Mulakan

Trendfollowing

Pivot Trend Flow [BigBeluga]🔵 OVERVIEW Pivot Trend Flow turns raw swing points into a clean, adaptive trend band. It averages recent pivot highs and lows to form two dynamic reference levels; when price crosses above the averaged highs, trend flips bullish and a green band is drawn; when it crosses below the averaged lows, trend flips bearish and a red band is drawn. During an uptrend the script highlights breakouts of previous pivot highs with ▲ labels, and during a downtrend it flags breakdowns of previous pivot lows with ▼ labels—making structure shifts and continuation signals obvious. 🔵 CONCEPTS Pivot-Based Averages : Recent pivot highs/lows are collected and averaged to create smoothed upper/lower reference levels. if not na(ph) phArray.push(ph) if not na(pl) plArray.push(pl) if phArray.size() > avgWindow upper := phArray.avg() phArray.shift() if plArray.size() > avgWindow lower := plArray.avg() plArray.shift() Trend State via Crosses : Close above the averaged-highs ⇒ bullish trend; close below the averaged-lows ⇒ bearish trend. Trend Band : A colored band (green/red) is plotted and optionally filled to visualize the active regime around price. Structure Triggers : In bull mode the tool watches for prior pivot-high breakouts (▲). In bear mode it watches for prior pivot-low breakdowns (▼). 🔵 FEATURES Adaptive Trend Detection from averaged pivot highs/lows. Clear Visuals : Green band in uptrends, red band in downtrends; optional fill for quick read. Breakout/Breakdown Labels : ▲ marks breaks of previous pivot highs in uptrends ▼ marks breaks of previous pivot lows in downtrends Minimal Clutter : Uses compact lines and labels that extend only on confirmation. Customizable Colors & Fill for trend states and band styling. 🔵 HOW TO USE Pivot Length : Sets how swing points are detected. Smaller = more reactive; larger = smoother. Avg Window (pivots) : How many recent pivot highs/lows are averaged. Increase to stabilize the band; decrease for agility. Read the Band : Green band active ⇒ prioritize longs, pullback buys toward the band. Red band active ⇒ prioritize shorts, pullback sells toward the band. Trade the Triggers : In bull mode, ▲ on a prior pivot-high break can confirm continuation. In bear mode, ▼ on a prior pivot-low break can confirm continuation. Combine with Context : Use HTF trend, S/R, or volume for confluence and to filter signals. Fill Color Toggle : Enable/disable band fill to match your chart style. 🔵 CONCLUSION Pivot Trend Flow converts swing structure into an actionable, low-lag trend framework. By blending averaged pivots with clean breakout/breakdown labels, it clarifies trend direction, timing, and continuation spots—ideal as a core bias tool or a confirmation layer in any trading system.
Penunjuk Pine Script®
oleh BigBeluga
8
Nirvana True Duel전략 이름 열반의 진검승부 (영문: Nirvana True Duel) 컨셉과 철학 “열반의 진검승부”는 시장 소음은 무시하고, 확실할 때만 진입하는 전략입니다. EMA 리본으로 추세 방향을 확인하고, 볼린저 밴드 수축/확장으로 변동성 돌파를 포착하며, OBV로 거래량 확인을 통해 가짜 돌파를 필터링합니다. 전략 로직 매수 조건 (롱) 20EMA > 50EMA (상승 추세) 밴드폭 수축 후 확장 시작 종가가 상단 밴드 돌파 OBV 상승 흐름 유지 매도 조건 (숏) 20EMA < 50EMA (하락 추세) 밴드폭 수축 후 확장 시작 종가가 하단 밴드 이탈 OBV 하락 흐름 유지 진입·청산 손절: ATR × 1.5 배수 익절: 손절폭의 1.5~2배에서 부분 청산 시간 청산: 설정한 최대 보유 봉수 초과 시 강제 청산 장점 ✅ 추세·변동성·거래량 3중 필터 → 노이즈 최소화 ✅ 백테스트·알람 지원 → 기계적 매매 가능 ✅ 5분/15분 차트에 적합 → 단타/스윙 트레이딩 활용 가능 주의점 ⚠ 횡보장에서는 신호가 적거나 실패 가능 ⚠ 수수료·슬리피지 고려 필요 📜 Nirvana True Duel — Strategy Description (English) Name: Nirvana True Duel (a.k.a. Nirvana Cross) Concept & Philosophy The “Nirvana True Duel” strategy focuses on trading only meaningful breakouts and avoiding unnecessary noise. Nirvana: A calm, patient state — waiting for the right opportunity without emotional trading. True Duel: When the signal appears, enter decisively and let the market reveal the outcome. In short: “Ignore market noise, trade only high-probability breakouts.” 🧩 Strategy Components Trend Filter (EMA Ribbon): Stay aligned with the main market trend. Volatility Squeeze (Bollinger Band): Detect volatility contraction & expansion to catch explosive moves early. Volume Confirmation (OBV): Filter out false breakouts by confirming with volume flow. ⚔️ Entry & Exit Conditions Long Setup: 20 EMA > 50 EMA (uptrend) BB width breaks out from recent squeeze Close > Upper Bollinger Band OBV shows positive flow Short Setup: 20 EMA < 50 EMA (downtrend) BB width breaks out from recent squeeze Close < Lower Bollinger Band OBV shows negative flow Risk Management: Stop Loss: ATR × 1.5 below/above entry Take Profit: 1.5–2× stop distance, partial take-profit allowed Time Stop: Automatically closes after max bars held (e.g. 8h on 5m chart) ✅ Strengths Triple Filtering: Trend + Volatility + Volume → fewer false signals Mechanical & Backtestable: Ideal for objective trading & performance validation Adaptable: Works well on Bitcoin, Nasdaq futures, and other high-volatility markets (5m/15m) ⚠️ Things to Note Low signal frequency or higher failure rate in sideways/range markets Commission & slippage should be factored in, especially on lower timeframes ATR multiplier and R:R ratio should be optimized per asset
Strategi Pine Script®
oleh fkgkekd1122
Machine Learning Gaussian Mixture Model | AlphaNattMachine Learning Gaussian Mixture Model | AlphaNatt A revolutionary oscillator that uses Gaussian Mixture Models (GMM) with unsupervised machine learning to identify market regimes and automatically adapt momentum calculations - bringing statistical pattern recognition techniques to trading. "Markets don't follow a single distribution - they're a mixture of different regimes. This oscillator identifies which regime we're in and adapts accordingly." ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🤖 THE MACHINE LEARNING Gaussian Mixture Models (GMM): Unlike K-means clustering which assigns hard boundaries, GMM uses probabilistic clustering : Models data as coming from multiple Gaussian distributions Each market regime is a different Gaussian component Provides probability of belonging to each regime More sophisticated than simple clustering Expectation-Maximization Algorithm: The indicator continuously learns and adapts using the E-M algorithm: E-step: Calculate probability of current market belonging to each regime M-step: Update regime parameters based on new data Continuous learning without repainting Adapts to changing market conditions ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🎯 THREE MARKET REGIMES The GMM identifies three distinct market states: Regime 1 - Low Volatility: Quiet, ranging markets Uses RSI-based momentum calculation Reduces false signals in choppy conditions Background: Pink tint Regime 2 - Normal Market: Standard trending conditions Uses Rate of Change momentum Balanced sensitivity Background: Gray tint Regime 3 - High Volatility: Strong trends or volatility events Uses Z-score based momentum Captures extreme moves Background: Cyan tint ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 💡 KEY INNOVATIONS 1. Probabilistic Regime Detection: Instead of binary regime assignment, provides probabilities: 30% Regime 1, 60% Regime 2, 10% Regime 3 Smooth transitions between regimes No sudden indicator jumps 2. Weighted Momentum Calculation: Combines three different momentum formulas Weights based on regime probabilities Automatically adapts to market conditions 3. Confidence Indicator: Shows how certain the model is (white line) High confidence = strong regime identification Low confidence = transitional market state Line transparency changes with confidence ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⚙️ PARAMETER OPTIMIZATION Training Period (50-500): 50-100: Quick adaptation to recent conditions 100: Balanced (default) 200-500: Stable regime identification Number of Components (2-5): 2: Simple bull/bear regimes 3: Low/Normal/High volatility (default) 4-5: More granular regime detection Learning Rate (0.1-1.0): 0.1-0.3: Slow, stable learning 0.3: Balanced (default) 0.5-1.0: Fast adaptation ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📊 TRADING STRATEGIES Visual Signals: Cyan gradient: Bullish momentum Magenta gradient: Bearish momentum Background color: Current regime Confidence line: Model certainty 1. Regime-Based Trading: Regime 1 (pink): Expect mean reversion Regime 2 (gray): Standard trend following Regime 3 (cyan): Strong momentum trades 2. Confidence-Filtered Signals: Only trade when confidence > 70% High confidence = clearer market state Avoid transitions (low confidence) 3. Adaptive Position Sizing: Regime 1: Smaller positions (choppy) Regime 2: Normal positions Regime 3: Larger positions (trending) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🚀 ADVANTAGES OVER OTHER ML INDICATORS vs K-Means Clustering: Soft clustering (probabilities) vs hard boundaries Captures uncertainty and transitions More mathematically robust vs KNN (K-Nearest Neighbors): Unsupervised learning (no historical labels needed) Continuous adaptation Lower computational complexity vs Neural Networks: Interpretable (know what each regime means) No overfitting issues Works with limited data ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📈 PERFORMANCE CHARACTERISTICS Best Market Conditions: Markets with clear regime shifts Volatile to trending transitions Multi-timeframe analysis Cryptocurrency markets (high regime variation) Key Strengths: Automatically adapts to market changes No manual parameter adjustment needed Smooth transitions between regimes Probabilistic confidence measure ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🔬 TECHNICAL BACKGROUND Gaussian Mixture Models are used extensively in: Speech recognition (Google Assistant) Computer vision (facial recognition) Astronomy (galaxy classification) Genomics (gene expression analysis) Finance (risk modeling at investment banks) The E-M algorithm was developed at Stanford in 1977 and is one of the most important algorithms in unsupervised machine learning. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 💡 PRO TIPS Watch regime transitions: Best opportunities often occur when regimes change Combine with volume: High volume + regime change = strong signal Use confidence filter: Avoid low confidence periods Multi-timeframe: Compare regimes across timeframes Adjust position size: Scale based on identified regime ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⚠️ IMPORTANT NOTES Machine learning adapts but doesn't predict the future Best used with other confirmation indicators Allow time for model to learn (100+ bars) Not financial advice - educational purposes Backtest thoroughly on your instruments ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🏆 CONCLUSION The GMM Momentum Oscillator brings institutional-grade machine learning to retail trading. By identifying market regimes probabilistically and adapting momentum calculations accordingly, it provides: Automatic adaptation to market conditions Clear regime identification with confidence levels Smooth, professional signal generation True unsupervised machine learning This isn't just another indicator with "ML" in the name - it's a genuine implementation of Gaussian Mixture Models with the Expectation-Maximization algorithm, the same technology used in: Google's speech recognition Tesla's computer vision NASA's data analysis Wall Street risk models "Let the machine learn the market regimes. Trade with statistical confidence." ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Developed by AlphaNatt | Machine Learning Trading Systems Version: 1.0 Algorithm: Gaussian Mixture Model with E-M Classification: Unsupervised Learning Oscillator Not financial advice. Always DYOR.
