def test_factor_correlation_methods(self, returns_length,
correlation_length):
"""
Ensure that `Factor.pearsonr` and `Factor.spearmanr` are consistent
with the built-in factors `RollingPearsonOfReturns` and
`RollingSpearmanOfReturns`.
"""
my_asset = self.my_asset
start_date = self.pipeline_start_date
end_date = self.pipeline_end_date
run_pipeline = self.run_pipeline
returns = Returns(window_length=returns_length, inputs=[self.col])
returns_slice = returns[my_asset]
pearson = returns.pearsonr(
target=returns_slice,
correlation_length=correlation_length,
)
spearman = returns.spearmanr(
target=returns_slice,
correlation_length=correlation_length,
)
expected_pearson = RollingPearsonOfReturns(
target=my_asset,
returns_length=returns_length,
correlation_length=correlation_length,
)
expected_spearman = RollingSpearmanOfReturns(
target=my_asset,
returns_length=returns_length,
correlation_length=correlation_length,
)
# These built-ins construct their own Returns factor to use as inputs,
# so the only way to set our own inputs is to do so after the fact.
# This should not be done in practice. It is necessary here because we
# want Returns to use our random data as an input, but by default it is
# using USEquityPricing.close.
expected_pearson.inputs = [returns, returns_slice]
expected_spearman.inputs = [returns, returns_slice]
columns = {
'pearson': pearson,
'spearman': spearman,
'expected_pearson': expected_pearson,
'expected_spearman': expected_spearman,
}
results = run_pipeline(Pipeline(columns=columns), start_date, end_date)
pearson_results = results['pearson'].unstack()
spearman_results = results['spearman'].unstack()
expected_pearson_results = results['expected_pearson'].unstack()
expected_spearman_results = results['expected_spearman'].unstack()
assert_frame_equal(pearson_results, expected_pearson_results)
assert_frame_equal(spearman_results, expected_spearman_results)
def test_factor_correlation_methods(self,
returns_length,
correlation_length):
"""
Ensure that `Factor.pearsonr` and `Factor.spearmanr` are consistent
with the built-in factors `RollingPearsonOfReturns` and
`RollingSpearmanOfReturns`.
"""
my_asset = self.my_asset
start_date = self.pipeline_start_date
end_date = self.pipeline_end_date
run_pipeline = self.run_pipeline
returns = Returns(window_length=returns_length, inputs=[self.col])
returns_slice = returns[my_asset]
pearson = returns.pearsonr(
target=returns_slice, correlation_length=correlation_length,
)
spearman = returns.spearmanr(
target=returns_slice, correlation_length=correlation_length,
)
expected_pearson = RollingPearsonOfReturns(
target=my_asset,
returns_length=returns_length,
correlation_length=correlation_length,
)
expected_spearman = RollingSpearmanOfReturns(
target=my_asset,
returns_length=returns_length,
correlation_length=correlation_length,
)
# These built-ins construct their own Returns factor to use as inputs,
# so the only way to set our own inputs is to do so after the fact.
# This should not be done in practice. It is necessary here because we
# want Returns to use our random data as an input, but by default it is
# using USEquityPricing.close.
expected_pearson.inputs = [returns, returns_slice]
expected_spearman.inputs = [returns, returns_slice]
columns = {
'pearson': pearson,
'spearman': spearman,
'expected_pearson': expected_pearson,
'expected_spearman': expected_spearman,
}
results = run_pipeline(Pipeline(columns=columns), start_date, end_date)
pearson_results = results['pearson'].unstack()
spearman_results = results['spearman'].unstack()
expected_pearson_results = results['expected_pearson'].unstack()
expected_spearman_results = results['expected_spearman'].unstack()
assert_frame_equal(pearson_results, expected_pearson_results)
assert_frame_equal(spearman_results, expected_spearman_results)
def test_factor_correlation_methods(self, returns_length,
correlation_length):
"""
Ensure that `Factor.pearsonr` and `Factor.spearmanr` are consistent
with the built-in factors `RollingPearsonOfReturns` and
`RollingSpearmanOfReturns`.
