Overall Statistics
Total Trades
207
Average Win
2.45%
Average Loss
-0.02%
Compounding Annual Return
34.151%
Drawdown
39.000%
Expectancy
103.972
Net Profit
2088.426%
Sharpe Ratio
1.529
Probabilistic Sharpe Ratio
82.448%
Loss Rate
1%
Win Rate
99%
Profit-Loss Ratio
105.01
Alpha
0.195
Beta
1.224
Annual Standard Deviation
0.252
Annual Variance
0.063
Information Ratio
1.637
Tracking Error
0.141
Treynor Ratio
0.314
Total Fees
$208.07
 
# Inspired by the theory here:
# https://seekingalpha.com/article/4299701-leveraged-etfs-for-long-term-investing

class Investment(object):
    def __init__(self, symbol, weight):
        self.symbol = symbol
        self.weight = weight

class MultidimensionalTransdimensionalPrism(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2010, 3, 1)
        self.SetCash(1600)
        self.AddEquity("SPY", Resolution.Hour)
        
        self.cashToAdd = 0
        self.investments = [
            Investment("TQQQ", 0.5),
            Investment("TLT", 0.5),
        ]
        
        # Add symbols
        for investment in self.investments:
            self.AddEquity(investment.symbol, Resolution.Hour)
        
        # Schedules
        self.Schedule.On(self.DateRules.MonthStart("SPY"), self.TimeRules.AfterMarketOpen("SPY", 150), self.Rebalance)
        self.Schedule.On(self.DateRules.MonthStart("SPY"), self.TimeRules.AfterMarketOpen("SPY", 140), self.AddCash)

    def OnData(self, data):
        pass       
        
    def Rebalance(self):
        for investment in self.investments:
            self.SetHoldings(investment.symbol, investment.weight)
            
    def AddCash(self):
        self.Portfolio.SetCash(self.Portfolio.Cash + self.cashToAdd)