Overall Statistics
Total Trades
767
Average Win
0.83%
Average Loss
-1.19%
Compounding Annual Return
6.661%
Drawdown
49.200%
Expectancy
0.304
Net Profit
322.285%
Sharpe Ratio
0.398
Probabilistic Sharpe Ratio
0.026%
Loss Rate
23%
Win Rate
77%
Profit-Loss Ratio
0.70
Alpha
0.016
Beta
0.675
Annual Standard Deviation
0.141
Annual Variance
0.02
Information Ratio
-0.031
Tracking Error
0.104
Treynor Ratio
0.083
Total Fees
$936.33
Estimated Strategy Capacity
$76000000.00
Lowest Capacity Asset
VNQ T2FCD04TATET
# https://quantpedia.com/strategies/sector-momentum-rotational-system/
#
# Use ten sector ETFs. Pick 3 ETFs with the strongest 12-month momentum into your portfolio and weight them equally. Hold them for one month and then rebalance.

class SectorMomentumAlgorithm(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2000, 1, 1)  
        self.SetCash(100000) 
        
        # Daily ROC data.
        self.data = {}
        
        self.period = 12 * 21
        self.SetWarmUp(self.period)
        
        self.symbols = [
                        "VNQ",  # Vanguard Real Estate Index Fund
                        "XLK",  # Technology Select Sector SPDR Fund
                        "XLE",  # Energy Select Sector SPDR Fund
                        "XLV",  # Health Care Select Sector SPDR Fund
                        "XLF",  # Financial Select Sector SPDR Fund
                        "XLI",  # Industrials Select Sector SPDR Fund
                        "XLB",  # Materials Select Sector SPDR Fund
                        "XLY",  # Consumer Discretionary Select Sector SPDR Fund
                        "XLP",  # Consumer Staples Select Sector SPDR Fund
                        "XLU"   # Utilities Select Sector SPDR Fund
                        ]  
                        
        for symbol in self.symbols:
            data = self.AddEquity(symbol, Resolution.Daily)
            data.SetFeeModel(CustomFeeModel(self))
            data.SetLeverage(5)
            
            self.data[symbol] = self.ROC(symbol, self.period, Resolution.Daily)
        
        self.data[self.symbols[0]].Updated += self.OnROCUpdated
        self.recent_month = -1
        self.rebalance_flag = False

    def OnROCUpdated(self, sender, updated):
        # set rebalance flag
        if self.recent_month != self.Time.month:
            self.recent_month = self.Time.month
            self.rebalance_flag = True
        
    def OnData(self, data):
        if self.IsWarmingUp: return

        # rebalance once a month
        if self.rebalance_flag:
            self.rebalance_flag = False
            
            sorted_by_momentum = sorted([x for x in self.data.items() if x[1].IsReady and x[0] in data and data[x[0]]], key = lambda x: x[1].Current.Value, reverse = True)
            long = [x[0] for x in sorted_by_momentum[:3]]
            
            # Trade execution.
            invested = [x.Key for x in self.Portfolio if x.Value.Invested]
            for symbol in invested:
                if symbol not in long:
                    self.Liquidate(symbol)
            
            for symbol in long:
                self.SetHoldings(symbol, 1 / len(long))
                
# Custom fee model
class CustomFeeModel(FeeModel):
    def GetOrderFee(self, parameters):
        fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
        return OrderFee(CashAmount(fee, "USD"))