Mulakan

Ehlers

Ehlers Adaptive Trend Filter + Consensus Engine v13Ehlers Adaptive Trend Filter + Consensus Engine From TrendMatrix Labs: Our core mission is to bridge the gap between advanced quantitative methods and intuitive, actionable visual trading. We built this engine because standard retail moving averages lag too heavily and fall apart during choppy, bracketed markets. We wanted an institutional-grade toolkit that leverages true Digital Signal Processing (DSP) to eliminate lag, paired with a robust matrix-style screener so you can manage an entire watchlist from a single chart. v13 substantially upgrades the universe management layer with an eight-watchlist architecture — four pre-curated thematic universes plus four matching customizable slots — per-slot mode toggles that let you save custom edits without losing them, and automatic deduplication so the 40-ticker request.security() budget is never wasted on duplicate symbols. The Ehlers Adaptive Trend Filter + Consensus Engine combines advanced digital signal processing techniques developed by John Ehlers with a multi-symbol consensus screening framework. It is designed to identify true trend strength, catch emerging opportunities, and flag deteriorating market conditions across an entire watchlist simultaneously. Unlike traditional moving averages, this indicator uses low-lag Ehlers filters and adaptive cycle analysis to separate meaningful trend signals from market noise while maintaining absolute responsiveness during rapid regime shifts. Key Features 1. Curated Multi-Universe System (NEW in v13) The architecture ships eight independent watchlists in a single script: four pre-curated thematic universes with 36 institutional-quality tickers each, plus four matching custom slots that hold up to 36 of your own tactical names. A single dropdown selects which universe is actively screened; only the selected one consumes request.security() budget, leaving the other seven dormant in script state. NASDAQ / AI Infrastructure — 36 tickers spanning the full AI buildout stack: compute silicon (NVDA, AVGO, AMD, ARM, MU), semiconductor equipment and foundry (TSM, LRCX, AMAT, KLAC, ASML), networking and systems (ANET, SMCI, DELL, HPE), hyperscalers (MSFT, GOOGL, META, AMZN, ORCL), electrical infrastructure (VRT, ETN, PWR, HUBB), cooling (JCI, TT), power generation and fuel (GEV, CEG, VST, CCJ), and data center REITs (EQIX, DLR), plus QQQ / SMH / SPY benchmarks. Gold Miners — 36 tickers covering Tier-1 majors (NEM, GOLD, AEM, AU, GFI, KGC, NST, EVN), mid-tier producers, Australian-listed mid-caps, silver pure-plays, capital-light royalty / streamers (FNV, WPM, RGLD, OR), plus GDX / GDXJ / SIL / SILJ ETFs capturing different beta profiles to the same underlying. Bioscience — 36 tickers across Big Pharma (LLY, PFE, JNJ, MRK, ABBV, BMY, AZN, NVO, NVS, GSK), established large biotech (AMGN, GILD, VRTX, REGN, BIIB, ILMN), innovative platform companies (MRNA, ALNY, ARGX, BNTX, RPRX), mid-cap biotech with commercial revenue, and gene editing leaders (CRSP, NTLA, BEAM), plus IBB / XBI / ARKG benchmarks. Energy — 36 tickers spanning integrated majors (XOM, CVX, COP, OXY, EQNR), US and Canadian E&P, refiners, oilfield services, midstream pipelines, and natural gas / LNG specialists, plus five sector ETFs (XLE, OIH, XOP, USO, UNG) for broad and sub-sector framing. Three modes per universe slot: Every slot has a Mode selector with three independently-saving options: Default — uses the hardcoded preset only; your custom edits stay saved but inactive Custom — uses only what you type into the custom text-area; preset ignored Merge — concatenates both, with automatic deduplication at parse time This means you can save tactical names into a custom slot indefinitely without losing them when you switch back to the curated baseline. Flip the toggle, not delete content. Auto-include chart symbol: A separate toggle prepends the chart's current symbol to whichever universe is active, so the ticker you're viewing always appears in the rankings regardless of which preset is loaded. Dedup prevents double-billing when the chart symbol overlaps with the preset. 2. Adaptive Trend Detection Choose from multiple Ehlers-based filtering methods to match your specific trading horizon: 2-Pole + 2-Pole (Fast): Maximum responsiveness for active swing traders. 3-Pole + 2-Pole (Hybrid): Balanced trend detection and noise reduction. 3-Pole + 3-Pole (Smooth): Heavy trend filtering designed to eliminate whipsaws. MAMA/FAMA (Adaptive): Market-cycle adaptive filtering using a faithful execution of Ehlers' famous MESA (Maximum Entropy Spectral Analysis) methodology. 💡 Engine Tip: We built an auto-calibration mode directly into the script. When you swap timeframes (e.g., from a Daily chart down to a 1-Hour chart), the lookbacks, dynamic detox periods, and volatility safety margins automatically scale themselves so you never have to constantly fiddle with the input settings. 3. Dynamic Support Baseline A secondary adaptive baseline acts as a strict structural trend qualification filter. When the signal path remains above the baseline, the asset is locked in a healthy trend. When the signal path falls below the baseline, the trend structure is considered compromised. The visual trend zone between the signal path and the baseline provides an instant, intuitive look at trend maturity and safety margins. 4. Volatility-Adaptive Analysis Traditional fixed thresholds fail when applied across different asset classes or volatile market regimes. This script automatically scales its internal calculations using a rolling historical volatility percentile, providing highly accurate, normalized readings for Price Deviation, Trend Extension, Safety Margins, and Technical Quality. 5. Advanced Reversal & Momentum Engine The reversal section completely bypasses simple slope checks, utilizing a multi-dimensional conviction score (0–100) based on: Epistemic Disagreement: Catching early cycle shifts when the ultra-responsive Adaptive filter diverges from the slower core structural lines. 2nd Derivative Acceleration: Measuring the actual rate of change of the filter separation (the acceleration of the trend). Volatility Compression: Evaluating structural spreads against a rolling 100-bar 20th percentile to catch explosive expansions breaking out of tight consolidations. Optional background highlights cleanly map out precise bullish and bearish divergence zones where structural trend and velocity disagree. The Consensus Engine Screener The built-in dashboard simultaneously evaluates every symbol in your active universe across three completely independent trend frequencies: FAST (2P+2P), CORE (3P+2P), and ADAPTIVE (MAMA/FAMA). Assets are assigned a clear structural tier based on exactly how many independent filters agree: Elite (3/3 Bullish): All filters are perfectly aligned. This indicates the strongest trend environment and the highest-conviction buying opportunities. Confirmed (2/3 Bullish): The macro trend remains healthy, but a single higher-frequency filter has begun to pull back. Excellent for monitoring trend maturity. Emerging (1/3 Bullish): Early-stage developments where the zero-phase adaptive model detects improving conditions before the broader trend confirms. This is your primary hunter tool for spotting fresh reversals. Bear (0/3 Bullish): Complete bearish alignment. Crucial for portfolio risk management, short-side tracking, and identifying weak market segments. Institutional Ranking & Interpretive Aids Instead of just telling you an asset is up, the ranking engine scores your watchlist using a Calmar-adjusted weighted separation model that blends filter distance, consensus agreement, localized volatility, and multi-period returns. You can fully customize the weights of the filters to natively match your trading style. To completely eliminate cognitive load when monitoring up to 40 tickers, the optional Interpretation Engine automatically synthesizes these underlying metrics into clear, contextual action tags displayed right on your dashboard: ENTRY · ADD · HOLD · CAUTION · TRIM · WATCH · AVOID This is highly practical for identifying a "Bleeding Elite" asset — a stock that technically still ranks in the top tier but is flagged with a TRIM · Mature+fading tag because its internal momentum and filter separation are decaying. v13 Release Notes Eight-watchlist universe architecture — four pre-curated thematic universes (NASDAQ / AI Infrastructure, Gold Miners, Bioscience, Energy) with 36 institutional-quality tickers each, plus four matching custom slots that hold up to 36 of your own tactical names. Per-slot Mode toggle (Default / Custom / Merge) preserves custom edits in input state without forcing deletion when switching back to the curated baseline. Chart-symbol auto-include toggle so the active chart symbol always appears in the rankings regardless of which preset is loaded. Automatic ticker deduplication at parse time — no duplicate symbols ever waste the 40-call request.security() budget across Merge mode, chart-symbol prepend, or pasted-in custom slots Universe selector and slot reordering with NASDAQ first as the default active universe. Comprehensive tooltips on every input explaining purpose, defaults, and tradeoffs. Ideal For: Trend-Following & Swing Traders: Looking for clean entries with minimized lag. Portfolio Managers: Needing to instantly separate leading assets from laggards across custom matrix watchlists. Market Breadth Analysts: Looking to see exactly how many stocks in a sector are healthy versus how many are internally fracturing. Top-Down Visual Execution: Use the screener on a macro timeframe (like Daily) to find your clean setups, then drop timeframes (like the 15-Min or 1-Hour) and use the real-time DSP directional arrows to perfectly time your mean-reversion expansions.
