text: # Copyright 2015 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
  from numbers import Integral
  from operator import itemgetter
  
  from logbook import Logger
  import numpy as np
  import pandas as pd
  from pandas import isnull
  from six import with_metaclass, string_types, viewkeys
  from six.moves import map as imap
  import sqlalchemy as sa
  
  from zipline.errors import (
      EquitiesNotFound,
      FutureContractsNotFound,
      MapAssetIdentifierIndexError,
      MultipleSymbolsFound,
      RootSymbolNotFound,
      SidsNotFound,
      SymbolNotFound,
  )
  from zipline.assets import (
      Asset, Equity, Future,
  )
  from zipline.assets.asset_writer import (
      check_version_info,
      split_delimited_symbol,
      asset_db_table_names,
  )
  from zipline.assets.asset_db_schema import (
      ASSET_DB_VERSION
  )
  from zipline.utils.control_flow import invert
  from zipline.utils.sqlite_utils import group_into_chunks
  
  log = Logger('assets.py')
  
  # A set of fields that need to be converted to strings before building an
  # Asset to avoid unicode fields
  _asset_str_fields = frozenset({
      'symbol',
      'asset_name',
      'exchange',
  })
  
  # A set of fields that need to be converted to timestamps in UTC
  _asset_timestamp_fields = frozenset({
      'start_date',
      'end_date',
      'first_traded',
      'notice_date',
      'expiration_date',
      'auto_close_date',
  })
  
  
  def _convert_asset_timestamp_fields(dict_):
      """
      Takes in a dict of Asset init args and converts dates to pd.Timestamps
      """
      for key in (_asset_timestamp_fields & viewkeys(dict_)):
          value = pd.Timestamp(dict_[key], tz='UTC')
          dict_[key] = None if isnull(value) else value
      return dict_
  
  
  class AssetFinder(object):
      """
      An AssetFinder is an interface to a database of Asset metadata written by
      an ``AssetDBWriter``.
  
      This class provides methods for looking up assets by unique integer id or
      by symbol.  For historical reasons, we refer to these unique ids as 'sids'.
  
      Parameters
      ----------
      engine : str or SQLAlchemy.engine
          An engine with a connection to the asset database to use, or a string
          that can be parsed by SQLAlchemy as a URI.
  
      See Also
      --------
      :class:`zipline.assets.asset_writer.AssetDBWriter`
      """
      # Token used as a substitute for pickling objects that contain a
      # reference to an AssetFinder.
      PERSISTENT_TOKEN = "<AssetFinder>"
  
      def __init__(self, engine):
          if isinstance(engine, str):
              engine = sa.create_engine('sqlite:///' + engine)
  
          self.engine = engine
          metadata = sa.MetaData(bind=engine)
          metadata.reflect(only=asset_db_table_names)
          for table_name in asset_db_table_names:
              setattr(self, table_name, metadata.tables[table_name])
  
          # Check the version info of the db for compatibility
          check_version_info(self.version_info, ASSET_DB_VERSION)
  
          # Cache for lookup of assets by sid, the objects in the asset lookup
          # may be shared with the results from equity and future lookup caches.
          #
          # The top level cache exists to minimize lookups on the asset type
          # routing.
          #
          # The caches are read through, i.e. accessing an asset through
          # retrieve_asset will populate the cache on first retrieval.
          self._caches = (self._asset_cache, self._asset_type_cache) = {}, {}
  
          # Populated on first call to `lifetimes`.
          self._asset_lifetimes = None
  
      def _reset_caches(self):
          """
          Reset our asset caches.
  
          You probably shouldn't call this method.
          """
          # This method exists as a workaround for the in-place mutating behavior
          # of `TradingAlgorithm._write_and_map_id_index_to_sids`.  No one else
          # should be calling this.
          for cache in self._caches:
              cache.clear()
  
      def lookup_asset_types(self, sids):
          """
          Retrieve asset types for a list of sids.
  
