Source code for zipline.data.data_portal

#
# 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 operator import mul

from logbook import Logger

import numpy as np
from numpy import float64, int64, nan
import pandas as pd
from pandas import isnull
from six import iteritems
from six.moves import reduce

from zipline.assets import (
    Asset,
    AssetConvertible,
    Equity,
    Future,
    PricingDataAssociable,
)
from zipline.assets.continuous_futures import ContinuousFuture
from zipline.data.continuous_future_reader import (
    ContinuousFutureSessionBarReader,
    ContinuousFutureMinuteBarReader
)
from zipline.assets.roll_finder import (
    CalendarRollFinder,
    VolumeRollFinder
)
from zipline.data.dispatch_bar_reader import (
    AssetDispatchMinuteBarReader,
    AssetDispatchSessionBarReader
)
from zipline.data.resample import (
    DailyHistoryAggregator,
    ReindexMinuteBarReader,
    ReindexSessionBarReader,
)
from zipline.data.history_loader import (
    DailyHistoryLoader,
    MinuteHistoryLoader,
)
from zipline.data.us_equity_pricing import NoDataOnDate
from zipline.utils.math_utils import (
    nansum,
    nanmean,
    nanstd
)
from zipline.utils.memoize import remember_last, weak_lru_cache
from zipline.utils.pandas_utils import (
    normalize_date,
    timedelta_to_integral_minutes,
)
from zipline.errors import HistoryWindowStartsBeforeData


log = Logger('DataPortal')

BASE_FIELDS = frozenset([
    "open",
    "high",
    "low",
    "close",
    "volume",
    "price",
    "contract",
    "sid",
    "last_traded",
])

OHLCV_FIELDS = frozenset([
    "open", "high", "low", "close", "volume"
])

OHLCVP_FIELDS = frozenset([
    "open", "high", "low", "close", "volume", "price"
])

HISTORY_FREQUENCIES = set(["1m", "1d"])

DEFAULT_MINUTE_HISTORY_PREFETCH = 1560
DEFAULT_DAILY_HISTORY_PREFETCH = 40

_DEF_M_HIST_PREFETCH = DEFAULT_MINUTE_HISTORY_PREFETCH
_DEF_D_HIST_PREFETCH = DEFAULT_DAILY_HISTORY_PREFETCH


[docs]class DataPortal(object): """Interface to all of the data that a zipline simulation needs. This is used by the simulation runner to answer questions about the data, like getting the prices of assets on a given day or to service history calls. Parameters ---------- asset_finder : zipline.assets.assets.AssetFinder The AssetFinder instance used to resolve assets. trading_calendar: zipline.utils.calendar.exchange_calendar.TradingCalendar The calendar instance used to provide minute->session information. first_trading_day : pd.Timestamp The first trading day for the simulation. equity_daily_reader : BcolzDailyBarReader, optional The daily bar reader for equities. This will be used to service daily data backtests or daily history calls in a minute backetest. If a daily bar reader is not provided but a minute bar reader is, the minutes will be rolled up to serve the daily requests. equity_minute_reader : BcolzMinuteBarReader, optional The minute bar reader for equities. This will be used to service minute data backtests or minute history calls. This can be used to serve daily calls if no daily bar reader is provided. future_daily_reader : BcolzDailyBarReader, optional The daily bar ready for futures. This will be used to service daily data backtests or daily history calls in a minute backetest. If a daily bar reader is not provided but a minute bar reader is, the minutes will be rolled up to serve the daily requests. future_minute_reader : BcolzFutureMinuteBarReader, optional The minute bar reader for futures. This will be used to service minute data backtests or minute history calls. This can be used to serve daily calls if no daily bar reader is provided. adjustment_reader : SQLiteAdjustmentWriter, optional The adjustment reader. This is used to apply splits, dividends, and other adjustment data to the raw data from the readers. last_available_session : pd.Timestamp, optional The last session to make available in session-level data. last_available_minute : pd.Timestamp, optional The last minute to make available in minute-level data. """ def __init__(self, asset_finder, trading_calendar, first_trading_day, equity_daily_reader=None, equity_minute_reader=None, future_daily_reader=None, future_minute_reader=None, adjustment_reader=None, last_available_session=None, last_available_minute=None, minute_history_prefetch_length=_DEF_M_HIST_PREFETCH, daily_history_prefetch_length=_DEF_D_HIST_PREFETCH): self.trading_calendar = trading_calendar self.asset_finder = asset_finder self._adjustment_reader = adjustment_reader # caches of sid -> adjustment list self._splits_dict = {} self._mergers_dict = {} self._dividends_dict = {} # Handle extra sources, like Fetcher. self._augmented_sources_map = {} self._extra_source_df = None self._first_available_session = first_trading_day if last_available_session: self._last_available_session = last_available_session else: # Infer the last session from the provided readers. last_sessions = [ reader.last_available_dt for reader in [equity_daily_reader, future_daily_reader] if reader is not None ] if last_sessions: self._last_available_session = min(last_sessions) else: self._last_available_session = None if last_available_minute: self._last_available_minute = last_available_minute else: # Infer the last minute from the provided readers. last_minutes = [ reader.last_available_dt for reader in [equity_minute_reader, future_minute_reader] if reader is not None ] if last_minutes: self._last_available_minute = max(last_minutes) else: self._last_available_minute = None aligned_equity_minute_reader = self._ensure_reader_aligned( equity_minute_reader) aligned_equity_session_reader = self._ensure_reader_aligned( equity_daily_reader) aligned_future_minute_reader = self._ensure_reader_aligned( future_minute_reader) aligned_future_session_reader = self._ensure_reader_aligned( future_daily_reader) self._roll_finders = { 'calendar': CalendarRollFinder(self.trading_calendar, self.asset_finder), } aligned_minute_readers = {} aligned_session_readers = {} if aligned_equity_minute_reader is not None: aligned_minute_readers[Equity] = aligned_equity_minute_reader if aligned_equity_session_reader is not None: aligned_session_readers[Equity] = aligned_equity_session_reader if aligned_future_minute_reader is not None: aligned_minute_readers[Future] = aligned_future_minute_reader aligned_minute_readers[ContinuousFuture] = \ ContinuousFutureMinuteBarReader( aligned_future_minute_reader, self._roll_finders, ) if aligned_future_session_reader is not None: aligned_session_readers[Future] = aligned_future_session_reader self._roll_finders['volume'] = VolumeRollFinder( self.trading_calendar, self.asset_finder, aligned_future_session_reader, ) aligned_session_readers[ContinuousFuture] = \ ContinuousFutureSessionBarReader( aligned_future_session_reader, self._roll_finders, ) _dispatch_minute_reader = AssetDispatchMinuteBarReader( self.trading_calendar, self.asset_finder, aligned_minute_readers, self._last_available_minute, ) _dispatch_session_reader = AssetDispatchSessionBarReader( self.trading_calendar, self.asset_finder, aligned_session_readers, self._last_available_session, ) self._pricing_readers = { 'minute': _dispatch_minute_reader, 'daily': _dispatch_session_reader, } self._daily_aggregator = DailyHistoryAggregator( self.trading_calendar.schedule.market_open, _dispatch_minute_reader, self.trading_calendar ) self._history_loader = DailyHistoryLoader( self.trading_calendar, _dispatch_session_reader, self._adjustment_reader, self.asset_finder, self._roll_finders, prefetch_length=daily_history_prefetch_length, ) self._minute_history_loader = MinuteHistoryLoader( self.trading_calendar, _dispatch_minute_reader, self._adjustment_reader, self.asset_finder, self._roll_finders, prefetch_length=minute_history_prefetch_length, ) self._first_trading_day = first_trading_day # Get the first trading minute self._first_trading_minute, _ = ( self.trading_calendar.open_and_close_for_session( self._first_trading_day ) if self._first_trading_day is not None else (None, None) ) # Store the locs of the first day and first minute self._first_trading_day_loc = ( self.trading_calendar.all_sessions.get_loc(self._first_trading_day) if self._first_trading_day is not None else None ) def _ensure_reader_aligned(self, reader): if reader is None: return if reader.trading_calendar.name == self.trading_calendar.name: return reader elif reader.data_frequency == 'minute': return ReindexMinuteBarReader( self.trading_calendar, reader, self._first_available_session, self._last_available_session ) elif reader.data_frequency == 'session': return ReindexSessionBarReader( self.trading_calendar, reader, self._first_available_session, self._last_available_session ) def _reindex_extra_source(self, df, source_date_index): return df.reindex(index=source_date_index, method='ffill')