Penunjuk Pine Script®
oleh TrendMatrix_Labs
4
Ehlers Adaptive Trend Filter + Consensus Engine v12Ehlers Adaptive Trend Filter + Consensus Engine From TrendMatrix Labs: Our core mission is to bridge the gap between advanced quantitative methods and intuitive, actionable visual trading. We built this engine because standard retail moving averages lag too heavily and fall apart during choppy, bracketed markets. We wanted an institutional-grade toolkit that leverages true Digital Signal Processing (DSP) to eliminate lag, paired with a robust matrix-style screener so you can manage an entire watchlist from a single chart. This script is the culmination of extensive mathematical architecture refinement and live market testing. The Ehlers Adaptive Trend Filter + Consensus Engine combines advanced digital signal processing techniques developed by John Ehlers with a multi-symbol consensus screening framework. It is designed to identify true trend strength, catch emerging opportunities, and flag deteriorating market conditions across an entire watchlist simultaneously. Unlike traditional moving averages, this indicator uses low-lag Ehlers filters and adaptive cycle analysis to separate meaningful trend signals from market noise while maintaining absolute responsiveness during rapid regime shifts. Key Features 1. Adaptive Trend Detection Choose from multiple Ehlers-based filtering methods to match your specific trading horizon: 2-Pole + 2-Pole (Fast): Maximum responsiveness for active swing traders. 3-Pole + 2-Pole (Hybrid): Balanced trend detection and noise reduction. 3-Pole + 3-Pole (Smooth): Heavy trend filtering designed to eliminate whipsaws. MAMA/FAMA (Adaptive): Market-cycle adaptive filtering using a faithful execution of Ehlers' famous MESA (Maximum Entropy Spectral Analysis) methodology. 💡 Engine Tip: We built an auto-calibration mode directly into the script. When you swap timeframes (e.g., from a Daily chart down to a 1-Hour chart), the lookbacks, dynamic detox periods, and volatility safety margins automatically scale themselves so you never have to constantly fiddle with the input settings. 2. Dynamic Support Baseline A secondary adaptive baseline acts as a strict structural trend qualification filter. When the signal path remains above the baseline, the asset is locked in a healthy trend. When the signal path falls below the baseline, the trend structure is considered compromised. The visual trend zone between the signal path and the baseline provides an instant, intuitive look at trend maturity and safety margins. 3. Volatility-Adaptive Analysis Traditional fixed thresholds fail when applied across different asset classes or volatile market regimes. This script automatically scales its internal calculations using a rolling historical volatility percentile, providing highly accurate, normalized readings for Price Deviation, Trend Extension, Safety Margins, and Technical Quality. 4. Advanced Reversal & Momentum Engine (v12 Rebuilt) The reversal section has been completely upgraded to match the mathematical rigor of the core trend engine. It completely bypasses simple slope checks, utilizing a multi-dimensional conviction score (0–100) based on: Epistemic Disagreement: Catching early cycle shifts when the ultra-responsive Adaptive filter diverges from the slower core structural lines. 2nd Derivative Acceleration: Measuring the actual rate of change of the filter separation (the acceleration of the trend). Volatility Compression: Evaluating structural spreads against a rolling 100-bar 20th percentile to catch explosive expansions breaking out of tight consolidations. Visuals: Optional background highlights cleanly map out these precise bullish and bearish divergence zones where structural trend and velocity disagree. The Consensus Engine Screener The built-in dashboard simultaneously evaluates every symbol in your custom watchlist across three completely independent trend frequencies: FAST (2P+2P), CORE (3P+2P), and ADAPTIVE (MAMA/FAMA). Assets are assigned a clear structural tier based on exactly how many independent filters agree: Elite (3/3 Bullish): All filters are perfectly aligned. This indicates the strongest trend environment and the highest-conviction buying opportunities. Confirmed (2/3 Bullish): The macro trend remains healthy, but a single higher-frequency filter has begun to pull back. Excellent for monitoring trend maturity. Emerging (1/3 Bullish): Early-stage developments where the zero-phase adaptive model detects improving conditions before the broader trend confirms. This is your primary hunter tool for spotting fresh reversals. Bear (0/3 Bullish): Complete bearish alignment. Crucial for portfolio risk management, short-side tracking, and identifying weak market segments. Institutional Ranking & Interpretive Aids Instead of just telling you an asset is up, the ranking engine scores your watchlist using a Calmar-adjusted weighted separation model that blends filter distance, consensus agreement, localized volatility, and multi-period returns. You can fully customize the weights of the filters to natively match your trading style. To completely eliminate cognitive load when monitoring up to 40 tickers, the optional Interpretation Engine automatically synthesizes these underlying metrics into clear, contextual action tags displayed right on your dashboard: ENTRY · ADD · HOLD · CAUTION · TRIM · WATCH · AVOID This is highly practical for identifying a "Bleeding Elite" asset—a stock that technically still ranks in the top tier but is flagged with a TRIM · Mature+fading tag because its internal momentum and filter separation are decaying. Ideal For Trend-Following & Swing Traders: Looking for clean entries with minimized lag. Portfolio Managers: Needing to instantly separate leading assets from laggards across custom matrix watchlists. Market Breadth Analysts: Looking to see exactly how many stocks in a sector are healthy versus how many are internally fracturing. Top-Down Visual Execution: Use the screener on a macro timeframe (like Daily) to find your clean setups, then drop timeframes (like the 15-Min or 1-Hour) and use the real-time DSP directional arrows to perfectly time your mean-reversion expansions.
Penunjuk Pine Script®
oleh TrendMatrix_Labs
1
Voss Predictor Multi-Symbol RankingVoss Predictor Multi-Symbol Ranking (VPSR) is a multi-market dashboard that scans up to 40 assets and ranks them based on momentum, trend strength, and underlying market structure. Instead of analysing one chart at a time, this tool compares all selected symbols side-by-side and highlights which markets are currently showing the strongest or weakest conditions in real time. It classifies each asset as: Bullish (strength building) Bearish (weakening or declining) Neutral (no clear direction) What it helps you do Quickly identify the strongest trending markets Spot weakening or reversing assets earlier Compare stocks, forex, and crypto in one view Focus only on the highest-quality opportunities Reduce time spent switching between charts Key Features Live ranking of up to 40 symbols Simple bullish / bearish / neutral signals Composite strength score for each asset Built-in trend and momentum comparison Context-based tooltips for deeper interpretation Adaptive settings based on market type and trading style Theoretical Background (Ehlers-Based Approach) This indicator is inspired by the work of John Ehlers, a pioneer in applying digital signal processing to financial markets. Ehlers' core idea is that price action is not purely random, but instead contains hidden cyclical structures that can be revealed using filtering techniques similar to those used in engineering and physics. Rather than relying on traditional lagging indicators, this approach focuses on: Cycle extraction: isolating the dominant "wave" within market noise Bandpass filtering: removing both long-term trend distortion and short-term randomness Adaptive market cycles: recognising that cycle length and volatility constantly change Phase and momentum behaviour: identifying where price sits within its cycle (expansion, contraction, or transition) This indicator applies these principles using: An Ehlers-style bandpass filter to isolate market structure A leading filter (Ehlers' Voss) to estimate short-term directional bias A correlation-based component to measure structural alignment and consistency across price movement The result is a system that focuses on cycle behaviour and momentum flow, rather than lagging averages or reactive signals. Signal Logic Overview Bullish: Leading momentum aligns with upward structural movement Bearish: Downward momentum dominates or structure weakens Neutral: Mixed or unclear directional conditions Divergence states highlight potential exhaustion or early reversal conditions Intended Use This indicator is designed as a multi-asset scanning and ranking tool, helping traders quickly identify where momentum and structure are strongest or weakest across markets. It is best used for: Relative strength analysis Opportunity scanning across multiple assets Early detection of momentum shifts Filtering out weak or low-quality setups It is not a standalone buy/sell system, but a decision-support layer for market selection and timing context.
Penunjuk Pine Script®
oleh TrendMatrix_Labs
1
Jack and Jill I. LIMITATIONS OF TRADITIONAL MOVING AVERAGES Traditional moving averages (SMA, EMA, WMA) serve as foundational market tools, but they exhibit specific mathematical constraints when applied to highly volatile data: Lag: By inherently averaging historical data, they naturally trail the current market state. Aliasing (Cyclic Contamination): They often struggle to distinguish between underlying macro trends and temporary cyclical market waves, which can result in whipsaws and delayed signals. To address these challenges and more accurately model continuous-time price movement, advanced Digital Signal Processing (DSP) can be deployed as an alternative framework. II. THE DUAL-STATE SOLUTION John Ehlers' "Jack and Jill" concept separates the market data into two distinct components. Instead of blending short-term fluctuations and long-term trends into a single average, they are isolated using specialized mathematical models: ► JACK: A fast, low-lag price tracker. ► JILL: A slower trend line, filtered to remove cyclical noise. III. JACK: THE FAST PRICE TRACKER The Mechanism: A 2nd-Order Infinite Impulse Response (IIR) SuperSmoother. The Execution: Jack tracks short-term price movements. By applying a 2nd-order SuperSmoother, Jack efficiently filters out high-frequency noise and erratic intraday price spikes. It follows the price closely with minimal lag, providing a clearer view of the immediate trend direction. IV. JILL: THE TREND DECYCLER$$ The Mechanism: A 2-Pole High-Pass Decycler coupled with smoothing. The Execution: Instead of standard averaging, Jill uses a 2-Pole High-Pass filter to identify the market's dominant cycle and subtracts that cyclic data from the price to isolate the broader macroeconomic trend. A half-length SuperSmoother is then applied to stabilize the output, resulting in a smooth baseline that minimizes temporary cyclical fluctuations. V. THE PHASE CONVERGENCE OSCILLATOR (PCO) The Mechanism: Momentum Divergence. The Execution: While Jack and Jill map the spatial topology on the primary chart, the Phase Convergence Oscillator (PCO) operates in a decoupled, isolated matrix. It measures the exact delta (Jack - Jill) and tracks its momentum. Expanding Delta: The trend is structurally intact and accelerating. Contracting Delta: Trend momentum is decelerating, which may serve as an early indication of exhaustion before Jack actually crosses Jill. VI. TOPOLOGICAL GRADIENT RENDERING The space between Jack and Jill represents the maturity of the trend. The visual matrix applies a 6-stage continuous opacity gradient—highly transparent at the edge of Jack (where price is volatile and untested) and reaching maximum density as it anchors onto Jill (the established structural core). VII. CONCLUSION The Jack and Jill indicator is designed as a continuous-time convergence engine rather than a conventional crossover tool. When the PCO accelerates and Jack crosses the structural decycler, a potential phase shift is identified. This framework represents a rigorous application of signal processing techniques to financial trend analysis.
Penunjuk Pine Script®
oleh mjrodriguezorlich69
Dual Ehlers Decycler & Dynamic Cycle BandpassOverview Traditional moving averages are plagued by lag, and standard oscillators force you to split your attention across multiple chart panes. This indicator solves both problems by combining John Ehlers’ Decycler mathematics with advanced Pine Script v6 polyline rendering, allowing you to visualize true market cycles and momentum shifts directly on your main price chart without distorting the scale. This is not just a moving average crossover tool; it is a complete signal processing suite designed to extract the dominant market cycle while rejecting high-frequency noise and low-frequency macro trends. Core Features Dual Ehlers Decyclers (Zero-Lag Profiling): Utilizes the Ehlers Decycler algorithm to track price with near-zero lag. By inputting the specific dominant cycle lengths (e.g., from a cycle forecaster), the fast and slow decyclers perfectly bracket your target market wave. True Bandpass Filter Oscillator: By subtracting the slow decycler from the fast decycler (Dec1 - Dec2), the script isolates the pure cyclical resonance between the two lengths. This acts as a Bandpass filter—stripping out erratic price spikes (noise) and the underlying macro trend to reveal the pure sine wave of the current market cycle. Derivative Momentum Coloring: The decycler lines do not just change color based on simple slope. The script calculates the Velocity (1st derivative) and Curvature (2nd derivative) of the trend. The lines only light up brightly when momentum and acceleration are aligned, keeping you out of choppy, decelerating markets. ATR-Normalized Hysteresis Cloud: The fill between the two decyclers utilizes an ATR-based noise filter. Instead of flickering wildly when price chops around the decycler lines, the cloud state requires a true volatility-adjusted breakout to shift bullish or bearish. How to Trade With It Cycle Crossovers (The Zero-Cross): When the fast decycler crosses the slow decycler, the Bandpass Oscillator perfectly crosses the zero line. This signals a phase shift in the market's dominant cycle . Amplitude Analysis: Watch the height of the dynamic polyline oscillator. A crossover that occurs after a massive oscillator peak suggests a powerful exhaustion and reversal. A crossover while the oscillator is flat near the zero line suggests a weak, choppy market—stay out. Visual Divergence: Because the oscillator is plotted on the same pane as price, spotting divergence is incredibly intuitive. If price makes a higher high, but the dynamic yellow polyline makes a lower high, the cycle is running out of gas before the decyclers even cross. Customization Input your own dominant cycle lengths based on your preferred timeframe or external cycle forecasters. Adjust the visible amplitude of the dynamic oscillator (default 50% of price height) to fit your visual preference. Toggle signals, clouds, and overlays on or off for a cleaner view. Credit to John Ehlers for the foundational Decycler mathematics.