          Parameters
          ----------
          sids : list[int]
  
          Returns
          -------
          types : dict[sid -> str or None]
              Asset types for the provided sids.
          """
          found = {}
          missing = set()
  
          for sid in sids:
              try:
                  found[sid] = self._asset_type_cache[sid]
              except KeyError:
                  missing.add(sid)
  
          if not missing:
              return found
  
          router_cols = self.asset_router.c
  
          for assets in group_into_chunks(missing):
              query = sa.select((router_cols.sid, router_cols.asset_type)).where(
                  self.asset_router.c.sid.in_(map(int, assets))
              )
              for sid, type_ in query.execute().fetchall():
                  missing.remove(sid)
                  found[sid] = self._asset_type_cache[sid] = type_
  
              for sid in missing:
                  found[sid] = self._asset_type_cache[sid] = None
  
          return found
  
      def group_by_type(self, sids):
          """
          Group a list of sids by asset type.
  
          Parameters
          ----------
          sids : list[int]
  
          Returns
          -------
          types : dict[str or None -> list[int]]
              A dict mapping unique asset types to lists of sids drawn from sids.
              If we fail to look up an asset, we assign it a key of None.
          """
          return invert(self.lookup_asset_types(sids))
  
      def retrieve_asset(self, sid, default_none=False):
          """
          Retrieve the Asset for a given sid.
          """
          return self.retrieve_all((sid,), default_none=default_none)[0]
  
      def retrieve_all(self, sids, default_none=False):
          """
          Retrieve all assets in `sids`.
  
          Parameters
          ----------
          sids : iterable of int
              Assets to retrieve.
          default_none : bool
              If True, return None for failed lookups.
              If False, raise `SidsNotFound`.
  
          Returns
          -------
          assets : list[Asset or None]
              A list of the same length as `sids` containing Assets (or Nones)
              corresponding to the requested sids.
  
          Raises
          ------
          SidsNotFound
              When a requested sid is not found and default_none=False.
          """
          hits, missing, failures = {}, set(), []
          for sid in sids:
              try:
                  asset = self._asset_cache[sid]
                  if not default_none and asset is None:
                      # Bail early if we've already cached that we don't know
                      # about an asset.
                      raise SidsNotFound(sids=[sid])
                  hits[sid] = asset
              except KeyError:
                  missing.add(sid)
  
          # All requests were cache hits.  Return requested sids in order.
          if not missing:
              return [hits[sid] for sid in sids]
  
          update_hits = hits.update
  
          # Look up cache misses by type.
          type_to_assets = self.group_by_type(missing)
  
          # Handle failures
          failures = {failure: None for failure in type_to_assets.pop(None, ())}
          update_hits(failures)
          self._asset_cache.update(failures)
  
          if failures and not default_none:
              raise SidsNotFound(sids=list(failures))
  
          # We don't update the asset cache here because it should already be
          # updated by `self.retrieve_equities`.
          update_hits(self.retrieve_equities(type_to_assets.pop('equity', ())))
          update_hits(
              self.retrieve_futures_contracts(type_to_assets.pop('future', ()))
          )
  
          # We shouldn't know about any other asset types.
          if type_to_assets:
              raise AssertionError(
                  "Found asset types: %s" % list(type_to_assets.keys())
              )
  
          return [hits[sid] for sid in sids]
  
      def retrieve_equities(self, sids):
          """
          Retrieve Equity objects for a list of sids.
  
          Users generally shouldn't need to this method (instead, they should
          prefer the more general/friendly `retrieve_assets`), but it has a
          documented interface and tests because it's used upstream.
  
          Parameters
          ----------
          sids : iterable[int]
  
          Returns
          -------
          equities : dict[int -> Equity]
  
          Raises
          ------
          EquitiesNotFound
              When any requested asset isn't found.
          """
          return self._retrieve_assets(sids, self.equities, Equity)
  
      def _retrieve_equity(self, sid):
          return self.retrieve_equities((sid,))[sid]
  
      def retrieve_futures_contracts(self, sids):
          """
          Retrieve Future objects for an iterable of sids.
  
