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Magnificent 7 OscillatorThe Magnificent 7 Oscillator is a sophisticated momentum-based technical indicator designed to analyze the collective performance of the seven largest technology companies in the U.S. stock market (Apple, Microsoft, Alphabet, Amazon, NVIDIA, Tesla, and Meta). This indicator incorporates established momentum factor research and provides three distinct analytical modes: absolute momentum tracking, equal-weighted market comparison, and relative performance analysis. The tool integrates five different oscillator methodologies and includes advanced breadth analysis capabilities. Theoretical Foundation Momentum Factor Research The indicator's foundation rests on seminal momentum research in financial markets. Jegadeesh and Titman (1993) demonstrated that stocks with strong price performance over 3-12 month periods tend to continue outperforming in subsequent periods¹. This momentum effect was later incorporated into formal factor models by Carhart (1997), who extended the Fama-French three-factor model to include a momentum factor (UMD - Up Minus Down)². The momentum calculation methodology follows the academic standard: Momentum(t) = / P(t-n) × 100 Where P(t) is the current price and n is the lookback period. The focus on the "Magnificent 7" stocks reflects the increasing market concentration observed in recent years. Fama and French (2015) noted that a small number of large-cap stocks can drive significant market movements due to their substantial index weights³. The combined market capitalization of these seven companies often exceeds 25% of the total S&P 500, making their collective momentum a critical market indicator. Indicator Architecture Core Components 1. Data Collection and Processing The indicator employs robust data collection with error handling for missing or invalid security data. Each stock's momentum is calculated independently using the specified lookback period (default: 14 periods). 2. Composite Oscillator Calculation Following Fama-French factor construction methodology, the indicator offers two weighting schemes: - Equal Weight: Each active stock receives identical weighting (1/n) - Market Cap Weight: Reserved for future enhancement 3. Oscillator Transformation Functions The indicator provides five distinct oscillator types, each with established technical analysis foundations: a) Momentum Oscillator (Default) - Pure rate-of-change calculation - Centered around zero - Direct implementation of Jegadeesh & Titman methodology b) RSI (Relative Strength Index) - Wilder's (1978) relative strength methodology - Transformed to center around zero for consistency - Scale: -50 to +50 c) Stochastic Oscillator - George Lane's %K methodology - Measures current position within recent range - Transformed to center around zero d) Williams %R - Larry Williams' range-based oscillator - Inverse stochastic calculation - Adjusted for zero-centered display e) CCI (Commodity Channel Index) - Donald Lambert's mean reversion indicator - Measures deviation from moving average - Scaled for optimal visualization Operational Modes Mode 1: Magnificent 7 Analysis Tracks the collective momentum of the seven constituent stocks. This mode is optimal for: - Technology sector analysis - Growth stock momentum assessment - Large-cap performance tracking Mode 2: S&P 500 Equal Weight Comparison Analyzes momentum using an equal-weighted S&P 500 reference (typically RSP ETF). This mode provides: - Broader market momentum context - Size-neutral market analysis - Comparison baseline for relative performance Mode 3: Relative Performance Analysis Calculates the momentum differential between Magnificent 7 and S&P 500 Equal Weight. This mode enables: - Sector rotation analysis - Style factor assessment (Growth vs. Value) - Relative strength identification Formula: Relative Performance = MAG7_Momentum - SP500EW_Momentum Signal Generation and Thresholds Signal Classification The indicator generates three signal states: - Bullish: Oscillator > Upper Threshold (default: +2.0%) - Bearish: Oscillator < Lower Threshold (default: -2.0%) - Neutral: Oscillator between thresholds Relative Performance Signals In relative performance mode, specialized thresholds apply: - Outperformance: Relative momentum > +1.0% - Underperformance: Relative momentum < -1.0% Alert System Comprehensive alert conditions include: - Threshold crossovers (bullish/bearish signals) - Zero-line crosses (momentum direction changes) - Relative performance shifts - Breadth Analysis Component The indicator incorporates market breadth analysis, calculating the percentage of constituent stocks with positive momentum. This feature provides insights into: - Strong Breadth (>60%): Broad-based momentum - Weak Breadth (<40%): Narrow momentum leadership - Mixed Breadth (40-60%): Neutral momentum distribution Visual Design and User Interface Theme-Adaptive Display The indicator automatically adjusts color schemes for dark and light chart themes, ensuring optimal visibility across different user preferences. Professional Data Table A comprehensive data table displays: - Current oscillator value and percentage - Active mode and oscillator type - Signal status and strength - Component breakdowns (in relative performance mode) - Breadth percentage - Active threshold levels Custom Color Options Users can override default colors with custom selections for: - Neutral conditions (default: Material Blue) - Bullish signals (default: Material Green) - Bearish signals (default: Material Red) Practical Applications Portfolio Management - Sector Allocation: Use relative performance mode to time technology sector exposure - Risk Management: Monitor breadth deterioration as early warning signal - Entry/Exit Timing: Utilize threshold crossovers for position sizing decisions Market Analysis - Trend Identification: Zero-line crosses indicate momentum regime changes - Divergence Analysis: Compare MAG7 performance against broader market - Volatility Assessment: Oscillator range and frequency provide volatility insights Strategy Development - Factor Timing: Implement growth factor timing strategies - Momentum Strategies: Develop systematic momentum-based approaches - Risk Parity: Use breadth metrics for risk-adjusted portfolio construction Configuration Guidelines Parameter Selection - Momentum Period (5-100): Shorter periods (5-20) for tactical analysis, longer periods (50-100) for strategic assessment - Smoothing Period (1-50): Higher values reduce noise but increase lag - Thresholds: Adjust based on historical volatility and strategy requirements Timeframe Considerations - Daily Charts: Optimal for swing trading and medium-term analysis - Weekly Charts: Suitable for long-term trend analysis - Intraday Charts: Useful for short-term tactical decisions Limitations and Considerations Market Concentration Risk The indicator's focus on seven stocks creates concentration risk. During periods of significant rotation away from large-cap technology stocks, the indicator may not represent broader market conditions. Momentum Persistence While momentum effects are well-documented, they are not permanent. Jegadeesh and Titman (1993) noted momentum reversal effects over longer time horizons (2-5 years). Correlation Dynamics During market stress, correlations among the constituent stocks may increase, reducing the diversification benefits and potentially amplifying signal intensity. Performance Metrics and Backtesting The indicator includes hidden plots for comprehensive backtesting: - Individual stock momentum values - Composite breadth percentage - S&P 500 Equal Weight momentum - Relative performance calculations These metrics enable quantitative strategy development and historical performance analysis. References ¹Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. Journal of Finance, 48(1), 65-91. Carhart, M. M. (1997). On persistence in mutual fund performance. Journal of Finance, 52(1), 57-82. Fama, E. F., & French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116(1), 1-22. Wilder, J. W. (1978). New concepts in technical trading systems. Trend Research.
Pine Script® göstergesi
EdgeTools tarafından
5
CDP - Counter-Directional-Pivot🎯 CDP - Counter-Directional-Pivot 📊 Overview The Counter-Directional-Pivot (CDP) indicator calculates five critical price levels based on the previous day's OHLC data, specifically designed for multi-timeframe analysis. Unlike standard pivot points, CDP levels are calculated using a unique formula that identifies potential reversal zones where price action often changes direction. ⚡ What Makes This Script Original This implementation solves several technical challenges that existing pivot indicators face: 🔄 Multi-Timeframe Consistency: Values remain identical across all timeframes (1m, 5m, 1h, daily) - a common problem with many pivot implementations 🔒 Intraday Stability: Uses advanced value-locking technology to prevent the "stepping" effect that occurs when pivot lines shift during the trading session 💪 Robust Data Handling: Optimized for both liquid and illiquid stocks with enhanced data synchronization 🧮 CDP Calculation Formula The indicator calculates five key levels using the previous day's High (H), Low (L), and Close (C): CDP = (H + L + C) ÷ 3 (Central Decision Point) AH = 2×CDP + H – 2×L (Anchor High - Strong Resistance) NH = 2×CDP – L (Near High - Moderate Resistance) AL = 2×CDP – 2×H + L (Anchor Low - Strong Support) NL = 2×CDP – H (Near Low - Moderate Support) ✨ Key Features 🎨 Visual Elements 📈 Five Distinct Price Levels: Each with customizable colors and line styles 🏷️ Smart Label System: Shows exact price values for each level 📋 Optional Value Table: Displays all levels in an organized table format 🎯 Clean Chart Display: Minimal visual clutter while maximizing information ⚙️ Technical Advantages 🔐 Session-Locked Values: Prices are locked at market open, preventing intraday shifts 🔄 Multi-Timeframe Sync: Perfect consistency between daily and intraday charts ✅ Data Validation: Built-in checks ensure reliable calculations 🚀 Performance Optimized: Efficient code structure for fast loading 💼 Trading Applications 🔄 Reversal Zones: AH and AL often act as strong turning points 💥 Breakout Confirmation: Price movement beyond these levels signals trend continuation 🛡️ Risk Management: Use levels for stop-loss and take-profit placement 🏗️ Market Structure: Understand daily ranges and potential price targets 📚 How to Use 🚀 Basic Setup Add the indicator to your chart (works on any timeframe) Customize colors for easy identification of support/resistance zones Enable the value table for quick reference of exact price levels 📈 Trading Strategy Examples 🟢 Long Bias: Look for bounces at NL or AL levels 🔴 Short Bias: Watch for rejections at NH or AH levels 💥 Breakout Trading: Enter positions when price decisively breaks through anchor levels ↔️ Range Trading: Use CDP as the central reference point for range-bound markets 🎯 Advanced Strategy Combinations RSI Integration for Enhanced Signals: 📊 📉 Oversold Bounces: Combine RSI below 30 with price touching AL/NL levels for high-probability long entries 📈 Overbought Rejections: Look for RSI above 70 with price rejecting AH/NH levels for short opportunities 🔍 Divergence Confirmation: When RSI shows bullish divergence at support levels (AL/NL) or bearish divergence at resistance levels (AH/NH), it often signals stronger reversal potential ⚡ Momentum Confluence: RSI crossing 50 while price breaks through CDP can confirm trend direction changes ⚙️ Configuration Options 🎨 Line Customization: Adjust width, style (solid/dashed/dotted), and colors 👁️ Display Preferences: Toggle individual levels, labels, and value table 📍 Table Position: Place the value table anywhere on your chart 🔔 Alert System: Get notifications when price crosses key levels 🔧 Technical Implementation Details 🎯 Data Reliability The script uses request.security() with lookahead settings to ensure historical accuracy while maintaining real-time functionality. The value-locking mechanism prevents the common issue where pivot levels shift during the trading day. 🔄 Multi-Timeframe Logic ⏰ Intraday Charts: Display previous day's calculated levels as stable horizontal lines 📅 Daily Charts: Show current day's levels based on yesterday's OHLC 🔍 Consistency Check: All timeframes reference the same source data 🤔 Why CDP vs Standard Pivots? Counter-Directional Pivots often provide more accurate reversal points than traditional pivot calculations because they incorporate the relationship between high/low ranges and closing prices more effectively. The formula creates levels that better reflect market psychology and institutional trading behaviors. 💡 Best Practices 💧 Use on liquid markets for most reliable results 📊 RSI Combination: Add RSI indicator for overbought/oversold confirmation and divergence analysis 📊 Combine with volume analysis for confirmation 🔍 Consider multiple timeframe analysis (daily levels on hourly charts) 📝 Test thoroughly in paper trading before live implementation 💪 Example Market Applications NASDAQ:AAPL AAPL - Tech stock breakouts through AH levels $NYSE:SPY SPY - Index trading with CDP range analysis NASDAQ:TSLA TSLA - Volatile stock reversals at AL/NL levels ⚠️ This indicator is designed for educational and analytical purposes. Always combine with proper risk management and additional technical analysis tools.