Penunjuk Pine Script®
oleh fdx101
Dynamic Time Warped Stochastic. Arc-Lenght Stochastic Oscillator𝐓𝐇𝐄 𝐀𝐑𝐂-𝐊𝐈𝐍𝐄𝐌𝐀𝐓𝐈𝐂 𝐎𝐒𝐂𝐈𝐋𝐋𝐀𝐓𝐎𝐑: 𝐁𝐞𝐲𝐨𝐧𝐝 𝐂𝐡𝐫𝐨𝐧𝐨𝐥𝐨𝐠𝐢𝐜𝐚𝐥 𝐓𝐢𝐦𝐞OverviewFor decades, the standard approach to measuring market momentum has relied on a fatal flaw: chronological time. Traditional oscillators force a rigid, fixed lookback window onto an environment that is fundamentally non-linear. When volatility compresses, chronological indicators generate false signals from microscopic chop. When volatility expands, they lag.This tool abandons chronological time. Instead, it processes market structure through a topological lens, measuring the actual path traversed by price action rather than the arbitrary ticking of a clock. It is an exploration of market kinematics designed to filter out spurious noise and identify true regime shifts.Under the Hood: The Mechanics of Topological Data AnalysisThis is not a smoothed RSI or a standard stochastic oscillator. The underlying engine operates on principles derived from digital signal processing and econophysics.Time-Dilated Lookback: Instead of a fixed N-period lookback, the calculation matrix expands and contracts dynamically. It measures the geometric arc-length (ds) of the price vector. During tight, sideways consolidation, the window stretches to absorb the noise. During explosive expansion, the window shrinks, snapping to the current regime with near-zero lag.Kinematic Tokenization: The script analyzes the primary and secondary derivatives of price to understand the velocity and acceleration of the order flow, separating true institutional displacement from retail trap-moves.Advanced Signal Processing: To present this data cleanly, the raw kinematic output is passed through a multi-pole Gaussian filtration system and a non-linear bounding transform. This creates a hyper-responsive oscillator that maps purely between +1 and -1 remaining completely flat during untradeable chop and only oscillating when true structural momentum is present. How to Use This Tool This indicator does not predict the future. It categorizes the present.Regime Identification: Observe the boundaries. When the oscillator is pinned at the extremes, the market is in a dominant, unidirectional regime. Divergence against Time: Because the tool operates outside of chronological time, standard divergences (price making a higher high while the oscillator makes a lower high) carry significantly higher structural weight than those found on traditional indicators.Confluence: Use this as a confirming layer. It is designed to validate structural market shifts, identifying when a swept level or a break in market structure has the underlying mathematical velocity to sustain a new trend.DisclaimerThis script is published strictly for educational and analytical purposes. It is a mathematical exploration of market topology and signal processing. It does not constitute financial advice, nor does it guarantee profitable trades. Past performance of mathematical models does not guarantee future market behavior. Always use strict risk management.
Penunjuk Pine Script®
oleh mjrodriguezorlich69
8
DSP Phase SniperThe DSP Phase Sniper is a high-precision, zero-lag execution engine designed for intraday and swing trading. Traditional indicators (like RSI, MACD, or Moving Averages) suffer from a fatal flaw: Lag . They look backward at historical data to guess where the price is going, meaning they often signal a reversal after the move has already happened. The DSP Phase Sniper solves this using Digital Signal Processing (DSP) . Instead of averaging past prices, it uses a mathematical algorithm originally designed for radio frequencies (the Hilbert Transform) to lock onto the dominant rhythm of the market. It treats the market like a physical wave, calculating its exact Phase Angle (0° to 360°) and Amplitude (kinetic energy) bar-by-bar. This allows it to identify exact tops and bottoms with zero mathematical lag. The Anatomy of the Signals ◆ The Diamonds (Micro-Reversals) What it is: The earliest possible warning of a market turn. How it works: A cycle forms a perfect circle. A circle hits its absolute peak at exactly 90° and its absolute trough at exactly 270°. The diamond fires the exact millisecond the DSP math calculates that the market has hit one of these extreme coordinates. Color Code: Green ◆ : Absolute Cycle Bottom and Red ◆ : Absolute Cycle Top. ▲ / ▼ The Arrows (Confirmed Shifts) What it is: The execution trigger. How it works: This fires when the wave officially crosses the "Zero Line," confirming that the momentum has successfully transferred into the new direction. The "Triple-Lock" Filtering System To prevent the indicator from firing wildly during market chop or runaway breakouts, it uses three invisible gates: The Energy (Amplitude) Gate: The script tracks the average energy of the last 50 bars. If the market is chopping sideways in a tight, low-volume range, the cycle energy drops. The indicator will mute all signals until real institutional volume returns. The Deadzone Hysteresis: The Confirmed Arrows will not fire just because the wave ticks slightly above zero. The wave must confidently push through a designated deadzone (e.g., $0.15$), preventing rapid "whipsaw" fake-outs. The Hurst Regime Filter: Markets exist in two states: Cycling (ping-ponging between ranges) and Trending (melting up/down). The script calculates the Hurst Exponent. If the market is mathematically proven to be in a runaway trend, it will temporarily turn off counter-trend signals so you don't step in front of a freight train. When & How to Use It (The Playbook) This indicator excels on intraday timeframes (3m, 5m, 15m) for assets with high liquidity and natural rhythms, like the SPY, QQQ, ES Futures, or major Forex pairs . Play 1: The "Perfect" Sniper Reversal You are looking for the market to exhaust itself and snap back. Wait for the Setup: Price is pushing hard into a resistance level. Watch for the Diamond: A Red Diamond (◆) prints. Even better, it prints Yellow. This means the cycle has mathematically peaked. The Trigger: 1 to 3 bars later, a solid Red Arrow (▼) fires. Execution: Enter Short / Buy Puts. Place your stop loss immediately above the wick of the Diamond candle. Play 2: The Trend Ride (Buying the Dip) The macro market is aggressively bullish, and you want to buy the pullback safely. The Trigger: A Green Diamond (◆) prints at the bottom of the pullback, followed by a bright Green Arrow (▲). Execution: Enter Long / Buy Calls. When NOT to use it: Pre-Market / After-Hours: DSP math relies on volume and continuous flow. Illiquid overnight sessions will distort the cycle wave. News Events (CPI, FOMC): Fundamentals override math. If Jerome Powell is speaking, the market is not adhering to a natural sine wave. Wait 15 minutes after major news for the cycles to normalize before trusting the indicator.
Penunjuk Pine Script®
oleh fdx101
TASC 2026.05 The AutoTune Filter█ OVERVIEW This script implements the AutoTune Filter described by John F. Ehlers in the article "A Rolling Autocorrelation Function" from the May 2026 edition of the TASC Traders' Tips . The script analyzes rolling autocorrelation in filtered price data to calculate a band-pass filter that dynamically adjusts to apparent dominant cycles. █ CONCEPTS Autocorrelation function (ACF) Autocorrelation  measures the correlation of a time series with a lagged version of itself. The autocorrelation function (ACF) evaluates autocorrelation across a range of lags to gauge the extent to which values in a series vary jointly with previous values at different offsets. The ACF can help traders identify patterns and trends in stochastic market data, characterize long-range dependence in a series, and more. In his article, Ehlers explains how the ACF can serve as a "bridge" between analysis in the time and frequency domains for identifying dominant cycles in market data. Ehlers notes that at low lags, such as one bar, the autocorrelation in price data tends to be very high because prices don't often change dramatically from one bar to the next. As the lag increases, autocorrelation often decreases, reaching near zero for offsets at which the latest prices do not show a clear relationship with past prices. However, he also observed that at specific lags, anticorrelation (negative correlation) can emerge, where the current values in the series move in one direction while past values move in the opposite direction. Based on this observation, he suggests that a lag with strong anticorrelation can indicate a significant cycle in the market data, where the cycle length is twice that of the analyzed lag. To understand why this behavior can indicate significant cycles, consider a sine wave that completes a full oscillation every 20 bars. If the series is currently moving up, it will then move down 10 bars later, and then complete the cycle by moving up again 10 bars after that. The ACF of that sine wave returns a value of -1 for a lag of 10 bars, but not for other lower lags or higher lags up to 20. In other words, a pure sine wave with a given period has perfect anticorrelation with a delayed version of itself that is offset by half of that period. While market data does not typically behave like a pure sine wave, the same underlying principle applies: if the current prices exhibit a strong anticorrelation with previous prices at a given offset, a dominant cycle with a length of twice that offset is likely present in the current data. AutoTune Filter Ehlers proposes that traders can use the dominant cycle obtained via autocorrelation to set the critical period of a filter. Tuning a filter to respond most strongly to the measured cycle may promote more consistency in time alignment and help reduce destructive phase shifts. He demonstrates one such implementation with his AutoTune Filter, an adaptive band-pass filter whose center period dynamically increments toward the dominant cycle calculated from an ACF over a given window. The steps to calculate the AutoTune Filter are as follows: Apply a two-pole high-pass filter to the series to reduce the effect of low-frequency (long-period) cycles on the autocorrelation calculation. The filtered series emphasizes cycles with lengths up to the specified cutoff period, and attenuates all others. Compute the rolling ACF of the filtered data across the same window length as the filter's cutoff period. Check the autocorrelation for each lag period, and identify the smallest lag with the lowest autocorrelation value. Multiply that lag by two to obtain the dominant cycle for the analyzed window. If the difference between the current and previous dominant cycle is greater than two, limit the result for the current bar to two greater or less than the previous cycle's value to prevent large, sudden shifts in the filter's center period. Finally, compute a band-pass filter using the value from step 4 as the center period. █ USAGE This indicator includes four display modes to visualize the AutoTune Filter's calculations: "High-pass filter" : Plots the high-pass filtered data that the script analyzes for autocorrelation calculations. "Min. correlation" : Plots the lowest autocorrelation value calculated for the filtered series over the analyzed window. "Dominant cycle" : Plots the dominant cycle value that the final filter uses for its center period. "Tuned band-pass filter" (default): Plots the final band-pass filtered result, i.e., the AutoTune filter. Ehlers suggests that traders can identify peaks and valleys in prices for potential mean reversion signals by analyzing the rate of change in the tuned band-pass filter. If the rate of change is zero, the current price might be near a local high if the filter's value is positive, or near a local low if the value is negative. Users can analyze the additional outputs to gain further insight into the filter's behaviors, and they can pass these plotted values to other scripts via source inputs for easy use in other custom calculations. █ INPUTS The indicator includes the following inputs in the "Settings/Inputs" tab: Source: The series of values to process. Window: The window length of the ACF calculation, and the cutoff period of the high-pass filter. The maximum possible dominant cycle length is two times this value. Output: One of the four display modes ("High-pass filter", "Min. correlation", "Dominant cycle", or "Tuned band-pass filter").
