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VWAP NYO DeviationVWAP - Deviation At NYO A clean, real-time overlay indicator that instantly shows how the major liquid markets are opening relative to their developing daily VWAP — plus powerful historical context in one compact table. What the table displays (updated live on every chart): Symbol – ES1!, CL1!, GC1!, 6E1!, BTCUSD Deviation (%) – Today’s RTH open (9:30 AM EST) vs. the VWAP up to that exact moment, expressed as a percentage. → Green background = opening above average deviation → Red background = opening below average deviation Avg Abs Dev (%) – Historical average absolute opening deviation (all completed days on your chart) Touch VWAP % – Percentage of completed trading days where price touched the daily VWAP at least once during Regular Trading Hours (9:30–16:00 EST) Additional features On your main chart symbol, a clean colored label automatically appears at the exact 9:30 AM EST bar showing that symbol’s opening VWAP deviation (green up / red down). Fully automatic EST/New York timezone handling — works on any timeframe and any symbol you’re viewing. Table text size is fully customizable (tiny → large / auto). Best for Futures & BTC day traders who use VWAP as a primary reference Quick cross-market bias check at the open Mean-reversion / VWAP magnet strength analysis Monitoring how “stretched” the open is versus historical norms Simply add the indicator, and the table appears in the top-right corner. The more history loaded on your chart, the more accurate the average deviation and touch percentage become. Perfect companion for any VWAP-based strategy. Enjoy! 🚀
Pine Script® インジケーター
Resonant_Traderの投稿
Trend Velocity Channel [BackQuant]Trend Velocity Channel Overview Trend Velocity Channel is a trend and momentum-acceleration overlay built around one idea, trend strength is the gap between a fast “lead” average and a slow “lag” average . When the lead line pulls away from the lag line, the market is accelerating in that direction. When that gap collapses, trend energy is fading and reversals become more likely. Instead of using a single moving average slope or crossover, this indicator measures: A leading trend line (DEMA) that reacts quickly. A lagging trend line (slower EMA) that represents slower consensus value. A normalized “velocity / crush” metric: the distance between them in ATR units . A trend regime based on the sign of that velocity. A dynamic channel defined by the lead line on one side and a padded lag boundary on the other. A reversal level engine that marks flip bars and tracks retests and invalidations. The result is a channel that visually answers: Are we accelerating or decelerating? How strong is the current acceleration relative to recent history? Where is the “danger edge” where a reversal would be confirmed? Which flip levels remain relevant and which got invalidated? Concept: lead vs lag as a proxy for trend velocity Markets trend when price doesn’t just move, it keeps moving faster than the slow baseline can follow . If a fast estimator (lead) separates from a slow estimator (lag), that separation is a practical proxy for “velocity”: Lead above lag, bullish acceleration. Lead below lag, bearish acceleration. Lead converging back into lag, trend energy compressing. This script calls that separation Crush , meaning the lead line is “crushing away” from the lag line. Core components 1) Leading line: DEMA The lead line is a Double Exponential Moving Average: dema = DEMA(price, maLen) Why DEMA: It reduces lag relative to a standard EMA. It reacts faster to genuine directional moves. It still smooths noise enough to act as a structural line. DEMA is used as the “inner” channel edge and the glow anchor. 2) Lagging line: Slow EMA The lag line is a slower EMA: lagMA = EMA(price, round(maLen * 1.5)) Why a slower EMA: It represents a slower-moving consensus baseline. It creates a meaningful “gap” against the lead line. It is less sensitive to micro-chop, so separation signals are cleaner. The lag line also becomes the basis for the channel’s outer edge. 3) Volatility normalization: ATR Raw MA distance is not comparable across regimes. A 50-point gap might be huge in a low-vol market and nothing in a high-vol market. So the gap is normalized by ATR: atr = ATR(14) rawCrush = (dema - lagMA) / atr Interpretation: rawCrush = “how many ATRs the lead line is away from the lag line.” This standardizes the signal across instruments and volatility states. 4) Crush smoothing The gap can still jitter, especially in choppy markets. So it is EMA-smoothed: crush = EMA(rawCrush, crushSmth) Lower crushSmth: Faster regime flips, more noise. Higher crushSmth: More stable regimes, slower reaction. Trend regime and flips Trend direction is derived directly from the sign of the smoothed crush: trend = crush > 0 ? +1 : -1 flip = trend != trend Meaning: Bull regime: lead (DEMA) is above lag baseline in ATR units. Bear regime: lead is below lag baseline. Flip: the velocity sign changed, meaning acceleration has switched direction. This is not a price crossover system, it is a lead-lag separation regime system . Measuring strength: crushNorm The script also grades how extreme current crush is relative to recent conditions: crushAbs = abs(crush) crushHigh = highest(crushAbs, 80) crushNorm = crushHigh > 0 ? min(crushAbs / crushHigh, 1) : 0 Interpretation: crushNorm near 0 means separation is small relative to recent extremes, trend is weak or compressing. crushNorm near 1 means separation is near the largest seen recently, trend acceleration is strong. This strength scale drives: Color intensity (gradient) Glow width “Peak Crush” alert condition Channel construction Inner edge The inner edge is the leading line: inner = dema This is the “fast structure” of the move. Outer edge The outer edge is built from the lag line plus an ATR padding: outer = (bull) lagMA - atr * chanPad outer = (bear) lagMA + atr * chanPad This is important. The lag line sits behind price, so the script offsets it outward by a user-defined fraction of ATR. This creates a more realistic boundary that accounts for volatility. Interpretation: In bull regimes, the outer boundary is below lagMA, creating a support-like corridor beneath price. In bear regimes, the outer boundary is above lagMA, creating a resistance-like corridor above price. The channel is intentionally asymmetric This channel is not “± ATR around a mean.” It is directional: Inner edge hugs price via fast DEMA. Outer edge is anchored to lagMA and padded outward. So it behaves like a trend corridor where: The inner edge shows where the trend is currently “being pulled.” The outer edge shows the boundary where the trend would be meaningfully compromised if crossed. Ribbon fill (3-layer depth) Two midpoints are created between inner and outer: mid1 = inner + (outer - inner) * 0.33 mid2 = inner + (outer - inner) * 0.66 Then the fill is layered: inner → mid1 (most opaque) mid1 → mid2 mid2 → outer (most transparent) This creates a depth effect that visually communicates where price is sitting within the corridor. When the corridor is tight and strong, the ribbon looks concentrated. When it expands, the ribbon spreads and fades. Color logic (trend + strength) The indicator uses a gradient color where direction sets the palette and crushNorm sets intensity: Bull: faint green → strong green as crushNorm increases Bear: faint red → strong red as crushNorm increases This means you can read two things instantly: Direction (bull vs bear) Acceleration strength (faded vs intense) Glow engine on DEMA Glow width scales with ATR and crushNorm: glowW = atr * 0.07 * (0.5 + crushNorm) So: High acceleration = larger glow, more “energy” around the lead line. Low acceleration = smaller glow. Glow is built as multiple invisible plots above and below DEMA with layered fills, forming a halo around the lead line that encodes strength. Flip-aware band breaking The outer boundary line is broken on flips: bandBrk = flip ? na : outer plot(..., plot.style_linebr) This prevents a misleading continuous line across regime changes, since the outer edge swaps sides on flip. Crush reversal levels (flip levels engine) This script includes a level system that plants a dashed horizontal level on every regime flip, then tracks: Whether price retests it (first touch marker) Whether price invalidates it (deletes it) How long it extends forward How many levels are kept 1) Level placement On a flip: If trend flips bullish, the level is placed at the flip bar’s low. If trend flips bearish, the level is placed at the flip bar’s high. That makes sense structurally: Bull flip low is a “pivot low” candidate. Bear flip high is a “pivot high” candidate. Then a dashed line is drawn forward ~60 bars. 2) Level storage and maxLvls Levels are stored in an array and capped by maxLvls. When the cap is exceeded, the oldest is deleted. This keeps the chart readable. 3) Level invalidation (broken logic) Each level is monitored: Bull flip level breaks if price closes far below it: close < level - atr * 2.5 Bear flip level breaks if price closes far above it: close > level + atr * 2.5 This is a volatility-scaled invalidation. If price pushes through a flip level by a large margin in ATR terms, it’s no longer acting like a meaningful reaction point. 4) Retest detection A “touch” is detected when: close is within 0.25 ATR of the level, and close two bars ago was not close (distance > 0.5 ATR), and the level hasn’t been marked retested yet. On first retest, an “x” marker is printed and the level’s retested flag is set to true so it won’t spam. What these levels represent They are not generic support/resistance. They are regime pivot levels created by a change in lead-lag acceleration. In practice: Untested flip levels can act like “memory zones” where price may react. Retested levels become less special, still relevant but not “naked.” Invalidated levels are removed to reduce noise. Signals and alerts The script provides: Crush Bull: flip into bullish regime (crush crosses above 0 via smoothing logic) Crush Bear: flip into bearish regime Peak Crush: crushNorm > 0.85, meaning separation is near recent max, strong acceleration Important: Peak Crush is not a reversal call. It flags strong trend energy. That can precede continuation or exhaustion, you use it as context, not a standalone trade trigger. How to use it Trend following framework Stay aligned with the regime color. In bull regime, treat the outer boundary as the “structure floor.” In bear regime, treat the outer boundary as the “structure ceiling.” The inner DEMA is your fast guide, the outer edge is your compromise boundary. Acceleration read Increasing color intensity and thicker glow imply acceleration is strengthening. Fading color and shrinking glow imply acceleration is decaying and the move is losing energy. A regime flip is a clean state change, not a micro-signal. Using reversal levels Treat naked flip levels as potential reaction zones. Watch first retest behavior, clean rejection suggests the flip level is holding. If the level invalidates by 2.5 ATR, it’s removed because structure has been overwritten. Key inputs explained MA Length (maLen) Sets both the lead line length and the lag line length (scaled by 1.5). Lower values: More sensitive, more flips. Higher values: Smoother, fewer flips, slower response. Crush Smoothing (crushSmth) Controls stability of the velocity signal. Lower: Fast flips, noisier regime. Higher: More confirmation, later flips. Channel Padding (chanPad) Controls how much extra ATR space is added beyond lagMA. Higher padding: Wider channel, fewer boundary touches. Lower padding: Tighter boundary, more reactive “risk edge.” Max Levels Controls how many historical flip levels are retained. Summary Trend Velocity Channel treats trend as lead-lag separation expressed in ATR units. A fast DEMA tracks the active move, a slower EMA defines baseline value, and their normalized gap (Crush) defines both direction and acceleration strength . That strength drives an adaptive visual language (gradient color, glow width, ribbon depth). The channel itself is directional, with the lead line as the inner edge and a volatility-padded lag boundary as the outer edge, acting as a structural “compromise line.” On every regime flip the script plants a pivot level, tracks retests, and deletes invalidated levels, giving you a clean map of acceleration-based reversal zones.
Pine Script® インジケーター
BackQuantの投稿
Auto Center ChartAutomatically centers the chart within the current visible chart window. This utility indicator scans the visible range to determine its High and Low, then adjusts autoscale so that the most recent completed candle remains visually centered — without drawing any visible objects or cluttering the chart. Why This Is Useful: - Prevents price from hugging the top or bottom of the screen - Reduces visual bias during analysis - Improves clarity when planning risk/reward - Keeps the active candle visually balanced - Eliminates constant manual scale adjustments The script only influences scaling at the right edge and does not change when hovering over historical candles. Important Notes: - Requires Auto (fits data to screen) to be enabled in chart settings - May not work as expected if other indicators are also forcing autoscale or adding extra chart space - Designed for standard time-based charts If another script modifies scaling or the chart is not in Auto mode, TradingView’s scaling engine can override this indicator.
Pine Script® インジケーター
OrdrFlowの投稿
VTS Strategy [Quision]Overview This strategy is built on top of BackQuant's Volatility Trend Score indicator , an open-source tool that quantifies trend persistence through a volatility-adjusted trailing structure and a rolling comparison score. The original indicator answers a critical question: "Is the market trending with conviction, or is it chopping?" - by scoring how consistently an ATR-based trailing level advances over a configurable lookback window. This strategy wraps that core logic into a fully tradeable system with proper risk management, flexible exit modes, and session filtering. All credit for the core indicator logic goes to BackQuant. This publication adds only the strategy execution layer. What This Strategy Adds 1. ATR-Based Stop Loss A dedicated ATR stop loss (independent of the indicator's core ATR) protects every trade with a volatility-scaled risk level. The SL ATR period and multiplier are fully configurable, allowing you to tune risk independently from the signal generation. 2. Risk:Reward Take Profit The take profit is calculated as a multiple of the stop loss distance. 3. Three Exit Modes The strategy offers three distinct exit modes to match different trading styles: - Signal Flip Only, Exits only when the VTS score flips to the opposite regime. No SL/TP. Pure trend-following. - SL/TP Only, Exits only when the stop loss or take profit is hit. Ignores signal flips. Pure risk management. - Signal Flip + SL/TP, Both mechanisms are active. Maximum flexibility. 4. Optional Trailing Stop When enabled, the trailing stop progressively tightens the stop loss as the trade moves in your favor. It only activates after the position is in profit. 5. Session Filter Restrict trading to specific hours. Configurable timezone support (Exchange, UTC, Europe/Rome, America/New_York, Europe/London, Asia/Tokyo). Recommended Usage This strategy works best on instruments with clear trending behavior and sufficient volatility. The VTS core logic excels at filtering out choppy conditions, making it particularly effective on: Crypto pairs (BTC, ETH) Gold (XAUUSD) Major forex pairs Index futures Suggested starting settings: ATR Period: 35, Factor: 1.2 Loop: 1–45 (default) Long Threshold: 40, Short Threshold: -10 (default) SL ATR Period: 14, SL Multiplier: 3.0 TP R:R: 6.0 Session: adjust to your instrument's active hours Important Notes The core indicator logic is entirely BackQuant's work. Please refer to the original publication for detailed documentation on the scoring mechanism, tuning guidelines, and theoretical foundations.
