def testRateOfReturnNoDivisor(self):
"""Test the rate of return function without a divisor"""
data = np.arange(1, 10)
ror = rateOfReturn(data)
exp_ror = [1, 1 / 2.0, 1 / 3.0, 1 / 4.0, 1 / 5.0, 1 / 6.0, 1 / 7.0, 1 / 8.0]
assert np.shape(ror) == np.shape(exp_ror), "Rate of return has incorrect size"
assert (ror == exp_ror).all(), "Rate of return calculated incorrectly"
data = np.random.rand(1000)
ror = rateOfReturn(data)
exp_ror = np.diff(data) / data[0:-1]
assert np.shape(ror) == np.shape(exp_ror), "Rate of return has incorrect size"
assert (ror == exp_ror).all(), "Rate of return calculated incorrectly"
def testNormalizeDataBasic(self):
"""Test the normalize data function with basic inputs"""
data = np.random.rand(3, 100)
ndata = normalizeData(data)
assert np.shape(ndata) == (3, 99), "Normalize Data gave incorrect shape"
for nd, d in izip(ndata, data):
assert close_enough(nd, rateOfReturn(d)), "Normalize Data did not use Rate of Return function correctly"
def testNormalizeDataWithSelectedDivisor(self):
"""Test the normalize data function with a selected RateOfReturn divisor row"""
data = np.random.rand(3, 100)
data[2] += 100
ndata = normalizeData(data, ror_divisor_row=2)
assert np.shape(ndata) == (3, 99), "Normalize Data gave incorrect shape"
for nd, d in izip(ndata, data):
assert close_enough(
nd, rateOfReturn(d, data[2][0:-1])
), "Normalize Data did not use Rate of Return with divisor function correctly"
def testNormalizeDataWithVolume(self):
"""Test the normalize data function with a selected RateOfReturn divisor row"""
data = np.random.rand(3, 100)
data = np.array(data.tolist() + np.random.randint(100, 10000, (1, 100)).tolist())
ndata = normalizeData(data, volume_row=3)
assert np.shape(ndata) == (4, 99), "Normalize Data gave incorrect shape"
for nd, d in izip(ndata[0:3], data[0:3]):
assert close_enough(nd, rateOfReturn(d)), "Normalize Data did not use Rate of Return correctly"
assert close_enough(
ndata[3], normalizeVolume(data[3][1:])
), "Normalize Data did not use Normalize Volume correctly"
def testRateOfReturnWithDivisor(self):
"""Test the rate of return function with a specified divisor"""
data = [1, 2, 2.5, 2]
div = [2, 2, 2]
bad_div = [2, 2, 2, 2]
ror = rateOfReturn(data, divisor=div)
assert np.shape(ror) == (3,), "Rate of return has incorrect size"
assert (np.array(ror) == np.array([0.5, 0.25, -0.25])).all(), "Rate of return is incorrect"
try:
ror = rateOfReturn(data, divisor=bad_div)
assert False, "Rate of return did not assert error for incorrect sized divisor"
except AssertionError:
pass
data = np.random.rand(100)
div = np.random.rand(99) + 100
ror = rateOfReturn(data, div)
assert np.shape(ror) == (99,), "Rate of return has incorrect size"
assert close_enough(ror, np.ones((99,))), "Rate of return is wrong!"
def testMakeNormalizeDataGenerator(self):
"""Test the normalize data generator function"""
data = np.random.rand(3, 100)
simple_norm = DataNormalizer()
ndata = simple_norm(data)
assert np.shape(ndata) == (3, 99), "Normalize Data gave incorrect shape"
for nd, d in izip(ndata, data):
assert close_enough(nd, rateOfReturn(d)), "Normalize Data did not use Rate of Return function correctly"
data = np.array(data.tolist() + np.random.randint(100, 10000, (1, 100)).tolist())
complex_norm = DataNormalizer(1, 3)
ndata = complex_norm(data)
assert np.shape(ndata) == (4, 99), "Normalize Data gave incorrect shape"
for nd, d in izip(ndata[0:3], data[0:3]):
assert close_enough(
nd, rateOfReturn(d, data[1][0:-1])
), "Normalize Data did not use Rate of Return correctly"
assert close_enough(
ndata[3], normalizeVolume(data[3][1:])
), "Normalize Data did not use Normalize Volume correctly"