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Psychological Levels + Buffer ZonesThis indicator automatically draws major (100-pip) and minor (50-pip) psychological levels on your Forex chart, along with optional buffer zones for smarter trade entries. Zones help you visually capture breakouts, retests, and fakeouts. Includes: Major & minor psych levels Adjustable buffer zones (±0.1%, etc.) Customizable zone color & transparency Optional ATR trailing lines for trend confirmation Perfect for scalpers, breakout traders, and zone-based strategies.
Pine Script®指標
由quiendum提供
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®指標
由EdgeTools提供
LyroMAsLibrary "LyroMAs" Custom Dynamic MA's that allow a dynamic calculation beginning from the first bar\ use getDynamicLength(maxLength) =>\\tmath.min(maxLength, bar_index + 1) \as length SMA(sourceData, maxLength)   Dynamic SMA   Parameters:      sourceData (float)      maxLength (int) EMA(src, length)   Dynamic EMA   Parameters:      src (float)      length (int) DEMA(src, length)   Dynamic DEMA   Parameters:      src (float)      length (int) TEMA(src, length)   Dynamic TEMA   Parameters:      src (float)      length (int) WMA(src, length)   Dynamic WMA   Parameters:      src (float)      length (int) HMA(src, length)   Dynamic HMA   Parameters:      src (float)      length (int) VWMA(src, volsrc, length)   Dynamic VWMA   Parameters:      src (float)      volsrc (float)      length (int) SMMA(src, length)   Dynamic SMMA   Parameters:      src (float)      length (int) LSMA(src, length, offset)   Dynamic LSMA   Parameters:      src (float)      length (int)      offset (int) RMA(src, length)   Dynamic RMA   Parameters:      src (float)      length (int) ALMA(src, length, offset_sigma, sigma)   Dynamic ALMA   Parameters:      src (float)      length (int)      offset_sigma (float)      sigma (float) ZLSMA(src, length)   Dynamic Zlsma   Parameters:      src (float)      length (int) Thank you to @QuantraSystems for the dynamic codes.
Pine Script®腳本庫
由LyroRS提供
Relative Strength Triple MA ConfluenceThis tool highlights moments of strong outperformance based on three customizable moving averages of an asset's relative strength vs a benchmark (SPY, BTC, etc). ✅ Green candles + triangle-up icon appear when relative strength is above all 3 MAs (short, medium, long) ❌ Red triangle-down appears when full confluence is lost 🔧 Fully customizable MA types (EMA or SMA), lengths, and benchmark Ideal for traders seeking high-conviction confirmation based on stacked RS strength.
Pine Script®指標
由kondor_trades提供
ML Super Divergence + ADX ConfirmationThe "ML Super Divergence + ADX Confirmation" indicator is a powerful tool designed for technical traders who want to combine machine learning-inspired divergence signals with the ADX (Average Directional Index) trend strength indicator for more reliable trade signals. This script is written in Pine Script v6 and is suitable for use on TradingView charts. It visually highlights potential buy and sell opportunities and also generates alerts when conditions are met. 📈 Core Components: 1. ADX System (Average Directional Index): Measures trend strength, not direction. Calculated using a 14-period (user-configurable) setting. DI+ (Positive Directional Index) and DI− (Negative Directional Index) are used to gauge bullish and bearish pressure. Signals are only valid when ADX > threshold (e.g., 20), ensuring that the trend is strong enough to act upon. 2. Simulated ML Super Divergence Signal: This is a placeholder for actual ML-based divergence detection. For demonstration, it simulates: Bullish divergence: when RSI crosses above 30 and OBV (On-Balance Volume) is rising. Bearish divergence: when RSI crosses below 70 and OBV is falling. This approximation helps visualize how real ML divergence signals could be integrated. ✅ Buy Signal Conditions: A buy signal is plotted when: A bullish divergence is detected. DI+ is greater than DI− (i.e., bullish directional strength). ADX is above the minimum threshold (confirming a strong trend). 📍 Visual cue: Green upward triangle below the price bar. 🔔 Alert Triggered: "Buy Signal: ML Super Divergence + DI+ + ADX Confirmation" ❌ Sell Signal Conditions: A sell signal is plotted when: A bearish divergence is detected. DI− is greater than DI+ (i.e., bearish directional strength). ADX is above the threshold. 📍 Visual cue: Red dot above the price bar. 🔔 Alert Triggered: "Sell Signal: ML Super Divergence + DI- + ADX Confirmation"
Pine Script®指標
由raghavprakash05提供
Daily 10, 50, 150, 200 DMAIrrespective of the Chart, i.e be it weekly or monthly DMA will be displayed on Daily Values. Do note that On a weekly chart, this gives you the DMA value from only one daily candle per week, usually Friday’s close. So if a DMA crossover (say, 10-DMA crossing 50-DMA) actually happens on Wednesday, you won’t see that reflected until Friday's value is displayed on the weekly chart. That causes crossover dates to appear wrong or delayed.
