Overall Statistics
Total Orders
9
Average Win
15.24%
Average Loss
-0.94%
Compounding Annual Return
8.480%
Drawdown
27.100%
Expectancy
11.864
Start Equity
100000
End Equity
150248.91
Net Profit
50.249%
Sharpe Ratio
0.253
Sortino Ratio
0.274
Probabilistic Sharpe Ratio
12.021%
Loss Rate
25%
Win Rate
75%
Profit-Loss Ratio
16.15
Alpha
-0.016
Beta
0.616
Annual Standard Deviation
0.112
Annual Variance
0.012
Information Ratio
-0.495
Tracking Error
0.088
Treynor Ratio
0.046
Total Fees
$32.46
Estimated Strategy Capacity
$200000000.00
Lowest Capacity Asset
SPY R735QTJ8XC9X
Portfolio Turnover
0.49%
Drawdown Recovery
793
 
#region imports
from AlgorithmImports import *
#endregion


class PairedSwitching(QCAlgorithm):
    
    def initialize(self):
        self.set_start_date(self.end_date - timedelta(5*365))
        self.set_cash(100000)
        # we select two etfs that are negatively correlated; equity and bond etfs
        self._equities = self.add_equity("SPY")
        self._bonds = self.add_equity("AGG")
        self._equities.roc = self.roc(self._equities, 90, Resolution.DAILY)
        self._bonds.roc = self.roc(self._bonds, 90, Resolution.DAILY)
        # monthly scheduled event but rebalancing will run on quarterly basis
        self._months = -1
        self.schedule.on(self.date_rules.month_start("SPY"), self.time_rules.after_market_open("SPY", 1), self._rebalance)

    def _rebalance(self):
        # Rebalance quarterly.
        self._months +=1
        if self._months%3 != 0:
            return
        # buys the fund that has the higher return during the period
        target = self._equities if self._equities.roc > self._bonds.roc else self._bonds
        if not target.invested:
            self.set_holdings([PortfolioTarget(target, 1)], True)