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AlphaEngine █ ALPHAENGINE v1.0 The Adaptive Consensus Trading Algorithm Self-optimizing signal engine that runs 6 independent "Expert" sub-systems simultaneously, tracks their real-time accuracy, and dynamically adjusts their influence using an Adaptive Consensus algorithm inspired by the Multiplicative Weight Update method. The result: an indicator that learns which strategies work best in the current market — and automatically amplifies them while suppressing underperformers. All in real-time, on every single bar. Free and Open Source. █ THE 6 EXPERTS 1. Trend Expert Triple EMA alignment (Fast/Mid/Slow) combined with ADX trend strength. Detects directional bias and amplifies signals when the trend is strong. 2. Momentum Expert Blends RSI zone analysis (40%), Stochastic crossover (30%), and Rate-of-Change velocity (30%) into a single momentum score. Catches acceleration and deceleration in price moves. 3. Volume Expert On-Balance Volume trend direction combined with real-time volume spike detection. Confirms signals with institutional participation. Volume must agree with price direction for full score. 4. Volatility Expert Bollinger Band position mapping combined with ATR expansion/contraction analysis. Includes Squeeze Detection that alerts before major breakout moves. 5. Supertrend Expert ATR-based dynamic support/resistance with distance scaling. Stronger signals when price is firmly above/below the supertrend line. 6. Structure Expert Higher-timeframe EMA bias using auto-selected HTF (e.g., 1H chart checks 4H, 5m chart checks 1H). Ensures signals align with the macro trend. █ THE ADAPTIVE WEIGHTING ENGINE Unlike traditional indicators that treat all components equally, AlphaEngine tracks which experts are actually performing well in the current market conditions: Each expert's predictions are validated against real price movement A rolling accuracy score (exponential decay) measures recent performance Weights are computed as accuracy squared — creating clear differentiation Experts with high accuracy get amplified, poor performers get dampened The engine adapts to any market environment automatically In a strong trending market, the Trend and Supertrend experts naturally gain weight. In a ranging/choppy market, Momentum and Volume experts take over. The engine adapts automatically. █ SIGNAL GRADING: A/B/C A-Grade (75%+ consensus) — High quality. Strong agreement among key experts. B-Grade (60%+ consensus) — Standard. Moderate agreement. Use with additional confirmation. C-Grade (50%+ consensus) — Weak. Minimal consensus. For analysis only. Filterable via settings. Signal labels are visual: A-grade signals are larger and prominent. B/C are smaller and subtle. A minimum grade filter lets you hide weaker signals. █ RISK MANAGEMENT Take Profit + Stop Loss: TP1 — ATR-based take profit target with visual line and hit detection SL — Dynamic ATR-based stop loss, adapts to current volatility Visual checkmark when TP1 is reached, X marker when SL triggers. Both levels are always proportional to market conditions — never fixed distances. █ VISUAL FEATURES Smart Candle Coloring — 6-level gradient from deep green to deep red based on consensus strength Trend Background — Subtle gradient overlay showing macro direction Volatility Bands — Dynamic Bollinger-based bands with squeeze highlight Squeeze Detection — Diamond dots below price when volatility contracts Supertrend Line — Color-coded dynamic support/resistance EMA Lines — Optional Fast/Mid/Slow EMA visualization █ DASHBOARD Compact 10-row real-time analytics panel: Current signal + grade + consensus %, visual consensus bar, each expert's direction + rolling accuracy %, and volatility state (Squeeze/Expand/Contract/Normal). █ ALERTS (7 CONDITIONS) Signal: Buy/Sell, A-Grade Buy/Sell Risk: TP1 Hit, SL Hit Volatility: Squeeze Start █ PRO VERSION The PRO version adds: VWAP Expert — 7th expert for institutional fair-value bias with timeframe auto-scaling Trading Style Presets — Auto/Scalping/Intraday/Swing/Position/Custom with automatic parameter tuning Asset Auto-Optimization — Crypto/Forex/Stocks/Futures multipliers for volatility and volume sensitivity RSI Divergence Detection — Regular + Hidden divergences with visual labels and connecting lines S-Grade Signals — Elite setup grade (90%+ consensus) for highest probability entries Multi-Target TP — TP1/TP2/TP3 system with sequential hit detection and R:R ratio display Premium Dashboard — 17-row analytics panel with all expert weights, divergence status, and TP tracking 17 Alert Conditions — Including divergence alerts, squeeze breakout, trend flip, and grade-specific triggers █ NON-REPAINTING All calculations use standard non-repainting indicators (EMA, RSI, ADX, ATR, OBV, Stochastic, Bollinger Bands). Multi-timeframe data uses confirmed prior-bar values. No repainting. █ WORKS ON Crypto, Forex, Stocks, Futures, Indices, Commodities and Bonds — any timeframe from 1 second to Monthly. █ DISCLAIMER This indicator is for educational and informational purposes only. It does not constitute financial advice. Past performance does not guarantee future results. No indicator can predict market movements with certainty. Always implement proper risk management. Use this tool as one component of a comprehensive trading strategy, not as a standalone decision-making system.
Chỉ báo Pine Script®
của alphachart_tv
AG Pro ATR Envelope Breakout Quality [AGPro Series]AG Pro ATR Envelope Breakout Quality Overview / What it does AG Pro ATR Envelope Breakout Quality is a volatility-aware breakout framework built around a dynamic ATR envelope rather than a static horizontal level, fixed box, or session-defined range. The script tracks when price closes outside an ATR-based outer band, then evaluates whether that move shows enough quality to be treated as a meaningful breakout instead of a weak expansion, short-lived overshoot, or low-conviction push. The core logic is centered on three linked questions. First, did price achieve a valid close outside the active envelope? Second, was that move supported by enough momentum and relative participation to deserve attention? Third, what happened when price came back toward the broken area? This progression allows the script to move beyond a simple breakout marker and present a more structured breakout-quality workflow. Because the reference structure is dynamic, the script adapts to changing market conditions instead of forcing all setups into a fixed box logic. In periods of contraction, the envelope tightens and makes outside acceptance more meaningful. In periods of expansion, the envelope widens and helps separate true continuation pressure from ordinary volatility noise. This makes the tool especially useful for traders who want to judge whether an expansion is merely visible or genuinely tradable. The visual design is intentionally clean and overlay-first. The envelope defines the active volatility shell, breakout markers show where price escapes that shell, the throwback zone highlights the key acceptance pocket after the move, and the optional target line provides a simple expansion objective. A compact panel then summarizes the current state without taking over the chart. The result is a script that aims to look premium while still keeping the main story readable in a publish screenshot. Unique Edge The main distinction of this script is that it does not evaluate breakout quality from a static support/resistance line, a consolidation rectangle, a Donchian extreme, or an opening range boundary. It evaluates breakout quality from a moving ATR envelope. That difference is not cosmetic. It changes the entire logic of what counts as a breakout, how follow-through is judged, and how retests are interpreted. In several classic breakout tools, the market is asked to escape a fixed historical structure. Here, the market is asked to achieve acceptance outside a live volatility shell. This creates a different analytical lens. A move that looks impressive relative to a flat level may not be meaningful relative to a volatility-adjusted envelope. On the other hand, a clean close outside an adaptive outer band can reveal expansion quality that a simple line break would miss. This also separates the script from our other AG Pro tools. It is not a consolidation breakout evaluator, because its reference structure is not a box. It is not a Donchian breakout tool, because it is not based on period highs and lows. It is not an opening-range breakout model, because it is not session-box dependent. It is not a standard break-retest script, because the retest here happens around a dynamic envelope acceptance area rather than around a static horizontal level. That distinction matters both analytically and visually. Analytically, the script focuses on volatility-adjusted breakout acceptance. Visually, it produces a different type of chart story: an active envelope, a breakout event, a throwback pocket, and a projected path. This gives the script its own place inside the AG Pro catalog rather than making it feel like a variation of an existing breakout family member. Methodology The script begins with an ATR-based envelope built around a moving basis. This creates an adaptive upper and lower band that expand or contract with market volatility. A bullish breakout candidate appears when price closes outside the upper band. A bearish breakout candidate appears when price closes outside the lower band. Wick-only excursions are not enough. The script is designed to care about acceptance, not mere contact. Once an outside close is detected, the script evaluates breakout quality through a compact scoring framework. Momentum contribution helps measure whether the breakout candle shows real displacement or just a hesitant push. Volume contribution helps detect whether the breakout is supported by stronger-than-usual participation or whether it lacks confirmation. The combined result becomes the displayed breakout-quality score. After the initial breakout, the script monitors the first return toward the broken band area. This is where the throwback logic becomes important. Instead of treating every pullback the same way, the script classifies what happens around the envelope area and updates the state accordingly. A successful hold suggests that the market accepted the breakout. A failure suggests that the move lost structural quality after the initial expansion. An optional target line can be used to project a simple post-breakout objective. This is not presented as a promise of outcome. It is a visual planning reference intended to show a possible expansion path if the breakout continues to behave constructively. Together, the envelope, the breakout signal, the throwback state, and the target framework create a full breakout-quality sequence rather than a single event label. Signals & Alerts The script is designed to organize the breakout workflow into visible states rather than flooding the chart with constant commentary. The main states include bullish breakout, bearish breakout, throwback monitoring, throwback hold, breakout failure, and target hit. This makes the chart easier to read and helps the user understand where the setup currently stands. Bullish and bearish breakout markers appear when price achieves a confirmed outside close beyond the relevant envelope band. These are the initial expansion events. They are then followed by a monitoring phase in which the script watches how price behaves around the broken band area. If the return is constructive, the script can label that behavior as a successful hold. If the move loses quality and breaks down, the script can classify it as a failure. The target marker is optional and functions as a planning aid, not as a certainty engine. It simply shows that the projected expansion objective has been reached based on the chosen configuration. In practical use, this can help traders separate the breakout event itself from the later progression of the move. The alert set is intended to remain deterministic and chart-state aware. It focuses on confirmed breakout events, throwback behavior, breakout failure, and target completion. This keeps the script aligned with workflow clarity instead of turning it into a noisy alert generator. Key Inputs The envelope settings control the moving basis, ATR length, and multiplier that define the adaptive breakout shell. These settings determine how sensitive the script is to changing volatility and how demanding the outside-close condition becomes. The breakout filter settings allow the user to regulate confirmation quality. Depending on the selected configuration, the script can require stronger momentum, clearer outside distance, and optional volume confirmation. This helps users decide whether they want a more selective or more responsive model. The throwback analysis settings define how the script interprets the first return toward the broken envelope area. These settings influence how deeply price can revisit the area before the move is treated as weak, failed, or still acceptable. The target settings control whether the projected objective is shown and how far it is placed from the breakout area. The visual settings then manage panel visibility, panel placement, font sizing, historical object behavior, and label density so the script can remain clean in live use and in publish screenshots. Limitations & Transparency This script is a breakout-quality framework, not a prediction engine. It does not know in advance whether a breakout will continue. It evaluates the quality of a breakout after a valid outside-close event occurs and then tracks how price behaves afterward. That distinction is important. The ATR envelope is an adaptive reference, which means the same market move may be classified differently under different volatility regimes. That is intentional. The script is designed to respond to changing market structure, but any adaptive model will also reflect the sensitivity of its settings. Users should therefore expect the behavior of the tool to vary across symbols, timeframes, and volatility environments. Volume inputs may also behave differently across markets and data feeds. On some instruments, volume can add useful confirmation. On others, it may be less informative. For that reason, volume should be treated as a supporting factor rather than as an absolute truth layer. The target projection is a chart-planning feature, not a guaranteed outcome. Likewise, a breakout failure label does not mean the market cannot later recover, and a target hit does not mean the move was universally optimal. The script is meant to help structure chart reading, not replace trade management, context analysis, or personal decision-making. How this script differs from our other AG Pro tools Within the AG Pro lineup, this script is intentionally positioned as a volatility-envelope breakout tool. It does not compete with our box-based breakout logic, our period-high/low breakout logic, or our static break-retest logic. Its role is to answer a different question: did price achieve meaningful acceptance outside an adaptive ATR shell, and did that acceptance survive the first return test? That makes it especially useful when traders want a volatility-adjusted view of expansion quality. In markets where static levels are repeatedly pierced, an adaptive envelope framework can provide a cleaner read on whether the move is truly escaping current volatility conditions or simply stretching within ordinary noise. In that sense, the script is not a replacement for our other breakout-oriented tools. It is a separate layer with a different reference model, different retest logic, and a different chart story. That separation is deliberate and is one of the reasons the script belongs in its own category inside the broader AG Pro collection. Risk Disclosure This script is an analytical chart tool designed to visualize volatility-adjusted breakout conditions, breakout quality, and post-breakout behavior. It is not financial advice, not a signal service, and not a guarantee of future price direction. All breakout conditions can fail. Momentum can fade, volume can be inconsistent, and throwback behavior can change quickly. Markets remain uncertain, and no indicator can eliminate risk. Users should always apply their own market judgment, risk controls, and execution rules. Use the script as a structured decision-support layer, not as a stand-alone trading instruction. Confirmation from broader context, trend conditions, liquidity structure, and personal risk management remains essential.
