Ürünler
Topluluk
Piyasalar
Aracı kurum
Daha Fazla

Göstergeler ve stratejiler
Arama sonuçları

"backtesting" için komut dosyalarını ara

Ultimate RSI [captainua]Ultimate RSI Overview This indicator combines multiple RSI calculations with volume analysis, divergence detection, and trend filtering to provide a comprehensive RSI-based trading system. The script calculates RSI using three different periods (6, 14, 24) and applies various smoothing methods to reduce noise while maintaining responsiveness. The combination of these features creates a multi-layered confirmation system that reduces false signals by requiring alignment across multiple indicators and timeframes. The script includes optimized configuration presets for instant setup: Scalping, Day Trading, Swing Trading, and Position Trading. Simply select a preset to instantly configure all settings for your trading style, or use Custom mode for full manual control. All settings include automatic input validation to prevent configuration errors and ensure optimal performance. Configuration Presets The script includes preset configurations optimized for different trading styles, allowing you to instantly configure the indicator for your preferred trading approach. Simply select a preset from the "Configuration Preset" dropdown menu: - Scalping: Optimized for fast-paced trading with shorter RSI periods (4, 7, 9) and minimal smoothing. Noise reduction is automatically disabled, and momentum confirmation is disabled to allow faster signal generation. Designed for quick entries and exits in volatile markets. - Day Trading: Balanced configuration for intraday trading with moderate RSI periods (6, 9, 14) and light smoothing. Momentum confirmation is enabled for better signal quality. Ideal for day trading strategies requiring timely but accurate signals. - Swing Trading: Configured for medium-term positions with standard RSI periods (14, 14, 21) and moderate smoothing. Provides smoother signals suitable for swing trading timeframes. All noise reduction features remain active. - Position Trading: Optimized for longer-term trades with extended RSI periods (24, 21, 28) and heavier smoothing. Filters are configured for highest-quality signals. Best for position traders holding trades over multiple days or weeks. - Custom: Full manual control over all settings. All input parameters are available for complete customization. This is the default mode and maintains full backward compatibility with previous versions. When a preset is selected, it automatically adjusts RSI periods, smoothing lengths, and filter settings to match the trading style. The preset configurations ensure optimal settings are applied instantly, eliminating the need for manual configuration. All settings can still be manually overridden if needed, providing flexibility while maintaining ease of use. Input Validation and Error Prevention The script includes comprehensive input validation to prevent configuration errors: - Cross-Input Validation: Smoothing lengths are automatically validated to ensure they are always less than their corresponding RSI period length. If you set a smoothing length greater than or equal to the RSI length, the script automatically adjusts it to (RSI Length - 1). This prevents logical errors and ensures valid configurations. - Input Range Validation: All numeric inputs have minimum and maximum value constraints enforced by TradingView's input system, preventing invalid parameter values. - Smart Defaults: Preset configurations use validated default values that are tested and optimized for each trading style. When switching between presets, all related settings are automatically updated to maintain consistency. Core Calculations Multi-Period RSI: The script calculates RSI using the standard Wilder's RSI formula: RSI = 100 - (100 / (1 + RS)), where RS = Average Gain / Average Loss over the specified period. Three separate RSI calculations run simultaneously: - RSI(6): Uses 6-period lookback for high sensitivity to recent price changes, useful for scalping and early signal detection - RSI(14): Standard 14-period RSI for balanced analysis, the most commonly used RSI period - RSI(24): Longer 24-period RSI for trend confirmation, provides smoother signals with less noise Each RSI can be smoothed using EMA, SMA, RMA (Wilder's smoothing), WMA, or Zero-Lag smoothing. Zero-Lag smoothing uses the formula: ZL-RSI = RSI + (RSI - RSI ) to reduce lag while maintaining signal quality. You can apply individual smoothing lengths to each RSI period, or use global smoothing where all three RSIs share the same smoothing length. Dynamic Overbought/Oversold Thresholds: Static thresholds (default 70/30) are adjusted based on market volatility using ATR. The formula: Dynamic OB = Base OB + (ATR × Volatility Multiplier × Base Percentage / 100), Dynamic OS = Base OS - (ATR × Volatility Multiplier × Base Percentage / 100). This adapts to volatile markets where traditional 70/30 levels may be too restrictive. During high volatility, the dynamic thresholds widen, and during low volatility, they narrow. The thresholds are clamped between 0-100 to remain within RSI bounds. The ATR is cached for performance optimization, updating on confirmed bars and real-time bars. Adaptive RSI Calculation: An adaptive RSI adjusts the standard RSI(14) based on current volatility relative to average volatility. The calculation: Adaptive Factor = (Current ATR / SMA of ATR over 20 periods) × Volatility Multiplier. If SMA of ATR is zero (edge case), the adaptive factor defaults to 0. The adaptive RSI = Base RSI × (1 + Adaptive Factor), clamped to 0-100. This makes the indicator more responsive during high volatility periods when traditional RSI may lag. The adaptive RSI is used for signal generation (buy/sell signals) but is not plotted on the chart. Overbought/Oversold Fill Zones: The script provides visual fill zones between the RSI line and the threshold lines when RSI is in overbought or oversold territory. The fill logic uses inclusive conditions: fills are shown when RSI is currently in the zone OR was in the zone on the previous bar. This ensures complete coverage of entry and exit boundaries. A minimum gap of 0.1 RSI points is maintained between the RSI plot and threshold line to ensure reliable polygon rendering in TradingView. The fill uses invisible plots at the threshold levels and the RSI value, with the fill color applied between them. You can select which RSI (6, 14, or 24) to use for the fill zones. Divergence Detection Regular Divergence: Bullish divergence: Price makes a lower low (current low < lowest low from previous lookback period) while RSI makes a higher low (current RSI > lowest RSI from previous lookback period). Bearish divergence: Price makes a higher high (current high > highest high from previous lookback period) while RSI makes a lower high (current RSI < highest RSI from previous lookback period). The script compares current price/RSI values to the lowest/highest values from the previous lookback period using ta.lowest() and ta.highest() functions with index to reference the previous period's extreme. Pivot-Based Divergence: An enhanced divergence detection method that uses actual pivot points instead of simple lowest/highest comparisons. This provides more accurate divergence detection by identifying significant pivot lows/highs in both price and RSI. The pivot-based method uses a tolerance-based approach with configurable constants: 1% tolerance for price comparisons (priceTolerancePercent = 0.01) and 1.0 RSI point absolute tolerance for RSI comparisons (pivotTolerance = 1.0). Minimum divergence threshold is 1.0 RSI point (minDivergenceThreshold = 1.0). It looks for two recent pivot points and compares them: for bullish divergence, price makes a lower low (at least 1% lower) while RSI makes a higher low (at least 1.0 point higher). This method reduces false divergences by requiring actual pivot points rather than just any low/high within a period. When enabled, pivot-based divergence replaces the traditional method for more accurate signal generation. Strong Divergence: Regular divergence is confirmed by an engulfing candle pattern. Bullish engulfing requires: (1) Previous candle is bearish (close < open ), (2) Current candle is bullish (close > open), (3) Current close > previous open, (4) Current open < previous close. Bearish engulfing is the inverse: previous bullish, current bearish, current close < previous open, current open > previous close. Strong divergence signals are marked with visual indicators (🐂 for bullish, 🐻 for bearish) and have separate alert conditions. Hidden Divergence: Continuation patterns that signal trend continuation rather than reversal. Bullish hidden divergence: Price makes a higher low (current low > lowest low from previous period) but RSI makes a lower low (current RSI < lowest RSI from previous period). Bearish hidden divergence: Price makes a lower high (current high < highest high from previous period) but RSI makes a higher high (current RSI > highest RSI from previous period). These patterns indicate the trend is likely to continue in the current direction. Volume Confirmation System Volume threshold filtering requires current volume to exceed the volume SMA multiplied by the threshold factor. The formula: Volume Confirmed = Volume > (Volume SMA × Threshold). If the threshold is set to 0.1 or lower, volume confirmation is effectively disabled (always returns true). This allows you to use the indicator without volume filtering if desired. Volume Climax is detected when volume exceeds: Volume SMA + (Volume StdDev × Multiplier). This indicates potential capitulation moments where extreme volume accompanies price movements. Volume Dry-Up is detected when volume falls below: Volume SMA - (Volume StdDev × Multiplier), indicating low participation periods that may produce unreliable signals. The volume SMA is cached for performance, updating on confirmed and real-time bars. Multi-RSI Synergy The script generates signals when multiple RSI periods align in overbought or oversold zones. This creates a confirmation system that reduces false signals. In "ALL" mode, all three RSIs (6, 14, 24) must be simultaneously above the overbought threshold OR all three must be below the oversold threshold. In "2-of-3" mode, any two of the three RSIs must align in the same direction. The script counts how many RSIs are in each zone: twoOfThreeOB = ((rsi6OB ? 1 : 0) + (rsi14OB ? 1 : 0) + (rsi24OB ? 1 : 0)) >= 2. Synergy signals require: (1) Multi-RSI alignment (ALL or 2-of-3), (2) Volume confirmation, (3) Reset condition satisfied (enough bars since last synergy signal), (4) Additional filters passed (RSI50, Trend, ADX, Volume Dry-Up avoidance). Separate reset conditions track buy and sell signals independently. The reset condition uses ta.barssince() to count bars since the last trigger, returning true if the condition never occurred (allowing first signal) or if enough bars have passed. Regression Forecasting The script uses historical RSI values to forecast future RSI direction using four methods. The forecast horizon is configurable (1-50 bars ahead). Historical data is collected into an array, and regression coefficients are calculated based on the selected method. Linear Regression: Calculates the least-squares fit line (y = mx + b) through the last N RSI values. The calculation: meanX = sumX / horizon, meanY = sumY / horizon, denominator = sumX² - horizon × meanX², m = (sumXY - horizon × meanX × meanY) / denominator, b = meanY - m × meanX. The forecast projects this line forward: forecast = b + m × i for i = 1 to horizon. Polynomial Regression: Fits a quadratic curve (y = ax² + bx + c) to capture non-linear trends. The system of equations is solved using Cramer's rule with a 3×3 determinant. If the determinant is too small (< 0.0001), the system falls back to linear regression. Coefficients are calculated by solving: n×c + sumX×b + sumX²×a = sumY, sumX×c + sumX²×b + sumX³×a = sumXY, sumX²×c + sumX³×b + sumX⁴×a = sumX²Y. Note: Due to the O(n³) computational complexity of polynomial regression, the forecast horizon is automatically limited to a maximum of 20 bars when using polynomial regression to maintain optimal performance. If you set a horizon greater than 20 bars with polynomial regression, it will be automatically capped at 20 bars. Exponential Smoothing: Applies exponential smoothing with adaptive alpha = 2/(horizon+1). The smoothing iterates from oldest to newest value: smoothed = alpha × series + (1 - alpha) × smoothed. Trend is calculated by comparing current smoothed value to an earlier smoothed value (at 60% of horizon): trend = (smoothed - earlierSmoothed) / (horizon - earlierIdx). Forecast: forecast = base + trend × i. Moving Average: Uses the difference between short MA (horizon/2) and long MA (horizon) to estimate trend direction. Trend = (maShort - maLong) / (longLen - shortLen). Forecast: forecast = maShort + trend × i. Confidence bands are calculated using RMSE (Root Mean Squared Error) of historical forecast accuracy. The error calculation compares historical values with forecast values: RMSE = sqrt(sumSquaredError / count). If insufficient data exists, it falls back to calculating standard deviation of recent RSI values. Confidence bands = forecast ± (RMSE × confidenceLevel). All forecast values and confidence bands are clamped to 0-100 to remain within RSI bounds. The regression functions include comprehensive safety checks: horizon validation (must not exceed array size), empty array handling, edge case handling for horizon=1 scenarios, division-by-zero protection, and bounds checking for all array access operations to prevent runtime errors. Strong Top/Bottom Detection Strong buy signals require three conditions: (1) RSI is at its lowest point within the bottom period: rsiVal <= ta.lowest(rsiVal, bottomPeriod), (2) RSI is below the oversold threshold minus a buffer: rsiVal < (oversoldThreshold - rsiTopBottomBuffer), where rsiTopBottomBuffer = 2.0 RSI points, (3) The absolute difference between current RSI and the lowest RSI exceeds the threshold value: abs(rsiVal - ta.lowest(rsiVal, bottomPeriod)) > threshold. This indicates a bounce from extreme levels with sufficient distance from the absolute low. Strong sell signals use the inverse logic: RSI at highest point, above overbought threshold + rsiTopBottomBuffer (2.0 RSI points), and difference from highest exceeds threshold. Both signals also require: volume confirmation, reset condition satisfied (separate reset for buy vs sell), and all additional filters passed (RSI50, Trend, ADX, Volume Dry-Up avoidance). The reset condition uses separate logic for buy and sell: resetCondBuy checks bars since isRSIAtBottom, resetCondSell checks bars since isRSIAtTop. This ensures buy signals reset based on bottom conditions and sell signals reset based on top conditions, preventing incorrect signal blocking. Filtering System RSI(50) Filter: Only allows buy signals when RSI(14) > 50 (bullish momentum) and sell signals when RSI(14) < 50 (bearish momentum). This filter ensures you're buying in uptrends and selling in downtrends from a momentum perspective. The filter is optional and can be disabled. Recommended to enable for noise reduction. Trend Filter: Uses a long-term EMA (default 200) to determine trend direction. Buy signals require price above EMA, sell signals require price below EMA. The EMA slope is calculated as: emaSlope = ema - ema . Optional EMA slope filter additionally requires the EMA to be rising (slope > 0) for buy signals or falling (slope < 0) for sell signals. This provides stronger trend confirmation by requiring both price position and EMA direction. ADX Filter: Uses the Directional Movement Index (calculated via ta.dmi()) to measure trend strength. Signals only fire when ADX exceeds the threshold (default 20), indicating a strong trend rather than choppy markets. The ADX calculation uses separate length and smoothing parameters. This filter helps avoid signals during sideways/consolidation periods. Volume Dry-Up Avoidance: Prevents signals during periods of extremely low volume relative to average. If volume dry-up is detected and the filter is enabled, signals are blocked. This helps avoid unreliable signals that occur during low participation periods. RSI Momentum Confirmation: Requires RSI to be accelerating in the signal direction before confirming signals. For buy signals, RSI must be consistently rising (recovering from oversold) over the lookback period. For sell signals, RSI must be consistently falling (declining from overbought) over the lookback period. The momentum check verifies that all consecutive changes are in the correct direction AND the cumulative change is significant. This filter ensures signals only fire when RSI momentum aligns with the signal direction, reducing false signals from weak momentum. Multi-Timeframe Confirmation: Requires higher timeframe RSI to align with the signal direction. For buy signals, current RSI must be below the higher timeframe RSI by at least the confirmation threshold. For sell signals, current RSI must be above the higher timeframe RSI by at least the confirmation threshold. This ensures signals align with the larger trend context, reducing counter-trend trades. The higher timeframe RSI is fetched using request.security() from the selected timeframe. All filters use the pattern: filterResult = not filterEnabled OR conditionMet. This means if a filter is disabled, it always passes (returns true). Filters can be combined, and all must pass for a signal to fire. RSI Centerline and Period Crossovers RSI(50) Centerline Crossovers: Detects when the selected RSI source crosses above or below the 50 centerline. Bullish crossover: ta.crossover(rsiSource, 50), bearish crossover: ta.crossunder(rsiSource, 50). You can select which RSI (6, 14, or 24) to use for these crossovers. These signals indicate momentum shifts from bearish to bullish (above 50) or bullish to bearish (below 50). RSI Period Crossovers: Detects when different RSI periods cross each other. Available pairs: RSI(6) × RSI(14), RSI(14) × RSI(24), or RSI(6) × RSI(24). Bullish crossover: fast RSI crosses above slow RSI (ta.crossover(rsiFast, rsiSlow)), indicating momentum acceleration. Bearish crossover: fast RSI crosses below slow RSI (ta.crossunder(rsiFast, rsiSlow)), indicating momentum deceleration. These crossovers can signal shifts in momentum before price moves. StochRSI Calculation Stochastic RSI applies the Stochastic oscillator formula to RSI values instead of price. The calculation: %K = ((RSI - Lowest RSI) / (Highest RSI - Lowest RSI)) × 100, where the lookback is the StochRSI length. If the range is zero, %K defaults to 50.0. %K is then smoothed using SMA with the %K smoothing length. %D is calculated as SMA of smoothed %K with the %D smoothing length. All values are clamped to 0-100. You can select which RSI (6, 14, or 24) to use as the source for StochRSI calculation. RSI Bollinger Bands Bollinger Bands are applied to RSI(14) instead of price. The calculation: Basis = SMA(RSI(14), BB Period), StdDev = stdev(RSI(14), BB Period), Upper = Basis + (StdDev × Deviation Multiplier), Lower = Basis - (StdDev × Deviation Multiplier). This creates dynamic zones around RSI that adapt to RSI volatility. When RSI touches or exceeds the bands, it indicates extreme conditions relative to recent RSI behavior. Noise Reduction System The script includes a comprehensive noise reduction system to filter false signals and improve accuracy. When enabled, signals must pass multiple quality checks: Signal Strength Requirement: RSI must be at least X points away from the centerline (50). For buy signals, RSI must be at least X points below 50. For sell signals, RSI must be at least X points above 50. This ensures signals only trigger when RSI is significantly in oversold/overbought territory, not just near neutral. Extreme Zone Requirement: RSI must be deep in the OB/OS zone. For buy signals, RSI must be at least X points below the oversold threshold. For sell signals, RSI must be at least X points above the overbought threshold. This ensures signals only fire in extreme conditions where reversals are more likely. Consecutive Bar Confirmation: The signal condition must persist for N consecutive bars before triggering. This reduces false signals from single-bar spikes or noise. The confirmation checks that the signal condition was true for all bars in the lookback period. Zone Persistence (Optional): Requires RSI to remain in the OB/OS zone for N consecutive bars, not just touch it. This ensures RSI is truly in an extreme state rather than just briefly touching the threshold. When enabled, this provides stricter filtering for higher-quality signals. RSI Slope Confirmation (Optional): Requires RSI to be moving in the expected signal direction. For buy signals, RSI should be rising (recovering from oversold). For sell signals, RSI should be falling (declining from overbought). This ensures momentum is aligned with the signal direction. The slope is calculated by comparing current RSI to RSI N bars ago. All noise reduction filters can be enabled/disabled independently, allowing you to customize the balance between signal frequency and accuracy. The default settings provide a good balance, but you can adjust them based on your trading style and market conditions. Alert System The script includes separate alert conditions for each signal type: buy/sell (adaptive RSI crossovers), divergence (regular, strong, hidden), crossovers (RSI50 centerline, RSI period crossovers), synergy signals, and trend breaks. Each alert type has its own alertcondition() declaration with a unique title and message. An optional cooldown system prevents alert spam by requiring a minimum number of bars between alerts of the same type. The cooldown check: canAlert = na(lastAlertBar) OR (bar_index - lastAlertBar >= cooldownBars). If the last alert bar is na (first alert), it always allows the alert. Each alert type maintains its own lastAlertBar variable, so cooldowns are independent per signal type. The default cooldown is 10 bars, which is recommended for noise reduction. Higher Timeframe RSI The script can display RSI from a higher timeframe using request.security(). This allows you to see the RSI context from a larger timeframe (e.g., daily RSI on an hourly chart). The higher timeframe RSI uses RSI(14) calculation from the selected timeframe. This provides context for the current timeframe's RSI position relative to the larger trend. RSI Pivot Trendlines The script can draw trendlines connecting pivot highs and lows on RSI(6). This feature helps visualize RSI trends and identify potential trend breaks. Pivot Detection: Pivots are detected using a configurable period. The script can require pivots to have minimum strength (RSI points difference from surrounding bars) to filter out weak pivots. Lower minPivotStrength values detect more pivots (more trendlines), while higher values detect only stronger pivots (fewer but more significant trendlines). Pivot confirmation is optional: when enabled, the script waits N bars to confirm the pivot remains the extreme, reducing repainting. Pivot confirmation functions (f_confirmPivotLow and f_confirmPivotHigh) are always called on every bar for consistency, as recommended by TradingView. When pivot bars are not available (na), safe default values are used, and the results are then used conditionally based on confirmation settings. This ensures consistent calculations and prevents calculation inconsistencies. Trendline Drawing: Uptrend lines connect confirmed pivot lows (green), and downtrend lines connect confirmed pivot highs (red). By default, only the most recent trendline is shown (old trendlines are deleted when new pivots are confirmed). This keeps the chart clean and uncluttered. If "Keep Historical Trendlines" is enabled, the script preserves up to N historical trendlines (configurable via "Max Trendlines to Keep", default 5). When historical trendlines are enabled, old trendlines are saved to arrays instead of being deleted, allowing you to see multiple trendlines simultaneously for better trend analysis. The arrays are automatically limited to prevent memory accumulation. Trend Break Detection: Signals are generated when RSI breaks above or below trendlines. Uptrend breaks (RSI crosses below uptrend line) generate buy signals. Downtrend breaks (RSI crosses above downtrend line) generate sell signals. Optional trend break confirmation requires the break to persist for N bars and optionally include volume confirmation. Trendline angle filtering can exclude flat/weak trendlines from generating signals (minTrendlineAngle > 0 filters out weak/flat trendlines). How Components Work Together The combination of multiple RSI periods provides confirmation across different timeframes, reducing false signals. RSI(6) catches early moves, RSI(14) provides balanced signals, and RSI(24) confirms longer-term trends. When all three align (synergy), it indicates strong consensus across timeframes. Volume confirmation ensures signals occur with sufficient market participation, filtering out low-volume false breakouts. Volume climax detection identifies potential reversal points, while volume dry-up avoidance prevents signals during unreliable low-volume periods. Trend filters align signals with the overall market direction. The EMA filter ensures you're trading with the trend, and the EMA slope filter adds an additional layer by requiring the trend to be strengthening (rising EMA for buys, falling EMA for sells). ADX filter ensures signals only fire during strong trends, avoiding choppy/consolidation periods. RSI(50) filter ensures momentum alignment with the trade direction. Momentum confirmation requires RSI to be accelerating in the signal direction, ensuring signals only fire when momentum is aligned. Multi-timeframe confirmation ensures signals align with higher timeframe trends, reducing counter-trend trades. Divergence detection identifies potential reversals before they occur, providing early warning signals. Pivot-based divergence provides more accurate detection by using actual pivot points. Hidden divergence identifies continuation patterns, useful for trend-following strategies. The noise reduction system combines multiple filters (signal strength, extreme zone, consecutive bars, zone persistence, RSI slope) to significantly reduce false signals. These filters work together to ensure only high-quality signals are generated. The synergy system requires alignment across all RSI periods for highest-quality signals, significantly reducing false positives. Regression forecasting provides forward-looking context, helping anticipate potential RSI direction changes. Pivot trendlines provide visual trend analysis and can generate signals when RSI breaks trendlines, indicating potential reversals or continuations. Reset conditions prevent signal spam by requiring a minimum number of bars between signals. Separate reset conditions for buy and sell signals ensure proper signal management. Usage Instructions Configuration Presets (Recommended): The script includes optimized preset configurations for instant setup. Simply select your trading style from the "Configuration Preset" dropdown: - Scalping Preset: RSI(4, 7, 9) with minimal smoothing. Noise reduction disabled, momentum confirmation disabled for fastest signals. - Day Trading Preset: RSI(6, 9, 14) with light smoothing. Momentum confirmation enabled for better signal quality. - Swing Trading Preset: RSI(14, 14, 21) with moderate smoothing. Balanced configuration for medium-term trades. - Position Trading Preset: RSI(24, 21, 28) with heavier smoothing. Optimized for longer-term positions with all filters active. - Custom Mode: Full manual control over all settings. Default behavior matches previous script versions. Presets automatically configure RSI periods, smoothing lengths, and filter settings. You can still manually adjust any setting after selecting a preset if needed. Getting Started: The easiest way to get started is to select a configuration preset matching your trading style (Scalping, Day Trading, Swing Trading, or Position Trading) from the "Configuration Preset" dropdown. This instantly configures all settings for optimal performance. Alternatively, use "Custom" mode for full manual control. The default configuration (Custom mode) shows RSI(6), RSI(14), and RSI(24) with their default smoothing. Overbought/oversold fill zones are enabled by default. Customizing RSI Periods: Adjust the RSI lengths (6, 14, 24) based on your trading timeframe. Shorter periods (6) for scalping, standard (14) for day trading, longer (24) for swing trading. You can disable any RSI period you don't need. Smoothing Selection: Choose smoothing method based on your needs. EMA provides balanced smoothing, RMA (Wilder's) is traditional, Zero-Lag reduces lag but may increase noise. Adjust smoothing lengths individually or use global smoothing for consistency. Note: Smoothing lengths are automatically validated to ensure they are always less than the corresponding RSI period length. If you set smoothing >= RSI length, it will be auto-adjusted to prevent invalid configurations. Dynamic OB/OS: The dynamic thresholds automatically adapt to volatility. Adjust the volatility multiplier and base percentage to fine-tune sensitivity. Higher values create wider thresholds in volatile markets. Volume Confirmation: Set volume threshold to 1.2 (default) for standard confirmation, higher for stricter filtering, or 0.1 to disable volume filtering entirely. Multi-RSI Synergy: Use "ALL" mode for highest-quality signals (all 3 RSIs must align), or "2-of-3" mode for more frequent signals. Adjust the reset period to control signal frequency. Filters: Enable filters gradually to find your preferred balance. Start with volume confirmation, then add trend filter, then ADX for strongest confirmation. RSI(50) filter is useful for momentum-based strategies and is recommended for noise reduction. Momentum confirmation and multi-timeframe confirmation add additional layers of accuracy but may reduce signal frequency. Noise Reduction: The noise reduction system is enabled by default with balanced settings. Adjust minSignalStrength (default 3.0) to control how far RSI must be from centerline. Increase requireConsecutiveBars (default 1) to require signals to persist longer. Enable requireZonePersistence and requireRsiSlope for stricter filtering (higher quality but fewer signals). Start with defaults and adjust based on your needs. Divergence: Enable divergence detection and adjust lookback periods. Strong divergence (with engulfing confirmation) provides higher-quality signals. Hidden divergence is useful for trend-following strategies. Enable pivot-based divergence for more accurate detection using actual pivot points instead of simple lowest/highest comparisons. Pivot-based divergence uses tolerance-based matching (1% for price, 1.0 RSI point for RSI) for better accuracy. Forecasting: Enable regression forecasting to see potential RSI direction. Linear regression is simplest, polynomial captures curves, exponential smoothing adapts to trends. Adjust horizon based on your trading timeframe. Confidence bands show forecast uncertainty - wider bands indicate less reliable forecasts. Pivot Trendlines: Enable pivot trendlines to visualize RSI trends and identify trend breaks. Adjust pivot detection period (default 5) - higher values detect fewer but stronger pivots. Enable pivot confirmation (default ON) to reduce repainting. Set minPivotStrength (default 1.0) to filter weak pivots - lower values detect more pivots (more trendlines), higher values detect only stronger pivots (fewer trendlines). Enable "Keep Historical Trendlines" to preserve multiple trendlines instead of just the most recent one. Set "Max Trendlines to Keep" (default 5) to control how many historical trendlines are preserved. Enable trend break confirmation for more reliable break signals. Adjust minTrendlineAngle (default 0.0) to filter flat trendlines - set to 0.1-0.5 to exclude weak trendlines. Alerts: Set up alerts for your preferred signal types. Enable cooldown to prevent alert spam. Each signal type has its own alert condition, so you can be selective about which signals trigger alerts. Visual Elements and Signal Markers The script uses various visual markers to indicate signals and conditions: - "sBottom" label (green): Strong bottom signal - RSI at extreme low with strong buy conditions - "sTop" label (red): Strong top signal - RSI at extreme high with strong sell conditions - "SyBuy" label (lime): Multi-RSI synergy buy signal - all RSIs aligned oversold - "SySell" label (red): Multi-RSI synergy sell signal - all RSIs aligned overbought - 🐂 emoji (green): Strong bullish divergence detected - 🐻 emoji (red): Strong bearish divergence detected - 🔆 emoji: Weak divergence signals (if enabled) - "H-Bull" label: Hidden bullish divergence - "H-Bear" label: Hidden bearish divergence - ⚡ marker (top of pane): Volume climax detected (extreme volume) - positioned at top for visibility - 💧 marker (top of pane): Volume dry-up detected (very low volume) - positioned at top for visibility - ↑ triangle (lime): Uptrend break signal - RSI breaks below uptrend line - ↓ triangle (red): Downtrend break signal - RSI breaks above downtrend line - Triangle up (lime): RSI(50) bullish crossover - Triangle down (red): RSI(50) bearish crossover - Circle markers: RSI period crossovers All markers are positioned at the RSI value where the signal occurs, using location.absolute for precise placement. Signal Priority and Interpretation Signals are generated independently and can occur simultaneously. Higher-priority signals generally indicate stronger setups: 1. Multi-RSI Synergy signals (SyBuy/SySell) - Highest priority: Requires alignment across all RSI periods plus volume and filter confirmation. These are the most reliable signals. 2. Strong Top/Bottom signals (sTop/sBottom) - High priority: Indicates extreme RSI levels with strong bounce conditions. Requires volume confirmation and all filters. 3. Divergence signals - Medium-High priority: Strong divergence (with engulfing) is more reliable than regular divergence. Hidden divergence indicates continuation rather than reversal. 4. Adaptive RSI crossovers - Medium priority: Buy when adaptive RSI crosses below dynamic oversold, sell when it crosses above dynamic overbought. These use volatility-adjusted RSI for more accurate signals. 5. RSI(50) centerline crossovers - Medium priority: Momentum shift signals. Less reliable alone but useful when combined with other confirmations. 6. RSI period crossovers - Lower priority: Early momentum shift indicators. Can provide early warning but may produce false signals in choppy markets. Best practice: Wait for multiple confirmations. For example, a synergy signal combined with divergence and volume climax provides the strongest setup. Chart Requirements For proper script functionality and compliance with TradingView requirements, ensure your chart displays: - Symbol name: The trading pair or instrument name should be visible - Timeframe: The chart timeframe should be clearly displayed - Script name: "Ultimate RSI " should be visible in the indicator title These elements help traders understand what they're viewing and ensure proper script identification. The script automatically includes this information in the indicator title and chart labels. Performance Considerations The script is optimized for performance: - ATR and Volume SMA are cached using var variables, updating only on confirmed and real-time bars to reduce redundant calculations - Forecast line arrays are dynamically managed: lines are reused when possible, and unused lines are deleted to prevent memory accumulation - Calculations use efficient Pine Script functions (ta.rsi, ta.ema, etc.) which are optimized by TradingView - Array operations are minimized where possible, with direct calculations preferred - Polynomial regression automatically caps the forecast horizon at 20 bars (POLYNOMIAL_MAX_HORIZON constant) to prevent performance degradation, as polynomial regression has O(n³) complexity. This safeguard ensures optimal performance even with large horizon settings - Pivot detection includes edge case handling to ensure reliable calculations even on early bars with limited historical data. Regression forecasting functions include comprehensive safety checks: horizon validation (must not exceed array size), empty array handling, edge case handling for horizon=1 scenarios, and division-by-zero protection in all mathematical operations The script should perform well on all timeframes. On very long historical data, forecast lines may accumulate if the horizon is large; consider reducing the forecast horizon if you experience performance issues. The polynomial regression performance safeguard automatically prevents performance issues for that specific regression type. Known Limitations and Considerations - Forecast lines are forward-looking projections and should not be used as definitive predictions. They provide context but are not guaranteed to be accurate. - Dynamic OB/OS thresholds can exceed 100 or go below 0 in extreme volatility scenarios, but are clamped to 0-100 range. This means in very volatile markets, the dynamic thresholds may not widen as much as the raw calculation suggests. - Volume confirmation requires sufficient historical volume data. On new instruments or very short timeframes, volume calculations may be less reliable. - Higher timeframe RSI uses request.security() which may have slight delays on some data feeds. - Regression forecasting requires at least N bars of history (where N = forecast horizon) before it can generate forecasts. Early bars will not show forecast lines. - StochRSI calculation requires the selected RSI source to have sufficient history. Very short RSI periods on new charts may produce less reliable StochRSI values initially. Practical Use Cases The indicator can be configured for different trading styles and timeframes: Swing Trading: Select the "Swing Trading" preset for instant optimal configuration. This preset uses RSI periods (14, 14, 21) with moderate smoothing. Alternatively, manually configure: Use RSI(24) with Multi-RSI Synergy in "ALL" mode, combined with trend filter (EMA 200) and ADX filter. This configuration provides high-probability setups with strong confirmation across multiple RSI periods. Day Trading: Select the "Day Trading" preset for instant optimal configuration. This preset uses RSI periods (6, 9, 14) with light smoothing and momentum confirmation enabled. Alternatively, manually configure: Use RSI(6) with Zero-Lag smoothing for fast signal detection. Enable volume confirmation with threshold 1.2-1.5 for reliable entries. Combine with RSI(50) filter to ensure momentum alignment. Strong top/bottom signals work well for day trading reversals. Trend Following: Enable trend filter (EMA) and EMA slope filter for strong trend confirmation. Use RSI(14) or RSI(24) with ADX filter to avoid choppy markets. Hidden divergence signals are useful for trend continuation entries. Reversal Trading: Focus on divergence detection (regular and strong) combined with strong top/bottom signals. Enable volume climax detection to identify capitulation moments. Use RSI(6) for early reversal signals, confirmed by RSI(14) and RSI(24). Forecasting and Planning: Enable regression forecasting with polynomial or exponential smoothing methods. Use forecast horizon of 10-20 bars for swing trading, 5-10 bars for day trading. Confidence bands help assess forecast reliability. Multi-Timeframe Analysis: Enable higher timeframe RSI to see context from larger timeframes. For example, use daily RSI on hourly charts to understand the larger trend context. This helps avoid counter-trend trades. Scalping: Select the "Scalping" preset for instant optimal configuration. This preset uses RSI periods (4, 7, 9) with minimal smoothing, disables noise reduction, and disables momentum confirmation for faster signals. Alternatively, manually configure: Use RSI(6) with minimal smoothing (or Zero-Lag) for ultra-fast signals. Disable most filters except volume confirmation. Use RSI period crossovers (RSI(6) × RSI(14)) for early momentum shifts. Set volume threshold to 1.0-1.2 for less restrictive filtering. Position Trading: Select the "Position Trading" preset for instant optimal configuration. This preset uses extended RSI periods (24, 21, 28) with heavier smoothing, optimized for longer-term trades. Alternatively, manually configure: Use RSI(24) with all filters enabled (Trend, ADX, RSI(50), Volume Dry-Up avoidance). Multi-RSI Synergy in "ALL" mode provides highest-quality signals. Practical Tips and Best Practices Getting Started: The fastest way to get started is to select a configuration preset that matches your trading style. Simply choose "Scalping", "Day Trading", "Swing Trading", or "Position Trading" from the "Configuration Preset" dropdown to instantly configure all settings optimally. For advanced users, use "Custom" mode for full manual control. The default configuration (Custom mode) is balanced and works well across different markets. After observing behavior, customize settings to match your trading style. Reducing Repainting: All signals are based on confirmed bars, minimizing repainting. The script uses confirmed bar data for all calculations to ensure backtesting accuracy. Signal Quality: Multi-RSI Synergy signals in "ALL" mode provide the highest-quality signals because they require alignment across all three RSI periods. These signals have lower frequency but higher reliability. For more frequent signals, use "2-of-3" mode. The noise reduction system further improves signal quality by requiring multiple confirmations (signal strength, extreme zone, consecutive bars, optional zone persistence and RSI slope). Adjust noise reduction settings to balance signal frequency vs. accuracy. Filter Combinations: Start with volume confirmation, then add trend filter for trend alignment, then ADX filter for trend strength. Combining all three filters significantly reduces false signals but also reduces signal frequency. Find your balance based on your risk tolerance. Volume Filtering: Set volume threshold to 0.1 or lower to effectively disable volume filtering if you trade instruments with unreliable volume data or want to test without volume confirmation. Standard confirmation uses 1.2-1.5 threshold. RSI Period Selection: RSI(6) is most sensitive and best for scalping or early signal detection. RSI(14) provides balanced signals suitable for day trading. RSI(24) is smoother and better for swing trading and trend confirmation. You can disable any RSI period you don't need to reduce visual clutter. Smoothing Methods: EMA provides balanced smoothing with moderate lag. RMA (Wilder's smoothing) is traditional and works well for RSI. Zero-Lag reduces lag but may increase noise. WMA gives more weight to recent values. Choose based on your preference for responsiveness vs. smoothness. Forecasting: Linear regression is simplest and works well for trending markets. Polynomial regression captures curves and works better in ranging markets. Exponential smoothing adapts to trends. Moving average method is most conservative. Use confidence bands to assess forecast reliability. Divergence: Strong divergence (with engulfing confirmation) is more reliable than regular divergence. Hidden divergence indicates continuation rather than reversal, useful for trend-following strategies. Pivot-based divergence provides more accurate detection by using actual pivot points instead of simple lowest/highest comparisons. Adjust lookback periods based on your timeframe: shorter for day trading, longer for swing trading. Pivot divergence period (default 5) controls the sensitivity of pivot detection. Dynamic Thresholds: Dynamic OB/OS thresholds automatically adapt to volatility. In volatile markets, thresholds widen; in calm markets, they narrow. Adjust the volatility multiplier and base percentage to fine-tune sensitivity. Higher values create wider thresholds in volatile markets. Alert Management: Enable alert cooldown (default 10 bars, recommended) to prevent alert spam. Each alert type has its own cooldown, so you can set different cooldowns for different signal types. For example, use shorter cooldown for synergy signals (high quality) and longer cooldown for crossovers (more frequent). The cooldown system works independently for each signal type, preventing spam while allowing different signal types to fire when appropriate. Technical Specifications - Pine Script Version: v6 - Indicator Type: Non-overlay (displays in separate panel below price chart) - Repainting Behavior: Minimal - all signals are based on confirmed bars, ensuring accurate backtesting results - Performance: Optimized with caching for ATR and volume calculations. Forecast arrays are dynamically managed to prevent memory accumulation. - Compatibility: Works on all timeframes (1 minute to 1 month) and all instruments (stocks, forex, crypto, futures, etc.) - Edge Case Handling: All calculations include safety checks for division by zero, NA values, and boundary conditions. Reset conditions and alert cooldowns handle edge cases where conditions never occurred or values are NA. - Reset Logic: Separate reset conditions for buy signals (based on bottom conditions) and sell signals (based on top conditions) ensure logical correctness. - Input Parameters: 60+ customizable parameters organized into logical groups for easy configuration. Configuration presets available for instant setup (Scalping, Day Trading, Swing Trading, Position Trading, Custom). - Noise Reduction: Comprehensive noise reduction system with multiple filters (signal strength, extreme zone, consecutive bars, zone persistence, RSI slope) to reduce false signals. - Pivot-Based Divergence: Enhanced divergence detection using actual pivot points for improved accuracy. - Momentum Confirmation: RSI momentum filter ensures signals only fire when RSI is accelerating in the signal direction. - Multi-Timeframe Confirmation: Optional higher timeframe RSI alignment for trend confirmation. - Enhanced Pivot Trendlines: Trendline drawing with strength requirements, confirmation, and trend break detection. Technical Notes - All RSI values are clamped to 0-100 range to ensure valid oscillator values - ATR and Volume SMA are cached for performance, updating on confirmed and real-time bars - Reset conditions handle edge cases: if a condition never occurred, reset returns true (allows first signal) - Alert cooldown handles na values: if no previous alert, cooldown allows the alert - Forecast arrays are dynamically sized based on horizon, with unused lines cleaned up - Fill logic uses a minimum gap (0.1) to ensure reliable polygon rendering in TradingView - All calculations include safety checks for division by zero and boundary conditions. Regression functions validate that horizon doesn't exceed array size, and all array access operations include bounds checking to prevent out-of-bounds errors - The script uses separate reset conditions for buy signals (based on bottom conditions) and sell signals (based on top conditions) for logical correctness - Background coloring uses a fallback system: dynamic color takes priority, then RSI(6) heatmap, then monotone if both are disabled - Noise reduction filters are applied after accuracy filters, providing multiple layers of signal quality control - Pivot trendlines use strength requirements to filter weak pivots, reducing noise in trendline drawing. Historical trendlines are stored in arrays and automatically limited to prevent memory accumulation when "Keep Historical Trendlines" is enabled - Volume climax and dry-up markers are positioned at the top of the pane for better visibility - All calculations are optimized with conditional execution - features only calculate when enabled (performance optimization) - Input Validation: Automatic cross-input validation ensures smoothing lengths are always less than RSI period lengths, preventing configuration errors - Configuration Presets: Four optimized preset configurations (Scalping, Day Trading, Swing Trading, Position Trading) for instant setup, plus Custom mode for full manual control - Constants Management: Magic numbers extracted to documented constants for improved maintainability and easier tuning (pivot tolerance, divergence thresholds, fill gap, etc.) - TradingView Function Consistency: All TradingView functions (ta.crossover, ta.crossunder, ta.atr, ta.lowest, ta.highest, ta.lowestbars, ta.highestbars, etc.) and custom functions that depend on historical results (f_consecutiveBarConfirmation, f_rsiSlopeConfirmation, f_rsiZonePersistence, f_applyAllFilters, f_rsiMomentum, f_forecast, f_confirmPivotLow, f_confirmPivotHigh) are called on every bar for consistency, as recommended by TradingView. Results are then used conditionally when needed. This ensures consistent calculations and prevents calculation inconsistencies.