          Users generally shouldn't need to this method (instead, they should
          prefer the more general/friendly `retrieve_assets`), but it has a
          documented interface and tests because it's used upstream.
  
          Parameters
          ----------
          sids : iterable[int]
  
          Returns
          -------
          equities : dict[int -> Equity]
  
          Raises
          ------
          EquitiesNotFound
              When any requested asset isn't found.
          """
          return self._retrieve_assets(sids, self.futures_contracts, Future)
  
      @staticmethod
      def _select_assets_by_sid(asset_tbl, sids):
          return sa.select([asset_tbl]).where(
              asset_tbl.c.sid.in_(map(int, sids))
          )
  
      @staticmethod
      def _select_asset_by_symbol(asset_tbl, symbol):
          return sa.select([asset_tbl]).where(asset_tbl.c.symbol == symbol)
  
      def _retrieve_assets(self, sids, asset_tbl, asset_type):
          """
          Internal function for loading assets from a table.
  
          This should be the only method of `AssetFinder` that writes Assets into
          self._asset_cache.
  
          Parameters
          ---------
          sids : iterable of int
              Asset ids to look up.
          asset_tbl : sqlalchemy.Table
              Table from which to query assets.
          asset_type : type
              Type of asset to be constructed.
  
          Returns
          -------
          assets : dict[int -> Asset]
              Dict mapping requested sids to the retrieved assets.
          """
          # Fastpath for empty request.
          if not sids:
              return {}
  
          cache = self._asset_cache
          hits = {}
  
          for assets in group_into_chunks(sids):
              # Load misses from the db.
              query = self._select_assets_by_sid(asset_tbl, assets)
  
              for row in imap(dict, query.execute().fetchall()):
                  asset = asset_type(**_convert_asset_timestamp_fields(row))
                  sid = asset.sid
                  hits[sid] = cache[sid] = asset
  
          # If we get here, it means something in our code thought that a
          # particular sid was an equity/future and called this function with a
          # concrete type, but we couldn't actually resolve the asset.  This is
          # an error in our code, not a user-input error.
          misses = tuple(set(sids) - viewkeys(hits))
          if misses:
              if asset_type == Equity:
                  raise EquitiesNotFound(sids=misses)
              else:
                  raise FutureContractsNotFound(sids=misses)
          return hits
  
      def _get_fuzzy_candidates(self, fuzzy_symbol):
          candidates = sa.select(
              (self.equities.c.sid,)
          ).where(self.equities.c.fuzzy_symbol == fuzzy_symbol).order_by(
              self.equities.c.start_date.desc(),
              self.equities.c.end_date.desc()
          ).execute().fetchall()
          return candidates
  
      def _get_fuzzy_candidates_in_range(self, fuzzy_symbol, ad_value):
          candidates = sa.select(
              (self.equities.c.sid,)
          ).where(
              sa.and_(
                  self.equities.c.fuzzy_symbol == fuzzy_symbol,
                  self.equities.c.start_date <= ad_value,
                  self.equities.c.end_date >= ad_value
              )
          ).order_by(
              self.equities.c.start_date.desc(),
              self.equities.c.end_date.desc(),
          ).execute().fetchall()
          return candidates
  
      def _get_split_candidates_in_range(self,
                                         company_symbol,
                                         share_class_symbol,
                                         ad_value):
          candidates = sa.select(
              (self.equities.c.sid,)
          ).where(
              sa.and_(
                  self.equities.c.company_symbol == company_symbol,
                  self.equities.c.share_class_symbol == share_class_symbol,
                  self.equities.c.start_date <= ad_value,
                  self.equities.c.end_date >= ad_value
              )
          ).order_by(
              self.equities.c.start_date.desc(),
              self.equities.c.end_date.desc(),
          ).execute().fetchall()
          return candidates
  
