Overall Statistics
Total Trades
986
Average Win
0.30%
Average Loss
-0.10%
Compounding Annual Return
11.462%
Drawdown
17.500%
Expectancy
1.224
Net Profit
81.824%
Sharpe Ratio
0.886
Probabilistic Sharpe Ratio
34.544%
Loss Rate
43%
Win Rate
57%
Profit-Loss Ratio
2.91
Alpha
0.094
Beta
0.06
Annual Standard Deviation
0.113
Annual Variance
0.013
Information Ratio
-0.003
Tracking Error
0.196
Treynor Ratio
1.682
Total Fees
$1171.51
 
import numpy as np
class TransdimensionalOptimizedAtmosphericScrubbers(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2015, 4, 29)  # Set Start Date
        self.SetCash(100000)  # Set Strategy Cash
        
        res = Resolution.Daily
        
        self.STOCKS = [self.AddEquity('QQQ', res).Symbol]
        self.BONDS = [self.AddEquity(ticker, res).Symbol for ticker in ['TLT', 'IEF']]
        
        self.XLI = self.AddEquity('XLI', res).Symbol
        self.XLU = self.AddEquity('XLU', res).Symbol
        self.MKT = self.STOCKS[0]
        
        self.VOLA = 126;
        self.BULL = 1;
        self.COUNT = 0;
        self.OUT_DAY = 0;
        self.RET_INITIAL = 80;
        self.LEV = 1.00;
    
        self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.AfterMarketOpen('QQQ', 140), self.daily_check)

    def daily_check(self):
        vola = self.History([self.MKT], self.VOLA + 1, Resolution.Daily).loc[self.MKT]['close'].pct_change().std() * np.sqrt(252)  
        WAIT_DAYS = int(vola * self.RET_INITIAL)  
        RET = int((1.0 - vola) * self.RET_INITIAL)
        
        P = self.History([self.XLI, self.XLU], RET + 2, Resolution.Daily)['close'].unstack(level=0).iloc[:-1].dropna()  
        if (len(P.columns) < 2):
            return
        ratio = (P[self.XLI].iloc[-1] / P[self.XLI].iloc[0]) / (P[self.XLU].iloc[-1] / P[self.XLU].iloc[0])  
        
        exit = ratio < 1.0
        if exit: 
            self.BULL = 0;
            self.OUT_DAY = self.COUNT;  
        elif (self.COUNT >= self.OUT_DAY + WAIT_DAYS):
            self.BULL = 1  
        self.COUNT += 1
        
        wt_stk = self.LEV if self.BULL else 0;  
        wt_bnd = 0 if self.BULL else self.LEV;
        
        wt = {}
        
        for sec in self.STOCKS:
            wt[sec] = wt_stk / len(self.STOCKS);
     
        for sec in self.BONDS:
            wt[sec] = wt_bnd / len(self.BONDS)         
    
        for sec, weight in wt.items():  
            self.SetHoldings(sec, weight)