EMA HeatmapEMA Heatmap — Indicator Description
The EMA Order Heatmap is a visual trend-structure tool designed to show whether the market is currently trending bullish, trending bearish, or moving through a neutral consolidation phase. It evaluates the alignment of multiple exponential moving averages (EMAs) at three different structural layers: short-term daily, medium-term daily, and weekly macro trend. This creates a quick and intuitive picture of how well price movement is organized across timeframes.
Each layer of the heatmap is scored from bearish to bullish based on how the EMAs are stacked relative to each other. When EMAs are in a fully bullish configuration, the row displays a bright green or lime color. Fully bearish alignment is shown in red. Yellow tones appear when the EMAs are mixed or compressing, indicating uncertainty, trend exhaustion, or a change in market character. The three rows combined offer a concise view of whether strength or weakness is isolated to one timeframe or broad across the market.
This indicator is best used as a trend filter before making trading decisions. Traders may find more consistent setups when the majority of the heatmap supports the direction of their trade. Green-dominant conditions suggest a trending bullish environment where long trades can be favored. Red-dominant conditions indicate bearish momentum and stronger potential for short opportunities. When yellow becomes more prominent, the market may be transitioning, ranging, or gearing up for a breakout, making timing more challenging and risk higher.
• Helps quickly identify directional bias
• Highlights when trends strengthen, weaken, or turn
• Provides insight into whether momentum is supported by higher timeframes
• Encourages traders to avoid fighting market structure
It is important to recognize the limitations. EMAs are lagging indicators, so the heatmap may confirm a trend after the initial move is underway, especially during fast reversals. In sideways or low-volume environments, the structure can shift frequently, reducing clarity. This tool does not generate entry or exit signals on its own and should be paired with price action, momentum studies, or support and resistance analysis for precise trade execution.
The EMA Order Heatmap offers a clean and reliable way to stay aligned with the broader market environment and avoid lower-quality trades in indecisive conditions. It supports more disciplined decision-making by helping traders focus on setups that match the prevailing structural trend.
Trendfollowing
Portfolio Strategy TesterThe Portfolio Strategy Tester is an institutional-grade backtesting framework that evaluates the performance of trend-following strategies on multi-asset portfolios. It enables users to construct custom portfolios of up to 30 assets and apply moving average crossover strategies across individual holdings. The model features a clear, color-coded table that provides a side-by-side comparison between the buy-and-hold portfolio and the portfolio using the risk management strategy, offering a comprehensive assessment of both approaches relative to the benchmark.
Portfolios are constructed by entering each ticker symbol in the menu, assigning its respective weight, and reviewing the total sum of individual weights displayed at the top left of the table. For strategy selection, users can choose between Exponential Moving Average (EMA), Simple Moving Average (SMA), Wilder’s Moving Average (RMA), Weighted Moving Average (WMA), Moving Average Convergence Divergence (MACD), and Volume-Weighted Moving Average (VWMA). Moving average lengths are defined in the menu and apply only to strategy-enabled assets.
To accurately replicate real-world portfolio conditions, users can choose between daily, weekly, monthly, or quarterly rebalancing frequencies and decide whether cash is held or redistributed. Daily rebalancing maintains constant portfolio weights, while longer intervals allow natural drift. When cash positions are not allowed, capital from bearish assets is automatically redistributed proportionally among bullish assets, ensuring the portfolio remains fully invested at all times. The table displays a comprehensive set of widely used institutional-grade performance metrics:
CAGR = Compounded annual growth rate of returns.
Volatility = Annualized standard deviation of returns.
Sharpe = CAGR per unit of annualized standard deviation.
Sortino = CAGR per unit of annualized downside deviation.
Calmar = CAGR relative to maximum drawdown.
Max DD = Largest peak-to-trough decline in value.
Beta (β) = Sensitivity of returns relative to benchmark returns.
Alpha (α) = Excess annualized risk-adjusted returns relative to benchmark.
Upside = Ratio of average return to benchmark return on up days.
Downside = Ratio of average return to benchmark return on down days.
Tracking = Annualized standard deviation of returns versus benchmark.
Turnover = Average sum of absolute changes in weights per year.
Cumulative returns are displayed on each label as the total percentage gain from the selected start date, with green indicating positive returns and red indicating negative returns. In the table, baseline metrics serve as the benchmark reference and are always gray. For portfolio metrics, green indicates outperformance relative to the baseline, while red indicates underperformance relative to the baseline. For strategy metrics, green indicates outperformance relative to both the baseline and the portfolio, red indicates underperformance relative to both, and gray indicates underperformance relative to either the baseline or portfolio. Metrics such as Volatility, Tracking Error, and Turnover ratio are always displayed in gray as they serve as descriptive measures.
In summary, the Portfolio Strategy Tester is a comprehensive backtesting tool designed to help investors evaluate different trend-following strategies on custom portfolios. It enables real-world simulation of both active and passive investment approaches and provides a full set of standard institutional-grade performance metrics to support data-driven comparisons. While results are based on historical performance, the model serves as a powerful portfolio management and research framework for developing, validating, and refining systematic investment strategies.
Curved Radius Supertrend [BOSWaves]Curved Radius Supertrend — Adaptive Parabolic Trend Framework with Dynamic Acceleration Geometry
Overview
The Curved Radius Supertrend introduces an evolution of the classic Supertrend indicator - engineered with a dynamic curvature engine that replaces rigid ATR bands with parabolic, radius-based motion. Traditional Supertrend systems rely on static band displacement, reacting linearly to volatility and often lagging behind emerging price acceleration. The Curved Radius Supertend model redefines this by integrating controlled acceleration and curvature geometry, allowing the trend bands to adapt fluidly to both velocity and duration of price movement.
The result is a smoother, more organic trend flow that visually captures the momentum curve of price action - not just its direction. Instead of sharp pivots or whipsaws, traders experience a structurally curved trajectory that mirrors real market inertia. This makes it particularly effective for identifying sustained directional phases, detecting early trend rotations, and filtering out noise that plagues standard Supertrend methodologies.
Unlike conventional band-following systems, the Curved Radius framework is time-reactive and velocity-aware, providing a nuanced signal structure that blends geometric precision with volatility sensitivity.
Theoretical Foundation
The Curved Radius Supertrend draws from the intersection of mathematical curvature dynamics and adaptive volatility processing. Standard Supertrend algorithms extend from Average True Range (ATR) envelopes - a linear measure of volatility that moves proportionally with price deviation. However, markets do not expand or contract linearly. Trend velocity typically accelerates and decelerates in nonlinear arcs, forming natural parabolas across price phases.
By embedding a radius-based acceleration function, the indicator models this natural behavior. The core variable, radiusStrength, controls how aggressively curvature accelerates over time. Instead of simply following price distance, the band now evolves according to temporal acceleration - each bar contributes incremental velocity, bending the trend line into a radius-like curve.
This structural design allows the indicator to anticipate rather than just respond to price action, capturing momentum transitions as curved accelerations rather than binary flips. In practice, this eliminates the stutter effect typical of standard Supertrends and replaces it with fluid directional motion that better reflects actual trend geometry.
How It Works
The Curved Radius Supertrend is constructed through a multi-stage process designed to balance price responsiveness with geometric stability:
1. Baseline Supertrend Core
The framework begins with a standard ATR-derived upper and lower band calculation. These define the volatility envelope that constrains potential price zones. Directional bias is determined through crossover logic - prices above the lower band confirm an uptrend, while prices below the upper band confirm a downtrend.
2. Curvature Acceleration Engine
Once a trend direction is established, a curvature engine is activated. This system uses radiusStrength as a coefficient to simulate acceleration per bar, incrementally increasing velocity over time. The result is a parabolic displacement from the anchor price (the price level at trend change), creating a curved motion path that dynamically widens or tightens as the trend matures.
Mathematically, this acceleration behaves quadratically - each new bar compounds the previous velocity, forming an exponential rate of displacement that resembles curved inertia.
3. Adaptive Smoothing Layer
After the radius curve is applied, a smoothing stage (defined by the smoothness parameter) uses a simple moving average to regulate curve noise. This ensures visual coherence without sacrificing responsiveness, producing flowing arcs rather than jagged band steps.
4. Directional Visualization and Outer Envelope
Directional state (bullish or bearish) dictates both the color gradient and band displacement. An outer envelope is plotted one ATR beyond the curved band, creating a layered trend visualization that shows the extent of volatility expansion.
5. Signal Events and Alerts
Each directional transition triggers a 'BUY' or 'SELL' signal, clearly labeling phase shifts in market structure. Alerts are built in for automation and backtesting.
Interpretation
The Curved Radius Supertrend reframes how traders visualize and confirm trends. Instead of simply plotting a trailing stop, it maps the dynamic curvature of trend development.
Uptrend Phases : The band curves upward with increasing acceleration, reflecting the market’s growing directional velocity. As curvature steepens, conviction strengthens.
Downtrend Phases : The band bends downward in a mirrored acceleration pattern, indicating sustained bearish momentum.
Trend Change Points : When the direction flips and a new anchor point forms, the curve resets - providing a clean, early visual confirmation of structural reversal.
Smoothing and Radius Interplay : A lower radius strength produces a tighter, more reactive curve ideal for scalping or short timeframes. Higher values generate broad, sweeping arcs optimized for swing or positional analysis.