Pine Script® göstergesi

captain_ua tarafından

Güncellendi

VMDM - Volume, Momentum & Divergence Master [BullByte]VMDM - Volume, Momentum and Divergence Master Educational Multi-Layer Market Structure Analysis System Multi-factor divergence engine that scores RSI momentum, volume pressure, and institutional footprints into one non-repainting confluence rating (0-100). WHAT THIS INDICATOR IS VMDM is an educational indicator designed to teach traders how to recognize high-probability reversal and continuation patterns by analyzing four independent market dimensions simultaneously. Instead of relying on a single indicator that may produce frequent false signals, VMDM creates a confluence-based scoring system that weights multiple confirmation factors, helping you understand which setups have stronger technical backing and which are lower quality. This is NOT a trading system or signal generator. It is a learning tool that visualizes complex market structure concepts in an accessible format for both coders and non-coders. THE PROBLEM IT SOLVES Most traders face these common challenges: Challenge 1 - Indicator Overload: Running RSI, volume analysis, and divergence detection separately creates chart clutter and conflicting signals. You waste time cross-referencing multiple windows trying to determine if all factors align. Challenge 2 - False Divergences: Standard divergence indicators trigger on every minor pivot, creating noise. Many divergences fail because they lack supporting evidence from volume or market structure. Challenge 3 - Missed Context: A bullish RSI divergence means nothing if it occurs during weak volume or in the middle of strong distribution. Context determines quality. Challenge 4 - Repainting Confusion: Many divergence scripts repaint, showing perfect historical signals that never actually triggered in real-time, leading to false confidence. Challenge 5 - Institutional Pattern Recognition: Absorption zones, stop hunts, and exhaustion patterns are taught in trading education but difficult to identify systematically without manual analysis. VMDM addresses all five challenges by combining complementary analytical layers into one transparent, non-repainting, confluence-weighted system with visual clarity. WHY THIS SPECIFIC COMBINATION - MASHUP JUSTIFICATION This indicator is NOT a random mashup of popular indicators. Each of the four layers serves a specific analytical purpose and together they create a complete market structure assessment framework. THE FOUR ANALYTICAL LAYERS LAYER 1 - RSI MOMENTUM DIVERGENCE (Trend Exhaustion Detection) Purpose: Identifies when price momentum is weakening before price itself reverses. Why RSI: The Relative Strength Index measures momentum on a bounded 0-100 scale, making divergence detection mathematically consistent across all assets and timeframes. Unlike raw price oscillators, RSI normalizes momentum regardless of volatility regime. How It Contributes: Divergence between price pivots and RSI pivots reveals early momentum exhaustion. A lower price low with a higher RSI low (bullish regular divergence) signals sellers are losing strength even as price makes new lows. This is the PRIMARY signal generator in VMDM. Limitation If Used Alone: RSI divergence by itself produces many false signals because momentum can remain weak during continued trends. It needs confirmation from volume and structural evidence. LAYER 2 - VOLUME PRESSURE ANALYSIS (Buying vs Selling Intensity) Purpose: Quantifies whether the current bar's volume reflects buying pressure or selling pressure based on where price closed within the bar's range. Methodology: Instead of just measuring volume size, VMDM calculates WHERE in the bar range the close occurred. A close near the high on high volume indicates strong buying absorption. A close near the low indicates selling pressure. The calculation accounts for wick size (wicks reduce pressure quality) and uses percentile ranking over a lookback period to normalize pressure strength on a 0-100 scale. Formula Concept: Buy Pressure = Volume × (Close - Low) / (High - Low) × Wick Quality Factor Sell Pressure = Volume × (High - Close) / (High - Low) × Wick Quality Factor Net Pressure = Buy Pressure - Sell Pressure Pressure Strength = Percentile Rank of Net Pressure over lookback period Why Percentile Ranking: Absolute volume varies by asset and session. Percentile ranking makes 85th percentile pressure on low-volume crypto comparable to 85th percentile pressure on high-volume forex. How It Contributes: When a bullish divergence occurs at a pivot low AND pressure strength is above 60 (strong buying), this adds 25 confluence points. It confirms that the divergence is occurring during actual accumulation, not just weak selling. Limitation If Used Alone: Pressure analysis shows current bar intensity but cannot identify trend exhaustion or reversal timing. High buying pressure can exist during a strong uptrend with no reversal imminent. LAYER 3 - BEHAVIORAL FOOTPRINT PATTERNS (Volume Anomaly Detection) CRITICAL DISCLAIMER: The terms "institutional footprint," "absorption," "stop hunt," and "exhaustion" used in this indicator are EDUCATIONAL LABELS for specific price and volume behavioral patterns. These patterns are detected through technical analysis of publicly available price, volume, and bar structure data. This indicator does NOT have access to actual institutional order flow, market maker data, broker stop-loss locations, or any non-public data source. These pattern names are used because they are common terminology in trading education to describe these technical behaviors. The analysis is interpretive and based on observable price action, not privileged information. Purpose: Detect volume anomalies and price patterns that historically correlate with potential reversal zones or trend continuation failure. Pattern Type 1 - Absorption (Labeled as "ACCUMULATION" or "DISTRIBUTION") Detection Criteria: Volume is more than 2x the moving average AND bar range is less than 50 percent of the average bar range. Interpretation: High volume compressed into a tight range suggests large participants are absorbing supply (accumulation) or distribution (distribution) without allowing price to move significantly. This often precedes directional moves once absorption completes. Visual: Colored box zone highlighting the absorption area. Pattern Type 2 - Stop Hunt (Labeled as "BULL HUNT" or "BEAR HUNT") Detection Criteria: Price penetrates a recent 10-bar high or low by a small margin (0.2 percent), then closes back inside the range on above-average volume (1.5x+). Interpretation: Price briefly spikes beyond recent structure (likely triggering stop losses placed just beyond obvious levels) then reverses. This is a classic false breakout pattern often seen before reversals. Visual: Label at the wick extreme showing hunt direction. Pattern Type 3 - Exhaustion (Labeled as "SELL EXHAUST" or "BUY EXHAUST") Detection Criteria: Lower wick is more than 2.5x the body size with volume above 1.8x average and RSI below 35 (sell exhaustion), OR upper wick more than 2.5x body size with volume above 1.8x average and RSI above 65 (buy exhaustion). Interpretation: Large wicks with high volume and extreme RSI suggest aggressive buying or selling was met with equally aggressive rejection. This exhaustion often marks short-term extremes. Visual: Label showing exhaustion type. How These Contribute: When a divergence forms at a pivot AND one of these behavioral patterns is active, the confluence score increases by 20 points. This confirms the divergence is occurring during structural anomaly activity, not just normal price flow. Limitation If Used Alone: These patterns can occur mid-trend and do not indicate direction without momentum context. Absorption in a strong uptrend may just be continuation accumulation. LAYER 4 - CONFLUENCE SCORING MATRIX (Quality Weighting System) Purpose: Translate all detected conditions into a single 0-100 quality score so you can objectively compare setups. Scoring Breakdown: Divergence Present: +30 points (primary signal) Pressure Confirmation: +25 points (volume supports direction) Behavioral Footprint Active: +20 points (structural anomaly present) RSI Extreme: +15 points (RSI below 30 or above 70 at pivot) Volume Spike: +10 points (current volume above 1.5x average) Maximum Possible Score: 100 points Why These Weights: The weights reflect reliability hierarchy based on backtesting observation. Divergence is the core signal (30 points), but without volume confirmation (25 points) many fail. Behavioral patterns add meaningful context (20 points). RSI extremes and volume spikes are secondary confirmations (15 and 10 points). Quality Tiers: 90-100: TEXTBOOK (all factors aligned) 75-89: HIGH QUALITY (strong confluence) 60-74: VALID (meets minimum threshold) Below 60: DEVELOPING (not displayed unless threshold lowered) How It Contributes: The confluence score allows you to filter noise. You can set your minimum quality threshold in settings. Higher thresholds (75+) show fewer but higher-quality patterns. Lower thresholds (50-60) show more patterns but include lower-confidence setups. This teaches you to distinguish strong setups from weak ones. Limitation: Confluence scoring is historical observation-based, not predictive guarantee. A 95-point setup can still fail. The score represents technical alignment, not future certainty. WHY THIS COMBINATION WORKS TOGETHER Each layer addresses a limitation in the others: RSI Divergence identifies WHEN momentum is exhausting (timing) Volume Pressure confirms WHETHER the exhaustion is accompanied by opposite-side accumulation (confirmation) Behavioral Footprint shows IF structural anomalies support the reversal hypothesis (context) Confluence Scoring weights ALL factors into an objective quality metric (filtering) Using only RSI divergence gives you timing without confirmation. Using only volume pressure gives you intensity without directional context. Using only pattern detection gives you anomalies without trend exhaustion context. Using all four together creates a complete analytical framework where each layer compensates for the others' weaknesses. This is not a mashup for the sake of combining indicators. It is a structured analytical system where each component has a defined role in a multi-dimensional market assessment process. HOW TO READ THE INDICATOR - VISUAL ELEMENTS GUIDE VMDM displays up to five visual layer types. You can enable or disable each layer independently in settings under "Visual Layers." VISUAL LAYER 1 - MARKET STRUCTURE (Pivot Points and Lines) What You See: Small labels at swing highs and lows marked "PH" (Pivot High) and "PL" (Pivot Low) with horizontal dashed lines extending right from each pivot. What It Means: These are CONFIRMED pivots, not real-time. A pivot low appears AFTER the required right-side confirmation bars pass (default 3 bars). This creates a delay but prevents repainting. The pivot only appears once it is mathematically confirmed. The horizontal lines represent support (from pivot lows) and resistance (from pivot highs) levels where price previously found significant rejection. Color Coding: Green label and line: Pivot Low (potential support) Red label and line: Pivot High (potential resistance) How To Use: These pivots are the foundation for divergence detection. Divergence is only calculated between confirmed pivots, ensuring all signals are non-repainting. The lines help you see historical structure levels. VISUAL LAYER 2 - PRESSURE ZONES (Background Color) What You See: Subtle background color shading on bars - light green or light red tint. What It Means: This visualizes volume pressure strength in real-time. Color Coding: Light Green Background: Pressure Strength above 70 (strong buying pressure - price closing near highs on volume) Light Red Background: Pressure Strength below 30 (strong selling pressure - price closing near lows on volume) No Color: Neutral pressure (pressure between 30-70) How To Use: When a bullish divergence pattern appears during green pressure zones, it suggests the divergence is forming during accumulation. When a bearish divergence appears during red zones, distribution is occurring. Pressure zones help you filter divergences - those forming in supportive pressure environments have higher probability. VISUAL LAYER 3 - DIVERGENCE LINES (Dotted Connectors) What You See: Dotted lines connecting two pivot points (either two pivot lows or two pivot highs). What It Means: A divergence has been detected between those two pivots. The line connects the price pivots where RSI showed opposite behavior. Color Coding: Bright Green Line: Bullish divergence (regular or hidden) Bright Red Line: Bearish divergence (regular or hidden) How To Use: The divergence line appears ONLY after the second pivot is confirmed (delayed by right-side confirmation bars). This is intentional to prevent repainting. When you see the line appear, it means: For Bullish Regular Divergence: Price made a lower low (second pivot lower than first) RSI made a higher low (RSI at second pivot higher than first) Interpretation: Downtrend losing momentum For Bullish Hidden Divergence: Price made a higher low (second pivot higher than first) RSI made a lower low (RSI at second pivot lower than first) Interpretation: Uptrend continuation likely (pullback within uptrend) For Bearish Regular Divergence: Price made a higher high (second pivot higher than first) RSI made a lower high (RSI at second pivot lower than first) Interpretation: Uptrend losing momentum For Bearish Hidden Divergence: Price made a lower high (second pivot lower than first) RSI made a higher high (RSI at second pivot higher than first) Interpretation: Downtrend continuation likely (bounce within downtrend) If "Show Consolidated Analysis Label" is disabled, a small label will appear on the divergence line showing the divergence type abbreviation. VISUAL LAYER 4 - BEHAVIORAL FOOTPRINT MARKERS What You See: Boxes, labels, and markers at specific bars showing pattern detection. ABSORPTION ZONES (Boxes): Colored rectangular boxes spanning one or more bars. Purple Box: Accumulation absorption zone (high volume, tight range, bullish close) Red Box: Distribution absorption zone (high volume, tight range, bearish close) If absorption continues for multiple consecutive bars, the box extends and a counter appears in the label showing how many bars the absorption lasted. What It Means: Large volume is being absorbed without significant price movement. This often precedes directional breakouts once the absorption phase completes. STOP HUNT MARKERS (Labels): Small labels below or above wicks labeled "BULL HUNT" or "BEAR HUNT" (may show bar count if consecutive). What It Means: BULL HUNT : Price spiked below recent lows then reversed back up on volume - likely triggered sell stops before reversing BEAR HUNT : Price spiked above recent highs then reversed back down on volume - likely triggered buy stops before reversing EXHAUSTION MARKERS (Labels): Labels showing "SELL EXHAUST" or "BUY EXHAUST." What It Means: SELL EXHAUST : Large lower wick with high volume and low RSI - aggressive selling met with strong rejection BUY EXHAUST : Large upper wick with high volume and high RSI - aggressive buying met with strong rejection How To Use: These markers help you identify WHERE structural anomalies occurred. When a divergence signal appears AT THE SAME TIME as one of these patterns, the confluence score increases. You are looking for alignment - divergence + behavioral pattern + pressure confirmation = high-quality setup. VISUAL LAYER 5 - CONSOLIDATED ANALYSIS LABEL (Main Pattern Signal) What You See: A large label appearing at pivot points (or in real-time mode, at current bar) containing full pattern analysis. Label Appearance: Depending on your "Use Compact Label Format" setting: COMPACT MODE (Single Line): Example: "BULLISH REGULAR | Q:HIGH QUALITY C:82" Breakdown: BULLISH REGULAR: Divergence type detected Q:HIGH QUALITY: Pattern quality tier C:82: Confluence score (82 out of 100) FULL MODE (Multi-Line Detailed): Example: PATTERN DETECTED ------------------- BULLISH REGULAR Quality: HIGH QUALITY Price: Lower Low Momentum: Higher Low Signal: Weakening Downtrend CONFLUENCE: 82/100 ------------------- Divergence: 30 Pressure: 25 Institutional: 20 RSI Extreme: 0 Volume: 10 Breakdown: Top section: Pattern type and quality Middle section: Divergence explanation (what price did vs what RSI did) Bottom section: Confluence score with itemized breakdown showing which factors contributed Label Position: In Confirmed modes: Label appears AT the pivot point (delayed by confirmation bars) In Real-time mode: Label appears at current bar as conditions develop Label Color: Gold: Textbook quality (90+ confluence) Green: High quality (75-89 confluence) Blue: Valid quality (60-74 confluence) How To Use: This is your primary decision-making label. When it appears: Check the divergence type (regular divergences are reversal signals, hidden divergences are continuation signals) Review the quality tier (textbook and high quality have better historical win rates) Examine the confluence breakdown to see which factors are present and which are missing Look at the chart context (trend, support/resistance, timeframe) Use this information to assess whether the setup aligns with your strategy The label does NOT tell you to buy or sell. It tells you a technical pattern has formed and provides the quality assessment. Your trading decision must incorporate risk management, market context, and your strategy rules. UNDERSTANDING THE THREE DETECTION MODES VMDM offers three signal detection modes in settings to accommodate different trading styles and learning objectives. MODE 1: "Confluence Only (Real-Time)" How It Works: Displays signals AS THEY DEVELOP on the current bar without waiting for pivot confirmation. The system calculates confluence score from pressure, volume, RSI extremes, and behavioral patterns. Divergence signals are NOT required in this mode. Delay: ZERO - signals appear immediately. Use Case: Real-time scanning for high-confluence zones without divergence requirement. Useful for intraday traders who want immediate alerts when multiple factors align. Tradeoff: More frequent signals but includes setups without confirmed divergence. Higher false signal rate. Signals can change as the bar develops (not repainting in historical bars, but current bar updates). Visual Behavior: Labels appear at the current bar. No divergence lines unless divergence happens to be present. MODE 2: "Divergence + Confluence (Confirmed)" - DEFAULT RECOMMENDED How It Works: Full system engagement. Signals appear ONLY when: A pivot is confirmed (requires right-side confirmation bars to pass) Divergence is detected between current pivot and previous pivot Total confluence score meets or exceeds your minimum threshold Delay: Equal to your "Pivot Right Bars" setting (default 3 bars). This means signals appear 3 bars AFTER the actual pivot formed. Use Case: Highest-quality, non-repainting signals for swing traders and learners who want to study confirmed pattern completion. Tradeoff: Delayed signals. You will not receive the signal until confirmation occurs. In fast-moving markets, price may have already moved significantly by the time the signal appears. Visual Behavior: Labels appear at the historical pivot location (in the past). Divergence lines connect the two pivots. This is the most educational mode because it shows completed, confirmed patterns. Non-Repainting Guarantee: Yes. Once a signal appears, it never disappears or changes. MODE 3: "Divergence + Confluence (Relaxed)" How It Works: Same as Confirmed mode but with adaptive thresholds. If confluence is very high (10 points above threshold), the signal may appear even if some factors are weak. If divergence is present but confluence is slightly below threshold (within 10 points), it may still appear. Delay: Same as Confirmed mode (right-side confirmation bars). Use Case: Slightly more signals than Confirmed mode for traders willing to accept near-threshold setups. Tradeoff: More signals but lower average quality than Confirmed mode. Visual Behavior: Same as Confirmed mode. DASHBOARD GUIDE - READING THE METRICS The dashboard appears in the corner of your chart (position selectable in settings) and provides real-time market state analysis. You can choose between four dashboard detail levels in settings: Off, Compact, Optimized (default), Full. DASHBOARD ROW EXPLANATIONS ROW 1 - Header Information Left: Current symbol and timeframe Center: "VMDM " Right: Version number ROW 2 - Mode and Delay Shows which detection mode you are using and the signal delay. Example: "CONFIRMED | Delay: 3 bars" This reminds you that signals in confirmed mode appear 3 bars after the pivot forms. ROW 3 - Market Regime Format: "TREND UP HV" or "RANGING NV" First Part - Trend State: TREND UP: 20 EMA above 50 EMA with strong separation TREND DOWN: 20 EMA below 50 EMA with strong separation RANGING: EMAs close together, low trend strength TRANSITION: Between trending and ranging states Second Part - Volatility State: HV: High Volatility (current ATR more than 1.3x the 50-bar average ATR) NV: Normal Volatility (current ATR between 0.7x and 1.3x average) LV: Low Volatility (current ATR less than 0.7x average) Third Column: Volatility ratio (example: "1.45x" means current ATR is 1.45 times normal) How To Use: Regime context helps you interpret signals. Reversal divergences are more reliable in ranging or transitional regimes. Continuation divergences (hidden) are more reliable in trending regimes. High volatility means wider stops may be needed. ROW 4 - Pressure Shows current volume pressure state. Format: "BUYING | ██████████░░░░░░░░░" States: BUYING : Pressure strength above 60 (closes near highs) SELLING : Pressure strength below 40 (closes near lows) NEUTRAL : Pressure strength between 40-60 Bar Visualization: Each block represents 10 percentile points. A full bar (10 filled blocks) = 100th percentile pressure. Color: Green for buying, red for selling, gray for neutral. How To Use: When pressure aligns with divergence direction (bullish divergence during buying pressure), confluence is stronger. ROW 5 - Volume and RSI Format: "1.8x | RSI 68 | OB" First Value: Current volume ratio (1.8x = volume is 1.8 times the moving average) Second Value: Current RSI reading Third Value: RSI state OB: Overbought (RSI above 70) OS: Oversold (RSI below 30) Blank: Neutral RSI How To Use: Volume spikes (above 1.5x) during divergence formation add confluence. RSI extremes at pivots add confluence. ROW 6 - Behavioral Footprint Format: "BULL HUNT | 2 bars" Shows the most recent behavioral pattern detected and how long ago. States: ACCUMULATION / DISTRIBUTION: Absorption detected BULL HUNT / BEAR HUNT: Stop hunt detected SELL EXHAUST / BUY EXHAUST: Exhaustion detected SCANNING: No recent pattern NOW: Pattern is active on current bar How To Use: When footprint activity is recent (within 50 bars) or active now, it adds context to divergence signals forming in that area. ROW 7 - Current Pattern Shows the divergence type currently detected (if any). Examples: "BULLISH REGULAR", "BEARISH HIDDEN", "Scanning..." Quality: Shows pattern quality (TEXTBOOK, HIGH QUALITY, VALID) How To Use: This tells you what type of signal is active. Regular divergences are reversal setups. Hidden divergences are continuation setups. ROW 8 - Session Summary Format: "14 events | A3 H8 E3" First Value: Total institutional events this session Breakdown: A: Absorption events H: Stop hunt events E: Exhaustion events How To Use: High event counts suggest an active, volatile session with frequent structural anomalies. Low counts suggest quiet, orderly price action. ROW 9 - Confluence Score (Optimized/Full mode only) Format: "78/100 | ████████░░" Shows current real-time confluence score even if no pattern is confirmed yet. How To Use: Watch this in real-time to see how close you are to pattern formation. When it exceeds your threshold and divergence forms, a signal will appear (after confirmation delay). ROW 10 - Patterns Studied (Optimized/Full mode only) Format: "47 patterns | 12 bars ago" First Value: Total confirmed patterns detected since chart loaded Second Value: How many bars since the last confirmed pattern appeared How To Use: Helps you understand pattern frequency on your selected symbol and timeframe. If many bars have passed since last pattern, market may be trending without reversal opportunities. ROW 11 - Bull/Bear Ratio (Optimized/Full mode only) Format: "28:19 | BULL" Shows count of bullish vs bearish patterns detected. Balance: BULL: More bullish patterns detected (suggests market has had more bullish reversals/continuations) BEAR: More bearish patterns detected BAL: Equal counts How To Use: Extreme imbalances can indicate directional bias in the studied period. A heavily bullish ratio in a downtrend might suggest frequent failed rallies (bearish continuation). Context matters. ROW 12 - Volume Ratio Detail (Optimized/Full mode only) Shows current volume vs average volume in absolute terms. Example: "1.4x | 45230 / 32300" How To Use: Confirms whether current activity is above or below normal. ROW 13 - Last Institutional Event (Full mode only) Shows the most recent institutional pattern type and how many bars ago it occurred. Example: "DISTRIBUTION | 23 bars" How To Use: Tracks recency of last anomaly for context. SETTINGS GUIDE - EVERY PARAMETER EXPLAINED PERFORMANCE SECTION Enable All Visuals (Master Toggle) Default: ON What It Does: Master kill switch for ALL visual elements (labels, lines, boxes, background colors, dashboard). When OFF, only plot outputs remain (invisible unless you open data window). When To Change: Turn OFF on mobile devices, 1-second charts, or slow computers to improve performance. You can still receive alerts even with visuals disabled. Impact: Dramatic performance improvement when OFF, but you lose all visual feedback. Maximum Object History Default: 50 | Range: 10-100 What It Does: Limits how many of each object type (labels, lines, boxes) are kept in memory. Older objects beyond this limit are deleted. When To Change: Lower to 20-30 on fast timeframes (1-minute charts) to prevent slowdown. Increase to 100 on daily charts if you want more historical pattern visibility. Impact: Lower values = better performance but less historical visibility. Higher values = more history visible but potential slowdown on fast timeframes. Alert Cooldown (Bars) Default: 5 | Range: 1-50 What It Does: Minimum number of bars that must pass before another alert of the same type can fire. Prevents alert spam when multiple patterns form in quick succession. When To Change: Increase to 20+ on 1-minute charts to reduce noise. Decrease to 1-2 on daily charts if you want every pattern alerted. Impact: Higher cooldown = fewer alerts. Lower cooldown = more alerts. USER EXPERIENCE SECTION Show Enhanced Tooltips Default: ON What It Does: Enables detailed hover-over tooltips on labels and visual elements. When To Change: Turn OFF if you encounter Pine Script compilation errors related to tooltip arguments (rare, platform-specific issue). Impact: Minimal. Just adds helpful hover text. MARKET STRUCTURE DETECTION SECTION Pivot Left Bars Default: 3 | Range: 2-10 What It Does: Number of bars to the LEFT of the center bar that must be higher (for pivot low) or lower (for pivot high) than the center bar for a pivot to be valid. Example: With value 3, a pivot low requires the center bar's low to be lower than the 3 bars to its left. When To Change: Increase to 5-7 on noisy timeframes (1-minute charts) to filter insignificant pivots Decrease to 2 on slow timeframes (daily charts) to catch more pivots Impact: Higher values = fewer, more significant pivots = fewer signals. Lower values = more frequent pivots = more signals but more noise. Pivot Right Bars Default: 3 | Range: 2-10 What It Does: Number of bars to the RIGHT of the center bar that must pass for confirmation. This creates the non-repainting delay. Example: With value 3, a pivot is confirmed 3 bars AFTER it forms. When To Change: Increase to 5-7 for slower, more confirmed signals (better for swing trading) Decrease to 2 for faster signals (better for intraday, but still non-repainting) Impact: Higher values = longer delay but more reliable confirmation. Lower values = faster signals but less confirmation. This setting directly controls your signal delay in Confirmed and Relaxed modes. Minimum Confluence Score Default: 60 | Range: 40-95 What It Does: The threshold score required for a pattern to be displayed. Patterns with confluence scores below this threshold are not shown. When To Change: Increase to 75+ if you only want high-quality textbook setups (fewer signals) Decrease to 50-55 if you want to see more developing patterns (more signals, lower average quality) Impact: This is your primary signal filter. Higher threshold = fewer, higher-quality signals. Lower threshold = more signals but includes weaker setups. Recommended starting point is 60-65. TECHNICAL PERIODS SECTION RSI Period Default: 14 | Range: 5-50 What It Does: Lookback period for RSI calculation. When To Change: Decrease to 9-10 for faster, more sensitive RSI that detects shorter-term momentum changes Increase to 21-28 for slower, smoother RSI that filters noise Impact: Lower values make RSI more volatile (more frequent extremes and divergences). Higher values make RSI smoother (fewer but more significant divergences). 14 is industry standard. Volume Moving Average Period Default: 20 | Range: 10-200 What It Does: Lookback period for calculating average volume. Current volume is compared to this average to determine volume ratio. When To Change: Decrease to 10-14 for shorter-term volume comparison (more sensitive to recent volume changes) Increase to 50-100 for longer-term volume comparison (smoother, less sensitive) Impact: Lower values make volume ratio more volatile. Higher values make it more stable. 20 is standard. ATR Period Default: 14 | Range: 5-100 What It Does: Lookback period for Average True Range calculation used for volatility measurement and label positioning. When To Change: Rarely needs adjustment. Use 7-10 for faster volatility response, 21-28 for slower. Impact: Affects volatility ratio calculation and visual label spacing. Minimal impact on signals. Pressure Percentile Lookback Default: 50 | Range: 10-300 What It Does: Lookback period for calculating volume pressure percentile ranking. Your current pressure is ranked against the pressure of the last X bars. When To Change: Decrease to 20-30 for shorter-term pressure context (more responsive to recent changes) Increase to 100-200 for longer-term pressure context (smoother rankings) Impact: Lower values make pressure strength more sensitive to recent bars. Higher values provide more stable, long-term pressure assessment. Capped at 300 for performance reasons. SIGNAL DETECTION SECTION Signal Detection Mode Default: "Divergence + Confluence (Confirmed)" Options: Confluence Only (Real-time) Divergence + Confluence (Confirmed) Divergence + Confluence (Relaxed) What It Does: Selects which detection logic mode to use (see "Understanding The Three Detection Modes" section above). When To Change: Use Confirmed for learning and non-repainting signals. Use Real-time for live scanning without divergence requirement. Use Relaxed for slightly more signals than Confirmed. Impact: Fundamentally changes when and how signals appear. VISUAL LAYERS SECTION All toggles default to ON. Each controls visibility of one visual layer: Show Market Structure: Pivot markers and support/resistance lines Show Pressure Zones: Background color shading Show Divergence Lines: Dotted lines connecting pivots Show Institutional Footprint Markers: Absorption boxes, hunt labels, exhaustion labels Show Consolidated Analysis Label: Main pattern detection label Use Compact Label Format Default: OFF What It Does: Switches consolidated label between single-line compact format and multi-line detailed format. When To Change: Turn ON if you find full labels too large or distracting. Impact: Visual clarity vs. information density tradeoff. DASHBOARD SECTION Dashboard Mode Default: "Optimized" Options: Off, Compact, Optimized, Full What It Does: Controls how much information the dashboard displays. Off: No dashboard Compact: 8 rows (essential metrics only) Optimized: 12 rows (recommended balance) Full: 13 rows (every available metric) Dashboard Position Default: "Top Right" Options: Top Right, Top Left, Bottom Right, Bottom Left What It Does: Screen corner where dashboard appears. HOW TO USE VMDM - PRACTICAL WORKFLOW STEP 1 - INITIAL SETUP Add VMDM to your chart Select your detection mode (Confirmed recommended for learning) Set your minimum confluence score (start with 60-65) Adjust pivot parameters if needed (default 3/3 is good for most timeframes) Enable the visual layers you want to see STEP 2 - CHART ANALYSIS Let the indicator load and analyze historical data Review the patterns that appear historically Examine the confluence scores - notice which patterns had higher scores Observe which patterns occurred during supportive pressure zones Notice the divergence line connections - understand what price vs RSI did STEP 3 - PATTERN RECOGNITION LEARNING When a consolidated analysis label appears: Read the divergence type (regular or hidden, bullish or bearish) Check the quality tier (textbook, high quality, or valid) Review the confluence breakdown - which factors contributed Look at the chart context - where is price relative to structure, trend, etc. Observe the behavioral footprint markers nearby - do they support the pattern STEP 4 - REAL-TIME MONITORING Watch the dashboard for real-time regime and pressure state Monitor the current confluence score in the dashboard When it approaches your threshold, be alert for potential pattern formation When a new pattern appears (after confirmation delay), evaluate it using the workflow above Use your trading strategy rules to decide if the setup aligns with your criteria STEP 5 - POST-PATTERN OBSERVATION After a pattern appears: Mark the level on your chart Observe what price does after the pattern completes Did price respect the reversal/continuation signal What was the confluence score of patterns that worked vs. those that failed Learn which quality tiers and confluence levels produce better results on your specific symbol and timeframe RECOMMENDED TIMEFRAMES AND ASSET CLASSES VMDM is timeframe-agnostic and works on any asset with volume data. However, optimal performance varies: BEST TIMEFRAMES 15-Minute to 1-Hour: Ideal balance of signal frequency and reliability. Pivot confirmation delay is acceptable. Sufficient volume data for pressure analysis. 4-Hour to Daily: Excellent for swing trading. Very high-quality signals. Lower frequency but higher significance. Recommended for learning because patterns are clearer. 1-Minute to 5-Minute: Works but requires adjustment. Increase pivot bars to 5-7 for filtering. Decrease max object history to 30 for performance. Expect more noise. Weekly/Monthly: Works but very infrequent signals. Increase confluence threshold to 70+ to ensure only major patterns appear. BEST ASSET CLASSES Forex Majors: Excellent volume data and clear trends. Pressure analysis works well. Crypto (Major Pairs): Good volume data. High volatility makes divergences more pronounced. Works very well. Stock Indices (SPY, QQQ, etc.): Excellent. Clean price action and reliable volume. Individual Stocks: Works well on high-volume stocks. Low-volume stocks may produce unreliable pressure readings. Commodities (Gold, Oil, etc.): Works well. Clear trends and reactions. WHAT THIS INDICATOR CANNOT DO - LIMITATIONS LIMITATION 1 - It Does Not Predict The Future VMDM identifies when technical conditions align historically associated with potential reversals or continuations. It does not predict what will happen next. A textbook 95-confluence pattern can still fail if fundamental events, news, or larger timeframe structure override the setup. LIMITATION 2 - Confirmation Delay Means You Miss Early Entry In Confirmed and Relaxed modes, the non-repainting design means you receive signals AFTER the pivot is confirmed. Price may have already moved significantly by the time you receive the signal. This is the tradeoff for non-repainting reliability. You can use Real-time mode for faster signals but sacrifice divergence confirmation. LIMITATION 3 - It Does Not Tell You Position Sizing or Risk Management VMDM provides technical pattern analysis. It does not calculate stop loss levels, take profit targets, or position sizing. You must apply your own risk management rules. Never risk more than you can afford to lose based on a technical signal. LIMITATION 4 - Volume Pressure Analysis Requires Reliable Volume Data On assets with thin volume or unreliable volume reporting, pressure analysis may be inaccurate. Stick to major liquid assets with consistent volume data. LIMITATION 5 - It Cannot Detect Fundamental Events VMDM is purely technical. It cannot predict earnings reports, central bank decisions, geopolitical events, or other fundamental catalysts that can override technical patterns. LIMITATION 6 - Divergence Requires Two Pivots The indicator cannot detect divergence until at least two pivots of the same type have formed. In strong trends without pullbacks, you may go long periods without signals. LIMITATION 7 - Institutional Pattern Names Are Interpretive The behavioral footprint patterns are named using common trading education terminology, but they are detected through technical analysis, not actual institutional data access. The patterns are interpretations based on price and volume behavior. CONCEPT FOUNDATION - WHY THIS APPROACH WORKS MARKET PRINCIPLE 1 - Momentum Divergence Precedes Price Reversal Price is the final output of market forces, but momentum (the rate of change in those forces) shifts first. When price makes a new low but the momentum behind that move is weaker (higher RSI low), it signals that sellers are losing strength even though they temporarily pushed price lower. This precedes reversal. This is a fundamental principle in technical analysis taught by Charles Dow, widely observed in market behavior. MARKET PRINCIPLE 2 - Volume Reveals Conviction Price can move on low volume (low conviction) or high volume (high conviction). When price makes a new low on declining volume while RSI shows improving momentum, it suggests the new low is not confirmed by participant conviction. Adding volume pressure analysis to momentum divergence adds a confirmation layer that filters false divergences. MARKET PRINCIPLE 3 - Anomalies Mark Structural Extremes When volume spikes significantly but range contracts (absorption), or when price spikes beyond structure then reverses (stop hunt), or when aggressive moves are met with large-wick rejection (exhaustion), these anomalies often mark short-term extremes. Combining these structural observations with momentum analysis creates context. MARKET PRINCIPLE 4 - Confluence Improves Probability No single technical factor is reliable in isolation. RSI divergence alone fails frequently. Volume analysis alone cannot time entries. Combining multiple independent factors into a weighted system increases the probability that observed patterns have structural significance rather than random noise. THE EDUCATIONAL VALUE By visualizing all four layers simultaneously and breaking down the confluence scoring transparently, VMDM teaches you to think in terms of multi-dimensional analysis rather than single-indicator reliance. Over time, you will learn to recognize these patterns manually and understand which combinations produce better results on your traded assets. INSTITUTIONAL TERMINOLOGY - IMPORTANT CLARIFICATION This indicator uses the following terms that are common in trading education: Institutional Footprint Absorption (Accumulation / Distribution) Stop Hunt Exhaustion CRITICAL DISCLAIMER: These terms are EDUCATIONAL LABELS for specific price action and volume behavior patterns detected through technical analysis of publicly available chart data (open, high, low, close, volume). This indicator does NOT have access to: Actual institutional order flow or order book data Market maker positions or intentions Broker stop-loss databases Non-public trading data Proprietary institutional information The patterns labeled as "institutional footprint" are interpretations based on observable price and volume behavior that educational trading literature often associates with potential large-participant activity. The detection is algorithmic pattern recognition, not privileged data access. When this indicator identifies "absorption," it means it detected high volume within a small range - a condition that MAY indicate large orders being filled but is not confirmation of actual institutional participation. When it identifies a "stop hunt," it means price briefly penetrated a structural level then reversed - a pattern that MAY have triggered stop losses but is not confirmation that stops were specifically targeted. When it identifies "exhaustion," it means high volume with large rejection wicks - a pattern that MAY indicate aggressive participation meeting strong opposition but is not confirmation of institutional involvement. These are technical analysis interpretations, not factual statements about market participant identity or intent. DISCLAIMER AND RISK WARNING EDUCATIONAL PURPOSE ONLY This indicator is designed as an educational tool to help traders learn to recognize technical patterns, understand multi-factor analysis, and practice systematic market observation. It is NOT a trading system, signal service, or financial advice. NO PERFORMANCE GUARANTEE Past pattern behavior does not guarantee future results. A pattern that historically preceded price movement in one direction may fail in the future due to changing market conditions, fundamental events, or random variance. Confluence scores reflect historical technical alignment, not future certainty. TRADING INVOLVES SUBSTANTIAL RISK Trading financial instruments involves substantial risk of loss. You can lose more than your initial investment. Never trade with money you cannot afford to lose. Always use proper risk management including stop losses, position sizing, and portfolio diversification. NO PREDICTIVE CLAIMS This indicator does NOT predict future price movement. It identifies when technical conditions align in patterns that historically have been associated with potential reversals or continuations. Market behavior is probabilistic, not deterministic. BACKTESTING LIMITATIONS If you backtest trading strategies using this indicator, ensure you account for: Realistic commission costs Realistic slippage (difference between signal price and actual fill price) Sufficient sample size (minimum 100 trades for statistical relevance) Reasonable position sizing (risking no more than 1-2 percent of account per trade) The confirmation delay inherent in the indicator (you cannot enter at the exact pivot in Confirmed mode) Backtests that do not account for these factors will produce unrealistic results. AUTHOR LIABILITY The author (BullByte) is not responsible for any trading losses incurred using this indicator. By using this indicator, you acknowledge that all trading decisions are your sole responsibility and that you understand the risks involved. NOT FINANCIAL ADVICE Nothing in this indicator, its code, its description, or its visual outputs constitutes financial, investment, or trading advice. Consult a licensed financial advisor before making investment decisions. FREQUENTLY ASKED QUESTIONS Q: Why do signals appear in the past, not at the current bar A: In Confirmed and Relaxed modes, signals appear at confirmed pivots, which requires waiting for right-side confirmation bars (default 3). This creates a delay but prevents repainting. Use Real-time mode if you want current-bar signals without pivot confirmation. Q: Can I use this for automated trading A: You can create alert-based automation, but understand that Confirmed mode signals appear AFTER the pivot with delay, so your entry will not be at the pivot price. Real-time mode signals can change as the current bar develops. Automation requires careful consideration of these factors. Q: How do I know which confluence score to use A: Start with 60. Observe which patterns work on your symbol/timeframe. If too many false signals, increase to 70-75. If too few signals, decrease to 55. Quality vs. quantity tradeoff. Q: Do regular divergences mean I should enter a reversal trade immediately A: No. Regular divergences indicate momentum exhaustion, which is a WARNING sign that trend may reverse, not a confirmation that it will. Use confluence score, market context, support/resistance, and your strategy rules to make entry decisions. Many divergences fail. Q: What's the difference between regular and hidden divergence A: Regular divergence = price and momentum move in opposite directions at extremes = potential reversal signal. Hidden divergence = price and momentum move in opposite directions during pullbacks = potential continuation signal. Hidden divergence suggests the pullback is just a correction within the larger trend. Q: Why does the pressure zone color sometimes conflict with the divergence direction A: Pressure is real-time current bar analysis. Divergence is confirmed pivot analysis from the past. They measure different things at different times. A bullish divergence confirmed 3 bars ago might appear during current selling pressure. This is normal. Q: Can I use this on stocks without volume data A: No. Volume is required for pressure analysis and behavioral pattern detection. Use only on assets with reliable volume reporting. Q: How often should I expect signals A: Depends on timeframe and settings. Daily charts might produce 5-10 signals per month. 1-hour charts might produce 20-30. 15-minute charts might produce 50-100. Adjust confluence threshold to control frequency. Q: Can I modify the code A: Yes, this is open source. You can modify for personal use. If you publish a modified version, please credit the original and ensure your publication meets TradingView guidelines. Q: What if I disagree with a pattern's confluence score A: The scoring weights are based on general observations and may not suit your specific strategy or asset. You can modify the code to adjust weights if you have data-driven reasons to do so. Final Notes VMDM - Volume, Momentum and Divergence Master is an educational multi-layer market analysis system designed to teach systematic pattern recognition through transparent, confluence-weighted signal detection. By combining RSI momentum divergence, volume pressure quantification, behavioral footprint pattern recognition, and quality scoring into a unified framework, it provides a comprehensive learning environment for understanding market structure. Use this tool to develop your analytical skills, understand how multiple technical factors interact, and learn to distinguish high-quality setups from noise. Remember that technical analysis is probabilistic, not predictive. No indicator replaces proper education, risk management, and trading discipline. Trade responsibly. Learn continuously. Risk only what you can afford to lose. -BullByte

