Martin Molinero
b5c216f7f3
- Adding stateless and stateful coarse universe selection benchmarks. Will be used to benchmark future performance improvement.
74 lines
2.8 KiB
Python
74 lines
2.8 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 clr import AddReference
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AddReference("System.Core")
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AddReference("System.Collections")
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AddReference("QuantConnect.Common")
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AddReference("QuantConnect.Algorithm")
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from System import *
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from System.Collections.Generic import List
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from QuantConnect import *
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from QuantConnect.Algorithm import QCAlgorithm
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from QuantConnect.Data.UniverseSelection import *
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class CoarseFineUniverseSelectionBenchmark(QCAlgorithm):
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def Initialize(self):
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self.SetStartDate(2017, 11, 1)
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self.SetEndDate(2018, 1, 1)
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self.SetCash(50000)
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self.UniverseSettings.Resolution = Resolution.Minute
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self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
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self.numberOfSymbols = 150
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self.numberOfSymbolsFine = 40
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self._changes = None
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# sort the data by daily dollar volume and take the top 'NumberOfSymbols'
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def CoarseSelectionFunction(self, coarse):
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selected = [x for x in coarse if (x.HasFundamentalData)]
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# sort descending by daily dollar volume
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sortedByDollarVolume = sorted(selected, key=lambda x: x.DollarVolume, reverse=True)
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# return the symbol objects of the top entries from our sorted collection
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return [ x.Symbol for x in sortedByDollarVolume[:self.numberOfSymbols] ]
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# sort the data by P/E ratio and take the top 'NumberOfSymbolsFine'
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def FineSelectionFunction(self, fine):
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# sort descending by P/E ratio
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sortedByPeRatio = sorted(fine, key=lambda x: x.ValuationRatios.PERatio, reverse=True)
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# take the top entries from our sorted collection
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return [ x.Symbol for x in sortedByPeRatio[:self.numberOfSymbolsFine] ]
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def OnData(self, data):
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# if we have no changes, do nothing
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if self._changes is None: return
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# liquidate removed securities
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for security in self._changes.RemovedSecurities:
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if security.Invested:
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self.Liquidate(security.Symbol)
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for security in self._changes.AddedSecurities:
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self.SetHoldings(security.Symbol, 0.02)
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self._changes = None;
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def OnSecuritiesChanged(self, changes):
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self._changes = changes |