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triggers.py
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triggers.py
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"""
Copyright 2019 Goldman Sachs.
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 typing import Optional
import warnings
from gs_quant.backtests.actions import Action, AddTradeAction, AddTradeActionInfo
from gs_quant.backtests.backtest_objects import BackTest, PredefinedAssetBacktest
from gs_quant.backtests.backtest_utils import make_list, CalcType
from gs_quant.backtests.data_sources import *
from gs_quant.datetime.relative_date import RelativeDateSchedule
from gs_quant.risk.transform import Transformer
from gs_quant.risk import RiskMeasure
class TriggerDirection(Enum):
ABOVE = 1
BELOW = 2
EQUAL = 3
class AggType(Enum):
ALL_OF = 1
ANY_OF = 2
class TriggerRequirements(object):
def __init__(self):
pass
class PeriodicTriggerRequirements(TriggerRequirements):
def __init__(self, start_date: dt.date = None, end_date: dt.date = None, frequency: str = None,
calendar: str = None):
super().__init__()
self.start_date = start_date
self.end_date = end_date
self.frequency = frequency
self.calendar = calendar
class IntradayTriggerRequirements(TriggerRequirements):
def __init__(self, start_time: dt.datetime, end_time: dt.datetime, frequency: str):
super().__init__()
self.start_time = start_time
self.end_time = end_time
self.frequency = frequency
class MktTriggerRequirements(TriggerRequirements):
def __init__(self, data_source: DataSource, trigger_level: float, direction: TriggerDirection):
super().__init__()
self.data_source = data_source
self.trigger_level = trigger_level
self.direction = direction
class RiskTriggerRequirements(TriggerRequirements):
def __init__(self, risk: RiskMeasure, trigger_level: float, direction: TriggerDirection,
risk_transformation: Optional[Transformer] = None):
super().__init__()
self.risk = risk
self.trigger_level = trigger_level
self.direction = direction
self.risk_transformation = risk_transformation
class AggregateTriggerRequirements(TriggerRequirements):
def __init__(self, triggers: Iterable[object], aggregate_type: AggType = AggType.ALL_OF):
super().__init__()
self.triggers = triggers
self.aggregate_type = aggregate_type
class NotTriggerRequirements(TriggerRequirements):
def __init__(self, trigger: object):
super().__init__()
self.trigger = trigger
class DateTriggerRequirements(TriggerRequirements):
def __init__(self, dates: Iterable[Union[dt.datetime, dt.date]], entire_day: bool = False):
super().__init__()
"""
:param dates: the list of dates on which to trigger
:param entire_day: flag that indicates whether to check against dates instead of datetimes
"""
self.dates = dates
self.entire_day = entire_day
class PortfolioTriggerRequirements(TriggerRequirements):
def __init__(self, data_source: str, trigger_level: float, direction: TriggerDirection):
"""
:param data_source: the portfolio property to check
:param trigger_level: the threshold level on which to trigger
:param direction: a direction for the trigger_level comparison
"""
super().__init__()
self.data_source = data_source
self.trigger_level = trigger_level
self.direction = direction
class MeanReversionTriggerRequirements(TriggerRequirements):
def __init__(self, data_source: DataSource,
z_score_bound: float,
rolling_mean_window: int,
rolling_std_window: int):
"""
This trigger will sell when the value hits the z score threshold on the up side, will close out a position
when the value crosses the rolling_mean and buy when the value hits the z score threshold on the down side.
