Mulai

Rata-Rata Pergerakan Adaptif Kaufman / Kaufman's Adaptive Moving Average (KAMA)

Gaussian Channel System [GCS]## DESCRIPTION Gaussian Ribbon Engine (GRE) is a multi-layer trend analysis system built on the Arnaud Legoux Moving Average (ALMA), which applies a Gaussian (bell curve) weighting function to price data instead of the linear or exponential weights used by traditional moving averages. **Mathematical Foundation** ALMA uses a Gaussian kernel — the same bell-curve distribution found in statistics and physics — to weight the prices in its lookback window. The Gaussian function is parameterized by two values: offset (which shifts the bell curve left or right, controlling responsiveness vs. smoothness) and sigma (which controls the width of the bell curve, determining how sharply weights decay from the center). The formula applies: w(i) = exp(-((i - offset * (N-1))^2) / (2 * sigma^2 * N^2)), where each price bar receives a weight according to its position on the Gaussian curve. This produces a moving average with mathematically optimal noise filtering properties. GRE constructs five ALMA layers with increasing periods (default 9, 21, 55, 100, 200), creating a visual ribbon. When all five layers align in order (fastest on top for bullish, fastest on bottom for bearish), the market is in full directional agreement. The spread between the outermost layers, measured as a percentage and compared to its own historical average, identifies squeeze (convergence) and expansion (divergence) conditions. **9-Point Confluence Scoring** The scoring matrix evaluates: price vs. Layer 1, Layer 1 vs. 2, Layer 2 vs. 3, Layer 3 vs. 4, Layer 4 vs. 5 alignment, Layer 1 slope direction, Layer 3 slope direction, RSI above/below 50, and DI+/DI- directional movement. Signals fire when the score crosses the configurable threshold with ADX confirmation. **Features** - Five-layer ALMA ribbon with Gaussian kernel weighting - Adjustable offset (0-1) and sigma parameters for fine-tuning the Gaussian bell shape - Ribbon spread analysis with squeeze and expansion detection - 9-point confluence scoring with visual dot notation in dashboard - ATR-based dual take-profit levels (TP1 and TP2) - Squeeze breakout signals when ribbon compresses then expands - ADX and volume confirmation filters - Full color-coded dashboard with regime classification - Multiple alert conditions ---
Indikator Pine Script®
oleh Jamtrader2020
Kaufman Efficiency Ratio Gate [NovaLens]Kaufman Efficiency Ratio Gate is a regime classifier that separates trending markets from choppy ones. Instead of plotting a raw ratio and leaving you to interpret thresholds, it ranks the current Kaufman Efficiency Ratio within its own recent history and outputs a binary gate: trend-favorable or chop-dominant. Five timeframe-specific presets ship ready to use - pick the one matching your chart. ◉ HOW IT WORKS The Efficiency Ratio measures how much of price's total movement was directional over N bars: ER = |Close - Close(N)| / Σ|Close(i) - Close(i-1)| A value near 1.0 means price moved in a straight line - maximum efficiency. A value near 0 means price covered distance but went nowhere net - noise. Perry Kaufman introduced this in "Trading Systems and Methods" (1995) as the foundation for his Adaptive Moving Average. Raw ER values are hard to threshold because what counts as "efficient" varies by asset and timeframe. This gate solves that with a three-stage pipeline: • Light EMA smoothing - removes single-bar noise from the raw ER without adding meaningful lag (Smoothing = 2 for most presets). • Percentile rank - ranks the smoothed ER within its own rolling window. A reading at the 70th percentile means the current efficiency is higher than 70% of recent history. This is what makes the gate self-normalizing. A "trending" efficiency ratio for Gold might sit at 0.45, while for a volatile altcoin it might be 0.25 - the gate adjusts automatically to each asset's own baseline, so you never need to guess at fixed thresholds. • Symmetric hysteresis - the gate opens when rank crosses above the median + Stability/2, and closes when rank drops below the median - Stability/2. This prevents flicker at the boundary. A small buffer (Stability = 2) is enough because KER is already a clean ratio. Other regime tools approach this differently. ADX measures trend strength through smoothed directional movement - it tells you how strong a trend is, but its fixed scale means a reading of 25 carries different weight on different instruments. The Choppiness Index compresses ATR relative to the window's price range into a 0-100 scale - useful, but sensitive to window length and not inherently normalized. The Efficiency Ratio takes a more direct route: what fraction of total movement was net directional? And the percentile-rank layer on top makes that reading self-normalizing across any asset or timeframe - no manual threshold tuning required. The result is a binary state: trend-favorable (gate open) or chop-dominant (gate closed). ◈ HOW TO READ IT • Teal background / teal hero line - Gate open. The market's directional efficiency is above its recent median. Trend-following setups tend to perform better in this environment. • Amber background / amber hero line - Gate closed. Efficiency is below the median - price is moving but not going anywhere. Trend-following setups historically tend to underperform in this environment. • Bright teal (strong trend) - Smoothed KER is in the top 25% of its recent rank window. The trend is unusually clean - continuation