Overall Statistics |
Total Trades 315 Average Win 0.01% Average Loss -0.01% Compounding Annual Return 52.921% Drawdown 1.100% Expectancy -0.188 Net Profit 7.732% Sharpe Ratio 5.403 Probabilistic Sharpe Ratio 99.168% Loss Rate 63% Win Rate 37% Profit-Loss Ratio 1.17 Alpha 0.069 Beta 0.988 Annual Standard Deviation 0.069 Annual Variance 0.005 Information Ratio 1.512 Tracking Error 0.043 Treynor Ratio 0.376 Total Fees $322.03 Estimated Strategy Capacity $13000000.00 Lowest Capacity Asset ORCL R735QTJ8XC9X |
from datetime import timedelta from QuantConnect.Data.UniverseSelection import * from Selection.FundamentalUniverseSelectionModel import FundamentalUniverseSelectionModel from Portfolio.EqualWeightingPortfolioConstructionModel import EqualWeightingPortfolioConstructionModel class SectorBalancedPortfolioConstruction(QCAlgorithm): def Initialize(self): self.SetStartDate(2016, 12, 28) self.SetEndDate(2017, 3, 1) self.SetCash(100000) self.UniverseSettings.Resolution = Resolution.Hour self.SetUniverseSelection(MyUniverseSelectionModel()) self.SetAlpha(ConstantAlphaModel(InsightType.Price, InsightDirection.Up, timedelta(1), 0.025, None)) self.SetPortfolioConstruction(MySectorWeightingPortfolioConstructionModel(Resolution.Daily)) self.SetExecution(ImmediateExecutionModel()) class MyUniverseSelectionModel(FundamentalUniverseSelectionModel): def __init__(self): super().__init__(True, None) def SelectCoarse(self, algorithm, coarse): filtered = [x for x in coarse if x.HasFundamentalData and x.Price > 0] sortedByDollarVolume = sorted(filtered, key=lambda x: x.DollarVolume, reverse=True) return [x.Symbol for x in sortedByDollarVolume][:100] def SelectFine(self, algorithm, fine): filtered = [f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.Technology] self.technology = sorted(filtered, key=lambda f: f.MarketCap, reverse=True)[:3] filtered = [f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.FinancialServices] self.financialServices = sorted(filtered, key=lambda f: f.MarketCap, reverse=True)[:2] filtered = [f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.ConsumerDefensive] self.consumerDefensive = sorted(filtered, key=lambda f: f.MarketCap, reverse=True)[:1] return [x.Symbol for x in self.technology + self.financialServices + self.consumerDefensive] class MySectorWeightingPortfolioConstructionModel(EqualWeightingPortfolioConstructionModel): def __init__(self, rebalance = Resolution.Daily): super().__init__(rebalance) self.symbolBySectorCode = dict() self.result = dict() def DetermineTargetPercent(self, activeInsights): #1. Set the self.sectorBuyingPower before by dividing one by the length of self.symbolBySectorCode self.sectorBuyingPower = 1/len(self.symbolBySectorCode) for sector, symbols in self.symbolBySectorCode.items(): #2. Search for the active insights in this sector. Save the variable self.insightsInSector self.insightsInSector = [insight for insight in activeInsights if insight.Symbol in symbols] #3. Divide the self.sectorBuyingPower by the length of self.insightsInSector to calculate the variable percent # The percent is the weight we'll assign the direction of the insight self.percent = self.sectorBuyingPower / len(self.insightsInSector) #4. For each insight in self.insightsInSector, assign each insight an allocation. # The allocation is calculated by multiplying the insight direction by the self.percent for insight in self.insightsInSector: self.result[insight] = insight.Direction * self.percent return self.result def OnSecuritiesChanged(self, algorithm, changes): for security in changes.AddedSecurities: sectorCode = security.Fundamentals.AssetClassification.MorningstarSectorCode if sectorCode not in self.symbolBySectorCode: self.symbolBySectorCode[sectorCode] = list() self.symbolBySectorCode[sectorCode].append(security.Symbol) for security in changes.RemovedSecurities: sectorCode = security.Fundamentals.AssetClassification.MorningstarSectorCode if sectorCode in self.symbolBySectorCode: symbol = security.Symbol if symbol in self.symbolBySectorCode[sectorCode]: self.symbolBySectorCode[sectorCode].remove(symbol) super().OnSecuritiesChanged(algorithm, changes)