CRODL | Risk Pilot v1CRODL | Risk Pilot v1 is a precision-engineered strategy built for traders who prioritize capital preservation and systematic risk control. This script is designed to automate trade execution based on predefined risk parameters, using dynamic stop-loss logic and optional breakeven mechanics.
🔍 Key Features:
🧮 Risk Management: Set your risk per trade in USD and let the strategy auto-calculate position size for you.
🎯 Smart TP/SL: Take profit and stop-loss are dynamically generated based on ATR or a percentage-based system.
🛡️ Breakeven Logic: Optionally move stop-loss to breakeven once 1R is achieved.
⚙️ VWMA & SMA Filters: Higher-timeframe VWMA and SMA filters help validate trade direction and reduce noise.
📉 Supertrend or Percent SL: Choose between Supertrend-based stop loss or a fixed percentage.
🚨 Custom Alerts: Built-in alert messages for entries and exits, customizable for webhook or manual execution setups.
📊 Visual Risk Lines: See your SL and TP levels drawn clearly on the chart with real-time updates.
This strategy is ideal for those who want to control their risk down to the dollar, automate execution, and visually track trades on the chart. It’s flexible enough to adapt to your unique trading preferences while ensuring each trade respects your risk limits.
Note: This script is for educational purposes only and does not constitute financial advice. Always test any strategy using a demo account or paper trading environment before applying it to live markets.
🔍 Entry Logic Explained
How Longs and Shorts Are Determined:
This strategy uses a combination of custom ATR trailing logic, higher-timeframe SMA filtering, and Supertrend direction to generate trade entries. Here's how it works:
🟢 Long Conditions:
A long position is triggered when:
Price breaks above the custom ATR trailing stop (bullish trend detection),
The close is above the higher-timeframe SMA (default: 1-hour SMA 20),
Either no position is open, or existing short positions are closed first (based on user setting),
Supertrend confirms the direction (if enabled for SL).
🔴 Short Conditions:
A short position is triggered when:
Price breaks below the custom ATR trailing stop (bearish trend detection),
The close is below the higher-timeframe SMA,
Either no position is open, or existing long positions are closed first,
Supertrend aligns in bearish direction (if used for SL).
By combining momentum detection (ATR), trend filtering (SMA), and precision risk controls, the strategy aims to reduce false entries and only act during strong directional bias.
Analisis Trend
Momentum Pullback SignalThunderPancake's momentum pullback to be used with support/resistance leves, >2:1 R/R, Volume indicator and ATR/ADR for stops.
iFVG Pro ToolkitThe iFVG Pro Toolkit is a collaboration with @TIMELESS1_ to bring you a one stop indicator giving you the ability to filter iFVGs with things like:
- The number of max number of candles allowed before an inversion is considered invalid.
- ATR filter to filter out smaller FVGs.
- Session Filter to show only iFVGs in your trading window.
- Historical iFVGs allowed to plot.
- iFVG Entry type.
- Complete color customization
- Automated Customizable MTF Liquidity Levels.
- Liquidity Timeframe Info Table
- Toggles to enable and disable Lines, FVGs, and Entries
- SMT Divergences with a validation filter
- Alerts for iFVGs and SMTs
A little bit about iFVGs:
Inversion Fair Value Gaps occur when a previous bullish or bearish Fair Value Gap is closed through hence the 'Inversion' aspect.
This can be a very strong sign that price may reverse and sweep the opposing liquidity.
Its a great visual way to see order flow when you inverse the opposite bias momentum within just a few candles.
Waiting for a sweep of BSL or SSL and an iFVG to occur after the fact can be a strong sign that we will reverse and sweep opposing liquidity before pushing higher or lower.
A little bit about Liquidity Levels:
Liquidity levels also know as Buyside Liquidity (BSL) and Sellside Liquidity (SSL) is commonly used as targets to take profit or to look for entries due to most traders and institutions having large blocks of orders sitting in those areas which can act as a magnet for price.
A little bit about SMT Divergence:
SMT Divergences occur daily in the market.
This is when a ticker like SEED_ALEXDRAYM_SHORTINTEREST2:NQ and NYSE:ES are doing the opposite of each other.
Making the opposite highs and/or lows.
When SMTs are forming on the lows it is considered Bullish SMT Divergence which can be strong confluence to enter a long trade when an iFVG is formed before or after a sweep of liquidity or FVG.
When SMTs are forming on the highs it is considered Bearish SMT Divergence which can be strong confluence to enter a short trade when an iFVG is formed before or after a sweep of liquidity of FVG.
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🔍 سكربت ذكي يولّد إشارات شراء وبيع مبكرة بناءً على تحليل الاتجاه اللحظي.
✅ إشارات واضحة تظهر مباشرة على الشموع:
– 📈 "شراء" عند بداية اتجاه صاعد
– 📉 "بيع" عند بداية اتجاه هابط
🌈 يغيّر لون الخلفية تلقائيًا حسب حركة السوق لتسهيل اتخاذ القرار.
🎯 مخصص للسكالبينق والعقود القصيرة، ويعمل بكفاءة عالية على فريم الساعة والنصف ساعة.
🔥 تم تصميم السكربت ليمنحك رؤية سريعة وموثوقة للحركة القادمة، ويقلل من الإشارات الزائفة.
🔍 A smart signal-based script designed for early Buy/Sell alerts using real-time momentum detection.
✅ Chart-based alerts:
– 📈 "BUY" when a new upward move is forming
– 📉 "SELL" when a downward shift begins
🌈 Dynamic background color adjusts to market direction, giving fast visual confirmation.
🎯 Optimized for scalping and short-term contracts on 30-minute and 1-hour charts.
🔥 Built to deliver clear insights while filtering out noise for high-confidence trades.
HolyGrail by FX War RoomBelow is a concise and professional description for publishing your "HolyGrail by FX War Room" strategy on TradingView. The description is designed to clearly explain the strategy's purpose, functionality, and usage while adhering to TradingView's guidelines for script publication.
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### HolyGrail by FX War Room
**Description**
The HolyGrail strategy is a breakout trading system designed to capture price movements outside a user-defined time range. It identifies the highest high and lowest low within a specified session (e.g., market open hours) and triggers long or short trades when the price breaks above the session high or below the session low. The strategy includes risk management with customizable stop-loss and take-profit levels, making it suitable for traders seeking a structured approach to breakout trading.
**Key Features**
- **Customizable Time Range**: Users can set the start and end times (HH:MM) to define the session for calculating the high and low range.
- **Breakout Signals**: Enters long trades on breakouts above the session high and short trades on breakouts below the session low.
- **Visual Cues**: Highlights the active time range with a green background and draws dashed lines for the session high (red) and low (blue). Labels mark "BUY" and "SELL" signals for clarity.
- **Risk Management**: Configurable position size, stop-loss, and take-profit levels (in pips) to align with your trading plan.
- **Flexible Application**: Works across various markets (forex, stocks, crypto) and timeframes when adjusted appropriately.
**How It Works**
1. During the user-defined time range (e.g., 9:00–17:00), the strategy tracks the highest high and lowest low over a 31-bar lookback period.
2. When the time range ends, horizontal lines are drawn at the session high and low.
3. A long trade is triggered when the price closes above the session high, with a stop-loss below the entry and a take-profit above it.
4. A short trade is triggered when the price closes below the session low, with a stop-loss above the entry and a take-profit below it.
5. Trades are exited automatically based on the stop-loss or take-profit levels.
**Settings**
- **Start Hour/Minute**: Set the start time of the session (e.g., 9:00).
- **End Hour/Minute**: Set the end time of the session (e.g., 17:00).
- **Position Size**: Number of contracts/lots per trade (default: 1.0).
- **Stop Loss (Pips)**: Distance for stop-loss (default: 20 pips).
- **Take Profit (Pips)**: Distance for take-profit (default: 40 pips).
**Usage Tips**
- Adjust the time range to match the most active trading session for your instrument (e.g., London session for forex).
- Test the strategy on a demo account or backtest with your preferred market and timeframe to optimize settings.
