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Merge pull request #1336 from quantopian/pipeline-docs-touchups

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DOC: Pipeline docstring edits
This commit is contained in:
David Michalowicz
2016-07-20 15:38:33 -04:00
committed by GitHub
parent adea192f02 9cc5796b3e
commit 71b2363775
1 changed files with 13 additions and 13 deletions
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zipline/pipeline/factors/factor.py
+13 -13
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@@ -636,9 +636,9 @@ class Factor(RestrictedDTypeMixin, ComputableTerm):
Parameters
----------
target : zipline.pipeline.Term with a numeric dtype
The term with which to compute correlations against each column of
data produced by `self`. This may be a Factor, a BoundColumn or a
Slice. If `target` is two-dimensional, correlations are computed
The term used to compute correlations against each column of data
produced by `self`. This may be a Factor, a BoundColumn or a Slice.
If `target` is two-dimensional, correlations are computed
asset-wise.
correlation_length : int
Length of the lookback window over which to compute each
@@ -661,7 +661,7 @@ class Factor(RestrictedDTypeMixin, ComputableTerm):
by doing the following::
returns = Returns(window_length=10)
returns_slice = returns[Asset(24)]
returns_slice = returns[sid(24)]
aapl_correlations = returns.pearsonr(
target=returns_slice, correlation_length=30,
)
@@ -669,7 +669,7 @@ class Factor(RestrictedDTypeMixin, ComputableTerm):
This is equivalent to doing::
aapl_correlations = RollingPearsonOfReturns(
target=Asset(24), returns_length=10, correlation_length=30,
target=sid(24), returns_length=10, correlation_length=30,
)
See Also
@@ -701,9 +701,9 @@ class Factor(RestrictedDTypeMixin, ComputableTerm):
Parameters
----------
target : zipline.pipeline.Term with a numeric dtype
The term with which to compute correlations against each column of
data produced by `self`. This may be a Factor, a BoundColumn or a
Slice. If `target` is two-dimensional, correlations are computed
The term used to compute correlations against each column of data
produced by `self`. This may be a Factor, a BoundColumn or a Slice.
If `target` is two-dimensional, correlations are computed
asset-wise.
correlation_length : int
Length of the lookback window over which to compute each
@@ -726,7 +726,7 @@ class Factor(RestrictedDTypeMixin, ComputableTerm):
by doing the following::
returns = Returns(window_length=10)
returns_slice = returns[Asset(24)]
returns_slice = returns[sid(24)]
aapl_correlations = returns.spearmanr(
target=returns_slice, correlation_length=30,
)
@@ -734,7 +734,7 @@ class Factor(RestrictedDTypeMixin, ComputableTerm):
This is equivalent to doing::
aapl_correlations = RollingSpearmanOfReturns(
target=Asset(24), returns_length=10, correlation_length=30,
target=sid(24), returns_length=10, correlation_length=30,
)
See Also
@@ -767,7 +767,7 @@ class Factor(RestrictedDTypeMixin, ComputableTerm):
----------
target : zipline.pipeline.Term with a numeric dtype
The term to use as the predictor/independent variable in each
regression. This may be a Factor, a BoundColumn or a Slice. If
regression. This may be a Factor, a BoundColumn or a Slice. If
`target` is two-dimensional, correlations are computed asset-wise.
correlation_length : int
Length of the lookback window over which to compute each
@@ -789,7 +789,7 @@ class Factor(RestrictedDTypeMixin, ComputableTerm):
regression over 30 days. This can be achieved by doing the following::
returns = Returns(window_length=10)
returns_slice = returns[Asset(24)]
returns_slice = returns[sid(24)]
aapl_regressions = returns.linear_regression(
target=returns_slice, regression_length=30,
)
@@ -797,7 +797,7 @@ class Factor(RestrictedDTypeMixin, ComputableTerm):
This is equivalent to doing::
aapl_regressions = RollingLinearRegressionOfReturns(
target=Asset(24), returns_length=10, regression_length=30,
target=sid(24), returns_length=10, regression_length=30,
)
See Also