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Адаптивная скользящая средняя Кауфмана (KAMA)

SENTINEL KAMA | AnonycryptousSentinel Kama | Anonycryptous Complete Description & User Manual Pine Script v6 · TradingView ═══════════════════════════════════════ Important note, read first! Sentinel Kama does not generate trading signals in the traditional sense. It does not tell you when to buy or sell. It does not predict market direction. It does not replace your own technical analysis or strategy. All references to possible entries, stop-loss levels, or targets in this manual are purely illustrative examples of how the indicator can be read. They are not trading recommendations. Always conduct your own research and consult a qualified financial advisor before making any trading decisions. ═══════════════════════════════════════ 1. Overview Sentinel Kama is an adaptive trend-following indicator built for precision entries across all timeframes. It combines a Kaufman Adaptive Moving Average with an ATR-based trailing stop, a macro trend filter via a configurable EMA, a session-anchored VWAP, and a real-time dashboard — forming a self-contained trend system that adapts to current market conditions automatically. Unlike fixed moving average systems, Sentinel Kama accelerates during trending conditions and slows during ranging conditions, filtering noise before it reaches the signal layer. Every component works together: the adaptive average identifies the trend, the ATR band confirms it, the EMA filter validates the macro direction, VWAP anchors price to session value, and the dashboard delivers instant confirmation of all states simultaneously. ═══════════════════════════════════════ 2. Core Components 2.1 Kama — Kaufman Adaptive Moving Average The engine of Sentinel Kama. The indicator calculates an Efficiency Ratio on every bar — a measure of how directionally efficient price movement is relative to total volatility over the lookback period. When the efficiency ratio is high (strong trend, low noise), Kama accelerates toward price using the fast smoothing constant. When it is low (choppy, ranging market), Kama slows to a near-flat line using the slow smoothing constant. It tightens during trends and dampens during consolidation — without manual intervention. Key inputs: - Kama Length — the efficiency ratio lookback window - Fast Length — speed during trending conditions (default 2) - Slow Length — speed during ranging conditions (default 30) 2.2 ATR Trailing Stop The ATR trailing stop is the primary signal line plotted on the chart. It trails the Kama line by a distance of ATR × Multiplier and uses a ratchet mechanism — in an uptrend it only moves up, never down, locking in the trend floor. When Kama crosses the trailing stop, the trend flips and the stop resets on the opposite side. Trend reversals are marked by dual-layer triangle signals — a solid inner marker for precision and a semi-transparent outer glow for visibility. Key inputs: - ATR Length — volatility measurement period - ATR Multiplier — distance of the trailing stop from Kama 2.3 Noise Filter An optional gate that prevents Kama from updating on insignificant price moves. Only price displacements larger than ATR × Noise Filter Threshold are allowed through, keeping the trend line stable during choppy conditions. When disabled, Kama updates every bar at full adaptive speed. On lower timeframes, keep the threshold low (0.1–0.2) to avoid filtering fast price moves. 2.4 EMA — Macro Trend Filter The EMA acts as a regime filter. Bullish reversal signals only fire when price is above it, confirming a macro uptrend. Bearish signals only fire when price is below it. This eliminates counter-trend entries and aligns every signal with the dominant market structure. The EMA supports a higher timeframe input. On lower timeframes (1–15m), setting it to 1H or 4H provides a far more meaningful macro reference. A label displaying the current EMA value is shown to the right of the last price candle. 2.5 VWAP — Session-Anchored Volume Weighted Average Price VWAP provides a volume-weighted price reference that resets at the start of each trading session. Supports Daily mode or Custom mode with precise start and end times for any session. A label displaying the current VWAP value is shown to the right of the last price candle. 2.6 Dashboard A real-time overlay table displaying the most critical indicator states simultaneously. Updates every bar and shows trend direction, Kama position relative to the ATR stop, EMA filter status, VWAP position, noise filter state, and the last signal fired. Available in Tiny, Small, or Normal size and placeable in any corner of the chart. Dashboard rows: - Trend — current bullish or bearish state - Kama — position above or below the ATR trailing stop - EMA 200 — price position relative to the macro filter - VWAP — price position relative to session value - Noise Filter — active or inactive state - Signal — last signal fired: buy, sell, or none ═══════════════════════════════════════ 3. Presets Three pre-built configurations cover the most common trading styles. Each preset overrides the core calculation parameters. Visual and VWAP settings remain under manual control. Default | Swing — 4H / Daily Kama 14 | Fast 2 | Slow 30 | ATR 14 | Mult 3.5 | Noise 0.3 Versatile baseline for swing trading. Balanced efficiency ratio period with standard fast/slow constants delivers adaptive behavior across most market conditions. Fast | Scalping — 1m to 15m Kama 6 | Fast 2 | Slow 15 | ATR 7 | Mult 1.8 | Noise 0.15 Built for scalping and intraday trading. Short efficiency ratio period reacts immediately to momentum shifts. Tight ATR band keeps stops close to price. Minimal noise filtering preserves responsiveness. Smooth | Position — Daily / Weekly Kama 21 | Fast 3 | Slow 40 | ATR 21 | Mult 5.0 | Noise 0.5 Designed for position trading and long-term trend following. Extended Kama period demands sustained momentum before accelerating. Aggressive noise filtering ensures only high-conviction moves generate signals. ═══════════════════════════════════════ 4. How to Use — Lower Timeframes (1m / 3m / 5m / 15m) Lower timeframes are fast and noisy. Sentinel Kama handles this through adaptive speed, tight ATR stops, and macro filtering via a higher timeframe EMA. The dashboard is especially valuable at these speeds — instant confirmation without needing to read chart lines. Recommended setup: - Use the Fast preset as your starting point - Set EMA timeframe to 60 (1H) or 240 (4H) — never leave it on the chart timeframe at these levels - Set VWAP to Daily or your active session with the correct timezone - Keep Noise Filter enabled at 0.15 What to watch: - Kama cloud widening — momentum building, trend is healthy - Kama cloud narrowing — Kama slowing down, possible consolidation ahead - ATR trailing stop flipping — confirmed trend change, signal fires here - Dashboard EMA and VWAP both aligned with signal direction — strongest confluence read ═══════════════════════════════════════ 5. How to Use — Higher Timeframes (1H / 4H / Daily) Higher timeframes produce fewer but higher-quality signals with larger reward potential. The adaptive nature of Kama is especially visible here — watch how it accelerates into trends and nearly flatlines during consolidation. Recommended setup: - Use Default preset on 1H and 4H - Use Smooth preset on Daily and Weekly - Leave EMA timeframe on the chart-native setting at these levels — the EMA 200 is meaningful on 1H and above - Set VWAP to Daily on 1H and 4H. On Daily charts VWAP is less relevant — consider hiding it for a cleaner chart What to watch: - A wide, sustained Kama cloud on 4H or Daily indicates a strong trend - Dashboard showing Kama above stop while EMA shows above confirms trend health at a glance - Noise filter at 0.3 is appropriate for 1H–4H. Increase to 0.4–0.5 on Daily ═══════════════════════════════════════ 6. Visual Guide ATR Trailing Line Bull: bull color, trails below price Bear: bear color, trails above price Kama Cloud Fill Bull: fill between Kama and stop in bull color Bear: fill between Kama and stop in bear color Triangle Signal Bull: up triangle at trailing line on bullish reversal Bear: down triangle at trailing line on bearish reversal Bar Color Tinted based on trend direction EMA Line White line with value label to the right VWAP Line Gold line with value label to the right Dashboard All rows reflect current trend state in real time ═══════════════════════════════════════ 7. Alerts Three alert conditions are available, compatible with TradingView's alert system. All alerts include exchange, ticker, and interval in the message. Alerts respect the EMA filter — bullish alerts only fire above the EMA, bearish alerts only fire below. Bullish Trend — bullish reversal signal fires above EMA Bearish Trend — bearish reversal signal fires below EMA Any Trend Change — either reversal detected ═══════════════════════════════════════ Educational purpose only. Sentinel Kama and all content in this manual are provided for educational and informational purposes only. Nothing in this document constitutes financial advice, investment advice, or any trading recommendation. Trading financial instruments involves substantial risk of loss. Past performance of any indicator is not indicative of future results. Always conduct your own independent research before making any trading or investment decisions. Anonycryptous accepts no responsibility or liability for any losses incurred as a result of using this indicator. ═══════════════════════════════════════ Sentinel Kama | Anonycryptous © 2026 Anonycryptous. All rights reserved.
