Overall Statistics |
Total Trades 15 Average Win 0.01% Average Loss -1.60% Compounding Annual Return -2.546% Drawdown 6.600% Expectancy -0.665 Net Profit -0.984% Sharpe Ratio -0.159 Probabilistic Sharpe Ratio 21.555% Loss Rate 67% Win Rate 33% Profit-Loss Ratio 0.01 Alpha -0.011 Beta 0.072 Annual Standard Deviation 0.101 Annual Variance 0.01 Information Ratio 0.134 Tracking Error 0.433 Treynor Ratio -0.223 Total Fees $29.15 |
from QuantConnect.Indicators import SimpleMovingAverage class MovingAverageAlphaModel(AlphaModel): '''Alpha model based on Price crossing an SMA''' def __init__(self, smaLength=200, resolution=Resolution.Daily): '''Initializes a new instance of the SmaAlphaModel class Args: period: The SMA period resolution: The reolution for the SMA''' self.smaLength = smaLength self.resolution = resolution self.symbolDataBySymbol = {} self.month = -1 self.riskOffAsset = "IEF" 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 = [] riskOffWeight = riskOnWeight = 1 / len(self.symbolDataBySymbol) for symbol, symbolData in self.symbolDataBySymbol.items(): if symbol.Value == self.riskOffAsset: algorithm.Log(self.symbolDataBySymbol[symbol].MovingAverage.Current.Value) continue price = algorithm.Securities[symbol].Price if price != 0 and symbolData.MovingAverage.IsReady: if price > symbolData.MovingAverage.Current.Value: insights.append( Insight.Price(symbol, Expiry.EndOfMonth, InsightDirection.Up, None, None, None, riskOnWeight)) elif price < symbolData.MovingAverage.Current.Value: insights.append( Insight.Price(symbol, Expiry.EndOfMonth, InsightDirection.Flat, None, None, None, 0) ) riskOffWeight += riskOnWeight insights.append( Insight.Price(self.riskOffAsset, Expiry.EndOfMonth, InsightDirection.Up, None, None, None, riskOffWeight) ) return insights def OnSecuritiesChanged(self, algorithm, changes): for added in changes.AddedSecurities: self.symbolDataBySymbol[added.Symbol] = SymbolData(added, algorithm, self.smaLength, 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, security, algorithm, smaLength, resolution): self.Security = security self.Symbol = security.Symbol self.MovingAverage = SimpleMovingAverage(smaLength) self.algorithm = algorithm # Warm up MA history = algorithm.History([self.Symbol], smaLength, resolution).loc[self.Symbol] for time, row in history.iterrows(): self.MovingAverage.Update(time, row["close"]) # Setup indicator consolidator self.consolidator = TradeBarConsolidator(resolution) self.consolidator.DataConsolidated += self.CustomDailyHandler algorithm.SubscriptionManager.AddConsolidator(self.Symbol, self.consolidator) def CustomDailyHandler(self, sender, consolidated): self.MovingAverage.Update(consolidated.Time, consolidated.Close) def dispose(self): self.algorithm.SubscriptionManager.RemoveConsolidator(self.Symbol, self.consolidator)
'''An implementation of Meb Faber's base model: Global Tactical Asset Allocation model (GTAA)(5) with 10-month SimpleMovingAverage Filter (200day) and (monthly rebalance), as found in the paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=962461 "A Quantitative Approach to Tactical Asset Allocation" published May 2006. Analysis only occurs at month End/Start, signals are NOT generated intra-month. ''' # self.Debug(str(dir( x ))) from alpha_model import MovingAverageAlphaModel class GlobalTacticalAssetAllocation(QCAlgorithm): def Initialize(self): self.SetStartDate(2020, 1, 2) # self.SetEndDate(2020, 5, 20) self.SetCash(100000) self.Settings.FreePortfolioValuePercentage = 0.02 self.SetBrokerageModel(BrokerageName.InteractiveBrokersBrokerage, AccountType.Margin) self.UniverseSettings.Resolution = Resolution.Daily symbols = [Symbol.Create(ticker, SecurityType.Equity, Market.USA) for ticker in [ "SPY", # US Large Cap ETF "VEA", # Developed Foreign Stocks (TradedSince: 2007/8)ETF "IEF", # US 10Y Gov.Bonds ETF "DBC", # GSCI Commodities ETF (TradedSince: 2006/3) "VNQ" # US RealEstate ETF ]] self.AddUniverseSelection( ManualUniverseSelectionModel(symbols) ) self.AddAlpha( MovingAverageAlphaModel() ) self.Settings.RebalancePortfolioOnInsightChanges = True self.Settings.RebalancePortfolioOnSecurityChanges = False self.SetPortfolioConstruction( InsightWeightingPortfolioConstructionModel(self.DateRules.MonthStart('SPY'), PortfolioBias.Long) ) self.SetExecution( ImmediateExecutionModel() ) self.AddRiskManagement( NullRiskManagementModel() )
class YourAlgorithm(QCAlgorithm): def Initialize(self): # 1) Setting a Benchmark to plot with equity self.benchmarkTicker = 'SPY' self.SetBenchmark(self.benchmarkTicker) self.initBenchmarkPrice = None def UpdateBenchmarkValue(self): ''' Simulate buy and hold the Benchmark ''' if self.initBenchmarkPrice is None: self.initBenchmarkCash = self.Portfolio.Cash self.initBenchmarkPrice = self.Benchmark.Evaluate(self.Time) self.benchmarkValue = self.initBenchmarkCash else: currentBenchmarkPrice = self.Benchmark.Evaluate(self.Time) self.benchmarkValue = (currentBenchmarkPrice / self.initBenchmarkPrice) * self.initBenchmarkCash def OnData(self, data): # 2) simulate buy and hold the benchmark and plot its daily value as we are using daily data. # Otherwise schedule when to call this function! self.UpdateBenchmarkValue() self.Plot('Strategy Equity', self.benchmarkTicker, self.benchmarkValue) ### _________________________________________________ # Plotting HoldingValues for kvp in self.Portfolio: symbol = kvp.Key holding = kvp.Value self.Debug(str(holding.Symbol)) self.Plot(f"{str(holding.Symbol)}HoldingValues", holding.HoldingsValue) ### _________________________________________________