Auf geht's

Trendanalyse

Multi-Timeframe Rsi-Mean Deviation (Normalized)═══════════════════════════════════════════════════════════════════ RSI SIGMOID OSCILLATOR + MULTI-TIMEFRAME Advanced RSI-EMA Deviation Analysis with Z-Score Normalization ═══════════════════════════════════════════════════════════════════ 📊 OVERVIEW ────────── This indicator measures the deviation of RSI from its EMA and transforms it into a normalized 0-100 oscillator using z-score and sigmoid function. It provides multi-timeframe analysis with a clean visual dashboard, making it easy to spot momentum shifts across different time horizons. 🎯 KEY FEATURES ────────────── ✓ Z-Score Normalized RSI-EMA Deviation ✓ Sigmoid Transformation (0-100 scale with smooth transitions) ✓ Multi-Timeframe Support (compare up to 3 timeframes simultaneously) ✓ Interactive Dashboard (real-time values and trend indicators) ✓ Dynamic Color Coding (red below 50, unique colors above 50) ✓ Timeframe Labels (clear identification of each line) ✓ RSI Bollinger Bands (hidden background extreme detection) ✓ Clean Minimalist Design ⚙️ HOW IT WORKS ────────────── 1. DEVIATION CALCULATION - Calculates: RSI - EMA(RSI) - Measures how far RSI deviates from its moving average 2. Z-SCORE NORMALIZATION - Converts deviation to z-score: (deviation) / stdev(deviation) - Makes signals comparable across different market conditions 3. SIGMOID TRANSFORMATION - Maps z-score to 0-100: sigmoid = 100 / (1 + e^(-k*z)) - Provides smooth, bounded oscillator with clear midline (50) 4. MULTI-TIMEFRAME ANALYSIS - Displays current TF + 2 higher timeframes - All calculations use identical parameters for consistency 📈 INTERPRETATION ──────────────── OSCILLATOR VALUES: • Above 50 = Bullish momentum (RSI > its EMA) • Below 50 = Bearish momentum (RSI < its EMA) • Near 70 = Strong bullish (potential overbought) • Near 30 = Strong bearish (potential oversold) COLOR CODING: • Blue line = Current timeframe • Orange line = Higher timeframe 1 (default: 4H) • Lime line = Higher timeframe 2 (default: 1D) • Red = All timeframes when below 50 MULTI-TIMEFRAME SIGNALS: • All 3 lines above 50 = Strong bullish alignment • All 3 lines below 50 = Strong bearish alignment • Mixed signals = Potential reversal or consolidation 🔧 PARAMETERS ───────────── RSI Period (14): Base RSI calculation period RSI EMA Period (14): EMA smoothing for RSI Standard Deviation Period (20): Window for z-score calculation Sigmoid Sensitivity (1.0): Controls oscillator responsiveness (0.1-10.0) Bollinger Band Multiplier (2.0): For background extreme detection Higher Timeframe 1 (240): First comparison timeframe Higher Timeframe 2 (D): Second comparison timeframe 💡 USAGE TIPS ──────────── 1. TREND CONFIRMATION - Use higher timeframes to confirm trend direction - Only take longs when 4H/1D also above 50 2. DIVERGENCE DETECTION - Watch for price making new highs/lows while oscillator doesn't - Classic bullish/bearish divergence signals 3. OVERBOUGHT/OVERSOLD - Values above 70: Consider taking profits or tightening stops - Values below 30: Watch for reversal or continuation 4. TIMEFRAME ALIGNMENT - Best trades occur when all timeframes align - Mixed signals suggest waiting for clarity ⚠️ IMPORTANT NOTES ───────────────── • Not a standalone trading system - use with other confirmations • Works best in trending markets • Adjust sensitivity (k) for different instruments • Higher k values = more responsive (more signals) • Lower k values = smoother (fewer false signals) 📊 DASHBOARD ─────────── The top-right table shows: • TF: Timeframe identifier • Signal: Current oscillator value (0-100) • Trend: Green circle (≥50) or Red circle (<50) ═══════════════════════════════════════════════════════════════════ Created for multi-timeframe momentum analysis Best used on 1H, 4H, or Daily charts Combines statistical normalization with sigmoid smoothing ═══════════════════════════════════════════════════════════════════ ⚠️ DISCLAIMER ───────────── This indicator is provided for educational and informational purposes only. It is NOT financial advice. Trading involves substantial risk of loss and is not suitable for everyone. Past performance does not guarantee future results. Always: • Use proper risk management • Combine with other analysis methods • Test thoroughly before live trading • Never risk more than you can afford to lose • Consult a financial advisor for personalized advice 👍 SUPPORT THIS WORK ─────────────────── If you find this indicator useful: 📊 Please give it a LIKE / BOOST 💬 Leave a COMMENT with your feedback 👤 FOLLOW me for more quality indicators and updates ⭐ Share with others who might benefit Your support motivates me to create more free tools for the trading community! ═══════════════════════════════════════════════════════════════════
Pine Script® Indikator
von hasanaksoy199264
Smart Trader, Episode 04, by Ata Sabanci, Candles and Z ScoresSmart Trader, Episode 04 Candles and Z-Scores: A Statistical Approach to Market Analysis ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ OVERVIEW This indicator applies Z-Score statistical analysis to measure how unusual current market conditions are compared to historical norms. It simultaneously analyzes five key metrics: Price, Total Volume, Buy Volume, Sell Volume, and Delta (Buy minus Sell) . The system detects 60 academically-researched market scenarios and provides visual feedback through Z-Lines (support/resistance levels), Event Markers, Trend Channels, and a comprehensive Dashboard. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ CORE CONCEPT: WHY Z-SCORE? A Z-Score measures how many standard deviations a value is from its mean. In financial markets, extreme Z-Scores indicate statistically rare events that often precede significant price movements. Mathematical Formula: Z = (Current Value - Mean) / Standard Deviation Interpretation: • Z ≥ +2.0: Extremely high (occurs approximately 2.5% of the time) • Z ≥ +1.0: Above average • Z ≈ 0: Normal (near the mean) • Z ≤ -1.0: Below average • Z ≤ -2.0: Extremely low (occurs approximately 2.5% of the time) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ACADEMIC FOUNDATION This indicator is inspired by / grounded in market microstructure literature (abbreviated citations in-script) from market microstructure literature: • Price-Volume Relationship - Karpoff (1987), Journal of Financial and Quantitative Analysis, Cambridge Volume is positively correlated with price change magnitude • Order Flow Imbalance - Cont, Kukanov, Stoikov (2014), Journal of Financial Econometrics Order imbalance drives price more reliably than raw volume • Informed Trading (PIN Model) - Easley, Kiefer, O'Hara, Paperman (1996), Journal of Finance Buy/Sell imbalance reveals informed trader activity • Mixture of Distributions - Tauchen & Pitts (1983), Clark (1973) Volume clusters with volatility regimes • Volume Predictability - Gervais, Kaniel, Mingelgrin (2001) Volume shocks predict future returns • Liquidity & Order Imbalance - Chordia, Roll, Subrahmanyam (2002) Order imbalance affects short-term returns • Volume-Return Dynamics - Llorente, Michaely, Saar, Wang (2002) Speculation vs. risk-sharing patterns • Reversal vs. Continuation - Campbell, Grossman, Wang (MIT) High volume predicts lower autocorrelation ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ VOLUME ENGINE The indicator offers two methods for decomposing total volume into Buy and Sell components: Method 1: Geometry (Approximation) Uses candle structure to estimate buying and selling pressure: Buy Volume = Total Volume × (Close - Low) / (High - Low) Sell Volume = Total Volume × (High - Close) / (High - Low) • Works on all instruments without additional data requirements • Fast calculation • Less precise than intrabar method Method 2: Intrabar (Precise) Uses Lower Timeframe (LTF) tick/second data to aggregate actual up-ticks versus down-ticks: • More accurate volume decomposition • Requires LTF data availability • Configurable LTF: 1T (tick), 1S, 15S, 1M Delta Calculation: Delta = Buy Volume - Sell Volume ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Z-SCORE SYSTEM The system calculates Z-Scores for five metrics simultaneously, using a configurable lookback period (default: 20 bars): • Zp (Price Z-Score): Measures price deviation from its mean • Zv (Volume Z-Score): Measures total volume deviation • Zbuy (Buy Volume Z-Score): Measures buying pressure deviation • Zsell (Sell Volume Z-Score): Measures selling pressure deviation • ZΔ (Delta Z-Score): Measures order flow imbalance deviation Threshold Constants: • ZH (Z High) = 2.0: Extreme threshold • ZM (Z Medium) = 1.0: Moderate threshold • Z0 (Z Zero) = 0.5: Near-zero threshold Group System: The analysis window is divided into groups (default: 5 groups × 20 bars = 100 bar total window). Group numbers (1, 2, 3...) are displayed above candles when enabled, helping identify the relative age of detected levels. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Z-LINES (SUPPORT/RESISTANCE LEVELS) When any metric reaches an extreme Z-Score, the system marks that price level as a significant support or resistance zone. Detection Logic: • Upper Z-Line: Drawn from the HIGH when Z ≥ upper threshold (default +2.0) • Lower Z-Line: Drawn from the LOW when Z ≤ lower threshold (default -2.0) Multi-Metric Detection: Z-Lines can be triggered by any of the five metrics (Price, Volume, Buy, Sell, Delta). When multiple metrics trigger at similar price levels, they are clustered together into a single combined label showing all contributing metrics. Persistence: Z-Lines persist for the entire analysis window (Period × Groups bars) and are NOT removed when price touches them. This allows traders to see historical support/resistance levels that may still be relevant. Anti-Overlap System: Labels are automatically repositioned to prevent overlap. The "Label Min Gap (%)" setting controls minimum vertical separation between ALL labels (both upper and lower), ensuring readability even when multiple levels cluster together. