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Quantum Liquidity Map [NikaQuant] DESCRIPTION: Quantum Liquidity Map combines three institutional analysis methods — Volume Profile, anchored VWAP with standard-deviation bands, and Cumulative Volume Delta (CVD) divergence — into a single overlay. The thesis behind this combination: volume profile reveals where institutional participants have positioned size, VWAP establishes the session's fair-value anchor, and CVD exposes whether current price movement has genuine order-flow conviction behind it. Used together, they answer the three questions institutional traders ask at every level: "Is there volume here?", "Is price stretched from fair value?", and "Is this move real?" █ HOW IT WORKS ── Volume Profile Heatmap ── The indicator builds a visible-range volume profile by distributing each bar's volume into horizontal price buckets across the lookback window. The bucket containing the highest accumulated volume becomes the Point of Control (POC) — the single price where the most trading activity occurred. This represents the market's consensus fair value for that window. From the POC, a value area is expanded outward using the CBOT method: alternately adding the next-highest-volume bucket above and below until 70% (adjustable) of total volume is captured. The upper boundary is Value Area High (VAH) and the lower is Value Area Low (VAL). These levels act as acceptance/rejection zones — price returning to the value area tends to find support or resistance at its edges. A "Naked POC" (nPOC) is the previous session's POC that price has not yet revisited. Because large volume clusters attract price, untested POCs often act as magnets. The profile renders as a gradient heatmap: hotter (more opaque) bins represent higher volume concentration. Each bin has a tooltip showing its zone type, price range, and volume percentage. ── VWAP with Standard-Deviation Bands ── The Volume-Weighted Average Price is calculated manually (no request.security) by accumulating price × volume and dividing by cumulative volume from the anchor reset. This gives the true average price weighted by where actual trading occurred — a more meaningful "average" than a simple moving average. Thirteen anchor periods are available (1H through Yearly) so the same tool adapts from scalping to swing contexts. Shorter anchors (1–4H) capture intraday institutional positioning; longer anchors (Weekly, Monthly) reveal macro fair value. Two standard-deviation bands (±1σ and ±2σ, multipliers adjustable) are derived from the running variance of the volume-weighted price distribution. In a normal distribution, roughly 68% of observations fall within ±1σ and 95% within ±2σ. When price reaches the outer bands, it is statistically extended from fair value — a condition that historically favors mean reversion. The bands only render after 5+ bars in the session to avoid misleading spikes at anchor resets. ── CVD Divergence Engine ── Cumulative Volume Delta estimates net buying vs. selling pressure per bar using the close-location-in-range method: buy volume ≈ volume × (close − low) / (high − low), sell volume is the remainder. Summing the difference cumulatively across the session produces the CVD line. The divergence detector uses confirmed swing pivots (ta.pivothigh / ta.pivotlow with equal left and right lookback bars) to identify structural price highs and lows. A bearish divergence fires when price prints a higher high but CVD prints a lower high — meaning the new price extreme was achieved on weaker buying pressure, suggesting exhaustion. Bullish divergence is the inverse: lower price low but higher CVD low, indicating selling pressure is diminishing. Two filters reduce noise: • Both-sided pivot confirmation (bars left AND right must confirm the swing) eliminates premature signals. • An ATR filter requires the swing to exceed 1.5× ATR(14) in magnitude, filtering out minor pivots that produce meaningless divergences. Divergence signals only fire on confirmed (closed) bars — no repainting. █ HOW THE COMPONENTS WORK TOGETHER Volume Profile identifies the key levels (POC, VAH, VAL). VWAP bands measure how far price has stretched from fair value. CVD divergence reveals whether the current move toward or away from those levels has genuine order-flow support. Example workflow: • Price approaches VAH from below → check VWAP band zone. If price is already at +2σ AND a bearish CVD divergence fires, three independent systems are aligned for a rejection. • Price tests a Naked POC → if CVD is trending in the direction of the test and price is between VWAP and ±1σ (not overextended), the level is more likely to hold as support/resistance. • No single component generates a trade signal. The value is in confluence — when multiple systems agree, the probability of a reaction increases. █ HOW TO USE IT 1. Start with the Volume Profile: identify where POC, VAH, and VAL sit relative to current price. These are your key decision levels. 2. Check the VWAP Band Zone (shown in the dashboard): if price is in the ±1σ zone, it is near fair value. If it is beyond ±2σ, it is statistically extended. 3. Watch for CVD divergence arrows near key VP levels or at VWAP band extremes — these confirm or deny the conviction behind the price move. 4. The dashboard shows Day Volume, Delta %, VWAP Distance, VAH/VAL Distance, current CVD signal, and Band Zone at a glance. Settings to adjust for your timeframe: • Lookback Bars: controls how much history the volume profile covers. 48 on a 30m chart ≈ 1 day. • VWAP Period: match to your trading horizon. Session for intraday, Week/Month for swing. • CVD Pivot Lookback: higher values (8–15) produce fewer but stronger divergence signals. █ ALERTS Four JSON-formatted alert conditions for webhook automation: • Price touches POC (within 0.5 ATR) • Price enters VAH zone • Price enters VAL zone • CVD divergence detected (bullish or bearish) Non-repainting. No lookahead. Pine Script v6. Built by NikaQuant.
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NikaQuant tarafından
Exponential Nadaraya Watson kernel regression [Jamallo](2025) Intro Nadaraya-Watson (N-W) kernel regression is a non-parametric smoothing technique that estimates the underlying trend of a price series without assuming any fixed model shape (like a straight line or curve). Unlike a simple moving average which weights bars equally, or an EMA which applies a fixed exponential decay, N-W regression derives its curve by computing a weighted average of all prices within a lookback window — where the weights are determined by a kernel function. The most common kernel used is the Gaussian kernel, which assigns weights in a bell-curve shape — prices closer to the center of the window receive higher weight, prices at the edges receive lower weight. Most N-W implementations use a pure symmetric Gaussian kernel, meaning every bar within the lookback window is weighted purely by its distance from the center, with no preference for recency. This indicator uses a hybrid Exponential Nadaraya-Watson (ENW) kernel that combines two weighting forces simultaneously: Gaussian spatial weight — bell-curve weighting centered on the window, same as standard N-W Exponential time-decay weight — progressively heavier weighting on recent bars, similar to how an EMA behaves Both weights are multiplied together for each bar, meaning a price bar must be both spatially central and recent to receive maximum influence. In practice this shifts the effective weight peak toward the recent end of the window, making the K Line more responsive to current price action than standard N-W. Breakdown K Line — ENW Kernel Regression The central baseline of the indicator. A smooth adaptive curve derived from the hybrid ENW kernel applied to closing prices. Bandwidth is controlled via the Kernel Length and Alpha inputs — higher alpha increases the recency bias, lower alpha brings behavior closer to standard N-W. Volatility Bands (Inner & Outer) Rather than using ATR or standard deviation for band width, this indicator measures the absolute deviation of price from the K Line and smooths that deviation through the same ENW kernel. This means bands are fully adaptive — they expand and contract organically based on how far price has been straying from the regression curve, not a fixed statistical formula. Inner and outer bands are independently scaled via deviation multipliers. A Line — Vervoort ATR Stop A trailing stop built on the HLC4 price source with an ATR-based loss distance. Flips direction on a close beyond the stop level. Serves as the primary trend bias line — when above the K Line the fill turns bullish, when below it turns bearish. Cross signals (triangles) are plotted whenever the A Line crosses the K Line, marking potential trend shifts. Signal Line — Vervoort ATR Stop (Secondary) A second independent Vervoort trailing stop running on its own ATR period and multiplier settings. Typically configured looser than the A Line — wider multiplier, longer or equal period — so it acts as a slower confirmation layer. Useful for filtering noise on the A Line crosses: an A Line cross that also aligns with the Signal Line's bias carries more weight than one that doesn't. ------- Nadaraya-Watson kernel regression concept — E. Nadaraya (1964), G.S. Watson (1964) Vervoort ATR Stop — Sylvain Vervoort
Pine Script® göstergesi
Jamallo22 tarafından
5
Lumina Trend Channels [Pineify]Lumina Trend Channels The Lumina Trend Channels is a dynamic, volatility-adaptive channel system that combines an Exponential Moving Average (EMA) baseline with Average True Range (ATR) band projections and slope-based trend detection to create a visually intuitive, all-in-one trend-following overlay. Unlike static channel indicators that use fixed-width bands, Lumina Trend Channels automatically expands and contracts its four-band envelope in real time as market volatility changes, keeping the channel structure relevant across all market conditions. The channel color shifts between bullish and bearish states based on the direction of the baseline slope, and buy/sell signals are generated only when price crosses the baseline in alignment with the confirmed trend — filtering out counter-trend noise and providing cleaner entry timing for trend traders. Key Features EMA baseline with ATR-scaled bands — a responsive center line surrounded by four symmetrical bands whose width adapts dynamically to current volatility via ATR measurement. Slope-based trend detection — trend direction is determined by whether the EMA baseline has risen or fallen for two consecutive bars, providing a simple yet effective trend filter. Trend-aligned buy/sell signals — BUY signals trigger only when price crosses above the baseline during a confirmed uptrend; SELL signals trigger only during a confirmed downtrend, eliminating counter-trend false entries. Gradient-style visual channel — layered fills with progressive transparency create a glowing channel effect that fades outward from the baseline, making trend direction and volatility state immediately visible. Trend change markers — circle markers appear on the baseline at the exact bar where the trend flips, providing clear visual anchors for regime changes. Built-in alert conditions — configurable alerts for buy signals, sell signals, and trend changes for hands-free monitoring. How It Works The indicator follows a three-stage calculation process to construct the channel and generate signals: Baseline calculation via EMA: The closing price is smoothed using an Exponential Moving Average with a configurable period (default: 21). The EMA was chosen over SMA because it assigns greater weight to recent prices, producing a center line that reacts faster to price changes while maintaining smoothness. This baseline serves as both the channel center and the reference line for signal generation. Volatility measurement via ATR: The Average True Range is calculated over the same lookback period as the EMA. ATR measures the average bar range (accounting for gaps), providing a robust volatility metric. Four bands are then projected symmetrically around the baseline: inner bands at basis ± ATR × inner multiplier (default: 1.0) and outer bands at basis ± ATR × outer multiplier (default: 2.0). As volatility expands, the bands widen; as it contracts, they narrow — keeping the channel proportional to actual market conditions. Trend detection via baseline slope: The trend state is determined by checking whether the EMA baseline has been rising (increasing for 2 consecutive bars) or falling (decreasing for 2 consecutive bars). If rising, the trend is set to bullish; if falling, bearish. If neither condition is met, the previous trend state is maintained. This persistence mechanism prevents rapid trend flipping during sideways consolidation. Trading Ideas and Insights The Lumina Trend Channels is designed to serve