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JD's Apollo ConfirmationsJD Apollo Confirmations Indicator is used as the confirmation indicators for a number of other algorithms. This has been specifically designed for Indicies, namely the US30. How to use; When the bars align, it means the price is heading in the direction of alignment. This indicator is intended to be used as a confirmation indicator for other algorithms for best effect. This algorithm combines a number of indicators with specifically tested and chosen settings that have shown to work on a number of timeframes. How to Access Gain access to JD Apollo Confirmations for your TradingView account through our website, links below. 7 day paid trials, subscriptions and lifetime access are all available.
Pine Script®指標
由refjamie提供
2
JD Progress ConfirmationsJD Progress Confirmations Indicator is used as the confirmation indicators for a number of other algorithms. This can be applied to Forex, Stocks and Crypto. How to use; When the bars align, it means the price is heading in the direction of alignment. This indicator is intended to be used as a confirmation indicator for other algorithms for best effect. This algorithm combines a number of indicators with specifically tested and chosen settings that have shown to work on a number of timeframes. How to Access Gain access to JD Progress Confirmations for your TradingView account through our website, links below. 7 day paid trials, subscriptions and lifetime access are all available. All tiers give you full instructions on how to trade this strategy.
Pine Script®指標
由refjamie提供
1
JD ConfirmationsJD Confirmations Indicator is used as the confirmation indicators for a number of other algorithms. This can be applied to Forex, Stocks and Crypto. How to use; When the bars align, it means the price is heading in the direction of alignment. This indicator is intended to be used as a confirmation indicator for other algorithms for best effect. This algorithm combines a number of indicators with specifically tested and chosen settings that have shown to work on a number of timeframes. How to Access Gain access to JD Core for your TradingView account through our website, links below. Both 7 day paid trials and lifetime access are available. Both tiers give you full instructions on how to trade this strategy.
Pine Script®指標
由refjamie提供
FIAfirecrest Trading System(INVITE ONLY indicator. TRIAL ONLY indicator Delay by 15 candles available here .) To access real time indicators click here for subscription. Please don't post comment to ask for invitation. This indicator based on our own smart signal algorithm: FIAfirecrest IS BASED ON OUR OWN ALGORITHM: FIAfirecrest is actually a trading system based on ‘trend following’ strategy. The system consists of several indicators which can give users trading signals as well as shows the validity of the trading signals. Since FIAfirecrest are very concerned about ‘trend following’, therefore we urge our trader to also focus on ‘trend following’ only. Besides thats, FIAfirecrest also features several important qualities: • The usage of colours improve trading clarity • Easily to determine the market structure • Enable to translate trading signal in many angle • Includes take profit and stop loss level • Early alert on any trend changes possibilities FIAfirecrest trading system originally based on 3 smart signal indicators: FIAbreakout, FIAmist and FIApierce. FIAbreakout initial calculation based on higher lower high/low price, FIAmist originally based on momentum thus forming the trend, while FIApierce use as filter based on non-lag adaptive moving averages. How to use: FIAbreakout | area Grey, Yellow & Blue FIAmist | area Green & Red FIApierce | cross Green & Red FIAosc | triangle Green & Red FIAbreakout and FIAmist condition: • LONG when price cross above Grey area and forming Yellow above Grey. Entry within FIAmist Green. Set your stop loss at lowest Grey. • SHORT when price cross below Grey area and forming Blue below Grey. Entry within FIAmist Red. Set your stop loss at highest Grey. FIApierce and FIAosc condition: • FIApierce cross Green or sharp turn formation from Grey area. Is early signal price reverse from long to short. Entry short within FIAmist Red. • FIApierce cross Red or sharp turn formation from Grey area. Is early signal price reverse from short to long. Entry short within FIAmist Green. • Use FIAosc to filter whether both setup are valid or not. That’s all for our FIAfirecrest Trading System.
Pine Script®指標
由FIRECREST提供
1
FIAfirecrest Trading System Trial(TRIAL ONLY indicator Delay by 15 candles.) To access real time indicators click here for subscription. Please don't post comment to ask for remove delay. This indicator based on our own smart signal algorithm: FIAfirecrest IS BASED ON OUR OWN ALGORITHM: FIAfirecrest is actually a trading system based on ‘trend following’ strategy. The system consists of several indicators which can give users trading signals as well as shows the validity of the trading signals. Since FIAfirecrest are very concerned about ‘trend following’, therefore we urge our trader to also focus on ‘trend following’ only. Besides thats, FIAfirecrest also features several important qualities: • The usage of colours improve trading clarity • Easily to determine the market structure • Enable to translate trading signal in many angle • Includes take profit and stop loss level • Early alert on any trend changes possibilities FIAfirecrest trading system originally based on 3 smart signal indicators: FIAbreakout, FIAmist and FIApierce. FIAbreakout initial calculation based on higher lower high/low price, FIAmist originally based on momentum thus forming the trend, while FIApierce use as filter based on non-lag adaptive moving averages. How to use: FIAbreakout | area Grey, Yellow & Blue FIAmist | area Green & Red FIApierce | cross Green & Red FIAosc | triangle Green & Red FIAbreakout and FIAmist condition: • LONG when price cross above Grey area and forming Yellow above Grey. Entry within FIAmist Green. Set your stop loss at lowest Grey. • SHORT when price cross below Grey area and forming Blue below Grey. Entry within FIAmist Red. Set your stop loss at highest Grey. FIApierce and FIAosc condition: • FIApierce cross Green or sharp turn formation from Grey area. Is early signal price reverse from long to short. Entry short within FIAmist Red. • FIApierce cross Red or sharp turn formation from Grey area. Is early signal price reverse from short to long. Entry short within FIAmist Green. • Use FIAosc to filter whether both setup are valid or not. That’s all for our FIAfirecrest Trading System.
Pine Script®指標
由FIRECREST提供
PRICE SATURATION INDEX / FİYAT YOĞUNLUK ENDEKSİEN: PRICE SATURATION INDEX is a momentum algorithm that measures price intensity. It helps us to determine the times when the price reaches intensity and calculates the latency in those moving averages. Moving averages have lag. The lag is necessary because the smoothing is done using past data. It shows you how to filtered a selected amount of lag from an exponential moving average (ema) and price movements. Removing all the lag is not necessarily a good thing, because with no lag, the indicator would just track out the price we were filtering, just as it is the moving average of 1 period; the amount of lag removed is a tradeoff with the amount of smoothing we are willing to forgo with golden ratio and multiline function. We show you the effects of lag removal in an indicator and then use the filter in an effective trading strategy with multiline function. The multiline function is inspired by Jhon Ehlers' zero lag formule, smooth moving average strategy and Schrödinger equation. The Schrödinger equation is a wave function based on quantum mechanics TR: FİYAT YOĞUNLUK ENDEKSİ, fiyat yoğunluğunu ölçen bir momentum algoritmasıdır. Fiyatın yoğunluğa ulaştığı zamanları belirlememize ve hareketli ortalamalardaki gecikmeyi hesaplamamıza yardımcı olur. Hareketli ortalamalar daima gecikir. Gecikme gereklidir çünkü yumuşatma geçmiş veriler kullanılarak yapılır. Bu algoritma hem fiyat hareketlerindeki hemde üstel hareketli ortalamadaki gecikme miktarının nasıl filtreleneceğini gösterir. Tüm gecikmenin kaldırılması iyi bir şey değildir, çünkü gecikme olmadığında gösterge sadece 1 periyodun hareketli ortalaması gibi davranacağı için filtrelediğimiz fiyatı izleyecektir; filtrelenen gecikme miktarı, terk etmek istediğimiz yumuşatma miktarına alternatif bir multiline fonksiyon ve altın orana uyarlanan frekans değirinden oluşur. Bu göstergede gecikmenin ortadan kaldırılmasının etkilerini gösteriyoruz ve daha sonra filtreyi multiline fonksiyona sahip etkili bir trading stratejisi olarak kullanıyoruz. Multiline fonksiyon, Jhon Ehler'in zero lag formülü, smooth hareketli ortalama stratejisi ve Schrödinger denkleminden esinlenmiştir. Schrödinger denklemi ise kuantum mekaniğini temel alan bir dalga fonksiyonudur.
Pine Script®指標
由nFinans提供
4
IME's Community First Presented FVGsIME's Community First Presented FVGs v1.5 - Advanced Implementation ORIGINALITY & INNOVATION This indicator advances beyond basic Fair Value Gap detection by implementing a sophisticated 24-hour FVG lifecycle management system aligned with institutional trading patterns. While many FVG indicators simply detect gaps and extend them indefinitely, this implementation introduces temporal intelligence that mirrors how institutional algorithms actually manage these inefficiencies. Key Innovations that set this apart: - 24-Hour Lifecycle Management: FVGs extend dynamically until 16:59, then freeze until removal at 17:00 next day - Institutional Day Alignment: Recognizes 18:00-16:59 trading cycles vs standard calendar days - Multi-Session Detection: Simultaneous monitoring of Midnight, London, NY AM, and NY PM sessions - Advanced Classification System: A.FVG detection with volume imbalance analysis vs classic FVG patterns - Volatility Settlement Logic: Blocks contamination from opening mechanics (3:01+, 0:01+, 13:31+ rules) - Visual Enhancement System: C.E. lines, contamination warnings, dark mode support with proper transparency handling BASED ON ICT CONCEPTS This indicator implements First Presented Fair Value Gap methodology taught by ICT (Inner Circle Trader). The original F.P. FVG concepts, timing rules, and session-based detection are credited to ICT's educational material. This implementation extends those foundational concepts with advanced lifecycle management and institutional alignment features. ICT's Core F.P. FVG Rules Implemented: - First clean FVG after session opening (avoids opening contamination) - 3-candle pattern requirement for valid detection - Session-specific timing windows and volatility settlement - Consequent Encroachment level identification IME's Advanced Enhancements: - Automated lifecycle management with institutional day recognition - Multi-session simultaneous monitoring with proper isolation - Advanced visual system with transparency states for aged FVGs - A.FVG classification with volume imbalance detection algorithms HOW IT WORKS Core Detection Engine The indicator monitors four key institutional sessions using precise timing windows: - Midnight Session: 00:01-00:30 (blocks 00:00 contamination) - London Session: 03:01-03:30 (blocks 03:00 contamination) - NY AM Session: 09:30-10:00 (configurable 9:30 detection) - NY PM Session: 13:31-14:00 (blocks 13:30 contamination) During each session window, the algorithm scans for the first valid FVG pattern using ICT's 3-candle rule while applying volatility settlement principles to avoid false signals from opening mechanics. Advanced Classification System Classic FVG Detection: Standard 3-candle wick-to-wick gap where candle 1 and 3 don't overlap, creating an inefficiency that institutions must eventually fill. A.FVG (Advanced FVG) Detection: Enhanced pattern recognition that includes volume imbalance analysis (deadpool detection) to identify more significant institutional inefficiencies. A.FVGs incorporate both the basic gap plus additional price imbalances between candle bodies, creating larger, more significant levels. 24-Hour Lifecycle Management Phase 1 - Dynamic Extension (Creation Day): From detection until 16:59 of creation day, FVGs extend in real-time as new bars form, maintaining their relevance as potential support/resistance levels. Phase 2 - Freeze Period (Next Day): At 16:59, FVGs stop extending and "freeze" at their final size, remaining visible as reference levels but no longer growing. This prevents outdated levels from contaminating fresh analysis. Phase 3 - Cleanup (17:00 Next Day): Exactly 24+ hours after creation, FVGs are automatically removed to maintain chart clarity. This timing aligns with institutional trading cycle completion. Institutional Day Logic The algorithm recognizes that institutional trading days run from 18:00-16:59 (not midnight-midnight). This alignment ensures FVGs are managed according to institutional timeframes rather than arbitrary calendar boundaries. Contamination Avoidance System Volatility Settlement Principle: Opening mechanics create artificial volatility that can produce false FVG signals. The indicator automatically blocks detection during exact session opening times (X:00) and requires settlement time (X:01+) before identifying clean institutional inefficiencies. Special NY AM Handling: Provides configurable 9:30 detection for advanced users who want to capture potential opening range FVGs, with clear visual warnings about contamination risk. VISUAL SYSTEM Color Intelligence - Current Day FVGs: Full opacity with session-specific colors - Previous Day FVGs: 70% transparency for historical reference - Special Timing (9:30): Dedicated warning color with alert labels - Dark Mode Support: Automatic text/line color adaptation Enhanced Visual Elements C.E. (Consequent Encroachment) Lines: Automatically calculated 50% levels within each FVG, representing the most likely fill point based on institutional behavior patterns. These levels extend and freeze with their parent FVG. Contamination Warnings: Visual alerts when FVGs are detected during potentially contaminated timing, helping traders understand signal quality. Session Identification: Clear labeling system showing FVG type (FVG/A.FVG), session origin (NY AM, London, etc.), and creation date for easy reference. HOW TO USE Basic Setup 1. Session Selection: Enable/disable specific sessions based on your trading strategy 2. FVG Type: Choose between Classic FVGs or A.FVGs depending on your analysis preference 3. Visual Preferences: Adjust colors, text size, and enable dark mode if needed Trading Applications Intraday Reference Levels: Use current day FVGs as potential support/resistance for price action analysis. The dynamic extension ensures levels remain relevant throughout the trading session. Multi-Session Analysis: Monitor how price interacts with FVGs from different sessions to understand institutional flow and market structure. C.E. Level Trading: Focus on the 