Pine Script® göstergesi
joericke tarafından
Momentum Flip Pro - Advanced ZigZag Trading SystemMomentum Flip Pro - Advanced ZigZag Trading System Complete User Guide 📊 What This Indicator Does The Momentum Flip Pro is an advanced position-flipping trading system that automatically identifies trend reversals using ZigZag patterns combined with momentum analysis. It's designed for traders who want to always be in the market, flipping between long and short positions at optimal reversal points. Key Features: Automatically flips positions at each ZigZag reversal point Dynamic stop loss placement at exact ZigZag levels Real-time trading dashboard with performance metrics Capital tracking and ROI calculation Three momentum engines to choose from 🎯 How It Works Entry Signal: When a ZigZag point appears (circle on chart), the indicator: Exits current position (if any) Immediately enters opposite position Places stop loss at the exact ZigZag price Exit Signal: Positions are closed when the next ZigZag appears, then immediately reversed Position Management: Long Entry: ZigZag bottom (momentum turns UP) Short Entry: ZigZag peak (momentum turns DOWN) Stop Loss: Always at the ZigZag entry price Take Profit: Next ZigZag point (automatic position flip) ⚙️ Recommended Settings For Day Trading (5m-15m timeframes): Momentum Engine: Quantum - RSI Length: 9-12 - Quantum Factor: 3.5-4.0 - RSI Smoothing: 3-5 - Threshold: 8-10 For Swing Trading (1H-4H timeframes): Momentum Engine: MACD - Fast Length: 12 - Slow Length: 26 - Signal Smoothing: 9 - MA Type: EMA For Position Trading (Daily): Momentum Engine: Moving Average - Average Type: EMA or HMA - Length: 20-50 📈 How to Use for Trading Add to Chart: Add indicator to your chart Set your starting capital Choose your preferred momentum engine Understanding Signals: Green circles: Strong bullish momentum reversal Red circles: Strong bearish momentum reversal Purple circles: Normal momentum reversal Entry labels: Show exact entry points with tooltips Trading Rules: Enter LONG when you see an up arrow + green/purple circle Enter SHORT when you see a down arrow + red/purple circle Stop loss is automatically at the ZigZag level Hold until next ZigZag appears (exit + reverse) Risk Management: Risk per trade = Entry Price - Stop Loss Position size = (Capital * Risk %) / Risk per trade Recommended risk: 1-2% per trade 💡 Best Practices Market Conditions: Works best in trending markets Excellent for volatile pairs (crypto, forex majors) Avoid during low volume/consolidation Timeframe Selection: Lower timeframes (5m-15m): More signals, higher noise Higher timeframes (1H+): Fewer signals, higher reliability Sweet spot: 15m-1H for most traders Momentum Engine Selection: Quantum: Best for volatile markets (crypto, indices) MACD: Best for trending markets (forex, stocks) Moving Average: Best for smooth trends (commodities) 📊 Dashboard Interpretation The trading dashboard shows: Current Capital: Your running balance Position: Current trade direction Entry/Stop: Your risk levels Statistics: Win rate and performance ROI: Overall return on investment ⚠️ Important Notes Always Active: This system is always in a position (long or short) No Neutral: You're either long or short, never flat Automatic Reversal: Positions flip at each signal Stop Loss: Fixed at entry ZigZag level (doesn't trail) 🎮 Quick Start Guide Beginners: Start with default settings on 1H timeframe Test First: Use paper trading to understand the signals Small Size: Begin with 1% risk per trade Track Results: Monitor the dashboard statistics Adjust: Fine-tune momentum settings based on results 🔧 Customization Tips Color Signals: Enable to see momentum strength Dashboard Position: Move to preferred screen location Visual Settings: Adjust colors for your theme Alerts: Set up for automated notifications This indicator is ideal for traders who prefer an always-in-market approach with clear entry/exit rules and automated position management. The key to success is choosing the right momentum engine for your market and maintaining disciplined risk management.
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MaximusGains tarafından
2
Open Interest-RSI + Funding + Fractal DivergencesIndicator — “Open Interest-RSI + Funding + Fractal Divergences” A multi-factor oscillator that fuses Open-Interest RSI, real-time Funding-Rate data and price/OI fractal divergences. It paints BUY/SELL arrows in its own pane and directly on the price chart, helping you spot spots where crowd positioning, leverage costs and price action contradict each other. 1 Purpose OI-RSI – measures conviction behind position changes instead of price momentum. Funding Rate – shows who pays to hold positions (longs → bull bias, shorts → bear bias). Fractal Divergences – detects HH/LL in price that are not confirmed by OI-RSI. Optional Funding filter – hides signals when funding is already extreme. Together these elements highlight exhaustion points and potential mean-reversion trades. 2 Inputs RSI / Divergence RSI length – default 14. High-OI level / Low-OI level – default 70 / 30. Fractal period n – default 2 (swing width). Fractals to compare – how many past swings to scan, default 3. Max visible arrows – keeps last 50 BUY/SELL arrows for speed. Funding Rate mode – choose FR, Avg Premium, Premium Index, Avg Prem + PI or FR-candle. Visual scale (×) – multiplies raw funding to fit 0-100 oscillator scale (default 10). specify symbol – enable only if funding symbol differs from chart. use lower tf – averages 1-min premiums for smoother intraday view. show table – tiny two-row widget at chart edge. Signal Filter Use Funding filter – ON hides long signals when funding > Buy-threshold and short signals when funding < Sell-threshold. BUY threshold (%) – default 0.00 (raw %). SELL threshold (%) – default 0.00 (raw %). (Enter funding thresholds as raw percentages, e.g. 0.01 = +0.01 %). 3 Visual Outputs Sub-pane Aqua OI-RSI curve with 70 / 50 / 30 reference lines. Funding visualised according to selected mode (green above 0, red below 0, or other). BUY / SELL arrows at oscillator extremes. Price chart Identical BUY / SELL arrows plotted with force_overlay = true above/below candles that formed qualifying fractals. Optional table Shows current asset ticker and latest funding value of the chosen mode. 4 Signal Logic (Summary) Load _OI series and compute RSI. Retrieve Funding-Rate + Premium Index (optionally from lower TF). Find fractal swings (n bars left & right). Check divergence: Bearish – price HH + OI-RSI LH. Bullish – price LL + OI-RSI HL. If Funding-filter enabled, require funding < Buy-thr (long) or > Sell-thr (short). Plot arrows and trigger two built-in alerts (Bearish OI-RSI divergence, Bullish OI-RSI divergence). Signals are fixed once the fractal bar closes; they do not repaint afterwards. 5 How to Use Attach to a liquid perpetual-futures chart (BTC, ETH, major Binance contracts). If _OI or funding series is missing you’ll see an error. Choose timeframe: 15 m – 4 h for intraday; 1 D+ for swing trades. Lower TFs → more signals; raise Fractals to compare or use Funding filter to trim noise. Trade checklist Funding positive and rising → longs overcrowded. Price makes higher high; OI-RSI makes lower high; Funding above Sell-threshold → consider short. Reverse logic for longs. Combine with trend filter (EMA ribbon, SuperTrend, etc.) so you fade only when price is stretched. Automation – set TradingView alerts on the two alertconditions and send to webhooks/bots. Performance tips Keep Max visible arrows ≤ 50. Disable lower-TF premium aggregation if script feels heavy. 6 Limitations Some symbols lack _OI or funding history → script stops with a console message. Binance Premium Index begins mid-2020; older dates show na. Divergences confirm only after n bars (no forward repaint). 7 Changelog v1.0 – 10 Jun 2025 Initial public release. Added price-chart arrows via force_overlay.
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RezzoRedPriest tarafından
6
Nova Volume Indicator Suite Edition (NVI SE) by SplitzMagicNova Volume Indicator Suite Edition (NVI SE) The Nova Volume Indicator Suite Edition (NVI SE) is an advanced upgrade to the original Nova tool — designed to elevate your trading strategy by combining volume momentum, volatility, and price structure into a single powerful indicator. This refined edition provides enhanced clarity, new visual cues, and smarter customization to help traders confidently identify high-probability trade setups. How It Works The Nova Volume Indicator Suite Edition uses advanced algorithms to analyse real-time price and volume relationships. Key features include: Volume Momentum Calculation By evaluating the relationship between price changes and volume, the indicator identifies momentum shifts — highlighting key moments when buyers or sellers gain control. These shifts are plotted as a dynamic histogram, allowing for precise trade timing. Trend Direction Filter A built-in price filter based on a moving average confirms the overall market direction. This ensures that trade signals only trigger when aligned with broader trend bias, improving win probability. Volatility Integration Nova SE uses price volatility as a normalizing factor to better detect when a momentum move is statistically significant — helping to filter out false signals in choppy conditions. Alert System Customizable alert thresholds notify users when bullish or bearish momentum crosses predefined levels. Traders can receive alerts for signal line crosses and background condition changes, making it easier to stay in sync with the market. Background Highlighting White or Blue background shading appears when valid bullish or bearish trade conditions are met. This instantly shows you when the market is in a high-probability setup zone — while a black background indicates no valid trade signal, helping you avoid low-conviction entries. Custom Symbol Support You can choose to pull data from another instrument - for example, tracking gold volume while trading a related pair. This adds a new layer of analysis for correlated market strategies. How to Use the Nova Volume Indicator for Entries Identifying the Trend - Check whether price is above or below the moving average. If above, focus on long setups. If below, focus on shorts. Spotting Entries Buy Signal : Look for a white histogram bar (bullish momentum) when price is above the moving average and momentum score exceeds the alert threshold. Sell Signal : Look for an aqua histogram bar (bearish momentum) when price is below the moving average and momentum score falls below the negative threshold. Background Confirmation - A white background confirms a bullish condition. A blue background confirms a bearish one. If there’s no background colour, skip the trade - the market is not offering a clear setup. Stops and Targets - Set stops below the swing low (for buys) or above the swing high (for sells). Aiming for at least a 1:2 risk-to-reward ratio is recommended. Customizable Settings - The Nova Volume Indicator Suite Edition offers full control over key parameters: Signal Period : Adjusts the EMA period of the momentum score (signal line). Volatility Period : Sets the window for measuring price volatility. Price Filter MA Length : Controls the moving average period used to determine trend bias. Alert Threshold : Defines the level at which bullish or bearish momentum is confirmed. Custom Symbol Feed : Optionally track volume and price from another instrument for advanced correlation analysis. The Nova Volume Indicator Suite Edition is a refined tool for modern traders - combining simplicity with smart design. Whether you're a beginner or experienced trader, Nova SE delivers clarity, customization, and a structured approach to volume-based trading. Use it to filter out the noise, lock into strong momentum moves, and execute your strategy with confidence. Start using Nova SE today and experience a smarter way to trade.
Pine Script® göstergesi
splitzmagic tarafından
4
Ashpi CVD + MACD AlertMACD Crossing + CVD Support Red arrows signal short entries above the MACD zero line. Green arrows signal long entries below the zero line. Blue arrows indicate a re-entry into an existing long trend (crossing above the zero line), typically on pull-backs. Yellow arrows indicate a re-entry into an existing short trend (crossing below the zero line). Time-Frame Setup - Entry on the 15-second chart - Confirmation on the 30-second chart - Trade management on the 1-minute chart (or higher) Always follow the primary trend: RED = Short, GREEN = Long. Sequence 1. RED signal appears → enter short 2. During the trade, if a GREEN arrow appears on the pull-back → exit trade, or add to position on a YELLOW arrow if the trend continues The same applies to long trades and BLUE arrows. Using EMAs (20, 50, 200) can help you spot structural breaks more clearly. Signal Strength (Delta Distance to Zero Line) The strength of each signal is enhanced by displaying the distance (delta) to the zero line in the chart: - Green numbers mean the delta is already above its 10-period moving average (MA10). - Red numbers mean the delta is below its MA10. Identifying Sideways Markets Use a standard MACD as an additional filter to spot ranging phases. If YELLOW and BLUE arrows occur frequently in succession, it indicates the two MACD lines are moving very close together—trading such conditions should generally be avoided.
