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Volatility Regime Classifier | ATRP Percentile Zones

This indicator helps you understand the current volatility environment of any asset by comparing recent ATR-based values to its historical range.
It defines four regimes:
🔴 Low Volatility: Volatility is decreasing
🟢 Normal: Volatility is increasing but still below average
🟠 High: Volatility is elevated
🟣 Extreme: Volatility is very high compared to recent history
⚙️ How it works
We calculate the Average True Range (ATR) as a percentage of price (ATRP), then compare a short-term ATR to a longer-term one. Their difference shows whether volatility is picking up or slowing down.
To make the signal more adaptive, we look at the distribution of recent volatility over a rolling window. We compute the 50th and 70th percentiles of that history to set dynamic thresholds.
About distribution & percentiles
Volatility in financial markets doesn't follow a normal (Gaussian) distribution, it's often skewed, with sudden spikes and fat tails. That means fixed thresholds (like "ATR > 20") can be misleading or irrelevant across assets and timeframes.
Using percentiles solves this:
The 50th percentile marks the middle of the recent volatility range.
The 70th percentile captures a zone where volatility is unusually high, but not too rare, which keeps the signal usable and not overly sensitive.
These levels offer a balance:
⚖️ not too reactive, not too slow — just enough to highlight meaningful shifts.
✅ Use cases
Spot changes in market conditions
Filter or adapt strategies depending on the regime
Adjust position sizing and risk dynamically
It defines four regimes:
🔴 Low Volatility: Volatility is decreasing
🟢 Normal: Volatility is increasing but still below average
🟠 High: Volatility is elevated
🟣 Extreme: Volatility is very high compared to recent history
⚙️ How it works
We calculate the Average True Range (ATR) as a percentage of price (ATRP), then compare a short-term ATR to a longer-term one. Their difference shows whether volatility is picking up or slowing down.
To make the signal more adaptive, we look at the distribution of recent volatility over a rolling window. We compute the 50th and 70th percentiles of that history to set dynamic thresholds.
About distribution & percentiles
Volatility in financial markets doesn't follow a normal (Gaussian) distribution, it's often skewed, with sudden spikes and fat tails. That means fixed thresholds (like "ATR > 20") can be misleading or irrelevant across assets and timeframes.
Using percentiles solves this:
The 50th percentile marks the middle of the recent volatility range.
The 70th percentile captures a zone where volatility is unusually high, but not too rare, which keeps the signal usable and not overly sensitive.
These levels offer a balance:
⚖️ not too reactive, not too slow — just enough to highlight meaningful shifts.
✅ Use cases
Spot changes in market conditions
Filter or adapt strategies depending on the regime
Adjust position sizing and risk dynamically
开源脚本
本着TradingView的真正精神,此脚本的创建者将其开源,以便交易者可以查看和验证其功能。向作者致敬!虽然您可以免费使用它,但请记住,重新发布代码必须遵守我们的网站规则。
免责声明
这些信息和出版物并不意味着也不构成TradingView提供或认可的金融、投资、交易或其它类型的建议或背书。请在使用条款阅读更多信息。
开源脚本
本着TradingView的真正精神,此脚本的创建者将其开源,以便交易者可以查看和验证其功能。向作者致敬!虽然您可以免费使用它,但请记住,重新发布代码必须遵守我们的网站规则。
免责声明
这些信息和出版物并不意味着也不构成TradingView提供或认可的金融、投资、交易或其它类型的建议或背书。请在使用条款阅读更多信息。