50% consequent encroachment levels for high-probability entry points when price approaches FVG zones. Historical Context: Previous day FVGs (shown with transparency) provide context for understanding market structure evolution across multiple trading days. Advanced Features 9:30 Special Detection: For experienced traders, enable 9:30 FVG detection to capture opening range inefficiencies, but understand the contamination risks indicated by warning labels. A.FVG vs Classic Toggle: Switch between detection modes based on market conditions - A.FVGs for trending environments, Classic FVGs for ranging conditions. Best Practices - Use on 1-minute to 15-minute timeframes for optimal session detection - Combine with other institutional concepts (order blocks, liquidity levels) for comprehensive analysis - Pay attention to transparency states - current day FVGs are more actionable than previous day references - Consider C.E. levels as primary targets rather than full FVG fills TECHNICAL SPECIFICATIONS Platform: Pine Script v6 for optimal performance and reliability Timeframe Compatibility: All timeframes (optimized for 1M-15M) Market Compatibility: 24-hour markets (Forex, Crypto, Futures) Session Management: Automatic trading day detection with weekend handling Memory Management: Intelligent capacity limits with automatic cleanup Performance: Optimized algorithms for smooth real-time operation CLOSED SOURCE JUSTIFICATION This indicator is published as closed source to protect the proprietary algorithms that enable: - Precise 24-hour lifecycle timing calculations with institutional day alignment - Advanced A.FVG classification with sophisticated volume imbalance detection - Complex multi-session coordination with contamination filtering - Optimized memory management preventing performance degradation - Specialized visual state management for transparency and extension logic The combination of these advanced systems creates a unique implementation that goes far beyond basic FVG detection, warranting protection of the underlying computational methods while providing full transparency about functionality and usage. PERFORMANCE CHARACTERISTICS Real-Time Operation: Smooth performance with minimal resource usage Accuracy: Precise session detection with timezone consistency Reliability: Robust error handling and edge case management Scalability: Supports multiple simultaneous FVGs without performance impact This advanced implementation represents significant evolution beyond basic FVG indicators, providing institutional-grade analysis tools for serious traders while maintaining the clean visual presentation essential for effective technical analysis. IMPORTANT DISCLAIMERS Past performance does not guarantee future results. This indicator is an educational tool based on ICT's Fair Value Gap concepts and should be used as part of a comprehensive trading strategy. Users should understand the risks involved in trading and consider their risk tolerance before making trading decisions. The indicator identifies potential support/resistance levels but does not predict market direction with certainty. 2 days ago Release Notes IME's Community First Presented FVGs v1.5.2 - Critical Bug Fixes Bug Fixes: v1.5.1 - Fixed 9:30 Contamination Blocking: Issue: When 9:30 detection toggle was OFF, script still detected 9:30 candles as F.P. FVGs Fix: Added proper contamination blocking logic that prevents 9:30 middle candle detection when toggle is OFF Result: Toggle OFF now correctly shows clean F.P. FVGs at 9:31+ (proper ICT volatility settlement) v1.5.2 - Fixed A.FVG Box Calculation Accuracy: Issue: A.FVG boxes incorrectly included ALL body levels even when no actual deadpool existed between specific candles Fix: Implemented selective body level inclusion - only adds body prices where actual volume imbalances exist Result: A.FVG boxes now accurately represent only areas with real institutional volume imbalances Impact: More Accurate Detection: 9:30 contamination properly blocked when disabled Precise A.FVG Zones: Boxes only include levels with actual deadpools/volume imbalances Institutional Accuracy: Both fixes align detection with true institutional trading principles Technical Details: Enhanced contamination blocking checks middle candle timing in normal mode A.FVG calculation now selectively includes body levels based on individual deadpool existence Maintains backward compatibility with all existing features and settings These fixes ensure the indicator provides institutionally accurate FVG detection and sizing for professional trading analysis.
Pine Script®指標
由IamtheIME提供
2
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®指標
由DskyzInvestments提供
11
VWAP/VOL [Extension] | FractalystWhat's the indicator's purpose and functionality? The VWAP/VOL Extension is designed specifically as a bias identification system for the Quantify Trading Model. This extension uses volume-weighted average price analysis combined with institutional volume classification to automatically detect market bias without requiring optimization periods that lead to overfitting. The system provides real-time bias signals (bullish/bearish/neutral) that integrate directly with Quantify's machine learning algorithms, enabling institutional-level backtesting and automated entry/exit identification based on genuine market structure rather than curve-fitted parameters. How does this extension work with the Quantify Trading Model? The VWAP/VOL Extension serves as the bias detection engine for Quantify's automated trading system. Instead of manually selecting bias direction, this extension automatically identifies market bias using: - Volume-weighted VWAP analysis with three-state detection (bullish/bearish/neutral) - Institutional volume classification using relative volume thresholds without optimization - Non-repainting architecture ensuring consistent bias signals for Quantify's machine learning The extension outputs bias signals that Quantify uses as input through the `input.source()` function, allowing the Trading Model to focus on optimal entry/exit timing while the extension handles bias identification. Why doesn't this use optimization periods like other indicators? The VWAP/VOL Extension deliberately avoids optimization periods to prevent overfitting bias that destroys out-of-sample performance. The system uses: - Fixed mathematical thresholds based on market structure principles rather than optimized parameters - Relative volume analysis using standard 2.0x/0.5x ratios that work across all market conditions - VWAP distance calculations based on percentage thresholds without curve-fitting - Gap enforcement using fixed 5-bar minimums for disciplined bias detection This approach ensures the bias signals remain robust across different market regimes without the performance degradation typical of over-optimized systems. Can this extension be used independently for discretionary trading? No, the VWAP/VOL Extension is specifically engineered to work as a component within the Quantify ecosystem. The extension is designed to: - Provide bias input for Quantify's machine learning algorithms - Enable automated backtesting through systematic bias identification - Support institutional-level analysis when combined with Quantify's ML entry model Using this extension independently would miss the primary value proposition of systematic entry/exit optimization that Quantify provides. The extension handles bias detection so Quantify can focus on probability-based trade timing and risk management. How does this enable institutional-level backtesting? The extension transforms discretionary bias identification into systematic institutional analysis by: - Eliminating subjective bias selection through automated VWAP/volume analysis - Providing consistent historical signals with non-repainting architecture for accurate backtesting - Integrating with Quantify's algorithms to identify optimal entry patterns based on objective bias states - Enabling performance analysis across multiple market regimes without optimization bias This combination allows Quantify to run institutional-grade backtests with consistent bias identification, generating reliable performance statistics and risk metrics that reflect genuine market edge rather than curve-fitted results. How do I integrate this with the Quantify Trading Model? Integration enables institutional-grade systematic trading through advanced machine learning and statistical validation: - Add both VWAP/VOL Extension and Quantify Trading Model to your chart - Select VWAP/VOL Extension as the bias source using input.source() - Quantify automatically uses the extension's bias signals for entry/exit analysis - The built-in machine learning algorithms score optimal entry and exit levels based on trend intensity, volume conviction, and market structure patterns identified by the extension The extension handles all bias detection complexity while Quantify focuses on optimal trade timing, position sizing, and risk management along with PineConnector automation What markets and assets does the VWAP/VOL Extension work best on? The VWAP/VOL Extension performs optimally on markets with consistent, high-volume participation since the system relies on institutional volume analysis for bias detection. Futures markets provide the most reliable performance due to their centralized volume data and continuous institutional participation. Recommended Futures Markets: - ES (S&P 500 E-mini) - Over 2 million contracts daily volume, excellent liquidity depth - NQ (NASDAQ-100 E-mini) - Around 600,000 contracts daily, strong tech sector representation - YM (Dow Jones E-mini) - Consistent institutional flow and volume patterns - RTY (Russell 2000 E-mini) - Small-cap exposure with reliable volume data - GC (Gold Futures) - High volume commodity with institutional participation - CL (Crude Oil Futures) - Energy sector representation with strong volume consistency Why Futures Markets Excel: - Futures markets provide centralized volume reporting, ensuring the extension's volume classification system receives accurate institutional participation data. The standardized contract specifications and continuous trading hours create consistent volume patterns that the extension's algorithms can analyze effectively. Acceptable Timeframes and Portfolio Integration: - Any timeframe that can be evaluated within Quantify Trading Model's backtesting engine is acceptable for live trading implementation. The extension is specifically designed to integrate with Quantify's portfolio management system, allowing multiple strategies across different timeframes and assets to operate simultaneously while maintaining consistent bias identification methodology across the entire automated trading portfolio. Legal Disclaimers and Risk Acknowledgments Trading Risk Disclosure The VWAP/VOL Extension is provided for informational, educational, and systematic bias detection purposes only and should not be construed as financial, investment, or trading advice. The extension provides volume-weighted institutional analysis but does not guarantee profitable outcomes, accurate bias predictions, or positive investment returns. Trading systems utilizing bias detection algorithms carry substantial risks including but not limited to total capital loss, incorrect bias identification, market regime changes, and adverse conditions that may invalidate volume-based analysis. The extension's performance depends on accurate volume data, TradingView infrastructure stability, and proper integration with Quantify Trading Model, any of which may experience data errors, technical failures, or service interruptions that could affect bias detection accuracy. System Dependency Acknowledgment The extension requires continuous operation of multiple interconnected systems: TradingView charts and real-time data feeds, accurate volume reporting from exchanges, Quantify Trading Model integration, and stable platform connectivity. Any interruption or malfunction in these systems may result in incorrect bias signals, missed transitions, or unexpected analytical behavior. Users acknowledge that neither Fractalyst nor the creator has control over third-party data providers, exchange volume reporting accuracy, or TradingView platform stability, and cannot guarantee data accuracy, service availability, or analytical performance. Market microstructure changes, volume reporting delays, exchange outages, and technical factors may significantly affect bias detection accuracy compared to theoretical or backtested performance. Intellectual Property Protection The VWAP/VOL Extension, including all proprietary algorithms, volume classification methodologies, three-state bias detection systems, and integration protocols, constitutes the exclusive intellectual property of Fractalyst. Unauthorized reproduction, reverse engineering, modification, or commercial exploitation of these proprietary technologies is strictly prohibited and may result in legal action. Liability Limitation By utilizing this extension, users acknowledge and agree that they assume full responsibility and liability for all trading decisions, financial outcomes, and potential losses resulting from reliance on the extension's bias detection signals. Fractalyst shall not be liable for any unfavorable outcomes, financial losses, missed opportunities, or damages resulting from the development, use, malfunction, or performance of this extension. Past performance of bias detection accuracy, volume classification effectiveness, or integration with Quantify Trading Model does not guarantee future results. Trading outcomes depend on numerous factors including market regime changes, volume pattern evolution, institutional behavior shifts, and proper system configuration, all of which are beyond the control of Fractalyst. User Responsibility Statement Users are solely responsible for understanding the risks associated with algorithmic bias detection, properly configuring system parameters, maintaining appropriate risk management protocols, and regularly monitoring extension performance. Users should thoroughly validate the extension's bias signals through comprehensive backtesting before live implementation and should never base trading decisions solely on automated bias detection. This extension is designed to provide systematic institutional flow analysis but does not replace the need for proper market understanding, risk management discipline, and comprehensive trading methodology. Users should maintain active oversight of bias detection accuracy and be prepared to implement manual overrides when market conditions invalidate volume-based analysis assumptions. Terms of Service Acceptance Continued use of the VWAP/VOL Extension constitutes acceptance of these terms, acknowledgment of associated risks, and agreement to respect all intellectual property protections. Users assume full responsibility for compliance with applicable laws and regulations governing automated trading system usage in their jurisdiction.