multiple trading approaches across different timeframes and markets: Trend-following entries: The primary use case — when a BUY triangle appears below a bar, it means price has crossed above the EMA baseline while the channel is green (bullish). This confirms that the immediate price action and the broader trend are aligned. Enter long and consider the upper inner or outer band as a potential profit target. The SELL triangle is the mirror setup for short entries during bearish channels. Volatility-based position sizing: The ATR-driven band width provides a built-in volatility gauge. When the channel is wide, the market is volatile — consider smaller position sizes or wider stops. When the channel is narrow, volatility is compressed — tighter stops may be appropriate, and a breakout from the narrow channel often precedes a strong directional move. Dynamic support and resistance: The inner and outer bands act as dynamic support/resistance levels. In an uptrend, pullbacks to the lower inner band often find support; in a downtrend, rallies to the upper inner band often meet resistance. The outer bands represent extreme volatility extensions where price is statistically stretched. Trend change detection: The circle markers on the baseline highlight the exact moment the trend flips. These are valuable for swing traders who want to exit positions when the trend turns against them, or for traders looking to enter early in a new trend direction. How Multiple Indicators Work Together The Lumina Trend Channels integrates three technical components into a unified system, each serving a distinct analytical role: Exponential Moving Average (trend center): The EMA provides the structural backbone of the channel. It defines the center line around which all bands are constructed and serves as the crossover reference for signal generation. Its low-lag property ensures the channel tracks price closely, keeping the entire system responsive to current market conditions. Average True Range (volatility scaling): ATR transforms the channel from a fixed-width envelope into a volatility-adaptive one. By scaling band distances with ATR, the channel automatically adjusts to the market's current behavior — wide during volatile periods, narrow during quiet ones. This means the bands always represent statistically meaningful distance from the baseline, regardless of the instrument or timeframe. Slope-based trend filter (directional bias): The trend detection layer adds a directional gate to the entire system. Without it, every EMA crossover would generate a signal — including counter-trend ones during choppy markets. By requiring the baseline to be actively rising (for buys) or falling (for sells), the trend filter ensures signals only fire when the broader directional context supports the trade. The synergy is layered: EMA establishes the trend center → ATR scales the channel to current volatility → slope detection determines the trend state → signals fire only when price action and trend direction agree. This multi-layer filtering produces a system where each component reinforces the others, resulting in higher-conviction signals than any single component could provide alone. Unique Aspects Volatility-adaptive channel with trend coloring: While many channel indicators use either fixed bands (like Bollinger Bands with standard deviation) or trend coloring separately, Lumina Trend Channels combines ATR-driven dynamic width with slope-based trend coloring in a single overlay. The result is a channel that communicates both volatility state and trend direction simultaneously through its shape and color. Gradient transparency design: The four-layer fill system uses progressive transparency — inner zones are more opaque, outer zones more transparent — creating a natural visual gradient that draws the eye toward the baseline. This design choice makes it immediately obvious where the channel center is and how far price has extended from it. Trend-gated signals: Rather than generating signals on every baseline crossover, the indicator requires trend confirmation before triggering entries. This simple but effective filter dramatically reduces false signals during sideways or transitional market phases, where most crossover-based systems struggle. Minimal parameter design: With only three calculation inputs (length, outer multiplier, inner multiplier), the indicator avoids over-parameterization. The single length parameter controls both the EMA and ATR simultaneously, ensuring the baseline and volatility measure are always in sync. How to Use Add the indicator to your chart. It overlays directly on the price chart, displaying a four-band channel with a central baseline, all colored according to the current trend direction. Observe the channel color: green indicates a bullish trend (baseline is rising), red indicates a bearish trend (baseline is falling). Trade in the direction of the channel color for higher-probability setups. Watch for BUY triangles (green, below bars) — these appear when price crosses above the baseline during a confirmed uptrend. Consider entering long with a stop below the lower inner or outer band. Watch for SELL triangles (red, above bars) — these appear when price crosses below the baseline during a confirmed downtrend. Consider entering short with a stop above the upper inner or outer band. Use the circle markers on the baseline to identify trend changes. These mark the exact bar where the channel flipped color, useful for timing exits or preparing for new trend entries. Monitor the channel width as a volatility gauge — wide channels mean high volatility, narrow channels mean low volatility and potential breakout setups. Set up alerts using the built-in alert conditions for buy signals, sell signals, and trend changes to automate your monitoring. Customization Channel Length (default: 21): Controls both the EMA baseline period and the ATR lookback. Lower values (e.g., 10–14) make the channel more responsive and generate more signals, suitable for shorter timeframes or scalping. Higher values (e.g., 34–55) produce a smoother, more stable channel for swing trading or higher timeframes. Outer Band Multiplier (default: 2.0): Scales the distance of the outer bands from the baseline. Increase for wider outer bands that capture more extreme price extensions; decrease for tighter outer bands that stay closer to price action. Inner Band Multiplier (default: 1.0): Scales the distance of the inner bands from the baseline. Adjust to control the width of the inner channel zone. A value of 0.5 creates a narrow inner zone; a value of 1.5 widens it. Bullish Color (default: green): The color applied to all channel elements during uptrends. Customize to match your chart theme. Bearish Color (default: red): The color applied to all channel elements during downtrends. Customize to match your chart theme. Conclusion The Lumina Trend Channels delivers a clean, volatility-adaptive channel overlay that combines EMA-based trend tracking, ATR-driven dynamic band scaling, and slope-based trend detection into a single, cohesive indicator. Its gradient-style visual design provides immediate, at-a-glance understanding of trend direction, volatility state, and price position within the channel. By gating buy and sell signals with trend confirmation, the indicator filters out counter-trend noise and delivers higher-conviction entries aligned with the prevailing market direction. Whether you trade stocks, forex, crypto, or futures, the Lumina Trend Channels adapts to your instrument and timeframe, offering a refined approach to trend-following and volatility-aware trading decisions.
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Pineify tarafından
Trend filter [Jamallo](2025) This is a trend qualification and filtering tool. Breakdown: Signal Line — Vervoort ATR Trailing Stop The signal line is an implementation of the ATR Trailing Stop developed by Sylvain Vervoort, first published in Technical Analysis of Stocks & Commodities, Volume 27, Issue 6 (June 2009), in his article "Average True Range Trailing Stops" . The stop uses a modified ATR calculated on the average price (O+H+L+C)/4 rather than the plain close. Baseline — Kalman Filter The baseline is derived from the Kalman Filter, a recursive algorithm developed in 1960 by Rudolf E. Kálmán, a Hungarian-American engineer and mathematician. Originally developed for aerospace applications in the early 1960s — including guidance of the Apollo spacecraft. In this indicator, the Kalman Filter acts as a smooth, low-lag trend estimate. Rather than applying fixed weights to historical data like conventional moving averages, it dynamically adjusts its trust between the predicted trend and observed prices. Bands — Volatility Channels Two pairs of bands (inner and outer) are built around the Kalman baseline using a 200-bar WMA.
Pine Script® göstergesi
Jamallo22 tarafından
LOWESS Reversal & Continuation [UAlgo]LOWESS Reversal & Continuation is a trend sensitive signal indicator built around a local LOWESS style smoothing engine that adapts to both price structure and volatility. Its core objective is to separate meaningful directional shifts from routine market noise by combining smoothed trend estimation, ATR normalized slope analysis, acceleration filtering, and disciplined signal gating. Instead of relying on a conventional moving average crossover model, the script fits a locally weighted regression over a rolling window of recent bars. This produces a smoother and more context aware estimate of price direction, while still reacting fast enough to identify emerging reversals and pullback continuation opportunities. Because the estimate is local and weighted, recent bars have greater influence than distant bars, which helps the line remain relevant to current market conditions. The indicator classifies signals into two practical categories. Reversal signals aim to identify transitions where directional pressure flips from negative to positive, or from positive to negative. Continuation signals aim to identify pullback resumption behavior inside an already established directional regime. This makes the script suitable for traders who want a single tool that can highlight both early trend change candidates and trend following re entry points. To improve robustness, the script also supports optional robust weighting passes. These passes reduce the influence of outlier bars on the LOWESS fit, which is especially useful during abnormal spikes, illiquid conditions, or isolated volatility shocks. In addition, all slope readings are normalized by ATR, allowing the trend filter to scale more consistently across instruments, timeframes, and volatility regimes. From a workflow perspective, the script is designed for clean chart usage. It colors the LOWESS line according to directional bias, draws an adaptive ATR based band around the curve, supports optional signal labels, limits on chart label clutter through an internal object manager, and provides alert conditions for all signal classes. The result is a visually compact but analytically rich framework for tracking trend reversals and continuation setups in real time. 🔹 Features 🔸 LOWESS Style Local Trend Estimation The heart of the script is a locally weighted linear regression model applied over a rolling lookback window. Each bar inside the window receives a distance based weight, meaning bars closer to the current bar have a larger impact on the estimate. This produces a smooth trend line that is more adaptive than many standard moving average techniques and better suited for identifying subtle turning points. 🔸 Optional Robust Regression Passes The indicator can apply additional robust weighting iterations after the initial fit. Residuals are measured relative to the first regression pass, and bars with unusually large residuals receive progressively lower influence in later passes. This reduces distortion from extreme candles and helps the LOWESS curve remain stable during irregular price events. 🔸 ATR Normalized Slope Filter The script does not use raw slope in isolation. Instead, the LOWESS slope is divided by ATR, creating a volatility adjusted slope metric. This makes the directional threshold more portable across markets and timeframes. A slope that may be meaningful on a slow instrument can be very different on a high volatility asset, so ATR normalization creates a more balanced regime filter. 🔸 Reversal Signal Detection Bullish and bearish reversal signals are triggered when the ATR normalized slope crosses the zero line and is confirmed by directional acceleration and price location relative to the LOWESS line. In other words, the script looks for a meaningful change in smoothed directional pressure, not simply a visual bend in the curve. This makes reversal signals more selective and better aligned with structural momentum shifts. 