Pine Script® ストラテジー
paolo_gaballoの投稿
LOWESS Adaptive Envelope [BackQuant]LOWESS Adaptive Envelope Overview LOWESS Adaptive Envelope is a nonparametric trend-fit and volatility envelope tool built around LOWESS (Locally Weighted Scatterplot Smoothing). Instead of smoothing price with a fixed-form moving average, this indicator performs a rolling set of local weighted linear regressions across a chosen historical window and stitches those local fits into a single smooth curve that adapts to changing market structure. On top of the fitted curve, the script builds an adaptive envelope whose width is driven by the local magnitude of the model’s residuals (how far price deviates from the fit). That means the envelope automatically expands when the market is noisy or trending aggressively, and contracts when price is stable or mean-reverting cleanly. The output is a complete “structure map”: A LOWESS fitted centerline (trend estimate). Upper and lower adaptive bands derived from smoothed residual spread. A filled region that changes color based on where price sits relative to the fit. Optional extrapolation of the fit and envelope into the future using last slope, with widening uncertainty. An info label showing fit quality (R²), position inside the envelope, and direction. Where LOWESS comes from (and why it is different from moving averages) LOWESS (also written LOESS) is a classic statistical smoothing technique used in exploratory data analysis and robust curve fitting. It became popular because it can approximate complex shapes without assuming a single global model. Instead of forcing the entire window to follow one equation (like a single linear regression or a single moving average kernel), LOWESS fits many small local regressions , each one tailored to its neighborhood. Key distinction: A moving average is a fixed smoother, it applies the same weighting rule everywhere, regardless of whether the market is trending, chopping, or accelerating. LOWESS is a locally re-fitted model, it re-estimates slope and intercept at each point based on nearby data. In price terms: LOWESS is better at “hugging structure” when the market curves or transitions. It can follow gradual regime shifts without the same lag profile as long-window MAs. It does not assume the trend is constant across the whole lookback, it assumes trend can vary locally. What the indicator is modeling Think of the lookback window as a dataset of points: x = bar index (0..length-1 inside the window) y = price For every point i inside that window, the indicator estimates the best local line: y ≈ a + b * x But it does this using only nearby points, and it weights them by distance from i. So the fitted value at i is a locally weighted regression prediction. The final fitted curve is the collection of those predictions across i = 0..length-1. Core mechanics: local weighted linear regression 1) Neighborhood size (bandwidth) The “locality” is controlled by a bandwidth parameter. In this script: h = max(bandwidth * length / 2, 2) Interpretation: h acts like a radius measured in bars inside the fitting window. Lower bandwidth → smaller h → more local fit (more responsive, can track curvature, more sensitive to noise). Higher bandwidth → larger h → more global fit (smoother, more stable, more lag in transitions). So bandwidth controls the bias-variance tradeoff: Small bandwidth: low bias, high variance. Large bandwidth: higher bias, lower variance. 2) Tricube kernel weighting LOWESS requires a weight function that decays smoothly with distance. This script uses the classic tricube kernel : For each candidate point j around target i: u = |i - j| / h If u < 1: - w = (1 - u³)³ If u ≥ 1: - w = 0 Why tricube: Weights go to zero smoothly at the boundary (no sharp cutoff artifacts). Nearby points dominate the fit, distant points contribute little or nothing. It is a standard LOWESS choice because it produces stable smooth curves. 3) Weighted least squares fit For each i, the script accumulates weighted sums over j in the neighborhood: sumW, sumWX, sumWY, sumWXX, sumWXY These correspond to the normal equations for weighted linear regression. From those, it computes: denom = sumW * sumWXX - sumWX² a and b derived from sums (intercept and slope) fitted = a + b * i If denom is too small (numerical instability, insufficient variation), it falls back to the raw price at that i. This entire process is repeated for every i in the window, which is why it is done only on the last bar (performance). Why it fits inside the window rather than a single line A single regression across 200 bars assumes one slope b explains the whole move. Markets rarely do that. LOWESS allows the slope to drift through time, which is exactly what “trend structure” actually does in real price. Residuals: turning model error into volatility structure Once the LOWESS fitted curve is computed, the script measures the residual at each point: res = price - fitted Residuals are the model’s error. In trading terms, residual magnitude is a proxy for: Local noise level. Deviations from trend structure (overextension/underextension). Regime instability (trend is less “explanatory”). The script takes absolute residuals: absRes = |res | This is important because envelope width should reflect spread size regardless of direction. R²: fit quality and regime information The indicator also computes R² over the window: ssRes = Σ(res²) ssTot = Σ((price - meanPrice)²) R² = 1 - ssRes/ssTot Interpretation: Higher R² means the LOWESS fit explains more of the variation inside the window. Lower R² means price is behaving in a way the smooth trend model cannot explain well (chop, shocks, irregular volatility). In markets, R² can be read as “how trend-like vs how noisy” the recent environment is, but remember it depends on your chosen length and bandwidth. Adaptive envelope construction (what makes it “adaptive”) A normal envelope uses a constant width (like ±k*ATR or ±k*stdev). This script does something different: it estimates a local envelope width based on smoothed residual magnitude. 1) Smooth residual magnitude locally It computes a residual averaging window: rWin = max(3, int(h * 0.8)) So the residual smoothing window is linked to the LOWESS locality. If the fit is local, the envelope adapts locally. If the fit is global, the envelope adapts more slowly. Then for each i: envW = mean(absRes over ) * envMult Interpretation: The envelope width is proportional to how much price typically deviates from the fit around that region. envMult is your “how many spreads” multiplier. This creates an envelope that expands and contracts along the curve, not a single constant band. 2) Upper and lower envelopes For each i: upper = fitted + envW lower = fitted - envW This is a model-driven channel. It is not ATR-based directly, it is “error-based.” That makes it very effective at responding to the actual behavior of the market relative to the fitted structure. How to interpret the envelope The centerline is the best local structural estimate. The envelope is the expected deviation range around that structure. Typical readings: Price near centerline: balanced relative to structure. Price riding upper band: strong bullish pressure, trend continuation or overextension depending on context. Price riding lower band: strong bearish pressure, continuation or overextension. Repeated band rejections: mean-reversion regime around the structural fit. Envelope widening: instability rising, volatility expanding, structure less reliable. Envelope tightening: compression, cleaner trend or coiling behavior. Because the band width is based on residuals, widening often coincides with “trend breaks” and regime transitions, not just higher ATR. Color logic and visual encoding The envelope fill color is based on price relative to the most recent fitted value: If close > fitted , bullish color. Else bearish color. So color is a regime/bias cue, not a volatility cue. The bands themselves are drawn with translucent versions of the same regime color, while the fit line is a subtle white. The fill polygon is constructed by: Walking forward through upper points. Then walking backward through lower points. So the shape is closed and can be filled cleanly using polyline fills. Extrapolation: forward projection with widening uncertainty This script can project the fitted line into future bars. This is not forecasting in a statistical sense, it is a deterministic extension based on the current slope. How it extrapolates It takes: slope = fitted - fitted lastFit = fitted Then for i = 1..extrapBars: futureFit = lastFit + slope * i This is a linear continuation of the most recent fit direction. It is meant as a visual guide for “if the current local trend continues.” Why the forward envelope widens The script also grows the envelope slightly with each projected bar: envGrow = lastEnv * 0.01 futureEnv = lastEnv + envGrow * i This is a simple uncertainty widening mechanism. As you move further into the future, you should assume less confidence. The envelope expansion encodes that visually without claiming statistical rigor. Info label: what it reports and how to read it When enabled, the label shows: 1) Direction arrow It computes a slope over the last few fitted points: recentSlope = fitted - fitted (or closest valid index) ▲ if slope >= 0 ▼ if slope < 0 This gives a slightly more stable direction read than one-bar slope. 2) R² Displayed as R²: 0.xxx, representing how well the LOWESS curve explains window variation. 3) Envelope Position (Env Pos) It measures where the current close sits inside the latest envelope: 0% = at lower band 50% = at centerline 100% = at upper band This is extremely useful as a normalized “over/under extension” metric because it is scaled by the adaptive band width, not raw price units. How to use it properly Trend structure and regime filtering Use the fit line as structural trend direction. Use the fill color as quick bias context. Use R² as a “trend quality” read: high R² tends to mean cleaner structure, low R² tends to mean chop or instability. Mean reversion vs continuation This tool can support both styles, but interpretation differs: Mean reversion framing If market repeatedly returns to the fit line, the fit is acting like value. Upper band touches can be “overbought relative to structure.” Lower band touches can be “oversold relative to structure.” Envelope position becomes your normalized stretch gauge. Trend continuation framing In strong trends, price can ride a band rather than revert to centerline. Band riding plus rising fit slope suggests persistence. A sudden failure to hold the band plus falling R² can flag transition risk. Breakdown/transition identification Because the envelope width is residual-driven: If price starts producing large residuals, the envelope expands. That expansion is often a signature of regime change, not just volatility. Combine expansion with slope flattening to identify trend exhaustion. Parameter tuning (what each input really does) Length Defines how much historical data is used for the full fit. Larger length: More stable curve. More computational load. Tends to represent macro structure. Bandwidth Controls locality: Low bandwidth (0.10–0.25): more reactive, tracks curvature and micro-structure, more sensitive to noise. Higher bandwidth (0.30–0.50+): smoother, more stable, more lag in fast turns. Envelope Width (envMult) Scales how wide the adaptive band is relative to the local residual spread: Lower values create a tighter channel, more band interactions. Higher values create a wider channel, fewer touches, better for regime filtering. Extrapolation Bars Purely visual. More bars gives a longer projected structure line and uncertainty region. Limitations and correct expectations LOWESS is powerful, but it is not a magic predictor. LOWESS is descriptive, it fits what happened, then projects linearly if extrapolation is enabled. In sudden shocks or gaps, the fit will update only after the new data is inside the window. Very small bandwidth can overfit local noise, producing misleading curvature. Very large bandwidth can underfit, behaving like a slow regression and missing turning points. R² is window-dependent, a low value does not mean “bad indicator,” it often means “market is not smooth right now.” Summary LOWESS Adaptive Envelope applies locally weighted linear regression (LOWESS) with a tricube kernel to build a smooth, structure-following fitted price curve that adapts to regime changes without relying on a fixed moving-average form. It then converts the model’s local residual spread into a dynamic envelope that expands and contracts with real deviation behavior, provides fit quality via R², normalizes price position inside the band, and optionally extrapolates the latest structural slope forward with widening uncertainty. The result is a robust trend-structure and deviation framework that is equally useful for regime filtering, mean-reversion context, and trend persistence assessment.
