AlexCatarino
fa179b4659
- ConstituentsQC500GeneratorAlgorithm: - Change monthly flag to be consistent with Selection Model that cannot use Schedule events. - Use a Dictionary keyed by `Symbol` instead of `string`. - Selector functions return `Universe.Unchanged` instead of empty list; -Refactoring and more informative logging. - QC500UniverseSelectionModel - SelectFine methods were performing all the logics every day and it should be only once per month - Log and return `Universe.Unchanged` before division by zero if universe drops to zero members after filtering before selection by sector. - Refactoring
104 lines
5.0 KiB
Python
104 lines
5.0 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("QuantConnect.Common")
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AddReference("QuantConnect.Algorithm")
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from System import *
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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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from math import ceil
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from itertools import groupby
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### <summary>
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### Demonstration of how to estimate constituents of QC500 index based on the company fundamentals
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### The algorithm creates a default tradable and liquid universe containing 500 US equities
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### which are chosen at the first trading day of each month.
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### </summary>
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### <meta name="tag" content="using data" />
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### <meta name="tag" content="universes" />
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### <meta name="tag" content="coarse universes" />
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### <meta name="tag" content="fine universes" />
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class ConstituentsQC500GeneratorAlgorithm(QCAlgorithm):
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def Initialize(self):
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'''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
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self.UniverseSettings.Resolution = Resolution.Daily
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self.SetStartDate(2018, 1, 1) # Set Start Date
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self.SetEndDate(2019, 1, 1) # Set End Date
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self.SetCash(100000) # Set Strategy Cash
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# this add universe method accepts two parameters:
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# - coarse selection function: accepts an IEnumerable<CoarseFundamental> and returns an IEnumerable<Symbol>
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# - fine selection function: accepts an IEnumerable<FineFundamental> and returns an IEnumerable<Symbol>
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self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
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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 CoarseSelectionFunction(self, coarse):
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if self.Time.month == self.lastMonth:
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return self.symbols
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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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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 FineSelectionFunction(self, fine):
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if self.Time.month == self.lastMonth:
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return self.symbols
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self.lastMonth = self.Time.month
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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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filtered = [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 (self.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(filtered, 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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self.Log(f"{self.Time} :: {code}-{c}: {','.join([x.Symbol.Value for x in y[:10]])}")
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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 |