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ EVENT DETECTION ENGINE (60 SCENARIOS) The system analyzes 60 distinct market scenarios based on Z-Score combinations. Each scenario is derived from academic research and assigned a confidence score based on signal strength and alignment. Notation: • Zp = Price Z-Score • Zv = Total Volume Z-Score • Zbuy = Buy Volume Z-Score • Zsell = Sell Volume Z-Score • ZΔ = Delta Z-Score • dirP = Price direction (+1 if Zp > 0.5, -1 if Zp < -0.5, else 0) • = Previous bar value • ZH = 2.0 (High threshold) • ZM = 1.0 (Medium threshold) • Z0 = 0.5 (Zero threshold) ───────────────────────────────────────────────────────────── CATEGORY A: PRICE-VOLUME (Events 1-10) Based on: Karpoff (1987), Tauchen-Pitts (1983), Clark (1973) ───────────────────────────────────────────────────────────── Event 1: Breakout Confirmed |Zp| ≥ ZH AND Zv ≥ ZH AND sign(ZΔ) = dirP AND dirP ≠ 0 Direction: Bullish/Bearish (follows price direction) Event 2: Trend Strength Confirmed |Zp| ≥ ZH AND Zv ≥ ZH Direction: Follows price direction Event 3: Fragile Move |Zp| ≥ ZH AND Zv ≤ -ZM Direction: Warning (price move without volume support) Event 4: Weak Rally Zp ≥ ZH AND Zv ≤ -ZH Direction: Warning (price up without volume) Event 5: Weak Selloff Zp ≤ -ZH AND Zv ≤ -ZH Direction: Warning (price down without volume) Event 6: Momentum Build ZM ≤ |Zp| < ZH AND Zv ≥ ZH Direction: Follows price direction Event 7: Churn |Zp| ≤ Z0 AND Zv ≥ ZH Direction: Neutral (high volume, low price movement) Event 8: Quiet Compression |Zp| ≤ Z0 AND Zv ≤ -ZH Direction: Neutral (low volume, low price movement) Event 9: High Volume Regime Zv ≥ ZH Direction: Neutral Event 10: Low Volume Regime Zv ≤ -ZH Direction: Neutral ───────────────────────────────────────────────────────────── CATEGORY B: ORDER-FLOW / DELTA (Events 11-16) Based on: Cont, Kukanov, Stoikov (2014), Easley, Kiefer, O'Hara, Paperman (1996) ───────────────────────────────────────────────────────────── Event 11: Imbalance Drives Price |ZΔ| ≥ ZH AND sign(ZΔ) = dirP AND dirP ≠ 0 Direction: Follows price direction (dirP), with delta alignment required Event 12: Divergence Top Zp ≥ ZH AND ZΔ ≤ -ZH Direction: Warning (distribution at top) Event 13: Divergence Bottom Zp ≤ -ZH AND ZΔ ≥ ZH Direction: Warning (accumulation at bottom) Event 14: Absorption Positive |Zp| ≤ Z0 AND Zv ≥ ZH AND ZΔ ≥ ZH Direction: Bullish (buy absorption, support forming) Event 15: Absorption Negative |Zp| ≤ Z0 AND Zv ≥ ZH AND ZΔ ≤ -ZH Direction: Bearish (sell absorption, resistance forming) Event 16: Depth Wall Zv ≥ ZH AND |ZΔ| ≥ ZH AND |Zp| ≤ Z0 Direction: Neutral (market depth absorbing) ───────────────────────────────────────────────────────────── CATEGORY C: BUY VS SELL (Events 17-23) Based on: Easley, Kiefer, O'Hara, Paperman (1996), Chordia, Roll, Subrahmanyam (2002) ───────────────────────────────────────────────────────────── Event 17: Aggressive Buy Dominance Zbuy ≥ ZH AND ZΔ ≥ ZH AND Zsell ≤ -ZM Direction: Bullish Event 18: Aggressive Sell Dominance Zsell ≥ ZH AND ZΔ ≤ -ZH AND Zbuy ≤ -ZM Direction: Bearish Event 19: Two-Sided Battle Zbuy ≥ ZH AND Zsell ≥ ZH AND |ZΔ| ≤ Z0 Direction: Neutral (buyers and sellers equally strong) Event 20: Battle with Buy Edge Zbuy ≥ ZH AND Zsell ≥ ZH AND ZM ≤ ZΔ < ZH Direction: Bullish Event 21: Battle with Sell Edge Zbuy ≥ ZH AND Zsell ≥ ZH AND -ZH < ZΔ ≤ -ZM Direction: Bearish Event 22: Hidden Accumulation Zbuy ≥ ZH AND |Zp| ≤ Z0 AND Zv ≥ ZH Direction: Bullish (buy shock without price movement) Event 23: Hidden Distribution Zsell ≥ ZH AND |Zp| ≤ Z0 AND Zv ≥ ZH Direction: Bearish (sell shock without price movement) ───────────────────────────────────────────────────────────── CATEGORY D: PREDICTABILITY (Events 24-26) Based on: Gervais, Kaniel, Mingelgrin (2001), Karpoff (1987) ───────────────────────────────────────────────────────────── Event 24: Volume Shock Positive Drift Zv ≥ ZH AND |Zp| ≤ ZM Direction: Follows price direction Event 25: Volume Shock Negative Drift Zv ≤ -ZH AND |Zp| ≤ ZM Direction: Opposite to price direction Event 26: Abnormal Volume Info Arrival Zv ≥ ZH Direction: Neutral ───────────────────────────────────────────────────────────── CATEGORY E: REVERSAL VS CONTINUATION (Events 27-30) Based on: Campbell, Grossman, Wang (MIT), Llorente, Michaely, Saar, Wang (2002) ───────────────────────────────────────────────────────────── Event 27: High Vol Reversal Risk Zv ≥ ZH Direction: Warning (high volume implies lower positive autocorrelation) Event 28: Low Vol Continuation Risk Zv ≤ -ZH Direction: Follows price direction (trend likely continues) Event 29: Speculation Continuation Zv ≥ ZH AND |ZΔ| ≥ ZM AND sign(ZΔ) = dirP AND dirP ≠ 0 Direction: Follows price direction Event 30: Risk Sharing Reversal Zv ≥ ZH AND |ZΔ| ≤ Z0 Direction: Warning (potential reversal) ───────────────────────────────────────────────────────────── CATEGORY F: IMBALANCE LAG (Events 31-33) Based on: Chordia, Roll, Subrahmanyam (2002) ───────────────────────────────────────────────────────────── Event 31: Persistent Imbalance Push |ZΔ| ≥ ZM AND |ZΔ | ≥ ZM AND sign(ZΔ) = sign(ZΔ ) Direction: Follows delta direction (persistent pressure) Event 32: Imbalance Pressure Decay (ZΔ ≥ ZM AND ZΔ ≤ -ZM) OR (ZΔ ≤ -ZM AND ZΔ ≥ ZM) Direction: Warning (imbalance sign flip) Event 33: Intraday Imbalance Predicts |ZΔ| ≥ ZM Direction: Follows delta direction ───────────────────────────────────────────────────────────── CATEGORY G: SUPPORT/RESISTANCE (Events 34-36) Based on: Peskir (Manchester) ───────────────────────────────────────────────────────────── Event 34: SR Barrier Event |Zp| ≤ Z0 AND Zv ≥ ZH Direction: Neutral (price stalls with high volume) Event 35: Volume Backed SR Level |Zp| ≤ Z0 AND Zv ≥ ZH AND |ZΔ| ≥ ZM Direction: Follows delta direction Event 36: Volume Poor SR Level |Zp| ≤ Z0 AND Zv ≤ -ZM Direction: Warning (weak S/R without volume) ───────────────────────────────────────────────────────────── CATEGORY H: EXTENDED ANALYSIS (Events 37-50) Based on: Extended market microstructure analysis ───────────────────────────────────────────────────────────── Event 37: Climax Buy Zbuy ≥ ZH AND Zp ≥ ZH AND Zv ≥ ZH Direction: Warning (extreme buying exhaustion, potential top) Event 38: Climax Sell Zsell ≥ ZH AND Zp ≤ -ZH AND Zv ≥ ZH Direction: Warning (extreme selling exhaustion, potential bottom) Event 39: Stealth Accumulation Zbuy ≥ ZM AND |Zp| ≤ Z0 AND Zv ≤ Z0 Direction: Bullish (quiet buying) Event 40: Stealth Distribution Zsell ≥ ZM AND |Zp| ≤ Z0 AND Zv ≤ Z0 Direction: Bearish (quiet selling) Event 41: Volume Divergence Bull Zp ≤ -ZM AND Zv ≤ -ZM Direction: Bullish (price down but volume declining) Event 42: Volume Divergence Bear Zp ≥ ZM AND Zv ≤ -ZM Direction: Bearish (price up but volume declining) Event 43: Delta Price Alignment |Zp| ≥ ZM AND |ZΔ| ≥ ZM AND sign(Zp) = sign(ZΔ) Direction: Follows price direction (strong trend confirmation) Event 44: Extreme Compression |Zp| ≤ Z0 AND Zv ≤ -ZH Direction: Neutral (very low volatility) Event 45: Volatility Expansion |Zp| ≥ ZH AND Zv ≥ ZH Direction: Follows price direction (breakout from compression) Event 46: Buy Exhaustion Zbuy ≥ ZH AND Zp ≤ Z0 Direction: Warning (high buy but price fails) Event 47: Sell Exhaustion Zsell ≥ ZH AND Zp ≥ -Z0 Direction: Warning (high sell but price holds) Event 48: Trend Acceleration |Zp| ≥ ZM AND |Zp| > |Zp | AND Zv ≥ ZM Direction: Follows price direction (increasing momentum) Event 49: Trend Deceleration |Zp| ≥ ZM AND |Zp| < |Zp | AND sign(Zp) = sign(Zp ) Direction: Warning (decreasing momentum) Event 50: Multi Divergence (Zp ≥ ZM AND ZΔ ≤ -ZM) OR (Zp ≤ -ZM AND ZΔ ≥ ZM) + |Zp| ≥ ZM AND Zv ≤ -ZM Direction: Warning (multiple divergence signals) ───────────────────────────────────────────────────────────── CATEGORY I: TREND-INTEGRATED (Events 51-60) Based on: Combined price-volume-delta trend analysis ───────────────────────────────────────────────────────────── Event 51: Trend Breakout Confirmed |Zp| ≥ ZH AND Zv ≥ ZH AND |ZΔ| ≥ ZM AND sign(ZΔ) = dirP AND dirP ≠ 0 Direction: Follows price direction Event 52: Trend Support Test Zp ≥ ZM AND Z0 ≤ Zp < ZM AND ZΔ ≥ Z0 Direction: Bullish (pullback in uptrend) Event 53: Trend Resistance Test Zp ≤ -ZM AND -ZM < Zp ≤ -Z0 AND ZΔ ≤ -Z0 Direction: Bearish (rally in downtrend) Event 54: Trend Reversal Signal sign(Zp) ≠ sign(Zp ) AND |Zp| ≥ ZM AND |Zp | ≥ ZM Direction: Follows new price direction (momentum flip) Event 55: Channel Absorption |Zp| ≤ Z0 AND Zv ≥ ZH Direction: Neutral (range-bound with volume) Event 56: Trend Continuation Volume |Zp| ≥ ZM AND Zv ≥ ZM AND sign(ZΔ) = dirP AND dirP ≠ 0 Direction: Follows price direction (healthy trend with volume) Event 57: Trend Exhaustion |Zp| ≥ ZM AND Zv ≤ -ZM AND |Zp| < |Zp | Direction: Warning (trend losing steam) Event 58: Range Breakout Pending |Zp| ≤ Z0 AND Zv ≤ -ZH AND |ZΔ| ≥ ZM Direction: Follows delta direction (compression with imbalance) Event 59: Trend Quality High |Zp| ≥ ZM AND sign(ZΔ) = dirP AND Zv ≥ Z0 AND dirP ≠ 0 Direction: Follows price direction (strong aligned signals) Event 60: Trend Quality Low |Zp| ≥ ZM AND sign(ZΔ) ≠ dirP AND dirP ≠ 0 Direction: Warning (conflicting signals) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ TREND CHANNEL SYSTEM The trend channel system is adapted from Smart Trader Episode 03 to provide consistent visual context for price action analysis. How It Works: • Divides the chart into blocks based on Z-Score groups • Calculates OHLC (Open, High, Low, Close) for each block • Detects Higher Highs/Higher Lows (uptrend) or Lower Highs/Lower Lows (downtrend) patterns • Draws channel lines connecting block extremes • Classifies by angle: steep angles indicate trends, flat angles indicate ranges Channel Classifications: • UPTREND: Higher highs and higher lows detected • DOWNTREND: Lower highs and lower lows detected • RANGE: Channel angle below threshold (default 10 degrees) Label Information: • Trend direction (UPTREND/DOWNTREND/RANGE) • Channel boundary prices • Distance from current price (absolute and percentage) • Channel angle in degrees ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ DASHBOARD The dashboard provides a comprehensive real-time view of all Z-Score metrics and detected events. Dashboard Sections: 1. Header Row Displays indicator name and current calculation mode (CLOSED or LIVE). 