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ashpi82 tarafından
IU Liquidity Flow TrackerDESCRIPTION The IU Liquidity Flow Tracker is a powerful market analysis tool designed to visualize hidden buying and selling activity by analyzing price action, volume behavior, market pressure, and depth. It provides a composite view of liquidity dynamics to help traders identify accumulation, distribution, and neutral phases with high clarity. This indicator is ideal for traders who want to gauge the flow of market participants and make informed entry/exit decisions based on the underlying liquidity structure. USER INPUTS: * Flow Analysis Period: Length used for analyzing price spread and volume flow. * Pressure Sensitivity: Adjusts the sensitivity of threshold detection for flow classification. * Flow Smoothing: Controls the smoothing applied to raw flow data. * Market Depth Analysis: Sets the depth range for rejection and wick analysis. * Colors: Customize colors for accumulation, distribution, neutral zones, and pressure visualization. INDICATOR LOGIC: The IU Liquidity Flow Tracker uses a multi-factor model to evaluate market behavior: 1. Liquidity Pressure: Combines price spread, price efficiency, and volume imbalance. 2. Flow Direction: Weighted momentum using short, medium, and long-term price changes adjusted for volume. 3. Market Depth: Wick-based rejection scoring to estimate buying/selling aggressiveness at price extremes. 4. Composite Flow Index: Blended value of flow direction, pressure, and depth—smoothed for clarity. 5. Dynamic Thresholds: Automatically adjusts based on volatility to classify the market into: * Accumulation: Strong buying signals. * Distribution: Strong selling signals. * Neutral: No significant flow dominance. 6. Entry Signals: Long/Short signals are generated when flow state shifts, supported by momentum, volume surge, and depth strength. WHY IT IS UNIQUE: Unlike typical indicators that rely solely on price or volume, this tool combines spread behavior, volume polarity, momentum weighting, and price rejection zones into a single visual interface. It dynamically adjusts sensitivity based on market volatility, helping avoid false signals during sideways or low-volume periods. It is not based on any traditional indicator (RSI, MACD, etc.), making it ideal for traders looking for an original and data-driven market read. HOW USER CAN BENEFIT FROM IT: * Understand Market Context: Know whether the market is being accumulated, distributed, or ranging. * Improve Entries/Exits: Use flow transitions combined with volume confirmation for high-probability setups. * Spot Institutional Activity: Detect subtle shifts in liquidity that precede major price moves. * Reduce Whipsaws: Dynamic thresholds and multi-factor confirmation help filter noise. * Use with Any Style: Whether you're a swing trader, day trader, or scalper, this tool adapts to different timeframes and strategies. DISCLAIMER: This indicator is created for educational and informational purposes only. It does not constitute financial advice or a recommendation to buy or sell any asset. All trading involves risk, and users should conduct their own analysis or consult with a qualified financial advisor before making any trading decisions. The creator is not responsible for any losses incurred through the use of this tool. Use at your own discretion.
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Shivam_Mandrai tarafından
MACD Support and Resistance [ChartPrime]⯁ OVERVIEW MACD Support and Resistance is a dynamic support/resistance mapping tool powered by MACD crossover logic. Each time the MACD line crosses the signal line, the indicator scans for recent price extremes and locks them in as potential support or resistance zones. These levels are automatically cleaned up if price breaks them, keeping the chart focused on active market structure. The system includes a built-in MACD display with visual markers, along with contextual highs and lows to help define the current environment. ⯁ MACD-BASED SUPPORT/RESISTANCE GENERATION The core logic uses the MACD oscillator crossover as a trigger event to generate structural levels: When MACD crosses above its signal line:   → The script scans the last 5 bars for the lowest low .   → A support level is plotted at that price. When MACD crosses below its signal line:   → The script scans the last 5 bars for the highest high .   → A resistance level is plotted at that price. These dynamic levels reflect where price recently reversed or paused, making them prime zones for reaction, continuation, or invalidation. ⯁ LEVEL MANAGEMENT AND VALIDATION To keep the chart clean and relevant: A maximum of 20 active levels are allowed at once. Older levels are automatically removed if the list exceeds the limit. If price closes below a support level or above a resistance level , the corresponding line is deleted. This ensures that only currently respected levels remain on the chart — a major advantage for active traders. ⯁ MACD VISUALIZATION + SIGNAL MARKERS A full MACD system is rendered on the lower panel for visual confirmation: The MACD line and Signal line are both plotted and color-coded dynamically. A filled area] highlights the spread between them to emphasize momentum strength. A diamond marker is drawn each time MACD crosses its signal line, alerting traders to potential trend shifts. These visuals make it easy to understand the timing of the support/resistance updates. ⯁ LOCAL EXTREME REFERENCE LINES To help contextualize current price position relative to recent market extremes: A Local High line is plotted based on the highest MACD value over the past 100 bars]. A Local Low line is plotted based on the lowest MACD value over the past 100 bars]. These levels are rendered lightly and serve as dynamic range boundaries. They assist traders in identifying overextended or compressed MACD behavior. ⯁ USAGE Use the generated S/R levels as breakout or reversal zones. Watch for MACD diamond markers to confirm the timing of new levels. Combine these reactive zones with other ChartPrime confluence tools for higher-confidence entries. Use the Local High/Low zones as a volatility envelope to guide risk and trend continuation potential. ⯁ CONCLUSION MACD Support and Resistance takes a classic momentum indicator and adds real-time structural awareness. By linking MACD crossover events to recent price extremes, it identifies the zones where market sentiment shifted — and continues to monitor their strength. Whether you're a breakout trader or looking to fade key reaction points, this tool delivers clean, actionable levels based on momentum and structure — not guesswork.
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ChartPrime tarafından
13
Market Arterial PressureIndicator Description: Pulse-Market – Market Blood Pressure "I slept and had a dream." In that dream, I wore a white lab coat and shiny black pointed shoes. I felt like a doctor—not of traditional medicine, but of the financial market itself. My mission was clear: to measure the market's blood pressure and diagnose its health. With this vision, I decided to turn the dream into code. Thus, Pulse-Market was born: an indicator designed to listen to the heartbeat of the blockchain, capturing signs of vitality or collapse, and anticipating the pulse of the next trend. But the journey did not stop there. At the core of this creation, I incorporated a profound theory: the cycle of existence — Alpha, Beta, and Omega — concepts that resonate both in science and sacred scriptures. Alpha (α) represents the beginning: the primary impulse, the market's accelerated pulse. Beta (β) symbolizes the middle: the vital rhythm, the stabilizing cadence of prices. Omega (Ω) indicates the end: structural collapse, the exhaustion of a cycle. This logical and symbolic triad forms the foundation of Pulse-Market — the beginning, middle, and end of every market cycle. How to Use the Indicator Pulse-Market works as a dynamic oscillator composed of three main forces: Alpha Pulse (α) Measures recent price acceleration. The stronger the pulse, the more intense the market movement. Beta Rhythm (β) Controls the smoothing of the price rhythm and can be adjusted in four modes: Fast – quick reactions with more sensitivity Normal – standard smoothing (simple moving average) Slow – slow and consistent movements Accelerated – Hull method: reactive and smooth Omega Collapse (Ω) Combines entropy and reversals to detect structural collapses where the market may be losing strength. Visual Interpretation Green line above zero: healthy pulse, buying pressure in control. Red line below zero: strong selling pressure, possible exhaustion. Crossing the zero line: potential trend reversal. Settings and Customization In the indicator settings panel, you can calibrate the pressure reading sensitivity: Systolic Pressure (α): controls the reaction to rapid price impulses. Increase to highlight aggressive moves; decrease to smooth spikes. Diastolic Pressure (β): regulates the importance of the underlying rhythm. Increase for smooth trends; decrease for quicker responses. Pulse Pressure (Ω): sensitivity to structural collapses and volatility. Increase to detect reversals; decrease to ignore market noise. Practical Applications Confirm entry and exit signals based on the balance between Alpha and Omega. Adjust the indicator to your trading style: scalper, day trader, or swing trader. Use on any asset: cryptocurrencies, stocks, indices, forex. Integrated Philosophy We live limited by time and matter, but markets, like life, follow natural cycles: they are born, mature, collapse, and are reborn. Pulse-Market is not just a technical indicator — it is a spiritual and analytical stethoscope that listens to the heartbeat of volatility and tries to anticipate what the eyes cannot see, but time always reveals. Original Creator This indicator was created by Canhoto-Medium, the sole inventor and namer of this tool. As long as time goes on, no other indicator will exist with this essence or name.
Pine Script® göstergesi
Canhoto-Medium tarafından
CCI Orbiting-VenusIndicator Description: CCI Orbiting-Venus This is a customized version of the Commodity Channel Index (CCI) that measures the price deviation relative to its smoothed moving average to help identify overbought or oversold market conditions. What does it do? Calculates the CCI based on various price sources (such as close, open, high, low, and several price averages). Applies customizable smoothing to the CCI using different types of moving averages (SMA, EMA, WMA, Hull, JMA, and SMMA). Visually highlights the CCI direction with different colors: Purple when CCI is above zero (positive momentum) Orange when CCI is below zero (negative momentum) Shows reference lines at +100 and -100 to help identify overbought and oversold zones. How to use this indicator? CCI Period Setting (CCI Period): Adjust the number of periods used to calculate the CCI. Lower values make the indicator more sensitive, while higher values smooth out fluctuations. Price Source (CCI Price Source): Choose which price to base the calculation on: close, open, high, low, or weighted averages. This allows you to adapt the indicator to your trading style or strategy. Smoothing Type (CCI Smoothing Type): Select from different smoothing methods for the CCI calculation, which affects how the indicator behaves: SMA (Simple Moving Average) – basic and traditional. EMA, WMA, Hull, JMA (more advanced averages) – provide different noise filtering or faster response to price movements. Interpreting CCI values: Values above +100 suggest the asset may be overbought and could be near a downward reversal. Values below -100 suggest the asset may be oversold and could be near an upward reversal. Crossing the zero line indicates a potential change in trend or momentum. Practical usage: Look for buy signals when CCI moves up from the oversold region (-100) and crosses above zero, turning purple (positive). Look for sell signals when CCI moves down from the overbought region (+100) and crosses below zero, turning orange (negative). Combine with other indicators or chart analysis to confirm signals and avoid false entries. Advantages of this custom indicator Flexibility in choosing the price source and smoothing method. Intuitive visual cues with colors indicating momentum direction. Clear reference lines for quick assessment of extreme conditions.
Pine Script® göstergesi
Canhoto-Medium tarafından
Chebyshev-Gauss Convergence DivergenceThe Chebyshev-Gauss Convergence Divergence is a momentum indicator that leverages the Chebyshev-Gauss Moving Average (CG-MA) to provide a smoother and more responsive alternative to traditional oscillators like the MACD. For more information see the moving average script: How it works: It calculates a fast CG-MA and a slow CG-MA. The CG-MA uses Gauss-Chebyshev quadrature to compute a weighted average, which can offer a better trade-off between lag and smoothness compared to simple or exponential MAs. The Oscillator line is the difference between the fast CG-MA and the slow CG-MA. A Signal Line, which is a simple moving average of the Oscillator line, is plotted to show the average trend of the oscillator. A Histogram is plotted, representing the difference between the Oscillator and the Signal Line. The color of the histogram bars changes to indicate whether momentum is strengthening or weakening. How to use: Crossovers: A buy signal can be generated when the Oscillator line crosses above the Signal line. A sell signal can be generated when it crosses below. Zero Line: When the Oscillator crosses above the zero line, it indicates upward momentum (fast MA is above slow MA).When it crosses below zero, it indicates downward momentum. Divergence: Like with the MACD, look for divergences between the oscillator and price action to spot potential reversals. Histogram: The histogram provides a visual representation of the momentum. When the bars are growing, momentum is increasing. When they are shrinking, momentum is fading.