Pine Script®指標
由Fractalyst提供
Trend Lines by CR86The basic construction algorithm: 1. The baseline trend line through the closing prices: First, the best fit line (linear regression) is calculated for the closing prices for a given period. The least squares method is used to find the optimal slope and intersection point. 2. Search for key deviation points: For each bar in the period, the deviation of the maximum and minimum from the regression baseline is calculated. The point with the maximum deviation of the maximum upward from the regression line (for the resistance line) is located The point with the maximum deviation of the minimum is located down from the regression line (for the support line) 3. Optimizing the slope of the lines: Lines with an optimized slope are drawn through the found key points. The algorithm selects the slope so that the line best "bends around" the corresponding extremes (maxima for resistance, minima for support) Numerical optimization is used to check the validity of the trend line. 4. The principle of validity: For the support line: all points must be above or at the line level (with a tolerance of 1e-5) For the resistance line: all points must be below or at the line level (with a tolerance of 1e-5) Key Features Adaptability: the lines automatically adjust to the actual price extremes Mathematical precision: a rigorous mathematical approach with optimization is used Logarithmic scaling: optional for dealing with highly volatile assets The basic construction algorithm 1. The baseline trend line through the closing prices: First, the best fit line (linear regression) is calculated for the closing prices for a given period. The least squares method is used to find the optimal slope and intersection point. 2. Search for key deviation points: For each bar in the period, the deviation of the maximum and minimum from the regression baseline is calculated. The point with the maximum deviation of the maximum upward from the regression line (for the resistance line) is located The point with the maximum deviation of the minimum is located down from the regression line (for the support line) 3. Optimizing the slope of the lines: Lines with an optimized slope are drawn through the found key points. The algorithm selects the slope so that the line best "bends around" the corresponding extremes (maxima for resistance, minima for support) Numerical optimization is used to check the validity of the trend line. 4. The principle of validity: For the support line: all points must be above or at the line level (with a tolerance of 1e-5) For the resistance line: all points must be below or at the line level (with a tolerance of 1e-5) Key Features Adaptability: the lines automatically adjust to the actual price extremes Mathematical precision: a rigorous mathematical approach with optimization is used Logarithmic scaling: optional for dealing with highly volatile assets *********************************************************************************************** Основной алгоритм построения: 1. Базовая линия тренда через цены закрытия: Сначала вычисляется линия наилучшего соответствия (линейная регрессия) для цен закрытия за заданный период Используется метод наименьших квадратов для нахождения оптимального наклона и точки пересечения 2. Поиск ключевых точек отклонения: Для каждого бара в периоде вычисляется отклонение максимума и минимума от базовой линии регрессии Находится точка с максимальным отклонением максимума вверх от линии регрессии (для линии сопротивления) Находится точка с максимальным отклонением минимума вниз от линии регрессии (для линии поддержки) 3. Оптимизация наклона линий: Через найденные ключевые точки проводятся линии с оптимизированным наклоном Алгоритм подбирает такой наклон, чтобы линия наилучшим образом "огибала" соответствующие экстремумы (максимумы для сопротивления, минимумы для поддержки) Используется численная оптимизация с проверкой валидности трендовой линии 4. Принцип валидности: Для линии поддержки: все точки должны быть выше или на уровне линии (с допуском 1e-5) Для линии сопротивления: все точки должны быть ниже или на уровне линии (с допуском 1e-5) Ключевые особенности Адаптивность: линии автоматически подстраиваются под фактические экстремумы цен Математическая точность: используется строгий математический подход с оптимизацией Логарифмическое масштабирование: опционально для работы с сильно волатильными активами
Pine Script®指標
由cryptorider86提供
RSI-Adaptive T3 [ChartPrime]The RSI-Adaptive T3 is a precision trend-following tool built around the legendary T3 smoothing algorithm developed by Tim Tillson , designed to enhance responsiveness while reducing lag compared to traditional moving averages. Current implementation takes it a step further by dynamically adapting the smoothing length based on real-time RSI conditions — allowing the T3 to “breathe” with market volatility. This dynamic length makes the curve faster in trending moves and smoother during consolidations. To help traders visualize volatility and directional momentum, adaptive volatility bands are plotted around the T3 line, with visual crossover markers and a dynamic info panel on the chart. It’s ideal for identifying trend shifts, spotting momentum surges, and adapting strategy execution to the pace of the market. HOIW IT WORKS At its core, this indicator fuses two ideas: The T3 Moving Average — a 6-stage recursively smoothed exponential average created by Tim Tillson , designed to reduce lag without sacrificing smoothness. It uses a volume factor to control curvature. A Dynamic Length Engine — powered by the RSI. When RSI is low (market oversold), the T3 becomes shorter and more reactive. When RSI is high (overbought), the T3 becomes longer and smoother. This creates a feedback loop between price momentum and trend sensitivity. // Step 1: Adaptive length via RSI rsi = ta.rsi(src, rsiLen) rsi_scale = 1 - rsi / 100 len = math.round(minLen + (maxLen - minLen) * rsi_scale) pine_ema(src, length) => alpha = 2 / (length + 1) sum = 0.0 sum := na(sum ) ? src : alpha * src + (1 - alpha) * nz(sum ) sum // Step 2: T3 with adaptive length e1 = pine_ema(src, len) e2 = pine_ema(e1, len) e3 = pine_ema(e2, len) e4 = pine_ema(e3, len) e5 = pine_ema(e4, len) e6 = pine_ema(e5, len) c1 = -v * v * v c2 = 3 * v * v + 3 * v * v * v c3 = -6 * v * v - 3 * v - 3 * v * v * v c4 = 1 + 3 * v + v * v * v + 3 * v * v t3 = c1 * e6 + c2 * e5 + c3 * e4 + c4 * e3 The result: an evolving trend line that adapts to market tempo in real-time. KEY FEATURES ⯁ RSI-Based Adaptive Smoothing The length of the T3 calculation dynamically adjusts between a Min Length and Max Length , based on the current RSI. When RSI is low → the T3 shortens, tracking reversals faster. When RSI is high → the T3 stretches, filtering out noise during euphoria phases. Displayed length is shown in a floating table, colored on a gradient between min/max values. ⯁ T3 Calculation (Tim Tillson Method) The script uses a 6-stage EMA cascade with a customizable Volume Factor (v) , as designed by Tillson (1998) . Formula: T3 = c1 * e6 + c2 * e5 + c3 * e4 + c4 * e3 This technique gives smoother yet faster curves than EMAs or DEMA/Triple EMA. ⯁ Visual Trend Direction & Transitions The T3 line changes color dynamically: Color Up (default: blue) → bullish curvature Color Down (default: orange) → bearish curvature Plot fill between T3 and delayed T3 creates a gradient ribbon to show momentum expansion/contraction. Directional shift markers (“🞛”) are plotted when T3 crosses its own delayed value — helping traders spot trend flips or pullback entries. ⯁ Adaptive Volatility Bands Optional upper/lower bands are plotted around the T3 line using a user-defined volatility window (default: 100). Bands widen when volatility rises, and contract during compression — similar to Bollinger logic but centered on the adaptive T3. Shaded band zones help frame breakout setups or mean-reversion zones. ⯁ Dynamic Info Table A live stats panel shows: Current adaptive length Maximum smoothing (▲ MaxLen) Minimum smoothing (▼ MinLen) All values update in real time and are color-coded to match trend direction. HOW TO USE Use T3 crossovers to detect trend transitions, especially during periods of volatility compression. Watch for volatility contraction in the bands — breakouts from narrow band periods often precede trend bursts. The adaptive smoothing length can also be used to assess current market tempo — tighter = faster; wider = slower. CONCLUSION RSI-Adaptive T3 modernizes one of the most elegant smoothing algorithms in technical analysis with intelligent RSI responsiveness and built-in volatility bands. It gives traders a cleaner read on trend health, directional shifts, and expansion dynamics — all in a visually efficient package. Perfect for scalpers, swing traders, and algorithmic modelers alike, it delivers advanced logic in a plug-and-play format.