2. Metric Rows (Price, Total Volume, Buy Volume, Sell Volume, Delta) Each row displays: • Value: Current metric value • Z: Calculated Z-Score • Visual: Graphical Z-bar showing position relative to mean • Status: Interpretation (Extreme High, Above Avg, Normal, Below Avg, Extreme Low) • Upper: Oldest active upper Z-Line in window (Label Mirror) • Lower: Oldest active lower Z-Line in window (Label Mirror) 3. Event Detection Section • Count of triggered events out of 60 total scenarios • Market Bias: Bull/Bear/Neutral percentage with visual bar • Strongest Event: Highest confidence event currently triggered • #2 Event: Second highest confidence event 4. Footer Shows engine type (Geometry/Intrabar), Z-Score period, calculation basis, and number of valid bars. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ALERT SYSTEM The indicator uses native alertcondition() functions, keeping the settings menu clean while providing comprehensive alert options in TradingView's alert dialog. Available Alert Categories: • Master Alerts: Any event, Any bullish, Any bearish, Any warning • Single Event Alerts: Individual alerts for key events (Breakout, Climax, Divergence, etc.) • Category Alerts: Alerts by event category (Price-Volume, Order-Flow, etc.) • Confluence Alerts: 2+, 3+, 4+, or 5+ aligned events • Bias Shift Alerts: 10%, 20%, or 30% shifts in market bias • High Confidence Alerts: Events with 60%+, 70%+, 80%+, or 90%+ confidence • Divergence Alerts: Price vs Volume or Price vs Delta divergences ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ DATA ACCURACY AND LIMITATIONS This indicator is 100% VOLUME-BASED and requires Lower Timeframe (LTF) intrabar data for accurate calculations when using the Intrabar method. Data Accuracy Levels: • 1T (Tick): Most accurate, real volume distribution per tick • 1S (1 Second): Reasonably accurate approximation • 15S (15 Seconds): Good approximation, longer historical data available • 1M (1 Minute): Rough approximation, maximum historical data range Backtest and Replay Limitations: • Replay mode results may differ from live trading due to data availability • For longer backtest periods, use higher LTF settings (15S or 1M) • Not all symbols/exchanges support tick-level data • Crypto and Forex typically have better LTF data availability than stocks A Note on Data Access: Higher TradingView plans provide access to more historical intrabar data, which directly impacts the accuracy of volume-based calculations. More precise volume data leads to more reliable calculations. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ LANGUAGE SUPPORT (TRI-LINGUAL UI) This indicator includes a built-in language switch with three interface languages : • English (EN) • Türkçe (TR) • 한국어 (KO) The selected language updates key interface text such as the Dashboard headers/rows , tooltips , and the Event Engine outputs (event names, category names, and direction labels). Turkish diacritics and Korean Hangul are supported for clean, native readability. Why only three languages? Each additional language requires duplicating strings throughout the code, which increases script size/memory usage and compilation time. To keep the indicator optimized and responsive, language options are intentionally limited to three. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⚠️ DISCLAIMER FOR EDUCATIONAL AND RESEARCH PURPOSES ONLY This indicator is designed as an educational and research tool based on academic market microstructure literature. It is NOT financial advice and should NOT be used as the sole basis for trading decisions. Important Notices: • Past performance does not guarantee future results • All trading involves risk of substantial loss • The indicator's signals are statistical probabilities, not certainties • Always conduct your own research and consult qualified financial advisors • The creator assumes no responsibility for trading losses Research Sources: This indicator is built upon peer-reviewed academic research from: • Journal of Financial and Quantitative Analysis (Cambridge University Press) • Journal of Finance • Journal of Financial Econometrics • MIT Working Papers • arXiv Financial Mathematics
Pine Script® Indikator
von ata_sabanci
1
Lanovyx# Lanovyx — Setup Window Confluence System ## The Problem This Solves Traditional confluence indicators require all conditions to align on the exact same bar: stochastic must be oversold AND price must touch support AND divergence must form — all simultaneously. In real markets, this rarely happens. Price touches VWAP -2σ, but stochastic doesn't reach oversold until 3 bars later. The opportunity is missed. **Lanovyx solves this with the Setup Window methodology.** --- ## Core Innovation: Setup Windows Instead of requiring simultaneous conditions, Lanovyx separates trading signals into two phases: **Phase 1 — Context Event (Setup Activation)** When a meaningful event occurs, it "opens a window" that stays active for a configurable number of bars: - Price touches VWAP ±2σ or ±3σ band → window opens - Price tests Previous Day High/Low → window opens - Stochastic divergence forms → window opens - Opening Range breakout occurs → window opens - Price reaches Support/Resistance level → window opens Each event adds to a cumulative "setup score" (capped at 8). Higher scores indicate stronger context. **Phase 2 — Trigger (Signal Generation)** Within the active window, when stochastic conditions confirm, a signal fires. The trigger doesn't need to occur on the same bar as the context — it just needs to occur while the window is open. This two-phase approach captures setups that traditional indicators miss entirely. --- ## Why Stochastic + VWAP Confluence Works **VWAP (Volume-Weighted Average Price)** tells us where institutional money has transacted. The standard deviation bands identify statistical extremes: - Price at VWAP -2σ is extended to the downside (potential mean reversion long) - Price at VWAP +2σ is extended to the upside (potential mean reversion short) **Stochastic Oscillator** measures momentum exhaustion. When price reaches a VWAP extreme AND stochastic shows momentum reversing, we have confluence of: 1. Price extension (VWAP bands) 2. Momentum exhaustion (Stochastic) 3. Context validation (Setup Window score) The multi-lane stochastic (14/21/55 periods) adds timeframe confluence — when fast, medium, and slow stochastics align, the signal is stronger. --- ## Five Signal Families Each family targets a specific market condition: ### 1. Trend Entry (T) — Blue Labels **When:** Stochastic pulls back to 25-55 zone (longs) or 45-75 zone (shorts) during established trend **Logic:** In trending markets, pullbacks to the "value zone" offer low-risk entries with trend **Best for:** Trending days with clear directional bias ### 2. Mean Reversion (R) — Green/Red Labels **When:** Stochastic exits oversold (<20) or overbought (>80) with active setup window **Logic:** At VWAP extremes with momentum exhaustion, price tends to revert to mean **Best for:** Range-bound, choppy markets **Requires:** Active setup window (context event must have occurred) ### 3. Breakout (B) — Orange Labels **When:** Stochastic lanes compress ("coil") then expand, crossing the 50 midline **Logic:** Compression precedes expansion; breakout from tight range signals new trend **Best for:** Transition days, post-squeeze moves ### 4. Momentum (M) — Green/Red Labels **When:** Stochastic crosses 50 from extreme zone (<25 or >75) within lookback period **Logic:** Catches V-shaped reversals where regime detection lags the move **Best for:** Fast reversals, news-driven moves ### 5. Counter-Signal / FADE (C) — Purple Labels **When:** A signal fires and immediately fails (stochastic reverses sharply against it) **Logic:** Failed signals often lead to strong moves in the opposite direction (trapped traders) **Confidence gating:** High-confidence fades generate signals; low-confidence show warnings only --- ## Institutional Key Levels Lanovyx incorporates levels that institutional traders use: - **PDH/PDL** (Previous Day High/Low) — Major support/resistance where stops cluster - **PDC** (Previous Day Close) — Settlement price, gap reference - **ORB** (Opening Range) — First 15 minutes high/low, breakout trigger - **IB** (Initial Balance) — First 60 minutes range, institutional benchmark These levels automatically activate setup windows when price interacts with them, adding to the setup score. --- ## Filtering System **ADX Filter:** In strong trends (ADX > 25), blocks counter-trend mean reversion signals to avoid fighting momentum. **HTF Bias Filter:** Optional alignment with higher timeframe (e.g., 1-hour) EMAs. Can block or demote signals that oppose the larger trend. **Regime Detection:** Classifies market as Uptrend, Downtrend, Sideways, or Squeeze using EMA alignment and market structure (HH/HL/LH/LL patterns). --- ## How to Use 1. **Wait for Setup** — Watch for context events (VWAP band touch, key level test, divergence) 2. **Check the Score** — Higher setup scores indicate stronger context (visible in debug mode) 3. **Wait for Trigger** — Let stochastic confirm within the window 4. **Confirm Regime** — Ensure signal type matches market condition 5. **Manage Risk** — Use the ATR-based stop/target levels shown after signals **Strong signals (★)** appear when multiple confluence factors align — these are highest probability setups. --- ## Settings Overview | Setting | Default | Purpose | |---------|---------|---------| | Setup Window | 10 bars | How long context events stay active | | Entry Zone | 25-55 | Stochastic zone for trend pullback entries | | OS/OB Levels | 20/80 | Stochastic extremes for mean reversion | | Stop Loss | 1.5 ATR | Risk management distance | | Target 1 | 2.0 ATR | First profit target (1.33:1 R:R) | Recommended timeframes: 5-minute and 15-minute charts. --- ## Disclaimer This indicator is for educational purposes only and does not constitute financial advice. Past performance does not guarantee future results. Always use proper risk management and never risk more than you can afford to lose. No indicator can predict the future — use this as one input in your trading decision process, not as a standalone system.
