Rozpocznij

Efficiencyratio

Adaptive Regime Filter + Divergence (AER-VN) [KEYALGOS]Adaptive Regime Filter + Divergence (AER-VN) Professional Grade Market Regime Classification with Advanced Divergence Detection Precision trend identification using Adaptive Efficiency Ratio methodology Volatility-normalized thresholds that adjust to real-time market conditions Automatic divergence detection (Regular and Hidden) with visual confirmation lines Four distinct market regimes with color-coded clarity Zero-lag signal generation with confirmation logic OVERVIEW The Adaptive Regime Filter with Volatility Normalization (AER-VN) represents a sophisticated evolution of traditional trend filtering methodologies. This proprietary indicator combines Kaufman's Efficiency Ratio principles with dynamic volatility adaptation to classify market conditions into four actionable regimes: Uptrend, Downtrend, Choppiness, and Consolidation. The integrated Divergence Detection System operates as a secondary analytical layer, identifying momentum exhaustion and trend continuation patterns through comparative analysis of price action versus efficiency metrics. Unlike standard oscillators that measure raw momentum, this system evaluates the quality of price movement, providing earlier and more reliable reversal signals. METHODOLOGY AND TECHNIQUE 1. Adaptive Efficiency Ratio (AER) Core Traditional efficiency ratios utilize static thresholds that fail across varying volatility environments. The AER-VN methodology introduces dynamic threshold calculation that self-adjusts based on current volatility relative to historical norms. Displacement Measurement: Calculates net price movement over the lookback period Path Distance Analysis: Sums absolute bar-to-bar movements to determine movement quality Efficiency Calculation: Ratio of displacement to path distance (0.0 to 1.0 scale) Threshold Adaptation: Baseline efficiency requirements scale proportionally with the ATR ratio When volatility expands (ATR above mean), the system automatically raises the efficiency threshold required to qualify as "trending." This prevents false trend signals during volatile chop. Conversely, during low volatility periods, the threshold contracts to capture subtle trending behavior. 2. Volatility Normalization Engine The Volatility Normalization component creates a relative volatility index by comparing current ATR readings against a rolling historical average. This produces an ATR Ratio that serves as the scaling factor for dynamic threshold calculation. Current ATR: Short-term volatility measurement (default 14 periods) Mean ATR: Long-term volatility baseline (default 50 periods) Adaptive Scaling: Raw threshold = Base Threshold x ATR Ratio Ceiling Protection: Maximum threshold cap prevents mathematically impossible requirements during extreme volatility events 3. Four Regime Classification System Uptrend (Teal): Efficiency exceeds dynamic threshold with positive price displacement. Indicates high-quality upward movement with minimal retracement. Downtrend (Maroon): Efficiency exceeds dynamic threshold with negative price displacement. Indicates sustained selling pressure with directional clarity. Choppiness (Orange): Efficiency below threshold during above-average volatility. Characterized by noisy, directionless movement with large wicks and whipsaws. Consolidation (Gray): Efficiency below threshold during below-average volatility. Represents quiet, range-bound markets with compressed price action. 4. Zero-Lag Divergence Detection The divergence system employs confirmed swing detection to identify pivotal highs and lows without repainting. Once a swing point is confirmed (price violates the extreme), the system evaluates four divergence classifications: Regular Bearish Divergence: Price records higher highs while Efficiency Ratio records lower highs. Indicates trend exhaustion and potential reversal to the downside. Regular Bullish Divergence: Price records lower lows while Efficiency Ratio records higher lows. Indicates selling exhaustion and potential reversal to the upside. Hidden Bearish Divergence: Price records lower highs while Efficiency Ratio records higher highs. Suggests continuation of the current downtrend after a pullback. Hidden Bullish Divergence: Price records higher lows while Efficiency Ratio records lower lows. Suggests continuation of the current uptrend after a retracement. Visual confirmation lines connect the relevant swing points on the indicator panel, allowing traders to verify divergence validity visually. INPUT PARAMETERS AND CONFIGURATION Efficiency Ratio Settings ER Lookback (N) Default: 10 | Range: 2+ The calculation period for efficiency measurement. Shorter values increase sensitivity to recent price action, suitable for scalping lower timeframes. Longer values smooth the oscillator, better for swing trading higher timeframes. Base ER Threshold Default: 0.25 | Range: 0.05 to 0.80 | Step: 0.05 The foundational efficiency level required in normalized volatility conditions. Higher values demand cleaner, more directional movement to trigger trending regime classification. Lower values allow noisier price action to qualify as trending. Max Threshold Cap Default: 0.65 | Range: 0.10 to 0.99 | Step: 0.05 The absolute ceiling for the dynamic threshold. This safety mechanism prevents the threshold from rising to levels mathematically impossible to achieve during extreme volatility expansion. Volatility Normalization Settings ATR Length Default: 14 | Range: 1+ The lookback period for Average True Range calculation. Determines how quickly the system responds to changing volatility conditions. ATR Mean Lookback Default: 50 | Range: 5+ The historical window for establishing the volatility baseline. Longer periods create a smoother volatility reference, while shorter periods adapt more quickly to regime changes in volatility. Divergence Detection Settings Swing Definition Length Default: 10 | Range: 3+ The lookback window for identifying swing highs and lows. Determines the minimum number of bars required to establish a pivot point. Lower values detect micro-swings (more signals, more noise). Higher values detect major swings (fewer signals, higher quality). Visual Display Toggles Show Regular Div Markers: Display circle markers for Regular Bearish and Regular Bullish divergences Show Hidden Div Markers: Display circle markers