@ab_trader Thankyou for sharing, your information and code is verry help me

i try to run you refer code above, but this python code return bellow. any suggestion, please?
source from :
https://community.backtrader.com/topic/870/plot-indicator-over-candles
https://community.backtrader.com/topic/773/zigzag-indicator/2

RecursionError: maximum recursion depth exceeded while calling a Python object

this my source code:

from datetime import datetime
import backtrader as bt

class ZigZag(bt.ind.PeriodN):
lines = (
'trend', 'last_pivot_t', 'last_pivot_x', 'last_pivot_ago',
'zigzag_peak', 'zigzag_valley', 'zigzag', 'last_zigzag',
)

# Fancy plotting name
# plotlines = dict(logreturn=dict(_name='log_ret'))
plotinfo = dict(
    subplot=False,
    plotlinelabels=True, plotlinevalues=True, plotvaluetags=True,
)

plotlines = dict(
    trend=dict(marker='', markersize=0.0, ls='', _plotskip=True),
    last_pivot_t=dict(marker='', markersize=0.0, ls='', _plotskip=True),
    last_pivot_x=dict(marker='', markersize=0.0, ls='', _plotskip=True),
    last_pivot_ago=dict(marker='', markersize=0.0, ls='', _plotskip=True),
    zigzag_peak=dict(marker='v', markersize=4.0, color='red',
                     fillstyle='full', ls=''),
    zigzag_valley=dict(marker='^', markersize=4.0, color='red',
                       fillstyle='full', ls=''),
    zigzag=dict(_name='zigzag', color='blue', ls='-', _skipnan=True),
    last_zigzag=dict(_name='last_zigzag', color='blue', ls='--', _skipnan=True),
)

# update value to standard for Moving Averages
params = (
    ('period', 2),
    ('up_retrace', 0.1),
    ('dn_retrace', 0.1),
    ('bardist', 0.015),  # distance to max/min in absolute perc
)

def __init__(self):
    super(ZigZag, self).__init__()

    if not self.p.up_retrace:
        raise ValueError('Upward retracement should not be zero.')

    if not self.p.dn_retrace:
        raise ValueError('Downward retracement should not be zero.')

    if self.p.up_retrace < 0:
        self.p.up_retrace = -self.p.up_retrace

    if self.p.dn_retrace > 0:
        self.p.dn_retrace = -self.p.dn_retrace

    self.p.up_retrace = self.p.up_retrace / 100
    self.p.dn_retrace = self.p.dn_retrace / 100

    self.missing_val = float('nan')
    self.zigzag = ZigZag()

def prenext(self):
    self.lines.trend[0] = 0
    self.lines.last_pivot_t[0] = 0
    self.lines.last_pivot_x[0] = self.data[0]
    self.lines.last_pivot_ago[0] = 0
    self.lines.zigzag_peak[0] = self.missing_val
    self.lines.zigzag_valley[0] = self.missing_val
    self.lines.zigzag[0] = self.missing_val
    self.lines.last_zigzag[0] = self.missing_val

def next(self):
    data = self.data
    trend = self.lines.trend
    last_pivot_t = self.lines.last_pivot_t
    last_pivot_x = self.lines.last_pivot_x
    last_pivot_ago = self.lines.last_pivot_ago
    zigzag_peak = self.lines.zigzag_peak
    zigzag_valley = self.lines.zigzag_valley
    zigzag = self.lines.zigzag
    last_zigzag = self.lines.last_zigzag

    x = data[0]
    r = x / last_pivot_x[-1] - 1
    curr_idx = len(data) - 1

    trend[0] = trend[-1]
    last_pivot_x[0] = last_pivot_x[-1]
    last_pivot_t[0] = last_pivot_t[-1]
    last_pivot_ago[0] = curr_idx - last_pivot_t[0]
    zigzag_peak[0] = self.missing_val
    zigzag_valley[0] = self.missing_val
    zigzag[0] = self.missing_val
    last_zigzag[0] = x

