Module backtrader.indicators.dema

Expand source code 
#!/usr/bin/env python
# -*- coding: utf-8; py-indent-offset:4 -*-
###############################################################################
#
# Copyright (C) 2015-2023 Daniel Rodriguez
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
#
###############################################################################
from __future__ import (absolute_import, division, print_function,
                        unicode_literals)


from . import Indicator, MovingAverageBase, MovAv


class DoubleExponentialMovingAverage(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 to Moving Averages

    Formula:
      - dema = (2.0 - ema(data, period) - ema(ema(data, period), period)

    See:
      (None)
    '''
    alias = ('DEMA', 'MovingAverageDoubleExponential',)

    lines = ('dema',)
    params = (('_movav', MovAv.EMA),)

    def __init__(self):
        ema = self.p._movav(self.data, period=self.p.period)
        ema2 = self.p._movav(ema, period=self.p.period)
        self.lines.dema = 2.0 * ema - ema2

        super(DoubleExponentialMovingAverage, self).__init__()


class TripleExponentialMovingAverage(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 to 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',)

    lines = ('tema',)
    params = (('_movav', MovAv.EMA),)

    def __init__(self):
        ema1 = self.p._movav(self.data, period=self.p.period)
        ema2 = self.p._movav(ema1, period=self.p.period)
        ema3 = self.p._movav(ema2, period=self.p.period)

        self.lines.tema = 3.0 * ema1 - 3.0 * ema2 + ema3
        super(TripleExponentialMovingAverage, self).__init__()

Classes

class DEMA

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 to Moving Averages

Formula

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

See

(None)

Ancestors

Class variables

var alias
var aliased
var frompackages
var linealias
var packages
var params
var plotinfo
var plotlines

Inherited members

class DoubleExponentialMovingAverage

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 to Moving Averages

Formula

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

See

(None)

Expand source code 
class DoubleExponentialMovingAverage(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 to Moving Averages

    Formula:
      - dema = (2.0 - ema(data, period) - ema(ema(data, period), period)

    See:
      (None)
    '''
    alias = ('DEMA', 'MovingAverageDoubleExponential',)

    lines = ('dema',)
    params = (('_movav', MovAv.EMA),)

    def __init__(self):
        ema = self.p._movav(self.data, period=self.p.period)
        ema2 = self.p._movav(ema, period=self.p.period)
        self.lines.dema = 2.0 * ema - ema2

        super(DoubleExponentialMovingAverage, self).__init__()

Ancestors

Subclasses

Class variables

var alias
var aliased
var frompackages
var linealias
var packages
var params
var plotinfo
var plotlines

Inherited members

class MovingAverageDoubleExponential

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 to Moving Averages

Formula

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

See

(None)

Ancestors

Class variables

var alias
var aliased
var frompackages
var linealias
var packages
var params
var plotinfo
var plotlines

Inherited members

class MovingAverageTripleExponential

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 to Moving Averages

Formula

  • ema1 = ema(data, period)
  • ema2 = ema(ema1, period)
  • ema3 = ema(ema2, period)
  • tema = 3 * ema1 - 3 * ema2 + ema3

See

(None)

Ancestors

Class variables

var alias
var aliased
var frompackages
var linealias
var packages
var params
var plotinfo
var plotlines

Inherited members

class TEMA

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 to Moving Averages

Formula

  • ema1 = ema(data, period)
  • ema2 = ema(ema1, period)
  • ema3 = ema(ema2, period)
  • tema = 3 * ema1 - 3 * ema2 + ema3

See

(None)

Ancestors

Class variables

var alias
var aliased
var frompackages
var linealias
var packages
var params
var plotinfo
var plotlines

Inherited members

class TripleExponentialMovingAverage

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 to Moving Averages

Formula

  • ema1 = ema(data, period)
  • ema2 = ema(ema1, period)
  • ema3 = ema(ema2, period)
  • tema = 3 * ema1 - 3 * ema2 + ema3

See

(None)

Expand source code 
class TripleExponentialMovingAverage(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 to 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',)

    lines = ('tema',)
    params = (('_movav', MovAv.EMA),)

    def __init__(self):
        ema1 = self.p._movav(self.data, period=self.p.period)
        ema2 = self.p._movav(ema1, period=self.p.period)
        ema3 = self.p._movav(ema2, period=self.p.period)

        self.lines.tema = 3.0 * ema1 - 3.0 * ema2 + ema3
        super(TripleExponentialMovingAverage, self).__init__()

Ancestors

Subclasses

Class variables

var alias
var aliased
var frompackages
var linealias
var packages
var params
var plotinfo
var plotlines

Inherited members