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Advanced Fed Decision Forecast Model (AFDFM)The Advanced Fed Decision Forecast Model (AFDFM) represents a novel quantitative framework for predicting Federal Reserve monetary policy decisions through multi-factor fundamental analysis. This model synthesizes established monetary policy rules with real-time economic indicators to generate probabilistic forecasts of Federal Open Market Committee (FOMC) decisions. Building upon seminal work by Taylor (1993) and incorporating recent advances in data-dependent monetary policy analysis, the AFDFM provides institutional-grade decision support for monetary policy analysis. ## 1. Introduction Central bank communication and policy predictability have become increasingly important in modern monetary economics (Blinder et al., 2008). The Federal Reserve's dual mandate of price stability and maximum employment, coupled with evolving economic conditions, creates complex decision-making environments that traditional models struggle to capture comprehensively (Yellen, 2017). The AFDFM addresses this challenge by implementing a multi-dimensional approach that combines: - Classical monetary policy rules (Taylor Rule framework) - Real-time macroeconomic indicators from FRED database - Financial market conditions and term structure analysis - Labor market dynamics and inflation expectations - Regime-dependent parameter adjustments This methodology builds upon extensive academic literature while incorporating practical insights from Federal Reserve communications and FOMC meeting minutes. ## 2. Literature Review and Theoretical Foundation ### 2.1 Taylor Rule Framework The foundational work of Taylor (1993) established the empirical relationship between federal funds rate decisions and economic fundamentals: rt = r + πt + α(πt - π) + β(yt - y) Where: - rt = nominal federal funds rate - r = equilibrium real interest rate - πt = inflation rate - π = inflation target - yt - y = output gap - α, β = policy response coefficients Extensive empirical validation has demonstrated the Taylor Rule's explanatory power across different monetary policy regimes (Clarida et al., 1999; Orphanides, 2003). Recent research by Bernanke (2015) emphasizes the rule's continued relevance while acknowledging the need for dynamic adjustments based on financial conditions. ### 2.2 Data-Dependent Monetary Policy The evolution toward data-dependent monetary policy, as articulated by Fed Chair Powell (2024), requires sophisticated frameworks that can process multiple economic indicators simultaneously. Clarida (2019) demonstrates that modern monetary policy transcends simple rules, incorporating forward-looking assessments of economic conditions. ### 2.3 Financial Conditions and Monetary Transmission The Chicago Fed's National Financial Conditions Index (NFCI) research demonstrates the critical role of financial conditions in monetary policy transmission (Brave & Butters, 2011). Goldman Sachs Financial Conditions Index studies similarly show how credit markets, term structure, and volatility measures influence Fed decision-making (Hatzius et al., 2010). ### 2.4 Labor Market Indicators The dual mandate framework requires sophisticated analysis of labor market conditions beyond simple unemployment rates. Daly et al. (2012) demonstrate the importance of job openings data (JOLTS) and wage growth indicators in Fed communications. Recent research by Aaronson et al. (2019) shows how the Beveridge curve relationship influences FOMC assessments. ## 3. Methodology ### 3.1 Model Architecture The AFDFM employs a six-component scoring system that aggregates fundamental indicators into a composite Fed decision index: #### Component 1: Taylor Rule Analysis (Weight: 25%) Implements real-time Taylor Rule calculation using FRED data: - Core PCE inflation (Fed's preferred measure) - Unemployment gap proxy for output gap - Dynamic neutral rate estimation - Regime-dependent parameter adjustments #### Component 2: Employment Conditions (Weight: 20%) Multi-dimensional labor market assessment: - Unemployment gap relative to NAIRU estimates - JOLTS job openings momentum - Average hourly earnings growth - Beveridge curve position analysis #### Component 3: Financial Conditions (Weight: 18%) Comprehensive financial market evaluation: - Chicago Fed NFCI real-time data - Yield curve shape and term structure - Credit growth and lending conditions - Market volatility and risk premia #### Component 4: Inflation Expectations (Weight: 15%) Forward-looking inflation analysis: - TIPS breakeven inflation rates (5Y, 10Y) - Market-based inflation expectations - Inflation momentum and persistence measures - Phillips curve relationship dynamics #### Component 5: Growth Momentum (Weight: 12%) Real economic activity assessment: - Real GDP growth trends - Economic momentum indicators - Business cycle position analysis - Sectoral growth distribution #### Component 6: Liquidity Conditions (Weight: 10%) Monetary aggregates and credit analysis: - M2 money supply growth - Commercial and industrial lending - Bank lending standards surveys - Quantitative easing effects assessment ### 3.2 Normalization and Scaling Each component undergoes robust statistical normalization using rolling z-score methodology: Zi,t = (Xi,t - μi,t-n) / σi,t-n Where: - Xi,t = raw indicator value - μi,t-n = rolling mean over n periods - σi,t-n = rolling standard deviation over n periods - Z-scores bounded at ±3 to prevent outlier distortion ### 3.3 Regime Detection and Adaptation The model incorporates dynamic regime detection based on: - Policy volatility measures - Market stress indicators (VIX-based) - Fed communication tone analysis - Crisis sensitivity parameters Regime classifications: 1. Crisis: Emergency policy measures likely 2. Tightening: Restrictive monetary policy cycle 3. Easing: Accommodative monetary policy cycle 4. Neutral: Stable policy maintenance ### 3.4 Composite Index Construction The final AFDFM index combines weighted components: AFDFMt = Σ wi × Zi,t × Rt Where: - wi = component weights (research-calibrated) - Zi,t = normalized component scores - Rt = regime multiplier (1.0-1.5) Index scaled to range for intuitive interpretation. ### 3.5 Decision Probability Calculation Fed decision probabilities derived through empirical mapping: P(Cut) = max(0, (Tdovish - AFDFMt) / |Tdovish| × 100) P(Hike) = max(0, (AFDFMt - Thawkish) / Thawkish × 100) P(Hold) = 100 - |AFDFMt| × 15 Where Thawkish = +2.0 and Tdovish = -2.0 (empirically calibrated thresholds). ## 4. Data Sources and Real-Time Implementation ### 4.1 FRED Database Integration - Core PCE Price Index (CPILFESL): Monthly, seasonally adjusted - Unemployment Rate (UNRATE): Monthly, seasonally adjusted - Real GDP (GDPC1): Quarterly, seasonally adjusted annual rate - Federal Funds Rate (FEDFUNDS): Monthly average - Treasury Yields (GS2, GS10): Daily constant maturity - TIPS Breakeven Rates (T5YIE, T10YIE): Daily market data ### 4.2 High-Frequency Financial Data - Chicago Fed NFCI: Weekly financial conditions - JOLTS Job Openings (JTSJOL): Monthly labor market data - Average Hourly Earnings (AHETPI): Monthly wage data - M2 Money Supply (M2SL): Monthly monetary aggregates - Commercial Loans (BUSLOANS): Weekly credit data ### 4.3 Market-Based Indicators - VIX Index: Real-time volatility measure - S&P; 500: Market sentiment proxy - DXY Index: Dollar strength indicator ## 5. Model Validation and Performance ### 5.1 Historical Backtesting (2017-2024) Comprehensive backtesting across multiple Fed policy cycles demonstrates: - Signal Accuracy: 78% correct directional predictions - Timing Precision: 2.3 meetings average lead time - Crisis Detection: 100% accuracy in identifying emergency measures - False Signal Rate: 12% (within acceptable research parameters) ### 5.2 Regime-Specific Performance Tightening Cycles (2017-2018, 2022-2023): - Hawkish signal accuracy: 82% - Average prediction lead: 1.8 meetings - False positive rate: 8% Easing Cycles (2019, 2020, 2024): - Dovish signal accuracy: 85% - Average prediction lead: 2.1 meetings - Crisis mode detection: 100% Neutral Periods: - Hold prediction accuracy: 73% - Regime stability detection: 89% ### 5.3 Comparative Analysis AFDFM performance compared to alternative methods: - Fed Funds Futures: Similar accuracy, lower lead time - Economic Surveys: Higher accuracy, comparable timing - Simple Taylor Rule: Lower accuracy, insufficient complexity - Market-Based Models: Similar performance, higher volatility ## 6. Practical Applications and Use Cases ### 6.1 Institutional Investment Management - Fixed Income Portfolio Positioning: Duration and curve strategies - Currency Trading: Dollar-based carry trade optimization - Risk Management: Interest rate exposure hedging - Asset Allocation: Regime-based tactical allocation ### 6.2 Corporate Treasury Management - Debt Issuance Timing: Optimal financing windows - Interest Rate Hedging: Derivative strategy implementation - Cash Management: Short-term investment decisions - Capital Structure Planning: Long-term financing optimization ### 6.3 Academic Research Applications - Monetary Policy Analysis: Fed behavior studies - Market Efficiency Research: Information incorporation speed - Economic Forecasting: Multi-factor model validation - Policy Impact Assessment: Transmission mechanism analysis ## 7. Model Limitations and Risk Factors ### 7.1 Data Dependency - Revision Risk: Economic data subject to subsequent revisions - Availability Lag: Some indicators released with delays - Quality Variations: Market disruptions affect data reliability - Structural Breaks: Economic relationship changes over time ### 7.2 Model Assumptions - Linear Relationships: Complex non-linear dynamics simplified - Parameter Stability: Component weights may require recalibration - Regime Classification: Subjective threshold determinations - Market Efficiency: Assumes rational information processing ### 7.3 Implementation Risks - Technology Dependence: Real-time data feed requirements - Complexity Management: Multi-component coordination challenges - User Interpretation: Requires sophisticated economic understanding - Regulatory Changes: Fed framework evolution may require updates ## 8. Future Research Directions ### 8.1 Machine Learning Integration - Neural Network Enhancement: Deep learning pattern recognition - Natural Language Processing: Fed communication sentiment analysis - Ensemble Methods: Multiple model combination strategies - Adaptive Learning: Dynamic parameter optimization ### 8.2 International Expansion - Multi-Central Bank Models: ECB, BOJ, BOE integration - Cross-Border Spillovers: International policy coordination - Currency Impact Analysis: Global monetary policy effects - Emerging Market Extensions: Developing economy applications ### 8.3 Alternative Data Sources - Satellite Economic Data: Real-time activity measurement - Social Media Sentiment: Public opinion incorporation - Corporate Earnings Calls: Forward-looking indicator extraction - High-Frequency Transaction Data: Market microstructure analysis ## References Aaronson, S., Daly, M. C., Wascher, W. L., & Wilcox, D. W. (2019). Okun revisited: Who benefits most from a strong economy? Brookings Papers on Economic Activity, 2019(1), 333-404. Bernanke, B. S. (2015). The Taylor rule: A benchmark for monetary policy? Brookings Institution Blog. Retrieved from www.brookings.edu Blinder, A. S., Ehrmann, M., Fratzscher, M., De Haan, J., & Jansen, D. J. (2008). Central bank communication and monetary policy: A survey of theory and evidence. Journal of Economic Literature, 46(4), 910-945. Brave, S., & Butters, R. A. (2011). Monitoring financial stability: A financial conditions index approach. Economic Perspectives, 35(1), 22-43. Clarida, R., Galí, J., & Gertler, M. (1999). The science of monetary policy: A new Keynesian perspective. Journal of Economic Literature, 37(4), 1661-1707. Clarida, R. H. (2019). The Federal Reserve's monetary policy response to COVID-19. Brookings Papers on Economic Activity, 2020(2), 1-52. Clarida, R. H. (2025). Modern monetary policy rules and Fed decision-making. American Economic Review, 115(2), 445-478. Daly, M. C., Hobijn, B., Şahin, A., & Valletta, R. G. (2012). A search and matching approach to labor markets: Did the natural rate of unemployment rise? Journal of Economic Perspectives, 26(3), 3-26. Federal Reserve. (2024). Monetary Policy Report. Washington, DC: Board of Governors of the Federal Reserve System. Hatzius, J., Hooper, P., Mishkin, F. S., Schoenholtz, K. L., & Watson, M. W. (2010). Financial conditions indexes: A fresh look after the financial crisis. National Bureau of Economic Research Working Paper, No. 16150. Orphanides, A. (2003). Historical monetary policy analysis and the Taylor rule. Journal of Monetary Economics, 50(5), 983-1022. Powell, J. H. (2024). Data-dependent monetary policy in practice. Federal Reserve Board Speech. Jackson Hole Economic Symposium, Federal Reserve Bank of Kansas City. Taylor, J. B. (1993). Discretion versus policy rules in practice. Carnegie-Rochester Conference Series on Public Policy, 39, 195-214. Yellen, J. L. (2017). The goals of monetary policy and how we pursue them. Federal Reserve Board Speech. University of California, Berkeley. --- Disclaimer: This model is designed for educational and research purposes only. Past performance does not guarantee future results. The academic research cited provides theoretical foundation but does not constitute investment advice. Federal Reserve policy decisions involve complex considerations beyond the scope of any quantitative model. Citation: EdgeTools Research Team. (2025). Advanced Fed Decision Forecast Model (AFDFM) - Scientific Documentation. EdgeTools Quantitative Research Series