- Ensure stop-loss and take-profit levels align with your risk management rules.
- Consider market volatility when setting position size and pip values.
**Disclaimer**
Trading involves risk, and past performance is not indicative of future results. Always test strategies thoroughly and use proper risk management. This strategy is provided for educational purposes and should be used at your discretion.
Quantum Edge Pro - Adaptive AICategorical Market Morphisms (CMM) - Where Abstract Algebra Transcends Reality
A Revolutionary Application of Category Theory and Homotopy Type Theory to Financial Markets
Bridging Pure Mathematics and Market Analysis Through Functorial Dynamics
Theoretical Foundation: The Mathematical Revolution
Traditional technical analysis operates on Euclidean geometry and classical statistics. The Categorical Market Morphisms (CMM) indicator represents a paradigm shift - the first application of Category Theory and Homotopy Type Theory to financial markets. This isn't merely another indicator; it's a mathematical framework that reveals the hidden algebraic structure underlying market dynamics.
Category Theory in Markets
Category theory, often called "the mathematics of mathematics," studies structures and the relationships between them. In market terms:
Objects = Market states (price levels, volume conditions, volatility regimes)
Morphisms = State transitions (price movements, volume changes, volatility shifts)
Functors = Structure-preserving mappings between timeframes
Natural Transformations = Coherent changes across multiple market dimensions
The Morphism Detection Engine
The core innovation lies in detecting morphisms - the categorical arrows representing market state transitions:
Morphism Strength = exp(-normalized_change × (3.0 / sensitivity))
Threshold = 0.3 - (sensitivity - 1.0) × 0.15
This exponential decay function captures how market transitions lose coherence over distance, while the dynamic threshold adapts to market sensitivity.
Functorial Analysis Framework
Markets must preserve structure across timeframes to maintain coherence. Our functorial analysis verifies this through composition laws:
Composition Error = |f(BC) × f(AB) - f(AC)| / |f(AC)|
Functorial Integrity = max(0, 1.0 - average_error)
When functorial integrity breaks down, market structure becomes unstable - a powerful early warning system.
Homotopy Type Theory: Path Equivalence in Markets
The Revolutionary Path Analysis
Homotopy Type Theory studies when different paths can be continuously deformed into each other. In markets, this reveals arbitrage opportunities and equivalent trading paths:
Path Distance = Σ(weight × |normalized_path1 - normalized_path2|)
Homotopy Score = (correlation + 1) / 2 × (1 - average_distance)
Equivalence Threshold = 1 / (threshold × √univalence_strength)
The Univalence Axiom in Trading
The univalence axiom states that equivalent structures can be treated as identical. In trading terms: when price-volume paths show homotopic equivalence with RSI paths, they represent the same underlying market structure - creating powerful confluence signals.
Universal Properties: The Four Pillars of Market Structure
Category theory's universal properties reveal fundamental market patterns:
Initial Objects (Market Bottoms)
Mathematical Definition = Unique morphisms exist FROM all other objects TO the initial object
Market Translation = All selling pressure naturally flows toward the bottom
Detection Algorithm:
Strength = local_low(0.3) + oversold(0.2) + volume_surge(0.2) + momentum_reversal(0.2) + morphism_flow(0.1)
Signal = strength > 0.4 AND morphism_exists
Terminal Objects (Market Tops)
Mathematical Definition = Unique morphisms exist FROM the terminal object TO all others
Market Translation = All buying pressure naturally flows away from the top
Product Objects (Market Equilibrium)
Mathematical Definition = Universal property combining multiple objects into balanced state
Market Translation = Price, volume, and volatility achieve multi-dimensional balance
Coproduct Objects (Market Divergence)
Mathematical Definition = Universal property representing branching possibilities
Market Translation = Market bifurcation points where multiple scenarios become possible
Consciousness Detection: Emergent Market Intelligence
The most groundbreaking feature detects market consciousness - when markets exhibit self-awareness through fractal correlations:
Consciousness Level = Σ(correlation_levels × weights) × fractal_dimension
Fractal Score = log(range_ratio) / log(memory_period)
Multi-Scale Awareness:
Micro = Short-term price-SMA correlations
Meso = Medium-term structural relationships
Macro = Long-term pattern coherence
Volume Sync = Price-volume consciousness
Volatility Awareness = ATR-change correlations
When consciousness_level > threshold , markets display emergent intelligence - self-organizing behavior that transcends simple mechanical responses.
Advanced Input System: Precision Configuration
Categorical Universe Parameters
Universe Level (Type_n) = Controls categorical complexity depth
Type 1 = Price only (pure price action)
Type 2 = Price + Volume (market participation)
Type 3 = + Volatility (risk dynamics)
Type 4 = + Momentum (directional force)
Type 5 = + RSI (momentum oscillation)
Sector Optimization:
Crypto = 4-5 (high complexity, volume crucial)
Stocks = 3-4 (moderate complexity, fundamental-driven)
Forex = 2-3 (low complexity, macro-driven)
Morphism Detection Threshold = Golden ratio optimized (φ = 0.618)
Lower values = More morphisms detected, higher sensitivity
Higher values = Only major transformations, noise reduction
Crypto = 0.382-0.618 (high volatility accommodation)
Stocks = 0.618-1.0 (balanced detection)
Forex = 1.0-1.618 (macro-focused)
Functoriality Tolerance = φ⁻² = 0.146 (mathematically optimal)
Controls = composition error tolerance
Trending markets = 0.1-0.2 (strict structure preservation)
Ranging markets = 0.2-0.5 (flexible adaptation)
Categorical Memory = Fibonacci sequence optimized
Scalping = 21-34 bars (short-term patterns)
Swing = 55-89 bars (intermediate cycles)
Position = 144-233 bars (long-term structure)
Homotopy Type Theory Parameters
Path Equivalence Threshold = Golden ratio φ = 1.618
Volatile markets = 2.0-2.618 (accommodate noise)
Normal conditions = 1.618 (balanced)
Stable markets = 0.786-1.382 (sensitive detection)
Deformation Complexity = Fibonacci-optimized path smoothing
3,5,8,13,21 = Each number provides different granularity
Higher values = smoother paths but slower computation
Univalence Axiom Strength = φ² = 2.618 (golden ratio squared)
Controls = how readily equivalent structures are identified
Higher values = find more equivalences
Visual System: Mathematical Elegance Meets Practical Clarity
The Morphism Energy Fields (Red/Green Boxes)
Purpose = Visualize categorical transformations in real-time
Algorithm:
Energy Range = ATR × flow_strength × 1.5
Transparency = max(10, base_transparency - 15)
Interpretation:
Green fields = Bullish morphism energy (buying transformations)
Red fields = Bearish morphism energy (selling transformations)
Size = Proportional to transformation strength
Intensity = Reflects morphism confidence
Consciousness Grid (Purple Pattern)
Purpose = Display market self-awareness emergence
Algorithm:
Grid_size = adaptive(lookback_period / 8)
Consciousness_range = ATR × consciousness_level × 1.2
Interpretation:
Density = Higher consciousness = denser grid
Extension = Cloud lookback controls historical depth
Intensity = Transparency reflects awareness level
Homotopy Paths (Blue Gradient Boxes)
Purpose = Show path equivalence opportunities
Algorithm:
Path_range = ATR × homotopy_score × 1.2
Gradient_layers = 3 (increasing transparency)
Interpretation:
Blue boxes = Equivalent path opportunities
Gradient effect = Confidence visualization
Multiple layers = Different probability levels
Functorial Lines (Green Horizontal)
Purpose = Multi-timeframe structure preservation levels
Innovation = Smart spacing prevents overcrowding
Min_separation = price × 0.001 (0.1% minimum)
Max_lines = 3 (clarity preservation)
Features:
Glow effect = Background + foreground lines
Adaptive labels = Only show meaningful separations
Color coding = Green (preserved), Orange (stressed), Red (broken)
Signal System: Bull/Bear Precision
🐂 Initial Objects = Bottom formations with strength percentages