for Hidden Bearish and Hidden Bullish divergences Line: Regular Bearish: Draw connecting lines between swing highs for Regular Bearish divergences (Red) Line: Regular Bullish: Draw connecting lines between swing lows for Regular Bullish divergences (Lime) Line: Hidden Bearish: Draw connecting lines for Hidden Bearish divergences (Orange) Line: Hidden Bullish: Draw connecting lines for Hidden Bullish divergences (Aqua) Visual Settings Color Price Bars Toggle to apply regime colors directly to price candles/bars on the main chart. Uptrend (Teal), Downtrend (Maroon), Choppiness (Orange), Consolidation (Gray). INTERPRETATION GUIDE Reading the Oscillator The main panel displays three critical elements: Efficiency Ratio Line: The primary oscillator colored by current regime. Values near 1.0 indicate perfect efficiency (strong trend). Values near 0.0 indicate complete inefficiency (chop). Dynamic Threshold: The white crossed line representing the current volatility-adjusted efficiency requirement. When the ER line crosses above this threshold, the regime shifts to trending. Base Threshold Reference: The gray dotted line showing the static baseline (0.25 default) for reference. Divergence Signal Interpretation Red Circle (Regular Bearish): Momentum divergence at highs. Consider reducing long exposure or preparing short entries. Highest probability when appearing near resistance or after extended uptrends. Lime Circle (Regular Bullish): Momentum divergence at lows. Consider reducing short exposure or preparing long entries. Highest probability when appearing near support or after extended downtrends. Orange Circle (Hidden Bearish): Trend continuation signal in downtrends. Pullback likely ending, downtrend resumption probable. Aqua Circle (Hidden Bullish): Trend continuation signal in uptrends. Retracement likely ending, uptrend resumption probable. TRADING APPLICATIONS Strategy 1: Regime-Based Trend Following Enter long positions only when the indicator displays Teal coloring (Uptrend regime) and short positions only during Maroon coloring (Downtrend regime). Exit positions when the regime shifts to Orange or Gray, indicating the trending condition has ended. Best for: Directional traders and trend followers Timeframe: M15 and higher recommended for stability Confluence: Combine with moving average alignment or breakout patterns Strategy 2: Divergence Reversal Trading Monitor for Regular Divergences (Red or Lime circles) as early warning systems. Wait for price confirmation (engulfing candles, pin bars) at the divergence point before entering. Use the connecting lines to visualize the divergence strength. Best for: Counter-trend scalpers and swing traders Timeframe: M5 to H1 depending on swing length settings Confluence: Combine with support/resistance levels and volume analysis Strategy 3: Chop Avoidance and Consolidation Breakout Use the Orange (Choppiness) and Gray (Consolidation) regimes as "No Trade" zones or reduction zones. Wait for a divergence to form within these regimes, then enter when the regime shifts back to trending (Teal or Maroon), capturing the breakout momentum. Best for: Patience-based traders seeking high-probability setups Timeframe: Effective across all timeframes Confluence: Combine with volume expansion on regime change Strategy 4: Hidden Divergence Continuation In established trends (Teal lasting 5+ bars), look for Hidden Bullish Divergences (Aqua) on pullbacks to enter additional long positions. In established downtrends (Maroon), look for Hidden Bearish Divergences (Orange) on rallies to add to shorts. Best for: Position traders adding to winners Timeframe: H1 to Daily for best results Confluence: Combine with Fibonacci retracement levels OPTIMIZATION GUIDELINES For Lower Timeframes (M1 to M5) Reduce ER Lookback to 6-8 for faster response Lower Base ER Threshold to 0.15-0.20 to account for noise Reduce Max Threshold Cap to 0.50-0.55 Shorten ATR Mean Lookback to 20-30 Reduce Swing Definition Length to 6-8 for micro-structure For Higher Timeframes (H1 to Daily) Increase ER Lookback to 14-21 for smoother readings Maintain or increase Base ER Threshold to 0.30+ for quality control Increase ATR Mean Lookback to 50-100 for stable volatility baselines Increase Swing Definition Length to 10-20 for major pivots only ALERTS AND NOTIFICATIONS The indicator includes built-in alert conditions for: Regular Bearish Divergence Detection Regular Bullish Divergence Detection Configure TradingView alerts to trigger on these conditions to monitor markets without constant chart watching. BEST PRACTICES AND RISK MANAGEMENT Always confirm divergence signals with price action patterns (engulfing candles, pin bars, break of structure) rather than entering immediately on marker appearance. Avoid trading divergence signals that occur deep within the Choppiness (Orange) regime without waiting for a regime shift confirmation. Use the regime colors as a position sizing guide: Full size in Teal/Maroon, half size in Gray, flat or minimal in Orange. The indicator excels when combined with support/resistance analysis. Divergences forming at key S/R levels carry significantly higher probability. In ranging markets, decrease the Base ER Threshold to reduce whipsaws. In strongly trending markets, consider increasing it to filter out minor retracements. TECHNICAL NOTES The indicator does not repaint. Swing points require confirmation on the subsequent bar to print, ensuring signals remain fixed after formation. All calculations utilize Pine Script v6 native functions for optimal performance and minimal resource usage. The Volatility Normalization component prevents the common failure mode of static efficiency indicators during periods of expanding volatility. Connecting lines for divergences are managed with automatic cleanup protocols to prevent chart clutter on extended runs. SUPPORT AND UPDATES This indicator is maintained by KeyAlgos. All users receive automatic updates as methodology improvements are implemented. For questions regarding parameter optimization or implementation strategies, utilize the TradingView comments section on this publication. Disclaimer: This indicator is a technical analysis tool designed to assist with market analysis, not a guaranteed profit system. Always practice proper risk management and use stop losses. Past performance of indicator signals does not guarantee future results.
