class MovingAlphaBeta(StatefulValueHolder): def __init__(self, window, dependency=('pRet', 'mRet', 'riskFree')): self._returnSize = 2 super(MovingAlphaBeta, self).__init__(window, dependency) self._pReturnMean = MovingAverage(window, dependency='x') self._mReturnMean = MovingAverage(window, dependency='y') self._pReturnVar = MovingVariance(window, dependency='x') self._mReturnVar = MovingVariance(window, dependency='y') self._correlationHolder = MovingCorrelation(window, dependency=['x', 'y']) def push(self, data): value = super(MovingAlphaBeta, self).push(data) if value is None: return pReturn = value[0] mReturn = value[1] rf = value[2] data = {'x': pReturn - rf, 'y': mReturn - rf} self._pReturnMean.push(data) self._mReturnMean.push(data) self._pReturnVar.push(data) self._mReturnVar.push(data) self._correlationHolder.push(data) def result(self): corr = self._correlationHolder.result() pStd = math.sqrt(self._pReturnVar.result()) mStd = math.sqrt(self._mReturnVar.result()) beta = corr * pStd / mStd alpha = self._pReturnMean.result() - beta * self._mReturnMean.result() return alpha, beta
class MovingAlphaBeta(StatefulValueHolder): def __init__(self, window, dependency=('pRet', 'mRet', 'riskFree')): self._returnSize = 2 super(MovingAlphaBeta, self).__init__(window, dependency) self._pReturnMean = MovingAverage(window, dependency='x') self._mReturnMean = MovingAverage(window, dependency='y') self._pReturnVar = MovingVariance(window, dependency='x') self._mReturnVar = MovingVariance(window, dependency='y') self._correlationHolder = MovingCorrelation(window, dependency=['x', 'y']) def push(self, data): value = super(MovingAlphaBeta, self).push(data) if value is None: return pReturn = value[0] mReturn = value[1] rf = value[2] data = {'x': pReturn - rf, 'y': mReturn - rf} self._pReturnMean.push(data) self._mReturnMean.push(data) self._pReturnVar.push(data) self._mReturnVar.push(data) self._correlationHolder.push(data) def result(self): corr = self._correlationHolder.result() pStd = math.sqrt(self._pReturnVar.result()) mStd = math.sqrt(self._mReturnVar.result()) beta = corr * pStd / mStd alpha = self._pReturnMean.result() - beta * self._mReturnMean.result() return alpha, beta
def __init__(self, window, dependency=('pRet', 'mRet', 'riskFree')): self._returnSize = 2 super(MovingAlphaBeta, self).__init__(window, dependency) self._pReturnMean = MovingAverage(window, dependency='x') self._mReturnMean = MovingAverage(window, dependency='y') self._pReturnVar = MovingVariance(window, dependency='x') self._mReturnVar = MovingVariance(window, dependency='y') self._correlationHolder = MovingCorrelation(window, dependency=['x', 'y'])
def testCompoundedOperator(self): ma5 = MovingAverage(5, 'x') maxer = Max('close') max5ma = Max('close') >> MovingAverage(5) max5ma2 = MovingAverage(5, Max('close')) average = Average('close') sumM = Sum('close') mvTest = Correlation(dependency=('x', 'y')) mvCorr = (Average('close') ^ Sum('close')) >> Correlation(dependency=('x', 'y')) for i, close in enumerate(self.sampleClose): data = {'close': close, 'open': 1.} maxer.push(data) data2 = {'x': maxer.result()} ma5.push(data2) max5ma.push(data) max5ma2.push(data) average.push(data) sumM.push(data) data3 = {'x': average.result(), 'y': sumM.result()} mvTest.push(data3) mvCorr.push(data) expected = ma5.result() calculated = max5ma.result() self.assertAlmostEqual( calculated, expected, 12, "at index {0:d}\n" "expected: {1:f}\n" "calculated: {2:f}".format(i, expected, calculated)) calculated = max5ma2.result() self.assertAlmostEqual( calculated, expected, 12, "at index {0:d}\n" "expected: {1:f}\n" "calculated: {2:f}".format(i, expected, calculated)) if i >= 1: expected = mvTest.result() calculated = mvCorr.result() self.assertAlmostEqual( calculated, expected, 12, "at index {0:d}\n" "expected: {1:f}\n" "calculated: {2:f}".format(i, expected, calculated)) with self.assertRaises(ValueError): _ = Max('close') >> math.sqrt with self.assertRaises(ValueError): _ = (Max('close') ^ Min('close')) >> MovingCorrelation( 20, dependency=('x', 'y', 'z')) (Max('close') ^ Min('close')) >> MovingCorrelation( 20, dependency=('x', 'y'))