Visually, this curvature system translates market inertia into shape - revealing how trends bend, accelerate, and ultimately exhaust.
Strategy Integration
The Curved Radius Supertrend is versatile enough to integrate seamlessly into multiple trading frameworks:
Trend Following : Use BUY/SELL flips to identify emerging directional bias. Strong curvature continuation confirms sustained momentum.
Momentum Entry Filtering : Combine with oscillators or volume tools to filter entries only when the curve slope accelerates (high momentum conditions).
Pullback and Re-entry Timing : The smooth curvature of the radius band allows traders to identify shallow retracements without premature exits. The band acts as a dynamic, self-adjusting support/resistance arc.
Volatility Compression and Expansion : Flattening curvature indicates volatility compression - a potential pre-breakout zone. Rapid re-steepening signals expansion and directional conviction.
Stop Placement Framework : The curved band can serve as a volatility-adjusted trailing stop. Because the curve reflects acceleration, it adapts naturally to market rhythm - widening during momentum surges and tightening during stagnation.
Technical Implementation Details
Curved Radius Engine : Parabolic acceleration algorithm that applies quadratic velocity based on bar count and radiusStrength.
Anchor Logic : Resets curvature at each trend change, establishing a new reference base for directional acceleration.
Smoothing Layer : SMA-based curve smoothing for noise reduction.
Outer Envelope : ATR-derived band offset visualizing volatility extension.
Directional Coloring : Candle and band coloration tied to current trend state.
Signal Engine : Built-in BUY/SELL markers and alert conditions for automation or script integration.
Optimal Application Parameters
Timeframe Guidance :
1-5 min (Scalping) : 0.08–0.12 radius strength, minimal smoothing for rapid responsiveness.
15 min : 0.12–0.15 radius strength for intraday trends.
1H : 0.15–0.18 radius strength for structured short-term swing setups.
4H : 0.18–0.22 radius strength for macro-trend shaping.
Daily : 0.20–0.25 radius strength for broad directional curves.
Weekly : 0.25–0.30 radius strength for smooth macro-level cycles.
The suggested radius strength ranges provide general structural guidance. Optimal values may vary across assets and volatility regimes, and should be refined through empirical testing to account for instrument-specific behavior and prevailing market conditions.
Asset Guidance :
Cryptocurrency : Higher radius and multiplier values to stabilize high-volatility environments.
Forex : Midrange settings (0.12-0.18) for clean curvature transitions.
Equities : Balanced curvature for trending sectors or momentum rotation setups.
Indices/Futures : Moderate radius values (0.15-0.22) to capture cyclical macro swings.
Performance Characteristics
High Effectiveness :
Trending environments with directional expansion.
Markets exhibiting clean momentum arcs and low structural noise.
Reduced Effectiveness :
Range-bound or low-volatility conditions with repeated false flips.
Ultra-short-term timeframes (<1m) where curvature acceleration overshoots.
Integration Guidelines
Confluence Framework : Combine with structure tools (order blocks, BOS, liquidity zones) for entry validation.
Risk Management : Trail stops along the curved band rather than fixed points to align with adaptive market geometry.
Multi-Timeframe Confirmation : Use higher timeframe curvature as a trend filter and lower timeframe curvature for execution timing.
Curve Compression Awareness : Treat flattening arcs as potential exhaustion zones - ideal for scaling out or reducing exposure.
Disclaimer
The Curved Radius Supertrend is a geometric trend model designed for professional traders and analysts. It is not a predictive system or a guaranteed profit method. Its performance depends on correct parameter calibration and sound risk management. BOSWaves recommends using it as part of a comprehensive analytical framework, incorporating volume, liquidity, and structural context to validate directional signals.
Background Trend Follower by exp3rtsThe Background Trend Follower indicator visually highlights the market’s daily directional bias using subtle background colors. It calculates the price change from the daily open and shades the chart background according to the current intraday momentum.
🟢 Green background → Price is significantly above the daily open (strong bullish trend)
🔴 Red background → Price is significantly below the daily open (strong bearish trend)
🟡 Yellow background → Price is trading near the daily open (neutral or consolidating phase)
The script automatically detects each new trading day.
It records the opening price at the start of the day.
As the session progresses, it continuously measures how far the current price has moved from that open.
When the move exceeds ±50 points (custom threshold), the background color adapts to reflect the trend strength.
Perfect for traders who want a quick visual sense of intraday bias — bullish, bearish, or neutral — without cluttering the chart with extra indicators.
Advanced Chandelier Exit with S/R [Alpha Extract]Advanced Chandelier Exit with S/R is a precision-crafted trailing stop and market structure detection system that fuses advanced Chandelier Exit logic with intelligent, multi-timeframe support and resistance tracking. This indicator delivers adaptive trend detection, volatility-aware exit positioning, and real-time structural mapping in a clean, responsive format. By combining directional filtering, pivot zone detection, and customizable styling, Advanced Chandelier Exit with S/R is designed to give traders reliable context, strong risk management, and visually intuitive confirmation signals across all timeframes and asset classes.
🔶 Adaptive Trailing Stop Architecture
At the core of Advanced Chandelier Exit with S/R is a refined Chandelier Exit mechanism that dynamically calculates trailing stops based on recent highs and lows, ATR volatility, and trend sensitivity. The system features directional memory, anchoring the stop to maintain position until a confirmed trend break occurs. This method prevents premature flips and keeps the trade aligned with sustained momentum.
longStop := close > longStop ? math.max(longStop, longStop ) : longStop
shortStop := close < shortStop ? math.min(shortStop, shortStop ) : shortStop
🔶 Volatility-Weighted Filtering
To reduce noise and improve reaction quality, Advanced Chandelier Exit with S/R includes an optional volatility normalization filter. This system adjusts ATR output based on how elevated it is relative to its own average, effectively down-weighting erratic price moves while maintaining responsiveness in directional phases.
volatilityFilter = enableVolatilityFilter ? ta.sma(baseATR, length) / baseATR : 1.0
atr = mult * baseATR * sensitivity * volatilityFilter
🔶 Trend Strength-Aware State Transitions
Trend flips in Advanced Chandelier Exit with S/R are not based solely on price crossing the stop level. Instead, the system includes a momentum-derived trend strength filter that validates the legitimacy of directional shifts. This guards against weak reversals and gives stronger confidence in breakout moves.
priceChange = math.abs(close - close )
avgPriceChange = ta.sma(priceChange, length)
trendStrength = math.min(priceChange / avgPriceChange * 100, 200)
🔶 Multi-Timeframe Support & Resistance Zones
Advanced Chandelier Exit with S/R embeds a sophisticated pivot-based structure mapping engine that automatically identifies significant price reaction levels and tracks their validity over time. It filters redundant zones, removes invalidated levels, and renders real-time support and resistance overlays based on market structure.
if isUniqueLevel(ph, resistanceLevels)
array.unshift(resistanceLevels, ph)
if isUniqueLevel(pl, supportLevels)
array.unshift(supportLevels, pl)
🔶 Dynamic Visual Encoding
The indicator uses strength-scaled fills, customizable colors, and line styling to convey directional bias with clarity. Color opacity intensifies as trend strength increases, offering intuitive context at a glance. Dynamic background fills mark trend states, while S/R zones are rendered with user-defined transparency for clean integration.
🔶 Signal Detection and Alerts
Directional signals are generated upon confirmed flips between long and short regimes, validated by stop crosses and strength filters. Additionally, the indicator provides S/R breakout alerts, identifying when price breaks through a key structural level.
🔶 Performance and Customization Optimizations
Advanced Chandelier Exit with S/R is built with modularity and efficiency in mind. It supports full customization of stop logic, volatility sensitivity, structural lookback, S/R zone filtering, and visual display. The use of array-based data structures for S/R levels ensures consistent performance even across high-activity assets and longer lookback periods.
Advanced Chandelier Exit with S/R represents the next evolution in trailing stop and structure-aware trading tools. By blending the proven logic of the Chandelier Exit system with intelligent trend strength filters and robust S/R detection, it becomes more than just a stop indicator—it becomes a complete trade management companion. Traders benefit from fewer false flips, clearer directional bias, and precise structural overlays that reinforce both breakout and reversal strategies. Whether used for swing entries, intraday positioning, or zone-based re-entries, Advanced Chandelier Exit with S/R empowers traders with responsive, intelligent logic that adapts to market conditions without compromise.
Smart Money Volume Activity [AlgoAlpha]🟠 OVERVIEW
This tool visualizes how Smart Money and Retail participants behave through lower-timeframe volume analysis. It detects volume spikes far beyond normal activity, classifies them as institutional or retail, and projects those zones as reactive levels. The script updates dynamically with each bar, showing when large players enter while tracking whether those events remain profitable. Each event is drawn as a horizontal line with bubble markers and summarized in a live P/L table comparing Smart Money versus Retail.
🟠 CONCEPTS
The core logic uses Z-score normalization on lower-timeframe volumes (like 5m inside a 1h chart). This lets the script detect statistically extreme bursts of buying or selling activity. It classifies each detected event as:
Smart Money — volume inside the candle body (suggesting hidden accumulation or distribution)
Retail — volume closing at bar extremes (suggesting chase entries or panic exits)
When new events appear, the script plots them as horizontal levels that persist until price interacts again. Each level acts as a potential reaction zone or liquidity footprint. The integrated P/L table then measures which class (Retail or Smart Money) is currently “winning” — comparing cumulative profitable versus losing volume.
