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Merge pull request #1826 from quantopian/add-engine-docs
Add engine docs
This commit is contained in:
Ana Ruelas
+60
-39
@@ -176,13 +176,48 @@ Pipeline API
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:members:
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:member-order: groupwise
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.. autoclass:: zipline.pipeline.filters.Filter
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:members: __and__, __or__
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:exclude-members: dtype
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.. autoclass:: zipline.pipeline.factors.Factor
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:members: top, bottom, rank, percentile_between, isnan, notnan, isfinite,
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eq, __add__, __sub__, __mul__, __div__, __mod__, __pow__, __lt__,
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:members: bottom, deciles, demean, linear_regression, pearsonr,
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percentile_between, quantiles, quartiles, quintiles, rank,
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spearmanr, top, winsorize, zscore, isnan, notnan, isfinite, eq,
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__add__, __sub__, __mul__, __div__, __mod__, __pow__, __lt__,
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__le__, __ne__, __ge__, __gt__
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:exclude-members: dtype
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:member-order: bysource
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.. autoclass:: zipline.pipeline.term.Term
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:members:
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:exclude-members: compute_extra_rows, dependencies, inputs, mask, windowed
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.. autoclass:: zipline.pipeline.data.USEquityPricing
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:members: open, high, low, close, volume
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:undoc-members:
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Built-in Factors
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````````````````
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.. autoclass:: zipline.pipeline.factors.AverageDollarVolume
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:members:
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.. autoclass:: zipline.pipeline.factors.BollingerBands
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:members:
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.. autoclass:: zipline.pipeline.factors.BusinessDaysSincePreviousEvent
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:members:
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.. autoclass:: zipline.pipeline.factors.BusinessDaysUntilNextEvent
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:members:
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.. autoclass:: zipline.pipeline.factors.ExponentialWeightedMovingAverage
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:members:
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.. autoclass:: zipline.pipeline.factors.ExponentialWeightedMovingStdDev
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:members:
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.. autoclass:: zipline.pipeline.factors.Latest
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:members:
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@@ -192,15 +227,18 @@ Pipeline API
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.. autoclass:: zipline.pipeline.factors.Returns
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:members:
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.. autoclass:: zipline.pipeline.factors.RollingLinearRegressionOfReturns
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:members:
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.. autoclass:: zipline.pipeline.factors.RollingPearsonOfReturns
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:members:
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.. autoclass:: zipline.pipeline.factors.RollingSpearmanOfReturns
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:members:
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.. autoclass:: zipline.pipeline.factors.RSI
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:members:
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.. autoclass:: zipline.pipeline.factors.BusinessDaysUntilNextEarnings
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:members:
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.. autoclass:: zipline.pipeline.factors.BusinessDaysSincePreviousEarnings
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:members:
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.. autoclass:: zipline.pipeline.factors.SimpleMovingAverage
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:members:
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@@ -210,39 +248,25 @@ Pipeline API
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.. autoclass:: zipline.pipeline.factors.WeightedAverageValue
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:members:
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.. autoclass:: zipline.pipeline.factors.ExponentialWeightedMovingAverage
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:members:
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Pipeline Engine
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```````````````
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.. autoclass:: zipline.pipeline.factors.ExponentialWeightedMovingStdDev
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:members:
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.. autoclass:: zipline.pipeline.engine.PipelineEngine
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:members: run_pipeline, run_chunked_pipeline
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:member-order: bysource
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.. autoclass:: zipline.pipeline.factors.AverageDollarVolume
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:members:
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.. autoclass:: zipline.pipeline.engine.SimplePipelineEngine
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:members: __init__, run_pipeline, run_chunked_pipeline
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:member-order: bysource
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.. autoclass:: zipline.pipeline.factors.BollingerBands
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:members:
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.. autofunction:: zipline.pipeline.engine.default_populate_initial_workspace
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.. autoclass:: zipline.pipeline.factors.RollingPearsonOfReturns
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:members:
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.. autoclass:: zipline.pipeline.factors.RollingSpearmanOfReturns
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:members:
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.. autoclass:: zipline.pipeline.factors.RollingLinearRegressionOfReturns
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:members:
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.. autoclass:: zipline.pipeline.filters.Filter
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:members: __and__, __or__
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:exclude-members: dtype
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.. autoclass:: zipline.pipeline.data.EarningsCalendar
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:members: next_announcement, previous_announcement
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:undoc-members:
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.. autoclass:: zipline.pipeline.data.USEquityPricing
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:members: open, high, low, close, volume
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:undoc-members:
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Data Loaders
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````````````
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.. autoclass:: zipline.pipeline.loaders.equity_pricing_loader.USEquityPricingLoader
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:members: __init__, from_files, load_adjusted_array
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:member-order: bysource
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Asset Metadata
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~~~~~~~~~~~~~~
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@@ -308,9 +332,6 @@ Readers
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.. autoclass:: zipline.assets.AssetFinder
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:members:
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.. autoclass:: zipline.assets.AssetFinderCachedEquities
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:members:
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.. autoclass:: zipline.data.data_portal.DataPortal
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:members:
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@@ -307,13 +307,13 @@ have.
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help some bundles generate queries for the days needed.
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``start_session``
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````````````
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`````````````````
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``start_session`` is a :class:`pandas.Timestamp` object indicating the first
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day that the bundle should load data for.
