Module backtrader.indicators.stochastic

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, Max, MovAv, Highest, Lowest, DivByZero


class _StochasticBase(Indicator):
    lines = ('percK', 'percD',)
    params = (('period', 14), ('period_dfast', 3), ('movav', MovAv.Simple),
              ('upperband', 80.0), ('lowerband', 20.0),
              ('safediv', False), ('safezero', 0.0))

    plotlines = dict(percD=dict(_name='%D', ls='--'),
                     percK=dict(_name='%K'))

    def _plotlabel(self):
        plabels = [self.p.period, self.p.period_dfast]
        plabels += [self.p.movav] * self.p.notdefault('movav')
        return plabels

    def _plotinit(self):
        self.plotinfo.plotyhlines = [self.p.upperband, self.p.lowerband]

    def __init__(self):
        highesthigh = Highest(self.data.high, period=self.p.period)
        lowestlow = Lowest(self.data.low, period=self.p.period)
        knum = self.data.close - lowestlow
        kden = highesthigh - lowestlow
        if self.p.safediv:
            self.k = 100.0 * DivByZero(knum, kden, zero=self.p.safezero)
        else:
            self.k = 100.0 * (knum / kden)
        self.d = self.p.movav(self.k, period=self.p.period_dfast)

        super(_StochasticBase, self).__init__()


class StochasticFast(_StochasticBase):
    '''
    By Dr. George Lane in the 50s. It compares a closing price to the price
    range and tries to show convergence if the closing prices are close to the
    extremes

      - It will go up if closing prices are close to the highs
      - It will roughly go down if closing prices are close to the lows

    It shows divergence if the extremes keep on growing but closing prices
    do not in the same manner (distance to the extremes grow)

    Formula:
      - hh = highest(data.high, period)
      - ll = lowest(data.low, period)
      - knum = data.close - ll
      - kden = hh - ll
      - k = 100 * (knum / kden)
      - d = MovingAverage(k, period_dfast)

    See:
      - http://en.wikipedia.org/wiki/Stochastic_oscillator
    '''
    def __init__(self):
        super(StochasticFast, self).__init__()
        self.lines.percK = self.k
        self.lines.percD = self.d


class Stochastic(_StochasticBase):
    '''
    The regular (or slow version) adds an additional moving average layer and
    thus:

      - The percD line of the StochasticFast becomes the percK line
      - percD becomes a  moving average of period_dslow of the original percD

    Formula:
      - k = k
      - d = d
      - d = MovingAverage(d, period_dslow)

    See:
      - http://en.wikipedia.org/wiki/Stochastic_oscillator
    '''
    alias = ('StochasticSlow',)
    params = (('period_dslow', 3),)

    def _plotlabel(self):
        plabels = [self.p.period, self.p.period_dfast, self.p.period_dslow]
        plabels += [self.p.movav] * self.p.notdefault('movav')
        return plabels

    def __init__(self):
        super(Stochastic, self).__init__()
        self.lines.percK = self.d
        self.l.percD = self.p.movav(self.l.percK, period=self.p.period_dslow)


class StochasticFull(_StochasticBase):
    '''
    This version displays the 3 possible lines:

      - percK
      - percD
      - percSlow

    Formula:
      - k = d
      - d = MovingAverage(k, period_dslow)
      - dslow =

    See:
      - http://en.wikipedia.org/wiki/Stochastic_oscillator
    '''
    lines = ('percDSlow',)
    params = (('period_dslow', 3),)

    plotlines = dict(percDSlow=dict(_name='%DSlow'))

    def _plotlabel(self):
        plabels = [self.p.period, self.p.period_dfast, self.p.period_dslow]
        plabels += [self.p.movav] * self.p.notdefault('movav')
        return plabels

    def __init__(self):
        super(StochasticFull, self).__init__()
        self.lines.percK = self.k
        self.lines.percD = self.d
        self.l.percDSlow = self.p.movav(
            self.l.percD, period=self.p.period_dslow)

Classes

class Stochastic

The regular (or slow version) adds an additional moving average layer and thus:

  • The percD line of the StochasticFast becomes the percK line
  • percD becomes a moving average of period_dslow of the original percD

