Library "f_maSelect" Easy to use drop-in facade function to lots of different moving average calculations, including some that are not natively available in PineScript v5 such as Zero-Lag EMA. Simply call f_maSelect(series float serie, simple string ma_type="sma", ma_length=14) instead of a ta.*ma() call and you get access to all MAs offered by PineScript and more.
zema(src, len) Zero-lag EMA (ZLMA) Parameters: src: Input series len: Lookback period Returns: Series smoothed with ZLMA
approximate_sma(x, ma_length) Approximate Standard Moving Average, which substracts the average instead of popping the oldest element, hence losing the base frequency and is why it is approximative. For some reason, this appears to give the same results as a standard RMA Parameters: x: Input series. ma_length: Lookback period. Returns: Approximate SMA series.
f_maSelect(serie, ma_type, ma_length) Generalized moving average selector Parameters: serie: Input series ma_type: String describing which moving average to use ma_length: Lookback period Returns: Serie smoothed with the selected moving average.
generalized_dev(src, length, avg, lmode) Generalized deviation calculation: Whereas other Bollinger Bands often just change the basis but not the stdev calculation, the correct way to change the basis is to also change it inside the stdev calculation. Parameters: src: Series to use (default: close) length: Lookback period avg: Average basis to use to calculate the standard deviation lmode: L1 or L2 regularization? (ie, lmode=1 uses abs() to cutoff negative values hence it calculates the Mean Absolute Deviation as does the ta.dev(), lmode=2 uses sum of squares hence it calculates the true Standard Deviation as the ta.stdev() function does). See also the research works of everget: Returns: stdev Standard deviation series
generalized_dev_discount(src, length, avg, lmode, temporal_discount) Standard deviation calculation but with different probabilities assigned to each bar, with newer bars having more weights en.wikipedia.org/wiki/Standard_deviation Parameters: src: Series to use (default: close) length: Lookback period avg: Average basis to use to calculate the standard deviation lmode: L1 or L2 regularization? (ie, lmode=1 uses abs() to cutoff negative values hence it calculates the Mean Absolute Deviation as does the ta.dev(), lmode=2 uses sum of squares hence it calculates the true Standard Deviation as the ta.stdev() function does). See also the research works of everget: temporal_discount: Probabilistic gamma factor to discount old values in favor of new ones, higher value = more weight to newer bars Returns: stdev Standard deviation series
median_absdev(src, length, median) Median Absolute Deviation Parameters: src: Input series length: Lookback period median: Median already calculated on the input series Returns: mad, the median absolute deviation value
發行說明
* Minor changes in text (remove unnecessary references to Bollinger Bands)