Pine Script® göstergesi

BullByte tarafından

Super-AO with Risk Management Alerts Template - 11-29-25 Super-AO with Risk Management: ALERTS & AUTOMATION Edition Signal Lynx | Free Scripts supporting Automation for the Night-Shift Nation 🌙 1. Overview This is the Indicator / Alerts companion to the Super-AO Strategy. While the Strategy version is built for backtesting (verifying profitability and checking historical performance), this Indicator version is built for Live Execution. We understand the frustration of finding a great strategy, only to realize you can't easily hook it up to your trading bot. This script solves that. It contains the exact same "Super-AO" logic and "Risk Management Engine" as the strategy version, but it is optimized to send signals to automation platforms like Signal Lynx, 3Commas, or any Webhook listener. 2. Quick Action Guide (TL;DR) Purpose: Live Signal Generation & Automation. Workflow: Use the Strategy Version to find profitable settings. Copy those settings into this Indicator Version. Set a TradingView Alert using the "Any Alert() function call" condition. Best Timeframe: 4 Hours (H4) and above. Compatibility: Works with any webhook-based automation service. 3. Why Two Scripts? Pine Script operates in two distinct modes: Strategy Mode: Calculates equity, drawdowns, and simulates orders. Great for research, but sometimes complex to automate. Indicator Mode: Plots visual data on the chart. This is the preferred method for setting up robust alerts because it is lighter weight and plots specific values that automation services can read easily. The Golden Rule: Always backtest on the Strategy, but trade on the Indicator. This ensures that what you see in your history matches what you execute in real-time. 4. How to Automate This Script This script uses a "Visual Spike" method to trigger alerts. Instead of drawing equity curves, it plots numerical values at the bottom of your chart when a trade event occurs. The Signal Map: Blue Spike (2 / -2): Entry Signal (Long / Short). Yellow Spike (1 / -1): Risk Management Close (Stop Loss / Trend Reversal). Green Spikes (1, 2, 3): Take Profit Levels 1, 2, and 3. Setup Instructions: Add this indicator to your chart. Open your TradingView "Alerts" tab. Create a new Alert. Condition: Select SAO - RM Alerts Template. Trigger: Select Any Alert() function call. Message: Paste your JSON webhook message (provided by your bot service). 5. The Logic Under the Hood Just like the Strategy version, this indicator utilizes: SuperTrend + Awesome Oscillator: High-probability swing trading logic. Non-Repainting Engine: Calculates signals based on confirmed candle closes to ensure the alert you get matches the chart reality. Advanced Adaptive Trailing Stop (AATS): Internally calculates volatility to determine when to send a "Close" signal. 6. About Signal Lynx Automation for the Night-Shift Nation 🌙 We are providing this code open source to help traders bridge the gap between manual backtesting and live automation. This code has been in action since 2022. If you are looking to automate your strategies, please take a look at Signal Lynx in your search. License: Mozilla Public License 2.0 (Open Source). If you make beneficial modifications, please release them back to the community!

Pine Script® göstergesi

SignalLynx tarafından

Daily % Change Table Daily % Change Table — Indicator Summary This indicator provides a compact performance summary for daily candles, designed for backtesting and daily-session analysis. It displays a table in the top-right corner of the chart showing three key percentage-change statistics based on the current candle: 1. Prior Change Percentage move from the close two days ago to the prior day’s close. Useful for understanding momentum and context heading into the current session. 2. Change Percentage move from the prior day's close to the current candle’s close. Shows today’s full-session change. 3. Premarket Percentage move from the prior day's close to the current day’s open. Helps quantify overnight sentiment and gap activity. Features Clean, unobtrusive table display Automatically updates on the most recent bar Designed for use on Daily timeframe Useful for gap analysis, backtesting, and volatility/momentum studies

Pine Script® göstergesi

better0ffread tarafından

able MACD Overview Purpose: The indicator combines the traditional MACD (Moving Average Convergence Divergence) with a short-term “forecast” (projection) of MACD/histogram values to give early warning of momentum changes. Typical outputs: MACD line (fastEMA − slowEMA) Signal line (EMA of MACD) Histogram (MACD − signal) Forecasted MACD or histogram projected N bars ahead Optional buy/sell markers and alert conditions Add the indicator to TradingView (Installation) Open TradingView and the chart you want to apply the indicator to. Click “Pine Editor” at the bottom of the chart. Copy the contents of able_macd_forecast.pine into the Pine Editor window. Click “Add to chart” (or Save then Add to chart). If it’s a study, it will appear on the chart below price. If you plan to re-use the script, click Save and give it a meaningful name. Inputs / Parameters (typical) Note: exact input names may differ in your script. Replace the names below with the script’s input labels when you inspect it. Source: price source for calculations (close, hl2, etc.). Fast Length: length for the fast EMA (commonly 12). Slow Length: length for the slow EMA (commonly 26). Signal Length: length for the MACD signal EMA (commonly 9). Forecast Length / Horizon: how many bars ahead the script projects the MACD/histogram (e.g., 1–5). Forecast Method / Smoothing: choice of projection method (linear regression, EMA extrapolation, simple slope * N, etc.) if available. Histogram Thresholds: numeric thresholds to emphasize significant momentum (optional). Show Forecast: toggle on/off the forecast plot. Alerts On/Off toggles: enable or disable alert conditions baked into the indicator. Visual / Style settings: colors, plot thickness, histogram style (columns/areas), show labels, show buy/sell arrows. How the indicator is typically calculated (summary) MACD line = EMA(source, fast) − EMA(source, slow) Signal line = EMA(MACD line, signal length) Histogram = MACD − Signal Forecast = method-specific short-term projection of MACD or histogram (for example: extend the last slope forward, apply linear regression to MACD values and extrapolate N bars, or apply an additional smoothing and extend that value) Note: For exact math, I need to inspect the script; this is the typical approach. How to read the indicator (signals & interpretation) Bullish signal: MACD line crossing above the signal line (MACD cross up). Histogram turns positive (cross above zero). Forecast shows MACD/histogram moving higher in the next N bars (if forecast is positive or trending up). Bearish signal: MACD line crossing below the signal line (MACD cross down). Histogram turns negative (cross below zero). Forecast shows MACD/histogram moving lower ahead. Confirmations: Use price action (higher highs/lows for bullish, lower highs/lows for bearish). Volume or other momentum/confluence indicators (RSI, ADX). Divergences: Bullish divergence: price makes lower low while MACD histogram makes higher low. Bearish divergence: price makes higher high while MACD histogram makes lower high. Forecast behavior: If the forecast leads the MACD cross (forecast crosses before the current MACD does), it’s an early warning. Use caution: forecasts are prone to false signals; always confirm. Common trading setups using this indicator Conservative: Wait for MACD to cross signal + histogram above zero + forecast already trending same direction. Use stop below recent swing low (for long) or above recent swing high (for short). Aggressive (early entry): Enter when forecast turns positive while MACD still below signal (anticipating cross). Use tighter stops and smaller position sizes. Exit rules: Opposite MACD cross, histogram flipping sign, or a target based on risk-reward. Use trailing stop based on ATR or structure. Example settings for different timeframes (starting points) Scalping / 5–15 min: Fast 8, Slow 21, Signal 5, Forecast 1–2 Intraday / 1H: Fast 12, Slow 26, Signal 9, Forecast 2–3 Swing / 4H–Daily: Fast 12, Slow 26, Signal 9, Forecast 3–5 Adjust based on the asset volatility and backtests. Adding alerts (TradingView) Click the “Alerts” button (clock icon) or press Alt + A. In the Condition dropdown, select the indicator name (able_macd_forecast) and choose a plotted series or built-in alert condition (if the script uses alertcondition). Common alert types: MACD crosses Signal (Crossing) Histogram crosses 0 (Crossing) Forecast crosses 0 or Forecast trend change (if provided) Message templates: “{{ticker}}: MACD crossed above signal on {{interval}}” “{{ticker}} Forecast positive: MACD forecast shows upward momentum” Customize the message for your trade automation or notifications. Configure frequency (Only once, Once per bar, or Once per bar close) — for signals like crossovers, “Once per bar close” is usually safer to avoid repainting issues. Note: If the script includes alertcondition() calls with explicit IDs/messages, use those directly — they are the most reliable for automation. Backtesting / Strategy conversion If this script is a study (indicator), you can: Convert it to a strategy by adding strategy.* order calls (strategy.entry, strategy.close) using the entry/exit logic you prefer, or Use TradingView’s “Bar Replay” to manually test signals across different markets/timeframes. If you want, I can help convert or write a strategy wrapper that uses the indicator’s signals to place backtest trades (I’ll need the code). Practical tips & best practices Use higher timeframe confirmation for lower-timeframe entries (e.g., check daily MACD momentum before trading 15m signals). Beware of choppy markets; MACD / forecast may produce whipsaws. Combine with trend filters (moving average direction, ADX). If you rely on forecasted values, prefer alerts “on bar close” when possible to reduce false alerts from intra-bar noise. Tune parameters for the specific asset (FX, crypto, stocks have different behavior). Record each signal and outcome for a sample period (20–100 trades) to evaluate performance. Troubleshooting Indicator won’t add: verify Pine version in script header (//@version=4 or //@version=5). TradingView may reject scripts with unsupported version syntax. Plots missing: check script inputs (Some scripts hide plots if toggles are off). Alerts firing too often: change alert frequency to “Once per bar close” or adjust threshold values. Forecast seems to repaint: some forecast methods can repaint (use “bar_index” or store values only on closed bars, or use non-repainting forecast methods). Ask me to inspect the script for repainting logic. What I can do next (recommended) If you paste the content of able_macd_forecast.pine here, I will: Produce a precise, line-by-line usage guide mapping to the exact input names and default values. Show the exact plotted series names and how to reference them for alerts. Point out any repainting risks and suggest fixes. Provide example alert messages that match the script’s alertcondition IDs (if any). Optionally convert it into a strategy for backtesting, or add non-repainting forecast logic if needed.

Pine Script® göstergesi

Able-terminal tarafından

Complete Harmonic Pattern Overview: The ultimate harmonic XABCD pattern identification, prediction, and backtesting system. Harmonic patterns are among the most accurate of trading signals, yet they're widely underutilized because they can be difficult to spot and tedious to validate. If you've ever come across a pattern and struggled with questions like "are these retracement ratios close enough to the harmonic ratios?" or "what are the Potential Reversal levels and are they confluent with point D?", then this tool is your new best friend. Or, if you've never traded harmonic patterns before, maybe it's time to start. Put away your drawing tools and calculators, relax, and let this indicator do the heavy lifting for you. - Identification - An exhaustive search across multiple pivot lengths ensures that even the sneakiest harmonic patterns are identified. Each pattern is evaluated and assigned a score, making it easy to differentiate weak patterns from strong ones. Tooltips under the pattern labels show a detailed breakdown of the pattern's score and retracement ratios (see the Scoring section below for details). - Prediction - After a pattern is identified, paths to potential targets are drawn, and Potential Reversal Zone (PRZ) levels are plotted based on the retracement ratios of the harmonic pattern. Targets are customizable by pattern type (e.g. you can specify one set of targets for a Gartley and another for a Bat, etc). - Backtesting - A table shows the results of all the patterns found in the chart. Change your target, stop-loss, and % error inputs and observe how it affects your success rate. //------------------------------------------------------ // Scoring //------------------------------------------------------ A percentage-based score is calculated from four components: (1) Retracement % Accuracy - this measures how closely the pattern's retracement ratios match the theoretical values (fibs) defined for a given harmonic pattern. You can change the "Allowed fib ratio error %" in Settings to be more or less inclusive. (2) PRZ Level Confluence - Potential Reversal Zone levels are projected from retracements of the XA and BC legs. The PRZ Level Confluence component measures the closeness of the closest XA and BC retracement levels, relative to the total height of the PRZ. (3) Point D / PRZ Confluence - this measures the closeness of point D to either of the closest two PRZ levels (identified in the PRZ Level Confluence component above), relative to the total height of the PRZ. In theory, the closer together these levels are, the higher the probability of a reversal. (4) Leg Length Symmetry - this measures the ΔX symmetry of each leg. You can change the "Allowed leg length asymmetry %" in settings to be more or less inclusive. So, a score of 100% would mean that (1) all leg retracements match the theoretical fib ratios exactly (to 16 decimal places), (2) the closest XA and BC PRZ levels are exactly the same, (3) point D is exactly at the confluent PRZ level, and (4) all legs are exactly the same number of bars. While this is theoretically possible, you have better odds of getting struck by lightning twice on a sunny day. Calculation weights of all four components can be changed in Settings. //------------------------------------------------------ // Targets //------------------------------------------------------ A hard-coded set of targets are available to choose from, and can be applied to each pattern type individually: (1) .618 XA = .618 retracement of leg XA, measured from point D (2) 1.272 XA = 1.272 retracement of leg XA, measured from point D (3) 1.618 XA = 1.618 retracement of leg XA, measured from point D (4) .618 CD = .618 retracement of leg CD, measured from point D (5) 1.272 CD = 1.272 retracement of leg CD, measured from point D (6) 1.618 CD = 1.618 retracement of leg CD, measured from point D (7) A = point A (8) B = point B (9) C = point C

Pine Script® göstergesi

Vmaster369 tarafından

Mirror Blocks: Strategy Mirror Blocks is an educational structural-wave model built around a unique concept: the interaction of mirrored weighted moving averages (“blocks”) that reflect shifts in market structure as price transitions between layered symmetry zones. Rather than attempting to “predict” markets, the Mirror Blocks framework visualizes how price behaves when it expands away from, contracts toward, or flips across stacked WMA structures. These mirrored layers form a wave-like block system that highlights transitional zones in a clean, mechanical way. This strategy version allows you to study how these structural transitions behave in different environments and on different timeframes. The goal is understanding wave structure, not generating signals. How It Works Mirror Blocks builds three mirrored layers: Top Block (Structural High Symmetry) Base Block (Neutral Wave) Bottom Block (Structural Low Symmetry) The relative position of these blocks — and how price interacts with them — helps visualize: Compression and expansion Reversal zones Wave stability Momentum transitions Structure flips A structure is considered bullish-stack aligned when: Top > Base > Bottom and bearish-stack aligned when: Bottom > Base > Top These formations create the core of the Mirror Blocks wave engine. What the Strategy Version Adds This version includes: Long Only, Short Only, or Long & Short modes Adjustable symmetry distance (Mirror Distance) Configurable WMA smoothing length Optional trend filter using fast/slow MA comparison ENTER / EXIT / LONG / SHORT labels for structural transitions Fixed stop-loss controls for research A clean, transparent structure with no hidden components It is optimized for educational chart study, not automated signals. Intended Purpose Mirror Blocks is meant to help traders: Study structural transitions Understand symmetry-based wave models Explore how price interacts with mirrored layers Examine reversals and expansions from a mechanical perspective Conduct long and short backtesting for research Develop a deeper sense of market rhythm This is not a prediction model. It is a visual and structural framework for understanding movement. Backtesting Disclaimer Backtest results can vary depending on: Slippage settings Commission settings Timeframe Asset volatility Structural sensitivity parameters Past performance does not guarantee future results. Use this as a research tool only. Warnings & Compliance This script is educational. It is not financial advice. It does not provide signals. It does not promise profitability. The purpose is to help visualize structure, not predict price. The strategy features are simply here to help users study how structural transitions behave under various conditions. License Released under the Michael Culpepper Gratitude License (2025). Use and modify freely for education and research with attribution. No resale. No promises of profitability. Purpose is understanding, not signals.

Pine Script® stratejisi

mikeyc747 tarafından

Time Colors Time Colors – Custom Trading Sessions Visualizer Time Colors is a simple visual helper for backtesting and intraday trading. It lets you define up to 10 custom time blocks and highlights the chart background during those periods. Use it to: Mark the exact times when you are realistically able to trade Visually separate different sessions (e.g. London, New York, Asia) Filter out “dream trades” that happened while you were sleeping or at work Features Up to 10 fully customizable time blocks Individual on/off toggle for each block Custom color for every block Works on any intraday timeframe Session resolution input for flexible time handling How to use Add the Time Colors indicator to your chart. Set each Time Block to your personal trading hours (based on your TradingView timezone). Disable blocks you don’t need with “Enable Block X”. When backtesting, only count trades that occur inside the colored areas – those are the times you could have actually taken trades.

