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FlexMAFlexMA – Time-based moving average FlexMA plots a moving average based on real-world time (like “5 days”) instead of fixed bar lengths. Choose the MA type (SMA, EMA, etc.), enter a timespan and unit, and the script automatically adjusts across any chart timeframe. This was created out of a demand for moving average indicator that was easy to configure across any time frame but the results end up consistent. For example, a 5 Day SMA where it looks the same at every interval. Powered by: Electrified/Time – Converts spans to lengths Electrified/MovingAverages – Provides modular MA logic Example: Want a 3-day EMA? Just set: Plot: EMA Timespan: 3 Unit: Days Clean, adaptive, and great for multi-timeframe setups.
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Tensor Market Analysis Engine (TMAE)# Tensor Market Analysis Engine (TMAE) ## Advanced Multi-Dimensional Mathematical Analysis System *Where Quantum Mathematics Meets Market Structure* --- ## 🎓 THEORETICAL FOUNDATION The Tensor Market Analysis Engine represents a revolutionary synthesis of three cutting-edge mathematical frameworks that have never before been combined for comprehensive market analysis. This indicator transcends traditional technical analysis by implementing advanced mathematical concepts from quantum mechanics, information theory, and fractal geometry. ### 🌊 Multi-Dimensional Volatility with Jump Detection **Hawkes Process Implementation:** The TMAE employs a sophisticated Hawkes process approximation for detecting self-exciting market jumps. Unlike traditional volatility measures that treat price movements as independent events, the Hawkes process recognizes that market shocks cluster and exhibit memory effects. **Mathematical Foundation:** ``` Intensity λ(t) = μ + Σ α(t - Tᵢ) ``` Where market jumps at times Tᵢ increase the probability of future jumps through the decay function α, controlled by the Hawkes Decay parameter (0.5-0.99). **Mahalanobis Distance Calculation:** The engine calculates volatility jumps using multi-dimensional Mahalanobis distance across up to 5 volatility dimensions: - **Dimension 1:** Price volatility (standard deviation of returns) - **Dimension 2:** Volume volatility (normalized volume fluctuations) - **Dimension 3:** Range volatility (high-low spread variations) - **Dimension 4:** Correlation volatility (price-volume relationship changes) - **Dimension 5:** Microstructure volatility (intrabar positioning analysis) This creates a volatility state vector that captures market behavior impossible to detect with traditional single-dimensional approaches. ### 📐 Hurst Exponent Regime Detection **Fractal Market Hypothesis Integration:** The TMAE implements advanced Rescaled Range (R/S) analysis to calculate the Hurst exponent in real-time, providing dynamic regime classification: - **H > 0.6:** Trending (persistent) markets - momentum strategies optimal - **H < 0.4:** Mean-reverting (anti-persistent) markets - contrarian strategies optimal - **H ≈ 0.5:** Random walk markets - breakout strategies preferred **Adaptive R/S Analysis:** Unlike static implementations, the TMAE uses adaptive windowing that adjusts to market conditions: ``` H = log(R/S) / log(n) ``` Where R is the range of cumulative deviations and S is the standard deviation over period n. **Dynamic Regime Classification:** The system employs hysteresis to prevent regime flipping, requiring sustained Hurst values before regime changes are confirmed. This prevents false signals during transitional periods. ### 🔄 Transfer Entropy Analysis **Information Flow Quantification:** Transfer entropy measures the directional flow of information between price and volume, revealing lead-lag relationships that indicate future price movements: ``` TE(X→Y) = Σ p(yₜ₊₁, yₜ, xₜ) log ``` **Causality Detection:** - **Volume → Price:** Indicates accumulation/distribution phases - **Price → Volume:** Suggests retail participation or momentum chasing - **Balanced Flow:** Market equilibrium or transition periods The system analyzes multiple lag periods (2-20 bars) to capture both immediate and structural information flows. --- ## 🔧 COMPREHENSIVE INPUT SYSTEM ### Core Parameters Group **Primary Analysis Window (10-100, Default: 50)** The fundamental lookback period affecting all calculations. Optimization by timeframe: - **1-5 minute charts:** 20-30 (rapid adaptation to micro-movements) - **15 minute-1 hour:** 30-50 (balanced responsiveness and stability) - **4 hour-daily:** 50-100 (smooth signals, reduced noise) - **Asset-specific:** Cryptocurrency 20-35, Stocks 35-50, Forex 40-60 **Signal Sensitivity (0.1-2.0, Default: 0.7)** Master control affecting all threshold calculations: - **Conservative (0.3-0.6):** High-quality signals only, fewer false positives - **Balanced (0.7-1.0):** Optimal risk-reward ratio for most trading styles - **Aggressive (1.1-2.0):** Maximum signal frequency, requires careful filtering **Signal Generation Mode:** - **Aggressive:** Any component signals (highest frequency) - **Confluence:** 2+ components agree (balanced approach) - **Conservative:** All 3 components align (highest quality) ### Volatility Jump Detection Group **Volatility Dimensions (2-5, Default: 3)** Determines the mathematical space complexity: - **2D:** Price + Volume volatility (suitable for clean markets) - **3D:** + Range volatility (optimal for most conditions) - **4D:** + Correlation volatility (advanced multi-asset analysis) - **5D:** + Microstructure volatility (maximum sensitivity) **Jump Detection Threshold (1.5-4.0σ, Default: 3.0σ)** Standard deviations required for volatility jump classification: - **Cryptocurrency:** 2.0-2.5σ (naturally volatile) - **Stock Indices:** 2.5-3.0σ (moderate volatility) - **Forex Major Pairs:** 3.0-3.5σ (typically stable) - **Commodities:** 2.0-3.0σ (varies by commodity) **Jump Clustering Decay (0.5-0.99, Default: 0.85)** Hawkes process memory parameter: - **0.5-0.7:** Fast decay (jumps treated as independent) - **0.8-0.9:** Moderate clustering (realistic market behavior) - **0.95-0.99:** Strong clustering (crisis/event-driven markets) ### Hurst Exponent Analysis Group **Calculation Method Options:** - **Classic R/S:** Original Rescaled Range (fast, simple) - **Adaptive R/S:** Dynamic windowing (recommended for trading) - **DFA:** Detrended Fluctuation Analysis (best for noisy data) **Trending Threshold (0.55-0.8, Default: 0.60)** Hurst value defining persistent market behavior: - **0.55-0.60:** Weak trend persistence - **0.65-0.70:** Clear trending behavior - **0.75-0.80:** Strong momentum regimes **Mean Reversion Threshold (0.2-0.45, Default: 0.40)** Hurst value defining anti-persistent behavior: - **0.35-0.45:** Weak mean reversion - **0.25-0.35:** Clear ranging behavior - **0.15-0.25:** Strong reversion tendency ### Transfer Entropy Parameters Group **Information Flow Analysis:** - **Price-Volume:** Classic flow analysis for accumulation/distribution - **Price-Volatility:** Risk flow analysis for sentiment shifts - **Multi-Timeframe:** Cross-timeframe causality detection **Maximum Lag (2-20, Default: 5)** Causality detection window: - **2-5 bars:** Immediate causality (scalping) - **5-10 bars:** Short-term flow (day trading) - **10-20 bars:** Structural flow (swing trading) **Significance Threshold (0.05-0.3, Default: 0.15)** Minimum entropy for signal generation: - **0.05-0.10:** Detect subtle information flows - **0.10-0.20:** Clear causality only - **0.20-0.30:** Very strong flows only --- ## 🎨 ADVANCED VISUAL SYSTEM ### Tensor Volatility Field Visualization **Five-Layer Resonance Bands:** The tensor field creates dynamic support/resistance zones that expand and contract based on mathematical field strength: - **Core Layer (Purple):** Primary tensor field with highest intensity - **Layer 2 (Neutral):** Secondary mathematical resonance - **Layer 3 (Info Blue):** Tertiary harmonic frequencies - **Layer 4 (Warning Gold):** Outer field boundaries - **Layer 5 (Success Green):** Maximum field extension **Field Strength Calculation:** ``` Field Strength = min(3.0, Mahalanobis Distance × Tensor Intensity) ``` The field amplitude adjusts to ATR and mathematical distance, creating dynamic zones that respond to market volatility. **Radiation Line Network:** During active tensor states, the system projects directional radiation lines showing field energy distribution: - **8 Directional Rays:** Complete angular coverage - **Tapering Segments:** Progressive transparency for natural visual flow - **Pulse Effects:** Enhanced visualization during volatility jumps ### Dimensional Portal System **Portal Mathematics:** Dimensional portals visualize regime transitions using category theory principles: - **Green Portals (◉):** Trending regime detection (appear below price for support) - **Red Portals (◎):** Mean-reverting regime (appear above price for resistance) - **Yellow Portals (○):** Random walk regime (neutral positioning) **Tensor Trail Effects:** Each portal generates 8 trailing particles showing mathematical momentum: - **Large Particles (●):** Strong mathematical signal - **Medium Particles (◦):** Moderate signal strength - **Small Particles (·):** Weak signal continuation - **Micro Particles (˙):** Signal dissipation ### Information Flow Streams **Particle Stream Visualization:** Transfer entropy creates flowing particle streams indicating information direction: - **Upward Streams:** Volume leading price (accumulation phases) - **Downward Streams:** Price leading volume (distribution phases) - **Stream Density:** Proportional to information flow strength **15-Particle Evolution:** Each stream contains 15 particles with progressive sizing and transparency, creating natural flow visualization that makes information transfer immediately apparent. ### Fractal Matrix Grid System **Multi-Timeframe Fractal Levels:** The system calculates and displays fractal highs/lows across five Fibonacci periods: - **8-Period:** Short-term fractal structure - **13-Period:** Intermediate-term patterns - **21-Period:** Primary swing levels - **34-Period:** Major structural levels - **55-Period:** Long-term fractal boundaries **Triple-Layer Visualization:** Each fractal level uses three-layer rendering: - **Shadow Layer:** Widest, darkest foundation (width 5) - **Glow Layer:** Medium white core line (width 3) - **Tensor Layer:** Dotted mathematical overlay (width 1) **Intelligent Labeling System:** Smart spacing prevents label overlap using ATR-based minimum distances. Labels include: - **Fractal Period:** Time-based identification - **Topological Class:** Mathematical complexity rating (0, I, II, III) - **Price Level:** Exact fractal price - **Mahalanobis Distance:** Current mathematical field strength - **Hurst Exponent:** Current regime classification - **Anomaly Indicators:** Visual strength representations (○ ◐ ● ⚡) ### Wick Pressure Analysis **Rejection Level Mathematics:** The system analyzes candle wick patterns to project future pressure zones: - **Upper Wick Analysis:** Identifies selling pressure and resistance zones - **Lower Wick Analysis:** Identifies buying pressure and support zones - **Pressure Projection:** Extends lines forward based on mathematical probability **Multi-Layer Glow Effects:** Wick pressure lines use progressive transparency (1-8 layers) creating natural glow effects that make pressure zones immediately visible without cluttering the chart. ### Enhanced Regime Background **Dynamic Intensity Mapping:** Background colors reflect mathematical regime strength: - **Deep Transparency (98% alpha):** Subtle regime indication - **Pulse Intensity:** Based on regime strength calculation - **Color Coding:** Green (trending), Red (mean-reverting), Neutral (random) **Smoothing Integration:** Regime changes incorporate 10-bar smoothing to prevent background flicker while maintaining responsiveness to genuine regime shifts. ### Color Scheme System **Six Professional Themes:** - **Dark (Default):** Professional trading environment optimization - **Light:** High ambient light conditions - **Classic:** Traditional technical analysis appearance - **Neon:** High-contrast visibility for active trading - **Neutral:** Minimal distraction focus - **Bright:** Maximum visibility for complex setups Each theme maintains mathematical accuracy while optimizing visual clarity for different trading environments and personal preferences. --- ## 📊 INSTITUTIONAL-GRADE DASHBOARD ### Tensor Field Status Section **Field Strength Display:** Real-time Mahalanobis distance calculation with dynamic emoji indicators: - **⚡ (Lightning):** Extreme field strength (>1.5× threshold) - **● (Solid Circle):** Strong field activity (>1.0× threshold) - **○ (Open Circle):** Normal field state **Signal Quality Rating:** Democratic algorithm assessment: - **ELITE:** All 3 components aligned (highest probability) - **STRONG:** 2 components aligned (good probability) - **GOOD:** 1 component active (moderate probability) - **WEAK:** No clear component signals **Threshold and Anomaly Monitoring:** - **Threshold Display:** Current mathematical threshold setting - **Anomaly Level (0-100%):** Combined volatility and volume spike measurement - **>70%:** High anomaly (red warning) - **30-70%:** Moderate