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.
Adaptive
Kernel Channel [BackQuant]Kernel Channel
A non-parametric, kernel-weighted trend channel that adapts to local structure, smooths noise without lagging like moving averages, and highlights volatility compressions, expansions, and directional bias through a flexible choice of kernels, band types, and squeeze logic.
What this is
This indicator builds a full trend channel using kernel regression rather than classical averaging. Instead of a simple moving average or exponential weighting, the midline is computed as a kernel-weighted expectation of past values. This allows it to adapt to local shape, give more weight to nearby bars, and reduce distortion from outliers.
You can think of it as a sliding local smoother where you define both the “window” of influence (Window Length) and the “locality strength” (Bandwidth). The result is a flexible midline with optional upper and lower bands derived from kernel-weighted ATR or kernel-weighted standard deviation, letting you visualize volatility in a structurally consistent way.
Three plotting modes help demonstrate this difference:
When the midline is shown alone, you get a smooth, adaptive baseline that behaves almost like a regression moving average, as shown in this view:
When full channels are enabled, you see how standard deviation reacts to local structure with dynamically widening and tightening bands, a mode illustrated here:
When ATR mode is chosen instead of StdDev, band width reflects breadth of movement rather than variance, creating a volatility-aware envelope like the example here:
Why kernels
Classical moving averages allocate fixed weights. Kernels let the user define weighting shape:
Epanechnikov — emphasizes bars near the current bar, fades fast, stable and smooth.
Triangular — linear decay, simple and responsive.
Laplacian — exponential decay from the current point, sharper reactivity.
Cosine — gentle periodic decay, balanced smoothness for trend filters.
Using these in combination with a bandwidth parameter gives fine control over smoothness vs responsiveness. Smaller bandwidths give sharper local sensitivity, larger bandwidths give smoother curvature.
How it works (core logic)
The indicator computes three building blocks:
1) Kernel-weighted midline
For every bar, a sliding window looks back Window Length bars. Each bar in this window receives a kernel weight depending on:
its index distance from the present
the chosen kernel shape
the bandwidth parameter (locality)
Weights form the denominator, weighted values form the numerator, and the resulting ratio is the kernel regression mean. This midline is the central trend.
2) Kernel-based width
You choose one of two band types:
Kernel ATR — ATR values are kernel-averaged, producing a smooth, volatility-based width that is not dependent on variance. Ideal for directional trend channels and regime separation.
Kernel StdDev — local variance around the midline is computed through kernel weighting. This produces a true statistical envelope that narrows in quiet periods and widens in noisy areas.
Width is scaled using Band Multiplier , controlling how far the envelope extends.
3) Upper and lower channels
Provided midline and width exist, the channel edges are:
Upper = midline + bandMult × width
Lower = midline − bandMult × width
These create smooth structures around price that adapt continuously.
Plotting modes
The indicator supports multiple visual styles depending on what you want to emphasize.
When only the midline is displayed, you get a pure kernel trend: a smooth regression-like curve that reacts to local structure while filtering noise, demonstrated here: This provides a clean read on direction and slope.
With full channels enabled, the behavior of the bands becomes visible. Standard deviation mode creates elastic boundaries that tighten during compressions and widen during turbulence, which you can see in the band-focused demonstration: This helps identify expansion events, volatility clusters, and breakouts.
ATR mode shifts interpretation from statistical variance to raw movement amplitude. This makes channels less sensitive to outliers and more consistent across trend phases, as shown in this ATR variation example: This mode is particularly useful for breakout systems and bar-range regimes.
Regime detection and bar coloring
The slope of the midline defines directional bias:
Up-slope → green
Down-slope → red
Flat → gray
A secondary regime filter compares close to the channel:
Trend Up Strong — close above upper band and midline rising.
Trend Down Strong — close below lower band and midline falling.
Trend Up Weak — close between midline and upper band with rising slope.
Trend Down Weak — close between lower band and midline with falling slope.
Compression mode — squeeze conditions.
Bar coloring is optional and can be toggled for cleaner charts.
Squeeze logic
The indicator includes non-standard squeeze detection based on relative width , defined as:
width / |midline|
This gives a dimensionless measure of how “tight” or “loose” the channel is, normalized for trend level.
A rolling window evaluates the percentile rank of current width relative to past behavior. If the width is in the lowest X% of its last N observations, the script flags a squeeze environment. This highlights compression regions that may precede breakouts or regime shifts.
Deviation highlighting
When using Kernel StdDev mode, you may enable deviation flags that highlight bars where price moves outside the channel:
Above upper band → bullish momentum overextension
Below lower band → bearish momentum overextension
This is turned off in ATR mode because ATR widths do not represent distributional variance.
Alerts included
Kernel Channel Long — midline turns up.
Kernel Channel Short — midline turns down.
Price Crossed Midline — crossover or crossunder of the midline.
Price Above Upper — early momentum expansion.
Price Below Lower — downward volatility expansion.
These help automate regime changes and breakout detection.
How to use it
Trend identification
The midline acts as a bias filter. Rising midline means trend strength upward, falling midline means downward behavior. The channel width contextualizes confidence.
Breakout anticipation
Kernel StdDev compressions highlight areas where price is coiling. Breakouts often follow narrow relative width. ATR mode provides structural expansion cues that are smooth and robust.
Mean reversion
StdDev mode is suitable for fade setups. Moves to outer bands during low volatility often revert to the midline.
Continuation logic
If price breaks above the upper band while midline is rising, the indicator flags strong directional expansion. Same logic for breakdowns on the lower band.
Volatility characterization
Kernel ATR maps raw bar movements and is excellent for identifying regime shifts in markets where variance is unstable.
Tuning guidance
For smoother long-term trend tracking
Larger window (150–300).
Moderate bandwidth (1.0–2.0).
Epanechnikov or Cosine kernel.
ATR mode for stable envelopes.
For swing trading / short-term structure
Window length around 50–100.
Bandwidth 0.6–1.2.
Triangular for speed, Laplacian for sharper reactions.
StdDev bands for precise volatility compression.
For breakout systems
Smaller bandwidth for sharp local detection.
ATR mode for stable envelopes.
Enable squeeze highlighting for identifying setups early.
For mean-reversion systems
Use StdDev bands.
Moderate window length.
Highlight deviations to locate overextended bars.
Settings overview
Kernel Settings
Source
Window Length
Bandwidth
Kernel Type (Epanechnikov, Triangular, Laplacian, Cosine)
Channel Width
Band Type (Kernel ATR or Kernel StdDev)
Band Multiplier
Visuals
Show Bands
Color Bars By Regime
Highlight Squeeze Periods
Highlight Deviation
Lookback and Percentile settings
Colors for uptrend, downtrend, squeeze, flat
Trading applications
Trend filtering — trade only in direction of the midline slope.
Breakout confirmation — expansion outside the bands while slope agrees.
Squeeze timing — compression periods often precede the next directional leg.
Volatility-aware stops — ATR mode makes channel edges suitable for adaptive stop placement.
Structural swing mapping — StdDev bands help locate midline pullbacks vs distributional extremes.
Bias rotation — bar coloring highlights when regime shifts occur.
Notes
The Kernel Channel is not a signal generator by itself, but a structural map. It helps classify trend direction, volatility environment, distribution shape, and compression cycles. Combine it with your entry and exit framework, risk parameters, and higher-timeframe confirmation.
It is designed to behave consistently across markets, to avoid the bluntness of classical averages, and to reveal subtle curvature in price that traditional channels miss. Adjust kernel type, bandwidth, and band source to match the noise profile of your instrument, then use squeeze logic and deviation highlighting to guide timing.
Lorentzian Length Adaptive Moving Average [LLAMA] Adaptation of "Machine Learning: Lorentzian Classification" by
Gradient color by base on work by
LLAMA: A regime-aware adaptive moving average that bends with the market.
Start with a problem traders know:
Traditional moving averages are either too slow (EMA200) or too fast (EMA9)
Adaptive MAs exist, but they often hug price too tightly or smooth too much, failing to balance bias and tactics
LLAMA uses a Lorentzian distance function to adapt its length dynamically. Instead of a fixed smoothing window, it stretches or contracts depending on market conditions. This distortion reduces lag while still providing a clear bias line.
The indicator looks back at recent bars and measures how similar they are using a Lorentzian distance (a log‑scaled absolute difference). It keeps track of the “nearest neighbors” — bars that most resemble the current regime. Each neighbor carries a label (long, short, neutral) based on simple price comparisons. By averaging these labels, LLAMA predicts whether the market is leaning bullish or bearish. That prediction is then mapped into a dynamic length between and .
Bullish bias -> length stretches toward max (smoother, more stable).
Bearish bias -> length contracts toward min (snappier, more reactive).
During breakouts, LLAMA tightens and comes into contact with bars, giving actionable signals. During chop, it stretches to avoid false triggers. It covers both ends of the spectrum (bias and tactics) in one line, something static MA's can't do.
Think of LLAMA as a lens that bends with the market:
Wide lens (max length) for big picture bias.
Narrow lens (min length) for tactical precision.
The "Lorentzian Loop" is the math that decides when to widen or narrow.
Adaptive Momentum Pressure (AMP)🔹 Adaptive Momentum Pressure (AMP)
A hybrid momentum oscillator that adapts to volatility and trend dynamics.
AMP measures the rate of change of price pressure and automatically adjusts its sensitivity based on market volatility.
It reacts faster in trending markets and smooths out noise during consolidation — helping traders identify genuine momentum shifts early while avoiding whipsaws.
🧠 Core Concept
AMP fuses three elements into one adaptive momentum model:
Normalized Momentum (ROC) – captures directional acceleration of price.
Adaptive Smoothing – the smoothing length dynamically contracts when volatility rises and expands when it falls.
Directional Bias – derived from the short-term EMA slope to weight momentum toward the prevailing trend.
Combined, these form a pressure value oscillating between –100 and +100, revealing when momentum expands or fades.
⚙️ How It Works
Calculates a normalized rate of change (ROC) relative to recent volatility.
Adjusts its effective length using the ATR — more volatile periods shorten the lookback for quicker reaction.
Applies a custom EMA that adapts in real time.
Modulates momentum by a normalized EMA slope (“trend bias”).
Produces a smoothed AMP line with a Signal line and crossover markers.
🔍 How to Read It
Green AMP line rising above Signal → Building bullish momentum.
Red AMP line falling below Signal → Fading or bearish momentum.
White Signal line = smoothed confirmation of trend energy.
Green dots = early bullish crossovers.
Red dots = early bearish crossovers.
Typical interpretations:
AMP crossing above 0 from below → early bullish impulse.
AMP peaking near +50–100 and curling down → potential momentum exhaustion.
Crosses below 0 with red pressure → bearish confirmation.
⚡ Advantages
✅ Adaptive across all markets and timeframes
✅ Built-in trend bias filters false signals
✅ Reacts earlier than RSI/MACD while reducing noise
✅ No manual retuning required
🧩 Suggested Use
Combine with structure or volume tools to confirm breakouts.
Works well as a momentum confirmation filter for entries/exits.
Optimal display: separate oscillator pane (not overlay).
Use it responsibly — AMP is an analytical tool, not financial advice.
Volatility-Targeted Momentum Portfolio [BackQuant]Volatility-Targeted Momentum Portfolio
A complete momentum portfolio engine that ranks assets, targets a user-defined volatility, builds long, short, or delta-neutral books, and reports performance with metrics, attribution, Monte Carlo scenarios, allocation pie, and efficiency scatter plots. This description explains the theory and the mechanics so you can configure, validate, and deploy it with intent.