Pine Script® göstergesi
uncleric0 tarafından
Market Pulse ProMarket Pulse Pro (Pulse‑X) — User Guide Market Pulse Pro, also known as Pulse‑X, is an advanced momentum indicator that combines SMI, Stochastic RSI, and a smoothed signal line to identify zones of buying and selling strength in the market. It is designed to assess the balance of power between bulls and bears with clear visualizations. How It Works The indicator calculates three main components: SMI (Stochastic Momentum Index) – measures price position relative to its recent range. Stochastic RSI – captures overbought/oversold extremes of the RSI. Smoothed Signal Line – based on closing price, smoothed using various methods (such as HMA, EMA, etc.). Each component is normalized to create two final values: Bull Herd (Buying Strength) – green line. Bear Winter (Selling Strength) – red line. Interpretation Bull Herd (high green values): Bulls dominate the market. May indicate the start or continuation of an uptrend. Bear Winter (high red values): Bears dominate. May indicate reversal or continuation of a downtrend. Convergence around 50%: Market is balanced. Signals are weaker or indecisive. Tip: Combine with price action analysis or support/resistance levels to confirm entries. Customizable Settings You can adjust: SMI Period, Smooth K, and D – control the sensitivity of the SMI. RSI Period – sets the RSI calculation window. Signal Period – period for the price-based signal line. Smoothing Methods – choose between HMA, EMA, WMA, JMA, SMMA, etc. Line Width – thickness of the plotted lines. Note: The JMA (Jurik Moving Average) used in this script is not the original proprietary version. It is a custom public version, based on open-source code shared by the TradingView community. The original JMA is copyrighted and owned by Jurik Research. How to Use It in Practice Buy Entries When the green Bull Herd line crosses above 60 and the red Bear Winter line falls below 40. Entry is more reliable if the green line is rising steadily. Sell Entries When the red Bear Winter line crosses above 60 and the green Bull Herd line falls. Signals are stronger when there is a clear crossover and divergence between the two lines. Avoid trading near the neutral zone (~50%), where the market shows indecision. Additional Tips Combine with volume analysis or reversal candlestick patterns for higher accuracy. Test different smoothing methods: HMA is more responsive, SMMA is smoother and slower.
Pine Script® göstergesi
Canhoto-Medium tarafından
1
Fear-Greed ThermometerFear-Greed Thermometer This indicator measures market sentiment between fear and greed by combining three key factors: volatility, average volume, and percentage price change. Each factor is normalized and averaged to produce an index ranging from 0 to 100 that reflects the overall level of market fear or greed. How to use: Index above 50: Indicates greed dominance. The market tends to be more optimistic, signaling potential bullish conditions or overbought levels. Index below 50: Indicates fear dominance. The market is more cautious or pessimistic, pointing to potential bearish conditions or oversold levels. Neutral line (50): Acts as a reference for transitions between fear and greed phases. Features: Dynamic background: The chart background changes color according to sentiment — green for greed, red for fear — making it easy to visually gauge the index. Customizable: Adjust the calculation periods for volatility, volume, and price change to fit your analysis style. Tips: Use alongside other technical tools to confirm entry and exit points. Watch for divergences between the index and price to anticipate possible reversals. Monitoring extreme levels can help identify market turning points. This indicator is not a buy or sell recommendation but an additional tool to help understand the overall market sentiment.
Pine Script® göstergesi
Canhoto-Medium tarafından
MACD Full [Titans_Invest]MACD Full — A Smarter, More Flexible MACD. Looking for a MACD with real customization power? We present one of the most complete public MACD indicators available on TradingView. It maintains the classic MACD structure but is enhanced with 20 fully customizable long entry conditions and 20 short entry conditions , giving you precise control over your strategy. Plus, it’s fully automation-ready, making it ideal for quantitative systems and algorithmic trading. Whether you're a discretionary trader or a bot developer, this tool is built to seamlessly adapt to your style. ⯁ WHAT IS THE MACD❓ The Moving Average Convergence Divergence (MACD) is a technical analysis indicator developed by Gerald Appel. It measures the relationship between two moving averages of a security’s price to identify changes in momentum, direction, and strength of a trend. The MACD is composed of three components: the MACD line, the signal line, and the histogram. ⯁ HOW TO USE THE MACD❓ The MACD is calculated by subtracting the 26-period Exponential Moving Average (EMA) from the 12-period EMA. A 9-period EMA of the MACD line, called the signal line, is then plotted on top of the MACD line. The MACD histogram represents the difference between the MACD line and the signal line. Here are the primary signals generated by the MACD: Bullish Crossover: When the MACD line crosses above the signal line, indicating a potential buy signal. Bearish Crossover: When the MACD line crosses below the signal line, indicating a potential sell signal. Divergence: When the price of the security diverges from the MACD, suggesting a potential reversal. Overbought/Oversold Conditions: Indicated by the MACD line moving far away from the signal line, though this is less common than in oscillators like the RSI. ⯁ ENTRY CONDITIONS The conditions below are fully flexible and allow for complete customization of the signal. ______________________________________________________ 🔹 CONDITIONS TO BUY 📈 ______________________________________________________  •   Signal Validity: The signal will remain valid for X bars .  •   Signal Sequence: Configurable as AND or OR . 🔹 MACD > Signal Smoothing 🔹 MACD < Signal Smoothing 🔹 Histogram > 0 🔹 Histogram < 0 🔹 Histogram Positive 🔹 Histogram Negative 🔹 MACD > 0 🔹 MACD < 0 🔹 Signal > 0 🔹 Signal < 0 🔹 MACD > Histogram 🔹 MACD < Histogram 🔹 Signal > Histogram 🔹 Signal < Histogram 🔹 MACD (Crossover) Signal 🔹 MACD (Crossunder) Signal 🔹 MACD (Crossover) 0 🔹 MACD (Crossunder) 0 🔹 Signal (Crossover) 0 🔹 Signal (Crossunder) 0 ______________________________________________________ ______________________________________________________ 🔸 CONDITIONS TO SELL 📉 ______________________________________________________  •   Signal Validity: The signal will remain valid for X bars .  •   Signal Sequence: Configurable as AND or OR . 🔸 MACD > Signal Smoothing 🔸 MACD < Signal Smoothing 🔸 Histogram > 0 🔸 Histogram < 0 🔸 Histogram Positive 🔸 Histogram Negative 🔸 MACD > 0 🔸 MACD < 0 🔸 Signal > 0 🔸 Signal < 0 🔸 MACD > Histogram 🔸 MACD < Histogram 🔸 Signal > Histogram 🔸 Signal < Histogram 🔸 MACD (Crossover) Signal 🔸 MACD (Crossunder) Signal 🔸 MACD (Crossover) 0 🔸 MACD (Crossunder) 0 🔸 Signal (Crossover) 0 🔸 Signal (Crossunder) 0 ______________________________________________________ ______________________________________________________ 🤖 AUTOMATION 🤖 • You can automate the BUY and SELL signals of this indicator. ______________________________________________________ ______________________________________________________ ⯁ UNIQUE FEATURES ______________________________________________________ Signal Validity: The signal will remain valid for X bars Signal Sequence: Configurable as AND/OR Condition Table: BUY/SELL Condition Labels: BUY/SELL Plot Labels in the Graph Above: BUY/SELL Automate and Monitor Signals/Alerts: BUY/SELL Signal Validity: The signal will remain valid for X bars Signal Sequence: Configurable as AND/OR Table of Conditions: BUY/SELL Conditions Label: BUY/SELL Plot Labels in the graph above: BUY/SELL Automate & Monitor Signals/Alerts: BUY/SELL ______________________________________________________ 📜 SCRIPT : MACD Full 🎴 Art by  : @Titans_Invest & @DiFlip 👨‍💻 Dev by : @Titans_Invest & @DiFlip 🎑 Titans Invest — The Wizards Without Gloves 🧤 ✨ Enjoy! ______________________________________________________ o Mission 🗺 • Inspire Traders to manifest Magic in the Market. o Vision 𐓏 • To elevate collective Energy 𐓷𐓏
Pine Script® göstergesi
Titans_Invest tarafından
15
(Mustang Algo) Stochastic RSI + Triple EMAStochastic RSI + Triple EMA (StochTEMA) Overview The Stochastic RSI + Triple EMA indicator combines the Stochastic RSI oscillator with a Triple Exponential Moving Average (TEMA) overlay to generate clear buy and sell signals on the price chart. By measuring RSI overbought/oversold conditions and confirming trend direction with TEMA, this tool helps traders identify high-probability entries and exits while filtering out noise in choppy markets. Key Features Stochastic RSI Calculation Computes a standard RSI over a user-defined period (default 50). Applies a Stochastic oscillator to the RSI values over a second user-defined period (default 50). Smooths the %K line by taking an SMA over a third input (default 3), and %D is an SMA of %K over another input (default 3). Defines oversold when both %K and %D are below 20, and overbought when both are above 80. Triple EMA (TEMA) Calculates three successive EMAs on the closing price with the same length (default 9). Combines them using TEMA = 3×(EMA1 – EMA2) + EMA3, producing a fast-reacting trend line. Bullish trend is identified when price > TEMA and TEMA is rising; bearish trend when price < TEMA and TEMA is falling; neutral/flat when TEMA change is minimal. Signal Logic Strong Buy: Previous bar’s Stoch RSI was oversold (both %K and %D < 20), %K crosses above %D, and TEMA is in a bullish trend. Medium Buy: %K crosses above %D (without requiring oversold), TEMA is bullish, and previous %K < 50. Weak Buy: Previous bar’s %K and %D were oversold, %K crosses above %D, TEMA is flat or bullish (not bearish). Strong Sell: Previous bar’s Stoch RSI was overbought (both %K and %D > 80), %K crosses below %D, and TEMA is bearish. Medium Sell: %K crosses below %D (without requiring overbought), TEMA is bearish, and previous %K > 50. Weak Sell: Previous bar’s %K and %D were overbought, %K crosses below %D, TEMA is flat or bearish (not bullish). Visual Elements on Chart TEMA Line: Plotted in cyan (#00BCD4) with a medium-thick line for clear trend visualization. Buy/Sell Markers: BUY STRONG: Lime label below the candle BUY MEDIUM: Green triangle below the candle BUY WEAK: Semi-transparent green circle below the candle SELL STRONG: Red label above the candle SELL MEDIUM: Orange triangle above the candle SELL WEAK: Semi-transparent orange circle above the candle Candle & Background Coloring: When a strong buy or sell signal occurs, the candle body is tinted (semi-transparent lime/red) and the chart background briefly flashes light green (buy) or light red (sell). Dynamic Support/Resistance: On a strong buy signal, a green dot is plotted under that bar’s low as a temporary support marker. On a strong sell signal, a red dot is plotted above that bar’s high as a temporary resistance marker. Alerts Strong Buy Alert: Triggered when Stoch RSI is oversold, %K crosses above %D, and TEMA is bullish. Strong Sell Alert: Triggered when Stoch RSI is overbought, %K crosses below %D, and TEMA is bearish. General Buy Alert: Triggered on any bullish crossover (%K > %D) when TEMA is not bearish. General Sell Alert: Triggered on any bearish crossover (%K < %D) when TEMA is not bullish. Inputs Stochastic RSI Settings (group “Stochastic RSI”): K (smoothK): Period length for smoothing the %K line (default 3, minimum 1) D (smoothD): Period length for smoothing the %D line (default 3, minimum 1) RSI Length (lengthRSI): Number of bars used for the RSI calculation (default 50, minimum 1) Stochastic Length (lengthStoch): Number of bars for the Stochastic oscillator applied to RSI (default 50, minimum 1) RSI Source (src): Price source for the RSI (default = close) TEMA Settings (group “Triple EMA”): TEMA Length (lengthTEMA): Number of bars used for each of the three EMAs (default 9, minimum 1) How to Use Add the Script Copy and paste the indicator code into TradingView’s Pine Editor (version 6). Save the script and add it to your chart as “Stochastic RSI + Triple EMA (StochTEMA).” Adjust Inputs Choose shorter lengths for lower timeframes (e.g., intraday scalping) and longer lengths for higher timeframes (e.g., swing trading). Fine-tune the Stochastic RSI parameters (K, D, RSI Length, Stochastic Length) to suit the volatility of the instrument. Modify TEMA Length if you prefer a faster or slower moving average response. Interpret Signals Primary Entries/Exits: Focus on “BUY STRONG” and “SELL STRONG” signals, as they require both oversold/overbought conditions and a confirming TEMA trend. Confirmation Signals: Use “BUY MEDIUM”/“BUY WEAK” to confirm or add to an existing position when the market is trending. Similarly, “SELL MEDIUM”/“SELL WEAK” can be used to scale out or confirm bearish momentum. Support/Resistance Dots: These help identify recent swing lows (green dots) and swing highs (red dots) that were tagged by strong signals—useful to place stop-loss or profit-target orders. Set Alerts Open the Alerts menu (bell icon) in TradingView, choose this script, and select the desired alert condition (e.g., “BUY Signal Strong”). Configure notifications (popup, email, webhook) according to your trading workflow. Notes & Best Practices Filtering False Signals: By combining Stoch RSI crossovers with TEMA trend confirmation, most false breakouts during choppy price action are filtered out. Timeframe Selection: This indicator works on all timeframes, but shorter timeframes may generate frequent signals—consider higher-timeframe confirmation when trading lower timeframes. Risk Management: Always use proper position sizing and stop-loss placement. An “oversold” or “overbought” reading can remain extended for some time in strong trends. Backtesting/Optimization: Before live trading, backtest different parameter combinations on historical data to find the optimal balance between sensitivity and reliability for your chosen instrument. No Guarantee of Profits: As with any technical indicator, past performance does not guarantee future results. Use in conjunction with other forms of analysis (volume, price patterns, fundamentals). Author: Your Name or Username Version: 1.0 (Pine Script v6) Published: June 2025 Feel free to customize input values and visual preferences. If you find bugs or have suggestions for improvements, open an issue or leave a comment below. Trade responsibly!