anomaly (orange caution) - **<30%:** Normal conditions (green confirmation) ### Tensor State Analysis Section **Mathematical State Classification:** - **↑ BULL (Tensor State +1):** Trending regime with bullish bias - **↓ BEAR (Tensor State -1):** Mean-reverting regime with bearish bias - **◈ SUPER (Tensor State 0):** Random walk regime (neutral) **Visual State Gauge:** Five-circle progression showing tensor field polarity: - **🟢🟢🟢⚪⚪:** Strong bullish mathematical alignment - **⚪⚪🟡⚪⚪:** Neutral/transitional state - **⚪⚪🔴🔴🔴:** Strong bearish mathematical alignment **Trend Direction and Phase Analysis:** - **📈 BULL / 📉 BEAR / ➡️ NEUTRAL:** Primary trend classification - **🌪️ CHAOS:** Extreme information flow (>2.0 flow strength) - **⚡ ACTIVE:** Strong information flow (1.0-2.0 flow strength) - **😴 CALM:** Low information flow (<1.0 flow strength) ### Trading Signals Section **Real-Time Signal Status:** - **🟢 ACTIVE / ⚪ INACTIVE:** Long signal availability - **🔴 ACTIVE / ⚪ INACTIVE:** Short signal availability - **Components (X/3):** Active algorithmic components - **Mode Display:** Current signal generation mode **Signal Strength Visualization:** Color-coded component count: - **Green:** 3/3 components (maximum confidence) - **Aqua:** 2/3 components (good confidence) - **Orange:** 1/3 components (moderate confidence) - **Gray:** 0/3 components (no signals) ### Performance Metrics Section **Win Rate Monitoring:** Estimated win rates based on signal quality with emoji indicators: - **🔥 (Fire):** ≥60% estimated win rate - **👍 (Thumbs Up):** 45-59% estimated win rate - **⚠️ (Warning):** <45% estimated win rate **Mathematical Metrics:** - **Hurst Exponent:** Real-time fractal dimension (0.000-1.000) - **Information Flow:** Volume/price leading indicators - **📊 VOL:** Volume leading price (accumulation/distribution) - **💰 PRICE:** Price leading volume (momentum/speculation) - **➖ NONE:** Balanced information flow - **Volatility Classification:** - **🔥 HIGH:** Above 1.5× jump threshold - **📊 NORM:** Normal volatility range - **😴 LOW:** Below 0.5× jump threshold ### Market Structure Section (Large Dashboard) **Regime Classification:** - **📈 TREND:** Hurst >0.6, momentum strategies optimal - **🔄 REVERT:** Hurst <0.4, contrarian strategies optimal - **🎲 RANDOM:** Hurst ≈0.5, breakout strategies preferred **Mathematical Field Analysis:** - **Dimensions:** Current volatility space complexity (2D-5D) - **Hawkes λ (Lambda):** Self-exciting jump intensity (0.00-1.00) - **Jump Status:** 🚨 JUMP (active) / ✅ NORM (normal) ### Settings Summary Section (Large Dashboard) **Active Configuration Display:** - **Sensitivity:** Current master sensitivity setting - **Lookback:** Primary analysis window - **Theme:** Active color scheme - **Method:** Hurst calculation method (Classic R/S, Adaptive R/S, DFA) **Dashboard Sizing Options:** - **Small:** Essential metrics only (mobile/small screens) - **Normal:** Balanced information density (standard desktop) - **Large:** Maximum detail (multi-monitor setups) **Position Options:** - **Top Right:** Standard placement (avoids price action) - **Top Left:** Wide chart optimization - **Bottom Right:** Recent price focus (scalping) - **Bottom Left:** Maximum price visibility (swing trading) --- ## 🎯 SIGNAL GENERATION LOGIC ### Multi-Component Convergence System **Component Signal Architecture:** The TMAE generates signals through sophisticated component analysis rather than simple threshold crossing: **Volatility Component:** - **Jump Detection:** Mahalanobis distance threshold breach - **Hawkes Intensity:** Self-exciting process activation (>0.2) - **Multi-dimensional:** Considers all volatility dimensions simultaneously **Hurst Regime Component:** - **Trending Markets:** Price above SMA-20 with positive momentum - **Mean-Reverting Markets:** Price at Bollinger Band extremes - **Random Markets:** Bollinger squeeze breakouts with directional confirmation **Transfer Entropy Component:** - **Volume Leadership:** Information flow from volume to price - **Volume Spike:** Volume 110%+ above 20-period average - **Flow Significance:** Above entropy threshold with directional bias ### Democratic Signal Weighting **Signal Mode Implementation:** - **Aggressive Mode:** Any single component triggers signal - **Confluence Mode:** Minimum 2 components must agree - **Conservative Mode:** All 3 components must align **Momentum Confirmation:** All signals require momentum confirmation: - **Long Signals:** RSI >50 AND price >EMA-9 - **Short Signals:** RSI <50 AND price 0.6):** - **Increase Sensitivity:** Catch momentum continuation - **Lower Mean Reversion Threshold:** Avoid counter-trend signals - **Emphasize Volume Leadership:** Institutional accumulation/distribution - **Tensor Field Focus:** Use expansion for trend continuation - **Signal Mode:** Aggressive or Confluence for trend following **Range-Bound Markets (Hurst <0.4):** - **Decrease Sensitivity:** Avoid false breakouts - **Lower Trending Threshold:** Quick regime recognition - **Focus on Price Leadership:** Retail sentiment extremes - **Fractal Grid Emphasis:** Support/resistance trading - **Signal Mode:** Conservative for high-probability reversals **Volatile Markets (High Jump Frequency):** - **Increase Hawkes Decay:** Recognize event clustering - **Higher Jump Threshold:** Avoid noise signals - **Maximum Dimensions:** Capture full volatility complexity - **Reduce Position Sizing:** Risk management adaptation - **Enhanced Visuals:** Maximum information for rapid decisions **Low Volatility Markets (Low Jump Frequency):** - **Decrease Jump Threshold:** Capture subtle movements - **Lower Hawkes Decay:** Treat moves as independent - **Reduce Dimensions:** Simplify analysis - **Increase Position Sizing:** Capitalize on compressed volatility - **Minimal Visuals:** Reduce distraction in quiet markets --- ## 🚀 ADVANCED TRADING STRATEGIES ### The Mathematical Convergence Method **Entry Protocol:** 1. **Fractal Grid Approach:** Monitor price approaching significant fractal levels 2. **Tensor Field Confirmation:** Verify field expansion supporting direction 3. **Portal Signal:** Wait for dimensional portal appearance 4. **ELITE/STRONG Quality:** Only trade highest quality mathematical signals 5. **Component Consensus:** Confirm 2+ components agree in Confluence mode **Example Implementation:** - Price approaching 21-period fractal high - Tensor field expanding upward (bullish mathematical alignment) - Green portal appears below price (trending regime confirmation) - ELITE quality signal with 3/3 components active - Enter long position with stop below fractal level **Risk Management:** - **Stop Placement:** Below/above fractal level that generated signal - **Position Sizing:** Based on Mahalanobis distance (higher distance = smaller size) - **Profit Targets:** Next fractal level or tensor field resistance ### The Regime Transition Strategy **Regime Change Detection:** 1. **Monitor Hurst Exponent:** Watch for persistent moves above/below thresholds 2. **Portal Color Change:** Regime transitions show different portal colors 3. **Background Intensity:** Increasing regime background intensity 4. **Mathematical Confirmation:** Wait for regime confirmation (hysteresis) **Trading Implementation:** - **Trending Transitions:** Trade momentum breakouts, follow trend - **Mean Reversion Transitions:** Trade range boundaries, fade extremes - **Random Transitions:** Trade breakouts with tight stops **Advanced Techniques:** - **Multi-Timeframe:** Confirm regime on higher timeframe - **Early Entry:** Enter on regime transition rather than confirmation - **Regime Strength:** Larger positions during strong regime signals ### The Information Flow Momentum Strategy **Flow Detection Protocol:** 1. **Monitor Transfer Entropy:** Watch for significant information flow shifts 2. **Volume Leadership:** Strong edge when volume leads price 3. **Flow Acceleration:** Increasing flow strength indicates momentum 4. **Directional Confirmation:** Ensure flow aligns with intended trade direction **Entry Signals:** - **Volume → Price Flow:** Enter during accumulation/distribution phases - **Price → Volume Flow:** Enter on momentum confirmation breaks - **Flow Reversal:** Counter-trend entries when flow reverses **Optimization:** - **Scalping:** Use immediate flow detection (2-5 bar lag) - **Swing Trading:** Use structural flow (10-20 bar lag) - **Multi-Asset:** Compare flow between correlated assets ### The Tensor Field Expansion Strategy **Field Mathematics:** The tensor field expansion indicates mathematical pressure building in market structure: **Expansion Phases:** 1. **Compression:** Field contracts, volatility decreases 2. **Tension Building:** Mathematical pressure accumulates 3. **Expansion:** Field expands rapidly with directional movement 4. **Resolution:** Field stabilizes at new equilibrium **Trading Applications:** - **Compression Trading:** Prepare for breakout during field contraction - **Expansion Following:** Trade direction of field expansion - **Reversion Trading:** Fade extreme field expansion - **Multi-Dimensional:** Consider all field layers for confirmation ### The Hawkes Process Event Strategy **Self-Exciting Jump Trading:** Understanding that market shocks cluster and create follow-on opportunities: **Jump Sequence Analysis:** 1. **Initial Jump:** First volatility jump detected 2. **Clustering Phase:** Hawkes intensity remains elevated 3. **Follow-On Opportunities:** Additional jumps more likely 4. **Decay Period:** Intensity gradually decreases **Implementation:** - **Jump Confirmation:** Wait for mathematical jump confirmation - **Direction Assessment:** Use other components for direction - **Clustering Trades:** Trade subsequent moves during high intensity - **Decay Exit:** Exit positions as Hawkes intensity decays ### The Fractal Confluence System **Multi-Timeframe Fractal Analysis:** Combining fractal levels across different periods for high-probability zones: **Confluence Zones:** - **Double Confluence:** 2 fractal levels align - **Triple Confluence:** 3+ fractal levels cluster - **Mathematical Confirmation:** Tensor field supports the level - **Information Flow:** Transfer entropy confirms direction **Trading Protocol:** 1. **Identify Confluence:** Find 2+ fractal levels within 1 ATR 2. **Mathematical Support:** Verify tensor field alignment 3. **Signal Quality:** Wait for STRONG or ELITE signal 4. **Risk Definition:** Use fractal level for stop placement 5. **Profit Targeting:** Next major fractal confluence zone --- ## ⚠️ COMPREHENSIVE RISK MANAGEMENT ### Mathematical Position Sizing **Mahalanobis Distance Integration:** Position size should inversely correlate with mathematical field strength: ``` Position Size = Base Size × (Threshold / Mahalanobis Distance) ``` **Risk Scaling Matrix:** - **Low Field Strength (<2.0):** Standard position sizing - **Moderate Field Strength (2.0-3.0):** 75% position sizing - **High Field Strength (3.0-4.0):** 50% position sizing - **Extreme Field Strength (>4.0):** 25% position sizing or no trade ### Signal Quality Risk Adjustment **Quality-Based Position Sizing:** - **ELITE Signals:** 100% of planned position size - **STRONG Signals:** 75% of planned position size - **GOOD Signals:** 50% of planned position size - **WEAK Signals:** No position or paper trading only **Component Agreement Scaling:** - **3/3 Components:** Full position size - **2/3 Components:** 75% position size - **1/3 Components:** 50% position size or skip trade ### Regime-Adaptive Risk Management **Trending Market Risk:** - **Wider Stops:** Allow for trend continuation - **Trend Following:** Trade with regime direction - **Higher Position Size:** Trend probability advantage - **Momentum Stops:** Trail stops based on momentum indicators **Mean-Reverting Market Risk:** - **Tighter Stops:** Quick exits on trend continuation - **Contrarian Positioning:** Trade against extremes - **Smaller Position Size:** Higher reversal failure rate - **Level-Based Stops:** Use fractal levels for stops **Random Market Risk:** - **Breakout Focus:** Trade only clear breakouts - **Tight Initial Stops:** Quick exit if breakout fails - **Reduced Frequency:** Skip marginal setups - **Range-Based Targets:** Profit targets at range boundaries ### Volatility-Adaptive Risk Controls **High Volatility Periods:** - **Reduced Position Size:** Account for wider price swings - **Wider Stops:** Avoid noise-based exits - **Lower Frequency:** Skip marginal setups - **Faster Exits:** Take profits more quickly **Low Volatility Periods:** - **Standard Position Size:** Normal risk parameters - **Tighter Stops:** Take advantage of compressed ranges - **Higher Frequency:** Trade more setups - **Extended Targets:** Allow for compressed volatility expansion ### Multi-Timeframe Risk Alignment **Higher Timeframe Trend:** - **With Trend:** Standard or increased position size - **Against Trend:** Reduced position size or skip - **Neutral Trend:** Standard position size with tight management **Risk Hierarchy:** 1. **Primary:** Current timeframe signal quality 2. **Secondary:** Higher timeframe trend alignment 3. **Tertiary:** Mathematical field strength 4. **Quaternary:** Market regime classification --- ## 📚 EDUCATIONAL VALUE AND MATHEMATICAL CONCEPTS ### Advanced Mathematical