BollingerBands Squeeze

[dasch]

If anyone needs this, here is a BBSqueeze indicator.

implementation is based on this: https://www.netpicks.com/squeeze-out-the-chop/

BBSqueeze

from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

import backtrader as bt

from . import KeltnerChannel


class BBSqueeze(bt.Indicator):

    '''
    https://www.netpicks.com/squeeze-out-the-chop/

    Both indicators are symmetrical, meaning that the upper and lower bands or channel lines are the same distance from the moving average. That means that we can focus on only one side in developing our indicator. In our case, we’ll just consider the upper lines.

    The basic formulas we need are:

        Bollinger Band = Moving Average + (Number of standard deviations X Standard Deviation)
        Keltner Channel = Moving Average + (Number of ATR’s X ATR)

    Or if we translate this into pseudo-code:

        BBUpper = Avg(close, period) + (BBDevs X StdDev(close, period))
        KCUpper = Avg(close, period) + (KCDevs X ATR(period))

    The squeeze is calculated by taking the difference between these two values:

        Squeeze = BBUpper – KCUpper

    Which simplifies down to this:

        Squeeze = (BBDevs X StdDev(close, period)) – (KCDevs X ATR(period))
    '''

    lines = ('squeeze',)
    params = (('period', 20), ('devfactor', 2.0), ('movav', bt.ind.MovAv.Simple),)

    plotinfo = dict(subplot=True)

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

    def __init__(self):
        bb = bt.ind.BollingerBands(
            period=self.p.period, devfactor=self.p.devfactor, movav=self.p.movav)
        kc = KeltnerChannel(
            period=self.p.period, devfactor=self.p.devfactor, movav=self.p.movav)
        self.lines.squeeze = bb.top - kc.top

KeltnerChannel

from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

import backtrader as bt


class KeltnerChannel(bt.Indicator):

    lines = ('mid', 'top', 'bot',)
    params = (('period', 20), ('devfactor', 1.5),
              ('movav', bt.ind.MovAv.Simple),)

    plotinfo = dict(subplot=False)
    plotlines = dict(
        mid=dict(ls='--'),
        top=dict(_samecolor=True),
        bot=dict(_samecolor=True),
    )

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

    def __init__(self):
        self.lines.mid = ma = self.p.movav(self.data, period=self.p.period)
        atr = self.p.devfactor * bt.ind.ATR(self.data, period=self.p.period)
        self.lines.top = ma + atr
        self.lines.bot = ma - atr

[dasch]

BBSqueeze with configureable multiplicators for bb and keltner

from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

import backtrader as bt

from . import KeltnerChannel


class BBSqueeze(bt.Indicator):

    '''
    https://www.netpicks.com/squeeze-out-the-chop/

    Both indicators are symmetrical, meaning that the upper and lower bands or channel lines are the same distance from the moving average. That means that we can focus on only one side in developing our indicator. In our case, we’ll just consider the upper lines.

    The basic formulas we need are:

        Bollinger Band = Moving Average + (Number of standard deviations X Standard Deviation)
        Keltner Channel = Moving Average + (Number of ATR’s X ATR)

    Or if we translate this into pseudo-code:

        BBUpper = Avg(close, period) + (BBDevs X StdDev(close, period))
        KCUpper = Avg(close, period) + (KCDevs X ATR(period))

    The squeeze is calculated by taking the difference between these two values:

        Squeeze = BBUpper – KCUpper

    Which simplifies down to this:

        Squeeze = (BBDevs X StdDev(close, period)) – (KCDevs X ATR(period))
    '''

    lines = ('squeeze',)
    params = (('period', 20), ('bbdevs', 2.0), ('kcdevs', 1.5), ('movav', bt.ind.MovAv.Simple),)

    plotinfo = dict(subplot=True)

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

    def __init__(self):
        bb = bt.ind.BollingerBands(
            period=self.p.period, devfactor=self.p.bbdevs, movav=self.p.movav)
        kc = KeltnerChannel(
            period=self.p.period, devfactor=self.p.kcdevs, movav=self.p.movav)
        self.lines.squeeze = bb.top - kc.top

[mics]

Thanks for sharing this @dasch.

Flipping positions between BBands is a part of my primary strategy whilst waiting for breakouts to occur. I've been using price action relative to dynamic highs and lows to identify breakouts from choppy zones.

I'll see how introducing the Keltner Channel performs.

M