[docs] def handle_extra_source(self, source_df, sim_params): """ Extra sources always have a sid column. We expand the given data (by forward filling) to the full range of the simulation dates, so that lookup is fast during simulation. """ if source_df is None: return # Normalize all the dates in the df source_df.index = source_df.index.normalize() # source_df's sid column can either consist of assets we know about # (such as sid(24)) or of assets we don't know about (such as # palladium). # # In both cases, we break up the dataframe into individual dfs # that only contain a single asset's information. ie, if source_df # has data for PALLADIUM and GOLD, we split source_df into two # dataframes, one for each. (same applies if source_df has data for # AAPL and IBM). # # We then take each child df and reindex it to the simulation's date # range by forward-filling missing values. this makes reads simpler. # # Finally, we store the data. For each column, we store a mapping in # self.augmented_sources_map from the column to a dictionary of # asset -> df. In other words, # self.augmented_sources_map['days_to_cover']['AAPL'] gives us the df # holding that data. source_date_index = self.trading_calendar.sessions_in_range( sim_params.start_session, sim_params.end_session ) # Break the source_df up into one dataframe per sid. This lets # us (more easily) calculate accurate start/end dates for each sid, # de-dup data, and expand the data to fit the backtest start/end date. grouped_by_sid = source_df.groupby(["sid"]) group_names = grouped_by_sid.groups.keys() group_dict = {} for group_name in group_names: group_dict[group_name] = grouped_by_sid.get_group(group_name) # This will be the dataframe which we query to get fetcher assets at # any given time. Get's overwritten every time there's a new fetcher # call extra_source_df = pd.DataFrame() for identifier, df in iteritems(group_dict): # Since we know this df only contains a single sid, we can safely # de-dupe by the index (dt). If minute granularity, will take the # last data point on any given day df = df.groupby(level=0).last() # Reindex the dataframe based on the backtest start/end date. # This makes reads easier during the backtest. df = self._reindex_extra_source(df, source_date_index) for col_name in df.columns.difference(['sid']): if col_name not in self._augmented_sources_map: self._augmented_sources_map[col_name] = {} self._augmented_sources_map[col_name][identifier] = df # Append to extra_source_df the reindexed dataframe for the single # sid extra_source_df = extra_source_df.append(df) self._extra_source_df = extra_source_df
def _get_pricing_reader(self, data_frequency): return self._pricing_readers[data_frequency]
[docs] def get_last_traded_dt(self, asset, dt, data_frequency): """ Given an asset and dt, returns the last traded dt from the viewpoint of the given dt. If there is a trade on the dt, the answer is dt provided. """ return self._get_pricing_reader(data_frequency).get_last_traded_dt( asset, dt)
@staticmethod def _is_extra_source(asset, field, map): """ Internal method that determines if this asset/field combination represents a fetcher value or a regular OHLCVP lookup. """ # If we have an extra source with a column called "price", only look # at it if it's on something like palladium and not AAPL (since our # own price data always wins when dealing with assets). return not (field in BASE_FIELDS and (isinstance(asset, (Asset, ContinuousFuture)))) def _get_fetcher_value(self, asset, field, dt): day = normalize_date(dt) try: return \ self._augmented_sources_map[field][asset].loc[day, field] except KeyError: return np.NaN def _get_single_asset_value(self, session_label, asset, field, dt, data_frequency): if self._is_extra_source( asset, field, self._augmented_sources_map): return self._get_fetcher_value(asset, field, dt) if field not in BASE_FIELDS: raise KeyError("Invalid column: " + str(field)) if dt < asset.start_date or \ (data_frequency == "daily" and session_label > asset.end_date) or \ (data_frequency == "minute" and session_label > asset.end_date): if field == "volume": return 0 elif field == "contract": return None elif field != "last_traded": return np.NaN if data_frequency == "daily": if field == "contract": return self._get_current_contract(asset, session_label) else: return self._get_daily_spot_value( asset, field, session_label, ) else: if field == "last_traded": return self.get_last_traded_dt(asset, dt, 'minute') elif field == "price": return self._get_minute_spot_value( asset, "close", dt, ffill=True, ) elif field == "contract": return self._get_current_contract(asset, dt) else: return self._get_minute_spot_value(asset, field, dt)
[docs] def get_spot_value(self, assets, field, dt, data_frequency): """ Public API method that returns a scalar value representing the value of the desired asset's field at either the given dt. Parameters ---------- assets : Asset, ContinuousFuture, or iterable of same. The asset or assets whose data is desired. field : {'open', 'high', 'low', 'close', 'volume', 'price', 'last_traded'} The desired field of the asset. dt : pd.Timestamp The timestamp for the desired value. data_frequency : str The frequency of the data to query; i.e. whether the data is 'daily' or 'minute' bars Returns ------- value : float, int, or pd.Timestamp The spot value of ``field`` for ``asset`` The return type is based on the ``field`` requested. If