class MovingAverageExitManager(ExitManager):
""" Exit on a moving average as a trailing stop
"""
def __init__(self, settings):
ExitManager.__init__(self)
self.period = settings.getint("MovingAverageExitManager", "period")
self.close = AdjustedClose()
self.ma = SimpleMovingAverage(metric=self.close, period=self.period)
def handle(self, perioddata):
ExitManager.handle(self, perioddata)
self.close.handle(perioddata)
self.ma.handle(perioddata)
def checkTrade(self, trade):
if trade is not None and self.ma.ready() and trade.exit is None \
and trade.entryPrice > trade.stop \
and self.perioddata.adjustedClose >= self.ma.value():
trade.exit = self.perioddata.date
trade.exitPrice = self.perioddata.adjustedClose
if trade is not None and self.ma.ready() and trade.exit is None \
and trade.entryPrice < trade.stop \
and self.perioddata.adjustedClose <= self.ma.value():
trade.exit = self.perioddata.date
trade.exitPrice = self.perioddata.adjustedClose
return trade
def recommendedPreload(self):
return self.period + 1
def __init__(self, settings):
DailyTrigger.__init__(self, settings)
# this is a hack until we implement subscribing to multiple streams of data
self.uptrendcheck = dict()
# if True, we are filtering on uptrending SPY, if false, downtrending SPY
# we are using the PREVIOUS bar data, hence the use of HistoricMetric.
# we do not now if TODAY will close above/below the ma before we enter our
# day trades. An alternative would have been to use the open
ticker = settings.get("TrendFilterDailyTrigger", "ticker")
self.uptrend = settings.getboolean("TrendFilterDailyTrigger", "uptrend")
period = settings.getint("TrendFilterDailyTrigger", "period")
close = AdjustedClose()
lastclose = HistoricMetric(close, 1)
ma = SimpleMovingAverage(metric=close, period=period)
lastma = HistoricMetric(ma, 1)
fromdt = datetime(1900, 1, 1)
todt = datetime.now()
datastore = datastorefactory.get_datastore()
for dd in datastore.getDailyData(ticker, fromdt, todt):
close.handle(dd)
lastclose.handle(dd)
ma.handle(dd)
lastma.handle(dd)
if lastma.ready() and lastclose.ready():
if lastclose.value() > lastma.value():
self.uptrendcheck[dd.date] = True
if lastclose.value() < lastma.value():
self.uptrendcheck[dd.date] = False
class RSIExitManager(ExitManager):
""" Exit on a target RSI value.
Handles long and short
"""
def __init__(self, settings):
ExitManager.__init__(self)
self.period = settings.getint("RSIExitManager", "period")
self.longExit = settings.getfloat("RSIExitManager", "longExit")
self.shortExit = settings.getfloat("RSIExitManager", "shortExit")
self.close = AdjustedClose()
self.rsi = RSI(metric=self.close, period=self.period)
def handle(self, perioddata):
ExitManager.handle(self, perioddata)
self.close.handle(perioddata)
self.rsi.handle(perioddata)
def checkTrade(self, trade):
if trade is not None and self.rsi.ready() and trade.is_open() \
and ((trade.is_long() and self.rsi.value() >= self.longExit) or
(trade.is_short() and self.rsi.value() <= self.shortExit)):
trade.close(self.perioddata.date, self.perioddata.adjustedClose)
return trade
def recommendedPreload(self):
return self.period + 3
class GapUpEntryManager:
def __init__(self, settings):
self.minPrice = settings.getfloat("GapAndGoSwingEntry", "minPrice")
self.minAvgVol = settings.getint("GapAndGoSwingEntry", "minAvgVol")
self.minPercent = settings.getfloat("GapAndGoSwingEntry", "minPercent")
targetstr = settings.get("GapAndGoSwingEntry", "target")
if targetstr == "None":
self.target = None
else:
self.target = float(targetstr)
self.volume = Volume()
self.avgvol = SimpleMovingAverage(metric=self.volume, period=21)
self.opn = AdjustedOpen()
self.close = AdjustedClose()
self.lastClose = HistoricMetric(metric=self.close, period=1)
self.high = AdjustedHigh()
self.lastHigh = HistoricMetric(metric=self.high, period=1)
self.low = AdjustedLow()
self.inBottomRange=0.1
self.inTopRange=None
def handle(self, perioddata):
self.close.handle(perioddata)
self.lastClose.handle(perioddata)
self.high.handle(perioddata)
self.lastHigh.handle(perioddata)
self.opn.handle(perioddata)
self.volume.handle(perioddata)
self.avgvol.handle(perioddata)
self.low.handle(perioddata)
self.lastdd = perioddata
def checkTrade(self):
if self.close.ready() and self.lastClose.ready() \
and self.opn.ready() and self.volume.ready() \
and self.avgvol.ready():
if self.lastdd.close >= self.minPrice and self.avgvol.value() >= self.minAvgVol \