Индикатор Pine Script®
от Anonycryptous
SENTINEL SHIFT | AnonycryptousSentinel Shift | Anonycryptous Complete Description & User Manual Pine Script v6 · TradingView ═══════════════════════════════════════ Important note, read first! Sentinel Shift does not generate trading signals in the traditional sense. It does not tell you when to buy or sell. It does not predict market direction. It does not replace your own technical analysis or strategy. All references to possible entries, stop-loss levels, or targets in this manual are purely illustrative examples of how the indicator can be read. They are not trading recommendations. Always conduct your own research and consult a qualified financial advisor before making any trading decisions. ═══════════════════════════════════════ 1. Overview Sentinel Shift is a Smart Money Concept structure indicator built in Pine Script v6 for TradingView. It automatically detects and visualizes market structure — specifically Break of Structure and Change of Character events — while tracking the adaptive trend through a Kaufman Adaptive Moving Average curve and dynamic cloud fill. It is built for traders who want to read price structure objectively, without manual drawing. It tracks swing highs and swing lows using pivot detection, identifies when the market breaks prior structure or reverses trend direction, and signals these events visually on the chart with colored lines, labels, and signal markers. ═══════════════════════════════════════ 2. Core Concepts 2.1 Kama — Kaufman Adaptive Moving Average The foundation of Sentinel Shift. Unlike a simple or exponential moving average, Kama adjusts its speed dynamically based on market efficiency. When price is trending strongly, Kama responds quickly and stays close to price. When the market is ranging or choppy, Kama slows down and filters noise. The Kama line is the primary trend filter: - Price above Kama — bullish environment - Price below Kama — bearish environment The Kama line color and the cloud fill between Kama and price both reflect the current trend state in real time. 2.2 Bos — Break of Structure A Break of Structure occurs when price closes beyond a previous confirmed swing high or swing low in the direction of the existing trend. Bullish BOS: price closes above the last confirmed swing high while structure trend is already bullish. Confirms trend continuation upward. Bearish BOS: price closes below the last confirmed swing low while structure trend is already bearish. Confirms trend continuation downward. BOS lines are drawn as dashed lines from the pivot bar to the candle that caused the break. The label appears at the start of the line. Green for bullish, red for bearish. 2.3 ChoCh — Change of Character A Change of Character is the most significant signal in Sentinel Shift. It occurs when price closes beyond a prior swing level in the opposite direction of the current structure trend — indicating a potential trend reversal. Bullish CHoCH: price closes above the last confirmed swing high while structure trend was bearish. Signals a possible shift from bearish to bullish. Bearish CHoCH: price closes below the last confirmed swing low while structure trend was bullish. Signals a possible shift from bullish to bearish. CHoCH lines are drawn as solid lines from the pivot bar to the breakout candle. The line stops exactly at that candle and does not extend further. After a CHoCH fires, the structure trend flips. 2.4 Trend Reversal Signal The trend reversal signal fires on every CHoCH event. It is displayed as a dual-layer triangle marker anchored to the Kama line at the signal bar. Bull signal: green triangle below the Kama line at the bullish CHoCH bar. Bear signal: red triangle above the Kama line at the bearish CHoCH bar. The dual-layer design uses a semi-transparent outer triangle as a glow and a solid inner triangle as the core, creating a clean visual hierarchy anchored to the Kama curve. 2.5 Target Lines Target lines are automatically drawn at the most recent confirmed pivot high and pivot low as dotted grey lines extending to the right. - The pivot high line — possible upside reference for longs - The pivot low line — possible downside reference for shorts When price closes through a target line, the line stops at that candle and changes color. A new pivot replaces the line with a fresh grey line. ═══════════════════════════════════════ 3. Settings Reference 3.1 Presets Three pre-built configurations cover the most common trading styles. Default | 1H–4H Pivot Left/Right 10/10 | ATR 14 | Mult 3.5 | Noise 0.3 Balanced structure detection for intraday and swing setups. Fast | 1m–15m Pivot Left/Right 5/5 | ATR 7 | Mult 1.8 | Noise 0.15 Tighter pivots and faster Kama response for scalping timeframes. Smooth | Daily–Weekly Pivot Left/Right 15/15 | ATR 21 | Mult 5.0 | Noise 0.5 Extended lookback with strong noise filtering. Fewer, more significant structural events. 3.2 Pivot Bars Left Bars / Right Bars — the number of bars required on each side for a pivot to be confirmed. Higher values produce fewer but more significant structure levels. Lower values produce more frequent but potentially less reliable levels. Starting point: 10/10 for most timeframes. Scalping: reduce to 5/5. Swing: increase to 15/15. Note: there is always a built-in delay equal to the Pivot Right Bars value. A confirmed pivot now was formed that many bars ago. This is the trade-off for confirmed structure — fewer false signals at the cost of some latency. 3.3 Max Structure Levels Controls how many BOS and CHoCH lines are visible at once. 3–5 keeps the chart clean. Older lines are removed first as new ones form. 3.4 Color Preset Classic uses the Anonycryptous standard green/red palette. Custom allows full color control. ═══════════════════════════════════════ 4. Dashboard Trend — current bullish or bearish state Structure — bull or bear structure direction Kama — position relative to price Signal — last signal fired: buy / sell / none BOS Count — number of consecutive structure confirmations ═══════════════════════════════════════ 5. Alerts Long — fires on bullish CHoCH Short — fires on bearish CHoCH Bull BOS — fires on bullish Break of Structure Bear BOS — fires on bearish Break of Structure All alert messages include exchange, ticker, and timeframe. ═══════════════════════════════════════ 6. How to Use — Step by Step 6.1 Reading the Chart - Kama line color tells you the current trend at a glance. Green = bullish. Red = bearish. - The cloud fill reinforces the trend. A large cloud means price is well extended from Kama. A thinning cloud may indicate weakening. - CHoCH signals — solid lines and triangle markers — indicate possible trend reversals. These are the highest-priority signals. - BOS signals — dashed lines — confirm trend continuation after a CHoCH has already established direction. - Grey target lines show the last swing high and low as possible reference areas. 6.2 Possible Long Setup — Educational Example Illustrative only. Not a trading recommendation. 1. Price was in a downtrend — red Kama, bearish structure, bear BOS events visible. 2. A bullish CHoCH fires — price closes above the last confirmed swing high. Green solid line appears. Green triangle signal appears on Kama. 3. Dashboard shows trend bullish, structure bullish, signal buy. 4. A possible long entry could be considered on the next pullback toward the Kama line or toward the CHoCH level. 5. A possible stop-loss could be placed below the CHoCH level or the most recent swing low. 6. A possible first target could be the grey pivot high target line. 6.3 Possible Short Setup — Educational Example Illustrative only. Not a trading recommendation. 1. Price was in an uptrend — green Kama, bullish structure, bull BOS events visible. 2. A bearish CHoCH fires — price closes below the last confirmed swing low. Red solid line appears. Red triangle signal appears on Kama. 3. Dashboard shows trend bearish, structure bearish, signal sell. 4. A possible short entry could be considered on a retest of the CHoCH level from below. 5. A possible stop-loss could be placed above the CHoCH level or the most recent swing high. 6. A possible first target could be the grey pivot low target line. ═══════════════════════════════════════ 7. Tips & Best Practices Timeframe guide: - 1m–3m: Fast preset, Pivot 5/5. Many signals, expect noise. - 5m–15m: Fast preset, Pivot 10/10. Recommended starting point. - 30m–1H: Default preset, Pivot 10/10. Balanced signal frequency. - 4H–Daily: Smooth preset, Pivot 15/15. Fewer, stronger signals. Not every signal is a trade. Sentinel Shift identifies structure. It does not filter by session, volume, or higher timeframe context. A CHoCH during low-volume hours may be less significant than one during the open of a major session. Always apply your own context and judgment. ═══════════════════════════════════════ Educational purpose only. Sentinel Shift and all content in this manual are provided for educational and informational purposes only. Nothing in this document constitutes financial advice, investment advice, or any trading recommendation. Trading financial instruments involves substantial risk of loss. Past performance of any indicator is not indicative of future results. Always conduct your own independent research before making any trading or investment decisions. Anonycryptous accepts no responsibility or liability for any losses incurred as a result of using this indicator. ═══════════════════════════════════════ Sentinel Shift | Anonycryptous · v1.0 © 2026 Anonycryptous. All rights reserved.
