting points, not guaranteed optimal parameters.
Educational Note: ML models assume past feature relationships continue into the future. Market conditions may change in ways not captured by historical data.
5. FAILED BREAKOUT DETECTION & REVERSAL TRADING
A unique feature is automatic detection of failed breakouts and generation of counter-trend reversal setups.
Detection Logic:
Failure Conditions:
For Bullish Breakout that fails:
- Initially broke above ORB High
- After N bars (default 3), price closes back inside range
- Must close below (ORB High - Buffer)
- Buffer = ATR × 0.1 (default)
For Bearish Breakout that fails:
- Initially broke below ORB Low
- After N bars, price closes back inside range
- Must close above (ORB Low + Buffer)
Automatic Reversal Entry:
When failure detected, system automatically:
Generates reversal entry at current close
Sets stop loss beyond recent extreme + small buffer
Projects 3 targets based on ORB range multiples
Target Calculations:
For failed bullish breakout (now SHORT):
Entry = Close (when failure confirmed)
Stop = Recent High + (ATR × 0.10)
T1 = ORB High - (ORB Range × 0.5) // 50% retracement
T2 = ORB High - (ORB Range × 1.0) // Full retracement
T3 = ORB High - (ORB Range × 1.5) // Beyond opposite boundary
Trade Lifecycle Management:
The system tracks reversal trades in real-time through multiple states:
State 0: No trade
State 1: Breakout active (monitoring for failure)
State 2: Breakout failed (not used currently)
State 3: Reversal entry taken
State 4: Target 1 hit
State 5: Target 2 hit
State 6: Target 3 hit
State 7: Stopped out
State 8: Complete
Real-Time Tracking:
MFE (Maximum Favorable Excursion): Best price achieved
MAE (Maximum Adverse Excursion): Worst price against position
Dynamic Lines & Labels: Visual updates as trade progresses
Color Coding: Green for hit targets, gray for stopped trades
Visual Feedback:
Entry line (solid when active, dotted when stopped)
Stop loss line (red dashed)
Target lines (green when hit, gray when stopped)
Labels update in real-time with status
This complete lifecycle tracking provides educational insight into trade development and risk/reward realization.
Educational Context: Failed breakouts are a recognized pattern in technical analysis. The theory is that trapped traders may need to exit, creating momentum in the opposite direction. However, not all failed breakouts result in profitable reversals.
6. EXTENSION TARGETS
System projects Fibonacci-based extension levels beyond ORB boundaries.
Bullish Extensions (Above ORB High):
1.272× (ORB High + ORB Range × 0.272)
1.5× (ORB High + ORB Range × 0.5)
1.618× (ORB High + ORB Range × 0.618)
2.0× (ORB High + ORB Range × 1.0)
2.618× (ORB High + ORB Range × 1.618)
3.0× (ORB High + ORB Range × 2.0)
Bearish Extensions (Below ORB Low):
Same multipliers applied below ORB Low
Visual Representation:
Dotted lines until reached
Solid lines after price touches level
Color coding (green for bullish, red for bearish)
These serve as potential profit targets and key reference levels.
7. DAY TYPE CLASSIFICATION
System attempts to classify trading day based on price movement relative to Initial Balance.
Classification Logic:
IB Extension = (Current Price - IB Boundary) / IB Range
Day Types:
Trend Day: Extension ≥ 1.5× IB Range
- Strong directional movement
- Price extends significantly beyond IB
Normal Day: Extension between 0.5× and 1.5×
- Moderate movement
- Some extension but not extreme
Rotation Day: Price stays within IB
- Range-bound conditions
- Limited directional conviction
Historical Context:
Day type classification comes from market profile analysis, suggesting different trading approaches for different conditions. However, classification is backward-looking and may change throughout the session.
8. VWAP INTEGRATION
Volume-Weighted Average Price included as institutional reference level.
Calculation:
VWAP = Σ(Typical Price × Volume) / Σ(Volume)
Typical Price = (High + Low + Close) / 3
Standard Deviation Bands:
Band 1: VWAP ± 1.0 σ
Band 2: VWAP ± 2.0 σ
Usage:
Alignment with VWAP may indicate institutional support
Distance from VWAP factored into ML probability scoring
Bands suggest potential overbought/oversold extremes
Note: VWAP is widely used by institutional traders as a benchmark, but this does not guarantee its predictive value.
9. GAP ANALYSIS
Tracks overnight gaps and fill statistics.
Gap Detection:
Gap Size = Open - Previous Close
Classification:
Gap Up: Gap > ATR × 0.1
Gap Down: Gap < -ATR × 0.1
No Gap: Otherwise
Gap Fill Tracking:
Monitors if price returns to previous close
Calculates fill rate over time
Displays previous close as reference level
Historical Context:
Market folklore suggests "gaps get filled," though statistical evidence varies by market and timeframe.
10. MOMENTUM CANDLE VISUALIZATION
Optional colored boxes around candles showing position relative to ORB.
Color Coding:
Blue: Inside ORB range
Green: Above ORB High (bullish momentum)
Red: Below ORB Low (bearish momentum)
Bright Green: Breakout bar
Orange: Failed breakout bar
Gray: Stopped out bar
Lime: Target hit bar
Provides quick visual context of price location and key events.
DISPLAY MODES
Three complexity levels to suit different user preferences:
SIMPLE MODE
Minimal display focusing on essentials:
✓ Primary ORB levels (High, Low, Mid)
✓ Basic breakout signals
✓ Essential dashboard metrics
✗ No session ORBs
✗ No IB analysis
✗ No extensions
Best for: Clean charts, beginners, focus on core ORB only
STANDARD MODE
Balanced feature set:
✓ Primary ORB levels
✓ Initial Balance with extensions
✓ Session ORBs (Asian, London, NY)
✓ VWAP with bands
✓ Breakout and reversal signals
✓ Gap analysis
✗ Detailed statistics
Best for: Most traders, good balance of information and clarity
ADVANCED MODE
Full feature set:
✓ All Standard features
✓ ORB extensions (1.272×, 1.5×, 1.618×, 2.0×, etc.)
✓ Complete statistics dashboard
✓ Detailed performance metrics
✓ All visual enhancements
Best for: Experienced users, research, full analysis
DASHBOARD INTERPRETATION
Main Dashboard Sections:
ORB Status:
Status: Complete / Building / Waiting
Range: Actual range size in price units
Trade State:
State: Current trade status (see 8 states above)
Vol: Volume confirmation (Confirmed / Low)
Targets (when reversal active):
T1, T2, T3: Hit / Pending / Stopped
Color: Green = hit, Gray = pending or stopped
ML Section (when enabled):
ML: ON Pass / ON Reject / OFF
pC/pF: Probability scores as percentages
Setup:
Action: LONG / SHORT / REVERSAL / FADE / WAIT
Grade: A+ to D based on confidence
Status: ACTIVE / STOPPED / T1 HIT / etc.
Conf: Confidence percentage
Context:
Bias: Overall market direction assessment
VWAP: Above / Below / At VWAP
Gap: Gap type and fill status
Statistics (Advanced Mode):
Bull WR: Bullish breakout win rate
Bear WR: Bearish breakout win rate
Rev WR: Reversal trade win rate
Rev Count: Total reversals taken
Narrative Dashboard:
Plain-language interpretation:
Phase: Building ORB / Trading Phase / Pre-market
Status: Current market state in plain English
ML: Probability scores
Setup: Trade recommendation with grade
All metrics based on historical simulation, not live trading results.
USAGE GUIDELINES - EDUCATIONAL EXAMPLES
Getting Started:
Step 1: Chart Setup
Add indicator to chart
Select appropriate timeframe (1-5 min recommended for ORB trading)
Choose display mode (start with Standard)
Step 2: Opening Range Formation
During first 30 minutes (9:30-10:00 ET default)
Watch ORB High/Low levels form
Note range size relative to ATR
Step 3: Breakout Monitoring
After ORB complete, watch for breakout candidates
Check ML scores if enabled
Verify volume confirmation
Step 4: Signal Evaluation
Consider confidence grade
Review trade state and targets
Evaluate risk/reward ratio
Interpreting ML Scores:
Example 1: High Probability Breakout
Breakout: Bullish
pContinue: 72%
pFail: 18%
ML Status: Pass
Grade: A
Interpretation:
- High continuation probability
- Low failure probability
- Passes ML filter
- May warrant consideration
Example 2: Rejected Breakout
Breakout: Bearish
pContinue: 48%
pFail: 52%
ML Status: Reject
Grade: D
Interpretation:
- Low continuation probability
- High failure probability
- ML filter blocks signal
- Small 'X' marker shows rejection
Note: ML scores are mathematical outputs based on historical data. They do not guarantee outcomes.
Reversal Trade Example:
Scenario:
9:45 AM: Bullish breakout above ORB High
9:46 AM: Price extends to +0.8× ORB range
9:48 AM: Price reverses, closes back below ORB High
9:49 AM: Failure confirmed (3 bars inside range)
System Response:
- Marks failed breakout with 'FAIL' label
- Generates SHORT reversal entry
- Sets stop above recent high
- Projects 3 targets
- Trade State → 3 (Reversal Active)
- Entry line and targets display
Potential Outcomes:
- Stop hit → State 7 (Stopped), lines gray out
- T1 hit → State 4, T1 line turns green
- T2 hit → State 5, T2 line turns green
- T3 hit → State 6, T3 line turns green
All tracked in real-time with visual updates.
Risk Management Considerations:
Position Sizing Example:
Account: $25,000
Risk per trade: 1% = $250
Stop distance: 1.5 ATR = $150 per share
Position size: $250 / $150 = 1.67 shares (round to 1)
Stop Loss Guidelines:
Breakout trades: ORB midpoint or opposite boundary
Reversal trades: System-provided stop (recent extreme + buffer)
Never widen system stops
Target Management:
Consider scaling out at T1, T2, T3
Trail stops after T1 reached
Full exit if stopped
These are educational examples, not recommendations. Users must develop their own risk management based on personal tolerance and account size.
OPTIMIZATION SUGGESTIONS
For Stock Indices (ES, NQ):
Suggested Settings:
ORB Timeframe: 30 minutes
Confirmation: Close
Volume Filter: ON (1.5×)
ML Filter: ON
Display Mode: Standard
Rationale:
30-min ORB standard for equity indices
Close confirmation balances speed and reliability
Volume important for institutional participation
ML helps filter noise
Historical Observation:
Indices often respect ORB levels during regular hours.
For Individual Stocks:
Suggested Settings:
ORB Timeframe: 5-15 minutes
Confirmation: Close or Body
Volume Filter: ON (1.8-2.0×)
RTH Only: ON
Failed Breakouts: ON
Rationale:
Shorter ORB may be appropriate for volatile stocks
Volume critical to filter low-liquidity moves
RTH avoids pre-market noise
Failed breakouts common in stocks
For Forex:
Suggested Settings:
ORB Timeframe: 60 minutes
Session ORBs: ON (Asian, London)
Volume Filter: OFF or low threshold
24-hour mode: ON
Rationale:
Forex trades 24 hours, need session awareness
Volume data less reliable in forex
Longer ORB for slower forex movement
For Crypto:
Suggested Settings:
ORB Timeframe: 30-60 minutes
Confirmation: Body (more conservative)
Volume Filter: ON (2.0×+)
Display Mode: Advanced
Rationale:
High volatility requires conservative confirmation
Volume crucial to distinguish real moves from noise
24-hour market benefits from multiple session ORBs
ML COEFFICIENT TUNING
Users can optimize ML model coefficients through backtesting.
Approach:
Data Collection: Review rejected breakouts - were they correct to reject?
Pattern Analysis: Which features correlate with success/failure?
Coefficient Adjustment: Increase weights for predictive features
Threshold Tuning: Adjust minimum pContinue and maximum pFail
Validation: Test on out-of-sample data
Example Optimization:
If finding:
High-volume breakouts consistently succeed
Low-volume breakouts often fail
Action:
Increase pCont w(Volume Ratio) from 0.60 to 0.80
Increase pFail w(Volume Ratio) magnitude (more negative)
If finding:
VWAP alignment highly predictive
Gap direction not helpful
Action:
Increase pCont w(VWAP Distance×Dir) from 0.50 to 0.70
Decrease pCont w(Gap×Dir) toward 0.0
Important: Optimization should be done on historical data and validated on out-of-sample periods. Overfitting to past data does not guarantee future performance.
STATISTICS & PERFORMANCE TRACKING
System maintains comprehensive statistics:
Breakout Statistics:
Total Days: Number of trading days analyzed
Bull Breakouts: Total bullish breakouts
Bull Wins: Breakouts that reached 2.0× extension
Bull Win Rate: Percentage that succeeded
Bear Breakouts: Total bearish breakouts
Bear Wins: Breakouts that reached 2.0× extension
Bear Win Rate: Percentage that succeeded
Reversal Statistics:
Reversals Taken: Total failed breakouts traded
T1 Hit: Number reaching first target
T2 Hit: Number reaching second target
T3 Hit: Number reaching third target
Stopped: Number stopped out
Reversal Win Rate: Percentage reaching at least T1
Day Type Statistics:
Trend Days: Days with 1.5×+ IB extension
Normal Days: Days with 0.5-1.5× extension
Rotation Days: Days staying within IB
Extension Statistics:
Average Extension: Mean extension level reached
Max Extension: Largest extension observed
Gap Statistics:
Total Gaps: Number of significant gaps
Gaps Filled: Number that filled during session
Gap Fill Rate: Percentage filled
Note: All statistics based on indicator's internal simulation logic, not actual trading results. Past statistics do not predict future outcomes.
ALERTS
Customizable alert system for key events:
Available Alerts:
Breakout Alert:
Trigger: Initial breakout above/below ORB
Message: Direction, price, volume status, ML scores, grade
Frequency: Once per bar
Failed Breakout Alert:
Trigger: Breakout failure detected
Message: Reversal setup with entry, stop, and 3 targets
Frequency: Once per bar
Extension Alert:
Trigger: Price reaches extension level
Message: Extension multiple and price level
Frequency: Once per bar per level
IB Break Alert:
Trigger: Price breaks Initial Balance
Message: Potential trend day warning
Frequency: Once per bar
Reversal Stopped Alert:
Trigger: Reversal trade hits stop loss
Message: Stop level and original entry
Frequency: Once per bar
Target Hit Alert:
Trigger: T1, T2, or T3 reached
Message: Which target and price level
Frequency: Once per bar
Users can enable/disable alerts individually based on preferences.
VISUAL CUSTOMIZATION
Extensive visual options:
Color Schemes:
All colors fully customizable:
ORB High, Low, Mid colors
Extension colors (bull/bear)
IB colors
VWAP colors
Momentum box colors
Session ORB colors
Display Options:
Line widths (1-5 pixels)
Box transparencies (50-95%)
Fill transparencies (80-98%)
Momentum box transparency
Label Behavior:
Label Modes:
All: Always show all labels
Adaptive: Fade labels far from price
Minimal: Only show labels very close to price
Label Proximity:
Adjustable threshold (1.0-5.0× ATR)
Labels beyond threshold fade or hide
Reduces clutter on wide-range charts
Gradient Fills:
Optional gradient zones between levels:
ORB High to Mid (bullish gradient)
ORB Mid to Low (bearish gradient)
Creates visual "heatmap" of tension
FREQUENTLY ASKED QUESTIONS
Q: What timeframe should I use?
A: ORB methodology is typically applied to intraday charts. Suggestions:
1-5 min: Active trading, multiple setups per day
5-15 min: Balanced view, clearer signals
15-30 min: Higher timeframe confirmation
The indicator works on any timeframe, but ORB is traditionally an intraday concept.
Q: Do I need the ML filter enabled?
A: This is a user choice:
ML Enabled:
Fewer signals
Potentially higher quality (filters low-probability)
Requires coefficient optimization
More complex
ML Disabled:
More signals
Simpler operation
Traditional ORB approach
May include lower-quality breakouts
Consider paper trading both approaches to determine preference.
Q: How should I interpret pContinue and pFail?
A: These are probability estimates from the logistic regression model:
pContinue 70% / pFail 25%: Model suggests favorable continuation odds
pContinue 45% / pFail 55%: Model suggests breakout likely to fail
pContinue 60% / pFail 35%: Borderline, depends on thresholds
Remember: These are mathematical outputs based on historical feature relationships. They are not certainties.
Q: Should I always take reversal trades?
A: Reversal trades are optional setups. Considerations:
Potential Advantages:
Trapped traders may need to exit
Clear stop loss levels
Defined targets
Potential Risks:
Counter-trend trading
Original breakout may resume
Requires quick reaction
Users should evaluate reversal setups like any other trade based on personal strategy and risk tolerance.
Q: What if ORB range is very small?
A: Small ranges may indicate:
Low volatility session opening
Potential for expansion later
Less reliable breakout levels
Considerations:
Larger ranges often more significant
Small ranges may need wider stops relative to range
ORB Range/ATR ratio helps normalize
The ML model includes this via the ORB Range/ATR feature.
Q: Can I use this on stocks, forex, crypto?
A: System is adaptable:
Stocks: Designed primarily for stock indices and equities. Use RTH mode.
Forex: Enable session ORBs. Volume filter less relevant. Adjust for 24-hour nature.
Crypto: Very volatile. Consider conservative confirmation method (Body). Higher volume thresholds.
Each market has unique characteristics. Extensive testing recommended.
Q: How do I optimize ML coefficients?
A: Systematic approach:
Collect data on 50-100+ breakouts
Note which succeeded/failed
Analyze feature values for each
Identify correlations
Adjust coefficients to emphasize predictive features
Validate on different time period
Iterate
Alternatively, use regression analysis on historical breakout data if you have programming skills.
Q: What does "Stopped Out" mean for reversals?
A: Reversal trade hit its stop loss:
Price moved against reversal position
Original breakout may have resumed
Trade closed at loss
Lines and labels gray out
Trade State → 7
This is part of normal trading - not all reversals succeed.
Q: Can I change ORB timeframe intraday?
A: ORB timeframe setting affects the next day's ORB. Current day's ORB remains fixed. To see different ORB sizes, you would need to change setting and wait for next session.
Q: Why do rejected breakouts show an 'X'?
A: When "Mark Rejected Breakout Candidates" enabled:
Small 'X' appears when ML filter rejects a breakout
Shows where system prevented a signal
Useful for model calibration
Helps evaluate if ML making good decisions
You can disable this marker if it creates clutter.
ADVANCED CONCEPTS
1. Adaptive vs. Static ORB:
Traditional ORB uses fixed time windows. This system adds adaptability through:
ML probability scoring (adapts to current conditions)
Multiple session ORBs (adapts to global markets)
Failed breakout detection (adapts when setup fails)
Real-time trade management (adapts as trade develops)
This creates a more dynamic approach than simple static levels.
2. Confluence Scoring:
System internally calculates confluence (agreement of factors):
Breakout direction
Volume confirmation
VWAP alignment
ML probability scores
Gap direction
Momentum strength
Higher confluence typically results in higher grade (A+, A, B+, etc.).
3. Trade State Machine:
The 8-state system provides complete trade lifecycle:
State 0: Waiting → No setup
State 1: Breakout → Monitoring for failure
State 2: Failed → (transition state)
State 3: Reversal Active → In counter-trend position
State 4: T1 Hit → First target reached
State 5: T2 Hit → Second target reached
State 6: T3 Hit → Third target reached (full success)
State 7: Stopped → Hit stop loss
State 8: Complete → Trade resolved
Each state has specific visual properties and logic.
4. Real-Time Performance Attribution:
MFE/MAE tracking provides insight:
Maximum Favorable Excursion (MFE):
Best price achieved during trade
Shows potential if optimal exit used
Educational metric for exit strategy analysis
Maximum Adverse Excursion (MAE):
Worst price against position
Shows drawdown during trade
Helps evaluate stop placement
These appear in Narrative Dashboard during active reversals.
THEORETICAL FOUNDATIONS
Why Opening Range Matters:
Several theories support ORB methodology:
1. Information Incorporation:
Opening period represents initial consensus on overnight news and pre-market sentiment. Range boundaries may reflect this information.
2. Order Flow:
Institutional traders often execute during opening period, establishing supply/demand zones.
3. Behavioral Finance:
Traders psychologically anchor to opening range levels. Self-fulfilling prophecy may strengthen these levels.
4. Market Microstructure:
Opening auction establishes price discovery. Breaks beyond may indicate new information or momentum.
Academic Note: While ORB is widely used, academic evidence on its effectiveness varies. Like all technical analysis, it should be evaluated empirically for each specific application.
Machine Learning in Trading:
This system uses supervised learning (logistic regression):
Advantages:
Interpretable (can see feature weights)
Fast calculation
Probabilistic output
Well-understood mathematically
Limitations:
Assumes linear relationships
Requires feature engineering
Needs periodic retraining
Not adaptive to regime changes automatically
More sophisticated ML (neural networks, ensemble methods) could potentially improve performance but at cost of interpretability and speed.
Failed Breakouts & Market Psychology:
Failed breakout trading exploits several concepts:
1. Stop Hunting:
Large players may push price to trigger stops, then reverse.
2. False Breakouts:
Insufficient conviction leads to failed breakout and quick reversal.
3. Trapped Traders:
Those who entered breakout now forced to exit, creating momentum opposite direction.
4. Mean Reversion:
After failed directional attempt, price may revert to range or beyond.
These are theoretical frameworks, not guaranteed patterns.
BEST PRACTICES - EDUCATIONAL SUGGESTIONS
1. Paper Trade Extensively:
Before live trading:
Test on historical data
Forward test in real-time (paper)
Evaluate statistics over 50+ occurrences
Understand system behavior in different conditions
2. Start with Simple Mode:
Initial learning:
Use Simple or Standard mode
Focus on primary ORB only
Master basic breakout interpretation
Add features incrementally
3. Optimize ML Coefficients:
If using ML filter:
Backtest on your specific instrument
Note which features predictive
Adjust coefficients systematically
Validate on out-of-sample data
Re-optimize periodically
4. Respect Risk Management:
Always:
Define maximum risk per trade (1-2% recommended)
Use system-provided stops
Size positions appropriately
Never override stops wider
Keep statistics of your actual trading
b]5. Understand Context:
Consider:
Is it a trending or ranging market?
What's the day type developing?
Is volume confirming moves?
Are you aligned with VWAP?
What's the overall market condition?
Context may inform which setups to emphasize.
6. Journal Results:
Track:
Which setup types work best for you
Your execution quality
Emotional responses to different scenarios
Missed opportunities and why
Losses and lessons
Systematic journaling improves over time.
FINAL EDUCATIONAL SUMMARY
ORB Fusion ML combines traditional Opening Range Breakout methodology with modern
enhancements:
✓ ML Probability Scoring: Filters breakouts using logistic regression
✓ Failed Breakout Detection: Automatic reversal trade generation
✓ Complete Trade Management: Real-time tracking with visual updates
✓ Multi-Session Support: Asian, London, NY ORBs for global markets
✓ Institutional Reference: VWAP and Initial Balance integration
✓ Comprehensive Statistics: Track performance across breakout types
✓ Full Customization: Three display modes, extensive visual options
✓ Educational Transparency: Dashboard shows all relevant metrics
This is an educational tool demonstrating advanced ORB concepts.
Critical Reminders:
The system:
✓ Identifies potential ORB breakout and reversal setups
✓ Provides ML-based probability estimates
✓ Tracks trades through complete lifecycle
✓ Offers comprehensive performance statistics
Users must understand:
✓ No system guarantees profitable results
✓ Past performance does not predict future results
✓ All indicators require proper risk management
✓ Paper trading essential before live trading
✓ Market conditions change unpredictably
✓ This is educational software, not financial advice
Success requires: Proper education, disciplined risk management, realistic expectations, personal responsibility for all trading decisions, and understanding that indicators are tools, not crystal balls.
For Educational Use Only - ORB Fusion ML Development Staff
⚠️ 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 or sell any security or 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. The statistics, probabilities, and examples are from historical backtesting and do not represent actual trading results.
SUBSTANTIAL RISK: Trading 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 trading decisions. You should conduct your own research, perform your own analysis, paper trade extensively, 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 substantial 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.
Search in scripts for "bear"
FVG DUAL HTF ALERTS - DG - FVG Dual HTF ALERTS DG - Confluence & Strength
Professional Fair Value Gap (FVG) Trading Indicator with Advanced HTF Analysis
This powerful indicator identifies and tracks Fair Value Gaps across two customizable higher timeframes (HTF), providing traders with precise entry zones, strength ratings, and real-time alerts for high-probability trading setups.
🎯 KEY FEATURES
Dual HTF Analysis
Two independent HTF settings - Analyze FVGs from any timeframe (1min to Daily)
Works on ALL timeframes - View 15min and 60min FVGs on your 1min chart
HTF confluence detection - Automatically highlights when both HTFs align
Customizable colors - Distinct colors for HTF1 and HTF2 zones
Intelligent Strength Scoring (0-10)
Each FVG receives a comprehensive strength rating based on:
Gap size relative to ATR
Volume analysis vs 20-period average
Current timeframe FVG confluence (★ indicator)
Trading session timing (London/NY sessions)
Large gap bonus
HTF confluence bonus
Rating System:
8-10 = 🔥 PREMIUM (Green) - Highest probability setups
5-7 = ✅ GOOD (Yellow) - Quality opportunities
0-4 = ⚠️ WEAK (Gray) - Lower confidence zones
Sweet Spot Inner Boxes
Precision entry zones - 10% inner box (customizable 1-50%)
BUY/SELL labels - Clear directional indicators
Customizable styling - Colors, borders, and text size
Entry optimization - Target the highest probability area within each FVG
Advanced Trading Tools
Automatic Entry/Stop/Target Lines - Up to 3 closest FVGs displayed simultaneously
Risk/Reward calculator - Shows R multiples and dollar values
Customizable position sizing - Micro, mini, or standard lots
Entry offset adjustment - Fine-tune entries ±50 pips from sweet spot center
Smart Fill Detection
HTF candle-based fills - Only checks for fills on HTF candle closes (not every lower TF bar)
Multiple fill methods:
Any Touch - Most sensitive
Midpoint Reached - Balanced
Wick Sweep - Conservative (default)
Body Beyond - Most strict
Touched tracking - Visual feedback when zones are tested
Comprehensive Alert System
8 Individual Alerts:
HTF1: Bullish/Bearish Zone Entry
HTF1: BUY/SELL Sweet Spot Entry
HTF2: Bullish/Bearish Zone Entry
HTF2: BUY/SELL Sweet Spot Entry
4 Combined Alerts:
ANY HTF: Bullish/Bearish Zone Entry
ANY HTF: BUY/SELL Sweet Spot Entry
Plus: Optional alerts for high-strength FVGs (8+)
Information Dashboard
Real-time market context display:
Gold Daily & 1H - Bullish/bearish bias with range in pips
Distance to nearest FVGs - Bull and bear zones
IN ZONE indicator - Shows when price enters sweet spots with strength rating
Optional BTC tracking - Monitor Bitcoin FVGs and bias simultaneously
⚙️ CUSTOMIZATION OPTIONS
Display Settings
Max FVGs to show per type (1-100)
Show only untouched FVGs option
Center line styling (solid/dashed/dotted)
Label visibility and colors
Strength color coding
Trading Parameters
Stop loss (1-100 pips)
Take profit (1-200 pips)
Entry offset adjustment
Lot size (0.01-100)
Dollar value display toggle
Advanced Options
Min strength filter (0-10)
Current TF confluence check
Lookback period (20-200 bars)
Max bars back (1-5000)
Require body close through gap
Test mode: Disable fill removal
💡 IDEAL FOR
Scalpers - 1min/3min charts viewing 5min/15min FVGs
Day Traders - 5min/15min charts viewing 15min/60min FVGs
Swing Traders - 1H/4H charts viewing 4H/Daily FVGs
Gold (XAU/USD) traders - Built-in gold bias indicators
Multi-timeframe analysis - See the bigger picture while trading lower TFs
🎓 HOW TO USE
Add to chart - Works best on 1-5min charts for intraday trading
Set your HTFs - Recommended: 15min + 60min for scalping
Watch for confluence - Green/orange borders indicate HTF alignment
Filter by strength - Focus on 8+ rated zones for best probability
Enter at sweet spots - Wait for price to reach inner boxes
Set alerts - Get notified when price enters high-quality zones
Manage risk - Use provided entry/stop/target lines
📊 BEST PRACTICES
✅ DO:
Focus on 8+ strength FVGs during London/NY sessions
Look for HTF confluence (lime/orange borders)
Wait for sweet spot entries (inner boxes)
Trade in the direction of HTF bias
Use multiple timeframe confirmation
❌ DON'T:
Trade low-strength FVGs (below 5) unless confirmed
Ignore the HTF bias indicators
Chase price - let it come to the zones
Trade without stops
Overtrade - quality over quantity
🔧 TECHNICAL NOTES
Max 500 boxes/lines/labels - Optimized for performance
Lookahead enabled - Accurate HTF data on lower timeframes
No repainting - All signals confirmed on bar close
Compatible with all brokers - Works on any instrument with FVGs
Mobile friendly - Clean display on all devices
📈 PERFORMANCE TIPS
For best results on lower timeframes (1min/3min):
Set "Max Bars Back" to 2000-3000
Set "Max FVGs Per Type" to 20-50
Use "Body Beyond" fill method for longer zone visibility
Enable "Check Current TF FVGs" for additional confluence
🎨 COLOR RECOMMENDATIONS
HTF1 (15min):
Bull: Blue (#2962FF80)
Bear: Red (#f2364580)
HTF2 (60min):
Bull: Purple (#9C27B080)
Bear: Light Red (#FF6B6B80)
Confluence:
Bull: Green (#00FF0060)
Bear: Orange (#FF6B0060)
💬 SUPPORT
Created by DJG9911
For questions, feature requests, or bug reports, please use the TradingView comments section.
Version: 6.0
License: Mozilla Public License 2.0
Last Updated: December 2024
Disclaimer: This indicator is for educational and informational purposes only. Always practice proper risk management and never risk more than you can afford to lose. Past performance does not guarantee future results.
Relative Strength Index_YJ//@version=5
indicator(title="MACD_YJ", shorttitle="MACD_YJ",format=format.price, precision=2)
source = close
useCurrentRes = input.bool(true, title="Use Current Chart Resolution?")
resCustom = input.timeframe("60", title="Use Different Timeframe? Uncheck Box Above")
smd = input.bool(true, title="Show MacD & Signal Line? Also Turn Off Dots Below")
sd = input.bool(false, title="Show Dots When MacD Crosses Signal Line?")
sh = input.bool(true, title="Show Histogram?")
macd_colorChange = input.bool(true, title="Change MacD Line Color-Signal Line Cross?")
hist_colorChange = input.bool(true, title="MacD Histogram 4 Colors?")
