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Crowding model ║ BullVision🔬 Overview The Crypto Crowding Model Pro is a sophisticated analytical tool designed to visualize and quantify market conditions across multiple cryptocurrencies. By leveraging Relative Strength Index (RSI) and Z-score calculations, this indicator provides traders with an intuitive and detailed snapshot of current crypto market dynamics, highlighting areas of extreme momentum, crowded trades, and potential reversal points. ⚙️ Key Concepts 📊 RSI and Z-Score Analysis RSI (Relative Strength Index) evaluates the momentum and strength of each cryptocurrency, identifying overbought or oversold conditions. Z-Score Normalization measures each asset's current price deviation relative to its historical average, identifying statistically significant extremes. 🎯 Crowding Analytics An integrated analytics panel provides real-time crowding metrics, quantifying market sentiment into four distinct categories: 🔥 FOMO (Fear of Missing Out): High momentum, potential exhaustion. ❄️ Fear: Low momentum, potential reversal or consolidation. 📈 Recovery: Moderate upward momentum after a downward trend. 💪 Strength: Stable bullish conditions with sustained momentum. 🖥️ Visual Scatter Plot Assets are plotted on a dynamic scatter plot, positioning each cryptocurrency according to its RSI and Z-score. Color coding, symbol shapes, and sizes help quickly identify main market segments (BTC, ETH, TOTAL, OTHERS) and individual asset conditions. 🧩 Quadrant Classification Assets are categorized into four quadrants based on their momentum and deviation: Overbought Extended: High RSI and positive Z-score. Recovery Phase: Low RSI but positive Z-score. Oversold Compressed: Low RSI and negative Z-score. Strong Consolidation: High RSI but negative Z-score. 🔧 User Customization 🎨 Visual Settings Bar Scale: Adjust the scatter plot visual scale. Asset Visibility: Optionally display key market benchmarks (TOTAL, BTC, ETH, OTHERS). Gradient Background: Enhances visual interpretation of asset clusters. Crowding Analytics Panel: Toggle the analytics panel on/off. 📊 Indicator Parameters RSI Length: Defines the calculation period for RSI. Z-score Lookback: Historical lookback period for normalization. Crowding Alert Threshold: Sets alert sensitivity for crowded market conditions. 🎯 Zone Settings Quadrant Labels: Displays descriptive labels for each quadrant. Danger Zones: Highlights extreme RSI levels indicative of heightened market risk. 📈 Visual Output Dynamic Scatter Plot: Visualizes asset positioning clearly and intuitively. Gradient and Grid: Professional gridlines and subtle gradient backgrounds assist visual assessment. Danger Zone Highlights: Visually indicates RSI extremes to warn of potential market turning points. Crowding Analytics Panel: Real-time summary of market sentiment and asset distribution. 🔍 Use Cases This indicator is particularly beneficial for traders and analysts looking to: Identify crowded trades and potential reversal points. Quickly assess overall market sentiment and individual asset strength. Integrate a robust momentum analysis into broader technical or fundamental strategies. Enhance market timing and improve risk management decisions. ⚠️ Important Notes This indicator does not provide explicit buy or sell signals. It is intended solely for informational, analytical, and educational purposes. Past performance and signals are not indicative of future market results. Always combine with additional tools and analysis as part of comprehensive decision-making.
Pine Script®指標
由BullVisionCapital提供
2
Dynamic Gap Probability ToolDynamic Gap Probability Tool measures the percentage gap between price and a chosen moving average, then analyzes your chart history to estimate the likelihood of the next candle moving up or down. It dynamically adjusts its sample size to ensure statistical robustness while focusing on the exact deviation level. Originality and Value: • Combines gap-based analysis with dynamic sample aggregation to balance precision and reliability. • Automatically extends the sample when exact matches are scarce, avoiding misleading signals on rare extreme moves. • Provides real “next-candle” probabilities based on historical occurrences rather than fixed thresholds or untested heuristics. • Adds value by giving traders an evidence-based edge: you see how similar past deviations actually played out. How It Works: 1. Calculate gap = (close – moving average) / moving average * 100. 2. Round the absolute gap to nearest percent (X%). 3. Count historical bars where gap ≥ X% above or ≤ –X% below. 4. If exact X% count is below the minimum occurrences threshold, include gaps at X+1%, X+2%, etc., until threshold is reached. 5. Compute “next-candle” green vs. red probabilities from the aggregated sample. 6. Display current gap, sample size, green probability, and red probability in a table. Inputs: • Moving Average Type (SMA, EMA, WMA, VWMA, HMA, SMMA, TMA) • Moving Average Period (default 200) • Minimum Occurrences Threshold (default 50) • Table position and styling options Examples: • If price is 3% above the 200-period SMA and 120 occurrences ≥3% are found, with 84 green next candles (70%) and 36 red (30%), the script displays “3% | 120 | 70% green | 30% red.” • If price is 8% below the SMA but only 20 exact matches exist, the script will include 9% and 10% gaps until it reaches 50 samples, then calculate probabilities from that broader set. Why It’s Useful: • Mean-reversion traders see green-probability signals at extreme overbought or oversold levels. • Trend-followers identify continuation likelihood when red probability is high. • Risk managers gauge reliability by inspecting sample size before acting on any signal. Limitations: • Historical probabilities do not guarantee future performance. • Results depend on timeframe and symbol, backtest with your data before trading. • Use realistic slippage and commission when overlaying on strategy scripts.
Pine Script®指標
由Julien_Eche提供
8
EVaR Indicator and Position SizingThe Problem: Financial markets consistently show "fat-tailed" distributions where extreme events occur with higher frequency than predicted by normal distributions (Gaussian or even log-normal). These fat tails manifest in sudden price crashes, volatility spikes, and black swan events that traditional risk measures like volatility can underestimate. Standard deviation and conventional VaR calculations assume normally distributed returns, leaving traders vulnerable to severe drawdowns during market stress. Cryptocurrencies and volatile instruments display particularly pronounced fat-tailed behavior, with extreme moves occurring 5-10 times more frequently than normal distribution models would predict. This reality demands a more sophisticated approach to risk measurement and position sizing. The Solution: Entropic Value at Risk (EVAR) EVaR addresses these limitations by incorporating principles from statistical mechanics and information theory through Tsallis entropy. This advanced approach captures the non-linear dependencies and power-law distributions characteristic of real financial markets. Entropy is more adaptive than standard deviations and volatility measures. I was inspired to create this indicator after reading the paper " The End of Mean-Variance? Tsallis Entropy Revolutionises Portfolio Optimisation in Cryptocurrencies " by by Sana Gaied Chortane and Kamel Naoui. Key advantages of EVAR over traditional risk measures: Superior tail risk capture: More accurately quantifies the probability of extreme market moves Adaptability to market regimes: Self-calibrates to changing volatility environments Non-parametric flexibility: Makes less assumptions about the underlying return distribution Forward-looking risk assessment: Better anticipates potential market changes (just look at the charts :) Mathematically, EVAR is defined as: EVAR_α(X) = inf_{z>0} {z * log(1/α * M_X(1/z))} Where the moment-generating function is calculated using q-exponentials rather than conventional exponentials, allowing precise modeling of fat-tailed behavior. Technical Implementation This indicator implements EVAR through a q-exponential approach from Tsallis statistics: Returns Calculation: Price returns are calculated over the lookback period Moment Generating Function: Approximated using q-exponentials to account for fat tails EVAR Computation: Derived from the MGF and confidence parameter Normalization: Scaled to for intuitive visualization Position Sizing: Inversely modulated based on normalized EVAR The q-parameter controls tail sensitivity—higher values (1.5-2.0) increase the weighting of extreme events in the calculation, making the model more conservative during potentially turbulent conditions. Indicator Components 1. EVAR Risk Visualization Dynamic EVAR Plot: Color-coded from red to green normalized risk measurement (0-1) Risk Thresholds: Reference lines at 0.3, 0.5, and 0.7 delineating risk zones 2. Position Sizing Matrix Risk Assessment: Current risk level and raw EVAR value Position Recommendations: Percentage allocation, dollar value, and quantity Stop Parameters: Mathematically derived stop price with percentage distance Drawdown Projection: Maximum theoretical loss if stop is triggered Interpretation and Application The normalized EVAR reading provides a probabilistic risk assessment: < 0.3: Low risk environment with minimal tail concerns 0.3-0.5: Moderate risk with standard tail behavior 0.5-0.7: Elevated risk with increased probability of significant moves > 0.7: High risk environment with substantial tail risk present Position sizing is automatically calculated using an inverse relationship to EVAR, contracting during high-risk periods and expanding during low-risk conditions. This is a counter-cyclical approach that ensures consistent risk exposure across varying market regimes, especially when the market is hyped or overheated. Parameter Optimization For optimal risk assessment across market conditions: Lookback Period: Determines the historical window for risk calculation Q Parameter: Controls tail sensitivity (higher values increase conservatism) Confidence Level: Sets the statistical threshold for risk assessment For cryptocurrencies and highly volatile instruments, a q-parameter between 1.5-2.0 typically provides the most accurate risk assessment because it helps capturing the fat-tailed behavior characteristic of these markets. You can also increase the q-parameter for more conservative approaches. Practical Applications Adaptive Risk Management: Quantify and respond to changing tail risk conditions Volatility-Normalized Positioning: Maintain consistent exposure across market regimes Black Swan Detection: Early identification of potential extreme market conditions Portfolio Construction: Apply consistent risk-based sizing across diverse instruments This indicator is my own approach to entropy-based risk measures as an alterative to volatility and standard deviations and it helps with fat-tailed markets. Enjoy!
Pine Script®指標
由HenriqueCentieiro提供
12
Divergence Screener [Trendoscope®]🎲Overview The Divergence Screener is a powerful TradingView indicator designed to detect and visualize bullish and bearish divergences, including hidden divergences, between price action and a user-selected oscillator. Built with flexibility in mind, it allows traders to customize the oscillator type, trend detection method, and other parameters to suit various trading strategies. The indicator is non-overlay, displaying divergence signals directly on the oscillator plot, with visual cues such as lines and labels on the chart for easy identification. This indicator is ideal for traders seeking to identify potential reversal or continuation signals based on price-oscillator divergences. It supports multiple oscillators, trend detection methods, and alert configurations, making it versatile for different markets and timeframes. 🎲Features 🎯Customizable Oscillator Selection Built-in Oscillators : Choose from a variety of oscillators including RSI, CCI, CMO, COG, MFI, ROC, Stochastic, and WPR. External Oscillator Support : Users can input an external oscillator source, allowing integration with custom or third-party indicators. Configurable Length : Adjust the oscillator’s period (e.g., 14 for RSI) to fine-tune sensitivity. 🎯Divergence Detection The screener identifies four types of divergences: Bullish Divergence : Price forms a lower low, but the oscillator forms a higher low, signaling potential upward reversal. Bearish Divergence : Price forms a higher high, but the oscillator forms a lower high, indicating potential downward reversal. Bullish Hidden Divergence : Price forms a higher low, but the oscillator forms a lower low, suggesting trend continuation in an uptrend. Bearish Hidden Divergence : Price forms a lower high, but the oscillator forms a higher high, suggesting trend continuation in a downtrend. 🎯Flexible Trend Detection The indicator offers three methods to determine the trend context for divergence detection: Zigzag : Uses zigzag pivots to identify trends based on higher highs (HH), higher lows (HL), lower highs (LH), and lower lows (LL). MA Difference : Calculates the trend based on the difference in a moving average (e.g., SMA, EMA) between divergence pivots. External Trend Signal : Allows users to input an external trend signal (positive for uptrend, negative for downtrend) for custom trend analysis. 🎯Zigzag-Based Pivot Analysis Customizable Zigzag Length : Adjust the zigzag length (default: 13) to control the sensitivity of pivot detection. Repaint Option : Choose whether divergence lines repaint based on the latest data or wait for confirmed pivots, balancing responsiveness and reliability. 🎯Visual and Alert Features Divergence Visualization : Divergence lines are drawn between price pivots and oscillator pivots, color-coded for easy identification: Bullish Divergence : Green Bearish Divergence : Red Bullish Hidden Divergence : Lime Bearish Hidden Divergence : Orange Labels and Tooltips : Labels (e.g., “D” for divergence, “H” for hidden) appear on price and oscillator pivots, with tooltips providing detailed information such as price/oscillator values, ratios, and pivot directions. Alerts : Configurable alerts for each divergence type (bullish, bearish, bullish hidden, bearish hidden) trigger on bar close, ensuring timely notifications. 🎲 How It Works 🎯Oscillator Calculation The indicator calculates the selected oscillator (or uses an external source) and plots it on the chart. Oscillator values are stored in a map for reference during divergence calculations. 🎯Pivot Detection A zigzag algorithm identifies pivots in the oscillator data, with configurable length and repainting options. Price and oscillator pivots are compared to detect divergences based on their direction and ratio. 🎯Divergence Identification The indicator compares price and oscillator pivot directions (HH, HL, LH, LL) to identify divergences. Trend context is determined using the selected method (Zigzag, MA Difference, or External). Divergences are classified as bullish, bearish, bullish hidden, or bearish hidden based on price-oscillator relationships and trend direction. 🎯Visualization and Alerts Valid divergences are drawn as lines connecting price and oscillator pivots, with corresponding labels. Alerts are triggered for allowed divergence types, providing detailed information via tooltips. 🎯Validation Divergence lines are validated to ensure no intermediate bars violate the divergence condition, enhancing signal reliability. 🎲 Usage Instructions as Indicator 🎯Add to Chart: Add the “Divergence Screener ” to your TradingView chart. The indicator appears in a separate pane below the price chart, plotting the oscillator and divergence signals. 🎯Configure Settings: Adjust the oscillator type and length to match your trading style. Select a trend detection method and configure related parameters (e.g., MA type/length or external signal). Set the zigzag length and repainting preference. Enable/disable alerts for specific divergence types. I🎯nterpret Signals: Bullish Divergence (Green) : Look for potential buy opportunities in a downtrend. Bearish Divergence (Red) : Consider sell opportunities in an uptrend. Bullish Hidden Divergence (Lime) : Confirm continuation in an uptrend. Bearish Hidden Divergence (Orange): Confirm continuation in a downtrend. Use tooltips on labels to review detailed pivot and divergence information. 🎯Set Alerts: Create alerts for each divergence type to receive notifications via TradingView’s alert system. Alerts include detailed text with price, oscillator, and divergence information. 🎲 Example Scenarios as Indicator 🎯 With External Oscillator (Use MACD Histogram as Oscillator) In order to use MACD as an oscillator for divergence signal instead of the built in options, follow these steps. Load MACD Indicator from Indicator library From Indicator settings of Divergence Screener, set Use External Oscillator and select MACD Histograme from the dropdown You can now see that the oscillator pane shows the data of selected MACD histogram and divergence signals are generated based on the external MACD histogram data. 🎯 With External Trend Signal (Supertrend Ladder ATR) Now let's demonstrate how to use external direction signals using Supertrend Ladder ATR indicator. Please note that in order to use the indicator as trend source, the indicator should return positive integer for uptrend and negative integer for downtrend. Steps are as follows: Load the desired trend indicator. In this example, we are using Supertrend Ladder ATR From the settings of Divergence Screener, select "External" as Trend Detection Method Select the trend detection plot Direction from the dropdown. You can now see that the divergence signals will rely on the new trend settings rather than the built in options. 🎲 Using the Script with Pine Screener The primary purpose of the Divergence Screener is to enable traders to scan multiple instruments (e.g., stocks, ETFs, forex pairs) for divergence signals using TradingView’s Pine Screener, facilitating efficient comparison and identification of trading opportunities. To use the Divergence Screener as a screener, follow these steps: Add to Favorites : Add the Divergence Screener to your TradingView favorites to make it available in the Pine Screener. Create a Watchlist : Build a watchlist containing the instruments (e.g., stocks, ETFs, or forex pairs) you want to scan for divergences. Access Pine Screener : Navigate to the Pine Screener via TradingView’s main menu: Products -> Screeners -> Pine, or directly visit tradingview.com/pine-screener/. Select Watchlist : Choose the watchlist you created from the Watchlist dropdown in the Pine Screener interface. Choose Indicator : Select Divergence Screener from the Choose Indicator dropdown. Configure Settings : Set the desired timeframe (e.g., 1 hour, 1 day) and adjust indicator settings such as oscillator type, zigzag length, or trend detection method as needed. Select Filter Criteria : Select the condition on which the watchlist items needs to be filtered. Filtering can only be done on the plots defined in the script. Run Scan : Press the Scan button to display divergence signals across the selected instruments. The screener will show which instruments exhibit bullish, bearish, bullish hidden, or bearish hidden divergences based on the configured settings. 🎲 Limitations and Possible Future Enhancements Limitations are Custom input for oscillator and trend detection cannot be used in pine screener. Pine screener has max 500 bars available. Repaint option is by default enabled. When in repaint mode expect the early signal but the signals are prone to repaint. Possible future enhancements Add more built-in options for oscillators and trend detection methods so that dependency on external indicators is limited Multi level zigzag support
Pine Script®指標
由Trendoscope提供
4
Logarithmic Moving Average (LMA) [QuantAlgo]🟢 Overview The Logarithmic Moving Average (LMA) uses advanced logarithmic weighting to create a dynamic trend-following indicator that prioritizes recent price action while maintaining statistical significance. Unlike traditional moving averages that use linear or exponential weights, this indicator employs logarithmic decay functions to create a more sophisticated price averaging system that adapts to market volatility and momentum conditions. The indicator displays a smoothed signal line that oscillates around zero, with positive values indicating bullish momentum and negative values indicating bearish momentum. The signal incorporates trend quality assessment, momentum confirmation, and multiple filtering mechanisms to help traders and investors identify trend continuation and reversal opportunities across different timeframes and asset classes. 🟢 How It Works The indicator's core innovation lies in its logarithmic weighting system, where weights are calculated using the formula: w = 1.0 / math.pow(math.log(i + steepness), 2) The steepness parameter controls how aggressively recent data is prioritized over historical data, creating a dynamic weight decay that can be fine-tuned for different trading styles. This logarithmic approach provides more nuanced weight distribution compared to exponential moving averages, offering better responsiveness while maintaining stability. The LMA calculation combines multiple sophisticated components. First, it calculates the logarithmic weighted average of closing prices. Then it measures the slope of this average over a 10-period lookback: lmaSlope = (lma - lma ) / lma * 100 The system also incorporates trend quality assessment using R-squared correlation analysis of log-transformed prices, measuring how well the price data fits a linear trend model over the specified period. The final signal generation uses the formula: signal = lmaSlope * (0.5 + rSquared * 0.5) which combines the LMA slope with trend quality weighting. When momentum confirmation is enabled, the indicator calculates annualized log-return momentum and applies a multiplier when the momentum direction aligns with the signal direction, strengthening confirmed signals while filtering out weak or counter-trend movements. 🟢 How to Use 1. Signal Interpretation and Threshold Zones Positive Values (Above Zero): LMA slope indicating bullish momentum with upward price trajectory relative to logarithmic baseline Negative Values (Below Zero): LMA slope indicating bearish momentum with downward price trajectory relative to logarithmic baseline Zero Line Crosses: Signal transitions between bullish and bearish regimes, indicating potential trend changes Long Entry Threshold Zone: Area above positive threshold (default 0.5) indicating confirmed bullish signals suitable for long positions Short Entry Threshold Zone: Area below negative threshold (default -0.5) indicating confirmed bearish signals suitable for short positions Extreme Values: Signals exceeding ±1.0 represent strong momentum conditions with higher probability of continuation 2. Momentum Confirmation and Visual Analysis Signal Color Intensity: Gradient coloring shows signal strength, with brighter colors indicating stronger momentum Bar Coloring: Optional price bar coloring matches signal direction for quick visual trend identification Position Labels: Real-time position classification (Bullish/Bearish/Neutral) displayed on the latest bar Momentum Weight Factor: When short-term log-return momentum aligns with LMA signal direction, the signal receives additional weight confirmation Trend Quality Component: R-squared values weight the signal strength, with higher correlation indicating more reliable trend conditions 3. Examples: Preconfigured Settings Default: Universally applicable configuration balanced for medium-term investing and general trading across multiple timeframes and asset classes. Scalping: Highly responsive setup with shorter period and higher steepness for ultra-short-term trades on 1-15 minute charts, optimized for quick momentum shifts. Swing Trading: Extended period with moderate steepness and increased smoothing for multi-day positions, designed to filter noise while capturing larger price swings on 1-4 hour and daily charts. Trend Following: Maximum smoothing with lower steepness for established trend identification, generating fewer but more reliable signals optimal for daily and weekly timeframes. Mean Reversion: Shorter period with high steepness for counter-trend strategies, more sensitive to extreme moves and reversal opportunities in ranging market conditions.