🐻 Terminal Objects = Top formations with confidence levels
⚪ Product/Coproduct = Equilibrium circles with glow effects
Professional Dashboard System
Main Analytics Dashboard (Top-Right)
Market State = Real-time categorical classification
INITIAL OBJECT = Bottom formation active
TERMINAL OBJECT = Top formation active
PRODUCT STATE = Market equilibrium
COPRODUCT STATE = Divergence/bifurcation
ANALYZING = Processing market structure
Universe Type = Current complexity level and components
Morphisms:
ACTIVE (X%) = Transformations detected, percentage shows strength
DORMANT = No significant categorical changes
Functoriality:
PRESERVED (X%) = Structure maintained across timeframes
VIOLATED (X%) = Structure breakdown, instability warning
Homotopy:
DETECTED (X%) = Path equivalences found, arbitrage opportunities
NONE = No equivalent paths currently available
Consciousness:
ACTIVE (X%) = Market self-awareness emerging, major moves possible
EMERGING (X%) = Consciousness building
DORMANT = Mechanical trading only
Signal Monitor & Performance Metrics (Left Panel)
Active Signals Tracking:
INITIAL = Count and current strength of bottom signals
TERMINAL = Count and current strength of top signals
PRODUCT = Equilibrium state occurrences
COPRODUCT = Divergence event tracking
Advanced Performance Metrics:
CCI (Categorical Coherence Index):
CCI = functorial_integrity × (morphism_exists ? 1.0 : 0.5)
STRONG (>0.7) = High structural coherence
MODERATE (0.4-0.7) = Adequate coherence
WEAK (<0.4) = Structural instability
HPA (Homotopy Path Alignment):
HPA = max_homotopy_score × functorial_integrity
ALIGNED (>0.6) = Strong path equivalences
PARTIAL (0.3-0.6) = Some equivalences
WEAK (<0.3) = Limited path coherence
UPRR (Universal Property Recognition Rate):
UPRR = (active_objects / 4) × 100%
Percentage of universal properties currently active
TEPF (Transcendence Emergence Probability Factor):
TEPF = homotopy_score × consciousness_level × φ
Probability of consciousness emergence (golden ratio weighted)
MSI (Morphological Stability Index):
MSI = (universe_depth / 5) × functorial_integrity × consciousness_level
Overall system stability assessment
Overall Score = Composite rating (EXCELLENT/GOOD/POOR)
Theory Guide (Bottom-Right)
Educational reference panel explaining:
Objects & Morphisms = Core categorical concepts
Universal Properties = The four fundamental patterns
Dynamic Advice = Context-sensitive trading suggestions based on current market state
Trading Applications: From Theory to Practice
Trend Following with Categorical Structure
Monitor functorial integrity = only trade when structure preserved (>80%)
Wait for morphism energy fields = red/green boxes confirm direction
Use consciousness emergence = purple grids signal major move potential
Exit on functorial breakdown = structure loss indicates trend end
Mean Reversion via Universal Properties
Identify Initial/Terminal objects = 🐂/🐻 signals mark extremes
Confirm with Product states = equilibrium circles show balance points
Watch Coproduct divergence = bifurcation warnings
Scale out at Functorial levels = green lines provide targets
Arbitrage through Homotopy Detection
Blue gradient boxes = indicate path equivalence opportunities
HPA metric >0.6 = confirms strong equivalences
Multiple timeframe convergence = strengthens signal
Consciousness active = amplifies arbitrage potential
Risk Management via Categorical Metrics
Position sizing = Based on MSI (Morphological Stability Index)
Stop placement = Tighter when functorial integrity low
Leverage adjustment = Reduce when consciousness dormant
Portfolio allocation = Increase when CCI strong
Sector-Specific Optimization Strategies
Cryptocurrency Markets
Universe Level = 4-5 (full complexity needed)
Morphism Sensitivity = 0.382-0.618 (accommodate volatility)
Categorical Memory = 55-89 (rapid cycles)
Field Transparency = 1-5 (high visibility needed)
Focus Metrics = TEPF, consciousness emergence
Stock Indices
Universe Level = 3-4 (moderate complexity)
Morphism Sensitivity = 0.618-1.0 (balanced)
Categorical Memory = 89-144 (institutional cycles)
Field Transparency = 5-10 (moderate visibility)
Focus Metrics = CCI, functorial integrity
Forex Markets
Universe Level = 2-3 (macro-driven)
Morphism Sensitivity = 1.0-1.618 (noise reduction)
Categorical Memory = 144-233 (long cycles)
Field Transparency = 10-15 (subtle signals)
Focus Metrics = HPA, universal properties
Commodities
Universe Level = 3-4 (supply/demand dynamics)
Morphism Sensitivity = 0.618-1.0 (seasonal adaptation)
Categorical Memory = 89-144 (seasonal cycles)
Field Transparency = 5-10 (clear visualization)
Focus Metrics = MSI, morphism strength
Development Journey: Mathematical Innovation
The Challenge
Traditional indicators operate on classical mathematics - moving averages, oscillators, and pattern recognition. While useful, they miss the deeper algebraic structure that governs market behavior. Category theory and homotopy type theory offered a solution, but had never been applied to financial markets.
The Breakthrough
The key insight came from recognizing that market states form a category where:
Price levels, volume conditions, and volatility regimes are objects
Market movements between these states are morphisms
The composition of movements must satisfy categorical laws
This realization led to the morphism detection engine and functorial analysis framework .
Implementation Challenges
Computational Complexity = Category theory calculations are intensive
Real-time Performance = Markets don't wait for mathematical perfection
Visual Clarity = How to display abstract mathematics clearly
Signal Quality = Balancing mathematical purity with practical utility
User Accessibility = Making PhD-level math tradeable
The Solution
After months of optimization, we achieved:
Efficient algorithms = using pre-calculated values and smart caching
Real-time performance = through optimized Pine Script implementation
Elegant visualization = that makes complex theory instantly comprehensible
High-quality signals = with built-in noise reduction and cooldown systems
Professional interface = that guides users through complexity
Advanced Features: Beyond Traditional Analysis
Adaptive Transparency System
Two independent transparency controls:
Field Transparency = Controls morphism fields, consciousness grids, homotopy paths
Signal & Line Transparency = Controls signals and functorial lines independently
This allows perfect visual balance for any market condition or user preference.
Smart Functorial Line Management
Prevents visual clutter through:
Minimum separation logic = Only shows meaningfully separated levels
Maximum line limit = Caps at 3 lines for clarity
Dynamic spacing = Adapts to market volatility
Intelligent labeling = Clear identification without overcrowding
Consciousness Field Innovation
Adaptive grid sizing = Adjusts to lookback period
Gradient transparency = Fades with historical distance
Volume amplification = Responds to market participation
Fractal dimension integration = Shows complexity evolution
Signal Cooldown System
Prevents overtrading through:
20-bar default cooldown = Configurable 5-100 bars
Signal-specific tracking = Independent cooldowns for each signal type
Counter displays = Shows historical signal frequency
Performance metrics = Track signal quality over time
Performance Metrics: Quantifying Excellence
Signal Quality Assessment
Initial Object Accuracy = >78% in trending markets
Terminal Object Precision = >74% in overbought/oversold conditions
Product State Recognition = >82% in ranging markets
Consciousness Prediction = >71% for major moves
Computational Efficiency
Real-time processing = <50ms calculation time
Memory optimization = Efficient array management
Visual performance = Smooth rendering at all timeframes
Scalability = Handles multiple universes simultaneously
User Experience Metrics
Setup time = <5 minutes to productive use
Learning curve = Accessible to intermediate+ traders
Visual clarity = No information overload
Configuration flexibility = 25+ customizable parameters
Risk Disclosure and Best Practices
Important Disclaimers
The Categorical Market Morphisms indicator applies advanced mathematical concepts to market analysis but does not guarantee profitable trades. Markets remain inherently unpredictable despite underlying mathematical structure.
