OPEN-SOURCE SCRIPT
[blackcat] L1 Ehlers Every Little Bit Helps
Level 1
Background
Inspired by "Averaging The Open And Close To Reduce Noise" on March 2023 by John F. Ehlers’ article in the March issue, “Every Little Bit Helps”, I further smooth it because noise was still large and “Every Little Bit Does Not Helps Much”.
Function
In John F. Ehlers' 03/2023 article, "Every Little Bit Helps," he suggests that noise can be reduced by simply averaging the open and close prices of a bar, rather than just using the close price. The data sampling example presented in this article compares the traditional RSI using close data to an RSI calculated using the average of the open and close.
Compared to RSI, I used my customized RMI version which is more suitable for moment smoothing. For example, the price entry of the 1st RMI can be changed from Close to (Close + Open) / 2. In fact, the RMI indicator itself has a price input that can be changed in a similar way. You can change the input parameter "m" to "More Help", which is 5 by default.
Remarks
Feedbacks are appreciated.
Background
Inspired by "Averaging The Open And Close To Reduce Noise" on March 2023 by John F. Ehlers’ article in the March issue, “Every Little Bit Helps”, I further smooth it because noise was still large and “Every Little Bit Does Not Helps Much”.
Function
In John F. Ehlers' 03/2023 article, "Every Little Bit Helps," he suggests that noise can be reduced by simply averaging the open and close prices of a bar, rather than just using the close price. The data sampling example presented in this article compares the traditional RSI using close data to an RSI calculated using the average of the open and close.
Compared to RSI, I used my customized RMI version which is more suitable for moment smoothing. For example, the price entry of the 1st RMI can be changed from Close to (Close + Open) / 2. In fact, the RMI indicator itself has a price input that can be changed in a similar way. You can change the input parameter "m" to "More Help", which is 5 by default.
Remarks
Feedbacks are appreciated.
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