setups tend to be cleaner in this state. • Bright amber (strong chop) - Smoothed KER is in the bottom 25%. Noise is dominant - even range-bound strategies may find fewer clean entries. Generally a low-opportunity environment. The info panel (top-right) shows the current gate state, smoothed KER value, percentile rank, and a momentum readout (strengthening / weakening / stable) based on how the rank has moved over the last few bars. ✦ HOW WE USE IT - REGIME FILTER In systematic trading, the Efficiency Ratio often serves as one of the regime filters applied before a trend-following signal gets capital allocation. The idea is to confirm that the market is actually trending efficiently, not just moving. When the gate is open (teal), directional efficiency is elevated. Pullback entries, breakout continuations, trend-following MA crosses - these setups tend to perform better because price is converting movement into net progress. When the gate closes (amber), the same setups historically tend to underperform. Price is volatile but going nowhere. In choppy regimes, trend-following systems generally struggle, and while mean reversion may be more favorable, it remains a harder environment to trade overall. Many systematic traders use this kind of regime awareness to reduce exposure or adjust position sizing rather than forcing directional bets. The gate works well as a context overlay alongside other entry signals. It doesn't indicate which direction to trade, but it helps characterize whether the current environment is rewarding directional movement at all. What the gate is NOT: a forward predictor. It classifies the recent past. A gate-open reading means efficiency has been high - it doesn't guarantee the next bar will trend. It's a filter, not a crystal ball. ◆ OTHER APPLICATIONS • Entry filter - pair with any trend-following signal (MA cross, breakout, RSI) and add a gate-open condition. Filtering out chop regimes can help reduce whipsaw entries. • Multi-timeframe confirmation - checking the gate on a higher timeframe before entering on a lower one can add confidence. For example, a Daily gate-open reading alongside a 4H trend entry. • Regime-aware sizing - some traders scale position size with regime state, increasing exposure during gate-open periods and reducing it when the gate closes. • Alert-driven workflow - set alerts on gate open/close transitions and check your trend setups only when the gate fires. ⚙ SETTINGS Preset (default: Daily) - Timeframe-specific parameter bundles. Select the one matching your chart resolution: • Weekly - KER 10, Smoothing 2, Rank Window 100, Stability 2. Long context window for position traders. • Daily - KER 8, Smoothing 2, Rank Window 50, Stability 2. The default. Works well on most daily charts. • 8H - KER 8, Smoothing 2, Rank Window 50, Stability 2. Starting point same as Daily - validate on your own 8H charts. • 4H - KER 14, Smoothing 2, Rank Window 30, Stability 2. Wider KER period compensates for noisier intraday data. • 30m - KER 5, Smoothing 5, Rank Window 30, Stability 2. Short KER period with heavier smoothing for fast charts. • Custom - Drive the gate from the four inputs below. KER Period - Lookback for the raw Efficiency Ratio. Shorter (5-8) reacts faster to regime changes. Longer (14-20) gives more stable readings but lags transitions. Smoothing - EMA applied to the raw KER. Set to 1 for no smoothing. KER is self-normalizing by construction, so low values (1-5) are usually enough. Rank Window - Rolling window for the percentile rank. Controls how much recent history defines "typical." Shorter windows adapt faster; longer windows give more stable context. Stability - Hysteresis half-width around the 50th percentile. At 0, the gate flips the instant rank crosses the median. At higher values, the gate requires a stronger signal to switch state. Low values (2-5) work well since KER is already a clean signal. Display toggles: • Show Raw KER - thin white line showing the unsmoothed ratio • Show Median - 50th-percentile reference line on the smoothed KER • Show Background - teal/amber background wash (turn off if your workspace already signals the regime elsewhere) • Show Info Panel - gate state, KER, rank, and momentum readout • Light Theme - flips panel colours for light chart backgrounds △ LIMITATIONS • Backward-looking - the gate classifies recent efficiency, not future direction. Regimes can shift faster than the rank window catches, especially around news events. • Directionless - both strong uptrends and strong downtrends produce gate-open readings. A separate directional indicator is needed to determine which side to trade. • Noise on thin instruments - short KER periods on low-volume assets can produce noisy readings even with smoothing. • History requirement - percentile rank needs sufficient data to be meaningful. The first ~50-100 bars on any chart (depending on preset) will have unstable rankings. ⌁ NOTES • Based on Perry Kaufman's Efficiency Ratio from "Trading Systems and Methods" (1995) • Cross-validated against our PyneCore Python reference implementation. • Parameters were tuned on gold (XAUUSD) via the NovaLens research pipeline. The same presets generalize reasonably to other assets - though testing on your own instruments is always recommended. • Regime palette: teal = trend-favorable, amber = chop-dominant. Not green/red - this is a state classifier, not a directional signal. If you find a Custom parameter set that works well on a different instrument, the comments are a good place to share it.