      def _get_split_candidates(self, company_symbol, share_class_symbol):
          candidates = sa.select(
              (self.equities.c.sid,)
          ).where(
              sa.and_(
                  self.equities.c.company_symbol == company_symbol,
                  self.equities.c.share_class_symbol == share_class_symbol
              )
          ).order_by(
              self.equities.c.start_date.desc(),
              self.equities.c.end_date.desc(),
          ).execute().fetchall()
          return candidates
  
      def _resolve_no_matching_candidates(self,
                                          company_symbol,
                                          share_class_symbol,
                                          ad_value):
          candidates = sa.select((self.equities.c.sid,)).where(
              sa.and_(
                  self.equities.c.company_symbol == company_symbol,
                  self.equities.c.share_class_symbol ==
                  share_class_symbol,
                  self.equities.c.start_date <= ad_value),
          ).order_by(
              self.equities.c.end_date.desc(),
          ).execute().fetchall()
          return candidates
  
      def _get_best_candidate(self, candidates):
          return self._retrieve_equity(candidates[0]['sid'])
  
      def _get_equities_from_candidates(self, candidates):
          sids = map(itemgetter('sid'), candidates)
          results = self.retrieve_equities(sids)
          return [results[sid] for sid in sids]
  
      def lookup_symbol(self, symbol, as_of_date, fuzzy=False):
          """
          Return matching Equity of name symbol in database.
  
          If multiple Equities are found and as_of_date is not set,
          raises MultipleSymbolsFound.
  
          If no Equity was active at as_of_date raises SymbolNotFound.
          """
          company_symbol, share_class_symbol, fuzzy_symbol = \
              split_delimited_symbol(symbol)
          if as_of_date:
              # Format inputs
              as_of_date = pd.Timestamp(as_of_date).normalize()
              ad_value = as_of_date.value
  
              if fuzzy:
                  # Search for a single exact match on the fuzzy column
                  candidates = self._get_fuzzy_candidates_in_range(fuzzy_symbol,
                                                                   ad_value)
  
                  # If exactly one SID exists for fuzzy_symbol, return that sid
                  if len(candidates) == 1:
                      return self._get_best_candidate(candidates)
  
              # Search for exact matches of the split-up company_symbol and
              # share_class_symbol
              candidates = self._get_split_candidates_in_range(
                  company_symbol,
                  share_class_symbol,
                  ad_value
              )
  
              # If exactly one SID exists for symbol, return that symbol
              # If multiple SIDs exist for symbol, return latest start_date with
              # end_date as a tie-breaker
              if candidates:
                  return self._get_best_candidate(candidates)
  
              # If no SID exists for symbol, return SID with the
              # highest-but-not-over end_date
              elif not candidates:
                  candidates = self._resolve_no_matching_candidates(
                      company_symbol,
                      share_class_symbol,
                      ad_value
                  )
                  if candidates:
                      return self._get_best_candidate(candidates)
  
              raise SymbolNotFound(symbol=symbol)
  
          else:
              # If this is a fuzzy look-up, check if there is exactly one match
              # for the fuzzy symbol
              if fuzzy:
                  candidates = self._get_fuzzy_candidates(fuzzy_symbol)
                  if len(candidates) == 1:
                      return self._get_best_candidate(candidates)
  
              candidates = self._get_split_candidates(company_symbol,
                                                      share_class_symbol)
              if len(candidates) == 1:
                  return self._get_best_candidate(candidates)
              elif not candidates:
                  raise SymbolNotFound(symbol=symbol)
              else:
                  raise MultipleSymbolsFound(
                      symbol=symbol,
                      options=self._get_equities_from_candidates(candidates)
                  )
  
      def lookup_future_symbol(self, symbol):
          """ Return the Future object for a given symbol.
  
          Parameters
          ----------
          symbol : str
              The symbol of the desired contract.
  
          Returns
          -------
          Future
              A Future object.
  
          Raises
          ------
          SymbolNotFound
              Raised when no contract named 'symbol' is found.
  
