| Overall Statistics |
|
Total Trades 401 Average Win 1.08% Average Loss -0.87% Compounding Annual Return 23.731% Drawdown 22.900% Expectancy 1.083 Net Profit 769.700% Sharpe Ratio 1.642 Probabilistic Sharpe Ratio 93.093% Loss Rate 7% Win Rate 93% Profit-Loss Ratio 1.24 Alpha 0.221 Beta 0.252 Annual Standard Deviation 0.154 Annual Variance 0.024 Information Ratio 0.657 Tracking Error 0.194 Treynor Ratio 1 Total Fees $898.83 |
# Inspired by the theory here:
# https://seekingalpha.com/article/4299701-leveraged-etfs-for-long-term-investing
# https://www.quantconnect.com/forum/discussion/7708/using-levered-etfs-in-ira-10-years-24-cagr-1-56-sharpe/p1
class MultidimensionalTransdimensionalPrism(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2010, 2, 1) # Earliest start date for all ETFs in universe 2/1/10
self.SetEndDate(2020, 3, 27)
self.SetCash(100000)
self.AddEquity("TQQQ", Resolution.Hour) # 3x QQQ
self.AddEquity("UBT", Resolution.Hour) # 3x 20yr Treasury
self.AddEquity("UST", Resolution.Hour) # 3x 10yr Treasury
self.AddEquity("TVIX", Resolution.Hour)
self.tkr = ["TQQQ", "UBT", "UST", "TVIX"]
self.Schedule.On(
self.DateRules.MonthStart("UST"),
self.TimeRules.AfterMarketOpen("UST", 150),
self.Rebalance
)
def OnData(self, data):
pass
def Rebalance(self):
for stock in self.tkr:
if stock =='TVIX':
weight = 0.05
else:
weight = 0.315
self.SetHoldings(stock, weight)