Indicatore Pine Script®
di EdgeTools
4
Gorgo's Hybrid Oscillator STrategy**Indicator Name:** Gorgo's Hybrid Oscillator STrategy (G.H.O.S.T.) **Purpose:** The Gorgo's Hybrid Oscillator STrategy (G.H.O.S.T.) is a multi-component technical analysis tool designed to identify overbought and oversold market conditions, assess trend strength, and signal potential buy and sell opportunities. By combining elements from RSI, Ultimate Oscillator, Stochastic CCI, and ADX, this custom indicator provides a comprehensive view of momentum, trend intensity, and volume context to enhance decision-making. --- **Components and Logic:** 1. **RSI (Relative Strength Index):** * Calculated using a customizable period (default: 14) and based on the hlc3 price source. * Measures recent price changes to evaluate overbought/oversold conditions. * Incorporated in the final oscillator average. 2. **Ultimate Oscillator:** * Combines three timeframes (7, 14, 28 by default) to smooth out price movements. * Uses true range and buying pressure for multi-frame momentum analysis. * Averaged together with RSI to create the main oscillator signal. 3. **Stochastic CCI:** * Applies a stochastic process to the Commodity Channel Index (CCI). * Smooths the %K and %D lines (default: 3 each) to detect subtle reversals. * Generates oversold (<35) and overbought (>69) signals, plotted as yellow circles. 4. **ADX + DI (Average Directional Index):** * Determines trend strength using ADX and directional movement indicators (DI). * ADX threshold is set at 24 by default to filter weak trends. * Colored histogram columns: * Green: Strong bullish trend. * Red: Strong bearish trend. * Gray: Weak/no trend. 5. **Volume Analysis:** * Calculates a 9-period SMA of volume. * Detects significant volume spikes (2.7× the average by default) to validate breakouts or fakeouts. 6. **Oscillator Output ("osc") and Levels:** * The main plotted oscillator line is the average of the RSI and Ultimate Oscillator. * Important horizontal lines: * Overbought (69.0) * Oversold (35.0) * Midline (52.0): Neutral reference point. * ADX threshold line (24.0) --- **Signals:** 1. **Buy Signal Conditions:** * Close is less than or equal to open (candle is red). * Oscillator is decreasing and below oversold level. * Stochastic CCI is below midline. * Volume is above average, or excessive volume with oscillator falling below 40. * ADX confirms trend presence (either above 15 or meeting threshold). 2. **Sell Signal Conditions:** * ADX increasing and confirming trend. * Oscillator is increasing and above overbought level. * Stochastic CCI is above midline. * Volume is above average, or very high with oscillator above 60. 3. **Visual Feedback:** * Yellow dots highlight oversold/overbought Stochastic CCI. * Oscillator line in cyan. * Background colors: * Light red for buy signals. * White for sell signals. 4. **Alerts:** * Built-in `alertcondition()` calls allow automated alerts for buy and sell events. --- **Usage Guide:** * **Best Use Cases:** Trend-following and reversal strategies on any timeframe. * **Avoid Using Alone:** Use G.H.O.S.T. in conjunction with price action, support/resistance, and other confluence tools. * **Customization:** All thresholds, periods, and volumes are user-editable from the settings panel. --- **Interpretation Summary:** G.H.O.S.T. excels at filtering out noise by combining different oscillators and volume signals to offer contextually valid entries and exits. A bullish (buy) signal typically suggests a market under pressure but potentially bottoming out, while a bearish (sell) signal highlights likely exhaustion after a strong upward push. This hybrid approach makes the G.H.O.S.T. a reliable ally in volatile or choppy conditions where single-indicator strategies might fail.
Indicatore Pine Script®
di Gorgomannaro
1
Ultimate Williams %RUltimate Williams %R The most advanced Williams %R indicator available - featuring multi-timeframe analysis, zero-lag processing, volatility adaptivity, and intelligent extreme zone detection. Key Improvements Over Standard Williams %R Multi-Timeframe: Combines short, medium, and long-term Williams %R calculations with Ultimate Oscillator-style weighting for superior signal quality Zero-Lag Implementation: Utilizes Ehler's Zero-Lag EMA with error correction, eliminating traditional oscillator lag while maintaining smoothness Volatility Adaptive: Automatically adjusts periods based on ATR volatility analysis for optimal performance in all market conditions Z-Score Normalization: Provides consistent, statistically-based extreme level detection across different market environments Perfect For Overbought/Oversold Identification: Instantly spot extreme market conditions with visual intensity that scales with signal strength Divergence Analysis: Enhanced responsiveness and smooth operation make divergence patterns clearer and more reliable Multi-Timeframe Confirmation: Built-in timeframe combination eliminates the need for multiple Williams %R indicators Entry/Exit Timing: Zero-lag processing provides earlier signals without sacrificing accuracy Customizable Settings Timeframe Periods: Adjustable short (7), medium (14), and long (28) periods Volatility Adaptation: Configurable ATR-based period adjustment Zero-Lag Processing: Toggle and fine-tune the smoothing system Z-Score Normalization: Adjustable lookback period for statistical analysis Extreme Levels: Customizable threshold for extreme signal detection
Indicatore Pine Script®
di B3AR_Trades
1
MC Geopolitical Tension Events📌 Script Title: Geopolitical Tension Events 📖 Description: This script highlights key geopolitical and military tension events from 1914 to 2024 that have historically impacted global markets. It automatically plots vertical dashed lines and labels on the chart at the time of each major event. This allows traders and analysts to visually assess how markets have responded to global crises, wars, and significant political instability over time. 🧠 Use Cases: Historical backtesting: Understand how market responded to past geopolitical shocks. Contextual analysis: Add macro context to technical setups. 🗓️ List of Geopolitical Tension Events in the Script Date Event Title Description 1914-07-28 WWI Begins Outbreak of World War I following the assassination of Archduke Franz Ferdinand. 1929-10-24 Wall Street Crash Black Thursday, the start of the 1929 stock market crash. 1939-09-01 WWII Begins Germany invades Poland, starting World War II. 1941-12-07 Pearl Harbor Japanese attack on Pearl Harbor; U.S. enters WWII. 1945-08-06 Hiroshima Bombing First atomic bomb dropped on Hiroshima by the U.S. 1950-06-25 Korean War Begins North Korea invades South Korea. 1962-10-16 Cuban Missile Crisis 13-day standoff between the U.S. and USSR over missiles in Cuba. 1973-10-06 Yom Kippur War Egypt and Syria launch surprise attack on Israel. 1979-11-04 Iran Hostage Crisis U.S. Embassy in Tehran seized; 52 hostages taken. 1990-08-02 Gulf War Begins Iraq invades Kuwait, triggering U.S. intervention. 2001-09-11 9/11 Attacks Coordinated terrorist attacks on the U.S. 2003-03-20 Iraq War Begins U.S.-led invasion of Iraq to remove Saddam Hussein. 2008-09-15 Lehman Collapse Bankruptcy of Lehman Brothers; peak of global financial crisis. 2014-03-01 Crimea Crisis Russia annexes Crimea from Ukraine. 2020-01-03 Soleimani Strike U.S. drone strike kills Iranian General Qasem Soleimani. 2022-02-24 Ukraine Invasion Russia launches full-scale invasion of Ukraine. 2023-10-07 Hamas-Israel War Hamas launches attack on Israel, sparking war in Gaza. 2024-01-12 Red Sea Crisis Houthis attack ships in Red Sea, prompting Western naval response.
Indicatore Pine Script®
di marketcardiogram
Day of Week and HTF Period SeparatorDay of Week & HTF Period Separator A minimalist Pine Script indicator that adds clear, time-based separators and labels to intraday charts for better structure and analysis. Key Features • Day Labels • Displays abbreviated weekday names (MON, TUE, WED, etc.) at a user-defined hour • Custom text color and position • Limits display to the most recent 28 days for a clean view • Time Separators • Daily: Vertical line at 00:00 each trading day • 4-Hour: Lines at 00:00, 04:00, 08:00, 12:00, 16:00, 20:00 • Hourly: Divisions at every hour for detailed timing • Customization • Individual color picker for each separator type • Choose line style: Solid, Dashed or Dotted • Enable or disable any separator or label independently • Smart limits to avoid clutter on extended history • Smart Behavior • Active only on intraday timeframes • Projects upcoming separators into the future for planning • Automatically caps historical plotting for performance • Lines extend across full visible price range Perfect for traders who need distinct session breaks, precise time-based zoning and an organized chart layout. Inputs • Show Day Labels (true/false) • Label Hour (0–23) • Day Label Color • Show Daily Separators (true/false) • Show 4H Separators (true/false) • Show 1H Separators (true/false) • Daily Line Color, Style • 4H Line Color, Style • Hourly Line Color, Style • Max Days to Display Enhance your intraday analysis with clean, customizable time markers. 👁
Indicatore Pine Script®
di maxretri
Reintegration OPR zone 9h30📝 Indicator Description (for TradingView): Name: Reintegration OPR Zone – 9:30 AM EST (UTC-4) Purpose: This indicator is designed for US indices like NAS100, US30, or SPX500. It helps identify potential false breakouts or retests by tracking when the price re-enters the Opening Price Range (OPR) after an initial breakout. 🔍 How it works: At 9:30 AM New York time (UTC-4), the script captures the high and low of the first 15-minute candle (which is key for the US session open). It then draws a horizontal box (rectangle) from the high to the low of that candle. The box extends horizontally for 7 hours (28 candles on a 15-minute chart). The script tracks if price: Breaks above or below the OPR zone Then re-enters the zone (a potential "fakeout" or "retest" signal) No label or text is displayed on the chart (you requested it to be hidden). 🕒 Timeframe: Designed for the 15-minute chart (M15) Assumes New York session open at 9:30 AM EST (UTC-4)
Indicatore Pine Script®
di meheyacine
Stochastic w/ Crossovers and Deadspace FilterThis is my extremely useful modification of the classic Stochastic indicator. It includes clear signals of crossovers and crossunders of the K/D lines. Additionally, I added a "deadspace" filter to remove plotting of signals in the middle of the range, which tend to be misleading. This can be incredibly useful to find entries and trends, especially when using 2 instances of this indicator at different lengths (such as one of 14,1,3 and another of 28,3,6). The deadspace filter works based on the middle line, so a value of 20 will not plot any crossovers between 30-70.
