Source code for pandas_ta.overlap.dema

# -*- coding: utf-8 -*-
from .ema import ema
from pandas_ta import Imports
from pandas_ta.utils import get_offset, verify_series

[docs]def dema(close, length=None, talib=None, offset=None, **kwargs): """Indicator: Double Exponential Moving Average (DEMA)""" # 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 DEMA dema = DEMA(close, length) else: ema1 = ema(close=close, length=length) ema2 = ema(close=ema1, length=length) dema = 2 * ema1 - ema2 # Offset if offset != 0: dema = dema.shift(offset) # Handle fills if "fillna" in kwargs: dema.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: dema.fillna(method=kwargs["fill_method"], inplace=True) # Name & Category = f"DEMA_{length}" dema.category = "overlap" return dema
dema.__doc__ = \ """Double Exponential Moving Average (DEMA) The Double Exponential Moving Average attempts to a smoother average with less lag than the normal Exponential Moving Average (EMA). Sources: Calculation: Default Inputs: length=10 EMA = Exponential Moving Average ema1 = EMA(close, length) ema2 = EMA(ema1, length) DEMA = 2 * ema1 - ema2 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: fillna (value, optional): pd.DataFrame.fillna(value) fill_method (value, optional): Type of fill method Returns: pd.Series: New feature generated. """