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60 lines
1.4 KiB
Python
60 lines
1.4 KiB
Python
# -*- coding: utf-8 -*-
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from pandas import DataFrame
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from ..utils import get_offset, verify_series
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def linear_decay(close, length=None, offset=None, **kwargs):
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"""Indicator: Linear Decay"""
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# Validate Arguments
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close = verify_series(close)
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length = int(length) if length and length > 0 else 5
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offset = get_offset(offset)
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# Calculate Result
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diff = close.shift(1) - (1 / length)
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diff[0] = close[0]
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tdf = DataFrame({'close': close, 'diff': diff, '0': 0})
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ld = tdf.max(axis=1)
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# Offset
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if offset != 0:
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ld = ld.shift(offset)
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# Handle fills
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if 'fillna' in kwargs:
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ld.fillna(kwargs['fillna'], inplace=True)
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if 'fill_method' in kwargs:
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ld.fillna(method=kwargs['fill_method'], inplace=True)
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# Name and Categorize it
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ld.name = f"LDECAY_{length}"
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ld.category = 'trend'
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return ld
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linear_decay.__doc__ = \
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"""Linear Decay
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Adds a linear decay moving forward from prior signals like crosses.
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Sources:
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https://tulipindicators.org/decay
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Calculation:
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Default Inputs:
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length=5
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max(close, close[-1] - (1 / length), 0)
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Args:
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close (pd.Series): Series of 'close's
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length (int): It's period. Default: 1
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offset (int): How many periods to offset the result. Default: 0
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Kwargs:
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fillna (value, optional): pd.DataFrame.fillna(value)
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fill_method (value, optional): Type of fill method
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Returns:
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pd.Series: New feature generated.
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""" |