# -*- coding: utf-8 -*-
from .ema import ema
from pandas_ta import Imports
from pandas_ta.utils import get_offset, verify_series
[docs]def tema(close, length=None, talib=None, offset=None, **kwargs):
"""Indicator: Triple Exponential Moving Average (TEMA)"""
# Validate Arguments
length = int(length) if length and length > 0 else 10
close = verify_series(close, length)
offset = get_offset(offset)
mode_tal = bool(talib) if isinstance(talib, bool) else True
if close is None: return
# Calculate Result
if Imports["talib"] and mode_tal:
from talib import TEMA
tema = TEMA(close, length)
else:
ema1 = ema(close=close, length=length, **kwargs)
ema2 = ema(close=ema1, length=length, **kwargs)
ema3 = ema(close=ema2, length=length, **kwargs)
tema = 3 * (ema1 - ema2) + ema3
# Offset
if offset != 0:
tema = tema.shift(offset)
# Handle fills
if "fillna" in kwargs:
tema.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
tema.fillna(method=kwargs["fill_method"], inplace=True)
# Name & Category
tema.name = f"TEMA_{length}"
tema.category = "overlap"
return tema
tema.__doc__ = \
"""Triple Exponential Moving Average (TEMA)
A less laggy Exponential Moving Average.
Sources:
https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/triple-exponential-moving-average-tema/
Calculation:
Default Inputs:
length=10
EMA = Exponential Moving Average
ema1 = EMA(close, length)
ema2 = EMA(ema1, length)
ema3 = EMA(ema2, length)
TEMA = 3 * (ema1 - ema2) + ema3
Args:
close (pd.Series): Series of 'close's
length (int): It's period. Default: 10
talib (bool): If TA Lib is installed and talib is True, Returns the TA Lib
version. Default: True
offset (int): How many periods to offset the result. Default: 0
Kwargs:
adjust (bool): Default: True
presma (bool, optional): If True, uses SMA for initial value.
fillna (value, optional): pd.DataFrame.fillna(value)
fill_method (value, optional): Type of fill method
Returns:
pd.Series: New feature generated.
"""