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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.
Penunjuk Pine Script®
oleh DskyzInvestments
2
NIFTY Option Buy Strategy MASTER v1This script is a complete option buying strategy framework for NIFTY, designed for both intraday and positional swing trades. 🔹 Built using multi-timeframe analysis (EMAs, MACD, RSI) 🔹 Combines key macro filters: India VIX, PCR, FII/DII net cash flows 🔹 Supports both Call (CE) and Put (PE) entries 🔹 Includes manual input dashboard for real-time market context 🔹 Trade logic includes: Bollinger Band breakouts Volume confirmation VWAP filtering EMA crossover + MACD alignment Resistance/support proximity from option chain (manual) 📈 Smart Trade Management: Multi-target system (e.g., exit 50% at RR=1, 50% at RR=2) Trailing stop-loss after target 1 hits Automatic exit on SL/TP or reverse signals Visual markers for all entries, exits, and stops 📊 Built-in Dashboard: Displays India VIX, PCR, FII/DII flows, and S/R levels Strike price selection (ATM + offset logic) 🧪 Ideal for backtesting, alerts, and real-time execution. Can be used with alerts + webhook for automated trading or signal generation. ⚠️ Note: This script is for educational purposes only. Always test on paper trading before going live.
Penunjuk Pine Script®
oleh aman_ghadge698
DCA Investment Tracker Pro [tradeviZion]DCA Investment Tracker Pro: Educational DCA Analysis Tool An educational indicator that helps analyze Dollar-Cost Averaging strategies by comparing actual performance with historical data calculations. --- 💡 Why I Created This Indicator As someone who practices Dollar-Cost Averaging, I was frustrated with constantly switching between spreadsheets, calculators, and charts just to understand how my investments were really performing. I wanted to see everything in one place - my actual performance, what I should expect based on historical data, and most importantly, visualize where my strategy could take me over the long term . What really motivated me was watching friends and family underestimate the incredible power of consistent investing. When Napoleon Bonaparte first learned about compound interest, he reportedly exclaimed "I wonder it has not swallowed the world" - and he was right! Yet most people can't visualize how their $500 monthly contributions today could become substantial wealth decades later. Traditional DCA tracking tools exist, but they share similar limitations: Require manual data entry and complex spreadsheets Use fixed assumptions that don't reflect real market behavior Can't show future projections overlaid on actual price charts Lose the visual context of what's happening in the market Make compound growth feel abstract rather than tangible I wanted to create something different - a tool that automatically analyzes real market history, detects volatility periods, and shows you both current performance AND educational projections based on historical patterns right on your TradingView charts. As Warren Buffett said: "Someone's sitting in the shade today because someone planted a tree a long time ago." This tool helps you visualize your financial tree growing over time. This isn't just another calculator - it's a visualization tool that makes the magic of compound growth impossible to ignore. --- 🎯 What This Indicator Does This educational indicator provides DCA analysis tools. Users can input investment scenarios to study: Theoretical Performance: Educational calculations based on historical return data Comparative Analysis: Study differences between actual and theoretical scenarios Historical Projections: Theoretical projections for educational analysis (not predictions) Performance Metrics: CAGR, ROI, and other analytical metrics for study Historical Analysis: Calculates historical return data for reference purposes --- 🚀 Key Features Volatility-Adjusted Historical Return Calculation Analyzes 3-20 years of actual price data for any symbol Automatically detects high-volatility stocks (meme stocks, growth stocks) Uses median returns for volatile stocks, standard CAGR for stable stocks Provides conservative estimates when extreme outlier years are detected Smart fallback to manual percentages when data insufficient Customizable Performance Dashboard Educational DCA performance analysis with compound growth calculations Customizable table sizing (Tiny to Huge text options) 9 positioning options (Top/Middle/Bottom + Left/Center/Right) Theme-adaptive colors (automatically adjusts to dark/light mode) Multiple display layout options Future Projection System Visual future growth projections Timeframe-aware calculations (Daily/Weekly/Monthly charts) 1-30 year projection options Shows projected portfolio value and total investment amounts Investment Insights Performance vs benchmark comparison ROI from initial investment tracking Monthly average return analysis Investment milestone alerts (25%, 50%, 100% gains) Contribution tracking