Concepts **Tensor Analysis in Markets:** The TMAE introduces traders to tensor analysis, a branch of mathematics typically reserved for physics and advanced engineering. Tensors provide a framework for understanding multi-dimensional market relationships that scalar and vector analysis cannot capture. **Information Theory Applications:** Transfer entropy implementation teaches traders about information flow in markets, a concept from information theory that quantifies directional causality between variables. This provides intuition about market microstructure and participant behavior. **Fractal Geometry in Trading:** The Hurst exponent calculation exposes traders to fractal geometry concepts, helping understand that markets exhibit self-similar patterns across multiple timeframes. This mathematical insight transforms how traders view market structure. **Stochastic Process Theory:** The Hawkes process implementation introduces concepts from stochastic process theory, specifically self-exciting point processes. This provides mathematical framework for understanding why market events cluster and exhibit memory effects. ### Learning Progressive Complexity **Beginner Mathematical Concepts:** - **Volatility Dimensions:** Understanding multi-dimensional analysis - **Regime Classification:** Learning market personality types - **Signal Democracy:** Algorithmic consensus building - **Visual Mathematics:** Interpreting mathematical concepts visually **Intermediate Mathematical Applications:** - **Mahalanobis Distance:** Statistical distance in multi-dimensional space - **Rescaled Range Analysis:** Fractal dimension measurement - **Information Entropy:** Quantifying uncertainty and causality - **Field Theory:** Understanding mathematical fields in market context **Advanced Mathematical Integration:** - **Tensor Field Dynamics:** Multi-dimensional market force analysis - **Stochastic Self-Excitation:** Event clustering and memory effects - **Categorical Composition:** Mathematical signal combination theory - **Topological Market Analysis:** Understanding market shape and connectivity ### Practical Mathematical Intuition **Developing Market Mathematics Intuition:** The TMAE serves as a bridge between abstract mathematical concepts and practical trading applications. Traders develop intuitive understanding of: - **How markets exhibit mathematical structure beneath apparent randomness** - **Why multi-dimensional analysis reveals patterns invisible to single-variable approaches** - **How information flows through markets in measurable, predictable ways** - **Why mathematical models provide probabilistic edges rather than certainties** --- ## 🔬 IMPLEMENTATION AND OPTIMIZATION ### Getting Started Protocol **Phase 1: Observation (Week 1)** 1. **Apply with defaults:** Use standard settings on your primary trading timeframe 2. **Study visual elements:** Learn to interpret tensor fields, portals, and streams 3. **Monitor dashboard:** Observe how metrics change with market conditions 4. **No trading:** Focus entirely on pattern recognition and understanding **Phase 2: Pattern Recognition (Week 2-3)** 1. **Identify signal patterns:** Note what market conditions produce different signal qualities 2. **Regime correlation:** Observe how Hurst regimes affect signal performance 3. **Visual confirmation:** Learn to read tensor field expansion and portal signals 4. **Component analysis:** Understand which components drive signals in different markets **Phase 3: Parameter Optimization (Week 4-5)** 1. **Asset-specific tuning:** Adjust parameters for your specific trading instrument 2. **Timeframe optimization:** Fine-tune for your preferred trading timeframe 3. **Sensitivity adjustment:** Balance signal frequency with quality 4. **Visual customization:** Optimize colors and intensity for your trading environment **Phase 4: Live Implementation (Week 6+)** 1. **Paper trading:** Test signals with hypothetical trades 2. **Small position sizing:** Begin with minimal risk during learning phase 3. **Performance tracking:** Monitor actual vs. expected signal performance 4. **Continuous optimization:** Refine settings based on real performance data ### Performance Monitoring System **Signal Quality Tracking:** - **ELITE Signal Win Rate:** Track highest quality signals separately - **Component Performance:** Monitor which components provide best signals - **Regime Performance:** Analyze performance across different market regimes - **Timeframe Analysis:** Compare performance across different session times **Mathematical Metric Correlation:** - **Field Strength vs. Performance:** Higher field strength should correlate with better performance - **Component Agreement vs. Win Rate:** More component agreement should improve win rates - **Regime Alignment vs. Success:** Trading with mathematical regime should outperform ### Continuous Optimization Process **Monthly Review Protocol:** 1. **Performance Analysis:** Review win rates, profit factors, and maximum drawdown 2. **Parameter Assessment:** Evaluate if current settings remain optimal 3. **Market Adaptation:** Adjust for changes in market character or volatility 4. **Component Weighting:** Consider if certain components should receive more/less emphasis **Quarterly Deep Analysis:** 1. **Mathematical Model Validation:** Verify that mathematical relationships remain valid 2. **Regime Distribution:** Analyze time spent in different market regimes 3. **Signal Evolution:** Track how signal characteristics change over time 4. **Correlation Analysis:** Monitor correlations between different mathematical components --- ## 🌟 UNIQUE INNOVATIONS AND CONTRIBUTIONS ### Revolutionary Mathematical Integration **First-Ever Implementations:** 1. **Multi-Dimensional Volatility Tensor:** First indicator to implement true tensor analysis for market volatility 2. **Real-Time Hawkes Process:** First trading implementation of self-exciting point processes 3. **Transfer Entropy Trading Signals:** First practical application of information theory for trade generation 4. **Democratic Component Voting:** First algorithmic consensus system for signal generation 5. **Fractal-Projected Signal Quality:** First system to predict signal quality at future price levels ### Advanced Visualization Innovations **Mathematical Visualization Breakthroughs:** - **Tensor Field Radiation:** Visual representation of mathematical field energy - **Dimensional Portal System:** Category theory visualization for regime transitions - **Information Flow Streams:** Real-time visual display of market information transfer - **Multi-Layer Fractal Grid:** Intelligent spacing and projection system - **Regime Intensity Mapping:** Dynamic background showing mathematical regime strength ### Practical Trading Innovations **Trading System Advances:** - **Quality-Weighted Signal Generation:** Signals rated by mathematical confidence - **Regime-Adaptive Strategy Selection:** Automatic strategy optimization based on market personality - **Anti-Spam Signal Protection:** Mathematical prevention of signal clustering - **Component Performance Tracking:** Real-time monitoring of algorithmic component success - **Field-Strength Position Sizing:** Mathematical volatility integration for risk management --- ## ⚖️ RESPONSIBLE USAGE AND LIMITATIONS ### Mathematical Model Limitations **Understanding Model Boundaries:** While the TMAE implements sophisticated mathematical concepts, traders must understand fundamental limitations: - **Markets Are Not Purely Mathematical:** Human psychology, news events, and fundamental factors create unpredictable elements - **Past Performance Limitations:** Mathematical relationships that worked historically may not persist indefinitely - **Model Risk:** Complex models can fail during unprecedented market conditions - **Overfitting Potential:** Highly optimized parameters may not generalize to future market conditions ### Proper Implementation Guidelines **Risk Management Requirements:** - **Never Risk More Than 2% Per Trade:** Regardless of signal quality - **Diversification Mandatory:** Don't rely solely on mathematical signals - **Position Sizing Discipline:** Use mathematical field strength for sizing, not confidence - **Stop Loss Non-Negotiable:** Every trade must have predefined risk parameters **Realistic Expectations:** - **Mathematical Edge, Not Certainty:** The indicator provides probabilistic advantages, not guaranteed outcomes - **Learning Curve Required:** Complex mathematical concepts require time to master - **Market Adaptation Necessary:** Parameters must evolve with changing market conditions - **Continuous Education Important:** Understanding underlying mathematics improves application ### Ethical Trading Considerations **Market Impact Awareness:** - **Information Asymmetry:** Advanced mathematical analysis may provide advantages over other market participants - **Position Size Responsibility:** Large positions based on mathematical signals can impact market structure - **Sharing Knowledge:** Consider educational contributions to trading community - **Fair Market Participation:** Use mathematical advantages responsibly within market framework ### Professional Development Path **Skill Development Sequence:** 1. **Basic Mathematical Literacy:** Understand fundamental concepts before advanced application 2. **Risk Management Mastery:** Develop disciplined risk control before relying on complex signals 3. **Market Psychology Understanding:** Combine mathematical analysis with behavioral market insights 4. **Continuous Learning:** Stay updated on mathematical finance developments and market evolution --- ## 🔮 CONCLUSION The Tensor Market Analysis Engine represents a quantum leap forward in technical analysis, successfully bridging the gap between advanced pure mathematics and practical trading applications. By integrating multi-dimensional volatility analysis, fractal market theory, and information flow dynamics, the TMAE reveals market structure invisible to conventional analysis while maintaining visual clarity and practical usability. ### Mathematical Innovation Legacy This indicator establishes new paradigms in technical analysis: - **Tensor analysis for market volatility understanding** - **Stochastic self-excitation for event clustering prediction** - **Information theory for causality-based trade generation** - **Democratic algorithmic consensus for signal quality enhancement** - **Mathematical field visualization for intuitive market understanding** ### Practical Trading Revolution Beyond mathematical innovation, the TMAE transforms practical trading: - **Quality-rated signals replace binary buy/sell decisions** - **Regime-adaptive strategies automatically optimize for market personality** - **Multi-dimensional risk management integrates mathematical volatility measures** - **Visual mathematical concepts make complex analysis immediately interpretable** - **Educational value creates lasting improvement in trading understanding** ### Future-Proof Design The mathematical foundations ensure lasting relevance: - **Universal mathematical principles transcend market evolution** - **Multi-dimensional analysis adapts to new market structures** - **Regime detection automatically adjusts to changing market personalities** - **Component democracy allows for future algorithmic additions** - **Mathematical visualization scales with increasing market complexity** ### Commitment to Excellence The TMAE represents more than an indicator—it embodies a philosophy of bringing rigorous mathematical analysis to trading while maintaining practical utility and visual elegance. Every component, from the multi-dimensional tensor fields to the democratic signal generation, reflects a commitment to mathematical accuracy, trading practicality, and educational value. ### Trading with Mathematical Precision In an era where markets grow increasingly complex and computational, the TMAE provides traders with mathematical tools previously available only to institutional quantitative research teams. Yet unlike academic mathematical models, the TMAE translates complex concepts into intuitive visual representations and practical trading signals. By combining the mathematical rigor of tensor analysis, the statistical power of multi-dimensional volatility modeling, and the information-theoretic insights of transfer entropy, traders gain unprecedented insight into market structure and dynamics. ### Final Perspective Markets, like nature, exhibit profound mathematical beauty beneath apparent chaos. The Tensor Market Analysis Engine serves as a mathematical lens that reveals this hidden order, transforming how traders perceive and interact with market structure. Through mathematical precision, visual elegance, and practical utility, the TMAE empowers traders to see beyond the noise and trade with the confidence that comes from understanding the mathematical principles governing market behavior. Trade with mathematical insight. Trade with the power of tensors. Trade with the TMAE. *"In mathematics, you don't understand things. You just get used to them." - John von Neumann* *With the TMAE, mathematical market understanding becomes not just possible, but intuitive.* — Dskyz, Trade with insight. Trade with anticipation.