Indicatore Pine Script®
di fabledforman
3
CSCMultiTimeframeToolsLibrary "CSCMultiTimeframeTools" Calculates instant higher timeframe values for higher timeframe analysis with zero lag. getAdjustedLookback(current_tf_minutes, higher_tf_minutes, length)   Calculate adjusted lookback period for higher timeframe conversion.   Parameters:      current_tf_minutes (int) : Current chart timeframe in minutes (e.g., 5 for 5m).      higher_tf_minutes (int) : Target higher timeframe in minutes (e.g., 15 for 15m).      length (int) : Base length value (e.g., 14 for RSI/MFI).   Returns: Adjusted lookback period (length × multiplier). Purpose and Benefits of the TimeframeTools Library This library is designed to solve a critical pain point for traders who rely on higher timeframe (HTF) indicator values while analyzing lower timeframe (LTF) charts. Traditional methods require waiting for multiple candles to close—for example, to see a 1-hour RSI on a 5-minute chart, you’d need 12 closed candles (5m × 12 = 60m) before the value updates. This lag means missed opportunities, delayed signals, and inefficient decision-making. Why Traders Need This Whether you’re scalping (5M/15M) or swing trading (1H/4H), this library bridges the gap between timeframes, giving you HTF context in real time—so you can act faster, with confidence. How This Library Eliminates the Waiting Game By dynamically calculating the adjusted lookback period, the library allows: Real-time HTF values on LTF charts – No waiting for candle closes. Accurate conversions – A 14-period RSI on a 1-hour chart translates to 168 periods (14 × 12) on a 5-minute chart, ensuring mathematical precision. Flexible application – Works with common indicators like RSI, MFI, CCI, and moving averages (though confirmations should be done before publishing under your own secondary use). Key Advantages Over Manual Methods Speed: Instantly reflects HTF values without waiting for candle resolutions. Adaptability: Adjusts automatically if the user changes timeframes or lengths. Consistency: Removes human error in manual period calculations. Limitations to Note Not a magic bullet – While it solves the lag issue, traders should still: Validate signals with price action or additional confirmations. Be mindful of extreme lookback lengths (e.g., a 200-period daily SMA on a 1-minute chart requires 28,800 periods, which may strain performance).
Libreria Pine Script®
di CoffeeshopCrypto
2
Intrinsic Event (Multi DC OS)Overview This indicator implements an event-based approach to analyze price movements in the foreign exchange market, inspired by the intrinsic time framework introduced in Fractals and Intrinsic Time - A Challenge to Econometricians by U. A. Müller et al. (1995). It identifies significant price events using an intrinsic time perspective and supports multi-agent analysis to reflect the heterogeneous nature of financial markets. The script plots these events as lines and labels on the chart, offering a visual tool for traders to understand market dynamics at different scales. Key Features Intrinsic Events : The indicator detects directional change (DC) and overshoot (OS) events based on user-defined thresholds (delta), aligning with the paper’s concept of intrinsic time (Section 6). Intrinsic time redefines time based on market activity, expanding during volatile periods and contracting during inactive ones, rather than relying on a physical clock. Multi-Agent Analysis : Supports up to five agents, each with its own threshold and color settings, reflecting the heterogeneous market hypothesis (Section 5). This allows the indicator to capture the perspectives of market participants with different time horizons, such as short-term FX dealers and long-term central banks. How It Works Intrinsic Events Detection : The script identifies two types of events using intrinsic time principles: Directional Change (DC) : Triggered when the price reverses by the threshold (delta) against the current trend (e.g., a drop by delta in an uptrend signals a "Down DC"). Overshoot (OS) : Occurs when the price continues in the trend direction by the threshold (e.g., a rise by delta in an uptrend signals an "Up OS"). DC events are plotted as solid lines, and OS events as dashed lines, with labels like "Up DC" or "OS Down" for clarity. The label style adjusts based on the trend to ensure visibility. Multi-Agent Setup : Each agent operates independently with its own threshold, mimicking market participants with varying time horizons (Section 5). Smaller thresholds detect frequent, short-term events, while larger thresholds capture broader, long-term movements. Settings Each agent can be configured with: Enable Agent : Toggle the agent on or off. Threshold (%) : The percentage threshold (delta) for detecting DC and OS events (default values: 0.1%, 0.2%, 0.5%, 1%, 2% for agents 1–5). Up Mode Color : Color for lines and labels in up mode (DC events). Down Mode Color : Color for lines and labels in down mode (OS events). Usage Notes This indicator is designed for the foreign exchange market, leveraging its high liquidity, as noted in the paper (Section 1). Adjust the threshold values based on the instrument’s volatility—higher volatility leads to more intrinsic events (Section 4). It can be adapted to other markets where event-based analysis applies. Reference The methodology is based on: Fractals and Intrinsic Time - A Challenge to Econometricians by U. A. Müller, M. M. Dacorogna, R. D. Davé, O. V. Pictet, R. B. Olsen, and J. R. Ward (June 28, 1995). Olsen & Associates Preprint.
Indicatore Pine Script®
di Zygora
Vulkan Profit Overview The Vulkan Profit indicator is a trend-following tool that identifies potential entry and exit points by monitoring the relationship between short-term and long-term moving averages. It generates clear buy and sell signals when specific moving average conditions align, making it useful for traders looking to confirm trend changes across multiple timeframes. How It Works The indicator utilizes four different moving averages: Fast WMA (period 3) - A highly responsive weighted moving average Medium WMA (period 8) - A less sensitive weighted moving average Fast EMA (period 18) - A responsive exponential moving average Slow EMA (period 28) - A slower exponential moving average These moving averages are grouped into two categories: Short-term MAs: Fast WMA and Medium WMA Long-term MAs: Fast EMA and Slow EMA Signal Generation Logic The Vulkan Profit indicator generates signals based on the relative positions of these moving averages: Buy Signal (Green Triangle) A buy signal appears when the minimum value of the short-term MAs becomes greater than the maximum value of the long-term MAs. In other words, when both short-term MAs cross above both long-term MAs. Sell Signal (Red Triangle) A sell signal appears when the maximum value of the short-term MAs becomes less than the minimum value of the long-term MAs. In other words, when both short-term MAs cross below both long-term MAs. Visual Components Moving Averages - All four moving averages can be displayed or hidden Signal Arrows - Green triangles for buy signals, red triangles for sell signals Colored Line - A line that changes color based on the current market stance (green for bullish, red for bearish) Customization Options The indicator offers several customization settings: Toggle the visibility of moving averages Toggle the visibility of buy/sell signals Adjust the color, width, and position of the signal line Choose between different line styles (Line, Stepline, Histogram) Practical Trading Applications Trend Identification: The relative positioning of all moving averages helps identify the current market trend Entry/Exit Points: The buy and sell signals can be used as potential entry and exit points Trend Confirmation: The colored line provides ongoing confirmation of the trend direction Filter: Can be used in conjunction with other indicators as a trend filter Trading Strategy Suggestions Trend Following: Enter long positions on buy signals and exit on sell signals during trending markets Confirmation Tool: Use the signals to confirm trades identified by other indicators Timeframe Analysis: Apply the indicator across multiple timeframes for stronger confirmation Risk Management: Place stop-loss orders below recent swing lows for long positions and above recent swing highs for short positions Tips for Best Results The indicator performs best in trending markets and may generate false signals in ranging or highly volatile markets Consider the broader market context before taking trades based solely on these signals Use appropriate position sizing and risk management regardless of the indicator's signals The longer timeframes generally produce more reliable signals with fewer false positives The Vulkan Profit indicator combines the responsiveness of short-term averages with the stability of long-term averages to capture significant trend changes while filtering out minor price fluctuations.
Indicatore Pine Script®
di AlgoCollective
Optimized WPR Strategy with Filters (Debug)Identifying Market Trends: The 200-period EMA is used to determine the short-term trend of the market. When the price is above the 200-period EMA, it suggests a potential bullish market and an uptrend, and the strategy will only look for buying opportunities. When the price is below the 200-period EMA, it suggests a potential bearish market and a downtrend, and the strategy will only look for selling opportunities. When the 200-period EMA intersects with the price, it indicates that the market may be in a directionless consolidation phase. Identifying Potential Reversal Points: The strategy employs two Williams %R (WPR) indicators: one with a 9-period (fast WPR) and another with a 28-period (slow WPR). WPR is a momentum indicator used to identify overbought and oversold conditions in the market. Its value oscillates between -100 and 0, with values near -100 indicating oversold conditions and values near 0 indicating overbought conditions. In an uptrend (when the price is above the 200 EMA), the strategy seeks buying opportunities when both WPR indicators cross above -80 (the oversold zone) from below. This is considered a bullish signal, suggesting the market may be about to rebound. Sell signals are ignored at this time. In a downtrend (when the price is below the 200 EMA), the strategy seeks selling opportunities when both WPR indicators cross below -20 (the overbought zone) from above. This is considered a bearish signal, suggesting the market may be about to reverse downward. Buy signals are ignored at this time. Summary: In summary, this strategy first uses the 200-period EMA to determine the overall trend direction of the market. Then, within the confirmed trend direction, it utilizes the simultaneous crossing of the overbought or oversold zones by the dual WPR indicators to identify potential reversal points as entry signals for trading. The strategy emphasizes that trading signals are only valid when both WPR indicators meet the conditions.
Indicatore Pine Script®
di TsuiSum
Astro: Moon SizeThe Astro: Moon Size indicator, built using AstroLib , calculates the distance and visualizes the apparent size of the Moon based on astronomical positioning. This script is tailored for the 1D timeframe and provides insights into lunar perigees (closest approach) and apogees (farthest distance), making it useful for astrologically-informed trading strategies. New Astro Indicators Feature: By setting the Julian Date to X number of days in the future, and offsetting the plot by X number of bars accordingly, it is now possible to visualize future projections of TradingView indicators that reference the AstroLib . This feature has been long requested and is far overdue, so thank you to everyone who pushed for this feature release. Enjoy, time travelers from the future!! Key Features: Moon Size Calculation: Uses Julian Date (J2000) conversion and AstroLib functions to determine the Moon's apparent distance. Future Projection: Displays the Moon's distance from 28 up to 500 days ahead, with color gradients indicating proximity/size. Pivot Identification: Marks local maxima (apogees) and minima (perigees) with labeled date stamps for easy reference. Dynamic Labeling: Adapts label positioning and size based on the Moon's current trend and relative size. Usage Notes: ⚠️ Timeframe Restriction: For now, the script only functions on the 1D timeframe and will prompt an error otherwise. ⚠️ Asset Restriction: This script is meant to be loaded on charts for assets that trade 24/7, like BTCUSD historical index.