Pine Script®指標
由ChartPrime提供
10
[blackcat] L3 Smart Money FlowCOMPREHENSIVE ANALYSIS OF THE L3 SMART MONEY FLOW INDICATOR 🌐 OVERVIEW: The L3 Smart Money Flow indicator represents a sophisticated multi-dimensional analytics tool combining traditional momentum measurements with advanced institutional investor tracking capabilities. It's particularly effective at identifying large-scale capital movement dynamics that often precede significant price shifts. Core Objectives: • Detect subtle but meaningful price action anomalies indicating major player involvement • Provide clear entry/exit markers based on multiple validated criteria • Offer risk-managed positioning strategies suitable for various account sizes • Maintain operational efficiency even during high volatility regimes THEORETICAL BACKDROP AND METHODOLOGY 🎓 Conceptual Foundation Principles: Utilizes Time-Varying Moving Averages (TVMA) responding adaptively to changing market states Implements Extended Smoothing Algorithm (XSA) providing enhanced filtration characteristics Employs asymmetric weight distribution favoring recent price observations over historical ones → Analyzes price-weighted closing prices incorporating volume influence indirectly ← Applies Asymmetric Local Maximum (ALMA) filters generating institution-specific trends ⟸ Combines multiple temporal perspectives producing robust directional assessments ✓ Calculates normalized momentum ratios comparing current state against extended range extremes ✗ Filters out insignificant fluctuations via double-stage verification process ⤾ Generates actionable alerts upon exceeding predefined significance boundaries CONFIGURABLE PARAMETERS IN DEPTH ⚙️ Input Customization Options Detailed Explanation: Temporal Resolution Control: → TVMA Length Setting: Minimum value constraint ensuring mathematical validity Higher numbers increase smoothing effect reducing reaction velocity Lower intervals enhance responsiveness potentially increasing noise exposure Validation Threshold Definition: ↓ Bull-Bear Boundary Level: Establishes fundamental acceptance/rejection zones Typically set near extreme values reflecting rare occurrence probability Can be adjusted per instrument liquidity profiles if necessary ADVANCED ALGORITHMIC PROCEDURES BREAKDOWN 💻 Internal Operation Architecture: Base Calculations Infrastructure: ☑ Raw Data Preparation and Normalization ☐ High/Low/Closing Aggregation Processes ☒ Range Estimation Algorithms Intermediate Transform Engine: 📈 Momentum Ratio Computation Workflow ↔ First Pass XSA Application Details ➖ Second Stage Refinement Mechanics Final Output Synthesis Framework: ➢ Composite Reading Compilation Logic ➣ Validation Status Determination Process ➤ Alert Trigger Decision Making Structure INTERACTIVE VISUAL INTERFACE COMPONENTS 🎨 User Experience Interface Elements: 🔵 Plotting Series Hierarchy: → Primary FundFlow Signal: White trace marking core oscillator progression ↑ Secondary Confirmation Overlay: Orange/Yellow highlighting validation status 🟥 Risk/Reward Boundaries: Aqua line delineating strategic areas requiring attention 🏷️ Interactive Marker System: ✔ "BUY": Green upward-pointing labels denoting confirmed long entries ❌ "SELL": Red downward-facing badges signaling short setups PRACTICAL APPLICATION STRATEGY GUIDE 📋 Operational Deployment Instructions: Strategic Planning Initiatives: • Define precise profit targets considering realistic reward/risk scenarios → Set maximum acceptable loss thresholds protecting available resources adequately ↓ Develop contingency plans addressing unexpected adverse developments promptly Live Trading Engagement Protocols: → Maintaining vigilant monitoring of label placement activities continuously ↓ Tracking order fill success rates across implemented grids regularly ↑ Evaluating system effectiveness compared alternative methodologies periodically Performance Optimization Techniques: ✔ Implement incremental improvements iteratively throughout lifecycle ❌ Eliminate ineffective component variations systematically ⟹ Ensure proportional growth capability matching user needs appropriately EFFICIENCY ENHANCEMENT APPROACHES 🚀 Ongoing Development Strategy: Resource Management Focus Areas: → Minimizing redundant computation cycles through intelligent caching mechanisms ↓ Leveraging parallel processing capabilities where feasible efficiently ↑ Optimizing storage access patterns improving response times substantially Scalability Consideration Factors: ✔ Adapting to varying account sizes/market capitalizations seamlessly ❌ Preventing bottlenecks limiting concurrent operation capacity ⟹ Ensuring balanced growth capability matching evolving requirements accurately Maintenance Routine Establishment: ✓ Regular codebase updates incorporation keeping functionality current ↓ Periodic performance audits conducting verifying continued effectiveness ↑ Documentation refinement updating explaining any material modifications made SYSTEMATIC RISK CONTROL MECHANISMS 🛡️ Comprehensive Protection Systems: Position Sizing Governance: ∅ Never exceed predetermined exposure limitations strictly observed ± Scale entries proportionally according to available resources carefully × Include slippage allowances within planning stages realistically Emergency Response Procedures: ↩ Well-defined exit strategies including trailing stops activation logic 🌀 Contingency plan formulation covering worst-case scenario contingencies ⇄ Recovery procedure documentation outlining restoration steps methodically
Pine Script®指標
由blackcat1402提供
[itradesize] ICT Opening range This indicator automatically annotates the opening ranges of the AM and PM sessions. It should be used on the 1-minute timeframe , although you can check and build a further models when using a 2-3-4 or even 5-minute timeframe. You can customize this under the settings tab. Additionally, it includes features such as standard deviations and the initial fair value gap presented. Everything is based on what ICT said in his algorithmic timing video. The algorithm will continue to adjust prices higher or lower until it reaches a predetermined target price. This process will occur within specific time frames: the last 10 minutes before the hour and the first 10 minutes after a new hour begins. For the  AM session opening range, this is from 9:30 to 10:00 , and for the  PM session, it's from 13:30 to 14:00 . Defining these ranges allows us to identify the first presented fair value gaps there, as the algorithm is designed to leave these signatures for smart money. This process of time-based delivery precision repeats every day. You can build a whole New York model on this. It's important to journal and backtest your results results. If the market breaks the opening range on either side and there is evident liquidity, it is highly likely that it will pursue that liquidity. However, before doing so, the market should retrace back to the first fair value gap if it hasn’t already occurred or back to the 0.75 or 0.5 level of the range at maximum. When does this happen? Typically, when a macro event occurs— for example, during the lunch macro from 11:30 to 12:00 . In most cases, you can expect a retracement during lunch macro. If the market retraces beyond these levels, there is a higher probability that the expected scenario will not play out. The algorithm primarily refers to the 30-minute opening range each time. The standard deviation levels can be used to establish algorithmic delivery targets and anticipate another run after the PM session opening range has occurred. The AM session often helps determine the likely direction of movement after the PM session range concludes. The PM macro runs from 15:15 to 15:45 . At this time, the market will typically operate within the narrative that is currently underway.
Pine Script®指標
由itradesize提供
1
Quantify [Trading Model] | FractalystNote: In this description, "TM" refers to Trading Model (not trademark) and "EM" refers to Entry Model What’s the indicator’s purpose and functionality? You know how to identify market bias but always struggle with figuring out the best exit method, or even hesitating to take your trades? I've been there. That's why I built this solution—once and for all—to help traders who know the market bias but need a systematic and quantitative approach for their entries and trade management. A model that shows you real-time market probabilities and insights, so you can focus on execution with confidence—not doubt or FOMO. How does this Quantify differentiate from Quantify ? Have you managed to code or even found an indicator that identifies the market bias for you, so you don’t have to manually spend time analyzing the market and trend? Then that’s exactly why you might need the Quantify Trading Model. With the Trading Model (TM) version, the script automatically uses your given bias identification method to determine the trend (bull vs bear and neutral), detect the bias, and provide instant insight into the trades you could’ve taken. To avoid complications from consecutive signals, it uses a kNN machine learning algorithm that processes market structure and probabilities to predict the best future patterns. (You don’t have to deal with any complexity—it’s all taken care of for you.) Quantify TM uses the k-Nearest Neighbors (kNN) machine learning algorithm to learn from historical market patterns and adapt to changing market structures. This means it can recognize similar market conditions from the past and apply those lessons to current trading decisions. On the other hand, Quantify EM requires you to manually select your directional bias. It then focuses solely on generating entry signals based on that pre-determined bias. While the entry model version (EM) uses your manual bias selection to determine the trend, it then provides insights into trades you could’ve taken and should be taking. Trading Model (TM) - Uses `input.source()` to incorporate your personal methodology for identifying market bias - Automates everything—from bias detection to entry and exit decisions - Adapts to market bias changes through kNN machine learning optimization - Reduces human intervention in trading decisions, limiting emotional interference Entry Model (EM) - Focuses specifically on optimizing entry points within your pre-selected directional bias - Requires manual input for determining market bias - Provides entry signals without automating alerts or bias rules Can the indicator be applied to any market approach/trading strategy? Yes, if you have clear rules for identifying the market bias, then you can code your bias detection and then use the input.source() user input to retrieve the direction from your own indicator, then the Quantify uses machine-learning identify the best setups for you. Here's an example: //@version=6 indicator('Moving Averages Bias', overlay = true) // Input lengths for moving averages ma10_length = input.int(10, title = 'MA 10 Length') ma20_length = input.int(20, title = 'MA 20 Length') ma50_length = input.int(50, title = 'MA 50 Length') // Calculate moving averages ma10 = ta.sma(close, ma10_length) ma20 = ta.sma(close, ma20_length) ma50 = ta.sma(close, ma50_length) // Identify bias var bias = 0 if close > ma10 and close > ma20 and close > ma50 and ma10 > ma20 and ma20 > ma50 bias := 1 // Bullish bias else if close < ma10 and close < ma20 and close < ma50 and ma10 < ma20 and ma20 < ma50 bias := -1 // Bearish bias else bias := 0 // Neutral bias // Plot the bias plot(bias, title = 'Identified Bias', color = color.blue,display = display.none) Once you've created your custom bias indicator, you can integrate it with Quantify : - Add your bias indicator to your chart - Open the Quantify settings - Set the Bias option to "Auto" - Select your custom indicator as the bias source The machine learning algorithms will then analyze historical price action and identify optimal setups based on your defined bias parameters. Performance statistics are displayed in summary tables, allowing you to evaluate effectiveness across different timeframes. Can the indicator be used for different timeframes or trading styles? Yes, regardless of the timeframe you’d like to take your entries, the indicator adapts to your trading style. Whether you’re a swing trader, scalper, or even a position trader, the algorithm dynamically evaluates market conditions across your chosen timeframe. How Quantify Helps You Trade Profitably? The Quantify Trading Model offers several powerful features that can significantly improve your trading profitability when used correctly: Real-Time Edge Assessment It displays real-time probability of price moving in your favor versus hitting your stoploss This gives you immediate insight into risk/reward dynamics before entering trades You can make more informed decisions by knowing the statistical likelihood of success Historical Edge Validation Instantly shows whether your trading approach has demonstrated an edge in historical data Prevents you from trading setups that historically haven't performed well Gives confidence when entering trades that have proven statistical advantages Optimized Position Sizing Analyzes each setup's success rate to determine the adjusted Kelly criterion formula Customizes position sizing based on your selected maximum drawdown tolerance Helps prevent account-destroying losses while maximizing growth potential Advanced Exit Management Utilizes market structure-based trailing stop-loss mechanisms Maximizes the average risk-reward ratio profit per winning trade Helps capture larger moves while protecting gains during market reversals Emotional Discipline Enforcement Eliminates emotional bias by adhering to your pre-defined rules for market direction Prevents impulsive decisions by providing objective entry and exit signals Creates psychological distance between your emotions and trading decisions Overtrading Prevention Highlights only setups that demonstrate positive expectancy Reduces frequency of low-probability trades Conserves capital for higher-quality opportunities Systematic Approach Benefits By combining machine learning algorithms with your personal bias identification methods, Quantify helps transform discretionary trading approaches into more systematic, probability-based strategies. What Entry Models are used in Quantify Trading Model version? The Quantify Trading Model utilizes two primary entry models to identify high-probability trade setups: Breakout Entry Model - Identifies potential trade entries when price breaks through significant swing highs and swing lows - Captures momentum as price moves beyond established trading ranges - Particularly effective in trending markets when combined with the appropriate bias detection - Optimized by machine learning to filter false breakouts based on historical performance Fractals Entry Model - Utilizes fractal patterns to identify potential reversal or continuation points - Also uses swing levels to determine optimal entry locations - Based on the concept that market structure repeats across different timeframes - Identifies local highs and lows that form natural entry points - Enhanced by machine learning to recognize the most profitable fractal formations - These entry models work in conjunction with your custom bias indicator to ensure trades are taken in the direction of the overall market trend. The machine learning component analyzes historical performance of these entry types across different market conditions to optimize entry timing and signal quality. How Does This Indicator Identify Market Structure? 1. Swing Detection • The indicator identifies key swing points on the chart. These are local highs or lows where the price reverses direction, forming the foundation of market structure. 2. Structural Break Validation • A structural break is flagged when a candle closes above a previous swing high (bullish) or below a previous swing low (bearish). • Break Confirmation Process: To confirm the break, the indicator applies the following rules: • Valid Swing Preceding the Break: There must be at least one valid swing point before the break. 3. Numeric Labeling • Each confirmed structural break is assigned a unique numeric ID starting from 1. • This helps traders track breaks sequentially and analyze how the market structure evolves over time. 4. Liquidity and Invalidation Zones • For every confirmed structural break, the indicator highlights two critical zones: 1. Liquidity Zone (LIQ): Represents the structural liquidity level. 2. Invalidation Zone (INV): Acts as Invalidation point if the structure fails to hold. How does the trailing stop-loss work? what are the underlying calculations? A trailing stoploss is a dynamic risk management tool that moves with the price as the market trend continues in the trader’s favor. Unlike a fixed take profit, which stays at a set level, the trailing stoploss automatically adjusts itself as the market moves, locking in profits as the price advances. In Quantify, the trailing stoploss is enhanced by incorporating market structure liquidity levels (explain above). This ensures that the stoploss adjusts intelligently based on key price levels, allowing the trader to stay in the trade as long as the trend remains intact, while also protecting profits if the market reverses. What is the Kelly Criterion, and how does it work in Quantify? The Kelly Criterion is a mathematical formula used to determine the optimal position size for each trade, maximizing long-term growth while minimizing the risk of large drawdowns. It calculates the percentage of your portfolio to risk on a trade based on the probability of winning and the expected payoff. Quantify integrates this with user-defined inputs to dynamically calculate the most effective position size in percentage, aligning with the trader’s risk tolerance and desired exposure. How does Quantify use the Kelly Criterion in practice? Quantify uses the Kelly Criterion to optimize position sizing based on the following factors: 1. Confidence Level: The model assesses the confidence level in the trade setup based on historical data and sample size. A higher confidence level increases the suggested position size because the trade has a higher probability of success. 2. Max Allowed Drawdown (User-Defined): Traders can set their preferred maximum allowed drawdown, which dictates how much loss is acceptable before reducing position size or stopping trading. Quantify uses this input to ensure that risk exposure aligns with the trader’s risk tolerance. 3. Probabilities: Quantify calculates the probabilities of success for each trade setup. The higher the probability of a successful trade (based on historical price action and liquidity levels), the larger the position size suggested by the Kelly Criterion. How can I get started to use the indicator? 1. Set Your Market Bias • Choose Auto. • Select the source you want Quantify to use as for bias identification method (explained above) 2. Choose Your Entry Timeframes • Specify the timeframes you want to focus on for trade entries. • The indicator will dynamically analyze these timeframes to provide optimal setups. 3. Choose Your Entry Model and BE/TP Levels • Choose a model that suits your personality • Choose a level where you'd like the script to take profit or move stop-loss to BE 4. Set and activate the alerts What tables are used in the Quantify? • Quarterly • Monthly • Weekly Terms and Conditions | Disclaimer Our charting tools are provided for informational and educational purposes only and should not be construed as financial, investment, or trading advice. They are not intended to forecast market movements or offer specific recommendations. Users should understand that past performance does not guarantee future results and should not base financial decisions solely on historical data. Built-in components, features, and functionalities of our charting tools are the intellectual property of @Fractalyst Unauthorized use, reproduction, or distribution of these proprietary elements is prohibited. - By continuing to use our charting tools, the user acknowledges and accepts the Terms and Conditions outlined in this legal disclaimer and agrees to respect our intellectual property rights and comply with all applicable laws and regulations.