🔸 Continuation Signal Detection Continuation logic is designed to capture trend resumption after a pullback. The script first requires an established directional regime, then checks whether price recently interacted with the LOWESS line, and finally waits for price to reclaim the trend direction with positive confirming acceleration. This helps distinguish genuine continuation behavior from random sideways oscillation around the curve. 🔸 Pullback Validation Window A dedicated pullback lookback parameter ensures that continuation signals only occur when price has interacted with the LOWESS line within a recent number of bars. This prevents stale continuation triggers and keeps the setup focused on recent retracement behavior rather than distant historical interactions. 🔸 Close Confirmation Option Signals can be gated so they only become valid after bar close. This is useful for traders who want to avoid intrabar flicker and premature triggers on live candles. When disabled, the script can respond more aggressively in real time, which may suit faster execution styles. 🔸 Signal Cooldown Logic To reduce repetitive clustering, the indicator tracks the last occurrence of each signal type and imposes a cooldown period before another signal of the same class can be printed. Separate cooldown tracking is maintained for bullish reversals, bearish reversals, bullish continuations, and bearish continuations. 🔸 Adaptive ATR Band An optional ATR based band can be plotted around the LOWESS line. This band provides a visual sense of dynamic range around the smoothed path and can help contextualize whether price is moving in a relatively stretched or balanced position around the trend estimate. 🔸 Directional Visual Coloring The LOWESS curve changes color according to directional bias derived from the ATR normalized slope. This gives the user an immediate visual read on whether the smoothed trend pressure is currently positive, negative, or unavailable due to insufficient historical data. 🔸 Lightweight Label Management When signal labels are enabled, the script uses an internal label book to store and manage plotted objects. Older labels are automatically deleted once the configured maximum is exceeded, helping keep the chart readable and preventing uncontrolled label buildup. 🔸 Full Alert Support Alert conditions are included for all four event classes: Bullish LOWESS Reversal Bearish LOWESS Reversal Bullish LOWESS Continuation Bearish LOWESS Continuation This allows the script to be used not only as a visual analysis tool, but also as an event driven signal framework for scanning and real time notification workflows. 🔹 Calculations 1) Rolling LOWESS Window Construction For every bar, the script loads the most recent length values of the selected source into an internal rolling window. This window becomes the data sample used for the local regression fit. method loadWindow(LowessEngine this, float seriesValue) => for i = 0 to this.length - 1 array.set(this.y, i, seriesValue ) Interpretation: The regression is always fit on the latest rolling block of data. The rightmost point in the window corresponds to the current estimation point. This makes the smoothing local rather than global. 2) Distance Based LOWESS Weights The script uses a tricube kernel to assign weights based on each point’s distance from the current bar inside the regression window. Bars nearer to the most recent observation receive a larger weight, while distant bars contribute less. method buildDistanceWeights(LowessEngine this, int spanBars) => int x0 = this.length - 1 float bandwidth = math.max(spanBars, 1) for i = 0 to this.length - 1 float u = math.abs(i - x0) / bandwidth float w = u < 1 ? math.pow(1 - math.pow(u, 3), 3) : 0.0 array.set(this.baseW, i, w) Interpretation of the conditions: x0 is the current evaluation point inside the rolling window. u is normalized distance from each historical point to the current point. The tricube weight decays smoothly as distance increases. Bars outside the effective span receive zero weight. This is what gives the LOWESS fit its local character and helps it stay focused on recent structure. 3) Weighted Local Linear Regression After weights are built, the script solves a weighted linear regression over the local window. The output is a local intercept and local slope. The final LOWESS estimate is the fitted value at the most recent point in the sample. method solveWeightedLinear(LowessEngine this, array weights) => float s0 = 0.0 float s1 = 0.0 float s2 = 0.0 float t0 = 0.0 float t1 = 0.0 for i = 0 to this.length - 1 float w = array.get(weights, i) float x = array.get(this.x, i) float y = array.get(this.y, i) s0 += w s1 += w * x s2 += w * x * x t0 += w * y t1 += w * x * y float den = s0 * s2 - s1 * s1 if s0 <= 1e-10 or math.abs(den) <= 1e-10 this.slope := 0.0 this.intercept := array.get(this.y, this.length - 1) else this.slope := (s0 * t1 - s1 * t0) / den this.intercept := (t0 - this.slope * s1) / s0 float x0 = this.length - 1 this.yhat := this.intercept + this.slope * x0 Interpretation: this.slope measures the local directional gradient of the LOWESS fit. this.yhat is the current LOWESS value plotted on the chart. If the weighted regression becomes numerically unstable, the script falls back to a flat slope and uses the latest source value as intercept. 4) Robust Reweighting Passes To reduce the impact of outliers, the script can run additional robust passes after the initial fit. It first calculates the absolute residual of each point relative to the fitted line, then computes a median based scale estimate, and finally applies a bisquare style robust weighting function. method updateRobustWeights(LowessEngine this) => for i = 0 to this.length - 1 float xi = array.get(this.x, i) float yi = array.get(this.y, i) float fit = this.intercept + this.slope * xi array.set(this.residualAbs, i, math.abs(yi - fit)) float med = this.residualAbs.median() float scale = med * 6.0 if na(scale) or scale <= 1e-10 for i = 0 to this.length - 1 array.set(this.robustW, i, 1.0) else for i = 0 to this.length - 1 float u = array.get(this.residualAbs, i) / scale float rw = u < 1 ? math.pow(1 - math.pow(u, 2), 2) : 0.0 array.set(this.robustW, i, rw) Interpretation: Large residual bars are treated as less trustworthy observations. The median residual acts as a robust scale anchor. Higher residuals receive smaller robust weights in subsequent fits. This improves stability during abnormal spikes and irregular candles. 5) ATR Normalized Slope and Acceleration Once the LOWESS fit is complete, the script converts raw slope into a volatility aware slope by dividing it by ATR. It also computes a first-difference style acceleration term to measure whether directional pressure is strengthening or weakening. float slopeAtr = not na(slope) and atr > 0 ? slope / atr : na float accelAtr = slopeAtr - nz(slopeAtr ) Interpretation: slopeAtr expresses trend slope in ATR units. accelAtr measures change in normalized slope from one bar to the next. Positive acceleration supports bullish developments. Negative acceleration supports bearish developments. This combination helps the script distinguish a genuine regime shift from a weak or decaying slope condition. 6) Directional Regime Classification The script uses a user defined ATR normalized threshold to determine whether the current smoothed state qualifies as a bullish or bearish directional regime. bool upRegime = not na(slopeAtr) and slopeAtr > slopeThreshold bool downRegime = not na(slopeAtr) and slopeAtr < -slopeThreshold Interpretation: A positive but very small slope is not automatically treated as a valid uptrend. A negative but very small slope is not automatically treated as a valid downtrend. The threshold acts as a noise filter that requires the trend estimate to have enough magnitude before continuation logic becomes eligible. 7) Pullback Detection Relative to LOWESS Continuation signals depend on recent interaction with the LOWESS line. The script checks how many bars have passed since price moved through the LOWESS curve in the opposite direction of the active regime. int bullPbBars = int(nz(ta.barssince(low < lowess), 100000)) int bearPbBars = int(nz(ta.barssince(high > lowess), 100000)) bool bullPullbackRecent = bullPbBars <= pullbackLookback bool bearPullbackRecent = bearPbBars <= pullbackLookback Interpretation: In a bullish regime, price must have recently dipped below the LOWESS line to qualify as a pullback. In a bearish regime, price must have recently pushed above the LOWESS line to qualify as a pullback. The pullbackLookback parameter controls how recent that interaction must be. 8) Reversal Signal Logic Bullish and bearish reversal signals are built from zero line slope crossings, directional acceleration, and price confirmation relative to the LOWESS curve. bool slopeCrossUp = ta.crossover(slopeAtr, 0) bool slopeCrossDown = ta.crossunder(slopeAtr, 0) bool bullRevRaw = enoughBars and slopeCrossUp and accelAtr > 0 and close > lowess bool bearRevRaw = enoughBars and slopeCrossDown and accelAtr < 0 and close < lowess Interpretation of the bullish reversal conditions: slopeCrossUp means the normalized LOWESS slope has crossed from negative to positive. accelAtr > 0 means the slope is improving, not merely touching zero. close > lowess confirms that price is positioned above the smoothed trend estimate. Interpretation of the bearish reversal conditions: slopeCrossDown means the normalized LOWESS slope has crossed from positive to negative. accelAtr < 0 confirms weakening trend pressure. close < lowess confirms price is positioned below the LOWESS line. This makes reversal signals more selective than a simple moving average crossover style event. 9) Continuation Signal Logic Continuation signals are only allowed when a directional regime already exists, a recent pullback has occurred, price crosses back through the LOWESS line in trend direction, and acceleration confirms that the move is regaining strength. bool priceCrossUp = ta.crossover(close, lowess) bool priceCrossDown = ta.crossunder(close, lowess) bool bullContRaw = enoughBars and upRegime and bullPullbackRecent and priceCrossUp and accelAtr > 0 and not bullRevRaw bool bearContRaw = enoughBars and downRegime and bearPullbackRecent and priceCrossDown and accelAtr < 0 and not bearRevRaw Interpretation of the bullish continuation conditions: The LOWESS slope must already define an uptrend regime. Price must have recently pulled back below the LOWESS line. Price must cross back above the LOWESS line. Acceleration must be positive. A reversal signal takes priority, so continuation does not print if the same bar qualifies as a bullish reversal. Interpretation of the bearish continuation conditions is the exact inverse. 10) Close Confirmation and Cooldown Control The final signal is gated by an optional bar close confirmation and a per-signal cooldown filter. bool gate = confirmClose ? barstate.isconfirmed : true bool bullRev = gate and bullRevRaw and canBullRev(signalState, cooldownBars) bool bearRev = gate and bearRevRaw and canBearRev(signalState, cooldownBars) bool bullCont = gate and bullContRaw and canBullCont(signalState, cooldownBars) bool bearCont = gate and bearContRaw and canBearCont(signalState, cooldownBars) Interpretation: When close confirmation is enabled, signals only become valid after the candle is closed. Cooldown logic prevents repeated printing of the same signal class within a short number of bars. This reduces visual clutter and avoids excessive re-triggering during choppy conditions. 11) Adaptive Band Construction The script can draw an ATR-based envelope around the LOWESS line to provide volatility context. float upperBand = showBand and not na(lowess) ? lowess + atr * bandAtrMult : na float lowerBand = showBand and not na(lowess) ? lowess - atr * bandAtrMult : na Interpretation: The band expands and contracts with ATR. This creates a dynamic visual frame around the LOWESS estimate. It is not a signal by itself, but it helps contextualize the distance between current price and the smoothed trend path. 12) Visual Output and Alerts The LOWESS line changes color according to slope direction, optional labels mark reversal and continuation events, and alert conditions are available for all four signal types. alertcondition(bullRev, "Bullish LOWESS Reversal", "Bullish LOWESS reversal on {{ticker}}") alertcondition(bearRev, "Bearish LOWESS Reversal", "Bearish LOWESS reversal on {{ticker}}") alertcondition(bullCont, "Bullish LOWESS Continuation", "Bullish LOWESS continuation on {{ticker}}") alertcondition(bearCont, "Bearish LOWESS Continuation", "Bearish LOWESS continuation on {{ticker}}") In practical terms, this means the indicator can serve both as a visual discretionary analysis tool and as an alert driven framework for identifying smoothed trend reversals and pullback continuation opportunities with a volatility aware filter structure.