          """
  
          data = self._select_asset_by_symbol(self.futures_contracts, symbol)\
                     .execute().fetchone()
  
          # If no data found, raise an exception
          if not data:
              raise SymbolNotFound(symbol=symbol)
          return self.retrieve_asset(data['sid'])
  
      def lookup_future_chain(self, root_symbol, as_of_date):
          """ Return the futures chain for a given root symbol.
  
          Parameters
          ----------
          root_symbol : str
              Root symbol of the desired future.
  
          as_of_date : pd.Timestamp or pd.NaT
              Date at which the chain determination is rooted. I.e. the
              existing contract whose notice date/expiration date is first
              after this date is the primary contract, etc. If NaT is
              given, the chain is unbounded, and all contracts for this
              root symbol are returned.
  
          Returns
          -------
          list
              A list of Future objects, the chain for the given
              parameters.
  
          Raises
          ------
          RootSymbolNotFound
              Raised when a future chain could not be found for the given
              root symbol.
          """
  
          fc_cols = self.futures_contracts.c
  
          if as_of_date is pd.NaT:
              # If the as_of_date is NaT, get all contracts for this
              # root symbol.
              sids = list(map(
                  itemgetter('sid'),
                  sa.select((fc_cols.sid,)).where(
                      (fc_cols.root_symbol == root_symbol),
                  ).order_by(
                      fc_cols.notice_date.asc(),
                  ).execute().fetchall()))
          else:
              as_of_date = as_of_date.value
  
              sids = list(map(
                  itemgetter('sid'),
                  sa.select((fc_cols.sid,)).where(
                      (fc_cols.root_symbol == root_symbol) &
  
                      # Filter to contracts that are still valid. If both
                      # exist, use the one that comes first in time (i.e.
                      # the lower value). If either notice_date or
                      # expiration_date is NaT, use the other. If both are
                      # NaT, the contract cannot be included in any chain.
                      sa.case(
                          [
                              (
                                  fc_cols.notice_date == pd.NaT.value,
                                  fc_cols.expiration_date >= as_of_date
                              ),
                              (
                                  fc_cols.expiration_date == pd.NaT.value,
                                  fc_cols.notice_date >= as_of_date
                              )
                          ],
                          else_=(
                              sa.func.min(
                                  fc_cols.notice_date,
                                  fc_cols.expiration_date
                              ) >= as_of_date
                          )
                      )
                  ).order_by(
                      # If both dates exist sort using minimum of
                      # expiration_date and notice_date
                      # else if one is NaT use the other.
                      sa.case(
                          [
                              (
                                  fc_cols.expiration_date == pd.NaT.value,
                                  fc_cols.notice_date
                              ),
                              (
                                  fc_cols.notice_date == pd.NaT.value,
                                  fc_cols.expiration_date
                              )
                          ],
                          else_=(
                              sa.func.min(
                                  fc_cols.notice_date,
                                  fc_cols.expiration_date
                              )
                          )
                      ).asc()
                  ).execute().fetchall()
              ))
  
          if not sids:
              # Check if root symbol exists.
              count = sa.select((sa.func.count(fc_cols.sid),)).where(
                  fc_cols.root_symbol == root_symbol,
              ).scalar()
              if count == 0:
                  raise RootSymbolNotFound(root_symbol=root_symbol)
  
          contracts = self.retrieve_futures_contracts(sids)
          return [contracts[sid] for sid in sids]
  
      def lookup_expired_futures(self, start, end):
          if not isinstance(start, pd.Timestamp):
              start = pd.Timestamp(start)
          start = start.value
          if not isinstance(end, pd.Timestamp):
              end = pd.Timestamp(end)
          end = end.value
  
          fc_cols = self.futures_contracts.c
  
          nd = sa.func.nullif(fc_cols.notice_date, pd.tslib.iNaT)
          ed = sa.func.nullif(fc_cols.expiration_date, pd.tslib.iNaT)
          date = sa.func.coalesce(sa.func.min(nd, ed), ed, nd)
  