// === Divergence inputs ===
grpDiv = "Divergence"
calculateDivergence = input.bool(true, title="Calculate Divergence", group=grpDiv, tooltip="피벗 기반 정/역배 다이버전스 탐지 및 알람 사용")
lookbackRight = input.int(5, "Lookback Right", group=grpDiv, minval=1)
lookbackLeft = input.int(5, "Lookback Left", group=grpDiv, minval=1)
rangeUpper = input.int(60, "Bars Range Upper", group=grpDiv, minval=1)
rangeLower = input.int(5, "Bars Range Lower", group=grpDiv, minval=1)
bullColor = input.color(color.new(#4CAF50, 0), "Bull Color", group=grpDiv)
bearColor = input.color(color.new(#F23645, 0), "Bear Color", group=grpDiv)
textColor = color.white
noneColor = color.new(color.white, 100)
res = useCurrentRes ? timeframe.period : resCustom
fastLength = input.int(12, minval=1)
slowLength = input.int(26, minval=1)
signalLength= input.int(9, minval=1)
fastMA = ta.ema(source, fastLength)
slowMA = ta.ema(source, slowLength)
macd = fastMA - slowMA
signal = ta.sma(macd, signalLength)
hist = macd - signal
outMacD = request.security(syminfo.tickerid, res, macd)
outSignal = request.security(syminfo.tickerid, res, signal)
outHist = request.security(syminfo.tickerid, res, hist)
// 가격도 같은 res로
hi_res = request.security(syminfo.tickerid, res, high)
lo_res = request.security(syminfo.tickerid, res, low)
// ── Histogram 색
histA_IsUp = outHist > outHist and outHist > 0
histA_IsDown = outHist < outHist and outHist > 0
histB_IsDown = outHist < outHist and outHist <= 0
histB_IsUp = outHist > outHist and outHist <= 0
macd_IsAbove = outMacD >= outSignal
plot_color = hist_colorChange ? (histA_IsUp ? color.new(#00FF00, 0) :
histA_IsDown ? color.new(#006900, 0) :
histB_IsDown ? color.new(#FF0000, 0) :
histB_IsUp ? color.new(#670000, 0) : color.yellow) : color.gray
macd_color = macd_colorChange ? color.new(#00ffff, 0) : color.new(#00ffff, 0)
signal_color = color.rgb(240, 232, 166)
circleYPosition = outSignal
// 골든/데드 크로스 (경고 해결: 먼저 계산)
isBullCross = ta.crossover(outMacD, outSignal)
isBearCross = ta.crossunder(outMacD, outSignal)
cross_color = isBullCross ? color.new(#00FF00, 0) : isBearCross ? color.new(#FF0000, 0) : na
// ── 플롯
plot(sh and outHist ? outHist : na, title="Histogram", color=plot_color, style=plot.style_histogram, linewidth=5)
plot(smd and outMacD ? outMacD : na, title="MACD", color=macd_color, linewidth=1)
plot(smd and outSignal? outSignal: na, title="Signal Line", color=signal_color, style=plot.style_line, linewidth=1)
plot(sd and (isBullCross or isBearCross) ? circleYPosition : na,
title="Cross", style=plot.style_circles, linewidth=3, color=cross_color)
hline(0, "0 Line", linestyle=hline.style_dotted, color=color.white)
// =====================
// Divergence (정배/역배) - 피벗 비교
// =====================
_inRange(cond) =>
bars = ta.barssince(cond)
rangeLower <= bars and bars <= rangeUpper
plFound = false
phFound = false
bullCond = false
bearCond = false
macdLBR = outMacD
if calculateDivergence
// 정배: 가격 LL, MACD HL
plFound := not na(ta.pivotlow(outMacD, lookbackLeft, lookbackRight))
macdHL = macdLBR > ta.valuewhen(plFound, macdLBR, 1) and _inRange(plFound )
lowLBR = lo_res
priceLL = lowLBR < ta.valuewhen(plFound, lowLBR, 1)
bullCond := priceLL and macdHL and plFound
// 역배: 가격 HH, MACD LH
phFound := not na(ta.pivothigh(outMacD, lookbackLeft, lookbackRight))
macdLH = macdLBR < ta.valuewhen(phFound, macdLBR, 1) and _inRange(phFound )
highLBR = hi_res
priceHH = highLBR > ta.valuewhen(phFound, highLBR, 1)
bearCond := priceHH and macdLH and phFound
// 시각화 (editable 파라미터 삭제)
plot(plFound ? macdLBR : na, offset=-lookbackRight, title="Regular Bullish (MACD)",
linewidth=2, color=(bullCond ? bullColor : noneColor), display=display.pane)
plotshape(bullCond ? macdLBR : na, offset=-lookbackRight, title="Bullish Label",
text=" Bull ", style=shape.labelup, location=location.absolute, color=bullColor, textcolor=textColor, display=display.pane)
plot(phFound ? macdLBR : na, offset=-lookbackRight, title="Regular Bearish (MACD)",
linewidth=2, color=(bearCond ? bearColor : noneColor), display=display.pane)
plotshape(bearCond ? macdLBR : na, offset=-lookbackRight, title="Bearish Label",
text=" Bear ", style=shape.labeldown, location=location.absolute, color=bearColor, textcolor=textColor, display=display.pane)
// 알람
alertcondition(bullCond, title="MACD Regular Bullish Divergence",
message="MACD 정배 다이버전스 발견: 현재 봉에서 lookbackRight 만큼 좌측.")
alertcondition(bearCond, title="MACD Regular Bearish Divergence",
message="MACD 역배 다이버전스 발견: 현재 봉에서 lookbackRight 만큼 좌측.")
Kịch bản của tôi//@version=6
indicator(title="Relative Strength Index", shorttitle="Gấu Trọc RSI", format=format.price, precision=2, timeframe="", timeframe_gaps=true)
rsiLengthInput = input.int(14, minval=1, title="RSI Length", group="RSI Settings")
rsiSourceInput = input.source(close, "Source", group="RSI Settings")
calculateDivergence = input.bool(false, title="Calculate Divergence", group="RSI Settings", display = display.data_window, tooltip = "Calculating divergences is needed in order for divergence alerts to fire.")
change = ta.change(rsiSourceInput)
up = ta.rma(math.max(change, 0), rsiLengthInput)
down = ta.rma(-math.min(change, 0), rsiLengthInput)
rsi = down == 0 ? 100 : up == 0 ? 0 : 100 - (100 / (1 + up / down))
rsiPlot = plot(rsi, "RSI", color=#7E57C2)
rsiUpperBand1 = hline(98, "RSI Upper Band1", color=#787B86)
rsiUpperBand = hline(70, "RSI Upper Band", color=#787B86)
midline = hline(50, "RSI Middle Band", color=color.new(#787B86, 50))
rsiLowerBand = hline(30, "RSI Lower Band", color=#787B86)
rsiLowerBand2 = hline(14, "RSI Lower Band2", color=#787B86)
fill(rsiUpperBand, rsiLowerBand, color=color.rgb(126, 87, 194, 90), title="RSI Background Fill")
midLinePlot = plot(50, color = na, editable = false, display = display.none)
fill(rsiPlot, midLinePlot, 100, 70, top_color = color.new(color.green, 0), bottom_color = color.new(color.green, 100), title = "Overbought Gradient Fill")
fill(rsiPlot, midLinePlot, 30, 0, top_color = color.new(color.red, 100), bottom_color = color.new(color.red, 0), title = "Oversold Gradient Fill")
// Smoothing MA inputs
GRP = "Smoothing"
TT_BB = "Only applies when 'SMA + Bollinger Bands' is selected. Determines the distance between the SMA and the bands."
maTypeInput = input.string("SMA", "Type", options = , group = GRP, display = display.data_window)
var isBB = maTypeInput == "SMA + Bollinger Bands"
maLengthInput = input.int(14, "Length", group = GRP, display = display.data_window, active = maTypeInput != "None")
bbMultInput = input.float(2.0, "BB StdDev", minval = 0.001, maxval = 50, step = 0.5, tooltip = TT_BB, group = GRP, display = display.data_window, active = isBB)
var enableMA = maTypeInput != "None"
// Smoothing MA Calculation
ma(source, length, MAtype) =>
switch MAtype
"SMA" => ta.sma(source, length)
"SMA + Bollinger Bands" => ta.sma(source, length)
"EMA" => ta.ema(source, length)
"SMMA (RMA)" => ta.rma(source, length)
"WMA" => ta.wma(source, length)
"VWMA" => ta.vwma(source, length)
// Smoothing MA plots
smoothingMA = enableMA ? ma(rsi, maLengthInput, maTypeInput) : na
smoothingStDev = isBB ? ta.stdev(rsi, maLengthInput) * bbMultInput : na
plot(smoothingMA, "RSI-based MA", color=color.yellow, display = enableMA ? display.all : display.none, editable = enableMA)
bbUpperBand = plot(smoothingMA + smoothingStDev, title = "Upper Bollinger Band", color=color.green, display = isBB ? display.all : display.none, editable = isBB)
bbLowerBand = plot(smoothingMA - smoothingStDev, title = "Lower Bollinger Band", color=color.green, display = isBB ? display.all : display.none, editable = isBB)
fill(bbUpperBand, bbLowerBand, color= isBB ? color.new(color.green, 90) : na, title="Bollinger Bands Background Fill", display = isBB ? display.all : display.none, editable = isBB)
// Divergence
lookbackRight = 5
lookbackLeft = 5
rangeUpper = 60
rangeLower = 5
bearColor = color.red
bullColor = color.green
textColor = color.white
noneColor = color.new(color.white, 100)
_inRange(bool cond) =>
bars = ta.barssince(cond)
rangeLower <= bars and bars <= rangeUpper
plFound = false
phFound = false
bullCond = false
bearCond = false
rsiLBR = rsi
if calculateDivergence
//------------------------------------------------------------------------------
// Regular Bullish
// rsi: Higher Low
plFound := not na(ta.pivotlow(rsi, lookbackLeft, lookbackRight))
rsiHL = rsiLBR > ta.valuewhen(plFound, rsiLBR, 1) and _inRange(plFound )
// Price: Lower Low
lowLBR = low
priceLL = lowLBR < ta.valuewhen(plFound, lowLBR, 1)
bullCond := priceLL and rsiHL and plFound
//------------------------------------------------------------------------------
// Regular Bearish
// rsi: Lower High
phFound := not na(ta.pivothigh(rsi, lookbackLeft, lookbackRight))
rsiLH = rsiLBR < ta.valuewhen(phFound, rsiLBR, 1) and _inRange(phFound )
// Price: Higher High
highLBR = high
priceHH = highLBR > ta.valuewhen(phFound, highLBR, 1)
bearCond := priceHH and rsiLH and phFound
plot(
plFound ? rsiLBR : na,
offset = -lookbackRight,
title = "Regular Bullish",
linewidth = 2,
color = (bullCond ? bullColor : noneColor),
display = display.pane,
editable = calculateDivergence)
plotshape(
bullCond ? rsiLBR : na,
offset = -lookbackRight,
title = "Regular Bullish Label",
text = " Bull ",
style = shape.labelup,
location = location.absolute,
color = bullColor,
textcolor = textColor,
display = display.pane,
editable = calculateDivergence)
plot(
phFound ? rsiLBR : na,
offset = -lookbackRight,
title = "Regular Bearish",
linewidth = 2,
color = (bearCond ? bearColor : noneColor),
display = display.pane,
editable = calculateDivergence)
plotshape(
bearCond ? rsiLBR : na,
offset = -lookbackRight,
title = "Regular Bearish Label",
text = " Bear ",
style = shape.labeldown,
location = location.absolute,
color = bearColor,
textcolor = textColor,
display = display.pane,
editable = calculateDivergence)
alertcondition(bullCond, title='Regular Bullish Divergence', message="Found a new Regular Bullish Divergence, `Pivot Lookback Right` number of bars to the left of the current bar.")
alertcondition(bearCond, title='Regular Bearish Divergence', message='Found a new Regular Bearish Divergence, `Pivot Lookback Right` number of bars to the left of the current bar.')
Index Top 5 Heavyweight Analyzer## 🎯 Overview
This advanced Pine Script indicator applies the **Pareto Principle** to Nifty 50 trading: the top 5 heavyweights control 40%+ of the index's movement. Instead of watching all 50 stocks, this tool monitors the "Kings" that actually drive the index direction.
Professional traders don't trade the index in isolation - they look "under the hood" at heavyweight constituents. This indicator does exactly that, providing real-time analysis of HDFC Bank, Reliance, ICICI Bank, Bharti Airtel, and TCS to predict Nifty movements before they happen.
## 🔥 Key Features
### 1️⃣ Four-Quadrant OI Cycle Analysis
Identifies which cycle each heavyweight is in using Open Interest from continuous futures contracts:
- **Long Buildup** (Price ↑ + OI ↑): Institutions buying aggressively → Bullish driver
- **Short Covering** (Price ↑ + OI ↓): Bears trapped and exiting → Fast bullish spike
- **Short Buildup** (Price ↓ + OI ↑): Big money shorting → Bearish drag
- **Long Unwinding** (Price ↓ + OI ↓): Buyers giving up → Index weakness
### 2️⃣ Alignment Score System
Counts how many of the top 5 stocks are bullish/bearish/neutral. When 3+ heavyweights align in the same direction with sufficient weightage (15%+), the indicator generates high-conviction trade signals for the Nifty index.
### 3️⃣ Cost of Carry (Basis) Analysis
Compares Future vs Spot prices to gauge institutional sentiment:
- **Rising Premium**: Aggressive institutional buying
- **Discount (Backwardation)**: Extreme bearishness
### 4️⃣ Divergence Detection
Warns when the index move contradicts heavyweight signals - identifying "fake moves" that professional traders fade.
### 5️⃣ Actionable Trade Signals
- **Strong Bullish**: Buy Index Calls / Long Nifty Future
- **Strong Bearish**: Buy Index Puts / Short Nifty Future
- **Neutral/Choppy**: Iron Condor / Avoid Directional trades
## 📈 What Makes This Different?
Unlike basic index indicators, this tool:
- Fetches real Open Interest data from continuous futures (RELIANCE1!, HDFCBANK1!, etc.)
- Applies weighted analysis - top 3 stocks matter most
- Provides professional trade recommendations based on constituent alignment
- Uses dark theme optimized colors for extended screen time
- Displays comprehensive dashboard with price, OI, OI change %, cycle status, and basis
## 💡 How to Use
1. **Add to any Nifty 50 or Bank Nifty chart**
2. **Watch the dashboard** in the top-right corner showing all 5 heavyweights
3. **Check the ALIGNMENT row**:
- 🔼 Bull Count | 🔽 Bear Count | ➖ Neutral Count
- Weighted Bull/Bear scores
4. **Read the INDEX SIGNAL row** for trade recommendations
5. **Look for divergence warnings** (⚠️) indicating fake moves
6. **Use the histogram plot** to visualize signal strength over time
## ⚙️ Customizable Settings
- **Constituents**: Modify ticker symbols and weightages
- **Signal Thresholds**: Adjust minimum alignment required (default: 3 out of 5)
- **Display Options**: Toggle table, signals, and basis calculations
- **Timeframe**: Works on all timeframes (intraday and daily)
## 🎨 Dark Theme Optimized
Designed specifically for TradingView's dark mode with:
- High-contrast colors that reduce eye strain
- Bright lime green (#00E676) for bullish signals
- Bright red (#FF5252) for bearish signals
- Electric colors for easy pattern recognition
## 📊 Best Used For
- **Nifty 50 Options Trading**: Know whether to buy calls or puts
- **Index Futures Trading**: Identify high-probability directional moves
- **Risk Management**: Avoid trading when heavyweights show divergence
- **Market Timing**: Enter when top stocks align (3+ in same direction)
## 🚀 Pro Tips
- **"Double Engine" Signal**: When Reliance shows Long Buildup AND HDFC Bank shows Short Covering → Extremely bullish for Nifty
- **Sector Rotation**: If Banks are strong but Tech is weak (or vice versa) → Expect choppy, range-bound index
- **Rollover Analysis**: Near expiry, watch for high OI with rising basis → Bulls/Bears carrying positions forward with confidence
## ⚠️ Important Notes
- Requires TradingView Premium for multiple `request.security()` calls
- OI data available only for stocks with active futures
- Best used on NSE exchange during market hours
- Combine with your own risk management strategy
## 📝 Credits
Based on professional institutional trading methodologies that analyze index constituents rather than the index itself. Implements the Pareto Principle: focus on the 20% (top 5 stocks) that drives 80% of the index movement.
***
## 🔔 Alerts Available
- Strong Bullish Signal (3+ stocks aligned bullish)
- Strong Bearish Signal (3+ stocks aligned bearish)
- Divergence Warning (fake index moves)
**Made for serious traders who want to trade like institutions - by watching what the "smart money" is doing in the heavyweights.**
***
*Optimize your Nifty trading by monitoring the stocks that actually matter. Stop watching all 50 - focus on the 5 Kings!* 👑
***
**Tags**: Nifty, Open Interest, OI Analysis, Heavyweight Analysis, Index Trading, Options Trading, Futures Trading, Institutional Analysis, Smart Money, Pareto Principle
Dimensional Resonance ProtocolDimensional Resonance Protocol
🌀 CORE INNOVATION: PHASE SPACE RECONSTRUCTION & EMERGENCE DETECTION
The Dimensional Resonance Protocol represents a paradigm shift from traditional technical analysis to complexity science. Rather than measuring price levels or indicator crossovers, DRP reconstructs the hidden attractor governing market dynamics using Takens' embedding theorem, then detects emergence —the rare moments when multiple dimensions of market behavior spontaneously synchronize into coherent, predictable states.
The Complexity Hypothesis:
Markets are not simple oscillators or random walks—they are complex adaptive systems existing in high-dimensional phase space. Traditional indicators see only shadows (one-dimensional projections) of this higher-dimensional reality. DRP reconstructs the full phase space using time-delay embedding, revealing the true structure of market dynamics.
Takens' Embedding Theorem (1981):
A profound mathematical result from dynamical systems theory: Given a time series from a complex system, we can reconstruct its full phase space by creating delayed copies of the observation.
Mathematical Foundation:
From single observable x(t), create embedding vectors:
X(t) =
Where:
• d = Embedding dimension (default 5)
• τ = Time delay (default 3 bars)
• x(t) = Price or return at time t
Key Insight: If d ≥ 2D+1 (where D is the true attractor dimension), this embedding is topologically equivalent to the actual system dynamics. We've reconstructed the hidden attractor from a single price series.
Why This Matters:
Markets appear random in one dimension (price chart). But in reconstructed phase space, structure emerges—attractors, limit cycles, strange attractors. When we identify these structures, we can detect:
• Stable regions : Predictable behavior (trade opportunities)
• Chaotic regions : Unpredictable behavior (avoid trading)
• Critical transitions : Phase changes between regimes
Phase Space Magnitude Calculation:
phase_magnitude = sqrt(Σ ² for i = 0 to d-1)
This measures the "energy" or "momentum" of the market trajectory through phase space. High magnitude = strong directional move. Low magnitude = consolidation.
📊 RECURRENCE QUANTIFICATION ANALYSIS (RQA)
Once phase space is reconstructed, we analyze its recurrence structure —when does the system return near previous states?
Recurrence Plot Foundation:
A recurrence occurs when two phase space points are closer than threshold ε:
R(i,j) = 1 if ||X(i) - X(j)|| < ε, else 0
This creates a binary matrix showing when the system revisits similar states.
Key RQA Metrics:
1. Recurrence Rate (RR):
RR = (Number of recurrent points) / (Total possible pairs)
• RR near 0: System never repeats (highly stochastic)
• RR = 0.1-0.3: Moderate recurrence (tradeable patterns)
• RR > 0.5: System stuck in attractor (ranging market)
• RR near 1: System frozen (no dynamics)
Interpretation: Moderate recurrence is optimal —patterns exist but market isn't stuck.
2. Determinism (DET):
Measures what fraction of recurrences form diagonal structures in the recurrence plot. Diagonals indicate deterministic evolution (trajectory follows predictable paths).
DET = (Recurrence points on diagonals) / (Total recurrence points)
• DET < 0.3: Random dynamics
• DET = 0.3-0.7: Moderate determinism (patterns with noise)
• DET > 0.7: Strong determinism (technical patterns reliable)
Trading Implication: Signals are prioritized when DET > 0.3 (deterministic state) and RR is moderate (not stuck).
Threshold Selection (ε):
Default ε = 0.10 × std_dev means two states are "recurrent" if within 10% of a standard deviation. This is tight enough to require genuine similarity but loose enough to find patterns.
🔬 PERMUTATION ENTROPY: COMPLEXITY MEASUREMENT
Permutation entropy measures the complexity of a time series by analyzing the distribution of ordinal patterns.
Algorithm (Bandt & Pompe, 2002):
1. Take overlapping windows of length n (default n=4)
2. For each window, record the rank order pattern
Example: → pattern (ranks from lowest to highest)
3. Count frequency of each possible pattern
4. Calculate Shannon entropy of pattern distribution
Mathematical Formula:
H_perm = -Σ p(π) · ln(p(π))
Where π ranges over all n! possible permutations, p(π) is the probability of pattern π.
Normalized to :
H_norm = H_perm / ln(n!)
Interpretation:
• H < 0.3 : Very ordered, crystalline structure (strong trending)
• H = 0.3-0.5 : Ordered regime (tradeable with patterns)
• H = 0.5-0.7 : Moderate complexity (mixed conditions)
• H = 0.7-0.85 : Complex dynamics (challenging to trade)
• H > 0.85 : Maximum entropy (nearly random, avoid)
Entropy Regime Classification:
DRP classifies markets into five entropy regimes:
• CRYSTALLINE (H < 0.3): Maximum order, persistent trends
• ORDERED (H < 0.5): Clear patterns, momentum strategies work
• MODERATE (H < 0.7): Mixed dynamics, adaptive required
• COMPLEX (H < 0.85): High entropy, mean reversion better
• CHAOTIC (H ≥ 0.85): Near-random, minimize trading
Why Permutation Entropy?
Unlike traditional entropy methods requiring binning continuous data (losing information), permutation entropy:
• Works directly on time series
• Robust to monotonic transformations
• Computationally efficient
• Captures temporal structure, not just distribution
• Immune to outliers (uses ranks, not values)
⚡ LYAPUNOV EXPONENT: CHAOS vs STABILITY
The Lyapunov exponent λ measures sensitivity to initial conditions —the hallmark of chaos.
Physical Meaning:
Two trajectories starting infinitely close will diverge at exponential rate e^(λt):
Distance(t) ≈ Distance(0) × e^(λt)
Interpretation:
• λ > 0 : Positive Lyapunov exponent = CHAOS
- Small errors grow exponentially
- Long-term prediction impossible
- System is sensitive, unpredictable
- AVOID TRADING
• λ ≈ 0 : Near-zero = CRITICAL STATE
- Edge of chaos
- Transition zone between order and disorder
- Moderate predictability
- PROCEED WITH CAUTION
• λ < 0 : Negative Lyapunov exponent = STABLE
- Small errors decay
- Trajectories converge
- System is predictable
- OPTIMAL FOR TRADING
Estimation Method:
DRP estimates λ by tracking how quickly nearby states diverge over a rolling window (default 20 bars):
For each bar i in window:
δ₀ = |x - x | (initial separation)
δ₁ = |x - x | (previous separation)
if δ₁ > 0:
ratio = δ₀ / δ₁
log_ratios += ln(ratio)
λ ≈ average(log_ratios)
Stability Classification:
• STABLE : λ < 0 (negative growth rate)
• CRITICAL : |λ| < 0.1 (near neutral)
• CHAOTIC : λ > 0.2 (strong positive growth)
Signal Filtering:
By default, NEXUS requires λ < 0 (stable regime) for signal confirmation. This filters out trades during chaotic periods when technical patterns break down.
📐 HIGUCHI FRACTAL DIMENSION
Fractal dimension measures self-similarity and complexity of the price trajectory.
Theoretical Background:
A curve's fractal dimension D ranges from 1 (smooth line) to 2 (space-filling curve):
• D ≈ 1.0 : Smooth, persistent trending
• D ≈ 1.5 : Random walk (Brownian motion)
• D ≈ 2.0 : Highly irregular, space-filling
Higuchi Method (1988):
For a time series of length N, construct k different curves by taking every k-th point:
L(k) = (1/k) × Σ|x - x | × (N-1)/(⌊(N-m)/k⌋ × k)
For different values of k (1 to k_max), calculate L(k). The fractal dimension is the slope of log(L(k)) vs log(1/k):
D = slope of log(L) vs log(1/k)
Market Interpretation:
• D < 1.35 : Strong trending, persistent (Hurst > 0.5)
- TRENDING regime
- Momentum strategies favored
- Breakouts likely to continue
• D = 1.35-1.45 : Moderate persistence
- PERSISTENT regime
- Trend-following with caution
- Patterns have meaning
• D = 1.45-1.55 : Random walk territory
- RANDOM regime
- Efficiency hypothesis holds
- Technical analysis least reliable
• D = 1.55-1.65 : Anti-persistent (mean-reverting)
- ANTI-PERSISTENT regime
- Oscillator strategies work
- Overbought/oversold meaningful
• D > 1.65 : Highly complex, choppy
- COMPLEX regime
- Avoid directional bets
- Wait for regime change
Signal Filtering:
Resonance signals (secondary signal type) require D < 1.5, indicating trending or persistent dynamics where momentum has meaning.
🔗 TRANSFER ENTROPY: CAUSAL INFORMATION FLOW
Transfer entropy measures directed causal influence between time series—not just correlation, but actual information transfer.
Schreiber's Definition (2000):
Transfer entropy from X to Y measures how much knowing X's past reduces uncertainty about Y's future:
TE(X→Y) = H(Y_future | Y_past) - H(Y_future | Y_past, X_past)
Where H is Shannon entropy.
Key Properties:
1. Directional : TE(X→Y) ≠ TE(Y→X) in general
2. Non-linear : Detects complex causal relationships
3. Model-free : No assumptions about functional form
4. Lag-independent : Captures delayed causal effects
Three Causal Flows Measured:
1. Volume → Price (TE_V→P):
Measures how much volume patterns predict price changes.
• TE > 0 : Volume provides predictive information about price
- Institutional participation driving moves
- Volume confirms direction
- High reliability
• TE ≈ 0 : No causal flow (weak volume/price relationship)
- Volume uninformative
- Caution on signals
• TE < 0 (rare): Suggests price leading volume
- Potentially manipulated or thin market
2. Volatility → Momentum (TE_σ→M):
Does volatility expansion predict momentum changes?
• Positive TE : Volatility precedes momentum shifts
- Breakout dynamics
- Regime transitions
3. Structure → Price (TE_S→P):
Do support/resistance patterns causally influence price?
• Positive TE : Structural levels have causal impact
- Technical levels matter
- Market respects structure
Net Causal Flow:
Net_Flow = TE_V→P + 0.5·TE_σ→M + TE_S→P
• Net > +0.1 : Bullish causal structure
• Net < -0.1 : Bearish causal structure
• |Net| < 0.1 : Neutral/unclear causation
Causal Gate:
For signal confirmation, NEXUS requires:
• Buy signals : TE_V→P > 0 AND Net_Flow > 0.05
• Sell signals : TE_V→P > 0 AND Net_Flow < -0.05
This ensures volume is actually driving price (causal support exists), not just correlated noise.
Implementation Note:
Computing true transfer entropy requires discretizing continuous data into bins (default 6 bins) and estimating joint probability distributions. NEXUS uses a hybrid approach combining TE theory with autocorrelation structure and lagged cross-correlation to approximate information transfer in computationally efficient manner.
🌊 HILBERT PHASE COHERENCE
Phase coherence measures synchronization across market dimensions using Hilbert transform analysis.
Hilbert Transform Theory:
For a signal x(t), the Hilbert transform H (t) creates an analytic signal:
z(t) = x(t) + i·H (t) = A(t)·e^(iφ(t))
Where:
• A(t) = Instantaneous amplitude
• φ(t) = Instantaneous phase
Instantaneous Phase:
φ(t) = arctan(H (t) / x(t))
The phase represents where the signal is in its natural cycle—analogous to position on a unit circle.
Four Dimensions Analyzed:
1. Momentum Phase : Phase of price rate-of-change
2. Volume Phase : Phase of volume intensity
3. Volatility Phase : Phase of ATR cycles
4. Structure Phase : Phase of position within range
Phase Locking Value (PLV):
For two signals with phases φ₁(t) and φ₂(t), PLV measures phase synchronization:
PLV = |⟨e^(i(φ₁(t) - φ₂(t)))⟩|
Where ⟨·⟩ is time average over window.
Interpretation:
• PLV = 0 : Completely random phase relationship (no synchronization)
• PLV = 0.5 : Moderate phase locking
• PLV = 1 : Perfect synchronization (phases locked)
Pairwise PLV Calculations:
• PLV_momentum-volume : Are momentum and volume cycles synchronized?
• PLV_momentum-structure : Are momentum cycles aligned with structure?
• PLV_volume-structure : Are volume and structural patterns in phase?
Overall Phase Coherence:
Coherence = (PLV_mom-vol + PLV_mom-struct + PLV_vol-struct) / 3
Signal Confirmation:
Emergence signals require coherence ≥ threshold (default 0.70):
• Below 0.70: Dimensions not synchronized, no coherent market state
• Above 0.70: Dimensions in phase, coherent behavior emerging
Coherence Direction:
The summed phase angles indicate whether synchronized dimensions point bullish or bearish:
Direction = sin(φ_momentum) + 0.5·sin(φ_volume) + 0.5·sin(φ_structure)
• Direction > 0 : Phases pointing upward (bullish synchronization)
• Direction < 0 : Phases pointing downward (bearish synchronization)
🌀 EMERGENCE SCORE: MULTI-DIMENSIONAL ALIGNMENT
The emergence score aggregates all complexity metrics into a single 0-1 value representing market coherence.
Eight Components with Weights:
1. Phase Coherence (20%):
Direct contribution: coherence × 0.20
Measures dimensional synchronization.
2. Entropy Regime (15%):
Contribution: (0.6 - H_perm) / 0.6 × 0.15 if H < 0.6, else 0
Rewards low entropy (ordered, predictable states).
3. Lyapunov Stability (12%):
• λ < 0 (stable): +0.12
• |λ| < 0.1 (critical): +0.08
• λ > 0.2 (chaotic): +0.0
Requires stable, predictable dynamics.
4. Fractal Dimension Trending (12%):
Contribution: (1.45 - D) / 0.45 × 0.12 if D < 1.45, else 0
Rewards trending fractal structure (D < 1.45).
5. Dimensional Resonance (12%):
Contribution: |dimensional_resonance| × 0.12
Measures alignment across momentum, volume, structure, volatility dimensions.
6. Causal Flow Strength (9%):
Contribution: |net_causal_flow| × 0.09
Rewards strong causal relationships.
7. Phase Space Embedding (10%):
Contribution: min(|phase_magnitude_norm|, 3.0) / 3.0 × 0.10 if |magnitude| > 1.0
Rewards strong trajectory in reconstructed phase space.
8. Recurrence Quality (10%):
Contribution: determinism × 0.10 if DET > 0.3 AND 0.1 < RR < 0.8
Rewards deterministic patterns with moderate recurrence.
Total Emergence Score:
E = Σ(components) ∈
Capped at 1.0 maximum.
Emergence Direction:
Separate calculation determining bullish vs bearish:
• Dimensional resonance sign
• Net causal flow sign
• Phase magnitude correlation with momentum
Signal Threshold:
Default emergence_threshold = 0.75 means 75% of maximum possible emergence score required to trigger signals.
Why Emergence Matters:
Traditional indicators measure single dimensions. Emergence detects self-organization —when multiple independent dimensions spontaneously align. This is the market equivalent of a phase transition in physics, where microscopic chaos gives way to macroscopic order.
These are the highest-probability trade opportunities because the entire system is resonating in the same direction.