Penunjuk Pine Script®
oleh AlphaNatt
3
High Probability Order Blocks [AlgoAlpha]🟠 OVERVIEW This script detects and visualizes high-probability order blocks by combining a volatility-based z-score trigger with a statistical survival model inspired by Kaplan-Meier estimation. It builds and manages bullish and bearish order blocks dynamically on the chart, displays live survival probabilities per block, and plots optional rejection signals. What makes this tool unique is its use of historical mitigation behavior to estimate and plot how likely each zone is to persist, offering traders a probabilistic perspective on order block strength—something rarely seen in retail indicators. 🟠 CONCEPTS Order blocks are regions of strong institutional interest, often marked by large imbalances between buying and selling. This script identifies those areas using z-score thresholds on directional distance (up or down candles), detecting statistically significant moves that signal potential smart money footprints. A bullish block is drawn when a strong up-move (zUp > 4) follows a down candle, and vice versa for bearish blocks. Over time, each block is evaluated: if price “mitigates” it (i.e., closes cleanly past the opposite side and confirmed with a 1 bar delay), it’s considered resolved and logged. These resolved blocks then inform a Kaplan-Meier-like survival curve, estimating the likelihood that future blocks of a given age will remain unbroken. The indicator then draws a probability curve for each side (bull/bear), updating it in real time. 🟠 FEATURES Live label inside each block showing survival probability or “N.E.D.” if insufficient data. Kaplan-Meier survival curves drawn directly on the chart to show estimated strength decay. Rejection markers (▲ ▼) if price bounces cleanly off an active order block. Alerts for zone creation and rejection signals, supporting rule-based trading workflows. 🟠 USAGE Read the label inside each block for Age | Survival% (or N.E.D. if there aren’t enough samples yet); higher survival % suggests blocks of that age have historically lasted longer. Use the right-side survival curves to gauge how probability decays with age for bull vs bear blocks, and align entries with the side showing stronger survival at current age. Treat ▲ (bullish rejection) and ▼ (bearish rejection) as optional confluence when price tests a boundary and fails to break. Turn on alerts for “Bullish Zone Created,” “Bearish Zone Created,” and rejection signals so you don’t need to watch constantly. If your chart gets crowded, enable Prevent Overlap ; tune Max Box Age to your timeframe; and adjust KM Training Window / Minimum Samples to trade off responsiveness vs stability.
Penunjuk Pine Script®
oleh AlgoAlpha
15
Adaptive Trend Following Suite [Alpha Extract]A sophisticated multi-filter trend analysis system that combines advanced noise reduction, adaptive moving averages, and intelligent market structure detection to deliver institutional-grade trend following signals. Utilizing cutting-edge mathematical algorithms and dynamic channel adaptation, this indicator provides crystal-clear directional guidance with real-time confidence scoring and market mode classification for professional trading execution. 🔶 Advanced Noise Reduction Filter Eliminates market noise using sophisticated Gaussian filtering with configurable sigma values and period optimization. The system applies mathematical weight distribution across price data to ensure clean signal generation while preserving critical trend information, automatically adjusting filter strength based on volatility conditions. advancedNoiseFilter(sourceData, filterLength, sigmaParam) => weightSum = 0.0 valueSum = 0.0 centerPoint = (filterLength - 1) / 2 for index = 0 to filterLength - 1 gaussianWeight = math.exp(-0.5 * math.pow((index - centerPoint) / sigmaParam, 2)) weightSum += gaussianWeight valueSum += sourceData * gaussianWeight valueSum / weightSum 🔶 Adaptive Moving Average Core Engine Features revolutionary volatility-responsive averaging that automatically adjusts smoothing parameters based on real-time market conditions. The engine calculates adaptive power factors using logarithmic scaling and bandwidth optimization, ensuring optimal responsiveness during trending markets while maintaining stability during consolidation phases. // Calculate adaptive parameters adaptiveLength = (periodLength - 1) / 2 logFactor = math.max(math.log(math.sqrt(adaptiveLength)) / math.log(2) + 2, 0) powerFactor = math.max(logFactor - 2, 0.5) relativeVol = avgVolatility != 0 ? volatilityMeasure / avgVolatility : 0 adaptivePower = math.pow(relativeVol, powerFactor) bandwidthFactor = math.sqrt(adaptiveLength) * logFactor 🔶 Intelligent Market Structure Analysis Employs fractal dimension calculations to classify market conditions as trending or ranging with mathematical precision. The system analyzes price path complexity using normalized data arrays and geometric path length calculations, providing quantitative market mode identification with configurable threshold sensitivity. 🔶 Multi-Component Momentum Analysis Integrates RSI and CCI oscillators with advanced Z-score normalization for statistical significance testing. Each momentum component receives independent analysis with customizable periods and significance levels, creating a robust consensus system that filters false signals while maintaining sensitivity to genuine momentum shifts. // Z-score momentum analysis rsiAverage = ta.sma(rsiComponent, zAnalysisPeriod) rsiDeviation = ta.stdev(rsiComponent, zAnalysisPeriod) rsiZScore = (rsiComponent - rsiAverage) / rsiDeviation if math.abs(rsiZScore) > zSignificanceLevel rsiMomentumSignal := rsiComponent > 50 ? 1 : rsiComponent < 50 ? -1 : rsiMomentumSignal ❓How It Works 🔶 Dynamic Channel Configuration Calculates adaptive channel boundaries using three distinct methodologies: ATR-based volatility, Standard Deviation, and advanced Gaussian Deviation analysis. The system automatically adjusts channel multipliers based on market structure classification, applying tighter channels during trending conditions and wider boundaries during ranging markets for optimal signal accuracy. dynamicChannelEngine(baselineData, channelLength, methodType) => switch methodType "ATR" => ta.atr(channelLength) "Standard Deviation" => ta.stdev(baselineData, channelLength) "Gaussian Deviation" => weightArray = array.new_float() totalWeight = 0.0 for i = 0 to channelLength - 1 gaussWeight = math.exp(-math.pow((i / channelLength) / 2, 2)) weightedVariance += math.pow(deviation, 2) * array.get(weightArray, i) math.sqrt(weightedVariance / totalWeight) 🔶 Signal Processing Pipeline Executes a sophisticated 10-step signal generation process including noise filtering, trend reference calculation, structure analysis, momentum component processing, channel boundary determination, trend direction assessment, consensus calculation, confidence scoring, and final signal generation with quality control validation. 🔶 Confidence Transformation System Applies sigmoid transformation functions to raw confidence scores, providing 0-1 normalized confidence ratings with configurable threshold controls. The system uses steepness parameters and center point adjustments to fine-tune signal sensitivity while maintaining statistical robustness across different market conditions. 🔶 Enhanced Visual Presentation Features dynamic color-coded trend lines with adaptive channel fills, enhanced candlestick visualization, and intelligent price-trend relationship mapping. The system provides real-time visual feedback through gradient fills and transparency adjustments that immediately communicate trend strength and direction changes. 🔶 Real-Time Information Dashboard Displays critical trading metrics including market mode classification (Trending/Ranging), structure complexity values, confidence scores, and current signal status. The dashboard updates in real-time with color-coded indicators and numerical precision for instant market condition assessment. 🔶 Intelligent Alert System Generates three distinct alert types: Bullish Signal alerts for uptrend confirmations, Bearish Signal alerts for downtrend confirmations, and Mode Change alerts for market structure transitions. Each alert includes detailed messaging and timestamp information for comprehensive trade management integration. 🔶 Performance Optimization Utilizes efficient array management and conditional processing to maintain smooth operation across all timeframes. The system employs strategic variable caching, optimized loop structures, and intelligent update mechanisms to ensure consistent performance even during high-volatility market conditions. This indicator delivers institutional-grade trend analysis through sophisticated mathematical modelling and multi-stage signal processing. By combining advanced noise reduction, adaptive averaging, intelligent structure analysis, and robust momentum confirmation with dynamic channel adaptation, it provides traders with unparalleled trend following precision. The comprehensive confidence scoring system and real-time market mode classification make it an essential tool for professional traders seeking consistent, high-probability trend following opportunities with mathematical certainty and visual clarity.
