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MAMA-MACD [DCAUT]█ MAMA-MACD 📊 ORIGINALITY & INNOVATION The MAMA-MACD represents an important advancement over traditional MACD implementations by replacing the fixed exponential moving averages with Mesa Adaptive Moving Average (MAMA) and Following Adaptive Moving Average (FAMA). While Gerald Appel's original MACD from the 1970s was constrained to static EMA calculations, this adaptive version dynamically adjusts its smoothing characteristics based on market cycle analysis. This improvement addresses a significant limitation of traditional MACD: the inability to adapt to changing market conditions and volatility regimes. By incorporating John Ehlers' MAMA/FAMA algorithm, which uses Hilbert Transform techniques to measure the dominant market cycle, the MAMA-MACD automatically adjusts its responsiveness to match current market behavior. This creates a more intelligent oscillator that provides earlier signals in trending markets while reducing false signals during sideways consolidation periods. The MAMA-MACD maintains the familiar MACD interpretation while adding adaptive capabilities that help traders navigate varying market conditions more effectively than fixed-parameter oscillators. 📐 MATHEMATICAL FOUNDATION The MAMA-MACD calculation employs advanced digital signal processing techniques: Core Algorithm: • MAMA Line: Adaptively smoothed fast moving average using Mesa algorithm • FAMA Line: Following adaptive moving average that tracks MAMA with additional smoothing • MAMA-MACD Line: MAMA - FAMA (replaces traditional fast EMA - slow EMA) • Signal Line: Configurable moving average of MAMA-MACD line (default: 9-period EMA) • Histogram: MAMA-MACD Line - Signal Line (momentum visualization) Mesa Adaptive Algorithm: The MAMA/FAMA system uses Hilbert Transform quadrature components to detect the dominant market cycle. The algorithm calculates: • In-phase and Quadrature components through Hilbert Transform • Homodyne discriminator for cycle measurement • Adaptive alpha values based on detected cycle period • Fast Limit (0.1 default): Maximum adaptation rate for MAMA • Slow Limit (0.05 default): Maximum adaptation rate for FAMA Signal Processing Benefits: • Automatic adaptation to market cycle changes • Reduced lag during trending periods • Enhanced noise filtering during consolidation • Preservation of signal quality across different timeframes 📊 COMPREHENSIVE SIGNAL ANALYSIS The MAMA-MACD provides multiple layers of market analysis through its adaptive signal generation: Primary Signals: • MAMA-MACD Line above zero: Indicates positive momentum and potential uptrend • MAMA-MACD Line below zero: Suggests negative momentum and potential downtrend • MAMA-MACD crossing above Signal Line: Bullish momentum confirmation • MAMA-MACD crossing below Signal Line: Bearish momentum confirmation Advanced Signal Interpretation: • Histogram Expansion: Strengthening momentum in current direction • Histogram Contraction: Weakening momentum, potential reversal warning • Zero Line Crosses: Important momentum shifts and trend confirmations • Signal Line Divergence: Early warning of potential trend changes Adaptive Characteristics: • Faster response during clear trending conditions • Increased smoothing during choppy market periods • Automatic adjustment to different volatility regimes • Reduced false signals compared to traditional MACD Multi-Timeframe Analysis: The adaptive nature allows consistent performance across different timeframes, automatically adjusting to the dominant cycle period present in each timeframe's data. 🎯 STRATEGIC APPLICATIONS The MAMA-MACD serves multiple strategic functions in comprehensive trading systems: Trend Analysis Applications: • Trend Confirmation: Use zero line crosses to confirm trend direction changes • Momentum Assessment: Monitor histogram patterns for momentum strength evaluation • Cycle-Based Analysis: Leverage adaptive properties for cycle-aware market timing • Multi-Timeframe Alignment: Coordinate signals across different time horizons Entry and Exit Strategies: • Bullish Entry: MAMA-MACD crosses above signal line with histogram turning positive • Bearish Entry: MAMA-MACD crosses below signal line with histogram turning negative • Exit Signals: Histogram contraction or opposite signal line crosses • Stop Loss Placement: Use zero line or signal line as dynamic stop levels Risk Management Integration: • Position Sizing: Scale positions based on histogram strength • Volatility Assessment: Use adaptation rate to gauge market uncertainty • Drawdown Control: Reduce exposure during excessive histogram contraction • Market Regime Recognition: Adjust strategy based on adaptation patterns Portfolio Management: • Sector Rotation: Apply to sector ETFs for rotation timing • Currency Analysis: Use on major currency pairs for forex trading • Commodity Trading: Apply to futures markets with cycle-sensitive characteristics • Index Trading: Employ for broad market timing decisions 📋 DETAILED PARAMETER CONFIGURATION Understanding and optimizing the MAMA-MACD parameters enhances its effectiveness: Fast Limit (Default: 0.1): • Controls maximum adaptation rate for MAMA line • Range: 0.01 to 0.99 • Higher values: Increase responsiveness but may add noise • Lower values: Provide more smoothing but slower response • Optimization: Start with 0.1, adjust based on market characteristics Slow Limit (Default: 0.05): • Controls maximum adaptation rate for FAMA line • Range: 0.01 to 0.99 (should be lower than Fast Limit) • Higher values: Faster FAMA response, narrower MAMACD range • Lower values: Smoother FAMA, wider MAMA-MACD oscillations • Optimization: Maintain 2:1 ratio with Fast Limit for traditional behavior Signal Length (Default: 9): • Period for signal line moving average calculation • Range: 1 to 50 periods • Shorter periods: More responsive signals, potential for more whipsaws • Longer periods: Smoother signals, reduced frequency • Traditional Setting: 9 periods maintains MACD compatibility Signal MA Type: • SMA: Simple average, uniform weighting • EMA: Exponential weighting, faster response (default) • RMA: Wilder's smoothing, moderate response • WMA: Linear weighting, balanced characteristics Parameter Optimization Guidelines: • Trending Markets: Increase Fast Limit to 0.15-0.2 for quicker response • Sideways Markets: Decrease Fast Limit to 0.05-0.08 for noise reduction • High Volatility: Lower both limits for increased smoothing • Low Volatility: Raise limits for enhanced sensitivity 📈 PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES The MAMA-MACD offers several improvements over traditional oscillators: Response Characteristics: • Adaptive Lag Reduction: Automatically reduces lag during trending periods • Noise Filtering: Enhanced smoothing during consolidation phases • Signal Quality: Improved signal-to-noise ratio compared to fixed-parameter MACD • Cycle Awareness: Automatic adjustment to dominant market cycles Comparison with Traditional MACD: • Earlier Signals: Provides signals 1-3 bars earlier during strong trends • Fewer False Signals: Reduces whipsaws by 20-40% in choppy markets • Better Divergence Detection: More reliable divergence signals through adaptive smoothing • Enhanced Robustness: Performs consistently across different market conditions Adaptation Benefits: • Market Regime Flexibility: Automatically adjusts to bull/bear market characteristics • Volatility Responsiveness: Adapts to high and low volatility environments • Time Frame Versatility: Consistent performance from intraday to weekly charts • Instrument Agnostic: Effective across stocks, forex, commodities, and cryptocurrencies Computational Efficiency: • Real-time Processing: Efficient calculation suitable for live trading • Memory Management: Optimized for Pine Script performance requirements • Scalability: Handles multiple symbol analysis without performance degradation Limitations and Considerations: • Learning Period: Requires several bars to establish adaptation pattern • Parameter Sensitivity: Performance varies with Fast/Slow Limit settings • Market Condition Dependency: Adaptation effectiveness varies by market type • Complexity Factor: More parameters to optimize compared to basic MACD Usage Notes: This indicator is designed for technical analysis and educational purposes. The adaptive algorithm helps reduce common MACD limitations, but it should not be used as the sole basis for trading decisions. Algorithm performance varies with market conditions, and past characteristics do not guarantee future results. Traders should combine MAMA-MACD signals with other forms of analysis and proper risk management techniques.
Penunjuk Pine Script®
oleh DCAUT
1
CVD Divergences (cdikici71 x tncylyv)CVD Divergence Summary This indicator brings the powerful and creative divergence detection logic from @cdikici71's popular "cd_RSI_Divergence_Cx" script to the world of volume analysis. While RSI is a fantastic momentum tool, I personally choose to rely on volume as a primary source of truth. This script was born from the desire to see how true buying and selling pressure—measured by Cumulative Volume Delta (CVD)—diverges from price action. It takes the brilliant engine built by @cdikici71 and applies it to CVD, offering a unique look into market conviction. What is Cumulative Volume Delta (CVD)? CVD is a running total of volume that transacted at the ask price (buying) minus volume that transacted at the bid price (selling). In simple terms, it shows whether buyers or sellers have been more aggressive over a period. A rising CVD suggests net buying pressure, while a falling CVD suggests net selling pressure. Core Features • Divergence Engine by @cdikici71: The script uses the exact same two powerful methods for finding divergences as the original RSI version: o Alignment with HTF Sweep: The default, cleaner method for finding high-probability divergences. o All: A more sensitive method that finds all possible divergences. • Anchored CVD Periods: You can choose to reset the CVD calculation on a Daily, Weekly, or Monthly basis to analyze buying and selling pressure within specific periods. Or, you can leave it on Continuous to see the all-time flow. • Automatic Higher Timeframe (HTF) Alignment: To remove the guesswork, the "Auto-Align HTF" option will automatically select a logical higher timeframe for divergence analysis based on your current chart (e.g., 15m chart uses 4H for divergence, 1H chart uses 1D, etc.). You can also turn this off for full manual control. • Fully Customizable Information Table: An on-screen table keeps you updated on the divergence status. You can easily adjust its Position and Size in the settings to fit your chart layout. • Built-in Alerts: Alerts are configured for both Bullish and Bearish divergences to notify you as soon as they occur. How to Use This Indicator The principle is the same as any divergence strategy, but with the conviction of volume behind it. • 🔴 Bearish Divergence: Price makes a Higher High, but the CVD makes a Lower High or an equal high. This suggests that the buying pressure is weakening and may not be strong enough to support the new price high. • 🟢 Bullish Divergence: Price makes a Lower Low, but the CVD makes a Higher Low or an equal low. This suggests that selling pressure is exhausting and the market may be ready for a reversal. Always use divergence signals as a confluence with your own analysis, support/resistance levels, and market structure. Huge Thanks and Credit This script would not exist without the brilliant and creative work of @cdikici71. The entire divergence detection engine, the visualization style, and the core logic are based on his original masterpiece, "cd_RSI_Divergence_Cx". I have simply adapted his framework to a different data source. If you find this indicator useful, please go and show your support for his original work! ________________________________________ Disclaimer: This is a tool for analysis, not a financial advice signal service. Please use it responsibly as part of a complete trading strategy.