    if trend[-1] == 0:
        if r >= self.p.up_retrace:
            piv = last_pivot_x[0] * (1 - self.p.bardist)
            zigzag_valley[-int(last_pivot_ago[0])] = piv
            zigzag[-int(last_pivot_ago[0])] = last_pivot_x[0]
            trend[0] = 1
            last_pivot_x[0] = x
            last_pivot_t[0] = curr_idx
        elif r <= self.p.dn_retrace:
            piv = last_pivot_x[0] * (1 + self.p.bardist)
            zigzag_peak[-int(last_pivot_ago[0])] = piv
            zigzag[-int(last_pivot_ago[0])] = last_pivot_x[0]
            trend[0] = -1
            last_pivot_x[0] = x
            last_pivot_t[0] = curr_idx
    elif trend[-1] == -1:
        if r >= self.p.up_retrace:
            piv = last_pivot_x[0] * (1 - self.p.bardist)
            zigzag_valley[-int(last_pivot_ago[0])] = piv
            zigzag[-int(last_pivot_ago[0])] = last_pivot_x[0]
            trend[0] = 1
            last_pivot_x[0] = x
            last_pivot_t[0] = curr_idx
        elif x < last_pivot_x[-1]:
            last_pivot_x[0] = x
            last_pivot_t[0] = curr_idx
    elif trend[-1] == 1:
        if r <= self.p.dn_retrace:
            piv = last_pivot_x[0] * (1 + self.p.bardist)
            zigzag_peak[-int(last_pivot_ago[0])] = piv
            zigzag[-int(last_pivot_ago[0])] = last_pivot_x[0]
            trend[0] = -1
            last_pivot_x[0] = x
            last_pivot_t[0] = curr_idx
        elif x > last_pivot_x[-1]:
            last_pivot_t[0] = curr_idx
            last_pivot_x[0] = x

    idx = 1
    while idx < len(self.zigzag) and math.isnan(zigzag[-idx]):
        last_zigzag[-idx] = self.missing_val
        idx += 1

    if idx < len(self.data):
        last_zigzag[-idx] = zigzag[-idx]

cerebro = bt.Cerebro()

data = bt.feeds.YahooFinanceData(dataname='MSFT', fromdate=datetime(2011, 1, 1),
todate=datetime(2012, 12, 31))
cerebro.adddata(data)

cerebro.addstrategy(ZigZag)
cerebro.run()
cerebro.plot()

i try to run you refer code above, but this python code return bellow. any suggestion?

RecursionError: maximum recursion depth exceeded while calling a Python object

this my source code:

from datetime import datetime
import backtrader as bt

class ZigZag(bt.ind.PeriodN):
lines = (
'trend', 'last_pivot_t', 'last_pivot_x', 'last_pivot_ago',
'zigzag_peak', 'zigzag_valley', 'zigzag', 'last_zigzag',
)

# Fancy plotting name
# plotlines = dict(logreturn=dict(_name='log_ret'))
plotinfo = dict(
    subplot=False,
    plotlinelabels=True, plotlinevalues=True, plotvaluetags=True,
)

plotlines = dict(
    trend=dict(marker='', markersize=0.0, ls='', _plotskip=True),
    last_pivot_t=dict(marker='', markersize=0.0, ls='', _plotskip=True),
    last_pivot_x=dict(marker='', markersize=0.0, ls='', _plotskip=True),
    last_pivot_ago=dict(marker='', markersize=0.0, ls='', _plotskip=True),
    zigzag_peak=dict(marker='v', markersize=4.0, color='red',
                     fillstyle='full', ls=''),
    zigzag_valley=dict(marker='^', markersize=4.0, color='red',
                       fillstyle='full', ls=''),
    zigzag=dict(_name='zigzag', color='blue', ls='-', _skipnan=True),
    last_zigzag=dict(_name='last_zigzag', color='blue', ls='--', _skipnan=True),
)

# update value to standard for Moving Averages
params = (
    ('period', 2),
    ('up_retrace', 0.1),
    ('dn_retrace', 0.1),
    ('bardist', 0.015),  # distance to max/min in absolute perc
)

def __init__(self):
    super(ZigZag, self).__init__()

    if not self.p.up_retrace:
        raise ValueError('Upward retracement should not be zero.')

    if not self.p.dn_retrace:
        raise ValueError('Downward retracement should not be zero.')

    if self.p.up_retrace < 0:
        self.p.up_retrace = -self.p.up_retrace

    if self.p.dn_retrace > 0:
        self.p.dn_retrace = -self.p.dn_retrace

    self.p.up_retrace = self.p.up_retrace / 100
    self.p.dn_retrace = self.p.dn_retrace / 100

    self.missing_val = float('nan')
    self.zigzag = ZigZag()

def prenext(self):
    self.lines.trend[0] = 0
    self.lines.last_pivot_t[0] = 0
    self.lines.last_pivot_x[0] = self.data[0]
    self.lines.last_pivot_ago[0] = 0
    self.lines.zigzag_peak[0] = self.missing_val
    self.lines.zigzag_valley[0] = self.missing_val
    self.lines.zigzag[0] = self.missing_val
    self.lines.last_zigzag[0] = self.missing_val