Pine Script® göstergesi

Twe3x tarafından

LapseBacktestingTable Library "LapseBacktestingMetrics" This library provides a robust set of quantitative backtesting and performance evaluation functions for Pine Script strategies. It’s designed to help traders, quants, and developers assess risk, return, and robustness through detailed statistical metrics — including Sharpe, Sortino, Omega, drawdowns, and trade efficiency. Built to enhance any trading strategy’s evaluation framework, this library allows you to visualize performance with the quantlapseTable() function, producing an interactive on-chart performance table. Credit to EliCobra and BikeLife76 for original concept inspiration. curve(disp_ind) Retrieves a selected performance curve of your strategy. Parameters: disp_ind (simple string): Type of curve to plot. Options include "Equity", "Open Profit", "Net Profit", "Gross Profit". Returns: (float) Corresponding performance curve value. cleaner(disp_ind, plot) Filters and displays selected strategy plots for clean visualization. Parameters: disp_ind (simple string): Type of display. plot (simple float): Strategy plot variable. Returns: (float) Filtered plot value. maxEquityDrawDown() Calculates the maximum equity drawdown during the strategy’s lifecycle. Returns: (float) Maximum equity drawdown percentage. maxTradeDrawDown() Computes the worst intra-trade drawdown among all closed trades. Returns: (float) Maximum intra-trade drawdown percentage. consecutive_wins() Finds the highest number of consecutive winning trades. Returns: (int) Maximum consecutive wins. consecutive_losses() Finds the highest number of consecutive losing trades. Returns: (int) Maximum consecutive losses. no_position() Counts the maximum consecutive bars where no position was held. Returns: (int) Maximum flat days count. long_profit() Calculates total profit generated by long positions as a percentage of initial capital. Returns: (float) Total long profit %. short_profit() Calculates total profit generated by short positions as a percentage of initial capital. Returns: (float) Total short profit %. prev_month() Measures the previous month’s profit or loss based on equity change. Returns: (float) Monthly equity delta. w_months() Counts the number of profitable months in the backtest. Returns: (int) Total winning months. l_months() Counts the number of losing months in the backtest. Returns: (int) Total losing months. checktf() Returns the time-adjusted scaling factor used in Sharpe and Sortino ratio calculations based on chart timeframe. Returns: (float) Annualization multiplier. stat_calc() Performs complete statistical computation including drawdowns, Sharpe, Sortino, Omega, trade stats, and profit ratios. Returns: (array) . f_colors(x, nv) Generates a color gradient for performance values, supporting dynamic table visualization. Parameters: x (simple string): Metric label name. nv (simple float): Metric numerical value. Returns: (color) Gradient color value for table background. quantlapseTable(option, position) Displays an interactive Performance Table summarizing all major backtesting metrics. Includes Sharpe, Sortino, Omega, Profit Factor, drawdowns, profitability %, and trade statistics. Parameters: option (simple string): Table type — "Full", "Simple", or "None". position (simple string): Table position — "Top Left", "Middle Right", "Bottom Left", etc. Returns: (table) On-chart performance visualization table. This library empowers advanced quantitative evaluation directly within Pine Script®, ideal for strategy developers seeking deeper performance diagnostics and intuitive on-chart metrics.

Pine Script® kütüphanesi

QuantLapse tarafından

Complete Dashboard PA+AI PRE/GO Trading Dashboard v0.1.2 - Publication Summary Overview A comprehensive multi-component trading system that combines technical analysis with an intelligent probability scoring framework to identify high-quality trade setups. The indicator features TTM Squeeze integration, volatility regime adaptation, and professional risk management tools—all presented in an intuitive 4-dashboard interface. Key Features 🎯 8-Component Probability Scoring System (0-100%) VWAP Position & Momentum - Price location and directional bias MACD Alignment - Trend confirmation and momentum strength EMA Trend Analysis - Multi-timeframe trend validation Volume Surge Detection - Relative volume analysis (RVOL) Price Extension Analysis - Distance from VWAP in ATR multiples TTM Squeeze Status - Volatility compression/expansion cycles Squeeze Momentum - Directional thrust measurement Confluence Scoring - Multi-indicator alignment bonus 🔥 TTM Squeeze Integration Squeeze Detection - Identifies consolidation phases (BB inside KC) Strength Classification - Distinguishes tight vs. loose squeezes Fire Signals - Premium entry alerts when squeeze releases Building Alerts - Early warnings when tight squeezes are coiling 📊 Volatility Regime Adaptation Dynamic Thresholds - Auto-adjusts based on ATR percentile (100-bar) Three Regimes - LOW VOL, NORMAL, HIGH VOL classification Adaptive Parameters - RVOL requirements and distance limits adjust automatically Context-Aware Scoring - Volume expectations scale with market volatility 💰 Professional Risk Management Position Sizing Calculator - Risk-based share calculation (% of account) ATR Trailing Stops - Dynamic stop-loss that tightens with profits Multiple Entry Strategies - VWAP reversion and pullback entries Complete Trade Info - Entry, stop, target, and size for every signal 📈 Multi-Timeframe Analysis Dashboard 4 Timeframes - Daily, 4H, 15m, 5m (customizable) 6 Metrics per TF - Price change, MACD, RSI, RVOL, EMA trend Alignment Visualization - Color-coded bull/bear indicators HTF Context - Understand broader market structure 🛡️ Reliability Features Confirm-on-Close - Eliminates intrabar repainting Minimum Bars Filter - Prevents premature signals on chart load NA-Safe Calculations - Works reliably on all symbols/timeframes Zero Division Protection - Bulletproof math across all market conditions What Makes This Indicator Unique Intelligent Probability Weighting Unlike binary "buy/sell" indicators, this system quantifies setup quality from 0-100%, allowing traders to: Filter by confidence - Only take 70%+ probability setups Size accordingly - Larger positions on higher probability signals Understand context - Know exactly why a signal fired Squeeze-Enhanced Entries The integration of TTM Squeeze analysis adds a powerful timing dimension: Premium Signals - 🔥 when squeeze fires + high probability (75%+) Regular Signals - Standard entries during trending conditions Avoid Chop - No entries during squeeze consolidation Strength Matters - Tight squeezes (BB width <20th percentile) get bonus points Adaptive Intelligence The volatility regime system ensures the indicator performs across all market conditions: Dead markets - Tighter thresholds prevent false signals Volatile markets - Loosened requirements catch real moves Automatic adjustment - No manual intervention needed Dashboard-Centric Design All critical information visible at a glance: Top-right - Probability breakdown & regime status Middle-right - Multi-timeframe alignment matrix Middle-left - RVOL status (volume confirmation) Bottom-right - Entry strategies with exact prices & sizes Ideal For ✅ Day Traders - Intraday setups with clear entry/exit ✅ Swing Traders - Multi-timeframe confirmation for position trades ✅ Options Traders - Squeeze timing for volatility expansion plays ✅ Systematic Traders - Quantified probabilities for rule-based systems ✅ Risk Managers - Built-in position sizing & stop placement Technical Specifications Indicator Type: Overlay (draws on price chart) Pine Script Version: v6 Calculation Method: Real-time, confirm-on-close option Alerts: 8 different alert types (premium entries, exits, squeeze warnings) Customization: 30+ input parameters Performance: Optimized for real-time updates Entry Strategies Included 1. VWAP Reversion Enter when price bounces off VWAP ± 0.7 ATR Targets mean reversion moves Best for range-bound or choppy markets 2. Pullback to Structure Enter on 50% retracement from swing high/low Targets trend continuation after healthy pullback Best for strong trending markets Both strategies include: Precise entry levels ATR-based stop placement Risk/reward targets Position size calculation Alert System 8 Alert Types: 🔥 Premium Long - Squeeze firing + bullish + high probability 🔥 Premium Short - Squeeze firing + bearish + high probability 🟢 High Probability Long - Standard bullish setup (70%+) 🔴 High Probability Short - Standard bearish setup (70%+) ⚡ Squeeze Coiling Long - Tight squeeze building, bullish bias ⚡ Squeeze Coiling Short - Tight squeeze building, bearish bias Exit Long - Long position exit signal Exit Short - Short position exit signal Settings & Customization Basic Settings ATR Length (default: 14) Confirm on Close (default: ON) Minimum Bars Required (default: 50) Squeeze Settings Bollinger Band Length & Multiplier Keltner Channel Length & Multiplier Momentum Length Squeeze strength classification Probability Settings MACD Parameters (12, 26, 9) Volume Surge Multiplier (1.5x) High/Medium Probability Thresholds (70%/50%) Volatility Regime Adaptation (ON/OFF) Risk Management Account Equity Risk % per Trade (default: 1%) ATR Trailing Stop (ON/OFF) Trail Multiplier (default: 2.0x) Visual Settings RVOL Period (20 bars) Fast/Slow EMA (9/21) Show/Hide each timeframe Dashboard positioning Use Cases Conservative Trading Set High Probability Threshold to 75%+ Enable Confirm-on-Close Only take Premium (🔥) entries Use 0.5% risk per trade Aggressive Trading Set Medium Probability Threshold to 50% Disable Confirm-on-Close (live signals) Take all High Probability entries Use 1.5-2% risk per trade Squeeze Specialist Focus exclusively on Premium entries (squeeze firing) Wait for "TIGHT SQUEEZE" status Monitor squeeze building alerts Enter immediately on fire signal Range Trading Use VWAP reversion entries only Lower probability threshold to 60% Tighter trailing stops (1.5x ATR) Focus on low volatility regime periods Performance Expectations Based on backtesting and design principles: Signal Quality: False signals reduced ~20-30% vs. single-indicator systems Win rate improvement ~5-10% from regime adaptation Average win size +15-20% from trailing stops Execution: Clear entry signals with exact prices Defined risk on every trade (stop loss) Consistent position sizing (% of account) Professional trade management Adaptability: Works across stocks, futures, forex, crypto Performs in trending and ranging markets Adjusts to changing volatility automatically Version History v0.1.2 (Current) Added squeeze momentum scoring (was calculated but unused) Implemented volatility regime adaptation Added confluence scoring (multi-indicator alignment) Enhanced squeeze strength classification (tight vs. loose) Improved reliability (confirm-on-close, NA-safe calculations) Added ATR trailing stops Added position sizing calculator Consolidated alert system v0.1.1 Initial release with 6-component probability system Basic TTM Squeeze integration Multi-timeframe analysis Entry strategy frameworks Limitations & Disclaimers ⚠️ Not a Holy Grail - No indicator is 100% accurate; losses will occur ⚠️ Requires Judgment - Use probability scores to guide, not replace, decision-making ⚠️ Backtesting Recommended - Test on paper/demo before live trading ⚠️ Market Dependent - Performance varies by asset class and market conditions ⚠️ Risk Management Essential - Always use stops; never risk more than you can afford to lose Installation & Setup Copy the Pine Script code Open TradingView chart Pine Editor → Paste code → "Add to Chart" Configure inputs for your trading style Set up alerts via TradingView alert menu Paper trade for 20+ signals before going live Future Development Roadmap Phase 3 (Planned) HTF alignment filter (require Daily + 4H confirmation) Session filters (avoid low-liquidity periods) Probability decay (signals lose value over time) Squeeze pre-alert enhancements Phase 4 (AI Integration) Feature vector export via webhooks ML-based parameter optimization Neural network regime classification Reinforcement learning for exits Support & Documentation Included Documentation: Complete changelog with implementation details Technical guide explaining all components Risk management best practices Alert configuration guide Best Practices: Start with default settings Enable Confirm-on-Close initially Use 1% risk per trade or less Focus on Premium (🔥) entries first Keep a trade journal to track performance Credits & Methodology Indicators Used: TTM Squeeze (John Carter) VWAP (Volume-Weighted Average Price) MACD (Gerald Appel) Exponential Moving Averages Average True Range (Wilder) Relative Volume Original Contributions: Multi-component probability weighting system Volatility regime adaptation framework Confluence scoring methodology Integrated risk management calculator Dashboard-centric visualization License & Terms Usage: Free for personal trading Modification: Open source, modify as needed Distribution: Credit original author if sharing modified versions Commercial Use: Contact author for licensing No Warranty: This indicator is provided "as-is" without guarantees of profitability. Trading involves substantial risk. Past performance does not guarantee future results. Quick Stats 📊 Components: 8 🎯 Probability Range: 0-100% 📈 Timeframes: 4 (customizable) 🔔 Alert Types: 8 ⚙️ Input Parameters: 30+ 📱 Dashboards: 4 💰 Entry Strategies: 2 (VWAP + Pullback) 🛡️ Risk Management: Integrated Status: Production Ready ✅ Version: 0.1.2 Last Updated: November 2025 Pine Script: v6 File Name: PA_AI_PRE_GO_v0.1.2_FIXED.pine One-Line Summary A professional-grade trading dashboard combining 8 technical components with TTM Squeeze analysis, volatility-adaptive thresholds, and integrated risk management—delivering quantified probability scores (0-100%) for every trade setup.

Pine Script® göstergesi

DayTradingMD1992 tarafından

No-Trade Zones UTC+7 This indicator helps you visualize and backtest your preferred trading hours. For example, if you have a 9-to-5 job, you obviously can’t trade during that time — and when backtesting, you should avoid those hours too. It also marks weekends if you prefer not to trade on those days. By highlighting no-trade periods directly on the chart, you can easily see when you shouldn’t be taking trades, without constantly checking the time or date by hovering over the chart. It makes backtesting smoother and more realistic for your personal schedule.

Pine Script® göstergesi

AightBett tarafından

4-Hour Range Scalping [v6.3]User Guide: 4-Hour Range Scalping Strategy Hello! Here is the guide for the Pine Script strategy. Please read it carefully to get the best results. 📈 This script automates the "4-Hour Range Scalping Strategy" from the video. The main idea is that the first four hours of a major trading day (like New York) set up a "trap zone." The strategy waits for the price to break out of this zone and then fail, giving us a signal that the breakout was false and the price is likely to reverse. Here’s the simple logic: Define the Range: It precisely calculates the highest high and lowest low during the first four hours of the selected trading session (e.g., 00:00 to 04:00 New York Time). Wait for a Breakout: It then monitors the 5-minute chart for a price breakout where a candle fully closes outside of this established range. Identify the Reversal: The trade trigger occurs when the price fails to continue its breakout and a subsequent 5-minute candle closes back inside the range. This signals a potential reversal or "failed breakout." Execute the Trade: ]A Short (Sell) trade is triggered after a failed breakout above the range high. A Long (Buy) trade is triggered after a failed breakout below the range low. Manage the Risk: The Stop Loss is automatically placed at the peak (for shorts) or trough (for longs) of the breakout move, and the Take Profit is set to a default 2:1 Risk/Reward Ratio. How to Use the Script (Step-by-Step) ⚙️ Follow these instructions to get it running perfectly. 1. Set Your Chart Timeframe This is the most important step. The strategy is designed to run on a 5-minute (5m) chart. Open your TradingView chart and make sure the timeframe is set to "5m". 2. Add the Script to Your Chart Open the Pine Editor tab at the bottom of TradingView, paste the entire script, and click the "Add to chart" button. 3. Configure the Settings On your chart, find the strategy's name (e.g., "4-Hour Range Scalping ") and click the gear icon ⚙️ to open its settings. Trading Session: Choose the session for the range. New York is the default and the one from the video. Risk/Reward Ratio: The default is 2.0, meaning your potential profit is twice your potential loss. You can adjust this to test other targets. Backtesting Period: To see how the strategy performed on all historical data, go to the "Strategy Tester" panel, click its own gear icon ⚙️, and uncheck the boxes for "Start Date" and "End Date." 4. Understand the Visuals on Your Chart Blue Background Area: This is the 4-hour calculation window. The script is identifying the day's high and low during this time. No trades will ever happen here. Red Line (Range High): The highest price of the 4-hour window. This is the upper boundary of the "trap zone." Green Line (Range Low): The lowest price of the 4-hour window. This is the lower boundary. Green Triangle (▲): Shows where a Long (Buy) trade was entered. Red Triangle (▼): Shows where a Short (Sell) trade was entered. A Very Important Note on Timezones 🕒 This is critical for you in the Philippines (PHT). The script is based on the New York session, which is 12 hours behind you. Your TradingView chart will still show your local time, but the script works on NY time in the background. The New York "day" begins at 12:00 PM (Noon) your time. The script's blue calculation window will be from 12:00 PM to 4:00 PM your local time. The red and green range lines will appear on your chart only after 4:00 PM your time. So, if you look at your chart in the morning or early afternoon, you will not see today's range yet. This is normal! The script is just waiting for the New York session to start. How to Set Up Trade Alerts 🔔 You can have TradingView send you a notification whenever the script enters a trade. Click the "Alert" button (looks like a clock) in the right-hand toolbar of TradingView. In the "Condition" dropdown, select the name of the script (e.g., "4-Hour Range Scalping..."). You will then see two options: "Long Signal" and "Short Signal". Select one (e.g., "Long Signal") and configure how you want to be notified (e.g., "Notify on app"). Click "Create". Repeat the process to create an alert for the other signal. ⚠️ Important Disclosure For Educational and Research Purposes Only. This script and all accompanying information are provided for educational and research purposes only. The strategy demonstrated is a technical concept and should not be misconstrued as financial, investment, legal, or tax advice. Trading financial markets involves substantial risk and is not suitable for every investor. There is a possibility that you could sustain a loss of some or all of your initial investment. Therefore, you should not invest money that you cannot afford to lose. Past performance is not indicative of future results. The backtesting results shown by this script are historical and do not guarantee future performance. Market conditions are constantly changing. By using this script, you acknowledge that you are solely responsible for any and all trading decisions you make. You should conduct your own thorough research and, if necessary, seek advice from an independent financial advisor before making any investment decisions. The creators of this script assume no liability for any of your trading results.

Pine Script® stratejisi

edisonquinones tarafından

Güncellendi

Portfolio Strategy Tester The Portfolio Strategy Tester is an institutional-grade backtesting framework that evaluates the performance of trend-following strategies on multi-asset portfolios. It enables users to construct custom portfolios of up to 30 assets and apply moving average crossover strategies across individual holdings. The model features a clear, color-coded table that provides a side-by-side comparison between the buy-and-hold portfolio and the portfolio using the risk management strategy, offering a comprehensive assessment of both approaches relative to the benchmark. Portfolios are constructed by entering each ticker symbol in the menu, assigning its respective weight, and reviewing the total sum of individual weights displayed at the top left of the table. For strategy selection, users can choose between Exponential Moving Average (EMA), Simple Moving Average (SMA), Wilder’s Moving Average (RMA), Weighted Moving Average (WMA), Moving Average Convergence Divergence (MACD), and Volume-Weighted Moving Average (VWMA). Moving average lengths are defined in the menu and apply only to strategy-enabled assets. To accurately replicate real-world portfolio conditions, users can choose between daily, weekly, monthly, or quarterly rebalancing frequencies and decide whether cash is held or redistributed. Daily rebalancing maintains constant portfolio weights, while longer intervals allow natural drift. When cash positions are not allowed, capital from bearish assets is automatically redistributed proportionally among bullish assets, ensuring the portfolio remains fully invested at all times. The table displays a comprehensive set of widely used institutional-grade performance metrics: CAGR = Compounded annual growth rate of returns. Volatility = Annualized standard deviation of returns. Sharpe = CAGR per unit of annualized standard deviation. Sortino = CAGR per unit of annualized downside deviation. Calmar = CAGR relative to maximum drawdown. Max DD = Largest peak-to-trough decline in value. Beta (β) = Sensitivity of returns relative to benchmark returns. Alpha (α) = Excess annualized risk-adjusted returns relative to benchmark. Upside = Ratio of average return to benchmark return on up days. Downside = Ratio of average return to benchmark return on down days. Tracking = Annualized standard deviation of returns versus benchmark. Turnover = Average sum of absolute changes in weights per year. Cumulative returns are displayed on each label as the total percentage gain from the selected start date, with green indicating positive returns and red indicating negative returns. In the table, baseline metrics serve as the benchmark reference and are always gray. For portfolio metrics, green indicates outperformance relative to the baseline, while red indicates underperformance relative to the baseline. For strategy metrics, green indicates outperformance relative to both the baseline and the portfolio, red indicates underperformance relative to both, and gray indicates underperformance relative to either the baseline or portfolio. Metrics such as Volatility, Tracking Error, and Turnover ratio are always displayed in gray as they serve as descriptive measures. In summary, the Portfolio Strategy Tester is a comprehensive backtesting tool designed to help investors evaluate different trend-following strategies on custom portfolios. It enables real-world simulation of both active and passive investment approaches and provides a full set of standard institutional-grade performance metrics to support data-driven comparisons. While results are based on historical performance, the model serves as a powerful portfolio management and research framework for developing, validating, and refining systematic investment strategies.

Pine Script® göstergesi

QuantitativeAlpha tarafından

T3 ATR [DCAUT]█ T3 ATR 📊 ORIGINALITY & INNOVATION The T3 ATR indicator represents an important enhancement to the traditional Average True Range (ATR) indicator by incorporating the T3 (Tilson Triple Exponential Moving Average) smoothing algorithm. While standard ATR uses fixed RMA (Running Moving Average) smoothing, T3 ATR introduces a configurable volume factor parameter that allows traders to adjust the smoothing characteristics from highly responsive to heavily smoothed output. This innovation addresses a fundamental limitation of traditional ATR: the inability to adapt smoothing behavior without changing the calculation period. With T3 ATR, traders can maintain a consistent ATR period while adjusting the responsiveness through the volume factor, making the indicator adaptable to different trading styles, market conditions, and timeframes through a single unified implementation. The T3 algorithm's triple exponential smoothing with volume factor control provides improved signal quality by reducing noise while maintaining better responsiveness compared to traditional smoothing methods. This makes T3 ATR particularly valuable for traders who need to adapt their volatility measurement approach to varying market conditions without switching between multiple indicator configurations. 📐 MATHEMATICAL FOUNDATION The T3 ATR calculation process involves two distinct stages: Stage 1: True Range Calculation The True Range (TR) is calculated using the standard formula: TR = max(high - low, |high - close |, |low - close |) This captures the greatest of the current bar's range, the gap from the previous close to the current high, or the gap from the previous close to the current low, providing a comprehensive measure of price movement that accounts for gaps and limit moves. Stage 2: T3 Smoothing Application The True Range values are then smoothed using the T3 algorithm, which applies six exponential moving averages in succession: First Layer: e1 = EMA(TR, period), e2 = EMA(e1, period) Second Layer: e3 = EMA(e2, period), e4 = EMA(e3, period) Third Layer: e5 = EMA(e4, period), e6 = EMA(e5, period) Final Calculation: T3 = c1×e6 + c2×e5 + c3×e4 + c4×e3 The coefficients (c1, c2, c3, c4) are derived from the volume factor (VF) parameter: a = VF / 2 c1 = -a³ c2 = 3a² + 3a³ c3 = -6a² - 3a - 3a³ c4 = 1 + 3a + a³ + 3a² The volume factor parameter (0.0 to 1.0) controls the weighting of these coefficients, directly affecting the balance between responsiveness and smoothness: Lower VF values (approaching 0.0): Coefficients favor recent data, resulting in faster response to volatility changes with minimal lag but potentially more noise Higher VF values (approaching 1.0): Coefficients distribute weight more evenly across the smoothing layers, producing smoother output with reduced noise but slightly increased lag 📊 COMPREHENSIVE SIGNAL ANALYSIS Volatility Level Interpretation: High Absolute Values: Indicate strong price movements and elevated market activity, suggesting larger position risks and wider stop-loss requirements, often associated with trending markets or significant news events Low Absolute Values: Indicate subdued price movements and quiet market conditions, suggesting smaller position risks and tighter stop-loss opportunities, often associated with consolidation phases or low-volume periods Rapid Increases: Sharp spikes in T3 ATR often signal the beginning of significant price moves or market regime changes, providing early warning of increased trading risk Sustained High Levels: Extended periods of elevated T3 ATR indicate sustained trending conditions with persistent volatility, suitable for trend-following strategies Sustained Low Levels: Extended periods of low T3 ATR indicate range-bound conditions with suppressed volatility, suitable for mean-reversion strategies Volume Factor Impact on Signals: Low VF Settings (0.0-0.3): Produce responsive signals that quickly capture volatility changes, suitable for short-term trading but may generate more frequent color changes during minor fluctuations Medium VF Settings (0.4-0.7): Provide balanced signal quality with moderate responsiveness, filtering out minor noise while capturing significant volatility changes, suitable for swing trading High VF Settings (0.8-1.0): Generate smooth, stable signals that filter out most noise and focus on major volatility trends, suitable for position trading and long-term analysis 🎯 STRATEGIC APPLICATIONS Position Sizing Strategy: Determine your risk per trade (e.g., 1% of account capital - adjust based on your risk tolerance and experience) Decide your stop-loss distance multiplier (e.g., 2.0x T3 ATR - this varies by market and strategy, test different values) Calculate stop-loss distance: Stop Distance = Multiplier × Current T3 ATR Calculate position size: Position Size = (Account × Risk %) / Stop Distance Example: $10,000 account, 1% risk, T3 ATR = 50 points, 2x multiplier → Position Size = ($10,000 × 0.01) / (2 × 50) = $100 / 100 points = 1 unit per point Important: The ATR multiplier (1.5x - 3.0x) should be determined through backtesting for your specific instrument and strategy - using inappropriate multipliers may result in stops that are too tight (frequent stop-outs) or too wide (excessive losses) Adjust the volume factor to match your trading style: lower VF for responsive stop distances in short-term trading, higher VF for stable stop distances in position trading Dynamic Stop-Loss Placement: Determine your risk tolerance multiplier (typically 1.5x to 3.0x T3 ATR) For long positions: Set stop-loss at entry price minus (multiplier × current T3 ATR value) For short positions: Set stop-loss at entry price plus (multiplier × current T3 ATR value) Trail stop-losses by recalculating based on current T3 ATR as the trade progresses Adjust the volume factor based on desired stop-loss stability: higher VF for less frequent adjustments, lower VF for more adaptive stops Market Regime Identification: Calculate a reference volatility level using a longer-period moving average of T3 ATR (e.g., 50-period SMA) High Volatility Regime: Current T3 ATR significantly above reference (e.g., 120%+) - favor trend-following strategies, breakout trades, and wider targets Normal Volatility Regime: Current T3 ATR near reference (e.g., 80-120%) - employ standard trading strategies appropriate for prevailing market structure Low Volatility Regime: Current T3 ATR significantly below reference (e.g., <80%) - favor mean-reversion strategies, range trading, and prepare for potential volatility expansion Monitor T3 ATR trend direction and compare current values to recent history to identify regime transitions early Risk Management Implementation: Establish your maximum portfolio heat (total risk across all positions, typically 2-6% of capital) For each position: Calculate position size using the formula Position Size = (Account × Individual Risk %) / (ATR Multiplier × Current T3 ATR) When T3 ATR increases: Position sizes automatically decrease (same risk %, larger stop distance = smaller position) When T3 ATR decreases: Position sizes automatically increase (same risk %, smaller stop distance = larger position) This approach maintains constant dollar risk per trade regardless of market volatility changes Use consistent volume factor settings across all positions to ensure uniform risk measurement 📋 DETAILED PARAMETER CONFIGURATION ATR Length Parameter: Default Setting: 14 periods This is the standard ATR calculation period established by Welles Wilder, providing balanced volatility measurement that captures both short-term fluctuations and medium-term trends across most markets and timeframes Selection Principles: Shorter periods increase sensitivity to recent volatility changes and respond faster to market shifts, but may produce less stable readings Longer periods emphasize sustained volatility trends and filter out short-term noise, but respond more slowly to genuine regime changes The optimal period depends on your holding time, trading frequency, and the typical volatility cycle of your instrument Consider the timeframe you trade: Intraday traders typically use shorter periods, swing traders use intermediate periods, position traders use longer periods Practical Approach: Start with the default 14 periods and observe how well it captures volatility patterns relevant to your trading decisions If ATR seems too reactive to minor price movements: Increase the period until volatility readings better reflect meaningful market changes If ATR lags behind obvious volatility shifts that affect your trades: Decrease the period for faster response Match the period roughly to your typical holding time - if you hold positions for N bars, consider ATR periods in a similar range Test different periods using historical data for your specific instrument and strategy before committing to live trading T3 Volume Factor Parameter: Default Setting: 0.7 This setting provides a reasonable balance between responsiveness and smoothness for most market conditions and trading styles Understanding the Volume Factor: Lower values (closer to 0.0) reduce smoothing, allowing T3 ATR to respond more quickly to volatility changes but with less noise filtering Higher values (closer to 1.0) increase smoothing, producing more stable readings that focus on sustained volatility trends but respond more slowly The trade-off is between immediacy and stability - there is no universally optimal setting Selection Principles: Match to your decision speed: If you need to react quickly to volatility changes for entries/exits, use lower VF; if you're making longer-term risk assessments, use higher VF Match to market character: Noisier, choppier markets may benefit from higher VF for clearer signals; cleaner trending markets may work well with lower VF for faster response Match to your preference: Some traders prefer responsive indicators even with occasional false signals, others prefer stable indicators even with some delay Practical Adjustment Guidelines: Start with default 0.7 and observe how T3 ATR behavior aligns with your trading needs over multiple sessions If readings seem too unstable or noisy for your decisions: Try increasing VF toward 0.9-1.0 for heavier smoothing If the indicator lags too much behind volatility changes you care about: Try decreasing VF toward 0.3-0.5 for faster response Make meaningful adjustments (0.2-0.3 changes) rather than small increments - subtle differences are often imperceptible in practice Test adjustments in simulation or paper trading before applying to live positions 📈 PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES Responsiveness Characteristics: The T3 smoothing algorithm provides improved responsiveness compared to traditional RMA smoothing used in standard ATR. The triple exponential design with volume factor control allows the indicator to respond more quickly to genuine volatility changes while maintaining the ability to filter noise through appropriate VF settings. This results in earlier detection of volatility regime changes compared to standard ATR, particularly valuable for risk management and position sizing adjustments. Signal Stability: Unlike simple smoothing methods that may produce erratic signals during transitional periods, T3 ATR's multi-layer exponential smoothing provides more stable signal progression. The volume factor parameter allows traders to tune signal stability to their preference, with higher VF settings producing remarkably smooth volatility profiles that help avoid overreaction to temporary market fluctuations. Comparison with Standard ATR: Adaptability: T3 ATR allows adjustment of smoothing characteristics through the volume factor without changing the ATR period, whereas standard ATR requires changing the period length to alter responsiveness, potentially affecting the fundamental volatility measurement Lag Reduction: At lower volume factor settings, T3 ATR responds more quickly to volatility changes than standard ATR with equivalent periods, providing earlier signals for risk management adjustments Noise Filtering: At higher volume factor settings, T3 ATR provides superior noise filtering compared to standard ATR, producing cleaner signals for long-term analysis without sacrificing volatility measurement accuracy Flexibility: A single T3 ATR configuration can serve multiple trading styles by adjusting only the volume factor, while standard ATR typically requires multiple instances with different periods for different trading applications Suitable Use Cases: T3 ATR is well-suited for the following scenarios: Dynamic Risk Management: When position sizing and stop-loss placement need to adapt quickly to changing volatility conditions Multi-Style Trading: When a single volatility indicator must serve different trading approaches (day trading, swing trading, position trading) Volatile Markets: When standard ATR produces too many false volatility signals during choppy conditions Systematic Trading: When algorithmic systems require a single, configurable volatility input that can be optimized for different instruments Market Regime Analysis: When clear identification of volatility expansion and contraction phases is critical for strategy selection Known Limitations: Like all technical indicators, T3 ATR has limitations that users should understand: Historical Nature: T3 ATR is calculated from historical price data and cannot predict future volatility with certainty Smoothing Trade-offs: The volume factor setting involves a trade-off between responsiveness and smoothness - no single setting is optimal for all market conditions Extreme Events: During unprecedented market events or gaps, T3 ATR may not immediately reflect the full scope of volatility until sufficient data is processed Relative Measurement: T3 ATR values are most meaningful in relative context (compared to recent history) rather than as absolute thresholds Market Context Required: T3 ATR measures volatility magnitude but does not indicate price direction or trend quality - it should be used in conjunction with directional analysis Performance Expectations: T3 ATR is designed to help traders measure and adapt to changing market volatility conditions. When properly configured and applied: It can help reduce position risk during volatile periods through appropriate position sizing It can help identify optimal times for more aggressive position sizing during stable periods It can improve stop-loss placement by adapting to current market conditions It can assist in strategy selection by identifying volatility regimes However, volatility measurement alone does not guarantee profitable trading. T3 ATR should be integrated into a comprehensive trading approach that includes directional analysis, proper risk management, and sound trading psychology. USAGE NOTES This indicator is designed for technical analysis and educational purposes. T3 ATR provides adaptive volatility measurement but has limitations and should not be used as the sole basis for trading decisions. The indicator measures historical volatility patterns, and past volatility characteristics do not guarantee future volatility behavior. Market conditions can change rapidly, and extreme events may produce volatility readings that fall outside historical norms. Traders should combine T3 ATR with directional analysis tools, support/resistance analysis, and other technical indicators to form a complete trading strategy. Proper backtesting and forward testing with appropriate risk management is essential before applying T3 ATR-based strategies to live trading. The volume factor parameter should be optimized for specific instruments and trading styles through careful testing rather than assuming default settings are optimal for all applications.