and next milestone indicators --- 📊 Step-by-Step Setup Guide 1. Investment Settings 💰 Initial Investment: Enter your starting lump sum (e.g., $60,000) Monthly Contribution: Set your regular DCA amount (e.g., $500/month) Return Calculation: Choose "Auto (Stock History)" for real data or "Manual" for fixed % Historical Period: Select 3-20 years for auto calculations (default: 10 years) Start Year: When you began investing (e.g., 2020) Current Portfolio Value: Your actual portfolio worth today (e.g., $150,000) 2. Display Settings 📊 Table Sizes: Choose from Tiny, Small, Normal, Large, or Huge Table Positions: 9 options - Top/Middle/Bottom + Left/Center/Right Visibility Toggles: Show/hide Main Table and Stats Table independently 3. Future Projection 🔮 Enable Projections: Toggle on to see future growth visualization Projection Years: Set 1-30 years ahead for analysis Live Example - NASDAQ:META Analysis: Settings shown: $60K initial + $500/month + Auto calculation + 10-year history + 2020 start + $150K current value --- 🔬 Pine Script Code Examples Core DCA Calculations: // Calculate total invested over time months_elapsed = (year - start_year) * 12 + month - 1 total_invested = initial_investment + (monthly_contribution * months_elapsed) // Compound growth formula for initial investment theoretical_initial_growth = initial_investment * math.pow(1 + annual_return, years_elapsed) // Future Value of Annuity for monthly contributions monthly_rate = annual_return / 12 fv_contributions = monthly_contribution * ((math.pow(1 + monthly_rate, months_elapsed) - 1) / monthly_rate) // Total expected value theoretical_total = theoretical_initial_growth + fv_contributions Volatility Detection Logic: // Detect extreme years for volatility adjustment extreme_years = 0 for i = 1 to historical_years yearly_return = ((price_current / price_i_years_ago) - 1) * 100 if yearly_return > 100 or yearly_return < -50 extreme_years += 1 // Use median approach for high volatility stocks high_volatility = (extreme_years / historical_years) > 0.2 calculated_return = high_volatility ? median_of_returns : standard_cagr Performance Metrics: // Calculate key performance indicators absolute_gain = actual_value - total_invested total_return_pct = (absolute_gain / total_invested) * 100 roi_initial = ((actual_value - initial_investment) / initial_investment) * 100 cagr = (math.pow(actual_value / initial_investment, 1 / years_elapsed) - 1) * 100 --- 📊 Real-World Examples See the indicator in action across different investment types: Stable Index Investments: AMEX:SPY (SPDR S&P 500) - Shows steady compound growth with standard CAGR calculations Classic DCA success story: $60K initial + $500/month starting 2020. The indicator shows SPY's historical 10%+ returns, demonstrating how consistent broad market investing builds wealth over time. Notice the smooth theoretical growth line vs actual performance tracking. MIL:VUAA (Vanguard S&P 500 UCITS) - Shows both data limitation and solution approaches Data limitation example: VUAA shows "Manual (Auto Failed)" and "No Data" when default 10-year historical setting exceeds available data. The indicator gracefully falls back to manual percentage input while maintaining all DCA calculations and projections. MIL:VUAA (Vanguard S&P 500 UCITS) - European ETF with successful 5-year auto calculation Solution demonstration: By adjusting historical period to 5 years (matching available data), VUAA auto calculation works perfectly. Shows how users can optimize settings for newer assets. European market exposure with EUR denomination, demonstrating DCA effectiveness across different markets and currencies. NYSE:BRK.B (Berkshire Hathaway) - Quality value investment with Warren Buffett's proven track record Value investing approach: Berkshire Hathaway's legendary performance through DCA lens. The indicator demonstrates how quality companies compound wealth over decades. Lower volatility than tech stocks = standard CAGR calculations used. High-Volatility Growth Stocks: NASDAQ:NVDA (NVIDIA Corporation) - Demonstrates volatility-adjusted calculations for extreme price swings High-volatility example: NVIDIA's explosive AI boom creates extreme years that trigger volatility detection. The indicator automatically switches to "Median (High Vol): 50%" calculations for conservative projections, protecting against unrealistic future estimates based on outlier performance periods. NASDAQ:TSLA (Tesla) - Shows how 10-year analysis can stabilize volatile tech stocks Stable long-term growth: Despite Tesla's reputation for volatility, the 10-year historical analysis (34.8% CAGR) shows consistent enough performance that volatility detection doesn't trigger. Demonstrates how longer timeframes can smooth out extreme periods for more reliable projections. NASDAQ:META (Meta Platforms) - Shows stable tech stock analysis using standard CAGR calculations Tech stock with stable growth: Despite being a tech stock and experiencing the 2022 