def next(self):
    data = self.data
    trend = self.lines.trend
    last_pivot_t = self.lines.last_pivot_t
    last_pivot_x = self.lines.last_pivot_x
    last_pivot_ago = self.lines.last_pivot_ago
    zigzag_peak = self.lines.zigzag_peak
    zigzag_valley = self.lines.zigzag_valley
    zigzag = self.lines.zigzag
    last_zigzag = self.lines.last_zigzag

    x = data[0]
    r = x / last_pivot_x[-1] - 1
    curr_idx = len(data) - 1

    trend[0] = trend[-1]
    last_pivot_x[0] = last_pivot_x[-1]
    last_pivot_t[0] = last_pivot_t[-1]
    last_pivot_ago[0] = curr_idx - last_pivot_t[0]
    zigzag_peak[0] = self.missing_val
    zigzag_valley[0] = self.missing_val
    zigzag[0] = self.missing_val
    last_zigzag[0] = x

    if trend[-1] == 0:
        if r >= self.p.up_retrace:
            piv = last_pivot_x[0] * (1 - self.p.bardist)
            zigzag_valley[-int(last_pivot_ago[0])] = piv
            zigzag[-int(last_pivot_ago[0])] = last_pivot_x[0]
            trend[0] = 1
            last_pivot_x[0] = x
            last_pivot_t[0] = curr_idx
        elif r <= self.p.dn_retrace:
            piv = last_pivot_x[0] * (1 + self.p.bardist)
            zigzag_peak[-int(last_pivot_ago[0])] = piv
            zigzag[-int(last_pivot_ago[0])] = last_pivot_x[0]
            trend[0] = -1
            last_pivot_x[0] = x
            last_pivot_t[0] = curr_idx
    elif trend[-1] == -1:
        if r >= self.p.up_retrace:
            piv = last_pivot_x[0] * (1 - self.p.bardist)
            zigzag_valley[-int(last_pivot_ago[0])] = piv
            zigzag[-int(last_pivot_ago[0])] = last_pivot_x[0]
            trend[0] = 1
            last_pivot_x[0] = x
            last_pivot_t[0] = curr_idx
        elif x < last_pivot_x[-1]:
            last_pivot_x[0] = x
            last_pivot_t[0] = curr_idx
    elif trend[-1] == 1:
        if r <= self.p.dn_retrace:
            piv = last_pivot_x[0] * (1 + self.p.bardist)
            zigzag_peak[-int(last_pivot_ago[0])] = piv
            zigzag[-int(last_pivot_ago[0])] = last_pivot_x[0]
            trend[0] = -1
            last_pivot_x[0] = x
            last_pivot_t[0] = curr_idx
        elif x > last_pivot_x[-1]:
            last_pivot_t[0] = curr_idx
            last_pivot_x[0] = x

    idx = 1
    while idx < len(self.zigzag) and math.isnan(zigzag[-idx]):
        last_zigzag[-idx] = self.missing_val
        idx += 1

    if idx < len(self.data):
        last_zigzag[-idx] = zigzag[-idx]

cerebro = bt.Cerebro()

data = bt.feeds.YahooFinanceData(dataname='MSFT', fromdate=datetime(2011, 1, 1),
todate=datetime(2012, 12, 31))
cerebro.adddata(data)

cerebro.addstrategy(ZigZag)
cerebro.run()
cerebro.plot()

i try to run you refer code above, but this python code return bellow. any suggestion?

RecursionError: maximum recursion depth exceeded while calling a Python object

this my source code:

from datetime import datetime
import backtrader as bt

class ZigZag(bt.ind.PeriodN):
lines = (
'trend', 'last_pivot_t', 'last_pivot_x', 'last_pivot_ago',
'zigzag_peak', 'zigzag_valley', 'zigzag', 'last_zigzag',
)

# Fancy plotting name
# plotlines = dict(logreturn=dict(_name='log_ret'))
plotinfo = dict(
    subplot=False,
    plotlinelabels=True, plotlinevalues=True, plotvaluetags=True,
)

plotlines = dict(
    trend=dict(marker='', markersize=0.0, ls='', _plotskip=True),
    last_pivot_t=dict(marker='', markersize=0.0, ls='', _plotskip=True),
    last_pivot_x=dict(marker='', markersize=0.0, ls='', _plotskip=True),
    last_pivot_ago=dict(marker='', markersize=0.0, ls='', _plotskip=True),
    zigzag_peak=dict(marker='v', markersize=4.0, color='red',
                     fillstyle='full', ls=''),
    zigzag_valley=dict(marker='^', markersize=4.0, color='red',
                       fillstyle='full', ls=''),
    zigzag=dict(_name='zigzag', color='blue', ls='-', _skipnan=True),
    last_zigzag=dict(_name='last_zigzag', color='blue', ls='--', _skipnan=True),
)

