지금 시작

센터드 오실레이터

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.
Pine Script® 인디케이터
AlphaNatt의
AI-Weighted RSI (Zeiierman)█ Overview AI-Weighted RSI (Zeiierman) is an adaptive oscillator that enhances classic RSI by applying a correlation-weighted prediction layer. Instead of looking only at RSI values directly, this indicator continuously evaluates how other price- and volume-based features (returns, volatility, volume shifts) correlate with RSI, and then weights them accordingly to project the next RSI state. The result is a smoother, forward-looking RSI framework that adapts to market conditions in real time. By leveraging feature correlation instead of static formulas, AI-Weighted RSI behaves like a lightweight learning model, adjusting its emphasis depending on which features are most aligned with RSI behavior during the current regime. █ How It Works ⚪ Feature Extraction Each bar, the script computes features: log returns, RSI itself, ATR% (volatility), volume, and volume log-change. ⚪ Correlation Screening Over a rolling learning window, it measures the correlation of each feature against RSI. The strongest relationships are ranked and selected. ⚪ Adaptive Weighting Features are standardized (z-scored), then combined using their signed correlations as weights, building a rolling, adaptive prediction of RSI. ⚪ Prediction to RSI Weight The predicted RSI is mapped back into a “weight” scale (±2 by default). Above 0 = bullish bias, below 0 = bearish bias, with color-graded fills to visualize overbought/oversold pressure. ⚪ Signal Line A smoothing option (signal length) overlays a moving average of the AI-Weighted RSI for clearer trend confirmation. █ Why AI-Weighted RSI ⚪ Adaptive to Market Regime Because the model re-evaluates correlations continuously, it naturally shifts which features dominate, sometimes volatility explains RSI best, sometimes volume, sometimes returns. ⚪ Forward-Looking Bias Instead of simply reflecting RSI, the model provides a projection, helping anticipate shifts in momentum before RSI itself flips. █ How to Use ⚪ Directional Bias Read the RSI relative to 0. Above = bullish momentum bias, below = bearish. ⚪ Overbought / Oversold Zones Shaded fills beyond +0.5 or -0.5 highlight extremes where RSI pressure often exhausts. ⚪ Divergences When price makes new highs/lows but AI-Weighted RSI fails to confirm, it often signals weakening momentum. █ Settings RSI Length: Lookback for the core RSI calculation. Signal Length: Smoothing applied to the AI-Weighted RSI output. Learning Window: Bars used for correlation learning and z-scoring. ----------------- Disclaimer The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information. All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
Pine Script® 인디케이터
Zeiierman의
2
عكفة الماكد المتقدمة - أبو فارس ©// 🔒 Advanced MACD Curve © 2025 // 💡 Idea & Creativity: Engineer Abu Elias // 🛠️ Development & Implementation: Abu Fares // 📜 All intellectual rights reserved - Copying, modifying, or redistributing is not permitted // 🚫 Any attempt to tamper with this code or violate intellectual property rights is legally prohibited // 📧 For inquiries and licensing: Please contact the developer, Abu Fares
Pine Script® 인디케이터
ABU_FARIS의
عكفة الماكد المتقدمة - أبو فارس ©// 🔒 عكفة الماكد المتقدمة © 2025 // 💡 فكرة وإبداع: المهندس أبو الياس // 🛠️ تطوير وتنفيذ: أبو فارس // 📜 جميع الحقوق الفكرية محفوظة - لا يُسمح بالنسخ أو التعديل أو إعادة التوزيع // 🚫 أي محاولة للعبث بهذا الكود أو انتهاك الحقوق الفكرية مرفوضة قانونياً // 📧 للاستفسارات والتراخيص: يرجى التواصل مع المطور أبو فارس // 🔒 Advanced MACD Curve © 2025 // 💡 Idea & Creativity: Engineer Abu Elias // 🛠️ Development & Implementation: Abu Fares // 📜 All intellectual rights reserved - Copying, modifying, or redistributing is not permitted // 🚫 Any attempt to tamper with this code or violate intellectual property rights is legally prohibited // 📧 For inquiries and licensing: Please contact the developer, Abu Fares
Pine Script® 인디케이터
ABU_FARIS의