Wskaźnik Pine Script®
od KeyAlgos
2
Adaptive MAD Supertrend | GForgeAdaptive MAD Supertrend | GForge The Adaptive MAD Supertrend is a trend-following indicator built on the classic Supertrend framework, but with two core innovations that address well-known weaknesses in the original design: how volatility is measured, and how the indicator behaves across different market conditions. The Problem With Standard Supertrend The classic Supertrend uses ATR (Average True Range) as its volatility measure and a fixed multiplier. ATR squares its deviation calculations internally, which means a single spike candle — a news wick, a liquidation cascade — can temporarily blow the bands wide and either trigger a false flip or delay a valid one. On top of that, a fixed multiplier means the indicator behaves identically in a clean trending market and a choppy ranging one. It has no awareness of what the market is actually doing. Innovation 1 — MAD Replaces ATR This indicator uses Mean Absolute Deviation as its volatility measure instead of ATR or Standard Deviation. MAD = mean( |close − mean(close, n)| , n ) The key difference is linearity. Each bar contributes its deviation to the average equally, without squaring. A spike candle influences the band width, but proportionally — it cannot disproportionately dominate the calculation the way it can in ATR or StdDev. The result is a more stable, consistent band width that responds to genuine volatility without overreacting to outlier bars. An optional EMA smoothing layer can be applied to the raw MAD output before it scales the bands, which further stabilizes band width during volatile periods. Innovation 2 — Kaufman Efficiency Ratio Scales the Multiplier The Efficiency Ratio (ER), developed by Perry Kaufman, measures how efficiently price is moving: ER = |net price change over n bars| / sum(|bar-to-bar changes|, n) ER → 1.0: price moved efficiently in one direction — a clean trend ER → 0.0: price moved a lot but went nowhere — chop and noise The adaptive multiplier uses ER to scale band width dynamically: adaptive_multiplier = Multiplier_Chop − ER × (Multiplier_Chop − Multiplier_Trend) During a clean trend, the multiplier contracts toward your Trend setting — bands tighten and the trailing stop follows price closely, capturing more of the move. During choppy conditions, the multiplier expands toward your Chop setting — bands widen and the stop absorbs noise without flipping unnecessarily. This means the indicator automatically adjusts its sensitivity to what the market is doing, rather than applying the same fixed behaviour to every bar. Basis MA The band centre line (basis) is fully configurable. Rather than raw hl2 like the classic Supertrend, any moving average from the menu can serve as the anchor — the band is built outward from it. The default is T3, a Tillson triple-smoothed MA that provides an extremely clean centre line with minimal lag overshoot. Other useful options include DEMA and TEMA for faster response, VWMA for volume-weighted anchoring, or RMA for higher timeframes. How to Read It Line colour: green/up colour = bullish trend, red/down colour = bearish trend Trend fill: shaded area between price and the trailing stop — visual confirmation of which side of the line price is on Inactive band: the faint dotted line on the opposite side shows where a flip would trigger if price reaches it Signal diamonds: markers at each trend flip — below bar for long entries, above bar for short/cash exits Notes Optimised and tested on Bitcoin 1D. Performs well on trending instruments across higher timeframes (4H and above). The MAD + ER combination is theoretically complementary: MAD handles what the band width is, ER handles how much of it to apply. They solve orthogonal problems. As with all trend-following tools, performance degrades in prolonged sideways markets — the Chop multiplier setting mitigates this but does not eliminate it. ⚠️ Disclaimer This indicator is a technical analysis tool provided for informational and educational purposes only. It is not financial advice, and nothing presented here should be construed as a recommendation to buy, sell, or hold any asset. Past performance does not guarantee future results. Developed by GForge
Wskaźnik Pine Script®
od GForge
FluxPulse Momentum [JOAT]FluxPulse Momentum - Adaptive Multi-Component Oscillator FluxPulse Momentum is a composite oscillator that blends three distinct momentum components into a single, smoothed signal line. Rather than relying on a single indicator, it synthesizes adaptive RSI, normalized rate of change, and a Kaufman-style efficiency ratio to provide a multi-dimensional view of momentum. What This Indicator Does Combines RSI, Rate of Change (ROC), and Efficiency Ratio into one weighted composite Applies EMA smoothing to reduce noise while preserving responsiveness Displays overbought/oversold zones with optional background highlighting Generates buy/sell signals when the oscillator crosses its signal line in favorable zones Provides a real-time dashboard showing current state, momentum direction, and efficiency Core Components Adaptive RSI (50% weight) — Standard RSI calculation normalized around the 50 level Normalized ROC (30% weight) — Rate of change scaled relative to its recent maximum range Efficiency Ratio (20% weight) — Measures directional movement efficiency, inspired by Kaufman's adaptive concepts The final composite is smoothed twice using EMA to create both a fast line and a signal line. Signal Logic // Buy signal: crossover in lower half buySignal = ta.crossover(qmo, qmoSmooth) and qmo < 50 // Sell signal: crossunder in upper half sellSignal = ta.crossunder(qmo, qmoSmooth) and qmo > 50 Signals are generated only when the oscillator is positioned favorably—buy signals occur below the 50 midline, sell signals occur above it. Dashboard Information The on-chart table displays: Current oscillator value with gradient coloring Momentum state (Overbought, Oversold, Bullish, Bearish, Neutral) Momentum direction and acceleration Efficiency ratio percentage Active signal status Inputs Overview RSI Length — Period for RSI calculation (default: 14) ROC Length — Period for rate of change (default: 10) Smoothing Length — EMA smoothing period (default: 3) Overbought/Oversold Levels — Threshold levels for zone detection Await Bar Confirmation — Wait for bar close before triggering alerts How to Use It Watch for crossovers between the main line and signal line Use overbought/oversold zones to identify potential reversal areas Monitor the histogram for momentum acceleration or deceleration Combine with price action analysis for confirmation Alerts Buy Signal — Bullish crossover in the lower zone Sell Signal — Bearish crossunder in the upper zone Overbought/Oversold Crosses — Level threshold crossings This indicator is provided for educational purposes. It does not constitute financial advice. Always conduct your own analysis before making trading decisions. — Made with passion by officialjackofalltrades
Wskaźnik Pine Script®
od officialjackofalltrades
ER-Adaptive ATR Limit Channels w/ States [Loxx]As simple as it gets, channels based on high, low and ATR distances, Shows possible short term support / resistance or can be used as a take profit/stop-loss in some trading systems. It does this by comparing high/low values of price to multiplied by a multiple of ATR to determine when the trend changes. States are included to change the sensitivity to trend changes. 1 is very sensitive, 3 is least sensitive. This uses Loxx's Expanded Source Types. You can read about them here: What is ER Adaptive ATR? Average True Range (ATR) is widely used indicator in many occasions for technical analysis . It is calculated as the RMA of true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range