Индикатор Pine Script®
от Anonycryptous
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.
Индикатор Pine Script®
от Jamtrader2020
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.
Индикатор Pine Script®
от Pineify
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.
Индикатор Pine Script®
от UAlgo
Trend Sniper v2.5 [Jamallo](2025) Intro Trend Sniper v2.5 is built around a novel-unique core construction — the Butterworth Stop : a ratcheting trailing stop anchored to a 2-Pole Butterworth Super Smoother and dynamically sized by Parkinson Volatility. While each algorithm used is individually well-documented, this specific combination is rare and not found in standard indicator libraries — most Butterworth implementations are simply plotted as trend lines, not used as the structural anchor of a volatility-adaptive stop mechanism. Break down: 2-Pole Butterworth Super Smoother (Core Trend Line) (smoothed price) The backbone of the indicator is a 2-Pole Butterworth Super Smoother, a concept brought into trading by engineer and author John Ehlers, first published in his 2004 book Cybernetic Analysis for Stocks and Futures In this indicator: The Butterworth filter runs on close with a user-defined period (default 20) and produces bw_trend — a clean, lag-minimized version of price. This smoothed value is not plotted directly. Instead, it acts as the anchor point for the trailing stop. The stop is placed at bw_trend ± stop_dist , meaning the stop hugs the filtered trend rather than raw price, making it far less susceptible to wick noise and erratic bars. Without this pre-filtering step, the trailing stop would oscillate erratically on volatile bars. Parkinson Volatility (Sizes the Stop Distance) "Developed by physicist Michael Parkinson in 1980, it estimates volatility using the natural logarithm of the high-to-low ratio each period, capturing intraday range rather than just close-to-close movement." In this indicator: Parkinson Volatility is calculated over a rolling window (default 50 bars) and multiplied by stop_mult (default 1.2) to produce stop_dist . When markets are volatile, stop_dist expands and the stop gives price more room. When markets are quiet, it contracts and the stop tightens. Butterworth Stop State Machine (The Main Line You Watch) This is the primary plotted line, It's a classic ratcheting trailing stop built on top of outputs from steps 1 and 2. In this indicator: The logic is a simple state machine with two modes — uptrend and downtrend. In an uptrend, the stop only moves up (never down), tracking at bw_trend − stop_dist . In a downtrend, it only moves down, tracking at bw_trend + stop_dist . When price closes on the wrong side of the stop, the state flips and the stop resets on the other side of the trend line. Because both the anchor bw_trend and the buffer stop_dist are noise-filtered and volatility-adjusted, the BW Stop flips far less often than a raw price-based trailing stop would, keeping you in trends longer. Chande Momentum Oscillator + Deadband (Colors the BW Stop Line) "The CMO measures momentum by calculating the difference between the sum of gains and the sum of losses over a specified period, dividing by their total to produce a normalized scale from -100 to +100 that accounts for both up and down days simultaneously." In this indicator: CMO is calculated on bw_stop itself (not raw price), over 14 bars by default. A deadband of ±10 is applied — the line only turns bull-colored when CMO exceeds +10, and bear-colored when it drops below −10. It does not change color in between. KAMA Midpoint (The Dynamic Midline and Fill Zone Anchor) "Introduced by Perry Kaufman in 1995, KAMA dynamically adjusts its smoothing based on the relative efficiency of price movement". In this indicator: Rather than applying KAMA to price directly, it's applied to the midpoint between the BW Stop and the Slow SuperTrend. This midpoint is a computed halfway value between the two lines. KAMA then smooths that midpoint adaptively, producing `kama_mid`. The fill colors you see on the chart are drawn between kama_mid and bw_stop — not between price and anything else. Dual SuperTrend (Signals and Fill State Logic) "Created by Olivier Seban, SuperTrend combines ATR-based volatility with trend detection into a single line that repositions dynamically as price confirms or breaks the current trend direction." In this indicator: Two SuperTrend lines run simultaneously. The Slow ST (9× ATR) is the macro trend reference. The Fast ST (6× ATR) is never plotted at full size — it's compressed halfway toward the BW Stop and shown as orange circles fast_shortened Putting It All Together The flow through the indicator is linear: Butterworth smooths price → Parkinson sizes the stop → the state machine builds the stop line → CMO colors it → KAMA anchors the midline fill → the dual SuperTrend fires signals and shades conviction. END Every component feeds the next, meaning the final signals and visuals are the product of five sequential filters rather than any single calculation — which is what gives the indicator its resistance to false signals in noisy market conditions.
Индикатор Pine Script®
от Jamallo22
6
KAMA Adaptive Trend System (Risk-Managed)Overview This script is a professional-grade adaptation of the Kaufman Adaptive Moving Average (KAMA). While standard KAMA scripts focus solely on price smoothing, this version (V5) is designed as a complete trading system with a focus on institutional-style risk management and trend hygiene. How It Works The strategy uses a three-layer confirmation process before entering a trade: The Core (KAMA): Tracks price efficiency to identify trend shifts. The Regime Filter: A 200-period SMA ensures you are only trading with the long-term trend (Longs above, Shorts below). Momentum Filter: An RSI check (Midline 50) confirms there is actual strength behind the move. Key Features Dynamic Position Sizing: Automatically calculates quantity based on a fixed % of account equity (default 1%). ATR-Based Volatility Stops: Uses a 2.5x ATR multiplier to set hard stop-losses that breathe with market volatility. Smart Alerts: Alerts are programmed to trigger on KAMA Color Changes only. This prevents "alert fatigue" caused by fluctuating filters while keeping you informed of trend flips. How to Use Bullish Setup: KAMA turns green, price is > 200 SMA, and RSI > 50. Bearish Setup: KAMA turns red, price is < 200 SMA, and RSI < 50. Settings: You can toggle the "Order Direction" to focus on Long-only or Short-only setups depending on your market bias. Credits Based on the original KAMA logic by tradedots. Modifications by Gemini Assistant include risk management integration and alert logic optimization.