crash, META's 10-year history shows consistent enough performance (23.98% CAGR) that volatility detection doesn't trigger. The indicator uses standard CAGR calculations, demonstrating how not all tech stocks require conservative median adjustments. Notice how the indicator automatically detects high-volatility periods and switches to median-based calculations for more conservative projections, while stable investments use standard CAGR methods. --- 📈 Performance Metrics Explained Current Portfolio Value: Your actual investment worth today Expected Value: What you should have based on historical returns (Auto) or your target return (Manual) Total Invested: Your actual money invested (initial + all monthly contributions) Total Gains/Loss: Absolute dollar difference between current value and total invested Total Return %: Percentage gain/loss on your total invested amount ROI from Initial Investment: How your starting lump sum has performed CAGR: Compound Annual Growth Rate of your initial investment (Note: This shows initial investment performance, not full DCA strategy) vs Benchmark: How you're performing compared to the expected returns --- ⚠️ Important Notes & Limitations Data Requirements: Auto mode requires sufficient historical data (minimum 3 years recommended) CAGR Limitation: CAGR calculation is based on initial investment growth only, not the complete DCA strategy Projection Accuracy: Future projections are theoretical and based on historical returns - actual results may vary Timeframe Support: Works ONLY on Daily (1D), Weekly (1W), and Monthly (1M) charts - no other timeframes supported Update Frequency: Update "Current Portfolio Value" regularly for accurate tracking --- 📚 Educational Use & Disclaimer This analysis tool can be applied to various stock and ETF charts for educational study of DCA mathematical concepts and historical performance patterns. Study Examples: Can be used with symbols like AMEX:SPY , NASDAQ:QQQ , AMEX:VTI , NASDAQ:AAPL , NASDAQ:MSFT , NASDAQ:GOOGL , NASDAQ:AMZN , NASDAQ:TSLA , NASDAQ:NVDA for learning purposes. EDUCATIONAL DISCLAIMER: This indicator is a study tool for analyzing Dollar-Cost Averaging strategies. It does not provide investment advice, trading signals, or guarantees. All calculations are theoretical examples for educational purposes only. Past performance does not predict future results. Users should conduct their own research and consult qualified financial professionals before making any investment decisions. --- © 2025 TradeVizion. All rights reserved.
Penunjuk Pine Script®
oleh TradeVizion
2
Mean Absolute Deviation Trend | Lyro RSMean Absolute Deviation Trend Introduction Mean Absolute Deviation (MAD) Trend is a precision tool designed to capture directional bias using the Mean Absolute Deviation from a dynamic moving average. It identifies trend shifts by measuring average volatility around price, highlighting bullish and bearish phases through adaptive bands. Signal Insight The 𝓜𝓐𝓓 𝓣𝓻𝓮𝓷𝓭 plots a dynamic bands around a user-defined moving average, using Mean Absolute Deviation (MAD) to reflect volatility-adjusted boundaries. A bullish signal is generated when price breaks above the upper MAD band—indicating positive momentum and potential trend continuation to the upside. A bearish signal occurs when price falls below the lower MAD band—signaling increased downside pressure and possible trend continuation to the downside. This approach gives traders a volatility-sensitive trend filter that can enhance signal quality across different market environments. Real-World Example 𝓜𝓐𝓓 𝓣𝓻𝓮𝓷𝓭 delivers a clear and timely long signal, capturing a +22.90% move. Upon exit, it seamlessly flips to a short position, securing an additional +13.34% —demonstrating its strength in both trending directions. Framework The 𝓜𝓐𝓓 𝓣𝓻𝓮𝓷𝓭 indicator identifies directional shifts by measuring price deviation from a dynamic moving average. At its core, it calculates the Mean Absolute Deviation (MAD) of price around a user-selected moving average. The indicator builds adaptive upper and lower bands by multiplying the MAD value above and below the moving average. When price crosses above the upper band, it triggers a bullish signal. When price crosses below the lower band, it signals bearish momentum which gives a bearish signal. This method provides an elegant balance between volatility sensitivity and trend clarity, adapting in real-time to changing market behavior. The moving average type and band sensitivity can be tuned to fit various strategies—from scalping to swing trading. Recommended Settings Long-Term Investing: 1D, EMA, 40, 2 Mid-Term Investing: 1D, Default Settings Swing Trading: 4h, EMA, 20, 2.5 Day/Intraday Trading: 15mins, 25, 2.5 ⚠️ WARNING ⚠️: THIS INDICATOR, OR ANY OTHER WE (LYRO RS) PUBLISH, IS NOT FINANCIAL OR INVESTMENT ADVICE. EVERY INDICATOR SHOULD BE COMBINED WITH PRICE ACTION, FUNDAMENTALS, OTHER TECHNICAL ANALYSIS TOOLS & PROPER RISK. MANAGEMENT.