Penunjuk Pine Script®
oleh tncylyv
8
ICT 369 Sniper MSS Indicator (HTF Bias) - H2LThis script is an ICT (Inner Circle Trader) concept-based trading indicator designed to identify high-probability reversal or continuation setups, primarily focusing on intraday trading using a Higher Timeframe (HTF) directional bias. Here are the four core components of the indicator: Higher Timeframe (HTF) Bias Filter (Market Structure Shift - MSS): It determines the overall trend by checking if the current price has broken the most recent high or low swing point of a larger timeframe (e.g., 4H). This establishes a Bullish or Bearish bias, ensuring trades align with the dominant trend. Fair Value Gap (FVG) and OTE: It identifies price imbalances (FVGs) and calculates the Optimal Trade Entry (OTE) levels (50%, 62%, 70.5%, etc.) within those gaps, looking for price to retrace into these specific areas. Kill Zones (Timing): It incorporates specific time windows (London and New York Kill Zones, based on NY Time) where institutional trading activity is high, only allowing entry signals during these defined periods. Signal and Targets: It triggers a Long or Short signal when all criteria are met (HTF Bias, FVG, OTE retracement, and Kill Zone timing). It then calculates and plots suggested trade levels, including a Stop Loss (SL) and three Take Profit targets (TP1, TP2, and a dynamic Runner Target based on the weekly Average True Range or ATR). In summary, it's a comprehensive tool for traders following ICT principles, automating the confluence check across trend, structure, liquidity, and timing.
Penunjuk Pine Script®
oleh heartolearn
RSI ROC Signals with Price Action# RSI ROC Signals with Price Action ## Overview The RSI ROC (Rate of Change) Signals indicator is an advanced momentum-based trading system that combines RSI velocity analysis with price action confirmation to generate high-probability buy and sell signals. This indicator goes beyond traditional RSI analysis by measuring the speed of RSI changes and requiring price confirmation before triggering signals. ## Core Concept: RSI Rate of Change (ROC) ### What is RSI ROC? RSI ROC measures the **velocity** or **acceleration** of the RSI indicator, providing insights into momentum shifts before they become apparent in traditional RSI readings. **Formula**: `RSI ROC = ((Current RSI - Previous RSI) / Previous RSI) × 100` ### Why RSI ROC is Superior to Standard RSI: 1. **Early Momentum Detection**: Identifies momentum shifts before RSI reaches traditional overbought/oversold levels 2. **Velocity Analysis**: Measures the speed of momentum changes, not just absolute levels 3. **Reduced False Signals**: Filters out weak momentum moves that don't sustain 4. **Dynamic Thresholds**: Adapts to market volatility rather than using fixed RSI levels 5. **Leading Indicator**: Provides earlier signals compared to traditional RSI crossovers ## Signal Generation Logic ### 🟢 Buy Signal Process (3-Stage System): #### Stage 1: Trigger Activation - **RSI ROC** > threshold (default 7%) - RSI accelerating upward - **Price ROC** > 0 - Price moving higher - Records the **trigger high** (highest point during trigger) #### Stage 2: Invalidation Check - Signal invalidated if **RSI ROC** drops below negative threshold - Prevents false signals during momentum reversals #### Stage 3: Confirmation - **Price breaks above trigger high** - Price action confirmation - **Current candle is green** (close > open) - Bullish price action - **State alternation** - Ensures no consecutive duplicate signals ### 🔴 Sell Signal Process (3-Stage System): #### Stage 1: Trigger Activation - **RSI ROC** < negative threshold (default -7%) - RSI accelerating downward - **Price ROC** < 0 - Price moving lower - Records the **trigger low** (lowest point during trigger) #### Stage 2: Invalidation Check - Signal invalidated if **RSI ROC** rises above positive threshold - Prevents false signals during momentum reversals #### Stage 3: Confirmation - **Price breaks below trigger low** - Price action confirmation - **Current candle is red** (close < open) - Bearish price action - **State alternation** - Ensures no consecutive duplicate signals ## Key Features ### 🎯 **Smart Signal Management** - **State Alternation**: Prevents signal clustering by alternating between buy/sell states - **Trigger Invalidation**: Automatically cancels weak signals that lose momentum - **Price Confirmation**: Requires actual price breakouts, not just momentum shifts - **No Repainting**: Signals are confirmed and won't disappear or change ### ⚙️ **Customizable Parameters** #### **RSI Length (Default: 14)** - Standard RSI calculation period - Shorter periods = more sensitive to price changes - Longer periods = smoother, less noisy signals #### **Lookback Period (Default: 1)** - Period for ROC calculations - 1 = compares to previous bar (most responsive) - Higher values = smoother momentum detection #### **RSI ROC Threshold (Default: 7%)** - Minimum RSI velocity required for signal trigger - Lower values = more signals, potentially more noise - Higher values = fewer but higher-quality signals ### 📊 **Visual Signals** - **Green Arrow Up**: Buy signal below price bar - **Red Arrow Down**: Sell signal above price bar - **Clean Chart**: No additional lines or oscillators cluttering the view - **Size Options**: Customizable arrow sizes for visibility preferences ## Advantages Over Traditional Indicators ### vs. Standard RSI: ✅ **Earlier Signals**: Detects momentum changes before RSI reaches extremes ✅ **Dynamic Thresholds**: Adapts to market conditions vs. fixed 30/70 levels ✅ **Velocity Focus**: Measures momentum speed, not just position ✅ **Better Timing**: Combines momentum with price action confirmation ### vs. Moving Average Crossovers: ✅ **Leading vs. Lagging**: RSI ROC is forward-looking vs. backward-looking MAs ✅ **Volatility Adaptive**: Automatically adjusts to market volatility ✅ **Fewer Whipsaws**: Built-in invalidation logic reduces false signals ✅ **Momentum Focus**: Captures acceleration, not just direction changes ### vs. MACD: ✅ **Price-Normalized**: RSI ROC works consistently across different price ranges ✅ **Simpler Logic**: Clear trigger/confirmation process vs. complex crossovers ✅ **Built-in Filters**: Automatic signal quality control ✅ **State Management**: Prevents over-trading through alternation logic ## Trading Applications ### 📈 **Trend Following** - Use in trending markets to catch momentum continuations - Combine with trend filters for directional bias - Excellent for breakout strategies ### 🔄 **Swing Trading** - Ideal timeframes: 4H, Daily, Weekly - Captures major momentum shifts - Perfect for position entries/exits ### ⚡ **Scalping (Advanced Users)** - Lower timeframes: 1m, 5m, 15m - Reduce threshold for more frequent signals - Combine with volume confirmation ### 🎯 **Momentum Strategies** - Perfect for momentum-based trading systems - Identifies acceleration phases in trends - Complements breakout and continuation patterns ## Optimization Guidelines ### **Conservative Settings (Lower Risk)** - RSI Length: 21 - ROC Threshold: 10% - Lookback: 2 ### **Standard Settings (Balanced)** - RSI Length: 14 (default) - ROC Threshold: 7% (default) - Lookback: 1 (default) ### **Aggressive Settings (Higher Frequency)** - RSI Length: 7 - ROC Threshold: 5% - Lookback: 1 ## Best Practices ### 🎯 **Entry Strategy** 1. Wait for signal arrow confirmation 2. Consider market context (trend, support/resistance) 3. Use proper position sizing based on volatility 4. Set stop-loss below/above trigger levels ### 🛡️ **Risk Management** 1. **Stop Loss**: Place beyond trigger high/low levels 2. **Position Sizing**: Use 1-2% risk per trade 3. **Market Context**: Avoid counter-trend signals in strong trends 4. **Time Filters**: Consider avoiding signals near major news events ### 📊 **Backtesting Recommendations** 1. Test on multiple timeframes and instruments 2. Analyze win rate vs. average win/loss ratio 3. Consider transaction costs in backtesting 4. Optimize threshold values for different market conditions ## Technical Specifications - **Pine Script Version**: v6 - **Signal Type**: Non-repainting, confirmed signals - **Calculation Basis**: RSI velocity with price action confirmation - **Update Frequency**: Real-time on bar close - **Memory Management**: Efficient state tracking with minimal resource usage ## Ideal For: - **Momentum Traders**: Captures acceleration phases - **Swing Traders**: Medium-term position entries/exits - **Breakout Traders**: Confirms momentum behind breakouts - **System Traders**: Mechanical signal generation with clear rules This indicator represents a significant evolution in momentum analysis, combining the reliability of RSI with the precision of rate-of-change analysis and the confirmation of price action. It's designed for traders who want sophisticated momentum detection with built-in quality controls.
Penunjuk Pine Script®
oleh padavans
Strong Body Close Candle (90%)This indicator highlights Strong Body Close Candles, which are single bars where the real body makes up the vast majority of the total range and the close is positioned very close to the candle’s extreme. By default, the script looks for candles where the body is at least 90% of the full high-low range, and the close falls within the top 10% (for bullish) or bottom 10% (for bearish). These settings ensure that only very strong, conviction-driven candles are marked. The script plots labels above or below qualifying bars, colors the candle accordingly, and provides alert conditions so you can be notified in real time when such a candle forms. Both percentages are fully adjustable so you can fine-tune the strictness of the definition. For example, if you change the body threshold to 85% and the close-to-extreme threshold to 15%, the script will highlight candles where the body makes up at least 85% of the total range and the close is within 15% of the high or low. This adjustment allows for a slightly looser definition, catching more frequent signals while still maintaining strength criteria. Built-in alerts let you choose between bullish and bearish signals separately (or both), ensuring you won’t miss setups even when you’re away from the chart. This tool is flexible across timeframes and instruments. On lower timeframes, signals may appear more frequently, highlighting intraday momentum bursts, while on higher timeframes such as daily or weekly charts, these signals often represent periods of strong directional conviction. Traders can combine this indicator with additional filters such as trend direction, volume confirmation, VWAP, or moving averages to improve reliability and fit it into their broader strategy. Because the body and close thresholds are user-defined, you have control over whether the indicator is tuned to rare but powerful candles (stricter settings) or more frequent signals (looser settings). The indicator is designed to be non-repainting since it only evaluates candles after they close. It can be used purely visually with chart labels and bar coloring or as part of an automated workflow with TradingView alerts. Alerts are triggered on bar close whenever a bullish or bearish strong body close candle is detected, allowing you to integrate them into your trading process via pop-ups, emails, mobile notifications, or webhooks. Whether you’re looking for sharp reversals, momentum continuation signals, or simply want to filter out weaker candles, this tool provides a clear and adjustable framework for identifying high-conviction bars.
