wehub-resource-sync
64 lines
3.2 KiB
Python
64 lines
3.2 KiB
Python
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
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# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from AlgorithmImports import *
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class CompositeRiskManagementModel(RiskManagementModel):
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'''Provides an implementation of IRiskManagementModel that combines multiple risk models
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into a single risk management model and properly sets each insights 'SourceModel' property.'''
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def __init__(self, *risk_management_models):
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'''Initializes a new instance of the CompositeRiskManagementModel class
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Args:
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risk_management_models: The individual risk management models defining this composite model.'''
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for model in risk_management_models:
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for attribute_names in [('ManageRisk', 'manage_risk'), ('OnSecuritiesChanged', 'on_securities_changed')]:
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if not hasattr(model, attribute_names[0]) and not hasattr(model, attribute_names[1]):
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raise Exception(f'IRiskManagementModel.{attribute_names[1]} must be implemented. Please implement this missing method on {model.__class__.__name__}')
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self.risk_management_models = risk_management_models
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def manage_risk(self, algorithm, targets):
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'''Manages the algorithm's risk at each time step
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Args:
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algorithm: The algorithm instance
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targets: The current portfolio targets to be assessed for risk'''
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for model in self.risk_management_models:
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# take into account the possibility of ManageRisk returning nothing
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risk_adjusted = model.manage_risk(algorithm, targets)
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# produce a distinct set of new targets giving preference to newer targets
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symbols = [x.symbol for x in risk_adjusted]
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for target in targets:
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if target.symbol not in symbols:
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risk_adjusted.append(target)
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targets = risk_adjusted
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return targets
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def on_securities_changed(self, algorithm, changes):
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'''Event fired each time the we add/remove securities from the data feed.
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This method patches this call through the each of the wrapped models.
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Args:
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algorithm: The algorithm instance that experienced the change in securities
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changes: The security additions and removals from the algorithm'''
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for model in self.risk_management_models:
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model.on_securities_changed(algorithm, changes)
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def add_risk_management(self, risk_management_model):
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'''Adds a new 'IRiskManagementModel' instance
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Args:
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risk_management_model: The risk management model to add'''
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self.risk_management_models.add(risk_management_model)
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