:param data_source: the asset values
:param z_score_bound: the threshold level on which to trigger
:param rolling_mean_window: the number of values to consider when calculating the rolling mean
:param rolling_std_window: the number of values to consider when calculating the standard deviation
"""
super().__init__()
self.data_source = data_source
self.z_score_bound = z_score_bound
self.rolling_mean_window = rolling_mean_window
self.rolling_std_window = rolling_std_window
class TriggerInfo(object):
def __init__(self, triggered: bool, info_dict: Optional[dict] = None):
self.triggered = triggered
self.info_dict = info_dict
def __eq__(self, other):
return self.triggered is other
def __bool__(self):
return self.triggered
class Trigger(object):
def __init__(self, trigger_requirements: Optional[TriggerRequirements], actions: Union[Action, Iterable[Action]]):
self._trigger_requirements = trigger_requirements
self._actions = make_list(actions)
self._risks = [x.risk for x in self.actions if x.risk is not None]
self._calc_type = CalcType.simple
def has_triggered(self, state: dt.date, backtest: BackTest = None) -> TriggerInfo:
"""
implemented by sub classes
:param state:
:param backtest:
:return:
TriggerInfo containing a bool indication of whether the trigger has triggered and optionally info
in the form of a dictionary of action type to object info understood by that action.
"""
raise RuntimeError('has_triggered to be implemented by subclass')
def get_trigger_times(self):
return []
@property
def calc_type(self):
return self._calc_type
@property
def actions(self):
return self._actions
@property
def trigger_requirements(self):
return self._trigger_requirements
@property
def risks(self):
return self._risks
class PeriodicTrigger(Trigger):
def __init__(self,
trigger_requirements: PeriodicTriggerRequirements,
actions: Union[Action, Iterable[Action]]):
super().__init__(trigger_requirements, actions)
self._trigger_dates = None
def get_trigger_times(self) -> [dt.date]:
if not self._trigger_dates:
self._trigger_dates = self._trigger_requirements.dates if \
hasattr(self._trigger_requirements, 'dates') else \
RelativeDateSchedule(self._trigger_requirements.frequency,
self._trigger_requirements.start_date,
self._trigger_requirements.end_date).apply_rule(
holiday_calendar=self.trigger_requirements.calendar)
return self._trigger_dates
def has_triggered(self, state: dt.date, backtest: BackTest = None) -> TriggerInfo:
if not self._trigger_dates:
self.get_trigger_times()
return TriggerInfo(state in self._trigger_dates)
class IntradayPeriodicTrigger(Trigger):
def __init__(self,
trigger_requirements: IntradayTriggerRequirements,
actions: Union[Action, Iterable[Action]]):
super().__init__(trigger_requirements, actions)
# generate all the trigger times
start = trigger_requirements.start_time
end = trigger_requirements.end_time
freq = trigger_requirements.frequency
self._trigger_times = []
time = start
while time <= end:
self._trigger_times.append(time)
time = (dt.datetime.combine(dt.date.today(), time) + dt.timedelta(minutes=freq)).time()
def get_trigger_times(self):
return self._trigger_times
def has_triggered(self, state: Union[dt.date, dt.datetime], backtest: BackTest = None) -> TriggerInfo:
return TriggerInfo(state.time() in self._trigger_times)
class MktTrigger(Trigger):
def __init__(self,
trigger_requirements: MktTriggerRequirements,
actions: Union[Action, Iterable[Action]]):
super().__init__(trigger_requirements, actions)
def has_triggered(self, state: dt.date, backtest: BackTest = None) -> TriggerInfo:
data_value = self._trigger_requirements.data_source.get_data(state)
if self._trigger_requirements.direction == TriggerDirection.ABOVE:
if data_value > self._trigger_requirements.trigger_level:
return TriggerInfo(True)
elif self._trigger_requirements.direction == TriggerDirection.BELOW:
if data_value < self._trigger_requirements.trigger_level:
