ssf#

API documentation for pandas_ta.overlap.ssf Python function.

ssf(close, length=None, poles=None, offset=None, **kwargs)[source]#

John F. Ehlers’s solution to reduce lag and remove aliasing noise with his research in aerospace analog filter design. This indicator comes with two versions determined by the keyword poles. By default, it uses two poles but there is an option for three poles. Since SSF is a (Resursive) Digital Filter, the number of poles determine how many prior recursive SSF bars to include in the design of the filter. So two poles uses two prior SSF bars and three poles uses three prior SSF bars for their filter calculations.

Sources:

http://www.stockspotter.com/files/PredictiveIndicators.pdf https://www.tradingview.com/script/VdJy0yBJ-Ehlers-Super-Smoother-Filter/ https://www.mql5.com/en/code/588 https://www.mql5.com/en/code/589

Calculation:

Default Inputs:: length=10, poles=[2, 3]

See the source code or Sources listed above.

Args:

close (pd.Series): Series of ‘close’s length (int): It’s period. Default: 10 poles (int): The number of poles to use, either 2 or 3. Default: 2 offset (int): How many periods to offset the result. Default: 0

Kwargs:

fillna (value, optional): pd.DataFrame.fillna(value) fill_method (value, optional): Type of fill method

Returns:

pd.Series: New feature generated.