def testIncremental_Recursive(self):
size = 20
ds1 = dataseries.SequenceDataSeries()
ds2 = dataseries.SequenceDataSeries()
ads1, ads2 = aligned.datetime_aligned(ds1, ds2)
# Align the aligned dataseries again with respect to each other.
ads1, ads2 = aligned.datetime_aligned(ads1, ads2)
now = datetime.datetime.now()
for i in range(size):
ds1.appendWithDateTime(now + datetime.timedelta(seconds=i), i)
ds2.appendWithDateTime(now + datetime.timedelta(seconds=i), i)
self.assertEqual(len(ads1), len(ads2))
self.assertEqual(ads1[:], ads2[:])
self.assertEqual(ads1.getDateTimes()[:], ads2.getDateTimes()[:])
def testSeqLikeOps(self):
seq = range(10)
ds = dataseries.SequenceDataSeries()
for value in seq:
ds.append(value)
# Test length and every item.
self.assertEqual(len(ds), len(seq))
for i in xrange(len(seq)):
self.assertEqual(ds[i], seq[i])
# Test negative indices
self.assertEqual(ds[-1], seq[-1])
self.assertEqual(ds[-2], seq[-2])
self.assertEqual(ds[-9], seq[-9])
# Test slices
sl = slice(0, 1, 2)
self.assertEqual(ds[sl], seq[sl])
sl = slice(0, 9, 2)
self.assertEqual(ds[sl], seq[sl])
sl = slice(0, -1, 1)
self.assertEqual(ds[sl], seq[sl])
for i in xrange(-100, 100):
self.assertEqual(ds[i:], seq[i:])
for step in xrange(1, 10):
for i in xrange(-100, 100):
self.assertEqual(ds[i::step], seq[i::step])
def testInvalidDataSeries(self):
with self.assertRaisesRegexp(
Exception,
"barDataSeries must be a dataseries.bards.BarDataSeries instance"
):
ds = dataseries.SequenceDataSeries()
linebreak.LineBreak(ds, 3, maxLen=2)
def __init__(self, dataSeries, period, div=4, interval=5):
self.ma = ma.SMA(dataSeries, period)
self.period = period
self.div = div
self.interval = interval
dataSeries.getNewValueEvent().subscribe(self.__onNewValue)
self.trend_data = dataseries.SequenceDataSeries()
def testInvalidDataSeries(self):
with self.assertRaisesRegexp(
Exception,
"barDataSeries must be a dataseries.bards.BarDataSeries instance"
):
ds = dataseries.SequenceDataSeries()
atr.ATR(ds, 14, True)
def testMaxLen(self):
ds = dataseries.SequenceDataSeries()
for i in xrange(3000):
ds.append(i)
self.assertEqual(len(ds), 2048)
self.assertEqual(ds[0], 952)
self.assertEqual(ds[-1], 2999)
def __init__(self, feed, smaShort, smaLong, aroonPeriod):
MyBasicStrategy.__init__(self, feed, smaShort, smaLong)
self._aroon = {}
self._aroonPeriod = aroonPeriod
for instrument in feed.getRegisteredInstruments():
self._aroon[instrument] = dataseries.SequenceDataSeries()
def load_test_csv(path):
inputSeq = []
expectedSeq = []
csvFile = open(path, "r")
reader = csv.DictReader(csvFile)
for row in reader:
inputSeq.append(float(row["Input"]))
expected = row["Expected"]
if not expected:
expected = None
else:
expected = float(expected)
expectedSeq.append(expected)
return (dataseries.SequenceDataSeries(inputSeq),
dataseries.SequenceDataSeries(expectedSeq))
def testStockChartsBollinger(self):
# Test data from http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:bollinger_bands
prices = [
86.1557, 89.0867, 88.7829, 90.3228, 89.0671, 91.1453, 89.4397, 89.1750, 86.9302, 87.6752, 86.9596,
89.4299, 89.3221, 88.7241, 87.4497, 87.2634, 89.4985, 87.9006, 89.1260, 90.7043, 92.9001, 92.9784,
91.8021, 92.6647, 92.6843, 92.3021, 92.7725, 92.5373, 92.9490, 93.2039, 91.0669, 89.8318, 89.7435,
90.3994, 90.7387, 88.0177, 88.0867, 88.8439, 90.7781, 90.5416, 91.3894, 90.6500
]
expectedMiddle = [
88.71, 89.05, 89.24, 89.39, 89.51, 89.69, 89.75, 89.91, 90.08, 90.38, 90.66, 90.86, 90.88, 90.91,
90.99, 91.15, 91.19, 91.12, 91.17, 91.25, 91.24, 91.17, 91.05
]
expectedUpper = [
91.29, 91.95, 92.61, 92.93, 93.31, 93.73, 93.90, 94.27, 94.57, 94.79, 95.04, 94.91, 94.90, 94.90,