          sids = list(map(
              itemgetter('sid'),
              sa.select((fc_cols.sid,)).where(
                  (date >= start) & (date < end)).order_by(
                  sa.func.coalesce(ed, nd).asc()
              ).execute().fetchall()
          ))
  
          return sids
  
      def _make_sids(tblattr):
          def _(self):
              return tuple(map(
                  itemgetter('sid'),
                  sa.select((
                      getattr(self, tblattr).c.sid,
                  )).execute().fetchall(),
              ))
  
          return _
  
      sids = property(
          _make_sids('asset_router'),
          doc='All the sids in the asset finder.',
      )
      equities_sids = property(
          _make_sids('equities'),
          doc='All of the sids for equities in the asset finder.',
      )
      futures_sids = property(
          _make_sids('futures_contracts'),
          doc='All of the sids for futures consracts in the asset finder.',
      )
      del _make_sids
  
      def _lookup_generic_scalar(self,
                                 asset_convertible,
                                 as_of_date,
                                 matches,
                                 missing):
          """
          Convert asset_convertible to an asset.
  
          On success, append to matches.
          On failure, append to missing.
          """
          if isinstance(asset_convertible, Asset):
              matches.append(asset_convertible)
  
          elif isinstance(asset_convertible, Integral):
              try:
                  result = self.retrieve_asset(int(asset_convertible))
              except SidsNotFound:
                  missing.append(asset_convertible)
                  return None
              matches.append(result)
  
          elif isinstance(asset_convertible, string_types):
              try:
                  matches.append(
                      self.lookup_symbol(asset_convertible, as_of_date)
                  )
              except SymbolNotFound:
                  missing.append(asset_convertible)
                  return None
          else:
              raise NotAssetConvertible(
                  "Input was %s, not AssetConvertible."
                  % asset_convertible
              )
  
      def lookup_generic(self,
                         asset_convertible_or_iterable,
                         as_of_date):
          """
          Convert a AssetConvertible or iterable of AssetConvertibles into
          a list of Asset objects.
  
          This method exists primarily as a convenience for implementing
          user-facing APIs that can handle multiple kinds of input.  It should
          not be used for internal code where we already know the expected types
          of our inputs.
  
          Returns a pair of objects, the first of which is the result of the
          conversion, and the second of which is a list containing any values
          that couldn't be resolved.
          """
          matches = []
          missing = []
  
          # Interpret input as scalar.
          if isinstance(asset_convertible_or_iterable, AssetConvertible):
              self._lookup_generic_scalar(
                  asset_convertible=asset_convertible_or_iterable,
                  as_of_date=as_of_date,
                  matches=matches,
                  missing=missing,
              )
              try:
                  return matches[0], missing
              except IndexError:
                  if hasattr(asset_convertible_or_iterable, '__int__'):
                      raise SidsNotFound(sids=[asset_convertible_or_iterable])
                  else:
                      raise SymbolNotFound(symbol=asset_convertible_or_iterable)
  
          # Interpret input as iterable.
          try:
              iterator = iter(asset_convertible_or_iterable)
          except TypeError:
              raise NotAssetConvertible(
                  "Input was not a AssetConvertible "
                  "or iterable of AssetConvertible."
              )
  
          for obj in iterator:
              self._lookup_generic_scalar(obj, as_of_date, matches, missing)
          return matches, missing
  
      def map_identifier_index_to_sids(self, index, as_of_date):
          """
          This method is for use in sanitizing a user's DataFrame or Panel
          inputs.
  
          Takes the given index of identifiers, checks their types, builds assets
          if necessary, and returns a list of the sids that correspond to the
          input index.
  