Wskaźnik Pine Script®
od loxx
6
STD-Filtered, Adaptive Exponential Hull Moving Average [Loxx]STD-Filtered, Adaptive Exponential Hull Moving Average is a Kaufman Efficiency Ratio Adaptive Hull Moving Average that uses EMA instead of WMA for its computation. I've also added standard deviation stepping to further smooth the signal. Using EMA instead of WMA turns the Hull into what's called the AEHMA. You can read more about the EHMA here: eceweb1.rutgers.edu What is the traditional Hull Moving Average? The Hull Moving Average (HMA) attempts to minimize the lag of a traditional moving average while retaining the smoothness of the moving average line. Developed by Alan Hull in 2005, this indicator makes use of weighted moving averages to prioritize more recent values and greatly reduce lag. The resulting average is more responsive and well-suited for identifying entry points. What is Kaufman's Efficiency Ratio? The Efficiency Ratio (ER) was first presented by Perry Kaufman in his 1995 book ‘Smarter Trading‘. It is calculated by dividing the price change over a period by the absolute sum of the price movements that occurred to achieve that change. The resulting ratio ranges between 0 and 1 with higher values representing a more efficient or trending market. The value of the ER ranges between 0 and 1. It has the value of 1 when prices move in the same direction for the full time over which the indicator is calculated, e.g. n bars period. It has a value of 0 when prices are unchanged over the n periods. When prices move in wide swings within the interval, the sum of the denominator becomes very large compared to the numerator and ER approaches zero. Some uses for ER: A qualifier for a trend following trade; a trend is considered “persistent” only when RE is above a certain value, e.g. 0.3 or 0.4 . A filter to screen out choppy stocks/markets, where breakouts are frequently “fakeouts”. In an adaptive trading system, helping to determine whether to apply a trend following algorithm or a mean reversion algorithm. It is used in the calculation of Kaufman’s Adaptive Moving Average (KAMA). How to calculate the Hull Adaptive Moving Average (HAMA) Find Signal to Noise ratio (SNR) Normalize SNR from 0 to 1 Calculate adaptive alphas Apply EMAs Included Bar coloring Signals Alerts Loxx's Expanded Source Types
Wskaźnik Pine Script®
od loxx
4
ER-Adaptive ATR, STD-Adaptive Damiani Volatmeter [Loxx]ER-Adaptive ATR, STD-Adaptive Damiani Volatmeter is a Damiani Volatmeter with both Efficiency-Ratio Adaptive ATR, used in place of ATR, and Adaptive Deviation, used in place of Standard Deviation. What is Adaptive Deviation? By definition, the Standard Deviation (STD, also represented by the Greek letter sigma σ or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis we usually use it to measure the level of current volatility . Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA , we can call it EMA deviation. And added to that, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting). The difference when compared to standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions. The green line is the Adaptive Deviation, the white line is regular Standard Deviation. This concept will be used in future indicators to further reduce noise and adapt to price volatility . See here for a comparison between Adaptive Deviation and Standard Deviation What is Efficiency Ratio Adaptive ATR? Average True Range (ATR) is widely used indicator in many occasions for technical analysis . It is calculated as the RMA of true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range See here for a comparison between Efficiency-Ratio Adaptive ATR, and ATR. What is the Damiani Volatmeter? Damiani Volatmeter uses ATR and Standard deviation to tease out ticker volatility so you can better understand when it's the ideal time to trade. The idea here is that you only take trades when volatility is high so this indicator is to be coupled with various other indicators to validate the other indicator's signals. This is also useful for detecting crabbing and chopping markets. Shoutout to user @xinolia for the DV function used here. Anything red means that volatility is low. Remember volatility doesn't have a direction. Anything green means volatility high despite the direction of price. The core signal line here is the green and red line that dips below two while threshold lines to "recharge". Maximum recharge happen when the core signal line shows a yellow ping. Soon after one or many yellow pings you should expect a massive upthrust of volatility . The idea here is you don't trade unless volatility is rising or green. This means that the Volatmeter has to dip into the recharge zone, recharge and then spike upward. You can also attempt to buy or sell reversals with confluence indicators when volatility is in the recharge zone, but I wouldn't recommend this. However, if you so choose to do this, then use the following indicator for confluence. And last reminder, volatility doesn't have a direction! Red doesn't mean short, and green doesn't mean long, Red means don't trade period regardless of direction long/short, and green means trade no matter the direction long/short. This means you'll have to add an indicator that does show direction such as a mean reversion indicator like Fisher Transform or a Gaussian Filter. You can search my public scripts for various Fisher Transform and Gaussian Filter indicators. Price-Filtered Spearman Rank Correl. w/ Floating Levels is considered the Mercedes Benz of reversal indicators Comparison between this indicator, ER-Adaptive ATR, STD-Adaptive Damiani Volatmeter , and the regular Damiani Volatmeter . Notice that the adaptive version catches more volatility than the regular version. How signals work RV = Rising Volatility VD = Volatility Dump Plots White line is signal Thick red/green line is the Volatmeter line The dotted lower lines are the zero line and minimum recharging line Included Bar coloring Alerts Signals Related indicators Variety Moving Average Waddah Attar Explosion (WAE) Damiani Volatmeter
Wskaźnik Pine Script®
od loxx
2
Adaptive Deviation [Loxx]Adaptive Deviation is an educational/conceptual indicator that is a new spin on the regular old standard deviation. By definition, the Standard Deviation (STD, also represented by the Greek letter sigma σ or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis we usually use it to measure the level of current volatility. Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we can call it EMA deviation. And added to that, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting). The difference when compared to standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions. The green line is the Adaptive Deviation, the white line is regular Standard Deviation. This concept will be used in future indicators to further reduce noise and adapt to price volatility. Included Loxx's Expanded Source Types
Wskaźnik Pine Script®
od loxx