🟠 FEATURES
Classifies flows into Smart Money or Retail based on candle-body context.
Displays live P/L comparison table for Smart vs Retail performance.
Alerts for each detected Smart or Retail buy/sell event.
🟠 USAGE
Setup : Add the script to any chart. Set Lower Timeframe Value (e.g., “5” for 5m) smaller than your main chart timeframe. The Period input controls how many bars are analyzed for the Z-score baseline. The Threshold (|Z|) decides how extreme a volume must be to plot a level.
Read the chart : Horizontal lines mark where heavy Smart or Retail volume occurred. Bright bubbles show the strongest events — their size reflects Z-score intensity. The on-chart table updates live: green cells show profitable flows, red cells show losing flows. A dominant green Smart Money row suggests institutions are currently controlling price.
See what others are doing :
Settings that matter : Raising Threshold (|Z|) filters noise, showing only large players. Increasing Period smooths results but reacts slower to new bursts. Use Show = “Both” for full comparison or isolate “Smart Money” / “Retail” to focus on one class.
Market Regime IndexThe Market Regime Index is a top-down macro regime nowcasting tool that offers a consolidated view of the market’s risk appetite. It tracks 32 of the world’s most influential markets across asset classes to determine investor sentiment by applying trend-following signals to each independent asset. It features adjustable parameters and a built-in alert system that notifies investors when conditions transition between Risk-On and Risk-Off regimes. The selected markets are grouped into equities (7), fixed income (9), currencies (7), commodities (5), and derivatives (4):
Equities = S&P 500 E-mini Index Futures, Nasdaq-100 E-mini Index Futures, Russell 2000 E-mini Index Futures, STOXX Europe 600 Index Futures, Nikkei 225 Index Futures, MSCI Emerging Markets Index Futures, and S&P 500 High Beta (SPHB)/Low Beta (SPLV) Ratio.
Fixed Income = US 10Y Treasury Yield, US 2Y Treasury Yield, US 10Y-02Y Yield Spread, German 10Y Bund Yield, UK 10Y Gilt Yield, US 10Y Breakeven Inflation Rate, US 10Y TIPS Yield, US High Yield Option-Adjusted Spread, and US Corporate Option-Adjusted Spread.
Currencies = US Dollar Index (DXY), Australian Dollar/US Dollar, Euro/US Dollar, Chinese Yuan/US Dollar, Pound Sterling/US Dollar, Japanese Yen/US Dollar, and Bitcoin/US Dollar.
Commodities = ICE Brent Crude Oil Futures, COMEX Gold Futures, COMEX Silver Futures, COMEX Copper Futures, and S&P Goldman Sachs Commodity Index (GSCI) Futures.
Derivatives = CBOE S&P 500 Volatility Index (VIX), ICE US Bond Market Volatility Index (MOVE), CBOE 3M Implied Correlation Index, and CBOE VIX Volatility Index (VVIX)/VIX.
All assets are directionally aligned with their historical correlation to the S&P 500. Each asset contributes equally based on its individual bullish or bearish signal. The overall market regime is calculated as the difference between the number of Risk-On and Risk-Off signals divided by the total number of assets, displayed as the percentage of markets confirming each regime. Green indicates Risk-On and occurs when the number of Risk-On signals exceeds Risk-Off signals, while red indicates Risk-Off and occurs when the number of Risk-Off signals exceeds Risk-On signals.
Bullish Signal = (Fast MA – Slow MA) > (ATR × ATR Margin)
Bearish Signal = (Fast MA – Slow MA) < –(ATR × ATR Margin)
Market Regime = (Risk-On signals – Risk-Off signals) ÷ Total assets
This indicator is designed with flexibility in mind, allowing users to include or exclude individual assets that contribute to the market regime and adjust the input parameters used for trend signal detection. These parameters apply to each independent asset, and the overall regime signal is smoothed by the signal length to reduce noise and enhance reliability. Investors can position according to the prevailing market regime by selecting factors that have historically outperformed under each regime environment to minimise downside risk and maximise upside potential:
Risk-On Equity Factors = High Beta > Cyclicals > Low Volatility > Defensives.
Risk-Off Equity Factors = Defensives > Low Volatility > Cyclicals > High Beta.
Risk-On Fixed Income Factors = High Yield > Investment Grade > Treasuries.
Risk-Off Fixed Income Factors = Treasuries > Investment Grade > High Yield.
Risk-On Commodity Factors = Industrial Metals > Energy > Agriculture > Gold.
Risk-Off Commodity Factors = Gold > Agriculture > Energy > Industrial Metals.
Risk-On Currency Factors = Cryptocurrencies > Foreign Currencies > US Dollar.
Risk-Off Currency Factors = US Dollar > Foreign Currencies > Cryptocurrencies.
In summary, the Market Regime Index is a comprehensive macro risk-management tool that identifies the current market regime and helps investors align portfolio risk with the market’s underlying risk appetite. Its intuitive, color-coded design makes it an indispensable resource for investors seeking to navigate shifting market conditions and enhance risk-adjusted performance by selecting factors that have historically outperformed. While it has proven historically valuable, asset-specific characteristics and correlations evolve over time as market dynamics change.
Trend Pivots Profile [BigBeluga]🔵 OVERVIEW
The Trend Pivots Profile is a dynamic volume profile tool that builds profiles around pivot points to reveal where liquidity accumulates during trend shifts. When the market is in an uptrend , the indicator generates profiles at low pivots . In a downtrend , it builds them at high pivots . Each profile is constructed using lower timeframe volume data for higher resolution, making it highly precise even in limited space. A colored trendline helps traders instantly recognize the prevailing trend and anticipate which type of profile (bullish or bearish) will form.
🔵 CONCEPTS
Pivot-Driven Profiles : Profiles are only created when a new pivot forms, aligning liquidity analysis with market structure shifts.
Trend-Contextual : Profiles form at low pivots in uptrends and at high pivots in downtrends.
Lower Timeframe Data : Volume and close values are pulled from smaller timeframes to provide detailed, high-resolution profiles inside larger pivot windows.
Adaptive Bin Sizing : Bin size is automatically calculated relative to ATR, ensuring consistent precision across different markets and volatility conditions.
Point of Control (PoC) : The highest-volume level within each profile is marked with a PoC line that extends until the next pivot forms.
Trendline Visualization : A wide, semi-transparent line follows the rolling average of highs and lows, colored blue in uptrends and orange in downtrends.
🔵 FEATURES
Pivot Length Control : Adjust how far back the script looks to detect pivots (e.g., length 5 → profiles cover 10 bars after pivot).
Pivot Profile toggle :
On → draw the filled pivot profile + PoC + pivot label.
Off → hide profiles; show only PoC level (clean S/R mode).
Trend Length Filter : Smooths trendline detection to ensure reliable up/down bias.
Precise Volume Distribution : Volume is aggregated into bins, creating a smooth volume curve around the pivot range.
PoC Extension : Automatically extends the most active price level until a new pivot is confirmed.
Profile Visualization : Profiles appear as filled shapes anchored at the pivot candle, colored based on trend.
Trendline Overlay : Thick, semi-transparent trendline provides visual guidance on directional bias.
Automatic Cleanup : Old profiles are deleted once they exceed the chart’s capacity (default 25 stored profiles).
🔵 HOW TO USE
Spotting Trend Liquidity : In an uptrend, monitor profiles at low pivots to see where buyers concentrated. In downtrends, use high-pivot profiles to spot sell-side pressure.
Watch the PoC : The PoC line highlights the strongest traded level of the pivot structure—expect reactions when price retests it.
Anticipate Trend Continuation/Reversal : Use the trendline (blue = bullish, orange = bearish) together with pivot profiles to forecast directional momentum.
Combine with HTF Context : Overlay with higher timeframe structure (order blocks, liquidity zones, or FVGs) for confluence.
Fine-Tune with Inputs : Adjust Pivot Length for sensitivity and Trend Length for smoother or faster trend shifts.
🔵 CONCLUSION
The Trend Pivots Profile blends pivot-based structure with precise volume profiling. By dynamically plotting profiles on pivots aligned with the prevailing trend, highlighting PoCs, and overlaying a directional trendline, it equips traders with a clear view of liquidity clusters and directional momentum—ideal for anticipating reactions, pullbacks, or breakouts.
Quant Trend + Donchian (Educational, Public-Safe)What this does
Educational, public-safe visualization of a quant regime model:
• Trend : EMA(64) vs EMA(256) (EWMAC proxy)
• Breakout : Donchian channel (200)
• Volatility-awareness : internal z-scores (not plotted) for concept clarity
Why it’s useful
• Shows when trend & breakout align (clean regimes) vs conflict (chop)
• Helps explain why volatility-aware systems size up in smooth trends and scale down in noise
How to read it
• EMA64 above EMA256 with price near/above Donchian high → trend-following alignment
• EMA64 below EMA256 with price near/below Donchian low → bearish alignment
• Inside channel with EMAs tangled → range/chop risk
Notes
• Indicator is educational only (no orders).