          Parameters
          ----------
          index : Iterable
              An iterable containing ints, strings, or Assets
          as_of_date : pandas.Timestamp
              A date to be used to resolve any dual-mapped symbols
  
          Returns
          -------
          List
              A list of integer sids corresponding to the input index
          """
          # This method assumes that the type of the objects in the index is
          # consistent and can, therefore, be taken from the first identifier
          first_identifier = index[0]
  
          # Ensure that input is AssetConvertible (integer, string, or Asset)
          if not isinstance(first_identifier, AssetConvertible):
              raise MapAssetIdentifierIndexError(obj=first_identifier)
  
          # If sids are provided, no mapping is necessary
          if isinstance(first_identifier, Integral):
              return index
  
          # Look up all Assets for mapping
          matches = []
          missing = []
          for identifier in index:
              self._lookup_generic_scalar(identifier, as_of_date,
                                          matches, missing)
  
          if missing:
              raise ValueError("Missing assets for identifiers: %s" % missing)
  
          # Return a list of the sids of the found assets
          return [asset.sid for asset in matches]
  
      def _compute_asset_lifetimes(self):
          """
          Compute and cache a recarry of asset lifetimes.
          """
          equities_cols = self.equities.c
          buf = np.array(
              tuple(
                  sa.select((
                      equities_cols.sid,
                      equities_cols.start_date,
                      equities_cols.end_date,
                  )).execute(),
              ), dtype='<f8',  # use doubles so we get NaNs
          )
          lifetimes = np.recarray(
              buf=buf,
              shape=(len(buf),),
              dtype=[
                  ('sid', '<f8'),
                  ('start', '<f8'),
                  ('end', '<f8')
              ],
          )
          start = lifetimes.start
          end = lifetimes.end
          start[np.isnan(start)] = 0  # convert missing starts to 0
          end[np.isnan(end)] = np.iinfo(int).max  # convert missing end to INTMAX
          # Cast the results back down to int.
          return lifetimes.astype([
              ('sid', '<i8'),
              ('start', '<i8'),
              ('end', '<i8'),
          ])
  
      def lifetimes(self, dates, include_start_date):
          """
          Compute a DataFrame representing asset lifetimes for the specified date
          range.
  
          Parameters
          ----------
          dates : pd.DatetimeIndex
              The dates for which to compute lifetimes.
          include_start_date : bool
              Whether or not to count the asset as alive on its start_date.
  
              This is useful in a backtesting context where `lifetimes` is being
              used to signify "do I have data for this asset as of the morning of
              this date?"  For many financial metrics, (e.g. daily close), data
              isn't available for an asset until the end of the asset's first
              day.
  
          Returns
          -------
          lifetimes : pd.DataFrame
              A frame of dtype bool with `dates` as index and an Int64Index of
              assets as columns.  The value at `lifetimes.loc[date, asset]` will
              be True iff `asset` existed on `date`.  If `include_start_date` is
              False, then lifetimes.loc[date, asset] will be false when date ==
              asset.start_date.
  
          See Also
          --------
          numpy.putmask
          zipline.pipeline.engine.SimplePipelineEngine._compute_root_mask
          """
          # This is a less than ideal place to do this, because if someone adds
          # assets to the finder after we've touched lifetimes we won't have
          # those new assets available.  Mutability is not my favorite
          # programming feature.
          if self._asset_lifetimes is None:
              self._asset_lifetimes = self._compute_asset_lifetimes()
          lifetimes = self._asset_lifetimes
  
          raw_dates = dates.asi8[:, None]
          if include_start_date:
              mask = lifetimes.start <= raw_dates
          else:
              mask = lifetimes.start < raw_dates
          mask &= (raw_dates <= lifetimes.end)
  
          return pd.DataFrame(mask, index=dates, columns=lifetimes.sid)
  
  
  class AssetConvertible(with_metaclass(ABCMeta)):
      """
      ABC for types that are convertible to integer-representations of
      Assets.
  
      Includes Asset, six.string_types, and Integral
      """
      pass
  
  
  AssetConvertible.register(Integral)
  AssetConvertible.register(Asset)
  # Use six.string_types for Python2/3 compatibility
  for _type in string_types:
      AssetConvertible.register(_type)
  
  
  class NotAssetConvertible(ValueError):
      pass
  
  
  class AssetFinderCachedEquities(AssetFinder):
      """
      An extension to AssetFinder that preloads all equities from equities table
      into memory and does lookups from there.
  