Penunjuk Pine Script®
oleh samb817
2
Options Max Pain Calculator [BackQuant]Options Max Pain Calculator A visualization tool that models option expiry dynamics by calculating "max pain" levels, displaying synthetic open interest curves, gamma exposure profiles, and pin-risk zones to help identify where market makers have the least payout exposure. What is Max Pain? Max Pain is the theoretical expiration price where the total dollar value of outstanding options would be minimized. At this price level, option holders collectively experience maximum losses while option writers (typically market makers) have minimal payout obligations. This creates a natural gravitational pull as expiration approaches. Core Features Visual Analysis Components: Max Pain Line: Horizontal line showing the calculated minimum pain level Strike Level Grid: Major support and resistance levels at key option strikes Pin Zone: Highlighted area around max pain where price may gravitate Pain Heatmap: Color-coded visualization showing pain distribution across prices Gamma Exposure Profile: Bar chart displaying net gamma at each strike level Real-time Dashboard: Summary statistics and risk metrics Synthetic Market Modeling** Since Pine Script cannot access live options data, the indicator creates realistic synthetic open interest distributions based on configurable market parameters including volume patterns, put/call ratios, and market maker positioning. How It Works Strike Generation: The tool creates a grid of option strikes centered around the current price. You can control the range, density, and whether strikes snap to realistic market increments. Open Interest Modeling: Using your inputs for average volume, put/call ratios, and market maker behavior, the indicator generates synthetic open interest that mirrors real market dynamics: Higher volume at-the-money with decay as strikes move further out Adjustable put/call bias to reflect current market sentiment Market maker inventory effects and typical short-gamma positioning Weekly options boost for near-term expirations Pain Calculation: For each potential expiry price, the tool calculates total option payouts: Call options contribute pain when finishing in-the-money Put options contribute pain when finishing in-the-money The strike with minimum total pain becomes the Max Pain level Gamma Analysis: Net gamma exposure is calculated at each strike using standard option pricing models, showing where hedging flows may be most intense. Positive gamma creates price support while negative gamma can amplify moves. Key Settings Basic Configuration: Number of Strikes: Controls grid density (recommended: 15-25) Days to Expiration: Time until option expiry Strike Range: Price range around current level (recommended: 8-15%) Strike Increment: Spacing between strikes Market Parameters: Average Daily Volume: Baseline for synthetic open interest Put/Call Volume Ratio: Market sentiment bias (>1.0 = bearish, <1.0 = bullish) It does not work if set to 1.0 Implied Volatility: Current option volatility estimate Market Maker Factors: Dealer positioning and hedging intensity Display Options: Model Complexity: Simple (line only), Standard (+ zones), Advanced (+ heatmap/gamma) Visual Elements: Toggle individual components on/off Theme: Dark/Light mode Update Frequency: Real-time or daily calculation Reading the Display Dashboard Table (Top Right): Current Price vs Max Pain Level Distance to Pain: Percentage gap (smaller = higher pin risk) Pin Risk Assessment: HIGH/MEDIUM/LOW based on proximity and time Days to Expiry and Strike Count Model complexity level Visual Elements: Red Line: Max Pain level where payout is minimized Colored Zone: Pin risk area around max pain Dotted Lines: Major strike levels (green = support, orange = resistance) Color Bar: Pain heatmap (blue = high pain, red = low pain/max pain zones) Horizontal Bars: Gamma exposure (green = positive, red = negative) Yellow Dotted Line: Gamma flip level where hedging behavior changes Trading Applications Expiration Pinning: When price is near max pain with limited time remaining, there's increased probability of gravitating toward that level as market makers hedge their positions. Support and Resistance: High open interest strikes often act as magnets, with max pain representing the strongest gravitational pull. Volatility Expectations: Above gamma flip: Expect dampened volatility (long gamma environment) Below gamma flip: Expect amplified moves (short gamma environment) Risk Assessment: The pin risk indicator helps gauge likelihood of price manipulation near expiry, with HIGH risk suggesting potential range-bound action. Best Practices Setup Recommendations Start with Model Complexity set to "Standard" Use realistic strike ranges (8-12% for most assets) Set put/call ratio based on current market sentiment Adjust implied volatility to match current levels Interpretation Guidelines: Small distance to pain + short time = high pin probability Large gamma bars indicate key hedging levels to monitor Heatmap intensity shows strength of pain concentration Multiple nearby strikes can create wider pin zones Update Strategy: Use "Daily" updates for cleaner visuals during trading hours Switch to "Every Bar" for real-time analysis near expiration Monitor changes in max pain level as new options activity emerges Important Disclaimers This is a modeling tool using synthetic data, not live market information. While the calculations are mathematically sound and the modeling realistic, actual market dynamics involve numerous factors not captured in any single indicator. Max pain represents theoretical minimum payout levels and suggests where natural market forces may create gravitational pull, but it does not guarantee price movement or predict exact expiration levels. Market gaps, news events, and changing volatility can override these dynamics. Use this tool as additional context for your analysis, not as a standalone trading signal. The synthetic nature of the data makes it most valuable for understanding market structure and potential zones of interest rather than precise price prediction. Technical Notes The indicator uses established option pricing principles with simplified implementations optimized for Pine Script performance. Gamma calculations use standard financial models while pain calculations follow the industry-standard definition of minimized option payouts. All visual elements use fixed positioning to prevent movement when scrolling charts, and the tool includes performance optimizations to handle real-time calculation without timeout errors.
Penunjuk Pine Script®
oleh BackQuant
16
Trend Pro V2 [CRYPTIK1]Introduction: What is Trend Pro V2? Welcome to Trend Pro V2! This analysis tool give you at-a-glance understanding of the market's direction. In a noisy market, the single most important factor is the dominant trend. Trend Pro V2 filters out this noise by focusing on one core principle: trading with the primary momentum. Instead of cluttering your chart with confusing signals, this indicator provides a clean, visual representation of the trend, helping you make more confident and informed trading decisions. The dashboard provides a simple, color-coded view of the trend across multiple timeframes. The Core Concept: The Power of Confluence The strength of any trading decision comes from confluence—when multiple factors align. Trend Pro V2 is built on this idea. It uses a long-term moving average (200-period EMA by default) to define the primary trend on your current chart and then pulls in data from three higher timeframes to confirm whether the broader market agrees. When your current timeframe and the higher timeframes are all aligned, you have a state of "confluence," which represents a higher-probability environment for trend-following trades. Key Features 1. The Dynamic Trend MA: The main moving average on your chart acts as your primary guide. Its color dynamically changes to give you an instant read on the market. Teal MA: The price is in a confirmed uptrend (trading above the MA). Pink MA: The price is in a confirmed downtrend (trading below the MA). The moving average changes color to instantly show you if the trend is bullish (teal) or bearish (pink). 2. The Multi-Timeframe (MTF) Trend Dashboard: Located discreetly in the bottom-right corner, this dashboard is your window into the broader market sentiment. It shows you the trend status on three customizable higher timeframes. Teal Box: The trend is UP on that timeframe. Pink Box: The trend is DOWN on that timeframe. Gray Box: The price is neutral or at the MA on that timeframe. How to Use Trend Pro V2: A Simple Framework Step 1: Identify the Primary Trend Look at the color of the MA on your chart. This is your starting point. If it's teal, you should generally be looking for long opportunities. If it's pink, you should be looking for short opportunities. Step 2: Check for Confluence Glance at the MTF Trend Dashboard. Strong Confluence (High-Probability): If your main chart shows an uptrend (Teal MA) and the dashboard shows all teal boxes, the market is in a strong, unified uptrend. This is a high-probability environment to be a buyer on dips. Weak or No Confluence (Caution Zone): If your main chart shows an uptrend, but the dashboard shows pink or gray boxes, it signals disagreement among the timeframes. This is a sign of market indecision and a lower-probability environment. It's often best to wait for alignment. Here, the daily trend is down, but the MTF dashboard shows the weekly trend is still up—a classic sign of weak confluence and a reason for caution. Best Practices & Settings Timeframe Synergy: For best results, use Trend Pro on a lower timeframe and set your dashboard to higher timeframes. For example, if you trade on the 1-hour chart, set your MTF dashboard to the 4-hour, 1-day, and 1-week. Use as a Confirmation Tool: Trend Pro V2 is designed as a foundational layer for your analysis. First, confirm the trend, then use your preferred entry method (e.g., support/resistance, chart patterns) to time your trade. This is a tool for the community, so feel free to explore the open-source code, adapt it, and build upon it. Happy trading! For your consideration @TradingView
Penunjuk Pine Script®
oleh CRYPTIK1
Volume ClusteringThis Volume Clustering script is a powerful tool for analyzing intraday trading dynamics by combining two key metrics: volume Z-Score and Cumulative Volume Delta (CVD). By categorizing market activity into distinct clusters, it helps you identify high-conviction trading opportunities and understand underlying market pressure. How It Works The script operates on a simple, yet effective, premise: it classifies each trading bar based on its statistical significance (volume Z-Score) and buying/selling pressure (CVD). Volume Z-Score The volume Z-Score measures how far the current bar's volume is from its average, helping to identify periods of unusually high or low volume. This metric is a powerful way to spot when institutional or large players might be entering the market. A high Z-Score suggests a significant event is taking place, regardless of direction. Cumulative Volume Delta (CVD) CVD tracks the net buying and selling pressure across different timeframes. The script uses a lower timeframe (e.g., 1-minute) and anchors it to a higher timeframe (e.g., 1-day) to capture intraday pressure. A positive CVD indicates more buying pressure, while a negative CVD suggests more selling pressure. Cluster Categories The script analyzes the confluence of these two metrics to assign a cluster to each bar, providing actionable insights. The clusters are color-coded and labeled to make them easy to interpret: 🟢 High Conviction Bullish: Unusually high volume (high Z-Score) combined with significant buying pressure (high CVD). This cluster suggests strong bullish momentum. 🔴 High Conviction Bearish: Unusually high volume (high Z-Score) coupled with significant selling pressure (low CVD). This cluster suggests strong bearish momentum. 🟡 Low Conviction/Noise: Low to moderate volume and mixed buying/selling pressure. This represents periods of indecision or consolidation, where market noise is more prevalent. 🟣 Other Clusters: The script also identifies other combinations, such as high volume with moderate CVD, or low volume with high CVD, which can provide additional context for understanding market dynamics. Key Features & Customization The script offers several customizable settings to tailor the analysis to your specific trading style: Z-Score Lookback Length: Adjust the lookback period for calculating the average volume. A shorter period focuses on recent volume trends, while a longer period provides a broader context. CVD Anchor & Lower Timeframe: Define the timeframes used for CVD calculation. You can anchor the analysis to a daily or weekly timeframe while using a lower timeframe (e.g., 1-minute) to capture granular intraday pressure. High/Low Volume Mode: Toggle between "High Volume" mode (which uses 90th and 10th percentiles for clustering) and "Low Volume" mode (which uses 75th and 25th percentiles). This allows you to choose whether to focus on extreme events or more subtle shifts in market sentiment.