Chỉ báo Pine Script®
của AGProLabs
2
Adaptive Pressure Trail [JOAT]Adaptive Pressure Trail Introduction Adaptive Pressure Trail is an open-source overlay indicator that combines an HMA-based adaptive ratchet trail with a custom volume-weighted Money Flow Index to classify bars into bull pressure, bear pressure, and neutral states. The system uses a three-layer visual architecture — an outer volatility cloud, an inner ratchet band fill, and a core gradient pressure fill between the HMA baseline and candle mid-body — to create a clear, spatially organized picture of momentum and direction on any chart. Volatility squeeze detection identifies compression phases before potential breakouts, and high-confidence signals fire when a squeeze releases simultaneously with pressure alignment. The core problem this indicator solves is that most trail-based systems are either too reactive (flipping constantly on noise) or too slow (missing meaningful moves). The HMA ratchet addresses this: the upper band only falls and the lower band only rises after a direction flip, preventing whipsaw while remaining responsive when momentum is genuine. Layering a volume-weighted MFI filter on top means a directional trail alone is not sufficient — volume-backed money flow must confirm the move before the indicator reports active pressure. Core Concepts 1. HMA Adaptive Ratchet Trail The trail baseline is computed using a Hull Moving Average, which provides low lag while remaining smooth. ATR-scaled upper and lower bands are applied around the HMA. The ratchet rule prevents band noise: the upper band can only move downward (or reset when price closes above it), and the lower band can only move upward (or reset when price closes below it). Direction flips when price closes through the active band. This creates a one-directional drift that is far more stable than a raw crossover trail: The trail direction variable persists with var and updates each bar. Direction == 1 means the lower band is the active trail (bullish), direction == -1 means the upper band is the active trail (bearish). 2. Custom Volume-Weighted MFI Rather than using a standard price-only momentum oscillator, the pressure engine uses a custom volume-weighted Money Flow Index. Positive flow is volume multiplied by HLC3 on bars where HLC3 increased; negative flow is volume multiplied by HLC3 on bars where HLC3 decreased. These are summed over the MFI length and converted to a 0-100 scale using the RSI formula. The result is smoothed with an HMA for responsiveness. This produces a momentum measure that is inherently volume-weighted — large-volume moves carry more influence than low-volume drift. The MFI is further smoothed to distinguish sustained pressure from transient spikes. 3. Pressure Regime Classification Bull pressure is active when the trail direction is bullish AND the smoothed MFI is above the bull threshold. Bear pressure is active when the trail direction is bearish AND MFI is below the bear threshold. Neutral is everything else. This dual-condition structure means you need both directional commitment from the ratchet trail AND volume-backed momentum to enter a pressure state. Either condition alone is insufficient. A rolling 50-bar history tracks what percentage of recent bars were in an active pressure state, producing a Pressure Strength percentage that indicates whether the current regime has been sustained or is a brief spike. 4. Squeeze Detection Band width — the distance between the upper and lower ratchet bands — is compared to its own SMA. When band width drops below 72% of its recent average, the market is compressing. A squeeze start fires a golden diamond marker at the trail level. A squeeze release fires a larger circle marker. The high-confidence signal fires when a squeeze release coincides with an active pressure state, identifying the highest-probability setups where compressed volatility breaks out in a confirmed directional context. 5. Three-Layer Visual Architecture The chart renders three nested visual layers: Outer Cloud: The ATR envelope (cloudMult * ATR from HMA center) filled with a very transparent directional color — gives spatial context to where price is within the volatility range Inner Band Fill: The ratchet upper and lower bands filled with medium transparency — shows the active directional channel Core Pressure Fill: A gradient fill between the HMA baseline and the candle mid-body — transparent at the HMA, saturated at the body, colored by pressure state The trail line itself uses three stacked plots at widths 10, 5, and 2 to create a neon glow shadow effect. Bar coloring uses color.from_gradient driven by MFI intensity, producing increasingly saturated candles as momentum builds. Features HMA Ratchet Trail with Triple-Layer Glow: Direction-persistent adaptive trail rendered as a neon glow (widths 10/5/2) using the bullish lime or bearish fuchsia color Outer ATR Volatility Cloud: Wide ATR envelope filled directionally, providing spatial context at a glance Inner Ratchet Band Fill: Gradient-filled active channel between upper and lower ratchet bands Core Pressure Gradient: Background-to-body gradient between HMA and mid-body, colored by current pressure state HMA Skeleton Reference: Subtle neutral line showing the raw HMA baseline beneath all fills Volatility Squeeze Markers: Golden diamonds during compression, circle flash on breakout High-Confidence Signal: Starred HC LONG / HC SHORT labels when squeeze releases into confirmed pressure alignment — the highest-quality setup the system produces Volume Impulse Labels: When a strong directional candle exceeds the volume threshold, a label shows the volume ratio (e.g., 2.1x vol) at the bar MFI Cross Markers: Small triangles on the trail when MFI crosses the 50 level, marking momentum regime shifts TP Signals: Labeled plotshapes when MFI reaches overbought/oversold extremes in the trail direction Pressure Strength Percentage: Rolling 50-bar % of time spent in active pressure — distinguishes sustained trends from brief spikes Gradient Bar Coloring: color.from_gradient driven by MFI intensity — bars saturate as momentum builds and fade as it weakens 11-Row Dashboard: Pressure state, trail direction, MFI reading, pressure score, pressure strength %, volatility state, band width, trend bars, trail price, ATR Input Parameters Adaptive Trail: Trail HMA Length: Period for the HMA baseline (default 21) Trail ATR Multiplier: Width of inner ratchet bands (default 1.8) Trail ATR Length: ATR lookback for band calculation (default 14) Outer Cloud ATR Width: Outer envelope width multiplier (default 3.2) Squeeze Reference Bars: SMA period for band-width baseline (default 20) Pressure Filter: MFI Length: Volume-weighted money flow lookback (default 14) MFI Smoothing: HMA smoothing on raw MFI (default 7) MFI Bull/Bear Thresholds: Activation levels for pressure states (default 62/38) Signals: TP Overbought/Oversold Levels: MFI levels that trigger TP signals (default 78/22) Impulse Volume Multiplier: Volume multiple above SMA required for impulse label (default 1.3) Visuals: Toggles for entry signals, TP signals, glow, cloud, pressure fill, squeeze markers, and dashboard Bull Color (default lime #a3e635), Bear Color (default fuchsia #e879f9), Neutral Color (default slate #94a3b8) How to Use This Indicator Primary Setup — Trend Following with Pressure Confirmation: Look for the trail to flip direction (circle marker on trail). Wait for MFI to cross the bull or bear threshold, confirming the pressure state activates. Enter in the trail direction once the pressure fill color saturates. Trail your stop at the active trail line. Exit on a TP signal or when the pressure state deactivates. High-Confidence Setup: Wait for squeeze markers (golden diamonds) to appear, indicating compression. When the squeeze releases (larger circle flash) and the pressure state is simultaneously active, the HC LONG or HC SHORT label fires. These are the setups where compressed volatility breaks out with momentum behind it. Filtering with Pressure Strength: The dashboard Pressure Strength percentage tells you how sustained the current move has been. Values above 60% indicate a mature trend. Values below 30% indicate the pressure state is new or unstable. Adjust position sizing accordingly. Reading Impulse Candles: Volume impulse labels (e.g., "2.1x vol") mark bars where a strong directional move was accompanied by significantly elevated volume. These often mark the start or acceleration of a pressure phase and can serve as reference points for support/resistance. APT dashboard showing bull pressure active, MFI at 71.2, P-Score 7.1/10, P-Strength at 64%, band width expanding after a squeeze release, and the trail at current price with ATR reference Indicator Limitations The ratchet trail requires a confirmed close through the active band to flip direction. On higher-timeframe charts with large candle bodies this can mean the flip is confirmed well after the actual turning point The volume-weighted MFI requires volume data. On instruments with unreliable volume reporting (some forex pairs, synthetic indices) the pressure filter may be less meaningful than on equities or futures Squeeze detection uses a 72% band-width threshold. In persistently low-volatility instruments this threshold may trigger too frequently; adjusting the Squeeze Reference Bars parameter can help High-confidence signals require both a squeeze release and active pressure simultaneously. On trending markets with no compression phase, HC signals will be rare MFI thresholds at 62/38 are defaults designed for balanced use; highly trending instruments may require raising the bull threshold and lowering the bear threshold to reduce false pressure activations Originality Statement This indicator is original in its combination of a ratchet-constrained HMA trail with a custom volume-weighted MFI, the three-layer nested visual system, and the squeeze-breakout confluence signal. While HMA trails and MFI oscillators exist independently, this publication is justified because: The ratchet logic applied to HMA (rather than ATR midline or EMA) reduces lag while preventing the constant flipping common in standard trail indicators The custom volume-weighted MFI differs from the standard MFI by using HLC3 as the price component with RSI-formula normalization, producing a smoother measure with better noise rejection The three-layer nested fill architecture (outer cloud, inner band, core pressure gradient) provides a spatially organized visual system where the distance between layers communicates volatility context Squeeze detection integrated with pressure confirmation for HC signals is a novel combination that identifies setups at the intersection of volatility compression and momentum alignment The Pressure Strength rolling percentage provides a trend maturity measure not present in standard trail indicators Disclaimer This indicator is provided for educational and informational purposes only and does not constitute financial advice or a recommendation to buy or sell any financial instrument. Past performance of any pattern or signal does not guarantee future results. All trading involves substantial risk. Always use proper risk management and conduct your own independent analysis. — Made with passion by officialjackofalltrades
Chỉ báo Pine Script®
của officialjackofalltrades
SENTINEL SHIFT ViprasolOverview The Sentinel Shift is an original market structure and trend analysis indicator that combines Kaufman Adaptive Moving Average (KAMA) for trend detection with Break of Structure (BOS) and Change of Character (CHoCH) identification for institutional market structure shifts. It is designed for traders seeking clear trend confirmation on all timeframes, from scalping to swing trading. How It Works Core Trend Engine (KAMA) Sentinel Shift uses Kaufman Adaptive Moving Average (KAMA) as its foundation. KAMA is a self-adjusting moving average that measures directional efficiency: ER = abs(close - close ) / sum(abs(close - close ), n) fastSC = 2.0 / (fast + 1) slowSC = 2.0 / (slow + 1) SC = (ER * (fastSC - slowSC) + slowSC)^2 KAMA = KAMA + SC * (close - KAMA ) KAMA responds dynamically to market conditions: - When price moves efficiently (trending), the efficiency ratio approaches 1.0, causing KAMA to use faster smoothing constants and hug price more closely - When price chops (ranging), the efficiency ratio approaches 0.0, causing KAMA to use slower smoothing and filter noise Trend Detection The current trend is determined by comparing close to KAMA: - BULLISH: close > KAMA - BEARISH: close < KAMA Preset Configurations (Fast / Default / Smooth) Three built-in presets auto-tune KAMA parameters for different timeframes: Fast Preset (optimized for 1-15 minute scalping): KAMA Length: 6 Fast Smoothing: 2 Slow Smoothing: 20 Default Preset (balanced for swing trading): KAMA Length: 10 Fast Smoothing: 2 Slow Smoothing: 30 Smooth Preset (optimized for position/daily trading): KAMA Length: 16 Fast Smoothing: 2 Slow Smoothing: 40 Pivot Detection The indicator detects swing highs and lows using configurable lookback periods (pivLeft and pivRight): pivHigh = ta.pivothigh(high, pivLeft, pivRight) pivLow = ta.pivotlow(low, pivLeft, pivRight) These pivot points form the reference levels for structure breaks. Market Structure Tracking Sentinel Shift maintains a continuous market structure state (bullish, bearish, or forming) by tracking the most recent swing high and swing low. A structure variable records the current directional bias: structTrend = 1 (bullish), -1 (bearish), or 0 (neutral) Break of Structure (BOS) A BOS is a directional continuation that breaks a recent swing level within the same trend: Bullish BOS: price closes above the most recent swing high while structTrend >= 0 Bearish BOS: price closes below the most recent swing low while structTrend <= 0 When a BOS fires, the indicator draws a dashed line at the structural level with a "BOS" label, and the signal is available as an alert condition. Change of Character (CHoCH) — Trend Reversal A CHoCH signals a reversal of trend direction — price breaks through the opposing structure level. To filter false signals, CHoCH requires a configurable number of BOS events to have fired first in the previous trend direction (BOS Required Before CHoCH Flip). For example, setting this to 1 requires at least one BOS in the prior trend before a CHoCH can flip the structure. When a CHoCH fires, the indicator: - Draws a solid line at the structural level with a "CHoCH" label - Updates structTrend to the new direction - Resets BOS counters for the new trend - Generates a trend reversal signal (triangular plotshape at KAMA +/- 0.5 ATR) Visualization KAMA Line and Cloud Fill - The KAMA line is plotted in the current trend color (bullish green / bearish red) - A cloud fill extends between close and KAMA, providing visual depth of the trend Bar Coloring - All candles are colored by the trend (bullish green / bearish red), making trend direction immediately obvious Target Pivot Lines - The most recent swing high and low are drawn as horizontal lines (default gray) - When price closes above/below these levels, the lines change color and stop extending forward Dashboard A real-time table displays: - Trend (BULLISH / BEARISH indicator) - Price vs KAMA (ABOVE / BELOW) - Market Structure (BULL STRUCT / BEAR STRUCT / FORMING) - BOS status (last event flagged) - CHoCH status (last event flagged) - Signal status (BUY / SELL / NONE from the trend reversal detection) - Color Mode (CLASSIC / CUSTOM) - Version The dashboard is repositionable (Top Right, Top Left, Bottom Right, Bottom Left) and resizable (Tiny, Small, Normal). Key Features - Kaufman Adaptive Moving Average (KAMA) with self-adjusting smoothing based on directional efficiency - Three preset timeframe configurations (Fast, Default, Smooth) for quick adaptation to scalping, swing, or position trading - Market structure detection with configurable pivot lookback periods (pivLeft, pivRight) - Break of Structure (BOS) identification with dashed level lines and labels - Change of Character (CHoCH) detection with solid level lines and trend reversal signals - Configurable BOS requirement before CHoCH flip (filters false reversals) - KAMA-anchored pivot target lines with color change on break-through - Cloud fill between close and KAMA for visual trend depth - Bar coloring by trend direction - Trend reversal signal (dual-layer triangular plotshape at location.absolute) - Real-time dashboard with trend, structure, BOS, CHoCH, signal status, and preset mode - Color preset selection (Classic green/red or Custom via color pickers) - Four alert conditions (Bullish Reversal, Bearish Reversal, Bull BOS, Bear BOS) with dynamic ticker/interval insertion - Full input groups with comprehensive tooltips for all parameters How to Use 1. Apply to a standard candlestick chart 2. Select a Preset (Fast for scalping, Default for swing, Smooth for position trading) 3. Adjust Pivot Left/Right bars for structure sensitivity (lower = more structures, higher = fewer/stronger) 4. Configure the BOS Required Before CHoCH Flip to filter whipsaw (default 0 = immediate reversal possible) 5. Watch for KAMA trend direction and monitor the dashboard for current structure state 6. Trade BOS events within-trend continuation 7. Trade CHoCH events as trend reversals 8. Use the pivot target lines as reference levels for price reversits Settings Calculation Parameters - Preset: Default, Fast, or Smooth — controls KAMA parameters for the selected timeframe - KAMA Length: efficiency ratio lookback (controls KAMA responsiveness) - Fast Period: KAMA fast smoothing constant (lower = faster KAMA in trends) - Slow Period: KAMA slow smoothing constant (higher = slower KAMA in ranges) Structure Settings - Pivot Left Bars: bars to the left for swing high/low detection - Pivot Right Bars: bars to the right for swing high/low detection - Show BOS: toggle BOS level lines and labels - Show CHoCH: toggle CHoCH level lines and labels - Show Trend Reversal Signal: toggle triangular reversal markers - Max Structure Levels: maximum number of BOS/CHoCH lines retained on chart - BOS Required Before CHoCH Flip: number of BOS events required before trend can reverse via CHoCH Target Lines - Show Pivot Lines: toggle last swing high/low reference lines - Pivot Line Color: color of the target lines - Pivot Line Width: line thickness (1 = thin, 2 = medium, 3 = thick) - Pivot Line Style: Solid, Dashed, or Dotted Visualization Settings - Color Preset: Classic (green/red) or Custom (enables color pickers) - Show Cloud Fill: toggle the cloud between close and KAMA - Color Candles: toggle bar coloring by trend - Show KAMA Line: toggle the KAMA plot - Bull Color: custom bullish color picker (if preset is Custom) - Bear Color: custom bearish color picker (if preset is Custom) Dashboard Settings - Show Dashboard: toggle the dashboard table - Position: Top Right, Top Left, Bottom Right, or Bottom Left - Size: Tiny, Small, or Normal Alerts Four alert conditions with dynamic message insertion ({{ticker}}, {{exchange}}, {{interval}}): 1. SS — Long: Bullish CHoCH reversal signal 2. SS — Short: Bearish CHoCH reversal signal 3. SS — Bull BOS: Break of structure in bullish direction 4. SS — Bear BOS: Break of structure in bearish direction Limitations & Disclaimer - Sentinel Shift is an analytical tool, not a trading system. Always use proper risk management - KAMA and pivot detection work on historical data; intra-bar updates may vary - Structure detection depends on accurate pivot identification — results vary with timeframe and pivot sensitivity settings - This indicator is best used on liquid instruments (stocks, forex, crypto, indices) where order flow is reliable - Past performance does not guarantee future results. This is not financial advice. Credits & Attribution Sentinel Shift is an original indicator created by Viprasol. All logic — KAMA adaptive engine, pivot detection, BOS/CHoCH classification, structure tracking with configurable BOS requirement, reversal signal generation, dashboard layout, and alert conditions — is original work developed specifically for multi-timeframe market structure analysis.
Chỉ báo Pine Script®
của viprasol
Kalman Trend Estimator [KTE] -FibonacciFlux OVERVIEW Optimal linear trend filter using scalar Kalman filtering. Treats price as a noisy measurement of a hidden trend state and recursively computes the optimal estimate. The Kalman gain adapts automatically — no manual threshold tuning required. HOW IT WORKS The filter balances two noise sources: - Process Noise (Q): how fast the true trend can change - Measurement Noise (R): how noisy price observations are The Kalman gain adjusts every bar: - During clean trends: gain tightens, tracks price closely - During choppy markets: gain loosens, smooths out noise Innovation magnitude (prediction error) serves as a built-in regime classifier: small innovations = trending, large innovations = noisy/ranging. FEATURES - Adaptive Kalman gain — zero manual tuning - Innovation-based regime coloring (green = trend, red = noise) - Confidence bands derived from estimation uncertainty (P matrix) - Signal dots at trend direction changes - Works on all timeframes and instruments SETTINGS Process Noise Q: Controls trend tracking speed. Higher = faster adaptation. Default 0.01. Measurement Noise R: Controls price smoothing. Higher = smoother output. Default 1.0. Show Bands: Toggle confidence bands on/off. Based on: R.E. Kalman (1960), "A New Approach to Linear Filtering and Prediction Problems", Journal of Basic Engineering.