Pine Script® インジケーター
DskyzInvestmentsの投稿
16
Grothendieck-Teichmüller Geometric SynthesisDskyz's Grothendieck-Teichmüller Geometric Synthesis (GTGS) THEORETICAL FOUNDATION: A SYMPHONY OF GEOMETRIES The 🎓 GTGS is built upon a revolutionary premise: that market dynamics can be modeled as geometric and topological structures. While not a literal academic implementation—such a task would demand computational power far beyond current trading platforms—it leverages core ideas from advanced mathematical theories as powerful analogies and frameworks for its algorithms. Each component translates an abstract concept into a practical market calculation, distinguishing GTGS by identifying deeper structural patterns rather than relying on standard statistical measures. 1. Grothendieck-Teichmüller Theory: Deforming Market Structure The Theory : Studies symmetries and deformations of geometric objects, focusing on the "absolute" structure of mathematical spaces. Indicator Analogy : The calculate_grothendieck_field function models price action as a "deformation" from its immediate state. Using the nth root of price ratios (math.pow(price_ratio, 1.0/prime)), it measures market "shape" stretching or compression, revealing underlying tensions and potential shifts. 2. Topos Theory & Sheaf Cohomology: From Local to Global Patterns The Theory : A framework for assembling local properties into a global picture, with cohomology measuring "obstructions" to consistency. Indicator Analogy : The calculate_topos_coherence function uses sine waves (math.sin) to represent local price "sections." Summing these yields a "cohomology" value, quantifying price action consistency. High values indicate coherent trends; low values signal conflict and uncertainty. 3. Tropical Geometry: Simplifying Complexity The Theory : Transforms complex multiplicative problems into simpler, additive, piecewise-linear ones using min(a, b) for addition and a + b for multiplication. Indicator Analogy : The calculate_tropical_metric function applies tropical_add(a, b) => math.min(a, b) to identify the "lowest energy" state among recent price points, pinpointing critical support levels non-linearly. 4. Motivic Cohomology & Non-Commutative Geometry The Theory : Studies deep arithmetic and quantum-like properties of geometric spaces. Indicator Analogy : The motivic_rank and spectral_triple functions compute weighted sums of historical prices to capture market "arithmetic complexity" and "spectral signature." Higher values reflect structured, harmonic price movements. 5. Perfectoid Spaces & Homotopy Type Theory The Theory : Abstract fields dealing with p-adic numbers and logical foundations of mathematics. Indicator Analogy : The perfectoid_conv and type_coherence functions analyze price convergence and path identity, assessing the "fractal dust" of price differences and price path cohesion, adding fractal and logical analysis. The Combination is Key : No single theory dominates. GTGS ’s Unified Field synthesizes all seven perspectives into a comprehensive score, ensuring signals reflect deep structural alignment across mathematical domains. 🎛️ INPUTS: CONFIGURING THE GEOMETRIC ENGINE The GTGS offers a suite of customizable inputs, allowing traders to tailor its behavior to specific timeframes, market sectors, and trading styles. Below is a detailed breakdown of key input groups, their functionality, and optimization strategies, leveraging provided tooltips for precision. Grothendieck-Teichmüller Theory Inputs 🧬 Deformation Depth (Absolute Galois) : What It Is : Controls the depth of Galois group deformations analyzed in market structure. How It Works : Measures price action deformations under automorphisms of the absolute Galois group, capturing market symmetries. Optimization : Higher Values (15-20) : Captures deeper symmetries, ideal for major trends in swing trading (4H-1D). Lower Values (3-8) : Responsive to local deformations, suited for scalping (1-5min). Timeframes : Scalping (1-5min) : 3-6 for quick local shifts. Day Trading (15min-1H) : 8-12 for balanced analysis. Swing Trading (4H-1D) : 12-20 for deep structural trends. Sectors : Stocks : Use 8-12 for stable trends. Crypto : 3-8 for volatile, short-term moves. Forex : 12-15 for smooth, cyclical patterns. Pro Tip : Increase in trending markets to filter noise; decrease in choppy markets for sensitivity. 🗼 Teichmüller Tower Height : What It Is : Determines the height of the Teichmüller modular tower for hierarchical pattern detection. How It Works : Builds modular levels to identify nested market patterns. Optimization : Higher Values (6-8) : Detects complex fractals, ideal for swing trading. Lower Values (2-4) : Focuses on primary patterns, faster for scalping. Timeframes : Scalping : 2-3 for speed. Day Trading : 4-5 for balanced patterns. Swing Trading : 5-8 for deep fractals. Sectors : Indices : 5-8 for robust, long-term patterns. Crypto : 2-4 for rapid shifts. Commodities : 4-6 for cyclical trends. Pro Tip : Higher towers reveal hidden fractals but may slow computation; adjust based on hardware. 🔢 Galois Prime Base : What It Is : Sets the prime base for Galois field computations. How It Works : Defines the field extension characteristic for market analysis. Optimization : Prime Characteristics : 2 : Binary markets (up/down). 3 : Ternary states (bull/bear/neutral). 5 : Pentagonal symmetry (Elliott waves). 7 : Heptagonal cycles (weekly patterns). 11,13,17,19 : Higher-order patterns. Timeframes : Scalping/Day Trading : 2 or 3 for simplicity. Swing Trading : 5 or 7 for wave or cycle detection. Sectors : Forex : 5 for Elliott wave alignment. Stocks : 7 for weekly cycle consistency. Crypto : 3 for volatile state shifts. Pro Tip : Use 7 for most markets; 5 for Elliott wave traders. Topos Theory & Sheaf Cohomology Inputs 🏛️ Temporal Site Size : What It Is : Defines the number of time points in the topological site. How It Works : Sets the local neighborhood for sheaf computations, affecting cohomology smoothness. Optimization : Higher Values (30-50) : Smoother cohomology, better for trends in swing trading. Lower Values (5-15) : Responsive, ideal for reversals in scalping. Timeframes : Scalping : 5-10 for quick responses. Day Trading : 15-25 for balanced analysis. Swing Trading : 25-50 for smooth trends. Sectors : Stocks : 25-35 for stable trends. Crypto : 5-15 for volatility. Forex : 20-30 for smooth cycles. Pro Tip : Match site size to your average holding period in bars for optimal coherence. 📐 Sheaf Cohomology Degree : What It Is : Sets the maximum degree of cohomology groups computed. How It Works : Higher degrees capture complex topological obstructions. Optimization : Degree Meanings : 1 : Simple obstructions (basic support/resistance). 2 : Cohomological pairs (double tops/bottoms). 3 : Triple intersections (complex patterns). 4-5 : Higher-order structures (rare events). Timeframes : Scalping/Day Trading : 1-2 for simplicity. Swing Trading : 3 for complex patterns. Sectors : Indices : 2-3 for robust patterns. Crypto : 1-2 for rapid shifts. Commodities : 3-4 for cyclical events. Pro Tip : Degree 3 is optimal for most trading; higher degrees for research or rare event detection. 🌐 Grothendieck Topology : What It Is : Chooses the Grothendieck topology for the site. How It Works : Affects how local data integrates into global patterns. Optimization : Topology Characteristics : Étale : Finest topology, captures local-global principles. Nisnevich : A1-invariant, good for trends. Zariski : Coarse but robust, filters noise. Fpqc : Faithfully flat, highly sensitive. Sectors : Stocks : Zariski for stability. Crypto : Étale for sensitivity. Forex : Nisnevich for smooth trends. Indices : Zariski for robustness. Timeframes : Scalping : Étale for precision. Swing Trading : Nisnevich or Zariski for reliability. Pro Tip : Start with Étale for precision; switch to Zariski in noisy markets. Unified Field Configuration Inputs ⚛️ Field Coupling Constant : What It Is : Sets the interaction strength between geometric components. How It Works : Controls signal amplification in the unified field equation. Optimization : Higher Values (0.5-1.0) : Strong coupling, amplified signals for ranging markets. Lower Values (0.001-0.1) : Subtle signals for trending markets. Timeframes : Scalping : 0.5-0.8 for quick, strong signals. Swing Trading : 0.1-0.3 for trend confirmation. Sectors : Crypto : 0.5-1.0 for volatility. Stocks : 0.1-0.3 for stability. Forex : 0.3-0.5 for balance. Pro Tip : Default 0.137 (fine structure constant) is a balanced starting point; adjust up in choppy markets. 📐 Geometric Weighting Scheme : What It Is : Determines the framework for combining geometric components. How It Works : Adjusts emphasis on different mathematical structures. Optimization : Scheme Characteristics : Canonical : Equal weighting, balanced. Derived : Emphasizes higher-order structures. Motivic : Prioritizes arithmetic properties. Spectral : Focuses on frequency domain. Sectors : Stocks : Canonical for balance. Crypto : Spectral for volatility. Forex : Derived for structured moves. Indices : Motivic for arithmetic cycles. Timeframes : Day Trading : Canonical or Derived for flexibility. Swing Trading : Motivic for long-term cycles. Pro Tip : Start with Canonical; experiment with Spectral in volatile markets. Dashboard and Visual Configuration Inputs 📋 Show Enhanced Dashboard, 📏 Size, 📍 Position : What They Are : Control dashboard visibility, size, and placement. How They Work : Display key metrics like Unified Field , Resonance , and Signal Quality . Optimization : Scalping : Small size, Bottom Right for minimal chart obstruction. Swing Trading : Large size, Top Right for detailed analysis. Sectors : Universal across markets; adjust size based on screen setup. Pro Tip : Use Large for analysis, Small for live trading. 📐 Show Motivic Cohomology Bands, 🌊 Morphism Flow, 🔮 Future Projection, 🔷 Holographic Mesh, ⚛️ Spectral Flow : What They Are : Toggle visual elements representing mathematical calculations. How They Work : Provide intuitive representations of market dynamics. Optimization : Timeframes : Scalping : Enable Morphism Flow and Spectral Flow for momentum. Swing Trading : Enable all for comprehensive analysis. Sectors : Crypto : Emphasize Morphism Flow and Future Projection for volatility. Stocks : Focus on Cohomology Bands for stable trends. Pro Tip : Disable non-essential visuals in fast markets to reduce clutter. 🌫️ Field Transparency, 🔄 Web Recursion Depth, 🎨 Mesh Color Scheme : What They Are : Adjust visual clarity, complexity, and color. How They Work : Enhance interpretability of visual elements. Optimization : Transparency : 30-50 for balanced visibility; lower for analysis. Recursion Depth : 6-8 for balanced detail; lower for older hardware. Color Scheme : Purple/Blue : Analytical focus. Green/Orange : Trading momentum. Pro Tip : Use Neon Purple for deep analysis; Neon Green for active trading. ⏱️ Minimum Bars Between Signals : What It Is : Minimum number of bars required between consecutive signals. How It Works : Prevents signal clustering by enforcing a cooldown period. Optimization : Higher Values (10-20) : Fewer signals, avoids whipsaws, suited for swing trading. Lower Values (0-5) : More responsive, allows quick reversals, ideal for scalping. Timeframes : Scalping : 0-2 bars for rapid signals. Day Trading : 3-5 bars for balance. Swing Trading : 5-10 bars for stability. Sectors : Crypto : 0-3 for volatility. Stocks : 5-10 for trend clarity. Forex : 3-7 for cyclical moves. Pro Tip : Increase in choppy markets to filter noise. Hardcoded Parameters Tropical, Motivic, Spectral, Perfectoid, Homotopy Inputs : Fixed to optimize performance but influence calculations (e.g., tropical_degree=4 for support levels, perfectoid_prime=5 for convergence). Optimization : Experiment with codebase modifications if advanced customization is needed, but defaults are robust across markets. 