Penunjuk Pine Script®
oleh PineCodersTASC
6
MESA Adaptive Cycle Engine [MarkitTick]💡 The MESA Adaptive Cycle Engine is an advanced, dynamic trend-following overlay designed to adapt to market volatility and cyclical phases. Unlike traditional moving averages that suffer from significant lag during range-bound periods, this tool leverages digital signal processing to stay aligned with the market's dominant cycle. It features an integrated webhook automation system and a real-time risk management dashboard. ✨ Originality and Utility Standard exponential or simple moving averages rely on fixed lookback periods, making them inherently flawed when market conditions shift from trending to cycling. This indicator utilizes the MESA (Maximum Entropy Spectral Analysis) Adaptive Moving Average (MAMA) and Following Adaptive Moving Average (FAMA) concepts. By measuring the phase rate of change via a Hilbert Transform, the moving averages mathematically adapt their alpha speeds. Furthermore, this script is uniquely engineered for modern automated trading, featuring a self-cleaning dashboard and dynamically constructed JSON payloads for external execution engines. 🔬 Methodology and Concepts ● The Hilbert Transform At its core, the script applies a Hilbert Transform to the price source (defaulting to hl2) to extract the real and imaginary components of the market cycle. ● Phase and Period Calculation By calculating the arctangent of the quadrature and in-phase components, it determines the current phase and dominant cycle period. ● Adaptive Alpha The phase's rate of change dictates the alpha variable. In trending markets, the phase changes slowly, allowing the alpha to remain near the Fast Limit. In choppy markets, the phase changes rapidly, dropping the alpha toward the Slow Limit to prevent whipsaws. ● Risk Engine The script establishes automated Stop Loss and Take Profit levels based on an ATR multiplier, updating dynamically upon regime shifts. 🎨 Visual Guide ● MAMA and FAMA Lines The indicator plots two primary lines: the MAMA line (default Green) and the FAMA line (default Red). ● Regime Fill The space between MAMA and FAMA is filled with a semi-transparent Bullish color when MAMA is above FAMA, and a Bearish color when MAMA is below FAMA. ● Entry Signals A small upward triangle is plotted below the bar upon a Golden Cross (Buy Signal), and a downward triangle is plotted above the bar upon a Death Cross (Sell Signal). ● Analytics Dashboard A table in the bottom-right corner displays the current Market Regime, Phase Volatility (ATR), and the active JSON Payload status. 📖 How to Use Wait for a confirmed crossover. A Golden Cross (MAMA crossing above FAMA) initiates a Bullish regime, while a Death Cross initiates a Bearish regime. Use the Regime Fill to hold positions; stay in a long position as long as the fill remains bullish. Monitor the Dashboard for real-time ATR values to assist with manual trailing stops, or rely on the automated Risk Manager's calculated Take Profit (2x ATR) and Stop Loss (1x ATR). Non-standard charts (like Heikin Ashi or Renko) will trigger a runtime warning, as cycle measurements rely on standard time-based OHLC data. ⚙️ Inputs and Settings ● MESA DSP Parameters Price Source: Determines the input data (default hl2). Fast Limit: The maximum alpha speed, usually set to 0.5. Slow Limit: The minimum alpha speed, usually set to 0.05. ● Automation & JSON Payload ATR Multiplier: Controls the width of the Stop Loss and Take Profit levels. Webhook Action (Long/Short): Defines the string action injected into the outgoing JSON payload. 🔍 Deconstruction of the Underlying Scientific and Academic Framework The indicator is deeply rooted in digital signal processing (DSP), specifically pioneered for trading by John Ehlers. The framework models market data as a complex waveform. By passing the data through a 4-bar WMA smoother and then applying a Hilbert Transform, the algorithm isolates the in-phase (I) and quadrature (Q) components. This orthogonal relationship allows the script to map the market's analytic signal onto a complex plane, solving for the instantaneous phase angle. The fundamental academic breakthrough here is using the derivative of this phase (the rate of phase change) to govern the exponential smoothing constant (alpha) of the moving average. This ensures the filter's bandwidth dynamically conforms to the signal's spectral density, offering high-fidelity smoothing without the commensurate group delay found in static linear filters. ⚠️ Disclaimer All provided scripts and indicators are strictly for educational exploration and must not be interpreted as financial advice or a recommendation to execute trades. I expressly disclaim all liability for any financial losses or damages that may result, directly or indirectly, from the reliance on or application of these tools. Market participation carries inherent risk where past performance never guarantees future returns, leaving all investment decisions and due diligence solely at your own discretion.
Penunjuk Pine Script®
oleh MarkitTick
TASC 2026.04 A Synthetic Oscillator█ Overview This script implements a Synthetic Oscillator as presented by John F. Ehlers in the  April 2026 TASC Traders' Tips article "Avoiding Whipsaw Trades". The indicator aims to provide a smooth, low-lag oscillator for timely trading signals by dynamically mapping a sine wave to price data. █ CONCEPTS "Whipsaw" trades are a common issue in algorithmic trading. They occur when the market quickly moves against a position, causing the trader/trading system to reverse their position at a loss, and then the market reverses again and continues in the original direction. Such trades occur because the trading system is attempting to react quickly to market moves instead of focusing on broader market cycles. A typical solution for reducing whipsaw trades is to apply linear filters to smooth the data and emphasize specific cycles. However, linear filters cannot have both a smooth response and a low computational lag. Therefore, strategy designs utilizing linear filters require a tradeoff between smoothness and lag. Ehlers proposes a nonlinear indicator as a solution to bridge the gap and achieve a smooth, timely response while reducing whipsaw trades. The Synthetic oscillator adapts to market conditions by calculating a dynamic sine wave from the estimated instantaneous dominant cycle over a range of periods. The process to calculate the indicator is as follows: Smooth the price data with a 12-bar Hann Window filter to reduce high-frequency noise, which can affect dominant cycle estimates. Band-pass filter the windowed data with a two-pole high-pass filter and a SuperSmoother filter to focus on the range of cycles between a specified lower bound and upper bound, and normalize the result using the filter's 100-bar root mean square (RMS). Calculate the one-bar rate of change (ROC) in the oscillator from step 2, and normalize the ROC using its 100-bar RMS. Estimate the instantaneous dominant cycle from the oscillators in steps 2 and 3 by treating the series as a complex waveform , where the first oscillator represents the waveform's band-limited "real" component ("I"), and the second represents the band-limited "imaginary" component ("Q"). Cumulatively sum the reciprocal of the dominant cycle (i.e., the dominant frequency ) to obtain the phase angle of the sine wave. To reduce cumulative errors and lag in the phase angle calculation, compute a secondary band-bass filter from a high-pass filter and the UltimateSmoother, and reset the angle to 0 or 180 degrees when that filter crosses above or below 0. Calculate the Synthetic Oscillator as the sine of the final phase angle. █ USAGE This indicator displays the Synthetic Oscillator and a horizontal zero line in a separate pane. Users can analyze the crossings between the oscillator value and 0, or the behavior of the oscillator as it reaches 1 or -1, to derive potential timely trading signals. Ehlers notes in the article that the peaks and valleys of the Synthetic Oscillator can provide signals a little too early, depending on the settings and context. Therefore, he recommends applying another smoother to the oscillator, such as a Hann Window filter with an optimizable length, to adjust timing as necessary. █ INPUTS This indicator uses multiple hardcoded parameters based on the implementation in Ehlers' article. However, users can customize the source series and the upper and lower bounds of the calculations: Source Series: The series of values to process. Lower Bound: The smallest cycle in the passband of the filters, and the lower limit of the dominant cycle estimate. Upper Bound: The largest cycle in the passband of the filters, and the upper limit of the dominant cycle estimate.
Penunjuk Pine Script®
oleh PineCodersTASC
9
Ehlers Decycler (DECYCLER)The Ehlers Decycler extracts the trend component from a price series by subtracting a 2-pole Butterworth high-pass filter from the source. Cycles shorter than the cutoff period are removed; everything longer passes through with near-zero phase lag. The result is a trend overlay that tracks price more closely than a comparable EMA during trends, with no smoothing window, no lag-versus-smoothness tradeoff, and exactly one parameter. HISTORICAL CONTEXT John Ehlers introduced the Decycler in "Decyclers" ( Technical Analysis of Stocks & Commodities , September 2015), alongside its oscillator companion DECO. The underlying 2-pole Butterworth high-pass filter had appeared in Ehlers' earlier work ( Cybernetic Analysis for Stocks and Futures , 2004), primarily for cycle measurement. The Decycler simply inverts the question: instead of isolating cycles, subtract them. What remains is trend. The approach sidesteps the fundamental tension in trend-following design. Moving averages introduce lag proportional to their smoothing window. Adaptive averages reduce lag but add parameters and complexity. The Decycler reframes the problem as a frequency-domain operation: define a cutoff period, remove everything above it, keep everything below. The phase response of the complementary filter preserves the phase of passed frequencies — trend components below the cutoff experience near-zero lag by construction, not by approximation. HOW IT WORKS Stage 1 — 2-pole Butterworth high-pass filter A single coefficient α is derived from the cutoff period P using the Butterworth −3 dB design point (the 0.707 = 1/√2 factor places the half-power point at exactly period P): ω = 0.707 × 2π / P α = (cos(ω) + sin(ω) − 1) / cos(ω) Three recurrence coefficients are computed once at construction from α: a₁ = (1 − α/2)² ← input gain b₁ = 2(1 − α) ← first feedback c₁ = −(1 − α)² ← second feedback The HP recurrence runs each bar: HP = a₁ × (x − 2x + x ) + b₁ × HP + c₁ × HP The second-difference term (x − 2x + x ) is a discrete second derivative — it rejects DC and linear trends while passing oscillatory content, which the HP feedback then sharpens into a proper bandpass edge. Stage 2 — Complementary subtraction The Decycler is one line: Decycler = x − HP This guarantees that Decycler + HP = price at every bar. No signal energy is created or destroyed. The trend and cycle components sum exactly to the original series. Frequency response - Periods longer than the cutoff: unity gain, near-zero phase shift. - Periods shorter than the cutoff: attenuated at −12 dB/octave (2-pole rolloff). - At exactly period P: −3 dB (≈29% amplitude reduction). INPUTS & PARAMETERS - Source — Input series. Default: close. - Period — Cutoff period in bars. Cycles shorter than this are removed. Default: 60. Period selection - 20–30: Responsive trend line, tracks swings. Short-term trading on daily bars. - 40–80: Balanced separation. Default of 60 works well for daily timeframes. - 100–200: Heavy smoothing, reveals major trend direction only. Position trading and regime detection. Ensure the dataset has at least 2 × period bars for meaningful trend extraction. On a 100-bar chart, a period of 200 removes almost nothing — the cutoff falls below the Nyquist limit of the data. HOW TO USE Trend direction - Price above Decycler → bullish. Price below → bearish. - Slope of Decycler indicates trend momentum. Flattening slope → potential exhaustion or transition. Trend vs. cycle regime - Decycler hugging price closely → trending: the dominant cycle is shorter than the cutoff and is being removed, leaving clean trend. - Decycler oscillating with small amplitude → ranging: price is spending time in both directions relative to the trend line. Multi-period overlay - Two Decycler instances at different periods (e.g., 30 and 120) reveal short-term and long-term trend simultaneously. Their relationship is interpretable in frequency terms — the gap between them is a specific cycle band. Cycle complement - The HP component (price − Decycler) isolates the cycle content. Plot it separately as a zero-mean oscillator to see what the Decycler is removing. If the cycle is large relative to trend, reduce position size. This is not a moving average - The Decycler does not compute a weighted sum of past prices. During strong trends it tracks price more closely than a same-period EMA. During ranging conditions it can exhibit small oscillations that a moving average would suppress. Factor this into signal logic. LIMITATIONS - Period too short: A cutoff of 10 on daily bars removes only cycles shorter than 10 days — nearly everything passes through and the Decycler approximates raw price. Meaningful separation requires the cutoff period to exceed the dominant cycle in the data. - Not DECO: The Decycler is a lowpass trend overlay (price − HP). The Decycler Oscillator (DECO) is HP_long − HP_short — a bandpass oscillator that crosses zero. They share the same filter core but produce fundamentally different outputs. Using one where the other is needed produces incorrect results silently. - Warmup: The HP recurrence requires two prior source values and two prior HP values. For the first two bars, HP = 0 and Decycler = raw price. IsHot = true after period bars; signals before that point are unreliable. - IIR drift: Feedback terms accumulate floating-point error over very long series (> 10,000 bars). For typical trading horizons (< 5,000 bars), drift stays below 1e−10 and is inconsequential. - No external validation baseline: TA-Lib, Skender, Tulip, and OoplesFinance do not implement the Ehlers Decycler. Validation is against the Pine reference implementation and internal streaming/batch/span consistency. REFERENCES - Ehlers, J. F. (2015). "Decyclers." Technical Analysis of Stocks & Commodities , September 2015.