Стратегия Pine Script®
от RadRik
KAMA SD Bands | RakoQuantKAMA SD Bands by RakoQuant is an adaptive volatility envelope built around a fully open-source implementation of Kaufman’s Adaptive Moving Average (KAMA). Instead of measuring raw candle volatility, this tool calculates Standard Deviation on residual structure (price minus KAMA), producing bands that reflect true deviation from adaptive equilibrium. The result: * Clean regime shifts * Intelligent volatility expansion * Clear bull/bear state transitions * Strategy-ready signal architecture This is not a cosmetic overlay — it is a structural framework. RakoQuant Architecture 1. Adaptive Core (KAMA Engine) The baseline is a manual implementation of Kaufman’s Adaptive Moving Average: * Efficiency Ratio (ER) governs responsiveness * Fast/Slow smoothing parameters define reaction bounds * Automatically contracts lag in trends * Smooths aggressively in consolidation This allows the baseline to behave dynamically across regimes instead of using fixed smoothing. 2. Residual Volatility Bands Bands are calculated as: Upper Band = KAMA + (Multiplier × StdDev of Residual)
Lower Band = KAMA − (Multiplier × StdDev of Residual) Where: Residual = Price − KAMA This approach measures structural deviation, not random price noise. Key effect: * Strong trends → Bands expand progressively * Range compression → Bands tighten sharply * Structural breaks → Clean directional triggers Signal Model (RakoQuant State Logic) The indicator uses a state-based architecture: Bullish Regime (1)
Confirmed break above upper band. Bearish Regime (-1)
Confirmed break below lower band. Signal modes: * Band Break (momentum continuation logic) * Re-Entry (mean reversion crossover logic) All signals are designed for: * Strategy conversion * Backtesting * Alert automation * System stacking No repainting. No dependency on external libraries. Visual System RakoQuant visual configuration includes: * Multiple professional color palettes * Optional glow layers * Trend-sensitive band coloring * Configurable markers (Labels / Triangles) * Pine-safe marker sizing * Optional candle painting * Toggleable baseline Every visual element can be disabled for clean strategy integration. Practical Use Cases Trend Continuation
Use band breaks to identify volatility expansion aligned with adaptive slope. Volatility Compression
Watch for tightening bands as pre-breakout structure forms. Mean Reversion
Use re-entry mode when price returns inside adaptive envelope. System Development
Directly compatible with RakoQuant regime logic architecture. Design Philosophy KAMA SD Bands reflects the RakoQuant approach: * Adaptive > Static * Structural > Cosmetic * Deterministic > Black-box * Backtest-ready > Visual-only Everything is manual, transparent, and open-source. Chart Example
Индикатор Pine Script®
от RakoQuant
1
Regime Switching [Pointalgo]Regime Switching Regime Switching is a market condition classifier designed to automatically detect whether price is in a Trending, Ranging (Mean Reversion), or Neutral/Noise environment. Instead of applying one strategy to all conditions, this script adapts its visual tools and signals based on the detected regime. How It Works: 1. ADX (Trend Strength) Measures directional strength. Higher values indicate stronger trends. 2. Efficiency Ratio (Kaufman Concept) Compares net price movement to total movement. High efficiency → directional move Low efficiency → choppy movement Using these two inputs, the script classifies the market into: TRENDING → Strong ADX + Efficient movement RANGING → Weak ADX + Inefficient movement NOISE → Transitional / unclear conditions Adaptive Visual System: The indicator automatically switches tools depending on the regime: Trending Mode Displays Donchian Channel (20-period highs/lows) Highlights breakout conditions Green background shading Ranging Mode Displays Bollinger Bands (20, 2 standard deviations) Highlights fade/reversal conditions Blue background shading Noise Mode 1.Neutral background 2.No active channel emphasis Signal Logic: Breakout above previous Donchian high Breakdown below previous Donchian low Range Mode: Buy when price crosses above lower Bollinger Band Sell when price crosses below upper Bollinger Band Signals are informational and intended for study and research. Dashboard: The built-in table shows: 1. Current detected regime 2. Efficiency value 3. State color coding This allows quick interpretation of market condition at a glance. Disclaimer: This script is for educational and research purposes only. It does not constitute financial advice or a trading recommendation. Always test and validate strategies before live trading.
Индикатор Pine Script®
от pointalgo
KAMA Momentum Extension WarningKAMA Momentum Extension Warning (Parabolic Exit) Description This indicator is designed for Momentum & Trend Following strategies. Its primary goal is to identify "Parabolic Blow-Off" tops—moments where price moves vertically away from the trend, creating an unsustainable "rubber band" effect. While standard trend-following tools (like moving averages) tell you when to enter or hold, this tool tells you exactly when to take profit into strength before a likely crash. Visual Signals Blue Line (KAMA 21): The "Floor." This represents the sustainable trend. In a healthy move, price should hug this line. Orange Line (Extension Limit): The "Ceiling." This is calculated as KAMA + (ATR * Multiplier). It represents the mathematical limit of a normal move. Yellow Candles: The "Climax Signal." This triggers when the High of the day pierces the Orange Extension Limit. It indicates the price is statistically over-extended (vertical). How to Trade It Trend Following: As long as candles are "Normal" colored and above the Blue Line, hold the position. The Warning: If a candle paints Yellow, the stock has gone parabolic. The Execution: Sell 50% of the position immediately (do not wait for the close). Tighten the stop loss on the remaining shares to the Low of the Previous Day. Settings / Inputs KAMA Length (Default: 21): Controls the baseline trend. Increase for longer-term trends, decrease for faster entries. ATR Length (Default: 21): The volatility lookback period (usually matches the KAMA length). Extension Multiplier (Default: 3.5): The "Sensitivity." 3.5 - 4.0: Best for volatile "Super Stocks" (Crypto, Biotech, Tech). 2.5 - 3.0: Best for slower, large-cap stocks. Tip: Adjust in 0.1 increments to fit the specific stock's personality.
