Overall Statistics |
Total Trades 832 Average Win 0.66% Average Loss -1.33% Compounding Annual Return 6.411% Drawdown 29.300% Expectancy 0.258 Net Profit 326.650% Sharpe Ratio 0.472 Probabilistic Sharpe Ratio 0.100% Loss Rate 16% Win Rate 84% Profit-Loss Ratio 0.50 Alpha 0.032 Beta 0.3 Annual Standard Deviation 0.105 Annual Variance 0.011 Information Ratio -0.058 Tracking Error 0.147 Treynor Ratio 0.166 Total Fees $2699.95 Estimated Strategy Capacity $14000000.00 Lowest Capacity Asset IEF SGNKIKYGE9NP Portfolio Turnover 1.07% |
# https://quantpedia.com/strategies/asset-class-trend-following/ # # Use 5 ETFs (SPY - US stocks, EFA - foreign stocks, IEF - bonds, VNQ - REITs, # GSG - commodities), equal weight the portfolio. Hold asset class ETF only when # it is over its 10 month Simple Moving Average, otherwise stay in cash. # # QC implementation: # - SMA with period of 210 days is used. #region imports from AlgorithmImports import * #endregion class AssetClassTrendFollowing(QCAlgorithm): def Initialize(self): self.SetStartDate(2000, 1, 1) self.SetCash(100000) self.sma = {} period = 10 * 21 self.SetWarmUp(period, Resolution.Daily) self.symbols = ["SPY", "EFA", "IEF", "VNQ", "GSG"] self.rebalance_flag = False self.tracked_symbol = None for symbol in self.symbols: self.AddEquity(symbol, Resolution.Minute) self.sma[symbol] = self.SMA(symbol, period, Resolution.Daily) self.recent_month = -1 def OnData(self, data): if self.IsWarmingUp: return if not (self.Time.hour == 9 and self.Time.minute == 31): return # rebalance once a month if self.Time.month == self.recent_month: return self.recent_month = self.Time.month long = [ symbol for symbol in self.symbols if symbol in data and data[symbol] and self.sma[symbol].IsReady and data[symbol].Value > self.sma[symbol].Current.Value ] # trade execution invested = [x.Key.Value 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))