Pine Script® インジケーター
BackQuantの投稿
Session Auction State Engine (SASE) Summary in one paragraph Session Auction State Engine (SASE) is a session context indicator for liquid futures, FX, equities, and crypto on intraday to daily timeframes. It classifies the current session into one of four auction regimes, Balanced, Early trend, Trend continuation, or Failed auction, so you act only when multiple acceptance and expansion conditions align. It is original because it outputs a single interpretable auction state, not buy and sell signals, by combining time acceptance, range expansion, and return to value logic into a compact dashboard. Add it to a clean chart, read the table for Action and permissions, and use the optional markers and bands for quick visual context. Shapes can move while the bar is open and settle on close, for conservative workflows use alerts on bar close. Scope and intent • Markets. Major index futures, major FX pairs, large cap equities, liquid crypto • Timeframes. 1 minute to daily • Default demo used in the publication. ES1! on 5 minute • Purpose. Prevent premature directional decisions by forcing regime confirmation before direction is considered actionable • Limits. Indicator only. No backtest, no execution, no performance claims Originality and usefulness This is not a mashup of common indicators. It is a session auction classifier with an explicit decision hierarchy. • Unique concept or fusion. A regime engine that gates trading by session auction state using Initial Balance, value acceptance, expansion, and return to value confirmation • What failure mode it addresses. False starts in chop, early breakouts that never gain acceptance, and trend chasing inside balanced sessions • Testability. Inputs expose each component and the table shows the state, direction, and pass or fail checklist so users can verify why a state appears and tune thresholds • Portable yardstick. Expansion is normalized by an expected range unit (ATR based), so thresholds scale across symbols with different point values Method overview in plain language SASE runs inside a user defined session window. It builds an Initial Balance (IB), then evaluates whether the market is accepting prices outside value and outside IB, whether the session is expanding beyond IB, and whether price returns to value after a failed excursion. Base measures • Range basis. Value width is ATR of the chart timeframe over the Width ATR length, multiplied by Width ATR multiplier • Normalization basis. Expected range is ATR, either daily or intraday based on Expected range mode, used to normalize session extension Components • Initial Balance (IB). High and low during the first IB minutes of the session. IB defines the first acceptance boundary • Value anchor. A session anchored VWAP or TWAP (or auto) used as a value center • Value width. ATR based band around value used to measure inside value and outside value • Acceptance. Two acceptance channels are tracked, closes outside value in the breakout direction and closes outside IB in the breakout direction. An EMA plus a short streak score turns this into an acceptance strength metric • Extension. Maximum distance beyond IB, normalized by expected range, measures range expansion • Balance. An EMA of time spent inside the value band measures how rotational versus directional the session is • Failed auction. Requires a real excursion outside value first, then a sustained return inside value, plus an acceptance collapse and a rebalance increase, optionally requiring price to be back inside IB Fusion rule SASE outputs exactly one state per bar inside the session. • Balanced. No confirmed breakout from IB, or conditions indicate rotational trade, high balance, low acceptance, low extension • Early trend. Breakout exists, but trend continuation checklist is not complete yet • Trend continuation. All trend gates pass at once: enough time since breakout, acceptance above threshold, extension above threshold, and balance below threshold • Failed auction. After an excursion outside value, price returns and holds inside value while acceptance collapses and balance rises, optionally requiring price back inside IB Signal rule This script does not issue trade signals. It outputs context. • Direction is shown only when state is not Balanced • The dashboard shows a Trend checklist (T, A, E). Trend continuation requires T pass, A pass, and E pass • Early trend indicates direction may be forming but confirmation is incomplete • Failed auction indicates the prior directional attempt was rejected and mean reversion conditions are stronger What you will see on the chart • Optional markers. Key markers appear on state transitions, typically when Trend continuation or Failed auction begins, depending on the Markers setting • Optional reference levels. IB high and IB low lines, and value center plus value bands • Optional background and bar color. Off by default for chart cleanliness • Compact dashboard. A table showing state, direction, Action, permissions, IB progress, checklist, and component gauges Table fields and quick reading guide • State badge. Current auction state • Direction. Up or Down when state is not Balanced, otherwise None • Action. Plain language instruction for the current state • Use. Two permissions, Trend and MeanRev, shown as ON or OFF • IB. Progress and status, Wait until Complete • Trend. Ready percent plus checklist T, A, E • Accept. Acceptance strength and whether it meets the trend threshold • Extend. Extension strength and whether it meets the trend threshold • Balance. Balance score and whether it is low enough for trend continuation • Fail. Excursion and return to value progress when relevant Reading tip. Trend continuation is only intended when T, A, and E are all passing. If Balanced, do not force direction. Inputs with guidance Setup • Session. Defines when the engine is active. Verify timezone and hours when changing symbol or venue • Session timezone. Exchange or a named timezone • Show last session snapshot when closed. When enabled, the table displays the last in session state after the session ends Logic • IB minutes. Typical 30 to 90. Higher reduces noise but delays classification • Value anchor. Auto is recommended. VWAP requires reliable volume, TWAP is volume agnostic • Value reference. Freeze at IB reduces value drift and makes acceptance and failed auction logic more stable • Width ATR length. Typical 14 to 30. Higher smooths value width • Width ATR multiplier. Typical 0.5 to 1.2. Higher widens value and increases Balanced time • Breakout buffer as width fraction. Typical 0.1 to 0.4. Higher requires cleaner breakouts • Acceptance EMA length. Typical 20 to 60. Higher reduces flicker • Acceptance streak bars. Typical 4 to 10. Higher requires sustained acceptance • Early trend acceptance threshold. Typical 0.25 to 0.45 • Trend continuation acceptance threshold. Typical 0.45 to 0.70 • Balance EMA length. Typical 30 to 80 • Trend continuation max balance. Typical 0.30 to 0.55. Lower makes trend continuation stricter • Balanced min balance. Typical 0.55 to 0.80 • Trend continuation min bars since breakout. Typical 10 to 30 • Extension thresholds. Early 0.10 to 0.25, Trend 0.20 to 0.50, in expected range units • Failed auction window bars. Typical 15 to 40. Larger windows require more sustained rejection • Failed auction thresholds. Acceptance max 0.10 to 0.30, Balance min 0.55 to 0.80 • State change confirm bars. Typical 1 to 4 • Return to Balanced confirm bars. Typical 4 to 12 • Minimum bars to keep non Balanced state. Typical 6 to 20, increases stability UI • Theme. Dark or Light • Dashboard position. Corner selection • Markers. Off, Key only, or All changes • Background tint and bar color. Off by default to keep charts readable • Show IB lines and Show value bands. Off by default, enable for learning or debugging Usage recipes Intraday trend focus • IB minutes 60 • Value reference Freeze at IB • Breakout buffer 0.25 • Trend confirm bars 18 • Accept trend threshold 0.55 • Extend trend threshold 0.30 • Trend max balance 0.40 Goal. Trade only when Trend continuation is active and checklist is fully passing Intraday mean reversion focus • IB minutes 30 to 60 • Value reference Freeze at IB • Wider value width, raise Width ATR multiplier to 0.9 to 1.2 • Raise Balanced min balance to 0.70 • Keep Markers on Key only Goal. Focus on Balanced and Failed auction states, avoid early trend and trend continuation trades Swing continuation • Use 60 minute or 240 minute chart • Expected range mode Daily ATR • Trend confirm bars 12 to 30 depending on timeframe • Raise Width ATR length to 30 Goal. Use Trend continuation as a higher level regime gate, then use your own entry timing tool inside that regime Realism and responsible publication • No performance claims. Past results never guarantee future outcomes • Intrabar motion reminder. Shapes and table values can update while a bar forms and settle on close • Session windows use the chart exchange time unless you override timezone. Verify hours when changing symbol or venue Honest limitations and failure modes • News releases and liquidity gaps can break normal auction behavior and can trigger rapid state transitions • Symbols with unreliable volume may work better using TWAP or Auto rather than VWAP only • Very quiet regimes can reduce contrast between Balanced and Early trend, consider longer windows or higher thresholds • Non standard chart types can distort session and IB calculations, use standard candles for evaluation Open source reuse and credits None Legal Education and research only. Not investment advice. You are responsible for your decisions. Test on historical data and in simulation before any live use. Use realistic costs in any external testing workflow.
Pine Script® インジケーター
exluxの投稿
Harmonic Frequency Visualizer [BackQuant]Harmonic Frequency Visualizer Overview Harmonic Frequency Visualizer is a cycle-analysis and cross-asset resonance tool that uses a simplified Discrete Fourier Transform (DFT) to measure how strongly specific cycle periods are present in price. It is not a “trend indicator” and it is not trying to predict direction by itself. Its job is to quantify rhythm: which repeating periods (in bars) are currently dominant, whether those cycles are expanding or contracting (phase direction), and whether multiple instruments are sharing the same dominant periods at the same time (resonance). This indicator has two main output modes: Spectrum : a frequency “snapshot” showing amplitude at each tested period for up to five instruments. Spectrogram : a history heatmap showing how the spectrum evolves through time (for the chart instrument). Spectrum Spectrogram On top of that, it produces a Dominant Cycle Oscillator derived from the dominant cycle’s phase, which gives a continuous cycle position metric (peak/trough style zones) without repainting. This is designed for traders who want cycle context the same way they want volatility context: not as a magic signal, but as structure. What “frequency” and “cycles” mean in trading terms A cycle period (say 21 bars) means: “a repeating pattern that tends to complete one full oscillation every 21 bars.” If price contains such a pattern, the DFT will detect a strong correlation between price and a 21-bar sine/cosine wave. Markets do not have perfectly stable periodic motion, but they often show: Mean-reverting swings around value. Trend pulses with pullback cadence. Volatility clustering that creates rhythmic expansions and contractions. Cycle tools are trying to measure those repeating components, and DFT is the standard mathematical way to do it. Where DFT comes from (the core idea) The Discrete Fourier Transform comes from Fourier analysis, a foundational signal processing concept: Fourier’s idea : any sufficiently well-behaved signal can be expressed as a sum of sine and cosine waves at different frequencies, each with: An amplitude (how strong that wave is). A phase (where you are within the wave cycle). In continuous math you get the Fourier Transform. In sampled data (like candles) you use the Discrete Fourier Transform. It converts a time series (price over time) into a frequency description (strength of different cycles). In markets: Time domain: candles and price series. Frequency domain: cycle periods and their strengths. Why sine and cosine, not just sine A sine wave alone cannot represent every phase alignment cleanly. DFT uses both cosine and sine components because together they form an orthogonal basis that can represent any phase shift. You can think of it like this: Cosine component captures “in-phase” alignment with the cycle. Sine component captures “quadrature” (90-degree shifted) alignment. Combining them gives full information: amplitude + phase. Mathematically, a single frequency component can be written as: A * cos(ωt + φ) But DFT estimates A and φ by separately accumulating cosine and sine projections. How this script implements the DFT (and what it is actually measuring) This is not a full-spectrum FFT across every frequency. It is a targeted DFT across a fixed set of cycle periods: Tested periods The script tests 8 predefined periods: 5, 8, 13, 21, 34, 55, 89, 120 These are Fibonacci-like cycle candidates commonly used in cycle/market structure work. The point is not that Fibonacci is magic. The point is that these represent a reasonable spread from short to long rhythms without needing hundreds of frequencies (which would be heavy in Pine). Normalization step (important) Before computing the DFT, the script normalizes the series: mn = SMA(src, lookback) sd = stdev(src, lookback) norm = (src - mn) / sd (if sd != 0) Why normalize: DFT amplitude depends on the scale of the input series. If you compare BTC and TLT raw prices, the magnitude is meaningless. Z-score normalization makes amplitude more comparable across instruments and regimes. So the spectrum is measuring “cyclical structure in standardized deviations,” not raw dollars. Projection onto cosine and sine For each tested period P: ω = 2π / P (angular frequency for that period) Compute: - sCos = Σ(norm * cos(ωk)) - sSin = Σ(norm * sin(ωk)) Interpretation: You are correlating the last window of normalized price with a cosine wave of period P. And also correlating it with a sine wave of period P. If the price has a strong P-bar rhythm, these sums grow in magnitude. Window length detail The script uses: window = min(lookback - 1, 99) So even if lookback is 200, the internal DFT accumulation caps at 100 bars for performance stability. This is a deliberate trade: stable computation in Pine, while still letting you define normalization lookback and overall context. Amplitude computation Once sCos and sSin are computed: raw magnitude = sqrt(sCos² + sSin²) This is the length of the vector (sCos, sSin). That vector length is the standard way to combine the orthogonal components into one strength metric. Then it scales it into a 0–100 “display amplitude”: amp = sqrt(sCos² + sSin²) / lookback * 100 * sensitivity amp is capped to 100 So: Higher amplitude means stronger alignment with that cycle period. Sensitivity is a user control to amplify or damp the display scaling. Important: amplitude here is not a probability, and it is not guaranteed “signal quality.” It is a standardized “how much of that cycle exists in the recent window” metric. Phase computation Phase is computed using atan2(sSin, sCos). That matters because: A simple atan(sin/cos) fails in different quadrants. atan2 correctly resolves the angle from -π to +π. Phase tells you where you are within the cycle: Two cycles can have same amplitude but opposite phase. Phase is what lets you infer “approaching peak vs trough” behavior. Dominant cycle selection The script chooses the dominant cycle as the period with the highest amplitude among the tested periods: domIdx = argmax(amp ) domAmp = max amplitude domPhase = phase at domIdx This dominant cycle is used for: Spectrogram history matrix (chart symbol). Dominant cycle oscillator. Data window outputs (dominant period, oscillator value). Spectrum View: what you see and how to read it In Spectrum mode, the indicator draws a frequency snapshot for up to five instruments. Each instrument gets a spectrum line (or bars/area depending on style) plotted across the 8 periods on the x-axis, with amplitude (0–100) on the y-axis. X-axis meaning Each x position corresponds to a period (5 → 120 bars). You are not looking at “frequency in Hz.” You are looking at “period in bars,” which is more intuitive in trading. Y-axis meaning Amplitude is a scaled measure of how strongly that period is present in the recent normalized data. Higher means stronger. Plot styles Waveform: connects amplitude points into a continuous shape, best for seeing spectrum shape. Bars: draws vertical bars per period, best for quick comparison. Area: similar to waveform but filled toward baseline for emphasis. Dominant peaks and phase direction labels The script highlights dominant cycles per symbol (if enabled): If max amplitude > 20, it labels that peak with the symbol name. If Show Phase Direction is enabled, it appends ▲ or ▼. Phase direction logic: rising = sin(phase) < 0 ▲ means cycle is in a “rising” phase segment ▼ means cycle is in a “falling” phase segment This is not “price will rise now.” It is “the dominant cycle’s