# -*- coding: utf-8 -*-
from . import (
dema, ema, hma, linreg, rma, sma, swma, t3, tema, trima, vidya, wma
)
from pandas_ta.utils import get_offset, verify_series
[docs]def zlma(close, length=None, mamode=None, offset=None, **kwargs):
"""Indicator: Zero Lag Moving Average (ZLMA)"""
# Validate Arguments
length = int(length) if length and length > 0 else 10
mamode = mamode.lower() if isinstance(mamode, str) else "ema"
close = verify_series(close, length)
offset = get_offset(offset)
if close is None: return
# Calculate Result
lag = int(0.5 * (length - 1))
close_ = 2 * close - close.shift(lag)
if mamode == "dema": zlma = dema(close_, length=length, **kwargs)
elif mamode == "hma": zlma = hma(close_, length=length, **kwargs)
elif mamode == "linreg": zlma = linreg(close_, length=length, **kwargs)
elif mamode == "rma": zlma = rma(close_, length=length, **kwargs)
elif mamode == "sma": zlma = sma(close_, length=length, **kwargs)
elif mamode == "swma": zlma = swma(close_, length=length, **kwargs)
elif mamode == "t3": zlma = t3(close_, length=length, **kwargs)
elif mamode == "tema": zlma = tema(close_, length=length, **kwargs)
elif mamode == "trima": zlma = trima(close_, length=length, **kwargs)
elif mamode == "vidya": zlma = vidya(close_, length=length, **kwargs)
elif mamode == "wma": zlma = wma(close_, length=length, **kwargs)
else: zlma = ema(close_, length=length, **kwargs) # "ema"
# Offset
if offset != 0:
zlma = zlma.shift(offset)
# Handle fills
if "fillna" in kwargs:
zlma.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
zlma.fillna(method=kwargs["fill_method"], inplace=True)
# Name & Category
zlma.name = f"ZL_{zlma.name}"
zlma.category = "overlap"
return zlma
zlma.__doc__ = \
"""Zero Lag Moving Average (ZLMA)
The Zero Lag Moving Average attempts to eliminate the lag associated
with moving averages. This is an adaption created by John Ehler and Ric Way.
Sources:
https://en.wikipedia.org/wiki/Zero_lag_exponential_moving_average
Calculation:
Default Inputs:
length=10, mamode=EMA
EMA = Exponential Moving Average
lag = int(0.5 * (length - 1))
SOURCE = 2 * close - close.shift(lag)
ZLMA = MA(kind=mamode, SOURCE, length)
Args:
close (pd.Series): Series of 'close's
length (int): It's period. Default: 10
mamode (str): Options: 'ema', 'hma', 'sma', 'wma'. Default: 'ema'
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.
"""