OPEN-SOURCE SCRIPT
已更新 Awesome Oscillator with AntiStep Correction

Here is the well-known Awesome Oscillator (AO), which I use to present the real purpose of this post: a function that provides step correction for simple moving averages (SMAs).
We all know that any indicator based on moving averages lags real-time movement. Normally this is fine, but just after large ("step") changes in level, the pre-step values that are still within the SMA window cause the result to falsely reflect continued movement, even when real-time values remain flat.
To counter this, when a step change of a configurable size is detected, I temporarily shrink the SMA window size to include only those values occurring since the step change, and then allow the size to increase to normal length as we move away from the step change. This is accomplished within the antistep_sma() function.
Note that this will cause SMAs of different lengths (e.g. those used in the AO) to be temporarily equal, until the shorter of the two reaches its normal size and begins to leave the longer one behind again. You can see this above, where the AO, which is the difference of two SMAs, goes to 0 immediately after a sufficiently large step change--configured to 0.5% in this case.
We all know that any indicator based on moving averages lags real-time movement. Normally this is fine, but just after large ("step") changes in level, the pre-step values that are still within the SMA window cause the result to falsely reflect continued movement, even when real-time values remain flat.
To counter this, when a step change of a configurable size is detected, I temporarily shrink the SMA window size to include only those values occurring since the step change, and then allow the size to increase to normal length as we move away from the step change. This is accomplished within the antistep_sma() function.
Note that this will cause SMAs of different lengths (e.g. those used in the AO) to be temporarily equal, until the shorter of the two reaches its normal size and begins to leave the longer one behind again. You can see this above, where the AO, which is the difference of two SMAs, goes to 0 immediately after a sufficiently large step change--configured to 0.5% in this case.
發行說明
I have generalized all flavors of exponential moving average (you may create your own alpha using my function) and used it to implement anti-step versions of the TradingView EMA and RMA, in addition to the SMA from version 1.Also, following a very good idea from Tracks, I added the option to base step detection on stochastic level (change the value from ~1% up to perhaps 50% for decent results). This still needs some work, though, so I am leaving that option disabled by default. Please feel free to toy with it, and let me know if you have any suggestions!
發行說明
Came up with an easier way to generalize step threshold for any tickerid or resolution.Removed stochastic method.
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。
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。