Table of contents
What the script does at a glance
Momentum, what it is, how to know if it is present
Volatility targeting, why and how it is done here
Portfolio construction modes: Long Only, Short Only, Delta Neutral
Regime filter and when the strategy goes to cash
Transaction cost modelling in this script
Backtest metrics and definitions
Performance attribution chart
Monte Carlo simulation
Scatter plot analysis modes
Asset allocation pie chart
Inputs, presets, and deployment checklist
Suggested workflow
1) What the script does at a glance
Pulls a list of up to 15 tickers, computes a simple momentum score on each over a configurable lookback, then volatility-scales their bar-to-bar return stream to a target annualized volatility.
Ranks assets by raw momentum, selects the top 3 and bottom 3, builds positions according to the chosen mode, and gates exposure with a fast regime filter.
Accumulates a portfolio equity curve with risk and performance metrics, optional benchmark buy-and-hold for comparison, and a full alert suite.
Adds visual diagnostics: performance attribution bars, Monte Carlo forward paths, an allocation pie, and scatter plots for risk-return and factor views.
2) Momentum: definition, detection, and validation
Momentum is the tendency of assets that have performed well to continue to perform well, and of underperformers to continue underperforming, over a specific horizon. You operationalize it by selecting a horizon, defining a signal, ranking assets, and trading the leaders versus laggards subject to risk constraints.
Signal choices . Common signals include cumulative return over a lookback window, regression slope on log-price, or normalized rate-of-change. This script uses cumulative return over lookback bars for ranking (variable cr = price/price - 1). It keeps the ranking simple and lets volatility targeting handle risk normalization.
How to know momentum is present .
Leaders and laggards persist across adjacent windows rather than flipping every bar.
Spread between average momentum of leaders and laggards is materially positive in sample.
Cross-sectional dispersion is non-trivial. If everything is flat or highly correlated with no separation, momentum selection will be weak.
Your validation should include a diagnostic that measures whether returns are explained by a momentum regression on the timeseries.
Recommended diagnostic tool . Before running any momentum portfolio, verify that a timeseries exhibits stable directional drift. Use this indicator as a pre-check: It fits a regression to price, exposes slope and goodness-of-fit style context, and helps confirm if there is usable momentum before you force a ranking into a flat regime.
3) Volatility targeting: purpose and implementation here
Purpose . Volatility targeting seeks a more stable risk footprint. High-vol assets get sized down, low-vol assets get sized up, so each contributes more evenly to total risk.
Computation in this script (per asset, rolling):
Return series ret = log(price/price ).
Annualized volatility estimate vol = stdev(ret, lookback) * sqrt(tradingdays).
Leverage multiplier volMult = clamp(targetVol / vol, 0.1, 5.0).
This caps sizing so extremely low-vol assets don’t explode weight and extremely high-vol assets don’t go to zero.
Scaled return stream sr = ret * volMult. This is the per-bar, risk-adjusted building block used in the portfolio combinations.
Interpretation . You are not levering your account on the exchange, you are rescaling the contribution each asset’s daily move has on the modeled equity. In live trading you would reflect this with position sizing or notional exposure.
4) Portfolio construction modes
Cross-sectional ranking . Assets are sorted by cr over the chosen lookback. Top and bottom indices are extracted without ties.
Long Only . Averages the volatility-scaled returns of the top 3 assets: avgRet = mean(sr_top1, sr_top2, sr_top3). Position table shows per-asset leverages and weights proportional to their current volMult.
Short Only . Averages the negative of the volatility-scaled returns of the bottom 3: avgRet = mean(-sr_bot1, -sr_bot2, -sr_bot3). Position table shows short legs.
Delta Neutral . Long the top 3 and short the bottom 3 in equal book sizes. Each side is sized to 50 percent notional internally, with weights within each side proportional to volMult. The return stream mixes the two sides: avgRet = mean(sr_top1,sr_top2,sr_top3, -sr_bot1,-sr_bot2,-sr_bot3).
Notes .
The selection metric is raw momentum, the execution stream is volatility-scaled returns. This separation is deliberate. It avoids letting volatility dominate ranking while still enforcing risk parity at the return contribution stage.
If everything rallies together and dispersion collapses, Long Only may behave like a single beta. Delta Neutral is designed to extract cross-sectional momentum with low net beta.
5) Regime filter
A fast EMA(12) vs EMA(21) filter gates exposure.
Long Only active when EMA12 > EMA21. Otherwise the book is set to cash.
Short Only active when EMA12 < EMA21. Otherwise cash.
Delta Neutral is always active.
This prevents taking long momentum entries during obvious local downtrends and vice versa for shorts. When the filter is false, equity is held flat for that bar.
6) Transaction cost modelling
There are two cost touchpoints in the script.
Per-bar drag . When the regime filter is active, the per-bar return is reduced by fee_rate * avgRet inside netRet = avgRet - (fee_rate * avgRet). This models proportional friction relative to traded impact on that bar.
Turnover-linked fee . The script tracks changes in membership of the top and bottom baskets (top1..top3, bot1..bot3). The intent is to charge fees when composition changes. The template counts changes and scales a fee by change count divided by 6 for the six slots.
Use case: increase fee_rate to reflect taker fees and slippage if you rebalance every bar or trade illiquid assets. Reduce it if you rebalance less often or use maker orders.
Practical advice .
If you rebalance daily, start with 5–20 bps round-trip per switch on liquid futures and adjust per venue.
For crypto perp microcaps, stress higher cost assumptions and add slippage buffers.
If you only rotate on lookback boundaries or at signals, use alert-driven rebalances and lower per-bar drag.
7) Backtest metrics and definitions
The script computes a standard set of portfolio statistics once the start date is reached.
Net Profit percent over the full test.
Max Drawdown percent, tracked from running peaks.
Annualized Mean and Stdev using the chosen trading day count.
Variance is the square of annualized stdev.
Sharpe uses daily mean adjusted by risk-free rate and annualized.
Sortino uses downside stdev only.
Omega ratio of sum of gains to sum of losses.
Gain-to-Pain total gains divided by total losses absolute.
CAGR compounded annual growth from start date to now.
Alpha, Beta versus a user-selected benchmark. Beta from covariance of daily returns, Alpha from CAPM.
Skewness of daily returns.
VaR 95 linear-interpolated 5th percentile of daily returns.
CVaR average of the worst 5 percent of daily returns.
Benchmark Buy-and-Hold equity path for comparison.
8) Performance attribution
Cumulative contribution per asset, adjusted for whether it was held long or short and for its volatility multiplier, aggregated across the backtest. You can filter to winners only or show both sides. The panel is sorted by contribution and includes percent labels.
9) Monte Carlo simulation
The panel draws forward equity paths from either a Normal model parameterized by recent mean and stdev, or non-parametric bootstrap of recent daily returns. You control the sample length, number of simulations, forecast horizon, visibility of individual paths, confidence bands, and a reproducible seed.
Normal uses Box-Muller with your seed. Good for quick, smooth envelopes.
Bootstrap resamples realized returns, preserving fat tails and volatility clustering better than a Gaussian assumption.
Bands show 10th, 25th, 75th, 90th percentiles and the path mean.
10) Scatter plot analysis
Four point-cloud modes, each plotting all assets and a star for the current portfolio position, with quadrant guides and labels.
Risk-Return Efficiency . X is risk proxy from leverage, Y is expected return from annualized momentum. The star shows the current book’s composite.
Momentum vs Volatility . Visualizes whether leaders are also high vol, a cue for turnover and cost expectations.
Beta vs Alpha . X is a beta proxy, Y is risk-adjusted excess return proxy. Useful to see if leaders are just beta.
Leverage vs Momentum . X is volMult, Y is momentum. Shows how volatility targeting is redistributing risk.
11) Asset allocation pie chart
Builds a wheel of current allocations.
Long Only, weights are proportional to each long asset’s current volMult and sum to 100 percent.
Short Only, weights show the short book as positive slices that sum to 100 percent.
Delta Neutral, 50 percent long and 50 percent short books, each side leverage-proportional.
Labels can show asset, percent, and current leverage.
12) Inputs and quick presets
Core
Portfolio Strategy . Long Only, Short Only, Delta Neutral.
Initial Capital . For equity scaling in the panel.
Trading Days/Year . 252 for stocks, 365 for crypto.
Target Volatility . Annualized, drives volMult.
Transaction Fees . Per-bar drag and composition change penalty, see the modelling notes above.
Momentum Lookback . Ranking horizon. Shorter is more reactive, longer is steadier.
Start Date . Ensure every symbol has data back to this date to avoid bias.
Benchmark . Used for alpha, beta, and B&H line.
Diagnostics
Metrics, Equity, B&H, Curve labels, Daily return line, Rolling drawdown fill.
Attribution panel. Toggle winners only to focus on what matters.
Monte Carlo mode with Normal or Bootstrap and confidence bands.
Scatter plot type and styling, labels, and portfolio star.
Pie chart and labels for current allocation.
Presets
Crypto Daily, Long Only . Lookback 25, Target Vol 50 percent, Fees 10 bps, Regime filter on, Metrics and Drawdown on. Monte Carlo Bootstrap with Recent 200 bars for bands.
Crypto Daily, Delta Neutral . Lookback 25, Target Vol 50 percent, Fees 15–25 bps, Regime filter always active for this mode. Use Scatter Risk-Return to monitor efficiency and keep the star near upper left quadrants without drifting rightward.
Equities Daily, Long Only . Lookback 60–120, Target Vol 15–20 percent, Fees 5–10 bps, Regime filter on. Use Benchmark SPX and watch Alpha and Beta to keep the book from becoming index beta.
13) Suggested workflow
Universe sanity check . Pick liquid tickers with stable data. Thin assets distort vol estimates and fees.
Check momentum existence . Run on your timeframe. If slope and fit are weak, widen lookback or avoid that asset or timeframe.
Set risk budget . Choose a target volatility that matches your drawdown tolerance. Higher target increases turnover and cost sensitivity.
Pick mode . Long Only for bull regimes, Short Only for sustained downtrends, Delta Neutral for cross-sectional harvesting when index direction is unclear.
Tune lookback . If leaders rotate too often, lengthen it. If entries lag, shorten it.
Validate cost assumptions . Increase fee_rate and stress Monte Carlo. If the edge vanishes with modest friction, refine selection or lengthen rebalance cadence.
Run attribution . Confirm the strategy’s winners align with intuition and not one unstable outlier.
Use alerts . Enable position change, drawdown, volatility breach, regime, momentum shift, and crash alerts to supervise live runs.
Important implementation details mapped to code
Momentum measure . cr = price / price - 1 per symbol for ranking. Simplicity helps avoid overfitting.
Volatility targeting . vol = stdev(log returns, lookback) * sqrt(tradingdays), volMult = clamp(targetVol / vol, 0.1, 5), sr = ret * volMult.
Selection . Extract indices for top1..top3 and bot1..bot3. The arrays rets, scRets, lev_vals, and ticks_arr track momentum, scaled returns, leverage multipliers, and display tickers respectively.
Regime filter . EMA12 vs EMA21 switch determines if the strategy takes risk for Long or Short modes. Delta Neutral ignores the gate.
Equity update . Equity multiplies by 1 + netRet only when the regime was active in the prior bar. Buy-and-hold benchmark is computed separately for comparison.
