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 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]))
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]))
def testDivOperator(self):
mc5 = MovingCorrelation(5, 'open', 'close')
minum = Min('open')
divRes = Min('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))
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