Индикатор Pine Script®
от rw76
Adaptive ML Trailing Stop [BOSWaves]Adaptive ML Trailing Stop – Regime-Aware Risk Control with KAMA Adaptation and Pattern-Based Intelligence Overview Adaptive ML Trailing Stop is a regime-sensitive trailing stop and risk control system that adjusts stop placement dynamically as market behavior shifts, using efficiency-based smoothing and pattern-informed biasing. Instead of operating with fixed ATR offsets or rigid trailing rules, stop distance, responsiveness, and directional treatment are continuously recalculated using market efficiency, volatility conditions, and historical pattern resemblance. This creates a live trailing structure that responds immediately to regime change - contracting during orderly directional movement, relaxing during rotational conditions, and applying probabilistic refinement when pattern confidence is present. Price is therefore assessed relative to adaptive, condition-aware trailing boundaries rather than static stop levels. Conceptual Framework Adaptive ML Trailing Stop is founded on the idea that effective risk control depends on regime context rather than price location alone. Conventional trailing mechanisms apply constant volatility multipliers, which often results in trend suppression or delayed exits. This framework replaces static logic with adaptive behavior shaped by efficiency state and observed historical outcomes. Three core principles guide the design: Stop distance should adjust in proportion to market efficiency. Smoothing behavior must respond to regime changes. Trailing logic benefits from probabilistic context instead of fixed rules. This shifts trailing stops from rigid exit tools into adaptive, regime-responsive risk boundaries. Theoretical Foundation The indicator combines adaptive averaging techniques, volatility-based distance modeling, and similarity-weighted pattern analysis. Kaufman’s Adaptive Moving Average (KAMA) is used to quantify directional efficiency, allowing smoothing intensity and stop behavior to scale with trend quality. Average True Range (ATR) defines the volatility reference, while a K-Nearest Neighbors (KNN) process evaluates historical price patterns to introduce directional weighting when appropriate. Three internal systems operate in tandem: KAMA Efficiency Engine : Evaluates directional efficiency to distinguish structured trends from range conditions and modulate smoothing and stop behavior. Adaptive ATR Stop Engine : Expands or contracts ATR-derived stop distance based on efficiency, tightening during strong trends and widening in low-efficiency environments. KNN Pattern Influence Layer : Applies distance-weighted historical pattern outcomes to subtly influence stop placement on both sides. This design allows stop behavior to evolve with market context rather than reacting mechanically to price changes. How It Works Adaptive ML Trailing Stop evaluates price through a sequence of adaptive processes: Efficiency-Based Regime Identification : KAMA efficiency determines whether conditions favor trend continuation or rotational movement, influencing stop sensitivity. Volatility-Responsive Scaling : ATR-based stop distance adjusts automatically as efficiency rises or falls. Pattern-Weighted Adjustment : KNN compares recent price sequences to historical analogs, applying confidence-based bias to stop positioning. Adaptive Stop Smoothing : Long and short stop levels are smoothed using KAMA logic to maintain structural stability while remaining responsive. Directional Trailing Enforcement : Stops advance only in the direction of the prevailing regime, preserving invalidation structure. Gradient Distance Visualization : Gradient fills reflect the relative distance between price and the active stop. Controlled Interaction Markers : Diamond markers highlight meaningful stop interactions, filtered through cooldown logic to reduce clustering. Together, these elements form a continuously adapting trailing stop system rather than a fixed exit mechanism. Interpretation Adaptive ML Trailing Stop should be interpreted as a dynamic risk envelope: Long Stop (Green) : Acts as the downside invalidation level during bullish regimes, tightening as efficiency improves. Short Stop (Red) : Serves as the upside invalidation level during bearish regimes, adjusting width based on efficiency and volatility. Trend State Changes : Regime flips occur only after confirmed stop breaches, filtering temporary price spikes. Gradient Depth : Deeper gradient penetration indicates increased extension from the stop rather than imminent reversal. Pattern Influence : KNN weighting affects stop behavior only when historical agreement is strong and remains neutral otherwise. Distance, efficiency, and context outweigh isolated price interactions. Signal Logic & Visual Cues Adaptive ML Trailing Stop presents two primary visual signals: Trend Transition Circles : Display when price crosses the opposing trailing stop, confirming a regime change rather than anticipating one. Stop Interaction Diamonds : Indicate controlled contact with the active stop, subject to cooldown filtering to avoid excessive signals. Alert generation is limited to confirmed trend transitions to maintain clarity. Strategy Integration Adaptive ML Trailing Stop fits within trend-following and risk-managed trading approaches: Dynamic Risk Framing : Use adaptive stops as evolving invalidation levels instead of fixed exits. Directional Alignment : Base execution on confirmed regime state rather than speculative reversals. Efficiency-Based Tolerance : Allow greater price fluctuation during inefficient movement while enforcing tighter control during clean trends. Pattern-Guided Refinement : Let KNN influence adjust sensitivity without overriding core structure. Multi-Timeframe Context : Apply higher-timeframe efficiency states to inform lower-timeframe stop responsiveness. Technical Implementation Details Core Engine : KAMA-based efficiency measurement with adaptive smoothing Volatility Model : ATR-derived stop distance scaled by regime Machine Learning Layer : Distance-weighted KNN with confidence modulation Visualization : Directional trailing stops with layered gradient fills Signal Logic : Regime-based transitions and controlled interaction markers Performance Profile : Optimized for real-time chart execution Optimal Application Parameters Timeframe Guidance: 1 - 5 min : Tight adaptive trailing for short-term momentum control 15 - 60 min : Structured intraday trend supervision 4H - Daily : Higher-timeframe regime monitoring Suggested Baseline Configuration: KAMA Length : 20 Fast/Slow Periods : 15 / 50 ATR Period : 21 Base ATR Multiplier : 2.5 Adaptive Strength : 1.0 KNN Neighbors : 7 KNN Influence : 0.2 These suggested parameters should be used as a baseline; their effectiveness depends on the asset volatility, liquidity, and preferred entry frequency, so fine-tuning is expected for optimal performance. Parameter Calibration Notes Use the following adjustments to refine behavior without altering the core logic: Excessive chop or overreaction : Increase KAMA Length, Slow Period, and ATR Period to reinforce regime filtering. Stops feel overly permissive : Reduce the Base ATR Multiplier to tighten invalidation boundaries. Frequent false regime shifts : Increase KNN Neighbors to demand stronger historical agreement. Delayed adaptation : Decrease KAMA Length and Fast Period to improve responsiveness during regime change. Adjustments should be incremental and evaluated over multiple market cycles rather than isolated sessions. Performance Characteristics High Effectiveness: Markets exhibiting sustained directional efficiency Instruments with recurring structural behavior Trend-oriented, risk-managed strategies Reduced Effectiveness: Highly erratic or event-driven price action Illiquid markets with unreliable volatility readings Integration Guidelines Confluence : Combine with BOSWaves structure or trend indicators Discipline : Follow adaptive stop behavior rather than forcing exits Risk Framing : Treat stops as adaptive boundaries, not forecasts Regime Awareness : Always interpret stop behavior within efficiency context Disclaimer Adaptive ML Trailing Stop is a professional-grade adaptive risk and regime management tool. It does not forecast price movement and does not guarantee profitability. Results depend on market conditions, parameter selection, and disciplined execution. BOSWaves recommends deploying this indicator within a broader analytical framework that incorporates structure, volatility, and contextual risk management.
Индикатор Pine Script®
от BOSWaves
3