Chỉ báo Pine Script®
của FibonacciFlux
Adaptive Regime Filter + Divergence (AER-VN) [KEYALGOS]Adaptive Regime Filter + Divergence (AER-VN) Professional Grade Market Regime Classification with Advanced Divergence Detection Precision trend identification using Adaptive Efficiency Ratio methodology Volatility-normalized thresholds that adjust to real-time market conditions Automatic divergence detection (Regular and Hidden) with visual confirmation lines Four distinct market regimes with color-coded clarity Zero-lag signal generation with confirmation logic OVERVIEW The Adaptive Regime Filter with Volatility Normalization (AER-VN) represents a sophisticated evolution of traditional trend filtering methodologies. This proprietary indicator combines Kaufman's Efficiency Ratio principles with dynamic volatility adaptation to classify market conditions into four actionable regimes: Uptrend, Downtrend, Choppiness, and Consolidation. The integrated Divergence Detection System operates as a secondary analytical layer, identifying momentum exhaustion and trend continuation patterns through comparative analysis of price action versus efficiency metrics. Unlike standard oscillators that measure raw momentum, this system evaluates the quality of price movement, providing earlier and more reliable reversal signals. METHODOLOGY AND TECHNIQUE 1. Adaptive Efficiency Ratio (AER) Core Traditional efficiency ratios utilize static thresholds that fail across varying volatility environments. The AER-VN methodology introduces dynamic threshold calculation that self-adjusts based on current volatility relative to historical norms. Displacement Measurement: Calculates net price movement over the lookback period Path Distance Analysis: Sums absolute bar-to-bar movements to determine movement quality Efficiency Calculation: Ratio of displacement to path distance (0.0 to 1.0 scale) Threshold Adaptation: Baseline efficiency requirements scale proportionally with the ATR ratio When volatility expands (ATR above mean), the system automatically raises the efficiency threshold required to qualify as "trending." This prevents false trend signals during volatile chop. Conversely, during low volatility periods, the threshold contracts to capture subtle trending behavior. 2. Volatility Normalization Engine The Volatility Normalization component creates a relative volatility index by comparing current ATR readings against a rolling historical average. This produces an ATR Ratio that serves as the scaling factor for dynamic threshold calculation. Current ATR: Short-term volatility measurement (default 14 periods) Mean ATR: Long-term volatility baseline (default 50 periods) Adaptive Scaling: Raw threshold = Base Threshold x ATR Ratio Ceiling Protection: Maximum threshold cap prevents mathematically impossible requirements during extreme volatility events 3. Four Regime Classification System Uptrend (Teal): Efficiency exceeds dynamic threshold with positive price displacement. Indicates high-quality upward movement with minimal retracement. Downtrend (Maroon): Efficiency exceeds dynamic threshold with negative price displacement. Indicates sustained selling pressure with directional clarity. Choppiness (Orange): Efficiency below threshold during above-average volatility. Characterized by noisy, directionless movement with large wicks and whipsaws. Consolidation (Gray): Efficiency below threshold during below-average volatility. Represents quiet, range-bound markets with compressed price action. 4. Zero-Lag Divergence Detection The divergence system employs confirmed swing detection to identify pivotal highs and lows without repainting. Once a swing point is confirmed (price violates the extreme), the system evaluates four divergence classifications: Regular Bearish Divergence: Price records higher highs while Efficiency Ratio records lower highs. Indicates trend exhaustion and potential reversal to the downside. Regular Bullish Divergence: Price records lower lows while Efficiency Ratio records higher lows. Indicates selling exhaustion and potential reversal to the upside. Hidden Bearish Divergence: Price records lower highs while Efficiency Ratio records higher highs. Suggests continuation of the current downtrend after a pullback. Hidden Bullish Divergence: Price records higher lows while Efficiency Ratio records lower lows. Suggests continuation of the current uptrend after a retracement. Visual confirmation lines connect the relevant swing points on the indicator panel, allowing traders to verify divergence validity visually. INPUT PARAMETERS AND CONFIGURATION Efficiency Ratio Settings ER Lookback (N) Default: 10 | Range: 2+ The calculation period for efficiency measurement. Shorter values increase sensitivity to recent price action, suitable for scalping lower timeframes. Longer values smooth the oscillator, better for swing trading higher timeframes. Base ER Threshold Default: 0.25 | Range: 0.05 to 0.80 | Step: 0.05 The foundational efficiency level required in normalized volatility conditions. Higher values demand cleaner, more directional movement to trigger trending regime classification. Lower values allow noisier price action to qualify as trending. Max Threshold Cap Default: 0.65 | Range: 0.10 to 0.99 | Step: 0.05 The absolute ceiling for the dynamic threshold. This safety mechanism prevents the threshold from rising to levels mathematically impossible to achieve during extreme volatility expansion. Volatility Normalization Settings ATR Length Default: 14 | Range: 1+ The lookback period for Average True Range calculation. Determines how quickly the system responds to changing volatility conditions. ATR Mean Lookback Default: 50 | Range: 5+ The historical window for establishing the volatility baseline. Longer periods create a smoother volatility reference, while shorter periods adapt more quickly to regime changes in volatility. Divergence Detection Settings Swing Definition Length Default: 10 | Range: 3+ The lookback window for identifying swing highs and lows. Determines the minimum number of bars required to establish a pivot point. Lower values detect micro-swings (more signals, more noise). Higher values detect major swings (fewer signals, higher quality). Visual Display Toggles Show Regular Div Markers: Display circle markers for Regular Bearish and Regular Bullish divergences Show Hidden Div Markers: Display circle markers for Hidden Bearish and Hidden Bullish divergences Line: Regular Bearish: Draw connecting lines between swing highs for Regular Bearish divergences (Red) Line: Regular Bullish: Draw connecting lines between swing lows for Regular Bullish divergences (Lime) Line: Hidden Bearish: Draw connecting lines for Hidden Bearish divergences (Orange) Line: Hidden Bullish: Draw connecting lines for Hidden Bullish divergences (Aqua) Visual Settings Color Price Bars Toggle to apply regime colors directly to price candles/bars on the main chart. Uptrend (Teal), Downtrend (Maroon), Choppiness (Orange), Consolidation (Gray). INTERPRETATION GUIDE Reading the Oscillator The main panel displays three critical elements: Efficiency Ratio Line: The primary oscillator colored by current regime. Values near 1.0 indicate perfect efficiency (strong trend). Values near 0.0 indicate complete inefficiency (chop). Dynamic Threshold: The white crossed line representing the current volatility-adjusted efficiency requirement. When the ER line crosses above this threshold, the regime shifts to trending. Base Threshold Reference: The gray dotted line showing the static baseline (0.25 default) for reference. Divergence Signal Interpretation Red Circle (Regular Bearish): Momentum divergence at highs. Consider reducing long exposure or preparing short entries. Highest probability when appearing near resistance or after extended uptrends. Lime Circle (Regular Bullish): Momentum divergence at lows. Consider reducing short exposure or preparing long entries. Highest probability when appearing near support or after extended downtrends. Orange Circle (Hidden Bearish): Trend continuation signal in downtrends. Pullback likely ending, downtrend resumption probable. Aqua Circle (Hidden Bullish): Trend continuation signal in uptrends. Retracement likely ending, uptrend resumption probable. TRADING APPLICATIONS Strategy 1: Regime-Based Trend Following Enter long positions only when the indicator displays Teal coloring (Uptrend regime) and short positions only during Maroon coloring (Downtrend regime). Exit positions when the regime shifts to Orange or Gray, indicating the trending condition has ended. Best for: Directional traders and trend followers Timeframe: M15 and higher recommended for stability Confluence: Combine with moving average alignment or breakout patterns Strategy 2: Divergence Reversal Trading Monitor for Regular Divergences (Red or Lime circles) as early warning systems. Wait for price confirmation (engulfing candles, pin bars) at the divergence point before entering. Use the connecting lines to visualize the divergence strength. Best for: Counter-trend scalpers and swing traders Timeframe: M5 to H1 depending on swing length settings Confluence: Combine with support/resistance levels and volume analysis Strategy 3: Chop Avoidance and Consolidation Breakout Use the Orange (Choppiness) and Gray (Consolidation) regimes as "No Trade" zones or reduction zones. Wait for a divergence to form within these regimes, then enter when the regime shifts back to trending (Teal or Maroon), capturing the breakout momentum. Best for: Patience-based traders seeking high-probability setups Timeframe: Effective across all timeframes Confluence: Combine with volume expansion on regime change Strategy 4: Hidden Divergence Continuation In established trends (Teal lasting 5+ bars), look for Hidden Bullish Divergences (Aqua) on pullbacks to enter additional long positions. In established downtrends (Maroon), look for Hidden Bearish Divergences (Orange) on rallies to add to shorts. Best for: Position traders adding to winners Timeframe: H1 to Daily for best results Confluence: Combine with Fibonacci retracement levels OPTIMIZATION GUIDELINES For Lower Timeframes (M1 to M5) Reduce ER Lookback to 6-8 for faster response Lower Base ER Threshold to 0.15-0.20 to account for noise Reduce Max Threshold Cap to 0.50-0.55 Shorten ATR Mean Lookback to 20-30 Reduce Swing Definition Length to 6-8 for micro-structure For Higher Timeframes (H1 to Daily) Increase ER Lookback to 14-21 for smoother readings Maintain or increase Base ER Threshold to 0.30+ for quality control Increase ATR Mean Lookback to 50-100 for stable volatility baselines Increase Swing Definition Length to 10-20 for major pivots only ALERTS AND NOTIFICATIONS The indicator includes built-in alert conditions for: Regular Bearish Divergence Detection Regular Bullish Divergence Detection Configure TradingView alerts to trigger on these conditions to monitor markets without constant chart watching. BEST PRACTICES AND RISK MANAGEMENT Always confirm divergence signals with price action patterns (engulfing candles, pin bars, break of structure) rather than entering immediately on marker appearance. Avoid trading divergence signals that occur deep within the Choppiness (Orange) regime without waiting for a regime shift confirmation. Use the regime colors as a position sizing guide: Full size in Teal/Maroon, half size in Gray, flat or minimal in Orange. The indicator excels when combined with support/resistance analysis. Divergences forming at key S/R levels carry significantly higher probability. In ranging markets, decrease the Base ER Threshold to reduce whipsaws. In strongly trending markets, consider increasing it to filter out minor retracements. TECHNICAL NOTES The indicator does not repaint. Swing points require confirmation on the subsequent bar to print, ensuring signals remain fixed after formation. All calculations utilize Pine Script v6 native functions for optimal performance and minimal resource usage. The Volatility Normalization component prevents the common failure mode of static efficiency indicators during periods of expanding volatility. Connecting lines for divergences are managed with automatic cleanup protocols to prevent chart clutter on extended runs. SUPPORT AND UPDATES This indicator is maintained by KeyAlgos. All users receive automatic updates as methodology improvements are implemented. For questions regarding parameter optimization or implementation strategies, utilize the TradingView comments section on this publication. Disclaimer: This indicator is a technical analysis tool designed to assist with market analysis, not a guaranteed profit system. Always practice proper risk management and use stop losses. Past performance of indicator signals does not guarantee future results.
Chỉ báo Pine Script®
của KeyAlgos
2
AG Pro KAMA Efficiency Zones [AGPro Series]AG Pro KAMA Efficiency Zones Overview KAMA stands for Kaufman’s Adaptive Moving Average. AG Pro KAMA Efficiency Zones is built around KAMA not as a simple trend-following line, but as an adaptive market reference for evaluating how efficiently price is moving. Instead of focusing only on direction, the script is designed to classify the quality of directional travel and separate cleaner movement from noisier, lower-clarity conditions. The core idea is straightforward: markets do not move with the same quality all the time. Some phases show relatively efficient directional travel where price stays organized around an adaptive path. Other phases become mixed, unstable, or reversion-prone, where direction weakens and noise becomes more dominant. This script is designed to map those changes visually through adaptive KAMA-based zones, state labels, and a compact panel that summarizes the current condition. This makes the tool structurally different from a basic moving average overlay. The objective is not to present KAMA as a one-line signal source. The objective is to use KAMA as the center of a state engine that helps users distinguish efficient trend phases from transitional or noisy environments. What this script does AG Pro KAMA Efficiency Zones evaluates price behavior around a Kaufman’s Adaptive Moving Average and organizes that behavior into visual market states. It does this by combining adaptive smoothing, slope behavior, distance from KAMA, and persistence around the KAMA path. The result is a chart framework that can help answer questions such as: • Is price moving in an efficient bullish or bearish path? • Is the market entering a mixed transition phase? • Has movement quality deteriorated into a noisier reversion-prone environment? • Is the adaptive path becoming stronger, weaker, or less stable? By turning those questions into zones and state-based chart feedback, the script aims to improve context rather than replace judgment. Unique edge The distinguishing feature of this script is that it does not treat KAMA as a standard moving average. Instead, it uses KAMA as the center of a layered efficiency model. That model focuses on the quality of movement, not just the existence of movement. Many tools emphasize momentum, volatility, volume pressure, or overbought/oversold conditions. This script is designed for a different purpose. It is a movement-quality map. It attempts to show whether price is traveling in a relatively efficient path or whether that path is degrading into a noisier condition where directional clarity may be weaker. This means the script is less about predicting a move and more about classifying the environment in which a move is taking place. How it works The script begins with KAMA, or Kaufman’s Adaptive Moving Average. KAMA is useful because it adapts its responsiveness according to market behavior. In cleaner directional phases it can respond more quickly, while in noisier phases it can become more conservative. That makes it a practical centerline for an efficiency-based state model. On top of KAMA, the script evaluates several components: 1. Efficiency behavior The script measures how directly price is moving relative to its recent path. This helps estimate whether price action is acting efficiently or becoming more erratic. 2. KAMA slope behavior The slope of KAMA is normalized so that directional angle can be evaluated in a more consistent way. Stronger and more persistent slope behavior supports higher-quality trend classifications. 3. Price-to-KAMA relationship Price position around KAMA helps determine whether movement is aligned with the adaptive path or drifting around it without clear structure. 4. Persistence The script also looks at how consistently price remains on one side of KAMA. That persistence can help distinguish a more stable move from a weaker and less durable one. These components are blended into a composite efficiency model that drives the active state and the corresponding visual zone. States and zones The script classifies market behavior into four main states: Efficient Bull Trend This state reflects a comparatively organized bullish environment where price and adaptive slope are aligned in a cleaner upward path. Efficient Bear Trend This state reflects a comparatively organized bearish environment where price and adaptive slope are aligned in a cleaner downward path. Transition This is a mixed condition. Direction may be weakening, changing, or failing to achieve the quality required for an efficient trend classification. Noise / Reversion This state reflects lower movement quality, weaker slope behavior, or a more unstable relationship between price and the adaptive path. The visual zone structure is designed to reinforce those classifications on the chart. Instead of using only one line, the script builds layered KAMA-centered bands so the user can read not only direction, but also how structured or fragile the current condition may be. How to read the chart The KAMA line is the adaptive spine of the script. The outer and inner bands represent zone structure around that adaptive path. In stronger trend states, the script increases the visual emphasis of the KAMA path and its supporting zone layers. In weaker or more mixed conditions, the script softens those visuals and allows the chart to communicate reduced clarity. State labels appear when the script confirms a meaningful shift in condition. These labels are intended to highlight a change in market state, not to promise a trade outcome. The on-chart panel summarizes the active reading using fields such as State, Efficiency, Score Band, Adaptive Bias, Active Zone, and Stability. This gives the user a compact interpretation layer without requiring every decision to be made directly from raw chart inspection. Key inputs KAMA Efficiency Length Controls the lookback used in the KAMA efficiency logic. Lower values react faster. Higher values smooth more noise. KAMA Fast Response and KAMA Slow Response Define the adaptive responsiveness range of the KAMA engine. ATR Length Used to normalize slope and distance so the tool behaves more consistently across different symbols and volatility conditions. KAMA Slope Lookback Controls how the script measures directional slope over time. Persistence Length Influences how much consistency price must show around KAMA before a move is treated as more structured. Efficient Trend Threshold and Noise Threshold These thresholds help determine when the model classifies a move as higher quality or lower quality. Zone Band ATR Width Adjusts the width of the adaptive visual zone. State Hold Bars Helps reduce rapid state flipping by requiring a condition to persist before the active state changes. Panel Font Size and Label Size Allow visual customization for different chart layouts and monitor sizes. Alerts The script includes state-oriented alerts intended to notify the user when market condition changes. These are designed around state transitions and movement-quality shifts rather than promotional “buy now” style messaging. Examples include bullish and bearish efficiency shifts, transition detection, noise-zone detection, efficiency recovery, efficiency breakdown, and trend strengthening. Alerts should be interpreted as contextual information. They are intended to support review and analysis, not to function as a standalone decision system. What this script is not This script is not a guarantee engine. It does not predict future price with certainty. It does not eliminate risk. It is not a substitute for broader market structure analysis, execution planning, or risk management. It should not be treated as a self-sufficient entry/exit system without additional confirmation and user judgment. Limitations and transparency All adaptive models are sensitive to parameter choices. Changing responsiveness, thresholds, smoothing, or persistence settings can materially affect the way states appear on the chart. Because the script is state-based, some shifts will naturally occur after the earliest turning point in price. That is part of the tradeoff involved in using confirmation and persistence to reduce noise. In highly erratic or news-driven conditions, classification can also become less stable. During those periods, transition or noise-oriented readings may occur more often, and users should interpret the visual output in that context. The script is best viewed as an analytical framework for movement quality and adaptive context, not as a promise of directional success. Practical use cases Users may find the script useful for: • separating cleaner trend phases from mixed or unstable phases • filtering chart environments before applying another workflow • evaluating whether direction is gaining or losing efficiency • adding adaptive context to discretionary analysis • comparing how different symbols behave around a KAMA-centered efficiency structure Risk disclosure This script is for analytical and educational use. It does not provide financial advice, investment advice, or guaranteed outcomes. Market conditions can change quickly, and any indicator can produce false, delayed, or incomplete signals. Users remain responsible for their own decisions, validation process, and risk management. In short, AG Pro KAMA Efficiency Zones is designed to help read the quality of movement, not just the direction of movement. It uses KAMA as an adaptive reference point and converts that reference into a structured zone and state model so users can assess whether price behavior appears efficient, transitional, or noisy.
