def __init__(self, settings):
PriceVolumeDailyTrigger.__init__(self, settings)
self.minatrmultiple = settings.getfloat("MomoDailyTrigger", "minatrmultiple")
self.closepercent = settings.get("MomoDailyTrigger", "closepercent")
self.openpercent = settings.get("MomoDailyTrigger", "openpercent")
self.takeLongs = settings.getboolean("MomoDailyTrigger", "takeLongs")
self.takeShorts = settings.getboolean("MomoDailyTrigger", "takeShorts")
minaprstr = settings.get("MomoDailyTrigger", "minapr")
self.minapr = None
if minaprstr != "None":
self.minapr = float(minaprstr)
dfilter = settings.get("MomoDailyTrigger", "donchianfilter")
self.high = High()
self.low = Low()
self.atr = ATR(14)
self.lastatr = HistoricMetric(self.atr, 1)
if dfilter == "None":
self.lasthighesthigh = None
self.lastlowestlow = None
else:
self.highesthigh = Highest(self.high, int(dfilter))
self.lasthighesthigh = HistoricMetric(self.highesthigh, 1)
self.lowestlow = Lowest(self.low, int(dfilter))
self.lastlowestlow = HistoricMetric(self.lowestlow, 1)
def findsetups(self, fromdt, todt): numstopouts = 0 stocks = self._getTickers(fromdt, datastore) for stock in stocks: # padded extra to make sure 200 day sma has enough trading days to work with before our window dailydata = datastore.getDailyData(stock, fromdt - timedelta(days=(self.slowma*2)), todt) close = Close() fastma = SimpleMovingAverage(period=self.fastma) slowma = SimpleMovingAverage(period=self.slowma) lastfastma = HistoricMetric(metric=fastma, period=1) lastslowma = HistoricMetric(metric=slowma, period=1) atr = ATR(period=14) lastdd = None trade = None for pd in dailydata: close.handle(pd) fastma.handle(pd) slowma.handle(pd) lastfastma.handle(pd) lastslowma.handle(pd) atr.handle(pd) # check for long stopout if trade != None and pd.low < trade.trailingstop: trade.exit = pd.date trade.exitPrice = min(pd.open, trade.trailingstop) self.tradeManager.addTrade(trade) numstopouts = numstopouts + 1 trade = None # check for long exit if trade != None and fastma.value() < slowma.value(): trade.exit = pd.date trade.exitPrice = pd.close self.tradeManager.addTrade(trade) trade = None if fastma.ready() and slowma.ready() and lastfastma.ready() and lastslowma.ready() and atr.ready(): pass # check for new long if trade == None and fastma.ready() and slowma.ready() \ and lastfastma.ready() and lastslowma.ready() \ and (self.atrStop == None or atr.ready()) \ and pd.date >= fromdt \ and fastma.value() > slowma.value() \ and lastfastma.value() <= lastslowma.value() \ and pd.close >= self.minprice: stop = 0 if self.atrStop != None: stop = max(0,pd.close - (float(self.atrStop) * atr.value())) if self.percentStop != None: stop = max(0, pd.close * (1.0 - self.percentStop)) trade = Trade(stock=stock, entry=pd.date, entryPrice=pd.close, stop=stop) if trade != None and trade.entry == None: trade.exit = lastdd.date trade.exitPrice = lastdd.close self.tradeManager.addTrade(trade) trade = None print "num stopouts was %d" % numstopouts return self.tradeManager.getStats()
def findsetups(self, fromdt, todt):
datastore = datastorefactory.get_datastore()
# stocks = datastore.filterStocksByAvgVolume(fromdt, minvolume)
stocks = self._getTickers(fromdt, datastore)
for stock in stocks:
dailydata = list()
volume = Volume()
avgvolume = SimpleMovingAverage(metric=volume, period=21)
dailyatr = ATR(20)
atrfromdt = fromdt - timedelta(days=max(self.duration, 40)) # 40 to give the atr time to normalize
dailydataiter = iter(datastore.getDailyData(stock, atrfromdt, todt))
dailydataday = None