Pine Script® göstergesi

DCAUT tarafından

RSI Bollinger Bands [DCAUT]█ RSI Bollinger Bands 📊 ORIGINALITY & INNOVATION The RSI Bollinger Bands indicator represents a meaningful advancement in momentum analysis by combining two proven technical tools: the Relative Strength Index (RSI) and Bollinger Bands. This combination addresses a significant limitation in traditional RSI analysis - the use of fixed overbought/oversold thresholds (typically 70/30) that fail to adapt to changing market volatility conditions. Core Innovation: Rather than relying on static threshold levels, this indicator applies Bollinger Bands statistical analysis directly to RSI values, creating dynamic zones that automatically adjust based on recent momentum volatility. This approach helps reduce false signals during low volatility periods while remaining sensitive to genuine extremes during high volatility conditions. Key Enhancements Over Traditional RSI: Dynamic Thresholds: Overbought/oversold zones adapt to market conditions automatically, eliminating the need for manual threshold adjustments across different instruments and timeframes Volatility Context: Band width provides immediate visual feedback about momentum volatility, helping traders distinguish between stable trends and erratic movements Reduced False Signals: During ranging markets, narrower bands filter out minor RSI fluctuations that would trigger traditional fixed-threshold signals Breakout Preparation: Band squeeze patterns (similar to price-based BB) signal potential momentum regime changes before they occur Self-Referencing Analysis: By measuring RSI against its own statistical behavior rather than arbitrary levels, the indicator provides more relevant context 📐 MATHEMATICAL FOUNDATION Two-Stage Calculation Process: Stage 1: RSI Calculation RSI = 100 - (100 / (1 + RS)) where RS = Average Gain / Average Loss over specified period The RSI normalizes price momentum into a bounded 0-100 scale, making it ideal for statistical band analysis. Stage 2: Bollinger Bands on RSI Basis = MA(RSI, BB Length) Upper Band = Basis + (StdDev(RSI, BB Length) × Multiplier) Lower Band = Basis - (StdDev(RSI, BB Length) × Multiplier) Band Width = Upper Band - Lower Band The Bollinger Bands measure RSI's standard deviation from its own moving average, creating statistically-derived dynamic zones. Statistical Interpretation: Under normal distribution assumptions with default 2.0 multiplier, approximately 95% of RSI values should fall within the bands Band touches represent statistically significant momentum extremes relative to recent behavior Band width expansion indicates increasing momentum volatility (strengthening trend or increasing uncertainty) Band width contraction signals momentum consolidation and potential regime change preparation 📊 COMPREHENSIVE SIGNAL ANALYSIS Visual Color Signals: This indicator features dynamic color fills that highlight extreme momentum conditions: Green Fill (Above Upper Band): Appears when RSI breaks above the upper band, indicating exceptionally strong bullish momentum Represents dynamic overbought zone - not necessarily a reversal signal but a warning of extreme conditions In strong uptrends, green fills can persist as RSI "rides the band" - this indicates sustained momentum strength Exit of green zone (RSI falling back below upper band) often signals initial momentum weakening Red Fill (Below Lower Band): Appears when RSI breaks below the lower band, indicating exceptionally weak bearish momentum Represents dynamic oversold zone - potential reversal or continuation signal depending on trend context In strong downtrends, red fills can persist as RSI "rides the band" - this indicates sustained selling pressure Exit of red zone (RSI rising back above lower band) often signals initial momentum recovery Position-Based Signals: Upper Band Interactions: RSI Touching Upper Band: Dynamic overbought condition - momentum is extremely strong relative to recent volatility, potential exhaustion or continuation depending on trend context RSI Riding Upper Band: Sustained strong momentum, often seen in powerful trends, not necessarily an immediate reversal signal but warrants monitoring for exhaustion RSI Crossing Below Upper Band: Initial momentum weakening signal, particularly significant if accompanied by price divergence Lower Band Interactions: RSI Touching Lower Band: Dynamic oversold condition - momentum is extremely weak relative to recent volatility, potential reversal or continuation of downtrend RSI Riding Lower Band: Sustained weak momentum, common in strong downtrends, monitor for potential exhaustion RSI Crossing Above Lower Band: Initial momentum strengthening signal, early indication of potential reversal or consolidation Basis Line Signals: RSI Above Basis: Bullish momentum regime - upward pressure dominant RSI Below Basis: Bearish momentum regime - downward pressure dominant Basis Crossovers: Momentum regime shifts, more significant when accompanied by band width changes RSI Oscillating Around Basis: Balanced momentum, often indicates ranging market conditions Volatility-Based Signals: Band Width Patterns: Narrow Bands (Squeeze): Momentum volatility compression, often precedes significant directional moves, similar to price coiling patterns Expanding Bands: Increasing momentum volatility, indicates trend acceleration or growing uncertainty Narrowest Band in 100 Bars: Extreme compression alert, high probability of upcoming volatility expansion Advanced Pattern Recognition: Divergence Analysis: Bullish Divergence: Price makes lower lows while RSI touches or stays above previous lower band touch, suggests downward momentum weakening Bearish Divergence: Price makes higher highs while RSI touches or stays below previous upper band touch, suggests upward momentum weakening Hidden Bullish: Price makes higher lows while RSI makes lower lows at the lower band, indicates strong underlying bullish momentum Hidden Bearish: Price makes lower highs while RSI makes higher highs at the upper band, indicates strong underlying bearish momentum Band Walk Patterns: Upper Band Walk: RSI consistently touching or staying near upper band indicates exceptionally strong trend, wait for clear break below basis before considering reversal Lower Band Walk: RSI consistently at lower band signals very weak momentum, requires break above basis for reversal confirmation 🎯 STRATEGIC APPLICATIONS Strategy 1: Mean Reversion Trading Setup Conditions: Market Type: Ranging or choppy markets with no clear directional trend Timeframe: Works best on lower timeframes (5m-1H) or during consolidation phases Band Characteristic: Normal to narrow band width Entry Rules: Long Entry: RSI touches or crosses below lower band, wait for RSI to start rising back toward basis before entry Short Entry: RSI touches or crosses above upper band, wait for RSI to start falling back toward basis before entry Confirmation: Use price action confirmation (candlestick reversal patterns) at band touches Exit Rules: Target: RSI returns to basis line or opposite band Stop Loss: Fixed percentage or below recent swing low/high Time Stop: Exit if position not profitable within expected timeframe Strategy 2: Trend Continuation Trading Setup Conditions: Market Type: Clear trending market with higher highs/lower lows Timeframe: Medium to higher timeframes (1H-Daily) Band Characteristic: Expanding or wide bands indicating strong momentum Entry Rules: Long Entry in Uptrend: Wait for RSI to pull back to basis line or slightly below, enter when RSI starts rising again Short Entry in Downtrend: Wait for RSI to rally to basis line or slightly above, enter when RSI starts falling again Avoid Counter-Trend: Do not fade RSI at bands during strong trends (band walk patterns) Exit Rules: Trailing Stop: Move stop to break-even when RSI reaches opposite band Trend Break: Exit when RSI crosses basis against trend direction with conviction Band Squeeze: Reduce position size when bands start narrowing significantly Strategy 3: Breakout Preparation Setup Conditions: Market Type: Consolidating market after significant move or at key technical levels Timeframe: Any timeframe, but longer timeframes provide more reliable breakouts Band Characteristic: Narrowest band width in recent 100 bars (squeeze alert) Preparation Phase: Identify band squeeze condition (bands at multi-period narrowest point) Monitor price action for consolidation patterns (triangles, rectangles, flags) Prepare bracket orders for both directions Wait for band expansion to begin Entry Execution: Breakout Confirmation: Enter in direction of RSI band breakout (RSI breaks above upper band or below lower band) Price Confirmation: Ensure price also breaks corresponding technical level Volume Confirmation: Look for volume expansion supporting the breakout Risk Management: Stop Loss: Place beyond consolidation pattern opposite extreme Position Sizing: Use smaller size due to false breakout risk Quick Exit: Exit immediately if RSI returns inside bands within 1-3 bars Strategy 4: Multi-Timeframe Analysis Timeframe Selection: Higher Timeframe: Daily or 4H for trend context Trading Timeframe: 1H or 15m for entry signals Confirmation Timeframe: 5m or 1m for precise entry timing Analysis Process: Trend Identification: Check higher timeframe RSI position relative to bands, trade only in direction of higher timeframe momentum Setup Formation: Wait for trading timeframe RSI to show pullback to basis in trending direction Entry Timing: Use confirmation timeframe RSI band touch or crossover for precise entry Alignment Confirmation: All timeframes should show RSI moving in same direction for highest probability setups 📋 DETAILED PARAMETER CONFIGURATION RSI Source: Close (Default): Standard price point, balances responsiveness and reliability HL2: Reduces noise from intrabar volatility, provides smoother RSI values HLC3 or OHLC4: Further smoothing for very choppy markets, slower to respond but more stable Volume-Weighted: Consider using VWAP or volume-weighted prices for additional liquidity context RSI Length Parameter: Shorter Periods (5-10): More responsive but generates more signals, suitable for scalping or very active trading, higher noise level Standard (14): Default and most widely used setting, proven balance between responsiveness and reliability, recommended starting point Longer Periods (21-30): Smoother momentum measurement, fewer but potentially more reliable signals, better for swing trading or position trading Optimization Note: Test across different market regimes, optimal length often varies by instrument volatility characteristics RSI MA Type Parameter: RMA (Default): Wilder's original smoothing method, provides traditional RSI behavior with balanced lag, most widely recognized and tested, recommended for standard technical analysis EMA: Exponential smoothing gives more weight to recent values, faster response to momentum changes, suitable for active trading and trending markets, reduces lag compared to RMA SMA: Simple average treats all periods equally, smoothest output with highest lag, best for filtering noise in choppy markets, useful for long-term position analysis WMA: Weighted average emphasizes recent data less aggressively than EMA, middle ground between SMA and EMA characteristics, balanced responsiveness for swing trading Advanced Options: Full access to 25+ moving average types including HMA (reduced lag), DEMA/TEMA (enhanced responsiveness), KAMA/FRAMA (adaptive behavior), T3 (smoothness), Kalman Filter (optimal estimation) Selection Guide: RMA for traditional analysis and backtesting consistency, EMA for faster signals in trending markets, SMA for stability in ranging markets, adaptive types (KAMA/FRAMA) for varying volatility regimes BB Length Parameter: Short Length (10-15): Tighter bands that react quickly to RSI changes, more frequent band touches, suitable for active trading styles Standard (20): Balanced approach providing meaningful statistical context without excessive lag Long Length (30-50): Smoother bands that filter minor RSI fluctuations, captures only significant momentum extremes, fewer but higher quality signals Relationship to RSI Length: Consider BB Length greater than RSI Length for cleaner signals BB MA Type Parameter: SMA (Default): Standard Bollinger Bands calculation using simple moving average for basis line, treats all periods equally, widely recognized and tested approach EMA: Exponential smoothing for basis line gives more weight to recent RSI values, creates more responsive bands that adapt faster to momentum changes, suitable for trending markets RMA: Wilder's smoothing provides consistent behavior aligned with traditional RSI when using RMA for both RSI and BB calculations WMA: Weighted average for basis line balances recent emphasis with historical context, middle ground between SMA and EMA responsiveness Advanced Options: Full access to 25+ moving average types for basis calculation, including HMA (reduced lag), DEMA/TEMA (enhanced responsiveness), KAMA/FRAMA (adaptive to volatility changes) Selection Guide: SMA for standard Bollinger Bands behavior and backtesting consistency, EMA for faster band adaptation in dynamic markets, matching RSI MA type creates unified smoothing behavior BB Multiplier Parameter: Conservative (1.5-1.8): Tighter bands resulting in more frequent touches, useful in low volatility environments, higher signal frequency but potentially more false signals Standard (2.0): Default setting representing approximately 95% confidence interval under normal distribution, widely accepted statistical threshold Aggressive (2.5-3.0): Wider bands capturing only extreme momentum conditions, fewer but potentially more significant signals, reduces false signals in high volatility Adaptive Approach: Consider adjusting multiplier based on instrument characteristics, lower multiplier for stable instruments, higher for volatile instruments Parameter Optimization Workflow: Start with default parameters (RSI:14, BB:20, Mult:2.0) Test across representative sample period including different market regimes Adjust RSI length based on desired responsiveness vs stability tradeoff Tune BB length to match your typical holding period Modify multiplier to achieve desired signal frequency Validate on out-of-sample data to avoid overfitting Document optimal parameters for different instruments and timeframes Reference Levels Display: Enabled (Default): Shows traditional 30/50/70 levels for comparison with dynamic bands, helps visualize the adaptive advantage Disabled: Cleaner chart focusing purely on dynamic zones, reduces visual clutter for experienced users Educational Value: Keeping reference levels visible helps understand how dynamic bands differ from fixed thresholds across varying market conditions 📈 PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES Comparison with Traditional RSI: Fixed Threshold RSI Limitations: In ranging low-volatility markets: RSI rarely reaches 70/30, missing tradable extremes In trending high-volatility markets: RSI frequently breaks through 70/30, generating excessive false reversal signals Across different instruments: Same thresholds applied to volatile crypto and stable forex pairs produce inconsistent results Threshold Adjustment Problem: Manually changing thresholds for different conditions is subjective and lagging RSI Bollinger Bands Advantages: Automatic Adaptation: Bands adjust to current volatility regime without manual intervention Consistent Logic: Same statistical approach works across different instruments and timeframes Reduced False Signals: Band width filtering helps distinguish meaningful extremes from noise Additional Information: Band width provides volatility context missing in standard RSI Objective Extremes: Statistical basis (standard deviations) provides objective extreme definition Comparison with Price-Based Bollinger Bands: Price BB Characteristics: Measures absolute price volatility Affected by large price gaps and outliers Band position relative to price not normalized Difficult to compare across different price scales RSI BB Advantages: Normalized Scale: RSI's 0-100 bounds make band interpretation consistent across all instruments Momentum Focus: Directly measures momentum extremes rather than price extremes Reduced Gap Impact: RSI calculation smooths price gaps impact on band calculations Comparable Analysis: Same RSI BB appearance across stocks, forex, crypto enables consistent strategy application Performance Characteristics: Signal Quality: Higher Signal-to-Noise Ratio: Dynamic bands help filter RSI oscillations that don't represent meaningful extremes Context-Aware Alerts: Band width provides volatility context helping traders adjust position sizing and stop placement Reduced Whipsaws: During consolidations, narrower bands prevent premature signals from minor RSI movements Responsiveness: Adaptive Lag: Band calculation introduces some lag, but this lag is adaptive to current conditions rather than fixed Faster Than Manual Adjustment: Automatic band adjustment is faster than trader's ability to manually modify thresholds Balanced Approach: Combines RSI's inherent momentum lag with BB's statistical smoothing for stable yet responsive signals Versatility: Multi-Strategy Application: Supports both mean reversion (ranging markets) and trend continuation (trending markets) approaches Universal Instrument Coverage: Works effectively across equities, forex, commodities, cryptocurrencies without parameter changes Timeframe Agnostic: Same interpretation applies from 1-minute charts to monthly charts Limitations and Considerations: Known Limitations: Dual Lag Effect: Combines RSI's momentum lag with BB's statistical lag, making it less suitable for very short-term scalping Requires Volatility History: Needs sufficient bars for BB calculation, less effective immediately after major regime changes Statistical Assumptions: Assumes RSI values are somewhat normally distributed, extreme trending conditions may violate this Not a Standalone System: Like all indicators, should be combined with price action analysis and risk management Optimal Use Cases: Best for swing trading and position trading timeframes Most effective in markets with alternating volatility regimes Ideal for traders who use multiple instruments and timeframes Suitable for systematic trading approaches requiring consistent logic Suboptimal Conditions: Very low timeframes (< 5 minutes) where lag becomes problematic Instruments with extreme volatility spikes (gap-prone markets) Markets in strong persistent trends where mean reversion rarely occurs Periods immediately following major structural changes (new trading regime) USAGE NOTES This indicator is designed for technical analysis and educational purposes to help traders understand the interaction between momentum measurement and statistical volatility bands. The RSI Bollinger Bands has limitations and should not be used as the sole basis for trading decisions. Important Considerations: No Predictive Guarantee: Past band touches and patterns do not guarantee future price behavior Market Regime Dependency: Indicator performance varies significantly between trending and ranging market conditions Complementary Analysis Required: Should be used alongside price action, support/resistance levels, and fundamental analysis Risk Management Essential: Always use proper position sizing, stop losses, and risk controls regardless of signal quality Parameter Sensitivity: Different instruments and timeframes may require parameter optimization for optimal results Continuous Monitoring: Band characteristics change with market conditions, requiring ongoing assessment Recommended Supporting Analysis: Price structure analysis (support/resistance, trend lines) Volume confirmation for breakout signals Multiple timeframe alignment Market context awareness (news events, session times) Correlation analysis with related instruments The indicator aims to provide adaptive momentum analysis that adjusts to changing market volatility, but traders must apply sound judgment, proper risk management, and comprehensive market analysis in their decision-making process.

Pine Script® göstergesi

DCAUT tarafından

Keltner Channel Enhanced [DCAUT]█ Keltner Channel Enhanced 📊 ORIGINALITY & INNOVATION The Keltner Channel Enhanced represents an important advancement over standard Keltner Channel implementations by introducing dual flexibility in moving average selection for both the middle band and ATR calculation. While traditional Keltner Channels typically use EMA for the middle band and RMA (Wilder's smoothing) for ATR, this enhanced version provides access to 25+ moving average algorithms for both components, enabling traders to fine-tune the indicator's behavior to match specific market characteristics and trading approaches. Key Advancements: Dual MA Algorithm Flexibility: Independent selection of moving average types for middle band (25+ options) and ATR smoothing (25+ options), allowing optimization of both trend identification and volatility measurement separately Enhanced Trend Sensitivity: Ability to use faster algorithms (HMA, T3) for middle band while maintaining stable volatility measurement with traditional ATR smoothing, or vice versa for different trading strategies Adaptive Volatility Measurement: Choice of ATR smoothing algorithm affects channel responsiveness to volatility changes, from highly reactive (SMA, EMA) to smoothly adaptive (RMA, TEMA) Comprehensive Alert System: Five distinct alert conditions covering breakouts, trend changes, and volatility expansion, enabling automated monitoring without constant chart observation Multi-Timeframe Compatibility: Works effectively across all timeframes from intraday scalping to long-term position trading, with independent optimization of trend and volatility components This implementation addresses key limitations of standard Keltner Channels: fixed EMA/RMA combination may not suit all market conditions or trading styles. By decoupling the trend component from volatility measurement and allowing independent algorithm selection, traders can create highly customized configurations for specific instruments and market phases. 📐 MATHEMATICAL FOUNDATION Keltner Channel Enhanced uses a three-component calculation system that combines a flexible moving average middle band with ATR-based (Average True Range) upper and lower channels, creating volatility-adjusted trend-following bands. Core Calculation Process: 1. Middle Band (Basis) Calculation: The basis line is calculated using the selected moving average algorithm applied to the price source over the specified period: basis = ma(source, length, maType) Supported algorithms include EMA (standard choice, trend-biased), SMA (balanced and symmetric), HMA (reduced lag), WMA, VWMA, TEMA, T3, KAMA, and 17+ others. 2. Average True Range (ATR) Calculation: ATR measures market volatility by calculating the average of true ranges over the specified period: trueRange = max(high - low, abs(high - close ), abs(low - close )) atrValue = ma(trueRange, atrLength, atrMaType) ATR smoothing algorithm significantly affects channel behavior, with options including RMA (standard, very smooth), SMA (moderate smoothness), EMA (fast adaptation), TEMA (smooth yet responsive), and others. 3. Channel Calculation: Upper and lower channels are positioned at specified multiples of ATR from the basis: upperChannel = basis + (multiplier × atrValue) lowerChannel = basis - (multiplier × atrValue) Standard multiplier is 2.0, providing channels that dynamically adjust width based on market volatility. Keltner Channel vs. Bollinger Bands - Key Differences: While both indicators create volatility-based channels, they use fundamentally different volatility measures: Keltner Channel (ATR-based): Uses Average True Range to measure actual price movement volatility Incorporates gaps and limit moves through true range calculation More stable in trending markets, less prone to extreme compression Better reflects intraday volatility and trading range Typically fewer band touches, making touches more significant More suitable for trend-following strategies Bollinger Bands (Standard Deviation-based): Uses statistical standard deviation to measure price dispersion Based on closing prices only, doesn't account for intraday range Can compress significantly during consolidation (squeeze patterns) More touches in ranging markets Better suited for mean-reversion strategies Provides statistical probability framework (95% within 2 standard deviations) Algorithm Combination Effects: The interaction between middle band MA type and ATR MA type creates different indicator characteristics: Trend-Focused Configuration (Fast MA + Slow ATR): Middle band uses HMA/EMA/T3, ATR uses RMA/TEMA, quick trend changes with stable channel width, suitable for trend-following Volatility-Focused Configuration (Slow MA + Fast ATR): Middle band uses SMA/WMA, ATR uses EMA/SMA, stable trend with dynamic channel width, suitable for volatility trading Balanced Configuration (Standard EMA/RMA): Classic Keltner Channel behavior, time-tested combination, suitable for general-purpose trend following Adaptive Configuration (KAMA + KAMA): Self-adjusting indicator responding to efficiency ratio, suitable for markets with varying trend strength and volatility regimes 📊 COMPREHENSIVE SIGNAL ANALYSIS Keltner Channel Enhanced provides multiple signal categories optimized for trend-following and breakout strategies. Channel Position Signals: Upper Channel Interaction: Price Touching Upper Channel: Strong bullish momentum, price moving more than typical volatility range suggests, potential continuation signal in established uptrends Price Breaking Above Upper Channel: Exceptional strength, price exceeding normal volatility expectations, consider adding to long positions or tightening trailing stops Price Riding Upper Channel: Sustained strong uptrend, characteristic of powerful bull moves, stay with trend and avoid premature profit-taking Price Rejection at Upper Channel: Momentum exhaustion signal, consider profit-taking on longs or waiting for pullback to middle band for reentry Lower Channel Interaction: Price Touching Lower Channel: Strong bearish momentum, price moving more than typical volatility range suggests, potential continuation signal in established downtrends Price Breaking Below Lower Channel: Exceptional weakness, price exceeding normal volatility expectations, consider adding to short positions or protecting against further downside Price Riding Lower Channel: Sustained strong downtrend, characteristic of powerful bear moves, stay with trend and avoid premature covering Price Rejection at Lower Channel: Momentum exhaustion signal, consider covering shorts or waiting for bounce to middle band for reentry Middle Band (Basis) Signals: Trend Direction Confirmation: Price Above Basis: Bullish trend bias, middle band acts as dynamic support in uptrends, consider long positions or holding existing longs Price Below Basis: Bearish trend bias, middle band acts as dynamic resistance in downtrends, consider short positions or avoiding longs Price Crossing Above Basis: Potential trend change from bearish to bullish, early signal to establish long positions Price Crossing Below Basis: Potential trend change from bullish to bearish, early signal to establish short positions or exit longs Pullback Trading Strategy: Uptrend Pullback: Price pulls back from upper channel to middle band, finds support, and resumes upward, ideal long entry point Downtrend Bounce: Price bounces from lower channel to middle band, meets resistance, and resumes downward, ideal short entry point Basis Test: Strong trends often show price respecting the middle band as support/resistance on pullbacks Failed Test: Price breaking through middle band against trend direction signals potential reversal Volatility-Based Signals: Narrow Channels (Low Volatility): Consolidation Phase: Channels contract during periods of reduced volatility and directionless price action Breakout Preparation: Narrow channels often precede significant directional moves as volatility cycles Trading Approach: Reduce position sizes, wait for breakout confirmation, avoid range-bound strategies within channels Breakout Direction: Monitor for price breaking decisively outside channel range with expanding width Wide Channels (High Volatility): Trending Phase: Channels expand during strong directional moves and increased volatility Momentum Confirmation: Wide channels confirm genuine trend with substantial volatility backing Trading Approach: Trend-following strategies excel, wider stops necessary, mean-reversion strategies risky Exhaustion Signs: Extreme channel width (historical highs) may signal approaching consolidation or reversal Advanced Pattern Recognition: Channel Walking Pattern: Upper Channel Walk: Price consistently touches or exceeds upper channel while staying above basis, very strong uptrend signal, hold longs aggressively Lower Channel Walk: Price consistently touches or exceeds lower channel while staying below basis, very strong downtrend signal, hold shorts aggressively Basis Support/Resistance: During channel walks, price typically uses middle band as support/resistance on minor pullbacks Pattern Break: Price crossing basis during channel walk signals potential trend exhaustion Squeeze and Release Pattern: Squeeze Phase: Channels narrow significantly, price consolidates near middle band, volatility contracts Direction Clues: Watch for price positioning relative to basis during squeeze (above = bullish bias, below = bearish bias) Release Trigger: Price breaking outside narrow channel range with expanding width confirms breakout Follow-Through: Measure squeeze height and project from breakout point for initial profit targets Channel Expansion Pattern: Breakout Confirmation: Rapid channel widening confirms volatility increase and genuine trend establishment Entry Timing: Enter positions early in expansion phase before trend becomes overextended Risk Management: Use channel width to size stops appropriately, wider channels require wider stops Basis Bounce Pattern: Clean Bounce: Price touches middle band and immediately reverses, confirms trend strength and entry opportunity Multiple Bounces: Repeated basis bounces indicate strong, sustainable trend Bounce Failure: Price penetrating basis signals weakening trend and potential reversal Divergence Analysis: Price/Channel Divergence: Price makes new high/low while staying within channel (not reaching outer band), suggests momentum weakening Width/Price Divergence: Price breaks to new extremes but channel width contracts, suggests move lacks conviction Reversal Signal: Divergences often precede trend reversals or significant consolidation periods Multi-Timeframe Analysis: Keltner Channels work particularly well in multi-timeframe trend-following approaches: Three-Timeframe Alignment: Higher Timeframe (Weekly/Daily): Identify major trend direction, note price position relative to basis and channels Intermediate Timeframe (Daily/4H): Identify pullback opportunities within higher timeframe trend Lower Timeframe (4H/1H): Time precise entries when price touches middle band or lower channel (in uptrends) with rejection Optimal Entry Conditions: Best Long Entries: Higher timeframe in uptrend (price above basis), intermediate timeframe pulls back to basis, lower timeframe shows rejection at middle band or lower channel Best Short Entries: Higher timeframe in downtrend (price below basis), intermediate timeframe bounces to basis, lower timeframe shows rejection at middle band or upper channel Risk Management: Use higher timeframe channel width to set position sizing, stops below/above higher timeframe channels 🎯 STRATEGIC APPLICATIONS Keltner Channel Enhanced excels in trend-following and breakout strategies across different market conditions. Trend Following Strategy: Setup Requirements: Identify established trend with price consistently on one side of basis line Wait for pullback to middle band (basis) or brief penetration through it Confirm trend resumption with price rejection at basis and move back toward outer channel Enter in trend direction with stop beyond basis line Entry Rules: Uptrend Entry: Price pulls back from upper channel to middle band, shows support at basis (bullish candlestick, momentum divergence) Enter long on rejection/bounce from basis with stop 1-2 ATR below basis Aggressive: Enter on first touch; Conservative: Wait for confirmation candle Downtrend Entry: Price bounces from lower channel to middle band, shows resistance at basis (bearish candlestick, momentum divergence) Enter short on rejection/reversal from basis with stop 1-2 ATR above basis Aggressive: Enter on first touch; Conservative: Wait for confirmation candle Trend Management: Trailing Stop: Use basis line as dynamic trailing stop, exit if price closes beyond basis against position Profit Taking: Take partial profits at opposite channel, move stops to basis Position Additions: Add to winners on subsequent basis bounces if trend intact Breakout Strategy: Setup Requirements: Identify consolidation period with contracting channel width Monitor price action near middle band with reduced volatility Wait for decisive breakout beyond channel range with expanding width Enter in breakout direction after confirmation Breakout Confirmation: Price breaks clearly outside channel (upper for longs, lower for shorts), channel width begins expanding from contracted state Volume increases significantly on breakout (if using volume analysis) Price sustains outside channel for multiple bars without immediate reversal Entry Approaches: Aggressive: Enter on initial break with stop at opposite channel or basis, use smaller position size Conservative: Wait for pullback to broken channel level, enter on rejection and resumption, tighter stop Volatility-Based Position Sizing: Adjust position sizing based on channel width (ATR-based volatility): Wide Channels (High ATR): Reduce position size as stops must be wider, calculate position size using ATR-based risk calculation: Risk / (Stop Distance in ATR × ATR Value) Narrow Channels (Low ATR): Increase position size as stops can be tighter, be cautious of impending volatility expansion ATR-Based Risk Management: Use ATR-based risk calculations, position size = 0.01 × Capital / (2 × ATR), use multiples of ATR (1-2 ATR) for adaptive stops Algorithm Selection Guidelines: Different market conditions benefit from different algorithm combinations: Strong Trending Markets: Middle band use EMA or HMA, ATR use RMA, capture trends quickly while maintaining stable channel width Choppy/Ranging Markets: Middle band use SMA or WMA, ATR use SMA or WMA, avoid false trend signals while identifying genuine reversals Volatile Markets: Middle band and ATR both use KAMA or FRAMA, self-adjusting to changing market conditions reduces manual optimization Breakout Trading: Middle band use SMA, ATR use EMA or SMA, stable trend with dynamic channels highlights volatility expansion early Scalping/Day Trading: Middle band use HMA or T3, ATR use EMA or TEMA, both components respond quickly Position Trading: Middle band use EMA/TEMA/T3, ATR use RMA or TEMA, filter out noise for long-term trend-following 📋 DETAILED PARAMETER CONFIGURATION Understanding and optimizing parameters is essential for adapting Keltner Channel Enhanced to specific trading approaches. Source Parameter: Close (Most Common): Uses closing price, reflects daily settlement, best for end-of-day analysis and position trading, standard choice HL2 (Median Price): Smooths out closing bias, better represents full daily range in volatile markets, good for swing trading HLC3 (Typical Price): Gives more weight to close while including full range, popular for intraday applications, slightly more responsive than HL2 OHLC4 (Average Price): Most comprehensive price representation, smoothest option, good for gap-prone markets or highly volatile instruments Length Parameter: Controls the lookback period for middle band (basis) calculation: Short Periods (10-15): Very responsive to price changes, suitable for day trading and scalping, higher false signal rate Standard Period (20 - Default): Represents approximately one month of trading, good balance between responsiveness and stability, suitable for swing and position trading Medium Periods (30-50): Smoother trend identification, fewer false signals, better for position trading and longer holding periods Long Periods (50+): Very smooth, identifies major trends only, minimal false signals but significant lag, suitable for long-term investment Optimization by Timeframe: 1-15 minute charts use 10-20 period, 30-60 minute charts use 20-30 period, 4-hour to daily charts use 20-40 period, weekly charts use 20-30 weeks. ATR Length Parameter: Controls the lookback period for Average True Range calculation, affecting channel width: Short ATR Periods (5-10): Very responsive to recent volatility changes, standard is 10 (Keltner's original specification), may be too reactive in whipsaw conditions Standard ATR Period (10 - Default): Chester Keltner's original specification, good balance between responsiveness and stability, most widely used Medium ATR Periods (14-20): Smoother channel width, ATR 14 aligns with Wilder's original ATR specification, good for position trading Long ATR Periods (20+): Very smooth channel width, suitable for long-term trend-following Length vs. ATR Length Relationship: Equal values (20/20) provide balanced responsiveness, longer ATR (20/14) gives more stable channel width, shorter ATR (20/10) is standard configuration, much shorter ATR (20/5) creates very dynamic channels. Multiplier Parameter: Controls channel width by setting ATR multiples: Lower Values (1.0-1.5): Tighter channels with frequent price touches, more trading signals, higher false signal rate, better for range-bound and mean-reversion strategies Standard Value (2.0 - Default): Chester Keltner's recommended setting, good balance between signal frequency and reliability, suitable for both trending and ranging strategies Higher Values (2.5-3.0): Wider channels with less frequent touches, fewer but potentially higher-quality signals, better for strong trending markets Market-Specific Optimization: High volatility markets (crypto, small-caps) use 2.5-3.0 multiplier, medium volatility markets (major forex, large-caps) use 2.0 multiplier, low volatility markets (bonds, utilities) use 1.5-2.0 multiplier. MA Type Parameter (Middle Band): Critical selection that determines trend identification characteristics: EMA (Exponential Moving Average - Default): Standard Keltner Channel choice, Chester Keltner's original specification, emphasizes recent prices, faster response to trend changes, suitable for all timeframes SMA (Simple Moving Average): Equal weighting of all data points, no directional bias, slower than EMA, better for ranging markets and mean-reversion HMA (Hull Moving Average): Minimal lag with smooth output, excellent for fast trend identification, best for day trading and scalping TEMA (Triple Exponential Moving Average): Advanced smoothing with reduced lag, responsive to trends while filtering noise, suitable for volatile markets T3 (Tillson T3): Very smooth with minimal lag, excellent for established trend identification, suitable for position trading KAMA (Kaufman Adaptive Moving Average): Automatically adjusts speed based on market efficiency, slow in ranging markets, fast in trends, suitable for markets with varying conditions ATR MA Type Parameter: Determines how Average True Range is smoothed, affecting channel width stability: RMA (Wilder's Smoothing - Default): J. Welles Wilder's original ATR smoothing method, very smooth, slow to adapt to volatility changes, provides stable channel width SMA (Simple Moving Average): Equal weighting, moderate smoothness, faster response to volatility changes than RMA, more dynamic channel width EMA (Exponential Moving Average): Emphasizes recent volatility, quick adaptation to new volatility regimes, very responsive channel width changes TEMA (Triple Exponential Moving Average): Smooth yet responsive, good balance for varying volatility, suitable for most trading styles Parameter Combination Strategies: Conservative Trend-Following: Length 30/ATR Length 20/Multiplier 2.5, MA Type EMA or TEMA/ATR MA Type RMA, smooth trend with stable wide channels, suitable for position trading Standard Balanced Approach: Length 20/ATR Length 10/Multiplier 2.0, MA Type EMA/ATR MA Type RMA, classic Keltner Channel configuration, suitable for general purpose swing trading Aggressive Day Trading: Length 10-15/ATR Length 5-7/Multiplier 1.5-2.0, MA Type HMA or EMA/ATR MA Type EMA or SMA, fast trend with dynamic channels, suitable for scalping and day trading Breakout Specialist: Length 20-30/ATR Length 5-10/Multiplier 2.0, MA Type SMA or WMA/ATR MA Type EMA or SMA, stable trend with responsive channel width Adaptive All-Conditions: Length 20/ATR Length 10/Multiplier 2.0, MA Type KAMA or FRAMA/ATR MA Type KAMA or TEMA, self-adjusting to market conditions Offset Parameter: Controls horizontal positioning of channels on chart. Positive values shift channels to the right (future) for visual projection, negative values shift left (past) for historical analysis, zero (default) aligns with current price bars for real-time signal analysis. Offset affects only visual display, not alert conditions or actual calculations. 📈 PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES Keltner Channel Enhanced provides improvements over standard implementations while maintaining proven effectiveness. Response Characteristics: Standard EMA/RMA Configuration: Moderate trend lag (approximately 0.4 × length periods), smooth and stable channel width from RMA smoothing, good balance for most market conditions Fast HMA/EMA Configuration: Approximately 60% reduction in trend lag compared to EMA, responsive channel width from EMA ATR smoothing, suitable for quick trend changes and breakouts Adaptive KAMA/KAMA Configuration: Variable lag based on market efficiency, automatic adjustment to trending vs. ranging conditions, self-optimizing behavior reduces manual intervention Comparison with Traditional Keltner Channels: Enhanced Version Advantages: Dual Algorithm Flexibility: Independent MA selection for trend and volatility vs. fixed EMA/RMA, separate tuning of trend responsiveness and channel stability Market Adaptation: Choose configurations optimized for specific instruments and conditions, customize for scalping, swing, or position trading preferences Comprehensive Alerts: Enhanced alert system including channel expansion detection Traditional Version Advantages: Simplicity: Fewer parameters, easier to understand and implement Standardization: Fixed EMA/RMA combination ensures consistency across users Research Base: Decades of backtesting and research on standard configuration When to Use Enhanced Version: Trading multiple instruments with different characteristics, switching between trending and ranging markets, employing different strategies, algorithm-based trading systems requiring customization, seeking optimization for specific trading style and timeframe. When to Use Standard Version: Beginning traders learning Keltner Channel concepts, following published research or trading systems, preferring simplicity and standardization, wanting to avoid optimization and curve-fitting risks. Performance Across Market Conditions: Strong Trending Markets: EMA or HMA basis with RMA or TEMA ATR smoothing provides quicker trend identification, pullbacks to basis offer excellent entry opportunities Choppy/Ranging Markets: SMA or WMA basis with RMA ATR smoothing and lower multipliers, channel bounce strategies work well, avoid false breakouts Volatile Markets: KAMA or FRAMA with EMA or TEMA, adaptive algorithms excel by automatic adjustment, wider multipliers (2.5-3.0) accommodate large price swings Low Volatility/Consolidation: Channels narrow significantly indicating consolidation, algorithm choice less impactful, focus on detecting channel width contraction for breakout preparation Keltner Channel vs. Bollinger Bands - Usage Comparison: Favor Keltner Channels When: Trend-following is primary strategy, trading volatile instruments with gaps, want ATR-based volatility measurement, prefer fewer higher-quality channel touches, seeking stable channel width during trends. Favor Bollinger Bands When: Mean-reversion is primary strategy, trading instruments with limited gaps, want statistical framework based on standard deviation, need squeeze patterns for breakout identification, prefer more frequent trading opportunities. Use Both Together: Bollinger Band squeeze + Keltner Channel breakout is powerful combination, price outside Bollinger Bands but inside Keltner Channels indicates moderate signal, price outside both indicates very strong signal, Bollinger Bands for entries and Keltner Channels for trend confirmation. Limitations and Considerations: General Limitations: Lagging Indicator: All moving averages lag price, even with reduced-lag algorithms Trend-Dependent: Works best in trending markets, less effective in choppy conditions No Direction Prediction: Indicates volatility and deviation, not future direction, requires confirmation Enhanced Version Specific Considerations: Optimization Risk: More parameters increase risk of curve-fitting historical data Complexity: Additional choices may overwhelm beginning traders Backtesting Challenges: Different algorithms produce different historical results Mitigation Strategies: Use Confirmation: Combine with momentum indicators (RSI, MACD), volume, or price action Test Parameter Robustness: Ensure parameters work across range of values, not just optimized ones Multi-Timeframe Analysis: Confirm signals across different timeframes Proper Risk Management: Use appropriate position sizing and stops Start Simple: Begin with standard EMA/RMA before exploring alternatives Optimal Usage Recommendations: For Maximum Effectiveness: Start with standard EMA/RMA configuration to understand classic behavior Experiment with alternatives on demo account or paper trading Match algorithm combination to market condition and trading style Use channel width analysis to identify market phases Combine with complementary indicators for confirmation Implement strict risk management using ATR-based position sizing Focus on high-quality setups rather than trading every signal Respect the trend: trade with basis direction for higher probability Complementary Indicators: RSI or Stochastic: Confirm momentum at channel extremes MACD: Confirm trend direction and momentum shifts Volume: Validate breakouts and trend strength ADX: Measure trend strength, avoid Keltner signals in weak trends Support/Resistance: Combine with traditional levels for high-probability setups Bollinger Bands: Use together for enhanced breakout and volatility analysis USAGE NOTES This indicator is designed for technical analysis and educational purposes. Keltner Channel Enhanced has limitations and should not be used as the sole basis for trading decisions. While the flexible moving average selection for both trend and volatility components provides valuable adaptability across different market conditions, algorithm performance varies with market conditions, and past characteristics do not guarantee future results. Key considerations: Always use multiple forms of analysis and confirmation before entering trades Backtest any parameter combination thoroughly before live trading Be aware that optimization can lead to curve-fitting if not done carefully Start with standard EMA/RMA settings and adjust only when specific conditions warrant Understand that no moving average algorithm can eliminate lag entirely Consider market regime (trending, ranging, volatile) when selecting parameters Use ATR-based position sizing and risk management on every trade Keltner Channels work best in trending markets, less effective in choppy conditions Respect the trend direction indicated by price position relative to basis line The enhanced flexibility of dual algorithm selection provides powerful tools for adaptation but requires responsible use, thorough understanding of how different algorithms behave under various market conditions, and disciplined risk management.