Tables . Position tables show current top or bottom assets with leverage and weights. Metric table prints all risk and performance figures.
Visualization panels . Attribution, Monte Carlo, scatter, and pie use the last bars to draw overlays that update as the backtest proceeds.
Final notes
Momentum is a portfolio effect. The edge comes from cross-sectional dispersion, adequate risk normalization, and disciplined turnover control, not from a single best asset call.
Volatility targeting stabilizes path but does not fix selection. Use the momentum regression link above to confirm structure exists before you size into it.
Always test higher lag costs and slippage, then recheck metrics, attribution, and Monte Carlo envelopes. If the edge persists under stress, you have something robust.
VWAP Kalman FilterOverview
This indicator applies Kalman filtering techniques to Volume Weighted Average Price (VWAP) calculations, providing a statistically optimized approach to VWAP analysis. The Kalman filter reduces noise while maintaining responsiveness to genuine price movements, addressing common VWAP limitations in volatile or low-volume conditions.
Technical Implementation
Kalman Filter Mathematics
The indicator implements a state-space model for VWAP estimation:
- Prediction Step: x̂(k|k-1) = x̂(k-1|k-1) + v(k-1)
- Update Step: x̂(k|k) = x̂(k|k-1) + K(k)
- Kalman Gain: K(k) = P(k|k-1) / (P(k|k-1) + R)
Where:
- x̂ = estimated VWAP state
- K = Kalman gain (adaptive weighting factor)
- P = error covariance
- R = measurement noise
- Q = process noise
- v = optional velocity component
Core Components
Dual VWAP System
- Standard VWAP: Traditional volume-weighted calculation
- Kalman-filtered VWAP: Noise-reduced estimation with optional velocity tracking
- Real-time divergence measurement between filtered and unfiltered values
Adaptive Filtering
- Process Noise (Q): Controls adaptation to price changes (0.001-1.0)
- Measurement Noise (R): Determines smoothing intensity (0.01-5.0)
- Optional velocity tracking for momentum-based filtering
Multi-Timeframe Anchoring
- Session, Weekly, Monthly, Quarterly, and Yearly anchor periods
- Automatic Kalman state reset on anchor changes
- Maintains VWAP integrity across timeframes
Features
Visual Components
- Dual VWAP Lines: Compare filtered vs. unfiltered in real-time
- Dynamic Bands: Three-level deviation bands (1σ, 2σ, 3σ)
- Trend Coloring: Automatic color adaptation based on price position
- Cloud Visualization: Highlights divergence between standard and Kalman VWAP
- Signal Markers: Crossover and band-touch indicators
Trading Signals
- VWAP crossover detection with Kalman filtering
- Band touch alerts at multiple standard deviation levels
- Velocity-based momentum confirmation (optional)
- Divergence warnings when filtered/unfiltered values separate
Information Display
- Real-time VWAP values (both standard and filtered)
- Trend direction indicator
- Velocity/momentum reading (when enabled)
- Divergence percentage calculation
- Anchor period display
Input Parameters
VWAP Settings
- Anchor Period: Choose calculation reset period
- Band Multipliers: Customize deviation band distances
- Display Options: Toggle standard VWAP and bands
Kalman Parameters
- Length: Base period for calculations (5-200)
- Process Noise (Q: Higher values increase responsiveness
- Measurement Noise (R): Higher values increase smoothing
- Velocity Tracking: Enable momentum-based filtering
Visual Controls
- Toggle filtered/unfiltered VWAP display
- Band visibility options
- Signal markers on/off
- Cloud fill between VWAPs
- Bar coloring by trend
Use Cases
Noise Reduction
Particularly effective during:
- Low volume periods (pre-market, lunch hours)
- Volatile market conditions
- Fast-moving markets where standard VWAP whipsaws
Trend Identification
- Cleaner trend signals with reduced false crosses
- Earlier trend detection through velocity component
- Confirmation through divergence analysis
Support/Resistance
- Filtered VWAP provides more stable S/R levels
- Bands adapt to filtered values for better zone identification
- Reduced false breakout signals
Technical Advantages
1. Optimal Estimation: Mathematically optimal under Gaussian noise assumptions
2. Adaptive Response: Self-adjusting to market conditions
3. Predictive Element: Velocity component provides forward-looking insight
4. Noise Immunity: Superior noise rejection vs. simple moving average smoothing
Limitations
- Assumes linear price dynamics
- Requires parameter optimization for different instruments
- May lag during sudden volatility regime changes
- Not suitable as standalone trading system
Mathematical Background
Based on control systems theory, the Kalman filter provides recursive Bayesian estimation originally developed for aerospace applications. This implementation adapts the algorithm specifically for financial time series, maintaining VWAP's volume-weighted properties while adding statistical filtering.
Comparison with Standard VWAP
Standard VWAP Issues Addressed:
- Choppy behavior in low volume
- Whipsaws around VWAP line
- Lag in trend identification
- Noise in deviation bands
Kalman VWAP Benefits:
- Smooth yet responsive line
- Fewer false signals
- Optional momentum tracking
- Statistically optimized filtering
Alert Conditions
The indicator includes several pre-configured alert conditions:
- Bullish/Bearish VWAP crosses
- Upper/Lower band touches
- High divergence warnings
- Velocity shifts (if enabled)
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This open-source indicator is provided as-is for educational and trading purposes. No guarantees are made regarding trading performance. Users should conduct their own testing and validation before using in live trading.
Dynamic Fractal Flow [Alpha Extract]An advanced momentum oscillator that combines fractal market structure analysis with adaptive volatility weighting and multi-derivative calculus to identify high-probability trend reversals and continuation patterns. Utilizing sophisticated noise filtering through choppiness indexing and efficiency ratio analysis, this indicator delivers entries that adapt to changing market regimes while reducing false signals during consolidation via multi-layer confirmation centered on acceleration analysis, statistical band context, and dynamic omega weighting—without any divergence detection.
🔶 Fractal-Based Market Structure Detection
Employs Williams Fractal methodology to identify pivotal market highs and lows, calculating normalized price position within the established fractal range to generate oscillator signals based on structural positioning. The system tracks fractal points dynamically and computes relative positioning with ATR fallback protection, ensuring continuous signal generation even during extended trending periods without fractal formation.
🔶 Dynamic Omega Weighting System
Implements an adaptive weighting algorithm that adjusts signal emphasis based on real-time volatility conditions and volume strength, calculating dynamic omega coefficients ranging from 0.3 to 0.9. The system applies heavier weighting to recent price action during high-conviction moves while reducing sensitivity during low-volume environments, mitigating lag inherent in fixed-period calculations through volatility normalization and volume-strength integration.
🔶 Cascading Robustness Filtering
Features up to five stages of progressive EMA smoothing with user-adjustable robustness steps, each layer systematically filtering microstructure noise while preserving essential trend information. Smoothing periods scale with the chosen fractal length and robustness steps using a fixed smoothing multiplier for consistent, predictable behavior.
🔶 Adaptive Noise Suppression Engine
Integrates dual-component noise filtering combining Choppiness Index calculation with Kaufman’s Efficiency Ratio to detect ranging versus trending market conditions. The system applies dynamic damping that maintains full signal strength during trending environments while suppressing signals during choppy consolidation, aligning output with the prevailing regime.
🔶 Acceleration and Jerk Analysis Framework
Calculates second-derivative acceleration and third-derivative jerk to identify explosive momentum shifts before they fully materialize on traditional indicators. Detects bullish acceleration when both acceleration and jerk turn positive in negative oscillator territory, and bearish acceleration when both turn negative in positive territory, providing early entry signals for high-velocity trend initiation phases.
🔶 Multi-Layer Signal Generation Architecture
Combines three primary signal types with hierarchical validation: acceleration signals, band crossover entries, and threshold momentum signals. Each signal category includes momentum confirmation, trend-state validation, and statistical band context; signals are further conditioned by band squeeze detection to avoid low-probability entries during compression phases. Divergence is intentionally excluded for a purely structure- and momentum-driven approach.
🔶 Dynamic Statistical Band System
Utilizes Bollinger-style standard deviation bands with configurable multiplier and length to create adaptive threshold zones that expand during volatile periods and contract during consolidation. Includes band squeeze detection to identify compression phases that typically precede expansion, with signal suppression during squeezes to prevent premature entries.
🔶 Gradient Color Visualization System
Features color gradient mapping that dynamically adjusts line intensity based on signal strength, transitioning from neutral gray to progressively intense bullish or bearish colors as conviction increases. Includes gradient fills between the signal line and zero with transparency scaling based on oscillator intensity for immediate visual confirmation of trend strength and directional bias.
All analysis provided by Alpha Extract is for educational and informational purposes only. The information and publications are not meant to be, and do not constitute, financial, investment, trading, or other types of advice or recommendations.
Adaptive Trend SelectorThe Adaptive Trend Selector is a comprehensive trend-following tool designed to automatically identify the optimal moving average crossover strategy. It features adjustable parameters and an integrated backtester that delivers institutional-grade insights into the recommended strategy. The model continuously adapts to new data in real time by evaluating multiple moving average combinations, determining the best performing lengths, and presenting the backtest results in a clear, color-coded table that benchmarks performance against the buy-and-hold strategy.
At its core, the model systematically backtests a wide range of moving average combinations to identify the configuration that maximizes the selected optimization metric. Users can choose to optimize for absolute returns or risk-adjusted returns using the Sharpe, Sortino, or Calmar ratios. Alternatively, users can enable manual optimization to test custom fast and slow moving average lengths and view the corresponding backtest results. The label displays the Compounded Annual Growth Rate (CAGR) of the strategy, with the buy-and-hold CAGR in parentheses for comparison. The table presents the backtest results based on the fast and slow lengths displayed at the top:
Sharpe = CAGR per unit of standard deviation.
Sortino = CAGR per unit of downside deviation.
Calmar = CAGR relative to maximum drawdown.
Max DD = Largest peak-to-trough decline in value.
Beta (β) = Return sensitivity relative to buy-and-hold.
Alpha (α) = Excess annualized risk-adjusted returns.
Win Rate = Ratio of profitable trades to total trades.
Profit Factor = Total gross profit per unit of losses.
Expectancy = Average expected return per trade.
Trades/Year = Average number of trades per year.
This indicator is designed with flexibility in mind, enabling users to specify the start date of the backtesting period and the preferred moving average strategy. Supported strategies include the Exponential Moving Average (EMA), Simple Moving Average (SMA), Wilder’s Moving Average (RMA), Weighted Moving Average (WMA), and Volume-Weighted Moving Average (VWMA). To minimize overfitting, users can define constraints such as a minimum and maximum number of trades per year, as well as an optional optimization margin that prioritizes longer, more robust combinations by requiring shorter-length strategies to exceed this threshold. The table follows an intuitive color logic that enables quick performance comparison against buy-and-hold (B&H):
Sharpe = Green indicates better than B&H, while red indicates worse.
Sortino = Green indicates better than B&H, while red indicates worse.
Calmar = Green indicates better than B&H, while red indicates worse.
Max DD = Green indicates better than B&H, while red indicates worse.
Beta (β) = Green indicates better than B&H, while red indicates worse.
Alpha (α) = Green indicates above 0%, while red indicates below 0%.
Win Rate = Green indicates above 50%, while red indicates below 50%.
Profit Factor = Green indicates above 2, while red indicates below 1.
Expectancy = Green indicates above 0%, while red indicates below 0%.
