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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.
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EdgeTools tarafından
TYSON / Risk EndThis indicator is specific to Risk End The indicator highlights 1- Showing entry signals at reversals and after the completion of the candlesticks 2- Helps the trader to determine the immediate direction of the candles 3- Helps the trader to determine the safest entry areas (where the stop loss is small compared to the take profit) Ratio 3-1 Description 1- You can wait for the entry signal to appear (whether it is a buy or sell) If the signal is consistent with your personal analysis, you can enter and commit to the goals and stop the loss 2- The indicator appears as an information panel on the right of the screen - showing you the general status of the indicator at every moment 3- When the buy or sell signal appears "This suggests that the accumulation process or the sideways trend has begun to end" Here the seller or buyer will prevail by moving the candles Settings 1- The default settings for buy and sell signals cannot be controlled 2- The indicator user can enable and disable some or all strategies 3- You can go into the settings and set the capital and specify the contract size and the dashboard will display A study of the profit or loss that occurred during a specific previous period This gives the trader a real-time study of the previous market movement Recommendation 1- Remember that financial markets and trading are full of risks, so be careful in managing your capital and managing risks when executing any deal 2- You can rely on indicator signals, but the most important thing is commitment and then capital management Comments 1- The free indicator works on the currency pair (EUR USD) ONLY 2- There is a paid version of the indicator that works on all Pairs, Commodities and Indices And it has many features 3- You can analyze the results on all pairs, commodities and indices on the free version. (You can contact technical support) For more information warning This indicator should not be relied upon only in trading (It only helps the trader to see the chart more clearly) 1- This indicator of buying and selling should not be relied upon only in trading (It only helps the trader to see sell signals , buy signals, momentum and liquidity) Notes 1- The indicator is subject to continuous updating. “You will be notified in the event of any update.”
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RiskEnd tarafından
NY Open 15-Min Candle Detector + EMAs & VWAP (BG Time) ➡️ NY Open 15-Min Candle Detector with EMAs & VWAP (BG Time) 🟢 This indicator is a powerful tool for traders looking to pinpoint and visualize the critical first 15-minute trading range of the New York session, precisely aligned with Bulgarian time (Europe/Sofia). It's perfect for those who trade around the NYSE open (09:30 AM New York time) but prefer to see these key levels mapped to their local time. In addition to the opening range, it integrates three Exponential Moving Averages (EMAs) and the Volume Weighted Average Price (VWAP) for a comprehensive trading perspective. 🔥 Key Features: Precise NY Open 15-Minute Range (Bulgarian Time): Automatically identifies and highlights the initial 15-minute candle that opens at 16:30 BG time, which directly corresponds to the 09:30 AM New York Stock Exchange (NYSE) opening bell. The background of this specific 15-minute period is clearly colored for immediate visual recognition. Draws durable horizontal lines marking the High, Low, and Mid-Point of this crucial opening range, extending them across the chart for the remainder of the trading day. Handles Daylight Saving Time (DST) changes automatically for the "Europe/Sofia" timezone. 🟢 Three Customizable Exponential Moving Averages (EMAs): Includes three distinct EMAs (default lengths: 20, 50, 200). Each EMA offers independent control over its length, data source (e.g., Close, Open, HLC3), color, and line width. Individual visibility toggles allow you to display only the EMAs relevant to your strategy. Default colors: EMA 20 (White), EMA 50 (Green), EMA 200 (Red) – all with a line width of 2 for optimal visibility. 