Penunjuk Pine Script®
oleh AlphaExtract
4
LogPressure Envelope [BOSWaves]LogPressure Envelope – Adaptive Volatility & Trend Visualizer Overview LogPressure Envelope is a specialized trading tool designed to normalize market behavior using logarithmic price scaling while providing an adaptive framework for volatility and trend detection. The indicator calculates a log-based moving average midline, surrounds it with asymmetric volatility envelopes, and replaces the conventional cloud with progressive fan lines to present price action in a more interpretable form. By integrating rate-of-change midline coloring, fading trend strength, and structured buy/sell markers, LogPressure Envelope simplifies the reading of complex market dynamics. Its design makes it suitable for multiple trading approaches, including scalping, intraday, and swing trading, where volatility behavior and trend shifts must be understood quickly and objectively. Unlike static envelope indicators, LogPressure Envelope adapts continuously to price scale and volatility conditions. It evaluates log-transformed prices, applies configurable moving average methods (EMA, SMA, WMA), and derives asymmetric standard-deviation bands for both upside and downside moves. These envelopes are projected as fan lines with adjustable opacity, producing a layered volatility map that evolves with the market. This system ensures each visual element—midline shading, candle coloring, fan structure, and signal markers—reflects real-time market conditions, allowing traders to interpret volatility expansion, contraction, and directional bias with clarity. How It Works The foundation of LogPressure Envelope is the logarithmic transformation of price. By operating in log space, the indicator removes distortions caused by large nominal price differences across assets, enabling consistent analysis of both low-priced and high-priced instruments. A moving average of log prices is calculated (EMA, SMA, or WMA depending on user input) and then re-converted to normal price scale, forming the log midline. Standard deviation of log prices is then measured over a separate period, with independent multipliers for upside and downside deviations. This asymmetry captures the fact that markets often expand differently in bullish versus bearish phases. Instead of plotting a filled cloud, the envelope is expressed as ten equidistant fan lines stretching from the lower to upper boundary. Each line is shaded progressively to visualize volatility clustering and directional strength without overloading the chart. Trend determination is smoothed using a fade mechanism: shifts in bias do not flip instantly but gradually move toward the new state, producing fewer false transitions. Buy and sell markers are generated when trend strength crosses confirmation thresholds, ensuring signals are event-driven and contextually meaningful. Signals and Visuals LogPressure Envelope provides multiple layers of structured signals: Midline Bias – Central moving average colored by rate-of-change, reflecting directional acceleration or deceleration. Volatility Fan – Ten progressive lines forming a gradient between lower and upper bands, visually encoding volatility spread. Buy Signals – Labels below bars when upward trend strength is confirmed. Sell Signals – Labels above bars when downward trend strength is confirmed. Candle Coloring – Optional shading of candles based on trend alignment with the log midline, highlighting bullish, bearish, or neutral conditions. These signals remain clear even during high-volatility phases, with visual hierarchy maintained through progressive opacity control. Interpretation Trend Analysis : Midline direction and candle coloring provide continuous feedback on prevailing bias. Upward-sloping midlines with blue shading indicate bullish phases, while downward slopes with orange shading confirm bearish conditions. Volatility and Risk Assessment : Expansion of fan lines indicates rising volatility and potential breakout conditions; contraction indicates consolidation and possible mean reversion. Signal Confirmation : Buy and sell markers validate transitions when trend strength thresholds are crossed, aligning with volatility envelope dynamics. Market Context : Asymmetric envelopes allow traders to see where bearish acceleration differs from bullish expansion, improving interpretation of liquidity conditions and institutional pressure. Strategy Integration LogPressure Envelope can be applied across trading styles: Trend Following : Enter trades in the direction of midline bias, confirmed by buy or sell markers. Pullback Entries : Use midline retests during trending conditions as lower-risk continuation points. Volatility Breakouts : Identify sharp expansions in fan line spacing as early signals of directional moves. Reversal Strategies : Fade extreme envelope touches when momentum shows exhaustion and fan contraction begins. Multi-Timeframe Confirmation : Align signals from higher and lower timeframes to reduce noise and validate trade setups. Stop-loss levels can be set near the opposite envelope boundary, while targets may be managed through progressive volatility zones or midline convergence. Advanced Techniques For greater precision, LogPressure Envelope can be combined with other analytical tools: Pair with volume or liquidity measures to validate breakout or reversal conditions. Use momentum indicators to confirm ROC-based midline bias. Track sequences of fan line expansions and contractions to anticipate regime shifts in volatility. Apply across multiple timeframes to monitor how volatility clusters align at different market scales. Adjusting parameters such as envelope multipliers, moving average type, and fade bars allows the indicator to adapt to diverse asset classes and volatility environments. Inputs and Customization Midline Type : Select EMA, SMA, or WMA. Line Opacity : Control visibility of fan lines. Enable Candle Coloring : Toggle trend-based bar shading. MA Length / StdDev Length : Define periods for midline and volatility calculation. Multipliers : Set asymmetric scaling for upside and downside envelopes. Fade Bars : Control smoothness of trend strength transitions. Fan Lines : Adjust number of envelope subdivisions for visualization granularity. Why Use LogPressure Envelope LogPressure Envelope translates complex volatility and trend interactions into a structured and adaptive framework. By combining logarithmic normalization, asymmetric standard deviation envelopes, and smoothed trend confirmation, it allows traders to: Normalize price analysis across assets of different scales. Visualize volatility expansion and contraction in real time. Identify and confirm directional shifts with objective signal markers. Apply a disciplined system for trend, breakout, and reversal strategies. This indicator is designed for traders who want a systematic, visually clear approach to volatility-based market analysis without relying on static bands or arbitrary scaling.
Penunjuk Pine Script®
oleh BOSWaves
6
Pasrsifal.RegressionTrendStateSummary The Parsifal.Regression.Trend.State Indicator analyzes the leading coefficients of linear and quadratic regressions of price (against time). It also considers their first- and second-order changes. These features are aggregated into a Trend-State background, shown as a gradient color. In addition, the indicator generates fast and slow signals that can be used as potential entry- or exit triggers. This tool is designed for advanced trend-following strategies, leveraging information from multiple trendline features. Background Trendlines provide insight into the state of a trend or the “trendiness” of a price process. While moving averages or pivot-based lines can serve as envelopes and breakout levels, they are often too lagging for swing traders, who need tools that adapt more closely to price swings, ideally using trendlines, around which the price process swings continuously. Regression lines address this by cutting directly through the data, making them a natural anchor for observing how price winds around a central trendline within a chosen lookback period. Regression Trendlines • Linear Regression: o Minimizes distance to all closing values over the lookback period. o The slope represents the short-term linear trend. o The change of slope indicates trend acceleration or deceleration. o Linear regression lags during phases of rapid market shifts. • Quadratic Regression: o Fits a second-degree polynomial to minimize deviation from closing prices. o The convexity term (leading coefficient) reflects curvature:  Positive convexity → accelerating uptrend or fading downtrend.  Negative convexity → accelerating downtrend or fading uptrend. o The change of convexity detects early shifts in momentum and often reacts faster than slope features. Features Extracted The indicator evaluates six features: • Linear features: slope, first derivative of slope, second derivative of slope. • Quadratic features: convexity term, first derivative of the convexity term, second derivative of the convexity term. • Linear features: capture broad, background trend behavior. • Quadratic features: detect deviations, accelerations, and smaller-scale dynamics. Quadratic terms generally react first to market changes, while linear terms provide stability and context. Dynamics of Market Moves as seen by linear and quadratic regressions • At the start of a rapid move: The change of convexity reacts first, capturing the shift in dynamics before other features. The convexity term then follows, while linear slope features lag further behind. Because convexity measures deviation from linearity, it reflects accelerating momentum more effectively than slope. • At the end of a rapid move: Again, the change of convexity responds first to fading momentum, signaling the transition from above-linear to below-linear dynamics. Even while a strong trend persists, the change of convexity may flip sign early, offering a warning of weakening strength. The convexity term itself adjusts more slowly but may still turn before the price process does. Linear features lag the most, typically only flipping after price has already reversed, thereby smoothing out the rapid, more sensitive reactions of quadratic terms. ________________________________________ Parsifal Regression.Trend.State Method 1. Feature Mapping: Each feature is mapped to a range between -1 and 1, preserving zero-crossings (critical for sign interpretation). 2. Aggregation: A heuristic linear combination*) produces a background information value, visualized as a gradient color scale: o Deep green → strong positive trend. o Deep red → strong negative trend. o Yellow → neutral or transitional states. 3. Signals: o Fast signal (oscillator): ranges from -1 to 1, reflecting short-term trend state. o Slow signal (smoothed): moving average of the fast signal. o Their interactions (crossovers, zero-crossings) provide actionable trading triggers. How to Use The Trend-State background gradient provides intuitive visual feedback on the aggregated regression features (slope, convexity, and their changes). Because these features reflect not only current trend strength but also their acceleration or deceleration, the color transitions help anticipate evolving market states: • Solid Green: All features near their highs. Indicates a strong, accelerating uptrend. May also reflect explosive or hyperbolic upside moves (including gaps). • Fading Solid Green: A recently strong uptrend is losing momentum. Price may shift into a slower uptrend, consolidation, or even a reversal. • Fading Green → Yellow: Often appears as a dirty yellow or a rapidly mixing pattern of green and red. Signals that the uptrend is weakening toward neutrality or beginning to turn negative. • Yellow → Deepening Red: Two possible scenarios: o Coming from a strong uptrend → suggests a sharp fade, though the trend may still technically be up. o Coming from a weaker uptrend or sideways market → suggests the start of an accelerating downtrend. • Solid Red: All features near their lows. Indicates a strong, accelerating downtrend. May also reflect crash-type conditions or downside gaps. • Fading Solid Red: A recently strong downtrend is losing strength. Market may move into a slower decline, consolidation, or early reversal upward. • Fading Red → Yellow : The downtrend is weakening toward neutral, with potential for a bullish shift. • Yellow → Increasing Green: Two possible scenarios: o Coming from a strong downtrend, it reflects a sharp fade of bearish momentum, though the market may still technically be trending down. o Coming from a weaker downtrend or sideways movement, it suggests the start of an accelerating uptrend. Note: Market evolution does not always follow this neat “color cycle.” It may jump between states, skip stages, or reverse abruptly depending on market conditions. This makes the background coloring particularly valuable as a contextual map of current and evolving price dynamics. Signal Crossovers: Although the fast signal is very similar (but not identical) to the background coloring, it provides a numerical representation indicating a bullish interpretation for rising values and bearish for falling. o High-confidence entries:  Fast signal rising from < -0.7 and crossing above the slow signal → potential long entry.  Fast signal falling from > +0.7 and crossing below the slow signal → potential short entry. o Low-confidence entries:  Crossovers near zero may still provide a valid trigger but may be noisy and should be confirmed with other signals. o Zero-crossings:  Indicate broader state changes, useful for conservative positioning or option strategies. For confirmation of a Fast signal 0-crossing, wait for the Slow signal to cross as well. ________________________________________ *) Note on Aggregation While the indicator currently uses a heuristic linear combination of features, alternatives such as Principal Component Analysis (PCA) could provide a more formal aggregation. However, while in the absence of matrix algebra, the required eigenvalue decomposition can be approximated, its computational expense does not justify the marginal higher insight in this case. The current heuristic approach offers a practical balance of clarity, speed, and accuracy.