Pips-Stepped, Adaptive-ER DSEMA w/ DSL [Loxx]Pips-Stepped, Adaptive-ER DSEMA w/ DSL is an Efficiency-Ratio-Adaptive, Double-Smoothed EMA with Pips Stepping and Discontinued Signal Lines. This combination reduces noise and improves signal quality. What is Double Smoothed Exponential Moving Average (DSEMA) ? The Double Smoothed Exponential Moving Average is a lot less laggy compared to a traditional EMA . It's also considered a leading indicator compared to the EMA , and is best utilized whenever smoothness and speed of reaction to market changes are required. What is the efficiency ratio? In statistical terms, the Efficiency Ratio tells us the fractal efficiency of price changes. ER fluctuates between 1 and 0, but these extremes are the exception, not the norm. ER would be 1 if prices moved up 10 consecutive periods or down 10 consecutive periods. ER would be zero if price is unchanged over the 10 periods. Included: Bar coloring Signals Alerts EMA and FEMA Signal/ DSL smoothing Loxx's Expanded Source Types
Wskaźnik Pine Script®
od loxx
Efficiency-Ratio-Adaptive EMA [Loxx]Efficiency ratio was invented by Perry Kaufman as a measure of volatility and as a way of making some calculations adaptive. In his adaptive moving average he uses 3 periods for calculation which makes it a bit "cryptic" and, by all means, not so simple to use. This version is simplifying the whole thing without an intention to clone the KAMA indicator--but with the intention to use the efficiency ratio for adapting the average calculations and to use only two parameters for that: period price Included: Bar coloring
Wskaźnik Pine Script®
od loxx
Adaptive Oscillator constructor [lastguru]Adaptive Oscillators use the same principle as Adaptive Moving Averages. This is an experiment to separate length generation from oscillators, offering multiple alternatives to be combined. Some of the combinations are widely known, some are not. Note that all Oscillators here are normalized to -1..1 range. This indicator is based on my previously published public libraries and also serve as a usage demonstration for them. I will try to expand the collection (suggestions are welcome), however it is not meant as an encyclopaedic resource , so you are encouraged to experiment yourself: by looking on the source code of this indicator, I am sure you will see how trivial it is to use the provided libraries and expand them with your own ideas and combinations. I give no recommendation on what settings to use, but if you find some useful setting, combination or application ideas (or bugs in my code), I would be happy to read about them in the comments section. The indicator works in three stages: Prefiltering, Length Adaptation and Oscillators. Prefiltering is a fast smoothing to get rid of high-frequency (2, 3 or 4 bar) noise. Adaptation algorithms are roughly subdivided in two categories: classic Length Adaptations and Cycle Estimators (they are also implemented in separate libraries), all are selected in Adaptation dropdown. Length Adaptation used in the Adaptive Moving Averages and the Adaptive Oscillators try to follow price movements and accelerate/decelerate accordingly (usually quite rapidly with a huge range). Cycle Estimators, on the other hand, try to measure the cycle period of the current market, which does not reflect price movement or the rate of change (the rate of change may also differ depending on the cycle phase, but the cycle period itself usually changes slowly). Chande (Price) - based on Chande's Dynamic Momentum Index (CDMI or DYMOI), which is dynamic RSI with this length Chande (Volume) - a variant of Chande's algorithm, where volume is used instead of price VIDYA - based on VIDYA algorithm. The period oscillates from the Lower Bound up (slow) VIDYA-RS - based on Vitali Apirine's modification of VIDYA algorithm (he calls it Relative Strength Moving Average). The period oscillates from the Upper Bound down (fast) Kaufman Efficiency Scaling - based on Efficiency Ratio calculation originally used in KAMA Deviation Scaling - based on DSSS by John F. Ehlers Median Average - based on Median Average Adaptive Filter by John F. Ehlers Fractal Adaptation - based on FRAMA by John F. Ehlers MESA MAMA Alpha - based on MESA Adaptive Moving Average by John F. Ehlers MESA MAMA Cycle - based on MESA Adaptive Moving Average by John F. Ehlers , but unlike Alpha calculation, this adaptation estimates cycle period Pearson Autocorrelation* - based on Pearson Autocorrelation Periodogram by John F. Ehlers DFT Cycle* - based on Discrete Fourier Transform Spectrum estimator by John F. Ehlers Phase Accumulation* - based on Dominant Cycle from Phase Accumulation by John F. Ehlers Length Adaptation usually take two parameters: Bound From (lower bound) and To (upper bound). These are the limits for Adaptation values. Note that the Cycle Estimators marked with asterisks(*) are very computationally intensive, so the bounds should not be set much higher than 50, otherwise you may receive a timeout error (also, it does not seem to be a useful thing to do, but you may correct me if I'm wrong). The Cycle Estimators marked with asterisks(*) also have 3 checkboxes: HP (Highpass Filter), SS (Super Smoother) and HW (Hann Window). These enable or disable their internal prefilters, which are recommended by their author - John F. Ehlers . I do not know, which combination works best, so you can experiment. Chande's Adaptations also have 3 additional parameters: SD Length (lookback length of Standard deviation), Smooth (smoothing length of Standard deviation) and Power ( exponent of the length adaptation - lower is smaller variation). These are internal tweaks for the calculation. Oscillators section offer you a choice of Oscillator algorithms: Stochastic - Stochastic Super Smooth Stochastic - Super Smooth Stochastic (part of MESA Stochastic) by John F. Ehlers CMO - Chande Momentum Oscillator RSI - Relative Strength Index Volume-scaled RSI - my own version of RSI. It scales price movements by the proportion of RMS of volume Momentum RSI - RSI of price momentum Rocket RSI - inspired by RocketRSI by John F. Ehlers (not an exact implementation) MFI - Money Flow Index LRSI - Laguerre RSI by John F. Ehlers LRSI with Fractal Energy - a combo oscillator that uses Fractal Energy to tune LRSI gamma Fractal Energy - Fractal Energy or Choppiness Index by E. W. Dreiss Efficiency ratio - based on Kaufman Adaptive Moving Average calculation DMI - Directional Movement Index (only ADX is drawn) Fast DMI - same as DMI, but without secondary smoothing If no Adaptation is selected (None option), you can set Length directly. If an Adaptation is selected, then Cycle multiplier can be set. Before an Oscillator, a High Pass filter may be executed to remove cyclic components longer than the provided Highpass Length (no High Pass filter, if Highpass Length = 0). Both before and after the Oscillator a Moving Average can be applied. The following Moving Averages are included: SMA, RMA, EMA, HMA , VWMA, 2-pole Super Smoother, 3-pole Super Smoother, Filt11, Triangle Window, Hamming Window, Hann Window, Lowpass, DSSS. For more details on these Moving Averages, you can check my other Adaptive Constructor indicator: The Oscillator output may be renormalized and postprocessed with the following Normalization algorithms: Stochastic - Stochastic Super Smooth Stochastic - Super Smooth Stochastic (part of MESA Stochastic) by John F. Ehlers Inverse Fisher Transform - Inverse Fisher Transform Noise Elimination Technology - a simplified Kendall correlation algorithm "Noise Elimination Technology" by John F. Ehlers Except for Inverse Fisher Transform, all Normalization algorithms can have Length parameter. If it is not specified (set to 0), then the calculated Oscillator length is used. More information on the algorithms is given in the code for the libraries used. I am also very grateful to other TradingView community members (they are also mentioned in the library code) without whom this script would not have been possible.