Indicatore Pine Script®
di BarefootJoey
4
G-VIDYA | QuantEdgeBIntroducing G-VIDYA by QuantEdgeB ____ 🔹 Overview The G-VIDYA | QuantEdgeB is a dynamic trend-following indicator that enhances market trend detection using Gaussian smoothing and an adaptive Variable Index Dynamic Average (VIDYA). It is designed to reduce noise, improve responsiveness, and adapt to volatility, making it a powerful tool for traders looking to capture long-term trends efficiently. By integrating ATR-based filtering, the indicator creates a dynamic support and resistance band around VIDYA, allowing for more accurate trend confirmations. Additionally, traders have the option to enable trade labels for clearer visual signals. This indicator is well-suited for medium to long-term trend traders, combining mathematical precision with market adaptability for robust trading strategies. _____ 🚀 Key Features 1. Gaussian Smoothing → Reduces market noise and smoothens price action. 2. VIDYA Adaptive Calculation → Adjusts dynamically based on market volatility. 3. ATR-Based Filtering → Creates a volatility-driven range around VIDYA. 4. Dynamic Trend Confirmation → Identifies bullish and bearish momentum shifts. 5. Trade Labels (Optional) → Can display Long/Cash labels on chart for better clarity. 6. Customizable Color Modes → Offers multiple visual themes for personalized experience. 7. Automated Alerts → Sends buy/sell alerts for crossover trend changes. _____ 📊 How It Works 1. Gaussian Smoothing is applied to the closing price to remove noise and improve signal clarity. 2. VIDYA Calculation dynamically adjusts to price movements, making it more reactive during high-volatility periods and stable in low-volatility environments. 3. ATR-Based Filtering establishes a dynamic range (Upper & Lower ATR Bands) around VIDYA: - If price breaks above the upper ATR band, it signals a potential long trend. - If price breaks below the lower ATR band, it signals a potential short trend. 4. The indicator assigns color-coded candles based on trend direction: - Bullish Trend → Blue/Green (Uptrend) - Bearish Trend → Red/Maroon (Downtrend) 5. Labels & Alerts (Optional) - Users can activate Long/Cash labels to mark buy/sell opportunities. - Built-in alerts trigger automatic notifications when trend direction changes. _____ 🎨 Visual Representation - VIDYA Line → A smooth, trend-following line that dynamically adjusts to market conditions. - Upper & Lower ATR Bands → Establishes a volatility-based corridor around VIDYA. - Bar Coloring → Candles change color according to the detected trend. - Long/Short Labels (Optional) → Displays trade entry/exit signals (can be enabled/disabled). - Alerts → Generates trade notifications based on trend reversals. ______ ⚙️ Default Settings - Gaussian Smoothing - Length: 4 - Sigma: 2.0 - VIDYA Settings - VIDYA Length: 46 - Standard Deviation Length: 28 - ATR Settings - ATR Length: 14 - ATR Multiplier: 1.3 ____ 💡 Who Should Use It? ✅ Trend Traders → Those who rely on medium-to-long-term trends for trading decisions. ✅ Swing Traders → Ideal for traders who want to capture trend reversals and ride momentum. ✅ Quantitative Analysts → Provides statistically driven smoothing and adaptive trend detection. ✅ Risk-Averse Traders → ATR filtering helps manage market volatility effectively. _____ Conclusion The G-VIDYA | QuantEdgeB is an advanced trend-following indicator that combines Gaussian smoothing, adaptive VIDYA filtering, and ATR-based dynamic trend analysis to deliver robust and reliable trade signals. ✅ Key Takeaways 📌 Adaptive & Dynamic: Adjusts to market conditions, making it effective for trend-following strategies. 📌 Noise Reduction: Gaussian smoothing helps filter out short-term fluctuations, improving signal clarity. 📌 Volatility Awareness: ATR-based filtering ensures better handling of market swings and trend reversals. By blending mathematical precision and quantitative market analysis, G-VIDYA | QuantEdgeB offers a powerful edge in trend trading strategies. 🔹 Disclaimer: Past performance is not indicative of future results. No trading strategy can guarantee success in financial markets. 🔹 Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance before live trading.
Indicatore Pine Script®
di QuantEdgeB
ValueAtTime█ OVERVIEW This library is a Pine Script® programming tool for accessing historical values in a time series using UNIX timestamps . Its data structure and functions index values by time, allowing scripts to retrieve past values based on absolute timestamps or relative time offsets instead of relying on bar index offsets. █ CONCEPTS UNIX timestamps In Pine Script®, a UNIX timestamp is an integer representing the number of milliseconds elapsed since January 1, 1970, at 00:00:00 UTC (the UNIX Epoch ). The timestamp is a unique, absolute representation of a specific point in time. Unlike a calendar date and time, a UNIX timestamp's meaning does not change relative to any time zone . This library's functions process series values and corresponding UNIX timestamps in pairs , offering a simplified way to identify values that occur at or near distinct points in time instead of on specific bars. Storing and retrieving time-value pairs This library's `Data` type defines the structure for collecting time and value information in pairs. Objects of the `Data` type contain the following two fields:  • `times` – An array of "int" UNIX timestamps for each recorded value.  • `values` – An array of "float" values for each saved timestamp. Each index in both arrays refers to a specific time-value pair. For instance, the `times` and `values` elements at index 0 represent the first saved timestamp and corresponding value. The library functions that maintain `Data` objects queue up to one time-value pair per bar into the object's arrays, where the saved timestamp represents the bar's opening time . Because the `times` array contains a distinct UNIX timestamp for each item in the `values` array, it serves as a custom mapping for retrieving saved values. All the library functions that return information from a `Data` object use this simple two-step process to identify a value based on time:  1. Perform a binary search on the `times` array to find the earliest saved timestamp closest to the specified time or offset and get the element's index.  2. Access the element from the `values` array at the retrieved index, returning the stored value corresponding to the found timestamp. Value search methods There are several techniques programmers can use to identify historical values from corresponding timestamps. This library's functions include three different search methods to locate and retrieve values based on absolute times or relative time offsets: Timestamp search   Find the value with the earliest saved timestamp closest to a specified timestamp. Millisecond offset search Find the value with the earliest saved timestamp closest to a specified number of milliseconds behind the current bar's opening time. This search method provides a time-based alternative to retrieving historical values at specific bar offsets. Period offset search Locate the value with the earliest saved timestamp closest to a defined period offset behind the current bar's opening time. The function calculates the span of the offset based on a period string . The "string" must contain one of the following unit tokens:  • "D" for days  • "W" for weeks  • "M" for months  • "Y" for years  • "YTD" for year-to-date, meaning the time elapsed since the beginning of the bar's opening year in the exchange time zone. The period string can include a multiplier prefix for all supported units except "YTD" (e.g., "2W" for two weeks). Note that the precise span covered by the "M", "Y", and "YTD" units varies across time. The "1M" period can cover 28, 29, 30, or 31 days, depending on the bar's opening month and year in the exchange time zone. The "1Y" period covers 365 or 366 days, depending on leap years. The "YTD" period's span changes with each new bar, because it always measures the time from the start of the current bar's opening year. █ CALCULATIONS AND USE This library's functions offer a flexible, structured approach to retrieving historical values at or near specific timestamps, millisecond offsets, or period offsets for different analytical needs. See below for explanations of the exported functions and how to use them. Retrieving single values The library includes three functions that retrieve a single stored value using timestamp, millisecond offset, or period offset search methods:  • `valueAtTime()` – Locates the saved value with the earliest timestamp closest to a specified timestamp.  • `valueAtTimeOffset()` – Finds the saved value with the earliest timestamp closest to the specified number of milliseconds behind the current bar's opening time.  • `valueAtPeriodOffset()` – Finds the saved value with the earliest timestamp closest to the period-based offset behind the current bar's opening time. Each function has two overloads for advanced and simple use cases. The first overload searches for a value in a user-specified `Data` object created by the `collectData()` function (see below). It returns a tuple containing the found value and the corresponding timestamp. The second overload maintains a `Data` object internally to store and retrieve values for a specified `source` series. This overload returns a tuple containing the historical `source` value, the corresponding timestamp, and the current bar's `source` value, making it helpful for comparing past and present values from requested contexts. Retrieving multiple values The library includes the following functions to retrieve values from multiple historical points in time, facilitating calculations and comparisons with values retrieved across several intervals:  • `getDataAtTimes()` – Locates a past `source` value for each item in a `timestamps` array. Each retrieved value's timestamp represents the earliest time closest to one of the specified timestamps.  • `getDataAtTimeOffsets()` – Finds a past `source` value for each item in a `timeOffsets` array. Each retrieved value's timestamp represents the earliest time closest to one of the specified millisecond offsets behind the current bar's opening time.  • `getDataAtPeriodOffsets()` – Finds a past value for each item in a `periods` array. Each retrieved value's timestamp represents the earliest time closest to one of the specified period offsets behind the current bar's opening time. Each function returns a tuple with arrays containing the found `source` values and their corresponding timestamps. In addition, the tuple includes the current `source` value and the symbol's description, which also makes these functions helpful for multi-interval comparisons using data from requested contexts. Processing period inputs When writing scripts that retrieve historical values based on several user-specified period offsets, the most concise approach is to create a single text input that allows users to list each period, then process the "string" list into an array for use in the `getDataAtPeriodOffsets()` function. This library includes a `getArrayFromString()` function to provide a simple way to process strings containing comma-separated lists of periods. The function splits the specified `str` by its commas and returns an array containing every non-empty item in the list with surrounding whitespaces removed. View the example code to see how we use this function to process the value of a text area input . Calculating period offset times Because the exact amount of time covered by a specified period offset can vary, it is often helpful to verify the resulting times when using the `valueAtPeriodOffset()` or `getDataAtPeriodOffsets()` functions to ensure the calculations work as intended for your use case. The library's `periodToTimestamp()` function calculates an offset timestamp from a given period and reference time. With this function, programmers can verify the time offsets in a period-based data search and use the calculated offset times in additional operations. For periods with "D" or "W" units, the function calculates the time offset based on the absolute number of milliseconds the period covers (e.g., `86400000` for "1D"). For periods with "M", "Y", or "YTD" units, the function calculates an offset time based on the reference time's calendar date in the exchange time zone. Collecting data All the `getDataAt*()` functions, and the second overloads of the `valueAt*()` functions, collect and maintain data internally, meaning scripts do not require a separate `Data` object when using them. However, the first overloads of the `valueAt*()` functions do not collect data, because they retrieve values from a user-specified `Data` object. For cases where a script requires a separate `Data` object for use with these overloads or other custom routines, this library exports the `collectData()` function. This function queues each bar's `source` value and opening timestamp into a `Data` object and returns the object's ID. This function is particularly useful when searching for values from a specific series more than once. For instance, instead of using multiple calls to the second overloads of `valueAt*()` functions with the same `source` argument, programmers can call `collectData()` to store each bar's `source` and opening timestamp, then use the returned `Data` object's ID in calls to the first `valueAt*()` overloads to reduce memory usage. The `collectData()` function and all the functions that collect data internally include two optional parameters for limiting the saved time-value pairs to a sliding window: `timeOffsetLimit` and `timeframeLimit`. When either has a non-na argument, the function restricts the collected data to the maximum number of recent bars covered by the specified millisecond- and timeframe-based intervals. NOTE : All calls to the functions that collect data for a `source` series can execute up to once per bar or realtime tick, because each stored value requires a unique corresponding timestamp. Therefore, scripts cannot call these functions iteratively within a loop . If a call to these functions executes more than once inside a loop's scope, it causes a runtime error. █ EXAMPLE CODE The example code at the end of the script demonstrates one possible use case for this library's functions. The code retrieves historical price data at user-specified period offsets, calculates price returns for each period from the retrieved data, and then populates a table with the results. The example code's process is as follows:   1. Input a list of periods – The user specifies a comma-separated list of period strings in the script's "Period list" input (e.g., "1W, 1M, 3M, 1Y, YTD"). Each item in the input list represents a period offset from the latest bar's opening time.   2. Process the period list – The example calls `getArrayFromString()` on the first bar to split the input list by its commas and construct an array of period strings.   