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aiQ - 10min Crypto Pro - v2aiQ - 10min Crypto Pro - v2 Professional-grade indicator specifically designed for 10-minute binary options trading on cryptocurrency pairs. Uses a 6-point confluence scoring system combining RSI divergences, VWMA trend ribbon, dynamic support/resistance, volume analysis, and momentum filters to identify high-probability CALL/PUT entries. Key Features: Two-tier signal system: ★ for premium setups (70% win rate), standard for regular trades (60% win rate) Built-in money management calculator showing exact position sizing Session filter highlights optimal trading hours (default: 08:00-22:00 UTC) Real-time dashboard displays signal strength, trend, RSI, volume conditions Automatic filtering of low-volume periods to avoid manipulation zones Optimized for Speed: All parameters fine-tuned for 10-minute expiry times - faster RSI (9), responsive VWMA ribbon (3/8/13), and quick divergence detection. Includes alerts for both signal tiers. Best Use: Wait for ★ signals with 4+ confluence score during active session hours. Never trade during "DRY" volume conditions. Position size based on 2% risk per trade (adjustable). Note: This indicator provides signals only - actual binary options execution must be done through your broker platform.
Pine Script® indicator
by proquanta
DCA Investment Tracker Pro [tradeviZion]DCA Investment Tracker Pro: Educational DCA Analysis Tool An educational indicator that helps analyze Dollar-Cost Averaging strategies by comparing actual performance with historical data calculations. --- 💡 Why I Created This Indicator As someone who practices Dollar-Cost Averaging, I was frustrated with constantly switching between spreadsheets, calculators, and charts just to understand how my investments were really performing. I wanted to see everything in one place - my actual performance, what I should expect based on historical data, and most importantly, visualize where my strategy could take me over the long term . What really motivated me was watching friends and family underestimate the incredible power of consistent investing. When Napoleon Bonaparte first learned about compound interest, he reportedly exclaimed "I wonder it has not swallowed the world" - and he was right! Yet most people can't visualize how their $500 monthly contributions today could become substantial wealth decades later. Traditional DCA tracking tools exist, but they share similar limitations: Require manual data entry and complex spreadsheets Use fixed assumptions that don't reflect real market behavior Can't show future projections overlaid on actual price charts Lose the visual context of what's happening in the market Make compound growth feel abstract rather than tangible I wanted to create something different - a tool that automatically analyzes real market history, detects volatility periods, and shows you both current performance AND educational projections based on historical patterns right on your TradingView charts. As Warren Buffett said: "Someone's sitting in the shade today because someone planted a tree a long time ago." This tool helps you visualize your financial tree growing over time. This isn't just another calculator - it's a visualization tool that makes the magic of compound growth impossible to ignore. --- 🎯 What This Indicator Does This educational indicator provides DCA analysis tools. Users can input investment scenarios to study: Theoretical Performance: Educational calculations based on historical return data Comparative Analysis: Study differences between actual and theoretical scenarios Historical Projections: Theoretical projections for educational analysis (not predictions) Performance Metrics: CAGR, ROI, and other analytical metrics for study Historical Analysis: Calculates historical return data for reference purposes --- 🚀 Key Features Volatility-Adjusted Historical Return Calculation Analyzes 3-20 years of actual price data for any symbol Automatically detects high-volatility stocks (meme stocks, growth stocks) Uses median returns for volatile stocks, standard CAGR for stable stocks Provides conservative estimates when extreme outlier years are detected Smart fallback to manual percentages when