      To have any changes in the underlying assets db reflected by this asset
      finder one must manually call the ``rehash_equities`` method.
      """
  
      def __init__(self, engine):
          super(AssetFinderCachedEquities, self).__init__(engine)
          self._fuzzy_symbol_cache = {}
          self._company_share_class_cache = {}
  
          self.rehash_equities()
  
      def rehash_equities(self):
          """Reload the underlying assets db into the in memory cache.
          """
          for equity in sa.select(self.equities.c).execute().fetchall():
              company_symbol = equity['company_symbol']
              share_class_symbol = equity['share_class_symbol']
              fuzzy_symbol = equity['fuzzy_symbol']
              asset = self._convert_row_to_equity(equity)
              self._company_share_class_cache.setdefault(
                  (company_symbol, share_class_symbol),
                  []
              ).append(asset)
              self._fuzzy_symbol_cache.setdefault(
                  fuzzy_symbol,
                  [],
              ).append(asset)
  
      def _convert_row_to_equity(self, row):
          """
          Converts a SQLAlchemy equity row to an Equity object.
          """
          return Equity(**_convert_asset_timestamp_fields(dict(row)))
  
      def _get_fuzzy_candidates(self, fuzzy_symbol):
          return self._fuzzy_symbol_cache.get(fuzzy_symbol, ())
  
      def _get_fuzzy_candidates_in_range(self, fuzzy_symbol, ad_value):
          return only_active_assets(
              ad_value,
              self._get_fuzzy_candidates(fuzzy_symbol),
          )
  
      def _get_split_candidates(self, company_symbol, share_class_symbol):
          return self._company_share_class_cache.get(
              (company_symbol, share_class_symbol),
              (),
          )
  
      def _get_split_candidates_in_range(self,
                                         company_symbol,
                                         share_class_symbol,
                                         ad_value):
          return sorted(
              only_active_assets(
                  ad_value,
                  self._get_split_candidates(company_symbol, share_class_symbol),
              ),
              key=lambda x: (x.start_date, x.end_date),
              reverse=True,
          )
  
      def _resolve_no_matching_candidates(self,
                                          company_symbol,
                                          share_class_symbol,
                                          ad_value):
          equities = self._get_split_candidates(
              company_symbol,
              share_class_symbol
          )
          partial_candidates = []
          for equity in equities:
              if equity.start_date.value <= ad_value:
                  partial_candidates.append(equity)
          if partial_candidates:
              partial_candidates = sorted(
                  partial_candidates,
                  key=lambda x: x.end_date,
                  reverse=True
              )
          return partial_candidates
  
      def _get_best_candidate(self, candidates):
          return candidates[0]
  
      def _get_equities_from_candidates(self, candidates):
          return candidates
  
  
  def was_active(reference_date_value, asset):
      """
      Whether or not `asset` was active at the time corresponding to
      `reference_date_value`.
  
      Parameters
      ----------
      reference_date_value : int
          Date, represented as nanoseconds since EPOCH, for which we want to know
          if `asset` was alive.  This is generally the result of accessing the
          `value` attribute of a pandas Timestamp.
      asset : Asset
          The asset object to check.
  
      Returns
      -------
      was_active : bool
          Whether or not the `asset` existed at the specified time.
      """
      return (
          asset.start_date.value
          <= reference_date_value
          <= asset.end_date.value
      )
  
  
  def only_active_assets(reference_date_value, assets):
      """
      Filter an iterable of Asset objects down to just assets that were alive at
      the time corresponding to `reference_date_value`.
  
      Parameters
      ----------
      reference_date_value : int
          Date, represented as nanoseconds since EPOCH, for which we want to know
          if `asset` was alive.  This is generally the result of accessing the
          `value` attribute of a pandas Timestamp.
      assets : iterable[Asset]
          The assets to filter.
  
      Returns
      -------
      active_assets : list
          List of the active assets from `assets` on the requested date.
      """
      return [a for a in assets if was_active(reference_date_value, a)]
  