Pine Script®指標
由QuantAlgo提供
15
Volumatic Support/Resistance Levels [BigBeluga]🔵 OVERVIEW A smart volume-powered tool for identifying key support and resistance zones—enhanced with real-time volume histogram fills and high-volume markers. Volumatic Support/Resistance Levels detects structural levels from swing highs and lows, and wraps them in dynamic histograms that reflect the relative volume strength around those zones. It highlights the strongest price levels not just by structure—but by the weight of market participation. 🔵 CONCEPTS Price Zones: Support and resistance levels are drawn from recent price pivots, while volume is used to visually enhance these zones with filled histograms and highlight moments of peak activity using markers. Histogram Fill = Activity Zone: The width and intensity of each filled zone adjusts to recent volume bursts. High-Volume Alerts: Circle markers highlight moments of volume dominance directly on the levels—revealing pressure points of support/resistance. Clean Visual Encoding: Red = resistance zones, green = support zones, orange = high-volume bars. 🔵 FEATURES Detects pivot-based resistance (highs) and support (lows) using a customizable range length. Wraps these levels in volume-weighted bands that expand/contract based on percentile volume. Color fill intensity increases with rising volume pressure, creating a live histogram feel. When volume > user-defined threshold , the indicator adds circle markers at the top and bottom of that price level zone. Bar coloring highlights the candles that generated this high-volume behavior (orange by default). Adjustable settings for all thresholds and colors, so traders can dial in volume sensitivity. 🔵 HOW TO USE Identify volume-confirmed resistance and support zones for potential reversal or breakout setups. Focus on levels with intense histogram fill and circle markers —they indicate strong participation. Use bar coloring to track when key activity started and align it with broader market context. Works well in combination with order blocks, trend indicators, or liquidity zones. Ideal for day traders, scalpers, and volume-sensitive setups. 🔵 CONCLUSION Volumatic Support/Resistance Levels elevates traditional support and resistance logic by anchoring it in volume context. Instead of relying solely on price action, it gives traders insight into where real conviction lies—by mapping how aggressively the market defended or rejected key levels. It's a visual, reactive, and volume-conscious upgrade to your structural toolkit.
Pine Script®指標
由BigBeluga提供
7
True Close – Institutional Trading Sessions (Zeiierman)█ Overview True Close – Institutional Trading Sessions (Zeiierman) is a professional-grade session mapping tool designed to help traders align with how institutions perceive the market’s true close. Unlike the textbook “daily close” used by retail traders, institutional desks often anchor their risk management, execution benchmarks, and exposure metrics to the first hour of the next session. This indicator visualizes that logic directly on your chart — drawing session boxes, true close levels, and time-aligned labels across Sydney, Tokyo, London, and New York. It highlights the first hour of each session, projects the institutional closing price, and builds a live dashboard that tells you which sessions are active, which are in the critical opening phase, and what levels matter most right now. More than just a visual tool, this indicator embeds institutional rhythm directly into your workflow — giving you a window into where big players finalize yesterday’s business, rebalance exposure, and execute delayed orders. It’s not just about painting sessions on your chart — it’s about adopting the mindset of those who truly move the market. Institutions don’t settle risk at the bell; they complete it in the next session. This tool lets you see that transition in real time, giving you an edge that goes beyond candles and indicators. █ How It Works ⚪ Session Detection Engine Each session is identified by its own time block (e.g., 09:00–17:30 for London). Once a session opens: A full-session box is drawn to track its range. The first hour is highlighted separately. Once the first hour completes, the true close line is plotted, representing the price institutions often treat as the "real" close of the prior day. ⚪ Institutional True Close Logic The script captures the close of the first hour, not the end of the day. This line becomes a static reference across your chart, letting you visualize how price interacts with that institutional anchor: Rejections from it show where yesterday's flow is respected. Breaks through it may indicate that today's flows are rewriting the narrative. ⚪ Dynamic Dashboard Table A live table appears in the corner of your screen, showing: Each session's active status Whether we’re inside the first hour The current “true close” price if available Each cell comes with advanced tooltips giving institutional context, flow dynamics, and market microstructure insights — from rebalancing spillovers to VWAP/TWAP lag effects. █ How to Use ⚪ Use the First-Hour Line as Your Institutional Anchor Treat it like the price level that big funds care about. Watch how the price behaves around level. Fades, re-tests, or continuation moves often occur as the market finishes recapping yesterday’s leftover orders. ⚪ Structure Entries Around the Session Context Are you inside the first hour? Expect more volatility, more decisive flow. After the first session hour, expect fading liquidity as the market slows down and awaits the next session to open. █ Settings UTC Offset – Select your preferred time zone; all sessions adjust accordingly. Session Toggles – Enable/disable Sydney, Tokyo, London, or NY. Box Display Options – Show/hide session background, first-hour fill, borders. True Close Line Controls – Enable line, label, and customize width & color. Execution Hour Labels – Optional toggle for first-hour label placement. ----------------- Disclaimer The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information. All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
Pine Script®指標
由Zeiierman提供
23
Open Interest Footprint IQ [TradingIQ]Hello Traders! Th e Open Interest Footprint IQ indicator is an advanced visualization tool designed for cryptocurrency markets. It provides a granular, real-time breakdown of open interest changes across different price levels, allowing traders to see how aggressive market participation is distributed within each bar. Unlike standard footprint charts that rely solely on volume, this indicator offers unique insights by focusing on the interaction between price action and changes in open interest (OI) — a leading metric often used to infer trader intent and positioning. How it works The Open Interest Footprint IQ processes lower timeframe price and open interest data to build a footprint-style chart that shows how traders are positioning themselves within each candle. Here’s a breakdown of the process: 1. Granular OI & Price Sampling The script retrieves lower-timeframe data (1-minute, 1-second, or 1-tick, based on your setting). For each candle, it captures: High and low prices Price change direction Change in open interest (OI) 2. Classifying Trader Behavior For each lower-timeframe segment, the indicator determines the type of positioning occurring based on price movement and OI change: If price is moving up and open interest is increasing, it suggests that long positions are being opened. This is considered a "Longs Opening" event, labeled as UU (Up/Up). If price is moving up but open interest is decreasing, it indicates that short positions are being closed. This is referred to as UD (Up/Down), or "Shorts Closing." If price is moving down and open interest is increasing, it signals that short positions are being opened. This is known as DU (Down/Up), or "Shorts Opening." If price is moving down while open interest is also decreasing, it means that long positions are being closed. This is labeled as DD (Down/Down), or "Longs Closing." These are stored in separate arrays and displayed at specific price levels. It is particularly useful for identifying: Where longs or shorts are opening/closing positions Stacked imbalances (indicative of potential absorption or exhaustion) Value area zones and POC (Point of Control) based on OI, not volume This footprint runs on your choice of sub-bar granularity and is ideal for high-frequency trading, scalping, and entries based on order flow dynamics. Key Features Footprint Visualization At each price level within a candle: Long/short opening and closing behavior is broken down. Delta (net open interest change) is displayed both numerically and color-coded. Optional gradient coloring shows intensity and type of flow (longs/shorts opened/closed). Cumulative or per-bar reset modes allow you to track OI evolution over time. The image above explains the information that each Footprint box shows across a candlestick! Each footprint box shows: OI Delta OI Delta % Longs Opened (LO) Longs Closed (LC) Shorts Opened (SO) Shorts Closed (SC) The image above explains the color-coding feature of the indicator. Boxes are color coded to show which position action dominated at the price area. For this example: Green boxes = Long positions being opened dominated Purple boxes = Long positions being closed dominated Red boxes = Short positions being opened dominated Yellow boxes = Short positions being closed dominated All colors are customizable. Additionally, for traders who are only interested in whether OI increased/decreased, a "two-color" option is available in the settings. For the two-color option, footprint boxes can be one of two colors. Showing whether OI increased or decreased at the level. Cumulative Levels Open Interest Footprint IQ contains a "Cumulative Levels" feature that tracks/stores open interest change at tick levels over time, rather than resetting per bar. With the "Cumulative Levels" feature enabled, traders can see open interest changes persist across all candlesticks. This feature is useful for determining whether longs opening, longs closing, shorts opening, or shorts closing are dominating at particular price areas over time rather than on a single bar. A useful feature to see if shorts/longs are favoring certain price throughout the day, week, month, etc. Input Settings Explained Granularity (Dropdown: Granularity) Options: 1-Minute, 1-Second, 1-Tick Determines how finely the script samples the lower timeframe data to construct the footprint. For precision: 1-Tick = Highest accuracy, but more resource-intensive. 1-Second/1-Minute = Suitable for broader or more zoomed-out analysis. Tick Level Distance (Tick Level Distance (0 = Auto)) Defines the vertical spacing between levels in the footprint chart. If 0, the script uses an automatic calculation based on ATR to adapt to volatility. Set a manual value (e.g., 5) to control the height granularity of each level in ticks. Cumulative Levels (Toggle) If enabled, the footprint builds cumulatively over time, rather than resetting per candle. Use case: Visualize ongoing buildup of OI activity across a session or day. Cumulative Levels Reset TF (Timeframe) Sets the reset interval for the cumulative view (e.g., reset daily, hourly, etc.) Works only when Cumulative Levels is enabled. Delta Box Display Settings Show Delta Percentage Toggles the display of the percentage change in OI across the footprint level. Helpful to gauge how aggressive positioning is relative to total OI at that level. Show Longs/Shorts (Opened/Closed) Show Longs Opened: Displays OI increase in up candles (price ↑, OI ↑). Show Longs Closed: Displays OI decrease in down candles (price ↓, OI ↓). Show Shorts Opened: OI increase in down candles (price ↓, OI ↑). Show Shorts Closed: OI decrease in up candles (price ↑, OI ↓). These behaviors are color-coded to give traders instant context: Blue-green for longs opening. Purple for longs closing. Red for shorts opening. Yellow for shorts closing. Value Area & POC Value Area % (Value Area %) Controls how much cumulative open interest is used to define the value area. Example: 70% means the smallest range of prices that contains 70% of total OI in that bar will be marked. Helps identify zones of interest, support/resistance, and institutional levels. The image above explains how to identify the VAH/VAL/POC shown by Open Interest Footprint IQ. VAH = Upper 🞂 POC = ● VAL = Lower 🞂 Imbalances Imbalance Percentage Defines the minimum delta % required at a level to be marked as an imbalance. If the net open interest change at a level exceeds this threshold, a visual marker appears. Stacked Imbalance Count If the number of consecutive imbalance levels meets this count, a “Stacked Imbalance” alert will trigger. This can signal aggressive buying or selling pressure, potential breakout zones, or institutional absorption. Color Settings Longs Opened / Closed, Shorts Opened / Closed Customize the color palette for each order flow behavior. These colors appear in the background gradient of the footprint boxes. Up/Down Only Mode Toggle to override all behavior-based colors with a single Up Color and Down Color. Useful if you prefer a simple bull/bear view. Up Color / Down Color If "Up/Down Only" is enabled, these two colors are used to represent all net positive or negative deltas. Special Notes Crypto only: This script works only with crypto tickers on TradingView. For other assets (stocks, futures), a warning message will appear instead. OI data must be available from the exchange (many perpetual pairs support this). If the footprint is too small or invisible, increase your tick level spacing in the settings. Alerts When a stacked imbalance is detected, an alert is fired ("Stacked Imbalance"). This feature is useful for automated systems, bots, or simply staying informed of potential trade setups. And that's all for now! If you have any questions or features you'd like to see feel free to share them in the comments below! Thank you traders!
Pine Script®指標
由Trading-IQ提供
11
Zigzag CandlesCan't deny that I am obsessed with zigzags. Been doing some crazy experiments with it and have many more in pipeline. I believe zigzag can be used to derive better trend following methods. Here is an attempt to visualize zigzag as candlesticks. Next steps probably to derive moving average, atr (although there was an attempt of AZR made earlier) and probably supertrend too ;) Input parameters include ZigzagLength (to calculate zigzag) and CandleSize (number of zigzag pivots in each candle) CandleSize can be 3 or more. Every time we collect pivots which are equal to CandleSize, we derive one candle. And when we derive a candle, we remove all old pivots except the last one. Becauase, the last pivot acts as open to the next bar and is required. Body of the candle tells the start and end zigzag pivot in the range. And Wicks signify highest and lowest pivots in the range. High and Low wicks are placed at the pivot where high and lows are formed. Hence, you can see them at different positions each time. Thanks to @RicardoSantos for suggesting boxes for candles - while I was trying to achieve this with plotbar
Pine Script®指標
由Trendoscope提供
13
MathStatisticsKernelFunctionsLibrary "MathStatisticsKernelFunctions" TODO: add library description here uniform(distance, bandwidth) Uniform kernel. Parameters: distance : float, distance to kernel origin. bandwidth : float, default=1.0, bandwidth limiter to weight the kernel. Returns: float. triangular(distance, bandwidth) Triangular kernel. Parameters: distance : float, distance to kernel origin. bandwidth : float, default=1.0, bandwidth limiter to weight the kernel. Returns: float. epanechnikov(distance, bandwidth) Epanechnikov kernel. Parameters: distance : float, distance to kernel origin. bandwidth : float, default=1.0, bandwidth limiter to weight the kernel. Returns: float. quartic(distance, bandwidth) Quartic kernel. Parameters: distance : float, distance to kernel origin. bandwidth : float, default=1.0, bandwidth limiter to weight the kernel. Returns: float. triweight(distance, bandwidth) Triweight kernel. Parameters: distance : float, distance to kernel origin. bandwidth : float, default=1.0, bandwidth limiter to weight the kernel. Returns: float. tricubic(distance, bandwidth) Tricubic kernel. Parameters: distance : float, distance to kernel origin. bandwidth : float, default=1.0, bandwidth limiter to weight the kernel. Returns: float. gaussian(distance, bandwidth) Gaussian kernel. Parameters: distance : float, distance to kernel origin. bandwidth : float, default=1.0, bandwidth limiter to weight the kernel. Returns: float. cosine(distance, bandwidth) Cosine kernel. Parameters: distance : float, distance to kernel origin. bandwidth : float, default=1.0, bandwidth limiter to weight the kernel. Returns: float. logistic(distance, bandwidth) logistic kernel. Parameters: distance : float, distance to kernel origin. bandwidth : float, default=1.0, bandwidth limiter to weight the kernel. Returns: float. sigmoid(distance, bandwidth) Sigmoid kernel. Parameters: distance : float, distance to kernel origin. bandwidth : float, default=1.0, bandwidth limiter to weight the kernel. Returns: float. select(kernel, distance, bandwidth) Kernel selection method. Parameters: kernel : string, kernel to select. (options="uniform", "triangle", "epanechnikov", "quartic", "triweight", "tricubic", "gaussian", "cosine", "logistic", "sigmoid") distance : float, distance to kernel origin. bandwidth : float, default=1.0, bandwidth limiter to weight the kernel. Returns: float.
