Overall Statistics 
Total Trades
60
Average Win
5.92%
Average Loss
1.77%
Compounding Annual Return
8.511%
Drawdown
23.000%
Expectancy
3.070
Net Profit
366.190%
Sharpe Ratio
0.66
Probabilistic Sharpe Ratio
3.229%
Loss Rate
6%
Win Rate
94%
ProfitLoss Ratio
3.34
Alpha
0.039
Beta
0.318
Annual Standard Deviation
0.095
Annual Variance
0.009
Information Ratio
0.083
Tracking Error
0.134
Treynor Ratio
0.197
Total Fees
$498.74
Estimated Strategy Capacity
$40000000.00
Lowest Capacity Asset
AGG SSC0EI5J2F6T

# 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 from AlgorithmImports import * # 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(2004, 1, 1) self.SetCash(100000) self.first_symbol = self.AddEquity("SPY", Resolution.Daily).Symbol self.second_symbol = self.AddEquity("AGG", Resolution.Daily).Symbol self.recent_month = 1 def OnData(self, data): if self.Time.month == self.recent_month: return self.recent_month = self.Time.month if(self.recent_month % 3 == 0): if self.first_symbol in data and self.second_symbol in data: history_call = self.History([self.first_symbol, self.second_symbol], timedelta(days=90)) if not history_call.empty: first_bars = history_call.loc[self.first_symbol.Value] last_p1 = first_bars["close"].iloc[0] second_bars = history_call.loc[self.second_symbol.Value] last_p2 = second_bars["close"].iloc[0] # Calculates performance of funds over the prior quarter. first_performance = (float(self.Securities[self.first_symbol].Price)  float(last_p1)) / (float(self.Securities[self.first_symbol].Price)) second_performance = (float(self.Securities[self.second_symbol].Price)  float(last_p2)) / (float(self.Securities[self.second_symbol].Price)) # Buys the fund that has the higher return during the period. if(first_performance > second_performance): if(self.Securities[self.second_symbol].Invested): self.Liquidate(self.second_symbol) self.SetHoldings(self.first_symbol, 1) else: if(self.Securities[self.first_symbol].Invested): self.Liquidate(self.first_symbol) self.SetHoldings(self.second_symbol, 1)