Pine Script® Indikator
von sfbayman
Dual HTF EMAMulti-timeframe Exponential Moving Average (EMA) indicator plots two separate higher timeframe (HTF) EMAs of your choice. Displays four EMAs per HTF while providing optional background coloring (bullish/bearish). The background coloring occurs when two EMA's cross per HTF. User can select two of the four EMAs to determine the trend direction as they cross creating the background color. User can configure timeframe, EMA lengths, EMA cross and background, source, and visibility; separately for each timeframe. Default lengths are 9, 21, 50, 200 with source as closed and EMA cross background from EMA 1 and EMA 3. Also clear visual distinction using thick solid lines for HTF 1 and thin dashed lines for HTF 2. Uses request.security() with gaps=barmerge.gaps_on to avoid staircase effects on lower timeframes. This script is ideal for multi-timeframe analysis, helping traders align shorter-term price movements with broader trends from higher timeframes without cluttering the chart.
Pine Script® Indikator
von Eightgent7
BTC - Power Law 1.5: Dynamic 50/50 Decay OVERVIEW Most Bitcoin models treat the asset as if it exists in a vacuum of infinite exponential growth. The classical Power Law (v1.0) was a groundbreaking start, but as Bitcoin matures into a multi-trillion dollar institutional asset, our models must account for the laws of physics and liquidity. The Power Law 1.5: Dynamic 50/50 Decay is a second-generation structural engine. It doesn't just draw a line; it calculates the structural "Center of Gravity" of Bitcoin’s adoption curve while accounting for the natural maturation (decay) of the network’s growth speed. THE MATHEMATICAL BACKBONE: QUANTILE MEDIAN CALCULATION The "Fair Value" line (blue) is derived using a Log-Log Linear Regression focused on the 50th percentile (Median). The script first transforms the price and the time (days since the Genesis Block) into a logarithmic scale. It then calculates a power-law constant by finding the Absolute Least Deviation across the entire historical dataset since 2011. Specifically, it uses the formula: Price = 10^(Intercept + Slope * log10(Days)) . To ensure the line is a true median, the script calculates the Median Offset of every historical price point from the raw regression line. By shifting the intercept by this median value, we guarantee that exactly 50% of all weekly bars fall above the curve and 50% fall below it, creating a robust, non-biased structural center. THE ALPHA SHADOW: DYNAMIC EXPONENT PROJECTION Unlike standard power-law projections that rely on a static slope, the "Alpha Shadow" (the projection extending from the blue backbone) utilizes a Time-Varying Exponent Model . The model acknowledges that Bitcoin's growth speed—the exponent 'b'—is a decaying function of time, reflecting the diminishing returns of a maturing asset. The script recalculated the Instantaneous Slope on every single bar using the formula: Future_Slope = Initial_Slope - (Decay_Rate * log10(Total_Days_from_Genesis)) . While the Decay Rate (default 0.045) serves as a structural sensitivity constant, its application ensures the growth speed is a dynamic variable rather than a fixed number. Each segment of the dashed green "Shadow" is a unique power-law arc calculated for its specific future time window. This ensures the projection isn't just a straight line drawn on a log chart, but a mathematically tethered curve that "feels" the weight of increasing market capitalization and respects the reality of global liquidity constraints as we approach 2029. HOW TO READ THE CHART • The Backbone (Solid Blue): This is the 50/50 Fair Value. When price is below this line, Bitcoin is structurally "cheap." When price is far above it, the asset is in a state of cyclical expansion. • The Alpha Shadow (Green): This is the mathematical projection of the current curve into 2029. It shows the path of "Fair Value" as the network continues to mature. • The Regime Audit (Dashboard): A real-time table in the middle-right of your chart provides an audit of the model's integrity, including the current slope (b) and the projected Fair Price for Jan 1, 2029. WHY THIS IS "FRESH" Most open-source Power Law scripts on TradingView utilize a Static Linear Regression —calculating a single constant slope that is applied equally to 2011 and 2029. Furthermore, common community models often rely on "Outer Band" fitting (connecting historical cycle peaks to cycle lows). While visually appealing, these methods can be highly sensitive to "Black Swan" outliers and often assume Bitcoin’s growth velocity is a permanent constant. This script stands out by introducing a Maturation Framework . Instead of fitting to volatile extremes, we anchor the logic to a 50/50 Quantile Median , creating a backbone that is mathematically centered regardless of cyclical noise. By then applying a Dynamic Decay Factor to the growth exponent, we move away from the "static bands" approach and toward a model that respects the physical reality of a maturing, multi-trillion-dollar asset class. This provides a structurally grounded, institutional-grade view of Bitcoin’s trajectory that accounts for the diminishing returns inherent in global adoption. DISCLAIMER This script is for educational and macro-analytical purposes only. It does not constitute financial advice. The 2029 projection is a mathematical extrapolation based on historical data and decay constants; it is not a guarantee of future price action. TAGS bitcoin, powerlaw, macro, regression, fairvalue, btc, projection, quantitative, math, structural, Rob Maths, robmaths, Rob_Maths
Pine Script® Indikator
von Rob_Maths
True FVGsThis script highlights 3-candle Fair Value Gaps (FVGs) on your chart, showing areas where price moved quickly and left potential gaps in market structure. Bullish FVGs are shown with green boxes and suggest possible support, while bearish FVGs are shown with red boxes and suggest possible resistance. It also includes doji candles—very small-bodied candles that indicate indecision—so these patterns are not missed. The script displays the most recent 5 FVGs, making it easy to spot recent potential areas where price may react.
Pine Script® Indikator
von andrew_blichasz
PSAR Laboratory [DAFE]PSAR Laboratory : The Ultimate Adaptive Trailing Stop & Reversal Engine 23 Advanced Algorithms. Adaptive Acceleration. Smart Flip Logic. Parabolic SAR Reimagined. █ PHILOSOPHY: WELCOME TO THE LABORATORY The standard Parabolic SAR, created by the legendary J. Welles Wilder Jr., is a tool of beautiful simplicity. But in today's complex, algorithm-driven markets, its simplicity is its fatal flaw. Its fixed acceleration and rigid flip logic cause it to fail precisely when you need it most: it whipsaws in choppy conditions and gives back too much profit in strong trends. The PSAR Laboratory was not created to be just another PSAR. It was engineered to be the definitive evolution of Wilder's original concept. This is not an indicator; it is a powerful, interactive research environment. It is a sandbox where you, the trader, can move beyond the static "one-size-fits-all" approach and forge a PSAR that is perfectly adapted to your specific market, timeframe, and trading style. We have deconstructed the very DNA of the Parabolic SAR and rebuilt it from the ground up, infusing it with modern quantitative techniques. The result is an institutional-grade suite of 23 distinct, mathematically diverse algorithms that dynamically control every aspect of the PSAR's behavior. █ WHAT MAKES THIS A "LABORATORY"? THE CORE INNOVATIONS This tool stands in a class of its own. It is a collection of what could be 23 separate indicators, all seamlessly integrated into one powerful engine. The 23 Algorithm Engine: This is the heart of the Laboratory. Instead of one rigid formula, you have a library of 23 unique mathematical engines at your command. These algorithms are not simple tweaks; they are complete re-imaginings of how the PSAR should behave, based on concepts from information theory, digital signal processing, fractal geometry, and institutional analysis. Truly Adaptive Acceleration (AF): The standard PSAR's "gas pedal" (the AF) is dumb; it accelerates at a fixed rate. Our algorithms make it intelligent. The AF can now speed up in clean, trending environments to lock in profits, and automatically slow down in choppy, chaotic conditions to avoid whipsaws. Advanced Flip Confirmation Logic: Say goodbye to noise-driven flips. You are no longer at the mercy of a single wick touching the SAR. The Laboratory provides multiple layers of flip confirmation, including requiring a bar close beyond the SAR, a volume spike to validate the reversal, or even a multi-bar confirmation . Comprehensive Noise Filtering Core: In a revolutionary step, you can apply one of over 30 advanced signal processing filters directly to the SAR output itself. From ultra-low-lag filters like the Hull MA and DAFE Spectral Laguerre to adaptive filters like KAMA and FRAMA , you can surgically remove noise while preserving the responsiveness of the core signal. Integrated Performance Engine: How do you know which of the 23 algorithms is best for your market? You test it. The built-in Performance Dashboard is a comprehensive backtesting and analytics engine that tracks every trade, providing real-time data on Win Rate, Profit Factor, Max Drawdown, and more. It allows you to scientifically validate your chosen configuration. █ A GUIDED TOUR OF THE ALGORITHMS: 23 PATHS TO AN EDGE b]These 23 algorithms are not simple settings; they are distinct mathematical philosophies for how a Parabolic SAR should adapt to the market. They are grouped into three primary categories: those that adapt the Acceleration Factor (AF) , those that enhance the Extreme Point (EP) detection, and those that redefine the Flip Logic . CATEGORY A: ACCELERATION FACTOR (AF) ADAPTATION These algorithms dynamically change the "gas pedal" of the PSAR. 1. Volatility-Scaled AF Core Concept: Treats volatility as market friction. The PSAR should be more forgiving in high-volatility environments. How It Works: It calculates a Volatility Ratio by comparing the short-term ATR to the long-term ATR. If current volatility is high (ratio > 1), it reduces the AF Step. If volatility is low (ratio < 1), it increases the AF Step to trail tighter. Ideal Use Case: The best all-rounder. Excellent for any market, especially those with clear shifts between high and low volatility regimes (like indices and crypto). 2. Efficiency Ratio (ER) AF Core Concept: The PSAR should accelerate aggressively in clean, efficient trends and slow down dramatically in choppy, inefficient markets. How It Works: It uses Kaufman's Efficiency Ratio (ER), which measures the net directional movement versus the total price movement. A high ER (near 1.0) signifies a pure trend, triggering a high AF multiplier. A low ER (near 0.0) signifies chop, triggering a low AF multiplier. Ideal Use Case: Markets that alternate between strong trends and sideways chop. It is exceptionally good at surviving ranging periods. 3. Shannon Entropy AF Core Concept: Uses Information Theory to measure market disorder. The PSAR should be conservative in chaos and aggressive in order. How It Works: It calculates the Shannon Entropy of recent price changes. High entropy means the market is unpredictable ("chaotic"), causing the AF to slow down. Low entropy means the market is organized and trending, causing the AF to speed up. Ideal Use Case: Advanced traders looking for a mathematically pure way to distinguish between a tradable trend and random noise. 4. Fractal Dimension (FD) AF Core Concept: Measures the "jaggedness" or complexity of the price path. A smooth path is a trend; a jagged, space-filling path is chop. How It Works: It calculates the Fractal Dimension of the price series. An FD near 1.0 is a smooth line (high AF). An FD near 1.5 is a random walk (low AF). Ideal Use Case: Visually identifying the moment a smooth trend begins to break down into chaotic, unpredictable movement. 