Penunjuk Pine Script®
oleh parsifaltrading
Reverse RSI Signals [AlgoAlpha]🟠 OVERVIEW This script introduces the Reverse RSI Signals system, an original approach that inverts traditional RSI values back into price levels and then overlays them directly on the chart as dynamic bands. Instead of showing RSI in a subwindow, the script calculates the exact price thresholds that correspond to common RSI levels (30/70/50) and displays them as upper, lower, and midline bands. These are further enhanced with an adaptive Supertrend filter and divergence detection, allowing traders to see overbought/oversold zones translated into actionable price ranges and trend signals. The script combines concepts of RSI inversion, volatility envelopes, and divergence tracking to provide a context-driven tool for spotting reversals and regime shifts. 🟠 CONCEPTS The script relies on inverting RSI math: by solving for the price that would yield a given RSI level, it generates real chart levels tied to oscillator conditions. These RSI-derived price bands act like support/resistance, adapting each bar as RSI changes. On top of this, a Supertrend built around the RSI midline introduces directional bias, switching regimes when the midline is breached. Regular bullish and bearish divergences are detected by comparing RSI pivots against price pivots, highlighting early reversal conditions. This layered approach means the indicator is not just RSI on price but a hybrid of oscillator translation, volatility-tracking midline envelopes, and divergence analysis. 🟠 FEATURES Inverted RSI bands: upper (70), lower (30), and midline (50), smoothed with EMA for noise reduction. Supertrend overlay on the RSI midline to confirm regime direction (bullish or bearish). Gradient-filled zones between outer and inner RSI bands to visualize proximity and exhaustion. Non-repainting bullish and bearish divergence markers plotted directly on chart highs/lows. 🟠 USAGE Apply the indicator to any chart and use the plotted RSI price bands as adaptive support/resistance. The midline defines equilibrium, while upper and lower bands represent classic RSI thresholds translated into real price action. In bullish regimes (green candles), long trades are stronger when price approaches or bounces from the lower band; in bearish regimes (red candles), shorts are favored near the upper band. Divergence markers (▲ for bullish, ▼ for bearish) flag potential reversal points early. Traders can combine the band proximity, divergence alerts, and Supertrend context to time entries, exits, or to refine ongoing trend trades. Adjust smoothing and Supertrend ATR settings to match the volatility of the instrument being analyzed.
Penunjuk Pine Script®
oleh AlgoAlpha
17
200 EMA w/ Ticker Memory200 EMA w/ Ticker Memory — Multi-Symbol & Multi-Timeframe EMA Tracker with Alerts Overview The 200 EMA w/ Ticker Memory indicator allows you to monitor the 200-period Exponential Moving Average (EMA) across multiple symbols and timeframes. Designed for traders managing multiple tickers, it provides customizable timeframe inputs per symbol and instant alerts on price touches of the 200 EMA. Key Features Multi-symbol support: Configure up to 20 different symbols, each with its own timeframe setting. Flexible timeframe input: Assign specific timeframes per symbol or use a default timeframe fallback. Accurate 200 EMA calculation: Uses request.security to fetch 200 EMA from the symbol-specific timeframe. Visual EMA plots: Displays both the EMA on the selected timeframe and the EMA on the current chart timeframe for comparison. Touch alerts: Configurable alerts when price “touches” the 200 EMA within a user-defined sensitivity percentage. Ticker memory: Remembers your configured symbols and displays them in an on-chart table. Compact info table: Displays current symbol status, alert settings, and timeframe in a clean, transparent table overlay. How to Use Configure Symbols and Timeframes: Input your desired symbols (up to 20) and their respective timeframes under the “Symbol Settings” groups in the indicator’s settings pane. Set Default Timeframe: Choose a default timeframe to be used when no specific timeframe is assigned for a symbol. Adjust Alert Settings: Enable or disable alerts and set the touch sensitivity (% distance from EMA to trigger alerts). Alerts Alerts trigger once per bar when the price touches the 200 EMA within the defined sensitivity threshold. Alert messages include: Symbol / Current price / EMA value / EMA timeframe used / Chart timeframe / Timestamp Customization 200 EMA Color: Change the line color for better visibility. Touch Sensitivity: Fine-tune how close price must be to the EMA to count as a touch (default 0.1%). Enable Touch Alerts: Turn on/off alert notifications easily. For: - Swing traders monitoring multiple stocks or assets. - Day traders watching key EMA levels on different timeframes. - Analysts requiring a quick visual and alert system for 200 EMA touches. - Portfolio managers tracking key technical levels across various securities. Limitations Supports up to 20 configured symbols (can be extended manually if needed). Works best on charts with reasonable bar frequency due to request.security usage. Alert frequency is limited to once per bar for clarity. Disclaimer This indicator is provided “as-is” for educational and informational purposes only. It does not guarantee trading success or financial gain.
Penunjuk Pine Script®
oleh BaidehnXBT
Mutanabby_AI | Ultimate Algo | Remastered+Overview The Mutanabby_AI Ultimate Algo Remastered+ represents a sophisticated trend-following system that combines Supertrend analysis with multiple moving average confirmations. This comprehensive indicator is designed specifically for identifying high-probability trend continuation and reversal opportunities across various market conditions. Core Algorithm Components **Supertrend Foundation**: The primary signal generation relies on a customizable Supertrend indicator with adjustable sensitivity (1-20 range). This adaptive trend-following tool uses Average True Range calculations to establish dynamic support and resistance levels that respond to market volatility. **SMA Confirmation Matrix**: Multiple Simple Moving Averages (SMA 4, 5, 9, 13) provide layered confirmation for signal strength. The algorithm distinguishes between regular signals and "Strong" signals based on SMA 4 vs SMA 5 relationship, offering traders different conviction levels for position sizing. **Trend Ribbon Visualization**: SMA 21 and SMA 34 create a visual trend ribbon that changes color based on their relationship. Green ribbon indicates bullish momentum while red signals bearish conditions, providing immediate visual trend context. **RSI-Based Candle Coloring**: Advanced 61-tier RSI system colors candles with gradient precision from deep red (RSI ≤20) through purple transitions to bright green (RSI ≥79). This visual enhancement helps traders instantly assess momentum strength and overbought/oversold conditions. Signal Generation Logic **Buy Signal Criteria**: - Price crosses above Supertrend line - Close price must be above SMA 9 (trend confirmation) - Signal strength determined by SMA 4 vs SMA 5 relationship - "Strong Buy" when SMA 4 ≥ SMA 5 - Regular "Buy" when SMA 4 < SMA 5 **Sell Signal Criteria**: - Price crosses below Supertrend line - Close price must be below SMA 9 (trend confirmation) - Signal strength based on SMA relationship - "Strong Sell" when SMA 4 ≤ SMA 5 - Regular "Sell" when SMA 4 > SMA 5 Advanced Risk Management System **Automated TP/SL Calculation**: The indicator automatically calculates stop loss and take profit levels using ATR-based measurements. Risk percentage and ATR length are fully customizable, allowing traders to adapt to different market conditions and personal risk tolerance. **Multiple Take Profit Targets**: - 1:1 Risk-Reward ratio for conservative profit taking - 2:1 Risk-Reward for balanced trade management - 3:1 Risk-Reward for maximum profit potential **Visual Risk Display**: All risk management levels appear as both labels and optional trend lines on the chart. Customizable line styles (solid, dashed, dotted) and positioning ensure clear visualization without chart clutter. **Dynamic Level Updates**: Risk levels automatically recalculate with each new signal, maintaining current market relevance throughout position lifecycles. Visual Enhancement Features **Customizable Display Options**: Toggle trend ribbon, TP/SL levels, and risk lines independently. Decimal precision adjustments (1-8 decimal places) accommodate different instrument price formats and personal preferences. **Professional Label System**: Clean, informative labels show entry points, stop losses, and take profit targets with precise price levels. Labels automatically position themselves for optimal chart readability. **Color-Coded Momentum**: The gradient RSI candle coloring system provides instant visual feedback on momentum strength, helping traders assess market energy and potential reversal zones. Implementation Strategy **Timeframe Optimization**: The algorithm performs effectively across multiple timeframes, with higher timeframes (4H, Daily) providing more reliable signals for swing trading. Lower timeframes work well for day trading with appropriate risk adjustments. **Sensitivity Adjustment**: Lower sensitivity values (1-5) generate fewer but higher-quality signals, ideal for conservative approaches. Higher sensitivity (15-20) increases signal frequency for active trading styles. **Risk Management Integration**: Use the automated risk calculations as baseline parameters, adjusting risk percentage based on account size and market conditions. The 1:1, 2:1, 3:1 targets enable systematic profit-taking strategies. Market Application **Trend Following Excellence**: Primary strength lies in capturing significant trend movements through the Supertrend foundation with SMA confirmation. The dual-layer approach reduces false signals common in single-indicator systems. **Momentum Assessment**: RSI-based candle coloring provides immediate momentum context, helping traders assess signal strength and potential continuation probability. **Range Detection**: The trend ribbon helps identify ranging conditions when SMA 21 and SMA 34 converge, alerting traders to potential breakout opportunities. Performance Optimization **Signal Quality**: The requirement for both Supertrend crossover AND SMA 9 confirmation significantly improves signal reliability compared to basic trend-following approaches. **Visual Clarity**: The comprehensive visual system enables rapid market assessment without complex calculations, ideal for traders managing multiple instruments. **Adaptability**: Extensive customization options allow fine-tuning for specific markets, trading styles, and risk preferences while maintaining the core algorithm integrity. ## Non-Repainting Design **Educational Note**: This indicator uses standard TradingView functions (Supertrend, SMA, RSI) with normal behavior patterns. Real-time updates on current candles are expected and standard across all technical indicators. Historical signals on closed candles remain fixed and unchanged, ensuring reliable backtesting and analysis. **Signal Confirmation**: Final signals are confirmed only when candles close, following standard technical analysis principles. The algorithm provides clear distinction between developing signals and confirmed entries. Technical Specifications **Supertrend Parameters**: Default sensitivity of 4 with ATR length of 11 provides balanced signal generation. Sensitivity range from 1-20 allows adaptation to different market volatilities and trading preferences. **Moving Average Configuration**: SMA periods of 8, 9, and 13 create multi-layered trend confirmation, while SMA 21 and 34 form the visual trend ribbon for broader market context. **Risk Management**: ATR-based calculations with customizable risk percentage ensure dynamic adaptation to market volatility while maintaining consistent risk exposure principles. Recommended Settings **Conservative Approach**: Sensitivity 4-5, RSI length 14, higher timeframes (4H, Daily) for swing trading with maximum signal reliability. **Active Trading**: Sensitivity 6-8, RSI length 8-10, intermediate timeframes (1H) for balanced signal frequency and quality. **Scalping Setup**: Sensitivity 10-15, RSI length 5-8, lower timeframes (15-30min) with enhanced risk management protocols. ## Conclusion The Mutanabby_AI Ultimate Algo Remastered+ combines proven trend-following principles with modern visual enhancements and comprehensive risk management. The algorithm's strength lies in its multi-layered confirmation approach and automated risk calculations, providing both novice and experienced traders with clear signals and systematic trade management. Success with this system requires understanding the relationship between signal strength indicators and adapting sensitivity settings to match current market conditions. The comprehensive visual feedback system enables rapid decision-making while the automated risk management ensures consistent trade parameters. Practice with different sensitivity settings and timeframes to optimize performance for your specific trading style and risk tolerance. The algorithm's systematic approach provides an excellent framework for disciplined trend-following strategies across various market environments.
