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Quantile Regression Bands [BackQuant]

Quantile Regression Bands [BackQuant]
Tail-aware trend channeling built from quantiles of real errors, not just standard deviations.
What it does
This indicator fits a simple linear trend over a rolling lookback and then measures how price has actually deviated from that trend during the window. It then places two pairs of bands at user-chosen quantiles of those deviations (inner and outer). Because bands are based on empirical quantiles rather than a symmetric standard deviation, they adapt to skewed and fat-tailed behaviour and often hug price better in trending or asymmetric markets.
Why “quantile” bands instead of Bollinger-style bands?
How it works (intuitive)
What you see
How to read it
A simple trend interpretation can be derived from the bar colouring

Good use-cases
Inputs (quick guide)
Practical settings
Signal ideas
Alerts included
Notes
Final thought
Quantile bands answer a simple question: “How unusual is this move given the current trend and the way price has been missing it lately?” By scoring that question with real, distribution-aware limits rather than one-size-fits-all volatility you get cleaner pullback zones in trends, more honest “extreme” tags in ranges, and a framework for risk that matches the market’s recent personality.
Tail-aware trend channeling built from quantiles of real errors, not just standard deviations.
What it does
This indicator fits a simple linear trend over a rolling lookback and then measures how price has actually deviated from that trend during the window. It then places two pairs of bands at user-chosen quantiles of those deviations (inner and outer). Because bands are based on empirical quantiles rather than a symmetric standard deviation, they adapt to skewed and fat-tailed behaviour and often hug price better in trending or asymmetric markets.
Why “quantile” bands instead of Bollinger-style bands?
- Bollinger Bands assume a (roughly) symmetric spread around the mean; quantiles don’t—upper and lower bands can sit at different distances if the error distribution is skewed.
- Quantiles are robust to outliers; a single shock won’t inflate the bands for many bars.
- You can choose tails precisely (e.g., 1%/99% or 5%/95%) to match your risk appetite.
How it works (intuitive)
- Center line — a rolling linear regression approximates the local trend.
- Residuals — for each bar in the lookback, the indicator looks at the gap between actual price and where the line “expected” price to be.
- Quantiles — those gaps are sorted; you select which percentiles become your inner/outer offsets.
- Bands — the chosen quantile offsets are added to the current end of the regression line to draw parallel support/resistance rails.
- Smoothing — a light EMA can be applied to reduce jitter in the line and bands.
What you see
- Center (linear regression) line (optional).
- Inner quantile bands (e.g., 25th/75th) with optional translucent fill.
- Outer quantile bands (e.g., 1st/99th) with a multi-step gradient to visualise “tail zones.”
- Optional bar coloring: bars trend-colored by whether price is rising above or falling below the center line.
- Alerts when price crosses the outer bands (upper or lower).
How to read it
- Trend & drift — the slope of the center line is your local trend. Persistent closes on the same side of the center line indicate directional drift.
- Pullbacks — tags of the inner band often mark routine pullbacks within trend. Reaction back to the center line can be used for continuation entries/partials.
- Tails & squeezes — outer-band touches highlight statistically rare excursions for the chosen window. Frequent outer-band activity can signal regime change or volatility expansion.
- Asymmetry — if the upper band sits much further from the center than the lower (or vice versa), recent behaviour has been skewed. Trade management can be adjusted accordingly (e.g., wider take-profit upslope than downslope).
A simple trend interpretation can be derived from the bar colouring
Good use-cases
- Volatility-aware mean reversion — fade moves into outer bands back toward the center when trend is flat.
- Trend participation — buy pullbacks to the inner band above a rising center; flip logic for shorts below a falling center.
- Risk framing — set dynamic stops/targets at quantile rails so position sizing respects recent tail behaviour rather than fixed ticks.
Inputs (quick guide)
- Source — price input used for the fit (default: close).
- Lookback Length — bars in the regression window and residual sample. Longer = smoother, slower bands; shorter = tighter, more reactive.
