@backtrader
oke, Thans for helping me, and it work complete.
:)
this is my fully code:
from datetime import datetime
import backtrader as bt
import math
%matplotlib notebook
class ZigZag(bt.ind.PeriodN):
'''
Identifies Peaks/Troughs of a timeseries
'''
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')
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]
class SmaCross(bt.SignalStrategy):
params = (('pfast', 10), ('pslow', 30),)
def init(self):
sma1, sma2 = bt.ind.SMA(period=self.p.pfast), bt.ind.SMA(period=self.p.pslow)
self.signal_add(bt.SIGNAL_LONG, bt.ind.CrossOver(sma1, sma2))
cerebro = bt.Cerebro()
data = bt.feeds.YahooFinanceData(dataname='MSFT', fromdate=datetime(2011, 1, 1),
todate=datetime(2012, 12, 31))
cerebro.adddata(data)
cerebro.addstrategy(SmaCross)
cerebro.addindicator(ZigZag)
cerebro.run()
cerebro.plot(style='candlestick')