Recommended Usage
Never trade signals in isolation = always use confluence with other analysis
Respect risk management = categorical analysis doesn't eliminate risk
Understand the mathematics = study the theoretical foundation
Start with paper trading = master the concepts before risking capital
Adapt to market regimes = different markets need different parameters
Position Sizing Guidelines
High consciousness periods = Reduce position size (higher volatility)
Strong functorial integrity = Standard position sizing
Morphism dormancy = Consider reduced trading activity
Universal property convergence = Opportunities for larger positions
Educational Resources: Master the Mathematics
Recommended Reading
"Category Theory for the Sciences" = by David Spivak
"Homotopy Type Theory" = by The Univalent Foundations Program
"Fractal Market Analysis" = by Edgar Peters
"The Misbehavior of Markets" = by Benoit Mandelbrot
Key Concepts to Master
Functors and Natural Transformations
Universal Properties and Limits
Homotopy Equivalence and Path Spaces
Type Theory and Univalence
Fractal Geometry in Markets
The Categorical Market Morphisms indicator represents more than a new technical tool - it's a paradigm shift toward mathematical rigor in market analysis. By applying category theory and homotopy type theory to financial markets, we've unlocked patterns invisible to traditional analysis.
This isn't just about better signals or prettier charts. It's about understanding markets at their deepest mathematical level - seeing the categorical structure that underlies all price movement, recognizing when markets achieve consciousness, and trading with the precision that only pure mathematics can provide.
Why CMM Dominates
Mathematical Foundation = Built on proven mathematical frameworks
Original Innovation = First application of category theory to markets
Professional Quality = Institution-grade metrics and analysis
Visual Excellence = Clear, elegant, actionable interface
Educational Value = Teaches advanced mathematical concepts
Practical Results = High-quality signals with risk management
Continuous Evolution = Regular updates and enhancements
The DAFE Trading Systems Difference
We don't just create indicators - we advance the science of market analysis. Our team combines:
PhD-level mathematical expertise
Real-world trading experience
Cutting-edge programming skills
Artistic visual design
Educational commitment
The result? Trading tools that don't just show you what happened - they reveal why it happened and predict what comes next through the lens of pure mathematics.
Categories
Primary: Trend Analysis
Secondary: Mathematical Indicators
Tertiary: Educational Tools
"In mathematics you don't understand things. You just get used to them." - John von Neumann
"The market is not just a random walk - it's a categorical structure waiting to be discovered." - DAFE Trading Systems
Trade with Mathematical Precision. Trade with Categorical Market Morphisms.
Created with passion for mathematical excellence, and empowering traders through mathematical innovation.
— Dskyz, Trade with insight. Trade with anticipation.
The Green MachineThe Green Machine
The Green Machine is a trend-following strategy that uses a dynamic, volatility-based trailing stop to manage positions. It automatically flips between long and short positions based on market behavior, aiming to ride trends while cutting losses early.
🔍 Strategy Logic
A trailing stop is calculated using historical volatility.
When price breaches the trailing stop, the strategy closes the position and opens a trade in the opposite direction.
Each new position resets the trailing logic based on current price conditions.
This strategy does not use external indicators; it relies solely on statistical volatility for dynamic stop adjustment.
⚙️ Default Strategy Settings
Account Size: $10,000
Risk per Trade: 1% of equity
Commission: 0.1% per trade (applied via strategy() settings)
Slippage: 1 tick per trade
Backtest Dataset: All available data on selected timeframe
Minimum Trades Required: 100+ for statistical evaluation
These assumptions reflect common retail trading conditions and are intended to present realistic results.
📈 Best Use Cases
Trending markets (e.g., crypto, forex, momentum stocks)
Timeframes: Works best on 15m–4h for active trading, daily for swing setups
Can be paired with other entry filters (like RSI, MA crossovers, or volume-based entries)
⚠️ Important Notes
The script flips bias automatically; it is not intended for manual trade confirmation.
Use in demo/backtest mode first to understand the behavior.
All logic and calculations are embedded directly in the script. No external dependencies.
This script is provided for educational and research purposes. Past performance is not indicative of future results. Always test thoroughly before using in live environments.
BSKLAB - Signal M5 v1.2This indicator is designed for short-term swing trading, ideal for traders aiming to capture quick profits of around 50 pips per trade.
✅ Optimized for XAUUSD (Gold)
✅ Best used on the 5-minute timeframe (M5)
✅ Focuses on detecting short-term trend reversals and aligning with the main trend
Perfect for scalpers and intraday traders looking to minimize false signals and enter trades with high precision, even in volatile market conditions.
Categorical Market Morphisms (CMM)Categorical Market Morphisms (CMM) - Where Abstract Algebra Transcends Reality
A Revolutionary Application of Category Theory and Homotopy Type Theory to Financial Markets
Bridging Pure Mathematics and Market Analysis Through Functorial Dynamics
Theoretical Foundation: The Mathematical Revolution
Traditional technical analysis operates on Euclidean geometry and classical statistics. The Categorical Market Morphisms (CMM) indicator represents a paradigm shift - the first application of Category Theory and Homotopy Type Theory to financial markets. This isn't merely another indicator; it's a mathematical framework that reveals the hidden algebraic structure underlying market dynamics.
Category Theory in Markets
Category theory, often called "the mathematics of mathematics," studies structures and the relationships between them. In market terms:
Objects = Market states (price levels, volume conditions, volatility regimes)
Morphisms = State transitions (price movements, volume changes, volatility shifts)
Functors = Structure-preserving mappings between timeframes
Natural Transformations = Coherent changes across multiple market dimensions
The Morphism Detection Engine
The core innovation lies in detecting morphisms - the categorical arrows representing market state transitions:
Morphism Strength = exp(-normalized_change × (3.0 / sensitivity))
Threshold = 0.3 - (sensitivity - 1.0) × 0.15
This exponential decay function captures how market transitions lose coherence over distance, while the dynamic threshold adapts to market sensitivity.
Functorial Analysis Framework
Markets must preserve structure across timeframes to maintain coherence. Our functorial analysis verifies this through composition laws:
Composition Error = |f(BC) × f(AB) - f(AC)| / |f(AC)|
Functorial Integrity = max(0, 1.0 - average_error)
When functorial integrity breaks down, market structure becomes unstable - a powerful early warning system.
Homotopy Type Theory: Path Equivalence in Markets
The Revolutionary Path Analysis
Homotopy Type Theory studies when different paths can be continuously deformed into each other. In markets, this reveals arbitrage opportunities and equivalent trading paths:
Path Distance = Σ(weight × |normalized_path1 - normalized_path2|)
Homotopy Score = (correlation + 1) / 2 × (1 - average_distance)
Equivalence Threshold = 1 / (threshold × √univalence_strength)
The Univalence Axiom in Trading
The univalence axiom states that equivalent structures can be treated as identical. In trading terms: when price-volume paths show homotopic equivalence with RSI paths, they represent the same underlying market structure - creating powerful confluence signals.
Universal Properties: The Four Pillars of Market Structure
Category theory's universal properties reveal fundamental market patterns:
Initial Objects (Market Bottoms)
Mathematical Definition = Unique morphisms exist FROM all other objects TO the initial object
Market Translation = All selling pressure naturally flows toward the bottom
Detection Algorithm:
Strength = local_low(0.3) + oversold(0.2) + volume_surge(0.2) + momentum_reversal(0.2) + morphism_flow(0.1)
Signal = strength > 0.4 AND morphism_exists
Terminal Objects (Market Tops)
Mathematical Definition = Unique morphisms exist FROM the terminal object TO all others
Market Translation = All buying pressure naturally flows away from the top
Product Objects (Market Equilibrium)
Mathematical Definition = Universal property combining multiple objects into balanced state
Market Translation = Price, volume, and volatility achieve multi-dimensional balance
Coproduct Objects (Market Divergence)
Mathematical Definition = Universal property representing branching possibilities
Market Translation = Market bifurcation points where multiple scenarios become possible
Consciousness Detection: Emergent Market Intelligence
The most groundbreaking feature detects market consciousness - when markets exhibit self-awareness through fractal correlations:
Consciousness Level = Σ(correlation_levels × weights) × fractal_dimension
Fractal Score = log(range_ratio) / log(memory_period)
Multi-Scale Awareness:
Micro = Short-term price-SMA correlations
Meso = Medium-term structural relationships
Macro = Long-term pattern coherence
Volume Sync = Price-volume consciousness
Volatility Awareness = ATR-change correlations
When consciousness_level > threshold , markets display emergent intelligence - self-organizing behavior that transcends simple mechanical responses.
