Overall Statistics |
Total Trades 0 Average Win 0% Average Loss 0% Compounding Annual Return 0% Drawdown 0% Expectancy 0 Net Profit 0% Sharpe Ratio 0 Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha 0 Beta 0 Annual Standard Deviation 0 Annual Variance 0 Information Ratio 0 Tracking Error 0 Treynor Ratio 0 Total Fees $0.00 |
from QuantConnect.Data.UniverseSelection import * from Selection.FundamentalUniverseSelectionModel import FundamentalUniverseSelectionModel from itertools import groupby from math import ceil class VentralDynamicContainmentField(QCAlgorithm): def Initialize(self): self.SetStartDate(2018, 12, 2) # Set Start Date self.SetEndDate(2019, 1, 8) self.SetCash(100000) # Set Strategy Cash self.UniverseSettings.Resolution = Resolution.Daily self.numberOfSymbolsCoarse = 1000 self.numberOfSymbolsFine = 500 self.dollarVolumeBySymbol = {} self.symbols = [] self.AddUniverseSelection(FineFundamentalUniverseSelectionModel(self.CoarseSelectionFunction, self.FineSelectionFunction, None, None)) self.week = 0 def OnData(self, data): pass # sort the data by daily dollar volume and take the top 'NumberOfSymbols' def CoarseSelectionFunction(self, coarse): current_week = self.Time.isocalendar()[1] if current_week == self.week: return self.symbols filtered = [x for x in coarse if x.HasFundamentalData and x.Volume > 0 and x.Price > 0] sortedByDollarVolume = sorted(filtered, key = lambda x: x.DollarVolume, reverse=True)[:self.numberOfSymbolsCoarse] self.symbols.clear() self.dollarVolumeBySymbol.clear() for x in sortedByDollarVolume: self.symbols.append(x.Symbol) self.dollarVolumeBySymbol[x.Symbol] = x.DollarVolume # return the symbol objects our sorted collection return self.symbols # sort the data by P/E ratio and take the top 'NumberOfSymbolsFine' def FineSelectionFunction(self, fine): current_week = self.Time.isocalendar()[1] if current_week == self.week: return self.symbols self.week = current_week filteredFine = [x for x in fine if x.CompanyReference.CountryId == "USA" and (x.CompanyReference.PrimaryExchangeID == "NYS" or x.CompanyReference.PrimaryExchangeID == "NAS") and (self.Time - x.SecurityReference.IPODate).days > 180 and x.EarningReports.BasicAverageShares.ThreeMonths * x.EarningReports.BasicEPS.TwelveMonths * x.ValuationRatios.PERatio > 5e8] sortedByDollarVolume = [] sortedBySector = sorted(filteredFine, key = lambda x: x.CompanyReference.IndustryTemplateCode) percent = self.numberOfSymbolsFine/float(len(sortedBySector)) # select stocks with top dollar volume in every single sector for code, g in groupby(sortedBySector, lambda x: x.CompanyReference.IndustryTemplateCode): y = sorted(g, key = lambda x: self.dollarVolumeBySymbol[x.Symbol], reverse = True) c = ceil(len(y) * percent) sortedByDollarVolume.extend(y[:c]) sortedByDollarVolume = sorted(sortedByDollarVolume, key = lambda x: self.dollarVolumeBySymbol[x.Symbol], reverse=True) self.symbols = [x.Symbol for x in sortedByDollarVolume[:self.numberOfSymbolsFine]] self.Log(f"selection complete: {self.Time}") return self.symbols def SelectSymbols(self): self.select = True