Pine Script® göstergesi
UAlgo tarafından
Nova Statistical Filtering Oscillator [Pineify]Nova Statistical Filtering Oscillator - Advanced Mean Reversion Trading Tool A sophisticated statistical oscillator that identifies overbought and oversold market conditions using Z-Score normalization and dynamic volatility bands The Nova Statistical Filtering Oscillator (NSFO) is an advanced technical analysis tool designed to help traders identify potential mean reversion opportunities in financial markets. By combining statistical price normalization with adaptive smoothing techniques, this indicator provides a unique approach to detecting extreme price conditions and anticipating momentum reversals. Z-Score based statistical analysis Triple Exponential Moving Average (TEMA) smoothing Dynamic volatility bands Automatic mean reversion signal generation Gradient color visualization Customizable parameters Statistical overbought/oversold detection Noise reduction through TEMA smoothing Adaptive bands that adjust to market volatility Clear buy and sell signal indicators Visual gradient coloring for quick analysis Configurable alert conditions The NSFO operates through a sophisticated three-step statistical process that transforms raw price data into actionable trading signals. Step 1: Z-Score Calculation The indicator first calculates the Z-Score, which measures how many standard deviations the current price is from its historical mean. This statistical normalization allows traders to objectively identify when price has moved to statistically extreme levels. The formula uses a configurable lookback period (default 20 bars) to compute both the simple moving average (mean) and standard deviation. A Z-Score greater than +2 or less than -2 indicates statistically rare extreme conditions, occurring approximately only 5% of the time. Step 2: TEMA Smoothing Raw Z-Score values can be volatile and produce choppy signals. The NSFO applies Triple Exponential Moving Average (TEMA) smoothing to reduce market noise while maintaining responsiveness. TEMA combines three successive EMAs using the formula: 3×EMA1 - 3×EMA2 + EMA3, achieving smoother results with less lag compared to traditional moving averages. Step 3: Dynamic Volatility Bands Unlike static overbought/oversold levels, the NSFO creates adaptive bands based on the volatility of the smoothed oscillator itself. This self-referential approach automatically adjusts to changing market conditions - becoming wider during volatile markets and narrower during calm periods. The bands are calculated using the standard deviation of the smoothed oscillator multiplied by a configurable factor (default 2.0). The NSFO is built on the fundamental principle of mean reversion - the statistical observation that prices tend to return to their average over time. This concept has been validated through decades of market research and forms the basis of many successful trading strategies. Statistical Extremes as Trade Setups : When the oscillator reaches the upper or lower bands, it indicates the price has moved to a statistically extreme level. These conditions often precede reversals as the market corrects itself back toward the mean. Momentum Confirmation : The gradient coloring system provides instant visual feedback on current momentum. When the oscillator transitions from extreme levels toward the center, it confirms that momentum is shifting and the reversal may be gaining strength. Time Frame Flexibility : The NSFO performs well across various time frames, from intraday charts to weekly frames. Shorter time frames generate more signals but require tighter risk management, while longer time frames produce fewer but potentially more reliable signals. The NSFO uniquely combines three distinct technical concepts into a cohesive system: Statistical Analysis (Z-Score) : Provides the mathematical foundation for identifying extreme price conditions using probability theory. This transforms subjective "overbought" or "oversold" labels into quantifiable statistical measurements. Moving Average Smoothing (TEMA) : Reduces the inherent noise in raw statistical values while maintaining sensitivity to genuine price movements. The triple-layer EMA approach minimizes lag that typically affects simple moving averages. Volatility-Based Bands : Creates adaptive thresholds that automatically adjust to current market conditions. This dynamic approach outperforms fixed-level indicators by accounting for the reality that market volatility changes over time. The synergy between these three components creates a robust system where each element addresses a weakness in the others. The Z-Score provides statistical rigor, TEMA adds smoothness for actionable signals, and dynamic bands ensure the indicator remains effective across varying market conditions. The Nova Statistical Filtering Oscillator offers several distinctive features that set it apart from traditional technical indicators: Self-Referential Adaptation : The volatility bands are calculated from the oscillator's own standard deviation, creating a truly adaptive system that automatically adjusts to current market conditions without manual parameter changes. Gradient Visualization : The color gradient provides at-a-glance understanding of current market position relative to statistical extremes, making it easy to identify both extreme conditions and transitions. Zero Baseline Clarity : The central baseline at zero represents the statistical mean, providing an immediate visual reference point for understanding whether price is above or below its historical average. Clean Signal Generation : Buy and sell signals are generated only when the oscillator crosses back from extreme levels, filtering out premature entries and focusing on confirmed reversals. Using the NSFO effectively requires understanding both its signals and its limitations. Here's how to integrate it into your trading strategy: Identifying Overbought Conditions : When the oscillator crosses below the upper band (red line), it indicates the price has extended statistically above its average. This suggests a potential selling opportunity as the market may revert toward the mean. Identifying Oversold Conditions : When the oscillator crosses above the lower band (green line), it indicates the price has dropped statistically below its average. This suggests a potential buying opportunity as the price may bounce back toward the mean. Signal Confirmation : Wait for the oscillator to cross back toward the center (zero line) before entering a trade. This confirms the reversal is underway rather than just touching the extreme level. Trend Context : Always consider the broader trend direction. In strong trending markets, the oscillator may remain at extreme levels for extended periods. Use additional trend indicators for confirmation. Important Note: No indicator guarantees profitable trades. Always use proper risk management and consider combining NSFO with other technical or fundamental analysis tools for better decision-making. The NSFO provides several customizable parameters to suit different trading styles and market conditions: Statistical Window (default: 20) : Controls the lookback period for calculating mean and standard deviation. Higher values produce more stable readings but respond slower to price changes. Lower values are more responsive but may generate more false signals. Smoothing Length (default: 10) : TEMA period for reducing noise in the Z-Score. Higher values create smoother lines with more lag, while lower values are more responsive but potentially noisier. Bands Multiplier (default: 2.0) : Controls the width of dynamic bands in standard deviations. Higher values create wider bands, reducing false signals but potentially missing some opportunities. Lower values generate more signals but with increased noise. Color Customization : Traders can customize bullish (green) and bearish (red) colors to match their visual preferences or other indicators in their chart setup. The Nova Statistical Filtering Oscillator represents a sophisticated approach to identifying statistical extremes in financial markets. By combining Z-Score normalization with TEMA smoothing and dynamic volatility bands, it provides traders with a powerful tool for detecting potential mean reversion opportunities. Its adaptive nature ensures effectiveness across various market conditions, while the clear visual signals make it accessible to traders of all experience levels. Whether used as a primary trading system or as a confirmation tool alongside other indicators, the NSFO offers a unique perspective on market dynamics that can enhance your trading decisions. Remember to always test any new indicator thoroughly on historical data before using it with real capital, and maintain disciplined risk management practices regardless of the signals generated. This indicator is provided for educational and informational purposes only. Past performance does not guarantee future results. Trading financial markets involves substantial risk, and you should only trade with capital you can afford to lose. Always conduct your own research and consider your financial situation before making any trading decisions.
Pine Script® göstergesi
Pineify tarafından
RSI & Stochastic Momentum Indicator [crlmx]Combined RSI and Stochastic Momentum Index (SMI) in a single pane, optimised for sub-1H momentum trading. Key Features - Independent show/hide toggles for each indicator - Dual band system: RSI at 70/30 with normalised SMI (typically +100 to -100) - Band gradient fills on range extremes - Streamlined inputs / UI brought to you by crlmx Trading Applications - Usefull for LFT Momentum Confluence (5min-1H) - SMI's responsiveness to momentum shifts suits fast-moving markets and higher volatility conditions - Beyond 1H, consider switching to RSI + DSS Bressert which handles slower swings with less lag, trend confirmation and cycle alignment - Recommended Settings Default: RSI: 14 | SMI: 10/3/3 | Chart: 15min-1H Scalping: RSI: 7 | SMI: 8/3/3 | Chart: 1-5min Swing: RSI: 21 | SMI: 13/5/3 | Chart: 1H-4H Version History (Latest - 10 Feb 2026) - Combined RSI + SMI oscillator on shared 0-100 scale - Individual show/hide toggles - Overbought/oversold gradient fills for both indicators
Pine Script® göstergesi
crlmx tarafından
Recursive Least Squares Forecast [LuxAlgo]The Recursive Least Squares Forecast indicator uses an adaptive linear regression algorithm to estimate price trends in real-time, projecting future movements via a "Ghost Line" and providing dynamic bands and mean reversion signals for identifying market extremes. By continuously updating its internal model with every new bar, the script provides a highly responsive framework for both trend forecasting and volatility-adjusted trading. 🔶 USAGE The indicator aims to identify trend direction and potential exhaustion points. The central RLS mean line represents the current equilibrium price based on the adaptive model, while the bands represent volatility-adjusted extremes. Users can utilize the tool for both trend following and mean reversion strategies: Trend Following: Observe the slope and direction of the RLS Mean and the "Ghost Line" projection to determine the prevailing market bias. Mean Reversion: Use the dynamic bands to identify when price has deviated significantly from its adaptive equilibrium. Responsiveness: Adjust the Forgetting Factor (λ) to control the model's memory. A lower value (e.g., 0.95) makes the model react quickly to new price pivots, while a higher value (e.g., 0.99) provides a smoother, more stable trend line. 🔹 Mean Reversion Signals The indicator identifies mean reversion opportunities using a two-step process: Overextension: A setup begins when the price crosses outside the Upper or Lower Band, indicating an overbought or oversold state. Entry Signal: A "BUY" or "SELL" signal is triggered when the price crosses back inside the band, suggesting a return to the RLS mean. Targets: The RLS mean line serves as the primary take-profit target for these mean reversion setups. 🔶 DETAILS The model assumes a linear relationship where the intercept and slope are updated recursively. The RLS algorithm is an adaptive filter that effectively "learns" the trend at every bar. It uses a state-space approach where the transition matrix is updated using a gain vector, ensuring the most efficient estimate of the current trend trajectory. Unlike standard Moving Averages, the Recursive Least Squares (RLS) algorithm minimizes the sum of squared prediction errors by giving more weight to recent data. This allows the mean line to pivot quickly when market conditions change without the lag associated with traditional smoothing techniques. The "Ghost Line" extends from the last bar into the future, providing a linear projection of where the current trend is headed. Surrounding this projection are "Standard Deviation Forecast Bands," which indicate the expected range of price movement based on the current model state. 🔶 SETTINGS Forgetting Factor (λ): Controls how quickly the model forgets old data. Values closer to 1.0 make the model stable, while lower values make it more adaptive to recent price changes. Band Multiplier: Standard deviation multiplier for the forecast bands, controlling the width of the mean reversion zones. Forecast Horizon: Number of bars to project the "Ghost Line" and uncertainty bands into the future. Show Ghost Line & Bands: Toggles the visibility of the future projection polylines. Show Mean Reversion Signals: Toggles the visibility of the BUY/SELL labels on the chart.