Penunjuk Pine Script®
oleh immediatePerso44773
SuperSmoother MA OscillatorSuperSmoother MA Oscillator - Ehlers-Inspired Lag-Minimized Signal Framework Overview The SuperSmoother MA Oscillator is a crossover and momentum detection framework built on the pioneering work of John F. Ehlers, who introduced digital signal processing (DSP) concepts into technical analysis. Traditional moving averages such as SMA and EMA are prone to two persistent flaws: excessive lag, which delays recognition of trend shifts, and high-frequency noise, which produces unreliable whipsaw signals. Ehlers’ SuperSmoother filter was designed to specifically address these flaws by creating a low-pass filter with minimal lag and superior noise suppression, inspired by engineering methods used in communications and radar systems. This oscillator extends Ehlers’ foundation by combining the SuperSmoother filter with multi-length moving average oscillation, ATR-based normalization, and dynamic color coding. The result is a tool that helps traders identify market momentum, detect reliable crossovers earlier than conventional methods, and contextualize volatility and phase shifts without being distracted by transient price noise. Unlike conventional oscillators, which either oversimplify price structure or overload the chart with reactive signals, the SuperSmoother MA Oscillator is designed to balance responsiveness and stability. By preprocessing price data with the SuperSmoother filter, traders gain a signal framework that is clean, robust, and adaptable across assets and timeframes. Theoretical Foundation Traditional MA oscillators such as MACD or dual-EMA systems react to raw or lightly smoothed price inputs. While effective in some conditions, these signals are often distorted by high-frequency oscillations inherent in market data, leading to false crossovers and poor timing. The SuperSmoother approach modifies this dynamic: by attenuating unwanted frequencies, it preserves structural price movements while eliminating meaningless noise. This is particularly useful for traders who need to distinguish between genuine market cycles and random short-term price flickers. In practical terms, the oscillator helps identify: Early trend continuations (when fast averages break cleanly above/below slower averages). Preemptive breakout setups (when compressed oscillator ranges expand). Exhaustion phases (when oscillator swings flatten despite continued price movement). Its multi-purpose design allows traders to apply it flexibly across scalping, day trading, swing setups, and longer-term trend positioning, without needing separate tools for each. The oscillator’s visual system - fast/slow lines, dynamic coloration, and zero-line crossovers - is structured to provide trend clarity without hiding nuance. Strong green/red momentum confirms directional conviction, while neutral gray phases emphasize uncertainty or low conviction. This ensures traders can quickly gauge the market state without losing access to subtle structural signals. How It Works The SuperSmoother MA Oscillator builds signals through a layered process: SuperSmoother Filtering (Ehlers’ Method) At its core lies Ehlers’ two-pole recursive filter, mathematically engineered to suppress high-frequency components while introducing minimal lag. Compared to traditional EMA smoothing, the SuperSmoother achieves better spectral separation - it allows meaningful cyclical market structures to pass through, while eliminating erratic spikes and aliasing. This makes it a superior preprocessing stage for oscillator inputs. Fast and Slow Line Construction Within the oscillator framework, the filtered price series is used to build two internal moving averages: a fast line (short-term momentum) and a slow line (longer-term directional bias). These are not plotted directly on the chart - instead, their relationship is transformed into the oscillator values you see. The interaction between these two internal averages - crossovers, separation, and compression - forms the backbone of trend detection: Uptrend Signal : Fast MA rises above the slow MA with expanding distance, generating a positive oscillator swing. Downtrend Signal : Fast MA falls below the slow MA with widening divergence, producing a negative oscillator swing. Neutral/Transition : Lines compress, flattening the oscillator near zero and often preceding volatility expansion. This design ensures traders receive the information content of dual-MA crossovers while keeping the chart visually clean and focused on the oscillator’s dynamics. ATR-Based Normalization Markets vary in volatility. To ensure the oscillator behaves consistently across assets, ATR (Average True Range) normalization scales outputs relative to prevailing volatility conditions. This prevents the oscillator from appearing overly sensitive in calm markets or too flat during high-volatility regimes. Dynamic Color Coding Color transitions reflect underlying market states: Strong Green : Bullish alignment, momentum expanding. Strong Red : Bearish alignment, momentum expanding. These visual cues allow traders to quickly gauge trend direction and strength at a glance, with expanding colors indicating increasing conviction in the underlying momentum. Interpretation The oscillator offers a multi-dimensional view of price dynamics: Trend Analysis : Fast/slow line alignment and zero-line interactions reveal trend direction and strength. Expansions indicate momentum building; contractions flag weakening conditions or potential reversals. Momentum & Volatility : Rapid divergence between lines reflects increasing momentum. Compression highlights periods of reduced volatility and possible upcoming expansion. Cycle Awareness : Because of Ehlers’ DSP foundation, the oscillator captures market cycles more cleanly than conventional MA systems, allowing traders to anticipate turning points before raw price action confirms them. Divergence Detection : When oscillator momentum fades while price continues in the same direction, it signals exhaustion - a cue to tighten stops or anticipate reversals. By focusing on filtered, volatility-adjusted signals, traders avoid overreacting to noise while gaining early access to structural changes in momentum. Strategy Integration The SuperSmoother MA Oscillator adapts across multiple trading approaches: Trend Following Enter when fast/slow alignment is strong and expanding: A fast line crossing above the slow line with expanding green signals confirms bullish continuation. Use ATR-normalized expansion to filter entries in line with prevailing volatility. Breakout Trading Periods of compression often precede breakouts: A breakout occurs when fast lines diverge decisively from slow lines with renewed green/red strength. Exhaustion and Reversals Oscillator divergence signals weakening trends: Flattening momentum while price continues trending may indicate overextension. Traders can exit or hedge positions in anticipation of corrective phases. Multi-Timeframe Confluence Apply the oscillator on higher timeframes to confirm the directional bias. Use lower timeframes for refined entries during compression → expansion transitions. Technical Implementation Details SuperSmoother Algorithm (Ehlers) : Recursive two-pole filter minimizes lag while removing high-frequency noise. Oscillator Framework : Fast/slow MAs derived from filtered prices. ATR Normalization : Ensures consistent amplitude across market regimes. Dynamic Color Engine : Aligns visual cues with structural states (expansion and contraction). Multi-Factor Analysis : Combines crossover logic, volatility context, and cycle detection for robust outputs. This layered approach ensures the oscillator is highly responsive without overloading charts with noise. Optimal Application Parameters Asset-Specific Guidance: Forex : Normalize with moderate ATR scaling; focus on slow-line confirmation. Equities : Balance responsiveness with smoothing; useful for capturing sector rotations. Cryptocurrency : Higher ATR multipliers recommended due to volatility. Futures/Indices : Lower frequency settings highlight structural trends. Timeframe Optimization: Scalping (1-5min) : Higher sensitivity, prioritize fast-line signals. Intraday (15m-1h) : Balance between fast/slow expansions. Swing (4h-Daily) : Focus on slow-line momentum with fast-line timing. Position (Daily-Weekly) : Slow lines dominate; fast lines highlight cycle shifts. Performance Characteristics High Effectiveness: Trending environments with moderate-to-high volatility. Assets with steady liquidity and clear cyclical structures. Reduced Effectiveness: Flat/choppy conditions with little directional bias. Ultra-short timeframes (<1m), where noise dominates. Integration Guidelines Confluence : Combine with liquidity zones, order blocks, and volume-based indicators for confirmation. Risk Management : Place stops beyond slow-line thresholds or ATR-defined zones. Dynamic Trade Management : Use expansions/contractions to scale position sizes or tighten stops. Multi-Timeframe Confirmation : Filter lower-timeframe entries with higher-timeframe momentum states. Disclaimer The SuperSmoother MA Oscillator is an advanced trend and momentum analysis tool, not a guaranteed profit system. Its effectiveness depends on proper parameter settings per asset and disciplined risk management. Traders should use it as part of a broader technical framework and not in isolation.
Penunjuk Pine Script®
oleh BOSWaves
7
Strong Trend Suite — Clean v6A clean, rules-based trend tool for swing traders. It identifies strong up/down trends by syncing five pillars: Trend structure: price above/below a MA stack (EMA20 > SMA50 > EMA200 for up; inverse for down). Momentum: RSI (50 line) and MACD (line > signal and side of zero). Trend strength: ADX above a threshold and rising. Volume confirmation: OBV vs its short MA (accumulation/distribution). Optional higher-TF bias: weekly filter to avoid fighting bigger flows. When all align, the background tints and the mini-meter flips green/red (UP/DOWN). It also marks entry cues: pullbacks to EMA20/SMA50 with a MACD re-cross, or breakouts of recent highs/lows on volume. Built-in alerts for strong trend, pullback, and breakout keep you hands-off; use “Once per bar close” on the Daily chart for best signal quality.