- Inner/Outer Quantiles (τ) — choose your “typical” vs “tail” levels (e.g., 0.25/0.75 inner, 0.01/0.99 outer).
- Show toggles — independently toggle center line, inner bands, outer bands, and their fills.
- Colors & transparency — customize band and fill appearance; gradient shading highlights the tail zone.
- Band Smoothing Length — small EMA on lines to reduce stair-step artefacts without meaningfully changing levels.
- Bar Coloring — optional trend tint from the center line’s momentum.
Practical settings
- Swing trading — Length 75–150; inner τ = 0.25/0.75, outer τ = 0.05/0.95.
- Intraday — Length 50–100 for liquid futures/FX; consider 0.20/0.80 inner and 0.02/0.98 outer in high-vol assets.
- Crypto — Because of fat tails, try slightly wider outers (0.01/0.99) and keep smoothing at 2–4 to tame weekend jumps.
Signal ideas
- Continuation — in an uptrend, look for pullback into the lower inner band with a close back above the center as a timing cue.
- Exhaustion probe — in ranges, first touch of an outer band followed by a rejection candle back inside the inner band often precedes mean-reversion swings.
- Regime shift — repeated closes beyond an outer band or a sharp re-tilt in the center line can mark a new trend phase; adjust tactics (stop-following along the opposite inner band).
Alerts included
- “Price Crosses Upper Outer Band” — potential overextension or breakout risk.
- “Price Crosses Lower Outer Band” — potential capitulation or breakdown risk.
Notes
- The fit and quantiles are computed on a fixed rolling window and do not repaint; bands update as the window moves forward.
- Quantiles are based on the recent distribution; if conditions change abruptly, expect band widths and skew to adapt over the next few bars.
- Parameter choices directly shape behaviour: longer windows favour stability, tighter inner quantiles increase touch frequency, and extreme outer quantiles highlight only the rarest moves.
Final thought
Quantile bands answer a simple question: “How unusual is this move given the current trend and the way price has been missing it lately?” By scoring that question with real, distribution-aware limits rather than one-size-fits-all volatility you get cleaner pullback zones in trends, more honest “extreme” tags in ranges, and a framework for risk that matches the market’s recent personality.
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Check out whop.com/signals-suite for Access to Invite Only Scripts!
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ข้อมูลและบทความไม่ได้มีวัตถุประสงค์เพื่อก่อให้เกิดกิจกรรมทางการเงิน, การลงทุน, การซื้อขาย, ข้อเสนอแนะ หรือคำแนะนำประเภทอื่น ๆ ที่ให้หรือรับรองโดย TradingView อ่านเพิ่มเติมที่ ข้อกำหนดการใช้งาน
สคริปต์โอเพนซอร์ซ
ด้วยเจตนารมณ์หลักของ TradingView ผู้สร้างสคริปต์นี้ได้ทำให้มันเป็นโอเพ่นซอร์ส เพื่อให้เทรดเดอร์สามารถตรวจสอบและยืนยันการทำงานของสคริปต์ได้ ขอแสดงความชื่นชมผู้เขียน! แม้ว่าคุณจะสามารถใช้งานได้ฟรี แต่อย่าลืมว่าการเผยแพร่โค้ดซ้ำนั้นจะต้องเป็นไปตามกฎระเบียบการใช้งานของเรา
Check out whop.com/signals-suite for Access to Invite Only Scripts!
คำจำกัดสิทธิ์ความรับผิดชอบ
ข้อมูลและบทความไม่ได้มีวัตถุประสงค์เพื่อก่อให้เกิดกิจกรรมทางการเงิน, การลงทุน, การซื้อขาย, ข้อเสนอแนะ หรือคำแนะนำประเภทอื่น ๆ ที่ให้หรือรับรองโดย TradingView อ่านเพิ่มเติมที่ ข้อกำหนดการใช้งาน