Pine Script® göstergesi
LuxAlgo tarafından
6
Daily maximum price range for Credit SpreadsVolatility & Momentum for Credit Spreads It is a specialized mean-reversion tool designed primarily for options traders focusing on Credit Spreads (specifically 0DTE on SPX) and intraday reversals. By combining Volume Weighted Average Price (VWAP) with VIX-adjusted volatility bands, this indicator identifies statistical extremes where price is likely to revert. Unlike standard Bollinger Bands or Keltner Channels, TITAN adapts its width based on real-time implied volatility (VIX), ensuring that your "overextended" zones are accurate whether the market is calm or chaotic. 🎯 Core Concept The indicator relies on the principle that price moves within a definable "Daily Range" relative to the VWAP. When price pushes to the outer limits of this range while simultaneously hitting RSI extremes; it signals a high-probability reversal setup ideal for selling premium. 🛠 How It Works The engine is built on three pillars: Volatility-Adaptive Bands: The bands are calculated using a 14-day Average Daily Range (ADR), which is then dynamically scaled by the current VIX relative to a baseline. If VIX spikes, the bands widen instantly to keep you safe from premature entries. Momentum Triggers: Signals are generated only when the RSI (14) hits extreme Overbought (>70) or Oversold (<30) levels. "Golden Hour" Filtering: To avoid market open noise or late-day chop, the indicator includes a customizable time filter (Default: 10:15 – 11:30 AM EST). Signals outside this window are suppressed to enforce trading discipline. 🚀 Key Features Visual Strategy Simulation: The indicator now includes a built-in "Strike Simulator." Upon the first valid signal of the session, it automatically plots a horizontal "Strike Line" at the Outer Band ± a user-defined buffer (e.g., 10 points). This helps you visualize your theoretical strike price for the rest of the day. Bull & Bear Zones: Color-coded fills (Green for Bullish Buy Zones, Red for Bearish Sell Zones) make it easy to see market context at a glance. Live Dashboard: A Heads-Up Display (HUD) in the bottom right shows real-time RSI values, Golden Hour status, and current signal state. Unified Alert System: A single master alert condition triggers if price hits an RSI extreme OR touches a volatility band during your active trading window. 📉 How to Trade It (Example Strategy) Wait for the Window: Ensure the "Golden Hour" on the dashboard reads ACTIVE (Default 10:15 AM EST). Identify the Zone: Short Setup (Call Credit Spread): Price pushes into the Red Zone (Outer High). Long Setup (Put Credit Spread): Price pushes into the Green Zone (Outer Low). Confirm the Signal: Look for the Diamond Icon. This confirms RSI has hit the extreme threshold. Check the "Strike Line": Use the simulated horizontal line to identify where your short strike would be (Outer Band + Buffer) to verify it is at a safe distance from current price. ⚙️ Settings ADR Length: Lookback period for daily range calculation (Default: 10). Baseline VIX:* The standard VIX level used for normalization (Default: 15.0). Inner/Outer Multipliers: Controls the width of the bands. Golden Hour: The specific time window for valid signals. Strike Buffer: Points added to the outer band to simulate your option strike price. ⚠️ Disclaimer This tool is for informational purposes only. Trading options, especially 0DTE credit spreads, involves significant risk. Always backtest strategies and manage risk accordingly.
Pine Script® göstergesi
oXXo1 tarafından
Session Anchored OIWAP [Arjo]The Session Anchored OIWAP (Open Interest Weighted Average Price) indicator shows you a weighted average price that uses Open Interest (OI) changes during different trading sessions . It divides the day into four clear sessions: Opening Hour , Morning Session , Mid-Day Session , and Closing Session . For each session , it calculates a weighted average price using both market price and open interest data from futures . This line updates as the session progresses and resets when a new session starts . You can also see optional deviation bands that you visually compare to how far the market price is moving away from the session’s weighted average. This indicator also helps you watch how Open Interest changes connect with price movements during specific market hours. Concepts This tool works on a few simple ideas: Session anchoring Each session starts fresh. The indicator resets and begins a new calculation when a new time block begins. This allows users to visually study each session independently. Open-interest weighting Instead of treating all price moves equally, price changes linked to higher open-interest activity have more influence on the OIWAP. This gives a weighted reflection of where the market has been trading during the session. Averaging and smoothing The OIWAP line blends many price data points into one smooth curve, making it easier to follow than raw price movement. Volatility display with bands The upper and lower bands are placed at ±0.5 standard deviation from the OIWAP line. These bands simply help you see when price stretches further away than usual from the session average. Features Four Independent Session Calculations: Shows separate OIWAP lines for Opening Hour (default: 09:15-10:15), Morning (10:15-11:30), Mid-Day (11:30-14:00), and Closing (14:00-15:30) sessions Open Interest Weighting: Uses absolute OI change as the weight instead of traditional volume Customizable Session Times: You can change the time ranges for each session to match your market or what you need Optional Deviation Bands: You can turn ±0.5 standard deviation bands on or off around each OIWAP line Color-Coded Sessions: Each session has its own color so you can tell them apart easily Selective Display: You can turn individual sessions and bands on or off Data Availability Check: Shows you a notification when Open Interest data isn't available for your symbol Adjustable Position Timeframe: You can calculate OI changes on different timeframes (Chart, Daily, 15min, 30min, 60min, 120min) How to use Add this indicator to a chart of any symbol that has Open Interest data ( from futures or derivatives contracts). Once you add it, you'll see colored lines showing the OIWAP for each session you enable, along with optional deviation bands. Adjusting Settings: Turn individual sessions on or off using the checkboxes in the " Sessions " section Change session colors to match your chart or what looks good to you Turn deviation bands on or off using the " Show Bands " option in the Display settings Change session time ranges in the " Session Times " section to match your market hours or what you want to analyze Change the Position Timeframe if you want to see OI changes calculated on a different time period Visual Interpretation: Each OIWAP line shows you the OI-weighted average price for that session The deviation bands show you how much prices spread out, weighted by OI changes You can watch how price interacts with these levels to see where significant OI activity happened Different sessions may show different OIWAP levels, showing you how the OI-price relationship changes throughout the trading day Note: This indicator needs Open Interest data to work. If OI data isn't available for your symbol, you'll see a message in the center of your chart. This indicator works only with derivatives markets like futures and options in the Indian Market where OI data is publicly available. Conclusion The Session Anchored OIWAP indicator is designed to support structured market observation by combining price, open interest, and session anchoring into a clear visual format. It helps users study market behavior during different parts of the day without generating trading instructions or outcomes. Disclaimer This indicator is for educational and visual-analysis purposes only. It does not provide trading signals , financial advice, or guaranteed outcomes . You should perform your own research and consult a licensed financial professional when needed. All trading decisions are solely the responsibility of the user. Happy Trading
Pine Script® göstergesi
ARJO_2019 tarafından
Open Interest Weighted Average Price [Arjo]Open Interest Weighted Average Price , or OIWAP , is a simple visual indicator that shows the average price of an asset based on changes in open interest . Instead of using trading volume like VWAP, this indicator gives more weight to prices where new futures contracts are being added or removed . This helps highlight the price levels where traders are actively building or closing positions. The indicator shows: A main line that represents the average price weighted by open interest changes. Upper and lower bands (standard deviation bands) that show how far the price moves away from this average. OIWAP is mainly useful for NSE futures markets , where open interest data is available. It helps traders visually understand where most market participation and positioning are taking place relative to price . Concepts: Applies statistical concepts, including weighted averaging and standard deviation, to open interest data Uses the absolute change in open interest as a weighting factor for each price point Creates a dynamic average that reflects where significant open interest activity has occurred during a given period Standard deviation bands are computed from this weighted average to show the statistical spread of prices around the OIWAP line Resets calculations based on user-selected time periods (daily, weekly, monthly, or session-based) Allows for fresh analysis at regular intervals Similar concept to volume-weighted average price (VWAP) indicators, but uses open interest changes as the weighting component Features: Weighted Average: Calculates a central line based on contract activity. Flexible Anchors: Allows users to choose the reset period for the calculation. Volatility Bands: Displays outer and mid-bands to visualize price stretches. Data Check: Built-in alerts notify you if Open Interest data is missing for a symbol. Visual Zones: Color-coded areas help identify price location at a glance. How To Use When you add the indicator to your chart, you will see: A main OIWAP line — the open-interest-weighted price level Mid-bands around the line (±0.5 standard deviations) Outer bands farther away (±2.0 standard deviations) Shaded background zones between these lines You can: Change the reset period to see how the average behaves over different time ranges Adjust the timeframe for open-interest data Turn mid-bands on or off Adjust colors and styles to improve readability Conclusion The OIWAP indicator serves as an educational tool for visualizing the relationship between price movements and open interest activity in futures markets Presents a weighted average price line along with statistical deviation bands Offers a structured framework for chart analysis Customizable settings allow users to adapt the display to their analytical preferences Maintains focus on visual interpretation rather than directional predictions Functions as a supplementary charting overlay that may complement other forms of technical and fundamental analysis Disclaimer This indicator is for educational and visual-analysis purposes only. It does not provide trading signals, financial advice, or guaranteed outcomes . You should perform your own research and consult a licensed financial professional when needed. All trading decisions are solely the responsibility of the user.