try:
while dailydataday == None or dailydataday.date < fromdt:
dailydataday = dailydataiter.next()
# have to fix it to a real object for the atr
dailyatr.handle(dailydataday)
dailydata.append(dailydataday)
except StopIteration:
pass
if len(dailydata) > self.duration:
dailydata = dailydata[len(dailydata) - self.duration :]
# ok, we find the highest high and lowest low first
high = 0
low = None
for ddhighfinder in dailydata:
if high < ddhighfinder.high:
high = ddhighfinder.high
if low == None or ddhighfinder.low < low:
low = ddhighfinder.low
# great, now we find how many lower highs are within the mush factor
atrmush = 0
if dailyatr.value() != None:
atrmush = dailyatr.value() * self.mushinessatr
taps = 0
shorttaps = 0
for ddtapfinder in dailydata:
delta = high - ddtapfinder.high
if delta <= atrmush or delta <= self.mushinessfixed:
taps = taps + 1
shortdelta = ddtapfinder.low - low
if shortdelta <= atrmush or delta <= self.mushinessfixed:
shorttaps = shorttaps + 1
# ok, now we can add the next dd - we go ahead and prep some things for the next loop pass
# since we are no longer using them now
for dailydataday in dailydataiter:
saveatr = dailyatr.value()
volume.handle(dailydataday)
avgvolume.handle(dailydataday)
dailyatr.handle(dailydataday)
dailydata.append(dailydataday)
dailydata = dailydata[1:]
trade = None
# as a hack, now we can check our peek ahead and see for free if we
# ever broke the high today. If not, we are done
if (
self.doLongs
and taps >= self.numtaps
and dailydataday.high > high
and high >= self.minprice
and (self.maxprice == None or high <= self.maxprice)
and avgvolume.ready()
and avgvolume.value() >= self.minavgvolume
and (
self.minAPR == None
or (dailyatr.ready() and dailyatr.value() / dailydataday.adjustedClose) >= self.minAPR
)
):
# ok, we need to scan the day
low = None
donchlow = None
if self.donchianstop != None:
low = Low()
donchlow = Lowest(low, self.donchianstop)
intrafromdt = dailydataday.date
intratodt = intrafromdt + timedelta(hours=24)
intradaydata = datastore.getIntradayData(stock, self.period, intrafromdt, intratodt)
if intradaydata != None and len(intradaydata) > 1:
intradaybar = intradaydata[0]
intralow = intradaybar.low
intrahigh = intradaybar.high
taps = 1
for i in range(1, len(intradaydata)):
intradaybar = intradaydata[i]
if trade == None and (
self.maxintradayrangeatr == None
or (saveatr != None and (intrahigh - intralow) < (saveatr * self.maxintradayrangeatr))
):
intralow = min(intralow, intradaybar.low)
if (
intradaybar.high <= intrahigh
and (intrahigh - intradaybar.high) <= self.mushinessfixed2m
):
taps = taps + 1
if intradaybar.high > intrahigh:
if (
taps >= self.taps2m
and intrahigh >= high
and (
self.maxhour == None
or intradaybar.date.hour < self.maxhour
or (intradaybar.date.hour == self.maxhour and intradaybar.date.minute == 0)
)
and (self.minhour == None or intradaybar.date.hour >= self.minhour)
):
# trade entry
if donchlow != None and donchlow.ready():
stop = donchlow.value() - 0.01
else:
stop = intralow - 0.01
entryPrice = min(intradaybar.open, intrahigh + 0.01)
if entryPrice > stop:
trade = Trade(
stock=stock,
entry=intradaybar.date,
entryPrice=min(intradaybar.open, intrahigh + 0.01),
stop=stop,
)
if self.target:
trade.target = trade.entryPrice + (
self.target * (trade.entryPrice - trade.stop)
)
else:
# need to recalculate taps off this new high as we had no signal yet
intrahigh = intradaybar.high
taps = 1
for j in range(0, i - 1):
if (intrahigh - intradaydata[j].high) < self.mushinessfixed2m:
taps = taps + 1