Pine Script®指標
由Fractalyst提供
1
Altcoins Screener [SwissAlgo]Introduction: The Altcoins Screener at a Glance The Altcoins Screener is a cryptocurrency analysis tool designed to provide an overview of potential trading opportunities across multiple crypto coins/tokens and categories. By combining technical analysis, price action assessment, and social metrics (via LunarCrush data), it presents market information and trading signals for a broad range of altcoins (approx. 300 USDT.P pairs of 9 crypto categories). The screener is designed to consolidate market information onto a single chart , aiming to streamline the analysis of market conditions. It provides a consolidated market overview, which can simplify the assessment of market conditions, compared to monitoring individual charts with several layered indicators. Key Features: 🔹 Multi-category analysis covering 300 crypto pairs of 9 categories on a single chart (Layer 1 & Top Coins, Layer2 & Scaling, Defi & Landing, Gaming & Metaverse, AI & Data, Exchanges & Trading, NFT & Social, Memes & Community, Other, User's Custom Portfolio). 🔹 Technical analysis with trade signals (Long/Short) based on an aggregated view of technical and social data points 🔹 Social sentiment integration through LunarCrush metrics (GalaxyScore, AltRank, Social Sentiment) 🔹 Real-time market scanning provides automated alerts when market conditions for specified coins/tokens potentially change. 🔹 Custom watchlist support for personalized monitoring (users can define a custom category containing a set of specific cryptocurrencies, i.e. own portfolio). The screener presents data in a table format, using color-coded indicators to aid visual analysis. Detailed technical information is also provided. The assessments/trade signals provided by this indicator should be considered as one input among many when forming your trading strategy. -------------------------------------- What It Does The Altcoins Screener is a cryptocurrency analysis tool that offers: Data Display and Analysis (Technical/Social): 🔹 Technical Metrics * Technical Raw Data : Displays raw values for a range of technical indicators, including RSI, Stochastic RSI, DMI/ADX, RVI, ATR, OBV, and Hull Moving Averages (including their recent trends and potential significance). Detailed view of key technical indicators, for further analysis and evaluation: * Technical Analysis (Summary) : Provides a summarized interpretation of technical conditions based on aggregated parameters: * Price Action * Trend * Momentum * Volatility * Volume Summarized view of confluences for potential long/short bias: 🔹 Social Metrics (LunarCrush) : Presents data from LunarCrush®, including Galaxy Score®, AltRank®, and Social Sentiment® (including their recent trends and potential significance). Lunarcrush data for the top 10 coins for each crypto category: 🔹 PVSRA (Price Volume & Market Makers Activity) Candles : Shows special candles highlighting potential market maker activity and volume anomalies, helping identify possible manipulation zones (including imbalance zones, i.e. price areas that market makers may revisit) -------------------------------------- Key Features: Automated trade signals (Long/Short) are generated based on algorithmic calculations and signal confidence levels across technical and social data points. These signals are intended to be used as one component of a broader trading strategy. Custom sensitivity settings allow users to adjust the analysis timeframe (options: 1D, 2D, or 1W). Higher timeframes may provide a broader perspective, while the 2D setting is the default configuration. Multi-category analysis covering a selection of approximately 300 crypto pairs across 9 predefined crypto categories. Custom symbol selection: Users can define a custom list of up to 10 symbols for focused monitoring. Automated Alerts to track potential trend changes across crypto categories (Long to Short to Neutral, or vice versa) Visual Interface: Organized table display with color-coded indicators to aid interpretation. Clear and efficient format for scanning market information. -------------------------------------- Target Audience 🔹 The screener is designed for cryptocurrency traders who:
 Need to efficiently monitor multiple USDT perpetual futures markets Use technical analysis in their trading decisions Want to track sector-wide movements across crypto categories 
 🔹 Suitable for different trading styles: Scalpers requiring quick market assessment Swing traders analyzing multi-day trends Position traders monitoring longer-term setups The color-coded interface makes it accessible for intermediate traders while providing detailed metrics for advanced users. A basic understanding of technical analysis and crypto trading is recommended. -------------------------------------- How It Works The Altcoins Screener evaluates cryptocurrencies through a multi-layered analysis: 🔹 Core Analysis Components Each parameter combines multiple indicators for comprehensive evaluation: Price Action EMA crossovers and momentum Support/resistance zones Candlestick patterns Trend Hull Moving Average system DMI/ADX trend strength Multi-timeframe confirmation Momentum RSI/Stochastic RSI readings MACD convergence/divergence Oscillator confirmations Volatility RVI/ATR measurements Bollinger Bands behavior Historical volatility trends Volume OBV trend analysis Volume/price correlations Volume profile assessment 🔹 Signal Generation Process 1. Real-time data collection across timeframes 2. Weighted indicator calculations 3. Parameter aggregation and analysis 4. Signal strength determination 5. Color-coding and alert generation -------------------------------------- How to Use 🔹 Initial Setup: Add the indicator to a chart (use the 1D timeframe) Select your preferred crypto category or create a custom list Choose between Technical Analysis or Technical Metrics view Set data sensitivity based on your trading style 🔹 Using the Technical Analysis View: Monitor color-coded dots for quick market assessment Green: bullish conditions Red: bearish conditions Gray: neutral conditions Check the "Trade Signal" column for potential Long/Short entries signaled by confluences among technical and/or social data points 🔹 Using the Technical Metrics View: Review detailed numerical values Monitor slopes (↑↓ arrows) for the most recent trend direction of each data point Watch for pivotal points (highlighted cells): these are data points that suggest potential trend reversals Focus on the confluence of multiple indicators The technical metrics view corroborates the conclusions shown in the Technical Analysis View, providing more details about some critical data points. 🔹 Alert Configuration: Enable Technical Alerts for signal notifications (which coin/token seems most suited for Long or Short trades, and which coin/token is in a neutral/uncertain state for trading = "No Trade") Configure alert conditions based on trading style Set timeframe-appropriate sensitivity Monitor alert messages for trade signals Instructions on how to set alerts are provided in the script (enable "Signals Setup Instructions" in User Interface to get a step-by-step guide about setting up alerts) Best Practices: Confirm signals across multiple timeframes Use appropriate sensitivity for your trading style Monitor multiple categories for sector rotation Combine signals with your trading strategy Verify signals with price action confirmation and deep dive into the charts of your potential targets -------------------------------------- About the Settings 🔹 Crypto Category Selection Layer 1 & Major: Top market cap coins (BTC, ETH, XRP,...), established protocols Layer 2 & Scaling: ETH L2s, scaling solutions DeFi & Lending: Decentralized finance protocols Gaming & Metaverse: Gaming and virtual world tokens AI & Data: Artificial intelligence and data projects Exchange & Trading: Exchange tokens, trading protocols NFT & Social: NFT platforms, social tokens Memes & Community: Community-driven tokens Others & Misc: Other categories Custom Category: User-defined list (up to 10 symbols) Data Type Options Technical Analysis: Color-coded summary view Technical Metrics: Detailed numerical values of some key technical data points Sensitivity Settings Higher: Shorter timeframe, more frequent signals Default: Balanced timeframe, standard signals Lower: Longer timeframe, stronger signals Alert Settings Technical Alerts: Trade signal notifications Data Timeframe: Minimum 1D required Theme: Dark/Light mode options Note: All analysis is performed on USDT Perpetual Futures pairs from Binance -------------------------------------- FAQ Q: Does the screener work on other exchanges besides Binance? A: No, it's designed specifically for Binance USDT Perpetual Futures pairs. Binance offers the highest liquidity and trading volume in the crypto derivatives market, making it ideal for technical analysis. The extensive range of trading pairs and reliable data streams help ensure more accurate signals and analysis. Using a single high-liquidity exchange also helps avoid inconsistencies that could arise from aggregating data across multiple platforms with varying liquidity levels. Q: What's the minimum timeframe required? A: The screener requires a minimum 1D (daily) timeframe. This requirement ensures that the technical analysis has sufficient data points for reliable signal generation. Lower timeframes can produce more noise and false signals, while daily timeframes help filter out market noise and identify stronger trends. Q: Why are some social metrics showing "NaN"? A: "NaN" (Not a Number) appears when cryptocurrencies don't have associated LunarCrush data. This typically occurs with newer tokens or those with lower market caps. The technical analysis remains fully functional regardless of social metric availability, as these are complementary data points. Q: How often are signals updated? A: Signals update with each new candle on the selected timeframe (1D, 2D, or 1W). For example, on the default 2D setting, signals are recalculated every two days as new candles form. This helps reduce noise while maintaining timely analysis of market conditions. Q: Can I add spot trading pairs? A: No, the screener is optimized for Binance USDT perpetual futures pairs for data consistency and analysis purposes. While spot and perpetual prices typically align closely due to arbitrage, using a single data source (Binance) and contract type (USDT perpetual) ensures uniform data quality and analysis across all pairs. This standardization helps maintain reliable technical analysis and signal generation. Q: How many coins can I add to my custom list? A: Users can add up to 10 custom symbols to their watchlist. This limit is designed to maintain optimal performance while allowing focused monitoring of specific assets. The custom list complements the predefined categories that cover over 300 pairs. Q: What determines signal confidence levels? A: Signal confidence is calculated through a weighted algorithm that considers multiple factors: trend strength (Hull MA, DMI/ADX), momentum indicators (RSI, SRSI), volatility measurements (RVI, ATR, BB), volume analysis (OBV, volume trends), and price action patterns. Higher confidence levels indicate stronger alignment across these factors. Q: Are signals guaranteed to work? A: No. Signals are analytical tools based on historical and current market data, not guaranteed predictions. They should be used as one component of a comprehensive trading strategy that includes proper risk management, position sizing, and additional confirmation factors. Past performance does not guarantee future results. Q: Why does the screener need higher timeframes? A: Higher timeframes (1D minimum) provide several benefits: reduced market noise, more reliable technical signals, better trend identification, and lower likelihood of false signals. They also align better with institutional trading patterns and allow for a more thorough analysis of market conditions across multiple indicators. -------------------------------------- Conclusion The Altcoins Screener is a comprehensive crypto market analysis tool that: Scans 300+ cryptocurrencies across 9 sectors on a single chart Combines technical indicators and social metrics for signal generation Identifies potential trading opportunities through color-coded visuals Saves time by eliminating the need to monitor multiple charts The tool is suited for: Market overview and sector rotation analysis Quick assessment of market conditions Technical and social sentiment tracking Systematic trading approach with alerts Use this screener with caution and as a complement to any other tool you use to define your trading strategy. -------------------------------------- Disclaimer This indicator is for informational and educational purposes only:
 Not financial advice: This indicator should not be considered investment advice. No guarantee of accuracy: The indicator's calculations and signals are based on specific algorithms and data sources, but accuracy cannot be guaranteed. Market conditions can change rapidly. Past performance is not predictive: Past performance of the indicator's signals or any specific asset is not indicative of future results. Substantial risk of loss: Trading cryptocurrencies involves a substantial risk of loss. You can lose money trading these assets. User responsibility: Users are solely responsible for their own trading decisions and should exercise caution. Independent research required: Always conduct thorough independent research (DYOR) before making any trading decisions. Technical analysis is one of many tools: Technical analysis, including the output of this indicator, is just one tool among many and should not be relied upon exclusively. Risk management is essential: Use proper risk management techniques, including position sizing and stop-loss orders. Comprehensive strategy: Use this tool as part of a comprehensive trading strategy, not as a standalone solution. No liability for trading results: The Author assumes no responsibility or liability for any trading results or losses incurred as a result of using this indicator. No TradingView affiliation: SwissAlgo is an independent entity and is not affiliated with or endorsed by TradingView. LunarCrush data: The indicator utilizes publicly available data from LunarCrush. LunarCrush data and trademarks are the property of LunarCrush. Consult a financial advisor: Consult with a qualified financial advisor before making any investment decisions. By using this indicator, you acknowledge and agree to these terms. If you do not agree with these terms, please refrain from using this indicator.