data insufficient Customizable Performance Dashboard Educational DCA performance analysis with compound growth calculations Customizable table sizing (Tiny to Huge text options) 9 positioning options (Top/Middle/Bottom + Left/Center/Right) Theme-adaptive colors (automatically adjusts to dark/light mode) Multiple display layout options Future Projection System Visual future growth projections Timeframe-aware calculations (Daily/Weekly/Monthly charts) 1-30 year projection options Shows projected portfolio value and total investment amounts Investment Insights Performance vs benchmark comparison ROI from initial investment tracking Monthly average return analysis Investment milestone alerts (25%, 50%, 100% gains) Contribution tracking and next milestone indicators --- 📊 Step-by-Step Setup Guide 1. Investment Settings 💰 Initial Investment: Enter your starting lump sum (e.g., $60,000) Monthly Contribution: Set your regular DCA amount (e.g., $500/month) Return Calculation: Choose "Auto (Stock History)" for real data or "Manual" for fixed % Historical Period: Select 3-20 years for auto calculations (default: 10 years) Start Year: When you began investing (e.g., 2020) Current Portfolio Value: Your actual portfolio worth today (e.g., $150,000) 2. Display Settings 📊 Table Sizes: Choose from Tiny, Small, Normal, Large, or Huge Table Positions: 9 options - Top/Middle/Bottom + Left/Center/Right Visibility Toggles: Show/hide Main Table and Stats Table independently 3. Future Projection 🔮 Enable Projections: Toggle on to see future growth visualization Projection Years: Set 1-30 years ahead for analysis Live Example - NASDAQ:META Analysis: Settings shown: $60K initial + $500/month + Auto calculation + 10-year history + 2020 start + $150K current value --- 🔬 Pine Script Code Examples Core DCA Calculations: // Calculate total invested over time months_elapsed = (year - start_year) * 12 + month - 1 total_invested = initial_investment + (monthly_contribution * months_elapsed) // Compound growth formula for initial investment theoretical_initial_growth = initial_investment * math.pow(1 + annual_return, years_elapsed) // Future Value of Annuity for monthly contributions monthly_rate = annual_return / 12 fv_contributions = monthly_contribution * ((math.pow(1 + monthly_rate, months_elapsed) - 1) / monthly_rate) // Total expected value theoretical_total = theoretical_initial_growth + fv_contributions Volatility Detection Logic: // Detect extreme years for volatility adjustment extreme_years = 0 for i = 1 to historical_years yearly_return = ((price_current / price_i_years_ago) - 1) * 100 if yearly_return > 100 or yearly_return < -50 extreme_years += 1 // Use median approach for high volatility stocks high_volatility = (extreme_years / historical_years) > 0.2 calculated_return = high_volatility ? median_of_returns : standard_cagr Performance Metrics: // Calculate key performance indicators absolute_gain = actual_value - total_invested total_return_pct = (absolute_gain / total_invested) * 100 roi_initial = ((actual_value - initial_investment) / initial_investment) * 100 cagr = (math.pow(actual_value / initial_investment, 1 / years_elapsed) - 1) * 100 --- 📊 Real-World Examples See the indicator in action across different investment types: Stable Index Investments: AMEX:SPY (SPDR S&P 500) - Shows steady compound growth with standard CAGR calculations Classic DCA success story: $60K initial + $500/month starting 2020. The indicator shows SPY's historical 10%+ returns, demonstrating how consistent broad market investing builds wealth over time. Notice the smooth theoretical growth line vs actual performance tracking. MIL:VUAA (Vanguard S&P 500 UCITS) - Shows both data limitation and solution approaches Data limitation example: VUAA shows "Manual (Auto Failed)" and "No Data" when default 10-year historical setting exceeds available data. The indicator gracefully falls back to manual percentage input while maintaining all DCA calculations and projections. MIL:VUAA (Vanguard S&P 500 UCITS) - European ETF with successful 5-year auto calculation Solution demonstration: By adjusting historical period to 5 years (matching available data), VUAA auto calculation works perfectly. Shows how users can optimize settings for newer