🎨 ADVANCED VISUAL SYSTEM: TRADING IN A GEOMETRIC UNIVERSE The GTTMTSF ’s visuals are direct representations of its mathematics, designed for intuitive and precise trading decisions. Motivic Cohomology Bands : What They Are : Dynamic bands ( H⁰ , H¹ , H² ) representing cohomological support/resistance. Color & Meaning : Colors reflect energy levels ( H⁰ tightest, H² widest). Breaks into H¹ signal momentum; H² touches suggest reversals. How to Trade : Use for stop-loss/profit-taking. Band bounces with Dashboard confirmation are high-probability setups. Morphism Flow (Webbing) : What It Is : White particle streams visualizing market momentum. Interpretation : Dense flows indicate strong trends; sparse flows signal consolidation. How to Trade : Follow dominant flow direction; new flows post-consolidation signal trend starts. Future Projection Web (Fractal Grid) : What It Is : Fibonacci-period fractal projections of support/resistance. Color & Meaning : Three-layer lines (white shadow, glow, colored quantum) with labels showing price, topological class, anomaly strength (φ), resonance (ρ), and obstruction ( H¹ ). ⚡ marks extreme anomalies. How to Trade : Target ⚡/● levels for entries/exits. High-anomaly levels with weakening Unified Field are reversal setups. Holographic Mesh & Spectral Flow : What They Are : Visuals of harmonic interference and spectral energy. How to Trade : Bright mesh nodes or strong Spectral Flow warn of building pressure before price movement. 📊 THE GEOMETRIC DASHBOARD: YOUR MISSION CONTROL The Dashboard translates complex mathematics into actionable intelligence. Unified Field & Signals : FIELD : Master value (-10 to +10), synthesizing all geometric components. Extreme readings (>5 or <-5) signal structural limits, often preceding reversals or continuations. RESONANCE : Measures harmony between geometric field and price-volume momentum. Positive amplifies bullish moves; negative amplifies bearish moves. SIGNAL QUALITY : Confidence meter rating alignment. Trade only STRONG or EXCEPTIONAL signals for high-probability setups. Geometric Components : What They Are : Breakdown of seven mathematical engines. How to Use : Watch for convergence. A strong Unified Field is reliable when components (e.g., Grothendieck , Topos , Motivic ) align. Divergence warns of trend weakening. Signal Performance : What It Is : Tracks indicator signal performance. How to Use : Assesses real-time performance to build confidence and understand system behavior. 🚀 DEVELOPMENT & UNIQUENESS: BEYOND CONVENTIONAL ANALYSIS The GTTMTSF was developed to analyze markets as evolving geometric objects, not statistical time-series. Why This Is Unlike Anything Else : Theoretical Depth : Uses geometry and topology, identifying patterns invisible to statistical tools. Holistic Synthesis : Integrates seven deep mathematical frameworks into a cohesive Unified Field . Creative Implementation : Translates PhD-level mathematics into functional Pine Script , blending theory and practice. Immersive Visualization : Transforms charts into dynamic geometric landscapes for intuitive market understanding. The GTTMTSF is more than an indicator; it’s a new lens for viewing markets, for traders seeking deeper insight into hidden order within chaos. " Where there is matter, there is geometry. " - Johannes Kepler — Dskyz , Trade with insight. Trade with anticipation.
Pine Script® インジケーター
DskyzInvestmentsの投稿
5
Copper to Gold Ratioratio = copper / gold: Calculates the ratio by dividing copper price by gold price. plot(ratio): Plots the ratio as a blue line. ma = ta.sma(ratio, 20): Adds a 20-period simple moving average (optional) to smooth the ratio, plotted as a red line. A rising Copper/Gold ratio often signals economic expansion (strong copper demand relative to gold), while a falling ratio may indicate economic uncertainty or recession fears, as gold outperforms copper. The ratio is also used as a leading indicator for 10-year U.S. Treasury yields, with a rising ratio often correlating with higher yields.
Pine Script® インジケーター
bfrahm73の投稿
AP Session Liquidity with EQH/EQL and Previous DayThis indicator plots key intraday session highs and lows, along with essential market structure levels, to help traders identify areas of interest, potential liquidity zones, and high-probability trade setups. It includes the Asia Session High and Low (typically 00:00–08:00 UTC), London Session High and Low (08:00–12:00 UTC), New York AM Session High and Low (12:00–15:00 UTC), and New York Lunch High and Low (15:00–17:00 UTC). Additionally, it displays the Previous Day’s High and Low for context on recent price action, as well as automatically detected Equal Highs and Lows based on configurable proximity settings to highlight potential liquidity pools or engineered price levels. These session levels are widely used by institutional traders and are critical for analyzing market behavior during time-based volatility windows. Traders can use this indicator to anticipate breakouts, fakeouts, and reversals around session boundaries—such as liquidity grabs at Asia highs/lows before the London or New York sessions—or to identify key consolidation and expansion zones. Equal Highs and Lows serve as magnets for price, offering insight into potential stop hunts or inducement zones. This tool is ideal for day traders, scalpers, and smart money concept practitioners, and includes full customization for session timings, color schemes, line styles, and alert conditions. Whether you're trading price action, ICT concepts, or supply and demand, this indicator provides a powerful framework for intraday analysis.
Pine Script® インジケーター
APTRADEZの投稿
2
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.
Pine Script® インジケーター
DskyzInvestmentsの投稿
9
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.
Pine Script® インジケーター
EdgeToolsの投稿
4
Consolidation Range with Signals (Zeiierman)█ Overview Consolidation Range with Signals (Zeiierman) is a precision tool for identifying and trading market consolidation zones, where price contracts into tight ranges before significant movement. It provides dynamic range detection using either ADX-based trend strength or volatility compression metrics, and offers built-in take profit and stop loss signals based on breakout dynamics. Whether you trade breakouts, range reversals, or trend continuation setups, this indicator visualizes the balance between supply and demand with clearly defined mid-bands, breakout zones, and momentum-sensitive TP/SL placements. █ How It Works ⚪ Multi-Method Range Detection ADX Mode Uses the Average Directional Index (ADX) to detect low-trend-strength environments. When ADX is below your selected threshold, price is considered to be in consolidation. Volatility Mode This mode detects consolidation by identifying periods of volatility compression. It evaluates whether the following metrics are simultaneously below their respective historical rolling averages: Standard Deviation Variance Average True Range (ATR) ⚪ Dynamic Range Band System Once a range is confirmed, the system builds a dynamic band structure using a volatility-based filter and price-jump logic: Middle Line (Trend Filter): Reacts to price imbalance using adaptive jump logic. Upper & Lower Bands: Calculated by expanding from the middle line using a configurable multiplier. This creates a clean, visual box that reflects current consolidation conditions and adapts as price fluctuates within or escapes the zone. ⚪ SL/TP Signal Engine On detection of a breakout from the range, the indicator generates up to 3 Take Profit levels and one Stop Loss, based on the breakout direction: All TP/SL levels are calculated using the filtered base range and multipliers. Cooldown logic ensures signals are not spammed bar-to-bar. Entries are visualized with colored lines and labeled levels. This feature is ideal for traders who want automated risk and reward reference points for range breakout plays. █ How to Use ⚪ Breakout Traders Use the SL/TP signals when the price breaks above or below the range bands, especially after extended sideways movement. You can customize how far TP1, TP2, and TP3 sit from the entry using your own risk/reward profile. ⚪ Mean Reversion Traders Use the bands to locate high-probability reversion zones. These serve as reference zones for scalping or fade entries within stable consolidation phases. █ Settings Range Detection Method – Choose between ADX or Volatility compression to define range criteria. Range Period – Determines how many bars are used to compute trend/volatility. Range Multiplier – Scales the width of the consolidation zone. SL/TP System – Optional levels that project TP1/TP2/TP3 and SL from the base price using multipliers. Cooldown – Prevents repeated SL/TP signals from triggering too frequently. ADX Threshold & Smoothing – Adjusts sensitivity of trend strength detection. StdDev / Variance / ATR Multipliers – Fine-tune compression detection logic. ----------------- Disclaimer The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information. All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
Pine Script® インジケーター
Zeiiermanの投稿
12
Shooting Star Detector[cryptovarthagam]🌠 Shooting Star Detector The Shooting Star Detector is a powerful price action tool that automatically identifies potential bearish reversal signals using the well-known Shooting Star candlestick pattern. Ideal for traders who rely on candlestick psychology to spot high-probability short setups, this script works across all markets and timeframes. 🔍 What is a Shooting Star? A Shooting Star is a single-candle pattern that typically forms at the top of an uptrend or resistance zone. It’s characterized by: A small body near the candle's low, A long upper wick, and Little or no lower wick. This pattern suggests that buyers pushed price higher but lost control by the close, hinting at potential bearish momentum ahead. ✅ Indicator Features: 🔴 Accurately detects Shooting Star candles in real-time 🔺 Plots a red triangle above every valid signal candle 🖼️ Optional background highlight for visual clarity 🕵️‍♂️ Strict ratio-based detection using: Wick-to-body comparisons Upper wick dominance Optional bearish candle confirmation ⚙️ Detection Logic (Rules Used): Upper wick > 60% of total candle range Body < 20% of total candle Lower wick < 15% of candle range Bearish candle (optional but included for accuracy) These rules ensure high-quality signals that filter out false positives. 📌 Best Use Cases: Spotting trend reversals at swing highs Confirming entries near resistance zones Enhancing price action or supply/demand strategies Works on: Crypto, Forex, Stocks, Commodities 🧠 Trading Tip: Pair this detector with volume confirmation, resistance zones, or bearish divergence for higher-probability entries. 📉 Clean, minimal, and non-repainting — designed for traders who value accuracy over noise. Created with ❤️ by Cryptovarthagam Follow for more real-time price action tools!