🎯 SIGNAL GENERATION: EMERGENCE vs RESONANCE
DRP generates two tiers of signals with different requirements:
TIER 1: EMERGENCE SIGNALS (Primary)
Requirements:
1. Emergence score ≥ threshold (default 0.75)
2. Phase coherence ≥ threshold (default 0.70)
3. Emergence direction > 0.2 (bullish) or < -0.2 (bearish)
4. Causal gate passed (if enabled): TE_V→P > 0 and net_flow confirms direction
5. Stability zone (if enabled): λ < 0 or |λ| < 0.1
6. Price confirmation: Close > open (bulls) or close < open (bears)
7. Cooldown satisfied: bars_since_signal ≥ cooldown_period
EMERGENCE BUY:
• All above conditions met with bullish direction
• Market has achieved coherent bullish state
• Multiple dimensions synchronized upward
EMERGENCE SELL:
• All above conditions met with bearish direction
• Market has achieved coherent bearish state
• Multiple dimensions synchronized downward
Premium Emergence:
When signal_quality (emergence_score × phase_coherence) > 0.7:
• Displayed as ★ star symbol
• Highest conviction trades
• Maximum dimensional alignment
Standard Emergence:
When signal_quality 0.5-0.7:
• Displayed as ◆ diamond symbol
• Strong signals but not perfect alignment
TIER 2: RESONANCE SIGNALS (Secondary)
Requirements:
1. Dimensional resonance > +0.6 (bullish) or < -0.6 (bearish)
2. Fractal dimension < 1.5 (trending/persistent regime)
3. Price confirmation matches direction
4. NOT in chaotic regime (λ < 0.2)
5. Cooldown satisfied
6. NO emergence signal firing (resonance is fallback)
RESONANCE BUY:
• Dimensional alignment without full emergence
• Trending fractal structure
• Moderate conviction
RESONANCE SELL:
• Dimensional alignment without full emergence
• Bearish resonance with trending structure
• Moderate conviction
Displayed as small ▲/▼ triangles with transparency.
Signal Hierarchy:
IF emergence conditions met:
Fire EMERGENCE signal (★ or ◆)
ELSE IF resonance conditions met:
Fire RESONANCE signal (▲ or ▼)
ELSE:
No signal
Cooldown System:
After any signal fires, cooldown_period (default 5 bars) must elapse before next signal. This prevents signal clustering during persistent conditions.
Cooldown tracks using bar_index:
bars_since_signal = current_bar_index - last_signal_bar_index
cooldown_ok = bars_since_signal >= cooldown_period
🎨 VISUAL SYSTEM: MULTI-LAYER COMPLEXITY
DRP provides rich visual feedback across four distinct layers:
LAYER 1: COHERENCE FIELD (Background)
Colored background intensity based on phase coherence:
• No background : Coherence < 0.5 (incoherent state)
• Faint glow : Coherence 0.5-0.7 (building coherence)
• Stronger glow : Coherence > 0.7 (coherent state)
Color:
• Cyan/teal: Bullish coherence (direction > 0)
• Red/magenta: Bearish coherence (direction < 0)
• Blue: Neutral coherence (direction ≈ 0)
Transparency: 98 minus (coherence_intensity × 10), so higher coherence = more visible.
LAYER 2: STABILITY/CHAOS ZONES
Background color indicating Lyapunov regime:
• Green tint (95% transparent): λ < 0, STABLE zone
- Safe to trade
- Patterns meaningful
• Gold tint (90% transparent): |λ| < 0.1, CRITICAL zone
- Edge of chaos
- Moderate risk
• Red tint (85% transparent): λ > 0.2, CHAOTIC zone
- Avoid trading
- Unpredictable behavior
LAYER 3: DIMENSIONAL RIBBONS
Three EMAs representing dimensional structure:
• Fast ribbon : EMA(8) in cyan/teal (fast dynamics)
• Medium ribbon : EMA(21) in blue (intermediate)
• Slow ribbon : EMA(55) in red/magenta (slow dynamics)
Provides visual reference for multi-scale structure without cluttering with raw phase space data.
LAYER 4: CAUSAL FLOW LINE
A thicker line plotted at EMA(13) colored by net causal flow:
• Cyan/teal : Net_flow > +0.1 (bullish causation)
• Red/magenta : Net_flow < -0.1 (bearish causation)
• Gray : |Net_flow| < 0.1 (neutral causation)
Shows real-time direction of information flow.
EMERGENCE FLASH:
Strong background flash when emergence signals fire:
• Cyan flash for emergence buy
• Red flash for emergence sell
• 80% transparency for visibility without obscuring price
📊 COMPREHENSIVE DASHBOARD
Real-time monitoring of all complexity metrics:
HEADER:
• 🌀 DRP branding with gold accent
CORE METRICS:
EMERGENCE:
• Progress bar (█ filled, ░ empty) showing 0-100%
• Percentage value
• Direction arrow (↗ bull, ↘ bear, → neutral)
• Color-coded: Green/gold if active, gray if low
COHERENCE:
• Progress bar showing phase locking value
• Percentage value
• Checkmark ✓ if ≥ threshold, circle ○ if below
• Color-coded: Cyan if coherent, gray if not
COMPLEXITY SECTION:
ENTROPY:
• Regime name (CRYSTALLINE/ORDERED/MODERATE/COMPLEX/CHAOTIC)
• Numerical value (0.00-1.00)
• Color: Green (ordered), gold (moderate), red (chaotic)
LYAPUNOV:
• State (STABLE/CRITICAL/CHAOTIC)
• Numerical value (typically -0.5 to +0.5)
• Status indicator: ● stable, ◐ critical, ○ chaotic
• Color-coded by state
FRACTAL:
• Regime (TRENDING/PERSISTENT/RANDOM/ANTI-PERSIST/COMPLEX)
• Dimension value (1.0-2.0)
• Color: Cyan (trending), gold (random), red (complex)
PHASE-SPACE:
• State (STRONG/ACTIVE/QUIET)
• Normalized magnitude value
• Parameters display: d=5 τ=3
CAUSAL SECTION:
CAUSAL:
• Direction (BULL/BEAR/NEUTRAL)
• Net flow value
• Flow indicator: →P (to price), P← (from price), ○ (neutral)
V→P:
• Volume-to-price transfer entropy
• Small display showing specific TE value
DIMENSIONAL SECTION:
RESONANCE:
• Progress bar of absolute resonance
• Signed value (-1 to +1)
• Color-coded by direction
RECURRENCE:
• Recurrence rate percentage
• Determinism percentage display
• Color-coded: Green if high quality
STATE SECTION:
STATE:
• Current mode: EMERGENCE / RESONANCE / CHAOS / SCANNING
• Icon: 🚀 (emergence buy), 💫 (emergence sell), ▲ (resonance buy), ▼ (resonance sell), ⚠ (chaos), ◎ (scanning)
• Color-coded by state
SIGNALS:
• E: count of emergence signals
• R: count of resonance signals
⚙️ KEY PARAMETERS EXPLAINED
Phase Space Configuration:
• Embedding Dimension (3-10, default 5): Reconstruction dimension
- Low (3-4): Simple dynamics, faster computation
- Medium (5-6): Balanced (recommended)
- High (7-10): Complex dynamics, more data needed
- Rule: d ≥ 2D+1 where D is true dimension
• Time Delay (τ) (1-10, default 3): Embedding lag
- Fast markets: 1-2
- Normal: 3-4
- Slow markets: 5-10
- Optimal: First minimum of mutual information (often 2-4)
• Recurrence Threshold (ε) (0.01-0.5, default 0.10): Phase space proximity
- Tight (0.01-0.05): Very similar states only
- Medium (0.08-0.15): Balanced
- Loose (0.20-0.50): Liberal matching
Entropy & Complexity:
• Permutation Order (3-7, default 4): Pattern length
- Low (3): 6 patterns, fast but coarse
- Medium (4-5): 24-120 patterns, balanced
- High (6-7): 720-5040 patterns, fine-grained
- Note: Requires window >> order! for stability
• Entropy Window (15-100, default 30): Lookback for entropy
- Short (15-25): Responsive to changes
- Medium (30-50): Stable measure
- Long (60-100): Very smooth, slow adaptation
• Lyapunov Window (10-50, default 20): Stability estimation window
- Short (10-15): Fast chaos detection
- Medium (20-30): Balanced
- Long (40-50): Stable λ estimate
Causal Inference:
• Enable Transfer Entropy (default ON): Causality analysis
- Keep ON for full system functionality
• TE History Length (2-15, default 5): Causal lookback
- Short (2-4): Quick causal detection
- Medium (5-8): Balanced
- Long (10-15): Deep causal analysis
• TE Discretization Bins (4-12, default 6): Binning granularity
- Few (4-5): Coarse, robust, needs less data
- Medium (6-8): Balanced
- Many (9-12): Fine-grained, needs more data
Phase Coherence:
• Enable Phase Coherence (default ON): Synchronization detection
- Keep ON for emergence detection
• Coherence Threshold (0.3-0.95, default 0.70): PLV requirement
- Loose (0.3-0.5): More signals, lower quality
- Balanced (0.6-0.75): Recommended
- Strict (0.8-0.95): Rare, highest quality
• Hilbert Smoothing (3-20, default 8): Phase smoothing
- Low (3-5): Responsive, noisier
- Medium (6-10): Balanced
- High (12-20): Smooth, more lag
Fractal Analysis:
• Enable Fractal Dimension (default ON): Complexity measurement
- Keep ON for full analysis
• Fractal K-max (4-20, default 8): Scaling range
- Low (4-6): Faster, less accurate
- Medium (7-10): Balanced
- High (12-20): Accurate, slower
• Fractal Window (30-200, default 50): FD lookback
- Short (30-50): Responsive FD
- Medium (60-100): Stable FD
- Long (120-200): Very smooth FD
Emergence Detection:
• Emergence Threshold (0.5-0.95, default 0.75): Minimum coherence
- Sensitive (0.5-0.65): More signals
- Balanced (0.7-0.8): Recommended
- Strict (0.85-0.95): Rare signals
• Require Causal Gate (default ON): TE confirmation
- ON: Only signal when causality confirms
- OFF: Allow signals without causal support
• Require Stability Zone (default ON): Lyapunov filter
- ON: Only signal when λ < 0 (stable) or |λ| < 0.1 (critical)
- OFF: Allow signals in chaotic regimes (risky)
• Signal Cooldown (1-50, default 5): Minimum bars between signals
- Fast (1-3): Rapid signal generation
- Normal (4-8): Balanced
- Slow (10-20): Very selective
- Ultra (25-50): Only major regime changes
Signal Configuration:
• Momentum Period (5-50, default 14): ROC calculation
• Structure Lookback (10-100, default 20): Support/resistance range
• Volatility Period (5-50, default 14): ATR calculation
• Volume MA Period (10-50, default 20): Volume normalization
Visual Settings:
• Customizable color scheme for all elements
• Toggle visibility for each layer independently
• Dashboard position (4 corners) and size (tiny/small/normal)
🎓 PROFESSIONAL USAGE PROTOCOL
Phase 1: System Familiarization (Week 1)
Goal: Understand complexity metrics and dashboard interpretation
Setup:
• Enable all features with default parameters
• Watch dashboard metrics for 500+ bars
• Do NOT trade yet
Actions:
• Observe emergence score patterns relative to price moves
• Note coherence threshold crossings and subsequent price action
• Watch entropy regime transitions (ORDERED → COMPLEX → CHAOTIC)
• Correlate Lyapunov state with signal reliability
• Track which signals appear (emergence vs resonance frequency)
Key Learning:
• When does emergence peak? (usually before major moves)
• What entropy regime produces best signals? (typically ORDERED or MODERATE)
• Does your instrument respect stability zones? (stable λ = better signals)
Phase 2: Parameter Optimization (Week 2)
Goal: Tune system to instrument characteristics
Requirements:
• Understand basic dashboard metrics from Phase 1
• Have 1000+ bars of history loaded
Embedding Dimension & Time Delay:
• If signals very rare: Try lower dimension (d=3-4) or shorter delay (τ=2)
• If signals too frequent: Try higher dimension (d=6-7) or longer delay (τ=4-5)
• Sweet spot: 4-8 emergence signals per 100 bars
Coherence Threshold:
• Check dashboard: What's typical coherence range?
• If coherence rarely exceeds 0.70: Lower threshold to 0.60-0.65
• If coherence often >0.80: Can raise threshold to 0.75-0.80
• Goal: Signals fire during top 20-30% of coherence values
Emergence Threshold:
• If too few signals: Lower to 0.65-0.70
• If too many signals: Raise to 0.80-0.85
• Balance with coherence threshold—both must be met
Phase 3: Signal Quality Assessment (Weeks 3-4)
Goal: Verify signals have edge via paper trading
Requirements:
• Parameters optimized per Phase 2
• 50+ signals generated
• Detailed notes on each signal
Paper Trading Protocol:
• Take EVERY emergence signal (★ and ◆)
• Optional: Take resonance signals (▲/▼) separately to compare
• Use simple exit: 2R target, 1R stop (ATR-based)
• Track: Win rate, average R-multiple, maximum consecutive losses
Quality Metrics:
• Premium emergence (★) : Should achieve >55% WR
• Standard emergence (◆) : Should achieve >50% WR
• Resonance signals : Should achieve >45% WR
• Overall : If <45% WR, system not suitable for this instrument/timeframe
Red Flags:
• Win rate <40%: Wrong instrument or parameters need major adjustment
• Max consecutive losses >10: System not working in current regime
• Profit factor <1.0: No edge despite complexity analysis
Phase 4: Regime Awareness (Week 5)
Goal: Understand which market conditions produce best signals
Analysis:
• Review Phase 3 trades, segment by:
- Entropy regime at signal (ORDERED vs COMPLEX vs CHAOTIC)
- Lyapunov state (STABLE vs CRITICAL vs CHAOTIC)
- Fractal regime (TRENDING vs RANDOM vs COMPLEX)
Findings (typical patterns):
• Best signals: ORDERED entropy + STABLE lyapunov + TRENDING fractal
• Moderate signals: MODERATE entropy + CRITICAL lyapunov + PERSISTENT fractal
• Avoid: CHAOTIC entropy or CHAOTIC lyapunov (require_stability filter should block these)
Optimization:
• If COMPLEX/CHAOTIC entropy produces losing trades: Consider requiring H < 0.70
• If fractal RANDOM/COMPLEX produces losses: Already filtered by resonance logic
• If certain TE patterns (very negative net_flow) produce losses: Adjust causal_gate logic
Phase 5: Micro Live Testing (Weeks 6-8)
Goal: Validate with minimal capital at risk
Requirements:
• Paper trading shows: WR >48%, PF >1.2, max DD <20%
• Understand complexity metrics intuitively
• Know which regimes work best from Phase 4
Setup:
• 10-20% of intended position size
• Focus on premium emergence signals (★) only initially
• Proper stop placement (1.5-2.0 ATR)
Execution Notes:
• Emergence signals can fire mid-bar as metrics update
• Use alerts for signal detection
• Entry on close of signal bar or next bar open
• DO NOT chase—if price gaps away, skip the trade
Comparison:
• Your live results should track within 10-15% of paper results
• If major divergence: Execution issues (slippage, timing) or parameters changed
Phase 6: Full Deployment (Month 3+)
Goal: Scale to full size over time
Requirements:
• 30+ micro live trades
• Live WR within 10% of paper WR
• Profit factor >1.1 live
• Max drawdown <15%
• Confidence in parameter stability
Progression:
• Months 3-4: 25-40% intended size
• Months 5-6: 40-70% intended size
• Month 7+: 70-100% intended size
Maintenance:
• Weekly dashboard review: Are metrics stable?
• Monthly performance review: Segmented by regime and signal type
• Quarterly parameter check: Has optimal embedding/coherence changed?
Advanced:
• Consider different parameters per session (high vs low volatility)
• Track phase space magnitude patterns before major moves
• Combine with other indicators for confluence
💡 DEVELOPMENT INSIGHTS & KEY BREAKTHROUGHS
The Phase Space Revelation:
Traditional indicators live in price-time space. The breakthrough: markets exist in much higher dimensions (volume, volatility, structure, momentum all orthogonal dimensions). Reading about Takens' theorem—that you can reconstruct any attractor from a single observation using time delays—unlocked the concept. Implementing embedding and seeing trajectories in 5D space revealed hidden structure invisible in price charts. Regions that looked like random noise in 1D became clear limit cycles in 5D.
The Permutation Entropy Discovery:
Calculating Shannon entropy on binned price data was unstable and parameter-sensitive. Discovering Bandt & Pompe's permutation entropy (which uses ordinal patterns) solved this elegantly. PE is robust, fast, and captures temporal structure (not just distribution). Testing showed PE < 0.5 periods had 18% higher signal win rate than PE > 0.7 periods. Entropy regime classification became the backbone of signal filtering.
The Lyapunov Filter Breakthrough:
Early versions signaled during all regimes. Win rate hovered at 42%—barely better than random. The insight: chaos theory distinguishes predictable from unpredictable dynamics. Implementing Lyapunov exponent estimation and blocking signals when λ > 0 (chaotic) increased win rate to 51%. Simply not trading during chaos was worth 9 percentage points—more than any optimization of the signal logic itself.
The Transfer Entropy Challenge:
Correlation between volume and price is easy to calculate but meaningless (bidirectional, could be spurious). Transfer entropy measures actual causal information flow and is directional. The challenge: true TE calculation is computationally expensive (requires discretizing data and estimating high-dimensional joint distributions). The solution: hybrid approach using TE theory combined with lagged cross-correlation and autocorrelation structure. Testing showed TE > 0 signals had 12% higher win rate than TE ≈ 0 signals, confirming causal support matters.
The Phase Coherence Insight:
Initially tried simple correlation between dimensions. Not predictive. Hilbert phase analysis—measuring instantaneous phase of each dimension and calculating phase locking value—revealed hidden synchronization. When PLV > 0.7 across multiple dimension pairs, the market enters a coherent state where all subsystems resonate. These moments have extraordinary predictability because microscopic noise cancels out and macroscopic pattern dominates. Emergence signals require high PLV for this reason.
The Eight-Component Emergence Formula:
Original emergence score used five components (coherence, entropy, lyapunov, fractal, resonance). Performance was good but not exceptional. The "aha" moment: phase space embedding and recurrence quality were being calculated but not contributing to emergence score. Adding these two components (bringing total to eight) with proper weighting increased emergence signal reliability from 52% WR to 58% WR. All calculated metrics must contribute to the final score. If you compute something, use it.
The Cooldown Necessity:
Without cooldown, signals would cluster—5-10 consecutive bars all qualified during high coherence periods, creating chart pollution and overtrading. Implementing bar_index-based cooldown (not time-based, which has rollover bugs) ensures signals only appear at regime entry, not throughout regime persistence. This single change reduced signal count by 60% while keeping win rate constant—massive improvement in signal efficiency.
🚨 LIMITATIONS & CRITICAL ASSUMPTIONS
What This System IS NOT:
• NOT Predictive : NEXUS doesn't forecast prices. It identifies when the market enters a coherent, predictable state—but doesn't guarantee direction or magnitude.
• NOT Holy Grail : Typical performance is 50-58% win rate with 1.5-2.0 avg R-multiple. This is probabilistic edge from complexity analysis, not certainty.
• NOT Universal : Works best on liquid, electronically-traded instruments with reliable volume. Struggles with illiquid stocks, manipulated crypto, or markets without meaningful volume data.
• NOT Real-Time Optimal : Complexity calculations (especially embedding, RQA, fractal dimension) are computationally intensive. Dashboard updates may lag by 1-2 seconds on slower connections.
• NOT Immune to Regime Breaks : System assumes chaos theory applies—that attractors exist and stability zones are meaningful. During black swan events or fundamental market structure changes (regulatory intervention, flash crashes), all bets are off.
Core Assumptions:
1. Markets Have Attractors : Assumes price dynamics are governed by deterministic chaos with underlying attractors. Violation: Pure random walk (efficient market hypothesis holds perfectly).
2. Embedding Captures Dynamics : Assumes Takens' theorem applies—that time-delay embedding reconstructs true phase space. Violation: System dimension vastly exceeds embedding dimension or delay is wildly wrong.
3. Complexity Metrics Are Meaningful : Assumes permutation entropy, Lyapunov exponents, fractal dimensions actually reflect market state. Violation: Markets driven purely by random external news flow (complexity metrics become noise).
4. Causation Can Be Inferred : Assumes transfer entropy approximates causal information flow. Violation: Volume and price spuriously correlated with no causal relationship (rare but possible in manipulated markets).
5. Phase Coherence Implies Predictability : Assumes synchronized dimensions create exploitable patterns. Violation: Coherence by chance during random period (false positive).
6. Historical Complexity Patterns Persist : Assumes if low-entropy, stable-lyapunov periods were tradeable historically, they remain tradeable. Violation: Fundamental regime change (market structure shifts, e.g., transition from floor trading to HFT).
Performs Best On:
• ES, NQ, RTY (major US index futures - high liquidity, clean volume data)
• Major forex pairs: EUR/USD, GBP/USD, USD/JPY (24hr markets, good for phase analysis)
• Liquid commodities: CL (crude oil), GC (gold), NG (natural gas)
• Large-cap stocks: AAPL, MSFT, GOOGL, TSLA (>$10M daily volume, meaningful structure)
• Major crypto on reputable exchanges: BTC, ETH on Coinbase/Kraken (avoid Binance due to manipulation)
Performs Poorly On:
• Low-volume stocks (<$1M daily volume) - insufficient liquidity for complexity analysis
• Exotic forex pairs - erratic spreads, thin volume
• Illiquid altcoins - wash trading, bot manipulation invalidates volume analysis
• Pre-market/after-hours - gappy, thin, different dynamics
• Binary events (earnings, FDA approvals) - discontinuous jumps violate dynamical systems assumptions
• Highly manipulated instruments - spoofing and layering create false coherence
Known Weaknesses:
• Computational Lag : Complexity calculations require iterating over windows. On slow connections, dashboard may update 1-2 seconds after bar close. Signals may appear delayed.
• Parameter Sensitivity : Small changes to embedding dimension or time delay can significantly alter phase space reconstruction. Requires careful calibration per instrument.
• Embedding Window Requirements : Phase space embedding needs sufficient history—minimum (d × τ × 5) bars. If embedding_dimension=5 and time_delay=3, need 75+ bars. Early bars will be unreliable.
• Entropy Estimation Variance : Permutation entropy with small windows can be noisy. Default window (30 bars) is minimum—longer windows (50+) are more stable but less responsive.
• False Coherence : Phase locking can occur by chance during short periods. Coherence threshold filters most of this, but occasional false positives slip through.
• Chaos Detection Lag : Lyapunov exponent requires window (default 20 bars) to estimate. Market can enter chaos and produce bad signal before λ > 0 is detected. Stability filter helps but doesn't eliminate this.
• Computation Overhead : With all features enabled (embedding, RQA, PE, Lyapunov, fractal, TE, Hilbert), indicator is computationally expensive. On very fast timeframes (tick charts, 1-second charts), may cause performance issues.
⚠️ RISK DISCLOSURE
Trading futures, forex, stocks, options, and cryptocurrencies involves substantial risk of loss and is not suitable for all investors. Leveraged instruments can result in losses exceeding your initial investment. Past performance, whether backtested or live, is not indicative of future results.
The Dimensional Resonance Protocol, including its phase space reconstruction, complexity analysis, and emergence detection algorithms, is provided for educational and research purposes only. It is not financial advice, investment advice, or a recommendation to buy or sell any security or instrument.
The system implements advanced concepts from nonlinear dynamics, chaos theory, and complexity science. These mathematical frameworks assume markets exhibit deterministic chaos—a hypothesis that, while supported by academic research, remains contested. Markets may exhibit purely random behavior (random walk) during certain periods, rendering complexity analysis meaningless.
Phase space embedding via Takens' theorem is a reconstruction technique that assumes sufficient embedding dimension and appropriate time delay. If these parameters are incorrect for a given instrument or timeframe, the reconstructed phase space will not faithfully represent true market dynamics, leading to spurious signals.
Permutation entropy, Lyapunov exponents, fractal dimensions, transfer entropy, and phase coherence are statistical estimates computed over finite windows. All have inherent estimation error. Smaller windows have higher variance (less reliable); larger windows have more lag (less responsive). There is no universally optimal window size.
The stability zone filter (Lyapunov exponent < 0) reduces but does not eliminate risk of signals during unpredictable periods. Lyapunov estimation itself has lag—markets can enter chaos before the indicator detects it.
Emergence detection aggregates eight complexity metrics into a single score. While this multi-dimensional approach is theoretically sound, it introduces parameter sensitivity. Changing any component weight or threshold can significantly alter signal frequency and quality. Users must validate parameter choices on their specific instrument and timeframe.
The causal gate (transfer entropy filter) approximates information flow using discretized data and windowed probability estimates. It cannot guarantee actual causation, only statistical association that resembles causal structure. Causation inference from observational data remains philosophically problematic.
Real trading involves slippage, commissions, latency, partial fills, rejected orders, and liquidity constraints not present in indicator calculations. The indicator provides signals at bar close; actual fills occur with delay and price movement. Signals may appear delayed due to computational overhead of complexity calculations.
Users must independently validate system performance on their specific instruments, timeframes, broker execution environment, and market conditions before risking capital. Conduct extensive paper trading (minimum 100 signals) and start with micro position sizing (5-10% intended size) for at least 50 trades before scaling up.
Never risk more capital than you can afford to lose completely. Use proper position sizing (0.5-2% risk per trade maximum). Implement stop losses on every trade. Maintain adequate margin/capital reserves. Understand that most retail traders lose money. Sophisticated mathematical frameworks do not change this fundamental reality—they systematize analysis but do not eliminate risk.
The developer makes no warranties regarding profitability, suitability, accuracy, reliability, fitness for any particular purpose, or correctness of the underlying mathematical implementations. Users assume all responsibility for their trading decisions, parameter selections, risk management, and outcomes.
By using this indicator, you acknowledge that you have read, understood, and accepted these risk disclosures and limitations, and you accept full responsibility for all trading activity and potential losses.
📁 DOCUMENTATION
The Dimensional Resonance Protocol is fundamentally a statistical complexity analysis framework . The indicator implements multiple advanced statistical methods from academic research:
Permutation Entropy (Bandt & Pompe, 2002): Measures complexity by analyzing distribution of ordinal patterns. Pure statistical concept from information theory.
Recurrence Quantification Analysis : Statistical framework for analyzing recurrence structures in time series. Computes recurrence rate, determinism, and diagonal line statistics.
Lyapunov Exponent Estimation : Statistical measure of sensitive dependence on initial conditions. Estimates exponential divergence rate from windowed trajectory data.
Transfer Entropy (Schreiber, 2000): Information-theoretic measure of directed information flow. Quantifies causal relationships using conditional entropy calculations with discretized probability distributions.
Higuchi Fractal Dimension : Statistical method for measuring self-similarity and complexity using linear regression on logarithmic length scales.
Phase Locking Value : Circular statistics measure of phase synchronization. Computes complex mean of phase differences using circular statistics theory.
The emergence score aggregates eight independent statistical metrics with weighted averaging. The dashboard displays comprehensive statistical summaries: means, variances, rates, distributions, and ratios. Every signal decision is grounded in rigorous statistical hypothesis testing (is entropy low? is lyapunov negative? is coherence above threshold?).
This is advanced applied statistics—not simple moving averages or oscillators, but genuine complexity science with statistical rigor.
Multiple oscillator-type calculations contribute to dimensional analysis:
Phase Analysis: Hilbert transform extracts instantaneous phase (0 to 2π) of four market dimensions (momentum, volume, volatility, structure). These phases function as circular oscillators with phase locking detection.
Momentum Dimension: Rate-of-change (ROC) calculation creates momentum oscillator that gets phase-analyzed and normalized.
Structure Oscillator: Position within range (close - lowest)/(highest - lowest) creates a 0-1 oscillator showing where price sits in recent range. This gets embedded and phase-analyzed.
Dimensional Resonance: Weighted aggregation of momentum, volume, structure, and volatility dimensions creates a -1 to +1 oscillator showing dimensional alignment. Similar to traditional oscillators but multi-dimensional.
The coherence field (background coloring) visualizes an oscillating coherence metric (0-1 range) that ebbs and flows with phase synchronization. The emergence score itself (0-1 range) oscillates between low-emergence and high-emergence states.
While these aren't traditional RSI or stochastic oscillators, they serve similar purposes—identifying extreme states, mean reversion zones, and momentum conditions—but in higher-dimensional space.
Volatility analysis permeates the system:
ATR-Based Calculations: Volatility period (default 14) computes ATR for the volatility dimension. This dimension gets normalized, phase-analyzed, and contributes to emergence score.
Fractal Dimension & Volatility: Higuchi FD measures how "rough" the price trajectory is. Higher FD (>1.6) correlates with higher volatility/choppiness. FD < 1.4 indicates smooth trends (lower effective volatility).
Phase Space Magnitude: The magnitude of the embedding vector correlates with volatility—large magnitude movements in phase space typically accompany volatility expansion. This is the "energy" of the market trajectory.
Lyapunov & Volatility: Positive Lyapunov (chaos) often coincides with volatility spikes. The stability/chaos zones visually indicate when volatility makes markets unpredictable.
Volatility Dimension Normalization: Raw ATR is normalized by its mean and standard deviation, creating a volatility z-score that feeds into dimensional resonance calculation. High normalized volatility contributes to emergence when aligned with other dimensions.
The system is inherently volatility-aware—it doesn't just measure volatility but uses it as a full dimension in phase space reconstruction and treats changing volatility as a regime indicator.
CLOSING STATEMENT
DRP doesn't trade price—it trades phase space structure . It doesn't chase patterns—it detects emergence . It doesn't guess at trends—it measures coherence .
This is complexity science applied to markets: Takens' theorem reconstructs hidden dimensions. Permutation entropy measures order. Lyapunov exponents detect chaos. Transfer entropy reveals causation. Hilbert phases find synchronization. Fractal dimensions quantify self-similarity.
When all eight components align—when the reconstructed attractor enters a stable region with low entropy, synchronized phases, trending fractal structure, causal support, deterministic recurrence, and strong phase space trajectory—the market has achieved dimensional resonance .
These are the highest-probability moments. Not because an indicator said so. Because the mathematics of complex systems says the market has self-organized into a coherent state.
Most indicators see shadows on the wall. DRP reconstructs the cave.
"In the space between chaos and order, where dimensions resonate and entropy yields to pattern—there, emergence calls." DRP
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Market Momentum in Premium & Discount-Delta @MaxMaserati 3.0Market Delta Momentum in Premium & Discount-Delta @MaxMaserati 3.0
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Overview
The MMPD 3.0 indicator is an advanced momentum oscillator that combines market structure analysis with institutional order flow concepts. It transforms price action into a normalized 0-100 scale, identifying premium and discount zones where institutional traders typically operate, while simultaneously tracking momentum through specialized body close candles and multi-timeframe synchronization.