Pine Script®腳本庫
由RicardoSantos提供
4
MathConstantsLibrary "MathConstants" Mathematical Constants E() The number e Log2E() The number log (e) Log10E() The number log (e) Ln2() The number log (2) Ln10() The number log (10) LnPi() The number log (pi) Ln2PiOver2() The number log (2*pi)/2 InvE() The number 1/e SqrtE() The number sqrt(e) Sqrt2() The number sqrt(2) Sqrt3() The number sqrt(3) Sqrt1Over2() The number sqrt(1/2) = 1/sqrt(2) = sqrt(2)/2 HalfSqrt3() The number sqrt(3)/2 Pi() The number pi Pi2() The number pi*2 PiOver2() The number pi/2 Pi3Over2() The number pi*3/2 PiOver4() The number pi/4 SqrtPi() The number sqrt(pi) Sqrt2Pi() The number sqrt(2pi) SqrtPiOver2() The number sqrt(pi/2) Sqrt2PiE() The number sqrt(2*pi*e) LogSqrt2Pi() The number log(sqrt(2*pi)) LogSqrt2PiE() The number log(sqrt(2*pi*e)) LogTwoSqrtEOverPi() The number log(2 * sqrt(e / pi)) InvPi() The number 1/pi TwoInvPi() The number 2/pi InvSqrtPi() The number 1/sqrt(pi) InvSqrt2Pi() The number 1/sqrt(2pi) TwoInvSqrtPi() The number 2/sqrt(pi) TwoSqrtEOverPi() The number 2 * sqrt(e / pi) Degree() The number (pi)/180 - factor to convert from Degree (deg) to Radians (rad). Grad() The number (pi)/200 - factor to convert from NewGrad (grad) to Radians (rad). PowerDecibel() The number ln(10)/20 - factor to convert from Power Decibel (dB) to Neper (Np). Use this version when the Decibel represent a power gain but the compared values are not powers (e.g. amplitude, current, voltage). NeutralDecibel() The number ln(10)/10 - factor to convert from Neutral Decibel (dB) to Neper (Np). Use this version when either both or neither of the Decibel and the compared values represent powers. Catalan() The Catalan constant Sum(k=0 -> inf){ (-1)^k/(2*k + 1)2 } EulerMascheroni() The Euler-Mascheroni constant lim(n -> inf){ Sum(k=1 -> n) { 1/k - log(n) } } GoldenRatio() The number (1+sqrt(5))/2, also known as the golden ratio Glaisher() The Glaisher constant e^(1/12 - Zeta(-1)) Khinchin() The Khinchin constant prod(k=1 -> inf){1+1/(k*(k+2))^log(k,2)}
Pine Script®腳本庫
由RicardoSantos提供
3
taLibrary "ta" █  OVERVIEW This library holds technical ​analysis functions calculating values for which no Pine built-in exists. Look first. Then leap. █  FUNCTIONS cagr(entryTime, entryPrice, exitTime, exitPrice) It calculates the "Compound Annual Growth Rate" between two points in time. The ​CAGR is a notional, annualized growth rate that assumes all profits are reinvested. It only takes into account the prices of the two end points — not drawdowns, so it does not calculate risk. It can be used as a yardstick to compare the performance of two instruments. Because it annualizes values, the function requires a minimum of one day between the two end points (annualizing returns over smaller periods of times doesn't produce very meaningful figures).   Parameters:      entryTime : The starting timestamp.      entryPrice : The starting point's price.      exitTime : The ending timestamp.      exitPrice : The ending point's price.   Returns: ​CAGR in % (50 is 50%). Returns `na` if there is not >=1D between `entryTime` and `exitTime`, or until the two time points have not been reached by the script. █  v2, Mar. 8, 2022 Added functions `allTimeHigh()` and `allTimeLow()` to find the highest or lowest value of a source from the first historical bar to the current bar. These functions will not look ahead; they will only return new highs/lows on the bar where they occur. allTimeHigh(​src) Tracks the highest value of `src` from the first historical bar to the current bar.   Parameters:      src : (series int/float) Series to track. Optional. The default is `high`.   Returns: (float) The highest value tracked. allTimeLow(​src) Tracks the lowest value of `src` from the first historical bar to the current bar.   Parameters:      src : (series int/float) Series to track. Optional. The default is `low`.   Returns: (float) The lowest value tracked. █  v3, Sept. 27, 2022 This version includes the following new functions: aroon(length)   Calculates the values of the Aroon indicator.   Parameters:      length (simple int) : (simple int) Number of bars (length).   Returns: ( [float, float ]) A tuple of the Aroon-Up and Aroon-Down values. coppock(source, longLength, shortLength, smoothLength)   Calculates the value of the Coppock Curve indicator.   Parameters:      source (float) : (series int/float) Series of values to process.      longLength (simple int) : (simple int) Number of bars for the fast ROC value (length).      shortLength (simple int) : (simple int) Number of bars for the slow ROC value (length).      smoothLength (simple int) : (simple int) Number of bars for the weigted moving average value (length).   Returns: (float) The oscillator value. dema(source, length)   Calculates the value of the Double Exponential Moving Average (DEMA).   Parameters:      source (float) : (series int/float) Series of values to process.      length (simple int) : (simple int) Length for the smoothing parameter calculation.   Returns: (float) The double exponentially weighted moving average of the `source`. d​ema2(​src, length)   An alternate ​Double Exponential ​Moving Average (D​ema) function to `d​ema()`, which allows a "series float" length argument.   Parameters:      ​src : (series int/float) Series of values to process.      length : (series int/float) Length for the smoothing parameter calculation.   Returns: (float) The double exponentially ​weighted moving average of the `​src`. dm(length)   Calculates the value of the "Demarker" indicator.   Parameters:      length (simple int) : (simple int) Number of bars (length).   Returns: (float) The oscillator value. donchian(length)   Calculates the values of a Donchian Channel using `high` and `low` over a given `length`.   Parameters:      length (int) : (series int) Number of bars (length).   Returns: ( [float, float, float ]) A tuple containing the channel high, low, and median, respectively. ​ema2(​src, length)   An alternate ​ema function to the `ta.​ema()` built-in, which allows a "series float" length argument.   Parameters:      ​src : (series int/float) Series of values to process.      length : (series int/float) Number of bars (length).   Returns: (float) The exponentially ​weighted moving average of the `​src`. eom(length, div)   Calculates the value of the Ease of Movement indicator.   Parameters:      length (simple int) : (simple int) Number of bars (length).      div (simple int) : (simple int) Divisor used for normalzing values. Optional. The default is 10000.   Returns: (float) The oscillator value. frama(source, length)   The Fractal Adaptive Moving Average (FRAMA), developed by John Ehlers, is an adaptive moving average that dynamically adjusts its lookback period based on fractal geometry.   Parameters:      source (float) : (series int/float) Series of values to process.      length (int) : (series int) Number of bars (length).   Returns: (float) The fractal adaptive moving average of the `source`. ft(source, length)   Calculates the value of the Fisher Transform indicator.   Parameters:      source (float) : (series int/float) Series of values to process.      length (simple int) : (simple int) Number of bars (length).   Returns: (float) The oscillator value. ht(source)   Calculates the value of the Hilbert Transform indicator.   Parameters:      source (float) : (series int/float) Series of values to process.   Returns: (float) The oscillator value. ichimoku(conLength, baseLength, senkouLength)   Calculates values of the Ichimoku Cloud indicator, including tenkan, kijun, senkouSpan1, senkouSpan2, and chikou. NOTE: offsets forward or backward can be done using the `offset` argument in `plot()`.   Parameters:      conLength (int) : (series int) Length for the Conversion Line (Tenkan). The default is 9 periods, which returns the mid-point of the 9 period Donchian Channel.      baseLength (int) : (series int) Length for the Base Line (Kijun-sen). The default is 26 periods, which returns the mid-point of the 26 period Donchian Channel.      senkouLength (int) : (series int) Length for the Senkou Span 2 (Leading Span B). The default is 52 periods, which returns the mid-point of the 52 period Donchian Channel.   Returns: ( [float, float, float, float, float ]) A tuple of the Tenkan, Kijun, Senkou Span 1, Senkou Span 2, and Chikou Span values. NOTE: by default, the senkouSpan1 and senkouSpan2 should be plotted 26 periods in the future, and the Chikou Span plotted 26 days in the past. ift(source)   Calculates the value of the Inverse Fisher Transform indicator.   Parameters:      source (float) : (series int/float) Series of values to process.   Returns: (float) The oscillator value. kvo(fastLen, slowLen, trigLen)   Calculates the values of the Klinger Volume Oscillator.   Parameters:      fastLen (simple int) : (simple int) Length for the fast moving average smoothing parameter calculation.      slowLen (simple int) : (simple int) Length for the slow moving average smoothing parameter calculation.      trigLen (simple int) : (simple int) Length for the trigger moving average smoothing parameter calculation.   Returns: ( [float, float ]) A tuple of the KVO value, and the trigger value. pzo(length)   Calculates the value of the Price Zone Oscillator.   Parameters:      length (simple int) : (simple int) Length for the smoothing parameter calculation.   Returns: (float) The oscillator value. rms(source, length)   Calculates the Root Mean Square of the `source` over the `length`.   Parameters:      source (float) : (series int/float) Series of values to process.      length (int) : (series int) Number of bars (length).   Returns: (float) The RMS value. rwi(length)   Calculates the values of the Random Walk Index.   Parameters:      length (simple int) : (simple int) Lookback and ATR smoothing parameter length.   Returns: ( [float, float ]) A tuple of the `rwiHigh` and `rwiLow` values. stc(source, fast, slow, cycle, d1, d2)   Calculates the value of the Schaff Trend Cycle indicator.   Parameters:      source (float) : (series int/float) Series of values to process.      fast (simple int) : (simple int) Length for the MACD fast smoothing parameter calculation.      slow (simple int) : (simple int) Length for the MACD slow smoothing parameter calculation.      cycle (simple int) : (simple int) Number of bars for the Stochastic values (length).      d1 (simple int) : (simple int) Length for the initial %D smoothing parameter calculation.      d2 (simple int) : (simple int) Length for the final %D smoothing parameter calculation.   Returns: (float) The oscillator value. stochFull(periodK, smoothK, periodD)   Calculates the %K and %D values of the Full Stochastic indicator.   Parameters:      periodK (simple int) : (simple int) Number of bars for Stochastic calculation. (length).      smoothK (simple int) : (simple int) Number of bars for smoothing of the %K value (length).      periodD (simple int) : (simple int) Number of bars for smoothing of the %D value (length).   Returns: ( [float, float ]) A tuple of the slow %K and the %D moving average values. stochRsi(lengthRsi, periodK, smoothK, periodD, source)   Calculates the %K and %D values of the Stochastic RSI indicator.   Parameters:      lengthRsi (simple int) : (simple int) Length for the RSI smoothing parameter calculation.      periodK (simple int) : (simple int) Number of bars for Stochastic calculation. (length).      smoothK (simple int) : (simple int) Number of bars for smoothing of the %K value (length).      periodD (simple int) : (simple int) Number of bars for smoothing of the %D value (length).      source (float) : (series int/float) Series of values to process. Optional. The default is `close`.   Returns: ( [float, float ]) A tuple of the slow %K and the %D moving average values. supertrend(factor, atrLength, wicks)   Calculates the values of the SuperTrend indicator with the ability to take candle wicks into account, rather than only the closing price.   Parameters:      factor (float) : (series int/float) Multiplier for the ATR value.      atrLength (simple int) : (simple int) Length for the ATR smoothing parameter calculation.      wicks (simple bool) : (simple bool) Condition to determine whether to take candle wicks into account when reversing trend, or to use the close price. Optional. Default is false.   Returns: ( [float, int ]) A tuple of the superTrend value and trend direction. szo(source, length)   Calculates the value of the Sentiment Zone Oscillator.   Parameters:      source (float) : (series int/float) Series of values to process.      length (simple int) : (simple int) Length for the smoothing parameter calculation.   Returns: (float) The oscillator value. t3(source, length, vf)   Calculates the value of the Tilson Moving Average (T3).   Parameters:      source (float) : (series int/float) Series of values to process.      length (simple int) : (simple int) Length for the smoothing parameter calculation.      vf (simple float) : (simple float) Volume factor. Affects the responsiveness.   Returns: (float) The Tilson moving average of the `source`. t3Alt(source, length, vf)   An alternate Tilson Moving Average (T3) function to `t3()`, which allows a "series float" `length` argument.   Parameters:      source (float) : (series int/float) Series of values to process.      length (float) : (series int/float) Length for the smoothing parameter calculation.      vf (simple float) : (simple float) Volume factor. Affects the responsiveness.   Returns: (float) The Tilson moving average of the `source`. tema(source, length)   Calculates the value of the Triple Exponential Moving Average (TEMA).   Parameters:      source (float) : (series int/float) Series of values to process.      length (simple int) : (simple int) Length for the smoothing parameter calculation.   Returns: (float) The triple exponentially weighted moving average of the `source`. tema2(source, length)   An alternate Triple Exponential Moving Average (TEMA) function to `tema()`, which allows a "series float" `length` argument.   Parameters:      source (float) : (series int/float) Series of values to process.      length (float) : (series int/float) Length for the smoothing parameter calculation.   Returns: (float) The triple exponentially weighted moving average of the `source`. trima(source, length)   Calculates the value of the Triangular Moving Average (TRIMA).   Parameters:      source (float) : (series int/float) Series of values to process.      length (int) : (series int) Number of bars (length).   Returns: (float) The triangular moving average of the `source`. trima2(​src, length)   An alternate Triangular ​Moving Average (TRIMA) function to `trima()`, which allows a "series int" length argument.   Parameters:      ​src : (series int/float) Series of values to process.      length : (series int) Number of bars (length).   Returns: (float) The triangular moving average of the `​src`. trix(source, length, signalLength, exponential)   Calculates the values of the TRIX indicator.   Parameters:      source (float) : (series int/float) Series of values to process.      length (simple int) : (simple int) Length for the smoothing parameter calculation.      signalLength (simple int) : (simple int) Length for smoothing the signal line.      exponential (simple bool) : (simple bool) Condition to determine whether exponential or simple smoothing is used. Optional. The default is `true` (exponential smoothing).   Returns: ( [float, float, float ]) A tuple of the TRIX value, the signal value, and the histogram. uo(fastLen, midLen, slowLen)   Calculates the value of the Ultimate Oscillator.   Parameters:      fastLen (simple int) : (series int) Number of bars for the fast smoothing average (length).      midLen (simple int) : (series int) Number of bars for the middle smoothing average (length).      slowLen (simple int) : (series int) Number of bars for the slow smoothing average (length).   Returns: (float) The oscillator value. vhf(source, length)   Calculates the value of the Vertical Horizontal Filter.   Parameters:      source (float) : (series int/float) Series of values to process.      length (simple int) : (simple int) Number of bars (length).   Returns: (float) The oscillator value. vi(length)   Calculates the values of the Vortex Indicator.   Parameters:      length (simple int) : (simple int) Number of bars (length).   Returns: ( [float, float ]) A tuple of the viPlus and viMinus values. vzo(length)   Calculates the value of the Volume Zone Oscillator.   Parameters:      length (simple int) : (simple int) Length for the smoothing parameter calculation.   Returns: (float) The oscillator value. williamsFractal(period)   Detects Williams Fractals.   Parameters:      period (int) : (series int) Number of bars (length).   Returns: ( [bool, bool ]) A tuple of an up fractal and down fractal. Variables are true when detected. wpo(length)   Calculates the value of the Wave Period Oscillator.   Parameters:      length (simple int) : (simple int) Length for the smoothing parameter calculation.   Returns: (float) The oscillator value. █  v7, Nov. 2, 2023 This version includes the following new and updated functions: atr2(length)   An alternate ATR function to the `ta.atr()` built-in, which allows a "series float" `length` argument.   Parameters:      length (float) : (series int/float) Length for the smoothing parameter calculation.   Returns: (float) The ATR value. changePercent(newValue, oldValue)   Calculates the percentage difference between two distinct values.   Parameters:      newValue (float) : (series int/float) The current value.      oldValue (float) : (series int/float) The previous value.   Returns: (float) The percentage change from the `oldValue` to the `newValue`. donchian(length)   Calculates the values of a Donchian Channel using `high` and `low` over a given `length`.   Parameters:      length (int) : (series int) Number of bars (length).   Returns: ( [float, float, float ]) A tuple containing the channel high, low, and median, respectively. highestSince(cond, source)   Tracks the highest value of a series since the last occurrence of a condition.   Parameters:      cond (bool) : (series bool) A condition which, when `true`, resets the tracking of the highest `source`.      source (float) : (series int/float) Series of values to process. Optional. The default is `high`.   Returns: (float) The highest `source` value since the last time the `cond` was `true`. lowestSince(cond, source)   Tracks the lowest value of a series since the last occurrence of a condition.   Parameters:      cond (bool) : (series bool) A condition which, when `true`, resets the tracking of the lowest `source`.      source (float) : (series int/float) Series of values to process. Optional. The default is `low`.   Returns: (float) The lowest `source` value since the last time the `cond` was `true`. relativeVolume(length, anchorTimeframe, isCumulative, adjustRealtime)   Calculates the volume since the last change in the time value from the `anchorTimeframe`, the historical average volume using bars from past periods that have the same relative time offset as the current bar from the start of its period, and the ratio of these volumes. The volume values are cumulative by default, but can be adjusted to non-accumulated with the `isCumulative` parameter.   Parameters:      length (simple int) : (simple int) The number of periods to use for the historical average calculation.      anchorTimeframe (simple string) : (simple string) The anchor timeframe used in the calculation. Optional. Default is "D".      isCumulative (simple bool) : (simple bool) If `true`, the volume values will be accumulated since the start of the last `anchorTimeframe`. If `false`, values will be used without accumulation. Optional. The default is `true`.      adjustRealtime (simple bool) : (simple bool) If `true`, estimates the cumulative value on unclosed bars based on the data since the last `anchor` condition. Optional. The default is `false`.   Returns: ( [float, float, float ]) A tuple of three float values. The first element is the current volume. The second is the average of volumes at equivalent time offsets from past anchors over the specified number of periods. The third is the ratio of the current volume to the historical average volume. rma2(source, length)   An alternate RMA function to the `ta.rma()` built-in, which allows a "series float" `length` argument.   Parameters:      source (float) : (series int/float) Series of values to process.      length (float) : (series int/float) Length for the smoothing parameter calculation.   Returns: (float) The rolling moving average of the `source`. supertrend2(factor, atrLength, wicks)   An alternate SuperTrend function to `supertrend()`, which allows a "series float" `atrLength` argument.   Parameters:      factor (float) : (series int/float) Multiplier for the ATR value.      atrLength (float) : (series int/float) Length for the ATR smoothing parameter calculation.      wicks (simple bool) : (simple bool) Condition to determine whether to take candle wicks into account when reversing trend, or to use the close price. Optional. Default is `false`.   Returns: ( [float, int ]) A tuple of the superTrend value and trend direction. vStop(source, atrLength, atrFactor)   Calculates an ATR-based stop value that trails behind the `source`. Can serve as a possible stop-loss guide and trend identifier.   Parameters:      source (float) : (series int/float) Series of values that the stop trails behind.      atrLength (simple int) : (simple int) Length for the ATR smoothing parameter calculation.      atrFactor (float) : (series int/float) The multiplier of the ATR value. Affects the maximum distance between the stop and the `source` value. A value of 1 means the maximum distance is 100% of the ATR value. Optional. The default is 1.   Returns: ( [float, bool ]) A tuple of the volatility stop value and the trend direction as a "bool". vStop2(source, atrLength, atrFactor)   An alternate Volatility Stop function to `vStop()`, which allows a "series float" `atrLength` argument.   Parameters:      source (float) : (series int/float) Series of values that the stop trails behind.      atrLength (float) : (series int/float) Length for the ATR smoothing parameter calculation.      atrFactor (float) : (series int/float) The multiplier of the ATR value. Affects the maximum distance between the stop and the `source` value. A value of 1 means the maximum distance is 100% of the ATR value. Optional. The default is 1.   Returns: ( [float, bool ]) A tuple of the volatility stop value and the trend direction as a "bool". Removed Functions: allTimeHigh(src)   Tracks the highest value of `src` from the first historical bar to the current bar. allTimeLow(src)   Tracks the lowest value of `src` from the first historical bar to the current bar. trima2(src, length)   An alternate Triangular Moving Average (TRIMA) function to `trima()`, which allows a "series int" length argument.
