# 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._shortsdef 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)
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