📈 Volume Weighted Average Price (VWAP): Displays the session-based VWAP, offering a crucial average price weighted by trading volume. Customizable color (default: Yellow) and line width (default: 2). Can be toggled on/off. Real-Time Breakout Alerts: Generates clear alerts when the price breaks above the 15-minute range's high or below its low, providing timely notifications for potential trading setups. ⚙️ How to Use: Apply to Chart: Simply add the indicator to any chart in TradingView. Verify Time: The "Market Start Hour (BG Time)" and "Market Start Minute (BG Time)" inputs are pre-set to 16:30, aligning with the 09:30 AM NY Open. You can adjust these if your specific market open differs. Customize Visuals: Tailor the colors, line widths, and background visibility of the opening range to match your chart theme. ➡️ Configure Indicators: Easily enable/disable, set lengths, sources, and colors for the EMAs and VWAP according to your technical analysis preferences. Set Alerts: Activate the breakout alerts to receive notifications directly from TradingView when significant price action occurs outside the initial NY Open range. This indicator is an indispensable tool for day traders and swing traders focusing on the New York session's opening momentum, combining precise time-based analysis with essential moving averages and volume-weighted pricing for a comprehensive trading edge. linktr.ee
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SaintGeorge tarafından
Google Trends: Bitcoin [Bitcoin CounterFlow]This script displays weekly Google Trends data for the term "Bitcoin". It can help visualize public interest over time and compare it with price action or other indicators. Data is manually updated each week based on Google Trends. Values range from 0 to 100, where 100 represents peak popularity for the selected term. Use this indicator to observe how shifts in search volume correlate with market movements. It is not a trading signal by itself but can be useful for sentiment analysis. Script created and published by Bitcoin CounterFlow.
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Bitcoin-CounterFlow-PRO tarafından
Smart Long Filtered Entries with Targets🔐 Smart Long Filtered Entries with Targets A professional indicator delivering high-precision long entry signals, based on dynamic trend behavior. Entry, stop loss, and multi-target levels are automatically drawn on the chart — optimized for crypto and fast-moving markets. Features: Long-only signals with precision. Auto-drawn entry, stop loss, and target lines. Optional filters: MACD, VWAP, SMA, Stoch RSI. Real-time alerts on entry signals. Fully customizable inputs. ⚠️ The script is protected. While the source code is hidden, the indicator is freely available to use on TradingView. 🔐 Smart Long Filtered Entries with Targets مؤشر احترافي يقدم إشارات شراء دقيقة ومفلترة، مبنية على تحليل الاتجاه الديناميكي مع مستويات دخول وخروج مرسومة تلقائيًا على الشارت. تم تطويره ليتناسب مع المضاربين المحترفين في الأسواق المالية وخاصة العملات الرقمية. المميزات: إشارات شراء فقط (Long Only) عالية الدقة. رسم تلقائي لخط الدخول ووقف الخسارة والأهداف. يدعم الفلاتر الفنية مثل: MACD – VWAP – SMA – Stochastic RSI. تنبيهات لحظية عند ظهور الإشارة. قابل للتخصيص الكامل. ⚠️ الكود محمي، لكن المؤشر متاح للجميع للاستخدام المباشر على TradingView.
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abusuhil tarafından
PLR-Z For Loop🧠 Overview PLR-Z For Loop is a trend-following indicator built on the Power Law Residual Z-score model of Bitcoin price behavior. By measuring how far price deviates from a long-term power law regression and applying a custom scoring loop, this tool identifies consistent directional pressure in market structure. Designed for BTC, this indicator helps traders align with macro trends. 🧩 Key Features Power Law Residual Model: Tracks deviations of BTC price from its long-term logarithmic growth curve. Z-Score Normalization: Applies long-horizon statistical normalization (400/1460 bars) to smooth residual deviations into a usable trend signal. Loop-Based Trend Filter: Iteratively scores how often the current Z-score exceeds prior values, emphasizing trend persistence over volatility. Optional Smoothing: Toggleable exponential smoothing helps filter noise in choppier market conditions. Directional Regime Coloring: Aqua (bullish) and Red (bearish) visuals reinforce trend alignment across plots and candles. 