def test_expiring_cache(self):
expiry_1 = Timestamp('2014')
before_1 = expiry_1 - Timedelta('1 minute')
after_1 = expiry_1 + Timedelta('1 minute')
expiry_2 = Timestamp('2015')
after_2 = expiry_1 + Timedelta('1 minute')
expiry_3 = Timestamp('2016')
cache = ExpiringCache()
cache.set('foo', 1, expiry_1)
cache.set('bar', 2, expiry_2)
self.assertEqual(cache.get('foo', before_1), 1)
# Unwrap on expiry is allowed.
self.assertEqual(cache.get('foo', expiry_1), 1)
with self.assertRaises(KeyError) as e:
self.assertEqual(cache.get('foo', after_1))
self.assertEqual(e.exception.args, ('foo', ))
# Should raise same KeyError after deletion.
with self.assertRaises(KeyError) as e:
self.assertEqual(cache.get('foo', before_1))
self.assertEqual(e.exception.args, ('foo', ))
# Second value should still exist.
self.assertEqual(cache.get('bar', after_2), 2)
# Should raise similar KeyError on non-existent key.
with self.assertRaises(KeyError) as e:
self.assertEqual(cache.get('baz', expiry_3))
self.assertEqual(e.exception.args, ('baz', ))
def test_expiring_cache(self):
expiry_1 = Timestamp('2014')
before_1 = expiry_1 - Timedelta('1 minute')
after_1 = expiry_1 + Timedelta('1 minute')
expiry_2 = Timestamp('2015')
after_2 = expiry_1 + Timedelta('1 minute')
expiry_3 = Timestamp('2016')
cache = ExpiringCache()
cache.set('foo', 1, expiry_1)
cache.set('bar', 2, expiry_2)
self.assertEqual(cache.get('foo', before_1), 1)
# Unwrap on expiry is allowed.
self.assertEqual(cache.get('foo', expiry_1), 1)
with self.assertRaises(KeyError) as e:
self.assertEqual(cache.get('foo', after_1))
self.assertEqual(e.exception.args, ('foo',))
# Should raise same KeyError after deletion.
with self.assertRaises(KeyError) as e:
self.assertEqual(cache.get('foo', before_1))
self.assertEqual(e.exception.args, ('foo',))
# Second value should still exist.
self.assertEqual(cache.get('bar', after_2), 2)
# Should raise similar KeyError on non-existent key.
with self.assertRaises(KeyError) as e:
self.assertEqual(cache.get('baz', expiry_3))
self.assertEqual(e.exception.args, ('baz',))
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
def __init__(self):
super(MarketImpactBase, self).__init__()
self._window_data_cache = ExpiringCache()
class MarketImpactBase(SlippageModel):
"""
Base class for slippage models which compute a simulated price impact
according to a history lookback.
"""
NO_DATA_VOLATILITY_SLIPPAGE_IMPACT = 10.0 / 10000
def __init__(self):
super(MarketImpactBase, self).__init__()
self._window_data_cache = ExpiringCache()
@abstractmethod
def get_txn_volume(self, data, order):
"""
Return the number of shares we would like to order in this minute.
Parameters
----------
data : BarData
order : Order
Return
------
int : the number of shares
"""
raise NotImplementedError('get_txn_volume')
@abstractmethod
def get_simulated_impact(self, order, current_price, current_volume,
txn_volume, mean_volume, volatility):
"""
Calculate simulated price impact.
Parameters
----------
order : The order being processed.
current_price : Current price of the asset being ordered.
current_volume : Volume of the asset being ordered for the current bar.
txn_volume : Number of shares/contracts being ordered.
mean_volume : Trailing ADV of the asset.
volatility : Annualized daily volatility of volume.
Return
------
int : impact on the current price.
"""
raise NotImplementedError('get_simulated_impact')
def process_order(self, data, order):
if order.open_amount == 0:
return None, None
minute_data = data.current(order.asset, ['volume', 'high', 'low'])
mean_volume, volatility = self._get_window_data(data, order.asset, 20)
# Price to use is the average of the minute bar's open and close.
price = np.mean([minute_data['high'], minute_data['low']])
volume = minute_data['volume']
if not volume:
return None, None
txn_volume = int(
min(self.get_txn_volume(data, order), abs(order.open_amount)))
# If the computed transaction volume is zero or a decimal value, 'int'
# will round it down to zero. In that case just bail.
if txn_volume == 0:
return None, None
if mean_volume == 0 or np.isnan(volatility):
# If this is the first day the contract exists or there is no
# volume history, default to a conservative estimate of impact.
simulated_impact = price * self.NO_DATA_VOLATILITY_SLIPPAGE_IMPACT
else:
simulated_impact = self.get_simulated_impact(
order=order,
current_price=price,
current_volume=volume,
txn_volume=txn_volume,
mean_volume=mean_volume,
volatility=volatility,
)
impacted_price = \
price + math.copysign(simulated_impact, order.direction)
if fill_price_worse_than_limit_price(impacted_price, order):
return None, None
return impacted_price, math.copysign(txn_volume, order.direction)
def _get_window_data(self, data, asset, window_length):
"""
Internal utility method to return the trailing mean volume over the
past 'window_length' days, and volatility of close prices for a
specific asset.