Pine Script® göstergesi
MustangAlgo tarafından
Risk-Adjusted Momentum Oscillator# Risk-Adjusted Momentum Oscillator (RAMO): Momentum Analysis with Integrated Risk Assessment ## 1. Introduction Momentum indicators have been fundamental tools in technical analysis since the pioneering work of Wilder (1978) and continue to play crucial roles in systematic trading strategies (Jegadeesh & Titman, 1993). However, traditional momentum oscillators suffer from a critical limitation: they fail to account for the risk context in which momentum signals occur. This oversight can lead to significant drawdowns during periods of market stress, as documented extensively in the behavioral finance literature (Kahneman & Tversky, 1979; Shefrin & Statman, 1985). The Risk-Adjusted Momentum Oscillator addresses this gap by incorporating real-time drawdown metrics into momentum calculations, creating a self-regulating system that automatically adjusts signal sensitivity based on current risk conditions. This approach aligns with modern portfolio theory's emphasis on risk-adjusted returns (Markowitz, 1952) and reflects the sophisticated risk management practices employed by institutional investors (Ang, 2014). ## 2. Theoretical Foundation ### 2.1 Momentum Theory and Market Anomalies The momentum effect, first systematically documented by Jegadeesh & Titman (1993), represents one of the most robust anomalies in financial markets. Subsequent research has confirmed momentum's persistence across various asset classes, time horizons, and geographic markets (Fama & French, 1996; Asness, Moskowitz & Pedersen, 2013). However, momentum strategies are characterized by significant time-varying risk, with particularly severe drawdowns during market reversals (Barroso & Santa-Clara, 2015). ### 2.2 Drawdown Analysis and Risk Management Maximum drawdown, defined as the peak-to-trough decline in portfolio value, serves as a critical risk metric in professional portfolio management (Calmar, 1991). Research by Chekhlov, Uryasev & Zabarankin (2005) demonstrates that drawdown-based risk measures provide superior downside protection compared to traditional volatility metrics. The integration of drawdown analysis into momentum calculations represents a natural evolution toward more sophisticated risk-aware indicators. ### 2.3 Adaptive Smoothing and Market Regimes The concept of adaptive smoothing in technical analysis draws from the broader literature on regime-switching models in finance (Hamilton, 1989). Perry Kaufman's Adaptive Moving Average (1995) pioneered the application of efficiency ratios to adjust indicator responsiveness based on market conditions. RAMO extends this concept by incorporating volatility-based adaptive smoothing, allowing the indicator to respond more quickly during high-volatility periods while maintaining stability during quiet markets. ## 3. Methodology ### 3.1 Core Algorithm Design The RAMO algorithm consists of several interconnected components: #### 3.1.1 Risk-Adjusted Momentum Calculation The fundamental innovation of RAMO lies in its risk adjustment mechanism: Risk_Factor = 1 - (Current_Drawdown / Maximum_Drawdown × Scaling_Factor) Risk_Adjusted_Momentum = Raw_Momentum × max(Risk_Factor, 0.05) This formulation ensures that momentum signals are dampened during periods of high drawdown relative to historical maximums, implementing an automatic risk management overlay as advocated by modern portfolio theory (Markowitz, 1952). #### 3.1.2 Multi-Algorithm Momentum Framework RAMO supports three distinct momentum calculation methods: 1. Rate of Change: Traditional percentage-based momentum (Pring, 2002) 2. Price Momentum: Absolute price differences 3. Log Returns: Logarithmic returns preferred for volatile assets (Campbell, Lo & MacKinlay, 1997) This multi-algorithm approach accommodates different asset characteristics and volatility profiles, addressing the heterogeneity documented in cross-sectional momentum studies (Asness et al., 2013). ### 3.2 Leading Indicator Components #### 3.2.1 Momentum Acceleration Analysis The momentum acceleration component calculates the second derivative of momentum, providing early signals of trend changes: Momentum_Acceleration = EMA(Momentum_t - Momentum_{t-n}, n) This approach draws from the physics concept of acceleration and has been applied successfully in financial time series analysis (Treadway, 1969). #### 3.2.2 Linear Regression Prediction RAMO incorporates linear regression-based prediction to project momentum values forward: Predicted_Momentum = LinReg_Value + (LinReg_Slope × Forward_Offset) This predictive component aligns with the literature on technical analysis forecasting (Lo, Mamaysky & Wang, 2000) and provides leading signals for trend changes. #### 3.2.3 Volume-Based Exhaustion Detection The exhaustion detection algorithm identifies potential reversal points by analyzing the relationship between momentum extremes and volume patterns: Exhaustion = |Momentum| > Threshold AND Volume < SMA(Volume, 20) This approach reflects the established principle that sustainable price movements require volume confirmation (Granville, 1963; Arms, 1989). ### 3.3 Statistical Normalization and Robustness RAMO employs Z-score normalization with outlier protection to ensure statistical robustness: Z_Score = (Value - Mean) / Standard_Deviation Normalized_Value = max(-3.5, min(3.5, Z_Score)) This normalization approach follows best practices in quantitative finance for handling extreme observations (Taleb, 2007) and ensures consistent signal interpretation across different market conditions. ### 3.4 Adaptive Threshold Calculation Dynamic thresholds are calculated using Bollinger Band methodology (Bollinger, 1992): Upper_Threshold = Mean + (Multiplier × Standard_Deviation) Lower_Threshold = Mean - (Multiplier × Standard_Deviation) This adaptive approach ensures that signal thresholds adjust to changing market volatility, addressing the critique of fixed thresholds in technical analysis (Taylor & Allen, 1992). ## 4. Implementation Details ### 4.1 Adaptive Smoothing Algorithm The adaptive smoothing mechanism adjusts the exponential moving average alpha parameter based on market volatility: Volatility_Percentile = Percentrank(Volatility, 100) Adaptive_Alpha = Min_Alpha + ((Max_Alpha - Min_Alpha) × Volatility_Percentile / 100) This approach ensures faster response during volatile periods while maintaining smoothness during stable conditions, implementing the adaptive efficiency concept pioneered by Kaufman (1995). ### 4.2 Risk Environment Classification RAMO classifies market conditions into three risk environments: - Low Risk: Current_DD < 30% × Max_DD - Medium Risk: 30% × Max_DD ≤ Current_DD < 70% × Max_DD - High Risk: Current_DD ≥ 70% × Max_DD This classification system enables conditional signal generation, with long signals filtered during high-risk periods—a approach consistent with institutional risk management practices (Ang, 2014). ## 5. Signal Generation and Interpretation ### 5.1 Entry Signal Logic RAMO generates enhanced entry signals through multiple confirmation layers: 1. Primary Signal: Crossover between indicator and signal line 2. Risk Filter: Confirmation of favorable risk environment for long positions 3. Leading Component: Early warning signals via acceleration analysis 4. Exhaustion Filter: Volume-based reversal detection This multi-layered approach addresses the false signal problem common in traditional technical indicators (Brock, Lakonishok & LeBaron, 1992). ### 5.2 Divergence Analysis RAMO incorporates both traditional and leading divergence detection: - Traditional Divergence: Price and indicator divergence over 3-5 periods - Slope Divergence: Momentum slope versus price direction - Acceleration Divergence: Changes in momentum acceleration This comprehensive divergence analysis framework draws from Elliott Wave theory (Prechter & Frost, 1978) and momentum divergence literature (Murphy, 1999). ## 6. Empirical Advantages and Applications ### 6.1 Risk-Adjusted Performance The risk adjustment mechanism addresses the fundamental criticism of momentum strategies: their tendency to experience severe drawdowns during market reversals (Daniel & Moskowitz, 2016). By automatically reducing position sizing during high-drawdown periods, RAMO implements a form of dynamic hedging consistent with portfolio insurance concepts (Leland, 1980). ### 6.2 Regime Awareness RAMO's adaptive components enable regime-aware signal generation, addressing the regime-switching behavior documented in financial markets (Hamilton, 1989; Guidolin, 2011). The indicator automatically adjusts its parameters based on market volatility and risk conditions, providing more reliable signals across different market environments. ### 6.3 Institutional Applications The sophisticated risk management overlay makes RAMO particularly suitable for institutional applications where drawdown control is paramount. The indicator's design philosophy aligns with the risk budgeting approaches used by hedge funds and institutional investors (Roncalli, 2013). ## 7. Limitations and Future Research ### 7.1 Parameter Sensitivity Like all technical indicators, RAMO's performance depends on parameter selection. While default parameters are optimized for broad market applications, asset-specific calibration may enhance performance. Future research should examine optimal parameter selection across different asset classes and market conditions. ### 7.2 Market Microstructure Considerations RAMO's effectiveness may vary across different market microstructure environments. High-frequency trading and algorithmic market making have fundamentally altered market dynamics (Aldridge, 2013), potentially affecting momentum indicator performance. ### 7.3 Transaction Cost Integration Future enhancements could incorporate transaction cost analysis to provide net-return-based signals, addressing the implementation shortfall documented in practical momentum strategy applications (Korajczyk & Sadka, 2004). ## References Aldridge, I. (2013). *High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems*. 2nd ed. Hoboken, NJ: John Wiley & Sons. Ang, A. (2014). *Asset Management: A Systematic Approach to Factor Investing*. New York: Oxford University Press. Arms, R. W. (1989). *The Arms Index (TRIN): An Introduction to the Volume Analysis of Stock and Bond Markets*. Homewood, IL: Dow Jones-Irwin. Asness, C. S., Moskowitz, T. J., & Pedersen, L. H. (2013). Value and momentum everywhere. *Journal of Finance*, 68(3), 929-985. Barroso, P., & Santa-Clara, P. (2015). Momentum has its moments. *Journal of Financial Economics*, 116(1), 111-120. Bollinger, J. (1992). *Bollinger on Bollinger Bands*. New York: McGraw-Hill. Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. *Journal of Finance*, 47(5), 1731-1764. Calmar, T. (1991). The Calmar ratio: A smoother tool. *Futures*, 20(1), 40. Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). *The Econometrics of Financial Markets*. Princeton, NJ: Princeton University Press. Chekhlov, A., Uryasev, S., & Zabarankin, M. (2005). Drawdown measure in portfolio optimization. *International Journal of Theoretical and Applied Finance*, 8(1), 13-58. Daniel, K., & Moskowitz, T. J. (2016). Momentum crashes. *Journal of Financial Economics*, 122(2), 221-247. Fama, E. F., & French, K. R. (1996). Multifactor explanations of asset pricing anomalies. *Journal of Finance*, 51(1), 55-84. Granville, J. E. (1963). *Granville's New Key to Stock Market Profits*. Englewood Cliffs, NJ: Prentice-Hall. Guidolin, M. (2011). Markov switching models in empirical finance. In D. N. Drukker (Ed.), *Missing Data Methods: Time-Series Methods and Applications* (pp. 1-86). Bingley: Emerald Group Publishing. Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. *Econometrica*, 57(2), 357-384. Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. *Journal of Finance*, 48(1), 65-91. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. *Econometrica*, 47(2), 263-291. Kaufman, P. J. (1995). *Smarter Trading: Improving Performance in Changing Markets*. New York: McGraw-Hill. Korajczyk, R. A., & Sadka, R. (2004). Are momentum profits robust to trading costs? *Journal of Finance*, 59(3), 1039-1082. Leland, H. E. (1980). Who should buy portfolio insurance? *Journal of Finance*, 35(2), 581-594. Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. *Journal of Finance*, 55(4), 1705-1765. Markowitz, H. (1952). Portfolio selection. *Journal of Finance*, 7(1), 77-91. Murphy, J. J. (1999). *Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications*. New York: New York Institute of Finance. Prechter, R. R., & Frost, A. J. (1978). *Elliott Wave Principle: Key to Market Behavior*. Gainesville, GA: New Classics Library. Pring, M. J. (2002). *Technical Analysis Explained: The Successful Investor's Guide to Spotting Investment Trends and Turning Points*. 4th ed. New York: McGraw-Hill. Roncalli, T. (2013). *Introduction to Risk Parity and Budgeting*. Boca Raton, FL: CRC Press. Shefrin, H., & Statman, M. (1985). The disposition to sell winners too early and ride losers too long: Theory and evidence. *Journal of Finance*, 40(3), 777-790. Taleb, N. N. (2007). *The Black Swan: The Impact of the Highly Improbable*. New York: Random House. Taylor, M. P., & Allen, H. (1992). The use of technical analysis in the foreign exchange market. *Journal of International Money and Finance*, 11(3), 304-314. Treadway, A. B. (1969). On rational entrepreneurial behavior and the demand for investment. *Review of Economic Studies*, 36(2), 227-239. Wilder, J. W. (1978). *New Concepts in Technical Trading Systems*. Greensboro, NC: Trend Research.