Pine Script®指標
由attasseril提供
Enhanced MFI Divergence with Pivot SignalsEnhanced MFI Divergence with Pivot Signals This custom Pine Script indicator identifies bullish and bearish divergences between price action and the Money Flow Index (MFI), enhancing the trader's ability to spot potential reversal zones with visual clarity and optional confirmation filters. 📊 Key Features: 🔹 MFI Divergence Detection The script detects: Bullish divergence when price forms a lower low but MFI forms a higher low. Bearish divergence when price forms a higher high but MFI forms a lower high. 🔹 Pivot-Based Logic To ensure high-confidence signals, the script uses pivot point logic to mark local highs and lows on both price and MFI. This avoids noise and focuses only on meaningful swing points. 🔹 Optional Confirmation Filter You can enable a filter that checks if MFI is above 50 during bullish divergence (implying buying pressure) and below 50 for bearish divergence (implying selling pressure), adding an extra layer of confirmation. 🔹 Signal Markers Signals are visually displayed on the chart using colored triangles: Green triangle up for bullish divergence Red triangle down for bearish divergence 🔹 Background Color Shading The background is optionally shaded green or red based on MFI’s relationship to its smoothed WMA, helping you visually interpret trend bias. 🔹 Pivot Point Debugging Tools Circles and crosses mark pivot points on price and MFI for debugging and visual clarity. 🔹 Alerts Ready Real-time alerts notify you instantly when a bullish or bearish MFI divergence occurs, allowing for quick decision-making. ⚙️ How It Helps This indicator is designed to help traders: Anticipate price reversals by identifying hidden strength or weakness in momentum, Avoid false breakouts, Confirm entries or exits based on volume-weighted momentum divergence. It works especially well when used alongside trend-following tools like moving averages, support/resistance zones, or additional volume indicators.
Pine Script®指標
由raghavprakash05提供
Bayram Günleri 2020-2025// This script highlights the days of Ramadan Eid and Eid al-Adha (including the day before) on the chart. // This indicator is designed to visually mark Ramadan Eid, Eid al-Adha, and their preceding days (Arefe) between 2020 and 2025. // It colors the background in orange on those specific dates, making it easy to identify and analyze holiday periods. // Works across all timeframes (1m, 1h, 1d, etc.). // Dates are checked using year, month, and dayofmonth values manually. // All times are based on Turkish local time (UTC+3). // Ramazan Bayramı ve Kurban Bayramı günlerini gösterir
Pine Script®指標
由turgut1844提供
Golden Key: Opening Channel DashboardGolden Key: Opening Channel Dashboard Complementary to the original Golden Key – The Frequency Upgrade of 10 Monday's 1H Avg Range + 30-Day Daily Range This indicator provides a structured dashboard to monitor the opening channel range and related metrics on 15m and 5m charts. Built to work alongside the Golden Key methodology, it focuses on pip precision, average volatility, and SL sizing. What It Does Detects first 4 candles of the session: 15m chart → first 4 Monday candles (1 hour) 5m chart → first 4 candles of each day (20 minutes) Calculates pip range of the opening move Stores and averages the last 10 such ranges Calculates daily range average over 10 or 30 days Generates SL size based on your multiplier setting Auto-adjusts for FX, JPY, and XAUUSD pip sizes Displays all values in a clean table in the top-right How to Use It Add to a 15m or 5m chart Compare the current opening range to the average Use the daily average to assess broader volatility Define SL size using the opening range x multiplier Customize display colors per table row About This Script This is not a visual box-style indicator. It is designed to complement the original “Golden Key – The Frequency” by focusing on metric output. It is also an upgraded version of the earlier "10 Monday’s 1H Avg Range" script, now supporting multi-timeframe logic and additional customization. Disclaimer This is a technical analysis tool. It does not provide trading advice. Use it in combination with your own research and strategy.