In summary, the Adaptive Trend Selector is a powerful tool designed to help investors make data-driven decisions when selecting moving average crossover strategies. By optimizing for risk-adjusted returns, investors can confidently identify the best lengths using institutional-grade metrics. While results are based on the selected historical period, users should be mindful of potential overfitting, as past results may not persist under future market conditions. Since the model recalibrates to incorporate new data, the recommended lengths may evolve over time.
Kalman VWAP Filter [BackQuant]Kalman VWAP Filter
A precision-engineered price estimator that fuses Kalman filtering with the Volume-Weighted Average Price (VWAP) to create a smooth, adaptive representation of fair value. This hybrid model intelligently balances responsiveness and stability, tracking trend shifts with minimal noise while maintaining a statistically grounded link to volume distribution.
If you would like to see my original Kalman Filter, please find it here:
Concept overview
The Kalman VWAP Filter is built on two core ideas from quantitative finance and control theory:
Kalman filtering — a recursive Bayesian estimator used to infer the true underlying state of a noisy system (in this case, fair price).
VWAP anchoring — a dynamic reference that weights price by traded volume, representing where the majority of transactions have occurred.
By merging these concepts, the filter produces a line that behaves like a "smart moving average": smooth when noise is high, fast when markets trend, and self-adjusting based on both market structure and user-defined noise parameters.
How it works
Measurement blend : Combines the chosen Price Source (e.g., close or hlc3) with either a Session VWAP or a Rolling VWAP baseline. The VWAP Weight input controls how much the filter trusts traded volume versus price movement.
Kalman recursion : Each bar updates an internal "state estimate" using the Kalman gain, which determines how much to trust new observations vs. the prior state.
Noise parameters :
Process Noise controls agility — higher values make the filter more responsive but also more volatile.
Measurement Noise controls smoothness — higher values make it steadier but slower to adapt.
Filter order (N) : Defines how many parallel state estimates are used. Larger orders yield smoother output by layering multiple one-dimensional Kalman passes.
Final output : A refined price trajectory that captures VWAP-adjusted fair value while dynamically adjusting to real-time volatility and order flow.
Why this matters
Most smoothing techniques (EMA, SMA, Hull) trade off lag for smoothness. Kalman filtering, however, adaptively rebalances that tradeoff each bar using probabilistic weighting, allowing it to follow market state changes more efficiently. Anchoring it to VWAP integrates microstructure context — capturing where liquidity truly lies rather than only where price moves.
Use cases
Trend tracking : Color-coded candle painting highlights shifts in slope direction, revealing early trend transitions.
Fair value mapping : The line represents a continuously updated equilibrium price between raw price action and VWAP flow.
Adaptive moving average replacement : Outperforms static MAs in variable volatility regimes by self-adjusting smoothness.
Execution & reversion logic : When price diverges from the Kalman VWAP, it may indicate short-term imbalance or overextension relative to volume-adjusted fair value.
Cross-signal framework : Use with standard VWAP or other filters to identify convergence or divergence between liquidity-weighted and state-estimated prices.
Parameter guidance
Process Noise : 0.01–0.05 for swing traders, 0.1–0.2 for intraday scalping.
Measurement Noise : 2–5 for normal use, 8+ for very smooth tracking.
VWAP Weight : 0.2–0.4 balances both price and VWAP influence; 1.0 locks output directly to VWAP dynamics.
Filter Order (N) : 3–5 for reactive short-term filters; 8–10 for smoother institutional-style baselines.
Interpretation
When price > Kalman VWAP and slope is positive → bullish pressure; buyers dominate above fair value.
When price < Kalman VWAP and slope is negative → bearish pressure; sellers dominate below fair value.
Convergence of price and Kalman VWAP often signals equilibrium; strong divergence suggests imbalance.
Crosses between Kalman VWAP and the base VWAP can hint at shifts in short-term vs. long-term liquidity control.
Summary
The Kalman VWAP Filter blends statistical estimation with market microstructure awareness, offering a refined alternative to static smoothing indicators. It adapts in real time to volatility and order flow, helping traders visualize balance, transition, and momentum through a lens of probabilistic fair value rather than simple price averaging.
IIR One-Pole Price Filter [BackQuant]IIR One-Pole Price Filter
A lightweight, mathematically grounded smoothing filter derived from signal processing theory, designed to denoise price data while maintaining minimal lag. It provides a refined alternative to the classic Exponential Moving Average (EMA) by directly controlling the filter’s responsiveness through three interchangeable alpha modes: EMA-Length , Half-Life , and Cutoff-Period .
Concept overview
An IIR (Infinite Impulse Response) filter is a type of recursive filter that blends current and past input values to produce a smooth, continuous output. The "one-pole" version is its simplest form, consisting of a single recursive feedback loop that exponentially decays older price information. This makes it both memory-efficient and responsive , ideal for traders seeking a precise balance between noise reduction and reaction speed.
Unlike standard moving averages, the IIR filter can be tuned in physically meaningful terms (such as half-life or cutoff frequency) rather than just arbitrary periods. This allows the trader to think about responsiveness in the same way an engineer or physicist would interpret signal smoothing.
Why use it
Filters out market noise without introducing heavy lag like higher-order smoothers.
Adapts to various trading speeds and time horizons by changing how alpha (responsiveness) is parameterized.
Provides consistent and mathematically interpretable control of smoothing, suitable for both discretionary and algorithmic systems.
Can serve as the core component in adaptive strategies, volatility normalization, or trend extraction pipelines.
Alpha Modes Explained
EMA-Length : Classic exponential decay with alpha = 2 / (L + 1). Equivalent to a standard EMA but exposed directly for fine control.
Half-Life : Defines the number of bars it takes for the influence of a price input to decay by half. More intuitive for time-domain analysis.
Cutoff-Period : Inspired by analog filter theory, defines the cutoff frequency (in bars) beyond which price oscillations are heavily attenuated. Lower periods = faster response.
Formula in plain terms
Each bar updates as:
yₜ = yₜ₋₁ + alpha × (priceₜ − yₜ₋₁)
Where alpha is the smoothing coefficient derived from your chosen mode.
Smaller alpha → smoother but slower response.
Larger alpha → faster but noisier response.
Practical application
Trend detection : When the filter line rises, momentum is positive; when it falls, momentum is negative.
Signal timing : Use the crossover of the filter vs its previous value (or price) as an entry/exit condition.
Noise suppression : Apply on volatile assets or lower timeframes to remove flicker from raw price data.
Foundation for advanced filters : The one-pole IIR serves as a building block for multi-pole cascades, adaptive smoothers, and spectral filters.
Customization options
Alpha Scale : Multiplies the final alpha to fine-tune aggressiveness without changing the mode’s core math.
Color Painting : Candles can be painted green/red by trend direction for visual clarity.
Line Width & Transparency : Adjust the visual intensity to integrate cleanly with your charting style.
Interpretation tips
A smooth yet reactive line implies optimal tuning — minimal delay with reduced false flips.
A sluggish line suggests alpha is too small (increase responsiveness).
A noisy, twitchy line means alpha is too large (increase smoothing).
Half-life tuning often feels more natural for aligning filter speed with price cycles or bar duration.
Summary
The IIR One-Pole Price Filter is a signal smoother that merges simplicity with mathematical rigor. Whether you’re filtering for entry signals, generating trend overlays, or constructing larger multi-stage systems, this filter delivers stability, clarity, and precision control over noise versus lag, an essential tool for any quantitative or systematic trading approach.
Adaptive Vol Gauge [ParadoxAlgo]This is an overlay tool that measures and shows market ups and downs (volatility) based on daily high and low prices. It adjusts automatically to recent price changes and highlights calm or wild market periods. It colors the chart background and bars in shades of blue to cyan, with optional small labels for changes in market mood. Use it for info only—combine with your own analysis and risk controls. It's not a buy/sell signal or promise of results.Key FeaturesSmart Volatility Measure: Tracks price swings with a flexible time window that reacts to market speed.
Market Mood Detection: Spots high-energy (wild) or low-energy (calm) phases to help see shifts.
Visual Style: Uses smooth color fades on the background and bars—cyan for calm, deep blue for wild—to blend nicely on your chart.
Custom Options: Change settings like time periods, sensitivity, colors, and labels.
Chart Fit: Sits right on your main price chart without extra lines, keeping things clean.
How It WorksThe tool figures out volatility like this:Adjustment Factor:Looks at recent price ranges compared to longer ones.
Tweaks the time window (between 10-50 bars) based on how fast prices are moving.
Volatility Calc:Adds up logs of high/low ranges over the adjusted window.
Takes the square root for the final value.
Can scale it to yearly terms for easy comparison across chart timeframes.
Mood Check:Compares current volatility to its recent average and spread.
Flags "high" if above your set level, "low" if below.
Neutral in between.
This setup makes it quicker in busy markets and steadier in quiet ones.Settings You Can ChangeAdjust in the tool's menu:Base Time Window (default: 20): Starting point for calculations. Bigger numbers smooth things out but might miss quick changes.
Adjustment Strength (default: 0.5): How much it reacts to price speed. Low = steady; high = quick changes.
Yearly Scaling (default: on): Makes values comparable across short or long charts. Turn off for raw numbers.
Mood Sensitivity (default: 1.0): How strict for calling high/low moods. Low = more shifts; high = only big ones.
Show Labels (default: on): Adds tiny "High Vol" or "Low Vol" tags when moods change. They point up or down from bars.
Background Fade (default: 80): How see-through the color fill is (0 = invisible, 100 = solid).
Bar Fade (default: 50): How much color blends into your candles or bars (0 = none, 100 = full).
How to Read and Use ItColor Shifts:Background and bars fade based on mood strength:Cyan shades mean calm markets (good for steady, back-and-forth trades).
Deep blue shades mean wild markets (watch for big moves or turns).
Smooth changes show volatility building or easing.
Labels:"High Vol" (deep blue, from below bar): Start of wild phase.
"Low Vol" (cyan, from above bar): Start of calm phase.
Only shows at changes to avoid clutter. Use for timing strategy tweaks.
Trading Ideas:Mood-Based Plays: In wild phases (deep blue), try chase-momentum or breakout trades since swings are bigger. In calm phases (cyan), stick to bounce-back or range trades.
Risk Tips: Cut trade sizes in wild times to handle bigger losses. Use calm times for longer holds with close stops.
Chart Time Tips: Turn on yearly scaling for matching short and long views. Test settings on past data—loosen for quick trades (more alerts), tighten for longer ones (fewer, stronger).
Mix with Others: Add trend lines or averages—buy in calm up-moves, sell in wild down-moves. Check with volume or key levels too.
Special Cases: In big news events, it reacts faster. On slow assets, it might overstate swings—ease the adjustment strength.
Limits and TipsIt looks back at past data, so it trails real-time action and can't predict ahead.
Results differ by stock or timeframe—test on history first.
Colors and tags are just visuals; set your own alerts if needed.
Follows TradingView rules: No win promises, for learning only. Open for sharing; share thoughts in forums.
With this, you can spot market energy and tweak your trades smarter. Start on practice charts.
Quantile-Based Adaptive Detection🙏🏻 Dedicated to John Tukey. He invented the boxplot, and I finalized it.