Pine Script® göstergesi
EdgeTools tarafından
2
AWR R & LR Oscillator with plots & tableHello trading viewers ! I'm glad to share with you one of my favorite indicator. It's the aggregate of many things. It is partly based on an indicator designed by gentleman goat. Many thanks to him. 1. Oscillator and Correlation Calculations Overview and Functionality: This part of the indicator computes up to 10 Pearson correlation coefficients between a chosen source (typically the close price, though this is user-configurable) and the bar index over various periods. Starting with an initial period defined by the startPeriod parameter and increasing by a set increment (periodIncrement), each correlation coefficient is calculated using the built-in ta.correlation function over successive ranges. These coefficients are stored in an array, and the indicator calculates their average (avgPR) to provide a complete view of the market trend strength. Display Features: Each individual coefficient, as well as the overall average, is plotted on the chart using a specific color. Horizontal lines (both dashed and solid) are drawn at levels 0, ±0.8, and ±1, serving as visual thresholds. Additionally, conditional fills in red or blue highlight when values exceed these thresholds, helping the user quickly identify potential extreme conditions (such as overbought or oversold situations). 2. Visual Signals and Automated Alerts Graphical Signal Enhancements: To reinforce the analysis, the indicator uses graphical elements like emojis and shape markers. For example: If all 10 curves drop below -0.79, a 🌋 emoji appears at the bottom of the chart; When curves 2 through 10 are below -0.79, a ⛰️ emoji is displayed below the bar, potentially serving as a buy signal accompanied by an alert condition; Likewise, symmetrical conditions for correlations exceeding 0.79 produce corresponding emojis (🤿 and 🏖️) at the top or bottom of the chart. Alerts and Notifications: Using these visual triggers, several alertcondition statements are defined within the script. This allows users to set up TradingView alerts and receive real-time notifications whenever the market reaches these predefined critical zones identified by the multi-period analysis. 3. Regression Channel Analysis Principles and Calculations: In addition to the oscillator, the indicator implements an analysis of regression channels. For each of the 8 configurable channels, the user can set a range of periods (for example, min1 to max1, etc.). The function calc_regression_channel iterates through the defined period range to find the optimal period that maximizes a statistical measure derived from a regression parameter calculated by the function r(p). Once this optimal period is identified, the indicator computes two key points (A and B) which define the main regression line, and then creates a channel based on the calculated deviation (an RMSE multiplied by a user-defined factor). The regression channels are not displayed on the chart but are used to plot shapes & fullfilled a table. Blue shapes are plotted when 6th channel or 7th channel are lower than 3 deviations Yellow shapes are plotted when 6th channel or 7th channel are higher than 3 deviations 4. Scores, Conditions, and the Summary Table Scoring System: The indicator goes further by assigning scores across multiple analytical categories, such as: 1. BigPear Score What It Represents: This score is based on a longer-term moving average of the Pearson correlation values (SMA 100 of the average of the 10 curves of correlation of Pearson). The BigPear category is designed to capture where this longer-term average falls within specific ranges. Conditions: The script defines nine boolean conditions (labeled BigPear1up through BigPear9up for the “up” direction). Here's the rules : BigPear1up = (bigsma_avgPR <= 0.5 and bigsma_avgPR > 0.25) BigPear2up = (bigsma_avgPR <= 0.25 and bigsma_avgPR > 0) BigPear3up = (bigsma_avgPR <= 0 and bigsma_avgPR > -0.25) BigPear4up = (bigsma_avgPR <= -0.25 and bigsma_avgPR > -0.5) BigPear5up = (bigsma_avgPR <= -0.5 and bigsma_avgPR > -0.65) BigPear6up = (bigsma_avgPR <= -0.65 and bigsma_avgPR > -0.7) BigPear7up = (bigsma_avgPR <= -0.7 and bigsma_avgPR > -0.75) BigPear8up = (bigsma_avgPR <= -0.75 and bigsma_avgPR > -0.8) BigPear9up = (bigsma_avgPR <= -0.8) Conditions: The script defines nine boolean conditions (labeled BigPear1down through BigPear9down for the “down” direction). BigPear1down = (bigsma_avgPR >= -0.5 and bigsma_avgPR < -0.25) BigPear2down = (bigsma_avgPR >= -0.25 and bigsma_avgPR < 0) BigPear3down = (bigsma_avgPR >= 0 and bigsma_avgPR < 0.25) BigPear4down = (bigsma_avgPR >= 0.25 and bigsma_avgPR < 0.5) BigPear5down = (bigsma_avgPR >= 0.5 and bigsma_avgPR < 0.65) BigPear6down = (bigsma_avgPR >= 0.65 and bigsma_avgPR < 0.7) BigPear7down = (bigsma_avgPR >= 0.7 and bigsma_avgPR < 0.75) BigPear8down = (bigsma_avgPR >= 0.75 and bigsma_avgPR < 0.8) BigPear9down = (bigsma_avgPR >= 0.8) Weighting: If BigPear1up is true, 1 point is added; if BigPear2up is true, 2 points are added; and so on up to 9 points from BigPear9up. Total Score: The positive score (posScoreBigPear) is the sum of these weighted conditions. Similarly, there is a negative score (negScoreBigPear) that is calculated using a mirrored set of conditions (named BigPear1down to BigPear9down), each contributing a negative weight (from -1 to -9). In essence, the BigPear score tells you—in a weighted cumulative way—where the longer-term correlation average falls relative to predefined thresholds. 2. Pear Score What It Represents: This category uses the immediate average of the Pearson correlations (avgPR) rather than a longer-term smoothed version. It reflects a more current picture of the market’s correlation behavior. How It’s Calculated: Conditions: There are nine conditions defined for the “up” scenario (named Pear1up through Pear9up), which partition the range of avgPR into intervals. For instance: Pear1up = (avgPR > -0.2 and avgPR <= 0) Pear2up = (avgPR > -0.4 and avgPR <= -0.2) Pear3up = (avgPR > -0.5 and avgPR <= -0.4) Pear4up = (avgPR > -0.6 and avgPR <= -0.5) Pear5up = (avgPR > -0.65 and avgPR <= -0.6) Pear6up = (avgPR > -0.7 and avgPR <= -0.65) Pear7up = (avgPR > -0.75 and avgPR <= -0.7) Pear8up = (avgPR > -0.8 and avgPR <= -0.75) Pear9up = (avgPR > -1 and avgPR <= -0.8) There are nine conditions defined for the “down” scenario (named Pear1down through Pear9down), which partition the range of avgPR into intervals. For instance: Pear1down = (avgPR >= 0 and avgPR < 0.2) Pear2down = (avgPR >= 0.2 and avgPR < 0.4) Pear3down = (avgPR >= 0.4 and avgPR < 0.5) Pear4down = (avgPR >= 0.5 and avgPR < 0.6) Pear5down = (avgPR >= 0.6 and avgPR < 0.65) Pear6down = (avgPR >= 0.65 and avgPR < 0.7) Pear7down = (avgPR >= 0.7 and avgPR < 0.75) Pear8down = (avgPR >= 0.75 and avgPR < 0.8) Pear9down = (avgPR >= 0.8 and avgPR <= 1) Weighting: Each condition has an associated weight, such as 0.9 for Pear1up, 1.9 for Pear2up, and so on, up to 9 for Pear9up. Sum up : Pear1up = 0.9 Pear2up = 1.9 Pear3up = 2.9 Pear4up = 3.9 Pear5up = 4.99 Pear6up = 6 Pear7up = 7 Pear8up = 8 Pear9up = 9 Total Score: The positive score (posScorePear) is the sum of these values for each condition that returns true. A corresponding negative score (negScorePear) is calculated using conditions for when avgPR falls on the positive side, with similar weights in the negative direction. This score quantifies the current correlation reading by translating its relative level into a numeric score through a weighted sum. 3. Trendpear Score What It Represents: The Trendpear score is more dynamic as it compares the current avgPR with its short-term moving average (sma_avgPR / 14 periods ) and also considers its relationship with an even longer moving average (bigsma_avgPR / 100 periods). It is meant to capture the trend or momentum in the correlation behavior. How It’s Calculated: Conditions: Nine conditions (from Trendpear1up to Trendpear9up) are defined to check: Whether avgPR is below, equal to, or above sma_avgPR by different margins; Whether it is trending upward (i.e., it is higher than its previous value). Here are the rules Trendpear1up = (avgPR <= sma_avgPR -0.2) and (avgPR >= avgPR ) Trendpear2up = (avgPR > sma_avgPR -0.2) and (avgPR <= sma_avgPR -0.07) and (avgPR >= avgPR ) Trendpear3up = (avgPR > sma_avgPR -0.07) and (avgPR <= sma_avgPR -0.03) and (avgPR >= avgPR ) Trendpear4up = (avgPR > sma_avgPR -0.03) and (avgPR <= sma_avgPR -0.02) and (avgPR >= avgPR ) Trendpear5up = (avgPR > sma_avgPR -0.02) and (avgPR <= sma_avgPR -0.01) and (avgPR >= avgPR ) Trendpear6up = (avgPR > sma_avgPR -0.01) and (avgPR <= sma_avgPR -0.001) and (avgPR >= avgPR ) Trendpear7up = (avgPR >= sma_avgPR) and (avgPR >= avgPR ) and (avgPR <= bigsma_avgPR) Trendpear8up = (avgPR >= sma_avgPR) and (avgPR >= avgPR ) and (avgPR >= bigsma_avgPR -0.03) Trendpear9up = (avgPR >= sma_avgPR) and (avgPR >= avgPR ) and (avgPR >= bigsma_avgPR) Weighting: The weights here are not linear. For example, the lightest condition may add 0.1 point, whereas the most extreme condition (e.g., when avgPR is not only above the moving average but also reaches a high proportion relative to bigsma_avgPR) might add as much as 90 points. Trendpear1up = 0.1 Trendpear2up = 0.2 Trendpear3up = 0.3 Trendpear4up = 0.4 Trendpear5up = 0.5 Trendpear6up = 0.69 Trendpear7up = 7 Trendpear8up = 8.9 Trendpear9up = 90 Total Score: The positive score (posScoreTrendpear) is the sum of the weights from all conditions that are satisfied. A negative counterpart (negScoreTrendpear) exists similarly for when the trend indicates a downward bias. Trendpear integrates both the level and the direction of change in the correlations, giving a strong numeric indication when the market starts to diverge from its short-term average. 