Wskaźnik Pine Script®
od lastguru
5
Efficiency RatioThe efficiency ratio (ER) is described by Perry Kaufman in his book, Trading Systems and Methods. It works by measuring the momentum of the market, that is, the absolute change from the current price to a past price, and divides it by the volatility, which is the sum of the absolute changes of each bar. That makes this a bounded indicator, going from 0 to 100, like an oscillator. Higher values mean less noise, while lower values mean more. Eg.: if the market moves from 10.0 to 15.0 in a directional manner, with every bar up, the ER is going to be at 100. However, if it moves up and down, and goes all over the place until finally reaching 15.0, the ER is going to be at around 20. It is very difficult for the ER to be at zero, because that would require 0 volatility, which is almost impossible to occur. This indicator is useful when planning for trades. If you notice the ER being higher than average, you may choose to increase the position size, because that would mean that the market is directional and has less chance of a whipsaw.
Wskaźnik Pine Script®
od EduardoMattje
2
Efficiency Ratio (Market Noise) by Alejandro PThis is an indicator based on the Efficiency Ratio by Perry Kaufman. Like Price Density, which we have published previously, Perry Kaufman's Efficiency Ratio is a quantifiable method of measuring market noise. This version of the indicator includes a feature to make the values of the indicator change based on thresholds to easier visualize different market conditions. Additionally there is a directional feature which factors in the direction of the price moves. We can use the Efficiency Ratio to set rules and only trade particular systems when noise is at an appropriate level. For example, if noise is high then we would want to avoid trend following strategies and instead trade mean-reversion strategies, and vice-versa when the opposite is true. The Efficiency Ratio can also be used to match assets to strategies. Some assets will be naturally more noisy than others and therefore we might have a principle where we only trade those noisy assets with our mean reversion strategies and the more quiet assets with trend following strategies. Calculation: Efficiency Ratio = Absolute net change in close price / absolute sum of the individual close price changes The numerator looks at the absolute close change in price. It subtracts the starting close price in the period from the final close price in the period. The denominator compares the close price of one bar to the close price of the previous bar, this is performed for each of the successive bars in the whole period and then the value is summed. The absolute price is used because there are positive and negative values because each bar may close above or below the previous bar close. The Efficiency Ratio provides an opposite interpretation of market noise compared to Price Density. With Price Density high values = high noise and low values = low noise. With the Efficiency Ratio high values = low noise and low values = high noise. Comparing Price Density to Perry Kaufman's Efficiency Ratio: Similarities Both use the sum of the individual bar moves - Price Density - High - Low - Efficiency Ratio - Close to Close Differences Price Density uses the full price range (to determine the height of the box) Efficiency Ratio uses the net close price change over the period The interpretation of the values is the reverse for each Full credits to the source of the above information and interpretation.
Wskaźnik Pine Script®
od BlueCapitalTrading
2
RedK Volume-Weighted Directional Efficiency Index (DXF)RedK Volume-Weighted Directional Efficiency Index (DXF) is a momentum indicator - that builds on Kaufman's Efficiency Ratio (ER) concept. DXF utilizes a restricted +100/-100 oscillator to represent the "quality" of a trend, and does a good job in detecting the possibility of an upcoming trend change (in both direction and quality), improving our ability to make decisions on trade entries and exits. Here's a quick background on Kaufman's Efficiency Ratio (ER) ------------------------------------------------------------------------------- Copied from internet sources ----------------------------- Developed by Perry Kaufman and introduced in his book “New Trading Systems and Methods”, the Efficiency Ratio reflects relative market speed to volatility. There are cases, when it is used as a filter, which helps a trader to avoid ”choppy” markets or trading ranges and to identify smoother trends. ER is the result of dividing the net change in price movement during n-periods by the sum of all bar-to-bar price changes during the same n-periods. In case the market is trending smoother, then the ratio will be higher. In case the ratio shows readings in proximity to zero, this implies that market movement is inefficient and ”choppy”. If the Efficiency Ratio shows a reading of +100, this means that the trading instrument is in a bull trend and trending with perfect efficiency. If the Efficiency Ratio shows a reading of -100, this means that the trading instrument is in a bear trend and trending with perfect efficiency. It is impossible for any instrument to have a perfect Efficiency ratio, because any movement against the major trend during the examined period of time would cause the ratio to drop. If the Efficiency Ratio shows a reading above +30 (common setting for the "Significant Level"), this is indicative of a quality bull trend. If the ratio shows a reading below -30, this is indicative of a quality bear trend. ------------------------------------------------------------------------------- End of Copy ------------------------------------------------------------------------------------------------------- Kaufman also used the ER as basis for his famous Kaufman Adaptive Moving Average (KAMA). Read more on ER & Kama here How is DXF different from other ER-based indicators? ------------------------------------------------------------------ - Let's get the easy part out of the way: DXF has a "volume-weighting" option ✔ This option is OFF by default (to avoid errors with instruments with no volume data) - once this option is applied, it provides the benefit of combining the volume effect into the calculation - those who appreciate the effect of volume on price action will hopefully find this option valuable - The calculation of ER and how it can be "best utilized": Let's examine the ER concept a bit closer: as a (math) concept, the (original) Efficiency Ratio (ER) takes the positive change of the price of an instrument during a certain period, and divide it by the sum of (absolute) price moves that were observed during that same period. So, in the trader's language, we will be saying "out of a total of $20 moves (up and down) that MSFT did in the past 10 days, MSFT only made a net change of $5 up during that period" - so the "10-day ER" for MSFT in that case is 5/20 = 25% -- then we continue to observe that ongoing "10-day ER" and if it increases, we can expect that MSFT is going to establish a strong move (trend) up --- right? the magic word here is to "observe the ongoing ER" - many of the ER based indicators just use the ER as calculated by Kaufman's original method. IMHO, these are just "point-in-time readings" - if we hope to get real insights from the ER, we need to take an average of that reading - for our "time window" we're interested in - and only then we can identify trends and patterns in the ER value as it changes during that windowss- DXF does that - and that allows a trader to say "the (weighted) 5-day average of the 10-day ER for MSFT is increasing, and that why i expect an up-trend" -- makes sense ? both the "Lookback" used to calculate the ER, and the Length of observed "window" for the Average ER are adjustable in DXF settings Other Uses and Settings : --------------------------------- - As a momentum indicator, DXF can predict an upcoming change of trend - cause that will reflect on the average ER value. There are few examples in the chart where the price move and ER trend *do not agree* - The trader can see these signs and take decisions accordingly - DXF can help reveal best entries and exits: assume we are long-term bullish on MSFT, and we want to "buy the dip" - DXF can help reveal the time where price is recovering from extreme weakness - and that would be the ideal buy opportunities for us - exampled marked on the chart - the Stepping & Smoothing options enable better visualization of the DXF plot. the "raw" DXF is still shown as a silver line. - The "Significant Levels" option is available and is set to -20/+20 by default .. also adjustable in indicator settings. - Please use DXF in combination with other trend and volume indicators, and with thorough chart / price action analysis and not in isolation to ensure you get proper signal confirmation for trades. In the chart above, you can see DXF combined with a moving average that can act as a filter and to confirm the price moves. --------------------------------------------------- As usual, feedback & comments are welcome - if you find this work useful in your trading arsenal, please share a comment - i would be more than happy to learn about that. Good luck!