Moving VWAP-KAMA CloudMoving VWAP-KAMA Cloud Overview The Moving VWAP-KAMA Cloud is a high-conviction trend filter designed to solve a major problem with standard indicators: Noise. By combining a smoothed Volume Weighted Average Price (MVWAP) with Kaufman’s Adaptive Moving Average (KAMA), this indicator creates a "Value Zone" that identifies the true structural trend while ignoring choppy price action. Unlike brittle lines that break constantly, this cloud is "slow" by design—making it exceptionally powerful for spotting genuine trend reversals and filtering out fakeouts. How It Works This script uses a unique "Double Smoothing" architecture: The Anchor (MVWAP): We take the standard VWAP and smooth it with a 30-period EMA. This represents the "Fair Value" baseline where volume has supported price over time. The Filter (KAMA): We apply Kaufman's Adaptive Moving Average to the already smoothed MVWAP. KAMA is unique because it flattens out during low-volatility (choppy) periods and speeds up during high-momentum trends. The Cloud: Green/Teal Cloud: Bullish Structure (MVWAP > KAMA) Purple Cloud: Bearish Structure (MVWAP < KAMA) 🔥 The "Reversal Slingshot" Strategy Backtests reveal a powerful behavior during major trend changes, particularly after long bear markets: The Resistance Phase: During a long-term downtrend, price will repeatedly rally into the Purple Cloud and get rejected. The flattened KAMA line acts as a "concrete ceiling," keeping the bearish trend intact. The Breakout & Flip: When price finally breaks above the cloud with conviction, and the cloud flips Green, it signals a structural regime change. The "Slingshot" Retest: Often, immediately after this flip, price will drop back into the top of the cloud. This is the "Slingshot" moment. The old resistance becomes new, hardened support. The Rally: From this support bounce, stocks often launch into a sustained, multi-month bull run. This setup has been observed repeatedly at the bottom of major corrections. How to Use This Indicator 1. Dynamic Support & Resistance The KAMA Wall: When price retraces into the cloud, the KAMA line often flattens out, acting as a hard "floor" or "wall." A break of this wall usually signals a genuine trend change, not just a stop hunt. 2. Trend Confirmation (Regime Filter) Bullish Regime: If price is holding above the cloud, only look for Long setups. Bearish Regime: If price is holding below the cloud, only look for Short setups. No-Trade Zone: If price is stuck inside the cloud, the market is traversing fair value. Stand aside until a clear winner emerges. 3. Multi-Timeframe Versatility While designed for trend confirmation on higher timeframes (4H, Daily), this indicator adapts beautifully to lower timeframes (5m, 15m) for intraday scalping. On Lower Timeframes: The cloud reacts much faster, acting as a dynamic "VWAP Band" that helps intraday traders stay on the right side of momentum during the session. Settings Moving VWAP Period (30): The lookback period for the base VWAP smoothing. KAMA Settings (10, 10, 30): Controls the sensitivity of the adaptive filter. Cloud Transparency: Adjust to keep your chart clean. Alerts Included Price Cross Over/Under MVWAP Price Cross Over/Under KAMA Cloud Flip (Bullish/Bearish Trend Change) Tip for Traders This is not a signal entry indicator. It is a Trend Conviction tool. Use it to filter your entries from faster indicators (like RSI or MACD). If your fast indicator signals "Buy" but the cloud is Purple, the probability is low. Wait for the Cloud Flip
Индикатор Pine Script®
от SovereignCharts
Tunç ŞatıroğluTunç Şatıroğlu's Technical Analysis Suite Description: This comprehensive Pine Script indicator, inspired by the technical analysis teachings of Tunç Şatıroğlu, integrates six powerful TradingView indicators into a single, user-friendly suite for robust trend, momentum, and divergence analysis. Each component has been carefully selected and enhanced by beytun to improve functionality, performance, and visual clarity, aligning with Şatıroğlu's approach to technical analysis. The default configuration is meticulously set to match the exact settings of the individual indicators as used by Tunç Şatıroğlu in his training, ensuring authenticity and ease of use for followers of his methodology. Whether you're a beginner or an experienced trader, this suite provides a versatile toolkit for analyzing markets across multiple timeframes. Included Indicators: 1. WaveTrend with Crosses (by LazyBear, modified): A momentum oscillator that identifies overbought/oversold conditions and trend reversals with clear buy/sell signals via crosses and bar color highlights. 2. Kaufman Adaptive Moving Average (KAMA) (by HPotter, modified): A dynamic moving average that adapts to market volatility, offering a smoother trend-following signal. 3. SuperTrend (by Alex Orekhov, modified): A trend-following indicator that plots dynamic support/resistance levels with buy/sell signals and optional wicks for enhanced accuracy. 4. Nadaraya-Watson Envelope (by LuxAlgo, modified): A non-linear envelope that highlights potential reversals with customizable repainting options for smoother outputs. 5. Divergence for Many Indicators v4 (by LonesomeTheBlue, modified): Detects regular and hidden divergences across multiple indicators (MACD, RSI, Stochastic, CCI, Momentum, OBV, VWMA, CMF, MFI, and more) for early reversal signals. 6. Ichimoku Cloud (TradingView built-in, modified): A multi-faceted indicator for trend direction, support/resistance, and momentum, with enhanced visuals for the Kumo Cloud. Key Features: - Authentic Default Settings : Pre-configured to mirror the exact parameters used by Tunç Şatıroğlu for each indicator, ensuring alignment with his proven technical analysis approach. - Customizable Settings : Enable/disable individual indicators and fine-tune parameters to suit your trading style while retaining the option to revert to Şatıroğlu’s defaults. - Enhanced User Experience : Modifications improve visual clarity, performance, and usability, with options like repainting smoothing for Nadaraya-Watson and adjustable Ichimoku projection periods. - Multi-Timeframe Analysis : Combines trend-following, momentum, and divergence tools for a holistic view of market dynamics. - Alert Conditions : Built-in alerts for SuperTrend direction changes, buy/sell signals, and divergence detections to keep you informed. - Visual Clarity : Overlays (KAMA, SuperTrend, Nadaraya-Watson, Ichimoku) and pane-based indicators (WaveTrend, Divergences) are clearly distinguished, with customizable colors and styles. Notes: - The Nadaraya-Watson Envelope and Ichimoku Cloud may repaint in their default modes. Use the "Repainting Smoothing" option for Nadaraya-Watson or adjust Ichimoku settings to mitigate repainting if preferred. - Published under the MIT License, with components licensed under GPL-3.0 (SuperTrend), CC BY-NC-SA 4.0 (Nadaraya-Watson), MPL 2.0 (Divergence), and TradingView's terms (Ichimoku Cloud). Usage: Add this indicator to your TradingView chart to leverage Tunç Şatıroğlu’s exact indicator configurations out of the box. Customize settings as needed to align with your strategy, and use the combined signals to identify trends, reversals, and divergences. Ideal for traders following Şatıroğlu’s methodologies or anyone seeking a powerful, all-in-one technical analysis tool. Credits: Original authors: LazyBear, HPotter, Alex Orekhov, LuxAlgo, LonesomeTheBlue, and TradingView. Modifications and integration by beytun . License: Published under the MIT License, incorporating code under GPL-3.0, CC BY-NC-SA 4.0, MPL 2.0, and TradingView’s terms where applicable.
Индикатор Pine Script®
от beytun
6
Adaptive Market Regime Identifier [LuciTech]What it Does: AMRI visually identifies and categorizes the market into six primary regimes directly on your chart using a color-coded background. These regimes are: -Strong Bull Trend: Characterized by robust upward momentum and low volatility. -Weak Bull Trend: Indicates upward momentum with less conviction or higher volatility. -Strong Bear Trend: Defined by powerful downward momentum and low volatility. -Weak Bear Trend: Suggests downward momentum with less force or increased volatility. -Consolidation: Periods of low volatility and sideways price action. -Volatile Chop: High volatility without clear directional bias, often seen during transitions or indecision. By clearly delineating these states, AMRI helps traders quickly grasp the overarching market context, enabling them to apply strategies best suited for the current conditions (e.g., trend-following in strong trends, range-bound strategies in consolidation, or caution in volatile chop). How it Works (The Adaptive Edge) AMRI achieves its adaptive classification by continuously analyzing three core market dimensions, with each component dynamically adjusting to current market conditions: 1.Adaptive Moving Average (KAMA): The indicator utilizes the Kaufman Adaptive Moving Average (KAMA) to gauge trend direction and strength. KAMA is unique because it adjusts its smoothing period based on market efficiency (noise vs. direction). In trending markets, it becomes more responsive, while in choppy markets, it smooths out noise, providing a more reliable trend signal than static moving averages. 2.Adaptive Average True Range (ATR): Volatility is measured using an adaptive version of the Average True Range. Similar to KAMA, this ATR dynamically adjusts its sensitivity to reflect real-time changes in market volatility. This helps AMRI differentiate between calm, ranging markets and highly volatile, directional moves or chaotic periods. 3.Normalized Slope Analysis: The slope of the KAMA is normalized against the Adaptive ATR. This normalization provides a robust measure of trend strength that is relative to the current market volatility, making the thresholds for strong and weak trends more meaningful across different instruments and timeframes. These adaptive components work in concert to provide a nuanced and responsive classification of the market regime, minimizing lag and reducing false signals often associated with fixed-parameter indicators. Key Features & Originality: -Dynamic Regime Classification: AMRI stands out by not just indicating trend or range, but by classifying the type of market regime, offering a higher-level analytical framework. This is a meta-indicator that provides context for all other trading tools. -Adaptive Core Metrics: The use of KAMA and an Adaptive ATR ensures that the indicator remains relevant and responsive across diverse market conditions, automatically adjusting to changes in volatility and trend efficiency. This self-adjusting nature is a significant advantage over indicators with static lookback periods. -Visual Clarity: The color-coded background provides an immediate, at-a-glance understanding of the current market regime, reducing cognitive load and allowing for quicker decision-making. -Contextual Trading: By identifying the prevailing regime, AMRI empowers traders to select and apply strategies that are most effective for that specific environment, helping to avoid costly mistakes of using a trend-following strategy in a ranging market, or vice-versa. -Originality: While components like KAMA and ATR are known, their adaptive integration into a comprehensive, multi-regime classification system, combined with normalized slope analysis for trend strength, offers a novel approach to market analysis not commonly found in publicly available indicators.