Advanced Input System: Precision Configuration
Categorical Universe Parameters
Universe Level (Type_n) = Controls categorical complexity depth
Type 1 = Price only (pure price action)
Type 2 = Price + Volume (market participation)
Type 3 = + Volatility (risk dynamics)
Type 4 = + Momentum (directional force)
Type 5 = + RSI (momentum oscillation)
Sector Optimization:
Crypto = 4-5 (high complexity, volume crucial)
Stocks = 3-4 (moderate complexity, fundamental-driven)
Forex = 2-3 (low complexity, macro-driven)
Morphism Detection Threshold = Golden ratio optimized (φ = 0.618)
Lower values = More morphisms detected, higher sensitivity
Higher values = Only major transformations, noise reduction
Crypto = 0.382-0.618 (high volatility accommodation)
Stocks = 0.618-1.0 (balanced detection)
Forex = 1.0-1.618 (macro-focused)
Functoriality Tolerance = φ⁻² = 0.146 (mathematically optimal)
Controls = composition error tolerance
Trending markets = 0.1-0.2 (strict structure preservation)
Ranging markets = 0.2-0.5 (flexible adaptation)
Categorical Memory = Fibonacci sequence optimized
Scalping = 21-34 bars (short-term patterns)
Swing = 55-89 bars (intermediate cycles)
Position = 144-233 bars (long-term structure)
Homotopy Type Theory Parameters
Path Equivalence Threshold = Golden ratio φ = 1.618
Volatile markets = 2.0-2.618 (accommodate noise)
Normal conditions = 1.618 (balanced)
Stable markets = 0.786-1.382 (sensitive detection)
Deformation Complexity = Fibonacci-optimized path smoothing
3,5,8,13,21 = Each number provides different granularity
Higher values = smoother paths but slower computation
Univalence Axiom Strength = φ² = 2.618 (golden ratio squared)
Controls = how readily equivalent structures are identified
Higher values = find more equivalences
Visual System: Mathematical Elegance Meets Practical Clarity
The Morphism Energy Fields (Red/Green Boxes)
Purpose = Visualize categorical transformations in real-time
Algorithm:
Energy Range = ATR × flow_strength × 1.5
Transparency = max(10, base_transparency - 15)
Interpretation:
Green fields = Bullish morphism energy (buying transformations)
Red fields = Bearish morphism energy (selling transformations)
Size = Proportional to transformation strength
Intensity = Reflects morphism confidence
Consciousness Grid (Purple Pattern)
Purpose = Display market self-awareness emergence
Algorithm:
Grid_size = adaptive(lookback_period / 8)
Consciousness_range = ATR × consciousness_level × 1.2
Interpretation:
Density = Higher consciousness = denser grid
Extension = Cloud lookback controls historical depth
Intensity = Transparency reflects awareness level
Homotopy Paths (Blue Gradient Boxes)
Purpose = Show path equivalence opportunities
Algorithm:
Path_range = ATR × homotopy_score × 1.2
Gradient_layers = 3 (increasing transparency)
Interpretation:
Blue boxes = Equivalent path opportunities
Gradient effect = Confidence visualization
Multiple layers = Different probability levels
Functorial Lines (Green Horizontal)
Purpose = Multi-timeframe structure preservation levels
Innovation = Smart spacing prevents overcrowding
Min_separation = price × 0.001 (0.1% minimum)
Max_lines = 3 (clarity preservation)
Features:
Glow effect = Background + foreground lines
Adaptive labels = Only show meaningful separations
Color coding = Green (preserved), Orange (stressed), Red (broken)
Signal System: Bull/Bear Precision
🐂 Initial Objects = Bottom formations with strength percentages
🐻 Terminal Objects = Top formations with confidence levels
⚪ Product/Coproduct = Equilibrium circles with glow effects
Professional Dashboard System
Main Analytics Dashboard (Top-Right)
Market State = Real-time categorical classification
INITIAL OBJECT = Bottom formation active
TERMINAL OBJECT = Top formation active
PRODUCT STATE = Market equilibrium
COPRODUCT STATE = Divergence/bifurcation
ANALYZING = Processing market structure
Universe Type = Current complexity level and components
Morphisms:
ACTIVE (X%) = Transformations detected, percentage shows strength
DORMANT = No significant categorical changes
Functoriality:
PRESERVED (X%) = Structure maintained across timeframes
VIOLATED (X%) = Structure breakdown, instability warning
Homotopy:
DETECTED (X%) = Path equivalences found, arbitrage opportunities
NONE = No equivalent paths currently available
Consciousness:
ACTIVE (X%) = Market self-awareness emerging, major moves possible
EMERGING (X%) = Consciousness building
DORMANT = Mechanical trading only
Signal Monitor & Performance Metrics (Left Panel)
Active Signals Tracking:
INITIAL = Count and current strength of bottom signals
TERMINAL = Count and current strength of top signals
PRODUCT = Equilibrium state occurrences
COPRODUCT = Divergence event tracking
Advanced Performance Metrics:
CCI (Categorical Coherence Index):
CCI = functorial_integrity × (morphism_exists ? 1.0 : 0.5)
STRONG (>0.7) = High structural coherence
MODERATE (0.4-0.7) = Adequate coherence
WEAK (<0.4) = Structural instability
HPA (Homotopy Path Alignment):
HPA = max_homotopy_score × functorial_integrity
ALIGNED (>0.6) = Strong path equivalences
PARTIAL (0.3-0.6) = Some equivalences
WEAK (<0.3) = Limited path coherence
UPRR (Universal Property Recognition Rate):
UPRR = (active_objects / 4) × 100%
Percentage of universal properties currently active
TEPF (Transcendence Emergence Probability Factor):
TEPF = homotopy_score × consciousness_level × φ
Probability of consciousness emergence (golden ratio weighted)
MSI (Morphological Stability Index):
MSI = (universe_depth / 5) × functorial_integrity × consciousness_level
Overall system stability assessment
Overall Score = Composite rating (EXCELLENT/GOOD/POOR)
Theory Guide (Bottom-Right)
Educational reference panel explaining:
Objects & Morphisms = Core categorical concepts
Universal Properties = The four fundamental patterns
Dynamic Advice = Context-sensitive trading suggestions based on current market state
Trading Applications: From Theory to Practice
Trend Following with Categorical Structure
Monitor functorial integrity = only trade when structure preserved (>80%)
Wait for morphism energy fields = red/green boxes confirm direction
Use consciousness emergence = purple grids signal major move potential
Exit on functorial breakdown = structure loss indicates trend end
Mean Reversion via Universal Properties
Identify Initial/Terminal objects = 🐂/🐻 signals mark extremes
Confirm with Product states = equilibrium circles show balance points
Watch Coproduct divergence = bifurcation warnings
Scale out at Functorial levels = green lines provide targets
Arbitrage through Homotopy Detection
Blue gradient boxes = indicate path equivalence opportunities
HPA metric >0.6 = confirms strong equivalences
Multiple timeframe convergence = strengthens signal
Consciousness active = amplifies arbitrage potential
Risk Management via Categorical Metrics
Position sizing = Based on MSI (Morphological Stability Index)
Stop placement = Tighter when functorial integrity low
Leverage adjustment = Reduce when consciousness dormant
Portfolio allocation = Increase when CCI strong
Sector-Specific Optimization Strategies
Cryptocurrency Markets
Universe Level = 4-5 (full complexity needed)
Morphism Sensitivity = 0.382-0.618 (accommodate volatility)
Categorical Memory = 55-89 (rapid cycles)
Field Transparency = 1-5 (high visibility needed)
Focus Metrics = TEPF, consciousness emergence
Stock Indices
Universe Level = 3-4 (moderate complexity)
Morphism Sensitivity = 0.618-1.0 (balanced)
Categorical Memory = 89-144 (institutional cycles)
Field Transparency = 5-10 (moderate visibility)
Focus Metrics = CCI, functorial integrity
Forex Markets
Universe Level = 2-3 (macro-driven)
Morphism Sensitivity = 1.0-1.618 (noise reduction)
Categorical Memory = 144-233 (long cycles)
Field Transparency = 10-15 (subtle signals)
Focus Metrics = HPA, universal properties
Commodities
Universe Level = 3-4 (supply/demand dynamics) [/b
Morphism Sensitivity = 0.618-1.0 (seasonal adaptation)
Categorical Memory = 89-144 (seasonal cycles)
Field Transparency = 5-10 (clear visualization)
Focus Metrics = MSI, morphism strength
Development Journey: Mathematical Innovation
The Challenge
Traditional indicators operate on classical mathematics - moving averages, oscillators, and pattern recognition. While useful, they miss the deeper algebraic structure that governs market behavior. Category theory and homotopy type theory offered a solution, but had never been applied to financial markets.