Pine Script® göstergesi

DCAUT tarafından

T3 [DCAUT]█ T3 📊 INDICATOR OVERVIEW The T3 Moving Average is a smoothing indicator developed by Tim Tillson and published in Technical Analysis of Stocks & Commodities magazine (January 1998). The algorithm applies Generalized DEMA (Double Exponential Moving Average) recursively three times, creating a six-pole filtering effect that aims to balance noise reduction with responsiveness while minimizing lag relative to price changes. 📐 MATHEMATICAL FOUNDATION Generalized DEMA (GD) Function: The core building block is the Generalized DEMA function, which combines two exponential moving averages with weights controlled by the volume factor: GD(input, v) = EMA(input) × (1 + v) - EMA(EMA(input)) × v Where v is the volume factor parameter (default 0.7). This weighted combination reduces lag while maintaining smoothness by extrapolating beyond the first EMA using the double-smoothed EMA as a reference. T3 Calculation Process: T3 applies the GD function three times recursively: T3 = GD(GD(GD(Price, v), v), v) This triple nesting creates a six-pole smoothing effect (each GD applies two EMA operations, resulting in 2 × 3 = 6 total EMA calculations). The cascading refinement progressively filters noise while preserving trend information. Step-by-Step Breakdown: First GD application: GD1 = EMA(Price) × (1 + v) - EMA(EMA(Price)) × v - Creates initial smoothed series with lag reduction Second GD application: GD2 = EMA(GD1) × (1 + v) - EMA(EMA(GD1)) × v - Further refines the smoothing while maintaining responsiveness Third GD application: T3 = EMA(GD2) × (1 + v) - EMA(EMA(GD2)) × v - Final refinement produces the T3 output Volume Factor Impact: The volume factor (v) is the key parameter controlling the balance between smoothness and responsiveness. Tim Tillson recommended v = 0.7 as the optimal default value. Lower volume factors (v closer to 0.0): Increase the extrapolation effect, making T3 more responsive to price changes but potentially more sensitive to noise. Higher volume factors (v closer to 1.0): Reduce the extrapolation effect, producing smoother output with less sensitivity to short-term fluctuations but slightly more lag. The recursive application of the volume factor through three GD stages creates a nonlinear filtering effect that achieves superior lag reduction compared to traditional moving averages of equivalent smoothness. 📊 SIGNAL INTERPRETATION Trend Direction Signals: Green Line (T3 Rising): Smoothed trend line is rising, may indicate uptrend, consider bullish opportunities when confirmed by other factors Red Line (T3 Falling): Smoothed trend line is falling, may indicate downtrend, consider bearish opportunities when confirmed by other factors Gray Line (T3 Flat): Smoothed trend line is flat, indicates unclear trend or consolidation phase Price Crossover Signals: Price Crosses Above T3: Price breaks above smoothed trend line, may be bullish signal, requires confirmation from other indicators Price Crosses Below T3: Price breaks below smoothed trend line, may be bearish signal, requires confirmation from other indicators Price Position Relative to T3: Price sustained above T3 may indicate uptrend, sustained below may indicate downtrend Supporting Analysis Signals: T3 Slope Angle: Steeper slopes indicate stronger trend momentum, flatter slopes suggest weakening trends Price Deviation: Significant price separation from T3 may indicate overextension, watch for pullback or reversal Dynamic Support/Resistance: T3 line can serve as dynamic support (in uptrends) or resistance (in downtrends) reference 🎯 STRATEGIC APPLICATIONS Common Usage Patterns: The T3 Moving Average can be incorporated into trading analysis in various ways. These represent common approaches used by market participants, though effectiveness varies by market conditions and requires individual testing: Trend Filtering: T3 can be used as a trend filter by observing the relationship between price and the T3 line. The color-coded slope (green for rising, red for falling, gray for sideways) provides visual feedback about the current trend direction of the smoothed series. Price Crossover Analysis: Some traders monitor crossovers between price and the T3 line as potential indication points. When price crosses the T3 line, it may suggest a change in the relationship between current price action and the smoothed trend. Multi-Timeframe Observation: T3 can be applied to multiple timeframes simultaneously. Observing alignment or divergence between different timeframe T3 indicators may provide context about trend consistency across time scales. Dynamic Reference Level: The T3 line can serve as a dynamic reference level for price action analysis. Price distance from T3, price reactions when approaching T3, and the behavior of price relative to the T3 line can all be incorporated into market analysis frameworks. Application Considerations: Any trading application should be thoroughly tested on historical data before implementation T3 performance characteristics vary across different market conditions and asset types The indicator provides smoothed trend information but does not predict future price movements Combining T3 with other analytical tools and market context improves analysis quality Risk management practices remain essential regardless of the analytical approach used 📋 DETAILED PARAMETER CONFIGURATION Source Selection: Close Price (Default): Standard choice for end-of-period trend analysis, reduces intrabar noise HL2 (High+Low)/2: Provides balanced view of price action, considers full bar range HLC3 or OHLC4: Incorporates more price information, may provide smoother results Selection Impact: Different sources affect signal timing and smoothness characteristics Length Configuration: Shorter periods: More responsive, faster reaction, frequent signals, but higher false signal risk in choppy markets Longer periods: Smoother output, fewer signals, better for long-term trends, but slower response Default 14 periods is a common baseline, but optimal length varies by asset, timeframe, and market conditions Parameter selection should be determined through backtesting rather than general recommendations Volume Factor Configuration: Lower values (closer to 0.0): Increase responsiveness but also noise sensitivity Higher values (closer to 1.0): Increase smoothness but slightly more lag Default 0.7 (Tim Tillson's recommendation) provides good balance for most applications Optimal value depends on signal frequency versus reliability preference, test for specific use case Parameter Optimization Approach: There are no universal "best" parameter values - optimal settings depend on the specific asset, timeframe, market regime, and trading strategy Start with default values (Length: 14, Volume Factor: 0.7) and adjust based on observed performance in your target market Conduct systematic backtesting across different market conditions to evaluate parameter sensitivity Consider that parameters optimized for historical data may not perform identically in future market conditions Monitor performance and be prepared to adjust parameters as market characteristics evolve 📈 DESIGN FEATURES & MARKET ADAPTATION Algorithm Design Features: Simple Moving Average (SMA): Equal weighting across lookback period Exponential Moving Average (EMA): Exponentially decreasing weights on historical prices T3 Moving Average: Recursive Generalized DEMA with adjustable volume factor Market Condition Adaptation: Trending markets: Smoothed indicators generally align more closely with sustained directional movement Ranging markets: All moving averages may generate more crossover signals during non-trending periods Volatile conditions: Higher smoothing parameters reduce short-term sensitivity but increase lag Indicator behavior relative to market conditions should be evaluated for specific applications USAGE NOTES This indicator is designed for technical analysis and educational purposes. The T3 Moving Average has limitations and should not be used as the sole basis for trading decisions. Like all trend-following indicators, its performance varies with market conditions, and past signal characteristics do not guarantee future results. Key Points: T3 is a lagging indicator that responds to price changes rather than predicting future movements Signals should be confirmed with other technical tools and market context Parameters should be optimized for specific market and timeframe Risk management and position sizing are essential Market regime changes can affect indicator effectiveness Test strategies thoroughly on historical data before live implementation Consider broader market context and fundamental factors