🔍 How It Works Power Law Curve: Price is compared against a logarithmic regression model fitted to historical BTC price evolution (starting July 2010), defining structural support, resistance, and centerline levels. Residual Z-Score: The residual is calculated as the log-difference between price and the power law center. This residual is then normalized using a rolling mean (400 days) and standard deviation (1460 days) to create a long-term Z-score. Loop Scoring Logic: A loop compares the current Z-score to a configurable number of past bars. Each higher comparison adds +1, and each lower one subtracts -1. The result is a trend persistence score (z_loop) that grows with consistent directional momentum. Smoothing Option: A user-defined EMA smooths the score, if enabled, to reduce short-term signal noise. Signal Logic: Long signal when trend score exceeds long_threshold. Short signal when score drops below short_threshold. Directional State (CD): Internally manages the current market regime (1 = long, -1 = short), controlling all visual output. 🔁 Use Cases & Applications Macro Trend Alignment: Ideal for traders and analysts tracking Bitcoin’s structural momentum over long timeframes. Trend Persistence Filter: Helps confirm whether the current move is part of a sustained trend or short-lived volatility. Best Suited for BTC: Built specifically on the BNC BLX price history and Bitcoin’s power law behavior. Not designed for use with other assets. ✅ Conclusion PLR-Z For Loop reframes Bitcoin’s long-term power law model into a trend-following tool by scoring the persistence of deviations above or below fair value. It shifts the focus from valuation-based mean reversion to directional momentum, making it a valuable signal for traders seeking high-conviction participation in BTC’s broader market cycles. ⚠️ Disclaimer The content provided by this indicator is for educational and informational purposes only. Nothing herein constitutes financial or investment advice. Trading and investing involve risk, including the potential loss of capital. Always backtest and apply risk management suited to your strategy.
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CipherDecoded tarafından
Congestion Indicator - Oscillator by saurabh maggoCore Functionality Market State Detection: Congestion: Identifies periods of low volatility (price consolidation) where the price range is tight relative to the Average True Range (ATR). Visualized with a blue background in the oscillator panel. Breakout Up: Detects upward breakouts from congestion zones, requiring conditions like price movement above the congestion high, volume spikes, and volatility increases. Visualized with a green background. Breakdown (Breakout Down): Detects downward breakouts from congestion zones, with similar conditions as Breakout Up but for downward movement. Visualized with a red background. Post-Congestion: Identifies the period after a congestion zone ends but before a breakout occurs (if extend_until_breakout is disabled). Visualized with a yellow background. Pullback: Detects pullbacks after breakouts or breakdowns, useful for identifying potential entry points (if use_pullback_entry is enabled). Visualized with a purple background. Visualization: Oscillator Panel: Displays the market state in a separate panel below the chart. Background Color: The panel’s background color changes to reflect the current state (e.g., blue for Congestion, green for Breakout Up). Histogram Plot: Optionally plots the state value as a histogram (e.g., 1 for Congestion, 2 for Breakout Up), toggleable via TradingView’s "Style" tab ("Market State"). The histogram provides a numerical representation of the state: Congestion: 1.0 Breakout Up: 2.0 Breakdown: -2.0 Post-Congestion: 0.5 Pullback: 1.5 None: 0.0 Alerts: Generates alerts for state changes (Congestion, Breakout Up, Breakdown). Supports enhanced alerts (if use_enhanced_alerts is enabled), including additional context like breakout level, volatility state, and trend direction. Includes an alert cooldown period (if use_alert_cooldown is enabled) to prevent excessive alerts. Key Features and Filters Customizable Parameters: Lookback Period: Adjusts the number of bars used to calculate the price range for congestion detection. Range Threshold: Sets the maximum price range (as a percentage of ATR) for