Pine Script®指標
由SwissAlgo提供
3
Finite Difference - Backward (mcbw_)In calculus there exists a 'derivative', which simply just measures the difference between two points on a curve. For well behaved mathematical functions there are infinitely many points and so there exists a derivative at every point. Where there are infinitely many points in a curve that curve is called 'continuous'. Continuous curves are very nice to deal with since each point on it exists almost exactly where its neighbors are. However, if the curve does not have infinitely many points on it, but instead has a finite number of points on it, that curve is called 'discrete' instead of continuous. Taking the derivative of discrete curves is much trickier business since there are none of the mathematical conveniences that a continuous offers. In the real world everything we measure is a discrete curve, including Price (since we measure it a finite number of times, aka each candlestick)! The branch of Discrete Mathematics has found an approach to measure the derivative along a discrete curve, that approach is aptly called " Finite Difference ". To get a more accurate approximation of a discrete derivative, the finite difference approach uses weighted combinations of neighboring points. The most common type of finite difference is a 'central' difference, this uses a combination of points before and after the point of interest to approximate the discrete derivative. This is great for historical analysis but is not of much use for trading algorithms since it technically means using future prices to calculate the derivative of the current point. Instead we can use a less common variant called a ' Backwards Difference ' that only uses a combination of points before the current one to help approximate the current derivative. In this script you can choose the " Order " of your derivative and the " Accuracy " of its approximation. This script is for educational purposes for folks building trading algorithms. Many trading algorithms often have an element of seeing how much Price has changed from the previous candle to the current candle. This approach is the lowest accuracy derivative possible, and using the backwards finite differences, made available for the first time on TradingView (!!), algorithms that use derivatives can now have higher orders of accuracy! Happy Trading/Developing!
Pine Script®指標
由mcbw_提供
Trading IQ - Razor IQIntroducing TradingIQ's first dip buying/shorting all-in-one trading system: Razor IQ. Razor IQ is an exclusive trading algorithm developed by TradingIQ, designed to trade upside/downside price dips of varying significance in trending markets. By integrating artificial intelligence and IQ Technology, Razor IQ analyzes historical and real-time price data to construct a dynamic trading system adaptable to various asset and timeframe combinations. Philosophy of Razor IQ Razor IQ operates on a single premise: Trends must retrace, and these retracements offer traders an opportunity to join in the overarching trend. At some point traders will enter against a trend in aggregate and traders in profitable positions entered during the trend will scale out. When occurring simultaneously, a trend will retrace against itself, offering an opportunity for traders not yet in the trend to join in the move and continue the trend. Razor IQ is designed to work straight out of the box. In fact, its simplicity requires just a few user settings to manage output, making it incredibly straightforward to manage. Long Limit Order Stop Loss and Minimum ATR TP/SL are the only settings that manage the performance of Razor IQ! Traders don’t have to spend hours adjusting settings and trying to find what works best - Razor IQ handles this on its own. Key Features of Razor IQ Self-Learning Retracement Detection Employs AI and IQ Technology to identify notable price dips in real-time. AI-Generated Trading Signals Provides retracement trading signals derived from self-learning algorithms. Comprehensive Trading System Offers clear entry and exit labels. Performance Tracking Records and presents trading performance data, easily accessible for user analysis. Self-Learning Trading Exits Razor IQ learns where to exit positions. Long and Short Trading Capabilities Supports both long and short positions to trade various market conditions. How It Works Razor IQ operates on a straightforward heuristic: go long during the retracement of significant upside price moves and go short during the retracement of significant downside price moves. IQ Technology, TradingIQ's proprietary AI algorithm, defines what constitutes a “trend” and a “retracement” and what’s considered a tradable dip buying/shorting opportunity. For Razor IQ, this algorithm evaluates all historical trends and retracements, how much trends generally retrace and how long trends generally persist. For instance, the "dip" following an uptrend is measured and learned from, including the significance of the identified trend level (how long it has been active, how much price has increased, etc). By analyzing these patterns, Razor IQ adapts to identify and trade similar future retracements and trends. In simple terms, Razor IQ clusters previous trend and retracement data in an attempt to trade similar price sequences when they repeat in the future. Using this knowledge, it determines the optimal, current price level where joining in the current trend (during a retracement) has a calculated chance of not stopping out before trend continuation. For long positions, Razor IQ enters using a market order at the AI-identified long entry price point. If price closes beneath this level a market order will be placed and a long position entered. Of course, this is how the algorithm trades, users can elect to use a stop-limit order amongst other order types for position entry. After the position is entered TP1 is placed (identifiable on the price chart). TP1 has a twofold purpose: Acts as a legitimate profit target to exit 50% of the position. Once TP1 is achieved, a stop-loss order is immediately placed at breakeven, and a trailing stop loss controls the remainder of the trade. With this, so long as TP1 is achieved, the position will not endure a loss. So long as price continues to uptrend, Razor IQ will remain in the position. For short positions, Razor IQ provides an AI-identified short entry level. If price closes above this level a market order will be placed and a short position entered. Again, this is how the algorithm trades, users can elect to use a stop-limit order amongst other order types for position entry. Upon entry Razor IQ implements a TP order and SL order (identifiable on the price chart). Downtrends, in most markets, usually operate differently than uptrends. With uptrends, price usually increases at a modest pace with consistency over an extended period of time. Downtrends behave in an opposite manner - price decreases rapidly for a much shorter duration. With this observation, the long dip entry heuristic differs slightly from the short dip entry heuristic. The long dip entry heuristic specializes in identifying larger, long-term uptrends and entering on retracement of the uptrends. With a dedicated trailing stop loss, so long as the uptrend persists, Razor IQ will remain in the position. The short dip entry heuristic specializes in identifying sharp, significant downside price moves, and entering short on upside volatility during these moves. A fixed stop loss and profit target are implemented for short positions - no trailing stop is used. As a trading system, Razor IQ exits all TP orders using a limit order, with all stop losses exited as stop market orders. What Classifies As a Tradable Dip? For Razor IQ, tradable price dips are not manually set but are instead learned by the system. What qualifies as an exploitable price dip in one market might not hold the same significance in another. Razor IQ continuously analyzes historical and current trends (if one exists), how far price has moved during the trend, the duration of the trend, the raw-dollar price move of price dips during trends, and more, to determine which future price retracements offer a smart chance to join in any current price trend. The image above illustrates the Razor Line Long Entry point. The green line represents the Long Retracement Entry Point. The blue upper line represents the first profit target for the trade. The blue lower line represents the trailing stop loss start point for the long position. The position is entered once price closes below the green line. The green Razor Lazor long entry point will only appear during uptrends. The image above shows a long position being entered after the Long Razor Lazor was closed beneath. Green arrows indicate that the strategy entered a long position at the highlighted price level. Blue arrows indicate that the strategy exited a position, whether at TP1, the initial stop loss, or at the trailing stop. Blue lines above the entry price indicate the TP1 level for the current long trade. Blue lines below the current price indicate the initial stop loss price. If price reaches TP1, a stop loss will be immediately placed at breakeven, and the in-built trailing stop will determine the future exit price. A blue line (similar to the blue line shown for TP1) will trail price and correspond to the trailing stop price of the trade. If the trailing stop is above the breakeven stop loss, then the trailing stop will be hit before the breakeven stop loss, which means the remainder of the trade will be exited at a profit. If the breakeven stop loss is above the trailing stop, then the breakeven stop loss will be hit first. In this case, the remainder of the position will be exited at breakeven. The image above shows the trailing stop price, represented by a blue line, and the breakeven stop loss price, represented by a pink line, used for the long position! You can also hover over the trade labels to get more information about the trade—such as the entry price and exit price. The image above exemplifies Razor IQ's output when a downtrend is active. When a downtrend is active, Razor IQ will switch to "short mode". In short mode, Razor IQ will display a neon red line. This neon red line indicates the Razor Lazor short entry point. When price closes above the red Razor Lazor line a short position is entered. The image above shows Razor IQ during an active short position. The image above shows Razor IQ after completing a short trade. Red arrows indicate that the strategy entered a short position at the highlighted price level. Blue arrows indicate that the strategy exited a position, whether at the profit target or the fixed stop loss. Blue lines indicate the profit target level for the current trade when below price. and blue lines above the current price indicate the stop loss level for the short trade. Short traders do not utilize a trailing stop - only a fixed profit target and fixed stop loss are used. You can also hover over the trade labels to get more information about the trade—such as the entry price and exit price. Minimum Profit Target And Stop Loss The Minimum ATR Profit Target and Minimum ATR Stop Loss setting control the minimum allowed profit target and stop loss distance. On most timeframes users won’t have to alter these settings; however, on very-low timeframes such as the 1-minute chart, users can increase these values so gross profits exceed commission. After changing either setting, Razor IQ will retrain on historical data - accounting for the newly defined minimum profit target or stop loss. AI Direction The AI Direction setting controls the trade direction Razor IQ is allowed to take. “Trade Longs” allows for long trades. “Trade Shorts” allows for short trades. Verifying Razor IQ’s Effectiveness Razor IQ automatically tracks its performance and displays the profit factor for the long strategy and the short strategy it uses. This information can be found in the table located in the top-right corner of your chart showing. This table shows the long strategy profit factor and the short strategy profit factor. The image above shows the long strategy profit factor and the short strategy profit factor for Razor IQ. A profit factor greater than 1 indicates a strategy profitably traded historical price data. A profit factor less than 1 indicates a strategy unprofitably traded historical price data. A profit factor equal to 1 indicates a strategy did not lose or gain money when trading historical price data. Using Razor IQ While Razor IQ is a full-fledged trading system with entries and exits - manual traders can certainly make use of its on chart indications and visualizations. The hallmark feature of Razor IQ is its ability to signal an acceptable dip entry opportunity - for both uptrends and downtrends. Long entries are often signaled near the bottom of a retracement for an uptrend; short entries are often signaled near the top of a retracement for a downtrend. Razor IQ will always operate on exact price levels; however, users can certainly take advantage of Razor IQ's trend identification mechanism and retracement identification mechanism to use as confluence with their personally crafted trading strategy. Of course, every trend will reverse at some point, and a good dip buying/shorting strategy will often trade the reversal in expectation of the prior trend continuing (retracement). It's important not to aggressively filter retracement entries in hopes of avoiding an entry when a trend reversal finally occurs, as this will ultimately filter out good dip buying/shorting opportunities. This is a reality of any dip trading strategy - not just Razor IQ. Of course, you can set alerts for all Razor IQ entry and exit signals, effectively following along its systematic conquest of price movement.