Wskaźnik Pine Script®
od RedKTrader
35
Z-Score of RSIA way to see whether RSI is overbought or oversold inside its Bollinger Bands in the form of an oscillator. Z-score tells you how far the data is from the mean in terms of standard deviations. The numbers shown in the indicator are the number of standard deviations away from the average or mean. Like Bollinger Bands, if it is above the standard deviation border lines then it is considered to be outside the distribution or Bollinger Band. If it is above the upper border line then is it is overbought, if it is below the lower border line then is is oversold. Example of how standard deviation works: 1 standard deviation contains 68% of the data, 2 SD (the default for BB) contains 95% of data, 3 SD for 99%, etc. If the data goes past that, it is considered to be outside the distribution. In statistics, data that is within the distribution is considered to be "significant". The z-score line is colored green if it is above zero and red if it is below zero. Above zero means that the RSI is above the average line or the middle line of the Bollinger Bands, and vice-versa if it is below. A red vertical bar is shown when the the z-score is above the upper border line to show that it is overbought and a green vertical bar is shown when the z-score is below the lower border line to show that it is oversold. This is the equivalent of when the RSI is above and below its Bollinger Bands.
Wskaźnik Pine Script®
od SparkyFlary
1
Trending True RangeDisplay a smoothed true range during trending markets, thus filtering any measurement occurring during ranging markets. Whether the market is trending or ranging is determined by the position of the efficiency ratio relative to its Wilder moving average. Settings Resolution : resolution of the indicator Length : period of the efficiency ratio and the Wilder moving averages used in the script Usage If you are not interested in volatility during ranging markets, this indicator might result useful to you. An interesting aspect is that it both measures volatility, but also determine whether the market is trending or ranging, with a zero value indicating a ranging market. Indicator against Atr, with both length = 14, our indicator might be easier to interpret. Note Thx to my twitter followers for their suggestions regarding this indicator. I apologize if it's a bit short, the original code was longer and included more options, but forcing a script to be lengthy is a really bad idea, so I stayed with something less flashy but certainly more practical, "classic Grover" some might say. Thx for reading!
Wskaźnik Pine Script®
od alexgrover
17
Efficient Trend Step ChannelIntroduction The efficient trend-step indicator is a trend indicator that make use of the efficiency ratio in order to adapt to the market trend strength, this indicator originally aimed to remain static during ranging states while fitting the price only when large variations occur. The trend step indicator family unlike most moving averages has a boxy appearance and could therefore not be classified as smooth, this makes it an indicator relatively uninteresting to use as input for other non-trending indicators such as oscillators. Today a channel indicator making use of the efficient trend-step is proposed, the indicator has an upper and a lower extremity who can be used for breakout or support and resistance methodologies, however we will see that the indicator is sometimes able to return accurate support and resistance levels. The Indicator The indicator has the same settings has the efficient trend step indicator, length control the period of the efficiency ratio, fast control the period of the rolling standard deviation used for trending states, slow control the period of the rolling standard deviation used for ranging states, fast should be lower than slow , if both are equal then the indicator is equal to the classical trend step indicator and length does no longer affect the indicator output. Lower values of fast/slow will make the indicator more reactive to small variations thus changing direction more often. The color changes you can see on the indicator are changed depending on the prior direction took by the indicator output, if the indicator where higher than its precedent value, then the color will be blue until the indicator is lower than its precedent value. Those colors help you have an estimate of the current trend direction. Channel Calculation And Role The extremities made from the efficient trend step allow for more advanced trading rules, they can act as stop/target level and can also give a rough estimate of the current market volatility, with wider extremities indicating a more volatile market. The extremities are made directly from the dev element used by the efficient trend-step, the upper extremity is made by summing the efficient trend step with the value of dev when the efficient trend step change, the lower extremity is made the same way but the value is subtracted instead. Is it a weird choice ? It sure is strange to see such approach, the absolute rolling average error between the price and the efficient trend step could have been a logical measure but using dev instead is more efficient and also allow for a more adaptive approach which can benefit the support and resistance methodology, the last reason is because i didn't wanted to "denature" the trend-step signature of the indicator. The figure above represent the measurement used for making the extremities (in green). Since the previously described measure change only when the efficient trend step change, we can conclude that such measure is representative of a relatively large variation, since the efficient trend step aim to only change when a large variations appear. We can see that the upper extremity acted as an accurate resistance in this upper variation of AMD, Here as well, however like other bands indicators it is safer to take into account the current trend direction, a strong uptrend will have less difficulties crossing the upper extremity, therefore it might be better to rely on the support (lower extremity) on an up-trending market (indicator in blue), and on the resistance (upper extremity) on an down-trending market (indicator in orange). The figure above show support and resistances signals, a cross represent a false signal, while green arrows represent correct ones with their respective direction. Conclusion The presented indicator add more possibilities to the interpretation of the efficient trend step, the extremities can act as stop/target level, however this use has to be controlled, and the level should be in accordance to your risk/reward ratio. Showcasing another trend-step indicator was a real pleasure. Thanks for reading :)
Wskaźnik Pine Script®
od alexgrover
5
Efficient Trend Step - Spotting Trends EfficientlyIntroduction The trend-step indicator (or auto-line) was based on volatility and aimed to spot trends in an adaptive way, however the indicator was only based on volatility and didn't gave much attention to the trend, later on i would publish an efficient version of it (efficient auto-line) based on the efficiency ratio who could adapt to the trend and eliminate potential whipsaws trades, however this approach included many settings that would require changes if the user switched markets, which reduce the utility of the indicator and make it actually super inefficient. This is why i had to propose this indicator who remove all the flaws the efficient auto-line had without removing the core idea of it. The Indicator The indicator is based on recursion, when the price is superior/inferior to the indicator precedent value +/- volatility metric, then the indicator is equal to the closing price, this allow the indicator to fit the price relatively well. The volatility metric used is based on 2 standard deviations, one fast and one slow and the efficiency ratio, basically when price is trending the volatility metric will be closer to the value of the fast standard deviations, which would allow the indicator to be closer to the price, else the metric will be closer to the slow standard deviation which restrain the indicator from changing, therefore the volatility metric act as a threshold. length control the period of the efficiency ratio, lower values of length will result in a volatility metric way closer to the fast standard deviation thus making the indicator more inclined toward making false signals. Lower values for slow will make the indicator more reactive. The indicator can be reactive but can also be really conservative, thus even remaining unchanged in some contrary movements of the main trend, this is called robustness and has its pro's and con's. Conclusion The trend-step indicators family might get to an end, or not, nonetheless they can provide precise entries and be extremely robust, which is great. Using low settings might prove to be useful to remove some noise. I hope this version find its use amongst the community. Thanks for reading !