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``end_session``
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````````````
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```````````````
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``end_session`` is a :class:`pandas.Timestamp` object indicating the last day
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that the bundle should load data for.
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@@ -81,7 +81,7 @@ Enhancements
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- Disallow regressions of length 1. (:issue:`1466`)
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Experimental
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~~~~~~~~~~~
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~~~~~~~~~~~~
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- Add support for comingled Future and Equity history windows, and enable other
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Future data access via data portal. (:issue:`1435`) (:issue:`1432`)
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@@ -111,7 +111,7 @@ Bug Fixes
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- Alpha and sharpe ratio are now annualized. (:issue:`1322`)
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- Fix units during reading and writing of daily bar ``first_trading_day ``
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- Fix units during reading and writing of daily bar ``first_trading_day``
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attribute. (:issue:`1245`)
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- Optional dispatch modules, when missing, no longer cause a `NameError`.
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@@ -131,7 +131,7 @@ Bug Fixes
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- Fix ``AverageDollarVolume`` NaN handling. (:issue:`1309`)
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Performance
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Performance
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~~~~~~~~~~~
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- Performance improvements to blaze core loader. (:issue:`1227`)
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@@ -157,7 +157,7 @@ Testing
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fixtures. (:issue:`1243`)
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Data Format Changes
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~~~~~~~~~~~~~~~~
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~~~~~~~~~~~~~~~~~~~
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- ``BcolzDailyBarReader`` and ``BcolzDailyBarWriter`` use trading calendar instance,
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instead of trading days serialized to ``JSON``. (:issue:`1330`)
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@@ -49,8 +49,8 @@ def rowwise_rank(array, mask=None):
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Take a 2D array and return the 0-indexed sorted position of each element in
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the array for each row.
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Example
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-------
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Examples
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||||
--------
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In [5]: data
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Out[5]:
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array([[-0.141, -1.103, -1.0171, 0.7812, 0.07 ],
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@@ -147,8 +147,8 @@ def to_dict(l):
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"""
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Convert a list to a dict with keys drawn from '0', '1', '2', ...
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|
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Example
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-------
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||||
Examples
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||||
--------
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||||
>>> to_dict([2, 3, 4]) # doctest: +SKIP
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{'0': 2, '1': 3, '2': 4}
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"""
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@@ -1374,7 +1374,9 @@ class DataPortal(object):
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Retrieves the future chain for the contract at the given `dt` according
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the `continuous_future` specification.
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Returns:
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Returns
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||||
-------
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future_chain : list[Future]
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A list of active futures, where the first index is the current
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contract specified by the continuous future definition, the second
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+30
-30
@@ -90,13 +90,13 @@ def _sid_subdir_path(sid):
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The number in each directory is designed to support at least 100000
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equities.
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Parameters:
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-----------
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Parameters
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----------
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sid : int
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Asset identifier.
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Returns:
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--------
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Returns
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-------
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out : string
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A path for the bcolz rootdir, including subdirectory prefixes based on
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the padded string representation of the given sid.
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@@ -517,13 +517,13 @@ class BcolzMinuteBarWriter(object):
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def sidpath(self, sid):
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"""
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Parameters:
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-----------
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Parameters
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----------
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sid : int
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Asset identifier.
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Returns:
|
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--------
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Returns
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-------
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out : string
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Full path to the bcolz rootdir for the given sid.
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"""
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@@ -532,13 +532,13 @@ class BcolzMinuteBarWriter(object):
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def last_date_in_output_for_sid(self, sid):
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"""
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Parameters:
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-----------
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Parameters
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----------
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sid : int
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Asset identifier.
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||||
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||||
Returns:
|
||||
--------
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Returns
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-------
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out : pd.Timestamp
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The midnight of the last date written in to the output for the
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||||
given sid.
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@@ -561,8 +561,8 @@ class BcolzMinuteBarWriter(object):
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"""
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Create empty ctable for given path.
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|
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Parameters:
|
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-----------
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Parameters
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----------
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path : string
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The path to rootdir of the new ctable.
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"""
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@@ -624,8 +624,8 @@ class BcolzMinuteBarWriter(object):
|
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including the specified date) will be padded with `minute_per_day`
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worth of zeros
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|
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Parameters:
|
||||
-----------
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Parameters
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----------
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sid : int
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The asset identifier for the data being written.
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date : datetime-like
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@@ -703,8 +703,8 @@ class BcolzMinuteBarWriter(object):
|
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If the length of the bcolz ctable is not exactly to the date before
|
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the first day provided, fill the ctable with 0s up to that date.
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
Parameters
|
||||
----------
|
||||
sid : int
|
||||
The asset identifer for the data being written.
|
||||
df : pd.DataFrame
|
||||
@@ -736,8 +736,8 @@ class BcolzMinuteBarWriter(object):
|
||||
If the length of the bcolz ctable is not exactly to the date before
|
||||
the first day provided, fill the ctable with 0s up to that date.
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
Parameters
|
||||
----------
|
||||
sid : int
|
||||
The asset identifier for the data being written.
|
||||
dts : datetime64 array
|
||||
@@ -763,8 +763,8 @@ class BcolzMinuteBarWriter(object):
|
||||
"""
|
||||
Internal method for `write_cols` and `write`.