return TriggerInfo(True)
else:
if data_value == self._trigger_requirements.trigger_level:
return TriggerInfo(True)
return TriggerInfo(False)
class StrategyRiskTrigger(Trigger):
def __init__(self,
trigger_requirements: RiskTriggerRequirements,
actions: Union[Action, Iterable[Action]]):
super().__init__(trigger_requirements, actions)
self._calc_type = CalcType.path_dependent
self._risks += [trigger_requirements.risk]
def has_triggered(self, state: dt.date, backtest: BackTest = None) -> TriggerInfo:
if self.trigger_requirements.risk_transformation is None:
risk_value = backtest.results[state][self._trigger_requirements.risk].aggregate()
else:
risk_value = backtest.results[state][self._trigger_requirements.risk].transform(
risk_transformation=self.trigger_requirements.risk_transformation).aggregate(
allow_mismatch_risk_keys=True)
if self._trigger_requirements.direction == TriggerDirection.ABOVE:
if risk_value > self._trigger_requirements.trigger_level:
return TriggerInfo(True)
elif self._trigger_requirements.direction == TriggerDirection.BELOW:
if risk_value < self._trigger_requirements.trigger_level:
return TriggerInfo(True)
else:
if risk_value == self._trigger_requirements.trigger_level:
return TriggerInfo(True)
return TriggerInfo(False)
class AggregateTrigger(Trigger):
def __init__(self,
trigger_requirements: Optional[AggregateTriggerRequirements] = None,
actions: Optional[Union[Action, Iterable[Action]]] = None,
triggers: Optional[Iterable[Trigger]] = None):
# support previous behaviour where a list of triggers was passed.
if not trigger_requirements and triggers is not None:
warnings.warn('triggers is deprecated; trigger_requirements', DeprecationWarning, 2)
trigger_requirements = AggregateTriggerRequirements(triggers)
actions = [] if not actions else actions
for t in trigger_requirements.triggers:
actions += [action for action in t.actions]
super().__init__(trigger_requirements, actions)
def has_triggered(self, state: dt.date, backtest: BackTest = None) -> TriggerInfo:
self._actions = []
info_dict = {}
if self._trigger_requirements.aggregate_type == AggType.ALL_OF:
for trigger in self._trigger_requirements.triggers:
t_info = trigger.has_triggered(state, backtest)
if not t_info:
return TriggerInfo(False)
else:
if t_info.info_dict:
info_dict.update(t_info.info_dict)
self._actions.extend(trigger.actions)
return TriggerInfo(True, info_dict)
elif self._trigger_requirements.aggregate_type == AggType.ANY_OF:
triggered = False
for trigger in self._trigger_requirements.triggers:
t_info = trigger.has_triggered(state, backtest)
if t_info:
triggered = True
if t_info.info_dict:
info_dict.update(t_info.info_dict)
self._actions.extend(trigger.actions)
return TriggerInfo(True, info_dict) if triggered else TriggerInfo(False)
else:
raise RuntimeError(f'Unrecognised aggregation type: {self._trigger_requirements.aggregate_type}')
@property
def triggers(self) -> Iterable[Trigger]:
return self._trigger_requirements.triggers
class NotTrigger(Trigger):
def __init__(self, trigger_requirements: NotTriggerRequirements, actions: Optional[Iterable[Action]] = None):
super().__init__(trigger_requirements, actions)
def has_triggered(self, state: dt.date, backtest: BackTest = None) -> TriggerInfo:
t_info = self.trigger_requirements.trigger.has_triggered(state, backtest)
if t_info:
return TriggerInfo(False)
else:
self._actions.extend(self.trigger_requirements.trigger.actions)
return TriggerInfo(True)
class DateTrigger(Trigger):
def __init__(self, trigger_requirements: DateTriggerRequirements, actions: Iterable[Action]):
super().__init__(trigger_requirements, actions)
self._dates_from_datetimes = [d.date() if isinstance(d, dt.datetime) else d
for d in self.trigger_requirements.dates] \
if self.trigger_requirements.entire_day else None
def has_triggered(self, state: Union[dt.date, dt.datetime], backtest: BackTest = None) -> TriggerInfo:
