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https://github.com/wassname/pandas-ta.git
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James Sawyer
71 lines
2.3 KiB
Python
71 lines
2.3 KiB
Python
# -*- coding: utf-8 -*-
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from numpy import NaN as npNaN
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from ..utils import get_offset, verify_series
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def ema(close, length=None, offset=None, **kwargs):
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"""Indicator: Exponential Moving Average (EMA)"""
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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 = kwargs.pop('min_periods', length)
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adjust = kwargs.pop("adjust", False)
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sma = kwargs.pop("sma", True)
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win_type = kwargs.pop("win_type", None)
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offset = get_offset(offset)
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# Calculate Result
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if sma:
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close = close.copy()
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sma_nth = close[0:length].sum() / length
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close[:length - 1] = npNaN
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close.iloc[length - 1] = sma_nth
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ema = close.ewm(span=length, adjust=adjust).mean()
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# Offset
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if offset != 0:
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ema = ema.shift(offset)
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# Name & Category
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ema.name = f"EMA_{length}"
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ema.category = "overlap"
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return ema
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ema.__doc__ = """Exponential Moving Average (EMA)
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The Exponential Moving Average is more responsive moving average compared to the
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Simple Moving Average (SMA). The weights are determined by alpha which is
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proportional to it's length. There are several different methods of calculating
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EMA. One method uses just the standard definition of EMA and another uses the
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SMA to generate the initial value for the rest of the calculation.
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Sources:
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https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:moving_averages
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https://www.investopedia.com/ask/answers/122314/what-exponential-moving-average-ema-formula-and-how-ema-calculated.asp
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Calculation:
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Default Inputs:
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length=10, adjust=False, sma=True
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if sma:
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sma_nth = close[0:length].sum() / length
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close[:length - 1] = np.NaN
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close.iloc[length - 1] = sma_nth
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EMA = close.ewm(span=length, adjust=adjust).mean()
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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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offset (int): How many periods to offset the result. Default: 0
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Kwargs:
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adjust (bool, optional): Default: False
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sma (bool, optional): If True, uses SMA for initial value. Default: True
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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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