Overall Statistics
Total Trades
58
Average Win
5.59%
Average Loss
-1.77%
Compounding Annual Return
9.210%
Drawdown
23.000%
Expectancy
2.888
Net Profit
414.338%
Sharpe Ratio
0.711
Probabilistic Sharpe Ratio
5.627%
Loss Rate
6%
Win Rate
94%
Profit-Loss Ratio
3.16
Alpha
0.043
Beta
0.317
Annual Standard Deviation
0.095
Annual Variance
0.009
Information Ratio
-0.072
Tracking Error
0.134
Treynor Ratio
0.213
Total Fees
$465.38
Estimated Strategy Capacity
$750000000.00
Lowest Capacity Asset
SPY R735QTJ8XC9X
# https://quantpedia.com/strategies/paired-switching/
#
# 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)