def __init__(self):
price = (self.data.high + self.data.low + self.data.close) / 3
pricema = self.p.movav(price, period=self.p.period)
posmf = btind.If(price(0) > price(-1), price*self.data.volume, 0)
negmf = btind.If(price(0) < price(-1), price*self.data.volume, 0)
mfi = bt.DivByZero(btind.SumN(posmf, period=self.p.period), btind.SumN(negmf, period=self.p.period))
self.lines.mfi = 100 - 100 / (1 + mfi)
def __init__(self):
dmi = btind.DirectionalMovementIndex(self.data, period=self.p.period)
osc = dmi.plusDI - dmi.minusDI
hh = btind.Highest(osc, period=self.p.sumperiod)
ll = btind.Lowest(osc, period=self.p.sumperiod)
self.lines.stoch = btind.SumN(osc - ll, period=self.p.sumperiod) / btind.SumN(hh - ll, period=self.p.sumperiod) * 100
def __init__(self):
price = (self.data.close+self.data.high+self.data.low) / 3
vol_sum = btind.SumN(self.data.volume, period=self.params.sumlength)
vol_weighted_price = btind.SumN(price*self.data.volume, period=self.params.sumlength)
self.lines.szo = SentimentZoneOscillator(vol_weighted_price/vol_sum, length=self.params.szolength)
self.lines.buy = btfunc.Max(self.p.signal_percent/self.params.szolength,0)
self.lines.sell = btfunc.Min(-self.p.signal_percent/self.params.szolength,0)
def __init__(self):
ma = self.p.movav(self.data, period=self.p.avglength)
over = btind.SumN(self.data > ma, period=self.p.sumlength)
under = btind.SumN(self.data < ma, period=self.p.sumlength)
self.lines.momentum = (over - under) / self.p.sumlength
self.lines.slope = self.lines.momentum(0) - self.lines.momentum(-1)
self.lines.zero = bt.LineNum(0)
def __init__(self):
sma = btind.SimpleMovingAverage(period=self.p.smaperiod)
sma1 = btind.SimpleMovingAverage(self.data, period=self.params.period1)
sma2 = btind.SimpleMovingAverage(period=self.params.period2)
myindicator = sma2 - sma1 + self.datas[0].close
self.sma = btind.SimpleMovingAverage(period=self.p.period)
# see the (delay) notation
self.cmpval = self.data.close(-1) > self.sma
datasum = btind.SumN(self.data, period=self.p.period1)
# using operators /
av = datasum / self.p.period1
self.line.sma = av
# cannot use "and" operators, backtrader provides several methods
close_over_sma = self.data.close > sma
self.sma_dist_to_high = self.data.high - sma
sma_dist_small = self.sma_dist_to_high < 3.5
sell_sig = bt.And(close_over_sma, sma_dist_small)
high_or_low = bt.If(sma1 > self.data.close, self.data.low,
self.data.high)
self.buysell = btind.CrossOver(self.data.close, sma, plot=True)
self.order = None
def __init__(self):
price = (self.data.close + self.data.high + self.data.low) / 3
vol_price = btind.SumN(
price * self.data.volume, period=self.p.sumlength) / btind.SumN(
self.data.volume + 1, period=self.p.sumlength)
r2 = price + self.data.high - self.data.low
s2 = price - self.data.high + self.data.low
rslope = r2(0) - r2(-1)
sslope = s2(0) - s2(-1)
diff = vol_price(0) - vol_price(-1)
resistance = btind.MovingAverageWilder(rslope, period=self.p.avglength)
support = btind.MovingAverageWilder(sslope, period=self.p.avglength)
difference = btind.MovingAverageWilder(diff, period=self.p.avglength)
self.lines.final = (resistance + support + difference * 5) / 3
# self.lines.final = difference
self.lines.zero = bt.LineNum(0)
def __init__(self):
ma = btind.MovingAverageSimple(self.data, period=self.p.avglength)
# self.lines.accum = btind.Accum((self.data - ma) / ma)
self.lines.accum = btind.SumN((self.data-ma) / ma, period=self.p.sumlength)
self.lines.slope = self.accum(0) - self.accum(-1)
def __init__(self):
price = (self.data.close+self.data.high+self.data.low) / 3
vol_sum = btind.SumN(self.data.volume, period=self.p.period)
self.lines.movav = btind.SumN(price*self.data.volume, period=self.p.period) / vol_sum
def __init__(self):
vel = self.data(0) - self.data(-1)
self.lines.vel = btind.SumN(vel / self.p.period, period=self.p.period)