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
Parameters
|
||||
----------
|
||||
sid : int
|
||||
The asset identifier for the data being written.
|
||||
dts : datetime64 array
|
||||
@@ -887,8 +887,8 @@ class BcolzMinuteBarReader(MinuteBarReader):
|
||||
"""
|
||||
Reader for data written by BcolzMinuteBarWriter
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
Parameters
|
||||
----------
|
||||
rootdir : string
|
||||
The root directory containing the metadata and asset bcolz
|
||||
directories.
|
||||
@@ -980,8 +980,8 @@ class BcolzMinuteBarReader(MinuteBarReader):
|
||||
based on the regular period of minutes per day and the market close
|
||||
do not match.
|
||||
|
||||
Returns:
|
||||
--------
|
||||
Returns
|
||||
-------
|
||||
List of DatetimeIndex representing the minutes to exclude because
|
||||
of early closes.
|
||||
"""
|
||||
@@ -1080,8 +1080,8 @@ class BcolzMinuteBarReader(MinuteBarReader):
|
||||
"""
|
||||
Retrieve the pricing info for the given sid, dt, and field.
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
Parameters
|
||||
----------
|
||||
sid : int
|
||||
Asset identifier.
|
||||
dt : datetime-like
|
||||
@@ -1090,8 +1090,8 @@ class BcolzMinuteBarReader(MinuteBarReader):
|
||||
The type of pricing data to retrieve.
|
||||
('open', 'high', 'low', 'close', 'volume')
|
||||
|
||||
Returns:
|
||||
--------
|
||||
Returns
|
||||
-------
|
||||
out : float|int
|
||||
|
||||
The market data for the given sid, dt, and field coordinates.
|
||||
|
||||
@@ -24,8 +24,8 @@ def naive_grouped_rowwise_apply(data,
|
||||
Array into which to write output. If not supplied, a new array of the
|
||||
same shape as ``data`` is allocated and returned.
|
||||
|
||||
Example
|
||||
-------
|
||||
Examples
|
||||
--------
|
||||
>>> data = np.array([[1., 2., 3.],
|
||||
... [2., 3., 4.],
|
||||
... [5., 6., 7.]])
|
||||
|
||||
@@ -22,8 +22,8 @@ def restrict_to_dtype(dtype, message_template):
|
||||
`message_template.format` will be called with keyword arguments
|
||||
`method_name`, `expected_dtype`, and `received_dtype`.
|
||||
|
||||
Usage
|
||||
-----
|
||||
Examples
|
||||
--------
|
||||
@restrict_to_dtype(
|
||||
dtype=float64_dtype,
|
||||
message_template=(
|
||||
|
||||
+40
-25
@@ -37,7 +37,8 @@ class PipelineEngine(with_metaclass(ABCMeta)):
|
||||
@abstractmethod
|
||||
def run_pipeline(self, pipeline, start_date, end_date):
|
||||
"""
|
||||
Compute values for `pipeline` between `start_date` and `end_date`.
|
||||
Compute values for ``pipeline`` between ``start_date`` and
|
||||
``end_date``.
|
||||
|
||||
Returns a DataFrame with a MultiIndex of (date, asset) pairs.
|
||||
|
||||
@@ -55,14 +56,14 @@ class PipelineEngine(with_metaclass(ABCMeta)):
|
||||
result : pd.DataFrame
|
||||
A frame of computed results.
|
||||
|
||||
The columns `result` correspond to the entries of
|
||||
The ``result`` columns correspond to the entries of
|
||||
`pipeline.columns`, which should be a dictionary mapping strings to
|
||||
instances of `zipline.pipeline.term.Term`.
|
||||
instances of :class:`zipline.pipeline.term.Term`.
|
||||
|
||||
For each date between `start_date` and `end_date`, `result` will
|
||||
contain a row for each asset that passed `pipeline.screen`. A
|
||||
screen of None indicates that a row should be returned for each
|
||||
asset that existed each day.
|
||||
For each date between ``start_date`` and ``end_date``, ``result``
|
||||
will contain a row for each asset that passed `pipeline.screen`.
|
||||
A screen of ``None`` indicates that a row should be returned for
|
||||
each asset that existed each day.
|
||||
"""
|
||||
raise NotImplementedError("run_pipeline")
|
||||
|
||||
@@ -90,18 +91,18 @@ class PipelineEngine(with_metaclass(ABCMeta)):
|
||||
result : pd.DataFrame
|
||||
A frame of computed results.
|
||||
|
||||
The columns `result` correspond to the entries of
|
||||
The ``result`` columns correspond to the entries of
|
||||
`pipeline.columns`, which should be a dictionary mapping strings to
|
||||
instances of `zipline.pipeline.term.Term`.
|
||||
instances of :class:`zipline.pipeline.term.Term`.
|
||||
|
||||
For each date between `start_date` and `end_date`, `result` will
|
||||
contain a row for each asset that passed `pipeline.screen`. A
|
||||
screen of None indicates that a row should be returned for each
|
||||
asset that existed each day.
|
||||
For each date between ``start_date`` and ``end_date``, ``result``
|
||||
will contain a row for each asset that passed `pipeline.screen`.
|
||||
A screen of ``None`` indicates that a row should be returned for
|
||||
each asset that existed each day.