QBAD (Quantile-Based Adaptive Detection) is ‘the’ adaptive (also optionally weighted = ready for timeseries) boxplot with more senseful fences. Instead of hardcoded multipliers for outer fences, I base em on a set of quantile-based asymmetry metrics (you can view it as an ‘algorithmic’ counter part of central & standardized moments). So outer bands are Not hardcoded, not optimized, not cross-validated etc, simply calculated at O(nlogn).
You can use it literally everywhere in any context with any continuous data, in any task that requires statistical control, novelty || outlier detection, without worrying and doubting the sense in arbitrary chosen thresholds. Obviously, given the robust nature of quantiles, it would fit best the cases where data has problems.
The thresholds are:
Basis: the model of the data (median in our case);
Deviations: represent typical spread around basis, together form “value” in general sense;
Extensions: estimate data’s extremums via combination of quantile-based asymmetry metrics without relying on actual blunt min and max, together form “range” / ”frame”. Datapoints outside the frame/range are novelties or outliers;
Limits: based also on quantile asymmetry metrics, estimate the bounds within which values can ‘ever’ emerge given the current data generating process stays the same, together form “field”. Datapoints outside the field are very rare, happen when a significant change/structural break happens in current data-generating process, or when a corrupt datapoint emerges.
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The first part of the post is for locals xd, the second is for the wanderers/wizards/creators/:
First part:
In terms of markets, mostly u gotta worry about dem instruments that represent crypto & FX assets: it’s either activity hence data sources there are decentralized, or data is fishy.
For a higher algocomplexity cost O(nlong), unlike MBAD that is 0(n), this thing (a control system in fact) works better with ishy data (contaminated with wrong values, incomplete, missing values etc). Read about the “ breakdown point of an estimator ” if you wanna understand it.
Even with good data, in cases when you have multiple instruments that represent the same asset, e.g. CL and BRN futures, and for some reason you wanna skip constructing a proper index of em (while you should), QBAD should be better put on each instrument individually.
Another reason to use this algo-based rather than math-based tool, might be in cases when data quality is all good, but the actual causal processes that generate the data are a bit inconsistent and/or possess ‘increased’ activity in a way. SO in high volatility periods, this tool should provide better.
In terms of built-ins you got 2 weightings: by sequence and by inferred volume delta. The former should be ‘On’ all the time when you work with timeseries, unless for a reason you want to consciously turn it off for a reason. The latter, you gotta keep it ‘On’ unless you apply the tool on another dataset that ain’t got that particular additional dimension.
Ain’t matter the way you gonna use it, moving windows, cumulative windows with or without anchors, that’s your freedom of will, but some stuff stays the same:
Basis and deviations are “value” levels. From process control perspective, if you pls, it makes sense to Not only fade or push based on these levels, but to also do nothing when things are ambiguous and/or don’t require your intervention
Extensions and limits are extreme levels. Here you either push or fade, doing nothing is not an option, these are decisive points in all the meanings
Another important thing, lately I started to see one kind of trend here on tradingview as well and in general in near quant sources, of applying averages, percentiles etc ‘on’ other stationary metrics, so called “indicators”. And I mean not for diagnostic or development reasons, for decision making xd
This is not the evil crime ofc, but hillbilly af, cuz the metrics are stationary it means that you can model em, fit a distribution, like do smth sharper. Worst case you have Bayesian statistics armed with high density intervals and equal tail intervals, and even some others. All this stuff is not hard to do, if u aint’t doing it, it’s on you.
So what I’m saying is it makes sense to apply QBAD on returns ‘of your strategy’, on volume delta, but Not on other metrics that already do calculations over their own moving windows.
...
Second part:
Looks like some finna start to have lil suspicions, that ‘maybe’ after all math entities in reality are more like blueprints, while actual representations are physical/mechanical/algorithmic. Std & centralized moments is a math entity that represents location, scale & asymmetry info, and we can use it no problem, when things are legit and consistent especially. Real world stuff tho sometimes deviates from that ideal, so we need smth more handy and real. Add to the mix the algo counter part of means: quantiles.
Unlike the legacy quantile-based asymmetry metrics from the previous century (check quantile skewness & kurtosis), I don’t use arbitrary sets of quantiles, instead we get a binary pattern that is totally geometric & natural (check the code if interested, I made it very damn explicit). In spirit with math based central & standardized moments, each consequent pair is wider empathizing tail info more and more for each higher order metric.
Unlike the classic box plot, where inner thresholds are quartiles and the rest are based on em, here the basis is median (minimises L1), I base inner thresholds on it, and we continue the pattern by basing the further set of levels on the previous set. So unlike the classic box plot, here we have coherency in construction, symmetry.
Another thing to pay attention to, tho for some reason ain’t many talk about it, it’s not conceptually right to think that “you got data and you apply std moments on it”. No, you apply it to ‘centered around smth’ data. That ‘smth’ should minimize L2 error in case of math, L1 error in case of algo, and L0 error in case of learning/MLish/optimizational/whatever-you-cal-it stuff. So in the case of L0, that’s actually the ‘mode’ of KDE, but that’s for another time. Anyways, in case of L2 it’s mean, so we center data around mean, and apply std moments on residuals. That’s the precise way of framing it. If you understand this, suddenly very interesting details like 0th and 1st central moments start to make sense. In case of quantiles, we center data around the median, and do further processing on residuals, same.
Oth moment (I call it init) is always 1, tho it’s interesting to extrapolate backwards the sequence for higher order moments construction, to understand how we actually end up with this zero.
1st moment (I call it bias) of residuals would be zero if you match centering and residuals analysis methods. But for some reason you didn’t do that (e.g centered data around midhinge or mean and applied QBAD on the centered data), you have to account for that bias.
Realizing stuff > understanding stuff
Learning 2981234 human invented fields < realizing the same unified principles how the Universe works
∞
Kalman Adjusted Average True Range [BackQuant]Kalman Adjusted Average True Range
A volatility-aware trend baseline that fuses a Kalman price estimate with ATR “rails” to create a smooth, adaptive guide for entries, exits, and trailing risk.
Built on my original Kalman
This indicator is based on my original Kalman Price Filter:
That core smoother is used here to estimate the “true” price path, then blended with ATR to control step size and react proportionally to market noise.
What it plots
Kalman ATR Line the main baseline that turns up/down with the filtered trend.
Optional Moving Average of the Kalman ATR a secondary line for confluence (SMA/Hull/EMA/WMA/DEMA/RMA/LINREG/ALMA).
Candle Coloring (optional) paint bars by the baseline’s current direction.
Why combine Kalman + ATR?
Kalman reduces measurement noise and produces a stable path without the lag of heavy MAs.
ATR rails scale the baseline’s step to current volatility, so it’s calm in chop and more responsive in expansion.
The result is a single, intelligible line you can trade around: slope-up = constructive; slope-down = caution.
How it works (plain English)
Each bar, the Kalman filter updates an internal state (tunable via Process Noise , Measurement Noise , and Filter Order ) to estimate the underlying price.
An ATR band (Period × Factor) defines the allowed per-bar adjustment. The baseline cannot “jump” beyond those rails in one step.
A direction flip is detected when the baseline’s slope changes sign (upturn/downturn), and alerts are provided for both.
Typical uses
Trend confirmation Trade in the baseline’s direction; avoid fading a firmly rising/falling line.
Pullback timing Look for entries when price mean-reverts toward a rising baseline (or exits on tags of a falling one).
Trailing risk Use the baseline as a dynamic guide; many traders set stops a small buffer beyond it (e.g., a fraction of ATR).
Confluence Enable the MA overlay of the Kalman ATR; alignment (baseline above its MA and rising) supports continuation.
Inputs & what they do
Calculation
Kalman Price Source which price the filter tracks (Close by default).
Process Noise how quickly the filter can adapt. Higher = more responsive (but choppier).
Measurement Noise how much you distrust raw price. Higher = smoother (but slower to turn).
Filter Order (N) depth of the internal state array. Higher = slightly steadier behavior.
Kalman ATR
Period ATR lookback. Shorter = snappier; longer = steadier.
Factor scales the allowed step per bar. Larger factors permit faster drift; smaller factors clamp movement.
Confluence (optional)
MA Type & Period compute an MA on the Kalman ATR line , not on price.
Sigma (ALMA) if ALMA is selected, this input controls the curve’s shape. (Ignored for other MA types.)
Visuals
Plot Kalman ATR toggle the main line.
Paint Candles color bars by up/down slope.
Colors choose long/short hues.
Signals & alerts
Trend Up baseline turns upward (slope crosses above 0).
Alert: “Kalman ATR Trend Up”
Trend Down baseline turns downward (slope crosses below 0).
Alert: “Kalman ATR Trend Down”
These are state flips , not “price crossovers,” so you avoid many one-bar head-fakes.
How to start (fast presets)
Swing (daily/4H) ATR Period 7–14, Factor 0.5–0.8, Process Noise 0.02–0.05, Measurement Noise 2–4, N = 3–5.
Intraday (5–15m) ATR Period 5–7, Factor 0.6–1.0, Process Noise 0.05–0.10, Measurement Noise 2–3, N = 3–5.
Slow assets / FX raise Measurement Noise or ATR Period for calmer lines; drop Factor if the baseline feels too jumpy.
Reading the line
Rising & curving upward momentum building; consider long bias until a clear downturn.
Flat & choppy regime uncertainty; many traders stand aside or tighten risk.
Falling & accelerating distribution lower; short bias until a clean upturn.
Practical playbook
Continuation entries After a Trend Up alert, wait for a minor pullback toward the baseline; enter on evidence the line keeps rising.
Exit/reduce If long and the baseline flattens then turns down, trim or exit; reverse logic for shorts.
Filters Add a higher-timeframe check (e.g., only take longs when the daily Kalman ATR is rising).
Stops Place stops just beyond the baseline (e.g., baseline − x% ATR for longs) to avoid “tag & reverse” noise.
Notes
This is a guide to state and momentum, not a guarantee. Combine with your process (structure, volume, time-of-day) for decisions.
Settings are asset/timeframe dependent; start with the presets and nudge Process/Measurement Noise until the baseline “feels right” for your market.
Summary
Kalman ATR takes the noise-reduction of a Kalman price estimate and couples it with volatility-scaled movement to produce a clean, adaptive baseline. If you liked the original Kalman Price Filter (), this is its trend-trading cousin purpose-built for cleaner state flips, intuitive trailing, and confluence with your existing
Adaptive Trend Following Suite [Alpha Extract]A sophisticated multi-filter trend analysis system that combines advanced noise reduction, adaptive moving averages, and intelligent market structure detection to deliver institutional-grade trend following signals. Utilizing cutting-edge mathematical algorithms and dynamic channel adaptation, this indicator provides crystal-clear directional guidance with real-time confidence scoring and market mode classification for professional trading execution.
🔶 Advanced Noise Reduction
Filter Eliminates market noise using sophisticated Gaussian filtering with configurable sigma values and period optimization. The system applies mathematical weight distribution across price data to ensure clean signal generation while preserving critical trend information, automatically adjusting filter strength based on volatility conditions.
advancedNoiseFilter(sourceData, filterLength, sigmaParam) =>
weightSum = 0.0
valueSum = 0.0
centerPoint = (filterLength - 1) / 2
for index = 0 to filterLength - 1
gaussianWeight = math.exp(-0.5 * math.pow((index - centerPoint) / sigmaParam, 2))
weightSum += gaussianWeight
valueSum += sourceData * gaussianWeight
valueSum / weightSum
🔶 Adaptive Moving Average Core Engine
Features revolutionary volatility-responsive averaging that automatically adjusts smoothing parameters based on real-time market conditions. The engine calculates adaptive power factors using logarithmic scaling and bandwidth optimization, ensuring optimal responsiveness during trending markets while maintaining stability during consolidation phases.