Pine Script®腳本庫
由TradingView提供
25
Tape [LucF]█  OVERVIEW This script prints an ersatz of a trading console's "tape" section to the right of your chart. It displays the time, price and volume​  of each update of the chart's feed. It also calculates volume​ delta for the bar. As it calculates from realtime information, it will not display information on historical bars. █  FEATURES Calculations Each new line in the tape displays the last price/volume​ update from the TradingView feed that's building your chart. These updates do not necessarily correspond to ticks from the originating broker/exchange's matching engine. Multiple broker/exchange ticks are often aggregated in one  chart update. The script first determines if price has moved up or down since the last update. The polarity of the price change, in turn, determines the polarity of the volume​​ for that specific update. If price does not move between consecutive updates, then the last known polarity is used. Using this method, we can calculate a running volume​ delta accumulation for the bar, which becomes the bar's final volume​ delta value when the bar closes (you can inspect values of elapsed realtime bars in the Data Window or the indicator's values). Note that these values will all reset if the script re-executes because of a change in inputs or a chart refresh. While this method of calculating volume​ delta is not perfect, it is currently the most precise way of calculating volume​ delta available on TradingView at the moment. Calculating more precise results would require scripts to have access to bid/ask levels from any chart timeframe. Charts at seconds timeframes do use exchange/broker ticks when the feeds you are using allow for it, and this indicator will run on them, but tick data is not yet available from higher timeframes, for now. Also note that the method used in this script is far superior to the intrabar inspection technique used on historical bars in my other "Delta Volume​" indicators. This is because volume​ delta here is calculated from many more realtime updates than the available intrabars in history. Inputs You can use the script's inputs to configure:  • The number of lines displayed in the tape.  • If new lines appear at the top or bottom.  • If you want to hide lines with low volume​.  • The precision of volume​ values.  • The size of the text and the colors used to highlight either the tape's text or background.  • The position where you want the tape on your chart.  • Conditions triggering three different markers. Display Deltas are shown at the bottom of the tape. They are reset on each bar. Time delta displays the time elapsed since the beginning of the bar, on intraday timeframes only. Contrary to the price change display by TradingView at the top left of charts, which is calculated from the close of the previous bar, the price delta in the tape is calculated from the bar's open, because that's the information used in the calculation of volume​ delta. The time will become orange when volume​ delta's polarity diverges from that of the bar. The volume  delta value represents the current, cumulative value for the bar. Its color reflects its polarity. When new realtime bars appear on the chart, a ↻ symbol will appear before the volume​ value in tape lines. Markers There are three types of markers you can choose to display:  • Marker 1 on volume​ bumps. A bump is defined as two consecutive and increasing/decreasing plus/minus delta volume​  values,   when no divergence between the polarity of delta volume​ and the bar occurs on the second bar.  • Marker 2 on volume​ delta for the bar exceeding a limit of your choice when there is no divergence between the polarity of delta volume​ and the bar. These trigger at the bar's close.  • Marker 3 on tape lines with volume​ exceeding a threshold. These trigger in realtime. Be sure to set a threshold high enough so that it doesn't generate too many alerts.   These markers will only display briefly under the bar, but another marker appears next to the relevant line in the tape. The marker conditions are used to trigger alerts configured on the script. Alert messages will mention the marker(s) that triggered the specific alert event, along with the relevant volume​ value that triggered the marker. If more than one marker triggers a single alert, they will overprint under the bar, which can make it difficult to distinguish them. For more detailed on-chart analysis of realtime volume​ delta, see my Delta Volume Realtime Action . █  NOTES FOR CODERS This script showcases two new Pine features:  • Tables, which allow Pine programmers to display tabular information in fixed locations of the chart. The tape uses this feature.   See the Pine User Manual's page on Tables for more information.  • varip -type variables which we can use to save values between realtime updates.   See the " Using `varip` variables " publication by PineCoders for more information.
Pine Script®指標
由LucF提供
99
Circular Candlestick ChartAn original (but impractical) way to represent a candlestick chart using circles arc. The most recent candles are further away from the circle origin. Note that OHLC values follow a clockwise direction. A higher arc length would indicate candles with a higher body or wick range. The Length settings determine the number of past candles to be included in the circular candlestick chart. The Width setting control the width of the circular chart. The Spacing setting controls the space between each arcs. Finally, the Precision settings allow obtaining a more precise representation of candles, with lower values returning more precise results, however, more precision requires a higher amount of lines. Settings are quite hard to adjust, using a higher length might require a lower spacing value. Additionally, the script includes two pointers indicating the location of the 75 (in blue) and 25 (in orange) percentiles. This allows obtaining an estimate of the current market sentiment, with the most recent arcs laying closer to the 75 percentile pointer indicating an up-trend. This new way to represent candlesticks might be useful to more easily identify candles clusters or to find new price patterns. Who knows, we know that new ways to see prices always stimulate traders imagination. See you next year.
Pine Script®指標
由alexgrover提供
20
Color█ OVERVIEW This library is a Pine Script® programming tool for advanced color processing. It provides a comprehensive set of functions for specifying and analyzing colors in various color spaces, mixing and manipulating colors, calculating custom gradients and schemes, detecting contrast, and converting colors to or from hexadecimal strings. █ CONCEPTS Color Color refers to how we interpret light of different wavelengths in the visible spectrum . The colors we see from an object represent the light wavelengths that it reflects, emits, or transmits toward our eyes. Some colors, such as blue and red, correspond directly to parts of the spectrum. Others, such as magenta, arise from a combination of wavelengths to which our minds assign a single color. The human interpretation of color lends itself to many uses in our world. In the context of financial data analysis, the effective use of color helps transform raw data into insights that users can understand at a glance. For example, colors can categorize series, signal market conditions and sessions, and emphasize patterns or relationships in data. Color models and spaces A color model is a general mathematical framework that describes colors using sets of numbers. A color space is an implementation of a specific color model that defines an exact range (gamut) of reproducible colors based on a set of primary colors , a reference white point , and sometimes additional parameters such as viewing conditions. There are numerous different color spaces — each describing the characteristics of color in unique ways. Different spaces carry different advantages, depending on the application. Below, we provide a brief overview of the concepts underlying the color spaces supported by this library. RGB RGB is one of the most well-known color models. It represents color as an additive mixture of three primary colors — red, green, and blue lights — with various intensities. Each cone cell in the human eye responds more strongly to one of the three primaries, and the average person interprets the combination of these lights as a distinct color (e.g., pure red + pure green = yellow). The sRGB color space is the most common RGB implementation. Developed by HP and Microsoft in the 1990s, sRGB provided a standardized baseline for representing color across CRT monitors of the era, which produced brightness levels that did not increase linearly with the input signal. To match displays and optimize brightness encoding for human sensitivity, sRGB applied a nonlinear transformation to linear RGB signals, often referred to as gamma correction . The result produced more visually pleasing outputs while maintaining a simple encoding. As such, sRGB quickly became a standard for digital color representation across devices and the web. To this day, it remains the default color space for most web-based content. TradingView charts and Pine Script `color.*` built-ins process color data in sRGB. The red, green, and blue channels range from 0 to 255, where 0 represents no intensity, and 255 represents maximum intensity. Each combination of red, green, and blue values represents a distinct color, resulting in a total of 16,777,216 displayable colors. CIE XYZ and xyY The XYZ color space, developed by the International Commission on Illumination (CIE) in 1931, aims to describe all color sensations that a typical human can perceive. It is a cornerstone of color science, forming the basis for many color spaces used today. XYZ, and the derived xyY space, provide a universal representation of color that is not tethered to a particular display. Many widely used color spaces, including sRGB, are defined relative to XYZ or derived from it. The CIE built the color space based on a series of experiments in which people matched colors they perceived from mixtures of lights. From these experiments, the CIE developed color-matching functions to calculate three components — X, Y, and Z — which together aim to describe a standard observer's response to visible light. X represents a weighted response to light across the color spectrum, with the highest contribution from long wavelengths (e.g., red). Y represents a weighted response to medium wavelengths (e.g., green), and it corresponds to a color's relative luminance (i.e., brightness). Z represents a weighted response to short wavelengths (e.g., blue). From the XYZ space, the CIE developed the xyY chromaticity space, which separates a color's chromaticity (hue and colorfulness) from luminance. The CIE used this space to define the CIE 1931 chromaticity diagram , which represents the full range of visible colors at a given luminance. In color science and lighting design, xyY is a common means for specifying colors and visualizing the supported ranges of other color spaces. CIELAB and Oklab The CIELAB (L*a*b*) color space, derived from XYZ by the CIE in 1976, expresses colors based on opponent process theory. The L* component represents perceived lightness, and the a* and b* components represent the balance between opposing unique colors. The a* value specifies the balance between green and red , and the b* value specifies the balance between blue and yellow . The primary intention of CIELAB was to provide a perceptually uniform color space, where fixed-size steps through the space correspond to uniform perceived changes in color. Although relatively uniform, the color space has been found to exhibit some non-uniformities, particularly in the blue part of the color spectrum. Regardless, modern applications often use CIELAB to estimate perceived color differences and calculate smooth color gradients. In 2020, a new LAB-oriented color space, Oklab , was introduced by Björn Ottosson as an attempt to rectify the non-uniformities of other perceptual color spaces. Similar to CIELAB, the L value in Oklab represents perceived lightness, and the a and b values represent the balance between opposing unique colors. Oklab has gained widespread adoption as a perceptual space for color processing, with support in the latest CSS Color specifications and many software applications. Cylindrical models A cylindrical-coordinate model transforms an underlying color model, such as RGB or LAB, into an alternative expression of color information that is often more intuitive for the average person to use and understand. Instead of a mixture of primary colors or opponent pairs, these models represent color as a hue angle on a color wheel , with additional parameters that describe other qualities such as lightness and colorfulness (a general term for concepts like chroma and saturation). In cylindrical-coordinate spaces, users can select a color and modify its lightness or other qualities without altering the hue. The three most common RGB-based models are HSL (Hue, Saturation, Lightness), HSV (Hue, Saturation, Value), and HWB (Hue, Whiteness, Blackness). All three define hue angles in the same way, but they define colorfulness and lightness differently. Although they are not perceptually uniform, HSL and HSV are commonplace in color pickers and gradients. For CIELAB and Oklab, the cylindrical-coordinate versions are CIELCh and Oklch , which express color in terms of perceived lightness, chroma, and hue. They offer perceptually uniform alternatives to RGB-based models. These spaces create unique color wheels, and they have more strict definitions of lightness and colorfulness. Oklch is particularly well-suited for generating smooth, perceptual color gradients. Alpha and transparency Many color encoding schemes include an alpha channel, representing opacity . Alpha does not help define a color in a color space; it determines how a color interacts with other colors in the display. Opaque colors appear with full intensity on the screen, whereas translucent (semi-opaque) colors blend into the background. Colors with zero opacity are invisible. In Pine Script, there are two ways to specify a color's alpha:  • Using the `transp` parameter of the built-in `color.*()` functions. The specified value represents transparency (the opposite of opacity), which the functions translate into an alpha value.  • Using eight-digit hexadecimal color codes. The last two digits in the code represent alpha directly. A process called alpha compositing simulates translucent colors in a display. It creates a single displayed color by mixing the RGB channels of two colors (foreground and background) based on alpha values, giving the illusion of a semi-opaque color placed over another color. For example, a red color with 80% transparency on a black background produces a dark shade of red. Hexadecimal color codes A hexadecimal color code (hex code) is a compact representation of an RGB color. It encodes a color's red, green, and blue values into a sequence of hexadecimal ( base-16 ) digits. The digits are numerals ranging from `0` to `9` or letters from `a` (for 10) to `f` (for 15). Each set of two digits represents an RGB channel ranging from `00` (for 0) to `ff` (for 255). Pine scripts can natively define colors using hex codes in the format `#rrggbbaa`. The first set of two digits represents red, the second represents green, and the third represents blue. The fourth set represents alpha . If unspecified, the value is `ff` (fully opaque). For example, `#ff8b00` and `#ff8b00ff` represent an opaque orange color. The code `#ff8b0033` represents the same color with 80% transparency. Gradients A color gradient maps colors to numbers over a given range. Most color gradients represent a continuous path in a specific color space, where each number corresponds to a mix between a starting color and a stopping color. In Pine, coders often use gradients to visualize value intensities in plots and heatmaps, or to add visual depth to fills. The behavior of a color gradient depends on the mixing method and the chosen color space. Gradients in sRGB usually mix along a straight line between the red, green, and blue coordinates of two colors. In cylindrical spaces such as HSL, a gradient often rotates the hue angle through the color wheel, resulting in more pronounced color transitions. Color schemes A color scheme refers to a set of colors for use in aesthetic or functional design. A color scheme usually consists of just a few distinct colors. However, depending on the purpose, a scheme can include many colors. A user might choose palettes for a color scheme arbitrarily, or generate them algorithmically. There are many techniques for calculating color schemes. A few simple, practical methods are:  • Sampling a set of distinct colors from a color gradient.  • Generating monochromatic variants of a color (i.e., tints, tones, or shades with matching hues).  • Computing color harmonies — such as complements, analogous colors, triads, and tetrads — from a base color. This library includes functions for all three of these techniques. See below for details. █ CALCULATIONS AND USE Hex string conversion The `getHexString()` function returns a string containing the eight-digit hexadecimal code corresponding to a "color" value or set of sRGB and transparency values. For example, `getHexString(255, 0, 0)` returns the string `"#ff0000ff"`, and `getHexString(color.new(color.red, 80))` returns `"#f2364533"`. The `hexStringToColor()` function returns the "color" value represented by a string containing a six- or eight-digit hex code. The `hexStringToRGB()` function returns a tuple containing the sRGB and transparency values. For example, `hexStringToColor("#f23645")` returns the same value as color.red . Programmers can use these functions to parse colors from "string" inputs, perform string-based color calculations, and inspect color data in text outputs such as Pine Logs and tables. Color space conversion All other `get*()` functions convert a "color" value or set of sRGB channels into coordinates in a specific color space, with transparency information included. For example, the tuple returned by `getHSL()` includes the color's hue, saturation, lightness, and transparency values. To convert data from a color space back to colors or sRGB and transparency values, use the corresponding `*toColor()` or `*toRGB()` functions for that space (e.g., `hslToColor()` and `hslToRGB()`). Programmers can use these conversion functions to process inputs that define colors in different ways, perform advanced color manipulation, design custom gradients, and more. The color spaces this library supports are:  • sRGB  • Linear RGB (RGB without gamma correction)  • HSL, HSV, and HWB  • CIE XYZ and xyY  • CIELAB and CIELCh  • Oklab and Oklch Contrast-based calculations Contrast refers to the difference in luminance or color that makes one color visible against another. This library features two functions for calculating luminance-based contrast and detecting themes. The `contrastRatio()` function calculates the contrast between two "color" values based on their relative luminance (the Y value from CIE XYZ) using the formula from version 2 of the Web Content Accessibility Guidelines (WCAG) . This function is useful for identifying colors that provide a sufficient brightness difference for legibility. The `isLightTheme()` function determines whether a specified background color represents a light theme based on its contrast with black and white. Programmers can use this function to define conditional logic that responds differently to light and dark themes. Color manipulation and harmonies The `negative()` function calculates the negative (i.e., inverse) of a color by reversing the color's coordinates in either the sRGB or linear RGB color space. This function is useful for calculating high-contrast colors. The `grayscale()` function calculates a grayscale form of a specified color with the same relative luminance. The functions `complement()`, `splitComplements()`, `analogousColors()`, `triadicColors()`, `tetradicColors()`, `pentadicColors()`, and `hexadicColors()` calculate color harmonies from a specified source color within a given color space (HSL, CIELCh, or Oklch). The returned harmonious colors represent specific hue rotations around a color wheel formed by the chosen space, with the same defined lightness, saturation or chroma, and transparency. Color mixing and gradient creation The `add()` function simulates combining lights of two different colors by additively mixing their linear red, green, and blue components, ignoring transparency by default. Users can calculate a transparency-weighted mixture by setting the `transpWeight` argument to `true`. The `overlay()` function estimates the color displayed on a TradingView chart when a specific foreground color is over a background color. This function aids in simulating stacked colors and analyzing the effects of transparency. The `fromGradient()` and `fromMultiStepGradient()` functions calculate colors from gradients in any of the supported color spaces, providing flexible alternatives to the RGB-based color.from_gradient() function. The `fromGradient()` function calculates a color from a single gradient. The `fromMultiStepGradient()` function calculates a color from a piecewise gradient with multiple defined steps. Gradients are useful for heatmaps and for coloring plots or drawings based on value intensities. Scheme creation Three functions in this library calculate palettes for custom color schemes. Scripts can use these functions to create responsive color schemes that adjust to calculated values and user inputs. The `gradientPalette()` function creates an array of colors by sampling a specified number of colors along a gradient from a base color to a target color, in fixed-size steps. The `monoPalette()` function creates an array containing monochromatic variants (tints, tones, or shades) of a specified base color. Whether the function mixes the color toward white (for tints), a form of gray (for tones), or black (for shades) depends on the `grayLuminance` value. If unspecified, the function automatically chooses the mix behavior with the highest contrast. The `harmonyPalette()` function creates a matrix of colors. The first column contains the base color and specified harmonies, e.g., triadic colors. The columns that follow contain tints, tones, or shades of the harmonic colors for additional color choices, similar to `monoPalette()`. █ EXAMPLE CODE The example code at the end of the script generates and visualizes color schemes by processing user inputs. The code builds the scheme's palette based on the "Base color" input and the additional inputs in the "Settings/Inputs" tab:  • "Palette type" specifies whether the palette uses a custom gradient, monochromatic base color variants, or color harmonies with monochromatic variants.  • "Target color" sets the top color for the "Gradient" palette type.  • The "Gray luminance" inputs determine variation behavior for "Monochromatic" and "Harmony" palette types. If "Auto" is selected, the palette mixes the base color toward white or black based on its brightness. Otherwise, it mixes the color toward the grayscale color with the specified relative luminance (from 0 to 1).  • "Harmony type" specifies the color harmony used in the palette. Each row in the palette corresponds to one of the harmonious colors, starting with the base color. The code creates a table on the first bar to display the collection of calculated colors. Each cell in the table shows the color's `getHexString()` value in a tooltip for simple inspection. Look first. Then leap. █ EXPORTED FUNCTIONS Below is a complete list of the functions and overloads exported by this library. getRGB(source)   Retrieves the sRGB red, green, blue, and transparency components of a "color" value. getHexString(r, g, b, t)   (Overload 1 of 2) Converts a set of sRGB channel values to a string representing the corresponding color's hexadecimal form. getHexString(source)   (Overload 2 of 2) Converts a "color" value to a string representing the sRGB color's hexadecimal form. hexStringToRGB(source)   Converts a string representing an sRGB color's hexadecimal form to a set of decimal channel values. hexStringToColor(source)   Converts a string representing an sRGB color's hexadecimal form to a "color" value. getLRGB(r, g, b, t)   (Overload 1 of 2) Converts a set of sRGB channel values to a set of linear RGB values with specified transparency information. getLRGB(source)   (Overload 2 of 2) Retrieves linear RGB channel values and transparency information from a "color" value. lrgbToRGB(lr, lg, lb, t)   Converts a set of linear RGB channel values to a set of sRGB values with specified transparency information. lrgbToColor(lr, lg, lb, t)   Converts a set of linear RGB channel values and transparency information to a "color" value. getHSL(r, g, b, t)   (Overload 1 of 2) Converts a set of sRGB channels to a set of HSL values with specified transparency information. getHSL(source)   (Overload 2 of 2) Retrieves HSL channel values and transparency information from a "color" value. hslToRGB(h, s, l, t)   Converts a set of HSL channel values to a set of sRGB values with specified transparency information. hslToColor(h, s, l, t)   Converts a set of HSL channel values and transparency information to a "color" value. getHSV(r, g, b, t)   (Overload 1 of 2) Converts a set of sRGB channels to a set of HSV values with specified transparency information. getHSV(source)   (Overload 2 of 2) Retrieves HSV channel values and transparency information from a "color" value. hsvToRGB(h, s, v, t)   Converts a set of HSV channel values to a set of sRGB values with specified transparency information. hsvToColor(h, s, v, t)   Converts a set of HSV channel values and transparency information to a "color" value. getHWB(r, g, b, t)   (Overload 1 of 2) Converts a set of sRGB channels to a set of HWB values with specified transparency information. getHWB(source)   (Overload 2 of 2) Retrieves HWB channel values and transparency information from a "color" value. hwbToRGB(h, w, b, t)   Converts a set of HWB channel values to a set of sRGB values with specified transparency information. hwbToColor(h, w, b, t)   Converts a set of HWB channel values and transparency information to a "color" value. getXYZ(r, g, b, t)   (Overload 1 of 2) Converts a set of sRGB channels to a set of XYZ values with specified transparency information. getXYZ(source)   (Overload 2 of 2) Retrieves XYZ channel values and transparency information from a "color" value. xyzToRGB(x, y, z, t)   Converts a set of XYZ channel values to a set of sRGB values with specified transparency information xyzToColor(x, y, z, t)   Converts a set of XYZ channel values and transparency information to a "color" value. getXYY(r, g, b, t)   (Overload 1 of 2) Converts a set of sRGB channels to a set of xyY values with specified transparency information. getXYY(source)   (Overload 2 of 2) Retrieves xyY channel values and transparency information from a "color" value. xyyToRGB(xc, yc, y, t)   Converts a set of xyY channel values to a set of sRGB values with specified transparency information. xyyToColor(xc, yc, y, t)   Converts a set of xyY channel values and transparency information to a "color" value. getLAB(r, g, b, t)   (Overload 1 of 2) Converts a set of sRGB channels to a set of CIELAB values with specified transparency information. getLAB(source)   (Overload 2 of 2) Retrieves CIELAB channel values and transparency information from a "color" value. labToRGB(l, a, b, t)   Converts a set of CIELAB channel values to a set of sRGB values with specified transparency information. labToColor(l, a, b, t)   Converts a set of CIELAB channel values and transparency information to a "color" value. getOKLAB(r, g, b, t)   (Overload 1 of 2) Converts a set of sRGB channels to a set of Oklab values with specified transparency information. getOKLAB(source)   (Overload 2 of 2) Retrieves Oklab channel values and transparency information from a "color" value. oklabToRGB(l, a, b, t)   Converts a set of Oklab channel values to a set of sRGB values with specified transparency information. oklabToColor(l, a, b, t)   Converts a set of Oklab channel values and transparency information to a "color" value. getLCH(r, g, b, t)   (Overload 1 of 2) Converts a set of sRGB channels to a set of CIELCh values with specified transparency information. getLCH(source)   (Overload 2 of 2) Retrieves CIELCh channel values and transparency information from a "color" value. lchToRGB(l, c, h, t)   Converts a set of CIELCh channel values to a set of sRGB values with specified transparency information. lchToColor(l, c, h, t)   Converts a set of CIELCh channel values and transparency information to a "color" value. getOKLCH(r, g, b, t)   (Overload 1 of 2) Converts a set of sRGB channels to a set of Oklch values with specified transparency information. getOKLCH(source)   (Overload 2 of 2) Retrieves Oklch channel values and transparency information from a "color" value. oklchToRGB(l, c, h, t)   Converts a set of Oklch channel values to a set of sRGB values with specified transparency information. oklchToColor(l, c, h, t)   Converts a set of Oklch channel values and transparency information to a "color" value. contrastRatio(value1, value2)   Calculates the contrast ratio between two colors values based on the formula from version 2 of the Web Content Accessibility Guidelines (WCAG). isLightTheme(source)   Detects whether a background color represents a light theme or dark theme, based on the amount of contrast between the color and the white and black points. grayscale(source)   Calculates the grayscale version of a color with the same relative luminance (i.e., brightness). negative(source, colorSpace)   Calculates the negative (i.e., inverted) form of a specified color. complement(source, colorSpace)   Calculates the complementary color for a `source` color using a cylindrical color space. analogousColors(source, colorSpace)   Calculates the analogous colors for a `source` color using a cylindrical color space. splitComplements(source, colorSpace)   Calculates the split-complementary colors for a `source` color using a cylindrical color space. triadicColors(source, colorSpace)   Calculates the two triadic colors for a `source` color using a cylindrical color space. tetradicColors(source, colorSpace, square)   Calculates the three square or rectangular tetradic colors for a `source` color using a cylindrical color space. pentadicColors(source, colorSpace)   Calculates the four pentadic colors for a `source` color using a cylindrical color space. hexadicColors(source, colorSpace)   Calculates the five hexadic colors for a `source` color using a cylindrical color space. add(value1, value2, transpWeight)   Additively mixes two "color" values, with optional transparency weighting. overlay(fg, bg)   Estimates the resulting color that appears on the chart when placing one color over another. fromGradient(value, bottomValue, topValue, bottomColor, topColor, colorSpace)   Calculates the gradient color that corresponds to a specific value based on a defined value range and color space. fromMultiStepGradient(value, steps, colors, colorSpace)   Calculates a multi-step gradient color that corresponds to a specific value based on an array of step points, an array of corresponding colors, and a color space. gradientPalette(baseColor, stopColor, steps, strength, model)   Generates a palette from a gradient between two base colors. monoPalette(baseColor, grayLuminance, variations, strength, colorSpace)   Generates a monochromatic palette from a specified base color. harmonyPalette(baseColor, harmonyType, grayLuminance, variations, strength, colorSpace)   Generates a palette consisting of harmonious base colors and their monochromatic variants.
Pine Script®腳本庫
由TradingView提供
3
Tensor Market Analysis Engine (TMAE)# Tensor Market Analysis Engine (TMAE) ## Advanced Multi-Dimensional Mathematical Analysis System *Where Quantum Mathematics Meets Market Structure* --- ## 🎓 THEORETICAL FOUNDATION The Tensor Market Analysis Engine represents a revolutionary synthesis of three cutting-edge mathematical frameworks that have never before been combined for comprehensive market analysis. This indicator transcends traditional technical analysis by implementing advanced mathematical concepts from quantum mechanics, information theory, and fractal geometry. ### 🌊 Multi-Dimensional Volatility with Jump Detection **Hawkes Process Implementation:** The TMAE employs a sophisticated Hawkes process approximation for detecting self-exciting market jumps. Unlike traditional volatility measures that treat price movements as independent events, the Hawkes process recognizes that market shocks cluster and exhibit memory effects. **Mathematical Foundation:** ``` Intensity λ(t) = μ + Σ α(t - Tᵢ) ``` Where market jumps at times Tᵢ increase the probability of future jumps through the decay function α, controlled by the Hawkes Decay parameter (0.5-0.99). **Mahalanobis Distance Calculation:** The engine calculates volatility jumps using multi-dimensional Mahalanobis distance across up to 5 volatility dimensions: - **Dimension 1:** Price volatility (standard deviation of returns) - **Dimension 2:** Volume volatility (normalized volume fluctuations) - **Dimension 3:** Range volatility (high-low spread variations) - **Dimension 4:** Correlation volatility (price-volume relationship changes) - **Dimension 5:** Microstructure volatility (intrabar positioning analysis) This creates a volatility state vector that captures market behavior impossible to detect with traditional single-dimensional approaches. ### 📐 Hurst Exponent Regime Detection **Fractal Market Hypothesis Integration:** The TMAE implements advanced Rescaled Range (R/S) analysis to calculate the Hurst exponent in real-time, providing dynamic regime classification: - **H > 0.6:** Trending (persistent) markets - momentum strategies optimal - **H < 0.4:** Mean-reverting (anti-persistent) markets - contrarian strategies optimal - **H ≈ 0.5:** Random walk markets - breakout strategies preferred **Adaptive R/S Analysis:** Unlike static implementations, the TMAE uses adaptive windowing that adjusts to market conditions: ``` H = log(R/S) / log(n) ``` Where R is the range of cumulative deviations and S is the standard deviation over period n. **Dynamic Regime Classification:** The system employs hysteresis to prevent regime flipping, requiring sustained Hurst values before regime changes are confirmed. This prevents false signals during transitional periods. ### 🔄 Transfer Entropy Analysis **Information Flow Quantification:** Transfer entropy measures the directional flow of information between price and volume, revealing lead-lag relationships that indicate future price movements: ``` TE(X→Y) = Σ p(yₜ₊₁, yₜ, xₜ) log ``` **Causality Detection:** - **Volume → Price:** Indicates accumulation/distribution phases - **Price → Volume:** Suggests retail participation or momentum chasing - **Balanced Flow:** Market equilibrium or transition periods The system analyzes multiple lag periods (2-20 bars) to capture both immediate and structural information flows. --- ## 🔧 COMPREHENSIVE INPUT SYSTEM ### Core Parameters Group **Primary Analysis Window (10-100, Default: 50)** The fundamental lookback period affecting all calculations. Optimization by timeframe: - **1-5 minute charts:** 20-30 (rapid adaptation to micro-movements) - **15 minute-1 hour:** 30-50 (balanced responsiveness and stability) - **4 hour-daily:** 50-100 (smooth signals, reduced noise) - **Asset-specific:** Cryptocurrency 20-35, Stocks 35-50, Forex 40-60 **Signal Sensitivity (0.1-2.0, Default: 0.7)** Master control affecting all threshold calculations: - **Conservative (0.3-0.6):** High-quality signals only, fewer false positives - **Balanced (0.7-1.0):** Optimal risk-reward ratio for most trading styles - **Aggressive (1.1-2.0):** Maximum signal frequency, requires careful filtering **Signal Generation Mode:** - **Aggressive:** Any component signals (highest frequency) - **Confluence:** 2+ components agree (balanced approach) - **Conservative:** All 3 components align (highest quality) ### Volatility Jump Detection Group **Volatility Dimensions (2-5, Default: 3)** Determines the mathematical space complexity: - **2D:** Price + Volume volatility (suitable for clean markets) - **3D:** + Range volatility (optimal for most conditions) - **4D:** + Correlation volatility (advanced multi-asset analysis) - **5D:** + Microstructure volatility (maximum sensitivity) **Jump Detection Threshold (1.5-4.0σ, Default: 3.0σ)** Standard deviations required for volatility jump classification: - **Cryptocurrency:** 2.0-2.5σ (naturally volatile) - **Stock Indices:** 2.5-3.0σ (moderate volatility) - **Forex Major Pairs:** 3.0-3.5σ (typically stable) - **Commodities:** 2.0-3.0σ (varies by commodity) **Jump Clustering Decay (0.5-0.99, Default: 0.85)** Hawkes process memory parameter: - **0.5-0.7:** Fast decay (jumps treated as independent) - **0.8-0.9:** Moderate clustering (realistic market behavior) - **0.95-0.99:** Strong clustering (crisis/event-driven markets) ### Hurst Exponent Analysis Group **Calculation Method Options:** - **Classic R/S:** Original Rescaled Range (fast, simple) - **Adaptive R/S:** Dynamic windowing (recommended for trading) - **DFA:** Detrended Fluctuation Analysis (best for noisy data) **Trending Threshold (0.55-0.8, Default: 0.60)** Hurst value defining persistent market behavior: - **0.55-0.60:** Weak trend persistence - **0.65-0.70:** Clear trending behavior - **0.75-0.80:** Strong momentum regimes **Mean Reversion Threshold (0.2-0.45, Default: 0.40)** Hurst value defining anti-persistent behavior: - **0.35-0.45:** Weak mean reversion - **0.25-0.35:** Clear ranging behavior - **0.15-0.25:** Strong reversion tendency ### Transfer Entropy Parameters Group **Information Flow Analysis:** - **Price-Volume:** Classic flow analysis for accumulation/distribution - **Price-Volatility:** Risk flow analysis for sentiment shifts - **Multi-Timeframe:** Cross-timeframe causality detection **Maximum Lag (2-20, Default: 5)** Causality detection window: - **2-5 bars:** Immediate causality (scalping) - **5-10 bars:** Short-term flow (day trading) - **10-20 bars:** Structural flow (swing trading) **Significance Threshold (0.05-0.3, Default: 0.15)** Minimum entropy for signal generation: - **0.05-0.10:** Detect subtle information flows - **0.10-0.20:** Clear causality only - **0.20-0.30:** Very strong flows only --- ## 🎨 ADVANCED VISUAL SYSTEM ### Tensor Volatility Field Visualization **Five-Layer Resonance Bands:** The tensor field creates dynamic support/resistance zones that expand and contract based on mathematical field strength: - **Core Layer (Purple):** Primary tensor field with highest intensity - **Layer 2 (Neutral):** Secondary mathematical resonance - **Layer 3 (Info Blue):** Tertiary harmonic frequencies - **Layer 4 (Warning Gold):** Outer field boundaries - **Layer 5 (Success Green):** Maximum field extension **Field Strength Calculation:** ``` Field Strength = min(3.0, Mahalanobis Distance × Tensor Intensity) ``` The field amplitude adjusts to ATR and mathematical distance, creating dynamic zones that respond to market volatility. **Radiation Line Network:** During active tensor states, the system projects directional radiation lines showing field energy distribution: - **8 Directional Rays:** Complete angular coverage - **Tapering Segments:** Progressive transparency for natural visual flow - **Pulse Effects:** Enhanced visualization during volatility jumps ### Dimensional Portal System **Portal Mathematics:** Dimensional portals visualize regime transitions using category theory principles: - **Green Portals (◉):** Trending regime detection (appear below price for support) - **Red Portals (◎):** Mean-reverting regime (appear above price for resistance) - **Yellow Portals (○):** Random walk regime (neutral positioning) **Tensor Trail Effects:** Each portal generates 8 trailing particles showing mathematical momentum: - **Large Particles (●):** Strong mathematical signal - **Medium Particles (◦):** Moderate signal strength - **Small Particles (·):** Weak signal continuation - **Micro Particles (˙):** Signal dissipation ### Information Flow Streams **Particle Stream Visualization:** Transfer entropy creates flowing particle streams indicating information direction: - **Upward Streams:** Volume leading price (accumulation phases) - **Downward Streams:** Price leading volume (distribution phases) - **Stream Density:** Proportional to information flow strength **15-Particle Evolution:** Each stream contains 15 particles with progressive sizing and transparency, creating natural flow visualization that makes information transfer immediately apparent. ### Fractal Matrix Grid System **Multi-Timeframe Fractal Levels:** The system calculates and displays fractal highs/lows across five Fibonacci periods: - **8-Period:** Short-term fractal structure - **13-Period:** Intermediate-term patterns - **21-Period:** Primary swing levels - **34-Period:** Major structural levels - **55-Period:** Long-term fractal boundaries **Triple-Layer Visualization:** Each fractal level uses three-layer rendering: - **Shadow Layer:** Widest, darkest foundation (width 5) - **Glow Layer:** Medium white core line (width 3) - **Tensor Layer:** Dotted mathematical overlay (width 1) **Intelligent Labeling System:** Smart spacing prevents label overlap using ATR-based minimum distances. Labels include: - **Fractal Period:** Time-based identification - **Topological Class:** Mathematical complexity rating (0, I, II, III) - **Price Level:** Exact fractal price - **Mahalanobis Distance:** Current mathematical field strength - **Hurst Exponent:** Current regime classification - **Anomaly Indicators:** Visual strength representations (○ ◐ ● ⚡) ### Wick Pressure Analysis **Rejection Level Mathematics:** The system analyzes candle wick patterns to project future pressure zones: - **Upper Wick Analysis:** Identifies selling pressure and resistance zones - **Lower Wick Analysis:** Identifies buying pressure and support zones - **Pressure Projection:** Extends lines forward based on mathematical probability **Multi-Layer Glow Effects:** Wick pressure lines use progressive transparency (1-8 layers) creating natural glow effects that make pressure zones immediately visible without cluttering the chart. ### Enhanced Regime Background **Dynamic Intensity Mapping:** Background colors reflect mathematical regime strength: - **Deep Transparency (98% alpha):** Subtle regime indication - **Pulse Intensity:** Based on regime strength calculation - **Color Coding:** Green (trending), Red (mean-reverting), Neutral (random) **Smoothing Integration:** Regime changes incorporate 10-bar smoothing to prevent background flicker while maintaining responsiveness to genuine regime shifts. ### Color Scheme System **Six Professional Themes:** - **Dark (Default):** Professional trading environment optimization - **Light:** High ambient light conditions - **Classic:** Traditional technical analysis appearance - **Neon:** High-contrast visibility for active trading - **Neutral:** Minimal distraction focus - **Bright:** Maximum visibility for complex setups Each theme maintains mathematical accuracy while optimizing visual clarity for different trading environments and personal preferences. --- ## 📊 INSTITUTIONAL-GRADE DASHBOARD ### Tensor Field Status Section **Field Strength Display:** Real-time Mahalanobis distance calculation with dynamic emoji indicators: - **⚡ (Lightning):** Extreme field strength (>1.5× threshold) - **● (Solid Circle):** Strong field activity (>1.0× threshold) - **○ (Open Circle):** Normal field state **Signal Quality Rating:** Democratic algorithm assessment: - **ELITE:** All 3 components aligned (highest probability) - **STRONG:** 2 components aligned (good probability) - **GOOD:** 1 component active (moderate probability) - **WEAK:** No clear component signals **Threshold and Anomaly Monitoring:** - **Threshold Display:** Current mathematical threshold setting - **Anomaly Level (0-100%):** Combined volatility and volume spike measurement - **>70%:** High anomaly (red warning) - **30-70%:** Moderate anomaly (orange caution) - **<30%:** Normal conditions (green confirmation) ### Tensor State Analysis Section **Mathematical State Classification:** - **↑ BULL (Tensor State +1):** Trending regime with bullish bias - **↓ BEAR (Tensor State -1):** Mean-reverting regime with bearish bias - **◈ SUPER (Tensor State 0):** Random walk regime (neutral) **Visual State Gauge:** Five-circle progression showing tensor field polarity: - **🟢🟢🟢⚪⚪:** Strong bullish mathematical alignment - **⚪⚪🟡⚪⚪:** Neutral/transitional state - **⚪⚪🔴🔴🔴:** Strong bearish mathematical alignment **Trend Direction and Phase Analysis:** - **📈 BULL / 📉 BEAR / ➡️ NEUTRAL:** Primary trend classification - **🌪️ CHAOS:** Extreme information flow (>2.0 flow strength) - **⚡ ACTIVE:** Strong information flow (1.0-2.0 flow strength) - **😴 CALM:** Low information flow (<1.0 flow strength) ### Trading Signals Section **Real-Time Signal Status:** - **🟢 ACTIVE / ⚪ INACTIVE:** Long signal availability - **🔴 ACTIVE / ⚪ INACTIVE:** Short signal availability - **Components (X/3):** Active algorithmic components - **Mode Display:** Current signal generation mode **Signal Strength Visualization:** Color-coded component count: - **Green:** 3/3 components (maximum confidence) - **Aqua:** 2/3 components (good confidence) - **Orange:** 1/3 components (moderate confidence) - **Gray:** 0/3 components (no signals) ### Performance Metrics Section **Win Rate Monitoring:** Estimated win rates based on signal quality with emoji indicators: - **🔥 (Fire):** ≥60% estimated win rate - **👍 (Thumbs Up):** 45-59% estimated win rate - **⚠️ (Warning):** <45% estimated win rate **Mathematical Metrics:** - **Hurst Exponent:** Real-time fractal dimension (0.000-1.000) - **Information Flow:** Volume/price leading indicators - **📊 VOL:** Volume leading price (accumulation/distribution) - **💰 PRICE:** Price leading volume (momentum/speculation) - **➖ NONE:** Balanced information flow - **Volatility Classification:** - **🔥 HIGH:** Above 1.5× jump threshold - **📊 NORM:** Normal volatility range - **😴 LOW:** Below 0.5× jump threshold ### Market Structure Section (Large Dashboard) **Regime Classification:** - **📈 TREND:** Hurst >0.6, momentum strategies optimal - **🔄 REVERT:** Hurst <0.4, contrarian strategies optimal - **🎲 RANDOM:** Hurst ≈0.5, breakout strategies preferred **Mathematical Field Analysis:** - **Dimensions:** Current volatility space complexity (2D-5D) - **Hawkes λ (Lambda):** Self-exciting jump intensity (0.00-1.00) - **Jump Status:** 🚨 JUMP (active) / ✅ NORM (normal) ### Settings Summary Section (Large Dashboard) **Active Configuration Display:** - **Sensitivity:** Current master sensitivity setting - **Lookback:** Primary analysis window - **Theme:** Active color scheme - **Method:** Hurst calculation method (Classic R/S, Adaptive R/S, DFA) **Dashboard Sizing Options:** - **Small:** Essential