Overall Statistics 
Total Trades
65
Average Win
6.76%
Average Loss
4.25%
Compounding Annual Return
7.057%
Drawdown
34.400%
Expectancy
0.862
Net Profit
188.654%
Sharpe Ratio
0.555
Probabilistic Sharpe Ratio
2.451%
Loss Rate
28%
Win Rate
72%
ProfitLoss Ratio
1.59
Alpha
0.069
Beta
0.047
Annual Standard Deviation
0.117
Annual Variance
0.014
Information Ratio
0.097
Tracking Error
0.219
Treynor Ratio
1.372
Total Fees
$605.52

# https://quantpedia.com/strategies/pairedswitching/ # # This strategy is very flexible. Investors could use stocks, funds, or ETFs as an investment vehicle. We show simple trading rules for a sample strategy # from the source research paper. The investor uses two Vanguard funds as his investment vehicles – one equity fund (VFINX) and one government bond # fund (VUSTX). These two funds have a negative correlation as they are proxies for two negatively correlated asset classes. The investor looks at the # performance of the two funds over the prior quarter and buys the fund that has a higher return during the ranking period. The position is held for one # quarter (the investment period). At the end of the investment period, the cycle is repeated. class PairedSwitching(QCAlgorithm): def Initialize(self): self.SetStartDate(2005, 1, 1) self.SetCash(100000) self.first = self.AddEquity("SPY", Resolution.Daily).Symbol self.second = self.AddEquity("AGG", Resolution.Daily).Symbol self.months = 3 self.Schedule.On(self.DateRules.MonthStart(self.first), self.TimeRules.AfterMarketOpen(self.first), self.Rebalance) def Rebalance(self): if(self.months % 3 == 0): if self.Securities.ContainsKey(self.first) and self.Securities.ContainsKey(self.second): history_call = self.History([self.first, self.second], timedelta(days=90)) if not history_call.empty: first_bars = history_call.loc[self.first.Value] last_p1 = first_bars["close"].iloc[0] second_bars = history_call.loc[self.second.Value] last_p2 = second_bars["close"].iloc[0] # Calculates performance of funds over the prior quarter. first_performance = (float(self.Securities[self.first].Price)  float(last_p1)) / (float(self.Securities[self.first].Price)) second_performance = (float(self.Securities[self.second].Price)  float(last_p2)) / (float(self.Securities[self.second].Price)) # Buys the fund that has the higher return during the period. if(first_performance > second_performance): if(self.Securities[self.second].Invested): self.Liquidate(self.second) self.SetHoldings(self.first, 1) else: if(self.Securities[self.first].Invested): self.Liquidate(self.first) self.SetHoldings(self.second, 1) self.months += 1