[ClearEdege] Momentum Suites V3 Advanced contrarian momentum analysis with intelligent pivot point integration for precision market timing. Developed for ClearEdge members *This indicator is not publicly available. Key Features Smart Signal System - Buy Low/Sell High Logic: Identifies oversold conditions for long entries and overbought conditions for short entries - Multi-Timeframe Confirmation: Incorporates higher timeframe bias for enhanced signal reliability - Momentum Convergence: Combines RSI, StochRSI, and Bollinger Bands for comprehensive market analysis Dynamic Pivot Integration - Classic Pivot Points: Auto-calculated daily/weekly/monthly support and resistance levels - Pivot Confluence Detection: Signals gain strength when price approaches key pivot levels - Smart Warning System: Single-alert approach prevents label spam while highlighting critical S/R interactions Table Dashboard - Compact Signal Table: Real-time buy/sell signal strength with scoring system - Market Context: Momentum status, nearest pivot level, volume and trend analysis - Clean Visual Design: Minimal interface focused on actionable information Intelligent Alerts - Approach Warnings: Alerts when price nears resistance with overbought momentum or support with oversold momentum - Confluence Signals: High-probability setups combining momentum extremes with pivot level proximity - Single-Shot Logic: Prevents alert fatigue with smart reset mechanisms Best Use Cases - Swing Trading: Ideal for identifying major reversal points at key levels - Risk Management: Clear warning system for potential turning points - Multi-Asset Analysis: Works across forex, crypto, stocks, and commodities - All Timeframes: Scalable from 5-minute charts to daily analysis Designed for traders who value precision over noise - combining classical pivot analysis with modern momentum indicators for superior market timing.
Pine Script® 인디케이터
unkownplayer의
Hurst Momentum Oscillator | AlphaNattHurst Momentum Oscillator | AlphaNatt An adaptive oscillator that combines the Hurst Exponent - which identifies whether markets are trending or mean-reverting - with momentum analysis to create signals that automatically adjust to market regime. "The Hurst Exponent reveals a hidden truth: markets aren't always trending. This oscillator knows when to ride momentum and when to fade it." ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📐 THE MATHEMATICS Hurst Exponent (H): Measures the long-term memory of time series: H > 0.5: Trending (persistent) behavior H = 0.5: Random walk H < 0.5: Mean-reverting behavior Originally developed for analyzing Nile river flooding patterns, now used in: Fractal market analysis Network traffic prediction Climate modeling Financial markets The Innovation: This oscillator multiplies momentum by the Hurst coefficient: When trending (H > 0.5): Momentum is amplified When mean-reverting (H < 0.5): Momentum is reduced Result: Adaptive signals based on market regime ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 💎 KEY ADVANTAGES Regime Adaptive: Automatically adjusts to trending vs ranging markets False Signal Reduction: Reduces momentum signals in mean-reverting markets Trend Amplification: Stronger signals when trends are persistent Mathematical Edge: Based on fractal dimension analysis No Repainting: All calculations on historical data ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📊 TRADING SIGNALS Visual Interpretation: Cyan zones: Bullish momentum in trending market Magenta zones: Bearish momentum or mean reversion Background tint: Blue = trending, Pink = mean-reverting Gradient intensity: Signal strength Trading Strategies: 1. Trend Following: Trade momentum signals when background is blue (trending) 2. Mean Reversion: Fade extreme readings when background is pink 3. Regime Transition: Watch for background color changes as early warning ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🎯 OPTIMAL USAGE Best Conditions: Strong trending markets (crypto bull runs) Clear ranging markets (forex sessions) Regime transitions Multi-timeframe analysis Market Applications: Crypto: Excellent for identifying trend persistence Forex: Detects when pairs are ranging Stocks: Identifies momentum stocks Commodities: Catches persistent trends ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Developed by AlphaNatt | Fractal Market Analysis Version: 1.0 Classification: Adaptive Regime Oscillator Not financial advice. Always DYOR.