metrics only (mobile/small screens) - **Normal:** Balanced information density (standard desktop) - **Large:** Maximum detail (multi-monitor setups) **Position Options:** - **Top Right:** Standard placement (avoids price action) - **Top Left:** Wide chart optimization - **Bottom Right:** Recent price focus (scalping) - **Bottom Left:** Maximum price visibility (swing trading) --- ## 🎯 SIGNAL GENERATION LOGIC ### Multi-Component Convergence System **Component Signal Architecture:** The TMAE generates signals through sophisticated component analysis rather than simple threshold crossing: **Volatility Component:** - **Jump Detection:** Mahalanobis distance threshold breach - **Hawkes Intensity:** Self-exciting process activation (>0.2) - **Multi-dimensional:** Considers all volatility dimensions simultaneously **Hurst Regime Component:** - **Trending Markets:** Price above SMA-20 with positive momentum - **Mean-Reverting Markets:** Price at Bollinger Band extremes - **Random Markets:** Bollinger squeeze breakouts with directional confirmation **Transfer Entropy Component:** - **Volume Leadership:** Information flow from volume to price - **Volume Spike:** Volume 110%+ above 20-period average - **Flow Significance:** Above entropy threshold with directional bias ### Democratic Signal Weighting **Signal Mode Implementation:** - **Aggressive Mode:** Any single component triggers signal - **Confluence Mode:** Minimum 2 components must agree - **Conservative Mode:** All 3 components must align **Momentum Confirmation:** All signals require momentum confirmation: - **Long Signals:** RSI >50 AND price >EMA-9 - **Short Signals:** RSI <50 AND price 0.6):** - **Increase Sensitivity:** Catch momentum continuation - **Lower Mean Reversion Threshold:** Avoid counter-trend signals - **Emphasize Volume Leadership:** Institutional accumulation/distribution - **Tensor Field Focus:** Use expansion for trend continuation - **Signal Mode:** Aggressive or Confluence for trend following **Range-Bound Markets (Hurst <0.4):** - **Decrease Sensitivity:** Avoid false breakouts - **Lower Trending Threshold:** Quick regime recognition - **Focus on Price Leadership:** Retail sentiment extremes - **Fractal Grid Emphasis:** Support/resistance trading - **Signal Mode:** Conservative for high-probability reversals **Volatile Markets (High Jump Frequency):** - **Increase Hawkes Decay:** Recognize event clustering - **Higher Jump Threshold:** Avoid noise signals - **Maximum Dimensions:** Capture full volatility complexity - **Reduce Position Sizing:** Risk management adaptation - **Enhanced Visuals:** Maximum information for rapid decisions **Low Volatility Markets (Low Jump Frequency):** - **Decrease Jump Threshold:** Capture subtle movements - **Lower Hawkes Decay:** Treat moves as independent - **Reduce Dimensions:** Simplify analysis - **Increase Position Sizing:** Capitalize on compressed volatility - **Minimal Visuals:** Reduce distraction in quiet markets --- ## 🚀 ADVANCED TRADING STRATEGIES ### The Mathematical Convergence Method **Entry Protocol:** 1. **Fractal Grid Approach:** Monitor price approaching significant fractal levels 2. **Tensor Field Confirmation:** Verify field expansion supporting direction 3. **Portal Signal:** Wait for dimensional portal appearance 4. **ELITE/STRONG Quality:** Only trade highest quality mathematical signals 5. **Component Consensus:** Confirm 2+ components agree in Confluence mode **Example Implementation:** - Price approaching 21-period fractal high - Tensor field expanding upward (bullish mathematical alignment) - Green portal appears below price (trending regime confirmation) - ELITE quality signal with 3/3 components active - Enter long position with stop below fractal level **Risk Management:** - **Stop Placement:** Below/above fractal level that generated signal - **Position Sizing:** Based on Mahalanobis distance (higher distance = smaller size) - **Profit Targets:** Next fractal level or tensor field resistance ### The Regime Transition Strategy **Regime Change Detection:** 1. **Monitor Hurst Exponent:** Watch for persistent moves above/below thresholds 2. **Portal Color Change:** Regime transitions show different portal colors 3. **Background Intensity:** Increasing regime background intensity 4. **Mathematical Confirmation:** Wait for regime confirmation (hysteresis) **Trading Implementation:** - **Trending Transitions:** Trade momentum breakouts, follow trend - **Mean Reversion Transitions:** Trade range boundaries, fade extremes - **Random Transitions:** Trade breakouts with tight stops **Advanced Techniques:** - **Multi-Timeframe:** Confirm regime on higher timeframe - **Early Entry:** Enter on regime transition rather than confirmation - **Regime Strength:** Larger positions during strong regime signals ### The Information Flow Momentum Strategy **Flow Detection Protocol:** 1. **Monitor Transfer Entropy:** Watch for significant information flow shifts 2. **Volume Leadership:** Strong edge when volume leads price 3. **Flow Acceleration:** Increasing flow strength indicates momentum 4. **Directional Confirmation:** Ensure flow aligns with intended trade direction **Entry Signals:** - **Volume → Price Flow:** Enter during accumulation/distribution phases - **Price → Volume Flow:** Enter on momentum confirmation breaks - **Flow Reversal:** Counter-trend entries when flow reverses **Optimization:** - **Scalping:** Use immediate flow detection (2-5 bar lag) - **Swing Trading:** Use structural flow (10-20 bar lag) - **Multi-Asset:** Compare flow between correlated assets ### The Tensor Field Expansion Strategy **Field Mathematics:** The tensor field expansion indicates mathematical pressure building in market structure: **Expansion Phases:** 1. **Compression:** Field contracts, volatility decreases 2. **Tension Building:** Mathematical pressure accumulates 3. **Expansion:** Field expands rapidly with directional movement 4. **Resolution:** Field stabilizes at new equilibrium **Trading Applications:** - **Compression Trading:** Prepare for breakout during field contraction - **Expansion Following:** Trade direction of field expansion - **Reversion Trading:** Fade extreme field expansion - **Multi-Dimensional:** Consider all field layers for confirmation ### The Hawkes Process Event Strategy **Self-Exciting Jump Trading:** Understanding that market shocks cluster and create follow-on opportunities: **Jump Sequence Analysis:** 1. **Initial Jump:** First volatility jump detected 2. **Clustering Phase:** Hawkes intensity remains elevated 3. **Follow-On Opportunities:** Additional jumps more likely 4. **Decay Period:** Intensity gradually decreases **Implementation:** - **Jump Confirmation:** Wait for mathematical jump confirmation - **Direction Assessment:** Use other components for direction - **Clustering Trades:** Trade subsequent moves during high intensity - **Decay Exit:** Exit positions as Hawkes intensity decays ### The Fractal Confluence System **Multi-Timeframe Fractal Analysis:** Combining fractal levels across different periods for high-probability zones: **Confluence Zones:** - **Double Confluence:** 2 fractal levels align - **Triple Confluence:** 3+ fractal levels cluster - **Mathematical Confirmation:** Tensor field supports the level - **Information Flow:** Transfer entropy confirms direction **Trading Protocol:** 1. **Identify Confluence:** Find 2+ fractal levels within 1 ATR 2. **Mathematical Support:** Verify tensor field alignment 3. **Signal Quality:** Wait for STRONG or ELITE signal 4. **Risk Definition:** Use fractal level for stop placement 5. **Profit Targeting:** Next major fractal confluence zone --- ## ⚠️ COMPREHENSIVE RISK MANAGEMENT ### Mathematical Position Sizing **Mahalanobis Distance Integration:** Position size should inversely correlate with mathematical field strength: ``` Position Size = Base Size × (Threshold / Mahalanobis Distance) ``` **Risk Scaling Matrix:** - **Low Field Strength (<2.0):** Standard position sizing - **Moderate Field Strength (2.0-3.0):** 75% position sizing - **High Field Strength (3.0-4.0):** 50% position sizing - **Extreme Field Strength (>4.0):** 25% position sizing or no trade ### Signal Quality Risk Adjustment **Quality-Based Position Sizing:** - **ELITE Signals:** 100% of planned position size - **STRONG Signals:** 75% of planned position size - **GOOD Signals:** 50% of planned position size - **WEAK Signals:** No position or paper trading only **Component Agreement Scaling:** - **3/3 Components:** Full position size - **2/3 Components:** 75% position size - **1/3 Components:** 50% position size or skip trade ### Regime-Adaptive Risk Management **Trending Market Risk:** - **Wider Stops:** Allow for trend continuation - **Trend Following:** Trade with regime direction - **Higher Position Size:** Trend probability advantage - **Momentum Stops:** Trail stops based on momentum indicators **Mean-Reverting Market Risk:** - **Tighter Stops:** Quick exits on trend continuation - **Contrarian Positioning:** Trade against extremes - **Smaller Position Size:** Higher reversal failure rate - **Level-Based Stops:** Use fractal levels for stops **Random Market Risk:** - **Breakout Focus:** Trade only clear breakouts - **Tight Initial Stops:** Quick exit if breakout fails - **Reduced Frequency:** Skip marginal setups - **Range-Based Targets:** Profit targets at range boundaries ### Volatility-Adaptive Risk Controls **High Volatility Periods:** - **Reduced Position Size:** Account for wider price swings - **Wider Stops:** Avoid noise-based exits - **Lower Frequency:** Skip marginal setups - **Faster Exits:** Take profits more quickly **Low Volatility Periods:** - **Standard Position Size:** Normal risk parameters - **Tighter Stops:** Take advantage of compressed ranges - **Higher Frequency:** Trade more setups - **Extended Targets:** Allow for compressed volatility expansion ### Multi-Timeframe Risk Alignment **Higher Timeframe Trend:** - **With Trend:** Standard or increased position size - **Against Trend:** Reduced position size or skip - **Neutral Trend:** Standard position size with tight management **Risk Hierarchy:** 1. **Primary:** Current timeframe signal quality 2. **Secondary:** Higher timeframe trend alignment 3. **Tertiary:** Mathematical field strength 4. **Quaternary:** Market regime classification --- ## 📚 EDUCATIONAL VALUE AND MATHEMATICAL CONCEPTS ### Advanced Mathematical Concepts **Tensor Analysis in Markets:** The TMAE introduces traders to tensor analysis, a branch of mathematics typically reserved for physics and advanced engineering. Tensors provide a framework for understanding multi-dimensional market relationships that scalar and vector analysis cannot capture. **Information Theory Applications:** Transfer entropy implementation teaches traders about information flow in markets, a concept from information theory that quantifies directional causality between variables. This provides intuition about market microstructure and participant behavior. **Fractal Geometry in Trading:** The Hurst exponent calculation exposes traders to fractal geometry concepts, helping understand that markets exhibit self-similar patterns across multiple timeframes. This mathematical insight transforms how traders view market structure. **Stochastic Process Theory:** The Hawkes process implementation introduces concepts from stochastic process theory, specifically self-exciting point processes. This provides mathematical framework for understanding why market events cluster and exhibit memory effects. ### Learning Progressive Complexity **Beginner Mathematical Concepts:** - **Volatility Dimensions:** Understanding multi-dimensional analysis - **Regime Classification:** Learning market personality types - **Signal Democracy:** Algorithmic consensus building - **Visual Mathematics:** Interpreting mathematical concepts visually **Intermediate Mathematical Applications:** - **Mahalanobis Distance:** Statistical distance in multi-dimensional space - **Rescaled Range Analysis:** Fractal dimension measurement - **Information Entropy:** Quantifying uncertainty and causality - **Field Theory:** Understanding mathematical fields in market context **Advanced Mathematical Integration:** - **Tensor Field Dynamics:** Multi-dimensional market force analysis - **Stochastic Self-Excitation:** Event clustering and memory effects - **Categorical Composition:** Mathematical signal combination theory - **Topological Market Analysis:** Understanding market shape and connectivity ### Practical Mathematical Intuition **Developing Market Mathematics Intuition:** The TMAE serves as a bridge between abstract mathematical concepts and practical trading applications. Traders develop intuitive understanding of: - **How markets exhibit mathematical structure beneath apparent randomness** - **Why multi-dimensional analysis reveals patterns invisible to single-variable approaches** - **How information flows through markets in measurable, predictable ways** - **Why mathematical models provide probabilistic edges rather than certainties** --- ## 🔬 IMPLEMENTATION AND OPTIMIZATION ### Getting Started Protocol **Phase 1: Observation (Week 1)** 1. **Apply with defaults:** Use standard settings on your primary trading timeframe 2. **Study visual elements:** Learn to interpret tensor fields, portals, and streams 3. **Monitor dashboard:** Observe how metrics change with market conditions 4. **No trading:** Focus entirely on pattern recognition and understanding **Phase 2: Pattern Recognition (Week 2-3)** 1. **Identify signal patterns:** Note what market conditions produce different signal qualities 2. **Regime correlation:** Observe how Hurst regimes affect signal performance 3. **Visual confirmation:** Learn to read tensor field expansion and portal signals 4. **Component analysis:** Understand which components drive signals in different markets **Phase 3: Parameter Optimization (Week 4-5)** 1. **Asset-specific tuning:** Adjust parameters for your specific trading instrument 2. **Timeframe optimization:** Fine-tune for your preferred trading timeframe 3. **Sensitivity adjustment:** Balance signal frequency with quality 4. **Visual customization:** Optimize colors and intensity for your trading environment **Phase 4: Live Implementation (Week 6+)** 1. **Paper trading:** Test signals with hypothetical trades 2. **Small position sizing:** Begin with minimal risk during learning phase 3. **Performance tracking:** Monitor actual vs. expected signal performance 4. **Continuous optimization:** Refine settings based on real performance data ### Performance Monitoring System **Signal Quality Tracking:** - **ELITE Signal Win Rate:** Track highest quality signals separately - **Component Performance:** Monitor which components provide best signals - **Regime Performance:** Analyze performance across different market regimes - **Timeframe Analysis:** Compare performance across different session times **Mathematical Metric Correlation:** - **Field Strength vs. Performance:** Higher field strength should correlate with better performance - **Component Agreement vs. Win Rate:** More component agreement should improve win rates - **Regime Alignment vs. Success:** Trading with mathematical regime should outperform ### Continuous Optimization Process **Monthly Review Protocol:** 1. **Performance Analysis:** Review win rates, profit factors, and maximum drawdown 2. **Parameter Assessment:** Evaluate if current settings remain optimal 3. **Market Adaptation:** Adjust for changes in market character or volatility 4. **Component Weighting:** Consider if certain components should receive more/less emphasis **Quarterly Deep Analysis:** 1. **Mathematical Model Validation:** Verify that mathematical relationships remain valid 2. **Regime Distribution:** Analyze time spent in different market regimes 3. **Signal Evolution:** Track how signal characteristics change over time 4. **Correlation Analysis:** Monitor correlations between different mathematical components --- ## 🌟 UNIQUE INNOVATIONS AND CONTRIBUTIONS ### Revolutionary Mathematical Integration **First-Ever Implementations:** 1. **Multi-Dimensional Volatility Tensor:** First indicator to implement true tensor analysis for market volatility 2. **Real-Time Hawkes Process:** First trading implementation of self-exciting point processes 3. **Transfer Entropy Trading Signals:** First practical application of information theory for trade generation 4. **Democratic Component Voting:** First algorithmic consensus system for signal generation 5. **Fractal-Projected Signal Quality:** First system to predict signal quality at future price levels ### Advanced Visualization Innovations **Mathematical Visualization Breakthroughs:** - **Tensor Field Radiation:** Visual representation of mathematical field energy - **Dimensional Portal System:** Category theory visualization for regime transitions - **Information Flow Streams:** Real-time visual display of market information transfer - **Multi-Layer Fractal Grid:** Intelligent spacing and projection system - **Regime Intensity Mapping:** Dynamic background showing mathematical regime strength ### Practical Trading Innovations **Trading System Advances:** - **Quality-Weighted Signal Generation:** Signals rated by mathematical confidence - **Regime-Adaptive Strategy Selection:** Automatic strategy optimization based on market personality - **Anti-Spam Signal Protection:** Mathematical prevention of signal clustering - **Component Performance Tracking:** Real-time monitoring of algorithmic component success - **Field-Strength Position Sizing:** Mathematical volatility integration for risk management --- ## ⚖️ RESPONSIBLE USAGE AND LIMITATIONS ### Mathematical Model Limitations **Understanding Model Boundaries:** While the TMAE implements sophisticated mathematical concepts, traders must understand fundamental limitations: - **Markets Are Not Purely Mathematical:** Human psychology, news events, and fundamental factors create unpredictable elements - **Past Performance Limitations:** Mathematical relationships that worked historically may not persist indefinitely - **Model Risk:** Complex models can fail during unprecedented market conditions - **Overfitting Potential:** Highly optimized parameters may not generalize to future market conditions ### Proper Implementation Guidelines **Risk Management Requirements:** - **Never Risk More Than 2% Per Trade:** Regardless of signal quality - **Diversification Mandatory:** Don't rely solely on mathematical signals - **Position Sizing Discipline:** Use mathematical field strength for sizing, not confidence - **Stop Loss Non-Negotiable:** Every trade must have predefined risk parameters **Realistic Expectations:** - **Mathematical Edge, Not Certainty:** The indicator provides probabilistic advantages, not guaranteed outcomes - **Learning Curve Required:** Complex mathematical concepts require time to master - **Market Adaptation Necessary:** Parameters must evolve with changing market conditions - **Continuous Education Important:** Understanding underlying mathematics improves application ### Ethical Trading Considerations **Market Impact Awareness:** - **Information Asymmetry:** Advanced mathematical analysis may provide advantages over other market participants - **Position Size Responsibility:** Large positions based on mathematical signals can impact market structure - **Sharing Knowledge:** Consider educational contributions to trading community - **Fair Market Participation:** Use mathematical advantages responsibly within market framework ### Professional Development Path **Skill Development Sequence:** 1. **Basic Mathematical Literacy:** Understand fundamental concepts before advanced application 2. **Risk Management Mastery:** Develop disciplined risk control before relying on complex signals 3. **Market Psychology Understanding:** Combine mathematical analysis with behavioral market insights 4. **Continuous Learning:** Stay updated on mathematical finance developments and market evolution --- ## 🔮 CONCLUSION The Tensor Market Analysis Engine represents a quantum leap forward in technical analysis, successfully bridging the gap between advanced pure mathematics and practical trading applications. By integrating multi-dimensional volatility analysis, fractal market theory, and information flow dynamics, the TMAE reveals market structure invisible to conventional analysis while maintaining visual clarity and practical usability. ### Mathematical Innovation Legacy This indicator establishes new paradigms in technical analysis: - **Tensor analysis for market volatility understanding** - **Stochastic self-excitation for event clustering prediction** - **Information theory for causality-based trade generation** - **Democratic algorithmic consensus for signal quality enhancement** - **Mathematical field visualization for intuitive market understanding** ### Practical Trading Revolution Beyond mathematical innovation, the TMAE transforms practical trading: - **Quality-rated signals replace binary buy/sell decisions** - **Regime-adaptive strategies automatically optimize for market personality** - **Multi-dimensional risk management integrates mathematical volatility measures** - **Visual mathematical concepts make complex analysis immediately interpretable** - **Educational value creates lasting improvement in trading understanding** ### Future-Proof Design The mathematical foundations ensure lasting relevance: - **Universal mathematical principles transcend market evolution** - **Multi-dimensional analysis adapts to new market structures** - **Regime detection automatically adjusts to changing market personalities** - **Component democracy allows for future algorithmic additions** - **Mathematical visualization scales with increasing market complexity** ### Commitment to Excellence The TMAE represents more than an indicator—it embodies a philosophy of bringing rigorous mathematical analysis to trading while maintaining practical utility and visual elegance. Every component, from the multi-dimensional tensor fields to the democratic signal generation, reflects a commitment to mathematical accuracy, trading practicality, and educational value. ### Trading with Mathematical Precision In an era where markets grow increasingly complex and computational, the TMAE provides traders with mathematical tools previously available only to institutional quantitative research teams. Yet unlike academic mathematical models, the TMAE translates complex concepts into intuitive visual representations and practical trading signals. By combining the mathematical rigor of tensor analysis, the statistical power of multi-dimensional volatility modeling, and the information-theoretic insights of transfer entropy, traders gain unprecedented insight into market structure and dynamics. ### Final Perspective Markets, like nature, exhibit profound mathematical beauty beneath apparent chaos. The Tensor Market Analysis Engine serves as a mathematical lens that reveals this hidden order, transforming how traders perceive and interact with market structure. Through mathematical precision, visual elegance, and practical utility, the TMAE empowers traders to see beyond the noise and trade with the confidence that comes from understanding the mathematical principles governing market behavior. Trade with mathematical insight. Trade with the power of tensors. Trade with the TMAE. *"In mathematics, you don't understand things. You just get used to them." - John von Neumann* *With the TMAE, mathematical market understanding becomes not just possible, but intuitive.* — Dskyz, Trade with insight. Trade with anticipation.