// Calculate adaptive parameters
adaptiveLength = (periodLength - 1) / 2
logFactor = math.max(math.log(math.sqrt(adaptiveLength)) / math.log(2) + 2, 0)
powerFactor = math.max(logFactor - 2, 0.5)
relativeVol = avgVolatility != 0 ? volatilityMeasure / avgVolatility : 0
adaptivePower = math.pow(relativeVol, powerFactor)
bandwidthFactor = math.sqrt(adaptiveLength) * logFactor
🔶 Intelligent Market Structure Analysis
Employs fractal dimension calculations to classify market conditions as trending or ranging with mathematical precision. The system analyzes price path complexity using normalized data arrays and geometric path length calculations, providing quantitative market mode identification with configurable threshold sensitivity.
🔶 Multi-Component Momentum Analysis
Integrates RSI and CCI oscillators with advanced Z-score normalization for statistical significance testing. Each momentum component receives independent analysis with customizable periods and significance levels, creating a robust consensus system that filters false signals while maintaining sensitivity to genuine momentum shifts.
// Z-score momentum analysis
rsiAverage = ta.sma(rsiComponent, zAnalysisPeriod)
rsiDeviation = ta.stdev(rsiComponent, zAnalysisPeriod)
rsiZScore = (rsiComponent - rsiAverage) / rsiDeviation
if math.abs(rsiZScore) > zSignificanceLevel
rsiMomentumSignal := rsiComponent > 50 ? 1 : rsiComponent < 50 ? -1 : rsiMomentumSignal
❓How It Works
🔶 Dynamic Channel Configuration
Calculates adaptive channel boundaries using three distinct methodologies: ATR-based volatility, Standard Deviation, and advanced Gaussian Deviation analysis. The system automatically adjusts channel multipliers based on market structure classification, applying tighter channels during trending conditions and wider boundaries during ranging markets for optimal signal accuracy.
dynamicChannelEngine(baselineData, channelLength, methodType) =>
switch methodType
"ATR" => ta.atr(channelLength)
"Standard Deviation" => ta.stdev(baselineData, channelLength)
"Gaussian Deviation" =>
weightArray = array.new_float()
totalWeight = 0.0
for i = 0 to channelLength - 1
gaussWeight = math.exp(-math.pow((i / channelLength) / 2, 2))
weightedVariance += math.pow(deviation, 2) * array.get(weightArray, i)
math.sqrt(weightedVariance / totalWeight)
🔶 Signal Processing Pipeline
Executes a sophisticated 10-step signal generation process including noise filtering, trend reference calculation, structure analysis, momentum component processing, channel boundary determination, trend direction assessment, consensus calculation, confidence scoring, and final signal generation with quality control validation.
🔶 Confidence Transformation System
Applies sigmoid transformation functions to raw confidence scores, providing 0-1 normalized confidence ratings with configurable threshold controls. The system uses steepness parameters and center point adjustments to fine-tune signal sensitivity while maintaining statistical robustness across different market conditions.
🔶 Enhanced Visual Presentation
Features dynamic color-coded trend lines with adaptive channel fills, enhanced candlestick visualization, and intelligent price-trend relationship mapping. The system provides real-time visual feedback through gradient fills and transparency adjustments that immediately communicate trend strength and direction changes.
🔶 Real-Time Information Dashboard
Displays critical trading metrics including market mode classification (Trending/Ranging), structure complexity values, confidence scores, and current signal status. The dashboard updates in real-time with color-coded indicators and numerical precision for instant market condition assessment.
🔶 Intelligent Alert System
Generates three distinct alert types: Bullish Signal alerts for uptrend confirmations, Bearish Signal alerts for downtrend confirmations, and Mode Change alerts for market structure transitions. Each alert includes detailed messaging and timestamp information for comprehensive trade management integration.
🔶 Performance Optimization
Utilizes efficient array management and conditional processing to maintain smooth operation across all timeframes. The system employs strategic variable caching, optimized loop structures, and intelligent update mechanisms to ensure consistent performance even during high-volatility market conditions.
This indicator delivers institutional-grade trend analysis through sophisticated mathematical modelling and multi-stage signal processing. By combining advanced noise reduction, adaptive averaging, intelligent structure analysis, and robust momentum confirmation with dynamic channel adaptation, it provides traders with unparalleled trend following precision. The comprehensive confidence scoring system and real-time market mode classification make it an essential tool for professional traders seeking consistent, high-probability trend following opportunities with mathematical certainty and visual clarity.
Adaptive Market Profile – Auto Detect & Dynamic Activity ZonesAdaptive Market Profile is an advanced indicator that automatically detects and displays the most relevant trend channel and market profile for any asset and timeframe. Unlike standard regression channel tools, this script uses a fully adaptive approach to identify the optimal period, providing you with the channel that best fits the current market dynamics. The calculation is based on maximizing the statistical significance of the trend using Pearson’s R coefficient, ensuring that the most relevant trend is always selected.
Within the selected channel, the indicator generates a dynamic market profile, breaking the price range into configurable zones and displaying the most active areas based on volume or the number of touches. This allows you to instantly identify high-activity price levels and potential support/resistance zones. The “most active lines” are plotted in real-time and always stay parallel to the channel, dynamically adapting to market structure.
Key features:
- Automatic detection of the optimal regression period: The script scans a wide range of lengths and selects the channel that statistically represents the strongest trend.
- Dynamic market profile: Visualizes the distribution of volume or price touches inside the trend channel, with customizable section count.
- Most active zones: Highlights the most traded or touched price levels as dynamic, parallel lines for precise support/resistance reading.
- Manual override: Optionally, users can select their own channel period for full control.
- Supports both linear and logarithmic charts: Simple toggle to match your chart scaling.
Use cases:
- Trend following and channel trading strategies.
- Quick identification of dynamic support/resistance and liquidity zones.
- Objective selection of the most statistically significant trend channel, without manual guesswork.
- Suitable for all assets and timeframes (crypto, stocks, forex, futures).
Originality:
This script goes beyond basic regression channels by integrating dynamic profile analysis and fully adaptive period detection, offering a comprehensive tool for modern technical analysts. The combination of trend detection, market profile, and activity zone mapping is unique and not available in TradingView built-ins.
Instructions:
Add Adaptive Market Profile to your chart. By default, the script automatically detects the optimal channel period and displays the corresponding regression channel with dynamic profile and activity zones. If you prefer manual control, disable “Auto trend channel period” and set your preferred period. Adjust profile settings as needed for your asset and timeframe.
For questions, suggestions, or further customization, contact Julien Eche (@Julien_Eche) directly on TradingView.
PRO Investing - Apex EnginePRO Investing - Apex Engine
1. Core Concept: Why Does This Indicator Exist?
Traditional momentum oscillators like RSI or Stochastic use a fixed "lookback period" (e.g., 14). This creates a fundamental problem: a 14-period setting that works well in a fast, trending market will generate constant false signals in a slow, choppy market, and vice-versa. The market's character is dynamic, but most tools are static.
The Apex Engine was built to solve this problem. Its primary innovation is a self-optimizing core that continuously adapts to changing market conditions. Instead of relying on one fixed setting, it actively tests three different momentum profiles (Fast, Mid, and Slow) in real-time and selects the one that is most synchronized with the current price action.
This is not just a random combination of indicators; it's a deliberate synthesis designed to create a more robust momentum tool. It combines:
Volatility analysis (ATR) to generate adaptive lookback periods.
Momentum measurement (ROC) to gauge the speed of price changes.
Statistical analysis (Correlation) to validate which momentum measurement is most effective right now.
Classic trend filters (Moving Average, ADX) to ensure signals are only taken in favorable market conditions.
The result is an oscillator that aims to be more responsive in volatile trends and more stable in quiet periods, providing a more intelligent and adaptive signal.
2. How It Works: The Engine's Three-Stage Process
To be transparent, it's important to understand the step-by-step logic the indicator follows on every bar. It's a process of Adapt -> Validate -> Signal.
Stage 1: Adapt (Dynamic Length Calculation)
The engine first measures market volatility using the Average True Range (ATR) relative to its own long-term average. This creates a volatility_factor. In high-volatility environments, this factor causes the base calculation lengths to shorten. In low-volatility, they lengthen. This produces three potential Rate of Change (ROC) lengths: dynamic_fast_len, dynamic_mid_len, and dynamic_slow_len.
Stage 2: Validate (Self-Optimizing Mode Selection)
This is the core of the engine. It calculates the ROC for all three dynamic lengths. To determine which is best, it uses the ta.correlation() function to measure how well each ROC's movement has correlated with the actual bar-to-bar price changes over the "Optimization Lookback" period. The ROC length with the highest correlation score is chosen as the most effective profile for the current moment. This "active" mode is reflected in the oscillator's color and the dashboard.
Stage 3: Signal (Normalized Velocity Oscillator)
The winning ROC series is then normalized into a consistent oscillator (the Velocity line) that ranges from -100 (extreme oversold) to +100 (extreme overbought). This ensures signals are comparable across any asset or timeframe. Signals are only generated when this Velocity line crosses its signal line and the trend filters (explained below) give a green light.
3. How to Use the Indicator: A Practical Guide
Reading the Visuals:
Velocity Line (Blue/Yellow/Pink): The main oscillator line. Its color indicates which mode is active (Fast, Mid, or Slow).
Signal Line (White): A moving average of the Velocity line. Crossovers generate potential signals.
Buy/Sell Triangles (▲ / ▼): These are your primary entry signals. They are intentionally strict and only appear when momentum, trend, and price action align.
Background Color (Green/Red/Gray): This is your trend context.
Green: Bullish trend confirmed (e.g., price above a rising 200 EMA and ADX > 20). Only Buy signals (▲) can appear.
Red: Bearish trend confirmed. Only Sell signals (▼) can appear.
Gray: No clear trend. The market is likely choppy or consolidating. No signals will appear; it is best to stay out.
Trading Strategy Example:
Wait for a colored background. A green or red background indicates the market is in a tradable trend.
Look for a signal. For a green background, wait for a lime Buy triangle (▲) to appear.
Confirm the trade. Before entering, confirm the signal aligns with your own analysis (e.g., support/resistance levels, chart patterns).
Manage the trade. Set a stop-loss according to your risk management rules. An exit can be considered on a fixed target, a trailing stop, or when an opposing signal appears.
4. Settings and Customization
This script is open-source, and its settings are transparent. You are encouraged to understand them.
Synaptic Engine Group:
Volatility Period: The master control for the adaptive engine. Higher values are slower and more stable.
Optimization Lookback: How many bars to use for the correlation check.
Switch Sensitivity: A buffer to prevent frantic switching between modes.
Advanced Configuration & Filters Group:
Price Source: The data source for momentum calculation (default close).
Trend Filter MA Type & Length: Define your long-term trend.
Filter by MA Slope: A key feature. If ON, allows for "buy the dip" entries below a rising MA. If OFF, it's stricter, requiring price to be above the MA.
ADX Length & Threshold: Filters out non-trending, choppy markets. Signals will not fire if the ADX is below this threshold.
5. Important Disclaimer
This indicator is a decision-support tool for discretionary traders, not an automated trading system or financial advice. Past performance is not indicative of future results. All trading involves substantial risk. You should always use proper risk management, including setting stop-losses, and never risk more than you are prepared to lose. The signals generated by this script should be used as one component of a broader trading plan.