a congestion zone. Dynamic Threshold: Optionally uses a percentile-based dynamic threshold for more adaptive congestion detection. Minimum Congestion Bars: Requires a minimum number of bars for a congestion zone to be confirmed. Volume Filter: Optionally requires low volume during congestion zones. Volume Breakout Filter: Requires a volume spike for breakouts/breakdowns. Volatility Breakout Filter: Requires an ATR spike for breakouts/breakdowns. Minimum Price Movement: Optionally requires a minimum price movement for breakouts/breakdowns. RSI Filter: Optionally requires RSI to be in a neutral range during congestion. Max Price Range Filter: Limits the absolute price range for congestion zones. Trend Filter: Optionally filters breakouts/breakdowns based on a higher timeframe trend (using a moving average). Momentum Filter: Optionally requires MACD momentum confirmation for breakouts/breakdowns. Pullback Detection: Optionally detects pullbacks after breakouts/breakdowns for entry opportunities. Timeframe Adjustment: Adjusts parameters based on the chart’s timeframe. Auto-Settings: Automatically adjusts parameters based on market volatility. Show Current Day Only: Optionally limits the indicator’s display to the current trading day (NSE session). Presets: Offers predefined configurations (Default, Aggressive, Conservative) for quick setup. Session Support: Operates within the NSE session (9:15 AM–3:30 PM IST) by default, ensuring relevance for Indian markets. Visual Output The oscillator panel uses color-coded backgrounds to indicate the market state: Blue: Congestion Green: Breakout Up Red: Breakdown Yellow: Post-Congestion Purple: Pullback Transparent (None): No state detected The histogram plot (optional) provides a numerical representation of the state, which can be toggled on/off in TradingView’s settings. Alerts Alerts are triggered for significant state changes (Congestion, Breakout Up, Breakdown). Enhanced alerts include additional details like price levels, volatility, and trend direction, making them more informative for traders. Step 2: Craft the Description for Publishing Based on the analysis, here’s a concise, user-friendly description you can use when publishing the indicator on TradingView: Congestion Indicator - Oscillator by Saurabh Maggo This indicator identifies market congestion zones, breakouts, breakdowns, post-congestion periods, and pullbacks in a separate oscillator panel below your chart. Designed for traders, it helps you spot key market states and potential trading opportunities with clear visual cues and customizable alerts. Key Features: Market States: Detects Congestion (Blue), Breakout Up (Green), Breakdown (Red), Post-Congestion (Yellow), and Pullbacks (Purple). Visual Display: Shows market states using background colors in an oscillator panel, with an optional histogram plot (toggleable in settings). Alerts: Generates alerts for state changes, with enhanced options to include price levels, volatility, and trend context. Customizable Filters: Includes volume, volatility, RSI, trend, momentum, and price movement filters to refine signals. Adaptable Settings: Supports dynamic thresholds, timeframe adjustments, auto-settings based on volatility, and predefined presets (Default, Aggressive, Conservative). NSE Session: Optimized for Indian markets with a default session time of 9:15 AM–3:30 PM IST. How can Grok help?
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srbhmaggo tarafından
New Mindset BreakOutSideway is my way!! Stoploss First bro This indicator is designed specifically for sideways or ranging market conditions. It combines the power of Bollinger Bands, momentum analysis, and EMA filters to identify high-probability reversal or breakout zones. Bollinger Bands are used to detect volatility contractions and price extremes. Momentum logic helps confirm whether price action is truly reversing or just retracing. EMA filters ensure trades are aligned with the dominant short-term trend when appropriate. This blend makes the indicator ideal for: Detecting reversal setups during consolidation phases Avoiding false signals in strong trending markets Helping traders identify low-risk zones with clearly defined structure You can apply this script across multiple timeframes and combine it with price action for more precise entries.