94.86, 94.67, 94.56, 94.68, 94.58, 94.53, 94.53, 94.37, 94.15
]
expectedLower = [
86.12, 86.14, 85.87, 85.85, 85.70, 85.65, 85.59, 85.56, 85.60, 85.98, 86.27, 86.82, 86.87, 86.91,
87.12, 87.63, 87.83, 87.56, 87.76, 87.97, 87.95, 87.96, 87.95
]
seqDS = dataseries.SequenceDataSeries()
bBands = bollinger.BollingerBands(seqDS, 20, 2)
for value in prices:
seqDS.append(value)
for i in xrange(19):
self.assertEqual(bBands.getMiddleBand()[i], None)
self.assertEqual(bBands.getUpperBand()[i], None)
self.assertEqual(bBands.getLowerBand()[i], None)
for i in xrange(19, len(seqDS)):
self.assertEqual(round(bBands.getMiddleBand()[i], 2), expectedMiddle[i-19])
self.assertEqual(round(bBands.getUpperBand()[i], 2), expectedUpper[i-19])
self.assertEqual(round(bBands.getLowerBand()[i], 2), expectedLower[i-19])
def testStockChartsBollinger_Bounded(self):
# Test data from http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:bollinger_bands
prices = [
86.1557, 89.0867, 88.7829, 90.3228, 89.0671, 91.1453, 89.4397, 89.1750, 86.9302, 87.6752, 86.9596,
89.4299, 89.3221, 88.7241, 87.4497, 87.2634, 89.4985, 87.9006, 89.1260, 90.7043, 92.9001, 92.9784,
91.8021, 92.6647, 92.6843, 92.3021, 92.7725, 92.5373, 92.9490, 93.2039, 91.0669, 89.8318, 89.7435,
90.3994, 90.7387, 88.0177, 88.0867, 88.8439, 90.7781, 90.5416, 91.3894, 90.6500
]
expectedMiddle = [91.24, 91.17, 91.05]
expectedUpper = [94.53, 94.37, 94.15]
expectedLower = [87.95, 87.96, 87.95]
seqDS = dataseries.SequenceDataSeries()
bBands = bollinger.BollingerBands(seqDS, 20, 2, 3)
for value in prices:
seqDS.append(value)
for i in xrange(3):
self.assertEqual(round(bBands.getMiddleBand()[i], 2), expectedMiddle[i])
self.assertEqual(round(bBands.getUpperBand()[i], 2), expectedUpper[i])
self.assertEqual(round(bBands.getLowerBand()[i], 2), expectedLower[i])
self.assertEqual(len(bBands.getMiddleBand()), 3)
self.assertEqual(len(bBands.getMiddleBand()[:]), 3)
self.assertEqual(len(bBands.getMiddleBand().getDateTimes()), 3)
self.assertEqual(len(bBands.getUpperBand()), 3)
self.assertEqual(len(bBands.getUpperBand()[:]), 3)
self.assertEqual(len(bBands.getUpperBand().getDateTimes()), 3)
self.assertEqual(len(bBands.getLowerBand()), 3)
self.assertEqual(len(bBands.getLowerBand()[:]), 3)
self.assertEqual(len(bBands.getLowerBand().getDateTimes()), 3)
def testStraightLine(self):
seqDS = dataseries.SequenceDataSeries()
lsReg = linreg.LeastSquaresRegression(seqDS, 3)
nextDateTime = datetime.datetime(2012, 1, 1)
seqDS.appendWithDateTime(nextDateTime, 1)
self.assertEqual(lsReg[-1], None)
nextDateTime = nextDateTime + datetime.timedelta(hours=1)
seqDS.appendWithDateTime(nextDateTime, 2)
self.assertEqual(lsReg[-1], None)
# Check current value.
nextDateTime = nextDateTime + datetime.timedelta(hours=1)
seqDS.appendWithDateTime(nextDateTime, 3)
self.assertEqual(round(lsReg[-1], 2), 3)
# Check future values.
futureDateTime = nextDateTime + datetime.timedelta(hours=1)
self.assertEqual(round(lsReg.getValueAt(futureDateTime), 2), 4)
futureDateTime = futureDateTime + datetime.timedelta(minutes=30)
self.assertEqual(round(lsReg.getValueAt(futureDateTime), 2), 4.5)
futureDateTime = futureDateTime + datetime.timedelta(minutes=30)
self.assertEqual(round(lsReg.getValueAt(futureDateTime), 2), 5)
# Move forward in sub-second increments.
nextDateTime = nextDateTime + datetime.timedelta(milliseconds=50)
seqDS.appendWithDateTime(nextDateTime, 4)
nextDateTime = nextDateTime + datetime.timedelta(milliseconds=50)
seqDS.appendWithDateTime(nextDateTime, 5)
self.assertEqual(round(lsReg[-1], 2), 5)
def datetime_aligned(ds1, ds2, maxLen=dataseries.DEFAULT_MAX_LEN):
"""
Returns two dataseries that exhibit only those values whose datetimes are in both dataseries.