instantaneous phase suggests you are on the upward vs downward half of that oscillation.” In real markets, you use this as context, not as a standalone trade trigger. It also draws small ▲/▼ markers on secondary peaks (amp > 15) to show phase direction of other meaningful cycles, giving you a richer picture than “one dominant period.” Resonance Zones: cross-asset harmonic alignment Resonance is where this tool becomes more than a single-chart curiosity. What resonance means here A resonance zone is flagged when at least 3 out of 5 instruments have strong amplitude at the same tested period. Mechanically: For each period i: - Count instruments with amp > 30 - If count >= 3, mark resonance at that period When resonance is detected: A vertical highlight box is drawn behind that period. A ⚡ marker is printed at the top. Interpretation: Multiple assets are expressing a similar cycle length at the same time. This can indicate macro rhythm, shared liquidity timing, or cross-market synchronization. This is especially useful when your instrument set includes: Rates proxy (TLT), commodities (oil, gold), and crypto indices. You can visually spot when markets are “vibrating” together at a shared period. Resonance is not automatically bullish or bearish. It is telling you “cycle length agreement,” which can help with timing models and contextual trade planning. Spectrogram View: frequency over time Spectrum mode is a snapshot. Spectrogram mode adds time evolution. What a spectrogram is A spectrogram is a 2D heatmap where: Rows = different periods (frequency bands). Columns = time history (bars ago → now). Color = amplitude strength. This allows you to see: Which cycles are persistent vs fleeting. When dominant cycle shifts occur (energy moves from one period to another). Cycle regime transitions (short cycles dominating in chop vs longer cycles dominating in trend). How the script builds the spectrogram matrix It maintains a matrix with: NUM_PERIODS rows (8 periods) histBars columns (history length) Each bar: Remove the oldest column. Append the newest amplitude array from chartSpec. So the spectrogram is always a rolling history of the chart symbol’s cycle amplitudes. It does not attempt to store five symbols (too heavy), it focuses on the active chart for time evolution. Heat coloring Amplitude values map to a custom gradient: Low = dark blue Mid = blue/cyan to orange High = yellow This makes dominant energy bands visually obvious. A stable bright band means persistent cycle dominance. Dominant Cycle Oscillator: phase mapped to a 0–100 oscillator The oscillator is derived from the dominant cycle phase (chart symbol): oscRaw = cos(domPhase) oscValue = 50 + 50 * oscRaw (maps -1..1 into 0..100) Interpretation: When cos(phase) ≈ +1, oscillator near 100 (cycle peak zone). When cos(phase) ≈ -1, oscillator near 0 (cycle trough zone). Midline 50 corresponds to the quarter-cycle transition points. It also colors the oscillator by phase direction: oscRising = sin(domPhase) < 0 Rising phase = green-ish Falling phase = red-ish This gives you a clean timing reference: The dominant period tells you the cycle length. The oscillator tells you where you are within that cycle. It is not forecasting price. It is telling you the current phase position of the strongest detected cycle component. Alerts and practical timing usage Alerts are based on the oscillator: Cross above 80: dominant cycle entering peak zone. Cross below 20: dominant cycle entering trough zone. Cross 50: midline cross (phase transition). In practice, you use these as “timing context” alerts, for example: If your trend model is bullish and cycle oscillator enters trough zone, it can hint at a favorable pullback timing window. If you are mean-reversion trading and cycle peak zone aligns with resistance, that confluence matters. Again: cycle timing needs structure confirmation. The oscillator alone is not a trade system. Multi-instrument design and non-repaint behavior The indicator requests five external instruments via request.security. It uses: close with lookahead_on This forces the data to be “previous confirmed close” so the spectral calculations do not repaint intra-bar. That matters because cycle measures can change drastically within a bar if you let them use live values. So: Spectra for external symbols are based on confirmed historical closes. Chart symbol spectrogram and oscillator are also stable in the sense they depend on confirmed series values (dominant phase updates bar-to-bar). Key parameters and how they change behavior Analysis Lookback Affects normalization and the DFT window cap: Higher lookback stabilizes mean/stdev normalization and reduces random shifts. Lower lookback makes the tool more reactive but more prone to regime noise. Because the inner DFT accumulation caps at 100 bars, very high lookback mostly affects normalization rather than the raw projection length. Sensitivity Scales displayed amplitude: Higher sensitivity makes peaks stand out more. Lower sensitivity compresses amplitude. It is a display control, not a physics constant. View Mode Spectrum: cross-asset snapshot comparison, resonance detection. Spectrogram: time evolution of cycle energy for chart symbol. Show Phase Direction Adds ▲/▼ markers derived from sin(phase). Useful for quick cycle position intuition, but do not treat ▲ as “buy.” Show Resonance Zones Marks periods where many instruments share strong energy. Useful for macro rhythm alignment. Highlight Dominant Cycles Labels peaks. If you disable it, the chart becomes cleaner but less informative. Spectrogram History Controls how many columns are stored. Higher makes a longer heatmap but costs more drawing. Limitations and what not to assume This tool is honest DSP applied to market data, but market data is not a stationary sine wave generator. Key limitations: Cycles drift. Dominant period can shift as regime changes. The tool only tests 8 candidate periods. If the true dominant period is 30, it will express as energy near 34 or distributed across neighbors. Normalization helps comparability, but does not make amplitude “absolute truth.” DFT assumes a stable frequency over the window. Markets often violate that. Phase-based oscillators are timing aids, not predictors. This is why the indicator is best used as: Context for entries/exits, not a standalone system. A way to see when cycle energy concentrates or disperses. A way to detect when multiple markets share a timing rhythm. How to use it properly (workflows) 1) Cycle regime identification If short periods (5–13) dominate, market is often choppy, reactive, and mean-reverting. If mid periods (21–55) dominate, market often shows swing structure. If long periods (89–120) dominate, market can be in slower macro drift, trend legs, or compressed volatility regimes. 2) Timing layer for an existing strategy Use your trend model to decide direction. Use dominant cycle oscillator to decide timing within that direction. Use spectrogram to avoid trading when dominant period is unstable or flipping rapidly. 3) Cross-asset confirmation If you see resonance at a period, watch whether your main instrument is also showing strength there. Resonance can justify holding a cycle-based timing thesis with more confidence because it is not isolated. 4) Expectation management If the spectrum is flat (no peaks above threshold), that is information: No clean dominant cycle, randomness dominates. Cycle-based timing will be unreliable. Summary Harmonic Frequency Visualizer uses a targeted Discrete Fourier Transform across predefined cycle periods to measure amplitude and phase of cyclical components in price. It supports multi-instrument spectrum comparison, resonance detection when several markets share strong energy at the same periods, and a spectrogram heatmap for the chart instrument showing how cycle dominance evolves over time. A dominant cycle oscillator maps phase into a 0–100 timing readout with alerts for peak/trough/midline transitions. It is a cycle context engine designed to complement trend, structure, and risk models, not replace them.
Pine Script® インジケーター
BackQuantの投稿
Ehlers Super Smoother Trend Score [BackQuant]Ehlers Super Smoother Trend Score Overview Ehlers Super Smoother Trend Score is a regime and trend-strength indicator built on a signal-processing filter created by John F. Ehlers. Instead of smoothing price with a standard moving average (which is mathematically crude and prone to noise and aliasing), this indicator applies the Ehlers Super Smoother, a Butterworth-style low-pass filter designed specifically for market data. The filtered series is then scored for directional persistence across a configurable lookback window, producing an oscillator-like trend score that measures how consistently the smoothed trend is advancing or deteriorating. This is not a simple “MA slope” tool. It is: A proper low-pass filter (Super Smoother) to reduce noise while preserving structure. A persistence score that converts the filtered trend into a quantitative regime signal. A threshold framework that turns the score into long/short regime transitions with clean state logic. Where the filter comes from (and why it matters) John F. Ehlers is known for applying digital signal processing (DSP) techniques to technical analysis. Traditional moving averages are not designed as proper frequency-selective filters. They blur price, lag heavily, and can introduce distortions, especially when the market contains high-frequency components (noise) near the Nyquist limit (the maximum representable frequency in sampled data). The Super Smoother is derived from a Butterworth low-pass filter design. Butterworth filters are engineered to have a maximally flat passband, meaning they smooth without introducing ripples in the filtered output. In trading terms: Less “wavy” smoothing artifacts than many MA variants. Better suppression of high-frequency noise. Cleaner trend structure for downstream logic. This script implements Ehlers’ recursive coefficient form, giving you a 2-pole (classic) or 3-pole (heavier) filter. What “Super Smoother” actually is The Super Smoother is a recursive IIR filter (Infinite Impulse Response). Unlike an SMA which averages a fixed window of past values, an IIR filter uses feedback from its own prior output values. That matters because it can achieve strong smoothing with less lag for a given “smoothness target.” Conceptually: Input: price series. Output: filtered estimate of the “low-frequency” component (trend structure). Mechanism: combine current input (or pre-filtered input) with previous filter outputs using coefficients derived from a chosen cutoff period. The coefficients (c1–c4) are not arbitrary, they are computed from exponential decay and cosine terms based on the cutoff period. This is what makes it a real DSP filter rather than “just another MA.” 2-pole vs 3-pole behavior 2-pole (classic) A standard Ehlers Super Smoother configuration. It offers a strong improvement over typical MAs in smoothness vs lag balance. 3-pole Adds an additional feedback term (one more prior filtered state). This increases smoothing and noise rejection, but introduces slightly more lag. The advantage is a cleaner structural line, which often improves regime stability when the market is noisy or mean-reverting. Anti-aliasing pre-filter step Before applying the recursive formula, the script averages the current and previous price: avg = (src + src ) / 2 This is a simple but important pre-filter that reduces high-frequency components that can alias into lower frequencies in sampled data. In practice, it helps stop “one-bar spikes” from contaminating the filter output as much. Inputs and what they really control Super Smoother Period (ssPeriod) This is the cutoff period used in the coefficient derivation. It is not the same as “MA length,” but it behaves similarly in that: Lower period = faster response, less smoothing, more sensitivity to noise. Higher period = smoother output, better noise rejection, more lag. Poles Selects filter order: 2 poles = balanced default. 3 poles = smoother, more conservative. Score Lookback Start/End Defines the persistence scoring window. The script compares the current filtered value to many prior filtered values across that range. A longer range makes the score more “confidence-based” and slower to change, while a shorter range makes it more reactive. Thresholds (Long/Short) Turns the score into a regime classification: Long threshold defines when bullish persistence is strong enough to be considered a trend regime. Short threshold defines when persistence has deteriorated enough to signal a bearish transition. How the trend score is computed After filtering, the indicator computes a directional persistence score on the filtered series (not raw price). That distinction matters because you are scoring structure, not noise. Mechanically: For each i in the scoring window: - If filt_now > filt , add +1 - Else add -1 Sum across the window to produce the score. Interpretation: High positive score means the filtered trend is consistently higher than many past points, persistent bullish structure. Low or negative score means the filtered trend is not advancing, or is consistently below prior points, bearish structure. Scores near the middle mean the filtered series is oscillating without clear persistence, chop or transition. This is a persistence metric, not a slope metric. It does not care about one-bar direction, it cares about consistency relative to history. Signal and state logic (why it stays clean) The indicator uses state logic to prevent constant flip-flopping: Long condition: score > long threshold. Short condition: score crosses below short threshold (uses prevScore and current score). That short logic is event-based, it triggers only on the breakdown transition, not on every bar below the threshold. Once a regime is set, it remains until a real threshold event forces change. Signals are plotted only on regime flips: Long marker when signal becomes +1 and prior was -1. Short marker when signal becomes -1 and prior was +1. This is designed for alerts and for clean backtesting interpretation. Visual layers The indicator can be used purely as a panel oscillator or as a structure overlay. Pane Trend Score line, colored by active regime. Optional reference lines at long/short thresholds for fast regime reading. On-chart (optional) Super Smoother line plotted over price, colored by regime. Optional candle painting and background shading to reflect active regime. This lets you treat the filter as a dynamic trend structure line while using the score as the regime classifier. How to interpret it properly 1) The Super Smoother line This is the cleaned trend structure estimate: When price respects the smoother line, trend structure is intact. When price repeatedly chops through it, structure is weak or range-bound. 2) The score This is the quantified persistence of that structure: Rising score implies strengthening trend persistence. Falling score implies deterioration, transition risk, or mean reversion. Score compression often shows consolidation before a regime shift. 3) Threshold regimes Above long threshold: bullish persistence regime, trend-following conditions. Below short threshold: bearish regime transition, defensive or short-biased conditions. Between thresholds: neutral/transition zone, where chop and fakeouts are common. Practical use cases Trend filter Only take long setups when score is above the long threshold. Reduce exposure or avoid trend trades in the neutral band. Treat a breakdown through the short threshold as regime invalidation. Trend quality assessment High score = continuation environment. Moderate score = trend exists but is fragile. Low/negative score = distribution, downtrend, or unstable structure. Trade management Use the Super Smoother line as a structure reference for trailing risk. Use score deterioration as an early warning before full regime flips. Use regime flips as hard exits or bias changes. Tuning guidelines If you want fewer signals and cleaner regimes Increase ssPeriod. Use 3 poles. Increase scoreEnd (longer scoring window). If you want faster reaction Decrease ssPeriod. Use 2 poles. Reduce the scoring window length. Keep in mind: faster settings increase sensitivity to chop. The filter is good, but no filter removes the reality of mean reversion. What makes this different from “just a smoothed MA score” The difference is the filter quality. The Super Smoother is a proper low-pass filter with coefficients derived from DSP principles, designed to suppress high-frequency noise and avoid common smoothing artifacts. Scoring that filtered structure gives you a regime metric that is more stable and more meaningful than scoring raw price or scoring a basic MA that still carries a lot of aliasing and distortion. Summary Ehlers Super Smoother Trend Score combines a DSP-derived Butterworth-style Super Smoother filter with a directional persistence scoring model. The filter provides a clean, low-noise trend structure series, and the score quantifies how consistently that structure is advancing or deteriorating across a defined window. Threshold-based regime logic converts the score into clean trend states and alerts, making it a practical tool for trend filtering, regime detection, and structure-aware trade management.