|
||||
|
||||
See Also
|
||||
--------
|
||||
:meth:`PipelineEngine.run_pipeline`
|
||||
:meth:`zipline.pipeline.engine.PipelineEngine.run_pipeline`
|
||||
"""
|
||||
raise NotImplementedError("run_chunked_pipeline")
|
||||
|
||||
@@ -217,15 +218,6 @@ class SimplePipelineEngine(PipelineEngine):
|
||||
"""
|
||||
Compute a pipeline.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
pipeline : zipline.pipeline.Pipeline
|
||||
The pipeline to run.
|
||||
start_date : pd.Timestamp
|
||||
Start date of the computed matrix.
|
||||
end_date : pd.Timestamp
|
||||
End date of the computed matrix.
|
||||
|
||||
The algorithm implemented here can be broken down into the following
|
||||
stages:
|
||||
|
||||
@@ -256,10 +248,33 @@ class SimplePipelineEngine(PipelineEngine):
|
||||
Step 2 is performed in ``SimplePipelineEngine.compute_chunk``.
|
||||
Steps 3, 4, and 5 are performed in ``SimplePiplineEngine._to_narrow``.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
pipeline : zipline.pipeline.Pipeline
|
||||
The pipeline to run.
|
||||
start_date : pd.Timestamp
|
||||
Start date of the computed matrix.
|
||||
end_date : pd.Timestamp
|
||||
End date of the computed matrix.
|
||||
|
||||
Returns
|
||||
-------
|
||||
result : pd.DataFrame
|
||||
A frame of computed results.
|
||||
|
||||
The ``result`` columns correspond to the entries of
|
||||
`pipeline.columns`, which should be a dictionary mapping strings to
|
||||
instances of :class:`zipline.pipeline.term.Term`.
|
||||
|
||||
For each date between ``start_date`` and ``end_date``, ``result``
|
||||
will contain a row for each asset that passed `pipeline.screen`.
|
||||
A screen of ``None`` indicates that a row should be returned for
|
||||
each asset that existed each day.
|
||||
|
||||
See Also
|
||||
--------
|
||||
:meth:`PipelineEngine.run_pipeline`
|
||||
:meth:`PipelineEngine.run_chunked_pipeline`
|
||||
:meth:`zipline.pipeline.engine.PipelineEngine.run_pipeline`
|
||||
:meth:`zipline.pipeline.engine.PipelineEngine.run_chunked_pipeline`
|
||||
"""
|
||||
if end_date < start_date:
|
||||
raise ValueError(
|
||||
|
||||
@@ -410,8 +410,8 @@ class Factor(RestrictedDTypeMixin, ComputableTerm):
|
||||
groupby : zipline.pipeline.Classifier, optional
|
||||
A classifier defining partitions over which to compute means.
|
||||
|
||||
Example
|
||||
-------
|
||||
Examples
|
||||
--------
|
||||
Let ``f`` be a Factor which would produce the following output::
|
||||
|
||||
AAPL MSFT MCD BK
|
||||
@@ -562,8 +562,8 @@ class Factor(RestrictedDTypeMixin, ComputableTerm):
|
||||
|
||||
``zscore()`` is only supported on Factors of dtype float64.
|
||||
|
||||
Example
|
||||
-------
|
||||
Examples
|
||||
--------
|
||||
See :meth:`~zipline.pipeline.factors.Factor.demean` for an in-depth
|
||||
example of the semantics for ``mask`` and ``groupby``.
|
||||
|
||||
@@ -674,8 +674,8 @@ class Factor(RestrictedDTypeMixin, ComputableTerm):
|
||||
A new Factor that will compute correlations between `target` and
|
||||
the columns of `self`.
|
||||
|
||||
Example
|
||||
-------
|
||||
Examples
|
||||
--------
|
||||
Suppose we want to create a factor that computes the correlation
|
||||
between AAPL's 10-day returns and the 10-day returns of all other
|
||||
assets, computing each correlation over 30 days. This can be achieved
|
||||
@@ -739,8 +739,8 @@ class Factor(RestrictedDTypeMixin, ComputableTerm):
|
||||
A new Factor that will compute correlations between `target` and
|
||||
the columns of `self`.
|
||||
|
||||
Example
|
||||
-------
|
||||
Examples
|
||||
--------
|
||||
Suppose we want to create a factor that computes the correlation
|
||||
between AAPL's 10-day returns and the 10-day returns of all other
|
||||
assets, computing each correlation over 30 days. This can be achieved
|
||||
@@ -803,8 +803,8 @@ class Factor(RestrictedDTypeMixin, ComputableTerm):
|
||||
A new Factor that will compute linear regressions of `target`
|
||||
against the columns of `self`.
|
||||
|
||||
Example
|
||||
-------
|
||||
Examples
|
||||
--------
|
||||
Suppose we want to create a factor that regresses AAPL's 10-day returns
|
||||
against the 10-day returns of all other assets, computing each
|
||||
regression over 30 days. This can be achieved by doing the following::
|
||||
@@ -881,34 +881,37 @@ class Factor(RestrictedDTypeMixin, ComputableTerm):
|
||||
winsorized : zipline.pipeline.Factor
|
||||
A Factor producing a winsorized version of self.