Pine Script®指標
由bramirez37提供
Combined ATR Bands + VWAP + Moving Averages🔥 Ultimate Trading Combo: ATR Bands + VWAP + Moving Averages This comprehensive indicator combines three powerful technical analysis tools in one clean interface: 📊 Features: • ATR Bands - Dynamic support/resistance levels with step-line styling • VWAP - Volume Weighted Average Price with orange dotted visualization • Moving Averages - 50, 100, 200 periods with customizable colors ⚙️ Customizable Settings: Toggle each component on/off independently Adjustable ATR periods and multipliers Multiple VWAP anchor periods (Session, Week, Month, Quarter, Year) Configurable MA sources and periods Custom colors and transparency levels 🎯 Perfect For: Day traders seeking dynamic support/resistance Swing traders using multiple timeframe analysis Anyone wanting clean, professional chart visualization 💡 Created with AI assistance (Claude Sonnet 4) Open source - feel free to modify and improve! 🔥 المؤشر الشامل: نطاقات ATR + VWAP + المتوسطات المتحركة هذا المؤشر الشامل يجمع ثلاث أدوات تحليل فني قوية في واجهة واحدة نظيفة: 📊 المميزات: • نطاقات ATR - مستويات دعم ومقاومة ديناميكية بتصميم خطوط متدرجة • VWAP - متوسط السعر المرجح بالحجم مع عرض نقطي برتقالي • المتوسطات المتحركة - فترات 50، 100، 200 مع ألوان قابلة للتخصيص ⚙️ إعدادات قابلة للتخصيص: تشغيل/إيقاف كل مكون بشكل مستقل فترات ومضاعفات ATR قابلة للتعديل فترات ربط VWAP متعددة (جلسة، أسبوع، شهر، ربع، سنة) مصادر وفترات MA قابلة للتكوين ألوان مخصصة ومستويات شفافية 🎯 مثالي لـ: المتداولين اليوميين الباحثين عن دعم/مقاومة ديناميكية متداولي التأرجح باستخدام تحليل الإطارات الزمنية المتعددة أي شخص يريد عرض مخططات نظيف ومهني 💡 تم إنشاؤه بمساعدة الذكاء الاصطناعي (Claude Sonnet 4) مفتوح المصدر - لا تتردد في التعديل والتحسين! #ATR #VWAP #MovingAverages #TechnicalAnalysis #Support #Resistance #DayTrading #SwingTrading #OpenSource #AI #تحليل_فني #دعم_مقاومة #متوسطات_متحركة #تداول_يومي
Pine Script®指標
由Redtoura提供
ATR Bands PRO + Sweep Label + Divergence [MASTER]🔰 ATR Bands PRO + Liquidity Sweep & Divergence (RSI/MACD) ## 🔰 ATR Bands PRO + Liquidity Sweep & Divergence A powerful institutional-grade toolkit that combines advanced ATR band visualization, customizable stop bands, dynamic grid lines, real-time liquidity sweep detection, and built-in swing-point divergence signals (RSI/MACD) – all fully adjustable. **Key Features:** - **Multi-Timeframe ATR Bands:** Visualize ATR-based bands from any higher timeframe, fully customizable in color, width, style, and extension. - **Smart Stops & Grid:** Add stop bands and dynamic ATR grid lines with user control over appearance and step. - **Liquidity Sweep Detection:** Instantly see “Bull Sweep” or “Bear Sweep” labels every time price touches high/low liquidity sweeps from your chosen timeframe. - **Divergence Alerts (RSI/MACD):** Detect bullish or bearish divergence at swing highs/lows (on the main timeframe) – complete with highly visible, color-customizable labels. - **Professional, Non-Cluttered Visuals:** All labels and lines are managed with smart array handling – zero repaint, zero overlay clutter. **How to Use:** 1. Choose your ATR “base” timeframe and customize band/stop/grid appearance. 2. Pick the Liquidity Sweep timeframe (e.g., H1, H4, D1) for institutional swing levels. 3. Enable divergence detection (RSI or MACD) to reveal hidden reversal signals at market pivots. 4. Adjust label offsets and colors for maximum clarity on your chart. **Perfect for:** - Liquidity-driven scalping, swing, and positional strategies. - Spotting liquidity grabs, institutional sweeps, and “trap” price action. - Fast visual confirmation of potential reversal zones using built-in divergence signals. - Traders who demand high-performance, flexible visuals without chart clutter. --- **Credit:** Original logic inspired by RunStrat, AlgoAlpha, and custom adaptations by MILO888. --- *For educational and professional use. Test on your own symbol/timeframe before live trading. Enjoy an edge!*
Pine Script®指標
由MILO888提供
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®指標
由AlwwAl提供
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®指標
由QuantByBoji提供
Sticky Notes📌 Sticky Notes - On-Chart Memo Tool A convenient indicator that lets you display trading ideas and important notes directly on your charts! ✨ Key Features: 📝 Create memos with custom text input 📍 Place anywhere on chart (top/middle/bottom) 🖥️ Screen-fixed display mode (corner positions) 🎨 Fully customizable text and background colors 📏 5 text size options (tiny to huge) ⏰ Time-based display functionality 📐 Text alignment options (left/center/right) 💡 Use Cases: Trading strategy reminders Important price level notes Economic event schedules Entry/exit point memos Simple and user-friendly design to enhance your trading analysis!
Pine Script®指標
由unchokori提供