:param ds1: A DataSeries instance.
:type ds1: :class:`DataSeries`.
:param ds2: A DataSeries instance.
:type ds2: :class:`DataSeries`.
:param maxLen: The maximum number of values to hold for the returned :class:`DataSeries`.
Once a bounded length is full, when new items are added, a corresponding number of items are discarded from the opposite end.
:type maxLen: int.
"""
aligned1 = dataseries.SequenceDataSeries(maxLen)
aligned2 = dataseries.SequenceDataSeries(maxLen)
Syncer(ds1, ds2, aligned1, aligned2)
return (aligned1, aligned2)
def testHighLow(self):
values = dataseries.SequenceDataSeries()
high = highlow.High(values, 5)
low = highlow.Low(values, 3)
for value in [1, 2, 3, 4, 5]:
values.append(value)
self.assertEqual(high[-1], 5)
self.assertEqual(low[-1], 3)
def testEmpty(self): ds = dataseries.SequenceDataSeries([]) self.assertTrue(ds.getFirstValidPos() == 0) self.assertTrue(ds.getLength() == 0) self.assertTrue(ds.getValue() == None) self.assertTrue(ds.getValue(1) == None) self.assertTrue(ds.getValueAbsolute(0) == None) self.assertTrue(ds.getValueAbsolute(1) == None)
def testBounded(self):
ds = dataseries.SequenceDataSeries(maxLen=2)
for i in xrange(100):
ds.append(i)
if i > 0:
self.assertEqual(ds[0], i - 1)
self.assertEqual(ds[1], i)
self.assertEqual(len(ds), 2)
def testStdDev_1(self):
values = [1, 1, 2, 3, 5]
seqDS = dataseries.SequenceDataSeries()
stdDev = stats.StdDev(seqDS, 1)
for value in values:
seqDS.append(value)
for i in stdDev:
self.assertEqual(i, 0)
def onBars(self, bars, instrument):
try:
values = self.__values[instrument]
except KeyError:
values = dataseries.SequenceDataSeries()
self.__values[instrument] = values
taValue = self.__calculator.calculate(self.__feed, instrument)
values.appendWithDateTime(bars.getDateTime(), taValue)
def testStdDev_Bounded(self):
values = [1, 1, 2, 3, 5]
seqDS = dataseries.SequenceDataSeries()
stdDev = stats.StdDev(seqDS, 2, maxLen=2)
for value in values:
seqDS.append(value)
self.assertEqual(stdDev[0], numpy.array([2, 3]).std())
self.assertEqual(stdDev[1], numpy.array([3, 5]).std())
def test_from_csv(testcase, filename, filterClassBuilder, roundDecimals=2, maxLen=dataseries.DEFAULT_MAX_LEN):
inputValues, expectedValues = load_test_csv(get_data_file_path(filename))
inputDS = dataseries.SequenceDataSeries(maxLen=maxLen)
filterDS = filterClassBuilder(inputDS)
for i in xrange(len(inputValues)):
inputDS.append(inputValues[i])
value = safe_round(filterDS[i], roundDecimals)
expectedValue = safe_round(expectedValues[i], roundDecimals)
testcase.assertEqual(value, expectedValue)
def __buildSlope(self,
values,
period,
slopeMaxLen=dataseries.DEFAULT_MAX_LEN):
seqDS = dataseries.SequenceDataSeries()
ret = trend.Slope(seqDS, period, slopeMaxLen)
for value in values:
seqDS.append(value)
return ret
def testFullyAligned(self):
size = 20
ds1 = dataseries.SequenceDataSeries()
ds2 = dataseries.SequenceDataSeries()
ads1, ads2 = aligned.datetime_aligned(ds1, ds2)
now = datetime.datetime.now()
for i in xrange(size):
ds1.appendWithDateTime(now + datetime.timedelta(seconds=i), i)
ds2.appendWithDateTime(now + datetime.timedelta(seconds=i), i)
self.assertEqual(len(ds1), len(ds2))
for ads in [ads1, ads2]:
self.assertEqual(len(ads), size)
for i in xrange(size):
self.assertEqual(ads.getValueAbsolute(i), i)
self.assertEqual(ads.getDateTimes()[i], now + datetime.timedelta(seconds=i))
def testNotAligned(self):
size = 20
ds1 = dataseries.SequenceDataSeries()
ds2 = dataseries.SequenceDataSeries()
ads1, ads2 = aligned.datetime_aligned(ds1, ds2)
now = datetime.datetime.now()
for i in range(size):
if i % 2 == 0:
ds1.appendWithDateTime(now + datetime.timedelta(seconds=i), i)
else:
ds2.appendWithDateTime(now + datetime.timedelta(seconds=i), i)
self.assertEqual(len(ds1), len(ds2))
for ads in [ads1, ads2]:
self.assertEqual(len(ads), 0)
self.assertEqual(ads.getValueAbsolute(0), None)
self.assertEqual(ads.getDateTimes(), [])
def __init__(self, dataSeries, fastEMA, slowEMA, signalEMA, maxLen=None):
assert(fastEMA > 0)
assert(slowEMA > 0)
assert(fastEMA < slowEMA)
assert(signalEMA > 0)
super(MACD, self).__init__(maxLen)