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niwfamily5 tarafından
Position Size Calculator ProPosition Size Calculator Pro is a professional risk management tool that helps traders calculate optimal position sizes based on their account size, risk tolerance, and trade setup. The indicator provides real-time calculations with interactive price lines and a comprehensive horizontal table display for quick decision-making. ✨ Key Features Multiple Entry Modes: Current price, manual price, or interactive buy line Flexible Stop Loss Options: LOD (Low of Day), manual price, percentage-based, or interactive stop line Advanced Risk Calculations: Includes brokerage impact and adjusted risk metrics Interactive Price Lines: Visual buy and stop loss lines with real-time updates Horizontal Table Display: Compact 2-row table showing all critical metrics Smart Color Coding: Visual feedback based on risk and allocation levels Professional UI: Clean, modern interface with intuitive controls Indian Market Ready: Optimized for Indian trading with ₹ currency display 🔧 Input Parameters 💰 Risk Management Account Size (₹): Total trading capital (default: 10,00,000) Risk per Trade (%): Maximum risk percentage per trade (default: 0.25%, range: 0.01-5%) Brokerage (%): Combined buy and sell brokerage (default: 0.12%, range: 0-2%) 📊 Entry & Stop Loss Entry Mode: Choose between Current Price, Manual Price, or Buy Line Manual Entry Price: Custom entry price (when Manual Price selected) Stop Loss Mode: LOD SL, Manual SL, Manual SL %, or SL Line Manual Stop Loss: Custom stop loss price SL Percentage (%): Percentage below entry for stop loss (default: 2%, range: 0.1-20%) 📈 Interactive Lines Buy Line Price: Interactive buy line (click on chart to set) Stop Loss Line: Interactive stop loss line (click on chart to set) Show Lines: Toggle line visibility 🎨 Display Options Show Table: Toggle calculation table visibility Table Size: Adjustable from tiny to huge Position: Top, middle, or bottom placement Alignment: Left, center, or right alignment Update Frequency: Real-time or bar close 📊 Calculation Methodology Position Size Formula Position Size = (Account Size × Risk %) ÷ (Adjusted Risk per Share) Risk Calculations Base Risk: |(Entry Price - Stop Loss)| ÷ Entry Price × 100 Adjusted Risk: Includes brokerage impact on both entry and exit Risk Amount: Position Size × Base Risk per Share Brokerage Impact Entry with Brokerage: Entry Price × (1 + Brokerage% ÷ 200) Exit with Brokerage: Stop Loss × (1 - Brokerage% ÷ 200) 🎮 How to Use Basic Setup Set your account size and risk percentage Configure brokerage percentage according to your broker Choose entry and stop loss modes The calculator automatically updates position size Interactive Lines Setup ⚠️ IMPORTANT: After selecting line modes, refresh the chart to ensure lines are visible For Buy Line: Select Entry Mode: "Buy Line" Set "Buy Line Price" or leave 0 for current price Refresh chart to see the green buy line Adjust price by clicking on chart or changing input value For Stop Loss Line: Select Stop Loss Mode: "SL Line" Set "Stop Loss Line" or leave 0 for current low Refresh chart to see the red stop loss line Adjust price by clicking on chart or changing input value Table Information The horizontal calculation table displays: SL: Stop Loss price Entry: Entry price level Risk%: Adjusted risk percentage (with brokerage) SL%: Base stop loss risk percentage Cap%: Account risk percentage setting Qty: Recommended quantity to buy Investment: Total investment amount required Alloc%: Portfolio allocation percentage Risk ₹: Total risk amount in Rupees Color Coding Guide Green Values: Positive/profitable metrics Red Values: Risk/loss related metrics Orange Values: Warning levels (high risk/allocation) Blue Headers: Table headers Bright Green Line: Buy line with target icon Bright Red Line: Stop loss line with shield icon 🚨 Alert Conditions Built-in Alerts High Allocation Warning: Triggers when position exceeds 20% of account High Risk Warning: Triggers when stop loss risk exceeds 5% Invalid Position: Triggers when calculation parameters are invalid Setting Up Alerts Click "Add Alert" on the chart Select "Position Size Calculator Pro" Choose desired alert condition Configure notification settings ⚠️ Important Notes & Troubleshooting Interactive Lines Lines not visible? Refresh the chart after selecting line modes Lines moving together? Each line