Generated by Cython 3.0.12
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 (
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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.utils.pandas_utils import normalize_date
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+030: from zipline.zipline_warnings import ZiplineDeprecationWarning
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031:
+032: cdef bool _is_iterable(obj):
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__Pyx_GOTREF(__pyx_t_10);
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__Pyx_GIVEREF(__pyx_t_10);
PyTuple_SET_ITEM(__pyx_t_1, 3, __pyx_t_10);
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PyBoolObject *(*_is_stale_for_asset)(struct __pyx_obj_7zipline_9_protocol_BarData *, PyObject *, PyObject *, PyObject *, PyObject *);
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118: """
119: Provides methods for accessing minutely and daily price/volume data from
120: Algorithm API functions.
121:
122: Also provides utility methods to determine if an asset is alive, and if it
123: has recent trade data.
124:
125: An instance of this object is passed as ``data`` to
126: :func:`~zipline.api.handle_data` and
127: :func:`~zipline.api.before_trading_start`.
128:
129: Parameters
130: ----------
131: data_portal : DataPortal
132: Provider for bar pricing data.
133: simulation_dt_func : callable
134: Function which returns the current simulation time.
135: This is usually bound to a method of TradingSimulation.
136: data_frequency : {'minute', 'daily'}
137: The frequency of the bar data; i.e. whether the data is
138: daily or minute bars
139: restrictions : zipline.finance.asset_restrictions.Restrictions
140: Object that combines and returns restricted list information from
141: multiple sources
142: """
143: cdef object data_portal
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146: cdef object restrictions
147: cdef dict _views
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149: cdef object _trading_calendar
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151:
152: cdef bool _adjust_minutes
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170: Internal utility method to get the current simulation time.
171:
172: Possible answers are:
173: - whatever the algorithm's get_datetime() method returns (this is what
174: `self.simulation_dt_func()` points to)
175: - sometimes we're knowingly not in a market minute, like if we're in
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187: # if we're in daily mode, take the given dt (which is the last
188: # minute of the session) and get the session label for it.
+189: dt = self.data_portal.trading_calendar.minute_to_session_label(dt)
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190:
+191: return dt
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192:
+193: @check_parameters(('assets', 'fields'),
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195: def current(self, assets, fields):
196: """
197: Returns the "current" value of the given fields for the given assets
198: at the current simulation time.
199:
200: Parameters
201: ----------
202: assets : zipline.assets.Asset or iterable of zipline.assets.Asset
203: The asset(s) for which data is requested.
204: fields : str or iterable[str].
205: Requested data field(s). Valid field names are: "price",
206: "open_price", "high_price", "low_price", "last_traded",
207: "open", "high", "low", "close", and "volume".
208:
209: Returns
210: -------
211: current_value : Scalar, pandas Series, or pandas DataFrame.
212: See notes below.
213:
214: Notes
215: -----
216: The return type of this function depends on the types of its inputs:
217:
218: - If a single asset and a single field are requested, the returned
219: value is a scalar (either a float or a ``pd.Timestamp`` depending on
220: the field).
221:
222: - If a single asset and a list of fields are requested, the returned
223: value is a :class:`pd.Series` whose indices are the requested fields.
224:
225: - If a list of assets and a single field are requested, the returned
226: value is a :class:`pd.Series` whose indices are the assets.
227:
228: - If a list of assets and a list of fields are requested, the returned
229: value is a :class:`pd.DataFrame`. The columns of the returned frame
230: will be the requested fields, and the index of the frame will be the
231: requested assets.
232:
233: The values produced for ``fields`` are as follows:
234:
235: - Requesting "price"/"open_price"/"high_price"/"low_price"
236: produces the last known "close"/"open"/"high"/"low" for the asset,
237: forward-filled from an earlier minute if there is no trade this
238: minute. If there is no last known value (either because the asset
239: has never traded, or because it has delisted) NaN is returned. If a
240: value is found, and we had to cross an adjustment boundary (split,
241: dividend, etc) to get it, the value is adjusted to the current
242: simulation time before being returned.
243:
244: - Requesting "open", "high", "low", or "close" produces the open, high,
245: low, or close for the current minute. If no trades occurred this
246: minute, ``NaN`` is returned.
247:
248: - Requesting "volume" produces the trade volume for the current
249: minute. If no trades occurred this minute, 0 is returned.
250:
251: - Requesting "last_traded" produces the datetime of the last minute in
252: which the asset traded, even if the asset has stopped trading. If
253: there is no last known value, ``pd.NaT`` is returned.
254:
255: If the current simulation time is not a valid market time for an asset,
256: we use the most recent market close instead.
257: """
+258: multiple_assets = _is_iterable(assets)
__pyx_t_1 = ((PyObject *)__pyx_f_7zipline_9_protocol__is_iterable(__pyx_v_assets)); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 258, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_v_multiple_assets = ((PyBoolObject *)__pyx_t_1); __pyx_t_1 = 0;
+259: multiple_fields = _is_iterable(fields)
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260:
261: # There's some overly verbose code in here, particularly around
262: # 'do something if self._adjust_minutes is False, otherwise do
263: # something else'. This could be less verbose, but the 99% case is that
264: # `self._adjust_minutes` is False, so it's important to keep that code
265: # path as fast as possible.