Penunjuk Pine Script®
oleh Mutanabby_AI
13
Zero Lag Liquidity [AlgoAlpha]🟠 OVERVIEW This script plots liquidity zones with zero lag using lower-timeframe wick profiles and high-volume wicks to mark key price reactions. It’s called Zero Lag Liquidity because it captures significant liquidity imbalances in real time by processing lower-TF price-volume distributions directly inside the wick of abnormal candles. The tool builds a volume histogram inside long upper/lower wicks, then calculates a local Point of Control (POC) to mark the price where most volume occurred. These levels act as visual liquidity zones, which can trigger labels, break signals, and trend detection depending on price interaction. 🟠 CONCEPTS The core concept relies on identifying high-volume candles with unusually long wicks—often a sign of opposing liquidity. When a large upper or lower wick appears with a strong volume spike, the script builds a histogram of lower-timeframe closes and volumes inside that wick. It bins the wick into segments, sums volume per bin, and finds the POC. This POC becomes the liquidity level. The script then dynamically tracks whether price breaks above or rejects off these levels, adjusts the active trend regime accordingly, and highlights bars to help users spot continuation or reversal behavior. The logic avoids repainting or subjective interpretation by using fixed thresholds and lower-TF price action. 🟠 FEATURES Dynamic liquidity levels rendered at POC of significant wicks, colored by bullish/bearish direction. Break detection that removes levels once price decisively crosses them twice in the same direction. Rejection detection that plots ▲/▼ markers when price bounces off levels intrabar. Volume labels for each level, shown either as raw volume or percentage of total level volume. Candle coloring based on trend direction (break-dominant). 🟠 USAGE Use this indicator to track where liquidity has most likely entered the market via abnormal wick events. When a long wick forms with high volume, the script looks inside it (using your chosen lower timeframe) and marks the most traded price within it. These levels can serve as expected reversal or breakout zones. Rejections are marked with small arrows, while breaks trigger trend shifts and remove the level. You can toggle trend coloring to see directional bias after a breakout. Use the wick multiplier to control how selective the detector is (higher = stricter). Alerts and label modes help customize the signal for different asset types and chart styles.
Penunjuk Pine Script®
oleh AlgoAlpha
12
Mean Reversion & Momentum Hybrid | D_QUANT 📌 Mean Reversion & Momentum Hybrid | D_QUANT 📖 Description: This indicator combines mean reversion logic, volatility filtering, and percentile-based momentum to deliver clear, context-aware buy/sell signals designed for trend-following and contrarian setups. At its core, it merges: A Bollinger Band % Positioning Model (BB%) A 75th/25th Percentile Momentum System A Volatility-Adjusted Trend Filter using RMA + ATR All tied together with a dynamic gradient-style oscillator that visualizes signal strength and persistence over time — making it easy to track high-conviction setups. Signals only trigger when all three core components align, filtering out noise and emphasizing high-probability turning points or trend continuations. ⚙️ Methodology Overview: Bollinger Bands % (BB%): Price is measured as a percentage between upper and lower Bollinger Bands (based on OHLC4). Entries are only considered when price exceeds custom BB% thresholds — emphasizing market extremes. Volatility-Based Trend Filter (RMA + ATR): A smoothed RMA baseline is paired with ATR to define trend bias. This ensures signals only occur when price deviates meaningfully beyond recent volatility. Percentile Momentum Model (75th/25th Rank): Price is compared against its rolling 75th and 25th percentile. If price breaks these statistical boundaries (adjusted by ATR), it triggers a directional momentum condition. Signal Consensus Engine: All three layers must agree — BB% condition, trend filter, and percentile momentum — before a buy or sell signal is plotted. Gradient Oscillator Visualization: Signals appear as a fading oscillator line with a gradient-filled area beneath it. The color intensity represents how “fresh” or “strong” the signal is, fading over time if not reconfirmed, offering both clarity and signal aging at a glance. 🔧 User Inputs: 🧠 Core Settings: Source: Select the price input (default: close) Bollinger Bands Length: Period for BB basis and deviation Bollinger Bands Multiplier: Width of the bands Minimum BB Width (% of Price): Prevents signals during low-volatility chop 📊 BB% Thresholds: BB% Long Threshold (L): Minimum %B to consider a long BB% Short Threshold (S): Maximum %B to consider a short 🔍 Trend Filter Parameters: RMA Length: Period for the smoothed trend baseline ATR Length: Lookback for ATR in trend deviation filter ⚡️ Momentum Parameters: Momentum Length: Period for percentile momentum calculation Mult_75 / Mult_25: ATR-adjusted thresholds for breakout above/below percentile levels 🎨 Visualization: Bar Coloring: Highlights candles during active signals Background Coloring: Optional background shading for signals Show Oscillator Plot: Toggle the gradient-style oscillator 🧪 Use Case: This indicator works well across all assets for trend identification. It is particularly effective when used on higher timeframes (e.g. 12H, 1D,2D) to capture mean reversion bounces or confirm breakouts backed by percentile momentum and volatility expansion. ⚠️ Notes: This is not financial advice. Use in combination with proper risk management and confluence from other tools.
Penunjuk Pine Script®
oleh D_QUANT
Hann Window FIR Filter Ribbon [BigBeluga]🔵 OVERVIEW The Hann Window FIR Filter Ribbon is a trend-following visualization tool based on a family of FIR filters using the Hann window function. It plots a smooth and dynamic ribbon formed by six Hann filters of progressively increasing length. Gradient coloring and filled bands reveal trend direction and compression/expansion behavior. When short-term trend shifts occur (via filter crossover), it automatically anchors visual support/resistance zones at the nearest swing highs or lows. 🔵 CONCEPTS Hann FIR Filter: A finite impulse response filter that uses a Hann (cosine-based) window for weighting past price values, resulting in a non-lag, ultra-smooth output. hannFilter(length)=> var float hann = na // Final filter output float filt = 0 float coef = 0 for i = 1 to length weight = 1 - math.cos(2 * math.pi * i / (length + 1)) filt += price * weight coef += weight hann := coef != 0 ? filt / coef : na Ribbon Stack: The indicator plots 6 Hann FIR filters with increasing lengths, creating a smooth "ribbon" that adapts to price shifts and visually encodes volatility. Gradient Coloring: Line colors and fill opacity between layers are dynamically adjusted based on the distance between the filters, showing momentum expansion or contraction. Dynamic Swing Zones: When the shortest filter crosses its nearest neighbor, a swing high/low is located, and a triangle-style level is anchored and projected to the right. Self-Extending Levels: These dynamic levels persist and extend until invalidated or replaced by a new opposite trend break. 🔵 FEATURES Plots 6 Hann FIR filters with increasing lengths (controlled by Ribbon Size input). Automatically colors each filter and the fill between them with smooth gradient transitions. Detects trend shifts via filter crossover and anchors visual resistance (red) or support (green) zones. Support/resistance zones are triangle-style bands built around recent swing highs/lows. Levels auto-extend right and adapt in real time until invalidated by price action. Ribbon responds smoothly to price and shows contraction or expansion behavior clearly. No lag in crossover detection thanks to FIR architecture. Adjustable sensitivity via Length and Ribbon Size inputs. 🔵 HOW TO USE Use the ribbon gradient as a visual trend strength and smooth direction cue. Watch for crossover of shortest filters as early trend change signals. Monitor support/resistance zones as potential high-probability reaction points. Combine with other tools like momentum or volume to confirm trend breaks. Adjust ribbon thickness and length to suit your trading timeframe and volatility preference. 🔵 CONCLUSION Hann Window FIR Filter Ribbon blends digital signal processing with trading logic to deliver a visually refined, non-lagging trend tool. The adaptive ribbon offers insight into momentum compression and release, while swing-based levels give structure to potential reversals. Ideal for traders who seek smooth trend detection with intelligent, auto-adaptive zone plotting.