Penunjuk Pine Script®
oleh LyroRS
GEEKSDOBYTE IFVG w/ Buy/Sell Signals1. Inputs & Configuration Swing Lookback (swingLen) Controls how many bars on each side are checked to mark a swing high or swing low (default = 5). Booleans to Toggle Plotting showSwings – Show small triangle markers at swing highs/lows showFVG – Show Fair Value Gap zones showSignals – Show “BUY”/“SELL” labels when price inverts an FVG showDDLine – Show a yellow “DD” line at the close of the inversion bar showCE – Show an orange dashed “CE” line at the midpoint of the gap area 2. Swing High / Low Detection isSwingHigh = ta.pivothigh(high, swingLen, swingLen) Marks a bar as a swing high if its high is higher than the highs of the previous swingLen bars and the next swingLen bars. isSwingLow = ta.pivotlow(low, swingLen, swingLen) Marks a bar as a swing low if its low is lower than the lows of the previous and next swingLen bars. Plotting If showSwings is true, small red downward triangles appear above swing highs, and green upward triangles below swing lows. 3. Fair Value Gap (3‐Bar) Identification A Fair Value Gap (FVG) is defined here using a simple three‐bar logic (sometimes called an “inefficiency” in price): Bullish FVG (bullFVG) Checks if, two bars ago, the low of that bar (low ) is strictly greater than the current bar’s high (high). In other words: bullFVG = low > high Bearish FVG (bearFVG) Checks if, two bars ago, the high of that bar (high ) is strictly less than the current bar’s low (low). In other words: bearFVG = high < low When either condition is true, it identifies a three‐bar “gap” or unfilled imbalance in the market. 4. Drawing FVG Zones If showFVG is enabled, each time a bullish or bearish FVG is detected: Bullish FVG Zone Draws a semi‐transparent green box from the bar two bars ago (where the gap began) at low up to the current bar’s high. Bearish FVG Zone Draws a semi‐transparent red box from the bar two bars ago at high down to the current bar’s low. These colored boxes visually highlight the “fair value imbalance” area on the chart. 5. Inversion (Fill) Detection & Entry Signals An inversion is defined as the price “closing through” that previously drawn FVG: Bullish Inversion (bullInversion) Occurs when a bullish FVG was identified on bar-2 (bullFVG), and on the current bar the close is greater than that old bar-2 low: bullInversion = bullFVG and close > low Bearish Inversion (bearInversion) Occurs when a bearish FVG was identified on bar-2 (bearFVG), and on the current bar the close is lower than that old bar-2 high: bearInversion = bearFVG and close < high When an inversion is true, the indicator optionally draws two lines and a label (depending on input toggles): Draw “DD” Line (yellow, solid) Plots a horizontal yellow line from the current bar’s close price extending five bars forward (bar_index + 5). This is often referred to as a “Demand/Daily Demand” line, marking where price inverted the gap. Draw “CE” Line (orange, dashed) Calculates the midpoint (ce) of the original FVG zone. For a bullish inversion: ce = (low + high) / 2 For a bearish inversion: ce = (high + low) / 2 Plots a horizontal dashed orange line at that midpoint for five bars forward. Plot Label (“BUY” / “SELL”) If showSignals is true, a green “BUY” label is placed at the low of the current bar when a bullish inversion occurs. Likewise, a red “SELL” label at the high of the current bar when a bearish inversion happens. 6. Putting It All Together Swing Markers (Optional): Visually confirm recent swing highs and swing lows with small triangles. FVG Zones (Optional): Highlight areas where price left a 3-bar gap (bullish in green, bearish in red). Inversion Confirmation: Wait for price to close beyond the old FVG boundary. Once that happens, draw the yellow “DD” line at the close, the orange dashed “CE” line at the zone’s midpoint, and place a “BUY” or “SELL” label exactly on that bar. User Controls: All of the above elements can be individually toggled on/off (showSwings, showFVG, showSignals, showDDLine, showCE). In Practice A bullish FVG forms whenever a strong drop leaves a gap in liquidity (three bars ago low > current high). When price later “fills” that gap by closing above the old low, the script signals a potential long entry (BUY), draws a demand line at the closing price, and marks the midpoint of that gap. Conversely, a bearish FVG marks a potential short zone (three bars ago high < current low). When price closes below that gap’s high, it signals a SELL, with similar lines drawn. By combining these elements, the indicator helps users visually identify inefficiencies (FVGs), confirm when price inverts/fills them, and place straightforward buy/sell labels alongside reference lines for trade management.
