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ENH: Add history for continuous futures.
Enable unadjusted history for continuous futures. The history array is filled by the values for the underlying contracts, where the contract used changes based on rolls. e.g., if a `1d` history window was over the range `2016-01-20` -> `2016-02-29` with contracts with a suffix of `F16` that rolls at the beginning of the session on `2016-01-26`, `G16` on `2016-02-26`, and `H16` on `2016-03-26`. The `2016-01-20` -> `2016-01-25` portion would use the values for `F16', the `2016-01-26` -> `2016-02-25` portion would use `G16` and the `2016-02-26` -> `2016-02-29` portion would use `H16`. Using the same contracts as above, a `1m` history window over the range (using a timezone of US/Eastern) `2016-01-25 4:00PM` -> `2016-01-25 7:00PM` would fill the `4:00PM` -> `6:00PM` portion with data for `F16` and the `6:01PM` -> `7:00PM` portion with data for `G16`, since the beginning of the `2016-01-26` session is `2016-01-25 6:01PM`. Supports `1d` and `1m`. Also adds the `sid` field to `history` to assist in showing the active contract at each dt in the window.
This commit is contained in:
Eddie Hebert
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import numpy as np
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from zipline.data.session_bars import SessionBarReader
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class ContinuousFutureSessionBarReader(SessionBarReader):
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def __init__(self, bar_reader, roll_finders):
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self._bar_reader = bar_reader
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self._roll_finders = roll_finders
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def load_raw_arrays(self, columns, start_date, end_date, assets):
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"""
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Parameters
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----------
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fields : list of str
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'sid'
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start_dt: Timestamp
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Beginning of the window range.
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end_dt: Timestamp
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End of the window range.
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sids : list of int
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The asset identifiers in the window.
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Returns
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-------
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list of np.ndarray
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A list with an entry per field of ndarrays with shape
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(minutes in range, sids) with a dtype of float64, containing the
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values for the respective field over start and end dt range.
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"""
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rolls_by_asset = {}
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for asset in assets:
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rf = self._roll_finders[asset.roll_style]
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rolls_by_asset[asset] = rf.get_rolls(
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asset.root_symbol, start_date, end_date, asset.offset)
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num_sessions = len(
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self.trading_calendar.sessions_in_range(start_date, end_date))
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shape = num_sessions, len(assets)
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results = []
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tc = self._bar_reader.trading_calendar
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sessions = tc.sessions_in_range(start_date, end_date)
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# Get partitions
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partitions_by_asset = {}
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for asset in assets:
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rolls_by_asset[asset] = rf.get_rolls(
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asset.root_symbol, start_date, end_date, asset.offset)
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partitions = []
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partitions_by_asset[asset] = partitions
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rolls = rolls_by_asset[asset]
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start = start_date
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for roll in rolls:
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sid, roll_date = roll
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start_loc = sessions.get_loc(start)
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if roll_date is not None:
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end = roll_date - sessions.freq
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end_loc = sessions.get_loc(end)
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else:
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end = end_date
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end_loc = len(sessions) - 1
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partitions.append((sid, start, end, start_loc, end_loc))
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if roll[-1] is not None:
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start = sessions[end_loc + 1]
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for column in columns:
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if column != 'volume' and column != 'sid':
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out = np.full(shape, np.nan)
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else:
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out = np.zeros(shape, dtype=np.int64)
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for i, asset in enumerate(assets):
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partitions = partitions_by_asset[asset]
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for sid, start, end, start_loc, end_loc in partitions:
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if column != 'sid':
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result = self._bar_reader.load_raw_arrays(
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[column], start, end, [sid])[0][:, 0]
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else:
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result = int(sid)
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out[start_loc:end_loc + 1, i] = result
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results.append(out)
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return results
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@property
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def last_available_dt(self):
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"""
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Returns
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-------
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dt : pd.Timestamp
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The last session for which the reader can provide data.
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"""
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return self._bar_reader.last_available_dt
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@property
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def trading_calendar(self):
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"""
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Returns the zipline.utils.calendar.trading_calendar used to read
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the data. Can be None (if the writer didn't specify it).
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"""
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return self._bar_reader.trading_calendar
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@property
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def first_trading_day(self):
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"""
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Returns
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-------
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dt : pd.Timestamp
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The first trading day (session) for which the reader can provide
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data.
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"""
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return self._bar_reader.first_trading_day
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def get_value(self, continuous_future, dt, field):
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"""
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Retrieve the value at the given coordinates.
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Parameters
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----------
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sid : int
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The asset identifier.
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dt : pd.Timestamp
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The timestamp for the desired data point.
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field : string
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The OHLVC name for the desired data point.
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Returns
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-------
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value : float|int
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The value at the given coordinates, ``float`` for OHLC, ``int``
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for 'volume'.
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Raises
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------
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NoDataOnDate
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If the given dt is not a valid market minute (in minute mode) or
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session (in daily mode) according to this reader's tradingcalendar.
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"""
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rf = self._roll_finders[continuous_future.roll]
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sid = (rf.get_contract_center(continuous_future.root_symbol,
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dt,
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continuous_future.offset))
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return self._bar_reader.get_value(sid, dt, field)
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def get_last_traded_dt(self, asset, dt):
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"""
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Get the latest minute on or before ``dt`` in which ``asset`` traded.
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If there are no trades on or before ``dt``, returns ``pd.NaT``.
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Parameters
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----------
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asset : zipline.asset.Asset
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The asset for which to get the last traded minute.
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dt : pd.Timestamp
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The minute at which to start searching for the last traded minute.
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Returns
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-------
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last_traded : pd.Timestamp
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The dt of the last trade for the given asset, using the input
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dt as a vantage point.