# update value to standard for Moving Averages
params = (
    ('period', 2),
    ('up_retrace', 0.1),
    ('dn_retrace', 0.1),
    ('bardist', 0.015),  # distance to max/min in absolute perc
)

def __init__(self):
    super(ZigZag, self).__init__()

    if not self.p.up_retrace:
        raise ValueError('Upward retracement should not be zero.')

    if not self.p.dn_retrace:
        raise ValueError('Downward retracement should not be zero.')

    if self.p.up_retrace < 0:
        self.p.up_retrace = -self.p.up_retrace

    if self.p.dn_retrace > 0:
        self.p.dn_retrace = -self.p.dn_retrace

    self.p.up_retrace = self.p.up_retrace / 100
    self.p.dn_retrace = self.p.dn_retrace / 100

    self.missing_val = float('nan')
    self.zigzag = ZigZag()

def prenext(self):
    self.lines.trend[0] = 0
    self.lines.last_pivot_t[0] = 0
    self.lines.last_pivot_x[0] = self.data[0]
    self.lines.last_pivot_ago[0] = 0
    self.lines.zigzag_peak[0] = self.missing_val
    self.lines.zigzag_valley[0] = self.missing_val
    self.lines.zigzag[0] = self.missing_val
    self.lines.last_zigzag[0] = self.missing_val

def next(self):
    data = self.data
    trend = self.lines.trend
    last_pivot_t = self.lines.last_pivot_t
    last_pivot_x = self.lines.last_pivot_x
    last_pivot_ago = self.lines.last_pivot_ago
    zigzag_peak = self.lines.zigzag_peak
    zigzag_valley = self.lines.zigzag_valley
    zigzag = self.lines.zigzag
    last_zigzag = self.lines.last_zigzag

    x = data[0]
    r = x / last_pivot_x[-1] - 1
    curr_idx = len(data) - 1

    trend[0] = trend[-1]
    last_pivot_x[0] = last_pivot_x[-1]
    last_pivot_t[0] = last_pivot_t[-1]
    last_pivot_ago[0] = curr_idx - last_pivot_t[0]
    zigzag_peak[0] = self.missing_val
    zigzag_valley[0] = self.missing_val
    zigzag[0] = self.missing_val
    last_zigzag[0] = x

    if trend[-1] == 0:
        if r >= self.p.up_retrace:
            piv = last_pivot_x[0] * (1 - self.p.bardist)
            zigzag_valley[-int(last_pivot_ago[0])] = piv
            zigzag[-int(last_pivot_ago[0])] = last_pivot_x[0]
            trend[0] = 1
            last_pivot_x[0] = x
            last_pivot_t[0] = curr_idx
        elif r <= self.p.dn_retrace:
            piv = last_pivot_x[0] * (1 + self.p.bardist)
            zigzag_peak[-int(last_pivot_ago[0])] = piv
            zigzag[-int(last_pivot_ago[0])] = last_pivot_x[0]
            trend[0] = -1
            last_pivot_x[0] = x
            last_pivot_t[0] = curr_idx
    elif trend[-1] == -1:
        if r >= self.p.up_retrace:
            piv = last_pivot_x[0] * (1 - self.p.bardist)
            zigzag_valley[-int(last_pivot_ago[0])] = piv
            zigzag[-int(last_pivot_ago[0])] = last_pivot_x[0]
            trend[0] = 1
            last_pivot_x[0] = x
            last_pivot_t[0] = curr_idx
        elif x < last_pivot_x[-1]:
            last_pivot_x[0] = x
            last_pivot_t[0] = curr_idx
    elif trend[-1] == 1:
        if r <= self.p.dn_retrace:
            piv = last_pivot_x[0] * (1 + self.p.bardist)
            zigzag_peak[-int(last_pivot_ago[0])] = piv
            zigzag[-int(last_pivot_ago[0])] = last_pivot_x[0]
            trend[0] = -1
            last_pivot_x[0] = x
            last_pivot_t[0] = curr_idx
        elif x > last_pivot_x[-1]:
            last_pivot_t[0] = curr_idx
            last_pivot_x[0] = x

    idx = 1
    while idx < len(self.zigzag) and math.isnan(zigzag[-idx]):
        last_zigzag[-idx] = self.missing_val
        idx += 1

    if idx < len(self.data):
        last_zigzag[-idx] = zigzag[-idx]

cerebro = bt.Cerebro()

data = bt.feeds.YahooFinanceData(dataname='MSFT', fromdate=datetime(2011, 1, 1),
todate=datetime(2012, 12, 31))
cerebro.adddata(data)

cerebro.addstrategy(ZigZag)
cerebro.run()
cerebro.plot()