operates independently - check you're adjusting the correct price input Default behavior: Buy line starts at current price, Stop line starts at current low Price = 0: Uses automatic defaults (current price/low) Risk Disclaimers This tool is for educational purposes only Always verify calculations independently Consider market conditions, gaps, and liquidity Past performance doesn't guarantee future results Technical Limitations Interactive lines require chart refresh for initial visibility Calculations update based on selected frequency Maximum 10 lines and 10 labels on chart simultaneously Best Practices Always set realistic account size Never risk more than you can afford to lose Consider slippage and market gaps in volatile conditions Review calculations before placing actual trades Use appropriate position sizing for your trading strategy Refresh chart when switching between line modes 🛠️ Technical Requirements TradingView account (any tier) Pine Script v6 compatibility Modern browser for interactive features Real-time or delayed data feed 📈 Performance Features The script includes several optimizations: Efficient calculation updates based on frequency setting Smart memory management for line drawings Conditional table updates to reduce resource usage Optimized number formatting for better readability 🎯 Use Cases Day Trading Quick position sizing for intraday setups Real-time risk assessment Interactive line placement for entry/exit planning Swing Trading Portfolio allocation management Multi-timeframe risk analysis Position size optimization for longer holds Investment Planning Capital allocation for stock purchases Risk-based position sizing Long-term portfolio management Disclaimer: This tool is for educational and informational purposes only. Trading involves substantial risk of loss and is not suitable for all investors. Always conduct your own research and consider seeking advice from qualified financial professionals.
Pine Script® göstergesi
vinshell tarafından
Money Markers AI Signal botMoney Markers AI Platinum Signals is a premium algorithmic tool designed to assist traders in identifying high-probability trade opportunities across Forex, Commodities, and Cryptocurrencies. This AI-enhanced bot utilizes multiple smart filters to deliver clean BUY and SELL alerts with visual trade levels, helping you act with confidence. ✅ Supports major Forex pairs, Gold, Oil & leading Cryptos ✅ Optimized for H1 and H4 timeframes ✅ Real-time alerts with SL/TP guidance ✅ Built for both swing & intraday traders 🔒 Source code is protected. Access is restricted to approved users only. 📩 To request access, send your TradingView username and registered email to: 📧 support@moneymarkers.net ⚠️ Use responsibly. This is not financial advice. Results may vary.
Pine Script® stratejisi
MoneyMarkers tarafından
abusuhil bullish breakAbusuhil Bullish Break is a price action-based confirmation tool that identifies a bullish reversal pattern consisting of: Two consecutive bearish candles followed by A strong bullish candle that closes above the high of both. The script includes: Optional dual MACD filter (current timeframe + higher timeframe) Configurable stop-loss and multiple take-profit levels Visual lines for targets and stop Custom styling for all elements It’s a clean, logic-driven entry confirmation tool for intraday and swing trading. ⚠️ Open-source and fully customizable. مؤشر Abusuhil Bullish Break هو أداة تأكيد لانعكاسات الاتجاه الصاعد بناءً على حركة السعر (Price Action)، ويكتشف نموذجًا يتكون من: شمعتين هابطتين متتاليتين تتبعهما شمعة صاعدة قوية تغلق فوق أعلى الشمعتين السابقتين يحتوي المؤشر على: فلتر MACD مزدوج اختياري (للفريم الحالي وفريم أعلى) إعدادات مخصصة للوقف والأهداف المتعددة خطوط مرئية احترافية للأهداف والوقف تحكم كامل في الألوان والنمط والعرض مناسب للتداول اللحظي والسوينج. ✅ مفتوح المصدر وقابل للتعديل بالكامل.
Pine Script® göstergesi
abusuhil tarafından
1
Breakout StrategyThis is my first script. This strategy detects breakout opportunities based on trend, RSI, Bollinger Bands, and volume filters. A trade is only executed if a breakout is confirmed after signal setup. Features: ✔️ RSI & BB filters to reduce noise ✔️ Volume spike confirmation (optional) ✔️ Trend filter using moving average ✔️ Stop loss and take profit in % terms ✔️ Ready-to-use alerts for automation Adjustable Inputs: - Stop Loss % - Take Profit %
Pine Script® stratejisi
saquibansari tarafından