5. ADX-Gated AF Core Concept: Uses the classic ADX indicator to confirm the presence of a trend before allowing the PSAR to accelerate. How It Works: If the ADX value is above a "Strong" threshold (e.g., 25), the AF accelerates normally. If the ADX is below a "Weak" threshold (e.g., 15), the AF is "frozen" and will not increase, preventing the SAR from tightening up in a non-trending market. Ideal Use Case: For classic trend-following purists who trust the ADX as their primary regime filter. 6. Kalman AF Estimator Core Concept: A sophisticated signal processing algorithm that predicts the "true" optimal AF by filtering out price "noise." How It Works: It treats the PSAR's AF as a state to be estimated. It makes a prediction, then corrects it based on how far the actual price deviates. It's like a GPS constantly refining its position. The "Process Noise" input controls how fast it thinks the AF can change, while "Measurement Noise" controls how much it trusts the price data. Ideal Use Case: Smooth, high-inertia markets like commodities or major forex pairs. It creates an incredibly smooth and responsive AF. 7. Volume-Momentum AF Core Concept: A trend's acceleration is only valid if confirmed by both volume and price momentum. How It Works: The AF will only increase if a new Extreme Point is made on above-average volume AND the Rate of Change (ROC) of the price is aligned with the trend's direction. Ideal Use Case: Any market with reliable volume data (stocks, futures, crypto). It's excellent for filtering out low-conviction moves. 8. Garman-Klass (GK) AF Core Concept: Uses a more advanced, statistically efficient measure of volatility (Garman-Klass, which uses OHLC data) to adapt the AF. How It Works: It modulates the AF based on whether the current GK volatility is higher or lower than its historical average. Unlike the standard Volatility-Scaled algo, it tends to slow down more in high volatility and speed up less in low volatility, making it more conservative. Ideal Use Case: Traders who want a volatility-adaptive model that is more focused on risk reduction during volatile periods. 9. RSI-Modulated AF Core Concept: The RSI can identify points of potential trend exhaustion or strong momentum. How It Works: If a trend is bullish but the RSI enters the "Overbought" zone, the AF slows down, anticipating a pullback. Conversely, if the RSI is in the strong momentum mid-range (40-60), the AF is boosted to trail more aggressively. Ideal Use Case: Mean-reversion traders or those who want to automatically loosen their trail stop near potential exhaustion points. 10. Bollinger Squeeze AF Core Concept: A Bollinger Band Squeeze signals a period of volatility compression, often preceding an explosive breakout. How It Works: When the algorithm detects that the Bollinger Band Width is in a "Squeeze" (below a certain historical percentile), it boosts the AF in anticipation of a fast move, allowing the PSAR to catch the breakout quickly. Ideal Use Case: Breakout traders. This algorithm primes the PSAR to be maximally responsive right at the moment a breakout is most likely. 11. Keltner Adaptive AF Core Concept: Keltner Channels provide a robust measure of a trend's "normal" volatility channel. How It Works: When price is trading strongly outside the Keltner Channel, it's considered a powerful trend, and the AF is boosted. When price falls back inside the channel, it's considered a consolidation or pullback, and the AF is slowed down. Ideal Use Case: Trend followers who use channel breakouts as their primary confirmation. 12. Choppiness-Gated AF Core Concept: Uses the Choppiness Index to quantify whether the market is trending or consolidating. How It Works: If the Choppiness Index is below the "Trend" threshold (e.g., 38.2), the AF is boosted. If it's above the "Range" threshold (e.g., 61.8), the AF is significantly reduced. Ideal Use Case: A more responsive alternative to the ADX-Gated algorithm for distinguishing between trending and ranging markets. 13. VIDYA-Style AF Core Concept: Uses a Chande Momentum Oscillator (CMO) to create a variable-speed acceleration factor. How It Works: The absolute value of the CMO is used to create a dynamic smoothing constant. Strong momentum (high absolute CMO) results in a faster, more responsive AF. Weak momentum results in a slower, smoother AF. Ideal Use Case: Momentum traders who want their trailing stop's speed directly tied to the momentum of the price itself. 14. Hilbert Cycle AF Core Concept: Uses Ehlers' Hilbert Transform to extract the dominant cycle period of the market and synchronizes the PSAR with it. How It Works: It dynamically adjusts the AF based on the detected cycle period (shorter cycles = faster AF) and can also modulate it based on the current phase within that cycle (e.g., accelerate faster near cycle tops/bottoms). Ideal Use Case: Markets with clear cyclical behavior, like commodities and some forex pairs. CATEGORY B: EXTREME POINT (EP) ENHANCEMENT These algorithms make the detection of new highs/lows more intelligent. 15. Volume-Weighted EP Core Concept: A new high or low is more significant if it occurs on high volume. How It Works: It can be configured to only accept a new EP if the volume on that bar is above average. It can also "weight" the EP by volume, pushing it further out on high-volume bars. Ideal Use Case: Filtering out weak, low-conviction price probes in markets with reliable volume. 16. Wavelet Filtered EP Core Concept: Uses wavelet decomposition (a signal processing technique) to separate the underlying trend from high-frequency noise. How It Works: It calculates a smoothed, wavelet-filtered version of the price. A new EP is only registered if the actual high/low significantly exceeds this smoothed baseline, effectively ignoring minor noise spikes. Ideal Use Case: Noisy markets where small, insignificant wicks can cause the AF to accelerate prematurely. 17. ATR-Validated EP Core Concept: A new EP should represent a meaningful move, not just a one-tick poke. How It Works: It requires a new high/low to exceed the previous EP by a minimum amount, defined as a multiple of the current ATR. This ensures only volatility-significant advances are counted. Ideal Use Case: A simple, robust way to filter out "noise" EPs and slow down the AF's acceleration in choppy conditions. 18. Statistical EP Filter Core Concept: A new EP is only valid if the price change that created it is statistically significant. How It Works: It calculates the Z-Score of the bar's price change relative to recent history. A new EP is only accepted if its Z-Score exceeds a certain threshold (e.g., 1.5 sigma), meaning it was an unusually strong move. Ideal Use Case: For quantitative traders who want to ensure their trailing stop only tightens in response to statistically meaningful price action. CATEGORY C: FLIP LOGIC & CONFIRMATION These algorithms change the very rules of when and why the PSAR reverses. 19. Dual-PSAR Gate Core Concept: Uses two PSARs—one fast and one slow—to confirm a reversal. How It Works: A flip signal for the main PSAR is only considered valid if both the fast (sensitive) PSAR and the slow (structural) PSAR have flipped. This acts as a powerful trend filter. Ideal Use Case: An excellent method for reducing whipsaws. It forces the PSAR to wait for both short-term and longer-term momentum to align before signaling a reversal. 20. MTF Coherence PSAR Core Concept: Do not flip against the higher timeframe macro trend. How It Works: It pulls PSAR data from two higher timeframes. A flip is only allowed if the new direction does not contradict the trend on at least one (or both) of those higher timeframes. It also boosts the AF when all timeframes are aligned. Ideal Use Case: The ultimate tool for multi-timeframe traders who want to ensure their entries and exits are in sync with the bigger picture. 21. Momentum-Gated Flip Core Concept: A reversal is only valid if it is supported by a significant surge of momentum. How It Works: A price cross of the SAR is not enough. The script also requires the Rate of Change (ROC) to exceed a certain threshold for a set number of bars, confirming that there is real force behind the reversal. Ideal Use Case: Filtering out weak, drifting reversals and only taking signals that are initiated with explosive power. 22. Close-Only PSAR Core Concept: Wicks are noise; the bar's close is the final decision. How It Works: This algorithm modifies the flip logic to ignore wicks. A flip only occurs if one or more bars close beyond the SAR line. Ideal Use Case: One of the most effective and simple ways to reduce false signals from volatile wicks. A fantastic default choice for any trader. 23. Ultimate PSAR Consensus Core Concept: The highest conviction signal comes from the agreement of multiple, diverse mathematical models. How It Works: This is the capstone algorithm. It runs a "vote" between a selection of the top-performing algorithms (e.g., Volatility-Scaled, Efficiency Ratio, Dual-PSAR). A flip is only signaled if a majority consensus is reached. It can even weight the votes based on each algorithm's recent performance. Ideal Use Case: For traders who want the absolute highest level of confirmation and are willing to accept fewer, but more robust, signals. █ PART II: THE NOISE FILTERING CORE - The Shield This is a revolutionary feature that allows you to apply a second layer of signal processing directly to the SAR line itself, surgically removing noise before the flip logic is even considered. FILTER CATEGORIES Basic Filters (SMA, EMA, WMA, RMA): The classic moving averages. They provide basic smoothing but introduce significant lag. Best used for educational purposes. Low-Lag Filters (DEMA, TEMA, Hull MA, ZLEMA): A family of filters designed to reduce the lag inherent in basic moving averages. The Hull MA is a standout, offering a superb balance of smoothness and responsiveness. Adaptive Filters (KAMA, VIDYA, FRAMA): These are "smart" filters. They automatically adjust their smoothing level based on market conditions. They will be very smooth in choppy markets and become highly responsive in trending markets. Advanced DSP & DAFE Filters: This is the pinnacle of signal processing. Ehlers Filters (SuperSmoother, 2-Pole, 3-Pole): Based on the work of John Ehlers, these use digital signal processing techniques to remove high-frequency noise with minimal lag. Gaussian & ALMA: These use a bell-curve weighting, giving the most importance to recent data in a smooth, non-linear fashion. DAFE Spectral Laguerre: A proprietary, non-linear filter that uses a feedback loop and adapts its "gamma" based on volatility, providing exceptional tracking in all market conditions. How to Choose a Filter Start with "None": First, find an algorithm you like with no filtering to understand its raw behavior. Introduce Low Lag: If you are getting too many whipsaws from noise, apply a short-length Hull MA (e.g., 5-8). This is often the best solution. Go Adaptive: If your market has very distinct trend/chop regimes, try an Adaptive KAMA . Maximum Purity: For the smoothest possible output with excellent responsiveness, use the DAFE Spectral Laguerre or Ehlers SuperSmoother . █ THE VISUAL EXPERIENCE: DATA AS ART The PSAR Laboratory is not just functional; it is beautiful. The visualization engine is designed to provide you with an intuitive, at-a-glance understanding of the market's state. Algorithm-Specific Theming: Each of the 23 algorithms comes with its own unique, professionally designed color palette. This not only provides visual variety but allows you to instantly recognize which engine is active. Dynamic Glow Effects: For many algorithms, the PSAR dots will emit a soft "glow." The brightness and color of this glow are not random; they are tied to a key metric of the active algorithm (e.g., trend strength, volatility, consensus), providing a subtle, visual cue about the health of the trend. Adaptive Volatility Bands: Certain algorithms will display dynamic bands around the PSAR. These are not standard deviation bands; their width is controlled by the specific logic of