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pandas-ta/pandas_ta/trend/linear_decay.py
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Python

# -*- coding: utf-8 -*-
from pandas import DataFrame
from ..utils import get_offset, verify_series
def linear_decay(close, length=None, offset=None, **kwargs):
"""Indicator: Linear Decay"""
# Validate Arguments
close = verify_series(close)
length = int(length) if length and length > 0 else 5
offset = get_offset(offset)
# Calculate Result
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
ld.name = f"LDECAY_{length}"
ld.category = 'trend'
return ld
linear_decay.__doc__ = \
"""Linear Decay
Adds a linear decay moving forward from prior signals like crosses.
Sources:
https://tulipindicators.org/decay
Calculation:
Default Inputs:
length=5
max(close, close[-1] - (1 / length), 0)
Args:
close (pd.Series): Series of 'close's
length (int): It's 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.
"""