mirror of
https://github.com/wassname/pandas-ta.git
synced 2026-08-09 12:20:21 +08:00
86 lines
2.8 KiB
Python
86 lines
2.8 KiB
Python
# -*- coding: utf-8 -*-
|
|
from numpy import NaN as npNaN
|
|
from ..utils import get_offset, verify_series
|
|
|
|
def ema(close, length=None, offset=None, **kwargs):
|
|
"""Indicator: Exponential Moving Average (EMA)"""
|
|
# Validate Arguments
|
|
close = verify_series(close)
|
|
length = int(length) if length and length > 0 else 10
|
|
min_periods = kwargs.pop('min_periods', length)
|
|
adjust = kwargs.pop('adjust', True)
|
|
offset = get_offset(offset)
|
|
sma = kwargs.pop('sma', True)
|
|
ewm = kwargs.pop('ewm', False)
|
|
|
|
# Calculate Result
|
|
if ewm:
|
|
# Mathematical Implementation of an Exponential Weighted Moving Average
|
|
ema = close.ewm(span=length, min_periods=min_periods, adjust=adjust).mean()
|
|
else:
|
|
alpha = 2 / (length + 1)
|
|
close = close.copy()
|
|
|
|
def ema_(series):
|
|
# Technical Anaylsis Definition of an Exponential Moving Average
|
|
# Slow for large series
|
|
series.iloc[1] = alpha * (series.iloc[1] - series.iloc[0]) + series.iloc[0]
|
|
return series.iloc[1]
|
|
|
|
seed = close[0:length].mean() if sma else close.iloc[0]
|
|
|
|
close[:length - 1] = npNaN
|
|
close.iloc[length - 1] = seed
|
|
ma = close[length - 1:].rolling(2, min_periods=2).apply(ema_, raw=False)
|
|
ema = close[:length].append(ma[1:])
|
|
|
|
# Offset
|
|
if offset != 0:
|
|
ema = ema.shift(offset)
|
|
|
|
# Name & Category
|
|
ema.name = f"EMA_{length}"
|
|
ema.category = 'overlap'
|
|
|
|
return ema
|
|
|
|
|
|
|
|
ema.__doc__ = \
|
|
"""Exponential Moving Average (EMA)
|
|
|
|
The Exponential Moving Average is more responsive moving average compared to the
|
|
Simple Moving Average (SMA). The weights are determined by alpha which is
|
|
proportional to it's length. There are several different methods of calculating
|
|
EMA. One method uses just the standard definition of EMA and another uses the
|
|
SMA to generate the initial value for the rest of the calculation.
|
|
|
|
Sources:
|
|
https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:moving_averages
|
|
https://www.investopedia.com/ask/answers/122314/what-exponential-moving-average-ema-formula-and-how-ema-calculated.asp
|
|
|
|
Calculation:
|
|
Default Inputs:
|
|
length=10
|
|
SMA = Simple Moving Average
|
|
if kwargs['presma']:
|
|
initial = SMA(close, length)
|
|
rest = close[length:]
|
|
close = initial + rest
|
|
|
|
EMA = close.ewm(span=length, adjust=adjust).mean()
|
|
|
|
Args:
|
|
close (pd.Series): Series of 'close's
|
|
length (int): It's period. Default: 10
|
|
offset (int): How many periods to offset the result. Default: 0
|
|
|
|
Kwargs:
|
|
adjust (bool, optional): Default: True
|
|
sma (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.
|
|
""" |