Overall Statistics |
Total Trades 418 Average Win 1.06% Average Loss -0.88% Compounding Annual Return 8.748% Drawdown 22.900% Expectancy 0.660 Net Profit 271.157% Sharpe Ratio 0.662 Probabilistic Sharpe Ratio 5.851% Loss Rate 25% Win Rate 75% Profit-Loss Ratio 1.22 Alpha 0.081 Beta -0.016 Annual Standard Deviation 0.12 Annual Variance 0.014 Information Ratio -0.049 Tracking Error 0.216 Treynor Ratio -4.974 Total Fees $1033.04 |
## A simple m odification to add leverage factor to the InsightWeightingPortfolioConstructionModel ## This appears to be triggering everyday - when I thought it would trigger EOM? class LeveragePCM(InsightWeightingPortfolioConstructionModel): leverage = 0.0 def CreateTargets(self, algorithm, insights): targets = super().CreateTargets(algorithm, insights) return [PortfolioTarget(x.Symbol, x.Quantity*(1+self.leverage)) for x in targets]
''' An ensemble approach to GEM - Global Equities Momentum. ''' from alpha_model import GEMEnsembleAlphaModel from pcm import LeveragePCM class GlobalTacticalAssetAllocation(QCAlgorithm): def Initialize(self): self.SetStartDate(2005, 1, 1) #self.SetEndDate(2020, 5, 20) self.SetCash(100000) self.Settings.FreePortfolioValuePercentage = 0.02 self.SetBrokerageModel(BrokerageName.InteractiveBrokersBrokerage, AccountType.Margin) # PNQI, TLT tickers = ['SPY', 'EFA', 'TLT'] #for plotting us_equity = Symbol.Create('SPY', SecurityType.Equity, Market.USA) foreign_equity = Symbol.Create('EFA', SecurityType.Equity, Market.USA) bond = Symbol.Create('TLT', SecurityType.Equity, Market.USA) symbols = [us_equity, foreign_equity, bond] self.UniverseSettings.Resolution = Resolution.Daily self.AddUniverseSelection( ManualUniverseSelectionModel(symbols) ) self.AddAlpha( GEMEnsembleAlphaModel(us_equity, foreign_equity, bond) ) self.Settings.RebalancePortfolioOnSecurityChanges = False self.Settings.RebalancePortfolioOnInsightChanges = False self.SetPortfolioConstruction(LeveragePCM(self.RebalanceFunction, PortfolioBias.Long)) self.lastRebalanceTime = None self.SetExecution( ImmediateExecutionModel() ) self.AddRiskManagement( NullRiskManagementModel() ) # Initialise plot assetWeightsPlot = Chart('AssetWeights %') for ticker in tickers: assetWeightsPlot.AddSeries(Series(ticker, SeriesType.Line, f'{ticker}%')) def RebalanceFunction(self, time): return Expiry.EndOfMonth(self.Time) def OnData(self, data): # Update Plot for kvp in self.Portfolio: symbol = kvp.Key holding = kvp.Value self.Plot('AssetWeights %', f"{str(holding.Symbol)}%", holding.HoldingsValue/self.Portfolio.TotalPortfolioValue)
import numpy as np class GEMEnsembleAlphaModel(AlphaModel): """ If the S&P 500 had positive returns over the past X-months (positive trend) the strategy allocates to stocks the next month; otherwise it allocates to bonds. When the trend is positive for stocks the strategy holds the equity index with the strongest total return over the same horizon. The Ensemble approach takes the average of all signals. """ def __init__(self, us_equity, foreign_equity, bond, resolution=Resolution.Daily): '''Initializes a new instance of the SmaAlphaModel class Args: resolution: The reolution for our indicators ''' self.us_equity = us_equity self.foreign_equity = foreign_equity self.bond = bond self.resolution = resolution self.symbolDataBySymbol = {} self.month = -1 def Update(self, algorithm, data): '''This is called each time the algorithm receives data for (@resolution of) subscribed securities Returns: The new insights generated. THIS: analysis only occurs at month start, so any signals intra-month are disregarded.''' if self.month == algorithm.Time.month: return [] self.month = algorithm.Time.month insights = [] strategies = {} weights = dict.fromkeys([self.us_equity, self.foreign_equity, self.bond], 0) for symbol, symbolData in self.symbolDataBySymbol.items(): strategies[symbol] = np.array([]) for lookback in symbolData.momp.keys(): strategies[symbol] = np.append(strategies[symbol], symbolData.momp[lookback].Current.Value) weights[self.us_equity] = sum( (strategies[self.us_equity] >= 0) & (strategies[self.us_equity] >= strategies[self.foreign_equity]) ) weights[self.foreign_equity] = sum( (strategies[self.us_equity] >= 0) & (strategies[self.foreign_equity] > strategies[self.us_equity]) ) weights[self.bond] = sum(strategies[self.us_equity] < 0) insights.append(Insight.Price(self.us_equity, Expiry.EndOfMonth, InsightDirection.Up, None, None, None, weights[self.us_equity])) insights.append(Insight.Price(self.foreign_equity, Expiry.EndOfMonth, InsightDirection.Up, None, None, None, weights[self.foreign_equity])) insights.append(Insight.Price(self.bond, Expiry.EndOfMonth, InsightDirection.Up, None, None, None, weights[self.bond])) return insights def OnSecuritiesChanged(self, algorithm, changes): for added in changes.AddedSecurities: self.symbolDataBySymbol[added.Symbol] = SymbolData(added.Symbol, algorithm, self.resolution) for removed in changes.RemovedSecurities: symbolData = self.symbolDataBySymbol.pop(removed.Symbol, None) if symbolData: # Remove consolidator symbolData.dispose() class SymbolData: def __init__(self, symbol, algorithm, resolution): self.algorithm = algorithm self.Symbol = symbol self.momp = {} for period in range(1, 13): self.momp[period] = MomentumPercent(period*21) # Warm up Indicators history = algorithm.History([self.Symbol], 21*13, resolution).loc[self.Symbol] # Use history to build our SMA for time, row in history.iterrows(): for period, momp in self.momp.items(): self.momp[period].Update(time, row["close"]) # Setup indicator consolidator self.consolidator = TradeBarConsolidator(timedelta(1)) self.consolidator.DataConsolidated += self.CustomDailyHandler algorithm.SubscriptionManager.AddConsolidator(self.Symbol, self.consolidator) def CustomDailyHandler(self, sender, consolidated): for period, momp in self.momp.items(): self.momp[period].Update(consolidated.Time, consolidated.Close) def dispose(self): self.algorithm.SubscriptionManager.RemoveConsolidator(self.Symbol, self.consolidator)