chore: import upstream snapshot with attribution

2026-07-13 13:02:50 +08:00
commit 0fc60fdcb1
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+# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
+# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
+#
+# 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 AlgorithmImports import *
+from itertools import groupby
+
+class MaximumSectorExposureRiskManagementModel(RiskManagementModel):
+    '''Provides an implementation of IRiskManagementModel that that limits the sector exposure to the specified percentage'''
+
+    def __init__(self, maximum_sector_exposure = 0.20):
+        '''Initializes a new instance of the MaximumSectorExposureRiskManagementModel class
+        Args:
+            maximum_drawdown_percent: The maximum exposure for any sector, defaults to 20% sector exposure.'''
+        if maximum_sector_exposure <= 0:
+            raise ValueError('MaximumSectorExposureRiskManagementModel: the maximum sector exposure cannot be a non-positive value.')
+
+        self.maximum_sector_exposure = maximum_sector_exposure
+        self.targets_collection = PortfolioTargetCollection()
+
+    def manage_risk(self, algorithm, targets):
+        '''Manages the algorithm's risk at each time step
+        Args:
+            algorithm: The algorithm instance'''
+        maximum_sector_exposure_value = float(algorithm.portfolio.total_portfolio_value) * self.maximum_sector_exposure
+
+        self.targets_collection.add_range(targets)
+
+        risk_targets = list()
+
+        # Group the securities by their sector
+        filtered = list(filter(lambda x: x.value.fundamentals is not None and x.value.fundamentals.has_fundamental_data, algorithm.universe_manager.active_securities))
+        filtered.sort(key = lambda x: x.value.fundamentals.company_reference.industry_template_code)
+        group_by_sector = groupby(filtered, lambda x: x.value.fundamentals.company_reference.industry_template_code)
+
+        for code, securities in group_by_sector:
+            # Compute the sector absolute holdings value
+            # If the construction model has created a target, we consider that
+            # value to calculate the security absolute holding value
+            quantities = {}
+            sector_absolute_holdings_value = 0
+
+            for security in securities:
+                symbol = security.value.symbol
+                quantities[symbol] = security.value.holdings.quantity
+                absolute_holdings_value = security.value.holdings.absolute_holdings_value
+
+                if self.targets_collection.contains_key(symbol):
+                    quantities[symbol] = self.targets_collection[symbol].quantity
+
+                    absolute_holdings_value = (security.value.price * abs(quantities[symbol]) *
+                        security.value.symbol_properties.contract_multiplier *
+                        security.value.quote_currency.conversion_rate)
+
+                sector_absolute_holdings_value += absolute_holdings_value
+
+            # If the ratio between the sector absolute holdings value and the maximum sector exposure value
+            # exceeds the unity, it means we need to reduce each security of that sector by that ratio
+            # Otherwise, it means that the sector exposure is below the maximum and there is nothing to do.
+            ratio = float(sector_absolute_holdings_value) / maximum_sector_exposure_value
+
+            if ratio > 1:
+                for symbol, quantity in quantities.items():
+                    if quantity != 0:
+                        risk_targets.append(PortfolioTarget(symbol, float(quantity) / ratio))
+
+        return risk_targets
+
+    def on_securities_changed(self, algorithm, changes):
+        '''Event fired each time the we add/remove securities from the data feed
+        Args:
+            algorithm: The algorithm instance that experienced the change in securities
+            changes: The security additions and removals from the algorithm'''
+        any_fundamental_data = any([
+            kvp.value.fundamentals is not None and
+            kvp.value.fundamentals.has_fundamental_data for kvp in algorithm.active_securities
+            ])
+
+        if not any_fundamental_data:
+            raise Exception("MaximumSectorExposureRiskManagementModel.on_securities_changed: Please select a portfolio selection model that selects securities with fundamental data.")