Pine Script® インジケーター
BackQuantの投稿
4
Step Generalized Moving Average [BackQuant]Step Generalized Moving Average Overview Step Generalized Moving Average (StepGMA) is a trend-structure moving average designed to solve two common problems with classic MAs: They overreact to noise in chop, causing constant micro-flips. They lag too much when you smooth them enough to stop that noise. StepGMA tackles this by combining two layers: A Generalized Moving Average (GMA) that increases responsiveness without simply shortening length. A Step Filter that converts the MA into discrete “steps” sized by ATR, suppressing insignificant movement and only updating when the move is meaningful. The output is a trend line that behaves more like market structure: it holds its level through noise, then “reprices” in chunks when volatility-adjusted movement is large enough. What the indicator is trying to represent Instead of showing every tiny MA wiggle, StepGMA tries to represent the idea that: Most price movement is noise relative to volatility. Trend only matters when it advances by a meaningful amount. A good trend line should stay stable until the market forces it to move. That makes this indicator useful as: A regime filter (trend vs chop). A trend-following bias line. A structure-like dynamic S/R reference. A signal generator with fewer low-quality flips. Component 1: Moving Average engine (selectable) The base smoothing is not fixed. You can choose between multiple MA types: SMA, EMA, WMA, VWMA: classic smoothing families. DEMA, TEMA: reduced-lag EMA variants. T3: smooth yet responsive, good for trend. HMA: very low lag, can be twitchy without filtering. ALMA: center-weighted smoothing, often “cleaner” visually. KAMA: adaptive smoothing based on efficiency ratio, good in mixed regimes. LSMA: regression-based, tends to track trend direction well. McGinley: dynamic smoothing designed to reduce lag during fast moves. This matters because the StepGMA is not “one MA.” It is a framework that lets you pick the underlying smoothing behavior, then applies the generalization and step logic on top. Component 2: Generalized Moving Average (GMA) Where the idea comes from Generalized MA here is essentially a form of two-stage smoothing compensation . A common trick in signal processing and technical analysis is: Apply a smoother once (MA1). Apply it again (MA2). Use MA2 as a “lag reference,” then combine MA1 and MA2 to reduce lag while keeping smoothness. This is related in spirit to reduced-lag filters (like DEMA/TEMA) and “zero-lag” style constructions that subtract part of the lag component. You are not magically removing lag, you are biasing the output toward the first-pass MA while subtracting some of the second-pass smoothing that represents delayed response. How this script does it It computes: ma1 = MA(src, len) ma2 = MA(ma1, len) Then combines them using a volume factor (vf): generalized = ma1 * (1 + vf) - ma2 * vf Interpretation: ma2 is a “more delayed” version of ma1. Subtracting vf * ma2 and adding (1+vf) * ma1 pushes the output toward responsiveness. vf controls how aggressive that push is. Volume Factor (vf) is really an aggressiveness knob The script clamps vf between 0.01 and 1.0 to keep it stable. Conceptually: Low vf: behaves closer to a normal MA1, smoother, more lag. High vf: more compensation, faster response, more risk of overshoot or noise sensitivity (which is then handled by the step filter). So the GMA stage tries to give you a cleaner, faster trend estimate without just shrinking the MA period. Component 3: Step Filter (the key behavior) What a step filter is A step filter turns a continuous signal (here, the generalized MA) into a discrete “staircase” signal. Instead of updating every bar, it updates only when the input has moved far enough to justify a new step. This is conceptually similar to: A quantizer in signal processing (rounding changes to discrete increments). A volatility threshold filter (ignore changes smaller than X). Market structure logic where levels matter more than micro movement. How it works in this script The filter maintains a persistent value: stepped . Each bar: diff = src - stepped If |diff| < stepSize, do nothing (hold the level). If |diff| >= stepSize, move stepped by a number of step increments. The step increment size is: stepSize = (stepMult / 100) * ATR(atrPeriod) This is critical: In higher volatility, ATR is larger, so steps are larger, fewer updates, more stability. In lower volatility, ATR is smaller, so steps are smaller, more updates, more sensitivity. So the step behavior automatically adapts to volatility. Multiple-step catching behavior If price jumps far beyond one step, the script does not move only one step. It moves by: floor(|diff| / stepSize) * stepSize So it “catches up” in discrete blocks, preserving the stepped character without lagging massively after large moves. Direction and regime Direction is determined by the stepped line, not the raw MA: direction = +1 if steppedMA is rising direction = -1 if steppedMA is falling otherwise direction stays the same Signals only trigger on direction state changes: Long when direction flips to +1 Short when direction flips to -1 This matters because it prevents repeated signals while the trend remains intact. You only get a signal when the market has moved enough (in ATR terms) to justify a structural step in the opposite direction. Secondary line and gradient fill The script also plots a secondary “slow MA” (length 25, same MA type). This is not the core logic, it is a visual context layer: StepGMA is the structure line (discrete, regime-driven). Slow MA is a smoother reference for the underlying drift. The gradient fill highlights separation and dominance. When StepGMA sits above the slow MA, the fill reinforces bullish bias. When below, it reinforces bearish bias. It is basically a “trend pressure” visual, not a separate signal. How to interpret it 1) StepGMA as trend structure Flat steps mean price is not making enough volatility-adjusted progress to move structure. Up-steps mean the market has advanced enough to reprice the trend line upward. Down-steps mean deterioration significant enough to reprice structure downward. 2) Direction is a regime, not a tick-by-tick call Because direction is derived from step changes, it is naturally a regime filter: Fewer flips in chop. Clearer regime transitions. Signals tend to occur later than ultra-fast tools, but with better confirmation quality. 3) Step size controls noise rejection StepMult is the main “anti-chop” control: Higher stepMult = bigger ATR steps = fewer updates, fewer signals, more confirmation, slower to react. Lower stepMult = smaller steps = more updates, more signals, more sensitivity, more chop risk. 4) Generalization controls responsiveness of the underlying trend estimate vf controls how “fast” the MA tries to be before stepping: Higher vf makes the MA respond faster to new price information. Lower vf makes the MA smoother and more conservative. The step filter then decides whether that change is meaningful enough to matter. Practical use cases Trend filter for entries Only take longs when direction is bullish. Only take shorts when direction is bearish. Avoid trades when StepGMA is flat for long periods, market is not repricing meaningfully. Dynamic support and resistance Because the line holds levels, it often behaves like structure: In uptrends it can act as a rising support reference. In downtrends it can act as falling resistance. Signal quality layer The step-based flip signals tend to be higher quality than basic MA crossovers because they require: A meaningful volatility-adjusted move. A confirmed direction change in the stepped trend structure. Trade management Use StepGMA as a trailing invalidation reference. Use direction flips as “hard” regime exits. Use separation vs slow MA as a “pressure” gauge for scaling decisions. Tuning guidelines MA Type Pick based on the character you want: T3, ALMA, KAMA are usually good defaults for clean trend representation. HMA/LSMA are faster but may need larger stepMult to avoid twitch. SMA is slow and stable but can be too laggy unless vf is increased. MA Period Sets the base smoothing horizon. Longer periods give “macro trend,” shorter periods give “tactical trend.” Volume Factor (vf) Sets responsiveness compensation: 0.05–0.25 is usually sensible. Higher than that can get aggressive, step filter will save you, but your steps may fire more often. ATR Period and StepMult These define your structure sensitivity: ATR Period controls how stable the volatility estimate is. StepMult controls how large a move must be to change structure. If you want fewer flips, increase StepMult or ATR Period. If you want quicker reaction, lower StepMult or ATR Period. What this indicator is and is not It is: A trend structure MA that ignores sub-threshold noise. A regime tool that uses volatility-adjusted repricing logic. A configurable framework that works across assets and timeframes. It is not: A predictive reversal tool. A scalping signal machine. A replacement for risk management. Summary Step Generalized Moving Average combines a lag-compensated moving average (generalization via MA1/MA2 blending) with a volatility-scaled step filter (ATR-based quantization). The result is a stable, structure-like trend line that updates only when price movement is meaningful relative to volatility, producing cleaner regimes, fewer chop flips, and clearer trend bias than conventional moving averages.
Pine Script® インジケーター
BackQuantの投稿
2
Laguerre Filter [BackQuant]Laguerre Filter Overview The Laguerre Filter is a powerful trend-following tool designed to smooth price action while maintaining responsiveness to market changes. It is based on the Laguerre recursive filter, which is a type of signal processing filter that adapts to both the current price dynamics and the underlying trend. The Laguerre Filter can be seen as a method to reduce market noise, enabling traders to more easily identify the strength and direction of trends while minimizing lag. The Laguerre Filter is well-suited for markets with varying volatility levels, offering a smoother representation of price action without the delay associated with traditional moving averages. By dynamically adjusting to price movements, the Laguerre Filter provides a more adaptive and reliable signal compared to simpler smoothing techniques. What is the Laguerre Filter? The Laguerre Filter is derived from the Laguerre polynomial, which is used in signal processing for smooth filtering of data. The Laguerre filter is a recursive filter, meaning that each new value is calculated based on both the current price data and previous values, with a weighting system that allows it to adapt to market conditions. This recursive nature helps reduce the impact of short-term fluctuations, enabling the filter to focus on the underlying trend. The Laguerre filter uses a feedback mechanism, where the input signal (price data) is smoothed iteratively. This iterative process helps avoid the lag that is typically associated with traditional moving averages while still capturing the overall trend direction. The filter is designed to have: Adaptive behavior: It reacts quickly to significant price changes while ignoring minor fluctuations. Reduced noise: By filtering out random short-term price movements, it provides a clearer view of the underlying trend. Customizability: Traders can adjust the filter’s sensitivity through user inputs, making it adaptable to different market conditions. Core Calculation Methodology The core of the Laguerre Filter lies in its recursive calculation: Each new value is calculated using the previous value along with the current price input. The recursive formula is governed by two key parameters: the damping factor (gamma) and the order of the filter (number of Laguerre elements). The damping factor controls how responsive the filter is to changes in price. A higher gamma value makes the filter smoother but introduces more lag, while a lower gamma value makes it more reactive to price changes but can introduce more noise. The order defines how many Laguerre elements are used in the calculation. A higher order results in a smoother output but with more delay, while a lower order provides a faster response but less smoothing. The filter works by weighting previous values with a binomial weighting system, which assigns more weight to recent values and less weight to older values. This creates a dynamic smoothing effect that adapts to price volatility, ensuring that the filter is neither too slow nor too noisy. Signal Logic and Trend Detection The Laguerre Filter continuously evaluates the strength and direction of the trend by comparing the current smoothed value to the previous value: If the current value is greater than the previous value, the trend is considered bullish, and the filter will signal a long condition. If the current value is less than the previous value, the trend is considered bearish, and the filter will signal a short condition. The trend detection logic is based on the recursive nature of the filter, which smooths price movements over time. This allows the filter to capture the broader trend while minimizing the influence of short-term price fluctuations. The trend state is also visually represented by color-coding: Green color represents an uptrend (bullish condition). Red color represents a downtrend (bearish condition). Neutral (white) indicates no clear trend direction. This color-coding helps traders easily identify the prevailing trend and decide whether to enter or exit trades based on the trend's strength. Laguerre Filter Behavior and Performance The performance of the Laguerre Filter can be influenced by several factors: Gamma (Damping Factor): A higher gamma value results in a smoother filter but increases lag. A lower gamma value allows for a faster response but may introduce more noise, making it more reactive to smaller price changes. Filter Order: The order determines how many Laguerre elements are used in the filter calculation. A higher order provides more smoothing but increases lag, while a lower order results in a quicker response but less smoothing. The sweet spot for gamma is typically between 0.7 and 0.85, where the filter offers a good balance between smoothness and responsiveness. The filter order is usually set to 4 for classic Laguerre filtering, but higher orders can be used for more smoothing if needed. The Laguerre Filter’s performance shines in markets with sustained trends, where the filter can effectively capture and represent the underlying direction without excessive lag. It is particularly useful in volatile markets, as it helps smooth out noise while providing a clear picture of the trend. Visual Presentation The Laguerre Filter provides a dynamic, color-coded line that follows the trend direction. This line can be displayed alongside price data to visually highlight the market trend. In addition to the main Laguerre line, several visual enhancements can be applied: Gradient fill between the price and the Laguerre Filter line, providing a visual cue for bullish or bearish market conditions. Candle coloring to reflect the current trend, making it easier to spot trend reversals or confirmations directly on the chart. Background shading to visually highlight areas of strong trend or consolidation. Edge glow effect that highlights trend boundaries, making it easy to spot key levels of support or resistance. These visual elements enhance the usability of the Laguerre Filter, allowing traders to quickly assess the market trend and make informed decisions. Practical Use Cases 1) Trend Following The Laguerre Filter is ideal for trend-following strategies. By using the filter to identify the prevailing trend, traders can: Enter long positions when the Laguerre Filter turns bullish (green). Enter short positions when the Laguerre Filter turns bearish (red). By aligning trades with the dominant trend, traders can improve their chances of success. 2) Trend Strength Assessment The Laguerre Filter can also be used to assess the strength of the trend: A rising Laguerre value indicates a strengthening uptrend. A falling Laguerre value indicates a strengthening downtrend. A flattening Laguerre value signals weakening momentum or consolidation. This information can be used to adjust position sizing or to decide when to enter or exit a trade. 3) Trade Management The Laguerre Filter can also assist in trade management: Use the Laguerre line as a trailing stop for long positions in an uptrend. Scale out of positions as the Laguerre value begins to flatten or reverse. Use the Laguerre Filter to avoid trades when the market is in consolidation or lacks a clear trend. Tuning Guidelines The Laguerre Filter can be adjusted for different market conditions using the following parameters: Gamma (Damping Factor): Adjust for the desired level of responsiveness versus smoothness. Typical values range from 0.7 to 0.85. Filter Order: Adjust to control the level of smoothing. The default value of 4 is a good starting point, but higher orders can be used for smoother filters. Summary The Laguerre Filter is a versatile and adaptive trend-following indicator that smooths price data and reduces noise, making it easier to identify and follow trends. By using recursive smoothing techniques and adjustable parameters, the Laguerre Filter provides an accurate representation of market conditions with minimal lag. It is especially useful in volatile markets where traditional moving averages may fail to capture the underlying trend. With its color-coded trend detection, gradient fills, and customizable settings, the Laguerre Filter is a powerful tool for traders looking to stay aligned with the prevailing market direction.