Chỉ báo Pine Script®
của AGProLabs
1
ADXVMA Multi-TF Overlay & Alerts [HYPR-run]DESCRIPTION: ADXVMA across three lookback periods on one chart. A moving average that uses the ADX (Average Directional Index) as its smoothing factor; fast in trends, flat in chop. Price crossing above or below a selected ADXVMA fires a webhook-ready alert for automated execution. Based on Linnsoft's ADXvma implementation, combining Chande's Variable Moving Average (VIDYA) with Wilder's ADX as the volatility measure. When ADX is high (strong trend), the MA tracks price closely. When ADX is low (choppy), the MA barely moves. This makes it naturally adaptive without manual adjustment. DISCOVERING EDGE Adaptive MAs are popular (KAMA, VIDYA, DEMA), but most still treat every directional change as a trend signal, producing false signals. This indicator adds a fuzzy factor dead zone that creates a third state, "fuzzy flat" that must exceed a noise threshold before registering a directional change. We found the fuzzy flat signal to be a powerful signal for confirming consolidation within a trend on shorter look back periods and with the longer period for identifying ranging distribution/accumulation regimes. Fuzzy ADXVMA vs ADXVMA The fuzzy dead zone forces a consolidation state (yellow flat) where a pivot or trend change would present otherwise. When the MA finally turns green or red, it exceeds the noise floor and considered a more reliable directional commitment, not a minor fluctuation. - Flat duration before the cross determines signal quality; XO after 15+ bars flat = base resolved (high conviction), XO after 3 bars flat = noise (low conviction). - 7-tier regime gradient (D Trend at score 5 down to Potential Chop at 0) shows the trend proving itself bar by bar across multiple lookbacks. - Two alert systems with multi-layer filtering (not vanilla crossovers). Regime-confirmed fires only at early pivots. Volatility-confirmed fires only when bar participation validates the MA shift. FEATURES - Three lookbacks: short, long, weekly - Fuzzy flat detection (dead zone prevents false trend changes in chop) - Optional ATR volatility scaling (shorter period in high-vol regimes) - Dashboard with 7-tier regime gradient and event badge - Two alert systems with multi-layer filtering (not vanilla crossovers) - Regime-confirmed: price vs ADXVMA, only at early pivots (score 1-2) - Volatility-confirmed: ADXVMA momentum shift + ATR bar expansion - Select which lookback triggers regime-confirmed alerts - Color-coded: green (up), red (down), yellow (flat) - Dashboard dark/light theme toggle for any chart background HOW IT WORKS ADX measures trend strength on a 0-1 scale and feeds it directly into the MA smoothing factor. High ADX = MA tracks price. Low ADX = MA holds still. The fuzzy factor adds a dead zone so tiny movements register as flat instead of false trend changes. Three simultaneous lookbacks give you short-term, medium-term, and weekly context without switching charts. DASHBOARD Regime state at a glance. The header row shows a badge that flags conflicting events; the second row shows the current regime label with a 7-tier color gradient; the third row shows direction pivot events. ALERTS Two independent alert systems, both multi-layer filtered. Regime-confirmed: the regime pivot is the signal; price crossing the selected ADXVMA is just the trigger. Only fires at early pivots (score 1-2), ignoring mid-trend crosses entirely. Volatility-confirmed: the ATR bar expansion is the signal; the ADXVMA momentum shift is the trigger. Only fires when the bar shows real participation (high/low extends beyond open +/- ATR), ignoring low-range bars. Both fire JSON payloads; works with any webhook receiver. CREDITS ADXVMA: Linnsoft ADX: J. Welles Wilder (1978) VIDYA: Tushar S. Chande, TASC March 1992
Chỉ báo Pine Script®
của HYPR-run
Adaptive Volatility Bands [AVB]Adaptive Volatility Bands (AVB) is a volatility-aware trend-following overlay indicator built on the Kaufman Adaptive Moving Average (KAMA) and dynamically adjusted Bollinger-style bands. **Mathematical Foundation:** The core of AVB is the Kaufman Efficiency Ratio (ER), which measures the ratio of directional price movement to total price movement over a lookback period. An ER near 1.0 indicates a strong trend with minimal noise; an ER near 0.0 indicates choppy, range-bound conditions. The KAMA uses this ratio to automatically adjust its smoothing constant — responding quickly during trends and slowly during consolidation. The bands around the KAMA are not static standard deviations. Instead, they use an adaptive standard deviation that widens when the Efficiency Ratio is low (noisy markets) and tightens when ER is high (trending markets). This creates bands that contract during consolidation (squeeze) and expand during breakouts. **Signal Logic:** Buy signals are generated when price touches the lower band with RSI in oversold territory during an uptrend, or when a squeeze releases with price above the KAMA. Sell signals fire at the upper band with RSI overbought during a downtrend, or at squeeze release below KAMA. Volume confirmation is applied to filter low-conviction signals. **Features:** - Kaufman Adaptive Moving Average with adjustable fast/slow smoothing periods - Adaptive volatility bands that respond to market efficiency - Volatility squeeze detection with bar coloring - RSI and volume filters for signal confirmation - ATR-based stop-loss and take-profit levels - Real-time dashboard showing efficiency ratio, RSI, volatility regime, and trend direction - Fully customizable colors and parameters **Use Cases:** Suitable for forex, crypto, commodities, and equities across all timeframes. Works well on 15-minute to daily charts.
Chỉ báo Pine Script®
của Jamtrader2020
Adaptive Trend ChannelAdaptive Trend Channel is designed to find the most reliable short-term and long-term trend channels automatically, instead of forcing the user to work with one arbitrary lookback length. The script scans a broad range of candidate periods, builds a regression-based channel for each one, and then compares them through a multi-factor selection process. The goal is not just to find a channel that looks clean, but one that is statistically solid and structurally meaningful. To do that, the indicator favors channels with strong linearity, efficient trend behavior, sufficient directional strength, good price containment inside the bands, controlled width, and stable quality across nearby lengths. This helps avoid weak or accidental fits and gives priority to channels that are more robust in practice. For best results, it is strongly recommended to use a logarithmic chart and to enable the option "Enable for logarithmic price scale" in the indicator settings. This is especially important on assets with large percentage moves over time, because the channel geometry then reflects percentage-based price movement more accurately. Color is also important and very simple to read: if a very robust channel is found, it is displayed in blue by default. This means the selected channel passed the eligibility filters and qualified as a strong structure. If no channel is robust enough, the script can still display the best available candidate, but it will appear in gray by default. OVERVIEW Adaptive Trend Channel helps identify the best short-term and long-term trend channels without manually testing many different lengths. Instead of relying on fixed settings, it adapts to the market structure by selecting the channels that best balance fit, strength, consistency, and usability. The indicator can display: - the best short-term channel - the best long-term channel - an optional midline - an optional data table with channel diagnostics HOW IT WORKS For each tested lookback period, the script builds a regression-based trend channel and measures its quality. Two selection modes are available: 1. Pearson r This mode focuses mainly on linear fit quality. 2. Robust Composite This mode uses a broader decision framework and combines several factors to favor channels that are not only well fitted, but also more reliable as usable trend structures. In Robust Composite mode, the selection can include: - Pearson correlation - trend efficiency - ADX trend strength - price containment inside the channel - channel width control - local stability across neighboring tested lengths A channel is considered eligible only if it passes the minimum filters defined by the user, such as: - minimum absolute Pearson r - minimum ADX - minimum containment ratio - maximum allowed channel width If at least one eligible channel is found, the strongest one is selected and displayed in blue by default. If none qualifies, the script still displays the best available fallback channel, but in gray by default. WHY THIS APPROACH A fixed-length channel can work well in one market condition and fail badly in another. This script addresses that problem by testing multiple candidate lengths and ranking them with a more complete selection logic. The method is designed to reduce three common issues: - choosing an arbitrary lookback period - overvaluing channels that only look good visually - accepting channels that fit price poorly or are too unstable By combining fit quality, structure, strength, containment, and stability, the indicator aims to produce channels that are more trustworthy and easier to interpret. HOW TO READ IT - The short-term channel helps track the active market structure. - The long-term channel helps frame the broader trend. - Blue by default means a robust eligible channel was found. - Gray by default means the displayed channel is the best available one, but it did not pass the eligibility filters. - The position of price inside the channel helps show whether price is near the upper band, lower band, or midline. FEATURES - Automatic search for the best short-term and long-term channels - Adaptive selection across multiple lookback lengths - Robust eligibility filtering - Blue default color for robust eligible channels - Gray default color for fallback non-eligible channels - Support for linear and logarithmic mode - Optional midline display - Optional table with channel metrics - Two detection methods: Pearson r or Robust Composite TABLE METRICS Depending on your settings, the table can display: - best length - selection metric - stability - trend efficiency - Pearson r - ADX - annualized channel return - annualized channel price return MAIN INPUTS - Show Best Short-Term Channel - Show Best Long-Term Channel - Enable for logarithmic price scale - Display Deviation Multiplier - Best Channel Detection - minimum eligibility filters for Pearson r, ADX, containment, and width - optional table settings NOTES - For best interpretation, use logarithmic mode on a logarithmic chart. - Blue by default means the channel passed the eligibility filters and was considered robust. - Gray by default means the script is showing the best fallback channel, but it is not eligible. - Annualized return metrics are intended for daily, weekly, and monthly timeframes. Adaptive Trend Channel is built for traders who want a more objective, adaptive, and robust way to identify high-quality trend channels.
Chỉ báo Pine Script®
của Julien_Exe
6
AI Neural Trend Predictor [identityKa]The AI Neural Trend Predictor is a professional-grade, zero-lag trend tracking system designed to keep traders in massive moves while aggressively filtering out market noise. Traditional moving averages suffer from two fatal flaws: they either lag heavily behind the price, or they whipsaw the trader out of positions during minor pullbacks. This script solves both issues by combining a zero-lag mathematical smoothing algorithm with a dynamic volatility shield. Core Mechanics & Detection Zero-Lag Base Engine: The core of the algorithm utilizes a highly responsive, smoothed proxy to track the live price instantly, eliminating the delayed entry problem found in SMA or EMA based indicators. Volatility Shield (Noise Filter): Instead of flipping signals the moment price crosses the baseline, the engine projects a dynamic ATR-based shield around the trend. During a bullish run, minor price drops will simply compress into the shield without triggering a premature SELL signal. The trend only flips when the institutional order flow breaks through the true volatility threshold. Clear BUY / SELL Labels: The engine prints highly visible, definitive BUY (Green) or SELL (Red) labels directly on the chart, taking the guesswork out of your entries. HUD Dashboard & AI Logic The strictly positioned on-chart intelligence panel evaluates the live market state: Dangerous (Orange): Displayed actively whenever the internal volatility ratio drops below the algorithmic threshold, indicating a Choppy or Ranging market. This warns the trader to avoid taking new positions until momentum returns. LONG / SHORT: The engine generates a clear directional bias when the market shifts to a "TRENDING" state and the volatility shield remains unbreached in the direction of the trend. How to Use It This tool is built for capturing massive swings. When an AI BUY label appears, you ride the trend until the opposing SELL label is printed. Do not panic-sell during minor red candles (pullbacks); trust the Volatility Shield to keep you in the trade. For optimal results, ignore signals generated while the dashboard reads "Dangerous."