• Built entirely with TradingView built-ins.
• For consistent visuals: enable “Indicator values on price scale” and disable “Scale price chart only” in Settings → Scales .
Fisher Transform Trend Navigator [QuantAlgo]🟢 Overview
The Fisher Transform Trend Navigator applies a logarithmic transformation to normalize price data into a Gaussian distribution, then combines this with volatility-adaptive thresholds to create a trend detection system. This mathematical approach helps traders identify high-probability trend changes and reversal points while filtering market noise in the ever-changing volatility conditions.
🟢 How It Works
The indicator's foundation begins with price normalization, where recent price action is scaled to a bounded range between -1 and +1:
highestHigh = ta.highest(priceSource, fisherPeriod)
lowestLow = ta.lowest(priceSource, fisherPeriod)
value1 = highestHigh != lowestLow ? 2 * (priceSource - lowestLow) / (highestHigh - lowestLow) - 1 : 0
value1 := math.max(-0.999, math.min(0.999, value1))
This normalized value then passes through the Fisher Transform calculation, which applies a logarithmic function to convert the data into a Gaussian normal distribution that naturally amplifies price extremes and turning points:
fisherTransform = 0.5 * math.log((1 + value1) / (1 - value1))
smoothedFisher = ta.ema(fisherTransform, fisherSmoothing)
The smoothed Fisher signal is then integrated with an exponential moving average to create a hybrid trend line that balances statistical precision with price-following behavior:
baseTrend = ta.ema(close, basePeriod)
fisherAdjustment = smoothedFisher * fisherSensitivity * close
fisherTrend = baseTrend + fisherAdjustment
To filter out false signals and adapt to market conditions, the system calculates dynamic threshold bands using volatility measurements:
dynamicRange = ta.atr(volatilityPeriod)
threshold = dynamicRange * volatilityMultiplier
upperThreshold = fisherTrend + threshold
lowerThreshold = fisherTrend - threshold
When price momentum pushes through these thresholds, the trend line locks onto the new level and maintains direction until the opposite threshold is breached:
if upperThreshold < trendLine
trendLine := upperThreshold
if lowerThreshold > trendLine
trendLine := lowerThreshold
🟢 Signal Interpretation
Bullish Candles (Green): indicate normalized price distribution favoring bulls with sustained buying momentum = Long/Buy opportunities
Bearish Candles (Red): indicate normalized price distribution favoring bears with sustained selling pressure = Short/Sell opportunities
Upper Band Zone: Area above middle level indicating statistically elevated trend strength with potential overbought conditions approaching mean reversion zones
Lower Band Zone: Area below middle level indicating statistically depressed trend strength with potential oversold conditions approaching mean reversion zones
Built-in Alert System: Automated notifications trigger when bullish or bearish states change, allowing you to act on significant developments without constantly monitoring the charts
Candle Coloring: Optional feature applies trend colors to price bars for visual consistency and clarity
Configuration Presets: Three parameter sets available - Default (balanced settings), Scalping (faster response with higher sensitivity), and Swing Trading (slower response with enhanced smoothing)
Color Customization: Four color schemes including Classic, Aqua, Cosmic, and Custom options for personalized chart aesthetics
Pivot Trend Flow [BigBeluga]🔵 OVERVIEW
Pivot Trend Flow turns raw swing points into a clean, adaptive trend band. It averages recent pivot highs and lows to form two dynamic reference levels; when price crosses above the averaged highs, trend flips bullish and a green band is drawn; when it crosses below the averaged lows, trend flips bearish and a red band is drawn. During an uptrend the script highlights breakouts of previous pivot highs with ▲ labels, and during a downtrend it flags breakdowns of previous pivot lows with ▼ labels—making structure shifts and continuation signals obvious.
🔵 CONCEPTS
Pivot-Based Averages : Recent pivot highs/lows are collected and averaged to create smoothed upper/lower reference levels.
if not na(ph)
phArray.push(ph)
if not na(pl)
plArray.push(pl)
if phArray.size() > avgWindow
upper := phArray.avg()
phArray.shift()
if plArray.size() > avgWindow
lower := plArray.avg()
plArray.shift()
Trend State via Crosses : Close above the averaged-highs ⇒ bullish trend; close below the averaged-lows ⇒ bearish trend.
Trend Band : A colored band (green/red) is plotted and optionally filled to visualize the active regime around price.
Structure Triggers :
In bull mode the tool watches for prior pivot-high breakouts (▲).
In bear mode it watches for prior pivot-low breakdowns (▼).
🔵 FEATURES
Adaptive Trend Detection from averaged pivot highs/lows.
Clear Visuals : Green band in uptrends, red band in downtrends; optional fill for quick read.
Breakout/Breakdown Labels :
▲ marks breaks of previous pivot highs in uptrends
▼ marks breaks of previous pivot lows in downtrends
Minimal Clutter : Uses compact lines and labels that extend only on confirmation.
Customizable Colors & Fill for trend states and band styling.
🔵 HOW TO USE
Pivot Length : Sets how swing points are detected. Smaller = more reactive; larger = smoother.
Avg Window (pivots) : How many recent pivot highs/lows are averaged. Increase to stabilize the band; decrease for agility.
Read the Band :
Green band active ⇒ prioritize longs, pullback buys toward the band.
Red band active ⇒ prioritize shorts, pullback sells toward the band.
Trade the Triggers :
In bull mode, ▲ on a prior pivot-high break can confirm continuation.
In bear mode, ▼ on a prior pivot-low break can confirm continuation.
Combine with Context : Use HTF trend, S/R, or volume for confluence and to filter signals.
Fill Color Toggle : Enable/disable band fill to match your chart style.
🔵 CONCLUSION
Pivot Trend Flow converts swing structure into an actionable, low-lag trend framework. By blending averaged pivots with clean breakout/breakdown labels, it clarifies trend direction, timing, and continuation spots—ideal as a core bias tool or a confirmation layer in any trading system.
Nirvana True Duel전략 이름
열반의 진검승부 (영문: Nirvana True Duel)
컨셉과 철학
“열반의 진검승부”는 시장 소음은 무시하고, 확실할 때만 진입하는 전략입니다.
EMA 리본으로 추세 방향을 확인하고, 볼린저 밴드 수축/확장으로 변동성 돌파를 포착하며, OBV로 거래량 확인을 통해 가짜 돌파를 필터링합니다.
전략 로직
매수 조건 (롱)
20EMA > 50EMA (상승 추세)
밴드폭 수축 후 확장 시작
종가가 상단 밴드 돌파
OBV 상승 흐름 유지
매도 조건 (숏)
20EMA < 50EMA (하락 추세)
밴드폭 수축 후 확장 시작
종가가 하단 밴드 이탈
OBV 하락 흐름 유지
진입·청산
손절: ATR × 1.5 배수
익절: 손절폭의 1.5~2배에서 부분 청산
시간 청산: 설정한 최대 보유 봉수 초과 시 강제 청산
장점
✅ 추세·변동성·거래량 3중 필터 → 노이즈 최소화
✅ 백테스트·알람 지원 → 기계적 매매 가능
✅ 5분/15분 차트에 적합 → 단타/스윙 트레이딩 활용 가능
주의점
⚠ 횡보장에서는 신호가 적거나 실패 가능
⚠ 수수료·슬리피지 고려 필요
📜 Nirvana True Duel — Strategy Description (English)
Name:
Nirvana True Duel (a.k.a. Nirvana Cross)
Concept & Philosophy
The “Nirvana True Duel” strategy focuses on trading only meaningful breakouts and avoiding unnecessary noise.
Nirvana: A calm, patient state — waiting for the right opportunity without emotional trading.
True Duel: When the signal appears, enter decisively and let the market reveal the outcome.
In short: “Ignore market noise, trade only high-probability breakouts.”
🧩 Strategy Components
Trend Filter (EMA Ribbon): Stay aligned with the main market trend.
Volatility Squeeze (Bollinger Band): Detect volatility contraction & expansion to catch explosive moves early.
Volume Confirmation (OBV): Filter out false breakouts by confirming with volume flow.
⚔️ Entry & Exit Conditions
Long Setup:
20 EMA > 50 EMA (uptrend)
BB width breaks out from recent squeeze
Close > Upper Bollinger Band
OBV shows positive flow
Short Setup:
20 EMA < 50 EMA (downtrend)
BB width breaks out from recent squeeze
Close < Lower Bollinger Band
OBV shows negative flow
Risk Management:
Stop Loss: ATR × 1.5 below/above entry
Take Profit: 1.5–2× stop distance, partial take-profit allowed
Time Stop: Automatically closes after max bars held (e.g. 8h on 5m chart)
✅ Strengths
Triple Filtering: Trend + Volatility + Volume → fewer false signals
Mechanical & Backtestable: Ideal for objective trading & performance validation
Adaptable: Works well on Bitcoin, Nasdaq futures, and other high-volatility markets (5m/15m)
⚠️ Things to Note
Low signal frequency or higher failure rate in sideways/range markets
Commission & slippage should be factored in, especially on lower timeframes
ATR multiplier and R:R ratio should be optimized per asset
Machine Learning Gaussian Mixture Model | AlphaNattMachine Learning Gaussian Mixture Model | AlphaNatt
A revolutionary oscillator that uses Gaussian Mixture Models (GMM) with unsupervised machine learning to identify market regimes and automatically adapt momentum calculations - bringing statistical pattern recognition techniques to trading.
