def get_equities(self):
assets = []
t = normalize_date(pd.Timestamp('now', tz=NY))
raw_assets = self._api.list_assets(asset_class='us_equity')
for raw_asset in raw_assets:
asset = Equity(
raw_asset.id, raw_asset.exchange,
symbol=raw_asset.symbol,
asset_name=raw_asset.symbol,
)
asset.start_date = t - one_day_offset
if raw_asset.status == 'active' and raw_asset.tradable:
asset.end_date = t + end_offset
else:
# if asset is not tradable, set end_date = day before
asset.end_date = t - one_day_offset
asset.auto_close_date = asset.end_date
assets.append(asset)
# register all unseen exchange name as
# alias of NYSE (e.g. AMEX, ARCA, NYSEARCA.)
if not default_calendar.has_calendar(raw_asset.exchange):
register_calendar_alias(raw_asset.exchange,
'NYSE', force=True)
return assets
def __init__(self, start=None, end=None, assets=None, exchange='NYSE'):
self.start = normalize_date(pd.Timestamp(start or '2018-08-13'))
self.end = normalize_date(pd.Timestamp(end or '2018-08-14'))
self._exchange = exchange
self._calendar = get_calendar(exchange)
self.assets = assets or ['asset-0', 'asset-1', 'asset-2']
minutes = self._calendar.minutes_for_sessions_in_range(
self.start, self.end)
self._minutely_bars = {}
for i, asset in enumerate(self.get_equities()):
bars = create_bars(minutes, i)
self._minutely_bars[asset] = bars
days = self._calendar.sessions_in_range(self.start, self.end)
self._daily_bars = {}
for i, asset in enumerate(self.get_equities()):
bars = create_bars(days, i)
self._daily_bars[asset] = bars
def get_equities(self):
assets = []
t = normalize_date(pd.Timestamp('now', tz=NY))
raw_assets = self._api.list_assets(asset_class='us_equity')
for raw_asset in raw_assets:
asset = Equity(
raw_asset.id,
raw_asset.exchange,
symbol=raw_asset.symbol,
asset_name=raw_asset.symbol,
)
asset.start_date = t - one_day_offset
if raw_asset.status == 'active' and raw_asset.tradable:
asset.end_date = t + end_offset
else:
# this is an experimental change, if an asset is not active or
# tradable, don't include it in the asset list. why?
# first the logical thing - if it's not tradable - we don't
# need it. now why bother?
# some symbols are included more than once in the Alpaca list.
# e.g VXX. one is tradable, one is not. as it happens, the
# first one we iterate on is the not tradable asset. which
# means when someone tries to trade it, even though there's a
# tradable asset, it rejects it because we test it against the
# untradable one. by doing this - we avoid this issue.
# but, it fear it may cause issues (e.g if an asset was
# tradable yesterday, but not tradable today. just a thought)
# so I do this with caution.
continue
# if asset is not tradable, set end_date = day before
asset.end_date = t - one_day_offset
asset.auto_close_date = asset.end_date
assets.append(asset)
# register all unseen exchange name as
# alias of NYSE (e.g. AMEX, ARCA, NYSEARCA.)
if not default_calendar.has_calendar(raw_asset.exchange):
register_calendar_alias(raw_asset.exchange, 'NYSE', force=True)
return assets
def _can_order_asset(self, asset):
if not isinstance(asset, Asset):
raise UnsupportedOrderParameters(
msg="Passing non-Asset argument to 'order()' is not supported."
" Use 'sid()' or 'symbol()' methods to look up an Asset.")
if asset.auto_close_date:
day = normalize_date(self.get_datetime())
if day > min(asset.end_date, asset.auto_close_date):
# If we are after the asset's end date or auto close date, warn
# the user that they can't place an order for this asset, and
# return None.
log.warn("Cannot place order for {0}"
", as it is not tradable.".format(asset.symbol))
return False
return True
def _is_stale_for_asset(self, asset, dt, adjusted_dt, data_portal):
session_label = normalize_date(dt) # FIXME
if not asset.is_alive_for_session(session_label):
return False
current_volume = data_portal.get_spot_value(asset, "volume",
adjusted_dt,
self.data_frequency)
if current_volume > 0:
# found a current value, so we know this asset is not stale.
return False
else:
# we need to distinguish between if this asset has ever traded
# (stale = True) or has never traded (stale = False)
last_traded_dt = \
data_portal.get_spot_value(asset, "last_traded", adjusted_dt,
self.data_frequency)
return not (last_traded_dt is pd.NaT)
def get_equities(self):
assets = []
t = normalize_date(pd.Timestamp('now', tz='America/New_York'))
raw_assets = self._api.list_assets(asset_class='us_equity')
for raw_asset in raw_assets:
asset = Equity(
raw_asset.id, raw_asset.exchange,
symbol=raw_asset.symbol,
asset_name=raw_asset.symbol,
)
asset.start_date = t - one_day_offset
if raw_asset.status == 'active' and raw_asset.tradable:
asset.end_date = t + end_offset
else:
# if asset is not tradable, set end_date = day before
asset.end_date = t - one_day_offset
asset.auto_close_date = asset.end_date
assets.append(asset)
return assets