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"""
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rf = self._roll_finders[asset.roll_style]
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sid = (rf.get_contract_center(asset.root_symbol,
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dt,
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asset.offset))
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contract = rf.asset_finder.retrieve_asset(sid)
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return self._bar_reader.get_last_traded_dt(contract, dt)
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@property
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def sessions(self):
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"""
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Returns
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-------
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sessions : DatetimeIndex
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All session labels (unionining the range for all assets) which the
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reader can provide.
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"""
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return self._bar_reader.sessions
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class ContinuousFutureMinuteBarReader(SessionBarReader):
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def __init__(self, bar_reader, roll_finders):
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self._bar_reader = bar_reader
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self._roll_finders = roll_finders
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def load_raw_arrays(self, columns, start_date, end_date, assets):
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"""
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Parameters
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----------
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fields : list of str
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'open', 'high', 'low', 'close', or 'volume'
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start_dt: Timestamp
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Beginning of the window range.
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end_dt: Timestamp
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End of the window range.
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sids : list of int
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The asset identifiers in the window.
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Returns
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-------
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list of np.ndarray
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A list with an entry per field of ndarrays with shape
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(minutes in range, sids) with a dtype of float64, containing the
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values for the respective field over start and end dt range.
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"""
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rolls_by_asset = {}
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tc = self.trading_calendar
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start_session = tc.minute_to_session_label(start_date)
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end_session = tc.minute_to_session_label(end_date)
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for asset in assets:
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rf = self._roll_finders[asset.roll_style]
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rolls_by_asset[asset] = rf.get_rolls(
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asset.root_symbol,
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start_session,
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end_session, asset.offset)
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sessions = tc.sessions_in_range(start_date, end_date)
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minutes = tc.minutes_in_range(start_date, end_date)
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num_minutes = len(minutes)
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shape = num_minutes, len(assets)
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results = []
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# Get partitions
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partitions_by_asset = {}
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for asset in assets:
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rolls_by_asset[asset] = rf.get_rolls(
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asset.root_symbol, start_date, end_date, asset.offset)
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partitions = []
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partitions_by_asset[asset] = partitions
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rolls = rolls_by_asset[asset]
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start = start_date
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for roll in rolls:
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sid, roll_date = roll
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start_loc = minutes.searchsorted(start)
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if roll_date is not None:
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_, end = tc.open_and_close_for_session(
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roll_date - sessions.freq)
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end_loc = minutes.searchsorted(end)
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else:
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end = end_date
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end_loc = len(minutes) - 1
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partitions.append((sid, start, end, start_loc, end_loc))
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if roll[-1] is not None:
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start, _ = tc.open_and_close_for_session(
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tc.minute_to_session_label(minutes[end_loc + 1]))
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for column in columns:
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if column != 'volume':
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out = np.full(shape, np.nan)
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else:
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out = np.zeros(shape, dtype=np.uint32)
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for i, asset in enumerate(assets):
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partitions = partitions_by_asset[asset]
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for sid, start, end, start_loc, end_loc in partitions:
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if column != 'sid':
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result = self._bar_reader.load_raw_arrays(
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[column], start, end, [sid])[0][:, 0]
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else:
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result = int(sid)
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out[start_loc:end_loc + 1, i] = result
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results.append(out)
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return results
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@property
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def last_available_dt(self):
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"""
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Returns
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-------
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dt : pd.Timestamp
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The last session for which the reader can provide data.
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"""
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return self._bar_reader.last_available_dt
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@property
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def trading_calendar(self):
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"""
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Returns the zipline.utils.calendar.trading_calendar used to read
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the data. Can be None (if the writer didn't specify it).
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"""
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return self._bar_reader.trading_calendar
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@property
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def first_trading_day(self):
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"""
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Returns
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-------
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dt : pd.Timestamp
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The first trading day (session) for which the reader can provide
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data.
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"""
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return self._bar_reader.first_trading_day
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def get_value(self, continuous_future, dt, field):
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"""
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Retrieve the value at the given coordinates.
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Parameters
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----------
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sid : int
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The asset identifier.
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dt : pd.Timestamp
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The timestamp for the desired data point.
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field : string
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The OHLVC name for the desired data point.
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Returns
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-------
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value : float|int
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The value at the given coordinates, ``float`` for OHLC, ``int``
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for 'volume'.
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Raises
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------
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NoDataOnDate
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If the given dt is not a valid market minute (in minute mode) or
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session (in daily mode) according to this reader's tradingcalendar.
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"""
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rf = self._roll_finders[continuous_future.roll_style]
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sid = (rf.get_contract_center(continuous_future.root_symbol,
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dt,
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continuous_future.offset))
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return self._bar_reader.get_value(sid, dt, field)
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def get_last_traded_dt(self, asset, dt):
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"""
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Get the latest minute on or before ``dt`` in which ``asset`` traded.
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If there are no trades on or before ``dt``, returns ``pd.NaT``.
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Parameters
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----------
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asset : zipline.asset.Asset
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The asset for which to get the last traded minute.
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dt : pd.Timestamp
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The minute at which to start searching for the last traded minute.
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Returns
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-------
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last_traded : pd.Timestamp
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The dt of the last trade for the given asset, using the input
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dt as a vantage point.
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"""
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rf = self._roll_finders[asset.roll_style]
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sid = (rf.get_contract_center(asset.root_symbol,
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dt,
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asset.offset))
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contract = rf.asset_finder.retrieve_asset(sid)
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return self._bar_reader.get_last_traded_dt(contract, dt)
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@property
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def sessions(self):
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return self._bar_reader.sessions
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