"Markets don't follow a single distribution - they're a mixture of different regimes. This oscillator identifies which regime we're in and adapts accordingly."
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🤖 THE MACHINE LEARNING
Gaussian Mixture Models (GMM):
Unlike K-means clustering which assigns hard boundaries, GMM uses probabilistic clustering :
Models data as coming from multiple Gaussian distributions
Each market regime is a different Gaussian component
Provides probability of belonging to each regime
More sophisticated than simple clustering
Expectation-Maximization Algorithm:
The indicator continuously learns and adapts using the E-M algorithm:
E-step: Calculate probability of current market belonging to each regime
M-step: Update regime parameters based on new data
Continuous learning without repainting
Adapts to changing market conditions
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🎯 THREE MARKET REGIMES
The GMM identifies three distinct market states:
Regime 1 - Low Volatility:
Quiet, ranging markets
Uses RSI-based momentum calculation
Reduces false signals in choppy conditions
Background: Pink tint
Regime 2 - Normal Market:
Standard trending conditions
Uses Rate of Change momentum
Balanced sensitivity
Background: Gray tint
Regime 3 - High Volatility:
Strong trends or volatility events
Uses Z-score based momentum
Captures extreme moves
Background: Cyan tint
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💡 KEY INNOVATIONS
1. Probabilistic Regime Detection:
Instead of binary regime assignment, provides probabilities:
30% Regime 1, 60% Regime 2, 10% Regime 3
Smooth transitions between regimes
No sudden indicator jumps
2. Weighted Momentum Calculation:
Combines three different momentum formulas
Weights based on regime probabilities
Automatically adapts to market conditions
3. Confidence Indicator:
Shows how certain the model is (white line)
High confidence = strong regime identification
Low confidence = transitional market state
Line transparency changes with confidence
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⚙️ PARAMETER OPTIMIZATION
Training Period (50-500):
50-100: Quick adaptation to recent conditions
100: Balanced (default)
200-500: Stable regime identification
Number of Components (2-5):
2: Simple bull/bear regimes
3: Low/Normal/High volatility (default)
4-5: More granular regime detection
Learning Rate (0.1-1.0):
0.1-0.3: Slow, stable learning
0.3: Balanced (default)
0.5-1.0: Fast adaptation
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📊 TRADING STRATEGIES
Visual Signals:
Cyan gradient: Bullish momentum
Magenta gradient: Bearish momentum
Background color: Current regime
Confidence line: Model certainty
1. Regime-Based Trading:
Regime 1 (pink): Expect mean reversion
Regime 2 (gray): Standard trend following
Regime 3 (cyan): Strong momentum trades
2. Confidence-Filtered Signals:
Only trade when confidence > 70%
High confidence = clearer market state
Avoid transitions (low confidence)
3. Adaptive Position Sizing:
Regime 1: Smaller positions (choppy)
Regime 2: Normal positions
Regime 3: Larger positions (trending)
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🚀 ADVANTAGES OVER OTHER ML INDICATORS
vs K-Means Clustering:
Soft clustering (probabilities) vs hard boundaries
Captures uncertainty and transitions
More mathematically robust
vs KNN (K-Nearest Neighbors):
Unsupervised learning (no historical labels needed)
Continuous adaptation
Lower computational complexity
vs Neural Networks:
Interpretable (know what each regime means)
No overfitting issues
Works with limited data
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📈 PERFORMANCE CHARACTERISTICS
Best Market Conditions:
Markets with clear regime shifts
Volatile to trending transitions
Multi-timeframe analysis
Cryptocurrency markets (high regime variation)
Key Strengths:
Automatically adapts to market changes
No manual parameter adjustment needed
Smooth transitions between regimes
Probabilistic confidence measure
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🔬 TECHNICAL BACKGROUND
Gaussian Mixture Models are used extensively in:
Speech recognition (Google Assistant)
Computer vision (facial recognition)
Astronomy (galaxy classification)
Genomics (gene expression analysis)
Finance (risk modeling at investment banks)
The E-M algorithm was developed at Stanford in 1977 and is one of the most important algorithms in unsupervised machine learning.
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💡 PRO TIPS
Watch regime transitions: Best opportunities often occur when regimes change
Combine with volume: High volume + regime change = strong signal
Use confidence filter: Avoid low confidence periods
Multi-timeframe: Compare regimes across timeframes
Adjust position size: Scale based on identified regime
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⚠️ IMPORTANT NOTES
Machine learning adapts but doesn't predict the future
Best used with other confirmation indicators
Allow time for model to learn (100+ bars)
Not financial advice - educational purposes
Backtest thoroughly on your instruments
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🏆 CONCLUSION
The GMM Momentum Oscillator brings institutional-grade machine learning to retail trading. By identifying market regimes probabilistically and adapting momentum calculations accordingly, it provides:
Automatic adaptation to market conditions
Clear regime identification with confidence levels
Smooth, professional signal generation
True unsupervised machine learning
This isn't just another indicator with "ML" in the name - it's a genuine implementation of Gaussian Mixture Models with the Expectation-Maximization algorithm, the same technology used in:
Google's speech recognition
Tesla's computer vision
NASA's data analysis
Wall Street risk models
"Let the machine learn the market regimes. Trade with statistical confidence."
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Developed by AlphaNatt | Machine Learning Trading Systems
Version: 1.0
Algorithm: Gaussian Mixture Model with E-M
Classification: Unsupervised Learning Oscillator
Not financial advice. Always DYOR.
High Probability Order Blocks [AlgoAlpha]🟠 OVERVIEW
This script detects and visualizes high-probability order blocks by combining a volatility-based z-score trigger with a statistical survival model inspired by Kaplan-Meier estimation. It builds and manages bullish and bearish order blocks dynamically on the chart, displays live survival probabilities per block, and plots optional rejection signals. What makes this tool unique is its use of historical mitigation behavior to estimate and plot how likely each zone is to persist, offering traders a probabilistic perspective on order block strength—something rarely seen in retail indicators.
🟠 CONCEPTS
Order blocks are regions of strong institutional interest, often marked by large imbalances between buying and selling. This script identifies those areas using z-score thresholds on directional distance (up or down candles), detecting statistically significant moves that signal potential smart money footprints. A bullish block is drawn when a strong up-move (zUp > 4) follows a down candle, and vice versa for bearish blocks. Over time, each block is evaluated: if price “mitigates” it (i.e., closes cleanly past the opposite side and confirmed with a 1 bar delay), it’s considered resolved and logged. These resolved blocks then inform a Kaplan-Meier-like survival curve, estimating the likelihood that future blocks of a given age will remain unbroken. The indicator then draws a probability curve for each side (bull/bear), updating it in real time.
🟠 FEATURES
Live label inside each block showing survival probability or “N.E.D.” if insufficient data.
Kaplan-Meier survival curves drawn directly on the chart to show estimated strength decay.
Rejection markers (▲ ▼) if price bounces cleanly off an active order block.
Alerts for zone creation and rejection signals, supporting rule-based trading workflows.
🟠 USAGE
Read the label inside each block for Age | Survival% (or N.E.D. if there aren’t enough samples yet); higher survival % suggests blocks of that age have historically lasted longer.
Use the right-side survival curves to gauge how probability decays with age for bull vs bear blocks, and align entries with the side showing stronger survival at current age.
Treat ▲ (bullish rejection) and ▼ (bearish rejection) as optional confluence when price tests a boundary and fails to break.
Turn on alerts for “Bullish Zone Created,” “Bearish Zone Created,” and rejection signals so you don’t need to watch constantly.
If your chart gets crowded, enable Prevent Overlap ; tune Max Box Age to your timeframe; and adjust KM Training Window / Minimum Samples to trade off responsiveness vs stability.
AMF PG Strategy AMF Command Center Strategy (Praetorian Guard)
The AMF PG Strategy (Praetorian Guard) is an advanced trading system built to adapt seamlessly across market conditions. Its unique structure balances precision entries with intelligent protection, giving traders confidence in both trending and volatile environments.
Key highlights include:
Adaptive Core (AMF Engine) – A dynamic framework that automatically adjusts and generates a powerful tracking line for clearer long and short opportunities.
Praetorian Guard – A built-in protective shield that activates in extreme conditions, helping stabilize performance when markets become turbulent.
Versatility – Effective across multiple timeframes, from scalping to swing trading, without constant parameter adjustments.
Clarity – Clean visual signals and color-coded tracking for instant decision-making.
This strategy was designed for traders who want more than just entries and exits — it offers a command center for disciplined, adaptive, and resilient trading.
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.
LogPressure Envelope [BOSWaves]LogPressure Envelope – Adaptive Volatility & Trend Visualizer
Overview
LogPressure Envelope is a specialized trading tool designed to normalize market behavior using logarithmic price scaling while providing an adaptive framework for volatility and trend detection. The indicator calculates a log-based moving average midline, surrounds it with asymmetric volatility envelopes, and replaces the conventional cloud with progressive fan lines to present price action in a more interpretable form.