Pine Script® göstergesi
ARJO_2019 tarafından
2
SuperBandsI've been seeing a lot of volatility band indicators pop up recently, and after watching this trend for a while, I figured it was time to throw my two chips in. The original spark for this idea came years ago from RicardoSantos's Vector Flow Channel script, which used decay channels with timed events in an interesting way. That concept stuck with me, and I kept thinking about how to build something that captured the same kind of dynamic envelope behavior but with a different mathematical foundation. What I ended up with is a hybrid that takes the core logic of supertrend trailing stops, smooths them heavily with exponential moving averages, and wraps them in Donchian-style filled bands with momentum-based color gradients. The basic mechanism here is pretty straightforward. Standard supertrend calculates a trailing stop based on ATR offset from price, then flips direction when price crosses the trail. This implementation does the same thing but adds EMA smoothing to the trail calculation itself, which removes a lot of the choppiness you get from raw supertrend during sideways periods. The smoothing period is adjustable, so you can tune how reactive versus stable you want the bands to be. Lower smoothing values make the bands track price more aggressively, higher values create wider, slower-moving envelopes that only respond to sustained directional moves. Where this diverges from typical supertrend implementations is in the visual presentation and the separate treatment of bullish and bearish conditions. Instead of a single flipping line, you get persistent upper and lower bands that each track their own trailing stops independently. The bullish band trails below price and stays active as long as price doesn't break below it. The bearish band trails above price and remains active until price breaks above. Both bands can be visible simultaneously, which gives you a dynamic channel that adapts to volatility on both sides of price action. When price is trending strongly, one band will dominate and the other will disappear. During consolidation, both bands tend to compress toward price. The color gradients are calculated by measuring the rate of change in each band's position and converting that delta into an angle using arctangent scaling. Steeper angles, which correspond to the band moving quickly to catch up with accelerating price, get brighter colors. Flatter angles, where the band is moving slowly or staying relatively stable, fade toward more muted tones. This gives you a visual sense of momentum within the bands themselves, not just from price movement. A rapidly brightening band often precedes expansion or breakout conditions, while fading colors suggest the trend is losing steam or entering consolidation. The filled regions between price and each band serve a similar function to Donchian channels or Keltner bands, creating clearly defined zones that represent normal price behavior relative to recent volatility. When price hugs one band and the fill area compresses, you're in a strong directional regime. When price bounces between both bands and the fills expand, you're in a ranging environment. The transparency gradients in the fills make it easier to see when price is near the edge of the envelope versus safely inside it. Configuration is split between bullish and bearish settings, which lets you asymmetrically tune the indicator if you find that your market or timeframe has different characteristics in uptrends versus downtrends. You can adjust ATR period, ATR multiplier, and smoothing independently for each direction. This flexibility is useful for instruments that exhibit different volatility profiles during bull and bear phases, or for strategies that want tighter trailing on longs than shorts, or vice versa. The ATR period controls the lookback window for volatility measurement. Shorter periods make the bands react quickly to recent volatility spikes, which can be beneficial in fast-moving markets but also leads to more frequent whipsaws. Longer periods smooth out volatility estimates and create more stable bands at the cost of slower adaptation. The multiplier scales the ATR offset, directly controlling how far the bands sit from price. Smaller multipliers keep the bands tight, triggering more frequent direction changes. Larger multipliers create wider envelopes that give price more room to move without breaking the trail. One thing to note is that this indicator doesn't generate explicit buy or sell signals in the traditional sense. It's a regime filter and envelope tool. You can use band breaks as directional cues if you want, but the primary value comes from understanding the current volatility environment and whether price is respecting or violating its recent behavioral boundaries. Pairing this with momentum oscillators or volume analysis tends to work better than treating band breaks as standalone entries. From an implementation perspective, the supertrend state machine tracks whether each direction's trail is active, handles resets when price breaks through, and manages the EMA smoothing on the trail points themselves rather than just post-processing the supertrend output. This means the smoothing is baked into the trailing logic, which creates a different response curve than if you just applied an EMA to a standard supertrend line. The angle calculations use RMS estimation for the delta normalization range, which adapts to changing volatility and keeps the color gradients responsive across different market conditions. What this really demonstrates is that there are endless ways to combine basic technical concepts into something that feels fresh without reinventing mathematics. ATR offsets, trailing stops, EMA smoothing, and Donchian fills are all standard building blocks, but arranging them in a particular way produces behavior that's distinct from each component alone. Whether this particular arrangement works better than other volatility band systems depends entirely on your market, timeframe, and what you're trying to accomplish. For me, it scratched the itch I had from seeing Vector Flow years ago and wanting to build something in that same conceptual space using tools I'm more comfortable with.
Pine Script® göstergesi
The_Peaceful_Lizard tarafından
7
Uptrick: Volatility Adjusted TrailIntroduction The "Uptrick: Volatility Adjusted Trail" is a dynamic trailing band indicator. It adapts in real time to changing market conditions by adjusting both to volatility and trend consistency. Inspired by Supertrend-style logic, it enhances traditional approaches by introducing adaptive mechanisms for more context-sensitive behavior in both trending and consolidating environments. Overview This indicator combines an exponential moving average (EMA) as its basis with an Average True Range (ATR)-derived multiplier that adjusts dynamically. Unlike fixed-multiplier tools, this indicator modifies its band distances in real time according to volatility expansion and trend persistence. The result is a trailing system that adapts to the prevailing market regime, providing traders with clearer signals for trend bias, stop placement, and potential momentum shifts. Originality The script’s originality lies in its multi-layered approach to trail calculation. It introduces a real-time ATR multiplier adjustment driven by two factors: a volatility expansion ratio and a trend persistence model. The expansion ratio compares the current ATR to its moving average, making the indicator more sensitive during volatile conditions and less sensitive during quieter periods. The trend persistence model assesses directional consistency to widen the bands during sustained trends. This dual adjustment method creates a system that evolves with market behavior, making it more responsive and adaptive than static-band or fixed-multiplier alternatives. Components & Inspiration This indicator was designed with specific components that work together: Exponential Moving Average (EMA): Chosen as the central baseline because it responds faster to recent price changes than a simple moving average, providing a more current reference for trailing bands. Average True Range (ATR): Used as the volatility measure because it accounts for both intraday and gap movement, making it a robust and widely accepted standard for market volatility. Dynamic Multiplier: The multiplier is adjusted by both volatility expansion and trend persistence to produce bands that tighten during low volatility and widen during consistent trends. This combination was chosen to give the indicator the ability to self-regulate across different market regimes. Trend Persistence Model: Integrated to assess directional consistency, ensuring the bands expand during strong trends, which can prevent premature stop-outs. Flip Confirmation Logic: Added to filter out noise by requiring multiple bar closes beyond a band before confirming a state change, reducing false reversals. For inspiration, the indicator draws on the core idea behind Supertrend—using a baseline and volatility-derived bands to define trailing stop levels. However, while Supertrend uses a fixed ATR multiplier, this indicator introduces a dynamic multiplier system and persistence weighting, making it more adaptive and suited for varying conditions. Inputs and Parameters Basis EMA Length Defines the period for the EMA that serves as the core price reference. ATR Length Sets the lookback period for the Average True Range calculation used in band spacing. Base ATR Mult The base multiplier applied to ATR before adjustments. Forms the starting scale of the band offset. Volatility Expansion Sensitivity Controls how strongly the band spacing reacts to short-term volatility bursts. Higher values create more pronounced band expansions or contractions. Trend Persistence Window Determines how many bars are used to calculate directional trend consistency using a smoothed step function. Persistence Impact Scales how much influence the trend persistence has on band widening. Values range from 0 (no effect) to 1 (maximum effect). Min Effective Mult Sets the minimum value that the adjusted multiplier can reach. Prevents the bands from becoming too narrow. Max Effective Mult Sets the maximum value the adjusted multiplier can reach. Prevents the bands from over-expanding during high volatility. Bars Above/Below to Confirm Flip Number of consecutive bars required to close above or below the opposing trail before confirming a bullish or bearish flip. Helps reduce noise and false signals. Show Flip Labels Enables or disables the display of flip markers on the chart. Label Size Allows users to adjust the size of flip labels from Tiny to Huge. Label ATR Offset Adjusts the vertical placement of flip labels in relation to the trail using an ATR-based offset. Features and Logic EMA Basis: All calculations stem from an EMA that tracks the centerline of price action. Dynamic ATR Multiplier: The ATR multiplier adjusts in real time based on volatility expansion and trend persistence. Clamped Multiplier: The adjusted multiplier is limited between user-defined minimum and maximum values to keep the band scale practical. Upper and Lower Bands: Bands are plotted above and below the EMA using the dynamic multiplier and ATR values. Trailing Logic: The script uses Supertrend-style trailing logic, updating the active band in the current trend direction and resetting the opposite band. Trend State Detection: A state variable tracks the current market regime (bullish, bearish, or neutral). Transitions are confirmed only after a user-specified number of bars close beyond the respective bands. Visual Elements: Trail lines and fill zones are color-coded (bullish cyan, bearish magenta). Candlestick and bar colors match the trend state. Optional flip labels mark confirmed transitions. Alerts: Built-in alert conditions allow users to receive real-time notifications for bullish or bearish flips. Usage Guidelines This indicator can be used for: Defining context-aware dynamic stop levels that adjust with market behavior. Identifying trend direction and reversal points based on adaptive logic. Filtering entry or exit signals during trending vs. consolidating conditions. Supplementing trade management strategies with responsive visual markers. Entering long or short positions based on the appearance of flip labels and managing stop losses by following the adaptive trail. Traders may tune the parameters to suit different trading styles or timeframes. For example, lower ATR and EMA values may suit intraday setups, while longer settings may benefit swing or positional trading. Summary The "Uptrick: Volatility Adjusted Trail" provides a flexible, adaptive trailing band system that accounts for both volatility and directional consistency. By combining an EMA baseline with a dynamic ATR multiplier influenced by volatility expansion and trend persistence, it creates a context-sensitive trailing system that aligns with changing market conditions. Customizable confirmation, flip labels, alerts, and dynamic visual cues make it a versatile tool for trend-following, breakout filtering, and trailing stop logic. Disclaimer This indicator is provided for educational and research purposes only. It does not constitute financial advice. Trading involves risk, and past performance does not guarantee future results. Always conduct your own analysis and risk management before making trading decisions.