Chỉ báo Pine Script®
của IdentityKa
Omni-Flow Consensus [LuxAlgo]The Omni-Flow Consensus indicator is a high-performance momentum and liquidity oscillator designed to visualize the directional pressure behind price movements through volume-weighted aggression and adaptive smoothing. It provides a comprehensive view of market regimes, identifying high-conviction capital injections while filtering out low-probability market noise. 🔶 USAGE The indicator is designed to be used as a primary trend-confirmation tool. Unlike standard oscillators that only track price, Omni-Flow incorporates volume and candle body relativity to determine if a move has "weight" behind it. 🔹 Momentum Regimes Bullish Flow (Cyan): Occurs when the main flow line is above zero and expanding toward the upper bands. This indicates aggressive buying pressure. Bearish Flow (Red): Occurs when the main flow line is below zero and expanding toward the lower bands. This indicates aggressive selling pressure. Accumulation/Neutral (Gray): When the flow line stays within the "Zero Zone" (±10), the market is in a contraction phase. Users should exercise caution as this is often a "no-trade" zone. 🔹 Impulse Injections The indicator plots small diamond shapes within the oscillator pane. These represent "Flow Injections"—moments where momentum has been confirmed by the Signal Strictness logic and has broken out of the neutral threshold. These are high-probability entry or trend-continuation points. 🔹 Gradient Candle Coloring The script features a cinematic gradient coloring system for price bars. Instead of binary "Up/Down" colors, the candles transition smoothly between Bullish, Neutral, and Bearish states based on the flow intensity. This allows traders to visually "feel" the momentum fading or building before a crossover actually occurs. 🔶 HOW TO USE Traders can utilize the Omni-Flow Consensus to filter entries and manage trend expectations: Trend Confirmation: Look for the main flow line to cross the Zero Axis. A cross above zero suggests a shift toward bullish dominance, while a cross below suggests bearish dominance. Identifying Injections: Pay attention to the Diamond symbols. These appear when the flow line crosses its signal line and maintains its direction for a set number of bars (defined by Strictness). These are often the start of volatile "expansion" phases. Volatility Monitoring: When the flow line remains flat near the zero line and the dashboard displays "ACCUMULATION," it indicates a lack of directional conviction. Traders may use this as a signal to avoid trend-following strategies until an injection occurs. Exhaustion Signals: When the flow line enters the glow bands (±70 to ±90), the market is in an extreme state. While trends can persist here, a crossover of the signal line within these bands often precedes a mean reversion or deep pullback. 🔶 DETAILS 🔹 Smart-Flow Engine To ensure the tool works across all assets (including Forex, which lacks centralized volume), the script uses a "Smart-Flow" proxy. If volume data is available, it calculates (Close - Open) / (High - Low) * Volume. If volume is missing, it automatically swaps to a Volatility Proxy using True Range, ensuring consistent behavior on any chart. 🔹 Adaptive Spectral Filter (ASF) The main flow line uses an adaptive alpha-based smoothing technique. This allows the indicator to remain reactive during high-volatility spikes (reducing lag) while smoothing out "whipsaws" during sideways consolidation. 🔹 Stochastic Normalization The values are mapped to a fixed -100 to +100 range using a double-lookback window. This ensures the oscillator utilizes the full vertical space of the pane, making Overbought and Oversold levels much easier to identify compared to standard Z-score oscillators. 🔶 SETTINGS 🔹 Core Logic Flow Sensitivity: Adjusts the primary lookback for the flow calculation. Higher values result in a slower, more macro view. Spectral Smoothing: Controls the responsiveness of the Adaptive Spectral Filter. Signal Boost: A non-linear multiplier that "magnifies" movements near the zero line to make crossings more distinct. 🔹 Signal Filtering Smooth Signals: When enabled, uses the Strictness and Threshold logic to remove false "whipsaw" signals. Signal Strictness: Defines how many bars the flow must maintain its position to confirm an "Impulse." Momentum Threshold: The level the flow must break (e.g., ±15) before a signal is considered valid. 🔹 Aesthetics Gradient Candle Coloring: Toggles the smooth color transitions on the price chart. Glow Intensity: Adjusts the visual brightness of the "Momentum Fill" between the main and signal lines. Flow Dashboard: Enables a real-time HUD showing the current Regime, Flow Intensity %, and Signal Status.
Chỉ báo Pine Script®
của LuxAlgo
2
Regime-Adaptive kNN Breakouts + Kalman Predictor [TechnicalZen]Regime-Adaptive kNN Breakout Classifier + Kalman Price Predictor Why This Indicator Exists Most breakout indicators treat every compression pattern equally. In reality, a volatility contraction forming during a high-ADX trending environment with surging volume behaves very differently from the same pattern in a choppy, low-volume consolidation. This indicator addresses that gap by combining three distinct analytical engines: Multi-Period Compression Detection — Scans across multiple bar periods to find the tightest range relative to recent history, identifying genuine volatility contraction zones where expansion is statistically likely. Regime-Adaptive kNN Classification — A machine learning gate that evaluates the market regime surrounding each compression zone using Kalman-filtered features. Only setups with sufficient similarity to historically successful breakouts are allowed through. Kalman Price Predictor — A state-space estimator tracking price position and velocity, enabling forward projection with a widening uncertainty cone. The result is an indicator that learns which market conditions produce successful breakouts and provides a probabilistic price forecast — not just pattern detection. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ HOW IT WORKS 1. Multi-Period Compression Detection The engine evaluates bar ranges across 2 to 20 periods, computing each period's range (highest high minus lowest low) and comparing it against the minimum range observed within an adaptive lookback window. When the current range is tighter than any historical range in the window, a compression zone is identified. The smallest qualifying period is selected — representing the most extreme volatility contraction. An optional Inside Bar filter adds a complementary signal when the current bar's range is entirely contained within the prior bar. 2. ADX-Adaptive Lookback Window The comparison window dynamically adjusts based on trend strength: High ADX (strong trend) — shorter lookback, more responsive to compression during momentum phases Low ADX (ranging market) — longer lookback, requiring more extreme contraction before triggering This prevents the indicator from being too sensitive in trending markets or too sluggish in ranging conditions. 3. Kalman-Filtered Feature Space Four market regime features are computed on every bar and smoothed through independent Kalman filters using a position + velocity state-space model. The Kalman filter reduces noise while tracking each feature's rate of change — achieving smoothing without the lag penalty of traditional moving averages. The kNN classifier operates entirely on these Kalman-filtered features: Relative Volume — Volume / SMA(Volume, 100) — captures participation surge or drought, Kalman-smoothed to filter out single-bar volume spikes Relative ATR — ATR(14) / SMA(ATR, 100) — captures volatility expansion vs contraction regime, Kalman-smoothed for stable regime identification ADX Normalized — ADX / 50 — measures trend strength (direction-agnostic), Kalman-smoothed to track trend momentum Distance from MA — (Close - Trend MA) / ATR — price position relative to trend, Kalman-smoothed to reduce whipsaw noise By filtering the feature space through the Kalman estimator before classification, the kNN operates on cleaner, denoised regime signals rather than raw noisy measurements. This is the critical link between the Kalman filter and the kNN — the classifier's accuracy depends on the quality of its input features. 4. kNN Breakout Classification When a compression zone triggers a breakout, the classifier: Constructs a feature vector from the four Kalman-filtered regime features Scans the history buffer using Manhattan distance to find similar past regime conditions Selects the k-nearest resolved neighbors — only TP (take-profit) and SL (stop-loss) outcomes vote; pending and time exits are excluded entirely Computes a distance-weighted classification score where closer neighbors have proportionally more influence Compares the score against the user-defined confidence threshold If the score falls below the threshold, the setup is silently skipped. The classifier has learned which combinations of volume regime, volatility regime, trend strength, and price position tend to produce winning breakouts. Key design choices: Adaptive k — k = floor(sqrt(resolved outcomes)), clamped between user-defined min/max. The number of neighbors consulted grows naturally as the classifier accumulates experience, preventing overfitting to sparse early data. Warmup phase — During the first N resolved outcomes, all setups pass through to build the training set. The classifier only begins filtering after accumulating sufficient data. Feedback loop — Every exit writes its outcome back to the history buffer. TP exits score 1.0, SL exits score 0.0. The classifier genuinely learns from the specific chart and timeframe it is applied to. Distance-weighted voting — Prevents outlier neighbors from distorting the classification. A very close TP neighbor outweighs several distant SL neighbors, producing more nuanced probability estimates. 5. Kalman Price Predictor A fifth Kalman filter runs on price itself, maintaining three estimates simultaneously: Filtered position — optimal smoothed price estimate Velocity — estimated rate of price change per bar Covariance matrix — estimation uncertainty and cross-correlations The velocity component enables forward projection: Predicted Price = Filtered Position + Velocity x Projection Bars . The uncertainty cone is scaled by ATR and widens proportionally to the square root of the projection horizon — reflecting the theoretical uncertainty growth of price over time. Projection trail: The last 5 projections are displayed with graduated transparency (50% to 90%), creating a visual history of how the forecast has evolved. A consistent, parallel trail suggests strong directional conviction; a diverging or oscillating trail signals uncertainty. 6. Trend-Aware Exit System The exit system uses four complementary mechanisms, each feeding outcomes back to the kNN: Take Profit — R-multiple target (default 2R, where R = compression zone range). Scored as 1.0 in kNN feedback. Stop Loss — Opposite side of compression zone, optionally requiring price to also be wrong-side of the Trend MA. This trend-aware condition reduces whipsaw stops in strong trends. Scored as 0.0 in kNN feedback. Trailing Stop — Activates after 1R profit, trails by ATR x multiplier. Dynamic protection that locks in gains. Time Exit — Maximum bars in trade before forced exit. Scored as 0.5 (neutral) — neither rewarding nor penalizing the kNN for inconclusive setups. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ VISUAL GUIDE Chart Elements Compression boxes — Colored zones marking detected volatility contraction (green = bullish breakout, red = bearish) Extended levels — Dotted lines projecting the high and low of each compression zone forward Entry labels — Direction and kNN confidence percentage (e.g., "Long 72.5%") Exit labels — TP / SL / T markers with R-multiple detail in tooltip Projection line — Dashed line extending forward from Kalman-filtered price Uncertainty cone — ATR-scaled filled area widening into the future Projection trail — 5 fading historical projections showing forecast evolution Kalman price line — Optional smoothed price curve (off by default) Dashboard (bottom-right) Win Rate — Percentage of resolved trades hitting TP (tinted green or red) Trades — Win / Loss count Mode — Distance-weighted classification Phase — Warmup (building data) or Active (filtering enabled) k — Current adaptive k value Score — Latest kNN confidence score History — Buffer fill level (e.g., 45/60) Projection — Predicted price with directional arrow ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ SETTINGS GUIDE Detection Enable Inside Bar (default: On) — Include Inside Bar patterns alongside compression detection Adaptive kNN Enable kNN Filter (default: On) — Toggle the ML classification gate k Min / k Max (default: 2 / 10) — Bounds for adaptive k. Auto-scales with sqrt of resolved outcomes Confidence Threshold (default: 0.25) — Minimum kNN score to accept a setup. Lower values are more permissive; higher values are more selective Min Resolved to Activate (default: 15) — TP/SL outcomes needed before the classifier begins filtering History Buffer Size (default: 60) — Maximum stored breakout patterns for comparison Kalman Filter Process Noise Q (default: 0.01) — Controls how much the filter expects the underlying signal to change between bars. Higher values make the filter more responsive but noisier Measurement Noise R (default: 0.10) — Controls how much the filter distrusts each new measurement. Higher values produce smoother output with more lag Show Price Projection (default: On) — Display the forward projection line and uncertainty cone Projection Bars (default: 10) — How far forward to project price Projection Color (default: Aqua) — Color for all projection elements Show Uncertainty Cone (default: On) — Display the ATR-scaled confidence band Cone Width (default: 1.0 ATR) — Width multiplier for the uncertainty cone. Adjustable per instrument Show Kalman Price Line (default: Off) — Display the smoothed Kalman price estimate on chart Trend Filter Enable Trend Filter (default: On) — Restrict breakouts to trend-aligned direction only Trend MA Mode (default: Adaptive) — Static = fixed MA length; Adaptive = MA length scales dynamically with the compression lookback MA Type (default: EMA) — Exponential or Wilder's (RMA) moving average Adaptive Multiplier (default: 2.0) — Lookback x Multiplier = MA length in adaptive mode Static MA Length (default: 200) — Fixed MA length when in static mode Adaptive Look Back Look Back Mode (default: ADX Adaptive) — Static = fixed comparison window; ADX Adaptive = window scales with trend strength ADX Length (default: 14) — Period for ADX calculation ADX Low / High (default: 10 / 35) — ADX range mapped to lookback bounds. Higher ADX compresses the lookback LB Min / LB Max (default: 20 / 120) — Minimum and maximum lookback window size Exits TP (R-multiple target) (default: On) — Take-profit at R-multiple of compression zone range SL (opposite side) (default: On) — Stop-loss at opposite boundary of compression zone Target R (default: 2.0) — Take-profit distance as multiple of range Trend-Aware SL (default: On) — SL only triggers when price is also wrong-side of Trend MA Trailing Stop (default: On) — Trails by ATR x multiplier after 1R profit Trail ATR Multiplier (default: 1.5) — Trail distance = ATR(14) x this value Time Exit (default: On, 50 bars) — Force exit after maximum bars in trade Visual Settings Bull / Bear / Time colors — Customizable directional colors Box Fill / Border Transparency — Compression zone box appearance Extend Levels (default: 50 bars) — Forward projection distance for compression zone levels Level Width / Style — Line appearance for projected levels Max Patterns Kept (default: 120) — Maximum drawing objects maintained on chart ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ THE KALMAN-kNN PIPELINE The two ML components are not independent — they form a pipeline: Kalman filters denoise the four regime features on every bar, producing clean estimates of volume regime, volatility regime, trend strength, and price position kNN classifier operates on these Kalman-filtered features, comparing the current denoised regime against historically successful and unsuccessful breakout conditions Kalman price filter independently tracks price dynamics, projecting the estimated trajectory forward with quantified uncertainty The classifier's accuracy fundamentally depends on the quality of its input features. By feeding Kalman-filtered signals rather than raw measurements, the kNN compares regime states rather than noisy observations — producing more stable and meaningful similarity assessments. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ CREDITS AND ACKNOWLEDGMENTS This indicator builds upon concepts from two published works: Smart NR2–NR20 and Inside Bar by Zeiierman — multi-period compression detection, adaptive lookback via ADX, and breakout trigger architecture kNN Market Architecture by LuxAlgo — application of k-nearest neighbors classification to filter market signals using relative volatility and volume features Original contributions in this indicator: Kalman filter state-space estimation for feature smoothing (position + velocity model with full covariance tracking) Kalman-to-kNN pipeline — classifier operates on denoised regime features, not raw measurements Regime-adaptive kNN classification with distance-weighted voting on resolved outcomes only Real-time feedback loop where exit outcomes update the kNN training data Adaptive k scaling based on accumulated classifier experience Kalman price predictor with forward projection and ATR-scaled uncertainty cone Graduated projection trail showing forecast evolution ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ This indicator is for educational and informational purposes only. It does not constitute financial advice. All investments involve risk, and past performance does not guarantee future results. The kNN classifier learns from historical patterns on the specific chart and timeframe it is applied to — its effectiveness may vary across different instruments and market conditions. Always conduct your own analysis and risk management.
