mirror of
https://github.com/wassname/pandas-ta.git
synced 2026-06-27 16:10:07 +08:00
James Sawyer
76 lines
2.2 KiB
Python
76 lines
2.2 KiB
Python
# -*- coding: utf-8 -*-
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from .ema import ema
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from .hma import hma
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from .rma import rma
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from .sma import sma
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from .wma import wma
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from ..utils import get_offset, verify_series
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def zlma(close, length=None, mamode=None, offset=None, **kwargs):
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"""Indicator: Zero Lag Moving Average (ZLMA)"""
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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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mamode = mamode.lower() if mamode else None
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# Calculate Result
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lag = int(0.5 * (length - 1))
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close = 2 * close - close.shift(lag)
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if mamode is None or mamode == "ema":
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zlma = ema(close, length=length, **kwargs)
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if mamode == "hma":
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zlma = hma(close, length=length, **kwargs)
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if mamode == "rma":
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zlma = rma(close, length=length, **kwargs)
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if mamode == "sma":
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zlma = sma(close, length=length, **kwargs)
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if mamode == "wma":
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zlma = wma(close, length=length, **kwargs)
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# Offset
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if offset != 0:
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zlma = zlma.shift(offset)
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# Name & Category
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zlma.name = f"ZL_{zlma.name}"
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zlma.category = "overlap"
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return zlma
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zlma.__doc__ = """Zero Lag Moving Average (ZLMA)
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The Zero Lag Moving Average attempts to eliminate the lag associated
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with moving averages. This is an adaption created by John Ehler and Ric Way.
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Sources:
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https://en.wikipedia.org/wiki/Zero_lag_exponential_moving_average
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Calculation:
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Default Inputs:
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length=10, mamode=EMA
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EMA = Exponential Moving Average
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lag = int(0.5 * (length - 1))
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SOURCE = 2 * close - close.shift(lag)
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ZLMA = MA(kind=mamode, SOURCE, length)
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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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mamode (str): Options: 'ema', 'hma', 'sma', 'wma'. Default: 'ema'
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offset (int): How many periods to offset the result. Default: 0
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Kwargs:
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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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