Pine Script® インジケーター
cryptovarthagamの投稿
Volume pressure by GSK-VIZAG-AP-INDIA🔍 Volume Pressure by GSK-VIZAG-AP-INDIA 🧠 Overview “Volume Pressure” is a multi-timeframe, real-time table-based volume analysis tool designed to give traders a clear and immediate view of buying and selling pressure across custom-selected timeframes. By breaking down buy volume, sell volume, total volume, and their percentages, this indicator helps traders identify demand/supply imbalances and volume momentum in the market. 🎯 Purpose / Trading Use Case This indicator is ideal for intraday and short-term traders who want to: Spot aggressive buying or selling activity Track volume dynamics across multiple timeframes *1 min time frame will give best results* Use volume pressure as a confirming tool alongside price action or trend-based systems It helps determine when large buying/selling activity is occurring and whether such behavior is consistent across timeframes—a strong signal of institutional interest or volume-driven trend shifts. 🧩 Key Features & Logic Real-Time Table Display: A clean, dynamic table showing: Buy Volume Sell Volume Total Volume Buy % of total volume Sell % of total volume Multi-Time frame Analysis: Supports 8 user-selectable custom time frames from 1 to 240 minutes, giving flexibility to analyze volume pressure at various granularities. Color-Coded Volume Bias: Green for dominant Buy pressure Red for dominant Sell pressure Yellow for Neutral Intensity-based blinking for extreme values (over 70%) Dynamic Data Calculation: Uses volume * (close > open) logic to estimate buy vs sell volumes bar-by-bar, then aggregates by timeframe. ⚙️ User Inputs & Settings Timeframe Selectors (TF1 to TF8): Choose any 8 timeframes you want to monitor volume pressure across. Text & Color Settings: Customize text colors for Buy, Sell, Total volumes Choose Buy/Sell bias colors Enable/disable blinking for visual emphasis on extremes Table Appearance: Set header color, metric background, and text size Table positioning: top-right, bottom-right, etc. Blinking Highlight Toggle: Enable this to visually highlight when Buy/Sell % exceeds 70%—a sign of strong pressure. 📊 Visual Elements Explained The table has 6 rows and 10 columns: Row 0: Headers for Today and TF1 to TF8 Rows 1–3: Absolute values (Buy Vol, Sell Vol, Total Vol) Rows 4–5: Relative percentages (Buy %, Sell %), with dynamic background color First column shows the metric names (e.g., “Buy Vol”) Cells blink using alternate background colors if volume pressure crosses thresholds 💡 How to Use It Effectively Use Buy/Sell % rows to confirm potential breakout trades or identify volume exhaustion zones Look for multi-timeframe confluence: If 5 or more TFs show >70% Buy pressure, buyers are in control Combine with price action (e.g., breakouts, reversals) to increase conviction Suitable for equities, indices, futures, crypto, especially on lower timeframes (1m to 15m) 🏆 What Makes It Unique Table-based MTF Volume Pressure Display: Most indicators only show volume as bars or histograms; this script summarizes and color-codes volume bias across timeframes in a tabular format. Customization-friendly: Full control over colors, themes, and timeframes Blinking Alerts: Rare visual feature to capture user attention during extreme pressure Designed with performance and readability in mind—even for fast-paced scalping environments. 🚨 Alerts / Extras While this script doesn’t include TradingView alert functions directly, the visual blinking serves as a strong real-time alert mechanism. Future versions may include built-in alert conditions for buy/sell bias thresholds. 🔬 Technical Concepts Used Volume Dissection using close > open logic (to estimate buyer vs seller pressure) Simple aggregation of volume over custom timeframes Table plotting using Pine Script table.new, table.cell Dynamic color logic for bias identification Custom blinking logic using na(bar_index % 2 == 0 ? colorA : colorB) ⚠️ Disclaimer This indicator is a tool for analysis, not financial advice. Always backtest and validate strategies before using any indicator for live trading. Past performance is not indicative of future results. Use at your own risk and apply proper risk management. ✍️ Author & Signature Indicator Name: Volume Pressure Author: GSK-VIZAG-AP-INDIA TradingView Username: prowelltraders
Pine Script® インジケーター
prowelltradersの投稿
Candle Volume Profile Marker# 📊 Candle Volume Profile Marker (CVPM) **Transform your chart analysis with precision volume profile levels on every candle!** The Candle Volume Profile Marker displays key volume profile levels (POC, VAH, VAL) for individual candles, giving you granular insights into price acceptance and rejection zones at the micro level. ## 🎯 **Key Features** ### **Core Levels** - **POC (Point of Control)** - The price level with highest volume concentration - **VAH (Value Area High)** - Upper boundary of the value area - **VAL (Value Area Low)** - Lower boundary of the value area - **Customizable Value Area** - Adjust percentage from 50% to 90% ### **Flexible Display Options** - **Current Candle Only** or **Historical Lookback** (1-50 candles) - **Multiple Visual Styles** - Lines, dots, crosses, triangles, squares, diamonds - **Smart Line Extensions** - Right only, both sides, or left only - **4 Line Length Modes** - Normal, Short, Ultra Short, Micro (for ultra-clean charts) - **Full Color Customization** - Colors, opacity, line width - **Adjustable Marker Sizes** - Tiny to Large ### **Advanced Calculation Methods** Choose your POC calculation: - **Weighted** - Smart estimation based on volume distribution (default) - **Close** - Uses closing price - **Middle** - High-Low midpoint - **VWAP** - Volume weighted average price ### **Professional Tools** - **Real-time Info Table** - Current levels display - **Smart Alerts** - POC crosses and Value Area breakouts - **Highlight Current Candle** - Extended dotted lines for current levels - **Developing Levels** - Real-time updates for active candle ## 🚀 **Why Use CVPM?** ### **Precision Trading** - Identify exact support/resistance on each candle - Spot volume acceptance/rejection zones - Plan entries and exits with micro-level precision ### **Clean & Customizable** - Lines extend only right (eliminates confusion) - Ultra-short line options for minimal chart clutter - Professional appearance with full customization ### **Multiple Timeframes** - Works on any timeframe from 1-minute to monthly - Historical analysis with adjustable lookback - Real-time developing levels ## 📈 **Perfect For** - **Day Traders** - Micro-level entry/exit points - **Swing Traders** - Key levels for position management - **Volume Analysis** - Understanding price acceptance zones - **Support/Resistance Trading** - Precise level identification - **Breakout Trading** - Value area breakout alerts ## ⚙️ **Easy Setup** 1. Add indicator to your chart 2. Choose your preferred visual style (lines/dots) 3. Select line extension (right-only recommended) 4. Adjust line length (try "Ultra Short" for clean charts) 5. Customize colors and enable alerts ## 🎨 **Customization Groups** - **Display Options** - What to show and how many candles - **Calculation** - POC method and value area percentage - **POC Visual** - Style, color, width, length for Point of Control - **Value Area Visual** - Style, color, width, length for VAH/VAL - **Line Settings** - Extension direction and length modes - **Size** - Marker sizes and opacity ## 🔔 **Built-in Alerts** - Price crosses above/below POC - Value Area breakouts (up/down) - Fully customizable alert messages ## 💡 **Pro Tips** - Use "Right Only" extension to avoid confusion about which candle owns the levels - Try "Ultra Short" or "Micro" line modes for cleaner charts - Enable "Highlight Current Candle" for extended reference lines - Combine with volume indicators for enhanced analysis - Use different colors for easy POC/VAH/VAL identification --- **Transform your volume analysis today with the most flexible and customizable candle-level volume profile indicator available!** *Perfect for traders who demand precision and clean, professional charts.*
Pine Script® インジケーター
padavansの投稿
1
Pin Bar Highlighter OnlyAbsolutely — here’s a full **description and breakdown** of what the script does: --- ## 📜 Script Name: **"Pin Bar Highlighter Only"** --- ## 🎯 Purpose: This script **detects and highlights classic pin bar reversal candles** directly on the chart — without plotting any entry, stop loss, or take profit levels. It’s designed for traders who: * Want to manually assess trades * Prefer a clean chart * Use pin bar price action as a signal for potential reversals --- ## ✅ What It Does: * **Scans each candle** to check if it qualifies as a **bullish** or **bearish pin bar** * If valid: * Plots a **green triangle below** bullish pin bars * Plots a **red triangle above** bearish pin bars * Keeps your chart **minimal and uncluttered** --- ## 📌 How It Detects a Pin Bar: ### 🔹 1. Candle Structure: * Measures the total candle range: `high - low` * Calculates the **body size**: `abs(close - open)` * Calculates the **upper and lower wick sizes** ### 🔹 2. Pin Bar Criteria: * The **wick (nose)** must be at least **2/3 of the total candle length** * The **body** must be small — **≤ 1/3** of the total range * The **body** must be located at **one end** of the candle * The wick must **pierce the high/low** of the previous candle --- ## 📍 Bullish Pin Bar Requirements: * Close > Open (green candle) * Lower wick ≥ 66% of candle range * Body ≤ 33% of range * Candle **makes a new low** (current low < previous low) ### 📍 Bearish Pin Bar Requirements: * Close < Open (red candle) * Upper wick ≥ 66% of candle range * Body ≤ 33% of range * Candle **makes a new high** (current high > previous high) --- ## 🖼️ Visual Output: * 🔻 Red triangle **above** bearish pin bars * 🔺 Green triangle **below** bullish pin bars --- ## 🛠️ Example Use Cases: * Identify **reversal points** at support/resistance * Confirm signals with **VWAP**, supply/demand zones, or AVWAP (manually plotted) * Use in **conjunction with other strategies** — without clutter ---
Pine Script® インジケーター
farimanga87の投稿
Liquidity LinesLiquidity Lines Indicator This advanced TradingView indicator identifies key liquidity zones on your price chart by detecting bullish and bearish engulfing candles, which often signify areas where liquidity accumulates. It helps traders visually spot potential support and resistance levels created by market participants’ stop-loss orders or pending orders. Key Features : -Aggregated Bars Option : Smooth out price data by grouping bars together, enabling clearer liquidity zone identification on higher timeframes or noisy charts. -Upper Liquidity Lines : Displays dashed lines at recent highs where bearish engulfing patterns indicate potential resistance or supply zones. -Lower Liquidity Lines : Displays dashed lines at recent lows where bullish engulfing patterns suggest potential support or demand zones. -Customizable Colors : Choose your preferred colors for bullish (default black) and bearish (default white) liquidity lines for better visual distinction. -Automatic Line Cleanup : Maintains chart clarity by automatically removing old liquidity lines after a configurable limit. -Dynamic Alerts : Trigger alerts when price breaches upper or lower liquidity lines, signaling potential breakout or reversal opportunities. Use Cases :
Pine Script® インジケーター
The_Forex_Stewardの投稿
BVB dominance bars Hello everyone, this is my first indicator. these candles shows you who's in control. I like to think its some what close to heikin ashi candles as it shows you the Trend but doesn't average it out. also shows you when there is indecision. please read the instructions on how it works. its not a stand alone strategy. but adds value to your own strategy. 📖 How It Works The BvB Dominance Bars indicator is a visual tool that colors candles based on market control—whether bulls or bears are in charge. It uses a custom metric comparing the price's relationship to a smoothed moving average (EMA), then normalizes that difference over time to express relative bullish or bearish pressure. Here’s the breakdown: Bulls vs Bears Logic: A short-term EMA (default: 14-period) is used to establish a midpoint reference. Bull Pressure is calculated as how far the high is above this EMA. Bear Pressure is how far the low is below this EMA. These are normalized over a lookback period (default: 120 bars) to produce percentile scores (0–100) for both bulls and bears. Dominance & Color Coding: The indicator compares normalized bull and bear scores. Candles are color-coded based on: Bright Lime: Strong Bull Dominance (with high confidence) Soft Lime/Yellow: Moderate Bull Control Bright Red: Strong Bear Dominance Soft Red/Yellow: Moderate Bear Control Gray: Neutral/Low conviction Optional Live Label: A small floating label shows who has control: “Bull Control,” “Bear Control,” or “Neutral.” 🧠 How to Use It (Example Strategy) The BvB Dominance Bars indicator is not a standalone buy/sell signal but a market sentiment overlay. It’s most effective when combined with your own strategy, like price action or trend-following tools. Here’s an example use case: 🧪 Reversal Confirmation Strategy Objective: Catch high-probability reversals during key kill zones or supply/demand levels. Setup: Mark your key support/resistance zones using your standard method (e.g., FVGs, liquidity sweeps, or ICT PD arrays). Wait for price to reach one of these zones. Watch candle colors from the BvB Dominance Bars: If you expect a bullish reversal, wait for a transition from red/gray candles to lime green or bright lime (bullish dominance taking over). If you expect a bearish reversal, look for a change from green/gray to red or bright red. Entry Filter: Only enter if the dominant color holds for 2+ candles. Avoid trades when candles are gray or yellow (indecision/neutral). Exit Option: Exit if dominance shifts against you (e.g., from lime to red), or use structure-based stops. ⚙️ Settings You Can Adjust: BvB Period: Controls how fast EMA responds. Bars Back: Determines how long the normalization looks back. Thresholds: Influence how strong the dominance must be to change candle color. ✅ Best Used When: You already have a bias and just want a confirmation of sentiment. You're trading intraday and want a feel for shifting momentum without relying on noisy indicators. You want a clean, color-coded overlay to help filter out fakeouts and indecision.
