OPEN-SOURCE SCRIPT
Standardized ROC Engine (EMA Version)

The purpose of this script is to create a standardized rate‑of‑change engine that compares the momentum of multiple structural anchors, specifically several EMAs, VWAP, price and volume. By converting each ROC stream into a z‑score, the indicator places all components on a common scale, allowing the trader to see when any anchor is accelerating or decelerating relative to its own long‑term distribution. This transforms raw ROC, which is naturally unstable and scale‑dependent, into a normalized momentum map that highlights extremes, clustering and regime shifts with far greater clarity.
The script works by first computing four EMAs of different lengths, along with VWAP, then calculating the percentage rate of change for each series over a user‑defined ROC window. Each ROC stream is then passed through a standardization function that subtracts its rolling mean and divides by its rolling standard deviation, producing a z‑score that expresses how unusual the current momentum is compared to the past. These standardized curves are plotted together, using consistent colors, while horizontal reference lines at one, two and three standard deviations provide visual thresholds for identifying statistically significant momentum events.
The rationale behind this architecture is that raw ROC values are not comparable across different structures because each anchor has its own volatility profile, amplitude and noise characteristics. Standardization solves this by converting every ROC stream into a dimensionless measure of deviation, enabling cross‑anchor comparison without distortion. This approach reveals when short‑term EMAs are accelerating faster than long‑term EMAs, when VWAP momentum diverges from trend momentum, and when volume expansion aligns with or contradicts price acceleration, all expressed in a unified statistical language that is robust across assets and timeframes.
The script works by first computing four EMAs of different lengths, along with VWAP, then calculating the percentage rate of change for each series over a user‑defined ROC window. Each ROC stream is then passed through a standardization function that subtracts its rolling mean and divides by its rolling standard deviation, producing a z‑score that expresses how unusual the current momentum is compared to the past. These standardized curves are plotted together, using consistent colors, while horizontal reference lines at one, two and three standard deviations provide visual thresholds for identifying statistically significant momentum events.
The rationale behind this architecture is that raw ROC values are not comparable across different structures because each anchor has its own volatility profile, amplitude and noise characteristics. Standardization solves this by converting every ROC stream into a dimensionless measure of deviation, enabling cross‑anchor comparison without distortion. This approach reveals when short‑term EMAs are accelerating faster than long‑term EMAs, when VWAP momentum diverges from trend momentum, and when volume expansion aligns with or contradicts price acceleration, all expressed in a unified statistical language that is robust across assets and timeframes.
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這些資訊和出版物並非旨在提供,也不構成TradingView提供或認可的任何形式的財務、投資、交易或其他類型的建議或推薦。請閱讀使用條款以了解更多資訊。
開源腳本
秉持TradingView一貫精神,這個腳本的創作者將其設為開源,以便交易者檢視並驗證其功能。向作者致敬!您可以免費使用此腳本,但請注意,重新發佈代碼需遵守我們的社群規範。
免責聲明
這些資訊和出版物並非旨在提供,也不構成TradingView提供或認可的任何形式的財務、投資、交易或其他類型的建議或推薦。請閱讀使用條款以了解更多資訊。