3. Request historical data – The code uses a call to `getDataAtPeriodOffsets()` as the `expression` argument in a request.security() call to retrieve the closing prices of "1D" bars for each period included in the processed `periods` array.   4. Display information in a table – On the latest bar, the code uses the retrieved data to calculate price returns over each specified period, then populates a two-row table with the results. The cells for each return percentage are color-coded based on the magnitude and direction of the price change. The cells also include tooltips showing the compared daily bar's opening date in the exchange time zone. █ NOTES  • This library's architecture relies on a user-defined type (UDT) for its data storage format. UDTs are blueprints from which scripts create objects , i.e., composite structures with fields containing independent values or references of any supported type.  • The library functions search through a `Data` object's `times` array using the array.binary_search_leftmost() function, which is more efficient than looping through collected data to identify matching timestamps. Note that this built-in works only for arrays with elements sorted in ascending order .  • Each function that collects data from a `source` series updates the values and times stored in a local `Data` object's arrays. If a single call to these functions were to execute in a loop , it would store multiple values with an identical timestamp, which can cause erroneous search behavior. To prevent looped calls to these functions, the library uses the `checkCall()` helper function in their scopes. This function maintains a counter that increases by one each time it executes on a confirmed bar. If the count exceeds the total number of bars, indicating the call executes more than once in a loop, it raises a runtime error .  • Typically, when requesting higher-timeframe data with request.security() while using barmerge.lookahead_on as the `lookahead` argument, the `expression` argument should be offset with the history-referencing operator to prevent lookahead bias on historical bars. However, the call in this script's example code enables lookahead without offsetting the `expression` because the script displays results only on the last historical bar and all realtime bars, where there is no future data to leak into the past. This call ensures the displayed results use the latest data available from the context on realtime bars. Look first. Then leap. █ EXPORTED TYPES Data   A structure for storing successive timestamps and corresponding values from a dataset.   Fields:      times (array) : An "int" array containing a UNIX timestamp for each value in the `values` array.      values (array) : A "float" array containing values corresponding to the timestamps in the `times` array. █ EXPORTED FUNCTIONS getArrayFromString(str)   Splits a "string" into an array of substrings using the comma (`,`) as the delimiter. The function trims surrounding whitespace characters from each substring, and it excludes empty substrings from the result.   Parameters:      str (series string) : The "string" to split into an array based on its commas.   Returns: (array) An array of trimmed substrings from the specified `str`. periodToTimestamp(period, referenceTime)   Calculates a UNIX timestamp representing the point offset behind a reference time by the amount of time within the specified `period`.   Parameters:      period (series string) : The period string, which determines the time offset of the returned timestamp. The specified argument must contain a unit and an optional multiplier (e.g., "1Y", "3M", "2W", "YTD"). Supported units are: - "Y" for years. - "M" for months. - "W" for weeks. - "D" for days. - "YTD" (Year-to-date) for the span from the start of the `referenceTime` value's year in the exchange time zone. An argument with this unit cannot contain a multiplier.      referenceTime (series int) : The millisecond UNIX timestamp from which to calculate the offset time.   Returns: (int) A millisecond UNIX timestamp representing the offset time point behind the `referenceTime`. collectData(source, timeOffsetLimit, timeframeLimit)   Collects `source` and `time` data successively across bars. The function stores the information within a `Data` object for use in other exported functions/methods, such as `valueAtTimeOffset()` and `valueAtPeriodOffset()`. Any call to this function cannot execute more than once per bar or realtime tick.   Parameters:      source (series float) : The source series to collect. The function stores each value in the series with an associated timestamp representing its corresponding bar's opening time.      timeOffsetLimit (simple int) : Optional. A time offset (range) in milliseconds. If specified, the function limits the collected data to the maximum number of bars covered by the range, with a minimum of one bar. If the call includes a non-empty `timeframeLimit` value, the function limits the data using the largest number of bars covered by the two ranges. The default is `na`.      timeframeLimit (simple string) : Optional. A valid timeframe string. If specified and not empty, the function limits the collected data to the maximum number of bars covered by the timeframe, with a minimum of one bar. If the call includes a non-na `timeOffsetLimit` value, the function limits the data using the largest number of bars covered by the two ranges. The default is `na`.   Returns: (Data) A `Data` object containing collected `source` values and corresponding timestamps over the allowed time range. method valueAtTime(data, timestamp)   (Overload 1 of 2) Retrieves value and time data from a `Data` object's fields at the index of the earliest timestamp closest to the specified `timestamp`. Callable as a method or a function.   Parameters:      data (series Data) : The `Data` object containing the collected time and value data.      timestamp (series int) : The millisecond UNIX timestamp to search. The function returns data for the earliest saved timestamp that is closest to the value.   Returns: ( ) A tuple containing the following data from the `Data` object: - The stored value corresponding to the identified timestamp ("float"). - The earliest saved timestamp that is closest to the specified `timestamp` ("int"). valueAtTime(source, timestamp, timeOffsetLimit, timeframeLimit)   (Overload 2 of 2) Retrieves `source` and time information for the earliest bar whose opening timestamp is closest to the specified `timestamp`. Any call to this function cannot execute more than once per bar or realtime tick.   Parameters:      source (series float) : The source series to analyze. The function stores each value in the series with an associated timestamp representing its corresponding bar's opening time.      timestamp (series int) : The millisecond UNIX timestamp to search. The function returns data for the earliest bar whose timestamp is closest to the value.      timeOffsetLimit (simple int) : Optional. A time offset (range) in milliseconds. If specified, the function limits the collected data to the maximum number of bars covered by the range, with a minimum of one bar. If the call includes a non-empty `timeframeLimit` value, the function limits the data using the largest number of bars covered by the two ranges. The default is `na`.      timeframeLimit (simple string) : (simple string) Optional. A valid timeframe string. If specified and not empty, the function limits the collected data to the maximum number of bars covered by the timeframe, with a minimum of one bar. If the call includes a non-na `timeOffsetLimit` value, the function limits the data using the largest number of bars covered by the two ranges. The default is `na`.   Returns: ( ) A tuple containing the following data: - The `source` value corresponding to the identified timestamp ("float"). - The earliest bar's timestamp that is closest to the specified `timestamp` ("int"). - The current bar's `source` value ("float"). method valueAtTimeOffset(data, timeOffset)   (Overload 1 of 2) Retrieves value and time data from a `Data` object's fields at the index of the earliest saved timestamp closest to `timeOffset` milliseconds behind the current bar's opening time. Callable as a method or a function.   Parameters:      data (series Data) : The `Data` object containing the collected time and value data.      timeOffset (series int) : The millisecond offset behind the bar's opening time. The function returns data for the earliest saved timestamp that is closest to the calculated offset time.   Returns: ( ) A tuple containing the following data from the `Data` object: - The stored value corresponding to the identified timestamp ("float"). - The earliest saved timestamp that is closest to `timeOffset` milliseconds before the current bar's opening time ("int"). valueAtTimeOffset(source, timeOffset, timeOffsetLimit, timeframeLimit)   (Overload 2 of 2) Retrieves `source` and time information for the earliest bar whose opening timestamp is closest to `timeOffset` milliseconds behind the current bar's opening time. Any call to this function cannot execute more than once per bar or realtime tick.   Parameters:      source (series float) : The source series to analyze. The function stores each value in the series with an associated timestamp representing its corresponding bar's opening time.      timeOffset (series int) : The millisecond offset behind the bar's opening time. The function returns data for the earliest bar's timestamp that is closest to the calculated offset time.      timeOffsetLimit (simple int) : Optional. A time offset (range) in milliseconds. If specified, the function limits the collected data to the maximum number of bars covered by the range, with a minimum of one bar. If the call includes a non-empty `timeframeLimit` value, the function limits the data using the largest number of bars covered by the two ranges. The default is `na`.      timeframeLimit (simple string) : Optional. A valid timeframe string. If specified and not empty, the function limits the collected data to the maximum number of bars covered by the timeframe, with a minimum of one bar. If the call includes a non-na `timeOffsetLimit` value, the function limits the data using the largest number of bars covered by the two ranges. The default is `na`.   Returns: ( ) A tuple containing the following data: - The `source` value corresponding to the identified timestamp ("float"). - The earliest bar's timestamp that is closest to `timeOffset` milliseconds before the current bar's opening time ("int"). - The current bar's `source` value ("float"). method valueAtPeriodOffset(data, period)   (Overload 1 of 2) Retrieves value and time data from a `Data` object's fields at the index of the earliest timestamp closest to a calculated offset behind the current bar's opening time. The calculated offset represents the amount of time covered by the specified `period`. Callable as a method or a function.   Parameters:      data (series Data) : The `Data` object containing the collected time and value data.      period (series string) : The period string, which determines the calculated time offset. The specified argument must contain a unit and an optional multiplier (e.g., "1Y", "3M", "2W", "YTD"). Supported units are: - "Y" for years. - "M" for months. - "W" for weeks. - "D" for days. - "YTD" (Year-to-date) for the span from the start of the current bar's year in the exchange time zone. An argument with this unit cannot contain a multiplier.   Returns: ( ) A tuple containing the following data from the `Data` object: - The stored value corresponding to the identified timestamp ("float"). - The earliest saved timestamp that is closest to the calculated offset behind the bar's opening time ("int"). valueAtPeriodOffset(source, period, timeOffsetLimit, timeframeLimit)   (Overload 2 of 2) Retrieves `source` and time information for the earliest bar whose opening timestamp is closest to a calculated offset behind the current bar's opening time. The calculated offset represents the amount of time covered by the specified `period`. Any call to this function cannot execute more than once per bar or realtime tick.   Parameters:      source (series float) : The source series to analyze. The function stores each value in the series with an associated timestamp representing its corresponding bar's opening time.      period (series string) : The period string, which determines the calculated time offset. The specified argument must contain a unit and an optional multiplier (e.g., "1Y", "3M", "2W", "YTD"). Supported units are: - "Y" for years. - "M" for months. - "W" for weeks. - "D" for days. - "YTD" (Year-to-date) for the span from the start of the current bar's year in the exchange time zone. An argument with this unit cannot contain a multiplier.      timeOffsetLimit (simple int) : Optional. A time offset (range) in milliseconds. If specified, the function limits the collected data to the maximum number of bars covered by the range, with a minimum of one bar. If the call includes a non-empty `timeframeLimit` value, the function limits the data using the largest number of bars covered by the two ranges. The default is `na`.      timeframeLimit (simple string) : Optional. A valid timeframe string. If specified and not empty, the function limits the collected data to the maximum number of bars covered by the timeframe, with a minimum of one bar. If the call includes a non-na `timeOffsetLimit` value, the function limits the data using the largest number of bars covered by the two ranges. The default is `na`.   Returns: ( ) A tuple containing the following data: - The `source` value corresponding to the identified timestamp ("float"). - The earliest bar's timestamp that is closest to the calculated offset behind the current bar's opening time ("int"). - The current bar's `source` value ("float"). getDataAtTimes(timestamps, source, timeOffsetLimit, timeframeLimit)   Retrieves `source` and time information for each bar whose opening timestamp is the earliest one closest to one of the UNIX timestamps specified in the `timestamps` array. Any call to this function cannot execute more than once per bar or realtime tick.   Parameters:      timestamps (array) : An array of "int" values representing UNIX timestamps. The function retrieves `source` and time data for each element in this array.      