Pine Script® インジケーター
BackQuantの投稿
1
Kalman Hull Trend Score [BackQuant]Kalman Hull Trend Score Overview Kalman Hull Trend Score is a trend-strength and regime-evaluation indicator that combines two ideas, Kalman filtering and Hull-style smoothing, then measures persistence of that filtered trend using a rolling score. The goal is to produce a cleaner, more stable trend read than typical moving average tools, while still reacting fast enough to be practical in live markets. Instead of treating a moving average as a simple line you cross, this indicator turns the filtered trend into an oscillator-like score that answers: “Is the smoothed trend consistently progressing, or is it stalling and degrading?” Core idea The indicator is built from two components: A Kalman-based smoothing engine that estimates price state and reduces noise adaptively. A Hull-style construction that uses multiple Kalman passes to create a responsive, low-lag trend filter. Once the Kalman Hull filter is built, a persistence score is calculated by comparing the current Kalman Hull value to many past values. The result is a trend score that rises in sustained trends and compresses or flips during deterioration. Why Kalman instead of standard smoothing Traditional moving averages apply fixed smoothing rules regardless of market conditions. A Kalman filter behaves differently, it is designed to estimate an underlying state in noisy data, adjusting how much it “trusts” new price information versus prior estimates. This script exposes that behavior through two key controls: Measurement Noise: how noisy the observed price is assumed to be. Process Noise: how much the underlying state is allowed to evolve from bar to bar. Together, these settings let you tune the balance between smoothness and responsiveness without relying on blunt averaging alone. Kalman filter mechanics (conceptual) Each update cycle follows the classic structure: Prediction: assume the state continues, and expand uncertainty by process noise. Update: compute Kalman Gain, then blend the new price observation into the estimate. Correction: reduce uncertainty based on how much the filter accepted the new information. When measurement noise is higher, the filter becomes more conservative, smoothing harder. When process noise is higher, the filter adapts faster to regime changes, but can become more reactive. Check out the original script: Kalman Hull construction The “Hull” component is not a standard HMA built from WMAs. Instead, it recreates the Hull idea using Kalman filtering as the smoothing primitive. The structure follows the same intent as HMA, reduce lag while keeping the line smooth, but does it with Kalman passes: Apply Kalman smoothing over multiple effective lengths. Combine them using the Hull-style weighting logic. Run the combined output through another Kalman pass to finalize smoothing. The result is a Kalman Hull filter that aims to track trend with less jitter than raw price, and less lag than slow averages. Another Kalman Hull with Supertrend Trend scoring logic The trend score is computed by comparing the current Kalman Hull value to past Kalman Hull values over a fixed lookback range (1 to 45 bars in this script): If current kalmanHMA > kalmanHMA , add +1 If current kalmanHMA < kalmanHMA , add -1 This produces a persistence score rather than a simple direction signal. Strong trends where the filter keeps advancing will accumulate positive comparisons. Weak trends, chop, or reversals will cause the score to flatten, decay, or flip negative. Interpreting the score Read the score as trend conviction and persistence: High positive values: bullish persistence, the filtered trend is progressing consistently. Low positive values: trend exists but is fragile, progress is slowing. Near zero: indecision, range behavior, frequent challenges to structure. Negative values: bearish persistence or sustained deterioration in the filtered trend. The rate of change matters: Score expansion suggests trend is gaining traction. Score compression often signals consolidation or exhaustion. Fast flips usually accompany regime transitions. Signal thresholds and regime transitions User-defined thresholds convert the score into regimes: Long threshold: score must exceed this level to confirm bullish persistence. Short threshold: a crossunder of the score triggers bearish regime transition. This is intentionally conservative. Long bias is maintained while the score holds above the long threshold. Short transitions are event-triggered on breakdown via crossunder, helping avoid constant flipping during minor noise. Signals are only plotted on regime changes (first bar of the flip), keeping them clean for alerts and backtests. Visual presentation The indicator provides multiple layers depending on how you want to use it: Kalman Hull Trend Score oscillator, color-coded by active regime. Optional Kalman Hull filter plotted on the price chart for structure context. Optional threshold reference lines for quick regime mapping. Optional candle coloring and background shading for instant readability. You can run it as a pure score panel or as a combined panel + on-chart trend overlay. How to use in practice Trend filtering Favor long setups when the score remains above the long threshold. Reduce directional aggression when score compresses toward zero. Treat a short-threshold breakdown as a regime risk event, not just a signal. Trend quality assessment Rising score supports continuation trades and adds confidence to breakouts. Flat or falling score warns that trend persistence is fading. If price trends but score fails to expand, trend may be weak or liquidity-driven. Trade management Use the Kalman Hull line as dynamic structure reference on chart. Use score deterioration to scale out before a full regime flip. Use regime flips as confirmation for bias shifts rather than prediction. Tuning guidelines Measurement Noise Higher: smoother filter, fewer false shifts, slower to adapt. Lower: more responsive, more sensitive to microstructure noise. Process Noise Higher: adapts quicker to sudden changes, but can become twitchy. Lower: steadier state estimate, but slower during sharp regime transitions. A practical approach is to first tune measurement noise until the Kalman Hull line matches the “clean trend structure” you want, then adjust process noise to control how quickly it reacts when the regime genuinely changes. Summary Kalman Hull Trend Score transforms a Kalman-based Hull-style trend filter into a quantified persistence oscillator. By combining adaptive Kalman smoothing with low-lag Hull logic and a rolling comparison score, it provides a cleaner read on trend quality than basic moving averages or single-condition trend tools. It is best used as a regime filter, trend strength gauge, and structure-aware trade management layer.
Pine Script® インジケーター
BackQuantの投稿
1
Volatility Trend Score [BackQuant]Volatility Trend Score Overview Volatility Trend Score is a trend-strength and regime-evaluation indicator built to measure directional persistence, not just direction. Most trend tools answer “up or down” using slope, crossovers, or a single condition. This indicator answers a more useful question for real trading: “How consistently is trend structure holding up once volatility is accounted for?” It does this by building a volatility-scaled trailing structure (ATR-based) and then scoring how that structure evolves over a configurable lookback range. The output is a continuous score that rises when trend is persistent and decays when price action becomes noisy, mean-reverting, or unstable. What it is measuring (the real goal) This indicator is not trying to predict reversals. It is trying to quantify whether the market is behaving like a trend market or a chop market. It focuses on: Persistence: does structure keep pushing in one direction bar after bar? Stability: are pullbacks being absorbed without breaking the trailing structure? Regime: is the market trending strongly enough to justify directional bias? If you already have entries from other systems, this becomes a high-quality trend filter and trade management layer. Core idea At its foundation, the indicator combines two parts: A volatility-adjusted trailing level derived from ATR and a user-defined factor. A rolling persistence score that compares the current trail to prior trail values over a configurable loop window. The trailing structure adapts to volatility and enforces one-sided movement, while the scoring logic converts that behavior into a numeric measure of trend quality. Inputs and what they actually control Average True Range Period (calc_p) Defines the ATR window used to estimate volatility. A higher value smooths the volatility estimate and makes the trailing structure less reactive. Factor (atr_factor) Scales the ATR band size. Higher values widen the trailing band, filtering more noise, reducing flip frequency, and generally producing slower but more stable regimes. For Loop Start/End (start/end) Defines the comparison window used to build the score. It effectively sets how many historical trail values the current trail is compared against. Shorter ranges produce a faster, more responsive score. Longer ranges produce a slower, more “confidence-based” score that only climbs when trend persistence is sustained. Long/Short Thresholds (thresL/thresS) Convert a continuous score into regime thresholds. Long threshold is a “trend quality requirement” for bullish bias. Short threshold is used as a deterioration / breakdown trigger via crossunder logic. Volatility-adjusted trailing structure The trailing line is built from ATR bands around price: up = close + ATR * factor dn = close - ATR * factor Then a trailing value is maintained with one-sided ratcheting behavior: If dn rises above the previous trail, the trail steps up (ratchets upward). If up drops below the previous trail, the trail steps down (ratchets downward). This “ratchet” behavior is important. It prevents the trail from oscillating with small countertrend moves, forcing the trail to represent meaningful structure rather than micro-noise. On-chart, this trail often behaves like dynamic support/resistance in trends. Why the trail is a better base than raw price Price itself is noisy, and volatility changes the meaning of “big move” vs “small move.” By anchoring structure to ATR: A move is interpreted relative to current volatility, not in absolute points. High-volatility chop is less likely to be misread as a trend. Trend structure is normalized across assets and timeframes more reliably. This is why the score remains usable even when switching from low-vol assets to high-vol crypto pairs. Trend scoring logic The score is built by repeatedly comparing the current trailing value to trailing values from prior bars across a loop window: If current trail > trail , add +1 If current trail < trail , add -1 This is a persistence test, not a momentum calculation. In a strong trend, the trail should generally keep stepping in the trend direction, so current values will be greater than many past values (bullish) or lower than many past values (bearish). In chop, the trail fails to progress meaningfully, so the score compresses, oscillates, or bleeds out. How to interpret the score Think of the score as a “trend conviction meter”: High positive values: bullish persistence, structure is advancing consistently. Low positive values: bullish bias may exist, but trend quality is weak or unstable. Near zero: indecision, range behavior, or frequent structure challenges. Negative values: bearish dominance or sustained deterioration in structure. The speed of score change matters too: Fast expansion suggests a fresh regime gaining traction. Slow grind suggests mature trend continuation. Rapid compression often signals consolidation, exhaustion, or a transition phase. Signals and regime transitions This script uses two different styles of conditions (important detail): Long condition: score > long threshold (state-based, persistent while true). Short condition: crossunder(score, short threshold) (event-based trigger). That means: Long bias can remain active as long as score stays above the long threshold. Short regime flips are triggered at the moment the score breaks down through the short threshold. On the chart, long/short shapes are only plotted when the regime flips (first bar of the change), not on every bar, using: Long shape when signal becomes 1 and previous signal was -1 Short shape when signal becomes -1 and previous signal was 1 This keeps signals clean and avoids spam, making it usable for alerts and regime tagging. Visual presentation The indicator is designed to work both as a panel oscillator and as an on-chart overlay: Score plot (oscillator): color reflects active regime state. Optional trail on price: volatility-scaled structure line on chart. Optional threshold reference lines: clear regime boundaries. Optional candle coloring: makes regime obvious without reading the panel. Optional background shading: useful for quick scanning and backtesting visually. You can use only the score, only the trail, or both together depending on your workflow. Practical use cases 1) Trend filter for systems Use the score as a regime gate: Allow long entries only when score is above the long threshold. Avoid longs when score compresses toward zero or loses the threshold. Treat the short threshold break as “trend is no longer healthy.” This often improves system expectancy by reducing exposure during low-conviction conditions. 