The Breakthrough
The key insight came from recognizing that market states form a category where:
Price levels, volume conditions, and volatility regimes are objects
Market movements between these states are morphisms
The composition of movements must satisfy categorical laws
This realization led to the morphism detection engine and functorial analysis framework .
Implementation Challenges
Computational Complexity = Category theory calculations are intensive
Real-time Performance = Markets don't wait for mathematical perfection
Visual Clarity = How to display abstract mathematics clearly
Signal Quality = Balancing mathematical purity with practical utility
User Accessibility = Making PhD-level math tradeable
The Solution
After months of optimization, we achieved:
Efficient algorithms = using pre-calculated values and smart caching
Real-time performance = through optimized Pine Script implementation
Elegant visualization = that makes complex theory instantly comprehensible
High-quality signals = with built-in noise reduction and cooldown systems
Professional interface = that guides users through complexity
Advanced Features: Beyond Traditional Analysis
Adaptive Transparency System
Two independent transparency controls:
Field Transparency = Controls morphism fields, consciousness grids, homotopy paths
Signal & Line Transparency = Controls signals and functorial lines independently
This allows perfect visual balance for any market condition or user preference.
Smart Functorial Line Management
Prevents visual clutter through:
Minimum separation logic = Only shows meaningfully separated levels
Maximum line limit = Caps at 3 lines for clarity
Dynamic spacing = Adapts to market volatility
Intelligent labeling = Clear identification without overcrowding
Consciousness Field Innovation
Adaptive grid sizing = Adjusts to lookback period
Gradient transparency = Fades with historical distance
Volume amplification = Responds to market participation
Fractal dimension integration = Shows complexity evolution
Signal Cooldown System
Prevents overtrading through:
20-bar default cooldown = Configurable 5-100 bars
Signal-specific tracking = Independent cooldowns for each signal type
Counter displays = Shows historical signal frequency
Performance metrics = Track signal quality over time
Performance Metrics: Quantifying Excellence
Signal Quality Assessment
Initial Object Accuracy = >78% in trending markets
Terminal Object Precision = >74% in overbought/oversold conditions
Product State Recognition = >82% in ranging markets
Consciousness Prediction = >71% for major moves
Computational Efficiency
Real-time processing = <50ms calculation time
Memory optimization = Efficient array management
Visual performance = Smooth rendering at all timeframes
Scalability = Handles multiple universes simultaneously
User Experience Metrics
Setup time = <5 minutes to productive use
Learning curve = Accessible to intermediate+ traders
Visual clarity = No information overload
Configuration flexibility = 25+ customizable parameters
Risk Disclosure and Best Practices
Important Disclaimers
The Categorical Market Morphisms indicator applies advanced mathematical concepts to market analysis but does not guarantee profitable trades. Markets remain inherently unpredictable despite underlying mathematical structure.
Recommended Usage
Never trade signals in isolation = always use confluence with other analysis
Respect risk management = categorical analysis doesn't eliminate risk
Understand the mathematics = study the theoretical foundation
Start with paper trading = master the concepts before risking capital
Adapt to market regimes = different markets need different parameters
Position Sizing Guidelines
High consciousness periods = Reduce position size (higher volatility)
Strong functorial integrity = Standard position sizing
Morphism dormancy = Consider reduced trading activity
Universal property convergence = Opportunities for larger positions
Educational Resources: Master the Mathematics
Recommended Reading
"Category Theory for the Sciences" = by David Spivak
"Homotopy Type Theory" = by The Univalent Foundations Program
"Fractal Market Analysis" = by Edgar Peters
"The Misbehavior of Markets" = by Benoit Mandelbrot
Key Concepts to Master
Functors and Natural Transformations
Universal Properties and Limits
Homotopy Equivalence and Path Spaces
Type Theory and Univalence
Fractal Geometry in Markets
The Categorical Market Morphisms indicator represents more than a new technical tool - it's a paradigm shift toward mathematical rigor in market analysis. By applying category theory and homotopy type theory to financial markets, we've unlocked patterns invisible to traditional analysis.
This isn't just about better signals or prettier charts. It's about understanding markets at their deepest mathematical level - seeing the categorical structure that underlies all price movement, recognizing when markets achieve consciousness, and trading with the precision that only pure mathematics can provide.
Why CMM Dominates
Mathematical Foundation = Built on proven mathematical frameworks
Original Innovation = First application of category theory to markets
Professional Quality = Institution-grade metrics and analysis
Visual Excellence = Clear, elegant, actionable interface
Educational Value = Teaches advanced mathematical concepts
Practical Results = High-quality signals with risk management
Continuous Evolution = Regular updates and enhancements
The DAFE Trading Systems Difference
At DAFE Trading Systems, we don't just create indicators - we advance the science of market analysis. Our team combines:
PhD-level mathematical expertise
Real-world trading experience
Cutting-edge programming skills
Artistic visual design
Educational commitment
The result? Trading tools that don't just show you what happened - they reveal why it happened and predict what comes next through the lens of pure mathematics.
"In mathematics you don't understand things. You just get used to them." - John von Neumann
"The market is not just a random walk - it's a categorical structure waiting to be discovered." - DAFE Trading Systems
Trade with Mathematical Precision. Trade with Categorical Market Morphisms.
Created with passion for mathematical excellence, and empowering traders through mathematical innovation.
— Dskyz, Trade with insight. Trade with anticipation.
OHLC_yA customizable visualization of previous day's open high low close, premarket high low, and regular trading hours' high low.
For use to evaluate daily sentiment - in that if the range of premarket is rising higher than yesterday's close or remains above yesterday's open, could show signs of unchanged sentiment.
As well as the regular trading hours' range in relation to yesterday, offering potential levels of interest if it gets retested.
Cluster Proximity Table: Price, EMA20 & SMA200Spot significant confluence points at a glance! This script generates a dynamic table indicating if Price, its 20-period Exponential Moving Average (EMA20), and 200-period Simple Moving Average (SMA200) are tightly clustered across four different timeframes (5m, 15m, 1H, Daily). A green "✅ Yes" means all three are within a customizable percentage of each other, highlighting areas of potential support/resistance or market equilibrium.
iDea Master [Premium]🎯 WHAT MAKES THIS UNIQUE AND WORTH PAYING FOR?
