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.
版本注释
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.
版本注释
v3版本注释
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.
版本注释
v5Changed the function name "analyse" to "analyze".
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Pine脚本库
本着真正的TradingView精神，作者将此Pine代码发布为开源库，以便我们社区的其他Pine程序员可以重复使用它。向作者致敬！您可以私密或在其他开源出版物中使用此库，但在出版物中重复使用此代码受网站规则约束。
免责声明
这些信息和出版物并不意味着也不构成TradingView提供或认可的金融、投资、交易或其它类型的建议或背书。请在使用条款阅读更多信息。