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 __init__(self, feed, instrument1, instrument2, windowSize):
super(StatArb, self).__init__(feed)
self.setUseAdjustedValues(True)
self.__statArbHelper = StatArbHelper(
feed[instrument1].getAdjCloseDataSeries(),
feed[instrument2].getAdjCloseDataSeries(), windowSize)
self.__i1 = instrument1
self.__i2 = instrument2
# These are used only for plotting purposes.
self.__spread = dataseries.SequenceDataSeries()
self.__hedgeRatio = dataseries.SequenceDataSeries()
def testIncremental(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(ads1), len(ads2))
self.assertEqual(ads1[:], ads2[:])
self.assertEqual(ads1.getDateTimes()[:], ads2.getDateTimes()[:])
def testBoundedSources(self):
ds1 = dataseries.SequenceDataSeries(1)
ds2 = dataseries.SequenceDataSeries(1)
ads1, ads2 = aligned.datetime_aligned(ds1, ds2)
now = datetime.datetime.now()
ds1.appendWithDateTime(now + datetime.timedelta(seconds=1), 1)
ds1.appendWithDateTime(now + datetime.timedelta(seconds=2), 2)
ds1.appendWithDateTime(now + datetime.timedelta(seconds=3), 3)
ds2.appendWithDateTime(now + datetime.timedelta(seconds=2), 2)
ds2.appendWithDateTime(now + datetime.timedelta(seconds=3), 3)
ds2.appendWithDateTime(now + datetime.timedelta(seconds=4), 4)
self.assertEqual(ads1[:], [2, 3])
self.assertEqual(ads2[:], [2, 3])
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 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 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 testSeqLikeOps(self):
seq = list(xrange(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.assertRaisesRegex(
Exception,
"barDataSeries must be a dataseries.bards.BarDataSeries instance"
):
ds = dataseries.SequenceDataSeries()
atr.ATR(ds, 14, True)
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 datetime_aligned(ds1, ds2, maxLen=None):
"""
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. If None then dataseries.DEFAULT_MAX_LEN is used.
:type maxLen: int.
"""
aligned1 = dataseries.SequenceDataSeries(maxLen)
aligned2 = dataseries.SequenceDataSeries(maxLen)
Syncer(ds1, ds2, aligned1, aligned2)
return (aligned1, aligned2)
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 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 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 __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 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 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 testNotAligned(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):
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 testUnderlyingDataSeries(self):
ds = dataseries.SequenceDataSeries()
testFilter = MockFilter(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 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 testStdDev(self):
values = [1, 1, 2, 3, 5]
seqDS = dataseries.SequenceDataSeries()
stdDev = stats.StdDev(seqDS, 2)
for value in values:
seqDS.append(value)
self.assertEqual(stdDev[0], None)
self.assertEqual(stdDev[1], numpy.array([1, 1]).std())
self.assertEqual(stdDev[2], numpy.array([1, 2]).std())
self.assertEqual(stdDev[3], numpy.array([2, 3]).std())
self.assertEqual(stdDev[4], numpy.array([3, 5]).std())
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
def test_from_csv(testcase,
filename,
filterClassBuilder,
roundDecimals=2,
maxLen=None):
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 testPartiallyAligned(self):
size = 20
commonDateTimes = []
ds1 = dataseries.SequenceDataSeries()
ds2 = dataseries.SequenceDataSeries()
ads1, ads2 = aligned.datetime_aligned(ds1, ds2)
now = datetime.datetime.now()
for i in xrange(size):
if i % 3 == 0:
commonDateTimes.append(now + datetime.timedelta(seconds=i))
ds1.appendWithDateTime(now + datetime.timedelta(seconds=i), i)
ds2.appendWithDateTime(now + datetime.timedelta(seconds=i), i)
elif i % 2 == 0:
ds1.appendWithDateTime(now + datetime.timedelta(seconds=i), i)
else:
ds2.appendWithDateTime(now + datetime.timedelta(seconds=i), i)
self.assertEqual(len(ads1), len(ads2))
self.assertEqual(ads1[:], ads2[:])
self.assertEqual(ads1.getDateTimes(), commonDateTimes)
self.assertEqual(ads2.getDateTimes(), commonDateTimes)
def testCumRet(self):
values = dataseries.SequenceDataSeries()
rets = cumret.CumulativeReturn(values)
for value in [1, 2, 3, 4, 4, 3, 1, 1.2]:
values.append(value)
self.assertEqual(rets[0], None)
self.assertEqual(rets[1], 1)
self.assertEqual(rets[2], 2)
self.assertEqual(rets[3], 3)
self.assertEqual(rets[4], 3)
self.assertEqual(rets[5], 2)
self.assertEqual(rets[6], 0)
self.assertEqual(round(rets[7], 1), 0.2)
def testResize1(self):
ds = dataseries.SequenceDataSeries(100)
for i in xrange(100):
ds.append(i)
self.assertEqual(len(ds), 100)
self.assertEqual(len(ds.getDateTimes()), 100)
self.assertEqual(ds[0], 0)
self.assertEqual(ds[-1], 99)
ds.setMaxLen(2)
self.assertEqual(len(ds), 2)
self.assertEqual(len(ds.getDateTimes()), 2)
self.assertEqual(ds[0], 98)
self.assertEqual(ds[1], 99)
def __init__(self, maxLen=None):
super(BarDataSeries, self).__init__(maxLen)
self.__openDS = dataseries.SequenceDataSeries(maxLen)
self.__closeDS = dataseries.SequenceDataSeries(maxLen)
self.__highDS = dataseries.SequenceDataSeries(maxLen)
self.__lowDS = dataseries.SequenceDataSeries(maxLen)
self.__volumeDS = dataseries.SequenceDataSeries(maxLen)
self.__adjCloseDS = dataseries.SequenceDataSeries(maxLen)
self.__extraDS = {}
self.__useAdjustedValues = False
def testNonEmpty(self):
ds = dataseries.SequenceDataSeries()
for value in xrange(10):
ds.append(value)
self.assertTrue(len(ds) == 10)
self.assertTrue(ds[-1] == 9)
self.assertTrue(ds[-2] == 8)
self.assertTrue(ds[0] == 0)
self.assertTrue(ds[1] == 1)
self.assertTrue(ds[-1:] == [9])
self.assertTrue(ds[-2:] == [8, 9])
self.assertTrue(ds[-2:-1] == [8])
self.assertTrue(ds[-3:-1] == [7, 8])
self.assertTrue(ds[1:4] == [1, 2, 3])
self.assertTrue(ds[9:10] == [9])
self.assertTrue(ds[9:11] == [9])
self.assertTrue(ds[9:] == [9])
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 testBoundedFilter(self):
values = [
22.2734, 22.1940, 22.0847, 22.1741, 22.1840, 22.1344, 22.2337,
22.4323, 22.2436, 22.2933, 22.1542, 22.3926, 22.3816, 22.6109,
23.3558, 24.0519, 23.7530, 23.8324, 23.9516, 23.6338, 23.8225,
23.8722, 23.6537, 23.1870, 23.0976, 23.3260, 22.6805, 23.0976,
22.4025, 22.1725
]
seqDS = dataseries.SequenceDataSeries()
ema = ma.EMA(seqDS, 10, 2)
for value in values:
seqDS.append(value)
self.assertEqual(round(ema[0], 5), 23.08068)
self.assertEqual(round(ema[1], 5), 22.91556)
self.assertEqual(len(ema), 2)
self.assertEqual(len(ema[:]), 2)
self.assertEqual(len(ema.getDateTimes()), 2)