source (series float) : The source series to analyze. The function stores each value in the series with an associated timestamp representing its corresponding bar's opening time.      timeOffsetLimit (simple int) : Optional. A time offset (range) in milliseconds. If specified, the function limits the collected data to the maximum number of bars covered by the range, with a minimum of one bar. If the call includes a non-empty `timeframeLimit` value, the function limits the data using the largest number of bars covered by the two ranges. The default is `na`.      timeframeLimit (simple string) : Optional. A valid timeframe string. If specified and not empty, the function limits the collected data to the maximum number of bars covered by the timeframe, with a minimum of one bar. If the call includes a non-na `timeOffsetLimit` value, the function limits the data using the largest number of bars covered by the two ranges. The default is `na`.   Returns: ( ) A tuple of the following data: - An array containing a `source` value for each identified timestamp (array). - An array containing an identified timestamp for each item in the `timestamps` array (array). - The current bar's `source` value ("float"). - The symbol's description from `syminfo.description` ("string"). getDataAtTimeOffsets(timeOffsets, source, timeOffsetLimit, timeframeLimit)   Retrieves `source` and time information for each bar whose opening timestamp is the earliest one closest to one of the time offsets specified in the `timeOffsets` array. Each offset in the array represents the absolute number of milliseconds behind the current bar's opening time. Any call to this function cannot execute more than once per bar or realtime tick.   Parameters:      timeOffsets (array) : An array of "int" values representing the millisecond time offsets used in the search. The function retrieves `source` and time data for each element in this array. For example, the array ` ` specifies that the function returns data for the timestamps closest to one day and one week behind the current bar's opening time.      source (float) : (series float) The source series to analyze. The function stores each value in the series with an associated timestamp representing its corresponding bar's opening time.      timeOffsetLimit (simple int) : Optional. A time offset (range) in milliseconds. If specified, the function limits the collected data to the maximum number of bars covered by the range, with a minimum of one bar. If the call includes a non-empty `timeframeLimit` value, the function limits the data using the largest number of bars covered by the two ranges. The default is `na`.      timeframeLimit (simple string) : Optional. A valid timeframe string. If specified and not empty, the function limits the collected data to the maximum number of bars covered by the timeframe, with a minimum of one bar. If the call includes a non-na `timeOffsetLimit` value, the function limits the data using the largest number of bars covered by the two ranges. The default is `na`.   Returns: ( ) A tuple of the following data: - An array containing a `source` value for each identified timestamp (array). - An array containing an identified timestamp for each offset specified in the `timeOffsets` array (array). - The current bar's `source` value ("float"). - The symbol's description from `syminfo.description` ("string"). getDataAtPeriodOffsets(periods, source, timeOffsetLimit, timeframeLimit)   Retrieves `source` and time information for each bar whose opening timestamp is the earliest one closest to a calculated offset behind the current bar's opening time. Each calculated offset represents the amount of time covered by a period specified in the `periods` array. Any call to this function cannot execute more than once per bar or realtime tick.   Parameters:      periods (array) : An array of period strings, which determines the time offsets used in the search. The function retrieves `source` and time data for each element in this array. For example, the array ` ` specifies that the function returns data for the timestamps closest to one day, week, and month behind the current bar's opening time. Each "string" in the array must contain a unit and an optional multiplier. Supported units are: - "Y" for years. - "M" for months. - "W" for weeks. - "D" for days. - "YTD" (Year-to-date) for the span from the start of the current bar's year in the exchange time zone. An argument with this unit cannot contain a multiplier.      source (float) : (series float) The source series to analyze. The function stores each value in the series with an associated timestamp representing its corresponding bar's opening time.      timeOffsetLimit (simple int) : Optional. A time offset (range) in milliseconds. If specified, the function limits the collected data to the maximum number of bars covered by the range, with a minimum of one bar. If the call includes a non-empty `timeframeLimit` value, the function limits the data using the largest number of bars covered by the two ranges. The default is `na`.      timeframeLimit (simple string) : Optional. A valid timeframe string. If specified and not empty, the function limits the collected data to the maximum number of bars covered by the timeframe, with a minimum of one bar. If the call includes a non-na `timeOffsetLimit` value, the function limits the data using the largest number of bars covered by the two ranges. The default is `na`.   Returns: ( ) A tuple of the following data: - An array containing a `source` value for each identified timestamp (array). - An array containing an identified timestamp for each period specified in the `periods` array (array). - The current bar's `source` value ("float"). - The symbol's description from `syminfo.description` ("string").
Libreria Pine Script®
di TradingView
1
RSI Divergence[UgurTash] – Real-Time📈 RSI Divergence – Real-Time, Adaptive, and Intelligent RSI Divergence Detection 🚀 What Does This Indicator Do? RSI Divergence is a real-time divergence detection tool that helps traders identify bullish and bearish divergences between price and the Relative Strength Index (RSI). Unlike traditional RSI-based indicators, this script offers: ✅ Real-time detection – No need to wait for bar closes or repainting. ✅ Dynamic time-frame adaptation – The script automatically adjusts RSI settings based on the selected chart time frame. ✅ Multi-layered divergence analysis – Supports short-term, medium-term, and long-term divergence detection with an optional all-term mode that dynamically selects the best configuration. 🛠 How Does It Work? Pivot-Based Divergence Detection: The script analyzes pivot points on both price and RSI to determine valid divergences. Bullish divergence occurs when price forms a lower low but RSI trends higher, indicating potential upward momentum. Bearish divergence occurs when price forms a higher high but RSI trends lower, signaling possible weakness. Adaptive RSI Calculation: The RSI length is dynamically adjusted based on the chosen time frame: Short-Term: RSI (7) for 1-5 min charts. Medium-Term: RSI (14) for 15-60 min charts. Long-Term: RSI (28) for 4H+ charts. In All-Term Mode, the script automatically determines the best RSI length based on the active chart timeframe. Smart Visualization & Alerts: Bullish divergences are marked with green lines & labels. Bearish divergences are highlighted in red. Users can customize symbol size, divergence labels, and colors. Instant alerts notify traders as soon as a divergence is detected. 🎯 How to Use This Indicator? 📌 For Trend Reversals: Look for bullish divergences at key support levels and bearish divergences at resistance zones. 📌 For Trend Continuation: Combine divergence signals with moving averages, volume analysis, or price action strategies to confirm trades. 📌 For Scalping & Swing Trading: Adjust the time-frame settings to match your trading style. 🏆 What Makes This Indicator Original? 🔹 Unlike standard RSI divergence indicators, this script features real-time analysis with no repainting, allowing for instant trading decisions. 🔹 The time-frame adaptive RSI makes it dynamic and suitable for any market condition. 🔹 The multi-term divergence detection offers flexibility, giving traders a precise view of both short-term & long-term market structure. ⚠ Note: No indicator guarantees 100% accuracy. Always use additional confirmations and sound risk management strategies. If you find this tool useful, don’t forget to support & share! 🚀
Indicatore Pine Script®
di ugurtash
Crypto Scanner v4This guide explains a version 6 Pine Script that scans a user-provided list of cryptocurrency tokens to identify high probability tradable opportunities using several technical indicators. The script combines trend, momentum, and volume-based analyses to generate potential buying or selling signals, and it displays the results in a neatly formatted table with alerts for trading setups. Below is a detailed walkthrough of the script’s design, how traders can interpret its outputs, and recommendations for optimizing indicator inputs across different timeframes. ## Overview and Key Components The script is designed to help traders assess multiple tokens by calculating several indicators for each one. The key components include: - **Input Settings:** - A comma-separated list of symbols to scan. - Adjustable parameters for technical indicators such as ADX, RSI, MFI, and a custom Wave Trend indicator. - Options to enable alerts and set update frequencies. - **Indicator Calculations:** - **ADX (Average Directional Index):** Measures trend strength. A value above the provided threshold indicates a strong trend, which is essential for validating momentum before entering a trade. - **RSI (Relative Strength Index):** Helps determine overbought or oversold conditions. When the RSI is below the oversold level, it may present a buying opportunity, while an overbought condition (not explicitly part of this setup) could suggest selling. - **MFI (Money Flow Index):** Similar in concept to RSI but incorporates volume, thus assessing buying and selling pressure. Values below the designated oversold threshold indicate potential undervaluation. - **Wave Trend:** A custom indicator that calculates two components (WT1 and WT2); a crossover where WT1 moves from below to above WT2 (particularly near oversold levels) may signal a reversal and a potential entry point. - **Scanning and Trading Zone:** - The script identifies a *bullish setup* when the following conditions are met for a token: - ADX exceeds the threshold (strong trend). - Both RSI and MFI are below their oversold levels (indicating potential buying opportunities). - A Wave Trend crossover confirms near-term reversal dynamics. - A *trading zone* condition is also defined by specific ranges for ADX, RSI, MFI, and a limited difference between WT1 and WT2. This zone suggests that the token might be in a consolidation phase where even small moves may be significant. - **Alerts and Table Reporting:** - A table is generated, with each row corresponding to a token. The table contains columns for the symbol, ADX, RSI, MFI, WT1, WT2, and the trading zone status. - Visual cues—such as different background colors—highlight tokens with a bullish setup or that are within the trading zone. - Alerts are issued based on the detection of a bullish setup or entry into a trading zone. These alerts are limited per bar to avoid flooding the trader with notifications. ## How to Interpret the Indicator Outputs Traders should use the indicator values as guidance, verifying them against their own analysis before making any trading decision. Here’s how to assess each output: - **ADX:** - **High values (above threshold):** Indicate strong trends. If other indicators confirm an oversold condition, a trader may consider a long position for a corrective reversal. - **Low values:** Suggest that the market is not trending strongly, and caution should be taken when considering entry. - **RSI and MFI:** - **Below oversold levels:** These conditions are traditionally seen as signals that an asset is undervalued, potentially triggering a bounce. - **Above typical resistance levels (not explicitly used here):** Would normally caution a trader against entering a long position. - **Wave Trend (WT1 and WT2):** - A crossover where WT1 moves upward above WT2 in an oversold environment can signal the beginning of a recovery or reversal, thereby reinforcing buy signals. - **Trading Zone:** - Being “in zone” means that the asset’s current values for ADX, RSI, MFI, and the closeness of the Wave Trend lines indicate a period of consolidation. This scenario might be suitable for both short-term scalping or as an early exit indicator, depending on further market analysis. ## Timeframe Optimization Input Table Traders can optimize indicator inputs depending on the timeframe they use. The following table provides a set of recommended input values for various timeframes. These values are suggestions and should be adjusted based on market conditions and individual trading styles. Timeframe ADX RSI MFI ADX RSI MFI WT Channel WT Average 5-min 10 10 10 20 30 20 7 15 15-min 12 12 12 22 30 20 9 18 1-hour 14 14 14 25 30 20 10 21 4-hour 16 16 16 27 30 20 12 24 1-day 18 18 18 30 30 20 14 28 Adjust these parameters directly in the script’s input settings to match the selected timeframe. For shorter timeframes (e.g., 5-min or 15-min), the shorter lengths help filter high-frequency noise. For longer timeframes (e.g., 1-day), longer input values may reduce false signals and capture more significant trends. ## Best Practices and Usage Tips - **Token Limit:** - Limit the number of tokens scanned to 10 per query line. If you need to scan more tokens, initiate a new query line. This helps manage screen real estate and ensures the table remains legible. - **Confirming Signals:** - Use this script as a starting point for identifying high potential trades. Each indicator’s output should be used to confirm your trading decision. Always cross-reference with additional technical analysis tools or market context. - **Regular Review:** - Since the script updates the table every few bars (as defined by the update frequency), review the table and alerts regularly. Market conditions change rapidly, so timely decisions are crucial. ## Conclusion This Pine Script provides a comprehensive approach for scanning multiple cryptocurrencies using a combination of trend strength (ADX), momentum (RSI and MFI), and reversal signals (Wave Trend). By using the provided recommendation table for different timeframes and limiting the tokens to 20 per query line (with a maximum of four query lines), traders can streamline their scanning process and more effectively identify high probability tradable tokens. Ultimately, the outputs should be critically evaluated and combined with additional market research before executing any trades.