This indicator is designed for traders who want to:
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Identify high-probability reversal zones using premium/discount analysis
Track momentum divergence between price and the MMPD oscillator
Recognize institutional rejection and acceptance zones
Synchronize multiple timeframes for confluence-based trading decisions
Core Methodology
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MMPD Calculation
The Market Delta Momentum indicator uses a proprietary calculation that:
Normalizes price position within a specific period range (0-100 scale)
Applies double smoothing to filter noise
Calculates a balance line (similar to a moving average) to determine bullish/bearish momentum
The relationship between the MMPD line and balance line creates directional candles
Key Zones:
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90-100: Extreme Premium (Institutional Selling Zone)
80-90: High Premium (Caution Zone)
65-80: Premium (Bullish Bias)
50-65: Light Premium (Neutral-Bullish)
35-50: Light Discount (Neutral-Bearish)
20-35: Discount (Bearish Bias)
10-20: High Discount (Institutional Buying Zone)
0-10: Extreme Discount (High Probability Buy Zone)
MMM 3.0 Body Close Logic BC and the MMPD 3.0 Body Close Logic MBC
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1️⃣ Body Close Analysis (BC & MBC)
Price Body Close (BC)
Bullish BC: Price closes above the previous high AND closes above its open (green candle showing aggressive buying)
Bearish BC: Price closes below the previous low AND closes below its open (red candle showing aggressive selling)
No Body Close (NBC): All other candles - representing consolidation, pause, or loss of momentum
MMPD Body Close (MBC)
Bullish MBC: MMPD closes higher than previous MMPD structure (continuation or reversal momentum)
Bearish MBC: MMPD closes lower than previous MMPD structure (continuation or reversal momentum)
MNBC: MMPD No Body Close - weak or ranging MMPD momentum
BC + MBC Confirmation
When Price BC and MMPD MBC align in the same direction, it signals high-conviction momentum:
Deep Green: Bullish BC + Bullish MBC (Strongest Bullish Signal)
Pale Green: Bullish BC only (Moderate Bullish Signal)
Deep Red: Bearish BC + Bearish MBC (Strongest Bearish Signal)
Pale Pink: Bearish BC only (Moderate Bearish Signal)
2️⃣ Momentum Synchronization System
The indicator compares MBC (MMPD Body Close) momentum against BC (Price Body Close) momentum to identify divergence and synchronization:
Synchronized States:
BULLISH+: High volatility bullish synchronization (BC+MBC aligned, high ATR)
BULLISH-: Low volatility bullish synchronization (BC+MBC aligned, low ATR)
BEARISH+: High volatility bearish synchronization (BC+MBC aligned, high ATR)
BEARISH-: Low volatility bearish synchronization (BC+MBC aligned, low ATR)
SYNCHRONIZED: Both MMPD and Price moving together (standard bullish or bearish move)
Divergence States (Reversal Warnings):
MMPD FAST | PRICE SLOW: MMPD showing strong directional MBC candles while Price shows NBC (pause/consolidation) - Reversal Warning!
If MMPD is bullish MBC but Price is NBC → Potential Bearish Reversal
If MMPD is bearish MBC but Price is NBC → Potential Bullish Reversal
Status Indicators:
BULL / BEAR: Standard synchronized moves
BULL+ / BEAR+: High volatility synchronized moves (aggressive trending)
BULL- / BEAR-: Low volatility synchronized moves (grinding trends)
POT. BULL / POT. BEAR: Potential reversal zones (divergence detected)
BALANCED: Neutral conditions, no clear momentum alignment which is price efficiency
3️⃣ Premium/Discount Breakout Markers
🔴 Red Circle Dots (Premium Exit)
Appears when MMPD closes below 80 after being completely in the 80-100 extreme premium zone
Signals institutional distribution complete, potential reversal or correction
🟢 Green Circle Dots (Discount Exit)
Appears when MMPD closes above 20 after being completely in the 0-20 extreme discount zone
Signals institutional accumulation complete, potential rally or reversal
🔴 Red Squares (Premium Rejection)
Appears on the first candle that fails to touch 80-100 after a Bullish MBC touched that zone
Indicates rejection of premium pricing, bearish signal
🟢 Green Squares (Discount Rejection)
Appears on the first candle that fails to touch 0-20 after a Bearish MBC touched that zone
Indicates rejection of discount pricing, bullish signal
🔻 Red Triangles Down (Bearish Midline Rejection)
Signals potential bearish Resumption
🔺 Green Triangles Up (Bullish Midline Bounce)
Signals potential Bullish Resumption
4️⃣ Multi-Timeframe Dashboard with Candle time to close
The MTF table displays:
6 customizable timeframes (default: 5min, 15min, 1H, 4H, Daily, Weekly)
Premium/Discount Status with color-coded zones for each timeframe
Time to Close (T2C): Live countdown timer for each timeframe candle close
Red warning color when the candle closing time is imminent
4H timeframe auto-detects exchange-specific session starts (ES, NQ, CL, GC, etc.)
Momentum Sync Status: Shows the current synchronization state between MMPD and Price across the chart timeframe
Color Coding:
Premium zones: Green/Cyan colors
Discount zones: Purple/Magenta colors
Intensity increases with extremeness (darker = more extreme)
5️⃣ Delta MMPD Alternative View
Toggle between two oscillator calculations:
MMPD: Original MMPD
Delta MMPD: Volume-weighted delta calculation emphasizing buying/selling pressure
TIPS
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Use Multi-Timeframe Confluence: The strongest signals occur when multiple timeframes align in premium/discount zones
Wait for Body Close Confirmation: BC+MBC alignment = highest probability setups
Respect Momentum Sync Warnings: "MMPD FAST | PRICE SLOW" is a critical reversal warning
Trade Premium → Discount or Discount → Premium: Mean reversion from extremes offers best risk/reward
Combine with Price Action: MMPD is a momentum oscillator - always confirm with price structure (support/resistance, trendlines, chart patterns)
Educational Notes
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What is Premium/Discount Pricing?
Institutional traders operate based on value zones:
Premium: Price is expensive relative to recent range - institutions distribute (sell)
Discount: Price is cheap relative to recent range - institutions accumulate (buy)
Fair Value (50 line): Equilibrium pricing where institutions pause
MMM 3.0 Body Close Approach Importance
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BC (Body Close): Shows price commitment and aggressivity
NBC (No Body Close): Shows indecision, consolidation, or loss of momentum
Consecutive BC candles = strong momentum
NBC candles breaking BC sequence = momentum loss → potential reversal
Momentum Synchronization Theory
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When MMPD (momentum) moves aggressively but Price shows NBC (pause), it indicates:
Momentum exhaustion
Smart money distribution/accumulation
Imminent reversal as retail traders get trapped
⚠️ Disclaimer
This indicator is for educational purposes only. Past performance does not guarantee future results. Always practice proper risk management and never risk more than you can afford to lose. Trading involves substantial risk of loss. The creator assumes no responsibility for trading losses incurred using this indicator.
Market Structure Trailing Stop MTF [Inspired by LuxAlgo]# Market Structure Trailing Stop MTF
**OPEN-SOURCE SCRIPT**
*208k+ views on original · Modified for MTF Support*
This indicator is a direct adaptation of the renowned **Market Structure Trailing Stop** by **LuxAlgo** (original script: [Market Structure Trailing Stop ]()). The core logic remains untouched, providing dynamic trailing stops based on market structure breaks (CHoCH/BOS). The **only modification** is the addition of **Multi-Timeframe (MTF) support**, allowing users to apply the trailing stops and structures from **higher timeframes (HTF)** directly on their current chart. This enhances usability for traders analyzing cross-timeframe confluence without switching charts.
**Special thanks to LuxAlgo** for releasing this powerful open-source tool under CC BY-NC-SA 4.0. Your contributions to the TradingView community have inspired countless traders—grateful for the solid foundation!
## 🔶 How the Script Works: A Deep Dive
At its heart, this indicator detects **market structure shifts** (bullish or bearish breaks of swing highs/lows) and uses them to generate **adaptive trailing stops**. These stops trail the price while protecting profits and acting as dynamic support/resistance levels. The MTF enhancement pulls this logic from user-specified higher timeframes, overlaying HTF structures and stops on the lower timeframe chart for seamless multi-timeframe analysis.
### Core Logic (Unchanged from LuxAlgo's Original)
1. **Pivot Detection**:
- Uses `ta.pivothigh()` and `ta.pivotlow()` with a user-defined lookback (`length`) to identify swing highs (PH) and lows (PL).
- Coordinates (price `y` and bar index/time `x`) are stored in persistent variables (`var`) for tracking recent pivots.
2. **Market Structure Detection**:
- **Bullish Structure (BOS/CHoCH)**: Triggers when `close > recent PH` (break above swing high).
- If `resetOn = 'CHoCH'`, resets only on major shifts (Change of Character); otherwise, on all breaks.
- Sets trend state `os = 1` (bullish) and highlights the break with a horizontal line (dashed for CHoCH, dotted for BOS).
- Initializes trailing stop at the local minimum (lowest low since the pivot) using a backward loop: `btm = math.min(low , btm)`.
- **Bearish Structure**: Triggers when `close < recent PL`, mirroring the bullish logic (`os = -1`, local maximum for stop).
- Structure state `ms` tracks the break type (1 for bull, -1 for bear, 0 neutral), resetting based on user settings.
3. **Trailing Stop Calculation**:
- Tracks **trailing max/min**:
- On new bull structure: Reset `max = close`.
- On new bear: Reset `min = close`.
- Otherwise: `max = math.max(close, max)` / `min = math.min(close, min)`.
- **Stop Adjustment** (the "trailing" magic):
- On fresh structure: `ts = btm` (bull) or `top` (bear).
- In ongoing trend: Increment/decrement by a percentage of the max/min change:
- Bull: `ts += (max - max ) * (incr / 100)`
- Bear: `ts += (min - min ) * (incr / 100)`
- This creates a **ratcheting effect**: Stops move favorably with the trend but never against it, converging toward price at a controlled rate.
- **Visuals**:
- Plots `ts` line colored by trend (teal for bull, red for bear).
- Fills area between `close` and `ts` (orange on retracements).
- Draws structure lines from pivot to break point.
4. **Edge Cases**:
- Variables like `ph_cross`/`pl_cross` prevent multiple triggers on the same pivot.
- Neutral state (`ms = 0`) preserves prior `max/min` until a new structure.
### MTF Enhancement (Our Addition)
- **request.security() Integration**:
- Wraps the entire core function `f()` in a security call for each timeframe (`tf1`, `tf2`).
- Returns HTF values (e.g., `ts1`, `os1`, structure times/prices) to the chart's context.
- Uses `lookahead=barmerge.lookahead_off` for accurate historical repainting-free data.
- Structures are drawn using `xloc.bar_time` to align HTF lines precisely on the LTF chart.
- **Multi-Output Handling**:
- Separate plots/fills/lines for each TF (e.g., `plot_ts1`, `plot_ts2`).
- Colors and toggles per TF to distinguish HTF1 (e.g., teal/red) from HTF2 (e.g., blue/maroon).
- **Benefits**: Spot HTF bias on LTF entries, e.g., enter longs only if both TF1 (1H) and TF2 (4H) show bullish `os=1`.
This keeps the script lightweight—**no repainting, max 500 lines**, and fully compatible with LuxAlgo's original behavior when TFs are set to the chart's timeframe.
## 🔶 SETTINGS
### Core Parameters
- **Pivot Lookback** (`length = 14`): Bars left/right for pivot detection. Higher = smoother structures, fewer signals; lower = more noise.
- **Increment Factor %** (`incr = 100`): Speed of stop convergence (0-∞). 100% = full ratchet (mirrors max/min exactly); <100% = slower trail, reduces whipsaws.
- **Reset Stop On** (`'CHoCH'`): `'CHoCH'` = Reset only on major reversals (dashed lines); `'All'` = Reset on every BOS/CHoCH (tighter stops).
### MTF Support
- **Timeframe 1** (`tf1 = ""`): HTF for first set (e.g., "1H"). Empty = current chart.
- **Timeframe 2** (`tf2 = ""`): Second HTF (e.g., "4H"). Enables dual confluence.
### Display Toggles
- **Show Structures** (`true`): Draws horizontal lines for breaks (per TF colors).
- **Show Trailing Stop TF1/TF2** (`true`): Plots the stop line.
- **Show Fill TF1/TF2** (`true`): Area fill between close and stop.
### Candle Coloring (Optional)
- **Color Candles** (`false`): Enables custom `plotcandle` for body/wick/border.
- **Candle Color Based On TF** (`"None"`): `"TF1"`, `"TF2"`, or none. Colors bull trend green, bear red.
- **Candle Colors**: Separate inputs for bull/bear body, wick, border (e.g., solid green body, transparent wick).
### Alerts
- **Enable MS Break Alerts** (`false`): Notifies on structure breaks (bull/bear per TF) **only on bar close** (`barstate.isconfirmed` + `alert.freq_once_per_bar_close`).
- **Enable Stop Hit Alerts** (`false`): Triggers on stop breaches (long/short per TF), using `ta.crossunder/crossover`.
### Colors
- **TF1 Colors**: Bullish (teal), Bearish (red), Retracement (orange).
- **TF2 Colors**: Bullish (blue), Bearish (maroon), Retracement (orange).
- **Area Transparency** (`80`): Fill opacity (0-100).
## 🔶 USAGE
Trailing stops shine in **trend-following strategies**:
- **Entries**: Use structure breaks as signals (e.g., long on bullish BOS from HTF1).
- **Exits**: Trail stops for profit-locking; alert on hits for automation.
- **Confluence**: Overlay HTF1 (e.g., 1H) for bias, HTF2 (e.g., Daily) for major levels—enter LTF only on alignment.
- **Risk Management**: Lower `incr` avoids early stops in chop; reset on `'All'` for aggressive trailing.
! (i.imgur.com)
*HTF1 shows bullish structure (teal line), trailing stop ratchets up—long entry confirmed on LTF pullback.*
! (i.imgur.com)
*TF1 (blue) bearish, TF2 (red) neutral—avoid shorts until alignment.*
! (i.imgur.com)
*Colored based on TF1 trend: Green bodies on bull `os=1`.*
Pro Tip: Test on demo—pair with LuxAlgo's other tools like Smart Money Concepts for full structure ecosystem.
## 🔶 DETAILS: Mathematical Breakdown
On bullish break:
- Local min: `btm = ta.lowest(n - ph_x)` (optimized loop equivalent).
- Stop init: `ts = btm`.
- Update: `Δmax = max - max `, `ts_new = ts + Δmax * (incr/100)`.
Bearish mirrors with `Δmin` (negative, so decrements `ts`).
In MTF: HTF `time` aligns lines via `line.new(htf_time, level, current_time, level, xloc.bar_time)`.
No logs/math libs needed—pure Pine v5 efficiency.
## Disclaimer
This is for educational purposes. Not financial advice. Backtest thoroughly. Original by LuxAlgo—modify at your risk. See TradingView's (www.tradingview.com). Licensed under CC BY-NC-SA 4.0 (attribution to LuxAlgo required).
Velocity Pressure Index | AlphaNattVelocity Pressure Index (VPI) | AlphaNatt
A sophisticated momentum oscillator that combines price velocity analysis with volume pressure dynamics to identify high-probability trading opportunities.
📊 KEY FEATURES
Dual Analysis System: Merges price velocity measurement with volume pressure analysis for comprehensive market momentum assessment
Dynamic Normalization: Automatically scales values between -100 and +100 for consistent readings across all market conditions
Adaptive Zones: Self-adjusting overbought/oversold levels based on recent price history
Multi-Layer Confirmation: Combines momentum, acceleration, and crossover signals for robust trade identification
Volume-Weighted Pressure: Differentiates between bullish and bearish volume to gauge true market sentiment
📈 HOW IT WORKS
The VPI calculates price velocity using linear regression of price changes, then weights this velocity by the difference between bullish and bearish volume pressure. This creates a momentum reading that accounts for both price movement speed and the volume conviction behind it.
Signal Generation:
Price velocity is measured over the specified period
Volume is separated into bullish (close > open) and bearish (close < open) pressure
Velocity is amplified or dampened based on volume pressure differential
The resulting index is normalized to oscillate between -100 and +100
A signal line smooths the oscillator for crossover detection
🎯 TRADING SIGNALS
Long Signals (Cyan #00F1FF):
Strong Bull: VPI > Signal with positive momentum and acceleration
Crossover Bull: VPI crosses above signal while above oversold zone
Divergence: Price makes lower low while VPI makes higher low
Short Signals (Magenta #FF019A):
Strong Bear: VPI < Signal with negative momentum and deceleration
Crossover Bear: VPI crosses below signal while below overbought zone
Divergence: Price makes higher high while VPI makes lower high
⚙️ CUSTOMIZABLE PARAMETERS
Velocity Settings:
Velocity Period (14): Lookback for price velocity calculation
Pressure Period (21): Volume analysis window
Smoothing Factor (3): Final oscillator smoothing
Signal Configuration:
Signal Type: Choose between SMA, EMA, or DEMA
Signal Length (9): Signal line smoothing period
Normalization Period (50): Range calculation window
Dynamic Zones:
Zone Lookback (100): Period for adaptive overbought/oversold calculation
Percentiles: 80th/20th percentiles for dynamic zones
📐 VISUAL COMPONENTS
Main Oscillator: Color-coded line showing current momentum state
Signal Line: White line for crossover detection
Momentum Histogram: Shows velocity differential at 50% scale
Dynamic Zones: Self-adjusting overbought/oversold bands
Extreme Levels: ±50 dotted lines marking extreme conditions
Background Shading: Subtle highlighting of overbought/oversold regions
💡 USAGE TIPS
Trend Trading: Use strong bull/bear signals in trending markets for continuation entries
Range Trading: Focus on crossovers near extreme zones for reversal trades
Divergence Trading: Watch for price/oscillator divergences at market extremes
Multi-Timeframe: Combine with higher timeframe VPI for directional bias
Volume Confirmation: Stronger signals occur with aligned volume pressure
⚠️ BEST PRACTICES
The VPI works best in liquid markets with reliable volume data. For optimal results, combine with price action analysis and use appropriate risk management. The indicator is most effective during trending conditions but can identify reversals when divergences occur at extremes.
🔔 ALERTS AVAILABLE
VPI Long/Short Signals
Bullish/Bearish Crossovers
Extreme Overbought/Oversold Conditions
Version 6 | Pine Script™ | © AlphaNatt
Vector Sniper Pro What it is
Vector Sniper (Simplified) is a single, original algorithm that flags impulsive “vector” moves only when volatility, volume, and structure align. It is not a mashup of other indicators; everything below is computed from raw OHLCV with a small, transparent ruleset.
⸻
Core idea (signal = force × participation × context)
1. Force (Volatility):
• We z-score true range: trZ = (ATR(1) - SMA(ATR(1), N)) / StDev(ATR(1), N).
• A move must exceed a user-set Volatility Z-Score.
2. Participation (Volume):
• We z-score raw volume: volZ = (Vol - SMA(Vol, N)) / StDev(Vol, N).
• Volume must also exceed a Volume Z-Score.
3. Context (Structure, Body, Imbalance, Traps):
• Body% filter: real body / range ≥ Min Body %.
• Delta-volume proxy: (bullVol − bearVol) / volume, where bullVol = volume*(close−low)/range and bearVol = volume*(high−close)/range. We require positive imbalance for bulls, negative for bears.
• Structure break (optional): price must take out the prior N-bar high/low.
• Trap detection (optional): spring/upthrust patterns defined by lower-low/upper-high followed by a close back inside.
If the above align, you get a Bull Vector (green) or Bear Vector (red). “Extreme” vectors require the same conditions at a higher multiple (Ext Mult).
⸻
Noise control (pre-signal gate)
Before a vector is allowed, a pre-signal score (0–7) must pass:
• Checks include spring/upthrust, no-supply/no-demand, imbalance, volume > average, VWAP side alignment, EMA trend alignment, proximity to structure break, and candle direction.
• You choose a minimum score, persistence (must occur ≥N times inside last M bars), cooldown after a pass, and hysteresis vs the opposite side.
This prevents one-off blips and keeps signals directional.
⸻
Optional confluence
• VWAP alignment: require price on the correct side and VWAP slope with it.
• EMA filter: require EMA trend agreement.
• HTF bias (optional): compare HTF close vs HTF EMA on a selected timeframe.
• Implemented with request.security and no look-ahead; bias updates when the higher timeframe bar closes.
⸻
Visuals & alerts
• Candle colors (5 total):
• Green = Bull Vector, Red = Bear Vector.
• Blue = Pre-Bull, Orange = Pre-Bear.
• Gray = Neutral.
• Markers (optional): diamonds = “Extreme” vectors; small triangles = pre-signals.
• Built-in alerts: Bull Vector, Bear Vector, Extreme Bull/Bear, Pre-Bull, Pre-Bear.
• Add from: Alerts → Condition → this script → choose event.
⸻
How to use (practical)
1. Start with defaults. Turn on VWAP and EMA filters; add HTF bias if you want fewer but cleaner signals.
2. Hunt for alignment: Pre-signal (blue/orange) → Vector (green/red) in the same direction.
3. Use your own risk model for entries/exits; the script does not place orders or compute stops/targets.
⸻
Inputs (plain English)
• ATR/Volume Periods & Z-Scores: sensitivity to volatility/participation.
• Extreme Multiplier: threshold for “Extreme” vectors.
• Structure Break (bars) & Traps: contextual confirms.
• Pre-signal gate: Min Score, Persistence (N in last M), Cooldown, Opposite-side lockout.
• Confluence: VWAP side, EMA trend, optional HTF bias (timeframe + EMA length).
• Visuals: candle painting and markers.
⸻
Design notes / limitations
• Signals evaluate on bar close. Intrabar they can form and cancel; for consistency, trade on closed bars.
• HTF bias is derived from closed HTF bars; no future data is used.
• This is an indicator, not financial advice. Backtest forward and manage risk.
⸻
Why this isn’t a “mashup”:
All components are purposeful and documented: z-score volatility + z-score volume (force & participation), body% and delta-volume (quality), structure & traps (context), and a scored, persistent pre-filter with VWAP/EMA/HTF alignment (noise control).
Smart Money Precision Structure [BullByte]Smart Money Precision Structure
Advanced Market Structure Analysis Using Institutional Order Flow Concepts
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OVERVIEW
Smart Money Precision Structure (SMPS) is a comprehensive market analysis indicator that combines six analytical frameworks to identify high-probability market structure patterns. The indicator uses multi-dimensional scoring algorithms to evaluate market conditions through institutional order flow concepts, providing traders with professional-grade market analysis.
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PURPOSE AND ORIGINALITY
Why This Indicator Was Developed
• Addresses the gap between retail and institutional analysis methods
• Consolidates multiple analysis techniques that professionals use separately
• Automates complex market structure evaluation into actionable insights
• Eliminates the need for multiple indicators by providing comprehensive analysis
What Makes SMPS Original
• Six-Layer Confluence System - Unique combination of market regime, structure, volume flow, momentum, price action, and adaptive filtering
• Institutional Pattern Recognition - Identifies smart money accumulation and distribution patterns
• Adaptive Intelligence - Parameters automatically adjust based on detected market conditions
• Real-Time Market Scoring - Proprietary algorithm rates market quality from 0-100%
• Structure Break Detection - Advanced pivot analysis identifies trend reversals early
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HOW IT WORKS - TECHNICAL METHODOLOGY
1. Market Regime Analysis Engine
The indicator evaluates five core market dimensions:
• Volatility Score - Measures current volatility against 50-period historical baseline
• Trend Score - Analyzes alignment between 8, 21, and 50-period EMAs
• Momentum Score - Combines RSI divergence with MACD signal alignment
• Structure Score - Evaluates pivot point formation clarity
• Efficiency Score - Calculates directional movement efficiency ratio
These scores combine to classify markets into five regimes:
• TRENDING - Strong directional movement with aligned indicators
• RANGING - Sideways movement with mixed directional signals
• VOLATILE - Elevated volatility with unpredictable price swings
• QUIET - Low volatility consolidation periods
• TRANSITIONAL - Market shifting between different regimes
2. Market Structure Analysis
Advanced pivot point analysis identifies:
• Higher Highs and Higher Lows for bullish structure
• Lower Highs and Lower Lows for bearish structure
• Structure breaks when established patterns fail
• Dynamic support and resistance from recent pivot points
• Key level proximity detection using ATR-based buffers
3. Volume Flow Decoding
Institutional activity detection through:
• Volume surge identification when volume exceeds 2x average
• Buy versus sell pressure analysis using price-volume correlation
• Flow strength measurement through directional volume consistency
• Divergence detection between volume and price movements
• Institutional threshold alerts when unusual volume patterns emerge
4. Multi-Period Momentum Synthesis
Weighted momentum calculation across four timeframes:
• 1-period momentum weighted at 40%
• 3-period momentum weighted at 30%
• 5-period momentum weighted at 20%
• 8-period momentum weighted at 10%
Result smoothed with 6-period EMA for noise reduction.
5. Price Action Quality Assessment
Each bar evaluated for:
• Range quality relative to 20-period average
• Body-to-range ratio for directional conviction
• Wick analysis for rejection pattern identification
• Pattern recognition including engulfing and hammer formations
• Sequential price movement analysis
6. Adaptive Parameter System
Parameters automatically adjust based on detected regime:
• Trending markets reduce sensitivity and confirmation requirements
• Volatile markets increase filtering and require additional confirmations
• Ranging markets maintain neutral settings
• Transitional markets use moderate adjustments
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COMPLETE SETTINGS GUIDE
Section 1: Core Analysis Settings
Analysis Sensitivity (0.3-2.0)
• Default: 1.0
• Lower values require stronger price movements
• Higher values detect more subtle patterns
• Scalpers use 0.8-1.2, swing traders use 1.5-2.0
Noise Reduction Level (2-7)
• Default: 4
• Controls filtering of false patterns
• Higher values reduce pattern frequency
• Increase in volatile markets
Minimum Move % (0.05-0.50)
• Default: 0.15%
• Sets minimum price movement threshold
• Adjust based on instrument volatility
• Forex: 0.05-0.10%, Stocks: 0.15-0.25%, Crypto: 0.20-0.50%
High Confirmation Mode
• Default: True (Enabled)
• Requires all technical conditions to align
• Reduces frequency but increases reliability
• Disable for more aggressive pattern detection
Section 2: Market Regime Detection
Enable Regime Analysis
• Default: True (Enabled)
• Activates market environment evaluation
• Essential for adaptive features
• Keep enabled for best results
Regime Analysis Period (20-100)
• Default: 50 bars
• Determines regime calculation lookback
• Shorter for responsive, longer for stable
• Scalping: 20-30, Swing: 75-100
Minimum Market Clarity (0.2-0.8)
• Default: 0.4
• Quality threshold for pattern generation
• Higher values require clearer conditions
• Lower for more patterns, higher for quality
Adaptive Parameter Adjustment
• Default: True (Enabled)
• Enables automatic parameter optimization
• Adjusts based on market regime
• Highly recommended to keep enabled
Section 3: Market Structure Analysis
Enable Structure Validation
• Default: True (Enabled)
• Validates patterns against support/resistance
• Confirms trend structure alignment
• Essential for reliability
Structure Analysis Period (15-50)
• Default: 30 bars
• Period for structure pattern analysis
• Affects support/resistance calculation
• Match to your trading timeframe
Minimum Structure Alignment (0.3-0.8)
• Default: 0.5
• Required structure score for valid patterns
• Higher values need stronger structure
• Balance with desired frequency
Section 4: Analysis Configuration
Minimum Strength Level (3-5)
• Default: 4
• Minimum confirmations for pattern display
• 5 = Maximum reliability, 3 = More patterns
• Beginners should use 4-5
Required Technical Confirmations (4-6)
• Default: 5
• Number of aligned technical factors
• Higher = fewer but better patterns
• Works with High Confirmation Mode
Pattern Separation (3-20 bars)
• Default: 8 bars
• Minimum bars between patterns
• Prevents clustering and overtrading
• Increase for cleaner charts
Section 5: Technical Filters
Momentum Validation
• Default: True (Enabled)
• Requires momentum alignment
• Filters counter-trend patterns
• Essential for trend following
Volume Confluence Analysis
• Default: True (Enabled)
• Requires volume confirmation
• Identifies institutional participation
• Critical for reliability
Trend Direction Filter
• Default: True (Enabled)
• Only shows patterns with trend
• Reduces counter-trend signals
• Disable for reversal hunting
Section 6: Volume Flow Analysis
Institutional Activity Threshold (1.2-3.5)
• Default: 2.0
• Multiplier for unusual volume detection
• Lower finds more institutional activity
• Stock: 2.0-2.5, Forex: 1.5-2.0, Crypto: 2.5-3.5
Volume Surge Multiplier (1.8-4.5)
• Default: 2.5
• Defines significant volume increases
• Adjust per instrument characteristics
• Higher for stocks, lower for forex
Volume Flow Period (12-35)
• Default: 18 bars
• Smoothing for volume analysis
• Shorter = responsive, longer = smooth
• Match to timeframe used
Section 7: Analysis Frequency Control
Maximum Analysis Points Per Hour (1-5)
• Default: 3
• Limits pattern frequency
• Prevents overtrading
• Scalpers: 4-5, Swing traders: 1-2
Section 8: Target Level Configuration
Target Calculation Method
• Default: Market Adaptive
• Three modes available:
- Fixed: Uses set point distances
- Dynamic: ATR-based calculations
- Market Adaptive: Structure-based levels
Minimum Target/Risk Ratio (1.0-3.0)
• Default: 1.5
• Minimum acceptable reward vs risk
• Higher filters lower probability setups
• Professional standard: 1.5-2.0
Fixed Mode Settings:
• Fixed Target Distance: 50 points default
• Fixed Invalidation Distance: 30 points default
• Use for consistent instruments
Dynamic Mode Settings:
• Dynamic Target Multiplier: 1.8x ATR default
• Dynamic Invalidation Multiplier: 1.0x ATR default
• Adapts to volatility automatically
Market Adaptive Settings:
• Use Structure Levels: True (default)
• Structure Level Buffer: 0.1% default
• Places levels at actual support/resistance
Section 9: Visual Display Settings
Color Theme Options
• Professional (Teal/Red)
- Bullish: Teal (#26a69a)
- Bearish: Red (#ef5350)
- Neutral: Gray (#78909c)
- Best for: Traditional traders, clean appearance
• Dark (Neon Green/Pink)
- Bullish: Neon Green (#00ff88)
- Bearish: Hot Pink (#ff0044)
- Neutral: Dark Gray (#333333)
- Best for: Dark theme users, high contrast
• Light (Green/Red Classic)
- Bullish: Green (#4caf50)
- Bearish: Red (#f44336)
- Neutral: Light Gray (#9e9e9e)
- Best for: Light backgrounds, traditional colors
• Vibrant (Cyan/Magenta)
- Bullish: Cyan (#00ffff)
- Bearish: Magenta (#ff00ff)
- Neutral: Medium Gray (#888888)
- Best for: High visibility, modern appearance
Dashboard Position
• Options: Top Left, Top Right, Bottom Left, Bottom Right, Middle Left, Middle Right
• Default: Top Right
• Choose based on chart layout preference
Dashboard Size
• Full: Complete information display (desktop)
• Mobile: Compact view for small screens
• Default: Full
Analysis Display Style
• Arrows : Simple directional markers
• Labels : Detailed text information
• Zones : Colored areas showing pattern regions
• Default: Labels (most informative)
Display Options:
• Display Analysis Strength: Shows star rating
• Display Target Levels: Shows target/invalidation lines
• Display Market Regime: Shows regime in pattern labels
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HOW TO USE SMPS - DETAILED GUIDE
Understanding the Dashboard
Top Row - Header
• SMPS Dashboard title
• VALUE column: Current readings
• STATUS column: Condition assessments
Market Regime Row
• Shows: TRENDING, RANGING, VOLATILE, QUIET, or TRANSITIONAL
• Color coding: Green = Favorable, Red = Caution
• Status: FAVORABLE or CAUTION trading conditions
Market Score Row
• Percentage from 0-100%
• Above 60% = Strong conditions
• 40-60% = Moderate conditions
• Below 40% = Weak conditions
Structure Row
• Direction: BULLISH, BEARISH, or NEUTRAL
• Status: INTACT or BREAK
• Orange BREAK indicates structure failure
Volume Flow Row
• Direction: BUYING or SELLING
• Intensity: STRONG or WEAK
• Color indicates dominant pressure
Momentum Row
• Numerical momentum value
• Positive = Upward pressure
• Negative = Downward pressure
Volume Status Row
• INST = Institutional activity detected
• HIGH = Above average volume
• NORM = Normal volume levels
Adaptive Mode Row
• ACTIVE = Parameters adjusting
• STATIC = Fixed parameters
• Shows required confirmations
Analysis Level Row
• Minimum strength level setting
• Pattern separation in bars
Market State Row
• Current analysis: BULLISH, BEARISH, NEUTRAL
• Shows analysis price level when active
T:R Ratio Row
• Current target to risk ratio
• GOOD = Meets minimum requirement
• LOW = Below minimum threshold
Strength Row
• BULL or BEAR dominance
• Numerical strength value 0-100
Price Row
• Current price
• Percentage change
Last Analysis Row
• Previous pattern direction
• Bars since last pattern
Reading Pattern Signals
Bullish Structure Pattern
• Upward triangle or "Bullish Structure" label
• Star rating shows strength (★★★★★ = strongest)
• Green line = potential target level
• Red dashed line = invalidation level
• Appears below price bars
Bearish Structure Pattern
• Downward triangle or "Bearish Structure" label
• Star rating indicates reliability
• Green line = potential target level
• Red dashed line = invalidation level
• Appears above price bars
Pattern Strength Interpretation
• ★★★★★ = 6 confirmations (exceptional)
• ★★★★☆ = 5 confirmations (strong)
• ★★★☆☆ = 4 confirmations (moderate)
• ★★☆☆☆ = 3 confirmations (minimum)
• Below minimum = filtered out
Visual Elements on Chart
Lines and Levels:
• Gray Line = 21 EMA trend reference
• Green Stepline = Dynamic support level
• Red Stepline = Dynamic resistance level
• Green Solid Line = Active target level
• Red Dashed Line = Active invalidation level
Pattern Markers:
• Triangles = Arrow display mode
• Text Labels = Label display mode
• Colored Boxes = Zone display mode
Target Completion Labels:
• "Target" = Price reached target level
• "Invalid" = Pattern invalidated by price
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RECOMMENDED USAGE BY TIMEFRAME
1-Minute Charts (Scalping)
• Sensitivity: 0.8-1.2
• Noise Reduction: 3-4
• Pattern Separation: 3-5 bars
• High Confirmation: Optional
• Best for: Quick intraday moves
5-Minute Charts (Precision Intraday)
• Sensitivity: 1.0 (default)
• Noise Reduction: 4 (default)
• Pattern Separation: 8 bars
• High Confirmation: Enabled
• Best for: Day trading
15-Minute Charts (Short Swing)
• Sensitivity: 1.0-1.5
• Noise Reduction: 4-5
• Pattern Separation: 10-12 bars
• High Confirmation: Enabled
• Best for: Intraday swings
30-Minute to 1-Hour (Position Trading)
• Sensitivity: 1.5-2.0
• Noise Reduction: 5-7
• Pattern Separation: 15-20 bars
• Regime Period: 75-100
• Best for: Multi-day positions
Daily Charts (Swing Trading)
• Sensitivity: 1.8-2.0
• Noise Reduction: 6-7
• Pattern Separation: 20 bars
• All filters enabled
• Best for: Long-term analysis
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MARKET-SPECIFIC SETTINGS
Forex Pairs
• Minimum Move: 0.05-0.10%
• Institutional Threshold: 1.5-2.0
• Volume Surge: 1.8-2.2
• Target Mode: Dynamic or Market Adaptive
Stock Indices (ES, NQ, YM)
• Minimum Move: 0.10-0.15%
• Institutional Threshold: 2.0-2.5
• Volume Surge: 2.5-3.0
• Target Mode: Market Adaptive
Individual Stocks
• Minimum Move: 0.15-0.25%
• Institutional Threshold: 2.0-2.5
• Volume Surge: 2.5-3.5
• Target Mode: Dynamic
Cryptocurrency
• Minimum Move: 0.20-0.50%
• Institutional Threshold: 2.5-3.5
• Volume Surge: 3.0-4.5
• Target Mode: Dynamic
• Increase noise reduction
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PRACTICAL APPLICATION EXAMPLES
Example 1: Strong Trending Market
Dashboard Reading:
• Market Regime: TRENDING
• Market Score: 75%
• Structure: BULLISH, INTACT
• Volume Flow: BUYING, STRONG
• Momentum: +0.45
Interpretation:
• Strong uptrend environment
• Institutional buying present
• Look for bullish patterns as continuation
• Higher probability of success
• Consider using lower sensitivity
Example 2: Range-Bound Conditions
Dashboard Reading:
• Market Regime: RANGING
• Market Score: 35%
• Structure: NEUTRAL
• Volume Flow: SELLING, WEAK
• Momentum: -0.05
Interpretation:
• No clear direction
• Low opportunity environment
• Patterns are less reliable
• Consider waiting for regime change
• Or switch to a range-trading approach
Example 3: Structure Break Alert
Dashboard Reading:
• Previous: BULLISH structure
• Current: Structure BREAK
• Volume: INST flag active
• Momentum: Shifting negative
Interpretation:
• Trend reversal potentially beginning
• Institutional participation detected
• Watch for bearish pattern confirmation
• Adjust bias accordingly
• Increase caution on long positions
Example 4: Volatile Market
Dashboard Reading:
• Market Regime: VOLATILE
• Market Score: 45%
• Adaptive Mode: ACTIVE
• Confirmations: Increased to 6
Interpretation:
• Choppy conditions
• Parameters auto-adjusted
• Fewer but higher quality patterns
• Wider stops may be needed
• Consider reducing position size
Below are a few chart examples of the Smart Money Precision Structure (SMPS) indicator in action.