the field is one of 'open', 'high', 'low', 'close', or 'price', the value will be a float. If the ``field`` is 'volume' the value will be a int. If the ``field`` is 'last_traded' the value will be a Timestamp. """ assets_is_scalar = False if isinstance(assets, (AssetConvertible, PricingDataAssociable)): assets_is_scalar = True else: # If 'assets' was not one of the expected types then it should be # an iterable. try: iter(assets) except TypeError: raise TypeError( "Unexpected 'assets' value of type {}." .format(type(assets)) ) session_label = self.trading_calendar.minute_to_session_label(dt) if assets_is_scalar: return self._get_single_asset_value( session_label, assets, field, dt, data_frequency, ) else: get_single_asset_value = self._get_single_asset_value return [ get_single_asset_value( session_label, asset, field, dt, data_frequency, ) for asset in assets ]
[docs] def get_scalar_asset_spot_value(self, asset, field, dt, data_frequency): """ Public API method that returns a scalar value representing the value of the desired asset's field at either the given dt. Parameters ---------- assets : Asset The asset or assets whose data is desired. This cannot be an arbitrary AssetConvertible. field : {'open', 'high', 'low', 'close', 'volume', 'price', 'last_traded'} The desired field of the asset. dt : pd.Timestamp The timestamp for the desired value. data_frequency : str The frequency of the data to query; i.e. whether the data is 'daily' or 'minute' bars Returns ------- value : float, int, or pd.Timestamp The spot value of ``field`` for ``asset`` The return type is based on the ``field`` requested. If the field is one of 'open', 'high', 'low', 'close', or 'price', the value will be a float. If the ``field`` is 'volume' the value will be a int. If the ``field`` is 'last_traded' the value will be a Timestamp. """ return self._get_single_asset_value( self.trading_calendar.minute_to_session_label(dt), asset, field, dt, data_frequency, )
[docs] def get_adjustments(self, assets, field, dt, perspective_dt): """ Returns a list of adjustments between the dt and perspective_dt for the given field and list of assets Parameters ---------- assets : list of type Asset, or Asset The asset, or assets whose adjustments are desired. field : {'open', 'high', 'low', 'close', 'volume', \ 'price', 'last_traded'} The desired field of the asset. dt : pd.Timestamp The timestamp for the desired value. perspective_dt : pd.Timestamp The timestamp from which the data is being viewed back from. Returns ------- adjustments : list[Adjustment] The adjustments to that field. """ if isinstance(assets, Asset): assets = [assets] adjustment_ratios_per_asset = [] def split_adj_factor(x): return x if field != 'volume' else 1.0 / x for asset in assets: adjustments_for_asset = [] split_adjustments = self._get_adjustment_list( asset, self._splits_dict, "SPLITS" ) for adj_dt, adj in split_adjustments: if dt < adj_dt <= perspective_dt: adjustments_for_asset.append(split_adj_factor(adj)) elif adj_dt > perspective_dt: break if field != 'volume': merger_adjustments = self._get_adjustment_list( asset, self._mergers_dict, "MERGERS" ) for adj_dt, adj in merger_adjustments: if dt < adj_dt <= perspective_dt: adjustments_for_asset.append(adj) elif adj_dt > perspective_dt: break dividend_adjustments = self._get_adjustment_list( asset, self._dividends_dict, "DIVIDENDS", ) for adj_dt, adj in dividend_adjustments: if dt < adj_dt <= perspective_dt: adjustments_for_asset.append(adj) elif adj_dt > perspective_dt: break ratio = reduce(mul, adjustments_for_asset, 1.0) adjustment_ratios_per_asset.append(ratio) return adjustment_ratios_per_asset
[docs] def get_adjusted_value(self, asset, field, dt, perspective_dt, data_frequency, spot_value=None): """ Returns a scalar value representing the value of the desired asset's field at the given dt with adjustments applied. Parameters ---------- asset : Asset The asset whose data is desired. field : {'open', 'high', 'low', 'close', 'volume', \ 'price', 'last_traded'} The desired field of the asset. dt : pd.Timestamp The timestamp for the desired value. perspective_dt : pd.Timestamp The timestamp from which the data is being viewed back from. data_frequency : str The frequency of the data to query; i.e. whether the data is 'daily' or 'minute' bars Returns ------- value : float, int, or pd.Timestamp The value of the given ``field`` for ``asset`` at ``dt`` with any adjustments known by ``perspective_dt`` applied. The return type is based on the ``field`` requested. If the field is one of 'open', 'high', 'low', 'close', or 'price', the value will be a float. If the ``field`` is 'volume' the value will be a int. If the ``field`` is 'last_traded' the value will be a Timestamp. """ if spot_value is None: # if this a fetcher field, we want to use perspective_dt (not dt) # because we want the new value as of midnight (fetcher only works # on a daily basis, all timestamps are on midnight) if self._is_extra_source(asset, field, self._augmented_sources_map): spot_value = self.get_spot_value(asset, field, perspective_dt, data_frequency) else: spot_value = self.get_spot_value(asset, field, dt, data_frequency) if isinstance(asset, Equity): ratio = self.get_adjustments(asset, field, dt, perspective_dt)[0] spot_value *= ratio return spot_value