Penunjuk Pine Script®
oleh BigBeluga
2
Trend Strength Index [Alpha Extract]The Trend Strength Index leverages Volume Weighted Moving Average (VWMA) and Average True Range (ATR) to quantify trend intensity in cryptocurrency markets, particularly Bitcoin. The combination of VWMA and ATR is particularly powerful because VWMA provides a more accurate representation of the market's true average price by weighting periods of higher trading volume more heavily—capturing genuine momentum driven by increased participation rather than treating all price action equally, which is crucial in volatile assets like Bitcoin where volume spikes often signal institutional interest or market shifts. Meanwhile, ATR normalizes this measurement for volatility, ensuring that trend strength readings remain comparable across different market conditions; without ATR's adjustment, raw price deviations from the mean could appear artificially inflated during high-volatility periods (like during news events or liquidations) or understated in low-volatility sideways markets, leading to misleading signals. Together, they create a volatility-adjusted, volume-sensitive metric that reliably distinguishes between meaningful trend developments and noise. This indicator measures the normalized distance between price and its volume-weighted mean, providing a clear visualization of trend strength while accounting for market volatility. It helps traders identify periods of strong directional movement versus consolidation, with color-coded gradients for intuitive interpretation. 🔶 CALCULATION The indicator processes price data through these analytical stages: Volume Weighted Moving Average: Computes a smoothed average weighted by trading volume Volatility Normalization: Uses ATR to account for market volatility Distance Measurement: Calculates absolute deviation between current price and VWMA Strength Normalization: Divides price deviation by ATR for a volatility-adjusted metric Formula: VWMA = Volume-Weighted Moving Average of Close over specified length ATR = Average True Range over specified length Price Distance = |Close - VWMA| Trend Strength = Price Distance / ATR 🔶 DETAILS Visual Features: VWMA Line: Blue line overlay on the price chart representing the volume-weighted mean Trend Strength Area: Histogram-style area plot with dynamic color gradient (red for weak trends, transitioning through orange and yellow to green for strong trends) Threshold Line: Horizontal red line at the customizable Trend Enter level Background Highlight: Subtle green background when trend strength exceeds the enter threshold for strong trend visualization Alert System: Triggers notifications for strong trend detection Interpretation: 0-Weak (Red): Minimal trend strength, potential consolidation or ranging market Mid-Range (Orange/Yellow): Building momentum, watch for breakout potential At/Above Enter Threshold (Green): Strong trend conditions, potential for continued directional moves Threshold Crossing: Trend strength crossing above the enter level signals increasing conviction in the current direction Color Transitions: Gradual shifts from warm (red/orange) to cool (green) tones indicate strengthening trends 🔶 EXAMPLES Strong Trend Entry: When trend strength crosses above the enter threshold (e.g., 1.2), it identifies the onset of a powerful move where price deviates significantly from the mean. Example: During a rally, trend strength rising from yellow (around 1.0) to green (1.2+) often precedes sustained upward momentum, providing entry opportunities for trend followers. Consolidation Detection: Low trend strength values in red shades (below 0.5) highlight periods of low volatility and mean reversion potential. Example: After a sharp sell-off, persistent red values signal a likely sideways phase, allowing traders to avoid whipsaws and wait for orange/yellow transitions as a precursor to recovery. Volatility-Adjusted Pullbacks: In volatile markets, the ATR component ensures trend strength remains accurate; a dip back to yellow from green during minor corrections can indicate healthy pullbacks within a strong trend. Example: Trend strength briefly falling to yellow levels (e.g., 0.8-1.1) after hitting green provides profit-taking signals without invalidating the overall bullish bias if the VWMA holds as support. Threshold Alert Integration: The alert condition combines strength value with the enter threshold for timely notifications. Example: Receiving a "Strong Trend Detected" alert when the area plot turns green helps confirm Bitcoin's breakout from consolidation, aligning with increased volume for higher-probability trades. 🔶 SETTINGS Customization Options: Lengths: VWMA length (default 14), ATR length (default 14) Thresholds: Trend enter (default 1.2, step 0.1), trend exit (default 1.15, for potential future signal enhancements) Visuals: Automatic color scaling with red at 0, transitioning to green at/above enter threshold Alert Conditions: Strong trend detection (when strength > enter) The Trend Strength Index equips traders with a robust, easy-to-interpret tool for gauging trend intensity in volatile markets like Bitcoin. By normalizing price deviations against volatility, it delivers reliable signals for identifying high-momentum opportunities while the gradient coloring and alerts facilitate quick assessments in both trending and choppy conditions.
Penunjuk Pine Script®
oleh AlphaExtract
9
Smart Money Breakout Channels [AlgoAlpha]🟠 OVERVIEW This script draws breakout detection zones called “Smart Money Breakout Channels” based on volatility-normalized price movement and visualizes them as dynamic boxes with volume overlays. It identifies temporary accumulation or distribution ranges using a custom normalized volatility metric and tracks when price breaks out of those zones—either upward or downward. Each channel represents a structured range where smart money may be active, helping traders anticipate key breakouts with added context from volume delta, up/down volume, and a visual gradient gauge for momentum bias. 🟠 CONCEPTS The script calculates normalized price volatility by measuring the standard deviation of price mapped to a scale using the highest and lowest prices over a set lookback period. When normalized volatility reaches a local low and flips upward, a boxed channel is drawn between the highest and lowest prices in that zone. These boxes persist until price breaks out, either with a strong candle close (configurable) or by touching the boundary. Volume analysis enhances interpretation by rendering delta bars inside the box, showing volume distribution during the channel. Additionally, a real-time visual “gauge” shows where volume delta sits within the channel range, helping users spot pressure imbalances. 🟠 FEATURES Automatic detection and drawing of breakout channels based on volatility-normalized price pivots. Optional nested channels to allow multiple simultaneous zones or a clean single-zone view. Gradient-filled volume gauge with dynamic pointer to show current delta pressure within the box. Three volume visualization modes: raw volume, comparative up/down volume, and delta. Alerts for new channel creation and confirmed bullish or bearish breakouts. 🟠 USAGE Apply the indicator to any chart. Wait for a new breakout box to form—this occurs when volatility behavior shifts and a stable range emerges. Once a box appears, monitor price relative to its boundaries. A breakout above suggests bullish continuation, below suggests bearish continuation; signals are stronger when “Strong Closes Only” is enabled. Watch the internal volume candles to understand where buy/sell pressure is concentrated during the box. Use the gauge on the right to interpret whether net pressure is building upward or downward before breakout to anticipate the direction. Use alerts to catch breakout events without needing to monitor the chart constantly 🚨.
Penunjuk Pine Script®
oleh AlgoAlpha
76
Stochastic Z-Score [AlgoAlpha]🟠 OVERVIEW This indicator is a custom-built oscillator called the Stochastic Z-Score , which blends a volatility-normalized Z-Score with stochastic principles and smooths it using a Hull Moving Average (HMA). It transforms raw price deviations into a normalized momentum structure, then processes that through a stochastic function to better identify extreme moves. A secondary long-term momentum component is also included using an ALMA smoother. The result is a responsive oscillator that reacts to sharp imbalances while remaining stable in sideways conditions. Colored histograms, dynamic oscillator bands, and reversal labels help users visually assess shifts in momentum and identify potential turning points. 🟠 CONCEPTS The Z-Score is calculated by comparing price to its mean and dividing by its standard deviation—this normalizes movement and highlights how far current price has stretched from typical values. This Z-Score is then passed through a stochastic function, which further refines the signal into a bounded range for easier interpretation. To reduce noise, a Hull Moving Average is applied. A separate long-term trend filter based on the ALMA of the Z-Score helps determine broader context, filtering out short-term traps. Zones are mapped with thresholds at ±2 and ±2.5 to distinguish regular momentum from extreme exhaustion. The tool is built to adapt across timeframes and assets. 🟠 FEATURES Z-Score histogram with gradient color to visualize deviation intensity (optional toggle). Primary oscillator line (smoothed stochastic Z-Score) with adaptive coloring based on momentum direction. Dynamic bands at ±2 and ±2.5 to represent regular vs extreme momentum zones. Long-term momentum line (ALMA) with contextual coloring to separate trend phases. Automatic reversal markers when short-term crosses occur at extremes with supporting long-term momentum. Built-in alerts for oscillator direction changes, zero-line crosses, overbought/oversold entries, and trend confirmation. 🟠 USAGE Use this script to track momentum shifts and identify potential reversal areas. When the oscillator is rising and crosses above the previous value—especially from deeply negative zones (below -2)—and the ALMA is also above zero, this suggests bullish reversal conditions. The opposite holds for bearish setups. Reversal labels ("▲" and "▼") appear only when both short- and long-term conditions align. The ±2 and ±2.5 thresholds act as momentum warning zones; values inside are typical trends, while those beyond suggest exhaustion or extremes. Adjust the length input to match the asset’s volatility. Enable the histogram to explore underlying raw Z-Score movements. Alerts can be configured to notify key changes in momentum or zone entries.
Penunjuk Pine Script®
oleh AlgoAlpha
13
RSI-Adaptive T3 + Squeeze Momentum Strategy✅ Strategy Guide: RSI-Adaptive T3 + Squeeze Momentum Strategy 📌 Overview The RSI-Adaptive T3 + Squeeze Momentum Strategy is a dynamic trend-following strategy based on an RSI-responsive T3 moving average and Squeeze Momentum detection . It adapts in real-time to market volatility to enhance entry precision and optimize risk. ⚠️ This strategy is provided for educational and research purposes only. Past performance does not guarantee future results. 🎯 Strategy Objectives The main objective of this strategy is to catch the early phase of a trend and generate consistent entry signals. Designed to be intuitive and accessible for traders from beginner to advanced levels. ✨ Key Features RSI-Responsive T3: T3 length dynamically adjusts according to RSI values for adaptive trend detection Squeeze Momentum: Combines Bollinger Bands and Keltner Channels to identify trend buildup phases Visual Triggers: Entry signals are generated from T3 crossovers and momentum strength after squeeze release 📊 Trading Rules Long Entry: When T3 crosses upward, momentum is positive, and the squeeze has just been released. Short Entry: When T3 crosses downward, momentum is negative, and the squeeze has just been released. Exit (Reversal): When the opposite condition to the entry is triggered, the position is reversed. 💰 Risk Management Parameters Pair & Timeframe: BTC/USD (30-minute chart) Capital (simulated): $30,00 Order size: `$100` per trade (realistic, low-risk sizing) Commission: 0.02% Slippage: 2 pips Risk per Trade: 5% Number of Trades (backtest period): 181 📊 Performance Overview Symbol: BTC/USD Timeframe: 30-minute chart Date Range: January 1, 2024 – July 3, 2025 Win Rate: 47.8% Profit Factor: 2.01 Net Profit: 173.16 (units not specified) Max Drawdown: 5.77% or 24.91 (0.79%) ⚙️ Indicator Parameters Indicator Name: RSI-Adaptive T3 + Squeeze Momentum RSI Length: 14 T3 Min Length: 5 T3 Max Length: 50 T3 Volume Factor: 0.7 BB Length: 27 (Multiplier: 2.0) KC Length: 20 (Multiplier: 1.5, TrueRange enabled) 🖼 Visual Support T3 slope direction, squeeze status, and momentum bars are visually plotted on the chart, providing high clarity for quick trend analysis and execution. 🔧 Strategy Improvements & Uniqueness Inspired by the RSI Adaptive T3 by ChartPrime and Squeeze Momentum Indicator by LazyBear , this strategy fuses both into a hybrid trend-reversal and momentum breakout detection system . Compared to traditional trend-following methods, it excels at capturing early trend signals with greater sensitivity . ✅ Summary The RSI-Adaptive T3 + Squeeze Momentum Strategy combines momentum detection with volatility-responsive risk management. With a strong balance between visual clarity and practicality, it serves as a powerful tool for traders seeking high repeatability. ⚠️ This strategy is based on historical data and does not guarantee future profits. Always use appropriate risk management when applying it.