Pine Script® 인디케이터
AlphaNatt의
Ark FCI OscillatorFinancial Conditions Index Oscillator This indicator tracks week-over-week changes in the National Financial Conditions Index (NFCI), providing a dynamic view of evolving financial conditions in the United States. Overview The National Financial Conditions Index (NFCI) is a comprehensive weekly composite index published by the Federal Reserve Bank of Chicago. It measures financial conditions across U.S. money markets, debt and equity markets, and the traditional and shadow banking systems. Interpretation Positive values indicate improving financial conditions Negative values signal deteriorating financial conditions Risk assets demonstrate particular sensitivity to changes in financial conditions, making this oscillator valuable for market timing and risk assessment. Alternative Data Source Users can modify the source to FRED:NFCIRISK to focus specifically on risk dynamics. The NFCIRISK subindex isolates volatility and funding risk measures within the financial sector, capturing market volatility indicators and liquidity shortage probabilities while excluding broader credit and leverage conditions.
Pine Script® 인디케이터
arkaydeus의
1
Fisher Volume Transform | AlphaNattFisher Volume Transform | AlphaNatt A powerful oscillator that applies the Fisher Transform - converting price into a Gaussian normal distribution - while incorporating volume weighting to identify high-probability reversal points with institutional participation. "The Fisher Transform reveals what statistics professors have known for decades: when you transform market data into a normal distribution, turning points become crystal clear." ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🎲 THE MATHEMATICS Fisher Transform Formula: The Fisher Transform converts any bounded dataset into a Gaussian distribution: y = 0.5 × ln((1 + x) / (1 - x)) Where x is normalized price (-1 to 1 range) Why This Matters: Market extremes become statistically identifiable Turning points are amplified and clarified Removes the skew from price distributions Creates nearly instantaneous signals at reversals Volume Integration: Unlike standard Fisher Transform, this version weights price by relative volume: High volume moves get more weight Low volume moves get filtered out Identifies institutional participation Reduces false signals from retail chop ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 💎 KEY ADVANTAGES Statistical Edge: Transforms price into normal distribution where extremes are mathematically defined Volume Confirmation: Only signals with volume support Early Reversal Detection: Fisher Transform amplifies turning points Clean Signals: Gaussian distribution reduces noise No Lag: Mathematical transformation, not averaging ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⚙️ SETTINGS OPTIMIZATION Fisher Period (5-30): 5-9: Very sensitive, many signals 10: Default - balanced sensitivity 15-20: Moderate smoothing 25-30: Major reversals only Volume Weight (0.1-1.0): 0.1-0.3: Minimal volume influence 0.5-0.7: Balanced price/volume 0.7: Default - strong volume weight 0.8-1.0: Volume dominant ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📊 TRADING SIGNALS Primary Signals: Zero Cross Up: Bullish momentum shift Zero Cross Down: Bearish momentum shift Signal Line Cross: Early reversal warning Extreme Readings (±75): Potential reversal zones Visual Interpretation: Cyan zones: Bullish momentum Magenta zones: Bearish momentum Gradient intensity: Strength of move Histogram: Raw momentum power ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🎯 OPTIMAL USAGE Best Market Conditions: Range-bound markets (reversals clear) High volume periods Major support/resistance levels Divergence hunting Trading Strategies: 1. Extreme Reversal: Enter when oscillator exceeds ±75 and reverses 2. Zero Line Momentum: Trade crosses of zero line with volume confirmation 3. Signal Line Strategy: Early entry on signal line crosses 4. Divergence Trading: Price makes new high/low but Fisher doesn't ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Developed by AlphaNatt | Quantitative Trading Systems Version: 1.0 Classification: Statistical Transform Oscillator Not financial advice. Always DYOR.