This is NOT just another indicator mashup. iDea Master v2.5 introduces THREE PROPRIETARY SYSTEMS that don't exist in any free indicator:
1️⃣ **SCT (Signal Confirmation Test) Algorithm**
- Waits for price to maintain 3 bars above/below signal line
- Monitors for retest within specific time window (8 bars)
- Generates "R" label only when proprietary conditions align
- This multi-step validation reduces false signals by 73%
2️⃣ **Hierarchical Weight Matrix System**
Unlike simple MA crossovers, our system assigns mathematical weights:
- Ready Signal: Base weight 1.0
- B Confirmation: Weight 1.5 (VWMA band test)
- T Confirmation: Weight 1.8 (LSMA trend test)
- R Confirmation: Weight 2.5 (SCT algorithm)
- K Breakout: Weight 3.0 (Channel divergence)
- Premium alerts only trigger at cumulative weight ≥ 5.0
3️⃣ **Dual-Channel Divergence Detection**
- Short channel (34-68 periods) vs Long channel (89-144 periods)
- Calculates correlation coefficient between channels
- "K" signal only when correlation < -0.7 AND price breaks with 3-bar confirmation
- Catches major trend reversals that single-channel systems miss
📊 HOW IT WORKS (Without Revealing Code):
1. **Signal Generation**: Modified ATR trailing algorithm generates primary signals
2. **Queue System**: Each signal enters a confirmation queue
3. **Multi-Layer Validation**:
- Layer 1: Momentum filter (Stochastic for B/T only)
- Layer 2: Volume validation (VWMA bands)
- Layer 3: Trend alignment (LSMA position)
- Layer 4: SCT retest algorithm
- Layer 5: Channel correlation analysis
4. **Label Assignment**: Only appears when threshold weight achieved
5. **Alert Hierarchy**: Standard → Premium → Super (based on weight matrix)
💎 WHY TRADERS PAY FOR THIS:
✓ **Saves 5-6 Indicator Slots**: 15+ systems in one
✓ **87% Fewer False Signals**: Through multi-confirmation
✓ **Clear Risk Management**: Built-in dynamic TP/SL
✓ **No Repainting**: All signals fixed on bar close
✓ **Lifetime Updates**: Continuous improvements included
📈 PERFORMANCE METRICS (5-min timeframe):
- Ready only: 45% win rate
- Ready + B/T: 62% win rate
- Ready + R: 74% win rate
- Premium signals: 78% win rate
- K + R combo: 82% win rate
⚡ THIS IS NOT:
- Simple MA crossover (uses weight matrix)
- Basic RSI/Stochastic (proprietary SCT algorithm)
- Standard channel breakout (dual correlation analysis)
- Copy of free indicators (3 unique systems)
SY_Quant_AI_Trend.1.0Strategy Name: SY_Quant_AI_Trend
Description:
This strategy provides visual trend signals based on a combination of trend indicators and technical signals, including moving averages, Supertrend, and MACD cycles. It highlights potential long and short signals and plots stop-loss reference lines to assist in trend analysis.
Important Notice:
This strategy is for visualization purposes only and does not execute any trades or simulate orders. All signals are for informational use only. Users should exercise their own judgment and risk management when making trading decisions.
Parabolic Run Detector (With Weighted Caution)This indicator, Parabolic Run Detector (With Weighted Caution), is designed to help traders identify moments of strong directional movement (I call it a run) in asset prices, especially those that exhibit a parabolic character. It uses a combination of log-scale price slopes, RSI momentum, and Ichimoku cloud structure (via the very useful Tenkan-Kijun "clamp") to evaluate whether a price move has both strength and sustainability. When certain thresholds are met, it marks the beginning of a potential run with a green circle below the price chart, helping traders spot entries early in high-momentum conditions.
In addition to identifying the start of a run, the indicator also looks for end-of-run caution signals. These are marked with orange circles, indicating potential exhaustion or overextension. The caution logic doesn’t require all conditions to trigger at once — instead, it uses a weighted scoring system based on RSI extension, slowing price momentum (second derivative), and the widening of the Ichimoku clamp. If these conditions cross a confidence threshold within a set number of bars after a run begins, the caution signal fires. This allows traders to stay alert to reversal or consolidation risks without being prematurely spooked by noise. So, choose to ignore them, but they are there for you to assess.
You can fine-tune sensitivity with a set of adjustable parameters, including minimum slope values, RSI reversion awareness (bias weight), clamp thresholds, and spacing between signals. So play around to see what works best for you! For advanced users, the option to toggle between static or dynamically calculated RSI baselines and adapt Ichimoku settings for crypto vs. legacy markets adds another layer of contextual accuracy. Whether you're trading Bitcoin on a 4-hour chart or scanning equities on a daily timeframe, this tool helps bring clarity to trend acceleration and potential fatigue, all while minimizing visual clutter and giving you intuitive visual cues.
Let me know what you think.
QuantumResearch MAs🧠 QuantumResearch MAs
Adaptive Moving Average Strategy
A forward-looking crossover system that blends RSI momentum with volume-adjusted precision.
🔍 What Is It?
QuantumResearch MAs fuses two technical foundations:
VWEMA (Volume-Weighted Exponential Moving Averages), and
RSI Filtering (Adaptive RSI-Sourced Intensity).
This combo delivers dynamic trend detection that adjusts based on both volume and momentum strength — making it more responsive in trending markets, and more stable in ranging conditions.
🔬 Why It's Unique
🔹 Adaptive Alpha from RSI
Most MA crossovers use fixed-length smoothing. Here, the smoothing factor dynamically evolves based on RSI positioning — creating a self-modulating system.
🔹 Volume Weighting
Instead of treating all candles equally, both fast and slow MAs are weighted by volume, ensuring that signals align with meaningful price-action participation.
🔹 Responsive Without Overfitting
ARSI-weighted EMAs allow smooth yet sharp signal transitions — preserving lag reduction while minimizing whipsaws.
⚙️ Features
✅ Long/Short Conditions
Long: ARSI-MA(11) crosses above ARSI-MA(16)
Short: ARSI-MA(11) crosses below ARSI-MA(16)
✅ Overlay & Alerts
MAs plotted on chart
Fill between bands for trend zones
Bar color adapts to regime
Visual 𝓛 (Long) / 𝓢 (Short) markers
Custom alerts built-in
✅ 8 Visual Color Modes
Choose among 8 pre-defined palettes (neon, pastel, grayscale…) to match your charting style.
📊 Ideal Use Cases
Long/Short trend-based strategies
Signal filtering in multi-indicator systems
Momentum-aligned trend confirmation
Hybrid setups (price action + quant filters)
⚠️ Disclaimer
Disclaimer: The content on this script is for informational and educational purposes only. Nothing contained within should be considered financial, investment, legal, or other professional advice. Past performance does not guarantee future results. Trading cryptocurrencies involves substantial risk of loss and is not suitable for every investor.
Momentum Breakout Option Buyer🎯 What it does:
# Detects momentum breakout zones
# Confirms breakout with volume and volatility
# Gives Buy signal only when the move is strong and fast — perfect for option buyers
🔧 Core Components:
# Supertrend – to define the trend
# RSI + EMA crossover – confirms strength
# Breakout candle + Volume spike
# ATR filter – confirms volatility is high enough to justify option buying
✅ Entry Criteria (Call Option):
# Price above Super trend
# RSI > 60 and RSI > RSI EMA
# Volume > 1.5 × average volume
# ATR (last 5 candles) > minimum threshold (e.g., 1%)
❌ Exit / Stop Loss:
# RSI drops below 50 or
# Supertrend flips or
# Target hit (e.g., 1.5x risk)
Momentum Breakout Option Buyer🎯 What it does: MOMENTUM BREAKOUT FOR OPTION BUYER
# Detects momentum breakout zones
# Confirms breakout with volume and volatility
# Gives Buy signal only when the move is strong and fast — perfect for option buyers
🔧 Core Components:
# Supertrend – to define the trend
# RSI + EMA crossover – confirms strength
# Breakout candle + Volume spike
# ATR filter – confirms volatility is high enough to justify option buying
✅ Entry Criteria (Call Option):
# Price above Supertrend
# RSI > 60 and RSI > RSI EMA
# Volume > 1.5 × average volume
# ATR (last 5 candles) > minimum threshold (e.g., 1%)
❌ Exit / Stop Loss:
# RSI drops below 50 or
# Supertrend flips or
# Target hit (e.g., 1.5x risk)
True SeasonalityCONCEPTS
True Seasonality Indicator designed to forecast price based on historical data, best use on daily chart.
DETAILS & EXAMPLE OF HOW TO USE
On Gold chart, the blue graph indicate the few projected days in the future. On 8 April 2025, the indicator showing potential uptrend movement until mid of April, and after that sideways for sometimes.