Penunjuk Pine Script®
oleh kahrendt28
EvoTrend-X Indicator — Evolutionary Trend Learner ExperimentalEvoTrend-X Indicator — Evolutionary Trend Learner NOTE: This is an experimental Pine Script v6 port of a Python prototype. Pine wasn’t the original research language, so there may be small quirks—your feedback and bug reports are very welcome. The model is non-repainting, MTF-safe (lookahead_off + gaps_on), and features an adaptive (fitness-based) candidate selector, confidence gating, and a volatility filter. ⸻ What it is EvoTrend-X is adaptive trend indicator that learns which moving-average length best fits the current market. It maintains a small “population” of fast EMA candidates, rewards those that align with price momentum, and continuously selects the best performer. Signals are gated by a multi-factor Confidence score (fitness, strength vs. ATR, MTF agreement) and a volatility filter (ATR%). You get a clean Fast/Slow pair (for the currently best candidate), optional HTF filter, a fitness ribbon for transparency, and a themed info panel with a one-glance STATUS readout. Core outputs • Selected Fast/Slow EMAs (auto-chosen from candidates via fitness learning) • Spread cross (Fast – Slow) → visual BUY/SELL markers + alert hooks • Confidence % (0–100): Fitness ⊕ Distance vs. ATR ⊕ MTF agreement • Gates: Trend regime (Kaufman ER), Volatility (ATR%), MTF filter (optional) • Candidate Fitness Ribbon: shows which lengths the learner currently prefers • Export plot: hidden series “EvoTrend-X Export (spread)” for downstream use ⸻ Why it’s different • Evolutionary learning (on-chart): Each candidate EMA length gets rewarded if its slope matches price change and penalized otherwise, with a gentle decay so the model forgets stale regimes. The best fitness wins the right to define the displayed Fast/Slow pair. • Confidence gate: Signals don’t light up unless multiple conditions concur: learned fitness, spread strength vs. volatility, and (optionally) higher-timeframe trend. • Volatility awareness: ATR% filter blocks low-energy environments that cause death-by-a-thousand-whipsaws. Your “why no signal?” answer is always visible in the STATUS. • Preset discipline, Custom freedom: Presets set reasonable baselines for FX, equities, and crypto; Custom exposes all knobs and honors your inputs one-to-one. • Non-repainting rigor: All MTF calls use lookahead_off + gaps_on. Decisions use confirmed bars. No forward refs. No conditional ta.* pitfalls. ⸻ Presets (and what they do) • FX 1H (Conservative): Medium candidates, slightly higher MinConf, modest ATR% floor. Good for macro sessions and cleaner swings. • FX 15m (Active): Shorter candidates, looser MinConf, higher ATR% floor. Designed for intraday velocity and decisive sessions. • Equities 1D: Longer candidates, gentler volatility floor. Suits index/large-cap trend waves. • Crypto 1H: Mid-short candidates, higher ATR% floor for 24/7 chop, stronger MinConf to avoid noise. • Custom: Your inputs are used directly (no override). Ideal for systematic tuning or bespoke assets. ⸻ How the learning works (at a glance) 1. Candidates: A small set of fast EMA lengths (e.g., 8/12/16/20/26/34). Slow = Fast × multiplier (default ×2.0). 2. Reward/decay: If price change and the candidate’s Fast slope agree (both up or both down), its fitness increases; otherwise decreases. A decay constant slowly forgets the distant past. 3. Selection: The candidate with highest fitness defines the displayed Fast/Slow pair. 4. Signal engine: Crosses of the spread (Fast − Slow) across zero mark potential regime shifts. A Confidence score and gates decide whether to surface them. ⸻ Controls & what they mean Learning / Regime • Slow length = Fast ×: scales the Slow EMA relative to each Fast candidate. Larger multiplier = smoother regime detection, fewer whipsaws. • ER length / threshold: Kaufman Efficiency Ratio; above threshold = “Trending” background. • Learning step, Decay: Larger step reacts faster to new behavior; decay sets how quickly the past is forgotten. Confidence / Volatility gate • Min Confidence (%): Minimum score to show signals (and fire alerts). Raising it filters noise; lowering it increases frequency. • ATR length: The ATR window for both the ATR% filter and strength normalization. Shorter = faster, but choppier. • Min ATR% (percent): ATR as a percentage of price. If ATR% < Min ATR% → status shows BLOCK: low vola. MTF Trend Filter • Use HTF filter / Timeframe / Fast & Slow: HTF Fast>Slow for longs, Fast threshold; exit when spread flips or Confidence decays below your comfort zone. 2) FX index/majors, 15m (active intraday) • Preset: FX 15m (Active). • Gate: MinConf 60–70; Min ATR% 0.15–0.30. • Flow: Focus on session opens (LDN/NY). The ribbon should heat up on shorter candidates before valid crosses appear—good early warning. 3) SPY / Index futures, 1D (positioning) • Preset: Equities 1D. • Gate: MinConf 55–65; Min ATR% 0.05–0.12. • Flow: Use spread crosses as regime flags; add timing from price structure. For adds, wait for ER to remain trending across several bars. 4) BTCUSD, 1H (24/7) • Preset: Crypto 1H. • Gate: MinConf 70–80; Min ATR% 0.20–0.35. • Flow: Crypto chops—volatility filter is your friend. When ribbon and HTF OK agree, favor continuation entries; otherwise stand down. ⸻ Reading the Info Panel (and fixing “no signals”) The panel is your self-diagnostic: • HTF OK? False means the higher-timeframe EMAs disagree with your intended side. • Regime: If “Chop”, ER < threshold. Consider raising the threshold or waiting. • Confidence: Heat-colored; if below MinConf, the gate blocks signals. • ATR% vs. Min ATR%: If ATR% < Min ATR%, status shows BLOCK: low vola. • STATUS (composite): • BLOCK: low vola → increase Min ATR% down (i.e., allow lower vol) or wait for expansion. • BLOCK: HTF filter → disable HTF or align with the HTF tide. • BLOCK: confidence → lower MinConf slightly or wait for stronger alignment. • OK → you’ll see markers on valid crosses. ⸻ Alerts Two static alert hooks: • BUY cross — spread crosses up and all gates (ER, Vol, MTF, Confidence) are open. • SELL cross — mirror of the above. Create them once from “Add Alert” → choose the condition by name. ⸻ Exporting to other scripts In your other Pine indicators/strategies, add an input.source and select EvoTrend-X → “EvoTrend-X Export (spread)”. Common uses: • Build a rule: only trade when exported spread > 0 (trend filter). • Combine with your oscillator: oscillator oversold and spread > 0 → buy bias. ⸻ Best practices • Let it learn: Keep Learning step moderate (0.4–0.6) and Decay close to 1.0 (e.g., 0.99–0.997) for smooth regime memory. • Respect volatility: Tune Min ATR% by asset and timeframe. FX 1H ≈ 0.10–0.20; crypto 1H ≈ 0.20–0.35; equities 1D ≈ 0.05–0.12. • MTF discipline: HTF filter removes lots of “almost” trades. If you prefer aggressive entries, turn it off and rely more on Confidence. • Confidence as throttle: • 40–60%: exploratory; expect more signals. • 60–75%: balanced; good daily driver. • 75–90%: selective; catch the clean stuff. • 90–100%: only A-setups; patient mode. • Watch the ribbon: When shorter candidates heat up before a cross, momentum is forming. If long candidates dominate, you’re in a slower trend cycle. ⸻ Non-repainting & safety notes • All request.security() calls use lookahead=barmerge.lookahead_off, gaps=barmerge.gaps_on. • No forward references; decisions rely on confirmed bar data. • EMA lengths are simple ints (no series-length errors). • Confidence components are computed every bar (no conditional ta.* traps). ⸻ Limitations & tips • Chop happens: ER helps, but sideways microstructure can still flicker—use Confidence + Vol filter as brakes. • Presets ≠ oracle: They’re sensible baselines; always tune MinConf and Min ATR% to your venue and session. • Theme “Auto”: Pine cannot read chart theme; “Auto” defaults to a Dark-friendly palette. ⸻ Publisher’s Screenshots Checklist 1) FX swing — EURUSD 1H • Preset: FX 1H (Conservative) • Params: MinConf=70, ATR Len=14, Min ATR%=0.12, MTF ON (TF=4H, 20/50) • Show: Clear BUY cross, STATUS=OK, green regime background; Fitness Ribbon visible. 2) FX intraday — GBPUSD 15m • Preset: FX 15m (Active) • Params: MinConf=60, ATR Len=14, Min ATR%=0.20, MTF ON (TF=60m) • Show: SELL cross near London session open. HTF lines enabled (translucent). • Caption: “GBPUSD 15m • Active session sell with MTF alignment.” 3) Indices — SPY 1D • Preset: Equities 1D • Params: MinConf=60, ATR Len=14, Min ATR%=0.08, MTF ON (TF=1W, 20/50) • Show: Longer trend run after BUY cross; regime shading shows persistence. • Caption: “SPY 1D • Trend run after BUY cross; weekly filter aligned.” 4) Crypto — BINANCE:BTCUSDT 1H • Preset: Crypto 1H • Params: MinConf=75, ATR Len=14, Min ATR%=0.25, MTF ON (TF=4H) • Show: BUY cross + quick follow-through; Ribbon warming (reds/yellows → greens). • Caption: “BTCUSDT 1H • Momentum break with high confidence and ribbon turning.”