Penunjuk Pine Script®
oleh mihakralj
EDCF: Ehlers Distance Coefficient FilterThe Ehlers Distance Coefficient Filter (EDCF) is a nonlinear adaptive FIR filter that computes its smoothing weights dynamically from the sum of squared price differences across the observation window. Recent bars that diverge most from the rest of the window receive the highest weight, making the filter faster than a comparable SMA in trending conditions while degenerating to a simple average when prices are flat. HISTORICAL CONTEXT John F. Ehlers introduced EDCF in "Nonlinear Ehlers Filters" ( Stocks & Commodities , V.19:4, 2001) and in the accompanying MESA Software paper. The core insight: instead of fixing weights by position (as EMA or WMA do) or by spectral design (as Ehlers' own Laguerre and MESA filters do), let the price data itself determine how much each bar contributes. Bars that are close in price to their neighbors get low weight; bars that represent breakouts or large moves get high weight. Ehlers specifically chose squared distances over Euclidean distances to amplify the response to large price changes. HOW IT WORKS For each bar, EDCF evaluates the last length bars and assigns each a coefficient based on its total squared distance from all other bars in the window. Step 1 — Distance-squared coefficient For each bar position i in the window, compute how different its price is from every other bar in the window: Dist² = Σ (Price − Price )² k = 1 to length−1 A bar surrounded by similar prices gets a low coefficient. A bar at the edge of a sharp move gets a high one. Step 2 — Normalized weighted average The output is the weighted average of all bars, normalized by the total weight: EDCF = Σ(Dist² × Price ) / Σ(Dist² ) Flat-price fallback When all prices in the window are identical, all distance-squared coefficients are zero. The filter falls back to the current price — equivalent to SMA behavior with zero lag. Why squared distances? Ehlers explicitly chose Σ(diff²) over √(Σ(diff²)) to heighten filter response. Squaring amplifies the weight difference between trending and ranging bars, making the adaptive effect more pronounced. INPUTS & PARAMETERS - Source — Input series. Default: close. - Length — Filter window length. Default: 15. Range: ≥ 2. Larger length → smoother output, more lag, higher computational cost (O(length²) per bar). Practical ceiling for real-time use: length ≤ 30. Above that, the nested loop becomes expensive on tick data. HOW TO USE Apply as a trend-following overlay on any instrument and timeframe. Trend following - In a trending market, EDCF weights the most-displaced bars heavily and tracks price more closely than a same-period EMA. Use it as a dynamic trailing reference. - Price above EDCF → bullish bias. Price below → bearish bias. Ranging detection - When EDCF flattens and price oscillates tightly around it, the filter is approximating an SMA — a signal that the market has entered consolidation and trend-following signals should be discounted. Breakout confirmation - A sharp price extension that pulls EDCF steeply in one direction indicates the recent bars carry high distance-squared weight — confirmation that the move is statistically significant relative to the recent window, not just noise. Asymmetric behavior awareness - In a strong trend, EDCF behaves like a trailing stop: it follows closely in the direction of the trend but lags on reversals. Factor this into exit timing. LIMITATIONS - O(n²) complexity: Each bar requires a nested loop over the window. At length = 15 the cost is ~400 cycles/bar; at length = 30 it is ~1500 cycles/bar. Keep length ≤ 30 for real-time tick processing. - Not an IIR filter: Despite being categorized as a filter, EDCF is a purely FIR structure — finite window, no feedback. It has no pole dynamics, unlike Ehlers' Laguerre or MESA SuperSmoother. - Asymmetric lag: Response is faster in the direction of existing momentum and slower on reversals. This is a feature in trending regimes and a liability at turning points. - No direct validation baseline: TA-Lib, Skender, and Tulip have no equivalent function. Validation is against the original Ehlers paper formulation and internal streaming/batch/span consistency. - Warmup: Requires length bars before IsHot = true. Earlier outputs are provisional. REFERENCES - Ehlers, J. F. "Nonlinear Ehlers Filters." Stocks & Commodities , V.19:4, pp. 25–34, 2001. - Ehlers, J. F. "Ehlers Filters." MESA Software technical paper.
Penunjuk Pine Script®
oleh mihakralj
Trend Sniper v2.5 [Jamallo](2025) Intro Trend Sniper v2.5 is built around a novel-unique core construction — the Butterworth Stop : a ratcheting trailing stop anchored to a 2-Pole Butterworth Super Smoother and dynamically sized by Parkinson Volatility. While each algorithm used is individually well-documented, this specific combination is rare and not found in standard indicator libraries — most Butterworth implementations are simply plotted as trend lines, not used as the structural anchor of a volatility-adaptive stop mechanism. Break down: 2-Pole Butterworth Super Smoother (Core Trend Line) (smoothed price) The backbone of the indicator is a 2-Pole Butterworth Super Smoother, a concept brought into trading by engineer and author John Ehlers, first published in his 2004 book Cybernetic Analysis for Stocks and Futures In this indicator: The Butterworth filter runs on close with a user-defined period (default 20) and produces bw_trend — a clean, lag-minimized version of price. This smoothed value is not plotted directly. Instead, it acts as the anchor point for the trailing stop. The stop is placed at bw_trend ± stop_dist , meaning the stop hugs the filtered trend rather than raw price, making it far less susceptible to wick noise and erratic bars. Without this pre-filtering step, the trailing stop would oscillate erratically on volatile bars. Parkinson Volatility (Sizes the Stop Distance) "Developed by physicist Michael Parkinson in 1980, it estimates volatility using the natural logarithm of the high-to-low ratio each period, capturing intraday range rather than just close-to-close movement." In this indicator: Parkinson Volatility is calculated over a rolling window (default 50 bars) and multiplied by stop_mult (default 1.2) to produce stop_dist . When markets are volatile, stop_dist expands and the stop gives price more room. When markets are quiet, it contracts and the stop tightens. Butterworth Stop State Machine (The Main Line You Watch) This is the primary plotted line, It's a classic ratcheting trailing stop built on top of outputs from steps 1 and 2. In this indicator: The logic is a simple state machine with two modes — uptrend and downtrend. In an uptrend, the stop only moves up (never down), tracking at bw_trend − stop_dist . In a downtrend, it only moves down, tracking at bw_trend + stop_dist . When price closes on the wrong side of the stop, the state flips and the stop resets on the other side of the trend line. Because both the anchor bw_trend and the buffer stop_dist are noise-filtered and volatility-adjusted, the BW Stop flips far less often than a raw price-based trailing stop would, keeping you in trends longer. Chande Momentum Oscillator + Deadband (Colors the BW Stop Line) "The CMO measures momentum by calculating the difference between the sum of gains and the sum of losses over a specified period, dividing by their total to produce a normalized scale from -100 to +100 that accounts for both up and down days simultaneously." In this indicator: CMO is calculated on bw_stop itself (not raw price), over 14 bars by default. A deadband of ±10 is applied — the line only turns bull-colored when CMO exceeds +10, and bear-colored when it drops below −10. It does not change color in between. KAMA Midpoint (The Dynamic Midline and Fill Zone Anchor) "Introduced by Perry Kaufman in 1995, KAMA dynamically adjusts its smoothing based on the relative efficiency of price movement". In this indicator: Rather than applying KAMA to price directly, it's applied to the midpoint between the BW Stop and the Slow SuperTrend. This midpoint is a computed halfway value between the two lines. KAMA then smooths that midpoint adaptively, producing `kama_mid`. The fill colors you see on the chart are drawn between kama_mid and bw_stop — not between price and anything else. Dual SuperTrend (Signals and Fill State Logic) "Created by Olivier Seban, SuperTrend combines ATR-based volatility with trend detection into a single line that repositions dynamically as price confirms or breaks the current trend direction." In this indicator: Two SuperTrend lines run simultaneously. The Slow ST (9× ATR) is the macro trend reference. The Fast ST (6× ATR) is never plotted at full size — it's compressed halfway toward the BW Stop and shown as orange circles fast_shortened Putting It All Together The flow through the indicator is linear: Butterworth smooths price → Parkinson sizes the stop → the state machine builds the stop line → CMO colors it → KAMA anchors the midline fill → the dual SuperTrend fires signals and shades conviction. END Every component feeds the next, meaning the final signals and visuals are the product of five sequential filters rather than any single calculation — which is what gives the indicator its resistance to false signals in noisy market conditions.
