# Copyright 2016 Quantopian, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from abc import ( ABCMeta, abstractmethod, abstractproperty, ) from numpy import concatenate from lru import LRU from pandas import isnull from pandas.tslib import normalize_date from toolz import sliding_window from six import with_metaclass from catalyst.assets import Equity, Future from catalyst.assets.continuous_futures import ContinuousFuture from catalyst.lib._int64window import AdjustedArrayWindow as Int64Window from catalyst.lib._float64window import AdjustedArrayWindow as Float64Window from catalyst.lib.adjustment import Float64Multiply, Float64Add from catalyst.utils.cache import ExpiringCache from catalyst.utils.math_utils import number_of_decimal_places from catalyst.utils.memoize import lazyval from catalyst.utils.numpy_utils import float64_dtype from catalyst.utils.pandas_utils import find_in_sorted_index # Default number of decimal places used for rounding asset prices. DEFAULT_ASSET_PRICE_DECIMALS = 9 class HistoryCompatibleUSEquityAdjustmentReader(object): def __init__(self, adjustment_reader): self._adjustments_reader = adjustment_reader def load_adjustments(self, columns, dts, assets): """ Returns ------- adjustments : list[dict[int -> Adjustment]] A list, where each element corresponds to the `columns`, of mappings from index to adjustment objects to apply at that index. """ out = [None] * len(columns) for i, column in enumerate(columns): adjs = {} for asset in assets: adjs.update(self._get_adjustments_in_range( asset, dts, column)) out[i] = adjs return out def _get_adjustments_in_range(self, asset, dts, field): """ Get the Float64Multiply objects to pass to an AdjustedArrayWindow. For the use of AdjustedArrayWindow in the loader, which looks back from current simulation time back to a window of data the dictionary is structured with: - the key into the dictionary for adjustments is the location of the day from which the window is being viewed. - the start of all multiply objects is always 0 (in each window all adjustments are overlapping) - the end of the multiply object is the location before the calendar location of the adjustment action, making all days before the event adjusted. Parameters ---------- asset : Asset The assets for which to get adjustments. dts : iterable of datetime64-like The dts for which adjustment data is needed. field : str OHLCV field for which to get the adjustments. Returns ------- out : dict[loc -> Float64Multiply] The adjustments as a dict of loc -> Float64Multiply """ sid = int(asset) start = normalize_date(dts[0]) end = normalize_date(dts[-1]) adjs = {} if field != 'volume': mergers = self._adjustments_reader.get_adjustments_for_sid( 'mergers', sid) for m in mergers: dt = m[0] if start < dt <= end: end_loc = dts.searchsorted(dt) adj_loc = end_loc mult = Float64Multiply(0, end_loc - 1, 0, 0, m[1]) try: adjs[adj_loc].append(mult) except KeyError: adjs[adj_loc] = [mult] divs = self._adjustments_reader.get_adjustments_for_sid( 'dividends', sid) for d in divs: dt = d[0] if start < dt <= end: end_loc = dts.searchsorted(dt) adj_loc = end_loc mult = Float64Multiply(0, end_loc - 1, 0, 0, d[1]) try: adjs[adj_loc].append(mult) except KeyError: adjs[adj_loc] = [mult] splits = self._adjustments_reader.get_adjustments_for_sid( 'splits', sid) for s in splits: dt = s[0] if start < dt <= end: if field == 'volume': ratio = 1.0 / s[1] else: ratio = s[1] end_loc = dts.searchsorted(dt) adj_loc = end_loc mult = Float64Multiply(0, end_loc - 1, 0, 0, ratio) try: adjs[adj_loc].append(mult) except KeyError: adjs[adj_loc] = [mult] return adjs class ContinuousFutureAdjustmentReader(object): """ Calculates adjustments for continuous futures, based on the close and open of the contracts on the either side of each roll. """ def __init__(self, trading_calendar, asset_finder, bar_reader, roll_finders, frequency): self._trading_calendar = trading_calendar self._asset_finder = asset_finder self._bar_reader = bar_reader self._roll_finders = roll_finders self._frequency = frequency def load_adjustments(self, columns, dts, assets): """ Returns ------- adjustments : list[dict[int -> Adjustment]] A list, where each element corresponds to the `columns`, of mappings from index to adjustment objects to apply at that index. """ out = [None] * len(columns) for i, column in enumerate(columns): adjs = {} for asset in assets: adjs.update(self._get_adjustments_in_range( asset, dts, column)) out[i] = adjs return out def _make_adjustment(self, adjustment_type, front_close, back_close, end_loc): adj_base = back_close - front_close if adjustment_type == 'mul': adj_value = 1.0 + adj_base / front_close adj_class = Float64Multiply elif adjustment_type == 'add': adj_value = adj_base adj_class = Float64Add return adj_class(0, end_loc, 0, 0, adj_value) def _get_adjustments_in_range(self, cf, dts, field): if field == 'volume' or field == 'sid': return {} if cf.adjustment is None: return {} rf = self._roll_finders[cf.roll_style] partitions = [] rolls = rf.get_rolls(cf.root_symbol, dts[0], dts[-1], cf.offset) tc = self._trading_calendar adjs = {} for front, back in sliding_window(2, rolls): front_sid, roll_dt = front back_sid = back[0] dt = tc.previous_session_label(roll_dt) if self._frequency == 'minute': dt = tc.open_and_close_for_session(dt)[1] roll_dt = tc.open_and_close_for_session(roll_dt)[0] partitions.append((front_sid, back_sid, dt, roll_dt)) for partition in partitions: front_sid, back_sid, dt, roll_dt = partition last_front_dt = self._bar_reader.get_last_traded_dt( self._asset_finder.retrieve_asset(front_sid), dt) last_back_dt = self._bar_reader.get_last_traded_dt( self._asset_finder.retrieve_asset(back_sid), dt) if isnull(last_front_dt) or isnull(last_back_dt): continue front_close = self._bar_reader.get_value( front_sid, last_front_dt, 'close') back_close = self._bar_reader.get_value( back_sid, last_back_dt, 'close') adj_loc = dts.searchsorted(roll_dt) end_loc = adj_loc - 1 adj = self._make_adjustment(cf.adjustment, front_close, back_close, end_loc) try: adjs[adj_loc].append(adj) except KeyError: adjs[adj_loc] = [adj] return adjs class SlidingWindow(object): """ Wrapper around an AdjustedArrayWindow which supports monotonically increasing (by datetime) requests for a sized window of data. Parameters ---------- window : AdjustedArrayWindow Window of pricing data with prefetched values beyond the current simulation dt. cal_start : int Index in the overall calendar at which the window starts. """ def __init__(self, window, size, cal_start, offset): self.window = window self.cal_start = cal_start self.current = next(window) self.offset = offset self.most_recent_ix = self.cal_start + size def get(self, end_ix): """ Returns ------- out : A np.ndarray of the equity pricing up to end_ix after adjustments and rounding have been applied. """ if self.most_recent_ix == end_ix: return self.current target = end_ix - self.cal_start - self.offset + 1 self.current = self.window.seek(target) self.most_recent_ix = end_ix return self.current class HistoryLoader(with_metaclass(ABCMeta)): """ Loader for sliding history windows, with support for adjustments. Parameters ---------- trading_calendar: TradingCalendar Contains the grouping logic needed to assign minutes to periods. reader : DailyBarReader, MinuteBarReader Reader for pricing bars. adjustment_reader : SQLiteAdjustmentReader Reader for adjustment data. """ FIELDS = ('open', 'high', 'low', 'close', 'volume', 'sid') def __init__(self, trading_calendar, reader, equity_adjustment_reader, asset_finder, roll_finders=None, sid_cache_size=1000, prefetch_length=0): self.trading_calendar = trading_calendar self._asset_finder = asset_finder self._reader = reader self._adjustment_readers = {} if