Overall Statistics |
Total Trades 130 Average Win 0.93% Average Loss -1.06% Compounding Annual Return 1.961% Drawdown 24.700% Expectancy 0.132 Net Profit 11.030% Sharpe Ratio 0.199 Probabilistic Sharpe Ratio 2.074% Loss Rate 40% Win Rate 60% Profit-Loss Ratio 0.88 Alpha 0.029 Beta -0.073 Annual Standard Deviation 0.115 Annual Variance 0.013 Information Ratio -0.316 Tracking Error 0.213 Treynor Ratio -0.314 Total Fees $187.73 |
## A simple m odification to add leverage factor to the InsightWeightingPortfolioConstructionModel 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(2015, 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', 'VEU', 'IEF'] #for plotting us_equity = Symbol.Create('SPY', SecurityType.Equity, Market.USA) foreign_equity = Symbol.Create('VEU', SecurityType.Equity, Market.USA) bond = Symbol.Create('IEF', 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 = {} 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) ## ISSUE: Rookie programmer trying to build a dict of dicts into a dataframe without losing a direct link between values and index. # I can build the DataFrame but then trouble accessing it using Symbols as the columns, Indicator lookbacks as the Index. ## Also, when attempting to do comparative analysis between assets on MomentumPercent Indicators ## I get another error as I did not build the DataFrame using .Current.Value ## Why didn't I? Because when I do that I lose my lookback indexing in the dataframe. ## I tried creating a list of lookbacks[] and appending the lookback periods and using this as an index but then I wasnt sure ## the values would always match up with their index? ## Trying to get my head around how to best implement Symbol objects and Indicator Object in a Dataframe. ## Is a DF even the best way to do this analysis??? # GEM Rules. Go to Bonds if SPY Momentum is -ve. bonds_weight = (strategies[self.us_equity] < 0).sum() # BUY SPY if it's +ve, AND its relative momentum is greater than that of Foreign Equities us_equity_weight = ( (strategies[self.us_equity] >= 0) & (strategies[self.us_equity] >= strategies[self.foreign_equity]) ).sum() # Else buy Foreign Equities. foreign_equity_weight = ( (strategies[self.us_equity] > 0) & (strategies[self.foreign_equity] > strategies[self.us_equity]) ).sum() insights.append(Insight.Price(self.bond, Expiry.EndOfMonth, InsightDirection.Up, None, None, None, bonds_weight)) insights.append(Insight.Price(self.us_equity, Expiry.EndOfMonth, InsightDirection.Up, None, None, None, us_equity_weight)) insights.append(Insight.Price(self.foreign_equity, Expiry.EndOfMonth, InsightDirection.Up, None, None, None, foreign_equity_weight)) 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(42, 253, 21): self.momp[period] = MomentumPercent(period) # Warm up Indicators history = algorithm.History([self.Symbol], 253, 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)