Индикатор Pine Script®
от TradesLuci
1
KAMA Trend Flip - SightLing LabsBuckle up, traders—this open-source KAMA Trend Flip indicator is your ticket to sniping trend reversals with a Kaufman Adaptive Moving Average (KAMA) that’s sharper than a Wall Street shark’s tooth. No voodoo, no fluff—just raw, volatility-adaptive math that dances with the market’s rhythm. It zips through trending rockets and chills in choppy waters, slashing false signals like a samurai. Not laggy like the others - this thing is the real deal! Core Mechanics: • Efficiency Ratio (ER): Reads the market’s pulse (0-1). High ER = turbo-charged MA, low ER = smooth operator. • Adaptive Smoothing: Mixes fast (default power 2) and slow (default 30) constants to match market mood swings. • Trend Signals: KAMA climbs = blue uptrend (bulls run wild). KAMA dips = yellow downtrend (bears take over). Flat = gray snooze-fest. • Alerts: Instant pings on flips—“Trend Flip Up” for long plays, “Down” for shorts. Plug into bots for set-and-forget domination. Why It Crushes: • Smokes static MAs in volatile arenas (crypto, stocks, you name it). Backtests show 20-30% fewer fakeouts than SMA50. • Visual Pop: Overlays price with bold blue/yellow signals. Slap it on BTC 1D to see trends light up like Times Square. • Tweakable: Dial ER length (default 50) to your timeframe. Short for scalps, long for swing trades. Example Settings in Action: • 10s Chart (Hyper-Scalping): Set Source: Close, ER Length: 100, Fast Power: 1, Slow Power: 6. Catches micro-trends in crypto like a heat-seeking missile. Blue/yellow flips scream entry/exit on fast moves. • 2m Chart (Quick Trades): Set Source: Close, ER Length: 14, Fast Power: 1, Slow Power: 6. Perfect for rapid trend shifts in stocks or forex. Signals align with momentum bursts—check historical flips for proof. Deployment: • Drop it on any chart. Backtest settings to match your asset’s volatility—tweak until it sings. • Pair with RSI or volume spikes for killer confirmation. Pro move: Enter on flip + volume pop, exit on reverse. • Strategy-Ready: Slap long/short logic on alerts to build a lean, mean trading machine. Open source from SightLing Labs—grab it, hack it, profit from it. Share your tweaks in the comments and let’s outsmart the market together. Trade hard, win big!
Индикатор Pine Script®
от SightLineLabs
Kaufman Trend Strength Signal█ Overview Kaufman Trend Strength Signal is an advanced trend detection tool that decomposes price action into its underlying directional trend and localized oscillation using a vector-based Kalman Filter. By integrating adaptive smoothing and dynamic weighting via a weighted moving average (WMA), this indicator provides real-time insight into both trend direction and trend strength — something standard moving averages often fail to capture. The core model assumes that observed price consists of two components: (1) a directional trend, and (2) localized noise or oscillation. Using a two-step Predict & Update cycle, the filter continuously refines its trend estimate as new market data becomes available. █ How It Works This indicator employs a Kalman Filter model that separates the trend from short-term fluctuations in a price series. Predict & Update Cycle : With each new bar, the filter predicts the price state and updates that prediction using the latest observed price, producing a smooth but adaptive trend line. Trend Strength Normalization : Internally, the oscillator component is normalized against recent values (N periods) to calculate a trend strength score between -100 and +100. (Note: The oscillator is not plotted on the chart but is used for signal generation.) Filtered MA Line : The trend component is plotted as a smooth Kalman Filter-based moving average (MA) line on the main chart. Threshold Cross Signals : When the internal trend strength crosses a user-defined threshold (default: ±60), visual entry arrows are displayed to signal momentum shifts. █ Key Features Adaptive Trend Estimation : Real-time filtering that adjusts dynamically to market changes. Visual Buy/Sell Signals : Entry arrows appear when the trend strength crosses above or below the configured threshold. Built-in Range Filter : The MA line turns blue when trend strength is weak (|value| < 10), helping you filter out choppy, sideways conditions. █ How to Use Trend Detection :  • Green MA = bullish trend  • Red MA = bearish trend  • Blue MA = no trend / ranging market Entry Signals :  • Green triangle = trend strength crossed above +Threshold → potential bullish entry  • Red triangle = trend strength crossed below -Threshold → potential bearish entry █ Settings Entry Threshold : Level at which the trend strength triggers entry signals (default: 60) Process Noise 1 & 2 : Control the filter’s responsiveness to recent price action. Higher = more reactive; lower = smoother. Measurement Noise : Sets how much the filter "trusts" price data. High = smoother MA, low = faster response but more noise. Trend Lookback (N2) : Number of bars used to normalize trend strength. Lower = more sensitive; higher = more stable. Trend Smoothness (R2) : WMA smoothing applied to the trend strength calculation. █ Visual Guide Green MA Line → Bullish trend Red MA Line → Bearish trend Blue MA Line → Sideways/range Green Triangle → Entry signal (trend strengthening) Red Triangle → Entry signal (trend weakening) █ Best Practices In high-volatility conditions, increase Measurement Noise to reduce false signals. Combine with other indicators (e.g., RSI, MACD, EMA) for confirmation and filtering. Adjust "Entry Threshold" and noise settings depending on your timeframe and trading style. ❗ Disclaimer This script is provided for educational purposes only and should not be considered financial advice or a recommendation to buy/sell any asset. Trading involves risk. Past performance does not guarantee future results. Always perform your own analysis and use proper risk management when trading.
Индикатор Pine Script®
от PakunFX
Kaufman Trend Strategy# ✅ Kaufman Trend Strategy – Full Description (Script Publishing Version) **Kaufman Trend Strategy** is a dynamic trend-following strategy based on Kaufman Filter theory. It detects real-time trend momentum, reduces noise, and aims to enhance entry accuracy while optimizing risk. ⚠️ _For educational and research purposes only. Past performance does not guarantee future results._ --- ## 🎯 Strategy Objective - Smooth price noise using Kaufman Filter smoothing - Detect the strength and direction of trends with a normalized oscillator - Manage profits using multi-stage take-profits and adaptive ATR stop-loss logic --- ## ✨ Key Features - **Kaufman Filter Trend Detection** Extracts directional signal using a state space model. - **Multi-Stage Profit-Taking** Automatically takes partial profits based on color changes and zero-cross events. - **ATR-Based Volatility Stops** Stops adjust based on swing highs/lows and current market volatility. --- ## 📊 Entry & Exit Logic **Long Entry** - `trend_strength ≥ 60` - Green trend signal - Price above the Kaufman average **Short Entry** - `trend_strength ≤ -60` - Red trend signal - Price below the Kaufman average **Exit (Long/Short)** - Blue trend color → TP1 (50%) - Oscillator crosses 0 → TP2 (25%) - Trend weakens → Final exit (25%) - ATR + swing-based stop loss --- ## 💰 Risk Management - Initial capital: `$3,000` - Order size: `$100` per trade (realistic, low-risk sizing) - Commission: `0.002%` - Slippage: `2 ticks` - Pyramiding: `1` max position - Estimated risk/trade: `~0.1–0.5%` of equity > ⚠️ _No trade risks more than 5% of equity. This strategy follows TradingView script publishing rules._ --- ## ⚙️ Default Parameters - **1st Take Profit**: 50% - **2nd Take Profit**: 25% - **Final Exit**: 25% - **ATR Period**: 14 - **Swing Lookback**: 10 - **Entry Threshold**: ±60 - **Exit Threshold**: ±40 --- ## 📅 Backtest Summary - **Symbol**: USD/JPY - **Timeframe**: 1H - **Date Range**: Jan 3, 2022 – Jun 4, 2025 - **Trades**: 924 - **Win Rate**: 41.67% - **Profit Factor**: 1.108 - **Net Profit**: +$1,659.29 (+54.56%) - **Max Drawdown**: -$1,419.73 (-31.87%) --- ## ✅ Summary This strategy uses Kaufman filtering to detect market direction with reduced lag and increased smoothness. It’s built with visual clarity and strong trade management, making it practical for both beginners and advanced users. --- ## 📌 Disclaimer This script is for educational and informational purposes only and should not be considered financial advice. Use with proper risk controls and always test in a demo environment before live trading.