By integrating rate-of-change midline coloring, fading trend strength, and structured buy/sell markers, LogPressure Envelope simplifies the reading of complex market dynamics. Its design makes it suitable for multiple trading approaches, including scalping, intraday, and swing trading, where volatility behavior and trend shifts must be understood quickly and objectively.
Unlike static envelope indicators, LogPressure Envelope adapts continuously to price scale and volatility conditions. It evaluates log-transformed prices, applies configurable moving average methods (EMA, SMA, WMA), and derives asymmetric standard-deviation bands for both upside and downside moves. These envelopes are projected as fan lines with adjustable opacity, producing a layered volatility map that evolves with the market.
This system ensures each visual element—midline shading, candle coloring, fan structure, and signal markers—reflects real-time market conditions, allowing traders to interpret volatility expansion, contraction, and directional bias with clarity.
How It Works
The foundation of LogPressure Envelope is the logarithmic transformation of price. By operating in log space, the indicator removes distortions caused by large nominal price differences across assets, enabling consistent analysis of both low-priced and high-priced instruments.
A moving average of log prices is calculated (EMA, SMA, or WMA depending on user input) and then re-converted to normal price scale, forming the log midline. Standard deviation of log prices is then measured over a separate period, with independent multipliers for upside and downside deviations. This asymmetry captures the fact that markets often expand differently in bullish versus bearish phases.
Instead of plotting a filled cloud, the envelope is expressed as ten equidistant fan lines stretching from the lower to upper boundary. Each line is shaded progressively to visualize volatility clustering and directional strength without overloading the chart.
Trend determination is smoothed using a fade mechanism: shifts in bias do not flip instantly but gradually move toward the new state, producing fewer false transitions. Buy and sell markers are generated when trend strength crosses confirmation thresholds, ensuring signals are event-driven and contextually meaningful.
Signals and Visuals
LogPressure Envelope provides multiple layers of structured signals:
Midline Bias – Central moving average colored by rate-of-change, reflecting directional acceleration or deceleration.
Volatility Fan – Ten progressive lines forming a gradient between lower and upper bands, visually encoding volatility spread.
Buy Signals – Labels below bars when upward trend strength is confirmed.
Sell Signals – Labels above bars when downward trend strength is confirmed.
Candle Coloring – Optional shading of candles based on trend alignment with the log midline, highlighting bullish, bearish, or neutral conditions.
These signals remain clear even during high-volatility phases, with visual hierarchy maintained through progressive opacity control.
Interpretation
Trend Analysis : Midline direction and candle coloring provide continuous feedback on prevailing bias. Upward-sloping midlines with blue shading indicate bullish phases, while downward slopes with orange shading confirm bearish conditions.
Volatility and Risk Assessment : Expansion of fan lines indicates rising volatility and potential breakout conditions; contraction indicates consolidation and possible mean reversion.
Signal Confirmation : Buy and sell markers validate transitions when trend strength thresholds are crossed, aligning with volatility envelope dynamics.
Market Context : Asymmetric envelopes allow traders to see where bearish acceleration differs from bullish expansion, improving interpretation of liquidity conditions and institutional pressure.
Strategy Integration
LogPressure Envelope can be applied across trading styles:
Trend Following : Enter trades in the direction of midline bias, confirmed by buy or sell markers.
Pullback Entries : Use midline retests during trending conditions as lower-risk continuation points.
Volatility Breakouts : Identify sharp expansions in fan line spacing as early signals of directional moves.
Reversal Strategies : Fade extreme envelope touches when momentum shows exhaustion and fan contraction begins.
Multi-Timeframe Confirmation : Align signals from higher and lower timeframes to reduce noise and validate trade setups.
Stop-loss levels can be set near the opposite envelope boundary, while targets may be managed through progressive volatility zones or midline convergence.
Advanced Techniques
For greater precision, LogPressure Envelope can be combined with other analytical tools:
Pair with volume or liquidity measures to validate breakout or reversal conditions.
Use momentum indicators to confirm ROC-based midline bias.
Track sequences of fan line expansions and contractions to anticipate regime shifts in volatility.
Apply across multiple timeframes to monitor how volatility clusters align at different market scales.
Adjusting parameters such as envelope multipliers, moving average type, and fade bars allows the indicator to adapt to diverse asset classes and volatility environments.
Inputs and Customization
Midline Type : Select EMA, SMA, or WMA.
Line Opacity : Control visibility of fan lines.
Enable Candle Coloring : Toggle trend-based bar shading.
MA Length / StdDev Length : Define periods for midline and volatility calculation.
Multipliers : Set asymmetric scaling for upside and downside envelopes.
Fade Bars : Control smoothness of trend strength transitions.
Fan Lines : Adjust number of envelope subdivisions for visualization granularity.
Why Use LogPressure Envelope
LogPressure Envelope translates complex volatility and trend interactions into a structured and adaptive framework. By combining logarithmic normalization, asymmetric standard deviation envelopes, and smoothed trend confirmation, it allows traders to:
Normalize price analysis across assets of different scales.
Visualize volatility expansion and contraction in real time.
Identify and confirm directional shifts with objective signal markers.
Apply a disciplined system for trend, breakout, and reversal strategies.
This indicator is designed for traders who want a systematic, visually clear approach to volatility-based market analysis without relying on static bands or arbitrary scaling.
Pasrsifal.RegressionTrendStateSummary
The Parsifal.Regression.Trend.State Indicator analyzes the leading coefficients of linear and quadratic regressions of price (against time). It also considers their first- and second-order changes. These features are aggregated into a Trend-State background, shown as a gradient color. In addition, the indicator generates fast and slow signals that can be used as potential entry- or exit triggers.
This tool is designed for advanced trend-following strategies, leveraging information from multiple trendline features.
Background
Trendlines provide insight into the state of a trend or the “trendiness” of a price process. While moving averages or pivot-based lines can serve as envelopes and breakout levels, they are often too lagging for swing traders, who need tools that adapt more closely to price swings, ideally using trendlines, around which the price process swings continuously.
Regression lines address this by cutting directly through the data, making them a natural anchor for observing how price winds around a central trendline within a chosen lookback period.
Regression Trendlines
• Linear Regression:
o Minimizes distance to all closing values over the lookback period.
o The slope represents the short-term linear trend.
o The change of slope indicates trend acceleration or deceleration.
o Linear regression lags during phases of rapid market shifts.
• Quadratic Regression:
o Fits a second-degree polynomial to minimize deviation from closing prices.
o The convexity term (leading coefficient) reflects curvature:
Positive convexity → accelerating uptrend or fading downtrend.
Negative convexity → accelerating downtrend or fading uptrend.
o The change of convexity detects early shifts in momentum and often reacts faster than slope features.
Features Extracted
The indicator evaluates six features:
• Linear features: slope, first derivative of slope, second derivative of slope.
• Quadratic features: convexity term, first derivative of the convexity term, second derivative of the convexity term.
• Linear features: capture broad, background trend behavior.
• Quadratic features: detect deviations, accelerations, and smaller-scale dynamics.
Quadratic terms generally react first to market changes, while linear terms provide stability and context.
Dynamics of Market Moves as seen by linear and quadratic regressions
• At the start of a rapid move:
The change of convexity reacts first, capturing the shift in dynamics before other features. The convexity term then follows, while linear slope features lag further behind. Because convexity measures deviation from linearity, it reflects accelerating momentum more effectively than slope.
• At the end of a rapid move:
Again, the change of convexity responds first to fading momentum, signaling the transition from above-linear to below-linear dynamics. Even while a strong trend persists, the change of convexity may flip sign early, offering a warning of weakening strength. The convexity term itself adjusts more slowly but may still turn before the price process does. Linear features lag the most, typically only flipping after price has already reversed, thereby smoothing out the rapid, more sensitive reactions of quadratic terms.
________________________________________
Parsifal Regression.Trend.State Method
1. Feature Mapping:
Each feature is mapped to a range between -1 and 1, preserving zero-crossings (critical for sign interpretation).
2. Aggregation:
A heuristic linear combination*) produces a background information value, visualized as a gradient color scale:
o Deep green → strong positive trend.
o Deep red → strong negative trend.
o Yellow → neutral or transitional states.
3. Signals:
o Fast signal (oscillator): ranges from -1 to 1, reflecting short-term trend state.
o Slow signal (smoothed): moving average of the fast signal.
o Their interactions (crossovers, zero-crossings) provide actionable trading triggers.
How to Use
The Trend-State background gradient provides intuitive visual feedback on the aggregated regression features (slope, convexity, and their changes). Because these features reflect not only current trend strength but also their acceleration or deceleration, the color transitions help anticipate evolving market states:
• Solid Green: All features near their highs. Indicates a strong, accelerating uptrend. May also reflect explosive or hyperbolic upside moves (including gaps).
• Fading Solid Green: A recently strong uptrend is losing momentum. Price may shift into a slower uptrend, consolidation, or even a reversal.
• Fading Green → Yellow: Often appears as a dirty yellow or a rapidly mixing pattern of green and red. Signals that the uptrend is weakening toward neutrality or beginning to turn negative.
• Yellow → Deepening Red: Two possible scenarios:
o Coming from a strong uptrend → suggests a sharp fade, though the trend may still technically be up.
o Coming from a weaker uptrend or sideways market → suggests the start of an accelerating downtrend.