Strategi Pine Script®
oleh PakunFX
Logarithmic Moving Average (LMA) [QuantAlgo]🟢 Overview The Logarithmic Moving Average (LMA) uses advanced logarithmic weighting to create a dynamic trend-following indicator that prioritizes recent price action while maintaining statistical significance. Unlike traditional moving averages that use linear or exponential weights, this indicator employs logarithmic decay functions to create a more sophisticated price averaging system that adapts to market volatility and momentum conditions. The indicator displays a smoothed signal line that oscillates around zero, with positive values indicating bullish momentum and negative values indicating bearish momentum. The signal incorporates trend quality assessment, momentum confirmation, and multiple filtering mechanisms to help traders and investors identify trend continuation and reversal opportunities across different timeframes and asset classes. 🟢 How It Works The indicator's core innovation lies in its logarithmic weighting system, where weights are calculated using the formula: w = 1.0 / math.pow(math.log(i + steepness), 2) The steepness parameter controls how aggressively recent data is prioritized over historical data, creating a dynamic weight decay that can be fine-tuned for different trading styles. This logarithmic approach provides more nuanced weight distribution compared to exponential moving averages, offering better responsiveness while maintaining stability. The LMA calculation combines multiple sophisticated components. First, it calculates the logarithmic weighted average of closing prices. Then it measures the slope of this average over a 10-period lookback: lmaSlope = (lma - lma ) / lma * 100 The system also incorporates trend quality assessment using R-squared correlation analysis of log-transformed prices, measuring how well the price data fits a linear trend model over the specified period. The final signal generation uses the formula: signal = lmaSlope * (0.5 + rSquared * 0.5) which combines the LMA slope with trend quality weighting. When momentum confirmation is enabled, the indicator calculates annualized log-return momentum and applies a multiplier when the momentum direction aligns with the signal direction, strengthening confirmed signals while filtering out weak or counter-trend movements. 🟢 How to Use 1. Signal Interpretation and Threshold Zones Positive Values (Above Zero): LMA slope indicating bullish momentum with upward price trajectory relative to logarithmic baseline Negative Values (Below Zero): LMA slope indicating bearish momentum with downward price trajectory relative to logarithmic baseline Zero Line Crosses: Signal transitions between bullish and bearish regimes, indicating potential trend changes Long Entry Threshold Zone: Area above positive threshold (default 0.5) indicating confirmed bullish signals suitable for long positions Short Entry Threshold Zone: Area below negative threshold (default -0.5) indicating confirmed bearish signals suitable for short positions Extreme Values: Signals exceeding ±1.0 represent strong momentum conditions with higher probability of continuation 2. Momentum Confirmation and Visual Analysis Signal Color Intensity: Gradient coloring shows signal strength, with brighter colors indicating stronger momentum Bar Coloring: Optional price bar coloring matches signal direction for quick visual trend identification Position Labels: Real-time position classification (Bullish/Bearish/Neutral) displayed on the latest bar Momentum Weight Factor: When short-term log-return momentum aligns with LMA signal direction, the signal receives additional weight confirmation Trend Quality Component: R-squared values weight the signal strength, with higher correlation indicating more reliable trend conditions 3. Examples: Preconfigured Settings Default: Universally applicable configuration balanced for medium-term investing and general trading across multiple timeframes and asset classes. Scalping: Highly responsive setup with shorter period and higher steepness for ultra-short-term trades on 1-15 minute charts, optimized for quick momentum shifts. Swing Trading: Extended period with moderate steepness and increased smoothing for multi-day positions, designed to filter noise while capturing larger price swings on 1-4 hour and daily charts. Trend Following: Maximum smoothing with lower steepness for established trend identification, generating fewer but more reliable signals optimal for daily and weekly timeframes. Mean Reversion: Shorter period with high steepness for counter-trend strategies, more sensitive to extreme moves and reversal opportunities in ranging market conditions.
Penunjuk Pine Script®
oleh QuantAlgo
16
Momentum Trail Oscillator [AlgoAlpha]🟠 OVERVIEW This script builds a Momentum Trail Oscillator designed to measure directional momentum strength and dynamically track shifts in trend bias using a combination of smoothed price change calculations and adaptive trailing bands. The oscillator aims to help traders visualize when momentum is expanding or contracting and to identify transitions between bullish and bearish conditions. 🟠 CONCEPTS The core idea combines two methods. First, the script calculates a normalized momentum measure by smoothing price changes relative to their absolute values, which creates a bounded oscillator that highlights whether moves are directional or choppy. Second, it uses a trailing band mechanism inspired by volatility stops, where bands adapt to the oscillator’s volatility, adjusting the thresholds that define a shift in directional bias. This dual approach seeks to address both the magnitude and persistence of momentum, reducing false signals in ranging markets. 🟠 FEATURES The momentum calculation applies Hull Moving Averages and double EMA smoothing to price changes, producing a smooth, responsive oscillator. The trailing bands are derived by offsetting a weighted moving average of the oscillator by a multiple of recent momentum volatility. A directional state variable tracks whether the oscillator is above or below the bands, updating when the momentum crosses these dynamic thresholds. Overbought and oversold zones are visually marked between fixed levels (+30/+40 and -30/-40), with color fills to highlight when momentum is in extreme areas. The script plots signals on both the oscillator pane and optionally overlays markers on the main price chart for clarity. 🟠 USAGE To use the indicator, apply it to any symbol and timeframe. The “Oscillator Length” controls how sensitive the momentum line is to recent price changes—lower values react faster, higher values smooth out noise. The “Trail Multiplier” sets how far the adaptive bands sit from the oscillator mid-line, which affects how often trend state changes occur. When the momentum line rises into the upper filled area and then crosses back below +40, it signals potential overbought exhaustion. The opposite applies for the oversold zone below -40. The plotted trailing bands switch visibility depending on the current directional state: when momentum is trending up, the lower band acts as the active trailing stop, and when trending down, the upper band becomes active. Trend changes are marked with circular symbols when the direction variable flips, and optional overlay arrows appear on the price chart to highlight overbought or oversold reversals. Traders can combine these signals with their own price action or volume analysis to confirm entries or exits.