Formula

  • k = k
  • d = d
  • d = MovingAverage(d, period_dslow)

See

Expand source code 
class Stochastic(_StochasticBase):
    '''
    The regular (or slow version) adds an additional moving average layer and
    thus:

      - The percD line of the StochasticFast becomes the percK line
      - percD becomes a  moving average of period_dslow of the original percD

    Formula:
      - k = k
      - d = d
      - d = MovingAverage(d, period_dslow)

    See:
      - http://en.wikipedia.org/wiki/Stochastic_oscillator
    '''
    alias = ('StochasticSlow',)
    params = (('period_dslow', 3),)

    def _plotlabel(self):
        plabels = [self.p.period, self.p.period_dfast, self.p.period_dslow]
        plabels += [self.p.movav] * self.p.notdefault('movav')
        return plabels

    def __init__(self):
        super(Stochastic, self).__init__()
        self.lines.percK = self.d
        self.l.percD = self.p.movav(self.l.percK, period=self.p.period_dslow)

Ancestors

Subclasses

Class variables

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

Inherited members

class StochasticFast

By Dr. George Lane in the 50s. It compares a closing price to the price range and tries to show convergence if the closing prices are close to the extremes

  • It will go up if closing prices are close to the highs
  • It will roughly go down if closing prices are close to the lows

It shows divergence if the extremes keep on growing but closing prices do not in the same manner (distance to the extremes grow)

Formula

  • hh = highest(data.high, period)
  • ll = lowest(data.low, period)
  • knum = data.close - ll
  • kden = hh - ll
  • k = 100 * (knum / kden)
  • d = MovingAverage(k, period_dfast)

See

Expand source code 
class StochasticFast(_StochasticBase):
    '''
    By Dr. George Lane in the 50s. It compares a closing price to the price
    range and tries to show convergence if the closing prices are close to the
    extremes

      - It will go up if closing prices are close to the highs
      - It will roughly go down if closing prices are close to the lows

    It shows divergence if the extremes keep on growing but closing prices
    do not in the same manner (distance to the extremes grow)

    Formula:
      - hh = highest(data.high, period)
      - ll = lowest(data.low, period)
      - knum = data.close - ll
      - kden = hh - ll
      - k = 100 * (knum / kden)
      - d = MovingAverage(k, period_dfast)

    See:
      - http://en.wikipedia.org/wiki/Stochastic_oscillator
    '''
    def __init__(self):
        super(StochasticFast, self).__init__()
        self.lines.percK = self.k
        self.lines.percD = self.d

Ancestors

Class variables

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

Inherited members

class StochasticFull

This version displays the 3 possible lines:

  • percK
  • percD
  • percSlow

Formula

  • k = d
  • d = MovingAverage(k, period_dslow)
  • dslow =

See

Expand source code 
class StochasticFull(_StochasticBase):
    '''
    This version displays the 3 possible lines:

      - percK
      - percD
      - percSlow

    Formula:
      - k = d
      - d = MovingAverage(k, period_dslow)
      - dslow =

    See:
      - http://en.wikipedia.org/wiki/Stochastic_oscillator
    '''
    lines = ('percDSlow',)
    params = (('period_dslow', 3),)

    plotlines = dict(percDSlow=dict(_name='%DSlow'))

    def _plotlabel(self):
        plabels = [self.p.period, self.p.period_dfast, self.p.period_dslow]
        plabels += [self.p.movav] * self.p.notdefault('movav')
        return plabels

    def __init__(self):
        super(StochasticFull, self).__init__()
        self.lines.percK = self.k
        self.lines.percD = self.d
        self.l.percDSlow = self.p.movav(
            self.l.percD, period=self.p.period_dslow)

Ancestors

Class variables

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

Inherited members

class StochasticSlow

The regular (or slow version) adds an additional moving average layer and thus:

  • The percD line of the StochasticFast becomes the percK line
  • percD becomes a moving average of period_dslow of the original percD

Formula

  • k = k
  • d = d
  • d = MovingAverage(d, period_dslow)

See

Ancestors

Class variables

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

Inherited members