OPEN-SOURCE SCRIPT
Quant Signals: Market Sentiment Monitor HUD

Wavelets & Scale Spectrum
This indicator is ideal for traders who adapt their strategy to market conditions — such as swing traders, intraday traders, and system developers.
Wavelets are like tiny “measuring rulers” for price changes. Instead of looking at the whole chart at once, a wavelet looks at differences in price over a specific time scale — for example, 2 bars, 4 bars, 8 bars, and so on.
The scale spectrum is what you get when you measure volatility at several of these scales and then plot them against scale size.
This approach works like a microscope, revealing whether the market’s behaviour is consistent across short-term and long-term horizons, and when that behaviour changes.
This tool applies a wavelet-based scale-spectrum analysis to price data to estimate three key market state measures inside a rolling window:
Hurst exponent (H) — measures persistence in price moves:
Volatility (σ) — the average size of price swings at your chart’s timeframe, optionally annualized. Rising volatility means larger price moves, falling volatility means smaller moves.
Fit residual — how well the observed multi-scale volatility fits a clean power-law line. Low residual = stable behaviour; high residual = structural change (possible regime shift).
This indicator is ideal for traders who adapt their strategy to market conditions — such as swing traders, intraday traders, and system developers.
- Trend-followers can use it to confirm trending conditions before entering.
- Mean-reversion traders can spot choppy markets where reversals are more likely.
- Risk managers can monitor volatility shifts and regime changes to adjust position size or pause trading.
It works best as a market context filter — telling you the “weather” before you decide on the trade.
Wavelets are like tiny “measuring rulers” for price changes. Instead of looking at the whole chart at once, a wavelet looks at differences in price over a specific time scale — for example, 2 bars, 4 bars, 8 bars, and so on.
The scale spectrum is what you get when you measure volatility at several of these scales and then plot them against scale size.
- If the spectrum forms a straight line on a log–log chart, it means price changes follow a consistent pattern across time scales (a power-law relationship).
- The slope of that line gives the Hurst exponent (H) — telling you whether moves tend to persist (trend) or reverse (mean-revert).
- The height of the line gives you the volatility (σ) — the average size of moves.
This approach works like a microscope, revealing whether the market’s behaviour is consistent across short-term and long-term horizons, and when that behaviour changes.
This tool applies a wavelet-based scale-spectrum analysis to price data to estimate three key market state measures inside a rolling window:
Hurst exponent (H) — measures persistence in price moves:
- H > ~0.55 → market is trending (moves tend to continue).
- H < ~0.45 → market is choppy/mean-reverting (moves tend to reverse).
- Values near 0.5 indicate a neutral, random-walk-like regime.
Volatility (σ) — the average size of price swings at your chart’s timeframe, optionally annualized. Rising volatility means larger price moves, falling volatility means smaller moves.
Fit residual — how well the observed multi-scale volatility fits a clean power-law line. Low residual = stable behaviour; high residual = structural change (possible regime shift).
오픈 소스 스크립트
진정한 트레이딩뷰 정신에 따라 이 스크립트 작성자는 트레이더가 기능을 검토하고 검증할 수 있도록 오픈소스로 공개했습니다. 작성자에게 찬사를 보냅니다! 무료로 사용할 수 있지만 코드를 다시 게시할 경우 하우스 룰이 적용된다는 점을 기억하세요.
면책사항
이 정보와 게시물은 TradingView에서 제공하거나 보증하는 금융, 투자, 거래 또는 기타 유형의 조언이나 권고 사항을 의미하거나 구성하지 않습니다. 자세한 내용은 이용 약관을 참고하세요.
오픈 소스 스크립트
진정한 트레이딩뷰 정신에 따라 이 스크립트 작성자는 트레이더가 기능을 검토하고 검증할 수 있도록 오픈소스로 공개했습니다. 작성자에게 찬사를 보냅니다! 무료로 사용할 수 있지만 코드를 다시 게시할 경우 하우스 룰이 적용된다는 점을 기억하세요.
면책사항
이 정보와 게시물은 TradingView에서 제공하거나 보증하는 금융, 투자, 거래 또는 기타 유형의 조언이나 권고 사항을 의미하거나 구성하지 않습니다. 자세한 내용은 이용 약관을 참고하세요.