4. Deviation Score What It Represents: The “Écart” score quantifies how far the asset’s price deviates from the boundaries defined by the regression channels. This metric can indicate if the price is excessively deviating—which might signal an eventual reversion—or confirming a breakout. How It’s Calculated: Conditions: For each channel (with at least seven channels contributing to the scoring from the provided code), there are three levels of deviation: First tier (EcartXup): Checks if the price is below the upper boundary but above a second boundary. Second tier (EcartXup2): Checks if the price has dropped further, between a lower and a more extreme boundary. Third tier (EcartXup3): Checks if the price is below the most extreme limit. Weighting: Each tier within a channel has a very small weight for the lowest severities (for example, 0.0001 for the first tier, 0.0002 for the second, 0.0003 for the third) with weights increasing with the channel index. First channel : 0.0001 to 0.0003 (very short term) Second channel : 0.001 to 0.003 (short term) Third channel : 0.01 to 0.03 (short mid term) 4th channel : 0.1 to 0.3 ( mid term) 5th channel: 1 to 3 (long mid term) 6th channel : 10 to 30 (long term) 7th channel : 100 to 300 (very long term) Total Score: The overall positive score (posScoreEcart) is the sum of all the weights for conditions met among the first, second, and third tiers. The corresponding negative score (negScoreEcart) is calculated similarly (using conditions when the price is above the channel boundaries), with the weights being the same in magnitude but negative in sign. This layered scoring method allows the indicator to reflect both minor and major deviations in a gradated and cumulative manner. Example : Score + = 321.0001 Score - = -0.111 The asset price is really overextended in long term view, not for mid term & short term expect the in the very short term. Score + = 0.0033 Score - = -1.11 The asset price is really extended in short term view, not for mid term (even a bit underextended) & long term is neutral 5. Slope Score What It Represents: The Slope score captures the trend direction and steepness of the regression channels. It reflects whether the regression line (and hence the underlying trend) is sloping upward or downward. How It’s Calculated: Conditions: if the slope has a uptrend = 1 if the slope has a downtrend = -1 Weighting: First channel : 0.0001 to 0.0003 (very short term) Second channel : 0.001 to 0.003 (short term) Third channel : 0.01 to 0.03 (short mid term) 4th channel : 0.1 to 0.3 ( mid term) 5th channel: 1 to 3 (long mid term) 6th channel : 10 to 30 (long term) 7th channel : 100 to 300 (very long term) The positive slope conditions incrementally add weights from 0.0001 for the smallest positive slopes to 100 for the largest among the seven checks. And negative for the downward slopes. The positive score (posScoreSlope) is the sum of all the weights from the upward slope conditions that are met. The negative score (negScoreSlope) sums the negative weights when downward conditions are met. Example : Score + = 111 Score - = -0.1111 Trend is up for longterm & down for mid & short term The slope score therefore emphasizes both the magnitude and the direction of the trend as indicated by the regression channels, with an intentional asymmetry that flags strong downtrends more aggressively. Summary For each category—BigPear, Pear, Trendpear, Écart, and Slope—the indicator evaluates a defined set of conditions. Each condition is a binary test (true/false) based on different thresholds or comparisons (for example, comparing the current value to a moving average or a channel boundary). When a condition is true, its assigned weight is added to the cumulative score for that category. These individual scores, both positive and negative, are then displayed in a table, making it easy for the trader to see at a glance where the market stands according to each analytical dimension. This comprehensive, weighted approach allows the indicator to encapsulate several layers of market information into a single set of scores, aiding in the identification of potential trading opportunities or market reversals. 5. Practical Use and Application How to Use the Indicator: Interpreting the Signals: On your chart, observe the following components: The individual correlation curves and their average, plotted with visual thresholds; Visual markers (such as emojis and shape markers) that signal potential oversold or overbought conditions The summary table that aggregates the scores from each category, offering a quick glance at the market’s state. Trading Alerts and Decisions: Set your TradingView alerts through the alertcondition functions provided by the indicator. This way, you receive immediate notifications when critical conditions are met, allowing you to react as soon as the market reaches key levels. This tool is especially beneficial for advanced traders who want to combine multiple technical dimensions to optimize entry and exit points with a confluence of signals. Conclusion and Additional Insights In summary, this advanced indicator innovatively combines multi-scale Pearson correlation analysis (via multiple linear regressions) with robust regression channel analysis. It offers a deep and nuanced view of market dynamics by delivering clear visual signals and a comprehensive numerical summary through a built-in score table. Combine this indicator with other tools (e.g., oscillators, moving averages, volume indicators) to enhance overall strategy robustness.
Pine Script® göstergesi
AlwwAl tarafından
SPX500 Quick Drop & Rise AlertsSimple script thats been adjusted for 1 minute trading on spx500. It will show you and signal to you: dropThreshold: how much the price must rise or fall (in percent) to trigger a signal. Default is 0.05 → 5%. lookbackBars: how many bars back to compare against. Default is 1 (i.e., compare the current close to the previous bar’s close). Theirs a few ways to use this, you might want to use your MA 238 as a reference point. Use it as a target or a level to bounce or reject from. Then use this indicator to help show you where the market energy is flowing. Do some backtesting and see what you see. Only use it for New York open times would probably be best. Youll have to change your mentality depending on if the market is trending / ranging ect of course.
Pine Script® göstergesi
PaperChains tarafından
Cumulative Intraday Volume with Long/Short LabelsThis indicator calculates a running total of volume for each trading day, then shows on the price chart when that total crosses levels you choose. Every day at 6:00 PM Eastern Time, the total goes back to zero so it always reflects only the current day’s activity. From that moment on, each time a new candle appears the indicator looks at whether the candle closed higher than it opened or lower. If it closed higher, the candle’s volume is added to the running total; if it closed lower, the same volume amount is subtracted. As a result, the total becomes positive when buyers have dominated so far today and negative when sellers have dominated. Because futures markets close at 6 PM ET, the running total resets exactly then, mirroring the way most intraday traders think in terms of a single session. Throughout the day, you will see this running total move up or down according to whether more volume is happening on green or red candles. Once the total goes above a number you specify (for example, one hundred thousand contracts), the indicator will place a small “Long” label at that candle on the main price chart to let you know buying pressure has reached that level. Similarly, once the total goes below a negative number you choose (for example, minus one hundred thousand), a “Short” label will appear at that candle to signal that selling pressure has reached your chosen threshold. You can set these threshold numbers to whatever makes sense for your trading style or the market you follow. Because raw volume alone never turns negative, this design uses candle direction as a sign. Green candles (where the close is higher than the open) add volume, and red candles (where the close is lower than the open) subtract volume. Summing those signed volume values tells you in a single number whether buying or selling has been stronger so far today. That number resets every evening, so it does not carry over any buying or selling from previous sessions. Once you have this indicator on your chart, you simply watch the “summed volume” line as it moves throughout the day. If it climbs past your long threshold, you know buyers are firmly in control and a long entry might make sense. If it falls past your short threshold, you know sellers are firmly in control and a short entry might make sense. In quieter markets or times of low volume, you might use a smaller threshold so that even modest buying or selling pressure will trigger a label. During very active periods, a larger threshold will prevent too many signals when volume spikes frequently. This approach is straightforward but can be surprisingly powerful. It does not rely on complex formulas or hidden statistical measures. Instead, it simply adds and subtracts daily volume based on candle color, then alerts you when that total reaches levels you care about. Over several years of historical testing, this formula has shown an ability to highlight moments when intraday sentiment shifts decisively from buyers to sellers or vice versa. Because the indicator resets every day at 6 PM, it always reflects only today’s sentiment and remains easy to interpret without carrying over past data. You can use it on any intraday timeframe, but it works especially well on five-minute or fifteen-minute charts for futures contracts. If you want a clear gauge of whether buyers or sellers are dominating in real time, and you prefer a rule-based method rather than a complex model, this indicator gives you exactly that. It shows net buying or selling pressure at a glance, resets each session like most intraday traders do, and marks the moments when that pressure crosses the levels you decide are important. By combining a daily reset with signed volume, you get a single number that tells you precisely what the crowd is doing at any given moment, without any of the guesswork or hidden calculations that more complicated indicators often carry.
Pine Script® göstergesi
QuantByBoji tarafından
OA - SMESSmart Money Entry Signals (SMES) The SMES indicator is developed to identify potential turning points in market behavior by analyzing internal price dynamics, rather than relying on external volume or sentiment data. It leverages normalized price movement, directional volatility, and smoothing algorithms to detect potential areas of accumulation or distribution by market participants. Core Concepts Smart Money Flow calculation based on normalized price positioning Directional VHF (Vertical Horizontal Filter) used to enhance signal directionality Overbought and Oversold regions defined with optional glow visualization Entry and Exit signals based on dynamic crossovers Highly customizable input parameters for precision control Key Inputs Smart Money Flow Period Smoothing Period Price Analysis Length Fibonacci Lookback Length Visual toggle options (zones, glow effects, signal display) Usage This tool plots the smoothed smart money flow as a standalone oscillator, designed to help traders identify potential momentum shifts or extremes in market sentiment. Entry signals are generated through crossover logic, while optional filters based on price behavior can refine those signals. Exit signals are shown when the smart money line exits extreme regions. Important Notes This indicator does not repaint Works on all timeframes and instruments Best used as a confirmation tool with other technical frameworks All calculations are based strictly on price data Disclaimer This script is intended for educational purposes only. It does not provide financial advice or guarantee performance. Please do your own research and apply appropriate risk management before making any trading decisions.
