Overall Statistics |
Total Trades
11152
Average Win
0.07%
Average Loss
-0.13%
Compounding Annual Return
5.729%
Drawdown
29.200%
Expectancy
0.127
Net Profit
161.817%
Sharpe Ratio
0.529
Probabilistic Sharpe Ratio
1.413%
Loss Rate
25%
Win Rate
75%
Profit-Loss Ratio
0.50
Alpha
0.053
Beta
-0.007
Annual Standard Deviation
0.098
Annual Variance
0.01
Information Ratio
-0.176
Tracking Error
0.199
Treynor Ratio
-7.869
Total Fees
$16612.40
|
# https://quantpedia.com/Screener/Details/1 # 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. import numpy as np from datetime import datetime class BasicTemplateAlgorithm(QCAlgorithm): def Initialize(self): self.SetStartDate(2003, 1, 1) self.SetEndDate(datetime.now()) self.SetCash(100000) self.data = {} period = 10*21 self.SetWarmUp(period) self.symbols = ["SPY", "EFA", "IEF", "VNQ", "GSG"] for symbol in self.symbols: self.AddEquity(symbol, Resolution.Daily) self.data[symbol] = self.SMA(symbol, period, Resolution.Daily) def OnData(self, data): if self.IsWarmingUp: return isUptrend = [] for symbol, sma in self.data.items(): if self.Securities[symbol].Price > sma.Current.Value: isUptrend.append(symbol) else: self.Liquidate(symbol) for symbol in isUptrend: self.SetHoldings(symbol, 1/len(isUptrend))