Penunjuk Pine Script®
oleh Jamallo22
6
RSI-Gauss-Velocity-Strategy-WiPStrategy Overview: RSI-Gauss-Velocity-Strategy-WiP This high-performance strategy captures trend expansions by combining N-Pole Gaussian Filter signal processing with Stochastic RSI momentum. Unlike traditional moving averages, the Gaussian Filter creates a responsive, low-lag baseline and volatility channel designed to filter market noise and focus on true directional energy. The Mechanics Gaussian Channel: Uses a 4-pole bell-curve weighting to create a "Filtered True Range". Momentum Confirmation: Entries require Stoch-RSI to be at extreme levels (>80 or <20) to ensure breakouts are backed by cyclical momentum. Trend Alignment: Longs are only taken when the Gaussian trend is sloping upward. Key Logic Upgrades Momentum Velocity Exit: Replaces static exits with a slope exhaustion filter that senses when a trend is "tired," locking in profits before a price crash. Asymmetric Profit Protection: Allows aggressive momentum exits only when in profit, giving losing trades room to breathe while protecting gains during "blow-off tops". Compounding Retention: Avoids hard trailing stops to stay in winning trades longer, significantly boosting compounding potential. Performance Evolution (Nov 2014 – Mar 2026) The optimized version represents a massive leap in efficiency over the original script: Total P&L: Increased from +2,610% to +45,383%. Profit Factor: Improved from 5.69 to 5.78. Drawdown: Maintained at a stable -20.28% despite the exponential profit increase. Optimized for: BTC, ETH, and high-beta trending assets on 1H, 4H, and Daily timeframes.
Strategi Pine Script®
oleh RS374
RSI-Gauss-WiPRSI-Gauss Momentum Breakout The Logic This strategy combines John Ehlers' N-Pole Gaussian Filter with Stoch-RSI momentum to capture high-probability trend expansions. It replaces lagging moving averages with a responsive Gaussian volatility channel to identify true price breakouts. Entry & Exit Long Entry: Gaussian trend is bullish (sloping up), price breaks above the Upper Channel, and Stoch-RSI confirms strong cyclical momentum. Long Exit: Price crosses back under the Gaussian Midline, providing a trailing exit that preserves gains during trend reversals. Key Features Gaussian Smoothing: Advanced signal processing filters market noise. Volatility Channels: Uses Filtered True Range for adaptive entries. Clean Chart: Optimized for a minimal UI by hiding labels and status line values. Best used on 1H, 4H, and Daily timeframes for trending assets.
Strategi Pine Script®
oleh RS374
RSI-Gauss-WiPSTRATEGY OVERVIEW: RSI-Gauss-WiP Momentum Breakout THE CONCEPT: This strategy is designed to capture high-probability trend expansions by combining advanced signal processing with classic momentum oscillators. Instead of using traditional moving averages (which suffer from significant lag), this strategy utilizes an N-Pole Gaussian Filter to create a smoother, more responsive baseline and volatility channel. THE MECHANICS: Gaussian Channel: We use a multi-pole Gaussian filter (default: 4 poles) to create a "Filtered True Range" channel. Unlike Bollinger Bands, this channel uses a bell-curve weighting to filter out market noise and focus on the true directional energy of the price. Stoch-RSI Momentum: We don't just buy breakouts; we confirm them with momentum. The strategy looks for the Stoch-RSI to be at extreme levels (above 80 or below 20), indicating that the breakout is backed by strong cyclical momentum rather than just a random price spike. +1 Trend Alignment: Long positions are only taken when the Gaussian Filter is sloping upwards (Green), ensuring we are trading in harmony with the underlying trend. ENTRY & EXIT LOGIC: Long Entry: Gaussian Trend is bullish (sloping up). Price crosses and closes above the High Band (Volatility Breakout). Stoch-RSI K-line is above the D-line. Stoch-RSI is at an extreme momentum reading (>80 or <20). Long Exit: Price crosses back under the Gaussian Filter (The midline). This acts as a trailing "equilibrium" stop to protect capital during a trend reversal. KEY FEATURES: Lag Reduction: Includes a "Reduced Lag Mode" and "Fast Response Mode" for traders looking to catch turns even earlier. Clean Interface: The script is optimized for a clean chart—status line values and execution labels are hidden by default to allow you to focus on the price action. Time-Filtering: Built-in back testing window to analyze performance across specific market regimes. HOW TO USE: Timeframes: Works best on the 1H, 4H, and Daily timeframes where Gaussian smoothing is most effective at removing "intra-day noise." Check each timeframe for your specific pair, e.g. BTC-USDC, ETH-USDC, LINK-USDC, etc. ASSETS: Optimized for trending assets like BTC, ETH, and high-beta stocks. Tip: If the market is in a heavy sideways chop, increase the "Poles" (N) to 6 or 8 to increase the smoothness of the filter.
Strategi Pine Script®
oleh RS374
Ehlers Super Smoother Trend Score [BackQuant]Ehlers Super Smoother Trend Score Overview Ehlers Super Smoother Trend Score is a regime and trend-strength indicator built on a signal-processing filter created by John F. Ehlers. Instead of smoothing price with a standard moving average (which is mathematically crude and prone to noise and aliasing), this indicator applies the Ehlers Super Smoother, a Butterworth-style low-pass filter designed specifically for market data. The filtered series is then scored for directional persistence across a configurable lookback window, producing an oscillator-like trend score that measures how consistently the smoothed trend is advancing or deteriorating. This is not a simple “MA slope” tool. It is: A proper low-pass filter (Super Smoother) to reduce noise while preserving structure. A persistence score that converts the filtered trend into a quantitative regime signal. A threshold framework that turns the score into long/short regime transitions with clean state logic. Where the filter comes from (and why it matters) John F. Ehlers is known for applying digital signal processing (DSP) techniques to technical analysis. Traditional moving averages are not designed as proper frequency-selective filters. They blur price, lag heavily, and can introduce distortions, especially when the market contains high-frequency components (noise) near the Nyquist limit (the maximum representable frequency in sampled data). The Super Smoother is derived from a Butterworth low-pass filter design. Butterworth filters are engineered to have a maximally flat passband, meaning they smooth without introducing ripples in the filtered output. In trading terms: Less “wavy” smoothing artifacts than many MA variants. Better suppression of high-frequency noise. Cleaner trend structure for downstream logic. This script implements Ehlers’ recursive coefficient form, giving you a 2-pole (classic) or 3-pole (heavier) filter. What “Super Smoother” actually is The Super Smoother is a recursive IIR filter (Infinite Impulse Response). Unlike an SMA which averages a fixed window of past values, an IIR filter uses feedback from its own prior output values. That matters because it can achieve strong smoothing with less lag for a given “smoothness target.” Conceptually: Input: price series. Output: filtered estimate of the “low-frequency” component (trend structure). Mechanism: combine current input (or pre-filtered input) with previous filter outputs using coefficients derived from a chosen cutoff period. The coefficients (c1–c4) are not arbitrary, they are computed from exponential decay and cosine terms based on the cutoff period. This is what makes it a real DSP filter rather than “just another MA.” 2-pole vs 3-pole behavior 2-pole (classic) A standard Ehlers Super Smoother configuration. It offers a strong improvement over typical MAs in smoothness vs lag balance. 3-pole Adds an additional feedback term (one more prior filtered state). This increases smoothing and noise rejection, but introduces slightly more lag. The advantage is a cleaner structural line, which often improves regime stability when the market is noisy or mean-reverting. Anti-aliasing pre-filter step Before applying the recursive formula, the script averages the current and previous price: avg = (src + src ) / 2 This is a simple but important pre-filter that reduces high-frequency components that can alias into lower frequencies in sampled data. In practice, it helps stop “one-bar spikes” from contaminating the filter output as much. Inputs and what they really control Super Smoother Period (ssPeriod) This is the cutoff period used in the coefficient derivation. It is not the same as “MA length,” but it behaves similarly in that: Lower period = faster response, less smoothing, more sensitivity to noise. Higher period = smoother output, better noise rejection, more lag. Poles Selects filter order: 2 poles = balanced default. 3 poles = smoother, more conservative. Score Lookback Start/End Defines the persistence scoring window. The script compares the current filtered value to many prior filtered values across that range. A longer range makes the score more “confidence-based” and slower to change, while a shorter range makes it more reactive. Thresholds (Long/Short) Turns the score into a regime classification: Long threshold defines when bullish persistence is strong enough to be considered a trend regime. Short threshold defines when persistence has deteriorated enough to signal a bearish transition. How the trend score is computed After filtering, the indicator computes a directional persistence score on the filtered series (not raw price). That distinction matters because you are scoring structure, not noise. Mechanically: For each i in the scoring window: - If filt_now > filt , add +1 - Else add -1 Sum across the window to produce the score. Interpretation: High positive score means the filtered trend is consistently higher than many past points, persistent bullish structure. Low or negative score means the filtered trend is not advancing, or is consistently below prior points, bearish structure. Scores near the middle mean the filtered series is oscillating without clear persistence, chop or transition. This is a persistence metric, not a slope metric. It does not care about one-bar direction, it cares about consistency relative to history. Signal and state logic (why it stays clean) The indicator uses state logic to prevent constant flip-flopping: Long condition: score > long threshold. Short condition: score crosses below short threshold (uses prevScore and current score). That short logic is event-based, it triggers only on the breakdown transition, not on every bar below the threshold. Once a regime is set, it remains until a real threshold event forces change. Signals are plotted only on regime flips: Long marker when signal becomes +1 and prior was -1. Short marker when signal becomes -1 and prior was +1. This is designed for alerts and for clean backtesting interpretation. Visual layers The indicator can be used purely as a panel oscillator or as a structure overlay. Pane Trend Score line, colored by active regime. Optional reference lines at long/short thresholds for fast regime reading. On-chart (optional) Super Smoother line plotted over price, colored by regime. Optional candle painting and background shading to reflect active regime. This lets you treat the filter as a dynamic trend structure line while using the score as the regime classifier. How to interpret it properly 1) The Super Smoother line This is the cleaned trend structure estimate: When price respects the smoother line, trend structure is intact. When price repeatedly chops through it, structure is weak or range-bound. 2) The score This is the quantified persistence of that structure: Rising score implies strengthening trend persistence. Falling score implies deterioration, transition risk, or mean reversion. Score compression often shows consolidation before a regime shift. 3) Threshold regimes Above long threshold: bullish persistence regime, trend-following conditions. Below short threshold: bearish regime transition, defensive or short-biased conditions. Between thresholds: neutral/transition zone, where chop and fakeouts are common. Practical use cases Trend filter Only take long setups when score is above the long threshold. Reduce exposure or avoid trend trades in the neutral band. Treat a breakdown through the short threshold as regime invalidation. Trend quality assessment High score = continuation environment. Moderate score = trend exists but is fragile. Low/negative score = distribution, downtrend, or unstable structure. Trade management Use the Super Smoother line as a structure reference for trailing risk. Use score deterioration as an early warning before full regime flips. Use regime flips as hard exits or bias changes. Tuning guidelines If you want fewer signals and cleaner regimes Increase ssPeriod. Use 3 poles. Increase scoreEnd (longer scoring window). If you want faster reaction Decrease ssPeriod. Use 2 poles. Reduce the scoring window length. Keep in mind: faster settings increase sensitivity to chop. The filter is good, but no filter removes the reality of mean reversion. What makes this different from “just a smoothed MA score” The difference is the filter quality. The Super Smoother is a proper low-pass filter with coefficients derived from DSP principles, designed to suppress high-frequency noise and avoid common smoothing artifacts. Scoring that filtered structure gives you a regime metric that is more stable and more meaningful than scoring raw price or scoring a basic MA that still carries a lot of aliasing and distortion. Summary Ehlers Super Smoother Trend Score combines a DSP-derived Butterworth-style Super Smoother filter with a directional persistence scoring model. The filter provides a clean, low-noise trend structure series, and the score quantifies how consistently that structure is advancing or deteriorating across a defined window. Threshold-based regime logic converts the score into clean trend states and alerts, making it a practical tool for trend filtering, regime detection, and structure-aware trade management.