Penunjuk Pine Script®
oleh AlgoAlpha
10
Volatility-Adjusted Momentum Score (VAMS) [QuantAlgo]🟢 Overview The Volatility-Adjusted Momentum Score (VAMS) measures price momentum relative to current volatility conditions, creating a normalized indicator that identifies significant directional moves while filtering out market noise. It divides annualized momentum by annualized volatility to produce scores that remain comparable across different market environments and asset classes. The indicator displays a smoothed VAMS Z-Score line with adaptive standard deviation bands and an information table showing real-time metrics. This dual-purpose design enables traders and investors to identify strong trend continuation signals when momentum persistently exceeds normal levels, while also spotting potential mean reversion opportunities when readings reach statistical extremes. 🟢 How It Works The indicator calculates annualized momentum using a simple moving average of logarithmic returns over a specified period, then measures annualized volatility through the standard deviation of those same returns over a longer timeframe. The raw VAMS score divides momentum by volatility, creating a risk-adjusted measure where high volatility reduces scores and low volatility amplifies them. This raw VAMS value undergoes Z-Score normalization using rolling statistical parameters, converting absolute readings into standardized deviations that show how current conditions compare to recent history. The normalized Z-Score receives exponential moving average smoothing to create the final VAMS line, reducing false signals while preserving sensitivity to meaningful momentum changes. The visualization includes dynamically calculated standard deviation bands that adjust to recent VAMS behavior, creating statistical reference zones. The information table provides real-time numerical values for VAMS Z-Score, underlying momentum percentages, and current volatility readings with trend indicators. 🟢 How to Use 1. VAMS Z-Score Bands and Signal Interpretation Above Mean Line: Momentum exceeds historical averages adjusted for volatility, indicating bullish conditions suitable for trend following Below Mean Line: Momentum falls below statistical norms, suggesting bearish conditions or downward pressure Mean Line Crossovers: Primary transition signals between bullish and bearish momentum regimes 1 Standard Deviation Breaks: Strong momentum conditions indicating statistically significant directional moves worth following 2 Standard Deviation Extremes: Rare momentum readings that often signal either powerful breakouts or exhaustion points 2. Information Table and Market Context Z-Score Values: Current VAMS reading displayed in standard deviations (σ), showing how far momentum deviates from its statistical norm Momentum Percentage: Underlying annualized momentum displayed as percentage return, quantifying the directional strength Volatility Context: Current annualized volatility levels help interpret whether VAMS readings occur in high or low volatility environments Trend Indicators: Directional arrows and change values provide immediate feedback on momentum shifts and market transitions 3. Strategy Applications and Alert System Trend Following: Use sustained readings beyond the mean line and 1σ band penetrations for directional trades, especially when VAMS maintains position in upper or lower statistical zones Mean Reversion: Focus on 2σ extreme readings for contrarian opportunities, particularly effective in sideways markets where momentum tends to revert to statistical norms Alert Notifications: Built-in alerts for mean crossovers (regime changes), 1σ breaks (strong signals), and 2σ touches (extreme conditions) help monitor multiple instruments for both continuation and reversal setups
Penunjuk Pine Script®
oleh QuantAlgo
10
Wavelet-Trend ML Integration [Alpha Extract]Alpha-Extract Volatility Quality Indicator The Alpha-Extract Volatility Quality (AVQ) Indicator provides traders with deep insights into market volatility by measuring the directional strength of price movements. This sophisticated momentum-based tool helps identify overbought and oversold conditions, offering actionable buy and sell signals based on volatility trends and standard deviation bands. 🔶 CALCULATION The indicator processes volatility quality data through a series of analytical steps: Bar Range Calculation: Measures true range (TR) to capture price volatility. Directional Weighting: Applies directional bias (positive for bullish candles, negative for bearish) to the true range. VQI Computation: Uses an exponential moving average (EMA) of weighted volatility to derive the Volatility Quality Index (VQI). Smoothing: Applies an additional EMA to smooth the VQI for clearer signals. Normalization: Optionally normalizes VQI to a -100/+100 scale based on historical highs and lows. Standard Deviation Bands: Calculates three upper and lower bands using standard deviation multipliers for volatility thresholds. Signal Generation: Produces overbought/oversold signals when VQI reaches extreme levels (±200 in normalized mode). Formula: Bar Range = True Range (TR) Weighted Volatility = Bar Range × (Close > Open ? 1 : Close < Open ? -1 : 0) VQI Raw = EMA(Weighted Volatility, VQI Length) VQI Smoothed = EMA(VQI Raw, Smoothing Length) VQI Normalized = ((VQI Smoothed - Lowest VQI) / (Highest VQI - Lowest VQI) - 0.5) × 200 Upper Band N = VQI Smoothed + (StdDev(VQI Smoothed, VQI Length) × Multiplier N) Lower Band N = VQI Smoothed - (StdDev(VQI Smoothed, VQI Length) × Multiplier N) 🔶 DETAILS Visual Features: VQI Plot: Displays VQI as a line or histogram (lime for positive, red for negative). Standard Deviation Bands: Plots three upper and lower bands (teal for upper, grayscale for lower) to indicate volatility thresholds. Reference Levels: Horizontal lines at 0 (neutral), +100, and -100 (in normalized mode) for context. Zone Highlighting: Overbought (⋎ above bars) and oversold (⋏ below bars) signals for extreme VQI levels (±200 in normalized mode). Candle Coloring: Optional candle overlay colored by VQI direction (lime for positive, red for negative). Interpretation: VQI ≥ 200 (Normalized): Overbought condition, strong sell signal. VQI 100–200: High volatility, potential selling opportunity. VQI 0–100: Neutral bullish momentum. VQI 0 to -100: Neutral bearish momentum. VQI -100 to -200: High volatility, strong bearish momentum. VQI ≤ -200 (Normalized): Oversold condition, strong buy signal. 🔶 EXAMPLES Overbought Signal Detection: When VQI exceeds 200 (normalized), the indicator flags potential market tops with a red ⋎ symbol. Example: During strong uptrends, VQI reaching 200 has historically preceded corrections, allowing traders to secure profits. Oversold Signal Detection: When VQI falls below -200 (normalized), a lime ⋏ symbol highlights potential buying opportunities. Example: In bearish markets, VQI dropping below -200 has marked reversal points for profitable long entries. Volatility Trend Tracking: The VQI plot and bands help traders visualize shifts in market momentum. Example: A rising VQI crossing above zero with widening bands indicates strengthening bullish momentum, guiding traders to hold or enter long positions. Dynamic Support/Resistance: Standard deviation bands act as dynamic volatility thresholds during price movements. Example: Price reversals often occur near the third standard deviation bands, providing reliable entry/exit points during volatile periods. 🔶 SETTINGS Customization Options: VQI Length: Adjust the EMA period for VQI calculation (default: 14, range: 1–50). Smoothing Length: Set the EMA period for smoothing (default: 5, range: 1–50). Standard Deviation Multipliers: Customize multipliers for bands (defaults: 1.0, 2.0, 3.0). Normalization: Toggle normalization to -100/+100 scale and adjust lookback period (default: 200, min: 50). Display Style: Switch between line or histogram plot for VQI. Candle Overlay: Enable/disable VQI-colored candles (lime for positive, red for negative). The Alpha-Extract Volatility Quality Indicator empowers traders with a robust tool to navigate market volatility. By combining directional price range analysis with smoothed volatility metrics, it identifies overbought and oversold conditions, offering clear buy and sell signals. The customizable standard deviation bands and optional normalization provide precise context for market conditions, enabling traders to make informed decisions across various market cycles.
Penunjuk Pine Script®
oleh AlphaExtract
8
Rolling Z-Score Trend [QuantAlgo]🟢 Overview The Rolling Z-Score Trend measures how far the current price deviates from its rolling mean in terms of standard deviations. It transforms price data into standardized scores to identify overbought and oversold conditions while tracking momentum shifts. The indicator displays a Z-Score line showing price deviation from statistical norms, with background momentum columns showing the rate of change in these deviations. This helps traders and investors identify mean reversion opportunities and momentum shifts across different asset classes and timeframes. 🟢 How It Works The indicator uses the Z-Score formula: Z = (X - μ) / σ, where X is the current closing price, μ is the rolling mean, and σ is the rolling standard deviation over a user-defined lookback period. This creates a dynamic baseline that adapts to changing market conditions and standardizes price movements for interpretation across different assets and volatility conditions. The raw Z-Score undergoes 3-period EMA smoothing to reduce noise while maintaining responsiveness to market signals. Beyond the basic Z-Score calculation, the indicator measures the rate of change in Z-Score values between successive bars, displayed as background momentum columns. This momentum component shows acceleration and deceleration of statistical deviations. All calculations are processed through confirmation filters, displaying signals only on confirmed bars to reduce premature signals based on incomplete price action. 🟢 How to Use 1. Z-Score Interpretation and Threshold Zones Positive Values (Above Zero) : Price trading above statistical mean, suggesting bullish momentum or potential overbought conditions Negative Values (Below Zero) : Price trading below statistical mean, suggesting bearish momentum or potential oversold conditions Zero Line Crosses : Signal transitions between statistical regimes and potential trend changes Upper Threshold Zone : Area above entry threshold (default 1.5) indicating potential overbought conditions Lower Threshold Zone : Area below negative entry threshold (default -1.5) indicating potential oversold conditions Extreme Values (±2.0 or higher) : Statistically significant deviations that may indicate reversal opportunities 2. Momentum Background Analysis and Info Table Green Columns : Accelerating positive momentum in Z-Score values Red Columns : Accelerating negative momentum in Z-Score values Column Height : Magnitude of momentum change between bars Momentum Divergence : When columns contradict primary Z-Score direction, often signals impending reversals Info Table : Displays real-time numerical values for both Z-Score and momentum, including trend direction indicators and bar-to-bar change calculations for position management 3. Preconfigured Settings Default : Balanced performance across multiple timeframes and asset classes for general trading and medium-term position management. Scalping : Responsive setup for ultra-short-term trading on 1-15 minute charts with frequent signals and increased sensitivity to quick price movements. Swing Trading : Optimized for multi-day positions with noise filtering, focusing on larger price swings. Most effective on 1-4 hour and daily timeframes. Trend Following : Maximum smoothing that prioritizes established trends over short-term volatility. Generates fewer signals for daily and weekly charts.
Penunjuk Pine Script®
oleh QuantAlgo
25
Trend Flow Trail [AlgoAlpha]OVERVIEW This script overlays a custom hybrid indicator called the Money Flow Trail which combines a volatility-based trend-following trail with a volume-weighted momentum oscillator. It’s built around two core components: the AlphaTrail—a dynamic band system influenced by Hull MA and volatility—and a smoothed Money Flow Index (MFI) that provides insights into buying or selling pressure. Together, these tools are used to color bars, generate potential reversal markers, and assist traders in identifying trend continuation or exhaustion phases in any market or timeframe. CONCEPTS The AlphaTrail calculates a volatility-adjusted channel around price using the Hull Moving Average as the base and an EMA of range as the spread. It adaptively shifts based on price interaction to capture trend reversals while avoiding whipsaws. The direction (bullish or bearish) determines both the band being tracked and how the trail locks in. The Money Flow Index (MFI) is derived from hlc3 and volume, measuring buying vs selling pressure, and is further smoothed with a short Hull MA to reduce noise while preserving structure. These two systems work in tandem: AlphaTrail governs directional context, while MFI refines the timing. FEATURES Dynamic AlphaTrail line with regime switching logic that controls directional bias and bar coloring. Smoothed MFI with gradient coloring to visually communicate pressure and exhaustion levels. Overbought/oversold thresholds (80/20), mid-level (50), and custom extreme zones (90/10) for deeper signal granularity. Built-in take-profit signal logic: crossover of MFI into overbought with bullish AlphaTrail, or into oversold with bearish AlphaTrail. Visual fills between price and AlphaTrail for clearer confirmation during trend phases. Alerts for regime shifts, MFI crossovers, trail interactions, and bar color regime changes. USAGE Add the indicator to any chart. Use the AlphaTrail plot to define trend context: bullish (trailing below price) or bearish (trailing above). MFI values give supporting confirmation—favor long setups when MFI is rising and above 50 in a bullish regime, and shorts when MFI is falling and below 50 in a bearish regime. The colored fills help visually track strength; sharp changes in MFI crossing 80/20 or 90/10 zones often precede pullbacks or reversals. Use the plotted circles as optional take-profit signals when MFI and trend are extended. Adjust AlphaTrail length/multiplier and MFI smoothing to better match the asset’s volatility profile.
Penunjuk Pine Script®
oleh AlgoAlpha
20