the active algorithm, showing you a visual representation of the market's expected range or energy level. Secondary Reference Lines: For algorithms like the Dual-PSAR or MTF Coherence, a secondary line will be plotted on the chart, giving you a clear visual of the underlying data (e.g., the slow PSAR, the HTF trend) that is driving the decision-making process. █ THE MASTER DASHBOARD: YOUR MISSION CONTROL The comprehensive dashboard is your unified command center for analysis and performance tracking. Engine Status: See the currently selected Algorithm, the active Noise Filter, the Trend direction, and a real-time progress bar of the current Acceleration Factor (AF). Algorithm-Specific Metrics: This is the most powerful section. It displays the key real-time data from the currently active algorithm. If you're using "Shannon Entropy," you'll see the Entropy score. If you're using "ADX-Gated," you'll see the ADX value. This gives you a direct, quantitative look under the hood. Performance Readout: When enabled, this section provides a full breakdown of your backtesting results, including Win Rate, Profit Factor, Net P&L, Max Drawdown, and your current trade status. █ DEVELOPMENT PHILOSOPHY The PSAR Laboratory was born from a deep respect for Wilder's original work and a relentless desire to push it into the 21st century. We believe that in modern markets, static tools are obsolete. The future of trading lies in adaptation. This indicator is for the serious trader, the tinkerer, the scientist—the individual who is not content with a black box, but who seeks to understand, test, and refine their edge with surgical precision. It is a tool for forging, not just following. The PSAR Laboratory is designed to be the ultimate tool for that evolution, allowing you to discover and codify the rules that truly fit you. █ DISCLAIMER AND BEST PRACTICES THIS IS A TOOL, NOT A STRATEGY: This indicator provides a sophisticated trailing stop and reversal signal. It must be integrated into a complete trading plan that includes risk management, position sizing, and your own contextual analysis. TEST, DON'T GUESS: The power of this tool is its adaptability. Use the Performance Dashboard to rigorously test different algorithms and settings on your chosen asset and timeframe. Find what works, and build your strategy around that data. START SIMPLE: Begin with the "Volatility-Scaled AF" algorithm, as it is a powerful and intuitive all-rounder. Once you are comfortable, begin experimenting with other engines. RISK MANAGEMENT IS PARAMOUNT: All trading involves substantial risk. The backtesting results are hypothetical and do not account for slippage or psychological factors. Never risk more capital than you are prepared to lose. "I don't think traders can follow rules for very long unless they reflect their own trading style. Eventually, a breaking point is reached and the trader has to quit or change, or find a new set of rules he can follow. This seems to be part of the process of evolution and growth of a trader." — Ed Seykota, Market Wizard Taking you to school. - Dskyz, Trade with Volume. Trade with Density. Trade with DAFE
Pine Script® Indikator
von DskyzInvestments
2
Luminous Market Flux [Pineify]Luminous Market Flux - Dynamic Volatility Channel with Breakout Detection The Luminous Market Flux indicator is a sophisticated volatility-based trading tool that combines dynamic channel analysis with breakout detection and squeeze identification. This indicator helps traders visualize market conditions by creating an adaptive envelope around price action, highlighting periods of compression (low volatility) and expansion (high volatility) while generating actionable buy and sell signals at key breakout moments. Key Features Dynamic volatility channel that adapts to changing market conditions using ATR-based calculations Visual squeeze detection system that warns traders when volatility is contracting Automatic breakout signal generation for both bullish and bearish scenarios Luminous gradient fill that provides instant visual feedback on price position within the channel Bar coloring feature that highlights strong volatility breakouts Built-in alert conditions for automated trading notifications How It Works The indicator operates on three core calculation layers: 1. Baseline Calculation (Central Tendency) The foundation uses a Running Moving Average (RMA) of the closing price over the specified Flux Length period. RMA was specifically chosen over SMA or EMA because it provides smoother trend detection similar to how RSI and ATR calculations work, reducing noise while maintaining responsiveness to genuine price movements. 2. Volatility Measurement The channel width is determined by the Average True Range (ATR) multiplied by the Flux Expansion Factor. ATR captures the true volatility of the market by accounting for gaps and limit moves, making the channel responsive to actual market conditions rather than just closing price variations. 3. Squeeze Detection Logic The indicator compares the current channel width against a 100-period simple moving average of historical channel widths. When the current range falls below 80% of this average, a squeeze condition is identified, signaling that volatility is compressing and a significant move may be imminent. Trading Ideas and Insights Breakout Trading: Enter long positions when price breaks above the upper flux channel with a BUY signal, and short positions when price breaks below the lower channel with a SELL signal. These breakouts indicate strong momentum in the direction of the move. Squeeze Anticipation: When squeeze circles appear at the top of the chart, prepare for a potential explosive move. Squeezes often precede significant breakouts as the market coils before releasing energy in one direction. Trend Confirmation: Use the bar coloring feature to confirm trend strength. Colored bars indicate that price is trading outside the volatility envelope, suggesting strong directional momentum. Mean Reversion: When price is within the channel (no bar coloring), the gradient fill helps identify whether price is closer to the upper or lower boundary, potentially useful for mean-reversion strategies. How Multiple Indicators Work Together This indicator integrates several technical concepts into a cohesive system: The RMA baseline provides the trend anchor, while the ATR-based envelope adapts to volatility conditions. These two components work together to create a channel that expands during volatile periods and contracts during quiet markets. The squeeze detection layer adds a third dimension by comparing current volatility to historical norms, alerting traders when the market is unusually quiet. The visual elements reinforce this analysis: the gradient fill shows price position within the channel at a glance, bar coloring confirms breakout strength, and shape markers provide discrete entry signals. This multi-layered approach ensures traders receive consistent information across different visualization methods. Unique Aspects The "Luminous" visual design uses color gradients that dynamically shift based on price position, creating an intuitive heat-map effect within the channel Unlike traditional Bollinger Bands that use standard deviation, this indicator uses ATR for volatility measurement, making it more responsive to actual price range movements The squeeze detection compares current volatility to a longer-term average (100 periods), providing context-aware compression signals rather than arbitrary thresholds Signal generation uses proper state tracking to ensure breakout signals only fire on the initial breakout, not on every bar during an extended move How to Use Add the indicator to your chart. It will overlay directly on price with the volatility channel visible. Watch for BUY labels appearing below bars when price breaks above the upper channel - these indicate bullish breakout opportunities. Watch for SELL labels appearing above bars when price breaks below the lower channel - these indicate bearish breakout opportunities. Monitor for small circles at the top of the chart indicating squeeze conditions - prepare for potential breakouts when these appear. Use the colored bars as confirmation of breakout strength - green bars confirm bullish momentum, red bars confirm bearish momentum. Set up alerts using the built-in alert conditions to receive notifications for buy signals, sell signals, and squeeze warnings. Customization Flux Length (default: 20): Controls the lookback period for both the baseline and ATR calculations. Lower values create more responsive but noisier channels; higher values create smoother but slower-reacting channels. Flux Expansion Factor (default: 2.0): Multiplier for the ATR value that determines channel width. Higher values create wider channels with fewer signals; lower values create tighter channels with more frequent signals. Smooth Signal : Toggle for signal smoothing preference. Bullish Energy : Customize the color for bullish breakouts and upper channel highlights. Bearish Energy : Customize the color for bearish breakouts and lower channel highlights. Compression/Neutral : Customize the color for squeeze indicators and neutral channel states. Conclusion The Luminous Market Flux indicator provides traders with a comprehensive volatility analysis tool that combines channel-based trend detection, squeeze identification, and breakout signaling into a single, visually intuitive package. By using ATR-based volatility measurement and RMA smoothing, the indicator adapts to changing market conditions while filtering out noise. Whether you are a breakout trader looking for momentum entries or a swing trader waiting for volatility expansion after compression periods, this indicator offers the visual clarity and signal precision needed to make informed trading decisions.
Pine Script® Indikator
von Pineify
XAUUSD: Ultimate Sniper v6.0 [Order Flow & Macro]This indicator is a comprehensive trading system designed specifically for XAUUSD (Gold). It moves away from lagging indicators by combining real-time Macro-Economic sentiment, Regression Analysis, and Institutional Order Flow logic into a single professional interface. ### Core Strategy & Features: 1. Macro Correlation Filter: Gold has a strong inverse correlation with the USD (DXY) and Treasury Yields (US10Y). This script monitors them in the background. If DXY/US10Y are Bullish, Gold Buy signals are filtered out to prevent trading against the trend. 2. Linear Regression Channel: Defines the "Fair Value" of price. We only look for reversal trades when price hits the extreme Upper or Lower bands. 3. Order Flow Pressure (New): Analyzes the internal structure of each candle (Wick vs Body). A signal is only confirmed if the "Buying Pressure" or "Selling Pressure" within the candle supports the move (e.g. >50%). 4. RSI Divergence: Automatically spots Bullish and Bearish divergences to identify momentum exhaustion. ### ⚙️ Recommended Settings / Best Practices To get the best results, adjust the settings based on your trading style: 🏎️ SCALPING (1min - 5min Charts) * Goal: Quick entries, smaller targets, higher frequency. * DXY/US10Y Timeframe: Set to "15" or "30" (Reacts faster to macro changes). * Regression Length: 50 or 80 (Adapts to short-term trends). * RSI Length: 9 or 14. 🛡️ INTRADAY (15min - 1h Charts) - * Goal: Balanced trading, capturing the daily range. * DXY/US10Y Timeframe: Set to "60" (1 Hour). * Regression Length: 100 (Standard setting). * RSI Length: 14. 🦅 SWING TRADING (4h - Daily Charts) * Goal: Catching major trend reversals. * DXY/US10Y Timeframe: Set to "240" (4 Hours) or "D" (Daily). * Regression Length: 200 (Long-term trend baseline). * Channel Width: Increase to 2.5 or 3.0. ### How to Trade: - BUY Signal: Valid when the Dashboard shows "BEARISH" DXY/US10Y and the Live Pressure is "BUYERS". - SELL Signal: Valid when the Dashboard shows "BULLISH" DXY/US10Y and the Live Pressure is "SELLERS". - Risk Management: The script automatically calculates ATR-based Stop Loss (SL) and Take Profit (TP) levels.