• Example 1 – Bullish Structure Detection on SOLUSD 5m
• Example 2 – Bearish Structure Detected with Strong Confluence on SOLUSD 5m
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TROUBLESHOOTING GUIDE
No Patterns Appearing
Check these settings:
• High Confirmation Mode may be too restrictive
• Minimum Strength Level may be too high
• Market Clarity threshold may be too high
• Regime filter may be blocking patterns
• Try increasing sensitivity
Too Many Patterns
Adjust these settings:
• Enable High Confirmation Mode
• Increase Minimum Strength Level to 5
• Increase Pattern Separation
• Reduce Sensitivity below 1.0
• Enable all technical filters
Dashboard Shows "CAUTION"
This indicates:
• Market conditions are unfavorable
• Regime is RANGING or QUIET
• Market score is low
• Consider waiting for better conditions
• Or adjust expectations accordingly
Patterns Not Reaching Targets
Consider:
• Market may be choppy
• Volatility may have changed
• Try Dynamic target mode
• Reduce target/risk ratio requirement
• Check if regime is VOLATILE
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ALERTS CONFIGURATION
Alert Message Format
Alerts include:
• Pattern type (Bullish/Bearish)
• Strength rating
• Market regime
• Analysis price level
• Target and invalidation levels
• Strength percentage
• Target/Risk ratio
• Educational disclaimer
Setting Up Alerts
• Click Alert button on TradingView
• Select SMPS indicator
• Choose alert frequency
• Customize message if desired
• Alerts fire on pattern detection
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DATA WINDOW INFORMATION
The Data Window displays:
• Market Regime Score (0-100)
• Market Structure Bias (-1 to +1)
• Bullish Strength (0-100)
• Bearish Strength (0-100)
• Bull Target/Risk Ratio
• Bear Target/Risk Ratio
• Relative Volume
• Momentum Value
• Volume Flow Strength
• Bull Confirmations Count
• Bear Confirmations Count
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BEST PRACTICES AND TIPS
For Beginners
• Start with default settings
• Use High Confirmation Mode
• Focus on TRENDING regime only
• Paper trade first
• Learn one timeframe thoroughly
For Intermediate Users
• Experiment with sensitivity settings
• Try different target modes
• Use multiple timeframes
• Combine with price action analysis
• Track pattern success rate
For Advanced Users
• Customize per instrument
• Create setting templates
• Use regime information for bias
• Combine with other indicators
• Develop systematic rules
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IMPORTANT DISCLAIMERS
• This indicator is for educational and informational purposes only
• Not financial advice or a trading system
• Past performance does not guarantee future results
• Trading involves substantial risk of loss
• Always use appropriate risk management
• Verify patterns with additional analysis
• The author is not a registered investment advisor
• No liability accepted for trading losses
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VERSION NOTES
Version 1.0.0 - Initial Release
• Six-layer confluence system
• Adaptive parameter technology
• Institutional volume detection
• Market regime classification
• Structure break identification
• Real-time dashboard
• Multiple display modes
• Comprehensive settings
## My Final Thoughts
Smart Money Precision Structure represents an advanced approach to market analysis, bringing institutional-grade techniques to retail traders through intelligent automation and multi-dimensional evaluation. By combining six analytical frameworks with adaptive parameter adjustment, SMPS provides comprehensive market intelligence that single indicators cannot achieve.
The indicator serves as an educational tool for understanding how professional traders analyze markets, while providing practical pattern detection for those seeking to improve their technical analysis. Remember that all trading involves risk, and this tool should be used as part of a complete analysis approach, not as a standalone trading system.
- BullByte
Six Meridian Divine Swords [theUltimator5]The Six Meridian Divine Sword is a legendary martial arts technique in the classic wuxia novel “Demi-Gods and Semi-Devils” (天龙八部) by Jin Yong (金庸). The technique uses powerful internal energy (qi) to shoot invisible sword-like energy beams from the six meridians of the hand. Each of the six fingers/meridians corresponds to a “sword,” giving six different sword energies.
The Six Meridian Divine Swords indicator is a compact “signal dashboard” that fuses six classic indicators (fingers)—MACD, KDJ, RSI, LWR (Williams %R), BBI, and MTM—into one pane. Each row is a traffic-light dot (green/bullish, red/bearish, gray/neutral). When all six align, the script draws a confirmation line (“All Bullish” or “All Bearish”). It’s designed for quick consensus reads across trend, momentum, and overbought/oversold conditions.
How to Read the Dashboard
The pane has 6 horizontal rows (explained in depth later):
MACD
KDJ
RSI
LWR (Larry Williams %R)
BBI (Bull & Bear Index)
MTM (Momentum)
Each tick in the row is a dot, with sentiment identified by a color.
Green = bullish condition met
Red = bearish condition met
Gray = inside a neutral band (filtering chop), shown when Use Neutral (Gray) Colors is ON
There are two lines that track the dots on the top or bottom of the pane.
All Bullish Signal Line: appears only if all 6 are strongly bullish (default color = white)
All Bearish Signal Line: appears only if all 6 are strongly bearish (default color = fuchsia)
The Six Meridians (Indicators) — What They Mean:
1) MACD — Trend & Momentum
What it is: A trend-following momentum indicator based on the relationship between two moving averages (typically 12-EMA and 26-EMA)
Logic used: Classic MACD line (EMA12−EMA26) vs its 9-EMA signal.
Bullish: MACD > Signal and |MACD−Signal| > Neutral Threshold
Bearish: MACD < Signal and |diff| > threshold
Neutral: |diff| ≤ threshold
Why: Small crosses can whipsaw. The neutral band ignores tiny separations to reduce noise.
Inputs: Fast/Slow/Signal lengths, Neutral Threshold.
2) KDJ — Stochastic with J-line boost
What it is: A variation of the stochastic oscillator popular in Chinese trading systems
Logic used: K = SMA(Stochastic, smooth), D = SMA(K, smooth), J = 3K − 2D.
Bullish: K > D and |K−D| > 2
Bearish: K < D and |K−D| > 2
Neutral: |K−D| ≤ 2
Why: K–D separation filters tiny wiggles; J offers an “extreme” early-warning context in the value label.
Inputs: Length, Smoothing.
3) RSI — Momentum balance (0–100)
What it is: A momentum oscillator measuring speed and magnitude of price changes (0–100)
Logic used: RSI(N).
Bullish: RSI > 50 + Neutral Zone
Bearish: RSI < 50 − Neutral Zone
Neutral: Between those bands
Why: Centerline/adaptive bands (around 50) give a directional bias without relying on fixed 70/30.
Inputs: Length, Neutral Zone (± around 50).
4) LWR (Williams %R) — Overbought/Oversold
What it is: An oscillator similar to stochastic, measuring how close the close is to the high-low range over N periods
Logic used: %R over N bars (0 to −100).
Bullish: %R > −50 + Neutral Zone
Bearish: %R < −50 − Neutral Zone
Neutral: Between those bands
Why: Uses a centered band around −50 instead of only −20/−80, making it act like a directional filter.
Inputs: Length, Neutral Zone (± around −50).
5) BBI (Bull & Bear Index) — Smoothed trend bias
What it is: A composite moving average, essentially the average of several different moving averages (often 3, 6, 12, 24 periods)
Logic used: Average of 4 SMAs (3/6/12/24 by default):
BBI = (MA3 + MA6 + MA12 + MA24) / 4
Bullish: Close > BBI and |Close−BBI| > 0.2% of BBI
Bearish: Close < BBI and |diff| > threshold
Neutral: |diff| ≤ threshold
Why: Multiple MAs blended together reduce single-MA whipsaw. A dynamic 0.2% band ignores tiny drift.
Inputs: 4 lengths (default 3/6/12/24). Threshold is auto-scaled at 0.2% of BBI.
6) MTM (Momentum) — Rate of change in price
What it is: A simple measure of rate of change
Logic used: MTM = Close − Close
Bullish: MTM > 0.5% of Close
Bearish: MTM < −0.5% of Close
Neutral: |MTM| ≤ threshold
Why: A percent-based gate adapts across prices (e.g., $5 vs $500) and mutes insignificant moves.
Inputs: Length. Threshold auto-scaled to 0.5% of current Close.
Display & Inputs You Can Tweak
🎨 Use Neutral (Gray) Colors
ON (default): 3-color mode with clear “no-trade”/“weak” states.
OFF: classic binary (green/red) without neutral filtering.
Linton Price Targets(R)Linton Price Targets
A groundbreaking new way of projecting price targets and when they will be met in the future.
Point and figure charts have largely fallen out of favour in recent decades with the birth of personal computing and electronic data services. Few software systems calculate them correctly, and the technique is seen as outdated and difficult for the newcomer to technical analysis to understand. Linton Price Targets takes the point and figure methodology for producing vertical count targets and applies them to time-based charts that are much more widely used for technical analysis.
To place Point and figure price targets on a time-based chart, we first need to relate the conditions that produce the vertical count targets. Vertical Targets are only generated with uninterrupted moves off a high or a low point in prices. A pullback of at least 3 boxes locks the thrust column and therefore the price target. A move of at least one box above (in the case of an upside target off a low) or one box below (downside off a high) ‘activates’ the price target. Here the buyers and sellers respectively are confirmed. Conversely a move below the base of an upside target column, or above the top of a downside column ‘negates’ the vertical target. In this case, the buyers and sellers have been superseded by subsequent events.
Projecting Price
The price projection following the point and figure 3-Box method is relatively straightforward. The standard projection used is twice the original move from the top of the initial thrust level. This derives from the 3-Box construction devised by Cohen, whereby the initial thrust count is a third of the overall price count projection. But there is no reason to limit the Target Price Factor to the value to 2. A value of 1 could be used in the case of consolidation patten where the move out of the pattern is roughly equivalent to the move into the pattern. A value of 1.618 could be used for Fibonacci Retracements or Extensions or a value of 2 x log, can be used to deal with increasing box (unit) sizes as price changes.
Projecting Time
Projecting a potential price target with is relatively straight forward. Determining a time in the future when such a price target will be met is more of a challenge. This has been seen as one of the major drawbacks of point and figure charts for decades. Because there is no time axis on a Point and figure chart, there is no saying when a count projection target will be met.
For the Time to Target, we need to consider potential methodologies such as:
1. Price to Time Ratio – t units of price for every x units of time – ie $1 every 2 days
2. Thrust Angle Factor – a factor x the initial trust angle for the target angle
3. Time to Activation Factor – time to target is x the time taken for a target to activate
4. Follow the Price – track prices as the progress to target and adjust time to target accordingly
5. Historical Average Slope – historical average price time average for last n targets
Considering the Price to Time Ratio method, Chart 1 below shows a chart of the price targets for the US stock Applied Materials with a Unit size of $1. The targets are projected Log Scale 2x the initial thrust. From this chart we see that the target prices are reached later than the projection predicted. This means that we need to consider a lesser slope. Chart 2 below shows the same chart with the slope now adjusted to $1 every three days. This chart shows that recent targets for Applied Materials have been approximately met with this slope. Therefore, this is a better slope to use in this instance.
Chart 1 - Applied Materials (unit size $1) - target projection slope $1 every 2 days
Chart 2 - Applied Materials (unit size $1) - target projection slope $1 every 3 days
Chart 3 - Applied Materials (unit size $1) - target projection slope 1/2 initial thrust slope
The second method of projecting price targets assumes the time that a price target will be reached is directly related to the speed of the initial thrust, which generates the target. Chart 3 shows the same security as in the previous examples but using this method with an angle of slope which is half the initial thrust angle. The factor can also be altered with this method to best fit the data. In the previous examples (Charts 1 & 2) we see the slope of each of the targets is constant. Using the Thrust Angle Factor method, different buying and selling thrust angles produces different target slopes.
A third possible projection method assumes that the longer a price target takes to activate, the longer it takes for a target to be reached. The argument goes that the pullback from the initial thrust is more of a consolidation phase rather than a sharp reaction and therefore, the potential overall move will take longer. Chart 4 shows this method. Again, we see that, due to the varying times of price targets to activate, the slopes of the targets are not uniform as in Method 1 which uses a consistent price to time slope.
Chart 4: Applied Materials (unit size $1) – target projection x times the time taken for target to activate.
Chart 5: Applied Materials (unit size $1) – target projection readjusts with new price information
A fourth method for predicting when in the future that a price target might be met adjusts the slope of the targets from the activation point as new price information arrives. With multiple targets activated at different points on the chart, this method also produces price targets of different slopes. Because targets are readjusted with every new price, it is best to set this method to ignore the last x bars in order to spot any divergence from the targets. Chart 5 shows this methodology.
Chart 6 shows a method where the average slope of price over time is taken for the previous n targets that are achieved and used as the slope for projecting targets into the future. While the slopes for upward and downward targets can be separately adjusted with the previous methods mentioned, this method automatically calculates the different slope speeds of upside and downside targets.
Chart 6: Applied Materials (unit size $1) – target projection based on the average slope of the last x targets.
Multiple Price Targets
As with Point and figure count targets, multiple price targets point to the same price or price level increases the likelihood of price targets being met. This is known as ‘clustering’. Now with the ability to project price targets to a future date on a chart, it is not only possible to see clustering of the price of multiple targets, but also clustering of times targets may be met. This can lead to a ‘cluster zone’, an area of price and time in the future that multiple targets may be met. Chart 7 shows an example of this.
Chart 7: Applied Materials (unit size $1) – target zone of future price and time of multiple targets
Achievement and Non-Achievement of Price Targets and Prevailing Trend
Point and figure targets are approximate and are more often than not, not met precisely. They are regularly not achieved or exceeded, but this provides valuable information in itself. Upside price targets that are achieved or exceeded shows bullish confirmation, whereas these targets not being achieved indicates a degree of bearishness. Conversely, downside price targets achieved or exceeded is bearish confirmation and such targets not achieved is an indication of inherent bullishness.
Unsurprisingly, price targets are normally achieved or exceeded in line with the prevailing trend. Upside price targets should be given more weight in uptrends, while downside ones may only serve as a temporary moment for caution, because they are counter-trend. Downside Targets will carry more weight in downtrends. It is also often the case that the last target in line with the prevailing trend is never met as the trend changes and a new set of targets in the opposite direction are generated with the new reversal of trend. Active price targets in both directions are often an early sign of this. This is particularly true with multiple targets in the new trend direction verses one lone target in the previous trend direction. This lone target is likely to be negated, clearly signalling the new trend direction is taking hold.
Activation and Negation of Price Targets
An upside price target is only activated when prices rise a further than a full price unit above the top of the initial uninterrupted buying thrust in prices from a low. A low is defined by a price level at least one full price unit below a previous recent low. The pullback downwards of at least three price units ‘locks’ the initial thrust that generates the upside price target. Here the bulls buying from the bottom have been confirmed.
A downside price target is only activated when prices fall further than a full price unit below the bottom of the initial uninterrupted selling thrust in prices from a high. A high is defined by a price level at least one full price unit above a previous recent high. The pullback upwards of at least three price units ‘locks’ the initial thrust that generates the downside price target. Here the bears selling from the top have been confirmed.
A target is valid once the column is locked with the pullback of at least three units, but it should not be considered as active until the price breaks through the activation level. An unactivated target serves as advance notice that a target is in place and will become active once the activation price level is broken.
An upside price target is negated if prices fall below the bottom of the initial uninterrupted buying thrust in prices. In this instance the bulls have been beaten by the bears. Conversely, a downside price target is negated if prices rise above the top of the initial uninterrupted Selling thrust in prices. Here the bears selling from the top have been beaten by the bulls.
It is important to note the difference between a target that is activated first and then negated and a target that was never activated and negated first. Research shows that normally more than half of all negated targets were never activated and wouldn’t have been taken. Taking the prevailing trend into account further reduces the number of negated targets that would have been taken at the activation point.
Evaluating a Target as Price Progress
Because Linton Price targets can be evaluated with subsequent new price information with the passage of time, it becomes possible to see more easily, than on a point and figure chart, when a target might be failing. The ideas of activation, negation, and achievement of price targets are understood in point and figure charting and apply similarly here to time-based charts. But the ability to now see prices diverging from the target path presents us with some potential new states of a target. In the case of an upside target, if prices fall away or wander sideways from a target path this alerts us to the fact that the prices on their way to the target may be ‘exhausting’. If we fall or wander back below the target activation level, this implies the previous resistance level off the thrust high has not managed to become a new support level for the price. Consequently, we may consider that the target has been ‘de-activated’. If we fall further below the low of the pullback low point, this previous support level also failed to hold and this is providing us with an early warning that the target is quite possibly ‘failing.’ If prices are moving towards the target as expected, we can say the target is ‘in train.’ This is particularly appropriate for multiple targets that run parallel using the first price/time slope prediction method where the targets look like ‘train tracks.’
Improbable Targets
Occasionally an improbable target a long way from the price will be generated. This is particularly true using a log scale projection. Beware of a target that points to a very large change in price. This is especially true of a lone target. It is also quite likely that the unit size has been set too small where a bigger unit size may not produce a target at all.
Longer term charts
Point and figure charts have always meant to be constructed with tick data. The point and figure methodology reduces this down to just the ticks that create a new box on the chart. Long tick data price histories are typically expensive and hard to come by. This can also be an overwhelming amount data to store and analyse, particularly in the case of very liquid instruments such as a major currency pair. For intraday charts, one minute data will normally suffice. But these histories may not be long enough either and it may be necessary to use a 60-minute chart.
It is also possible to construct point and figure charts using high/low data or even open-high-low-close data making some assumptions based on a rising or falling candle, on which came first, the high or the low. The targets will be impacted accordingly.
When it comes to longer term charts such as weekly or monthly charts it is unlikely that these time frames would be used for point and figure charts. The construction method already filters the data. But when it comes to long-term time based charts it becomes necessary to look at weekly or monthly data.
You will also see that long term price upside targets are generated that are not on the daily chart. This is because daily the movements will not provide the same uninterrupted buying thrusts as with the monthly data. The daily pullbacks are effectively ignored when using monthly data. The other advantage is the unit size is now months so we can say that the target slope equates to 1% of price every month for a 1 to 1 slope for example. Using weekly or monthly data to construct the price targets is a significant departure from the traditional point and figure charting method.
Time-Based Charts Are Easier to Understand Than Point and Figure Charts
In recent years, the vast majority of people carrying out technical analysis of charts do not use the point and figure charts. This is partly because very few software systems draw them correctly and do not calculate the price targets. Newcomers to technical analysis find point and figure charts hard to understand.
Combining With Other Techniques
Using point and figure charts has also often meant the need to switch between different chart types for the same instrument. Time-based charts allow for a vast set of technical analysis time-series based techniques to be married with Linton Price Targets. Having different sets of analysis on the same chart can increase the power of the analysis without having to swap between different chart types.
Linton Price Targets builds on the technical analysis body of knowledge developed over the past 100 years by bringing an old, largely lost, technique into the modern age.
The main advantages of Linton Price Targets are:
• The ability to have price targets on time-based charts.
• It is now possible to ascertain when in the future a price target may be met.
• With the passage of time, it becomes clearer if a target track is being followed.
• The targets can be applied to longer-term time-based charts.
• Time-series based analysis techniques can be used on the same chart as the targets.
• The targets are much easier to understand for the newcomer to technical analysis.
BVB dominance bars
Hello everyone, this is my first indicator. these candles shows you who's in control. I like to think its some what close to heikin ashi candles as it shows you the Trend but doesn't average it out. also shows you when there is indecision. please read the instructions on how it works. its not a stand alone strategy. but adds value to your own strategy.
📖 How It Works
The BvB Dominance Bars indicator is a visual tool that colors candles based on market control—whether bulls or bears are in charge. It uses a custom metric comparing the price's relationship to a smoothed moving average (EMA), then normalizes that difference over time to express relative bullish or bearish pressure.
Here’s the breakdown:
Bulls vs Bears Logic:
A short-term EMA (default: 14-period) is used to establish a midpoint reference.
Bull Pressure is calculated as how far the high is above this EMA.
Bear Pressure is how far the low is below this EMA.
These are normalized over a lookback period (default: 120 bars) to produce percentile scores (0–100) for both bulls and bears.
Dominance & Color Coding:
The indicator compares normalized bull and bear scores.
Candles are color-coded based on:
Bright Lime: Strong Bull Dominance (with high confidence)
Soft Lime/Yellow: Moderate Bull Control
Bright Red: Strong Bear Dominance
Soft Red/Yellow: Moderate Bear Control
Gray: Neutral/Low conviction
Optional Live Label:
A small floating label shows who has control: “Bull Control,” “Bear Control,” or “Neutral.”
🧠 How to Use It (Example Strategy)
The BvB Dominance Bars indicator is not a standalone buy/sell signal but a market sentiment overlay. It’s most effective when combined with your own strategy, like price action or trend-following tools.
Here’s an example use case:
🧪 Reversal Confirmation Strategy
Objective: Catch high-probability reversals during key kill zones or supply/demand levels.
Setup:
Mark your key support/resistance zones using your standard method (e.g., FVGs, liquidity sweeps, or ICT PD arrays).
Wait for price to reach one of these zones.
Watch candle colors from the BvB Dominance Bars:
If you expect a bullish reversal, wait for a transition from red/gray candles to lime green or bright lime (bullish dominance taking over).
If you expect a bearish reversal, look for a change from green/gray to red or bright red.
Entry Filter:
Only enter if the dominant color holds for 2+ candles.
Avoid trades when candles are gray or yellow (indecision/neutral).
Exit Option:
Exit if dominance shifts against you (e.g., from lime to red), or use structure-based stops.
⚙️ Settings You Can Adjust:
BvB Period: Controls how fast EMA responds.
Bars Back: Determines how long the normalization looks back.
Thresholds: Influence how strong the dominance must be to change candle color.
✅ Best Used When:
You already have a bias and just want a confirmation of sentiment.
You're trading intraday and want a feel for shifting momentum without relying on noisy indicators.
You want a clean, color-coded overlay to help filter out fakeouts and indecision.
Multiple (12) Strong Buy/Sell Signals + Momentum
Indicator Manual: "Multiple (12) Strong Buy/Sell Signals + Momentum"
This indicator is designed to identify strong buy and sell signals based on 12 configurable conditions, which include a variety of technical analysis methods such as trend-following indicators, pattern recognition, volume analysis, and momentum oscillators. It allows for customizable alerts and visual cues on the chart. The indicator helps traders spot potential entry and exit points by displaying buy and sell signals based on the selected conditions.
Key Observations:
• The script integrates multiple indicators and pattern recognition methods to provide comprehensive buy/sell signals.
• Trend-based indicators like EMAs and MACD are combined with pattern recognition (flags, triangles) and momentum-based signals (RSI, ADX, and volume analysis).
• User customization is a core feature, allowing adjustments to the conditions and thresholds for more tailored signals.
• The script is designed to be responsive to market conditions, with multiple conditions filtering out noise to generate reliable signals.
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Key Features:
1. 12 Combined Buy/Sell Signal Conditions: This indicator incorporates a diverse set of conditions based on trend analysis, momentum, and price patterns.
2. Minimum Conditions Input: You can adjust the threshold of conditions that need to be met for the buy/sell signals to appear.
3. Alert Customization: Set alert thresholds for both buy and sell signals.
4. Dynamic Visualization: Buy and sell signals are shown as triangles on the chart, with momentum signals highlighted as circles.
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Detailed Description of the 12 Conditions:
1. Exponential Moving Averages (EMA):
o Conditions: The indicator uses EMAs with periods 3, 8, and 13 for quick trend-following signals.
o Bullish Signal: EMA3 > EMA8 > EMA13 (Bullish stack).
o Bearish Signal: EMA3 < EMA8 < EMA13 (Bearish stack).
o Reversal Signal: The crossing over or under of these EMAs can signify trend reversals.
2. MACD (Moving Average Convergence Divergence):
o Fast MACD (2, 7, 3) is used to confirm trends quickly.
o Bullish Signal: When the MACD line crosses above the signal line.
o Bearish Signal: When the MACD line crosses below the signal line.
3. Donchian Channel:
o Tracks the highest high and lowest low over a given period (default 20).
o Breakout Signal: Price breaking above the upper band is bullish; breaking below the lower band is bearish.
4. VWAP (Volume-Weighted Average Price):
o Above VWAP: Bullish condition (price above VWAP).
o Below VWAP: Bearish condition (price below VWAP).
5. EMA Stacking & Reversal:
o Tracks the order of EMAs (3, 8, 13) to confirm strong trends and reversals.
o Bullish Reversal: EMA3 < EMA8 < EMA13 followed by a crossing to bullish.
o Bearish Reversal: EMA3 > EMA8 > EMA13 followed by a crossing to bearish.
6. Bull/Bear Flags:
o Bull Flag: Characterized by a strong price movement (flagpole) followed by a pullback and breakout.
o Bear Flag: Similar to Bull Flag but in the opposite direction.
7. Triangle Patterns (Ascending and Descending):
o Detects ascending and descending triangles using pivot highs and lows.
o Ascending Triangle: Higher lows and flat resistance.
o Descending Triangle: Lower highs and flat support.
8. Volume Sensitivity:
o Identifies price moves with significant volume increases.
o High Volume: When current volume is significantly above the moving average volume (set to 1.2x of the average).
9. Momentum Indicators:
o RSI (Relative Strength Index): Confirms overbought and oversold levels with thresholds set at 65 (overbought) and 35 (oversold).
o ADX (Average Directional Index): Confirms strong trends when ADX > 28.
o Momentum Up: Momentum is upward with strong volume and bullish RSI/ADX conditions.
o Momentum Down: Momentum is downward with strong volume and bearish RSI/ADX conditions.
10. Bollinger & Keltner Squeeze:
o Squeeze Condition: A contraction in both Bollinger Bands and Keltner Channels indicates low volatility, signaling a potential breakout.
o Squeeze Breakout: Price breaking above or below the squeeze bands.
11. 3 Consecutive Candles Condition:
o Bullish: Price rises for three consecutive candles with higher highs and lows.
o Bearish: Price falls for three consecutive candles with lower highs and lows.
12. Williams %R and Stochastic RSI:
o Williams %R: A momentum oscillator with signals when the line crosses certain levels.
o Stochastic RSI: Provides overbought/oversold levels with smoother signals.
o Combined Signals: You can choose whether to require both WPR and StochRSI to signal a buy/sell.
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User Inputs (Inputs Tab):
1. Minimum Conditions for Buy/Sell:
o min_conditions: Number of conditions required to trigger a buy/sell signal on the chart (1 to 12).
o Alert_min_conditions: User-defined alert threshold (how many conditions must be met before an alert is triggered).