Penunjuk Pine Script®
oleh RayenGeeksdoByte
Psychological Levels + Buffer ZonesThis indicator automatically draws major (100-pip) and minor (50-pip) psychological levels on your Forex chart, along with optional buffer zones for smarter trade entries. Zones help you visually capture breakouts, retests, and fakeouts. Includes: Major & minor psych levels Adjustable buffer zones (±0.1%, etc.) Customizable zone color & transparency Optional ATR trailing lines for trend confirmation Perfect for scalpers, breakout traders, and zone-based strategies.
Penunjuk Pine Script®
oleh quiendum
Risk-Adjusted Momentum Oscillator# Risk-Adjusted Momentum Oscillator (RAMO): Momentum Analysis with Integrated Risk Assessment ## 1. Introduction Momentum indicators have been fundamental tools in technical analysis since the pioneering work of Wilder (1978) and continue to play crucial roles in systematic trading strategies (Jegadeesh & Titman, 1993). However, traditional momentum oscillators suffer from a critical limitation: they fail to account for the risk context in which momentum signals occur. This oversight can lead to significant drawdowns during periods of market stress, as documented extensively in the behavioral finance literature (Kahneman & Tversky, 1979; Shefrin & Statman, 1985). The Risk-Adjusted Momentum Oscillator addresses this gap by incorporating real-time drawdown metrics into momentum calculations, creating a self-regulating system that automatically adjusts signal sensitivity based on current risk conditions. This approach aligns with modern portfolio theory's emphasis on risk-adjusted returns (Markowitz, 1952) and reflects the sophisticated risk management practices employed by institutional investors (Ang, 2014). ## 2. Theoretical Foundation ### 2.1 Momentum Theory and Market Anomalies The momentum effect, first systematically documented by Jegadeesh & Titman (1993), represents one of the most robust anomalies in financial markets. Subsequent research has confirmed momentum's persistence across various asset classes, time horizons, and geographic markets (Fama & French, 1996; Asness, Moskowitz & Pedersen, 2013). However, momentum strategies are characterized by significant time-varying risk, with particularly severe drawdowns during market reversals (Barroso & Santa-Clara, 2015). ### 2.2 Drawdown Analysis and Risk Management Maximum drawdown, defined as the peak-to-trough decline in portfolio value, serves as a critical risk metric in professional portfolio management (Calmar, 1991). Research by Chekhlov, Uryasev & Zabarankin (2005) demonstrates that drawdown-based risk measures provide superior downside protection compared to traditional volatility metrics. The integration of drawdown analysis into momentum calculations represents a natural evolution toward more sophisticated risk-aware indicators. ### 2.3 Adaptive Smoothing and Market Regimes The concept of adaptive smoothing in technical analysis draws from the broader literature on regime-switching models in finance (Hamilton, 1989). Perry Kaufman's Adaptive Moving Average (1995) pioneered the application of efficiency ratios to adjust indicator responsiveness based on market conditions. RAMO extends this concept by incorporating volatility-based adaptive smoothing, allowing the indicator to respond more quickly during high-volatility periods while maintaining stability during quiet markets. ## 3. Methodology ### 3.1 Core Algorithm Design The RAMO algorithm consists of several interconnected components: #### 3.1.1 Risk-Adjusted Momentum Calculation The fundamental innovation of RAMO lies in its risk adjustment mechanism: Risk_Factor = 1 - (Current_Drawdown / Maximum_Drawdown × Scaling_Factor) Risk_Adjusted_Momentum = Raw_Momentum × max(Risk_Factor, 0.05) This formulation ensures that momentum signals are dampened during periods of high drawdown relative to historical maximums, implementing an automatic risk management overlay as advocated by modern portfolio theory (Markowitz, 1952). #### 3.1.2 Multi-Algorithm Momentum Framework RAMO supports three distinct momentum calculation methods: 1. Rate of Change: Traditional percentage-based momentum (Pring, 2002) 2. Price Momentum: Absolute price differences 3. Log Returns: Logarithmic returns preferred for volatile assets (Campbell, Lo & MacKinlay, 1997) This multi-algorithm approach accommodates different asset characteristics and volatility profiles, addressing the heterogeneity documented in cross-sectional momentum studies (Asness et al., 2013). ### 3.2 Leading Indicator Components #### 3.2.1 Momentum Acceleration Analysis The momentum acceleration component calculates the second derivative of momentum, providing early signals of trend changes: Momentum_Acceleration = EMA(Momentum_t - Momentum_{t-n}, n) This approach draws from the physics concept of acceleration and has been applied successfully in financial time series analysis (Treadway, 1969). #### 3.2.2 Linear Regression Prediction RAMO incorporates linear regression-based prediction to project momentum values forward: Predicted_Momentum = LinReg_Value + (LinReg_Slope × Forward_Offset) This predictive component aligns with the literature on technical analysis forecasting (Lo, Mamaysky & Wang, 2000) and provides leading signals for trend changes. #### 3.2.3 Volume-Based Exhaustion Detection The exhaustion detection algorithm identifies potential reversal points by analyzing the relationship between momentum extremes and volume patterns: Exhaustion = |Momentum| > Threshold AND Volume < SMA(Volume, 20) This approach reflects the established principle that sustainable price movements require volume confirmation (Granville, 1963; Arms, 1989). ### 3.3 Statistical Normalization and Robustness RAMO employs Z-score normalization with outlier protection to ensure statistical robustness: Z_Score = (Value - Mean) / Standard_Deviation Normalized_Value = max(-3.5, min(3.5, Z_Score)) This normalization approach follows best practices in quantitative finance for handling extreme observations (Taleb, 2007) and ensures consistent signal interpretation across different market conditions. ### 3.4 Adaptive Threshold Calculation Dynamic thresholds are calculated using Bollinger Band methodology (Bollinger, 1992): Upper_Threshold = Mean + (Multiplier × Standard_Deviation) Lower_Threshold = Mean - (Multiplier × Standard_Deviation) This adaptive approach ensures that signal thresholds adjust to changing market volatility, addressing the critique of fixed thresholds in technical analysis (Taylor & Allen, 1992). ## 4. Implementation Details ### 4.1 Adaptive Smoothing Algorithm The adaptive smoothing mechanism adjusts the exponential moving average alpha parameter based on market volatility: Volatility_Percentile = Percentrank(Volatility, 100) Adaptive_Alpha = Min_Alpha + ((Max_Alpha - Min_Alpha) × Volatility_Percentile / 100) This approach ensures faster response during volatile periods while maintaining smoothness during stable conditions, implementing the adaptive efficiency concept pioneered by Kaufman (1995). ### 4.2 Risk Environment Classification RAMO classifies market conditions into three risk environments: - Low Risk: Current_DD < 30% × Max_DD - Medium Risk: 30% × Max_DD ≤ Current_DD < 70% × Max_DD - High Risk: Current_DD ≥ 70% × Max_DD This classification system enables conditional signal generation, with long signals filtered during high-risk periods—a approach consistent with institutional risk management practices (Ang, 2014). ## 5. Signal Generation and Interpretation ### 5.1 Entry Signal Logic RAMO generates enhanced entry signals through multiple confirmation layers: 1. Primary Signal: Crossover between indicator and signal line 2. Risk Filter: Confirmation of favorable risk environment for long positions 3. Leading Component: Early warning signals via acceleration analysis 4. Exhaustion Filter: Volume-based reversal detection This multi-layered approach addresses the false signal problem common in traditional technical indicators (Brock, Lakonishok & LeBaron, 1992). ### 5.2 Divergence Analysis RAMO incorporates both traditional and leading divergence detection: - Traditional Divergence: Price and indicator divergence over 3-5 periods - Slope Divergence: Momentum slope versus price direction - Acceleration Divergence: Changes in momentum acceleration This comprehensive divergence analysis framework draws from Elliott Wave theory (Prechter & Frost, 1978) and momentum divergence literature (Murphy, 1999). ## 6. Empirical Advantages and Applications ### 6.1 Risk-Adjusted Performance The risk adjustment mechanism addresses the fundamental criticism of momentum strategies: their tendency to experience severe drawdowns during market reversals (Daniel & Moskowitz, 2016). By automatically reducing position sizing during high-drawdown periods, RAMO implements a form of dynamic hedging consistent with portfolio insurance concepts (Leland, 1980). ### 6.2 Regime Awareness RAMO's adaptive components enable regime-aware signal generation, addressing the regime-switching behavior documented in financial markets (Hamilton, 1989; Guidolin, 2011). The indicator automatically adjusts its parameters based on market volatility and risk conditions, providing more reliable signals across different market environments. ### 6.3 Institutional Applications The sophisticated risk management overlay makes RAMO particularly suitable for institutional applications where drawdown control is paramount. The