|
||||
|
||||
Example
|
||||
-------
|
||||
Examples
|
||||
--------
|
||||
.. code-block:: python
|
||||
|
||||
price = USEquityPricing.close.latest
|
||||
columns={
|
||||
'PRICE': price,
|
||||
'WINSOR_1: price.winsorize(
|
||||
min_percentile=0.25, max_percentile=0.75
|
||||
),
|
||||
'WINSOR_2': price.winsorize(
|
||||
min_percentile=0.50, max_percentile=1.0
|
||||
),
|
||||
'WINSOR_3': price.winsorize(
|
||||
min_percentile=0.0, max_percentile=0.5
|
||||
),
|
||||
price = USEquityPricing.close.latest
|
||||
columns={
|
||||
'PRICE': price,
|
||||
'WINSOR_1: price.winsorize(
|
||||
min_percentile=0.25, max_percentile=0.75
|
||||
),
|
||||
'WINSOR_2': price.winsorize(
|
||||
min_percentile=0.50, max_percentile=1.0
|
||||
),
|
||||
'WINSOR_3': price.winsorize(
|
||||
min_percentile=0.0, max_percentile=0.5
|
||||
),
|
||||
|
||||
}
|
||||
}
|
||||
|
||||
Given a pipeline with columns, defined above, the result for a
|
||||
given day could look like:
|
||||
|
||||
'PRICE' 'WINSOR_1' 'WINSOR_2' 'WINSOR_3'
|
||||
Asset_1 1 2 4 3
|
||||
Asset_2 2 2 4 3
|
||||
Asset_3 3 3 4 3
|
||||
Asset_4 4 4 4 4
|
||||
Asset_5 5 5 5 4
|
||||
Asset_6 6 5 5 4
|
||||
::
|
||||
|
||||
'PRICE' 'WINSOR_1' 'WINSOR_2' 'WINSOR_3'
|
||||
Asset_1 1 2 4 3
|
||||
Asset_2 2 2 4 3
|
||||
Asset_3 3 3 4 3
|
||||
Asset_4 4 4 4 4
|
||||
Asset_5 5 5 5 4
|
||||
Asset_6 6 5 5 4
|
||||
|
||||
See Also
|
||||
--------
|
||||
|
||||
@@ -237,14 +237,14 @@ class RollingPearsonOfReturns(RollingPearson):
|
||||
A Filter describing which assets should have their correlation with the
|
||||
target asset computed each day.
|
||||
|
||||
Note
|
||||
----
|
||||
Notes
|
||||
-----
|
||||
Computing this factor over many assets can be time consuming. It is
|
||||
recommended that a mask be used in order to limit the number of assets over
|
||||
which correlations are computed.
|
||||
|
||||
Example
|
||||
-------
|
||||
Examples
|
||||
--------
|
||||
Let the following be example 10-day returns for three different assets::
|
||||
|
||||
SPY MSFT FB
|
||||
@@ -327,8 +327,8 @@ class RollingSpearmanOfReturns(RollingSpearman):
|
||||
A Filter describing which assets should have their correlation with the
|
||||
target asset computed each day.
|
||||
|
||||
Note
|
||||
----
|
||||
Notes
|
||||
-----
|
||||
Computing this factor over many assets can be time consuming. It is
|
||||
recommended that a mask be used in order to limit the number of assets over
|
||||
which correlations are computed.
|
||||
@@ -397,8 +397,8 @@ class RollingLinearRegressionOfReturns(RollingLinearRegression):
|
||||
For more help on factors with multiple outputs, see
|
||||
:class:`zipline.pipeline.factors.CustomFactor`.
|
||||
|
||||
Example
|
||||
-------
|
||||
Examples
|
||||
--------
|
||||
Let the following be example 10-day returns for three different assets::
|
||||
|
||||
SPY MSFT FB
|
||||
|
||||
@@ -226,8 +226,8 @@ class _ExponentialWeightedFactor(SingleInputMixin, CustomFactor):
|
||||
Forwards `decay_rate` as `1 - (2.0 / (1 + span))`. This provides the
|
||||
behavior equivalent to passing `span` to pandas.ewma.
|
||||
|
||||
Example
|
||||
-------
|
||||
Examples
|
||||
--------
|
||||
.. code-block:: python
|
||||
|
||||
# Equivalent to:
|
||||
@@ -242,8 +242,8 @@ class _ExponentialWeightedFactor(SingleInputMixin, CustomFactor):
|
||||
span=15,
|
||||
)
|
||||
|
||||
Note
|
||||
----
|
||||
Notes
|
||||
-----
|
||||
This classmethod is provided by both
|
||||
:class:`ExponentialWeightedMovingAverage` and
|
||||
:class:`ExponentialWeightedMovingStdDev`.
|
||||
@@ -273,8 +273,8 @@ class _ExponentialWeightedFactor(SingleInputMixin, CustomFactor):
|
||||
Forwards ``decay_rate`` as ``exp(log(.5) / halflife)``. This provides
|
||||
the behavior equivalent to passing `halflife` to pandas.ewma.