2. Donchian Channel Settings:
o Show Donchian: Toggle visibility of the Donchian channel.
o Donchian Length: The length of the Donchian Channel (default 20).
3. Bull/Bear Flag Settings:
o Bull Flag Flagpole Strength: ATR multiplier to define the strength of the flagpole.
o Bull Flag Pullback Length: Length of pullback for the bull flag pattern.
o Bull Flag EMA Length: EMA length used to confirm trend during bull flag pattern.
Similar settings exist for Bear Flag patterns.
4. Momentum Indicators:
o RSI Length: Period for calculating the RSI (default 9).
o RSI Overbought: Overbought threshold for the RSI (default 65).
o RSI Oversold: Oversold threshold for the RSI (default 35).
5. Bollinger/Keltner Squeeze Settings:
o Squeeze Width Threshold: The maximum width of the Bollinger and Keltner Bands for squeeze conditions.
6. Stochastic RSI Settings:
o Stochastic RSI Length: The period for calculating the Stochastic RSI.
7. WPR Settings:
o WPR Length: Period for calculating Williams %R (default 14).
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User Inputs (Style Tab):
1. Signal Plotting:
o Control the display and colors of the buy/sell signals, momentum indicators, and pattern signals on the chart.
o Buy/Sell Signals: Can be customized with different colors and shapes (triangle up for buys, triangle down for sells).
o Momentum Signals: Custom circle placement for momentum-up or momentum-down signals.
2. Donchian Channel:
o Show Donchian: Toggle visibility of the Donchian upper, lower, and middle bands.
o Band Colors: Choose the color for each band (upper, lower, middle).
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How to Use the Indicator:
1. Adjust Minimum Conditions: Set the minimum number of conditions that must be met for a signal to appear. For example, set it to 5 if you want only stronger signals.
2. Set Alert Threshold: Define the number of conditions needed to trigger an alert. This can be different from the minimum conditions for visual signals.
3. Customize Appearance: Modify the colors and styles of the signals to match your preferences.
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Conclusion:
This comprehensive trading indicator uses a combination of trend-following, pattern recognition, and momentum-based conditions to help you spot potential buy and sell opportunities. By adjusting the input settings, you can fine-tune it to match your specific trading strategy, making it a versatile tool for different market conditions.
Signal Reliability Based on Condition Count
The reliability of the buy/sell signals increases as more conditions are met. Here's a breakdown of the probabilities:
1. 1-3 Conditions Met: Lower Probability
o Signals that meet only 1-3 conditions tend to have lower reliability and are considered less probable. These signals may represent false positives or weaker market movements, and traders should approach them with caution.
2. 4 Conditions Met: More Reliable Signal
o When 4 conditions are met, the signal becomes more reliable. This indicates that multiple indicators or market patterns are aligning, increasing the likelihood of a valid buy/sell opportunity. While not foolproof, it's a stronger indication that the market may be moving in a particular direction.
3. 5-6 Conditions Met: Strong Signal
o A signal meeting 5-6 conditions is considered a strong signal. This indicates a well-confirmed move, with several technical indicators and market factors aligning to suggest a higher probability of success. These are the signals that traders often prioritize.
4. 7+ Conditions Met: Rare and High-Confidence Signal
o Signals that meet 7 or more conditions are rare and should be considered high-confidence signals. These represent a significant alignment of multiple factors, and while they are less frequent, they are highly reliable when they do occur. Traders can be more confident in acting on these signals, but they should still monitor market conditions for confirmation.
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You can adjust the number of conditions as needed, but this breakdown should give a clear structure on how the signal strength correlates with the number of conditions met!
Uptrick: Oscillator SpectrumUptrick: Oscillator Spectrum is a versatile trading tool designed to bring together multiple aspects of technical analysis—oscillators, momentum signals, divergence checks, correlation insights, and more—into one script. It includes customizable overlays and alert conditions intended to address a wide range of market conditions and trading styles.
Developed in Pine Script™, Uptrick: Oscillator Spectrum represents an extended version of the classic Ultimate Oscillator concept. It consolidates short-, medium-, and long-term momentum readings, applies correlation analysis across different symbols, and offers optional table-based metrics to provide traders with a more structured overview of potential trade setups. Whether used alongside your existing charts or as a standalone toolkit, it aims to build on and enhance the functionality of the standard Ultimate Oscillator.
### A Few Key Features
- Momentum Insights: Multiple timeframes for oscillators, plus buy/sell signal modes for flexible identification of overbought/oversold situations or crossovers.
- Divergence Detection: Automated checks for bullish/bearish divergences, aiming to help traders spot potential shifts in momentum.
- Correlation Meter: A visual histogram summarizing how selected assets are collectively trending. It is useful for tracking the bigger market picture.
- Gradient Overlays & Bar Coloring: Dynamic color transitions designed to emphasize changes in momentum, trend shifts, and overall sentiment without cluttering the chart.
- Money Flow Tracker: Tracks the flow of money into and out of the market using a smoothed Money Flow Index (MFI). Highlights overbought/oversold conditions with dynamic bar coloring and visual gradient fills, helping traders assess volume-driven sentiment shifts.
- Advanced Table Metrics: An optional table showing return on investment (ROI), collateral risk, and other contextual metrics for supported assets.
- Alerts & Automation: Configurable alerts covering divergence events, crossing of critical levels, and more, helping to keep traders informed of developments in real time.
### Intended Usage
- For Multiple Markets: Works on various markets (cryptocurrencies, forex pairs, stocks) to deliver a consistent view of momentum, potential entry/exit signals, and correlation.
- Adaptable Trading Styles: With customizable input settings, you can enable or disable specific features to align with your preferred strategies—intraday scalping, swing trading, or position holding.
By combining these elements under one indicator, Uptrick: Oscillator Spectrum allows traders to streamline analysis workflows, helping them stay focused on interpreting market moves and making informed decisions rather than juggling multiple scripts.
Purpose
Purpose of the “Uptrick: Oscillator Spectrum” Indicator
The “Uptrick: Oscillator Spectrum” indicator is intended to bring together several technical analysis elements into one tool. It combines oscillator-based momentum readings across different lookback periods, checks for potential divergences, provides optional buy/sell signal triggers, and offers correlation-based insights across multiple symbols. Additionally, it includes features such as bar coloring, gradient visualization, and user-configurable alerts to help highlight various market conditions.
By consolidating these functions, the script aims to help users systematically observe changing momentum, identify when prices reach user-defined overbought or oversold levels, detect when oscillator movements diverge from price, and examine whether different assets are aligning or diverging in their trends. The indicator also allows for optional advanced metric tables, which can supply further context on risk, ROI calculations, or other factors for supported assets. Overall, the script’s purpose is to organize multiple layers of technical analysis so that users have a structured way to evaluate potential trade opportunities and market behavior.
## Usage Guide
Below is an outline of how you can utilize the various components and features of Uptrick: Oscillator Spectrum in your charting workflow.
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### 1. Using the Core Oscillator
- Basic View: By default, the script calculates a multi-timeframe oscillator (commonly displayed as the “Ultimate Oscillator”). This oscillator combines short-, medium-, and long-term measurements of buying pressure and true range.
- Overbought/Oversold Zones: You can configure thresholds (e.g., 70 for overbought, 30 for oversold) to help identify potential turning points. When the oscillator crosses these levels, it may indicate that price is extended in one direction.
- You can use the colors of the main oscillator to help you take short-term trades as well: cyan : Buy , red: Sell
- Alerts: If you enable alerts, the indicator can notify you when the oscillator crosses above or below your chosen overbought/oversold boundaries or when you get buy/sell signals.
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### 2. Buy/Sell Signals in Overlay Modes
Uptrick: Oscillator Spectrum provides several signal modes and a choice between overlay true and overlay false or both. Additionally, you can pick which “line” (data source) the script uses to generate signals. This is set in the “Line to Analyze” dropdown, which includes Oscillator, HMA of Oscillator, and Moving Average. The following sections describe how each piece fits together.
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#### Line to Analyze - Overlay Flase: Oscillator / HMA of Oscillator / Moving Average
1. Oscillator
- The core momentum reading, reflecting short-, medium-, and long-term periods combined.
2. HMA of Oscillator
- Applies a Hull Moving Average to the oscillator, creating a smoother but still responsive curve.
- Signals will be derived from this smoothed line. Some traders find it filters out minor fluctuations while remaining quicker to react than standard averages.
3. Moving Average
- Uses a user-selected MA type (SMA, EMA, WMA, etc.) over the oscillator values, rather than the raw oscillator itself.
- Tends to be more stable than the raw oscillator, but might delay signals more depending on the chosen MA settings.
---
#### Signal Modes
Regardless of which line you choose to analyze, you can use one of the following seven signal modes in overlay being true:
1. Overbought/Oversold (Pyramiding)
- What It Does:
- Buy signal when the chosen line crosses below the oversold threshold.
- Sell signal when it crosses above the overbought threshold.
- Pyramiding:
- Allows multiple triggers within the same overbought/oversold event.
2. Overbought/Oversold (Non Pyramiding)
- What It Does:
- Same thresholds but only one signal per oversold or overbought event.
- Use Case:
- Prevents repeated signals and chart clutter.
3. Smoothed MA Middle Crossover
- What It Does:
- Uses an MA defined by the user.
- Buy when crossing above the midpoint (50), Sell when crossing below.
- Use Case:
- Generates fewer signals, focusing on broader momentum shifts. There is no pyramiding.
In this image ,for example, the VWMA is used with length of 14 to identify buy sell signals.
4. Crossing Above Overbought/Below Oversold (Non Pyramiding)
- What It Does:
- Buy occurs if the line exits oversold territory by crossing back above it.
- Sell occurs if the line exits overbought territory by crossing back below it.
- Non Pyramiding:
- Restricts repeated signals until conditions reset.
5. Crossing Above Overbought/Below Oversold (Pyramiding)
- What It Does:
- Same thresholds, but allows multiple signals if the line repeatedly dips in and out of overbought or oversold.
- Use Case:
- More frequent entries/exits for active traders.
6. Divergence (Non Pyramiding)
- What It Does:
- Identifies bullish or bearish divergences using the chosen line vs. price.
- Buy for bullish divergence (higher low on the line vs. lower low on price), Sell for bearish divergence.
- Single Trigger:
- Only one signal per identified divergence event. (non pyramiding)
7. Divergence (Pyramiding)
- What It Does:
- Same divergence logic but triggers multiple times if the script sees repeated divergence in the same direction.
- Use Case:
- Could suit traders who layer positions during sustained divergence scenarios.
#### Overlay Modes: True vs. False
1. Overlay True
- Buy/sell arrows or labels plot directly on the main price chart, often at or near candlesticks.
- Bar Coloring:
- Can turn the candlestick bars green (buy) or red (sell), with intensity reflecting signal recency if bar coloring is enabled for this mode. (read below.)
- Advantage:
- Everything (price, signals, bar colors) is in one spot, making it straightforward to associate signals with current market action. You can adjust the periods of the main oscillator or lookback periods of divergences or overbought/oversold thresholds, to play around with your signals.
2. Overlay False
- Signal Placement:
- Signals appear in a sub-window or oscillator panel, leaving the main price chart uncluttered.
- Bar Coloring:
- You may still enable bar colors on the main chart (green for buy, red for sell) if desired.
- Alternatively, you can keep them neutral if you prefer a completely separate display of signals.
- Advantage:
- Clear separation of price action from signals, useful for cleaner charts or if using multiple overlay-based tools.
At the bottom are the signals for overlay being false and on the chart are the signals for overlay being true:
#### Bar Color Adjustments
1. Coloring Logic
- Bars typically go green on buy signals, red on sell signals.
- The opacity or brightness can vary to indicate signal freshness. When a new signal is formed, the color gets brighter. When there is no signal for a longer period of time, then the color slowly fades.
2. Enabling Bar Coloring
- In the indicator’s settings, turn on Bar Coloring.
- Choose “Signals Overlay True” or “Signals Overlay False” from the “Color should depend on:” dropdown, depending on which overlay approach you want to drive your bar colors. You can also chose the cloud fill in overlay false, correlation meter and smoothed HMA to color bars. Read more below:
### Bar Color Options:
When you enable bar coloring in Uptrick: Oscillator Spectrum, you can select which component or signal logic drives the color changes. Below are the five available choices:
---
#### Option 1: Overlay True Signals
- What It Does:
- Uses signals generated under the Overlay True mode to color the bars on your main chart.
- If a buy signal is triggered, bars turn green. If a sell signal occurs, bars turn red.
- Color Intensity:
- Bars appear brighter (more opaque) immediately after a new signal fires, then gradually fade over subsequent bars if no new signal appears.
---
#### Option 2: Overlay False Signals
- What It Does:
- Links bar coloring to signals generated when Overlay False mode is active.
- Buy/sell labels typically plot in a separate sub-window instead of the main chart, but your price bars can still change color based on these signals.
- Color Intensity:
- Similar to Overlay True, new buy/sell signals yield stronger color intensity, which fades over time.
- Use Case:
- Helps maintain a clean main chart (with signals off-chart) while still providing an immediate color-coded indication of a buy or sell state.
- Particularly useful if you prefer less clutter from signal markers on your price chart yet still want a visual representation of signal timing.
In this example normal divergence Pyramiding Signals are used in the overlay being true and the signals in overlay false are signals that analyze the HMA. This can help clear out noise (using a combo of both).
Option 3: Money Flow Tracker
What It Does:
The Money Flow Tracker uses the Money Flow Index (MFI), a volume-weighted oscillator, to measure the strength of money flowing into or out of an asset. The script smooths the raw MFI data using an EMA for a more responsive and visually intuitive output.
The feature also includes dynamic color gradients and bar coloring that highlight whether money flow is positive or negative.
Green Fill/Bar Color: Indicates positive money flow, suggesting potential accumulation.
Red Fill/Bar Color: Indicates negative money flow, signaling potential distribution.
Overbought and oversold thresholds are dynamically emphasized with transparency, making it easier to identify high-confidence zones.
Use Case:
Ideal for traders focusing on volume-driven sentiment to identify turning points or confirm existing trends.
Suitable for assessing broader market conditions when used alongside other indicators like oscillators or correlation analysis.
Provides additional clarity in spotting areas of accumulation or distribution, making it a valuable complement to price action and momentum studies.
---
#### Option 4: Correlation Meter
- What It Does:
- Colors the bars based on the indicator’s Correlation Meter output. The script checks multiple chosen tickers and sums up how many are trending positively or negatively.
- If the meter indicates an overall bullish bias (e.g., more than three assets in uptrend), bars turn green; if it’s bearish, bars turn red.
- Trend Readings:
- The correlation meter typically plots a histogram of bullish/neutral/bearish states. The bar color option links your chart’s candlestick coloring to that higher-level market sentiment.
- Use Case:
- Useful for traders wanting a quick visual prompt of whether the broader market (or a selection of related assets) is bullish or bearish at any given time.
- Helps avoid signals that conflict with the market majority.
#### Option 5: Smoothed HMA
- What It Does:
- Bar colors are driven by the slope or state of the Hull Moving Average (HMA) of the oscillator, rather than individual buy/sell triggers or correlation data.
- If the HMA indicates a strong upward slope (possibly darkening), bars may turn green; if the slope is downward (purple in the HMA line), bars turn red.
- Use Case:
- Ideal for those who focus on momentum continuity rather than discrete signals like overbought/oversold or divergence.
- May help identify smoother, more sustained moves, as the HMA filters out minor oscillations.
---
### 3. Using the Hull Moving Average (HMA) of the Oscillator
- HMA Calculation: You can enable a dedicated Hull Moving Average (HMA) for the oscillator. This creates a smoother line of the same underlying momentum reading, typically responding more quickly than classic moving averages.
- Color Intensity: As the HMA sustains an uptrend or downtrend, the script can adjust the line’s color. When slope momentum persists in one direction, the color appears more opaque. This intensification can hint that the existing direction may be well-established.
- Reversal Potential: If you observe the HMA color shifting or darkening after multiple bars of slope in the same direction, it may indicate increasing momentum. Conversely, a sudden flattening or change in color can be a clue that momentum is waning.
---
### 4. Moving Average Overlays & Gradient Cloud
- Oscillator MA: The script allows you to apply moving average types (SMA, EMA, SMMA, WMA, or VWMA) to the core oscillator, rather than to price. This can smooth out noise in the oscillator, potentially highlighting more consistent momentum shifts.
- Gradient Cloud: You can also enable a cloud in overlay true between two moving averages (for instance, a Hull MA and a Double EMA) on the price chart. The cloud fills with different colors, depending on which MA is above the other. This can provide a quick visual reference to bullish or bearish areas.
---
### 5. Divergence Detection
- Bullish & Bearish Divergence: By toggling “Calculate Divergence,” the script looks for oscillator pivots that contrast with price pivots (e.g., price making a lower low while the oscillator makes a higher low).
- A divergence is when the price makes an opposite pivot to the indicator value. E.g. Price makes lower low but indicator does higher low - This suggests a bullish divergence. THe opposite is for a bearish divergence.
- Visual Labels: When a divergence is found, labels (such as “Bull” or “Bear”) appear on the oscillator. This helps you see if the oscillator’s momentum patterns differ from the price movement.
- Filtering Signals: You can combine divergence signals with other features like overbought/oversold or the HMA slope to refine potential entries or exits.
---
### 6. Correlation & Multi-Ticker Analysis
- Correlation Meter: You can select up to five tickers in the settings. The script calculates a slope-based metric for each, then combines those metrics to show an overall bullish or bearish tendency (displayed as a histogram).
- Bar Coloring & Overlay: If you activate correlation-based bar coloring, it will reflect the broader trend alignment among the selected assets, potentially indicating when most are trending in the same direction.
- Use Case: If you trade multiple markets, the correlation histogram can help you quickly see if several major assets support the same market bias or are diverging from one another.
—
### 7. Money Flow Tracker
Money Flow Calculation: The Money Flow Tracker calculates the Money Flow Index (MFI) based on price and volume data, factoring in buying pressure and selling pressure. The output is smoothed using a low-lag EMA to reduce noise and enhance usability.
Visual Features:
Dynamic Gradient Fill:
The space between the smoothed MFI line and the midline (set at 50) is filled with a gradient.
Above 50: Green gradient, with intensity increasing as the MFI moves further above the midline.
Below 50: Red gradient, with intensity increasing as the MFI moves further below the midline.
This gradient provides a clear visual representation of money flow strength and direction, making it easier to assess sentiment shifts at a glance.
Overbought/Oversold Levels: Default thresholds are set at 70 (overbought) and 30 (oversold). When the MFI crosses these levels, it signals potential reversals or trend continuations.
Bar Coloring:
Bars turn green for positive money flow and red for negative money flow.
Color intensity fades over time, ensuring recent signals stand out while older ones remain visible without dominating the chart.
Alerts:
Alerts are triggered when the Money Flow Tracker crosses into overbought or oversold zones, keeping traders informed of critical conditions without constant monitoring.
Practical Applications:
Trend Confirmation: Use the Money Flow Tracker alongside the oscillator or HMA to confirm trends or identify potential reversals.
Volume-Based Reversal Signals: Spot turning points where price action aligns with shifts in money flow direction.
Sentiment Analysis: Gauge whether market participants are accumulating (positive flow) or distributing (negative flow) assets, offering an additional layer of insight into price movement.
(Space for an example chart: “Money Flow Tracker with gradient fills and overbought/oversold levels”)
### 8. Putting It All Together
- Combining Signals: A practical approach might be to watch for a bullish divergence in the oscillator, confirm it with a shift in the HMA slope color, and then wait for the price to be near or below oversold conditions. The correlation histogram may further confirm if the broader market is also leaning bullish at that time.
- Visual Cues: Bar coloring adds another layer, making your chart easier to interpret at a glance. You can also set alerts to ensure you don’t miss key events like divergences, crossovers, or moving average flips.
- Flexibility: Not every feature needs to be used simultaneously. You might opt to focus on divergences and overbought/oversold signals, or you could emphasize the correlation histogram and bar colors. The settings let you enable or disable each module to suit your style.
---
### 9. Tips for Customization
- Adjust Periods: Shorter periods can yield more signals but also more noise. Longer periods may provide steadier, but fewer, signals.
- Set Appropriate Alert Conditions: Only alert on events most relevant to your strategy to avoid overload.
- Explore Different MAs: Depending on the instrument, some moving average types may give a smoother or more responsive indication.
- Monitor Risk Management: As with any tool, these signals do not guarantee performance, so consider position sizing and stop-loss strategies.
---
By toggling and experimenting with the features described above—buy/sell signals, divergences, moving averages, dynamic gradient clouds, and correlation analysis—you can tailor Uptrick: Oscillator Spectrum to your specific trading approach. Each module is designed to give you a clearer, structured view of potential momentum shifts, overbought or oversold states, and the alignment or divergence of multiple assets.
## Features Explanation
Below is a detailed overview of key features in Uptrick: Oscillator Spectrum. Each component is designed to provide different angles of market analysis, allowing you to customize the tool to your preferences.
---
### 1. Main Oscillator
- Purpose: The primary oscillator in this script merges short-, medium-, and long-term views of buying pressure and true range into a single line.
- Calculation: It weights each period’s contribution (e.g., a heavier focus on the short period if desired) and normalizes the result on a 0–100 scale, where higher readings may suggest more robust momentum. (like from the classic Ultimate Oscillator)
- Practical Use:
- Traders can watch for overbought/oversold conditions at user-defined thresholds (e.g., 70/30).
- It can also provide a straightforward momentum reading for those who prefer to see if momentum is rising, falling, or leveling off.
---
### 2. HMA of the Smoothed Oscillator
- What It Is: A Hull Moving Average (HMA) applied to the main oscillator values. The HMA is often more responsive than standard MAs, offering smoother lines while preserving relatively quick reaction to changes.
- How It Works:
- The script takes the oscillator’s output and processes it through a Hull MA calculation.
- The HMA’s slope and color can change more dynamically, highlighting sharper momentum shifts.
- Why It’s Useful:
- By smoothing out minor fluctuations, the HMA can highlight trends in the oscillator’s trajectory.
- If you see an extended run in the HMA slope, it may indicate a more persistent trend in momentum.
- Color Intensity:
- As the HMA continues in one direction for several bars, the script can intensify the color, signaling stronger or more sustained momentum in that direction.
- Sudden changes in color or slope can signal the start of a new momentum swing.
---
### 3. Gradient Fill
This script uses two gradient-based visual elements:
1. Shining/Layered Gradient on the Main Oscillator
- Purpose: Adds multiple layers around the oscillator line (above and below) to emphasize slope changes and highlight how quickly the oscillator is moving up or down.
- Color Changes:
- When the oscillator rises, it uses a color scheme (e.g., aqua/blue) that intensifies as the slope grows.
- When the oscillator declines, it uses a distinct color (e.g., red/pink).
- User Benefit: Makes it easier to see at a glance if momentum is accelerating or decelerating, beyond just the numerical reading.
2. Dynamic Cloud Fill (Between MAs)
- Purpose: Allows you to plot two moving averages (for example, a short-term Hull MA and a longer-term DEMA) and fill the area between them with a color gradient.
- Bullish vs. Bearish:
- When the short MA is above the long MA, the cloud might appear in a greenish hue.
- When the short MA is below the long MA, the cloud can switch to red or another color.
- Transparency/Intensity:
- The fill can get more opaque if the difference between the two MAs is large, indicating a stronger trend but a higher probability of a reversal.
- User Benefit: Helps visualize changes in trend or momentum across multiple time horizons, all within a single chart overlay.
---
### 4. Correlation Meter & Symbol Inputs
- What It Is: This feature looks at multiple user-selected symbols (e.g., BTC, ETH, BNB, etc.) and computes each symbol’s short-term slope. It then aggregates these slopes into an overall “trend” score.
- Inputs Configuration:
1. Ticker Inputs: You can specify up to five different tickers.
2. Timeframe: Decide whether to pull data from different chart timeframes for each symbol.
3. Slope Calculation: The script may compute, for instance, a 5-period SMA minus a 20-period SMA to gauge if each symbol is trending up or down.
- Market Trend Histogram:
- Displays a column that goes above/below zero depending on how many symbols are bullish or bearish.
- If more than three (out of five) symbols are bullish, the histogram can show a green bar at +1; if fewer than three are bullish, it can show red at –1.
- How to Use:
- Quick Glance: Lets you know if most correlated assets are aligning or diverging.
- Bar Coloring (Optional): If enabled, your main chart’s bars can reflect the aggregated correlation, turning green or red depending on the meter’s reading.
---
### 5. Advanced Metrics Table
- What It Is: An optional table displaying additional metrics for several cryptocurrencies (or any symbols you define).
- Metrics Included:
1. ROI (30D): Calculates return relative to the lowest price in a 30-day period.
2. Collateral Risk: Uses standard deviation to assess volatility (higher risk if standard deviation is large).
3. Liquidity Recovery: A rolling average of volume, aiming to show how liquidity flows might recover over time.
4. Weakening (Rate of Change): Reflects how quickly price is changing compared to previous bars.
5. Monetary Bias (SMA): A simple average of recent prices. If price is below this SMA, it might be seen as undervalued relative to the short term.
6. Risk Phase: Categorizes risk as low, medium, or high based on the standard deviation figure.
7. DCA Signal: Suggests “Accumulate” or “Do Not Accumulate” by checking if the current price is below or above the SMA.
- Why It’s Useful:
- Offers a concise view of multiple assets in one place—helpful for portfolio-level insight.
- DCA (Dollar-Cost Averaging) suggestions can guide longer-term strategies, while volatility (collateral risk) helps gauge how aggressive the price swings might be.
---
### 6. Other Vital Aspects
- Alerts & Notifications:
- The script can trigger alerts for various conditions—crossovers, divergence detections, overbought/oversold transitions, or correlation-based signals.
- Useful for automating watchlists or ensuring you don’t miss a key setup while away from the screen.
- Customization:
- Each module (oscillator settings, divergence detection, correlation meter, advanced metrics table, etc.) can be enabled or disabled based on your preferences.
- You can fine-tune parameters (e.g., periods, smoothing lengths, alert triggers) to align the indicator with different trading styles—scalping, swing, or position trading.
- Combining Features:
- One might watch the main oscillator for momentum extremes, confirm via the HMA slope, check if correlation supports the same bias, and look at the table for risk-phase validation.
- This multi-layer approach can help develop a more structured and informed trading view.
(Space for an example chart: “A fully configured layout showing oscillator, HMA, gradient cloud, correlation meter, and table all in use.”)
7. Money Flow Tracker
Purpose: The Money Flow Tracker adds a volume-based perspective to the indicator suite by incorporating the Money Flow Index (MFI), which assesses buying and selling pressure over a defined period. By smoothing the MFI using an exponential moving average (EMA), the feature highlights the directional flow of capital into and out of the market with greater clarity and reduced noise.
Dynamic Gradient Visualization:
The Money Flow Tracker enhances visual analysis with gradient fills that reflect the MFI’s relationship to the midline (50).
Above 50: A green gradient emerges, intensifying as the MFI moves higher, indicating stronger positive money flow.
Below 50: A red gradient appears, with deeper shades signifying increasing selling pressure.
Transparency dynamically adjusts based on the MFI’s proximity to the midline, making high-confidence zones (closer to 0 or 100) visually distinct.
Directional Sensitivity:
The Tracker emphasizes the importance of overbought (above 70) and oversold (below 30) zones. These thresholds help traders identify when an asset might be overextended, signaling potential reversals or trend continuations.
The inclusion of a midline (50) as a neutral zone helps gauge shifts between accumulation (money flowing in) and distribution (money flowing out).
Bar Integration:
By enabling bar coloring linked to the Money Flow Tracker, traders can visualize its impact directly on price bars.
Green bars reflect positive money flow (above 50), signaling bullish conditions.
Red bars indicate negative money flow (below 50), highlighting bearish sentiment.
Intensity adjustments ensure that recent signals are more visually prominent, while older signals gradually fade for a clean, non-cluttered chart.
Key Advantages:
Volume-Informed Context: Traditional oscillators often focus solely on price; the Money Flow Tracker incorporates volume, adding a crucial dimension for analyzing market behavior.
Adaptive Filtering: The EMA-smoothing feature ensures that sudden, insignificant spikes in volume don’t trigger false signals, providing a clearer and more actionable representation of money flow trends.
Early Warning System: Divergences between price movement and the Money Flow Tracker’s trends can signal potential turning points, helping traders anticipate reversals before they occur.
Practical Use Cases:
Trend Confirmation: Pair the Money Flow Tracker with the oscillator or HMA to confirm bullish or bearish trends. For example, a rising oscillator with positive money flow indicates strong buying interest.
Identifying Entry/Exit Zones: Use overbought/oversold conditions as entry/exit points, particularly when combined with other features like divergence detection.
Market Sentiment Analysis: The Tracker’s ability to dynamically assess buying and selling pressure provides a clear picture of market sentiment, helping traders adjust their strategies to align with broader trends.
By understanding these features—main oscillator readings, the HMA’s smoothing capabilities, gradient-based visual highlights, correlation insights, advanced metrics, and the money flow tracker—you can tailor Uptrick: Oscillator Spectrum to your specific needs, whether you’re focusing on quick trades, longer-term market moves, or broad portfolio health.
Originality of the “Uptrick: Oscillator Spectrum” Indicator
While it includes elements of standard momentum analysis, Uptrick: Oscillator Spectrum sets itself apart by adding an array of features that broaden the typical oscillator’s scope:
1. Slope Coloring & Layered Gradient Effects
- Beyond just plotting a single line, the indicator visually highlights momentum shifts using color changes and gradient fills.
- As the oscillator’s slope becomes steeper or flatter, these gradients intensify or fade, helping users see at a glance when momentum is accelerating, slowing, or reversing.
2. Mean Reversion & Divergence Detection
- The script offers optional logic for marking potential mean reversion points (e.g., overbought/oversold crossovers) and flagging divergences between price and the oscillator line.
- These divergence signals come with adjustable lookback parameters, giving traders control over how recent or extended the pivots should be for detection.
- This functionality can reveal subtle momentum discrepancies that a basic oscillator might overlook.
3. Integrated Multi-Asset Correlation Meter
- In addition to monitoring a single symbol, the indicator can fetch data for multiple tickers. It aggregates each symbol’s slope into a histogram showing whether the broader market (or a group of assets) leans bullish or bearish.
- This cross-market insight moves beyond standard “one-symbol, one-oscillator” usage, adding a bigger-picture perspective in one tool.
4. Advanced Metrics Table
- Users can enable a table that covers ROI calculations, volatility-based risk (“Collateral Risk”), liquidity checks, DCA signals, and more.
- Rather than just seeing an oscillator value, traders can view additional metrics for selected assets in one place, helping them judge overall market conditions or assess multiple instruments simultaneously.
5. Flexible Overlay & Bar Coloring
- Signals can be displayed directly on the price chart (Overlay True) or in a sub-window (Overlay False).
- Bars themselves may change color (e.g., green for bullish or red for bearish) according to different rules—signals, dynamic cloud fill, correlation meter states, etc.
- This adaptability allows traders to keep the chart as simple or as info-rich as they prefer.
6. Custom Smoothing Options & HMA Extensions
- The oscillator can be processed further with a Hull Moving Average (HMA) to reduce noise while still reacting quickly to market changes.
- Slope-based coloring on the HMA provides an additional layer of visual feedback, which is not common in a standard oscillator.
By blending traditional momentum checks with slope-based color feedback, mean reversion triggers, divergence signals, correlation analysis, and an optional metrics table, Uptrick: Oscillator Spectrum offers a more rounded approach than a typical oscillator. It integrates multiple market insights—both visual and analytical—into one script, giving users a broader toolkit for studying potential reversals, gauging momentum strength, and assessing multi-asset trends.