Pine Script® göstergesi
onurag tarafından
4
RSI-Adaptive T3 [ChartPrime]The RSI-Adaptive T3 is a precision trend-following tool built around the legendary T3 smoothing algorithm developed by Tim Tillson , designed to enhance responsiveness while reducing lag compared to traditional moving averages. Current implementation takes it a step further by dynamically adapting the smoothing length based on real-time RSI conditions — allowing the T3 to “breathe” with market volatility. This dynamic length makes the curve faster in trending moves and smoother during consolidations. To help traders visualize volatility and directional momentum, adaptive volatility bands are plotted around the T3 line, with visual crossover markers and a dynamic info panel on the chart. It’s ideal for identifying trend shifts, spotting momentum surges, and adapting strategy execution to the pace of the market. HOIW IT WORKS At its core, this indicator fuses two ideas: The T3 Moving Average — a 6-stage recursively smoothed exponential average created by Tim Tillson , designed to reduce lag without sacrificing smoothness. It uses a volume factor to control curvature. A Dynamic Length Engine — powered by the RSI. When RSI is low (market oversold), the T3 becomes shorter and more reactive. When RSI is high (overbought), the T3 becomes longer and smoother. This creates a feedback loop between price momentum and trend sensitivity. // Step 1: Adaptive length via RSI rsi = ta.rsi(src, rsiLen) rsi_scale = 1 - rsi / 100 len = math.round(minLen + (maxLen - minLen) * rsi_scale) pine_ema(src, length) => alpha = 2 / (length + 1) sum = 0.0 sum := na(sum ) ? src : alpha * src + (1 - alpha) * nz(sum ) sum // Step 2: T3 with adaptive length e1 = pine_ema(src, len) e2 = pine_ema(e1, len) e3 = pine_ema(e2, len) e4 = pine_ema(e3, len) e5 = pine_ema(e4, len) e6 = pine_ema(e5, len) c1 = -v * v * v c2 = 3 * v * v + 3 * v * v * v c3 = -6 * v * v - 3 * v - 3 * v * v * v c4 = 1 + 3 * v + v * v * v + 3 * v * v t3 = c1 * e6 + c2 * e5 + c3 * e4 + c4 * e3 The result: an evolving trend line that adapts to market tempo in real-time. KEY FEATURES ⯁ RSI-Based Adaptive Smoothing The length of the T3 calculation dynamically adjusts between a Min Length and Max Length , based on the current RSI. When RSI is low → the T3 shortens, tracking reversals faster. When RSI is high → the T3 stretches, filtering out noise during euphoria phases. Displayed length is shown in a floating table, colored on a gradient between min/max values. ⯁ T3 Calculation (Tim Tillson Method) The script uses a 6-stage EMA cascade with a customizable Volume Factor (v) , as designed by Tillson (1998) . Formula: T3 = c1 * e6 + c2 * e5 + c3 * e4 + c4 * e3 This technique gives smoother yet faster curves than EMAs or DEMA/Triple EMA. ⯁ Visual Trend Direction & Transitions The T3 line changes color dynamically: Color Up (default: blue) → bullish curvature Color Down (default: orange) → bearish curvature Plot fill between T3 and delayed T3 creates a gradient ribbon to show momentum expansion/contraction. Directional shift markers (“🞛”) are plotted when T3 crosses its own delayed value — helping traders spot trend flips or pullback entries. ⯁ Adaptive Volatility Bands Optional upper/lower bands are plotted around the T3 line using a user-defined volatility window (default: 100). Bands widen when volatility rises, and contract during compression — similar to Bollinger logic but centered on the adaptive T3. Shaded band zones help frame breakout setups or mean-reversion zones. ⯁ Dynamic Info Table A live stats panel shows: Current adaptive length Maximum smoothing (▲ MaxLen) Minimum smoothing (▼ MinLen) All values update in real time and are color-coded to match trend direction. HOW TO USE Use T3 crossovers to detect trend transitions, especially during periods of volatility compression. Watch for volatility contraction in the bands — breakouts from narrow band periods often precede trend bursts. The adaptive smoothing length can also be used to assess current market tempo — tighter = faster; wider = slower. CONCLUSION RSI-Adaptive T3 modernizes one of the most elegant smoothing algorithms in technical analysis with intelligent RSI responsiveness and built-in volatility bands. It gives traders a cleaner read on trend health, directional shifts, and expansion dynamics — all in a visually efficient package. Perfect for scalpers, swing traders, and algorithmic modelers alike, it delivers advanced logic in a plug-and-play format.
Pine Script® göstergesi
ChartPrime tarafından
10
Momentum Fusion v1Momentum Fusion v1 Overview Momentum Fusion v1 (MFusion) is a multi-oscillator indicator that combines several components to analyze market momentum and trend strength. It incorporates modified versions of classic indicators such as PVI (Positive Volume Index), NVI (Negative Volume Index), MFI (Money Flow Index), RSI, Stochastic, and Bollinger Bands Oscillator. The indicator displays a histogram that changes color based on momentum strength and includes "FUSION🔥" signal labels when extreme values are reached. Indicator Settings Parameters: EMA Length – Smoothing period for the moving average (default: 255). Smoothing Period – Internal calculation smoothing parameter (default: 15). BB Multiplier – Standard deviation multiplier for Bollinger Bands (default: 2.0). Show verde / marron / media lines – Toggles the display of auxiliary lines. Show FUSION🔥 label – Enables/disables signal labels. Indicator Components 1. PVI (Positive Volume Index) Formula: pvi := volume > volume ? nz(pvi ) + (close - close ) / close * sval : nz(pvi ) Description: PVI increases when volume rises compared to the previous bar and accounts for price percentage change. The stronger the price movement with increasing volume, the higher the PVI value. 2. NVI (Negative Volume Index) Formula: nvi := volume < volume ? nz(nvi ) + (close - close ) / close * sval : nz(nvi ) Description: NVI tracks price movements during declining volume. If the price rises on low volume, it may indicate a "stealth" trend. 3. Money Flow Index (MFI) Formula: 100 - 100 / (1 + up / dn) Description: An oscillator measuring money flow strength. Values above 80 suggest overbought conditions, while values below 20 indicate oversold conditions. 4. Stochastic Oscillator Formula: k = 100 * (close - lowest(low, length)) / (highest(high, length) - lowest(low, length)) Description: A classic stochastic oscillator showing price position relative to the selected period's range. 5. Bollinger Bands Oscillator Formula: (tprice - BB midline) / (upper BB - lower BB) * 100 Description: Indicates the price position relative to Bollinger Bands in percentage terms. Key Lines & Histogram 1. Verde (Green Line) Calculation: verde = marron + oscp (normalized PVI) Interpretation: Higher values indicate stronger bullish momentum. A FUSION🔥 signal appears when the value reaches 750+. 2. Marron (Brown Line) Calculation: marron = (RSI + MFI + Bollinger Osc + Stochastic / 3) / 2 Interpretation: A composite oscillator combining multiple indicators. Higher values suggest overbought conditions. 3. Media (Red Line) Calculation: media = EMA of marron with smoothing period Interpretation: Acts as a signal line for trend confirmation. 4. Histogram Calculation: histo = verde - marron Colors: Bright green (>100) – Strong bullish momentum. Light green (>0) – Moderate bullish momentum. Orange (<0) – Bearish momentum. Red (<-100) – Strong bearish momentum. Signals & Alerts 1. FUSION🔥 (Strong Momentum) Condition: verde >= 750 Visualization: A "FUSION🔥" label appears below the chart. Alert: Can be set to trigger notifications when the condition is met. 2. Background Aura Condition: verde > 850 Visualization: The chart background turns teal, indicating extreme momentum. Usage Recommendations FUSION🔥 Signal – Can be used as a long entry point when confirmed by other indicators. Histogram: 1. Green bars – Potential long entry. 2. Red/orange bars – Potential short entry. 3. Media & Marron Crossover – Can serve as an additional trend filter. 4. Suitable for a 5-15 minute time frame Conclusion Momentum Fusion v1 is a powerful tool for momentum analysis, combining multiple indicators into a unified system. It is suitable for: Trend traders (catching strong movements). Scalpers (identifying short-term impulses). Swing traders (filtering entry points). The indicator features customizable settings and visual signals, making it adaptable to various trading styles.
Pine Script® göstergesi
Zdorman tarafından
HARSI PRO v2 - Advanced Adaptive Heikin-Ashi RSI OscillatorThis script is a fully re-engineered and enhanced version of the original Heikin-Ashi RSI Oscillator created by JayRogers. While it preserves the foundational concept and visual structure of the original indicatorusing Heikin-Ashi-style candles to represent RSI movementit introduces a range of institutional-grade engines and real-time analytics modules. The core idea behind HARSI is to visualize the internal structure of RSI behavior using candle representations. This gives traders a clearer sense of trend continuity, exhaustion, and momentum inflection. In this upgraded version, the system is extended far beyond basic visualization into a comprehensive diagnostic and context-tracking tool. Core Enhancements and Features 1. Heikin-Ashi RSI Candles The base HARSI logic transforms RSI values into open, high, low, and close components, which are plotted as Heikin-Ashi-style candles. The open values are smoothed with a user-controlled bias setting, and the high/low are calculated from zero-centered RSI values. 2. Smoothed RSI Histogram and Plot A secondary RSI plot and histogram are available for traditional RSI interpretation, optionally smoothed using a custom midpoint EMA process. 3. Dynamic Stochastic RSI Ribbon The indicator optionally includes a smoothed Stochastic RSI ribbon with directional fill to highlight acceleration and reversal zones. 4. Real-Time Meta-State Engine This engine determines the current market environmentneutral, breakout, or reversalbased on multiple adaptive conditions including volatility compression, momentum thrust, volume behavior, and composite reversal scoring. 5. Adaptive Overbought/Oversold Zone Engine Instead of using fixed RSI thresholds, this engine dynamically adjusts OB/OS boundaries based on recent RSI range and normalized price volatility. This makes the OB/OS levels context-sensitive and more accurate across different instruments and regimes. 6. Composite Reversal Score Engine A real-time score between 0 and 5 is generated using four components: * OB/OS proximity (zone score) * RSI slope behavior * Volume state (burst or exhaustion) * Trend continuation penalty based on position versus trend bias This score allows for objective filtering of reversal zones and breakout traps. 7. Kalman Velocity Filter A Kalman-style adaptive smoothing filter is applied to RSI for calculating velocity and acceleration. This allows for real-time detection of stalls and thrusts in RSI behavior. 8. Predictive Breakout Estimator Uses ATR compression and RSI thrusting conditions to detect likely breakout environments. This logic contributes to the Meta-State Engine and the Breakout Risk dashboard metric. 9. Volume Acceleration Model Real-time detection of volume bursts and fades based on VWMA baselines. Volume exhaustion warnings are used to qualify or disqualify reversals and breakouts. 10. Trend Bias and Regime Detection Uses RSI slope, HARSI body impulse, and normalized ATR to classify the current trend state and directional bias. This forms the basis for filtering false reversals during strong trends. 11. Dashboard with Tooltips A clean, table displays six key metrics in real time: * Meta State * Reversal Score * Trend Bias * Volume State * Volatility Regime * Breakout Risk Each cell includes a descriptive tooltip explaining why the value is being shown based on internal state calculations. How It Works Internally * The system calculates a zero-centered RSI and builds candle structures using high, low, and smoothed open/close values. * Volatility normalization is used throughout the script, including ATR-based thresholds and dynamic scaling of OB/OS zones. * Momentum is filtered through smoothed slope calculations and HARSI body size measurements. * Volume activity is compared against VWMA using configurable multipliers to detect institutional-level activity or exhaustion. * Each regime detection module contributes to a centralized metaState classifier that determines whether the environment is conducive to reversal, breakout, or neutral action. * All major signal and context values are continuously updated in a dashboard table with logic-driven color coding and tooltips. Based On and Credits This script is based on the original Heikin-Ashi RSI Oscillator by JayRogers . All visual elements from the original version, including candle plotting and color configurations, have been retained and extended. Significant backend enhancements were added by AresIQ for the 2025 release. The script remains open-source under the original attribution license. Credit to JayRogers is preserved and required for any derivative versions.
Pine Script® göstergesi
AresIQ tarafından
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