Chỉ báo Pine Script®
của TechnicalZen
Adaptive Statistical Smoother [Pineify]Adaptive Statistical Smoother The Adaptive Statistical Smoother is an overlay trend-following indicator that combines a forward-backward zero-lag EMA approximation with an R-Squared trend filter to produce an adaptive moving average that tightly tracks price during trending markets and deliberately diverges during ranging conditions — solving the core problem of traditional moving averages that generate excessive whipsaw signals in sideways price action. Instead of using a fixed smoothing period or a single-pass EMA, the indicator first constructs a bidirectional (zero-phase-shift) EMA baseline that virtually eliminates the lag inherent in standard exponential averages, then modulates how closely the final adaptive MA follows this baseline based on the real-time R-Squared coefficient of determination. When R-Squared confirms a strong linear trend, the MA converges toward the zero-lag target proportionally to trend strength; when R-Squared indicates a ranging market, the MA actively pushes away from price in the last known trend direction, creating a natural buffer zone that suppresses false crossovers. Dynamic standard-deviation volatility bands and R-Squared-filtered buy/sell signals complete the system, giving traders a statistically grounded, self-adjusting trend tool with built-in noise rejection. Key Features Forward-backward zero-lag EMA approximation — a two-pass EMA computation (forward pass followed by a backward iteration over historical values) that closely approximates a bidirectional filter, virtually eliminating the phase lag that causes standard EMAs to react late to trend changes. R-Squared adaptive trend filter — the Pearson correlation coefficient squared (R²) between price and bar index measures how well a linear trend fits recent data. Values above 0.5 indicate trending conditions; values below indicate ranging. This statistical metric drives the core adaptive behavior of the MA. Dual-regime moving average — during trending markets (R² > 0.5), the adaptive MA blends toward the zero-lag target proportionally to R², tracking price closely. During ranging markets (R² ≤ 0.5), the MA diverges from price in the last known direction, creating a buffer that prevents whipsaw crossovers. Dynamic volatility bands — standard deviation of the source price over the statistical window, scaled by a user-defined multiplier, creates upper and lower bands that automatically expand during volatile periods and contract during quiet ones. R-Squared-filtered buy/sell signals — crossover signals between price and the adaptive MA are only generated when R² exceeds 0.3, ensuring signals fire only when there is statistically meaningful trend strength and suppressing noise during flat markets. Trend-adaptive coloring — the MA line, volatility cloud fill, and bar colors all dynamically switch between bullish and bearish colors based on the current trend state, providing instant visual identification of the prevailing direction. How It Works The indicator follows a multi-stage calculation pipeline that transforms raw price data into an adaptive, statistically filtered trend line: Forward-backward zero-lag baseline: A standard EMA is first computed on the source price. Then a second pass iterates backward over the historical EMA values, applying the same EMA alpha (2 / (smooth + 1)) at each step across the lookback window. This two-pass approach approximates a zero-phase-shift filter — the resulting baseline tracks price turns almost immediately, without the half-period delay of a conventional EMA. This baseline serves as the "target" that the adaptive MA will converge toward when the market is trending. R-Squared trend detection: The Pearson correlation between closing prices and bar indices over the statistical window is squared to produce R². This coefficient of determination measures the proportion of price variance explained by a linear trend. R² near 1.0 means price is moving in a clean, directional manner; R² near 0.0 means price is oscillating without a clear direction. The 0.5 threshold divides the market into "trending" and "ranging" regimes. Adaptive MA computation: In trending mode (R² > 0.5), the adaptive MA is computed as a weighted blend: R² × target + (1 − R²) × previous MA. Stronger trends (higher R²) pull the MA closer to the zero-lag target; weaker trends allow it to lag slightly, providing natural smoothing. In ranging mode (R² ≤ 0.5), the MA moves away from price by the magnitude of the target's recent change, in the direction of the last known trend bias. This deliberate divergence creates separation between price and the MA, preventing the repeated false crossovers that plague fixed-parameter moving averages in choppy markets. Volatility bands and signal generation: Standard deviation bands are added around the adaptive MA to visualize the current volatility regime. Buy and sell signals are generated on price crossovers of the MA, but only when R² exceeds 0.3 — a secondary filter that ensures even the crossover signals carry minimum statistical trend evidence. Trading Ideas and Insights Trend-following entries with lag reduction: The zero-lag baseline allows the adaptive MA to respond to trend initiations significantly faster than a standard EMA of equivalent smoothing. When a BUY signal fires (price crosses above the MA with R² > 0.3), the entry is closer to the actual trend start than what a conventional moving average crossover would provide, improving the risk/reward ratio of trend-following trades. Whipsaw avoidance in ranging markets: The adaptive divergence mechanism during low-R² periods is specifically designed to prevent the most common failure mode of moving average systems — repeated false crossovers during sideways consolidation. Traders can trust that when a signal does fire, the statistical environment supports a directional move. Volatility band breakout confirmation: When price breaks above the upper band or below the lower band while the adaptive MA is already in the corresponding trend state, it confirms a high-volatility directional expansion. These breakouts can be used to add to existing positions or to set trailing stops at the opposite band. R-Squared as a standalone filter: Even without acting on the buy/sell signals, traders can use the implicit R-Squared regime (visible through the MA's behavior — tight tracking vs. divergence) as a filter for other strategies. Apply your existing entry rules only when the MA is tightly tracking price (trending regime), and stand aside when the MA visibly separates from price (ranging regime). Multi-timeframe trend alignment: Apply the indicator on both a higher timeframe (e.g., daily) and a lower timeframe (e.g., 1-hour). Take lower-timeframe BUY signals only when the higher-timeframe adaptive MA is in bullish state, and SELL signals only when the higher-timeframe is bearish. This multi-timeframe alignment leverages the adaptive nature of the indicator across different time horizons. How Multiple Indicators Work Together The Adaptive Statistical Smoother integrates three distinct analytical components into a unified adaptive system, each addressing a specific weakness of traditional moving averages: Forward-backward zero-lag EMA (lag elimination): Standard moving averages inherently lag price by approximately half their lookback period. The bidirectional EMA approximation addresses this by running a second smoothing pass in reverse over historical values, canceling out the phase shift. This gives the adaptive MA a responsive baseline to track during trends — without the noise sensitivity that comes from simply using a very short-period EMA. R-Squared trend filter (regime detection): The R-Squared coefficient provides an objective, statistical answer to the question "is the market trending right now?" This replaces subjective visual assessment or fixed-threshold approaches (like ADX) with a measure rooted in linear regression theory. R² directly controls how the adaptive MA behaves — it is not merely a signal filter but the core adaptive mechanism that switches the MA between trend-tracking and range-diverging modes. Standard deviation volatility bands (context visualization): The bands add a volatility dimension that neither the zero-lag baseline nor the R-Squared filter provides. They show traders the expected range of price movement around the adaptive MA, helping to distinguish between normal retracements within a trend (price stays within bands) and genuine trend reversals (price breaks through bands and crosses the MA). The synergy is structural: zero-lag EMA (responsive baseline) → R-Squared (regime classification) → adaptive blending/divergence (the adaptive MA itself) → volatility bands (context envelope) → R²-filtered crossover signals (actionable entries/exits). The zero-lag baseline ensures the MA has a fast, accurate target to track; R-Squared determines whether to track it or diverge; and the volatility bands provide the visual context for interpreting the MA's position relative to price. Each component compensates for a specific weakness — lag, false signals in ranges, and lack of volatility context — that would undermine the system if any single component were used alone. Unique Aspects Statistical regime switching: Unlike adaptive moving averages that use volatility or momentum to adjust their speed (e.g., KAMA, VIDYA), the Adaptive Statistical Smoother uses R-Squared — a measure of trend linearity — to switch between two fundamentally different behaviors: convergence toward a target during trends and deliberate divergence during ranges. This is a qualitatively different approach that directly addresses the root cause of whipsaw (lack of trend) rather than a symptom (high volatility). Bidirectional EMA approximation in Pine Script: True zero-phase-shift filters require processing the entire dataset in both directions, which is not natively possible in real-time bar-by-bar computation. The forward-backward loop in this indicator approximates this by iterating over historical forward-EMA values within the lookback window, achieving near-zero lag without requiring future data — a practical implementation of signal processing theory within Pine Script's constraints. Directional divergence mechanism: During ranging markets, the adaptive MA does not simply freeze or slow down — it actively moves away from price in the last known trend direction. This creates increasing separation that requires a genuine trend resumption (not just noise) to produce a crossover, providing a self-adjusting buffer proportional to the ranging market's volatility. Dual-threshold R-Squared filtering: The indicator uses two R-Squared thresholds for different purposes: 0.5 for the MA's adaptive regime switch (trending vs. ranging behavior) and 0.3 for signal generation (minimum trend evidence for crossover signals). This layered approach means the MA adapts its behavior at a stricter threshold while still allowing signals in moderately trending conditions, balancing responsiveness with noise rejection. How to Use Add the indicator to your chart. It overlays directly on the price chart, displaying the adaptive MA line, upper and lower volatility bands, and a shaded volatility cloud between the bands. Observe the adaptive MA line (thick colored line). When it is green and tightly tracking price, the market is in a statistically confirmed uptrend. When it is red and tracking price closely, the market is in a confirmed downtrend. When the MA visibly separates from price, the R-Squared filter has detected a ranging market and the MA is in divergence mode. Watch for BUY signals (green "BUY" labels below bars) — these fire when price crosses above the adaptive MA and R-Squared exceeds 0.3, indicating a bullish crossover with minimum statistical trend support. Consider entering long positions or closing short positions. Watch for SELL signals (red "SELL" labels above bars) — these fire when price crosses below the adaptive MA and R-Squared exceeds 0.3, indicating a bearish crossover with trend confirmation. Consider entering short positions or closing long positions. Use the volatility bands (shaded cloud) to gauge the expected price range around the adaptive MA. Price touching the upper band in an uptrend suggests extended momentum; price touching the lower band in a downtrend suggests extended selling pressure. Reversals from band extremes back toward the MA can serve as mean-reversion opportunities within the prevailing trend. Monitor bar colors for a quick visual scan of the current trend state across the chart — green bars indicate bullish trend, red bars indicate bearish trend. Adjust the Statistical Window to match your trading timeframe. Shorter windows (10–15) make the R-Squared filter more responsive to recent price behavior — suitable for intraday or short-term swing trading. Longer windows (25–50) provide a more stable trend assessment — suitable for position trading on daily or weekly charts. Customization Statistical Window (default: 20): The lookback period for both the R-Squared calculation and the standard deviation bands. This is the most impactful parameter. Shorter values make the indicator more responsive — the R-Squared filter reacts faster to regime changes and the volatility bands adjust more quickly. Longer values produce smoother, more stable readings that filter out short-term noise but may delay regime detection. Start with 20 for daily charts and adjust based on your asset's typical trend duration. Forward-Backward Smoothing (default: 10): Controls the EMA period used in the zero-lag approximation. Lower values (5–7) produce a baseline that tracks price very closely, making the adaptive MA highly responsive during trends but potentially more sensitive to noise. Higher values (15–20) produce a smoother baseline with slightly more residual lag but better noise rejection. The interaction between this parameter and the Statistical Window determines the overall character of the indicator. Volatility Multiplier (default: 1.5): Scales the standard deviation bands around the adaptive MA. Higher values (2.0–3.0) produce wider bands that contain more price action — useful for volatile assets or for identifying only extreme deviations. Lower values (0.5–1.0) produce tighter bands that price breaks more frequently — useful for identifying smaller volatility expansions or for more active trading styles. Bullish / Bearish Colors: Fully customizable colors applied to the adaptive MA line, volatility bands, cloud fill, signal labels, and bar coloring. Adjust to match your chart theme or to improve visibility on different background colors. Conclusion The Adaptive Statistical Smoother brings a statistically rigorous approach to trend following by combining a forward-backward zero-lag EMA approximation with an R-Squared-driven adaptive regime filter. The zero-lag baseline eliminates the inherent delay of conventional moving averages, while the R-Squared coefficient provides an objective, real-time assessment of whether the market is trending or ranging. During trends, the adaptive MA converges toward the responsive baseline proportionally to trend strength; during ranges, it deliberately diverges to create a whipsaw-resistant buffer zone. Dynamic volatility bands add a contextual envelope, and dual-threshold R-Squared filtering ensures that buy and sell signals carry minimum statistical trend evidence. Whether used as a standalone trend-following system or as an adaptive trend filter for other strategies, the Adaptive Statistical Smoother provides a self-adjusting framework that adapts its behavior to the current market regime — tracking trends closely when they exist and stepping aside when they do not.
Chỉ báo Pine Script®
của Pineify
Kalman Regime [Jamallo](2025) Intro Kalman Regime filters price through two stages — a Gaussian kernel weighted average followed by an adaptive Kalman filter — to produce a smooth, noise-resistant baseline. ATR is used throughout to make the system volatility-aware. Breakdown The Gaussian kernel pre-smooths price using a bell-shaped weight profile that blends recency bias (recent bars matter more) with center localization (edge bars contribute less). This pre-smoothed value is then passed into an adaptive Kalman filter, which recursively estimates the "true" price state by balancing how much to trust the new measurement versus the prior state — with that balance dynamically scaled by current ATR. The resulting baseline drives regime detection through a two-tier state machine. A strong signal fires when price breaks the ATR envelope and the baseline slope confirms direction. A weaker signal fires when price crosses the baseline and slope exceeds an ATR-scaled threshold. This dual-gate structure reduces whipsaws without adding lag. END A two-stage price filter combining a Gaussian kernel pre-smoother with an adaptive Kalman filter, using ATR envelopes and slope confirmation for regime detection.
