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.
版本注释
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!
版本注释
Came up with an easier way to generalize step threshold for any tickerid or resolution.Removed stochastic method.
开源脚本
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免责声明
这些信息和出版物并非旨在提供,也不构成TradingView提供或认可的任何形式的财务、投资、交易或其他类型的建议或推荐。请阅读使用条款了解更多信息。
开源脚本
秉承TradingView的精神,该脚本的作者将其开源,以便交易者可以查看和验证其功能。向作者致敬!您可以免费使用该脚本,但请记住,重新发布代码须遵守我们的网站规则。
免责声明
这些信息和出版物并非旨在提供,也不构成TradingView提供或认可的任何形式的财务、投资、交易或其他类型的建议或推荐。请阅读使用条款了解更多信息。