2) Trend quality grading Instead of treating all uptrends as equal: Higher score = higher persistence, better continuation odds. Score plateau = trend losing pressure, continuation becomes less reliable. Score decay while price rises = trend is getting weaker under the hood. This is useful for position sizing or deciding whether to add to winners. 3) Trade management and exits Two complementary tools exist here: Trail line can act as a dynamic stop reference or structure invalidation level. Score behavior can be used to scale out when persistence fades (before a full flip). Many traders use the trail for “hard structure” and the score for “soft deterioration.” 4) Breakout confirmation vs fakeouts A breakout that immediately fails to build score is often low quality. Healthy breakouts usually come with score expansion as structure advances. Fakeouts often revert quickly, score fails to climb, and regime stays unstable. Tuning guidelines These are general behaviors you can expect when adjusting settings: Higher ATR period and factor: slower regimes, fewer flips, cleaner structure. Lower ATR period and factor: faster reaction, more sensitivity, more noise risk. Longer loop range: score becomes more “confidence-based,” slower to change. Shorter loop range: score becomes more “tactical,” faster but more jittery. A good way to tune is to pick the trail behavior first (ATR period and factor), then tune the score window (loop) to match how quickly you want “trend conviction” to build. Market behavior focus Volatility Trend Score is most valuable in markets where volatility shifts frequently and fake trends are common, especially crypto. It is designed to: Stay out of low-quality chop where most indicators whipsaw. Quantify when volatility is being expressed directionally (constructive trend). Provide a clean regime framework for filtering, alignment, and management. Summary Volatility Trend Score converts volatility-adjusted structure into a quantified measure of trend persistence. By combining an ATR-based trailing mechanism with a rolling comparison score, it provides a more reliable read on trend quality than single-condition indicators. It is best used as a regime filter, a trend strength gauge, and a trade management layer, helping you stay aligned with strong directional phases while avoiding low-conviction envir
Pine Script® インジケーター
BackQuantの投稿
13
Zero-Lag ATR Trend [BackQuant]Zero-Lag ATR Trend Overview Zero-Lag ATR Trend is a volatility-adaptive trend-following overlay designed to identify directional market regimes with minimal delay while preserving structural clarity. The indicator combines a zero-lag moving average framework with a zero-lag volatility model to produce a trailing trend line that reacts quickly to meaningful price changes without becoming unstable or overly sensitive. Unlike conventional ATR-based trend tools that rely on lagging averages and delayed volatility estimates, this indicator applies zero-lag logic to both the trend centerline and the volatility calculation. The result is a trend structure that aligns more closely with real-time price action while still maintaining the discipline required for trend continuation trading. Core design philosophy The core idea behind Zero-Lag ATR Trend is simple: Reduce signal delay without sacrificing trend integrity. Adapt dynamically to changing volatility regimes. Provide a single, clean structure that defines trend direction, continuation, and invalidation. Instead of stacking multiple indicators, the script builds a complete trend framework from two tightly integrated components: a zero-lag trend spine and a zero-lag ATR trailing mechanism. Zero-lag trend spine The trend spine is constructed using a zero-lag moving average (ZLMA). This is achieved by applying a corrective step to a traditional moving average, effectively compensating for smoothing delay. Conceptually, the process works as follows: A base moving average is calculated from the selected price source. That moving average is then passed through a zero-lag correction. The correction pulls the line closer to current price without introducing noise. This produces a trend line that reacts faster than standard EMA, SMA, or HMA signals, particularly during early trend acceleration phases. Multiple moving-average types can be used inside the zero-lag framework, allowing traders to fine-tune responsiveness based on asset behavior and timeframe. Zero-lag volatility model Volatility is measured using True Range, but instead of applying classic ATR smoothing, the indicator uses a zero-lag smoothing pass on the True Range itself. This approach offers several advantages: Volatility expands more quickly during impulse moves. Volatility contracts faster during consolidations. Band width adjusts in near real-time to changing conditions. The smoothed zero-lag ATR is multiplied by a user-defined factor to create adaptive upper and lower boundaries around the trend spine. These boundaries define how much counter-movement price is allowed before the trend structure is invalidated. Volatility-aware trailing structure The trailing output is the defining feature of the indicator. It behaves as a one-directional trailing structure: In bullish conditions, the trailing line can only move upward. In bearish conditions, the trailing line can only move downward. Minor pullbacks inside the volatility envelope do not flip the trend. This logic prevents the indicator from reacting to shallow retracements and focuses instead on structural trend changes. Because the trailing behavior is volatility-scaled, the indicator remains stable during high volatility while still responding promptly during regime shifts. Trend flips and regime transitions Trend direction is determined by changes in the trailing structure itself rather than raw price crosses. A trend flip occurs only when price movement is strong enough, relative to current volatility, to force the trailing line to reverse direction. This means: Bullish flips represent genuine transitions into upward regimes. Bearish flips represent genuine transitions into downward regimes. Sideways noise is largely filtered out. As a result, the indicator is well suited for identifying medium-to-long trend phases rather than short-term oscillations. Visual structure and chart clarity The visual design is intentionally minimal and functional: The main trailing line is color-coded by trend direction. An optional ribbon or cloud reinforces directional bias. Optional candle coloring aligns price bars with the active trend. These elements allow traders to assess trend state instantly without interpreting multiple signals or overlays. How to use for trend following Trend bias Maintain a bullish bias while price holds above the trailing line. Maintain a bearish bias while price holds below the trailing line. Entries Trend flips can be used as initial directional entries. Pullbacks toward the trailing line often act as continuation opportunities. Momentum confirmation can be layered on top for additional confluence. Trend management The trailing line naturally functions as a dynamic stop reference. As long as price respects the trailing structure, the trend remains valid. A flip in direction signals a full regime transition rather than a minor correction. Why zero-lag matters for trend trading Traditional trend indicators often react late, especially during fast expansions, resulting in delayed entries and early exits. By reducing lag in both the trend calculation and the volatility model, Zero-Lag ATR Trend aims to capture a larger portion of directional moves while maintaining consistency and discipline. This makes it particularly effective for momentum-based trend following, breakout continuation strategies, and traders who prioritize staying aligned with dominant market structure rather than predicting reversals. Summary Zero-Lag ATR Trend is a complete trend-following framework built around responsiveness, adaptability, and clarity. Its zero-lag architecture allows it to respond earlier to meaningful price changes, while its volatility-aware trailing logic ensures that trends are only invalidated when structure truly breaks. The result is a clean, intuitive tool that supports disciplined trend participation across assets and timeframes.
Pine Script® インジケーター
BackQuantの投稿
5
Adaptive Trend Envelope [BackQuant]Adaptive Trend Envelope Overview Adaptive Trend Envelope is a volatility-aware trend-following overlay designed to stay responsive in fast markets while remaining stable during slower conditions. It builds a dynamic trend spine from two exponential moving averages and surrounds it with an adaptive envelope whose width expands and contracts based on realized return volatility. The result is a clean, self-adjusting trend structure that reacts to market conditions instead of relying on fixed parameters. This indicator is built to answer three core questions directly on the chart: Is the market trending or neutral? If trending, in which direction is the dominant pressure? Where is the dynamic trend boundary that price should respect? Core trend spine At the heart of the indicator is a blended trend spine: A fast EMA captures short-term responsiveness. A slow EMA captures structural direction. A volatility-based blend weight dynamically shifts influence between the two. When short-term volatility is low relative to long-term volatility, the fast EMA has more influence, keeping the trend responsive. When volatility rises, the blend shifts toward the slow EMA, reducing noise and preventing overreaction. This blended output is then smoothed again to form the final trend spine, which acts as the structural backbone of the system. Volatility-adaptive envelope The envelope surrounding the trend spine is not based on ATR or fixed percentages. Instead, it is derived from: Log returns of price. An exponentially weighted variance estimate. A configurable multiplier that scales envelope width. This creates bands that automatically widen during volatile expansions and tighten during compression. The envelope therefore reflects the true statistical behavior of price rather than an arbitrary distance. Inner hysteresis band Inside the main envelope, an inner band is constructed using a hysteresis fraction. This inner zone is used to stabilize regime transitions: It prevents rapid flipping between bullish and bearish states. It allows trends to persist unless price meaningfully invalidates them. It reduces whipsaws in sideways conditions. Trend regime logic The indicator operates with three regime states: Bullish Bearish Neutral Regime changes are confirmed using a configurable number of bars outside the adaptive envelope: A bullish regime is confirmed when price closes above the upper envelope for the required number of bars. A bearish regime is confirmed when price closes below the lower envelope for the required number of bars. A trend exits back to neutral when price reverts through the trend spine. This structure ensures that trends are confirmed by sustained pressure rather than single-bar spikes. Active trend line Once a regime is active, the indicator plots a single dominant trend line: In a bullish regime, the lower envelope becomes the active trend support. In a bearish regime, the upper envelope becomes the active trend resistance. In neutral conditions, price itself is used as a placeholder. This creates a simple, actionable visual reference for trend-following decisions. Directional energy visualization The indicator uses layered fills to visualize directional pressure: Bullish energy fills appear when price holds above the active trend line. Bearish energy fills appear when price holds below the active trend line. Opacity gradients communicate strength and persistence rather than binary states. A subtle “rim” effect is added using ATR-based offsets to give depth and reinforce the active side of the trend without cluttering the chart. Signals and trend starts Discrete signals are generated only when a new trend regime begins: Buy signals appear at the first confirmed transition into a bullish regime. Sell signals appear at the first confirmed transition into a bearish regime. Signals are intentionally sparse. They are designed to mark regime shifts, not every pullback or continuation, making them suitable for higher-quality trend entries rather than frequent trading. Candle coloring Optional candle coloring reinforces regime context: Bullish regimes tint candles toward the bullish color. Bearish regimes tint candles toward the bearish color. Neutral states remain visually muted. This allows the chart to communicate trend state even when the envelope itself is partially hidden or de-emphasized. Alerts Built-in alerts are provided for key trend events: Bull trend start. Bear trend start. Transition from trend to neutral. Price crossing the trend spine. These alerts support hands-off trend monitoring across multiple instruments and timeframes. How to use it for trend following Trend identification Only trade in the direction of the active regime. Ignore counter-trend signals during confirmed trends. Entry alignment Use the first regime signal as a structural entry. Use pullbacks toward the active trend line as continuation opportunities. Trend management As long as price respects the active envelope boundary, the trend remains valid. A move back through the spine signals loss of trend structure. Market filtering Periods where the indicator remains neutral highlight non-trending environments. This helps avoid forcing trades during chop or compression. Adaptive Trend Envelope is designed to behave like a living trend structure. Instead of forcing price into static rules, it adapts to volatility, confirms direction through sustained pressure, and presents trend information in a clean, readable form that supports disciplined trend-following workflows.
