Overall Statistics |
Total Trades
810
Average Win
0.65%
Average Loss
-1.30%
Compounding Annual Return
7.436%
Drawdown
29.300%
Expectancy
0.278
Net Profit
399.639%
Sharpe Ratio
0.581
Probabilistic Sharpe Ratio
0.759%
Loss Rate
15%
Win Rate
85%
Profit-Loss Ratio
0.50
Alpha
0.041
Beta
0.255
Annual Standard Deviation
0.096
Annual Variance
0.009
Information Ratio
-0.024
Tracking Error
0.148
Treynor Ratio
0.218
Total Fees
$2292.36
Estimated Strategy Capacity
$870000.00
Lowest Capacity Asset
GSG TKH7EPK7SRC5
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#region imports from AlgorithmImports import * #endregion # 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 121 days is used. class AssetClassTrendFollowing(QCAlgorithm): def Initialize(self): self.SetStartDate(2000, 1, 1) self.SetCash(100000) self.sma = {} period = 10 * 21 self.SetWarmUp(period) 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): # rebalance once a month if self.Time.month == self.recent_month: return if self.Time.hour != 9 and self.Time.minute != 31: 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))