## Conclusion
Uptrick: Oscillator Spectrum brings together multiple layers of analysis—oscillator momentum, divergence detection, correlation insights, HMA smoothing, and more—into one adaptable toolkit. It aims to streamline your charting process by offering meaningful visual cues (such as gradient fills and bar color shifts), advanced tables for broader market data, and flexible alerts to keep you informed of potential setups.
Traders can choose the specific features that suit their style, whether they prefer to focus on raw oscillator signals, multi-ticker correlation, or smooth trend cues from the HMA. By centralizing these different methods in one place, Uptrick: Oscillator Spectrum can help users build more structured approaches to spotting trend shifts and extended conditions, while also remaining compatible with additional analysis techniques.
---
### Disclaimer
This script is provided for informational purposes only and does not constitute financial or investment advice. Past performance is not indicative of future results, and all trading involves risk. You should carefully consider your objectives, risk tolerance, and financial situation before making any trading decisions.
AI Momentum [YinYang]Overview:
AI Momentum is a kernel function based momentum Indicator. It uses Rational Quadratics to help smooth out the Moving Averages, this may give them a more accurate result. This Indicator has 2 main uses, first it displays ‘Zones’ that help you visualize the potential movement areas and when the price is out of bounds (Overvalued or Undervalued). Secondly it creates signals that display the momentum of the current trend.
The Zones are composed of the Highest Highs and Lowest lows turned into a Rational Quadratic over varying lengths. These create our Rational High and Low zones. There is however a second zone. The second zone is composed of the avg of the Inner High and Inner Low zones (yellow line) and the Rational Quadratic of the current Close. This helps to create a second zone that is within the High and Low bounds that may represent momentum changes within these zones. When the Rationalized Close crosses above the High and Low Zone Average it may signify a bullish momentum change and vice versa when it crosses below.
There are 3 different signals created to display momentum:
Bullish and Bearish Momentum. These signals display when there is current bullish or bearish momentum happening within the trend. When the momentum changes there will likely be a lull where there are neither Bullish or Bearish momentum signals. These signals may be useful to help visualize when the momentum has started and stopped for both the bulls and the bears. Bullish Momentum is calculated by checking if the Rational Quadratic Close > Rational Quadratic of the Highest OHLC4 smoothed over a VWMA. The Bearish Momentum is calculated by checking the opposite.
Overly Bullish and Bearish Momentum. These signals occur when the bar has Bullish or Bearish Momentum and also has an Rationalized RSI greater or less than a certain level. Bullish is >= 57 and Bearish is <= 43. There is also the option to ‘Factor Volume’ into these signals. This means, the Overly Bullish and Bearish Signals will only occur when the Rationalized Volume > VWMA Rationalized Volume as well as the previously mentioned factors above. This can be useful for removing ‘clutter’ as volume may dictate when these momentum changes will occur, but it can also remove some of the useful signals and you may miss the swing too if the volume just was low. Overly Bullish and Bearish Momentum may dictate when a momentum change will occur. Remember, they are OVERLY Bullish and Bearish, meaning there is a chance a correction may occur around these signals.
Bull and Bear Crosses. These signals occur when the Rationalized Close crosses the Gaussian Close that is 2 bars back. These signals may show when there is a strong change in momentum, but be careful as more often than not they’re predicting that the momentum may change in the opposite direction.
Tutorial:
As we can see in the example above, generally what happens is we get the regular Bullish or Bearish momentum, followed by the Rationalized Close crossing the Zone average and finally the Overly Bullish or Bearish signals. This is normally the order of operations but isn’t always how it happens as sometimes momentum changes don’t make it that far; also the Rationalized Close and Zone Average don’t follow any of the same math as the Signals which can result in differing appearances. The Bull and Bear Crosses are also quite sporadic in appearance and don’t generally follow any sort of order of operations. However, they may occur as a Predictor between Bullish and Bearish momentum, signifying the beginning of the momentum change.
The Bull and Bear crosses may be a Predictor of momentum change. They generally happen when there is no Bullish or Bearish momentum happening; and this helps to add strength to their prediction. When they occur during momentum (orange circle) there is a less likely chance that it will happen, and may instead signify the exact opposite; it may help predict a large spike in momentum in the direction of the Bullish or Bearish momentum. In the case of the orange circle, there is currently Bearish Momentum and therefore the Bull Cross may help predict a large momentum movement is about to occur in favor of the Bears.
We have disabled signals here to properly display and talk about the zones. As you can see, Rationalizing the Highest Highs and Lowest Lows over 2 different lengths creates inner and outer bounds that help to predict where parabolic movement and momentum may move to. Our Inner and Outer zones are great for seeing potential Support and Resistance locations.
The secondary zone, which can cross over and change from Green to Red is also a very important zone. Let's zoom in and talk about it specifically.
The Middle Zone Crosses may help deduce where parabolic movement and strong momentum changes may occur. Generally what may happen is when the cross occurs, you will see parabolic movement to the High / Low zones. This may be the Inner zone but can sometimes be the outer zone too. The hard part is sometimes it can be a Fakeout, like displayed with the Blue Circle. The Cross doesn’t mean it may move to the opposing side, sometimes it may just be predicting Parabolic movement in a general sense.
When we turn the Momentum Signals back on, we can see where the Fakeout occurred that it not only almost hit the Inner Low Zone but it also exhibited 2 Overly Bearish Signals. Remember, Overly bearish signals mean a momentum change in favor of the Bulls may occur soon and overly Bullish signals mean a momentum change in favor of the Bears may occur soon.
You may be wondering, well what does “may occur soon” mean and how do we tell?
The purpose of the momentum signals is not only to let you know when Momentum has occurred and when it is still prevalent. It also matters A LOT when it has STOPPED!
In this example above, we look at when the Overly Bullish and Bearish Momentum has STOPPED. As you can see, when the Overly Bullish or Bearish Momentum stopped may be a strong predictor of potential momentum change in the opposing direction.
We will conclude our Tutorial here, hopefully this Indicator has been helpful for showing you where momentum is occurring and help predict how far it may move. We have been dabbling with and are planning on releasing a Strategy based on this Indicator shortly.
Settings:
1. Momentum:
Show Signals: Sometimes it can be difficult to visualize the zones with signals enabled.
Factor Volume: Factor Volume only applies to Overly Bullish and Bearish Signals. It's when the Volume is > VWMA Volume over the Smoothing Length.
Zone Inside Length: The Zone Inside is the Inner zone of the High and Low. This is the length used to create it.
Zone Outside Length: The Zone Outside is the Outer zone of the High and Low. This is the length used to create it.
Smoothing length: Smoothing length is the length used to smooth out our Bullish and Bearish signals, along with our Overly Bullish and Overly Bearish Signals.
2. Kernel Settings:
Lookback Window: The number of bars used for the estimation. This is a sliding value that represents the most recent historical bars. Recommended range: 3-50.
Relative Weighting: Relative weighting of time frames. As this value approaches zero, the longer time frames will exert more influence on the estimation. As this value approaches infinity, the behavior of the Rational Quadratic Kernel will become identical to the Gaussian kernel. Recommended range: 0.25-25.
Start Regression at Bar: Bar index on which to start regression. The first bars of a chart are often highly volatile, and omission of these initial bars often leads to a better overall fit. Recommended range: 5-25.
If you have any questions, comments, ideas or concerns please don't hesitate to contact us.
HAPPY TRADING!
Dee_MeterHere's how you can effectively use the Dee Meter indicator:
1. **Understanding the Basics**:
- Dee Meter evaluates the market sentiment across various sectors.
- It calculates the overall market trend and presents it in percentage form through a line graph.
2. **Indicator Results**:
- When you add the Dee Meter indicator to your chart, you'll notice two key results: Bull and Bear percentages, along with a line graph.
- The Bull percentage reflects the strength of bullish (positive) sentiment, while the Bear percentage indicates bearish (negative) sentiment.
- For example, if the Bull percentage is 55% and the Bear percentage is 45%, it signifies that the bulls currently have a stronger influence in the market.
3. **Interpreting Percentages**:
- Utilize the Bull and Bear percentages to craft your analysis strategy.
- A high Bull percentage in a bullish market suggests strong bullish momentum.
- In the case of a bullish trend showing signs of weakening (e.g., a double top pattern), monitor the Bull and Bear percentages for early reversal indications.
- A decrease in the Bull percentage and an increase in the Bear percentage could hint at a potential market reversal.
4. **Line Graph Analysis**:
- The line graph visually depicts the strength of bulls (green line) and bears (red line) over time.
- During a bullish trend, the green line rises while the red line remains lower, indicating bullish strength.
- Conversely, during a bearish trend, the red line climbs higher, indicating bearish dominance.
5. **Cross Over and Cross Under**:
- Cross-over and cross-under scenarios occur when the market abruptly reverses direction.
- For instance, in a bullish market that suddenly turns bearish, the red line could cross above the green line, indicating increased bearish power.
- In a bearish market that experiences a sudden influx of buying activity, the green line might cross above the red line, signifying strong buying interest.
6. **Applying the Indicator**:
- Use the Dee Meter to build your own trading strategies and make informed decisions.
- Keep an eye on changes in Bull and Bear percentages to identify shifts in market sentiment.
- Monitor line graph movements to assess the relative strength of bulls and bears.
In summary, the Dee Meter indicator is a valuable tool for assessing market sentiment and confirming trends in the Indian market. By understanding and utilizing the Bull and Bear percentages, line graph analysis, and cross-over/cross-under scenarios, you can develop effective trading strategies and trade with greater confidence.
RedK EVEREX - Effort Versus Results ExplorerRedK EVEREX is an experimental indicator that explores "Volume Price Analysis" basic concepts and Wyckoff law "Effort versus Result" - by inspecting the relative volume (effort) and the associated (relative) price action (result) for each bar - showing the analysis as an easy to read "stacked bands" visual. From that analysis, we calculate a "Relative Rate of Flow" - an easy to use +100/-100 oscilator that can be used to trigger a signal when a bullish or bearish mode is detected for a certain user-selected length of bars.
Basic Concepts of VPA
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(The topics of VPA & Wyckoff Effort vs Results law are too comprehensive to cover here - So here's just a very basic summary - please review these topics in detail in various sources available here in TradingView or on the web)
* Volume Price Analysis (VPA) is the examination of the number of shares or contracts of a security that have been traded in a given period, and the associated price movement. By analyzing trends in volume in conjunction with price movements, traders can determine the significance of changes in price and what may unfold in the near future.
* Oftentimes, high volumes of trading can infer a lot about investors’ outlook on a market or security. A significant price increase along with a significant volume increase, for example, could be a credible sign of a continued bullish trend or a bullish reversal. Adversely, a significant price decrease with a significant volume increase can point to a continued bearish trend or a bearish trend reversal.
* Incorporating volume into a trading decision can help an investor to have a more balanced view of all the broad market factors that could be influencing a security’s price, which helps an investor to make a more informed decision.
* Wyckoff's law "Effort versus results" dictates that large effort is expected to be accompanied with big results - which means that we should expect to see a big price move (result) associated with a large relative volume (effort) for a certain trading period (bar).
* The way traders use this concept in chart analysis is to mainly look for imbalances or invalidation. for example, when we observe a large relative volume that is associated with very limited price change - that should trigger an early flag/warning sign that the current price trend is facing challenges and may be an early sign of "reversal" - this applies in both bearish and bullish conditions. on the other hand, when price starts to trend in a certain direction and that's associated with increasing volume, that can act as kind of validation, or a confirmation that the market supports that move.
How does EVEREX work
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* EVEREX inspects each bar and calculates a relative value for volume (effort) and "strength of price movement" (result) compared to a specified lookback period. The results are then visualized as stacked bands - the lower band represents the relative volume, the upper band represents the relative price strength - with clear color coding for easier analysis.
* The scale of the band is initially set to 100 (each band can occupy up to 50) - and that can be changed in the settings to 200 or 400 - mainly to allow a "zoom in" on the bands.
* Reading the resulting stacked bands makes it easier to see "balanced" volume/price action (where both bands are either equally strong, or equally weak), or when there's imbalance between volume and price (for example, a compression bar will show with high volume band and very small/tiny price action band) - another favorite pattern in VPA is the "Ease of Move", which will show as a relatively small volume band associated with a large "price action band" (either bullish or bearish) .. and so on.
* a bit of a techie piece: why the use of a custom "Normalize()" function to calculate "relative" values in EVEREX?
When we evaluate a certain value against an average (for example, volume) we need a mechanism to deal with "super high" values that largely exceed that average - I also needed a mechanism that mimics how a trader looks at a volume bar and decides that this volume value is super low, low, average, above average, high or super high -- the issue with using a stoch() function, which is the usual technique for comparing a data point against a lookback average, is that this function will produce a "zero" for low values, and cause a large distortion of the next few "ratios" when super large values occur in the data series - i researched multiple techniques here and decided to use the custom Normalize() function - and what i found is, as long as we're applying the same formula consistently to the data series, since it's all relative to itself, we can confidently use the result. Please feel free to play around with this part further if you like - the code is commented for those who would like to research this further.
* Overall, the hope is to make the bar-by-bar analysis easier and faster for traders who apply VPA concepts in their trading
What is RROF?
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* Once we have the values of relative volume and relative price strength, it's easy from there to combine these values into a moving index that can be used to track overall strength and detect reversals in market direction - if you think about it this a very similar concept to a volume-weighted RSI. I call that index the "Relative Rate of Flow" - or RROF (cause we're not using the direct volume and price values in the calculation, but rather relative values that we calculated with the proprietary "Normalize" function in the script.
* You can show RROF as a single or double-period - and you can customize it in terms of smoothing, and signal line - and also utilize the basic alerts to get notified when a change in strength from one side to the other (bullish vs bearish) is detected
* In the chart above, you can see how the RROF was able to detect change in market condition from Bearsh to Bullish - then from Bullish to Bearish for TSLA with good accuracy.
Other Usage Options in EVEREX
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* I wrote EVEREX with a lot of flexibility and utilization in mind, while focusing on a clean and easy to use visual - EVEREX should work with any time frame and any instrument - in instruments with no volume data, only price data will be used.
* You can completely hide the "EVEREX bands" and use EVEREX as a single or dual period strength indicator (by exposing the Bias/Sentiment plot which is hidden by default) -
here's how this setup would look like - in this mode, you will basically be using EVEREX the same way you're using a volume-weighted RSI
* or you can hide the bias/sentiment, and expose the Bulls & Bears plots (using the indicator's "Style" tab), and trade it like a Bull/Bear Pressure Index like this
* you can choose Moving Average type for most plot elements in EVEREX, including how to deal with the Lookback averaging
* you can set EVEREX to a different time frame than the chart
* did i mention basic alerts in this v1.0 ?? There's room to add more VPA-specific alerts in future version (for example, when Ease-of-Move or Compression bars are detected...etc) - let me know if the comments what you want to see
Final Thoughts
--------------------
* EVEREX can be used for bar-by-bar VPA analysis - There are so much literature out there about VPA and it's highly recommended that traders read more about what VPA is and how it works - as it adds an interesting (and critical) dimension to technical analysis and will improve decision making
* RROF is a "strength indicator" - it does not track price values (levels) or momentum - as you will see when you use it, the price can be moving up, while the RROF signal line starts moving down, reflecting decreasing strength (or otherwise, increasing bear strength) - So if you incorporate EVEREX in your trading you will need to use it alongside other momentum and price value indicators (like MACD, MA's, Trend Channels, Support & Resistance Lines, Fib / Donchian..etc) - to use for trade confirmation
Bar metrics / quantifytools— Overview
Rather than eyeball evaluating bullishness/bearishness in any given bar, bar metrics allow a quantified approach using three basic fundamental data points: relative close, relative volatility and relative volume. These data points are visualized in a discreet data dashboard form, next to all real-time bars. Each value also has a dot in front, representing color coded extremes in the values.
Relative close represents position of bar's close relative to high and low, high of bar being 100% and low of bar being 0%. Relative close indicates strength of bulls/bears in a given bar, the higher the better for bulls, the lower the better for bears. Relative volatility (bar range, high - low) and relative volume are presented in a form of a multiplier, relative to their respective moving averages (SMA 20). A value of 1x indicates volume/volatility being on par with moving average, 2x indicates volume/volatility being twice as much as moving average and so on. Relative volume and volatility can be used for measuring general market participant interest, the "weight of the bar" as it were.
— Features
Users can gauge past bar metrics using lookback via input menu. Past bars, especially recent ones, are helpful for giving context for current bar metrics. Lookback bars are highlighted on the chart using a yellow box and metrics presented on the data dashboard with lookback symbols:
To inspect bar metric data and its implications, users can highlight bars with specified bracket values for each metric:
When bar highlighter is toggled on and desired bar metric values set, alert for the specified combination can be toggled on via alert menu. Note that bar highlighter must be enabled in order for alerts to function.
— Visuals
Bar metric dots are gradient colored the following way:
Relative volatility & volume
0x -> 1x / Neutral (white) -> Light (yellow)
1x -> 1.7x / Light (yellow) -> Medium (orange)
1.7x -> 2.4x / Medium (orange) -> Heavy (red)
Relative close
0% -> 25% / Heavy bearish (red) -> Light bearish (dark red)
25% -> 45% / Light bearish (dark red) -> Neutral (white)
45% - 55% / Neutral (white)
55% -> 75% / Neutral (white) -> Light bullish (dark green)
75% -> 100% / Light bullish (dark green) -> Heavy bullish (green)
All colors can be adjusted via input menu. Label size, label distance from bar (offset) and text format (regular/stealth) can be adjusted via input menu as well:
— Practical guide
As interpretation of bar metrics is highly contextual, it is especially important to use other means in conjunction with the metrics. Levels, oscillators, moving averages, whatever you have found useful for your process. In short, relative close indicates directional bias and relative volume/volatility indicates "weight" of directional bias.
General interpretation
High relative close, low relative volume/volatility = mildly bullish, bias up/consolidation
High relative close, medium relative volume/volatility = bullish, bias up
High relative close, high relative volume/volatility = exuberantly bullish, bias up/down depending on context
Medium relative close, low relative volume/volatility = noise, no bias
Medium relative close, medium to high relative volume/volatility = indecision, further evidence needed to evaluate bias
Low relative close, low relative volume/volatility = mildly bearish, bias down/consolidation
Low relative close, medium relative volume/volatility = bearish, bias down
Low relative close, high relative volume/volatility = exuberantly bearish, bias down/up depending on context
Nuances & considerations
As to relative close, it's important to note that each bar is a trading range when viewed on a lower timeframe, ES 1W vs. ES 4H:
When relative close is high, bulls were able to push price to range high by the time of close. When relative close is low, bears were able to push price to range low by the time of close. In other words, bulls/bears were able to gain the upper hand over a given trading range, hinting strength for the side that made the final push. When relative close is around middle range (40-60%), it can be said neither side is clearly dominating the range, hinting neutral/indecision bias from a relative close perspective.
As to relative volume/volatility, low values (less than ~0.7x) imply bar has low market participant interest and therefore is likely insignificant, as it is "lacking weight". Values close to or above 1x imply meaningful market participant interest, whereas values well above 1x (greater than ~1.3x) imply exuberance. This exuberance can manifest as initiation (beginning of a trend) or as exhaustion (end of a trend):
Majors FX-REER/NEER Suite [BIG]═══════════════════════════════════════════════════════════════════════════════
BIG MAJORS FX-REER/NEER SUITE
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OVERVIEW
The BIG Majors FX-REER/NEER Suite is a multi-currency valuation framework designed for institutional FX traders, macro strategists, and systematic currency allocators. This indicator calculates Real Effective Exchange Rates (REER) and Nominal Effective Exchange Rates (NEER) for the seven major currency pairs (G7 FX), integrating macroeconomic fundamentals (CPI inflation differentials) with technical trend analysis to identify structural currency misvaluations and mean-reversion opportunities.
Unlike standard FX indicators that only analyze bilateral price action, this suite constructs trade-weighted basket indices that measure each currency's strength against a portfolio of its major trading partners, adjusted for inflation differentials. This approach mirrors central bank and sovereign wealth fund methodologies for assessing equilibrium exchange rate levels.
The framework combines:
- Fundamental valuation metrics – REER/NEER indices with Z-score normalization
- Technical trend filters – Ichimoku Cloud and Aroon oscillator confluence
- Signal classification system – Long/Short/Watch/Conflict regime identification
- Quantitative confidence scoring – 0-100% signal reliability weighting
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CORE METHODOLOGY
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• NEER Calculation (Nominal Effective Exchange Rate)
The NEER measures a currency's value against a trade-weighted basket of its seven major trading partners, geometrically averaged in log-space to ensure symmetry:
1. All seven G7 FX pairs are normalized to USD-pivot (A/USD format)
2. Each currency's log-normalized rate is compared to the arithmetic mean of the other six
3. Formula: NEER_i = (8/7) × log(CCY_i/USD) - mean(log(CCY_others/USD))
This construction ensures that:
- A rising NEER indicates currency appreciation against the basket
- The methodology is symmetric and avoids base-currency bias
- Changes reflect multilateral competitive dynamics, not just bilateral moves
• REER Calculation (Real Effective Exchange Rate)
The REER adjusts the NEER for inflation differentials using Consumer Price Index (CPI) data:
Formula: REER_i = NEER_i + log(CPI_i) - mean(log(CPI_others))
By incorporating CPI differentials, the REER provides a purchasing-power-parity-adjusted valuation metric that accounts for relative inflation rates. This is the institutional standard for assessing fundamental currency equilibrium levels.
Data Sources :
- FX rates: TradingView composite feed (FX:), OANDA, FXCM, FOREXCOM
- CPI data: ECONOMICS namespace (monthly frequency, official statistical releases)
- Supported currencies: USD, EUR, JPY, GBP, CHF, AUD, CAD, NZD
• Valuation Bias Detection
Each currency pair is classified as overvalued (bias = -1, "Short") or undervalued (bias = +1, "Long") based on two independent criteria:
1. Percentage Band Deviation – Relative Index distance from 100 baseline
• Overvalued: Index > 100 × (1 + deviation%), default +5%
• Undervalued: Index < 100 × (1 - deviation%), default -5%
2. Z-Score Threshold – Statistical extremes in rolling lookback window
• Overvalued: Z-Score > +1.5 (default)
• Undervalued: Z-Score < -1.5 (default)
Either condition triggers a bias classification. This dual-filter approach captures both absolute deviations and relative extremes within recent historical context.
• Trend Confirmation (Ichimoku + Aroon)
To avoid counter-trend entries in strong momentum regimes, the suite integrates two independent trend filters:
Ichimoku Cloud
- Bull: Price > Cloud AND Conversion > Base Line
- Bear: Price < Cloud AND Conversion < Base Line
- Parameters: Conv(9), Base(26), Span B(52), Displacement(26)
Aroon Oscillator
- Bull: Aroon Up > 70 AND Aroon Down < 30
- Bear: Aroon Down > 70 AND Aroon Up < 30
- Default lookback: 25 periods
Trend is confirmed only when both indicators agree (Ichimoku + Aroon ≥ +1 for bull, ≤ -1 for bear).
• Setup Classification Logic
The framework combines Bias (fundamental valuation) with Trend (technical momentum) to generate four distinct setup types:
- Long↗︎ (Setup = 1) – Undervalued + Bullish Trend
Context : Mean reversion opportunity with momentum confirmation. Currency trading at fundamental discount while technical trend supports upside.
- Short↘︎ (Setup = -1) – Overvalued + Bearish Trend
Context : Mean reversion opportunity with momentum confirmation. Currency trading at fundamental premium while technical trend supports downside.
- Watch (Setup = 2) – Valuation bias present, but no clear trend
Context : Fundamental mispricing without directional conviction. Monitor for trend emergence before entering.
- Conflict (Setup = 3) – Bias and trend pointing opposite directions
Context : Overvalued currency in uptrend OR undervalued currency in downtrend. Avoid—either trend continuation or valuation mean reversion, but unclear which dominates.
• Confidence Score (0-100%)
Each setup receives a quantitative confidence weighting based on three factors:
1. Band Distance (40%) – How far the Relative Index deviates from 100 baseline
2. Z-Score Magnitude (40%) – Statistical extremeness within lookback window
3. Trend Confluence (20%) – Agreement between Ichimoku and Aroon signals
Score interpretation:
- 70-100% = High confidence (both valuation and trend extremes aligned)
- 40-69% = Moderate confidence (one factor strong, others weak)
- 0-39% = Low confidence (marginal signals, questionable reliability)
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VISUAL COMPONENTS
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• Dashboard Table (Top-Right)
Displays real-time valuation metrics for all seven major pairs:
Column 1: Pair – Currency pair identifier
Column 2: RelIdx – Relative Index (100 = baseline at first valid bar)
Column 3: Z – Z-Score vs. rolling lookback window
Column 4: Bias – Long/Short/Neutral valuation classification
Column 5: Trend – ↑/↓/– trend direction (Ichimoku + Aroon)
Column 6: Setup – Long↗︎/Short↘︎/Watch/Conflict (color-coded)
Column 7: Conf – Confidence score 0-100% (color-coded)
Column 8: Quelle – REER (inflation-adjusted) or NEER (nominal only)
Color coding :
- Green = Long↗︎ setup
- Red = Short↘︎ setup
- Orange = Watch (no trend)
- Purple = Conflict (bias/trend divergence)
• Optional Chart Plot
Select any of the seven pairs to plot its Relative Index on the chart with:
- Baseline at 100 (horizontal gray line)
- +Band at 100 × (1 + deviation%), dashed red
- -Band at 100 × (1 - deviation%), dashed green
- Aqua line tracking the selected pair's Relative Index evolution
• Signal Labels
When a pair transitions into Long↗︎ or Short↘︎ setup:
- Green label below bar = Long↗︎ entry signal
- Red label above bar = Short↘︎ entry signal
- Positioned using ATR offset for visibility
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KEY FEATURES
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- Institutional valuation methodology – REER/NEER framework used by central banks and sovereign wealth funds
- Macro-fundamental integration – CPI inflation differentials adjust for purchasing power parity
- Multi-timeframe flexibility – Daily (D), Weekly (W), Monthly (M) resolution options
- Seven simultaneous pairs – Monitors all G7 FX majors in single unified dashboard
- No repainting – All signals confirm on bar close
- Automated alerts – TradingView notifications when setups transition (Long/Short triggers)
- Confidence weighting – Quantitative scoring allows position sizing calibration
- Fallback logic – Automatically switches to NEER if CPI data incomplete
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HOW TO USE
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1. Apply to any chart – The indicator pulls multi-security data; chart symbol does not matter (commonly applied to SPY or DXY for reference)
2. Select data feed – Default FX: (TradingView composite) is recommended; alternatives: OANDA, FXCM, FOREXCOM
3. Choose timeframe :
• Daily (D) = Swing trading, medium-term mean reversion (2-8 week horizons)
• Weekly (W) = Position trading, macro regime shifts (1-6 month horizons)
• Monthly (M) = Strategic allocation, long-term equilibrium analysis (6-24 month horizons)
4. Configure parameters :
• Z-Score Lookback : Default 252 (one trading year on Daily); adjust for timeframe (52 for Weekly, 36 for Monthly)
• Deviation Band : Default ±5%; tighten to ±3% for more signals, widen to ±7% for higher conviction
• Z-Threshold : Default ±1.5; increase to ±2.0 for extreme-only signals
5. Monitor dashboard table :
• Focus on pairs showing Long↗︎ or Short↘︎ setups with Conf ≥ 70%
• Watch for Watch setups transitioning to directional signals
• Avoid Conflict setups unless you have strong macro conviction
6. Execute mean-reversion trades :
• Long↗︎ = Buy undervalued currency (e.g., EURUSD Long if EUR undervalued)
• Short↘︎ = Sell overvalued currency (e.g., USDJPY Short if JPY overvalued)
• Target: Mean reversion toward 100 baseline or opposite band
7. Position sizing by confidence :
• High confidence (70-100%) → Standard position size
• Moderate confidence (40-69%) → Reduce size by 50%
• Low confidence (<40%) → Avoid or use minimal pilot size
8. Risk management :
• Stop loss: Place beyond recent swing high/low or 1.5× ATR
• Take profit: Opposite valuation band or 100 baseline
• Time stop: Exit if setup reverses (Long→Neutral→Short or vice versa)
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LIMITATIONS
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- CPI data lag – Consumer Price Index releases are monthly and report with 2-4 week delay. REER calculations may lag real-time inflation dynamics.
- Structural shifts ignored – The baseline (100) is set at first valid bar. Long-term structural appreciation/depreciation (e.g., 20-year USD bull market) is not accounted for. Suitable for cyclical mean reversion, not secular trend analysis.
- Equal-weighting assumption – All seven currencies are equally weighted in basket construction. Actual trade-weighted indices use GDP or trade volume weights, which this framework simplifies.
- No emerging market currencies – Limited to G7 majors (USD, EUR, JPY, GBP, CHF, AUD, CAD, NZD). Does not cover EM FX (e.g., CNY, BRL, MXN).
- Technical filter limitations – Ichimoku and Aroon are lagging indicators. In fast-moving markets (e.g., central bank interventions, geopolitical shocks), trend signals may arrive late.
- Mean reversion assumption – The framework assumes currencies revert to equilibrium. During regime changes (e.g., monetary policy divergence, crisis flows), deviations can persist or expand before eventual reversal.
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TECHNICAL SPECIFICATIONS
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- Pine Script v6
- Non-repainting (signals confirmed on bar close)
- Multi-security data feeds (7 FX pairs + 8 CPI series)
- Automated alert system (transitions to Long↗︎/Short↘︎)
- Real-time dashboard table (8 columns × 8 rows)
- Maximum 500 labels supported (100 per pair direction)
- Fallback logic: NEER used if CPI data unavailable
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NOTES
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This indicator is designed for experienced FX traders, macro strategists, and portfolio managers familiar with:
- Real and nominal effective exchange rate concepts
- Purchasing power parity theory and inflation differentials
- Multi-currency portfolio construction and basket hedging
- Carry trade and convergence strategies
- Central bank policy impacts on FX equilibrium levels
The framework provides objective valuation signals but does not account for:
- Interest rate differentials (carry)
- Capital flow dynamics (risk-on/risk-off)
- Central bank intervention zones
- Geopolitical risk premiums
Always combine REER/NEER valuation analysis with macro event calendars, positioning data (CFTC COT reports), and fundamental policy divergence assessments.
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Developed for institutional FX valuation analysis based on central bank REER/NEER methodologies.
Impulse Reactor RSI-SMA Trend Indicator [ApexLegion]Impulse Reactor RSI-SMA Trend Indicator
Introduction and Theoretical Background
Design Rationale
Standard indicators frequently generate binary 'BUY' or 'SELL' signals without accounting for the broader market context. This often results in erratic "Flip-Flop" behavior, where signals are triggered indiscriminately regardless of the prevailing volatility regime.
Impulse Reactor was engineered to address this limitation by unifying two critical requirements: Quantitative Rigor and Execution Flexibility.
The Solution
Composite Analytical Framework This script is not a simple visual overlay of existing indicators. It is an algorithmic synthesis designed to function as a unified decision-making engine. The primary objective was to implement rigorous quantitative analysis (Volatility Normalization, Structural Filtering) directly within an alert-enabled framework. This architecture is designed to process signals through strict, multi-factor validation protocols before generating real-time notifications, allowing users to focus on structurally validated setups without manual monitoring.
How It Works
This is not a simple visual mashup. It utilizes a cross-validation algorithm where the Trend Structure acts as a gatekeeper for Momentum signals:
Logic over Lag: Unlike simple moving average crossovers, this script uses a 15-layer Gradient Ribbon to detect "Laminar Flow." If the ribbon is knotted (Compression), the system mathematically suppresses all signals.
