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 *
+
+### <summary>
+### Regression algorithm testing portfolio construction model control over rebalancing,
+### specifying a custom rebalance function that returns null in some cases, see GH 4075.
+### </summary>
+class PortfolioRebalanceOnCustomFuncRegressionAlgorithm(QCAlgorithm):
+    def initialize(self):
+        ''' Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
+
+        self.universe_settings.resolution = Resolution.DAILY
+
+        # Order margin value has to have a minimum of 0.5% of Portfolio value, allows filtering out small trades and reduce fees.
+        # Commented so regression algorithm is more sensitive
+        #self.settings.minimum_order_margin_portfolio_percentage = 0.005
+
+        self.set_start_date(2015, 1, 1)
+        self.set_end_date(2018, 1, 1)
+
+        self.settings.rebalance_portfolio_on_insight_changes = False
+        self.settings.rebalance_portfolio_on_security_changes = False
+
+        self.set_universe_selection(CustomUniverseSelectionModel("CustomUniverseSelectionModel", lambda time: [ "AAPL", "IBM", "FB", "SPY", "AIG", "BAC", "BNO" ]))
+        self.set_alpha(ConstantAlphaModel(InsightType.PRICE, InsightDirection.UP, TimeSpan.from_minutes(20), 0.025, None))
+        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel(self.rebalance_function))
+        self.set_execution(ImmediateExecutionModel())
+        self.last_rebalance_time = self.start_date
+
+    def rebalance_function(self, time):
+        # for performance only run rebalance logic once a week, monday
+        if time.weekday() != 0:
+            return None
+
+        if self.last_rebalance_time == self.start_date:
+            # initial rebalance
+            self.last_rebalance_time = time
+            return time
+
+        deviation = 0
+        count = sum(1 for security in self.securities.values() if security.invested)
+        if count > 0:
+            self.last_rebalance_time = time
+            portfolio_value_per_security = self.portfolio.total_portfolio_value / count
+            for security in self.securities.values():
+                if not security.invested:
+                    continue
+                reserved_buying_power_for_current_position = (security.buying_power_model.get_reserved_buying_power_for_position(
+                    ReservedBuyingPowerForPositionParameters(security)).absolute_used_buying_power
+                                                         * security.buying_power_model.get_leverage(security)) # see GH issue 4107
+                # we sum up deviation for each security
+                deviation += (portfolio_value_per_security - reserved_buying_power_for_current_position) / portfolio_value_per_security
+
+            # if securities are deviated 1.5% from their theoretical share of TotalPortfolioValue we rebalance
+            if deviation >= 0.015:
+                return time
+        return None
+
+    def on_order_event(self, order_event):
+        if order_event.status == OrderStatus.SUBMITTED:
+            if self.utc_time != self.last_rebalance_time or self.utc_time.weekday() != 0:
+                raise ValueError(f"{self.utc_time} {order_event.symbol}")