Chỉ báo Pine Script®
của Jamallo22
FRAMA Zone Trapper [NHELD]🔰 INSPIRATION: • Wyckoff Method (Spring/Upthrust) - Smart money positioning • John Ehlers (FRAMA) - Fractal adaptive calculations • Market Profile - Previous value areas as magnets 📚 PUBLICATION NOTES: This indicator combines classical Wyckoff psychology with modern adaptive mathematics. The unique "Previous Bar Box / Next Bar Line" separation creates a natural confirmation delay, filtering out false signals and aligning with institutional order flow. ✨ UNIQUE FEATURES: 1. Time-Shifted Box Placement - Box appears on PREVIOUS candle, line on CURRENT 2. Body-Only Detection - Only counts crosses WITHIN candle bodies (ignores wick noise) 3. Adaptive FRAMA Levels - Self-adjusts to market fractal efficiency 4. Touch Block Mechanism - Auto-deactivates after N touches (prevents repainting) 5. Dual Visualization - Lines, Boxes, or Both with intelligent color coding ⚙️ HOW IT WORKS: 1. FRAMA calculates adaptive support/resistance based on market fractal efficiency 2. When price crosses FRAMA WITHIN the candle body, a signal is generated 3. BOX appears on the PREVIOUS candle (marking the "Smart Money Zone") 4. LINE appears at the FRAMA level on the CURRENT candle (the "Trigger Level") 5. Each subsequent touch increments counter until block threshold is reached 6. Blocked elements turn gray and stop expanding 🧠 PSYCHOLOGICAL EDGE: • Box on previous bar = "Where smart money positioned" • Line on current bar = "Level to watch for confirmation" • Break of line = "Smart money move is activating" • Block after N touches = "Level is now exhausted/obvious to everyone"
Chỉ báo Pine Script®
của nheldboyca3b
Adaptive Velocity Oscillator [UAlgo]Adaptive Velocity Oscillator is a momentum and reversal framework built around the rate of change of an Adaptive Moving Average. Instead of using a fixed smoothing engine, the script first creates a Kaufman style adaptive average whose responsiveness changes according to market efficiency, then measures how fast that adaptive baseline is moving from one bar to the next. That velocity becomes the core oscillator. The main idea is straightforward. When the adaptive average starts accelerating upward, the oscillator rises above zero. When the adaptive average starts decelerating or turning lower, the oscillator falls below zero. This gives the user a direct view of directional pressure, but in a way that remains sensitive to changing market conditions because the underlying average itself is adaptive rather than static. To make the oscillator more practical, the script surrounds the velocity with dynamic filter bands derived from the standard deviation of the AMA series. These bands act like a contextual noise threshold. Small fluctuations inside the band are treated as less important, while stronger moves through the band can be interpreted as meaningful directional expansion or reversal activity. The script also supports two signal styles. Standard mode reacts to velocity transitions through the regular filter band. Extreme Reversal mode requires a deeper stretch into an expanded threshold before signaling a reversal style response. Optional price confirmation can then require bullish candle structure for buy signals and bearish candle structure for sell signals. A cooldown filter is added on top so the same type of signal cannot repeat too rapidly. The result is an oscillator that can be used for trend context, reversal spotting, and momentum transition analysis. It is especially useful for traders who want something more adaptive than a traditional moving average crossover or a simple rate of change calculation. 🔹 Features 🔸 Adaptive AMA Core The script uses an adaptive moving average whose smoothing constant changes according to the Efficiency Ratio. When price is moving efficiently in one direction, the average becomes more responsive. When price is noisy and directionless, the average becomes slower and more stable. 🔸 Velocity Based Oscillator The oscillator is not built from price directly. It is built from the bar to bar change of the adaptive average. This means the indicator measures how quickly the smoothed baseline itself is moving, which creates a cleaner momentum signal than raw price change alone. 🔸 Dynamic Filter Bands A statistical filter band is calculated from the standard deviation of the AMA series and then scaled by the user selected gamma value. This creates a noise threshold that expands and contracts with market conditions. 🔸 Standard and Extreme Reversal Modes In Standard mode, signals are generated when velocity crosses back through the regular filter threshold. In Extreme Reversal mode, the script requires a deeper stretch using an expanded band before a signal can trigger. This gives the user a choice between more responsive and more selective behavior. 🔸 Optional Price Confirmation Signals can require candle confirmation. Bullish signals may be restricted to bars where close is above open, and bearish signals may be restricted to bars where close is below open. This can help reduce signals that appear without supportive candle structure. 🔸 Cooldown Protection A built in cooldown logic prevents repeated same side signals from firing too close together. This helps reduce clustering during noisy or oscillatory phases. 🔸 Trend State From Zero Crosses The script also tracks velocity crosses through zero. These zero transitions can be interpreted as broader positive or negative momentum regime shifts. 🔸 Visual Context Through Color and Bands The histogram and line coloring change according to whether velocity is strongly positive, strongly negative, or neutral relative to the filter zone. This makes the oscillator easy to read at a glance. 🔸 Signal Labels and Alerts The indicator places buy and sell labels around qualifying signal bars and includes alert conditions for bullish signals, bearish signals, positive trend shifts, and negative trend shifts. 🔹 Calculations 1) AMA State Container type AMACalculator float value = na float value_prev = na float velocity = 0.0 float filterBand = 0.0 This object stores the internal state of the adaptive average engine. value holds the latest AMA value. value_prev stores the previous AMA value. velocity stores the bar to bar change of that AMA. filterBand stores the active dynamic threshold used later for signal filtering. So before any signal logic begins, the script already has a dedicated structure for the adaptive average, its momentum, and its statistical band. 2) Efficiency Ratio Calculation float change = math.abs(src - src ) float volatility = math.sum(math.abs(src - src ), lengthER) float ER = volatility == 0 ? 0 : change / volatility This is the first major step inside the AMA calculation. The script compares two quantities. change measures the net directional move from the current price back to the price lengthER bars ago. volatility measures the total path traveled during that same interval by summing all absolute bar to bar changes. The Efficiency Ratio is then: ER = change / volatility If price moved smoothly in one direction, net change will be large relative to total movement, and ER will be high. If price moved in a noisy back and forth way, total movement will be large but net change will be smaller, so ER will be low. This ratio tells the AMA how efficiently price has been moving, which directly controls how responsive the adaptive average should become. 3) Building the Adaptive Smoothing Constant float fastest = 2.0 / (fastLen + 1) float slowest = 2.0 / (slowLen + 1) float sc = math.pow(ER * (fastest - slowest) + slowest, 2) This block converts the Efficiency Ratio into a smoothing constant. First, the script computes the EMA style constants for the chosen fast and slow lengths. Then it blends between them using ER. When ER is high, the result moves closer to the fast setting. When ER is low, the result stays closer to the slow setting. Finally, the blended value is squared. This is a classic AMA technique that makes the adaptive response more sensitive to efficiency changes. So the smoothing constant automatically shifts between fast and slow behavior depending on market structure. 4) Updating the Adaptive Moving Average this.value_prev := this.value float prevAma = na(this.value_prev) ? src : this.value_prev this.value := prevAma + sc * (src - prevAma) This is the actual AMA update formula. The script first stores the previous AMA value. If no previous value exists yet, it uses the current source as the starting point. Then it updates the average using: new AMA = previous AMA + smoothing constant × (source minus previous AMA) So the adaptive average moves toward price, but the speed of that movement depends entirely on the previously calculated smoothing constant. When the market is efficient, the average reacts more quickly. When the market is noisy, it reacts more slowly. 5) Computing Velocity this.velocity := this.value - prevAma This single line is the core of the oscillator. Velocity here is simply the difference between the current AMA value and the previous AMA value. If the adaptive average is rising, velocity is positive. If the adaptive average is falling, velocity is negative. If the adaptive average is barely moving, velocity stays close to zero. So the oscillator is not measuring price change directly. It is measuring the momentum of the adaptive baseline itself. 6) Creating the Dynamic Filter Band float amaGlobalSeries = amaObj.value float sigma = ta.stdev(amaGlobalSeries, n) amaObj.filterBand := gamma * sigma After the AMA is calculated, the script builds a dynamic filter threshold from its standard deviation. sigma measures how much the AMA has been varying over the selected period. That value is then scaled by gamma to create the final filter band. So the band expands when the adaptive average becomes more variable and contracts when the average becomes quieter. This creates a context aware threshold that helps separate meaningful momentum movement from smaller background fluctuations. 7) Regular Band and Extreme Band float currentVelocity = amaObj.velocity float currentFilter = amaObj.filterBand float extremeBand = currentFilter * extMulti This block prepares the two signal thresholds used later. currentFilter is the normal band. extremeBand is a larger band created by multiplying the normal band by the user selected extreme multiplier. So the script supports two layers of selectivity: a regular threshold for Standard mode, and a deeper threshold for Extreme Reversal mode. 8) Raw Signal Logic bool rawBullSignal = sigMode == "Standard" ? ta.crossover(currentVelocity, -currentFilter) : ta.crossover(currentVelocity, -extremeBand) bool rawBearSignal = sigMode == "Standard" ? ta.crossunder(currentVelocity, currentFilter) : ta.crossunder(currentVelocity, extremeBand) This is the primary signal engine. In Standard mode: a bullish signal appears when velocity crosses upward through the negative regular filter level. a bearish signal appears when velocity crosses downward through the positive regular filter level. In Extreme Reversal mode: a bullish signal requires velocity to recover upward through the deeper negative extreme band. a bearish signal requires velocity to fall downward through the deeper positive extreme band. The important idea is that signals are not based on zero line crosses alone. They are based on velocity reentering from stretched territory. That makes the logic more reversal oriented than a simple trend flip model. 9) Optional Price Confirmation if reqPriceConf rawBullSignal := rawBullSignal and close > open rawBearSignal := rawBearSignal and close < open This block adds an extra candle structure filter. If price confirmation is enabled: bullish signals are only allowed when the bar closes above its open. bearish signals are only allowed when the bar closes below its open. This can help reduce signals that occur mathematically in the oscillator but do not have supportive price behavior on the actual candle. So the oscillator can be used either in pure indicator form or with a stricter candle aligned confirmation layer. 10) Cooldown Filter Logic method filterSignal(array arr, bool cond, int waitBars) => bool isValid = false if cond int lastSignalBar = arr.size() > 0 ? arr.get(0) : -waitBars - 1 if (bar_index - lastSignalBar) >= waitBars isValid := true arr.unshift(bar_index) if arr.size() > 2 arr.pop() isValid This method prevents signals from firing too frequently. When a new raw signal appears, the script looks at the most recent stored signal bar for that direction. If enough bars have passed since the previous signal, the new one is accepted. Otherwise it is ignored. The accepted signal bar index is then stored at the front of the array. Only a small recent history is kept. So this method acts as a timing gate that stops repetitive same side signals during choppy conditions. 11) Final Signal Construction var array lastBull = array.new() var array lastBear = array.new() bool finalBullSignal = lastBull.filterSignal(rawBullSignal, cooldown) bool finalBearSignal = lastBear.filterSignal(rawBearSignal, cooldown) This is where raw signals become final trade style signals. Bullish signals are passed through the bullish cooldown array. Bearish signals are passed through the bearish cooldown array. That means buy and sell signals are filtered independently. A recent bullish signal only blocks another bullish signal, and a recent bearish signal only blocks another bearish signal. So the script maintains clean directional spacing for both sides. 12) Histogram Coloring Logic color histColor = currentVelocity > currentFilter ? color.new(colorUp, 20) : currentVelocity < -currentFilter ? color.new(colorDn, 20) : currentVelocity > 0 ? color.new(colorUp, 70) : color.new(colorDn, 70) This block assigns visual meaning to oscillator strength. If velocity is above the upper regular filter, the histogram uses a stronger bullish color. If velocity is below the lower regular filter, it uses a stronger bearish color. If velocity is still positive but inside the filter region, it uses a softer bullish color. If velocity is negative but inside the filter region, it uses a softer bearish color. So the color logic tells the user both direction and intensity at the same time. 13) Drawing the Filter Bands plot(currentFilter, "Upper Filter Band", color=color.new(colorDn, 40), linewidth=1, style=plot.style_line) plot(-currentFilter, "Lower Filter Band", color=color.new(colorUp, 40), linewidth=1, style=plot.style_line) plot(extremeBand, "Upper Extreme Band", color=color.new(colorDn, 60), linewidth=1, style=plot.style_line, display=sigMode == "Extreme Reversal" ? display.all : display.none) plot(-extremeBand, "Lower Extreme Band", color=color.new(colorUp, 60), linewidth=1, style=plot.style_line, display=sigMode == "Extreme Reversal" ? display.all : display.none) These plots create the visual threshold system. The regular upper and lower filter bands are always shown. The wider extreme bands are shown only when Extreme Reversal mode is selected. This makes it easy to see whether current velocity is operating inside the neutral zone, beyond the regular band, or at deeper stretch levels. 14) Filling the Neutral Filter Region p_upper = plot(currentFilter, display=display.none) p_lower = plot(-currentFilter, display=display.none) fill(p_upper, p_lower, color=color.new(colorNeu, 95), title="Filter Band Fill") This block shades the area between the upper and lower regular filter bands. The filled zone visually represents the neutral or lower conviction region. When velocity remains inside this zone, momentum is more muted relative to recent adaptive average behavior. So the fill acts as a quick background cue for whether velocity is still inside normal fluctuation territory. 15) Plotting Velocity as Histogram and Line plot(currentVelocity, "AMA Velocity", color=histColor, style=plot.style_columns) plot(currentVelocity, "Velocity Line", color=histColor, linewidth=2) The same velocity value is drawn in two forms. The column plot gives a strong histogram style momentum read. The line plot overlays the same data as a smoother continuous path. Using both together makes the oscillator easier to read because the columns highlight amplitude while the line emphasizes turning points and transitions. 16) Signal Label Placement if finalBullSignal label.new(x=bar_index, y=math.min(0, currentVelocity) - math.abs(sigMode == "Extreme Reversal" ? extremeBand : currentFilter) - (math.abs(currentFilter) * 1.5), text="▲ BUY", color=color.new(color.white, 100), textcolor=colorUp, style=label.style_none, size=size.small) if finalBearSignal label.new(x=bar_index, y=math.max(0, currentVelocity) + math.abs(sigMode == "Extreme Reversal" ? extremeBand : currentFilter) + (math.abs(currentFilter) * 1.5), text="SELL ▼", color=color.new(color.white, 100), textcolor=colorDn, style=label.style_none, size=size.small) These blocks place the signal labels outside the oscillator body rather than directly on top of the bars. For bullish signals, the label is positioned below the relevant lower threshold area. For bearish signals, the label is positioned above the relevant upper threshold area. This helps keep the chart readable and visually separates the signal from the oscillator itself. 17) Alert Conditions alertcondition(finalBullSignal, "Buy Signal", "AMA Velocity Buy Signal") alertcondition(finalBearSignal, "Sell Signal", "AMA Velocity Sell Signal") bool posTrend = ta.crossover(currentVelocity, 0) bool negTrend = ta.crossunder(currentVelocity, 0) alertcondition(posTrend, "Positive Trend", "AMA Velocity crossed above zero") alertcondition(negTrend, "Negative Trend", "AMA Velocity crossed below zero") The script provides four alert types. The first two alert on final buy and sell signals after all filters and cooldown checks are applied. The second two alert when velocity crosses the zero line, which can be interpreted as broader momentum regime shifts. So the indicator supports both reversal style event monitoring and general trend transition monitoring.
Chỉ báo Pine Script®
của UAlgo
Fractal Retracement [Jamallo](2025) Intro FRAMA is a moving average that adapts its speed based on fractal geometry — specifically, the fractal dimension (D) of recent price action. When price is trending strongly (low fractal dimension), it moves fast. When price is choppy/ranging (high fractal dimension), it slows down. This makes it far more responsive than a standard EMA or SMA. Breakdown: The indicator wraps this with a continuous range logic layer: the filtered line = k only moves if price breaks beyond the FRAMA ± ATR-based range, creating a stepped/ratcheting effect that filters out noise. Two sets of bands are plotted around the filtered line, scaled by ATR multiplied by user-defined multipliers (tight at 0.5×, medium at 1.0×). They're smoothed with a short EMA to reduce jitter, and filled with gradient colors for visual clarity. Direction is simply determined by whether k is rising or falling, and colors everything green (uptrend) or pink/red (downtrend). END In short, it's a noise-filtered trend indicator useful for identifying trend direction, dynamic support/resistance , and gauging how far price has retraced from the trend baseline.
Chỉ báo Pine Script®
của Jamallo22
Hodrick-Prescott filter [Jamallo](2025) Intro The Hodrick-Prescott (HP) filter is a tool originally developed by economists to remove short-term noise from data and reveal the underlying long-term trend — think of it like a moving average, but mathematically smarter. Instead of just averaging recent prices, it solves for the smoothest possible trendline that still follows the data. Applied to price, it produces a cleaner baseline than most traditional moving averages. --- However it comes with 3 problems in practical day trading. Problem #1 Standard HP Repaints History (core issue) The standard HP filter is non-causal — the value at any point in time depends on future values CEPR, meaning past signals can change retroactively. How I solved it: Switching to a one-sided implementation fixes this — it only looks at past data, so history never changes once a bar closes. Problem #2 One-Sided HP Hooks at Turns Going one-sided introduces an endpoint artifact — near turning points the trend line briefly curves in the wrong direction before reversing, producing a false signal. How I solved it: Feeding the HP output into a range filter solves this. The line only moves when HP breaks outside a volatility band, so the false hook gets absorbed into a flat zone and never fires a signal. Problem #3 Fixed Band Would Be Too Rigid A fixed band would whipsaw in volatile markets and lag in calm ones. How I solved it: Instead the band is sized dynamically using a percentile of True Range, scaling automatically with current market conditions.
Chỉ báo Pine Script®
của Jamallo22
J - ATR Trend Channel(2022) A trend-following overlay that combines an ATR-based trailing stop with a smoothed price average to identify trend direction and generate signals. The trailing stop dynamically trails price using ATR volatility bands, tightening in trending conditions The smoothed line (a) blends open/close/high/low into a volatility-weighted average, acting as a trend filter Best used on trending markets to ride momentum and spot early reversals.
Chỉ báo Pine Script®
của Jamallo22
Adaptive Quasimodo + Confluence Engine - PhenLabs📊Quasimodo Pattern (QM) Detector - PhenLabs Recognizing reversal patterns like the Quasimodo can be challenging and time-consuming, requiring a keen eye for specific market structures. Missing a key swing point or misinterpreting a retracement can lead to missed opportunities or false signals. This indicator automates the precise identification of Quasimodo patterns, providing clear, actionable signals directly on your chart, so you can focus on execution. 🚨OVERVIEW🚨 The Quasimodo Pattern (QM) Detector is an advanced, non-repainting indicator designed to automatically spot high-probability bullish and bearish Quasimodo reversal patterns. It integrates customizable swing point detection, confluence filters (Higher Timeframe trend and volume confirmation), and precise entry, stop-loss, and take-profit calculations directly from the pattern’s structure. Get automated alerts and a real-time dashboard to enhance your pattern-based trading strategy. 🔷 WHAT IS A QUASIMODO (QM) PATTERN? The Quasimodo pattern is a powerful reversal formation characterized by a specific sequence of price action: Bearish QM: A Higher High (HH) is followed by a Lower Low (LL), then a Higher High (HH), and finally a Lower Low (LL). The pattern suggests a reversal from an uptrend to a downtrend, often at the level of the previous Higher High. Bullish QM: A Lower Low (LL) is followed by a Higher High (HH), then a Lower Low (LL), and finally a Higher High (HH). This suggests a reversal from a downtrend to an uptrend, often at the level of the previous Lower Low. It’s a cousin to the Head & Shoulders pattern but with a distinct structure focused on specific swing points. 🔶 KEY FEATURES Automated QM Detection: Accurately identifies both bullish and bearish Quasimodo patterns as they form on any timeframe. Customizable Swing Points: Define how swing highs and lows are detected, choosing between using wick extremes or candle bodies. Confluence Filters: Incorporates a Higher Timeframe (HTF) Trend filter to confirm pattern alignment using a non-repainting EMA, and a Volume Confirmation filter to validate patterns with significant volume spikes at key structural points. Dynamic Trade Planning: Automatically calculates suggested Entry, Stop Loss, and Take Profit levels based on the detected QM structure and your desired Risk-to-Reward ratio. Non-Repainting Logic: All detected patterns and signals are permanent and do not shift or disappear on subsequent bars. Informative Dashboard: Provides a real-time summary of HTF trend, volume confirmation, and the last detected QM. Visual Clarity: Plots QM lines, potential entry/SL/TP zones, and clear labels for pattern identification. Alerts: Customizable alerts for new QM patterns and when price enters the suggested entry zone. ⚙️ SETTINGS GUIDE Pattern Recognition: Adjust settings like Use Wick As Extremes (for swing points), Swing Lookback (how many bars to scan for swings), Min Head Height/Right Shoulder Depth Factor (magnitude requirements), Strict Higher/Lower High/Low (stricter swing definitions), and Max RSL/RSH Bars (time limit for right shoulder formation). Trade Management: Configure Entry/SL/TP Offset % to fine-tune entry, stop-loss, and take-profit levels as a percentage buffer, and set your Desired Risk:Reward ratio for calculating Take Profit. Confluence Filters: Enable or disable Use HTF Confirmation and Use Volume Confirmation . For HTF, set HTF Timeframe and EMA Length . For Volume, adjust Volume Lookback and Multiplier . Visual Settings: Toggles are available for showing QM lines, entry/SL/TP zones, alerts, and dashboard position. 📈 TRADING WORKFLOW Identify: Wait for a Bullish or Bearish QM pattern to be detected and labeled on your chart. Confirm: Check the dashboard and pattern details. Ensure the HTF trend and Volume confirmation are aligned with the pattern’s direction (e.g., Bullish QM + Bullish HTF). Enter: Wait for price to retest the suggested entry zone. The indicator plots these zones for you. Manage: Use the automatically calculated Stop Loss and Take Profit levels, adjusting them as needed based on market context and your risk management strategy. Alerts: Set up alerts to be notified immediately when a QM pattern is found or when price enters an entry zone. ⚠️ RISK DISCLAIMER Trading involves substantial risk, and past performance is not indicative of future results. The Quasimodo Pattern Detector is a tool to assist in identifying potential trade setups but does not guarantee profitability. Always conduct your own analysis and implement proper risk management. Does it repaint? No. This indicator is coded with non-repainting logic. Once a pattern is confirmed and a signal is generated on a closed bar, it will remain on your chart permanently. The Higher Timeframe trend filter also uses a non-repainting method.
