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 Orders.Slippage.VolumeShareSlippageModel import VolumeShareSlippageModel
+
+### <summary>
+### Example algorithm implementing VolumeShareSlippageModel.
+### </summary>
+class VolumeShareSlippageModelAlgorithm(QCAlgorithm):
+    _longs = []
+    _shorts = []
+
+    def initialize(self) -> None:
+        self.set_start_date(2020, 11, 29)
+        self.set_end_date(2020, 12, 2)
+        # To set the slippage model to limit to fill only 30% volume of the historical volume, with 5% slippage impact.
+        self.set_security_initializer(lambda security: security.set_slippage_model(VolumeShareSlippageModel(0.3, 0.05)))
+
+        self.universe_settings.resolution = Resolution.DAILY
+        # Add universe to trade on the most and least weighted stocks among SPY constituents.
+        self.add_universe(self.universe.etf("SPY", universe_filter_func=self.selection))
+
+    def selection(self, constituents: list[ETFConstituentUniverse]) -> list[Symbol]:
+        sorted_by_weight = sorted(constituents, key=lambda c: c.weight)
+        # Add the 10 most weighted stocks to the universe to long later.
+        self._longs = [c.symbol for c in sorted_by_weight[-10:]]
+        # Add the 10 least weighted stocks to the universe to short later.
+        self._shorts = [c.symbol for c in sorted_by_weight[:10]]
+
+        return self._longs + self._shorts
+
+    def on_data(self, slice: Slice) -> None:
+        # Equally invest into the selected stocks to evenly dissipate capital risk.
+        # Dollar neutral of long and short stocks to eliminate systematic risk, only capitalize the popularity gap.
+        targets = [PortfolioTarget(symbol, 0.05) for symbol in self._longs]
+        targets += [PortfolioTarget(symbol, -0.05) for symbol in self._shorts]
+
+        # Liquidate the ones not being the most and least popularity stocks to release fund for higher expected return trades.
+        self.set_holdings(targets, liquidate_existing_holdings=True)