assets. European market exposure with EUR denomination, demonstrating DCA effectiveness across different markets and currencies. NYSE:BRK.B (Berkshire Hathaway) - Quality value investment with Warren Buffett's proven track record Value investing approach: Berkshire Hathaway's legendary performance through DCA lens. The indicator demonstrates how quality companies compound wealth over decades. Lower volatility than tech stocks = standard CAGR calculations used. High-Volatility Growth Stocks: NASDAQ:NVDA (NVIDIA Corporation) - Demonstrates volatility-adjusted calculations for extreme price swings High-volatility example: NVIDIA's explosive AI boom creates extreme years that trigger volatility detection. The indicator automatically switches to "Median (High Vol): 50%" calculations for conservative projections, protecting against unrealistic future estimates based on outlier performance periods. NASDAQ:TSLA (Tesla) - Shows how 10-year analysis can stabilize volatile tech stocks Stable long-term growth: Despite Tesla's reputation for volatility, the 10-year historical analysis (34.8% CAGR) shows consistent enough performance that volatility detection doesn't trigger. Demonstrates how longer timeframes can smooth out extreme periods for more reliable projections. NASDAQ:META (Meta Platforms) - Shows stable tech stock analysis using standard CAGR calculations Tech stock with stable growth: Despite being a tech stock and experiencing the 2022 crash, META's 10-year history shows consistent enough performance (23.98% CAGR) that volatility detection doesn't trigger. The indicator uses standard CAGR calculations, demonstrating how not all tech stocks require conservative median adjustments. Notice how the indicator automatically detects high-volatility periods and switches to median-based calculations for more conservative projections, while stable investments use standard CAGR methods. --- 📈 Performance Metrics Explained Current Portfolio Value: Your actual investment worth today Expected Value: What you should have based on historical returns (Auto) or your target return (Manual) Total Invested: Your actual money invested (initial + all monthly contributions) Total Gains/Loss: Absolute dollar difference between current value and total invested Total Return %: Percentage gain/loss on your total invested amount ROI from Initial Investment: How your starting lump sum has performed CAGR: Compound Annual Growth Rate of your initial investment (Note: This shows initial investment performance, not full DCA strategy) vs Benchmark: How you're performing compared to the expected returns --- ⚠️ Important Notes & Limitations Data Requirements: Auto mode requires sufficient historical data (minimum 3 years recommended) CAGR Limitation: CAGR calculation is based on initial investment growth only, not the complete DCA strategy Projection Accuracy: Future projections are theoretical and based on historical returns - actual results may vary Timeframe Support: Works ONLY on Daily (1D), Weekly (1W), and Monthly (1M) charts - no other timeframes supported Update Frequency: Update "Current Portfolio Value" regularly for accurate tracking --- 📚 Educational Use & Disclaimer This analysis tool can be applied to various stock and ETF charts for educational study of DCA mathematical concepts and historical performance patterns. Study Examples: Can be used with symbols like AMEX:SPY , NASDAQ:QQQ , AMEX:VTI , NASDAQ:AAPL , NASDAQ:MSFT , NASDAQ:GOOGL , NASDAQ:AMZN , NASDAQ:TSLA , NASDAQ:NVDA for learning purposes. EDUCATIONAL DISCLAIMER: This indicator is a study tool for analyzing Dollar-Cost Averaging strategies. It does not provide investment advice, trading signals, or guarantees. All calculations are theoretical examples for educational purposes only. Past performance does not predict future results. Users should conduct their own research and consult qualified financial professionals before making any investment decisions. --- © 2025 TradeVizion. All rights reserved.
Pine Script® indicator
by TradeVizion
2
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® indicator
by EdgeTools
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.
Pine Script® indicator
by CipherDecoded
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?