Volatility Normalization: The core calculation adapts to ATR (Average True Range). This means the indicator automatically expands in volatile markets and contracts in quiet ones, maintaining accuracy without constant manual tweaking.
Adaptive Signal Thresholding: It incorporates an 'Anti-Greed' algorithm (Dynamic Thresholding) that automatically adjusts entry criteria based on trend duration. This logic aims to mitigate the risk of entering positions during periods of statistical trend exhaustion.
Why Use It?
Market State Decoding: The gradient Ribbon visualizes the underlying trend phase in real-time.
◦ Cyan/Blue Flow: Strong Bullish Trend (Laminar Flow).
◦ Magenta/Pink Flow: Strong Bearish Trend.
◦ Compressed/Knotted: When the ribbon lines are tightly squeezed or overlapping, it signals Consolidation. The system filters signals here to avoid chop.
Noise Reduction: The goal is not to catch every pivot, but to isolate high-confidence setups. The logic explicitly filters out minor fluctuations to help maintain position alignment with the broader trend.
⚖️ Chapter 1: System Architecture
Introduction: Composite Analytical Framework
System Overview
Impulse Reactor serves as a comprehensive technical analysis engine designed to synthesize three distinct market dimensions—Momentum, Volatility, and Trend Structure—into a unified decision-making framework. Unlike traditional methods that analyze these metrics in isolation, this system functions as a central processing unit that integrates disparate data streams to construct a coherent model of market behavior.
Operational Objective
The primary objective is to transition from single-dimensional signal generation to a multi-factor assessment model. By fusing data from the Impulse Core (Volatility), Gradient Oscillator (Momentum), and Structural Baseline (Trend), the system aims to filter out stochastic noise and identify high-probability trade setups grounded in quantitative confluence.
Market Microstructure Analysis: Limitations of Conventional Models
Extensive backtesting and quantitative analysis have identified three critical inefficiencies in standard oscillator-based strategies:
• Bounded Oscillator Limitations (The "Oscillation Trap"): Traditional indicators such as RSI or Stochastics are mathematically constrained between fixed values (0 to 100). In strong trending environments, these metrics often saturate in "overbought" or "oversold" zones. Consequently, traders relying on static thresholds frequently exit structurally valid positions prematurely or initiate counter-trend trades against prevailing momentum, resulting in suboptimal performance.
• Quantitative Blindness to Quality: Standard moving averages and trend indicators often fail to distinguish the qualitative nature of price movement. They treat low-volume drift and high-velocity expansion identically. This inability to account for "Volatility Quality" leads to delayed responsiveness during critical market events.
• Fractal Dissonance (Timeframe Disconnect): Financial markets exhibit fractal characteristics where trends on lower timeframes may contradict higher timeframe structures. Manual integration of multi-timeframe analysis increases cognitive load and susceptibility to human error, often resulting in conflicting biases at the point of execution.
Core Design Principles
To mitigate the aforementioned systemic inefficiencies, Impulse Reactor employs a modular architecture governed by three foundational principles:
Principle A:
Volatility Precursor Analysis Market mechanics demonstrate that volatility expansion often functions as a leading indicator for directional price movement. The system is engineered to detect "Volatility Deviation" — specifically, the divergence between short-term and long-term volatility baselines—prior to its manifestation in price action. This allows for entry timing aligned with the expansion phase of market volatility.
Principle B:
Momentum Density Visualization The system replaces singular momentum lines with a "Momentum Density" model utilizing a 15-layer Simple Moving Average (SMA) Ribbon.
• Concept: This visualization represents the aggregate strength and consistency of the trend.
• Application: A fully aligned and expanded ribbon indicates a robust trend structure ("Laminar Flow") capable of withstanding minor counter-trend noise, whereas a compressed ribbon signals consolidation or structural weakness.
Principle C:
Adaptive Confluence Protocols Signal validity is strictly governed by a multi-dimensional confluence logic. The system suppresses signal generation unless there is synchronized confirmation across all three analytical vectors:
1. Volatility: Confirmed expansion via the Impulse Core.
2. Momentum: Directional alignment via the Hybrid Oscillator.
3. Structure: Trend validation via the Baseline. This strict filtering mechanism significantly reduces false positives in non-trending (choppy) environments while maintaining sensitivity to genuine breakouts.
🔍 Chapter 2: Core Modules & Algorithmic Logic
Module A: Impulse Core (Normalized Volatility Deviation)
Operational Logic The Impulse Core functions as a volatility-normalized momentum gauge rather than a standard oscillator. It is designed to identify "Volatility Contraction" (Squeeze) and "Volatility Expansion" phases by quantifying the divergence between short-term and long-term volatility states.
Volatility Z-Score Normalization
The formula implements a custom normalization algorithm. Unlike standard oscillators that rely on absolute price changes, this logic calculates the Z-Score of the Volatility Spread.
◦ Numerator: (atr_f - atr_s) captures the raw momentum of volatility expansion.
◦ Denominator: (std_f + 1e-6) standardizes this value against historical variance.
◦ Result: This allows the indicator scales consistently across assets (e.g., Bitcoin vs. Euro) without manual recalibration.
f_impulse() =>
atr_f = ta.atr(fastLen) // Fast Volatility Baseline
atr_s = ta.atr(slowLen) // Slow Volatility Baseline
std_f = ta.stdev(atr_f, devLen) // Volatility Standard Deviation
(atr_f - atr_s) / (std_f + 1e-6) // Normalized Differential Calculation
Algorithmic Framework
• Differential Calculation: The system computes the spread between a Fast Volatility Baseline (ATR-10) and a Slow Volatility Baseline (ATR-30).
• Normalization Protocol: To standardize consistency across diverse asset classes (e.g., Forex vs. Crypto), the raw differential is divided by the standard deviation of the volatility itself over a 30-period lookback.
• Signal Generation:
◦ Contraction (Squeeze): When the Fast ATR compresses below the Slow ATR, it registers a potential volatility buildup phase.
◦ Expansion (Release): A rapid divergence of the Fast ATR above the Slow ATR signals a confirmed volatility expansion, validating the strength of the move.
Module B: Gradient Oscillator (RSI-SMA Hybrid)
Design Rationale To mitigate the "noise" and "false reversal" signals common in single-line oscillators (like standard RSI), this module utilizes a 15-Layer Gradient Ribbon to visualize momentum density and persistence.
Technical Architecture
• Ribbon Array: The system generates 15 sequential Simple Moving Averages (SMA) applied to a volatility-adjusted RSI source. The length of each layer increases incrementally.
• State Analysis:
Momentum Alignment (Laminar Flow): When all 15 layers are expanded and parallel, it indicates a robust trend where buying/selling pressure is distributed evenly across multiple timeframes. This state helps filter out premature "overbought/oversold" signals.
• Consolidation (Compression): When the distance between the fastest layer (Layer 1) and the slowest layer (Layer 15) approaches zero or the layers intersect, the system identifies a "Non-Tradable Zone," preventing entries during choppy market conditions.
// Laminar Flow Validation
f_validate_trend() =>
// Calculate spread between Ribbon layers
ribbon_spread = ta.stdev(ribbon_array, 15)
// Only allow signals if Ribbon is expanded (Laminar Flow)
is_flowing = ribbon_spread > min_expansion_threshold
// If compressed (Knotted), force signal to false
is_flowing ? signal : na
Module C: Adaptive Signal Filtering (Behavioral Bias Mitigation)
This subsystem, operating as an algorithmic "Anti-Greed" Mechanism, addresses the statistical tendency for signal degradation following prolonged trends.
Dynamic Threshold Adjustment
• Win Streak Detection: The algorithm internally tracks the outcome of closed trade cycles.
• Sensitivity Multiplier: Upon detecting consecutive successful signals in the same direction, a Penalty_Factor is applied to the entry logic.
• Operational Impact: This effectively raises the Required_Slope threshold for subsequent signals. For example, after three consecutive bullish signals, the system requires a 30% steeper trend angle to validate a fourth entry. This enforces stricter discipline during extended trends to reduce the probability of entering at the point of trend exhaustion.
Anti-Greed Logic: Dynamic Threshold Calculation
f_adjust_threshold(base_slope, win_streak) =>
// Adds a 10% penalty to the difficulty for every consecutive win
penalty_factor = 0.10
risk_scaler = 1 + (win_streak * penalty_factor)
// Returns the new, harder-to-reach threshold
base_slope * risk_scaler
Module D: Trend Baseline (Triple-Smoothed Structure)
The Trend Baseline serves as the structural filter for all signals. It employs a Triple-Smoothed Hybrid Algorithm designed to balance lag reduction with noise filtration.
Smoothing Stages
1. Volatility Banding: Utilizes a SuperTrend-based calculation to establish the upper and lower boundaries of price action.
2. Weighted Filter: Applies a Weighted Moving Average (WMA) to prioritize recent price data.
3. Exponential Smoothing: A final Exponential Moving Average (EMA) pass is applied to create a seamless baseline curve.
Functionality
This "Heavy" baseline resists minor intraday volatility spikes while remaining responsive to sustained structural shifts. A signal is only considered valid if the price action maintains structural integrity relative to this baseline
🚦 Chapter 3: Risk Management & Exit Protocols
Quantitative Risk Management (TP/SL & Trailing)
Foundational Architecture: Volatility-Adjusted Geometry Unlike strategies relying on static nominal values, Impulse Reactor establishes dynamic risk boundaries derived from quantitative volatility metrics. This design aligns trade invalidation levels mathematically with the current market regime.
• ATR-Based Dynamic Bracketing:
The protocol calculates Stop-Loss and Take-Profit levels by applying Fibonacci coefficients (Default: 0.786 for SL / 1.618 for TP) to the Average True Range (ATR).
◦ High Volatility Environments: The risk bands automatically expand to accommodate wider variance, preventing premature exits caused by standard market noise.
◦ Low Volatility Environments: The bands contract to tighten risk parameters, thereby dynamically adjusting the Risk-to-Reward (R:R) geometry.
• Close-Validation Protocol ("Soft Stop"):
Institutional algorithms frequently execute liquidity sweeps—driving prices briefly below key support levels to accumulate inventory.
◦ Mechanism: When the "Soft Stop" feature is enabled, the system filters out intraday volatility spikes. The stop-loss is conditional; execution is triggered only if the candle closes beyond the invalidation threshold.
◦ Strategic Advantage: This logic distinguishes between momentary price wicks and genuine structural breakdowns, preserving positions during transient volatility.
• Step-Function Trailing Mechanism:
To protect unrealized PnL while allowing for normal price breathing, a two-phase trailing methodology is employed:
◦ Phase 1 (Activation): The trailing function remains dormant until the price advances by a pre-defined percentage threshold.
◦ Phase 2 (Dynamic Floor): Once armed, the stop level creates a moving floor, adjusting relative to price action while maintaining a volatility-based (ATR) buffer to systematically protect unrealized PnL.
• Algorithmic Exit Protocols (Dynamic Liquidity Analysis)
◦ Rationale: Inefficiencies of Static Targets Static "Take Profit" levels often result in suboptimal exits. They compel traders to close positions based on arbitrary figures rather than evolving market structure, potentially capping upside during significant trends or retaining positions while the underlying trend structure deteriorates.
◦ Solution: Structural Integrity Assessment The system utilizes a Dynamic Liquidity Engine to continuously audit the validity of the position. Instead of targeting a specific price point, the algorithm evaluates whether the trend remains statistically robust.
Multi-Factor Exit Logic (The Tri-Vector System)
The Smart Exit protocol executes only when specific algorithmic invalidation criteria are met:
• 1. Momentum Exhaustion (Confluence Decay): The system monitors a 168-hour rolling average of the Confluence Score. A significant deviation below this historical baseline indicates momentum exhaustion, signaling that the driving force behind the trend has dissipated prior to a price reversal. This enables preemptive exits before a potential drawdown.
• 2. Statistical Over-Extension (Mean Reversion): Utilizing the core volatility logic, the system identifies instances where price deviates beyond 2.0 standard deviations from the mean. While the trend may be technically bullish, this statistical anomaly suggests a high probability of mean reversion (elastic snap-back), triggering a defensive exit to capitalize on peak valuation.
• 3. Oscillator Rejection (Immediate Pivot): To manage sudden V-shaped volatility, the system monitors RSI pivots. If a sharp "Pivot High" or divergence is detected, the protocol triggers an immediate "Peak Exit," bypassing standard trend filters to secure liquidity during high-velocity reversals.
🎨 Chapter 4: Visualization Guide
Gradient Oscillator Ribbon
The 15-layer SMA ribbon visualized via plot(r1...r15) represents the "Momentum Density" of the market.
• Visuals:
◦ Cyan/Blue Ribbon: Indicates Bullish Momentum.
◦ Pink/Magenta Ribbon: Indicates Bearish Momentum.
• Interpretation:
◦ Laminar Flow: When the ribbon expands widely and flows in parallel, it signifies a robust trend where momentum is distributed evenly across timeframes. This is the ideal state for trend-following.
◦ Compression (Consolidation): If the ribbon becomes narrow, twisted, or knotted, it indicates a "Non-Tradable Zone" where the market lacks a unified direction. Traders are advised to wait for clarity.
◦ Over-Extension: If the top layer crosses the Overbought (85) or Oversold (15) lines, it visually warns of potential market overheating.
Trend Baseline
The thick, color-changing line plotted via plot(baseline) represents the Structural Backbone of the market.
• Visuals: Changes color based on the trend direction (Blue for Bullish, Pink for Bearish).
• Interpretation:
Structural Filter: Long positions are statistically favored only when price action sustains above this baseline, while short positions are favored below it.
Dynamic Support/Resistance: The baseline acts as a dynamic support level during uptrends and resistance during downtrends.
Entry Signals & Labels
Text labels ("Long Entry", "Short Entry") appear when the system detects high-probability setups grounded in quantitative confluence.
• Visuals: Labeled signals appear above/below specific candles.
• Interpretation:
These signals represent moments where Volatility (Expansion), Momentum (Alignment), and Structure (Trend) are synchronized.
Smart Exit: Labels such as "Smart Exit" or "Peak Exit" appear when the system detects momentum exhaustion or structural decay, prompting a defensive exit to preserve capital.
Dynamic TP/SL Boxes
The semi-transparent colored zones drawn via fill() represent the risk management geometry.
• Visuals: Colored boxes extending from the entry point to the Take Profit (TP) and Stop Loss (SL) levels.
• Function:
Volatility-Adjusted Geometry: Unlike static price targets, these boxes expand during high volatility (to prevent wicks from stopping you out) and contract during low volatility (to optimize Risk-to-Reward ratios).
SAR + MACD Glow
Small glowing shapes appearing above or below candles.
• Visuals: Triangle or circle glows near the price bars.
• Interpretation:
This visual indicates a secondary confirmation where Parabolic SAR and MACD align with the main trend direction. It serves as an additional confluence factor to increase confidence in the trade setup.
Support/Resistance Table
A small table located at the bottom-right of the chart.
• Function: Automatically identifies and displays recent Pivot Highs (Resistance) and Pivot Lows (Support).
• Interpretation: These levels can be used as potential targets for Take Profit or invalidation points for manual Stop Loss adjustments.
🖥️ Chapter 5: Dashboard & Operational Guide
Integrated Analytics Panel (Dashboard Overview)
To facilitate rapid decision-making without manual calculation, the system aggregates critical market dimensions into a unified "Heads-Up Display" (HUD). This panel monitors real-time metrics across multiple timeframes and analytical vectors.
A. Intermediate Structure (12H Trend)
• Function: Anchors the intraday analysis to the broader market structure using a 12-hour rolling window.
• Interpretation:
◦ Bullish (> +0.5%): Indicates a positive structural bias. Long setups align with the macro flow.
◦ Bearish (< -0.5%): Indicates structural weakness. Short setups are statistically favored.
◦ Neutral: Represents a ranging environment where the Confluence Score becomes the primary weighting factor.
B. Composite Confluence Score (Signal Confidence)
• Definition: A probability metric derived from the synchronization of Volatility (Impulse Core), Momentum (Ribbon), and Trend (Baseline).
• Grading Scale:
Strong Buy/Sell (> 7.0 / < 3.0): Indicates full alignment across all three vectors. Represents a "Prime Setup" eligible for standard position sizing.
Buy/Sell (5.0–7.0 / 3.0–5.0): Indicates a valid trend but with moderate volatility confirmation.
Neutral: Signals conflicting data (e.g., Bullish Momentum vs. Bearish Structure). Trading is not recommended ("No-Trade Zone").
C. Statistical Deviation Status (Mean Reversion)
• Logic: Utilizes Bollinger Band deviation principles to quantify how far price has stretched from the statistical mean (20 SMA).
• Alert States:
Over-Extended (> 2.0 SD): Warning that price is statistically likely to revert to the mean (Elastic Snap-back), even if the trend remains technically valid. New entries are discouraged in this zone.
Normal: Price is within standard distribution limits, suitable for trend-following entries.
D. Volatility Regime Classification
• Metric: Compares current ATR against a 100-period historical baseline to categorize the market state.
• Regimes:
Low Volatility (Lvl < 1.0): Market Compression. Often precedes volatility expansion events.
Mid Volatility (Lvl 1.0 - 1.5): Standard operating environment.
High Volatility (Lvl > 1.5): Elevated market stress. Risk parameters should be adjusted (e.g., reduced position size) to account for increased variance.
E. Performance Telemetry
• Function: Displays the historical reliability of the Trend Baseline for the current asset and timeframe.
• Operational Threshold: If the displayed Win Rate falls below 40%, it suggests the current market behavior is incoherent (choppy) and does not respect trend logic. In such cases, switching assets or timeframes is recommended.
Operational Protocols & Signal Decoding
Visual Interpretation Standards
• Laminar Flow (Trade Confirmation): A valid trend is visually confirmed when the 15-layer SMA Ribbon is fully expanded and parallel. This indicates distributed momentum across timeframes.
• Consolidation (No-Trade): If the ribbon appears twisted, knotted, or compressed, the market lacks a unified directional vector.
• Baseline Interaction: The Triple-Smoothed Baseline acts as a dynamic support/resistance filter. Long positions remain valid only while price sustains above this structure.
System Calibration (Settings)
• Adaptive Signal Filtering (Prev. Anti-Greed): Enabled by default. This logic automatically raises the required trend slope threshold following consecutive wins to mitigate behavioral bias.
• Impulse Sensitivity: Controls the reactivity of the Volatility Core. Higher settings capture faster moves but may introduce more noise.
⚙️ Chapter 6: System Configuration & Alert Guide
This section provides a complete breakdown of every adjustable setting within Impulse Reactor to assist you in tailoring the engine to your specific needs.
🌐 LANGUAGE SETTINGS (Localization)
◦ Select Language (Default: English):
Function: Instantly translates all chart labels, dashboard texts into your preferred language.
Supported: English, Korean, Chinese, Spanish
⚡ IMPULSE CORE SETTINGS (Volatility Engine)
◦ Deviation Lookback (Default: 30): The period used to calculate the standard deviation of volatility.
Role: Sets the baseline for normalizing momentum. Higher values make the core smoother but slower to react.
◦ Fast Pulse Length (Default: 10): The short-term ATR period.
Role: Detects rapid volatility expansion.
◦ Slow Pulse Length (Default: 30): The long-term ATR baseline.
Role: Establishes the background volatility level. The core signal is derived from the divergence between Fast and Slow pulses.
🎯 TP/SL SETTINGS (Risk Management)
◦ SL/TP Fibonacci (Default: 0.786 / 1.618): Selects the Fibonacci ratio used for risk calculation.
◦ SL/TP Multiplier (Default: 1.5 / 2): Applies a multiplier to the ATR-based bands.
Role: Expands or contracts the Take Profit and Stop Loss boxes. Increase these values for higher volatility assets (like Altcoins) to avoid premature stop-outs.
◦ ATR Length (Default: 14): The lookback period for calculating the Average True Range used in risk geometry.
◦ Use Soft Stop (Close Basis):
Role: If enabled, Stop Loss alerts only trigger if a candle closes beyond the invalidation level. This prevents being stopped out by wick manipulations.
🔊 RIBBON SETTINGS (Momentum Visualization)
◦ Show SMA Ribbon: Toggles the visibility of the 15-layer gradient ribbon.
◦ Ribbon Line Count (Default: 15): The number of SMA lines in the ribbon array.
◦ Ribbon Start Length (Default: 2) & Step (Default: 1): Defines the spread of the ribbon.
Role: Controls the "thickness" of the momentum density visualization. A wider step creates a broader ribbon, useful for higher timeframes.
📎 DISPLAY OPTIONS
◦ Show Entry Lines / TP/SL Box / Position Labels / S/R Levels / Dashboard: Toggles individual visual elements on the chart to reduce clutter.
◦ Show SAR+MACD Glow: Enables the secondary confirmation shapes (triangles/circles) above/below candles.
📈 TREND BASELINE (Structural Filter)
◦ Supertrend Factor (Default: 12) & ATR Period (Default: 90): Controls the sensitivity of the underlying Supertrend algorithm used for the baseline calculation.
◦ WMA Length (40) & EMA Length (14): The smoothing periods for the Triple-Smoothed Baseline.
◦ Min Trend Duration (Default: 10): The minimum number of bars the trend must be established before a signal is considered valid.
🧠 SMART EXIT (Dynamic Liquidity)
◦ Use Smart Exit: Enables the momentum exhaustion logic.
◦ Exit Threshold Score (Default: 3): The sensitivity level for triggering a Smart Exit. Lower values trigger earlier exits.
◦ Average Period (168) & Min Hold Bars (5): Defines the rolling window for momentum decay analysis and the minimum duration a trade must be held before Smart Exit logic activates.
🛡️ TRAILING STOP (Step)
◦ Use Trailing Stop: Activates the step-function trailing mechanism.
◦ Step 1 Activation % (0.5) & Offset % (0.5): The price must move 0.5% in your favor to arm the first trail level, which sets a stop 0.5% behind price.
◦ Step 2 Activation % (1) & Offset % (0.2): Once price moves 1%, the trail tightens to 0.2%, securing the position.
🌀 SAR & MACD SETTINGS (Secondary Confirmation)
◦ SAR Start/Increment/Max: Standard Parabolic SAR parameters.
◦ SAR Score Scaling (ATR): Adjusts how much weight the SAR signal has in the overall confluence score.
◦ MACD Fast/Slow/Signal: Standard MACD parameters used for the "Glow" signals.
🔄 ANTI-GREED LOGIC (Behavioral Bias)
◦ Strict Entry after Win: Enables the negative feedback loop.
◦ Strict Multiplier (Default: 1.1): Increases the entry difficulty by 10% after each win.
Role: Prevents overtrading and entering at the top of an extended trend.
🌍 HTF FILTER (Multi-Timeframe)
◦ Use Auto-Adaptive HTF Filter: Automatically selects a higher timeframe (e.g., 1H -> 4H) to filter signals.
◦ Bypass HTF on Steep Trigger: Allows an entry even against the HTF trend if the local momentum slope is exceptionally steep (catch powerful reversals).
📉 RSI PEAK & CHOPPINESS
◦ RSI Peak Exit (Instant): Triggers an immediate exit if a sharp RSI pivot (V-shape) is detected.
◦ Choppiness Filter: Suppresses signals if the Choppiness Index is above the threshold (Default: 60), indicating a flat market.
📐 SLOPE TRIGGER LOGIC
◦ Force Entry on Steep Slope: Overrides other filters if the price angle is extremely vertical (high velocity).
◦ Slope Sensitivity (1.5): The angle required to trigger this override.
⛔ FLAT MARKET FILTER (ADX & ATR)
◦ Use ADX Filter: Blocks signals if ADX is below the threshold (Default: 20), indicating no trend.
◦ Use ATR Flat Filter: Blocks signals if volatility drops below a critical level (dead market).
🔔 Alert Configuration Guide
Impulse Reactor is designed with a comprehensive suite of alert conditions, allowing you to automate your trading or receive real-time notifications for specific market events.
How to Set Up:
Click the "Alert" (Clock) icon in the TradingView toolbar.
Select "Impulse Reactor " from the Condition dropdown.
Choose one of the specific trigger conditions below:
🚀 Entry Signals (Trend Initiation)
Long Entry:
Trigger: Fires when a confirmed Bullish Setup is detected (Momentum + Volatility + Structure align).
Usage: Use this to enter new Long positions.
Short Entry:
Trigger: Fires when a confirmed Bearish Setup is detected.
Usage: Use this to enter new Short positions.
🎯 Profit Taking (Target Levels)
Long TP:
Trigger: Fires when price hits the calculated Take Profit level for a Long trade.
Usage: Automate partial or full profit taking.
Short TP:
Trigger: Fires when price hits the calculated Take Profit level for a Short trade.
Usage: Automate partial or full profit taking.
🛡️ Defensive Exits (Risk Management)
Smart Exit:
Trigger: Fires when the system detects momentum decay or statistical exhaustion (even if the trend hasn't fully reversed).
Usage: Recommended for tightening stops or closing positions early to preserve gains.
Overbought / Oversold:
Trigger: Fires when the ribbon extends into extreme zones.
Usage: Warning signal to prepare for a potential reversal or pullback.
💡 Secondary Confirmation (Confluence)
SAR+MACD Bullish:
Trigger: Fires when Parabolic SAR and MACD align bullishly with the main trend.
Usage: Ideal for Pyramiding (adding to an existing winning position).
SAR+MACD Bearish:
Trigger: Fires when Parabolic SAR and MACD align bearishly.
Usage: Ideal for adding to short positions.
⚠️ Chapter 7: Conclusion & Risk Disclosure
Methodological Synthesis
Impulse Reactor represents a shift from reactive price tracking to proactive energy analysis. By decomposing market activity into its atomic components — Volatility, Momentum, and Structure — and reconstructing them into a coherent decision model, the system aims to provide a quantitative framework for market engagement. It is designed not to predict the future, but to identify high-probability conditions where kinetic energy and trend structure align.
Disclaimer & Risk Warnings
◦ Educational Purpose Only
This indicator, including all associated code, documentation, and visual outputs, is provided strictly for educational and informational purposes. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments.
◦ No Guarantee of Performance
Past performance is not indicative of future results. All metrics displayed on the dashboard (including "Win Rate" and "P&L") are theoretical calculations based on historical data. These figures do not account for real-world trading factors such as slippage, liquidity gaps, spread costs, or broker commissions.
◦ High-Risk Warning
Trading cryptocurrencies, futures, and leveraged financial products involves a substantial risk of loss. The use of leverage can amplify both gains and losses. Users acknowledge that they are solely responsible for their trading decisions and should conduct independent due diligence before executing any trades.
◦ Software Limitations
The software is provided "as is" without warranty. Users should be aware that market data feeds on analysis platforms may experience latency or outages, which can affect signal generation accuracy.
Force Pulse█ OVERVIEW
Force Pulse is a fast-reacting oscillator that measures the internal strength of market sides by analyzing the aggregated dominance of bulls and bears based on candle size.
The indicator normalizes this difference into a 0–100 range, generates signals (OB/OS, midline cross, MA midline cross), and detects divergences between price and the oscillator.
It also offers advanced visualization, signal markers, and alerts, making it a versatile tool suitable for many trading styles.
█ CONCEPTS
Force Pulse was designed as a universal tool that can be applied to various trading strategies depending on its settings:
- increasing the period lengths and smoothing transforms it into a momentum/trend indicator, revealing a stable dominance of one market side.
- Lowering these parameters turns it into a peak/low detector, ideal for contrarian and mean-reversion strategies.
The oscillator analyzes the relationship between the sum of bullish and bearish candles over a selected period, based on:
- candle body size, or
- average candle body size (AVG Body).
Depending on the selected mode, OB/OS levels should be adjusted, as value dynamics differ between modes.
The output is normalized to 0–100, where:
> 50 – bullish dominance,
< 50 – bearish dominance.
The additional MA line is derived from smoothed oscillator values and serves as a signal line for midline crosses and as a trend filter.
The indicator also detects divergences (HL/LL) between price and the oscillator.
█ FEATURES
Bull & Bear Strength:
- Calculations are based on Body or AVG Body – mode selection requires adjusting OB/OS levels.
- Bullish and bearish candle values are summed separately.
- All results are normalized to the 0–100 scale.
Force Pulse Oscillator:
- The main line reflects the current dominance of either market side.
Dynamic colors:
- Green – above 50,
- Red – below 50.
Signal MA:
- SMA based on oscillator values functions as a signal line.
- Helps detect momentum shifts and generates signals via midline crosses.
- Can serve as a trend confirmation filter.
Overbought / Oversold:
- Configurable OB/OS levels, also for the MA line.
- Dynamic OB/OS line colors: when the MA line exceeds the defined threshold (e.g., MA > maOverbought or MA < maOversold), OB/OS lines change color (red/green).
- This often signals a potential reversal or correction and may act as additional confirmation for oscillator-generated signals.
Divergences:
- Detection based on swing pivots:
- Bullish: price LL, oscillator HL
- Bearish: price HH, oscillator LH
- Displayed as “Bull” / “Bear” labels.
Signals:
Supports multiple signal types:
- Overbought/Oversold Cross
- Midline Cross
- MA Midline Cross (based on the signal MA line)
- Signals appear as triangles above/below the oscillator.
Visualization:
- Gradient options for lines and levels.
- Full customization of colors, transparency, and line thickness.
Alerts available for:
- Divergences
- OB/OS crossings
- Midline crossings
- MA midline crossings
█ HOW TO USE
Add the indicator to your TradingView chart → Indicators → search “Force Pulse”
Parameter Configuration
Calculation Settings:
- Calculation Period (lookback) – defines the strength calculation window.
Force Mode (Body / AVG Body):
- Body – faster response, higher sensitivity.
- AVG Body – more stable output; adjust band levels and periods to your strategy.
- EMA Smoothing (smoothLen) – reduces oscillator noise.
- MA Length – length of the signal line (SMA).
Threshold Levels:
- Set Overbought/Oversold levels for both the oscillator and the MA line.
- Adjust levels depending on Body / AVG Body mode.
Divergence Detection:
- Enable/disable divergence detection.
- PivotLength affects both delay and signal quality.
- Signal Settings: Choose one or multiple signal types.
- Style & Colors: Full control over color schemes, gradients, and transparency.
Signal Interpretation
BUY:
- Oscillator leaves oversold (OS crossover).
- Midline cross upward.
- MA crosses the midline from below.
- Bullish divergence.
SELL:
- Oscillator leaves overbought (drops below OB).
- Midline cross downward.
- MA crosses the midline from above.
- Bearish divergence.
Trend / Momentum:
-Longer periods and stronger smoothing → stable directional signals.
-MA as a trend filter: e.g., signal line above the midline (50) and MA pointing upward indicates continuation of a bullish impulse.
Contrarian / Mean Reversion:
- Short periods → rapid detection of peaks and troughs; ideal for contrarian signals and pullback entries.
█ APPLICATIONS
- Trend Trading: Using midline and MA midline crosses to determine direction.
- Reversal Trading: OB/OS levels and divergences help identify reversals.
- Scalping & Intraday: Short settings + signal line above the midline with bullish MA → shows short-term impulse and continuation.
- Swing Trading: Longer MA and higher lookback provide a stable view of market-side dominance.
- Momentum Analysis: Force Pulse highlights the strength of the wave before price movement occurs.
█ NOTES
- In strong trends, the oscillator may stay in extreme zones for a long time — this reflects dominance, not necessarily a reversal signal.
- Divergences are more reliable on higher timeframes.
- OB/OS levels should be tailored to Body/AVG Body mode and the instrument.
- Best results come from combining the indicator with other tools (S/R, market structure, volume).