assert isinstance(state, dt.datetime) or isinstance(state, dt.date)
if self.trigger_requirements.entire_day:
if isinstance(state, dt.datetime):
return TriggerInfo(state.date() in self._dates_from_datetimes)
elif isinstance(state, dt.date):
return TriggerInfo(state in self._dates_from_datetimes)
return TriggerInfo(state in self._trigger_requirements.dates)
def get_trigger_times(self):
return self._dates_from_datetimes or self._trigger_requirements.dates
class PortfolioTrigger(Trigger):
def __init__(self, trigger_requirements: PortfolioTriggerRequirements, actions: Iterable[Action] = None):
super().__init__(trigger_requirements, actions)
def has_triggered(self, state: dt.date, backtest: BackTest = None) -> TriggerInfo:
if self._trigger_requirements.data_source == 'len':
value = len(backtest.portfolio_dict)
if self._trigger_requirements.direction == TriggerDirection.ABOVE:
if value > self._trigger_requirements.trigger_level:
return TriggerInfo(True)
elif self._trigger_requirements.direction == TriggerDirection.BELOW:
if value < self._trigger_requirements.trigger_level:
return TriggerInfo(True)
else:
if value == self._trigger_requirements.trigger_level:
return TriggerInfo(True)
return TriggerInfo(False)
class MeanReversionTrigger(Trigger):
def __init__(self,
trigger_requirements: MeanReversionTriggerRequirements,
actions: Union[Action, Iterable[Action]]):
super().__init__(trigger_requirements, actions)
self._current_position = 0
def has_triggered(self, state: dt.date, backtest: BackTest = None) -> TriggerInfo:
trigger_req = self._trigger_requirements
rolling_mean = trigger_req.data_source.get_data_range(state, trigger_req.rolling_mean_window).mean()
rolling_std = trigger_req.data_source.get_data_range(state, trigger_req.rolling_std_window).std()
current_price = trigger_req.data_source.get_data(state)
if self._current_position == 0:
if abs((current_price - rolling_mean) / rolling_std) > self.trigger_requirements.z_score_bound:
if current_price > rolling_mean:
self._current_position = -1
return TriggerInfo(True, {AddTradeAction: AddTradeActionInfo(scaling=-1)})
else:
self._current_position = 1
return TriggerInfo(True, {AddTradeAction: AddTradeActionInfo(scaling=1)})
elif self._current_position == 1:
if current_price > rolling_mean:
self._current_position = 0
return TriggerInfo(True, {AddTradeAction: AddTradeActionInfo(scaling=-1)})
elif self._current_position == -1:
if current_price > rolling_mean:
self._current_position = 0
return TriggerInfo(True, {AddTradeAction: AddTradeActionInfo(scaling=1)})
else:
raise RuntimeWarning(f'unexpected current position: {self._current_position}')
return TriggerInfo(False)
class OrdersGeneratorTrigger(Trigger):
"""Base class for triggers used with the PredefinedAssetEngine."""
def __init__(self):
super().__init__(None, Action())
def get_trigger_times(self) -> list:
"""
Returns the set of times when orders can be generated e.g. every 30 min
:return: list
"""
raise RuntimeError('get_trigger_times must be implemented by subclass')
def generate_orders(self, state: dt.datetime, backtest: PredefinedAssetBacktest = None) -> list:
"""
Returns the orders generated at state
:param state: the time when orders are generated
:param backtest: the backtest, used to access the holdings and orders generated so far
:return: list
"""
raise RuntimeError('generate_orders must be implemented by subclass')
def has_triggered(self, state: dt.datetime, backtest: PredefinedAssetBacktest = None) -> TriggerInfo:
"""
Calls generate_orders if state is among the trigger times
:param state: the time of the trigger
:param backtest: the backtest, used to access the holdings and orders generated so far
:return: list
"""
if state.time() not in self.get_trigger_times():
return TriggerInfo(False)
else:
orders = self.generate_orders(state, backtest)
return TriggerInfo(True, {type(a): orders for a in self.actions}) if len(orders) else TriggerInfo(False)