Source code for pandas_ta.overlap.tema

# -*- 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 = f"TEMA_{length}" tema.category = "overlap" return tema
tema.__doc__ = \ """Triple Exponential Moving Average (TEMA) A less laggy Exponential Moving Average. Sources: 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. """