Source code for pandas_ta.momentum.bias

# -*- coding: utf-8 -*-
from pandas_ta.overlap import ma
from pandas_ta.utils import get_offset, verify_series


[docs]def bias(close, length=None, mamode=None, offset=None, **kwargs): """Indicator: Bias (BIAS)""" # Validate Arguments length = int(length) if length and length > 0 else 26 mamode = mamode if isinstance(mamode, str) else "sma" close = verify_series(close, length) offset = get_offset(offset) if close is None: return # Calculate Result bma = ma(mamode, close, length=length, **kwargs) bias = (close / bma) - 1 # Offset if offset != 0: bias = bias.shift(offset) # Handle fills if "fillna" in kwargs: bias.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: bias.fillna(method=kwargs["fill_method"], inplace=True) # Name and Categorize it bias.name = f"BIAS_{bma.name}" bias.category = "momentum" return bias
bias.__doc__ = \ """Bias (BIAS) Rate of change between the source and a moving average. Sources: Few internet resources on definitive definition. Request by Github user homily, issue #46 Calculation: Default Inputs: length=26, MA='sma' BIAS = (close - MA(close, length)) / MA(close, length) = (close / MA(close, length)) - 1 Args: close (pd.Series): Series of 'close's length (int): The period. Default: 26 mamode (str): See ```help(ta.ma)```. Default: 'sma' drift (int): The short period. Default: 1 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. """