Noise Gate
This Pine Script code defines an indicator called "Noise Gate" which filters out "noise" from a given signal. The indicator takes four input parameters: source, length, ratio, and level. The source parameter specifies the source data for the indicator (e.g., close prices), the length parameter specifies the length of a moving average, the ratio parameter specifies the attenuation ratio, and the level parameter specifies the threshold for attenuating the signal.
The core of the indicator is the noise_gate function, which takes three input parameters: signal, ratio, and level. The signal parameter represents the input signal that needs to be filtered. The ratio parameter specifies the amount by which the signal will be attenuated (reduced in amplitude) if it falls below the level parameter. The level parameter is a threshold that determines whether the signal will be attenuated or not.
The noise_gate function first calculates the absolute value of the signal using the math.abs() function. This is done because the filtering only applies to the magnitude of the signal, not its sign (positive or negative value).
The function then checks if the absolute value of the signal is above the level threshold using an if statement. If it is, the signal is returned as is. If the absolute value of the signal is below the level threshold, the function calculates a value called soft_knee_ratio using the formula 1 - (level - abs_signal) / level. This value represents the amount by which the signal will be attenuated. The signal is then reduced in amplitude by this soft_knee_ratio and the resulting value is returned as the output of the function.
The noise_gate function applies the transformation symmetrically to both positive and negative values of the signal parameter. This is because the transformation only depends on the absolute value of the signal, not its sign. The transformation first calculates the absolute value of the signal using the math.abs() function and then applies the filtering based on the magnitude of the signal. The sign of the signal is not taken into account in this process. As a result, the transformation is applied symmetrically to both positive and negative values of the signal.
The noise_gate function can be a valuable tool for anyone looking to filter out noise or unwanted variations from a signal. It is flexible and easy to use, and can be applied to a wide range of situations where signal noise reduction is needed. For example, it can be used to smooth out financial time series data or to remove background noise from an audio recording.
The noise_gate function in this code has been modified to include an additional input parameter called knee_type, which allows the user to specify whether to use a hard knee or a soft knee. A hard knee means that the compressor triggers simply at the threshold, whereas a soft knee means that the compressor triggers smoothly, gradually increasing the attenuation as the signal falls further below the threshold.
To use a hard knee, the user can set the knee_type parameter to "hard". To use a soft knee, the user can set the knee_type parameter to "soft". The default value for the knee_type parameter is "soft", so if the user does not specify a value for knee_type, the noise_gate function will use a soft knee by default.
The noise_gate function includes a check for the value of the knee_type parameter and applies the appropriate knee type. If the knee_type parameter is set to "hard", the function applies a hard knee by simply triggering at the threshold and dividing the input by the ratio if the signal falls below the threshold. If the knee_type parameter is set to "soft" (or if it is not specified and the default value is used), the function applies a soft knee by gradually increasing the attenuation of the signal as it falls further below the threshold.
The noise_gate function can be a valuable tool for anyone looking to filter out noise or unwanted variations from a signal. It is flexible and easy to use, and can be applied to a wide range of situations where signal noise reduction is needed. For example, it can be used to smooth out financial time series data or to remove background noise from an audio recording.
The core of the indicator is the noise_gate function, which takes three input parameters: signal, ratio, and level. The signal parameter represents the input signal that needs to be filtered. The ratio parameter specifies the amount by which the signal will be attenuated (reduced in amplitude) if it falls below the level parameter. The level parameter is a threshold that determines whether the signal will be attenuated or not.
The noise_gate function first calculates the absolute value of the signal using the math.abs() function. This is done because the filtering only applies to the magnitude of the signal, not its sign (positive or negative value).
The function then checks if the absolute value of the signal is above the level threshold using an if statement. If it is, the signal is returned as is. If the absolute value of the signal is below the level threshold, the function calculates a value called soft_knee_ratio using the formula 1 - (level - abs_signal) / level. This value represents the amount by which the signal will be attenuated. The signal is then reduced in amplitude by this soft_knee_ratio and the resulting value is returned as the output of the function.
The noise_gate function applies the transformation symmetrically to both positive and negative values of the signal parameter. This is because the transformation only depends on the absolute value of the signal, not its sign. The transformation first calculates the absolute value of the signal using the math.abs() function and then applies the filtering based on the magnitude of the signal. The sign of the signal is not taken into account in this process. As a result, the transformation is applied symmetrically to both positive and negative values of the signal.
The noise_gate function can be a valuable tool for anyone looking to filter out noise or unwanted variations from a signal. It is flexible and easy to use, and can be applied to a wide range of situations where signal noise reduction is needed. For example, it can be used to smooth out financial time series data or to remove background noise from an audio recording.
The noise_gate function in this code has been modified to include an additional input parameter called knee_type, which allows the user to specify whether to use a hard knee or a soft knee. A hard knee means that the compressor triggers simply at the threshold, whereas a soft knee means that the compressor triggers smoothly, gradually increasing the attenuation as the signal falls further below the threshold.
To use a hard knee, the user can set the knee_type parameter to "hard". To use a soft knee, the user can set the knee_type parameter to "soft". The default value for the knee_type parameter is "soft", so if the user does not specify a value for knee_type, the noise_gate function will use a soft knee by default.
The noise_gate function includes a check for the value of the knee_type parameter and applies the appropriate knee type. If the knee_type parameter is set to "hard", the function applies a hard knee by simply triggering at the threshold and dividing the input by the ratio if the signal falls below the threshold. If the knee_type parameter is set to "soft" (or if it is not specified and the default value is used), the function applies a soft knee by gradually increasing the attenuation of the signal as it falls further below the threshold.
The noise_gate function can be a valuable tool for anyone looking to filter out noise or unwanted variations from a signal. It is flexible and easy to use, and can be applied to a wide range of situations where signal noise reduction is needed. For example, it can be used to smooth out financial time series data or to remove background noise from an audio recording.