Pine Script®指標
由Trading-IQ提供
3
AiTrend Pattern Matrix for kNN Forecasting (AiBitcoinTrend)The AiTrend Pattern Matrix for kNN Forecasting (AiBitcoinTrend) is a cutting-edge indicator that combines advanced mathematical modeling, AI-driven analytics, and segment-based pattern recognition to forecast price movements with precision. This tool is designed to provide traders with deep insights into market dynamics by leveraging multivariate pattern detection and sophisticated predictive algorithms. 👽 Core Features Segment-Based Pattern Recognition At its heart, the indicator divides price data into discrete segments, capturing key elements like candle bodies, high-low ranges, and wicks. These segments are normalized using ATR-based volatility adjustments to ensure robustness across varying market conditions. AI-Powered k-Nearest Neighbors (kNN) Prediction The predictive engine uses the kNN algorithm to identify the closest historical patterns in a multivariate dictionary. By calculating the distance between current and historical segments, the algorithm determines the most likely outcomes, weighting predictions based on either proximity (distance) or averages. Dynamic Dictionary of Historical Patterns The indicator maintains a rolling dictionary of historical patterns, storing multivariate data for: Candle body ranges, High-low ranges, Wick highs and lows. This dynamic approach ensures the model adapts continuously to evolving market conditions. Volatility-Normalized Forecasting Using ATR bands, the indicator normalizes patterns, reducing noise and enhancing the reliability of predictions in high-volatility environments. AI-Driven Trend Detection The indicator not only predicts price levels but also identifies market regimes by comparing current conditions to historically significant highs, lows, and midpoints. This allows for clear visualizations of trend shifts and momentum changes. 👽 Deep Dive into the Core Mathematics 👾 Segment-Based Multivariate Pattern Analysis The indicator analyzes price data by dividing each bar into distinct segments, isolating key components such as: Body Ranges: Differences between the open and close prices. High-Low Ranges: Capturing the full volatility of a bar. Wick Extremes: Quantifying deviations beyond the body, both above and below. Each segment contributes uniquely to the predictive model, ensuring a rich, multidimensional understanding of price action. These segments are stored in a rolling dictionary of patterns, enabling the indicator to reference historical behavior dynamically. 👾 Volatility Normalization Using ATR To ensure robustness across varying market conditions, the indicator normalizes patterns using Average True Range (ATR). This process scales each component to account for the prevailing market volatility, allowing the algorithm to compare patterns on a level playing field regardless of differing price scales or fluctuations. 👾 k-Nearest Neighbors (kNN) Algorithm The AI core employs the kNN algorithm, a machine-learning technique that evaluates the similarity between the current pattern and a library of historical patterns. Euclidean Distance Calculation: The indicator computes the multivariate distance across four distinct dimensions: body range, high-low range, wick low, and wick high. This ensures a comprehensive and precise comparison between patterns. Weighting Schemes: The contribution of each pattern to the forecast is either weighted by its proximity (distance) or averaged, based on user settings. 👾 Prediction Horizon and Refinement The indicator forecasts future price movements (Y_hat) by predicting logarithmic changes in the price and projecting them forward using exponential scaling. This forecast is smoothed using a user-defined EMA filter to reduce noise and enhance actionable clarity. 👽 AI-Driven Pattern Recognition Dynamic Dictionary of Patterns: The indicator maintains a rolling dictionary of N multivariate patterns, continuously updated to reflect the latest market data. This ensures it adapts seamlessly to changing market conditions. Nearest Neighbor Matching: At each bar, the algorithm identifies the most similar historical pattern. The prediction is based on the aggregated outcomes of the closest neighbors, providing confidence levels and directional bias. Multivariate Synthesis: By combining multiple dimensions of price action into a unified prediction, the indicator achieves a level of depth and accuracy unattainable by single-variable models. Visual Outputs Forecast Line (Y_hat_line): A smoothed projection of the expected price trend, based on the weighted contribution of similar historical patterns. Trend Regime Bands: Dynamic high, low, and midlines highlight the current market regime, providing actionable insights into momentum and range. Historical Pattern Matching: The nearest historical pattern is displayed, allowing traders to visualize similarities 👽 Applications Trend Identification: Detect and follow emerging trends early using dynamic trend regime analysis. Reversal Signals: Anticipate market reversals with high-confidence predictions based on historically similar scenarios. Range and Momentum Trading: Leverage multivariate analysis to understand price ranges and momentum, making it suitable for both breakout and mean-reversion strategies. Disclaimer: This information is for entertainment purposes only and does not constitute financial advice. Please consult with a qualified financial advisor before making any investment decisions.
Pine Script®指標
由aibitcointrend提供
5
Bayesian Price Projection Model [Pinescriptlabs]📊 Dynamic Price Projection Algorithm 📈 This algorithm combines **statistical calculations**, **technical analysis**, and **Bayesian theory** to forecast a future price while providing **uncertainty ranges** that represent upper and lower bounds. The calculations are designed to adjust projections by considering market **trends**, **volatility**, and the historical probabilities of reaching new highs or lows. Here’s how it works: 🚀 Future Price Projection A dynamic calculation estimates the future price based on three key elements: 1. **Trend**: Defines whether the market is predisposed to move up or down. 2. **Volatility**: Quantifies the magnitude of the expected change based on historical fluctuations. 3. **Time Factor**: Uses the logarithm of the projected period (`proyeccion_dias`) to adjust how time impacts the estimate. 🧠 **Bayesian Probabilistic Adjustment** - Conditional probabilities are calculated using **Bayes' formula**: \ This models future events using conditional information: - **Probability of reaching a new all-time high** if the price is trending upward. - **Probability of reaching a new all-time low** if the price is trending downward. - These probabilities refine the future price estimate by considering: - **Higher volatility** increases the likelihood of hitting extreme levels (highs/lows). - **Market trends** influence the expected price movement direction. 🌟 **Volatility Calculation** - Volatility is measured using the **ATR (Average True Range)** indicator with a 14-period window. This reflects the average amplitude of price fluctuations. - To express volatility as a percentage, the ATR is normalized by dividing it by the closing price and multiplying it by 200. - Volatility is then categorized into descriptive levels (e.g., **Very Low**, **Low**, **Moderate**, etc.) for better interpretation. --- 🎯 **Deviation Limits (Upper and Lower)** - The upper and lower limits form a **projected range** around the estimated future price, providing a framework for uncertainty. - These limits are calculated by adjusting the ATR using: - A user-defined **multiplier** (`factor_desviacion`). - **Bayesian probabilities** calculated earlier. - The **square root of the projected period** (`proyeccion_dias`), incorporating the principle that uncertainty grows over time. 🔍 **Interpreting the Model** This can be seen as a **dynamic probabilistic model** that: - Combines **technical analysis** (trends and ATR). - Refines probabilities using **Bayesian theory**. - Provides a **visual projection range** to help you understand potential future price movements and associated uncertainties. ⚡ Whether you're analyzing **volatile markets** or confirming **bullish/bearish scenarios**, this tool equips you with a robust, data-driven approach! 🚀 Español : 📊 Algoritmo de Proyección de Precio Dinámico 📈 Este algoritmo combina **cálculos estadísticos**, **análisis técnico** y **la teoría de Bayes** para proyectar un precio futuro, junto con rangos de **incertidumbre** que representan los límites superior e inferior. Los cálculos están diseñados para ajustar las proyecciones considerando la **tendencia del mercado**, **volatilidad** y las probabilidades históricas de alcanzar nuevos máximos o mínimos. Aquí se explica su funcionamiento: 🚀 **Proyección de Precio Futuro** Se realiza un cálculo dinámico del precio futuro estimado basado en tres elementos clave: 1. **Tendencia**: Define si el mercado tiene predisposición a subir o bajar. 2. **Volatilidad**: Determina la magnitud del cambio esperado en función de las fluctuaciones históricas. 3. **Factor de Tiempo**: Usa el logaritmo del período proyectado (`proyeccion_dias`) para ajustar cómo el tiempo afecta la estimación. 🧠 **Ajuste Probabilístico con la Teoría de Bayes** - Se calculan probabilidades condicionales mediante la fórmula de **Bayes**: \ Esto permite modelar eventos futuros considerando información condicional: - **Probabilidad de alcanzar un nuevo máximo histórico** si el precio sube. - **Probabilidad de alcanzar un nuevo mínimo histórico** si el precio baja. - Estas probabilidades ajustan la estimación del precio futuro considerando: - **Mayor volatilidad** aumenta la probabilidad de alcanzar niveles extremos (máximos/mínimos). - **La tendencia del mercado** afecta la dirección esperada del movimiento del precio. 🌟 **Cálculo de Volatilidad** - La volatilidad se mide usando el indicador **ATR (Average True Range)** con un período de 14 velas. Este indicador refleja la amplitud promedio de las fluctuaciones del precio. - Para obtener un valor porcentual, el ATR se normaliza dividiéndolo por el precio de cierre y multiplicándolo por 200. - Además, se clasifica esta volatilidad en categorías descriptivas (e.g., **Muy Baja**, **Baja**, **Moderada**, etc.) para facilitar su interpretación. 🎯 **Límites de Desviación (Superior e Inferior)** - Los límites superior e inferior representan un **rango proyectado** en torno al precio futuro estimado, proporcionando un marco para la incertidumbre. - Estos límites se calculan ajustando el ATR según: - Un **multiplicador** definido por el usuario (`factor_desviacion`). - Las **probabilidades condicionales** calculadas previamente. - La **raíz cuadrada del período proyectado** (`proyeccion_dias`), lo que incorpora el principio de que la incertidumbre aumenta con el tiempo. --- 🔍 **Interpretación del Modelo** Este modelo se puede interpretar como un **modelo probabilístico dinámico** que: - Integra **análisis técnico** (tendencias y ATR). - Ajusta probabilidades utilizando **la teoría de Bayes**. - Proporciona un **rango de proyección visual** para ayudarte a entender los posibles movimientos futuros del precio y su incertidumbre. ⚡ Ya sea que estés analizando **mercados volátiles** o confirmando **escenarios alcistas/bajistas**, ¡esta herramienta te ofrece un enfoque robusto y basado en datos! 🚀
Pine Script®指標
由PINE_LABS提供
3
TradingIQ - Counter Strike IQIntroducing "Counter Strike IQ" by TradingIQ Counter Strike IQ is an exclusive trading algorithm developed by TradingIQ, designed to trade upside/downside breakouts of varying significance. By integrating artificial intelligence and IQ Technology, Counter Strike IQ analyzes historical and real-time price data to construct a dynamic trading system adaptable to various asset and timeframe combinations. Philosophy of Counter Strike IQ Counter Strike IQ operates on a single premise: Support and resistance levels cannot hold forever. At some point either side must break for the underlying asset to exhibit trends; otherwise, prices would be confined to an infinitely narrowing range. Counter Strike IQ is designed to work straight out of the box. In fact, its simplicity requires just four user settings to manage output, making it incredibly straightforward to manage. Minimum ATR Profit, Minimum ATR Stop, EMA Filter and EMA Filter Length are the only settings that manage the performance of Counter Strike IQ! Traders don’t have to spend hours adjusting settings and trying to find what works best - Counter Strike IQ handles this on its own. Key Features of Counter Strike IQ Self-Learning Breakout Detection Employs AI and IQ Technology to identify notable breakouts in real-time. AI-Generated Trading Signals Provides breakout trading signals derived from self-learning algorithms. Comprehensive Trading System Offers clear entry and exit labels. Performance Tracking Records and presents trading performance data, easily accessible for user analysis. Self-Learning Trading Exits Counter Strike IQ learns where to exit positions. Long and Short Trading Capabilities Supports both long and short positions to trade various market conditions. Strike Channel The Strike Channel represents what Counter Strike IQ considers a tradable long opportunity or a tradable short opportunity. The Strike Channel is dynamic and adjusts from chart to chart. IQ Graph Gradient Introduces the IQ Graph Gradient, designed to classify extreme values in price on a grand scale. How It Works Counter Strike IQ operates on a straightforward heuristic: go long during significant upside price moves that break established resistance levels and go short during significant downside price moves that break established support levels. IQ Technology, TradingIQ's proprietary AI algorithm, defines what constitutes a “significant price move” and what’s considered a tradable breakout. For Counter Strike IQ, this algorithm evaluates all historical support/resistance breaks and any subsequent breakouts. For instance, the price move following up to a breakout is measured and learned from, including the significance of the identified support/resistance level (how long it’s been active, how far price moved away from it, etc). By analyzing these patterns, Counter Strike IQ adapts to identify and trade similar future breakout sequences. In simple terms, Counter Strike IQ learns from violations of historical support/resistance levels to identify potential entry points at currently established support/resistance levels. Using this knowledge, it