class MovingAlphaBeta(StatefulValueHolder): def __init__(self, window, dependency=('pRet', 'mRet', 'riskFree')): super(MovingAlphaBeta, self).__init__(window, dependency) self._returnSize = 2 self._pReturnMean = MovingAverage(window, dependency='x') self._mReturnMean = MovingAverage(window, dependency='y') self._pReturnVar = MovingVariance(window, dependency='x') self._mReturnVar = MovingVariance(window, dependency='y') self._correlationHolder = MovingCorrelation(window, dependency=['x', 'y']) def push(self, data): value = super(MovingAlphaBeta, self).push(data) if np.any(np.isnan(value)): return np.nan pReturn = value[0] mReturn = value[1] rf = value[2] data = {'x': pReturn - rf, 'y': mReturn - rf} self._pReturnMean.push(data) self._mReturnMean.push(data) self._pReturnVar.push(data) self._mReturnVar.push(data) self._correlationHolder.push(data) def result(self): corr = self._correlationHolder.result() tmp = self._pReturnVar.result() if not isClose(tmp, 0.): pStd = math.sqrt(tmp) else: pStd = 0. tmp = self._mReturnVar.result() if not isClose(tmp, 0.): mStd = math.sqrt(tmp) else: mStd = 0. if not isClose(tmp, 0.): beta = corr * pStd / mStd else: beta = 0. alpha = self._pReturnMean.result() - beta * self._mReturnMean.result() return alpha, beta
def testDivOperator(self): mc5 = MovingCorrelation(5, ['open', 'close']) minum = Minimum('open') divRes = Minimum('open') / MovingCorrelation(5, ['open', 'close']) for i, (open, close) in enumerate(zip(self.sampleOpen, self.sampleClose)): data = {'close': close, 'open': open} mc5.push(data) minum.push(data) divRes.push(data) if i >= 1: expected = minum.result() / mc5.result() calculated = divRes.result() self.assertAlmostEqual(calculated, expected, 12, "at index {0:d}\n" "expected: {1:f}\n" "calculated: {2:f}".format(i, expected, calculated))
def testDivOperator(self): mc5 = MovingCorrelation(5, ['open', 'close']) minum = Minimum('open') divRes = Minimum('open') / MovingCorrelation(5, ['open', 'close']) concated = (Minimum('open') ^ MovingCorrelation(5, ['open', 'close'])) / MovingCorrelation(5, ['open', 'close']) concated2 = MovingCorrelation(5, ['open', 'close']) / ( Minimum('open') ^ MovingCorrelation(5, ['open', 'close'])) for i, (open, close) in enumerate(zip(self.sampleOpen, self.sampleClose)): data = {'close': close, 'open': open} mc5.push(data) minum.push(data) divRes.push(data) concated.push(data) concated2.push(data) if i >= 1: expected = minum.result() / mc5.result() calculated = divRes.result() self.assertAlmostEqual(calculated, expected, 12, "at index {0:d}\n" "expected: {1:f}\n" "calculated: {2:f}".format(i, expected, calculated)) expected = (minum.result() / mc5.result(), mc5.result() / mc5.result()) calculated = concated.result() self.assertAlmostEqual(calculated[0], expected[0], 12, "at index {0:d}\n" "expected: {1:f}\n" "calculated: {2:f}".format(i, expected[0], calculated[0])) self.assertAlmostEqual(calculated[1], expected[1], 12, "at index {0:d}\n" "expected: {1:f}\n" "calculated: {2:f}".format(i, expected[1], calculated[1])) expected = (mc5.result() / minum.result(), mc5.result() / mc5.result()) calculated = concated2.result() self.assertAlmostEqual(calculated[0], expected[0], 12, "at index {0:d}\n" "expected: {1:f}\n" "calculated: {2:f}".format(i, expected[0], calculated[0])) self.assertAlmostEqual(calculated[1], expected[1], 12, "at index {0:d}\n" "expected: {1:f}\n" "calculated: {2:f}".format(i, expected[1], calculated[1]))