Pine Script® Indikator
von dokuyucuali006
Impulse Trend Levels [BOSWaves]Impulse Trend Levels - Momentum-Adaptive Trend Detection with Impulse-Driven Confidence Bands Overview Impulse Trend Levels is a momentum-aware trend identification system that tracks directional price movement through adaptive confidence bands, where band width dynamically adjusts based on impulse strength and freshness to reflect real-time conviction in the current trend direction. Instead of relying on fixed moving average crossovers or static band multipliers, trend state, band positioning, and zone thickness are determined through impulse detection patterns, exponential decay modeling, and volatility-normalized momentum measurement. This creates dynamic trend boundaries that reflect actual momentum intensity rather than arbitrary technical levels - contracting during fresh impulse conditions when trend conviction is high, expanding during impulse decay periods when directional confidence weakens, and incorporating momentum freshness calculations to reveal whether trends are accelerating or deteriorating. Price is therefore evaluated relative to bands that adapt to momentum state rather than conventional static thresholds. Conceptual Framework Impulse Trend Levels is founded on the principle that meaningful trend signals emerge when price momentum intensity reaches significant thresholds relative to recent volatility rather than when price simply crosses moving averages. Traditional trend-following methods identify directional changes through price-indicator crossovers, which often ignore the underlying momentum dynamics and conviction levels that sustain those moves. This framework replaces static-threshold logic with impulse-driven band construction informed by actual momentum strength and decay characteristics. Three core principles guide the design: Trend direction should be determined by volatility-normalized momentum breaches, not simple price crossovers alone. Band width must adapt to impulse freshness, reflecting real-time confidence in the current trend. Momentum decay modeling reveals whether trends are maintaining strength or losing conviction. This shifts trend analysis from static indicator levels into adaptive, momentum-anchored confidence boundaries. Theoretical Foundation The indicator combines exponential moving average smoothing, mean absolute deviation measurement, impulse detection methodology, and exponential decay tracking. An EMA-based trend baseline provides directional reference, while Mean Absolute Deviation (MAD) offers volatility-normalized scaling for momentum measurement. Impulse detection identifies significant price movements relative to recent volatility, triggering fresh momentum readings that decay exponentially over time. Band multipliers interpolate between tight and wide settings based on calculated impulse freshness. Four internal systems operate in tandem: Trend Baseline Engine : Computes EMA-smoothed price levels for directional reference and band anchoring. Volatility Measurement System : Calculates MAD to provide adaptive scaling that normalizes momentum across varying market conditions. Impulse Detection Logic : Identifies volatility-normalized price movements exceeding threshold levels, capturing momentum intensity and direction. Decay-Based Confidence Modeling : Applies exponential decay to impulse readings, converting raw momentum into time-weighted freshness metrics that drive band adaptation. This design allows trend confidence to reflect actual momentum behavior rather than reacting mechanically to price formations. How It Works Impulse Trend Levels evaluates price through a sequence of momentum-aware processes: Baseline Calculation : EMA smoothing of open and close creates a directional trend reference that filters short-term noise. Volatility Normalization : MAD calculation over a specified lookback provides dynamic scaling for momentum measurement. Raw Impulse Detection : Price change over impulse lookback divided by MAD creates volatility-normalized momentum readings. Threshold-Based Activation : When normalized momentum exceeds threshold (1.0), impulse registers with absolute magnitude and directional sign. Exponential Decay Application : Between impulse events, stored impulse value decays exponentially via configurable decay rate. Freshness Conversion : Decaying impulse transforms into freshness metric (0-100%) representing current momentum conviction. Adaptive Band Construction : Band multiplier interpolates between minimum (fresh) and maximum (stale) settings based on freshness, then scales MAD to determine band width. Trend State Logic : Price crossing above upper band triggers bullish state; crossing below lower band triggers bearish state; state persists until opposite breach. Signal Generation : Trend state switches from bearish to bullish produce buy signals; bullish to bearish switches produce sell signals. Retest Identification : Price touching inner band edge after signal buffer period marks retests, with cooldown periods preventing excessive plotting. Together, these elements form a continuously updating trend framework anchored in momentum reality. Interpretation Impulse Trend Levels should be interpreted as momentum-anchored trend confidence boundaries: Bullish Trend State (Cyan) : Established when price closes above adaptive upper band, indicating upward momentum breach with associated confidence level. Bearish Trend State (Magenta) : Established when price closes below adaptive lower band, signaling downward momentum breach with directional conviction. Trend Cloud : Visual gradient zone displays between outer and inner band edges, with opacity reflecting current trend state and confidence. Band Width Dynamics : Tighter bands indicate fresh impulse (high confidence), wider bands indicate impulse decay (reduced confidence). ▲ Buy Signals : Green upward triangles mark bullish trend state initiations at crossovers above upper band. ▼ Sell Signals : Red downward triangles mark bearish trend state initiations at crossovers below lower band. ✦ Retest Markers : Small diamonds identify price retouching inner band edge after sufficient buffer period from initial signal. Retest Extension Lines : Horizontal projections from retest points extend forward, marking potential support/resistance levels. Colored Candles : Optional bar coloring reflects current trend state for immediate visual reference. Note: The original chart candles must be disabled in chart settings for the trend-colored candles to display properly. Impulse freshness, band width dynamics, and momentum normalization outweigh isolated price movements. Signal Logic & Visual Cues Impulse Trend Levels presents two primary interaction signals: Buy Signal (▲) : Green label appears when trend state switches from bearish to bullish via upper band crossover, suggesting momentum shift to upside. Sell Signal (▼) : Red label displays when trend state switches from bullish to bearish via lower band crossunder, indicating momentum shift to downside. Retest detection provides secondary confirmation when price revisits inner band boundaries after signal buffer cooldown expires. Alert generation covers trend state switches (long/short), retest occurrences, and impulse freshness decay below 50% threshold for systematic monitoring. Strategy Integration Impulse Trend Levels fits within momentum-informed and adaptive trend-following approaches: Momentum-Confirmed Entries : Use band crossovers as high-probability trend initiation points where volatility-normalized momentum exceeded threshold. Freshness-Based Position Sizing : Scale exposure based on impulse freshness - larger positions during fresh impulse periods, reduced sizing as impulse decays. Band-Width Risk Management : Expect wider price ranges when bands expand during decay, tighter ranges when bands contract during fresh impulse. Retest-Based Re-entry : Use inner band retests as lower-risk entry opportunities within established trends after initial signal cooldown. Cloud-Aligned Directional Bias : Favor trades aligning with current trend state rather than counter-trend positions. Multi-Timeframe Momentum Confirmation : Apply higher-timeframe impulse trend state to filter lower-timeframe entry precision. Technical Implementation Details Core Engine : EMA-based baseline with MAD volatility measurement Impulse Model : Volatility-normalized momentum detection with directional sign capture Decay System : Exponential decay application (0.8-0.99 range) with freshness conversion Band Construction : Linear interpolation between min/max multipliers scaled by MAD Visualization : Gradient-filled cloud zones with bar coloring and signal labels Signal Logic : State-switch detection with retest buffer and cooldown mechanisms Performance Profile : Optimized for real-time execution across all timeframes Optimal Application Parameters Timeframe Guidance: 1 - 5 min : Micro-trend detection for scalping with responsive impulse settings 15 - 60 min : Intraday momentum tracking with balanced decay characteristics 4H - Daily : Swing-level trend identification with sustained impulse persistence Suggested Baseline Configuration: Trend Length : 19 Impulse Lookback : 5 Decay Rate : 0.99 MAD Length : 20 Band Min (Fresh) : 1.5 Band Max (Stale) : 1.9 Signal Buffer Period : 10 Show Trend Cloud : Enabled Color Bars : Enabled (requires disabling original chart candles in chart settings) Show Buy/Sell Signals : Enabled These suggested parameters should be used as a baseline; their effectiveness depends on the asset's volatility profile, momentum characteristics, and preferred signal frequency, so fine-tuning is expected for optimal performance. Parameter Calibration Notes Use the following adjustments to refine behavior without altering the core logic: Excessive signal noise : Increase Trend Length to demand smoother baseline crossovers or increase Impulse Lookback for less reactive momentum detection. Missed momentum shifts : Decrease Impulse Lookback to capture shorter-term momentum changes or reduce Decay Rate to allow faster impulse fade. Bands too tight/wide : Adjust Band Min and Band Max multipliers to modify confidence zone thickness across freshness spectrum. Impulse decays too quickly : Increase Decay Rate toward 0.99 to sustain impulse readings longer between fresh events. Impulse decays too slowly : Decrease Decay Rate toward 0.8 for faster momentum fade and more frequent band expansion. Unstable volatility scaling : Increase MAD Length to smooth volatility measurement and reduce sensitivity to short-term spikes. Too many retest markers : Increase retest cooldown period (55 bars hardcoded) or increase Signal Buffer Period to space out signals. Adjustments should be incremental and evaluated across multiple session types rather than isolated market conditions. Performance Characteristics High Effectiveness: Trending markets with clear momentum phases and directional persistence Instruments with consistent volatility characteristics where MAD scaling normalizes effectively Momentum continuation strategies entering on fresh impulse signals Trend-following approaches benefiting from adaptive confidence measurement Reduced Effectiveness: Choppy, range-bound markets with frequent whipsaw crossovers Extremely low volatility environments where impulse threshold becomes difficult to breach News-driven or gapped markets with discontinuous momentum patterns Mean-reversion dominant conditions where momentum breaches quickly reverse Consolidation and sideways price action where trend-following methodologies inherently struggle due to lack of sustained directional movement Integration Guidelines Confluence : Combine with BOSWaves structure, volume analysis, or traditional trend indicators Freshness Respect : Trust signals occurring during high impulse freshness periods with contracted bands Decay Awareness : Reduce position sizing or tighten stops as impulse decays and bands widen Retest Utilization : Treat inner band retests as continuation confirmation rather than reversal signals State Discipline : Maintain directional bias aligned with current trend state until opposite band breach occurs Disclaimer Impulse Trend Levels is a professional-grade momentum and trend analysis tool. It uses volatility-normalized impulse detection with exponential decay modeling but does not predict future price movements. Results depend on market conditions, volatility characteristics, parameter selection, and disciplined execution. BOSWaves recommends deploying this indicator within a broader analytical framework that incorporates price structure, volume context, and comprehensive risk management.