|
||||
|
||||
Example
|
||||
-------
|
||||
Examples
|
||||
--------
|
||||
.. code-block:: python
|
||||
|
||||
# Equivalent to:
|
||||
@@ -289,8 +289,8 @@ class _ExponentialWeightedFactor(SingleInputMixin, CustomFactor):
|
||||
halflife=15,
|
||||
)
|
||||
|
||||
Note
|
||||
----
|
||||
Notes
|
||||
-----
|
||||
This classmethod is provided by both
|
||||
:class:`ExponentialWeightedMovingAverage` and
|
||||
:class:`ExponentialWeightedMovingStdDev`.
|
||||
@@ -322,8 +322,8 @@ class _ExponentialWeightedFactor(SingleInputMixin, CustomFactor):
|
||||
Forwards `decay_rate` as `1 - (1 / 1 + center_of_mass)`. This provides
|
||||
behavior equivalent to passing `center_of_mass` to pandas.ewma.
|
||||
|
||||
Example
|
||||
-------
|
||||
Examples
|
||||
--------
|
||||
.. code-block:: python
|
||||
|
||||
# Equivalent to:
|
||||
@@ -338,8 +338,8 @@ class _ExponentialWeightedFactor(SingleInputMixin, CustomFactor):
|
||||
center_of_mass=15,
|
||||
)
|
||||
|
||||
Note
|
||||
----
|
||||
Notes
|
||||
-----
|
||||
This classmethod is provided by both
|
||||
:class:`ExponentialWeightedMovingAverage` and
|
||||
:class:`ExponentialWeightedMovingStdDev`.
|
||||
|
||||
@@ -370,8 +370,8 @@ class ExecutionPlan(TermGraph):
|
||||
which describes how many additional rows of `term`'s inputs we need to
|
||||
load, and which is determined entirely by `Term` itself.
|
||||
|
||||
Example
|
||||
-------
|
||||
Examples
|
||||
--------
|
||||
Our graph contains the following terms:
|
||||
|
||||
A = SimpleMovingAverage([USEquityPricing.high], window_length=5)
|
||||
|
||||
@@ -219,8 +219,8 @@ def format_attrs(attrs):
|
||||
"""
|
||||
Format key, value pairs from attrs into graphviz attrs format
|
||||
|
||||
Example
|
||||
-------
|
||||
Examples
|
||||
--------
|
||||
>>> format_attrs({'key1': 'value1', 'key2': 'value2'}) # doctest: +SKIP
|
||||
'[key1=value1, key2=value2]'
|
||||
"""
|
||||
|
||||
@@ -285,8 +285,8 @@ def chrange(start, stop):
|
||||
chars: iterable[str]
|
||||
Iterable of strings beginning with start and ending with stop.
|
||||
|
||||
Example
|
||||
-------
|
||||
Examples
|
||||
--------
|
||||
>>> chrange('A', 'C')
|
||||
['A', 'B', 'C']
|
||||
"""
|
||||
@@ -1106,8 +1106,8 @@ def parameter_space(__fail_fast=False, **params):
|
||||
The decorated test function will be called with the cross-product of all
|
||||
possible inputs
|
||||
|
||||
Usage
|
||||
-----
|
||||
Examples
|
||||
--------
|
||||
>>> from unittest import TestCase
|
||||
>>> class SomeTestCase(TestCase):
|
||||
... @parameter_space(x=[1, 2], y=[2, 3])
|
||||
|
||||
@@ -68,8 +68,8 @@ def require_not_initialized(exception):
|
||||
TradingAlgorithm.initialize. `exception` will be raised if the method is
|
||||
called after initialize.
|
||||
|
||||
Usage
|
||||
-----
|
||||
Examples
|
||||
--------
|
||||
@require_not_initialized(SomeException("Don't do that!"))
|
||||
def method(self):
|
||||
# Do stuff that should only be allowed during initialize.
|
||||
@@ -90,8 +90,8 @@ def require_initialized(exception):
|
||||
TradingAlgorithm.initialize. `exception` will be raised if the method is
|
||||
called before initialize has completed.
|
||||
|
||||
Usage
|
||||
-----
|
||||
Examples
|
||||
--------
|
||||
@require_initialized(SomeException("Don't do that!"))
|
||||
def method(self):
|
||||
# Do stuff that should only be allowed after initialize.
|
||||
@@ -112,8 +112,8 @@ def disallowed_in_before_trading_start(exception):
|
||||
TradingAlgorithm.before_trading_start. `exception` will be raised if the
|
||||
method is called inside `before_trading_start`.
|
||||
|
||||
Usage
|
||||
-----
|
||||
Examples
|
||||
--------
|
||||
@disallowed_in_before_trading_start(SomeException("Don't do that!"))
|
||||
def method(self):
|
||||
# Do stuff that is not allowed inside before_trading_start.
|
||||
|
||||
@@ -32,8 +32,8 @@ class CachedObject(object):
|
||||
Expiration date of `value`. The cache is considered invalid for dates
|
||||
**strictly greater** than `expires`.
|
||||
|
||||
Usage
|
||||
-----
|
||||
Examples
|
||||
--------
|
||||
>>> from pandas import Timestamp, Timedelta
|
||||
>>> expires = Timestamp('2014', tz='UTC')
|
||||
>>> obj = CachedObject(1, expires)
|
||||
@@ -86,8 +86,8 @@ class ExpiringCache(object):
|
||||
`__del__`, `__getitem__`, `__setitem__`
|
||||
If `None`, than a dict is used as a default.
