mirror of
https://github.com/wassname/catalyst.git
synced 2026-06-28 18:09:32 +08:00
Scott Sanderson
26fd6fda8b
- Fixes an error where Modeling API data known as of the close of `day N` would be shown to algorithms during `before_trading_start` as of the close of the same day. Algorithms should now only receive data during `before_trading_start/handle_data` that was known as of the simulation time at which the function would be called. - All Term instances now have a `mask` attribute that must be a `Filter` or an instance of `AssetExists()`. `mask` can be used to specify that a Factor should be computed in a manner that ignores the values that were not `True` in the mask. - Changed the interface for `FFCLoader.load_adjusted_array` and `Term._compute` from `(columns, mask)`, with mask as a DataFrame, to `(columns, dates, assets, mask)`, where mask is a numpy array. This is primarily to avoid having to reconstruct extra DataFrames when using masks produced by non `AssetExists` filters. - Adds `BoundColumn.latest`, which gives the most-recently-known value of a column.
180 lines
6.7 KiB
Python
180 lines
6.7 KiB
Python
"""
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Tests for Factor terms.
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"""
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from numpy import array, eye, nan, ones
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from zipline.errors import UnknownRankMethod
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from zipline.modelling.factor import Factor
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from zipline.modelling.filter import Filter
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from zipline.utils.test_utils import check_arrays
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from .base import BaseFFCTestCase
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class F(Factor):
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inputs = ()
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window_length = 0
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class Mask(Filter):
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inputs = ()
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window_length = 0
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class FactorTestCase(BaseFFCTestCase):
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def setUp(self):
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super(FactorTestCase, self).setUp()
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self.f = F()
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def test_bad_input(self):
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with self.assertRaises(UnknownRankMethod):
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self.f.rank("not a real rank method")
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def test_rank_ascending(self):
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# Generated with:
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# data = arange(25).reshape(5, 5).transpose() % 4
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data = array([[0, 1, 2, 3, 0],
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[1, 2, 3, 0, 1],
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[2, 3, 0, 1, 2],
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[3, 0, 1, 2, 3],
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[0, 1, 2, 3, 0]], dtype=float)
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expected_ranks = {
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'ordinal': array([[1., 3., 4., 5., 2.],
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[2., 4., 5., 1., 3.],
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[3., 5., 1., 2., 4.],
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[4., 1., 2., 3., 5.],
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[1., 3., 4., 5., 2.]]),
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'average': array([[1.5, 3., 4., 5., 1.5],
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[2.5, 4., 5., 1., 2.5],
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[3.5, 5., 1., 2., 3.5],
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[4.5, 1., 2., 3., 4.5],
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[1.5, 3., 4., 5., 1.5]]),
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'min': array([[1., 3., 4., 5., 1.],
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[2., 4., 5., 1., 2.],
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[3., 5., 1., 2., 3.],
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[4., 1., 2., 3., 4.],
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[1., 3., 4., 5., 1.]]),
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'max': array([[2., 3., 4., 5., 2.],
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[3., 4., 5., 1., 3.],
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[4., 5., 1., 2., 4.],
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[5., 1., 2., 3., 5.],
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[2., 3., 4., 5., 2.]]),
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'dense': array([[1., 2., 3., 4., 1.],
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[2., 3., 4., 1., 2.],
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[3., 4., 1., 2., 3.],
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[4., 1., 2., 3., 4.],
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[1., 2., 3., 4., 1.]]),
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}
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def check(terms):
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results = self.run_terms(
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terms,
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initial_workspace={self.f: data},
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mask=self.build_mask(ones((5, 5))),
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)
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for method in terms:
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check_arrays(results[method], expected_ranks[method])
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check({meth: self.f.rank(method=meth) for meth in expected_ranks})
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check({
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meth: self.f.rank(method=meth, ascending=True)
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for meth in expected_ranks
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})
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# Not passing a method should default to ordinal.
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check({'ordinal': self.f.rank()})
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check({'ordinal': self.f.rank(ascending=True)})
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def test_rank_descending(self):
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# Generated with:
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# data = arange(25).reshape(5, 5).transpose() % 4
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data = array([[0, 1, 2, 3, 0],
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[1, 2, 3, 0, 1],
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[2, 3, 0, 1, 2],
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[3, 0, 1, 2, 3],
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[0, 1, 2, 3, 0]], dtype=float)
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expected_ranks = {
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'ordinal': array([[4., 3., 2., 1., 5.],
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[3., 2., 1., 5., 4.],
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[2., 1., 5., 4., 3.],
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[1., 5., 4., 3., 2.],
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[4., 3., 2., 1., 5.]]),
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'average': array([[4.5, 3., 2., 1., 4.5],
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[3.5, 2., 1., 5., 3.5],
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[2.5, 1., 5., 4., 2.5],
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[1.5, 5., 4., 3., 1.5],
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[4.5, 3., 2., 1., 4.5]]),
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'min': array([[4., 3., 2., 1., 4.],
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[3., 2., 1., 5., 3.],
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[2., 1., 5., 4., 2.],
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[1., 5., 4., 3., 1.],
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[4., 3., 2., 1., 4.]]),
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'max': array([[5., 3., 2., 1., 5.],
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[4., 2., 1., 5., 4.],
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[3., 1., 5., 4., 3.],
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[2., 5., 4., 3., 2.],
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[5., 3., 2., 1., 5.]]),
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'dense': array([[4., 3., 2., 1., 4.],
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[3., 2., 1., 4., 3.],
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[2., 1., 4., 3., 2.],
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[1., 4., 3., 2., 1.],
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[4., 3., 2., 1., 4.]]),
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}
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def check(terms):
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results = self.run_terms(
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terms,
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initial_workspace={self.f: data},
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mask=self.build_mask(ones((5, 5))),
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)
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for method in terms:
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check_arrays(results[method], expected_ranks[method])
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check({
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meth: self.f.rank(method=meth, ascending=False)
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for meth in expected_ranks
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})
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# Not passing a method should default to ordinal.
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check({'ordinal': self.f.rank(ascending=False)})
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def test_rank_after_mask(self):
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# data = arange(25).reshape(5, 5).transpose() % 4
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data = array([[0, 1, 2, 3, 0],
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[1, 2, 3, 0, 1],
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[2, 3, 0, 1, 2],
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[3, 0, 1, 2, 3],
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[0, 1, 2, 3, 0]], dtype=float)
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mask_data = ~eye(5, dtype=bool)
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initial_workspace = {self.f: data, Mask(): mask_data}
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terms = {
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"ascending_nomask": self.f.rank(ascending=True),
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"ascending_mask": self.f.rank(ascending=True, mask=Mask()),
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# "descending_nomask": self.f.rank(ascending=False),
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# "descending_mask": self.f.rank(ascending=False, mask=Mask()),
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}
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expected = {
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"ascending_nomask": array([[1., 3., 4., 5., 2.],
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[2., 4., 5., 1., 3.],
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[3., 5., 1., 2., 4.],
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[4., 1., 2., 3., 5.],
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[1., 3., 4., 5., 2.]]),
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# Diagonal should be all nans, and anything whose rank was less
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# than the diagonal in the unmasked calc should go down by 1.
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"ascending_mask": array([[nan, 2., 3., 4., 1.],
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[2., nan, 4., 1., 3.],
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[2., 4., nan, 1., 3.],
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[3., 1., 2., nan, 4.],
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[1., 2., 3., 4., nan]]),
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}
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results = self.run_terms(
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terms,
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initial_workspace,
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mask=self.build_mask(ones((5, 5))),
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)
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for method in results:
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check_arrays(expected[method], results[method])
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