def _get_minute_spot_value(self, asset, column, dt, ffill=False): reader = self._get_pricing_reader('minute') if not ffill: try: return reader.get_value(asset.sid, dt, column) except NoDataOnDate: if column != 'volume': return np.nan else: return 0 # At this point the pairing of column='close' and ffill=True is # assumed. try: # Optimize the best case scenario of a liquid asset # returning a valid price. result = reader.get_value(asset.sid, dt, column) if not pd.isnull(result): return result except NoDataOnDate: # Handling of no data for the desired date is done by the # forward filling logic. # The last trade may occur on a previous day. pass # If forward filling, we want the last minute with values (up to # and including dt). query_dt = reader.get_last_traded_dt(asset, dt) if pd.isnull(query_dt): # no last traded dt, bail return np.nan result = reader.get_value(asset.sid, query_dt, column) if (dt == query_dt) or (dt.date() == query_dt.date()): return result # the value we found came from a different day, so we have to # adjust the data if there are any adjustments on that day barrier return self.get_adjusted_value( asset, column, query_dt, dt, "minute", spot_value=result ) def _get_daily_spot_value(self, asset, column, dt): reader = self._get_pricing_reader('daily') if column == "last_traded": last_traded_dt = reader.get_last_traded_dt(asset, dt) if isnull(last_traded_dt): return pd.NaT else: return last_traded_dt elif column in OHLCV_FIELDS: # don't forward fill try: return reader.get_value(asset, dt, column) except NoDataOnDate: return np.nan elif column == "price": found_dt = dt while True: try: value = reader.get_value( asset, found_dt, "close" ) if not isnull(value): if dt == found_dt: return value else: # adjust if needed return self.get_adjusted_value( asset, column, found_dt, dt, "minute", spot_value=value ) else: found_dt -= self.trading_calendar.day except NoDataOnDate: return np.nan @remember_last def _get_days_for_window(self, end_date, bar_count): tds = self.trading_calendar.all_sessions end_loc = tds.get_loc(end_date) start_loc = end_loc - bar_count + 1 if start_loc < self._first_trading_day_loc: raise HistoryWindowStartsBeforeData( first_trading_day=self._first_trading_day.date(), bar_count=bar_count, suggested_start_day=tds[ self._first_trading_day_loc + bar_count ].date(), ) return tds[start_loc:end_loc + 1] def _get_history_daily_window(self, assets, end_dt, bar_count, field_to_use, data_frequency): """ Internal method that returns a dataframe containing history bars of daily frequency for the given sids. """ session = self.trading_calendar.minute_to_session_label(end_dt) days_for_window = self._get_days_for_window(session, bar_count) if len(assets) == 0: return pd.DataFrame(None, index=days_for_window, columns=None) data = self._get_history_daily_window_data( assets, days_for_window, end_dt, field_to_use, data_frequency ) return pd.DataFrame( data, index=days_for_window, columns=assets ) def _get_history_daily_window_data(self, assets, days_for_window, end_dt, field_to_use, data_frequency): if data_frequency == 'daily': # two cases where we use daily data for the whole range: # 1) the history window ends at midnight utc. # 2) the last desired day of the window is after the # last trading day, use daily data for the whole range. return self._get_daily_window_data( assets, field_to_use, days_for_window, extra_slot=False ) else: # minute mode, requesting '1d' daily_data = self._get_daily_window_data( assets, field_to_use, days_for_window[0:-1] ) if field_to_use == 'open': minute_value = self._daily_aggregator.opens( assets, end_dt) elif field_to_use == 'high': minute_value = self._daily_aggregator.highs( assets, end_dt) elif field_to_use == 'low': minute_value = self._daily_aggregator.lows( assets, end_dt) elif field_to_use == 'close': minute_value = self._daily_aggregator.closes( assets, end_dt) elif field_to_use == 'volume': minute_value = self._daily_aggregator.volumes( assets, end_dt) elif field_to_use == 'sid': minute_value = [ int(self._get_current_contract(asset, end_dt)) for asset in assets] # append the partial day. daily_data[-1] = minute_value return daily_data def _handle_minute_history_out_of_bounds(self, bar_count): cal = self.trading_calendar first_trading_minute_loc = ( cal.all_minutes.get_loc( self._first_trading_minute ) if self._first_trading_minute is not None else None ) suggested_start_day = cal.minute_to_session_label( cal.all_minutes[ first_trading_minute_loc + bar_count ] + cal.day ) raise HistoryWindowStartsBeforeData( first_trading_day=self._first_trading_day.date(), bar_count=bar_count, suggested_start_day=suggested_start_day.date(), ) def _get_history_minute_window(self, assets, end_dt, bar_count, field_to_use): """ Internal method that returns a dataframe containing history bars of minute frequency for the given sids. """ # get all the minutes for this window try: minutes_for_window = self.trading_calendar.minutes_window( end_dt, -bar_count ) except KeyError: self._handle_minute_history_out_of_bounds(bar_count) if minutes_for_window[0] < self._first_trading_minute: self._handle_minute_history_out_of_bounds(bar_count) asset_minute_data = self._get_minute_window_data( assets, field_to_use, minutes_for_window, ) return pd.DataFrame( asset_minute_data, index=minutes_for_window, columns=assets )
[docs] def get_history_window(self, assets, end_dt, bar_count, frequency, field, data_frequency, ffill=True): """ Public API method that returns a dataframe containing the requested history window. Data is fully adjusted. Parameters ---------- assets : list of zipline.data.Asset objects The assets whose data is desired. bar_count: int The number of bars desired. frequency: string "1d" or "1m" field: string The desired field of the asset. data_frequency: string The frequency of the data to query; i.e. whether the data is 'daily' or 'minute' bars. ffill: boolean Forward-fill missing values. Only has effect if field is 'price'. Returns ------- A dataframe containing the requested data. """ if field not in OHLCVP_FIELDS and field != 'sid': raise ValueError("Invalid field: {0}".format(field)) if bar_count < 1: raise ValueError( "bar_count must be >= 1, but got {}".format(bar_count) ) if frequency == "1d": if field == "price": df = self._get_history_daily_window(assets, end_dt, bar_count, "close", data_frequency) else: df = self._get_history_daily_window(assets, end_dt, bar_count, field, data_frequency) elif frequency == "1m": if field == "price": df = self._get_history_minute_window(assets, end_dt, bar_count, "close") else: df = self._get_history_minute_window(assets, end_dt, bar_count, field) else: raise ValueError("Invalid frequency: {0}".format(frequency)) # forward-fill price if field == "price": if frequency == "1m": ffill_data_frequency = 'minute' elif frequency == "1d": ffill_data_frequency = 'daily' else: raise Exception( "Only 1d and 1m are supported for forward-filling.") assets_with_leading_nan = np.where(isnull(df.iloc[0]))[0] history_start, history_end = df.index[[0, -1]] if ffill_data_frequency == 'daily' and data_frequency == 'minute': # When we're looking for a daily value, but we haven't seen any # volume in today's minute bars yet, we need to use the # previous day's ffilled daily price. Using today's daily price # could yield a value from later today. history_start -= self.trading_calendar.day initial_values = [] for asset in df.columns[assets_with_leading_nan]: last_traded = self.get_last_traded_dt( asset, history_start, ffill_data_frequency, ) if isnull(last_traded): initial_values.append(nan) else: initial_values.append( self.get_adjusted_value( asset, field, dt=last_traded, perspective_dt=history_end, data_frequency=ffill_data_frequency, ) ) # Set leading values for assets that were missing data, then ffill. df.ix[0, assets_with_leading_nan] = np.array( initial_values, dtype=np.float64 ) df.fillna(method='ffill', inplace=True) # forward-filling will incorrectly produce values after the end of # an asset's lifetime, so write NaNs back over the asset's # end_date. normed_index = df.index.normalize() for asset in df.columns: if history_end >= asset.end_date: # if the window extends past the asset's end date, set # all post-end-date values to NaN in that asset's series df.loc[normed_index > asset.end_date, asset] = nan return df