and self.lastClose.value() > 0 and self.opn.value() > 0 \
and ((self.opn.value()-self.lastClose.value())/self.lastClose.value()) >= self.minPercent \
and self.low.value() >= self.lastHigh.value() \
and (self.inBottomRange == None or ((self.lastdd.adjustedHigh != self.lastdd.adjustedLow) and (self.close.value()-self.lastdd.adjustedLow)/(self.lastdd.adjustedHigh-self.lastdd.adjustedLow)) <= self.inBottomRange) \
and (self.inTopRange == None or ((self.lastdd.adjustedHigh != self.lastdd.adjustedLow) and (self.close.value()-self.lastdd.adjustedLow)/(self.lastdd.adjustedHigh-self.lastdd.adjustedLow)) >= self.inTopRange):
stop = max(0.0, self.low.value() - 0.01)
# stop = max(0.0, self.lastHigh.value())
trade = Trade(self.lastdd.stock, self.lastdd.date, self.close.value(), stop)
if self.target != None:
target = self.close.value() + ((self.close.value()-stop)*self.target)
trade.target = target
return trade
return None
def recommendedPreload(self):
return 22
class MomoPullbackDailyTrigger(DailyTrigger):
def __init__(self, settings):
DailyTrigger.__init__(self, settings)
self.minprice = settings.getfloat("Strategy", "minprice")
self.minvolume = settings.getint("Strategy", "minvolume")
self.minmove = settings.getfloat("Strategy", "minmove")
smatrend = settings.get("Strategy", "dailysmatrendfilter")
if smatrend == "None":
self.dailysmatrendfilter = None
else:
self.dailysmatrendfilter = int(smatrend)
self.volume = Volume()
self.avgvol = SimpleMovingAverage(self.volume, 21)
self.close = AdjustedClose()
if self.dailysmatrendfilter is not None:
self.ma = SimpleMovingAverage(metric=self.close, period=self.dailysmatrendfilter)
else:
self.ma = None
def ready(self):
return (self.ma is None or self.ma.ready()) and self.avgvol.ready() and self.close.ready()
def check(self):
if self.ready() and self.lastpd is not None:
# todo would be a lot faster if I could implement a peek at today to check the move is big enough
if self.lastpd.open >= self.minprice \
and (self.ma is None or self.close.value() > self.ma.value()) \
and self.avgvol.value() >= self.minvolume \
and ((self.peekpd.high - self.peekpd.open) / self.peekpd.open) >= self.minmove:
logger.debug("Saw minimum move in MomoPullbackDailyTrigger, indicating we have a good day")
return True
return False
def handle(self, perioddata):
if self.ma is not None:
self.ma.handle(perioddata)
self.volume.handle(perioddata)
self.avgvol.handle(perioddata)
self.close.handle(perioddata)
self.lastpd = perioddata
def recommendedPreload(self):
mapreload = 0
if self.ma is not None:
mapreload = self.ma.recommendedPreload()
return max(mapreload, self.avgvol.recommendedPreload())
class MyTrendCheck(Check):
def __init__(self):
Check.__init__(self)
# adjusted close accounts for splits and dividends
self.close = AdjustedClose()
self.ma = SimpleMovingAverage(metric=self.close, period=200)
def handle(self, periodData):
self.close.handle(periodData)
self.ma.handle(periodData)
def ready(self):
return self.close.ready() and self.ma.ready()
def check(self):
if not self.ready():
return False
return self.close.value() > self.ma.value()
class MyTrendCheck(Check):
def __init__(self):
Check.__init__(self)
# adjusted close accounts for splits and dividends
self.close = AdjustedClose()
self.ma = SimpleMovingAverage(metric=self.close, period=200)
def handle(self, periodData):
self.close.handle(periodData)
self.ma.handle(periodData)
def ready(self):
return self.close.ready() and self.ma.ready()
def check(self):
if not self.ready():
return False
return self.close.value() > self.ma.value()
def generate_random_perioddata(dt):
a = random()*50+50
b = random()*50+50
c = random()*50+50
d = random()*50+50
retval = PeriodData(date=dt,period=Period.DAILY, stock="AAPL", open=a,high=max((a,b,c,d)),low=min(a,b,c,d),close=d,volume=100000,adjustedClose=d)
return retval
def generate_random_series():
retval = list()
dt = datetime(year=2015,month=1,day=1)
for i in range(1000):
dt = dt + timedelta(days=1)
retval.append(generate_random_perioddata(dt))
return retval
# Here begins the example code
# Track the adjusted close and 20 period simple moving average
close = AdjustedClose()
sma = SimpleMovingAverage(metric=close, period=20)
print "Date,SMA"
for periodData in generate_random_series():
close.handle(periodData)
sma.handle(periodData)
if sma.ready() and close.ready():
print "%s,%f" % (periodData.date, sma.value())
