Source code for pandas_ta.statistics.mad

# -*- coding: utf-8 -*-
from numpy import fabs as npfabs
from pandas_ta.utils import get_offset, verify_series

[docs]def mad(close, length=None, offset=None, **kwargs): """Indicator: Mean Absolute Deviation""" # Validate Arguments length = int(length) if length and length > 0 else 30 min_periods = int(kwargs["min_periods"]) if "min_periods" in kwargs and kwargs["min_periods"] is not None else length close = verify_series(close, max(length, min_periods)) offset = get_offset(offset) if close is None: return # Calculate Result def mad_(series): """Mean Absolute Deviation""" return npfabs(series - series.mean()).mean() mad = close.rolling(length, min_periods=min_periods).apply(mad_, raw=True) # Offset if offset != 0: mad = mad.shift(offset) # Handle fills if "fillna" in kwargs: mad.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: mad.fillna(method=kwargs["fill_method"], inplace=True) # Name & Category = f"MAD_{length}" mad.category = "statistics" return mad
mad.__doc__ = \ """Rolling Mean Absolute Deviation Sources: Calculation: Default Inputs: length=30 mad = close.rolling(length).mad() Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 30 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. """