if trade and trade.exit == None:
if intradaybar.low < trade.trailingstop:
# taken out
trade.exit = intradaybar.date
trade.exitPrice = min(intradaybar.open, trade.trailingstop)
if trade.target != None and intradaybar.high > trade.target:
trade.exit = intradaybar.date
trade.exitPrice = max(intradaybar.open, trade.target)
if low != None:
low.handle(intradaybar)
if donchlow != None:
donchlow.handle(intradaybar)
if trade != None and trade.exit == None:
trade.exit = intradaybar.date
trade.exitPrice = intradaybar.close
if trade:
self.tradeManager.addTrade(trade)
trade = None
trade = None
# SHORTS
# as a hack, now we can check our peek ahead and see for free if we
# ever broke the low today. If not, we are done
if (
self.doShorts
and shorttaps >= self.numtaps
and dailydataday.low < low
and low >= self.minprice
and avgvolume.ready()
and avgvolume.value() >= self.minavgvolume
and (
self.minAPR == None
or (dailyatr.ready() and dailyatr.value() / dailydataday.adjustedClose) >= self.minAPR
)
):
# ok, we need to scan the day
high = None
donchhigh = None
if self.donchianstop != None:
high = High()
donchhigh = Highest(high, self.donchianstop)
intrafromdt = dailydataday.date
intratodt = intrafromdt + timedelta(hours=24)
intradaydata = datastore.getIntradayData(stock, 300, intrafromdt, intratodt)
if intradaydata != None and len(intradaydata) > 1:
intradaybar = intradaydata[0]
intralow = intradaybar.low
intrahigh = intradaybar.high
taps = 1
for i in range(1, len(intradaydata)):
intradaybar = intradaydata[i]
if trade == None and (
self.maxintradayrangeatr == None
or (saveatr != None and (intrahigh - intralow) < (saveatr * self.maxintradayrangeatr))
):
intrahigh = max(intrahigh, intradaybar.high)
if (
intradaybar.low >= intralow
and (intradaybar.low - intralow) <= self.mushinessfixed2m
):
taps = taps + 1
if intradaybar.low < intralow:
if (
taps >= self.taps2m
and intralow <= low
and (
self.maxhour == None
or intradaybar.date.hour < self.maxhour
or (intradaybar.date.hour == self.maxhour and intradaybar.date.minute == 0)
)
and (self.minhour == None or intradaybar.date.hour >= self.minhour)
):
# trade entry
if donchhigh != None and donchhigh.ready():
stop = donchhigh.value() + 0.01
else:
stop = intrahigh + 0.01
entryPrice = min(intradaybar.open, intralow - 0.01)
if entryPrice < stop:
trade = Trade(
stock=stock, entry=intradaybar.date, entryPrice=entryPrice, stop=stop
)
if self.target:
trade.target = trade.entryPrice - (
self.target * (trade.stop - trade.entryPrice)
)
else:
# need to recalculate taps off this new high as we had no signal yet
intralow = intradaybar.low
taps = 1
for j in range(0, i - 1):
if (intralow - intradaydata[j].low) < self.mushinessfixed2m:
taps = taps + 1
if trade and trade.exit == None:
if intradaybar.high >= trade.trailingstop:
# taken out
trade.exit = intradaybar.date
trade.exitPrice = max(intradaybar.open, trade.trailingstop)
if trade.target != None and intradaybar.low < trade.target:
trade.exit = intradaybar.date
trade.exitPrice = min(intradaybar.open, trade.target)
if high != None:
high.handle(intradaybar)
if donchhigh != None:
donchhigh.handle(intradaybar)
if trade != None and trade.exit == None:
trade.exit = intradaybar.date
trade.exitPrice = intradaybar.close
if trade:
self.tradeManager.addTrade(trade)
trade = None
trade = None
# redo daily setup for the next day, already loaded in above the intraday loop
# ok, we find the highest high first
high = 0
low = None
for ddhighfinder in dailydata:
if high < ddhighfinder.high:
high = ddhighfinder.high