|
||||
|
||||
Usage
|
||||
-----
|
||||
Examples
|
||||
--------
|
||||
>>> from pandas import Timestamp, Timedelta
|
||||
>>> expires = Timestamp('2014', tz='UTC')
|
||||
>>> value = 1
|
||||
|
||||
@@ -859,8 +859,8 @@ def days_at_time(days, t, tz, day_offset=0):
|
||||
day_offset : int
|
||||
The number of days we want to offset @days by
|
||||
|
||||
Example
|
||||
-------
|
||||
Examples
|
||||
--------
|
||||
In the example below, the times switch from 13:45 to 12:45 UTC because
|
||||
March 13th is the daylight savings transition for US/Eastern. All the
|
||||
times are still 8:45 when interpreted in US/Eastern.
|
||||
|
||||
@@ -28,8 +28,8 @@ def deprecated(msg=None, stacklevel=2):
|
||||
stacklevel : int
|
||||
How far up the stack the warning needs to go, before
|
||||
showing the relevant calling lines.
|
||||
Usage
|
||||
-----
|
||||
Examples
|
||||
--------
|
||||
@deprecated(msg='function_a is deprecated! Use function_b instead.')
|
||||
def function_a(*args, **kwargs):
|
||||
"""
|
||||
|
||||
+10
-10
@@ -74,8 +74,8 @@ def mapall(funcs, seq):
|
||||
elem : object
|
||||
Concatenated result of mapping each ``func`` over ``seq``.
|
||||
|
||||
Example
|
||||
-------
|
||||
Examples
|
||||
--------
|
||||
>>> list(mapall([lambda x: x + 1, lambda x: x - 1], [1, 2, 3]))
|
||||
[2, 3, 4, 0, 1, 2]
|
||||
"""
|
||||
@@ -90,8 +90,8 @@ def same(*values):
|
||||
|
||||
Returns True on empty sequences.
|
||||
|
||||
Example
|
||||
-------
|
||||
Examples
|
||||
--------
|
||||
>>> same(1, 1, 1, 1)
|
||||
True
|
||||
>>> same(1, 2, 1)
|
||||
@@ -128,8 +128,8 @@ def dzip_exact(*dicts):
|
||||
ValueError
|
||||
If dicts don't all have the same keys.
|
||||
|
||||
Example
|
||||
-------
|
||||
Examples
|
||||
--------
|
||||
>>> result = dzip_exact({'a': 1, 'b': 2}, {'a': 3, 'b': 4})
|
||||
>>> result == {'a': (1, 3), 'b': (2, 4)}
|
||||
True
|
||||
@@ -273,8 +273,8 @@ def getattrs(value, attrs, default=_no_default):
|
||||
result : object
|
||||
Result of the lookup sequence.
|
||||
|
||||
Example
|
||||
-------
|
||||
Examples
|
||||
--------
|
||||
>>> class EmptyObject(object):
|
||||
... pass
|
||||
...
|
||||
@@ -309,8 +309,8 @@ def set_attribute(name, value):
|
||||
|
||||
Doesn't change the behavior of the wrapped function.
|
||||
|
||||
Usage
|
||||
-----
|
||||
Examples
|
||||
--------
|
||||
>>> @set_attribute('__name__', 'foo')
|
||||
... def bar():
|
||||
... return 3
|
||||
|
||||
@@ -91,8 +91,8 @@ def optionally(preprocessor):
|
||||
optional_preprocessor : callable[callable, str, any -> any]
|
||||
A preprocessor that delegates to `preprocessor` when `arg is not None`.
|
||||
|
||||
Usage
|
||||
-----
|
||||
Examples
|
||||
--------
|
||||
>>> def preprocessor(func, argname, arg):
|
||||
... if not isinstance(arg, int):
|
||||
... raise TypeError('arg must be int')
|
||||
@@ -136,8 +136,8 @@ def ensure_dtype(func, argname, arg):
|
||||
"""
|
||||
Argument preprocessor that converts the input into a numpy dtype.
|
||||
|
||||
Usage
|
||||
-----
|
||||
Examples
|
||||
--------
|
||||
>>> import numpy as np
|
||||
>>> from zipline.utils.preprocess import preprocess
|
||||
>>> @preprocess(dtype=ensure_dtype)
|
||||
@@ -163,8 +163,8 @@ def ensure_dtype(func, argname, arg):
|
||||
def ensure_timezone(func, argname, arg):
|
||||
"""Argument preprocessor that converts the input into a tzinfo object.
|
||||
|
||||
Usage
|
||||
-----
|
||||
Examples
|
||||
--------
|
||||
>>> from zipline.utils.preprocess import preprocess
|
||||
>>> @preprocess(tz=ensure_timezone)
|
||||
... def foo(tz):
|
||||
@@ -191,8 +191,8 @@ def ensure_timestamp(func, argname, arg):
|
||||
"""Argument preprocessor that converts the input into a pandas Timestamp
|
||||
object.
|
||||
|
||||
Usage
|
||||
-----
|
||||
Examples
|
||||
--------
|
||||
>>> from zipline.utils.preprocess import preprocess
|
||||
>>> @preprocess(ts=ensure_timestamp)
|
||||
... def foo(ts):
|
||||
@@ -220,8 +220,8 @@ def expect_dtypes(__funcname=_qualified_name, **named):
|
||||
"""
|
||||
Preprocessing decorator that verifies inputs have expected numpy dtypes.
