D
Updated it to this:
'''
//PRC_Reversal point indicator | indicator
//09.04.2017
//Nicolas @ www.prorealcode.com
//Sharing ProRealTime knowledge
// https://www.prorealcode.com/topic/need-help-coding-the-following-reversal-indicator/
// https://www.prorealcode.com/prorealtime-indicators/reversal-point-indicator/
// LONG
//Red/Green candle (reversal)
//Bullish candle close above the open of the previous red candle
//Space to the left (the low of the last 3 candles lower than the low of the last 50 candles)
//default stochastic (8,3,3) was in the oversold area within the last 3 candles
// SHORT
//Green/Red candle (reversal)
//Bearish candle close below the close of the previous green candle
//Space to the left (the high of the last 3 candles higher than the high of the last 50 candles)
//default stochastic (8,3,3) was in the overbought area within the last 3 candles.
'''
from __future__ import (absolute_import, division, print_function,
unicode_literals)
# Import the backtrader platform
import backtrader as bt
import backtrader.indicators as btind
class ReversalPoint(bt.Indicator):
lines = ('revpoint',)
plotinfo = dict(
# Add extra margins above and below the 1s and -1s
plotymargin=0.15,
# Plot a reference horizontal line at 1.0 and -1.0
plothlines=[1.0, -1.0],
# Simplify the y scale to 1.0 and -1.0
plotyticks=[1.0, -1.0])
# Plot the line "overunder" (the only one) with dash style
# ls stands for linestyle and is directly passed to matplotlib
plotlines = dict(revpoint=dict(ls='--'))
params = dict(
sto_period=8,
sto_period_dfast=3,
sto_oversold=20,
sto_overbought=80,
lperiod=50,
speriod=3,
)
def __init__(self):
self.sto = btind.Stochastic(period=self.p.sto_period, period_dfast=self.p.sto_period_dfast)
self.addminperiod(self.p.lperiod + self.p.speriod)
self.c1 = bt.And(self.data.close(-1) < self.data.open(-1), self.data.close(0) > self.data.open(0))
self.c2 = self.data.close(0) > self.data.open(-1)
self.c5 = bt.And(self.data.close(-1) > self.data.open(-1), self.data.close(0) < self.data.open(0))
self.c6 = self.data.close(0) < self.data.open(-1)
def next(self):
sList = self.sto.get(size=self.p.speriod)
sLow = min(self.data.low.get(size=self.p.speriod))
lLow1 = min(self.data.low.get(ago=-1, size=self.p.lperiod))
lLow2 = min(self.data.low.get(ago=-2, size=self.p.lperiod))
lLow3 = min(self.data.low.get(ago=-3, size=self.p.lperiod))
sHigh = max(self.data.high.get(size=self.p.speriod))
lHigh1 = max(self.data.high.get(ago=-1, size=self.p.lperiod))
lHigh2 = max(self.data.high.get(ago=-2, size=self.p.lperiod))
lHigh3 = max(self.data.high.get(ago=-3, size=self.p.lperiod))
c1 = self.c1
c2 = self.c2
c3 = sLow < lLow1 or sLow < lLow2 or sLow < lLow3
c4 = len(list(filter(lambda x: (x < self.p.sto_oversold), sList))) > 0
tLong = c1 and c2 and c3 and c4
c5 = self.c5
c6 = self.c6
c7 = sHigh > lHigh1 or sHigh > lHigh2 or sHigh > lHigh3
c8 = len(list(filter(lambda x: (x > self.p.sto_overbought), sList))) > 0
tShort = c5 and c6 and c7 and c8
if tLong:
self.revpoint[0] = 1.0
elif tShort:
self.revpoint[0] = -1.0
else:
self.revpoint[0] = 0.0
i also changed this calculation:
c8 = False
for i in self.sto.get(size=3):
if i > 80:
c8 = True
break
to this
c8 = len(list(filter(lambda x: (x > self.p.sto_overbought), sList))) > 0