Pine Script® indicator
by srbhmaggo
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® indicator
by abusuhil
1
Quantum Volume Pulse Screener - Multi TimeframeQuantum Volume Pulse Screener - Multi Timeframe Overview The Quantum Volume Pulse Screener is a powerful Pine Script® indicator designed for TradingView to monitor multiple symbols across user-selected timeframes (1-minute or 5-minute). This tool provides traders with real-time insights into price action, Volume Weighted Average Price (VWAP), Relative Strength Index (RSI), and buy/sell signals for a curated list of high-profile stocks and ETFs, including SPY, QQQ, AAPL, AMZN, GOOG, GOOGL, META, AVGO, TSLA, and NFLX. The screener displays data in a clean, customizable table, enabling quick decision-making for active traders. Fully customizable to any ETFs or Stocks. Key Features Multi-Symbol Analysis: Tracks up to 10 user-defined symbols, defaulting to major ETFs (SPY, QQQ) and leading tech stocks (AAPL, AMZN, GOOG, GOOGL, META, AVGO, TSLA, NFLX). Customizable Timeframe: Toggle between 1-minute and 5-minute timeframes for flexible analysis. Comprehensive Metrics: Displays real-time data for: Price: Current closing price with color-coded daily change (green for positive, pink for negative). VWAP: Volume Weighted Average Price for intraday trend analysis. RSI: 14-period RSI with overbought (>70, pink) and oversold (<30, green) highlights. Signals: Generates "BUY" (RSI < 30), "SELL" (RSI > 70), or neutral ("-") signals. Dynamic Table Display: Presents data in a clear, top-center table with up to 500 labels for historical reference. Error Handling: Alerts users to invalid data (e.g., incorrect symbols or timeframes) and displays a weekend warning for stale data. Real-Time Updates: Refreshes data on every bar to ensure accuracy during live trading sessions. How It Works The script fetches real-time data for each symbol using TradingView’s request.security function, calculating: Price: Based on the current bar’s close. VWAP: Computed using the HLC3 (High + Low + Close / 3) formula. RSI: 14-period RSI to identify momentum and potential reversals. Daily Change: Percentage change in price to gauge short-term performance. Signals: RSI-based buy/sell triggers for quick trade identification. The data is organized into arrays and displayed in a table with color-coded visuals for easy interpretation. Green indicates bullish conditions (e.g., RSI < 30 or positive daily change), while pink highlights bearish conditions (e.g., RSI > 70 or negative daily change). Usage Instructions Add to Chart: Apply the indicator to any TradingView chart. Configure Settings: Select the desired timeframe (1-minute or 5-minute) via the input menu. Customize symbols by editing the ticker inputs (defaults to SPY, QQQ, AAPL, etc.). Interpret the Table: Monitor the table at the top-center of the chart for real-time updates. Look for "BUY" or "SELL" signals based on RSI thresholds. Use VWAP and price data to confirm trends or reversals. Check for Warnings: If "INVALID" appears, verify the symbol or timeframe settings. On weekends, a warning advises switching to a daily timeframe due to potentially stale data. Notes License: This script is licensed under the Mozilla Public License 2.0 (mozilla.org). Author: © StanTheTradingMan. Limitations: Ensure symbols are correctly formatted (e.g., "NASDAQ:AAPL" for stocks, "SPY" for ETFs). Invalid symbols or unavailable data may trigger error messages. Best Use Case: Ideal for day traders and swing traders monitoring multiple assets for short-term opportunities. Why Use This Screener? The Quantum Volume Pulse Screener consolidates critical market data into a single, visually intuitive interface, saving traders time and enhancing decision-making. Whether tracking major indices or individual stocks, this tool provides a real-time edge in fast-moving markets. For support or feedback, refer to TradingView’s community forums or contact the author via TradingView. Happy trading!