# We need to skip some values when calculating the fast EMA in order for both EMA
# to calculate their first values at the same time.
# I'M FORCING THIS BEHAVIOUR ONLY TO MAKE THIS FITLER MATCH TA-Lib MACD VALUES.
self.__fastEMASkip = slowEMA - fastEMA
self.__fastEMAWindow = ma.EMAEventWindow(fastEMA)
self.__slowEMAWindow = ma.EMAEventWindow(slowEMA)
self.__signalEMAWindow = ma.EMAEventWindow(signalEMA)
self.__signal = dataseries.SequenceDataSeries(maxLen)
self.__histogram = dataseries.SequenceDataSeries(maxLen)
dataSeries.getNewValueEvent().subscribe(self.__onNewValue)
def __buildWMA(self,
weights,
values,
seqMaxLen=dataseries.DEFAULT_MAX_LEN,
wmaMaxLen=dataseries.DEFAULT_MAX_LEN):
seqDS = dataseries.SequenceDataSeries(maxLen=seqMaxLen)
ret = ma.WMA(seqDS, weights, wmaMaxLen)
for value in values:
seqDS.append(value)
return ret
def __init__(self, feed, instrument, ema1_period, ema2_period, ema3_period):
super(AS001Strategy, self).__init__(feed, Broker(100000, feed, commission=TradePercentage(0.00075)))
self.__instrument = instrument
self.__ema1_period = ema1_period
self.__ema2_period = ema2_period
self.__ema3_period = ema3_period
self.__hlc3 = dataseries.SequenceDataSeries()
self.__position = None
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 testEmpty(self):
ds = dataseries.SequenceDataSeries()
self.assertTrue(len(ds) == 0)
with self.assertRaises(IndexError):
ds[-1]
with self.assertRaises(IndexError):
ds[-2]
with self.assertRaises(IndexError):
ds[0]
with self.assertRaises(IndexError):
ds[1]
def testUnderlyingDataSeries(self):
ds = dataseries.SequenceDataSeries()
testFilter = TestFilter(ds)
for i in range(10):
ds.append(i)
ds.append(None)
self.assertEqual(testFilter.getDataSeries(), ds)
for i in range(0, len(testFilter)):
self.assertEqual(testFilter[i], ds[i])
self.assertEqual(testFilter.getDataSeries()[i], ds[i])
def testZScore(self):
values = [1.10, 2.20, 4.00, 5.10, 6.00, 7.10, 8.20, 9.00, 10.10, 3.00, 4.10, 5.20, 7.00, 8.10, 9.20, 16.00, 17.10, 18.20, 19.30, 20.40]
expected = [None, None, None, None, 1.283041407, 1.317884611, 1.440611043, 1.355748299, 1.4123457, -1.831763202, -0.990484842, -0.388358578, 0.449889908, 1.408195169, 1.332948099, 1.867732104, 1.334258333, 1.063608066, 0.939656572, 1.414213562]
seqDS = dataseries.SequenceDataSeries()
zscore = stats.ZScore(seqDS, 5)
i = 0
for value in values:
seqDS.append(value)
if i >= 4:
self.assertEqual(round(zscore[-1], 4), round(expected[i], 4))
i += 1
def __buildTrend(self,
values,
trendDays,
positiveThreshold,
negativeThreshold,
trendMaxLen=None):
seqDS = dataseries.SequenceDataSeries()
ret = linreg.Trend(seqDS, trendDays, positiveThreshold,
negativeThreshold, trendMaxLen)
for value in values:
seqDS.append(value)
return ret
