PINE LIBRARY
已更新 ApproximateGaussianSmoothing

Library "ApproximateGaussianSmoothing"
This library provides a novel smoothing function for time-series data, serving as an alternative to SMA and EMA. Additionally, it provides some statistical processing, using moving averages as expected values in statistics.
'Approximate Gaussian Smoothing' (AGS) is designed to apply weights to time-series data that closely resemble Gaussian smoothing weights. it is easier to calculate than the similar ALMA.
In case AGS is used as a moving average, I named it 'Approximate Gaussian Weighted Moving Average' (AGWMA).
The formula is:
AGWMA = (EMA + EMA(EMA) + EMA(EMA(EMA)) + EMA(EMA(EMA(EMA)))) / 4
The EMA parameter alpha is 5 / (N + 4), using time period N (or length).
ma(src, length)
Calculate moving average using AGS (AGWMA).
Parameters:
src (float): Series of values to process.
length (simple int): Number of bars (length).
Returns: Moving average.
analyse(src, length)
Calculate mean and variance using AGS.
Parameters:
src (float): Series of values to process.
length (simple int): Number of bars (length).
Returns: Mean and variance.
analyse(dimensions, sources, length)
Calculate mean and variance covariance matrix using AGS.
Parameters:
dimensions (simple int): Dimensions of sources to process.
sources (array<float>): Series of values to process.
length (simple int): Number of bars (length).
Returns: Mean and variance covariance matrix.
trend(src, length)
Calculate intercept (LSMA) and slope using AGS.
Parameters:
src (float): Series of values to process.
length (simple int): Number of bars (length).
Returns: Intercept and slope.
This library provides a novel smoothing function for time-series data, serving as an alternative to SMA and EMA. Additionally, it provides some statistical processing, using moving averages as expected values in statistics.
'Approximate Gaussian Smoothing' (AGS) is designed to apply weights to time-series data that closely resemble Gaussian smoothing weights. it is easier to calculate than the similar ALMA.
In case AGS is used as a moving average, I named it 'Approximate Gaussian Weighted Moving Average' (AGWMA).
The formula is:
AGWMA = (EMA + EMA(EMA) + EMA(EMA(EMA)) + EMA(EMA(EMA(EMA)))) / 4
The EMA parameter alpha is 5 / (N + 4), using time period N (or length).
ma(src, length)
Calculate moving average using AGS (AGWMA).
Parameters:
src (float): Series of values to process.
length (simple int): Number of bars (length).
Returns: Moving average.
analyse(src, length)
Calculate mean and variance using AGS.
Parameters:
src (float): Series of values to process.
length (simple int): Number of bars (length).
Returns: Mean and variance.
analyse(dimensions, sources, length)
Calculate mean and variance covariance matrix using AGS.
Parameters:
dimensions (simple int): Dimensions of sources to process.
sources (array<float>): Series of values to process.
length (simple int): Number of bars (length).
Returns: Mean and variance covariance matrix.
trend(src, length)
Calculate intercept (LSMA) and slope using AGS.
Parameters:
src (float): Series of values to process.
length (simple int): Number of bars (length).
Returns: Intercept and slope.
發行說明
v2更新:
trend(src, length)
Calculate trend statistics using AGS.
Parameters:
src (float): Series of values to process.
length (simple int): Number of bars (length).
Returns: Slope, intercept, correlation and RSS.
發行說明
v3發行說明
v4Add:
linreg(src1, src2, length)
Calculate linear regression using AGS.
Parameters:
src1 (float): Series of values to process.
src2 (float): Series of values to process.
length (simple int): Number of bars (length).
Returns: Slope, intercept and MSE.
correlation(src1, src2, length)
Calculate correlation using AGS.
Parameters:
src1 (float): Series of values to process.
src2 (float): Series of values to process.
length (simple int): Number of bars (length).
Returns: Correlation coefficient.
Delete:
trend(src, length)
Calculate trend statistics using AGS.
To get trend statistics, use the linreg method with bar_index as the first argument.
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Pine腳本庫
秉持 TradingView 一貫的共享精神,作者將此 Pine 程式碼發佈為開源庫,讓社群中的其他 Pine 程式設計師能夠重複使用。向作者致敬!您可以在私人專案或其他開源發佈中使用此庫,但在公開發佈中重複使用該程式碼需遵守社群規範。
免責聲明
這些資訊和出版物並不意味著也不構成TradingView提供或認可的金融、投資、交易或其他類型的意見或建議。請在使用條款閱讀更多資訊。