Indikator Pine Script®
oleh NovaLens
AG Pro KAMA Efficiency Zones [AGPro Series]AG Pro KAMA Efficiency Zones Overview KAMA stands for Kaufman’s Adaptive Moving Average. AG Pro KAMA Efficiency Zones is built around KAMA not as a simple trend-following line, but as an adaptive market reference for evaluating how efficiently price is moving. Instead of focusing only on direction, the script is designed to classify the quality of directional travel and separate cleaner movement from noisier, lower-clarity conditions. The core idea is straightforward: markets do not move with the same quality all the time. Some phases show relatively efficient directional travel where price stays organized around an adaptive path. Other phases become mixed, unstable, or reversion-prone, where direction weakens and noise becomes more dominant. This script is designed to map those changes visually through adaptive KAMA-based zones, state labels, and a compact panel that summarizes the current condition. This makes the tool structurally different from a basic moving average overlay. The objective is not to present KAMA as a one-line signal source. The objective is to use KAMA as the center of a state engine that helps users distinguish efficient trend phases from transitional or noisy environments. What this script does AG Pro KAMA Efficiency Zones evaluates price behavior around a Kaufman’s Adaptive Moving Average and organizes that behavior into visual market states. It does this by combining adaptive smoothing, slope behavior, distance from KAMA, and persistence around the KAMA path. The result is a chart framework that can help answer questions such as: • Is price moving in an efficient bullish or bearish path? • Is the market entering a mixed transition phase? • Has movement quality deteriorated into a noisier reversion-prone environment? • Is the adaptive path becoming stronger, weaker, or less stable? By turning those questions into zones and state-based chart feedback, the script aims to improve context rather than replace judgment. Unique edge The distinguishing feature of this script is that it does not treat KAMA as a standard moving average. Instead, it uses KAMA as the center of a layered efficiency model. That model focuses on the quality of movement, not just the existence of movement. Many tools emphasize momentum, volatility, volume pressure, or overbought/oversold conditions. This script is designed for a different purpose. It is a movement-quality map. It attempts to show whether price is traveling in a relatively efficient path or whether that path is degrading into a noisier condition where directional clarity may be weaker. This means the script is less about predicting a move and more about classifying the environment in which a move is taking place. How it works The script begins with KAMA, or Kaufman’s Adaptive Moving Average. KAMA is useful because it adapts its responsiveness according to market behavior. In cleaner directional phases it can respond more quickly, while in noisier phases it can become more conservative. That makes it a practical centerline for an efficiency-based state model. On top of KAMA, the script evaluates several components: 1. Efficiency behavior The script measures how directly price is moving relative to its recent path. This helps estimate whether price action is acting efficiently or becoming more erratic. 2. KAMA slope behavior The slope of KAMA is normalized so that directional angle can be evaluated in a more consistent way. Stronger and more persistent slope behavior supports higher-quality trend classifications. 3. Price-to-KAMA relationship Price position around KAMA helps determine whether movement is aligned with the adaptive path or drifting around it without clear structure. 4. Persistence The script also looks at how consistently price remains on one side of KAMA. That persistence can help distinguish a more stable move from a weaker and less durable one. These components are blended into a composite efficiency model that drives the active state and the corresponding visual zone. States and zones The script classifies market behavior into four main states: Efficient Bull Trend This state reflects a comparatively organized bullish environment where price and adaptive slope are aligned in a cleaner upward path. Efficient Bear Trend This state reflects a comparatively organized bearish environment where price and adaptive slope are aligned in a cleaner downward path. Transition This is a mixed condition. Direction may be weakening, changing, or failing to achieve the quality required for an efficient trend classification. Noise / Reversion This state reflects lower movement quality, weaker slope behavior, or a more unstable relationship between price and the adaptive path. The visual zone structure is designed to reinforce those classifications on the chart. Instead of using only one line, the script builds layered KAMA-centered bands so the user can read not only direction, but also how structured or fragile the current condition may be. How to read the chart The KAMA line is the adaptive spine of the script. The outer and inner bands represent zone structure around that adaptive path. In stronger trend states, the script increases the visual emphasis of the KAMA path and its supporting zone layers. In weaker or more mixed conditions, the script softens those visuals and allows the chart to communicate reduced clarity. State labels appear when the script confirms a meaningful shift in condition. These labels are intended to highlight