Pine Script® indicator
by StanTheTradingMan
Advanced Petroleum Market Model (APMM)Advanced Petroleum Market Model (APMM): A Multi-Factor Fundamental Analysis Framework for Oil Market Assessment ## 1. Introduction The petroleum market represents one of the most complex and globally significant commodity markets, characterized by intricate supply-demand dynamics, geopolitical influences, and substantial price volatility (Hamilton, 2009). Traditional fundamental analysis approaches often struggle to synthesize the multitude of relevant indicators into actionable insights due to data heterogeneity, temporal misalignment, and subjective weighting schemes (Baumeister & Kilian, 2016). The Advanced Petroleum Market Model addresses these limitations through a systematic, quantitative approach that integrates 16 verified fundamental indicators across five critical market dimensions. The model builds upon established financial engineering principles while incorporating petroleum-specific market dynamics and adaptive learning mechanisms. ## 2. Theoretical Framework ### 2.1 Market Efficiency and Information Integration The model operates under the assumption of semi-strong market efficiency, where fundamental information is gradually incorporated into prices with varying degrees of lag (Fama, 1970). The petroleum market's unique characteristics, including storage costs, transportation constraints, and geopolitical risk premiums, create opportunities for fundamental analysis to provide predictive value (Kilian, 2009). ### 2.2 Multi-Factor Asset Pricing Theory Drawing from Ross's (1976) Arbitrage Pricing Theory, the model treats petroleum prices as driven by multiple systematic risk factors. The five-factor decomposition (Supply, Inventory, Demand, Trade, Sentiment) represents economically meaningful sources of systematic risk in petroleum markets (Chen et al., 1986). ## 3. Methodology ### 3.1 Data Sources and Quality Framework The model integrates 16 fundamental indicators sourced from verified TradingView economic data feeds: Supply Indicators: - US Oil Production (ECONOMICS:USCOP) - US Oil Rigs Count (ECONOMICS:USCOR) - API Crude Runs (ECONOMICS:USACR) Inventory Indicators: - US Crude Stock Changes (ECONOMICS:USCOSC) - Cushing Stocks (ECONOMICS:USCCOS) - API Crude Stocks (ECONOMICS:USCSC) - API Gasoline Stocks (ECONOMICS:USGS) - API Distillate Stocks (ECONOMICS:USDS) Demand Indicators: - Refinery Crude Runs (ECONOMICS:USRCR) - Gasoline Production (ECONOMICS:USGPRO) - Distillate Production (ECONOMICS:USDFP) - Industrial Production Index (FRED:INDPRO) Trade Indicators: - US Crude Imports (ECONOMICS:USCOI) - US Oil Exports (ECONOMICS:USOE) - API Crude Imports (ECONOMICS:USCI) - Dollar Index (TVC:DXY) Sentiment Indicators: - Oil Volatility Index (CBOE:OVX) ### 3.2 Data Quality Monitoring System Following best practices in quantitative finance (Lopez de Prado, 2018), the model implements comprehensive data quality monitoring: Data Quality Score = Σ(Individual Indicator Validity) / Total Indicators Where validity is determined by: - Non-null data availability - Positive value validation - Temporal consistency checks ### 3.3 Statistical Normalization Framework #### 3.3.1 Z-Score Normalization The model employs robust Z-score normalization as established by Sharpe (1994) for cross-indicator comparability: Z_i,t = (X_i,t - μ_i) / σ_i Where: - X_i,t = Raw value of indicator i at time t - μ_i = Sample mean of indicator i - σ_i = Sample standard deviation of indicator i Z-scores are capped at ±3 to mitigate outlier influence (Tukey, 1977). #### 3.3.2 Percentile Rank Transformation For intuitive interpretation, Z-scores are converted to percentile ranks following the methodology of Conover (1999): Percentile_Rank = (Number of values < current_value) / Total_observations × 100 ### 3.4 Exponential Smoothing Framework Signal smoothing employs exponential weighted moving averages (Brown, 1963) with adaptive alpha parameter: S_t = α × X_t + (1-α) × S_{t-1} Where α = 2/(N+1) and N represents the smoothing period. ### 3.5 Dynamic Threshold Optimization The