wehub-resource-sync
90 lines
4.7 KiB
Python
90 lines
4.7 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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from itertools import groupby
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class MaximumSectorExposureRiskManagementModel(RiskManagementModel):
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'''Provides an implementation of IRiskManagementModel that that limits the sector exposure to the specified percentage'''
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def __init__(self, maximum_sector_exposure = 0.20):
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'''Initializes a new instance of the MaximumSectorExposureRiskManagementModel class
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Args:
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maximum_drawdown_percent: The maximum exposure for any sector, defaults to 20% sector exposure.'''
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if maximum_sector_exposure <= 0:
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raise ValueError('MaximumSectorExposureRiskManagementModel: the maximum sector exposure cannot be a non-positive value.')
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self.maximum_sector_exposure = maximum_sector_exposure
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self.targets_collection = PortfolioTargetCollection()
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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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maximum_sector_exposure_value = float(algorithm.portfolio.total_portfolio_value) * self.maximum_sector_exposure
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self.targets_collection.add_range(targets)
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risk_targets = list()
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# Group the securities by their sector
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filtered = list(filter(lambda x: x.value.fundamentals is not None and x.value.fundamentals.has_fundamental_data, algorithm.universe_manager.active_securities))
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filtered.sort(key = lambda x: x.value.fundamentals.company_reference.industry_template_code)
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group_by_sector = groupby(filtered, lambda x: x.value.fundamentals.company_reference.industry_template_code)
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for code, securities in group_by_sector:
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# Compute the sector absolute holdings value
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# If the construction model has created a target, we consider that
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# value to calculate the security absolute holding value
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quantities = {}
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sector_absolute_holdings_value = 0
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for security in securities:
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symbol = security.value.symbol
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quantities[symbol] = security.value.holdings.quantity
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absolute_holdings_value = security.value.holdings.absolute_holdings_value
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if self.targets_collection.contains_key(symbol):
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quantities[symbol] = self.targets_collection[symbol].quantity
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absolute_holdings_value = (security.value.price * abs(quantities[symbol]) *
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security.value.symbol_properties.contract_multiplier *
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security.value.quote_currency.conversion_rate)
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sector_absolute_holdings_value += absolute_holdings_value
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# If the ratio between the sector absolute holdings value and the maximum sector exposure value
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# exceeds the unity, it means we need to reduce each security of that sector by that ratio
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# Otherwise, it means that the sector exposure is below the maximum and there is nothing to do.
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ratio = float(sector_absolute_holdings_value) / maximum_sector_exposure_value
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if ratio > 1:
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for symbol, quantity in quantities.items():
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if quantity != 0:
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risk_targets.append(PortfolioTarget(symbol, float(quantity) / ratio))
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return risk_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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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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any_fundamental_data = any([
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kvp.value.fundamentals is not None and
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kvp.value.fundamentals.has_fundamental_data for kvp in algorithm.active_securities
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])
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if not any_fundamental_data:
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raise Exception("MaximumSectorExposureRiskManagementModel.on_securities_changed: Please select a portfolio selection model that selects securities with fundamental data.")
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