|
||||
|
||||
Usage
|
||||
-----
|
||||
Examples
|
||||
--------
|
||||
>>> from numpy import dtype, arange, int8, float64
|
||||
>>> @expect_dtypes(x=dtype(int8))
|
||||
... def foo(x, y):
|
||||
@@ -288,8 +288,8 @@ def expect_kinds(**named):
|
||||
"""
|
||||
Preprocessing decorator that verifies inputs have expected dtype kinds.
|
||||
|
||||
Usage
|
||||
-----
|
||||
Examples
|
||||
--------
|
||||
>>> from numpy import int64, int32, float32
|
||||
>>> @expect_kinds(x='i')
|
||||
... def foo(x):
|
||||
@@ -351,8 +351,8 @@ def expect_types(__funcname=_qualified_name, **named):
|
||||
"""
|
||||
Preprocessing decorator that verifies inputs have expected types.
|
||||
|
||||
Usage
|
||||
-----
|
||||
Examples
|
||||
--------
|
||||
>>> @expect_types(x=int, y=str)
|
||||
... def foo(x, y):
|
||||
... return x, y
|
||||
@@ -478,8 +478,8 @@ def expect_element(__funcname=_qualified_name, **named):
|
||||
Preprocessing decorator that verifies inputs are elements of some
|
||||
expected collection.
|
||||
|
||||
Usage
|
||||
-----
|
||||
Examples
|
||||
--------
|
||||
>>> @expect_element(x=('a', 'b'))
|
||||
... def foo(x):
|
||||
... return x.upper()
|
||||
@@ -537,8 +537,8 @@ def expect_bounded(__funcname=_qualified_name, **named):
|
||||
``None`` may be passed as ``min_value`` or ``max_value`` to signify that
|
||||
the input is only bounded above or below.
|
||||
|
||||
Usage
|
||||
-----
|
||||
Examples
|
||||
--------
|
||||
>>> @expect_bounded(x=(1, 5))
|
||||
... def foo(x):
|
||||
... return x + 1
|
||||
@@ -616,8 +616,8 @@ def expect_strictly_bounded(__funcname=_qualified_name, **named):
|
||||
``None`` may be passed as ``min_value`` or ``max_value`` to signify that
|
||||
the input is only bounded above or below.
|
||||
|
||||
Usage
|
||||
-----
|
||||
Examples
|
||||
--------
|
||||
>>> @expect_strictly_bounded(x=(1, 5))
|
||||
... def foo(x):
|
||||
... return x + 1
|
||||
@@ -711,8 +711,8 @@ def expect_dimensions(__funcname=_qualified_name, **dimensions):
|
||||
Preprocessing decorator that verifies inputs are numpy arrays with a
|
||||
specific dimensionality.
|
||||
|
||||
Usage
|
||||
-----
|
||||
Examples
|
||||
--------
|
||||
>>> from numpy import array
|
||||
>>> @expect_dimensions(x=1, y=2)
|
||||
... def foo(x, y):
|
||||
@@ -770,8 +770,8 @@ def coerce(from_, to, **to_kwargs):
|
||||
**to_kwargs
|
||||
Additional keywords to forward to every call to ``to``.
|
||||
|
||||
Usage
|
||||
-----
|
||||
Examples
|
||||
--------
|
||||
>>> @preprocess(x=coerce(float, int), y=coerce(float, int))
|
||||
... def floordiff(x, y):
|
||||
... return x - y
|
||||
@@ -803,8 +803,8 @@ def coerce_types(**kwargs):
|
||||
Keyword arguments mapping function parameter names to pairs of
|
||||
(from_type, to_type).
|
||||
|
||||
Usage
|
||||
-----
|
||||
Examples
|
||||
--------
|
||||
>>> @coerce_types(x=(float, int), y=(int, str))
|
||||
... def func(x, y):
|
||||
... return (x, y)
|
||||
|
||||
@@ -48,8 +48,8 @@ def preprocess(*_unused, **processors):
|
||||
`argname` is the name of the argument we're processing.
|
||||
`argvalue` is the value of the argument we're processing.
|
||||
|
||||
Usage
|
||||
-----
|
||||
Examples
|
||||
--------
|
||||
>>> def _ensure_tuple(func, argname, arg):
|
||||
... if isinstance(arg, tuple):
|
||||
... return argvalue
|
||||
@@ -124,8 +124,8 @@ def call(f):
|
||||
f : function
|
||||
Function accepting a single argument and returning a replacement.
|
||||
|
||||
Usage
|
||||
-----
|
||||
Examples
|
||||
--------
|
||||
>>> @preprocess(x=call(lambda x: x + 1))
|
||||
... def foo(x):
|
||||
... return x
|
||||
|
||||
@@ -86,8 +86,8 @@ def templated_docstring(**docs):
|
||||
"""
|
||||
Decorator allowing the use of templated docstrings.
|
||||
|
||||
Usage
|
||||
-----
|
||||
Examples
|
||||
--------
|
||||
>>> @templated_docstring(foo='bar')
|
||||
... def my_func(self, foo):
|
||||
... '''{foo}'''
|
||||
|
||||
Reference in New Issue
Block a user