Source code for pandas_ta.trend.decay

# -*- coding: utf-8 -*-
from numpy import exp as npExp
from pandas import DataFrame
from pandas_ta.utils import get_offset, verify_series

[docs]def decay(close, kind=None, length=None, mode=None, offset=None, **kwargs): """Indicator: Decay""" # Validate Arguments length = int(length) if length and length > 0 else 5 mode = mode.lower() if isinstance(mode, str) else "linear" close = verify_series(close, length) offset = get_offset(offset) if close is None: return # Calculate Result _mode = "L" if mode == "exp" or kind == "exponential": _mode = "EXP" diff = close.shift(1) - npExp(-length) else: # "linear" diff = close.shift(1) - (1 / length) diff[0] = close[0] tdf = DataFrame({"close": close, "diff": diff, "0": 0}) ld = tdf.max(axis=1) # Offset if offset != 0: ld = ld.shift(offset) # Handle fills if "fillna" in kwargs: ld.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: ld.fillna(method=kwargs["fill_method"], inplace=True) # Name and Categorize it = f"{_mode}DECAY_{length}" ld.category = "trend" return ld
decay.__doc__ = \ """Decay Creates a decay moving forward from prior signals like crosses. The default is "linear". Exponential is optional as "exponential" or "exp". Sources: Calculation: Default Inputs: length=5, mode=None if mode == "exponential" or mode == "exp": max(close, close[-1] - exp(-length), 0) else: max(close, close[-1] - (1 / length), 0) Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 1 mode (str): If 'exp' then "exponential" decay. Default: 'linear' 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. """