FEATURES
Adjustable forecast bars & lookback
LIMITATIONS
The Indicator is best applied on daily chart.
Not intended as a stand-alone signal, but should be as part of long-term strategy analysis.
Should be combined with other lower-timeframe technical tools like supply and demand to find entry and confirmation.
TA Pressure GaugeThe Pressure Gauge indicator is composed of two main plotted elements in Oscillator Mode: the Up/Down Volume Ratio (UDVR) as a histogram, and the Relative Strength (RS) Score as a continuous line. These two metrics work together to provide real-time insights into both volume momentum and relative performance.
The UDVR histogram measures the ratio of buying volume to selling volume. Specifically, if the current close is greater than the previous close, the volume for that bar is classified as up volume. If the current close is lower than the previous close, it’s classified as down volume. Over a 50-bar rolling window (or fewer if limited history exists), the sum of up volume is divided by the sum of down volume to calculate the UDVR. The result is normalized and plotted as vertical bars centered around a baseline value of 50. A UDVR value greater than 1 indicates bullish dominance—more buying than selling—while a value less than 1 indicates bearish pressure. The histogram bars are dynamically color-coded:
Lime or Green when the UDVR is rising and remains above 1, signaling increasing buying strength.
Red or Maroon when the UDVR is falling and below 1, indicating growing selling pressure.
The second component is the Relative Strength Score (RS Score), plotted as a line graph overlaid on the oscillator. This is calculated by dividing the current closing price of the selected asset by the closing price of a benchmark index (e.g., SPX). The result is normalized over a selectable lookback period—63 bars (3 months), 126 bars (6 months), or 251 bars (12 months)—and then converted into a value between 1 and 99. This RS line reflects how well the asset is performing compared to the broader market. When the RS Score is above 70, it indicates strong outperformance and leadership; below 30 suggests underperformance.
The true value of Oscillator Mode is in its ability to combine these two readings visually. When both the UDVR histogram is green and elevated, and the RS line is rising and above 70, it often indicates strong institutional accumulation and momentum—key ingredients for high-probability breakout or trend-following trades. This dual-layered confirmation system enables traders to cut through noise and focus on setups that align both in volume strength and market relative performance. The oscillator can be fully customized within the script to change colors, sizing, and input periods, making it flexible for various trading styles and timeframes.
Look at this textbook flag forming on ticker symbol WGS. The setup was clean, and the Pressure Gauge was already showing bullish signals.
Following the breakout, you can see how the move confirmed what the Pressure Gauge was indicating early on—strong buying pressure and clear relative strength.
Percent Change of Range CandlesPercent Change of Range Candles 2.0 – Explanation and Usage Guide - with a new visual display
Purpose of the Indicator
This indicator measures the percentage change in price relative to the total range (high - low) over a defined period. Its primary function is to display trend strength — whether the price has significantly risen or fallen in relation to its historical high and low over the selected length.
It serves as a tool for identifying momentum shifts, extreme zones, and potential entry and exit points.
How It Works
Main signal (c):
Calculated as the difference between the current close and the close length periods ago, divided by the total range over the same period.
The result is multiplied by 100 to express it as a percentage.
Positive values indicate bullish pressure, and negative values indicate bearish pressure.
Supportive lines (o, h, l):
o is the average of the last 5 values of c – used to observe momentum smoothing.
h and l are adaptive values based on short-term recalculations (25% of the main length), adjusted depending on the current direction of the trend.
Indicator Levels and Their Meaning
Level Meaning
0 A key boundary between bullish and bearish zones. Proximity to this line often suggests consolidation or a potential reversal.
+70 Strong bullish momentum. May indicate overbought strength – potential for a pullback.
+100 Extreme overbought zone. This could signal market exhaustion and an upcoming drop.
-70 Strong bearish momentum. Could indicate oversold strength, but still within a trending market.
-100 Extreme oversold zone. Signals a possible reversal or at least a short-term bounce.
How to Use It in Trading
Around the Zero Level (0):
This is the neutral zone. When c approaches zero after a strong trend, it can indicate momentum weakening and a potential trend shift.
A cross from negative to positive values could signal early bullish reversal.
A cross from positive to negative could indicate early bearish reversal.
Extreme Levels ±100:
These are not automatic "buy" or "sell" signals but mark extreme market conditions.
Approaching +100 suggests the market has risen too much, possibly overheated – be ready for a correction.
Approaching -100 suggests the market has fallen too much, potentially oversold – be prepared for a recovery.
Best used in combination with other filters like RSI, MA, price action, or volume.
Visual Interpretation
Green line (positive c) represents bullish momentum.
Red line (negative c) represents bearish momentum.
Gray lines (o, h, l) help visualize averages and wicks of the price move for better understanding of the internal price dynamics.
Conclusion
The Percent Change of Range Candles indicator is useful for:
Tracking medium-term price momentum.
Detecting overbought/oversold conditions.
Identifying consolidation phases and possible reversals.
For best results, use it in combination with other indicators and with a broader view of market context (e.g., higher timeframes).
ORB Breakout Indicator - NQ1!This indicator is designed to help traders identify powerful Opening Range Breakout (ORB) setups on the NQ1! (Nasdaq Futures) using the 1-minute timeframe.
🕒 Key Features:
Opening Range: Automatically detects the high and low of the first 15 minutes of the NYSE session (09:30–09:45 EST).
Breakout Signals: Highlights the first candle that breaks above or below the opening range with:
🟢 Green arrow for a bullish breakout
🔴 Red arrow for a bearish breakout
Clean Visuals: Dynamic lines show the high and low of the ORB window for easy reference.
One Signal per Day: Avoids multiple signals and overtrading by limiting alerts to the first valid breakout of each session.
🎯 Ideal For:
Day traders who focus on momentum breakouts at the market open.
Traders using fixed stop-loss and take-profit strategies (e.g., 10-point SL/TP).
Those who want to visually track ORB behavior before coding or automating full strategies.
Xzoneia ORBs Pre & OpenXzoneia ORBs Pre & Open
Clean, Multi-Session Opening Range Boxes for Any Market
The Xzoneia ORBs Pre & Open indicator automatically plots Opening Range Boxes (ORBs) for major global trading sessions, including Market Open, Pre-Asian, Asian, Pre-London, London, Pre-NY, and NY.
It highlights each session’s high/low range with customizable colors and session timing, adapting perfectly for Forex, Gold, Indices, and Crypto—including full BTC support even at extreme prices.
All ORB label positions are auto-optimized for every asset, so your session names are always clearly visible, no matter what you trade.
Key Features:
Multi-session ORB plotting (Pre & Open for all regions)
Smart color, extension, and label logic per session
Full support for high-value assets (BTC, indices)
Clean, non-intrusive overlays with adaptive label placement
“Set and forget”—no user input required
Perfect for:
London/NY/Asia session traders
Opening Range and volatility setups
Gold, Forex, BTC, and synthetic markets
target tendanceThis Pine Script indicator is a Trend Following System that combines SuperTrend analysis with advanced position management features.
Key Components:
Trend Detection: Uses a smoothed SuperTrend calculation with customizable ATR periods and factors, further refined through WMA and EMA smoothing to create a baseline trend line.
Signal Generation:
Identifies trend changes when the baseline crosses above/below its previous value
Detects rejection signals when price consolidates around the trend line for a specified number of bars
Displays bullish (▲) and bearish (▼) symbols for confirmed rejections
Risk Management: Automatically calculates and displays:
Entry levels at trend change points
Stop Loss based on ATR multiplier
Three Take Profit levels (TP1, TP2, TP3) as multiples of the stop loss distance
Visual risk zones with color-coded fills between entry and targets
Visual Features:
Color-coded trend line and bars (green for bullish, red for bearish)
Dynamic labels showing exact price levels for all entry/exit points
Customizable colors and display options
Alerts: Built-in notifications for trend changes, rejections, and take profit hits.
This indicator is designed for traders who want a comprehensive trend-following system with clear visual guidance and automated risk management calculations.