indicator's design philosophy aligns with the risk budgeting approaches used by hedge funds and institutional investors (Roncalli, 2013). ## 7. Limitations and Future Research ### 7.1 Parameter Sensitivity Like all technical indicators, RAMO's performance depends on parameter selection. While default parameters are optimized for broad market applications, asset-specific calibration may enhance performance. Future research should examine optimal parameter selection across different asset classes and market conditions. ### 7.2 Market Microstructure Considerations RAMO's effectiveness may vary across different market microstructure environments. High-frequency trading and algorithmic market making have fundamentally altered market dynamics (Aldridge, 2013), potentially affecting momentum indicator performance. ### 7.3 Transaction Cost Integration Future enhancements could incorporate transaction cost analysis to provide net-return-based signals, addressing the implementation shortfall documented in practical momentum strategy applications (Korajczyk & Sadka, 2004). ## References Aldridge, I. (2013). *High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems*. 2nd ed. Hoboken, NJ: John Wiley & Sons. Ang, A. (2014). *Asset Management: A Systematic Approach to Factor Investing*. New York: Oxford University Press. Arms, R. W. (1989). *The Arms Index (TRIN): An Introduction to the Volume Analysis of Stock and Bond Markets*. Homewood, IL: Dow Jones-Irwin. Asness, C. S., Moskowitz, T. J., & Pedersen, L. H. (2013). Value and momentum everywhere. *Journal of Finance*, 68(3), 929-985. Barroso, P., & Santa-Clara, P. (2015). Momentum has its moments. *Journal of Financial Economics*, 116(1), 111-120. Bollinger, J. (1992). *Bollinger on Bollinger Bands*. New York: McGraw-Hill. Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. *Journal of Finance*, 47(5), 1731-1764. Calmar, T. (1991). The Calmar ratio: A smoother tool. *Futures*, 20(1), 40. Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). *The Econometrics of Financial Markets*. Princeton, NJ: Princeton University Press. Chekhlov, A., Uryasev, S., & Zabarankin, M. (2005). Drawdown measure in portfolio optimization. *International Journal of Theoretical and Applied Finance*, 8(1), 13-58. Daniel, K., & Moskowitz, T. J. (2016). Momentum crashes. *Journal of Financial Economics*, 122(2), 221-247. Fama, E. F., & French, K. R. (1996). Multifactor explanations of asset pricing anomalies. *Journal of Finance*, 51(1), 55-84. Granville, J. E. (1963). *Granville's New Key to Stock Market Profits*. Englewood Cliffs, NJ: Prentice-Hall. Guidolin, M. (2011). Markov switching models in empirical finance. In D. N. Drukker (Ed.), *Missing Data Methods: Time-Series Methods and Applications* (pp. 1-86). Bingley: Emerald Group Publishing. Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. *Econometrica*, 57(2), 357-384. Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. *Journal of Finance*, 48(1), 65-91. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. *Econometrica*, 47(2), 263-291. Kaufman, P. J. (1995). *Smarter Trading: Improving Performance in Changing Markets*. New York: McGraw-Hill. Korajczyk, R. A., & Sadka, R. (2004). Are momentum profits robust to trading costs? *Journal of Finance*, 59(3), 1039-1082. Leland, H. E. (1980). Who should buy portfolio insurance? *Journal of Finance*, 35(2), 581-594. Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. *Journal of Finance*, 55(4), 1705-1765. Markowitz, H. (1952). Portfolio selection. *Journal of Finance*, 7(1), 77-91. Murphy, J. J. (1999). *Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications*. New York: New York Institute of Finance. Prechter, R. R., & Frost, A. J. (1978). *Elliott Wave Principle: Key to Market Behavior*. Gainesville, GA: New Classics Library. Pring, M. J. (2002). *Technical Analysis Explained: The Successful Investor's Guide to Spotting Investment Trends and Turning Points*. 4th ed. New York: McGraw-Hill. Roncalli, T. (2013). *Introduction to Risk Parity and Budgeting*. Boca Raton, FL: CRC Press. Shefrin, H., & Statman, M. (1985). The disposition to sell winners too early and ride losers too long: Theory and evidence. *Journal of Finance*, 40(3), 777-790. Taleb, N. N. (2007). *The Black Swan: The Impact of the Highly Improbable*. New York: Random House. Taylor, M. P., & Allen, H. (1992). The use of technical analysis in the foreign exchange market. *Journal of International Money and Finance*, 11(3), 304-314. Treadway, A. B. (1969). On rational entrepreneurial behavior and the demand for investment. *Review of Economic Studies*, 36(2), 227-239. Wilder, J. W. (1978). *New Concepts in Technical Trading Systems*. Greensboro, NC: Trend Research.