def _get_minute_window_data(self, assets, field, minutes_for_window): """ Internal method that gets a window of adjusted minute data for an asset and specified date range. Used to support the history API method for minute bars. Missing bars are filled with NaN. Parameters ---------- assets : iterable[Asset] The assets whose data is desired. field: string The specific field to return. "open", "high", "close_price", etc. minutes_for_window: pd.DateTimeIndex The list of minutes representing the desired window. Each minute is a pd.Timestamp. Returns ------- A numpy array with requested values. """ return self._minute_history_loader.history(assets, minutes_for_window, field, False) def _get_daily_window_data(self, assets, field, days_in_window, extra_slot=True): """ Internal method that gets a window of adjusted daily data for a sid and specified date range. Used to support the history API method for daily bars. Parameters ---------- asset : Asset The asset whose data is desired. start_dt: pandas.Timestamp The start of the desired window of data. bar_count: int The number of days of data to return. field: string The specific field to return. "open", "high", "close_price", etc. extra_slot: boolean Whether to allocate an extra slot in the returned numpy array. This extra slot will hold the data for the last partial day. It's much better to create it here than to create a copy of the array later just to add a slot. Returns ------- A numpy array with requested values. Any missing slots filled with nan. """ bar_count = len(days_in_window) # create an np.array of size bar_count dtype = float64 if field != 'sid' else int64 if extra_slot: return_array = np.zeros((bar_count + 1, len(assets)), dtype=dtype) else: return_array = np.zeros((bar_count, len(assets)), dtype=dtype) if field != "volume": # volumes default to 0, so we don't need to put NaNs in the array return_array[:] = np.NAN if bar_count != 0: data = self._history_loader.history(assets, days_in_window, field, extra_slot) if extra_slot: return_array[:len(return_array) - 1, :] = data else: return_array[:len(data)] = data return return_array def _get_adjustment_list(self, asset, adjustments_dict, table_name): """ Internal method that returns a list of adjustments for the given sid. Parameters ---------- asset : Asset The asset for which to return adjustments. adjustments_dict: dict A dictionary of sid -> list that is used as a cache. table_name: string The table that contains this data in the adjustments db. Returns ------- adjustments: list A list of [multiplier, pd.Timestamp], earliest first """ if self._adjustment_reader is None: return [] sid = int(asset) try: adjustments = adjustments_dict[sid] except KeyError: adjustments = adjustments_dict[sid] = self._adjustment_reader.\ get_adjustments_for_sid(table_name, sid) return adjustments
[docs] def get_splits(self, assets, dt): """ Returns any splits for the given sids and the given dt. Parameters ---------- assets : container Assets for which we want splits. dt : pd.Timestamp The date for which we are checking for splits. Note: this is expected to be midnight UTC. Returns ------- splits : list[(asset, float)] List of splits, where each split is a (asset, ratio) tuple. """ if self._adjustment_reader is None or not assets: return [] # convert dt to # of seconds since epoch, because that's what we use # in the adjustments db seconds = int(dt.value / 1e9) splits = self._adjustment_reader.conn.execute( "SELECT sid, ratio FROM SPLITS WHERE effective_date = ?", (seconds,)).fetchall() splits = [split for split in splits if split[0] in assets] splits = [(self.asset_finder.retrieve_asset(split[0]), split[1]) for split in splits] return splits
[docs] def get_stock_dividends(self, sid, trading_days): """ Returns all the stock dividends for a specific sid that occur in the given trading range. Parameters ---------- sid: int The asset whose stock dividends should be returned. trading_days: pd.DatetimeIndex The trading range. Returns ------- list: A list of objects with all relevant attributes populated. All timestamp fields are converted to pd.Timestamps. """ if self._adjustment_reader is None: return [] if len(trading_days) == 0: return [] start_dt = trading_days[0].value / 1e9 end_dt = trading_days[-1].value / 1e9 dividends = self._adjustment_reader.conn.execute( "SELECT * FROM stock_dividend_payouts WHERE sid = ? AND " "ex_date > ? AND pay_date < ?", (int(sid), start_dt, end_dt,)).\ fetchall() dividend_info = [] for dividend_tuple in dividends: dividend_info.append({ "declared_date": dividend_tuple[1], "ex_date": pd.Timestamp(dividend_tuple[2], unit="s"), "pay_date": pd.Timestamp(dividend_tuple[3], unit="s"), "payment_sid": dividend_tuple[4], "ratio": dividend_tuple[5], "record_date": pd.Timestamp(dividend_tuple[6], unit="s"), "sid": dividend_tuple[7] }) return dividend_info
def contains(self, asset, field): return field in BASE_FIELDS or \ (field in self._augmented_sources_map and asset in self._augmented_sources_map[field])
[docs] def get_fetcher_assets(self, dt): """ Returns a list of assets for the current date, as defined by the fetcher data. Returns ------- list: a list of Asset objects. """ # return a list of assets for the current date, as defined by the # fetcher source if self._extra_source_df is None: return [] day = normalize_date(dt) if day in self._extra_source_df.index: assets = self._extra_source_df.loc[day]['sid'] else: return [] if isinstance(assets, pd.Series): return [x for x in assets if isinstance(x, Asset)] else: return [assets] if isinstance(assets, Asset) else []