RSI Mansfield +RSI Mansfield+ – Adaptive Relative Strength Indicator with Divergences
Overview
RSI Mansfield+ is an advanced relative strength indicator that compares your instrument’s performance against a configurable benchmark index or asset (e.g., Bitcoin Dominance, S&P 500). It combines Mansfield normalization, adaptive smoothing techniques, and automatic detection of bullish and bearish divergences (regular and hidden), delivering a comprehensive tool for assessing relative strength across any market and timeframe.
Originality and Motivation
Unlike traditional relative strength scripts, this indicator introduces several distinctive improvements:
Mansfield Normalization: Scales the ratio between the asset and the benchmark relative to its moving average, transforming it into a normalized oscillator that fluctuates around zero, making it easier to spot outperformance or underperformance.
Adaptive Smoothing: Automatically selects whether to use EMA or SMA based on the market type (crypto or stocks) and timeframe (intraday, daily, weekly, monthly), avoiding manual configuration and providing more robust results under varying volatility conditions.
Divergence Detection: Identifies four types of divergences in the Mansfield oscillator to help anticipate potential reversal points or trend confirmations.
Multi-Market Support: Offers benchmark selection among major crypto and global stock indices from a single input.
These enhancements make RSI Mansfield+ more practical and powerful than conventional relative strength scripts with static benchmarks or without divergence capabilities.
Core Concepts
Relative Strength (RS): Compares price evolution between your asset and the selected benchmark.
Mansfield Normalization: Measures how much the RS deviates from its historical moving average, expressed as a scaled oscillator.
Divergences: Detects regular and hidden bullish or bearish divergences within the Mansfield oscillator.
Timeframe Adaptation: Dynamically adjusts moving average lengths based on timeframe and market type.
How It Works
Benchmark Selection
Choose among over 10 indices or market domains (BTC Dominance, ETH Dominance, S&P 500, European indices, etc.).
Ratio Calculation
Computes the price-to-benchmark ratio and smooths it with the adaptive moving average.
Normalization and Scaling
Transforms deviations into a Mansfield oscillator centered around zero.
Dynamic Coloring
Green indicates relative outperformance, red signals underperformance.
Divergence Detection
Automatically identifies bullish and bearish (regular and hidden) divergences by comparing oscillator pivots against price pivots.
Baseline Reference
A clear zero line helps interpret relative strength trends.
Usage Guidelines
Benchmark Comparison
Ideal for traders analyzing whether an asset is outperforming or lagging its sector or market.
Divergence Analysis
Helps detect potential reversal or continuation signals in relative strength.
Multi-Timeframe Compatibility
Can be applied to intraday, daily, weekly, or monthly charts.
Interpretation
Oscillator >0 and green: outperforming the benchmark.
Oscillator <0 and red: underperforming.
Bullish divergences: potential relative strength reversal to the upside.
Bearish divergences: possible loss of momentum or reversal to the downside.
Credits
The concept of Mansfield Relative Strength is based on Stan Weinstein’s original work on relative performance analysis. This script was built entirely from scratch in TradingView Pine Script v6, incorporating original logic for adaptive smoothing, normalized scaling, and divergence detection, without reusing any external open-source code.
Adaptive Squeeze Momentum +Adaptive Squeeze Momentum+ (Auto-Timeframe Version)
Overview
Adaptive Squeeze Momentum+ is an enhanced volatility and momentum indicator designed to identify compression and expansion phases in price action. It is inspired by the classic Squeeze Momentum Indicator by LazyBear but introduces automatic parameter adaptation to any timeframe, making it simpler to use across different markets without manual configuration.
Concepts and Methodology
The script combines Bollinger Bands (BB) and Keltner Channels (KC) to detect periods when volatility contracts (squeeze) or expands (release).
A squeeze occurs when BB are inside KC, suggesting low volatility and potential breakout scenarios.
A squeeze release is detected when BB expand outside KC.
Momentum is derived using a linear regression applied to the difference between price and a midrange reference level.
Original Improvements
Compared to the original Squeeze Momentum Indicator, this version offers several enhancements:
Automatic Adaptation: BB and KC lengths and multipliers are dynamically adjusted based on the chart’s timeframe (from 1 minute up to 1 month), removing the need for manual tuning.
Simplified Visualization: A clean, minimalist histogram and clear squeeze state cross markers allow for faster interpretation.
Flexible Application: Designed to work consistently on intraday, daily, and higher timeframes across crypto, forex, stocks, and indices.
Features
Dynamic Squeeze Detection:
Gray Cross: Neutral (no squeeze detected)
Blue Cross: Active squeeze
Yellow Cross: Squeeze released
Momentum Histogram:
Positive/negative momentum shown with slope-based coloring.
Timeframe-Aware Parameters:
Automatically sets optimal BB/KC configurations.
Usage
Watch for blue crosses indicating an active squeeze phase that may precede a directional move.
Use the histogram color and slope to gauge momentum strength and direction.
Combine squeeze release signals with momentum confirmation for potential entries or exits.
Credits and Licensing
This script was inspired by LazyBear’s OLD “Squeeze Momentum Indicator” (). The implementation here significantly expands upon the original by introducing auto-adaptive parameters, restructured logic, and a new visualization approach. Published under the Mozilla Public License 2.0.
Disclaimer
This indicator is for educational purposes only and does not constitute financial advice. Use at your own risk.
Active PMI Support/Resistance Levels [EdgeTerminal]The PMI Support & Resistance indicator revolutionizes traditional technical analysis by using Pointwise Mutual Information (PMI) - a statistical measure from information theory - to objectively identify support and resistance levels. Unlike conventional methods that rely on visual pattern recognition, this indicator provides mathematically rigorous, quantifiable evidence of price levels where significant market activity occurs.
- The Mathematical Foundation: Pointwise Mutual Information
Pointwise Mutual Information measures how much more likely two events are to occur together compared to if they were statistically independent. In our context:
Event A: Volume spikes occurring (high trading activity)
Event B: Price being at specific levels
The PMI formula calculates: PMI = log(P(A,B) / (P(A) × P(B)))
Where:
P(A,B) = Probability of volume spikes occurring at specific price levels
P(A) = Probability of volume spikes occurring anywhere
P(B) = Probability of price being at specific levels
High PMI scores indicate that volume spikes and certain price levels co-occur much more frequently than random chance would predict, revealing genuine support and resistance zones.
- Why PMI Outperforms Traditional Methods
Subjective interpretation: What one trader sees as significant, another might ignore
Confirmation bias: Tendency to see patterns that confirm existing beliefs
Inconsistent criteria: No standardized definition of "significant" volume or price action
Static analysis: Doesn't adapt to changing market conditions
No strength measurement: Can't quantify how "strong" a level truly is
PMI Advantages:
✅ Objective & Quantifiable: Mathematical proof of significance, not visual guesswork
✅ Statistical Rigor: Levels backed by information theory and probability
✅ Strength Scoring: PMI scores rank levels by statistical significance
✅ Adaptive: Automatically adjusts to different market volatility regimes
✅ Eliminates Bias: Computer-calculated, removing human interpretation errors
✅ Market Structure Aware: Reveals the underlying order flow concentrations
- How It Works
Data Processing Pipeline:
Volume Analysis: Identifies volume spikes using configurable thresholds
Price Binning: Divides price range into discrete levels for analysis
Co-occurrence Calculation: Measures how often volume spikes happen at each price level
PMI Computation: Calculates statistical significance for each price level
Level Filtering: Shows only levels exceeding minimum PMI thresholds
Dynamic Updates: Refreshes levels periodically while maintaining historical traces
Visual System:
Current Levels: Bright, thick lines with PMI scores - your actionable levels
Historical Traces: Faded previous levels showing market structure evolution
Strength Tiers: Line styles indicate PMI strength (solid/dashed/dotted)
Color Coding: Green for support, red for resistance
Info Table: Real-time display of strongest levels with scores
- Indicator Settings:
Core Parameters
Lookback Period (Default: 200)
Lower (50-100): More responsive to recent price action, catches short-term levels
Higher (300-500): Focuses on major historical levels, more stable but less responsive
Best for: Day trading (100-150), Swing trading (200-300), Position trading (400-500)
Volume Spike Threshold (Default: 1.5)
Lower (1.2-1.4): More sensitive, catches smaller volume increases, more levels detected
Higher (2.0-3.0): Only major volume surges count, fewer but stronger signals
Market dependent: High-volume stocks may need higher thresholds (2.0+), low-volume stocks lower (1.2-1.3)
Price Bins (Default: 50)
Lower (20-30): Broader price zones, less precise but captures wider areas
Higher (70-100): More granular levels, precise but may be overly specific
Volatility dependent: High volatility assets benefit from more bins (70+)
Minimum PMI Score (Default: 0.5)
Lower (0.2-0.4): Shows more levels including weaker ones, comprehensive view
Higher (1.0-2.0): Only statistically strong levels, cleaner chart
Progressive filtering: Start with 0.5, increase if too cluttered
Max Levels to Show (Default: 8)
Fewer (3-5): Clean chart focusing on strongest levels only
More (10-15): Comprehensive view but may clutter chart
Strategy dependent: Scalpers prefer fewer (3-5), swing traders more (8-12)
Historical Tracking Settings
Update Frequency (Default: 20 bars)
Lower (5-10): More frequent updates, captures rapid market changes
Higher (50-100): Less frequent updates, focuses on major structural shifts
Timeframe scaling: 1-minute charts need lower frequency (5-10), daily charts higher (50+)
Show Historical Levels (Default: True)
Enables the "breadcrumb trail" effect showing evolution of support/resistance
Disable for cleaner charts focusing only on current levels
Max Historical Marks (Default: 50)
Lower (20-30): Less memory usage, shorter history
Higher (100-200): Longer historical context but more resource intensive
Fade Strength (Default: 0.8)
Lower (0.5-0.6): Historical levels more visible
Higher (0.9-0.95): Historical levels very subtle
Visual Settings
Support/Resistance Colors: Choose colors that contrast well with your chart theme Line Width: Thicker lines (3-4) for better visibility on busy charts Show PMI Scores: Toggle labels showing statistical strength Label Size: Adjust based on screen resolution and chart zoom level
- Most Effective Usage Strategies
For Day Trading:
Setup: Lookback 100-150, Volume Threshold 1.8-2.2, Update Frequency 10-15
Use PMI levels as bounce/rejection points for scalp entries
Higher PMI scores (>1.5) offer better probability setups
Watch for volume spike confirmations at levels
For Swing Trading:
Setup: Lookback 200-300, Volume Threshold 1.5-2.0, Update Frequency 20-30
Enter on pullbacks to high PMI support levels
Target next resistance level with PMI score >1.0
Hold through minor levels, exit at major PMI levels
For Position Trading:
Setup: Lookback 400-500, Volume Threshold 2.0+, Update Frequency 50+
Focus on PMI scores >2.0 for major structural levels
Use for portfolio entry/exit decisions
Combine with fundamental analysis for timing
- Trading Applications:
Entry Strategies:
PMI Bounce Trades
Price approaches high PMI support level (>1.0)
Wait for volume spike confirmation (orange triangles)
Enter long on bullish price action at the level
Stop loss just below the PMI level
Target: Next PMI resistance level
PMI Breakout Trades
Price consolidates near high PMI level
Volume increases (watch for orange triangles)
Enter on decisive break with volume
Previous resistance becomes new support
Target: Next major PMI level
PMI Rejection Trades
Price approaches PMI resistance with momentum
Watch for rejection signals and volume spikes
Enter short on failure to break through
Stop above the PMI level
Target: Next PMI support level
Risk Management:
Stop Loss Placement
Place stops 0.1-0.5% beyond PMI levels (adjust for volatility)
Higher PMI scores warrant tighter stops
Use ATR-based stops for volatile assets
Position Sizing
Larger positions at PMI levels >2.0 (highest conviction)
Smaller positions at PMI levels 0.5-1.0 (lower conviction)
Scale out at multiple PMI targets
- Key Warning Signs & What to Watch For
Red Flags:
🚨 Very Low PMI Scores (<0.3): Weak statistical significance, avoid trading
🚨 No Volume Confirmation: PMI level without recent volume spikes may be stale
🚨 Overcrowded Levels: Too many levels close together suggests poor parameter tuning
🚨 Outdated Levels: Historical traces are reference only, not tradeable
Optimization Tips:
✅ Regular Recalibration: Adjust parameters monthly based on market regime changes
✅ Volume Context: Always check for recent volume activity at PMI levels
✅ Multiple Timeframes: Confirm PMI levels across different timeframes
✅ Market Conditions: Higher thresholds during high volatility periods
Interpreting PMI Scores
PMI Score Ranges:
0.5-1.0: Moderate statistical significance, proceed with caution
1.0-1.5: Good significance, reliable for most trading strategies
1.5-2.0: Strong significance, high-confidence trade setups
2.0+: Very strong significance, institutional-grade levels
Historical Context: The historical trace system shows how support and resistance evolve over time. When current levels align with multiple historical traces, it indicates persistent market memory at those prices, significantly increasing the level's reliability.
