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