Penunjuk Pine Script®
oleh BackQuant
4
Ehlers Adaptive Trend FilterEHLERS ADAPTIVE TREND FILTER | Lag-Compensated SuperSmoother Based on John Ehlers' "Smoothing The Data" (2014), this indicator extends the SuperSmoother with hybrid Butterworth filters and dynamic lag compensation. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ KEY FEATURES: ✓ 3 FILTER MODES (lag-measured empirically) • 2p+2p (Fast): 62 bars lag — responsive, great for scalping • 3p+2p (Hybrid): 70 bars lag — RECOMMENDED, best risk/reward • 3p+3p (Smooth): 88 bars lag — ultra-smooth for macro trends ✓ LAG-COMPENSATED MOMENTUM Automatically extends momentum lookback to account for filter delay. Keeps momentum signals responsive despite heavy smoothing. ✓ CONFIRMATION-BASED REVERSALS Requires 2+ bars confirmation before signaling reversals. ~60% fewer false signals than single-bar detection. Reduces whipsaws on volatile assets. ✓ VOLATILITY-ADAPTIVE THRESHOLDS Automatically scales all deviation levels based on asset volatility. Works seamlessly across: - Crypto (20%+ volatility) - Equities (10-15% volatility) - Forex (2-5% volatility) - Bonds (<2% volatility) ✓ MULTI-TIMEFRAME AUTO-CALIBRATION Automatically optimizes filter periods for your trading style: - Scalping (<1H): 2p+2p (Fast) - Swing Trading (1D): 3p+2p (Hybrid) ← Default - Position Trading (1W+): 3p+3p (Smooth) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ WHAT YOU GET IN THE DASHBOARD: • TREND STATUS: Good/Bad (signal above/below baseline) • MOMENTUM: Strong/Steady/Weak/Opposing (lag-compensated) • MOMENTUM TREND: Increasing/Decreasing/Stable • SUPPORT BASELINE: Bull Reversal/Bear Reversal/Aligned • SUPPORT SLOPE: Positive/Negative/Neutral (with %) • SAFETY MARGIN: % distance from baseline • PRICE DEVIATION: Extended/Expanding/On Course/Lagging • TECHNICAL RATING: Perfect/Transition/Dangerous/Critical • VOLATILITY: Live % + historical baseline • FILTER CONFIG: Active mode + exact lag metric • THRESHOLD LEVELS: Adaptive or Fixed mode • ANALYSIS MODE: Auto-calibrated or Manual ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ PERFORMANCE (Backtested 2020-2024): ES 1D (3p+2p Hybrid): ✓ 68% Win Rate | 2.2:1 Profit Factor ✓ 12% Max Drawdown | Avg Trade: +45 points BTC 4H (3p+2p Hybrid): ✓ 62% Win Rate | 1.9:1 Profit Factor ✓ 18% Max Drawdown | Avg Trade: +$280 EURUSD 1H (2p+2p Fast): ✓ 55% Win Rate | 1.7:1 Profit Factor ✓ 8% Max Drawdown | Avg Trade: +45 pips ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ HOW TO USE: 1. Add indicator to chart (any asset, any timeframe) 2. Select Filter Configuration: → 3p+2p (Hybrid) recommended for most traders 3. Read the dashboard (bottom-right table) 4. Trade signals: → ENTER: Trend Status = "Good" + Momentum = "Strong" → EXIT: Trend Status = "Bad" OR background highlight appears 5. Combine with your own trade plan (entries, sizing, risk management) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ WHY THIS INDICATOR? Most traders face a painful choice: → Fast MA (like EMA20): Responsive but too many false signals → Slow MA (like EMA100): Smooth but miss 20% of moves Ehlers SuperSmoother solves this using 40+ years of digital signal processing research. Butterworth filters preserve trend direction while removing high-frequency noise more efficiently than moving averages. The innovation: LAG COMPENSATION By measuring the exact delay of each filter and dynamically adjusting momentum lookback windows, you get BOTH clean trends AND responsive signals. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ TECHNICAL DETAILS: Filter Type: Ehlers 2-Pole & 3-Pole SuperSmoother (Butterworth) Lag Compensation: Empirically measured via step response Momentum Adjustment: 1.0x (2p+2p) / 1.15x (3p+2p) / 1.45x (3p+3p) Volatility Model: 75th percentile of rolling 252-day returns Reversal Confirmation: 2-bar minimum (reduces noise) Repainting: NO (Pine Script v6, confirmed bars only) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ DISCLAIMER: This indicator is for educational and analytical purposes only. NOT financial advice, investment recommendations, or profit guarantees. • Past performance does NOT guarantee future results • All trading involves risk, including loss of principal • Test extensively on historical data before live trading • "Safety" and "Risk" metrics measure technical deviation, NOT capital protection • Start with small position sizes and proper risk management ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ REFERENCE: Ehlers, J. (2014). "Smoothing The Data." Stocks & Commodities Magazine. Oppenheim & Schafer. "Discrete-Time Signal Processing" (3rd ed.)
Penunjuk Pine Script®
oleh TrendMatrix_Labs
TASC 2026.01 The Reversion Index█ OVERVIEW This script implements the Reversion Index as presented by John F. Ehlers in the January 2026 edition of the TASC Traders' Tips , "Identifying Peaks And Valleys In Ranging Markets”. This indicator was created to provide timely buy and sell signals for mean reversion strategies. █ CONCEPTS Ehlers came up with the idea for the Reversion Index following the development of the "Continuation Index" (featured in the September 2025 edition). While the Continuation Index provides indications for trend onset, continuation, and exhaustion; the Reversion Index serves as its counterpart for mean-reversion trading. The raw Reversion Index value is calculated as the net change in price normalized to the sum of the absolute value of change in price over the same period; for clarity, it is then smoothed using Ehlers' SuperSmoother. The Smooth Reversion Index value is led by a "Trigger" line, which is created by smoothing the raw data to half the smoothing period of the smoothed index. Note: Ehlers suggests the smoothing lengths be left at 8 and 4 (Reversion Index & Trigger). For this reason these lengths are hard-coded in the script but can be easily modified in the code. █ USAGE In order to identify peaks and valleys effectively, the "Length" should ideally be set to half of that of the expected cycle of the data. If the expected cycle of your trading data is 20 bars, a 10 bar length should be set. Note: The Reversion Index is intended to identify peaks and valleys within a cycle, not over a large sample period. Ehlers suggests that this would create an estimation of trend, which is not the goal here. Once the length is set, peaks and valleys are interpreted as the cross of the "Trigger" and "Smooth" lines.
Penunjuk Pine Script®
oleh PineCodersTASC
MAMA - FAMA (Ehlers) [KN]MAMA - FAMA (Ehlers) Surprisingly, I couldn't find a proper Pine Script implementation of this classic indicator on TradingView, so here's my version. This indicator implements John Ehlers' MESA Adaptive Moving Average (MAMA) and Following Adaptive Moving Average (FAMA) from his book "Rocket Science for Traders." How It Works Unlike traditional moving averages with fixed periods, MAMA adapts its smoothing based on the market's dominant cycle. It uses the Hilbert Transform to measure the instantaneous phase of price, then adjusts its responsiveness according to how fast that phase is changing. When price is trending strongly (rapid phase change), MAMA speeds up to follow closely. During consolidation (slow phase change), it slows down to filter noise. FAMA is a further smoothed version of MAMA that serves as a signal line. Signals 🔵 Bullish : MAMA crosses above FAMA 🟠 Bearish : MAMA crosses below FAMA The adaptive nature makes this particularly effective at avoiding whipsaws during ranging markets while still catching trends early. Inputs - Fast Limit (default 0.5): Maximum alpha, controls fastest response - Slow Limit (default 0.05): Minimum alpha, controls slowest response - Source (default hl2): Price input Credits Original concept by John F. Ehlers.
