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'])
class MovingSharp(StatefulValueHolder):
def __init__(self, window, dependency=('ret', 'riskFree')):
super(MovingSharp, self).__init__(window, dependency)
self._mean = MovingAverage(window, dependency='x')
self._var = MovingVariance(window, dependency='x', isPopulation=False)
def push(self, data):
value = super(MovingSharp, self).push(data)
ret = value[0]
benchmark = value[1]
data = {'x': ret - benchmark}
self._mean.push(data)
self._var.push(data)
def result(self):
return self._mean.result() / math.sqrt(self._var.result())
class MovingSharp(StatefulValueHolder):
def __init__(self, window, dependency=('ret', 'riskFree')):
super(MovingSharp, self).__init__(window, dependency)
self._mean = MovingAverage(window, dependency='x')
self._var = MovingVariance(window, dependency='x', isPopulation=False)
def push(self, data):
value = super(MovingSharp, self).push(data)
ret = value[0]
benchmark = value[1]
data = {'x': ret - benchmark}
self._mean.push(data)
self._var.push(data)
def result(self):
return self._mean.result() / math.sqrt(self._var.result())
def testMultiplyOperator(self):
mv5 = MovingVariance(5, 'close')
average = Average('open')
mulRes = MovingVariance(5, 'close') * Average('open')
for i, (open, close) in enumerate(zip(self.sampleOpen, self.sampleClose)):
data = {'close': close, 'open': open}
mv5.push(data)
average.push(data)
mulRes.push(data)
if i >= 1:
expected = mv5.result() * average.result()
calculated = mulRes.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')):
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 testMultiplyOperator(self):
mv5 = MovingVariance(5, 'close')
average = Average('open')
mulRes = MovingVariance(5, 'close') * Average('open')
concated = (Average('open') ^ mv5) * Average('open')
concated2 = Average('open') * (Average('open') ^ mv5)
for i, (open, close) in enumerate(zip(self.sampleOpen, self.sampleClose)):
data = {'close': close, 'open': open}
mv5.push(data)
average.push(data)
mulRes.push(data)
concated.push(data)
concated2.push(data)
if i >= 1:
expected = mv5.result() * average.result()
calculated = mulRes.result()
self.assertAlmostEqual(calculated, expected, 12, "at index {0:d}\n"
"expected: {1:f}\n"
"calculated: {2:f}".format(i, expected, calculated))
expected = (average.result() * average.result(), mv5.result() * average.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]))
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]))
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 __init__(self, window, dependency=('ret', 'riskFree')):
super(MovingSharp, self).__init__(window, dependency)
self._mean = MovingAverage(window, dependency='x')
self._var = MovingVariance(window, dependency='x', isPopulation=False)
def __init__(self, window, dependency=('ret', 'riskFree')):
super(MovingSharp, self).__init__(window, dependency)
self._mean = MovingAverage(window, dependency='x')
self._var = MovingVariance(window, dependency='x', isPopulation=False)