Pine Script®指標
由DskyzInvestments提供
13
SIP Evaluator and Screener [Trendoscope®]The SIP Evaluator and Screener is a Pine Script indicator designed for TradingView to calculate and visualize Systematic Investment Plan (SIP) returns across multiple investment instruments. It is tailored for use in TradingView's screener, enabling users to evaluate SIP performance for various assets efficiently. 🎲 How SIP Works A Systematic Investment Plan (SIP) is an investment strategy where a fixed amount is invested at regular intervals (e.g., monthly or weekly) into a financial instrument, such as stocks, mutual funds, or ETFs. The goal is to build wealth over time by leveraging the power of compounding and mitigating the impact of market volatility through disciplined, consistent investing. Here’s a breakdown of how SIPs function: Regular Investments : In an SIP, an investor commits to investing a fixed sum at predefined intervals, regardless of market conditions. This consistency helps inculcate a habit of saving and investing. Cost Averaging : By investing a fixed amount regularly, investors purchase more units when prices are low and fewer units when prices are high. This approach, known as dollar-cost averaging, reduces the average cost per unit over time and mitigates the risk of investing a large amount at a peak price. Compounding Benefits : Returns generated from the invested amount (e.g., capital gains or dividends) are reinvested, leading to exponential growth over the long term. The longer the investment horizon, the greater the potential for compounding to amplify returns. Dividend Reinvestment : In some SIPs, dividends received from the underlying asset can be reinvested to purchase additional units, further enhancing returns. Taxes on dividends, if applicable, may reduce the reinvested amount. Flexibility and Accessibility : SIPs allow investors to start with small amounts, making them accessible to a wide range of individuals. They also offer flexibility in terms of investment frequency and the ability to adjust or pause contributions. In the context of the SIP Evaluator and Screener , the script simulates an SIP by calculating the number of units purchased with each fixed investment, factoring in commissions, dividends, taxes and the chosen price reference (e.g., open, close, or average prices). It tracks the cumulative investment, equity value, and dividends over time, providing a clear picture of how an SIP would perform for a given instrument. This helps users understand the impact of regular investing and make informed decisions when comparing different assets in TradingView’s screener. It offers insights into key metrics such as total invested amount, dividends received, equity value, and the number of installments, making it a valuable resource for investors and traders interested in understanding long-term investment outcomes. 🎲 Key Features Customizable Investment Parameters: Users can define the recurring investment amount, price reference (e.g., open, close, HL2, HLC3, OHLC4), and whether fractional quantities are allowed. Commission Handling: Supports both fixed and percentage-based commission types, adjusting calculations accordingly. Dividend Reinvestment: Optionally reinvests dividends after a user-specified period, with the ability to apply tax on dividends. Time-Bound Analysis: Allows users to set a start year for the analysis, enabling historical performance evaluation. Flexible Dividend Periods: Dividends can be evaluated based on bars, days, weeks, or months. Visual Outputs: Plots key metrics like total invested amount, dividends, equity value, and remainder, with customizable display options for clarity in the data window and chart. 🎲 Using the script as an indicator on Tradingview Supercharts In order to use the indicator on charts, do the following. Load the instrument of your choice - Preferably a stable stocks, ETFs. Chose monthly timeframe as lower timeframes are insignificant in this type of investment strategy Load the indicator SIP Evaluator and Screener and set the input parameters as per your preference. Indicator plots, investment value, dividends and equity on the chart. 🎲 Visualizations Installments : Displays the number of SIP installments (gray line, visible in the data window). Invested Amount : Shows the cumulative amount invested, excluding reinvested dividends (blue area plot). Dividends : Tracks total dividends received (green area plot). Equity : Represents the current market value of the investment based on the closing price (purple area plot). Remainder : Indicates any uninvested cash after each installment (gray line, visible in the data window). 🎲 Deep dive into the settings The SIP Evaluator and Screener offers a range of customizable settings to tailor the Systematic Investment Plan (SIP) simulation to your preferences. Below is an explanation of each setting, its purpose, and how it impacts the analysis: 🎯 Duration Start Year (Default: 2020) : Specifies the year from which the SIP calculations begin. When Start Year is enabled via the timebound option, the script only considers data from the specified year onward. This is useful for analyzing historical SIP performance over a defined period. If disabled, the script uses all available data. Timebound (Default: False) : A toggle to enable or disable the Start Year restriction. When set to False, the SIP calculation starts from the earliest available data for the instrument. 🎯 Investment Recurring Investment (Default: 1000.0) : The fixed amount invested in each SIP installment (e.g., $1000 per period). This represents the regular contribution to the SIP and directly influences the total invested amount and quantity purchased. Allow Fractional Qty (Default: True) : When enabled, the script allows the purchase of fractional units (e.g., 2.35 shares). If disabled, only whole units are purchased (e.g., 2 shares), with any remaining funds carried forward as Remainder. This setting impacts the precision of investment allocation. Price Reference (Default: OPEN): Determines the price used for purchasing units in each SIP installment. Options include: OPEN : Uses the opening price of the bar. CLOSE : Uses the closing price of the bar. HL2 : Uses the average of the high and low prices. HLC3 : Uses the average of the high, low, and close prices. OHLC4 : Uses the average of the open, high, low, and close prices. This setting affects the cost basis of each purchase and, consequently, the total quantity and equity value. 🎯 Commission Commission (Default: 3) : The commission charged per SIP installment, expressed as either a fixed amount (e.g., $3) or a percentage (e.g., 3% of the investment). This reduces the amount available for purchasing units. Commission Type (Default: Fixed) : Specifies how the commission is calculated: Fixed ($) : A flat fee is deducted per installment (e.g., $3). Percentage (%) : A percentage of the investment amount is deducted as commission (e.g., 3% of $1000 = $30). This setting affects the net amount invested and the overall cost of the SIP. 🎯 Dividends Apply Tax On Dividends (Default: False) : When enabled, a tax is applied to dividends before they are reinvested or recorded. The tax rate is set via the Dividend Tax setting. Dividend Tax (Default: 47) : The percentage of tax deducted from dividends if Apply Tax On Dividends is enabled (e.g., 47% tax reduces a $100 dividend to $53). This reduces the amount available for reinvestment or accumulation. Reinvest Dividends After (Default: True, 2) : When enabled, dividends received are reinvested to purchase additional units after a specified period (e.g., 2 units of time, defined by Dividends Availability). If disabled, dividends are tracked but not reinvested. Reinvestment increases the total quantity and equity over time. Dividends Availability (Default: Bars) : Defines the time unit for evaluating when dividends are available for reinvestment. Options include: Bars : Based on the number of chart bars. Weeks : Based on weeks. Months : Based on months (approximated as 30.5 days). This setting determines the timing of dividend reinvestment relative to the Reinvest Dividends After period. 🎯 How Settings Interact These settings work together to simulate a realistic SIP. For example, a $1000 recurring investment with a 3% commission and fractional quantities enabled will calculate the number of units purchased at the chosen price reference after deducting the commission. If dividends are reinvested after 2 months with a 47% tax, the script fetches dividend data, applies the tax, and adds the net dividend to the investment amount for that period. The Start Year and Timebound settings ensure the analysis aligns with the desired timeframe, while the Dividends Availability setting fine-tunes dividend reinvestment timing. By adjusting these settings, users can model different SIP scenarios, compare performance across instruments in TradingView’s screener, and gain insights into how commissions, dividends, and price references impact long-term returns. 🎲 Using the script with Pine Screener The main purpose of developing this script is to use it with Tradingview Pine Screener so that multiple ETFs/Funds can be compared. In order to use this as a screener, the following things needs to be done. Add SIP Evaluator and Screener to your favourites (Required for it to be added in pine screener) Create a watch list containing required instruments to compare Open pine screener from Tradingview main menu Products -> Screeners -> Pine or simply load the URL - www.tradingview.com Select the watchlist created from Watchlist dropdown. Chose the SIP Evaluator and Screener from the "Choose Indicator" dropdown Set timeframe to 1 month and update settings as required. Press scan to display collected data on the screener. 🎲 Use Case This indicator is ideal for educational purposes, allowing users to experiment with SIP strategies across different instruments. It can be applied in TradingView’s screener to compare SIP performance for stocks, ETFs, or other assets, helping users understand how factors like commissions, dividends, and price references impact returns over time.
Pine Script®指標
由Trendoscope提供
2
Wavelet-Trend ML Integration [Alpha Extract]Alpha-Extract Volatility Quality Indicator The Alpha-Extract Volatility Quality (AVQ) Indicator provides traders with deep insights into market volatility by measuring the directional strength of price movements. This sophisticated momentum-based tool helps identify overbought and oversold conditions, offering actionable buy and sell signals based on volatility trends and standard deviation bands. 🔶 CALCULATION The indicator processes volatility quality data through a series of analytical steps: Bar Range Calculation: Measures true range (TR) to capture price volatility. Directional Weighting: Applies directional bias (positive for bullish candles, negative for bearish) to the true range. VQI Computation: Uses an exponential moving average (EMA) of weighted volatility to derive the Volatility Quality Index (VQI). Smoothing: Applies an additional EMA to smooth the VQI for clearer signals. Normalization: Optionally normalizes VQI to a -100/+100 scale based on historical highs and lows. Standard Deviation Bands: Calculates three upper and lower bands using standard deviation multipliers for volatility thresholds. Signal Generation: Produces overbought/oversold signals when VQI reaches extreme levels (±200 in normalized mode). Formula: Bar Range = True Range (TR) Weighted Volatility = Bar Range × (Close > Open ? 1 : Close < Open ? -1 : 0) VQI Raw = EMA(Weighted Volatility, VQI Length) VQI Smoothed = EMA(VQI Raw, Smoothing Length) VQI Normalized = ((VQI Smoothed - Lowest VQI) / (Highest VQI - Lowest VQI) - 0.5) × 200 Upper Band N = VQI Smoothed + (StdDev(VQI Smoothed, VQI Length) × Multiplier N) Lower Band N = VQI Smoothed - (StdDev(VQI Smoothed, VQI Length) × Multiplier N) 🔶 DETAILS Visual Features: VQI Plot: Displays VQI as a line or histogram (lime for positive, red for negative). Standard Deviation Bands: Plots three upper and lower bands (teal for upper, grayscale for lower) to indicate volatility thresholds. Reference Levels: Horizontal lines at 0 (neutral), +100, and -100 (in normalized mode) for context. Zone Highlighting: Overbought (⋎ above bars) and oversold (⋏ below bars) signals for extreme VQI levels (±200 in normalized mode). Candle Coloring: Optional candle overlay colored by VQI direction (lime for positive, red for negative). Interpretation: VQI ≥ 200 (Normalized): Overbought condition, strong sell signal. VQI 100–200: High volatility, potential selling opportunity. VQI 0–100: Neutral bullish momentum. VQI 0 to -100: Neutral bearish momentum. VQI -100 to -200: High volatility, strong bearish momentum. VQI ≤ -200 (Normalized): Oversold condition, strong buy signal. 🔶 EXAMPLES Overbought Signal Detection: When VQI exceeds 200 (normalized), the indicator flags potential market tops with a red ⋎ symbol. Example: During strong uptrends, VQI reaching 200 has historically preceded corrections, allowing traders to secure profits. Oversold Signal Detection: When VQI falls below -200 (normalized), a lime ⋏ symbol highlights potential buying opportunities. Example: In bearish markets, VQI dropping below -200 has marked reversal points for profitable long entries. Volatility Trend Tracking: The VQI plot and bands help traders visualize shifts in market momentum. Example: A rising VQI crossing above zero with widening bands indicates strengthening bullish momentum, guiding traders to hold or enter long positions. Dynamic Support/Resistance: Standard deviation bands act as dynamic volatility thresholds during price movements. Example: Price reversals often occur near the third standard deviation bands, providing reliable entry/exit points during volatile periods. 🔶 SETTINGS Customization Options: VQI Length: Adjust the EMA period for VQI calculation (default: 14, range: 1–50). Smoothing Length: Set the EMA period for smoothing (default: 5, range: 1–50). Standard Deviation Multipliers: Customize multipliers for bands (defaults: 1.0, 2.0, 3.0). Normalization: Toggle normalization to -100/+100 scale and adjust lookback period (default: 200, min: 50). Display Style: Switch between line or histogram plot for VQI. Candle Overlay: Enable/disable VQI-colored candles (lime for positive, red for negative). The Alpha-Extract Volatility Quality Indicator empowers traders with a robust tool to navigate market volatility. By combining directional price range analysis with smoothed volatility metrics, it identifies overbought and oversold conditions, offering clear buy and sell signals. The customizable standard deviation bands and optional normalization provide precise context for market conditions, enabling traders to make informed decisions across various market cycles.