Стратегия Pine Script®
от PakunFX
Trailing Monster StrategyTrailing Monster Strategy This is an experimental trend-following strategy that incorporates a custom adaptive moving average (PKAMA), RSI-based momentum filtering, and dynamic trailing stop-loss logic. It is designed for educational and research purposes only, and may require further optimization or risk management considerations prior to live deployment. Strategy Logic The strategy attempts to participate in sustained price trends by combining: - A Power Kaufman Adaptive Moving Average (PKAMA) for dynamic trend detection, - RSI and Simple Moving Average (SMA) filters for market condition confirmation, - A delayed trailing stop-loss to manage exits once a trade is in profit. Entry Conditions Long Entry: - RSI exceeds the overbought threshold (default: 70), - Price is trading above the 200-period SMA, - PKAMA slope is positive (indicating upward momentum), - A minimum number of bars have passed since the last entry. Short Entry: - RSI falls below the oversold threshold (default: 30), - Price is trading below the 200-period SMA, - PKAMA slope is negative (indicating downward momentum), -A minimum number of bars have passed since the last entry. Exit Conditions - A trailing stop-loss is applied once the position has been open for a user-defined number of bars. - The trailing distance is calculated as a fixed percentage of the average entry price. Technical Notes This script implements a custom version of the Power Kaufman Adaptive Moving Average (PKAMA), conceptually inspired by alexgrover’s public implementation on TradingView . Unlike traditional moving averages, PKAMA dynamically adjusts its responsiveness based on recent market volatility, allowing it to better capture trend changes in fast-moving assets like altcoins. Disclaimer This strategy is provided for educational purposes only. It is not financial advice, and no guarantee of profitability is implied. Always conduct thorough backtesting and forward testing before using any strategy in a live environment. Adjust inputs based on your individual risk tolerance, asset class, and trading style. Feedback is encouraged. You are welcome to fork and modify this script to suit your own preferences and market approach.
Стратегия Pine Script®
от wielkieef
1
Litecoin Trailing-Stop StrategyAltcoins Trailing-Stop Strategy This strategy is based on a momentum breakout approach using PKAMA (Powered Kaufman Adaptive Moving Average) as a trend filter, and a delayed trailing stop mechanism to manage risk effectively. It has been designed and fine-tuned Altcoins, which historically shows consistent volatility patterns and clean trend structures, especially on intraday timeframes like 15m and 30m. Strategy Logic: Entry Conditions: Long when PKAMA indicates an upward move Short when PKAMA detects a downward trend Minimum spacing of 30 bars between trades to avoid overtrading Trailing Stop: Activated only after a customizable delay (delayBars) User can set trailing stop % and delay independently Helps avoid premature exits due to short-term volatility Customizable Parameters: This strategy uses a custom implementation of PKAMA (Powered Kaufman Adaptive Moving Average), inspired by the work of alexgrover PKAMA is a volatility-aware moving average that adjusts dynamically to market conditions, making it ideal for altcoins where trend strength and direction change frequently. This script is for educational and experimental purposes only. It is not financial advice. Please test thoroughly before using it in live conditions, and always adapt parameters to your specific asset and time frame. Feedback is welcome! Feel free to clone and adapt it for your own trading style.
Стратегия Pine Script®
от wielkieef
Fourier Transformed & Kalman Filtered EMA Crossover [Mattes]The Fourier Transformed & Kalman Filtered EMA Crossover (FTKF EMAC) is a trend-following indicator that leverages Fourier Transform approximation, Kalman Filtration, and two Exponential Moving Averages (EMAs) of different lengths to provide accurate and smooth market trend signals. By combining these three components, it captures the underlying market cycles, reduces noise, and produces actionable insights, making it suitable for detecting both emerging trends and confirming existing ones. TECHNICALITIES: >>> The Fourier Transform approximation is designed to identify dominant cyclical patterns in price action by focusing on key frequencies, while filtering out noise and less significant movements. It emphasizes the most meaningful price cycles, enabling the indicator to isolate important trends while ignoring minor fluctuations. This cyclical awareness adds an extra layer of depth to trend detection, allowing the EMAs to work with a cleaner and more reliable data set. >>> The Kalman Filter adds dynamic noise reduction, adjusting its predictions of future price trends based on past and current data. As new price data comes in, the filter recalibrates itself to ensure that the price action remains smooth and devoid of erratic movements. This real-time adjustment is key to minimizing lag while avoiding false signals, which ensures that the EMAs react to more accurate and stable market data. The Kalman Filter’s ability to smooth price data without losing sensitivity to trend changes complements the Fourier approximation, ensuring a high level of precision in volatile and stable market environments. >>> The EMA Crossover involves using two EMAs: a shorter EMA that reacts quickly to price movements and a longer EMA that responds more slowly. The shorter EMA is responsible for capturing immediate market shifts, detecting potential bullish or bearish trends. The longer EMA smooths out price fluctuations and provides trend confirmation, working with the shorter EMA to ensure the signals are reliable. When the shorter EMA crosses above the longer EMA, it indicates a bullish trend, likewise when it goes below the longer EMA, it signals a bearish trend. This setup provides a clear way to track market direction, with color-coded signals (green for bullish, red for bearish) for visual clarity. The flexibility of adjusting the EMA periods allows traders to fine-tune the indicator to their preferred timeframe and strategy, making it adaptable to different market conditions. |-> A key technical aspect is that the first EMA should always be shorter than the second one. If the first EMA is longer than the second, the tool’s effectiveness is compromised because the faster EMA is designed to signal long conditions, while the longer one is made for signaling a bearish trend. Reversing their roles would lead to delayed or confused signals, reducing the indicator’s ability to detect trend shifts early and making it less efficient in volatile markets. This is the only key weakness of the indicator, failure to submit to this rule will result in confusion. >>> These components work together like a clock to create a comprehensive and effective trend-following system. The Fourier approximation highlights key cyclical movements, the Kalman Filter refines these movements by removing noise, and the EMAs interpret the filtered data to generate actionable trend signals. Each component enhances the next, ensuring that the final output is both responsive and reliable, with minimal false signals or lag. creating an indicator using widespread concepts which haven't been combined before. Summary This indicator combines Fourier Transform approximation, Kalman Filtration, and two EMAs of different lengths to deliver accurate and timely trend-following signals. The Fourier approximation identifies dominant market cycles, while the Kalman Filter dynamically removes noise and refines the price data in real time. The two EMAs then use this filtered data to generate buy and sell signals based on their crossovers. The shorter EMA reacts quickly to price changes, while the longer EMA provides smoother trend confirmation. The components work in synergy to capture trends with minimal false signals or lag, ensuring traders can act promptly on market shifts. Customizable EMA periods make the tool adaptable to different market conditions, enhancing its versatility for various trading strategies. To use the indicator, traders should adjust the EMA lengths based on their timeframe and strategy, ensuring that the shorter EMA remains shorter than the longer EMA to preserve the tool’s responsiveness. The color-coded signals offer visual clarity, making it easy to identify potential entry and exit points. This confluence of Fourier, Kalman, and EMA methodologies provides a smooth, highly effective trend-following tool that excels in both trending and ranging markets.
Индикатор Pine Script®
от Mattes00
3