mirror of
https://github.com/wassname/pandas-ta.git
synced 2026-06-27 16:10:07 +08:00
James Sawyer
62 lines
1.7 KiB
Python
62 lines
1.7 KiB
Python
# -*- coding: utf-8 -*-
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from .ema import ema
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from ..utils import get_offset, verify_series
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def tema(close, length=None, offset=None, **kwargs):
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"""Indicator: Triple Exponential Moving Average (TEMA)"""
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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 10
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min_periods = (int(kwargs["min_periods"]) if "min_periods" in kwargs and
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kwargs["min_periods"] is not None else length)
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offset = get_offset(offset)
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# Calculate Result
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ema1 = ema(close=close, length=length, **kwargs)
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ema2 = ema(close=ema1, length=length, **kwargs)
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ema3 = ema(close=ema2, length=length, **kwargs)
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tema = 3 * (ema1 - ema2) + ema3
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# Offset
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if offset != 0:
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tema = tema.shift(offset)
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# Name & Category
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tema.name = f"TEMA_{length}"
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tema.category = "overlap"
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return tema
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tema.__doc__ = """Triple Exponential Moving Average (TEMA)
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A less laggy Exponential Moving Average.
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Sources:
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https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/triple-exponential-moving-average-tema/
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Calculation:
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Default Inputs:
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length=10
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EMA = Exponential Moving Average
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ema1 = EMA(close, length)
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ema2 = EMA(ema1, length)
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ema3 = EMA(ema2, length)
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TEMA = 3 * (ema1 - ema2) + ema3
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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: 10
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offset (int): How many periods to offset the result. Default: 0
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Kwargs:
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adjust (bool): Default: True
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presma (bool, optional): If True, uses SMA for initial value.
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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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