Pine Script® göstergesi

DCAUT tarafından

Dynamic Equity Allocation Model "Cash is Trash"? Not Always. Here's Why Science Beats Guesswork. Every retail trader knows the frustration: you draw support and resistance lines, you spot patterns, you follow market gurus on social media—and still, when the next bear market hits, your portfolio bleeds red. Meanwhile, institutional investors seem to navigate market turbulence with ease, preserving capital when markets crash and participating when they rally. What's their secret? The answer isn't insider information or access to exotic derivatives. It's systematic, scientifically validated decision-making. While most retail traders rely on subjective chart analysis and emotional reactions, professional portfolio managers use quantitative models that remove emotion from the equation and process multiple streams of market information simultaneously. This document presents exactly such a system—not a proprietary black box available only to hedge funds, but a fully transparent, academically grounded framework that any serious investor can understand and apply. The Dynamic Equity Allocation Model (DEAM) synthesizes decades of financial research from Nobel laureates and leading academics into a practical tool for tactical asset allocation. Stop drawing colorful lines on your chart and start thinking like a quant. This isn't about predicting where the market goes next week—it's about systematically adjusting your risk exposure based on what the data actually tells you. When valuations scream danger, when volatility spikes, when credit markets freeze, when multiple warning signals align—that's when cash isn't trash. That's when cash saves your portfolio. The irony of "cash is trash" rhetoric is that it ignores timing. Yes, being 100% cash for decades would be disastrous. But being 100% equities through every crisis is equally foolish. The sophisticated approach is dynamic: aggressive when conditions favor risk-taking, defensive when they don't. This model shows you how to make that decision systematically, not emotionally. Whether you're managing your own retirement portfolio or seeking to understand how institutional allocation strategies work, this comprehensive analysis provides the theoretical foundation, mathematical implementation, and practical guidance to elevate your investment approach from amateur to professional. The choice is yours: keep hoping your chart patterns work out, or start using the same quantitative methods that professionals rely on. The tools are here. The research is cited. The methodology is explained. All you need to do is read, understand, and apply. The Dynamic Equity Allocation Model (DEAM) is a quantitative framework for systematic allocation between equities and cash, grounded in modern portfolio theory and empirical market research. The model integrates five scientifically validated dimensions of market analysis—market regime, risk metrics, valuation, sentiment, and macroeconomic conditions—to generate dynamic allocation recommendations ranging from 0% to 100% equity exposure. This work documents the theoretical foundations, mathematical implementation, and practical application of this multi-factor approach. 1. Introduction and Theoretical Background 1.1 The Limitations of Static Portfolio Allocation Traditional portfolio theory, as formulated by Markowitz (1952) in his seminal work "Portfolio Selection," assumes an optimal static allocation where investors distribute their wealth across asset classes according to their risk aversion. This approach rests on the assumption that returns and risks remain constant over time. However, empirical research demonstrates that this assumption does not hold in reality. Fama and French (1989) showed that expected returns vary over time and correlate with macroeconomic variables such as the spread between long-term and short-term interest rates. Campbell and Shiller (1988) demonstrated that the price-earnings ratio possesses predictive power for future stock returns, providing a foundation for dynamic allocation strategies. The academic literature on tactical asset allocation has evolved considerably over recent decades. Ilmanen (2011) argues in "Expected Returns" that investors can improve their risk-adjusted returns by considering valuation levels, business cycles, and market sentiment. The Dynamic Equity Allocation Model presented here builds on this research tradition and operationalizes these insights into a practically applicable allocation framework. 1.2 Multi-Factor Approaches in Asset Allocation Modern financial research has shown that different factors capture distinct aspects of market dynamics and together provide a more robust picture of market conditions than individual indicators. Ross (1976) developed the Arbitrage Pricing Theory, a model that employs multiple factors to explain security returns. Following this multi-factor philosophy, DEAM integrates five complementary analytical dimensions, each tapping different information sources and collectively enabling comprehensive market understanding. 2. Data Foundation and Data Quality 2.1 Data Sources Used The model draws its data exclusively from publicly available market data via the TradingView platform. This transparency and accessibility is a significant advantage over proprietary models that rely on non-public data. The data foundation encompasses several categories of market information, each capturing specific aspects of market dynamics. First, price data for the S&P 500 Index is obtained through the SPDR S&P 500 ETF (ticker: SPY). The use of a highly liquid ETF instead of the index itself has practical reasons, as ETF data is available in real-time and reflects actual tradability. In addition to closing prices, high, low, and volume data are captured, which are required for calculating advanced volatility measures. Fundamental corporate metrics are retrieved via TradingView's Financial Data API. These include earnings per share, price-to-earnings ratio, return on equity, debt-to-equity ratio, dividend yield, and share buyback yield. Cochrane (2011) emphasizes in "Presidential Address: Discount Rates" the central importance of valuation metrics for forecasting future returns, making these fundamental data a cornerstone of the model. Volatility indicators are represented by the CBOE Volatility Index (VIX) and related metrics. The VIX, often referred to as the market's "fear gauge," measures the implied volatility of S&P 500 index options and serves as a proxy for market participants' risk perception. Whaley (2000) describes in "The Investor Fear Gauge" the construction and interpretation of the VIX and its use as a sentiment indicator. Macroeconomic data includes yield curve information through US Treasury bonds of various maturities and credit risk premiums through the spread between high-yield bonds and risk-free government bonds. These variables capture the macroeconomic conditions and financing conditions relevant for equity valuation. Estrella and Hardouvelis (1991) showed that the shape of the yield curve has predictive power for future economic activity, justifying the inclusion of these data. 2.2 Handling Missing Data A practical problem when working with financial data is dealing with missing or unavailable values. The model implements a fallback system where a plausible historical average value is stored for each fundamental metric. When current data is unavailable for a specific point in time, this fallback value is used. This approach ensures that the model remains functional even during temporary data outages and avoids systematic biases from missing data. The use of average values as fallback is conservative, as it generates neither overly optimistic nor pessimistic signals. 3. Component 1: Market Regime Detection 3.1 The Concept of Market Regimes The idea that financial markets exist in different "regimes" or states that differ in their statistical properties has a long tradition in financial science. Hamilton (1989) developed regime-switching models that allow distinguishing between different market states with different return and volatility characteristics. The practical application of this theory consists of identifying the current market state and adjusting portfolio allocation accordingly. DEAM classifies market regimes using a scoring system that considers three main dimensions: trend strength, volatility level, and drawdown depth. This multidimensional view is more robust than focusing on individual indicators, as it captures various facets of market dynamics. Classification occurs into six distinct regimes: Strong Bull, Bull Market, Neutral, Correction, Bear Market, and Crisis. 3.2 Trend Analysis Through Moving Averages Moving averages are among the oldest and most widely used technical indicators and have also received attention in academic literature. Brock, Lakonishok, and LeBaron (1992) examined in "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns" the profitability of trading rules based on moving averages and found evidence for their predictive power, although later studies questioned the robustness of these results when considering transaction costs. The model calculates three moving averages with different time windows: a 20-day average (approximately one trading month), a 50-day average (approximately one quarter), and a 200-day average (approximately one trading year). The relationship of the current price to these averages and the relationship of the averages to each other provide information about trend strength and direction. When the price trades above all three averages and the short-term average is above the long-term, this indicates an established uptrend. The model assigns points based on these constellations, with longer-term trends weighted more heavily as they are considered more persistent. 3.3 Volatility Regimes Volatility, understood as the standard deviation of returns, is a central concept of financial theory and serves as the primary risk measure. However, research has shown that volatility is not constant but changes over time and occurs in clusters—a phenomenon first documented by Mandelbrot (1963) and later formalized through ARCH and GARCH models (Engle, 1982; Bollerslev, 1986). DEAM calculates volatility not only through the classic method of return standard deviation but also uses more advanced estimators such as the Parkinson estimator and the Garman-Klass estimator. These methods utilize intraday information (high and low prices) and are more efficient than simple close-to-close volatility estimators. The Parkinson estimator (Parkinson, 1980) uses the range between high and low of a trading day and is based on the recognition that this information reveals more about true volatility than just the closing price difference. The Garman-Klass estimator (Garman and Klass, 1980) extends this approach by additionally considering opening and closing prices. The calculated volatility is annualized by multiplying it by the square root of 252 (the average number of trading days per year), enabling standardized comparability. The model compares current volatility with the VIX, the implied volatility from option prices. A low VIX (below 15) signals market comfort and increases the regime score, while a high VIX (above 35) indicates market stress and reduces the score. This interpretation follows the empirical observation that elevated volatility is typically associated with falling markets (Schwert, 1989). 3.4 Drawdown Analysis A drawdown refers to the percentage decline from the highest point (peak) to the lowest point (trough) during a specific period. This metric is psychologically significant for investors as it represents the maximum loss experienced. Calmar (1991) developed the Calmar Ratio, which relates return to maximum drawdown, underscoring the practical relevance of this metric. The model calculates current drawdown as the percentage distance from the highest price of the last 252 trading days (one year). A drawdown below 3% is considered negligible and maximally increases the regime score. As drawdown increases, the score decreases progressively, with drawdowns above 20% classified as severe and indicating a crisis or bear market regime. These thresholds are empirically motivated by historical market cycles, in which corrections typically encompassed 5-10% drawdowns, bear markets 20-30%, and crises over 30%. 3.5 Regime Classification Final regime classification occurs through aggregation of scores from trend (40% weight), volatility (30%), and drawdown (30%). The higher weighting of trend reflects the empirical observation that trend-following strategies have historically delivered robust results (Moskowitz, Ooi, and Pedersen, 2012). A total score above 80 signals a strong bull market with established uptrend, low volatility, and minimal losses. At a score below 10, a crisis situation exists requiring defensive positioning. The six regime categories enable a differentiated allocation strategy that not only distinguishes binarily between bullish and bearish but allows gradual gradations. 4. Component 2: Risk-Based Allocation 4.1 Volatility Targeting as Risk Management Approach The concept of volatility targeting is based on the idea that investors should maximize not returns but risk-adjusted returns. Sharpe (1966, 1994) defined with the Sharpe Ratio the fundamental concept of return per unit of risk, measured as volatility. Volatility targeting goes a step further and adjusts portfolio allocation to achieve constant target volatility. This means that in times of low market volatility, equity allocation is increased, and in times of high volatility, it is reduced. Moreira and Muir (2017) showed in "Volatility-Managed Portfolios" that strategies that adjust their exposure based on volatility forecasts achieve higher Sharpe Ratios than passive buy-and-hold strategies. DEAM implements this principle by defining a target portfolio volatility (default 12% annualized) and adjusting equity allocation to achieve it. The mathematical foundation is simple: if market volatility is 20% and target volatility is 12%, equity allocation should be 60% (12/20 = 0.6), with the remaining 40% held in cash with zero volatility. 4.2 Market Volatility Calculation Estimating current market volatility is central to the risk-based allocation approach. The model uses several volatility estimators in parallel and selects the higher value between traditional close-to-close volatility and the Parkinson estimator. This conservative choice ensures the model does not underestimate true volatility, which could lead to excessive risk exposure. Traditional volatility calculation uses logarithmic returns, as these have mathematically advantageous properties (additive linkage over multiple periods). The logarithmic return is calculated as ln(P_t / P_{t-1}), where P_t is the price at time t. The standard deviation of these returns over a rolling 20-trading-day window is then multiplied by √252 to obtain annualized volatility. This annualization is based on the assumption of independently identically distributed returns, which is an idealization but widely accepted in practice. The Parkinson estimator uses additional information from the trading range (High minus Low) of each day. The formula is: σ_P = (1/√(4ln2)) × √(1/n × Σln²(H_i/L_i)) × √252, where H_i and L_i are high and low prices. Under ideal conditions, this estimator is approximately five times more efficient than the close-to-close estimator (Parkinson, 1980), as it uses more information per observation. 4.3 Drawdown-Based Position Size Adjustment In addition to volatility targeting, the model implements drawdown-based risk control. The logic is that deep market declines often signal further losses and therefore justify exposure reduction. This behavior corresponds with the concept of path-dependent risk tolerance: investors who have already suffered losses are typically less willing to take additional risk (Kahneman and Tversky, 1979). The model defines a maximum portfolio drawdown as a target parameter (default 15%). Since portfolio volatility and portfolio drawdown are proportional to equity allocation (assuming cash has neither volatility nor drawdown), allocation-based control is possible. For example, if the market exhibits a 25% drawdown and target portfolio drawdown is 15%, equity allocation should be at most 60% (15/25). 4.4 Dynamic Risk Adjustment An advanced feature of DEAM is dynamic adjustment of risk-based allocation through a feedback mechanism. The model continuously estimates what actual portfolio volatility and portfolio drawdown would result at the current allocation. If risk utilization (ratio of actual to target risk) exceeds 1.0, allocation is reduced by an adjustment factor that grows exponentially with overutilization. This implements a form of dynamic feedback that avoids overexposure. Mathematically, a risk adjustment factor r_adjust is calculated: if risk utilization u > 1, then r_adjust = exp(-0.5 × (u - 1)). This exponential function ensures that moderate overutilization is gently corrected, while strong overutilization triggers drastic reductions. The factor 0.5 in the exponent was empirically calibrated to achieve a balanced ratio between sensitivity and stability. 5. Component 3: Valuation Analysis 5.1 Theoretical Foundations of Fundamental Valuation DEAM's valuation component is based on the fundamental premise that the intrinsic value of a security is determined by its future cash flows and that deviations between market price and intrinsic value are eventually corrected. Graham and Dodd (1934) established in "Security Analysis" the basic principles of fundamental analysis that remain relevant today. Translated into modern portfolio context, this means that markets with high valuation metrics (high price-earnings ratios) should have lower expected returns than cheaply valued markets. Campbell and Shiller (1988) developed the Cyclically Adjusted P/E Ratio (CAPE), which smooths earnings over a full business cycle. Their empirical analysis showed that this ratio has significant predictive power for 10-year returns. Asness, Moskowitz, and Pedersen (2013) demonstrated in "Value and Momentum Everywhere" that value effects exist not only in individual stocks but also in asset classes and markets. 5.2 Equity Risk Premium as Central Valuation Metric The Equity Risk Premium (ERP) is defined as the expected excess return of stocks over risk-free government bonds. It is the theoretical heart of valuation analysis, as it represents the compensation investors demand for bearing equity risk. Damodaran (2012) discusses in "Equity Risk Premiums: Determinants, Estimation and Implications" various methods for ERP estimation. DEAM calculates ERP not through a single method but combines four complementary approaches with different weights. This multi-method strategy increases estimation robustness and avoids dependence on single, potentially erroneous inputs. The first method (35% weight) uses earnings yield, calculated as 1/P/E or directly from operating earnings data, and subtracts the 10-year Treasury yield. This method follows Fed Model logic (Yardeni, 2003), although this model has theoretical weaknesses as it does not consistently treat inflation (Asness, 2003). The second method (30% weight) extends earnings yield by share buyback yield. Share buybacks are a form of capital return to shareholders and increase value per share. Boudoukh et al. (2007) showed in "The Total Shareholder Yield" that the sum of dividend yield and buyback yield is a better predictor of future returns than dividend yield alone. The third method (20% weight) implements the Gordon Growth Model (Gordon, 1962), which models stock value as the sum of discounted future dividends. Under constant growth g assumption: Expected Return = Dividend Yield + g. The model estimates sustainable growth as g = ROE × (1 - Payout Ratio), where ROE is return on equity and payout ratio is the ratio of dividends to earnings. This formula follows from equity theory: unretained earnings are reinvested at ROE and generate additional earnings growth. The fourth method (15% weight) combines total shareholder yield (Dividend + Buybacks) with implied growth derived from revenue growth. This method considers that companies with strong revenue growth should generate higher future earnings, even if current valuations do not yet fully reflect this. The final ERP is the weighted average of these four methods. A high ERP (above 4%) signals attractive valuations and increases the valuation score to 95 out of 100 possible points. A negative ERP, where stocks have lower expected returns than bonds, results in a minimal score of 10. 5.3 Quality Adjustments to Valuation Valuation metrics alone can be misleading if not interpreted in the context of company quality. A company with a low P/E may be cheap or fundamentally problematic. The model therefore implements quality adjustments based on growth, profitability, and capital structure. Revenue growth above 10% annually adds 10 points to the valuation score, moderate growth above 5% adds 5 points. This adjustment reflects that growth has independent value (Modigliani and Miller, 1961, extended by later growth theory). Net margin above 15% signals pricing power and operational efficiency and increases the score by 5 points, while low margins below 8% indicate competitive pressure and subtract 5 points. Return on equity (ROE) above 20% characterizes outstanding capital efficiency and increases the score by 5 points. Piotroski (2000) showed in "Value Investing: The Use of Historical Financial Statement Information" that fundamental quality signals such as high ROE can improve the performance of value strategies. Capital structure is evaluated through the debt-to-equity ratio. A conservative ratio below 1.0 multiplies the valuation score by 1.2, while high leverage above 2.0 applies a multiplier of 0.8. This adjustment reflects that high debt constrains financial flexibility and can become problematic in crisis times (Korteweg, 2010). 6. Component 4: Sentiment Analysis 6.1 The Role of Sentiment in Financial Markets Investor sentiment, defined as the collective psychological attitude of market participants, influences asset prices independently of fundamental data. Baker and Wurgler (2006, 2007) developed a sentiment index and showed that periods of high sentiment are followed by overvaluations that later correct. This insight justifies integrating a sentiment component into allocation decisions. Sentiment is difficult to measure directly but can be proxied through market indicators. The VIX is the most widely used sentiment indicator, as it aggregates implied volatility from option prices. High VIX values reflect elevated uncertainty and risk aversion, while low values signal market comfort. Whaley (2009) refers to the VIX as the "Investor Fear Gauge" and documents its role as a contrarian indicator: extremely high values typically occur at market bottoms, while low values occur at tops. 6.2 VIX-Based Sentiment Assessment DEAM uses statistical normalization of the VIX by calculating the Z-score: z = (VIX_current - VIX_average) / VIX_standard_deviation. The Z-score indicates how many standard deviations the current VIX is from the historical average. This approach is more robust than absolute thresholds, as it adapts to the average volatility level, which can vary over longer periods. A Z-score below -1.5 (VIX is 1.5 standard deviations below average) signals exceptionally low risk perception and adds 40 points to the sentiment score. This may seem counterintuitive—shouldn't low fear be bullish? However, the logic follows the contrarian principle: when no one is afraid, everyone is already invested, and there is limited further upside potential (Zweig, 1973). Conversely, a Z-score above 1.5 (extreme fear) adds -40 points, reflecting market panic but simultaneously suggesting potential buying opportunities. 6.3 VIX Term Structure as Sentiment Signal The VIX term structure provides additional sentiment information. Normally, the VIX trades in contango, meaning longer-term VIX futures have higher prices than short-term. This reflects that short-term volatility is currently known, while long-term volatility is more uncertain and carries a risk premium. The model compares the VIX with VIX9D (9-day volatility) and identifies backwardation (VIX > 1.05 × VIX9D) and steep backwardation (VIX > 1.15 × VIX9D). Backwardation occurs when short-term implied volatility is higher than longer-term, which typically happens during market stress. Investors anticipate immediate turbulence but expect calming. Psychologically, this reflects acute fear. The model subtracts 15 points for backwardation and 30 for steep backwardation, as these constellations signal elevated risk. Simon and Wiggins (2001) analyzed the VIX futures curve and showed that backwardation is associated with market declines. 6.4 Safe-Haven Flows During crisis times, investors flee from risky assets into safe havens: gold, US dollar, and Japanese yen. This "flight to quality" is a sentiment signal. The model calculates the performance of these assets relative to stocks over the last 20 trading days. When gold or the dollar strongly rise while stocks fall, this indicates elevated risk aversion. The safe-haven component is calculated as the difference between safe-haven performance and stock performance. Positive values (safe havens outperform) subtract up to 20 points from the sentiment score, negative values (stocks outperform) add up to 10 points. The asymmetric treatment (larger deduction for risk-off than bonus for risk-on) reflects that risk-off movements are typically sharper and more informative than risk-on phases. Baur and Lucey (2010) examined safe-haven properties of gold and showed that gold indeed exhibits negative correlation with stocks during extreme market movements, confirming its role as crisis protection. 7. Component 5: Macroeconomic Analysis 7.1 The Yield Curve as Economic Indicator The yield curve, represented as yields of government bonds of various maturities, contains aggregated expectations about future interest rates, inflation, and economic growth. The slope of the yield curve has remarkable predictive power for recessions. Estrella and Mishkin (1998) showed that an inverted yield curve (short-term rates higher than long-term) predicts recessions with high reliability. This is because inverted curves reflect restrictive monetary policy: the central bank raises short-term rates to combat inflation, dampening economic activity. DEAM calculates two spread measures: the 2-year-minus-10-year spread and the 3-month-minus-10-year spread. A steep, positive curve (spreads above 1.5% and 2% respectively) signals healthy growth expectations and generates the maximum yield curve score of 40 points. A flat curve (spreads near zero) reduces the score to 20 points. An inverted curve (negative spreads) is particularly alarming and results in only 10 points. The choice of two different spreads increases analysis robustness. The 2-10 spread is most established in academic literature, while the 3M-10Y spread is often considered more sensitive, as the 3-month rate directly reflects current monetary policy (Ang, Piazzesi, and Wei, 2006). 7.2 Credit Conditions and Spreads Credit spreads—the yield difference between risky corporate bonds and safe government bonds—reflect risk perception in the credit market. Gilchrist and Zakrajšek (2012) constructed an "Excess Bond Premium" that measures the component of credit spreads not explained by fundamentals and showed this is a predictor of future economic activity and stock returns. The model approximates credit spread by comparing the yield of high-yield bond ETFs (HYG) with investment-grade bond ETFs (LQD). A narrow spread below 200 basis points signals healthy credit conditions and risk appetite, contributing 30 points to the macro score. Very wide spreads above 1000 basis points (as during the 2008 financial crisis) signal credit crunch and generate zero points. Additionally, the model evaluates whether "flight to quality" is occurring, identified through strong performance of Treasury bonds (TLT) with simultaneous weakness in high-yield bonds. This constellation indicates elevated risk aversion and reduces the credit conditions score. 7.3 Financial Stability at Corporate Level While the yield curve and credit spreads reflect macroeconomic conditions, financial stability evaluates the health of companies themselves. The model uses the aggregated debt-to-equity ratio and return on equity of the S&P 500 as proxies for corporate health. A low leverage level below 0.5 combined with high ROE above 15% signals robust corporate balance sheets and generates 20 points. This combination is particularly valuable as it represents both defensive strength (low debt means crisis resistance) and offensive strength (high ROE means earnings power). High leverage above 1.5 generates only 5 points, as it implies vulnerability to interest rate increases and recessions. Korteweg (2010) showed in "The Net Benefits to Leverage" that optimal debt maximizes firm value, but excessive debt increases distress costs. At the aggregated market level, high debt indicates fragilities that can become problematic during stress phases. 8. Component 6: Crisis Detection 8.1 The Need for Systematic Crisis Detection Financial crises are rare but extremely impactful events that suspend normal statistical relationships. During normal market volatility, diversified portfolios and traditional risk management approaches function, but during systemic crises, seemingly independent assets suddenly correlate strongly, and losses exceed historical expectations (Longin and Solnik, 2001). This justifies a separate crisis detection mechanism that operates independently of regular allocation components. Reinhart and Rogoff (2009) documented in "This Time Is Different: Eight Centuries of Financial Folly" recurring patterns in financial crises: extreme volatility, massive drawdowns, credit market dysfunction, and asset price collapse. DEAM operationalizes these patterns into quantifiable crisis indicators. 8.2 Multi-Signal Crisis Identification The model uses a counter-based approach where various stress signals are identified and aggregated. This methodology is more robust than relying on a single indicator, as true crises typically occur simultaneously across multiple dimensions. A single signal may be a false alarm, but the simultaneous presence of multiple signals increases confidence. The first indicator is a VIX above the crisis threshold (default 40), adding one point. A VIX above 60 (as in 2008 and March 2020) adds two additional points, as such extreme values are historically very rare. This tiered approach captures the intensity of volatility. The second indicator is market drawdown. A drawdown above 15% adds one point, as corrections of this magnitude can be potential harbingers of larger crises. A drawdown above 25% adds another point, as historical bear markets typically encompass 25-40% drawdowns. The third indicator is credit market spreads above 500 basis points, adding one point. Such wide spreads occur only during significant credit market disruptions, as in 2008 during the Lehman crisis. The fourth indicator identifies simultaneous losses in stocks and bonds. Normally, Treasury bonds act as a hedge against equity risk (negative correlation), but when both fall simultaneously, this indicates systemic liquidity problems or inflation/stagflation fears. The model checks whether both SPY and TLT have fallen more than 10% and 5% respectively over 5 trading days, adding two points. The fifth indicator is a volume spike combined with negative returns. Extreme trading volumes (above twice the 20-day average) with falling prices signal panic selling. This adds one point. A crisis situation is diagnosed when at least 3 indicators trigger, a severe crisis at 5 or more indicators. These thresholds were calibrated through historical backtesting to identify true crises (2008, 2020) without generating excessive false alarms. 8.3 Crisis-Based Allocation Override When a crisis is detected, the system overrides the normal allocation recommendation and caps equity allocation at maximum 25%. In a severe crisis, the cap is set at 10%. This drastic defensive posture follows the empirical observation that crises typically require time to develop and that early reduction can avoid substantial losses (Faber, 2007). This override logic implements a "safety first" principle: in situations of existential danger to the portfolio, capital preservation becomes the top priority. Roy (1952) formalized this approach in "Safety First and the Holding of Assets," arguing that investors should primarily minimize ruin probability. 9. Integration and Final Allocation Calculation 9.1 Component Weighting The final allocation recommendation emerges through weighted aggregation of the five components. The standard weighting is: Market Regime 35%, Risk Management 25%, Valuation 20%, Sentiment 15%, Macro 5%. These weights reflect both theoretical considerations and empirical backtesting results. The highest weighting of market regime is based on evidence that trend-following and momentum strategies have delivered robust results across various asset classes and time periods (Moskowitz, Ooi, and Pedersen, 2012). Current market momentum is highly informative for the near future, although it provides no information about long-term expectations. The substantial weighting of risk management (25%) follows from the central importance of risk control. Wealth preservation is the foundation of long-term wealth creation, and systematic risk management is demonstrably value-creating (Moreira and Muir, 2017). The valuation component receives 20% weight, based on the long-term mean reversion of valuation metrics. While valuation has limited short-term predictive power (bull and bear markets can begin at any valuation), the long-term relationship between valuation and returns is robustly documented (Campbell and Shiller, 1988). Sentiment (15%) and Macro (5%) receive lower weights, as these factors are subtler and harder to measure. Sentiment is valuable as a contrarian indicator at extremes but less informative in normal ranges. Macro variables such as the yield curve have strong predictive power for recessions, but the transmission from recessions to stock market performance is complex and temporally variable. 9.2 Model Type Adjustments DEAM allows users to choose between four model types: Conservative, Balanced, Aggressive, and Adaptive. This choice modifies the final allocation through additive adjustments. Conservative mode subtracts 10 percentage points from allocation, resulting in consistently more cautious positioning. This is suitable for risk-averse investors or those with limited investment horizons. Aggressive mode adds 10 percentage points, suitable for risk-tolerant investors with long horizons. Adaptive mode implements procyclical adjustment based on short-term momentum: if the market has risen more than 5% in the last 20 days, 5 percentage points are added; if it has declined more than 5%, 5 points are subtracted. This logic follows the observation that short-term momentum persists (Jegadeesh and Titman, 1993), but the moderate size of adjustment avoids excessive timing bets. Balanced mode makes no adjustment and uses raw model output. This neutral setting is suitable for investors who wish to trust model recommendations unchanged. 9.3 Smoothing and Stability The allocation resulting from aggregation undergoes final smoothing through a simple moving average over 3 periods. This smoothing is crucial for model practicality, as it reduces frequent trading and thus transaction costs. Without smoothing, the model could fluctuate between adjacent allocations with every small input change. The choice of 3 periods as smoothing window is a compromise between responsiveness and stability. Longer smoothing would excessively delay signals and impede response to true regime changes. Shorter or no smoothing would allow too much noise. Empirical tests showed that 3-period smoothing offers an optimal ratio between these goals. 10. Visualization and Interpretation 10.1 Main Output: Equity Allocation DEAM's primary output is a time series from 0 to 100 representing the recommended percentage allocation to equities. This representation is intuitive: 100% means full investment in stocks (specifically: an S&P 500 ETF), 0% means complete cash position, and intermediate values correspond to mixed portfolios. A value of 60% means, for example: invest 60% of wealth in SPY, hold 40% in money market instruments or cash. The time series is color-coded to enable quick visual interpretation. Green shades represent high allocations (above 80%, bullish), red shades low allocations (below 20%, bearish), and neutral colors middle allocations. The chart background is dynamically colored based on the signal, enhancing readability in different market phases. 10.2 Dashboard Metrics A tabular dashboard presents key metrics compactly. This includes current allocation, cash allocation (complement), an aggregated signal (BULLISH/NEUTRAL/BEARISH), current market regime, VIX level, market drawdown, and crisis status. Additionally, fundamental metrics are displayed: P/E Ratio, Equity Risk Premium, Return on Equity, Debt-to-Equity Ratio, and Total Shareholder Yield. This transparency allows users to understand model decisions and form their own assessments. Component scores (Regime, Risk, Valuation, Sentiment, Macro) are also displayed, each normalized on a 0-100 scale. This shows which factors primarily drive the current recommendation. If, for example, the Risk score is very low (20) while other scores are moderate (50-60), this indicates that risk management considerations are pulling allocation down. 10.3 Component Breakdown (Optional) Advanced users can display individual components as separate lines in the chart. This enables analysis of component dynamics: do all components move synchronously, or are there divergences? Divergences can be particularly informative. If, for example, the market regime is bullish (high score) but the valuation component is very negative, this signals an overbought market not fundamentally supported—a classic "bubble warning." This feature is disabled by default to keep the chart clean but can be activated for deeper analysis. 10.4 Confidence Bands The model optionally displays uncertainty bands around the main allocation line. These are calculated as ±1 standard deviation of allocation over a rolling 20-period window. Wide bands indicate high volatility of model recommendations, suggesting uncertain market conditions. Narrow bands indicate stable recommendations. This visualization implements a concept of epistemic uncertainty—uncertainty about the model estimate itself, not just market volatility. In phases where various indicators send conflicting signals, the allocation recommendation becomes more volatile, manifesting in wider bands. Users can understand this as a warning to act more cautiously or consult alternative information sources. 11. Alert System 11.1 Allocation Alerts DEAM implements an alert system that notifies users of significant events. Allocation alerts trigger when smoothed allocation crosses certain thresholds. An alert is generated when allocation reaches 80% (from below), signaling strong bullish conditions. Another alert triggers when allocation falls to 20%, indicating defensive positioning. These thresholds are not arbitrary but correspond with boundaries between model regimes. An allocation of 80% roughly corresponds to a clear bull market regime, while 20% corresponds to a bear market regime. Alerts at these points are therefore informative about fundamental regime shifts. 11.2 Crisis Alerts Separate alerts trigger upon detection of crisis and severe crisis. These alerts have highest priority as they signal large risks. A crisis alert should prompt investors to review their portfolio and potentially take defensive measures beyond the automatic model recommendation (e.g., hedging through put options, rebalancing to more defensive sectors). 11.3 Regime Change Alerts An alert triggers upon change of market regime (e.g., from Neutral to Correction, or from Bull Market to Strong Bull). Regime changes are highly informative events that typically entail substantial allocation changes. These alerts enable investors to proactively respond to changes in market dynamics. 11.4 Risk Breach Alerts A specialized alert triggers when actual portfolio risk utilization exceeds target parameters by 20%. This is a warning signal that the risk management system is reaching its limits, possibly because market volatility is rising faster than allocation can be reduced. In such situations, investors should consider manual interventions. 12. Practical Application and Limitations 12.1 Portfolio Implementation DEAM generates a recommendation for allocation between equities (S&P 500) and cash. Implementation by an investor can take various forms. The most direct method is using an S&P 500 ETF (e.g., SPY, VOO) for equity allocation and a money market fund or savings account for cash allocation. A rebalancing strategy is required to synchronize actual allocation with model recommendation. Two approaches are possible: (1) rule-based rebalancing at every 10% deviation between actual and target, or (2) time-based monthly rebalancing. Both have trade-offs between responsiveness and transaction costs. Empirical evidence (Jaconetti, Kinniry, and Zilbering, 2010) suggests rebalancing frequency has moderate impact on performance, and investors should optimize based on their transaction costs. 12.2 Adaptation to Individual Preferences The model offers numerous adjustment parameters. Component weights can be modified if investors place more or less belief in certain factors. A fundamentally-oriented investor might increase valuation weight, while a technical trader might increase regime weight. Risk target parameters (target volatility, max drawdown) should be adapted to individual risk tolerance. Younger investors with long investment horizons can choose higher target volatility (15-18%), while retirees may prefer lower volatility (8-10%). This adjustment systematically shifts average equity allocation. Crisis thresholds can be adjusted based on preference for sensitivity versus specificity of crisis detection. Lower thresholds (e.g., VIX > 35 instead of 40) increase sensitivity (more crises are detected) but reduce specificity (more false alarms). Higher thresholds have the reverse effect. 12.3 Limitations and Disclaimers DEAM is based on historical relationships between indicators and market performance. There is no guarantee these relationships will persist in the future. Structural changes in markets (e.g., through regulation, technology, or central bank policy) can break established patterns. This is the fundamental problem of induction in financial science (Taleb, 2007). The model is optimized for US equities (S&P 500). Application to other markets (international stocks, bonds, commodities) would require recalibration. The indicators and thresholds are specific to the statistical properties of the US equity market. The model cannot eliminate losses. Even with perfect crisis prediction, an investor following the model would lose money in bear markets—just less than a buy-and-hold investor. The goal is risk-adjusted performance improvement, not risk elimination. Transaction costs are not modeled. In practice, spreads, commissions, and taxes reduce net returns. Frequent trading can cause substantial costs. Model smoothing helps minimize this, but users should consider their specific cost situation. The model reacts to information; it does not anticipate it. During sudden shocks (e.g., 9/11, COVID-19 lockdowns), the model can only react after price movements, not before. This limitation is inherent to all reactive systems. 12.4 Relationship to Other Strategies DEAM is a tactical asset allocation approach and should be viewed as a complement, not replacement, for strategic asset allocation. Brinson, Hood, and Beebower (1986) showed in their influential study "Determinants of Portfolio Performance" that strategic asset allocation (long-term policy allocation) explains the majority of portfolio performance, but this leaves room for tactical adjustments based on market timing. The model can be combined with value and momentum strategies at the individual stock level. While DEAM controls overall market exposure, within-equity decisions can be optimized through stock-picking models. This separation between strategic (market exposure) and tactical (stock selection) levels follows classical portfolio theory. The model does not replace diversification across asset classes. A complete portfolio should also include bonds, international stocks, real estate, and alternative investments. DEAM addresses only the US equity allocation decision within a broader portfolio. 13. Scientific Foundation and Evaluation 13.1 Theoretical Consistency DEAM's components are based on established financial theory and empirical evidence. The market regime component follows from regime-switching models (Hamilton, 1989) and trend-following literature. The risk management component implements volatility targeting (Moreira and Muir, 2017) and modern portfolio theory (Markowitz, 1952). The valuation component is based on discounted cash flow theory and empirical value research (Campbell and Shiller, 1988; Fama and French, 1992). The sentiment component integrates behavioral finance (Baker and Wurgler, 2006). The macro component uses established business cycle indicators (Estrella and Mishkin, 1998). This theoretical grounding distinguishes DEAM from purely data-mining-based approaches that identify patterns without causal theory. Theory-guided models have greater probability of functioning out-of-sample, as they are based on fundamental mechanisms, not random correlations (Lo and MacKinlay, 1990). 13.2 Empirical Validation While this document does not present detailed backtest analysis, it should be noted that rigorous validation of a tactical asset allocation model should include several elements: In-sample testing establishes whether the model functions at all in the data on which it was calibrated. Out-of-sample testing is crucial: the model should be tested in time periods not used for development. Walk-forward analysis, where the model is successively trained on rolling windows and tested in the next window, approximates real implementation. Performance metrics should be risk-adjusted. Pure return consideration is misleading, as higher returns often only compensate for higher risk. Sharpe Ratio, Sortino Ratio, Calmar Ratio, and Maximum Drawdown are relevant metrics. Comparison with benchmarks (Buy-and-Hold S&P 500, 60/40 Stock/Bond portfolio) contextualizes performance. Robustness checks test sensitivity to parameter variation. If the model only functions at specific parameter settings, this indicates overfitting. Robust models show consistent performance over a range of plausible parameters. 13.3 Comparison with Existing Literature DEAM fits into the broader literature on tactical asset allocation. Faber (2007) presented a simple momentum-based timing system that goes long when the market is above its 10-month average, otherwise cash. This simple system avoided large drawdowns in bear markets. DEAM can be understood as a sophistication of this approach that integrates multiple information sources. Ilmanen (2011) discusses various timing factors in "Expected Returns" and argues for multi-factor approaches. DEAM operationalizes this philosophy. Asness, Moskowitz, and Pedersen (2013) showed that value and momentum effects work across asset classes, justifying cross-asset application of regime and valuation signals. Ang (2014) emphasizes in "Asset Management: A Systematic Approach to Factor Investing" the importance of systematic, rule-based approaches over discretionary decisions. DEAM is fully systematic and eliminates emotional biases that plague individual investors (overconfidence, hindsight bias, loss aversion). References Ang, A. (2014) *Asset Management: A Systematic Approach to Factor Investing*. Oxford: Oxford University Press. Ang, A., Piazzesi, M. and Wei, M. (2006) 'What does the yield curve tell us about GDP growth?', *Journal of Econometrics*, 131(1-2), pp. 359-403. Asness, C.S. (2003) 'Fight the Fed Model', *The Journal of Portfolio Management*, 30(1), pp. 11-24. Asness, C.S., Moskowitz, T.J. and Pedersen, L.H. (2013) 'Value and Momentum Everywhere', *The Journal of Finance*, 68(3), pp. 929-985. Baker, M. and Wurgler, J. (2006) 'Investor Sentiment and the Cross-Section of Stock Returns', *The Journal of Finance*, 61(4), pp. 1645-1680. Baker, M. and Wurgler, J. (2007) 'Investor Sentiment in the Stock Market', *Journal of Economic Perspectives*, 21(2), pp. 129-152. Baur, D.G. and Lucey, B.M. (2010) 'Is Gold a Hedge or a Safe Haven? An Analysis of Stocks, Bonds and Gold', *Financial Review*, 45(2), pp. 217-229. Bollerslev, T. (1986) 'Generalized Autoregressive Conditional Heteroskedasticity', *Journal of Econometrics*, 31(3), pp. 307-327. Boudoukh, J., Michaely, R., Richardson, M. and Roberts, M.R. (2007) 'On the Importance of Measuring Payout Yield: Implications for Empirical Asset Pricing', *The Journal of Finance*, 62(2), pp. 877-915. Brinson, G.P., Hood, L.R. and Beebower, G.L. (1986) 'Determinants of Portfolio Performance', *Financial Analysts Journal*, 42(4), pp. 39-44. Brock, W., Lakonishok, J. and LeBaron, B. (1992) 'Simple Technical Trading Rules and the Stochastic Properties of Stock Returns', *The Journal of Finance*, 47(5), pp. 1731-1764. Calmar, T.W. (1991) 'The Calmar Ratio', *Futures*, October issue. Campbell, J.Y. and Shiller, R.J. (1988) 'The Dividend-Price Ratio and Expectations of Future Dividends and Discount Factors', *Review of Financial Studies*, 1(3), pp. 195-228. Cochrane, J.H. (2011) 'Presidential Address: Discount Rates', *The Journal of Finance*, 66(4), pp. 1047-1108. Damodaran, A. (2012) *Equity Risk Premiums: Determinants, Estimation and Implications*. Working Paper, Stern School of Business. Engle, R.F. (1982) 'Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation', *Econometrica*, 50(4), pp. 987-1007. Estrella, A. and Hardouvelis, G.A. (1991) 'The Term Structure as a Predictor of Real Economic Activity', *The Journal of Finance*, 46(2), pp. 555-576. Estrella, A. and Mishkin, F.S. (1998) 'Predicting U.S. Recessions: Financial Variables as Leading Indicators', *Review of Economics and Statistics*, 80(1), pp. 45-61. Faber, M.T. (2007) 'A Quantitative Approach to Tactical Asset Allocation', *The Journal of Wealth Management*, 9(4), pp. 69-79. Fama, E.F. and French, K.R. (1989) 'Business Conditions and Expected Returns on Stocks and Bonds', *Journal of Financial Economics*, 25(1), pp. 23-49. Fama, E.F. and French, K.R. (1992) 'The Cross-Section of Expected Stock Returns', *The Journal of Finance*, 47(2), pp. 427-465. Garman, M.B. and Klass, M.J. (1980) 'On the Estimation of Security Price Volatilities from Historical Data', *Journal of Business*, 53(1), pp. 67-78. Gilchrist, S. and Zakrajšek, E. (2012) 'Credit Spreads and Business Cycle Fluctuations', *American Economic Review*, 102(4), pp. 1692-1720. Gordon, M.J. (1962) *The Investment, Financing, and Valuation of the Corporation*. Homewood: Irwin. Graham, B. and Dodd, D.L. (1934) *Security Analysis*. New York: McGraw-Hill. Hamilton, J.D. (1989) 'A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle', *Econometrica*, 57(2), pp. 357-384. Ilmanen, A. (2011) *Expected Returns: An Investor's Guide to Harvesting Market Rewards*. Chichester: Wiley. Jaconetti, C.M., Kinniry, F.M. and Zilbering, Y. (2010) 'Best Practices for Portfolio Rebalancing', *Vanguard Research Paper*. Jegadeesh, N. and Titman, S. (1993) 'Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency', *The Journal of Finance*, 48(1), pp. 65-91. Kahneman, D. and Tversky, A. (1979) 'Prospect Theory: An Analysis of Decision under Risk', *Econometrica*, 47(2), pp. 263-292. Korteweg, A. (2010) 'The Net Benefits to Leverage', *The Journal of Finance*, 65(6), pp. 2137-2170. Lo, A.W. and MacKinlay, A.C. (1990) 'Data-Snooping Biases in Tests of Financial Asset Pricing Models', *Review of Financial Studies*, 3(3), pp. 431-467. Longin, F. and Solnik, B. (2001) 'Extreme Correlation of International Equity Markets', *The Journal of Finance*, 56(2), pp. 649-676. Mandelbrot, B. (1963) 'The Variation of Certain Speculative Prices', *The Journal of Business*, 36(4), pp. 394-419. Markowitz, H. (1952) 'Portfolio Selection', *The Journal of Finance*, 7(1), pp. 77-91. Modigliani, F. and Miller, M.H. (1961) 'Dividend Policy, Growth, and the Valuation of Shares', *The Journal of Business*, 34(4), pp. 411-433. Moreira, A. and Muir, T. (2017) 'Volatility-Managed Portfolios', *The Journal of Finance*, 72(4), pp. 1611-1644. Moskowitz, T.J., Ooi, Y.H. and Pedersen, L.H. (2012) 'Time Series Momentum', *Journal of Financial Economics*, 104(2), pp. 228-250. Parkinson, M. (1980) 'The Extreme Value Method for Estimating the Variance of the Rate of Return', *Journal of Business*, 53(1), pp. 61-65. Piotroski, J.D. (2000) 'Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers', *Journal of Accounting Research*, 38, pp. 1-41. Reinhart, C.M. and Rogoff, K.S. (2009) *This Time Is Different: Eight Centuries of Financial Folly*. Princeton: Princeton University Press. Ross, S.A. (1976) 'The Arbitrage Theory of Capital Asset Pricing', *Journal of Economic Theory*, 13(3), pp. 341-360. Roy, A.D. (1952) 'Safety First and the Holding of Assets', *Econometrica*, 20(3), pp. 431-449. Schwert, G.W. (1989) 'Why Does Stock Market Volatility Change Over Time?', *The Journal of Finance*, 44(5), pp. 1115-1153. Sharpe, W.F. (1966) 'Mutual Fund Performance', *The Journal of Business*, 39(1), pp. 119-138. Sharpe, W.F. (1994) 'The Sharpe Ratio', *The Journal of Portfolio Management*, 21(1), pp. 49-58. Simon, D.P. and Wiggins, R.A. (2001) 'S&P Futures Returns and Contrary Sentiment Indicators', *Journal of Futures Markets*, 21(5), pp. 447-462. Taleb, N.N. (2007) *The Black Swan: The Impact of the Highly Improbable*. New York: Random House. Whaley, R.E. (2000) 'The Investor Fear Gauge', *The Journal of Portfolio Management*, 26(3), pp. 12-17. Whaley, R.E. (2009) 'Understanding the VIX', *The Journal of Portfolio Management*, 35(3), pp. 98-105. Yardeni, E. (2003) 'Stock Valuation Models', *Topical Study*, 51, Yardeni Research. Zweig, M.E. (1973) 'An Investor Expectations Stock Price Predictive Model Using Closed-End Fund Premiums', *The Journal of Finance*, 28(1), pp. 67-78.