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Uptrick tarafından
15
Uptrick: Volatility Weighted CloudIntroduction The Volatility Weighted Cloud (VWC) is a trend-tracking overlay that combines adaptive volatility-based bands with a multi-source smoothed price cloud to visualize market bias. It provides users with a dynamic structure that adapts to volatility conditions while maintaining a persistent visual record of trend direction. By incorporating configurable smoothing techniques, percentile-ranked volatility, and multi-line cloud construction, the indicator allows traders to interpret price context more effectively without relying on raw price movement alone. Overview The script builds a smoothed price basis using the open, and close prices independently, and uses these to construct a layered visual cloud. This cloud serves both as a reference for price structure and a potential area of dynamic support and resistance. Alongside this cloud, adaptive upper and lower bands are plotted using volatility that scales with percentile rank. When price closes above or below these bands, the script interprets that as a breakout and updates the trend bias accordingly. Candle coloring is persistent and reflects the most recent confirmed signal. Labels can optionally be placed on the chart when the trend bias flips, giving traders additional visual reference points. The indicator is designed to be both flexible and visually compact, supporting different strategies and timeframes through its detailed configuration options. Originality This script introduces originality through its combined use of percentile-ranked volatility, adaptive envelope sizing, and multi-source cloud construction. Unlike static-band indicators, the Volatility Weighted Cloud adjusts its band width based on where current volatility ranks within a defined lookback range. This dynamic scaling allows for smoother signal behavior during low-volatility environments and more responsive behavior during high-volatility phases. Additionally, instead of using a single basis line, the indicator computes two separate smoothed lines for open and close. These are rendered into a shaded visual cloud that reflects price structure more completely than traditional moving average overlays. The use of ALMA and MAD, both less commonly applied in volatility-band overlays, adds further control over smoothing behavior and volatility measurement, enhancing its adaptability across different market types. Inputs Group: Core Basis Length (short-term): The number of bars used for calculating the primary basis line. Affects how quickly the basis responds to price changes. Basis Type: Option to choose between EMA and ALMA. EMA provides a standard exponential average; ALMA offers a centered, Gaussian-weighted average with reduced lag. ALMA Offset: Determines the balance point of the ALMA window. Only applies when ALMA is selected. Sigma: Sets the width of the ALMA smoothing window, influencing how much smoothing is applied. Basis Smoothing EMA: Adds additional EMA-based smoothing to the computed basis line for noise reduction. Group: Volatility & Bands Volatility: Choose between StDev (standard deviation) and MAD (median absolute deviation) for measuring price volatility. Vol Length (short-term): Length of the window used for calculating volatility. Vol Smoothing EMA: Smooths the raw volatility value to stabilize band behavior. Min Multiplier: Minimum multiplier applied to volatility when forming the adaptive bands. Max Multiplier: Maximum multiplier applied at high volatility percentile. Volatility Rank Lookback: Number of bars used to calculate the percentile rank of current volatility. Show Adaptive Bands: Enables or disables the display of upper and lower volatility bands on the chart. Group: Trend Switch Labels Show Trend Switch Labels: Toggles the appearance of labels when the trend direction changes. Label Anchor: Defines whether the labels are anchored to recent highs/lows or to the main basis line. ATR Length (offset): Length used for calculating ATR, which determines label offset distance. ATR Offset (multiplier): Multiplies the ATR value to place labels away from price bars for better visibility. Label Size: Allows selection of label size (tiny to huge) to suit different chart setups. Features Adaptive Volatility Bands: The indicator calculates volatility using either standard deviation or MAD. It then applies an EMA smoothing layer and scales the band width dynamically based on the percentile rank of volatility over a user-defined lookback window. This avoids fixed-width bands and allows the indicator to adapt to changing volatility regimes in real time. Volatility Method Options: Users can switch between two volatility measurement methods: ➤ Standard Deviation (StDev): Captures overall price dispersion, but may be sensitive to spikes. ➤ Median Absolute Deviation (MAD): A more robust measure that reduces the effect of outliers, making the bands less jumpy during erratic price behavior. Basis Type Options: The core price basis used for cloud and bands can be built from: ➤ Exponential Moving Average (EMA): Fast-reacting and widely used in trend systems. ➤ Arnaud Legoux Moving Average (ALMA): A smoother, more centered alternative that offers greater control through offset and sigma parameters. Multi-Line Basis Cloud: The cloud is formed by plotting two individually smoothed basis lines from open and close prices. A filled area is created between the open and close basis lines. This cloud serves as a dynamic support or resistance zone, allowing users to identify possible reversal areas. Price moving through or rejecting from the cloud can be interpreted contextually, especially when combined with band-based signals. Persistent Trend Bias Coloring: The indicator uses the last confirmed breakout (above upper band or below lower band) to determine bias. This bias is reflected in the color of every subsequent candle, offering a persistent visual cue until a new signal is triggered. It helps simplify trend recognition, especially in choppy or sideways markets. Trend Switch Labels: When enabled, the script places labeled markers at the exact bar where the bias direction switches. Labels are anchored either to recent highs/lows or to the main basis line, and spaced vertically using an ATR-based offset. This allows the trader to quickly locate historical trend transitions. Alert Conditions: Two built-in alert conditions are available: ➤ Long Signal: Triggered when the close crosses above the upper adaptive band. ➤ Short Signal: Triggered when the close crosses below the lower adaptive band. These conditions can be used for custom alerts, automation, or external signaling tools. Display Control and Flexibility: Users can disable the adaptive bands for a cleaner layout while keeping the basis cloud and candle coloring active. The indicator can be tuned for fast or slow response depending on the strategy in use, and is suitable for intraday, swing, or position trading. Summary The Volatility Weighted Cloud is a configurable trend-following overlay that uses adaptive volatility bands and a structured cloud system to help visualize market bias. By combining EMA or ALMA smoothing with percentile-ranked volatility and a four-line price structure, it provides a flexible and informative charting layer. Its key strengths lie in the use of dynamic envelopes, visually persistent trend indication, and clearly defined breakout zones that adapt to current volatility conditions. Disclaimer This indicator is for informational and educational purposes only. Trading involves risk and may not be suitable for all investors. Past performance does not guarantee future results.
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Uptrick tarafından
25
Adaptive Trend FinderAdaptive Trend Finder - The Ultimate Trend Detection Tool Introducing Adaptive Trend Finder, the next evolution of trend analysis on TradingView. This powerful indicator is an enhanced and refined version of Adaptive Trend Finder (Log), designed to offer even greater flexibility, accuracy, and ease of use. What’s New? Unlike the previous version, Adaptive Trend Finder allows users to fully configure and adjust settings directly within the indicator menu, eliminating the need to modify chart settings manually. A major improvement is that users no longer need to adjust the chart's logarithmic scale manually in the chart settings; this can now be done directly within the indicator options, ensuring a smoother and more efficient experience. This makes it easier to switch between linear and logarithmic scaling without disrupting the analysis. This provides a seamless user experience where traders can instantly adapt the indicator to their needs without extra steps. One of the most significant improvements is the complete code overhaul, which now enables simultaneous visualization of both long-term and short-term trend channels without needing to add the indicator twice. This not only improves workflow efficiency but also enhances chart readability by allowing traders to monitor multiple trend perspectives at once. The interface has been entirely redesigned for a more intuitive user experience. Menus are now clearer, better structured, and offer more customization options, making it easier than ever to fine-tune the indicator to fit any trading strategy. Key Features & Benefits Automatic Trend Period Selection: The indicator dynamically identifies and applies the strongest trend period, ensuring optimal trend detection with no manual adjustments required. By analyzing historical price correlations, it selects the most statistically relevant trend duration automatically. Dual Channel Display: Traders can view both long-term and short-term trend channels simultaneously, offering a broader perspective of market movements. This feature eliminates the need to apply the indicator twice, reducing screen clutter and improving efficiency. Fully Adjustable Settings: Users can customize trend detection parameters directly within the indicator settings. No more switching chart settings – everything is accessible in one place. Trend Strength & Confidence Metrics: The indicator calculates and displays a confidence score for each detected trend using Pearson correlation values. This helps traders gauge the reliability of a given trend before making decisions. Midline & Channel Transparency Options: Users can fine-tune the visibility of trend channels, adjusting transparency levels to fit their personal charting style without overwhelming the price chart. Annualized Return Calculation: For daily and weekly timeframes, the indicator provides an estimate of the trend’s performance over a year, helping traders evaluate potential long-term profitability. Logarithmic Adjustment Support: Adaptive Trend Finder is compatible with both logarithmic and linear charts. Traders who analyze assets like cryptocurrencies, where log scaling is common, can enable this feature to refine trend calculations. Intuitive & User-Friendly Interface: The updated menu structure is designed for ease of use, allowing quick and efficient modifications to settings, reducing the learning curve for new users. Why is this the Best Trend Indicator? Adaptive Trend Finder stands out as one of the most advanced trend analysis tools available on TradingView. Unlike conventional trend indicators, which rely on fixed parameters or lagging signals, Adaptive Trend Finder dynamically adjusts its settings based on real-time market conditions. By combining automatic trend detection, dual-channel visualization, real-time performance metrics, and an intuitive user interface, this indicator offers an unparalleled edge in trend identification and trading decision-making. Traders no longer have to rely on guesswork or manually tweak settings to identify trends. Adaptive Trend Finder does the heavy lifting, ensuring that users are always working with the strongest and most reliable trends. The ability to simultaneously display both short-term and long-term trends allows for a more comprehensive market overview, making it ideal for scalpers, swing traders, and long-term investors alike. With its state-of-the-art algorithms, fully customizable interface, and professional-grade accuracy, Adaptive Trend Finder is undoubtedly one of the most powerful trend indicators available. Try it today and experience the future of trend analysis. This indicator is a technical analysis tool designed to assist traders in identifying trends. It does not guarantee future performance or profitability. Users should conduct their own research and apply proper risk management before making trading decisions. // Created by Julien Eche - @Julien_Eche
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Julien_Exe tarafından
26
Volatility Price FlowCapitalize on market volatility with our new volatility price flow indicator. We have designed this indicator to process historical price movements and indicate when price may have reached exhaustion in the context of current volatility. This is achieved by taking the price deviation from a user defined moving average, and applying a weighting to the deviations from the candle body and candle wick on both buy side and sell side, over a user defined period. The period of the base moving average, type of moving average and the period of the historical price deviations can all be modified. This creates a typical 'band' style indicator, though with a unique characteristic that the buy and sell side vary independently as well as the band expansion being based on weighted variables tied to the actual price changes, rather than just a standard deviation the moves uniformly. Additionally, these bands can be merged with an anchored vwap - we do this so that the deviations of price from the moving average can include a more volume based approach to identifying potential pivots. The end result is an indicator that reflects the current market price movements, identifies and capitalizes on impulsive or beginning moves to indicate potential tops / bottoms / reversals. The signals are simple - anytime price closes within a band, having been outside the band, a signal is displayed. As a basic guide to setting the indicator up for the first time, we suggest reducing all of the multipliers to a value less than 1. Then gradually increase each one, until the signals reduce in quantity and improve in quality, starting with the price deviation multiplier, then the volatility multiplier and finally the expansion multiplier. Last of all, alerts can be created based on the current chart timeframe and indicator settings, simply by adding an alert that uses the built in buy or sell signal. Note: We cannot guarantee the accuracy of the signals provided, since the user creates the signals by modifying the settings, and as such we can take no responsibility for any trading losses incurred using the indicator and highly encourage all users to manage their risk and only risk what you can afford to lose.