Pine Script® インジケーター
barton_evanの投稿
1
FVG (Nephew sam remake)Hello i am making my own FVG script inspired by Nephew Sam as his fvg code is not open source. My goal is to replicate his Script and then add in alerts and more functions. Thus, i spent few days trying to code. There is bugs such as lower time frame not showing higher time frame FVG. This script automatically detects and visualizes Fair Value Gaps (FVGs) — imbalances between demand and supply — across multiple timeframes (15-minute, 1-hour, and 4-hour). 15m chart shows: 15m FVGs (green/red boxes) 1H FVGs (lime/maroon) 4H FVGs (faded green/red with borders) (Bugged For now i only see 1H appearing) 1H chart shows: 1H FVGs 4H FVGs 4H chart shows: 4H FVGs only There is the function to auto close FVG when a future candle fully disrespected it. You're welcome to: 🔧 Customize the appearance: adjust box colors, transparency, border style 🧪 Add alerts: e.g., when price enters or fills a gap 📅 Expand to Daily/Weekly: just copy the logic and plug in "D" or "W" as new layers 📈 Build confluence logic: combine this with order blocks, liquidity zones, or ICT concepts 🧠 Experiment with entry signals: e.g., candle confirmation on return to FVG 🚀 Improve performance: if you find a lighter way to track gaps, feel free to optimize!
Pine Script® インジケーター
ATKHの投稿
2
Bitcoin Open Interest [SAKANE]Bitcoin Open Interest — Unveiling the True Flow of Capital PurposeVisualize and compare Bitcoin open interest (OI) from CME and Binance, the leading derivatives exchanges, in a single intuitive chart, providing traders with clear insights into crypto market capital dynamics. Background & MotivationIn the 24/7 crypto market, price movements alone reveal only part of the story. Open interest (OI)—the total outstanding futures contracts—offers critical clues to the market’s next move. Yet, accessing and interpreting OI data is challenging: CME Constraints: Commitment of Traders (COT) reports are weekly, and standalone BTC1! or BTC2! OI is noisy due to contract rollovers, obscuring true OI changes. Existing Tool Limitations: Most OI indicators are fixed to either USD or BTC, limiting flexible analysis. This indicator overcomes these hurdles, enabling seamless comparison of CME and Binance OI to track the market’s “capital center of gravity” in real time. Key Features Synthetic CME OI: Combines BTC1! and BTC2! to deliver high-accuracy OI, eliminating rollover noise. Multi-Timeframe Analysis: Displays daily CME OI as pseudo-candlestick (OHLC) on any timeframe (e.g., 4H), allowing intuitive capital flow tracking across timeframes. CME/Binance One-Click Toggle: Instantly compare institutional-driven CME and retail-driven Binance OI. USD/BTC Flexibility: Switch between BTC (real demand) and USD (margin) perspectives for OI analysis. Robust Design: Concise, global-scope code ensures stability and adaptability to TradingView updates. Insights & Use Cases Holistic Market Sentiment: Analyze capital flows by region and exchange for a multidimensional view. Signal Detection: E.g., a sharp drop in CME OI during a sell-off may signal institutional withdrawal. Retail Trends: A surge in Binance OI suggests retail-driven inflows. Event-Driven Insights: E.g., during a hypothetical April 2025 “Trump Tariff Shock,” instantly identify which exchange drives capital shifts. Unique ValueUnlike price-centric indicators, this tool focuses on capital flow (OI). It’s the only indicator offering one-click multi-timeframe and multi-exchange OI comparison, empowering traders to uncover the market’s “true intent” and gain a strategic edge. ConclusionBitcoin Open Interest makes the market’s hidden capital movements accessible to all. By capturing market dynamics and pinpointing the “leading forces” during events, it sets a new standard for traders seeking a revolutionary perspective.
Pine Script® インジケーター
amusedMagpie26867の投稿
SOFR Spread (proxy: FEDFUNDS - US03MY)📊 SOFR Spread (Proxy: FEDFUNDS - US03MY) – Monitoring USD Money Market Liquidity In 2008, the spread exhibits a sharp vertical spike, signaling a severe liquidity dislocation: investors rushed into short-term U.S. Treasuries, pushing their yields down dramatically, while the FEDFUNDS rate remained relatively high. This behavior indicates extreme systemic stress in the interbank lending market, preceding massive Federal Reserve interventions such as rate cuts, emergency liquidity operations, and the launch of quantitative easing (QE). Description: This indicator plots the spread between the Effective Federal Funds Rate (FEDFUNDS) and the 3-Month US Treasury Bill yield (US03MY), used here as a proxy for the SOFR spread. It serves as a simple yet powerful tool to detect liquidity dislocations and stress signals in the US short-term funding markets. Interpretation: 🔴 Spread > 0.20% → Possible liquidity stress: elevated repo rates, cash shortage, interbank distrust. 🟡 Spread ≈ 0% → Normal market conditions, balanced liquidity. 🟢 Spread < 0% → Excess liquidity: strong demand for T-Bills, “flight to safety”, or distortion due to expansionary monetary policy. Ideal for: Monitoring Fed policy impact Anticipating market-wide liquidity squeezes Correlation with DXY, SPX, VIX, MOVE Index, and risk sentiment 🧠 Note: As SOFR is not directly available on TradingView, FEDFUNDS is used as a reliable proxy, closely tracking the same trends in most macro conditions.
Pine Script® インジケーター
bordimichele00の投稿
LANZ Strategy 2.0 [Backtest]🔷 LANZ Strategy 2.0 — Structural Breakout Logic with Dynamic Swing Protection LANZ Strategy 2.0 is a precision-focused backtesting system built for intraday traders who rely on structural confirmations before the London session to guide directional bias. This tool uses smart swing detection, risk-defined position sizing, and strict time-based execution to simulate real trading conditions with clarity and control. 🧠 Core Components: Structural Confirmation (Trend & BoS): Detects trend direction and break of structure (BoS) using a three-swing logic, aligning trade entries with valid structural movement. Time-Based Execution: Trades are triggered exclusively at 02:00 a.m. New York time, ensuring disciplined and repeatable intraday testing. Swing-Based SL Models: Traders can select between three stop-loss protection types: First Swing: Most recent structural level Second Swing: Prior level Full Coverage: All recent swing levels + configurable pip buffer Dynamic TP Calculation: Take-Profit is projected as a risk-based multiple (RR), fully adjustable via input. Capital-Based Risk Management: Risk is defined as a percentage of a fixed account size (e.g., $100 per trade from $10,000), and lot size is automatically calculated based on SL distance. Fallback Entry Logic: If structural breakout is present but trend is not confirmed, a secondary entry is triggered. End-of-Session Management: Any open trades are automatically closed at 11:45 a.m. NY time, with optional manual labeling or review. 📊 Visual Features (Optional in Indicator Version): (Note: Visuals apply to the indicator version of LANZ 2.0, not this backtest script) Swing level labels (1st, 2nd) and dynamic SL/TP lines. Real-time session coloring for clarity: Pre-London, Entry Window, and NY Close. Outcome labels: +RR, -RR, or net % at close. Auto-cleanup of previous drawings for a clean chart per session. ⚙️ How It Works: Detects last trend and BoS using swing logic before 02:00 a.m. NY. At 02:00 a.m., evaluates directional bias and executes BUY or SELL if confirmed. Applies selected SL logic (1st, 2nd, or full swing protection). Sets TP based on the RR multiplier. Closes the trade either on SL, TP, or at 11:45 a.m. NY manually. 🔔 Alerts: Time-of-day alert at 02:00 a.m. NY to monitor execution. Can be extended to cover SL/TP triggers or new BoS events. 📝 Notes: Designed for backtesting precision and discretionary decision-making. Ideal for Forex pairs, indices, or assets active during the London session. Fully customizable: session timing, swing logic, SL buffer, and RR. 👤 Credits: Strategy built by @rau_u_lanz using Pine Script v6, combining structural logic, capital-based risk control, and London-session timing in a backtest-ready framework for traders who demand accuracy and structure.
Pine Script® ストラテジー
rau_u_lanzの投稿
Interest Zones | @CRYPTOKAZANCEVEnglish Description. 🧠 What This Script Does This script automatically detects price interest zones — areas where the price repeatedly reacts by forming local swing highs or lows , suggesting heightened supply/demand or market attention. It uses a custom volatility-adjusted range (pseudo-ATR) to dynamically group significant swing points and highlights these zones visually on the chart. The script is not a mashup or copy of built-in indicators. It’s an original implementation that performs a meaningful calculation based on market structure and volatility to help traders identify important price areas. ⚙️ How It Works 1. Swing Point Detection: The script identifies swing highs and lows using a configurable lookback window. 2. Zone Candidate Evaluation: Each swing is checked against a custom zone width (based on ATR and your multiplier). If multiple swings fall within this range, it’s marked as a potential zone. 3. Filtering: The script keeps only those zones that: • Contain at least a user-defined number of swing points. • Do not overlap with stronger (higher swing count) zones. 4. Visualization: • The strongest zones are drawn as semi-transparent boxes. • Zones are limited by time (last X candles). • Optional: Swing highs/lows can be shown on chart. 📊 How to Use • Use it on any timeframe or asset to identify price regions of interest. • Combine with volume, trend, or candlestick analysis for entries/exits. • The number of touches (swing points in a zone) gives insight into zone significance. This tool is particularly useful for identifying support/resistance areas based on actual price structure rather than arbitrary levels. 🔧 Settings • Swing Lookback Period: Controls how many candles on each side of a pivot the script checks to detect a local high/low. • Zone Width Multiplier: Adjusts the volatility-based range. Larger values create wider zones. • Min Swing Count: Zones with fewer swing points than this won't be shown. • Max Zones Displayed: Limits the number of zones shown on screen. • Max Candles for Analysis: Old swing points beyond this range are ignored. 📌 Notes • No third-party code or mashups used. • This is a standalone implementation of a concept similar to market structure mapping, tailored to be dynamic and responsive to volatility. • Ideal for traders who prefer clean, price-action-based analysis. 🇷🇺 Русское описание 🧠 Что делает этот индикатор: Индикатор автоматически определяет зоны интереса цены — области, где цена многократно формирует локальные максимумы или минимумы (свинги) . Эти зоны могут сигнализировать о повышенном внимании рынка, предложении или спросе. Скрипт использует псевдо-ATR (волатильность на основе среднего диапазона), чтобы динамически определять такие области и выделяет их на графике. Это не копия стандартных индикаторов и не микс чужих скриптов — это оригинальная разработка , полезная для всех, кто ищет автоматическую разметку важных ценовых уровней. ⚙️ Как работает индикатор 1. Поиск свинг-точек: Определяются локальные экстремумы с учетом указанного периода. 2. Формирование кандидатов в зоны: Каждая свинг-точка проверяется, есть ли в её диапазоне другие свинги. Если таких достаточно — зона считается потенциальной. 3. Фильтрация зон: • Учитываются только зоны с минимумом заданных свингов. • Перекрывающиеся зоны удаляются в пользу более значимых. 4. Визуализация: • Отображаются зоны с наибольшим числом касаний. • Зоны ограничиваются последними X свечами. • При желании можно отобразить сами свинг-точки. 📊 Как использовать • Работает на любом таймфрейме и инструменте. • Используйте совместно с объёмами, трендом или свечным анализом. • Количество касаний помогает оценить важность зоны. Полезен тем, кто предпочитает анализ на основе структуры цены, а не произвольных уровней. 🔧 Настройки • Период свингов: Сколько свечей учитывается по бокам для поиска экстремумов. • Множитель зоны: Увеличивает диапазон зоны на основе волатильности. • Мин. количество свингов: Минимум точек в зоне для её отображения. • Макс. зон на графике: Ограничение по количеству отображаемых зон. • Макс. свечей анализа: Старые точки за пределами не учитываются. 📌 Примечания • Не содержит чужих индикаторов или шаблонов. • Самостоятельная реализация механизма анализа структуры рынка.