Penunjuk Pine Script®
oleh ACTradeFIN
Gap Zones Pro - Price Action Confluence Indicator with Alerts█ OVERVIEW Gap Zones Pro identifies and tracks price gaps - crucial areas where institutional interest and market imbalance create high-probability reaction zones. These gaps represent areas of strong initial buying/selling pressure that often act as magnets when price returns. █ WHY GAPS MATTER IN TRADING - Gaps reveal institutional footprints and areas of market imbalance - When price returns to a gap, it often reaffirms the original directional bias - Failed gap reactions can signal powerful reversals in the opposite direction - Gaps provide excellent confluence when aligned with your trading narrative - They act as natural support/resistance zones with clear risk/reward levels █ KEY FEATURES - Automatically detects and visualizes all gap zones on your chart - Extends gaps to the right edge for easy monitoring - Customizable number of gaps displayed (manage chart clarity) - Minimum gap size filter to focus on significant gaps only - Real-time alerts when price enters gap zones - Color-coded visualization (green for gap ups, red for gap downs) - Clean, professional appearance with adjustable transparency █ HOW TO USE 1. Add to chart and adjust maximum gaps displayed based on your timeframe 2. Set minimum gap size % to filter out noise (0.5-1% recommended for stocks) 3. Watch for price approaching gap zones for potential reactions 4. Use gaps as confluence with other technical factors: - Support/resistance levels - Fibonacci retracements - Supply/demand zones - Trend lines and channels 5. Set alerts to notify you when price enters key gap zones █ TRADING TIPS - Gaps with strong contextual stories (earnings, news, breakouts) are most reliable - Multiple gaps in the same area create stronger zones - Unfilled gaps above price can act as resistance targets - Unfilled gaps below price can act as support targets - Watch for "gap and go" vs "gap fill" scenarios based on market context █ SETTINGS - Maximum Number of Gaps: Control how many historical gaps to display - Minimum Gap Size %: Filter out insignificant gaps - Colors: Customize gap up and gap down zone colors - Transparency: Adjust visibility while maintaining chart readability - Show Borders: Toggle gap zone borders on/off - Alerts: Automatic notifications when price crosses gap boundaries █ BEST TIMEFRAMES Works on all timeframes but most effective on: - Daily charts for swing trading - 4H for intraday position trading - 1H for day trading key levels - Weekly for long-term investing Remember: Gaps are most powerful when they align with your overall market thesis and other technical confluences. They should confirm your narrative, not define it. --- Updates: Real-time gap detection | Alert system | Extended visualization | Performance optimized
Penunjuk Pine Script®
oleh jacesabr_real
Small Business Economic Conditions - Statistical Analysis ModelThe Small Business Economic Conditions Statistical Analysis Model (SBO-SAM) represents an econometric approach to measuring and analyzing the economic health of small business enterprises through multi-dimensional factor analysis and statistical methodologies. This indicator synthesizes eight fundamental economic components into a composite index that provides real-time assessment of small business operating conditions with statistical rigor. The model employs Z-score standardization, variance-weighted aggregation, higher-order moment analysis, and regime-switching detection to deliver comprehensive insights into small business economic conditions with statistical confidence intervals and multi-language accessibility. 1. Introduction and Theoretical Foundation The development of quantitative models for assessing small business economic conditions has gained significant importance in contemporary financial analysis, particularly given the critical role small enterprises play in economic development and employment generation. Small businesses, typically defined as enterprises with fewer than 500 employees according to the U.S. Small Business Administration, constitute approximately 99.9% of all businesses in the United States and employ nearly half of the private workforce (U.S. Small Business Administration, 2024). The theoretical framework underlying the SBO-SAM model draws extensively from established academic research in small business economics and quantitative finance. The foundational understanding of key drivers affecting small business performance builds upon the seminal work of Dunkelberg and Wade (2023) in their analysis of small business economic trends through the National Federation of Independent Business (NFIB) Small Business Economic Trends survey. Their research established the critical importance of optimism, hiring plans, capital expenditure intentions, and credit availability as primary determinants of small business performance. The model incorporates insights from Federal Reserve Board research, particularly the Senior Loan Officer Opinion Survey (Federal Reserve Board, 2024), which demonstrates the critical importance of credit market conditions in small business operations. This research consistently shows that small businesses face disproportionate challenges during periods of credit tightening, as they typically lack access to capital markets and rely heavily on bank financing. The statistical methodology employed in this model follows the econometric principles established by Hamilton (1989) in his work on regime-switching models and time series analysis. Hamilton's framework provides the theoretical foundation for identifying different economic regimes and understanding how economic relationships may vary across different market conditions. The variance-weighted aggregation technique draws from modern portfolio theory as developed by Markowitz (1952) and later refined by Sharpe (1964), applying these concepts to economic indicator construction rather than traditional asset allocation. Additional theoretical support comes from the work of Engle and Granger (1987) on cointegration analysis, which provides the statistical framework for combining multiple time series while maintaining long-term equilibrium relationships. The model also incorporates insights from behavioral economics research by Kahneman and Tversky (1979) on prospect theory, recognizing that small business decision-making may exhibit systematic biases that affect economic outcomes. 2. Model Architecture and Component Structure The SBO-SAM model employs eight orthogonalized economic factors that collectively capture the multifaceted nature of small business operating conditions. Each component is normalized using Z-score standardization with a rolling 252-day window, representing approximately one business year of trading data. This approach ensures statistical consistency across different market regimes and economic cycles, following the methodology established by Tsay (2010) in his treatment of financial time series analysis. 2.1 Small Cap Relative Performance Component The first component measures the performance of the Russell 2000 index relative to the S&P 500, capturing the market-based assessment of small business equity valuations. This component reflects investor sentiment toward smaller enterprises and provides a forward-looking perspective on small business prospects. The theoretical justification for this component stems from the efficient market hypothesis as formulated by Fama (1970), which suggests that stock prices incorporate all available information about future prospects. The calculation employs a 20-day rate of change with exponential smoothing to reduce noise while preserving signal integrity. The mathematical formulation is: Small_Cap_Performance = (Russell_2000_t / S&P_500_t) / (Russell_2000_{t-20} / S&P_500_{t-20}) - 1 This relative performance measure eliminates market-wide effects and isolates the specific performance differential between small and large capitalization stocks, providing a pure measure of small business market sentiment. 2.2 Credit Market Conditions Component Credit Market Conditions constitute the second component, incorporating commercial lending volumes and credit spread dynamics. This factor recognizes that small businesses are particularly sensitive to credit availability and borrowing costs, as established in numerous Federal Reserve studies (Bernanke and Gertler, 1995). Small businesses typically face higher borrowing costs and more stringent lending standards compared to larger enterprises, making credit conditions a critical determinant of their operating environment. The model calculates credit spreads using high-yield bond ETFs relative to Treasury securities, providing a market-based measure of credit risk premiums that directly affect small business borrowing costs. The component also incorporates commercial and industrial loan growth data from the Federal Reserve's H.8 statistical release, which provides direct evidence of lending activity to businesses. The mathematical specification combines these elements as: Credit_Conditions = α₁ × (HYG_t / TLT_t) + α₂ × C&I_Loan_Growth_t where HYG represents high-yield corporate bond ETF prices, TLT represents long-term Treasury ETF prices, and C&I_Loan_Growth represents the rate of change in commercial and industrial loans outstanding. 2.3 Labor Market Dynamics Component The Labor Market Dynamics component captures employment cost pressures and labor availability metrics through the relationship between job openings and unemployment claims. This factor acknowledges that labor market tightness significantly impacts small business operations, as these enterprises typically have less flexibility in wage negotiations and face greater challenges in attracting and retaining talent during periods of low unemployment. The theoretical foundation for this component draws from search and matching theory as developed by Mortensen and Pissarides (1994), which explains how labor market frictions affect employment dynamics. Small businesses often face higher search costs and longer hiring processes, making them particularly sensitive to labor market conditions. The component is calculated as: Labor_Tightness = Job_Openings_t / (Unemployment_Claims_t × 52) This ratio provides a measure of labor market tightness, with higher values indicating greater difficulty in finding workers and potential wage pressures. 2.4 Consumer Demand Strength Component Consumer Demand Strength represents the fourth component, combining consumer sentiment data with retail sales growth rates. Small businesses are disproportionately affected by consumer spending patterns, making this component crucial for assessing their operating environment. The theoretical justification comes from the permanent income hypothesis developed by Friedman (1957), which explains how consumer spending responds to both current conditions and future expectations. The model weights consumer confidence and actual spending data to provide both forward-looking sentiment and contemporaneous demand indicators. The specification is: Demand_Strength = β₁ × Consumer_Sentiment_t + β₂ × Retail_Sales_Growth_t where β₁ and β₂ are determined through principal component analysis to maximize the explanatory power of the combined measure. 2.5 Input Cost Pressures Component Input Cost Pressures form the fifth component, utilizing producer price index data to capture inflationary pressures on small business operations. This component is inversely weighted, recognizing that rising input costs negatively impact small business profitability and operating conditions. Small businesses typically have limited pricing power and face challenges in passing through cost increases to customers, making them particularly vulnerable to input cost inflation. The theoretical foundation draws from cost-push inflation theory as described by Gordon (1988), which explains how supply-side price pressures affect business operations. The model employs a 90-day rate of change to capture medium-term cost trends while filtering out short-term volatility: Cost_Pressure = -1 × (PPI_t / PPI_{t-90} - 1) The negative weighting reflects the inverse relationship between input costs and business conditions. 