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Texmoonbeam tarafından
1
Standard Deviation-Based Fibonacci Band by zdmre This indicator is designed to better understand market dynamics by focusing on standard deviation and the Fibonacci sequence. This indicator includes the following components to assist investors in analyzing price movements: Weighted Moving Average (WMA) : The indicator creates a central band by utilizing the weighted moving average of standard deviation. WMA provides a more current and accurate representation by giving greater weight to recent prices. This central band offers insights into the general trend of the market, helping to identify potential buying and selling opportunities. Fibonacci Bands : The Fibonacci bands located above and below the central band illustrate potential support and resistance levels for prices. These bands enable investors to pinpoint areas where the price may exhibit indecisiveness. When prices move within these bands, it may be challenging for investors to discern the market's preferred direction. Indecisiveness Representation : When prices fluctuate between the Fibonacci bands, they may reflect a state of indecisiveness. This condition is critical for identifying potential reversal points and trend changes. Investors can evaluate these periods of indecisiveness to develop suitable buying and selling strategies. This indicator is designed to assist investors in better analyzing market trends and supporting their decision-making processes. The integration of standard deviation and the Fibonacci sequence offers a new perspective on understanding market movements. #DYOR
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zdmre tarafından
Fibonacci HH LL TRAMA BandLuxAlgo's Trend Moving Adaptive Moving Average was used as a reference to create bands by reading the highest and lowest prices of past bars based on Fibonacci numbers and then multiplying them by the Fibonacci ratio. LuxAlgo/ LuxAlgo/ In particular, the so-called TRAMA is characterized by its adaptation to the average of the highest and lowest prices over a specific period of time and is used to identify support/resistance. In order to apply this feature to the maximum extent possible, I used the high or low prices as the source of input, rather than the closing price. For example, src = high not original like src = close In addition, I created 6 levels by multiplying the Fibonacci ratio //Midline mah = ama1 mal = ama2 m = (mah + mal)/2 //Half Mean Range dist = (mah - mal)/2 //Levels h6 = m + dist * 11.089 h5 = m + dist * 6.857 h4 = m + dist * 4.235 h3 = m + dist * 2.618 h2 = m + dist * 1.618 h1 = m + dist * 0.618 l1 = m - dist * 0.618 l2 = m - dist * 1.618 l3 = m - dist * 2.618 l4 = m - dist * 4.235 l5 = m - dist * 6.857 l6 = m - dist * 11.089 If you want to use it for scalping, such as 15 minutes, you can include Fibonacci numbers such as 21,34,55 for a quick reaction type to detect the trend. Also, by including Fibonacci numbers such as 89,144,233, you can see where you stand in the larger trend. Some examples are included below. For Investors BTCUSDT 1day Chart Fibonacci number "55" For Daytraders BTCUSDT 4hour Chart Fibonacci number "34" For Scalpers BTCUSDT 15min Chart Fibonacci number "55" BTCUSDT 15min Chart Fibonacci number "89" BTCUSDT 15min Chart Fibonacci number "233" Fibonacci numbers are 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, etc., Fibonacci ratios are 0.618, 1.618, 2.618, 4.236, 6.854, 11.089, etc.,
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FibonacciFlux tarafından
Bollinger Bands Heatmap (BBH)The Bollinger Bands Heatmap (BBH) Indicator provides a unique visualization of Bollinger Bands by displaying the full distribution of prices as a heatmap overlaying your price chart. Unlike traditional Bollinger Bands, which plot the mean and standard deviation as lines, BBH illustrates the entire statistical distribution of prices based on a normal distribution model. This heatmap indicator offers traders a visually appealing way to understand the probabilities associated with different price levels. The lower the weight of a certain level, the more transparent it appears on the heatmap, making it easier to identify key areas of interest at a glance. Key Features Dynamic Heatmap: Changes in real-time as new price data comes in. Fully Customizable: Adjust the scale, offset, alpha, and other parameters to suit your trading style. Visually Engaging: Uses gradients of colors to distinguish between high and low probabilities. Settings Scale Tooltip: Scale the size of the heatmap. Purpose: The 'Scale' setting allows you to adjust the dimensions of each heatmap box. A higher value will result in larger boxes and a more generalized view, while a lower value will make the boxes smaller, offering a more detailed look at price distributions. Values: You can set this from a minimum of 0.125, stepping up by increments of 0.125. Scale ATR Length Tooltip: The ATR used to scale the heatmap boxes. Purpose: This setting is designed to adapt the heatmap to the instrument's volatility. It determines the length of the Average True Range (ATR) used to size the heatmap boxes. Values: Minimum allowable value is 5. You can increase this to capture more bars in the ATR calculation for greater smoothing. Offset Tooltip: Offset mean by ATR. Purpose: The 'Offset' setting allows you to shift the mean value by a specified ATR. This could be useful for strategies that aim to capitalize on extreme price movements. Values: The value can be any floating-point number. Positive values shift the mean upward, while negative values shift it downward. Multiplier Tooltip: Bollinger Bands Multiplier. Purpose: The 'Multiplier' setting determines how wide the Bollinger Bands are around the mean. A higher value will result in a wider heatmap, capturing more extreme price movements. A lower value will tighten the heatmap around the mean price. Values: The minimum is 0, and you can increase this in steps of 0.2. Length Tooltip: Length of Simple Moving Average (SMA). Purpose: This setting specifies the period for the Simple Moving Average that serves as the basis for the Bollinger Bands. A higher value will produce a smoother average, while a lower value will make it more responsive to price changes. Values: Can be set to any integer value. Heat Map Alpha Tooltip: Opacity level of the heatmap. Purpose: This controls the transparency of the heatmap. A lower value will make the heatmap more transparent, allowing you to see the price action more clearly. A higher value will make the heatmap more opaque, emphasizing the bands. Values: Ranges from 0 (completely transparent) to 100 (completely opaque). Color Settings High Color & Low Color: These settings allow you to customize the gradient colors of the heatmap. Purpose: Use contrasting colors for better visibility or colors that you prefer. The 'High Color' is used for areas with high density (high probability), while the 'Low Color' is for low-density areas (low probability). Usage Scenarios for Settings For Volatile Markets: Increase 'Scale ATR Length' for better smoothing and set a higher 'Multiplier' to capture wider price movements. For Trend Following: You might want to set a larger 'Length' for the SMA and adjust 'Scale' and 'Offset' to focus on more probable price zones. These are just recommendations; feel free to experiment with these settings to suit your specific trading requirements. How To Interpret The heatmap gives a visual representation of the range within which prices are likely to move. Areas with high density (brighter color) indicate a higher probability of the price being in that range, whereas areas with low density (more transparent) indicate a lower probability. Bright Areas: Considered high-probability zones where the price is more likely to be. Transparent Areas: Considered low-probability zones where the price is less likely to be. Tips For Use Trend Confirmation: Use the heatmap along with other trend indicators to confirm the strength and direction of a trend. Volatility: Use the density and spread of the heatmap as an indication of market volatility. Entry and Exit: High-density areas could be potential support and resistance levels, aiding in entry and exit decisions. Caution The Bollinger Bands Heatmap assumes a normal distribution of prices. While this is a standard assumption in statistics, it is crucial to understand that real-world price movements may not always adhere to a normal distribution. Conclusion The Bollinger Bands Heatmap Indicator offers traders a fresh perspective on Bollinger Bands by transforming them into a visual, real-time heatmap. With its customizable settings and visually engaging display, BBH can be a useful tool for traders looking to understand price probabilities in a dynamic way. Feel free to explore its features and adjust the settings to suit your trading strategy. Happy trading!
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The_Peaceful_Lizard tarafından
3
Ultimate Trend ChannelThe "Ultimate Trend Channel" indicator is a comprehensive trend analysis tool that calculates and displays a series of upper and lower bands based on user-defined input lengths. It uses linear regression and standard deviation to determine these bands for each of the 21 different group lengths. The indicator then computes the averages of these upper and lower bands, as well as the average of all the bands combined. The visualization on the chart includes the plotting of the average upper and lower bands, with the space between these bands shaded for easy visualization of the overall trend. Additionally, the average of all the bands, referred to as the "Ultimate Trend Line," is also plotted on the chart. This indicator provides a robust way of assessing market trends and volatility over varying periods, which can be extremely useful for both short-term and long-term trading strategies.
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Julien_Exe tarafından
7
(Very promising) [Abdullah Ahmed] Momentum indicator V.1Description: MOM-LRC is a powerful technical analysis indicator designed to provide traders with signals based on the momentum of an asset's price and its deviation from its mean value. The indicator calculates the exponential RSI and uses a custom function to determine the percentage change from the mean. The upper and lower bands of the momentum channel are then calculated using linear regression of the rate of change from the mean. The channel multiplier can be adjusted to increase or decrease the sensitivity of the indicator. How to use : 1 - Using MOM-LRC , look for buy signals when the price of the asset is below the lower border of the channel and retracing up. The opposite is true in the case of sell signals. 2 - It is also used in the case of negative and positive divergences, just as you use RSI The indicator can be used on any time frame and any asset, making it a versatile tool for traders of all levels. features: Calculates exponential RSI and percentage change from the mean Uses linear regression to calculate upper and lower bands of momentum channel Adjustable channel multiplier for increased sensitivity Suitable for any time frame and any asset Happy trading!
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AbdullahAhmedEgypt tarafından
6
Faytterro Bandswhat is Faytterro Bands? it is a channel indicator like "Bollinger Bands". what it does? creates a channel using standard deviations and means. thus giving users an idea about the expensive and cheap zones. It uses a special weighted moving average different from standard bollinger bands, it also averages not only price but also deviations. how it does it? it uses this formulas: how to use it? its usage is the same as "bollinger band". length represents the number of candles to be taken into account, source represents the source of those candles and stdev represents the coefficient of the standard deviation. you can use it with other indicators:
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faytterro tarafından
147