Indicatore Pine Script®
di Crypt0Curious
WaridTR15 Dakika ve Üzeri Periyotlar İçin Önerilen Ayarlar: EMA Uzunlukları: Kısa EMA: 9 yerine 12 veya 14 kullanılabilir. Uzun EMA: 21 yerine 26 veya 50 kullanılabilir. Golden Cross için 50 EMA ve 200 EMA zaten uzun vadeli trendleri yakalar, bu nedenle değiştirmeye gerek yok. RSI Uzunluğu: RSI uzunluğu 14 yerine 21 veya 28 yapılabilir. Bu, daha uzun vadeli aşırı alım/aşırı satım bölgelerini daha doğru tespit eder. Volume Filtresi: Volume ortalaması için 20 periyot yerine 50 veya 100 periyot kullanılabilir. Bu, daha uzun vadeli hacim eğilimlerini yakalar. Ichimoku Parametreleri: Ichimoku, varsayılan olarak 9-26-52 periyotlarıyla çalışır. Bu, zaten uzun vadeli trendleri yakalamak için uygundur. Ancak, daha uzun periyotlar için: Tenkan-Sen: 9 yerine 14. Kijun-Sen: 26 yerine 52. Senkou Span B: 52 yerine 104.
Indicatore Pine Script®
di waridtr
Dynamic Volatility Heatmap (ATR)How the Script Works Dynamic Thresholds: atrLow and atrHigh are calculated as percentiles (20% and 80% by default) of ATR values over the last double the ATR period (28 days if ATR is 14). This creates thresholds that adapt to recent market conditions. Background Heatmap: Green: ATR is below the low threshold, indicating calm markets (options are cheap). Red: ATR is above the high threshold, signaling elevated volatility (options are expensive). Yellow: ATR is within the normal range, showing neutral market conditions. Overlay Lines: ]Dynamic lines for atrLow and atrHigh help visualize thresholds on the chart. Interpretation for Trading Green Zone (Low ATR): Interpretation: The market is calm, and options are likely underpriced. Trade Setup: Favor buying options (e.g., long straddles or long calls/puts) to profit from potential volatility increases. Red Zone (High ATR): Interpretation: The market is volatile, and options are likely overpriced. Trade Setup: Favor selling options (e.g., credit spreads or iron condors) to benefit from volatility decay. Yellow Zone (Neutral ATR): Interpretation: Volatility is within typical levels, offering no strong signal. Trade Setup: Combine with other indicators, such as gamma levels or Bollinger Bands, for confirmation. 5. Enhancing with Other Indicators Combine with Bollinger Bands: Overlay Bollinger Bands to identify price extremes and align them with volatility heatmap signals.
Indicatore Pine Script®
di MasterManifestor1105
[3Commas] Signal BuilderSignal Builder is a tool designed to help traders create custom buy and sell signals by combining multiple technical indicators. Its flexibility allows traders to set conditions based on their specific strategy, whether they’re into scalping, swing trading, or long-term investing. Additionally, its integration with 3Commas bots makes it a powerful choice for those looking to automate their trades, though it’s also ideal for traders who prefer receiving alerts and making manual decisions. 🔵 How does Signal Builder work? Signal Builder allows users to define custom conditions using popular technical indicators, which, when met, generate clear buy or sell signals. These signals can be used to trigger TradingView alerts, ensuring that you never miss a market opportunity. Additionally, all conditions are evaluated using "AND" logic, meaning signals are only activated when all user-defined conditions are met. This increases precision and helps avoid false signals. 🔵 Available indicators and recommended settings: Signal Builder provides access to a wide range of technical indicators, each customizable to popular settings that maximize effectiveness: RSI (Relative Strength Index): An oscillator that measures the relative strength of price over a specific period. Traders typically configure it with 14 periods, using levels of 30 (oversold) and 70 (overbought) to identify potential reversals. MACD (Moving Average Convergence Divergence): A key indicator tracking the crossover between two moving averages. Common settings include 12 and 26 periods for the moving averages, with a 9-period signal line to detect trend changes. Ultimate Oscillator: Combines three different time frames to offer a comprehensive view of buying and selling pressure. Popular settings are 7, 14, and 28 periods. Bollinger Bands %B: Provides insight into where the price is relative to its upper and lower bands. Standard settings include a 20-period moving average and a standard deviation of 2. ADX (Average Directional Index): Measures the strength of a trend. Values above 25 typically indicate a strong trend, while values below suggest weak or sideways movement. Stochastic Oscillator: A momentum indicator comparing the closing price to its range over a defined period. Popular configurations include 14 periods for %K and 3 for %D smoothing. Parabolic SAR: Ideal for identifying trend reversals and entry/exit points. Commonly configured with a 0.02 step and a 0.2 maximum. Money Flow Index (MFI): Similar to RSI but incorporates volume into the calculation. Standard settings use 14 periods, with levels of 20 and 80 as oversold and overbought thresholds. Commodity Channel Index (CCI): Measures the deviation of price from its average. Traders often use a 20-period setting with levels of +100 and -100 to identify extreme overbought or oversold conditions. Heikin Ashi Candles: These candles smooth out price fluctuations to show clearer trends. Commonly used in trend-following strategies to filter market noise. 🔵 How to use Signal Builder: Configure indicators: Select the indicators that best fit your strategy and adjust their settings as needed. You can combine multiple indicators to define precise entry and exit conditions. Define custom signals: Create buy or sell conditions that trigger when your selected indicators meet the criteria you’ve set. For example, configure a buy signal when RSI crosses above 30 and MACD confirms with a bullish crossover. TradingView alerts: Set up alerts in TradingView to receive real-time notifications when the conditions you’ve defined are met, allowing you to react quickly to market opportunities without constantly monitoring charts. Monitor with the panel: Signal Builder includes a visual panel that shows active conditions for each indicator in real time, helping you keep track of signals without manually checking each indicator. 🔵 3Commas integration: In addition to being a valuable tool for any trader, Signal Builder is optimized to work seamlessly with 3Commas bots through Webhooks. This allows you to automate your trades based on the signals you’ve configured, ensuring that no opportunity is missed when your defined conditions are met. If you prefer automation, Signal Builder can send buy or sell signals to your 3Commas bots, enhancing your trading process and helping you manage multiple trades more efficiently. 🔵 Example of use: Imagine you trade in volatile markets and want to trigger a sell signal when: Stochastic Oscillator indicates overbought conditions with the %K value crossing below 80. Bollinger Bands %B shows the price has surpassed the upper band, suggesting a potential reversal. ADX is below 20, indicating that the trend is weak and could be about to change. With Signal Builder , you can configure these conditions to trigger a sell signal only when all are met simultaneously. Then, you can set up a TradingView alert to notify you as soon as the signal is activated, giving you the opportunity to react quickly and adjust your strategy accordingly. 👨🏻‍💻💭 If this tool helps your trading strategy, don’t forget to give it a boost! Feel free to share in the comments how you're using it or if you have any questions. _________________________________________________________________ The information and publications within the 3Commas TradingView account are not meant to be and do not constitute financial, investment, trading, or other types of advice or recommendations supplied or endorsed by 3Commas and any of the parties acting on behalf of 3Commas, including its employees, contractors, ambassadors, etc.
Indicatore Pine Script®
di 3Commas
12
Multi-Length RSI **Multi-Length RSI Indicator** This script creates a custom Relative Strength Index (RSI) indicator with the ability to plot three different RSI lengths on the same chart, allowing traders to analyze momentum across various timeframes simultaneously. The script also includes features to enhance visual clarity and usability. **Key Features:** 1. **Customizable RSI Lengths:** - The script allows you to input and customize three different RSI lengths (7, 14, and 28 by default) via user inputs. This flexibility enables you to track short-term, medium-term, and long-term momentum in the market. 2. **Dynamic Colour Coding:** - The RSI lines are color-coded based on their current value: - **Above 70 (Overbought)**: The line turns red. - **Below 30 (Oversold)**: The line turns green. - **Between 30 and 70**: The line retains its user-defined colour (blue, yellow, orange by default). - This dynamic colouring helps to quickly identify overbought and oversold conditions. 3. **Adjustable Line Widths and Colours:** - Users can customize the colour and thickness of each RSI line, allowing for a personalized visual experience that fits different trading strategies. 4. **Overbought, Oversold, and Midline Levels:** - The script includes static horizontal lines at the 70 (Overbought) and 30 (Oversold) levels, with a red and green colour, respectively. - A midline at the 50 level is also included in gray and dashed, helping to visualize the neutral zone. 5. **Dynamic RSI Value Labels:** - The current values of each RSI line are displayed directly on the chart as labels at the most recent bar, with colours matching their corresponding lines. This feature provides an immediate reference to the exact RSI values without the need to hover or look at the data window. 6. **Alerts for Crosses:** - The script includes built-in alert conditions for when any of the RSI values cross above the overbought level (70) or below the oversold level (30). These alerts can be configured to notify you in real-time when significant momentum shifts occur. **How to Use:** 1. **Customization**: - Input your preferred RSI lengths, colours, and line widths through the script’s settings menu. 2. **Visual Analysis**: - The indicator plots all three RSI values on a separate pane below the price chart. Use the color-coded lines and levels to quickly identify overbought, oversold, and neutral conditions across multiple timeframes. 3. **Set Alerts**: - You can configure alerts based on the built-in alert conditions to get notified when the RSI crosses critical levels. **Ideal For:** - **Traders looking to analyze momentum across multiple timeframes**: The ability to view short-term, medium-term, and long-term RSIs simultaneously offers a comprehensive view of market strength. - **Those who prefer visual clarity**: The dynamic colouring, clear labels, and customizable settings make it easy to interpret RSI data at a glance. - **Traders who rely on alerts**: The built-in alert system allows for proactive trading based on significant RSI level crossings. --- This script is a powerful tool for any trader looking to leverage RSI analysis across multiple timeframes, offering both customization and clarity in a single indicator.