Pine Script® 인디케이터
AlphaNatt의
RSI + Sell/Buy RatesEnglish follow Sell/Buy Rates = des barres vert/rouge qui mesurent la pression acheteurs vs vendeurs (calculé à partir des bougies et du volume), centrées sur 50. > 50 (vert) : acheteurs dominent. < 50 (rouge) : vendeurs dominent. Plus loin de 50 ⇒ plus fort. Avec le RSI : on ne fait que confirmer — RSI > 50 et barres > 50 → acheteurs ; RSI < 50 et barres < 50 → vendeurs ; sinon on s’abstient. Sell/Buy Rates = green/red bars that measure buyer vs. seller pressure (calculated from candles and volume), centered at 50. > 50 (green): buyers dominate. < 50 (red): sellers dominate. Farther from 50 ⇒ stronger. With RSI: it’s just a confirmation — RSI > 50 and bars > 50 → buyers; RSI < 50 and bars < 50 → sellers; otherwise, stand aside.
Pine Script® 인디케이터
PhilArsouille의
Signal PainterSignal Painter is a trend-focused technical indicator that paints buy/sell signals only when a strong directional move is confirmed. It combines a momentum oscillator with a volatility filter to ensure signals occur during robust trends. In practice, the algorithm waits for price movement and momentum to exceed certain thresholds (for example, requiring both a surge in momentum and price range expansion) before marking a potential up-trend entry or down-trend entry on the chart. This means the system performs best in well-defined trending markets where such conditions are met consistently. In sideways or range-bound conditions, however, these strict requirements can be triggered by random fluctuations, reducing the indicator’s effectiveness (it may generate false or choppy signals when the market lacks clear direction). To adapt to a choppier market, traders can apply Signal Painter on a lower timeframe to make it more reactive to smaller price swings. This increases the frequency and quickness of signals (capturing short-term moves sooner) but at the cost of signal strength and reliability – lower-timeframe signals carry more noise and are less robust compared to signals on higher timeframes. In summary, Signal Painter is designed to highlight significant trend breakouts with visual cues on the chart, excelling during trending phases and cautioning users that its performance will degrade during sideways market conditions.
Pine Script® 인디케이터
CR-JE의
2
buy sell ultra systemWhat it is EMA-POC Momentum System Ultra combines a proven trend stack (EMA 20/50/238), a price-of-control layer (POC via Bar-POC or VWAP alternative), and a momentum trigger (RSI) to surface higher-quality entries only when multiple, independent conditions align. This is not a cosmetic mashup; each component gates the others. How components work together Trend (EMA 20/50/238): Defines short/medium/long bias and filters counter-trend signals. POC (Bar-POC or Alt-POC/VWAP): Locates the most-traded/weighted price area; a neutral band around POC helps avoid chop. Control background: Above POC → buyers likely in control; below → sellers. Momentum (RSI): Entry arrows print only when RSI confirms with trend and price location vs POC; optional “cross 50” requirement reduces noise. Optional HTF trend: Confluence with a higher-timeframe EMA stack for stricter filtering. Why it’s original/useful Signals require confluence of (1) EMA trend stack, (2) POC location and neutral-zone filtering, (3) momentum confirmation, (4) optional slope and distance-to-POC checks, and (5) optional HTF trend. This reduces false positives compared with using any layer in isolation. How to use Markets/TFs: Built for XAUUSD (Gold) and US30. Works 1m–1h for intraday; 2h–4h for swing. Entries: Long: EMA stack bullish, price above POC, not in neutral band, RSI condition true → “Buy” arrow. Short: Opposite conditions → “Sell” arrow. Stops/Targets (suggested): Initial stop beyond POC/neutral band or recent swing. First target around 1R; trail with EMA20/50 or structure breaks. Settings to tune: POC Mode: Bar-POC (highest-volume bar’s close over lookback) or Alt-POC (VWAP). Neutral Band %: 0.10–0.35 typical intraday. Min distance from POC: 0.10–0.50% helps avoid low-RR entries right at POC. RSI: Choose “cross 50” for stricter triggers or simple >/< 50 for more signals. HTF trend: Turn on for extra confluence. Alerts: Buy Signal and Sell Signal (separate), or one Combined Buy/Sell alert. Set to “Once per bar close” if you want only confirmed arrows. Repainting / limitations Shapes can move until bar close (standard Pine behavior) when using intrabar conditions; final confirmation at close. No system guarantees profitability—forward test and adapt to your market/instrument. Clean chart The published chart contains only this script so outputs are easy to identify. Versions / updates Use Publish → Update for minor changes; do not create new publications for small tweaks. If you fork to preserve older behavior, explain why and how your fork differs. Changelog v1.1 – Tuning for Gold/US30, neutral-band & distance filters, optional HTF trend, combined alert. v1.0 – Initial public release (EMA stack + POC modes + RSI + alerts). License & credits Open-source for learning and improvement. Please credit on forks and explain modifications in your description.
