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 testIncremental(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): 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 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 testMaxLen(self): ds = dataseries.SequenceDataSeries() for i in range(3000): ds.append(i) self.assertEqual(len(ds), 2048) self.assertEqual(ds[0], 952) self.assertEqual(ds[-1], 2999)
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 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 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 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 testFullyAligned(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): 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 range(size): self.assertEqual(ads.getValueAbsolute(i), i) self.assertEqual(ads.getDateTimes()[i], now + datetime.timedelta(seconds=i))
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 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 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 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 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 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 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 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 range(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 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 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)
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 testNonEmpty(self): ds = dataseries.SequenceDataSeries() for value in range(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 testInvalidPosNotCached(self): ds = dataseries.SequenceDataSeries() testFilter = TestFilter(ds) for i in range(10): ds.append(i) ds.append(None) # Interleave Nones. self.assertEqual(testFilter[-1], None) self.assertEqual(testFilter[-2], 9) self.assertEqual( testFilter[-4], 8 ) # We go 3 instead of 2 because we need to skip the interleaved None values. self.assertEqual(testFilter[18], 9) self.assertEqual(testFilter[19], None) # Absolut pos 20 should have the next value once we insert it, but right now it should be invalid. with self.assertRaises(IndexError): testFilter[20] ds.append(10) self.assertEqual(testFilter[20], 10)
def testMACD(self): values = [ 16.39, 16.4999, 16.45, 16.43, 16.52, 16.51, 16.423, 16.41, 16.47, 16.45, 16.32, 16.36, 16.34, 16.59, 16.54, 16.52, 16.44, 16.47, 16.5, 16.45, 16.28, 16.07, 16.08, 16.1, 16.1, 16.09, 16.43, 16.4899, 16.59, 16.65, 16.78, 16.86, 16.86, 16.76 ] # These expected values were generated using TA-Lib macdValues = [ None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, 0.0067, 0.0106, 0.0028, -0.0342, -0.0937, -0.1214, -0.1276, -0.125, -0.1195, -0.0459, 0.0097, 0.0601, 0.0975, 0.139, 0.1713, 0.1816, 0.1598 ] signalValues = [ None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, 0.0036, 0.0056, 0.0048, -0.0064, -0.0313, -0.057, -0.0772, -0.0909, -0.0991, -0.0839, -0.0571, -0.0236, 0.011, 0.0475, 0.0829, 0.1111, 0.125 ] histogramValues = [ None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, 0.0031, 0.005, -0.002, -0.0279, -0.0624, -0.0643, -0.0504, -0.0342, -0.0205, 0.0379, 0.0668, 0.0838, 0.0865, 0.0914, 0.0884, 0.0705, 0.0348 ] ds = dataseries.SequenceDataSeries() macdDs = macd.MACD(ds, 5, 13, 6) for i, value in enumerate(values): ds.append(value) self.assertEqual(common.safe_round(macdDs[i], 4), macdValues[i]) self.assertEqual(common.safe_round(macdDs.getSignal()[i], 4), signalValues[i]) self.assertEqual(common.safe_round(macdDs.getHistogram()[i], 4), histogramValues[i])
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 __buildRSI(self, values, period, rsiMaxLen=None): seqDS = dataseries.SequenceDataSeries() ret = rsi.RSI(seqDS, period, rsiMaxLen) for value in values: seqDS.append(value) return ret
def testInvalidThreshold(self): seqDS = dataseries.SequenceDataSeries() with self.assertRaisesRegexp(Exception, "Invalid thresholds"): linreg.Trend(seqDS, 10, 0.2, 0.5, 5)