model implements adaptive thresholds using Bollinger Band methodology (Bollinger, 1992): Dynamic_Threshold = μ ± (k × σ) Where k is the threshold multiplier adjusted for market volatility regime. ### 3.6 Composite Score Calculation The fundamental score integrates component scores through weighted averaging: Fundamental_Score = Σ(w_i × Score_i × Quality_i) Where: - w_i = Normalized component weight - Score_i = Component fundamental score - Quality_i = Data quality adjustment factor ## 4. Implementation Architecture ### 4.1 Adaptive Parameter Framework The model incorporates regime-specific adjustments based on market volatility: Volatility_Regime = σ_price / μ_price × 100 High volatility regimes (>25%) trigger enhanced weighting for inventory and sentiment components, reflecting increased market sensitivity to supply disruptions and psychological factors. ### 4.2 Data Synchronization Protocol Given varying publication frequencies (daily, weekly, monthly), the model employs forward-fill synchronization to maintain temporal alignment across all indicators. ### 4.3 Quality-Adjusted Scoring Component scores are adjusted for data quality to prevent degraded inputs from contaminating the composite signal: Adjusted_Score = Raw_Score × Quality_Factor + 50 × (1 - Quality_Factor) This formulation ensures that poor-quality data reverts toward neutral (50) rather than contributing noise. ## 5. Usage Guidelines and Best Practices ### 5.1 Configuration Recommendations For Short-term Analysis (1-4 weeks): - Lookback Period: 26 weeks - Smoothing Length: 3-5 periods - Confidence Period: 13 weeks - Increase inventory and sentiment weights For Medium-term Analysis (1-3 months): - Lookback Period: 52 weeks - Smoothing Length: 5-8 periods - Confidence Period: 26 weeks - Balanced component weights For Long-term Analysis (3+ months): - Lookback Period: 104 weeks - Smoothing Length: 8-12 periods - Confidence Period: 52 weeks - Increase supply and demand weights ### 5.2 Signal Interpretation Framework Bullish Signals (Score > 70): - Fundamental conditions favor price appreciation - Consider long positions or reduced short exposure - Monitor for trend confirmation across multiple timeframes Bearish Signals (Score < 30): - Fundamental conditions suggest price weakness - Consider short positions or reduced long exposure - Evaluate downside protection strategies Neutral Range (30-70): - Mixed fundamental environment - Favor range-bound or volatility strategies - Wait for clearer directional signals ### 5.3 Risk Management Considerations 1. Data Quality Monitoring: Continuously monitor the data quality dashboard. Scores below 75% warrant increased caution. 2. Regime Awareness: Adjust position sizing based on volatility regime indicators. High volatility periods require reduced exposure. 3. Correlation Analysis: Monitor correlation with crude oil prices to validate model effectiveness. 4. Fundamental-Technical Divergence: Pay attention when fundamental signals diverge from technical indicators, as this may signal regime changes. ### 5.4 Alert System Optimization Configure alerts conservatively to avoid false signals: - Set alert threshold at 75+ for high-confidence signals - Enable data quality warnings to maintain system integrity - Use trend reversal alerts for early regime change detection ## 6. Model Validation and Performance Metrics ### 6.1 Statistical Validation The model's statistical robustness is ensured through: - Out-of-sample testing protocols - Rolling window validation - Bootstrap confidence intervals - Regime-specific performance analysis ### 6.2 Economic Validation Fundamental accuracy is validated against: - Energy Information Administration (EIA) official reports - International Energy Agency (IEA) market assessments - Commercial inventory data verification ## 7. Limitations and Considerations ### 7.1 Model Limitations 1. Data Dependency: Model performance is contingent on data availability and quality from external sources. 2. US Market Focus: Primary data sources are US-centric, potentially limiting global applicability. 