mirror of
https://github.com/wassname/pandas-ta.git
synced 2026-06-27 16:10:07 +08:00
James Sawyer
73 lines
1.9 KiB
Python
73 lines
1.9 KiB
Python
# -*- coding: utf-8 -*-
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from math import exp
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from pandas import DataFrame
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from pandas_ta.utils import get_offset, verify_series
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def decay(close, kind=None, length=None, mode=None, offset=None, **kwargs):
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"""Indicator: 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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mode = mode.lower() if isinstance(mode, str) else "linear"
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offset = get_offset(offset)
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# Calculate Result
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_mode = "L"
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if mode == "exp" or kind == "exponential":
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_mode = "EXP"
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diff = close.shift(1) - exp(-length)
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else: # "linear"
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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"{_mode}DECAY_{length}"
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ld.category = "trend"
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return ld
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decay.__doc__ = """Decay
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Creates a decay moving forward from prior signals like crosses. The default is
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"linear". Exponential is optional as "exponential" or "exp".
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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, mode=None
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if mode == "exponential" or mode == "exp":
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max(close, close[-1] - exp(-length), 0)
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else:
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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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mamode (str): Option "exponential" ("exp"). Default: 'linear' or None
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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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"""
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