def __init__(self, feed, fastSMA, slowSMA):
strategy.BacktestingStrategy.__init__(self, feed, 1000)
ds = feed["orcl"].getPriceDataSeries()
self.__fastSMADS = ma.SMA(ds, fastSMA)
self.__slowSMADS = ma.SMA(ds, slowSMA)
self.__longPos = None
self.__shortPos = None
self.__finalValue = None
def __init__(self, feed, smaPeriod):
strategy.BacktestingStrategy.__init__(self, feed)
assert (smaPeriod > 3)
self.__lead = "^n225"
self.__lag = "spy"
self.__adjClose = feed[self.__lead].getAdjCloseDataSeries()
# Exit signal is more sensitive than entry.
self.__fastSMA = ma.SMA(self.__adjClose, int(smaPeriod / 2))
self.__slowSMA = ma.SMA(self.__adjClose, smaPeriod)
self.__pos = None
def testCrossAboveWithSMA(self):
ds1 = dataseries.SequenceDataSeries()
ds2 = dataseries.SequenceDataSeries()
sma1 = ma.SMA(ds1, 15)
sma2 = ma.SMA(ds2, 25)
for i in range(100):
ds1.append(i)
ds2.append(50)
if i == 58:
self.assertEqual(cross.cross_above(sma1[:], sma2[:], -2, None),
1)
else:
self.assertEqual(cross.cross_above(sma1[:], sma2[:], -2, None),
0)
def __init__(self, feed, instrument, entrySMA, exitSMA, rsiPeriod,
overBoughtThreshold, overSoldThreshold):
super(RSI2, self).__init__(feed)
self.__instrument = instrument
# We'll use adjusted close values, if available, instead of regular close values.
if feed.barsHaveAdjClose():
self.setUseAdjustedValues(True)
self.__priceDS = feed[instrument].getPriceDataSeries()
self.__entrySMA = ma.SMA(self.__priceDS, entrySMA)
self.__exitSMA = ma.SMA(self.__priceDS, exitSMA)
self.__rsi = rsi.RSI(self.__priceDS, rsiPeriod)
self.__overBoughtThreshold = overBoughtThreshold
self.__overSoldThreshold = overSoldThreshold
self.__longPos = None
self.__shortPos = None
def __init__(self, feed, instrument, smaPeriod):
super(SMACrossOver, self).__init__(feed)
self.__instrument = instrument
self.__position = None
# We'll use adjusted close values instead of regular close values.
self.setUseAdjustedValues(True)
self.__prices = feed[instrument].getPriceDataSeries()
self.__sma = ma.SMA(self.__prices, smaPeriod)
def __init__(self, dataSeries, period, numStdDev, maxLen=None):
self.__sma = ma.SMA(dataSeries, period, maxLen=maxLen)
self.__stdDev = stats.StdDev(dataSeries, period, maxLen=maxLen)
self.__upperBand = dataseries.SequenceDataSeries(maxLen)
self.__lowerBand = dataseries.SequenceDataSeries(maxLen)
self.__numStdDev = numStdDev
# It is important to subscribe after sma and stddev since we'll use those values.
dataSeries.getNewValueEvent().subscribe(self.__onNewValue)
def testStockChartsSMA_BoundedSeq(self):
# Test data from http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:moving_averages
common.test_from_csv(self,
"sc-sma-10.csv",
lambda inputDS: ma.SMA(inputDS, 10),
maxLen=1)
common.test_from_csv(self,
"sc-sma-10.csv",
lambda inputDS: ma.SMA(inputDS, 10),
maxLen=2)
common.test_from_csv(self,
"sc-sma-10.csv",
lambda inputDS: ma.SMA(inputDS, 10),
maxLen=4)
common.test_from_csv(self,
"sc-sma-10.csv",
lambda inputDS: ma.SMA(inputDS, 10),
maxLen=1000)
def testEventWindow(self):
ds = dataseries.SequenceDataSeries()
smaEW = ma.SMAEventWindow(10)
sma = ma.SMA(ds, 10)
smaEW.onNewValue(None, None) # This value should get skipped
for i in xrange(100):
ds.append(i)
smaEW.onNewValue(None, i)
self.assertEqual(sma[-1], smaEW.getValue())
smaEW.onNewValue(None, None) # This value should get skipped
def __init__(self, feed, instrumentsByClass, initialCash):
super(MarketTiming, self).__init__(feed, initialCash)
self.setUseAdjustedValues(True)
self.__instrumentsByClass = instrumentsByClass
self.__rebalanceMonth = None
self.__sharesToBuy = {}
# Initialize indicators for each instrument.
self.__sma = {}
for assetClass in instrumentsByClass:
for instrument in instrumentsByClass[assetClass]:
priceDS = feed[instrument].getPriceDataSeries()
self.__sma[instrument] = ma.SMA(priceDS, 200)
def __init__(self, feed, brk):
super(Strategy, self).__init__(feed, brk)
smaPeriod = 20
self.__instrument = "BTC"
self.__prices = feed[self.__instrument].getCloseDataSeries()
self.__sma = ma.SMA(self.__prices, smaPeriod)
self.__bid = None
self.__ask = None
self.__position = None
self.__posSize = 0.05
# Subscribe to order book update events to get bid/ask prices to trade.
feed.getOrderBookUpdateEvent().subscribe(self.__onOrderBookUpdate)
def __init__(self,
barDataSeries,
period,
dSMAPeriod=3,
useAdjustedValues=False,
maxLen=None):
assert dSMAPeriod > 1, "dSMAPeriod must be > 1"
assert isinstance(barDataSeries, bards.BarDataSeries), \
"barDataSeries must be a dataseries.bards.BarDataSeries instance"
super(StochasticOscillator,
self).__init__(barDataSeries,
SOEventWindow(period, useAdjustedValues), maxLen)
self.__d = ma.SMA(self, dSMAPeriod, maxLen)
def __init__(self, feed):
super(BuyOnGap, self).__init__()
stdDevPeriod = 90
smaPeriod = 20
self.__returns = {}
self.__stdDev = {}
self.__ma = {}
for instrument in feed.getRegisteredInstruments():
priceDS = feed[instrument].getAdjCloseDataSeries()
# Returns over the adjusted close values.
self.__returns[instrument] = roc.RateOfChange(priceDS, 1)
# StdDev over those returns.
self.__stdDev[instrument] = stats.StdDev(
self.__returns[instrument], stdDevPeriod)
# MA over the adjusted close values.
self.__ma[instrument] = ma.SMA(priceDS, smaPeriod)
def __init__(self, feed, instrument):
super(MyStrategy, self).__init__(feed)
self.__rsi = rsi.RSI(feed[instrument].getCloseDataSeries(), 14)
self.__sma = ma.SMA(self.__rsi, 15)
self.__instrument = instrument
def __buildSMA(self, period, values, smaMaxLen=None):
seqDs = dataseries.SequenceDataSeries()
ret = ma.SMA(seqDs, period, smaMaxLen)
for value in values:
seqDs.append(value)
return ret
def testNinjaTraderSMA(self):
common.test_from_csv(self, "nt-sma-15.csv",
lambda inputDS: ma.SMA(inputDS, 15), 3)
def __init__(self, feed, instrument):
super(MyStrategy, self).__init__(feed)
# We want a 15 period SMA over the closing prices.
self.__sma = ma.SMA(feed[instrument].getCloseDataSeries(), 15)
self.__instrument = instrument