Wskaźnik Pine Script®
od alexgrover
25
Kaufman Adaptive Least Squares Moving AverageIntroduction It is possible to use a wide variety of filters for the estimation of a least squares moving average, one of the them being the Kaufman adaptive moving average (KAMA) which adapt to the market trend strength, by using KAMA in an lsma we therefore allow for an adaptive low lag filter which might provide a smarter way to remove noise while preserving reactivity. The Indicator The lsma aim to minimize the sum of the squared residuals, paired with KAMA we obtain a great adaptive solution for smoothing while conserving reactivity. Length control the period of the efficiency ratio used in KAMA, higher values of length allow for overall smoother results. The pre-filtering option allow for even smoother results by using KAMA as input instead of the raw price. The proposed indicator without pre-filtering in green, a simple moving average in orange, and a lsma with all of them length = 200. The proposed filter allow for fast and precise crosses with the moving average while eliminating major whipsaws. Same setup with the pre-filtering option, the result are overall smoother. Conclusion The provided code allow for the implementation of any filter instead of KAMA, try using your own filters. Thanks for reading :)
Wskaźnik Pine Script®
od alexgrover
17
Powered Kaufman Adaptive Moving AverageIntroduction The ability the Kaufman adaptive moving average (KAMA) has to be flat during ranging markets and close to the price during trending markets is what make this moving average one of the most useful in technical analysis. KAMA is calculated by using exponential averaging using the efficiency ratio (ER) as smoothing variable where 1 > ER > 0 . An increasing efficiency ratio indicate a trending market. Based on one of my latest indicator (see Kaufman Adaptive Bands) i propose this modified KAMA that allow to emphasis the abilities of KAMA by powering the efficiency ratio. I also added a new option that allow for even more adaptivity. The Indicator The indicator is a simple KAMA of period length that use a powered ER with exponent factor . When factor = 1 the indicator is a simple KAMA, however when factor > 1 there can be more emphasis on the flattening effect of KAMA. You can also restrain this effect by using 1 > factor > 0 Note that when the exponent is lower than 1 and greater than 0 you are basically applying a nth square root to the value, for example pow(2,0.5) = sqrt(2) because 1/0.5 = 2, in our case : pow(ER,factor > 1) < ER and pow(ER,1 > factor > 0) > ER Self Powered P-KAMA When the self powered option is checked you are basically powering ER with the reciprocal of ER as exponent, however factor does no longer change anything. This can give interesting results since the exponent depend on the market trend strength. In orange the self powered KAMA of period length = 50 and in blue a basic powered KAMA with a factor of 3 and a period of length = 50. Conclusion Applying basic math to indicators is always fun and easy to do, if you have adaptive moving averages using exponential averaging try powering your smoothing variable in order to see interesting results. I hope you like this indicator. Thanks for reading !
Wskaźnik Pine Script®
od alexgrover
32
Kaufman Adaptive BandsIntroduction Bands are quite efficient in technical analysis, they can provide support and resistance levels, provide breakouts points, trailing stop loss/take profits positions and can show the current market volatility to the user. Most of the time bands are made from a central tendency estimator like a moving average plus/minus a volatility indicator. Therefore bands can be made out of pretty much everything thus allowing for any kind of flavors. So i propose a band indicator made from a Kaufman adaptive moving average using an estimate of the standard deviation. Construction The Kaufman moving average is an exponential averager using the efficiency ratio as smoothing variable, length control the period of kama and in order to provide more smoothness a power parameter has been introduced, higher values of power will return smoother results. The volatility indicator is made from a biased estimation of the standard deviation by using the square root of the mean of the square minus the square of the mean method, except that we use kama instead of a mean. The bands are made by adding/subtracting this volatility indicator with kama. How To Use The ability of the indicator to adapt to the current market state is what makes him a great tool for avoiding major exposition during ranging market, therefore the indicator will have a greater motion during trending market, or more simply the bands will move during trending markets while staying "flat" during ranging ones. Therefore the indicator might be more suited to breakouts, even if some cases will return what where turning points, this is particularly true during ranging markets. Of course the efficiency ratio is not an "unbiased" trend metric indicator, it can consider high volatility markets as trending markets. Its one of his downsides. High values of power will create smoother bands. When using a low power parameter use an higher mult. In general using a low power value will make the bands move more freely as well as making them closer to each others. Conclusion At least the indicator is really nice to the eyes when using high power values, its ability to adapt to the market is a great addition to other more classical bands indicators, i also introduced a volatility estimator based on kama, some might have used the following estimation : kama(abs(price - kama)) which would have created a slower result. A trailing stop might be made from it if i see request about such addition. If you are curious here are some more images of the indicator performing on different markets. Thanks for reading !
Wskaźnik Pine Script®
od alexgrover
18