if low == None or ddhighfinder.low < low:
low = ddhighfinder.low
# great, now we find how many lower highs are within the mush factor
atrmush = 0
if dailyatr.value() != None:
atrmush = dailyatr.value() * self.mushinessatr
taps = 0
shorttaps = 0
for ddtapfinder in dailydata:
delta = high - ddtapfinder.high
shortdelta = ddtapfinder.low - low
if delta <= atrmush or delta <= self.mushinessfixed:
taps = taps + 1
if shortdelta <= atrmush or shortdelta <= self.mushinessfixed:
shorttaps = shorttaps + 1
return self.tradeManager.getStats()
class MomoDailyTrigger(PriceVolumeDailyTrigger):
def __init__(self, settings):
PriceVolumeDailyTrigger.__init__(self, settings)
self.minatrmultiple = settings.getfloat("MomoDailyTrigger", "minatrmultiple")
self.closepercent = settings.get("MomoDailyTrigger", "closepercent")
self.openpercent = settings.get("MomoDailyTrigger", "openpercent")
self.takeLongs = settings.getboolean("MomoDailyTrigger", "takeLongs")
self.takeShorts = settings.getboolean("MomoDailyTrigger", "takeShorts")
minaprstr = settings.get("MomoDailyTrigger", "minapr")
self.minapr = None
if minaprstr != "None":
self.minapr = float(minaprstr)
dfilter = settings.get("MomoDailyTrigger", "donchianfilter")
self.high = High()
self.low = Low()
self.atr = ATR(14)
self.lastatr = HistoricMetric(self.atr, 1)
if dfilter == "None":
self.lasthighesthigh = None
self.lastlowestlow = None
else:
self.highesthigh = Highest(self.high, int(dfilter))
self.lasthighesthigh = HistoricMetric(self.highesthigh, 1)
self.lowestlow = Lowest(self.low, int(dfilter))
self.lastlowestlow = HistoricMetric(self.lowestlow, 1)
def ready(self):
if PriceVolumeDailyTrigger.ready(self) and self.perioddata is not None and self.lastperioddata is not None:
return True
def check(self):
if self.lastperioddata.close <= 0:
return False
if self.minapr is None or (self.lastatr.value() / self.lastperioddata.close) < self.minapr:
return False
if (self.lastperioddata.close - self.lastperioddata.open) < (self.lastatr.value() * self.minatrmultiple):
return False
if self.lastperioddata.close > self.lastperioddata.open and self.takeLongs:
if ((self.lastperioddata.high - self.lastperioddata.close) / (
self.lastperioddata.high - self.lastperioddata.low)) > self.closepercent:
return False
if ((self.lastperioddata.open - self.lastperioddata.low) / (
self.lastperioddata.high - self.lastperioddata.low)) > self.openpercent:
return False
return True
if self.lastperioddata.close < self.lastperioddata.open and self.takeShorts:
if ((self.lastperioddata.close - self.lastperioddata.low) / (
self.lastperioddata.high - self.lastperioddata.low)) > self.closepercent:
return False
if ((self.lastperioddata.high - self.lastperioddata.open) / (
self.lastperioddata.high - self.lastperioddata.low)) > self.openpercent:
return False
return True
def handle(self, perioddata):
# store previous perioddata before we call superclass
self.lastperioddata = self.perioddata
self.high.handle(perioddata)
self.low.handle(perioddata)
self.atr.handle(perioddata)
self.lastatr.handle(perioddata)
if self.lasthighesthigh is not None:
self.highesthigh.handle(perioddata)
self.lasthighesthigh.handle(perioddata)
self.lowestlow.handle(perioddata)
self.lastlowestlow.handle(perioddata)
PriceVolumeDailyTrigger.handle(self, perioddata)
def getLongPrice(self):
if self.lastperioddata.close > self.lastperioddata.open:
return self.lastperioddata.close
return None
def getShortPrice(self):
if self.lastperioddata.open > self.lastperioddata.close:
return self.lastperioddata.close
return None
