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Click on a line that starts with a "+" to see the C code that Cython generated for it.
Raw output: _protocol.c
+001: #
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002: # Copyright 2016 Quantopian, Inc.
003: #
004: # Licensed under the Apache License, Version 2.0 (the "License");
005: # you may not use this file except in compliance with the License.
006: # You may obtain a copy of the License at
007: #
008: # http://www.apache.org/licenses/LICENSE-2.0
009: #
010: # Unless required by applicable law or agreed to in writing, software
011: # distributed under the License is distributed on an "AS IS" BASIS,
012: # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
013: # See the License for the specific language governing permissions and
014: # limitations under the License.
+015: import warnings
__pyx_t_1 = __Pyx_Import(__pyx_n_s_warnings, 0, 0); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 15, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); if (PyDict_SetItem(__pyx_d, __pyx_n_s_warnings, __pyx_t_1) < 0) __PYX_ERR(0, 15, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
+016: from contextlib import contextmanager
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+017: from functools import wraps
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+018: import pandas as pd
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+019: import numpy as np
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020:
021: from cpython cimport bool
+022: from collections.abc import Iterable
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023:
+024: from zipline.assets import (
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+025: PricingDataAssociable,
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026: )
027: from zipline.assets._assets cimport Asset
+028: from zipline.assets.continuous_futures import ContinuousFuture
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+029: from zipline.zipline_warnings import ZiplineDeprecationWarning
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030:
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192: """Returns the "current" value of the given fields for the given assets
193: at the current simulation time.
194:
195: Parameters
196: ----------
197: assets : zipline.assets.Asset or iterable of zipline.assets.Asset
198: The asset(s) for which data is requested.
199: fields : str or iterable[str].
200: Requested data field(s). Valid field names are: "price",
201: "last_traded", "open", "high", "low", "close", and "volume".
202:
203: Returns
204: -------
205: current_value : Scalar, pandas Series, or pandas DataFrame.
206: See notes below.
207:
208: Notes
209: -----
210: The return type of this function depends on the types of its inputs:
211:
212: - If a single asset and a single field are requested, the returned
213: value is a scalar (either a float or a ``pd.Timestamp`` depending on
214: the field).
215:
216: - If a single asset and a list of fields are requested, the returned
217: value is a :class:`pd.Series` whose indices are the requested fields.
218:
219: - If a list of assets and a single field are requested, the returned
220: value is a :class:`pd.Series` whose indices are the assets.
221:
222: - If a list of assets and a list of fields are requested, the returned
223: value is a :class:`pd.DataFrame`. The columns of the returned frame
224: will be the requested fields, and the index of the frame will be the
225: requested assets.
226:
227: The values produced for ``fields`` are as follows:
228:
229: - Requesting "price" produces the last known close price for the asset,
230: forward-filled from an earlier minute if there is no trade this
231: minute. If there is no last known value (either because the asset
232: has never traded, or because it has delisted) NaN is returned. If a
233: value is found, and we had to cross an adjustment boundary (split,
234: dividend, etc) to get it, the value is adjusted to the current
235: simulation time before being returned.
236:
237: - Requesting "open", "high", "low", or "close" produces the open, high,
238: low, or close for the current minute. If no trades occurred this
239: minute, ``NaN`` is returned.
240:
241: - Requesting "volume" produces the trade volume for the current
242: minute. If no trades occurred this minute, 0 is returned.
243:
244: - Requesting "last_traded" produces the datetime of the last minute in
245: which the asset traded, even if the asset has stopped trading. If
246: there is no last known value, ``pd.NaT`` is returned.
247:
248: If the current simulation time is not a valid market time for an asset,
249: we use the most recent market close instead.
250: """
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254: # There's some overly verbose code in here, particularly around
255: # 'do something if self._adjust_minutes is False, otherwise do
256: # something else'. This could be less verbose, but the 99% case is that
257: # `self._adjust_minutes` is False, so it's important to keep that code
258: # path as fast as possible.
259:
260: # There's probably a way to make this method (and `history`) less
261: # verbose, but this is OK for now.
262:
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378: """For the given asset or iterable of assets, returns True if all of the
379: following are true:
380:
381: 1. The asset is alive for the session of the current simulation time
382: (if current simulation time is not a market minute, we use the next
383: session).
384: 2. The asset's exchange is open at the current simulation time or at
385: the simulation calendar's next market minute.
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388: Parameters
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391: Asset(s) for which tradability should be determined.
392:
393: Notes
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396:
397: - If the asset's exchange calendar is identical to the simulation
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399: - If there are market minutes in the simulation calendar outside of
400: this asset's exchange's trading hours (for example, if the simulation
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403: (for example, 3:15 am Eastern on a weekday, during which the CMES is
404: open but the NYSE is closed).
405:
406: Returns
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538: """Returns a trailing window of length ``bar_count`` with data for
539: the given assets, fields, and frequency, adjusted for splits, dividends,
540: and mergers as of the current simulation time.
541:
542: The semantics for missing data are identical to the ones described in
543: the notes for :meth:`current`.
544:
545: Parameters
546: ----------
547: assets: zipline.assets.Asset or iterable of zipline.assets.Asset
548: The asset(s) for which data is requested.
549: fields: string or iterable of string.
550: Requested data field(s). Valid field names are: "price",
551: "last_traded", "open", "high", "low", "close", and "volume".
552: bar_count: int
553: Number of data observations requested.
554: frequency: str
555: String indicating whether to load daily or minutely data
556: observations. Pass '1m' for minutely data, '1d' for daily data.
557:
558: Returns
559: -------
560: history : pd.Series or pd.DataFrame or pd.Panel
561: See notes below.
562:
563: Notes
564: -----
565: The return type of this function depends on the types of ``assets`` and
566: ``fields``:
567:
568: - If a single asset and a single field are requested, the returned
569: value is a :class:`pd.Series` of length ``bar_count`` whose index is
570: :class:`pd.DatetimeIndex`.
571:
572: - If a single asset and multiple fields are requested, the returned
573: value is a :class:`pd.DataFrame` with shape
574: ``(bar_count, len(fields))``. The frame's index will be a
575: :class:`pd.DatetimeIndex`, and its columns will be ``fields``.
576:
577: - If multiple assets and a single field are requested, the returned
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580: :class:`pd.DatetimeIndex`, and its columns will be ``assets``.
581:
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585: It has shape``(bar_count * len(assets), len(fields))``. The names of the pd.MultiIndex are
586: - ``date`` if frequency == '1d'`` or ``date_time`` if frequency == '1m``, and
587: - ``asset``
588:
589: If the current simulation time is not a valid market time, we use the last market close instead.
590: """
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688: )
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