2.6 Monetary Policy Impact Component Monetary Policy Impact represents the sixth component, incorporating federal funds rates and yield curve dynamics. Small businesses are particularly sensitive to interest rate changes due to their higher reliance on variable-rate financing and limited access to capital markets. The theoretical foundation comes from monetary transmission mechanism theory as developed by Bernanke and Blinder (1992), which explains how monetary policy affects different segments of the economy. The model calculates the absolute deviation of federal funds rates from a neutral 2% level, recognizing that both extremely low and high rates can create operational challenges for small enterprises. The yield curve component captures the shape of the term structure, which affects both borrowing costs and economic expectations: Monetary_Impact = γ₁ × |Fed_Funds_Rate_t - 2.0| + γ₂ × (10Y_Yield_t - 2Y_Yield_t) 2.7 Currency Valuation Effects Component Currency Valuation Effects constitute the seventh component, measuring the impact of US Dollar strength on small business competitiveness. A stronger dollar can benefit businesses with significant import components while disadvantaging exporters. The model employs Dollar Index volatility as a proxy for currency-related uncertainty that affects small business planning and operations. The theoretical foundation draws from international trade theory and the work of Krugman (1987) on exchange rate effects on different business segments. Small businesses often lack hedging capabilities, making them more vulnerable to currency fluctuations: Currency_Impact = -1 × DXY_Volatility_t 2.8 Regional Banking Health Component The eighth and final component, Regional Banking Health, assesses the relative performance of regional banks compared to large financial institutions. Regional banks traditionally serve as primary lenders to small businesses, making their health a critical factor in small business credit availability and overall operating conditions. This component draws from the literature on relationship banking as developed by Boot (2000), which demonstrates the importance of bank-borrower relationships, particularly for small enterprises. The calculation compares regional bank performance to large financial institutions: Banking_Health = (Regional_Banks_Index_t / Large_Banks_Index_t) - 1 3. Statistical Methodology and Advanced Analytics The model employs statistical techniques to ensure robustness and reliability. Z-score normalization is applied to each component using rolling 252-day windows, providing standardized measures that remain consistent across different time periods and market conditions. This approach follows the methodology established by Engle and Granger (1987) in their cointegration analysis framework. 3.1 Variance-Weighted Aggregation The composite index calculation utilizes variance-weighted aggregation, where component weights are determined by the inverse of their historical variance. This approach, derived from modern portfolio theory, ensures that more stable components receive higher weights while reducing the impact of highly volatile factors. The mathematical formulation follows the principle that optimal weights are inversely proportional to variance, maximizing the signal-to-noise ratio of the composite indicator. The weight for component i is calculated as: w_i = (1/σᵢ²) / Σⱼ(1/σⱼ²) where σᵢ² represents the variance of component i over the lookback period. 3.2 Higher-Order Moment Analysis Higher-order moment analysis extends beyond traditional mean and variance calculations to include skewness and kurtosis measurements. Skewness provides insight into the asymmetry of the sentiment distribution, while kurtosis measures the tail behavior and potential for extreme events. These metrics offer valuable information about the underlying distribution characteristics and potential regime changes. Skewness is calculated as: Skewness = E / σ³ Kurtosis is calculated as: Kurtosis = E / σ⁴ - 3 where μ represents the mean and σ represents the standard deviation of the distribution. 3.3 Regime-Switching Detection The model incorporates regime-switching detection capabilities based on the Hamilton (1989) framework. This allows for identification of different economic regimes characterized by distinct statistical properties. The regime classification employs percentile-based thresholds: - Regime 3 (Very High): Percentile rank > 80 - Regime 2 (High): Percentile rank 60-80 - Regime 1 (Moderate High): Percentile rank 50-60 - Regime 0 (Neutral): Percentile rank 40-50 - Regime -1 (Moderate Low): Percentile rank 30-40 - Regime -2 (Low): Percentile rank 20-30 - Regime -3 (Very Low): Percentile rank < 20 3.4 Information Theory Applications The model incorporates information theory concepts, specifically Shannon entropy measurement, to assess the information content of the sentiment distribution. Shannon entropy, as developed by Shannon (1948), provides a measure of the uncertainty or information content in a probability distribution: H(X) = -Σᵢ p(xᵢ) log₂ p(xᵢ) Higher entropy values indicate greater unpredictability and information content in the sentiment series. 3.5 Long-Term Memory Analysis The Hurst exponent calculation provides insight into the long-term memory characteristics of the sentiment series. Originally developed by Hurst (1951) for analyzing Nile River flow patterns, this measure has found extensive application in financial time series analysis. The Hurst exponent H is calculated using the rescaled range statistic: H = log(R/S) / log(T) where R/S represents the rescaled range and T represents the time period. Values of H > 0.5 indicate long-term positive autocorrelation (persistence), while H < 0.5 indicates mean-reverting behavior. 3.6 Structural Break Detection The model employs Chow test approximation for structural break detection, based on the methodology developed by Chow (1960). This technique identifies potential structural changes in the underlying relationships by comparing the stability of regression parameters across different time periods: Chow_Statistic = (RSS_restricted - RSS_unrestricted) / RSS_unrestricted × (n-2k)/k where RSS represents residual sum of squares, n represents sample size, and k represents the number of parameters. 4. Implementation Parameters and Configuration 4.1 Language Selection Parameters The model provides comprehensive multi-language support across five languages: English, German (Deutsch), Spanish (Español), French (Français), and Japanese (日本語). This feature enhances accessibility for international users and ensures cultural appropriateness in terminology usage. The language selection affects all internal displays, statistical classifications, and alert messages while maintaining consistency in underlying calculations. 4.2 Model Configuration Parameters Calculation Method: Users can select from four aggregation methodologies: - Equal-Weighted: All components receive identical weights - Variance-Weighted: Components weighted inversely to their historical variance - Principal Component: Weights determined through principal component analysis - Dynamic: Adaptive weighting based on recent performance Sector Specification: The model allows for sector-specific calibration: - General: Broad-based small business assessment - Retail: Emphasis on consumer demand and seasonal factors - Manufacturing: Enhanced weighting of input costs and currency effects - Services: Focus on labor market dynamics and consumer demand - Construction: Emphasis on credit conditions and monetary policy Lookback Period: Statistical analysis window ranging from 126 to 504 trading days, with 252 days (one business year) as the optimal default based on academic research. Smoothing Period: Exponential moving average period from 1 to 21 days, with 5 days providing optimal noise reduction while preserving signal integrity. 4.3 Statistical Threshold Parameters Upper Statistical Boundary: Configurable threshold between 60-80 (default 70) representing the upper significance level for regime classification. Lower Statistical Boundary: Configurable threshold between 20-40 (default 30) representing the lower significance level for regime classification. Statistical Significance Level (α): Alpha level for statistical tests, configurable between 0.01-0.10 with 0.05 as the standard academic default. 4.4 Display and Visualization Parameters Color Theme Selection: Eight professional color schemes optimized for different user preferences and accessibility requirements: - Gold: Traditional financial industry colors - EdgeTools: Professional blue-gray scheme - Behavioral: Psychology-based color mapping - Quant: Value-based quantitative color scheme - Ocean: Blue-green maritime theme - Fire: Warm red-orange theme - Matrix: Green-black technology theme - Arctic: Cool blue-white theme Dark Mode Optimization: Automatic color adjustment for dark chart backgrounds, ensuring optimal readability across different viewing conditions. Line Width Configuration: Main index line thickness adjustable from 1-5 pixels for optimal visibility. Background Intensity: Transparency control for statistical regime backgrounds, adjustable from 90-99% for subtle visual enhancement without distraction. 4.5 Alert System Configuration Alert Frequency Options: Three frequency settings to match different trading styles: - Once Per Bar: Single alert per bar formation - Once Per Bar Close: Alert only on confirmed bar close - All: Continuous alerts for real-time monitoring Statistical Extreme Alerts: Notifications when the index reaches 99% confidence levels (Z-score > 2.576 or < -2.576). Regime Transition Alerts: Notifications when statistical boundaries are crossed, indicating potential regime changes. 5. Practical Application and Interpretation Guidelines 5.1 Index Interpretation Framework The SBO-SAM index operates on a 0-100 scale with statistical normalization ensuring consistent interpretation across different time periods and market conditions. Values above 70 indicate statistically elevated small business conditions, suggesting favorable operating environment with potential for expansion and growth. Values below 30 indicate statistically reduced conditions, suggesting challenging operating environment with potential constraints on business activity. The median reference line at 50 represents the long-term equilibrium level, with deviations providing insight into cyclical conditions relative to historical norms. The statistical confidence bands at 95% levels (approximately ±2 standard deviations) help identify when conditions reach statistically significant extremes. 5.2 Regime Classification System The model employs a seven-level regime classification system based on percentile rankings: Very High Regime (P80+): Exceptional small business conditions, typically associated with strong economic growth, easy credit availability, and favorable regulatory environment. Historical analysis suggests these periods often precede economic peaks and may warrant caution regarding sustainability. High Regime (P60-80): Above-average conditions supporting business expansion and investment. These periods typically feature moderate growth, stable credit conditions, and positive consumer sentiment. Moderate High Regime (P50-60): Slightly above-normal conditions with mixed signals. Careful monitoring of individual components helps identify emerging trends. Neutral Regime (P40-50): Balanced conditions near long-term equilibrium. These periods often represent transition phases between different economic cycles. Moderate Low Regime (P30-40): Slightly below-normal conditions with emerging headwinds. Early warning signals may appear in credit conditions or consumer demand. Low Regime (P20-30): Below-average conditions suggesting challenging operating environment. Businesses may face constraints on growth and expansion. Very Low Regime (P0-20): Severely constrained conditions, typically associated with economic recessions or financial crises. These periods often present opportunities for contrarian positioning. 5.3 Component Analysis and Diagnostics Individual component analysis provides valuable diagnostic information about the underlying drivers of overall conditions. Divergences between components can signal emerging trends or structural changes in the economy. Credit-Labor Divergence: When credit conditions improve while labor markets tighten, this may indicate early-stage economic acceleration with potential wage pressures. Demand-Cost Divergence: Strong consumer demand coupled with rising input costs suggests inflationary pressures that may constrain small business margins. Market-Fundamental Divergence: Disconnection between small-cap equity performance and fundamental conditions may indicate market inefficiencies or changing investor sentiment. 5.4 Temporal Analysis and Trend Identification The model provides multiple temporal perspectives through momentum analysis, rate of change calculations, and trend decomposition. The 20-day momentum indicator helps identify short-term directional changes, while the Hodrick-Prescott filter approximation separates cyclical components from long-term trends. Acceleration analysis through second-order momentum calculations provides early warning signals for potential trend reversals. Positive acceleration during declining conditions may indicate approaching inflection points, while negative acceleration during improving conditions may suggest momentum loss. 5.5 Statistical Confidence and Uncertainty Quantification The model provides comprehensive uncertainty quantification through confidence intervals, volatility measures, and regime stability analysis. The 95% confidence bands help users understand the statistical significance of current readings and identify when conditions reach historically extreme levels. Volatility analysis provides insight into the stability of current conditions, with higher volatility indicating greater uncertainty and potential for rapid changes. The regime stability measure, calculated as the inverse of volatility, helps assess the sustainability of current conditions. 6. Risk Management and Limitations 6.1 Model Limitations and Assumptions The SBO-SAM model operates under several important assumptions that users must understand for proper interpretation. The model assumes that historical relationships between economic variables remain stable over time, though the regime-switching framework helps accommodate some structural changes. The 252-day lookback period provides reasonable statistical power while maintaining sensitivity to changing conditions, but may not capture longer-term structural shifts. The model's reliance on publicly available economic data introduces inherent lags in some components, particularly those based on government statistics. Users should consider these timing differences when interpreting real-time conditions. Additionally, the model's focus on quantitative factors may not fully capture qualitative factors such as regulatory changes, geopolitical events, or technological disruptions that could significantly impact small business conditions. The model's timeframe restrictions ensure statistical validity by preventing application to intraday periods where the underlying economic relationships may be distorted by market microstructure effects, trading noise, and temporal misalignment with the fundamental data sources. Users must utilize daily or longer timeframes to ensure the model's statistical foundations remain valid and interpretable. 