# cache size picked somewhat loosely. this code exists purely to # handle deprecated API. @weak_lru_cache(20) def _get_minute_count_for_transform(self, ending_minute, days_count): # This function works in three steps. # Step 1. Count the minutes from ``ending_minute`` to the start of its # session. # Step 2. Count the minutes from the prior ``days_count - 1`` sessions. # Step 3. Return the sum of the results from steps (1) and (2). # Example (NYSE Calendar) # ending_minute = 2016-12-28 9:40 AM US/Eastern # days_count = 3 # Step 1. Calculate that there are 10 minutes in the ending session. # Step 2. Calculate that there are 390 + 210 = 600 minutes in the prior # two sessions. (Prior sessions are 2015-12-23 and 2015-12-24.) # 2015-12-24 is a half day. # Step 3. Return 600 + 10 = 610. cal = self.trading_calendar ending_session = cal.minute_to_session_label( ending_minute, direction="none", # It's an error to pass a non-trading minute. ) # Assume that calendar days are always full of contiguous minutes, # which means we can just take 1 + (number of minutes between the last # minute and the start of the session). We add one so that we include # the ending minute in the total. ending_session_minute_count = timedelta_to_integral_minutes( ending_minute - cal.open_and_close_for_session(ending_session)[0] ) + 1 if days_count == 1: # We just need sessions for the active day. return ending_session_minute_count # XXX: We're subtracting 2 here to account for two offsets: # 1. We only want ``days_count - 1`` sessions, since we've already # accounted for the ending session above. # 2. The API of ``sessions_window`` is to return one more session than # the requested number. I don't think any consumers actually want # that behavior, but it's the tested and documented behavior right # now, so we have to request one less session than we actually want. completed_sessions = cal.sessions_window( cal.previous_session_label(ending_session), 2 - days_count, ) completed_sessions_minute_count = ( self.trading_calendar.minutes_count_for_sessions_in_range( completed_sessions[0], completed_sessions[-1] ) ) return ending_session_minute_count + completed_sessions_minute_count def get_simple_transform(self, asset, transform_name, dt, data_frequency, bars=None): if transform_name == "returns": # returns is always calculated over the last 2 days, regardless # of the simulation's data frequency. hst = self.get_history_window( [asset], dt, 2, "1d", "price", data_frequency, ffill=True, )[asset] return (hst.iloc[-1] - hst.iloc[0]) / hst.iloc[0] if bars is None: raise ValueError("bars cannot be None!") if data_frequency == "minute": freq_str = "1m" calculated_bar_count = int(self._get_minute_count_for_transform( dt, bars )) else: freq_str = "1d" calculated_bar_count = bars price_arr = self.get_history_window( [asset], dt, calculated_bar_count, freq_str, "price", data_frequency, ffill=True, )[asset] if transform_name == "mavg": return nanmean(price_arr) elif transform_name == "stddev": return nanstd(price_arr, ddof=1) elif transform_name == "vwap": volume_arr = self.get_history_window( [asset], dt, calculated_bar_count, freq_str, "volume", data_frequency, ffill=True, )[asset] vol_sum = nansum(volume_arr) try: ret = nansum(price_arr * volume_arr) / vol_sum except ZeroDivisionError: ret = np.nan return ret
[docs] def get_current_future_chain(self, continuous_future, dt): """ Retrieves the future chain for the contract at the given `dt` according the `continuous_future` specification. Returns ------- future_chain : list[Future] A list of active futures, where the first index is the current contract specified by the continuous future definition, the second is the next upcoming contract and so on. """ rf = self._roll_finders[continuous_future.roll_style] session = self.trading_calendar.minute_to_session_label(dt) contract_center = rf.get_contract_center( continuous_future.root_symbol, session, continuous_future.offset) oc = self.asset_finder.get_ordered_contracts( continuous_future.root_symbol) chain = oc.active_chain(contract_center, session.value) return self.asset_finder.retrieve_all(chain)
def _get_current_contract(self, continuous_future, dt): rf = self._roll_finders[continuous_future.roll_style] contract_sid = rf.get_contract_center(continuous_future.root_symbol, dt, continuous_future.offset) if contract_sid is None: return None return self.asset_finder.retrieve_asset(contract_sid) @property def adjustment_reader(self): return self._adjustment_reader