Pine Script® インジケーター
BackQuantの投稿
12
Adaptive Strength Overlay (MTF) [BackQuant]Adaptive Strength Overlay (MTF) A multi-timeframe RSI strength visualizer that projects oscillator “pressure” directly onto price using adaptive gradient fills between percent bands. Built to make strength, exhaustion, and regime context readable at a glance, without needing to stare at a separate oscillator panel. Mean-Reversion mode example What this indicator does This indicator converts RSI strength into a chart overlay that reacts to momentum and extremes, then visualizes it as colored “pressure zones” around price. Instead of plotting RSI in a sub-window, it: Builds 1 to 3 symmetric percent bands above and below price. Computes RSI strength on up to 3 different timeframes (MTF). Smooths RSI with your selected moving average type. Maps RSI values into discrete transparency “buckets”. Fills between the bands with a gradient whose opacity reflects strength or exhaustion. Displays a compact RSI table for all enabled timeframes. Provides alert conditions for extremes and midline shifts on each timeframe. The result is an overlay that looks like a dynamic envelope. When strength rises, the envelope “lights up” in the direction of the move. When strength becomes stretched, the outer zones become visually prominent. Core idea: “Strength as an overlay” RSI is normally interpreted in a separate oscillator panel. That makes context-switching slow: You check price action. You look down at RSI. You mentally translate RSI into risk or trend bias. This script removes that translation step by projecting strength directly onto the price area, using band fills as a visual language: More visible fill = stronger strength or more extreme condition (depending on mode). Less visible fill = weak strength or neutral state. Two operating modes 1) Trend mode Trend mode emphasizes strength aligned with direction: When RSI is strong on the upside, upper bands become more visible. When RSI is strong on the downside, lower bands become more visible. Neutral RSI fades, so the chart de-clutters during chop. Use Trend mode when: You want a clean trend-following overlay. You want to quickly see which timeframe(s) are powering the move. You want to filter entries to moments when strength confirms direction. 2) Mean-Reversion mode Mean-Reversion mode flips the emphasis to highlight exhaustion against the move : Upper extremes become a “potential exhaustion” cue. Lower extremes become a “potential exhaustion” cue. The overlay is tuned to make stretched conditions obvious. This is not an automatic “short overbought / long oversold” system. It is a visualization mode that makes “extended” conditions stand out faster, especially when multiple timeframes align. How the bands work (Percent Bands) The indicator constructs up to three symmetric envelopes around price: Band 1: percent1 scaled by scale Band 2: percent2 scaled by scale (optional) Band 3: percent3 scaled by scale (optional) The percent bands are simple deviations from the selected price source: Upper = price * (1 + (percent * scaling)/100) Lower = price * (1 - (percent * scaling)/100) Why this matters: It anchors “strength visualization” to meaningful price distance. It makes the overlay comparable across assets because it’s percent-based. It gives you a consistent spatial frame for reading momentum versus extension. Multi-timeframe engine (MTF) The script runs the same strength calculation on up to three timeframes: Timeframe 1 uses the chart timeframe by default (empty string input). Timeframe 2 is optional and defaults to Daily. Timeframe 3 is optional and defaults to Weekly. Each timeframe has: Its own RSI period (len, len2, len3). Its own smoothing length (slen, slen2, slen3). The same smoothing type selection (EMA, HMA, etc). This creates a layered view: TF1 often reflects tactical pressure (entries/exits). TF2 reflects structural pressure (swing context). TF3 reflects macro bias (regime context). When multiple timeframes agree, the fills stack and the overlay becomes visually louder. When they disagree, the overlay looks mixed or muted, which is exactly the point. Smoothing options (why so many) Raw RSI can be noisy. This script lets you smooth RSI with multiple MA types, which changes how “responsive” the overlay feels: EMA/RMA smooth without lagging as hard as SMA. HMA responds faster but can be twitchy. LINREG can feel more “structural”. ALMA and T3/TEMA provide heavier smoothing profiles with different lag characteristics. This isn’t cosmetic. Your smoothing choice affects: How early the overlay “lights up” in Trend mode. How long extremes remain highlighted in Mean-Reversion mode. How often fills flicker in chop. Strength mapping (the transparency buckets) Instead of mapping RSI to a continuous color scale, the script uses a discrete transparency ladder. That creates a clean, readable visual that avoids constant flickering. The logic assigns two transparency values per timeframe: Upper-side transparency responds to lower RSI zones (weak upside strength). Lower-side transparency responds to higher RSI zones (strong upside strength). Then the script uses those transparencies differently depending on mode: Trend mode shows “strength aligned with direction”. Mean-Reversion mode swaps the emphasis so “extremes” stand out as potential stretch. You can think of it as: Trend mode highlights continuation strength. Mean-Reversion mode highlights potential exhaustion. Fill stacking (how the overlay is built) The overlay uses layered fills: Fill from price to Band 1 Fill from Band 1 to Band 2 (if enabled) Fill from Band 2 to Band 3 (if enabled) Upper side uses the negative color (typically red) and lower side uses the positive color (typically green), because upper bands represent “above price” space and lower bands represent “below price” space. The intensity is controlled by the computed transparency per timeframe and selected mode. Important behavior: Disabling Band 2 or Band 3 can change how the stacked fills look, because you are removing fill segments. If you want a clean look, run only Band 1. If you want a “regime heat” look, run Bands 1–3 with higher scaling. Table (MTF RSI dashboard) A compact table prints RSI values for each configured timeframe: Row labels show TF. Values show the smoothed RSI output that drives the overlay. Use it for quick confirmation: If overlay looks strong but table RSI is neutral, your band settings might be too tight. If TF3 RSI is extreme while TF1 is neutral, you are likely in a macro stretched regime with local consolidation. Alerts (built-in) Alerts are provided for each timeframe separately, covering: Entering upper extreme (cross above 70) Exiting upper extreme (cross below 70) Entering lower extreme (cross below 30) Exiting lower extreme (cross above 30) Bullish midline cross (cross above 50) Bearish midline cross (cross below 50) This enables workflows like: Notify when TF2 enters extreme, then wait for TF1 mean-reversion confirmation. Notify when TF3 crosses midline, then only take TF1 trend setups in that direction. How to use it (practical reads) Trend mode reads Strong continuation: TF1 and TF2 fills become clearly visible on the same side. Healthy pullback: TF1 fades but TF2 stays visible, suggesting underlying structure remains strong. Chop warning: fills alternate or remain mostly invisible, indicating neutral strength. Mean-Reversion mode reads Exhaustion zones: outer fills become prominent near the extremes, signaling stretched conditions. Compression after extreme: fill fades while price stabilizes, suggesting “cooling off” rather than immediate reversal. Multi-TF stretch: TF2 and TF3 extremes together often mark higher significance zones. Recommended setup presets Preset A: Clean trend overlay Mode: Trend Bands: only Band 1 Scale: 1–2 Smoothing: EMA, moderate slen (6–10) TF2: Daily on intraday charts Preset B: Regime and exhaustion mapper Mode: Mean-Reversion Bands: Bands 1–3 Scale: 2–4 Smoothing: T3 or RMA, slightly higher slen TF2: Daily, TF3: Weekly Limitations This is a strength visualization tool, not a full entry/exit system. Percent bands are not volatility-adjusted, they are distance frames. In very high vol conditions, you may need higher band percentages or higher scaling. MTF values update on their own timeframe closes, so higher timeframes will step rather than update every bar.
Pine Script® インジケーター
BackQuantの投稿
MarketMind LITEM🜁rketMind LITE ──────────────────── Essential Market Awareness, Reduced to Its Core M🜁rketMind LITE is a lightweight market awareness tool designed to display essential situational context . It provides basic orientation and movement awareness without interpretation, risk framing, diagnostics, or decision guidance. This script is designed as a standalone awareness layer. It does not evaluate trade quality, issue signals, or influence decision-making. WHAT IT DOES ──────────────────── M🜁rketMind LITE presents a minimal, static view of current market conditions focused entirely on awareness rather than analysis. The system displays only essential context, allowing traders to stay oriented without introducing judgment, noise, or implied direction. The script provides visibility into: Time-of-day session context Basic market regime classification (trending, range-bound, mixed) Short-term momentum direction only (up, down, neutral) A clean, static HUD display M🜁rketMind LITE also includes a minimal visual state indicator that reflects recent price responsiveness, intended to be observed over time alongside the trader’s own experience. The goal is to support awareness without influence . HOW TO USE IT ──────────────────── M🜁rketMind LITE is not a signal generator. It is designed to remain visible in the background of any chart, offering quiet orientation while traders rely entirely on their own process for analysis and execution. Common use cases include: Maintaining session awareness Preserving context during focused trading periods Reducing cognitive load while monitoring markets M🜁rketMind LITE does not evaluate risk, alignment, or opportunity. It simply shows what is happening. DESIGN PHILOSOPHY ──────────────────── M🜁rketMind LITE is intentionally minimal. It includes only essential awareness elements and excludes all interpretive or evaluative logic: Situational context only Directional momentum (up / down / neutral) No diagnostics, confidence, or conviction framing No process, risk, or quality assessment Presentation controls only (HUD on/off, size, position) Nothing is inferred. Nothing is suggested. This script shows market state without interpretation. WHO IT IS FOR ──────────────────── M🜁rketMind LITE is suited for traders who: Want passive situational awareness Prefer minimal on-chart information Already operate with a defined decision process It is not designed for: Analytical or diagnostic use Risk evaluation or context synthesis Traders seeking guidance or confirmation IMPORTANT NOTES ──────────────────── M🜁rketMind LITE does not provide financial advice No system can predict future price behavior This tool is designed for awareness only Used appropriately, M🜁rketMind LITE helps traders stay oriented without interference.
Pine Script® インジケーター
nedra-codesの投稿
Open Interest Bubbles [BackQuant]Open Interest Bubbles A visual OI positioning overlay that aggregates futures open interest across major venues, normalizes it into a consistent “signal strength” scale, then plots extreme events as bubbles, labels, and optional horizontal levels directly on price. What this is for Open interest is one of the cleanest ways to track when positioning is building, unwinding, or aggressively shifting. The problem is raw OI is noisy, exchange-specific, and hard to compare across time. This script solves that by: - Aggregating OI across multiple exchanges. - Letting you choose what “OI signal” you care about (raw, delta, percent versions). - Normalizing the signal so “big events” are easy to spot. - Plotting those events as bubbles and levels at the exact price they occurred. You end up with a clean, fast visual map of where large positioning changes occurred, and where those events may later matter as reaction points. ──────────────────────────────────────────────────────────── Plotting types (what you can display) Bubbles This mode plots OI events as size-bucketed circles on the chart. Bigger bubbles represent stronger normalized events. You can tune: - Bubble sizing by bucket (Tiny → Huge). - Heatmap vs solid color styling. - Signed vs unsigned coloring (positive/negative separation or magnitude-only). Best use: - Spotting “where something changed” at a glance. - Identifying clusters of positioning events around key price zones. - Seeing whether the market is repeatedly building/closing positions at similar levels. Levels Levels mode draws a horizontal line at the anchor price when an extreme OI event triggers. These act like “positioning memory” levels: - They do not claim to be support/resistance by themselves. - They highlight prices where the derivatives market clearly did something meaningful. Best use: - Marking potential reaction zones. - Combining with your price action tools (structure, OBs, FVGs) to confirm whether an OI level aligns with a technical level. - Building a “map” of where leverage likely entered or exited. Modes available in the script: - Off - Bubbles - Bubbles + Labels - Labels Only - Levels + Labels ──────────────────────────────────────────────────────────── Aggregated Open Interest source (multi-exchange) This indicator builds a single aggregated OI series by requesting OI data from multiple exchanges and summing it. You can toggle exchanges on/off: - Binance, Bybit, OKX, Bitget, Kraken, HTX, Deribit You can also choose OI units: - COIN , OI in base units (native sizing) - USD , converted for a dollar-value representation Important note: Not every symbol has OI data on every venue. If the script cannot build an aggregated series for the symbol, it will throw an error rather than quietly plotting garbage. ──────────────────────────────────────────────────────────── OI Source, what the bubbles are measuring You control what “signal” is normalized and plotted: - Delta , change in aggregated OI from the prior bar. Use when you want to highlight bursts of new positioning or sudden unwind events. - Raw OI , the aggregated open interest level itself. Use when you want to highlight absolute positioning build-up periods. - Delta % , percent change in OI. Use when you want moves normalized to the current OI regime, useful across different market eras. - Raw OI % , percent change form of the raw series. Use when you want relative changes rather than absolute size. Practical guidance: - Delta modes are best for “event detection”. - Raw modes are better for “regime context” and whether positioning is structurally rising or fading. ──────────────────────────────────────────────────────────── Normalization (the key to making it readable) Because OI varies massively across assets and time, the script includes multiple normalization modes to convert your chosen OI source into a comparable “strength” value. Options: - ZScore , deviation from a rolling mean in standard deviation units. - StdNorm , scaled by rolling standard deviation. - AbsZScore , absolute value version for magnitude-only mapping. - AbsStdNorm , absolute value version for magnitude-only mapping. - None , plots raw values (advanced users only, often too noisy visually). Why this matters: Normalization makes a “1.5” or “3.0” threshold mean something across different assets and timeframes, instead of being stuck to raw OI units. ──────────────────────────────────────────────────────────── Threshold system (when bubbles/levels trigger) The plot is driven by two user thresholds: - Base Threshold Controls where “meaningful” events start. Raising this reduces noise and focuses on larger deviations. - Extreme Threshold Controls what qualifies as a top-tier event. Extreme events are what you typically want to convert into labels and levels. You also control side filtering: - Both , show positive and negative events. - Positive Only , show only increases (or positive signal side depending on source). - Negative Only , show only decreases (or negative signal side). In practice: - Use Base Threshold to tune chart cleanliness. - Use Extreme Threshold to mark only the “big stuff” that tends to matter later. ──────────────────────────────────────────────────────────── Anchor Source (where the bubble/level is placed) The indicator places bubbles, labels, and levels at a price anchor you choose: - HL2, Close, Open, High, Low, VWAP This is important because “where you pin the event” changes how it reads: - Close is clean and consistent for backtesting and candle-close logic. - High/Low can better represent where the fight occurred intrabar. - VWAP can be useful for “fair price” anchoring in active markets. ──────────────────────────────────────────────────────────── Style system (theme, palette, signed logic) This script is built to look good and stay readable on busy charts. Themes - BackQuant, Classic, Ice, Fire, Mono, Custom Palette Mode - Solid , one consistent color - Heatmap , intensity increases with magnitude - Single Color Adaptive , adapts to chart background for clarity Side Coloring - Signed , positive and negative events can use different ramps - Unsigned , magnitude-only coloring Negative theme handling: - Auto (mirrors your chosen theme), - Invert (flips the ramp), - Custom (fully user-defined negative palette). What this gives you: - You can run a clean “mono” look for professional charts. - Or a high-contrast heatmap for fast scanning. - Or fully custom branding colors for BackQuant-style presentation. ──────────────────────────────────────────────────────────── Labels (what’s inside the label) When labels are enabled, the script can display: - OI , the aggregated OI value - OI + Norm , OI plus normalized strength - Norm Only , just the normalized strength - Src + Norm , the selected source value (Delta, Raw, %) plus normalized strength You can also control: - Left/Center/Right label alignment - Number formatting style (Raw, Compact, Volume format) Best practice: - Use “Src + Norm” when you want both the raw event size and its rarity. - Use “Norm Only” when you want a clean, minimal chart. ──────────────────────────────────────────────────────────── Levels and object limits (performance and cleanliness) Because this script draws objects, it includes a hard cleanup system: - You set Max Levels / Labels to control chart clutter. - The script deletes older lines/labels when the limit is exceeded. This is critical if you trade lower timeframes, where OI events can trigger frequently. ──────────────────────────────────────────────────────────── How to interpret the signals What a large bubble usually means: - A statistically large positioning change relative to recent history. - This can represent fresh leverage entering, forced liquidations, or aggressive de-risking, depending on direction and context. How to use levels: - Treat them as “attention levels”, not automatic entries. - Combine them with structure and liquidity tools: - If price revisits an OI level and shows rejection, it often confirms that level mattered. - If price slices through with no reaction, it often indicates the OI event was transitional, not defended. Common setups: - Clustered extreme bubbles near a breakout zone, then retest later. - Extreme negative event at capitulation low, followed by structure flip. - Extreme positive build into resistance, then unwind and mean reversion. Also, please check out @NoveltyTrade for the OI Aggregation logic & pulling the data source! Here is the original script:
Pine Script® インジケーター
BackQuantの投稿
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