• Solid Red: All features near their lows. Indicates a strong, accelerating downtrend. May also reflect crash-type conditions or downside gaps.
• Fading Solid Red: A recently strong downtrend is losing strength. Market may move into a slower decline, consolidation, or early reversal upward.
• Fading Red → Yellow : The downtrend is weakening toward neutral, with potential for a bullish shift.
• Yellow → Increasing Green: Two possible scenarios:
o Coming from a strong downtrend, it reflects a sharp fade of bearish momentum, though the market may still technically be trending down.
o Coming from a weaker downtrend or sideways movement, it suggests the start of an accelerating uptrend.
Note: Market evolution does not always follow this neat “color cycle.” It may jump between states, skip stages, or reverse abruptly depending on market conditions. This makes the background coloring particularly valuable as a contextual map of current and evolving price dynamics.
Signal Crossovers:
Although the fast signal is very similar (but not identical) to the background coloring, it provides a numerical representation indicating a bullish interpretation for rising values and bearish for falling.
o High-confidence entries:
Fast signal rising from < -0.7 and crossing above the slow signal → potential long entry.
Fast signal falling from > +0.7 and crossing below the slow signal → potential short entry.
o Low-confidence entries:
Crossovers near zero may still provide a valid trigger but may be noisy and should be confirmed with other signals.
o Zero-crossings:
Indicate broader state changes, useful for conservative positioning or option strategies. For confirmation of a Fast signal 0-crossing, wait for the Slow signal to cross as well.
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*) Note on Aggregation
While the indicator currently uses a heuristic linear combination of features, alternatives such as Principal Component Analysis (PCA) could provide a more formal aggregation. However, while in the absence of matrix algebra, the required eigenvalue decomposition can be approximated, its computational expense does not justify the marginal higher insight in this case. The current heuristic approach offers a practical balance of clarity, speed, and accuracy.
Reverse RSI Signals [AlgoAlpha]🟠 OVERVIEW
This script introduces the Reverse RSI Signals system, an original approach that inverts traditional RSI values back into price levels and then overlays them directly on the chart as dynamic bands. Instead of showing RSI in a subwindow, the script calculates the exact price thresholds that correspond to common RSI levels (30/70/50) and displays them as upper, lower, and midline bands. These are further enhanced with an adaptive Supertrend filter and divergence detection, allowing traders to see overbought/oversold zones translated into actionable price ranges and trend signals. The script combines concepts of RSI inversion, volatility envelopes, and divergence tracking to provide a context-driven tool for spotting reversals and regime shifts.
🟠 CONCEPTS
The script relies on inverting RSI math: by solving for the price that would yield a given RSI level, it generates real chart levels tied to oscillator conditions. These RSI-derived price bands act like support/resistance, adapting each bar as RSI changes. On top of this, a Supertrend built around the RSI midline introduces directional bias, switching regimes when the midline is breached. Regular bullish and bearish divergences are detected by comparing RSI pivots against price pivots, highlighting early reversal conditions. This layered approach means the indicator is not just RSI on price but a hybrid of oscillator translation, volatility-tracking midline envelopes, and divergence analysis.
🟠 FEATURES
Inverted RSI bands: upper (70), lower (30), and midline (50), smoothed with EMA for noise reduction.
Supertrend overlay on the RSI midline to confirm regime direction (bullish or bearish).
Gradient-filled zones between outer and inner RSI bands to visualize proximity and exhaustion.
Non-repainting bullish and bearish divergence markers plotted directly on chart highs/lows.
🟠 USAGE
Apply the indicator to any chart and use the plotted RSI price bands as adaptive support/resistance. The midline defines equilibrium, while upper and lower bands represent classic RSI thresholds translated into real price action. In bullish regimes (green candles), long trades are stronger when price approaches or bounces from the lower band; in bearish regimes (red candles), shorts are favored near the upper band. Divergence markers (▲ for bullish, ▼ for bearish) flag potential reversal points early. Traders can combine the band proximity, divergence alerts, and Supertrend context to time entries, exits, or to refine ongoing trend trades. Adjust smoothing and Supertrend ATR settings to match the volatility of the instrument being analyzed.
200 EMA w/ Ticker Memory200 EMA w/ Ticker Memory — Multi-Symbol & Multi-Timeframe EMA Tracker with Alerts
Overview
The 200 EMA w/ Ticker Memory indicator allows you to monitor the 200-period Exponential Moving Average (EMA) across multiple symbols and timeframes. Designed for traders managing multiple tickers, it provides customizable timeframe inputs per symbol and instant alerts on price touches of the 200 EMA.
Key Features
Multi-symbol support: Configure up to 20 different symbols, each with its own timeframe setting.
Flexible timeframe input: Assign specific timeframes per symbol or use a default timeframe fallback.
Accurate 200 EMA calculation: Uses request.security to fetch 200 EMA from the symbol-specific timeframe.
Visual EMA plots: Displays both the EMA on the selected timeframe and the EMA on the current chart timeframe for comparison.
Touch alerts: Configurable alerts when price “touches” the 200 EMA within a user-defined sensitivity percentage.
Ticker memory: Remembers your configured symbols and displays them in an on-chart table.
Compact info table: Displays current symbol status, alert settings, and timeframe in a clean, transparent table overlay.
How to Use
Configure Symbols and Timeframes:
Input your desired symbols (up to 20) and their respective timeframes under the “Symbol Settings” groups in the indicator’s settings pane.
Set Default Timeframe:
Choose a default timeframe to be used when no specific timeframe is assigned for a symbol.
Adjust Alert Settings:
Enable or disable alerts and set the touch sensitivity (% distance from EMA to trigger alerts).
Alerts
Alerts trigger once per bar when the price touches the 200 EMA within the defined sensitivity threshold.
Alert messages include:
Symbol / Current price / EMA value / EMA timeframe used / Chart timeframe / Timestamp
Customization
200 EMA Color: Change the line color for better visibility.
Touch Sensitivity: Fine-tune how close price must be to the EMA to count as a touch (default 0.1%).
Enable Touch Alerts: Turn on/off alert notifications easily.
For:
- Swing traders monitoring multiple stocks or assets.
- Day traders watching key EMA levels on different timeframes.
- Analysts requiring a quick visual and alert system for 200 EMA touches.
- Portfolio managers tracking key technical levels across various securities.
Limitations
Supports up to 20 configured symbols (can be extended manually if needed).
Works best on charts with reasonable bar frequency due to request.security usage.
Alert frequency is limited to once per bar for clarity.
Disclaimer
This indicator is provided “as-is” for educational and informational purposes only. It does not guarantee trading success or financial gain.
Mutanabby_AI | Ultimate Algo | Remastered+Overview
The Mutanabby_AI Ultimate Algo Remastered+ represents a sophisticated trend-following system that combines Supertrend analysis with multiple moving average confirmations. This comprehensive indicator is designed specifically for identifying high-probability trend continuation and reversal opportunities across various market conditions.
Core Algorithm Components
**Supertrend Foundation**: The primary signal generation relies on a customizable Supertrend indicator with adjustable sensitivity (1-20 range). This adaptive trend-following tool uses Average True Range calculations to establish dynamic support and resistance levels that respond to market volatility.
**SMA Confirmation Matrix**: Multiple Simple Moving Averages (SMA 4, 5, 9, 13) provide layered confirmation for signal strength. The algorithm distinguishes between regular signals and "Strong" signals based on SMA 4 vs SMA 5 relationship, offering traders different conviction levels for position sizing.
**Trend Ribbon Visualization**: SMA 21 and SMA 34 create a visual trend ribbon that changes color based on their relationship. Green ribbon indicates bullish momentum while red signals bearish conditions, providing immediate visual trend context.
**RSI-Based Candle Coloring**: Advanced 61-tier RSI system colors candles with gradient precision from deep red (RSI ≤20) through purple transitions to bright green (RSI ≥79). This visual enhancement helps traders instantly assess momentum strength and overbought/oversold conditions.
Signal Generation Logic
**Buy Signal Criteria**:
- Price crosses above Supertrend line
- Close price must be above SMA 9 (trend confirmation)
- Signal strength determined by SMA 4 vs SMA 5 relationship
- "Strong Buy" when SMA 4 ≥ SMA 5
- Regular "Buy" when SMA 4 < SMA 5
**Sell Signal Criteria**:
- Price crosses below Supertrend line
- Close price must be below SMA 9 (trend confirmation)
- Signal strength based on SMA relationship
- "Strong Sell" when SMA 4 ≤ SMA 5
- Regular "Sell" when SMA 4 > SMA 5
Advanced Risk Management System
**Automated TP/SL Calculation**: The indicator automatically calculates stop loss and take profit levels using ATR-based measurements. Risk percentage and ATR length are fully customizable, allowing traders to adapt to different market conditions and personal risk tolerance.
**Multiple Take Profit Targets**:
- 1:1 Risk-Reward ratio for conservative profit taking
- 2:1 Risk-Reward for balanced trade management
- 3:1 Risk-Reward for maximum profit potential
**Visual Risk Display**: All risk management levels appear as both labels and optional trend lines on the chart. Customizable line styles (solid, dashed, dotted) and positioning ensure clear visualization without chart clutter.