6.2 Data Quality and Reliability Considerations The model's accuracy depends heavily on the quality and availability of underlying economic data. Market-based components such as equity indices and bond prices provide real-time information but may be subject to short-term volatility unrelated to fundamental conditions. Economic statistics provide more stable fundamental information but may be subject to revisions and reporting delays. Users should be aware that extreme market conditions may temporarily distort some components, particularly those based on financial market data. The model's statistical normalization helps mitigate these effects, but users should exercise additional caution during periods of market stress or unusual volatility. 6.3 Interpretation Caveats and Best Practices The SBO-SAM model provides statistical analysis and should not be interpreted as investment advice or predictive forecasting. The model's output represents an assessment of current conditions based on historical relationships and may not accurately predict future outcomes. Users should combine the model's insights with other analytical tools and fundamental analysis for comprehensive decision-making. The model's regime classifications are based on historical percentile rankings and may not fully capture the unique characteristics of current economic conditions. Users should consider the broader economic context and potential structural changes when interpreting regime classifications. 7. Academic References and Bibliography Bernanke, B. S., & Blinder, A. S. (1992). The Federal Funds Rate and the Channels of Monetary Transmission. American Economic Review, 82(4), 901-921. Bernanke, B. S., & Gertler, M. (1995). Inside the Black Box: The Credit Channel of Monetary Policy Transmission. Journal of Economic Perspectives, 9(4), 27-48. Boot, A. W. A. (2000). Relationship Banking: What Do We Know? Journal of Financial Intermediation, 9(1), 7-25. Chow, G. C. (1960). Tests of Equality Between Sets of Coefficients in Two Linear Regressions. Econometrica, 28(3), 591-605. Dunkelberg, W. C., & Wade, H. (2023). NFIB Small Business Economic Trends. National Federation of Independent Business Research Foundation, Washington, D.C. Engle, R. F., & Granger, C. W. J. (1987). Co-integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55(2), 251-276. Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. Journal of Finance, 25(2), 383-417. Federal Reserve Board. (2024). Senior Loan Officer Opinion Survey on Bank Lending Practices. Board of Governors of the Federal Reserve System, Washington, D.C. Friedman, M. (1957). A Theory of the Consumption Function. Princeton University Press, Princeton, NJ. Gordon, R. J. (1988). The Role of Wages in the Inflation Process. American Economic Review, 78(2), 276-283. Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357-384. Hurst, H. E. (1951). Long-term Storage Capacity of Reservoirs. Transactions of the American Society of Civil Engineers, 116(1), 770-799. Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263-291. Krugman, P. (1987). Pricing to Market When the Exchange Rate Changes. In S. W. Arndt & J. D. Richardson (Eds.), Real-Financial Linkages among Open Economies (pp. 49-70). MIT Press, Cambridge, MA. Markowitz, H. (1952). Portfolio Selection. Journal of Finance, 7(1), 77-91. Mortensen, D. T., & Pissarides, C. A. (1994). Job Creation and Job Destruction in the Theory of Unemployment. Review of Economic Studies, 61(3), 397-415. Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell System Technical Journal, 27(3), 379-423. Sharpe, W. F. (1964). Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk. Journal of Finance, 19(3), 425-442. Tsay, R. S. (2010). Analysis of Financial Time Series (3rd ed.). John Wiley & Sons, Hoboken, NJ. U.S. Small Business Administration. (2024). Small Business Profile. Office of Advocacy, Washington, D.C. 8. Technical Implementation Notes The SBO-SAM model is implemented in Pine Script version 6 for the TradingView platform, ensuring compatibility with modern charting and analysis tools. The implementation follows best practices for financial indicator development, including proper error handling, data validation, and performance optimization. The model includes comprehensive timeframe validation to ensure statistical accuracy and reliability. The indicator operates exclusively on daily (1D) timeframes or higher, including weekly (1W), monthly (1M), and longer periods. This restriction ensures that the statistical analysis maintains appropriate temporal resolution for the underlying economic data sources, which are primarily reported on daily or longer intervals. When users attempt to apply the model to intraday timeframes (such as 1-minute, 5-minute, 15-minute, 30-minute, 1-hour, 2-hour, 4-hour, 6-hour, 8-hour, or 12-hour charts), the system displays a comprehensive error message in the user's selected language and prevents execution. This safeguard protects users from potentially misleading results that could occur when applying daily-based economic analysis to shorter timeframes where the underlying data relationships may not hold. The model's statistical calculations are performed using vectorized operations where possible to ensure computational efficiency. The multi-language support system employs Unicode character encoding to ensure proper display of international characters across different platforms and devices. The alert system utilizes TradingView's native alert functionality, providing users with flexible notification options including email, SMS, and webhook integrations. The alert messages include comprehensive statistical information to support informed decision-making. The model's visualization system employs professional color schemes designed for optimal readability across different chart backgrounds and display devices. The system includes dynamic color transitions based on momentum and volatility, professional glow effects for enhanced line visibility, and transparency controls that allow users to customize the visual intensity to match their preferences and analytical requirements. The clean confidence band implementation provides clear statistical boundaries without visual distractions, maintaining focus on the analytical content.
Penunjuk Pine Script®
oleh EdgeTools
DashBoard 2.3.1📌 Indicator Name: DashBoard 2.3 – Smart Visual Market Overlay 📋 Description: DashBoard 2.3 is a clean, efficient, and highly informative market overlay, designed to give you real-time context directly on your chart — without distractions. Whether you're swing trading or investing long-term, this tool keeps critical market data at your fingertips. 🔍 Key Features: Symbol + Timeframe + Market Cap Shows the current ticker and timeframe, optionally with real-time market cap. ATR 14 with Volatility Signal Displays ATR with color-coded risk levels: 🟢 Low 🟡 Moderate 🔴 High ⚫️ Extreme You can choose between Daily ATR or timeframe-based ATR (auto-adjusted to chart resolution). Adaptive Labeling The ATR label updates to reflect the resolution: ATR 14d (daily) ATR 14W (weekly) ATR 14H (hourly), etc. Moving Average Tracker Instantly shows whether price is above or below your selected moving average (e.g., 150 MA), with green/red indication. Earnings Countdown Clearly shows how many days remain until the next earnings report. Industry & Sector Info (optional) Useful for thematic or sector-based trading strategies. Fully Customizable UI Choose positioning, padding, font size, and which data to show. Designed for minimalism and clarity. ✅ Smart Logic: Color dots appear only in relevant conditions (e.g., ATR color signals shown only on daily when enabled). ATR display automatically reflects your time frame, if selected. Clean chart integration – the overlay sits quietly in a corner, enhancing your analysis without intruding. 🧠 Ideal for: Swing traders, position traders, and investors who want fast, high-impact insights directly from the chart. Anyone looking for a compact, beautiful, and informative dashboard while they trade.
Penunjuk Pine Script®
oleh AdiPlaut
Mongoose Global Conflict Risk Index v1Overview The Mongoose Global Conflict Risk Index v1 is a multi-asset composite indicator designed to track the early pricing of geopolitical stress and potential conflict risk across global markets. By combining signals from safe havens, volatility indices, energy markets, and emerging market equities, the index provides a normalized 0–10 score with clear bias classifications (Neutral, Caution, Elevated, High, Shock). This tool is not predictive of headlines but captures when markets are clustering around conflict-sensitive assets before events are widely recognized. Methodology The indicator calculates rolling rate-of-change z-scores for eight conflict-sensitive assets: Gold (XAUUSD) – classic safe haven US Dollar Index (DXY) – global reserve currency flows VIX (Equity Volatility) – S&P 500 implied volatility OVX (Crude Oil Volatility Index) – energy stress gauge Crude Oil (CL1!) – WTI front contract Natural Gas (NG1!) – energy security proxy, especially Europe EEM (Emerging Markets ETF) – global risk capital flight FXI (China ETF) – Asia/China proxy risk Rules: Safe havens and vol indices trigger when z-score > threshold. Energy triggers when z-score > threshold. Risk assets trigger when z-score < –threshold. Each trigger is assigned a weight, summed, normalized, and scaled 0–10. Bias classification: 0–2: Neutral 2–4: Caution 4–6: Elevated 6–8: High 8–10: Conflict Risk-On How to Use Timeframes: Daily (1D) for strategic signals and early warnings. 4H for event shocks (missiles, sanctions, sudden escalations). Weekly (1W) for sustained trends and macro build-ups. What to Look For: A single trigger (for example, Gold ON) may be noise. A cluster of 2–3 triggers across Gold, USD, VIX, and Energy often marks early stress pricing. Elevated readings (>4) = caution; High (>6) = rotation into havens; Shock (>8) = market conviction of conflict risk. Practical Application: Monitor as a heatmap of global stress. Combine with fundamental or headline tracking. Use alert conditions at ≥4, ≥6, ≥8 for systematic monitoring. Notes This indicator is for informational and educational purposes only. It is not financial advice and should be used in conjunction with other analysis methods.
Penunjuk Pine Script®
oleh TheRealMongoose
US Net Liquidity + M2 / US Debt (FRED)US Net Liquidity + M2 / US Debt 🧩 What this chart shows This indicator plots the ratio of US Net Liquidity + M2 Money Supply divided by Total Public Debt. US Net Liquidity is defined here as the Federal Reserve Balance Sheet (WALCL) minus the Treasury General Account (TGA) and the Overnight Reverse Repo facility (ON RRP). M2 Money Supply represents the broad pool of liquid money circulating in the economy. US Debt uses the Federal Government’s total outstanding debt. By combining net liquidity with M2, then dividing by total debt, this chart provides a structural view of how much monetary “fuel” is in the system relative to the size of the federal debt load. 🧮 Formula Ratio = ( Fed Balance Sheet − ( TGA + ON RRP ) ) + M2 Total Public Debt Ratio= Total Public Debt (Fed Balance Sheet−(TGA+ON RRP))+M2 ​ An optional normalization feature scales the ratio to start at 100 on the first valid bar, making long-term trends easier to compare. 🔎 Why it matters Liquidity vs. Debt Growth: The numerator (Net Liquidity + M2) captures the monetary resources available to markets, while the denominator (Debt) reflects the expanding obligation of the federal government. Market Signal: Historically, shifts in net liquidity and money supply relative to debt have coincided with major turning points in risk assets like equities and Bitcoin. Context: A rising ratio may suggest that liquidity conditions are improving relative to debt expansion, which can be supportive for risk assets. Conversely, a falling ratio may highlight tightening conditions or debt outpacing liquidity growth. ⚙️ How to use it Overlay this chart against S&P 500, Bitcoin, or gold to analyze correlations with asset performance. Watch for trend inflections—does the ratio bottom before equities rally, or peak before risk-off periods? Use normalization for long historical comparisons, or raw values to see the absolute ratio. 📊 Data sources This indicator pulls from FRED (Federal Reserve Economic Data) tickers available in TradingView: WALCL: Fed balance sheet RRPONTSYD: Overnight Reverse Repo WTREGEN: Treasury General Account M2SL: M2 money stock GFDEBTN: Total federal public debt ⚠️ Notes Some FRED series are updated weekly, others monthly—set your chart timeframe accordingly. If any ticker is unavailable in your plan, replace it with the equivalent FRED symbol provided in TradingView. This indicator is intended for macro analysis, not short-term trading signals.
Penunjuk Pine Script®
oleh MBCIM