equity_adjustment_reader is not None: self._adjustment_readers[Equity] = \ HistoryCompatibleUSEquityAdjustmentReader( equity_adjustment_reader) if roll_finders: self._adjustment_readers[ContinuousFuture] =\ ContinuousFutureAdjustmentReader(trading_calendar, asset_finder, reader, roll_finders, self._frequency) self._window_blocks = { field: ExpiringCache(LRU(sid_cache_size)) for field in self.FIELDS } self._prefetch_length = prefetch_length @abstractproperty def _frequency(self): pass @abstractproperty def _calendar(self): pass @abstractmethod def _array(self, start, end, assets, field): pass def _decimal_places_for_asset(self, asset, reference_date): if isinstance(asset, Future) and asset.tick_size: return number_of_decimal_places(asset.tick_size) elif isinstance(asset, ContinuousFuture): # Tick size should be the same for all contracts of a continuous # future, so arbitrarily get the contract with next upcoming auto # close date. oc = self._asset_finder.get_ordered_contracts(asset.root_symbol) contract_sid = oc.contract_before_auto_close(reference_date.value) if contract_sid is not None: contract = self._asset_finder.retrieve_asset(contract_sid) if contract.tick_size: return number_of_decimal_places(contract.tick_size) return DEFAULT_ASSET_PRICE_DECIMALS def _ensure_sliding_windows(self, assets, dts, field, is_perspective_after): """ Ensure that there is a Float64Multiply window for each asset that can provide data for the given parameters. If the corresponding window for the (assets, len(dts), field) does not exist, then create a new one. If a corresponding window does exist for (assets, len(dts), field), but can not provide data for the current dts range, then create a new one and replace the expired window. Parameters ---------- assets : iterable of Assets The assets in the window dts : iterable of datetime64-like The datetimes for which to fetch data. Makes an assumption that all dts are present and contiguous, in the calendar. field : str The OHLCV field for which to retrieve data. is_perspective_after : bool see: `PricingHistoryLoader.history` Returns ------- out : list of Float64Window with sufficient data so that each asset's window can provide `get` for the index corresponding with the last value in `dts` """ end = dts[-1] size = len(dts) asset_windows = {} needed_assets = [] cal = self._calendar assets = self._asset_finder.retrieve_all(assets) end_ix = find_in_sorted_index(cal, end) for asset in assets: try: window = self._window_blocks[field].get( (asset, size, is_perspective_after), end) except KeyError: needed_assets.append(asset) else: if end_ix < window.most_recent_ix: # Window needs reset. Requested end index occurs before the # end index from the previous history call for this window. # Grab new window instead of rewinding adjustments. needed_assets.append(asset) else: asset_windows[asset] = window if needed_assets: offset = 0 start_ix = find_in_sorted_index(cal, dts[0]) prefetch_end_ix = min(end_ix + self._prefetch_length, len(cal) - 1) prefetch_end = cal[prefetch_end_ix] prefetch_dts = cal[start_ix:prefetch_end_ix + 1] if is_perspective_after: adj_end_ix = min(prefetch_end_ix + 1, len(cal) - 1) adj_dts = cal[start_ix:adj_end_ix + 1] else: adj_dts = prefetch_dts prefetch_len = len(prefetch_dts) array = self._array(prefetch_dts, needed_assets, field) if field == 'sid': window_type = Int64Window else: window_type = Float64Window view_kwargs = {} if field == 'volume': array = array.astype(float64_dtype) for i, asset in enumerate(needed_assets): adj_reader = None try: adj_reader = self._adjustment_readers[type(asset)] except KeyError: adj_reader = None if adj_reader is not None: adjs = adj_reader.load_adjustments( [field], adj_dts, [asset])[0] else: adjs = {} window = window_type( array[:, i].reshape(prefetch_len, 1), view_kwargs, adjs, offset, size, int(is_perspective_after), self._decimal_places_for_asset(asset, dts[-1]), ) sliding_window = SlidingWindow(window, size, start_ix, offset) asset_windows[asset] = sliding_window self._window_blocks[field].set( (asset, size, is_perspective_after), sliding_window, prefetch_end) return [asset_windows[asset] for asset in assets] def history(self, assets, dts, field, is_perspective_after): """ A window of pricing data with adjustments applied assuming that the end of the window is the day before the current simulation time. Parameters ---------- assets : iterable of Assets The assets in the window. dts : iterable of datetime64-like The datetimes for which to fetch data. Makes an assumption that all dts are present and contiguous, in the calendar. field : str The OHLCV field for which to retrieve data. is_perspective_after : bool True, if the window is being viewed immediately after the last dt in the sliding window. False, if the window is viewed on the last dt. This flag is used for handling the case where the last dt in the requested window immediately precedes a corporate action, e.g.: - is_perspective_after is True When the viewpoint is after the last dt in the window, as when a daily history window is accessed from a simulation that uses a minute data frequency, the history call to this loader will not include the current simulation dt. At that point in time, the raw data for the last day in the window will require adjustment, so the most recent adjustment with respect to the simulation time is applied to the last dt in the requested window. An example equity which has a 0.5 split ratio dated for 05-27, with the dts for a history call of 5 bars with a '1d' frequency at 05-27 9:31. Simulation frequency is 'minute'. (In this case this function is called with 4 daily dts, and the calling function is responsible for stitching back on the 'current' dt) | | | | | last dt | <-- viewer is here | | | 05-23 | 05-24 | 05-25 | 05-26 | 05-27 9:31 | | raw | 10.10 | 10.20 | 10.30 | 10.40 | | | adj | 5.05 | 5.10 | 5.15 | 5.25 | | The adjustment is applied to the last dt, 05-26, and all previous dts. - is_perspective_after is False, daily When the viewpoint is the same point in time as the last dt in the window, as when a daily history window is accessed from a simulation that uses a daily data frequency, the history call will include the current dt. At that point in time, the raw data for the last day in the window will be post-adjustment, so no adjustment is applied to the last dt. An example equity which has a 0.5 split ratio dated for 05-27, with the dts for a history call of 5 bars with a '1d' frequency at 05-27 0:00. Simulation frequency is 'daily'. | | | | | | <-- viewer is here | | | | | | | last dt | | | 05-23 | 05-24 | 05-25 | 05-26 | 05-27 | | raw | 10.10 | 10.20 | 10.30 | 10.40 | 5.25 | | adj | 5.05 | 5.10 | 5.15 | 5.20 | 5.25 | Adjustments are applied 05-23 through 05-26 but not to the last dt, 05-27 Returns ------- out : np.ndarray with shape(len(days between start, end), len(assets)) """ block = self._ensure_sliding_windows(assets, dts, field, is_perspective_after) end_ix = self._calendar.searchsorted(dts[-1]) return concatenate( [window.get(end_ix) for window in block], axis=1, ) class DailyHistoryLoader(HistoryLoader): @property def _frequency(self): return 'daily' @property def _calendar(self): return self._reader.sessions def _array(self, dts, assets, field): return self._reader.load_raw_arrays( [field], dts[0], dts[-1], assets, )[0] class MinuteHistoryLoader(HistoryLoader): @property def _frequency(self): return 'minute' @lazyval def _calendar(self): mm = self.trading_calendar.all_minutes start = mm.searchsorted(self._reader.first_trading_day) end = mm.searchsorted(self._reader.last_available_dt, side='right') return mm[start:end] def _array(self, dts, assets, field): return self._reader.load_raw_arrays( [field], dts[0], dts[-1], assets, )[0]