determines the optimal, current support/resistance price level where a breakout has a higher chance of occurring. For long positions, Counter Strike IQ places a stop-market order at the AI-identified resistance point. If price violates this level a market order will be placed and a long position entered. Of course, this is how the algorithm trades, users can elect to use a stop-limit order amongst other order types for position entry. After the position is entered TP1 is placed (identifiable on the price chart). TP1 has a twofold purpose: Acts as a legitimate profit target to exit 50% of the position. Once TP1 is closed over, the initial stop loss is converted to a trailing stop, and the long position remains active so long as price continues to uptrend. For short positions, Counter Strike IQ places a stop-market order at the AI-identified support point. If price violates this level a market order will be placed and a short position entered. Again, this is how the algorithm trades, users can elect to use a stop-limit order amongst other order types for position entry. Upon entry TP1 is placed (identifiable on the price chart). TP1 has a twofold purpose: Acts as a legitimate profit target to exit 50% of the position. Once TP1 is closed over, the initial stop loss is converted to a trailing stop, and the short position remains active so long as price continues to downtrend. As a trading system, Counter Strike IQ exits TP1 using a limit order, with all stop losses exited as stop market orders. What Classifies As a Tradable Upside Breakout or Tradable Downside Breakout? For Counter Strike IQ, tradable price breakouts are not manually set but are instead learned by the system. What qualifies as a significant upside or downside breakout in one market might not hold the same significance in another. Counter Strike IQ continuously analyzes historical and current support/resistance levels, how far price has extended from those levels, the raw-dollar price move leading up to a violation of those levels, their longevity, and more, to determine which future levels have a higher chance of breaking out when retested! The image above illustrates the Strike Channel and explains the corresponding prices and levels The green upper line represents the Long Breakout Point. The pink lower line represents the Short Breakout Point. Any price between the two deviation points is considered “Acceptable”. The image above shows a long position being entered after the Upside Breakout Point was reached. Green arrows indicate that the strategy entered a long position at the highlighted price level. Blue arrows indicate that the strategy exited a position, whether at TP1, the initial stop loss, or at the trailing stop. Blue lines indicate the TP1 level for the current trade. Red lines indicate the initial stop loss price. If price closes above TP1, the initial stop loss will be replaced with a trailing stop. A blue line (similar to the blue line shown for TP1) will trail price and correspond to the trailing stop price of the trade. The image above shows the trailing stop price, represented by a blue line, used for the long position! You can also hover over the trade labels to get more information about the trade—such as the entry price and exit price. The image above shows a short position being entered after the Downside Breakout Point was reached. Red arrows indicate that the strategy entered a short position at the highlighted price level. Blue arrows indicate that the strategy exited a position, whether at TP1, the initial stop loss, or at the trailing stop. Blue lines indicate the TP1 level for the current trade. Red lines indicate the initial stop loss price. If price closes below TP1, the initial stop loss will be replaced with a trailing stop. A blue line (similar to the blue line shown for TP1) will trail price and correspond to the trailing stop price of the trade. The image above shows the trailing stop price, represented by a blue line, used for the short position! You can also hover over the trade labels to get more information about the trade—such as the entry price and exit price. IQ Gradient Graph The IQ Gradient Graph provides a macro characterization of extreme prices. The lower macro extremity of the IQ Gradient Graph is colored green, while the upper macro extremity is colored red. Minimum Profit Target And Stop Loss The Minimum ATR Profit Target and Minimum ATR Stop Loss setting control the minimum allowed profit target and stop loss distance. On most timeframes users won’t have to alter these settings; however, on very-low timeframes such as the 1-minute chart, users can increase these values so gross profits exceed commission. After changing either setting, Counter Strike IQ will retrain on historical data - accounting for the newly defined minimum profit target or stop loss. AI Direction The AI Direction setting controls the trade direction Counter Strike IQ is allowed to take. “Trade Longs” allows for long trades. “Trade Shorts” allows for short trades. EMA Filter The EMA Filter setting controls whether the AI should implement an EMA trading filter. Simply, if the EMA Filter is active, long trades can only initiate if price is trading above the user-defined EMA. Conversely, short trades can only initiate if price is trading below the user-defined EMA. The image above shows the EMA Filter in action! Verifying Counter Strike IQ’s Effectiveness Counter Strike IQ automatically tracks its performance and displays the profit factor for the long strategy and the short strategy it uses. This information can be found in the table located in the top-right corner of your chart showing. This table shows the long strategy profit factor and the short strategy profit factor. The image above shows the long strategy profit factor and the short strategy profit factor for Counter Strike IQ. A profit factor greater than 1 indicates a strategy profitably traded historical price data. A profit factor less than 1 indicates a strategy unprofitably traded historical price data. A profit factor equal to 1 indicates a strategy did not lose or gain money when trading historical price data. Using Counter Strike IQ While Counter Strike IQ is a full-fledged trading system with entries and exits - manual traders can certainly make use of its on chart indications and visualizations. The hallmark feature of Counter Strike IQ is its ability to signal a breakout near its origin point. Long entries are often signaled near the start of a large upside price move; short entries are often signaled near the start of a large downside price move. For live analysis, the Strike Channel serves as a valuable tool for identifying breakout points. The further price moves toward the Upside Breakout Point (green), the stronger the indication that price might breakout to the upside. Conversely, the deeper price reaches toward the Downside Breakout Point (red), the stronger the indication that price might breakout to the downside. Of course, should buying or selling pressure stall, price may fail to breakout at the identified breakout level. This is a natural consequence of any breakout trading strategy! With this information at hand, traders can quickly switch between charts and timeframes to identify optimized areas of interest.
Pine Script®指標
由Trading-IQ提供
5
Market Trades PinescriptlabsThis algorithm is designed to emulate the true order book of exchanges by showing the quantity of transactions of an asset in real-time, while identifying patterns of high activity and volatility in the market through the analysis of volume and price movements. 📈 Below, I explain how to understand and use the information provided by the chart, along with the trades table: Identification of High Activity Zones 🚀 The algorithm calculates the average volume and the rate of price change to detect areas with spikes in activity. This is visualized on the chart with labels "Volatility Spike Buy" and "Volatility Spike Sell": Volatility Spike Buy: Indicates an unusual increase in volatility in the buying market, suggesting a potential surge in buying interest. 🟢 Volatility Spike Sell: Signals an increase in volatility in the selling market, which may indicate selling pressure or a sudden massive sell-off. 🔴 Market Trades Table 📋 The table provides a detailed view of the latest trades: Price: Displays the price at which each trade was executed. 💵 Quantity (Traded): Indicates the amount of the asset traded. 💰 Type of Trade (Buy/Sell): Differentiates between buy (Buy) and sell (Sell) operations based on volume and strength. 🔄 Date and Time: Refers to the start of the calculated trading candle. ⏰ Recency: Identifies the most recent trade to facilitate tracking of current activity. 🔍 Analysis of Trade Imbalance ⚖️ The imbalance between buys and sells is calculated based on the volume of both. This indicator helps to understand whether the market has a tendency toward buying or selling, showing if there is greater strength on one side of the market. A positive imbalance suggests more buying pressure. 📊 A negative imbalance indicates greater selling pressure. 📉 Volume Presentation Visualizes the volume of buying and selling in the market, allowing the identification of buying or selling strength through the size of the volume candle. 🔍 Español : "Este algoritmo está diseñado para emular el verdadero libro de órdenes de los intercambios al mostrar la cantidad de transacciones de un activo en tiempo real, mientras identifica patrones de alta actividad y volatilidad en el mercado a través del análisis de volumen y movimientos de precios. 📈 A continuación, explico cómo entender y usar la información proporcionada por el gráfico, junto con la tabla de operaciones:" Identificación de Zonas de Alta Actividad 🚀 El algoritmo calcula el volumen promedio y la velocidad de cambio de precio para detectar zonas con picos de actividad. Esto se visualiza en el gráfico con etiquetas de "Volatility Spike Buy" y "Volatility Spike Sell": Volatility Spike Buy: Indica un incremento inusual de volatilidad en el mercado de compra, sugiriendo un posible interés de compra elevado. 🟢 Volatility Spike Sell: Señala un incremento de volatilidad en el mercado de venta, lo cual puede indicar presión de venta o una venta masiva repentina. 🔴 Tabla de Operaciones en el Mercado (Market Trades) 📋 La tabla proporciona una vista detallada de las últimas operaciones: Precio: Muestra el precio al cual se realizó cada operación. 💵 Cantidad (Transaccionada): Indica la cantidad del activo transaccionada. 💰 Tipo de operación (Buy/Sell): Diferencia entre operaciones de compra (Buy) y de venta (Sell), dependiendo del volumen y fuerza. 🔄 Fecha y Hora: Refleja el inicio de la vela de negociación calculada. ⏰ Recency: Identifica la operación más reciente para facilitar el seguimiento de la actividad actual. 🔍 Análisis de Desequilibrio de Operaciones (Imbalance) ⚖️ El desequilibrio entre compras y ventas se calcula con base en el volumen de ambas. Este indicador ayuda a entender si el mercado tiene una tendencia hacia la compra o venta, mostrando si hay una mayor fuerza en uno de los lados del mercado. Un desequilibrio positivo sugiere más presión de compra. 📊 Un desequilibrio negativo indica mayor presión de venta. 📉 Presentación en Volumen Visualiza el volumen de compra y venta en el mercado, permitiendo identificar mediante el tamaño de la vela de volumen la fuerza, ya sea compradora o vendedora. 🔍
Pine Script®指標
由PINE_LABS提供
2
Seasonality normalizedThis custom indicator provides an in-depth analysis of historical price performance to identify potential seasonal patterns and correlations. By examining data from the past 10 years, the indicator filters out outlier performances and focuses on the most consistent seasonal trends. Key Features: Intelligent Clustering Algorithm: The indicator employs a custom clustering algorithm to group similar yearly performances together. This approach effectively filters out anomalous years, such as those affected by black swan events like the COVID-19 pandemic, providing a more accurate representation of typical seasonal behavior. Seasonal Correlation Measurement: The indicator calculates the percentage of years exhibiting similar performance patterns for each week. This measurement helps traders assess the strength of seasonal correlations and make informed decisions based on the consistency of historical data. High and Low Seasonality Bands: The indicator plots two distinct bands on the chart, representing the expected range of price movement based on historical highs and lows. These bands offer valuable insight into potential support and resistance levels during specific weeks. Enhanced Visualization: Weeks with high seasonal correlations are prominently highlighted, making it easy for traders to identify periods with the strongest historical patterns. The seasonality bands extend to cover the last and future 3 months, divided into weekly segments, providing a comprehensive view of the current market context. Dynamic Adaptation: The seasonality bands are dynamically tied to the current high and low prices, ensuring that the indicator remains relevant and responsive to the latest market conditions. Under the Hood: The indicator begins by calculating the performance of the asset for each week, going back 10 years. The custom clustering algorithm groups similar performances together, effectively filtering out outlier years. The percentage of years falling into the largest performance cluster is calculated, representing the seasonal correlation for each week. The average performance of the largest cluster is used to plot the high and low seasonality bands, anchored to the current high and low prices. The bands are color-coded based on the strength of the seasonal correlation, with darker colors indicating higher consistency. This indicator is designed to help professional traders identify and capitalize on seasonal patterns in the market. By providing a robust and adaptable framework for analyzing historical performance, the Seasonality Indicator offers valuable insights for making informed trading decisions. We believe this tool will be a valuable addition to your trading arsenal, complementing your existing strategies and enhancing your market analysis capabilities. As a professional trader, your feedback and ideas are invaluable to us. Please share your thoughts, experiences, and suggestions for improvement as you incorporate the Seasonality Indicator into your trading workflow. Together, we can refine this powerful tool to better serve the needs of the trading community.
Pine Script®指標
由dsvaryts提供
1