| Overall Statistics |
|
Total Trades 128 Average Win 0.26% Average Loss -0.02% Compounding Annual Return 8.710% Drawdown 18.300% Expectancy 11.994 Net Profit 55.577% Sharpe Ratio 0.948 Probabilistic Sharpe Ratio 41.843% Loss Rate 8% Win Rate 92% Profit-Loss Ratio 13.11 Alpha 0.048 Beta 0.446 Annual Standard Deviation 0.098 Annual Variance 0.01 Information Ratio -0.062 Tracking Error 0.115 Treynor Ratio 0.207 Total Fees $167.24 |
class SPYTLT6040InsightWeighted(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2015, 1, 1) # Set Start Date
self.SetCash(1000000) # Set Strategy Cash
self.trigger = False
self.Settings.RebalancePortfolioOnInsightChanges = False
self.Settings.RebalancePortfolioOnSecurityChanges = True
self.spy = self.AddEquity("SPY", Resolution.Minute).Symbol
self.tlt = self.AddEquity("TLT", Resolution.Minute).Symbol
self.SetBenchmark('SPY')
# Scheduled event
self.Schedule.On(self.DateRules.MonthStart(self.spy), self.TimeRules.AfterMarketOpen(self.spy, 5), self.emitNewInsights)
# Use the Alpha Streams Brokerage Model
self.SetBrokerageModel(AlphaStreamsBrokerageModel())
# Immediate execution model
self.SetExecution(ImmediateExecutionModel())
# Insight Weighted portfolio construction
self.SetPortfolioConstruction(InsightWeightingPortfolioConstructionModel(self.RebalanceFunction, PortfolioBias.Long))
def emitNewInsights(self):
insights = []
# Up insight for SPY with weight 60%
insights.append(Insight.Price(self.spy, timedelta(days=35), InsightDirection.Up, None, None, None, 0.6))
# Up insight for TLT with weight 40%
insights.append(Insight.Price(self.tlt, timedelta(days=35), InsightDirection.Up, None, None, None, 0.4))
self.trigger = True
self.EmitInsights(insights)
def RebalanceFunction(self, time):
if self.trigger:
self.trigger = False
return time
return None