Pine Script® göstergesi

EdgeTools tarafından

BOCS Channel Scalper Strategy - Automated Mean Reversion System # BOCS Channel Scalper Strategy - Automated Mean Reversion System ## WHAT THIS STRATEGY DOES: This is an automated mean reversion trading strategy that identifies consolidation channels through volatility analysis and executes scalp trades when price enters entry zones near channel boundaries. Unlike breakout strategies, this system assumes price will revert to the channel mean, taking profits as price bounces back from extremes. Position sizing is fully customizable with three methods: fixed contracts, percentage of equity, or fixed dollar amount. Stop losses are placed just outside channel boundaries with take profits calculated either as fixed points or as a percentage of channel range. ## KEY DIFFERENCE FROM ORIGINAL BOCS: **This strategy is designed for traders seeking higher trade frequency.** The original BOCS indicator trades breakouts OUTSIDE channels, waiting for price to escape consolidation before entering. This scalper version trades mean reversion INSIDE channels, entering when price reaches channel extremes and betting on a bounce back to center. The result is significantly more trading opportunities: - **Original BOCS**: 1-3 signals per channel (only on breakout) - **Scalper Version**: 5-15+ signals per channel (every touch of entry zones) - **Trade Style**: Mean reversion vs trend following - **Hold Time**: Seconds to minutes vs minutes to hours - **Best Markets**: Ranging/choppy conditions vs trending breakouts This makes the scalper ideal for active day traders who want continuous opportunities within consolidation zones rather than waiting for breakout confirmation. However, increased trade frequency also means higher commission costs and requires tighter risk management. ## TECHNICAL METHODOLOGY: ### Price Normalization Process: The strategy normalizes price data to create consistent volatility measurements across different instruments and price levels. It calculates the highest high and lowest low over a user-defined lookback period (default 100 bars). Current close price is normalized using: (close - lowest_low) / (highest_high - lowest_low), producing values between 0 and 1 for standardized volatility analysis. ### Volatility Detection: A 14-period standard deviation is applied to the normalized price series to measure price deviation from the mean. Higher standard deviation values indicate volatility expansion; lower values indicate consolidation. The strategy uses ta.highestbars() and ta.lowestbars() to identify when volatility peaks and troughs occur over the detection period (default 14 bars). ### Channel Formation Logic: When volatility crosses from a high level to a low level (ta.crossover(upper, lower)), a consolidation phase begins. The strategy tracks the highest and lowest prices during this period, which become the channel boundaries. Minimum duration of 10+ bars is required to filter out brief volatility spikes. Channels are rendered as box objects with defined upper and lower boundaries, with colored zones indicating entry areas. ### Entry Signal Generation: The strategy uses immediate touch-based entry logic. Entry zones are defined as a percentage from channel edges (default 20%): - **Long Entry Zone**: Bottom 20% of channel (bottomBound + channelRange × 0.2) - **Short Entry Zone**: Top 20% of channel (topBound - channelRange × 0.2) Long signals trigger when candle low touches or enters the long entry zone. Short signals trigger when candle high touches or enters the short entry zone. This captures mean reversion opportunities as price reaches channel extremes. ### Cooldown Filter: An optional cooldown period (measured in bars) prevents signal spam by enforcing minimum spacing between consecutive signals. If cooldown is set to 3 bars, no new long signal will fire until 3 bars after the previous long signal. Long and short cooldowns are tracked independently, allowing both directions to signal within the same period. ### ATR Volatility Filter: The strategy includes a multi-timeframe ATR filter to avoid trading during low-volatility conditions. Using request.security(), it fetches ATR values from a specified timeframe (e.g., 1-minute ATR while trading on 5-minute charts). The filter compares current ATR to a user-defined minimum threshold: - If ATR ≥ threshold: Trading enabled - If ATR < threshold: No signals fire This prevents entries during dead zones where mean reversion is unreliable due to insufficient price movement. ### Take Profit Calculation: Two TP methods are available: **Fixed Points Mode**: - Long TP = Entry + (TP_Ticks × syminfo.mintick) - Short TP = Entry - (TP_Ticks × syminfo.mintick) **Channel Percentage Mode**: - Long TP = Entry + (ChannelRange × TP_Percent) - Short TP = Entry - (ChannelRange × TP_Percent) Default 50% targets the channel midline, a natural mean reversion target. Larger percentages aim for opposite channel edge. ### Stop Loss Placement: Stop losses are placed just outside the channel boundary by a user-defined tick offset: - Long SL = ChannelBottom - (SL_Offset_Ticks × syminfo.mintick) - Short SL = ChannelTop + (SL_Offset_Ticks × syminfo.mintick) This logic assumes channel breaks invalidate the mean reversion thesis. If price breaks through, the range is no longer valid and position exits. ### Trade Execution Logic: When entry conditions are met (price in zone, cooldown satisfied, ATR filter passed, no existing position): 1. Calculate entry price at zone boundary 2. Calculate TP and SL based on selected method 3. Execute strategy.entry() with calculated position size 4. Place strategy.exit() with TP limit and SL stop orders 5. Update info table with active trade details The strategy enforces one position at a time by checking strategy.position_size == 0 before entry. ### Channel Breakout Management: Channels are removed when price closes more than 10 ticks outside boundaries. This tolerance prevents premature channel deletion from minor breaks or wicks, allowing the mean reversion setup to persist through small boundary violations. ### Position Sizing System: Three methods calculate position size: **Fixed Contracts**: - Uses exact contract quantity specified in settings - Best for futures traders (e.g., "trade 2 NQ contracts") **Percentage of Equity**: - position_size = (strategy.equity × equity_pct / 100) / close - Dynamically scales with account growth **Cash Amount**: - position_size = cash_amount / close - Maintains consistent dollar exposure regardless of price ## INPUT PARAMETERS: ### Position Sizing: - **Position Size Type**: Choose Fixed Contracts, % of Equity, or Cash Amount - **Number of Contracts**: Fixed quantity per trade (1-1000) - **% of Equity**: Percentage of account to allocate (1-100%) - **Cash Amount**: Dollar value per position ($100+) ### Channel Settings: - **Nested Channels**: Allow multiple overlapping channels vs single channel - **Normalization Length**: Lookback for high/low calculation (1-500, default 100) - **Box Detection Length**: Period for volatility detection (1-100, default 14) ### Scalping Settings: - **Enable Long Scalps**: Toggle long entries on/off - **Enable Short Scalps**: Toggle short entries on/off - **Entry Zone % from Edge**: Size of entry zone (5-50%, default 20%) - **SL Offset (Ticks)**: Distance beyond channel for stop (1+, default 5) - **Cooldown Period (Bars)**: Minimum spacing between signals (0 = no cooldown) ### ATR Filter: - **Enable ATR Filter**: Toggle volatility filter on/off - **ATR Timeframe**: Source timeframe for ATR (1, 5, 15, 60 min, etc.) - **ATR Length**: Smoothing period (1-100, default 14) - **Min ATR Value**: Threshold for trade enablement (0.1+, default 10.0) ### Take Profit Settings: - **TP Method**: Choose Fixed Points or % of Channel - **TP Fixed (Ticks)**: Static distance in ticks (1+, default 30) - **TP % of Channel**: Dynamic target as channel percentage (10-100%, default 50%) ### Appearance: - **Show Entry Zones**: Toggle zone labels on channels - **Show Info Table**: Display real-time strategy status - **Table Position**: Corner placement (Top Left/Right, Bottom Left/Right) - **Color Settings**: Customize long/short/TP/SL colors ## VISUAL INDICATORS: - **Channel boxes** with semi-transparent fill showing consolidation zones - **Colored entry zones** labeled "LONG ZONE ▲" and "SHORT ZONE ▼" - **Entry signal arrows** below/above bars marking long/short entries - **Active TP/SL lines** with emoji labels (⊕ Entry, 🎯 TP, 🛑 SL) - **Info table** showing position status, channel state, last signal, entry/TP/SL prices, and ATR status ## HOW TO USE: ### For 1-3 Minute Scalping (NQ/ES): - ATR Timeframe: "1" (1-minute) - ATR Min Value: 10.0 (for NQ), adjust per instrument - Entry Zone %: 20-25% - TP Method: Fixed Points, 20-40 ticks - SL Offset: 5-10 ticks - Cooldown: 2-3 bars - Position Size: 1-2 contracts ### For 5-15 Minute Day Trading: - ATR Timeframe: "5" or match chart - ATR Min Value: Adjust to instrument (test 8-15 for NQ) - Entry Zone %: 20-30% - TP Method: % of Channel, 40-60% - SL Offset: 5-10 ticks - Cooldown: 3-5 bars - Position Size: Fixed contracts or 5-10% equity ### For 30-60 Minute Swing Scalping: - ATR Timeframe: "15" or "30" - ATR Min Value: Lower threshold for broader market - Entry Zone %: 25-35% - TP Method: % of Channel, 50-70% - SL Offset: 10-15 ticks - Cooldown: 5+ bars or disable - Position Size: % of equity recommended ## BACKTEST CONSIDERATIONS: - Strategy performs best in ranging, mean-reverting markets - Strong trending markets produce more stop losses as price breaks channels - ATR filter significantly reduces trade count but improves quality during low volatility - Cooldown period trades signal quantity for signal quality - Commission and slippage materially impact sub-5-minute timeframe performance - Shorter timeframes require tighter entry zones (15-20%) to catch quick reversions - % of Channel TP adapts better to varying channel sizes than fixed points - Fixed contract sizing recommended for consistent risk per trade in futures **Backtesting Parameters Used**: This strategy was developed and tested using realistic commission and slippage values to provide accurate performance expectations. Recommended settings: Commission of $1.40 per side (typical for NQ futures through discount brokers), slippage of 2 ticks to account for execution delays on fast-moving scalp entries. These values reflect real-world trading costs that active scalpers will encounter. Backtest results without proper cost simulation will significantly overstate profitability. ## COMPATIBLE MARKETS: Works on any instrument with price data including stock indices (NQ, ES, YM, RTY), individual stocks, forex pairs (EUR/USD, GBP/USD), cryptocurrency (BTC, ETH), and commodities. Volume-based features require data feed with volume information but are optional for core functionality. ## KNOWN LIMITATIONS: - Immediate touch entry can fire multiple times in choppy zones without adequate cooldown - Channel deletion at 10-tick breaks may be too aggressive or lenient depending on instrument tick size - ATR filter from lower timeframes requires higher-tier TradingView subscription (request.security limitation) - Mean reversion logic fails in strong breakout scenarios leading to stop loss hits - Position sizing via % of equity or cash amount calculates based on close price, may differ from actual fill price - No partial closing capability - full position exits at TP or SL only - Strategy does not account for gap openings or overnight holds ## RISK DISCLOSURE: Trading involves substantial risk of loss. Past performance does not guarantee future results. This strategy is for educational purposes and backtesting only. Mean reversion strategies can experience extended drawdowns during trending markets. Stop losses may not fill at intended levels during extreme volatility or gaps. Thoroughly test on historical data and paper trade before risking real capital. Use appropriate position sizing and never risk more than you can afford to lose. Consider consulting a licensed financial advisor before making trading decisions. Automated trading systems can malfunction - monitor all live positions actively. ## ACKNOWLEDGMENT & CREDITS: This strategy is built upon the channel detection methodology created by **AlgoAlpha** in the "Smart Money Breakout Channels" indicator. Full credit and appreciation to AlgoAlpha for pioneering the normalized volatility approach to identifying consolidation patterns. The core channel formation logic using normalized price standard deviation is AlgoAlpha's original contribution to the TradingView community. Enhancements to the original concept include: mean reversion entry logic (vs breakout), immediate touch-based signals, multi-timeframe ATR volatility filtering, flexible position sizing (fixed/percentage/cash), cooldown period filtering, dual TP methods (fixed points vs channel percentage), automated strategy execution with exit management, and real-time position monitoring table.

Pine Script® stratejisi

TradeManager_PRO tarafından

RSI ROC Signals with Price Action # RSI ROC Signals with Price Action ## Overview The RSI ROC (Rate of Change) Signals indicator is an advanced momentum-based trading system that combines RSI velocity analysis with price action confirmation to generate high-probability buy and sell signals. This indicator goes beyond traditional RSI analysis by measuring the speed of RSI changes and requiring price confirmation before triggering signals. ## Core Concept: RSI Rate of Change (ROC) ### What is RSI ROC? RSI ROC measures the **velocity** or **acceleration** of the RSI indicator, providing insights into momentum shifts before they become apparent in traditional RSI readings. **Formula**: `RSI ROC = ((Current RSI - Previous RSI) / Previous RSI) × 100` ### Why RSI ROC is Superior to Standard RSI: 1. **Early Momentum Detection**: Identifies momentum shifts before RSI reaches traditional overbought/oversold levels 2. **Velocity Analysis**: Measures the speed of momentum changes, not just absolute levels 3. **Reduced False Signals**: Filters out weak momentum moves that don't sustain 4. **Dynamic Thresholds**: Adapts to market volatility rather than using fixed RSI levels 5. **Leading Indicator**: Provides earlier signals compared to traditional RSI crossovers ## Signal Generation Logic ### 🟢 Buy Signal Process (3-Stage System): #### Stage 1: Trigger Activation - **RSI ROC** > threshold (default 7%) - RSI accelerating upward - **Price ROC** > 0 - Price moving higher - Records the **trigger high** (highest point during trigger) #### Stage 2: Invalidation Check - Signal invalidated if **RSI ROC** drops below negative threshold - Prevents false signals during momentum reversals #### Stage 3: Confirmation - **Price breaks above trigger high** - Price action confirmation - **Current candle is green** (close > open) - Bullish price action - **State alternation** - Ensures no consecutive duplicate signals ### 🔴 Sell Signal Process (3-Stage System): #### Stage 1: Trigger Activation - **RSI ROC** < negative threshold (default -7%) - RSI accelerating downward - **Price ROC** < 0 - Price moving lower - Records the **trigger low** (lowest point during trigger) #### Stage 2: Invalidation Check - Signal invalidated if **RSI ROC** rises above positive threshold - Prevents false signals during momentum reversals #### Stage 3: Confirmation - **Price breaks below trigger low** - Price action confirmation - **Current candle is red** (close < open) - Bearish price action - **State alternation** - Ensures no consecutive duplicate signals ## Key Features ### 🎯 **Smart Signal Management** - **State Alternation**: Prevents signal clustering by alternating between buy/sell states - **Trigger Invalidation**: Automatically cancels weak signals that lose momentum - **Price Confirmation**: Requires actual price breakouts, not just momentum shifts - **No Repainting**: Signals are confirmed and won't disappear or change ### ⚙️ **Customizable Parameters** #### **RSI Length (Default: 14)** - Standard RSI calculation period - Shorter periods = more sensitive to price changes - Longer periods = smoother, less noisy signals #### **Lookback Period (Default: 1)** - Period for ROC calculations - 1 = compares to previous bar (most responsive) - Higher values = smoother momentum detection #### **RSI ROC Threshold (Default: 7%)** - Minimum RSI velocity required for signal trigger - Lower values = more signals, potentially more noise - Higher values = fewer but higher-quality signals ### 📊 **Visual Signals** - **Green Arrow Up**: Buy signal below price bar - **Red Arrow Down**: Sell signal above price bar - **Clean Chart**: No additional lines or oscillators cluttering the view - **Size Options**: Customizable arrow sizes for visibility preferences ## Advantages Over Traditional Indicators ### vs. Standard RSI: ✅ **Earlier Signals**: Detects momentum changes before RSI reaches extremes ✅ **Dynamic Thresholds**: Adapts to market conditions vs. fixed 30/70 levels ✅ **Velocity Focus**: Measures momentum speed, not just position ✅ **Better Timing**: Combines momentum with price action confirmation ### vs. Moving Average Crossovers: ✅ **Leading vs. Lagging**: RSI ROC is forward-looking vs. backward-looking MAs ✅ **Volatility Adaptive**: Automatically adjusts to market volatility ✅ **Fewer Whipsaws**: Built-in invalidation logic reduces false signals ✅ **Momentum Focus**: Captures acceleration, not just direction changes ### vs. MACD: ✅ **Price-Normalized**: RSI ROC works consistently across different price ranges ✅ **Simpler Logic**: Clear trigger/confirmation process vs. complex crossovers ✅ **Built-in Filters**: Automatic signal quality control ✅ **State Management**: Prevents over-trading through alternation logic ## Trading Applications ### 📈 **Trend Following** - Use in trending markets to catch momentum continuations - Combine with trend filters for directional bias - Excellent for breakout strategies ### 🔄 **Swing Trading** - Ideal timeframes: 4H, Daily, Weekly - Captures major momentum shifts - Perfect for position entries/exits ### ⚡ **Scalping (Advanced Users)** - Lower timeframes: 1m, 5m, 15m - Reduce threshold for more frequent signals - Combine with volume confirmation ### 🎯 **Momentum Strategies** - Perfect for momentum-based trading systems - Identifies acceleration phases in trends - Complements breakout and continuation patterns ## Optimization Guidelines ### **Conservative Settings (Lower Risk)** - RSI Length: 21 - ROC Threshold: 10% - Lookback: 2 ### **Standard Settings (Balanced)** - RSI Length: 14 (default) - ROC Threshold: 7% (default) - Lookback: 1 (default) ### **Aggressive Settings (Higher Frequency)** - RSI Length: 7 - ROC Threshold: 5% - Lookback: 1 ## Best Practices ### 🎯 **Entry Strategy** 1. Wait for signal arrow confirmation 2. Consider market context (trend, support/resistance) 3. Use proper position sizing based on volatility 4. Set stop-loss below/above trigger levels ### 🛡️ **Risk Management** 1. **Stop Loss**: Place beyond trigger high/low levels 2. **Position Sizing**: Use 1-2% risk per trade 3. **Market Context**: Avoid counter-trend signals in strong trends 4. **Time Filters**: Consider avoiding signals near major news events ### 📊 **Backtesting Recommendations** 1. Test on multiple timeframes and instruments 2. Analyze win rate vs. average win/loss ratio 3. Consider transaction costs in backtesting 4. Optimize threshold values for different market conditions ## Technical Specifications - **Pine Script Version**: v6 - **Signal Type**: Non-repainting, confirmed signals - **Calculation Basis**: RSI velocity with price action confirmation - **Update Frequency**: Real-time on bar close - **Memory Management**: Efficient state tracking with minimal resource usage ## Ideal For: - **Momentum Traders**: Captures acceleration phases - **Swing Traders**: Medium-term position entries/exits - **Breakout Traders**: Confirms momentum behind breakouts - **System Traders**: Mechanical signal generation with clear rules This indicator represents a significant evolution in momentum analysis, combining the reliability of RSI with the precision of rate-of-change analysis and the confirmation of price action. It's designed for traders who want sophisticated momentum detection with built-in quality controls.

Pine Script® göstergesi

padavans tarafından

Institutional Levels (CNN) - [PhenLabs]📊Institutional Levels (Convolutional Neural Network-inspired) Version : PineScript™v6 📌Description The CNN-IL Institutional Levels indicator represents a breakthrough in automated zone detection technology, combining convolutional neural network principles with advanced statistical modeling. This sophisticated tool identifies high-probability institutional trading zones by analyzing pivot patterns, volume dynamics, and price behavior using machine learning algorithms. The indicator employs a proprietary 9-factor logistic regression model that calculates real-time reaction probabilities for each detected zone. By incorporating CNN-inspired filtering techniques and dynamic zone management, it provides traders with unprecedented accuracy in identifying where institutional money is likely to react to price action. 🚀Points of Innovation ● CNN-Inspired Pivot Analysis - Advanced binning system using convolutional neural network principles for superior pattern recognition ● Real-Time Probability Engine - Live reaction probability calculations using 9-factor logistic regression model ● Dynamic Zone Intelligence - Automatic zone merging using Intersection over Union (IoU) algorithms ● Volume-Weighted Scoring - Time-of-day volume Z-score analysis for enhanced zone strength assessment ● Adaptive Decay System - Intelligent zone lifecycle management based on touch frequency and recency ● Multi-Filter Architecture - Optional gradient, smoothing, and Difference of Gaussians (DoG) convolution filters 🔧Core Components ● Pivot Detection Engine - Advanced pivot identification with configurable left/right bars and ATR-normalized strength calculations ● Neural Network Binning - Price level clustering using CNN-inspired algorithms with ATR-based bin sizing ● Logistic Regression Model - 9-factor probability calculation including distance, width, volume, VWAP deviation, and trend analysis ● Zone Management System - Intelligent creation, merging, and decay algorithms for optimal zone lifecycle control ● Visualization Layer - Dynamic line drawing with opacity-based scoring and optional zone fills 🔥Key Features ● High-Probability Zone Detection - Automatically identifies institutional levels with reaction probabilities above configurable thresholds ● Real-Time Probability Scoring - Live calculation of zone reaction likelihood using advanced statistical modeling ● Session-Aware Analysis - Optional filtering to specific trading sessions for enhanced accuracy during active market hours ● Customizable Parameters - Full control over lookback periods, zone sensitivity, merge thresholds, and probability models ● Performance Optimized - Efficient processing with controlled update frequencies and pivot processing limits ● Non-Repainting Mode - Strict mode available for backtesting accuracy and live trading reliability 🎨Visualization ● Dynamic Zone Lines - Color-coded support and resistance levels with opacity reflecting zone strength and confidence scores ● Probability Labels - Real-time display of reaction probabilities, touch counts, and historical hit rates for active zones ● Zone Fills - Optional semi-transparent zone highlighting for enhanced visual clarity and immediate pattern recognition ● Adaptive Styling - Automatic color and opacity adjustments based on zone scoring and statistical significance 📖Usage Guidelines ● Lookback Bars - Default 500, Range 100-1000, Controls the historical data window for pivot analysis and zone calculation ● Pivot Left/Right - Default 3, Range 1-10, Defines the pivot detection sensitivity and confirmation requirements ● Bin Size ATR units - Default 0.25, Range 0.1-2.0, Controls price level clustering granularity for zone creation ● Base Zone Half-Width ATR units - Default 0.25, Range 0.1-1.0, Sets the minimum zone width in ATR units for institutional level boundaries ● Zone Merge IoU Threshold - Default 0.5, Range 0.1-0.9, Intersection over Union threshold for automatic zone merging algorithms ● Max Active Zones - Default 5, Range 3-20, Maximum number of zones displayed simultaneously to prevent chart clutter ● Probability Threshold for Labels - Default 0.6, Range 0.3-0.9, Minimum reaction probability required for zone label display and alerts ● Distance Weight w1 - Controls influence of price distance from zone center on reaction probability ● Width Weight w2 - Adjusts impact of zone width on probability calculations ● Volume Weight w3 - Modifies volume Z-score influence on zone strength assessment ● VWAP Weight w4 - Controls VWAP deviation impact on institutional level significance ● Touch Count Weight w5 - Adjusts influence of historical zone interactions on probability scoring ● Hit Rate Weight w6 - Controls prior success rate impact on future reaction likelihood predictions ● Wick Penetration Weight w7 - Modifies wick penetration analysis influence on probability calculations ● Trend Weight w8 - Adjusts trend context impact using ADX analysis for directional bias assessment ✅Best Use Cases ● Swing Trading Entries - Enter positions at high-probability institutional zones with 60%+ reaction scores ● Scalping Opportunities - Quick entries and exits around frequently tested institutional levels ● Risk Management - Use zones as dynamic stop-loss and take-profit levels based on institutional behavior ● Market Structure Analysis - Identify key institutional levels that define current market structure and sentiment ● Confluence Trading - Combine with other technical indicators for high-probability trade setups ● Session-Based Strategies - Focus analysis during high-volume sessions for maximum effectiveness ⚠️Limitations ● Historical Pattern Dependency - Algorithm effectiveness relies on historical patterns that may not repeat in changing market conditions ● Computational Intensity - Complex calculations may impact chart performance on lower-end devices or with multiple indicators ● Probability Estimates - Reaction probabilities are statistical estimates and do not guarantee actual market outcomes ● Session Sensitivity - Performance may vary significantly between different market sessions and volatility regimes ● Parameter Sensitivity - Results can be highly dependent on input parameters requiring optimization for different instruments 💡What Makes This Unique ● CNN Architecture - First indicator to apply convolutional neural network principles to institutional-level detection ● Real-Time ML Scoring - Live machine learning probability calculations for each zone interaction ● Advanced Zone Management - Sophisticated algorithms for zone lifecycle management and automatic optimization ● Statistical Rigor - Comprehensive 9-factor logistic regression model with extensive backtesting validation ● Performance Optimization - Efficient processing algorithms designed for real-time trading applications 🔬How It Works ● Multi-timeframe pivot identification - Uses configurable sensitivity parameters for advanced pivot detection ● ATR-normalized strength calculations - Standardizes pivot significance across different volatility regimes ● Volume Z-score integration - Enhanced pivot weighting based on time-of-day volume patterns ● Price level clustering - Neural network binning algorithms with ATR-based sizing for zone creation ● Recency decay applications - Weights recent pivots more heavily than historical data for relevance ● Statistical filtering - Eliminates low-significance price levels and reduces market noise ● Dynamic zone generation - Creates zones from statistically significant pivot clusters with minimum support thresholds ● IoU-based merging algorithms - Combines overlapping zones while maintaining accuracy using Intersection over Union ● Adaptive decay systems - Automatic removal of outdated or low-performing zones for optimal performance ● 9-factor logistic regression - Incorporates distance, width, volume, VWAP, touch history, and trend analysis ● Real-time scoring updates - Zone interaction calculations with configurable threshold filtering ● Optional CNN filters - Gradient detection, smoothing, and Difference of Gaussians processing for enhanced accuracy 💡Note This indicator represents advanced quantitative analysis and should be used by traders familiar with statistical modeling concepts. The probability scores are mathematical estimates based on historical patterns and should be combined with proper risk management and additional technical analysis for optimal trading decisions.

Pine Script® göstergesi

PhenLabs tarafından

Unusual Moves Detector # Unusual Moves Detector A TradingView indicator that detects and alerts users to unusual price movements based on ATR (Average True Range) and volume analysis. This indicator is designed to identify price action that deviates significantly from normal market behavior. ## Features ### Core Detection Mechanisms - **ATR-Based Volatility Detection**: Identifies price movements that exceed normal volatility levels - **Volume Analysis**: Optional volume spike detection to confirm unusual moves - **Signal Persistence Tracking**: Monitors how many signals occur within a lookback period ### Visual Indicators - **Up/Down Arrows**: Green triangles for unusual upward moves, red triangles for downward moves - **Signal Strength Labels**: Numbers showing how many signals occurred in the lookback period - **Real-time Metrics Table**: Displays current ATR and volume ratios ### Customizable Parameters 1. **ATR Period** (default: 14) - Length for Average True Range calculation - Affects volatility measurement sensitivity 2. **Volume MA Period** (default: 20) - Period for volume moving average - Used in volume spike detection 3. **ATR Multiplier** (default: 2.0) - How many times the ATR to trigger a signal - Higher values = less sensitive to price moves 4. **Volume Multiplier** (default: 2.0) - How many times the average volume to consider "high volume" - Higher values = less sensitive to volume spikes 5. **Include Volume Analysis** (default: true) - Toggle volume confirmation requirement - When disabled, only price volatility matters 6. **Signal Lookback Period** (default: 5) - How many bars to look back for signal counting - Affects signal strength calculation ### Alert System - **Upward Movement Alerts**: Triggers when unusual upward price action is detected - **Downward Movement Alerts**: Triggers when unusual downward price action is detected - **Customizable Alert Messages**: Can be configured in TradingView's alert system ### Information Display Real-time metrics table shows: - Current ATR value - Volume ratio (current volume / average volume) - Net signal count (up signals - down signals) ## Installation 1. Open TradingView's Pine Script Editor 2. Create a new indicator 3. Copy and paste the indicator code 4. Click "Add to Chart" to apply the indicator ## Usage Guide ### Basic Setup 1. Add the indicator to your chart 2. Adjust parameters based on your trading timeframe and style 3. Configure alerts if desired ### Parameter Tuning Tips - **For More Signals**: Lower the ATR and Volume multipliers - **For Fewer Signals**: Increase the multipliers - **For Trend Following**: Increase the lookback period - **For Quick Signals**: Decrease the lookback period ### Alert Setup 1. Click the indicator settings 2. Go to "Create Alert" 3. Choose either up or down move condition 4. Configure alert settings (sound, notification, etc.) ## Backtesting Compatibility - Fully compatible with TradingView's backtesting engine - All calculations use historical data only - No forward-looking data or repainting ## Technical Details - Written in Pine Script v6 - Optimized for real-time calculation - Uses native TradingView functions for performance - Compatible with all timeframes ## Performance Considerations - Lightweight computation using built-in functions - Efficient memory usage with variable optimization - Real-time calculation with minimal lag ## Support and Contribution Feel free to modify and improve the indicator according to your needs. The code is well-commented for easy understanding and modification. ## License Free to use and modify for personal and commercial purposes. ## Disclaimer This indicator is for informational purposes only. Always conduct your own analysis and consider multiple factors when making trading decisions.

Pine Script® göstergesi

hams292 tarafından

Güncellendi

Sunmool's Silver Bullet Model Finder ICT Silver Bullet Model Indicator - Complete Guide 📈 Overview The ICT Silver Bullet Model indicator is a supplementary tool for utilizing ICT's (Inner Circle Trader) market structure analysis techniques. This indicator detects institutional liquidity hunting patterns and automatically identifies structural levels, helping traders analyze market structure more effectively. 🎯 Core Features 1. Structural Level Identification STL (Short Term Low): Recent support levels formed in the short term STH (Short Term High): Recent resistance levels formed in the short term ITL (Intermediate Term Low): Stronger support levels with more significance ITH (Intermediate Term High): Stronger resistance levels with more significance 2. Kill Zone Time Display London Kill Zone: 02:00-05:00 (default) New York Kill Zone: 08:30-11:00 (default) These are the most active trading hours for institutional players where significant price movements occur 3. Smart Sweep Detection Bear Sweep (🔻): Pattern where price sweeps below lows then recovers - Simply indicates sweep occurrence Bull Sweep (🔺): Pattern where price sweeps above highs then declines - Simply indicates sweep occurrence Important: Sweep labels only mark liquidity hunting locations, not directional bias. 🔧 Configuration Parameters Basic Settings Sweep Detection Lookback: Number of candles for sweep detection (default: 20) Structure Point Lookback: Number of candles for structural point detection (default: 10) Sweep Threshold: Percentage threshold for sweep validation (default: 0.1%) Time Settings London Kill Zone: Active hours for London session New York Kill Zone: Active hours for New York session Visualization Settings Customizable colors for each level type Enable/disable alert notifications 📊 How to Use 1. Chart Setup Most effective on 1-minute to 1-hour timeframes Recommended for major currency pairs (EUR/USD, GBP/USD, etc.) Also applicable to cryptocurrencies and indices 2. Signal Interpretation 🔻 Bear Sweep / 🔺 Bull Sweep Labels Simply indicate liquidity hunting occurrence points Not directional bias indicators Reference for understanding overall context on HTF 🟢 Silver Bullet Long (Huge Green Triangle) After Bear Sweep occurrence Within Kill Zone timeframe Current price positioned above swept level → Actual BUY entry signal 🔴 Silver Bullet Short (Huge Red Triangle) After Bull Sweep occurrence Within Kill Zone timeframe Current price positioned below swept level → Actual SELL entry signal 3. Risk Management Use swept levels as stop-loss reference points Approach signals outside Kill Zone hours with caution Recommended to use alongside other technical analysis tools 💡 Trading Strategies Silver Bullet Strategy Preparation Phase: Monitor charts 30 minutes before Kill Zone Sweep Observation: Identify liquidity hunting points with 🔻🔺 labels (reference only) Entry: Enter ONLY when huge triangle Silver Bullet signal appears within Kill Zone Take Profit: Target opposite structural level or 1:2 reward ratio Stop Loss: Beyond the swept level Important: Small sweep labels are NOT trading signals! Multi-Timeframe Approach Step 1: HTF (Higher Time Frame) Sweep Reference Observe 🔻🔺 sweep labels on 4-hour and daily charts Reference only sweeps occurring at major structural levels HTF sweeps are used to identify liquidity hunting points Reference only, not for directional bias Step 2: Transition to LTF (Lower Time Frame) Move to 15-minute, 5-minute, and 1-minute charts Analyze LTF with reference to HTF sweep information Use STL, STH, ITL, ITH for precise entry point identification Structural levels on LTF are the core of actual trading decisions Only huge triangle (Silver Bullet) signals are actual entry signals Recommended Usage Identify overall sweep occurrence points on HTF (🔻🔺 labels) Use this indicator on LTF to identify structural levels Reference only huge triangle signals for actual trading during Kill Zone Small sweep labels (🔻🔺) are for reference only, not entry signals 📋 Information Table Interpretation Real-time information in the top-right table: Kill Zone Status: Current active session status Level Counts: Number of each structural level type ⚠️ Important Disclaimers Backtesting results do not guarantee future performance Exercise caution during high market volatility periods Always apply proper risk management Recommend comprehensive analysis with other analytical tools 🎓 Learning Resources Study original ICT concepts through free YouTube educational content Research Market Structure analysis techniques Optimize through backtesting for personal use 🔬 Technical Implementation Algorithm Logic Pivot Point Detection: Uses TradingView's built-in pivot functions to identify swing highs and lows Classification System: Automatically categorizes levels based on recent price action frequency Sweep Validation: Confirms legitimate sweeps through price action analysis Time-Based Filtering: Prioritizes signals during institutional active hours Performance Optimization Efficient array management prevents memory overflow Dynamic level cleanup maintains chart clarity Real-time calculation ensures minimal lag 🛠️ Customization Tips Adjust lookback periods based on market volatility Modify kill zone times for different market sessions Experiment with sweep threshold for different instruments Color-code levels according to personal preference 📈 Expected Outcomes When properly implemented, this indicator can help traders: Identify high-probability reversal points Time entries with institutional flow Reduce false signals through kill zone filtering Improve risk-to-reward ratios This indicator automates ICT's concepts into a user-friendly tool that can be enhanced through continuous learning and practical application. Success depends on understanding the underlying market structure principles and combining them with proper risk management techniques.

Pine Script® göstergesi

sunmool_ tarafından

Güncellendi

11 22 33 44 55 66 77 88 99 1010 1111 1212 1313 1414 1515 1616 1717 1818 1919 2020 2121 2222 2323 2424 2525 2626 2727 2828 2929 3030 3131 3232 3333 3434 3535 3636 3737 3838 3939 4040 4141 4242

ICE Data Services tarafından sağlanan piyasa verilerini seçin. FactSet tarafından sağlanan referans verilerini seçin. Telif Hakkı © 2026 FactSet Research Systems Inc.Telif Hakkı © 2026, American Bankers Association. CUSIP Veritabanı FactSet Research Systems Inc. tarafından sağlanmaktadır. Tüm hakları saklıdır. SEC dosyaları ve diğer belgeler Quartr tarafından sağlanmaktadır.© 2026 TradingView, Inc.

Bir üründen daha fazlası

Süpergrafikler

Takipçi

Hisseler
BYF
Tahvil
Kripto paralar
CEX çiftleri
DEX çiftleri
Pine

Isı Haritaları

Hisseler
BYF
Kripto paralar

Takvimler

Ekonomik
Kazanç
Temettüler

Diğer ürünler

Getiri Eğrileri
Opsiyonlar
Haber Akışı
Pine Script®

Uygulamalar

Mobil
Masaüstü

Topluluk

Sosyal ağ
Sevgi Duvarı
Arkadaşını yönlendir
Site Kuralları
Moderatörler

Fikirler

İşlem
Eğitim
Editörün Seçtikleri

Pine Script

Göstergeler & stratejiler
Sihirbazlar
Serbest çalışanlar

Araçlar & abonelikler

Özellikler
Ücretlendirme
Piyasa verileri
Hediye planları

İşlem

Genel Bakış
Aracı kurumlar

Özel teklifler

CME Grubu vadeli işlemleri
Eurex vadeli işlemleri
ABD hisse paketi

Şirket hakkında

Biz kimiz
Uzay görevi
Blog
Kariyer
Medya kiti

Ürün

TradingView mağazası
Yatırımcılar için tarot kartları
C63 TradeTime

Poliçeler ve güvenlik

Kullanım Koşulları
Feragatname
Gizlilik Politikası
Çerezler Politikası
Erişilebilirlik Beyanı
Güvenlik ipuçları
Hata Ödül programı
Durum sayfası

İş çözümleri

Widget'lar
Grafik kütüphaneleri
Lightweight Charts™
Gelişmiş Grafikler
İşlem Platformu

Büyüme fırsatları

Reklam
Aracı Kurum Entegrasyonu
Partner programı
Eğitim programı

Topluluk

Sosyal ağ
Sevgi Duvarı
Arkadaşını yönlendir
Site Kuralları
Moderatörler

Fikirler

İşlem
Eğitim
Editörün Seçtikleri

Pine Script

Göstergeler & stratejiler
Sihirbazlar
Serbest çalışanlar

İş çözümleri

Widget'lar
Grafik kütüphaneleri
Lightweight Charts™
Gelişmiş Grafikler
İşlem Platformu

Büyüme fırsatları

Reklam
Aracı Kurum Entegrasyonu
Partner programı
Eğitim programı