Pine Script® インジケーター
cryptokazancevの投稿
Dskyz (DAFE) GENESIS Dskyz (DAFE) GENESIS: Adaptive Quant, Real Regime Power Let’s be honest: Most published strategies on TradingView look nearly identical—copy-paste “open-source quant,” generic “adaptive” buzzwords, the same shallow explanations. I’ve even fallen into this trap with my own previously posted strategies. Not this time. What Makes This Unique GENESIS is not a black-box mashup or a pre-built template. It’s the culmination of DAFE’s own adaptive, multi-factor, regime-aware quant engine—built to outperform, survive, and visualize live edge in anything from NQ/MNQ to stocks and crypto. True multi-factor core: Volume/price imbalances, trend shifts, volatility compression/expansion, and RSI all interlock for signal creation. Adaptive regime logic: Trades only in healthy, actionable conditions—no “one-size-fits-all” signals. Momentum normalization: Uses rolling, percentile-based fast/slow EMA differentials, ALWAYS normalized, ALWAYS relevant—no “is it working?” ambiguity. Position sizing that adapts: Not fixed-lot, not naive—not a loophole for revenge trading. No hidden DCA or pyramiding—what you see is what you trade. Dashboard and visual system: Directly connected to internal logic. If it’s shown, it’s used—and nothing cosmetic is presented on your chart that isn’t quantifiable. 📊 Inputs and What They Mean (Read Carefully) Maximum Raw Score: How many distinct factors can contribute to regime/trade confidence (default 4). If you extend the quant logic, increase this. RSI Length / Min RSI for Shorts / Max RSI for Longs: Fine-tunes how “overbought/oversold” matters; increase the length for smoother swings, tighten floors/ceilings for more extreme signals. ⚡ Regime & Momentum Gates Min Normed Momentum/Score (Conf): Raise to demand only the strongest trends—your filter to avoid algorithmic chop. 🕒 Volatility & Session ATR Lookback, ATR Low/High Percentile: These control your system’s awareness of when the market is dead or ultra-volatile. All sizing and filter logic adapts in real time. Trading Session (hours): Easy filter for when entries are allowed; default is regular trading hours—no surprise overnight fills. 📊 Sizing & Risk Max Dollar Risk / Base-Max Contracts: All sizing is adaptive, based on live regime and volatility state—never static or “just 1 contract.” Control your max exposures and real $ risk. ATR will effect losses in high volatility times. 🔄 Exits & Scaling Stop/Trail/Scale multipliers: You choose how dynamic/flexible risk controls and profit-taking need to be. ATR-based, so everything auto-adjusts to the current market mode. Visuals That Actually Matter Dashboard (Top Right): Shows only live, relevant stats: scoring, status, position size, win %, win streak, total wins—all from actual trade engine state (not “simulated”). Watermark (Bottom Right): Momentum bar visual is always-on, regime-aware, reflecting live regime confidence and momentum normalization. If the bar is empty, you’re truly in no-momentum. If it glows lime, you’re riding the strongest possible edge. *No cosmetics, no hidden code distractions. Backtest Settings Initial capital: $10,000 Commission: Conservative, realistic roundtrip cost: 15–20 per contract (including slippage per side) I set this to $25 Slippage: 3 ticks per trade Symbol: CME_MINI:NQ1! Timeframe: 1 min (but works on all timeframes) Order size: Adaptive, 1–3 contracts No pyramiding, no hidden DCA Why these settings? These settings are intentionally strict and realistic, reflecting the true costs and risks of live trading. The 10,000 account size is accessible for most retail traders. 25/contract including 3 ticks of slippage are on the high side for NQ, ensuring the strategy is not curve-fit to perfect fills. If it works here, it will work in real conditions. Why It Wins While others put out “AI-powered” strategies with little logic or soul, GENESIS is ruthlessly practical. It is built around what keeps traders alive: - Context-aware signals, not just patterns - Tight, transparent risk - Inputs that adapt, not confuse - Visuals that clarify, not distract - Code that runs clean, efficient, and with minimal overfitting risk (try it on QQQ, AMD, SOL, etc. out of the box) Disclaimer (for TradingView compliance): Trading is risky. Futures, stocks, and crypto can result in significant losses. Do not trade with funds you cannot afford to lose. This is for educational and informational purposes only. Use in simulation/backtest mode before live trading. No past performance is indicative of future results. Always understand your risk and ownership of your trades. This will not be my last—my goal is to keep raising the bar until DAFE is a brand or I’m forced to take this private. Use with discipline, use with clarity, and always trade smarter. — Dskyz , powered by DAFE Trading Systems.
Pine Script® ストラテジー
DskyzInvestmentsの投稿
6
REVELATIONS (VoVix - PoC) REVELATIONS (VoVix - POC): True Regime Detection Before the Move Let’s not sugarcoat it: Most strategies on TradingView are recycled—RSI, MACD, OBV, CCI, Stochastics. They all lag. No matter how many overlays you stack, every one of these “standard” indicators fires after the move is underway. The retail crowd almost always gets in late. That’s never been enough for my team, for DAFE, or for anyone who’s traded enough to know the real edge vanishes by the time the masses react. How is this different? REVELATIONS (VoVix - POC) was engineered from raw principle, structured to detect pre-move regime change—before standard technicals even light up. We built, tested, and refined VoVix to answer one hard question: What if you could see the spike before the trend? Here’s what sets this system apart, line-by-line: o True volatility-of-volatility mathematics: It’s not just "ATR of ATR" or noise smoothing. VoVix uses normalized, multi-timeframe v-vol spikes, instantly detecting orderbook stress and "outlier" market events—before the chart shows them as trends. o Purist regime clustering: Every trade is enabled only during coordinated, multi-filter regime stress. No more signals in meaningless chop. o Nonlinear entry logic: No trade is ever sent just for a “good enough” condition. Every entry fires only if every requirement is aligned—local extremes, super-spike threshold, regime index, higher timeframe, all must trigger in sync. o Adaptive position size: Your contracts scale up with event strength. Tiny size during nominal moves, max leverage during true regime breaks—never guesswork, never static exposure. o All exits governed by regime decay logic: Trades are closed not just on price targets but at the precise moment the market regime exhausts—the hardest part of systemic trading, now solved. How this destroys the lag: Standard indicators (RSI, MACD, OBV, CCI, and even most “momentum” overlays) simply tell you what already happened. VoVix triggers as price structure transitions—anyone running these generic scripts will trade behind the move while VoVix gets in as stress emerges. Real alpha comes from anticipation, not confirmation. The visuals only show what matters: Top right, you get a live, live quant dashboard—regime index, current position size, real-time performance (Sharpe, Sortino, win rate, and wins). Bottom right: a VoVix "engine bar" that adapts live with regime stress. Everything you see is a direct function of logic driving this edge—no cosmetics, no fake momentum. Inputs/Signals—explained carefully for clarity: o ATR Fast Length & ATR Slow Length: These are the heart of VoVix’s regime sensing. Fast ATR reacts to sharp volatility; Slow ATR is stability baseline. Lower Fast = reacts to every twitch; higher Slow = requires more persistent, “real” regime shifts. Tip: If you want more signals or faster markets, lower ATR Fast. To eliminate noise, raise ATR Slow. o ATR StdDev Window: Smoothing for volatility-of-volatility normalization. Lower = more jumpy, higher = only the cleanest spikes trigger. Tip: Shorten for “jumpy” assets, raise for indices/futures. o Base Spike Threshold: Think of this as your “minimum event strength.” If the current move isn’t volatile enough (normalized), no signal. Tip: Higher = only biggest moves matter. Lower for more signals but more potential noise. o Super Spike Multiplier: The “are you sure?” test—entry only when the current spike is this multiple above local average. Tip: Raise for ultra-selective/swing-trading; lower for more active style. Regime & MultiTF: o Regime Window (Bars): How many bars to scan for regime cluster “events.” Short for turbo markets, long for big swings/trends only. o Regime Event Count: Only trade when this many spikes occur within the Regime Window—filters for real stress, not isolated ticks. Tip: Raise to only ever trade during true breakouts/crashes. o Local Window for Extremes: How many bars to check that a spike is a local max. Tip: Raise to demand only true, “clearest” local regime events; lower for early triggers. o HTF Confirm: Higher timeframe regime confirmation (like 45m on an intraday chart). Ensures any event you act on is visible in the broader context. Tip: Use higher timeframes for only major moves; lower for scalping or fast regimes. Adaptive Sizing: o Max Contracts (Adaptive): The largest size your system will ever scale to, even on extreme event. Tip: Lower for small accounts/conservative risk; raise on big accounts or when you're willing to go big only on outlier events. o Min Contracts (Adaptive): The “toe-in-the-water.” Smallest possible trade. Tip: Set as low as your broker/exchange allows for safety, or higher if you want to always have meaningful skin in the game. Trade Management: o Stop %: Tightness of your stop-loss relative to entry. Lower for tighter/safer, higher for more breathing room at cost of greater drawdown. o Take Profit %: How much you'll hold out for on a win. Lower = more scalps. Higher = only run with the best. o Decay Exit Sensitivity Buffer: Regime index must dip this far below the trading threshold before you exit for “regime decay.” Tip: 0 = exit as soon as stress fails, higher = exits only on stronger confirmation regime is over. o Bars Decay Must Persist to Exit: How long must decay be present before system closes—set higher to avoid quick fades and whipsaws. Backtest Settings Initial capital: $10,000 Commission: Conservative, realistic roundtrip cost: 15–20 per contract (including slippage per side) I set this to $25 Slippage: 3 ticks per trade Symbol: CME_MINI:NQ1! Timeframe: 1 min (but works on all timeframes) Order size: Adaptive, 1–3 contracts No pyramiding, no hidden DCA Why these settings? These settings are intentionally strict and realistic, reflecting the true costs and risks of live trading. The 10,000 account size is accessible for most retail traders. 25/contract including 3 ticks of slippage are on the high side for NQ, ensuring the strategy is not curve-fit to perfect fills. If it works here, it will work in real conditions. Tip: Set to 1 for instant regime exit; raise for extra confirmation (less whipsaw risk, exits held longer). ________________________________________ Bottom line: Tune the sensitivity, selectivity, and risk of REVELATIONS by these inputs. Raise thresholds and windows for only the best, most powerful signals (institutional style); lower for activity (scalpers, fast cryptos, signals in constant motion). Sizing is always adaptive—never static or martingale. Exits are always based on both price and regime health. Every input is there for your control, not to sell “complexity.” Use with discipline, and make it your own. This strategy is not just a technical achievement: It’s a statement about trading smarter, not just more. * I went back through the code to make sure no the strategy would not suffer from repainting, forward looking, or any frowned upon loopholes. Disclaimer: Trading is risky and carries the risk of substantial loss. Do not use funds you aren’t prepared to lose. This is for research and informational purposes only, not financial advice. Backtest, paper trade, and know your risk before going live. Past performance is not a guarantee of future results. Expect more: We’ll keep pushing the standard, keep evolving the bar until “quant” actually means something in the public code space. Use with clarity, use with discipline, and always trade your edge. — Dskyz , for DAFE Trading Systems
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