Martin Molinero
85 lines
4.3 KiB
Python
85 lines
4.3 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")
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AddReference("QuantConnect.Common")
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AddReference("QuantConnect.Algorithm.Framework")
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from QuantConnect.Data.UniverseSelection import *
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from Selection.FundamentalUniverseSelectionModel import FundamentalUniverseSelectionModel
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from itertools import groupby
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from math import ceil
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class QC500UniverseSelectionModel(FundamentalUniverseSelectionModel):
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'''Defines the QC500 universe as a universe selection model for framework algorithm
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For details: https://github.com/QuantConnect/Lean/pull/1663'''
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def __init__(self, filterFineData = True, universeSettings = None, securityInitializer = None):
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'''Initializes a new default instance of the QC500UniverseSelectionModel'''
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super().__init__(filterFineData, universeSettings, securityInitializer)
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self.numberOfSymbolsCoarse = 1000
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self.numberOfSymbolsFine = 500
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self.dollarVolumeBySymbol = {}
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self.symbols = []
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self.lastMonth = -1
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def SelectCoarse(self, algorithm, coarse):
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'''Performs coarse selection for the QC500 constituents.
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The stocks must have fundamental data
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The stock must have positive previous-day close price
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The stock must have positive volume on the previous trading day'''
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if algorithm.Time.month == self.lastMonth:
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return self.symbols
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filtered = [x for x in coarse if x.HasFundamentalData and x.Volume > 0 and x.Price > 0]
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sortedByDollarVolume = sorted(filtered, key = lambda x: x.DollarVolume, reverse=True)[:self.numberOfSymbolsCoarse]
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self.symbols.clear()
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self.dollarVolumeBySymbol.clear()
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for x in sortedByDollarVolume:
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self.symbols.append(x.Symbol)
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self.dollarVolumeBySymbol[x.Symbol] = x.DollarVolume
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# return the symbol objects our sorted collection
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return self.symbols
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def SelectFine(self, algorithm, fine):
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'''Performs fine selection for the QC500 constituents
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The company's headquarter must in the U.S.
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The stock must be traded on either the NYSE or NASDAQ
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At least half a year since its initial public offering
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The stock's market cap must be greater than 500 million'''
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if algorithm.Time.month == self.lastMonth:
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return self.symbols
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self.lastMonth = algorithm.Time.month
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filteredFine = [x for x in fine if x.CompanyReference.CountryId == "USA"
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and (x.CompanyReference.PrimaryExchangeID == "NYS" or x.CompanyReference.PrimaryExchangeID == "NAS")
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and (algorithm.Time - x.SecurityReference.IPODate).days > 180
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and x.EarningReports.BasicAverageShares.ThreeMonths * x.EarningReports.BasicEPS.TwelveMonths * x.ValuationRatios.PERatio > 5e8]
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sortedByDollarVolume = []
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sortedBySector = sorted(filteredFine, key = lambda x: x.CompanyReference.IndustryTemplateCode)
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percent = self.numberOfSymbolsFine/float(len(sortedBySector))
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# select stocks with top dollar volume in every single sector
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for code, g in groupby(sortedBySector, lambda x: x.CompanyReference.IndustryTemplateCode):
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y = sorted(g, key = lambda x: self.dollarVolumeBySymbol[x.Symbol], reverse = True)
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c = ceil(len(y) * percent)
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sortedByDollarVolume.extend(y[:c])
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sortedByDollarVolume = sorted(sortedByDollarVolume, key = lambda x: self.dollarVolumeBySymbol[x.Symbol], reverse=True)
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self.symbols = [x.Symbol for x in sortedByDollarVolume[:self.numberOfSymbolsFine]]
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return self.symbols |