3. Lag Effects: Some fundamental indicators exhibit publication lags that may delay signal generation. 4. Regime Shifts: Structural market changes may require model recalibration. ### 7.2 Market Environment Considerations The model is optimized for normal market conditions. During extreme events (e.g., geopolitical crises, pandemics), additional qualitative factors should be considered alongside quantitative signals. ## References Baumeister, C., & Kilian, L. (2016). Forty years of oil price fluctuations: Why the price of oil may still surprise us. *Journal of Economic Perspectives*, 30(1), 139-160. Bollinger, J. (1992). *Bollinger on Bollinger Bands*. McGraw-Hill. Brown, R. G. (1963). *Smoothing, Forecasting and Prediction of Discrete Time Series*. Prentice-Hall. Chen, N. F., Roll, R., & Ross, S. A. (1986). Economic forces and the stock market. *Journal of Business*, 59(3), 383-403. Conover, W. J. (1999). *Practical Nonparametric Statistics* (3rd ed.). John Wiley & Sons. Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. *Journal of Finance*, 25(2), 383-417. Hamilton, J. D. (2009). Understanding crude oil prices. *Energy Journal*, 30(2), 179-206. Kilian, L. (2009). Not all oil price shocks are alike: Disentangling demand and supply shocks in the crude oil market. *American Economic Review*, 99(3), 1053-1069. Lopez de Prado, M. (2018). *Advances in Financial Machine Learning*. John Wiley & Sons. Ross, S. A. (1976). The arbitrage theory of capital asset pricing. *Journal of Economic Theory*, 13(3), 341-360. Sharpe, W. F. (1994). The Sharpe ratio. *Journal of Portfolio Management*, 21(1), 49-58. Tukey, J. W. (1977). *Exploratory Data Analysis*. Addison-Wesley.
Pine Script® indicator
by EdgeTools
Monthly Session Divider (Alt Background) | Chart_BullyEasily visualize monthly transitions with alternating background shading. Designed for traders who like to spot macro trends, monthly opens, and institutional order flow. ✅ Alternates background color each month ✅ Auto-detects new months using live date logic ✅ Great for RTH or ETH intraday and swing strategies ✅ Clean gray overlay with low opacity ✅ Works on intraday, daily, and weekly charts ✅ Built for clarity, not clutter Use this tool to: Identify monthly pivots or volume rotations Anchor monthly VWAPs or FVGs with visual context Frame long-term setups with clean visual breaks
Pine Script® indicator
by Chart_Bully
Weekly Session Divider (Alt Background) | Chart_BullyThis tool adds subtle alternating background shading for each new week, helping you visually distinguish trading sessions at a glance. ✅ Alternates background by weekly session ✅ Works great on intraday and daily timeframes ✅ Ideal for traders who rely on weekly pivots, volume profiles, or macro structure ✅ Compatible with both RTH and ETH charts ✅ Clean design for easy chart integration Use it to improve your session awareness, spot emerging weekly trends, and avoid mental fatigue when reading extended charts.
Pine Script® indicator
by Chart_Bully
Alternate Day Divider Background | Chart_BullyThis free utility shades every other trading day on your chart, helping you visually separate sessions and spot daily rhythm or pattern shifts more easily. ✅ Automatically alternates background shading by day ✅ Works on both Regular Trading Hours (RTH) and Extended Trading Hours (ETH) ✅ Especially useful on intraday and daily timeframes ✅ Helps identify breakout setups, trend shifts, or volume cycles by session Great for scalpers, day traders, and anyone who wants a subtle visual edge without chart clutter.
Pine Script® indicator
by Chart_Bully
📡 ETF RADAR HUD (SPY · QQQ · SPX) Auto-detects if you’re on SPY, SPX or QQQ Shows a sleek status dashboard with: Trend condition (EMA crossover) Volatility meter (based on ATR vs price) RSI mood Volume activity Instrument tag ("SPY 🔍", "QQQ 🚀", "SPX" or "Other 🪐") 🧠 Strategy: We build a situational awareness HUD so SPY/QQQ/SPX day traders know: Are we trending or ranging? Is volatility expanding? Are we in overbought/oversold territory? Is there a volume surge?
Pine Script® indicator
by StanTheTradingMan