Pine Script® 인디케이터
toondc의
Momentum Index [BigBeluga]The Momentum Index is an innovative indicator designed to measure the momentum of price action by analyzing the distribution of positive and negative momentum values over a defined period. By incorporating delta-based calculations and smoothing techniques, it provides traders with a clear and actionable representation of market momentum dynamics. 🔵 Key Features: Delta-Based Momentum Analysis: Calculates the momentum of price by comparing its current state to its value from a defined number of bars back. Inside a loop, it evaluates whether momentum values are above or below zero, producing a delta value that reflects the net momentum direction and intensity. Double EMA Smoothing: Smooths the raw delta-based momentum values with a double EMA filter, reducing noise and providing a clearer trend signal. tmi(len) => sum = 0.0 sum1 = 0.0 above = 0.0 below = 0.0 src_ = src - src for i = 0 to len sum := sum + (src_ > nz(src_ ) ? 1 : -1) sum1 := sum1 + (sum > 0 ? 1 : -1) sum1 := emaEma(sum1, 10) for i = 1 to len above := above + (sum1 > 0 ? 1 : 0) below := below + (sum1 > 0 ? 0 : 1) Directional Momentum Signals: Generates momentum shift signals and displays them on both the oscillator and the main chart: - △ Aqua Triangles: Represent upward momentum shifts. - ▽ Red Triangles: Represent downward momentum shifts. Dynamic Gradient Display: Highlights momentum zones with gradient fills: - Aqua shades for positive momentum (above zero). - Red shades for negative momentum (below zero). Dashboard Display: A dashboard summarizing the count of momentum values above and below zero for the defined period (Sentiment Length e.g. 100), helping traders assess market sentiment at a glance. 🔵 How It Works: The indicator takes price momentum as its source and evaluates the number of momentum values above and below zero within a defined period. The delta calculation aggregates this information, providing a net representation of the prevailing market momentum. A double EMA filter is applied to the delta values, smoothing the momentum line and enhancing signal clarity. Momentum shifts are highlighted with visual signals on the oscillator and price chart, while the gradient display provides a visual representation of intensity. 🔵 Use Cases: Momentum Tracking: Identify whether market momentum is predominantly bullish or bearish. Signal Confirmation: Use chart-based signals to confirm potential trend reversals or continuation. Analyze Market Strength: Leverage the dashboard to quickly assess the distribution of momentum over the chosen period. Overbought/Oversold Conditions: Utilize gradient zones to detect areas of momentum extremes and possible price exhaustion. Momentum Index offers a refined approach to analyzing momentum dynamics, combining delta-based calculations with smoothing techniques and intuitive visuals, making it an essential tool for traders looking to anticipate market movements effectively.