Adaptive Causal Wavelet Trend FilterThe Adaptive Causal Wavelet Trend Filter is a technical indicator implementing causal approximations of wavelet transform properties for better trend detection with adaptive volatility response.
The Adaptive Causal Wavelet Trend Filter (ACWTF) applies mathematical principles derived from wavelet analysis to financial time series, providing robust trend identification with minimal lag. Unlike conventional moving averages, it preserves significant price movements while filtering market noise through signal processing that i describe below.
I was inspired to build this indicator after reading " Wavelet-Based Trend Identification in Financial Time Series " by In, F., & Kim, S. 2013 and reading about Mexican Hat wavelet filters.
The ACWTF maintains optimal performance across varying market regimes without requiring parameter adjustments by adapting filter characteristics to current volatility conditions.
Mathematical Foundation
Inspired by the Mexican Hat wavelet (Ricker wavelet), this indicator implements causal approximations of wavelet filters optimized for real-time financial analysis. The multi-resolution approach identifies features at different scales and the adaptive component dynamically adjusts filtering characteristics based on local volatility measurements.
Key mathematical properties include:
Non-linear frequency response adaptation
Edge-preserving signal extraction
Scale-space analysis through dual filter implementation
Volatility-dependent coefficient adjustment, which I love
Filter Methods
Adaptive: Implements a volatility-weighted combination of multiple filter types to optimize the time-frequency resolution trade-off
Hull: Provides a causal approximation of wavelet edge detection properties with forward-projection characteristics
VWMA: Incorporates volume information into the filtering process for enhanced signal detection
EMA Cascade: Creates a multi-pole filter structure that approximates certain wavelet scaling properties
Suggestion: try all as they will provide slightly different signals. Try also different time-frames.
Practical Applications
Trend Direction Identification: Clear visual trend direction with reduced noise and lag
Regime Change Detection: Early identification of significant trend reversals
Market Condition Analysis: Integrated volatility metrics provide context for current market behavior
Multi-timeframe Confirmation: Alignment between primary and secondary filters offers additional confirmation
Entry/Exit Timing: Filter crossovers and trend changes provide potential trading signals
The comprehensive information panel provides:
Current filter method and trend state
Trend alignment between timeframes
Real-time volatility assessment
Price position relative to filter
Overall trading bias based on multiple factors
Implementation Notes
Log returns option provides improved statistical properties for financial time series
Primary and secondary filter lengths can be adjusted to optimize for specific instruments and timeframes
The indicator performs particularly well during trend transitions and regime changes
The indicator reduces the need for using additional indicators to check trend reversion
Adaptive Quadratic Kernel EnvelopeThis study draws a fair-value curve from a quadratic-weighted (Nadaraya-Watson) regression. Alpha sets how sharply weights decay inside the look-back window, so you trade lag against smoothness with one slider. Band half-width is ATRslow times a bounded fast/slow ATR ratio, giving an instant response to regime shifts without overshooting on spikes. Work in log space when an instrument grows exponentially, equal percentage moves then map to equal vertical steps. NearBase and FarBase define a progression of adaptive thresholds, useful for sizing exits or calibrating mean-reversion logic. Non-repaint mode keeps one-bar delay for clean back-tests, predictive mode shows the zero-lag curve for live decisions.
Key points
- Quadratic weights cut phase error versus Gaussian or SMA-based envelopes.
- Dual-ATR scaling updates width on the next bar, no residual lag.
- Log option preserves envelope symmetry across multi-decade data.
- Alpha provides direct control of curvature versus noise.
- Built-in alerts trigger on the first adaptive threshold, ready for automation.
Typical uses
Trend bias from the slope of the curve.
Entry timing when price pierces an inner threshold and momentum stalls.
Breakout confirmation when closes hold beyond outer thresholds while volatility expands.
Stops and targets anchored to chosen thresholds, automatically matching current noise.
Adaptive MACD Deluxe [AlgoAlpha]OVERVIEW
This script is an advanced rework of the classic MACD indicator, designed to be more adaptive, visually informative, and customizable. It enhances the original MACD formula using a dynamic feedback loop and a correlation-based weighting system that adjusts in real-time based on how deterministic recent price action is. The signal line is flexible, offering several smoothing types including Heiken Ashi, while the histogram is color-coded with gradients to help users visually identify momentum shifts. It also includes optional normalization by volatility, allowing MACD values to be interpreted as relative percentage moves, making the indicator more consistent across different assets and timeframes.
CONCEPTS
This version of MACD introduces a deterministic weight based on R-squared correlation with time, which modulates how fast or slow the MACD adapts to price changes. Higher correlation means smoother, slower MACD responses, and low correlation leads to quicker reaction. The momentum calculation blends traditional EMA math with feedback and damping components to create a smoother, less noisy series. Heiken Ashi is optionally used for signal smoothing to better visualize short-term trend bias. When normalization is enabled, the MACD is scaled by an EMA of the high-low range, converting it into a bounded, volatility-relative indicator. This makes extreme readings more meaningful across markets.
FEATURES
The script offers six distinct options for signal line smoothing: EMA, SMA, SMMA (RMA), WMA, VWMA, and a custom Heiken Ashi mode based on the MACD series. Each option provides a different response speed and smoothing behavior, allowing traders to match the indicator’s behavior to their strategy—whether it's faster reaction or reduced noise.
Normalization is another key feature. When enabled, MACD values are scaled by a volatility proxy, converting the indicator into a relative percentage. This helps standardize the MACD across different assets and timeframes, making overbought and oversold readings more consistent and easier to interpret.
Threshold zones can be customized using upper and lower boundaries, with inner zones for early warnings. These zones are highlighted on the chart with subtle background fills and directional arrows when MACD enters or exits key levels. This makes it easier to spot strong or weak reversals at a glance.
Lastly, the script includes multiple built-in alerts. Users can set alerts for MACD crossovers, histogram flips above or below zero, and MACD entries into strong or weak reversal zones. This allows for hands-free monitoring and quick decision-making without staring at the chart.
USAGE
To use this script, choose your preferred signal smoothing type, enable normalization if you want MACD values relative to volatility, and adjust the threshold zones to fit your asset or timeframe. Use the colored histogram to detect changes in momentum strength—brighter colors indicate rising strength, while faded colors imply weakening. Heiken Ashi mode smooths out noise and provides clearer signals, especially useful in choppy conditions. Use alert conditions for crossover and reversal detection, or monitor the arrow markers for entries into potential exhaustion zones. This setup works well for trend following, momentum trading, and reversal spotting across all market types.
CNN Statistical Trading System [PhenLabs]📌 DESCRIPTION
An advanced pattern recognition system utilizing Convolutional Neural Network (CNN) principles to identify statistically significant market patterns and generate high-probability trading signals.
CNN Statistical Trading System transforms traditional technical analysis by applying machine learning concepts directly to price action. Through six specialized convolution kernels, it detects momentum shifts, reversal patterns, consolidation phases, and breakout setups simultaneously. The system combines these pattern detections using adaptive weighting based on market volatility and trend strength, creating a sophisticated composite score that provides both directional bias and signal confidence on a normalized -1 to +1 scale.
🚀 CONCEPTS
• Built on Convolutional Neural Network pattern recognition methodology adapted for financial markets
• Six specialized kernels detect distinct price patterns: upward/downward momentum, peak/trough formations, consolidation, and breakout setups
• Activation functions create non-linear responses with tanh-like behavior, mimicking neural network layers
• Adaptive weighting system adjusts pattern importance based on current market regime (volatility < 2% and trend strength)
• Multi-confirmation signals require CNN threshold breach (±0.65), RSI boundaries, and volume confirmation above 120% of 20-period average
🔧 FEATURES
Six-Kernel Pattern Detection:
Simultaneous analysis of upward momentum, downward momentum, peak/resistance, trough/support, consolidation, and breakout patterns using mathematically optimized convolution kernels.
Adaptive Neural Architecture:
Dynamic weight adjustment based on market volatility (ATR/Price) and trend strength (EMA differential), ensuring optimal performance across different market conditions.
Professional Visual Themes:
Four sophisticated color palettes (Professional, Ocean, Sunset, Monochrome) with cohesive design language. Default Monochrome theme provides clean, distraction-free analysis.
Confidence Band System:
Upper and lower confidence zones at 150% of threshold values (±0.975) help identify high-probability signal areas and potential exhaustion zones.
Real-Time Information Panel:
Live display of CNN score, market state with emoji indicators, net momentum, confidence percentage, and RSI confirmation with dynamic color coding based on signal strength.
Individual Feature Analysis:
Optional display of all six kernel outputs with distinct visual styles (step lines, circles, crosses, area fills) for advanced pattern component analysis.
User Guide
• Monitor CNN Score crossing above +0.65 for long signals or below -0.65 for short signals with volume confirmation
• Use confidence bands to identify optimal entry zones - signals within confidence bands carry higher probability
• Background intensity reflects signal strength - darker backgrounds indicate stronger conviction
• Enter long positions when blue circles appear above oscillator with RSI < 75 and volume > 120% average
• Enter short positions when dark circles appear below oscillator with RSI > 25 and volume confirmation
• Information panel provides real-time confidence percentage and momentum direction for position sizing decisions
• Individual feature plots allow granular analysis of specific pattern components for strategy refinement
💡Conclusion
CNN Statistical Trading System represents the evolution of technical analysis, combining institutional-grade pattern recognition with retail accessibility. The six-kernel architecture provides comprehensive market pattern coverage while adaptive weighting ensures relevance across all market conditions. Whether you’re seeking systematic entry signals or advanced pattern confirmation, this indicator delivers mathematically rigorous analysis with intuitive visual presentation.
