Generated by Cython 3.0.10
Yellow lines hint at Python interaction.
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_2 = __Pyx_ImportDottedModule(__pyx_n_s_warnings, NULL); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 15, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); if (PyDict_SetItem(__pyx_d, __pyx_n_s_warnings, __pyx_t_2) < 0) __PYX_ERR(0, 15, __pyx_L1_error) __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 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 (
__pyx_t_2 = __Pyx_Import(__pyx_n_s_zipline_assets, __pyx_t_3, 0); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 24, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_3 = __Pyx_ImportFrom(__pyx_t_2, __pyx_n_s_PricingDataAssociable); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 24, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); if (PyDict_SetItem(__pyx_d, __pyx_n_s_PricingDataAssociable, __pyx_t_3) < 0) __PYX_ERR(0, 25, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
+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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115:
+116: cdef class BarData:
struct __pyx_obj_7zipline_9_protocol_BarData {
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struct __pyx_vtabstruct_7zipline_9_protocol_BarData *__pyx_vtab;
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PyObject *data_frequency;
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PyBoolObject *_daily_mode;
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struct __pyx_vtabstruct_7zipline_9_protocol_BarData {
PyObject *(*_get_current_minute)(struct __pyx_obj_7zipline_9_protocol_BarData *);
PyBoolObject *(*_can_trade_for_asset)(struct __pyx_obj_7zipline_9_protocol_BarData *, PyObject *, PyObject *, PyObject *, PyObject *);
PyBoolObject *(*_is_stale_for_asset)(struct __pyx_obj_7zipline_9_protocol_BarData *, PyObject *, PyObject *, PyObject *, PyObject *);
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117: """Provides methods for accessing minutely and daily price/volume data from
118: Algorithm API functions.
119:
120: Also provides utility methods to determine if an asset is alive, and if it
121: has recent trade data.
122:
123: An instance of this object is passed as ``data`` to
124: :func:`~zipline.api.handle_data` and
125: :func:`~zipline.api.before_trading_start`.
126:
127: Parameters
128: ----------
129: data_portal : DataPortal
130: Provider for bar pricing data.
131: simulation_dt_func : callable
132: Function which returns the current simulation time.
133: This is usually bound to a method of TradingSimulation.
134: data_frequency : {'minute', 'daily'}
135: The frequency of the bar data; i.e. whether the data is
136: daily or minute bars
137: restrictions : zipline.finance.asset_restrictions.Restrictions
138: Object that combines and returns restricted list information from
139: multiple sources
140: """
141: cdef object data_portal
142: cdef object simulation_dt_func
143: cdef object data_frequency
144: cdef object restrictions
145: cdef dict _views
146: cdef bool _daily_mode
147: cdef object _trading_calendar
148: cdef object _is_restricted
149:
150: cdef bool _adjust_minutes
151:
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153: trading_calendar, restrictions):
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165:
+166: cdef _get_current_minute(self):
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167: """Internal utility method to get the current simulation time.
168:
169: Possible answers are:
170: - whatever the algorithm's get_datetime() method returns (this is what
171: `self.simulation_dt_func()` points to)
172: - sometimes we're knowingly not in a market minute, like if we're in
173: before_trading_start. In that case, `self._adjust_minutes` is
174: True, and we get the previous market minute.
175: - if we're in daily mode, get the session label for this minute.
176: """
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183: # if we're in daily mode, take the given dt (which is the last
184: # minute of the session) and get the session label for it.
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188:
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191: def current(self, assets, fields):
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: """
+251: multiple_assets = _is_iterable(assets)
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253:
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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269: # return scalar value
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272: asset,
273: field,
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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.
386: 3. There is a known last price for the asset.
387:
388: Parameters
389: ----------
390: assets: zipline.assets.Asset or iterable of zipline.assets.Asset
391: Asset(s) for which tradability should be determined.
392:
393: Notes
394: -----
395: The second condition above warrants some further explanation:
396:
397: - If the asset's exchange calendar is identical to the simulation
398: calendar, then this condition always returns True.
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
401: is running on the CMES calendar but the asset is MSFT, which trades
402: on the NYSE), during those minutes, this condition will return False
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
407: -------
408: can_trade : bool or pd.Series[bool]
409: Bool or series of bools indicating whether the requested asset(s)
410: can be traded in the current minute.
411: """
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433:
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435: cdef object session_label
436: cdef object dt_to_use_for_exchange_check,
437:
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444: # asset isn't alive
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451: # Find the next market minute for this calendar, and check if this
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+459: return False
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460:
461: # is there a last price?
+462: return not np.isnan(
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465: )
466: )
467:
+468: @check_parameters(('assets',), (Asset,))
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469: def is_stale(self, assets):
470: """For the given asset or iterable of assets, returns True if the asset
471: is alive and there is no trade data for the current simulation time.
472:
473: If the asset has never traded, returns False.
474:
475: If the current simulation time is not a valid market time, we use the
476: current time to check if the asset is alive, but we use the last
477: market minute/day for the trade data check.
478:
479: Parameters
480: ----------
481: assets: zipline.assets.Asset or iterable of zipline.assets.Asset
482: Asset(s) for which staleness should be determined.
483:
484: Returns
485: -------
486: is_stale : bool or pd.Series[bool]
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488: are stale.
489: """
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537: def history(self, assets, fields, bar_count, frequency):
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
578: value is a :class:`pd.DataFrame` with shape
579: ``(bar_count, len(assets))``. The frame's index will be a
580: :class:`pd.DatetimeIndex`, and its columns will be ``assets``.
581:
582: - If multiple assets and multiple fields are requested, the returned
583: value is a :class:`pd.DataFrame` with a pd.MultiIndex containing
584: pairs of :class:`pd.DatetimeIndex`, and ``assets``, while the columns
585: while contain the field(s). It has shape ``(bar_count * len(assets),
586: len(fields))``. The names of the pd.MultiIndex are
587:
588: - ``date`` if frequency == '1d'`` or ``date_time`` if frequency == '1m``, and
589: - ``asset``
590:
591: If the current simulation time is not a valid market time, we use the last market close instead.
592: """
593:
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604: asset_list,
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606: bar_count,
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608: fields,
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618: )
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