Overall Statistics |
Total Trades
87
Average Win
11.41%
Average Loss
-4.39%
Compounding Annual Return
8.674%
Drawdown
77.000%
Expectancy
0.482
Net Profit
174.827%
Sharpe Ratio
0.426
Loss Rate
59%
Win Rate
41%
Profit-Loss Ratio
2.60
Alpha
0.177
Beta
-2.669
Annual Standard Deviation
0.294
Annual Variance
0.086
Information Ratio
0.359
Tracking Error
0.294
Treynor Ratio
-0.047
Total Fees
$885.21
|
# https://quantpedia.com/Screener/Details/2 # Use 5 ETFs (SPY - US stocks, EFA - foreign stocks, BND - bonds, VNQ - REITs, GSG - commodities). # Pick 3 ETFs with strongest 12 month momentum into your portfolio and weight them equally. # Hold for 1 month and then rebalance. import pandas as pd from datetime import datetime class AssetClassMomentumAlgorithm(QCAlgorithm): def Initialize(self): self.SetStartDate(2007, 5, 1) self.SetEndDate(datetime.now()) self.SetCash(100000) # create a dictionary to store momentum indicators for all symbols self.data = {} period = 12*21 self.symbols = ["SPY", "EFA", "BND", "VNQ", "GSG"] # warm up the MOM indicator self.SetWarmUp(period) for symbol in self.symbols: self.AddEquity(symbol, Resolution.Daily) self.data[symbol] = self.MOM(symbol, period, Resolution.Daily) # shcedule the function to fire at the month start self.Schedule.On(self.DateRules.MonthStart("SPY"), self.TimeRules.AfterMarketOpen("SPY"), self.Rebalance) def OnData(self, data): pass def Rebalance(self): if self.IsWarmingUp: return top3 = pd.Series(self.data).sort_values(ascending = False)[:3] for kvp in self.Portfolio: security_hold = kvp.Value # liquidate the security which is no longer in the top3 momentum list if security_hold.Invested and (security_hold.Symbol.Value not in top3.index): self.Liquidate(security_hold.Symbol) added_symbols = [] for symbol in top3.index: if not self.Portfolio[symbol].Invested: added_symbols.append(symbol) for added in added_symbols: self.SetHoldings(added, 1/len(added_symbols))