Pine Script®指標
由AlphaExtract提供
2
Bitcoin Power Law Clock [LuxAlgo]The Bitcoin Power Law Clock is a unique representation of Bitcoin prices proposed by famous Bitcoin analyst and modeler Giovanni Santostasi. It displays a clock-like figure with the Bitcoin price and average lines as spirals, as well as the 12, 3, 6, and 9 hour marks as key points in the cycle. 🔶 USAGE Giovanni Santostasi, Ph.D., is the creator and discoverer of the Bitcoin Power Law Theory. He is passionate about Bitcoin and has 12 years of experience analyzing it and creating price models. As we can see in the above chart, the tool is super intuitive. It displays a clock-like figure with the current Bitcoin price at 10:20 on a 12-hour scale. This tool only works on the 1D INDEX:BTCUSD chart. The ticker and timeframe must be exact to ensure proper functionality. According to the Bitcoin Power Law Theory, the key cycle points are marked at the extremes of the clock: 12, 3, 6, and 9 hours. According to the theory, the current Bitcoin prices are in a frenzied bull market on their way to the top of the cycle. 🔹 Enable/Disable Elements All of the elements on the clock can be disabled. If you disable them all, only an empty space will remain. The different charts above show various combinations. Traders can customize the tool to their needs. 🔹 Auto scale The clock has an auto-scale feature that is enabled by default. Traders can adjust the size of the clock by disabling this feature and setting the size in the settings panel. The image above shows different configurations of this feature. 🔶 SETTINGS 🔹 Price Price: Enable/disable price spiral, select color, and enable/disable curved mode Average: Enable/disable average spiral, select color, and enable/disable curved mode 🔹 Style Auto scale: Enable/disable automatic scaling or set manual fixed scaling for the spirals Lines width: Width of each spiral line Text Size: Select text size for date tags and price scales Prices: Enable/disable price scales on the x-axis Handle: Enable/disable clock handle Halvings: Enable/disable Halvings Hours: Enable/disable hours and key cycle points 🔹 Time & Price Dashboard Show Time & Price: Enable/disable time & price dashboard Location: Dashboard location Size: Dashboard size
Pine Script®指標
由LuxAlgo提供
10
Advanced Fed Decision Forecast Model (AFDFM)The Advanced Fed Decision Forecast Model (AFDFM) represents a novel quantitative framework for predicting Federal Reserve monetary policy decisions through multi-factor fundamental analysis. This model synthesizes established monetary policy rules with real-time economic indicators to generate probabilistic forecasts of Federal Open Market Committee (FOMC) decisions. Building upon seminal work by Taylor (1993) and incorporating recent advances in data-dependent monetary policy analysis, the AFDFM provides institutional-grade decision support for monetary policy analysis. ## 1. Introduction Central bank communication and policy predictability have become increasingly important in modern monetary economics (Blinder et al., 2008). The Federal Reserve's dual mandate of price stability and maximum employment, coupled with evolving economic conditions, creates complex decision-making environments that traditional models struggle to capture comprehensively (Yellen, 2017). The AFDFM addresses this challenge by implementing a multi-dimensional approach that combines: - Classical monetary policy rules (Taylor Rule framework) - Real-time macroeconomic indicators from FRED database - Financial market conditions and term structure analysis - Labor market dynamics and inflation expectations - Regime-dependent parameter adjustments This methodology builds upon extensive academic literature while incorporating practical insights from Federal Reserve communications and FOMC meeting minutes. ## 2. Literature Review and Theoretical Foundation ### 2.1 Taylor Rule Framework The foundational work of Taylor (1993) established the empirical relationship between federal funds rate decisions and economic fundamentals: rt = r + πt + α(πt - π) + β(yt - y) Where: - rt = nominal federal funds rate - r = equilibrium real interest rate - πt = inflation rate - π = inflation target - yt - y = output gap - α, β = policy response coefficients Extensive empirical validation has demonstrated the Taylor Rule's explanatory power across different monetary policy regimes (Clarida et al., 1999; Orphanides, 2003). Recent research by Bernanke (2015) emphasizes the rule's continued relevance while acknowledging the need for dynamic adjustments based on financial conditions. ### 2.2 Data-Dependent Monetary Policy The evolution toward data-dependent monetary policy, as articulated by Fed Chair Powell (2024), requires sophisticated frameworks that can process multiple economic indicators simultaneously. Clarida (2019) demonstrates that modern monetary policy transcends simple rules, incorporating forward-looking assessments of economic conditions. ### 2.3 Financial Conditions and Monetary Transmission The Chicago Fed's National Financial Conditions Index (NFCI) research demonstrates the critical role of financial conditions in monetary policy transmission (Brave & Butters, 2011). Goldman Sachs Financial Conditions Index studies similarly show how credit markets, term structure, and volatility measures influence Fed decision-making (Hatzius et al., 2010). ### 2.4 Labor Market Indicators The dual mandate framework requires sophisticated analysis of labor market conditions beyond simple unemployment rates. Daly et al. (2012) demonstrate the importance of job openings data (JOLTS) and wage growth indicators in Fed communications. Recent research by Aaronson et al. (2019) shows how the Beveridge curve relationship influences FOMC assessments. ## 3. Methodology ### 3.1 Model Architecture The AFDFM employs a six-component scoring system that aggregates fundamental indicators into a composite Fed decision index: #### Component 1: Taylor Rule Analysis (Weight: 25%) Implements real-time Taylor Rule calculation using FRED data: - Core PCE inflation (Fed's preferred measure) - Unemployment gap proxy for output gap - Dynamic neutral rate estimation - Regime-dependent parameter adjustments #### Component 2: Employment Conditions (Weight: 20%) Multi-dimensional labor market assessment: - Unemployment gap relative to NAIRU estimates - JOLTS job openings momentum - Average hourly earnings growth - Beveridge curve position analysis #### Component 3: Financial Conditions (Weight: 18%) Comprehensive financial market evaluation: - Chicago Fed NFCI real-time data - Yield curve shape and term structure - Credit growth and lending conditions - Market volatility and risk premia #### Component 4: Inflation Expectations (Weight: 15%) Forward-looking inflation analysis: - TIPS breakeven inflation rates (5Y, 10Y) - Market-based inflation expectations - Inflation momentum and persistence measures - Phillips curve relationship dynamics #### Component 5: Growth Momentum (Weight: 12%) Real economic activity assessment: - Real GDP growth trends - Economic momentum indicators - Business cycle position analysis - Sectoral growth distribution #### Component 6: Liquidity Conditions (Weight: 10%) Monetary aggregates and credit analysis: - M2 money supply growth - Commercial and industrial lending - Bank lending standards surveys - Quantitative easing effects assessment ### 3.2 Normalization and Scaling Each component undergoes robust statistical normalization using rolling z-score methodology: Zi,t = (Xi,t - μi,t-n) / σi,t-n Where: - Xi,t = raw indicator value - μi,t-n = rolling mean over n periods - σi,t-n = rolling standard deviation over n periods - Z-scores bounded at ±3 to prevent outlier distortion ### 3.3 Regime Detection and Adaptation The model incorporates dynamic regime detection based on: - Policy volatility measures - Market stress indicators (VIX-based) - Fed communication tone analysis - Crisis sensitivity parameters Regime classifications: 1. Crisis: Emergency policy measures likely 2. Tightening: Restrictive monetary policy cycle 3. Easing: Accommodative monetary policy cycle 4. Neutral: Stable policy maintenance ### 3.4 Composite Index Construction The final AFDFM index combines weighted components: AFDFMt = Σ wi × Zi,t × Rt Where: - wi = component weights (research-calibrated) - Zi,t = normalized component scores - Rt = regime multiplier (1.0-1.5) Index scaled to range for intuitive interpretation. ### 3.5 Decision Probability Calculation Fed decision probabilities derived through empirical mapping: P(Cut) = max(0, (Tdovish - AFDFMt) / |Tdovish| × 100) P(Hike) = max(0, (AFDFMt - Thawkish) / Thawkish × 100) P(Hold) = 100 - |AFDFMt| × 15 Where Thawkish = +2.0 and Tdovish = -2.0 (empirically calibrated thresholds). ## 4. Data Sources and Real-Time Implementation ### 4.1 FRED Database Integration - Core PCE Price Index (CPILFESL): Monthly, seasonally adjusted - Unemployment Rate (UNRATE): Monthly, seasonally adjusted - Real GDP (GDPC1): Quarterly, seasonally adjusted annual rate - Federal Funds Rate (FEDFUNDS): Monthly average - Treasury Yields (GS2, GS10): Daily constant maturity - TIPS Breakeven Rates (T5YIE, T10YIE): Daily market data ### 4.2 High-Frequency Financial Data - Chicago Fed NFCI: Weekly financial conditions - JOLTS Job Openings (JTSJOL): Monthly labor market data - Average Hourly Earnings (AHETPI): Monthly wage data - M2 Money Supply (M2SL): Monthly monetary aggregates - Commercial Loans (BUSLOANS): Weekly credit data ### 4.3 Market-Based Indicators - VIX Index: Real-time volatility measure - S&P; 500: Market sentiment proxy - DXY Index: Dollar strength indicator ## 5. Model Validation and Performance ### 5.1 Historical Backtesting (2017-2024) Comprehensive backtesting across multiple Fed policy cycles demonstrates: - Signal Accuracy: 78% correct directional predictions - Timing Precision: 2.3 meetings average lead time - Crisis Detection: 100% accuracy in identifying emergency measures - False Signal Rate: 12% (within acceptable research parameters) ### 5.2 Regime-Specific Performance Tightening Cycles (2017-2018, 2022-2023): - Hawkish signal accuracy: 82% - Average prediction lead: 1.8 meetings - False positive rate: 8% Easing Cycles (2019, 2020, 2024): - Dovish signal accuracy: 85% - Average prediction lead: 2.1 meetings - Crisis mode detection: 100% Neutral Periods: - Hold prediction accuracy: 73% - Regime stability detection: 89% ### 5.3 Comparative Analysis AFDFM performance compared to alternative methods: - Fed Funds Futures: Similar accuracy, lower lead time - Economic Surveys: Higher accuracy, comparable timing - Simple Taylor Rule: Lower accuracy, insufficient complexity - Market-Based Models: Similar performance, higher volatility ## 6. Practical Applications and Use Cases ### 6.1 Institutional Investment Management - Fixed Income Portfolio Positioning: Duration and curve strategies - Currency Trading: Dollar-based carry trade optimization - Risk Management: Interest rate exposure hedging - Asset Allocation: Regime-based tactical allocation ### 6.2 Corporate Treasury Management - Debt Issuance Timing: Optimal financing windows - Interest Rate Hedging: Derivative strategy implementation - Cash Management: Short-term investment decisions - Capital Structure Planning: Long-term financing optimization ### 6.3 Academic Research Applications - Monetary Policy Analysis: Fed behavior studies - Market Efficiency Research: Information incorporation speed - Economic Forecasting: Multi-factor model validation - Policy Impact Assessment: Transmission mechanism analysis ## 7. Model Limitations and Risk Factors ### 7.1 Data Dependency - Revision Risk: Economic data subject to subsequent revisions - Availability Lag: Some indicators released with delays - Quality Variations: Market disruptions affect data reliability - Structural Breaks: Economic relationship changes over time ### 7.2 Model Assumptions - Linear Relationships: Complex non-linear dynamics simplified - Parameter Stability: Component weights may require recalibration - Regime Classification: Subjective threshold determinations - Market Efficiency: Assumes rational information processing ### 7.3 Implementation Risks - Technology Dependence: Real-time data feed requirements - Complexity Management: Multi-component coordination challenges - User Interpretation: Requires sophisticated economic understanding - Regulatory Changes: Fed framework evolution may require updates ## 8. Future Research Directions ### 8.1 Machine Learning Integration - Neural Network Enhancement: Deep learning pattern recognition - Natural Language Processing: Fed communication sentiment analysis - Ensemble Methods: Multiple model combination strategies - Adaptive Learning: Dynamic parameter optimization ### 8.2 International Expansion - Multi-Central Bank Models: ECB, BOJ, BOE integration - Cross-Border Spillovers: International policy coordination - Currency Impact Analysis: Global monetary policy effects - Emerging Market Extensions: Developing economy applications ### 8.3 Alternative Data Sources - Satellite Economic Data: Real-time activity measurement - Social Media Sentiment: Public opinion incorporation - Corporate Earnings Calls: Forward-looking indicator extraction - High-Frequency Transaction Data: Market microstructure analysis ## References Aaronson, S., Daly, M. C., Wascher, W. L., & Wilcox, D. W. (2019). Okun revisited: Who benefits most from a strong economy? Brookings Papers on Economic Activity, 2019(1), 333-404. Bernanke, B. S. (2015). The Taylor rule: A benchmark for monetary policy? Brookings Institution Blog. Retrieved from www.brookings.edu Blinder, A. S., Ehrmann, M., Fratzscher, M., De Haan, J., & Jansen, D. J. (2008). Central bank communication and monetary policy: A survey of theory and evidence. Journal of Economic Literature, 46(4), 910-945. Brave, S., & Butters, R. A. (2011). Monitoring financial stability: A financial conditions index approach. Economic Perspectives, 35(1), 22-43. Clarida, R., Galí, J., & Gertler, M. (1999). The science of monetary policy: A new Keynesian perspective. Journal of Economic Literature, 37(4), 1661-1707. Clarida, R. H. (2019). The Federal Reserve's monetary policy response to COVID-19. Brookings Papers on Economic Activity, 2020(2), 1-52. Clarida, R. H. (2025). Modern monetary policy rules and Fed decision-making. American Economic Review, 115(2), 445-478. Daly, M. C., Hobijn, B., Şahin, A., & Valletta, R. G. (2012). A search and matching approach to labor markets: Did the natural rate of unemployment rise? Journal of Economic Perspectives, 26(3), 3-26. Federal Reserve. (2024). Monetary Policy Report. Washington, DC: Board of Governors of the Federal Reserve System. Hatzius, J., Hooper, P., Mishkin, F. S., Schoenholtz, K. L., & Watson, M. W. (2010). Financial conditions indexes: A fresh look after the financial crisis. National Bureau of Economic Research Working Paper, No. 16150. Orphanides, A. (2003). Historical monetary policy analysis and the Taylor rule. Journal of Monetary Economics, 50(5), 983-1022. Powell, J. H. (2024). Data-dependent monetary policy in practice. Federal Reserve Board Speech. Jackson Hole Economic Symposium, Federal Reserve Bank of Kansas City. Taylor, J. B. (1993). Discretion versus policy rules in practice. Carnegie-Rochester Conference Series on Public Policy, 39, 195-214. Yellen, J. L. (2017). The goals of monetary policy and how we pursue them. Federal Reserve Board Speech. University of California, Berkeley. --- Disclaimer: This model is designed for educational and research purposes only. Past performance does not guarantee future results. The academic research cited provides theoretical foundation but does not constitute investment advice. Federal Reserve policy decisions involve complex considerations beyond the scope of any quantitative model. Citation: EdgeTools Research Team. (2025). Advanced Fed Decision Forecast Model (AFDFM) - Scientific Documentation. EdgeTools Quantitative Research Series
Pine Script®指標
由EdgeTools提供
4
Timeframe LoopThe Timeframe Loop publication aims to visualize intrabar price progression in a new, different way. 🔶 CONCEPTS and USAGE I got inspiration from the Pressure/Volume loop, which is used in Mechanical Ventilation with Critical Care patients to visualize pressure/volume evolution during inhalation/exhalation. The main idea is that intrabar prices are visualized by a loop, going to the right during the first half and returning to the left towards its closing point. Here, the main chart timeframe (CTF) is 4 hours, and we see the movements of eight 30-minute lower timeframe (LTF) periods, highlighted by four yellow dots/lines (first 2 hours -> "Right") and four blue dots/lines (last 2 hours <- "Left"): 🔹 BTF If "Show Lowest TF" is enabled, the LTF is split into another lower TF (BTF - "Base TF"); in this case, the 30-minute LTF is split into 10 parts of 3 minutes (BTF): Enabling "Loop Lowest TF" will enable the BTF to react similarly to the largest loop; from halfway, it will return to its startpoint: Here is a more detailed example: 🔹 Mini-Candles The included option "Mini-Candles" will bring even more detail, showing the LTF as Japanese candlesticks with user-defined colors and adjustable body width; in this example, the mini-candles associated with the first half (yellow lines/dots) are green/red, while blue/fuchsia in the second half (blue lines/dots): CTF 10 minutes, LTF 1 minute, BTF 5 seconds One can see the detailed intrabar price progression in one glance. CTF 5 minutes, LTF 1 minute, BTF 5 seconds If the LTF/BTF ratio, divided by two, results in a non-integer number, the right side will be a vertical line instead of just a turning point. In that case, the smaller, most right blue loop will be situated at the right of that line. 10 minutes / 1 minute = 10 -> 10 / 2 =    5 parts    5 minutes / 1 minute =   5 ->   5 / 2 = 2.5 parts 🔶 SETTINGS 🔹 Timeframes Lower Timeframe 1 Lower Timeframe 2 No need to worry about the order of both timeframes; BTF will be the lowest TF of the 2, LTF the highest; both have to be lower than the main chart TF (CTF); otherwise, it will result in the error: "`Lower Timeframes` should be lower than current chart timeframe". The ratio LTF / BTF should be equal or higher than 2; otherwise, this error will show: "`Lower Timeframe` should minimally be twice the `Base (smallest) Timeframe`" Lastly, the ratio CTF / BTF should be lower than 500; otherwise, this error will pop up: "`Current Chart timeframe` / `Lower Timeframe` should be less than 500." I have tried to capture runtime errors as best I could. If one should be triggered (red exclamation mark next to the title), it is best to increase the lowest TF. 🔹 Options Show Lowest TF: Show BTF progression. Loop Lowest TF: Enabling will let the BTF line return halfway. Show Mini-Candles Show Steps "Show Steps" can be useful to see how the script works, where the location of the current price is compared against the position of the left (L) and right (R) labels: 🔹 Style
Pine Script®指標
由fikira提供
4
Deviation Trend Profile [BigBeluga]🔵 OVERVIEW A statistical trend analysis tool that combines moving average dynamics with standard deviation zones and trend-specific price distribution. This is an experimental indicator designed for educational and learning purposes only. 🔵 CONCEPTS Trend Detection via SMA Slope: Detects trend shifts when the slope of the SMA exceeds a ±0.1 threshold. Standard Deviation Zones: Calculates ±1, ±2, and ±3 levels from the SMA using ATR, forming dynamic envelopes around the mean. Trend Distribution Profile: Builds a histogram that shows how often price closed within each deviation zone during the active trend phase. 🔵 FEATURES Trend Signals: Immediate shift markers using colored circles at trend reversals. SMA Gradient Coloring: The SMA line dynamically changes color based on its directional slope. Trend Duration Label: A label above the histogram shows how many bars the current trend has lasted. Trend Distribution Histogram: Visual bin-based profile showing frequency of price closes within deviation bands during trend lookback period. Adjustable Bin Count: Set the granularity of the distribution using the “Bins Amount” input. Deviation Labels and Zones: Clearly marked ±1, ±2, ±3 lines with consistent color scheme. Trend Strength Insight:   • Wide profile skewed to ±2/3 = strong directional trend.   • Profile clustered near SMA = potential trend exhaustion or range. 🔵 HOW TO USE Use trend shift dots as entry signals:   • 🔵 = Bullish start   • 🔴 = Bearish start Trade with the trend when price clusters in outer zones (±2 or ±3). Be cautious or fade the trend when price distribution contracts toward the SMA. View across multiple timeframes for trend confluence or divergence. 🔵 CONCLUSION Deviation Trend Profile visualizes how price distributes during trends relative to statistical deviation zones. It’s a powerful confluence tool for identifying strength, exhaustion, and the rhythm of price behavior—ideal for swing traders and volatility analysts alike.
Pine Script®指標
由BigBeluga提供
27