Penunjuk Pine Script®
oleh KatherinaNote
Ehlers Dominant Cycle Stochastic RSIEhlers Enhanced Cycle Stochastic RSI OVERVIEW The Ehlers Enhanced Cycle Stochastic RSI is a momentum oscillator that automatically adjusts its lookback periods based on the dominant market cycle. Unlike traditional Stochastic RSI which uses fixed periods, this indicator detects the current cycle length and scales its calculations—making it responsive in fast markets and stable in slow ones. The indicator combines John Ehlers' digital signal processing research with the classic Stochastic RSI indicator, then adds a confirmation system to ensure cycle measurements are reliable. THE THEORY Traditional oscillators use fixed lookback periods (ie, 14-bar RSI). This creates a fundamental problem: markets don't move in fixed cycles. A 14-period RSI might capture the rhythm perfectly during one market phase, then completely miss it when conditions change. Ehlers' research demonstrated that price data contains measurable cyclical components. If you can detect the dominant cycle length, you can tune your indicators to match it—like tuning a radio to the right frequency. This indicator takes that concept further by using three independent cycle detection methods and only trusting the measurement when they agree: Hilbert Transform — A mathematical technique from signal processing that extracts cycle period from the phase relationship between price and its derivative. It is fast but can be noisy. Autocorrelation Periodogram — Measures how similar the price series is to lagged versions of itself. The lag with highest correlation reveals the dominant cycle. More stable than Hilbert, but slightly slower to adapt. Goertzel Algorithm (DFT) — A frequency-domain approach that calculates spectral power at each candidate period. Identifies which frequencies contain the most energy. When all three methods converge on similar period estimates, confidence is high. When they disagree, the market may be in a non-cyclical or in transition. HOW IT CHANGES THE STOCHASTIC RSI Standard Stochastic RSI: 1. Calculate RSI with fixed period (14 bars) 2. Apply Stochastic formula over fixed period (14 bars) 3. Smooth with fixed periods Ehlers Enhanced Cycle Stochastic RSI: 1. Detect dominant cycle using three methods 2. Confirm cycle measurement (methods must agree) 3. Calculate RSI with period scaled to the detected cycle 4. Apply Stochastic formula with cycle-scaled lookback 5. Smooth adaptively The result: when the market is cycling quickly (say, 15-bar cycles), the indicator uses shorter periods and responds faster. When the market stretches into longer cycles (such as 40-bar cycles), it automatically extends its lookback to avoid whipsaws. The Period Multipliers let you fine-tune this relationship: • 1.0 = Use the full detected cycle (smoother, fewer signals) • 0.5 = Use half the cycle (more responsive, catches turns earlier) INTERPRETATION Reading the Oscillator: • K Line (Blue) — The main signal line. Moves between 0 and 100. • D Line (Orange) — Smoothed version of K. Use for confirmation. • Above 80 — Overbought. Momentum stretched to upside. • Below 20 — Oversold. Momentum stretched to downside. • Crossovers — K crossing above D suggests bullish momentum shift; K crossing below D suggests bearish. Spectral Dilation (optional): When enabled, applies a bandpass filter before cycle detection. This isolates the frequency band of interest and reduces noise. Useful for: • Very noisy instruments • Lower timeframes • When confidence stays persistently low
Penunjuk Pine Script®
oleh B3AR_Trades
MESA Adaptive Ehlers Flow | AlphaNattMESA Adaptive Ehlers Flow | AlphaNatt An advanced adaptive indicator based on John Ehlers' MESA (Maximum Entropy Spectrum Analysis) algorithm that automatically adjusts to market cycles in real-time, providing superior trend identification with minimal lag across all market conditions. 🎯 What Makes This Indicator Revolutionary? Unlike traditional moving averages with fixed parameters, this indicator uses Hilbert Transform mathematics to detect the dominant market cycle and adapts its responsiveness accordingly: Automatically detects market cycles using advanced signal processing MAMA (MESA Adaptive Moving Average) adapts from fast to slow based on cycle phase FAMA (Following Adaptive Moving Average) provides confirmation signals Dynamic volatility bands that expand and contract with cycle detection Zero manual optimization required - the indicator tunes itself 📊 Core Components 1. MESA Adaptive Moving Average (MAMA) The MAMA is the crown jewel of adaptive indicators. It uses the Hilbert Transform to measure the market's dominant cycle and adjusts its smoothing factor in real-time: During trending phases: Responds quickly to capture moves During choppy phases: Smooths heavily to filter noise Transition is automatic and seamless based on price action Parameters: Fast Limit: Maximum responsiveness (default: 0.5) - how fast the indicator can adapt Slow Limit: Minimum responsiveness (default: 0.05) - maximum smoothing during consolidation 2. Following Adaptive Moving Average (FAMA) The FAMA is a slower version of MAMA that follows the primary signal. The relationship between MAMA and FAMA provides powerful trend confirmation: MAMA > FAMA: Bullish trend in progress MAMA < FAMA: Bearish trend in progress Crossovers signal potential trend changes 3. Hilbert Transform Cycle Detection The indicator employs sophisticated DSP (Digital Signal Processing) techniques: Detects the dominant cycle period (1.5 to 50 bars) Measures phase relationships in the price data Calculates adaptive alpha values based on cycle dynamics Continuously updates as market character changes ⚡ Key Features Adaptive Alpha Calculation The indicator's "intelligence" comes from its adaptive alpha: Alpha dynamically adjusts between Fast Limit and Slow Limit based on the rate of phase change in the market cycle. Rapid phase changes trigger faster adaptation, while stable cycles maintain smoother response. Dynamic Volatility Bands Unlike static bands, these adapt to both ATR volatility AND the current cycle state: Bands widen when the indicator detects fast adaptation (trending) Bands narrow during slow adaptation (consolidation) Band Multiplier controls overall width (default: 1.5) Provides context-aware support and resistance Intelligent Color Coding Cyan: Bullish regime (MAMA > FAMA and price > MAMA) Magenta: Bearish regime (MAMA < FAMA and price < MAMA) Gray: Neutral/transitional state 📈 Trading Strategies Trend Following Strategy The MESA indicator excels at identifying and riding strong trends while automatically reducing sensitivity during choppy periods. Entry Signals: Long: MAMA crosses above FAMA with price closing above MAMA Short: MAMA crosses below FAMA with price closing below MAMA Exit/Management: Exit longs when MAMA crosses below FAMA Exit shorts when MAMA crosses above FAMA Use dynamic bands as trailing stop references Mean Reversion Strategy When price extends beyond the dynamic bands during established trends, look for bounces back toward the MAMA line. Setup Conditions: Strong trend confirmed by MAMA/FAMA alignment Price touches or exceeds outer band Enter on first sign of reversal toward MAMA Target: Return to MAMA line or opposite band Cycle-Based Swing Trading The indicator's cycle detection makes it ideal for swing trading: Enter on MAMA/FAMA crossovers Hold through the detected cycle period Exit on counter-crossover or band extremes Works exceptionally well on 4H to Daily timeframes 🔬 Technical Background The Hilbert Transform The Hilbert Transform is a mathematical operation used in signal processing to extract instantaneous phase and frequency information from a signal. In trading applications: Separates trend from cycle components Identifies the dominant market cycle without curve-fitting Provides leading indicators of trend changes MESA Algorithm Components Smoothing: 4-bar weighted moving average for noise reduction Detrending: Removes linear price trend to isolate cycles InPhase & Quadrature: Orthogonal components for phase measurement Homodyne Discriminator: Calculates instantaneous period Adaptive Alpha: Converts period to smoothing factor MAMA/FAMA: Final adaptive moving averages ⚙️ Optimization Guide Fast Limit (0.1 - 0.9) Higher values (0.5-0.9): More responsive, better for volatile markets and lower timeframes Lower values (0.1-0.3): Smoother response, better for stable markets and higher timeframes Default 0.5: Balanced for most applications Slow Limit (0.01 - 0.1) Higher values (0.05-0.1): Less smoothing during consolidation, more signals Lower values (0.01-0.03): Heavy smoothing during chop, fewer but cleaner signals Default 0.05: Good noise filtering while maintaining responsiveness Band Multiplier (0.5 - 3.0) Adjust based on instrument volatility Backtest to find optimal value for your specific market 1.5 works well for most forex and equity indices Consider higher values (2.0-2.5) for cryptocurrencies 🎨 Visual Interpretation The gradient visualization shows probability zones around the MESA line: MESA line: The adaptive trend center Band expansion: Indicates strong cycle detection and trending Band contraction: Indicates consolidation or ranging market Color intensity: Shows confidence in trend direction 💡 Best Practices Let it adapt: Give the indicator 50+ bars to properly calibrate to the market Combine timeframes: Use higher timeframe MESA for trend bias, lower for entries Respect the bands: Price rarely stays outside bands for extended periods Watch for compression: Narrow bands often precede explosive moves Volume confirmation: Combine with volume for higher probability setups 📊 Optimal Timeframes 15m - 1H: Day trading with Fast Limit 0.6-0.8 4H - Daily: Swing trading with Fast Limit 0.4-0.6 (recommended) Weekly: Position trading with Fast Limit 0.2-0.4 ⚠️ Important Considerations The indicator needs time to "learn" the market - avoid trading the first 50 bars after applying Extreme gap events can temporarily disrupt cycle calculations Works best in markets with detectable cyclical behavior Less effective during news events or extreme volatility spikes Consider the detected cycle period for position holding times 🔍 What Makes MESA Superior? Compared to traditional indicators: vs. Fixed MAs: Automatically adjusts to market conditions instead of using one-size-fits-all parameters vs. Other Adaptive MAs: Uses true DSP mathematics rather than simple volatility adjustments vs. Manual Optimization: Continuously re-optimizes itself in real-time vs. Lagging Indicators: Hilbert Transform provides earlier trend change detection 🎓 Understanding Adaptation The magic of MESA is that it solves the eternal dilemma of technical analysis: be fast and get whipsawed in chop, or be smooth and miss the early move. MESA does both by detecting when to be fast and when to be smooth. Adaptation in Action: Strong trend starts → MESA quickly detects phase change → Fast Limit kicks in → Early entry Trend continues → Phase stabilizes → MESA maintains moderate speed → Smooth ride Consolidation begins → Phase changes slow → Slow Limit engages → Whipsaw avoidance 🚀 Advanced Applications Multi-timeframe confluence: Use MESA on 3 timeframes for high-probability setups Divergence detection: Watch for MAMA/price divergences at band extremes Cycle period analysis: The internal period calculation can guide position duration Band squeeze trading: Narrow bands + MAMA/FAMA cross = high-probability breakout Created by AlphaNatt - Based on John Ehlers' MESA research. For educational purposes. Always practice proper risk management. Not financial advice. Always DYOR.
Penunjuk Pine Script®
oleh AlphaNatt
2
Cycle-Synced Channel Breakout📌 Cycle-Synced Channel Breakout – Detect Breakouts Confirmed by Candles and Momentum Cycles 📖 Overview The Cycle-Synced Channel Breakout indicator is a precision breakout detection tool that combines the power of: • Adaptive Keltner Channels • Dominant Cycle Period Analysis (Ehlers-inspired) • Candlestick Pattern Recognition (Engulfing) This multi-layered approach helps identify true breakout opportunities by filtering out noise and false signals, making it ideal for swing traders and intraday traders seeking high-probability directional moves. ⚙️ How It Works 1. Keltner Channel Envelope A dynamic volatility channel based on the EMA and ATR defines the upper and lower bounds of price movement. 2. Engulfing Candle Detection The script detects strong bullish and bearish engulfing patterns, which often signal trend reversals or momentum continuations. 3. Dominant Cycle Momentum (Ehlers-inspired) Using a smoothed power oscillator derived from a detrended price series, the indicator assesses whether momentum is accelerating during the breakout — filtering out weak moves. 4. Signal Confirmation Logic A signal is only shown when: • An engulfing pattern is detected, and • Price breaks out of the Keltner Channel, and • Momentum (cycle power) is rising 5. Visual Feedback • Breakout signals are plotted with “BUY” or “SELL” labels • Faded green/red background highlights confirmed breakouts • Optional display of engulfing candles with triangle markers ⸻ 🛠️ Key Features • ✅ Adaptive Keltner Channels • ✅ Bullish/Bearish Engulfing Candle Recognition • ✅ Ehlers-style Cycle Momentum Confirmation • ✅ Background highlights for confirmed breakouts • ✅ Optional candle pattern visualization • ✅ Lightweight and Pine v6 compatible ⸻ 🧪 Inputs • Keltner Length – EMA period for channel basis • Multiplier – Multiplied with ATR to determine band width • Cycle Lookback – Used to calculate smoothed cycle power • Show Engulfing Candles? – Toggles candlestick signals • Show Breakout Signals? – Toggles breakout labels and backgrounds ⸻ 🧠 How to Use • Look for “BUY” or “SELL” labels when: • An engulfing candle breaks through the Keltner Channel • Cycle momentum confirms strength behind the move • The background color will faintly highlight the breakout direction. • Use in combination with other trend or volume indicators for added confluence. 🔒 Notes • This indicator is not repainting. • It is designed for educational and research purposes only. • Works across all timeframes and asset classes (stocks, crypto, forex, etc.)
Penunjuk Pine Script®
oleh TradeTechanalysis