**Dynamic Level Updates**: Risk levels automatically recalculate with each new signal, maintaining current market relevance throughout position lifecycles.
Visual Enhancement Features
**Customizable Display Options**: Toggle trend ribbon, TP/SL levels, and risk lines independently. Decimal precision adjustments (1-8 decimal places) accommodate different instrument price formats and personal preferences.
**Professional Label System**: Clean, informative labels show entry points, stop losses, and take profit targets with precise price levels. Labels automatically position themselves for optimal chart readability.
**Color-Coded Momentum**: The gradient RSI candle coloring system provides instant visual feedback on momentum strength, helping traders assess market energy and potential reversal zones.
Implementation Strategy
**Timeframe Optimization**: The algorithm performs effectively across multiple timeframes, with higher timeframes (4H, Daily) providing more reliable signals for swing trading. Lower timeframes work well for day trading with appropriate risk adjustments.
**Sensitivity Adjustment**: Lower sensitivity values (1-5) generate fewer but higher-quality signals, ideal for conservative approaches. Higher sensitivity (15-20) increases signal frequency for active trading styles.
**Risk Management Integration**: Use the automated risk calculations as baseline parameters, adjusting risk percentage based on account size and market conditions. The 1:1, 2:1, 3:1 targets enable systematic profit-taking strategies.
Market Application
**Trend Following Excellence**: Primary strength lies in capturing significant trend movements through the Supertrend foundation with SMA confirmation. The dual-layer approach reduces false signals common in single-indicator systems.
**Momentum Assessment**: RSI-based candle coloring provides immediate momentum context, helping traders assess signal strength and potential continuation probability.
**Range Detection**: The trend ribbon helps identify ranging conditions when SMA 21 and SMA 34 converge, alerting traders to potential breakout opportunities.
Performance Optimization
**Signal Quality**: The requirement for both Supertrend crossover AND SMA 9 confirmation significantly improves signal reliability compared to basic trend-following approaches.
**Visual Clarity**: The comprehensive visual system enables rapid market assessment without complex calculations, ideal for traders managing multiple instruments.
**Adaptability**: Extensive customization options allow fine-tuning for specific markets, trading styles, and risk preferences while maintaining the core algorithm integrity.
## Non-Repainting Design
**Educational Note**: This indicator uses standard TradingView functions (Supertrend, SMA, RSI) with normal behavior patterns. Real-time updates on current candles are expected and standard across all technical indicators. Historical signals on closed candles remain fixed and unchanged, ensuring reliable backtesting and analysis.
**Signal Confirmation**: Final signals are confirmed only when candles close, following standard technical analysis principles. The algorithm provides clear distinction between developing signals and confirmed entries.
Technical Specifications
**Supertrend Parameters**: Default sensitivity of 4 with ATR length of 11 provides balanced signal generation. Sensitivity range from 1-20 allows adaptation to different market volatilities and trading preferences.
**Moving Average Configuration**: SMA periods of 8, 9, and 13 create multi-layered trend confirmation, while SMA 21 and 34 form the visual trend ribbon for broader market context.
**Risk Management**: ATR-based calculations with customizable risk percentage ensure dynamic adaptation to market volatility while maintaining consistent risk exposure principles.
Recommended Settings
**Conservative Approach**: Sensitivity 4-5, RSI length 14, higher timeframes (4H, Daily) for swing trading with maximum signal reliability.
**Active Trading**: Sensitivity 6-8, RSI length 8-10, intermediate timeframes (1H) for balanced signal frequency and quality.
**Scalping Setup**: Sensitivity 10-15, RSI length 5-8, lower timeframes (15-30min) with enhanced risk management protocols.
## Conclusion
The Mutanabby_AI Ultimate Algo Remastered+ combines proven trend-following principles with modern visual enhancements and comprehensive risk management. The algorithm's strength lies in its multi-layered confirmation approach and automated risk calculations, providing both novice and experienced traders with clear signals and systematic trade management.
Success with this system requires understanding the relationship between signal strength indicators and adapting sensitivity settings to match current market conditions. The comprehensive visual feedback system enables rapid decision-making while the automated risk management ensures consistent trade parameters.
Practice with different sensitivity settings and timeframes to optimize performance for your specific trading style and risk tolerance. The algorithm's systematic approach provides an excellent framework for disciplined trend-following strategies across various market environments.
Zero Lag Liquidity [AlgoAlpha]🟠 OVERVIEW
This script plots liquidity zones with zero lag using lower-timeframe wick profiles and high-volume wicks to mark key price reactions. It’s called Zero Lag Liquidity because it captures significant liquidity imbalances in real time by processing lower-TF price-volume distributions directly inside the wick of abnormal candles. The tool builds a volume histogram inside long upper/lower wicks, then calculates a local Point of Control (POC) to mark the price where most volume occurred. These levels act as visual liquidity zones, which can trigger labels, break signals, and trend detection depending on price interaction.
🟠 CONCEPTS
The core concept relies on identifying high-volume candles with unusually long wicks—often a sign of opposing liquidity. When a large upper or lower wick appears with a strong volume spike, the script builds a histogram of lower-timeframe closes and volumes inside that wick. It bins the wick into segments, sums volume per bin, and finds the POC. This POC becomes the liquidity level. The script then dynamically tracks whether price breaks above or rejects off these levels, adjusts the active trend regime accordingly, and highlights bars to help users spot continuation or reversal behavior. The logic avoids repainting or subjective interpretation by using fixed thresholds and lower-TF price action.
🟠 FEATURES
Dynamic liquidity levels rendered at POC of significant wicks, colored by bullish/bearish direction.
Break detection that removes levels once price decisively crosses them twice in the same direction.
Rejection detection that plots ▲/▼ markers when price bounces off levels intrabar.
Volume labels for each level, shown either as raw volume or percentage of total level volume.
Candle coloring based on trend direction (break-dominant).
🟠 USAGE
Use this indicator to track where liquidity has most likely entered the market via abnormal wick events. When a long wick forms with high volume, the script looks inside it (using your chosen lower timeframe) and marks the most traded price within it. These levels can serve as expected reversal or breakout zones. Rejections are marked with small arrows, while breaks trigger trend shifts and remove the level. You can toggle trend coloring to see directional bias after a breakout. Use the wick multiplier to control how selective the detector is (higher = stricter). Alerts and label modes help customize the signal for different asset types and chart styles.
Mean Reversion & Momentum Hybrid | D_QUANT 📌 Mean Reversion & Momentum Hybrid | D_QUANT
📖 Description:
This indicator combines mean reversion logic, volatility filtering, and percentile-based momentum to deliver clear, context-aware buy/sell signals designed for trend-following and contrarian setups.
At its core, it merges:
A Bollinger Band % Positioning Model (BB%)
A 75th/25th Percentile Momentum System
A Volatility-Adjusted Trend Filter using RMA + ATR
All tied together with a dynamic gradient-style oscillator that visualizes signal strength and persistence over time — making it easy to track high-conviction setups.
Signals only trigger when all three core components align, filtering out noise and emphasizing high-probability turning points or trend continuations.
⚙️ Methodology Overview:
Bollinger Bands % (BB%):
Price is measured as a percentage between upper and lower Bollinger Bands (based on OHLC4). Entries are only considered when price exceeds custom BB% thresholds — emphasizing market extremes.
Volatility-Based Trend Filter (RMA + ATR):
A smoothed RMA baseline is paired with ATR to define trend bias. This ensures signals only occur when price deviates meaningfully beyond recent volatility.
Percentile Momentum Model (75th/25th Rank):
Price is compared against its rolling 75th and 25th percentile. If price breaks these statistical boundaries (adjusted by ATR), it triggers a directional momentum condition.
Signal Consensus Engine:
All three layers must agree — BB% condition, trend filter, and percentile momentum — before a buy or sell signal is plotted.
Gradient Oscillator Visualization:
Signals appear as a fading oscillator line with a gradient-filled area beneath it. The color intensity represents how “fresh” or “strong” the signal is, fading over time if not reconfirmed, offering both clarity and signal aging at a glance.
🔧 User Inputs:
🧠 Core Settings:
Source: Select the price input (default: close)
Bollinger Bands Length: Period for BB basis and deviation
Bollinger Bands Multiplier: Width of the bands
Minimum BB Width (% of Price): Prevents signals during low-volatility chop
📊 BB% Thresholds:
BB% Long Threshold (L): Minimum %B to consider a long
BB% Short Threshold (S): Maximum %B to consider a short
🔍 Trend Filter Parameters:
RMA Length: Period for the smoothed trend baseline
ATR Length: Lookback for ATR in trend deviation filter
⚡️ Momentum Parameters:
Momentum Length: Period for percentile momentum calculation
Mult_75 / Mult_25: ATR-adjusted thresholds for breakout above/below percentile levels
🎨 Visualization:
Bar Coloring: Highlights candles during active signals
Background Coloring: Optional background shading for signals
Show Oscillator Plot: Toggle the gradient-style oscillator
🧪 Use Case:
This indicator works well across all assets for trend identification. It is particularly effective when used on higher timeframes (e.g. 12H, 1D,2D) to capture mean reversion bounces or confirm breakouts backed by percentile momentum and volatility expansion.
⚠️ Notes:
This is not financial advice. Use in combination with proper risk management and confluence from other tools.