a change in market state, not to promise a trade outcome. The on-chart panel summarizes the active reading using fields such as State, Efficiency, Score Band, Adaptive Bias, Active Zone, and Stability. This gives the user a compact interpretation layer without requiring every decision to be made directly from raw chart inspection. Key inputs KAMA Efficiency Length Controls the lookback used in the KAMA efficiency logic. Lower values react faster. Higher values smooth more noise. KAMA Fast Response and KAMA Slow Response Define the adaptive responsiveness range of the KAMA engine. ATR Length Used to normalize slope and distance so the tool behaves more consistently across different symbols and volatility conditions. KAMA Slope Lookback Controls how the script measures directional slope over time. Persistence Length Influences how much consistency price must show around KAMA before a move is treated as more structured. Efficient Trend Threshold and Noise Threshold These thresholds help determine when the model classifies a move as higher quality or lower quality. Zone Band ATR Width Adjusts the width of the adaptive visual zone. State Hold Bars Helps reduce rapid state flipping by requiring a condition to persist before the active state changes. Panel Font Size and Label Size Allow visual customization for different chart layouts and monitor sizes. Alerts The script includes state-oriented alerts intended to notify the user when market condition changes. These are designed around state transitions and movement-quality shifts rather than promotional “buy now” style messaging. Examples include bullish and bearish efficiency shifts, transition detection, noise-zone detection, efficiency recovery, efficiency breakdown, and trend strengthening. Alerts should be interpreted as contextual information. They are intended to support review and analysis, not to function as a standalone decision system. What this script is not This script is not a guarantee engine. It does not predict future price with certainty. It does not eliminate risk. It is not a substitute for broader market structure analysis, execution planning, or risk management. It should not be treated as a self-sufficient entry/exit system without additional confirmation and user judgment. Limitations and transparency All adaptive models are sensitive to parameter choices. Changing responsiveness, thresholds, smoothing, or persistence settings can materially affect the way states appear on the chart. Because the script is state-based, some shifts will naturally occur after the earliest turning point in price. That is part of the tradeoff involved in using confirmation and persistence to reduce noise. In highly erratic or news-driven conditions, classification can also become less stable. During those periods, transition or noise-oriented readings may occur more often, and users should interpret the visual output in that context. The script is best viewed as an analytical framework for movement quality and adaptive context, not as a promise of directional success. Practical use cases Users may find the script useful for: • separating cleaner trend phases from mixed or unstable phases • filtering chart environments before applying another workflow • evaluating whether direction is gaining or losing efficiency • adding adaptive context to discretionary analysis • comparing how different symbols behave around a KAMA-centered efficiency structure Risk disclosure This script is for analytical and educational use. It does not provide financial advice, investment advice, or guaranteed outcomes. Market conditions can change quickly, and any indicator can produce false, delayed, or incomplete signals. Users remain responsible for their own decisions, validation process, and risk management. In short, AG Pro KAMA Efficiency Zones is designed to help read the quality of movement, not just the direction of movement. It uses KAMA as an adaptive reference point and converts that reference into a structured zone and state model so users can assess whether price behavior appears efficient, transitional, or noisy.
Indikator Pine Script®
oleh AGProLabs
1
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.
Indikator Pine Script®
oleh 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.
Indikator Pine Script®
oleh 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.
Indikator Pine Script®
oleh 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.
Indikator Pine Script®
oleh 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.
Strategi Pine Script®
oleh 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
Indikator Pine Script®
oleh 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.
Indikator Pine Script®
oleh 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.
Indikator Pine Script®
oleh 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.
Indikator Pine Script®
oleh 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
Indikator Pine Script®
oleh 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.
Indikator Pine Script®
oleh 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.
Indikator Pine Script®
oleh 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!
Indikator Pine Script®
oleh 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.
Indikator Pine Script®
oleh 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.
Strategi Pine Script®
oleh 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.
Strategi Pine Script®
oleh 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.
Strategi Pine Script®
oleh wielkieef