266:
267: # There's probably a way to make this method (and `history`) less
268: # verbose, but this is OK for now.
269:
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+271: asset = assets
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272:
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275:
276: # return scalar value
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384: def can_trade(self, assets):
385: """
386: For the given asset or iterable of assets, returns True if all of the
387: following are true:
388:
389: 1. The asset is alive for the session of the current simulation time
390: (if current simulation time is not a market minute, we use the next
391: session).
392: 2. The asset's exchange is open at the current simulation time or at
393: the simulation calendar's next market minute.
394: 3. There is a known last price for the asset.
395:
396: Parameters
397: ----------
398: assets: zipline.assets.Asset or iterable of zipline.assets.Asset
399: Asset(s) for which tradability should be determined.
400:
401: Notes
402: -----
403: The second condition above warrants some further explanation:
404:
405: - If the asset's exchange calendar is identical to the simulation
406: calendar, then this condition always returns True.
407: - If there are market minutes in the simulation calendar outside of
408: this asset's exchange's trading hours (for example, if the simulation
409: is running on the CMES calendar but the asset is MSFT, which trades
410: on the NYSE), during those minutes, this condition will return False
411: (for example, 3:15 am Eastern on a weekday, during which the CMES is
412: open but the NYSE is closed).
413:
414: Returns
415: -------
416: can_trade : bool or pd.Series[bool]
417: Bool or series of bools indicating whether the requested asset(s)
418: can be traded in the current minute.
419: """
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431: assets, dt, adjusted_dt, data_portal
432: )
433: else:
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436: asset, dt, adjusted_dt, data_portal
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441:
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478: def is_stale(self, assets):
479: """
480: For the given asset or iterable of assets, returns True if the asset
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482:
483: If the asset has never traded, returns False.
484:
485: If the current simulation time is not a valid market time, we use the
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492: Asset(s) for which staleness should be determined.
493:
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547: def history(self, assets, fields, bar_count, frequency):
548: """
549: Returns a trailing window of length ``bar_count`` with data for
550: the given assets, fields, and frequency, adjusted for splits, dividends,
551: and mergers as of the current simulation time.
552:
553: The semantics for missing data are identical to the ones described in
554: the notes for :meth:`current`.
555:
556: Parameters
557: ----------
558: assets: zipline.assets.Asset or iterable of zipline.assets.Asset
559: The asset(s) for which data is requested.
560: fields: string or iterable of string.
561: Requested data field(s). Valid field names are: "price",
562: "last_traded", "open", "high", "low", "close", and "volume".
563: bar_count: int
564: Number of data observations requested.
565: frequency: str
566: String indicating whether to load daily or minutely data
567: observations. Pass '1m' for minutely data, '1d' for daily data.
568:
569: Returns
570: -------
571: history : pd.Series or pd.DataFrame or pd.Panel
572: See notes below.
573:
574: Notes
575: -----
576: The return type of this function depends on the types of ``assets`` and
577: ``fields``:
578:
579: - If a single asset and a single field are requested, the returned
580: value is a :class:`pd.Series` of length ``bar_count`` whose index is
581: :class:`pd.DatetimeIndex`.
582:
583: - If a single asset and multiple fields are requested, the returned
584: value is a :class:`pd.DataFrame` with shape
585: ``(bar_count, len(fields))``. The frame's index will be a
586: :class:`pd.DatetimeIndex`, and its columns will be ``fields``.
587:
588: - If multiple assets and a single field are requested, the returned
589: value is a :class:`pd.DataFrame` with shape
590: ``(bar_count, len(assets))``. The frame's index will be a
591: :class:`pd.DatetimeIndex`, and its columns will be ``assets``.
592:
593: - If multiple assets and multiple fields are requested, the returned
594: value is a :class:`pd.DataFrame` with a pd.MultiIndex containing pairs of
595: :class:`pd.DatetimeIndex`, and ``assets``, while the columns while contain the field(s).
596: It has shape``(bar_count * len(assets), len(fields))``. The names of the pd.MultiIndex are
597: - ``date`` if frequency == '1d'`` or ``date_time`` if frequency == '1m``, and
598: - ``asset``
599:
600: If the current simulation time is not a valid market time, we use the last market close instead.
601: """
602:
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/* … */
goto __pyx_L3;
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610:
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/* … */
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613: asset_list,
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616: frequency,
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619: )
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627: )
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630:
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632: # single asset, single field: return pd.Series with pd.DateTimeIndex
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634: else:
635: # multiple assets, single field: return DataFrame with pd.DateTimeIndex
636: # and assets in columns.
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638: else: # multiple fields
639: # if single_asset:
640: # todo: optimize by querying multiple fields
641: # Make multiple history calls, one per field, then combine results
642:
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