"""
my_asset = self.asset_finder.retrieve_asset(self.sids[0])
returns = Returns(window_length=returns_length, inputs=[self.col])
returns_slice = returns[my_asset]
pearson = returns.pearsonr(
target=returns_slice,
correlation_length=correlation_length,
)
spearman = returns.spearmanr(
target=returns_slice,
correlation_length=correlation_length,
)
expected_pearson = RollingPearsonOfReturns(
target=my_asset,
returns_length=returns_length,
correlation_length=correlation_length,
)
expected_spearman = RollingSpearmanOfReturns(
target=my_asset,
returns_length=returns_length,
correlation_length=correlation_length,
)
# These built-ins construct their own Returns factor to use as inputs,
# so the only way to set our own inputs is to do so after the fact.
# This should not be done in practice. It is necessary here because we
# want Returns to use our random data as an input, but by default it is
# using USEquityPricing.close.
expected_pearson.inputs = [returns, returns_slice]
expected_spearman.inputs = [returns, returns_slice]
columns = {
'pearson': pearson,
'spearman': spearman,
'expected_pearson': expected_pearson,
'expected_spearman': expected_spearman,
}
results = self.run_pipeline(
Pipeline(columns=columns),
self.pipeline_start_date,
self.pipeline_end_date,
)
pearson_results = results['pearson'].unstack()
spearman_results = results['spearman'].unstack()
expected_pearson_results = results['expected_pearson'].unstack()
expected_spearman_results = results['expected_spearman'].unstack()
assert_frame_equal(pearson_results, expected_pearson_results)
assert_frame_equal(spearman_results, expected_spearman_results)
# Make sure we cannot call the correlation methods on factors or slices
# of dtype `datetime64[ns]`.
class DateFactor(CustomFactor):
window_length = 1
inputs = []
dtype = datetime64ns_dtype
window_safe = True
def compute(self, today, assets, out):
pass
date_factor = DateFactor()
date_factor_slice = date_factor[my_asset]
with self.assertRaises(TypeError):
date_factor.pearsonr(
target=returns_slice,
correlation_length=correlation_length,
)
with self.assertRaises(TypeError):
date_factor.spearmanr(
target=returns_slice,
correlation_length=correlation_length,
)
with self.assertRaises(TypeError):
returns.pearsonr(
target=date_factor_slice,
correlation_length=correlation_length,
)
with self.assertRaises(TypeError):
returns.pearsonr(
target=date_factor_slice,
correlation_length=correlation_length,
)
def test_factor_correlation_methods(self,
returns_length,
correlation_length):
"""
Ensure that `Factor.pearsonr` and `Factor.spearmanr` are consistent
with the built-in factors `RollingPearsonOfReturns` and
`RollingSpearmanOfReturns`.
"""
my_asset = self.asset_finder.retrieve_asset(self.sids[0])
returns = Returns(window_length=returns_length, inputs=[self.col])
returns_slice = returns[my_asset]
pearson = returns.pearsonr(
target=returns_slice, correlation_length=correlation_length,
)
spearman = returns.spearmanr(
target=returns_slice, correlation_length=correlation_length,
)
expected_pearson = RollingPearsonOfReturns(
target=my_asset,
returns_length=returns_length,
correlation_length=correlation_length,
)
expected_spearman = RollingSpearmanOfReturns(
target=my_asset,
returns_length=returns_length,
correlation_length=correlation_length,
)
# These built-ins construct their own Returns factor to use as inputs,
# so the only way to set our own inputs is to do so after the fact.
# This should not be done in practice. It is necessary here because we
# want Returns to use our random data as an input, but by default it is
# using USEquityPricing.close.
expected_pearson.inputs = [returns, returns_slice]
expected_spearman.inputs = [returns, returns_slice]
columns = {
'pearson': pearson,
'spearman': spearman,
'expected_pearson': expected_pearson,
'expected_spearman': expected_spearman,
}
results = self.run_pipeline(
Pipeline(columns=columns),
self.pipeline_start_date,
self.pipeline_end_date,
)
pearson_results = results['pearson'].unstack()
spearman_results = results['spearman'].unstack()
expected_pearson_results = results['expected_pearson'].unstack()
expected_spearman_results = results['expected_spearman'].unstack()
assert_frame_equal(pearson_results, expected_pearson_results)
assert_frame_equal(spearman_results, expected_spearman_results)
# Make sure we cannot call the correlation methods on factors or slices
# of dtype `datetime64[ns]`.
class DateFactor(CustomFactor):
window_length = 1
inputs = []
dtype = datetime64ns_dtype
window_safe = True
def compute(self, today, assets, out):
pass
date_factor = DateFactor()
date_factor_slice = date_factor[my_asset]
with self.assertRaises(TypeError):
date_factor.pearsonr(
target=returns_slice, correlation_length=correlation_length,
)
with self.assertRaises(TypeError):
date_factor.spearmanr(
target=returns_slice, correlation_length=correlation_length,
)
with self.assertRaises(TypeError):
returns.pearsonr(
target=date_factor_slice,
correlation_length=correlation_length,
)
with self.assertRaises(TypeError):
returns.pearsonr(
target=date_factor_slice,
correlation_length=correlation_length,
)
