Overall Statistics |
Total Orders
905
Average Win
0.62%
Average Loss
-1.32%
Compounding Annual Return
6.190%
Drawdown
29.400%
Expectancy
0.232
Start Equity
100000
End Equity
440627.12
Net Profit
340.627%
Sharpe Ratio
0.251
Sortino Ratio
0.247
Probabilistic Sharpe Ratio
0.059%
Loss Rate
16%
Win Rate
84%
Profit-Loss Ratio
0.47
Alpha
0.013
Beta
0.305
Annual Standard Deviation
0.104
Annual Variance
0.011
Information Ratio
-0.112
Tracking Error
0.144
Treynor Ratio
0.086
Total Fees
$3131.29
Estimated Strategy Capacity
$2500000.00
Lowest Capacity Asset
EFA S79U6IHK5HLX
Portfolio Turnover
1.09%
|
# 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 changes: # - 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) tickers: List[str] = ["SPY", "EFA", "IEF", "VNQ", "GSG"] period: int = 10 * 21 self.sma: Dict[Symbol, SimpleMovingAverage] = { self.AddEquity(ticker, Resolution.Minute).Symbol : self.SMA(ticker, period, Resolution.Daily) for ticker in tickers } self.recent_month: int = -1 self.SetWarmUp(period, Resolution.Daily) self.Settings.MinimumOrderMarginPortfolioPercentage = 0. def OnData(self, data: Slice) -> None: 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: List[Symbol] = [ symbol for symbol, sma in self.sma.items() if symbol in data and data[symbol] and sma.IsReady and data[symbol].Value > sma.Current.Value ] portfolio: List[PortfolioTarget] = [PortfolioTarget(symbol, 1. / len(long)) for symbol in long] self.SetHoldings(portfolio, True)