backtrader.indicators.dema module

DEMA/TEMA Indicator Module - Double/Triple Exponential Moving Average.

This module provides DEMA and TEMA indicators introduced by Patrick G. Mulloy in 1994 to reduce the lag associated with traditional moving averages.

Classes:

DoubleExponentialMovingAverage: DEMA indicator (alias: DEMA). TripleExponentialMovingAverage: TEMA indicator (alias: TEMA).

示例

class MyStrategy(bt.Strategy):

def __init__(self):

self.dema = bt.indicators.DEMA(self.data.close, period=20)

def next(self):

if self.data.close[0] > self.dema[0]:

self.buy()

elif self.data.close[0] < self.dema[0]:

self.sell()

class backtrader.indicators.dema.DoubleExponentialMovingAverage

基类：MovingAverageBase

DEMA was first time introduced in 1994, in the article "Smoothing Data with Faster-Moving Averages" by Patrick G. Mulloy in "Technical Analysis of Stocks & Commodities" magazine.

It attempts to reduce the inherent lag associated with Moving Averages

Formula:

• dema = (2.0 - ema(data, period) - ema(ema(data, period), period)

See:

(None)

alias = ('DEMA', 'MovingAverageDoubleExponential')

__init__(*args, **kwargs)

next()

Calculate DEMA for the current bar.

Formula: DEMA = 2 * EMA1 - EMA(EMA1)

once(start, end)

Calculate DEMA in runonce mode.

frompackages = ()

packages = ()

class backtrader.indicators.dema.TripleExponentialMovingAverage

基类：MovingAverageBase

TEMA was first time introduced in 1994, in the article "Smoothing Data with Faster-Moving Averages" by Patrick G. Mulloy in "Technical Analysis of Stocks & Commodities" magazine.

It attempts to reduce the inherent lag associated with Moving Averages

Formula:

• ema1 = ema(data, period)

• ema2 = ema(ema1, period)

• ema3 = ema(ema2, period)

• tema = 3 * ema1 - 3 * ema2 + ema3

See:

(None)

alias = ('TEMA', 'MovingAverageTripleExponential')

__init__(*args, **kwargs)

next()

Calculate TEMA for the current bar.

Formula: TEMA = 3 * EMA1 - 3 * EMA2 + EMA3

once(start, end)

Calculate TEMA in runonce mode.

frompackages = ()

packages = ()