Pine Script® Indikator
von BOSWaves
8
Keltner-Aroon-EFI FlowKeltner-Aroon-EFI Flow (KAE) KAE Flow is a quantitative composite indicator designed to identify dominant market trends by fusing three distinct dimensions of price action: Volatility, Trend Age, and Volume Pressure. Unlike standard indicators that rely on a single data point (like a moving average crossover), KAE Flow aggregates three independent logic engines into a single normalized "Flow" score. This score is then smoothed using an Arnaud Legoux Moving Average (ALMA) to filter out noise while retaining responsiveness to genuine trend reversals. This script operates strictly on the current chart timeframe, ensuring all signals are causal, non-repainting, and reliable for real-time analysis. 1. The Quantitative Engine (How it Works) The indicator polls three separate components. Each component votes "1" (Bullish), "-1" (Bearish), or "0" (Neutral). These votes are averaged to create the raw signal. K — Keltner Channels (Volatility Dimension) Concept: Measures volatility expansion. Logic: The script calculates Keltner Channels using an EMA center line and ATR bands. Bullish (+1): Price closes above the Upper Channel. Bearish (-1): Price closes below the Lower Channel. This component ensures we only trade when price is breaking out of its expected volatility range. A — Aroon (Trend Age Dimension) Concept: Measures the strength and "freshness" of a trend. Logic: We utilize the Aroon Up and Aroon Down metrics. Bullish (+1): Aroon Up is greater than Aroon Down AND Aroon Up is > 70. Bearish (-1): Aroon Down is greater than Aroon Up AND Aroon Down > 70. This filters out weak or aging trends, ensuring the move has mathematical momentum. E — Elder’s Force Index (Volume Dimension) Concept: Measures volume-weighted price change. Logic: We calculate the raw Force Index (Close - Close ) * Volume and smooth it with an EMA. Bullish (+1): Smoothed EFI > 0. Bearish (-1): Smoothed EFI < 0. This component confirms that price movement is supported by actual volume flow (accumulation/distribution). 2. Signal Processing (ALMA Smoothing) Raw aggregation can be noisy. The composite score is passed through an ALMA (Arnaud Legoux Moving Average) filter. Why ALMA? It uses a Gaussian distribution to provide smoothness without the significant lag associated with SMA or EMA. This creates the "Flow" line that resists false flips during choppy consolidation. 3. How to Use The indicator plots a signal line and dynamically colors the price bars and background to reflect the dominant bias. Deep Blue (Bullish Flow): The KAE Score is > 0.1. All three engines (or the majority) are aligned bullishly. Traders typically look for long entries or hold existing long positions. White (Bearish Flow): The KAE Score is < -0.1. The majority of engines detect bearish volatility and volume. Traders typically look for short entries. Gray (Neutral): The score is between -0.1 and 0.1. The market is in equilibrium or transition. Trend-following strategies should be paused. 4. Configuration Logic Engine: You can toggle individual components (K, A, or E) on or off to isolate specific market dimensions. Smoothing: Adjust the ALMA Window and Offset to fine-tune the sensitivity of the signal line. Lengths: Fully customizable periods for Keltner, Aroon, and EFI to adapt to different asset classes (e.g., Crypto vs. Forex).
Pine Script® Indikator
von D_QUANT
1
Future Swing [BigBeluga]🔵 OVERVIEW Future Swing is a swing-based projection tool that estimates the potential size and price target of the next swing move using historical swing behavior. Instead of predicting direction randomly, it analyzes completed swing legs, measures their percentage moves, and projects a statistically derived swing target into the future. The indicator combines swing structure, high/low zones, volume context, and a real-time dashboard to help traders anticipate where price may travel next. 🔵 CONCEPTS Swing Detection — Swing highs and lows are identified using a configurable lookback length. Swing Percentage Tracking — Each completed swing leg is converted into a percentage move and stored. Statistical Projection — Future swing size is estimated using Average, Median, or Mode of past swing percentages. Directional Awareness — Projections adapt automatically based on current swing direction. 🔵 FEATURES Historical Swing Sampling — • Uses a user-defined number of completed swings. • More samples = smoother projection, fewer samples = faster adaptation. Future Swing Projection — • Dashed line projects the estimated swing target forward in time. • Projection distance is visual-only and does not affect calculations. High/Low Swing Zones — • Upper and lower swing zones expand using ATR distance. • Zones visualize potential reaction and rejection areas. Volume Context per Swing — • Buy and sell volume are accumulated during each swing leg. • Delta and total volume are displayed in the dashboard. Smart Dashboard — • Displays each stored swing percentage. • Shows calculated swing projection value. Flexible Projection Method — • Average: smooth and balanced. • Median: filters out extreme outliers. • Mode: focuses on the most common swing size. Extendable Zones — • Swing zones can optionally extend forward indefinitely. 🔵 HOW TO USE Anticipate Swing Targets — Use the projected swing line as a probabilistic price objective. Combine with Structure — Align projections with support, resistance, or liquidity zones. Filter by Volume — Confirm swing quality using delta and total volume metrics. Adjust Sensitivity — Tune swing length and historical sample size to match timeframe and volatility. Context, Not Certainty — Use projections as guidance, not fixed take-profit levels. 🔵 CONCLUSION Future Swing transforms past swing behavior into a forward-looking projection model. By combining swing structure, statistical aggregation, ATR zones, and volume analysis, it offers traders a structured way to estimate where the next meaningful price move may reach — without relying on fixed targets or subjective assumptions.
Pine Script® Indikator
von BigBeluga
9
Multi-Timeframe EMA Bundle (576/676/144/169/12)A comprehensive EMA (Exponential Moving Average) indicator combining five key moving averages used by professional traders for trend identification and dynamic support/resistance levels. Included EMAs: EMA 576 & EMA 676 (Blue) — Long-term trend filters commonly used on lower timeframes to represent higher timeframe structure. Acts as major support/resistance zones. EMA 144 & EMA 169 (White) — Mid-term trend indicators derived from Fibonacci numbers. When price respects this zone, it often signals strong trend continuation. EMA 12 (Yellow) — Short-term momentum tracker for entries and exits. Useful for identifying pullback opportunities within the trend.
Pine Script® Indikator
von dylancui9
AI Adaptive Trend Navigator Strategy Echo EditionAI Adaptive Trend Navigator Strategy This is a professional long-only automated strategy optimized for Taiwan Index Futures (TX). Based on the LuxAlgo clustering framework, this version features advanced logic iteration for institutional-grade backtesting and execution. 1. Realistic Cost Modeling To ensure backtest reliability, this strategy is pre-configured with: Slippage: 2 ticks (Approx. 400 TWD per side). Commission: 100 TWD per side. Total Cost: 500 TWD per side. This provides a rigorous stress test for real-world trading environments. 2. State Consistency & Logic Continuity Optimized the underlying array handling to ensure "State Persistence." This eliminates the logic gaps common in real-time script execution, ensuring that historical signals are 100% consistent with live alerts. 3. Adaptive AI Clustering Utilizes K-means clustering to dynamically select the optimal ATR factors based on current market volatility, allowing the strategy to "evolve" as market regimes shift. 🧠 開發理念：追求實戰一致性的量化策略 本策略旨在為台指期（TX）提供一套具備真實參考價值的自動化系統。 ✨ Echo 版核心優化點 數據連續性迭代：修正底層邏輯，確保訊號在即時盤勢中穩定不跳斷。 真實交易成本模擬：預設 2 點滑價 與 單邊 100 TWD 手續費，單邊總成本對標 500 TWD，拒絕虛假神單，挑戰最嚴苛的回測環境。 台指期專屬參數調校：融入針對台灣市場波動特性的預設參數與過濾邏輯。 🛡️ 進階實戰過濾 空間緩衝區 (Buffer Strategy)：價格需有效突破緩衝區才觸發，精準過濾盤整雜訊。 AI 信心評分系統：只有當動能穩定度達標時才會發進場訊號。 冷卻保護機制：有效抑制訊號在洗盤區間過度頻繁跳動。 ⚠️ Disclaimer: Backtest results do not guarantee future performance.
Pine Script® Strategie
von EchoQuan
5
Euro Day StrategyThis is a false breakout reversal strategy that fades short-term breakouts when they conflict with longer-term momentum. Here's the detailed breakdown: Strategy Overview Type: Counter-trend/Fade strategy disguised as breakout trading Core Logic: Enter against immediate breakouts when longer-term momentum suggests the move is exhausted. Strategy Classification This is a FADE/EXHAUSTION strategy, NOT a breakout-following strategy Enters against the immediate breakout direction Bets on mean reversion when short-term price action diverges from longer-term momentum Works best in ranging/choppy markets where breakouts frequently fail Will get hurt in strong trending markets where breakouts are genuine This strategy is designed for intraday mean-reversion trading on instruments that tend to range (likely forex or futures). It requires markets where false breakouts are common and price tends to snap back quickly.
Pine Script® Strategie
von trungnam2101998
MTF Equals v1.0MTF Equals is a professional-grade tool designed to identify significant price levels across multiple timeframes. It scans the current chart and higher timeframes (HTF) for identical highs and lows ("Equals"), which often act as price magnets or liquidity pools. Key Features: Multi-Timeframe Analysis: Automatically detects identical highs and lows on the current chart, as well as M5, M15, M30, H1, H4, and Daily. NQ Auto-Detection: Specialized logic for Nasdaq (NQ) that automatically determines the ideal starting point for analysis based on volume, efficiency, and price density. Live Statistics: Displays the number of touches and the bar distance from the first touchpoint directly on the price level. Smart Cleaning: Levels are automatically removed once they are significantly breached by price, keeping your chart clutter-free. Advanced Visuals: Fully customizable colors, line styles, and label positioning (e.g., Align to Margin). How to use: Perfect for spotting "Equal Highs/Lows" (Liquidity) or confirming institutional support and resistance zones.
Pine Script® Indikator
von kp_eu_de
AI Adaptive Trend Navigator Echo EditionAI Adaptive Trend Navigator This is an advanced trend-following system optimized for high-volatility index futures (TX). Built upon the LuxAlgo clustering framework, this version introduces several critical enhancements to meet professional trading standards: 1. State Consistency Iteration Enhanced the underlying logic for dynamic arrays and User-Defined Types (UDTs) to ensure stable "State Persistence." This fix eliminates logic gaps during real-time price fluctuations, ensuring that historical backtests perfectly align with live execution. 2. Adaptive Factor Tuning (K-means) The system simulates dozens of parameter paths in real-time, using K-means clustering to automatically select the optimal factor suited for the current market volatility. 3. Advanced Practical Filters Dynamic Buffer Strategy: Filters out market noise during consolidation and early session volatility. Confidence Threshold: Only triggers signals when the AI performance score meets the required quality. Cooldown Logic: Prevents rapid signal flipping in choppy markets. 🧠 開發理念：將 AI 自適應力帶入台指期實戰 針對台指期（TX）高波動特性開發，透過機器學習演算法動態尋優，解決傳統指標參數固定的滯後性。 ✨ Echo 版核心優化點 數據連續性迭代：底層邏輯優化，確保訊號在即時盤勢中穩定不跳斷，回測與實戰高度吻合。 自適應動態尋優：透過 K-means 聚類自動鎖定當前最佳 ATR 因子。 實戰多重濾網：包含空間緩衝區 (Buffer) 與信心門檻，大幅提升訊號品質。 📊 視覺說明 🚀 Rocket: AI confirms trend momentum. ⚡ Lightning: Trend exhaustion or reversal warning. ⚠️ Disclaimer: For educational and technical analysis purposes only.
Pine Script® Indikator
von EchoQuan