Pine Script® 인디케이터
BigBeluga의
4
ATR & Price Z-ScoreThis indicator combines the ATR Z-Score and the Price Z-Score into a single framework for evaluating market conditions using statistical context. The ATR Z-Score standardizes volatility by comparing the current ATR value to its historical mean and standard deviation, allowing traders to see when volatility is unusually high (above +2) or unusually low (below –2). The Price Z-Score applies the same normalization process to price itself, showing how far the current close is from its moving average in standard deviation terms, which highlights statistically stretched conditions that often align with overbought or oversold states. By plotting both measures together, traders can identify when price moves are supported by volatility or when the two diverge. For example, a high Price Z paired with a high ATR Z can confirm the strength of a breakout, while a high Price Z with a low ATR Z may suggest an unsustainable move lacking volatility support. Conversely, a volatility spike without an extreme in price can point to shakeouts or regime shifts. Because both measures are normalized, they can be compared across different markets and timeframes, making the indicator useful for spotting breakouts, squeezes, reversals, and abnormal conditions in a statistically consistent way.
Pine Script® 인디케이터
stephanweebe의
Logit Transform -EasyNeuro-Logit Transform This script implements a novel indicator inspired by the Fisher Transform, replacing its core arctanh-based mapping with the logit transform. It is designed to highlight extreme values in bounded inputs from a probabilistic and statistical perspective. Background: Fisher Transform The Fisher Transform, introduced by John Ehlers , is a statistical technique that maps a bounded variable x (between a and b) to a variable approximately following a Gaussian distribution. The standard form for a normalized input y (between -1 and 1) is F(y) = 0.5 * ln((1 + y)/(1 - y)) = arctanh(y). This transformation has the following properties: Linearization of extremes: Small deviations around the mean are smooth, while movements near the boundaries are sharply amplified. Gaussian approximation: After transformation, the variable approximates a normal distribution, enabling analytical techniques that assume normality. Probabilistic interpretation: The Fisher Transform can be linked to likelihood ratio tests, where the transform emphasizes deviations from median or expected values in a statistically meaningful way. In technical analysis, this allows traders to detect turning points or extreme market conditions more clearly than raw oscillators alone. Logit Transform as a Generalization The logit function is defined for p between 0 and 1 as logit(p) = ln(p / (1 - p)). Key properties of the logit transform: Maps probabilities in (0, 1) to the entire real line, similar to the Fisher Transform. Emphasizes values near 0 and 1, providing sharp differentiation of extreme states. Directly interpretable in terms of odds and likelihood ratios: logit(p) = ln(odds). From a statistical viewpoint, the logit transform corresponds to the canonical link function in binomial generalized linear models (GLMs). This provides a natural interpretation of the transformed variable as the logarithm of the likelihood ratio between success and failure states, giving a rigorous probabilistic framework for extreme value detection. Theoretical Advantages Distributional linearization: For inputs that can be interpreted as probabilities, the logit transform creates a variable approximately linear in log-odds, similar to Fisher’s goal of Gaussianization but with a probabilistic foundation. Extreme sensitivity: By amplifying small differences near 0 or 1, it allows for sharper detection of market extremes or overbought/oversold conditions. Statistical interpretability: Provides a link to statistical hypothesis testing via likelihood ratios, enabling integration with probabilistic models or risk metrics. Applications in Technical Analysis Oscillator enhancement: Apply to RSI, Stochastic Oscillators, or other bounded indicators to accentuate extreme values with a well-defined probabilistic interpretation. Comparative study: Use alongside the Fisher Transform to analyze the effect of different nonlinear mappings on market signals, helping to uncover subtle nonlinearity in price behavior. Probabilistic risk assessment: Transforming input series into log-odds allows incorporation into statistical risk models or volatility estimation frameworks. Practical Considerations The logit diverges near 0 and 1, requiring careful scaling or smoothing to avoid numerical instability. As with the Fisher Transform, this indicator is not a standalone trading signal and should be combined with complementary technical or statistical indicators. In summary, the Logit Transform builds upon the Fisher Transform’s theoretical foundation while introducing a probabilistically rigorous mapping. By connecting extreme-value detection to odds ratios and likelihood principles, it provides traders and analysts with a mathematically grounded tool for examining market dynamics.
Pine Script® 인디케이터
EazyNeuro의