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)