def get_spot_values(self,
assets,
field,
dt,
data_frequency,
reset_reader=False):
values = []
try:
reader = self.get_reader(data_frequency)
if reset_reader:
del self._readers[reader._rootdir]
reader = self.get_reader(data_frequency)
for asset in assets:
value = reader.get_value(sid=asset.sid, dt=dt, field=field)
values.append(value)
return values
except Exception:
symbols = [asset.symbol.encode('utf-8') for asset in assets]
raise PricingDataNotLoadedError(
field=field,
first_trading_day=min([asset.start_date for asset in assets]),
exchange=self.exchange.name,
symbols=symbols,
symbol_list=','.join(symbols),
data_frequency=data_frequency)
def get_spot_values(self,
assets,
field,
dt,
data_frequency,
reset_reader=False
):
"""
The spot values for the gives assets, field and date. Reads from
the exchange data bundle.
Parameters
----------
assets: list[TradingPair]
field: str
dt: pd.Timestamp
data_frequency: str
reset_reader:
Returns
-------
float
"""
values = []
try:
reader = self.get_reader(data_frequency)
if reset_reader:
del self._readers[reader._rootdir]
reader = self.get_reader(data_frequency)
for asset in assets:
value = reader.get_value(
sid=asset.sid,
dt=dt,
field=field
)
values.append(value)
return values
except Exception:
symbols = [asset.symbol for asset in assets]
raise PricingDataNotLoadedError(
field=field,
first_trading_day=min([asset.start_date for asset in assets]),
exchange=self.exchange_name,
symbols=symbols,
symbol_list=','.join(symbols),
data_frequency=data_frequency,
start_dt=dt,
end_dt=dt
)
def get_spot_values(self,
assets,
field,
dt,
data_frequency,
reset_reader=False):
"""
The spot values for the gives assets, field and date. Reads from
the exchange data bundle.
:param assets:
:param field:
:param dt:
:param data_frequency:
:param reset_reader:
:return:
"""
values = []
try:
reader = self.get_reader(data_frequency)
if reset_reader:
del self._readers[reader._rootdir]
reader = self.get_reader(data_frequency)
for asset in assets:
value = reader.get_value(sid=asset.sid, dt=dt, field=field)
values.append(value)
return values
except Exception:
symbols = [asset.symbol for asset in assets]
raise PricingDataNotLoadedError(
field=field,
first_trading_day=min([asset.start_date for asset in assets]),
exchange=self.exchange.name,
symbols=symbols,
symbol_list=','.join(symbols),
data_frequency=data_frequency)
def get_history_window_series(self,
assets,
end_dt,
bar_count,
field,
data_frequency,
trailing_bar_count=None,
reset_reader=False):
start_dt = get_start_dt(end_dt, bar_count, data_frequency, False)
start_dt, _ = self.get_adj_dates(start_dt, end_dt, assets,
data_frequency)
if trailing_bar_count:
delta = get_delta(trailing_bar_count, data_frequency)
end_dt += delta
# This is an attempt to resolve some caching with the reader
# when auto-ingesting data.
# TODO: needs more work
reader = self.get_reader(data_frequency)
if reset_reader:
del self._readers[reader._rootdir]
reader = self.get_reader(data_frequency)
if reader is None:
symbols = [asset.symbol for asset in assets]
raise PricingDataNotLoadedError(
field=field,
first_trading_day=min([asset.start_date for asset in assets]),
exchange=self.exchange_name,
symbols=symbols,
symbol_list=','.join(symbols),
data_frequency=data_frequency,
start_dt=start_dt,
end_dt=end_dt)
series = dict()
for asset in assets:
asset_start_dt, _ = self.get_adj_dates(start_dt, end_dt, assets,
data_frequency)
in_bundle = range_in_bundle(asset, asset_start_dt, end_dt, reader)
if not in_bundle:
raise PricingDataNotLoadedError(
field=field,
first_trading_day=asset.start_date,
exchange=self.exchange_name,
symbols=asset.symbol,
symbol_list=asset.symbol,
data_frequency=data_frequency,
start_dt=asset_start_dt,
end_dt=end_dt)
periods = self.get_calendar_periods_range(asset_start_dt, end_dt,
data_frequency)
# This does not behave well when requesting multiple assets
# when the start or end date of one asset is outside of the range
# looking at the logic in load_raw_arrays(), we are not achieving
# any performance gain by requesting multiple sids at once. It's
# looping through the sids and making separate requests anyway.
arrays = reader.load_raw_arrays(sids=[asset.sid],
fields=[field],
start_dt=start_dt,
end_dt=end_dt)
if len(arrays) == 0:
raise DataCorruptionError(exchange=self.exchange_name,
symbols=asset.symbol,
start_dt=asset_start_dt,
end_dt=end_dt)
field_values = arrays[0][:, 0]
try:
value_series = pd.Series(field_values, index=periods)
series[asset] = value_series
except ValueError as e:
raise PricingDataValueError(exchange=asset.exchange,
symbol=asset.symbol,
start_dt=asset_start_dt,
end_dt=end_dt,
error=e)
return series
def get_history_window_series(self,
assets,
end_dt,
bar_count,
field,
data_frequency,
reset_reader=False):
start_dt = get_start_dt(end_dt, bar_count, data_frequency, False)
start_dt, end_dt = self.get_adj_dates(start_dt, end_dt, assets,
data_frequency)
reader = self.get_reader(data_frequency)
if reset_reader:
del self._readers[reader._rootdir]
reader = self.get_reader(data_frequency)
if reader is None:
symbols = [asset.symbol for asset in assets]
raise PricingDataNotLoadedError(
field=field,
first_trading_day=min([asset.start_date for asset in assets]),
exchange=self.exchange.name,
symbols=symbols,
symbol_list=','.join(symbols),
data_frequency=data_frequency,
start_dt=start_dt,
end_dt=end_dt)
for asset in assets:
asset_start_dt, asset_end_dt = self.get_adj_dates(
start_dt, end_dt, assets, data_frequency)
in_bundle = range_in_bundle(asset, asset_start_dt, asset_end_dt,
reader)
if not in_bundle:
raise PricingDataNotLoadedError(
field=field,
first_trading_day=asset.start_date,
exchange=self.exchange.name,
symbols=asset.symbol,
symbol_list=asset.symbol,
data_frequency=data_frequency,
start_dt=asset_start_dt,
end_dt=asset_end_dt)
series = dict()
try:
arrays = reader.load_raw_arrays(
sids=[asset.sid for asset in assets],
fields=[field],
start_dt=start_dt,
end_dt=end_dt)
except Exception:
symbols = [asset.symbol.encode('utf-8') for asset in assets]
raise PricingDataNotLoadedError(
field=field,
first_trading_day=min([asset.start_date for asset in assets]),
exchange=self.exchange.name,
symbols=symbols,
symbol_list=','.join(symbols),
data_frequency=data_frequency,
start_dt=start_dt,
end_dt=end_dt)
periods = self.get_calendar_periods_range(start_dt, end_dt,
data_frequency)
for asset_index, asset in enumerate(assets):
asset_values = arrays[asset_index]
value_series = pd.Series(asset_values.flatten(), index=periods)
series[asset] = value_series
return series