Overall Statistics |
Total Trades
578
Average Win
1.28%
Average Loss
-1.37%
Compounding Annual Return
6.895%
Drawdown
49.100%
Expectancy
0.378
Net Profit
385.072%
Sharpe Ratio
0.408
Probabilistic Sharpe Ratio
0.022%
Loss Rate
29%
Win Rate
71%
Profit-Loss Ratio
0.94
Alpha
0.017
Beta
0.678
Annual Standard Deviation
0.141
Annual Variance
0.02
Information Ratio
-0.023
Tracking Error
0.104
Treynor Ratio
0.085
Total Fees
$1067.23
Estimated Strategy Capacity
$32000000.00
Lowest Capacity Asset
XLK RGRPZX100F39
Portfolio Turnover
1.16%
|
#region imports from AlgorithmImports import * #endregion # 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()) 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"))