Chỉ báo Pine Script®
của PhenLabs
11
Machine Learning PSAR [BOSWaves]Machine Learning PSAR - Adaptive Parabolic Stop and Reverse with K-Means Regime Detection and KNN Signal Validation Overview Machine Learning PSAR is a regime-aware trend reversal system that tracks directional price movement through an adaptive Parabolic SAR, where acceleration parameters dynamically adjust based on market regime classification and each reversal signal is validated against historically similar setups using a K-Nearest Neighbors scoring model. Instead of relying on fixed acceleration factors or unfiltered SAR flips, trend state, parameter scaling, and signal confidence are determined through K-Means flip-frequency clustering, KNN outcome weighting, and Kalman-filtered output smoothing that maintains visual clarity without sacrificing reversal responsiveness. This creates a SAR system that reflects actual market conditions rather than applying the same parameters regardless of context - tightening in trending environments where acceleration should build quickly, relaxing in choppy conditions where early flips are noise, and scoring every reversal against the historical record so confidence is quantified rather than assumed. Price is therefore evaluated relative to a SAR that adapts to regime dynamics and historically validated reversal patterns rather than conventional fixed-parameter parabolic logic. Conceptual Framework Machine Learning PSAR is founded on the principle that meaningful reversal signals emerge when the SAR acceleration factor is calibrated to current market conditions, and when each flip is cross-referenced against similar historical flips to assess its probability of success. Traditional PSAR implementations use fixed start, increment, and maximum AF values that ignore whether the market is trending or ranging. This framework replaces static acceleration logic with regime-driven parameter adaptation informed by flip frequency clustering, then layers a KNN validation pass on top to score each signal before it is presented. Three core principles guide the design: Acceleration factor behavior should adapt to the detected market regime, becoming more aggressive during trending conditions and more conservative during choppy ones. Every SAR flip should be scored against historically similar setups so confidence is expressed as a quantified probability rather than a binary signal. The displayed SAR line should be smooth enough for clean visual interpretation while remaining responsive enough that reversals are never delayed. This shifts SAR analysis from a fixed-parameter trailing stop into an adaptive, regime-anchored reversal system with integrated signal confidence measurement. Theoretical Foundation The indicator combines classical Parabolic SAR logic, K-Means-inspired regime classification, K-Nearest Neighbors outcome scoring, exponential AF smoothing, and Kalman filter output processing. Flip frequency over a configurable training period provides the feature for regime classification, with centroid distances determining whether the market is trending, neutral, or choppy. KNN validation uses a five-dimensional feature vector at each flip — prior trend duration, bars since last flip, AF at flip, flip frequency, and EP progress — to find the most similar historical flips and weight their outcomes by proximity. The Kalman filter then smooths the final SAR output while snapping to new values instantly on every reversal. Four internal systems operate in tandem: Adaptive PSAR Engine : Computes classical parabolic SAR with optional AF smoothing and minimum bars filter to suppress whipsaw flips. K-Means Regime Classifier : Measures flip frequency relative to its historical range, assigns the current bar to the nearest of three regime centroids, and adjusts AF start, increment, and maximum accordingly. KNN Signal Validator : On each flip, searches historical flips for the k most similar setups by Euclidean distance, computes an inverse-distance-weighted confidence score, and filters low-confidence signals from high-confidence alerts. Kalman Smoothing Layer : Applies a recursive Kalman filter to the SAR output for display, balancing smoothness with responsiveness and resetting on every reversal so flips are never visually delayed. This design allows reversal signals to reflect actual market behavior and historical precedent rather than reacting mechanically to fixed acceleration rules. How It Works Machine Learning PSAR evaluates price through a sequence of regime-aware and historically-validated processes: PSAR Initialization : Classical parabolic SAR begins with base AF start value, tracking EP and advancing the stop in the trend direction. AF Smoothing : Instead of stepping AF in discrete increments, exponential smoothing ramps it gradually toward the target, producing a more fluid SAR trajectory. Minimum Bars Filter : Trend must persist for a configurable minimum number of bars before a flip is allowed, preventing immediate whipsaw reversals. Flip Detection : Price crossing the SAR triggers a raw flip, resetting AF, capturing the new EP, and recording trend duration and context features. Flip Frequency Measurement : Rolling count of flips over the training period, normalized to its historical range, provides the regime classification feature. Regime Assignment : Flip frequency is compared against three percentile-anchored centroids; the nearest centroid determines whether the market is choppy, neutral, or trending. Parameter Adaptation : Regime assignment scales AF start, increment, and maximum — reducing them in choppy conditions to slow the SAR, increasing them in trending conditions to accelerate it. KNN Feature Construction : At each flip, a five-dimensional vector is built from current context and compared against all historical flips of the same direction within the lookback window. Neighbor Scoring : The k closest historical flips by Euclidean distance are retrieved; each is weighted by inverse distance and its five-bar forward outcome determines a weighted success rate. Confidence Assignment : Weighted success rate expressed as a 0–100% confidence score, with flips below the minimum threshold classified as low-confidence. Kalman Filtering : SAR value is passed through a Kalman filter for display smoothing, with process and measurement noise configurable; filter snaps to new SAR position on every flip. Confidence Fill : Fill opacity between SAR and price anchor reflects current confidence score — denser fill indicates higher conviction in the active trend. Together, these elements form a continuously updating reversal framework anchored in regime awareness and historically validated signal quality. Interpretation Machine Learning PSAR should be interpreted as a confidence-weighted trend reversal system with regime-adaptive sensitivity: Bullish State (Blue) : Established when price closes above the SAR after a validated bullish flip, with SAR acting as a dynamic trailing support level below price. Bearish State (Red) : Established when price closes below the SAR after a validated bearish flip, with SAR acting as a dynamic trailing resistance level above price. Confidence Fill : Gradient zone between SAR and price reflects KNN confidence — vivid, dense fill indicates high historical precedent for the current flip; faint fill indicates low confidence. Confidence Score Labels : Percentage label at each flip displays the KNN confidence score. Green (70%+) indicates strong historical backing; orange (50–69%) indicates moderate backing; red (below 50%) indicates low historical support. Regime Labels : Numbers displayed alongside the SAR indicate current market regime — 3 for trending, 2 for neutral, 1 for choppy — reflecting the K-Means classifier output in real time. High-Confidence Flips : Flips meeting or exceeding the minimum confidence threshold trigger alerts and represent the primary actionable signals. Low-Confidence Flips : Flips below the confidence threshold are still displayed but excluded from high-confidence alerts, flagging setups with weak historical precedent. Regime classification, KNN confidence, and Kalman-smoothed SAR position together outweigh any isolated price movement against the stop. Signal Logic & Visual Cues Machine Learning PSAR presents two primary signal categories: High-Confidence Flip : SAR reversal with KNN score at or above the minimum confidence threshold. These represent setups where historically similar conditions produced successful reversals at a statistically meaningful rate and form the basis for alert-driven systematic monitoring. Low-Confidence Flip : SAR reversal with KNN score below the minimum confidence threshold. The signal is displayed for awareness but is not included in high-confidence alert conditions, reflecting limited historical precedent. Regime labels provide continuous market context between flips, allowing real-time awareness of whether the K-Means system is operating in a trending, neutral, or choppy environment. Confidence fill intensity provides a passive, non-disruptive view of trend conviction without requiring active label reading. Alert generation covers high-confidence bullish and bearish flips, separately triggerable 70%+ confidence signals, and regime transition events for systematic monitoring of market state changes. Strategy Integration Machine Learning PSAR fits within adaptive trend-following and signal-quality-filtered reversal approaches: Confidence-Gated Entries : Enter reversals only on high-confidence flips, using the minimum confidence threshold as a quality gate that filters historically weak setups. Regime-Aware Sizing : Increase position sizing during trending regime (label 3) where the K-Means system detects low flip frequency and sustained directional conviction. Choppy Market Avoidance : Reduce or pause activity during choppy regime (label 1) where frequent flips indicate low directional conviction and elevated whipsaw risk. SAR as Stop Placement : Use the Kalman-smoothed SAR as a trailing stop reference — exit longs when price closes below the bullish SAR, exit shorts when price closes above the bearish SAR. Confidence Fill Monitoring : Use fill intensity as a passive conviction gauge — fading fill during an active trend may indicate the next flip is likely to be lower confidence. Multi-Timeframe Regime Alignment : Apply higher-timeframe regime label as a directional filter, entering signals only when the regime aligns across timeframes. Alert-Based Systematic Monitoring : Configure high-confidence and regime-change alerts for systematic notification without requiring active chart monitoring. Technical Implementation Details Core Engine : Classical Parabolic SAR with configurable base AF start, increment, and maximum, optional exponential AF smoothing, and minimum bars flip filter Regime Model : Flip frequency normalized to training-period range with three percentile-anchored centroids (choppy, neutral, trending) and nearest-centroid assignment KNN Validator : Five-dimensional feature vector with configurable k and lookback, inverse-distance weighting, and five-bar forward outcome labeling Smoothing Layer : Kalman filter with configurable process and measurement noise, hard snap to SAR on every flip to preserve reversal timing Visualization : Dual-plot SAR circles with confidence-opacity fill, percentage confidence labels, every-other-bar regime labels Signal Logic : High/low confidence classification with configurable minimum threshold, raw flip detection decoupled from display Performance Profile : Optimized for real-time execution across all timeframes with efficient array-based KNN search and FIFO distance sorting Optimal Application Parameters Timeframe Guidance: 1 - 5 min : Scalping with tighter AF values, shorter KNN lookback, and lower minimum confidence threshold 15 - 60 min : Intraday trend following with balanced regime sensitivity and moderate confidence filtering 4H - Daily : Swing and position trading with wider AF maximum, longer training period, and higher confidence threshold Suggested Baseline Configuration: Base AF Start : 0.02 Base AF Increment : 0.02 Base AF Maximum : 0.10 Training Data Period : 100 Choppy Regime Percentile : 0.75 Trending Regime Percentile : 0.25 K - Number of Neighbors : 8 Historical Lookback Period : 200 Minimum Confidence Filter : 15% AF Smoothing Factor : 0.01 Kalman Process Noise : 0.015 Kalman Measurement Noise : 0.5 Minimum Bars Before Flip : 3 These suggested parameters should be used as a baseline; their effectiveness depends on the asset's volatility profile, trending characteristics, and preferred signal frequency, so fine-tuning is expected for optimal performance. Parameter Calibration Notes Use the following adjustments to refine behavior without altering the core logic: Too many flips in ranging markets : Increase Minimum Bars Before Flip to require longer trend duration before a reversal is allowed, or increase Base AF Maximum to widen the SAR distance. SAR too slow to reverse : Decrease Base AF Start and increase Base AF Increment so the acceleration factor builds more quickly during new trends. Excessive low-confidence signals : Increase Minimum Confidence Filter to raise the threshold for high-confidence classification, focusing only on the strongest historical precedents. KNN scores feel unstable : Increase K - Number of Neighbors to average over more historical examples, smoothing out per-flip score variance. Regime changing too rapidly : Increase Training Data Period to smooth regime classification over a longer flip-frequency history. Regime too slow to update : Decrease Training Data Period for more responsive regime detection that reacts faster to market character shifts. SAR line too jumpy visually : Increase Kalman Measurement Noise for more aggressive smoothing, or decrease Kalman Process Noise to make the filter trust its own estimate more. Kalman lagging reversals : Decrease Kalman Measurement Noise or increase Kalman Process Noise to make the filter more responsive to SAR changes between flips. AF ramping too abruptly : Decrease AF Smoothing Factor toward 0.01 for a more gradual exponential ramp from start to target AF on each new trend. Adjustments should be incremental and evaluated across multiple market sessions rather than isolated conditions. Performance Characteristics High Effectiveness: Trending markets with sustained directional conviction where flip frequency remains low and regime classification stabilizes at label 3 Instruments with consistent volatility where ATR-normalized KNN features generalize well across historical flips Momentum continuation strategies using SAR as a trailing stop with confidence-filtered entries at reversals Systematic approaches benefiting from quantified signal confidence and regime-based parameter adaptation Multi-timeframe frameworks where regime labels provide higher-timeframe directional context for lower-timeframe entries Reduced Effectiveness: Choppy, range-bound markets with high flip frequency causing frequent low-confidence signals and regime label 1 classification Extremely thin historical data environments where the KNN lookback contains insufficient comparable flips for reliable scoring News-driven or gapped markets where discrete price discontinuities bypass SAR logic and invalidate ATR-normalized tension features Very low volatility instruments where ATR scaling compresses feature vectors and reduces KNN discriminative power Consolidation phases where mean-reversion dominance causes repeated SAR whipsaws regardless of minimum bars filtering Integration Guidelines Confluence : Combine with BOSWaves volume analysis, structure detection, or supply and demand zone identification for multi-factor confirmation SAR Respect : Honor the trailing SAR as the primary risk boundary — avoid holding positions against the active stop regardless of confidence score Confidence Awareness : Treat confidence scores as probabilistic context, not certainty — high scores improve odds but do not guarantee outcome Regime Discipline : Reduce activity during persistent choppy regime classification rather than fighting repeated low-confidence flips Alert Utilization : Configure high-confidence and regime-change alerts to enable systematic monitoring without requiring active chart observation Lookback Sufficiency : Ensure sufficient historical bars are loaded for the KNN lookback period before relying on confidence scores, particularly on shorter timeframes Multi-Timeframe Alignment : Use higher timeframe regime label and trend direction as a filter for lower timeframe flip entries to ensure directional confluence Disclaimer Machine Learning PSAR is a professional-grade adaptive reversal and trend-following tool. It uses K-Means regime classification and KNN signal validation to adapt classical Parabolic SAR behavior to current market conditions but does not predict future price movements. Results depend on market conditions, volatility characteristics, parameter selection, and disciplined execution. BOSWaves recommends deploying this indicator within a broader analytical framework that incorporates price structure, volume context, and comprehensive risk management.
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