Penunjuk Pine Script®
oleh EdgeTools
Combined ATR Bands + VWAP + Moving Averages🔥 Ultimate Trading Combo: ATR Bands + VWAP + Moving Averages This comprehensive indicator combines three powerful technical analysis tools in one clean interface: 📊 Features: • ATR Bands - Dynamic support/resistance levels with step-line styling • VWAP - Volume Weighted Average Price with orange dotted visualization • Moving Averages - 50, 100, 200 periods with customizable colors ⚙️ Customizable Settings: Toggle each component on/off independently Adjustable ATR periods and multipliers Multiple VWAP anchor periods (Session, Week, Month, Quarter, Year) Configurable MA sources and periods Custom colors and transparency levels 🎯 Perfect For: Day traders seeking dynamic support/resistance Swing traders using multiple timeframe analysis Anyone wanting clean, professional chart visualization 💡 Created with AI assistance (Claude Sonnet 4) Open source - feel free to modify and improve! 🔥 المؤشر الشامل: نطاقات ATR + VWAP + المتوسطات المتحركة هذا المؤشر الشامل يجمع ثلاث أدوات تحليل فني قوية في واجهة واحدة نظيفة: 📊 المميزات: • نطاقات ATR - مستويات دعم ومقاومة ديناميكية بتصميم خطوط متدرجة • VWAP - متوسط السعر المرجح بالحجم مع عرض نقطي برتقالي • المتوسطات المتحركة - فترات 50، 100، 200 مع ألوان قابلة للتخصيص ⚙️ إعدادات قابلة للتخصيص: تشغيل/إيقاف كل مكون بشكل مستقل فترات ومضاعفات ATR قابلة للتعديل فترات ربط VWAP متعددة (جلسة، أسبوع، شهر، ربع، سنة) مصادر وفترات MA قابلة للتكوين ألوان مخصصة ومستويات شفافية 🎯 مثالي لـ: المتداولين اليوميين الباحثين عن دعم/مقاومة ديناميكية متداولي التأرجح باستخدام تحليل الإطارات الزمنية المتعددة أي شخص يريد عرض مخططات نظيف ومهني 💡 تم إنشاؤه بمساعدة الذكاء الاصطناعي (Claude Sonnet 4) مفتوح المصدر - لا تتردد في التعديل والتحسين! #ATR #VWAP #MovingAverages #TechnicalAnalysis #Support #Resistance #DayTrading #SwingTrading #OpenSource #AI #تحليل_فني #دعم_مقاومة #متوسطات_متحركة #تداول_يومي
Penunjuk Pine Script®
oleh Redtoura
Sterke Trendwissel SignalenOverview The indicator combines three technical analysis tools to generate strong buy and sell signals: Moving Averages (MA): Short-term (9-period) and long-term (21-period) simple moving averages. Relative Strength Index (RSI): A 14-period RSI to measure overbought and oversold conditions. Volume: A volume multiplier (1.2x) compared to the average volume over 20 periods. Signals The indicator generates two types of signals: Strong Buy: When the short-term MA crosses above the long-term MA (bullish crossover), the relative volume is above the threshold (1.2x average), and the RSI is oversold (< 30). Strong Sell: When the short-term MA crosses below the long-term MA (bearish crossover), the relative volume is above the threshold (1.2x average), and the RSI is overbought (> 70). Visualizations The indicator plots: The short-term MA (blue line) The long-term MA (orange line) "STRONG BUY" labels (green) below the bar when a strong buy signal is generated "STRONG SELL" labels (red) above the bar when a strong sell signal is generated Alerts The indicator sets up alerts for strong buy and sell signals, which can be configured in TradingView's alert system. In summary, this indicator aims to identify strong trend changes by combining moving average crossovers, high volume, and RSI overbought/oversold conditions.
Penunjuk Pine Script®
oleh Fiona_Dutch