Parameters
----------
data : The BarData from which to fetch the daily windows.
asset : The Asset whose data we are fetching.
window_length : Number of days of history used to calculate the mean
volume and close price volatility.
Returns
-------
(mean volume, volatility)
"""
try:
values = self._window_data_cache.get(asset, data.current_session)
except KeyError:
try:
# Add a day because we want 'window_length' complete days,
# excluding the current day.
volume_history = data.history(
asset,
'volume',
window_length + 1,
'1d',
)
close_history = data.history(
asset,
'close',
window_length + 1,
'1d',
)
except HistoryWindowStartsBeforeData:
# If there is not enough data to do a full history call, return
# values as if there was no data.
return 0, np.NaN
# Exclude the first value of the percent change array because it is
# always just NaN.
close_volatility = close_history[:-1].pct_change()[1:].std(
skipna=False, )
values = {
'volume': volume_history[:-1].mean(),
'close': close_volatility * SQRT_252,
}
self._window_data_cache.set(asset, values, data.current_session)
return values['volume'], values['close']
def __init__(self):
super(MarketImpactBase, self).__init__()
self._window_data_cache = ExpiringCache()
class MarketImpactBase(SlippageModel):
"""
Base class for slippage models which compute a simulated price impact
according to a history lookback.
"""
NO_DATA_VOLATILITY_SLIPPAGE_IMPACT = 10.0 / 10000
def __init__(self):
super(MarketImpactBase, self).__init__()
self._window_data_cache = ExpiringCache()
@abstractmethod
def get_txn_volume(self, data, order):
"""
Return the number of shares we would like to order in this minute.
Parameters
----------
data : BarData
order : Order
Return
------
int : the number of shares
"""
raise NotImplementedError('get_txn_volume')
@abstractmethod
def get_simulated_impact(self,
order,
current_price,
current_volume,
txn_volume,
mean_volume,
volatility):
"""
Calculate simulated price impact.
Parameters
----------
order : The order being processed.
current_price : Current price of the asset being ordered.
current_volume : Volume of the asset being ordered for the current bar.
txn_volume : Number of shares/contracts being ordered.
mean_volume : Trailing ADV of the asset.
volatility : Annualized daily volatility of volume.
Return
------
int : impact on the current price.
"""
raise NotImplementedError('get_simulated_impact')
def process_order(self, data, order):
if order.open_amount == 0:
return None, None
minute_data = data.current(order.asset, ['volume', 'high', 'low'])
mean_volume, volatility = self._get_window_data(data, order.asset, 20)
# Price to use is the average of the minute bar's open and close.
price = np.mean([minute_data['high'], minute_data['low']])
volume = minute_data['volume']
if not volume:
return None, None
txn_volume = int(
min(self.get_txn_volume(data, order), abs(order.open_amount))
)
# If the computed transaction volume is zero or a decimal value, 'int'
# will round it down to zero. In that case just bail.
if txn_volume == 0:
return None, None
if mean_volume == 0 or np.isnan(volatility):
# If this is the first day the contract exists or there is no
# volume history, default to a conservative estimate of impact.
simulated_impact = price * self.NO_DATA_VOLATILITY_SLIPPAGE_IMPACT
else:
simulated_impact = self.get_simulated_impact(
order=order,
current_price=price,
current_volume=volume,
txn_volume=txn_volume,
mean_volume=mean_volume,
volatility=volatility,
)
impacted_price = \
price + math.copysign(simulated_impact, order.direction)
if fill_price_worse_than_limit_price(impacted_price, order):
return None, None
return impacted_price, math.copysign(txn_volume, order.direction)
def _get_window_data(self, data, asset, window_length):
"""
Internal utility method to return the trailing mean volume over the
past 'window_length' days, and volatility of close prices for a
specific asset.
Parameters
----------
data : The BarData from which to fetch the daily windows.
asset : The Asset whose data we are fetching.
window_length : Number of days of history used to calculate the mean
volume and close price volatility.
Returns
-------
(mean volume, volatility)
"""
try:
values = self._window_data_cache.get(asset, data.current_session)
except KeyError:
try:
# Add a day because we want 'window_length' complete days,
# excluding the current day.
volume_history = data.history(
asset, 'volume', window_length + 1, '1d',
)
close_history = data.history(
asset, 'close', window_length + 1, '1d',
)
except HistoryWindowStartsBeforeData:
# If there is not enough data to do a full history call, return
# values as if there was no data.
return 0, np.NaN
# Exclude the first value of the percent change array because it is
# always just NaN.
close_volatility = close_history[:-1].pct_change()[1:].std(
skipna=False,
)
values = {
'volume': volume_history[:-1].mean(),
'close': close_volatility * SQRT_252,
}
self._window_data_cache.set(asset, values, data.current_session)
return values['volume'], values['close']