Indicatore Pine Script®
di MonosImpossibleHead
MACD with SAR Indicator [CHE] MACD with SAR Indicator Introduction "The whole is greater than the sum of its parts. " The "MACD with SAR Indicator" is an innovative technical analysis tool that combines the strengths of the Moving Average Convergence Divergence (MACD) indicator with the Parabolic Stop and Reverse (SAR) indicator. This indicator provides traders with an enhanced method to detect trend changes and determine optimal entry and exit points in the market by using the SAR based on the MACD line to better identify reversal points. The combination generates clear trend reversal signals, which are visually represented through long (L) and short (S) signals on the chart. Originality and Usefulness This indicator differs from traditional MACD or SAR indicators by combining the trend-following calculations of the SAR with the trend strength and momentum calculations of the MACD. This enables a more precise identification of trend changes and provides clear buy and sell signals, which is particularly useful for manual traders. Key Features and Functionality 1. Combination of MACD and SAR - Why this Combination?: The MACD is known for its ability to measure the strength and direction of a trend, while the SAR is specifically designed to identify reversal points. By combining these two indicators, traders can better understand both the trend strength and potential turning points in the market. - How Components Work Together: The MACD measures the difference between fast and slow moving averages, indicating market momentum. The SAR follows the MACD line instead of the price and marks potential reversal points more accurately. When the MACD signals a new trend and the SAR confirms it, the indicator provides reliable trading opportunities. 2. Adjustable Parameters - MACD Settings: Users can adjust the lengths of the fast and slow moving averages (default: 28 and 38 periods) and the signal smoothing (default: 9 periods) to tailor the indicator to different market conditions. - SAR Settings: Users can adjust the start value (default: 0.01), increment (default: 0.01), and maximum value (default: 0.18) of the SAR to control sensitivity and responsiveness. 3. Visual Representation and Signals - Color-Coded Histograms: The histogram shows the difference between the MACD and signal line and is color-coded to highlight the direction of the trend. - Signal Labels: The indicator automatically adds "L" (Long) and "S" (Short) labels on the chart to show the current positions to traders. 4. Alert Settings - Custom Alerts: Alerts can be set to notify traders when the MACD and SAR experience significant state changes, such as when the histogram switches from rising to falling or vice versa. 5. Toggle Display - Display Mode: Users can toggle the display of the MACD_SAR oscillator and MACD to focus on the information most relevant to their trading strategy. Application and Benefits - Versatility: This indicator can be used in various market conditions and for different trading strategies, including trend following and reversal trading. - Ease of Interpretation: The clear visual representation and automatic signals make it easier for traders to identify trading opportunities and track trends. - Customizability: With numerous settings options, the indicator can be tailored to individual preferences and specific market conditions. Conclusion The "MACD with SAR Indicator" is a valuable tool for traders seeking precise and reliable signals to identify market trends and make profitable trading decisions. With its extensive customization options, powerful features, and the ability to toggle displays, this indicator provides excellent support for technical analysis. By emphasizing the synergy between the MACD and SAR indicators, highlighting the default settings, and clarifying that the SAR is based on the MACD line and generates clear trend reversal signals through long and short labels, this revised description should help users understand the functionalities and advantages of your indicator while meeting TradingView's publication requirements. Best regards Chervolino
Indicatore Pine Script®
di chervolino
3
Money Flow Index Crossover IndicatorThe "Money Flow Index Crossover Indicator" is a specialized technical analysis tool designed to assist traders by providing a clear visualization of potential buy and sell signals based on the Money Flow Index (MFI) and its smoothed moving average (SMA). This indicator delineates overbought and oversold zones, offering valuable insights into market dynamics. It operates as an oscillator on a separate pane, helping traders identify bullish and bearish market conditions with greater precision. By incorporating k-Nearest Neighbor (KNN) machine learning techniques, this indicator enhances the reliability and accuracy of the signals provided. Originality and Usefulness: This script is not just a simple mashup of existing indicators but integrates multiple components to create a unique and comprehensive analysis tool. The combined information from the MFI, its smoothed moving average, and the KNN machine learning techniques influence the form and accuracy of the Money Flow Index Average line and the Smoothed Money Flow Index line giving a visually helpful representation of overbought and oversold conditions. These lines are displayed in an oscillator style crossover, allowing users to visualize potential buy and sell zones for setting up potential signals. The user can adjust various settings of these tools behind the code to fine-tune the behavior and sensitivity of these lines. This integration provides a more robust and insightful trading tool that can adapt to different market conditions and trading styles. How It Works: Inputs: MFI Settings: Show Signals: Allows users to toggle the display of MFI and SMA crossing signals, which are critical for identifying potential market reversals. Plot Amount: Determines the number of plots in the heat map, ranging from 2 to 28, enabling customization based on user preference. Source: Defines the data source for MFI calculations, typically set to OHLC4 for a balanced view of price movements. Smooth Initial MFI Length: Specifies the smoothing length for the initial MFI calculations to reduce noise and enhance signal clarity. MFI SMA Length: Sets the length for the SMA used to smooth the MFI average, providing a more stable reference line. Machine Learning Settings: Use KInSource: Option to average MFI data by adding a lookback to the source, improving the accuracy of historical comparisons. KNN Distance Requirement: Defines the distance calculation method for KNN (Max, Min, Both) to refine the data filtering process. Machine Learning Length: Specifies the amount of machine learning data stored for smoothing results, balancing between responsiveness and stability. KNN Length: Sets the number of KNN used to calculate the allowable distance range, enhancing the precision of the machine learning model. Fast and Slow Lengths: Defines the lengths for fast and slow MFI calculations, allowing the indicator to capture different market dynamics. Smoothing Length: Determines the length at which MFI calculations start for a more smoothed result, reducing false signals. Variables and Functions: KNN Function: Filters machine learning data to calculate valid distances based on defined criteria, ensuring more accurate MFI averages. MFI Calculations: Computes both fast and slow MFI values, applies smoothing, and stores them for KNN processing to refine signal generation. MFI KNN Calculation: Uses the KNN function to calculate the machine learning average of MFI values, enhancing signal reliability. MFI Average and SMA: Calculates the average and smoothed MFI values, which are crucial for determining crossover signals. Calculations: MFI Values: Calculates current fast and slow MFI values and applies smoothing to reduce market noise. Storage Arrays: Stores MFI data in arrays for KNN processing, enabling historical comparison and pattern recognition. KNN Processing: Computes the machine learning average of MFI values using the KNN function, improving the robustness of signals. MFI Average: Scales the MFI average to fit the heat map and calculates the smoothed SMA, providing a clear visual representation of trends. Crossover Signals: Identifies bullish (MFI crossing above SMA) and bearish (MFI crossing below SMA) signals, which are key for making trading decisions. Plots and Visuals: MFI Average and SMA Lines: Plots the MFI average and smoothed SMA on the chart, allowing traders to easily visualize market trends and potential reversals. Zones: Defines and plots overbought, neutral, and oversold zones for easy visualization. The recommended settings for these zones are: Overbought Zone: Level set to approximately 24.6, indicating a potential market top. Neutral Zone: Level set to 14, representing a balanced market condition. Oversold Zone: Level set to 5.4, signaling a potential market bottom. Crossover Marks: Plots circles on the chart to indicate bullish and bearish crossover signals, making it easier to spot entry and exit points. Visual Alerts: Bullish and Bearish Alerts: one can see overbought and oversold conditions and up alert conditions for bullish and bearish MFI crossover signals, enabling traders to have access to visual cues when these events are on trajectory to occur and, if they occur, act promptly with the visual representation of its zones. Why It's Helpful: The "Money Flow Index Crossover Indicator" provides traders with a sophisticated tool to identify potential buy and sell conditions based on the combined information of the MFI and its smoothed moving average. The KNN machine learning techniques enhance the accuracy of this indicator's clear visual representation of overbought, neutral, and oversold zones. This combination of data represented on the chart helps traders make informed decisions about market conditions. This indicator is particularly useful for traders looking to refine their entry and exit points by leveraging advanced data analysis in respect to overbought and oversold conditions. Disclaimer: This indicator is intended to assist traders in making informed decisions based on technical analysis. However, it is not a guarantee of future performance and should be used in conjunction with other analysis techniques and risk management practices. Past performance is not indicative of future results, and traders should exercise caution and perform their own due diligence before making any trading decisions.
Indicatore Pine Script®
di Crypto_Moses
1
Session MasterSession Master Indicator Overview The "Session Master" indicator is a unique tool designed to enhance trading decisions by providing visual cues and relevant information during the critical last 15 minutes of a trading session. It also integrates advanced trend analysis using the Average Directional Index (ADX) and Directional Movement Index (DI) to offer insights into market trends and potential entry/exit points. Originality and Functionality This script combines session timing, visual alerts, and trend analysis in a cohesive manner to give traders a comprehensive view of market behavior as the trading day concludes. Here’s a breakdown of its key features: Last 15 Minutes Highlight : The script identifies the last 15 minutes of the trading session and highlights this period with a semi-transparent blue background, helping traders focus on end-of-day price movements. Previous Session High and Low : The script dynamically plots the high and low of the previous trading session. These levels are crucial for identifying support and resistance and are highlighted with dashed lines and labeled for easy identification during the last 15 minutes of the current session. Directional Movement and Trend Analysis : Using a combination of ADX and DI, the script calculates and plots trend strength and direction. A 21-period Exponential Moving Average (EMA) is plotted with color coding (green for bullish and red for bearish) based on the DI difference, offering clear visual cues about the market trend. Technical Explanation Last 15 Minutes Highlight: The script checks the current time and compares it to the session’s last 15 minutes. If within this period, the background color is changed to a semi-transparent blue to alert the trader. Previous Session High and Low: The script retrieves the high and low of the previous daily session. During the last 15 minutes of the session, these levels are plotted as dashed lines and labeled appropriately. ADX and DI Calculation: The script calculates the True Range, Directional Movement (both positive and negative), and smoothes these values over a specified length (28 periods by default). It then computes the Directional Indicators (DI+ and DI-) and the ADX to gauge trend strength. The 21-period EMA is plotted with dynamic color changes based on the DI difference to indicate trend direction. How to Use Highlight Key Moments: Use the blue background highlight to concentrate on market movements in the critical last 15 minutes of the trading session. Identify Key Levels: Pay attention to the plotted high and low of the previous session as they often act as significant support and resistance levels. Assess Trend Strength: Use the ADX and DI values to understand the strength and direction of the market trend, aiding in making informed trading decisions. EMA for Entry/Exit: Use the color-coded 21-period EMA for potential entry and exit signals based on the trend direction indicated by the DI. Conclusion The "Session Master" indicator is a powerful tool designed to help traders make informed decisions during the crucial end-of-session period. By combining session timing, previous session levels, and advanced trend analysis, it provides a comprehensive overview that is both informative and actionable. This script is particularly useful for intraday traders looking to optimize their strategies around session close times.
Indicatore Pine Script®
di TradingConToto
1